{ // 获取包含Hugging Face文本的span元素 const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap'); spans.forEach(span => { if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) { span.textContent = 'AI快站'; } }); }); // 替换logo图片的alt属性 document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => { if (img.alt.match(/Hugging\s*Face/i)) { img.alt = 'AI快站 logo'; } }); } // 替换导航栏中的链接 function replaceNavigationLinks() { // 已替换标记,防止重复运行 if (window._navLinksReplaced) { return; } // 已经替换过的链接集合,防止重复替换 const replacedLinks = new Set(); // 只在导航栏区域查找和替换链接 const headerArea = document.querySelector('header') || document.querySelector('nav'); if (!headerArea) { return; } // 在导航区域内查找链接 const navLinks = headerArea.querySelectorAll('a'); navLinks.forEach(link => { // 如果已经替换过,跳过 if (replacedLinks.has(link)) return; const linkText = link.textContent.trim(); const linkHref = link.getAttribute('href') || ''; // 替换Spaces链接 - 仅替换一次 if ( (linkHref.includes('/spaces') || linkHref === '/spaces' || linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) && linkText !== 'GitHub加速' && linkText !== 'GitHub加速' ) { link.textContent = 'GitHub加速'; link.href = 'https://githubproxy.cc'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } // 替换Docs链接 - 仅替换一次 else if ( (linkHref.includes('/docs') || linkHref === '/docs' || linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) && linkText !== 'Vibevoice' ) { link.textContent = 'Vibevoice'; link.href = 'https://vibevoice.info/'; replacedLinks.add(link); } // 替换Pricing链接 - 仅替换一次 else if ( (linkHref.includes('/pricing') || linkHref === '/pricing' || linkText === 'Pricing' || linkText.match(/^s*Pricings*$/i)) && linkText !== 'SoraWatermarkRemover' ) { link.textContent = 'SoraWatermarkRemover'; link.href = 'https://sora2watermarkremover.net/'; replacedLinks.add(link); } // 替换Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i)) && linkText !== 'VoxCPM' ) { link.textContent = 'VoxCPM'; link.href = 'https://voxcpm.net'; replacedLinks.add(link); } }); // 查找可能嵌套的Spaces和Posts文本 const textNodes = []; function findTextNodes(element) { if (element.nodeType === Node.TEXT_NODE) { const text = element.textContent.trim(); if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') { textNodes.push(element); } } else { for (const child of element.childNodes) { findTextNodes(child); } } } // 只在导航区域内查找文本节点 findTextNodes(headerArea); // 替换找到的文本节点 textNodes.forEach(node => { const text = node.textContent.trim(); if (text === 'Spaces') { node.textContent = node.textContent.replace(/Spaces/g, 'GitHub加速'); } else if (text === 'Posts') { // 删除Posts文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } else if (text === 'Enterprise') { // 删除Enterprise文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } }); // 标记已替换完成 window._navLinksReplaced = true; } // 替换代码区域中的域名 function replaceCodeDomains() { // 特别处理span.hljs-string和span.njs-string元素 document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换hljs-string类的span中的域名(移除多余的转义符号) document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换pre和code标签中包含git clone命令的域名 document.querySelectorAll('pre, code').forEach(element => { if (element.textContent && element.textContent.includes('git clone')) { const text = element.innerHTML; if (text.includes('huggingface.co')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 处理特定的命令行示例 document.querySelectorAll('pre, code').forEach(element => { const text = element.innerHTML; if (text.includes('huggingface.co')) { // 针对git clone命令的专门处理 if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 特别处理模型下载页面上的代码片段 document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => { const content = container.innerHTML; if (content && content.includes('huggingface.co')) { container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 特别处理模型仓库克隆对话框中的代码片段 try { // 查找包含"Clone this model repository"标题的对话框 const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]'); if (cloneDialog) { // 查找对话框中所有的代码片段和命令示例 const codeElements = cloneDialog.querySelectorAll('pre, code, span'); codeElements.forEach(element => { if (element.textContent && element.textContent.includes('huggingface.co')) { if (element.innerHTML.includes('huggingface.co')) { element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); } // 更精确地定位克隆命令中的域名 document.querySelectorAll('[data-target]').forEach(container => { const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string'); codeBlocks.forEach(block => { if (block.textContent && block.textContent.includes('huggingface.co')) { if (block.innerHTML.includes('huggingface.co')) { block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); }); } catch (e) { // 错误处理但不打印日志 } } // 当DOM加载完成后执行替换 if (document.readyState === 'loading') { document.addEventListener('DOMContentLoaded', () => { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); }); } else { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); } // 增加一个MutationObserver来处理可能的动态元素加载 const observer = new MutationObserver(mutations => { // 检查是否导航区域有变化 const hasNavChanges = mutations.some(mutation => { // 检查是否存在header或nav元素变化 return Array.from(mutation.addedNodes).some(node => { if (node.nodeType === Node.ELEMENT_NODE) { // 检查是否是导航元素或其子元素 if (node.tagName === 'HEADER' || node.tagName === 'NAV' || node.querySelector('header, nav')) { return true; } // 检查是否在导航元素内部 let parent = node.parentElement; while (parent) { if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') { return true; } parent = parent.parentElement; } } return false; }); }); // 只在导航区域有变化时执行替换 if (hasNavChanges) { // 重置替换状态,允许再次替换 window._navLinksReplaced = false; replaceHeaderBranding(); replaceNavigationLinks(); } }); // 开始观察document.body的变化,包括子节点 if (document.body) { observer.observe(document.body, { childList: true, subtree: true }); } else { document.addEventListener('DOMContentLoaded', () => { observer.observe(document.body, { childList: true, subtree: true }); }); } })(); \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%cython --annotate\\n\",\n \"\\n\",\n \"cimport cython\\n\",\n \"\\n\",\n \"from libc.math cimport floor, ceil\\n\",\n \"from numpy.math cimport INFINITY\\n\",\n \"\\n\",\n \"cpdef double c_raytrace_fast(double[::1] line, double ex1, double ex2, double ey1, double ey2, double[:, ::1] pixels):\\n\",\n \" # Fixed issue, alphax[0] in Filip Jacob's paper means first alphax in siddon array, not alphax at zero.\\n\",\n \" cdef:\\n\",\n \" double d12 = 0\\n\",\n \" double alphaxmin, alphaxmax\\n\",\n \" double alphaymin, alphaymax\\n\",\n \" double p1x, p1y, p2x, p2y\\n\",\n \" double bx, by\\n\",\n \" int Nx, Ny\\n\",\n \" double dx, dy\\n\",\n \" double alphax, alphay\\n\",\n \" double alphamid\\n\",\n \" int i, j, iu, ju\\n\",\n \" int imax, imin, jmax, jmin\\n\",\n \" int Np\\n\",\n \" \\n\",\n \" p1x = line[0]\\n\",\n \" p1y = line[1]\\n\",\n \" p2x = line[2]\\n\",\n \" p2y = line[3]\\n\",\n \" bx, by = ex1, ey1\\n\",\n \" Nx, Ny = pixels.shape[0] + 1, pixels.shape[1] + 1\\n\",\n \" dx, dy = (ex2 - ex1) / pixels.shape[0], (ey2 - ey1) / pixels.shape[1]\\n\",\n \"\\n\",\n \" if p1x == p2x:\\n\",\n \" alphaxmin = 0\\n\",\n \" alphaxmax = 0\\n\",\n \" else:\\n\",\n \" alphaxmin = min(((bx + 0 * dx) - p1x) / (p2x - p1x),\\n\",\n \" ((bx + (Nx - 1) * dx) - p1x) / (p2x - p1x))\\n\",\n \" alphaxmax = max(((bx + 0 * dx) - p1x) / (p2x - p1x),\\n\",\n \" ((bx + (Nx - 1) * dx) - p1x) / (p2x - p1x))\\n\",\n \"\\n\",\n \" if p1y == p2y:\\n\",\n \" alphaymin = 0\\n\",\n \" alphaymax = 0\\n\",\n \" else:\\n\",\n \" alphaymin = min(((by + 0 * dy) - p1y) / (p2y - p1y),\\n\",\n \" ((by + (Ny - 1) * dy) - p1y) / (p2y - p1y))\\n\",\n \" alphaymax = max(((by + 0 * dy) - p1y) / (p2y - p1y),\\n\",\n \" ((by + (Ny - 1) * dy) - p1y) / (p2y - p1y))\\n\",\n \"\\n\",\n \" if p1x == p2x:\\n\",\n \" alphamin = max(0, alphaymin)\\n\",\n \" alphamax = min(1, alphaymax)\\n\",\n \" elif p1y == p2y:\\n\",\n \" alphamin = max(0, alphaxmin)\\n\",\n \" alphamax = min(1, alphaxmax)\\n\",\n \" else:\\n\",\n \" alphamin = max(0, alphaxmin, alphaymin)\\n\",\n \" alphamax = min(1, alphaxmax, alphaymax)\\n\",\n \"\\n\",\n \" if p1x < p2x:\\n\",\n \" if alphamin == alphaxmin:\\n\",\n \" imin = 1\\n\",\n \" else:\\n\",\n \" imin = ceil(((p1x + alphamin * (p2x - p1x)) - bx) / dx)\\n\",\n \"\\n\",\n \" if alphamax == alphaxmax:\\n\",\n \" imax = Nx - 1\\n\",\n \" else:\\n\",\n \" imax = floor(((p1x + alphamax * (p2x - p1x)) - bx) / dx)\\n\",\n \"\\n\",\n \" if p1x == p2x:\\n\",\n \" alphax = INFINITY\\n\",\n \" else:\\n\",\n \" alphax = ((bx + imin * dx) - p1x) / (p2x - p1x)\\n\",\n \"\\n\",\n \" else:\\n\",\n \" if alphamin == alphaxmin:\\n\",\n \" imax = Nx - 2\\n\",\n \" else:\\n\",\n \" imax = floor(((p1x + alphamin * (p2x - p1x)) - bx) / dx)\\n\",\n \"\\n\",\n \" if alphamax == alphaxmax:\\n\",\n \" imin = 0\\n\",\n \" else:\\n\",\n \" imin = ceil(((p1x + alphamax * (p2x - p1x)) - bx) / dx)\\n\",\n \"\\n\",\n \" if p1x == p2x:\\n\",\n \" alphax = INFINITY\\n\",\n \" else:\\n\",\n \" alphax = ((bx + imax * dx) - p1x) / (p2x - p1x)\\n\",\n \"\\n\",\n \" if p1y < p2y:\\n\",\n \" if alphamin == alphaymin:\\n\",\n \" jmin = 1\\n\",\n \" else:\\n\",\n \" jmin = ceil(((p1y + alphamin * (p2y - p1y)) - by) / dy)\\n\",\n \"\\n\",\n \" if alphamax == alphaymax:\\n\",\n \" jmax = Ny - 1\\n\",\n \" else:\\n\",\n \" jmax = floor(((p1y + alphamax * (p2y - p1y)) - by) / dy)\\n\",\n \"\\n\",\n \" if p1y == p2y:\\n\",\n \" alphay = INFINITY\\n\",\n \" else:\\n\",\n \" alphay = ((by + jmin * dy) - p1y) / (p2y - p1y)\\n\",\n \"\\n\",\n \" else:\\n\",\n \" if alphamin == alphaymin:\\n\",\n \" jmax = Ny - 2\\n\",\n \" else:\\n\",\n \" jmax = floor(((p1y + alphamin * (p2y - p1y)) - by) / dy)\\n\",\n \"\\n\",\n \" if alphamax == alphaymax:\\n\",\n \" jmin = 0\\n\",\n \" else:\\n\",\n \" jmin = ceil(((p1y + alphamax * (p2y - p1y)) - by) / dy)\\n\",\n \"\\n\",\n \" if p1y == p2y:\\n\",\n \" alphay = INFINITY\\n\",\n \" else:\\n\",\n \" alphay = ((by + jmax * dy) - p1y) / (p2y - p1y)\\n\",\n \"\\n\",\n \" Np = (imax - imin + 1) + (jmax - jmin + 1)\\n\",\n \"\\n\",\n \" alphamid = (min(alphax, alphay) + alphamin) / 2\\n\",\n \"\\n\",\n \" i = floor(((p1x + alphamid * (p2x - p1x)) - bx) / dx)\\n\",\n \" j = floor(((p1y + alphamid * (p2y - p1y)) - by) / dy)\\n\",\n \"\\n\",\n \" if p1x == p2x:\\n\",\n \" alphaxu = 0\\n\",\n \" else:\\n\",\n \" alphaxu = dx / abs(p2x - p1x)\\n\",\n \" if p1y == p2y:\\n\",\n \" alphayu = 0\\n\",\n \" else:\\n\",\n \" alphayu = dy / abs(p2y - p1y)\\n\",\n \"\\n\",\n \" dconv = ((p2x - p1x)**2 + (p2y - p1y)**2)**0.5\\n\",\n \" alphac = alphamin\\n\",\n \"\\n\",\n \" if p1x < p2x:\\n\",\n \" iu = 1\\n\",\n \" else:\\n\",\n \" iu = -1\\n\",\n \"\\n\",\n \" if p1y < p2y:\\n\",\n \" ju = 1\\n\",\n \" else:\\n\",\n \" ju = -1\\n\",\n \"\\n\",\n \" for k in range(Np):\\n\",\n \"\\n\",\n \" if alphax < alphay:\\n\",\n \" lij = (alphax - alphac) * dconv\\n\",\n \" d12 = d12 + lij * pixels[i, j]\\n\",\n \" i = i + iu\\n\",\n \" alphac = alphax\\n\",\n \" alphax = alphax + alphaxu\\n\",\n \" else:\\n\",\n \" lij = (alphay - alphac) * dconv\\n\",\n \" d12 = d12 + lij * pixels[i, j]\\n\",\n \" j = j + ju\\n\",\n \" alphac = alphay\\n\",\n \" alphay = alphay + alphayu\\n\",\n \"\\n\",\n \" # have to think about this for case of line in and outside of image\\n\",\n \" # alphamax == 1 means last point is in image\\n\",\n \" # alphamin == 0 means first point is in image\\n\",\n \" # print(alphamax, alphamin, alphaxmin, alphaxmax)\\n\",\n \" if alphamax == 1:\\n\",\n \"\\n\",\n \" lij = (alphamax - alphac) * dconv\\n\",\n \" d12 = d12 + lij * pixels[i, j]\\n\",\n \"\\n\",\n \" return d12\\n\",\n \"\\n\",\n \"@cython.boundscheck(False)\\n\",\n \"@cython.wraparound(False)\\n\",\n \"cpdef void c_raytrace_fast_bulk(double[:, ::1] lines, double ex1, double ex2, double ey1, double ey2, double[:, ::1] pixels, double[::1] cache):\\n\",\n \" cdef:\\n\",\n \" int i\\n\",\n \" \\n\",\n \" for i in range(lines.shape[0]):\\n\",\n \" cache[i] = c_raytrace_fast(lines[i], ex1, ex2, ey1, ey2, pixels)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"_cache = np.zeros((10000), dtype=np.double)\\n\",\n \"\\n\",\n \"def raytrace_fast(line, extent, pixels):\\n\",\n \" return c_raytrace_fast(line, extent[0], extent[1], extent[2], extent[3], pixels)\\n\",\n \"\\n\",\n \"def raytrace_bulk_fast(lines, extent, pixels):\\n\",\n \" c_raytrace_fast_bulk(lines, extent[0], extent[1], extent[2], extent[3], pixels, _cache)\\n\",\n \" return _cache[:np.size(lines, 0)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def draw_algorithm(extent, pixels, draw_lines=True):\\n\",\n \"\\n\",\n \" plt.imshow(pixels.T, extent=extent, origin='lower')\\n\",\n \"\\n\",\n \" if draw_lines:\\n\",\n \" # vertical lines\\n\",\n \" for i in range(np.size(pixels, 0) + 1):\\n\",\n \" x = extent[0] + (extent[1] - extent[0]) / np.size(pixels, 0) * i\\n\",\n \" plt.plot([x, x], [extent[2], extent[3]], 'g')\\n\",\n \"\\n\",\n \" # horizontal lines\\n\",\n \" for i in range(np.size(pixels, 1) + 1):\\n\",\n \" y = extent[2] + (extent[3] - extent[2]) / np.size(pixels, 1) * i\\n\",\n \" plt.plot([extent[0], extent[1]], [y, y], 'g')\\n\",\n \"\\n\",\n \"\\n\",\n \"def draw_line(line, draw_option='c-'):\\n\",\n \" plt.plot([line[0], line[2]], [line[1], line[3]], draw_option, lw=1)\\n\",\n \"\\n\",\n \"\\n\",\n \"def draw_alpha(line, alpha, color='red'):\\n\",\n \" point_x = line[0] + alpha * (line[2] - line[0])\\n\",\n \" point_y = line[1] + alpha * (line[3] - line[1])\\n\",\n \"\\n\",\n \" plt.scatter(point_x, point_y, color=color)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def test_outside(seed=8675309, nruns=5000, npixels=50, debug=False):\\n\",\n \" import random\\n\",\n \"\\n\",\n \" def distance(line):\\n\",\n \" return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\\n\",\n \"\\n\",\n \" def outside_intersection_distance(line, extent):\\n\",\n \" x1, y1, x2, y2 = line\\n\",\n \" x3, x4, y3, y4 = extent\\n\",\n \"\\n\",\n \" def intersection(x1, x2, y1, y2, x3, x4, y3, y4):\\n\",\n \" denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)\\n\",\n \" if denom != 0:\\n\",\n \" t = (x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)\\n\",\n \" t /= denom\\n\",\n \" u = - ((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3))\\n\",\n \" u /= denom\\n\",\n \"\\n\",\n \" if 0 <= t <= 1 and 0 <= u <= 1:\\n\",\n \" return [x1 + t * (x2 - x1), y1 + t * (y2 - y1)]\\n\",\n \"\\n\",\n \" return None\\n\",\n \"\\n\",\n \" top_intersection = intersection(x1, x2, y1, y2, x3, x4, y4, y4)\\n\",\n \" bottom_intersection = intersection(x1, x2, y1, y2, x3, x4, y3, y3)\\n\",\n \" left_intersection = intersection(x1, x2, y1, y2, x3, x3, y3, y4)\\n\",\n \" right_intersection = intersection(x1, x2, y1, y2, x4, x4, y3, y4)\\n\",\n \"\\n\",\n \" intersections = [i for i in [top_intersection, bottom_intersection,\\n\",\n \" left_intersection, right_intersection] if i is not None]\\n\",\n \"\\n\",\n \" true_line = [intersections[0][0], intersections[0][1],\\n\",\n \" intersections[1][0], intersections[1][1]]\\n\",\n \"\\n\",\n \" return distance(true_line)\\n\",\n \"\\n\",\n \" image = np.ones((npixels, npixels))\\n\",\n \" extent = [-5, 5, -5, 5]\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" draw_algorithm(extent, image)\\n\",\n \"\\n\",\n \" random.seed(seed)\\n\",\n \"\\n\",\n \" differences = []\\n\",\n \"\\n\",\n \" for i in range(nruns):\\n\",\n \" if random.choice([True, False]):\\n\",\n \" line = [extent[0] - 1,\\n\",\n \" random.uniform(extent[2], extent[3]),\\n\",\n \" extent[1] + 1,\\n\",\n \" random.uniform(extent[2], extent[3])]\\n\",\n \" true_value = outside_intersection_distance(line, extent)\\n\",\n \" else:\\n\",\n \" line = [random.uniform(extent[0], extent[1]),\\n\",\n \" extent[2] - 1,\\n\",\n \" random.uniform(extent[0], extent[1]),\\n\",\n \" extent[3] + 1]\\n\",\n \" true_value = outside_intersection_distance(line, extent)\\n\",\n \"\\n\",\n \" d12 = raytrace(line, extent, image, debug)\\n\",\n \" difference = abs(d12 - true_value)\\n\",\n \" if difference > 1e-12:\\n\",\n \" # print(d12, distance(line), abs(d12 - distance(line)))\\n\",\n \" draw_line(line, 'r-')\\n\",\n \" else:\\n\",\n \" draw_line(line)\\n\",\n \" differences.append(difference)\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" plt.hist(differences)\\n\",\n \" print()\\n\",\n \" \\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"\\n\",\n \"def test_innoutside(seed=8675309, nruns=5000, npixels=50, debug=False):\\n\",\n \" import random\\n\",\n \"\\n\",\n \" def distance(line):\\n\",\n \" return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\\n\",\n \"\\n\",\n \" image = np.ones((npixels, npixels))\\n\",\n \" extent = [-5, 5, -5, 5]\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" draw_algorithm(extent, image)\\n\",\n \"\\n\",\n \" random.seed(seed)\\n\",\n \"\\n\",\n \" differences = []\\n\",\n \"\\n\",\n \" for i in range(nruns):\\n\",\n \" inside_pt = [random.uniform(extent[0], extent[1]),\\n\",\n \" random.uniform(extent[2], extent[3])]\\n\",\n \" side = random.choice(['left', 'right', 'top', 'bottom'])\\n\",\n \" if side == 'left':\\n\",\n \" outside_pt = [extent[0]-1, random.uniform(extent[2], extent[3])]\\n\",\n \" elif side == 'right':\\n\",\n \" outside_pt = [extent[1]+1, random.uniform(extent[2], extent[3])]\\n\",\n \" elif side == 'top':\\n\",\n \" outside_pt = [random.uniform(extent[0], extent[1]), extent[3]+1]\\n\",\n \" elif side == 'bottom':\\n\",\n \" outside_pt = [random.uniform(extent[2], extent[3]), extent[2]-1]\\n\",\n \"\\n\",\n \" if random.choice([True, False]):\\n\",\n \" line = [inside_pt[0], inside_pt[1], outside_pt[0], outside_pt[1]]\\n\",\n \" else:\\n\",\n \" line = [outside_pt[0], outside_pt[1], inside_pt[0], inside_pt[1]]\\n\",\n \"\\n\",\n \" def intersection(x1, x2, y1, y2, x3, x4, y3, y4):\\n\",\n \" denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)\\n\",\n \" if denom != 0:\\n\",\n \" t = (x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)\\n\",\n \" t /= denom\\n\",\n \" u = - ((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3))\\n\",\n \" u /= denom\\n\",\n \"\\n\",\n \" if 0 <= t <= 1 and 0 <= u <= 1:\\n\",\n \" return [x1 + t * (x2 - x1), y1 + t * (y2 - y1)]\\n\",\n \"\\n\",\n \" return None\\n\",\n \"\\n\",\n \" if side == 'left':\\n\",\n \" intersect = intersection(line[0], line[2], line[1], line[3],\\n\",\n \" extent[0], extent[0], extent[2], extent[3])\\n\",\n \" elif side == 'right':\\n\",\n \" intersect = intersection(line[0], line[2], line[1], line[3],\\n\",\n \" extent[1], extent[1], extent[2], extent[3])\\n\",\n \" elif side == 'top':\\n\",\n \" intersect = intersection(line[0], line[2], line[1], line[3],\\n\",\n \" extent[0], extent[1], extent[3], extent[3])\\n\",\n \" elif side == 'bottom':\\n\",\n \" intersect = intersection(line[0], line[2], line[1], line[3],\\n\",\n \" extent[0], extent[1], extent[2], extent[2])\\n\",\n \"\\n\",\n \" true_value = distance([inside_pt[0], inside_pt[1], intersect[0], intersect[1]])\\n\",\n \" d12 = raytrace(line, extent, image, debug)\\n\",\n \" difference = abs(d12 - true_value)\\n\",\n \" if difference > 1e-12:\\n\",\n \" # print(d12, true_value, difference)\\n\",\n \" draw_line(line, 'r-')\\n\",\n \" else:\\n\",\n \" draw_line(line)\\n\",\n \" differences.append(difference)\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" plt.hist(differences)\\n\",\n \" print()\\n\",\n \" \\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"\\n\",\n \"def test_inside(seed=8675309, nruns=5000, npixels=50, debug=False, display_pixels=False):\\n\",\n \" import random\\n\",\n \"\\n\",\n \" def distance(line):\\n\",\n \" return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\\n\",\n \"\\n\",\n \" if display_pixels:\\n\",\n \" image = np.zeros((npixels, npixels))\\n\",\n \" else:\\n\",\n \" image = np.ones((npixels, npixels))\\n\",\n \" extent = [-5, 5, -5, 5]\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" draw_algorithm(extent, image)\\n\",\n \"\\n\",\n \" random.seed(seed)\\n\",\n \"\\n\",\n \" differences = []\\n\",\n \"\\n\",\n \" for i in range(nruns):\\n\",\n \" line = [random.uniform(extent[0], extent[1]),\\n\",\n \" random.uniform(extent[2], extent[3]),\\n\",\n \" random.uniform(extent[0], extent[1]),\\n\",\n \" random.uniform(extent[2], extent[3])]\\n\",\n \"\\n\",\n \" d12 = raytrace(line, extent, image, debug, display_pixels)\\n\",\n \" true_value = distance(line)\\n\",\n \" difference = abs(d12 - true_value)\\n\",\n \" if difference > 1e-12:\\n\",\n \" draw_line(line, 'r-')\\n\",\n \" else:\\n\",\n \" draw_line(line)\\n\",\n \" differences.append(difference)\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" plt.hist(differences)\\n\",\n \" print()\\n\",\n \" \\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"\\n\",\n \"def test_special_cases(nruns=100, npixels=50, run_cython=False):\\n\",\n \" def distance(line):\\n\",\n \" return ((line[2] - line[0]) ** 2 + (line[3] - line[1]) ** 2) ** 0.5\\n\",\n \"\\n\",\n \" image = np.ones((npixels, npixels))\\n\",\n \" extent = [-5, 5, -4, 4]\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" draw_algorithm(extent, image)\\n\",\n \"\\n\",\n \" def test_line(line, extent, image, true_value):\\n\",\n \" if run_cython:\\n\",\n \" d12 = raytrace_fast(line, extent, image)\\n\",\n \" else:\\n\",\n \" d12 = raytrace(line, extent, image)\\n\",\n \" difference = abs(d12 - true_value)\\n\",\n \" if difference > 1e-12:\\n\",\n \" print(d12, true_value, difference)\\n\",\n \" draw_line(line, 'r-')\\n\",\n \" else:\\n\",\n \" draw_line(line)\\n\",\n \"\\n\",\n \" # outside lines\\n\",\n \" line = [-6.2, -7.1, -5.4, 7.4]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [-5.4, -7.1, -6.1, 7.4]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [-5.4, -7.1, 6.1, -7.4]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [-5.4, -7.4, 6.1, -7.1]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [6.2, -7.1, 5.4, 7.4]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [5.4, -7.1, 6.1, 7.4]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [-5.4, 7.1, 6.1, 7.4]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" line = [-5.4, 7.4, 6.1, 7.1]\\n\",\n \" test_line(line, extent, image, 0)\\n\",\n \"\\n\",\n \" # horizontal lines\\n\",\n \" line = [-5.4, 2.11, 6.1, 2.11]\\n\",\n \" test_line(line, extent, image, 10)\\n\",\n \"\\n\",\n \" line = [5.4, -2.11, -6.1, -2.11]\\n\",\n \" test_line(line, extent, image, 10)\\n\",\n \"\\n\",\n \" # vertical lines\\n\",\n \" line = [2.11, 6.1, 2.11, -5.4]\\n\",\n \" test_line(line, extent, image, 10)\\n\",\n \"\\n\",\n \" line = [-2.11, -6.1, -2.11, 5.4]\\n\",\n \" test_line(line, extent, image, 10)\\n\",\n \"\\n\",\n \" # horizontal line inside\\n\",\n \" line = [-4.4, 1.11, -4.4+3, 1.11]\\n\",\n \" test_line(line, extent, image, 3)\\n\",\n \"\\n\",\n \" # horizontal line inside / outside\\n\",\n \" line = [-6.4, 3.5, -5 + 3, 3.5]\\n\",\n \" test_line(line, extent, image, 3)\\n\",\n \"\\n\",\n \" # horizontal line outside / inside\\n\",\n \" line = [6.4, 3.5, 5-3, 3.5]\\n\",\n \" test_line(line, extent, image, 3)\\n\",\n \"\\n\",\n \" # vertical line inside\\n\",\n \" line = [1.11, -4.4, 1.11, -4.4+3]\\n\",\n \" test_line(line, extent, image, 3)\\n\",\n \"\\n\",\n \" # vertical line inside / outside\\n\",\n \" line = [3.5, -6.4, 3.5, -5 + 3]\\n\",\n \" test_line(line, extent, image, 3)\\n\",\n \"\\n\",\n \" # vertical line outside / inside\\n\",\n \" line = [3.5, 6.4, 3.5, 5-3]\\n\",\n \" test_line(line, extent, image, 3)\\n\",\n \"\\n\",\n \" # ray within a single pixel\\n\",\n \" line = [1.1, 1.1, 1.12, 1.12]\\n\",\n \" test_line(line, extent, image, distance(line))\\n\",\n \"\\n\",\n \" # ray crosses the origin only\\n\",\n \" line = [0.1, 0.1, -0.1, -0.1]\\n\",\n \" test_line(line, extent, image, distance(line))\\n\",\n \" \\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"\\n\",\n \"def test_object(nrays=100):\\n\",\n \" import pytracer.transmission as transmission\\n\",\n \" import pytracer.geometry as geo\\n\",\n \" from scripts.assemblies import shielded_assembly\\n\",\n \"\\n\",\n \" xs = np.linspace(-11, 11, 200)\\n\",\n \" ys = np.linspace(-6, 6, 200)\\n\",\n \"\\n\",\n \" assembly = shielded_assembly()\\n\",\n \" assembly_flat = geo.flatten(assembly)\\n\",\n \"\\n\",\n \" # truth\\n\",\n \" mu_image, extent = transmission.absorbance_image(xs, ys, assembly_flat)\\n\",\n \" mu_image = mu_image.T\\n\",\n \"\\n\",\n \" draw_algorithm(extent, mu_image, draw_lines=False)\\n\",\n \"\\n\",\n \" radians = np.array([0.]) #np.linspace(0, np.pi, 100)\\n\",\n \" arc_radians = np.linspace(-np.pi / 8, np.pi / 8, nrays)\\n\",\n \" start, end = geo.fan_beam_paths(60, arc_radians, radians, extent=False)\\n\",\n \" for (s, e) in zip(start[:, 0], end[:, 0]):\\n\",\n \" plt.plot([s[0], e[0]], [s[1], e[1]], color='blue', ls='-', lw=0.2)\\n\",\n \"\\n\",\n \" d12_values = np.zeros(nrays)\\n\",\n \" d12_index = 0\\n\",\n \"\\n\",\n \" for (s, e) in zip(start[:, 0], end[:, 0]):\\n\",\n \" line = [s[0], s[1], e[0], e[1]]\\n\",\n \"\\n\",\n \" d12_values[d12_index] = raytrace(line, extent, mu_image)\\n\",\n \" d12_index += 1\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" plt.plot(arc_radians, d12_values)\\n\",\n \" \\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def test_speedup(npixels=50, nruns=100, seed=8675309, run_cython=True):\\n\",\n \" import cProfile\\n\",\n \" import random\\n\",\n \"\\n\",\n \" random.seed(seed)\\n\",\n \"\\n\",\n \" image = np.ones((npixels, npixels), dtype=np.double)\\n\",\n \" extent = [-5, 5, -5, 5]\\n\",\n \"\\n\",\n \" lines = np.zeros((nruns, 4), dtype=np.double)\\n\",\n \"\\n\",\n \" for i in range(nruns):\\n\",\n \" lines[i] = [random.uniform(extent[0], extent[1]),\\n\",\n \" random.uniform(extent[2], extent[3]),\\n\",\n \" random.uniform(extent[0], extent[1]),\\n\",\n \" random.uniform(extent[2], extent[3])]\\n\",\n \"\\n\",\n \" integral_python = 0\\n\",\n \" integral_cython = 0\\n\",\n \"\\n\",\n \" def test_python_raytrace(lines, extent, image):\\n\",\n \" integral = 0\\n\",\n \" for line in lines:\\n\",\n \" integral += raytrace(line, extent, image)\\n\",\n \" print(integral)\\n\",\n \" print()\\n\",\n \"\\n\",\n \" def test_cython_raytrace(lines, extent, image):\\n\",\n \" integral = 0.\\n\",\n \"# for line in lines:\\n\",\n \"# integral += raytrace_fast(line, extent, image)\\n\",\n \" integral = np.sum(raytrace_bulk_fast(lines, extent, image))\\n\",\n \" print(integral)\\n\",\n \" print()\\n\",\n \"\\n\",\n \" g = globals()\\n\",\n \" l = locals()\\n\",\n \" if run_cython:\\n\",\n \" cProfile.runctx('test_cython_raytrace(lines, extent, image)', globals=g, locals=l, sort='time')\\n\",\n \" else:\\n\",\n \" cProfile.runctx('test_python_raytrace(lines, extent, image)', globals=g, locals=l, sort='time')\\n\",\n \" # do cython and compare stats, also compare answers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"7.999999999999984 10 2.000000000000016\\n\",\n \"7.999999999999984 10 2.000000000000016\\n\",\n \"2.6000000000000005 3 0.39999999999999947\\n\",\n \"2.0 3 1.0\\n\",\n \"2.0 3 1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"# test_inside(nruns=1, debug=True, display_pixels=True)\\n\",\n \"# test_outside(nruns=100)\\n\",\n \"# test_innoutside(nruns=100)\\n\",\n \"test_special_cases()\\n\",\n \"# test_object()\\n\",\n \"\\n\",\n \"# test_speedup(nruns=10000, run_cython=True)\\n\",\n \"# test_speedup(nruns=10000, run_cython=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Edit Metadata\",\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":230,"cells":{"repo_name":{"kind":"string","value":"mne-tools/mne-tools.github.io"},"path":{"kind":"string","value":"0.18/_downloads/c92aa91c680730c756234cdbc466c558/plot_introduction.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"24435"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\nOverview of MEG/EEG analysis with MNE-Python\\n============================================\\n\\nThis tutorial covers the basic EEG/MEG pipeline for event-related analysis:\\nloading data, epoching, averaging, plotting, and estimating cortical activity\\nfrom sensor data. It introduces the core MNE-Python data structures\\n:class:`~mne.io.Raw`, :class:`~mne.Epochs`, :class:`~mne.Evoked`, and\\n:class:`~mne.SourceEstimate`, and covers a lot of ground fairly quickly (at the\\nexpense of depth). Subsequent tutorials address each of these topics in greater\\ndetail.\\n :depth: 1\\n\\nWe begin by importing the necessary Python modules:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\nimport numpy as np\\nimport mne\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Loading data\\n^^^^^^^^^^^^\\n\\nMNE-Python data structures are based around the FIF file format from\\nNeuromag, but there are reader functions for `a wide variety of other\\ndata formats `. MNE-Python also has interfaces to a\\nvariety of :doc:`publicly available datasets <../../manual/datasets_index>`,\\nwhich MNE-Python can download and manage for you.\\n\\nWe'll start this tutorial by loading one of the example datasets (called\\n\\\"`sample-dataset`\\\"), which contains EEG and MEG data from one subject\\nperforming an audiovisual experiment, along with structural MRI scans for\\nthat subject. The :func:`mne.datasets.sample.data_path` function will\\nautomatically download the dataset if it isn't found in one of the expected\\nlocations, then return the directory path to the dataset (see the\\ndocumentation of :func:`~mne.datasets.sample.data_path` for a list of places\\nit checks before downloading). Note also that for this tutorial to run\\nsmoothly on our servers, we're using a filtered and downsampled version of\\nthe data (:file:`sample_audvis_filt-0-40_raw.fif`), but an unfiltered version\\n(:file:`sample_audvis_raw.fif`) is also included in the sample dataset and\\ncould be substituted here when running the tutorial locally.\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sample_data_folder = mne.datasets.sample.data_path()\\nsample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',\\n 'sample_audvis_filt-0-40_raw.fif')\\nraw = mne.io.read_raw_fif(sample_data_raw_file)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default, :func:`~mne.io.read_raw_fif` displays some information about the\\nfile it's loading; for example, here it tells us that there are four\\n\\\"projection items\\\" in the file along with the recorded data; those are\\n:term:`SSP projectors ` calculated to remove environmental noise\\nfrom the MEG signals, plus a projector to mean-reference the EEG channels;\\nthese are discussed\\nin a later tutorial. In addition to the information displayed during loading,\\nyou can get a glimpse of the basic details of a :class:`~mne.io.Raw` object\\nby printing it; even more is available by printing its ``info`` attribute\\n(a :class:`dictionary-like object ` that is preserved across\\n:class:`~mne.io.Raw`, :class:`~mne.Epochs`, and :class:`~mne.Evoked`\\nobjects). The ``info`` data structure keeps track of channel locations,\\napplied filters, projectors, etc. Notice especially the ``chs`` entry,\\nshowing that MNE-Python detects different sensor types and handles each\\nappropriately.\\n\\n.. TODO edit prev. paragraph when projectors tutorial is added: ...those are\\n discussed in the tutorial `projectors-tutorial`. (or whatever link)\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"print(raw)\\nprint(raw.info)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \":class:`~mne.io.Raw` objects also have several built-in plotting methods;\\nhere we show the power spectral density (PSD) for each sensor type with\\n:meth:`~mne.io.Raw.plot_psd`, as well as a plot of the raw sensor traces with\\n:meth:`~mne.io.Raw.plot`. In the PSD plot, we'll only plot frequencies below\\n50 Hz (since our data are low-pass filtered at 40 Hz). In interactive Python\\nsessions, :meth:`~mne.io.Raw.plot` is interactive and allows scrolling,\\nscaling, bad channel marking, annotation, projector toggling, etc.\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"raw.plot_psd(fmax=50)\\nraw.plot(duration=5, n_channels=30)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Preprocessing\\n^^^^^^^^^^^^^\\n\\nMNE-Python supports a variety of preprocessing approaches and techniques\\n(maxwell filtering, signal-space projection, independent components analysis,\\nfiltering, downsampling, etc); see the full list of capabilities in the\\n:mod:`mne.preprocessing` and :mod:`mne.filter` submodules. Here we'll clean\\nup our data by performing independent components analysis\\n(:class:`~mne.preprocessing.ICA`); for brevity we'll skip the steps that\\nhelped us determined which components best capture the artifacts (see\\n:doc:`../preprocessing/plot_artifacts_correction_ica` for a detailed\\nwalk-through of that process).\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# set up and fit the ICA\\nica = mne.preprocessing.ICA(n_components=20, random_state=97, max_iter=800)\\nica.fit(raw)\\nica.exclude = [1, 2] # details on how we picked these are omitted here\\nica.plot_properties(raw, picks=ica.exclude)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once we're confident about which component(s) we want to remove, we pass them\\nas the ``exclude`` parameter and then apply the ICA to the raw signal. The\\n:meth:`~mne.preprocessing.ICA.apply` method requires the raw data to be\\nloaded into memory (by default it's only read from disk as-needed), so we'll\\nuse :meth:`~mne.io.Raw.load_data` first. We'll also make a copy of the\\n:class:`~mne.io.Raw` object so we can compare the signal before and after\\nartifact removal side-by-side:\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"orig_raw = raw.copy()\\nraw.load_data()\\nica.apply(raw)\\n\\n# show some frontal channels to clearly illustrate the artifact removal\\nchs = ['MEG 0111', 'MEG 0121', 'MEG 0131', 'MEG 0211', 'MEG 0221', 'MEG 0231',\\n 'MEG 0311', 'MEG 0321', 'MEG 0331', 'MEG 1511', 'MEG 1521', 'MEG 1531',\\n 'EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006',\\n 'EEG 007', 'EEG 008']\\nchan_idxs = [raw.ch_names.index(ch) for ch in chs]\\norig_raw.plot(order=chan_idxs, start=12, duration=4)\\nraw.plot(order=chan_idxs, start=12, duration=4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Detecting experimental events\\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n\\nThe sample dataset includes several :term:`\\\"STIM\\\" channels `\\nthat recorded electrical\\nsignals sent from the stimulus delivery computer (as brief DC shifts /\\nsquarewave pulses). These pulses (often called \\\"triggers\\\") are used in this\\ndataset to mark experimental events: stimulus onset, stimulus type, and\\nparticipant response (button press). The individual STIM channels are\\ncombined onto a single channel, in such a way that voltage\\nlevels on that channel can be unambiguously decoded as a particular event\\ntype. On older Neuromag systems (such as that used to record the sample data)\\nthis summation channel was called ``STI 014``, so we can pass that channel\\nname to the :func:`mne.find_events` function to recover the timing and\\nidentity of the stimulus events.\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"events = mne.find_events(raw, stim_channel='STI 014')\\nprint(events[:5]) # show the first 5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting events array is an ordinary 3-column :class:`NumPy array\\n`, with sample number in the first column and integer event ID\\nin the last column; the middle column is usually ignored. Rather than keeping\\ntrack of integer event IDs, we can provide an *event dictionary* that maps\\nthe integer IDs to experimental conditions or events. In this dataset, the\\nmapping looks like this:\\n\\n\\n+----------+----------------------------------------------------------+\\n| Event ID | Condition |\\n+==========+==========================================================+\\n| 1 | auditory stimulus (tone) to the left ear |\\n+----------+----------------------------------------------------------+\\n| 2 | auditory stimulus (tone) to the right ear |\\n+----------+----------------------------------------------------------+\\n| 3 | visual stimulus (checkerboard) to the left visual field |\\n+----------+----------------------------------------------------------+\\n| 4 | visual stimulus (checkerboard) to the right visual field |\\n+----------+----------------------------------------------------------+\\n| 5 | smiley face (catch trial) |\\n+----------+----------------------------------------------------------+\\n| 32 | subject button press |\\n+----------+----------------------------------------------------------+\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,\\n 'visual/right': 4, 'smiley': 5, 'buttonpress': 32}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Event dictionaries like this one are used when extracting epochs from\\ncontinuous data; the ``/`` character in the dictionary keys allows pooling\\nacross conditions by requesting partial condition descriptors (i.e.,\\nrequesting ``'auditory'`` will select all epochs with Event IDs 1 and 2;\\nrequesting ``'left'`` will select all epochs with Event IDs 1 and 3). An\\nexample of this is shown in the next section. There is also a convenient\\n:func:`~mne.viz.plot_events` function for visualizing the distribution of\\nevents across the duration of the recording (to make sure event detection\\nworked as expected). Here we'll also make use of the :class:`~mne.Info`\\nattribute to get the sampling frequency of the recording (so our x-axis will\\nbe in seconds instead of in samples).\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig = mne.viz.plot_events(events, event_id=event_dict, sfreq=raw.info['sfreq'])\\nfig.subplots_adjust(right=0.7) # make room for the legend\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For paradigms that are not event-related (e.g., analysis of resting-state\\ndata), you can extract regularly spaced (possibly overlapping) spans of data\\nby creating events using :func:`mne.make_fixed_length_events` and then\\nproceeding with epoching as described in the next section.\\n\\n\\nEpoching continuous data\\n^^^^^^^^^^^^^^^^^^^^^^^^\\n\\nThe :class:`~mne.io.Raw` object and the events array are the bare minimum\\nneeded to create an :class:`~mne.Epochs` object, which we create with the\\n:class:`mne.Epochs` class constructor. Here we'll also specify some data\\nquality constraints: we'll reject any epoch where peak-to-peak signal\\namplitude is beyond reasonable limits for that channel type. This is done\\nwith a *rejection dictionary*; you may include or omit thresholds for any of\\nthe channel types present in your data. The values given here are reasonable\\nfor this particular dataset, but may need to be adapted for different\\nhardware or recording conditions. For a more automated approach, consider\\nusing the `autoreject package`_.\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"reject_criteria = dict(mag=4000e-15, # 4000 fT\\n grad=4000e-13, # 4000 fT/cm\\n eeg=150e-6, # 150 \\u03bcV\\n eog=250e-6) # 250 \\u03bcV\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll also pass the event dictionary as the ``event_id`` parameter (so we can\\nwork with easy-to-pool event labels instead of the integer event IDs), and\\nspecify ``tmin`` and ``tmax`` (the time relative to each event at which to\\nstart and end each epoch). As mentioned above, by default\\n:class:`~mne.io.Raw` and :class:`~mne.Epochs` data aren't loaded into memory\\n(they're accessed from disk only when needed), but here we'll force loading\\ninto memory using the ``preload=True`` parameter so that we can see the\\nresults of the rejection criteria being applied:\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"epochs = mne.Epochs(raw, events, event_id=event_dict, tmin=-0.2, tmax=0.5,\\n reject=reject_criteria, preload=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next we'll pool across left/right stimulus presentations so we can compare\\nauditory versus visual responses. To avoid biasing our signals to the\\nleft or right, we'll use :meth:`~mne.Epochs.equalize_event_counts` first to\\nrandomly sample epochs from each condition to match the number of epochs\\npresent in the condition with the fewest good epochs.\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"conds_we_care_about = ['auditory/left', 'auditory/right',\\n 'visual/left', 'visual/right']\\nepochs.equalize_event_counts(conds_we_care_about) # this operates in-place\\naud_epochs = epochs['auditory']\\nvis_epochs = epochs['visual']\\ndel raw, epochs # free up memory\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like :class:`~mne.io.Raw` objects, :class:`~mne.Epochs` objects also have a\\nnumber of built-in plotting methods. One is :meth:`~mne.Epochs.plot_image`,\\nwhich shows each epoch as one row of an image map, with color representing\\nsignal magnitude; the average evoked response and the sensor location are\\nshown below the image:\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"aud_epochs.plot_image(picks=['MEG 1332', 'EEG 021'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"

Note

Both :class:`~mne.io.Raw` and :class:`~mne.Epochs` objects have\\n :meth:`~mne.Epochs.get_data` methods that return the underlying data\\n as a :class:`NumPy array `. Both methods have a ``picks``\\n parameter for subselecting which channel(s) to return; ``raw.get_data()``\\n has additional parameters for restricting the time domain. The resulting\\n matrices have dimension ``(n_channels, n_times)`` for\\n :class:`~mne.io.Raw` and ``(n_epochs, n_channels, n_times)`` for\\n :class:`~mne.Epochs`.

\\n\\n\\nTime-frequency analysis\\n^^^^^^^^^^^^^^^^^^^^^^^\\n\\nThe :mod:`mne.time_frequency` submodule provides implementations of several\\nalgorithms to compute time-frequency representations, power spectral density,\\nand cross-spectral density. Here, for example, we'll compute for the auditory\\nepochs the induced power at different frequencies and times, using Morlet\\nwavelets. On this dataset the result is not especially informative (it just\\nshows the evoked \\\"auditory N100\\\" response); see `here\\n` for a more extended example on a dataset with richer\\nfrequency content.\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"frequencies = np.arange(7, 30, 3)\\npower = mne.time_frequency.tfr_morlet(aud_epochs, n_cycles=2, return_itc=False,\\n freqs=frequencies, decim=3)\\npower.plot(['MEG 1332'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Estimating evoked responses\\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n\\nNow that we have our conditions in ``aud_epochs`` and ``vis_epochs``, we can\\nget an estimate of evoked responses to auditory versus visual stimuli by\\naveraging together the epochs in each condition. This is as simple as calling\\nthe :meth:`~mne.Epochs.average` method on the :class:`~mne.Epochs` object,\\nand then using a function from the :mod:`mne.viz` module to compare the\\nglobal field power for each sensor type of the two :class:`~mne.Evoked`\\nobjects:\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"aud_evoked = aud_epochs.average()\\nvis_evoked = vis_epochs.average()\\n\\nmne.viz.plot_compare_evokeds(dict(auditory=aud_evoked, visual=vis_evoked),\\n show_legend='upper left',\\n show_sensors='upper right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also get a more detailed view of each :class:`~mne.Evoked` object\\nusing other plotting methods such as :meth:`~mne.Evoked.plot_joint` or\\n:meth:`~mne.Evoked.plot_topomap`. Here we'll examine just the EEG channels,\\nand see the classic auditory evoked N100-P200 pattern over dorso-frontal\\nelectrodes, then plot scalp topographies at some additional arbitrary times:\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# sphinx_gallery_thumbnail_number = 13\\naud_evoked.plot_joint(picks='eeg')\\naud_evoked.plot_topomap(times=[0., 0.08, 0.1, 0.12, 0.2], ch_type='eeg')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Evoked objects can also be combined to show contrasts between conditions,\\nusing the :func:`mne.combine_evoked` function. A simple difference can be\\ngenerated by negating one of the :class:`~mne.Evoked` objects passed into the\\nfunction. We'll then plot the difference wave at each sensor using\\n:meth:`~mne.Evoked.plot_topo`:\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"evoked_diff = mne.combine_evoked([aud_evoked, -vis_evoked], weights='equal')\\nevoked_diff.pick_types('mag').plot_topo(color='r', legend=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Inverse modeling\\n^^^^^^^^^^^^^^^^\\n\\nFinally, we can estimate the origins of the evoked activity by projecting the\\nsensor data into this subject's :term:`source space` (a set of points either\\non the cortical surface or within the cortical volume of that subject, as\\nestimated by structural MRI scans). MNE-Python supports lots of ways of doing\\nthis (dynamic statistical parametric mapping, dipole fitting, beamformers,\\netc.); here we'll use minimum-norm estimation (MNE) to generate a continuous\\nmap of activation constrained to the cortical surface. MNE uses a linear\\n:term:`inverse operator` to project EEG+MEG sensor measurements into the\\nsource space. The inverse operator is computed from the\\n:term:`forward solution` for this subject and an estimate of `the\\ncovariance of sensor measurements `. For this\\ntutorial we'll skip those computational steps and load a pre-computed inverse\\noperator from disk (it's included with the `sample data\\n`). Because this \\\"inverse problem\\\" is underdetermined (there\\nis no unique solution), here we further constrain the solution by providing a\\nregularization parameter specifying the relative smoothness of the current\\nestimates in terms of a signal-to-noise ratio (where \\\"noise\\\" here is akin to\\nbaseline activity level across all of cortex).\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# load inverse operator\\ninverse_operator_file = os.path.join(sample_data_folder, 'MEG', 'sample',\\n 'sample_audvis-meg-oct-6-meg-inv.fif')\\ninv_operator = mne.minimum_norm.read_inverse_operator(inverse_operator_file)\\n# set signal-to-noise ratio (SNR) to compute regularization parameter (\\u03bb\\u00b2)\\nsnr = 3.\\nlambda2 = 1. / snr ** 2\\n# generate the source time course (STC)\\nstc = mne.minimum_norm.apply_inverse(vis_evoked, inv_operator,\\n lambda2=lambda2,\\n method='MNE') # or dSPM, sLORETA, eLORETA\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, in order to plot the source estimate on the subject's cortical\\nsurface we'll also need the path to the sample subject's structural MRI files\\n(the ``subjects_dir``):\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# path to subjects' MRI files\\nsubjects_dir = os.path.join(sample_data_folder, 'subjects')\\n# plot\\nstc.plot(initial_time=0.1, hemi='split', views=['lat', 'med'],\\n subjects_dir=subjects_dir)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The remaining tutorials have *much more detail* on each of these topics (as\\nwell as many other capabilities of MNE-Python not mentioned here:\\nconnectivity analysis, encoding/decoding models, lots more visualization\\noptions, etc). Read on to learn more!\\n\\n\\n.. LINKS\\n\\n\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":231,"cells":{"repo_name":{"kind":"string","value":"ProfessorKazarinoff/staticsite"},"path":{"kind":"string","value":"content/code/webscrape/webscrape_html_table_with_pandas.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"7004"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import sys\\n\",\n \"import urllib3,certifi\\n\",\n \"import requests\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"system: win32\\n\",\n \"python version: 3.6.3 |Anaconda, Inc.| (default, Oct 27 2017, 12:22:41) [MSC v.1900 64 bit (AMD64)]\\n\",\n \"pandas version: 0.20.3\\n\",\n \"urllib3 version: 1.22\\n\",\n \"certifi version: 2017.07.27.1\\n\",\n \"requests version: 2.18.4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('system: ', sys.platform)\\n\",\n \"print('python version: ', sys.version)\\n\",\n \"print('pandas version: ', pd.__version__)\\n\",\n \"print('urllib3 version: ', urllib3.__version__)\\n\",\n \"print('certifi version: ', certifi.__version__)\\n\",\n \"print('requests version: ', requests.__version__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#Force certificate check and use certifi to handle the certificate. \\n\",\n \"https = urllib3.PoolManager( cert_reqs='CERT_REQUIRED', ca_certs=certifi.where(),) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"page = 'https://www.pcc.edu/schedule/default.cfm?fa=dspCourse2&thisTerm=201801&crsCode=ENGR&subjCode=ENGR&crsNum=114&topicCode=GE&subtopicCode=%20'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" #x = requests.get(url=url, certs= certs).content\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"url = https.urlopen('GET', page)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"No text parsed from document: \",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mdf_list\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mpd\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mread_html\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 2\\u001b[0m \\u001b[0mdf_list\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32m~\\\\AppData\\\\Local\\\\Continuum\\\\Anaconda3\\\\envs\\\\webscrape\\\\lib\\\\site-packages\\\\pandas\\\\io\\\\html.py\\u001b[0m in \\u001b[0;36mread_html\\u001b[1;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, tupleize_cols, thousands, encoding, decimal, converters, na_values, keep_default_na)\\u001b[0m\\n\\u001b[0;32m 904\\u001b[0m \\u001b[0mthousands\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mthousands\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mattrs\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mattrs\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mencoding\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mencoding\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 905\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mdecimal\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mconverters\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mconverters\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mna_values\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mna_values\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 906\\u001b[1;33m keep_default_na=keep_default_na)\\n\\u001b[0m\",\n \"\\u001b[1;32m~\\\\AppData\\\\Local\\\\Continuum\\\\Anaconda3\\\\envs\\\\webscrape\\\\lib\\\\site-packages\\\\pandas\\\\io\\\\html.py\\u001b[0m in \\u001b[0;36m_parse\\u001b[1;34m(flavor, io, match, attrs, encoding, **kwargs)\\u001b[0m\\n\\u001b[0;32m 741\\u001b[0m \\u001b[1;32mbreak\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 742\\u001b[0m \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 743\\u001b[1;33m \\u001b[0mraise_with_traceback\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mretained\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 744\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 745\\u001b[0m \\u001b[0mret\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;33m[\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32m~\\\\AppData\\\\Local\\\\Continuum\\\\Anaconda3\\\\envs\\\\webscrape\\\\lib\\\\site-packages\\\\pandas\\\\compat\\\\__init__.py\\u001b[0m in \\u001b[0;36mraise_with_traceback\\u001b[1;34m(exc, traceback)\\u001b[0m\\n\\u001b[0;32m 342\\u001b[0m \\u001b[1;32mif\\u001b[0m \\u001b[0mtraceback\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[0mEllipsis\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 343\\u001b[0m \\u001b[0m_\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0m_\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtraceback\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0msys\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mexc_info\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 344\\u001b[1;33m \\u001b[1;32mraise\\u001b[0m \\u001b[0mexc\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mwith_traceback\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtraceback\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 345\\u001b[0m \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 346\\u001b[0m \\u001b[1;31m# this version of raise is a syntax error in Python 3\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: No text parsed from document: \"\n ]\n }\n ],\n \"source\": [\n \"df_list = pd.read_html(url)\\n\",\n \"df_list\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":232,"cells":{"repo_name":{"kind":"string","value":"VandyAstroML/Vanderbilt_Computational_Bootcamp"},"path":{"kind":"string","value":"notebooks/Week_05/05_Numpy_Matplotlib.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"92420"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Week 5 - Numpy & Matplotlib\\n\",\n \"\\n\",\n \"## Today's Agenda\\n\",\n \"* Numpy\\n\",\n \"* Matplotlib\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## [Numpy](http://www.numpy.org/) - Numerical Python\\n\",\n \"\\n\",\n \"From their website ([http://www.numpy.org/](http://www.numpy.org/)):\\n\",\n \"\\n\",\n \"> NumPy is the fundamental package for scientific computing with Python. \\n\",\n \"> * a powerful N-dimensional array object\\n\",\n \"> * sophisticated (broadcasting) functions\\n\",\n \"> * tools for integrating C/C++ and Fortran code\\n\",\n \"> * useful linear algebra, Fourier transform, and random number capabilities\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"You can import __\\\"`numpy`\\\"__ as\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Numpy arrays\\n\",\n \"\\n\",\n \"In standard Python, data is stored as lists, and multidimensional data as **lists of lists**. In `numpy`, however, we can now work with arrays. To get these arrays, we can use `np.asarray` to convert a list into an array. Below we take a quick look at how a list behaves differently from an array.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1 2 3 4 5 6 7 8 9 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# We first create an array `x`\\n\",\n \"start = 1\\n\",\n \"stop = 11\\n\",\n \"step = 1\\n\",\n \"\\n\",\n \"x = np.arange(start, stop, step)\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also manipulate the array. For example, we can:\\n\",\n \"\\n\",\n \"- Multiply by two:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x * 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Take the square of all the values in the array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x ** 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Or even do some math on it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 6.33333333, 14.66666667, 25. , 37.33333333,\\n\",\n \" 51.66666667, 68. , 86.33333333, 106.66666667,\\n\",\n \" 129. , 153.33333333])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"(x**2) + (5*x) + (x / 3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we want to set up an array in numpy, we can use range to make a list and then convert it to an array, but we can also just create an array directly in numpy. `np.arange` will do this with integers, and `np.linspace` will do this with floats, and allows for non-integer steps.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 1 2 3 4 5 6 7 8 9]\\n\",\n \"[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.arange(10))\\n\",\n \"\\n\",\n \"print(np.linspace(1,10,10))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Last week we had to use a function or a loop to carry out math on a list. However with numpy we can do this a lot simpler by making sure we're working with an array, and carrying out the mathematical operations on that array.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 1 2 3 4 5 6 7 8 9]\\n\",\n \"[ 0 1 4 9 16 25 36 49 64 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x=np.arange(10)\\n\",\n \"print(x)\\n\",\n \"\\n\",\n \"print(x**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In numpy, we also have more options for quickly (and without much code) examining the contents of an array. One of the most helpful tools for this is `np.where`. `np.where` uses a conditional statement on the array and returns an array that contains indices of all the values that were true for the conditional statement. We can then call the original array and use the new array to get all the **values** that were true for the conditional statement.\\n\",\n \"\\n\",\n \"There are also functions like `max` and `min` that will give the maximum and minimum, respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5\\n\",\n \" 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. 10.5 11. 11.5\\n\",\n \" 12. 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5 17. 17.5\\n\",\n \" 18. 18.5 19. 19.5 20. 20.5 21. 21.5 22. 22.5 23. 23.5\\n\",\n \" 24. 24.5 25. 25.5 26. 26.5 27. 27.5 28. 28.5 29. 29.5\\n\",\n \" 30. 30.5 31. 31.5 32. 32.5 33. 33.5 34. 34.5 35. 35.5\\n\",\n \" 36. 36.5 37. 37.5 38. 38.5 39. 39.5 40. 40.5 41. 41.5\\n\",\n \" 42. 42.5 43. 43.5 44. 44.5 45. 45.5 46. 46.5 47. 47.5\\n\",\n \" 48. 48.5 49. 49.5 50. 50.5 51. 51.5 52. 52.5 53. 53.5\\n\",\n \" 54. 54.5 55. 55.5 56. 56.5 57. 57.5 58. 58.5 59. 59.5\\n\",\n \" 60. 60.5 61. 61.5 62. 62.5 63. 63.5 64. 64.5 65. 65.5\\n\",\n \" 66. 66.5 67. 67.5 68. 68.5 69. 69.5 70. 70.5 71. 71.5\\n\",\n \" 72. 72.5 73. 73.5 74. 74.5 75. 75.5 76. 76.5 77. 77.5\\n\",\n \" 78. 78.5 79. 79.5 80. 80.5 81. 81.5 82. 82.5 83. 83.5\\n\",\n \" 84. 84.5 85. 85.5 86. 86.5 87. 87.5 88. 88.5 89. 89.5\\n\",\n \" 90. 90.5 91. 91.5 92. 92.5 93. 93.5 94. 94.5 95. 95.5\\n\",\n \" 96. 96.5 97. 97.5 98. 98.5 99. 99.5 100. ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Defining starting and ending values of the array, as well as the number of elements in the array.\\n\",\n \"start = 0\\n\",\n \"stop = 100\\n\",\n \"n_elements = 201\\n\",\n \"\\n\",\n \"x = np.linspace(start, stop, n_elements)\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And we can select only those values that are divisible by *5*:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120,\\n\",\n \" 130, 140, 150, 160, 170, 180, 190, 200]),)\\n\",\n \"[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55. 60. 65.\\n\",\n \" 70. 75. 80. 85. 90. 95. 100.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This function returns the indices that match the criteria of `x % 5 == 0`:\\n\",\n \"x_5 = np.where(x%5 == 0)\\n\",\n \"\\n\",\n \"print(x_5)\\n\",\n \"\\n\",\n \"# And one can use those indices to *only* select those values:\\n\",\n \"print(x[x_5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or similarly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.,\\n\",\n \" 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[x%5 == 0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And you can find the `max` and ``min`` values of the array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The minimum of `x` is `0.0`\\n\",\n \"The maximum of `x` is `100.0`\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('The minimum of `x` is `{0}`'.format(x.min()))\\n\",\n \"\\n\",\n \"print('The maximum of `x` is `{0}`'.format(x.max()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Numpy also provides some tools for loading and saving data, loadtxt and savetxt. Here I'm using a function called transpose so that instead of each array being a row, they each get treated as a column.\\n\",\n \"\\n\",\n \"When we load the information again, it's now a 2D array. We can select parts of those arrays just as we could for 1D arrays.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2D-array from file `myfile.txt`:\\n\",\n \"\\n\",\n \" [[ 0.000000e+00 3.000000e+00]\\n\",\n \" [ 2.000000e-01 2.000400e+00]\\n\",\n \" [ 4.000000e-01 1.001600e+00]\\n\",\n \" ...\\n\",\n \" [ 9.960000e+01 -3.957984e+02]\\n\",\n \" [ 9.980000e+01 -3.963996e+02]\\n\",\n \" [ 1.000000e+02 -3.970000e+02]] \\n\",\n \"\\n\",\n \"Selecting certain elements from `data`:\\n\",\n \"\\n\",\n \" [[0. 3. ]\\n\",\n \" [0.2 2.0004]\\n\",\n \" [0.4 1.0016]] \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = 0\\n\",\n \"stop = 100\\n\",\n \"n_elem = 501\\n\",\n \"\\n\",\n \"x = np.linspace(start, stop, n_elem)\\n\",\n \"\\n\",\n \"# We can now create another array from `x`:\\n\",\n \"y = (.1*x)**2 - (5*x) + 3\\n\",\n \"\\n\",\n \"# And finally, we can dump `x` and `y` to a file:\\n\",\n \"np.savetxt('myfile.txt', np.transpose([x,y]))\\n\",\n \"\\n\",\n \"# We can also load the data from `myfile.txt` and display it:\\n\",\n \"data = np.loadtxt('myfile.txt')\\n\",\n \"print('2D-array from file `myfile.txt`:\\\\n\\\\n', data, '\\\\n')\\n\",\n \"\\n\",\n \"# You can also select certain elements of the 2D-array\\n\",\n \"print('Selecting certain elements from `data`:\\\\n\\\\n', data[:3,:], '\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Resources\\n\",\n \"* Scientific Lectures on Python - __Numpy__: [iPython Notebook](https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-2-Numpy.ipynb)\\n\",\n \"* Data Science iPython Notebooks - __Numpy__: [iPython Notebook](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## [Matplotlib](http://matplotlib.org/)\\n\",\n \"\\n\",\n \"Matplotlib is a Python 2D plotting library which\\n\",\n \"* produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms\\n\",\n \"* Quick way to visualize data from Python\\n\",\n \"* Main plotting utility in Python\\n\",\n \"\\n\",\n \"From their website ([http://matplotlib.org/](http://matplotlib.org/)):\\n\",\n \"\\n\",\n \"> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\\n\",\n \"\\n\",\n \"A great starting point to figuring out how to make a particular figure is to start from the [Matplotlib gallery](http://matplotlib.org/gallery.html) and look for what you want to make.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"## Importing modules\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# Importing LaTeX\\n\",\n \"from matplotlib import rc\\n\",\n \"rc('text', usetex=True)\\n\",\n \"\\n\",\n \"# Importing matplotlib and other modules\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now load in the data from `myfile.txt`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = np.loadtxt('myfile.txt')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The simplest figure is to simply make a plot. We can have multiple figures, but for now, just one. The plt.plot function will connect the points, but if we want a scatter plot, then plt.scatter will work.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(1, figsize=(8,8))\\n\",\n \"plt.plot(data[:,0],data[:,1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also pass the `*data.T` value instead:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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zKGxMmhMlg55wTllWdfHLsiiSp5xnMyhtlZTBpUug5n3JK6DkffHDsqiSpZxnMyitlZTB5clg+dcghIZzHjIldlST1HCd/Ke+Ul8MNN8Bmm8Gee4ZJYZJUKgxm5aXvfAduuw1+9KMwa/tPf4pdkST1DINZeWvppUNveeWVYfvt4emnY1ckSblnMCuvLbdc2Fd78cXD1p0vvhi7IknKLYNZee+73w3h/MknsNVW8MYbsSuSpNwxmFUQfvjDcOjFm2+GE6k89EJSsTKYVTB+9jOYMSMcerHddvDBB7ErkqTuZzCroAwbBjfeCGkKo0aF4W1JKiYGswrOqFFw1VXwwAOw667w+eexK5Kk7mMwqyDtsQdcfHEY2v75zz2RSlLxcEtOFazx4+Hdd+H442GZZeCiizyRSlLhM5hV0I49Flpb4Ve/gv794YwzYlckSdkxmFXQyspg4sQQzmeeGXrOv/xl7KokadEZzCp4ZWVw2WXw3nvhTOdlloFx42JXJUmLxmBWUejdGxoawtrmAw4I+2zvumvsqiSp65yVraKx2GIwbRpssgnU18Odd8auSJK6rluCOUmSqjmeT8z8Oa5DW12SJNVJkkzojveUOtOvH9x+O/zkJ1BXBw89FLsiSeqarIM5SZJqYNoczeOSJHkBaM68pgogTdNGoHXOIJe6U/txkautBjvsAH/5S+yKJGnhZR3MmbBtnqN5bJqmQzJfA9gFaM08bgaqs31faX4GDYL77gt/brMN/OMfsSuSpIWTq3vMlXMMW/cHOp4HNDBH7yt9bfBgaGyEvn3DcZHNc/7zUZLyUE5mZadpOgkgSZKtMkPd89XS0kJ9ff1c7TU1NdTW1uagQpWKIUPCWc6bbRbC+ZFHYKWVYlclSfO2wGDuOIGrg+YOw9SdvX52mqbTgVlAJWEYe0DmJf0z7V+rqKigoaGhK3VLC23ttcNZzsOGhbOcf//7MMQtSflogcGcpunkLn7PlG/uOQ8BLs+0JZm2SqDTUJdyZf314bbbwjnOw4eHk6mWWSZ2VZI0t+6YlV0X/kjqANI0bQJ2zjx/IU3Tpkxb+wzu1vbnUk/aYgu46SZ4+ukQ0B98ELsiSZpbWVtbW+waqK+vb3MoWz3lpptg551h883hjjvC2mdJ6mlJkjyRpmkyZ7s7f6nkjB4NV10FDz4IO+0En30WuyJJ+obBrJJUXx8OvrjzTth9d/jii9gVSVLgIRYqWfvvDx99BEccEYazr7oKevlPVUmRGcwqaYcfHiaBnXQSLLEEXHppOEZSkmIxmFXyTjgBPvwQJk4M4fyrXxnOkuIxmFXyysrg7LNDOJ97bgjnU0+NXZWkUmUwS4RwvuCCEM6nnRbCeYIHlEqKwGCWMnr1giuugI8/hqOPDuE8fnzsqiSVGoNZ6qB3b7j66hDOBx8Miy8O++wTuypJpcTFIdIcFlsMpk4NB17st194LEk9xWCWOtG3L9xyCwwdCnvsEQ7AkKSeYDBL87D44mEv7aqqsHXnfffFrkhSKTCYpflYeulwlvNaa8GoUfDII7ErklTsDGZpAQYMCL3l//kf2H57ePzx2BVJKmYGs7QQllsO7r8fll0Whg+HJ5+MXZGkYmUwSwtp8GB44IEwvF1dDU8/HbsiScXIYJa6YNVVQzj36wfDhsEzz8SuSFKxMZilLhoyJAxr9+4NW24J//pX7IokFRODWVoEa64ZwvnLL0M4NzfHrkhSsTCYpUX0gx9AY2PYvnPLLeHll2NXJKkYGMxSFn7847CU6t13Qzi/9lrsiiQVOoNZylJVFdxzD7z9dgjnN9+MXZGkQmYwS91g/fXDDmGvvRZma7/9duyKJBUqg1nqJkOHhr21X3wxrHOeNSt2RZIKkcEsdaPNNw8nUT33XDg2srU1dkWSCo3BLHWz6mq4+eawM9g228B778WuSFIhMZilHNhuO5g2DZ54Ijz+4IPYFUkqFAazlCOjRsH118Of/wwjRsBHH8WuSFIhMJilHKqrg6uvhocfhh13hE8+iV2RpHxnMEs5tttucOWVYZew0aPh009jVyQpnxnMUg/Yay+4/HKYORN23hk++yx2RZLylcEs9ZCxY+GSS8Jyql12gc8/j12RpHxkMEs96KCD4KKL4NZbDWdJnTOYpR528MFwwQVwyy0wZozhLOnbDGYpgkMPhV//Gm66KUwOM5wltSuPXYBUqg47DNra4IgjoFcvuPZaKPcTKZU8fw1IER1+eAjnI48M4dzQYDhLpc5fAVJkRxwBX30Fv/wllJWFDUkMZ6l0+fGX8sBRR4VwPvrob8K5d+/YVUmKwWCW8sSECSGcjz02DGtfdZXhLJUig1nKI8ccE8L5+ONDz/l3vzOcpVKTdTAnSTIu83BImqZHZ9rqgFagKk3TSfNqkzS3444L4XziiaHn/NvfGs5SKclqHXOSJNVAY5qmk4HKJEmqkySpAkjTtBFoTZKkqrO2bAuXitkJJ8Cpp8KUKbDffiGoJZWGbHvMlZn/JgPNmcdbAfdlvt4MVAMDO2lryvK9paJ20kkhkE89NfScr7gi/CmpuGUVzJmecrsqYCqwLjC7Q/tAoH8nbZIW4OSTQziffnoI5csvN5ylYtctk78yQ9NNaZo2JUnS5b/f0tJCfX39XO01NTXU1tZ2Q4VSYSorCz3mr76CM88Mz//v/wxnqZgtMJg7TO7qqDlzv7hddfvEL8IErwGZx/2BWZnHnbUBUFFRQUNDw0IXLZWSsrLQY25rg7POCqF86aWGs1SsFhjMcwxXzyVJknEdZl5XE4az27vNlUB7gHfWJmkhlJXBGWeEnvP//i98+aXD2lKx6o5Z2ROTJHkhSZIWgDRNmzp8rTVN06bO2rKsWyo5ZWWhx3z88fCb38DYsc7WlopRtpO/GoGKTtrn6mUvqOctacHah7V794bTTgs9Z9c5S8XFnb+kAtM+IaxXLzjllNBrdocwqXgYzFKBOvnkEMYnnhh6zlOmeCqVVAz8GEsF7IQTQjgfd1wI52uuMZylQudHWCpwxx4bwvnoo0M4X3cdLLZY7KokLSqDWSoCEyaEcG4/1/n666FPn9hVSVoUroKUisSRR8Kvfw033ww77wyffRa7IkmLwmCWishhh8GFF8KMGVBXB59+GrsiSV1lMEtF5pBD4JJL4PbbYfRo+OST2BVJ6gqDWSpCBx0UDru4806oqTGcpUJiMEtFav/9YfJkuPtuGDUKPv44dkWSFobBLBWxsWPDlp333QcjR8JHH8WuSNKCGMxSkfv5z8OWnfffDzvsAB9+GLsiSfNjMEslYK+9wpadDz0E228PH3wQuyJJ82IwSyWivh4aGuCRR2D4cHj33dgVSeqMwSyVkN12g6lT4fHHYautYPbs2BVJmpPBLJWYurqwO9hTT8GwYfD227ErktSRwSyVoBEj4Lbb4NlnYYst4M03Y1ckqZ3BLJWo4cNh5kx48UXYbDN49dXYFUkCg1kqaVtsAffcA2+8AZtuCi+9FLsiSQazVOI23hgaG6GlJfSc//3v2BVJpc1glsT668MDD4TNRzbdNNx7lhSHwSwJgHXWgd//Hr76KvScn346dkVSaTKYJX1t7bXD7mDl5bD55tDUFLsiqfQYzJK+Zc014eGHYcklYcst4bHHYlcklRaDWdJchgwJ4TxoEFRXh208JfUMg1lSp1ZdNQxrDx4M22wTJodJyj2DWdI8DR4cwrmyMpxKdffdsSuSip/BLGm+ll8eHnwQvv99GDUKZsyIXZFU3AxmSQs0aBDcfz/89KfhEIypU2NXJBUvg1nSQqmogPvugw03DMdHXnll7Iqk4mQwS1poSy8d7jNXV8O++8KFF8auSCo+BrOkLll88XBkZE0N/OIXcOaZ0NYWuyqpeBjMkrqsb1+48UbYYw844QQ49ljDWeou5bELkFSYysthypSwQ9jEifDBB2Fou5f/3JeyYjBLWmS9esGll4ZwPuecEM6/+U0IbUmLxo+PpKyUlcGkSWFi2EknhXC+7jro0yd2ZVJhctBJUtbKyuDEE+G88+Cmm2DHHeHjj2NXJRUmg1lStzn8cJg8OSyp2nZbeP/92BVJhcdgltStxo6Fa6+FP/whrHeePTt2RVJhMZgldbsxY+Dmm+Gvf4XNN4e33opdkVQ4DGZJOTFyJNx5J7zwAmyyCfznP7ErkgpD1sGcJMm4zH8TO7RNbP9ah7a6JEmqkySZkO17SioM1dVw772hx7zJJvDvf8euSMp/WQVzkiTVQGOappOBysxzgHFJkrwANGdeVwWQpmkj0Nr+XFLxGzo0HBv5wQchnP/+99gVSfkt2x5zJdAexs2Z5wBj0zQdkgligF2A1g6vq0ZSyaiqgocfDsuqNtsM0jR2RVL+ymqDkUxPuV0V0H5Ka3vvuSpN00lAf6Dj3MyBHb9PS0sL9fX1c33/mpoaamtrsylRUp74wQ/CTO1hw2CLLeD228PEMEnf1i07f2WGppvSNG0CyIQxSZJs1WF4e54qKipoaGjojlIk5bHKyhDOW28N22wTDsIYOTJ2VVJ+WWAwd5zA1UFzh2FqgOo0TY/u8PrZaZpOB2YRhrdbgQGZ1/bPtEsqQYMHh2Ht7baD2lq48krYc8/YVUn5Y4HBPMdw9VySJBnXoYdcDaRkJn0BQ4DLM21Jpq0SaJzz+0gqHQMHQmNj2Lpzr72gpSWc7Sype2ZlT0yS5IUkSVoAMsPZOydJUge8kKbp10Pcmde3tj+XVLqWWiqsc66pgcMOg1NO8UxnCbKf/NUIVHTSPlcve0E9b0ml5zvfCfeZx46FU08N23eef75nOqu0eeyjpKjKy+G3v4UBA8LpVC0t4b7zYovFrkyKw2CWFF2vXnDOOSGcTzgB3n0Xpk6Ffv1iVyb1PAeMJOWFsjI4/ni49FK4445wbOR778WuSup5BrOkvHLggeHYyD/+MWxE8vbbsSuSepbBLCnvjBkDM2bAP/8Z9td+5ZXYFUk9x2CWlJe22y6cTPXGG7DxxvDcc7ErknqGwSwpb22yCfz+9/DJJ+FxkzsgqAQYzJLy2jrrhP21+/ULh148/HDsiqTcMpgl5b011giTwQYPhuHDw6xtqVgZzJIKwsorwyOPwNprhz22p0yJXZGUGwazpIIxaBA88EBYRrX33vCrX8WuSOp+BrOkgrLUUmEoe5ddYMIEOOoo+Oqr2FVJ3cctOSUVnL594brrYNll4dxz4b//Dfttu7+2ioHBLKkg9eoFF14Iyy8PJ54I77wD06bBEkvErkzKjkPZkgpWWVk49OLyy+Gee6C6GmbNil2VlB2DWVLBGzcu9JaffDJsRPKf/8SuSFp0BrOkolBbG3rNr70GG20U9tmWCpHBLKlobLYZPPQQfP556Dk/+mjsiqSuM5glFZWf/hT+9CeoqIBhw2DmzNgVSV1jMEsqOpWVYQvPtdaCkSOhoSF2RdLCM5glFaXllw8nU222Gey5Z1jvLBUCg1lS0Vp66TCUXVcXdgibMAHa2mJXJc2fwSypqPXtCzfcAAceGPbW3mefMDlMylfu/CWp6PXuDZdcEoa3Tzkl7BI2daq7hCk/2WOWVBLKyuDkk+Gyy8Lw9rBh8PbbsauS5mYwSyopBxwAN90ETz0FQ4dCc3PsiqRvM5gllZyaGmhsDEPaG24ITzwRuyLpGwazpJI0dGhY69yvX1hSdffdsSuSAoNZUsn6/vfDLmGrrw4jRsCUKbErkgxmSSVupZXg4YdDr3nvveGss1zrrLgMZkklr30jkt13h+OPh/Hj4csvY1elUuU6ZkkC+vSBq6+GwYNh0iR44w247rpwD1rqSfaYJSmjVy+YOBEuuABmzIDqapg1K3ZVKjUGsyTN4dBD4cYbwzKqoUPhpZdiV6RSYjBLUifq6uDee+Gtt8Ja57/+NXZFKhUGsyTNw6abwh/+AOXl4XFjY+yKVAoMZkmajx/+EB59FL77Xdh2W7j22tgVqdgZzJK0ACuvDI88AhtvDHvsESaIudZZuWIwS9JCWGaZsG3nrrvCMceE852/+CJ2VSpGrmOWpIXUt28Yyv7ud+F//xf+859wrvOSS8auTMUk62BOkqQ683CrNE2PzrTVAa1AVZqmk+bVJkmFplcvOPvsEM4HHRS28nOa98cAAA+ASURBVLzjDlhxxdiVqVhkNZSdCeWd0jRtBKqSJKlKkqQKINPWOq+2bAuXpJj23x9uvx2eew422AD+8Y/YFalYZBXMaZo2pmm6f+ZpZZqmTcAuhJ4xQDNQPY82SSpo220XDsD47LOwEcmDD8auSMWgW+4xJ0kyAWgP6P7A7A5fHjiPtq+1tLRQX18/1/etqamhtra2O0qUpJyoqoI//zmE9PDhcOWVYea2tKi6JZjTNJ2UJMm0JEnSRfn7FRUVNDQ0dEcpktTjVl0V/vhHqK2F+np48UU44QQoK4tdmQrRAoM5SZJxnTQ3p2na2OHecRNhiHocYch6QOZ1/YH2LeA7a5OkotC/f1hOtd9+cNJJYX/t//s/WGyx2JWp0CwwmNM0nTyfL1cDTZnH/YG/AI1AkmmrzDxnHm2SVDT69IEpU8KM7dNPh1dfhWnTwnnP0sLKdoORyUBle686TdPpmd5z+4zt1jRNmzpry/J9JSkvlZXBaafBb38LDzwAm2wSAlpaWGVtebCvXH19fZv3mCUVm3vvDadULb00zJwJP/5x7IqUT5IkeSJN02TOdrfklKQc2XrrcDoVhH227703bj0qDAazJOXQj38cllOtthpsv31YTiXNj8EsSTnWfjrVllvCvvvCccfBV1/Frkr5ymCWpB6w9NJhT+1x48Je27vuCh9/HLsq5SNPl5KkHrLYYmFt8xprwC9/Ca+8AjNmwPLLx65M+cQesyT1oLIyOPJIuOkm+NvfPABDczOYJSmCmppwAMYnn8BGG8F998WuSPnCYJakSJIEHn887BS27bYweX77LKpkGMySFNEqq4S1zltvHc54Puoo+PLL2FUpJoNZkiJbaim47TYYPx7OPTfsFvbhh7GrUiwGsyTlgfJyuPhiuOCCENKbbQavvx67KsVgMEtSHjn00LCE6tln4Wc/g6eeil2ReprBLEl5Zocdwk5hbW1hj+2ZM2NXpJ5kMEtSHlpnHXjsMfje92DEiDDMrdJgMEtSnho8OKx13mEHOOSQMMz9xRexq1KuGcySlMeWXBJuvhkOPxwuughGjoR3341dlXLJYJakPNe7N5x3Xthn+777wk5hzc2xq1KuGMySVCD23x/uuQfeeAPWXz8Mc6v4GMySVEC23DJMChs0CKqr4corY1ek7mYwS1KB+d734M9/hs03h333dRvPYmMwS1IB6t8/rG8++OCwjeeoUfDee7GrUncwmCWpQJWXh5nal1wCd98NQ4fCSy/FrkrZMpglqcAddFAI5ldfDZPC/vCH2BUpGwazJBWB6upw37l/fxg2DKZMiV2RFpXBLElFYs01QzhvsgnsvTccfbSTwgqRwSxJRWTAALjrLjjgAJg0CWpr4YMPYlelrjCYJanILLYYXHppmBh2xx1hUtgrr8SuSgvLYJakIlRWFpZS3XUXvPwyrLcePPpo7Kq0MAxmSSpiW28d7jsvtVTYkOSqq2JXpAUxmCWpyK21Fjz+OGy6KeyzTzipyuMj85fBLEkloH1S2C9+AeefD9ttB7Nnx65KnTGYJalElJeHUL7ySnjoobAZyT//GbsqzclglqQSs88+8Pvfh2VUG2wAt98euyJ1ZDBLUgnacENIU1hjjXAAxllnQVtb7KoEBrMklayVV4ZHHoExY+D448OfH30UuyoZzJJUwvr1g2uugYkT4cYbYeON3YwkNoNZkkpcWRlMmBDuNb/wQtiMxBOq4jGYJUkAbL992IxkmWVgyy3hiitiV1SaDGZJ0te+/3147LEQzOPGhW09P/88dlWlxWCWJH1LRUU4/OLII+GSS2D4cHjnndhVlY6sgzlJkurMfxM7tE3M/DmuQ1td5nUTsn1PSVJulZfDOefAlCnwpz+F+85PPRW7qtKQVTAnSVIN7JSmaSNQlSRJVeZL45IkeQFozryuCiDzutYOr5Mk5bE99wy7hH32WVj7fMMNsSsqfuXZ/OVM0DZmnlamadqUeTw2TdPpHV66C3Bf5nEzUA00IUnKez/7GTzxBNTVhbXOTzwBZ58detXqft1yjzkzPL1/h6bKOYat+wMdt0sf2B3vK0nqGSusAA88AAceGIa4t90WZs2KXVVx6pZ/76RpOilJkmlJkqRpmramaToJIEmSrTLD3fPV0tJCfX39XO01NTXU1tZ2R4mSpCz16QOXXgrrrgsHHQRJArfcAj/9aezKissCg7njBK4OmtM0bexw77iJMEQ9LkmSVmB2Zih7FlAJtAIDMn+3f6b9axUVFTQ0NCz6/4Ukqcfsuy/86EdQWwsbbQS/+Q3stlvsqorHAoM5TdPJ8/lyx3vF/YG/EAK6OdM2BLgcSIEk01bJN/elJUkFaP31w73mnXaC3XcPjydO9L5zd8j2HvNkwv3kcQBpmk7P9J53TpKkDnghTdOm9klhmWHt1g6TxCRJBWr55aGxMWxCct55rnfuLmVteXDOV319fZtD2ZJUuK66Cg44IEwSu+UWWGed2BXlvyRJnkjTNJmz3Z2/JElZ23vvcPDFl1+G+87XXhu7osJlMEuSukWShHvN668Pe+wBhx8OX3wRu6rCYzBLkrrNcsuF+86HHgrnnw9bbQVvvx27qsJiMEuSutVii8EFF4R9tv/852960lo4BrMkKSf23DPcdwYYOhR++9u49RQKg1mSlDPrrht6y5tuCvvtB2PHwiefxK4qvxnMkqScGjQI7roLjj8+7BK28cbw8suxq8pfBrMkKed694YzzoAZM+D556GqCu65J3ZV+clgliT1mJEjw9D24MHhhKrTT4evvopdVX4xmCVJPWr11cNs7d13h5NOCmHd0hK7qvxhMEuSetzii8PVV8PFF8O994YlVU89Fbuq/GAwS5KiKCuD8ePhoYfCTO0NNghhXeoMZklSVBtuCE1NIZj32gsOOgg+/TR2VfEYzJKk6JZfHu67D375S7jssrDu+T//iV1VHAazJCkvlJfDpEkwfTo880xYUnX//bGr6nkGsyQpr4weDX/5SzgQY+ut4eyzS2tJlcEsSco7a64Jjz0GO+0Exx0Ho0bB7Nmxq+oZBrMkKS8tuSRcfz1cdFHYJayqKvSki53BLEnKW2VlcPDB4ZSqtrZwStXFF4fHxcpgliTlvfXXhyefDPecDzkExoyB99+PXVVuGMySpIIwYADcdluYDDZtWtgt7OmnY1fV/QxmSVLB6NULjjkGHngA3nsPfvYzuOqq2FV1L4NZklRwNtssDG1vsAHssw/suy98/HHsqrqHwSxJKkgrrBB2Czv+eLjyyhDS//pX7KqyZzBLkgpW795wxhkwcya89lq47zxtWuyqsmMwS5IK3rbbhqHtH/4Qdt4ZDj0UPvssdlWLxmCWJBWFVVYJR0gedljYlGSTTeDll2NX1XUGsySpaPTpA7/+dTgI49lnYZ114I47YlfVNQazJKnojB4NTzwBq64KI0bAkUcWztC2wSxJKkqrrw6PPgrjx8N554Wh7RdfjF3VghnMkqSi9Z3vhL21p0+H554LQ9s33xy7qvkzmCVJRW/0aGhqgjXWCI8POQQ++SR2VZ0zmCVJJaGyMpxSdcQRoRe90Ubw/POxq5qbwSxJKhl9+sC554bDMF5+OZzxfP31sav6NoNZklRyRoyAv/4VfvIT2G03GDsWPvoodlWBwSxJKkmrrAIPPgjHHgu/+U04qeqf/4xdlcEsSSphiy0GZ50Fd98Nb70F660XjpFsa4tXk8EsSSp5w4fDU0+FXvM++8Bee8EHH8SpxWCWJAlYccVwjOSpp8K114aTqp56qufrMJglScro3RtOOgnuvx/eey/0oC+9tGeHtrstmJMkmdDhcV2SJNULapMkKR9tvnmYtb3llmFLz9GjYfbsnnnvbgnmJEmqga0yj6sA0jRtBFqTJKnqrK073leSpFxZbrlwMtW558LMmfC3v/XM++ZiKHsXoDXzuBmonkebJEl5rVevsFPYSy+FXnSPvGe23yBJkqpMT7hdf6Bjh3/gPNokSSoIK6zQc+9V3g3fY0C236ClpYX6+vq52mtqaqitrc3220uSVDAWGMxJkozrpLk5TdPGTnrLEIas28O6PzAr87izNgAqKipoaGhY+KolSSpSCwzmNE0nz+fLlUmSVBJCd0BmUtdUIGn/OtAe3J21SZKkDrK6x5ym6fQ0TadnnvbPtDXB1zO1W9M0beqsLZv3lSSpWHXHPeb2XvXkOZ539hpJkjQf7vwlSVIeMZglScojBrMkSXnEYJYkKY8YzJIk5RGDWZKkPFK0wXzzzTfHLqHgeQ2z5zXMntcwe17D7tFT17Fog/mWW26JXULB8xpmz2uYPa9h9ryG3aOnrmPRBrMkSYXIYJYkKY8YzJIk5RGDWZKkPFLW1tYWuwaSJHkbeLmbv+0g4J1u/p6lxmuYPa9h9ryG2fMado/uvo6rpmm67JyNeRHMkiQpcChbyoEkSSZ0eFyXJEl1xzZJ+StJkqo5ns/1Gc7l57pbzmPOJ0mS1AGtQFWappNi11NIkiQZl3k4JE3TozNtXs8uSpKkGtgKmNT+AU/TtDFJksokSarSNG2KW2F+y1yzSoA0Tadn2vw57IIO16syTdPJc7R5Decj8/m9HBiSeT7XZ7j9tbn6XBdVj7njBQRa5/xXj+Yt88PYmPkQV2b+Jej1zN4uhF+GAM1AdcRaCsWxmUCuTJKkyp/Drslcn+bM9Wr2GnZN+3Xr0NTZZzinn+uiCmb8JZiNSr65Xs2Z517PLsr8y7mxQ1N/YHaH5wN7uKSCkunV/QUgTdNJmV6IP4ddNzHzZ6XXMGudfYZz+rkutmD2l+AiStN0cvuQF1AFpHg9F8WA2AUUuPWAgZleXvu9O38OuyATxM1JkrTwzXXzGhaQYgtmZSkzxNXkfdCu66S3DKGX0h7W/YFZPVtVQZrV/vOX6UGrC5Ik6U/4uTsbuCJJksrIJRW6zj7DOf1cF9vkL38JZq+6feIXXs+uqsz8EhwADMj8I2cqkLR/HZgzuPVts/jm/l4roQftz2HXjAPOTtO0NUmSZqB90pfXcNHM6zOcs891sfWYp5KZzYm/BLssSZJx7bM1M5PBvJ5dkKbp9PZZxIRffnTo+VUDrY5ELNB0vvmZ60+43+zP4SLK/Dy24jVcaJlRmqR9tKazz3CuP9dFt8FIZslPMx2WCWjBMj9g0wj3oQYAO2WWAng91aMyP3OzgfU6LNvz57ALMvfnm4EBHZZLeQ0LRNEFsyRJhazYhrIlSSpoBrMkSXnEYJYkKY8YzJIk5RGDWZKkPGIwS5KURwxmSZLyiMEsSVIe+X+1mPBaYcivsgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(1, figsize=(8,8))\\n\",\n \"plt.plot(*data.T)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can take that same figure and add on the needed labels and titles.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Creating figure\\n\",\n \"plt.figure(figsize=(8,8))\\n\",\n \"plt.plot(*data.T)\\n\",\n \"plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)\\n\",\n \"plt.xlabel('x value', fontsize=20)\\n\",\n \"plt.ylabel('y value', fontsize=20)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's a large number of options available for plotting, so try using the initial code below, combined with the information [here](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) to try out a few of the following things: changing the line width, changing the line color\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"plt.plot(data[:,0],data[:,1])\\n\",\n \"plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)\\n\",\n \"plt.xlabel('x value', fontsize=20)\\n\",\n \"plt.ylabel('y value', fontsize=20)\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":233,"cells":{"repo_name":{"kind":"string","value":"google/earthengine-api"},"path":{"kind":"string","value":"python/examples/ipynb/UNET_regression_demo.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"31375"},"content":{"kind":"string","value":"{\"nbformat\":4,\"nbformat_minor\":0,\"metadata\":{\"colab\":{\"name\":\"UNET_regression_demo.ipynb\",\"provenance\":[{\"file_id\":\"https://github.com/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb\",\"timestamp\":1586992475463}],\"private_outputs\":true,\"collapsed_sections\":[],\"toc_visible\":true,\"machine_shape\":\"hm\"},\"kernelspec\":{\"name\":\"python3\",\"display_name\":\"Python 3\"},\"accelerator\":\"GPU\"},\"cells\":[{\"cell_type\":\"code\",\"metadata\":{\"id\":\"esIMGVxhDI0f\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"#@title Copyright 2020 Google LLC. { display-mode: \\\"form\\\" }\\n\",\"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n\",\"# you may not use this file except in compliance with the License.\\n\",\"# You may obtain a copy of the License at\\n\",\"#\\n\",\"# https://www.apache.org/licenses/LICENSE-2.0\\n\",\"#\\n\",\"# Unless required by applicable law or agreed to in writing, software\\n\",\"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n\",\"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n\",\"# See the License for the specific language governing permissions and\\n\",\"# limitations under the License.\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"aV1xZ1CPi3Nw\",\"colab_type\":\"text\"},\"source\":[\"
\\n\",\"\\n\",\" Run in Google Colab\\n\",\"\\n\",\" View source on GitHub
\"]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"_SHAc5qbiR8l\",\"colab_type\":\"text\"},\"source\":[\"# Introduction\\n\",\"\\n\",\"This is an Earth Engine <> TensorFlow demonstration notebook. Suppose you want to predict a continuous output (regression) from a stack of continuous inputs. In this example, the output is impervious surface area from [NLCD](https://www.mrlc.gov/data) and the input is a Landsat 8 composite. The model is a [fully convolutional neural network (FCNN)](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf), specifically [U-net](https://arxiv.org/abs/1505.04597). This notebook shows:\\n\",\"\\n\",\"1. Exporting training/testing patches from Earth Engine, suitable for training an FCNN model.\\n\",\"2. Preprocessing.\\n\",\"3. Training and validating an FCNN model.\\n\",\"4. Making predictions with the trained model and importing them to Earth Engine.\"]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"_MJ4kW1pEhwP\",\"colab_type\":\"text\"},\"source\":[\"# Setup software libraries\\n\",\"\\n\",\"Authenticate and import as necessary.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"neIa46CpciXq\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Cloud authentication.\\n\",\"from google.colab import auth\\n\",\"auth.authenticate_user()\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"jat01FEoUMqg\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Import, authenticate and initialize the Earth Engine library.\\n\",\"import ee\\n\",\"ee.Authenticate()\\n\",\"ee.Initialize()\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"8RnZzcYhcpsQ\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Tensorflow setup.\\n\",\"import tensorflow as tf\\n\",\"print(tf.__version__)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"n1hFdpBQfyhN\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Folium setup.\\n\",\"import folium\\n\",\"print(folium.__version__)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"iT8ycmzClYwf\",\"colab_type\":\"text\"},\"source\":[\"# Variables\\n\",\"\\n\",\"Declare the variables that will be in use throughout the notebook.\"]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"qKs6HuxOzjMl\",\"colab_type\":\"text\"},\"source\":[\"## Specify your Cloud Storage Bucket\\n\",\"You must have write access to a bucket to run this demo! To run it read-only, use the demo bucket below, but note that writes to this bucket will not work.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"obDDH1eDzsch\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# INSERT YOUR BUCKET HERE:\\n\",\"BUCKET = 'your-bucket-name'\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"wmfKLl9XcnGJ\",\"colab_type\":\"text\"},\"source\":[\"## Set other global variables\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"psz7wJKalaoj\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Specify names locations for outputs in Cloud Storage. \\n\",\"FOLDER = 'fcnn-demo'\\n\",\"TRAINING_BASE = 'training_patches'\\n\",\"EVAL_BASE = 'eval_patches'\\n\",\"\\n\",\"# Specify inputs (Landsat bands) to the model and the response variable.\\n\",\"opticalBands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']\\n\",\"thermalBands = ['B10', 'B11']\\n\",\"BANDS = opticalBands + thermalBands\\n\",\"RESPONSE = 'impervious'\\n\",\"FEATURES = BANDS + [RESPONSE]\\n\",\"\\n\",\"# Specify the size and shape of patches expected by the model.\\n\",\"KERNEL_SIZE = 256\\n\",\"KERNEL_SHAPE = [KERNEL_SIZE, KERNEL_SIZE]\\n\",\"COLUMNS = [\\n\",\" tf.io.FixedLenFeature(shape=KERNEL_SHAPE, dtype=tf.float32) for k in FEATURES\\n\",\"]\\n\",\"FEATURES_DICT = dict(zip(FEATURES, COLUMNS))\\n\",\"\\n\",\"# Sizes of the training and evaluation datasets.\\n\",\"TRAIN_SIZE = 16000\\n\",\"EVAL_SIZE = 8000\\n\",\"\\n\",\"# Specify model training parameters.\\n\",\"BATCH_SIZE = 16\\n\",\"EPOCHS = 10\\n\",\"BUFFER_SIZE = 2000\\n\",\"OPTIMIZER = 'SGD'\\n\",\"LOSS = 'MeanSquaredError'\\n\",\"METRICS = ['RootMeanSquaredError']\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"hgoDc7Hilfc4\",\"colab_type\":\"text\"},\"source\":[\"# Imagery\\n\",\"\\n\",\"Gather and setup the imagery to use for inputs (predictors). This is a three-year, cloud-free, Landsat 8 composite. Display it in the notebook for a sanity check.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"-IlgXu-vcUEY\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Use Landsat 8 surface reflectance data.\\n\",\"l8sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\\n\",\"\\n\",\"# Cloud masking function.\\n\",\"def maskL8sr(image):\\n\",\" cloudShadowBitMask = ee.Number(2).pow(3).int()\\n\",\" cloudsBitMask = ee.Number(2).pow(5).int()\\n\",\" qa = image.select('pixel_qa')\\n\",\" mask1 = qa.bitwiseAnd(cloudShadowBitMask).eq(0).And(\\n\",\" qa.bitwiseAnd(cloudsBitMask).eq(0))\\n\",\" mask2 = image.mask().reduce('min')\\n\",\" mask3 = image.select(opticalBands).gt(0).And(\\n\",\" image.select(opticalBands).lt(10000)).reduce('min')\\n\",\" mask = mask1.And(mask2).And(mask3)\\n\",\" return image.select(opticalBands).divide(10000).addBands(\\n\",\" image.select(thermalBands).divide(10).clamp(273.15, 373.15)\\n\",\" .subtract(273.15).divide(100)).updateMask(mask)\\n\",\"\\n\",\"# The image input data is a cloud-masked median composite.\\n\",\"image = l8sr.filterDate('2015-01-01', '2017-12-31').map(maskL8sr).median()\\n\",\"\\n\",\"# Use folium to visualize the imagery.\\n\",\"mapid = image.getMapId({'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3})\\n\",\"map = folium.Map(location=[38., -122.5])\\n\",\"folium.TileLayer(\\n\",\" tiles=mapid['tile_fetcher'].url_format,\\n\",\" attr='Map Data &copy; Google Earth Engine',\\n\",\" overlay=True,\\n\",\" name='median composite',\\n\",\" ).add_to(map)\\n\",\"\\n\",\"mapid = image.getMapId({'bands': ['B10'], 'min': 0, 'max': 0.5})\\n\",\"folium.TileLayer(\\n\",\" tiles=mapid['tile_fetcher'].url_format,\\n\",\" attr='Map Data &copy; Google Earth Engine',\\n\",\" overlay=True,\\n\",\" name='thermal',\\n\",\" ).add_to(map)\\n\",\"map.add_child(folium.LayerControl())\\n\",\"map\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"gHznnctkJsZJ\",\"colab_type\":\"text\"},\"source\":[\"Prepare the response (what we want to predict). This is impervious surface area (in fraction of a pixel) from the 2016 NLCD dataset. Display to check.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"e0wHDyxVirec\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"nlcd = ee.Image('USGS/NLCD/NLCD2016').select('impervious')\\n\",\"nlcd = nlcd.divide(100).float()\\n\",\"\\n\",\"mapid = nlcd.getMapId({'min': 0, 'max': 1})\\n\",\"map = folium.Map(location=[38., -122.5])\\n\",\"folium.TileLayer(\\n\",\" tiles=mapid['tile_fetcher'].url_format,\\n\",\" attr='Map Data &copy; Google Earth Engine',\\n\",\" overlay=True,\\n\",\" name='nlcd impervious',\\n\",\" ).add_to(map)\\n\",\"map.add_child(folium.LayerControl())\\n\",\"map\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"CTS7_ZzPDhhg\",\"colab_type\":\"text\"},\"source\":[\"Stack the 2D images (Landsat composite and NLCD impervious surface) to create a single image from which samples can be taken. Convert the image into an array image in which each pixel stores 256x256 patches of pixels for each band. This is a key step that bears emphasis: to export training patches, convert a multi-band image to [an array image](https://developers.google.com/earth-engine/arrays_array_images#array-images) using [`neighborhoodToArray()`](https://developers.google.com/earth-engine/api_docs#eeimageneighborhoodtoarray), then sample the image at points.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"eGHYsdAOipa4\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"featureStack = ee.Image.cat([\\n\",\" image.select(BANDS),\\n\",\" nlcd.select(RESPONSE)\\n\",\"]).float()\\n\",\"\\n\",\"list = ee.List.repeat(1, KERNEL_SIZE)\\n\",\"lists = ee.List.repeat(list, KERNEL_SIZE)\\n\",\"kernel = ee.Kernel.fixed(KERNEL_SIZE, KERNEL_SIZE, lists)\\n\",\"\\n\",\"arrays = featureStack.neighborhoodToArray(kernel)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"F4djSxBRG2el\",\"colab_type\":\"text\"},\"source\":[\"Use some pre-made geometries to sample the stack in strategic locations. Specifically, these are hand-made polygons in which to take the 256x256 samples. Display the sampling polygons on a map, red for training polygons, blue for evaluation.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"ure_WaD0itQY\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"trainingPolys = ee.FeatureCollection('projects/google/DemoTrainingGeometries')\\n\",\"evalPolys = ee.FeatureCollection('projects/google/DemoEvalGeometries')\\n\",\"\\n\",\"polyImage = ee.Image(0).byte().paint(trainingPolys, 1).paint(evalPolys, 2)\\n\",\"polyImage = polyImage.updateMask(polyImage)\\n\",\"\\n\",\"mapid = polyImage.getMapId({'min': 1, 'max': 2, 'palette': ['red', 'blue']})\\n\",\"map = folium.Map(location=[38., -100.], zoom_start=5)\\n\",\"folium.TileLayer(\\n\",\" tiles=mapid['tile_fetcher'].url_format,\\n\",\" attr='Map Data &copy; Google Earth Engine',\\n\",\" overlay=True,\\n\",\" name='training polygons',\\n\",\" ).add_to(map)\\n\",\"map.add_child(folium.LayerControl())\\n\",\"map\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"ZV890gPHeZqz\",\"colab_type\":\"text\"},\"source\":[\"# Sampling\\n\",\"\\n\",\"The mapped data look reasonable so take a sample from each polygon and merge the results into a single export. The key step is sampling the array image at points, to get all the pixels in a 256x256 neighborhood at each point. It's worth noting that to build the training and testing data for the FCNN, you export a single TFRecord file that contains patches of pixel values in each record. You do NOT need to export each training/testing patch to a different image. Since each record potentially contains a lot of data (especially with big patches or many input bands), some manual sharding of the computation is necessary to avoid the `computed value too large` error. Specifically, the following code takes multiple (smaller) samples within each geometry, merging the results to get a single export.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"FyRpvwENxE-A\",\"colab_type\":\"code\",\"cellView\":\"both\",\"colab\":{}},\"source\":[\"# Convert the feature collections to lists for iteration.\\n\",\"trainingPolysList = trainingPolys.toList(trainingPolys.size())\\n\",\"evalPolysList = evalPolys.toList(evalPolys.size())\\n\",\"\\n\",\"# These numbers determined experimentally.\\n\",\"n = 200 # Number of shards in each polygon.\\n\",\"N = 2000 # Total sample size in each polygon.\\n\",\"\\n\",\"# Export all the training data (in many pieces), with one task \\n\",\"# per geometry.\\n\",\"for g in range(trainingPolys.size().getInfo()):\\n\",\" geomSample = ee.FeatureCollection([])\\n\",\" for i in range(n):\\n\",\" sample = arrays.sample(\\n\",\" region = ee.Feature(trainingPolysList.get(g)).geometry(), \\n\",\" scale = 30,\\n\",\" numPixels = N / n, # Size of the shard.\\n\",\" seed = i,\\n\",\" tileScale = 8\\n\",\" )\\n\",\" geomSample = geomSample.merge(sample)\\n\",\"\\n\",\" desc = TRAINING_BASE + '_g' + str(g)\\n\",\" task = ee.batch.Export.table.toCloudStorage(\\n\",\" collection = geomSample,\\n\",\" description = desc,\\n\",\" bucket = BUCKET,\\n\",\" fileNamePrefix = FOLDER + '/' + desc,\\n\",\" fileFormat = 'TFRecord',\\n\",\" selectors = BANDS + [RESPONSE]\\n\",\" )\\n\",\" task.start()\\n\",\"\\n\",\"# Export all the evaluation data.\\n\",\"for g in range(evalPolys.size().getInfo()):\\n\",\" geomSample = ee.FeatureCollection([])\\n\",\" for i in range(n):\\n\",\" sample = arrays.sample(\\n\",\" region = ee.Feature(evalPolysList.get(g)).geometry(), \\n\",\" scale = 30,\\n\",\" numPixels = N / n,\\n\",\" seed = i,\\n\",\" tileScale = 8\\n\",\" )\\n\",\" geomSample = geomSample.merge(sample)\\n\",\"\\n\",\" desc = EVAL_BASE + '_g' + str(g)\\n\",\" task = ee.batch.Export.table.toCloudStorage(\\n\",\" collection = geomSample,\\n\",\" description = desc,\\n\",\" bucket = BUCKET,\\n\",\" fileNamePrefix = FOLDER + '/' + desc,\\n\",\" fileFormat = 'TFRecord',\\n\",\" selectors = BANDS + [RESPONSE]\\n\",\" )\\n\",\" task.start()\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"rWXrvBE4607G\",\"colab_type\":\"text\"},\"source\":[\"# Training data\\n\",\"\\n\",\"Load the data exported from Earth Engine into a `tf.data.Dataset`. The following are helper functions for that.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"WWZ0UXCVMyJP\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"def parse_tfrecord(example_proto):\\n\",\" \\\"\\\"\\\"The parsing function.\\n\",\" Read a serialized example into the structure defined by FEATURES_DICT.\\n\",\" Args:\\n\",\" example_proto: a serialized Example.\\n\",\" Returns:\\n\",\" A dictionary of tensors, keyed by feature name.\\n\",\" \\\"\\\"\\\"\\n\",\" return tf.io.parse_single_example(example_proto, FEATURES_DICT)\\n\",\"\\n\",\"\\n\",\"def to_tuple(inputs):\\n\",\" \\\"\\\"\\\"Function to convert a dictionary of tensors to a tuple of (inputs, outputs).\\n\",\" Turn the tensors returned by parse_tfrecord into a stack in HWC shape.\\n\",\" Args:\\n\",\" inputs: A dictionary of tensors, keyed by feature name.\\n\",\" Returns:\\n\",\" A tuple of (inputs, outputs).\\n\",\" \\\"\\\"\\\"\\n\",\" inputsList = [inputs.get(key) for key in FEATURES]\\n\",\" stacked = tf.stack(inputsList, axis=0)\\n\",\" # Convert from CHW to HWC\\n\",\" stacked = tf.transpose(stacked, [1, 2, 0])\\n\",\" return stacked[:,:,:len(BANDS)], stacked[:,:,len(BANDS):]\\n\",\"\\n\",\"\\n\",\"def get_dataset(pattern):\\n\",\" \\\"\\\"\\\"Function to read, parse and format to tuple a set of input tfrecord files.\\n\",\" Get all the files matching the pattern, parse and convert to tuple.\\n\",\" Args:\\n\",\" pattern: A file pattern to match in a Cloud Storage bucket.\\n\",\" Returns:\\n\",\" A tf.data.Dataset\\n\",\" \\\"\\\"\\\"\\n\",\" glob = tf.io.gfile.glob(pattern)\\n\",\" dataset = tf.data.TFRecordDataset(glob, compression_type='GZIP')\\n\",\" dataset = dataset.map(parse_tfrecord, num_parallel_calls=5)\\n\",\" dataset = dataset.map(to_tuple, num_parallel_calls=5)\\n\",\" return dataset\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"Xg1fa18336D2\",\"colab_type\":\"text\"},\"source\":[\"Use the helpers to read in the training dataset. Print the first record to check.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"rm0qRF0fAYcC\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"def get_training_dataset():\\n\",\"\\t\\\"\\\"\\\"Get the preprocessed training dataset\\n\",\" Returns: \\n\",\" A tf.data.Dataset of training data.\\n\",\" \\\"\\\"\\\"\\n\",\"\\tglob = 'gs://' + BUCKET + '/' + FOLDER + '/' + TRAINING_BASE + '*'\\n\",\"\\tdataset = get_dataset(glob)\\n\",\"\\tdataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()\\n\",\"\\treturn dataset\\n\",\"\\n\",\"training = get_training_dataset()\\n\",\"\\n\",\"print(iter(training.take(1)).next())\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"j-cQO5RL6vob\",\"colab_type\":\"text\"},\"source\":[\"# Evaluation data\\n\",\"\\n\",\"Now do the same thing to get an evaluation dataset. Note that unlike the training dataset, the evaluation dataset has a batch size of 1, is not repeated and is not shuffled.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"ieKTCGiJ6xzo\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"def get_eval_dataset():\\n\",\"\\t\\\"\\\"\\\"Get the preprocessed evaluation dataset\\n\",\" Returns: \\n\",\" A tf.data.Dataset of evaluation data.\\n\",\" \\\"\\\"\\\"\\n\",\"\\tglob = 'gs://' + BUCKET + '/' + FOLDER + '/' + EVAL_BASE + '*'\\n\",\"\\tdataset = get_dataset(glob)\\n\",\"\\tdataset = dataset.batch(1).repeat()\\n\",\"\\treturn dataset\\n\",\"\\n\",\"evaluation = get_eval_dataset()\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"9JIE7Yl87lgU\",\"colab_type\":\"text\"},\"source\":[\"# Model\\n\",\"\\n\",\"Here we use the Keras implementation of the U-Net model. The U-Net model takes 256x256 pixel patches as input and outputs per-pixel class probability, label or a continuous output. We can implement the model essentially unmodified, but will use mean squared error loss on the sigmoidal output since we are treating this as a regression problem, rather than a classification problem. Since impervious surface fraction is constrained to [0,1], with many values close to zero or one, a saturating activation function is suitable here.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"wsnnnz56yS3l\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"from tensorflow.python.keras import layers\\n\",\"from tensorflow.python.keras import losses\\n\",\"from tensorflow.python.keras import models\\n\",\"from tensorflow.python.keras import metrics\\n\",\"from tensorflow.python.keras import optimizers\\n\",\"\\n\",\"def conv_block(input_tensor, num_filters):\\n\",\"\\tencoder = layers.Conv2D(num_filters, (3, 3), padding='same')(input_tensor)\\n\",\"\\tencoder = layers.BatchNormalization()(encoder)\\n\",\"\\tencoder = layers.Activation('relu')(encoder)\\n\",\"\\tencoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)\\n\",\"\\tencoder = layers.BatchNormalization()(encoder)\\n\",\"\\tencoder = layers.Activation('relu')(encoder)\\n\",\"\\treturn encoder\\n\",\"\\n\",\"def encoder_block(input_tensor, num_filters):\\n\",\"\\tencoder = conv_block(input_tensor, num_filters)\\n\",\"\\tencoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)\\n\",\"\\treturn encoder_pool, encoder\\n\",\"\\n\",\"def decoder_block(input_tensor, concat_tensor, num_filters):\\n\",\"\\tdecoder = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same')(input_tensor)\\n\",\"\\tdecoder = layers.concatenate([concat_tensor, decoder], axis=-1)\\n\",\"\\tdecoder = layers.BatchNormalization()(decoder)\\n\",\"\\tdecoder = layers.Activation('relu')(decoder)\\n\",\"\\tdecoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)\\n\",\"\\tdecoder = layers.BatchNormalization()(decoder)\\n\",\"\\tdecoder = layers.Activation('relu')(decoder)\\n\",\"\\tdecoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)\\n\",\"\\tdecoder = layers.BatchNormalization()(decoder)\\n\",\"\\tdecoder = layers.Activation('relu')(decoder)\\n\",\"\\treturn decoder\\n\",\"\\n\",\"def get_model():\\n\",\"\\tinputs = layers.Input(shape=[None, None, len(BANDS)]) # 256\\n\",\"\\tencoder0_pool, encoder0 = encoder_block(inputs, 32) # 128\\n\",\"\\tencoder1_pool, encoder1 = encoder_block(encoder0_pool, 64) # 64\\n\",\"\\tencoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) # 32\\n\",\"\\tencoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) # 16\\n\",\"\\tencoder4_pool, encoder4 = encoder_block(encoder3_pool, 512) # 8\\n\",\"\\tcenter = conv_block(encoder4_pool, 1024) # center\\n\",\"\\tdecoder4 = decoder_block(center, encoder4, 512) # 16\\n\",\"\\tdecoder3 = decoder_block(decoder4, encoder3, 256) # 32\\n\",\"\\tdecoder2 = decoder_block(decoder3, encoder2, 128) # 64\\n\",\"\\tdecoder1 = decoder_block(decoder2, encoder1, 64) # 128\\n\",\"\\tdecoder0 = decoder_block(decoder1, encoder0, 32) # 256\\n\",\"\\toutputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)\\n\",\"\\n\",\"\\tmodel = models.Model(inputs=[inputs], outputs=[outputs])\\n\",\"\\n\",\"\\tmodel.compile(\\n\",\"\\t\\toptimizer=optimizers.get(OPTIMIZER), \\n\",\"\\t\\tloss=losses.get(LOSS),\\n\",\"\\t\\tmetrics=[metrics.get(metric) for metric in METRICS])\\n\",\"\\n\",\"\\treturn model\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"uu_E7OTDBCoS\",\"colab_type\":\"text\"},\"source\":[\"# Training the model\\n\",\"\\n\",\"You train a Keras model by calling `.fit()` on it. Here we're going to train for 10 epochs, which is suitable for demonstration purposes. For production use, you probably want to optimize this parameter, for example through [hyperparamter tuning](https://cloud.google.com/ml-engine/docs/tensorflow/using-hyperparameter-tuning).\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"NzzaWxOhSxBy\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"m = get_model()\\n\",\"\\n\",\"m.fit(\\n\",\" x=training, \\n\",\" epochs=EPOCHS, \\n\",\" steps_per_epoch=int(TRAIN_SIZE / BATCH_SIZE), \\n\",\" validation_data=evaluation,\\n\",\" validation_steps=EVAL_SIZE)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"U2XrwZHp66j4\",\"colab_type\":\"text\"},\"source\":[\"Note that the notebook VM is sometimes not heavy-duty enough to get through a whole training job, especially if you have a large buffer size or a large number of epochs. You can still use this notebook for training, but may need to set up an alternative VM ([learn more](https://research.google.com/colaboratory/local-runtimes.html)) for production use. Alternatively, you can package your code for running large training jobs on Google's AI Platform [as described here](https://cloud.google.com/ml-engine/docs/tensorflow/trainer-considerations). The following code loads a pre-trained model, which you can use for predictions right away.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"-RJpNfEUS1qp\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Load a trained model. 50 epochs. 25 hours. Final RMSE ~0.08.\\n\",\"MODEL_DIR = 'gs://ee-docs-demos/fcnn-demo/trainer/model'\\n\",\"m = tf.keras.models.load_model(MODEL_DIR)\\n\",\"m.summary()\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"J1ySNup0xCqN\",\"colab_type\":\"text\"},\"source\":[\"# Prediction\\n\",\"\\n\",\"The prediction pipeline is:\\n\",\"\\n\",\"1. Export imagery on which to do predictions from Earth Engine in TFRecord format to a Cloud Storge bucket.\\n\",\"2. Use the trained model to make the predictions.\\n\",\"3. Write the predictions to a TFRecord file in a Cloud Storage.\\n\",\"4. Upload the predictions TFRecord file to Earth Engine.\\n\",\"\\n\",\"The following functions handle this process. It's useful to separate the export from the predictions so that you can experiment with different models without running the export every time.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"M3WDAa-RUpXP\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"def doExport(out_image_base, kernel_buffer, region):\\n\",\" \\\"\\\"\\\"Run the image export task. Block until complete.\\n\",\" \\\"\\\"\\\"\\n\",\" task = ee.batch.Export.image.toCloudStorage(\\n\",\" image = image.select(BANDS),\\n\",\" description = out_image_base,\\n\",\" bucket = BUCKET,\\n\",\" fileNamePrefix = FOLDER + '/' + out_image_base,\\n\",\" region = region.getInfo()['coordinates'],\\n\",\" scale = 30,\\n\",\" fileFormat = 'TFRecord',\\n\",\" maxPixels = 1e10,\\n\",\" formatOptions = {\\n\",\" 'patchDimensions': KERNEL_SHAPE,\\n\",\" 'kernelSize': kernel_buffer,\\n\",\" 'compressed': True,\\n\",\" 'maxFileSize': 104857600\\n\",\" }\\n\",\" )\\n\",\" task.start()\\n\",\"\\n\",\" # Block until the task completes.\\n\",\" print('Running image export to Cloud Storage...')\\n\",\" import time\\n\",\" while task.active():\\n\",\" time.sleep(30)\\n\",\"\\n\",\" # Error condition\\n\",\" if task.status()['state'] != 'COMPLETED':\\n\",\" print('Error with image export.')\\n\",\" else:\\n\",\" print('Image export completed.')\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"zb_9_FflygVw\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"def doPrediction(out_image_base, user_folder, kernel_buffer, region):\\n\",\" \\\"\\\"\\\"Perform inference on exported imagery, upload to Earth Engine.\\n\",\" \\\"\\\"\\\"\\n\",\"\\n\",\" print('Looking for TFRecord files...')\\n\",\"\\n\",\" # Get a list of all the files in the output bucket.\\n\",\" filesList = !gsutil ls 'gs://'{BUCKET}'/'{FOLDER}\\n\",\"\\n\",\" # Get only the files generated by the image export.\\n\",\" exportFilesList = [s for s in filesList if out_image_base in s]\\n\",\"\\n\",\" # Get the list of image files and the JSON mixer file.\\n\",\" imageFilesList = []\\n\",\" jsonFile = None\\n\",\" for f in exportFilesList:\\n\",\" if f.endswith('.tfrecord.gz'):\\n\",\" imageFilesList.append(f)\\n\",\" elif f.endswith('.json'):\\n\",\" jsonFile = f\\n\",\"\\n\",\" # Make sure the files are in the right order.\\n\",\" imageFilesList.sort()\\n\",\"\\n\",\" from pprint import pprint\\n\",\" pprint(imageFilesList)\\n\",\" print(jsonFile)\\n\",\"\\n\",\" import json\\n\",\" # Load the contents of the mixer file to a JSON object.\\n\",\" jsonText = !gsutil cat {jsonFile}\\n\",\" # Get a single string w/ newlines from the IPython.utils.text.SList\\n\",\" mixer = json.loads(jsonText.nlstr)\\n\",\" pprint(mixer)\\n\",\" patches = mixer['totalPatches']\\n\",\"\\n\",\" # Get set up for prediction.\\n\",\" x_buffer = int(kernel_buffer[0] / 2)\\n\",\" y_buffer = int(kernel_buffer[1] / 2)\\n\",\"\\n\",\" buffered_shape = [\\n\",\" KERNEL_SHAPE[0] + kernel_buffer[0],\\n\",\" KERNEL_SHAPE[1] + kernel_buffer[1]]\\n\",\"\\n\",\" imageColumns = [\\n\",\" tf.io.FixedLenFeature(shape=buffered_shape, dtype=tf.float32) \\n\",\" for k in BANDS\\n\",\" ]\\n\",\"\\n\",\" imageFeaturesDict = dict(zip(BANDS, imageColumns))\\n\",\"\\n\",\" def parse_image(example_proto):\\n\",\" return tf.io.parse_single_example(example_proto, imageFeaturesDict)\\n\",\"\\n\",\" def toTupleImage(inputs):\\n\",\" inputsList = [inputs.get(key) for key in BANDS]\\n\",\" stacked = tf.stack(inputsList, axis=0)\\n\",\" stacked = tf.transpose(stacked, [1, 2, 0])\\n\",\" return stacked\\n\",\"\\n\",\" # Create a dataset from the TFRecord file(s) in Cloud Storage.\\n\",\" imageDataset = tf.data.TFRecordDataset(imageFilesList, compression_type='GZIP')\\n\",\" imageDataset = imageDataset.map(parse_image, num_parallel_calls=5)\\n\",\" imageDataset = imageDataset.map(toTupleImage).batch(1)\\n\",\"\\n\",\" # Perform inference.\\n\",\" print('Running predictions...')\\n\",\" predictions = m.predict(imageDataset, steps=patches, verbose=1)\\n\",\" # print(predictions[0])\\n\",\"\\n\",\" print('Writing predictions...')\\n\",\" out_image_file = 'gs://' + BUCKET + '/' + FOLDER + '/' + out_image_base + '.TFRecord'\\n\",\" writer = tf.io.TFRecordWriter(out_image_file)\\n\",\" patches = 0\\n\",\" for predictionPatch in predictions:\\n\",\" print('Writing patch ' + str(patches) + '...')\\n\",\" predictionPatch = predictionPatch[\\n\",\" x_buffer:x_buffer+KERNEL_SIZE, y_buffer:y_buffer+KERNEL_SIZE]\\n\",\"\\n\",\" # Create an example.\\n\",\" example = tf.train.Example(\\n\",\" features=tf.train.Features(\\n\",\" feature={\\n\",\" 'impervious': tf.train.Feature(\\n\",\" float_list=tf.train.FloatList(\\n\",\" value=predictionPatch.flatten()))\\n\",\" }\\n\",\" )\\n\",\" )\\n\",\" # Write the example.\\n\",\" writer.write(example.SerializeToString())\\n\",\" patches += 1\\n\",\"\\n\",\" writer.close()\\n\",\"\\n\",\" # Start the upload.\\n\",\" out_image_asset = user_folder + '/' + out_image_base\\n\",\" !earthengine upload image --asset_id={out_image_asset} {out_image_file} {jsonFile}\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"LZqlymOehnQO\",\"colab_type\":\"text\"},\"source\":[\"Now there's all the code needed to run the prediction pipeline, all that remains is to specify the output region in which to do the prediction, the names of the output files, where to put them, and the shape of the outputs. In terms of the shape, the model is trained on 256x256 patches, but can work (in theory) on any patch that's big enough with even dimensions ([reference](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)). Because of tile boundary artifacts, give the model slightly larger patches for prediction, then clip out the middle 256x256 patch. This is controlled with a kernel buffer, half the size of which will extend beyond the kernel buffer. For example, specifying a 128x128 kernel will append 64 pixels on each side of the patch, to ensure that the pixels in the output are taken from inputs completely covered by the kernel.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"FPANwc7B1-TS\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Output assets folder: YOUR FOLDER\\n\",\"user_folder = 'users/username' # INSERT YOUR FOLDER HERE.\\n\",\"\\n\",\"# Base file name to use for TFRecord files and assets.\\n\",\"bj_image_base = 'FCNN_demo_beijing_384_'\\n\",\"# Half this will extend on the sides of each patch.\\n\",\"bj_kernel_buffer = [128, 128]\\n\",\"# Beijing\\n\",\"bj_region = ee.Geometry.Polygon(\\n\",\" [[[115.9662455210937, 40.121362012835235],\\n\",\" [115.9662455210937, 39.64293313749715],\\n\",\" [117.01818643906245, 39.64293313749715],\\n\",\" [117.01818643906245, 40.121362012835235]]], None, False)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"lLNEOLkXWvSi\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Run the export.\\n\",\"doExport(bj_image_base, bj_kernel_buffer, bj_region)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"KxACnxKFrQ_J\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"# Run the prediction.\\n\",\"doPrediction(bj_image_base, user_folder, bj_kernel_buffer, bj_region)\"],\"execution_count\":0,\"outputs\":[]},{\"cell_type\":\"markdown\",\"metadata\":{\"id\":\"uj_G9OZ1xH6K\",\"colab_type\":\"text\"},\"source\":[\"# Display the output\\n\",\"\\n\",\"One the data has been exported, the model has made predictions and the predictions have been written to a file, and the image imported to Earth Engine, it's possible to display the resultant Earth Engine asset. Here, display the impervious area predictions over Beijing, China.\"]},{\"cell_type\":\"code\",\"metadata\":{\"id\":\"Jgco6HJ4R5p2\",\"colab_type\":\"code\",\"colab\":{}},\"source\":[\"out_image = ee.Image(user_folder + '/' + bj_image_base)\\n\",\"mapid = out_image.getMapId({'min': 0, 'max': 1})\\n\",\"map = folium.Map(location=[39.898, 116.5097])\\n\",\"folium.TileLayer(\\n\",\" tiles=mapid['tile_fetcher'].url_format,\\n\",\" attr='Map Data &copy; Google Earth Engine',\\n\",\" overlay=True,\\n\",\" name='predicted impervious',\\n\",\" ).add_to(map)\\n\",\"map.add_child(folium.LayerControl())\\n\",\"map\"],\"execution_count\":0,\"outputs\":[]}]}"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":234,"cells":{"repo_name":{"kind":"string","value":"Cyb3rWard0g/HELK"},"path":{"kind":"string","value":"docker/helk-jupyter/notebooks/sigma/win_susp_powershell_empire_launch.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"3455"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Empire PowerShell Launch Parameters\\n\",\n \"Detects suspicious powershell command line parameters used in Empire\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Rule Content\\n\",\n \"```\\n\",\n \"- title: Empire PowerShell Launch Parameters\\n\",\n \" id: 79f4ede3-402e-41c8-bc3e-ebbf5f162581\\n\",\n \" description: Detects suspicious powershell command line parameters used in Empire\\n\",\n \" status: experimental\\n\",\n \" references:\\n\",\n \" - https://github.com/EmpireProject/Empire/blob/c2ba61ca8d2031dad0cfc1d5770ba723e8b710db/lib/common/helpers.py#L165\\n\",\n \" - https://github.com/EmpireProject/Empire/blob/e37fb2eef8ff8f5a0a689f1589f424906fe13055/lib/modules/powershell/persistence/powerbreach/deaduser.py#L191\\n\",\n \" - https://github.com/EmpireProject/Empire/blob/e37fb2eef8ff8f5a0a689f1589f424906fe13055/lib/modules/powershell/persistence/powerbreach/resolver.py#L178\\n\",\n \" - https://github.com/EmpireProject/Empire/blob/e37fb2eef8ff8f5a0a689f1589f424906fe13055/data/module_source/privesc/Invoke-EventVwrBypass.ps1#L64\\n\",\n \" author: Florian Roth\\n\",\n \" date: 2019/04/20\\n\",\n \" tags:\\n\",\n \" - attack.execution\\n\",\n \" - attack.t1086\\n\",\n \" logsource:\\n\",\n \" category: process_creation\\n\",\n \" product: windows\\n\",\n \" service: null\\n\",\n \" detection:\\n\",\n \" selection:\\n\",\n \" CommandLine:\\n\",\n \" - '* -NoP -sta -NonI -W Hidden -Enc *'\\n\",\n \" - '* -noP -sta -w 1 -enc *'\\n\",\n \" - '* -NoP -NonI -W Hidden -enc *'\\n\",\n \" condition: selection\\n\",\n \" level: critical\\n\",\n \"\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Querying Elasticsearch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Import Libraries\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from elasticsearch import Elasticsearch\\n\",\n \"from elasticsearch_dsl import Search\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Initialize Elasticsearch client\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"es = Elasticsearch(['http://helk-elasticsearch:9200'])\\n\",\n \"searchContext = Search(using=es, index='logs-*', doc_type='doc')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Run Elasticsearch Query\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"s = searchContext.query('query_string', query='process_command_line.keyword:(*\\\\ \\\\-NoP\\\\ \\\\-sta\\\\ \\\\-NonI\\\\ \\\\-W\\\\ Hidden\\\\ \\\\-Enc\\\\ * OR *\\\\ \\\\-noP\\\\ \\\\-sta\\\\ \\\\-w\\\\ 1\\\\ \\\\-enc\\\\ * OR *\\\\ \\\\-NoP\\\\ \\\\-NonI\\\\ \\\\-W\\\\ Hidden\\\\ \\\\-enc\\\\ *)')\\n\",\n \"response = s.execute()\\n\",\n \"if response.success():\\n\",\n \" df = pd.DataFrame((d.to_dict() for d in s.scan()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Show Results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.head()\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":235,"cells":{"repo_name":{"kind":"string","value":"TomAugspurger/pyowa"},"path":{"kind":"string","value":"flights.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"2604828"},"content":{"kind":"null"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":236,"cells":{"repo_name":{"kind":"string","value":"ES-DOC/esdoc-jupyterhub"},"path":{"kind":"string","value":"notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"209021"},"content":{"kind":"string","value":"{\n \"nbformat_minor\": 0, \n \"nbformat\": 4, \n \"cells\": [\n {\n \"source\": [\n \"# ES-DOC CMIP6 Model Properties - Atmos \\n\", \n \"**MIP Era**: CMIP6 \\n\", \n \"**Institute**: TEST-INSTITUTE-2 \\n\", \n \"**Source ID**: SANDBOX-3 \\n\", \n \"**Topic**: Atmos \\n\", \n \"**Sub-Topics**: Dynamical Core, Radiation, Turbulence Convection, Microphysics Precipitation, Cloud Scheme, Observation Simulation, Gravity Waves, Solar, Volcanos. \\n\", \n \"**Properties**: 156 (127 required) \\n\", \n \"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmos?client=jupyter-notebook) \\n\", \n \"**Initialized From**: -- \\n\", \n \"\\n\", \n \"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \\n\", \n \"**Notebook Initialised**: 2018-02-15 16:54:45\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Setup \\n\", \n \"**IMPORTANT: to be executed each time you run the notebook** \"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# DO NOT EDIT ! \\n\", \n \"from pyesdoc.ipython.model_topic import NotebookOutput \\n\", \n \"\\n\", \n \"# DO NOT EDIT ! \\n\", \n \"DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-3', 'atmos')\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Authors \\n\", \n \"*Set document authors*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# Set as follows: DOC.set_author(\\\"name\\\", \\\"email\\\") \\n\", \n \"# TODO - please enter value(s)\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Contributors \\n\", \n \"*Specify document contributors* \"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# Set as follows: DOC.set_contributor(\\\"name\\\", \\\"email\\\") \\n\", \n \"# TODO - please enter value(s)\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Publication \\n\", \n \"*Specify document publication status* \"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# Set publication status: \\n\", \n \"# 0=do not publish, 1=publish. \\n\", \n \"DOC.set_publication_status(0)\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Table of Contents \\n\", \n \"[1. Key Properties --&gt; Overview](#1.-Key-Properties---&gt;-Overview) \\n\", \n \"[2. Key Properties --&gt; Resolution](#2.-Key-Properties---&gt;-Resolution) \\n\", \n \"[3. Key Properties --&gt; Timestepping](#3.-Key-Properties---&gt;-Timestepping) \\n\", \n \"[4. Key Properties --&gt; Orography](#4.-Key-Properties---&gt;-Orography) \\n\", \n \"[5. Grid --&gt; Discretisation](#5.-Grid---&gt;-Discretisation) \\n\", \n \"[6. Grid --&gt; Discretisation --&gt; Horizontal](#6.-Grid---&gt;-Discretisation---&gt;-Horizontal) \\n\", \n \"[7. Grid --&gt; Discretisation --&gt; Vertical](#7.-Grid---&gt;-Discretisation---&gt;-Vertical) \\n\", \n \"[8. Dynamical Core](#8.-Dynamical-Core) \\n\", \n \"[9. Dynamical Core --&gt; Top Boundary](#9.-Dynamical-Core---&gt;-Top-Boundary) \\n\", \n \"[10. Dynamical Core --&gt; Lateral Boundary](#10.-Dynamical-Core---&gt;-Lateral-Boundary) \\n\", \n \"[11. Dynamical Core --&gt; Diffusion Horizontal](#11.-Dynamical-Core---&gt;-Diffusion-Horizontal) \\n\", \n \"[12. Dynamical Core --&gt; Advection Tracers](#12.-Dynamical-Core---&gt;-Advection-Tracers) \\n\", \n \"[13. Dynamical Core --&gt; Advection Momentum](#13.-Dynamical-Core---&gt;-Advection-Momentum) \\n\", \n \"[14. Radiation](#14.-Radiation) \\n\", \n \"[15. Radiation --&gt; Shortwave Radiation](#15.-Radiation---&gt;-Shortwave-Radiation) \\n\", \n \"[16. Radiation --&gt; Shortwave GHG](#16.-Radiation---&gt;-Shortwave-GHG) \\n\", \n \"[17. Radiation --&gt; Shortwave Cloud Ice](#17.-Radiation---&gt;-Shortwave-Cloud-Ice) \\n\", \n \"[18. Radiation --&gt; Shortwave Cloud Liquid](#18.-Radiation---&gt;-Shortwave-Cloud-Liquid) \\n\", \n \"[19. Radiation --&gt; Shortwave Cloud Inhomogeneity](#19.-Radiation---&gt;-Shortwave-Cloud-Inhomogeneity) \\n\", \n \"[20. Radiation --&gt; Shortwave Aerosols](#20.-Radiation---&gt;-Shortwave-Aerosols) \\n\", \n \"[21. Radiation --&gt; Shortwave Gases](#21.-Radiation---&gt;-Shortwave-Gases) \\n\", \n \"[22. Radiation --&gt; Longwave Radiation](#22.-Radiation---&gt;-Longwave-Radiation) \\n\", \n \"[23. Radiation --&gt; Longwave GHG](#23.-Radiation---&gt;-Longwave-GHG) \\n\", \n \"[24. Radiation --&gt; Longwave Cloud Ice](#24.-Radiation---&gt;-Longwave-Cloud-Ice) \\n\", \n \"[25. Radiation --&gt; Longwave Cloud Liquid](#25.-Radiation---&gt;-Longwave-Cloud-Liquid) \\n\", \n \"[26. Radiation --&gt; Longwave Cloud Inhomogeneity](#26.-Radiation---&gt;-Longwave-Cloud-Inhomogeneity) \\n\", \n \"[27. Radiation --&gt; Longwave Aerosols](#27.-Radiation---&gt;-Longwave-Aerosols) \\n\", \n \"[28. Radiation --&gt; Longwave Gases](#28.-Radiation---&gt;-Longwave-Gases) \\n\", \n \"[29. Turbulence Convection](#29.-Turbulence-Convection) \\n\", \n \"[30. Turbulence Convection --&gt; Boundary Layer Turbulence](#30.-Turbulence-Convection---&gt;-Boundary-Layer-Turbulence) \\n\", \n \"[31. Turbulence Convection --&gt; Deep Convection](#31.-Turbulence-Convection---&gt;-Deep-Convection) \\n\", \n \"[32. Turbulence Convection --&gt; Shallow Convection](#32.-Turbulence-Convection---&gt;-Shallow-Convection) \\n\", \n \"[33. Microphysics Precipitation](#33.-Microphysics-Precipitation) \\n\", \n \"[34. Microphysics Precipitation --&gt; Large Scale Precipitation](#34.-Microphysics-Precipitation---&gt;-Large-Scale-Precipitation) \\n\", \n \"[35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics](#35.-Microphysics-Precipitation---&gt;-Large-Scale-Cloud-Microphysics) \\n\", \n \"[36. Cloud Scheme](#36.-Cloud-Scheme) \\n\", \n \"[37. Cloud Scheme --&gt; Optical Cloud Properties](#37.-Cloud-Scheme---&gt;-Optical-Cloud-Properties) \\n\", \n \"[38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution](#38.-Cloud-Scheme---&gt;-Sub-Grid-Scale-Water-Distribution) \\n\", \n \"[39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution](#39.-Cloud-Scheme---&gt;-Sub-Grid-Scale-Ice-Distribution) \\n\", \n \"[40. Observation Simulation](#40.-Observation-Simulation) \\n\", \n \"[41. Observation Simulation --&gt; Isscp Attributes](#41.-Observation-Simulation---&gt;-Isscp-Attributes) \\n\", \n \"[42. Observation Simulation --&gt; Cosp Attributes](#42.-Observation-Simulation---&gt;-Cosp-Attributes) \\n\", \n \"[43. Observation Simulation --&gt; Radar Inputs](#43.-Observation-Simulation---&gt;-Radar-Inputs) \\n\", \n \"[44. Observation Simulation --&gt; Lidar Inputs](#44.-Observation-Simulation---&gt;-Lidar-Inputs) \\n\", \n \"[45. Gravity Waves](#45.-Gravity-Waves) \\n\", \n \"[46. Gravity Waves --&gt; Orographic Gravity Waves](#46.-Gravity-Waves---&gt;-Orographic-Gravity-Waves) \\n\", \n \"[47. Gravity Waves --&gt; Non Orographic Gravity Waves](#47.-Gravity-Waves---&gt;-Non-Orographic-Gravity-Waves) \\n\", \n \"[48. Solar](#48.-Solar) \\n\", \n \"[49. Solar --&gt; Solar Pathways](#49.-Solar---&gt;-Solar-Pathways) \\n\", \n \"[50. Solar --&gt; Solar Constant](#50.-Solar---&gt;-Solar-Constant) \\n\", \n \"[51. Solar --&gt; Orbital Parameters](#51.-Solar---&gt;-Orbital-Parameters) \\n\", \n \"[52. Solar --&gt; Insolation Ozone](#52.-Solar---&gt;-Insolation-Ozone) \\n\", \n \"[53. Volcanos](#53.-Volcanos) \\n\", \n \"[54. Volcanos --&gt; Volcanoes Treatment](#54.-Volcanos---&gt;-Volcanoes-Treatment) \\n\", \n \"\\n\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"# 1. Key Properties --&gt; Overview \\n\", \n \"*Top level key properties*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 1.1. Model Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview of atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 1.2. Model Name\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.overview.model_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 1.3. Model Family\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Type of atmospheric model.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.overview.model_family') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"AGCM\\\" \\n\", \n \"# \\\"ARCM\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 1.4. Basic Approximations\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Basic approximations made in the atmosphere.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"primitive equations\\\" \\n\", \n \"# \\\"non-hydrostatic\\\" \\n\", \n \"# \\\"anelastic\\\" \\n\", \n \"# \\\"Boussinesq\\\" \\n\", \n \"# \\\"hydrostatic\\\" \\n\", \n \"# \\\"quasi-hydrostatic\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 2. Key Properties --&gt; Resolution \\n\", \n \"*Characteristics of the model resolution*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 2.1. Horizontal Resolution Name\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 2.2. Canonical Horizontal Resolution\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 2.3. Range Horizontal Resolution\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 2.4. Number Of Vertical Levels\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Number of vertical levels resolved on the computational grid.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 2.5. High Top\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 3. Key Properties --&gt; Timestepping \\n\", \n \"*Characteristics of the atmosphere model time stepping*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 3.1. Timestep Dynamics\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Timestep for the dynamics, e.g. 30 min.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 3.2. Timestep Shortwave Radiative Transfer\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Timestep for the shortwave radiative transfer, e.g. 1.5 hours.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 3.3. Timestep Longwave Radiative Transfer\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Timestep for the longwave radiative transfer, e.g. 3 hours.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 4. Key Properties --&gt; Orography \\n\", \n \"*Characteristics of the model orography*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 4.1. Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Time adaptation of the orography.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.orography.type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"present day\\\" \\n\", \n \"# \\\"modified\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 4.2. Changes\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *If the orography type is modified describe the time adaptation changes.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.key_properties.orography.changes') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"related to ice sheets\\\" \\n\", \n \"# \\\"related to tectonics\\\" \\n\", \n \"# \\\"modified mean\\\" \\n\", \n \"# \\\"modified variance if taken into account in model (cf gravity waves)\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 5. Grid --&gt; Discretisation \\n\", \n \"*Atmosphere grid discretisation*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 5.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of grid discretisation in the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 6. Grid --&gt; Discretisation --&gt; Horizontal \\n\", \n \"*Atmosphere discretisation in the horizontal*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 6.1. Scheme Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Horizontal discretisation type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"spectral\\\" \\n\", \n \"# \\\"fixed grid\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 6.2. Scheme Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Horizontal discretisation method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"finite elements\\\" \\n\", \n \"# \\\"finite volumes\\\" \\n\", \n \"# \\\"finite difference\\\" \\n\", \n \"# \\\"centered finite difference\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 6.3. Scheme Order\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Horizontal discretisation function order*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"second\\\" \\n\", \n \"# \\\"third\\\" \\n\", \n \"# \\\"fourth\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 6.4. Horizontal Pole\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Horizontal discretisation pole singularity treatment*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"filter\\\" \\n\", \n \"# \\\"pole rotation\\\" \\n\", \n \"# \\\"artificial island\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 6.5. Grid Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Horizontal grid type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Gaussian\\\" \\n\", \n \"# \\\"Latitude-Longitude\\\" \\n\", \n \"# \\\"Cubed-Sphere\\\" \\n\", \n \"# \\\"Icosahedral\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 7. Grid --&gt; Discretisation --&gt; Vertical \\n\", \n \"*Atmosphere discretisation in the vertical*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 7.1. Coordinate Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Type of vertical coordinate system*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"isobaric\\\" \\n\", \n \"# \\\"sigma\\\" \\n\", \n \"# \\\"hybrid sigma-pressure\\\" \\n\", \n \"# \\\"hybrid pressure\\\" \\n\", \n \"# \\\"vertically lagrangian\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 8. Dynamical Core \\n\", \n \"*Characteristics of the dynamical core*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 8.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of atmosphere dynamical core*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.2. Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name for the dynamical core of the model.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.3. Timestepping Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Timestepping framework type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Adams-Bashforth\\\" \\n\", \n \"# \\\"explicit\\\" \\n\", \n \"# \\\"implicit\\\" \\n\", \n \"# \\\"semi-implicit\\\" \\n\", \n \"# \\\"leap frog\\\" \\n\", \n \"# \\\"multi-step\\\" \\n\", \n \"# \\\"Runge Kutta fifth order\\\" \\n\", \n \"# \\\"Runge Kutta second order\\\" \\n\", \n \"# \\\"Runge Kutta third order\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.4. Prognostic Variables\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *List of the model prognostic variables*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"surface pressure\\\" \\n\", \n \"# \\\"wind components\\\" \\n\", \n \"# \\\"divergence/curl\\\" \\n\", \n \"# \\\"temperature\\\" \\n\", \n \"# \\\"potential temperature\\\" \\n\", \n \"# \\\"total water\\\" \\n\", \n \"# \\\"water vapour\\\" \\n\", \n \"# \\\"water liquid\\\" \\n\", \n \"# \\\"water ice\\\" \\n\", \n \"# \\\"total water moments\\\" \\n\", \n \"# \\\"clouds\\\" \\n\", \n \"# \\\"radiation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 9. Dynamical Core --&gt; Top Boundary \\n\", \n \"*Type of boundary layer at the top of the model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 9.1. Top Boundary Condition\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Top boundary condition*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"sponge layer\\\" \\n\", \n \"# \\\"radiation boundary condition\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.2. Top Heat\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Top boundary heat treatment*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.3. Top Wind\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Top boundary wind treatment*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 10. Dynamical Core --&gt; Lateral Boundary \\n\", \n \"*Type of lateral boundary condition (if the model is a regional model)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 10.1. Condition\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Type of lateral boundary condition*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"sponge layer\\\" \\n\", \n \"# \\\"radiation boundary condition\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 11. Dynamical Core --&gt; Diffusion Horizontal \\n\", \n \"*Horizontal diffusion scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 11.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Horizontal diffusion scheme name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 11.2. Scheme Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Horizontal diffusion scheme method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"iterated Laplacian\\\" \\n\", \n \"# \\\"bi-harmonic\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 12. Dynamical Core --&gt; Advection Tracers \\n\", \n \"*Tracer advection scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 12.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Tracer advection scheme name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Heun\\\" \\n\", \n \"# \\\"Roe and VanLeer\\\" \\n\", \n \"# \\\"Roe and Superbee\\\" \\n\", \n \"# \\\"Prather\\\" \\n\", \n \"# \\\"UTOPIA\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 12.2. Scheme Characteristics\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Tracer advection scheme characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Eulerian\\\" \\n\", \n \"# \\\"modified Euler\\\" \\n\", \n \"# \\\"Lagrangian\\\" \\n\", \n \"# \\\"semi-Lagrangian\\\" \\n\", \n \"# \\\"cubic semi-Lagrangian\\\" \\n\", \n \"# \\\"quintic semi-Lagrangian\\\" \\n\", \n \"# \\\"mass-conserving\\\" \\n\", \n \"# \\\"finite volume\\\" \\n\", \n \"# \\\"flux-corrected\\\" \\n\", \n \"# \\\"linear\\\" \\n\", \n \"# \\\"quadratic\\\" \\n\", \n \"# \\\"quartic\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 12.3. Conserved Quantities\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Tracer advection scheme conserved quantities*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"dry mass\\\" \\n\", \n \"# \\\"tracer mass\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 12.4. Conservation Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Tracer advection scheme conservation method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"conservation fixer\\\" \\n\", \n \"# \\\"Priestley algorithm\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 13. Dynamical Core --&gt; Advection Momentum \\n\", \n \"*Momentum advection scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 13.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Momentum advection schemes name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"VanLeer\\\" \\n\", \n \"# \\\"Janjic\\\" \\n\", \n \"# \\\"SUPG (Streamline Upwind Petrov-Galerkin)\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.2. Scheme Characteristics\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Momentum advection scheme characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"2nd order\\\" \\n\", \n \"# \\\"4th order\\\" \\n\", \n \"# \\\"cell-centred\\\" \\n\", \n \"# \\\"staggered grid\\\" \\n\", \n \"# \\\"semi-staggered grid\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.3. Scheme Staggering Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Momentum advection scheme staggering type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Arakawa B-grid\\\" \\n\", \n \"# \\\"Arakawa C-grid\\\" \\n\", \n \"# \\\"Arakawa D-grid\\\" \\n\", \n \"# \\\"Arakawa E-grid\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.4. Conserved Quantities\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Momentum advection scheme conserved quantities*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Angular momentum\\\" \\n\", \n \"# \\\"Horizontal momentum\\\" \\n\", \n \"# \\\"Enstrophy\\\" \\n\", \n \"# \\\"Mass\\\" \\n\", \n \"# \\\"Total energy\\\" \\n\", \n \"# \\\"Vorticity\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.5. Conservation Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Momentum advection scheme conservation method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"conservation fixer\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 14. Radiation \\n\", \n \"*Characteristics of the atmosphere radiation process*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 14.1. Aerosols\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Aerosols whose radiative effect is taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.aerosols') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"sulphate\\\" \\n\", \n \"# \\\"nitrate\\\" \\n\", \n \"# \\\"sea salt\\\" \\n\", \n \"# \\\"dust\\\" \\n\", \n \"# \\\"ice\\\" \\n\", \n \"# \\\"organic\\\" \\n\", \n \"# \\\"BC (black carbon / soot)\\\" \\n\", \n \"# \\\"SOA (secondary organic aerosols)\\\" \\n\", \n \"# \\\"POM (particulate organic matter)\\\" \\n\", \n \"# \\\"polar stratospheric ice\\\" \\n\", \n \"# \\\"NAT (nitric acid trihydrate)\\\" \\n\", \n \"# \\\"NAD (nitric acid dihydrate)\\\" \\n\", \n \"# \\\"STS (supercooled ternary solution aerosol particle)\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 15. Radiation --&gt; Shortwave Radiation \\n\", \n \"*Properties of the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 15.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of shortwave radiation in the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.2. Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name for the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.3. Spectral Integration\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Shortwave radiation scheme spectral integration*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"wide-band model\\\" \\n\", \n \"# \\\"correlated-k\\\" \\n\", \n \"# \\\"exponential sum fitting\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.4. Transport Calculation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Shortwave radiation transport calculation methods*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"two-stream\\\" \\n\", \n \"# \\\"layer interaction\\\" \\n\", \n \"# \\\"bulk\\\" \\n\", \n \"# \\\"adaptive\\\" \\n\", \n \"# \\\"multi-stream\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.5. Spectral Intervals\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Shortwave radiation scheme number of spectral intervals*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 16. Radiation --&gt; Shortwave GHG \\n\", \n \"*Representation of greenhouse gases in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 16.1. Greenhouse Gas Complexity\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"CO2\\\" \\n\", \n \"# \\\"CH4\\\" \\n\", \n \"# \\\"N2O\\\" \\n\", \n \"# \\\"CFC-11 eq\\\" \\n\", \n \"# \\\"CFC-12 eq\\\" \\n\", \n \"# \\\"HFC-134a eq\\\" \\n\", \n \"# \\\"Explicit ODSs\\\" \\n\", \n \"# \\\"Explicit other fluorinated gases\\\" \\n\", \n \"# \\\"O3\\\" \\n\", \n \"# \\\"H2O\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 16.2. ODS\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"CFC-12\\\" \\n\", \n \"# \\\"CFC-11\\\" \\n\", \n \"# \\\"CFC-113\\\" \\n\", \n \"# \\\"CFC-114\\\" \\n\", \n \"# \\\"CFC-115\\\" \\n\", \n \"# \\\"HCFC-22\\\" \\n\", \n \"# \\\"HCFC-141b\\\" \\n\", \n \"# \\\"HCFC-142b\\\" \\n\", \n \"# \\\"Halon-1211\\\" \\n\", \n \"# \\\"Halon-1301\\\" \\n\", \n \"# \\\"Halon-2402\\\" \\n\", \n \"# \\\"methyl chloroform\\\" \\n\", \n \"# \\\"carbon tetrachloride\\\" \\n\", \n \"# \\\"methyl chloride\\\" \\n\", \n \"# \\\"methylene chloride\\\" \\n\", \n \"# \\\"chloroform\\\" \\n\", \n \"# \\\"methyl bromide\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 16.3. Other Flourinated Gases\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"HFC-134a\\\" \\n\", \n \"# \\\"HFC-23\\\" \\n\", \n \"# \\\"HFC-32\\\" \\n\", \n \"# \\\"HFC-125\\\" \\n\", \n \"# \\\"HFC-143a\\\" \\n\", \n \"# \\\"HFC-152a\\\" \\n\", \n \"# \\\"HFC-227ea\\\" \\n\", \n \"# \\\"HFC-236fa\\\" \\n\", \n \"# \\\"HFC-245fa\\\" \\n\", \n \"# \\\"HFC-365mfc\\\" \\n\", \n \"# \\\"HFC-43-10mee\\\" \\n\", \n \"# \\\"CF4\\\" \\n\", \n \"# \\\"C2F6\\\" \\n\", \n \"# \\\"C3F8\\\" \\n\", \n \"# \\\"C4F10\\\" \\n\", \n \"# \\\"C5F12\\\" \\n\", \n \"# \\\"C6F14\\\" \\n\", \n \"# \\\"C7F16\\\" \\n\", \n \"# \\\"C8F18\\\" \\n\", \n \"# \\\"c-C4F8\\\" \\n\", \n \"# \\\"NF3\\\" \\n\", \n \"# \\\"SF6\\\" \\n\", \n \"# \\\"SO2F2\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 17. Radiation --&gt; Shortwave Cloud Ice \\n\", \n \"*Shortwave radiative properties of ice crystals in clouds*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 17.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General shortwave radiative interactions with cloud ice crystals*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 17.2. Physical Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical representation of cloud ice crystals in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"bi-modal size distribution\\\" \\n\", \n \"# \\\"ensemble of ice crystals\\\" \\n\", \n \"# \\\"mean projected area\\\" \\n\", \n \"# \\\"ice water path\\\" \\n\", \n \"# \\\"crystal asymmetry\\\" \\n\", \n \"# \\\"crystal aspect ratio\\\" \\n\", \n \"# \\\"effective crystal radius\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 17.3. Optical Methods\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Optical methods applicable to cloud ice crystals in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"T-matrix\\\" \\n\", \n \"# \\\"geometric optics\\\" \\n\", \n \"# \\\"finite difference time domain (FDTD)\\\" \\n\", \n \"# \\\"Mie theory\\\" \\n\", \n \"# \\\"anomalous diffraction approximation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 18. Radiation --&gt; Shortwave Cloud Liquid \\n\", \n \"*Shortwave radiative properties of liquid droplets in clouds*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 18.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General shortwave radiative interactions with cloud liquid droplets*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 18.2. Physical Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical representation of cloud liquid droplets in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"cloud droplet number concentration\\\" \\n\", \n \"# \\\"effective cloud droplet radii\\\" \\n\", \n \"# \\\"droplet size distribution\\\" \\n\", \n \"# \\\"liquid water path\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 18.3. Optical Methods\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"geometric optics\\\" \\n\", \n \"# \\\"Mie theory\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 19. Radiation --&gt; Shortwave Cloud Inhomogeneity \\n\", \n \"*Cloud inhomogeneity in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 19.1. Cloud Inhomogeneity\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Method for taking into account horizontal cloud inhomogeneity*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Monte Carlo Independent Column Approximation\\\" \\n\", \n \"# \\\"Triplecloud\\\" \\n\", \n \"# \\\"analytic\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 20. Radiation --&gt; Shortwave Aerosols \\n\", \n \"*Shortwave radiative properties of aerosols*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 20.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General shortwave radiative interactions with aerosols*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 20.2. Physical Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical representation of aerosols in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"number concentration\\\" \\n\", \n \"# \\\"effective radii\\\" \\n\", \n \"# \\\"size distribution\\\" \\n\", \n \"# \\\"asymmetry\\\" \\n\", \n \"# \\\"aspect ratio\\\" \\n\", \n \"# \\\"mixing state\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 20.3. Optical Methods\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Optical methods applicable to aerosols in the shortwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"T-matrix\\\" \\n\", \n \"# \\\"geometric optics\\\" \\n\", \n \"# \\\"finite difference time domain (FDTD)\\\" \\n\", \n \"# \\\"Mie theory\\\" \\n\", \n \"# \\\"anomalous diffraction approximation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 21. Radiation --&gt; Shortwave Gases \\n\", \n \"*Shortwave radiative properties of gases*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 21.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General shortwave radiative interactions with gases*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 22. Radiation --&gt; Longwave Radiation \\n\", \n \"*Properties of the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 22.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of longwave radiation in the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.2. Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name for the longwave radiation scheme.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.3. Spectral Integration\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Longwave radiation scheme spectral integration*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"wide-band model\\\" \\n\", \n \"# \\\"correlated-k\\\" \\n\", \n \"# \\\"exponential sum fitting\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.4. Transport Calculation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Longwave radiation transport calculation methods*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"two-stream\\\" \\n\", \n \"# \\\"layer interaction\\\" \\n\", \n \"# \\\"bulk\\\" \\n\", \n \"# \\\"adaptive\\\" \\n\", \n \"# \\\"multi-stream\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.5. Spectral Intervals\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Longwave radiation scheme number of spectral intervals*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 23. Radiation --&gt; Longwave GHG \\n\", \n \"*Representation of greenhouse gases in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 23.1. Greenhouse Gas Complexity\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"CO2\\\" \\n\", \n \"# \\\"CH4\\\" \\n\", \n \"# \\\"N2O\\\" \\n\", \n \"# \\\"CFC-11 eq\\\" \\n\", \n \"# \\\"CFC-12 eq\\\" \\n\", \n \"# \\\"HFC-134a eq\\\" \\n\", \n \"# \\\"Explicit ODSs\\\" \\n\", \n \"# \\\"Explicit other fluorinated gases\\\" \\n\", \n \"# \\\"O3\\\" \\n\", \n \"# \\\"H2O\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 23.2. ODS\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"CFC-12\\\" \\n\", \n \"# \\\"CFC-11\\\" \\n\", \n \"# \\\"CFC-113\\\" \\n\", \n \"# \\\"CFC-114\\\" \\n\", \n \"# \\\"CFC-115\\\" \\n\", \n \"# \\\"HCFC-22\\\" \\n\", \n \"# \\\"HCFC-141b\\\" \\n\", \n \"# \\\"HCFC-142b\\\" \\n\", \n \"# \\\"Halon-1211\\\" \\n\", \n \"# \\\"Halon-1301\\\" \\n\", \n \"# \\\"Halon-2402\\\" \\n\", \n \"# \\\"methyl chloroform\\\" \\n\", \n \"# \\\"carbon tetrachloride\\\" \\n\", \n \"# \\\"methyl chloride\\\" \\n\", \n \"# \\\"methylene chloride\\\" \\n\", \n \"# \\\"chloroform\\\" \\n\", \n \"# \\\"methyl bromide\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 23.3. Other Flourinated Gases\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"HFC-134a\\\" \\n\", \n \"# \\\"HFC-23\\\" \\n\", \n \"# \\\"HFC-32\\\" \\n\", \n \"# \\\"HFC-125\\\" \\n\", \n \"# \\\"HFC-143a\\\" \\n\", \n \"# \\\"HFC-152a\\\" \\n\", \n \"# \\\"HFC-227ea\\\" \\n\", \n \"# \\\"HFC-236fa\\\" \\n\", \n \"# \\\"HFC-245fa\\\" \\n\", \n \"# \\\"HFC-365mfc\\\" \\n\", \n \"# \\\"HFC-43-10mee\\\" \\n\", \n \"# \\\"CF4\\\" \\n\", \n \"# \\\"C2F6\\\" \\n\", \n \"# \\\"C3F8\\\" \\n\", \n \"# \\\"C4F10\\\" \\n\", \n \"# \\\"C5F12\\\" \\n\", \n \"# \\\"C6F14\\\" \\n\", \n \"# \\\"C7F16\\\" \\n\", \n \"# \\\"C8F18\\\" \\n\", \n \"# \\\"c-C4F8\\\" \\n\", \n \"# \\\"NF3\\\" \\n\", \n \"# \\\"SF6\\\" \\n\", \n \"# \\\"SO2F2\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 24. Radiation --&gt; Longwave Cloud Ice \\n\", \n \"*Longwave radiative properties of ice crystals in clouds*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 24.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General longwave radiative interactions with cloud ice crystals*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 24.2. Physical Reprenstation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical representation of cloud ice crystals in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"bi-modal size distribution\\\" \\n\", \n \"# \\\"ensemble of ice crystals\\\" \\n\", \n \"# \\\"mean projected area\\\" \\n\", \n \"# \\\"ice water path\\\" \\n\", \n \"# \\\"crystal asymmetry\\\" \\n\", \n \"# \\\"crystal aspect ratio\\\" \\n\", \n \"# \\\"effective crystal radius\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 24.3. Optical Methods\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Optical methods applicable to cloud ice crystals in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"T-matrix\\\" \\n\", \n \"# \\\"geometric optics\\\" \\n\", \n \"# \\\"finite difference time domain (FDTD)\\\" \\n\", \n \"# \\\"Mie theory\\\" \\n\", \n \"# \\\"anomalous diffraction approximation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 25. Radiation --&gt; Longwave Cloud Liquid \\n\", \n \"*Longwave radiative properties of liquid droplets in clouds*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 25.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General longwave radiative interactions with cloud liquid droplets*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 25.2. Physical Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical representation of cloud liquid droplets in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"cloud droplet number concentration\\\" \\n\", \n \"# \\\"effective cloud droplet radii\\\" \\n\", \n \"# \\\"droplet size distribution\\\" \\n\", \n \"# \\\"liquid water path\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 25.3. Optical Methods\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Optical methods applicable to cloud liquid droplets in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"geometric optics\\\" \\n\", \n \"# \\\"Mie theory\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 26. Radiation --&gt; Longwave Cloud Inhomogeneity \\n\", \n \"*Cloud inhomogeneity in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 26.1. Cloud Inhomogeneity\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Method for taking into account horizontal cloud inhomogeneity*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Monte Carlo Independent Column Approximation\\\" \\n\", \n \"# \\\"Triplecloud\\\" \\n\", \n \"# \\\"analytic\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 27. Radiation --&gt; Longwave Aerosols \\n\", \n \"*Longwave radiative properties of aerosols*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 27.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General longwave radiative interactions with aerosols*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 27.2. Physical Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical representation of aerosols in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"number concentration\\\" \\n\", \n \"# \\\"effective radii\\\" \\n\", \n \"# \\\"size distribution\\\" \\n\", \n \"# \\\"asymmetry\\\" \\n\", \n \"# \\\"aspect ratio\\\" \\n\", \n \"# \\\"mixing state\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 27.3. Optical Methods\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Optical methods applicable to aerosols in the longwave radiation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"T-matrix\\\" \\n\", \n \"# \\\"geometric optics\\\" \\n\", \n \"# \\\"finite difference time domain (FDTD)\\\" \\n\", \n \"# \\\"Mie theory\\\" \\n\", \n \"# \\\"anomalous diffraction approximation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 28. Radiation --&gt; Longwave Gases \\n\", \n \"*Longwave radiative properties of gases*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 28.1. General Interactions\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General longwave radiative interactions with gases*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"scattering\\\" \\n\", \n \"# \\\"emission/absorption\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 29. Turbulence Convection \\n\", \n \"*Atmosphere Convective Turbulence and Clouds*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 29.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of atmosphere convection and turbulence*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 30. Turbulence Convection --&gt; Boundary Layer Turbulence \\n\", \n \"*Properties of the boundary layer turbulence scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 30.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Boundary layer turbulence scheme name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Mellor-Yamada\\\" \\n\", \n \"# \\\"Holtslag-Boville\\\" \\n\", \n \"# \\\"EDMF\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 30.2. Scheme Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Boundary layer turbulence scheme type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"TKE prognostic\\\" \\n\", \n \"# \\\"TKE diagnostic\\\" \\n\", \n \"# \\\"TKE coupled with water\\\" \\n\", \n \"# \\\"vertical profile of Kz\\\" \\n\", \n \"# \\\"non-local diffusion\\\" \\n\", \n \"# \\\"Monin-Obukhov similarity\\\" \\n\", \n \"# \\\"Coastal Buddy Scheme\\\" \\n\", \n \"# \\\"Coupled with convection\\\" \\n\", \n \"# \\\"Coupled with gravity waves\\\" \\n\", \n \"# \\\"Depth capped at cloud base\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 30.3. Closure Order\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Boundary layer turbulence scheme closure order*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 30.4. Counter Gradient\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Uses boundary layer turbulence scheme counter gradient*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 31. Turbulence Convection --&gt; Deep Convection \\n\", \n \"*Properties of the deep convection scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 31.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Deep convection scheme name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 31.2. Scheme Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Deep convection scheme type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"mass-flux\\\" \\n\", \n \"# \\\"adjustment\\\" \\n\", \n \"# \\\"plume ensemble\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 31.3. Scheme Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Deep convection scheme method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"CAPE\\\" \\n\", \n \"# \\\"bulk\\\" \\n\", \n \"# \\\"ensemble\\\" \\n\", \n \"# \\\"CAPE/WFN based\\\" \\n\", \n \"# \\\"TKE/CIN based\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 31.4. Processes\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical processes taken into account in the parameterisation of deep convection*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"vertical momentum transport\\\" \\n\", \n \"# \\\"convective momentum transport\\\" \\n\", \n \"# \\\"entrainment\\\" \\n\", \n \"# \\\"detrainment\\\" \\n\", \n \"# \\\"penetrative convection\\\" \\n\", \n \"# \\\"updrafts\\\" \\n\", \n \"# \\\"downdrafts\\\" \\n\", \n \"# \\\"radiative effect of anvils\\\" \\n\", \n \"# \\\"re-evaporation of convective precipitation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 31.5. Microphysics\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Microphysics scheme for deep convection. Microphysical processes directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"tuning parameter based\\\" \\n\", \n \"# \\\"single moment\\\" \\n\", \n \"# \\\"two moment\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 32. Turbulence Convection --&gt; Shallow Convection \\n\", \n \"*Properties of the shallow convection scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 32.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Shallow convection scheme name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 32.2. Scheme Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *shallow convection scheme type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"mass-flux\\\" \\n\", \n \"# \\\"cumulus-capped boundary layer\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 32.3. Scheme Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *shallow convection scheme method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"same as deep (unified)\\\" \\n\", \n \"# \\\"included in boundary layer turbulence\\\" \\n\", \n \"# \\\"separate diagnosis\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 32.4. Processes\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Physical processes taken into account in the parameterisation of shallow convection*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"convective momentum transport\\\" \\n\", \n \"# \\\"entrainment\\\" \\n\", \n \"# \\\"detrainment\\\" \\n\", \n \"# \\\"penetrative convection\\\" \\n\", \n \"# \\\"re-evaporation of convective precipitation\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 32.5. Microphysics\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Microphysics scheme for shallow convection*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"tuning parameter based\\\" \\n\", \n \"# \\\"single moment\\\" \\n\", \n \"# \\\"two moment\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 33. Microphysics Precipitation \\n\", \n \"*Large Scale Cloud Microphysics and Precipitation*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 33.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of large scale cloud microphysics and precipitation*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 34. Microphysics Precipitation --&gt; Large Scale Precipitation \\n\", \n \"*Properties of the large scale precipitation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 34.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name of the large scale precipitation parameterisation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 34.2. Hydrometeors\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Precipitating hydrometeors taken into account in the large scale precipitation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"liquid rain\\\" \\n\", \n \"# \\\"snow\\\" \\n\", \n \"# \\\"hail\\\" \\n\", \n \"# \\\"graupel\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics \\n\", \n \"*Properties of the large scale cloud microphysics scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 35.1. Scheme Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name of the microphysics parameterisation scheme used for large scale clouds.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 35.2. Processes\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Large scale cloud microphysics processes*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"mixed phase\\\" \\n\", \n \"# \\\"cloud droplets\\\" \\n\", \n \"# \\\"cloud ice\\\" \\n\", \n \"# \\\"ice nucleation\\\" \\n\", \n \"# \\\"water vapour deposition\\\" \\n\", \n \"# \\\"effect of raindrops\\\" \\n\", \n \"# \\\"effect of snow\\\" \\n\", \n \"# \\\"effect of graupel\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 36. Cloud Scheme \\n\", \n \"*Characteristics of the cloud scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 36.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of the atmosphere cloud scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.2. Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name for the cloud scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.3. Atmos Coupling\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *Atmosphere components that are linked to the cloud scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"atmosphere_radiation\\\" \\n\", \n \"# \\\"atmosphere_microphysics_precipitation\\\" \\n\", \n \"# \\\"atmosphere_turbulence_convection\\\" \\n\", \n \"# \\\"atmosphere_gravity_waves\\\" \\n\", \n \"# \\\"atmosphere_solar\\\" \\n\", \n \"# \\\"atmosphere_volcano\\\" \\n\", \n \"# \\\"atmosphere_cloud_simulator\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.4. Uses Separate Treatment\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Different cloud schemes for the different types of clouds (convective, stratiform and boundary layer)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.5. Processes\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Processes included in the cloud scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.processes') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"entrainment\\\" \\n\", \n \"# \\\"detrainment\\\" \\n\", \n \"# \\\"bulk cloud\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.6. Prognostic Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Is the cloud scheme a prognostic scheme?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.7. Diagnostic Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Is the cloud scheme a diagnostic scheme?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 36.8. Prognostic Variables\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *List the prognostic variables used by the cloud scheme, if applicable.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"cloud amount\\\" \\n\", \n \"# \\\"liquid\\\" \\n\", \n \"# \\\"ice\\\" \\n\", \n \"# \\\"rain\\\" \\n\", \n \"# \\\"snow\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 37. Cloud Scheme --&gt; Optical Cloud Properties \\n\", \n \"*Optical cloud properties*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 37.1. Cloud Overlap Method\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Method for taking into account overlapping of cloud layers*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"random\\\" \\n\", \n \"# \\\"maximum\\\" \\n\", \n \"# \\\"maximum-random\\\" \\n\", \n \"# \\\"exponential\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 37.2. Cloud Inhomogeneity\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Method for taking into account cloud inhomogeneity*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution \\n\", \n \"*Sub-grid scale water distribution*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 38.1. Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sub-grid scale water distribution type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"prognostic\\\" \\n\", \n \"# \\\"diagnostic\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 38.2. Function Name\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sub-grid scale water distribution function name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 38.3. Function Order\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sub-grid scale water distribution function type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 38.4. Convection Coupling\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Sub-grid scale water distribution coupling with convection*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"coupled with deep\\\" \\n\", \n \"# \\\"coupled with shallow\\\" \\n\", \n \"# \\\"not coupled with convection\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution \\n\", \n \"*Sub-grid scale ice distribution*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 39.1. Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sub-grid scale ice distribution type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"prognostic\\\" \\n\", \n \"# \\\"diagnostic\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 39.2. Function Name\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sub-grid scale ice distribution function name*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 39.3. Function Order\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sub-grid scale ice distribution function type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 39.4. Convection Coupling\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Sub-grid scale ice distribution coupling with convection*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"coupled with deep\\\" \\n\", \n \"# \\\"coupled with shallow\\\" \\n\", \n \"# \\\"not coupled with convection\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 40. Observation Simulation \\n\", \n \"*Characteristics of observation simulation*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 40.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of observation simulator characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 41. Observation Simulation --&gt; Isscp Attributes \\n\", \n \"*ISSCP Characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 41.1. Top Height Estimation Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Cloud simulator ISSCP top height estimation methodUo*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"no adjustment\\\" \\n\", \n \"# \\\"IR brightness\\\" \\n\", \n \"# \\\"visible optical depth\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 41.2. Top Height Direction\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator ISSCP top height direction*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"lowest altitude level\\\" \\n\", \n \"# \\\"highest altitude level\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 42. Observation Simulation --&gt; Cosp Attributes \\n\", \n \"*CFMIP Observational Simulator Package attributes*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 42.1. Run Configuration\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator COSP run configuration*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Inline\\\" \\n\", \n \"# \\\"Offline\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 42.2. Number Of Grid Points\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator COSP number of grid points*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 42.3. Number Of Sub Columns\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator COSP number of sub-cloumns used to simulate sub-grid variability*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 42.4. Number Of Levels\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator COSP number of levels*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 43. Observation Simulation --&gt; Radar Inputs \\n\", \n \"*Characteristics of the cloud radar simulator*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 43.1. Frequency\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator radar frequency (Hz)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 43.2. Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator radar type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"surface\\\" \\n\", \n \"# \\\"space borne\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 43.3. Gas Absorption\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator radar uses gas absorption*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 43.4. Effective Radius\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator radar uses effective radius*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 44. Observation Simulation --&gt; Lidar Inputs \\n\", \n \"*Characteristics of the cloud lidar simulator*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 44.1. Ice Types\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Cloud simulator lidar ice type*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"ice spheres\\\" \\n\", \n \"# \\\"ice non-spherical\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 44.2. Overlap\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Cloud simulator lidar overlap*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"max\\\" \\n\", \n \"# \\\"random\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 45. Gravity Waves \\n\", \n \"*Characteristics of the parameterised gravity waves in the atmosphere, whether from orography or other sources.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 45.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of gravity wave parameterisation in the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 45.2. Sponge Layer\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Sponge layer in the upper levels in order to avoid gravity wave reflection at the top.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Rayleigh friction\\\" \\n\", \n \"# \\\"Diffusive sponge layer\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 45.3. Background\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Background wave distribution*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.background') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"continuous spectrum\\\" \\n\", \n \"# \\\"discrete spectrum\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 45.4. Subgrid Scale Orography\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Subgrid scale orography effects taken into account.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"effect on drag\\\" \\n\", \n \"# \\\"effect on lifting\\\" \\n\", \n \"# \\\"enhanced topography\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 46. Gravity Waves --&gt; Orographic Gravity Waves \\n\", \n \"*Gravity waves generated due to the presence of orography*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 46.1. Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name for the orographic gravity wave scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 46.2. Source Mechanisms\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Orographic gravity wave source mechanisms*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"linear mountain waves\\\" \\n\", \n \"# \\\"hydraulic jump\\\" \\n\", \n \"# \\\"envelope orography\\\" \\n\", \n \"# \\\"low level flow blocking\\\" \\n\", \n \"# \\\"statistical sub-grid scale variance\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 46.3. Calculation Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Orographic gravity wave calculation method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"non-linear calculation\\\" \\n\", \n \"# \\\"more than two cardinal directions\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 46.4. Propagation Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Orographic gravity wave propogation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"linear theory\\\" \\n\", \n \"# \\\"non-linear theory\\\" \\n\", \n \"# \\\"includes boundary layer ducting\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 46.5. Dissipation Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Orographic gravity wave dissipation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"total wave\\\" \\n\", \n \"# \\\"single wave\\\" \\n\", \n \"# \\\"spectral\\\" \\n\", \n \"# \\\"linear\\\" \\n\", \n \"# \\\"wave saturation vs Richardson number\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 47. Gravity Waves --&gt; Non Orographic Gravity Waves \\n\", \n \"*Gravity waves generated by non-orographic processes.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 47.1. Name\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Commonly used name for the non-orographic gravity wave scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 47.2. Source Mechanisms\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Non-orographic gravity wave source mechanisms*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"convection\\\" \\n\", \n \"# \\\"precipitation\\\" \\n\", \n \"# \\\"background spectrum\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 47.3. Calculation Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Non-orographic gravity wave calculation method*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"spatially dependent\\\" \\n\", \n \"# \\\"temporally dependent\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 47.4. Propagation Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Non-orographic gravity wave propogation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"linear theory\\\" \\n\", \n \"# \\\"non-linear theory\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 47.5. Dissipation Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Non-orographic gravity wave dissipation scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"total wave\\\" \\n\", \n \"# \\\"single wave\\\" \\n\", \n \"# \\\"spectral\\\" \\n\", \n \"# \\\"linear\\\" \\n\", \n \"# \\\"wave saturation vs Richardson number\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 48. Solar \\n\", \n \"*Top of atmosphere solar insolation characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 48.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of solar insolation of the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 49. Solar --&gt; Solar Pathways \\n\", \n \"*Pathways for solar forcing of the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 49.1. Pathways\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Pathways for the solar forcing of the atmosphere model domain*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"SW radiation\\\" \\n\", \n \"# \\\"precipitating energetic particles\\\" \\n\", \n \"# \\\"cosmic rays\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 50. Solar --&gt; Solar Constant \\n\", \n \"*Solar constant and top of atmosphere insolation characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 50.1. Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Time adaptation of the solar constant.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.solar_constant.type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"fixed\\\" \\n\", \n \"# \\\"transient\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 50.2. Fixed Value\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If the solar constant is fixed, enter the value of the solar constant (W m-2).*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 50.3. Transient Characteristics\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *solar constant transient characteristics (W m-2)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 51. Solar --&gt; Orbital Parameters \\n\", \n \"*Orbital parameters and top of atmosphere insolation characteristics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 51.1. Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Time adaptation of orbital parameters*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"fixed\\\" \\n\", \n \"# \\\"transient\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 51.2. Fixed Reference Date\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Reference date for fixed orbital parameters (yyyy)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 51.3. Transient Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Description of transient orbital parameters*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 51.4. Computation Method\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Method used for computing orbital parameters.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Berger 1978\\\" \\n\", \n \"# \\\"Laskar 2004\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 52. Solar --&gt; Insolation Ozone \\n\", \n \"*Impact of solar insolation on stratospheric ozone*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 52.1. Solar Ozone Impact\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Does top of atmosphere insolation impact on stratospheric ozone?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 53. Volcanos \\n\", \n \"*Characteristics of the implementation of volcanoes*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 53.1. Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview description of the implementation of volcanic effects in the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.volcanos.overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 54. Volcanos --&gt; Volcanoes Treatment \\n\", \n \"*Treatment of volcanoes in the atmosphere*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 54.1. Volcanoes Implementation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *How volcanic effects are modeled in the atmosphere.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"high frequency solar constant anomaly\\\" \\n\", \n \"# \\\"stratospheric aerosols optical thickness\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### \\u00a92017 [ES-DOC](https://es-doc.org) \\n\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }\n ], \n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\", \n \"name\": \"python2\", \n \"language\": \"python\"\n }, \n \"language_info\": {\n \"mimetype\": \"text/x-python\", \n \"nbconvert_exporter\": \"python\", \n \"name\": \"python\", \n \"file_extension\": \".py\", \n \"version\": \"2.7.10\", \n \"pygments_lexer\": \"ipython2\", \n \"codemirror_mode\": {\n \"version\": 2, \n \"name\": \"ipython\"\n }\n }\n }\n}"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":237,"cells":{"repo_name":{"kind":"string","value":"winpython/winpython_afterdoc"},"path":{"kind":"string","value":"docs/installing_julia_and_ijulia.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"13034"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Installating Julia/IJulia \\n\",\n \"\\n\",\n \"### 1 - Downloading and Installing the right Julia binary in the right place\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"import io\\n\",\n \"import re\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import urllib.request as request # Python 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get latest stable release info, download link and hashes\\n\",\n \"g = request.urlopen(\\\"https://julialang.org/downloads/\\\")\\n\",\n \"s = g.read().decode()\\n\",\n \"g.close;\\n\",\n \"\\n\",\n \"r = r'([^<]+) ' + \\\\\\n\",\n \" r'

Checksums for this release are available in both MD5 and SHA256 formats.

' + \\\\\\n\",\n \" r'[^W]*Windows .help. 64-bit .installer., 64-bit .portable.' + \\\\\\n\",\n \" r' 32-bit .installer., 32-bit .portable.'\\n\",\n \"\\n\",\n \"release_str, md5link, sha256link, ziplink64bit, ziplink32bit = re.findall(r,s)[0]\\n\",\n \"julia_version=re.findall(r\\\"v([^\\\\s]+)\\\",release_str)[0]\\n\",\n \"print(release_str)\\n\",\n \"print(julia_version)\\n\",\n \"print(ziplink64bit)\\n\",\n \"print(ziplink32bit)\\n\",\n \"print(md5link)\\n\",\n \"print(sha256link)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"overwrite links, since v1.5.3 installation does not work properly due to\\n\",\n \"https://github.com/JuliaLang/julia/issues/38411\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if julia_version=='1.5.3':\\n\",\n \" julia_version='1.6.0-rc1'\\n\",\n \" ziplink64bit='https://julialang-s3.julialang.org/bin/winnt/x64/1.6/julia-1.6.0-rc1-win64.zip'\\n\",\n \" md5link='https://julialang-s3.julialang.org/bin/checksums/julia-1.6.0-rc1.md5'\\n\",\n \" sha256link='https://julialang-s3.julialang.org/bin/checksums/julia-1.6.0-rc1.sha256'\\n\",\n \" print(julia_version)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# download checksums\\n\",\n \"g = request.urlopen(md5link)\\n\",\n \"md5hashes = g.read().decode()\\n\",\n \"g.close;\\n\",\n \"\\n\",\n \"g = request.urlopen(sha256link)\\n\",\n \"sha256hashes = g.read().decode()\\n\",\n \"g.close;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# downloading julia (may take 1 minute or 2)\\n\",\n \"\\n\",\n \"if 'amd64' in sys.version.lower():\\n\",\n \" julia_zip=ziplink64bit.split(\\\"/\\\")[-1]\\n\",\n \" julia_url=ziplink64bit\\n\",\n \"else:\\n\",\n \" julia_zip=ziplink32bit.split(\\\"/\\\")[-1]\\n\",\n \" julia_url=ziplink32bit\\n\",\n \" \\n\",\n \"hashes=(re.findall(r\\\"([0-9a-f]{32})\\\\s\\\"+julia_zip, md5hashes)[0] , re.findall(r\\\"([0-9a-f]{64})\\\\s+\\\"+julia_zip, sha256hashes)[0])\\n\",\n \" \\n\",\n \"julia_zip_fullpath = os.path.join(os.environ[\\\"WINPYDIRBASE\\\"], \\\"t\\\", julia_zip)\\n\",\n \"\\n\",\n \"g = request.urlopen(julia_url) \\n\",\n \"with io.open(julia_zip_fullpath, 'wb') as f:\\n\",\n \" f.write(g.read())\\n\",\n \"g.close\\n\",\n \"g = None\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#checking it's there\\n\",\n \"assert os.path.isfile(julia_zip_fullpath)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# checking the hashes\\n\",\n \"import hashlib\\n\",\n \"def give_hash(of_file, with_this):\\n\",\n \" with io.open(julia_zip_fullpath, 'rb') as f:\\n\",\n \" return with_this(f.read()).hexdigest() \\n\",\n \"print (\\\" \\\"*12+\\\"MD5\\\"+\\\" \\\"*(32-12-3)+\\\" \\\"+\\\" \\\"*15+\\\"SHA-256\\\"+\\\" \\\"*(40-15-5)+\\\"\\\\n\\\"+\\\"-\\\"*32+\\\" \\\"+\\\"-\\\"*64)\\n\",\n \"\\n\",\n \"print (\\\"%s %s %s\\\" % (give_hash(julia_zip_fullpath, hashlib.md5) , give_hash(julia_zip_fullpath, hashlib.sha256),julia_zip))\\n\",\n \"assert give_hash(julia_zip_fullpath, hashlib.md5) == hashes[0].lower() \\n\",\n \"assert give_hash(julia_zip_fullpath, hashlib.sha256) == hashes[1].lower()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# will be in env next time\\n\",\n \"os.environ[\\\"JUPYTER\\\"] = os.path.join(os.environ[\\\"WINPYDIR\\\"],\\\"Scripts\\\",\\\"jupyter.exe\\\")\\n\",\n \"os.environ[\\\"JULIA_HOME\\\"] = os.path.join(os.environ[\\\"WINPYDIRBASE\\\"], \\\"t\\\", \\\"julia-\\\"+julia_version)\\n\",\n \"os.environ[\\\"JULIA_EXE_PATH\\\"] = os.path.join(os.environ[\\\"JULIA_HOME\\\"], \\\"bin\\\")\\n\",\n \"os.environ[\\\"JULIA_EXE\\\"] = \\\"julia.exe\\\"\\n\",\n \"os.environ[\\\"JULIA\\\"] = os.path.join(os.environ[\\\"JULIA_EXE_PATH\\\"],os.environ[\\\"JULIA_EXE\\\"])\\n\",\n \"os.environ[\\\"JULIA_PKGDIR\\\"] = os.path.join(os.environ[\\\"WINPYDIRBASE\\\"],\\\"settings\\\",\\\".julia\\\")\\n\",\n \"os.environ[\\\"JULIA_DEPOT_PATH\\\"] = os.environ[\\\"JULIA_PKGDIR\\\"] \\n\",\n \"os.environ[\\\"JULIA_HISTORY\\\"] = os.path.join(os.environ[\\\"JULIA_PKGDIR\\\"],\\\"logs\\\",\\\"repl_history.jl\\\")\\n\",\n \"os.environ[\\\"CONDA_JL_HOME\\\"] = os.path.join(os.environ[\\\"JULIA_HOME\\\"], \\\"conda\\\", \\\"3\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"# move JULIA_EXE_PATH to the beginning of PATH, since a julia installation may be present on the machine\\n\",\n \"os.environ[\\\"PATH\\\"] = os.environ[\\\"JULIA_EXE_PATH\\\"] + \\\";\\\" + os.environ[\\\"PATH\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if not os.path.isdir(os.environ[\\\"JULIA_PKGDIR\\\"]):\\n\",\n \" os.mkdir(os.environ[\\\"JULIA_PKGDIR\\\"])\\n\",\n \" \\n\",\n \"if not os.path.isdir(os.path.join(os.environ[\\\"JULIA_PKGDIR\\\"],\\\"logs\\\")):\\n\",\n \" os.mkdir(os.path.join(os.environ[\\\"JULIA_PKGDIR\\\"],\\\"logs\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if not os.path.isfile(os.environ[\\\"JULIA_HISTORY\\\"]):\\n\",\n \" open(os.environ[\\\"JULIA_HISTORY\\\"], 'a').close() # create empty file\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# extract the zip archive\\n\",\n \"import zipfile\\n\",\n \"try:\\n\",\n \" with zipfile.ZipFile(julia_zip_fullpath) as z:\\n\",\n \" z.extractall(os.path.join(os.environ[\\\"WINPYDIRBASE\\\"], \\\"t\\\"))\\n\",\n \" print(\\\"Extracted all files\\\")\\n\",\n \"except:\\n\",\n \" print(\\\"Invalid file\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# delete zip file\\n\",\n \"os.remove(julia_zip_fullpath)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Initialize Julia , IJulia, and make them link to winpython\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# connecting Julia to WinPython (only once, or everytime you move things)\\n\",\n \"# see the Windows terminal window for the detailed status. This may take \\n\",\n \"# a minute or two.\\n\",\n \"import julia\\n\",\n \"julia.install()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%load_ext julia.magic\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"info = julia.juliainfo.JuliaInfo.load()\\n\",\n \"print(info.julia)\\n\",\n \"print(info.sysimage)\\n\",\n \"print(info.version_raw)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from julia.api import Julia\\n\",\n \"jl = Julia(compiled_modules=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# sanity check\\n\",\n \"assert jl.eval(\\\"1+2\\\") == 3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Print julia's versioninfo()\\n\",\n \"The environment should point to the usb drive and not to C:\\\\\\\\ (your local installation of julia maybe...)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"jl.eval(\\\"using InteractiveUtils\\\")\\n\",\n \"jl.eval('file = open(\\\"julia_versioninfo.txt\\\",\\\"w\\\")') \\n\",\n \"jl.eval(\\\"versioninfo(file,verbose=false)\\\")\\n\",\n \"jl.eval(\\\"close(file)\\\")\\n\",\n \"\\n\",\n \"with open('julia_versioninfo.txt', 'r') as f:\\n\",\n \" print(f.read())\\n\",\n \" \\n\",\n \"os.remove('julia_versioninfo.txt')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Install julia Packages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%julia\\n\",\n \"using Pkg\\n\",\n \"\\n\",\n \"Pkg.instantiate()\\n\",\n \"Pkg.update()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%julia\\n\",\n \"# add useful packages. Again, this may take a while...\\n\",\n \"Pkg.add(\\\"IJulia\\\")\\n\",\n \"Pkg.add(\\\"Plots\\\")\\n\",\n \"Pkg.add(\\\"Interact\\\")\\n\",\n \"Pkg.add(\\\"Compose\\\")\\n\",\n \"Pkg.add(\\\"SymPy\\\")\\n\",\n \"\\n\",\n \"using Compose\\n\",\n \"using SymPy\\n\",\n \"using IJulia\\n\",\n \"using Plots\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Fix the kernel.json to allow arbitrary drive letters and modify the env.bat\\n\",\n \"\\n\",\n \"the path to kernel.jl is hardcoded in the kernel.json file\\n\",\n \"this will cause trouble, if the drive letter of the usb drive changes\\n\",\n \"use relative paths instead\\n\",\n \"rewrite kernel.json and delete the one created from IJulia.jl Package\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"kernel_path = os.path.join(os.environ[\\\"WINPYDIRBASE\\\"], \\\"settings\\\", \\\"kernels\\\", \\\"julia-\\\"+julia_version[0:3])\\n\",\n \"assert os.path.isdir(kernel_path)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open(os.path.join(kernel_path,\\\"kernel.json\\\"), 'r') as f:\\n\",\n \" kernel_str = f.read()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_kernel_str = kernel_str.replace(os.environ[\\\"WINPYDIRBASE\\\"].replace(\\\"\\\\\\\\\\\",\\\"\\\\\\\\\\\\\\\\\\\"),\\\"{prefix}\\\\\\\\\\\\\\\\..\\\")\\n\",\n \"print(new_kernel_str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open(os.path.join(kernel_path,\\\"kernel.json\\\"), 'w') as f:\\n\",\n \" f.write(new_kernel_str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# add JULIA env variables to env.bat\\n\",\n \"inp_str = r\\\"\\\"\\\"\\n\",\n \"rem ******************\\n\",\n \"rem handle Julia {0} if included\\n\",\n \"rem ******************\\n\",\n \"\\n\",\n \"if not exist \\\"%WINPYDIRBASE%\\\\t\\\\julia-{0}\\\\bin\\\" goto julia_bad_{0}\\n\",\n \"set JULIA_PKGDIR=%WINPYDIRBASE%\\\\settings\\\\.julia\\n\",\n \"set JULIA_DEPOT_PATH=%JULIA_PKGDIR%\\n\",\n \"set JULIA_EXE=julia.exe\\n\",\n \"set JULIA_HOME=%WINPYDIRBASE%\\\\t\\\\julia-{0}\\n\",\n \"set JULIA_HISTORY=%JULIA_PKGDIR%\\\\logs\\\\repl_history.jl\\n\",\n \":julia_bad_{0}\\n\",\n \"\\n\",\n \"\\\"\\\"\\\".format(julia_version)\\n\",\n \"\\n\",\n \"# append to env.bat\\n\",\n \"with open(os.path.join(os.environ[\\\"WINPYDIRBASE\\\"],\\\"scripts\\\",\\\"env.bat\\\"), 'a') as file :\\n\",\n \" file.write(inp_str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Launching a Julia Notebook \\n\",\n \"\\n\",\n \"choose a Julia Kernel from Notebook, or Julia from Jupyterlab Launcher\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Julia Magic \\n\",\n \"or use %load_ext julia.magic then %julia or %%julia\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":238,"cells":{"repo_name":{"kind":"string","value":"hakonsbm/nest-simulator"},"path":{"kind":"string","value":"doc/topology/examples/grid_iaf.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"1796"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\nNEST Topology Module Example\\n\\nCreate layer of 4x3 iaf_psc_alpha neurons, visualize\\n\\nBCCN Tutorial @ CNS*09\\nHans Ekkehard Plesser, UMB\\n\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import nest\\nimport pylab\\nimport nest.topology as topo\\n\\npylab.ion()\\n\\nnest.ResetKernel()\\n\\nl1 = topo.CreateLayer({'columns': 4, 'rows': 3,\\n 'extent': [2.0, 1.5],\\n 'elements': 'iaf_psc_alpha'})\\n\\nnest.PrintNetwork()\\nnest.PrintNetwork(2)\\nnest.PrintNetwork(2, l1)\\n\\ntopo.PlotLayer(l1, nodesize=50)\\n\\n# beautify\\npylab.axis([-1.0, 1.0, -0.75, 0.75])\\npylab.axes().set_aspect('equal', 'box')\\npylab.axes().set_xticks((-0.75, -0.25, 0.25, 0.75))\\npylab.axes().set_yticks((-0.5, 0, 0.5))\\npylab.grid(True)\\npylab.xlabel('4 Columns, Extent: 1.5')\\npylab.ylabel('2 Rows, Extent: 1.0')\\n\\n# pylab.savefig('grid_iaf.png')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"license":{"kind":"string","value":"gpl-2.0"}}},{"rowIdx":239,"cells":{"repo_name":{"kind":"string","value":"mathnathan/notebooks"},"path":{"kind":"string","value":"mpfi/probability blog post.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"124466"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"Machine learning literature makes heavy use of probabilistic graphical models\\n\",\n \"and bayesian statistics. In fact, state of the art (SOTA) architectures, such as\\n\",\n \"[variational autoencoders][vae-blog] (VAE) or [generative adversarial\\n\",\n \"networks][gan-blog] (GAN), are intrinsically stochastic by nature. To\\n\",\n \"wholesomely understand research in this field not only do we need a broad\\n\",\n \"knowledge of mathematics, probability, and optimization but we somehow need\\n\",\n \"intuition about how these concepts are applied to real world problems. For\\n\",\n \"example, one of the most common applications of deep learning techniques is\\n\",\n \"vision. We may want to classify images or generate new ones. Most SOTA\\n\",\n \"techniques pose these problems in a probabilistic framework. We frequently see\\n\",\n \"things like $p(\\\\mathbf{x}|\\\\mathbf{z})$ where $\\\\mathbf{x}$ is an image and\\n\",\n \"$\\\\mathbf{z}$ is a latent variable. What do we mean by the probability of an\\n\",\n \"image? What is a latent variable, and why is it necessary[^Bishop2006] to pose\\n\",\n \"the problems this way?\\n\",\n \" \\n\",\n \"Short answer, it is necessary due to the inherent uncertainty of our universe.\\n\",\n \"In this case, uncertainty in image acquisition can be introduced via many\\n\",\n \"sources, such as the recording apparatus, the finite precision of our\\n\",\n \"measurements, as well as the intrinsic stochasticity of the process being\\n\",\n \"measured. Perhaps the most important source of uncertainty we will consider is\\n\",\n \"due to there being sources of variability that are themselves unobserved.\\n\",\n \"Probability theory provides us with a framework to reason in the presence of\\n\",\n \"uncertainty and information theory allows us to quantify uncertainty. As we \\n\",\n \"elluded earlier the field of machine learning makes heavy use of both, and\\n\",\n \"this is no coincidence.\\n\",\n \"\\n\",\n \"\\n\",\n \"## Representations\\n\",\n \"\\n\",\n \"How do we describe a face? The word \\\"face\\\" is a symbol and this symbol means\\n\",\n \"different things to different people. Yet, there is enough commonality between\\n\",\n \"our interpretations that we are able to effectively communicate with one\\n\",\n \"another using the word. How is that? What are the underlying features of faces\\n\",\n \"that we all hold common? Why is a simple smiley face clip art so obviously\\n\",\n \"perceived as a face? To make it more concrete, why are two simple ellipses\\n\",\n \"decorated underneath by a short curve so clearly a face, while an eye lid,\\n\",\n \"lower lip, one ear and a nostril, not? \\n\",\n \"\\n\",\n \"\\n\",\n \"**Insert Image of Faces**\\n\",\n \"*Left: Most would likely agree, this is clearly a face. Middle:\\n\",\n \"With nearly all of the details removed, a mere two circles and\\n\",\n \"curve are enough to create what the author still recognizes\\n\",\n \"as a face. Right: Does this look like a face to you? An ear, \\n\",\n \"nostril, eyelid, and lip do not seem to convey a face as clearly\\n\",\n \"as the eyes and the mouth do. We will quantify this demonstration\\n\",\n \"shortly.*\\n\",\n \"\\n\",\n \"Features, or representations, are built on the idea that characteristics of the\\n\",\n \"symbol \\\"face\\\" are not a property of any one face. Rather, they only arise from\\n\",\n \"the myriad of things we use the symbol to represent. In other words, a\\n\",\n \"particular face is not ascribed meaning by the word \\\"face\\\" - the word \\\"face\\\"\\n\",\n \"derives meaning from the many faces it represents. This suggests that facial\\n\",\n \"characteristics can be described through the statistical properties of all\\n\",\n \"faces. Loosely speaking, these underlying statistical characteristics are what\\n\",\n \"the machine learning field often calls latent variables.\\n\",\n \"\\n\",\n \"\\n\",\n \"## Probability of an Image\\n\",\n \"\\n\",\n \"Most images are contaminated with noise that must be addressed. At the\\n\",\n \"highest level, we have noise being added to the data by the imaging device. The\\n\",\n \"next level of uncertainty comes as a consequence of discretization.\\n\",\n \"Images in reality are continuous but in the process of imaging we only measure\\n\",\n \"certain points along the face. Consider for example a military satellite\\n\",\n \"tracking a vehicle. If one wishes to predict the future location of the van,\\n\",\n \"the prediction is limited to be within one of the discrete cells that make up\\n\",\n \"its measurements. However, the true location of the van could be anywhere\\n\",\n \"within that grid cell. There is also intrinsic stochasticity at the atomic\\n\",\n \"level that we ignore. The fluctuations taking place at that scale are assumed\\n\",\n \"to be averaged out in our observations.\\n\",\n \"\\n\",\n \"The unobserved sources of variability will be our primary focus. Before we\\n\",\n \"address that, let us lay down some preliminary concepts. We are going to assume\\n\",\n \"that there exists some true unknown process that determines what faces look\\n\",\n \"like. A dataset of faces can then be considered as a sample of this process at \\n\",\n \"various points throughout its life. This suggests that these snapshots are a\\n\",\n \"outputs of the underlying data generating process. Considering the many\\n\",\n \"sources of uncertainty outlined above, it is natural to describe this process\\n\",\n \"as a probability distribution. There will be many ways to interpret the data as\\n\",\n \"a probability, but we will begin by considering any one image to be the result\\n\",\n \"of a data generating distribution, $P_{data}(\\\\mathbf{x})$. Here $\\\\mathbf{x}$ is considered to be\\n\",\n \"an image of a face with $n$ pixels. So $P_{data}$ is a joint distribution over\\n\",\n \"each pixel of the frame with a probability density function (pdf),\\n\",\n \"$p_{data}(x_1,x_2,\\\\dots,x_n)$.\\n\",\n \"\\n\",\n \"To build intuition about what $p_{data}(\\\\mathbf{x})$ is and how it relates to\\n\",\n \"the assumed data generating process, we will explore a simple example. Take an\\n\",\n \"image with only 2 pixels... [$x_1$,$x_2$] where both $x_1$ and $x_2$ are in\\n\",\n \"[0,1]. Each image can be considered as a two dimensional point, in\\n\",\n \"$\\\\mathbb{R}^2$. All possible images would occupy a square in the 2 dimensional\\n\",\n \"plane. An example of what this might look like can be seen in Figure\\n\",\n \"\\\\ref{fig:images_in_2dspace} on page \\\\pageref{fig:images_in_2dspace}. Any one\\n\",\n \"point inside the unit square would represent an image. For example the image\\n\",\n \"associated with the point $(0.25,0.85)$ is shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NiGXu/XXJrPjwf3E9Ws41Vz/Obn56No2I6Q0GhH89N5M2VEp0eoLS+m\\ntqIuNzg4LgVkcsLb9Ha4juyBScyZN5r8nCzbLuyFF1/GrNchU6r99nfk5+dTGx7nMs6I2+uUd97i\\nFwmJb05wTBLoc6nVnbAtXm5UpOq4f3HUajUDBw5k2/YdRD84zeUlEsVagp1W+86ExDXCXOreCSgP\\ni/YdLlmYgUxxIYberNdRdno/vXv3duvEtzo5nVd64H9WeWpqKmZ9ATXFbqO3L1yvvID4+HgHgRz2\\nf6+g7jyEqtJCjxnLmkdmUFtWgKW60uO1xYh4MjIyHP42YdxYpk4YA+VF5H72HLqVUyne+DG6tEkU\\nLn6BKaOe9yiQfGWPRz84DUPGQcx6ndvzrRFIiiitW0Fpz5V0/Gq1WuTlOiLveJDEZz9H1aIrsrBI\\nVC26kjj830Te8SDm0jzkYTF14y658N/22Cs4tVrNsGHDmD59Gob1cyg/sMHnbjNY25xly5ah1Wqx\\nlLg+J4Ig2MZYW1FK1Zm9dEgKR5ebzcTx4656dNrVRFIcNwDewh/lYdGYS7wLiBBjIWL5ObcCSdWs\\nM1VZh7wLq7xjtu+2OswB7r//foyZf7ic68nJacVeYHhCrVYzceJEqs7u9zq2yuzD9OnTx0Ug+zMG\\nRUILjMd3eRxDzblscnIcBZIgCLwyYTznCvL58L13GHB7M+5tGso7k0ZwrlDHxAmeBZK339FiMlKt\\nO4VMqUKXNtnlnq0moIhOf0OIqO9WUNojK8u/Yo7f1NRU2+8uU6gIb9ObyDseJrxN7/O+LR2m7MOo\\nmnd2+G93OCu4CePGMmHkM5TvXOrxt7NSq9by/AsvciYjk6os1+fQ9h1VBmrLComIS6Zz5843rHnK\\nHklx3AB4C39UNeuMKdvzS2MVrpMnT8awbrbLcZYqA4I8GN2KKW6FlW7lVGTqWEw5RylYNZPcT4aj\\nuqkH6kZt2bhxI5MnvYJh/RyHc2srSpBHxFJ+aBP6n1dRfmgTFpPR8Xv9WBFPmzKZvn37okub5HZs\\nVjv1Dz/84CKQ/TLB1UumpijT7WdmvQ5T7lESE91nPVtXyGlpaaSlpfH000/7FEjufkdRFNH/vIac\\nD5/EeOwnQlvcCTI5uZ8MR7dyGsWbP6Vg9SzyFr9IWJu7CG97DxhLMWb+7vU3P3f8V06dybgilWet\\nwRv69JleFZylsgzdiilEdPobspBQt9dyjmyymmjffectnyHpNSW5qDo9whsf/Zvu3e8kf/lEyvak\\nOzxz1vGEt+tHVe5RunfvfukT8BdA8nHcAHjL67A6AXUrpqAZNMujE3DCuLEoFAo3oaC/I6utQdWi\\nb50tOKFlXVhtcQ7VuUcJb9efsr1fEd6qJ0GRGkTRQlBEfcSaCjIyMmjQoAFd2rZg8+LnCU+5BTEy\\nAePRndRU6KktK3LIUo/o9AgRt6ciCIJfoZCCIPDd1/9j5qw5zJs/EkXSTciikwgqL8CY9YfNTr1w\\n4UIXgSwLCaMq0221fxs1RZnUnMtG3b6/WwdvaIyWlJQUH7+O/7j7HT05gKsLMyhYOa0uAfOmO6n3\\nf6Mp3/8dOR8+iULblGBtc4+/ecGqmag7DuDNjxajUCiYOH7cZc/3mDBuLKdPn2bZ0rrnqUZVH1PB\\nGUy5R1EmtEBecJSSvasQasyoWnRxew1vkU1Dhw5l1Njx3hMHsw5hyjmCuv29bNv+NVgsVJ76heD6\\nKVSdPUjx94tAkBHe/j7Es78y6ZWJhIa6V2A3GpLiuAHwldehatGF0u1fUrD4BSKbtHfID7CPYLJ3\\ntufl5REfH09qairvvr+I+e98hGbofFtYbYimCdG9/knRugVEdR1C5B0PAlBTcAZz+TlqMn5jzrwN\\nqBu3xaLWom7cnvIzB2jY0EBViIL6Qxa5FWgAYS27+hUKaRV2ocoQ3n7zdQRBoLS01DZu6wrfXiBb\\nQzj1u1aAxewieKw5ATVFWVRlHSK8XX+3Dt7wdv0x//G9xzFejCB2/h29JbyF1E9B+9gC8ha/RP0B\\n4zH89i0VhzbaosCs95n775F1iqR+CubSfJtzOuL2VGrL+jFn7mhMJhMLX3v9suZ7CILAo0MG88Zr\\nC23PU1RUfwRBoKSkxOnZcvXr+Ips8pUwWbBqJpFdhxLWsiu6ldOwyEKI/8frrkEQK6ZQdeAbZkyf\\nxoRxY9m6dWvA9/pXRFIcNwBqtZpxY8Yw+81JLs5es15H4epZRNw2APMf3zP3xSccXlznl1KtVrtE\\n2liduTNmjEfQNCekfsr5XcLHNiFkpaYkF0pyqamqdNtf4XTaJFQtu3vICZlK3uIXqT262WsopKfy\\nIMbMul3GY4895iDs7AVyxdGdttyMiqM7beGY8oi4CzkBia0IitISlnILFQe/J7z9fQTXT8FiLEEV\\n14ioHn+ncPUsenfr5pL5fimlS5yFYVXWIZ9+mBBtU86tnUNl9hGHCDWr41fdrh/lBzdQuu0/RN81\\nzJZDYz1frmnK/DfeRtXxYcxiLfKwaEI7DUZRZbgs+R7unid7vCU9+opssn42bfoLyOOaEqxpjLkk\\nz0E5CoKA5pEZ5C1+EVlohMv8aQbNomRpXQmYG9kZ7oykOG4QUhomE6IM9Rj+GHF7KkZDLkqlknHj\\nAhME1t3IE48/RqMmTUHTBFWLrg5CCOqUVFXWIaitcQkLhgtJeXmLXyKy8yMudu2gSA0hmib0u6Ol\\nV4ERaIMoq0Ce99YsyksKbaty+3BMWXg9sJjd5gTolk+i8sw+VI1utSlMT5nvl9q8asTwZ9m3dy/r\\nPh+BoAwjpIn3vmYhsQ1IqT7D2UZt3SoYmUJFxG2pVGUeQggKcZhzURQxVVZSbaxAlnXIxWwYfv8r\\nzJk75orle4BrWLn9TtfXd1rPPXbkMMu+3oIyLIqQuMYuz2VdrlFrjMd3uYT8BkVqUDVoc8PWpPKE\\npDhuEHQ6HcqU9tTrNBjjid0uWdpw6SGYCQkJTJ8+nfnvfISyfX8XpaFbMQWZpYYwD0IMLiQMunuJ\\nAVTxTejY4VaPqz9P5UGs1/aU3DZh3Fj27d3LV3uOuazKw1r1IPfT4SQ8+a57ZTdkDrmfv4CgUDko\\nTLO+n8N3+RpbWL9RTJ8+DnO1iYYNGzqYr5x3Kupb76W6MIOK3zdiLitE1aIrYc27OIQ9Q52vo2mH\\npmR7j5gmODrBljdhpWxPOrXlxST8632PZkN3+R6X4g+xPzcyMhIAvV5/UX4V67Vyc3ORh0ejbnev\\ny/x4u38rNar65Ob6mMAbDElxXEcYDAY2bNjAnj17An6RrHZ8a/ijOy5H7R13poWaokxM2YdJHfAA\\nN7duxVvf7Pd6DW8vsa8xXmzDI0EQuK1jBzZnmV3OqTq7H6WHzHnrNa2f28+t83d5Gpt9aYwQbQve\\n+GofQRXfOZivZs6aw4L3P3FROpHnI9fK9qRTuvlTBxNMXWTXMSJ734rs2DGPcwbnI4ziGtn+bTEZ\\n0e9a4TEzvs5s+BKRN/eyLTYuxQznfG6NKs7BWa4KC+PZES9wX/9+dOxwq23X4e75d0nmDIsDCsn5\\n8EmH+fF2//aYCjLYu0/hdf5uNCTFcR1g/yIoElsiRCUG7KD0t/DhpdbecWdaKC1twSuv/Eh4eDhf\\nfPEF5nP/83qNmuIcty+xNXHQ2xj9LSnvbmflKfrMr9BcD8rO/rs8jc1TZJTVfLVx02Z+/PFHz+a9\\nR2aSt/glNI++StG6BUBdAEHBqpkoE5rTqFEjjKvWeP3tTdl/EHv/GNvfyg9sQKFt5nNnWJN7hPj4\\nh4FLM8N5Ote6u6kIUmKqqeWrX47zQ0Y1wcYCj8+/u2tF4rhTsgZreLp/+8+qdSf5+tsz111b3iuJ\\npDiuAy7VLg6+o0wud+0de6fnli1bbNc9czYTw5kDtgJ3zqXcQzRNqDp7gKieTzhcz5o46Ms96U9J\\neWPeKX7dG4woih6d5PbzIw+LxpR9xOs1Pa1Y7XdIzmOzmIxUHNmK/qflJDz5nkfT2uaPnyG0UXuf\\nQrw6/xRxD08l97MR6HetILLzIOQFR0lJSfH62xevno4cC5Xfv2VzPpef2IuqbT+332f73uh4qrIO\\nkJqaetEmQvBtXox7eCq5nz6H9ok3HEqquCtpb03m9HatvMUvoT5vSjXrdRSsmAyCHEtlmat5deVU\\nIjr9jaDC46Snp18X3RX/DCTFcY1zKS+kM5cSoRLIeN11AQQoKytj3rz5qDsOQPffGaia3UG5tU9G\\ndAKmrMMUb3gfeVQ8+V+OQ5ncxsWJLy846tVR6c/Oqlp3iu+26V0c156Uq6pZZ4o3fuw9JyD7sMuK\\n1XkXZx1bSGk+FYe3UPbzGoIi4lAk3uRVKQRFxRMU7aN96vkdT1CkBkVyG1TNOhHaqD2l+9aQmppq\\ni+5y99tPOd9Ya926dTbnc2VlJZPe+cLrd9YUZDBwwADCw8NZsmTJRfdE98e8qGzYlur8UzbF4amk\\nfdkzz/osuhmibULx+lcJVSooO/UbFlkwYbf0dVu2nSAFonhh5ygpjjokxXGNc7E2e3dcSoSKL7zZ\\nt4cOGUyPHj3411NPI9M2J+rOv2M8uQfjke1uo5QKVs0k4vZUgiLiXJz4hq2fe3Xg24T/27NRD5js\\n/tqdHiGsZVePTnKAmbNeQpXcClONGdO5XERzDbqVU13Kx1ubToW36+eyWnXexanVaia9MpGpM19G\\nCAkj4cl3qTi6HUuF3uvcBkXGUeOrf4XdjiekXjLm0nyX7/f12zs7uF8eM86rArYUnOCzT7cBl2Yi\\n9Ofc4JhEB1Ogp5L2pi3/RjTXeL9WvQZU7P+GvgMeYMNZOfUfq1uURXV+xCFwJKrnE+iWjqds93+J\\nadTmhu294Q5JcVzjXMoL6QlfsfMXgzdzWlr6TJIS57Bu3XqUbfvVVT4tySPhafc2e6s5IXH45y4h\\nuf448CeMG8tv+/ax6rPn6hpEuQk9FgTBo5N8wrixHD92jOVpK1AktULV9HZMBRmY8o6R+/nzKBvc\\n4rAqbdq0KdmHNlBhyPW5i6s2mbDUmEh44q26PAk/zGAIAqbsw37veKoLM7DkH2X69Oku3+/vb++P\\naXP6tKk257S/XSfd/XbezrWaMo2nf0WZ1NpWesZT4mOtvgBLpcHrOMx6HZE9nuC7zesJirvgx3EX\\nOKJIao25rBBDxkFSU1P59ddfvV77RkFSHNc4l/JCuuNKtAr1ZU6LTJ3KvFdfJKReAuaSPPQ7l6FI\\n9lFA0E1Irr8OfEEQ6NjhVjaeMSKLa+Y29Bg8K9z5Cxay6ptNxDolKJr1OvLTJmEuyUUw5FOrL2Dq\\nlMlMmzKZ8vJyn7u4unmag7JhOwczmK+GQdW5x4i44yGv/Sus9ZzMeh1iwQnOnjmNVut9weGL8WPH\\n2Do0KpJaExzb4HyE3B/07duX8WMvmOYuJfjC3bkOTZgSWxGa0g6zXkfOh0+ibHQrIYmtXL7HYjJS\\neWaf7ft8BQLUVugRzSavcxAcnUBtZRm9e/WQihvaISmOa5zLFQ0VaKhkIArGH3OaIrElZrMZU/Yf\\nBNdvRIimsdfxBkXHO5gmAnXga7Va5MZzhLV5xuMx7hSuLyWoHTyHwi9G8tbbbzJkyBAHM5Svlfyy\\nZcsQwuoRHHPBX+GrYZDVDBbZ+REEefD53hAtCI5JrCsRknPYtouyztG0qVMuWWkAvLrwNXYfOOLQ\\noTFE04Tou55i97ev8+rC12w+oksJvnB3rrcmTLqVU5GHu5ZZtzYVUyS19jyfK6agbNQBWUgowbHJ\\nVPzxo9c5qCnJRWauYsjgwQHP318ZSXFc41yuaCh/I7OcFUytqh41uUd46ulnGDDgAT779BMiIhxL\\nM/hjTpNFJ8HZ34jo9Aj6XSsR5HKvx9foTlNdqUdWVXZRDvxAFK69kjx58qRLNzh7giI1RDRuh1Kp\\nDHgFunnzZoKitC79S+wz1K0lROSGfKrzjtG7Wzd27f+V2rJ+thIhxhO7KP99I6bsw6gb3YKsqhTD\\n+tmYco5e1iAHewXq3KFR5iYo43KUB5k9ZxTKxJboTx1wSTyEC+HHuZ+NwFJd6bCDtIZNO8+nvaky\\nuH4KtVUGyg9tQtmwHcXff+Bzd6IMlvPggw+6fH4jc1UVhyAI/YC3ATnwqSiK850+jwS+BBpQN9bX\\nRFH8958+0KuM/Ut1IY/Df2EaSGSWtWBh1NDXMR77ydavWdm2DV/9cpzYOC0DHrjfIQnLH3NaUHkB\\nRn0BUS1/jkKCAAAgAElEQVS6YKkyUvbLau8vbO5RQlRqbo+uZOhzzwTswPdL4U6cyLvvL3LYhVWd\\n2Ys86Rav176UDHtRtFB9voy9p7pR+q1f8OEHixgyZAhhYWHnFbmjMJYVn2XAAw+gCAkGILlrN6ZP\\n33jZzCkXE5RxOcqDPP/ccEaOHEmasdL7Dja+OeUHviPitgs7bau/yH4+naskFKbPAQSMx36iZNMn\\nBIWGo0tzX8OtYNUMkMnp1q2bZKZy4qopDkEQ5MD7QF8gG/hFEIT1oigetjtsBHBYFMX7BUGoDxwT\\nBGGpKIrVV2HIVw37l2revHlERUUFFA3lrxBYtmyZTcFYi/1ZTQW2qrF5J/hqj2MS1rgxY6g4e8j7\\n6j77MBMnTuTNj+oqnVbrTqBLm4xm8Gy3tZ9kERpC2/Zl0+ZlpA4ceFF9IXytgEVR5NV3PnZQqEK9\\nhhiP7vR63YvNsO/duzf/TV+HusMDbk0plioDhr1f0b79rQwePNhtNFRubi57f9vP15m/s/OEzmZy\\nLP/6W+rVj7voarXOBBKU4c6sebHBF2q1GqPR6LMrZXD9hhh2/xdV8y4ew6adnd1mvY7q/FO2oAur\\nKdBiLCH3sxEo4psTXD+FmnPZmLL/QEQEBH788UcMBsMl+wL/SlzNHcftwElRFE8DCIKQBgwA7BWH\\nCKiFujchHCgGXGtC3CCo1WruvvtuevbsGdB5/gqBTZs2oWrQ2m2/Zk/hjwq9jjc+mkP37t3Zvd79\\n6l6fPtO1p0dSK0qqDOR+Orwu8ikmkZqSXKqyDhGibUp1zhGqMg6gbHM3417/jJfHjAu4jLe3FbAo\\nimgTk21Kwxq9Y9YXUJV5gOrCDAfzjEM59VO/0adPnwB+gTqGDh3KCy+NwnhiN6pmnS6YpqK01BSc\\nwZR7jBBtU85UyNEmJtvut7y83CaY9+77jQ3b9xD96JsXnQzqD/4GZfy6dx/axOTLWnIdoKY428fn\\nOcRGhpP72QiUDdsSFBVPZcZvdWHTKyajGeS6ILEPIoALRTVzPxuBuuNAyg9+jxCsQKYMI/qupwlr\\n1cPWTOpfTw9jZdryi7qXvyJXU3EkAvbB6dmAc6nP94D1QC6gBgaJomj5c4b310Gj0WD2kQcgM+gQ\\nhCQsaq1L21RvfR+spq6dy0YzZvQoFr7murofOmSwS0+P5cuX89wLLxJn53hVxTUiJL45xiPbXMpr\\nmP0QjJ4c+u4c19aENXlEHPqf19hMcsHRCSiS2pC/ZBTqjgOI7P44hl/WOpRTVya3pmmLmwIWjmq1\\nmunTpjJz/msYfvsGReJN1JTkYMo5TGTnR6j/0BSbUAs9f79btm5jx44dNn9T6f7vHcqjO/8O/iaD\\n+sIfH1HZ6f18dy7nkioauMO6M/MVftz/sSGs23UYoUlXKo5uB0stCU8twnjspwtKOTq+TinnHSey\\nyxCHEv9wIYLP8Gu623mVhYSiGTSLdV+MlEqO2HGtO8fvAfYDvYEmwA+CIGwXRbHM+UBBEIYBw6BO\\nUG7ZssWvLygvL/f72GsBd+M1Go1s376d4uJiYmJi6N69OyrVhSqgm7dsdSjz4YxZr6M84yCJ99yB\\neGAnFmWEQ20mf/pvhyS0oLqqkv+mLWPHjh3k5eVRVFRN/dsfRB2m4ttvv3UY05kzZ1A1aENI/RTb\\nyt5iMpLz4ZNeFdTMWS/R7pabHTqxiaLIsrQVfLl0GaHJrRAjExD0uTzz3PM89uhQhg4e5CLcd+7c\\niTncR/TO8klUHNqEEBLq9vO5b87k9OnTPDrE/4ibznfczuODHuI/Xy5FqCiiVl9IwlOeFcH3nwwn\\nsutgzDIZ5pK8utW1j99h7ty53H333X6PyRNDhwwmLX0mkamu0Uml6TOxWCxEDJwS0G9lj/OzbH2O\\ndTodgmhBt3IamkdmuPU9yGUC9913H8tXriLytr9hyjyI+tb7MWX/gbpdPwf/RlCTjpjyT6Fu39+t\\nkg+OScJcVugjKvAm5s6dS5cuXa57eXE5uJqKIwewz99POv83e/4JzBfrDNwnBUE4A7QE9jhfTBTF\\nj4GPATp27Cj6a87ZsmVLwKafq4n9eN2H2B7infc/cDBz3PfAQNQdB3gN+Zw8fhyjXnqRDz9JRhHf\\nxiHyx58if0JUIqdOnSIyMpITJ0/x9bffEdawDZaiEMTSw3z06ecOK/Q9e/YQHNvA4Rr+KKjwlFso\\nKiqy7SAMBgP/euppvvrxJ+o9/pbDuSq9jpXr59C4cWOXle+xY8dY+r8FGAuzier+ODKl2uW76j80\\nmfwvRpHw2EKP+SnLl43mzddfC2iF36tXL954bSEjR45k1baDbu/XWlJDkNUl/4XUS6Yq+zChKe28\\nXluISiQqKuqyPNM9evSgcWNXx7wx8xD39e/H5kOZAf1WzlifZXfPcXijthjOHCTnsxEoG9xCiL0p\\nUxnK1OnTePDBB7nzs3/zw/JXUCS1AkutzentXAW3KvOQx1L9NYUZBEXU9zoXwbENiIqKIjw8/LqV\\nF5eTq6k4fgGaCYLQiDqFMRgY6nRMJnAXsF0QBA3QAjj9p47yGsZTiG1wYQYz589g29YtJCcloUxs\\nSfidf0emVLvtC66Mqk+jhg3sGhp9QFVJoc1U4E92szHvFN9kVbDhTDWmwgxMNbWERDdBHhGLqIxA\\nEd+GeW99ANSZL9zZ0P1RUFaHrFXYzJo9hyqTyW/zzQUhNQdLTArhbftRlXUI/a4VLsKmWnfKZ6Ki\\nv+Ve7BFFkfcWfcDSZWkob3a/M7D5lOzuKzgu5Yo57t3hzUe0aNEiNmV6L+3hb/SZp+dYbXVeF2VQ\\nU2NEXmtCGSxnysSxTBg3lvkLFrL7wBG3Zk3nKrjOeUH2x1bnHSM4vrnn+zAZqc76nX37gikpKaFD\\nhw43vKP8qikOURTNgiA8D2ygLhz3c1EU/xAE4dnzn38IzAIWC4LwOyAA40VRLLpaY76WsGYh2ztJ\\nnbNtdxYEIZz4DcPZQ9T+vAZBqDumtrwYsboSS3UloigiKtS2F/xCu80Z6FZMQTNoln/ZzbpT1B/+\\nb2QhoYiiSOm2JZTtXoEi+WZCYhvUrRZLCpk+YybPPzfcrQ3dHwVlFYxWYaO87W+Qdchv4X5BSL3p\\nU9jUVpS47Iqcsag1ZGRksGTJEr+z8a1jCO/6aF1HROdrevAp+fM7XI7S+M648xFdrooGvkLFNYPn\\nULTkRSY+9QgpKSm2SEJ/KuraV8GtKThDUJOODsdZzV4Wi0hN3nFqSvMJjroQRGLff16hbcoPZ2sQ\\nD/zosKO/UdvJXlUfhyiK3wDfOP3tQ7v/zgUu3Vj7F0MURZ7811NYYho5vDSe7PVqvY68L8chCwl1\\niYqyZtPu/a2uuZJ1lTli+LM8NewZ1i1+AVWDNig0jW2KxJ3NOaLThVavZXvSqTy5x+1K0D5CxTnP\\nwl/B2KdPH5q2uOl82PB2v3cpgQobeVg0VRkHvP4OVRm/MWfeBtSN2/oVVWQ/BmRBlO74kpItiwmO\\nTUbVrDMyhcqjyc5XlvnlLo3vjctV0cCfUHF1o7akpKQ4KC+/qhWcL1ujTG6NKe84pvyTVGUecqld\\nVpX5O7WFpzi3fAKxQ191iiTc6PaduZwRbNcj17pzXMIN8xcs5H/fbSSk5Z22v5kN59DvXIr61vup\\nyjqESqm2tcmUKdWINVVoHlvgflU3aBZfLx1FeXm5TehERESwMm05BoPBMX9g6cuoGtyMRa3BUpyN\\n/swBIrs+aotW8RWBZR+h4i7PIkRdz2NCllUw/vDDDzahEcguJRBhE96mNyGaJhRveN+jIivdtoTq\\nqko0T7iGKHsSLOnp6YQmt6Li6M7zkVxtEM3VGI/utNnmRbGWoGj3K3XrPFvzDlQJTev6Z2QcZOqU\\nyZcla9wb9pFr9/a7hw1rZ7k4yANRYhdbxNOvirrRCdQUZaLfWRdGG3HbgwRFxlJbUeJQu8xSUQL1\\nG1J18FuKv3yZsIY3U6uKQf8nRbBdj0iK4zrDaDTWORE7PkxV1iFqqyoo3vAexhO7CY6OR6ypcnEQ\\nWmv4eBOYqgZt3Nrqnc0UVkWSl5fHvn3BfB+pJcKum5o/Dm6VnenItrt5ehhr164jJDYRIVRN7mfP\\noUhoiSKuEcHGQocs+YULF9qERiDmm0WLFvklbGorijHrdVR89wZ9+/Z1m59SXZiB4dd1HrvyeRIs\\n+fn5GCsqMHupw2SpriKknvv+G9as6MpTv3CLNpSHUu8gPj6e2NhY+vfv7/XeLgVPtc5MpQUULH4B\\nVXJriEpE0OdSnXeUyZMm+aXELtbk5c951YVnMOUcIbLro6hadKFw9SzC2txF5B0POxxXU5KLqkVX\\nQhq3Y87IvxMaGspXX33FBr33CLaL8W/9VZAUx3XG9u3bUTVojfKWuynduYycRU+gSGiJ+tb7MJfk\\nUXF4CxGdHiH6rqcpXD3r/FkWv8057vCUH7FkyRI2LfjI4Vh/HNyymCSH73r/gw/ZsOMX4p68UJvI\\nYjJSfnADxl/Subt/Xz7f/Z1NAGu1Wlteii/zTcmaGYwbPZrw8HD/hE3RWYJyyjj3y2qmTpnM+LFj\\neHXhay6RRWWn9qFuFLhgiYyMpCr3mMeVrOaRmeR++hy1upNelaFYdJr03cfZuHEjeXl5HD58mG7d\\nugXktA2kkKW3Wme6tElUFmQgN1Uir60ikCT/izV5+XOeKfc4CcM+Ieh8QURnU6TtuPMl6SvOZbBp\\n0yY6dOgAgDwmyevYL6X0zPWOpDiuM4qLi7GotZQf2IA8PMZtYyGrk9f6okT1+Lvf5hx7fFXUHTH8\\n2UtycBsMBpYtW8a0qdMI7/qoQ0isTKEi4rZUVM278M2y0Q7n9+nTB8O/nrblpbgUCYyOp0Z3GlPe\\ncULrxfPqwoWEKELcjtces16HKesPauUCQ4cOdUlatI8sysjI4K1v9nu9T0+CRZHQ0kfOQEv6d2jK\\nJg+Z+GXrZtOzZ0+atWxl+13E0hy/nbYXUynZlwM7b/FLaB6aZivlcaVbGlvPm/3GDKIfnOb2HYjs\\nMtimNGxza2eKdM4mr8g5zrpjBfxv92EshgIIj8ObGr6cEWzXG5LiuM6IiYmB0gOUZXzv0Y9gv7JS\\nJLUCBKqyDgUcjeNPRd2LcXAbMw9xOuMs2sRkguNboLz5bo8hse5W7hs3biQ0NtFhl+FQJHD3apQN\\nbiHp+f+4CDJvQqpg1Uwiuz1KWMuurE6fSQu71rJqtZqBAweSnp5OXl4eWVlZCGWBm1j0ej0KN73J\\n7VHENeKO22/jtttvd5tD0a1bN3YfOOLyu/grsAPtYe+PbygkoQVFX7+BPDQCuToWeYtezJ4z94q2\\nNJ4wbizbtm1jg63siJaac1lU5x23PUfuxlp+YAPGYzttznH1bQMp2foF1bnHUTZqR3B0IjUhKirP\\n7Kdk6xdE3fl3F0V8pSLYrhckxXGd0b17d954620UiTf75eQNjk7AXJpPiDKU4tXTiXloul+rOvtV\\npkyppvzQJmorSpCHRaNq1tlmw8/LzgQcX3plVH2vDu5u3brxxof/div4nENiwXXlnp+fj6JhO0JC\\no113GbpTRHYe5KJ8XMf7MpaYRgTHNcJckleXcXzbQGSqiLpIrVZ9bILvQoXaCyt0SnMoOfkbygCV\\nsVarJdhY4PU3DjYWkpCQwOOPP+6y07GPKLsYp+3F9LD31xFtyj+BWF1FxeGtyCM1mCsrefKpp1mx\\nfJnXHdDFVtUVBIEhgx5hz+kihCZdqTy1h9qKUhLPh4W7o6boLEKIClWLrjbneMnWLzAe3eHSkbIu\\nOXYyANE9nnD4+58ZwXYtIimO6wyVSkXvXj3ZWRDs9Tirk9faRnTChAkcPnyY9efDa2UxSV5Xda7R\\nP3V1nEzZR2yOd2VSK9auXevy0mu1AzhzNpMFC+uUSW14HPLyAoyZhxg7ZjQLX3vd75BYcF25a7Va\\n5OU6wnr+y1ZaovLUHmqrykl6brFboWHduVjHGxMVyZhZryEPiyIkrhEh2uYYfkl36N9QVVXFk089\\nTft27Zn/rusK3bz1C58RYKIoOuR49OnTB2MANn3n4ARrja2LddpeTLl0f3xDZr0Odbv+jiagjgP4\\navNO5tvt3LxxMS2Nrb6O6J7DUDXrRM6HT2KpLHP7DNT1ST+JXC5HCA2h4lwGtUVnKTtzkMSnP/Rg\\nhptN7qfDEYpOI49tiFiac1n7nlyvSIrjOmTwoEH8NOd9r8fUlOSiVMcgFpxg9OjRvPb6G6gatCbs\\n5ruoyT2CKeMA7du1474xo0iM11JeXu7gGPUV/VOwaiZBYZG2nYC7l37USy+ydu1aduzYQbduqaSm\\nprJmzZqAQmKdBanBYKCyshL9iX3U7kknvO09hLfpXVeTSF3f40oTHHcuer2ekORbUN/xMPqf12A8\\nus3tff7vv1NZt2499f/xrsuYo+78OwC5nwy/YOI434e8b98+tgq89n4E4/Mj6dypE7vWuLfN+1rJ\\nXmoP+os5f+DAgTz9zHDvviG7nuf2CwDNo/OZM3fSFQtbtVU7eHs2QsOOiJZaj/lGxaunM33aVIdF\\nzurV2Rys9bF7T25Dy3pBPJh6B6WlpbzyyuXre3K9IimO6wiDwcCGDRtQqVSYsg/77FwWVK6jR48e\\nvP/vZW7NQvtWTOH3z1cTFh7u4hj1Ff0T9/BUcj8bQXR0tMfxWpVJcnKyrV6OP4LLWh7CXpCGhYUx\\n79UFNnORqm0/Ks/spfSnNCI7D0Km8u2UpzSHU6dOsWDBAk6ePAmlOT7zTkJv6Ufl6V/dzrMgCET3\\neILqgjPIw2KQhUWiimtEVM8n2LJsHLv3H3WYd1EUqd62hE2b16KIia/LxUi8ieB6SQhleZjzT/hc\\nyV5qxvbFnP/+Bx8iKFQUrJpB3MPuHdH25crhwgKgOv/UFQ9bnTBuLJs2/cjWPduJ/+e77qvj5h4j\\nOEjO888Nd1jk7N27lyO1VV6vHxyTREpKKOPGjWPLli03vNIASXFcF9hHwdg6AKpjPZpJdCumIMfC\\n6OH/4tWFC4l57E332/BBs8hb/BKxD36OorLMxTHqM/onoUXA92IfSuuJmsIMxNwKSvam2wSpJ4eu\\n9X6DwqMx5bsPYbWWQDGc/I3VMjniwSKEsnxKTv5G5VevE5LYyuN9imItwbENHf5m7c1h9fkER2mR\\nR8Ta8gMsJiM1ZjP1Bzg64K0Z9VazSN11dlNTlEl14VnGjxnt06RzqRnbgZ5v9YnUG/o6xRs/tvVP\\nCYqO9+2Ijo6n8tQeRKOer776ioEDB6JWq21hwDt37iQzM9NniRbrOJxDh6HO9Hb27Fm2btuK5p/v\\nOQRKuFTHbdiGtWvXOiiw3r17s/7nD7x+d03RWe56ZoTXY240JMVxHWAvNOURcZTtSadKX4RcHXt+\\n1dqS4HrJiCV1RQtTBzzAZ598THp6OmENvSf+2ZuF7B2j/kb/lJSUBHQvzqG0zljNHgveeI1//vOf\\nftUl0gyaReEXIxl4//1sWjcHtZPALt22pM756ZSspzyfgyD3UhlVHhZN1dmDgFMtMDufT9XZ/YQ2\\n7YQoigiC4JAEWZfRvwyzXkdV1iE0Q+fbxmDfoc7cvj+vvT6aMaNe9rqivdQe9P6cf2+/e1i0aBFa\\nrZbKysq6viWhEVTnHEZ7vn+KX45o3Wlqq8pRJrdhw/4MNAlJdO/e3dZfxByuYc3Oj7yWaHEXOiyU\\n5fHUM8MRgIgm7aisMhEU38LhXpy7/1VlHqKqxuxiwhs6dCgjXxrldfduKTjJkCFDPP4mNyKS4rjG\\ncRaa+p/XOHTis1+11hRnMXHiBKZPnQL4Hw1jrRpq7xj1N/on0Dh2d6G0Vqxmj9DYRFsJa/DPoRvR\\nuB0DB9zPbbff5hDhRWkOhpO/eczwrusA9xyW6kq3AlDVrDPF3y+qyyR3aqfrPO6yPelE3vFg3U4k\\nKgHdqpmYMvajSG5t27UULHsFRUo76j84GZlM5jAWf006I4Y/y2/79tnqiMlikgJy2noKfy079Rsi\\nsPlQJpsya5AZ8ik7tR+5pimW43XK0No/xR9HdHXBaQfFUrL1Czbv2oEmgMZP7naa+p/XQEEO9c+b\\nzcw/r0Jeofd6z8HRCZgz97k8r3XNtaYx6w33EYfFq6czfepUyTzlhKQ4rnHshaY7e7y3Vas/9uya\\nklxUdjsLq2N0+PDhl6WInTPuQmmdi86JFefIyMhwOMcfh25+fr5LhNepU6dYLZP7NLmVH/iOiNtc\\n78VSZSBILqcsfRblpYUuBe+s17CPBhMEOZXHdyALDvUY4lm4Zjaah6e63IO3TGTn1feFQIeDdOnS\\nmf/tyfIrc9xd+Ouve/fx3bkcl7pTivOmQPHodkLqXWif4ytj37nwpcVkpHz/t16bdDmHAefm5jJ9\\n+nSUbe+z1V8DXN4Bf5JOa4pzqNUXun1eJ4wfC0JdiHZIfEtbI7DqvKNM8bN0yo2GpDiuceyFpj91\\noOxXrX6VZbCLhoELjtFATCKBlK5wF0prbRtrjavXrZjMjFnf8NOuXQweNIjIyMiAHLr2zs8FCxYg\\nHvReiV8R1xjjL+momndx6z9BFGmoieSkMtKn2a/8wHdU/74BS1UF8Y+/7jXE01xe7JDZ7CsT2Zuf\\nZ2/6TN5b9EFA1Vqt82QwGNAkJKG87W9UHN1uy9WRKVQ2U2DuZ88h2O2QANeM/UgNNUVnqco+jDKl\\nPerbBtqODeTZfeyxx5i/YCEzZsxEpm3p0KBJmdLe5Tr+JJ1WnT3A1CmTXMKjrc/qxeSR3MhIiuMa\\nx37XEEijI/Btz7aPhrGYjJQf2ED5yb1UVlZiMBgYP3YMW7Zu44dPh9ftDGIbUFOUiSn7D/r27cu4\\nMaMdIp38KV1RWVlJ2an9NmXm3JHNqswUybewsyCYn+a8T03eMWrN5ova/URGRlJdkOF1zgR9Ls0b\\nJ3Pk3yMI0rZwSAqM6PQIqhZdOLFsHEq7asTuCIrUUL5jKe3a3sLv4bHelUziTZTuWEpsvxd83gP4\\nTtyLTJ3qd7VWe0Wv0WhYu249VSYTZB1yydWJuD21LjNc25yqswcchLO14KI1Y79023+IvmsY0X2G\\nUbRuAYZf1jr2NvHz2bUqyNgnXMuZ5y0ZTVirHg7n+dr96NIm0bfvXShCFC7h0fbP6sXkkdyoSIrj\\nGsd+1xBIHSgrzvbsGlV9TAUZmPJPENl5EOrbBqL/eU1ds5r4Zqhu6cekd77g5THj6NatG7v2X3CI\\n1lYUE6JpQvRdT7H729e59/4BbktfONusRVFk3qsLmDV7DvLIOCzKCPKWjEYzeLat3zhcMOOoOw5w\\nydQtWj4+oMx3q1ln6rTp1NbWEulF6ejPHERodzfyuFqqco6APJiwm7rbdkAAqo6pVJ7+1evcyw35\\nvP/eO6SnpxMshng9Nji2AZVnfrON4dx/p/Hy88+zZs0atzu3i0ncc8ado9l8LquuH33HAQ6lNZyz\\n+JXaJphyjrgNybVUGSg/uJHIbo+hbtcPcC0o6M+zaynOJirqHl4eM86zgrzjIbe/g/3uJ0TbDEX9\\nBnV+n+wjTJ44geDgYLdJnN78KxKekRTHNY79riGs36iAO8A527Nzc3PZu0/BV9+cJqjwGEUrJ1NT\\nVuRiu1fodWxOm4SqZXebQ9SKxWRE3qIX329YTGS3R93267a3WS9dnsaXK9dQXVOLMrQeoQltqCnK\\nIu+LUQRF1kfZqAO1+nyqzuxHfdsAW2Kd/fXqDZ5P0ZIXKbHrB2LNfB83ZgwJWg0LFiywCdz3Fn3A\\nvLc+oBYZ6o7/53k1unwSEZ3+RkSXQURwQWDWVugdnL7hbe+hdPt/vNfgyvqDIUOG8Msvv1BzxLuS\\nMZfqMOvzyV8+EVPuceRKFfMXLKiruFsv2WU1fKmJf+C9RWvBqpnIlOoLrVad/Tb6XMI0DQlu0eNC\\n++HoBFvOkHNIrnPEnl81zLL+APCqIMPb3kPpji9drmPd/YQ2vpVzy8Yx+slUUlIeIjU11ZaIebFl\\nWiRckRTHdcCFXcMkFBH10K2c6rYqrrdQTHd9NZYvX84LL76Mxk1WtDXiKG/xS0R2fsTWEtY+HFXd\\n/j5M2YfJ2bPGbXHCYG1znnjiCdZ+9Q3yiDgShrzqKrhXTqN8/3eEtboTRcNbHHYa9gRHaYmw65dQ\\nV9pEy+mMsyx47TUn88ML1NSYCes8GGXWH0TZ9Vt3dsbLwushV8c63Le7sicAIoKXJLgZWI1y06dP\\n59PPU7wnaOYcqRtLVDzKhu0wHtmG9rHXPK6GE+P9T9zzlPMQSPdD69+tfhtj1h+EtbmLiPOmqXPf\\nvUu17hThbe9x2Jk5XNeuz7dMoSK8XX90aZPRDJ7t1pf0f/37sWnTJioMZYiHNtn8LA73qFAREt/c\\n4ztQ8d0bTJ8+zWH3cKllWiRckRTHdYD9rmHu3LnsP/g7P34xEnWjtlgitH5VEnVGrVajVCqJaNLO\\n7zwPT61pPRUnrFVrWf/jLkSL6GreMBnrImWadaLs13XUVhoconbcYVFrKC0tZdiwYQDMe3UBb360\\n2H2PiBVTqDp7kJB6yQ62eGdnvH7HMixGx1wU5/uG887dxJsIbXSrx2gwecFRm/Bp1qwpp1dMRTPI\\nfdn7iE5/w1JRgqBQYfgl3We00Ymjh33WuLKvOuxsx7+33z0BlXqx/T06HuMv6Qy4///48Y8LPVBC\\nm3TEeOwnFx+VPTUFZxDNRgxVZRjzTlGVdwJlchuX+avM/B05Fr7/4QeC41sSVL+xSzMyh/wOQYal\\nrJCCxS8Q2aS9z2q6l2O3JuGIpDiuI9RqNffccw/z5s1z6MR3sREggeR5+CrN4alJjkypJigq3naO\\nuyQ6ZXJrTBn7MZfmEyU+6bGSqr3/xp+kwNzPRjhEAjknhYFrOLLzfduOK8oiJDbZowKShYRi2Pq5\\nTfhMHD+OZ8dN86hkIm5PpXDNbORh0X5FG23cuNFroIM+fabHqsMKvY7/LR3r07nvfM9QJ/wf6N+X\\nT8m4XWIAACAASURBVD/5mPikBjbF5Y/pSVacwRuvL6SkpIRf9wbz3TY9EX2fQaZU2+ZPqY7BUnAS\\nmUJFtBv/lfOCxKzXUZ17jOjGt1CZe5xerZPocGt7EhISPL4Dl1qmRcKVq6o4BEHoB7wNyIFPRVGc\\n7+aYnsBbQDBQJIpiD+djbkQuRwSIX3kexTmo4hr5FU7pXJzQlH0YRYObCbIzBXnbtejSJlG6bYlb\\nc5Wz/8av/uHJbajK9N6HxDkc2XbfdgrFrNdRfWyrzc/jTgGBo/B56KGHGDHyJWKGzrcFFtgrGet3\\nqzsO8FqYES6shr31rXj4oQdZtXqNR0Xqj3PfWYlahf/nn+0gPDzcRXF5i2QyrJ/D5Emv8PTTTwP2\\njvm6sYtqDbVFmVRl/YGISOzQBT4XJJbKsrqeKV2Hor7jQUL1Ojasn0P7W2/1+i5capkWCVdkvg+5\\nMgiCIAfeB/oDrYAhgiC0cjomClgEPCCKYmvgb3/6QP/CpKamYsz8A7Ne5/Zza/x7iLaJX+GU9v26\\nreaY0JT21BSdBbDtWpwFDVzwqRh+XU/1+ePtx+Hsv/Frt1QvCUVyawpWzXS5R7NeR8F/Z7gU57O/\\nb0vBSSq+mkfJ0lGMf/kFzPnHvc6Vczn0sWNGU7JmBjVFmedzIzrZlIZ1foIi61NTkuv1PmRl+Zw6\\ndYqFCxeSGK/lxNHDvDH+Gcan3sEb459Bl5tNglbj06lsyj1aV0LDZKT80Cb0P6+i/NAmLCajTZGp\\nmnd2mPPJk16xzfmEcWOZMPIZSpaOouKreQiVJQQHycj9dDgla6Zj2Pq5bb4mjHzGwWRkNbfmZWfS\\nq3UShn3fUFtRTHBCS4Lib/K6AAjRNiN/6XjyFr9Y52c574S/YMqbR3l5OVC3E/3oo48YNGgQgwYN\\n4qOP6lobT570Cob1c9w+Bzd6b42L4WruOG4HToqieBpAEIQ0YABw2O6YocAaURQzAURR9F4DQyIg\\n/Eny69u3DztWvoIsIg5ziPes5Oqis9SeOUfZ7lU2c4xYXUnJls9ttZp87VpUDVpRuORlopp19Oq/\\n8Wu3VHAGZeMOIOJaLTXnKIIyHFWLLg7nWO+7R7cu9O/X1cEMqFAo/EqItK6uF772OiHa5ohmExV/\\nbKH4+w+QR8ZhLs4j4vaBF+bHh8nn3PFfWW2pRTxYZCvN7pwnY20p7AmZQoUivjm6pWOprTahTG5j\\ny9ko3vgxgjwIRWQ9KnYt9zjn7hsupdKnTx9b73NfZlPn/vL6n1dh8VUupF4SMmUY2kdfdVHyVlPe\\nmjVryMnNZ/qMGcg0zQiObUjNuUz+u2YtI0a+yCsTJzLhhWeYPdd3l8FAElpvVC5KcQiC0FcUxR8u\\n8bsTAfsyqdnAHU7HNAeCBUHYAqiBt0VRXHKJ3ythhz9tO8vLy20RWN4EnDn3CIqYeGLs6hMJChWR\\nXQajWzkNVbNOPnctirhGjP9HKikpKV4FkV9Z8XnHMeWfRJnchrBWPanK/J3KU7+gbNyRhBFLqDiw\\ngfwvXrrggzjfS2Pa1Cnc2a0rvXr1CniuwHuGty5tEoqkllSe3IO6XT+fJh9rXku4nfnOXe5BTEwM\\nMsMhr3Mrqy5HHhKK5tGFLt9TvHo6/9fjDjp2uNWn8HdnJvXHbOrOL+VfuZBswlr19GjSs6g1LF+x\\nkq2/HHTpm2KN2Jo1ey53392XvOxM1q1b5/bZ8qcXu0QdgiiKgZ8kCJmiKDa4pC8WhIeBfqIoPnX+\\n348Dd4ii+LzdMe8BHYG7gFBgF3CfKIrH3VxvGDAMQKPRdEhLS/NrHOXl5dfVFvVKjddoNLJjxw7O\\nnTtHvXr16N69O6Ghji/q0uVppK37jshUNwJuxRREYwkCEPePdwmOurD6tTrES3d8iTKpFZpBsz2O\\no2zdLJ5N7c3dd9/tc8zexlOwaiZhbe5ycGRXHd6KIIjE/eM927HWIpG1FcXIw2KoPbGN4Q/1pUuX\\nLh7n2dtcGY1GHnpkMPUef8ujQsv9dDiKKA0mfSGKxJYExSRRnXmQmtJ8whreTHBsA8TSXPSnfnNJ\\nzLO/zrn/vMSa/64gNDSUgoICnnjyKY/fW12YQf6SUW6LPbq7nv29bt++neLiYmJiYujevTsqlcr2\\n9/z8fAoLC4mLi0Oj0dg+d8eGDRv4aO2PRAyY4jD/2R/8k6gugxHFWodyJ7b5+mwECcM+pirjN4f2\\nxdZj9GumU5F1mDg3YeXWa+QtfhFBoeLxh/6PJzwoOW/PU/GqaQy6/24eevD6KkMSiLzo1avXXlEU\\nO/pzrMcdhyAI6z19BNTzayTeyQHs4y+Tzv/NnmzgnCiKFUCFIAjbgLaAi+IQRfFj4GOAjh07itbG\\nQb7YsmUL/h57LXCx4/Vn+33vvfd6vUaPHj1o3Hgh06e/gEzTnOC4FJfSHCVrZnAubQKxdjkbgiAQ\\n1rIr5sMbqTxvZ/f0glfnHvO7w5p1PLPnjEKV3BpTaD3MpTpMOYcdwjitznrjz/8lrM1dDtdwdnQb\\nzmXYKvN6m2dPc7VkyRLCU7x3lKvXvCMPdb+ZxMREcnJySExMJCXlEQeTz6lTp1gt4LDTcL5OeMot\\nFBUV8fjjj7NlyxamTpns0ZRWsmZGXXKhl3HZX8/96vsQ77z/Ad26dWPbtm3I1LFUnsura0aVL0Mo\\nq/vcU4n0PXv2IEQl2v4tiiKG/d+BaKHy9F6C41Icyp2oWnTBsH4OgkBdHxA785r9Mcasw36EGrdG\\n2aANy9NW8O7bb7s8XwaDgfseGOgxuCDm4Rl88elwRODfn33qtYf6tcSVkm/eTFXdgceAcqe/C9T5\\nJy6VX4BmgiA0ok5hDKbOp2HPOuA9QRCCgBDqTFlvXobv/ktiVQ5nz54lKyuL5ORkGjRowJmzmSx8\\n7XW/6kl5QxAEnn9uOLNmz0HZpAOipZaQuMYOCWAxD02n8IuRnPvPSwRFaamVKxBqjFjKCrnzzjv5\\n8ccfPbb2DNRJ6WxzX7Z8OVtOnUHz6KuE2DVfsl7bPhfBE5calulXzkCElsaNGzNunGOJC4PBgNUC\\nUFhYSKXJRO3Pq1xW2LbrOOUeeDOl9ezWhT0l/kVvgWdzW11FgcnIYxtTU1FKwr/ed/kdPZXwcPZL\\nWSPsEp58z+0OtmJ3Gr169WL7r2a3pWYKVs2g8rf19O7Vg11FCq/3FhydgGipRZHY0iHRz/rOfP31\\n1wQ79fSwJyhSg7JhO9JWr6NFixY3fHkSb4pjN/D/7J1nYFRl2oavMy2TTCY9mfSEoIB0BUQFVBQU\\n0FXARtPdzw5Y6UoLJdIUy1pXcBFXBFcBXVdFioiigqggXSkhfQKkTdqkzPl+DGeYcubMSYBVIfcf\\nyMwp7ynzPu9T7vupFkXxK+8vBEE4eKYnFkWxQRCER4B1OMtx3xJFca8gCA+f+v51URT3C4LwOfAL\\n4MBZsqscyL0A4b461MW3QQyLp/5kHvb8dzBExGEvKSSsx2BC3EIezdXokZpDmWQkyAG0YXHowuNo\\nKC9Ga4pEG56IozSP+tIivvp2GzH3vEjNr995JauzcVh/JTNzZsA4sj/P6e6773apqs7Nelo2BzF2\\n9MMeXARvuFdG7dihXLrqD83hDLg/v+CU9lRXVVFbcJCghLY0Vpb6iA5Kz9D7OPLJ6wRXr/cdC99Q\\nNa6CggJmzZrtEhn07ngYO3QqRcvHEf9X+c6S/iQ83PNSGqNZkRdkuWsOJf96km+++ZqokfLnibt9\\nprOB16238t3itxSvzVVqHJlMYWGhj0dVZatAF5uheAx9VBLEpJL1zLwLXp7Er+EQRXGgwnfKTCKV\\nEEXxU+BTr89e9/p7EbDobJzvfIVSMrb4g9mE9RhMzaHtPlpE7j9wURRVVZIEWlFXbF9DXV0dFhll\\nU+v7M6ne/xURVw3zau3ZDXvJUR4dO8av96Mmcak0cUo/8mlTn2beC3PQXXK9R0zdUWvz6/E0pcpG\\nTdK+OmePB2fA/flVHdhKw56NPr3evclwStwDueS1Wi7D4aPZ3N/6IjTx7dCGxVG+bbVPx8PSjW+i\\nDYvDXnjIw7OT4E/Cw72KT9u2b8AKO224Ba0pUnGbsIyuCIJAfaFyCFTi69Qc/Z6EhASf34y4ZyPV\\nB7+VPY+E+tICQtr2wtAiT9LCHP+zIxCDWiJQWUbOx/ruFMyXDgRRdK0ghdAY7v7r3/hi/QZVoSyl\\nFXUgdrnlzlkULBkNokD4VXd65BaqrAcUf4xKoRNvz8kfOVIURRDBXlZM/ZEd6GPSqD32CyVfvIpO\\nqyVz5gwPj0etsXJHoBJn66rpCPX1/P2VV10Va9LzC7QKl55lcOtuVH32XJPCempKr3v16sXzbyzD\\n2OUmcDQqkzVXzaD64DeYO10vdzq/Eh7S/Z05YybGTsoFEI3aILThgaXYy8rKmDZ1KnOek1dPdkm8\\n1FRQk7ePfv36cVHbSzzepZCLr6Rkwz+o2L7Gb5JeMj5VJ7MveHmSFsPxJ4cqBnVye+qKDmNIuoST\\nn79M7dEfXWGiBr2Jjz7+D8b0SzH2exyt0QT4D2UprVydek7tA8aJK3d+iqDTe+haKWkFBTKOatVN\\n5y9cxPy/vyFbsmn7KAtBEDwMQVOMlTukyVGpiGD+S88ATvFC6flV7tkYcBVuiL+Ik+9OIDMzs8nl\\noUo5kEkTJrBg0SKiRj1Pbe4eqvZ/Td3u9QqhpNkUvPWo35a7kkS6NySvMCoinInPLlEcr7bRDuUB\\nyJGnwmujRo0CEabPHIs+Qb6niuRRrl+/3uM3oyZJf/zDOS6yaIs8ye/IHG/B2UFT9KbE+hrqrIdI\\n+NuLxN02najr7scyLIvE+1+j/mQu+a/9jfJtqxFFUZaVC6dXrnIs3DrrEXQRAcYSlYSpQ18qvv83\\njroa1+dKP8am9KLwB8n4eK+2pf3Nt3pea3V1tfL2MvdGglREoNXpCG7dDa0pgpC2vUi491W0pnCq\\nD36Dtm1f5mY9w7Fjx1zPr7GqFF2k8oRkjEtj2tNP8dTkSU2u7JEm7aL8XB/meav0VExpHV06VPbc\\n3YEXASkdqP71O5/vpB4nT4yfyLwFC5Er+R8xYgT1hQcVmfiOimJXCMrfNlK4ThAEnpoyiZLjVgZ2\\nu4jqn/+LaCvG3OFaNIV7OfH2o1zVpS2J8RaPew6eSXrLsLlEXXc/cbdNJ+FvL1K563MK//moi7He\\nIk/iRIvH8SeH2r7iwVFJ1BX+6hM7h9PJyMJlj1O528nrDO85FI3RjGCO5f7772fQoEGu2L73yrUx\\nNA77sZ1UF+dgTO0UcCwhbXv56Fop/RiLiopoDImmcs9G2Tp+CKxu2tRGSF9//fUZSXG7FxFIPJbC\\nt8Z4CB7W1tbyyX8/RbAJiKJIXdFhHNXKLGpHaR55eXHYbLZms5nlQnnuCxBNUAhBKZ0CLgJ0EQnU\\nn8jx+EwKDYX3GoGpXS/mvziXn3/6ie7dLvPID5nNZkaNHMH7H/sPnU2b+jSAKra+hLCwMFZ/+IEr\\nL/Xeqvf5ct8ezBmXsr00mB2L/oHtyC604XGEXi0i1tUETNIXLnsc86UDaawopnzN7BZ5EpR5HLsB\\nOXagAIiiKHY+Z6NqgWqo7SselNCWoMR2qmrdy7auQmysx/bDGgwJbfj8iJ2NC9/wiO17lMGuXMWW\\no40k/O1FrCumqEpS1hcfdelaKZXhiqLIjh9/omznFxjTimVbmwqCEDB80FRp7UASHt7bK51PKVew\\n+8NMGmwnqd+yHLvVaTiU7p/t2F4+3KLlX0kpTS6nVoL3AiSkXR+q929R3EfqL15X+Ct6S4ZHaEh6\\nLuZbp/HB0rFsOFqDtvqExzs0YthdZGRkBGTiQ2C2vjfMZjP5hUV8u3O/TwvaIDdBTX10cuDwYEIb\\nSt9/GkdFMSOGD2thkKPscdz8PxtFC5oNNX3FQ7sOoOKH1Zg6+O+dAKdr3bXmaKr2fukz0XnH9s1m\\nM4MHD+bhsY+68gBK8hnuPc7rThxDV2Cj9Mc1ihPA/IWLWPf1dsUqI1O7XgHDB00tk1Ul4aFgrKTz\\nBSoYiLotk+Jlj2Lb8RGJ979G1YGtyt0Ke95O6FV3YWxmObU/eC9ATG2upGyTsoaWePwwNw+8kfXb\\ndqM1RfhweqRrNKZ1QRN3EaaOD3q8Q1f2vDxgFZx0fYG28UZA2f1hWRQsGUPopQMDy+DEpDIgI4gl\\nS5awY8eOPw3571xCqRzXJVEqCEIacLEoihsEQQhW2q8F/3u4h4508RcjhiWc4nHsJzg6gfo967hl\\n4A18uTdPdn+pTr/6yA4nd6Dc6kPsAvlEtHcIyL33sxSWqTt+FHvBr4RfNcwVJ3YUHWTxi88zfPhw\\nvxOAuoqxx2k8sClg+KCp0tp9+vThpVdea7YUt3S+xl3rAsvRRyehN5ichlfm/tWXFlB7bCeC0ezq\\nVuj9LM4UcguQQLLpM2dMZ8tXmzGkdiG85+1+j+3e58N93P9eucK1jZQHEUVRNifS1DYCakKT5lad\\nqNr5X/RJHRWPpa8+zk03PXTBh6fcEdAACILwAE4NqCigNU5pkNdx6ke14A8Ab/5CdnY2+fmJJCU5\\nxQLd+y67ryA9mioltSc4vSu1uXsJSmijOrZfVFREfUicx1i8mx1xQkPY5UNc/IOKtXO45eabKC0t\\nZfXq1X55EWp+/AZLawb0bBcwfKCmHNXd+ISEhPhsLxnY+hO51B38isnjxykKAU6b+jSzshZgCNBA\\nqVEbhD46ze/9C4lrhS4sjrriIx7dCt2fRXR0NMuXLz8jRdcpkyaCCJmzHkVjuRhddCqi6CD/zYcx\\nJl1CkCUDffVxV5hozMMPMX36dAypXRWP693nQxr3li1b2PnL7iaVO6vFsWPHaDDFKW6ji05lyp03\\n8uziFxQ9q5ZkuC/UeA5jcUqMbAMQRfE3QRCUn0gLfhfI9RVfs2YNr776KhaLhYSEBI6tnIplWBa6\\ncIts7L182wc0VpUpnsc9th8eHo69+Kjzc3s1Vfu/ovbYLgCMqV0wdx1E7ZGfsOfuxfrvWdQe24Ve\\nr2Pzvjw25TYoThRq8hIhCa3p3u2ygBOMzWYjMd7CVV0vUd12V/p7ztwnEUKjT+syRSdjiE1n0bPP\\nERQU5HeCmzJpIj/9+COfbFcWWtA22nGUenqD3hpaxQe34qirQWuK8tjOYbY4pVa2fENoeqczlpRB\\ngKCIOHStuyM6Ggm/4g6M6V2p3v8V1T+s4YaB/Xnr+88JDQ3ljjvvQpfQFnv+viY3y3KYLWzc9CUH\\n8443udxZCS7uzTPz0MS3I1xhW43NStu2g5k5c4bfBUXJh5ncPOBGWS/oQoYaw2EXRbFOevlO6Ua1\\n3MU/MOSIa/XHs7EdPUTY5UMoXPY4hsR22HP3+ISk1Mhce8f2a/P3U7J5GZU//xdEhzPEEpVM9cFv\\nKP1yKaLDgTGjGxqdAV1YDHFeOlVB5VbmPJfJzz/9xNIlb7pWymej5afcvTCldaTiyM9c1/daho9+\\nkKFDh8p6DpInV2evY+ErbzZJl0na/62lS4hPSlacWB0VxYjl1gCT715EEVejJdd3J3PYfOCYjypu\\ncyZfpdBgWI8hhLS5ik9XjHdt+9FHH2PsMoDgjB6q8lru0FQUsfPQLldfDnc0hZvjDYl7Ez1iUcBC\\nDcmTMJmc3CUpAV8fEou9+Cj2ggMYE9uyaU8O8aeKEa64vIfqsZzPUGM4vhIE4WkgWBCE/sAY4D/n\\ndlgtOBPIEdcq92wkGB2R1/yV8CvuoPTLt0Am9q6ml7S7615eXo7OHE3V7vXowmKJu32mb1J35VTE\\nBju1ubsVk8QfLB3LJ59+xvRpU5kyaaKqvITt6C769evXpHsBEFxu5duPs7j6miLFHMuKFSvIysoi\\ntNdINEbP0I+aCc4ZspqqqFo7acIEDEEGBebzLNAFEe7VZtZ5/b80WTPKH5pSsiyKIkGxyTSUFhLZ\\n9z7Aq1mW9Qh262HCr7zLlbfxGHf2L5jSOza53FlJ/sXb8AXK0biHJqVQ7333P8DHn60npMcQYodO\\nc91vqRjhzpuP+PRpuRChhgA4BTgO7AYewqktNe1cDqoFzYc/oltjValTpA1nGEQXGY8hxreliiYo\\nxPWDk2uzaX3vaXp0747JZEIURb77fhsN5ccRGxt8jAacrmCx5+whKDkQq7wLxsvvYP5LbzB/4SJF\\nsqFT+2oG2rBYLmp7iSzRLCDpzw+JTxRF3n1vJfFJKUxe/BbGTjdQm7uH/NfvdREk3Y8jRz602Wws\\nX76chQsXkhhvYdxD/0fpu+MoXZ1JyYY3sK6aTsHSsaDRsmDRIkRRZPq4sRxf9ijWlVMp2fAPrO/P\\noGDpGOpLizB16OsxAUtGJzgmSVYvSmls/tCUkuWioiL0iZdgz9tLY0Ux4T2HkvTwW4S07YXWFIEm\\nJAJtaCSmdr08QmVSdVhaSgpEpiicyTMkKooi8xYsJD4phfEL32DBmm2MX/gG8UkprmcvV6hh6ng9\\nhcsep/jDOZRuWop11XROvP2YT2tbCf/97HNiRi4irMcQn+ow8y1T+de7K2RJnxca1HgcMaIovgm8\\nKX0gCEJb4IwVcltw9uFv1egdgpL+9lY+Dbn4StcEVbBkDEHJl6CPTqH+ZC72/ANowy1s/uorBtz0\\nF665ug+fb9qCPiYVXVisolHQhcWgi1BmRUvlwO4rZd+KMWenPnv+PozplxGU3oXG8mJmz38Wu91O\\n5ozTTYKaSvqTMH/hIlZ+9Llf0UjAr1yKP32r6py9XHHFFXz7w08YotIxdehL7JCnXT3IF/w9iymP\\nPcSJ4iLee+89Nm3aBFzMdVMeoqDIyqJnn6O6stCDx3B1n95sO2lQvKeBiJHuaEpoUBRFtNUnfVb1\\nUl6mvqyIonfGO/topHVFH5VEfWkB9ry9hHYdyJE9GzA2yDd88j4XqJN/0Qp4GD65QgNtiZbJT02W\\nDd+pe1/aX/ACh6DOcHwtCMJ0URTfBxAEYTxwH9D+nI6sBc2Cv1WjewhKYzTjqLdTk72TvNf+z2+D\\nnLKtK6g7kYPBchGmDtcRO2Sqa6LbtHIqm778kpAug7Dn7w9YC68Lj6P+VBLdH6TqG+8J/anJk7hn\\n1EjSWmXQ6NiFPjoFU4fraCjJo+zLpRgS2iBEpTN7zlwEQWDGtKkIgtBk0h9AQUEBmZmZGLvcRG3u\\nHkKMZhdDXSoBLnjrUUztr0VndiaqlSY4yTBrg2PZtPETEv72IobYdK974xlWevDBB3nwwQc9thn/\\n5BNnJJWuBk0pWRZFkdGPPEZE73sBmfLh3D2IDfXE37OY+uIjruowiedhan8NhW8/iV5BVFA6l1qt\\nssWLFsgaPvdCg6rjv5Geni57/WreF8ITL3iBQ1BnOK4F/iEIwh2ABdjP2Wnk1IJzAH+rRk1QCOae\\nd1D4r0mI9bVozTHoQqOwDJsrG1Ov2P4hgiCQMGqR3/BTwdIx6CIs1Bz9ifpSZTE6EYG6ot9UV994\\nT+hPjhuPEBpDkkwDKGeb2B5E3/AwC1+ehcFg4KnJk5q8gp6/cBGzZs1GE98OHI1UH/zWh6GuC7cQ\\nlNiGgjcfILzXSELaXiU7wXlLkusiEjCmdsK6YopPXw3pnipJmJyJVLraUtKmliw7t3WGAt1X9UZz\\nFLX5BzCmtiMoLp2guHSP84iiSM2RnxAEgZojO9DHtXItWszdhyDo9dTsWMtfBtwgG4LyhnTvAKpz\\n9vrcD/cy6trDP/vNial5XygvuOAFDkGF4RBFsfBUM6WncDZTmiKKYkuQ7w8KpclEEEBjMBJ75yys\\nK6YoyHfPpGDJGDQR8VQd+FpWG0oXbiEo6RJAoOFURZCSUagv+o2wnrdT/MEs2QS6d/WN+0rZZrPx\\n0cf/wSLTU/o0EfAJzJcOJHLoTNfKXboX+uPZ1FkP++hcSRNrv379uGvYcD758lsfeQq58JQhthUG\\ny0VU7dlAzc8fM/PUZLp8+XLXBFe+bbVfmRG5cBc0LawETZ/o1UBJQde7ZNl7W9FsQWezUnH4Z7TG\\nMHSR8bL6Yi5RQbf+56IoUrZlORXfryIoqR3GdlezeV8e8UkpXN37KlWeo1Ne/fT90IbFefCUdBHx\\nGFM6cFHbS5g0YQLpaSlYrVZXkl2dId7XwulAHQFwA1AAdMTZI3ypIAhbRFGcoLxnC34P+JtMHPZq\\nyr97n8T/e4na3D2B2cyJbWmsLsdRVe63A50+OhWHvZLwK++iYsdHfo2C9f0Zp/fV6ilYOgZDQlsM\\nMvpG0j7uK+U1a9YQnKpcgeMumiit3EeNGkXv3r1Zv3ycK84uXUto14E4sn+gV69etG7Tjlq73a8A\\npLth0hiCXUKN5ksHcvztx3hkzGjgdKgjkMyI9/EkNEeuW5q8Z895gtD0zqq1nPxBTTOsQNt+uHo1\\n/9mwhardG2isOOERBjX3GEzF9jUk/p+nga7YvoaaQ9s9jAk4cxibV07xCe95w0NeHacxwxRFXV2d\\nrPGe+/xUDMZgjOmXevBeevfuxaaV02Q9cevKaXTt2LGFQY66UNXLoiiuPfX/MkEQrsLpfbTgDwp3\\n4po23EKjNoiGsiKCLK3RhVtoPPB1wJyE3pKB0RThkpKQWynXH89GFx6LuftgJwv9u5WnEuod0Ecn\\nU388G3vRby6pESlZ2VhRTM2vW6kpPoz5qmEe+kZyK+WioiK0UcnK43WTtZBW7vMXLuL7Xft9JiOp\\nRLhVfBTf79qPsccdkLtHlWEypnRwhdQ0hmDCMrqydu1a7r77bleoo/q37wIbZjdDJ42pOQxlafLu\\n0qkjJ0+eVK3lFAhNkfhw39Zms3Hvgw+jCQrDct8imUXETGeY1CuUpGRoI4fOpGj5OMwqQnLS/fjr\\n3aPIuKiNTydK6ZiWYVkULnuC6CuHozEEO5PsL86luqSIkEtv9snZSEn93b98RmVl5QVvPNSEqtZ6\\n/d0AzDlnI2rBWYMgOKunNGEJ1FecRB/rLNtUQ/JrKC3EEHe6B7P3StlRU4G98CD1FcWEtLmKATlj\\nXAAAIABJREFUiCtuI+zSgZRseIOaIz+iMZoIueRqYm+b7kP+aqg4TpClNX3axLLlmw/QWg8orpTV\\nSsdLshYam5WIiAienDBJUeTu0JLRxP/1eWoO/xDYkEYmUn8ih4ptH3qE1NzDS1KoQxeaqOp4kqFr\\nbljJHSEhIQwaNKhZ+55NrFixgsbGRhKHZ8nf9ztnUfDWIx4NoAIZWkNsOsExSZSunkXkUF+P1vZx\\nFoMG3Mirr77qCjtt2LABc0YX1cZbF27BfOs0bEvHEn7lnYRfcYeH5Iu0UKgoy2mpqkJZVv0bURR7\\nC4Jgw5MpLsmqh53z0bXAL9yJUOHhTmGF8vJy4uPjOZJ9jOffWEbkyNPEML0lg+oDWwF1JD85mQjp\\nx1a563Mqf9lAeK8RgEDBPx8jKP5iZyK0oZJqexWR1/5N8dimpIsYMXw4769aFTAkolY6PuYvE1yr\\nT0B1Z0Q1hrTu+FHs+fsJ7zXSg0/hHl6SwoSz5z+LEN3K36FOHS8bbYmWquO/NTusdK7RlF7rEjZt\\n2qQqDOrubTVWlQZWqE3rSmz5AX5bMtrpCcSkUn8iB3vuXgStho27j7Exp94VdlKTF3E33q6xuRkT\\nd8kXF1qqqgBlddzep/5tXrcYFRAEYQDwIqAFloiiON/Pdj2A74Bhoih+cK7G82eARISam/UMwSnt\\nqa6qorbgIEGJ7QiKa4WuqpiSX3dg7n4r2rDTkmLexkKt/Lk3dOEWyra8Q3jvUad7LnQdQOUv66jc\\n+i6v/P0lDv56NS++ORWL16rTXebdtuMj+vfvHzAkIk1eV/fpzRY/K073ntLSyr2srCzwxBGdRmNV\\nCeaugwIb0oJfSXzwTXShUR6fe4eXpkyaiN1uZ/acuYrHayw6yPjxT9K2bdszDiudbTSn17o79IHC\\nitEpHg2g1BjuxpJcco6fIP6exdRZneW9BktrIq+/nxMfLcBhae8KoarNi9SX5qOtq6Z82weu5L0+\\nKtnDmPigpaoKUPY4jMDDwEXAL8Bbp8JUZwWCIGiBV4D+QB7wgyAIH4uiuE9muwXAF2fr3H9mrFi5\\nivc/WU/kyMVUHdhKw56NPkld46nJVGM0u35M7ozwuNtneMp3J7VHF2FBLM2nKnevz6raHfWlBURe\\n/yDmrgNcn2mCQgjrMQSt9QBGo5FOHTsgiA1+yV/Vv20jOCaJ9evX+zUa3pNXY6gFNFoKlozG3Koz\\nQkSyh56QtvgApT9+6JrY3nnnncDhrZPHMMS39rk3cklR82U3+RgNufCSIAhkzpiOgMDCV+QNnfX9\\nGRgiLDz/0stMm/q0Syvpj4Lm9loHuO666/h422uKx68vyaf++DHMlw5EF25R5QFL0iqGmDQfgxB3\\n+0yPYgO1eZHa7J0EpXZGYwhxJe81IeGEdvHtlS7t01JV5YRSjuNtoB74GhgEdAAeP4vnvhw4JIri\\nEQBBEFYCtwL7vLZ7FPgQuODVxWw2G/96dwXRd7+AxmhucuWOixG+dCxByR0wxKRiSGxHTfZPdOrQ\\ngUcmj+GJ8RN9ZCIkNJRbqSs4SOytk2XH5x7vD72kDw59KBXb3qe+rBBdWByGxHZU7vyMsCvuRKgp\\nVXT5pckr/I4s6qyHEatKCe42GHNUEuX/WcC1F8Uy5P4xLqJfXl4eyckDSEqIp7KyUnV4K/L6Bzzu\\njbfeUmPxIa7r25evv15Hla1ANhcjF9KZMX0qhiCDB+O9oawIe/4+VwVZY0XxWW3GdDaglmznT/9q\\nxIgRPPbEOGXvLXcvoZcO8khAa0Kjsb4n76VaV05Fa45VlFbxLjYIlBexrpyGufutRF7zV59z1fz4\\nEaa2vWRzKaNGjvhDeYe/F5QMR3tRFDsBCIKwFNh+ls+dBOS6/Z0H9HTfQBCEJGAI0JcWw+EsS01x\\n6j1V7tnY5ModqaqpNucXdOYYNKZwgs1RaI7/xrdbvyE0NJSTpWV+eQHWVdP9hrDAS46i0krYzfcR\\n1v0vp+UeTFHE3joZjSGYqk/m+XX5bTYbc+ZmobvkOoreGY82LA5dWBwIAnUFBwjtOpAt33zB+6tW\\n8fKrrzF/0XOnQion0Ng+d4VUJk4YzyI/q/7S1bPQhYRx4qOFLi9Dkqeo/GUd5d9/iE6v45WXXuCB\\nBx7AZrP55GJMJlPAkM5f7x5Fq9YXYeySTki73sTcMtF1/6SJOHPWY0RHRjB8+HCfHEJz8gxngubK\\ntEgwm81kzpzJnMXygo0lH2bSv38/vvlmE2Ep7ah3NNKQ8xOCzcrVV1/D18seRWNpgz4unYbSQmpz\\nfgFBg6mTfyFL8M1XgGdeRPJ8604cozZnN2E9BhNx9T0+12YZlsXxtx+j5F9PYkrrhMNsgbJ8KrN/\\n4bq+1xITFXlG/d7PFygZjnrpP6IoNvxO7RJfACaLougIdH5BEB7E2XAKi8XC5s2bVZ2gsrJS9ba/\\nN7Zu3YoY5pxs1SQU5X5MLq9h9GQcNRWUr5nNiOHD2LFjBwBXXN6DO28+wrJ/PoLOcrFHL2nR0Yix\\n1aWypK6GciuV2b8QExODKIpUZu92rfa9k4zu28rd+88//5x6QU/tz5/6DXXpgyO45dbB7Nh32Gd1\\nbCgrIvOZyTTWVmGIsDg9rKR26KNTEMsKaCg6yLA772TFylUY2/f1W3pp/+UzkpOTXWNMSUkhJcUp\\nzLdjxw7efW+lrKZVULmVZ56fzZEjR4iJisSU3pmwa/8m+4x04RY08W15MnMBj4+bwKiRIxgx7C7A\\nGZb817srCE5pjxieiFBewENjHnFt4/6bOFvv8datW2kIlTcaEhpD4/jmm29c98IbV/Tswcghg3hn\\nuVO+XxOZhKM0n7qCA9w9aiQjht1FzSNj2LBhA1VVVURHR9OnTx9EUeSbrVsJbt0N0dGIPiqZ2tw9\\nRFw1jNpc5Ta+3s2iAOpP5HA457BHXkRbF0tQcgcPT8MdunALprSO3HtTb/R6PZu+3MzP2bsxpXXm\\n+xIj36/5khf+/orsM/gj4lzNb0qGo4sgCBWn/i/glFWv4OxVVeXjJBRKSD71mTu6AytPPZwYYJAg\\nCA3eJcI4B/QP4B8A3bt3F6+99lpVg9i8eTNqt/29kZOTw7+3vAqoSyjWl+R7/Jgkr0EXGsnJFRNo\\nKC/mqaeeYub0aR4/gO7du/PuivcQ9Uaq9m7G3P0WjBndse34mKK3n8CY1hVDTKoHmU48toMZ06cx\\ncOBAAGZMn6bIaHbf1htZ8+aDRi/Lvyj+YDYhF/ek4sf/sPW748TJsMmrD36LaDARP2Khm17U99Sf\\nyKH+ZA6TJ00ic8Z02rRty/yX3sAyYr5rYgmJa0XENfdg+2QhM2dM9ztGm83GTbcM9hvSCR8yg/dW\\njGfyhHEIEUmKz8kQk4bG1BlTuz68/3EWGRnOMuj3P1nv02cjpNzq2sY9vHW23uOcnBxWb1XWv9JW\\nFtO79xDF8/Xt25fFzy5SrJgLCQnxOMby5csxt+qMqYczbFi5ZyPGlI6EdrmR8u9WKYa/anP3Ety6\\nB+XbPkBjMOGoqaDy6C50EQnOMOmpvEj5tg/QGpTFFYWIJOLi4mgU4UBusc87ZvLzDP6IOFfzm1JV\\nlfasn80TPwAXC4LQCqfBGAaM8BqDa9YTBGEZ8Imc0bhQMGTIEB4a8wgh5VZVCcXaYzsJ0WuwnczG\\nUZKHLfsXRFEkOLUj2ugUgiuLZbvYrVmzBlNaR4IHTeH46rlUbF1JUHJ7gltdSkNZglOZNq0LsX3v\\npbGiGOvKqVx3ZTePctKxox/m559+Yu0/H0EbbkEwhqJttNNYbnX12wDfUEy/fv3Y/NUWLP/3st/c\\nTcFbjyLogzBYMny2kSOTuYvcNVw6kGefG8+EcU+6SWZMJSS1IxqzBeH4b5RvWcqI4cMUy2PVhnTy\\n8vJUc1Ck0NXcrHGIokjUqLPTZ6MpOJv6V03tE+4tMih51UrFC/VlRRT9awKIjdQc/gFdRDy1x3/G\\nnn+AoMR2oNOT//q9rryS2kZlgXhA5/IZ/Bmghjl+TnAq/PUIsA5nOe5boijuFQTh4VPfv/57je2P\\nCrPZzKiRI3j/Y+dKPlCjmhnTp7Fv717Wrv0IITgUTJEk3OUppSA1qIHTCVrpB2z7YS2N5VYSH5Bf\\n+YOTRW4ZlsXWFeOpqqpyxf3nzM1CCI2mwSGiDY5AF5OCWJqPIDj7avgr+ax46GGCktoF4AG0oaG8\\nGE2Ub6hEDWtbH9+GFStW8OCDD/qV19ixY4diGMK717oc6kNiSU5OpjpnlSoOijQ+Q0JbGqpKmp1n\\nOBM0R//qbOVhvIme7pO8d/GCFFasyf4ZnSkKy93Pyb6jpo7XEzPgEdf7au46QFWjMgjMAzpXz+DP\\ngN/NcACIovgpzsZQ7p/JGgxRFP/2vxjTHx0jht1FRkYGc7PGEZzSAZ0pnIKlY1w8Dn31cVfFjyiK\\nrPvmB2JGPXtK1HCBqtVTfHw8lOVTkf2Fqqot9x9RXkEh8196A33HG53aQ37arW7+agvf79rvs6Kr\\n27wMsaFO8R4YYluhMYb6NHcCdbmfRnM8jzz6OCdLy5gyaWKTV8bg2WvdH+zFR4mPH6Q4EctxZsTw\\nRBprq11/y/VMaaogYlOgVujwTPke3vD2dry9au/eGkHBodRm/+xTiQW+76j7/wMtuCaOH8/GjRsD\\nLgzO5TP4o+N3NRwtaDrkhOUiIiIQBIHS0lLXilkUReKTUogcudglaqgxmmUT296rpyFDhvDAQw8T\\nlNTJ9cOSm7zcq7YaQ6JYu3Yt//nkU0w976DihzV+jY75lqmsPyX34f29PibFxXD3h/rSAoJb96Ds\\nq2U+K0dVcirlVkJ7j2T+S85YfnPj1PaCAwFIg85eZ+4TsSGhLY3meFcfcXdxRwlCeQHaxlqn/pek\\n7npqlS3llYwRscTH39qscQeCWqFDf71HdKGJso21AkHO2/Ge5KWwY0O5lZMrp2BupV5WRPp/2OVD\\ncNTaXJwgXXSqyzD27t2bBYsWoQ2Po8GgnMZtjijl+YIWw/EnRaBVsrvEd8P+LTjqqsl//V6fCUia\\nuNxXT2azmev6XsvWYr3i5KWPa0W97QTl21ZTvvMLvijpiLHTDVQd3IrhlKCiHJw/6A7UFR32qc1v\\nihyKw17ts3IMufhKSjb8Q93+ba5qdpy6vLwcY2JbRfa9MbENpaWlHhPxe++9xyOPPk5o75FE3TiW\\n2uyfqdj+4WlPotZGXeFBl8x4zaHtsuqu1vemMifrGURRZOjQoXJDPGMovWNKvUf0kYkI0a18Gmup\\ngbe3I4TGoddpZCf5a3tfxfZS+dJwCe6VhbpwC7adn1H/6xYain5jxvRpZKSnUVRUREJCAoePZrP4\\ntbcw9rgDR30tVdvXBAxpXahkwBbDcZ7CPdFYX3yURttJxd4QOq/V07C77uLbrFdcvRNkJ69V06k5\\n+C001nmw18u3fUBjVZni+PQxqbLSDlIi1Pr+DCx3zlYM7bjHvQ0JbQiKSUVffRydIFLyoTyPwH1/\\njSG4WXFqm83GoUOHEGrK0MekUfDPx1xdFKVy3rAr7kRbfMDjnprNZoYPH876DRtZ+/kayrau9Oi+\\nWLLhHxiMwcx4+inq6uqYPWeuT2UZnOIbDM/i8NJHeHTOS4x59HFGDB/GNddc0+zy0KbmKdyLA5R6\\nj7g31lIDeW9nCP369WPDhg1N7oDoXqZbX1qAJiiUuuPZTJ4w3sMbys/P5/8yWuMQBYKO7UIfnYw2\\nLBbryqlYhvmSEs9UlPLPjhbDcZ5CSjQ67NXUHv0pQK7icYx6rcfqaejQoYx+5DFqc/b59E6Q9rXc\\nNYcCmZCTqlLhEzkYLK1lvwu7fAh1B77kxNuPYW7VhXpTLPbjx6grOuQR2nHvKX1y5RTS7A1c0esK\\nZn7xPm+/8y8yMx9Fk9AOQ0yax4TuHhpqSpzaPaYfnNIBbUoXGsqsCIKAoDcihIS5lFQdNRWU/bTa\\ndU/d98UUCdogEv/PV3a85MNMBARapaViTu+s6LUZ0zpjaNsLY/+xrFwzm4yMRU0OuzU3TyEtTBps\\nJynf+i7my/4i22rXvbFWUyZZOW+nOR0Q3cUv6woOkjT6LRw1Fa7KOqmYY/qMmQghUSS69eGQvL6C\\nJaMJSumEPioJygtotP72hxSl/F9C83sPoAXnBkOGDKE6Zy+Vu9YFrDIyWC7ipoEDPH7YZrOZmwYO\\nICjh4oAx5Lqiwx6fh1x8Jfa8vbLJa8AV3zfEyxuOxopihOpSjh7+jcVTHmb8X7rhKDqIZeQCwnsO\\n9ZjIHPZqKneto7o4h0M2LR9s2cXF7doD8OLzz6Grq0BjCiekbS+SRv/TZ/+mxKndY/qhf3maqH4P\\nEXf7DBL+9iL1x7MRBC2hHa/zEFuU7qm0b9jtWdSUWH0aBUn3M+q2TLLmzSMnJweNyh4kEm8k65l5\\nVFY2rTmn+zWZbn4a8zX3Yrr5aSJHLmb+S28wf+Ei2f0sFgu12T87J9Wk9q5Wu/mv30v5ttWIoui6\\nJsmrO1uw2WwsX76c1157jZsGDsD20Vyfd81b/NLd03Qf0/yFi5j3wms4BK3PMxEEgchr/kr8PYud\\n77P1N7olh2ItyOOpyZP+8OS/c4kWj+M8hZRonJW1AEO7qxW3DYpLp9tll/p83r3bZazPVq5w0sek\\n+YScAokG2j7Oon///nz/2XNoFEo+ExISXKtMnSGI+S+d3l7KvZR/t4qg+IsxX3YTDWVFlGU7Wd/z\\nXnyd8Q/fi6PiOKZ2fc44Th1Iw8nJL3kEbdE+avL3e6xI3fct3byMoIS2qvgfjlJvPqwnvNVdgxLb\\nNinsdia6VNnHcqmrrSHx3r/7PD/vhl9nq/pIzjsSKgqpKbVSu+xRwlp3pT4kDrv1CPaCgxgS21Cb\\nu4eK79+X9TSzs7OZv+g5grrfjlGhkZchNh1jWhe0NSVceeWVF2x4yh0thuM8xpRJE/npxx/5ZPtB\\nxe2E8gIiIyN9Pk9ISEBfXay4r7+Qk0tQcckYgpIuQR+TglBRSEOR082fPHECCxY9q6q3tXQtcDpp\\nWlVpo778uE8YTZq4gi+6nEXPPcfECeN5/o0z78mthvBnTuvA7dd04dln13scU9pXYzRTc2gboZcq\\nN1xymC0kJydjz3tXmeDppe5ae2wXK1auYtSoUapWw83VpbLZbCx89lksCkbUXWDzbFUf+VPtNZZb\\nqVg7h74dUuje7TL274/g3+sq0Wd0c+qjuemDSdDYrOTl5RGS2oEGsTFgCbcuMoHa3F306dPnjK/j\\nfEBLqOo8hiAIvLV0CZqSo4pho/Kjv/DE+InMW7DQFWKA0+Gu5oScBEHA1K4XOp2Wvu0TmHhrD16e\\n/pjLzddoNDw1eRJF+bksnvwQk4f0ZPHkh/yGAaSkaVF+LlmP3UN90a9+wz1xt8+gcufnGBPbkZGe\\nxpTHHqL03XFUfTIP21dvUfXJPErfHceUxx5SHaf2ZjXLQRedSkZGho8hkvat/u07dFHJNJQqr77r\\nirNJT0/nqSlTsK6cKhuGkdRdLXdkEnndfcTdNp3E+15hy47dfsNLzbkmOW9BjcGRSl/PVvWR5B15\\nLwAANEYzukuuZ+1HHxEREUFWVhZi5UlM7foQ2vE63w6Up8aUnJyMwxyP1hRJfWmB4vnri7MZfOut\\nBAcrV3FdKGjxOM5zOENWUxUJaOG9RmBq18uHQa6GRRwo5DQrcyZX9rzcr15OU8l3ZrMZo9FISGqn\\ngBNXbX0DRUVFqjgJgaCmfa3cylqqwKo9+iOUFWNIuJiag1sVPYmqnD30798fi8XCt99/z/olozGm\\ndUEXEU9DWRG12bsw97hVVt21Kcno5l5TYWFhQHKc1Gq3/ue1Z6X6SM5YeZeKGzvewKTnlvLkhEn0\\n7t2b7z6aS9it0/x6mkkJ8WhsnxN8xbCAJeCO4t9YumQLP/744xldx/mCFsNxAWDyxAls/moL65eM\\nJii5PfroNOpPHsOeu4+g9K6YewxGo9HIxrQDsYjVhJy++uqrs3o9RUVFaCKVhQP1kYk05Pzk0dZV\\niZPgXYrqjaZqOPlUYCV3pr6kAPvhHwhK7YL137Ow3CHTJ2LVdHQhYYwdO5bo6Gh6XXE5IcYg/vvp\\npzRYD9FQZycotZOiuqvWchGrV6/mnnvukd2mudckYcePP2E/nq147Lrj2TiKDpCZmano1aktA5bz\\njpRKxb//OIsru7bnm3f9v5eVlZXO66+1KefkPppL5swZF7yUujtaDMcFgAWLnuX7Xfs9227Gtyby\\n+gc48dFCbD+sJbznUNmYthoWcXNW9GeibxQfH4+2MkDupSSfxvLjiiESpVJUb15EUzWc/MXjnV7e\\nLHQR8T66S7W5exAdjRhiW7Hm4/+eMvJFCBVF6A0Gbhp4Azt37qTIrByPF8MSeW/lyoCGo7m6VP/9\\n7HPs9Y2KK3Sx+DeOHT3ilK/xc+/ffW8lN90y2OPePzz2UW4aOIDu3S5zvUdms9nHO5ITs5QgJfa3\\nrhjPoYP7ffgf0vW4X3/oX54GvBp5FWfjsP5KZubMC7r0Vg4thuM8h3fljG/bTc9Epr8KmEAhJbUh\\np7OhbyStlI2KysC7mDF9qqLxUmqRKseLUKvhFLgCayaFyx4n4d5XqT22k8aqEozmKOpLC9BFJNBQ\\nkkfi/a/6TOTrPs7ixt492LQ7ByU0lBWxae8eKisrA4aI1F6TBEk5OSi2nf9Wu6umM+QvN/s1GuC8\\n9+69TKSwU+2hXXzyw6+sz65DX13sei/Gjn7YwztSI2YZnNKBDRs2KL6Xp69/PCGpHQnv1Jf6ggPU\\n5uxi8OBbWbpkS4unIYMWw3Eewn01f+jQIYyB1GaT21O17ysEnZ6awz/w2Wcl2O120tLSznrHuab2\\ns/bnmSitlK0rp9K/fz9mTp/mdxyBJvfwITOYNftxGuo874Ma70pN8tgQl0HxB5kEp3VxkRPN3YdQ\\n8cNqv4RL8y1T+e+742ior1c0mvb8/USmdw5YmivdW60AixctQBAEysrKFD1GKWTkT63WnreXIEsG\\n3S7t2qR7L4WdvK/d/b1wf+ZqxCzVlAHLe9R3NDkHdqGhxXCcR5BbzTeczMWWvRvHttWEXT7EZzUv\\niiKOumpKv1zqLJtN7cr32Xl8NecZgqMTeHjso67+GWdKeGoKbyBQW9bJE50y5HOzxhGS0oGG0Dgc\\npXnY8/Yz7akpAfWR1EzuQtzFLHj7Y4KNBg+PKJB3paZaSR+bjlBW6CQnnmKbV+78nKB4ZcJlSGpH\\nEnSV7A8gySLWlvmdNP15fdU5e5k29WnFcl4pZOTO2pfUaqXrqPniBRIT5Sd1m83G+PHj0cW3cY1d\\nTdgp65nxFOY5Pa25WePQhMXRYFBe0DSlDLg5CskXMloMx3kEf6t5swwpS0LF9jU02k6SeO/LspNQ\\ncMcbz0hF1mazsW7dOrZv386hQ4ecUvAqeAOSPHsgz6S51VJqJndDbDoaUzimnrf79YjkoKZaqfFk\\nDjXHdiM2NqCPTqFq72bseXsI7SrfcVCCw2zhktQkfi0odWp0JbYDUaShopjGimLCejqJbmVrZpGQ\\nMFj2GE31+twxePBgHnhotCtk5N4kC/wn1D0kV0Ii0Kac9kjUhp3Wrl3rIRb56ONPnnMRQm+PNyYm\\n5oyOd76ghcdxnkCpzl3iNlR8/28cdTWuzx32asq/W4XlrjmKfAjTwPFNlrMQRZF5CxYSn5TCG2u/\\nZMGabbz78RfUBUcr7icxepWuxbkCPT0eiXsiiqIHD0UJaib3+tICtKYop95X7h5IuYzMzFkBwx9q\\n+C81+ftBH4QmOIyGypM0VpcRef2DNJQFLo/t1asX9pJCTJ36Y8/bB0Bwq24Y07pg27GGsi3LqTiy\\ni/79+/vsL/eeOOzVVO7ZSNWBr9G27cvcrGf8PutXXnsdISiE4g9myfJLSj7MZOrTTyGKIsuXL2fh\\nwoUsX76cWXPmuoxVcPehHtfZ1LCT2WzmwQcfJDNzJraPs2TH4S+x7z4mm83m93zu7+/4hW+wYM02\\nxi98g9vuHObDd7oQ0eJxnCdQRcpKusTVmwBw6ljFX6RKi6qpKrJyq1ohOi1grw13Rm+gFei9993P\\nfz/7HGNSO+z1DYiVJ3jgoYdl+6h7Q00pam3uXgwJbTzk6DXxbWnV+mJmzpzhN3wXKAfj5M6MxNSu\\nl7OH+iV9sB/bRVBSO9keI+771uTuxWg0ojeFU/Pb97KSH9aV09AajDzxxBMMGjTII0/l/p7ISebX\\nl+ZTW1NLrz59GPfEEwwdOtS1r2R0okc8R/XBb2WrwvQaqK2tJT4pxU0WpIiSX3dg7n4r2rA4H+l8\\nte1cvcNOTWk4NWvOXObNm4cuPA4hNIYgvU6xIONMvLILAS2G4zyBKmZzhIWaHR8insxGY7NS+duP\\nhHQZoLiPJKSnaYLekL9chppeG05G7404zCcUz1EfEstHn32BscMNlO38zDmBpV6GWJLP7Dlz+e77\\nbXz+3//4NR5qJveg5PZU798iyxMINHmcntSexBHVCn1cKxpKCz0UegVBOF3V1n0IJz5eRGjXgYoa\\nX1Offgqr1UpDTaVPd0U4pVo8bC4FS8fw2W9VbFz4hscE6f6eKPEg9r0/gzFTZjHm0cdd+0pGRx8R\\n7ze/Uf7JAha+8DLRMrIgxR/MRmM0E95zqAdvQu174R12UlMqLooiA276C+vXr8eY1hVdVBL1pQUe\\nmmbez/FMNLwuFLQYjvMEgUIvDns1YuF+OqXHk5aq4/rr7wHuYepLbyseV+pnIBz/TXWi0Z/3o0b8\\n0J3RqwR78VE0cRf7bXS0aeVUZs/NUqysmjJpInX2OmbNGe2cVCITqD/hlG83dx+CbYdyF0OlyUOa\\n1KIiwpkw51m0pggMcRnE/GWChwSG5NXpwmMwdbye8u9WoQ2NomDpWGexQnTyKY2vX7lp4EA0iGzc\\nuJGgpEsUPTJjWlf0CW0wdbzOY5XsvLdFARPSljtnU7jsCSwj5zP/pecA0Ap4LE688xuGzSG5AAAg\\nAElEQVQAYlgChqj0gBpWLi2zfz7mLEQIMmNdOc1HRkaNpphSYnv2nCw2ffejT18Td02zrGfmeTzH\\n5mp4XUhoyXGcJ/AXVxdFkfJtq8l77f9o0Jv4TUhi0948npwwicKiYqqO7QmgRbUPQ3zrJiUalbyf\\nsMuHYOp4PQVLx1K6OlNWO0qVRlb+AWqzfya0Uz80Rs/qGueqO4t58xco5mUEQaBVeiqRF11KSLve\\naIyh2PP3Yxm5AF14TMCErcTQVkJ5eTmGlM6E97xdVjcJJK+ulPCeQ0ke/U+MRiND/jKIv954OY8N\\n6EzbSA11djufbf2RBR9u5Yf92eij1UmuS2OV8kL9+/dXLbcvhSmlfSMiIgLnhU7moY9JVTxm9a/f\\nuT4TBAFtaBTBrbuj0ekpWDLa73vRVNhsNubNn+/TiEkai7ummbvse3M1vC4k/K4ehyAIA4AXAS2w\\nRBTF+V7fjwQmAwJgA0aLorjrfz7QPzDcqz4GDbiRdWvnEDZ4uld9/AbZ+vjFb2TRp08fvv/Yf7gm\\ntOsAqj57rkl6Q0rej1TGqSnayx3XdCUjI0OR0SvL03hvKtqwWIJbXUZt7h7Kv1vlEf4BKafTLuCq\\nsKioCCKSXCtnQRfEiY8WEty6R8CErRiWwJy5WXzzzTekpKTI8l7UJuGlLnWOWhui7TjLli3j5Vdf\\nIzNzFpr4NgR3GUhDaSHVu7/E2Ooy6kvVH1O6HxIhTq3cvnu/j+CUDgBU5+xVbpxUcIDYIU8HPKa/\\nMFnd8WxKV8/iqotiGTHmoTPiU6xZs+aUNxdY08zdCDRXw+tCwu9mOARB0AKvAP2BPOAHQRA+FkVx\\nn9tmR4FrRFEsFQRhIPAPoOf/frR/PPirxbeXFVO87FHCMrriMEVTvvMLj7auEtxlGSaMH8eiZ8eh\\ni78YMSyB+pN52PP3ExydQP2edS4eh1qo6sxWcJBnn93gd1KQS3w2nMzBdvQXzN2dAn+SkZDrAeH8\\nI0l2VehNkKTsdN8LKYRSvvVdgpIuUbzOhjIrh44eJftkDQ1lhRjMETw05hFmTJ/mSrg2tUudFJZ5\\n+dXXmP/i68T+Tb7fRUNFsaq+6u6QVslTJk1k+7ZtfP7TIcXr8zBoZgtlZWWKBr109SyCo+JlvSrX\\nMUvyCY5KouyrZbJhMkNsOtHD5rPl3XEMGTyYV199tcmSNBKao2kGzdfwupDwe3oclwOHRFE8AiAI\\nwkrgVsBlOERR/NZt++8BZf/8AoJS1Yfto7lc1ykVQXSwrrxLwFht61bpFOXnsnbtWrKzs8nPTyQp\\naQjp6enNWvE1RwPJG96Jz+zsbOY+8xnxf30eQ0yaz3V4S6cA1BUfJTj4etd28o2Aiig99DMNX73t\\nMkbhPYdian8NBW8+pNwPI+cXEJxFB8Gtu1FfnI294ACz5i1CFEWenjI5sPe0ajpBllbUfPGCqxpo\\n7OiHSUhODdg0yvrvTCx3KPdVd4fGZiU+Pp75Cxexbv0G7Ha7auMjrbBHjRoFyFcyTZowgQWLFinf\\ns2M70VafxJDQRvG9dESlM372IgwpnZssSSOhuZpmZ+P9Pd/xexqOJCDX7e88lL2J+4DPzumI/iQI\\nWPVx6zQ+XTGeyRPGsSGnQfFY0ir0bDNn3T2GoKR2CBFJihpI3vAmXsXFxRHW+lIfoyHBPX4e2vE6\\n58RXeJAJk6ZQWV3DlEkTFRsBWVc6k+iS6qzOHI0hsa3fhG3xB7MQ9EGYO9/goVTbUG6l+N+zmDV7\\nDo89MpbQ0FCFstHdDBk0iG6XdiUxMdFlpJcvXx4wOWtM7YSgMzqT6IltMVpaUWs9ir3oEOFX3uXR\\n7U4aV03uXo5kH+P5N5YRNep5qg5s9Vuo4G58GsqtVBzZyeDBgwNWMhmCDIoT7szp09m/bw+fH7Er\\nPn99XCu0pgjMPW8HmlcGeyaaZv6eWWX2Ly6P8kLGn6KqShCEvjgNR2+FbR4EHgRnP+TNmzerOnZl\\nZaXqbf8IqKys5JlnniEogP6UIbEt27dvRyw7rng8sSyfsrKyc3IPrux5Of9euYINGzZQVVVFdHRb\\n+vSZSnBwsKzUenV1NV9//TUbN33Jzp27MKV1hKgUhPICbId/JqTrQEwK55Pi595ciWeen82vBw+y\\n8v1/E333C/5LWJeMRlNyFCEyBbGsgIb8/TTirPwxpnT00GMKu+JOQtpeRdHbTxJ+5Z2u1b0u3ELc\\nHTMpWDqWzMxMbr75Zo978c0333Dy5EmPeyFhx44dAGzdupWGUPln6z7m+tJC9AltqD+Ziz13D+np\\nrSgOi8LUrpfHqryh3Er5mtnccdtQFixc5LoH3npTusgE6q1HqCs+4soZnTaSRsZNmMjI4cNcx01J\\nSSElJcVj7Fdc3oM7bz7Cv955guCU9hCeCOUF1OTuY9TIEVzdpxe1NVVodn2peH0NpYUY4jI8rtd8\\ny1Rmz3mCrp07qW6oNGL4MFaumU34EE/jWHc8G+vKqSQmWLDXVPPpp58SEhLisa/cM7v00seJjY09\\n660CzhXO1fz2exqOfCDF7e/kU595QBCEzsASYKAoiif9HUwUxX/gzIHQvXt30V/jIG9s3rzZb5Oh\\nPyI2b95MZGQkQoRy7FaISOLyy7uy6avnFEMHdQUHefpp/7mGs4GQkBDFe+weQtLFt0EMS0CX0pmK\\n3H2EJXXGdMUw7F+8QvWh7ejjMgi5+Eo0QSE+x6k7cYzGY2VUfP+BR7I8fMgM3lv+OKakdtTm7qHx\\nwNdoTZEex9GFW4hu053b+nQiIyODsrIy4uNHMHnxW8QOmerDV5AMhbuXI0HyfvLy8nyue9Ag5bax\\nADk5Oaze+obiNg1lVuqLj+KoqyZ57HIcNRUcXzuHXt07++1BkRhv4ZPtB5yhIHs11b99BziIuOav\\ngIDDXkltVSma0BgcVaUcXz3Xw0i+994Enn/u2YDvSt++fVn87CK/3Iru3bvz0iuvNjlHowu3EJre\\nmRMnTqj2jq+55hoyMha5NM3qTbHUZv9MXWkR5vTO1MSksuQ/W3jpldf8hsLcn9mfcb44F+P9PQ3H\\nD8DFgiC0wmkwhgEj3DcQBCEVWA3cLYrir//7If4xYbFYaDiZq7iNxmYlPT39nMVqz6Sfhjf8hZDq\\ny4ooency5d++hzG1E8EZ3ak+sJXSjW/6VFG5VGH73oep/TUe8X1tWByYoijN3o1R1KKPTMSet9/n\\nOI6weDIyMpg0aRKbN29m+/btBMW1kuUrSHAve/X4PKr56ThVCfX8/VhGzqfoHefkqgu3ED54umIP\\nioULF9IYaqF822oPtrg9b7/LQAS1ugxHZYmH+KJ0L5vCXVAKfZrNZm4aOJA1q6b7yN0o5Wig6WWw\\n3qG1FStXseUYPryOFkZ40/C7GQ5RFBsEQXgEWIezHPctURT3CoLw8KnvXwdmANHAq6cmiAZRFLv/\\nXmP+o+DosRxsR3dhVlH1YTI5gztq+y0Ewtnop+EOpXxN9cFv0RpNxI1aKDu5gLOKyplknkH4VcMx\\nd/VlwldsX0NjY6NPdZn3cbxLLOPj49FXB0iuepW9uj4/cYzrHxqr4g74QkrOzl08i8ihvl0CpYnV\\nEJOGMbWzy+NxL7mVm7Tj4+OxH9tJfYNDljRZ/MFsRCC8522yhvJMuAveC42OHdrzn237PGRL6k7m\\nYs/dTXivkT45Ggkam5WIiAiWL1/e5EVLdXU1GzdsJLTXSFnuTwsjXD1+1xyHKIqfAp96ffa62//v\\nB+7/X4/rj4zq6moWPfsc5u63+m+ks3Iq0yaMP6MOff5wtjV8/LF0AzGbpcqi2pxfsOftQxsW65ps\\npDBMY1UpmiBTwOMULnuC4NbdXAlgCU0ppfX+3FF8iOHDh/vso9ZTmzJpIlu2bGHd0rEY07r45Fek\\na/X2eJQm9379+lFz4gGf1bbHPV0yGmO6fC+N5nAX/C00bEd2oTHHkPjQUmoObXOGAaOSsOfv98nR\\nSKgvK6LiyE6enLD71CIo8KLF/fz6hLYYO92gyP250BnhavGnSI634DS+/vprQlI7EHL1PWiMZtlG\\nOsaIWFqlebJ3z0bV1LnQ8PHH0lUjtR2U1B6dOYbo+1+n8K0xNJRbqT74rUcYpmrfFgyW1q7juBsV\\nKc8RlHQJxatmoDME8/Krr7kMn5qGUaFdB3iEVCSF2MwZMzzuQVM9NUEQGH7XnWw/cgKhdS/Z/Ar4\\nejxKk/uGDRswt1Iuzw5K6Uht9k4fj6O53AWlhYZ15VQqtn3gUZXmsFf7XRCdXDkFbWgUkbdlql60\\nKLfw9eX+XOiMcLVoMRx/MpSUlDj5BwqNdKq+e8/JiD7LOBcaPv5Yumqktg0xqWhM4ejMUYRdcSdF\\n705GazR5eBeabR/QWFUmqwQr5Tn0MWnoYtOJvmE0Wc9M4NGxY1znUCqlvaR1a/b//AmN1kNoo1Nw\\nlObTaP1NljDZHE9N8ngir31QlccTaHIvKipCF50i+50EfVQy9Sc8W9PK5cPUeE6BFhqWYVkULBmD\\nqcO1rjLrsMuH4Ki1UbBkNOZWndFGJDhbuRbngEYgZvgC1YuWwC18fbk/FzojXC1aDMefDFFRUWhs\\ne1x/yyVuz9XL3xQNH/eJpbS0lG7dusmGZPyFg9RIbdeX5LtW26FdbqR86wqffIh0HCUlWOuqaQj6\\nYGfb08S2rFmzxlVm6o+3MHjwYF5+9TXmzM1CqLNRX3wEbWOt7Dib66mpUfB151oEKnZQI6UhVBRQ\\nm7uXqspC2XxYUzwnNQsNc6tOnHx3AmGtL3OdryFnD9OnTWX/vn189PF/CE7tSFBqGI2NjiYtWlS1\\nGvDi/lzojHC1aDEcfzL06dOHl1557XeRQ1A38RSx46efPfoxiGX5fssd/U2OIRdfScmGfwRkIYfo\\nNdhOZlOXu5ugxLY+20rHqc3d47eXt+WuuRQsfYSq/V9jz93DipWrmDRhvGsb79W1ZDTmv/QGEXc+\\nQ531sCv0ZbC0Zv5Li4HTHsSZeGpyHk+t9Qg1OXvRRyYg1JRS9ck8VcQ0NTmbRushso8clq3KApg1\\ney4LX3lTleekSuo/OpVpd99Cenq6x/n+/sqrrPvmB5fkSvm2D9BWlSseyzvMpKqFrxv3R41X1QIn\\nWgzHnwwhISG/mxyCmonHdnQX60qLZGPK/kIysuGgiiI0YiMn3p9OzJ2+JZsSCzmjVRqFhYX89JOe\\n9cfqfcakCQohuNVlOGpsykzstM6EtO1F9A2j2bJ6FskrV3Httdf6WV0/Sl19A8FdBmFdMcUn9BXa\\ndSBzs55xeRBnorYq7/EMpl+/fh6Te0xMDAMHKredVSulIXlUa9asobCwkNWrVzN48GAWv/Ais+fM\\n9Ztc9/ac1IoFpqenexhMdw9NYzRTuWcjdUWHcNTJe3Tux/Kuigt0/roTx9AV2Cj9cY0qr2rE8GFc\\nc801TaocPB/RYjj+hFDb+exsI9DEU756Fg5HI+ZblVq++oZkAoWDlK5T+gEvX76cjQvlSXMGS2sa\\nq0oVr81dCTZy6Ez+9c4TJCcns/j1f/pJ7E6jau+Xfsta9aYolwcRHx8PZflU7tnokZR3JzEqhRfl\\nPB7vYge17OBA787kiROYt2Chz6T5wEOjcWi0BKUG1j6Trrs5YoE2m43x48ejtVxM1YGtrpyULiyW\\nmsPrm9TsSc35HUUHWfzi8wwfPtz1Ts5bsNBvPmrlmtlkZCy64LkeLYbjTwg1nc/OFeQmHqHCWSbp\\naGxAn9T85Llc5Zfa61SaJLShka7e3P7gXp2kC7dgTGzLvHnzifmrn/DWsLkULnscTXCYz3dSqXB2\\ndjaiKHI0O4fSQz9jrBfRRyX5kA8bK4plw4vN4cwESloHeneUJk3rqumIddWK99G7N7jiQmPNbJd3\\n7H6thETQaDDj8MpJacPiKP5gFnG3+3Jb5DxtNR5W5swZPPDAAx73TykfFT5kRgvXgxbD8adGoBLb\\ns8nuliA38ez46WfWlRZB6mXgaFTcvznljtJ12mw2VqxYwX333QfAddddx4gRIzCbzYqThMHSmpJ1\\nrzRJ4qKuUVTRy6GDj9yI67vEduTn5zN/4SIWv/FPvx3oHLU2yPlRNrzYlEosURQ9PIXGkGjqC/Zz\\n/wMPceutt7B0yZuEhTmNnJwHExoaGrgK6q45FCwZTdnWVYRfdadsuMbbc1LycEYMH+b63v1aa47+\\nROmmJT6ETfeugUGW1hjjM9BVHVf0tJvqnbd0/1OHFsNxHuJss7sl+EsWxielEDlyMbW5e6g++K3i\\nMdwnFrWGTZoUM2fNpqGx0ZlTiErm422v8dgT48icOZMpkycqThL9+/dXbFjlLXEh2orRtOqmeC3+\\n5EYA9NHJxMTEBCwHLVgyWjap3dRKrBUrV/H+J+uJGPGcB5fF2KUjn/zwK7GWBDJnzkREJOuZebLv\\nRWK8JbAqb1pXKnd+iqDTe/Y+QT5cpOTh7NixA0EQfK9VhKBEXxFP9xL0onfGc12yhsGDlZs9NdU7\\nb+n+pw4thuM8xNlmdysZokEDbnRNNiFGM6Ub3wwYhx48eLBsHN2fYZu/cBGz5z8LoTEkyoQp5izO\\nBMF5Tf4mCZPJdOoanGJ39uBoGsqs2PP3eTCxpWM2lB8nJFAvBz9yIwBCRSHHjycEnIij23Sndat0\\nH0PelJXv4MGD+de7K4i++wWqDmz1W3Y8Z3EmjroaYvy8F1d1vSRwFVJUEvqYVMq/W4UmKBiHvcpV\\nTVbxyUK/hRlK3rH3tTrqqtDHpru+lyNthmR0o2vXroiiqKrZk9L5lRp7yaGF69FiOM47nAt2t5Ih\\n+s+7EzGeakGqCQoh7Io7/TJ/pTj0c8+/wILn/46hfX/EmBRMpxLFcobNZrMxZ24WdfWNJPohf0Xd\\nlsncrHGua/I3SXiI3b33HpsPH8UycoFHjw9pnMNHDOeDD1cr9nKw5+31kRtxfZd/gKNHI2kwxSne\\nW0dYvGv16j6B/fjjjzSGRCvve2rlu2bNGoJT2v9/e2ceX1V57f3vyjwdEgJkIATQiFpxAEHR6tsP\\nitwyVCm9VgG5DvUVRfTV1pmI4gAoWq12uO0VvWq9DU4g1qIUFapcASEKQlRUkCEkJEBmyEie949z\\nTjw5494hOYeQ9f188sk+ez9nn98enmc941pEJTiCuldJ//e5AcdlHJfls+rl20gZODTobzZXlpB0\\nygXEZ+ZRW/gOCYOH0bBzEwff+wNjxozhnrt+uB9WW5TetXz32pugizYd6Twy7z1STzq7wy1qYwwP\\nPfIoCxYsICY1A0npS3xMDJXb2wf28kTXejhRw9HN8c6c9fX1ndpHG8oQJY2cTP2OjW37vGM8xPbu\\nT9P+72ks/pKxYy+hsaGRhx99lIRBw6D1CIe3fdJuoNjbsC1dupTo1AwSEvsEvaa47FMsXZPbqEyf\\nPt3VApntsyJ84vjxxMfGMHH8OFYsexTHpPt9jOCB1+YQHRtHa32Nj8uRfQX3ER2bwOpCpw+t1CB6\\n3FH5vFtgRyqKqfl+Mya5Xzt/St7fzc7OprS0FJPa35qbliDjMo7BZ1Kz43NLxrKpfAcJyWmkugIt\\ntVSXseHteTz+xJNtQbOstii9p80mDTmfyg+eo+qjl6n/7lP/izYL8kk8azzJF13ftt9Oi9oYw7iJ\\nl7Jy5UoSBg0jJj2H5soSqnYV4Rg5iUNffQzgE6TLc0C/J6OGo5sSqPuo6ptCYgcMpWX9G36nfQIc\\nSUrnnXfeobS0NGQTP1SXScpZP6VqzStt3VPerlCaD+ymce/XZExbwOrXH2BN4daAA8Xg9Bvkadj2\\n7dvHkeiEkO5HTGr/o3K3XVJSQuFnn/POri2sKtpDS0om0bX7qK8so+HFW+mVN4zmpAway7+nseRr\\nEvqfTJSHa4yYPgNpPrCL2h3OcLKSlkV0Sh8a934VsuvOHZXP2zinuIIoue+Lv+9OnjyZJUuWINUl\\nHElMDXmfgo3LtPbK4uKLRvPREv9eectenYNpPULt5+/SXFHiN9DSvPl30NTUFHAas7+C3XtGXFR8\\nEo6Rk6lZ92rANSOZU+dR+uLtpF0wrV0QrUCuR7xbPk89/Qwfri0M+C4m/+j/ULtxGTEHd9LY2oqp\\nO0BLdTlTpkzpsunu3YmoSAtQ7FNbW8sVV07hoXmPEz/ychLH3k7KT66jpd+pHJEojGml9VA1h7d9\\nwt4//4rq9UswxmCMoXr9Eqo2/ZMVm3by+NL13LHwL2Tl5LLg8YUYY3x+K9RgYVR8Egn9T+HA6w/Q\\nUl3Wfn/uUOq3byD1gmnEpefS3NLiUyDBDwPFNetep7Wpvt3gY1ZWFtFHGmiuLAl6T6S6pEP9zu4W\\nSCvCijUbSJ/+NMk/m03q6OtJuTSfjGt/T3xaBlnRh2j4+iMS80Yy4JZXyJiygIxr/0DW1U/RtH8X\\nw1NqqNu5BYkSEgcPJ+nEkUTFxoNEUfZa+3sDbieJ93PeqFFOb8eX+V/7knH5g9Sse43Wpvp23/Wc\\nfjp58mTq93yJSHTI+9RcWUJ0crrfY1G1ZUydMoW7Z91AyXMzKVucT8WHiyh/8xFKX7yNlLPGkX3d\\n7zm09X0a92wh6eTzffTGu6YxB7oeZ8G+gLq6unbP4P782dS+Pa/tPkX36kt87hmW3IV473dXPNyT\\nKrJycrlj4V/a3vfM/gN4dP48MqfMC/gu1m16j4T0bGp2byU6Koq4QcNxnDic115/g8cWPuE3r/Qk\\ntMXRjTDG8D8Fi5l42SRa008g7tSftLmIjss5jZaqffT/1R8C1uYBDm1932eaY7AmvpXVt0nJKeT0\\njmJLEBfgh4o+tNCN4iwIPAcfJ0+ezE2zbqXhQGnwSIal2zrc7xyqO67Xz+dQtGgmWdc87RPzPK7f\\nYPpMeYxVz99MdHI6mVPbF0buYFQlz88iPvtkYjNPpKWylMbiIlKGjeejj96hV97w4N1wmXlUvjab\\nuNwz/E4ldTgcTL9qGq+8+Q6NdVUhph0HHpep31PEL37xC4wxzJs/n8S8kZjWI8RlnNjOK2/G5Q9S\\n8sItfvU2NreEnMbsLtjd/sDAd9psfW01sf1O9HsON4FaT+6KR6CxuSOfLsXs2Bii6/NkGst3kH1d\\n+/yUoAGfAG1xdCseW/gEi5e9R++rniZzyjzSL/6/ZPz7HLKvfYaWg7tJGjIqYA2qeu1rVK991Wfx\\nlDuNv5ogOAvuw7uLfGrMblqqy2go/pJZM2fSO+8skk65wBk97pQLyJn536SO+gUi4vR2GyIqXmzv\\n/jQf2E3Njk3U19dTW1uLw+Fgzv35xCUkUv7GQ35r7hVvzuX+/Nkd7ne25gxvKE37tvs9HpXgwBjj\\nYzQAYtOyyJ6+EKKiaaoooWHnJqKT08ic/lti+wyA2HjqGxpobQy8sC4pO4+JF57NPZNH8dQ9N1JW\\nUsx999zdbpzg55ddSktdBeZIC2UF+f5bOAX5xMYl0Fpf43PMswWzdOlSUk8aTq9zJpM66nJSTr+4\\n3ThOTGpmWwApb45UlxPVO3hYY3/TWd1dh/v27uG3d8/gpLRomg/sCnqeQK0nd7CnR+fN99vyMeZI\\nu1lb/ohN70/K6WNs5ZWehLY4uglWXFSXvng7qedf4RNyMyY1kxhHOtHJabYHza36N5o6dSq/vvNu\\nvy7AWxsP01S2g6bSb6nLygsYM7y5Yi+Npd8Qn5FH/rMv8es7725zg2GMYe5DD1Py/M1t6ziaD+yi\\ntexb5zqOo+h3tuQMr+/AgGMDh79dS3zOj4Ibnsw8jhyuJn7gmTTs2syhrbcSP/AMEk75Cc0Hd7P3\\nz7/yCSzkJqq2jIkTbww68L9mzRriemdB8xGShoxqH6elYi8NuzYRneTg0nGXsDxATHL3PbTjHNCT\\nluoyGir3Qdn3IScEpKWlsWLFCj799NN242wOh4O9pfvYWV5NY+V+23HJ3S0nIGBlICoumYaKLUGv\\nr/ngHpKH+g8XrIsA1XB0G+y6iPYmOtFBfEbwpr8/l+hZWVnMmnkTEHz1rYj4GBj3dMrqta8SnzWE\\nxLzgMcMbdm2i13lXtq1K9uxCm33vPdw662YKCgr48MMPARhz46x2Poa8sTod1Ep3XPOB3cRl5vk9\\nduRQJbFeXVjexGaeSEJyGhCFAP1vCD5BwHO/lemf+/bto/5gKf2v/6PTNcZ5v2wXpyVt9DXs++ud\\nnHn6UF5Y9FzQxXCW7oeHS3tP/annT6Fmw5KgBX7Vd58x65aNxPXNIX7AUKIPH2ybcTVr5k1tFaTo\\nr/83aJTLQEG05sy+j6qqKh/j5/k+0toS3Cjt3Ua/yfkBr7+nLwJUw9FN6Ggt0E1MaxNUBx84jaot\\nY+369cx5cO4P89pjY9oydWnxbpYtWxawwPHupz5UV0tzzQEfd+Z+Y4a/9iC9zr+StB9f+YNmr1ky\\nDoeDGTNmMGPGjKDXYXflvLUQsUX0HuM/irFI6G6VlspSYtMHUPWvF0OGsXUHFrLj7Xj//v3tWj3+\\n4rS43aCEclVj5X407NpEa9Nhmsp3tI3ZtFUGYmID+pQqK8iH5D4k5o2kpbKU6i2r6HXeFaRd+Cse\\ne3Y+nxUWkpjrrCD5m9rdXFlCw65NxPQeQO3ny2k+sPuH/Xu2EhsFt9w8k6VLl/oYP3dMlv7XPcuh\\nYEapIJ+4tH4+LXdPevoiQDUc3YSO1ALdtFSX0VpTTmtNWdBaVuW3n/HW1y0kDh7+w7z2nc5B3AXP\\nOEPBBxsQ9JziWlBQwK23/ZpMVzwFT9rcbbxwC6Z4MzW7tpJ6wVXtVm97prXbLWB35byV7rixY8ey\\n7t3fEuXneMtXH9BaVR6yWyUx75yQEwTisk6i4u3HSUyIt+XtOCMjg9h9wYcsY/sMICcn+PgDWLsf\\nky79Ge9/+gXRyWk+g+dtPqWev5nUE84iKn0ALQd3U/v9FzhGTmq3sM6zEuG4LJ9l/z2L5LPGAfhM\\n7Xa3nmJ6ZRDdqw+OYRN8ol/W//N3vPXWWz7GzzuGvbdRiumdTXP5DlrLt3PXbwcYTtQAAA5cSURB\\nVH7N75551pYn3p5GRA2HiIwDngGigUXGmMe8jovr+ATgMHCtMeazsAs9BrBWC9xM2uhrfPbXvj2P\\n+/NnAwQsDCrenIuJiSMnwKysxJPOZd78BZZWnDscDhISEuiVNyxoIekYNJRT06P5pvdP6eW1TsET\\nO90CHV05791aOpKSQXRdeTt3448/8WTA7joMPPb74L6wWhvrQq6zSMgYxPl9Gpk6daotb8eZmZm0\\nVm4Omqa1spjBg39p6XyhnANGYfioFByuBYCeuAt8ObSffzsxiaFDh/Lo/Hf9zkrzbmnF982lxSt0\\nrXfrqXzb/xKXPcRvq8r9rngbv4Y9W9sZbX9GybTU89QzT3PDDTfgcDgiEvOmuxAxwyEi0cAfgbFA\\nMbBBRN42xnj6vx4PDHH9jQL+0/W/x+HOCPOffpjUyb7N64o353L66aex/bX7SBp4RlAvoP7iZzc1\\nNpHlNfUQ2mfs3oOt1/ytRn8bNDCG7UXFQdPZ6RboqHdT7wWBa9as4cILJ7crvIM5yzPGgNDmC6sl\\nJYOWA7s5tKeorTV1qOjDkOFwYw7tZ+otwQfC/TFixAgOP/lU0Fry4d1FjB071tL5QjkHfPnll0O2\\ngKMPVzBx4i8xxtArb7iP0XDjOT4X2/9Uar8IHnfD36C4G893xdP4kZRGTO4w3/Qexqe2oYbKykqf\\n73rmFSuRFnsCkWxxnAt8Z4zZASAii4FJgKfhmAS8bJyrbdaJSJqIZBtjeuSo1L1338WOHTv4m59Z\\nMXNcxqGuri7owKe/wqC+vp67nlxEbJr/gt6dsRuaWyzX/K1Gfxsz5mreeffuTguFe7TeTd39/7m5\\nuYwePTrgcW/8FbRpaeO5/Y67SD71AkSkzZVGV3SBFBYWktg3J2C/ffkbD5PYN4eVK1faMkqBrtdO\\nkKY//elPlsfnYg5XcOnEifx9cb7PAj33+IP3oLi/34T2z+TOO+9k8arPg2rwNDqBDKeVSIs9gUga\\njhxgj8fnYnxbE/7S5AA90nCICFdNncJTTz4R0DiEGvj0l2bhwoUh597H9u5Py+7PLNf8rRYs06ZN\\n42BlVad1C1g1WF01sOl9b72vzYoTyI50gVRUVBA/aBhxib19BpPdA9dSX9lpM4GsTtO2GkK2ubKE\\nBEc69XuKeGntu1x+5VRWLppJwqBhxLrG2xqLi4jNzOPQVx/jGDbO8v1zOBw8+eSTvJKTa9todzTS\\n4vHOcTM4LiIzgBng7O+1+oDr6uq61ctQV1dHYWEhubm5bStvN27cGOJbwamsrITq4AVKc8VeWirL\\n6Nu3r+X7NW3qFBYv9d+1Vr30YaZOuZKNGzdy3rnncMXPdvDKX28nMfc0SO0P1SXU7/mS6VdN47xz\\nz7H8m3379qVu55agBqtu5xchr6Oz3gufa+uVTUwUlCyaSfKgM4jtO7DD1+pJcnIyVG8mdfT1PoPJ\\n7oHrmmWPUFVV1Wnvu9XnZuWZNBYXEVVdwtQpV1JYWMi9d91BVmYGBX8rgJpSxJFBr4GnUl/8NcPP\\nOIOtf72NxNyhtt4Vq+9jMLpjedEVeiVSPldE5HxgrjHmp67P9wEYYxZ4pPkLsNoYU+D6vA0YHaqr\\nauTIkcZqYbp69Wq/XRLHKl2ht7a2ti0YU6CMXbLoZh6Yk8/cB+ZYPq97WuzDjzxKyuAzg8YMd+vo\\njFC47vCngWrC9/6/G0O6i+js++x9bZdccgnvv/9+p4X9Xb58Ob+cMi3oM6z62x3s27un0wd1rTy3\\nYM+k7NU5SH0VD8190PI70ZF3pf007eAx7ANxPJcXIlJojBlpKW0EDUcM8A0wBtgLbACmGWOKPNJM\\nBG7BOatqFPCsMebcUOdWw2GfoBl7cT4Xnz+C9/7x9w5FDly+fDkHDx4MW2z0nlpArF3/6VEbzK7C\\n3zNpPrCbxuIvnaFtn/uvow5rbJWjqaB0x/eiKwxHxLqqjDEtInILsALndNwXjDFFInKT6/ifgeU4\\njcZ3OKfjXhcpvcc77WaRuGYFtVYW01j8Ffffdy8P3J/fIaMBkJSUxIQJEzpTblDshgs9XrAbXzuc\\n+HsmVVWnMHv2qrA/EyvjgEpwIjrGYYxZjtM4eO77s8e2AWaFW1dP5HgsbHtaAdEdnqHnM1m9evUx\\no0uxx3EzOK50Dj2tsD0e0WeodDXqVl1RFEWxhRoORVEUxRZqOBRFURRbqOFQFEVRbKGGQ1EURbGF\\nGg5FURTFFmo4FEVRFFuo4VAURVFsoYZDURRFsYUaDkVRFMUWajgURVEUW6jhUBRFUWyhhkNRFEWx\\nhRoORVEUxRZqOBRFURRbqOFQFEVRbKGGQ1EURbGFGg5FURTFFmo4FEVRFFtExHCISLqIrBSRb13/\\ne/tJkysiq0TkSxEpEpHbIqFVURRFaU+kWhz3Ah8YY4YAH7g+e9MC3GGMOQ04D5glIqeFUaOiKIri\\nh0gZjknAS67tl4CfeycwxpQaYz5zbdcCXwE5YVOoKIqi+CVShiPTGFPq2t4HZAZLLCKDgeHA+q6V\\npSiKooRCjDFdc2KR94EsP4fygZeMMWkeaSuNMT7jHK5jKcC/gHnGmCVBfm8GMAMgMzNzxOLFiy3p\\nrKurIyUlxVLaY4HuphdUczjobnpBNYcDO3ovuuiiQmPMSEuJjTFh/wO2Admu7WxgW4B0scAK4Dd2\\nzj9ixAhjlVWrVllOeyzQ3fQao5rDQXfTa4xqDgd29AIbjcUyNlJdVW8D17i2rwGWeScQEQGeB74y\\nxjwVRm2KoihKECJlOB4DxorIt8Alrs+ISH8RWe5KcwHwH8DFIrLJ9TchMnIVRVEUNzGR+FFjzEFg\\njJ/9JcAE1/YaQMIsTVEURQmBrhxXFEVRbKGGQ1EURbFFl03HjSQish/YZTF5X+BAF8rpbLqbXlDN\\n4aC76QXVHA7s6B1kjOlnJeFxaTjsICIbjdW5y8cA3U0vqOZw0N30gmoOB12lV7uqFEVRFFuo4VAU\\nRVFsoYYD/ivSAmzS3fSCag4H3U0vqOZw0CV6e/wYh6IoimIPbXEoiqIotuhxhqO7RB8UkXEisk1E\\nvhMRn0BX4uRZ1/EvROTscGv0oymU5qtcWreIyCciclYkdHroCarXI905ItIiIpeHU18ALSE1i8ho\\nl4ueIhH5V7g1+tET6r1IFZG/i8hml+brIqHTQ88LIlIuIlsDHD+m8p4FvZ2f76x6Qzxe/oCFwL2u\\n7XuBx/2kyQbOdm07gG+A08KoMRrYDpwIxAGbvX8fp2uWd3G6ZTkPWB/h+2pF84+B3q7t8ZHUbEWv\\nR7oPgeXA5d3gHqcBXwIDXZ8zuoHm2e58CPQDKoC4CGr+CXA2sDXA8WMt74XS2+n5rse1OOge0QfP\\nBb4zxuwwxjQBi3Hq9mQS8LJxsg5IE5HsMGr0JqRmY8wnxphK18d1wIAwa/TEyj0GuBV4EygPp7gA\\nWNE8DVhijNkNYIyJtG4rmg3gcHnETsFpOFrCK9NDjDEfuTQE4pjKe6H0dkW+64mGoztEH8wB9nh8\\nLsbXcFlJE07s6rkeZ60tUoTUKyI5wGTgP8OoKxhW7vHJQG8RWS0ihSJyddjU+ceK5j8APwJKgC3A\\nbcaY1vDI6xDHWt6zQ6fku4h4x+1qQkQfbMMYY0Qk4LQyV/TBN4HbjTE1nauy5yIiF+F8gS+MtJYQ\\n/A64xxjT6qwMdwtigBE4vU8nAmtFZJ0x5pvIygrKT4FNwMVAHrBSRD7WPNe5dGa+Oy4NhzHmkkDH\\nRKRMRLKNMaWu5qXfpryIxOI0Gv9jgoSs7SL2Arkenwe49tlNE04s6RGRM4FFwHjjdK8fKazoHQks\\ndhmNvsAEEWkxxrwVHok+WNFcDBw0xhwCDonIR8BZOMfpIoEVzdcBjxlnJ/x3IvI9cCrwaXgk2uZY\\ny3sh6ex81xO7qrpD9MENwBAROUFE4oApOHV78jZwtWuGx3lAtUcXXCQIqVlEBgJLgP84BmrAIfUa\\nY04wxgw2xgwG3gBujqDRAGvvxTLgQhGJEZEkYBTOMbpIYUXzblzxeUQkEzgF2BFWlfY41vJeULok\\n30VyNkAk/oA+wAfAt8D7QLprf39guWv7QpwDdl/gbEJvAiaEWecEnLXE7UC+a99NwE2ubQH+6Dq+\\nBRh5DNzbUJoXAZUe99RyjONI6PVK+yIRnlVlVTNwF86ZVVtxdrMe05pdee+frvd4KzA9wnoLgFKg\\nGWcL7vpjOe9Z0Nvp+U5XjiuKoii26IldVYqiKMpRoIZDURRFsYUaDkVRFMUWajgURVEUW6jhUBRF\\nUWyhhkNRbCAiR1yeZ7eKyOuutRKIyCcdPN9gf15NRWSYiKx1eYv9QkSuPFrtitJZqOFQFHvUG2OG\\nGWNOB5pwzpfHGPPjTv6dw8DVxpihwDjgdyKS1sm/oSgdQg2HonScj4GTAESkzvV/soh84FpVnC0i\\n34hIlohEi8gTIrLB1YK4MdiJjTHfGGO+dW2X4HSN06+Lr0dRLKGGQ1E6gIjE4IxtsMVzvzFmKc5V\\nvLOA54AHjTH7cK7mrTbGnAOcA9wgIidY/K1zccay2N55V6AoHee4dHKoKF1Ioohscm1/jNOnmTe3\\n4nSdsc4YU+Da92/AmfJDFMFUYAghnA+6HHH+FbjGHNuuxpUehBoORbFHvTFmWIg0A4BWIFNEolwF\\nvgC3GmNWeCZ0xXvxi4j0Av6B07/TuqNSrSidiHZVKUon4urCegGYitMr7W9ch1YAM13u+hGRk0Uk\\nOch54oClOCPNvdG1qhXFHtriUJTOZTbwsTFmjYhsBjaIyD9weigdDHzmctu/Hz9hiz24Amcs6T4i\\ncq1r37XGmE2Bv6Io4UG94yqKoii20K4qRVEUxRZqOBRFURRbqOFQFEVRbKGGQ1EURbGFGg5FURTF\\nFmo4FEVRFFuo4VAURVFsoYZDURRFscX/B9veEn2IvSUDAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = np.random.uniform(size=500)\\n\",\n \"x2 = np.random.uniform(size=500)\\n\",\n \"fig = plt.figure();\\n\",\n \"ax = fig.add_subplot(1,1,1);\\n\",\n \"ax.scatter(x1,x2, edgecolor='black', s=80);\\n\",\n \"ax.grid();\\n\",\n \"ax.set_axisbelow(True);\\n\",\n \"ax.set_xlim(-0.25,1.25); ax.set_ylim(-0.25,1.25)\\n\",\n \"ax.set_xlabel('Pixel 2'); ax.set_ylabel('Pixel 1'); plt.savefig('images_in_2dspace.pdf')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Any one point inside the unit square would represent an image. For example the image associated with the point $(0.25,0.85)$ is shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAWQAAADHCAYAAAAu5CnUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"im = [(0.25, 0.85)]\\n\",\n \"plt.imshow(im, cmap='gray',vmin=0,vmax=1)\\n\",\n \"plt.tick_params(\\n\",\n \" axis='both', # changes apply to the x-axis\\n\",\n \" which='both', # both major and minor ticks are affected\\n\",\n \" bottom='off', # ticks along the bottom edge are off\\n\",\n \" top='off', # ticks along the top edge are off\\n\",\n \" left='off',\\n\",\n \" right='off'\\n\",\n \")\\n\",\n \"plt.xticks([])\\n\",\n \"plt.yticks([])\\n\",\n \"plt.xlabel('Pixel 1 = 0.25 Pixel 2 = 0.85')\\n\",\n \"plt.savefig('sample_2dspace_image.pdf')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now consider the case where there is some \\n\",\n \"process correlating the two variables. This \\n\",\n \"would be similar to their being some rules behind\\n\",\n \"the structure of faces. We know, that this must be\\n\",\n \"the case because if it weren't then faces would\\n\",\n \"be created randomly and we would not see the \\n\",\n \"patterns that was do. In \\n\",\n \"this case, the pixels would be correlated in \\n\",\n \"some manner due to the mechanism driving the\\n\",\n \"construction of faces. In this simple case, \\n\",\n \"let's consider a direct correlation of the \\n\",\n \"form $x_1 = \\\\frac{1}{2} \\\\cos(2\\\\pi x_2)+\\\\frac{1}{2}+\\\\epsilon$ \\n\",\n \"where $\\\\epsilon$ is a noise term coming from\\n\",\n \"a low variability normal distribution \\n\",\n \"$\\\\epsilon \\\\sim N(0,\\\\frac{1}{10})$. We see \\n\",\n \"in Figure \\\\ref{fig:structured_images_in_2dspace}\\n\",\n \"on page \\\\pageref{fig:structured_images_in_2dspace}\\n\",\n \"that in this case, the images plotted\\n\",\n \"in two dimensions resulting from this \\n\",\n \"relationship form a distinct pattern.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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VdM3z8BEtOBzX6fM5hkWRUH1ETY07XTZ88jvNflHsetZhOl377rqBs5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\\nERhmgAzDCAwzQIZhBMb/AWekUT7WaapgAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = lambda x2: 0.5*np.cos(2*np.pi*x2)+0.5\\n\",\n \"x2 = np.linspace(0,1,200)\\n\",\n \"eps = np.random.normal(scale=0.1, size=200)\\n\",\n \"fig = plt.figure();\\n\",\n \"ax = fig.add_subplot(1,1,1);\\n\",\n \"ax.scatter(x2,x1(x2)+eps, edgecolor='black', s=80);\\n\",\n \"ax.grid();\\n\",\n \"ax.set_axisbelow(True);\\n\",\n \"ax.set_xlim(-0.25,1.25); ax.set_ylim(-0.25,1.25); plt.axes().set_aspect('equal')\\n\",\n \"ax.set_xlabel('Pixel 2'); ax.set_ylabel('Pixel 1'); plt.savefig('structured_images_in_2dspace.pdf')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will refer to the structure suggested by \\n\",\n \"the two dimensional points as the 'manifold'.\\n\",\n \"This is a common practice when analyzing images.\\n\",\n \"A 28 by 28 dimensional image will be a point in\\n\",\n \"784 dimensional space. If we are examining \\n\",\n \"images with structure, various images of the\\n\",\n \"number 2 for example, then it turns out that \\n\",\n \"these images will form a manifold in 784 \\n\",\n \"dimensional space. In most cases, as is the \\n\",\n \"case in our contrived example, this manifold \\n\",\n \"exists in a lower dimensional space than that\\n\",\n \"of the images themselves. The goal is to 'learn'\\n\",\n \"this manifold. In our simple case we can describe\\n\",\n \"the manifold as a function of only 1 variable \\n\",\n \"$$f(t) = $$ \\n\",\n \"This is what we would call the underlying data \\n\",\n \"generating process. In practice we usually \\n\",\n \"describe the manifold in terms of a probability\\n\",\n \"distribution. We will refer to the data \\n\",\n \"generating distribution in our example as \\n\",\n \"$p_{test}(x_1, x_2)$. Why did we choose a \\n\",\n \"probability to describe the manifold created \\n\",\n \"by the data generating process? How might this\\n\",\n \"probability be interpreted?\\n\",\n \"\\n\",\n \"Learning the actual distribution turns out to \\n\",\n \"be a difficult task. Here we will use a\\n\",\n \"common non parametric technique for describing\\n\",\n \"distributions, the histogram. Looking at a \\n\",\n \"histogram of the images, or two dimensional points,\\n\",\n \"will give us insight into the structure of the \\n\",\n \"distribution from which they came. Notice here \\n\",\n \"though that the histogram merely describes the \\n\",\n \"distribution, we do not know what it is.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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M91OYPcN9I+N/ivzd8eygvTuwHoSfvbtqzOe881RgxJzy9whrcta1aG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\\nKFx2GyXZngAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from matplotlib.colors import LogNorm\\n\",\n \"x2 = np.random.uniform(size=100000)\\n\",\n \"eps = np.random.normal(scale=0.1, size=100000)\\n\",\n \"hist2d = plt.hist2d(x2,x1(x2)+eps, bins=50, norm=LogNorm())\\n\",\n \"plt.xlim(0.0,1.0); plt.ylim(-0.3,1.3); plt.axes().set_aspect('equal')\\n\",\n \"plt.xlabel('Pixel 2'); plt.ylabel('Pixel 1')\\n\",\n \"plt.colorbar();\\n\",\n \"plt.savefig('histogram_of_structured_images.pdf')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As our intuition might have suggested, the data\\n\",\n \"generating distribution looks very similar to \\n\",\n \"the structure suggested by the two dimensional\\n\",\n \"images plotted above. There is high probability\\n\",\n \"very near the actual curve \\n\",\n \"$x_1 = \\\\frac{1}{2} \\\\cos(2\\\\pi x_2)+\\\\frac{1}{2}$ \\n\",\n \"and low probability as we move away. We imposed\\n\",\n \"the uncertainty via the Gaussian noise term \\n\",\n \"$\\\\epsilon$. However, in real data the \\n\",\n \"uncertainty can be due to the myriad sources\\n\",\n \"outlined above. In these cases a complex \\n\",\n \"probability distribution isn't an arbitrary \\n\",\n \"choice for representing the data, it becomes \\n\",\n \"necessary. \\n\",\n \"\\n\",\n \"Hopefully we're now beginning to understand how\\n\",\n \"to interpret $p_{test}(x_1, x_2)$. One might say\\n\",\n \"$p_{test}$ measures how likely a certain \\n\",\n \"configuration of $x_1$ and $x_2$ is to have \\n\",\n \"arisen from the data generating process $f(t)$.\\n\",\n \"Therefore if one can learn the data generating\\n\",\n \"distribution, then they have a descriptive\\n\",\n \"measure of the true underlying data generating\\n\",\n \"process. This intuition extends to the \\n\",\n \"$p_{data}(x)$ for faces that was presented \\n\",\n \"above. A sample from the LFW dataset is shown in \\n\",\n \"Figure \\\\ref{fig:Agnelo_Queiroz_0001} on page\\n\",\n \"\\\\pageref{fig:Agnelo_Queiroz_0001}. \\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":240,"cells":{"repo_name":{"kind":"string","value":"ESMG/ESMG-configs"},"path":{"kind":"string","value":"CCS1/preprocessing/CreateFMSgridTopo.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"156388"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy\\n\",\n \"import scipy.io\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#Open ROMS grid file\\n\",\n \"if 'nc' not in vars(): nc = scipy.io.netcdf_file('CCS_7k_0-360_fred_grd.nc')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nj=480, nj=180\\n\"\n ]\n }\n ],\n \"source\": [\n \"nj,ni = nc.variables['lon_rho'].shape\\n\",\n \"nj -=2; ni -=2\\n\",\n \"print('nj=%i, nj=%i'%(nj,ni))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Supergrid shape\\n\",\n \"snj,sni = 2*numpy.array([nj,ni]) # Smallest useful super-grid has a multiplier of 2\\n\",\n \"\\n\",\n \"# Declare shapes\\n\",\n \"lon = numpy.zeros((snj+1,sni+1))\\n\",\n \"lat = numpy.zeros((snj+1,sni+1))\\n\",\n \"area = numpy.zeros((snj,sni))\\n\",\n \"dx = numpy.zeros((snj+1,sni))\\n\",\n \"dy = numpy.zeros((snj,sni+1))\\n\",\n \"angle = numpy.zeros((snj+1,sni+1))\\n\",\n \"\\n\",\n \"# Copy in data from ROMS file\\n\",\n \"lon[::2,::2] = nc.variables['lon_psi'][:,:] # Cell corners\\n\",\n \"lon[1::2,1::2] = nc.variables['lon_rho'][1:-1,1:-1] # Cell centers (drop outside row and column)\\n\",\n \"lon[1::2,::2] = nc.variables['lon_u'][1:-1,:] # U-points (drop outside row)\\n\",\n \"lon[::2,1::2] = nc.variables['lon_v'][:,1:-1] # V-points (drop outside column)\\n\",\n \"lat[::2,::2] = nc.variables['lat_psi'][:,:] # Cell corners\\n\",\n \"lat[1::2,1::2] = nc.variables['lat_rho'][1:-1,1:-1] # Cell centers (drop outside row and column)\\n\",\n \"lat[1::2,::2] = nc.variables['lat_u'][1:-1,:] # U-points (drop outside row)\\n\",\n \"lat[::2,1::2] = nc.variables['lat_v'][:,1:-1] # V-points (drop outside column)\\n\",\n \"\\n\",\n \"def angle_p1p2(p1, p2):\\n\",\n \" \\\"\\\"\\\"Angle at center of sphere between two points on the surface of the sphere.\\n\",\n \" Positions are given as (latitude,longitude) tuples measured in degrees.\\\"\\\"\\\"\\n\",\n \" phi1 = numpy.deg2rad( p1[0] )\\n\",\n \" phi2 = numpy.deg2rad( p2[0] )\\n\",\n \" dphi_2 = 0.5 * ( phi2 - phi1 )\\n\",\n \" dlambda_2 = 0.5 * numpy.deg2rad( p2[1] - p1[1] )\\n\",\n \" a = numpy.sin( dphi_2 )**2 + numpy.cos( phi1 ) * numpy.cos( phi2 ) * ( numpy.sin( dlambda_2 )**2 )\\n\",\n \" c = 2. * numpy.arctan2( numpy.sqrt(a), numpy.sqrt( 1. - a ) )\\n\",\n \" return c\\n\",\n \"# Approximate edge lengths as great arcs\\n\",\n \"R = 6370.e3 # Radius of sphere\\n\",\n \"dx[:,:] = R*angle_p1p2( (lat[:,1:],lon[:,1:]), (lat[:,:-1],lon[:,:-1]) )\\n\",\n \"dy[:,:] = R*angle_p1p2( (lat[1:,:],lon[1:,:]), (lat[:-1,:],lon[:-1,:]) )\\n\",\n \"\\n\",\n \"# Approximate angles using centered differences in interior\\n\",\n \"angle[:,1:-1] = numpy.arctan( (lat[:,2:]-lat[:,:-2]) / ((lon[:,2:]-lon[:,:-2])*numpy.cos(numpy.deg2rad(lat[:,1:-1]))) )\\n\",\n \"# Approximate angles using side differences on left/right edges\\n\",\n \"angle[:,0] = numpy.arctan( (lat[:,1]-lat[:,0]) / ((lon[:,1]-lon[:,0])*numpy.cos(numpy.deg2rad(lat[:,0]))) )\\n\",\n \"angle[:,-1] = numpy.arctan( (lat[:,-1]-lat[:,-2]) / ((lon[:,-1]-lon[:,-2])*numpy.cos(numpy.deg2rad(lat[:,-1]))) )\\n\",\n \"\\n\",\n \"def spherical_angle(v1, v2, v3):\\n\",\n \" \\\"\\\"\\\"Returns angle v2-v1-v3 i.e betweeen v1-v2 and v1-v3.\\\"\\\"\\\"\\n\",\n \" # vector product between v1 and v2\\n\",\n \" px = v1[1]*v2[2] - v1[2]*v2[1]\\n\",\n \" py = v1[2]*v2[0] - v1[0]*v2[2]\\n\",\n \" pz = v1[0]*v2[1] - v1[1]*v2[0]\\n\",\n \" # vector product between v1 and v3\\n\",\n \" qx = v1[1]*v3[2] - v1[2]*v3[1]\\n\",\n \" qy = v1[2]*v3[0] - v1[0]*v3[2]\\n\",\n \" qz = v1[0]*v3[1] - v1[1]*v3[0]\\n\",\n \"\\n\",\n \" ddd = (px*px+py*py+pz*pz)*(qx*qx+qy*qy+qz*qz)\\n\",\n \" ddd = (px*qx+py*qy+pz*qz) / numpy.sqrt(ddd)\\n\",\n \" angle = numpy.arccos( ddd );\\n\",\n \" return angle\\n\",\n \"def spherical_quad(lat,lon):\\n\",\n \" \\\"\\\"\\\"Returns area of spherical quad (bounded by great arcs).\\\"\\\"\\\"\\n\",\n \" # x,y,z are 3D coordinates\\n\",\n \" d2r = numpy.deg2rad(1.)\\n\",\n \" x = numpy.cos(d2r*lat)*numpy.cos(d2r*lon)\\n\",\n \" y = numpy.cos(d2r*lat)*numpy.sin(d2r*lon)\\n\",\n \" z = numpy.sin(d2r*lat)\\n\",\n \" c0 = (x[:-1,:-1],y[:-1,:-1],z[:-1,:-1])\\n\",\n \" c1 = (x[:-1,1:],y[:-1,1:],z[:-1,1:])\\n\",\n \" c2 = (x[1:,1:],y[1:,1:],z[1:,1:])\\n\",\n \" c3 = (x[1:,:-1],y[1:,:-1],z[1:,:-1])\\n\",\n \" a0 = spherical_angle( c1, c0, c2)\\n\",\n \" a1 = spherical_angle( c2, c1, c3)\\n\",\n \" a2 = spherical_angle( c3, c2, c0)\\n\",\n \" a3 = spherical_angle( c0, c3, c1)\\n\",\n \" return a0+a1+a2+a3-2.*numpy.pi\\n\",\n \"# Approximate cell areas as that of spherical polygon\\n\",\n \"area[:,:] = R*R*spherical_quad(lat,lon)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create a mosaic file\\n\",\n \"rg = scipy.io.netcdf_file('ocean_hgrid.nc','w')\\n\",\n \"# Dimensions\\n\",\n \"rg.createDimension('nx',sni)\\n\",\n \"rg.createDimension('nxp',sni+1)\\n\",\n \"rg.createDimension('ny',snj)\\n\",\n \"rg.createDimension('nyp',snj+1)\\n\",\n \"rg.createDimension('string',5)\\n\",\n \"# Variables\\n\",\n \"hx = rg.createVariable('x','float32',('nyp','nxp',))\\n\",\n \"hx.units = 'degrees'\\n\",\n \"hy = rg.createVariable('y','float32',('nyp','nxp',))\\n\",\n \"hy.units = 'degrees'\\n\",\n \"hdx = rg.createVariable('dx','float32',('nyp','nx',))\\n\",\n \"hdx.units = 'meters'\\n\",\n \"hdy = rg.createVariable('dy','float32',('ny','nxp',))\\n\",\n \"hdy.units = 'meters'\\n\",\n \"harea = rg.createVariable('area','float32',('ny','nx',))\\n\",\n \"harea.units = 'meters^2'\\n\",\n \"hangle = rg.createVariable('angle_dx','float32',('nyp','nxp',))\\n\",\n \"hangle.units = 'degrees'\\n\",\n \"htile = rg.createVariable('tile','c',('string',))\\n\",\n \"# Values\\n\",\n \"hx[:] = lon\\n\",\n \"hy[:] = lat\\n\",\n \"hdx[:] = dx\\n\",\n \"hdy[:] = dy\\n\",\n \"harea[:] = area\\n\",\n \"hangle[:] = angle\\n\",\n \"htile[:5] = 'tile1'\\n\",\n \"rg.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create a topography file\\n\",\n \"rg = scipy.io.netcdf_file('ocean_topog.nc','w')\\n\",\n \"# Dimensions\\n\",\n \"rg.createDimension('nx',ni)\\n\",\n \"rg.createDimension('ny',nj)\\n\",\n \"rg.createDimension('ntiles',1)\\n\",\n \"# Variables\\n\",\n \"hdepth = rg.createVariable('depth','float32',('ny','nx',))\\n\",\n \"hdepth.units = 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[],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":241,"cells":{"repo_name":{"kind":"string","value":"ES-DOC/esdoc-jupyterhub"},"path":{"kind":"string","value":"notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"99839"},"content":{"kind":"string","value":"{\n \"nbformat_minor\": 0, \n \"nbformat\": 4, \n \"cells\": [\n {\n \"source\": [\n \"# ES-DOC CMIP6 Model Properties - Seaice \\n\", \n \"**MIP Era**: CMIP6 \\n\", \n \"**Institute**: EC-EARTH-CONSORTIUM \\n\", \n \"**Source ID**: EC-EARTH3-CC \\n\", \n \"**Topic**: Seaice \\n\", \n \"**Sub-Topics**: Dynamics, Thermodynamics, Radiative Processes. \\n\", \n \"**Properties**: 80 (63 required) \\n\", \n \"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \\n\", \n \"**Initialized From**: -- \\n\", \n \"\\n\", \n \"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \\n\", \n \"**Notebook Initialised**: 2018-02-15 16:53:59\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Setup \\n\", \n \"**IMPORTANT: to be executed each time you run the notebook** \"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# DO NOT EDIT ! \\n\", \n \"from pyesdoc.ipython.model_topic import NotebookOutput \\n\", \n \"\\n\", \n \"# DO NOT EDIT ! \\n\", \n \"DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-cc', 'seaice')\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Authors \\n\", \n \"*Set document authors*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# Set as follows: DOC.set_author(\\\"name\\\", \\\"email\\\") \\n\", \n \"# TODO - please enter value(s)\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Contributors \\n\", \n \"*Specify document contributors* \"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# Set as follows: DOC.set_contributor(\\\"name\\\", \\\"email\\\") \\n\", \n \"# TODO - please enter value(s)\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Publication \\n\", \n \"*Specify document publication status* \"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# Set publication status: \\n\", \n \"# 0=do not publish, 1=publish. \\n\", \n \"DOC.set_publication_status(0)\"\n ], \n \"outputs\": [], \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### Document Table of Contents \\n\", \n \"[1. Key Properties --&gt; Model](#1.-Key-Properties---&gt;-Model) \\n\", \n \"[2. Key Properties --&gt; Variables](#2.-Key-Properties---&gt;-Variables) \\n\", \n \"[3. Key Properties --&gt; Seawater Properties](#3.-Key-Properties---&gt;-Seawater-Properties) \\n\", \n \"[4. Key Properties --&gt; Resolution](#4.-Key-Properties---&gt;-Resolution) \\n\", \n \"[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \\n\", \n \"[6. Key Properties --&gt; Key Parameter Values](#6.-Key-Properties---&gt;-Key-Parameter-Values) \\n\", \n \"[7. Key Properties --&gt; Assumptions](#7.-Key-Properties---&gt;-Assumptions) \\n\", \n \"[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \\n\", \n \"[9. Grid --&gt; Discretisation --&gt; Horizontal](#9.-Grid---&gt;-Discretisation---&gt;-Horizontal) \\n\", \n \"[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \\n\", \n \"[11. Grid --&gt; Seaice Categories](#11.-Grid---&gt;-Seaice-Categories) \\n\", \n \"[12. Grid --&gt; Snow On Seaice](#12.-Grid---&gt;-Snow-On-Seaice) \\n\", \n \"[13. Dynamics](#13.-Dynamics) \\n\", \n \"[14. Thermodynamics --&gt; Energy](#14.-Thermodynamics---&gt;-Energy) \\n\", \n \"[15. Thermodynamics --&gt; Mass](#15.-Thermodynamics---&gt;-Mass) \\n\", \n \"[16. Thermodynamics --&gt; Salt](#16.-Thermodynamics---&gt;-Salt) \\n\", \n \"[17. Thermodynamics --&gt; Salt --&gt; Mass Transport](#17.-Thermodynamics---&gt;-Salt---&gt;-Mass-Transport) \\n\", \n \"[18. Thermodynamics --&gt; Salt --&gt; Thermodynamics](#18.-Thermodynamics---&gt;-Salt---&gt;-Thermodynamics) \\n\", \n \"[19. Thermodynamics --&gt; Ice Thickness Distribution](#19.-Thermodynamics---&gt;-Ice-Thickness-Distribution) \\n\", \n \"[20. Thermodynamics --&gt; Ice Floe Size Distribution](#20.-Thermodynamics---&gt;-Ice-Floe-Size-Distribution) \\n\", \n \"[21. Thermodynamics --&gt; Melt Ponds](#21.-Thermodynamics---&gt;-Melt-Ponds) \\n\", \n \"[22. Thermodynamics --&gt; Snow Processes](#22.-Thermodynamics---&gt;-Snow-Processes) \\n\", \n \"[23. Radiative Processes](#23.-Radiative-Processes) \\n\", \n \"\\n\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"# 1. Key Properties --&gt; Model \\n\", \n \"*Name of seaice model used.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 1.1. Model Overview\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Overview of sea ice model.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 1.2. Model Name\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.model.model_name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 2. Key Properties --&gt; Variables \\n\", \n \"*List of prognostic variable in the sea ice model.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 2.1. Prognostic\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *List of prognostic variables in the sea ice component.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Sea ice temperature\\\" \\n\", \n \"# \\\"Sea ice concentration\\\" \\n\", \n \"# \\\"Sea ice thickness\\\" \\n\", \n \"# \\\"Sea ice volume per grid cell area\\\" \\n\", \n \"# \\\"Sea ice u-velocity\\\" \\n\", \n \"# \\\"Sea ice v-velocity\\\" \\n\", \n \"# \\\"Sea ice enthalpy\\\" \\n\", \n \"# \\\"Internal ice stress\\\" \\n\", \n \"# \\\"Salinity\\\" \\n\", \n \"# \\\"Snow temperature\\\" \\n\", \n \"# \\\"Snow depth\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 3. Key Properties --&gt; Seawater Properties \\n\", \n \"*Properties of seawater relevant to sea ice*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 3.1. Ocean Freezing Point\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"TEOS-10\\\" \\n\", \n \"# \\\"Constant\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 3.2. Ocean Freezing Point Value\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If using a constant seawater freezing point, specify this value.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 4. Key Properties --&gt; Resolution \\n\", \n \"*Resolution of the sea ice grid*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 4.1. Name\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.resolution.name') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 4.2. Canonical Horizontal Resolution\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 4.3. Number Of Horizontal Gridpoints\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 5. Key Properties --&gt; Tuning Applied \\n\", \n \"*Tuning applied to sea ice model component*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 5.1. Description\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 5.2. Target\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 5.3. Simulations\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Which simulations had tuning applied, e.g. all, not historical, only pi-control? *\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 5.4. Metrics Used\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *List any observed metrics used in tuning model/parameters*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 5.5. Variables\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Which variables were changed during the tuning process?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 6. Key Properties --&gt; Key Parameter Values \\n\", \n \"*Values of key parameters*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 6.1. Typical Parameters\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *What values were specificed for the following parameters if used?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Ice strength (P*) in units of N m{-2}\\\" \\n\", \n \"# \\\"Snow conductivity (ks) in units of W m{-1} K{-1} \\\" \\n\", \n \"# \\\"Minimum thickness of ice created in leads (h0) in units of m\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 6.2. Additional Parameters\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\\n\", \n \"### *If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 7. Key Properties --&gt; Assumptions \\n\", \n \"*Assumptions made in the sea ice model*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 7.1. Description\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *General overview description of any *key* assumptions made in this model.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 7.2. On Diagnostic Variables\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 7.3. Missing Processes\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *List any *key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 8. Key Properties --&gt; Conservation \\n\", \n \"*Conservation in the sea ice component*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 8.1. Description\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Provide a general description of conservation methodology.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.conservation.description') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.2. Properties\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Properties conserved in sea ice by the numerical schemes.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Energy\\\" \\n\", \n \"# \\\"Mass\\\" \\n\", \n \"# \\\"Salt\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.3. Budget\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.4. Was Flux Correction Used\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Does conservation involved flux correction?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 8.5. Corrected Conserved Prognostic Variables\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *List any variables which are conserved by *more* than the numerical scheme alone.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 9. Grid --&gt; Discretisation --&gt; Horizontal \\n\", \n \"*Sea ice discretisation in the horizontal*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 9.1. Grid\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Grid on which sea ice is horizontal discretised?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Ocean grid\\\" \\n\", \n \"# \\\"Atmosphere Grid\\\" \\n\", \n \"# \\\"Own Grid\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.2. Grid Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the type of sea ice grid?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Structured grid\\\" \\n\", \n \"# \\\"Unstructured grid\\\" \\n\", \n \"# \\\"Adaptive grid\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.3. Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the advection scheme?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Finite differences\\\" \\n\", \n \"# \\\"Finite elements\\\" \\n\", \n \"# \\\"Finite volumes\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.4. Thermodynamics Time Step\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the time step in the sea ice model thermodynamic component in seconds.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.5. Dynamics Time Step\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the time step in the sea ice model dynamic component in seconds.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 9.6. Additional Details\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Specify any additional horizontal discretisation details.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 10. Grid --&gt; Discretisation --&gt; Vertical \\n\", \n \"*Sea ice vertical properties*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 10.1. Layering\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Zero-layer\\\" \\n\", \n \"# \\\"Two-layers\\\" \\n\", \n \"# \\\"Multi-layers\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 10.2. Number Of Layers\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *If using multi-layers specify how many.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 10.3. Additional Details\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Specify any additional vertical grid details.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 11. Grid --&gt; Seaice Categories \\n\", \n \"*What method is used to represent sea ice categories ?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 11.1. Has Mulitple Categories\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Set to true if the sea ice model has multiple sea ice categories.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 11.2. Number Of Categories\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *If using sea ice categories specify how many.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 11.3. Category Limits\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *If using sea ice categories specify each of the category limits.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 11.4. Ice Thickness Distribution Scheme\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe the sea ice thickness distribution scheme*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 11.5. Other\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 12. Grid --&gt; Snow On Seaice \\n\", \n \"*Snow on sea ice details*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 12.1. Has Snow On Ice\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Is snow on ice represented in this model?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 12.2. Number Of Snow Levels\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Number of vertical levels of snow on ice?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 12.3. Snow Fraction\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe how the snow fraction on sea ice is determined*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 12.4. Additional Details\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Specify any additional details related to snow on ice.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 13. Dynamics \\n\", \n \"*Sea Ice Dynamics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 13.1. Horizontal Transport\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the method of horizontal advection of sea ice?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Incremental Re-mapping\\\" \\n\", \n \"# \\\"Prather\\\" \\n\", \n \"# \\\"Eulerian\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.2. Transport In Thickness Space\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the method of sea ice transport in thickness space (i.e. in thickness categories)?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Incremental Re-mapping\\\" \\n\", \n \"# \\\"Prather\\\" \\n\", \n \"# \\\"Eulerian\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.3. Ice Strength Formulation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Which method of sea ice strength formulation is used?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Hibler 1979\\\" \\n\", \n \"# \\\"Rothrock 1975\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.4. Redistribution\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Which processes can redistribute sea ice (including thickness)?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.dynamics.redistribution') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Rafting\\\" \\n\", \n \"# \\\"Ridging\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 13.5. Rheology\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Rheology, what is the ice deformation formulation?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.dynamics.rheology') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Free-drift\\\" \\n\", \n \"# \\\"Mohr-Coloumb\\\" \\n\", \n \"# \\\"Visco-plastic\\\" \\n\", \n \"# \\\"Elastic-visco-plastic\\\" \\n\", \n \"# \\\"Elastic-anisotropic-plastic\\\" \\n\", \n \"# \\\"Granular\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 14. Thermodynamics --&gt; Energy \\n\", \n \"*Processes related to energy in sea ice thermodynamics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 14.1. Enthalpy Formulation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the energy formulation?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Pure ice latent heat (Semtner 0-layer)\\\" \\n\", \n \"# \\\"Pure ice latent and sensible heat\\\" \\n\", \n \"# \\\"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\\\" \\n\", \n \"# \\\"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 14.2. Thermal Conductivity\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What type of thermal conductivity is used?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Pure ice\\\" \\n\", \n \"# \\\"Saline ice\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 14.3. Heat Diffusion\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the method of heat diffusion?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Conduction fluxes\\\" \\n\", \n \"# \\\"Conduction and radiation heat fluxes\\\" \\n\", \n \"# \\\"Conduction, radiation and latent heat transport\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 14.4. Basal Heat Flux\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Method by which basal ocean heat flux is handled?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Heat Reservoir\\\" \\n\", \n \"# \\\"Thermal Fixed Salinity\\\" \\n\", \n \"# \\\"Thermal Varying Salinity\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 14.5. Fixed Salinity Value\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 14.6. Heat Content Of Precipitation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe the method by which the heat content of precipitation is handled.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 14.7. Precipitation Effects On Salinity\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 15. Thermodynamics --&gt; Mass \\n\", \n \"*Processes related to mass in sea ice thermodynamics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 15.1. New Ice Formation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe the method by which new sea ice is formed in open water.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.2. Ice Vertical Growth And Melt\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe the method that governs the vertical growth and melt of sea ice.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.3. Ice Lateral Melting\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the method of sea ice lateral melting?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Floe-size dependent (Bitz et al 2001)\\\" \\n\", \n \"# \\\"Virtual thin ice melting (for single-category)\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.4. Ice Surface Sublimation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe the method that governs sea ice surface sublimation.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 15.5. Frazil Ice\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Describe the method of frazil ice formation.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 16. Thermodynamics --&gt; Salt \\n\", \n \"*Processes related to salt in sea ice thermodynamics.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 16.1. Has Multiple Sea Ice Salinities\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 16.2. Sea Ice Salinity Thermal Impacts\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Does sea ice salinity impact the thermal properties of sea ice?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 17. Thermodynamics --&gt; Salt --&gt; Mass Transport \\n\", \n \"*Mass transport of salt*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 17.1. Salinity Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *How is salinity determined in the mass transport of salt calculation?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Constant\\\" \\n\", \n \"# \\\"Prescribed salinity profile\\\" \\n\", \n \"# \\\"Prognostic salinity profile\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 17.2. Constant Salinity Value\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If using a constant salinity value specify this value in PSU?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 17.3. Additional Details\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Describe the salinity profile used.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 18. Thermodynamics --&gt; Salt --&gt; Thermodynamics \\n\", \n \"*Salt thermodynamics*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 18.1. Salinity Type\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *How is salinity determined in the thermodynamic calculation?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Constant\\\" \\n\", \n \"# \\\"Prescribed salinity profile\\\" \\n\", \n \"# \\\"Prognostic salinity profile\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 18.2. Constant Salinity Value\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *If using a constant salinity value specify this value in PSU?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 18.3. Additional Details\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Describe the salinity profile used.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 19. Thermodynamics --&gt; Ice Thickness Distribution \\n\", \n \"*Ice thickness distribution details.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 19.1. Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *How is the sea ice thickness distribution represented?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Explicit\\\" \\n\", \n \"# \\\"Virtual (enhancement of thermal conductivity, thin ice melting)\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 20. Thermodynamics --&gt; Ice Floe Size Distribution \\n\", \n \"*Ice floe-size distribution details.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 20.1. Representation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *How is the sea ice floe-size represented?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Explicit\\\" \\n\", \n \"# \\\"Parameterised\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 20.2. Additional Details\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Please provide further details on any parameterisation of floe-size.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 21. Thermodynamics --&gt; Melt Ponds \\n\", \n \"*Characteristics of melt ponds.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 21.1. Are Included\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Are melt ponds included in the sea ice model?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 21.2. Formulation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What method of melt pond formulation is used?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Flocco and Feltham (2010)\\\" \\n\", \n \"# \\\"Level-ice melt ponds\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 21.3. Impacts\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *What do melt ponds have an impact on?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Albedo\\\" \\n\", \n \"# \\\"Freshwater\\\" \\n\", \n \"# \\\"Heat\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 22. Thermodynamics --&gt; Snow Processes \\n\", \n \"*Thermodynamic processes in snow on sea ice*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 22.1. Has Snow Aging\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Set to True if the sea ice model has a snow aging scheme.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.2. Snow Aging Scheme\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Describe the snow aging scheme.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.3. Has Snow Ice Formation\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Set to True if the sea ice model has snow ice formation.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(value) \\n\", \n \"# Valid Choices: \\n\", \n \"# True \\n\", \n \"# False \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.4. Snow Ice Formation Scheme\\n\", \n \"**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\\n\", \n \"### *Describe the snow ice formation scheme.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.5. Redistribution\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the impact of ridging on snow cover?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 22.6. Heat Diffusion\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *What is the heat diffusion through snow methodology in sea ice thermodynamics?*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Single-layered heat diffusion\\\" \\n\", \n \"# \\\"Multi-layered heat diffusion\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"# 23. Radiative Processes \\n\", \n \"*Sea Ice Radiative Processes*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"source\": [\n \"### 23.1. Surface Albedo\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\\n\", \n \"### *Method used to handle surface albedo.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE: \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Delta-Eddington\\\" \\n\", \n \"# \\\"Parameterized\\\" \\n\", \n \"# \\\"Multi-band albedo\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### 23.2. Ice Radiation Transmission\\n\", \n \"**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\\n\", \n \"### *Method by which solar radiation through sea ice is handled.*\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }, \n {\n \"execution_count\": null, \n \"cell_type\": \"code\", \n \"source\": [\n \"# PROPERTY ID - DO NOT EDIT ! \\n\", \n \"DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \\n\", \n \"\\n\", \n \"# PROPERTY VALUE(S): \\n\", \n \"# Set as follows: DOC.set_value(\\\"value\\\") \\n\", \n \"# Valid Choices: \\n\", \n \"# \\\"Delta-Eddington\\\" \\n\", \n \"# \\\"Exponential attenuation\\\" \\n\", \n \"# \\\"Ice radiation transmission per category\\\" \\n\", \n \"# \\\"Other: [Please specify]\\\" \\n\", \n \"# TODO - please enter value(s)\", \n \"\\n\"\n ], \n \"outputs\": [], \n \"metadata\": {\n \"collapsed\": true\n }\n }, \n {\n \"source\": [\n \"### \\u00a92017 [ES-DOC](https://es-doc.org) \\n\"\n ], \n \"cell_type\": \"markdown\", \n \"metadata\": {}\n }\n ], \n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\", \n \"name\": \"python2\", \n \"language\": \"python\"\n }, \n \"language_info\": {\n \"mimetype\": \"text/x-python\", \n \"nbconvert_exporter\": \"python\", \n \"name\": \"python\", \n \"file_extension\": \".py\", \n \"version\": \"2.7.10\", \n \"pygments_lexer\": \"ipython2\", \n \"codemirror_mode\": {\n \"version\": 2, \n \"name\": \"ipython\"\n }\n }\n }\n}"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":242,"cells":{"repo_name":{"kind":"string","value":"greenca/diy-spectrometer"},"path":{"kind":"string","value":"peak-detection.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"75028"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Detecting Peaks in a Spectrum\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Populating the interactive namespace from numpy and matplotlib\\n\"\n ]\n }\n ],\n \"source\": [\n \"import spectrumlib\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"%pylab inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"filename = 'shear.png'\\n\",\n \"spectrum = spectrumlib.getSpectrum(filename)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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d8NvKKgWsrkAVqrnMPeytRq2N9FurTlWOcBWqucw95KkS/gMRV4qoWn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\\nADjszcwGwP8HSpEwZ+lAfvkAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(spectrum)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, find the relative maxima of the spectrum.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy.signal import argrelextrema\\n\",\n \"maxes = argrelextrema(spectrum, np.greater, order=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 5 14 20 32 49 58 71 74 94 119 125 155 159 188 194 204 210 217\\n\",\n \" 223 229 238 249 263 270 282 308 329 358 370 380]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print maxes[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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MPe8tRo2L+2g7XkqfwB2og1pMCfNOI1szZz2FueJpLGca/HMnqnZV/p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\\nAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for x in maxes[0]:\\n\",\n \" plt.axvline(x)\\n\",\n \"plt.plot(spectrum, color='r')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This clearly gives us way too many local maxima. \\n\",\n \"\\n\",\n \"So, next, we try the `find_peaks_cwt` function from `scipy.signal`, which uses wavelets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2507: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\\n\",\n \" VisibleDeprecationWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from scipy.signal import find_peaks_cwt\\n\",\n \"cwt_peaks = find_peaks_cwt(spectrum, np.arange(10,15))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[8, 33, 97, 159, 202, 233, 330, 374]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print cwt_peaks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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DAFaaiH9eSlUcveYW8957C3PL2KNOdNYxEbKc9Y+1pn0DrsLTcOe8tT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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for x in cwt_peaks:\\n\",\n \" plt.axvline(x)\\n\",\n \"plt.plot(spectrum, color='r')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is better, in that we lost all the spurious values, but it doesn't match that well, and we don't get the\\n\",\n \"double-peak (near 150) anymore.\\n\",\n \"\\n\",\n \"https://gist.github.com/endolith/250860 has a python translation of a matlab peak-detection script. Downloaded as `peakdetect.py`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import peakdetect\\n\",\n \"peaks, valleys = peakdetect.peakdet(spectrum, 3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 32. 33.5952381 ]\\n\",\n \" [ 94. 27.52380952]\\n\",\n \" [ 155. 172.04761905]\\n\",\n \" [ 159. 214.54761905]\\n\",\n \" [ 194. 63.82142857]\\n\",\n \" [ 204. 73.08333333]\\n\",\n \" [ 210. 63.13095238]\\n\",\n \" [ 229. 143.82142857]\\n\",\n \" [ 249. 66.58333333]\\n\",\n \" [ 270. 33.80952381]\\n\",\n \" [ 282. 26.75 ]\\n\",\n \" [ 308. 21.11904762]\\n\",\n \" [ 329. 32.79761905]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print peaks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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6a9k9Zy4bC3PDUT9k8BE0ty2b56LXuHvXWdw97y9BrSDsn60s7Lshxr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\\nihaRUqYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for index, val in peaks:\\n\",\n \" plt.axvline(index)\\n\",\n \"plt.plot(spectrum, color='r')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is a pretty decent result, which we should be able to use for matching with known spectra.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Calibration\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The sample spectrum above is for a fluorescent lamp. This is a known spectrum, that we can use for calibration. Here is a labelled plot of the spectrum: \\n\",\n \"\\n\",\n \"![Alt text](640px-Fluorescent_lighting_spectrum_peaks_labelled.gif)\\n\",\n \"\\n\",\n \"\\\"Fluorescent lighting spectrum peaks labelled\\\". Licensed under CC BY-SA 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Fluorescent_lighting_spectrum_peaks_labelled.gif#/media/File:Fluorescent_lighting_spectrum_peaks_labelled.gif\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Visually, this appears to match pretty well with our spectrum. We calibrate the x-axis by matching two points with the known spectrum. Let's use the strongest two peaks: 5, at 546.5 nm (from Mercury) and 12, at 611.6 nm (from Europium). In our spectrum, peak 4 has a higher intensity than peak 12, but we'll use peak 12 anyway, because peaks 4 and 5 are too close together to get an accurate calibration.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"159.0 229.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"intensities = sorted([intensity for index, intensity in peaks])\\n\",\n \"peak5 = [index for index, intensity in peaks if intensity == intensities[-1]][0]\\n\",\n \"peak12 = [index for index, intensity in peaks if intensity == intensities[-3]][0]\\n\",\n \"print peak5, peak12\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Linear scale between index numbers and wavelengths:\\n\",\n \"
`wavelength = m*index + b`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.93 398.63\\n\"\n ]\n }\n ],\n \"source\": [\n \"peak5_wl = 546.5\\n\",\n \"peak12_wl = 611.6\\n\",\n \"m = (peak12_wl - peak5_wl)/(peak12 - peak5)\\n\",\n \"b = peak5_wl - m*peak5\\n\",\n \"print m, b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"wavelengths = [m*index + b for index in range(len(spectrum))]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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sNK10qvp0Udfs+NI2J5frycVaNiN2X1pLAMD+yz8SsaQwGpMXuGhApK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\\n9NwRX/8AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(wavelengths, spectrum)\\n\",\n \"plt.xlabel('Wavelength (nm)')\\n\",\n \"plt.ylabel('Intensity')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":243,"cells":{"repo_name":{"kind":"string","value":"tanghaibao/goatools"},"path":{"kind":"string","value":"notebooks/cell_cycle.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"115266"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Cell Cycle genes\\n\",\n \"Using Gene Ontologies (GO), create an up-to-date list of all human protein-coding genes that are know to be associated with cell cycle.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Download Ontologies, if necessary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" EXISTS: go-basic.obo\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Get http://geneontology.org/ontology/go-basic.obo\\n\",\n \"from goatools.base import download_go_basic_obo\\n\",\n \"obo_fname = download_go_basic_obo()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Download Associations, if necessary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" EXISTS: gene2go\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz\\n\",\n \"from goatools.base import download_ncbi_associations\\n\",\n \"gene2go = download_ncbi_associations()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Read associations\\n\",\n \"Normally, when reading associations, GeneID2GOs are returned. We get the reverse, GO2GeneIDs, by adding the key-word arg, \\\"go2geneids=True\\\" to the call to read_ncbi_gene2go.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"HMS:0:00:05.295771 323,107 annotations, 19,649 genes, 18,246 GOs, 1 taxids READ: gene2go \\n\",\n \"12285 IDs in loaded association branch, BP\\n\",\n \"12285 GO terms associated with human NCBI Entrez GeneIDs\\n\"\n ]\n }\n ],\n \"source\": [\n \"from goatools.anno.genetogo_reader import Gene2GoReader\\n\",\n \"\\n\",\n \"objanno = Gene2GoReader(\\\"gene2go\\\", taxids=[9606])\\n\",\n \"go2geneids_human = objanno.get_id2gos(namespace='BP', go2geneids=True)\\n\",\n \"\\n\",\n \"print(\\\"{N:} GO terms associated with human NCBI Entrez GeneIDs\\\".format(N=len(go2geneids_human)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Initialize Gene-Search Helper\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"go-basic.obo: fmt(1.2) rel(2020-01-01) 47,337 GO Terms; optional_attrs(comment def relationship synonym xref)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from goatools.go_search import GoSearch\\n\",\n \"\\n\",\n \"srchhelp = GoSearch(\\\"go-basic.obo\\\", go2items=go2geneids_human)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Find human all genes related to \\\"cell cycle\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5a. Prepare \\\"cell cycle\\\" text searches\\n\",\n \"We will need to search for both *cell cycle* and *cell cycle-independent*. Those GOs that contain the text *cell cycle-independent* are specifically **not** related to *cell cycle* and must be removed from our list of *cell cycle* GO terms.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import re\\n\",\n \"\\n\",\n \"# Compile search pattern for 'cell cycle'\\n\",\n \"cell_cycle_all = re.compile(r'cell cycle', flags=re.IGNORECASE)\\n\",\n \"cell_cycle_not = re.compile(r'cell cycle.independent', flags=re.IGNORECASE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5b. Find NCBI Entrez GeneIDs related to \\\"cell cycle\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2062 human NCBI Entrez GeneIDs related to 'cell cycle' found.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Find ALL GOs and GeneIDs associated with 'cell cycle'.\\n\",\n \"\\n\",\n \"# Details of search are written to a log file\\n\",\n \"fout_allgos = \\\"cell_cycle_gos_human.log\\\" \\n\",\n \"with open(fout_allgos, \\\"w\\\") as log:\\n\",\n \" # Search for 'cell cycle' in GO terms\\n\",\n \" gos_cc_all = srchhelp.get_matching_gos(cell_cycle_all, prt=log)\\n\",\n \" # Find any GOs matching 'cell cycle-independent' (e.g., \\\"lysosome\\\")\\n\",\n \" gos_no_cc = srchhelp.get_matching_gos(cell_cycle_not, gos=gos_cc_all, prt=log)\\n\",\n \" # Remove GO terms that are not \\\"cell cycle\\\" GOs\\n\",\n \" gos = gos_cc_all.difference(gos_no_cc)\\n\",\n \" # Add children GOs of cell cycle GOs\\n\",\n \" gos_all = srchhelp.add_children_gos(gos)\\n\",\n \" # Get Entrez GeneIDs for cell cycle GOs\\n\",\n \" geneids = srchhelp.get_items(gos_all)\\n\",\n \"print(\\\"{N} human NCBI Entrez GeneIDs related to 'cell cycle' found.\\\".format(N=len(geneids)))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Print the \\\"cell cycle\\\" protein-coding gene Symbols\\n\",\n \"In this example, the background is all human protein-codinge genes. \\n\",\n \"\\n\",\n \"Follow the instructions in the `background_genes_ncbi` notebook to download a set of background population genes from NCBI.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from genes_ncbi_9606_proteincoding import GENEID2NT\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RNF212B ring finger protein 212B\\n\",\n \"TTYH1 tweety family member 1\\n\",\n \"CABLES2 Cdk5 and Abl enzyme substrate 2\\n\",\n \"SEH1L SEH1 like nucleoporin\\n\",\n \"KIF18A kinesin family member 18A\\n\",\n \"CHAF1B chromatin assembly factor 1 subunit B\\n\",\n \"SDE2 SDE2 telomere maintenance homolog\\n\",\n \"ABL1 ABL proto-oncogene 1, non-receptor tyrosine kinase\\n\",\n \"CLTCL1 clathrin heavy chain like 1\\n\",\n \"AICDA activation induced cytidine deaminase\\n\",\n \"USP9X ubiquitin specific peptidase 9 X-linked\\n\",\n \"SMC1A structural maintenance of chromosomes 1A\\n\",\n \"HIPK1 homeodomain interacting protein kinase 1\\n\",\n \"ACTA1 actin alpha 1, skeletal muscle\\n\",\n \"ACTB actin beta\\n\",\n \"SPC25 SPC25 component of NDC80 kinetochore complex\\n\",\n \"NAA10 N-alpha-acetyltransferase 10, NatA catalytic subunit\\n\",\n \"ACVR1 activin A receptor type 1\\n\",\n \"ACVR1B activin A receptor type 1B\\n\",\n \"TUBGCP5 tubulin gamma complex associated protein 5\\n\",\n \"BIRC6 baculoviral IAP repeat containing 6\\n\",\n \"ADARB1 adenosine deaminase RNA specific B1\\n\",\n \"LIN9 lin-9 DREAM MuvB core complex component\\n\",\n \"ESCO1 establishment of sister chromatid cohesion N-acetyltransferase 1\\n\",\n \"ADCYAP1 adenylate cyclase activating polypeptide 1\\n\",\n \"MYORG myogenesis regulating glycosidase (putative)\\n\",\n \"AXIN2 axin 2\\n\",\n \"BAP1 BRCA1 associated protein 1\\n\",\n \"CDC7 cell division cycle 7\\n\",\n \"CDC45 cell division cycle 45\\n\",\n \"CNOT6 CCR4-NOT transcription complex subunit 6\\n\",\n \"FZD9 frizzled class receptor 9\\n\",\n \"GFI1B growth factor independent 1B transcriptional repressor\\n\",\n \"GMNC geminin coiled-coil domain containing\\n\",\n \"SYCE1L synaptonemal complex central element protein 1 like\\n\",\n \"NLN neurolysin\\n\",\n \"MTA3 metastasis associated 1 family member 3\\n\",\n \"HECW2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2\\n\",\n \"RPTOR regulatory associated protein of MTOR complex 1\\n\",\n \"KLHL13 kelch like family member 13\\n\",\n \"HACE1 HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1\\n\",\n \"MAD1L1 mitotic arrest deficient 1 like 1\\n\",\n \"AHR aryl hydrocarbon receptor\\n\",\n \"KLHL42 kelch like family member 42\\n\",\n \"AIF1 allograft inflammatory factor 1\\n\",\n \"TP53I13 tumor protein p53 inducible protein 13\\n\",\n \"AKT1 AKT serine/threonine kinase 1\\n\",\n \"AKT2 AKT serine/threonine kinase 2\\n\",\n \"MICAL3 microtubule associated monooxygenase, calponin and LIM domain containing 3\\n\",\n \"TAOK1 TAO kinase 1\\n\",\n \"UXT ubiquitously expressed prefoldin like chaperone\\n\",\n \"CEP126 centrosomal protein 126\\n\",\n \"RAD54L RAD54 like\\n\",\n \"ALOX15B arachidonate 15-lipoxygenase type B\\n\",\n \"DYRK3 dual specificity tyrosine phosphorylation regulated kinase 3\\n\",\n \"DYRK2 dual specificity tyrosine phosphorylation regulated kinase 2\\n\",\n \"CUL4B cullin 4B\\n\",\n \"CUL4A cullin 4A\\n\",\n \"CUL3 cullin 3\\n\",\n \"CUL2 cullin 2\\n\",\n \"CUL1 cullin 1\\n\",\n \"TICRR TOPBP1 interacting checkpoint and replication regulator\\n\",\n \"KLF11 Kruppel like factor 11\\n\",\n \"BIN1 bridging integrator 1\\n\",\n \"FKBP6 FKBP prolyl isomerase 6\\n\",\n \"RAE1 ribonucleic acid export 1\\n\",\n \"OFD1 OFD1 centriole and centriolar satellite protein\\n\",\n \"ANK3 ankyrin 3\\n\",\n \"C6orf89 chromosome 6 open reading frame 89\\n\",\n \"PPM1D protein phosphatase, Mg2+/Mn2+ dependent 1D\\n\",\n \"ANXA1 annexin A1\\n\",\n \"USP28 ubiquitin specific peptidase 28\\n\",\n \"KLHL40 kelch like family member 40\\n\",\n \"KNSTRN kinetochore localized astrin (SPAG5) binding protein\\n\",\n \"PKP4 plakophilin 4\\n\",\n \"APAF1 apoptotic peptidase activating factor 1\\n\",\n \"USP29 ubiquitin specific peptidase 29\\n\",\n \"APBB1 amyloid beta precursor protein binding family B member 1\\n\",\n \"CDKL4 cyclin dependent kinase like 4\\n\",\n \"APC APC regulator of WNT signaling pathway\\n\",\n \"APBB2 amyloid beta precursor protein binding family B member 2\\n\",\n \"APEX1 apurinic/apyrimidinic endodeoxyribonuclease 1\\n\",\n \"BIRC2 baculoviral IAP repeat containing 2\\n\",\n \"GAS7 growth arrest specific 7\\n\",\n \"BIRC5 baculoviral IAP repeat containing 5\\n\",\n \"TRIM71 tripartite motif containing 71\\n\",\n \"CAMK1 calcium/calmodulin dependent protein kinase I\\n\",\n \"BARX2 BARX homeobox 2\\n\",\n \"USP37 ubiquitin specific peptidase 37\\n\",\n \"APP amyloid beta precursor protein\\n\",\n \"FAS Fas cell surface death receptor\\n\",\n \"FASLG Fas ligand\\n\",\n \"MAPKAPK5 MAPK activated protein kinase 5\\n\",\n \"PIAS1 protein inhibitor of activated STAT 1\\n\",\n \"CDC14B cell division cycle 14B\\n\",\n \"CDC14A cell division cycle 14A\\n\",\n \"KIAA1614 KIAA1614\\n\",\n \"CDK10 cyclin dependent kinase 10\\n\",\n \"GADD45GIP1 GADD45G interacting protein 1\\n\",\n \"THOC5 THO complex 5\\n\",\n \"ARF1 ADP ribosylation factor 1\\n\",\n \"MADD MAP kinase activating death domain\\n\",\n \"ARF6 ADP ribosylation factor 6\\n\",\n \"SPC24 SPC24 component of NDC80 kinetochore complex\\n\",\n \"RHOA ras homolog family member A\\n\",\n \"RHOB ras homolog family member B\\n\",\n \"RHOC ras homolog family member C\\n\",\n \"ARL2 ADP ribosylation factor like GTPase 2\\n\",\n \"ARL3 ADP ribosylation factor like GTPase 3\\n\",\n \"ARNTL aryl hydrocarbon receptor nuclear translocator like\\n\",\n \"CCDC124 coiled-coil domain containing 124\\n\",\n \"CNOT6L CCR4-NOT transcription complex subunit 6 like\\n\",\n \"RUVBL1 RuvB like AAA ATPase 1\\n\",\n \"KASH5 KASH domain containing 5\\n\",\n \"HAUS1 HAUS augmin like complex subunit 1\\n\",\n \"CDC26 cell division cycle 26\\n\",\n \"ASCL1 achaete-scute family bHLH transcription factor 1\\n\",\n \"CDK13 cyclin dependent kinase 13\\n\",\n \"TP63 tumor protein p63\\n\",\n \"STK33 serine/threonine kinase 33\\n\",\n \"ASNS asparagine synthetase (glutamine-hydrolyzing)\\n\",\n \"MARK4 microtubule affinity regulating kinase 4\\n\",\n \"SSNA1 SS nuclear autoantigen 1\\n\",\n \"ASPH aspartate beta-hydroxylase\\n\",\n \"PHACTR4 phosphatase and actin regulator 4\\n\",\n \"BRINP2 BMP/retinoic acid inducible neural specific 2\\n\",\n \"DDRGK1 DDRGK domain containing 1\\n\",\n \"LAMTOR3 late endosomal/lysosomal adaptor, MAPK and MTOR activator 3\\n\",\n \"POLD4 DNA polymerase delta 4, accessory subunit\\n\",\n \"CCAR2 cell cycle and apoptosis regulator 2\\n\",\n \"DYNLL1 dynein light chain LC8-type 1\\n\",\n \"ZFHX3 zinc finger homeobox 3\\n\",\n \"TNKS tankyrase\\n\",\n \"ATM ATM serine/threonine kinase\\n\",\n \"CCNB1IP1 cyclin B1 interacting protein 1\\n\",\n \"RBM24 RNA binding motif protein 24\\n\",\n \"BECN1 beclin 1\\n\",\n \"PEA15 proliferation and apoptosis adaptor protein 15\\n\",\n \"ATP2B4 ATPase plasma membrane Ca2+ transporting 4\\n\",\n \"BOLL boule homolog, RNA binding protein\\n\",\n \"CDC23 cell division cycle 23\\n\",\n \"TRADD TNFRSF1A associated via death domain\\n\",\n \"TPRA1 transmembrane protein adipocyte associated 1\\n\",\n \"EED embryonic ectoderm development\\n\",\n \"GBF1 golgi brefeldin A resistant guanine nucleotide exchange factor 1\\n\",\n \"ATR ATR serine/threonine kinase\\n\",\n \"ATRX ATRX chromatin remodeler\\n\",\n \"CRADD CASP2 and RIPK1 domain containing adaptor with death domain\\n\",\n \"TNFSF14 TNF superfamily member 14\\n\",\n \"RIPK1 receptor interacting serine/threonine kinase 1\\n\",\n \"TNFSF10 TNF superfamily member 10\\n\",\n \"AVPR1A arginine vasopressin receptor 1A\\n\",\n \"STAC3 SH3 and cysteine rich domain 3\\n\",\n \"BACH1 BTB domain and CNC homolog 1\\n\",\n \"BAD BCL2 associated agonist of cell death\\n\",\n \"RAB11A RAB11A, member RAS oncogene family\\n\",\n \"ADGRB1 adhesion G protein-coupled receptor B1\\n\",\n \"MEIOB meiosis specific with OB-fold\\n\",\n \"ADGRB2 adhesion G protein-coupled receptor B2\\n\",\n \"BAK1 BCL2 antagonist/killer 1\\n\",\n \"RIPK2 receptor interacting serine/threonine kinase 2\\n\",\n \"FADD Fas associated via death domain\\n\",\n \"BAX BCL2 associated X, apoptosis regulator\\n\",\n \"BARD1 BRCA1 associated RING domain 1\\n\",\n \"BBS4 Bardet-Biedl syndrome 4\\n\",\n \"BCAT1 branched chain amino acid transaminase 1\\n\",\n \"CCND1 cyclin D1\\n\",\n \"BCL2 BCL2 apoptosis regulator\\n\",\n \"BCL2L1 BCL2 like 1\\n\",\n \"BCL3 BCL3 transcription coactivator\\n\",\n \"TNFRSF10B TNF receptor superfamily member 10b\\n\",\n \"BCL6 BCL6 transcription repressor\\n\",\n \"TNFRSF10A TNF receptor superfamily member 10a\\n\",\n \"BCL9 BCL9 transcription coactivator\\n\",\n \"BCR BCR activator of RhoGEF and GTPase\\n\",\n \"CCNK cyclin K\\n\",\n \"CDKL1 cyclin dependent kinase like 1\\n\",\n \"BANF1 BAF nuclear assembly factor 1\\n\",\n \"BDNF brain derived neurotrophic factor\\n\",\n \"BGLAP bone gamma-carboxyglutamate protein\\n\",\n \"IQGAP1 IQ motif containing GTPase activating protein 1\\n\",\n \"BID BH3 interacting domain death agonist\\n\",\n \"BLM BLM RecQ like helicase\\n\",\n \"CXCR5 C-X-C motif chemokine receptor 5\\n\",\n \"CFLAR CASP8 and FADD like apoptosis regulator\\n\",\n \"BMI1 BMI1 proto-oncogene, polycomb ring finger\\n\",\n \"BMP2 bone morphogenetic protein 2\\n\",\n \"BMP4 bone morphogenetic protein 4\\n\",\n \"BMP7 bone morphogenetic protein 7\\n\",\n \"KAT2B lysine acetyltransferase 2B\\n\",\n \"CDK5R1 cyclin dependent kinase 5 regulatory subunit 1\\n\",\n \"BOK BCL2 family apoptosis regulator BOK\\n\",\n \"BRCA1 BRCA1 DNA repair associated\\n\",\n \"PER2 period circadian regulator 2\\n\",\n \"BRCA2 BRCA2 DNA repair associated\\n\",\n \"BRDT bromodomain testis associated\\n\",\n \"ZFP36L1 ZFP36 ring finger protein like 1\\n\",\n \"ZFP36L2 ZFP36 ring finger protein like 2\\n\",\n \"CDC123 cell division cycle 123\\n\",\n \"SPHK1 sphingosine kinase 1\\n\",\n \"BTC betacellulin\\n\",\n \"KLF5 Kruppel like factor 5\\n\",\n \"CDC16 cell division cycle 16\\n\",\n \"ZPR1 ZPR1 zinc finger\\n\",\n \"NAE1 NEDD8 activating enzyme E1 subunit 1\\n\",\n \"BTG1 BTG anti-proliferation factor 1\\n\",\n \"ANGEL2 angel homolog 2\\n\",\n \"BUB1 BUB1 mitotic checkpoint serine/threonine kinase\\n\",\n \"BUB1B BUB1 mitotic checkpoint serine/threonine kinase B\\n\",\n \"WRAP73 WD repeat containing, antisense to TP73\\n\",\n \"CCNA1 cyclin A1\\n\",\n \"TIMELESS timeless circadian regulator\\n\",\n \"OSGIN2 oxidative stress induced growth inhibitor family member 2\\n\",\n \"EME2 essential meiotic structure-specific endonuclease subunit 2\\n\",\n \"PHOX2B paired like homeobox 2B\\n\",\n \"UHRF2 ubiquitin like with PHD and ring finger domains 2\\n\",\n \"CDK5R2 cyclin dependent kinase 5 regulatory subunit 2\\n\",\n \"BTRC beta-transducin repeat containing E3 ubiquitin protein ligase\\n\",\n \"PTTG1IP PTTG1 interacting protein\\n\",\n \"SENP5 SUMO specific peptidase 5\\n\",\n \"USP17L2 ubiquitin specific peptidase 17 like family member 2\\n\",\n \"NUDT16 nudix hydrolase 16\\n\",\n \"CALM1 calmodulin 1\\n\",\n \"CALM2 calmodulin 2\\n\",\n \"CDKL2 cyclin dependent kinase like 2\\n\",\n \"CALM3 calmodulin 3\\n\",\n \"CALR calreticulin\\n\",\n \"CAMK2A calcium/calmodulin dependent protein kinase II alpha\\n\",\n \"CAPN2 calpain 2\\n\",\n \"CAPN3 calpain 3\\n\",\n \"BRSK2 BR serine/threonine kinase 2\\n\",\n \"RNF8 ring finger protein 8\\n\",\n \"CASP1 caspase 1\\n\",\n \"CASP2 caspase 2\\n\",\n \"HIP1R huntingtin interacting protein 1 related\\n\",\n \"CASP4 caspase 4\\n\",\n \"CASP5 caspase 5\\n\",\n \"CASP3 caspase 3\\n\",\n \"TM4SF5 transmembrane 4 L six family member 5\\n\",\n \"CASP8 caspase 8\\n\",\n \"CASP9 caspase 9\\n\",\n \"CASP10 caspase 10\\n\",\n \"WBP2NL WBP2 N-terminal like\\n\",\n \"WHAMM WASP homolog associated with actin, golgi membranes and microtubules\\n\",\n \"CTDSP1 CTD small phosphatase 1\\n\",\n \"UBA3 ubiquitin like modifier activating enzyme 3\\n\",\n \"CAV2 caveolin 2\\n\",\n \"CAV3 caveolin 3\\n\",\n \"PRC1 protein regulator of cytokinesis 1\\n\",\n \"RUNX3 RUNX family transcription factor 3\\n\",\n \"CCK cholecystokinin\\n\",\n \"DIRAS3 DIRAS family GTPase 3\\n\",\n \"CCNA2 cyclin A2\\n\",\n \"CCNB1 cyclin B1\\n\",\n \"CCNC cyclin C\\n\",\n \"CCND2 cyclin D2\\n\",\n \"PKMYT1 protein kinase, membrane associated tyrosine/threonine 1\\n\",\n \"CCND3 cyclin D3\\n\",\n \"CCNE1 cyclin E1\\n\",\n \"CCNF cyclin F\\n\",\n \"CCNG1 cyclin G1\\n\",\n \"CCNG2 cyclin G2\\n\",\n \"CCNH cyclin H\\n\",\n \"UNC119 unc-119 lipid binding chaperone\\n\",\n \"CCNT1 cyclin T1\\n\",\n \"CCNT2 cyclin T2\\n\",\n \"DNAJA3 DnaJ heat shock protein family (Hsp40) member A3\\n\",\n \"USP2 ubiquitin specific peptidase 2\\n\",\n \"USP10 ubiquitin specific peptidase 10\\n\",\n \"USP8 ubiquitin specific peptidase 8\\n\",\n \"LATS1 large tumor suppressor kinase 1\\n\",\n \"MS4A3 membrane spanning 4-domains A3\\n\",\n \"CNOT9 CCR4-NOT transcription complex subunit 9\\n\",\n \"SMC3 structural maintenance of chromosomes 3\\n\",\n \"AIFM1 apoptosis inducing factor mitochondria associated 1\\n\",\n \"CD28 CD28 molecule\\n\",\n \"CCNB2 cyclin B2\\n\",\n \"CCNE2 cyclin E2\\n\",\n \"CTDP1 CTD phosphatase subunit 1\\n\",\n \"CD44 CD44 molecule (Indian blood group)\\n\",\n \"CD53 CD53 molecule\\n\",\n \"EXO1 exonuclease 1\\n\",\n \"CD74 CD74 molecule\\n\",\n \"CDK1 cyclin dependent kinase 1\\n\",\n \"CDK11B cyclin dependent kinase 11B\\n\",\n \"CDC5L cell division cycle 5 like\\n\",\n \"ARHGEF2 Rho/Rac guanine nucleotide exchange factor 2\\n\",\n \"CDC6 cell division cycle 6\\n\",\n \"ZW10 zw10 kinetochore protein\\n\",\n \"CDC20 cell division cycle 20\\n\",\n \"CDC25A cell division cycle 25A\\n\",\n \"CDC25B cell division cycle 25B\\n\",\n \"CDC25C cell division cycle 25C\\n\",\n \"CDC27 cell division cycle 27\\n\",\n \"BUB3 BUB3 mitotic checkpoint protein\\n\",\n \"CDC34 cell division cycle 34, ubiqiutin conjugating enzyme\\n\",\n \"CDC42 cell division cycle 42\\n\",\n \"DCUN1D3 defective in cullin neddylation 1 domain containing 3\\n\",\n \"CDH13 cadherin 13\\n\",\n \"CDK2 cyclin dependent kinase 2\\n\",\n \"CDK3 cyclin dependent kinase 3\\n\",\n \"CDK4 cyclin dependent kinase 4\\n\",\n \"CDK5 cyclin dependent kinase 5\\n\",\n \"AURKB aurora kinase B\\n\",\n \"CDK7 cyclin dependent kinase 7\\n\",\n \"CDK6 cyclin dependent kinase 6\\n\",\n \"PHF13 PHD finger protein 13\\n\",\n \"CDK9 cyclin dependent kinase 9\\n\",\n \"CDKN1A cyclin dependent kinase inhibitor 1A\\n\",\n \"CDKN1B cyclin dependent kinase inhibitor 1B\\n\",\n \"CDKN1C cyclin dependent kinase inhibitor 1C\\n\",\n \"CDKN2A cyclin dependent kinase inhibitor 2A\\n\",\n \"CDKN2B cyclin dependent kinase inhibitor 2B\\n\",\n \"CDKN2C cyclin dependent kinase inhibitor 2C\\n\",\n \"CDKN2D cyclin dependent kinase inhibitor 2D\\n\",\n \"CDKN3 cyclin dependent kinase inhibitor 3\\n\",\n \"NOLC1 nucleolar and coiled-body phosphoprotein 1\\n\",\n \"TMEM67 transmembrane protein 67\\n\",\n \"PTTG1 PTTG1 regulator of sister chromatid separation, securin\\n\",\n \"CCPG1 cell cycle progression 1\\n\",\n \"TBRG4 transforming growth factor beta regulator 4\\n\",\n \"CEACAM5 CEA cell adhesion molecule 5\\n\",\n \"CEBPA CCAAT enhancer binding protein alpha\\n\",\n \"CENPA centromere protein A\\n\",\n \"CENPC centromere protein C\\n\",\n \"CENPE centromere protein E\\n\",\n \"CENPF centromere protein F\\n\",\n \"CETN1 centrin 1\\n\",\n \"CETN2 centrin 2\\n\",\n \"CETN3 centrin 3\\n\",\n \"CFL1 cofilin 1\\n\",\n \"PIWIL1 piwi like RNA-mediated gene silencing 1\\n\",\n \"RCC1 regulator of chromosome condensation 1\\n\",\n \"CHD3 chromodomain helicase DNA binding protein 3\\n\",\n \"CHEK1 checkpoint kinase 1\\n\",\n \"FOXN3 forkhead box N3\\n\",\n \"KLF4 Kruppel like factor 4\\n\",\n \"TRIP13 thyroid hormone receptor interactor 13\\n\",\n \"RHOU ras homolog family member U\\n\",\n \"NLRC4 NLR family CARD domain containing 4\\n\",\n \"CNOT8 CCR4-NOT transcription complex subunit 8\\n\",\n \"RPRD1B regulation of nuclear pre-mRNA domain containing 1B\\n\",\n \"TAOK2 TAO kinase 2\\n\",\n \"RPL23 ribosomal protein L23\\n\",\n \"BOD1 biorientation of chromosomes in cell division 1\\n\",\n \"CKS1B CDC28 protein kinase regulatory subunit 1B\\n\",\n \"CKS2 CDC28 protein kinase regulatory subunit 2\\n\",\n \"H1-8 H1.8 linker histone\\n\",\n \"SLC9A3R1 SLC9A3 regulator 1\\n\",\n \"KIF3B kinesin family member 3B\\n\",\n \"WIZ WIZ zinc finger\\n\",\n \"RRAGD Ras related GTP binding D\\n\",\n \"CLIC1 chloride intracellular channel 1\\n\",\n \"SUN2 Sad1 and UNC84 domain containing 2\\n\",\n \"RECQL5 RecQ like helicase 5\\n\",\n \"CLTA clathrin light chain A\\n\",\n \"RAD54B RAD54 homolog B\\n\",\n \"CLTC clathrin heavy chain\\n\",\n \"FBXO7 F-box protein 7\\n\",\n \"EIF2AK3 eukaryotic translation initiation factor 2 alpha kinase 3\\n\",\n \"NIPBL NIPBL cohesin loading factor\\n\",\n \"PLK3 polo like kinase 3\\n\",\n \"HOMER1 homer scaffold protein 1\\n\",\n \"ANAPC13 anaphase promoting complex subunit 13\\n\",\n \"CATSPERZ catsper channel auxiliary subunit zeta\\n\",\n \"ROCK2 Rho associated coiled-coil containing protein kinase 2\\n\",\n \"COL4A3 collagen type IV alpha 3 chain\\n\",\n \"ANKK1 ankyrin repeat and kinase domain containing 1\\n\",\n \"PSMF1 proteasome inhibitor subunit 1\\n\",\n \"KIF23 kinesin family member 23\\n\",\n \"POC1A POC1 centriolar protein A\\n\",\n \"ADAMTS1 ADAM metallopeptidase with thrombospondin type 1 motif 1\\n\",\n \"INTS7 integrator complex subunit 7\\n\",\n \"GDF15 growth differentiation factor 15\\n\",\n \"MAP3K8 mitogen-activated protein kinase kinase kinase 8\\n\",\n \"CNOT10 CCR4-NOT transcription complex subunit 10\\n\",\n \"EEF1E1 eukaryotic translation elongation factor 1 epsilon 1\\n\",\n \"ANAPC15 anaphase promoting complex subunit 15\\n\",\n \"AHCTF1 AT-hook containing transcription factor 1\\n\",\n \"VPS4B vacuolar protein sorting 4 homolog B\\n\",\n \"RTTN rotatin\\n\",\n \"RMI2 RecQ mediated genome instability 2\\n\",\n \"NEK7 NIMA related kinase 7\\n\",\n \"NOX4 NADPH oxidase 4\\n\",\n \"SYCP3 synaptonemal complex protein 3\\n\",\n \"NSL1 NSL1 component of MIS12 kinetochore complex\\n\",\n \"MACROH2A1 macroH2A.1 histone\\n\",\n \"SIN3A SIN3 transcription regulator family member A\\n\",\n \"ZNF385A zinc finger protein 385A\\n\",\n \"EPGN epithelial mitogen\\n\",\n \"SYF2 SYF2 pre-mRNA splicing factor\\n\",\n \"KANK2 KN motif and ankyrin repeat domains 2\\n\",\n \"CLOCK clock circadian regulator\\n\",\n \"BABAM2 BRISC and BRCA1 A complex member 2\\n\",\n \"ATF2 activating transcription factor 2\\n\",\n \"CREBL2 cAMP responsive element binding protein like 2\\n\",\n \"KIF20B kinesin family member 20B\\n\",\n \"SH2B1 SH2B adaptor protein 1\\n\",\n \"MAD2L1BP MAD2L1 binding protein\\n\",\n \"WTAP WT1 associated protein\\n\",\n \"CHMP2B charged multivesicular body protein 2B\\n\",\n \"CRY1 cryptochrome circadian regulator 1\\n\",\n \"HINFP histone H4 transcription factor\\n\",\n \"ULK3 unc-51 like kinase 3\\n\",\n \"RNF167 ring finger protein 167\\n\",\n \"CTCFL CCCTC-binding factor like\\n\",\n \"MAPK14 mitogen-activated protein kinase 14\\n\",\n \"L3MBTL1 L3MBTL histone methyl-lysine binding protein 1\\n\",\n \"FAM32A family with sequence similarity 32 member A\\n\",\n \"ARHGEF10 Rho guanine nucleotide exchange factor 10\\n\",\n \"KLHDC3 kelch domain containing 3\\n\",\n \"CSNK1A1 casein kinase 1 alpha 1\\n\",\n \"CSNK1D casein kinase 1 delta\\n\",\n \"CSNK1E casein kinase 1 epsilon\\n\",\n \"CSNK2A1 casein kinase 2 alpha 1\\n\",\n \"CSNK2A2 casein kinase 2 alpha 2\\n\",\n \"MDC1 mediator of DNA damage checkpoint 1\\n\",\n \"CEP135 centrosomal protein 135\\n\",\n \"CUZD1 CUB and zona pellucida like domains 1\\n\",\n \"MARF1 meiosis regulator and mRNA stability factor 1\\n\",\n \"SENP6 SUMO specific peptidase 6\\n\",\n \"ANKRD17 ankyrin repeat domain 17\\n\",\n \"GIGYF2 GRB10 interacting GYF protein 2\\n\",\n \"NKX2-5 NK2 homeobox 5\\n\",\n \"APPL1 adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1\\n\",\n \"CTBP1 C-terminal binding protein 1\\n\",\n \"CCN2 cellular communication network factor 2\\n\",\n \"ZNF830 zinc finger protein 830\\n\",\n \"SLFN11 schlafen family member 11\\n\",\n \"POLDIP2 DNA polymerase delta interacting protein 2\\n\",\n \"CTNNB1 catenin beta 1\\n\",\n \"CROCC ciliary rootlet coiled-coil, rootletin\\n\",\n \"PUM1 pumilio RNA binding family member 1\\n\",\n \"ESPL1 extra spindle pole bodies like 1, separase\\n\",\n \"CEP57 centrosomal protein 57\\n\",\n \"CTSH cathepsin H\\n\",\n \"UTP14C UTP14C small subunit processome component\\n\",\n \"TDRD12 tudor domain containing 12\\n\",\n \"RAB11FIP3 RAB11 family interacting protein 3\\n\",\n \"CCDC69 coiled-coil domain containing 69\\n\",\n \"CYLD CYLD lysine 63 deubiquitinase\\n\",\n \"CDCA3 cell division cycle associated 3\\n\",\n \"HDAC9 histone deacetylase 9\\n\",\n \"KNTC1 kinetochore associated 1\\n\",\n \"CYP1A1 cytochrome P450 family 1 subfamily A member 1\\n\",\n \"CCP110 centriolar coiled-coil protein 110\\n\",\n \"NLRP12 NLR family pyrin domain containing 12\\n\",\n \"ZFP42 ZFP42 zinc finger protein\\n\",\n \"RIPOR2 RHO family interacting cell polarization regulator 2\\n\",\n \"ROMO1 reactive oxygen species modulator 1\\n\",\n \"HDAC4 histone deacetylase 4\\n\",\n \"PCLAF PCNA clamp associated factor\\n\",\n \"RASSF2 Ras association domain family member 2\\n\",\n \"MCIDAS multiciliate differentiation and DNA synthesis associated cell cycle protein\\n\",\n \"LIN54 lin-54 DREAM MuvB core complex component\\n\",\n \"CYP27B1 cytochrome P450 family 27 subfamily B member 1\\n\",\n \"DLGAP5 DLG associated protein 5\\n\",\n \"CKAP5 cytoskeleton associated protein 5\\n\",\n \"DACH1 dachshund family transcription factor 1\\n\",\n \"MAML1 mastermind like transcriptional coactivator 1\\n\",\n \"DAP death associated protein\\n\",\n \"OR1A2 olfactory receptor family 1 subfamily A member 2\\n\",\n \"DAPK3 death associated protein kinase 3\\n\",\n \"DAZL deleted in azoospermia like\\n\",\n \"BRINP1 BMP/retinoic acid inducible neural specific 1\\n\",\n \"NUF2 NUF2 component of NDC80 kinetochore complex\\n\",\n \"SFI1 SFI1 centrin binding protein\\n\",\n \"CUL7 cullin 7\\n\",\n \"RB1CC1 RB1 inducible coiled-coil 1\\n\",\n \"SPAG8 sperm associated antigen 8\\n\",\n \"LIN52 lin-52 DREAM MuvB core complex component\\n\",\n \"DCTN1 dynactin subunit 1\\n\",\n \"MELK maternal embryonic leucine zipper kinase\\n\",\n \"NEK9 NIMA related kinase 9\\n\",\n \"DDB1 damage specific DNA binding protein 1\\n\",\n \"GINS1 GINS complex subunit 1\\n\",\n \"GADD45A growth arrest and DNA damage inducible alpha\\n\",\n \"FBXL3 F-box and leucine rich repeat protein 3\\n\",\n \"DDIT3 DNA damage inducible transcript 3\\n\",\n \"PHGDH phosphoglycerate dehydrogenase\\n\",\n \"MYEF2 myelin expression factor 2\\n\",\n \"DDX3X DEAD-box helicase 3 X-linked\\n\",\n \"DDX5 DEAD-box helicase 5\\n\",\n \"CABLES1 Cdk5 and Abl enzyme substrate 1\\n\",\n \"KIAA0753 KIAA0753\\n\",\n \"DDX11 DEAD/H-box helicase 11\\n\",\n \"CDCA2 cell division cycle associated 2\\n\",\n \"PSMD6 proteasome 26S subunit, non-ATPase 6\\n\",\n \"CHMP7 charged multivesicular body protein 7\\n\",\n \"MAGI2 membrane associated guanylate kinase, WW and PDZ domain containing 2\\n\",\n \"SLC26A8 solute carrier family 26 member 8\\n\",\n \"TLK1 tousled like kinase 1\\n\",\n \"TAS2R13 taste 2 receptor member 13\\n\",\n \"FBXO22 F-box protein 22\\n\",\n \"FBXO6 F-box protein 6\\n\",\n \"FBXO5 F-box protein 5\\n\",\n \"FBXO4 F-box protein 4\\n\",\n \"PARD6A par-6 family cell polarity regulator alpha\\n\",\n \"WASHC5 WASH complex subunit 5\\n\",\n \"KLHL21 kelch like family member 21\\n\",\n \"ANKRD2 ankyrin repeat domain 2\\n\",\n \"DHCR24 24-dehydrocholesterol reductase\\n\",\n \"DHFR dihydrofolate reductase\\n\",\n \"NCAPD2 non-SMC condensin I complex subunit D2\\n\",\n \"KIF14 kinesin family member 14\\n\",\n \"DLG1 discs large MAGUK scaffold protein 1\\n\",\n \"DCLRE1A DNA cross-link repair 1A\\n\",\n \"SESN2 sestrin 2\\n\",\n \"DMPK DM1 protein kinase\\n\",\n \"DMRT1 doublesex and mab-3 related transcription factor 1\\n\",\n \"DNA2 DNA replication helicase/nuclease 2\\n\",\n \"USP3 ubiquitin specific peptidase 3\\n\",\n \"TNFSF15 TNF superfamily member 15\\n\",\n \"RHNO1 RAD9-HUS1-RAD1 interacting nuclear orphan 1\\n\",\n \"DYNC1H1 dynein cytoplasmic 1 heavy chain 1\\n\",\n \"FAM162A family with sequence similarity 162 member A\\n\",\n \"DYNC1I2 dynein cytoplasmic 1 intermediate chain 2\\n\",\n \"DNM2 dynamin 2\\n\",\n \"RBX1 ring-box 1\\n\",\n \"DNMT3A DNA methyltransferase 3 alpha\\n\",\n \"THOC1 THO complex 1\\n\",\n \"REC8 REC8 meiotic recombination protein\\n\",\n \"DMTF1 cyclin D binding myb like transcription factor 1\\n\",\n \"CASP8AP2 caspase 8 associated protein 2\\n\",\n \"DRD2 dopamine receptor D2\\n\",\n \"DRD3 dopamine receptor D3\\n\",\n \"ARID3A AT-rich interaction domain 3A\\n\",\n \"MARVELD1 MARVEL domain containing 1\\n\",\n \"HDAC5 histone deacetylase 5\\n\",\n \"PDCD6 programmed cell death 6\\n\",\n \"BCL2L10 BCL2 like 10\\n\",\n \"BCL2L11 BCL2 like 11\\n\",\n \"RCAN1 regulator of calcineurin 1\\n\",\n \"PDCD6IP programmed cell death 6 interacting protein\\n\",\n \"L3MBTL2 L3MBTL histone methyl-lysine binding protein 2\\n\",\n \"DUSP1 dual specificity phosphatase 1\\n\",\n \"CHAF1A chromatin assembly factor 1 subunit A\\n\",\n \"DUSP3 dual specificity phosphatase 3\\n\",\n \"PARP3 poly(ADP-ribose) polymerase family member 3\\n\",\n \"TOM1L1 target of myb1 like 1 membrane trafficking protein\\n\",\n \"DYRK1A dual specificity tyrosine phosphorylation regulated kinase 1A\\n\",\n \"SMC4 structural maintenance of chromosomes 4\\n\",\n \"E2F1 E2F transcription factor 1\\n\",\n \"E2F2 E2F transcription factor 2\\n\",\n \"E2F3 E2F transcription factor 3\\n\",\n \"E2F4 E2F transcription factor 4\\n\",\n \"E2F5 E2F transcription factor 5\\n\",\n \"E2F6 E2F transcription factor 6\\n\",\n \"E4F1 E4F transcription factor 1\\n\",\n \"CCNQ cyclin Q\\n\",\n \"USH1C USH1 protein network component harmonin\\n\",\n \"ECT2 epithelial cell transforming 2\\n\",\n \"NUPR1 nuclear protein 1, transcriptional regulator\\n\",\n \"GMNN geminin DNA replication inhibitor\\n\",\n \"ACTR3 actin related protein 3\\n\",\n \"ACTR2 actin related protein 2\\n\",\n \"EDN1 endothelin 1\\n\",\n \"EDN3 endothelin 3\\n\",\n \"HEXIM2 HEXIM P-TEFb complex subunit 2\\n\",\n \"PHC1 polyhomeotic homolog 1\\n\",\n \"RPS27L ribosomal protein S27 like\\n\",\n \"CTDSP2 CTD small phosphatase 2\\n\",\n \"RAD50 RAD50 double strand break repair protein\\n\",\n \"KIF20A kinesin family member 20A\\n\",\n \"ESCO2 establishment of sister chromatid cohesion N-acetyltransferase 2\\n\",\n \"USP26 ubiquitin specific peptidase 26\\n\",\n \"FEM1B fem-1 homolog B\\n\",\n \"ACTR1B actin related protein 1B\\n\",\n \"ACTR1A actin related protein 1A\\n\",\n \"SETDB2 SET domain bifurcated histone lysine methyltransferase 2\\n\",\n \"MLXIPL MLX interacting protein like\\n\",\n \"CNTD1 cyclin N-terminal domain containing 1\\n\",\n \"OPTN optineurin\\n\",\n \"YAF2 YY1 associated factor 2\\n\",\n \"SH3GLB1 SH3 domain containing GRB2 like, endophilin B1\\n\",\n \"LATS2 large tumor suppressor kinase 2\\n\",\n \"AKAP9 A-kinase anchoring protein 9\\n\",\n \"EGF epidermal growth factor\\n\",\n \"HMCN1 hemicentin 1\\n\",\n \"TBC1D10A TBC1 domain family member 10A\\n\",\n \"EGFR epidermal growth factor receptor\\n\",\n \"SBDS SBDS ribosome maturation factor\\n\",\n \"CNEP1R1 CTD nuclear envelope phosphatase 1 regulatory subunit 1\\n\",\n \"EIF4E eukaryotic translation initiation factor 4E\\n\",\n \"EIF4EBP1 eukaryotic translation initiation factor 4E binding protein 1\\n\",\n \"EIF4G1 eukaryotic translation initiation factor 4 gamma 1\\n\",\n \"EIF4G2 eukaryotic translation initiation factor 4 gamma 2\\n\",\n \"HASPIN histone H3 associated protein kinase\\n\",\n \"RBM7 RNA binding motif protein 7\\n\",\n \"DYNC1LI1 dynein cytoplasmic 1 light intermediate chain 1\\n\",\n \"ING4 inhibitor of growth family member 4\\n\",\n \"MRNIP MRN complex interacting protein\\n\",\n \"AATF apoptosis antagonizing transcription factor\\n\",\n \"SENP2 SUMO specific peptidase 2\\n\",\n \"PSME3 proteasome activator subunit 3\\n\",\n \"NME6 NME/NM23 nucleoside diphosphate kinase 6\\n\",\n \"EMD emerin\\n\",\n \"EML1 EMAP like 1\\n\",\n \"CKAP2 cytoskeleton associated protein 2\\n\",\n \"RASSF4 Ras association domain family member 4\\n\",\n \"TOPORS TOP1 binding arginine/serine rich protein, E3 ubiquitin ligase\\n\",\n \"FLOT1 flotillin 1\\n\",\n \"PSMD14 proteasome 26S subunit, non-ATPase 14\\n\",\n \"TUBD1 tubulin delta 1\\n\",\n \"TUBE1 tubulin epsilon 1\\n\",\n \"ENG endoglin\\n\",\n \"CTDSPL CTD small phosphatase like\\n\",\n \"ENSA endosulfine alpha\\n\",\n \"EP300 E1A binding protein p300\\n\",\n \"EPB41 erythrocyte membrane protein band 4.1\\n\",\n \"EPB41L2 erythrocyte membrane protein band 4.1 like 2\\n\",\n \"HERC5 HECT and RLD domain containing E3 ubiquitin protein ligase 5\\n\",\n \"NIN ninein\\n\",\n \"NUSAP1 nucleolar and spindle associated protein 1\\n\",\n \"ANKRD31 ankyrin repeat domain 31\\n\",\n \"DUSP13 dual specificity phosphatase 13\\n\",\n \"RIDA reactive intermediate imine deaminase A homolog\\n\",\n \"EPS8 epidermal growth factor receptor pathway substrate 8\\n\",\n \"SPRY1 sprouty RTK signaling antagonist 1\\n\",\n \"SPRY2 sprouty RTK signaling antagonist 2\\n\",\n \"ERCC1 ERCC excision repair 1, endonuclease non-catalytic subunit\\n\",\n \"CCDC8 coiled-coil domain containing 8\\n\",\n \"EREG epiregulin\\n\",\n \"BRIP1 BRCA1 interacting protein C-terminal helicase 1\\n\",\n \"CDK2AP2 cyclin dependent kinase 2 associated protein 2\\n\",\n \"ERCC4 ERCC excision repair 4, endonuclease catalytic subunit\\n\",\n \"ERCC2 ERCC excision repair 2, TFIIH core complex helicase subunit\\n\",\n \"ERCC3 ERCC excision repair 3, TFIIH core complex helicase subunit\\n\",\n \"ERCC6 ERCC excision repair 6, chromatin remodeling factor\\n\",\n \"ZMPSTE24 zinc metallopeptidase STE24\\n\",\n \"AKAP8 A-kinase anchoring protein 8\\n\",\n \"ERH ERH mRNA splicing and mitosis factor\\n\",\n \"ERF ETS2 repressor factor\\n\",\n \"CENPW centromere protein W\\n\",\n \"STAG1 stromal antigen 1\\n\",\n \"ERN1 endoplasmic reticulum to nucleus signaling 1\\n\",\n \"SHISA5 shisa family member 5\\n\",\n \"ESRRB estrogen related receptor beta\\n\",\n \"MAEA macrophage erythroblast attacher, E3 ubiquitin ligase\\n\",\n \"PAK4 p21 (RAC1) activated kinase 4\\n\",\n \"KATNB1 katanin regulatory subunit B1\\n\",\n \"CDKL3 cyclin dependent kinase like 3\\n\",\n \"CCNO cyclin O\\n\",\n \"TFDP3 transcription factor Dp family member 3\\n\",\n \"MECOM MDS1 and EVI1 complex locus\\n\",\n \"EVI2B ecotropic viral integration site 2B\\n\",\n \"IKZF1 IKAROS family zinc finger 1\\n\",\n \"MCMDC2 minichromosome maintenance domain containing 2\\n\",\n \"KLHL41 kelch like family member 41\\n\",\n \"RRAGB Ras related GTP binding B\\n\",\n \"RXFP3 relaxin family peptide receptor 3\\n\",\n \"MND1 meiotic nuclear divisions 1\\n\",\n \"EZH2 enhancer of zeste 2 polycomb repressive complex 2 subunit\\n\",\n \"F2R coagulation factor II thrombin receptor\\n\",\n \"HORMAD1 HORMA domain containing 1\\n\",\n \"CNTROB centrobin, centriole duplication and spindle assembly protein\\n\",\n \"F3 coagulation factor III, tissue factor\\n\",\n \"RAB6C RAB6C, member RAS oncogene family\\n\",\n \"NPM2 nucleophosmin/nucleoplasmin 2\\n\",\n \"HMG20B high mobility group 20B\\n\",\n \"WAC WW domain containing adaptor with coiled-coil\\n\",\n \"PYHIN1 pyrin and HIN domain family member 1\\n\",\n \"SYCE2 synaptonemal complex central element protein 2\\n\",\n \"FANCA FA complementation group A\\n\",\n \"FANCD2 FA complementation group D2\\n\",\n \"CITED2 Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2\\n\",\n \"USP44 ubiquitin specific peptidase 44\\n\",\n \"TUBA1B tubulin alpha 1b\\n\",\n \"DACT1 dishevelled binding antagonist of beta catenin 1\\n\",\n \"PCGF6 polycomb group ring finger 6\\n\",\n \"TUBB3 tubulin beta 3 class III\\n\",\n \"TUBB4A tubulin beta 4A class IVa\\n\",\n \"FZR1 fizzy and cell division cycle 20 related 1\\n\",\n \"SCGB3A1 secretoglobin family 3A member 1\\n\",\n \"TUBB4B tubulin beta 4B class IVb\\n\",\n \"BTN2A2 butyrophilin subfamily 2 member A2\\n\",\n \"FAP fibroblast activation protein alpha\\n\",\n \"SYCP2 synaptonemal complex protein 2\\n\",\n \"TAOK3 TAO kinase 3\\n\",\n \"NOD1 nucleotide binding oligomerization domain containing 1\\n\",\n \"ANAPC10 anaphase promoting complex subunit 10\\n\",\n \"DLC1 DLC1 Rho GTPase activating protein\\n\",\n \"NDRG1 N-myc downstream regulated 1\\n\",\n \"ATRIP ATR interacting protein\\n\",\n \"RACK1 receptor for activated C kinase 1\\n\",\n \"NDC80 NDC80 kinetochore complex component\\n\",\n \"CEP78 centrosomal protein 78\\n\",\n \"ABRAXAS1 abraxas 1, BRCA1 A complex subunit\\n\",\n \"STRADA STE20 related adaptor alpha\\n\",\n \"DUX4 double homeobox 4\\n\",\n \"CRLF3 cytokine receptor like factor 3\\n\",\n \"PRMT5 protein arginine methyltransferase 5\\n\",\n \"WNT16 Wnt family member 16\\n\",\n \"TUBGCP3 tubulin gamma complex associated protein 3\\n\",\n \"FEN1 flap structure-specific endonuclease 1\\n\",\n \"RWDD1 RWD domain containing 1\\n\",\n \"RBM14 RNA binding motif protein 14\\n\",\n \"FER FER tyrosine kinase\\n\",\n \"FES FES proto-oncogene, tyrosine kinase\\n\",\n \"PPME1 protein phosphatase methylesterase 1\\n\",\n \"FGF8 fibroblast growth factor 8\\n\",\n \"FGF10 fibroblast growth factor 10\\n\",\n \"FGFR1 fibroblast growth factor receptor 1\\n\",\n \"FGFR2 fibroblast growth factor receptor 2\\n\",\n \"GPNMB glycoprotein nmb\\n\",\n \"MAD2L2 mitotic arrest deficient 2 like 2\\n\",\n \"TACC3 transforming acidic coiled-coil containing protein 3\\n\",\n \"PRKAG2 protein kinase AMP-activated non-catalytic subunit gamma 2\\n\",\n \"PIBF1 progesterone immunomodulatory binding factor 1\\n\",\n \"FHL1 four and a half LIM domains 1\\n\",\n \"FHIT fragile histidine triad diadenosine triphosphatase\\n\",\n \"SNX9 sorting nexin 9\\n\",\n \"ANAPC5 anaphase promoting complex subunit 5\\n\",\n \"ANAPC7 anaphase promoting complex subunit 7\\n\",\n \"TADA3 transcriptional adaptor 3\\n\",\n \"YTHDF2 YTH N6-methyladenosine RNA binding protein 2\\n\",\n \"FOXG1 forkhead box G1\\n\",\n \"FOXC1 forkhead box C1\\n\",\n \"ARAP1 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1\\n\",\n \"LCMT1 leucine carboxyl methyltransferase 1\\n\",\n \"FOXE3 forkhead box E3\\n\",\n \"FOXM1 forkhead box M1\\n\",\n \"CARM1 coactivator associated arginine methyltransferase 1\\n\",\n \"CHMP4C charged multivesicular body protein 4C\\n\",\n \"TRIM72 tripartite motif containing 72\\n\",\n \"FLNA filamin A\\n\",\n \"MYBBP1A MYB binding protein 1a\\n\",\n \"FLT3LG fms related receptor tyrosine kinase 3 ligand\\n\",\n \"CIB1 calcium and integrin binding 1\\n\",\n \"SLF1 SMC5-SMC6 complex localization factor 1\\n\",\n \"KAT5 lysine acetyltransferase 5\\n\",\n \"IPO7 importin 7\\n\",\n \"SPOUT1 SPOUT domain containing methyltransferase 1\\n\",\n \"ZNRD2 zinc ribbon domain containing 2\\n\",\n \"UBD ubiquitin D\\n\",\n \"BATF basic leucine zipper ATF-like transcription factor\\n\",\n \"TRIAP1 TP53 regulated inhibitor of apoptosis 1\\n\",\n \"DCTN2 dynactin subunit 2\\n\",\n \"ANP32B acidic nuclear phosphoprotein 32 family member B\\n\",\n \"LAMTOR5 late endosomal/lysosomal adaptor, MAPK and MTOR activator 5\\n\",\n \"RTF2 replication termination factor 2\\n\",\n \"SLC25A33 solute carrier family 25 member 33\\n\",\n \"CHMP5 charged multivesicular body protein 5\\n\",\n \"GTSE1 G2 and S-phase expressed 1\\n\",\n \"DTL denticleless E3 ubiquitin protein ligase homolog\\n\",\n \"ANAPC11 anaphase promoting complex subunit 11\\n\",\n \"ZC3HC1 zinc finger C3HC-type containing 1\\n\",\n \"HBP1 HMG-box transcription factor 1\\n\",\n \"PDCD2L programmed cell death 2 like\\n\",\n \"TACC2 transforming acidic coiled-coil containing protein 2\\n\",\n \"SIRT7 sirtuin 7\\n\",\n \"CHORDC1 cysteine and histidine rich domain containing 1\\n\",\n \"CINP cyclin dependent kinase 2 interacting protein\\n\",\n \"SMC2 structural maintenance of chromosomes 2\\n\",\n \"ERN2 endoplasmic reticulum to nucleus signaling 2\\n\",\n \"USP16 ubiquitin specific peptidase 16\\n\",\n \"AKAP8L A-kinase anchoring protein 8 like\\n\",\n \"P3H4 prolyl 3-hydroxylase family member 4 (inactive)\\n\",\n \"HEXIM1 HEXIM P-TEFb complex subunit 1\\n\",\n \"SPAG5 sperm associated antigen 5\\n\",\n \"CYFIP2 cytoplasmic FMR1 interacting protein 2\\n\",\n \"STAMBP STAM binding protein\\n\",\n \"TXNIP thioredoxin interacting protein\\n\",\n \"CNPPD1 cyclin Pas1/PHO80 domain containing 1\\n\",\n \"BEX3 brain expressed X-linked 3\\n\",\n \"GAS2L1 growth arrest specific 2 like 1\\n\",\n \"RGS14 regulator of G protein signaling 14\\n\",\n \"MLH3 mutL homolog 3\\n\",\n \"KHDRBS1 KH RNA binding domain containing, signal transduction associated 1\\n\",\n \"SND1 staphylococcal nuclease and tudor domain containing 1\\n\",\n \"CTCF CCCTC-binding factor\\n\",\n \"MTOR mechanistic target of rapamycin kinase\\n\",\n \"CGRRF1 cell growth regulator with ring finger domain 1\\n\",\n \"CGREF1 cell growth regulator with EF-hand domain 1\\n\",\n \"RRAGA Ras related GTP binding A\\n\",\n \"ANKRD1 ankyrin repeat domain 1\\n\",\n \"SSX2IP SSX family member 2 interacting protein\\n\",\n \"NR5A2 nuclear receptor subfamily 5 group A member 2\\n\",\n \"ECRG4 ECRG4 augurin precursor\\n\",\n \"CHMP3 charged multivesicular body protein 3\\n\",\n \"CDK5RAP1 CDK5 regulatory subunit associated protein 1\\n\",\n \"MRGPRX2 MAS related GPR family member X2\\n\",\n \"MTBP MDM2 binding protein\\n\",\n \"NR5A1 nuclear receptor subfamily 5 group A member 1\\n\",\n \"RAB11FIP4 RAB11 family interacting protein 4\\n\",\n \"USP39 ubiquitin specific peptidase 39\\n\",\n \"POLD3 DNA polymerase delta 3, accessory subunit\\n\",\n \"DOT1L DOT1 like histone lysine methyltransferase\\n\",\n \"LZTS2 leucine zipper tumor suppressor 2\\n\",\n \"BRSK1 BR serine/threonine kinase 1\\n\",\n \"NFAT5 nuclear factor of activated T cells 5\\n\",\n \"NUDC nuclear distribution C, dynein complex regulator\\n\",\n \"BBC3 BCL2 binding component 3\\n\",\n \"PLK4 polo like kinase 4\\n\",\n \"STAG3 stromal antigen 3\\n\",\n \"STAG2 stromal antigen 2\\n\",\n \"HECA hdc homolog, cell cycle regulator\\n\",\n \"SLX4 SLX4 structure-specific endonuclease subunit\\n\",\n \"SMC1B structural maintenance of chromosomes 1B\\n\",\n \"PTTG2 pituitary tumor-transforming 2\\n\",\n \"KCNH5 potassium voltage-gated channel subfamily H member 5\\n\",\n \"GIPC1 GIPC PDZ domain containing family member 1\\n\",\n \"CIDEB cell death inducing DFFA like effector b\\n\",\n \"CAB39 calcium binding protein 39\\n\",\n \"UIMC1 ubiquitin interaction motif containing 1\\n\",\n \"NES nestin\\n\",\n \"PLK2 polo like kinase 2\\n\",\n \"ZMYND11 zinc finger MYND-type containing 11\\n\",\n \"GAK cyclin G associated kinase\\n\",\n \"SPIRE2 spire type actin nucleation factor 2\\n\",\n \"ARPP19 cAMP regulated phosphoprotein 19\\n\",\n \"WWOX WW domain containing oxidoreductase\\n\",\n \"NEK6 NIMA related kinase 6\\n\",\n \"MCM8 minichromosome maintenance 8 homologous recombination repair factor\\n\",\n \"RTEL1 regulator of telomere elongation helicase 1\\n\",\n \"TUBG2 tubulin gamma 2\\n\",\n \"TMEM8B transmembrane protein 8B\\n\",\n \"ZNF268 zinc finger protein 268\\n\",\n \"CDK12 cyclin dependent kinase 12\\n\",\n \"VPS4A vacuolar protein sorting 4 homolog A\\n\",\n \"STRA8 stimulated by retinoic acid 8\\n\",\n \"SDCCAG8 SHH signaling and ciliogenesis regulator SDCCAG8\\n\",\n \"ENTR1 endosome associated trafficking regulator 1\\n\",\n \"GAS1 growth arrest specific 1\\n\",\n \"GAS2 growth arrest specific 2\\n\",\n \"GAS6 growth arrest specific 6\\n\",\n \"MAP3K20 mitogen-activated protein kinase kinase kinase 20\\n\",\n \"GATA3 GATA binding protein 3\\n\",\n \"DNER delta/notch like EGF repeat containing\\n\",\n \"GATA6 GATA binding protein 6\\n\",\n \"PARD6G par-6 family cell polarity regulator gamma\\n\",\n \"SIK1 salt inducible kinase 1\\n\",\n \"KAT2A lysine acetyltransferase 2A\\n\",\n \"SIX4 SIX homeobox 4\\n\",\n \"TUBGCP2 tubulin gamma complex associated protein 2\\n\",\n \"TUBGCP4 tubulin gamma complex associated protein 4\\n\",\n \"OPN1MW opsin 1, medium wave sensitive\\n\",\n \"TUBA8 tubulin alpha 8\\n\",\n \"LILRB1 leukocyte immunoglobulin like receptor B1\\n\",\n \"CHMP2A charged multivesicular body protein 2A\\n\",\n \"GEM GTP binding protein overexpressed in skeletal muscle\\n\",\n \"C10orf99 chromosome 10 open reading frame 99\\n\",\n \"GFI1 growth factor independent 1 transcriptional repressor\\n\",\n \"PDCD4 programmed cell death 4\\n\",\n \"JMY junction mediating and regulatory protein, p53 cofactor\\n\",\n \"USP19 ubiquitin specific peptidase 19\\n\",\n \"PARD6B par-6 family cell polarity regulator beta\\n\",\n \"GJA1 gap junction protein alpha 1\\n\",\n \"TUBB6 tubulin beta 6 class V\\n\",\n \"JTB jumping translocation breakpoint\\n\",\n \"TTBK1 tau tubulin kinase 1\\n\",\n \"BLCAP BLCAP apoptosis inducing factor\\n\",\n \"AFAP1L2 actin filament associated protein 1 like 2\\n\",\n \"TXNL4A thioredoxin like 4A\\n\",\n \"SUGT1 SGT1 homolog, MIS12 kinetochore complex assembly cochaperone\\n\",\n \"GADD45G growth arrest and DNA damage inducible gamma\\n\",\n \"APEX2 apurinic/apyrimidinic endodeoxyribonuclease 2\\n\",\n \"EHMT2 euchromatic histone lysine methyltransferase 2\\n\",\n \"DBF4 DBF4 zinc finger\\n\",\n \"SPIN1 spindlin 1\\n\",\n \"GLI1 GLI family zinc finger 1\\n\",\n \"EHD1 EH domain containing 1\\n\",\n \"SPDYC speedy/RINGO cell cycle regulator family member C\\n\",\n \"RPS6KA6 ribosomal protein S6 kinase A6\\n\",\n \"BTG3 BTG anti-proliferation factor 3\\n\",\n \"UBE2S ubiquitin conjugating enzyme E2 S\\n\",\n \"PRPF19 pre-mRNA processing factor 19\\n\",\n \"INSM2 INSM transcriptional repressor 2\\n\",\n \"GML glycosylphosphatidylinositol anchored molecule like\\n\",\n \"PPP1R9B protein phosphatase 1 regulatory subunit 9B\\n\",\n \"GNAI1 G protein subunit alpha i1\\n\",\n \"GNAI2 G protein subunit alpha i2\\n\",\n \"SPATA22 spermatogenesis associated 22\\n\",\n \"GNAI3 G protein subunit alpha i3\\n\",\n \"SGSM3 small G protein signaling modulator 3\\n\",\n \"BEX2 brain expressed X-linked 2\\n\",\n \"CCNI cyclin I\\n\",\n \"COPS5 COP9 signalosome subunit 5\\n\",\n \"GOLGA2 golgin A2\\n\",\n \"PSRC1 proline and serine rich coiled-coil 1\\n\",\n \"SFN stratifin\\n\",\n \"KIF2C kinesin family member 2C\\n\",\n \"TLK2 tousled like kinase 2\\n\",\n \"HORMAD2 HORMA domain containing 2\\n\",\n \"GPR3 G protein-coupled receptor 3\\n\",\n \"RAB35 RAB35, member RAS oncogene family\\n\",\n \"TDRKH tudor and KH domain containing\\n\",\n \"PIM2 Pim-2 proto-oncogene, serine/threonine kinase\\n\",\n \"GPER1 G protein-coupled estrogen receptor 1\\n\",\n \"HTRA2 HtrA serine peptidase 2\\n\",\n \"TENT4A terminal nucleotidyltransferase 4A\\n\",\n \"EML4 EMAP like 4\\n\",\n \"KMT5A lysine methyltransferase 5A\\n\",\n \"TUBA1C tubulin alpha 1c\\n\",\n \"GRK5 G protein-coupled receptor kinase 5\\n\",\n \"CNTRL centriolin\\n\",\n \"UBE2C ubiquitin conjugating enzyme E2 C\\n\",\n \"GPX1 glutathione peroxidase 1\\n\",\n \"TOPBP1 DNA topoisomerase II binding protein 1\\n\",\n \"TRIOBP TRIO and F-actin binding protein\\n\",\n \"PLPP7 phospholipid phosphatase 7 (inactive)\\n\",\n \"PARD3B par-3 family cell polarity regulator beta\\n\",\n \"TMEM119 transmembrane protein 119\\n\",\n \"GRIN2A glutamate ionotropic receptor NMDA type subunit 2A\\n\",\n \"NR3C1 nuclear receptor subfamily 3 group C member 1\\n\",\n \"MEI1 meiotic double-stranded break formation protein 1\\n\",\n \"KATNA1 katanin catalytic subunit A1\\n\",\n \"PRDM7 PR/SET domain 7\\n\",\n \"PRDM5 PR/SET domain 5\\n\",\n \"CIT citron rho-interacting serine/threonine kinase\\n\",\n \"CEP43 centrosomal protein 43\\n\",\n \"GSPT1 G1 to S phase transition 1\\n\",\n \"KIF3A kinesin family member 3A\\n\",\n \"ZWINT ZW10 interacting kinetochore protein\\n\",\n \"ZNF503 zinc finger protein 503\\n\",\n \"KLHL22 kelch like family member 22\\n\",\n \"CDC37 cell division cycle 37, HSP90 cochaperone\\n\",\n \"DMC1 DNA meiotic recombinase 1\\n\",\n \"PLAAT3 phospholipase A and acyltransferase 3\\n\",\n \"ZBTB49 zinc finger and BTB domain containing 49\\n\",\n \"SULT1A4 sulfotransferase family 1A member 4\\n\",\n \"GTF2H1 general transcription factor IIH subunit 1\\n\",\n \"NUDT6 nudix hydrolase 6\\n\",\n \"WDHD1 WD repeat and HMG-box DNA binding protein 1\\n\",\n \"FAM107A family with sequence similarity 107 member A\\n\",\n \"TBRG1 transforming growth factor beta regulator 1\\n\",\n \"LZTS1 leucine zipper tumor suppressor 1\\n\",\n \"WDR6 WD repeat domain 6\\n\",\n \"RASSF1 Ras association domain family member 1\\n\",\n \"CEP250 centrosomal protein 250\\n\",\n \"CHEK2 checkpoint kinase 2\\n\",\n \"TTL tubulin tyrosine ligase\\n\",\n \"MASTL microtubule associated serine/threonine kinase like\\n\",\n \"H2AX H2A.X variant histone\\n\",\n \"ZFYVE19 zinc finger FYVE-type containing 19\\n\",\n \"MAEL maelstrom spermatogenic transposon silencer\\n\",\n \"AVPI1 arginine vasopressin induced 1\\n\",\n \"WEE2 WEE2 oocyte meiosis inhibiting kinase\\n\",\n \"AJUBA ajuba LIM protein\\n\",\n \"LSM10 LSM10, U7 small nuclear RNA associated\\n\",\n \"PMF1 polyamine modulated factor 1\\n\",\n \"HCFC1 host cell factor C1\\n\",\n \"EDA2R ectodysplasin A2 receptor\\n\",\n \"HTT huntingtin\\n\",\n \"DCTN3 dynactin subunit 3\\n\",\n \"HELLS helicase, lymphoid specific\\n\",\n \"HGF hepatocyte growth factor\\n\",\n \"TREX1 three prime repair exonuclease 1\\n\",\n \"HHEX hematopoietically expressed homeobox\\n\",\n \"NANOGP8 Nanog homeobox retrogene P8\\n\",\n \"HIC1 HIC ZBTB transcriptional repressor 1\\n\",\n \"HIP1 huntingtin interacting protein 1\\n\",\n \"HINT1 histidine triad nucleotide binding protein 1\\n\",\n \"SMYD1 SET and MYND domain containing 1\\n\",\n \"ECD ecdysoneless cell cycle regulator\\n\",\n \"HLA-G major histocompatibility complex, class I, G\\n\",\n \"PHB2 prohibitin 2\\n\",\n \"TUSC2 tumor suppressor 2, mitochondrial calcium regulator\\n\",\n \"CBX3 chromobox 3\\n\",\n \"OIP5 Opa interacting protein 5\\n\",\n \"HMGA1 high mobility group AT-hook 1\\n\",\n \"HMMR hyaluronan mediated motility receptor\\n\",\n \"NR4A1 nuclear receptor subfamily 4 group A member 1\\n\",\n \"PPP1R10 protein phosphatase 1 regulatory subunit 10\\n\",\n \"FOXA1 forkhead box A1\\n\",\n \"HNRNPU heterogeneous nuclear ribonucleoprotein U\\n\",\n \"HAUS8 HAUS augmin like complex subunit 8\\n\",\n \"RINT1 RAD50 interactor 1\\n\",\n \"USP51 ubiquitin specific peptidase 51\\n\",\n \"HPGD 15-hydroxyprostaglandin dehydrogenase\\n\",\n \"HRAS HRas proto-oncogene, GTPase\\n\",\n \"PRMT2 protein arginine methyltransferase 2\\n\",\n \"PRMT1 protein arginine methyltransferase 1\\n\",\n \"HES1 hes family bHLH transcription factor 1\\n\",\n \"LSM11 LSM11, U7 small nuclear RNA associated\\n\",\n \"POC5 POC5 centriolar protein\\n\",\n \"HSF1 heat shock transcription factor 1\\n\",\n \"INCA1 inhibitor of CDK, cyclin A1 interacting protein 1\\n\",\n \"HSPA1A heat shock protein family A (Hsp70) member 1A\\n\",\n \"HSPA1B heat shock protein family A (Hsp70) member 1B\\n\",\n \"HSPA2 heat shock protein family A (Hsp70) member 2\\n\",\n \"SYCE1 synaptonemal complex central element protein 1\\n\",\n \"HSP90AA1 heat shock protein 90 alpha family class A member 1\\n\",\n \"HSP90AB1 heat shock protein 90 alpha family class B member 1\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MIIP migration and invasion inhibitory protein\\n\",\n \"HSPD1 heat shock protein family D (Hsp60) member 1\\n\",\n \"HSPE1 heat shock protein family E (Hsp10) member 1\\n\",\n \"HUS1 HUS1 checkpoint clamp component\\n\",\n \"HYAL1 hyaluronidase 1\\n\",\n \"ICAM1 intercellular adhesion molecule 1\\n\",\n \"ID1 inhibitor of DNA binding 1, HLH protein\\n\",\n \"ID2 inhibitor of DNA binding 2\\n\",\n \"ID3 inhibitor of DNA binding 3, HLH protein\\n\",\n \"ID4 inhibitor of DNA binding 4, HLH protein\\n\",\n \"SNX33 sorting nexin 33\\n\",\n \"PRKAB2 protein kinase AMP-activated non-catalytic subunit beta 2\\n\",\n \"IFI16 interferon gamma inducible protein 16\\n\",\n \"PRKAG1 protein kinase AMP-activated non-catalytic subunit gamma 1\\n\",\n \"TUBGCP6 tubulin gamma complex associated protein 6\\n\",\n \"IFNG interferon gamma\\n\",\n \"IFNW1 interferon omega 1\\n\",\n \"MISP mitotic spindle positioning\\n\",\n \"NANOS2 nanos C2HC-type zinc finger 2\\n\",\n \"IGF1 insulin like growth factor 1\\n\",\n \"IGF2 insulin like growth factor 2\\n\",\n \"CCNB3 cyclin B3\\n\",\n \"LRRCC1 leucine rich repeat and coiled-coil centrosomal protein 1\\n\",\n \"TNKS1BP1 tankyrase 1 binding protein 1\\n\",\n \"MYOCD myocardin\\n\",\n \"CEP295 centrosomal protein 295\\n\",\n \"IK IK cytokine\\n\",\n \"IL1A interleukin 1 alpha\\n\",\n \"IL1B interleukin 1 beta\\n\",\n \"IL4R interleukin 4 receptor\\n\",\n \"CXCL8 C-X-C motif chemokine ligand 8\\n\",\n \"IL10 interleukin 10\\n\",\n \"TRNP1 TMF1 regulated nuclear protein 1\\n\",\n \"IL12A interleukin 12A\\n\",\n \"IL12B interleukin 12B\\n\",\n \"BRINP3 BMP/retinoic acid inducible neural specific 3\\n\",\n \"ILK integrin linked kinase\\n\",\n \"INCENP inner centromere protein\\n\",\n \"ING1 inhibitor of growth family member 1\\n\",\n \"ING2 inhibitor of growth family member 2\\n\",\n \"INHA inhibin subunit alpha\\n\",\n \"INHBA inhibin subunit beta A\\n\",\n \"TUBB8 tubulin beta 8 class VIII\\n\",\n \"CXCL10 C-X-C motif chemokine ligand 10\\n\",\n \"INS insulin\\n\",\n \"PLK5 polo like kinase 5 (inactive)\\n\",\n \"INSM1 INSM transcriptional repressor 1\\n\",\n \"INSR insulin receptor\\n\",\n \"PPP2R3B protein phosphatase 2 regulatory subunit B''beta\\n\",\n \"IRF1 interferon regulatory factor 1\\n\",\n \"RAD21L1 RAD21 cohesin complex component like 1\\n\",\n \"IRF6 interferon regulatory factor 6\\n\",\n \"TUBB2B tubulin beta 2B class IIb\\n\",\n \"ZNF385B zinc finger protein 385B\\n\",\n \"ITGB1 integrin subunit beta 1\\n\",\n \"JAK2 Janus kinase 2\\n\",\n \"JUN Jun proto-oncogene, AP-1 transcription factor subunit\\n\",\n \"JUNB JunB proto-oncogene, AP-1 transcription factor subunit\\n\",\n \"JUND JunD proto-oncogene, AP-1 transcription factor subunit\\n\",\n \"MEI4 meiotic double-stranded break formation protein 4\\n\",\n \"CCNYL1 cyclin Y like 1\\n\",\n \"KCNA5 potassium voltage-gated channel subfamily A member 5\\n\",\n \"PPP1R1C protein phosphatase 1 regulatory inhibitor subunit 1C\\n\",\n \"SGO2 shugoshin 2\\n\",\n \"KEL Kell metallo-endopeptidase (Kell blood group)\\n\",\n \"KIF2A kinesin family member 2A\\n\",\n \"KIF11 kinesin family member 11\\n\",\n \"KIFC1 kinesin family member C1\\n\",\n \"KIF25 kinesin family member 25\\n\",\n \"KIF22 kinesin family member 22\\n\",\n \"KPNB1 karyopherin subunit beta 1\\n\",\n \"IPO5 importin 5\\n\",\n \"CCSAP centriole, cilia and spindle associated protein\\n\",\n \"ACTR8 actin related protein 8\\n\",\n \"KRT18 keratin 18\\n\",\n \"C10orf90 chromosome 10 open reading frame 90\\n\",\n \"STMN1 stathmin 1\\n\",\n \"LCK LCK proto-oncogene, Src family tyrosine kinase\\n\",\n \"LEP leptin\\n\",\n \"LFNG LFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase\\n\",\n \"IHO1 interactor of HORMAD1 1\\n\",\n \"LIF LIF interleukin 6 family cytokine\\n\",\n \"LIG1 DNA ligase 1\\n\",\n \"LIG3 DNA ligase 3\\n\",\n \"LIG4 DNA ligase 4\\n\",\n \"LIMS1 LIM zinc finger domain containing 1\\n\",\n \"LMNA lamin A/C\\n\",\n \"LRP6 LDL receptor related protein 6\\n\",\n \"LRP5 LDL receptor related protein 5\\n\",\n \"MAD2L1 mitotic arrest deficient 2 like 1\\n\",\n \"SMAD3 SMAD family member 3\\n\",\n \"TMPRSS11A transmembrane serine protease 11A\\n\",\n \"MAGEA2 MAGE family member A2\\n\",\n \"CACUL1 CDK2 associated cullin domain 1\\n\",\n \"SPRED2 sprouty related EVH1 domain containing 2\\n\",\n \"TP53INP1 tumor protein p53 inducible nuclear protein 1\\n\",\n \"MAP4 microtubule associated protein 4\\n\",\n \"MAPT microtubule associated protein tau\\n\",\n \"MAX MYC associated factor X\\n\",\n \"MCM2 minichromosome maintenance complex component 2\\n\",\n \"MCM3 minichromosome maintenance complex component 3\\n\",\n \"MCM4 minichromosome maintenance complex component 4\\n\",\n \"MCM5 minichromosome maintenance complex component 5\\n\",\n \"MCM6 minichromosome maintenance complex component 6\\n\",\n \"MCM7 minichromosome maintenance complex component 7\\n\",\n \"SKA2 spindle and kinetochore associated complex subunit 2\\n\",\n \"SGO1 shugoshin 1\\n\",\n \"MDM2 MDM2 proto-oncogene\\n\",\n \"MDM4 MDM4 regulator of p53\\n\",\n \"MECP2 methyl-CpG binding protein 2\\n\",\n \"PSMA8 proteasome 20S subunit alpha 8\\n\",\n \"MEF2C myocyte enhancer factor 2C\\n\",\n \"MEIS2 Meis homeobox 2\\n\",\n \"MEN1 menin 1\\n\",\n \"AGO4 argonaute RISC component 4\\n\",\n \"MIF macrophage migration inhibitory factor\\n\",\n \"CXCL9 C-X-C motif chemokine ligand 9\\n\",\n \"MKI67 marker of proliferation Ki-67\\n\",\n \"MLF1 myeloid leukemia factor 1\\n\",\n \"MLH1 mutL homolog 1\\n\",\n \"FOXO4 forkhead box O4\\n\",\n \"MME membrane metalloendopeptidase\\n\",\n \"MMP14 matrix metallopeptidase 14\\n\",\n \"MNAT1 MNAT1 component of CDK activating kinase\\n\",\n \"KLHDC8B kelch domain containing 8B\\n\",\n \"MNT MAX network transcriptional repressor\\n\",\n \"DEUP1 deuterosome assembly protein 1\\n\",\n \"MOS MOS proto-oncogene, serine/threonine kinase\\n\",\n \"MRE11 MRE11 homolog, double strand break repair nuclease\\n\",\n \"LAMTOR2 late endosomal/lysosomal adaptor, MAPK and MTOR activator 2\\n\",\n \"HUS1B HUS1 checkpoint clamp component B\\n\",\n \"GIT1 GIT ArfGAP 1\\n\",\n \"BTBD18 BTB domain containing 18\\n\",\n \"RGCC regulator of cell cycle\\n\",\n \"MCTS1 MCTS1 re-initiation and release factor\\n\",\n \"HIPK2 homeodomain interacting protein kinase 2\\n\",\n \"PIWIL4 piwi like RNA-mediated gene silencing 4\\n\",\n \"MSH2 mutS homolog 2\\n\",\n \"MSH3 mutS homolog 3\\n\",\n \"MSH4 mutS homolog 4\\n\",\n \"MSH5 mutS homolog 5\\n\",\n \"NUPR2 nuclear protein 2, transcriptional regulator\\n\",\n \"PRKAG3 protein kinase AMP-activated non-catalytic subunit gamma 3\\n\",\n \"MSX1 msh homeobox 1\\n\",\n \"MSX2 msh homeobox 2\\n\",\n \"SETD2 SET domain containing 2, histone lysine methyltransferase\\n\",\n \"MAPK15 mitogen-activated protein kinase 15\\n\",\n \"CHMP4A charged multivesicular body protein 4A\\n\",\n \"BABAM1 BRISC and BRCA1 A complex member 1\\n\",\n \"LAMTOR4 late endosomal/lysosomal adaptor, MAPK and MTOR activator 4\\n\",\n \"PYCARD PYD and CARD domain containing\\n\",\n \"BRD7 bromodomain containing 7\\n\",\n \"RACGAP1 Rac GTPase activating protein 1\\n\",\n \"UHRF1 ubiquitin like with PHD and ring finger domains 1\\n\",\n \"CENPV centromere protein V\\n\",\n \"FLCN folliculin\\n\",\n \"PLD6 phospholipase D family member 6\\n\",\n \"MUC1 mucin 1, cell surface associated\\n\",\n \"GEN1 GEN1 Holliday junction 5' flap endonuclease\\n\",\n \"TRIM37 tripartite motif containing 37\\n\",\n \"MAGEA2B MAGE family member A2B\\n\",\n \"MX2 MX dynamin like GTPase 2\\n\",\n \"MAJIN membrane anchored junction protein\\n\",\n \"MYB MYB proto-oncogene, transcription factor\\n\",\n \"MYBL1 MYB proto-oncogene like 1\\n\",\n \"MYBL2 MYB proto-oncogene like 2\\n\",\n \"CDC14C cell division cycle 14C\\n\",\n \"MYC MYC proto-oncogene, bHLH transcription factor\\n\",\n \"THAP5 THAP domain containing 5\\n\",\n \"ACER2 alkaline ceramidase 2\\n\",\n \"GADD45B growth arrest and DNA damage inducible beta\\n\",\n \"MYF5 myogenic factor 5\\n\",\n \"MYF6 myogenic factor 6\\n\",\n \"NUGGC nuclear GTPase, germinal center associated\\n\",\n \"MYH9 myosin heavy chain 9\\n\",\n \"MYH10 myosin heavy chain 10\\n\",\n \"MYO6 myosin VI\\n\",\n \"MYOD1 myogenic differentiation 1\\n\",\n \"NEK10 NIMA related kinase 10\\n\",\n \"MYOG myogenin\\n\",\n \"PPP1R12A protein phosphatase 1 regulatory subunit 12A\\n\",\n \"PPP1R12B protein phosphatase 1 regulatory subunit 12B\\n\",\n \"NASP nuclear autoantigenic sperm protein\\n\",\n \"NUBP1 nucleotide binding protein 1\\n\",\n \"NBN nibrin\\n\",\n \"SPICE1 spindle and centriole associated protein 1\\n\",\n \"DRG1 developmentally regulated GTP binding protein 1\\n\",\n \"NEDD9 neural precursor cell expressed, developmentally down-regulated 9\\n\",\n \"NEK1 NIMA related kinase 1\\n\",\n \"NEK2 NIMA related kinase 2\\n\",\n \"NEK3 NIMA related kinase 3\\n\",\n \"NEUROD1 neuronal differentiation 1\\n\",\n \"NEUROG1 neurogenin 1\\n\",\n \"PELO pelota mRNA surveillance and ribosome rescue factor\\n\",\n \"NF2 neurofibromin 2\\n\",\n \"NFATC1 nuclear factor of activated T cells 1\\n\",\n \"NFATC2 nuclear factor of activated T cells 2\\n\",\n \"NFATC3 nuclear factor of activated T cells 3\\n\",\n \"NFATC4 nuclear factor of activated T cells 4\\n\",\n \"NFE2L1 nuclear factor, erythroid 2 like 1\\n\",\n \"BHLHA15 basic helix-loop-helix family member a15\\n\",\n \"NGF nerve growth factor\\n\",\n \"NGFR nerve growth factor receptor\\n\",\n \"ANAPC16 anaphase promoting complex subunit 16\\n\",\n \"NKX3-1 NK3 homeobox 1\\n\",\n \"SLC39A5 solute carrier family 39 member 5\\n\",\n \"CNOT2 CCR4-NOT transcription complex subunit 2\\n\",\n \"CNOT3 CCR4-NOT transcription complex subunit 3\\n\",\n \"CNOT4 CCR4-NOT transcription complex subunit 4\\n\",\n \"NOTCH1 notch receptor 1\\n\",\n \"NOTCH2 notch receptor 2\\n\",\n \"CCN3 cellular communication network factor 3\\n\",\n \"NPAT nuclear protein, coactivator of histone transcription\\n\",\n \"NPM1 nucleophosmin 1\\n\",\n \"NPPC natriuretic peptide C\\n\",\n \"NPR2 natriuretic peptide receptor 2\\n\",\n \"MIS18A MIS18 kinetochore protein A\\n\",\n \"NUMA1 nuclear mitotic apparatus protein 1\\n\",\n \"NUP88 nucleoporin 88\\n\",\n \"NUP43 nucleoporin 43\\n\",\n \"ARL8A ADP ribosylation factor like GTPase 8A\\n\",\n \"POLE3 DNA polymerase epsilon 3, accessory subunit\\n\",\n \"ODF2 outer dense fiber of sperm tails 2\\n\",\n \"CHAMP1 chromosome alignment maintaining phosphoprotein 1\\n\",\n \"OPA1 OPA1 mitochondrial dynamin like GTPase\\n\",\n \"SEPTIN7 septin 7\\n\",\n \"ORC1 origin recognition complex subunit 1\\n\",\n \"ORC2 origin recognition complex subunit 2\\n\",\n \"ORC4 origin recognition complex subunit 4\\n\",\n \"ORC5 origin recognition complex subunit 5\\n\",\n \"OVOL1 ovo like transcriptional repressor 1\\n\",\n \"P2RX1 purinergic receptor P2X 1\\n\",\n \"PAFAH1B1 platelet activating factor acetylhydrolase 1b regulatory subunit 1\\n\",\n \"UHMK1 U2AF homology motif kinase 1\\n\",\n \"SERPINE1 serpin family E member 1\\n\",\n \"CYCS cytochrome c, somatic\\n\",\n \"PAX6 paired box 6\\n\",\n \"PBX1 PBX homeobox 1\\n\",\n \"PCM1 pericentriolar material 1\\n\",\n \"PCNA proliferating cell nuclear antigen\\n\",\n \"PCNT pericentrin\\n\",\n \"CHMP1A charged multivesicular body protein 1A\\n\",\n \"CDK16 cyclin dependent kinase 16\\n\",\n \"CDK17 cyclin dependent kinase 17\\n\",\n \"CDK18 cyclin dependent kinase 18\\n\",\n \"PDCD2 programmed cell death 2\\n\",\n \"PDE3A phosphodiesterase 3A\\n\",\n \"DCDC1 doublecortin domain containing 1\\n\",\n \"REC114 REC114 meiotic recombination protein\\n\",\n \"CDK8 cyclin dependent kinase 8\\n\",\n \"PDGFB platelet derived growth factor subunit B\\n\",\n \"PDGFRB platelet derived growth factor receptor beta\\n\",\n \"PDK2 pyruvate dehydrogenase kinase 2\\n\",\n \"TMOD3 tropomodulin 3\\n\",\n \"E2F7 E2F transcription factor 7\\n\",\n \"NCAPH2 non-SMC condensin II complex subunit H2\\n\",\n \"WNT4 Wnt family member 4\\n\",\n \"SYCE3 synaptonemal complex central element protein 3\\n\",\n \"CDK14 cyclin dependent kinase 14\\n\",\n \"PGGT1B protein geranylgeranyltransferase type I subunit beta\\n\",\n \"ABCB1 ATP binding cassette subfamily B member 1\\n\",\n \"PNPT1 polyribonucleotide nucleotidyltransferase 1\\n\",\n \"SENP1 SUMO specific peptidase 1\\n\",\n \"MIS12 MIS12 kinetochore complex component\\n\",\n \"FBXL22 F-box and leucine rich repeat protein 22\\n\",\n \"PIK3C3 phosphatidylinositol 3-kinase catalytic subunit type 3\\n\",\n \"ANLN anillin actin binding protein\\n\",\n \"PIM1 Pim-1 proto-oncogene, serine/threonine kinase\\n\",\n \"NUP37 nucleoporin 37\\n\",\n \"ZNF655 zinc finger protein 655\\n\",\n \"PIN1 peptidylprolyl cis/trans isomerase, NIMA-interacting 1\\n\",\n \"MOV10L1 Mov10 like RISC complex RNA helicase 1\\n\",\n \"ANAPC2 anaphase promoting complex subunit 2\\n\",\n \"CNOT7 CCR4-NOT transcription complex subunit 7\\n\",\n \"NABP2 nucleic acid binding protein 2\\n\",\n \"FBXW5 F-box and WD repeat domain containing 5\\n\",\n \"PKD1 polycystin 1, transient receptor potential channel interacting\\n\",\n \"PKD2 polycystin 2, transient receptor potential cation channel\\n\",\n \"ETAA1 ETAA1 activator of ATR kinase\\n\",\n \"SPIN2A spindlin family member 2A\\n\",\n \"ASPM assembly factor for spindle microtubules\\n\",\n \"PKHD1 PKHD1 ciliary IPT domain containing fibrocystin/polyductin\\n\",\n \"PSMC3IP PSMC3 interacting protein\\n\",\n \"TERB1 telomere repeat binding bouquet formation protein 1\\n\",\n \"NLE1 notchless homolog 1\\n\",\n \"GPSM2 G protein signaling modulator 2\\n\",\n \"PLAGL1 PLAG1 like zinc finger 1\\n\",\n \"PIMREG PICALM interacting mitotic regulator\\n\",\n \"TMEM109 transmembrane protein 109\\n\",\n \"PLK1 polo like kinase 1\\n\",\n \"DSCC1 DNA replication and sister chromatid cohesion 1\\n\",\n \"PLRG1 pleiotropic regulator 1\\n\",\n \"GPR132 G protein-coupled receptor 132\\n\",\n \"RPA4 replication protein A4\\n\",\n \"IQGAP3 IQ motif containing GTPase activating protein 3\\n\",\n \"DDX4 DEAD-box helicase 4\\n\",\n \"PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1\\n\",\n \"ANAPC4 anaphase promoting complex subunit 4\\n\",\n \"PML PML nuclear body scaffold\\n\",\n \"DNMT3L DNA methyltransferase 3 like\\n\",\n \"SERTAD1 SERTA domain containing 1\\n\",\n \"CDIP1 cell death inducing p53 target 1\\n\",\n \"DDIT4 DNA damage inducible transcript 4\\n\",\n \"DONSON DNA replication fork stabilization factor DONSON\\n\",\n \"SLC2A8 solute carrier family 2 member 8\\n\",\n \"PHF23 PHD finger protein 23\\n\",\n \"NOP53 NOP53 ribosome biogenesis factor\\n\",\n \"POLA1 DNA polymerase alpha 1, catalytic subunit\\n\",\n \"POLD1 DNA polymerase delta 1, catalytic subunit\\n\",\n \"POLD2 DNA polymerase delta 2, accessory subunit\\n\",\n \"POLE DNA polymerase epsilon, catalytic subunit\\n\",\n \"POLE2 DNA polymerase epsilon 2, accessory subunit\\n\",\n \"FBXL15 F-box and leucine rich repeat protein 15\\n\",\n \"RAD9B RAD9 checkpoint clamp component B\\n\",\n \"BRCC3 BRCA1/BRCA2-containing complex subunit 3\\n\",\n \"POU4F1 POU class 4 homeobox 1\\n\",\n \"POU4F2 POU class 4 homeobox 2\\n\",\n \"FSD1 fibronectin type III and SPRY domain containing 1\\n\",\n \"INO80 INO80 complex ATPase subunit\\n\",\n \"CCNJ cyclin J\\n\",\n \"PPARG peroxisome proliferator activated receptor gamma\\n\",\n \"MED1 mediator complex subunit 1\\n\",\n \"PAF1 PAF1 homolog, Paf1/RNA polymerase II complex component\\n\",\n \"PPAT phosphoribosyl pyrophosphate amidotransferase\\n\",\n \"MAP10 microtubule associated protein 10\\n\",\n \"PPM1A protein phosphatase, Mg2+/Mn2+ dependent 1A\\n\",\n \"PPM1G protein phosphatase, Mg2+/Mn2+ dependent 1G\\n\",\n \"PPP1CA protein phosphatase 1 catalytic subunit alpha\\n\",\n \"PPP1CB protein phosphatase 1 catalytic subunit beta\\n\",\n \"PPP1CC protein phosphatase 1 catalytic subunit gamma\\n\",\n \"PPP2CA protein phosphatase 2 catalytic subunit alpha\\n\",\n \"PIM3 Pim-3 proto-oncogene, serine/threonine kinase\\n\",\n \"PPP2R1A protein phosphatase 2 scaffold subunit Aalpha\\n\",\n \"PPP2R2A protein phosphatase 2 regulatory subunit Balpha\\n\",\n \"PPP2R2B protein phosphatase 2 regulatory subunit Bbeta\\n\",\n \"PPP2R2C protein phosphatase 2 regulatory subunit Bgamma\\n\",\n \"PTPA protein phosphatase 2 phosphatase activator\\n\",\n \"PPP2R5B protein phosphatase 2 regulatory subunit B'beta\\n\",\n \"PPP3CA protein phosphatase 3 catalytic subunit alpha\\n\",\n \"PPP3CB protein phosphatase 3 catalytic subunit beta\\n\",\n \"PPP3CC protein phosphatase 3 catalytic subunit gamma\\n\",\n \"PPP3R1 protein phosphatase 3 regulatory subunit B, alpha\\n\",\n \"PPP5C protein phosphatase 5 catalytic subunit\\n\",\n \"PPP6C protein phosphatase 6 catalytic subunit\\n\",\n \"MEIOC meiosis specific with coiled-coil domain\\n\",\n \"PRCC proline rich mitotic checkpoint control factor\\n\",\n \"PRELP proline and arginine rich end leucine rich repeat protein\\n\",\n \"PRIM1 DNA primase subunit 1\\n\",\n \"PRIM2 DNA primase subunit 2\\n\",\n \"PRKAA1 protein kinase AMP-activated catalytic subunit alpha 1\\n\",\n \"PRKAA2 protein kinase AMP-activated catalytic subunit alpha 2\\n\",\n \"PRKAB1 protein kinase AMP-activated non-catalytic subunit beta 1\\n\",\n \"PRKACA protein kinase cAMP-activated catalytic subunit alpha\\n\",\n \"PRKACB protein kinase cAMP-activated catalytic subunit beta\\n\",\n \"CEP41 centrosomal protein 41\\n\",\n \"PKIA cAMP-dependent protein kinase inhibitor alpha\\n\",\n \"PRKAR1A protein kinase cAMP-dependent type I regulatory subunit alpha\\n\",\n \"MPLKIP M-phase specific PLK1 interacting protein\\n\",\n \"PRKAR2B protein kinase cAMP-dependent type II regulatory subunit beta\\n\",\n \"PRKCA protein kinase C alpha\\n\",\n \"PRKCD protein kinase C delta\\n\",\n \"PRKCE protein kinase C epsilon\\n\",\n \"PRKCB protein kinase C beta\\n\",\n \"PKN2 protein kinase N2\\n\",\n \"PRKDC protein kinase, DNA-activated, catalytic subunit\\n\",\n \"MAPK1 mitogen-activated protein kinase 1\\n\",\n \"MAPK3 mitogen-activated protein kinase 3\\n\",\n \"MAPK4 mitogen-activated protein kinase 4\\n\",\n \"MAPK6 mitogen-activated protein kinase 6\\n\",\n \"MAPK7 mitogen-activated protein kinase 7\\n\",\n \"MAPK13 mitogen-activated protein kinase 13\\n\",\n \"MAP2K1 mitogen-activated protein kinase kinase 1\\n\",\n \"MAP2K6 mitogen-activated protein kinase kinase 6\\n\",\n \"BTG4 BTG anti-proliferation factor 4\\n\",\n \"PRNP prion protein\\n\",\n \"PROX1 prospero homeobox 1\\n\",\n \"ALKBH4 alkB homolog 4, lysine demethylase\\n\",\n \"DAB2IP DAB2 interacting protein\\n\",\n \"BHLHE41 basic helix-loop-helix family member e41\\n\",\n \"TET2 tet methylcytosine dioxygenase 2\\n\",\n \"HMGN5 high mobility group nucleosome binding domain 5\\n\",\n \"LGMN legumain\\n\",\n \"HAUS6 HAUS augmin like complex subunit 6\\n\",\n \"KLK10 kallikrein related peptidase 10\\n\",\n \"NDE1 nudE neurodevelopment protein 1\\n\",\n \"ERCC6L ERCC excision repair 6 like, spindle assembly checkpoint helicase\\n\",\n \"SLC38A9 solute carrier family 38 member 9\\n\",\n \"PSMA1 proteasome 20S subunit alpha 1\\n\",\n \"PSMA2 proteasome 20S subunit alpha 2\\n\",\n \"PSMA3 proteasome 20S subunit alpha 3\\n\",\n \"PSMA4 proteasome 20S subunit alpha 4\\n\",\n \"PSMA5 proteasome 20S subunit alpha 5\\n\",\n \"PSMA6 proteasome 20S subunit alpha 6\\n\",\n \"PSMA7 proteasome 20S subunit alpha 7\\n\",\n \"PSMB1 proteasome 20S subunit beta 1\\n\",\n \"PSMB2 proteasome 20S subunit beta 2\\n\",\n \"PSMB3 proteasome 20S subunit beta 3\\n\",\n \"PSMB4 proteasome 20S subunit beta 4\\n\",\n \"PSMB5 proteasome 20S subunit beta 5\\n\",\n \"PSMB6 proteasome 20S subunit beta 6\\n\",\n \"PSMB7 proteasome 20S subunit beta 7\\n\",\n \"PSMB8 proteasome 20S subunit beta 8\\n\",\n \"FBXL12 F-box and leucine rich repeat protein 12\\n\",\n \"PSMB9 proteasome 20S subunit beta 9\\n\",\n \"PSMB10 proteasome 20S subunit beta 10\\n\",\n \"PSMC1 proteasome 26S subunit, ATPase 1\\n\",\n \"PSMC2 proteasome 26S subunit, ATPase 2\\n\",\n \"PSMC3 proteasome 26S subunit, ATPase 3\\n\",\n \"PSMC4 proteasome 26S subunit, ATPase 4\\n\",\n \"PSMC5 proteasome 26S subunit, ATPase 5\\n\",\n \"PSMC6 proteasome 26S subunit, ATPase 6\\n\",\n \"PSMD1 proteasome 26S subunit, non-ATPase 1\\n\",\n \"PSMD2 proteasome 26S subunit, non-ATPase 2\\n\",\n \"PSMD3 proteasome 26S subunit, non-ATPase 3\\n\",\n \"PSMD4 proteasome 26S subunit, non-ATPase 4\\n\",\n \"PSMD5 proteasome 26S subunit, non-ATPase 5\\n\",\n \"HAUS3 HAUS augmin like complex subunit 3\\n\",\n \"PSMD7 proteasome 26S subunit, non-ATPase 7\\n\",\n \"PSMD8 proteasome 26S subunit, non-ATPase 8\\n\",\n \"PSMD10 proteasome 26S subunit, non-ATPase 10\\n\",\n \"PSMD11 proteasome 26S subunit, non-ATPase 11\\n\",\n \"PSMD9 proteasome 26S subunit, non-ATPase 9\\n\",\n \"PSMD13 proteasome 26S subunit, non-ATPase 13\\n\",\n \"PAGR1 PAXIP1 associated glutamate rich protein 1\\n\",\n \"PSME1 proteasome activator subunit 1\\n\",\n \"PSME2 proteasome activator subunit 2\\n\",\n \"PSMD12 proteasome 26S subunit, non-ATPase 12\\n\",\n \"CNTLN centlein\\n\",\n \"PTCH1 patched 1\\n\",\n \"PTEN phosphatase and tensin homolog\\n\",\n \"BOD1L2 biorientation of chromosomes in cell division 1 like 2\\n\",\n \"NSUN2 NOP2/Sun RNA methyltransferase 2\\n\",\n \"NCAPG2 non-SMC condensin II complex subunit G2\\n\",\n \"KLLN killin, p53 regulated DNA replication inhibitor\\n\",\n \"PTGS2 prostaglandin-endoperoxide synthase 2\\n\",\n \"TTC19 tetratricopeptide repeat domain 19\\n\",\n \"PTK6 protein tyrosine kinase 6\\n\",\n \"SPDL1 spindle apparatus coiled-coil protein 1\\n\",\n \"CHTF8 chromosome transmission fidelity factor 8\\n\",\n \"PTPN3 protein tyrosine phosphatase non-receptor type 3\\n\",\n \"PTPN6 protein tyrosine phosphatase non-receptor type 6\\n\",\n \"HAUS4 HAUS augmin like complex subunit 4\\n\",\n \"PTPN11 protein tyrosine phosphatase non-receptor type 11\\n\",\n \"CEP120 centrosomal protein 120\\n\",\n \"PTPRC protein tyrosine phosphatase receptor type C\\n\",\n \"PTPRK protein tyrosine phosphatase receptor type K\\n\",\n \"TXNL4B thioredoxin like 4B\\n\",\n \"RAD1 RAD1 checkpoint DNA exonuclease\\n\",\n \"TIPIN TIMELESS interacting protein\\n\",\n \"OR2A4 olfactory receptor family 2 subfamily A member 4\\n\",\n \"BANP BTG3 associated nuclear protein\\n\",\n \"MAMSTR MEF2 activating motif and SAP domain containing transcriptional regulator\\n\",\n \"PINX1 PIN2 (TERF1) interacting telomerase inhibitor 1\\n\",\n \"AURKAIP1 aurora kinase A interacting protein 1\\n\",\n \"CDC73 cell division cycle 73\\n\",\n \"LAMTOR1 late endosomal/lysosomal adaptor, MAPK and MTOR activator 1\\n\",\n \"MUL1 mitochondrial E3 ubiquitin protein ligase 1\\n\",\n \"CEP97 centrosomal protein 97\\n\",\n \"PHIP pleckstrin homology domain interacting protein\\n\",\n \"WDR62 WD repeat domain 62\\n\",\n \"USP47 ubiquitin specific peptidase 47\\n\",\n \"RAD9A RAD9 checkpoint clamp component A\\n\",\n \"RAD17 RAD17 checkpoint clamp loader component\\n\",\n \"RAD21 RAD21 cohesin complex component\\n\",\n \"RAD23A RAD23 homolog A, nucleotide excision repair protein\\n\",\n \"RAD51 RAD51 recombinase\\n\",\n \"RAD51C RAD51 paralog C\\n\",\n \"CCNJL cyclin J like\\n\",\n \"RAD51B RAD51 paralog B\\n\",\n \"RAD51D RAD51 paralog D\\n\",\n \"RAD52 RAD52 homolog, DNA repair protein\\n\",\n \"RNASEH2B ribonuclease H2 subunit B\\n\",\n \"MOK MOK protein kinase\\n\",\n \"RALA RAS like proto-oncogene A\\n\",\n \"RALB RAS like proto-oncogene B\\n\",\n \"RAN RAN, member RAS oncogene family\\n\",\n \"RANBP1 RAN binding protein 1\\n\",\n \"ZWILCH zwilch kinetochore protein\\n\",\n \"EGLN3 egl-9 family hypoxia inducible factor 3\\n\",\n \"RARA retinoic acid receptor alpha\\n\",\n \"CHMP6 charged multivesicular body protein 6\\n\",\n \"ASZ1 ankyrin repeat, SAM and basic leucine zipper domain containing 1\\n\",\n \"MCPH1 microcephalin 1\\n\",\n \"RASA1 RAS p21 protein activator 1\\n\",\n \"RB1 RB transcriptional corepressor 1\\n\",\n \"RBBP4 RB binding protein 4, chromatin remodeling factor\\n\",\n \"IFT57 intraflagellar transport 57\\n\",\n \"RBBP7 RB binding protein 7, chromatin remodeling factor\\n\",\n \"RBBP8 RB binding protein 8, endonuclease\\n\",\n \"RBL1 RB transcriptional corepressor like 1\\n\",\n \"RBL2 RB transcriptional corepressor like 2\\n\",\n \"SMC6 structural maintenance of chromosomes 6\\n\",\n \"OPN1LW opsin 1, long wave sensitive\\n\",\n \"RDX radixin\\n\",\n \"MZT1 mitotic spindle organizing protein 1\\n\",\n \"PIWIL2 piwi like RNA-mediated gene silencing 2\\n\",\n \"CEP192 centrosomal protein 192\\n\",\n \"C11orf80 chromosome 11 open reading frame 80\\n\",\n \"UPF1 UPF1 RNA helicase and ATPase\\n\",\n \"RFC1 replication factor C subunit 1\\n\",\n \"RFC2 replication factor C subunit 2\\n\",\n \"RFC3 replication factor C subunit 3\\n\",\n \"RFC4 replication factor C subunit 4\\n\",\n \"FIGN fidgetin, microtubule severing factor\\n\",\n \"CHMP4B charged multivesicular body protein 4B\\n\",\n \"RFC5 replication factor C subunit 5\\n\",\n \"RFPL1 ret finger protein like 1\\n\",\n \"HAUS2 HAUS augmin like complex subunit 2\\n\",\n \"CDCA8 cell division cycle associated 8\\n\",\n \"THAP1 THAP domain containing 1\\n\",\n \"SUV39H2 suppressor of variegation 3-9 homolog 2\\n\",\n \"RGS2 regulator of G protein signaling 2\\n\",\n \"MSTO1 misato mitochondrial distribution and morphology regulator 1\\n\",\n \"E2F8 E2F transcription factor 8\\n\",\n \"RFWD3 ring finger and WD repeat domain 3\\n\",\n \"RHEB Ras homolog, mTORC1 binding\\n\",\n \"CEP55 centrosomal protein 55\\n\",\n \"RING1 ring finger protein 1\\n\",\n \"PRMT6 protein arginine methyltransferase 6\\n\",\n \"RIF1 replication timing regulatory factor 1\\n\",\n \"PBRM1 polybromo 1\\n\",\n \"RNF2 ring finger protein 2\\n\",\n \"APPL2 adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 2\\n\",\n \"RNF4 ring finger protein 4\\n\",\n \"ARL8B ADP ribosylation factor like GTPase 8B\\n\",\n \"RCBTB1 RCC1 and BTB domain containing protein 1\\n\",\n \"FANCI FA complementation group I\\n\",\n \"FBXO31 F-box protein 31\\n\",\n \"SNX18 sorting nexin 18\\n\",\n \"EHMT1 euchromatic histone lysine methyltransferase 1\\n\",\n \"STEAP3 STEAP3 metalloreductase\\n\",\n \"ROBO1 roundabout guidance receptor 1\\n\",\n \"ROCK1 Rho associated coiled-coil containing protein kinase 1\\n\",\n \"SPRED1 sprouty related EVH1 domain containing 1\\n\",\n \"YY1AP1 YY1 associated protein 1\\n\",\n \"EIF2AK4 eukaryotic translation initiation factor 2 alpha kinase 4\\n\",\n \"KDM8 lysine demethylase 8\\n\",\n \"RPA1 replication protein A1\\n\",\n \"RPA2 replication protein A2\\n\",\n \"RPA3 replication protein A3\\n\",\n \"CSPP1 centrosome and spindle pole associated protein 1\\n\",\n \"NUDT15 nudix hydrolase 15\\n\",\n \"NEK11 NIMA related kinase 11\\n\",\n \"TUBAL3 tubulin alpha like 3\\n\",\n \"BORA BORA aurora kinase A activator\\n\",\n \"FBXW7 F-box and WD repeat domain containing 7\\n\",\n \"CCNI2 cyclin I family member 2\\n\",\n \"RPL24 ribosomal protein L24\\n\",\n \"RPL26 ribosomal protein L26\\n\",\n \"MAP9 microtubule associated protein 9\\n\",\n \"SMIM22 small integral membrane protein 22\\n\",\n \"MCMBP minichromosome maintenance complex binding protein\\n\",\n \"MIS18BP1 MIS18 binding protein 1\\n\",\n \"MNS1 meiosis specific nuclear structural 1\\n\",\n \"EXD1 exonuclease 3'-5' domain containing 1\\n\",\n \"FBXL8 F-box and leucine rich repeat protein 8\\n\",\n \"ATAD5 ATPase family AAA domain containing 5\\n\",\n \"RPS6 ribosomal protein S6\\n\",\n \"RPS6KA1 ribosomal protein S6 kinase A1\\n\",\n \"RPS6KA2 ribosomal protein S6 kinase A2\\n\",\n \"RPS6KA3 ribosomal protein S6 kinase A3\\n\",\n \"RPS6KB1 ribosomal protein S6 kinase B1\\n\",\n \"HJURP Holliday junction recognition protein\\n\",\n \"CCNP cyclin P\\n\",\n \"RPS15A ribosomal protein S15a\\n\",\n \"PIDD1 p53-induced death domain protein 1\\n\",\n \"TUBA3E tubulin alpha 3e\\n\",\n \"CEP76 centrosomal protein 76\\n\",\n \"JADE1 jade family PHD finger 1\\n\",\n \"MCM10 minichromosome maintenance 10 replication initiation factor\\n\",\n \"RRM1 ribonucleotide reductase catalytic subunit M1\\n\",\n \"RRM2 ribonucleotide reductase regulatory subunit M2\\n\",\n \"RTKN rhotekin\\n\",\n \"WDR76 WD repeat domain 76\\n\",\n \"CLIP1 CAP-Gly domain containing linker protein 1\\n\",\n \"DSN1 DSN1 component of MIS12 kinetochore complex\\n\",\n \"SPRED3 sprouty related EVH1 domain containing 3\\n\",\n \"RYR1 ryanodine receptor 1\\n\",\n \"EHD2 EH domain containing 2\\n\",\n \"ANKRD53 ankyrin repeat domain 53\\n\",\n \"SORT1 sortilin 1\\n\",\n \"PIK3R4 phosphoinositide-3-kinase regulatory subunit 4\\n\",\n \"S100A8 S100 calcium binding protein A8\\n\",\n \"S100A9 S100 calcium binding protein A9\\n\",\n \"PHC3 polyhomeotic homolog 3\\n\",\n \"STRADB STE20 related adaptor beta\\n\",\n \"CAMK2N1 calcium/calmodulin dependent protein kinase II inhibitor 1\\n\",\n \"FBXL18 F-box and leucine rich repeat protein 18\\n\",\n \"MAPK12 mitogen-activated protein kinase 12\\n\",\n \"CCL2 C-C motif chemokine ligand 2\\n\",\n \"CCL8 C-C motif chemokine ligand 8\\n\",\n \"TUBA4B tubulin alpha 4b\\n\",\n \"SMPD3 sphingomyelin phosphodiesterase 3\\n\",\n \"TRIM36 tripartite motif containing 36\\n\",\n \"TERB2 telomere repeat binding bouquet formation protein 2\\n\",\n \"TUBB8B tubulin beta 8B\\n\",\n \"SDCBP syndecan binding protein\\n\",\n \"RBM38 RNA binding motif protein 38\\n\",\n \"VCPIP1 valosin containing protein interacting protein 1\\n\",\n \"HAUS7 HAUS augmin like complex subunit 7\\n\",\n \"ZNF703 zinc finger protein 703\\n\",\n \"CNOT11 CCR4-NOT transcription complex subunit 11\\n\",\n \"SETMAR SET domain and mariner transposase fusion gene\\n\",\n \"SFPQ splicing factor proline and glutamine rich\\n\",\n \"SFRP1 secreted frizzled related protein 1\\n\",\n \"CENPT centromere protein T\\n\",\n \"ATF5 activating transcription factor 5\\n\",\n \"SRSF5 serine and arginine rich splicing factor 5\\n\",\n \"PHLDA1 pleckstrin homology like domain family A member 1\\n\",\n \"CTC1 CST telomere replication complex component 1\\n\",\n \"DBF4B DBF4 zinc finger B\\n\",\n \"MYO19 myosin XIX\\n\",\n \"CEP290 centrosomal protein 290\\n\",\n \"TUBB tubulin beta class I\\n\",\n \"VASH1 vasohibin 1\\n\",\n \"PRR5 proline rich 5\\n\",\n \"C14orf39 chromosome 14 open reading frame 39\\n\",\n \"SHH sonic hedgehog signaling molecule\\n\",\n \"MUS81 MUS81 structure-specific endonuclease subunit\\n\",\n \"SHOX2 short stature homeobox 2\\n\",\n \"SIAH1 siah E3 ubiquitin protein ligase 1\\n\",\n \"SIAH2 siah E3 ubiquitin protein ligase 2\\n\",\n \"NLRP1 NLR family pyrin domain containing 1\\n\",\n \"NAA50 N-alpha-acetyltransferase 50, NatE catalytic subunit\\n\",\n \"STIL STIL centriolar assembly protein\\n\",\n \"SIPA1 signal-induced proliferation-associated 1\\n\",\n \"SIX1 SIX homeobox 1\\n\",\n \"SIX3 SIX homeobox 3\\n\",\n \"SKI SKI proto-oncogene\\n\",\n \"SKIL SKI like proto-oncogene\\n\",\n \"SKP1 S-phase kinase associated protein 1\\n\",\n \"SKP2 S-phase kinase associated protein 2\\n\",\n \"KHDC1L KH domain containing 1 like\\n\",\n \"PRPF40A pre-mRNA processing factor 40 homolog A\\n\",\n \"URGCP upregulator of cell proliferation\\n\",\n \"CEP164 centrosomal protein 164\\n\",\n \"CARD8 caspase recruitment domain family member 8\\n\",\n \"CEP63 centrosomal protein 63\\n\",\n \"SLC6A4 solute carrier family 6 member 4\\n\",\n \"MAPRE1 microtubule associated protein RP/EB family member 1\\n\",\n \"MAPRE3 microtubule associated protein RP/EB family member 3\\n\",\n \"PLA2R1 phospholipase A2 receptor 1\\n\",\n \"SIRT2 sirtuin 2\\n\",\n \"YJU2 YJU2 splicing factor homolog\\n\",\n \"CDK5RAP3 CDK5 regulatory subunit associated protein 3\\n\",\n \"NDC1 NDC1 transmembrane nucleoporin\\n\",\n \"SNW1 SNW domain containing 1\\n\",\n \"SLC16A1 solute carrier family 16 member 1\\n\",\n \"SLF2 SMC5-SMC6 complex localization factor 2\\n\",\n \"P2RX2 purinergic receptor P2X 2\\n\",\n \"TRIM32 tripartite motif containing 32\\n\",\n \"CEP72 centrosomal protein 72\\n\",\n \"INTS13 integrator complex subunit 13\\n\",\n \"CHTF18 chromosome transmission fidelity factor 18\\n\",\n \"EPC1 enhancer of polycomb homolog 1\\n\",\n \"TPX2 TPX2 microtubule nucleation factor\\n\",\n \"SNAI2 snail family transcriptional repressor 2\\n\",\n \"CHFR checkpoint with forkhead and ring finger domains\\n\",\n \"CEP70 centrosomal protein 70\\n\",\n \"PAXIP1 PAX interacting protein 1\\n\",\n \"CCAR1 cell division cycle and apoptosis regulator 1\\n\",\n \"SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1\\n\",\n \"NINL ninein like\\n\",\n \"DMRTC2 DMRT like family C2\\n\",\n \"CDK5RAP2 CDK5 regulatory subunit associated protein 2\\n\",\n \"CEP295NL CEP295 N-terminal like\\n\",\n \"SMARCD3 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 3\\n\",\n \"SMO smoothened, frizzled class receptor\\n\",\n \"CEP131 centrosomal protein 131\\n\",\n \"CEP152 centrosomal protein 152\\n\",\n \"SNAI1 snail family transcriptional repressor 1\\n\",\n \"REEP4 receptor accessory protein 4\\n\",\n \"PRR11 proline rich 11\\n\",\n \"SNCA synuclein alpha\\n\",\n \"CLSPN claspin\\n\",\n \"KIF13A kinesin family member 13A\\n\",\n \"RIOK2 RIO kinase 2\\n\",\n \"CDCA5 cell division cycle associated 5\\n\",\n \"CNOT1 CCR4-NOT transcription complex subunit 1\\n\",\n \"TXLNG taxilin gamma\\n\",\n \"FIGNL1 fidgetin like 1\\n\",\n \"MYO16 myosin XVI\\n\",\n \"PCID2 PCI domain containing 2\\n\",\n \"KDM1A lysine demethylase 1A\\n\",\n \"PIWIL3 piwi like RNA-mediated gene silencing 3\\n\",\n \"SOD1 superoxide dismutase 1\\n\",\n \"SON SON DNA and RNA binding protein\\n\",\n \"MITD1 microtubule interacting and trafficking domain containing 1\\n\",\n \"SOX2 SRY-box transcription factor 2\\n\",\n \"SOX4 SRY-box transcription factor 4\\n\",\n \"SOX9 SRY-box transcription factor 9\\n\",\n \"PDS5B PDS5 cohesin associated factor B\\n\",\n \"SOX11 SRY-box transcription factor 11\\n\",\n \"SOX15 SRY-box transcription factor 15\\n\",\n \"SP100 SP100 nuclear antigen\\n\",\n \"WAPL WAPL cohesin release factor\\n\",\n \"CENPJ centromere protein J\\n\",\n \"DIS3L2 DIS3 like 3'-5' exoribonuclease 2\\n\",\n \"SPAST spastin\\n\",\n \"PPP2R2D protein phosphatase 2 regulatory subunit Bdelta\\n\",\n \"TOPAZ1 testis and ovary specific PAZ domain containing 1\\n\",\n \"TRIM35 tripartite motif containing 35\\n\",\n \"ACTR10 actin related protein 10\\n\",\n \"SPTBN1 spectrin beta, non-erythrocytic 1\\n\",\n \"CDK19 cyclin dependent kinase 19\\n\",\n \"SRC SRC proto-oncogene, non-receptor tyrosine kinase\\n\",\n \"TSPYL2 TSPY like 2\\n\",\n \"HDAC8 histone deacetylase 8\\n\",\n \"PBK PDZ binding kinase\\n\",\n \"PERP p53 apoptosis effector related to PMP22\\n\",\n \"SRF serum response factor\\n\",\n \"CUL9 cullin 9\\n\",\n \"SRPK2 SRSF protein kinase 2\\n\",\n \"TRIM21 tripartite motif containing 21\\n\",\n \"CLASP2 cytoplasmic linker associated protein 2\\n\",\n \"POGZ pogo transposable element derived with ZNF domain\\n\",\n \"STOX1 storkhead box 1\\n\",\n \"PHF8 PHD finger protein 8\\n\",\n \"SMOC2 SPARC related modular calcium binding 2\\n\",\n \"KMT2E lysine methyltransferase 2E (inactive)\\n\",\n \"SMC5 structural maintenance of chromosomes 5\\n\",\n \"NEDD1 NEDD1 gamma-tubulin ring complex targeting factor\\n\",\n \"SSTR5 somatostatin receptor 5\\n\",\n \"BIN3 bridging integrator 3\\n\",\n \"ANKLE2 ankyrin repeat and LEM domain containing 2\\n\",\n \"CENPK centromere protein K\\n\",\n \"RCC2 regulator of chromosome condensation 2\\n\",\n \"SEPTIN6 septin 6\\n\",\n \"STAT3 signal transducer and activator of transcription 3\\n\",\n \"STAT5B signal transducer and activator of transcription 5B\\n\",\n \"RRAGC Ras related GTP binding C\\n\",\n \"CCNY cyclin Y\\n\",\n \"GJD4 gap junction protein delta 4\\n\",\n \"TTLL12 tubulin tyrosine ligase like 12\\n\",\n \"NEK4 NIMA related kinase 4\\n\",\n \"ABRAXAS2 abraxas 2, BRISC complex subunit\\n\",\n \"AURKA aurora kinase A\\n\",\n \"LPIN1 lipin 1\\n\",\n \"CDKL5 cyclin dependent kinase like 5\\n\",\n \"CEP68 centrosomal protein 68\\n\",\n \"STK10 serine/threonine kinase 10\\n\",\n \"AURKC aurora kinase C\\n\",\n \"STK11 serine/threonine kinase 11\\n\",\n \"LIN37 lin-37 DREAM MuvB core complex component\\n\",\n \"KLHL9 kelch like family member 9\\n\",\n \"NCAPG non-SMC condensin I complex subunit G\\n\",\n \"FBXL7 F-box and leucine rich repeat protein 7\\n\",\n \"UBXN2B UBX domain protein 2B\\n\",\n \"PSME4 proteasome activator subunit 4\\n\",\n \"NSFL1C NSFL1 cofactor\\n\",\n \"SULT1A3 sulfotransferase family 1A member 3\\n\",\n \"RRS1 ribosome biogenesis regulator 1 homolog\\n\",\n \"DPEP3 dipeptidase 3\\n\",\n \"SUV39H1 suppressor of variegation 3-9 homolog 1\\n\",\n \"SVIL supervillin\\n\",\n \"SYCP1 synaptonemal complex protein 1\\n\",\n \"PLCB1 phospholipase C beta 1\\n\",\n \"PDS5A PDS5 cohesin associated factor A\\n\",\n \"BOP1 BOP1 ribosomal biogenesis factor\\n\",\n \"TACC1 transforming acidic coiled-coil containing protein 1\\n\",\n \"ADAM17 ADAM metallopeptidase domain 17\\n\",\n \"TAF1 TATA-box binding protein associated factor 1\\n\",\n \"TAF2 TATA-box binding protein associated factor 2\\n\",\n \"TAF6 TATA-box binding protein associated factor 6\\n\",\n \"MLST8 MTOR associated protein, LST8 homolog\\n\",\n \"TAF10 TATA-box binding protein associated factor 10\\n\",\n \"MGA MAX dimerization protein MGA\\n\",\n \"TAL1 TAL bHLH transcription factor 1, erythroid differentiation factor\\n\",\n \"KLHL18 kelch like family member 18\\n\",\n \"TBX1 T-box transcription factor 1\\n\",\n \"TBCD tubulin folding cofactor D\\n\",\n \"TBCE tubulin folding cofactor E\\n\",\n \"FBXW11 F-box and WD repeat domain containing 11\\n\",\n \"TBX2 T-box transcription factor 2\\n\",\n \"UBR2 ubiquitin protein ligase E3 component n-recognin 2\\n\",\n \"TBX3 T-box transcription factor 3\\n\",\n \"NCAPD3 non-SMC condensin II complex subunit D3\\n\",\n \"TCF7L2 transcription factor 7 like 2\\n\",\n \"TENT4B terminal nucleotidyltransferase 4B\\n\",\n \"USP22 ubiquitin specific peptidase 22\\n\",\n \"TTC28 tetratricopeptide repeat domain 28\\n\",\n \"CLASP1 cytoplasmic linker associated protein 1\\n\",\n \"SUN1 Sad1 and UNC84 domain containing 1\\n\",\n \"HAUS5 HAUS augmin like complex subunit 5\\n\",\n \"RNF212 ring finger protein 212\\n\",\n \"OBSL1 obscurin like cytoskeletal adaptor 1\\n\",\n \"PPP1R13B protein phosphatase 1 regulatory subunit 13B\\n\",\n \"ESX1 ESX homeobox 1\\n\",\n \"DYNLT3 dynein light chain Tctex-type 3\\n\",\n \"DYNLT1 dynein light chain Tctex-type 1\\n\",\n \"RRP8 ribosomal RNA processing 8\\n\",\n \"AZI2 5-azacytidine induced 2\\n\",\n \"SPECC1L sperm antigen with calponin homology and coiled-coil domains 1 like\\n\",\n \"MAU2 MAU2 sister chromatid cohesion factor\\n\",\n \"TEX15 testis expressed 15, meiosis and synapsis associated\\n\",\n \"TEX14 testis expressed 14, intercellular bridge forming factor\\n\",\n \"TEX12 testis expressed 12\\n\",\n \"TEX11 testis expressed 11\\n\",\n \"TEP1 telomerase associated protein 1\\n\",\n \"NCAPH non-SMC condensin I complex subunit H\\n\",\n \"TERF2 telomeric repeat binding factor 2\\n\",\n \"TDRD1 tudor domain containing 1\\n\",\n \"TERT telomerase reverse transcriptase\\n\",\n \"TERF1 telomeric repeat binding factor 1\\n\",\n \"CTDNEP1 CTD nuclear envelope phosphatase 1\\n\",\n \"TFAP4 transcription factor AP-4\\n\",\n \"NR2F2 nuclear receptor subfamily 2 group F member 2\\n\",\n \"TFDP1 transcription factor Dp-1\\n\",\n \"SIRT1 sirtuin 1\\n\",\n \"TFDP2 transcription factor Dp-2\\n\",\n \"KHDC1 KH domain containing 1\\n\",\n \"ITGB3BP integrin subunit beta 3 binding protein\\n\",\n \"TGFA transforming growth factor alpha\\n\",\n \"TGFB1 transforming growth factor beta 1\\n\",\n \"TGFB2 transforming growth factor beta 2\\n\",\n \"RYBP RING1 and YY1 binding protein\\n\",\n \"TGFBR1 transforming growth factor beta receptor 1\\n\",\n \"DDIAS DNA damage induced apoptosis suppressor\\n\",\n \"TGM1 transglutaminase 1\\n\",\n \"LMOD3 leiomodin 3\\n\",\n \"TARDBP TAR DNA binding protein\\n\",\n \"THBS1 thrombospondin 1\\n\",\n \"TIMP2 TIMP metallopeptidase inhibitor 2\\n\",\n \"NACC2 NACC family member 2\\n\",\n \"CBX5 chromobox 5\\n\",\n \"SUSD2 sushi domain containing 2\\n\",\n \"BRD4 bromodomain containing 4\\n\",\n \"PES1 pescadillo ribosomal biogenesis factor 1\\n\",\n \"NR2E1 nuclear receptor subfamily 2 group E member 1\\n\",\n \"MEPCE methylphosphate capping enzyme\\n\",\n \"TAS1R2 taste 1 receptor member 2\\n\",\n \"NANOS3 nanos C2HC-type zinc finger 3\\n\",\n \"KIF4B kinesin family member 4B\\n\",\n \"ZFYVE26 zinc finger FYVE-type containing 26\\n\",\n \"TNF tumor necrosis factor\\n\",\n \"TNFAIP3 TNF alpha induced protein 3\\n\",\n \"SCRIB scribble planar cell polarity protein\\n\",\n \"SUZ12 SUZ12 polycomb repressive complex 2 subunit\\n\",\n \"MORC3 MORC family CW-type zinc finger 3\\n\",\n \"PARD3 par-3 family cell polarity regulator\\n\",\n \"SKA1 spindle and kinetochore associated complex subunit 1\\n\",\n \"TOP2A DNA topoisomerase II alpha\\n\",\n \"TOP2B DNA topoisomerase II beta\\n\",\n \"TOP3A DNA topoisomerase III alpha\\n\",\n \"TP53 tumor protein p53\\n\",\n \"TP53BP1 tumor protein p53 binding protein 1\\n\",\n \"TP53BP2 tumor protein p53 binding protein 2\\n\",\n \"TP73 tumor protein p73\\n\",\n \"TPD52L1 TPD52 like 1\\n\",\n \"CDK20 cyclin dependent kinase 20\\n\",\n \"TPR translocated promoter region, nuclear basket protein\\n\",\n \"GTPBP4 GTP binding protein 4\\n\",\n \"TRAF2 TNF receptor associated factor 2\\n\",\n \"METTL3 methyltransferase like 3\\n\",\n \"CASP14 caspase 14\\n\",\n \"CCNDBP1 cyclin D1 binding protein 1\\n\",\n \"ORC6 origin recognition complex subunit 6\\n\",\n \"ORC3 origin recognition complex subunit 3\\n\",\n \"CD2AP CD2 associated protein\\n\",\n \"STXBP4 syntaxin binding protein 4\\n\",\n \"PHLDA3 pleckstrin homology like domain family A member 3\\n\",\n \"SPO11 SPO11 initiator of meiotic double stranded breaks\\n\",\n \"TSC1 TSC complex subunit 1\\n\",\n \"TSC2 TSC complex subunit 2\\n\",\n \"TSG101 tumor susceptibility 101\\n\",\n \"NUP62 nucleoporin 62\\n\",\n \"HEPACAM2 HEPACAM family member 2\\n\",\n \"RABGAP1 RAB GTPase activating protein 1\\n\",\n \"PPP1R15A protein phosphatase 1 regulatory subunit 15A\\n\",\n \"POLA2 DNA polymerase alpha 2, accessory subunit\\n\",\n \"TTK TTK protein kinase\\n\",\n \"TTN titin\\n\",\n \"TUBA4A tubulin alpha 4a\\n\",\n \"TUBB2A tubulin beta 2A class IIa\\n\",\n \"TUBG1 tubulin gamma 1\\n\",\n \"CASP12 caspase 12 (gene/pseudogene)\\n\",\n \"HIRA histone cell cycle regulator\\n\",\n \"TWIST1 twist family bHLH transcription factor 1\\n\",\n \"TYMS thymidylate synthetase\\n\",\n \"TUBB1 tubulin beta 1 class VI\\n\",\n \"HEPACAM hepatic and glial cell adhesion molecule\\n\",\n \"UBB ubiquitin B\\n\",\n \"UBE2B ubiquitin conjugating enzyme E2 B\\n\",\n \"UBE2D1 ubiquitin conjugating enzyme E2 D1\\n\",\n \"RPRM reprimo, TP53 dependent G2 arrest mediator homolog\\n\",\n \"GSPT2 G1 to S phase transition 2\\n\",\n \"UBE2E2 ubiquitin conjugating enzyme E2 E2\\n\",\n \"UBE2E1 ubiquitin conjugating enzyme E2 E1\\n\",\n \"UBE2I ubiquitin conjugating enzyme E2 I\\n\",\n \"UBE2L3 ubiquitin conjugating enzyme E2 L3\\n\",\n \"ANAPC1 anaphase promoting complex subunit 1\\n\",\n \"EID1 EP300 interacting inhibitor of differentiation 1\\n\",\n \"SPIN2B spindlin family member 2B\\n\",\n \"BCL2L13 BCL2 like 13\\n\",\n \"MTCH1 mitochondrial carrier 1\\n\",\n \"TAF1L TATA-box binding protein associated factor 1 like\\n\",\n \"UVRAG UV radiation resistance associated\\n\",\n \"SMYD3 SET and MYND domain containing 3\\n\",\n \"TEX19 testis expressed 19\\n\",\n \"VCP valosin containing protein\\n\",\n \"TOM1L2 target of myb1 like 2 membrane trafficking protein\\n\",\n \"AEN apoptosis enhancing nuclease\\n\",\n \"VRK1 VRK serine/threonine kinase 1\\n\",\n \"CEP85 centrosomal protein 85\\n\",\n \"CYP26B1 cytochrome P450 family 26 subfamily B member 1\\n\",\n \"DIABLO diablo IAP-binding mitochondrial protein\\n\",\n \"WEE1 WEE1 G2 checkpoint kinase\\n\",\n \"ASAH2 N-acylsphingosine amidohydrolase 2\\n\",\n \"WNT5A Wnt family member 5A\\n\",\n \"WNT10B Wnt family member 10B\\n\",\n \"WNT9A Wnt family member 9A\\n\",\n \"WRN WRN RecQ like helicase\\n\",\n \"XBP1 X-box binding protein 1\\n\",\n \"BCCIP BRCA2 and CDKN1A interacting protein\\n\",\n \"XDH xanthine dehydrogenase\\n\",\n \"MARCHF7 membrane associated ring-CH-type finger 7\\n\",\n \"POLE4 DNA polymerase epsilon 4, accessory subunit\\n\",\n \"XK X-linked Kx blood group\\n\",\n \"YTHDC2 YTH domain containing 2\\n\",\n \"XPC XPC complex subunit, DNA damage recognition and repair factor\\n\",\n \"NIBAN2 niban apoptosis regulator 2\\n\",\n \"DCLRE1B DNA cross-link repair 1B\\n\",\n \"XRCC1 X-ray repair cross complementing 1\\n\",\n \"XRCC2 X-ray repair cross complementing 2\\n\",\n \"XRCC3 X-ray repair cross complementing 3\\n\",\n \"NABP1 nucleic acid binding protein 1\\n\",\n \"XPO1 exportin 1\\n\",\n \"NSMCE2 NSE2 (MMS21) homolog, SMC5-SMC6 complex SUMO ligase\\n\",\n \"YWHAE tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon\\n\",\n \"YWHAG tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma\\n\",\n \"OPN1MW2 opsin 1, medium wave sensitive 2\\n\",\n \"ZNF16 zinc finger protein 16\\n\",\n \"FBXO43 F-box protein 43\\n\",\n \"FMN2 formin 2\\n\",\n \"MRPL41 mitochondrial ribosomal protein L41\\n\",\n \"FAM9A family with sequence similarity 9 member A\\n\",\n \"FAM9B family with sequence similarity 9 member B\\n\",\n \"FAM9C family with sequence similarity 9 member C\\n\",\n \"KIF18B kinesin family member 18B\\n\",\n \"EME1 essential meiotic structure-specific endonuclease 1\\n\",\n \"PCGF2 polycomb group ring finger 2\\n\",\n \"ZBTB17 zinc finger and BTB domain containing 17\\n\",\n \"TDRD9 tudor domain containing 9\\n\",\n \"CDK15 cyclin dependent kinase 15\\n\",\n \"RNF112 ring finger protein 112\\n\",\n \"MDM1 Mdm1 nuclear protein\\n\",\n \"TCIM transcriptional and immune response regulator\\n\",\n \"MEIKIN meiotic kinetochore factor\\n\",\n \"CDK11A cyclin dependent kinase 11A\\n\",\n \"KIF4A kinesin family member 4A\\n\",\n \"SPIRE1 spire type actin nucleation factor 1\\n\",\n \"ZNF207 zinc finger protein 207\\n\",\n \"SMARCAD1 SWI/SNF-related, matrix-associated actin-dependent regulator of chromatin, subfamily a, containing DEAD/H box 1\\n\",\n \"ZNF318 zinc finger protein 318\\n\",\n \"TIPRL TOR signaling pathway regulator\\n\",\n \"KCTD11 potassium channel tetramerization domain containing 11\\n\",\n \"INTS3 integrator complex subunit 3\\n\",\n \"SMYD2 SET and MYND domain containing 2\\n\",\n \"PTP4A1 protein tyrosine phosphatase 4A1\\n\",\n \"EVI5 ecotropic viral integration site 5\\n\",\n \"GDPD5 glycerophosphodiester phosphodiesterase domain containing 5\\n\",\n \"PRDM9 PR/SET domain 9\\n\",\n \"PRDM11 PR/SET domain 11\\n\",\n \"EFHC1 EF-hand domain containing 1\\n\",\n \"BTG2 BTG anti-proliferation factor 2\\n\",\n \"PSMG2 proteasome assembly chaperone 2\\n\",\n \"NDEL1 nudE neurodevelopment protein 1 like 1\\n\",\n \"ALMS1 ALMS1 centrosome and basal body associated protein\\n\",\n \"KIF15 kinesin family member 15\\n\",\n \"RSPH1 radial spoke head component 1\\n\",\n \"TUBA1A tubulin alpha 1a\\n\",\n \"LMLN leishmanolysin like peptidase\\n\",\n \"CCNL1 cyclin L1\\n\",\n \"PDXP pyridoxal phosphatase\\n\",\n \"FAM83D family with sequence similarity 83 member D\\n\",\n \"CAB39L calcium binding protein 39 like\\n\",\n \"CDT1 chromatin licensing and DNA replication factor 1\\n\",\n \"SHCBP1L SHC binding and spindle associated 1 like\\n\",\n \"NLRP2B NLR family pyrin domain containing 2B\\n\",\n \"PCBP4 poly(rC) binding protein 4\\n\",\n \"BAG6 BAG cochaperone 6\\n\",\n \"DDX39B DExD-box helicase 39B\\n\",\n \"PCNP PEST proteolytic signal containing nuclear protein\\n\",\n \"CCNL2 cyclin L2\\n\",\n \"FZD3 frizzled class receptor 3\\n\",\n \"CHMP1B charged multivesicular body protein 1B\\n\",\n \"KAT6A lysine acetyltransferase 6A\\n\",\n \"NR4A3 nuclear receptor subfamily 4 group A member 3\\n\",\n \"GJC2 gap junction protein gamma 2\\n\",\n \"PSMB11 proteasome subunit beta 11\\n\",\n \"NUP214 nucleoporin 214\\n\",\n \"ZMIZ1 zinc finger MIZ-type containing 1\\n\",\n \"SAPCD2 suppressor APC domain containing 2\\n\",\n \"SELENON selenoprotein N\\n\",\n \"REEP3 receptor accessory protein 3\\n\",\n \"NLRP3 NLR family pyrin domain containing 3\\n\",\n \"FOSL1 FOS like 1, AP-1 transcription factor subunit\\n\",\n \"CUL5 cullin 5\\n\",\n \"M1AP meiosis 1 associated protein\\n\",\n \"AAAS aladin WD repeat nucleoporin\\n\",\n \"YEATS4 YEATS domain containing 4\\n\",\n \"HMGA2 high mobility group AT-hook 2\\n\",\n \"CDK2AP1 cyclin dependent kinase 2 associated protein 1\\n\",\n \"TMEM14B transmembrane protein 14B\\n\",\n \"MED25 mediator complex subunit 25\\n\",\n \"SASS6 SAS-6 centriolar assembly protein\\n\",\n \"SPDYA speedy/RINGO cell cycle regulator family member A\\n\",\n \"LACRT lacritin\\n\",\n \"SKA3 spindle and kinetochore associated complex subunit 3\\n\",\n \"KAT14 lysine acetyltransferase 14\\n\"\n ]\n }\n ],\n \"source\": [\n \"for geneid in geneids: # geneids associated with cell-cycle\\n\",\n \" nt = GENEID2NT.get(geneid, None)\\n\",\n \" if nt is not None:\\n\",\n \" print(\\\"{Symbol:<10} {desc}\\\".format(\\n\",\n \" Symbol = nt.Symbol, \\n\",\n \" desc = nt.description))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copyright 2016-present, DV Klopfenstein, Haibao Tang. All rights reserved.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"bsd-2-clause"}}},{"rowIdx":244,"cells":{"repo_name":{"kind":"string","value":"AkshanshChahal/BTP"},"path":{"kind":"string","value":"Baseline 2.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"18442"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Establishing a Baseline for the Problem\\n\",\n \"## Using variety of regression algorithms (non linear)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"from math import sqrt\\n\",\n \"\\n\",\n \"import pprint\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"from sklearn import metrics\\n\",\n \"\\n\",\n \"from sklearn.svm import SVR\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Unnamed: 0State_Nameind_districtCrop_YearSeasonCropAreaProductionphosphorusX1X2X3X4
015Andhra Pradeshanantapur1999kharifRice37991.0105082.00.096800.075400.0643.720881.473
116Andhra Pradeshanantapur2000kharifRice39905.0117680.00.0105082.096800.0767.351643.720
217Andhra Pradeshanantapur2001kharifRice32878.095609.00.0117680.0105082.0579.338767.351
318Andhra Pradeshanantapur2002kharifRice29066.066329.00.095609.0117680.0540.070579.338
421Andhra Pradeshanantapur2005kharifRice25008.069972.00.085051.044891.0819.700564.500
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Unnamed: 0 State_Name ind_district Crop_Year Season Crop Area \\\\\\n\",\n \"0 15 Andhra Pradesh anantapur 1999 kharif Rice 37991.0 \\n\",\n \"1 16 Andhra Pradesh anantapur 2000 kharif Rice 39905.0 \\n\",\n \"2 17 Andhra Pradesh anantapur 2001 kharif Rice 32878.0 \\n\",\n \"3 18 Andhra Pradesh anantapur 2002 kharif Rice 29066.0 \\n\",\n \"4 21 Andhra Pradesh anantapur 2005 kharif Rice 25008.0 \\n\",\n \"\\n\",\n \" Production phosphorus X1 X2 X3 X4 \\n\",\n \"0 105082.0 0.0 96800.0 75400.0 643.720 881.473 \\n\",\n \"1 117680.0 0.0 105082.0 96800.0 767.351 643.720 \\n\",\n \"2 95609.0 0.0 117680.0 105082.0 579.338 767.351 \\n\",\n \"3 66329.0 0.0 95609.0 117680.0 540.070 579.338 \\n\",\n \"4 69972.0 0.0 85051.0 44891.0 819.700 564.500 \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# importing the dataset we prepared and saved using Baseline 1 Notebook\\n\",\n \"ricep = pd.read_csv(\\\"/Users/macbook/Documents/BTP/Notebook/BTP/ricep.csv\\\")\\n\",\n \"ricep.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ricep = ricep.drop([\\\"Unnamed: 0\\\"],axis=1)\\n\",\n \"ricep[\\\"phosphorus\\\"] = ricep[\\\"phosphorus\\\"]*10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ricep[\\\"value\\\"] = ricep[\\\"Production\\\"]/ricep[\\\"Area\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X = ricep[[\\\"X1\\\",\\\"X2\\\",\\\"X3\\\",\\\"X4\\\",\\\"phosphorus\\\"]]\\n\",\n \"y = ricep[[\\\"value\\\"]]*1000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" \\n\"\n ]\n }\n ],\n \"source\": [\n \"# Z-Score Normalization OR try using the sklearn internal normalizing by setting mormalize flag = true !!!\\n\",\n \"\\n\",\n \"cols = list(X.columns)\\n\",\n \"for col in cols:\\n\",\n \" col_zscore = col + '_zscore'\\n\",\n \" X[col_zscore] = (X[col] - X[col].mean())/X[col].std(ddof=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
X1_zscoreX2_zscoreX3_zscoreX4_zscorephosphorus_zscore
0-0.285176-0.374714-0.4578000.021735-0.837691
1-0.247120-0.276111-0.198113-0.496827-0.837691
2-0.189232-0.237950-0.593035-0.227176-0.837691
3-0.290648-0.179903-0.675518-0.637250-0.837691
4-0.339162-0.515288-0.088153-0.669613-0.837691
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" X1_zscore X2_zscore X3_zscore X4_zscore phosphorus_zscore\\n\",\n \"0 -0.285176 -0.374714 -0.457800 0.021735 -0.837691\\n\",\n \"1 -0.247120 -0.276111 -0.198113 -0.496827 -0.837691\\n\",\n \"2 -0.189232 -0.237950 -0.593035 -0.227176 -0.837691\\n\",\n \"3 -0.290648 -0.179903 -0.675518 -0.637250 -0.837691\\n\",\n \"4 -0.339162 -0.515288 -0.088153 -0.669613 -0.837691\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_ = X[[\\\"X1_zscore\\\", \\\"X2_zscore\\\", \\\"X3_zscore\\\", \\\"X4_zscore\\\", \\\"phosphorus_zscore\\\"]]\\n\",\n \"X_.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train, X_test, y_train, y_test = train_test_split(X_, y, test_size=0.2, random_state=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### First checking the avg RMSE for Linear Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1030.92314374 1109.37929379 972.36266895 1487.52744177 491.48595541]\\n\",\n \"\\n\",\n \"\\n\",\n \"Avg RMSE is 1018.33570073\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\\n\",\n \" warnings.warn(mesg, RuntimeWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"clf = LinearRegression()\\n\",\n \"scores = cross_val_score(clf, X_, y, cv=5, scoring='neg_mean_squared_error')\\n\",\n \"for i in range(0,5):\\n\",\n \" scores[i] = sqrt(-1*scores[i])\\n\",\n \" \\n\",\n \"print(scores)\\n\",\n \"avg_rmse = scores.mean()\\n\",\n \"print(\\\"\\\\n\\\\nAvg RMSE is \\\",scores.mean())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Epsilon-Support Vector Regression (SVR)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RBF Kernel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 5 Fold CV, to calculate avg RMSE\\n\",\n \"clf = SVR(C=500000.0, epsilon=0.1, kernel='rbf', gamma=0.0008)\\n\",\n \"scores = cross_val_score(clf, X_, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\\n\",\n \"for i in range(0,5):\\n\",\n \" scores[i] = sqrt(-1*scores[i])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 904.09013921 940.99998887 981.97853142 1616.00179024 568.93419484]\\n\",\n \"\\n\",\n \"\\n\",\n \"Avg RMSE is 1002.40092892\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(scores)\\n\",\n \"avg_rmse = scores.mean()\\n\",\n \"print(\\\"\\\\n\\\\nAvg RMSE is \\\",scores.mean())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Just the 4 original features (no soil data)\\n\",\n \"X_old = X[[\\\"X1_zscore\\\", \\\"X2_zscore\\\", \\\"X3_zscore\\\", \\\"X4_zscore\\\"]]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 903.93008696 753.88394413 765.69751566 1574.251674 636.95214188]\\n\",\n \"\\n\",\n \"\\n\",\n \"Avg RMSE is 926.943072526\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 5 Fold CV, to calculate avg RMSE\\n\",\n \"clf = SVR(C=1000.0, epsilon=0.1, kernel='rbf', gamma=0.027)\\n\",\n \"scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\\n\",\n \"for i in range(0,5):\\n\",\n \" scores[i] = sqrt(-1*scores[i])\\n\",\n \"\\n\",\n \"print(scores)\\n\",\n \"avg_rmse = scores.mean()\\n\",\n \"print(\\\"\\\\n\\\\nAvg RMSE is \\\",scores.mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### SVR : 927\\n\",\n \"#### LR : 1018\\n\",\n \"#### SVR (RBF kernel) works better than Linear Regression.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Also, the soil feature, for now, does more harm than good (Phosphorous content)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Lets check the importance of Rain Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Just 2 features (no rain data)\\n\",\n \"X_nr = X[[\\\"X1_zscore\\\", \\\"X2_zscore\\\"]]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1039.57563055 863.77364865 944.40471 1492.31174906 672.96822263]\\n\",\n \"\\n\",\n \"\\n\",\n \"Avg RMSE is 1002.60679218\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 5 Fold CV, to calculate avg RMSE\\n\",\n \"clf = SVR(C=1000.0, epsilon=0.1, kernel='rbf', gamma=0.027)\\n\",\n \"scores = cross_val_score(clf, X_nr, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\\n\",\n \"for i in range(0,5):\\n\",\n \" scores[i] = sqrt(-1*scores[i])\\n\",\n \"\\n\",\n \"print(scores)\\n\",\n \"avg_rmse = scores.mean()\\n\",\n \"print(\\\"\\\\n\\\\nAvg RMSE is \\\",scores.mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### The Rain data does helps us\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Lets try for SVR with other kernels ...\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Degree 3 Polynomial\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 906.20976415 837.77643762 1049.76326739 1568.88777167 504.49443066]\\n\",\n \"\\n\",\n \"\\n\",\n \"Avg RMSE is 973.426334297\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 5 Fold CV, to calculate avg RMSE\\n\",\n \"clf = SVR(kernel='poly', gamma='auto', degree=3, coef0=2)\\n\",\n \"scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\\n\",\n \"for i in range(0,5):\\n\",\n \" scores[i] = sqrt(-1*scores[i])\\n\",\n \" \\n\",\n \"print(scores)\\n\",\n \"avg_rmse = scores.mean()\\n\",\n \"print(\\\"\\\\n\\\\nAvg RMSE is \\\",scores.mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Polynomial Kernel also does better than Linear Regression\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Degree 4 Polynomial\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 907.10874357 787.20784909 848.64917648 1570.06140194 557.83575489]\\n\",\n \"\\n\",\n \"\\n\",\n \"Avg RMSE is 934.172585194\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 5 Fold CV, to calculate avg RMSE\\n\",\n \"clf = SVR(kernel='poly', gamma='auto', degree=4, coef0=2)\\n\",\n \"scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')\\n\",\n \"for i in range(0,5):\\n\",\n \" scores[i] = sqrt(-1*scores[i])\\n\",\n \" \\n\",\n \"print(scores)\\n\",\n \"avg_rmse = scores.mean()\\n\",\n \"print(\\\"\\\\n\\\\nAvg RMSE is \\\",scores.mean())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":245,"cells":{"repo_name":{"kind":"string","value":"rbiswas4/Cadence"},"path":{"kind":"string","value":"LSSTmetrics/readingLightCurves.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"185222"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import sncosmo\\n\",\n \"import analyzeSN as ans\\n\",\n \"from analyzeSN import LightCurve\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"lc_field309_mjd_49923.ascii lc_field744_mjd_49923.ascii\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls *.ascii\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fname = 'lc_field744_mjd_49923.ascii'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"lcdf = pd.read_csv(fname, delim_whitespace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
indextimebandfluxfluxerrzpzpsysSNRfinSeeingairmassfiltSkyBrightnessfiveSigmaDepthpropIDnightDetectionEfficiencymodelFluxdeviation
041637349921.234141r8.922394e-103.173156e-110.0ab28.1183570.7056361.28627121.09410024.5797163665680.9877468.922394e-101.764052
141637249921.233713r8.922386e-103.177184e-110.0ab28.0826860.7061651.28822521.09286924.5780923665680.9877268.922386e-100.400157
241637149921.233285r8.922378e-103.181244e-110.0ab28.0468240.7066981.29019121.09163324.5764583665680.9877068.922378e-100.978738
341637049921.232857r8.922370e-103.185325e-110.0ab28.0108590.7072331.29216721.09039224.5748183665680.9876868.922370e-102.240893
441636949921.232428r8.922362e-103.189429e-110.0ab27.9747910.7077711.29415521.08914824.5731723665680.9876668.922362e-101.867558
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" index time band flux fluxerr zp zpsys \\\\\\n\",\n \"0 416373 49921.234141 r 8.922394e-10 3.173156e-11 0.0 ab \\n\",\n \"1 416372 49921.233713 r 8.922386e-10 3.177184e-11 0.0 ab \\n\",\n \"2 416371 49921.233285 r 8.922378e-10 3.181244e-11 0.0 ab \\n\",\n \"3 416370 49921.232857 r 8.922370e-10 3.185325e-11 0.0 ab \\n\",\n \"4 416369 49921.232428 r 8.922362e-10 3.189429e-11 0.0 ab \\n\",\n \"\\n\",\n \" SNR finSeeing airmass filtSkyBrightness fiveSigmaDepth propID \\\\\\n\",\n \"0 28.118357 0.705636 1.286271 21.094100 24.579716 366 \\n\",\n \"1 28.082686 0.706165 1.288225 21.092869 24.578092 366 \\n\",\n \"2 28.046824 0.706698 1.290191 21.091633 24.576458 366 \\n\",\n \"3 28.010859 0.707233 1.292167 21.090392 24.574818 366 \\n\",\n \"4 27.974791 0.707771 1.294155 21.089148 24.573172 366 \\n\",\n \"\\n\",\n \" night DetectionEfficiency modelFlux deviation \\n\",\n \"0 568 0.987746 8.922394e-10 1.764052 \\n\",\n \"1 568 0.987726 8.922386e-10 0.400157 \\n\",\n \"2 568 0.987706 8.922378e-10 0.978738 \\n\",\n \"3 568 0.987686 8.922370e-10 2.240893 \\n\",\n \"4 568 0.987666 8.922362e-10 1.867558 \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lcdf.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"banddict = dict((x, 'lsst_' + x) for x in 'ugrizy')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"lightcurve = LightCurve(lcdf[['time', 'flux', 'band', 'fluxerr','zp', 'zpsys', 'SNR']], bandNameDict=banddict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mjdfluxbandfluxerrzpzpsysSNR
049921.2341418.922394e-10lsst_r3.173156e-110.0ab28.118357
149921.2337138.922386e-10lsst_r3.177184e-110.0ab28.082686
249921.2332858.922378e-10lsst_r3.181244e-110.0ab28.046824
349921.2328578.922370e-10lsst_r3.185325e-110.0ab28.010859
449921.2324288.922362e-10lsst_r3.189429e-110.0ab27.974791
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\"\n ],\n \"text/plain\": [\n \" mjd flux band fluxerr zp zpsys SNR\\n\",\n \"0 49921.234141 8.922394e-10 lsst_r 3.173156e-11 0.0 ab 28.118357\\n\",\n \"1 49921.233713 8.922386e-10 lsst_r 3.177184e-11 0.0 ab 28.082686\\n\",\n \"2 49921.233285 8.922378e-10 lsst_r 3.181244e-11 0.0 ab 28.046824\\n\",\n \"3 49921.232857 8.922370e-10 lsst_r 3.185325e-11 0.0 ab 28.010859\\n\",\n \"4 49921.232428 8.922362e-10 lsst_r 3.189429e-11 0.0 ab 27.974791\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lightcurve.lightCurve.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mjdfluxbandfluxerrzpzpsysSNR
049921.2341418.922394e-10lsst_r3.173156e-110.0ab28.118357
149921.2337138.922386e-10lsst_r3.177184e-110.0ab28.082686
249921.2332858.922378e-10lsst_r3.181244e-110.0ab28.046824
349921.2328578.922370e-10lsst_r3.185325e-110.0ab28.010859
449921.2324288.922362e-10lsst_r3.189429e-110.0ab27.974791
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" mjd flux band fluxerr zp zpsys SNR\\n\",\n \"0 49921.234141 8.922394e-10 lsst_r 3.173156e-11 0.0 ab 28.118357\\n\",\n \"1 49921.233713 8.922386e-10 lsst_r 3.177184e-11 0.0 ab 28.082686\\n\",\n \"2 49921.233285 8.922378e-10 lsst_r 3.181244e-11 0.0 ab 28.046824\\n\",\n \"3 49921.232857 8.922370e-10 lsst_r 3.185325e-11 0.0 ab 28.010859\\n\",\n \"4 49921.232428 8.922362e-10 lsst_r 3.189429e-11 0.0 ab 27.974791\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lightcurve.coaddedLC().head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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63rnG/c+LeFmMaMGdN63sy6p7Kykvr6eiorK3t1nfRvtLZv3976099o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\\nmVlB/C95GqJwuVu+VwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = sncosmo.plot_lc(lightcurve.snCosmoLC(), color='k')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you want to plot the model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from lsst.sims.catUtils.supernovae import SNObject\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from astropy.cosmology import FlatLambdaCDM\\n\",\n \"cosmo = FlatLambdaCDM(H0=73., Om0=0.25)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"snobject = SNObject(ra=0., dec=np.degrees(0.794553))\\n\",\n \"paramDict = dict(x1=0., z=0.5, c=0., t0=49923.)\\n\",\n \"mwebv = snobject.ebvofMW\\n\",\n \"#snobject.set_MWebv(0.)\\n\",\n \"snobject.set(**paramDict)\\n\",\n \"snobject.set_source_peakabsmag(-19.3, 'BessellB', 'AB')\\n\",\n \"snobject.set_MWebv(0.)\\n\",\n \"sncosmoModel = snobject.equivalentSNCosmoModel()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"source:\\n\",\n \" class : SALT2Source\\n\",\n \" name : 'salt2-extended'\\n\",\n \" version : 1.0\\n\",\n \" phases : [-20, .., 50] days\\n\",\n \" wavelengths: [300, .., 18000] Angstroms\\n\",\n \"effect (name='host' frame='rest'):\\n\",\n \" class : OD94Dust\\n\",\n \" wavelength range: [909.09, 33333.3] Angstroms\\n\",\n \"effect (name='mw' frame='obs'):\\n\",\n \" class : OD94Dust\\n\",\n \" wavelength range: [909.09, 33333.3] Angstroms\\n\",\n \"parameters:\\n\",\n \" z = 0.5\\n\",\n \" t0 = 49923.0\\n\",\n \" x0 = 1.0068661711630977e-05\\n\",\n \" x1 = 0.0\\n\",\n \" c = 0.0\\n\",\n \" hostebv = 0.0\\n\",\n \" hostr_v = 3.1000000000000001\\n\",\n \" mwebv = 0.0\\n\",\n \" mwr_v = 3.1000000000000001\\n\"\n ]\n }\n ],\n \"source\": [\n \"print sncosmoModel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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metk7QPtc8iV5V8ZJjLR5mZNWNuEJiZWV3dDtyY2ZDUFuhNsujj6cDf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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = sncosmo.plot_lc(lightcurve.snCosmoLC(), color='k', model=sncosmoModel)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":246,"cells":{"repo_name":{"kind":"string","value":"HWNi/data-512-a1"},"path":{"kind":"string","value":"hcds-a1-data-curation.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"173304"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# A1 Data Curation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step1: Data Acquisition\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Import packages that will be used in this assignment\\n\",\n \"import requests\\n\",\n \"import json\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline \\n\",\n \"import seaborn as sns\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To get the monthly traffic data on English Wikipedia from January 2008 through September 2017, we need to use 2 API endpoints, the Pagecounts API and the Pageviews API. The Pagecounts API provides monthy desktop and mobile traffic data from January 2008 through July 2016, and the Pageviews API provides monthy desktop, mobile-web, and mobile-app traffic data from July 2015 through September 2017. Once the user finishes the parameter settings for the API request, the traffic data will be returned in JSON format. The codes below will get you all pagecounts for English Wikipedia accessed through desktop from January 2008 through July 2016.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Collect desktop traffic data from January 2008 through July 2016 using the Pagecounts API\\n\",\n \"endpoint_pagecounts = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{access}/{granularity}/{start}/{end}'\\n\",\n \"\\n\",\n \"params_pc_desktop = {\\n\",\n \" 'project' : 'en.wikipedia.org',\\n\",\n \" 'access' : 'desktop-site',\\n\",\n \" 'granularity' : 'monthly',\\n\",\n \" 'start' : '2008010100',\\n\",\n \" 'end' : '2016080100'#use the first day of the following month to ensure a full month of data is collected\\n\",\n \" }\\n\",\n \"\\n\",\n \"api_call = requests.get(endpoint_pagecounts.format(**params_pc_desktop))\\n\",\n \"response_pc_desktop = api_call.json()\\n\",\n \"with open('pagecounts_desktop-site_200801-201607.json', 'w') as outfile:\\n\",\n \" json.dump(response_pc_desktop, outfile)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The codes below will get you all pagecounts for English Wikipedia accessed through mobile from January 2008 through July 2016.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Collect mobile traffic data from January 2008 through July 2016 using the Pagecounts API\\n\",\n \"endpoint_pagecounts = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{access}/{granularity}/{start}/{end}'\\n\",\n \"\\n\",\n \"params_pc_mobile = {\\n\",\n \" 'project' : 'en.wikipedia.org',\\n\",\n \" 'access' : 'mobile-site',\\n\",\n \" 'granularity' : 'monthly',\\n\",\n \" 'start' : '2008010100',\\n\",\n \" 'end' : '2016080100'\\n\",\n \" }\\n\",\n \"\\n\",\n \"api_call = requests.get(endpoint_pagecounts.format(**params_pc_mobile))\\n\",\n \"response_pc_mobile = api_call.json()\\n\",\n \"with open('pagecounts_mobile-site_200801-201607.json', 'w') as outfile:\\n\",\n \" json.dump(response_pc_mobile, outfile)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The codes below will get you all pageviews for English Wikipedia accessed through desktop from July 2015 through September 2017. Note that the data doesn't count traffic by web crawlers or spiders.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Collect desktop traffic data from July 2015 through September 2017 using the Pageviews API\\n\",\n \"endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'\\n\",\n \"\\n\",\n \"headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : 'haowen2@uw.edu'}\\n\",\n \"\\n\",\n \"params_pv_desktop = {\\n\",\n \" 'project' : 'en.wikipedia.org',\\n\",\n \" 'access' : 'desktop',\\n\",\n \" 'agent' : 'user',\\n\",\n \" 'granularity' : 'monthly',\\n\",\n \" 'start' : '2015070100',\\n\",\n \" 'end' : '2017100100'\\n\",\n \" }\\n\",\n \"\\n\",\n \"api_call = requests.get(endPoint_pageviews.format(**params_pv_desktop))\\n\",\n \"response_pv_desktop = api_call.json()\\n\",\n \"with open('pageviews_desktop_201507-201709.json', 'w') as outfile:\\n\",\n \" json.dump(response_pv_desktop, outfile)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The codes below will get you all pageviews for English Wikipedia accessed through mobile website from July 2015 through September 2017. Again, note that the data doesn't count traffic by web crawlers or spiders.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Collect mobile web traffic data from July 2015 through September 2017 using the Pageviews API\\n\",\n \"endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'\\n\",\n \"\\n\",\n \"headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : 'haowen2@uw.edu'}\\n\",\n \"\\n\",\n \"params_pv_mobile_web = {\\n\",\n \" 'project' : 'en.wikipedia.org',\\n\",\n \" 'access' : 'mobile-web',\\n\",\n \" 'agent' : 'user',\\n\",\n \" 'granularity' : 'monthly',\\n\",\n \" 'start' : '2015070100',\\n\",\n \" 'end' : '2017100100'\\n\",\n \" }\\n\",\n \"\\n\",\n \"api_call = requests.get(endPoint_pageviews.format(**params_pv_mobile_web))\\n\",\n \"response_pv_mobile_web = api_call.json()\\n\",\n \"with open('pageviews_mobile-web_201507-201709.json', 'w') as outfile:\\n\",\n \" json.dump(response_pv_mobile_web, outfile)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The codes below will get you all pageviews for English Wikipedia accessed through mobile app from July 2015 through September 2017. Again, note that the data doesn't count traffic by web crawlers or spiders.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Collect mobile app traffic data from July 2015 through September 2017 using the Pageviews API\\n\",\n \"endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'\\n\",\n \"\\n\",\n \"headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : 'haowen2@uw.edu'}\\n\",\n \"\\n\",\n \"params_pv_mobile_app = {\\n\",\n \" 'project' : 'en.wikipedia.org',\\n\",\n \" 'access' : 'mobile-app',\\n\",\n \" 'agent' : 'user',\\n\",\n \" 'granularity' : 'monthly',\\n\",\n \" 'start' : '2015070100',\\n\",\n \" 'end' : '2017100100'\\n\",\n \" }\\n\",\n \"\\n\",\n \"api_call = requests.get(endPoint_pageviews.format(**params_pv_mobile_app))\\n\",\n \"response_pv_mobile_app = api_call.json()\\n\",\n \"with open('pageviews_mobile-app_201507-201709.json', 'w') as outfile:\\n\",\n \" json.dump(response_pv_mobile_app, outfile)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Data processing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, we have 5 JSON files containing the traffic data we're interested in. In this step, we first iterate these 5 JSON files one by one and combine the data into a Python dictionary. Eventually, the key of the dictionary will be the list of time stamps (from January 2008 to September 2017). For each key (time stamp), we will append a list which contains 5 values: pagecounts accessed through desktop, pagecounts accessed through mobile, pageviews accessed through desktop, pageviews accessed through mobile web, and pageviews accessed through mobile app. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data_cleaned = {}\\n\",\n \"\\n\",\n \"for item in response_pc_desktop['items']:\\n\",\n \" timeStamp = item['timestamp']\\n\",\n \" data_cleaned[timeStamp] = [item['count'], 0, 0, 0, 0]\\n\",\n \" \\n\",\n \"for item in response_pc_mobile['items']:\\n\",\n \" timeStamp = item['timestamp']\\n\",\n \" if timeStamp in data_cleaned:\\n\",\n \" data_cleaned[timeStamp][1] = item['count'] \\n\",\n \" else:\\n\",\n \" data_cleaned[timeStamp] = [0, item['count'], 0, 0, 0]\\n\",\n \" \\n\",\n \"for item in response_pv_desktop['items']:\\n\",\n \" timeStamp = item['timestamp']\\n\",\n \" if timeStamp in data_cleaned:\\n\",\n \" data_cleaned[timeStamp][2] = item['views'] \\n\",\n \" else:\\n\",\n \" data_cleaned[timeStamp] = [0, 0, item['views'], 0, 0]\\n\",\n \" \\n\",\n \"for item in response_pv_mobile_web['items']:\\n\",\n \" timeStamp = item['timestamp']\\n\",\n \" if timeStamp in data_cleaned:\\n\",\n \" data_cleaned[timeStamp][3] = item['views'] \\n\",\n \" else:\\n\",\n \" data_cleaned[timeStamp] = [0, 0, 0, item['views'], 0]\\n\",\n \" \\n\",\n \"for item in response_pv_mobile_app['items']:\\n\",\n \" timeStamp = item['timestamp']\\n\",\n \" if timeStamp in data_cleaned:\\n\",\n \" data_cleaned[timeStamp][4] = item['views'] \\n\",\n \" else:\\n\",\n \" data_cleaned[timeStamp] = [0, 0, 0, 0, item['views']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we get the dictionary, we could convert it into a Pandas dataframe and save the dataframe to a csv file\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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yearmonthpagecount_all_viewspagecount_desktop_viewspagecount_mobile_viewspageview_all_viewspageview_desktop_viewspageview_mobile_views
2008010100200801493090257049309025700000
2008020100200802481839376348183937630000
2008030100200803495540580949554058090000
2008040100200804515916218351591621830000
2008050100200805558469109255846910920000
2008060100200806571210427957121042790000
2008070100200807530630287453063028740000
2008080100200808514015551951401555190000
2008090100200809547953382354795338230000
2008100100200810567944078256794407820000
2008110100200811541583207154158320710000
2008120100200812521170845152117084510000
2009010100200901580268155158026815510000
2009020100200902554732086055473208600000
2009030100200903629515905762951590570000
2009040100200904598881732159888173210000
2009050100200905626751673362675167330000
2009060100200906581892418258189241820000
2009070100200907580164697858016469780000
2009080100200908579085038457908503840000
2009090100200909405751576840575157680000
2009100100200910601610714760161071470000
2009110100200911576848691057684869100000
2009120100200912542650597754265059770000
2010010100201001570346528557034652850000
2010020100201002576245141857624514180000
2010030100201003666134794666613479460000
2010040100201004661855215266185521520000
2010050100201005641057877564105787750000
2010060100201006489803501448980350140000
...........................
2015040100201504942103557461989456573222089917000
2015050100201505965787129763238018143334069483000
2015060100201506820357610351654136403038162463000
2015070100201507848369871752292260223254472695766542198043766666863288755294
2015080100201508830402203150355344493268487582763481522143324821833302333038
2015090100201509858206118254096313553172429827765569503744854917043170203333
2015100100201510878178697655357044713246082505774603188744775327553268499132
2015110100201511851519062852969561163218234512752432129042877202203236601070
2015120100201512865185803652644461733387411863747628734441000120373376275307
2016010100201601930926124455696325023739628742815401630344361794573717836846
2016020100201602868094075353477093613333231392758585945742509971853334862272
2016030100201603882752969254076760563419853636767327461742865904263386684191
2016040100201604887362052355722353993301385124740814785941493838573258764002
2016050100201605874896813953305323343418435805758681133041917780943395033236
2016060100201606834771051049750924473372618063724363065638888397113354790945
2016070100201607886462756053639664393500661121783443958943378658273496573762
2016080100201608000821086551946950462163515819303
2016090100201609000752829227941350064983393285781
2016100100201610000787102158143617376903509283891
2016110100201611000798311316143920682363591044925
2016120100201612000798615243342096085783776543855
2017010100201701000875394194045219803984231961542
2017020100201702000773846356240267021633711761399
2017030100201703000822346589143199719023903493989
2017040100201704000759108011139514569923639623119
2017050100201705000787455829941878705793686687720
2017060100201706000712393419036045509973519383193
2017070100201707000729050379735654445443725059253
2017080100201708000719697861535755723133621406302
2017090100201709000707905226135474478923531604369
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117 rows × 8 columns

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\"\n ],\n \"text/plain\": [\n \" year month pagecount_all_views pagecount_desktop_views \\\\\\n\",\n \"2008010100 2008 01 4930902570 4930902570 \\n\",\n \"2008020100 2008 02 4818393763 4818393763 \\n\",\n \"2008030100 2008 03 4955405809 4955405809 \\n\",\n \"2008040100 2008 04 5159162183 5159162183 \\n\",\n \"2008050100 2008 05 5584691092 5584691092 \\n\",\n \"2008060100 2008 06 5712104279 5712104279 \\n\",\n \"2008070100 2008 07 5306302874 5306302874 \\n\",\n \"2008080100 2008 08 5140155519 5140155519 \\n\",\n \"2008090100 2008 09 5479533823 5479533823 \\n\",\n \"2008100100 2008 10 5679440782 5679440782 \\n\",\n \"2008110100 2008 11 5415832071 5415832071 \\n\",\n \"2008120100 2008 12 5211708451 5211708451 \\n\",\n \"2009010100 2009 01 5802681551 5802681551 \\n\",\n \"2009020100 2009 02 5547320860 5547320860 \\n\",\n \"2009030100 2009 03 6295159057 6295159057 \\n\",\n \"2009040100 2009 04 5988817321 5988817321 \\n\",\n \"2009050100 2009 05 6267516733 6267516733 \\n\",\n \"2009060100 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\"df.to_csv('en-wikipedia_traffic_200801-201709.csv', index=False)\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Step 3: Analysis\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the final step, we make a time series plot for the data we processed before. The X-axis of the plot will be a date range, and Y-axis of the plot will be the amount of traffic times 1 million(The author downscaled the traffic data by 1 million).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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AhgwIABhIWFYTabCQsLw9/fn+HDh9dtCw4OZvDgweTn5zN9+nTS09N5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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"dateRange = pd.date_range('2008-01', '2017-10', freq='M')\\n\",\n \"scale = 1e-6\\n\",\n \"sns.set_style(\\\"whitegrid\\\")\\n\",\n \"fig = plt.figure(figsize=(18, 12))\\n\",\n \"plt.plot(dateRange, df['pagecount_all_views'] * scale, linestyle = ':')\\n\",\n \"plt.plot(dateRange, df['pagecount_desktop_views'] * scale)\\n\",\n \"plt.plot(dateRange, df['pagecount_mobile_views'] * scale)\\n\",\n \"plt.plot(dateRange, df['pageview_all_views'] * scale, linestyle = ':')\\n\",\n \"plt.plot(dateRange, df['pagecount_desktop_views'] * scale)\\n\",\n \"plt.plot(dateRange, df['pagecount_mobile_views'] * scale)\\n\",\n \"plt.legend()\\n\",\n \"plt.xlabel('Year')\\n\",\n \"plt.ylabel('Amount of Traffic (* 1,000,000)')\\n\",\n \"fig.savefig('en-wikipedia_traffic_200801-201709.jpg')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":247,"cells":{"repo_name":{"kind":"string","value":"gregcaporaso/Extensible-Evaluation-Framework-Presentation"},"path":{"kind":"string","value":"1-evaluation-framework-results.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"129671"},"content":{"kind":"string","value":"{\n \"metadata\": {\n \"name\": \"1-evaluation-framework-results\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n {\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Taxonomic assignment method evaluation framework\\n\",\n \"================================================\\n\",\n \"\\n\",\n \"This [IPython Notebook](http://ipython.org/notebook.html) illustrates how to apply the evaluation framework described in (Bokulich, Rideout, et al. (in preparation)). Given a set of precomputed taxonomic assignment method evaluation results (this will likely be the ones included in the [short-read-tax-assignment GitHub repository](https://github.com/gregcaporaso/short-read-tax-assignment/)) and a set of results generated by the user for a new method, the results from the new method are analyzed in the context of the precomputed results. This allows users to rapidly determine how a new method performs, relative to the precomputed results. \\n\",\n \"\\n\",\n \"The following sections present different evaluations with an example usearch-based taxonomic assigner. A parameter sweep was performed using this new taxonomic assigner (as described in the [usearch-tax-assigner notebook](http://nbviewer.ipython.org/urls/raw.github.com/gregcaporaso/short-read-tax-assignment/master/demo/eval-demo/0-usearch-tax-analyzer.ipynb)), and the evaluations that are run here allow us to determine how the new assigner performs relative to the precomputed results and summarize the results in a report, which will be this notebook after all cells have been executed. This provides a convenient interactive framework for analyzing new methods. \\n\",\n \"\\n\",\n \"This notebook could easily be applied to the results of a different taxonomic assigner, and it is therefore intended to serve as a template for report generation. Only one change would need to be made to support that, and that change is discussed inline below.\\n\",\n \"\\n\",\n \"This code and other components of this framework can be found in the [short-read-tax-assignment GitHub repository](https://github.com/gregcaporaso/short-read-tax-assignment/).\\n\",\n \"\\n\",\n \"Requirements\\n\",\n \"------------\\n\",\n \"\\n\",\n \"To run this notebook, you'll need to have the following installed:\\n\",\n \"\\n\",\n \" * [IPython](http://ipython.org/) >= 1.0.0\\n\",\n \" * [QIIME](http://qiime.org/)\\n\",\n \" * [PyCogent](https://github.com/pycogent/pycogent)\\n\",\n \" * [short-read-tax-assignment](https://github.com/gregcaporaso/short-read-tax-assignment)\\n\",\n \" * [matplotlib](http://matplotlib.org/)\\n\",\n \" \\n\",\n \"Terminology\\n\",\n \"-----------\\n\",\n \"\\n\",\n \"Throughout the notebook and associated code, we refer to the precomputed results obtained from GitHub as the *subject results*, and the results generated by the user as the *query results*.\\n\",\n \"\\n\",\n \"Structuring query results for comparison to subject results\\n\",\n \"-----------------------------------------------------------\\n\",\n \"\\n\",\n \"To prepare results from another classifier for analysis, you'll need to have [BIOM](http://www.biom-format.org) files with taxonomy assignments as an observation metadata category called ``taxonomy``. These should be generated for all analyses (mock community, natural community), datasets, reference databases, methods, and parameter combinations of interest. An example of how to generate these is presented in the [usearch-tax-assigner notebook](http://nbviewer.ipython.org/urls/raw.github.com/gregcaporaso/short-read-tax-assignment/master/demo/eval-demo/0-usearch-tax-analyzer.ipynb)).\\n\",\n \"\\n\",\n \"Your BIOM tables should be called ``table.biom``, and nested in the following directory structure:\\n\",\n \"\\n\",\n \"```\\n\",\n \"query_results_dir/\\n\",\n \" analysis/\\n\",\n \" dataset-id/ \\n\",\n \" reference-db-id/\\n\",\n \" method-id/\\n\",\n \" parameter-combination-id/\\n\",\n \" table.biom\\n\",\n \"```\\n\",\n \"\\n\",\n \"``query_results_dir`` is the name of the top level directory, and you will set this value in the first code cell of this notebook. You can name this directory whatever you want to.\\n\",\n \"\\n\",\n \"This directory structure is identical to that for the [subject results](https://github.com/gregcaporaso/short-read-tax-assignment/tree/master/data/eval-subject-results). You can review that directory structure for an example of how this should look.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Configure local environment-specific values\\n\",\n \"-------------------------------------------\\n\",\n \"\\n\",\n \"**This is the only cell that you will need to edit to generate reports locally.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"## short_read_tax_dir should be the directory where you've downloaded (or cloned) the \\n\",\n \"## short-read-tax-assignment repository. \\n\",\n \"short_read_tax_dir = \\\"/home/jrideout/dev/short-read-tax-assignment/\\\"\\n\",\n \"\\n\",\n \"## query_results_dir should be the directory where the results of the method(s) to be\\n\",\n \"## compared to the pre-computed results can be found. \\n\",\n \"query_results_dir = \\\"/home/jrideout/dev/short-read-tax-assignment/usearch_parameter_sweep/\\\"\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 1\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Prepare the environment\\n\",\n \"-----------------------\\n\",\n \"\\n\",\n \"First we'll configure IPython to add matplotlib plots inline. Then we'll import various functions that we'll need for generating the report. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from os.path import join, exists\\n\",\n \"from taxcompare.eval_framework import (get_expected_tables_lookup, \\n\",\n \" find_and_process_result_tables,\\n\",\n \" compute_prfs,\\n\",\n \" compute_pearson_spearman,\\n\",\n \" compute_procrustes,\\n\",\n \" generate_pr_scatter_plots,\\n\",\n \" generate_prf_box_plots,\\n\",\n \" generate_precision_box_plots,\\n\",\n \" generate_recall_box_plots,\\n\",\n \" generate_fmeasure_box_plots,\\n\",\n \" generate_prf_table,\\n\",\n \" generate_correlation_box_plots,\\n\",\n \" generate_pearson_box_plots,\\n\",\n \" generate_spearman_box_plots,\\n\",\n \" generate_pearson_spearman_table,\\n\",\n \" generate_procrustes_table)\\n\",\n \"from cogent.draw.distribution_plots import generate_box_plots\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 2\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"# Define the subdirectories where the query mock and natural community data should be, and confirm that they exist.\\n\",\n \"mock_query_results_dir = join(query_results_dir,\\\"mock\\\")\\n\",\n \"natural_query_results_dir = join(query_results_dir,\\\"natural\\\")\\n\",\n \"\\n\",\n \"assert exists(mock_query_results_dir), \\\"Mock community query directory doesn't exist: %s\\\" % mock_query_results_dir\\n\",\n \"assert exists(natural_query_results_dir), \\\"Natural community query directory doesn't exist: %s\\\" % natural_query_results_dir\\n\",\n \"\\n\",\n \"# Define the subdirectories where the subject mock and natural community data should be, and confirm that they exist.\\n\",\n \"mock_subject_results_dir = join(short_read_tax_dir,'data/eval-subject-results/mock/')\\n\",\n \"natural_subject_results_dir = join(short_read_tax_dir,'data/eval-subject-results/natural/')\\n\",\n \"\\n\",\n \"assert exists(mock_query_results_dir), \\\"Mock community subject directory doesn't exist: %s\\\" % mock_subject_results_dir\\n\",\n \"assert exists(natural_query_results_dir), \\\"Natural community subject directory doesn't exist: %s\\\" % natural_subject_results_dir\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 3\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Find mock community pre-computed tables, expected tables, and \\\"query\\\" tables\\n\",\n \"----------------------------------------------------------------------------\\n\",\n \"\\n\",\n \"Next we'll use the paths defined above to find all of the tables that will be compared. These include the *pre-computed result* tables (i.e., the ones that the new methods will be compared to), the *expected result* tables (i.e., the tables containing the known composition of the mock microbial communities), and the *query result* tables (i.e., the tables generated with the new method(s) that we want to compare to the *pre-computed result* tables).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"query_results = find_and_process_result_tables(mock_query_results_dir)\\n\",\n \"subject_results = find_and_process_result_tables(mock_subject_results_dir)\\n\",\n \"expected_L6_tables = get_expected_tables_lookup(mock_subject_results_dir)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 4\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Evalution 1: Compute and summarize precision, recall, and F-measure for mock communities\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"\\n\",\n \"In this evaluation, we compute and summarize precision, recall, and F-measure of each result (pre-computed and query) based on the known composition of the mock communities. We then summarize the results in two ways: first with a scatter plot, where pre-computed results are represented by blue points, and query results are represented by red points; and second with a table of the top twenty-five methods based on their F-measures. \\n\",\n \"\\n\",\n \"This is a qualitative evaluation, effectively telling us about the ability of the different methods to report the taxa that are present in each sample. These metrics are not concerned with the abundance of the different taxa.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"subject_prf = list(compute_prfs(subject_results,expected_L6_tables))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 5\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"query_prf = list(compute_prfs(query_results,expected_L6_tables))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 6\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_pr_scatter_plots(query_prf,subject_prf)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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PH7/RUix5233SY2kFBDGcyfP/+0bZ977jlxGm0dIBMnTjxr/4P69hWr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\\n\",\n \"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 7\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_precision_box_plots(query_prf,subject_prf)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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8HmWkgMWVkMKiKyZjKZYDKZet2vx/Q5fPiw1Urnzc3Nlm2NRoP6+vpu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\\nZMkSDB06FPxbqGdz586Fr68vFi9eDEmS8O6772Lu3Ll4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\"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 8\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_recall_box_plots(query_prf,subject_prf)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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FyN6WRr3DZeTPc1gqmHy3COie3B0ukW4YMHnU4B1K+u93ta0BvBbZ9E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\\nLy1dulRVVVUqLCzU/PnzWeYHcEj37t31xBNP6Pbbb5fP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\"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 9\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_fmeasure_box_plots(query_prf,subject_prf)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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nmMRiOMRqPV7WQVqcjISKvb2DI9ff78+Xj99dctzbvOuvTSePEmIiKH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\\nKd2wAAAAAElFTkSuQmCC\\n\",\n \"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 10\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_prf_table(query_prf,subject_prf)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \" Data set | Precision | Recall | F-measure | Method | Parameters \\n\",\n \"ITS2-SAG1 | 0.615 | 0.889 | 0.727 |rtax |single \\n\",\n \"ITS2-SAG1 | 0.571 | 0.889 | 0.696 |rdp |0.0 \\n\",\n \"ITS2-SAG1 | 0.571 | 0.889 | 0.696 |mothur |0.0 \\n\",\n \"ITS2-SAG1 | 0.533 | 0.889 | 0.667 |mothur |0.2 \\n\",\n \"ITS2-SAG1 | 0.533 | 0.889 | 0.667 |mothur |0.1 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.889 | 0.640 |rdp |0.2 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.889 | 0.640 |rdp |0.1 \\n\",\n \"ITS1 | 0.600 | 0.667 | 0.632 |rdp |0.0 \\n\",\n \"ITS1 | 0.600 | 0.667 | 0.632 |mothur |0.0 \\n\",\n \"S16S-2 | 0.500 | 0.826 | 0.623 |blast |1.0 \\n\",\n \"S16S-2 | 0.500 | 0.826 | 0.623 |blast |100.0 \\n\",\n \"S16S-2 | 0.500 | 0.826 | 0.623 |blast |0.0001 \\n\",\n \"S16S-2 | 0.500 | 0.826 | 0.623 |blast |1e-06 \\n\",\n \"S16S-2 | 0.500 | 0.826 | 0.623 |blast |1e-10 \\n\",\n \"S16S-2 | 0.487 | 0.826 | 0.613 |blast |1e-30 \\n\",\n \"S16S-2 | 0.475 | 0.826 | 0.603 |rdp |0.0 \\n\",\n \"S16S-2 | 0.475 | 0.826 | 0.603 |mothur |0.0 \\n\",\n \"ITS1 | 0.545 | 0.667 | 0.600 |rdp |0.1 \\n\",\n \"ITS1 | 0.545 | 0.667 | 0.600 |mothur |0.1 \\n\",\n \"S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e1.000000_qf0.500000_ma1_c1.000000\\n\",\n \"S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e0.001000_qf0.500000_ma1_c0.750000\\n\",\n \"S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e1.000000_qf0.500000_ma1_c0.750000\\n\",\n \"S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e1.000000_qf0.500000_ma1_c0.510000\\n\",\n \"S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e0.001000_qf0.500000_ma1_c0.510000\\n\",\n \"S16S-2 | 0.452 | 0.826 | 0.585 |usearch |e0.001000_qf0.500000_ma1_c1.000000\\n\",\n \"S16S-2 | 0.462 | 0.783 | 0.581 |usearch |e0.001000_qf0.500000_ma3_c0.510000\\n\",\n \"S16S-2 | 0.462 | 0.783 | 0.581 |usearch |e1.000000_qf0.500000_ma3_c0.510000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e1.000000_qf0.750000_ma1_c0.510000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e1.000000_qf0.750000_ma1_c1.000000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e0.001000_qf0.750000_ma1_c1.000000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e0.001000_qf0.750000_ma1_c0.510000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e0.001000_qf0.750000_ma1_c0.750000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |usearch |e1.000000_qf0.750000_ma1_c0.750000\\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |rdp |0.1 \\n\",\n \"S16S-2 | 0.442 | 0.826 | 0.576 |mothur |0.1 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |1.0 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |100.0 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |0.0001 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |1e-06 \\n\",\n \"ITS2-SAG1 | 0.500 | 0.667 | 0.571 |blast |1e-10 \\n\",\n \"ITS2-SAG1 | 0.421 | 0.889 | 0.571 |mothur |0.3 \\n\",\n \"S16S-2 | 0.432 | 0.826 | 0.567 |mothur |0.2 \\n\",\n \"S16S-2 | 0.459 | 0.739 | 0.567 |usearch |e1.000000_qf0.500000_ma5_c0.510000\\n\",\n \"S16S-2 | 0.459 | 0.739 | 0.567 |usearch |e0.001000_qf0.500000_ma5_c0.510000\\n\",\n \"S16S-2 | 0.439 | 0.783 | 0.562 |usearch |e1.000000_qf0.750000_ma3_c0.510000\\n\",\n \"S16S-2 | 0.439 | 0.783 | 0.562 |usearch |e0.001000_qf0.750000_ma3_c0.510000\\n\",\n \"S16S-2 | 0.422 | 0.826 | 0.559 |rdp |0.3 \\n\",\n \"S16S-2 | 0.422 | 0.826 | 0.559 |rdp |0.2 \\n\",\n \"S16S-2 | 0.447 | 0.739 | 0.557 |usearch |e0.001000_qf0.750000_ma5_c0.510000\\n\",\n \"S16S-2 | 0.447 | 0.739 | 0.557 |usearch |e1.000000_qf0.750000_ma5_c0.510000\\n\"\n ]\n }\n ],\n \"prompt_number\": 11\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Evaluation 2: Compute and summarize correlations between observed and known mock community structure\\n\",\n \"----------------------------------------------------------------------------------------------------\\n\",\n \"\\n\",\n \"In this evaluation, we compute and summarize the correlation between each result (pre-computed and query) and the known composition of the mock communities. We then summarize the results in two ways: first with a series of boxplots of correlation coefficients by method; and second with a table of the top twenty-five methods based on their Pearson correlation coefficient. \\n\",\n \"\\n\",\n \"This is a quantitative evaluation, which tells us about the ability of the different methods to report the taxa that are present in each sample and accurately assess their abundance. Because many factors can affect the observed abundance of taxa beyond the accuracy of the taxonomic assigner (e.g., primer bias), the correlation coefficients are frequently low, but we expect that their relative values are informative in understanding which taxonomic assigners are more correct than others.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"subject_pearson_spearman = list(compute_pearson_spearman(subject_results,expected_L6_tables))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 12\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"query_pearson_spearman = list(compute_pearson_spearman(query_results,expected_L6_tables))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 13\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_pearson_box_plots(query_pearson_spearman,subject_pearson_spearman)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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vcQ7FhgFgDgKBaYBQC4EiEFADAWIQUAMBYhBQAwFiEFADAWIQUAMBYh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\\n\",\n \"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 14\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_spearman_box_plots(query_pearson_spearman,subject_pearson_spearman)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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l7PnJACEBS8tvN1C2v3OYjhPrQFDPcFhnYKjIntxHAfAMCTCCnAcFyk\\niraM4T4AQcHEYSwTmdhODPcBACR5r2dOSAEICl7b+brFa7MgGe4DALjqbPvyUIdrAfB/Pp/P1vfj\\ngA3BiJACXEKoAM3jnBQAwFiEFAC0IV6bOEFIAQgKXtv5uoW1+xzE7D4AJ5h4kaqJTGwnZvcBQBsQ\\n6IzRQCeWmtAJIKQAIEiYECp2I6QAGM/OHkIw7siDGRMnABjPsqxmHx9+2Pw2BJRUUOB2BeeGkAIQ\\nFLy283WL19qJkAIAGItzUgA8q6Dgu57Bydf/pKY2PNDAy+3EdVIAgkJWFhf0BiI11bwhP256CADw\\nJIb7AAQF04et3HTycF9h4Xc9Ti8M9xFSABDkTg6jkhJvDYsy3AcgKJh2nsVUJSVuV3BuCCkAaEPi\\n4tyu4Nww3AfAs7w8tdpJJ7dTTs53QeWFdiKkAHjWqTtZL51rcZKX24nhPgCAsQgpAEHB9GErU3it\\nnRwNqeXLl6t///4KCQnRP//5zya3y8/PV3x8vHr37q3HH3/cwQpbroApRgGhnQJHWwWqwO0CPKLA\\n7QLOiaMhlZCQoJUrV+qaa65pcpu6ujrNmDFD+fn5Ki4u1pIlS7Rt2zYHq2wZdiiBoZ0CR1sFhnYK\\njNfaydGJE/Hx8c1us2HDBvXq1Utx/59+MmHCBL355pvq27dvK1cHADCNceekysvLFRsb638eExOj\\n8vJyFysCALjF9p7UiBEjtHfv3tNenzNnjsaOHdvs9wd6m+jz3d4J2SdfsIEm0U6Bo60CQzsFxkvt\\nZHtIvffeey36/ujoaJWWlvqfl5aWKiYm5ozbcpsOAAhurg33NRUwQ4YM0Y4dO1RSUqJjx45p6dKl\\nSk9Pd7g6AIAJHA2plStXKjY2VuvXr9eYMWM0atQoSdKePXs0ZswYSVJoaKjmz5+vkSNHql+/fho/\\nfjyTJgCgjfL0nXkBAMHNuNl9XvP++++f9lpOTo4LlZhv+/btmjp1qkaMGKG0tDSlpaXpuuuuc7ss\\n43zxxRcBvQapsrJSVVVVbpdhtPr6ei1btsztMs4bPakWSklJ0YABAzR37lxVVVVp6tSpuvDCC7Vi\\nxQq3SzPOwIEDNX36dCUnJyskJERSw+zMwYMHu1yZWZKSklRUVNTotcGDB2vTpk0uVWSeTz/9VJmZ\\nmaqsrJQkRUVF6c9//rOGDBnicmVm8vLfD6ugt1BhYaHmzZunxMRE+Xw+ZWdn65ZbbnG7LCOFhYVp\\n+vTpbpdhrG3btqm4uFiHDh3SG2+8Icuy5PP5VFlZqSNHjrhdnlEyMzP17LPPKiUlRZK0du1aZWZm\\nasuWLS5XZqYRI0Zo7ty5Gj9+vMLDw/2vd+3a1cWqAkNItVBFRYU+/fRTXX755SorK9Pu3bv9Oxc0\\nOHDggCzL0tixY7VgwQJlZGSoXbt2/q974YPihM8//1yrVq3SoUOHtGrVKv/rERERevHFF12szDyh\\noaH+gJKk4cOHKzSU3VlTXnvtNfl8Pi1YsKDR619++aVLFQWO4b4WuuKKK3TfffdpypQpOnz4sO67\\n7z5t2rRJ69atc7s0Y8TFxZ01tL3wQXHSxx9/rKuvvtrtMox21113qaamRhMnTpQkLV26VO3bt9et\\nt94qSUpOTnazPNiIkGqhXbt2qUePHo1eKyws1LXXXutSRfC6e++9Vw8++KA6dOig66+/Xps3b9aT\\nTz7p3wFDSk1NbXTgc+roxYcffuhGWcbKyck544HipEmTXKjm3BBSNqioqNDnn3+uo0eP+l8720rv\\nbdWCBQt0yy23qEuXLpIa2m3JkiX69a9/7XJlZklMTNTmzZu1cuVKvfXWW3riiSeUkpLC+Ractxkz\\nZvhDqqamRh988IGSk5P1+uuvu1xZ8wipFnrxxRf19NNPq6ysTIMGDdL69et19dVX64MPPnC7NOOc\\n2PmebNCgQfrXv/7lUkVm6t+/v7Zu3aopU6bopptu0qhRo87Ydm3RvHnzJDW9Zufvf/97J8vxrIMH\\nD2r8+PF655133C6lWVwn1UJPPfWUNmzYoB49eujDDz9UUVGROnfu7HZZRqqvr1d9fb3/eV1dnY4f\\nP+5iRWYaO3as4uPjtWnTJv34xz/W/v371b59e7fLMkJVVZW+/fZbbdy4Uc8995zKy8tVVlam559/\\n/qw3UkVjHTt29My5YHpSLTRkyBBt3LjR34tq3769+vXrp+LiYrdLM84999yj3bt3a9q0abIsSwsX\\nLtQPfvAD/9ExvvPNN98oKipKISEhqq6uVlVVlbp37+52WcZISUnR6tWrFRERIakhvEaPHq2PPvrI\\n5crMdPIdKOrr61VcXKybb77ZE3c+Z85mC8XExKiiokLjxo3TiBEj1KVLF/8NG9HYH//4Ry1cuFDP\\nPfecpIZrN+644w6XqzLTnj179Le//U01NTX+oS0vnOR2yv79+xUWFuZ/HhYWpv3797tYkdnuvvtu\\n/79DQ0PVo0ePRvftMxk9KRsVFBSosrJS119/vS688EK3yzFKbW2tBgwYoM8++8ztUoyXlZWlwsJC\\nbd26VWPGjNGaNWs0fPhwT5zkdoJlWfrDH/6gFStWKCMjQ5ZlKTc3V+PHj9cDDzzgdnmwGeekWujk\\nacGpqalKT0/XlClTXKzITKGhoerTp4927drldinGe/311/X+++/r0ksv1aJFi7R582YdPHjQ7bKM\\nsnz5ci1evFhRUVHq2rWrFi9eTECdxYoVK9S7d29FRkYqIiJCERERioyMdLusgDDc10L//ve/Gz2v\\nra317BpZre3AgQPq37+/hg4d6l+axefzKS8vz+XKzNKhQweFhIQoNDRUhw4d0iWXXNLoRqBt3Yn1\\nHmtra3XXXXe5XY4nzJo1S2+99ZYnb3tESJ2nOXPm6NFHH1VNTY3/5K3UMDb+q1/9ysXKzPXwww+7\\nXYInXHnllaqoqNDUqVM1ZMgQhYeHa9iwYW6XZZT169frlVdeUY8ePRod8HAt2Zl1797dkwElcU6q\\nRSzLUu/evbVz5063S0GQ+vLLL1VZWanExES3SzFKSUnJGV9n0lJjJ+7G8Pe//1179+7VuHHj/OfL\\nfT6fMjIy3CwvIIRUC02ePFl33nmnhg4d6nYpxvv44481c+ZMbdu2TUePHlVdXZ06derkv91CW7dp\\n06YzXqR6Yskf1qPDubrtttv8f1NnWvh60aJFbpR1TgipFurTp4927tzJsEMABg8erNdee00333yz\\nNm7cqJdfflnbt2/XY4895nZpRjh1PbpTsR4dztfatWs1fPjwZl8zESHVQgw7BO7EjdcGDhzoD3GW\\nRTpdTU2Nnn32Wa1du1Y+n0/Dhw/X9OnT1aFDB7dLg0clJyeftiLHmV4zERMnWogwClx4eLiOHj2q\\nxMREzZo1S927dxfHSKebNGmSIiMjNXPmTFmWpb/+9a+aNGmSli9f7nZp8JiPP/5Y69at0/79+/XE\\nE0/4P29VVVWqq6tzubrAEFJwzMsvv6z6+nrNnz9fTz75pMrKyvwndvGdrVu3NlpW67rrrlO/fv1c\\nrAhedezYMX8gVVVV+V+PjIz0zMXhDPfBUYcPH1Zpaan69OnjdinG+uUvf6k777zTf+PD9evXa8GC\\nBfrLX/7icmXwqpKSEsXFxfmD6uTLZkxHTwqOycvL07333qujR4+qpKRERUVFmj17Nhfz/l9CQoKk\\nhgvCf/SjHyk2NlY+n0+7d+8m1NEiVVVVSkpK0jfffCNJuvjii5WTk6MBAwa4XFnz6EnBMcnJyfrg\\ngw+UlpamoqIiSdKAAQNOW7WjrWpqEo7UMGP01DtAA4G6+uqrNWfOHKWlpUlqWGf0gQce0Lp161yu\\nrHn0pOCYsLAwRUVFNXrtggtYPvIEJuGgtRw+fNgfUFLD5Q7V1dUuVhQ4QgqO6d+/v1599VXV1tZq\\nx44devrpp1nuB3BAz5499fDDD+vWW2+VZVl69dVXddlll7ldVkA4jIVjnnnmGW3dulXt2rXTxIkT\\nFRkZqT/96U9ulwUEvZdeeklfffWVMjIydNNNN+nrr7/WSy+95HZZAeGcFFxRV1enb7/9Vp07d3a7\\nFCDoffrpp5ozZ46+/PJL//VRXlkZh5CCYyZOnKiFCxcqJCREV155pQ4dOqTf/va3mjVrltulAUHt\\niiuu0Ny5c5WQkNBo6S0vnAdluA+OKS4uVmRkpHJzczVq1CiVlJRw7Q/ggIsvvljp6enq2bOn4uLi\\n/A8vYOIEHFNbW6vjx48rNzdXd955p8LCws66oCoAe8yePVtTpkzRT37yE8/dqoOQgmOmTZumuLg4\\nDRw4UNdee6127drFOSnAATk5Odq+fbtqa2sbXfbhhZDinBQck52d3eh5fX296urq9Mgjj7hUEdA2\\n9OnTR5999pknRy44JwXHhIeHq1OnTurUqZNCQkL0zjvvaM+ePW6XBQS9YcOGNVq02EvoScE1R48e\\n1U9/+lMVFha6XQoQ1OLj4/Wf//xHPXv2VLt27SR5Zwo656TgmurqapWXl7tdBhD08vPz3S7hvBFS\\ncMyJVb6lhvNR+/fv10MPPeRiRUDb4JXp5mfCcB8cc/Iq36GhoerWrZvCwsLcKwiA8QgpAICxmN0H\\nADAWIQUAMBYhBQAwFiEFtIILLrhAt956q/95bW2tLr74Yo0dO/as37d582atWbPG/zwrK0vz5s07\\n7zpa+v2A2wgpoBWEh4dr69atOnLkiCTpvffeU0xMTLPL0hQVFWn16tX+5y1dxsaLy+AAJyOkgFYy\\nevRovf3225KkJUuWaOLEiToxmba6ulqZmZm66qqrlJycrLy8PB0/flwPPfSQli5dqqSkJC1btkxS\\nwy1O0tLSdPnll+uZZ57xv/8TTzyhhIQEJSQk6KmnnvK//uijj6pPnz5KSUnR9u3bHfyNgVZgAbBd\\np06drC1btlg33XSTdeTIEWvQoEFWQUGBdcMNN1iWZVn333+/9corr1iWZVkVFRXWFVdcYVVXV1uL\\nFy+2fvOb3/jfZ/bs2dawYcOsY8eOWV9//bV10UUXWbW1tdbGjRuthIQE6/Dhw9a3335r9e/f3yoq\\nKvK/XlNTY1VWVlq9evWy5s2b50obAHZgxQmglSQkJKikpERLlizRmDFjGn3t3Xff1apVqzR37lxJ\\nDesY7t69W5Zl+XtbUsNw3Q033KCwsDBddNFFuuSSS7R3716tXbtWGRkZ6tChg6SGWy589NFHqq+v\\nV0ZGhtq3b6/27dsrPT290fsBXkNIAa0oPT1d99xzjwoLC/XVV181+tobb7yh3r17N3rtk08+Oe09\\nTtykTpJCQkJUW1srn8/XKHwC+TfgRZyTAlpRZmamsrKy1L9//0avjxw5Uk8//bT/eVFRkSQpIiJC\\nVVVVZ31Pn8+nlJQU5ebmqqamRtXV1crNzdU111yja665Rrm5uTpy5Iiqqqr01ltvMXkCnkZIAa3g\\nRDBER0drxowZ/tdOvP7ggw/q+PHjGjhwoAYMGKDZs2dLktLS0lRcXNxo4sSZQiYpKUm33Xabhg4d\\nqh/+8IeaOnWqEhMTlZSUpPHjxysxMVGjR4/W0KFDnfh1gVbD2n0AAGPRkwIAGIuQAgAYi5ACABiL\\nkAIAGIuQAgAYi5ACABjrfyDkidXp707fAAAAAElFTkSuQmCC\\n\",\n \"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 15\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_pearson_spearman_table(query_pearson_spearman,subject_pearson_spearman)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \" Data set | r | rho | Method | Parameters \\n\",\n \"ITS2-SAG1 | 0.679 | 0.219 |rdp |0.5 \\n\",\n \"ITS2-SAG1 | 0.678 | 0.181 |rdp |0.6 \\n\",\n \"ITS2-SAG1 | 0.632 | 0.096 |mothur |0.7 \\n\",\n \"ITS2-SAG1 | 0.588 | 0.383 |mothur |0.5 \\n\",\n \"ITS2-SAG1 | 0.586 | 0.178 |rdp |0.4 \\n\",\n \"ITS2-SAG1 | 0.574 | 0.089 |mothur |0.6 \\n\",\n \"Turnbaugh-2 | 0.562 | -0.223 |rdp |1.0 \\n\",\n \"ITS2-SAG1 | 0.557 | 0.357 |mothur |0.3 \\n\",\n \"Turnbaugh-2 | 0.532 | -0.422 |mothur |1.0 \\n\",\n \"Turnbaugh-3 | 0.524 | 0.053 |mothur |0.9 \\n\",\n \"ITS1 | 0.513 | 0.025 |blast |1.0 \\n\",\n \"ITS1 | 0.513 | 0.025 |blast |100.0 \\n\",\n \"ITS1 | 0.513 | 0.025 |blast |0.0001 \\n\",\n \"ITS1 | 0.513 | 0.025 |blast |1e-06 \\n\",\n \"ITS1 | 0.513 | 0.025 |blast |1e-10 \\n\",\n \"ITS2-SAG1 | 0.493 | -0.531 |mothur |0.7 \\n\",\n \"ITS2-SAG1 | 0.483 | -0.532 |rdp |0.7 \\n\",\n \"ITS2-SAG1 | 0.477 | 0.373 |rdp |0.3 \\n\",\n \"ITS1 | 0.473 | -0.402 |rdp |0.5 \\n\",\n \"ITS1 | 0.472 | -0.019 |blast |1e-30 \\n\",\n \"Broad-2 | 0.465 | 0.427 |mothur |0.9 \\n\",\n \"ITS1 | 0.464 | -0.391 |rdp |0.6 \\n\",\n \"Broad-2 | 0.464 | 0.336 |rdp |0.9 \\n\",\n \"Turnbaugh-1 | 0.463 | 0.227 |blast |1e-10 \\n\",\n \"Turnbaugh-1 | 0.462 | 0.227 |blast |1.0 \\n\",\n \"Turnbaugh-1 | 0.462 | 0.227 |blast |100.0 \\n\",\n \"Turnbaugh-1 | 0.462 | 0.227 |blast |0.0001 \\n\",\n \"Turnbaugh-1 | 0.462 | 0.227 |blast |1e-06 \\n\",\n \"Turnbaugh-2 | 0.459 | 0.158 |mothur |0.8 \\n\",\n \"ITS2-SAG1 | 0.458 | 0.285 |mothur |0.2 \\n\",\n \"Turnbaugh-1 | 0.458 | 0.232 |blast |1e-30 \\n\",\n \"ITS1 | 0.455 | 0.187 |mothur |0.8 \\n\",\n \"Turnbaugh-2 | 0.453 | 0.154 |blast |1.0 \\n\",\n \"Turnbaugh-2 | 0.453 | 0.154 |blast |100.0 \\n\",\n \"Turnbaugh-2 | 0.453 | 0.154 |blast |0.0001 \\n\",\n \"Turnbaugh-2 | 0.453 | 0.154 |blast |1e-06 \\n\",\n \"Turnbaugh-2 | 0.453 | 0.154 |blast |1e-10 \\n\",\n \"Broad-3 | 0.448 | 0.392 |rdp |0.9 \\n\",\n \"ITS2-SAG1 | 0.447 | 0.137 |rdp |0.8 \\n\",\n \"ITS1 | 0.447 | -0.405 |rdp |0.7 \\n\",\n \"ITS2-SAG1 | 0.445 | 0.058 |rdp |0.7 \\n\",\n \"ITS1 | 0.443 | -0.416 |mothur |0.7 \\n\",\n \"ITS2-SAG1 | 0.442 | -0.071 |blast |1e-30 \\n\",\n \"ITS1 | 0.441 | 0.087 |rdp |0.3 \\n\",\n \"Turnbaugh-2 | 0.440 | 0.107 |rdp |0.9 \\n\",\n \"Broad-3 | 0.439 | 0.373 |mothur |0.9 \\n\",\n \"ITS2-SAG1 | 0.436 | 0.029 |blast |1.0 \\n\",\n \"ITS2-SAG1 | 0.436 | 0.029 |blast |100.0 \\n\",\n \"ITS2-SAG1 | 0.436 | 0.029 |blast |0.0001 \\n\",\n \"ITS2-SAG1 | 0.436 | 0.029 |blast |1e-06 \\n\"\n ]\n }\n ],\n \"prompt_number\": 16\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Find natural community pre-computed tables, expected tables, and \\\"query\\\" tables\\n\",\n \"-------------------------------------------------------------------------------\\n\",\n \"\\n\",\n \"Next we'll use the paths defined above to find all of the natural community tables that will be compared. These include the *pre-computed result* tables (i.e., the ones that the new methods will be compared to), the *expected result* tables (i.e., the OTU table), and the *query result* tables (i.e., the tables generated with the new method(s) that we want to compare to the *pre-computed result* tables).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"query_results = find_and_process_result_tables(natural_query_results_dir,filename_pattern=\\\"table.biom\\\")\\n\",\n \"subject_results = find_and_process_result_tables(natural_subject_results_dir)\\n\",\n \"expected_pcs = get_expected_tables_lookup(natural_subject_results_dir,\\n\",\n \" filename_pattern='bray_curtis_pc.txt')\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 17\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Evaluation 3: Compute and summarize Procrustes analysis results based on real community data\\n\",\n \"--------------------------------------------------------------------------------------------\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"query_procrustes = list(compute_procrustes(query_results,expected_pcs))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 18\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"subject_procrustes = list(compute_procrustes(subject_results,expected_pcs))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 19\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"generate_procrustes_table(query_procrustes,subject_procrustes)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \" Data set | M^2 | Method | Parameters \\n\",\n \"study_449 | 0.047 |rtax |single \\n\",\n \"study_449 | 0.050 |usearch |e0.001000_qf0.500000_ma3_c0.510000\\n\",\n \"study_449 | 0.050 |usearch |e1.000000_qf0.500000_ma3_c0.510000\\n\",\n \"study_449 | 0.054 |usearch |e1.000000_qf0.750000_ma3_c0.510000\\n\",\n \"study_449 | 0.054 |usearch |e0.001000_qf0.750000_ma3_c0.510000\\n\",\n \"study_449 | 0.104 |blast |1.0 \\n\",\n \"study_449 | 0.104 |blast |100.0 \\n\",\n \"study_449 | 0.104 |blast |1e-30 \\n\",\n \"study_449 | 0.104 |blast |0.0001 \\n\",\n \"study_449 | 0.104 |blast |1e-06 \\n\",\n \"study_449 | 0.104 |blast |1e-10 \\n\",\n \"study_449 | 0.116 |usearch |e1.000000_qf0.750000_ma1_c0.510000\\n\",\n \"study_449 | 0.116 |usearch |e1.000000_qf0.750000_ma1_c1.000000\\n\",\n \"study_449 | 0.116 |usearch |e0.001000_qf0.750000_ma1_c1.000000\\n\",\n \"study_449 | 0.116 |usearch |e0.001000_qf0.750000_ma1_c0.510000\\n\",\n \"study_449 | 0.116 |usearch |e0.001000_qf0.750000_ma1_c0.750000\\n\",\n \"study_449 | 0.116 |usearch |e1.000000_qf0.750000_ma1_c0.750000\\n\",\n \"study_449 | 0.120 |rdp |1.0 \\n\",\n \"study_449 | 0.121 |usearch |e1.000000_qf0.750000_ma3_c1.000000\\n\",\n \"study_449 | 0.121 |usearch |e0.001000_qf0.750000_ma3_c1.000000\\n\",\n \"study_449 | 0.122 |usearch |e0.001000_qf0.750000_ma3_c0.750000\\n\",\n \"study_449 | 0.122 |usearch |e1.000000_qf0.750000_ma3_c0.750000\\n\",\n \"study_449 | 0.126 |usearch |e1.000000_qf0.500000_ma1_c1.000000\\n\",\n \"study_449 | 0.126 |usearch |e0.001000_qf0.500000_ma1_c0.750000\\n\",\n \"study_449 | 0.126 |usearch |e1.000000_qf0.500000_ma1_c0.750000\\n\",\n \"study_449 | 0.126 |usearch |e1.000000_qf0.500000_ma1_c0.510000\\n\",\n \"study_449 | 0.126 |usearch |e0.001000_qf0.500000_ma1_c0.510000\\n\",\n \"study_449 | 0.126 |usearch |e0.001000_qf0.500000_ma1_c1.000000\\n\",\n \"study_449 | 0.126 |usearch |e0.001000_qf0.750000_ma5_c0.510000\\n\",\n \"study_449 | 0.126 |usearch |e1.000000_qf0.750000_ma5_c0.510000\\n\",\n \"study_449 | 0.127 |usearch |e1.000000_qf0.500000_ma3_c1.000000\\n\",\n \"study_449 | 0.127 |usearch |e0.001000_qf0.500000_ma3_c1.000000\\n\",\n \"study_449 | 0.128 |usearch |e1.000000_qf0.500000_ma3_c0.750000\\n\",\n \"study_449 | 0.128 |usearch |e0.001000_qf0.500000_ma3_c0.750000\\n\",\n \"study_449 | 0.134 |usearch |e1.000000_qf0.500000_ma5_c0.510000\\n\",\n \"study_449 | 0.134 |usearch |e0.001000_qf0.500000_ma5_c0.510000\\n\",\n \"study_449 | 0.134 |mothur |1.0 \\n\",\n \"study_449 | 0.156 |usearch |e0.001000_qf0.750000_ma5_c0.750000\\n\",\n \"study_449 | 0.156 |usearch |e1.000000_qf0.750000_ma5_c0.750000\\n\",\n \"study_449 | 0.158 |usearch |e0.001000_qf0.500000_ma5_c1.000000\\n\",\n \"study_449 | 0.158 |usearch |e1.000000_qf0.500000_ma5_c1.000000\\n\",\n \"study_449 | 0.160 |usearch |e1.000000_qf0.750000_ma5_c1.000000\\n\",\n \"study_449 | 0.160 |usearch |e0.001000_qf0.750000_ma5_c1.000000\\n\",\n \"study_449 | 0.163 |usearch |e1.000000_qf0.500000_ma5_c0.750000\\n\",\n \"study_449 | 0.163 |usearch |e0.001000_qf0.500000_ma5_c0.750000\\n\",\n \"study_449 | 0.195 |rdp |0.8 \\n\",\n 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force=False):\\n\",\n \" if force or not os.path.exists(filename + '.txt'): \\n\",\n \" urlretrieve(url, filename + '.txt')\\n\",\n \" if force or not os.path.exists(filename + '.csv'):\\n\",\n \" clean_data(filename)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Get data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#http://www.bom.gov.au/climate/change/acorn-sat/#tabs=Data-and-networks\\n\",\n \"\\n\",\n \"minURL = 'http://www.bom.gov.au/climate/change/acorn/sat/data/acorn.sat.minT.059040.daily.txt'\\n\",\n \"minFile = 'coffs-harbor-min'\\n\",\n \"get_data(minURL, minFile)\\n\",\n \"\\n\",\n \"maxURL = 'http://www.bom.gov.au/climate/change/acorn/sat/data/acorn.sat.maxT.059040.daily.txt'\\n\",\n \"maxFile = 'coffs-harbor-max'\\n\",\n \"get_data(maxURL, maxFile)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n 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Temp_MinTemp_MaxTemp_Diff
Date
1943-02-0221.830.58.7
1943-02-0321.029.78.7
1943-02-0419.828.38.5
1943-02-0519.828.28.4
1943-02-0620.928.57.6
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Temp_MinTemp_MaxTemp_Diff
Date
2015-08-2214.124.910.8
2015-08-2312.526.213.7
2015-08-2416.022.06.0
2015-08-2514.327.613.3
2015-08-267.522.114.6
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Temp_MinTemp_MaxTemp_Diff
count23406.00000023485.00000023339.000000
mean14.06377923.2689939.228587
std5.1792923.8412123.533022
min-3.20000012.0000000.000000
25%10.40000020.2000006.600000
50%14.80000023.3000008.800000
75%18.20000026.00000011.600000
max26.70000043.30000027.300000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Temp_Min Temp_Max Temp_Diff\\n\",\n \"count 23406.000000 23485.000000 23339.000000\\n\",\n \"mean 14.063779 23.268993 9.228587\\n\",\n \"std 5.179292 3.841212 3.533022\\n\",\n \"min -3.200000 12.000000 0.000000\\n\",\n \"25% 10.400000 20.200000 6.600000\\n\",\n \"50% 14.800000 23.300000 8.800000\\n\",\n \"75% 18.200000 26.000000 11.600000\\n\",\n \"max 26.700000 43.300000 27.300000\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exploratory visualisation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def apply_common(title=''):\\n\",\n \" ax.set_ylim(-5,45)\\n\",\n \" ax.set_title(title)\\n\",\n \" ax.set_xlabel('Date')\\n\",\n \" ax.set_ylabel('°Centrigrade')\\n\",\n \" ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[,\\n\",\n \" ],\\n\",\n \" [,\\n\",\n \" ]], dtype=object)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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QAAJkZQAwBgYgQ1AAAmRlADAGBiMX0yGQBg+E6s3Vs2e1juBwMR1ACC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\\nTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"data.hist()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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tb7e0/0Vino9dKEsXCTn58tua2eau9Q3p/d5RCs12LpFr54+e2Rc99q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\\niIiIiCLg/wOtwSDkFWzQ9gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.plot()\\n\",\n \"apply_common('All data')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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OlY7HpLQb6PpZAGIdczuqFeRlK9SFJl+qlfAOncYh+/92aApFH80Y5h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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(data['Temp_Max'], data.index, marker='.')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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CoXuxLGROLTt1WhtXdiSbiZaVKM3W9dl48rPX6MTk3cgNS4/MZqN/Zt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\\nSUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(data['Temp_Min'], data.index, marker='.')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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ZauAJTG/Oe/a1IV75gqBlpI6lEz0oDwUowlEhgNJzBwA0qqdICVlfk4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\\n1ARBEASRxpChJgiCIIg05v8DEAvVFj8F+dEAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(data['Temp_Diff'], data.index, marker='.')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Ex8ASAz8QWAzMQXADITXwDITHwBIDPxBYDMxBcAMhNfAMhMfAEgM/EF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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"filtered_data = data.dropna()\\n\",\n \"boxplot_data = [filtered_data['Temp_Max'], filtered_data['Temp_Min'], filtered_data['Temp_Diff']]\\n\",\n \"plt.boxplot(boxplot_data)\\n\",\n \"plt.xticks([1, 2, 3], ['maximum', 'minimum', 'difference'])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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LNgcNAUknX//fcjlUrh+eefx+uvv47zzjsPGzZsUP+n7K1bQnf3QDOq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\\niIiIiBTw//0Azh2WQcCkAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.resample('m').mean().plot()\\n\",\n \"apply_common('Monthly mean')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Ro0AMDb25vatWsD4OPjQ2JiIgCBgYGYTCYCAgJwd3cnKioKPz+/m5bV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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.resample('d').mean().rolling(365).mean().plot()\\n\",\n \"apply_common('Rolling 365-day mean')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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QghhBDCAQqyCCGEEEI4QEEWIYQQQggHKMgihBBCCOEABVmEEEIIIRyg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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.loc['2007-3-1':, 'Temp_Max'].resample('m').mean().plot()\\n\",\n \"apply_common('Ten year monthly mean')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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AUAAGACQhYAAIAJCFkAAAAmIGQBAACYgJAFAABgAkIWAACACQhZAAAA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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.loc['2014-3-1':, 'Temp_Max'].resample('m').mean().plot()\\n\",\n \"apply_common('One year monthly mean')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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aLt6R70tO3qadsD9MxtImKHJCLrk08+gUwmw88//4xjx47h8ccfR11d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\\nAGAAIQsAAMAAQhYAAIABhCwAAAAD/j/yOughw1H8zAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.loc['2007-2-28':, 'Temp_Max'].resample('w').mean().plot()\\n\",\n \"apply_common('One year weekly mean')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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oLrn/wGz36wsauHQhBEF0MiiyCIDqeqrg2BkNTVw+gS/EH1c2/YU9/F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\\nAMAE/x/lWpHPlcWIDAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = data.loc['2014-3-1':,'Temp_Max'].plot()\\n\",\n \"apply_common('One year daily')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gtZzzzzDFOnTiU+Ph6A7OxsBg0aBMDw4cNZu3att5oWERERaXVe2V24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\\nTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#https://www.kaggle.com/miguelferia/visualization-of-temperature-data\\n\",\n \"#http://benalexkeen.com/resampling-time-series-data-with-pandas/\\n\",\n \"ax = data.resample('AS').mean().plot()\\n\",\n \"apply_common('Annual summary mean')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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1mcIbPjHdmDbmcJTNuCSX1EUI+gGJXjkmlH9ZSVK86kBzwvy/deuc+1\\n6U1kLZPfWNpNjIT3KcpoG8/br48pls0j3988qv4Zfqdeu6PCtuvSFlscI3pHEQqmi5vcX02YPWYj\\nWxVj3n7n34mhexYf5UXzsxnVG68/Ni1m4xU7KLvetW6dubEhcvOIx3jhlhSu4HA24/tvj9m/d+LN\\nk9ggoQYF79du3aYmLJfEB6z5GB2rCPs+p4mRplmCCvZ4HTTEYBAJaPCoCH42QyUi0oW4uslCu1lL\\ngnDzzoLJYdlZvP6U+/wU+fp6jIgi6jqv56mNS3uKfFWVtmm69ZuTjJd47DIVJNT4ZoJqfOEuZ3gJ\\nyVfEUy3vEl13Q8S27Wo4iyLBY7SLM4TVnGQxIWOFeUT5MvUbt/Op1zSCateOSoUosSNsjVgsIoEb\\ni5u8MX2LyteIKl4VEUHCo0nNz2Y04wOax7x/5rOn5O5BlXtlzcp73phXvL18f0o8167FDu5kvu7+\\nPdp7Z1064Rk2EhvLPHnGneLTNidtdLw1e/uJAo8ttngSfOjcpT9KjOctVyeeVR07jchjbvs79+9y\\nfd/z//3wFo0LZ8Wbm1TX2ZQwn3cFJx6BDV+f8NhJzNfG5rie8WlsntvZqmW/nHF3Xp78oHZ5uDFu\\nyj3qSfz4AQs+rOemKD4EXLskhgoT2s6Fvl4XVAQNEa1rwnyGVBXOhRPLVzkWXxXRcbPYp/IVJp0i\\n5pTF/ICBJAKss4zaU+eu9i2ihohFUdqmRekMi815ivaE+JviNoeHP+T6skTN8+u696x46ch3k0Nm\\nNFKsGuaHi454VVEiGWBCQFSxTcHALLGEbkcXT8jt2O186sJufP8n5CvdTa2CMZZ4Sk13p7zL3Ri4\\nHQPeLaiX14i+axbtj8ZIU9Pcukn95hvc+M9/5QdHV8/szB6F02q9lY9UIfDWvCPd5hEurBCF/WnV\\nNaIW5d+vHXH3qES8eyiHeRNXOp0W1d6/j7t//8zn/DMkoG88CPYZyLeJkR/MChZP6Hk8a+Ys2iWz\\n9tF1WrfY4mn4tzfHfO+tH23B/Hp9jxdtt+RHEdrqHsGdva9bLxwtI7NV4NbBWS/asc5j87w/qsbA\\nxjqOnoji1uE2VYghYH2BfYRYakO+y+WEuqwoiwm6joeqCqIB18pxfYH4QFnIDaJ0nkbVzor1ol3d\\nhVATo9C2gToqs9pTuZJJXFFoTetaxrfu0rYn67CTtcWu0nn8wj7GNtDvxt/FjU/VaVbt9gvSJRY1\\n69SqqMqqaanigDLs4b0wn5Wniix1NnA8VWjDNQfM6jmzakH5nHuyPwteOvLd7KpciNyb19wZLxHR\\nrp2ghG5Cst69JRZrAhv1wf7Riv98e3Ici1i5hvur+9wvD4gST8hXpFPmSSCiGFWsMYieXKA2tmxu\\noY3KTsUT5nMmb7zB4upV/LgrZ9aEBkRZuQJV5bA6IkpkumxoTl10Fz1NaNkvx5RuRVShfYIb+GjR\\ncGN/yXzV0rhI4wLL0lF873sU3/vemc8+2DlERbrE9/WNvXEFtc+gvDwuPvIYVcfSrbi5vI2q0oSu\\nEk0dHm+VbCz+Njw5sX+LLZ6G+Aybx3cLVT1WAz8JG8u7cQIIcfEa48PrzKc3u7FFIfhI6ztjwccu\\nlBVDQK+9hh7ePd4cbwhPRWhc4P/5wX3Gi46QijffxI8PcOKYtSvuFHdpvVuPM9AWJZPF4RlREZxs\\nmqt1CcoQ9UyqDhpo6rU4CSVsjJUHzmndtlStYnxH+hHFqunWzBBpW2VJzo1FzbXFikY9S2mYrcaY\\nG7dJF5PjOFc4jjuDRkXiw0JYjZEokZVvGFd0Y5RunC52LvKpD9wqlKgWYxLuHwqTSUFVd3UeVACT\\nHJedFFG8Wnz0iCqVe/FNF168o/t9whiDGsutKtCoMA2evWlFWTRwwZJgMNIJyjUFgwfNQJVl2TC0\\nKU0bOKxW/NviHpezhL3VPVx0fLK/1+3AUKxEXBQWteFCGqlCROoabwyZMbThtIt4E6ONrPbHHDWe\\n1C35xLC/fl0JLlKFiqS13FrexofI7TvKIE/52Z84Ty83qAqLpuComeCjYZi/sl5MHu0S8aG7QX0U\\nkjW5+ce43o4f6k21L7deTLTzAvj1Q+g3GxfvMWnapQ08Bo9b6MbVhFkz49XhFUQ3Hagejw35Nk+p\\nqrPFFo/C6bBG4yKj/vO1KVx1j9DO6O99miQdPvZzde1pWwdOCMsVe/2a5dGMUnv8ysdgvL+iKhyN\\n76zUEDstSWx9lw87HZ8qlrHEm0PE/zS35gcclpb0KOXKuQFuMSOmI6p+SlNGyBQXA32rhBCYDc4h\\nUlGsPXEbbCzfTpRlaW3SicJUUGORGGldIMZArBpCv5vrhgjn79xE1DBeOtrbC7LYEn7uVaIRajMk\\nosS1MRGxHB6t6C9qPhEhMUrRlOQxQdV3DRfQYzLUdVqori3hzk98XEmJ/zj8AQdNS7ocIDNHsmz4\\n5KUMJyflQGonaGqxGOp5TTGZ02QDdrJIQucO77yXytSnHDSOiMVL5G6x5ELbsNfrv7+b5V3gpSNf\\nACElrhfq6AOtc7SmwUgfqx35Ruliscm6rxF0rayGOfjWU3iHiHI0h14puJ5HxR/HhVUCRzUcTKE3\\nniP9XZq9PnOTcaWf0cSIj5HWQFgscfMp6acvUky6Ums+6x2Pt6kTbt9x7IxKUs0oxoHllRrVHu/c\\nW1C7wGcHS9rZPeIrfey0pNYe+UXP/rjAXtph1D8rg1dV/DoeFKPSmu4WbNtH79AftHxPu6VjCKdi\\nSEpTFIQfvkZ2+TKDz/zMyTG0SxMwRUVy+z7xcwM49/Bvbdzn3UbGHn/3cdikG7Rb8t3iPSCe8g7V\\nbXjoWXm/CG2XZyqheiL5Ho1LggrFItCzNWmvonfqfe8izgd86Nyg+BlGX0W8Y1YZeslJ5kSUGjWR\\npr5N0yg2JJRuiIaAqAEViklFM4/0Xq0IISCpds0JDCi26xgU4/FBN3UDgkaCyajTPtdry0/bjoyL\\nomR6kNKUNUkUQlNB3ocY8SFy9+o9vIHaGqyPtKLcL6bUZKzMec4p2Ju3iD507QKXK/K2JkThXDpg\\n6RyhBM13Owdm2AjEDKlr2alLeOV8N9ZT+3oRIURPVMEEYTmvqJeO3azCjfoEoKocy1UgHyVc1ILe\\n9dtUlYFRfmwlJ6YihBJDgpMubh5EEQ1IUJp6yl7vE8/lnnkWvHTkq6rs31vgawUGaAwcyhFNUmGk\\n18nJpcvvVeNQUowaBCWESKWeH9y4R6qxi1WI0CyFeKFzO4fjes6RKhrC0tNmhp3bB+j/dJ5WOksu\\niUvS8ho6bjmK99k7PyJJb1E7T4xKFgOTWIHCoq6YllMOi4zxfmQ5D4wPxmS7Vxi3njAr2Tm4S8wr\\n9LV98vuH2CtXKLKGcnqXYnqO/+1/+ckz56G9eYPww3cwH/8MUXbQtcXr286NYtfqbGO6HeXCh87F\\nFSNXp29yvoIlCYrhp0I4fuhVoV4uKVpP/+5N8p/6FEmSA3BQO/arknjtFksfyA7H8LHPPnSNNpZs\\n1EjcWL5PMH2P3c7RHY95iy2eFeGUHqJx7190tXIFqU0YpIMH3jm5L9u7d0l2dhDXQoxkr34MHyPG\\ndgmJBqH1nt7aadU9e4LzHqsNqRZITCEsqOqS9tjDqxSLFctVyTAD5xruTxWJOcF7JITjOsUxBogO\\nGxwhNmiix52BohjqVUs9foOwXNL/4v/O/UnJYKc7XxEDRgiqnVjUJFRFzv1pxJuAwRB8JGbCW9Vt\\nFlcTsmXDzm5K8BYfR9yyAz5WH7CySoVnqMrs7lVW0RI1JYndutKGSEWBuoyoORJAbYKIw0VHTyxp\\n8JgQIDSgEKqGxGeYLO1iwdo1WvAxEmKX4+t8gxMliFDVHle7zqruRWwCSeLwOlxXuOou39FSWElC\\nDAFUcCLUITBtzvEpHb3ve+fd4KUj3+Adi/khvhmi6R6EgF8v7EaFaCImgRAiJglIu4tYi2aC9563\\np/dQcXxCaqomYgIsC88rwSPiTpowa2BZGxalcuW8QQErjqAZisWEisF0wrJWfB6ZrybMZMBR1aMu\\nPTuDlIPsiFVxRD2vqc4nvD6tydtdKHvMZ4orI742zCYw6JfsXYFmuiKKosETnRCNwbVLal8Cu0hT\\nMykq5jduEUQwTcV02bA76Hb76j1BIE/o0qjSlFnrudu07Kni6yVBb3Iwb/F8AgVC647rogqwcpFV\\n07Ca3SHe+j6f+OlfBKAKER8DbRSidjvgR16j9c3eqcVZH/eUdfLO26B6bFU3wTFrPefyjDY6+mnv\\noWNuscXjEE7pFJr2/ZFv5WvemL5FlmT8r1d+oQsZKaTmVKu8pqG9c5tIRdIbYGKKufzqeoepoGBN\\nRFXQjaxGY5dSFG/SZ0xCio9Quobx8gDvS/J01BkXb1+jmk7oXxnRtI4QlD0zZVI67q0CKTCrIyvf\\ndetRMYj4TiAqXeZH9HBwrebn97o48ff/22vshxI7B6IiZpMrrMRYUs9qbtw9z6q9gM0VbNc9qNSW\\nMja8sz/h1UYYDgNRoOnv0gtK5RIWqZL3M+bTBdJEymSIhBRnEtzFV9id38OkluAdVnMkQi0e6xtM\\nXWHZQ0Rp+31WGhksFriqIQZLlmbUzoN0hZO6kpOKohQSODBm7b4HkYj6QNUb0BsM2EtqWiLZYobk\\nSlUpB0cNt23k3PAWJj1Pq+CisoyDtVz3xeGlI98kzZg3gvcBYwXXOoQEUQgoDohpQhtSRFrEJ7TO\\nEoPj6KjEJQYrkVnrKJtAz0A4WtBcSbmzusbKl5CNiDFwY+k5TDyvxIRXrTJb3WVsd7mgfZAVsS5o\\n0hHF5T0u3LyBkyWLZo8cQ1sV+F5D0ZaIj2i9YuUcmXMYGVFpnyos0MTSr4f4tAASRKR7aFXxERSh\\nkYaD+SHy6qvU77zDwf6YohUOF0ova2jikvM7PZJyiTQNvteRr8SISRLcRgkJtO2MYd52ysT1Oa2b\\nCYkLeHuBo0XLx/sBCZ6icjC5zoVLFxns/TRelNiUx7FeFxz18m2Oqh3O7V1gb5ijqpS+onLVuvDJ\\nSVoBgJvN8ONOlTr4zM8QJHBYtzSxo+ct+W7xbnHW8n2yalUeo9QX6Txq145ugjnJjNivHXdKy8+P\\n5KTs6zpkE8wCCQ25XMKH2MUpk055a8SxqoVsYCHrUiRjFEQcKpFEA3VZU0yPIMQuXVINKko7niJV\\nhcgAYmehCd0xG1czwFA6pW4cCZboznEuho58VbGJB9N1BWI3I2ikuXeDhaZcGC0pmguwNuotnVXZ\\nthCCRaKsUzMNEiOV1CR39hGfUPg+QQJBDSYIxnlKk7Aa9tlpLaNaMKsGSUd4m1KmPfLRLsF0mhsT\\nYrcJV2UnrwjWnVS7EqEd9dC6YlXXiDFMXzlHjAl+1XVQEhV0bbUHgUXIaVtHGG42OmDU0LSGtFZw\\nkVnWY3LxPK3cp18L85jh2OPi7X3iqqC+cIHWQRn02Ov5ovDSkW+Iyqq1uNZhKCnalmgSzGiPYuci\\nr3SyNryYLhgfDEqKSwJ16aiCox9rbB7A57Q2orOW8uaYg+xNAsJqdAlbRiZJjzRRVkmg1IRVcCzC\\nhP06YdQ09H1NsjvAVw0VhmQ5Ie5ZplXkQuvxfkWDkCqYuqWuE2wthGipo8H5SIolUBGN4NpImQyo\\n+xlRbZe7aA8g9ijqQIwRKQpQZdHCooVh2WKHymJe0p/v46rI8nzK6FKfe4uKo1BxbpR37qzg0Lhk\\nyEZE0T1kbbsESZiMl6yKyMU94ZJ3XUswv6Cav05/96fwItSHY2arimWv5RNFn7a5wvU7S159JWfv\\nUzlBI3dXd0nLhuVYsT/7eeCkNlh5/cbxtVRV2uCYVzU1DVEzXptc5XMXf44L/fMv9sba4qXFafKt\\nn+J2vja/jveBn9n7NL1TseGjgxXeBY6mK/Y+npKtQy1u7eL0AoNTYkVFmTSOxAiv5krrI6AEGziM\\nJUnrsQFcSCHpFMLQiSSVSI4DMuplAcF07uoYCXVDXOs2RIQYPajtnh8RQui8VBHFNZ4sMxDTzhsV\\nIiEEsiTSy4S0mdGsKm61K6ZzQ9a7wKC/omLIUh09tSQa1nXw6Vy7QdebDEXEEcIhaawYHt2izT5N\\nE5SQJSR1DUHRxqDnemjoClhIFCQ1CGa98VZC1iOaBitd9oYxkUu9AnYb7tbggyLGEgnUoWEoAVGP\\nl4o62WOAFF6rAAAgAElEQVTlHf3gaEPEKwTtBFoxRvLJGJcPwORgDWpSKm/pB4vQndd5YuhHxUXw\\n6pGoRCLOe6QOiMno9VLS9MUm/zyVfGOM/OEf/iHXr1/HGMMf//Ef0+v1+L3f+z2MMfzsz/4sX/va\\n17BPUMU+T1hrEJOS11OqZUFjBJdYEtvvkqlFIBpiMWKqRRff0IyYByQE2spDGnFlQCXF+4yFZsjh\\nIdOdBENCqYe0pUWKn2I0WLIYGq7nFedij3YWGc9zouZQXMBdNNQh4EygV3lau0cVlwyqFj+KNCmM\\nAI0gbSBIjnMOLxaJBlUD0RFNZLIIFMmAJksQE6hxBARrlLJyVMsSKQtYlSyKHerGkNaeLCrF/A5l\\nOSXUKRMDuUl5J55nzgp7xZP5T1BWDQNX09uBqvZE49nNU2ofAcvRuERtwnwVOO8dzirXjvoUzZKf\\nPPpvTPc+zt3b+ygtjTpmxRjnP42xveNKM2Edv925dUQ8t4POZrB34djFFYqT5hTEyOu3J9yfFIx2\\nc0oKVplh1i4eS77bmPAWD+K02zmK0NSectVy8crooXulCjX37szI++f5mc+90qmNo7CY1fSGyalW\\npZsSryfhGB8d16Zv8rHCdnFE7RZxAO8CiLA7aDi3m2KMI2oKJuGgNVydV5xXS6GGpQyxoUZJcOLJ\\nQoKxEIqS8ua/4eoVag0HYtmVgGqGIlirlMUdjsqEEBPO5Z5aLIhB1VO3nXWdqICB2DrKdIEiRO/J\\n+6POZWs8wUCy9rK1laeMnjYtiWEHEzze9IhEJmJJkkgijkJbmpDi+zm5dLnBYe0+t2nnxnbRErBE\\nNWs/gcGnOcSWIJZVOlpXCFnHpm2KSw2pGsgVn1q8ETRPaL1wKBmSB36ymTFpd2nLwLCp0QhBBSFF\\nQoAkJUqkSXMy22lZDB2pN+rIVInREInY0MXKfQr09rpkmJiuN1AvDk8l33/+538G4O/+7u/47ne/\\ny5//+Z+jqnz5y1/ml37pl/jqV7/Kt771LX7t137tAx8sQGINru1aZ6Vlgc8yikQZtSnBZ12BCZd2\\nromQA0LwSXehjKMNUEVllCkyWvCxxKLTIY2vaYJhJ/TJ+yXTeoh3ljSHHBjVJfO0oI0DWpOijSFI\\nZAVIktCmAQ2Bpvbs9RypX7JyhjpPGaaWLIkELzjXyetlOEHoo00fJOBkHRPCoAZQpTEtGEtiApN5\\nzc0b+3DndQ5rpU4+iwBtFXHLKfu3jthJGpQ93qmhqFrmzR3qC/fI2k8RQknVCLSBowKEHv1kyZHN\\nuVXtoShWFd8ISx8oagMITbAsVxUr+zrTNqGoWlLTxbW8BJyrQXvHC+DpkpNR4nGkV7WLR2+WQo2B\\nUKyYliWiESs5vSQSJDy2YcPKFbwxfYvPXvw5dvIXK47Y4tkRQncv570X41jbWL6ptQQRZtOaYlEz\\n3MkZDPMzn42n7i8RJUkMdXlS6OEkuyUi3uGaGkWpY4vxJaUrKcrAaN2iDqtEjayqhugj1dKSno9Y\\niQhp51uya8VuWzAWg2hKT1NEDUECWTTrmKXg25IQPNoDK0pYF8BQFKORRVEzK/ewWcEoFep2RAhd\\nPDTGiLWRy9mchQwoSlgQYC/t1L7SoE1FSCNdBfxu7CJKGQy1SyF2jRKqdoTtBQaxoSUhsRanwpH3\\nNDogEwhqKZseXkBDl6J4y4+Y1wmDXoqqBZSQ5rQqLPKM4BRrOve4USUkOXtNQUyEkOTUQJtmmJ6y\\n7wfUacIkOD4pQllFmkbwNqdRx2VJSNebJ208BEMllt4g4voFmRViaqg10peMVLrNUs87jAZQgzol\\nkuASuFU0/JdzQwbpi6l29VRz9Vd/9Vf5kz/5EwDu3bvH3t4er732Gl/4whcA+OIXv8h3vvOdD3aU\\npxCjUNUBFxRTFUQJtP2EVnq4todznmHiSYwCFlEleEsdBU/b1UBtWkqB4IdUoUdMW1rTEDRiYkJW\\nJERvSYi0RklbZXjkSOc1iXpyIxgvxKgk3tFvA46Mum/J0xYbhdqlLOIubW+XxgzRMCQrW87Plui6\\nbqnYFu3NERyTckHp2q6YOAaNnmgCGEGTiPeR+XRCJZ5VBdV0gUSPr0rKeYEEx6oO1OJYhpQyWkbx\\nFnYxZn+yxIVA00Ysnv3VXVahYGFapuJZVIHbtxrMbEJWL8g5YuVqbN+TmhYvkVYVV5TE4PCmu3G8\\nRhofuOlusl/dx0XHqi0w65zjqPG4kpigxyU9Afx4TPXaDwjiERGG1nBpcIkokcrXBAn8cPIGr01e\\nP/7OJm+xfErTa1XB12Me2a90iw8ch/dW3Lkxe/oHnxNCFBYuUG264sgJuZ6GrLUUXXxVT7Xh26Th\\nxZNqi6o0169T37zOsl1xvxhTuM5rE9u2S52TrpjGUV1z9faMphaiE2g7suwSiky3UW0W3Du6hysd\\n4nxXAlHXJW9DpHVCNB7flLgoBAPUJXq4DzHQZbMK+/OKSdG157PIOjQUcOLwIdLPuvTDnawhVQch\\nEI3iNSA6A999vqoiPnYK7Ok4UJQp8yZjNKy4tNOS5hUFLYl0rnWf5rTRctR2oiYjQktC0IQYLFbB\\niZAXB8Q8EkLDzuQQwdBmfeqs8/JF7Sz44ONx+mHqAyHrEXyGk4xV0qcajqi1jzu3R3X+HM4JbRUw\\n4qitcL65zSG2+2ysSZYT0iCdN9EErFpIus2RNwoxR7xBfEq+UhI1uH6KBiVZVpjEEAzU4cWtGc+0\\nNU3TlK985Sv80z/9E3/xF3/Bv/zLvxy7c0ajEavV6onfv3BhSPqcdhMxCrNVYJgIo7bA7IzwvSHS\\nsyS2wdDQ1imNt2jWjbGqI+JysN3zogAhJU+77keVKLs2doIHrxiGjBIh9iLeQhoEyEi9I08dw0Sx\\nUWkko5rm5GmffmswvYZB4jla7tL3kaXu4RWGu4G9rMUuK84XSmNSmtEI03eQrHBDpW6Vc42QaCQb\\nJKhpUNNHiFgTyH1DNZuSiDIxu5QCWTUja4SGEpVIrhGXWEI0BGOIzhEMxHnD3C3IxGCaKVoOmEvK\\n4IrB9qCtPG2EkTqGeUEdS36GW8QmYpIRMU0oZZc6KiYGJE1J6UQic22YS42GMe80KUu/YqSQ5Sl5\\nP6HXS0j3+gzThIupsAAGuzkyEQa5weSBftryE72Kfi9nnlgOwn0+YS+SDLqH88qVXVz0vFnO8WnD\\n7l7OlQu7j71HivkNXFzQ7+cM937izHvX7y2YLBp+8bOvfCDu6ytXHj+ujwKeZX6zcUViLJcu7WAf\\no4h/HpDocc2c4cAS0wQzzNhLUoajnAzLhfMjzl04SRdywbFbD1hMa0ajnIsXd+j1UxBoCs+iXTEY\\n5OzudYK/kTMktcXh6PVzsoGwmwwZHZUMbEoWLMaU1I1nsrxEnhhI6DSzpiNfk1gym5BlCYdrRW4C\\n6Lrw3sKO8EBqakw/YBOHtT0whrSusLsWGz2ago8rbtcVabJiVxRBsAaUBGMNSWLoZesevViGTUXa\\nLpAL56i0xqYZppfggyW4iLcWrKUKCUYUkyh56hE1nBvW1HYHEwKp7cr8WGOISSd6MiKoZd3btUva\\nVR9QlFw8PS9YFHZyQpYT1WCiIYjFJkJCJASL8xZrwGZg0wGrNMftjdhxJZrtEpIEE5X7N++QpJ/E\\nWsOr9oheU0Mz5ygbojZn19Xk9kI3UO3KXbpen2Yn5WImJDEhBEPc+TgSLLmUhKRHICFvHUlxSJZc\\n4mOv7HKu92JUz8/sF/r617/O7/7u7/Ibv/EbtO1JMYSyLNnb23vid2ez51e6q2o9bRkIuaEfE0wE\\nyRLEQGY6a/RgkTJxOVky4Hxf8KWQF91O12okqqKSkNtAaiMeaK0haFe0w2hGlnlUWl5xLT0BNSlJ\\nEAaThKw2+DCgoY9GoSJFrOGovkB7ueBioQyMoMbQLi9hewsCJRdaRULKcJ6S93q0tiSJYGyfSB8b\\nPJEUEwwZFiMgREIdCfsFy+Yd6twyTwfMRwl7RU1GipWGEJSBiTR5ipWMeevw3pCNBHOuJNRTejJA\\nQiS4jGXYpU0zLp4LRO9pfcqerfDqIKyQXqCeg0kdZD3eLke8U0V2fU0GvJJ7pgr/MbtLEQy+UG4f\\njihuv42fNBz5AeeSmv5shR81tInhyNe8Xt3mti/Ya5f89GSXcWxIY0RDQrtaUOExeG4c7B9LpA8O\\nF9y4c5Xv3Pg27IwwPmMYHlHdY3OPLI7Q6KmaBWV79nM3785ZVY5753qkqWFcT7gyuPRMdaqfhitX\\ndhmPn7wRfZnxrPObTUtiFA4PliTPScRy+7Dg/qTkF3/+Com1iHjqxZugyv5hSlH2SSwsZktuBOUz\\nl4YMjjLcqY44//3m/839eo6pL7OQivF4SUXJD66/wwV/BZOWrFYV2bTCWMuNo5S3ikjtGs4R6duG\\nwhrS6bJzpS4LUjclLhKapCYJoYsBS8GIwEQs86rE5Y6iNBTq8CaSiuCd0PoU7ywqQj+3+Fooy0CU\\nDKtKVMGH7m/ta1QdJgrOOERTNt0FolMWTUvmK7Ks8y4JMCju0WpJJXu0IbDTUShiUiR2wqWy9ti1\\nsZfauG5MAN548ixAExDNMAEwkdZ0de9N8ISsR+YcLqQ4qckbh7c5GQ5LDmq6Ah1ZzlSG7LQtLlps\\nCr6JKAlBDB6o/YDU9ulWvBRvc+reiJYEY4RbzZBmkJInBrU5mu6SSaeNsSQgoYsYqDLAsTewFMVF\\nXh2uGORCuHiJedOSRO1i2FlENSFLIgZH3q6YTucsp5dxz9Ht/KTN6lOfjH/8x3/kr/7qrwAYDAYY\\nY/iFX/gFvvvd7wLw7W9/m89//vPPaahPR5ZYKomU0XBk+pimh/UZ0dTIqOhcxT4lEClCgtPOvx9w\\nYGv6g4LdkGI1cC5tyHNHnjjKmOIlp/WG1EDfe141NZcSRy+xVNKHIqExOStjab0SbQqmR2E9k36K\\nGwYSSUmTmiEBawUbc6w1DEVJU4OoYRgc5xNPXyJGI/16wE6dIQwIkkMckPqEc0VL2qzzfVVww8ss\\nRj9BSHuQeiwBkygmtWQZ9PqGvnqcQl2taIKnXeSM3lkwmuyT24aghsoZjDVYH7B1S9t4jAif3Jvh\\niVgVYhppohBcV5xj7hxNKMiahp+cHdE3kb5RZkVJG2u8BhbFlGUZmLg9Cj3P3WnLfFrjYhdXqos5\\n/312xA+LJVUQZq6mjY7E9DGY9dJgscsS6s59FvyCYvEWi8WY0ntu31ny+rWbFKvHV8Pa1OjmEZ1K\\nQtiU2OvaQ95e3mHavLsG6N7HMz1Ynzc64crL6TLfFJOA99YkXlVpyzvEBxoD3D0quh6t6zxeje54\\nc9b6rn5vvV+w2C+oncfLpkZxhxA9dbVP08xQhaX33Ckars2vE0OkdEt8NaWez4izMbaZ8cMicL+K\\n6zKIXfnX4Dy3DxYsygZtGkyoMVIjQTAmUiSQJTWigo8JUZXal8yXM+Ja879u+kewKV668xSSlGnI\\nub3IumjsOtYbgiDqsFphJCDBEs3agFjnD8bAOry2wsVOWJRIxBIJBuaxQaKQmC7lUNMEsRBtwsRb\\nglgwSq/nSbKAB9QoKQFruqwIGyOXdmrO7VRdx6QQsT4yaBvSRY24Gtt4YpqDsai1XW0E76n7uxyc\\n/xQ+6+FiQpIoiRVi3kPSEcu4S9b0sGSdaNYkeJsiWQJW6WeBQTok8b2uk1LaJyYZiQpGwaqQ4oDO\\nir6QtAz7lguXHXtpSzQ5NX3m/cv4tkdFn6K3R4gpYlKMMWQxkmR97AsUcz7V8v31X/91fv/3f5/f\\n+Z3fIYTAH/zBH/CZz3yGP/qjP+LP/uzP+PSnP82XvvSlFzFWANoYacSzYwXJoCxSdqaRNJ1g7S4i\\nQqUGn7SoGFqXEgcV0bYkGHq9kvZSwLqcXh9cr0++EJwbEc/tEYcZu3LEucYzkwGJdOXcjDeoZOzu\\nKgPjkTLFpxHJ+hSJJzXnyAYl5xPHXvTY0ZDh+YysqbGpMFCPy3vUkrKjgUWIpNEwqj29xtETQdMM\\niR5FCHZEEgqSuiVmfS5nFSEf0NgdWnIcDQkBTI8QEmwSybOEc4njUAXjPdJL8PWAVg2Dec0on1Eb\\nQxkMtq+otcQIUgsaIBlBaJW0tchQiUlK5YZoa2nyFrFA33VijeApUmEVLFYGeBtxV28SQp9gM6pB\\nzr27A65XCy65N/ipT13AXPseKye0mnFUwSCp8d6QS4bBklqoiyU71w6Qeoh++pNUjWOcFMyXJfXB\\nHgvxZD4wnyyJ+Xn6aUIvOdlDqurxoqz6sHrxWBgWhTrWiPdnukltIKIUjSdLLINTwqG28dx6Z8ru\\nXp+P/cTjre/3g3HjOKgcnz0/Ik9ebPrD+8Xp3rTvhXwlVIR23gkNH6owxbEa+fS1dSGgTcRY08V1\\nRREBHwLXFzf5+OhVfHGT0oPzgUojhbTEosD0BBWwpitHayWwWAhZ8OzPPGUw7FzowlWhLnD/4xrl\\nJONIDBfSTnTlvNLEht004FF6CF5TSA3WJESbEu4tiGnEZB352hGcS1ZMXU4WPaQZ873zSJKyExeY\\nGJGkK3+IaUmMMLAWiRlZP2BMgkgnWpJocTQgB7hg8C3sJR4jnpAKtQgIJKp4YyGx4ARvUhonOJPS\\nSyMDG1mVOYflDpcvFvSj79KPDKCWfgarJIXY5SPbKGAtqYu8cn5O4z2SZpg0ZzArSHxg2b9C3O39\\n/9S92ZJkV3am9+3xDD7ElJlIFIpFqJtsiTTjjfSkfAe+ga50Kd1oaJNMarLJIllAAcgEcogI93D3\\nM+1pbV2cAArFYjdpNLKssa/SMsLdwtwiztprrf//fqoVZt2S6/qsclaIpdIHS3TX1Ku6jtpzohhL\\nMg60QpnK1hUa5ZnCakdKNtGiWYfrFVcFVRNVKQwVkwFr2WxHoqqcYkMMGvEt0c0oNMk6rNZEoJq1\\nwbC/ZxfFP1l8+77nz//8z3/n///iL/7i3+QH+qfOxjvu7ELfBWTJcLa0IXN1lZjtOs5ZqqBsQpWG\\nKa8eX4rgiqVwQ7ptuFKF1DQYhNRZYlRIBrtRPJQX7O7vmZOiNoa+QioGnx0/2yt8yHx4hOAyiUhT\\nHdO24rpr3GXGqj3JG46yQxXhftmidcR3wunqBf74ET0aXKvwbLkbBqZty+INzZJIRiGlwY0BI4nO\\nJnZVSKpFjCU3a3ZxFLvC1EVTjYbS0ORVTT21Hr2fUWnBhI5FWpZT4rJxSNF4VfExQMmY6jFZcFUz\\nJ0UnUDDc33zGcumxeaZ6QWlLfrlB25kHhIgQVaWdPVOojB8FubLM3rFEx4ew5SpfOD4ZXpgjKZ4J\\nekNUmYNdVmbuR03Ve6a9ZbZnvntz5LNvTzx9fMMYd3zsWp7yzOPHgZBBVc2wWL787u+wD47m+jX/\\n0x/9AlhTZQ5PZ+Q84bxha383zPx7MU4uwuV4z/L2a4Lew+bVb33fl+/OPDynyPyPf/wS78xKH3p7\\nBuByXnj9j4Gt/xVOeAaORJGfXPH98UTgxzm18DyRUOa/umsXyRxC5MokGlg74B8J534TxP6b/4s5\\nI0uCxlDqKqCqtfLd8IHYDaQ8cZsvPEnDKBFdM0YJD8sDlAPbcrXuYvUaMqBKoC6VGHbkYilBMR5g\\n15xJU6bGSEgeZVaGfBYwJeLdmdaeUUqxZIPzgIZJDmxPj7jrPWI1BZhMpteCtTNNjgS3Y3EtRmVK\\nfUSVQqyCtAbIGLOuZrRoPmkDm1pJs6MZIk87i3eBCCyhY1EG3x5oaiA4zSLrg16xKns3ceQxOYRV\\n4T2aBq8jOhYeZ88slSgeH0dy0FSjAIVBiE1HInPZTPjJU7Vmnxc2H87oWfN4bVEIPgZqquhYySmB\\nFk7jCxSCcRdUydgCpmrKtqG0Ey5UXC5kKslavBLQcb1gaE/vI0dVKbYwN5lTc4u49bKtSgYFRgqm\\nQlUKZYQSLE4Mc7OF6hHlUR4KhUla5uLIHpao+fjdR84/f8mLze9e+v4tzk8OspFKpM8DWiyfdCNz\\nDagKKjh6b5mjh1J4fbrQ9I45NthaqFiM0hS9wTYDNyrQpwCXhU7dsQTPcGjpZaYqxeHUsHhFvN5g\\no2MxLTdWoa1wljvK3kPvcbZifMQ1ht4EOqXJSnHIDfOg2VwbRixfTJ/R2MrYNbRzoJnheuzJTU/Y\\nBCaniPkKG2aM0jhlaEpLrzPVV5RtCNIwNC3kTGs9xTargKyCqpVSLA2QgaVTXPcjf/A08Ni+YPZX\\n+BpoDpleEjefOXbzzDJrsB1aa4I0SK2YogimY5AtxXqyTDQ18anWXPlKNYo2Gc7JrXaK0qMKYBXj\\n9o5sPDpCwpJSwpTEmA2nXAj9jmIyTyFAOGEf9nywnqdJaF9/RE5CLqBVJn13JP3hCz6EmVOzI9Ye\\nmR8YlebvL5l/3yZ0eeDxsy2lFoanhq+/fscLPeJsQmlP9yM5Qvk+sYk1CWoa13FzPJ/gs9/+PVvC\\nj+Ij0zpVCEsmHA7Qdvj+3y795Pvm8Z+R7vjf3Plx8a1SGVPhm3Hm804h469RueGbyxXbXc/PX21/\\n5/VPIXBJhcs48adXsJy/eH6va5S2PyKmPQuLaiVm4Tw/8DhX0tjRdBaplRBnbLf+bSTJa7RlrJAT\\nrs98N33DtosQHE1d+HDJpFBomkhmDRMIFB7vZxrXcDkldN7SysBiDVLLD2NSXRN37hFTAiFuGJLD\\nOSiuMKeZiza0VkPJiDH0IVBbh60ZIwkpQnJm7ZhTAVMRA0WvnmKrhYzGVcXWZBrRlNhANpha0aJX\\n1ffSIDZSQqU6RdSKUMFjUdmjO8XeLeznkbNsV6KeUphSCNETtcNMA5vjgO8jVWtEwOfEIpmsW4qN\\nlJqQJlLUnuvhiFOBs+yJbsMmBxSsEzIB5QQzFkrSKA3GCmTNfp45y8JTuycZhSdic1knbAqcLjg7\\nQ1GoCq0r6GdP9LHdUxuDlNXjW9Vq0XS14HJGURBdeBr30Hl0Y/FToRTNhkqbJz70NxjlyH7PJB32\\nPvDrd/e8eL7M/1ufn9a1GoiXYd1dCGTfcu0XXFw4jy2Hc8sptLiguF40dVAsxnJytxzKSxazpSPx\\n8/HIJ+NHXr75SDcK1/VMUyK7ANPoyTEh1aN1RiXLqBvmzhJfvWQxr1DV0XaBiqZojTXQm8hNrOjZ\\nMijHWA0779l5x86cSVERnxXWF9/TTomXfUImy0Pd8Zg/ISZLUh4xCbTCZIfX0CEM9Zr7vGc8ZRqd\\n2e01toWoDJIV42I428r97Y56Y8hbg5o0NTmy6fhknyhRoaLw2k5c5Ym+Tti+4nSGFr7tt6s5XQzR\\n94zVgNLMyVGrwhuFzhs68dyFDSk39FlANC/HQu42ZO1ocsSpdSgUBIZ05HS5EB4Sbs5UpchaE2tD\\nsC1FWYYp8fH9hunkOHPLdi/s2omQFfdTJtWKiAbryKrwLmtiqqRS+PXha94+fcE8vqeWhXE5UcsT\\nX71/+C3cYM51pYhdAnMMpLjuleP4uyKi9CPLwfej6nAe4O2X8PDht8ar/9rn++7uv5YE9fs8QxpZ\\n/plZyyX/uPOtfD3MxFJ5N5yoJTN+/Zfcf/OOx/Pyj74+/ZDvWn7Y2T7FkePw7Q/v+f3XAXK1lLJS\\nk2KqzLmQklAEQgrY9ISRyFTSM+509aKLyOpDryA5scSJeYmkDGe5cEoDqi5EAlJgGCPN5Ug9z5RS\\nmYwmKk2peh1+1oxWGdFwPjkkeXKCGAM2LeR2i1iDKkLNhTYJLlesZIxWNHpBq0ownmVUqOeQhqrq\\nsx+3pVZFYxRGFXi2HmVjUaWhVrN22cmSa0anNV7v/u4PmbafoLhG25bQXYPStCpg1Rp8kIomG89T\\n/xrrDXeXI26KlOKJdJRqsBTEGLDCbg68ehowKlJVgaJIBU7dHtENPgXQz5ysXNGSydEjVVPbiLZC\\nEzM+J7zJoA1qyLgS8DVQyuplNEowekYrhVaKVgKNqcxWYzvFL26PtCav3G0P9nLhxcN7bBZ0EWIW\\nnnIhNB3KVOySqAJOs1qNsqdisJKZS0W04erf8FL9D89PrvO1vsHYlqAqwTr8NjEWyzA3KOPRA2zz\\njLaGzmU+uivm2LINA7fNCKXwSRjQ2nLJmqw6Gr2wtw9Ichgc56XFagdNQdQOUZncOCyZ7ZwpUTGH\\nhDILTimaMGOWDq13TFIJ/kKeK6rxOGPwbxZ+sTxxYUNzozl7w6IV9twCitwanNLYvDBvElpDpy3D\\n1DDubng9j4zagSv05kyp0HaGZWOos0IZCF2i7XusaNoGNo3DnH/BVD+unXAwxM5yUIaf3Y3raKgG\\nArcoEZw9cyzCImvxP+pbQnFUBXHqGLLCNw2z1fxsf+I7eUUII6+6AZ0i2W7QdUGqplsKsVGgFbMF\\nJZnjfGQvV3Sz4rTPFG0otQOtKalgOiFnhVt6qlcoFRl15iBrFnKfF6ps0NpjnCVI5aFobqdvWd5k\\nmh6ehjPD3BJSIBXLUBPvDwOfv75+Di8XDverV/g0PZHDEwpLjgsSI9qvQIb6/L3fn+//vZzOPLoV\\nobl/Ton6t7Ar/ZBM899A8a218nfHL9jYnj/49MU/+f2/PXZeIRExFb54f6G9SmhR1FLWFJqQfwfE\\nUZ737wahPme9fjueuJ8z19uf/3AxOQ8zrank4pFSsZpn36yQpRByRMuCySNZIn83HBnyREVBWEjT\\nI8MmsW1BUiLmyjQLDYWTu2GN3pmBlhwNdQw0y7KmBWmDkkIArBiWpiPsNpBWYpNOgnKZjKHkkRu7\\n0Gx3ZG2RLCRjEOOgKnyJVAP7G+HGXnice6I1XMcF2XWUUphrR6l3mBrIrgE1o2XlGZuS0Mli/bqq\\nsKpABVsqh+ufE6WHxdHvwG8Nxbp15CuCqQlJEJLG+kwxhiYv2I3jtK/sq6ZWvw6r9Yw2iqwybSqo\\nbFEiKCsUq3m4+XcM6ZaqLW6JoNZxsCmFnDw1G9AKaVoMJ6Q+hzs4jV8CUyhs9YnGCsYWtFY0utCp\\nCa1xJkoAACAASURBVNFbltmxt0803nM0lrv9CayidVCXlY6lp8jVcgFumJLj/ihcbjJ2I3jJnPYZ\\nT8ZYhRRLLh6jKl2c2XIkt5a7298f1vYn1/kGNF9c/YJ7fUfB0vYFpSspahqluM4TGwJiNM5mmkbj\\nS+HF8sin+oitmeV2w2N/zbvtzxnxLKZw40ZetweyNcyyJfV3LNs/QvwVsXrEOiyFT89vqacJ0RBC\\nwiShLo5PT0eiVE6lXQlYvsE1whq55OlSZh8nmpLY9pXSdYS8wWqF3mnabcJvH1i8gFrh5tF4rO4Y\\n0g3VV5p+Zn91z00/oFCIXy1Wxmpq34HZYpKnPUReLQElDcV72rsnrrYzWSq1MZT+lrflBb+211yM\\nR4DN9EQtCq17crEMuieKIVZL0Z6740T3eOTj0nBytzyWLaZe054qewEtBl01dkjoSVBKQCtKNajQ\\noOaW6CwGi5OE0golhWIKrRvIUtA6Y4uicZXgDRnNVRm4LRNlmJBiUb5H+4asNUOKDPlCOHzHeBk5\\nDu94J18RS2I5LEipTPPasf3Vlwf++teHH36Pjk9fEsOR4+VIyInyI696LvWHWEb4jUJ6uEycnGPR\\nmuNzaESt9Z/sUOv3ytT/wpE8I/Kb/fS/RvGN0zvi/OFf/PrvT5aMSGEuv9up1lo5LIlvx4WlPINV\\ncv2tr9cK50sghsTffhcRWa01jw8jb79+Wj+/77tZEVIann2aQi2Rc4an8JwmJAmRymkIfP3+xOF4\\nWItmWLvAqhSqRjwHQl5VueMQCNMHnh4Ty7KOiatklhhZ4sTh/IGUB+YlrEJHyRQMyig+6c7omvFK\\nYzTMtEiFojRVBUJeQ+PFGZTKzN26pql5DUUpUhA7U0lYpwFNSgpEUXQPsiIQj3e3PHZbsskEOxO1\\nxZSIqQmbAlUcmkquFqsBbZ6JdOseV2ewVIqCvs7stQZliW4D1dEmheksoi12SbTTzDYvNJLRWVOC\\nQ3RElIHqCHrL7DactobRaEQZtreZ2+sDd83CVe+o5ZM1SEIXYttTRZN1gzIw+4azVtSyUFUiLKAF\\ntBb6Gqg6UhGiVygluBhozxe2dUCVQtcUGi/0dsEiVNGM0WFSotMFtGazCdDNdGbNOq5OUW2i0YKi\\nslRBbIc3nn66sAtn8JHbdqLxglRDFbdSzVC8tDPVn3+v6NqfXOdbRdBLxs0J80Ij28JNuGdOibbt\\naExl2N6Q9IK+bmljopsmbhgIzZZ0vaGazKA6JHuyqozuiRulse3Ee2NIuqFrItW2hCXTWouumurg\\nzfySBzZs65mbuCE1LVYi1lZOoyb4RF8qxXt2fWFaFO3zg7XzE6eg2O88JQi6ryw7hy4KrTLnOYIx\\ntL6slJzG0phC66FrZqYm8OWHPbepY7urBOdZGk2797y2M/2uYMpAGhc2NRDchv5nhnnrGNKeSV8j\\nohhM5enimfHsG0NjYBMi7bJB48h+S4odEipOFbS2tEZIZsbkG46pRdeEE+HSvqQxCrlk2hDhamLR\\n695ZaxBVIM9UN5Nti6nQpERWQs0V303UxeLVgnYDTe3RLoItqACftgdsPPNV7TnMlSwGvRNe9SM3\\n6YSJE31+YvSJpTZIbllCwJxa8IGn08QSEl9/OP9miVozw3TA5BmtNFMYkGmi3t4C68h5HhOmVvzG\\n/4AvHOcZKizngcVdyPkVb5ZEEOFPrv/LuMt3b0+kWHjxasvxcWT/6ZZ9s3bZIon5/AXaeLqr/wD8\\nuPj+y/9OUjiglMF3n/yzX1NLQeYZs/3NLjY/d5+5pN8RUJ1i5s24FmUF/Gxjfqfzhe8vLxUETtGA\\nCMfLwpv7C6W3nIbIn33imP76/2a4FYx8gP4zak18tygeoocakZIoUglZyGXhl29Grm8H3h8zSSrV\\nVairDayyMI8zlyljEA6PPZOrdLeZXg9s28hHLcRcyKUg2RKqoJRZf1SlnoETlbRY4gKpWhSJpW3Z\\nq0LJhdk6lgQxC1zpdcyaK0tOGKto1uUUwpqvHaNgW6jaomqkmGaF/7B+RM5Uiqkgq8jpyWzJOBwQ\\nTKHoDKw/o5dMtwxMO7/qz4JgdCW4LWNXSIOmjAU2jmIbVJhXwIUSXBW2aUG1lioGVVcjlBLAVXYm\\nIgZOxeC2r9CpUBhQdY8XTbAKU6AgZNsQJYHSK1ijcWzuFl6Q2foLX0mHq4obfUaZAaUWqhaW5hYi\\nWApqmWlv14tG1QVlhFQjJmraMPM09Cw7TacSt62nz5qiM12xoIRkQXcTdoDZwlANOnbspjMv8wXr\\nN3x+zNiXPZNuqNajoqLUmb7M5O0NkioxnKD5pyc8/xrnJ1d8dVW4ywPbUlGqMreVrOCmTkTO6xWL\\nzLzt2BmhfTyxxIbj9Z5ro2maAtJQR08zxHVfs3Rop0hXGpNAckv1FpsrogRxLTYlOGeelo5iLDfN\\nicN4R5GGKpZTaLgoUFtNnyKmLbRkjs7g6gUrC7kT6tKgjGF3VRldQzVwpQaakEiXHWUvaFOxrmCL\\ncOVmUs7MVXiaRpTcEETolUOsQm083hWuNhUlCx2B9jbR2sxFYFI9j7qh9Xs0FROEy2gxsqapzBia\\nNtPLhCxbLuKJtiFFixsDemsRp4jWUxlpc2aeHZqIJxBoqDYzJqitx/kBS4/NkYTHSQSBVt5S3DU2\\nJvpp5LDdcGwKn5qMqpW+WYizoXeJ2XUMMZLEoZLQlcC29jwmBVZjSkJVTa4wFXjpMlUij0VTRTOV\\nGUJLvGQuXz1io+H4tJBS4rPhI3458PCHI9fZkucrcpN4fBgY5X9jv3tF2/x7To8jnYUxCy+fKUnz\\nOKzggNMTKRV+dXhN9quXOH2PKswT2nQ/3KC/+tUDH747c/tiw/Ew8dVlxnvF//BCs/eWEk8ALCli\\nimC14ntB77/EqgPPquJaqfzGjjOmibeX7/jF/rN/JCR+PfMXvyIfj/R/8qfYZ3DOj1nd/3Dve/ke\\nRD+NZLvCBH5cfEsRqq4ch4X5tPCZzZziGlV3mjJv1Uxz3YGC8HTmPJ/5628zNls+vT7ys82Br4Y3\\nPMyZHZYxXfibw98x1RPdXDlP8FASKa9FR8pa6IcsxNM7vsiR20YI05oKVJYMc8BKocqaNkTUXE6C\\ns7Iqm62lCKAzois6WhCNFkGq4vH6BbSKlxwpBSbjQFVMSpgUsSVzkcrsRrxNbLTQVkhKrdGAWa8m\\nGWNRGZJtV7GkFmpRWAPFO2SBwbSc7RVRmtV7b6DaZzFTBVsLrQq0KiBVoUuhGs0RxycolNUwZSRv\\nWazlKi+IXhXBVgp7WTiXLbVqpFqqWm1F3kDnDKOKDGrDXikG1/DxeGHCc10E74Q5eURtyNZjJeEs\\nVFV4fZu4Xk7U4rE28cIHktErMOjqAiLMraI0DTrNxF7Y6QGjhagGYt7QSaSLla13lFiobeW8WG6q\\ncNsqutOeRa0876ozF5fomowdFVUJi7Y0RG4ev0OuthRdYadBrZ9jNh5TC/3pzE5N5OqAWx7PT7ze\\n/36K709u7Fwl8bkZ6FzBGCGiOeQGrw1NTFRTkBpJTYcuCXO+MLcbzu6aUjXeCEpDEkVSmiwQY8vY\\n9kjn2e1huzf4nGlrwlRwVFRWxOBI2ZDQWL3eIHOxDPOGw6mjiEbrlhebwLbJKNYHvqkTw43jcnfL\\npdtQjSZ3huIMRlU2cWZTJj7ZZnoFRaBqjTaFT9JXNPM7LuVM0JpOIoV7NAnXK0rj2PaJF+N7rocD\\nS3bE0uBCRBsYmz1oy5JmruJ7XD6RJ0HVjCkGO4zc6gMvZOaT43vaMIBW+HHBKaFlxUk+XV2zNHds\\nYqBMYEqmbydsWnDjE5tuQbtAqAv4REqCchWnEiDQvF5JNLlyfXhAqmbrGroxYmyi70dYHG0vjM2e\\nD+kWFQtVgRbBF0EjoBRepbWbSR3vTOBgFD5ltM5oFRjdRHKFSSpP88Jf/V9fsbx7JN3fM797y4eY\\nkccnZNzQ1UQYNX/75u94eP+RD+evOA0z88M9333zgY9PE+chEueBmA7UPNPHFfDw7cNvEppCnHh8\\n9//x5v4L4nNBBQhLJi6ZLIXT5cJYK1KE+L3lKa6K628Ww9fDjNTKkieOyz35H3Sa//CEWHh/+F16\\nXH3Oji25/CBMelwOPMwH/ucv/xeewul3XgOQjyuPufwoeSr9aBz+ffG9pMyX54mnkOB8YvzV37N8\\n8w337y+cj6s969v5A//Hd3/J4zLzzfuBUyq8XYSLCPPwiOSJeTmjZB3355T49RA4j4ZQhMfzxF/9\\nzf9OlEQqmZQVp9OFh/HAx/meQzySRHGchCwVres6LSqQQ+H+8p4hjHwIkXNSxARJNHN0qFxIKmMi\\n1DOkWAlnS40OLKjnRBxrC51KKF3RIkTbMNx+Qum6tYCKJolHaUuXJmxYVvV1chhVMKbgVUFjKWql\\nPemk0FWTrCFrQ9UGw8pyroDTQu5aqqpMtaWUVReSlEG0wepMM864IBgLpi0YMrqsXONi9MpvxlBV\\nheVZnOospRhUzmgp2JzxOlPKKi0WPBqhaIXpHVULjwoW25CUsDjLlA3xZNG54HyhSwltK1YLVmv+\\nXfuO//Dynq2Nq82orOuAu/6IMgvzVlG6O07XLzltX1GVps+PdOUD5rogZOzpTEqFLh64qYISy9J2\\nSNvihwUOgo6aHB3DuFsfLTrQqsynfqZzM4OG0XtcHFY6mLXMNw5eQ9gaBt1wX7cUU3E1IdEwPRVs\\n0vB7ClWAn2Dn69oW+hErmU4Ml3lPjIrWfYAiRBqKbVfM2XnkY3+NGEVdHLmAtBmpmlEpFOtnHYtG\\n6UrrBWcUzBpTMuhK4wpaBJMCJmRwCt0WEEFXoSGzaA3FUnFYbbgUjysKCQf64YgAl6uerRaC35Lr\\ngHIaiRWVMwpFNB5/bWlHTcl2BbTZQpOEZrkQPjXYWNjGjBnAx0LbKDbXkbYGMAaq4TD12PGM3gm2\\niWAtfZ2Ix49Iu6eEliIznQ+Y0OEm4Rf5iX0zwLnyIh15sJpyhkU5TKlYKeRcqXTkVkMxNC5T2KLQ\\nXHKDUhVHQYeC5BPFtKjWkvWGxa57PQmWMhdSbJGL4Uo32DRimxNtO9PoDfSOrDQpa8rG0ZAxZQUJ\\nOLXmmPoyoVTLWAVjNfeL5SpHdjtBdZpAIfRCZWSSe8piuHqZKd7zZtkydJF+UbzYKEjCEmFOj3zS\\nJQ5o/tfLW85Nw9U042Pk7fk9n592RKWRGPEpgVRSKCi1diGX4Q1iRw5Tou0XXjfw/nFkmNbi9cXT\\nd7wfz3z2yR+RU2IZvyObl5Q4E/72S7K7Iv3xH3A/H3h7+RKAl+0VaZmwzc06Dv0H5//91QOVyqa1\\n7HqP1Mqv3525ahPjw8hSNbINXLUtNQeWsqyXhss7rpsrxr/6S8xuR/uHn//2G5cfKcS/B5CkzDQP\\nNFzxsCSOIfHdFPj86cBBO9TxhDK3XM6B/XXL18N7klKYsgZciIMS4akmmrKg2bB3j5jLiaA+p+yE\\nQ7FoKxRRRBH+Uzihl445KNyp0qplLTAvKkvJyJKZaiYl6DTPPOVKSoK5irwylbka5mx41Q04SYzF\\ngKrkmnBRYxaFWio3x3uedneoPUgRmpppw4wA3iScSUTtqQrENPhnglYSh7HCRgWkQkwNOfeYHLE1\\nokRTZWUbOwGV1RpjaBRaaZRSdDIzqn59vulVwCRWM9bdyoupiljLCqKpCT8ukB26XT26PAfUV4Gs\\nNT5lxGt0jRitqCWQnOYh3lAlIOmCrYpc18+6YhjyHqcryRjyRpObyJN4GqWf99yKZmro5gXTCe5K\\nuNpM/L2bqGmHqgpTMs615Lny4ZtKW8DcFTywayNDNTS0LL4lp4iXmS6emXc98api55FJMv3Tt+Su\\no2KZjedDclRb2c0L4dJTlSNhwFpSFLxJvNCCzwvzuWXcBHK/Iz3vsSdveJkHvKpkZTlkx6wd57bS\\nKMeLY4ClYHrYb35/gqufXPHNtfCw27Kf4e1ly5FrrnYnXCg/YBNL02I1TKYjzoaEo6fgaiRpQ3SK\\nB6NwveF6jqSsqN5gKJgGjElE52jDyGwbXo7vURmkZqqxWCeIaJTReCWMTjPse0yvMZ2jxE/QMdPm\\nkW29UN0dsbVoRpwzSJE1xKGCiQvzopjYsblTOFtZgqOqyqZMmCAkp+jrhRI125xpNUi5xTaCaQ16\\nWhAMOWh28cTNfGbIlth5gvFA4mJ7QvRc+YmtE9ruwiZDnwqvNiNGArFOpFYz5B2n2FK1I/cDxvdr\\nSkhwiNOIWSPGfIWzVtii8TazH2aOfYMrkUzG78+UoSUBw6xoTwsX3dKWPfVSmTqPcQs6Bop9hXvV\\nsfiKGgW1RMrNOlpf5g4tlZ2eSamuUHS9YyKyqYZR7zjKGV8Tvnqq9pzjhmwERUBtNNJYahPom4WP\\nxrDUz/nwcGTbKC7s2TZPWAY+HBuKPlJ2LY+xwY1HmkPljT0yVss0BLpYVmBBrVw7yzFmYha8KlSl\\n+Dglvjn9kl//cqY8Zj5l5rvjwqXxvCiZLs6Mp2+ZTORvPvyKu8uFrCpTSqT8+CziEublnqgyVRK+\\n/3T9/T89rcv0fkuqglWKmArv3jxRG8t//JuPbJvEn1wJX2fH5Wnkz/Ynyvg19XmEvJTAcv6GdH7k\\n+1xVAGUNNa9JVEsutNb8MHa2v37Lu9OA/cP/nuMsLLKhAstwAQyLXmH0JQvfHWceL5HtrmWYByoe\\n49TqPa0V5wNdHkhLYHwqXNJ/5j++f+ApvkTpwLgoVKu4aMP0MTInQzdlihdqEIbzAO8GaviUZCPL\\nXDEJdLOmpqQCmIxFc2Mz+9ZS0Nz4xBItWKEQMMUiuafNmf3LhZIGgtlAWUMD5tLRRMfd3cQrd+FN\\n7hELVRtsKisasiiaRrhuFhSVmAy5gs4aazJGGubUU/RafEXUuuBVFWEtvk1dsEWQwVOvNigPybQs\\n2TMkTyyavn9COUuDrO+RFZpCqhrlYeivqCmSlaWRwG48oWzmpHt2+YAvW8oEyY+rEMp6smRsHiBq\\n5txhXSVXQ+17FuOp42ohonimS4vTeU0myoq886u/WZ+Z5AZXLFIU74Zb5N0TxTbUqnEnQ+0a2o2g\\n2g7vMxcNtoCLAyKKdIHsFZJWT7EAUjS5TSzzhbZUxCi8XgFBpSSaduB8c0d5EK4O99T+zFISqmgk\\njzD05N1L0rKwtJZrRkxt0ErhXWLAUUrg41FxO26QU6XdVF73v79glJ9c8W2d4w8aDyXybTQoV2kd\\nmDlDqqhOYfYZLRYVQLprMJWiC1uTOWOICjCO5DxlCkh+DoA2DiUV1y3k4snKoLIgkeeIQkEZQaHI\\npkXr1QdbjGa56WmlgNGEpqcPT1wzM2qFik80c4O3Azf1BpUF2hWPdn/o+Th2NLc9+zlwXR6QyVEN\\nvOKAqoXUWrRk+pyxuccKmDCxdA6njkxS6QIsF8er9BHlOkK7QYdKTXCyDajV53hdPKVo3FUmd4Ur\\nUcRB0xRN88KhvCecPdH2WG3ZFgX6mbM7COefbVkax+PUsLEToxGsVdxKIR8tOVqUF2y8rOksVbPl\\ngpoSB3Y0XnhhA0FtKKolK0stHmhwG4iqYs8J5gUTJjpnmVMDtXDbjOinjOszuSYSlhAd1SoeTeYV\\nF5axZZh3FAWiMxFo24aghaKF1/6eN/IppXqeTMsn7RNLTLS6cMl7Hn2Dqke8vuNiClNc2A2VY02c\\nkkXngQ8yoXWhSZmdUnwXMqfTzG0zsbw58eve8mF4oMZA8+j5RgWedKT9gz2xFEqeKR4O4ztknPlS\\n4HCYOP7NF3z+aWUqM2MaeaGFt9NbXtz9GXf9p9ScmX75S4pU3v13f8rHkrmumv/nV/d85h1v3z+S\\niuF+WnitA8dFwJyJbaHUAs9RjCFNpFIoacD+KKCiVM3p8czpck/qbvn8ul/HzrUSQuY/3d/T+Jaw\\njOxPPen6mofhA4u/w8q6wSpFOOW02n0kcY4Dpu7RDhaBWQIqZxq7kAIEWVDuIw9vCqfGsXMLmsIc\\ngbYSimBiQIeGp2Ro7Ix9fyA/RQgT6XrdtyKCdi0oIRZNVavYaeMSc1p3vrNxOGVXXCIZQqEsLVeb\\nM7VWNvuFxWwhy5oiVFssin0zo0Kh7RXVFHovlLkjlgpOo6i0OuNJ6xi3gg2Gz0+JqetZcM+X7Qxi\\nGYJHtpauRLwoGpVxKXOeFf4K8LD4llI1MRiwiloi2hmayhoKnyyeDDqTNx2z7WibipsKWmlqAPoG\\npaH1gXYqlEPh8iKxVEeDBYmICAaDVIOtgs2ZYh0VjWOP1MLTvWVjPY23DO2WeLboIfDST9z0EycL\\nmxTwCk6x5yYfSB4qMzbeUR4cvsvgezDpOVVOsZtnSrDIUuDOYLMhZoOk1bud3Yy7ZK6XwF2c6Gtm\\n2rdkBz5XCIX9cmHz9UjUI+NWgxPKxtLqAvqKNwbscuElgustXVWrqK1UdM6UCB+HPS/rA4s58e7p\\nzC/ubn8fpeynV3xLEdqgmbNj2ySaG2AEU9d9WXHgUCgv1M0VXXOFKSd8F/BWU6SSxGJrJaotk1rI\\nXvi5m0EpjmVH6ivbOKIHCMuFSiJbjREojUdlC75Fi1m5okajPTQIyha0WBzCnWx4bALaTmzH94Td\\nHlcrKmRUY9gOZz7aG1zX4mzlfOm4dZYbF5EIjSp0p5Hlj1/iwoVOVazT6ARtjuziPQnLeSrU9zva\\nVGHTcuqukFPF5ANy1+JTYDLtGkAhZiVPKVAOvIciliW1iI4oo1miQ7THU+hixs2VJgRsl+gQPl6/\\nIiWDWgq1gSEDZ+Hx6iWzRK5qZhNm8sWiqsWKZ988UXTi1c3Cz02gZ+KN9CzqGqca3DSxd2ce/RUl\\nLqS9JceIO2k2eWJJhtdqwNAyzSupyMYNWitivTCoyvlyR0kVpRyuVBorPIiF2mAiDGokP+eX1aSY\\nlSEmhbcZrRVvm09IKiCyTl4LiVwNj1Hxpa+EYWb/NHDaGup0Zv/1L3m4euI/TxNbWeD4K9LSM36T\\nONxd46dIbAJZKfrthb1SpPGRaGeCPa8P5Dly0htOref8eOL9RkGfIRcu799TbyPz5ZF6U0kPD9Ra\\nefsgvMkPyJVnyIUyJeYwMRy+QcwN2mn+9ssTJ9Oxu1oYQ0TSyDYeOestUcIKZUCoPxoxv72f+Oqh\\ncPMiInPi/3x84PU+AIVHlXnMheZpIlxmvj56Xqf3pHniXDvAkmKhZEHcSpMaSiDEimUB0675zsOJ\\nsJzxdxuiEtAVnQOfqMTRRrQUss7kbNaibye8BEzdkxYYdWUTM4FKUxIyL3RVKBniEjGqolUmF0Nr\\noDWFuWrqUgn9DUY7qjwyi0ZCw6e7GSsLcbZUtxCphDXICzEKawqqKooC1RhQ0NlCwZHzCvKHiteZ\\najKLqSTdYHPCl0zOBdFr92qyYLQmYRlKR6MGdBU6naiyQm2aulqGTqpFibB/OpJVJt0EvOroSFQq\\nU/L0OtG3E+dOUZPiqgw86RZEWFJHJ5F+n8m+QqlMRbNoTU0erTRGFjCV5BwNgS5OBLNFMMzqhr14\\nlnhCYoM4iF2DbPboMTBHONeem37gU3nHtVEgBpKAyhgb0VWjp0TRhjQVyk5YlCKjYQzsk2KKhrbX\\n5E2FWaOiofZhVYdnTV4WfArsK6At6qow+z3dYeL6cORqeaK2His7NnFm8ZVtWDi1PdpkGhp0vCeq\\nhdfxhuTdCu2A9bJE5bRVzN0VZgPLZYa7308t+8kVX6M1zhQmAy9fJGJT+C5smQFsRNwGw4p8q7sr\\n2tDwsx7UtiDJQw3oormqL7nMwuAadiqANTStwsiabel8hc5gwkQ1ic82Cw9hj32xw50TtlFsXaGI\\nUDo4OOFGz6xGAI1VGrf/FBu+ITUGXSEpz9ZXpsnwanjkEHs0O3a7yk0fmMeGq11DmCOla2DaIO2G\\nq7JlShNeHPtNx//P3pv82rKmdXrP10e3ut2e/vZJ0le5yiRYFvIEwQSJMUNmCAnlEDFIxij/GEuM\\nzQSZASPLKiVgyHR29959ztndaqP52hrEybyZUCSUDWkh+R3trbUj4osVa6/3e7vnV/mJcdCwjJAT\\nx9DRCo2RE2jNqBvgRBVvkKFFKE12inFsGLRBUDhIzebqkeXBwK1CCM30ILgTS0owaCupc48Ie4TY\\ngDG01RFRHnh4dBRnWDycuKOl+IKLmWwMwkfAUBVPGCyL1Yl4GFm4SHO259x4lDe0MrAxgQe1fDdr\\neKSowvXwhs98TaoMPmeG0ZB8JEdoO0/tDKOXxGlG6uV6iRkl475B6MTCnJhGMCljU2SwNbGssPoN\\nSQ085CUFg3WzfvMxO2od+Z44J3oQbFHmnMSE4JGiIEyWkcTq888hZsSFoU53pMN3+T/+6m958+Jn\\nCGrH+XGLj4VHUTOSSNZydHty6VgZyKJQp0/ZD5mVigi/5+5Npt54NnXFY6g5jBoVE2J34v7OEbY7\\ngvom3n6Jb3/7/+TtQ0/2S3a7gVOM+CmT+8B5c89YPDo98NjP86CZxNG/5fvfv8Md7tier5EmMcUJ\\nhJvncONMkhpOnr95EzDAdsqU48T2MFL23+fpRSLxBisFu+kZbx9qVJas7k+U6EmNoA+Zb3zzryEp\\n+suaqUyUwTKmCZ0tCQ3RkxHocWI93iNC4aA1GwI0GicnZJ7hJTGCGj2ploh3uNFYJKS5CafIyJgc\\nLgZ0Vgxy3jF1NqJMQEuoVUSlWej9fnQIqRHWMh41x8lwbgtOR/wusd0vyENiqzRWJUJWRApVPZKU\\nZru4IErFVdtTcJxiQzSZLDQLTkgFXlZMrmUyK8Q0E6tUKqA0j5Ph8WB5hiRLPTsXIVHZIwWkJDkI\\nwXmZz3koHSqnmYZVThykpcsSQyYj8EEjcsBViiQkVR6R4YRfBsbUUE2ShoixBY/gdIJcJL4oRBHo\\nkrD5iJKBJAoqFxb+CBU8pjV9aWlCpGwNIif8eGBaazbTwLIJtP4t4+GK6VxTuYmcarY6UsqOB/qS\\ncAAAIABJREFU0Xqk2eLkFfLOw+SZGk3/RJGyJT3uOXv7FnEuqFRkbWHaCR4ePSpNhOiRUuHfOFRM\\nTJuO5jgwnRJCRJRM+LrB7HZU45HUWsa8YYqGnQF99HRe0lx5Fv4ROT6QGkPoDVMxKCsRzPjKrART\\nLbjQhet0yVn9/6ed/0mTAj5a79gGw3FxgWDks7DmGB2p0kgtsDox6oplt8C6lnV+5D7UjFnMWo4Y\\nLtKsED03WzmcdRQBGx4ZSoNxBqqEWkVIAk3kxcWR+/YlaIvJFU/XI46OT63GW0EnJ0LOkFqSbMEG\\nTBAcljVuSKAWNKqw1xXj2DKqK55aSb8sOAQkMEXz8uKEUprW1/jXZ7zuDHGQ2Nix0Fe4umH8bEey\\nJ27Eik3dI/U5Ko+UUXDsWvwyEJ+umbDIDNlaEvP87VQck3/Gh/I1qi6EyZGLZa8UWdc8DZ6pCEx4\\nAA1V06OCYRAblEpUY2DTnagnz/MsONt+xnj24TxInxLKSJTUPFn3oC3B77EODtogfGEaC8WBqTWd\\nK4hjT4qFTIO4T4TREJ1B2AJe8lnccM0j8QhD0pwy+KbCSSgJ6gF2yVCJzIWLZDPSZ0OnJ0wJkEZi\\nGTFeE+MSaRRWBpKVHEqNNp7BF2To2VUWZS2UiMuRUEZskSy+czePfGhBloWUEkfdE3MFw8hUZUYf\\nmXzgTStJ9BSr8abHvqP7BKs45ETr4eQL5rs3DH1N1S0xJWJUpE+WjYgMdxoRCw/3kvp84ltv3vDN\\nR8+3Xp/oSoZqzTYHTjGy9ooH9rzVkiZFTqeE7CIlFSYfOG7vea0dY68INqP0NGMB34kTHB9P/F9/\\nc0s/JhbNRJCOcByJuztStUX1W5ixDjw+DOSkkaIQDj2lVRyiJ4yGw/hIOwZyFgiZkapGhgndekLR\\nqOhJWTIZiY4DOlT4LZS2EHWNFZGS55piKbMAgR7mrnwhM1MpyABGS45BkJHIDClLgoogA0YW0rt5\\n2VpmxFBIUXAqNW0BpzL3qYIyUeu5I/dx+07CT5iZk1wKHk1UEdXsiaaGk8CYGf6Rc8OBDrksnLBc\\nl1ukhFgUwWpEVkhZc6zOCd5y8jVZRIRICGkISs8bjCQxRHISZBHISOLkMEgyFZqJEhPRZarRY8W7\\neWRTc6qX9Pmex9ogZcYOE4OC3aKiPSUGkUkGJJn7qNEpIYomnTZIdUAwMA0TIt2TVY1+7LF1wGiP\\nnPZMpaHejbhYEDoQVprK7nH79p1s34nHRrKfDMtakaNh7zv0ElLbE7XEJI1XAImtFNyFliwjZ/FA\\nag2UgDEFa0bGnUH6gtY9Ljbc3V6xvN0hzJqg1jzmt1yMb7ma3jDqBWFRU/ojUVv85Niv30M8PBD0\\n58QgebrdcqkS8TBw22guTEMcNCVCUQpVEkV6tCzgBS9Mha4XhPDTo8r9u3O+IgW6Zk3qO9AbarPl\\nQtYoDImEcwm9tmgvUMqxXtcsU82wG/AobGWpbMdiWhCmE7uTwZlEL6+R4oArR7Rs0JVGTRIbLXp1\\nyXCXKLqjPmt5tVlg73fkOCKy5rJzDKLQoMlIxtTgVxVi+hQhBMfnF3hp+WiqUQXqWvOt8gs4HXgi\\njwQl0UWShKQfa3InWFeCanLQBGrt2TmDuf6Q+un/gKPF/q//G+5x4GkL7UWNW2Sa7YhIicXGctfP\\nsJDJtSRRkDmQDNwdWoqztK5Qi4ZsC30xLE2muVS0cQfCoOSC5DoelGG9fOCsl+wOl6SkeWV6pkpT\\nmoqf6d9y0oI3rsWIWUs4a8lqEalqxemkiTvFdlHRdYKS9AwaUD/QQ/bociBujxS9ZDjWJF8TREEr\\nTU6KSdbEtCN5ICoiDWlfM9iZy1r5yGOUJKnIUVHlCcWEKtBNA0cvefHdey5Wkm+5Z0gDarGljGuO\\no2ApPVqCbTLFLNipgh095BlGf60fQKVZPq4OSK0hZqLMNLag9AGRHVMRyDQRzQxjiNJQWk9XjqBq\\nPAJDZjtpfAmYNxOiqRmdwJ0iZ2x5Xcw8KpIyQ1Rsc6QNib/bHvh0WjA5jd15ZJw4tnsme8DewOv3\\nHB5LmBL7cGIZ8qzYFSEMPVNXgw8wDfTDjnK+4gfNVp99+pbPXz8wpUA0O1yskPsTtX6gjweO/Y5M\\nJhVN6CMhKySBkj1jgfHzQJYDVmfa3S19kZSL5+Qk0cMRsXCIPJFzIArBJkaiNPhYUU0eXWl2puMu\\nbNjwSI6FogAEy2OPSh1CZpJMXDUjGwd9bxiOiSLm+dQsCk55NIJQNEZDOs69IDLI+Us6SlwBqRUy\\nRGrliNPMhZ65JgUByJIIyZKEpG1GgjbkqBmyxsSeU7rGOYkbJ3xUYACVyamgjEF5SbSGk11xO5wx\\n+Aqp74i6kI2asZMikpOGWN7NF2dE8cQ3A8Ws5+anEJj3bYpuAKUjEw29WzLZirf1JdEISh6xhwOP\\nVpNVRZ0zMUX6AjpJTlJy4QdGGkS/5KFE0ug5dD2xWrGtt6wfttyOmb2+YDclir8nTQ2qCKyIbCqP\\nlIlquEdrgZEKqTNTqvh2bOm8heZAVRmktyRVE51FyyMnsyO5a4osCDKPXcNFsyV+2qPUPAueB0eu\\nHSJVLLzmGCQyzd3XJVk+v3yCY+AinnjwR3y14dw/zlqPWnNRyznruM3cL2qaCKvbW7TKNKXlqGt0\\nVXEcLBGN0wErM0JDyZbN2YQ4a6nqn55L/HfnfJVrWb3/azx+D7xVVM5SGUlWek43tRKlNaY4gjSc\\nbxpcb2hGSy4Ra2uqruE61Hz/dU/rFGYV0WdnpGlAEzFKYVpDbROMK2Iq6L7ntDqjrhp+/vLLeJUY\\njv83IlpOtUOPCfQZbcpUsqV5Iin7gd49sKyXdGLBMnZU3DDploPRdGuF7k/YFNBaMSEJXjFOLVw+\\noTN7SlOh43cQUmKrFVfrDb4Y9Pklsn+LVi2tqfiFX1mx/9+/A+uXmAUcD4qYFUppZK4pMlBURhiD\\nXRveu3yOULMIOFKj9Ui9NiwbRxnfcKye0O4VD2zRJtO4Qj9E2rqgRsUdDYtn4F4PHC0kE1nJPVtx\\nyaALbZmB59pHdDjyeXxJOcxAdak1RvYUlfFFUMcDvfJ8+rjhfNwTdc3dlLFYUi+JSpGCwzMijUZH\\ni4iAExSfiadAEoa+ajhOnv2jR6wiVxnOj5HkA05WJN0BkgU9Nm056pqTWnBbGbL2BCKiHGgHzRjW\\nOCExwbMOA+3aEt54trKQskRnMLUDaRHJk4XFC4PSnip7UiicLQaSEMggEM7SJ4ebAodBgc84YfGd\\nfadyM9BZQUwr+n3FOHli1NynCkdiP3km68g+ktIEyePVPU8e7jnXibvpPUKzoPiBIAwRj04TMc4b\\n1kSeIRCmEKPn7c33Od7AcnXgb1ZbHmLiqHqyHGjka85v7khPBLusEIeeUhxJzRmgvmqYjpE8By+I\\nnAnBY9NIEJK9lURjYQKVI1JmRM6kkkhRUE8jySw45oYmTYgIMc112dd9B3GgW7+LQCJz6tglis60\\nVeCkzihyx9hWSGOQzES4xnpM0tyHhpPpyA89m6pwKB2xtgypYLNg4RSOEXHoiBMgC0dTsSjvauFy\\njrzHpLFWksoX1CvvB3IRJKVJxaPcEc0EOVNkgkpgciRniRQWbRSLqWexe8PNxYaoNEVIisjz5iwp\\nxkoy9oHKe7oxkQdJ1hYtIg9txcp71mMg20ASetY0zglZIErL2fhIGk70q0scCpktJQXuxxazD8jN\\nhEkDQznDqcwkFH2csxPHKiJE5vX5Aie26OPI0QqUmMhyhcmRtZwlBqedoxy3CHfC65qDFuRkCUJy\\n3CSc69k0DfJGMNWSaATSOLSZiJuaIuXcia23NOMjop+4D4ZD0FTFznKLaaQeNKVWgCZFWOz2TFcO\\nlEGKwBWPpGxYKY8qnp2RrDWo6yXD54IxtCgRKcmhuohlQNsG07UMdw1CCVa6obMX2FZhqpata3h/\\nsfhHrPF/S/t353wBmvMvI25u+Oiiw9LzeHZi93lHaTxFO6RUnNmag1B8uKy5yRc8f35NNX6b/dTz\\n/PqXuNveIQVooVANVE1F8B2xHOhUwGkH04lWLVgvnnCzWZNyxGiJkZqgNFIrNJbNqqYRJyojaU4N\\n1bLhSy+e8fePCVntuZYrXL9AUhGL5Kxb460mbQoL8ZbTKaLO1yhvEYce11zw8pNX1I2lvP4u+luf\\nol3LarnkvWWHU5bXz94j3T4g3cSFqVleX/LwH34ZLc/46O413x93HBYCKUEWN+MenSBIxXK54b16\\nzSkLejKuuqGyCrGUhNpx87Hm4+2Cu5OnGlcsygO2GmiaASUcZ+sLLkzivjvDT4m0H1gMN6wvMzfl\\nBVmEuaYkK0QaOMmCqTQyRPpgycsGVWXQhalIagasnhhLD5UGU82R3wC2eHKCgZpdnAjdXKvUaaRx\\nMJ0C+zA/Dy0se7ckdgdCW7GSkWXj2XqNWnUc7Jp0kLRyz4KRzJbD0BL0hPcTNY9UC8966nj9kLAi\\nYqJEO0UqFbI+sJjecpzWkDv8sCBIQT8EsrEkfcGZu+OqG5l8QYqWzgsSnsKJYXLopBFExBTY6wbX\\n6XdSdwIrT+A9U78kBEEyhlOseZSeUQhQgmwNQRZKyogkUcIzyEh8B5tyIiCsnAlIU4BHS1SaFApK\\nF6RM1DcPvB16bg9rVvsdp8pxGGCioFCM8kDKBeU7Riq+IyS2KBaIOfUpFLaMDEeomsSM9C9Uk2B3\\n/pKhzYgCsp9LKciMzIWcA+YY0FJwtG7uAk4CmWCUBp8lg5loQwAUShViqTDFgggUJZmMI6klJ1PI\\nQiBFJjo4kyNtNdH0iWVQ3CZ4dpR893jGdFaTJIisCEGitOHaFkYEw/hurKiuaKcJBBSZyBl2uYHK\\nUPee5AVKK0gJESaOwtJbhcnbmSxFYdCeqk4cR8hZk0SNSAWVAypMXPg7iA2JBSTNHGlHfDWx2yrc\\n3pFUZCiWQENREyejWE+GZtgyVpak56Yvu92iNhEjDOvjHfcxE9QzbBLErEkYhElI0VP3gRQNwVqU\\nLCQEx0XguG6Z8pK6txgPQRa0nlB6xvhSJBpFrQMhLuEAmbfc1+AmQcgSskaVQmh3VKbHek9OiVOx\\nDN7SbgxNEOi6p1ae89MjtlYYL8gmU4qkyS1+2XC6fsX1t76JCQ+4IOhsM4NBtERKiZUWpxLXnWCp\\ne5KQ7J5dYo41nVvQtIn90CCwHKm4pkI1noaI7xxaKFZuyS5POGt5trlgfRaZbiP+7Ckff/yUtrY/\\nNT/279L5xjDXqmqrEcnwCx+1DOmabx+OfGpariqH5pyF0aysYXX1AQD9dmDdetr6jDfiESXAaMu5\\nXnJWXfE9WZFk4KJRPF1a/u54oJaOztacqTVHf6RVsxi4bZ4SzQMiSnSred4a+pPAnBRKK1au4xcu\\nfhafI1ZqytTQbCr8KbFabhChYJ8WOBYkhU1XM9wV6kry4n2H0bMwgFk47NUZ1XSi6xxGzo9MGoN0\\nC4r0NKrCWId68QH67QG932EKnImaR62oOSdmQd1m3o6Gq+sF68pyKk+ppOT8eqBNI+LijMlv+ZCe\\npw2ks5q7W2iUpRKBShRoVmwurygXzyg3f8dbDFoV6uOEFQ5TVWQylYIcC3WO3FY1xhRIkrEopHBk\\np0HNUHOVA1UoWBUYXUPbVOjHRBSC1o04CkyWt3aNaVpiTrhwoi2SVARTmcdHlBWM65pDp0AI3k4F\\nvUnU0TO0l5QgMfsRZRPRa9y0x7xWqGCwyhCxVATO/JGboJHJIG2ip6UTsO9aOFiSh7YTODQxJ6yq\\nSRSOU0frMrpkKpM4xIrlVtCIiZu0YeEDJxoaHuc6mm3ojGBMcJdbVn5AkKCfm/OS0dxWz1FdjziF\\nubtX1UQnmKIipBVBf5NRCmIIqFSoWk8bFLqPs9hFL5lWllQKcSqYojgNmjteYFREDxPbveLgBUIk\\niHqug4mewBoGQdaSHkFXwOqMUAJHIApNLy2TLrQ2I9DIWqCkZCUidgrsnSaryKokymlPKBq/qBiN\\nYyiWnCQkOFETi0KbQlIjmYYGyXWrOO4VVk2cVxNRLaFIkqyRFCqVSC7zzA4kO8H3EudN5GH0WOe5\\nL2eoUig5ogfYhQ6WZzwXN9gSET7S1wZhoERBKQIVBEkrpgSLWBAhIZIlT4mYBnzws0asiSzuD0iT\\n8ApGoxBKz4pIwLjX2HFEn04UWVgdjoR+h2kXSFkTE1T5wMNpooQzVAkkI5h0Rcwtb9tE8QMmQzX0\\nDGWBdw4v5kbTxfTAsjjqfmA0EvP4lk4kAtf4lWS6mGh2O+rJEYsiGYmJimwHmjLiHs8ofs2QA4KE\\nbxUnKxE2IceCKhJjJZaIHDI6C5JNlGVDPFYIr5FIVI64MlIlQU4TUZ+YaPHRwvPEGGqKTVTTa5o0\\noUSNGiN9ZQiywqaaZBtEt6TabBDjlkVM1DhWzzZsVQLhsWhybnhyuaJSK/YPCZkmUAZlHE01sWvP\\n2STFw/IV6IhxN8gEqnI4tUJohxABqwXVYs1HL56gnm3RT56i5E8X+PgTnW8IgT/+4z/ms88+w3vP\\n7//+7/Pxxx/zR3/0Rwgh+OSTT/iTP/kT5E950fHdNl9pSSkapSKmNrzqC2frludX1zz6BZfrH2fY\\n1suPKDkipObVlz/g061noRpePV1SjKWWnl7X1HVLo2s2i4pyrLhaN/NoShjozLwzMtU5bv0eId5x\\ntX6G71/zsr3mu6fPqBdzx1ylK560lzwMj/zilz7gtPdoc8nbmwNXS8NeTvgXzwluRKUGVyYWtaDS\\nD0zHWfoul3luz5gWJTVSzO/1clNzf6dQClbNisXZz/OiDYjhW+SmwQ0Tm81LlvUz5MLg7wVZv+V5\\nMnx8veSjyyXj45ECfPzJU8LdW5YXL9i9PvBcrqmtYHmx4soHuvoccaropICrX+L6P77i9vUB032M\\nW2UGcWT58D2EcJjK0gdJZ95S+kIloX5e81pYZCyIKWNKQRqBlBkjBFoEbBII6ZnUgvXZmrPpkaOW\\nLASzutIukIWj1gXfSaQ/4d05fhQMziJKP8NPWkH2V2SZ+bTsuC4nQjPrIuchsuiPqBDxURFPI5Ao\\ng0NUiiwt4V4T6onRrWiGE0olglAETtwLRxBP0FFQJs2gFNnMXavOJnIyHPySqk6s1Q5zOEDnoE8c\\nY4ubjjjrUJWgsoWcFSEpBmHoMQy9JfSzPrNQAu0kKWum3GCmkZdvP+Pm6j3GxhCLpAiJwDLVDeQ0\\nq/XomVOoR4uWARUSo+sYUegUKVET1YZTabAmUI1Htg8VwySpbMZONWXyyHripFr6xiKKZAJMiGzS\\nkc/0ExSJyTQgEtElVIEkZqHzjdzTTD1D05JkISpPM0REnbk1Fb6dxUQmDDlLPIoRRcqCyiiKCTxO\\nGukMF6JQrCc2gqLbdx31iqxqivQ4P+LOEwRD3GXaNOER1PvMpATZVMiYcbtHlvtMVAcOP/cB+/4V\\n6n5HiYWp1kg1IHVBFJiOjlJJUInxKKlOGXd3YOhags6Id58pkTPjoyM9m3Biwokz9smgpiOSCjEG\\n2uMJ4onRSIoUJFGowgmfNQyRx7cndq3CJkEdAtNKUJwhHSWj92iRUFEy5BoRYPH6kf7K0a8kjRyQ\\nPmL7TLAWLStEVkzOIUVhLG9RRFSu5k2PK+ixoEVHwxYRJJN8zVASp+opkz3DLjxLHRHFEKXE2RkR\\nW00DISkOq8j1848Yvj/h/cyE1qnHZqizQpRIcBYjNI0dWV8sMMdr3oiCUAIpNV0QXDUrXreaszeK\\nU5Qko6nrio8/2vCwvWUII2q1YnNRodkg/S0ffRDI2nLxwf9MeZgYNn9L3r2FaoVyjvpqSbof6IaI\\n1AbXXqDyDQWLaCrW9QWvZUAUqK1ELZfUbYPo/mlRlH9L+4nO98/+7M9Yr9d8/etfZ7vd8ju/8zt8\\n+ctf5qtf/Spf+cpX+NrXvsaf//mf8xu/8Rs/rfUCX0S+SksKC3IckU1F1a9ZLhuq5poXq+4fHSek\\nRryLHKva8kv/8WO++fmOrjZsuoq9OrEQz/l4s0IpWLcHfv7V+wxJcrf3CAEL+0Vawr18hXvxEiEl\\nX64WHELEPX9B+yN1gw+W7/H+8hVSSKrKzilTo6gaw988PBCdxVYSV60x9ZZPXnm0lu/ur4HY8wuX\\nv8g3Tw8/jHrni2ueNoZBVqyqFUIILiqL36wZnj3j0kTU+hU3MSO0ZrlYsJv2OLFAq5ms86KtKIBb\\nvMReXqIWC/4TjuHhrzEXZ3zw/H0++pWPCYe/5vVffxs1VTz/ZFbJkXLG5OXNU0R5TSs0u6Glvjgj\\nDAOL6hyn9yQB1bP5y2XcSqxZYZeeplKMyrFMdxgtqOXM1W2qBm0s1UvHt3PB7xxiGFELCMEQgfPu\\nyLFIxDRQ0oIiM7HWUJ3mWcOSZj6vbDkWjdYFISOFCYpHnnpOThCPAU1AeEmxoINjGiN9G9FSsm9a\\nhAqokhhCRolC0hIhDEN2lCGwrE9s7YqF8ehG0o+WU0i8TJ6z7S1vL865r1pELKh4IusOGksVE2+3\\njj48ZWtALKC73yIj9LHgbJ4VdIrAx0J9esBMAzpnZNswbRNpiAz6GZX1mHEkZU8Ihn4EgsXZEUlg\\nki1THFgkgdRl3uHHhDctnzMhQ8JoA1FScoU8DoybFWN2eGnRSVGkwfQeVwJPb28IxuBNxb6ytP4B\\nJQ2+aJwstAxIX/Bj5sqdiFNEJs1oK3SlQBhymgH3/ckSXOSoBYFCpwSiCYQycZgUbdRIU7iNS3KY\\nN9pKzGn1ohWuP3ImMqdiuXpzQ2839N0Kd/o+EcPkauzxjnW/RYYF3fEO2a8JwTA1a+r+lrxukWFH\\nyjUqR1SwEA54NdJuFXkMjJMlukQRDbJ4CkeCheNiwTerJ1xUB/a7C06LmipvaYcbXKyRwuOV5PPF\\nUzB7dJFoRkSuUNOAz1CPnvPDPcNS8PryjO4kiEBgwCKRWRKpkSIjVQY5EU2DLxIXMg/NConF2BUx\\ntEQDdW3xVKSsEcWgtSdXluwH8AKBgZgQ9sjk1iSxwuYeaXe0XSEog+kzXeVRkycVRdGa9tkTLs6e\\ncX/3KceDxGeNyokqZpaiBS3I0iCLxMlE1bZoaRCHe4oqGF3PIz4XlyyuztmMe77zMBGV5soeOF84\\nuF5zus9MWtBo0Isl8s0b2rWjeu99jFsT9T1aKhAa5WraDz+gfbFEffOGRSmsxIDrZ4nE0iheLJ5x\\nsXjJm4s35GFg01T88pPzn6qE4D+0n+h8f+u3fovf/M3fBGZtTqUU3/jGN/iVX/kVAH7913+dv/zL\\nv/zpO993+qpaS4pYEYZb9OUZ1Yf/GWWq/yYH979lP3ByRksuakclJKgFz5ZXlFK4qM9RUjHliJaa\\nD5bPeNEtf3i8EAJ+5OHVStFoydqaH/ubmSL9xe9t5wD4cLXgG/1bPr60POmWVFPCmT0IgbZLXPeK\\nkiOt1HweRhrzRST//LLl+4/P+FKs6J6/98U9rdcIIYhFYLTh0hYW64YxSdbyQ7Yp8v5iPs/afbFO\\nzPyzWSzR/+k/I/QXH41sGs4+fEkxH/9w7T/40CqjsY2jXD9luHzG5uwMefwMUzTSS8wS6k1HvTdM\\nC8vF9RPS8IZaWp7JSF8ysmkwakWkplutMZXl4vmHHL79mqh6TLxhsoq+swxK8spMtFFwFBF9giIF\\notbU6oR68/f09TW5foIUS0ZOLCvP6BVjMWw4cZxG7u8r1jFQ+UwmY0pEx8Q2CvY6U8RAMmekcCAF\\nQZUM1o1cLvZQNKN6ij4cqeMdJ+OwlSVUFVko8kOkVgnpJaEofNY478l7T9KFrBQnfcFBSlRoKGIg\\nUphcjTaato7E7FiwI/qOcOtx969x4w4b3uch16Qw18yjNzgXEaHHTQP70iJLQorCrjTknGiFJvse\\nFTKyEayaWcz+obpiCOmd+IXEjTXSCkzIJGUYoiJqAWMLRdOblmgdi4eJ0Vv6paakxDINKGfwwSAr\\ngfGB+0OD2A4srgZ8EEQUn+FwUdJXc7e3rgMHsSaWFb1XSDJawPndntcrR/SCMWt0U/AetCkMo6aL\\nCSHkjITtPRLJ8rij0om7i3mN1ejwEiiZIj2KjMwSVzyX3/xb9psrHqsN44slSuY5pa9bdBoRomOZ\\nJnbBs3xzwgwjt9UrshgAgcgRMz4yVBf0y3mme0g1tWuQPkMuaHFEyAmlEn1dc1osCSqzGCTN4UhW\\nhuZwT5GzHu/ZNPHttuG0crTDPA6WZcRFi4+KIC0pRTQn1NBhhoHDuWFIFu8E+9pRD7OWuJQSYTKN\\nvIZxoGhLqjJ5qWjKQDtF3KFiEru5xFDOkKLCaIFyI1l7XFfwZxVtecQIw0p3GBE4Xj4lMFK5giAR\\n6Aj9lnNW0DqcSky+Y6omLtYNxixIw4RkS9GFcL2mWE8+v0AMJ3IpaK1I2vBy6ZAJdN0ixZGcC0JK\\nNk82VOufpW6OmMUlUlmQCiMNWheWbceL98+IOVNpEFkjlSQFgwmGfL7EKIurG66fLRGi5fzp8qee\\nZv6H9hOdb9vO4fjxeOQP//AP+epXv8qf/umf/vCLt21bDj8iQv5P2WbToP8V1SK2dz2bTcPV1ewI\\nH/gUgPXVOVKZn3Toj9l607CfEh88W3G2rPiZDyJSCC4vf3zQ2k2Be1G4alY8+wm6rQBPr5c/8fUf\\ntVI6WnmDdR3LzTVyTMTJs157zq7fR9svrvW/nP+PIPhh2vniouNnP75ClYQ0P3rPC9T3Wn7GFsqz\\nFR+93PDoAw/qyO7oea+zfPx88y9eI0C5+GUoBSG/eIYiQxgT0UqUOKJ/9svos2vevzpnpQ39f7nl\\nyfWHPIbP+PjJNbd5olkuedWueNxm5DDSqjsullekvqU0FRvbsLi+4vz9J7x4tebbNz2nS8fUHPgc\\nQ7bPeOUSX7Ij33h94qyz7HrBccxInVjmCRFOHMw9w3CBKXlOOSrBmCShgJAJOXk2e4uMkCCyAAAg\\nAElEQVRWDp1GTipTl0iKkpgd4+SwFtAFIyJTnseMXjY9QWTIGTP6WX4tw3onEGtDyZIqZcYxc8yG\\nTmbqfks6Vrhjzy6CTnug4ZRrsJoYJG0+sqMmKostGiEKC3ui0iPdEYoXZCp6/Uj75jWiNsikqJse\\nJSThGDElM54S0UicSKzFyE5oojGkbLBBkYbMXlquhkdaMXInBNFJjAfKOy46EuskSVi8lDOLud8g\\nvCFsTlgBSlqcj6A1Lo2cy5GjWjIFgcgeJTPHwWJjwIg4t5sZy10WnIvINDkehCQvGwYEZtohfI9U\\nc5RXlKI9zTVsvEO1UJkCMsIYiaPFNpaoFEpPM63JCh6fXTK2EU4Zz5qpUUTjOSZYYRmVoVGJZn9P\\nlI677pypvqTogJlu0SYSj0eUrhjVTIEzYSDImqR7fDWSlMWcBlRMpMMj9Sljpx15cU1WCh0mvFQY\\nKUGPbF9cYFRP03uOqQaZ0Kngxge6EulFRgYI0nB7doEk0y8fZwbxGGiDpiDwo6GWA8poVDK4/ki8\\niIRqxZAL1UbSbmA3LeimHldp2sUZJb4hadh3Ha41XBvHB68rhqrnVvaE3NJtlhS1IrkD7nLJMIFT\\nkdVCYOKCJY7/6b01D4d7/stmgwfqdcdwc8KOmTg5UqtoVytWi4Zx8IzNLR9++UsUfc5be4MZ3yAM\\nSFvRPXnJZlWRYqG8d4F9/YbNZsOH12ua4x736or7XWT1ZE21WfKLv/ycxvy4qxrLgL1fsF5+iYtf\\n/Qq6quYXPromh0BIcOgTTf0J/fOGq/OXnF9tCGPmw0pz9XSJ/P8w6oV/QcPVzc0Nf/AHf8Dv/u7v\\n8tu//dt8/etf/+Frp9OJ5fKfdzaPj/9Y9uz/jcWQOZ08t7ez40/lmhR70sMIjP9d53p13pCmwO1t\\nYPNuxusH5/2B5VJwPqFF4Db885uN/x4r8iVjEEy3B9aXDYvuSwzDwOMuA//Sa/34PZcPf5anQiCU\\nYjyO1MA4ePaHASPKP7q//ye23fbsDwNjzsiLS3AWGTMqJZ53l0wffYX02ee8+NIv8vr0La5Xhm00\\nPHtxRl17dn+biCbTWI3SHcMYWGqLXXdILXh47Ck5IyJ8cPkenz0eMF3Lz68ll4eB95rvky9bDgfB\\nawqawjpObBcb1tMZTCO2TxhbaNdlRkmKkf06c7aXXIYtMWq2LhNKod8rxrIgy0zyFQsnuGgeOIZE\\n0BJ8oiOQtOY0SYQ/0WIoYs2yePQJpsWSFBMpSx6mmmYxMEnD3kpU1RIPHnt4IPYDW/lsFq6LgiZF\\nDkTUSSNkQccjH3TfYgygpwXbvOaEI9iKFBSmH9FG4P3IyieiVmgJOgc2rWdlR16oHW+k41GckY6R\\nxW7iPtYka3jM7ax2UxLIiBQZI2GxHtgKjV+ukcUz5ABFzSzxLEkZgtIgI0MUIAV1mFjqCT94fBS4\\ncUcUkjhJKivxdU1EsBcWQWQ7VfjkyJuKLgTcLmKOd8hqjWhAppqUOxDQN9egHGf9G1aNJurCcVLE\\nKGmmHm8a4qKjm15z754xlJ4gDqjcEqqOJCTaj4Rs+ezsA9Qu06obhmjIBYSbZqWvEtE1EAcWMlOq\\nE9VpS+cVWRoOdSK194RqhaLi2AWuDkfKKeDLrCXNEEmdoY17jjgadcLITCgtCyF44o7cHZ4Ts0CU\\nIy4cUCLhdWGQDVk2VFiyGPEGnJlYHDx2MlTG0saJdTnR06GoEPQICaJk+iS5qFukfs55HLF3J0xT\\nc4bk4BSjjPjNCq0l3bRCxS1NbTgXHadiOfvF95nGjiEOeJ2wtDAeebEqpHuJKoLTaUAoAcEyxhOV\\nqVASlCpzM9NmQZYgikQbjTaKaZolFCtzgWk+R4aBU+oYvOSwnxtKT9byuNmghKFq3scP3yXGxNOv\\n/AfuQ+G0WrN/6DnJH3eUOWomt8Q+ecLjIXBZVdzeHkgvPyIPA9Pf/x1WQMgVtVwz9Irb2wPNYi4b\\n3v+IHOi/pf3DQO5H7Sc637u7O37v936Pr33ta/zar/0aAD/3cz/HX/3VX/GVr3yFv/iLv+BXf/VX\\n/3VX+89YKYUUZym5H5gyC5T5t8OCSSF43lb/Juf+0RS5MYrFsmKcwk844l9wTv2PH6sz83WM/tdJ\\ntch3/wxKgHzXsHCpC58sW6QQ1OeXcH4JQKs+QofvzHgt4KrtyJcF6zNt27B+ucQ/DBhpWL78Amq+\\n6Cx+l2HxlE/cGc1lx0sRkAfD1dkL7ozk5z7JjOOS+OYee8iIZ2vc8JTF5xOeecTFKhC5IHThsXUY\\n0XB2yhxtR7CS0f1X9t4s1pLrrPv+raHmqj2euef2kHiISUKAT2J60QcKQgkRElwgpFwkQkkkBBEK\\nZBZEthAIxA0iAqRcAfogygUXn4QCN7z5ECS8GBIndjy33e7h9Bn2Pnuqudb6LnZ3u9vddhzbuH06\\n9bs5Z9feVXs9tXbVv9Z6nvU8HraKUAasTJkJl9WgoKdShGhInQjtSJpa4hhBKEE4M2p3hUxsIOQO\\nflPh1xlZ45LjcND49HXOdu6Rpz4rlGRK0Gv2GY8V9CwVClsbrHXplgVp6aM8TZKPCeIK11hcPyU/\\nCCiMxpEROS6qTgmsQUxLulqypwMS1bAajhh3AoJ5ReAZlPFhCouZZFuvENUGF0GGS6U1jT8mnO7R\\nG1eka0fJrKZ2BeOZxleWqsyRiUtYNZiyYTF3sVLSCI/CUZiyITQlZaVZzEtMY5BeTlZLmsaiu4pZ\\n0GERQ15IPBNglUPjK3Ar3NEBZR4yVwkI0DJFNQGN6mKMg7EB1pFoZdDesnIUaUVZe7hOTSNKat+j\\nFgG1FRTKUuSCQCtEZTFCQTWjcrtkxsHDknmK/aaDCBU2aXCtpTNriGyDW2e4aYPNSg6UxK3VskKX\\nO6dMFFI6KOuTOi51pRD5DLSgAeLpiKyIWVQ+TWJRc8MkCEkrBydycKUkchzGXo3VDR4ZVpTgOtRN\\nlzxy6Fmoyci8AJG7hIFkWHcR6x1Onn8Ki6ExDT3foXQEtbD4GOrGJejcTUdtcZRLjIyP7PXwrKY8\\neQepzDm5MWBRVSglcfqgPZdsauj5AcHGEO0mHAkHfP3if3LS6zDJDIOqJk0VXmrBGLxhQuzGTOsF\\n8VDTOxKTX1ggZAxRTCVTIiWQ0kFrFyUEJzs9slri5xlnRo+TCQ91WUd912PczEkCl6Nr67hugLv5\\ndsygoEGyN8+RApS8cYQqHYfg9OkbtqsgQAUBhetiyhIdL+8nSt+aoKpX4hXF9y/+4i+YTqd88Ytf\\n5Itf/CIAn/3sZ3nooYf40z/9U06fPn3VJ/xm0TQvBlu1vHquiq96Y86bXaYEQiGuaCqOlOibXShC\\noq4pXbcSRIwjgastnvahtjjSQXredfudONHDXpxhgGOdiEE3ZBh4GEfi+IK9554gCV22kiGLLKU3\\nNeTeCqof4Y5hv8wJ6xpThcShwIYNM1+TbZ3iIBE040tICtKhS3AQ41YHFLYhdQIWoQPNAtEY/KBC\\nGolrK5TWy/q0UhN3J+TTkCoIsNlF0sk6NYJKCyZOyDPzmkrVxEVJEhmqANxFyMqBogwUc20pbc7C\\nFHSy5bQ4sSKSM9JUEmlLpGuSsGB3HlHZLjWCRE/Y6DXYSuAaiZY1rjAEdUVqlonsx24fr1rmsJqU\\nlrlf4eqKvu+Q1w5lZejtjWAh6CMobEMtFHnjQlkjMourclztYLVmmns0pYBSohxJ1QhCMUeZgv1J\\nwMx3UGmNndVMiKi0i0kcUlNTlzm2kWgnpKkVoqmx9RTp5oxUF6eWKMCrGnw7Z9FNqHNJLT0q5TAJ\\nV+kyJ2pScuHjymqZF9md4QQuOQlVLigcy1T2cApwRANNhfEccDVVCh6S/SRm6inW/RS/WiArS2BS\\nvHqMLkd0a8WB7FOGA0rXI56cI40lRazQ7hzPJJSpT2oMXg4yWuZx9+oMJTNmjU9Ql+ysrxCU4GbP\\nYbsrdLxTzEJDz1HEaYRDhiwrHHeTSjq4LiglkRgcDRvREVZTDcdWOHEqJuQJ6v0xa80cGa4z1YpR\\n1jCtFNp1iMMuiXUR0RbrgaXwFeogZ/34FvNAMFyLWc9qgnKXlSCmcAPCcoKz0mUlilHapeu5DP2l\\nS6ofH6HMJZW9iBQzrK1x1jY56Q1Im32UEEQrK4ROQq0sM3OA0A4ulihch+5yRcrAD5BCcnDQwcoB\\nPeURO8vBxanuCS7yKELAaudFV5j0PHS1vKu4r9EvK6MIU5Y43TV0t7f0E7/FeEXx/dznPsfnPve5\\nG7b/zd/8zf9Yg74XSkm6gwBzOS9ty6ujn3gMOz6Dzhszgm+apZg6Ul4VX3uNwF6LxXKtJgfaxdEB\\nFOBqH3dznfLZC/h33HHdfp3IQ6rl9NAg8jgeXw44W12jqRac2OggBbiqy7n5LrFzinvveIDdSnNx\\n6yzpC9sMmxRR1zSdLoWaErkNdWMIo5gslcslCl0HT/j0iprJXNO4LkUAppLUjcIoSZIUpCYmEDV6\\nkZJZTQ50wz2mmYeoDDbNMImHNIJaK4wA3VR4uaX0Y5zaImqfvm0oFzlZJ4KmZGoFnQqCJqMwijIG\\n70DxXNHj+OAiUbBgfxFhGo9+NONItA9BFycFUwFGYUyJK0o6sxFz0UP4DYFsCHLLvvEQGHRYYoWm\\ndjR1Bn0qMukTJiVaFFSVRy0VTlkiUkPgGpp5TeEvUy3qykIDxqmR1rJZ7SLyimnVIR9ALy/Ym/iU\\ngYBAUOJiCoGjUzASiUCmENUZJp4xdl1yv0alAm0MWiW40xmTTpemsTSNppSaqROR1BOUbFjWA6wR\\nHvjDGX7TXZYElYq575M3LkWeI7VGG4sKQ4raIwgM3rRBGBerLKFMqTIHgSRxUuZuRndS46UNjSeY\\nB0O0adD3BGRYSl8QCBdPe6jxPsVC0jQOlZYEsUT4XUIT4qQGqTRZs4wW9gLNZnyaobvFNjtIC8dE\\nj2DUcMlN0GwRM6cTSDK3Aunj6Ij1eIutMGJUwx3HuqSjVRaTCVWcIBEcEwOS6S6afYrVhMgLiGpN\\nSoOUgq1ugBCSSCvmenmvPNmPOBoLsqenyNUhjl/iJxGrUYS4HEuipF4mExISEQTQD+gPV/DX1rCy\\nYj0MOb9YyobWLrbjIlWFOJguY0+ky6n1Luunt6hNczVGJdIxQ2edrtPjrr5PqEN87bEZb2CMIfCu\\nF0fn8g3DfY2DBac/wCzm6N5bU3jhECbZEEJw7OTgDfFb/iChleSuo7037HjdfkCeVaysxYxNw3Za\\nEr5MUF3HTViLBvTDhDv7MWZesNq7m9QJcZWP293Ae8+RG/YLfY0UAmMtcXB9IJ2QDo6SCOVwPO7S\\n2QgoVzwGww7pviXoDRmOJwxnYw6CCCUlntToSKFln0FlmZYCb6WL2+ngHukRXNxG+h5utBzdagx0\\nfWopCcKCyhp8U1NPBNPa5+ngNBvNiF41x8t9fLXAppLM+hill+kYC4tb5KRFF02NQeM0I/ojScqy\\n5FqqJJWtkFrRBDmTTkLjSvYuOHi1gx0Y3JnFGsuJ/g6qrhnVkki5y7J7NajGInWDbqplHuSsIUl3\\nccsYSZ+EBcJIjBEoBa6jsKXC82tcleOZHOqGKEmoJ4ZSavzCAdmQSg9lcrpyzowEV+ckTs1qWfH8\\nxZhGOfi+QMoDZl7MIvDpkVE5DtkkwCsqitBBG7DlctQjxwULHSO1IfSWkdiR9VAIdNXg1jlkNXM3\\nxihYuJrANlSORiiQoSQUJSkgSqiAWjjY2qLqDK2XqRGN4zC1GarQCAmykXjW0rWCKB2zN1AMwgXF\\ngYsnIhpRMwmHuGWBX8+ZD49QeT6qOsPQ9lHKEOuanbUBo3JIqQ6wpsJ4ffxGsSUqVFByToXopmRF\\nH2fDO47vu3RrhyyvGR49Qtbp0k0FOxczpBVUQQxyhuv08LwVunVMNwk4tRKRxAHNygbV0ZK5degl\\nIVESUP/nkzSOJY5CTiQd7EJyJbrmVC/Evexv3N2bEsQegQEdD/BPnMTtBmg9WyYqEi8K3H3DtzEt\\nZ4xLn7Q2JGsBoe6DAprlcs3TvZPMyjkbvQGj3QWDjSHyAqz2E3ZfSNGOInLC6y9mK+noPo5SDPwX\\nR7mBXg4GtHP9vcOTkoHn0HVfm0Q5q6s4q6uvad83i0Mnvi1vDZSSbB1bivk6mq6rUS8TPSiF5HR3\\nuRxqGLjszgtW4k2IN1/xO6QQhL5mnlU3FV+pXJSTIKSD5zp4jkUIjZI10g84eudpyplPpnxIF/Tk\\nGmEHMtPBPxlQch7/xAZuHeFFMbvxHXiLnH6uEOOcvapLHUzZDCRhaDGVZpI77NUx21ub5HXMtHFw\\nqgafGb6wiLSgE3mksqJAEdsc3QiqSqIdifJdRFkTmHOsX5SoOmE/uJwYw5shQgsSRL2cgq+yiCDT\\nxN2SRoHthpRpSTxWNJEgVx6qrIn8HJCoosSGoJqadFyQlpokzDFZH6kKbKBxnYYyrbC2QqkGRE2N\\nIMgXuLMddptkuWRKK9zSYIMGv5siO5eoM4s2moic7XQNZXPmQ3AZMeo5zIsOuvJYhBMytyY2iioL\\ncFyLbmpKCVZbbA4iBMepUaphWM4JFgVIgxIZiS0ohINXpkgbkOUBi1EJw4zM65DolMoIhKy5qCO0\\ntTRpg54EeJUino+IegWNNyTcr3nS19jKRWtF0oX+yhZmMWY1NfiRZCO8A9W5AFGFfyBhtIvqKybN\\nEXyl6eDi1FOOOhmN71CvnKSfas5MU7S0OMbDCVw6NmU9DKh3DUWaUyQ9pFSsrCccdweMFhl3njpG\\nOR6zVyqeuvQ4K50OUwlJp0t/dYAjHTbcLmJW0ekHCKnwO8cpgl0oFOr4Oqu9iKcec6GR9HorHIu7\\nbJcvBra614iZlhJHSRyWS3fcjQ2aOr1pHIurXFaCIZaStC5InGVea9tUIARCSAZ+/6qAbl6+B9x1\\n4ujy9zqUeP6NsmKMvXpN3wznJeIrhOBY/D8TZ/NWoRXfljcEX71xS8mu5eRGh6yor7uZwPLiDLp3\\nX30tpYNpSpBqKShAFDjUhUeI5OhmQOz7iEQwLRssLvHb70JJwQ+tvI2z84xRtQzWWXOHKLPAUzt4\\nUY9uCKGOqc2U/VKTDlcJVyLUSOI4oH2FK1yGjceBI6mFJNUNrqxJijmV6VJVCm0UomsxWJRrUI3A\\nFeBZgTIQ6gW1zrG4VHm6rIHUeMymltX+AhG4GBEQipymOSBliNAGJy7o2ZpSSDAlUXpAoBcsDCxC\\nD+FZ3EKgbEE828XtCWa1pqgE8UCwsBF2LqlLQSebk7oBmfWwRiHyClfWrPQzRKIZTSc0c4NRGtO4\\nzLqCoNhBFA1OZ51YRgTaYR6VVF5NYaf0KkHeGMJRReoPqISLW/cJK1ByvvR/+13CvTEiMERlhqgE\\nWkvCRUopQkoRor0C6xQoq5YpLptlmbhKC6w2aAO154NvkaMd1lRBdXCRkQlwlINwZ/RdnxPxkLjj\\nUkQBs72cpLfCqt8nFTMWuUWPalzj0E3WsEohPB9lPYaNQ0+m0N8kWDlBPllwcabQUhP5MSePHCWp\\nM3S2T1TvU0gP7flsHu2SdH3u9TZY1A1h6BOGAVFteMe7GsJ6ysxsU8Q+TbDFiueylgT0j774wCnd\\nECWg73vEnQSJZe34PUypODk4jRDiZYXtysztdfEY9pVL560GLqFW6Cqlzq9cc99bLlY3bh74eiXQ\\nM/Bufp/Qzg9eDE8rvi1vaeLAuWHUezOUE2OZIYTGc5biu95PmBxs88Bmj95wFaUU5+cvEOqUWFvm\\nVcA7196BlppOaREIPKXp+x5uYnBMh7ArSWJDIwS68Hl+P0e7LrETMvEtMpcEboQvXJrFgqLxaRyJ\\njnKEFTBzsI7CUxJHGaogxqdL4zbUWiAPwFFQ2RgdjghMhleVjNIAX9TEdcE406ikAqtpEBxMDZ2D\\nPbJuB+uAVA2yMlS4ywpTzRS3rBCmoo4F1qtxTIP1Z5T5HDcwDOuCadBB+j1MJeibBUqUqFwQDSpk\\nYzFVjdANvp6y7s4hh3QuqBbgdDKUOwZTUhU1jScR0icWAicsKZuYMl/QBHuMhj5q5IMboERNldcI\\nFTDUOcafgeNQyCOMBj5BMaNMBcYLCBSoeUa+OIAmpMoizJbF1zWFoylzxcZuTrnaZ17ndKVkrd+H\\nQrN1t0dw8ByV0QS9Pr5T4g5KToiYTT/CSRKqOqQbVTh+QdLrMS8U+bjAOC8gpeSBt3fYriLG1sPx\\nenh5RlBY6EakgON7HJF9Bo6gt3oMPwxxyxpVSrQSCCWJYpfO5TS3A99hwIu/ZUdLfuwdm1w469AT\\nc843mlLK5YzPS1w4yg1wg3U8oYn7dwCS496EmoYkWo4+r2iv/5I1sVdmpPQ108tShyg3Qbsvv+Y/\\nchRV8+Kxrl3n//1ybC1GKcHG4Prp6CMn+jS1uaWZpm4Vrfi23BY4wSZOsIkQgn7icc+JAZ3QYW34\\n7usy2YyLgEW1YF7N8bWPvpyys+v5CMCVDieHMSeOD5nsz0iLnPWuQ1HneHFATcqu7JAayaysqJ0A\\npzsg2rmIlTXS1Kxu1uThAdkiYXMWshM7KAf8Xkx2ZBMdeCzkeepRgJtbpNG4VmI9j9qp2Jt2CW2G\\nL0tcJVirKpx5hpNoMB7PDTc4Mi9JU4PnNSinpklrbFiSuQ3KdrBNCk2K52U0TkQ8nEIl2PMTTGNJ\\njswopg5JA9rUTFRC3ukwKBcM8hH7TUws52h/xmLo0XE9moWL8ZfFGCJZYp0Kawz9Zsy5jVUKYnrz\\nCXgSR4RURU0SV6SRJqsV2mricsa+9XEkbGnDVHgof87Ixsz6Gm9mCKcz0m4XNysJnAk4czZHJQtf\\nMPcE6yseTCNcbfHjMR3tUeQVSsADay47KbiDGLoh7jYEnRW62QLVE9zjJiRRwKITko5yEA5O5z6i\\n2NCRx9g++C6x2+BpQ7cXsr3nEAtF4mv6cYc4LzCdu9nfAeVqjgQJgbTocOkmcFyF62lW1xPcKiM5\\n8cp+RyEER06ssj81RDmEylkGEb501YAUCLkMHJJqOR3rCIWDQlxOebvSCygrw2o/eMmul8X3mmMK\\nIfDjE3wvrl0KKeSrT2D0UqQUHF29MeVvGL01g6HeDFrxbbktuPbJWQhB9/JF/VI/dNfrsJvuAVwX\\nFNJxXIbhgFPa5a5+jKckstT0kxilQ2QxAuD4iiLNI7wi4wUtwJEM1wasqBwjM2RqcAMHz0/pNn3W\\nt0JClXNgJfbICoOtLkNPwXiC7HaZxhlr8xwpLX7TI5SwGwtKIfFm4NUeHdGQuYLNYMGoCrCepQol\\nVdbQJA61X9FDYRxN3nisyoCZN2W7nxCGFi2gr/vMAoFezKibmDidMU8bmsSSeS75vMso7uJNthkW\\nF5gpTSgLjFNTez282EXpVXQzJ/cXBGWJMQ2N62Hnhlp0UMohkDXKNJQ6oiMatO/QTY5wVhbkVjK6\\nNMQXOa6rcD3JUAaUbkScbHBWXsJZ7NEdJpj5jO0gQoSb3JNVELyA67tcCjt0oyF5vsDLNUpO2QgK\\npvUCTw5IlGBPQGNzZKQ5em+PPIuR50uEFnTWVpFYhsNNtscppVjD8xPcMOB4T/H0s8/i65j1FYvj\\nOFgUWgiORR6ecNG9HwJnFXZ2QUB3YxUB+LFHVdY4noYZBKFH1vTw/FcX5OgFQ2yVARBqdcNIUCcd\\nvKPH0P0Xjxfeey8mTZGXxVcKwdG1GwUuUBIr5dUI4u+La0bLSt947JbXTiu+LT9QdNwEKdUy57Hz\\n4sJ7KQQ/vrGF5EUhF1JhTYP2BnjJCZpqgTEF9+pLzJTgzLQAoJuEdOuEdOpAYYj9k7yrt073aIfV\\ntTuZPPZdnn3mInthwtFhzMD6NPOjbA62+PbF5wiKfUpr6PqSRAjOILFuSLCmONHpMFjf4OGLzzAR\\ncPagoookxlV41lL6XeLEMoojdseSJrC8S0oelQ5Z4xCWDp4vsf6QRHhM8pjVMKf2jjJozrGTDZj3\\nVki7XcpCsb9xkhXrcGyyi7SCsRvQCyL8MCAM72JLXUQfn9FNj+LujbhQpszX7qfrriK1INENUZqi\\nYk3jrrPaGSCHW+xX50l7EOcBcVMhBj5lr8IpXVIHfuWH7+L/+d97yLrCdQQGSdZz6IuYoYZJMEOH\\nDqvJCsMgYhxUDGeaTuSiT6+xPl5F70FulkFBypEIIUm8AY7wWbFHSNyA+N47L/etZLDyNi6OFlfd\\nGsNuyB0n72a3EUT+AQjB/afXOJgXdDuWfHaAdhKUozi2lhD7GmV6GGOpyoaDUYPnO1SA40p8r0Pc\\nuX7t+suhrxHb6GVWDXhHrl8RoJMOJN87w+BW6LGymrymrE7XjnyV04rvG0krvi0/UEgh6bodxvmY\\n2Lk+681LR8lefJKmnKDcZdUo7XaoyylSgO+4KLEMWok8B+E4eJ4DssZPQrbuuh/EcgTTu/8dOBsn\\n2BCS490A1wr2pqc5ttGlOS3ZUyE7B3M8d0RHKzwlKIwhFJr1k3fTW9ugp6bYIqNXVyR1gNd1KV1L\\nrULWYsnz5QlkOEE6M0QnoTw7oWMUihoaD6+T4KHYSBVx0FDiIMYCd5pSRcsZACUFcVczc07SKebY\\nMkfZLpHosNKNwK4xqDO81Q7v3LifvW99h8mZx0lWuiSeYleCNMuqSf1kBWNdfvTeDS6KlL0qJa80\\nUQJDT7EvSsqioBOtEESn8B2PtfUB6VmBFysWRpD4R/iRe44Rn32O2SghO7HBShwx8FyiMOAdA4+8\\nljA4wemNhCceeYqG5YOU8CKUs4bn9VDaIZSKXhIgrnFBnNhI2FqJrsv6dmytw9bqu6nSiwgM3jUx\\nB2Hv7Veroh1Zuf63Y62lNwwhS2l2PdyNDTrrG6/al3ltFqfIeWODF18pGOt773uNn1i9ugeJlldH\\nK74tP3AcS44w8HvXVYm6GUoHKH39Z67cfF3XRQlFYxsiz0dojRe6bAwFcce/+r+v93AAACAASURB\\nVDkApRS+51I0BldKAq34oTtXADh555D9nTlOkSOcEN9RhK6gFpr1jSNsnvphmvKAE8kKF8w23VDR\\nLwaMLHTWa1QDvcAytgW1FHgezB2HunEIjMBngWxqNrod3EYiFjOsjIhDl/XuFseFw381KaNkiJlD\\nJ3aJXcUoOkHijwm6qwy2BiSRYrA2xPEVneHSVz7Y2uS+g/Ns90O01Eyyhjp0MWjuedsGu3sFK4OY\\nInXYirYojEM4yHlHP+b/O/s4F4sOjjzOxuV+GMQx865LZS219YlUzF0n1sjmlwimIUmQ0A1jpKhZ\\n9WIkFZEbk3S3MNaiPA9TFFhrcFyF7w0QQuD5Du+8b40ovtG/eLN0q0oIVLR1w/Zr+/SG94RAKQFx\\nTPzOd73i7+pmXDvyDf+HVg68Fq74edtR7xtPK74tP3C4ysFVry3hyJXlFkppXOlSmILAc8BxEFqj\\nXYNwbrzJR1pRG3NDxp4o8Xjnjx3j4LtTPG9IFaSsySlSRNx1132Xy1Yq1v3uMmPQuKQQknqwQuRd\\n4mgIqfHpNzlWGTzfQ/sOjnVwTE2soScK3nNknUmqyRYjlNPhbZsh4ak7sLsN5cEeT/tdOl0oTc2R\\nfg/R92nyY6hujxPuDlIto3aLqr7adt3tEPU2GK4kVIVG59vYvsJ1Au48NuSu4y8G+vS9hP2i4s7u\\nGiuBj6sUILBC4V9eZrIedTnjeqjKwSqHfi9YVvfZOkJY5xDE9IOISTkidENggvBe9Hfqfp9mseDk\\naoKNKja9CKoZQmrWN//ncr+/EVx1dXDzXMa3CiE1Ye/t8CrLtLa8elrxbWn5PhDSQUiFVC5HwqNk\\nVYmrJU2ngzMcUnUFejC4Yb+tyGMjdG+aiKTTCwjvvxshJJODMcdlxor2SNzldLC4PH19vHuEMpzy\\nQj6h2dhE5Ds4UhPqANnkrAQxJsiQSrNW95iZbQLtcXztNG4Y05M59x8bEIQrCCnQTgfVj/m/eBvR\\naEaxP6IZ7+FGIdJZPpz0XE1f33zKUfoBybt+GLcx7O2OOTk6Q9lZ0D9x6rrpViUFjpKc7gQcjXyE\\nEJzsHmU0HwPgX/ZxriUJnfgO6r1LqJXoahIXZzDkjjhiUab4rsdiMqOne8AE6b7YLp0kqDjhzl6I\\nrxRltkNVzV51fe9bzX39CHjrCO8VXmnE3/Laac9qS8v3gRACv3MnQkjuOWZomuUaRd3tsXLnMezL\\npD1VQrxsBjC4HDwDyJlLx/ExTkLovCi+sBT+6L77acZzgnqBU7soqfGDIff6EKy41MLBVxuMjxke\\n2T6PQNDZPL48tvJxtUI7Edq7fuSvpaDp94i7CcU1NbG1FLjB2iueE09J7lvtU2RbCPcU3uXvu9Z2\\ngECpq77Ho8k6j6ulz9y/7OOMfAftB8sM4a7L8Jpp4sD1CdzlEpt3rt5PM5uxgKvLbADu6oSUxlxN\\n+HJFNF5Ncoi3AvoWF3dveXM5HL/Klpa3EPKyHywO3vgRlXZ7bDgdTiTHca6Ih3IRykHpCO26oB36\\nusdG/GN49SW020dVy0hWz/FxtEfouHhhRF2VBKsbwNJvF3TuROob0/ZpsVzeZF0NjUEI2Ay8V51b\\n11US99Q9N33vapKHa6ZTA1chERgs4WXxFUIQ9LoU25omjOkGL78GVEYRznB4Xf7eQCsCXuyTK+th\\nxVs0sX7LDzat+La0vIUQQoKQV4Xjyraw+7YbPhuH60TOFmW6TXNZfIVQCGf5cNDprZLbCi+8ZknV\\nTYQXXhTIVc/hQlpwKglInDfm9hBoiRDXR/E6UqCVpGwaAvfF7d0k4uk77iV09NXp6JshpCS4865X\\n/F6lQ4LOHQh1e+cIbjmctOLb0vIWwouP0lQzpHrlSGwA/3Kk7rU+OSEU4vK063E5xGqJ0N/7Mh94\\nDloKhr7D0Hfe0HR/vlI8MLg+4EkKge9IGmMIrhHZQejCCBLvjbk1Sf29z2NLy62gFd+WlrcQ2u2h\\n3VeOxD7dCaiNvTpavdanqZylyKk4RuT59xwdXqHnOfS8154+8LVwdBixqBqcayLAVy7XnT7aC19h\\nz5aWw08rvi0th4yXTgcrJ8Hxh2i3f3VaObznXoDrkkq81TjWCagae10CiNDR/MiRPsFbaK1rS8v/\\nBK34trQccoRUuOHmS7a9dUX3Comj4SaD7fgN8jW3tLyVeetfoS0tLS0tLbcZrfi2tLS0tLS8yQhr\\nrb3VjWhpaWlpaflBoh35trS0tLS0vMm04tvS0tLS0vIm04pvS0tLS0vLm0wrvi0tLS0tLW8yrfi2\\ntLS0tLS8ybTi29LS0tLS8iZzaFLJGGP4/d//fZ544glc1+Whhx7ixIkTt7pZr5tf+qVfIo5jAI4e\\nPcpHP/pRPvWpTyGE4K677uL3fu/3kIcgW9G1fOtb3+JP/uRP+Ou//muef/75m9rz5S9/mb/7u79D\\na83HPvYxfuZnfuZWN/tVc619jz32GB/5yEc4efIkAL/6q7/KL/zCLxxK+6qq4jOf+Qznz5+nLEs+\\n9rGPceedd94W/Xcz2zY3N2+bvmuahs997nOcOXMGIQRf+MIX8Dzvtug7uLl9dV0f7v6zh4SvfvWr\\n9pOf/KS11tr//u//th/96EdvcYteP3me2w984APXbfvIRz5iv/71r1trrf385z9v/+mf/ulWNO01\\n81d/9Vf2fe97n/2VX/kVa+3N7dnZ2bHve9/7bFEUdjqdXv3/MPBS+7785S/bL33pS9d95rDa95Wv\\nfMU+9NBD1lprx+Ox/emf/unbpv9uZtvt1Hf//M//bD/1qU9Za639+te/bj/60Y/eNn1n7c3tO+z9\\nd2iGVA8//DA/+ZM/CcA73/lOvvOd79ziFr1+Hn/8cbIs40Mf+hAf/OAH+eY3v8mjjz7Kj/7ojwLw\\nUz/1U/zbv/3bLW7l98fx48f5sz/7s6uvb2bPI488wrve9S5c1yVJEo4fP87jjz9+q5r8ffFS+77z\\nne/wL//yL/zar/0an/nMZ5jP54fWvp//+Z/nt37rtwCw1qKUum3672a23U5997M/+7M8+OCDAFy4\\ncIFOp3Pb9B3c3L7D3n+HRnzn8/nV6VkApRR1Xd/CFr1+fN/nwx/+MF/60pf4whe+wCc+8QmstVdr\\nqUZRxGw2u8Wt/P5473vfi76mfuzN7JnP5yTJi/VdoyhiPp+/6W19LbzUvgceeIDf/d3f5W//9m85\\nduwYf/7nf35o7YuiiDiOmc/n/OZv/iYf//jHb5v+u5ltt1PfAWit+eQnP8mDDz7I+9///tum767w\\nUvsOe/8dGvGN45jFYnH1tTHmupvgYeTUqVP84i/+IkIITp06Ra/XY39//+r7i8WCTqdzC1v4+rnW\\nX33Fnpf25WKxuO6COUz83M/9HPfff//V/x977LFDbd/Fixf54Ac/yAc+8AHe//7331b991Lbbre+\\nA/ijP/ojvvrVr/L5z3+eoiiubj/sfXeFa+37iZ/4iUPdf4dGfN/97nfzta99DYBvfvOb3H333be4\\nRa+fr3zlK/zhH/4hAJcuXWI+n/PjP/7jfOMb3wDga1/7Gu95z3tuZRNfN/fee+8N9jzwwAM8/PDD\\nFEXBbDbjmWeeObT9+eEPf5hHHnkEgH//93/nvvvuO7T27e3t8aEPfYjf+Z3f4Zd/+ZeB26f/bmbb\\n7dR3//AP/8Bf/uVfAhAEAUII7r///tui7+Dm9v3Gb/zGoe6/Q1NY4Uq085NPPom1lj/4gz/gjjvu\\nuNXNel2UZcmnP/1pLly4gBCCT3ziE/T7fT7/+c9TVRWnT5/moYceQh2ywuLnzp3jt3/7t/nyl7/M\\nmTNnbmrPl7/8Zf7+7/8eay0f+chHeO9733urm/2quda+Rx99lAcffBDHcVhZWeHBBx8kjuNDad9D\\nDz3EP/7jP3L69Omr2z772c/y0EMPHfr+u5ltH//4x/njP/7j26Lv0jTl05/+NHt7e9R1za//+q9z\\nxx133DbX3s3s29zcPNTX3qER35aWlpaWltuFQzPt3NLS0tLScrvQim9LS0tLS8ubzJsSLry7+8Yu\\nl+n3Q8bj9A095luF29k2uL3tu51tg9a+w8ztbBu8de1bXX35SOtDOfLV+nAFIH0/3M62we1t3+1s\\nG7T2HWZuZ9vgcNp3KMW3paWlpaXlMHPbia+xhkvpLsaaW92UlpaWlpaWm3LoxNday5mDBfPq5qkl\\nx/kBL0zPsZft3/T9lpaWlpaWW82hE9/aWnbSgv28uun7RVNe97elpaWlpeWtxqETX3U5UXj9MrlB\\nKrMU5VZ8W1paWlreqhw68ZVCoISgNi8nvsvp6PJNFF9b15SXtrHmRj9zs1hQHxx838esjeG5WUbR\\ntL7rlpaWltuNQye+AI4UNC8z8k3rgv2iIq1erOjRmOYVA7D20zFF/drFOnvqSRbPPk21u3Pd9p10\\nj+3/+jem3/h35t/+1k3F+eWYVQ2TsuagvPn0+ivRGEPzfXxXS0tLy2FmPM3JiuvjgIqq4dkLU+rv\\nMYCxtqEuD3izMy0fSvHVUr7syPfsbM68ahiXBY1pAHh6+z945tLDVz9jrOVgvhTntMh5+JGn+MZT\\n377hWFf2h2Wg18W0YDstmBQpWTljXizYzUou7TzP89NzLBaT5X5NjbGGswdn2U33aeZzmtmc5ia1\\neXeygml5Y/DYFftqYzHG8tS5A87tvrq6lN99bsx3nxu/qs+2tLS0HHYeOzPiuYvT67btHWTsHKQv\\n3uvrhizbpalmXMoKnt0ZUVw4T37peWaPP0yTT97UNh/KgrhaCizQGIvBYix4SpJWNUWzHCkKoDQl\\ns72a+WQf31OURYXrOTx9bsJoknKHW9D0Eqyx1JkhrTIOSoNFoZnywvwCW9EGW/EGs6phJ1uOjs9O\\nH+UkexgGjOsBnWyCFJClU7yL53j62/+Kf+QYRMuF36YsqaqKvf0xvcjnuelZhv4GF7YLnigKNnoB\\nP7U5AOD8eMG3nt1lYy0mr1OyMuT/feIxZgcOd22ucmQluloguzI1u+ke6+EqSi6/Kytq5peD0cqy\\nJq9ypCuIneh1nXNTldi6QQXB6zpOS0tLC8B4ViAFdGPvdR9LCCjr60e4RbV8XdeG2bkXOINgtHeO\\nI7FPNbyT+aUdou3nUbLAihJzsoE38fZ2SMV3OWAvGsP/OXeJpmn4X6ePcH48xhiDlBJrLXvTBd/4\\nj6fRumKjb3g+2+ZYXzH7r8eQ/VXy6oB8vgJAU1n+c+cRZlXCarhOfXCe8W7JuPMUzz71LXo/9AB1\\nJ0LQUDYWIy0H5YTm6RdQl7axW6s0oxGT8QFFPiJ9ag9WN1G2oTE1Fw7GPF9M8fMZYVLw+LO7ZGcK\\nMt9hPvSZFVOK4SqPnpuwvT3noD5gKvfYEz6jfMIohWgvYp5VBOWCcn/E3kCyUxxwUEy4d/g2gKtP\\neQDfevoMo8mI/nGH+9fejpI+rhRXxftasrphNy85GvnIl7zfpCmLbz+CkJL4PT+CEAJrLaOiouc6\\nKHnj8VpaWlpeiWfOT1BS8K67V1/3sbSW1M31s6FltZy5rCYTZheepzQ5+4TYosIcnEdePEdmCyI3\\nx7v3JE7cfd3t+L7a/KZ+2xuEFpA9/hiPDYY8ubdPbjLC7UdpRgXzFY+wk3Dw7Jzv+OcxpmSxqDFx\\nzXw64TtP7CFsjZqOMb5gMZkRBXuktcdzkwA1PWCWb3P+IMOvYnqX9ulMMrb/98N4/+s9BNJSV5YF\\nhurSmM5zF/G6HcZNyf40I5uOqXWBwsDogNoqctNwdnaJs5MI4b3ARqZ45uIz+HWPxUGOSB1Gzwr2\\nVzfIbEihXJ4/8yTRsQ2ypmIvramNZJ5VnN9bsPbCdxlXIy7ugH98lRQ4KCb0vC4HswLHbAOCvUUD\\nFsqy4lu7T+I5JznVieh7DsBVX4hWkicny7yoka4Y+u7Vc22tIX3qyeX/xmDrCuG4zKqGc4uC2lrW\\ng9f/5NrS0nI4qYzhmWnGZujRdV+dpFS1oTaG2oAxFvkyD/C2rkFKhJSk84L5rGCwGt2QTtJR8ur9\\nzFiLtVDWDUVe8uSzZzjmpMzmc1gJaBDsPn2BZjRhY8WgTc7FuUMyK9mKJZ56c7yxh1J8Z9OM8d4u\\nZdlQVoacBRfTA5yioFOuku9UmFnB9MIl3ATsPCOXc16wit1pwZ39iG6+TyZWKbIDJj3LfFozm0PS\\nVJT5lHwRkZmUHJinGUUVsv7db3GRkN2qwgjwzuYkec5kfZ0z8xmD2QXcckbl17hK4qmMaVZjcpeR\\nrJjYBLk3Ij4/oQgKJr19bJ4xqmKKKmBRlEybhrTaR6sKub/L82hmB+DRAc/y5NkD0p0Jz5YFIrCs\\n9Euinst+NqbjdrgwPUdP7COtR5ppQlcwzedk9ZS1aEDRLOdVrLV87dtPYw383++++4ZzPF2UBJ6i\\nGD1CWVzAYbjcr27AWV5wAGXTloNuabldaaoFUgcI8fKClNaGojHMyvqq+FprbzrDdoWiaq77P/Bu\\nlKIrM27OcEhw512M9lKytGQ2KTh2R5/tUcbmMMLREq0lFkvdGHaKiovTjGdfOODC9oh4fx/iMeM6\\nBC/l2PGQ89/MaIqKg8keJgp44pxhK8oYBm4rvi9HYwz/+t/Pc2bngJ5t0OMZgYaJruhWNQc7hmlj\\ncMuSbGxIigWebUgdw04zZ1x6PJ5Ouac5R5OW7MwFhdXMDjzEaELe12TGpzEK8hxTZJwU57FiSlWs\\nUdSCnd0+W05BmJc0tuFgfJGuAF0JUiNQFhZpTWGmZFaQ5jm5jMmcmsHuFFVkRANJFkiawGEuDZN5\\nw7PKMq626R2MmPU3afI5B41F5YKt3R02T3lcOlCQn6N2B+SVYas2jNMF5y5WsJmwqM4yEbvILGK2\\nu0b3eIesqSlETWNrGmupyylndh/h3M4Mq2LG83UwNUgXKQTTtORbz+5zpC9ZaTIaMUUKB2kSbF0B\\nAdXlgLBXWgplTU1VjHD8lRsu3trU1KbB1+2ouaXlrUhdTslnz8O0Jth4+8vGe9RXHsQv/33iYMF4\\nlhMaQW8YcCzybxDi7dmIoi7xtEteLsXXmpq6GKP9FbCW7MknAKj29wnuvOtqNLIxhkt7KRfGKUpJ\\njqxEZNZQ2qX4pnXD7tlL7D1+hhofkWekqiZVHr0058zeBTJyUj9gVmvSuSYNDDqrSJw3r0DDoRPf\\nqm7YfvY5skpTTgzBOMX3Xcqhi1mkTE1JicRMG2xpSfQ+SKjVHKSmLiV1PsdmOfXiBWp/FVkKPB/m\\nB5oyjBCiwTQgPQcTdCln0PFyvEsXmfsxiS8QdYY1hokbIJsaXA9hakZxB1Xv4Ex6MPIJvPOY0seE\\nXaSu8BcWaoOXRvizHlmicVTG2M25JBq64wKdOYi4IJsLbOGxvr8DtUQXl+jJVUrRkJmGopE8+8IB\\n81qROYbnz9Z0jEGFDrZKsfMcbVycaofGOGTljO/sPoHwUv7j4nmK0sW4IWcvPobjOVThHRhjeeL5\\nMTsXt6GS9MMUI3KIJc10zGi04IULOSJW7DaXWA/XgfAm/WTYvvgkg8gghMLxh9e9/+j+E1RNybvX\\nfwj5Ck/VLS0/CFRVg1ICKW/ttVAVI6p8lyC5A1OnNOMJ5dltxFwRveMBiryiaSxh9KJr6sqDeGks\\nZVWT1jXbk5x8UXJcwaq22P3z+BunkUphrOHhM89Rp4ZTw02y+QI3nzOrLpH4BoRE1h6mWMavLISD\\nSEuausHWNUJr8ny5QiTNKy5dmPAf0xco8ooHqh4XHnuO0b8/AsIhjnNcWTCzLkUjaao5F85ZSutg\\njGEqPTIvolCSnlavOFp/ozl04quMQVf7GOFQFw1uAWkjEOse0unSzEHIHJXXiMrgkJIpD9ISFRiU\\naqjKhlmtQEpwJftZiNCKVDjIxuDJBmlAdXy8xZwi7lKs5vjPzxEjhb+pKIsaOhY7iEgYMVtoJqsd\\n9lVNb0+DFNA4BGVNjcIpKzxf49dggU5WMEEjSpe69njWn9E0OW5aoKoat6hhVNNpNFXpISko5iOi\\n2MeWE05xwN7qKgfziINdn7475pIjaGrFShlSpDWlGrFXPY9SNa6RjPYeQ1Yr/Kt3np20JGIVieJg\\nNmZYOqiz32TvxH0888zTqAuXqLOS2brF0RKjJUYsOBhPqZyEC2eeIe1XaOnQ2AFKCEydYzEoHfLN\\nR1/g0vkp77jHZ9O/ca1ydU0a0ED7b/KvqKXlrYO1lrPP7GOM5ejJPkHofu+d3gDSeYHr6+v8p6Za\\nYJsK02SYqiR99HGkG9DkOdZaLl2YUeQVJ9+2inN5era+OgvW8H+e3OWSaTDGMsfyzP/P3pv02pdd\\n1Z6/Ve3qVLf6F1E6ItIGAwkkenoYOmR2EHSQaNOkh5CQmwgs06GDLNG0+AB8gaSPlEIP5QMeL4Fw\\ngaP817c85S5XnY0bjrBxEBFAEA8Lj+Y5++y797nr7LHmnGOOue15fnqH+fYxsjBUd15h8o71xsGw\\nYSpG2vEuD3YXTEcDn7tzj+3b/8Dzq1eYA1OM/PVVD+5bFFct05T4iS++ivM9vPG3XJzeZZ8k6+C5\\n1pf83//tEf7hCiVhMbMEYSh0ZOtLeusxRHJMSKkhJvok2cwEsUjE+OHzAv698ENHvqYqkWxZ1Ee0\\nRUbtPGqKhDRjKht8VpSTJaBAgS4jQsPBKqRJlMYTW8l1dcruZMEqH4g2kwFlBFJnVoxUpWJKCm8T\\nV2rGkBIvzDpE23OsCkxlkMqRZcmj8S7H8cDFrKBTJbP1nCLDWGSKrElJI1xEDBqVA9PZGUd9R2Mm\\ndpR4NFdxRm1Hyskjc8AHRR8VrpoTRWK1DDR2w9UwcBI7uuKEsYtEEUkBapU5mta0co5rPBttId22\\nP8lCIbIhupEptSQx0cdMgWMpS/zoaPsNY76PfecNXL9FcYVKA/thRsspeep5terZjy3qaoNYfxuf\\nTpnqGTZ4GlMwte+QUqRefZ7t9kAKmWFI5PT95Pvo8ITrccOd+gQb7adCvt9NSX2WO9cf4Uf4NBBj\\nIr1HYDeXHS+9evIvPsdoA28/3fPa8yvqUuH6x0g9+4GM03fhXeTpox3Lo5p7zy/ffz3nWwJK0RG2\\nG9IwIlBcnHccHu5oO8tFP9FdH/jZeyuyH9k9eJ2weJ5cLVh3lkkLhAgMoWezc/y/7oZfypHKjjy5\\n6sjSYTYXkDwplgzpwCS2XA8ND97seGF6h/GNZ8z0EZdlwaP8lHx4xlosMfOSe9eXSLmmu9lycyVZ\\n1AnOarytefum5XioqaVA6UyZLEZnxkkQvaeTNdmMiGTRqSCJhMsVylsO+0/mo/Bp4YeOfHfbNcpY\\nVM6IGAhFIsYasS/YnpRg1sx7RSwiVleMZ0tyl+hbg64Ci2biRt1hJxL3maiip7SO1lSIuWFWB6TM\\n+DYzJE8OBaYo6XaKFkvZHHiumiGyRJFwWTJQs6sd10mhhELnU2TeEFXipp7j9QkHeUZqD8TKkI3G\\nLY6QMaNUIAtBFBXKDpQ20DWK3hdM2jHMBvSsJCuFz5mpVDwufpw4TbgpcBkNKSSmsqBxPWvdcPAZ\\nRKb0Ej9lxrFnWdwlToHJO6yLZK+wuceUhtB1bGroU+SyH0iHlrvWkm3gBkMIkLLnHVnx2A988d13\\nmYqOcJB0Q0E/XFEvnyfnhLPXTJdXRH8EgPeZlD5ofzpMLVfDNa07vEe+H+0s1vuBMUyc1R/9QHpr\\n9w5jmPjps5/8EQH/CD9USN8jWgzhX+dMd7EZ6CbPdx5t+dnPHxHcAZXC++Q7+YlH2wvGtuS155es\\nrztiTEyjJ+y2ICR6taLfXHF98ZTl3RL7dI2MGWJGpHOGwx3eOtdYlwjzgr/eTPTP3kGnPUkfuFxV\\n3DxO6CwwM+iMJ0ZBpyRXStJvRm5Ey9NH58yfXFKUETPPHKqe7RC5qeek5LmcSu6eX/Bw1fD0hQUu\\nzZmlCS8UOUW+8+Q7HIdA7DNDHJgmgTlRFKKk6wp6GxgXgvmipe4Es8pj7QJvJMYohplGyi33ZUOt\\nPLIeWVQCHY8+lf/nJ8VHkq/3nt/7vd/j6dOnOOf4rd/6LT7/+c/zu7/7uwgh+MIXvsAf/MEffKZ1\\nijErlvWCGA27wWKrgPSacspECUJXNHLHVBrKxqK1QhAZpWQVE2du5JmUTJ3kWA04IRnjDOMSaWmo\\nxEg7GgapMGTKpePIZA5tQ6+OOa1GhqDQElISjFoyzgbWqSJGTeXnZJkRCKKB6eQYF48Ik2ahMlkk\\nDJ6GRB8bDAklbj2r9RRJSbJTZ/TMEUVLnMPSB9pxRqsm6rpgO1V44ckBymyxxtBS0iwC2iVCEpSh\\npOoh1A1p2bLeBYwyYAN9r0EmkJZ+e8FK7Xm2WXGTNTJ5ZkqAUSQf6ZxGY5FS8x17TNSJnetZnE2M\\nVPTjwMX+nHceOXBr5rOH5KRIvkQIg/Pq/cj35rKlbde3UWoKPLk88O6TC379504w6jb1FXzkMHrq\\nUlOXmje3b+NTQCTB6ez4Q9dE7weu2z0+ZMJpxIgfuj3lj/CfGCllQg5IJDn9y5+lISYO/e0mdnKB\\nh29eMKsmFkcfpK//x7PXeXC9o3L3qKVgf9Gxbyfu3hH0bz4kdNeUL73E4wff4qn3rA5PWU0TYTAs\\ndUcWkTztuLQ1o7UMl5p3p5HN1SVVNpSrnv10zvJ8wJZ3CINBP5fJI0yi5LGUHH3zb9m/+DPsNyM6\\nz8EOtE96NjNPLyt2oqQsM7kuOS9WnC+PCASCMKSpoKTGm55uNjB/GlgcO8aDZkwFIiVMkcm6xAGN\\nSSQhKaaWcqmZhcBlqqjkRBaSLCS6iGQkRYaj8oAd2o9VaX+a+Min1J/92Z9xdHTE1772NXa7Hb/+\\n67/OF7/4Rb785S/zpS99ia9+9av8+Z//Ob/8y7/8mVwswN3VDBUyVZEwyuGlRCKIXpAiVEFgYmAQ\\nBVllMgrfGHppWDnHTE+Y1NGj6b1GiJIpSpqYqFTE4IEChUDKzLL2hFCwtFWBwAAAIABJREFUHyqa\\nskaUNSJnQgAdE0k6cpGQfU0WK2ISpJwRyiBEolKS1muClVgEVhfMFDipMSmjhaCZeqrBs9g6WpZs\\n8hEJaJRkYSyVjKzbhrk64X6VyTbTq5oqjMyCZRU8ZSMIUbA0lsnO0b1FZI+dDN1hztDOOF6OKCI+\\nZKTQ6HJE9o+50ILeQdKeLATBGqI27LPgQMcqVlgvcDowjyNC9yAn7pRXXEyaf7x4h+sn5xgxcHLv\\nQGNOuUJQ5MRyTDwePHdVz81Ny5v5EllbDuunjIeMmTUcpsDpTGGt47/9z2/S+pqfeOUeP/7yMSEF\\nuoPlb8//kf/zp/4LQxpYlcv3RVrjW2/ydLziKhjGMeNeDJhP2Gv4I/wInyb2neXhZcsXXz6m+ISq\\n2ZwTIUYe2HeZqwXPyec+9Bg3XBD3W4rjF1HVArg1ywlux9sPH/DoakFVGiol2YzP8OPIfDVjevQQ\\nf7hkvDsQYkHOMI57Jp95SmJyE83o4fpd2su3OURLXCzoc0sxRsZQEYOkZuLhVceFvyTLTGpLtt2A\\n3gSaeMVYKdTQk3KNUwETBTM7sk+KsBVsK9j3c/rXHxF0zUt3PT6U9J1Btnvy0RxXjNRK4KgQr0hG\\nb0BsEWRWjacpJeeTopeKsZhIoiIXcDKfaGVNIQzVvITgQGZSzOjoiZQUxxVMNUU5QVY4pZE1kCRK\\nSGxMbNsrQkiYz0jx/JFPqV/91V/lV37lV4DbmppSim9+85v8/M//PAC/9Eu/xF/+5V9+puQbXWQY\\nFaKC+3PLOkWcuK2bZO/QWZB1JqbMPkh6e5+mXuOzYp0iz9PTpI62aNBATpJJSqTVLHMiiFszCVNk\\n5qUlbQNjWJJT4tBXpKIg+wqlA5LbXrUCWHY12xUkJQncWlQKApMvCAKqxjO2Gi1qcjFj1l2hooQp\\nkFNGBIe0E8NyyWrhyAqOjhWZiKtHbkTJIdaIAxymgsSEKSI+Ks5MR+9LwFDqxDAEstBEFenCjHaX\\nUSnhvOJk3vHidEDKBURPtb0mpGNicYaUkaQVCI0XJTZJxjSQvaTNDcn2lNIx1AKZNJMIjEjSJBC+\\nYyM87bqkbASq90xO8s71A15PitMycBZrtjPF6C45pEiRwPqBb9+8y8+Zl3n9/Dts+o6LjWNWCl69\\nd0uw0+DoB8fD/VN2cYcfNP/1pZ/EaInfrHHtBbZ5ARB0o0NFxdV5y/0XlhTf0z/oNxuydxT37n9m\\n6/VH+M+DN5/sCSnxbN3zyv3lxx6fomU8vMkUjhgmi9CKbPIPRF/Rt9j2CeMb36G+16Huz8h9pKxf\\nJFUD09RBKolJE8SBdf86cRio9E9x5FYM2wekxYwpRA7Dmyx8w24DQ3HKGDwy9JzqiclKDq3AyZHY\\ntDRjCWSskkxT4vHNiFE9Ukq2yxviMqJvBMFDfRhIhSSpgr6QHF9dMPOJ7XMvEPceiwZVoqxDhR55\\nnHFFTehBCkOSEmU90mSylMRs0NLiU4sSDUlYihSp5ILeVEzFAQ2kShIWI1mCsSB0gZ97kgnIYNHK\\nY4uC3BQ0UrHQFleUbKLBhcAMgxAl7eDJOjP2FnP0g90b/x74SPKdzW79gLuu43d+53f48pe/zB/9\\n0R+9vzBmsxnthwwL+Kc4Pm5+wJHkX4v1zcjkFSWJeVLkWcnlVhJ9xrWRqrFQC1ISHETGYEBlgpBs\\n6yWIG2bRIUyNSvq2XjqL6A1MnQRxW3xP0pKHllOfuFALjosdky24ullymM94cbWFnCFLmqhx0aGT\\nR6QTnJJkpZDaETAUAooiYsrMlGukrhiKmur6gNIlk9FQe/yxpFCSyiTmq4kgGkpbsRcOZTxiL9nr\\nhqwEVlUMRpJjZBSaISvy6DAyUomJvW5IsmZ0Gh8zc1qqPjN/YY/ZTzQL8ALclDnkJSFrcn3bBoWW\\nZCkYfQUuInMiikxOgsvJoIsFlTgQxZKV1RxiZkLgBBzWNYvUME+RlCKuGFm+dY57rqBdvILdr8n6\\nmkQmx4APPVZFnoUn9H7g3G8IHJPjDqUUqfBc77dUNExhTe2uuBwbbmzHT5/dRSwq9CCocyDWDUVj\\nWD/pOGxG7tydc/r8nKcPt8yXFVw9gZyY9Eh/2vBjZ69BhsdvfYOju6+wWC7+WaedT4o7dxafyjr/\\nj4r/zPeXc8YONxT1MVJq3tk8otIFq/oMGxInxw2Ti8zm1YeeZ3PT07eWFz53jO2vuHjw/1A0Z3x7\\nd8Vh6lBKs3yu5vRkjtIfpJ/Htkf0EldKyjyx3szI7z7ixZcE5sfvUksoS8F8XkPaEvYBOXZsH71N\\nau5RmwS95TDsOHm8Y7x/RDFbMokaxJwDlmVOZCA5y41dgNrQ+Du0coaKGZEzUQpOt3v84ohBRHJO\\nzE2HHCB5TVieQtSEQjLMM4voCXbH5Oc0LpNMwSIP5JgIMhO0RgRPLgWTycjJI+eZjCQDM3HNwe+o\\n1AwVJNVgaeoFgzHEOjAtPdpmvBYknZAhkOYJUWYqX7DabsFobCWJKnJXX1PFnphOaYREehAGpBa0\\ng0Ese3z2n9ka/9j83Pn5Ob/927/Nb/zGb/Brv/ZrfO1rX3v/vb7vWS4/foe33Q7/tqv8HrhJkIXA\\njaDrBhUyQmWETeAhRklQEq8S3q7QhSQHQZ4JVKxJtqCO0OgaiPjg8MUEpiENll0qmcvAvH7AbN3D\\n7B5HizVdtIRCwXqAUNLvBNF5phriUCPSxEsy4IIk5BlCQVpMWHOGCFCViZnwPBsayiKRheZws6Ia\\nIuNCMKIIZQNJsqgHkoyQBEM/o3UZWS3o2oI6RaIGJ8BWgpBqiqiJI6QQKIpMqeDGGpyJhCQxMtOM\\n4FJNuEwYFdExoZxnSse4UBNKiVYRNylkKchS0I4LgmvIMnJ06tlsC3ZiybHxaLlnwwJJwni4QiDy\\nDYglPkhaF0F7yiDATQwX73KTK06aA4MPJBLCjkQx5+qyZ3vY8fiiZ9uOSJ+4fgR/lx/ynWODHUBo\\nx83NuzRaI6bA3z98g4t3t6zOt+wmS1QHDgn+5pvf4Y49pttbHj/UdJ1ltxkgw73udh3+3eZN6ldf\\no/IL+vUVj//nfyeUD3j5x36O5146ItgtOUdMdfYvWpt37iy4vv74zegPK34Y7y/nzIOLljtHNfPa\\nfOSxH3d/wR2w3SNMfZeivst3Lh4AYMytSEr2jn7y5Bi5M/+g3rpd9yglefpwRwiRq82ek9kTuq7n\\n5vySfVqx9xEdPRe7nvLZlqP69vM+RB49fsQstdz0nptxS5jPSeuOUW2Rzx5zebXhfCl5Ts5YFB4R\\nPSIEps0VhzZyVGue6VP2g+Hlomd+uedc3cUvFmAtW69ZugVthKtQcPfJDWNTs5ENoirpxYKoJDl6\\nZApkWVCEmlBMiCojO8nUzDHudoM+0xVnxzWn445aWJ6UGgZFLBSagE9glUaohJeRXEQm7W9bgHQk\\nhgqyYDYb6PvA0jpKZUmppkmRRheoUoEUxDoAGiEhmZFsZsymSBEySc8pfM9NLvFkypxQWVC7gCQj\\nEyQp0TqTJ8UwCR7cPOH0+NPzeP4oIv9I8r25ueE3f/M3+epXv8ov/uIvAvCTP/mT/NVf/RVf+tKX\\n+Iu/+At+4Rd+4VO70E+CIBVDZSiGDEqjRWAmepblSFU1CFXiMXiZEOWOnOZ4r9CrjEkLBl/hG4Mx\\nC2IcCUOHFIbQFITgqfKIWCW8UaQItipoFwu2UWImECphckA5zyI7HnpofKCWktnoub/c8mBT4FxG\\n9XOKosEBSkeMFCivmSbBWQldqKkbR+lbrlpD1QiqwtFHxVFy+OA56GNULBGFJIpEUILT5cDNVGPs\\nllysmHxFIGKIVMKSZY1Kknk9UOkOvekoJAz5tppNasj5djGbOzPkNpOVQyrDIlvErCaFiNWa3lbI\\nJrLUjrLJjGNgV2t8XnC1XlCqSFlmpDpCVR5GTdsbGp/RhtsaspDERhGzYqQhIAlZMLaK/iDZVhZr\\nLbYIjFawEJGhd1xdjVybFTrMqFSmmwaaqqSQd7noWva55MwLypAwbmSYaW4uJ2amJ6UT9ruR97N3\\n7/XwDflWmJK9v3UnO1wRQiSKga69VWXb/inAv5h8/6Phu/Ojxf9i44b/leinwOV7m/+PI9+PQ063\\nxDq8/jr5tZ/+gfdDSqQQ2b3xNumFBdLc/r31VU9Kkasnazb5BpMFP3UkmbYt7RgY6JnUGY9NiR06\\n/E7zRY549HiH7RxZHfBYhEjc9C2z2uImz+X1AT8+pRUTo3uBKUfumEQX30vKxUQ7ebpppK/nGDI4\\nj7UDz+qOIe/RfsHeGx77Cqsl2ezRPhKd5rb6OtAeH2PFnKQtWUeSkhQuY0uFlODnMxIKOY5koTE5\\nUmhFVUZKH7geRvJQwxGkukQNPUGV2Kplmu1R+gjpPbZsKXIi5IJyPJCbgVI67sUDOUneiMecFgMn\\nCgozJ+FRGkSSiAxa9mh5jEqZKBW71R1Otw47JHw/sG4XCFMxnwe8MWQmksyYMmKsYRmOOBaf3W/+\\nI8n3T/7kTzgcDnz961/n61//OgC///u/zx/+4R/yx3/8x7z22mvv14Q/K3R2AhEQKaK8RRrB4jRy\\nMo6knNnEilEYlBoxMoHoIRboVJGGRDdq6rliQSZ7RRAgc00qJMIGajsx5IYxnSLKW9GTLxROKaZ6\\nTvFCSbWfEF1krGYkb1AqosnMY8aEjIwOHyN+NFCDUBCjICtNYQRxEKyHhrYynOmJRgec9ZRVgcya\\nQ1JIGyi7A2p2QlHXOCxlNWBmijruqa5gXHmK2QEvCnqRWYiOueiY1BFSSF42N6zUhJpLrqoT+tFg\\nVcZUkq1uqH0ka4laRqyURK04KTOx1MRC4pwhB0hWsA81xAnjemIjOLgKEKQp4KPC+IA1FTEukEkQ\\nhkRpetCKUNxmGWRlCCKSQ2QaC4IrCV7Q7wNEiKMkBYkiEHNissATSXI1o+hw3R6GM55fOt4cBLW9\\n5uAT8ynT5A2naSQNS7oi4FzBDTAUktPKkMeJg3Ncm9uZncl7bLD03Y5kLZHxE69BZwNKye9LDf5H\\nRP+N18nWsvivP/+pnvezVIT+WxHfsz+NH+JB3rqOp+05nz9+FS0//FEYYmLfObQS1NKRrSOOPX6/\\ng38ypdP5hH37Ldz1NZu/9hx/6f8AUdymq7/1OuHBJfvTiuO7Zzw+rDn2gRgiQxBMEYLO7GPL1I38\\nzeOWcReYVYqyGdA4hEics+H4cs+dfqA1M3q3oVcBp/a07cCVvSbrEi8LpBjIciQkiINH1hKhFIPO\\nTMHc2r+mkeAbtrEiqIBclOROkZGI7CAMlI0lHRLJDyStENlTuQPWz4hqgZsXyCgw+w39fE7T7xHH\\nEKSmGB11tuTBkRYl08qw8BOhnJFlIhQGhcDYCHWiBOpxjY+JLjqSzxRKYHNB1AVs99RHgiJVhCzR\\nuienBpkEVW7RpaOQmRwyoZwxFDPiECgKRZ06oqwpsmNEMeWM0Al0Yqw6XlYvcHI8/7SX4D+LjyTf\\nr3zlK3zlK1/5gdf/9E//9N/tgj4OFZml6xCTRZYKaSpyLYljIqwT6m4iK80iG1q5wqiM8QXlePvQ\\n3NV3OQkjp9qzaTO2WCLlbbokSZjJZ0zTgiCPKPOKIAxRGJKuiR5iWeJrwaEv2OialDNSeoIssKYk\\n+8SQNVGUzOxAFQa0LolTIs8EpUpMQyYgaBqPo+LH7l4zyw6rVrR7TZUcogocXffIpqPXJclqGg5s\\no2TYr6gzxGTwtmcQp0Th8UUk+kQ2iUJ5zhiR2THO7jI1czSCtW+o4sgdJp4U9wlFRqQWWULKiW6m\\nqHXEUxCFgpSRIhFyBesrVKqgLLBdhREwjz3O1izbcy7jMTJIhAngJ4ienWso/ZKcIqEELQQxZ4Tk\\nNm2UYFxPFKsFVg2Qe/CWJDNiaznOA7vlnHC4YDCGm7EkJQ/djt4uwWsCc+6FDWGv0H7C5pG1nFEM\\nJf440xWC63da/sEfeOXzCnrIwbO1e3K7w3BF6gbEu99mbAZ4r5KSc/oBT+qUMo/f3RBC4t7zS45O\\nPhtxxr8UOWfS+Mk3FJ8U7+4fsR7X/Nzdn3l/hvR/ZHx3zNx3J96EwwFyQq+OeHv3Lm9u3+GkPuZu\\ncxvxbKYtta7wTpEzrPcT59sDfWz5L8+VmJjIKZDirdgyZjDAZAM3+5Hct1wWPS9trhDb71AVZ/Q+\\n4NuOKTuUlLh+z9o8Y7upOIt7kiyJBRzpCS0U7+4Tx7sBlRrSeEkqb7iZArNxYBomNl3gtLUE+Ra6\\nHsi5IKvA5K4ozJ4pQBKgJfisGcSMFCCKkj5WjO2O4WjOFAp0SJiomNwMUY4E7YipQAtJloFpWaGU\\nQ9eCKQhcZRA5sGrXaBsYlWKqFUWAdn6K8RatHEolQqqR1NRobIpUhzXyLGBPPVIGyAIhG4pgWFnJ\\ndCRREVIKICNbFzC+ACNIHpowITqPWFUgQZJwZUbaApHByIkitsxFJowTHC24LhaEJKhCZBkmOqvR\\n2pJURXvQVIsdIc9JjefunRnNpzBb+JPih64nQ0bL8f6GIWnGvKDgljTXArTsyGEBuoCsaUQNOhOE\\nRrQWh0Kq2/rAKu04sCCpEiEjpXBIcU0WE7PRUNIgY2BCEkRFKufIaOkczAnIAobCMF+tmQO9K+iQ\\ntPEILSNWeQYkWguaIpMPkMpEqTPJelStQUUKMpGS2UJThgJh4XNiR6sMhVH4/RVO1Cibmbc9W3VM\\n62qSCgRvqJTH5wmUYjx9kfXQcV/umNew0y+RlceXNUMuOTp2xE2kEzMaIdi6il5altqgCkkYPMq3\\nhPkCtMJHhbYeN5sT0SynkSE0xKRwSXBqRp5TW86DI9gBtSkpc0Y2E1SCwUu8VmzVHeb5QJIRn0qG\\n4kWEntDGIUePHGpM6SGBIDATjhgLxqmilhNxOkfR0RaWMZQ80TO0MIwWcsxQL3ku3+bVJZ5J7tlq\\nOLLA2PBON9EODgqJKUuWO0frPZvDJYv9I6aUiGbDjVtzuLpmKWYc6/sM7sCd+9/feB/8rXXe5qZH\\nSkFTHYihQzSv8s6up0kZmSLdP/w95fPPU9x/jv12JMbEydnsQ9f0945N+7SQhg90FjmlT+3c63EN\\ngEue+mPIN+dMyBHzz0SVnya+/XBLypmfeuX7zVi+a/ofUyanxPDtbwGw/NIv8KQ7J5PYTTvuNmeM\\nbuCNq/8PoWry/lUA6kKzD1v2Yc1mOuJujOQciTFgnz3Fy4Ly+Ve52Yxc7yb6SZDnc56lkc3hCael\\n5/oQkVPHKBOjyBQ3A5taYXqYCUFXHkicYnJFBCYfycnRXH+bxXJNd72gz+pWJRwTndMEP3BctMSy\\nJPT3CdZRqi3DNCDSrdZjv7zLYGfsekko5qALcqMR1wmRa5I0ROlRUhLVjKQU2Rhi3lCKRG88/fIE\\nVUTKBPqkQNRLioOGG8+i67CnFb65FVflPhKDIBeaVFlcLCjwNCbgABEmFOBnFZW0tz67uuaWhzUz\\nqUhpgvd6nV0MzGKNMIJ6aLnTBWwQyJRQKTHKgGAJsUYAKidkd8nZPnJjjiElNmVJmhyFSOiYOTq/\\nRNeWfKchzyXO1ZTdyNIE3t2d87/HVzjhszHb+I+dN/sQRCROFDitsE4jkUgVeFrWbM8kYeYYB/C6\\nRGSobYcLilasyF4gbUQlx8KtqcKIrCRCOxbxGSpeQ1Lc3Zc8bw9omQnW0U6CLOWtNI6EU4K0MEx1\\nZtcsmVanZCW45oSDajDLzNHLGvnSCr0ymOQogoWouScuOMpbtNWImcYEsKHAqgpNpopQV4l5hLEq\\nyX5ATR3VYYuxAp8jQTtC9shQ0e9LiImsGoI4IoqCUc5IVUksS57EO5yLY1rbsA0zdCEIWrHRc3Ih\\n8Eh6OUfr22ERarRokUhKIhSs/JagCvIQ0INC9hrrCwgFqncsbA8i05cNJiQa23Fn9xSTAjnf1lBd\\nuSDVK2QY0dqiZML1Bb1YoI/m5OQxvUONEe0qqig4JEVPosSzTJacM6vLJXUn6IbEztxnMke4URJq\\nST/VWJfZ55G9yaAmbLmnmQXszZbttMdXgSleMuYbxqnDbh6R/cCUWjKOvbpk79esD094dnnBbt0R\\nwgejz4bJ83dv3rBpLSll1uOWN9ffIoWJm3HkerDsnMc+e0K2lunhw1uBzfmB9dWHW9flnOlf/3um\\nB+9+ur+T7nuEQ+nDXZNi15Gm6fuuJabvT88+6y7ofP+vuoZH7RP+/up1xvDpR+B7eyDlD+5r39vb\\nDdY/QfyeyDdsN7S7kcNupHM9IQUy0B825JxZbx8SfE+wm/c+m9gfRux739HkR3qbiAnC1BO6Hep/\\nfIP+9X9g6gb8bktPJBvJIy0hZy6HG4Kd8D5ihaBQjrkccU7gfaBPAScmvNgioocICk83eozYE7LE\\nRsUTd5etaNAq4TP0ZUlAE/I9SKfocUZhLb2t6SfF1EtCPSPVJaOqcdZgCIQkKLxh3gcKF5DRApn4\\nnj1rMU9kZZgo8SIyzGYMRqJkQBpByAVjNUNpSektJ+EcHa4Zmg2mbqlWA0kaVJEYRUnKAmkSyiS6\\nZU3SAXfckKsJJT2qKEFLVnWHIEPISAQqCURIFFkj862SWeREExwiZxBwZ9ijg8e5ipQ1xgtiGxHR\\noguJihOdup1Y1C3nKBk4UXua7DGFYV5kBgHCOe65A52CIXx64uCPww8d+eqiYH1yhi0V/SAgC0rj\\ncRLcUtMUnvks3E7E6B3L7TmMgZwkpXeoy5Z77QNygtq2RJnIeqSfRa5PCoqsWOnIkWtZ+pZsE6E/\\ngHeI6G9JtBJ08zmq0ASzIhQVcdEQ0IiUKJeeshaczDWzOtEXM6RKiBQZDhItAsvhGXKKNNPAuT2l\\n7Y7xowYhUDIiYiKVhjIN3Nm9way/QrhMTplYjEBCITgZampq6mJGVg0ezbU9ZpwMej8yjyPGBrTL\\nTEnRzRVImLLAF4kgNS5rlIKVbMkZytyRRaaZWY7YIfueZrenmSQ+augdvqhY5j0FGSUSy7liMcsI\\nI1EN1GEgm4QKPTELnL79MRS7S+ivCaNERoEoEmmuESKSZUDJDApETmQ66rRDJEFyFcpnVu3Iardj\\n7DOejCg7UrvhejxiCplAxAqQOdHiue53MPaELPCioe81bT/yTndF7Fv2diTlxPUwZy93hDQxTVsG\\n/xBIxPfs/pyP/MM7a7rBsestKSbevr7g757tePOw5e+v36Czjm88eMLm2/+Iu7kGYLd+j3iyxQ5r\\nhhB56zC8H5Fl70nOf1+k+mkgdh+Qff4e8s35Nmp3o2X41jcZ3hvbtp4c39h2fHPbvT8mcgwTz7pz\\nLvvrHzj/d720PwrXww0Anft07+1yuObN7ds8aZ996PvJWuyzZ7eR93fTzTEzXT7ksu15trM86Z4R\\nUsD1I/sn7+I312wOt+dbHxLb1rK/GThc9bjtrZvczWR51Cb++3XJo6uWNOzJw0jsO+yhJXlHVTvO\\nmj2ePXo4J04Hbnyi0wavFatZREqPzIK9OWVjSrKQty2CwzWqGxm6zC6CUg4hoTcNUzQcygU6ZyKZ\\nblniZU3GIIBizJAE3bhg96yhT7fEl8bEzHmGqIkWbFJkI4mypBknlJ+4Izd8obngqBwxpSFJw6QN\\nT+8ckYoGw4QQDjLsrSGHjDwpKJ4XlLXmWFuU1AiRee6O5f7ytpvCe40NElMM+JlArCa60wphDFIL\\ntIhQFugSKuGQKUK8VSELGSmmQO0MMntyAgHMXAcxkzDImEmHhE8FPmg0hsZ5OudR1USRLDHs6ZuK\\n3ghiBYVOqAI4KYjzgm4RKHbPWHmHpiTw4WMT/z3wQ5d2zkkzHuYU3qEwPN4eIeueuvRokdAkEj1F\\nVOTdnriQ6H7CDJJ62BNVZC1nVEpw2ZwQZUBrhycRS8GMTB0nsiwYs0alBrOLlKsBYwrmcWIvNEMu\\nkHoOwpPzwGIWEL1D2w7VFDhRUlcFNgT2omF49Zh7wyWH+Qs4s+f0quV0veZIdJyLewwHg0JSiB6Z\\nbus/YlEyXnoqAaEQTFKS1QzhRmwxIsSM9uQuK5nYmhIxBuwARaUYvKIe9jxXbbisXkR7Q1sZXJGR\\nNhNjIopEDuDTCgWs6pGbMDBLBdYvkC5hZEDbAwvfoVOmNzWYzModKKUjScVCRA5BU5lMmCVOmkwV\\nLOuyAOUZho5YCSq/xl+V1EawPzMgMjoFclMwto5BO7SI5OjJqSDhkdJSJMhJ4RQ4kZmlQBU8zSrR\\nGI8fLFtdoIQixsDGVsxDRa891+0VLiUqC7QSXw5UZSKlikf7kddEwkfDlCs2B4mRHSda4Bk4tFus\\nvcN1Z3nWW7z3TE+/BWqFKxeMo2OczfnGwZIlrDZvMm4Czw4jL6YJcuBmu4MsMekxUzenbypaF/nm\\nkx2297xypKjgtl/7nbeJbcvsZ36WyUUeX3W89vwS/a8Y7h3774m0v4d899uR9VVH6zuOcyaPI+Fw\\nYJ0V3w16Ox8oVYGNt5mLmCP/FIlP7kEs/43irPPHO6bB8rkvnCClYTvt6HvHQd3eY3pvIzC0jr97\\n6w1E/5jP9QVpHAjVGb2P1CnT797hsdXsxMhP2AOjjawPIyshiPZAX0yQM7shkLuWmRMoKck6Mks7\\nbqYFdxz4BJd7S9AtlIJVcgztlsk5zMoRRUarwNXWcSfuOYkbruSMqZqxFD3SeyIVCbg2DbpwaMAK\\ny+giOjtWJeQAQmZ6anIWWFlyv3PYA4RXa9pSE7xGkKlzoHQT234Go6RTx7frCpBKIpNHAiFrMIYs\\nFKUbiMpidIOUkmUluM4luTCoNJGaBaVQaC9up5YlxRgUTe9ACHyhKaLlc1HwQMyRuUOIjDSRkMBG\\nxa5ecJR67MmILBxCVjS5wnJMEQeCNgQHKmdySOSckChmfaaJc3QQaOfwUSBUpIqW/S5hzypKL1jZ\\nib4UeK9JRnHiPbM60RYjQ7sAbqjjnuxfxJWGpEAUBoxEoVC+5UGGVRI/AAAgAElEQVSqeGmq6FcF\\nPn92lPhDR77SSISWlLZAC8Pj0CBsgRETlZKQFRjHvbZlo084dCUJR1UITvIWMxu5VnOGrqI3C3I1\\noFQmRDgWgaWMaKNxLCBJYlFS6xI1eRZjy3x+oE0lMRga6QlRUIfES+6Cri/pDgq7MihpMCfHTDdb\\nQlBUhUJQI63AL89IraAcdog6Y6sSsYPr4oSX55ZpXVK/pDkqA4v36tYHcatMLuSMmCzSWEp6QnnE\\nqARJCeRk8fKIxu7JqmRjS54rW1IpQBuKUUG5pNYdPg1EG2nTDFym8Z4qC44qx8o5or9hOBgCoHNA\\ne49KmYREqYjKgiJGRMg0JeyzRAi4f+YpkyBOkTpmxATzfYccHbPTLTteIKQSEydMSpg+Mi5Ldidz\\nhIu8cHgXWc0IAfpU4tKB0Qn6mWJfN4g8MQ9wIjqirmGZOX37LRoveHj3ZbbjgjAd0YtA1Fd0oiCK\\nmqN2h4gwnFZkMSMPEIbMFApcEAwSXICxX5BWIzFbtk/f5G8vYG8D1bziNb0nty3Rd6TnXsUqRcyO\\nGAQ1PdurwOWFxe8986OW8/4t3gznvNx8ji8YCCGyOYw8u+kp0Cy0oj2MVMAwTHDoqArNcHHF3zyy\\nVPOCutS8dPcDBaa7uCALgTq786GkHGLC9Q/w/Q1K3n7ueyPfcbj12Q67Pbw3TGo6P2c8e55oA+v1\\nhqdmzf/1v/00U/iAfGMYcB/Mx3g/8h38wF9fPuDHju/x/OzDJ+h8VxmdU8I+foReHaGPfrCuFlLg\\nSfeM52b3Ud2ArGpkWdK1FpGuGXbXzI6+yM36wGHfU7kDF/KIWX38/7P3Zk+SZNed3nd33yIiMzIr\\na+lqdBMLAYLkjGQmjdn88zLTixYbmUYjcrgAINFAV1VX5RarL3fXQxQAUqSGko0GMpjpPsVDpIe7\\nW7qfe875ne/HcQy8//kj9/d7fnA7M354oC6e043gcZG4+4/chGeKH4gtxGniSmw5iHfMQPQPTNKT\\nCjzFB0RylNNrJpnp3CduViMnL3ieNat+z3hU3EtF22ZcOrF4RcjQyHIByCARJ395h7gzhwZcaClF\\nUlAYkYkSohDcFM0qQ1H1M24SvK5Um/G1YSmGfjrj2sjYrSg2kK2lSEFR4GqgFZ6Tban3Hl0rB3qq\\nn0FUjMy0BASSWjTVNQTlWM97pF6gZLxxuOhZi4buVvG007TdxShmDgKLZAmXSmOcImYoGFlo84yU\\nGeQWhaAiSTUjciYUgW87iunhGKk2YUqmpkqmZ9YtEoEWAd0XwlnQuorKE4JESY5+ieASQbQUrWj0\\niMFznno+tT/iLtxTnSAvAiE1m1RpV4KdEDwmg7DQLJEq4XkeeOsltYOh7qjJ802QSDHgxWu6anjd\\n/v+Z7//1KoKuEbRnSbWKrAQpa7QGWTWlSExj6P/M8vy+YX6O9OGAFpeeh1BPLP4toii0rizCcIwt\\nKmeu84jpW87imvzl1yx/L+hERaSRZvYMYUF2GRMS25IYnGTyErcIYn3BPEZCgaYszFVjuzVW7+lb\\niSoSkTVm3bFZFKopmPMzk3qBMhq6kWhXnM0brj8FFrWiMQFzU4lNw/rXj/juCqd7lIlIJ1ktT0QR\\nWOwKGRZ0DHTCkNGUxnIWWw5NhxKGYiyLEHSyYAmYDCJFnmhpLEihaTtoT5qWhRfLMx/8liwEqhSU\\nz2RZcToiVCFjOJlbxLLgmgoVpPJoZYnK0peR29PA4dzSyoLzkjloEhKK5NYsqFpJT4LTWhAQuDTS\\n2AWCh9oTi2Yn7EVAolvCYmlMZtGFPgWqcMSokUkgqfhFchYDVgmsn1kdT9yuPJ/kW3ypXJUZfINv\\nLdcuYZOnYniMFU4TK6UR3RZfMhTPfhp5f95jVCH7Bbn/S2YT+NS29PGZpDWlZOICqmp2+wtusxcw\\nC895gnc5U/QDN/Oav/x25IFfMAyZwd6wWl8R5ks/+/3HI7VWXl33nP/D3/Egt9SUuLn+3cugpsTf\\n/O9/x+NuovuzP+ff/PTlP8oqfcz8+5/ds1IHXoUR1XwO2v8g+AZ/mXdWyxkxtAilmfZ7uH2DnRJP\\nT/dA4d3VR1x3eT083p+4Of8NsvSI7KnK/bbf+h8e/46dh18en3nT3/C87AgxsLK/2zD8JlDHhwfC\\nx4/E+/t/Mv50DiP/03f/C51pCcvM3d8/o5Ri+G/+299cHaUYSomcxglIlJj47//qrxDqmqd7zeE0\\n85WZCO/ekYK7BPuYL2NsYeEQNbUW4jLx8Kt3fPQS2oynsoSRaBPPjwun1PMqfOTF8SP/62qFNN8x\\nhZaVeiJNgROORSSe545NU9llGKvAtw0bsUNGwd15YSiC0gqULaxLICXPoh2L6RH5QKyBUgQ2a5oU\\nOTcS6gUUtKiZse2Y2BB9xfqJimBpBoreIRSwQDGFVsyUVnI2Ldkk8IKkNMSKkAJEpRURWQxVGBa3\\nIlWFDhFXFhICAYiqaXImmIawblDVIDIMpfAUO9LeI+RCSRJBpSAQCgyRGkArSy4ekzyFSoqKxVnW\\nFJxdWKTCLp6ungitwEuDVprGRKJQWBWQtSBEoUpBv9tg2oWkFJMa0G4mXLWYLqFFphTDLK6x54k6\\nR3qZGGwgSMnJS6KG1KwZlj1ZS6as8abBWoucR0KKLLKhkwaSoDZHUlmA38+40R9c8G2d5F9/afju\\nMTGbBpsj87mlbiZaLdjPDZthTxaKVZtJbcFmQScjWcK5aalzgy2G9bDCktnhwUVqGsjuFf4H/xW+\\nWl78MGL9E4OceH6v6EpiHgyvfMOUC1cyUo57OvUFuSwUFMTCbrdidfV9Xl2/xX16x2GArAq2MwRj\\n6Esl3bUMk+aXm1v0OWNwVBEZvv4K0a5oe4HkgP56RTqccddvqN/7IdezIjUdBMWfqJlvU+QhJkot\\nGCVpRCEMt9jbFebbZ2YpmKtgpSTOFWI5EXIliA6FQBSH0+nzzJ1Ev1xgX+i8YigzoRhs9chp4SQc\\nso+oKtAViuoIqXAtRrScmX3l6AcOQvBqU9mozP1ssboiQ2HyAxVBayu5QpWCIhRxzmQr6ZYJpzIh\\na1ZhJhiJ2RgYLIE1YS/oCSympS2Zzh+Yzg1PL37CQ6nMR1janjfLe0Q4IZOiPSxcmZFYMkYknFqQ\\nRnKKBV80G1XZbgRx8ogpwfPIuNaQMzvV4GOkILnJ98joqSUydYYc92S5pUwLdZ8oN1fMQbJkyaQl\\nu5g5C8FsrnioiV+/z6Q6cvfKsws3WHnkw6Ph6fCEMctvA9T9fqIrlam7osTM//jLRyZR+eMbQ3w+\\n8TQXfrkLdN8deX3TM/nEj7+8QkrB/uypJfF4WHhZflcqrp8DZUqZ/WEhLgv1/oC7u2bTWfzpDCmi\\nMigqwXtO+8D9bg9NYpw8C5pcGox/YjEvKLVwCmeWlAEFGGJI/O39XzOd3/N6+JpqFQLBEgO/+OZn\\nXJ8PGH6XiZ+ev+O8jPjmFf/x3c+Z3RE/n9kOHX/xmGlV4V+Fy3WImqhFX3xmcwYyiYIohXHec9y3\\nzCVfNjOukkthCoUPcyLlGTUeeTSVaDLeKH4xGcKpYqWkNTNTHDkcJE8fR1LnMA664cBZJLYBYky0\\nuVyAMQUWDDF48sby0AtOR4lOBVEFJsxozpe+blhRuoWbsnCaR6rYMomOImakOSNKxRbHJoyMuqdU\\niVxGFtXwbngDVGIMdPOeXDYgNFJUVuPEPDvGTWGQC3vdsAiNVIUkFChJShcNhtSCdZioS8NUDdls\\noAj0Emn7zKkWQrXcLs+kzuB7C8agrEAmiF7TqcihKBQCx0IMDX2XadeFmivD0xmlV1QiIk8ULknA\\nUiS6SHoTyUbRHXa8DB856o5fmS+xOmJMpuSCE5FaK7IWqpKoKtEyQ6Mwp8wqnMkrw01+RKWJR/MF\\nUTWUIi5irVjRJVKiIs8Lom0RWhP1CmE050biQ0OvMnXes68DrWhBQGojul3hy7+sZfh/a/3BBV8h\\nNa+c4OFaIVpNcxBE0dPmzNpE9kmCbojasd1sOJ2eaE1hY0Yma6irr5jtLbbA61WLFZJlPhBFZnv7\\nI1r7I25XW2Iu5E1Bs4Z9w9v1E8F7uOupDyvm+Zm27HilHE+sWKaFPE1IJdGs+eLuDVppbLdCfkY2\\nrmRlnysYQydH/L/+Kf5k6J4zg2sIg8RutuibW+7YkTzEx8CyVwhVeHslCa3gfuw5CsGL/o6nc6UN\\nC0VopDPUVJACvrjqeff+GQ8oV1EeejI+ntktGwQCJyumChIaKQIhKaSppFJRi+ClOLObDCmfAc8s\\nBnzMDFYjgYqkZIFyiZfF8/fHNY9ug7KJw3LNYAO5c4wRrsqJuzbySKaRhblIihC43rPVZ7wXXKlM\\nkxfKvEa7hO40XjpEa6lBIgco0RC0IMuZzdMnFvElqoGjN2TruDKB7CZWR0UsmfLk2GzO+FxZiYAW\\nC/tkeLdIXj0X3HWlxIrsBDtaBu0xvvLLsCKWAjUhzjPr+R3BnvC5QVRByJGUEzEU7r2jLYESNRVH\\nlIIP+opEACkJCY4x8arf4UQihUI1r6Am+vgNZ2+gOvauxSvF19NELAtP5xOb9Yb/+btnFr+Qf/7I\\nYUrMS+L0PPK//eKBl9uO8xJZd5Zx9Ni/+2vU6RPBeXh1dwGBfA52v/5w5P3jiFxG+pD4cC6srx1L\\nLtR5RmaNihP56ZmnKMmvFLoqKola1aX8fNrz8LhwfLjmT36wIdVL5u1z5Zd//8jueY8dZqgVvyws\\nE4jjA+7DI09S8qpzHMvM7rv3/OXP/4Jf5cp2XYlhQfiR9jyRbq9JBU6l8vN/95eYcYLXhZIV+/OZ\\nkivTecL0gvO0MB4V0zlfepJRgimci2HcS57LmUZ9oJP3nHcndi83LLphnAR6VhTveHaGD/s9n4Jj\\n9W7H+ssbSgNBeaI0KAVOFHa+xQeF1hoGxd2vR0TT8qnriKVgwxGnZkrreRy+RC73HM8bbK8wdoSa\\nKUJRhaBK+KI5c35uWZcDayJNsiRVaOXIYzKXQrGEep7JErwSWCyuFiRQisadPVl5pt4yl4RVGilm\\nlMnk6smupU1gUyIViMFSVw4RQKWENYLN+ZG5NKh2phDApItwUhlEFJQoURUUEpU1ndgz+oHbVb30\\n/oXkTp+JrSUVeNwpZucoOiO1wNNg6oSh0hNRZBq5sDp9IA6ZRVhWaaYvhTMOTURJgTEF1RSQl95x\\n2mo24ZHtaY887RivG2K5IViHzJn9s2O1ZLyprFZnVCkcxTVBDwjnaJsz6STIxl5GHUuhmwdcEsyr\\nhRu7xajm9xbL/uCCb86FcQddFryLLwhF4JpK+2pgvb5l/fQzjFvTmC2dUFS5A+BHVx0P1vDNcEcD\\nDBVedxmHoTFvOIjM6y9u+OHN17xoDf/ub+4RQrBtLF9u3vLz+wNiWNO+fUsqDfljZJWeeC2uqI3l\\nb5fXNN07Hsyazhlur1qkVoS7G24fJ6TUvO09d/1rTk9PPMprPvqEVD2d8KzbhmnTopVAI9DaQpJE\\npamlRYhILgItNS/bNSIZ3rze8oufvUdSEKVinCGXiABeXq8QK0esIztpKRJaDPo8cBICXRIhaK6G\\nEaslWhRkNnwMmq+TxgBZK67ymV3sWIyhaJAiUBeBbiVeONJGoZfE4dtI6dcUodAIxirZGIHWgiUb\\nVquZXYHGJmRW1EUgGw2m0seRt/0ExrCKN8RiaSQECaMe6OXn8lZvYJ8IRkCK5N0DdfMKs8mUpFkN\\niX4TSGeBjRGP5Tm19Ivnzcs9bVdYciVlwbY1PF2/4rX4e1w9Ewucbm9I00zMGx5ECzXTMdOkA7iZ\\n++0dadmTUqXmizFE9oW8QAkCqwvFVrxLdELg1UDnKz6debCBt3qhkYlWOZTf8erNluePkZRPMGaW\\n21d4c8OYC5+CZymZupy5GTb8/CnRftxzvxeMSyIuidMY2W4Svz6850+a73F62nP2TzQicHq3Q6Rv\\nkeuOoVRCzHzzzRNKCl6sNMuj5OfPEdkl2loR04JkQPiRKiN+f0KVwPhmi3p6T+w3LK8d5MzjUfFq\\nnXj3MPLNd4HVjSXpwi9//ciBxIsBfJi5f3hkWlrUStDcj6z/5PuY7cAv/ua/42//hxPflDO6t3jz\\nV1Aim+SZD4mzOQMdwUe++/DM94yH856HY+DT9QUZKch8Ok0cveKYO1IsCFFZsqBYx872qCUTphmj\\nPDFL6nbLUTcQzoilRQZBOrU09Zlf2gHBA53ydDWRhaDWgm6vWTqLT49Ip3h8WrPtMkYnkpCopZJS\\nIZ4iS6zQRbK6jDkemzvyXMlZ4gRUDVkKlmCZsFzJAlWzJMVKpUtPeFXZNAu74wF1FehDZoktuSrO\\nztHmwlpc+rckcN7DKpClYimFagzGaqROqDCTREPVFVMjuVZCVagCqgZmCSsFxWcWMVFFoVZBcAYj\\n5cXeoBSWSTO0GZt6REyI9gFK5Tha+qHAAqkIcAlRJFOwFDRaFqqsLKXFqiu0z1Q7Y04LwQvKmJCr\\nytlZ+jHThEiVmaFMJNvTpYqUmqU69qLBS0s77Rk9VC1w3USQr8FJygQxadK5EKzB9Ik+T0x5S41r\\nqrB0OrFoQ9QN1he2eYsAruXIbddypQeG3yO57Q8u+Col6dxF3KOVpnOQreTmBv7o7Zcc6gkfnpEi\\nYp3jSvVsRGbdF/ZVIY3D2MiPrjVXDUwz9HrA2TV33TVWS5SUvN72fPc88vbFwNXK8Xo+88E+4Zxl\\nbFq2ZuCPwxo59Nx9+ZbTjeZ5PaA+PbJ+seXrV2uMUSxoHg6CzJZm+wOgcNc+4ZfCwRpq0ay2kn7T\\nYK2mNxpVKtq0FA/FbBAigdLkdIGW//HXXxBjoVvucZ9JNjIVtDPkJWOEwPUNtu1oz9+xLJ5SJT4P\\nlKsX2PEd+32HDYYfX0WiX9jaHXId8WXAqIpKCVnAWJBRISi09owWDdkr9HlmfvmCvsnIXJm6gcf1\\nDWtXaRBMfSbNPVoXos/otbwQyFRBSEGdLdUqUJ6ByHXdc3YvOLBF9AuDGDmYnlmu6OXFpEHLntJo\\noo4cUbztFV0zU7OiiMrKZJzKdBoEilGv6GpllQXCarKsjItDNQWvN+TecvAbXqqJaX3LYR6YiiWc\\nGk61oYSEmRJWJOZNSzCJKQ7EpC7ziLpcnJlKSyMzOV/GuHLNZJXRwLAYSoVjG1DrTKrgxExYFPOH\\nX2EonENmvB44dA1eKa5zIRWgZK7jO2TS7H2kzpGxFKIsJFHY54h/956vG8mqdYzHHXM5U6JnfTqj\\n+4/km2v2T7/g9P4G87O/YPtnf84LZ3loDep8ZLeTmBwQuwN106GLR8jIst8hfeAwn5CmcPIn9ONI\\njQnjJ+LumQ/5iphh9xwpIjGdzkQZuc6FadoTpu+oh8C7+6/5fjFMs+T07hvKdw/sc0NoKnUuTP0B\\nnRXnMdOXi/hM1Jbn7w5UqXh5k+nP98RTon777zlt70Au+DQS6kCMAi0zkkxMhadlhFPAeIdcbWnM\\nSC4SKeEqHCEcKXJgYwpPQVKWSoojUkT6a0mrwcuKVJnOamStzH7ApEAukpQLrc0X57IMzbywIMnD\\nSFUZmwte94QqmUVGV4FUEnSkaEkJgrEoFBUlDbthiygNMRiEuMAiTIq0eY+qW2QCqqCgqEKhbUFQ\\nyFmT9pFZFO43jiQFeRVQduBazNhxJmwHbKk0SvIkNbPsaEXAlgl/01FVZFGCY5VsW01SiqRBiUsJ\\nPQbJ6dxSSqAphaYuNCag8SAqByFRrWUWW2onac8FqsDUgAwRUQeeUwdC8EFqbuUnclUEL/AzNEhM\\n8kQqbfRc5YVYNcLai55BVo70JGdZKPizw3cWJQP2VvH6wzueVzd80g0ISUVTjEbVSC0KmQrQMmWD\\nqJHT+hrLhnCGIlc0G8kgA65zrKXGzwn7e/ID/4MLvgCblcFpwV2fafqWcNVy3Wo60/L13RW/+Bi4\\nXsGqGq5sixUj2lrSzZa3qy94zgdumkrjZr7Xa/76QeI6hxQS89lS7su7gRdXLV1zuUW3f/SWj49H\\nOtNRhw6pJcI1qK7FbTdcETi9uOV7L2+43g60n31ktTJoKUlFMpeC0Ypt1/K0OyGlYvNyzd2qYegM\\ni8/0NyveP57QZiD13yctZ7Tc4ZWilkqrHVfXLcZqlm92rGRhUxJLtmhniMJjjUYqhW4abo+aUDOz\\nC6TGkKcjXQ8iFpzKbNtXvL6LCPb8nI651osIYT4iZaRerVlNESNnFt1ik6eIFikuM8fKVFZhQVfL\\n9XJCGUejJ6pYcy6KqqCzC5M1jLWyNiMxajCKJFq0zsgUESUw2obx3vHaLrTXDfuxXmhcbWD1PHHQ\\nF4iIzBFvJdO2ZVseCKPm2/g9SqnkTtBYjeoKfZOIe4WSmcerN0wnwbK3dP1ImzxtWDj1NzQyIKug\\nnhVRt5SqSFiqsai8QzbqIpSphhFJShIXFEWDdiNJNpha6frCu9zRVoGolaIETpxR3mDszBQsvu1p\\n9cyD6nD7d4yzxVZJQZCqIETPOUVy8ZRYkHMl7J/JCKpOJBFQDm6vdihzYrc0/PB84kFfU5aRCzCh\\nQCmcqqb4Qv70wOP+yIknfrCuiDmhlaTfPRJERITvyPpE+dEa6oj1jyzyDrKmZI/SsFTNaTqjnnds\\n3kdi6piFJmtJKgG9LCxtZQ47PkxnftB2lJIQVOJyJIhrTE386unvGKczQV1mqHXNBO9Rc0cuhZAE\\nfv+MpocU0WVP3O05DpnjSRLFQtqfyZtMyIVpNpTF4WQmkwhoclJwHhFSocKI0OkiQkSz6WawhdMx\\nMZs1tQYmPXCYWgwLizSfDe0ESioacdnIhWLolhPqPDM0AScr0lwqRNdp5Kw6mmQRbodKmlovQKAl\\nF4Z6IYyZtlCUp5ZMpWBF5mhX+KyZtSEXjaDgcqXJGo2mFof0mYogVw21optL3zxgWRrFAxMBjYwt\\nsY0oFbFnj4pgl4pOlbkZyDJSqdgy0rl7buSJs9HMgE8SLxOiFFJVGAUlSfanlpI8S4TrcuZKHDhX\\nsI1HqApVkZ2BYqBeJiJkURSVUTVSZCJow8fYYvWZBckoOiaf0D7SHh7wquV5tmzEmXRfUDeWiL5Y\\nwWqYpCVZTxcD5+0rrvQj08sO51aoLyvaaPSssE8VVS60rGoUWil0SnipCHNEFkmumlPYUP0TtbmU\\n75uu4vqGu7sNXW//uZDzX2T9QQbfzlmuVxXRZYIXCNvwZv0SIyV//uqHdI3ijzbfoywHHn+taJuA\\nuSu86TdMDPxgu2baf6BpNV2vyc3AQV8Yvfpz2eGCDvzd7Wm04+3qC9Z2QHSW6fQeNd7QfP/76Ndb\\nhJv56U3Lp+eZ26vf9Q1s/xbtLGmuVGBtFde25800s10Jti/v/pFidf85+Gsh0LoFEzDy4g8M0KgG\\n9dkbWSiJ1YqN9JxsxQ6GsDOsVy1WCdQwsHHfQ+UjH+RH4kmhlohxms0KUq2stx0//smK3c++5ZA9\\nUUFwL0lywjQds2tZ6XukbGiFYxAG0RS0uUA62iHDg2QzzYSmw2NZPCQ0S+kQ4uIcMlfHvnFYdaaO\\nIJsASVyyxMORh5J57zqg5Sv3HjKYRSGaBo1nmM+cpz21vUIJkK6QqgORCElRU0JKWKKhVZbNcGJv\\nNCaC0ZnRC5YCx85gFosXhk19YhIbJrfBxYUxNRRnuBYTVypRysJxCCQUMSlSkShVMLYgqyZW6EUm\\nKsEYQSsFoUCViOUCG9FGIJZKT2ZSPQrBYlqW6PhOdfjDwugUL3vPFConn5nOHilHRJXIttL7ExMr\\nzqVg7cRSLLIuGOm4EiN//beS7fV71p++pcqEMwtCZ5JN5LxwGJ/YHypRnDinR1SAkz9zfz1gqufV\\np/dM8gbRfgvTA0IpkiysdCYgmNqJx5wZ93A7jyRWxEPmuE48moRxmau0MLhETSei93i7AAUhKznP\\nxLoiisQyLoRUKKperHdI9KeRYZ+YV5D2I9/RcxV2KP9E6Aoft2em1R0hOq53T8xCU02Pr5b02dvb\\nqcBSgSJZtwujECQEShRkLTzqgVZCo8ZLa0QrcoZSLCSHrwYXW0I/X9jPWuAcWFERCDyS3dFhaqHR\\nmZUJjDaTy4VK5fIa5SY0AlEkFUWpklIuamuQCJupNSGIlFwpVYPVhCcQLUhRaMNC6xeGusbkhslf\\n4BnFKGRVVJnBaWoq7NcvCauEnj5RVUCbDuufWC0jKnjOeYWKPUVlgpGofI9NlaYuFDVzlplcFalk\\n7CJYloqQ8XJeNZKzxQZPLJAXaLaJ7Ab85Okl6BoJxeKKRAdPKRGT9KWyJSAYdRESaomoFlLhuToG\\nsVARqCzJu8RkDoRwzTJOmKxBCoqWyKZSlGQ0Fy1K0Z6sVkR5SydOGBpaN/OcK/rKYoy4bJyrQbQN\\nrTXcWMtjdeQcOJWe3m2ws2CwDtlaDlcnrNQoY3jxYv17NQz5gwu+tVak0FzfCoKu3KZCUBIhBUYK\\nGm3509ufXL6rW+5+2LDuDKutYagFpVtKKZyaCS0vQpRNbxjTZWZS/yfM1F/1d7/9rL//FdV77Os3\\nANy+vMjT3979Y5m6lIa76xV9n1FKsjIaq7Z8cbPHNLf/BEDwm99XUqCrAOfQ6zXps2LVGfM7w3cp\\nMUohleHV0LL0V4jvDbz+YsMfDS21OfMxwfamZ0kt+8MZqVve9Bv2p0CRha5vaforOun4V6Jj41p+\\nNQ3EzQtMeWRRjrbfcNKOc2nZ2iNFTTTOUewCpqF8hsuzajh2LR/mNW5aszGSGDK6Rg6zQxaBEQO6\\nzgg1E1LHHD1XMjEvEh8kqzZypQ8cWJGaHhkldjZMs2Y+VFqzgMtcCuGOORvO1WCXEcvMOHyBkAJt\\nIrIR5FPHQxgosSGpiWQSBQ0lc2wcRRmiapBzZEahzSVz6suClAu0AXesLNqBBGcVVnmsLTxHhSyX\\nLFdYyNGiQqVIQ0AjgkVqQfUes75kQhKIQmFswrrI/aoliqtflYgAACAASURBVMJYZw5HwZgENl3u\\nqdKRJCRhqiwlMAUN/UgicPZXvDae+clzLpbIt3wxfEJQGQ+C2Fx65s/B4OxC8QGlBR92v+Db4OnP\\nB3zzJZPWLNPC3mbO3/2CdC2Z7QY6Qe8CppX8ajKclECFTJKFiKTMC6dTpF5XFJHjqaJKQslCyoWn\\ncGaWgVVbmDH40yONf8M0e4SsFGPItVBr5WZ3YsYgp5mJC04xBnipLhso1wpmtUaIxKIKVe3RecNc\\nL6MzVleKhFIMFUdFoI3AR4nve5LOnOkpjaAzT5SqaFQh1IqIglokGdCfg25aMksrkeYiSqQKFjTj\\nZGiaQusSjQh0pnDwGbVIqI5Gn1DpYnhSakMphYy4kC4AZSBTKaIguXzHygKnBcYD6zzS3ERkKUgi\\nPkBMCpUS1WRsTmSrEEqRkyRag6agFo2sFnc4MviZ1FYWo6muZZgdojtThaJKQzUjFY2JjqUolIqM\\nOqKyRBwTRu2w5pF+3XLOhr4WplQIWWHWCyI1KGmgQFEZKRtMLAQEZEktFSECQlYkCj7/z1stMT4x\\nLxCkuMA4AmQH8i7wZT4TdoaYJcZ2pL6lKk8wipAlg9ug7Q1aVOrcY/XA2jQYGejLxEn3OOfQKmFr\\nYBw62qHBLhIvHQ/HGW8HOtUglkvbcn2j8Y3AVoUx7vfu1PWHF3xTQiqD0JchcO00p80FWP9/DpxC\\nCP7kq+t/cgwpJaura+bTDiktzq7hMv7428z3X1pm+88DBf65ZbWifD7uYBRKGtqrHyPEP739a6cx\\nUtAqSSr1EmB/8lOGbz5gpMb8w36EEBh5Gefob16wSMVmY7laNTRa8dVX1zx88452o/iqvEZPe/TR\\nspLgRabvG37yRy9QugOjqTHy1WA5xRXca5LqmNU17aBZsuDU3vJ6GWlc4qQbepHQriNrQ689bq0h\\nC1QHzdqRzoKpwCafOS0WnSK2yWztiWgF+ShIraNNnrPU6FAY1gWS5tRsiGWABcwxsMfRmUgvCqoT\\nRFWoxfBplmQ0NVmC3FGK4JJlCKRKFCpSKqascBpcSRShmTwMWqBKxauGMi0YUZElEKugKYFqIqul\\nUhZDMfLz06IoQqMaBblFzxZhBMZVfDAoESlFknCIrJBW0aaIlVCUYMGQEfRqJEQumbSrpNKTfEQK\\nyNKRqAgtOJ8Dk2rxviJFh8wtRWbOdcewr0zJoPCYdMLLhc4Y7seObhUJCQ6jYm0TrVqYRORx/45J\\nbylpoeQdLjmO+pqD0Sg/811y9CuLCJKKRAMqXMxLHnJCimuet69xnwLl/TOuKlqz4LuBohOaADFz\\n7wMOwWArrjlz8/NPTL/O+DRSACcLJReKSJ8NNTK1FkbZ4HGoqtBvEu1K4pNlnhwNhSgqK2aW3CKC\\npcqG4DR6LhjAK8U0NvTjiQ83lvnKYuWKsFc4Wbm3r1DpMtIm88L6nBidAAmNKggt0LmgE1Ql0KKS\\nqmBBEWVh5SpSgUwJJVpqsegwoq3EiIqOkKQjS0XNhYqh5uXCK1YKyBcbUzRp2aBMgbLg956+ichc\\nLjxqUSlZkqJClIQRDccoCVZQimAWjuQaVM5otcEtXEAcFk79FTZWtIjkoBBNpkhNqoZiPbNJ9OGC\\nmsxkdjaxrbA/WmIyDP2JbTgQs8YVw4GWKiVFnnEoLAZk4rlXmGBYycJUes4Pmk19YLU64deG4+ka\\niaConqEkBjETpWTsLTpW1AliE4itYtO9Zekm9vET7eoVdaNwdcZLRfSCK7nCuI4qM8EfKOJMrQ6p\\n7tiWXyHKR0z/muVuSzECNteo65YuDMh0y1Pe8XpraauiFS3xAZq2su4tjAVjfn9uRr9Zf3DBVwiB\\ntC326jVvXm0RQvJrMRBK/b8dOAGkbumv//RyzJxhmjBS/BfZ/fzmkE4JzGd3GSn/eWPvm9bx08+e\\nklJcsl2tFNvmQgRabX5X0tZXVzSba/TQsVpv2EWQkt9iAvvB8eWXtzzPO77afI90VOQgGGpC2RYh\\noW0bhLxYM+YYMcbwx19/yX/0moezZ/aCTfMNTJ7aDpTa0bYzz9IwhPnicHSStKIitUHHStMIhran\\nhIVzUiyLIfqKGNrL7B6SLFralaQrAUvECoH2lUU6nuc1NgkMFRXChfPcaGypqODplkCyZyiOp8kx\\nmASyEptMxQMSrIBaabRnSfbCzK4VlzLVXYQwj9M1d8IglWbIJ4wqF9p5qBSbUTKRhcMnSf4skEFK\\nMg6BRJmMidDnQFGORYLREQKEeKEDWaHQ645oDVEaatL4ZHHSI+eJIF7gYiAEsNrTqoXT1JGExqjI\\nIWlEr7C9oj6Vy6hKEaS4EMYdXbpm1hpRKrp46tnQnCtx6+Dk0eJMewONC5ymQF4Kba08rgvFjMgm\\nYmNlnCE2HjJooUhmYFI9kjNaC2Q0HE+O6QaUlNxLS0oQH8/I7cV+rnYQF0cVBiMC66ZgZcW6gl87\\n3u0EyTb0ZUaUwuvxyObkqdZRVSZVyVk34AMiZco0kNcL+3FDyAYlZgwKkSWFQogCWRQmBGqpuNOB\\nuHbshlvGFz1Tv0FJ8NqRq2KKEuMXbFEMOuHmE0lLctZoqXAikatFlIvq11fJ5dEVRHFRES+rNeci\\nWdcT1lXkfNk0GpPp82UDMFpDVhKipJqIzBKlK0prRLlkv0pYcnKgJLpGBr/A0DFqhxGRVkeWbCGC\\nqFxKuRVSlcSiSdJQgawktRlwS6apEa46RKvJZUbPkhgUUCmyEKq5uKGRMLKSRSXUgheJIhWqwlIE\\noUo0kdfpZyTzNddIRlEpJSBLoVUCoQqzBhUlSoEykjk0rChcaYid5jAriAHtCl2aWQkux7GCsFQk\\nkr4KDtbyorthePNv+NuwR5snTk1mXq+YkiBNYKq8CKGqZqWvcCRE7unNS5b6kZu6pnz/Fbl/pj7s\\nQDUoo7h7/QY139C2ilk+olSlHi0RMLLQoIHw/0nw/YMzVhBac/tv/y3Nl1/91mvVqn+5ZPyfWlr8\\n5/39v7TCZ2eVTv8/8z9Vn0/HqN+d1/ofBF/V9Wx+8lPa7Q8Y+g03Sl0Yrf8gO/5q9SU/3v6IN8Mr\\n/vT2x7zoXwAXH922Vyh92QRIfSEpaXfNy5sVb754QdsNGGtYrl5iX71l23fMP/mvUX/8hruXhrd6\\nwuwjWTec1ldIKiYJerPiqlvRNZaNLVgJoVnRuIZxvGMJG0wpvLk+c90eqAgQBisFh7zi29WXlK7h\\n1fSRYfdMEJ83RVaTiiEuBVkqOUmSUERTKK3nvDJIQOhE0YZWZrarCaRkNo4bM7E2GWTEG08WsMz5\\n4nVaBYPzDMqjUgJRscUjhCALAVIgEGhZGbNDp8gqjahScSViVUHoipPxIrbJhhQv85y6uZT8opCU\\nKDnOHT9fWh7KilIFtnU4Mp1d0LpwbRYac7FOS1XQ2cBVE3n5MpGQqGIhwVkFvNBUeTEuP8gtRUpc\\nk9EpoQ4z7XSmWUaQiikY7ksixgRCkKSkyHLpWevMrBdSrcxLJjYNh2bF0V5hjSCJQGoAoYhKs3QD\\nhXJxkpGSFCqlgKiXZ8nVjKuVAiRhmLWDlBiLuVxziNxOM7036Dxw1WUylYyg2N/wkypi6Ymlp8ZK\\nXiAWfQH2z1DOHp0S5Mpw3NHME+35TP3sRNWPMzZFapVUBNNTprnfI06Jmiqr/oTNiZIFIXDZwElD\\nLVALBKGgCCQCaypcb8Ea9nlDDoKmSdzcRvg/2HuPXduyrFz3a62bYeecy69twkekx51zdYHCFdV8\\nBAQS70CNChJFHiCLVBEF3oEKBSSkq6OLEPdKxyFIE2abZaYbY3R3C33FjgAyMohDZELoxC9taY9p\\n1hzTjN56a+1v/z+s6HtLkzImCUdpyWrIHBGBzh4JjeXgz5AMMgdcdCzigULDVFm63jC5SjRqTcBk\\nOB6raGORArmQS1Wv2klfjy0kBJszV8cPyFLozUvGIZLGFfPYETEUgcmmyotCsKbQ2IKkgsSegkcz\\naE4c7lv2AXCedhMY0sJgdhRJCIVG62e12MrO1lxQLax8glmwxROsZ25aummhCXd0rqFBKKK8v1ie\\n3xqyUVbiuZSTh+DXQin4VBhKpIwtZRSGYaHL4J2pn6e5QOIbTMsjTNuxKSMDPbZz5KdXhLMNaIuK\\n4GzPe69tOF/1rEala/2rbMhqwU+pah50vxhVq0/jKxd8AcQYrP+knHzVea47/yqr/KKwKozOsHY/\\nn0LAx3ZdnfliwXewhovWcdF6+sFzctZX0YRPwVkFgVXnGIzl2+uON8dPBWg1r6T+jDWIGHIWUioM\\nwydBvT1/B+tPaE7fQkV47aoytkWF5voSsz7j3YuRs4sNZbOi74TOGr4zb+m84X59gpRCly2n5jHr\\noaH1hq6xGG9RA84AxpKLZZGOe9OSsqmLtnEYqbOyx3ZFUoOosBjDLjukFKxklmLZHxsIkELVmg59\\nQ+gXsvNsxwuib1lcLft+sLfsgycXod9voRSaGCkhIiWiKUJKpFLJVFfttrqrUOj3L5ECaai/C5MK\\nBiHQIgmGXEdYGhLewul4wGqqZBpq1lQyGCco1NnRkBDJJNuz0w6KQlLWekBPE2ihywutBV0KqNA0\\nBTWG7CxZQKPBHw2LVSZjmRtPsp6buWUOBb/KFATZzqxuDmhKGBPRznLQDZOkqoCEsqD8MJxybzsS\\niSyFnSrReUoSUjEIyuwWcpcfeodCcLW/Z7MQsyNRHYSKwJWD8yajS2K3CFmFqW3IOaPUz3p9mDiZ\\nFFLhenOkbxKNy0SxpCREESJV0CQbZVqUu6llKo4lWpgzJzc7/BI484FJLS4k7LLQ393y2g//G2fv\\n/yOrww2aAhITJm1x84H56JFjrIIqLJCVzswk2xJoyMWSi2OmoWRDSRYyoJUFXfaJHBWTA02TYBjp\\nm0hbLDYbjtLj4wHJgpYNUhLbZoNS+8slRGyBgIGQaHSmmFqtiSpkBJcDm/kAoa4dGUVy7R/frUde\\n9mtyhgmLCGzyjoEDPtzj9EijgvgCWvu1OSeiSWQfKbmOPnWmkHMiB89SPCZbXCoMdx3m1rF2jtJk\\nmnzPih2jNGiprYiEkIzDNYIBcrA4LYzJknLHnenxg2PTXfAoNFw5ZUwLTguzKpINqXdM1xf4ssH6\\na5p+hZo6H+yMx6iCNdjzyOn1yNPO04oyqCHljpzBdSMpjJgy4i8ucE++hX/tl1AZafqnONNgjfJr\\n717zn177Jr9y/U2a0WNtxCwLTSho131ddv4isH7DcngfgJWzrP6NgfPddf9lnNZPhVA5F4P7YsFX\\nRHg61ED69Kf0rgFOVw3vPF5zvmm5POmwRj/TRcYYQY1Q2EB5wbD6xNy9uXhMc/H41bFVeUXs6kfP\\n0Hu+9XTDRxbuX1iKKsZZhli4Pj9lO2WOWFYmsjnteLzqMPdCcsJilN5krAizdMxpoC9Hjp1n9TIR\\ng+e898g6ctMkAsohNzRFSCiH7HAaaNzCdBjZHT3bcgqLofhEspHYZLrGYNfKUXpW8VArByWSSnV1\\nCTaBJMBiJ4sPFmMjuVUWYylWmNs9tAEpER/2FDPBaOjFYrJlDBPPo6FYGKcdc7ZYrVKZmIyRRMoO\\nkxWTFyQV1NZFM2TBHxeafOAoiTwn4tEwZ8PeCkUzoTH0zJzdvmTb1f5y64W8QBEFBY2F1K64X1ma\\nRUm2fk/72SA2EZoOswgzHUPco8Ejg7A6sfS7FUtRiBFUmFWwqhRNZFewuTBjazVpKa+Yu3eTww6W\\nUhJFlOwEKRPXumfXX7EPmbIIimUlGZMaDh9Gwik0Gjj4jpQypSQOs6VLBoPFLhaTApILFEWLYIYT\\nwtZhvSA21cBxFFKBXW7Y5ZaWA8yR1Cr74jmeNWzu5/p3ghCNEBU2yy1xGun3mcXNuJjBOJYEGhPa\\nCOPQcs1zdu0ptgRAKBgWamaXskEylDKRIvj9nqk4mj4SrVCwSCnoqkWnA1NpWceZfLAUsaRoyWKQ\\nUhi3N9h0XXvpU8a6wEW6Z9udIl7IUr9nJdd/c6SUyrjWIoBgjBJxpAhlPuDCgevlfbI3uC4yZ0Gl\\nPGwWCi/mgekQmdaFVGYIDcVHQsns1VKAY5fRbaaZHY3PlIMhi+HQOHIXeeJ3hLQmFqFYITQNRYSh\\ns/jliJsC7bxncPAynPBSLBenhnfeuGSfLCcfvcRax35ZuDWGtjiatWd5csHrb73GW69/G1HhH968\\nol8ydpzojjckf04mYNTy3c3Aoxl2M0Cs5C5jsI9/mZOzkQ8aYaRlvXb8ZL/DGlMD+APWfkU0meFs\\nIHURmQN+7CjjgNVffCj8Sma+UGUmm/EN2tVb/96n8rn4xqbntaH5wmXnfw1EhKvTHqOKd+YTJvRP\\ngRqtIiVjz/XrT1idvvbZj1V55ZrjncE1houLoZbm1ZBXPc3ZGc1rr3G9HjhrHZ0T1meOJ5cjo3N0\\nRmidwTth7BKGgliLGI8phc4OFB0ZdCBsTllpYBV2OFNIWBbfMLmWUsCQMVJQWygI26nhmCwmQdaH\\nEpJ4KIWMISNYzSRbiTM5JO7ulcNtIhwLujgyCbcDTZlFHbPz7PxAMRm37MjG4KctTU4gireZIczY\\nkOi298iDbGOjGRMz3X5HPiyklB8y1ACx1L5hKYQgzMFjcsRO97jdHewnshSOriMDU2eJTthMd9gl\\nU7LFJiUcC6EI1pXam3OnvDieE4xgXUTJzLNyPAiHxfF8N3Jn1xQRNCVkSYz3R5oEhBYzbfDTiKGg\\nPiKmkBpLtg2xv0S0ILlQsnIsLffphN3RYZZqJ6euEG3BlwPSKSKuyhDmglE4LJ67wyPm6NGQIApH\\n23HTPmbqL7EhgSYgUGKt3qhYsB4xHt00UGqlQYribcT5TEQrm3gxSBF0CgQUrENFagsjFrIxPF+f\\nVM/r2+cM+x0+LtiQOLiOgGKWiGk9vcvs/JpUKplJJVFECGoRBUJGpoLc3yNLZMFxe9txPBYWY8gY\\nNEN2QrCOmA03tz3zpJUpLSso0E07+l1kdUj0aaLEgqTI6CJOasA8RmXbDCCZEgWfIpBIYmgkkbNy\\njB5wVTV0PuCmW8Z8QJrEvReWJbI96iuzlWIDs+zw1tHqCpctt1PPRzIyTVdoMSQb2HkPKnTWkvLA\\nPg7cWEMaHCtbeH1VsNR+uNGWMz8Q1o85ho7N9kBXIkbBqqHVjLeOddfi7QPJVRyPsufKjlz0ibHL\\nyLjm5OwK6wzGKONJT1qf1R6vesR1r5KJpnkYt2w/4cvkXCjG1nLyAy43LdbYKvX7z0itH69pp80j\\nXl8/xYhFhwH3GRycnyf+VcH3b/7mb/i93/s9AP7hH/6B3/md3+F3f/d3+aM/+iPypxxTftGwfo1x\\nv/ha/RdFZw3n7S9uePuzYB9K1k1n8U1D2392qcVZxQivxEJab2tAVkHVgrWc/ur3GP/z/8H67bd5\\nl4WnTUe/PqXzjtbWclhrBDEWT+FiiKwGg6hicqaLFr83LN1ItJ4n5o5V2FUjBi3smxVz2+BN9SJt\\nXMaYuvuXIpQiaHbV0ALBiwLpVbBuDBQyWRMhCrudMvz9juG/v8QWB0RKiehUnbrFRBKCyQFTJooq\\n9rBlk+7QIqx8xEphnPYM85b4MEbiJUGGOWb8dKwOMblgYkJjLWFLzizzwrNwIKdMuzWQHO7B7H3r\\nOhZby/OzN6S8UCYFFJky4W7huCuk3JCikoJitXBEyKUQkiEmw7M7z0+2G57bE+7dQC6CKRk7LRQR\\n2hjJpnCbBw7TyLmNnJ9MtbethuJ6shiGcItdIrsXhg+OA0tsWWbP7b0FTWQbye1c57UtIMoRx5It\\nWiBODZEVuhRCUijK0q0QMVjT0IU9aGT2DTlVT16F6o6VM22eyIdMKtV7ViSRBDIOUwoaqykHRYnZ\\n0C8zRQ1FlCiefT9wP4xE48jJoGRcrFn7fVcJbRIipq/Z7ewsAjTxiF12LM6SHgiYZSmY7Z6SBTNn\\nsliWqMSQSFbr1EFIWApHN2IWkBwJCUiZWftqMTgvhAXW6Z6GuXIZcsbZTNdAEdgvyrHp2HcNKSlG\\nq36yUejjxId3A7dLQynV1L7liGgilMILaziWiWex4Y4WDRE7z6gsPFm9ZEyBLiqdFvAZXCTrGfLA\\nZziMA9NoWW06OndCdmcomeI9tvc4Ux3JlmiJvsebS+gHjmmNSP3tokJrDJ0TrDo60yCAloJV5aq0\\nON8RL06YNyswhsF/UnU0KsRSrTFb4/HO8XE64ZsaIK1T2q7+/6P3t5RSsFZ5Y2x5Y2y5vl7x9LVT\\nVGrb7Z/jV9+94N0nZzh1uK5/KHV/+YnR5+Fzg++f/Mmf8Id/+IfMczXz/OM//mN+//d/nz/7sz+j\\nlMJf/MVf/NxP8mt8OfCNffWj/fj4s2BU+fXvXPHkol4Ylw9ELwHU9HTNgHM9drVGvGf96BFvDS2/\\nwsy3z1Z4VUQNgxdOeoNGoXcgxpKtJwVhfP4SdQN3J1c8vrlnVQLfXt9xZfcMeY8ISM6kOWO1MKcG\\ndQXEfGyCxpI6wjxii8cag5YIWTBSmGNPEkH0waascSymx6upYgA5Izkw7ythqUhm3H/A6v4jePCG\\nTkYYyy1v25eMTawWbZQagB48jBuT0FKZ2V3c0h3v8GmqRCzq+JCUQoiGEjIhzVWJJ1lM7Z4xiUAx\\nqBSyKUgSSgBBSceEjwspFMgNcxbMg1/CsRicSbWsnSo5bKcbnpWRn4zn7PuBeJvJ8ePSuDDbiWPy\\nHOfaU0UK1oOmlhlLBjbhtgqd5DqLW3BIEkiBoJGgiWIgt6BSM9RUlCyVeb1EC6rYUijewuCxXvBe\\nOCHgcmDfGtRXGz1yZGFEc+G8DThXuN+cstMzbMyYhzn3IBZKrZoXCgXDTMbmmWwMWWov+9g2xK7n\\nxfqCWRuKJkxOBNuQcER6YvRVklCEeyzBCI0cMWUBDNtoOURDTkKICsWiMVYyVrHMS3U5wgp+ewQj\\nTL7DCBTJLGJhDsQHwhfTQliEE+5pdUsQoWTIbqRpIEthH+pGsnghasHLDrFHnC74cKT3CaeGhCPH\\n6sy1WMvdYri1wl0MdaDYWUw6Mmx3uCL4snCW7xg1Mbg6LlWycHy4qIUqCKO90o5COd1gxfOGGbk4\\nHTkZG3x/Res9a1sYXM+6fYIzyqobQQxqC93VGXJ9jreezo6sm+GhilY/F9eMuK7FvfGIdHEBQG8/\\nCb7WKEJ+mA32NE7xUtce96m23easZ3PW03V1DRtWDaeN47RxiMiroGvkXwbVrrH4h+y5WdcpEm9+\\n8cnR5wbfN954gx/84Aevjv/u7/6OX//16sX5W7/1W/zVX/3Vz+/svsaXjs2n/GE/b6zKe/vqMVcP\\nzzvGhHEjg2swrvaM5WP1rZwYpGCde8iQFSvg1NVgKQpND5sLYjkjpUvc4yecXp7TLcLuxUhIDa5E\\nvMnV0SVlptkwTZaUHL7JYCxGch0fQtF0iYtrcNVntCTh2X7FYTrB5krckiTs3WOerx7XsZEkZI1Y\\nP7McqzxntBN2TrXs7SzPrjfsesfUFIxAFsWWhImJm1hLl9ZkjAiaq9mCSmIj97gucN48Y3ALVjPd\\nvK3OQbGwhAV3mGmagtGCzpFZysPGps4DlxxIekRDZJmlbipKwVqpm5KHsZNjqlaRYoScDDXP9kzZ\\ncGw6bk9OiItwvCsUqZuSlDJZC43AzXbN88OAV4HdKexOsQewIZBLqdWFOSEojkg2gamE+l1aoTiH\\nlESTEi5m2lRoD4k5KEUUR2LCMQ99dfMBkqnEsVA8xgEIL8wT7sslxnY4LThf368GMAmk+udQI0Ui\\n5wfyk1Fie8d+uKE8pM6lFNIqo1bIxrBtRtQnBJhMDzmz7wbuzIr4QD68DZacA+/PK9IDWS6kwjFZ\\nKJlpbqAoJgQKUIyyLYYpCWrBTHNlSzupGaUkFnEwJ6bsWYpDYyAtgqU89KYLOYF6S9MuhBzISQFB\\nnGPRTHe8ZfKKLwlbCt5GMp6YlFwSYoTj2PCjkzU/FktKB5pUaOKMCngrlFbIQOuOnNt7TGcICZ7v\\n+yrmYwxGqnlJ1xmsFczlORTBq3D5aKRrHKYYGjUMxnHWdmz8Ke+dfot1v0LVYGyhXY+o95y157y5\\n/gZPhiu+ffou5vVv0F2/y3D9DteXa55861ucvfMa55cjvftkTTJGSHQIQmke8b3zd3mtfYPBmjpm\\n+rECoFW63tGPnm9895qLfyZu9HHw/ayMVh5uf3z9Nm9t3mRwPz/Oz2fhc7vM3//+9/nRj3706riU\\n8mpBHoaB7Xb7uS9yetpjv+R+5+Xl6kv9e/+R8PN8bxfnI9YY1puO0/PP/8F9a0o03vDk0RqA7/WO\\nv79t+PbJUzZDvW3KR7Yv6+5Unef8as0mZabbD/jg5sCNWtT1DFfKXNZcDiPx2Y5LL9x/412Gly9p\\nGiX4nmN+Wnf6pEoaKRlvoO0FNRlzBiepZbd9znbeVMUoNdw7y7FdOI1VsnAXHNlGDqHBWEUyBGnw\\nbSKWEYOhSGAqFpcTje6IZYdGi7WZbAUd1/zkUeTyJVR1+zpHLUDIigPOmyPbw4oJIUeqFrAKrZ1o\\n5wPHCF4i437LyYd73m8e04bEC8ms/BF/FMqUyKK15C0QnGe72eC3gdlmWCKWjJSEUSFJD8VQSl1U\\nB51JdORSZRCnXDcmiLLTlqTVDhAeiDhL1fg1UiBrtZpLnkxG1dLNR9RHJvEUVYbtHUvf0+iClEgM\\ngpWG0p0RmwWbM21aaJY1/dxhxhtuQ8diBjZJidmzqOO2dzT7QBZDNtAcI6X3LGI40iEiZOPQEjA2\\nQ8xIKOSsNAQO4hEtlFiz/oJBnSM9KNVlW+8LCL5Z8KZAgaU4Wg1IEl6kEZkjh2HFc3cO6ijbmWXd\\n8A93nnV0jBkkQ46+6gTnzJItPoPEhdiNHIYN++aGi2RYmYxKJJdEFEVtJezF0vGj9opV12DtLZJr\\nST1bi0ghZyF4i8tC5w98FB0uCIJBBWYnuFxYOofP0M8ntgAAIABJREFUmfyQok6pr8UXWcAGvFP2\\n2SGygGSuimEKM2fpQ8SOYJray20bNhvL/UvHj+9HZgInXcccG1oNXHaBVXeCPV2xunxM+uEN3sJq\\n1eIOBhsSjUSKc5R+oF13PL084e5qZPe8ofHC+bvfYyXKk2+fMfo1Z53nPGWeHWYu37vi9v/+L1y/\\n/ZT1k9f5fz/6r6go11ebV+vN/ZyYE8Av86vvnHGx7vj2xQkiwmrd8uKDHTFkzi4HTk47usG/ikef\\nXjfvzYqwPXJ9sWHd/sv1dPzmW8TdjtXbr3+B1fPLxRemeOmn2GP7/Z71ev25z7m5OXzRl/mZuLxc\\n8ezZ5wf9ryJ+Ee+tX3liTv+q1zl9KOt8/FgB3nKO5SA8O9Tbwt3E8X4CwK49+eGxbcy0WhfRyz6T\\nLaxWA20+8tHTt7i3gPMk4yj9GmeUZRg5FkvPFiXiCVy6wL3z+LYQ1iv8ckozfQQpU0yhnPaYDLYU\\nNFc3poOxhMWRjUFFKTmTxGCbQJlbpBSKjYRYs/v18YY7EyAZWvF431Jcg48bRG+qQlmpCR8UcoFT\\nd6SxifvJE4+5OtJoAYWRO7xEzBJwB8PdvuU+WkwRVm1kZ4QICIJZJpLvyKXgUyBLJvgOdK4jJqUw\\n2onneQDXEIcGoZbTV+y52t4RyoY7IFupHs4FrGZ22rKEKrRwcAOaCiWk2gM1cNYeyarEuWWXHCuN\\nuJKYvWWJliKKjRGbCs4udGYmZoMxBpGG0KzoSiAXKhvX+VoCF8tiF57pNcMxkrwFLWjOJFGSdxy0\\np7PKXbMmZ6WoUvShf8+EtILGAhlaXdiV7uGzL1WBShzGCNkkSMLu3JCeW0KpY1m+JpGkUHAcCblF\\nU4udA6a3r0Z6jkeY5SPa2JPwUCqzOOWGYmEnCz5kpK3f1dwr0QuLD6QyYEvA+YQJW17KCn/c4pl5\\nmVeoEUgz25vEsxtPL5GM4RBdFemIdcQOKZzeZDpj+PF+pPiJ7CeeXRi0qYFvKbVaNUUPKaN2j/gj\\n4bYnpkBxBTGFcVrT8IyWG44egmk5LyMn3rIyLc9LIcf628u5YMOIkyOqliQN6WSkdQPJroj5nv3x\\nwNl6YHmxMDQ7zPkpP16EclzY3R05ObnEyg+5un7KtNT1olsa0jLzrFKTccAtUL75SyRVbl4ceKRP\\nKZR/sg5t74/cb491rVkiNy92r+6bngV2u5llidhGca1hf1yAf7lu5tmQJ+Vwl5h/WoLoV3C2Yvo5\\nr7U/K5H6wsH3u9/9Ln/913/Nb/zGb/CXf/mX/OZv/ua/6eS+xlcP/3yUST5V1dDukxKSqsEZ4c3z\\nhu2USOKwRjlbt9wGwwL0VknjivjkTc7eeYPpWFgfbilZWHfKu8OWJnheWMuxzXiz4rQdyGWsWUEL\\nwQmyBAaJlCN1JIUOUyJiGsRUMk8RpfELB3p2wZLIzAI9hbPtDdOc0FWPlZF8f0bcZHzTwKGjyEMv\\nyhSEQiowmMBcDFEdq9sPiONCf7zhYMcqFDEpw/sv0JeG/2FOUZTe3dN44U4dB6esFJplT7Ybciog\\n9kGhLJFtDZrkzGq1pTMtuenI25YYwRpY6xGjQpsjWQcmfJ2bRrDANnf80F7SrRJLc4bJE7DFLTN9\\nK2yMwdrANicirhJjTGFxhpQKi+tALDYXLAGnEc2JbMBYi8Ph0kLHhErGGB5E84UmzuzymqUxDLYg\\nzJgQiaoEW0uxthGyHYiFyiiPCW0KF8cPoGuZ7SUkpaG+YdWIIow2oCUzPfTfJcMyRtKtYU6O4hyd\\nRHqzsHjLUQ13rsVkOM/3HIKjiNCz5yb0lKZKMZaY8aXBBCVgCCUTbhNdKrgQGaZ7ljwjtoBdKKlg\\n04zbVGKTn+4IsQFTvbeTybTbO8J2z4dhw6bL9GFhaz1HD+f3ibKxWCl004QdlPvs2E8NbUw0Q2bV\\n3tOkhlBaCBZTAJnJ9oh3SiknRHMgOcFY4f/6lW8x/9DxP0bPj6ctXTtxlc74zuklz8yCb28ZGqU1\\nJ/RygmK4WgsdnmwcrasTGh9dnfHyg3vEgbu8xJz1LDc/RBpf+99GsSr0Y893npzTPrrgOIz49rPD\\ninwqeRORB+7GJyg/Y52pz5eHteVnt8xOmg0nzeZnPubfG1941OgP/uAP+MEPfsBv//ZvE0Lg+9//\\n/s/jvL7GVwjyKfEQbT/Vv7GVxGBMi3XUkQwVjD701ABjFe896ckbLFrVZwbbUWgQ7ZifXJC+dUnp\\nCmnIePX41uJtX5WHGsFKxueAitA5y7QYovO1dLxyTONUheaNo5wUDlSJxjm27JPDlEwqiguJmAvi\\nchXE2DnO55axM2ALohFVQ2cjqWSO8UHzmUK3zPS7j+r7lUK0SlRL3heOHxW2tz0uW5qSySXiNUE2\\nWAreBEi39PElPmdUDRl96GFW3eOxnxnSj1nbO4zlVf/Upo/1yJUw9OzGizpeJApSKLawOMtLt6IU\\nRVXID0tcbgyKYkRofcA+ZIpW6/2zOIooizZYzYgkjBZsToxpW8VDFGyc8SwoiaKWGY8WrRZu2ta+\\nqomspwkTErPzzG0NUNF3JASZF0wO9HPGpoRoQkoiUt23HIWmBABOwz1vjbecjzNZS604ZAcIzckR\\ney40zjLYKihhNHBcWxbnKQ/90IEjIVmON4VZHxSkEMJygqWF6AhJWEqBFCkPfrPkSMkBcVCcR2aw\\nZSKbyCIt+8kSs3K7dBgMNs/o/a72aAWyGkIyFM0ktxAi1UpxD8tpR9cJrYsYByYGxCgnbSQdEyF3\\nSAKXH4xXVEiz0pSGQ9OCgrUtT568ybU/ZeMHRBVNiY3pse1I61quxmu+cXXOe+vX6X2HseA6w+pK\\nGU9a1NaNw9PLR/TnhicXFzTrt1m//p/Bu2pug0MVnChmXNFeXuAvLjm7HBhX/+uCFR+H1HX/0wlQ\\nHwddNT87+H4V8K/KfF977TX+/M//HIC3336bP/3TP/25ntTX+GpBzCc/o09nvsatcN0VcYl05iV7\\nDGtv0bmODiXAGEWNEkgc5oiKsO48dweDAZJEurMWmlvI4NTQ9J6ja7jubrjtB+6BdRpozIGNd9yX\\nQ5339Q5pPHsP8y6DHdj3F3Rmh4meLIVFlJyUQINlT0yJ4jJLKtijp9OIawPZQV60aitfDcx3gZkF\\n57VmuVqlJGsCFwmmSgyy1DJ0wdGUwmo/wwLHlcU1Led5R2LB5JkVB0K5wGjt1xoHhFpyzk1EtwtX\\nx8QLHpNEabJgqwgW5mHGdTYdReoile1MdgfusmWxTfUZBpbOEegwmhEpxGRQB85k1AhOMwtaCWW5\\nkGwdFdNSXYucCL2bEXV1tZwTlljVvVQ4Lr4GehFsyWQFLRmXEzuTobGVQJMgeU85lDqTqj1FCqZM\\nJLeQxZAK1ZKORKsH7DGwClvMmJFiyJoxpeCzZ+UCcxNJk2Olwn1OpKMlkSiacItjCZbeZUY9sIQO\\npmckW5WgQCmpEJzD72ai96SwpSNizZEuLmTxJI5kZxE/Uo6CukjMnuPBMCfldqlBr5FCsompb1gW\\nRzsFnObauvATubPcrQJTWNCdJ560DLYw2YItDpcTyTla4yjSIOKxYshU2U6rpfpqmxmPI4pn5dav\\nNr2n2mMwpJTYjB7jPT5FiibWzZpcFGZBRGn6DePFisENHNNEaxpU4L2ztwGwbo31LVIl0xGaV5mv\\nGEP3jW98KWvJ4/OBAjw6++l8lI+Drv4vqhn+R8JXVuHqa/zHwT/JfPtPgu9q3RKWDded4e9/9IJz\\n1vyfb5zz4T/eYOcZY2smXIMvhJTpvKX1HvcMND3MJXvD4/Qm7x8/5KQ5o3Uee9LSNcrheqQrihSL\\noWHTVMs/lYw6pdiBtiRyduSizMnTah29iArZZIqEKmOoDbII2VaFJGuEZVXVgEx0WBcpRyGmDKpM\\nk6NMBvGRpTO0k6GYSCeF26Lc24Gu7EkqBGNZy6GKDqWAT5kihSZHJhsrizqnuiHRKtLR+UIgcu62\\nFAqLd6y3Ry62H7Er13hTJQENin3IZqPWTBz7sUmIZbKBo2kIMdID0VnKYrC+9uNCeSCzmYwYg5HM\\nXVREIrE0JK0EIZsDVjKlVKWtXhdQkGmhkYDzkSjKbu7JInQh0NrALQ3OBGw6sGt6jMtMzuPSXNWS\\ncqHEjHnQLn65d7Smw/sq6CAKyRzR1mIPGdcIxTpEqx+0lFqi7G1mEuVmatiFNRqFOVpCs6O4iMtw\\nLMq8OOxNxhwz+diRB4su54h0OE1MNLjdjDQT0kcGmViZPXLsa3+cGsAsVehfU9WStiUhyRJMi7FC\\ndkohoRGkOA79GY95TrLK3CvWKu1KWW7OcHlLcYpRh7eZVWpoZ8e8WtFlQZxl3zjGxjFPhSQLJ73D\\nEpG2zgpb62lti6gyfO+XuN6/5Onf3ZAoPLm4JHVnnHFByNW28TbMQMCrpevqNfxouGbTrCpPQi2u\\nPUdNh/GbWiZWpaSM0Spq4r9kPXxV4bXLz9Zu+N8u8/0aX+Nn4tPB131SLrLOcPW4EvJe53sMneN8\\n05EfRV7vDbtQFy1vhI8peY0z9K1j1axJR8HoSxpvuShXeHlE5xxd5zi/HPHjm/S+4XoZeXlYoanD\\nNR5d75GyUFTI6nC5sEmJbUkctoa1CH2T8N3ElBvafEDtQskePye0L1h/g/SGMvTcl4FVKjSa0DaR\\nywCM7A7KYWtw/T0mBbQo69mxa6sU4tSPxK7Ovi5UUQTVQiGStZBjwUhEfK7TtSVRitA1M7oO2F1m\\nShlTEpqV4gvxJYwyc9rs8Y3Dbv1DqVgwppBUMQWiUxwCySPtniTC/cGx7oQmRKac0X5BxoUMlOJw\\nWrC26oPN2SCSWVI1r1CJ2Bioxe6afa3NkYv8ErN/idtoNbvAsFt6cqvYlHHWshkiXgpFE1M0+Kgc\\nveOaPYuDudTXTLkKTZRFuckDPh8QU8v4IlW2srgjrcRKOzeKlgVTwKA4k0kYptTw399fsW6OHCVi\\nVweaaMk2UgRiEvKimJQoyZPCGZI6DFsMhnmvGBNo9x4XLON8j1CwKRKNYXIGLTMuFTQJcVZcW7i7\\ng+IsagrJedSbet77HUdjWVzLy7ZhHrYY3WKs0jpPNIHoDC/MCcWdsmq3XN5HYIP3awY3kdYjoldo\\n6XAyY2ShH1ouZOEw7rmxew4LjF0d/zPjyNo5xodZ/vHyKdN4DcCVLvzo9obzi56f3E/0tqHr5eFa\\ndLVlQe3J+v4TyVmAcfMGdzf3nBnH1UlP+3NQ7ftZ0H9lz/ergK+D79f4N0NE0KZBm8/u9VycdP/k\\n+Krz7EJlNTbmkxKSd4bWG/z4JsFF+mWicYpEC2S8EZxRvvnNC26OE/cHWA89d40QimJUUe/QeAQZ\\nKQq+ZGxnWUIhpIigDG3BrgNe73HPYdEZSQ0mGGBBfCQcgHXDXhuQSFMmjrlhVsu06VlPESlKOXiW\\npBhTkOIpjakewwI3q0dkHDkImqo0ZCgZSiDlDiVSvKCz4RBbpBVahMEtTMYhkpGc0BTxCvutcNJO\\nhNWO3K7Id0pURxZDM5RKmLIQLTSaSIuiQ80Mwywsi+XifuJlDnhTM/Z7MxDvC6Lg2+olewxASeQi\\nZDWIZvooaK59+SigDpplJoeAT6aqkpWGvO6IMeJCqGXaUGVB7VDNImJS7l3DFbbqjZcEZLRkkmRM\\nguOsvKDjSnb4bNDe0RBpfKBoj7IgtsoLKg99ai0YG/EOpmKIUYnjgg0t+ZCZ5yrQUbIiBWbXsNOW\\noIYSJjCp9tvFYHJgc9yy3k5EuzCZiMGy95ZkQcsWdKAJid3LltgouRxpPPRt5iDKeDlgY0M7DLgP\\nCj7DE2f4xxwZdWGVDeftCbPuef70mpvNGf29pVGDGVqKcTRDR3d9Sn7tKfZ9x9k+8N575/yX7Q3G\\nn3K+OGZuaLxndgcery5fXUumaXjPXJFLxp+dMT2IEXa95813zylS+Nv/+ZLetFhb++nuczSOh82G\\npmto11c4/4tXhVptWlIqNO0vXg7yy8bXwfdrfCkYf+0/faHHr5zlvXXPLkbKMb26vWvMK0lL11g6\\naXFGKcYBM421fPdkwMrM3QSPOsGYhp8YzzFGirnG2H/EhIgYT++gd1vuW0tbCinVjAmNiCi+CeAd\\nhyZig8eEqgI1rpVwDLyMjrHx3KtyDA02W0LnKMlguoDVmThHSlkTmi3tCDdiWLLy/NBija9jNCj6\\nUBqu6tMLS4JSAtkVJApp32E2oFI9j1EhS0EpSAy0BZJ4zG1ic3HkPvWUrlrT5aIkp4ztjEyOvSba\\nbsGrkLSlGZRyJxyDq+M2WbAmgxZmq8xRmELDkAO7SR5mbSP7UshqQSNZW7SUSuIpGbGZGLTqP7+f\\nseOeYjt8B4cMPiwk2WDnyCEYxg5GG9iWOh4kKCqF+NBytSWTpBBcA+pYspLnhLWACikLRRw8fJpi\\nC8SFkYXeKVFAsmJsZjFwDODszLJ44t4zx5Y2A0mRkmmawu7YkExGM8hoiDshGkMTLDktNPOR4bAj\\nrD1FA5NvSKq4cotIiw+KZkGy0JSGIpnWg14axktIzxzOeZB7ss9014Z+MjQjnJE53Zzy/xlDcg6S\\npXEDb643fOedJ/yt/38wHDHW4Vcr+HDh/HrkO7/8lP/2tz8k5swQHTYrnesQsTwd/lmmKg0I+JMN\\nvPxk5PNjdbtvvXfBOBg+LP8T4HMNBnz3CNcm7L9D4IW6ceg+g4z1VcPXwfdr/LthcIbBGQ4SaL3l\\ndGy4Pu3J5ZOBg3cur2iaRIyOj1h4Z+w4az1LqmWn87YhYOj7FffhyIfpOWeN4+YYKBhMtswmk0XQ\\nJJxYWDnAzhQcPkbURfZjy+lxRxsCpoy0rBjsxFIOuGw4xIwrkRgHOjcRjUc0YeNCLglISOnwuqVp\\nIvvo6aLDSmCXGzIJNaBJkAjLBMdxS8iJ5MBQaGMhThG6yiKe1h12XihS2N6eo+aeRSwpKW4/oWMi\\nDg2LdxxDSzKKaqBfF8K04CQSrSUWj5oOq4EpGUyxrP3EYAOgzMViNTFFxzRnWrTOUC+BonW4uRTh\\nuCiitcuQEzhNBPHY1CAx0S0H7nXB47FTJIUMothimaMQSqYziV1OpF4oy4PtYimUbOjTkahajRRM\\nYs6RmDxYJRlDCEqnC1XxulTyVz7wtN8zKrwQZesT0U8c2i3NDlLMcCxoMij+lVlDXsA2EIuhSMJp\\nwfsCAsEqJVYZUS0ZmxZssuxay957BMfticeKkGId6XI2kkrdGOAtbmNZ+4ajW9DY4J3QesPFt1Z8\\n9PwDzCx0wXExrIhxpkE4ZKXzPY+GDX03oqcnuGOi7XrssAae03iLinKxaUm5sH7tDdYf/Vdu9A5L\\nw+OH0vKra+xXfhVy/ie8jE/jO++cAfDiI0vM8VXJ+bPwVdDS/6rg6+D7Nf7d0beOX3vv4tWxIqx7\\nj1Hhm0+uERFu7Mx8iFyNtbTtjePRcM3oB35yEN7cXPHB9jmb1URaBi5ufsxU5iqO4BtyY5i2wtDB\\nG0R2SXnrw5lkA9YLh27N8PZjpo9+DE2PYLD/P3tvGmPZVR3+/vZwpnvuUPdWVY/ubrfbNtgQICR/\\nCC8hUaQgeAiCIiXKJPgAigAlAisiYQgEiC0CIt9QRoVPSVCw+BBFT4rIoERIIaD3SAyxjcdut3uu\\nue50pj28D7equqq7enTb3VWc35fuW/fcc/Y6+5y99lp77bWkoSlzhJPYUpJ46FchTV1SBg7lK4Is\\nI7IV5VryQ4UhkgXCeGJfUaRNykJihvnE7hXgTUiwlq84JyRXDi08sXTMv5izpztC7dGIQNIIS3wJ\\n4xykTCblf5Ho8RCZWlwjIIsblKUmdI4cgdGaOMoQpkTKGOFjrNWEusJbiRQK5Q1KORwK4wRxotCN\\nyQDtNXjrMaWB2OHUpJCFlg4TBPRaFTa3eCSlCNBGYp1HCYt3k4CssJgM+BaI8xxhMorUkkhHUpSU\\nImDUTkgBK8FWgkiXCFeSxy1iHAMhGBIQ+8n9WhSGjs8J1rYXSQypWyYxJUGkETKmkJJhUuKtxQYF\\noZzUmw2kw0iPwOAQ+EogERg/qS0beUsQaEQArpgErSkMLvAQWAgsg05EHkcooQhciykDy6qN7HQ4\\nKF7EiwY2CNFxwIE04Whb4YZQjkLiCPa0J1HlcUNjXUDDxfRaXbrDFZTzDMUkwQiAEdCZ7RGNC/Yc\\neR1FpVF6ibQVIYWkuZajPW52OBy/BrPyAg/27qcbT215t1SydbnnSvzYzIP4Lbtsa15uauVbc0fy\\n4N29LZ+7rYjupvUsgLtaBwCYz0d0phrErb3k5Wlc2KY5U9DqewpvkKLB7IEGL/Y93huiRGFLSWeQ\\nYu0AmTRIOwfptGboT+1hOF4m9gVKSSJf4H0IQ8+CaeHKmJYYUCqwwhGUOV47BII8SPBiBRkZ4qzA\\nmibtuGSMptISUxaUwlJGnilyjBfkMqBSFSEetKLKgXLiyvUCAlmSFQpjPEoovFXkVhMMSlS3wusG\\nRoARCqUmxeytloSuJKgqtNSUvkVuJ25vKyZlCYV1SO1xCKzzmCBFaYk1crKn2Rg8Q7yYLAlUQcg4\\nbNBvCrTyREGF9woThuQ2RGDIrUAoh50YkFgdTyKl1zKCVTakKQRNW7Hq5KR+MZYSSew9o77CaIMI\\nBIGeVGvKREgiFYEssEKxMIiZqhxhXKLDnJ4aEWlBaDIC6/AkCAKcn1SzCpQmbeR4E5PjJkUrcAgj\\nyUYxUipilRMGBVZ3MM0xIhcMuwlVIDmyeJ7ldsxyL2Gx0UDnIU2XcFDHBAsFP0zUZO+zEVgBQSBw\\ngUIriXMF+1oJ5wqNQNAMFUJAM2yS7pmlZ9t09t7FsUHJMBujZIJwksp5RtYh4h4z3R5BmKICz7H7\\nZ5mdirdYp4HSHIz3k6iIPY2t78eNsF31n5qXl1r51ux4WoFmbEpCGVIKSFSD+/ffx2qYMZaGXivh\\nTQdez/+z8Bzns5woEHiZ0PWSzDrMdJv93VmMmEaYDivOYPOYB5vLFGWFk444U2QqoNlQaB/QK8bI\\ncpGomSFdQWE7ZCplcf8skgJvJ9l71KTiHHYtSjbN+8hEo5WlacdYHZBrharAagXWAW5i+niJEoZ+\\nqdEOQuEpnabyEm8mAUrSG7wXWAVSWILQEwhPV44oigolLV6oSdEHoPQSpf2knJ5wlMCk0vJaakwA\\nMXEv+2iEFyXCB+RRwhJTTDFE4tHGYwOFVZ7FuEvWiCmL8cRi9g4XaMZxiAsC4lgxUCmVK2hqiQol\\n7TJHpJqxMxgBIY4QicFBMwaTkZcWqxXWKbz3uMDiRUVRBMhEoaXDRZNiGkJUyMqD60wqQ1mFUJY4\\nTPE+R5WTheVMOe7KxvSk4JxuIKUgCCoC4YhUSZY6wvEYlVYMqwShI5SuKOIIpQpSGhxs9jlquiwO\\nHGFk0anCqA4dO80glnTa02gx8YTsaXZgLFkOFcHa9q9AKCIdk84cREjJvuYe+iYjUJbVquD8uKAq\\nKhCCaK3ogBSCe++6PGNTIAMCqdmb7nlF3rWaW0etfGt2PM1AcSGbRF1PyqsKukkH06tI0TSCSdL+\\nQ/vb9M9doLhrBtUXTHd72BNPcHZ2mqaYxlWCkVL0uj2GK+doElC4VRbLHkZBr2VQLiLLOwgPXbmA\\n1TFBUSKko/CalVGXbvM8bZVhfItAe7TQSO9ZjjpUCloyQ0SCQBvKUJL7mG4uJ9V+vMN7sAWMaJDP\\nW9o2pKlXka5BHvhJRSJjsRa0szjnMF5TBoJKVSTCkYgRlXYIJ0AIfGUmDk0vsEFIXFWTNJFeYAjB\\nCbSYfI4UJGXM2DuEN2gdEEcOUTnAo7FIJ7AIKhHgfUkRRchxH/DkSuNjEA2JUzDvUkRpKPQImXp0\\nrJjSBZULGZbgZE7gPW1Keq0+o9YeGMUMjEM7xXAcMe9b+EN9wmgZMZjCCUXRCDCBZUU6IGB5RRIW\\nUxgV4fKCMB7TLlZYXi1IbIjXCUZMkoGEoUaFliqAJpbQVaSR41B3gVbDE6qUxV6KLVqEfkyoEsJA\\nMtWwzOqcfUGPrGOJghLRUpS9JtPJvci8wieCbrQCwN5Wj+XF1UmReS1ohk2W8mW0VERrZex0MLFk\\nEylhXxvvLrp/E331NdjbUQS+5tZQK9+aHU+6aa/hg737WcjnuKt5gMzkDMvhxlrW3Xd3eU5KxpGm\\nc9chpuIGopcwI6YwQzg5P0LgODC9j9P9AqlChtmYZRoEDYsUhkg6Sh8S6IpYxgyGhnGhkI0M5wVV\\n2SBKIjrCIGJD0KoIrCaoFKXT5GFElxwlJEobMisJyxTtDINikvfLOkHLZ4gVS1J6GnFO6iuEH7EQ\\ndBFMth6Z0mOsIDBgRQDCYr0ltBlOelRgkflaDWaT463AexgHTcKWJ3ZjzrqAzEk6TiCZpNkUynE0\\nzDi1lDBIDUhQ2tBuFaSJRY4KjFFURIxExGK7SdEI8cWAsZhC6QgRG5Q2eClx3hMbjc2mGIWTCkBa\\nOVIyzlfxpNyddWhpSGPLUIBDU4kUiUN7yHxCQ1iUA+Une40n7leNk4ZxKch9AATosEmUDkiFoKUr\\nGGrSQnOh4dHhmHjs8ElMGAZMicke6z2dkl5vCWsF7vAeZjRMG488F3BmNULrmFQKwqkRqvQkkaS3\\nr0WYLYCSNHTMnmYCFvZ2FEsSAhXSXNt+t39mmqMHE+7rHmJ/uo/FfIl2OEm6v16nNlUSWiHOwz3t\\nhEhKgmtkcrrW1qCaO5e652p2PEIIOqFmWFlaYUI7uhuAY1NHeWH1RWYb0wC04oBuY5qVYom9aYNm\\nEHEgnaTPq5qWc8sZnSSCOMDIFjk9fDFH4gpcrEhwNH1B3g6IE8vdRcpSUfLE3vuQ8ikMAq9iVuMW\\nrp2QFBCGgii1BKsShyQIHImMiFSBYYQTDl+k9AcFmVNINbFQFZ5GXpJUgCoInUHvGWNliFzyyKIg\\nMGNwbXIfkdtgUuA+LwiNwCaT/b6SisDlBKamJ4sOAAAgAElEQVScFI/3kwo6Rgdkw4izpjVx7aKQ\\nFFgpkKS0tGK6UCxFYJQgYIgIJCMbIZcykjJDJhHDosUoTkH0GYcBwmu0ByUtzUZGGUp0YggrjRSK\\nrIowmSfqOJKyQA0lRaBQTmIxDAqNLQRDF6ObEIiCIItASAIR05FTiMgwlgGJCnB4vINhpvAR2FIj\\n0DRlwKFQMJ3FuCGYRoe0kRNNW9rhXlABmoDpWNBwBQf2CVrNJh29lzknoZxntjVNP54jWBEYHYC0\\nJIFHlIpQQ5y2adkxqin4mXseRBYeV1im0xaNQDEd99BKEijF3fv28KrDkziGTtSiE12sdrO+X1Zp\\nxZ4kxDhPK7jGftswJTP5NWty19y51Mq3ZldwpDmpJ7x5MAqk5r7uPRuflRAc6Rxiutw/yd6zKblH\\noBWvv2+G06NikrhBa2wpQGui5RGqqbmrHJHbWdLI0NQ5swemiVs5z+ZTDOQenFvFBI5+5sk0NIzC\\nSYGK/GS71DggCgVKBCQyJok7jGmTmQayMASBxUtJWSiqwhMISXtoGMWCvNsmnhmR2woVSUymoT9G\\n7KuYLzsULqBwFcJljFSbVEpiCUU4KTjQiCroT5JQGiMpgoCs0BgdIaKEygoCUUzSfHqB15qRSMBA\\nxy0Sj8eYXjxR0tZSji3L/ZiVOMVKh0UgdQh2UnoR7+iZcxRBiyHT6MAS64JRHmG8YzVPkV4zVVSA\\nxsgYK8bgNP1cU+gIFRa4IMeNIAoSJBaTNAikxLsArTK0CbFGEa4G+BmPKQuEyfGBoBlIctvlfC/l\\nQLOJTE8x1enQktN4YwnDBo3+HFkrIAotadBgX7qf2bBHNQgIZEg2O0XoQ+KkZCpZxZkYhEUrgYxi\\nXj1zkJn9Lfb1WizNjwAIQ02ncXHLz4N3d7et0LPx7K1ZvkpJ9ibXV5Tggd7913VczZ1LrXxrdgXX\\nawHMRAGjtbSWwSUp6tZdfFIKoiTADipylaLTBfbsE8SnPKaC0JVMaUE0cwCaIxpnA4ZWIiVU3qDL\\nkMABSFSY4MIhYRrijSIUMSIoiHQDEcToqQZhP8GrAS2ZYRsNMhcwWCzpJiVdb4lXEtxMh8xXDHxI\\nWxlAMh4KhvMWMxUglEaUHmGGuCSishqlPFUzoCoFoY0IhcFLQeWgMp7z8Qw+bCK9pxiLSRpMD2Oj\\nGKkGldA0yhEuT3BaTYpLOEksc4Kg4ExQUIYVjKHKNYHsILRGrq0nB7LAqAZGglbVZHtTWIEXJPEi\\nagSxlXSzAK8Ufq3alRMCpxRKeVRQQqpQuoFwltQ7ymaDBkMcYpIb3MFBlzEfTpM0HbJ0SA+hbMLM\\nNAc6mvs6+8lXh/SaHWQeYk3Gq193D52TOXN+TLa2dCpkQCNsMZaT9dhWu0HaT2klI6o4RgyX2EOC\\nUJJ7H9yLEBefvU4vIYz1ZUkg1pPGXAkdKKZnm8RJPRz/KFH3ds2PFJ3w4iMfXrKepjcp43Y7YjSo\\niMVebDpmb3sPQWOFII9pGcP+pEty731E1jHTf5HhKECHGlFCZFOKHKIgBDlJaSgbAU3bxKQSUc3R\\n1A1azSbng2kCN6KVKXoJVChO5gGVk7TwtFoxOmxQaEm/CBmLgF6QTZJ06RKdlciOxmtB4EOs8Bhf\\nUKkWXmsqVSHzDOkjysQRBxYCT7sckIkIkATG4amQchLwVVnJQLUgtezLVlkwMVWRI9HYKKQVZjR0\\nRhGBtBXeKqSb5LTUuqJXDihcxvlxSBQrnBHkVtNGoIRChRYtPUY5QudJlGExaRGLFcbNBLRHBxCF\\nAiy4lqchSmwoaFvBWFpKqRmVAoKcQIcke3J8QxKYiGYnJhkNmTnQY9E2mA4kbZGwd3SYA2kPHQqy\\nccF0NyWZej3ZC4Y8WqYTt1A63TKRi4I2R2YE55oR41BzpAo4etdR4kOHL8svrJS86XJ6vdn0pn5X\\ns3OplW/NjxRCCF411WBQWpJLksLrTYPunl6DE2eGpLpBmB7lQEuQdVPK0YhERvSiFkJrlIZmI6Dj\\nQ7IoYOg83gY0bECUKCqlwUKQCKZUyKHeDOOFVZo6YjadImrspds4z7TJsUYhvSJMQ8o8pNMUpEGL\\n7uwBnh1mnBsX+JkCLQxaeYLEY51COYHyhkJICCaVdwoDRazJ7RjRgtC08dUIF2n2pCu4VU+ORQ6W\\n6Y0GeBEj4hZFIDE+ZBQ4TMdhYkPeV4xTOGSXaAQWdSgmqyTlSE/Wl7VFmEl2J1c5dDDGyopCxhij\\nkSikNKg8RrdgulVBLqiUI3WWRlxSNQX9/QcpcoMqoAxyQlcyLT29XpukDBnnnqCnaNsuLwxKjO6T\\niIBmEpJNx5QmBK3RWjN7cJp9Pc3SMnR7IffO7qUx3SJJAoSAYVatJaoImLnnNcSDF5hO21jV2vJM\\nKNUCNaLXiuhEEQfuupdGc2sKx5qam6FWvjU/csRKESeXJxVQmyyZvZ0GJ5UgQTHT28++mTZ2KuDp\\npe/TkAbZuGipNFsRU3oGEQq8GVMuORIpmTkoWImAZUFrSnCs2aFjFMv9Fs1AEzUb3D87xSDXiLPP\\nk0qPVgI13ePsixX79zeYPfAAZVGin7iAcAOEMizf0yVlst5JVBE4j4gtQkt8tJ+yXEBUCzRVl9w6\\nrBDEUx2aaQNzZg7ZBm8rGuWYRLXZc+ECq9P7sYmgIQ1j4xiXGqkNzZanGzt6HUczsKR2FWVSRtpT\\nVY7AC7rSYVowzhyld6RRSWZBC08gPUiJCASJz/AR7E36mMixEmdEAwVCciAdcMG36CchJlKMA03s\\nJUo49vWmWBzvxdqltaIKEUmSMJIF052Ku3t7ecwMKYZ+suasC47MHOHAoQNciP+XNGgQxQFHD17c\\nJ7u3u7nnPaGK1krmTZ6LqHkYvKXUc8CISE9yOhe2vMVPY82PKrXyralZQ22yfDuxJm6EZMOCPb0U\\nFTRRATz4tp8EUyGDi/srjxybprMSMw57PF4epyEFvRgIFLYqkUqRNsPJut6iJDnwGpp6ROO+o+z3\\nknxZUgSzNNyYRuyYPhRzYOYAvWqV6NBdqMVF7ppZ5PxcRRyFzLYifHyYbKFA2z5TnRWy7l6agaKQ\\nU5TGkeWrpNow8gppoEXE/rjJuWqeM4MevXwelUEaa6LKorxDCE8jFhS5xuJJoxJlJd1YkjR6zOzp\\nMl625MuLZFVKmjsSmRM6RyljVAKruWNYJGhV0AgM02bIyExDNcYISaca0KJCNzu0kxa+/wKOKXp7\\nO4yqGO8CRFggjcC7Bnl4FBqHccYRCEUoI+JWwIyLaDYd02mXA9Ov5dzi0ywUY1QQEwlDt9GksAV4\\nT0NfPcWiVJPv43QPo0mJY3Q4KYVZ6WUAjnWPciI7x/46mUXNLaJWvjU1m9jXCFFCoKSkO9sgSAPS\\n9GIAjVIS1NZ1vTgJ2JdMEeoZsnOSVi9mqnOOuVGTlf6IKPHEcUA7iTh4X4fhoKDTvQshBImxSCUw\\n8SyqWiBKLI1Wg5ljxxBraQRdljHTipgdxsw3LTpwJEGHol3SGg+5a0YySiqiKCIjJKsqGnSZjrrk\\nXiNsm5YOETpgKgxZsSWjoEFaFnTGI7wAZS1pNcLGMd1Oyrwt6SiLsA0WsxnavsGBVhszPIOIAuIs\\n4mhpWG1GyHKI0p5uaMmXDFkVsj82zKiI/RrOKsOikBSVpi0tWko6jSlmZl/HUm4ZjlY4uOcIfjiF\\nmR+w7E+QDyNUOksTz3ScMrAFKlYo7YnimFffvY/nV3KCICWOp4h1zKs6klOrAiUkThScH/cBaAZX\\nX09VQUrSuY9Ge4bR/GDLd9GB/ahWk7DZ4/XN3hXOUFNz49TKt6ZmE5u3eiRRQFZaoussn9aeirnn\\nnr3ESYAQs7RnYF85YunUEOchDTQ6UEz1Ghu/iZQkDhRlI4XhAknaJem9ekPxAuhuj2B6mp+69xjz\\ni98GZ2gnB1nKT9OLJMc6HUZyxLzq4FyCVWOixl7ayQyRWaEVJMzImNlKsLRvmgtz5+iHAZ1RSa8c\\nUQYxWSOiHUsOjBdZ2befmcaYjqqwhWbZNokbTd589xEev3CO5bgBUtBKFIMgIFRDer6k0YThbE6V\\nCxINUaBpTbXoNmP6ywHNWBHqkjhWHNzfozk9QzR3D4U4Szq1n4PpNPLUExAd4EQcspJKIhWQC8GR\\n6SajwRLCWpSONkrfRXotKjlskgaOe149zWOnnyeXq4yzMUnQoBd3uRZSbR8oJeOEML6+4gQ1NTfC\\nTSlf5xyf+9znePrppwnDkEceeYQjR47c6rbV1NxWptsxlXWk8fW9JkKIy7aZhCpAS0HlPIm+PBWg\\nFIJXTaXMHdmDHZ0DIS4r/yakJLlvsq8zWgkROuKBPYd5XGUEZoU4bWKNQCGxznJ3+1Xk5QsIIZht\\nTHMg3ccDrSnMoCTpHGR+OKLMBNNikSQqyRQ0e2P2reZ42UalbZz3aGXZO51yoh+Tpg2ajSlCKVFx\\nQrh3D0mYMnvuFGXaIG22UUGfPfsM1SggGCmajYAjr/9p/HLBiSefRTUt7Tggadi1qGJJMLUXSkHc\\nPUK2XCDjGAq4J9Do1l4WnGM9KD1WCiwoHdLQCWmYbijW+6Ym+7mdd1wwAeNqUrv2UOtAnYii5o7k\\nppTvv/7rv1KWJV//+td57LHH+OIXv8if//mf3+q21dTcVqY7MdOd+CWdQ0vNdBTg/JVTAUoh2Heo\\nR//sxMISV0kp+Ob9P4FAsjdNKcM9nF6YJ5Ca2XQfS4VH6y5KKkI1UTgKSUMnRJGmm0a4viY6cIh2\\nWdJIQtLBMuNuj+DuAwS2QzXWzLRTbCmoRqsUKuTwniaN2SZSwNTe+7lw5ixatxHGQ7NLW8Kr7m3S\\n6r6GpxbPcurEcYLIMNVtECYNDqo2hw/mdLo5bQ9TjXtJOvcBEN11iOjAQYTWCFkgkwST5wjn0VIy\\nIwTjtTJ7jSDBmhwdNFBSbUk0sa5glVAEUlPZikhHGykca2ruNG5K+X7ve9/jrW99KwBveMMbePzx\\nx29po2pqdgtSSCKl8d6hrlGoPH3Na/HGXPWYfenFzEn7GnuRzTkSFaKDJq9tHeDZ1YnFFyiNdYZA\\nBQghWA/kjuOAJNIEzhIdeRU6rDggBGpqivGgoGMNx/ZPcXoQcm4woskUUq0lsxCC6T2z3Nfq8NTx\\nRXqBoBXEtCJFM1U0mlM8EDTJlkNMscie/Q2kimk3JD91/xHiUGGru5Aq3nCrCylZN22FEOipLr6q\\nUJ1JZLIWgoPNGCvARE1KEaO28SBsRqwp61rx1tzJ3JTyHQ6HNJvNjc9KKYwxaL396brdBlrf2nqR\\ns7O798XazbLB7pZvO9lmyha5Kdm3d2qbX2z+8Y3fl73T/4dx/wzNqbupUJz3jlBKjrQfpLAlJ1cl\\nQkr2zrYJlSTIS444R3N+yFuOzPBCNdk686rpFoOsoq0UjTQk6DeYnx8TNmJS72i1Y6QQzM62mAV6\\nSrNwYYi3liBIaLcTerMtvPecmJsFZrnvgf0El7337SvKoqWkGBmYOrrl7w8cmkZpyfOFJxtVTM80\\nr/oM/UTrQU6vnuX+mWOE6saq/vyoPZu7iZ0m300p32azyWg02vjsnLui4gVYXh7fzGWuyOxsi/lL\\nohJ3C7tZNtjd8l1JtmosqIx/GeXeR7GUA7AHQSwEdiTRJOyTlpGxrC5N3tfCOoajgv3dBtY5msYj\\ngPFKhgJGGEbjgrwyVNax3M+oFIjB5PzrMozGBf1BBl6Rpi0qcdfGd1VRkZWGlRt87/sr2eScQHc6\\nZXlx0ubFpSFCCPr9nGxcEq1qxFXn8oK98iCrSzmQX/f1fxSfzd3CnSrf1SYEN6V83/jGN/Lv//7v\\nvPOd7+Sxxx7j/vvrJN81NVfins4RvPfXPvAW0LykGk4aKNLgoqaKlOTBbrqRx7obbW8ZJkoSxJpy\\nVGGt37IHGqDZjrlwtg9CoMI9W6KFf+yeadxNyBsnAVpLZva2ttyv9fVcubaOfWlax5qanchNKd+3\\nve1t/Od//ie/9mu/hveeL3zhC7e6XTU1uwYpJNxB+uJaNWJh4gIOI81oMMk6cam+k1LQm0lZWhgR\\nXRINLqVA3oTAYaQ5ev8swMZ1Lz3v5n9ranYyN6V8pZT80R/90a1uS01NzR1EsxmyOjdCh2ojiGkz\\n03uaNJohcXJj66rXg9KXTxDWla6olW/NLqBOslFTU7MtrUbI1MEWKlCXWb7rXLqv+VaxnfJVa/WX\\nlbq25V5Tc6dTK9+ampptiZUkWLNq01u8W+Fa6G2Ub6eXEITqMjd3Tc1OpH6Ka2pqtiXeZGHOxC+P\\nhXslhBB0p1OCTak9tVa0p+pUjzW7g1r51tTUbEusJu7mdqAJb4Ord2Zv89oH1dTsUGrlW1NTsy1a\\nCh6cal5xvbempubmqZVvTU3NFVG15q2peVmowwZrampqampeYYR/pVLv1NTU1NTU1AC15VtTU1NT\\nU/OKUyvfmpqampqaV5ha+dbU1NTU1LzC1Mq3pqampqbmFaZWvjU1NTU1Na8wtfKtqampqal5hamV\\nb01NTU1NzSvMjslw5Zzjc5/7HE8//TRhGPLII49w5MiR292sl8wv/dIv0WxOctjeddddfOhDH+IT\\nn/gEQgjuu+8+PvvZzyKvo/j5ncT3v/99/uRP/oS/+Zu/4eTJk9vK8+ijj/L3f//3aK358Ic/zM//\\n/M/f7mZfN5vle/LJJ/ngBz/I3XffDcCv//qv8853vnNHyldVFZ/61Kc4c+YMZVny4Q9/mHvvvXdX\\n9N92su3fv3/X9J21lk9/+tOcOHECIQSf//zniaJoV/QdbC+fMWZn95/fIXzzm9/0H//4x7333v/P\\n//yP/9CHPnSbW/TSyfPcv+c979nytw9+8IP+O9/5jvfe+8985jP+n//5n29H026av/qrv/Lvete7\\n/K/8yq9477eXZ25uzr/rXe/yRVH4fr+/8f+dwKXyPfroo/6rX/3qlmN2qnzf+MY3/COPPOK99355\\nedn/3M/93K7pv+1k20199y//8i/+E5/4hPfe++985zv+Qx/60K7pO++3l2+n99+OMam+973v8da3\\nvhWAN7zhDTz++OO3uUUvnaeeeoosy3j/+9/P+973Ph577DGeeOIJ3vSmNwHwsz/7s3z729++za28\\nMQ4fPsxXvvKVjc/byfODH/yAH//xHycMQ1qtFocPH+app566XU2+IS6V7/HHH+c//uM/+M3f/E0+\\n9alPMRwOd6x873jHO/joRz8KgPcepdSu6b/tZNtNffcLv/ALPPzwwwCcPXuWdru9a/oOtpdvp/ff\\njlG+w+Fwwz0LoJTCGHMbW/TSieOYD3zgA3z1q1/l85//PB/72Mfw3iPEJJl9mqYMBoPb3Mob4+1v\\nfztaX1zN2E6e4XBIq9XaOCZNU4bD4Sve1pvhUvle97rX8fu///v83d/9HYcOHeJP//RPd6x8aZrS\\nbDYZDod85CMf4aGHHto1/bedbLup7wC01nz84x/n4Ycf5t3vfveu6bt1LpVvp/ffjlG+zWaT0Wi0\\n8dk5t2UQ3IkcPXqUX/zFX0QIwdGjR5mammJxcXHj+9FoRLvdvo0tfOlsXq9el+fSvhyNRltemJ3E\\n2972Nl772tdu/P/JJ5/c0fKdO3eO973vfbznPe/h3e9+967qv0tl2219B/ClL32Jb37zm3zmM5+h\\nKIqNv+/0vltns3w/8zM/s6P7b8co3ze+8Y1861vfAuCxxx7j/vvvv80teul84xvf4Itf/CIAFy5c\\nYDgc8tM//dN897vfBeBb3/oWP/mTP3k7m/iSefDBBy+T53Wvex3f+973KIqCwWDA888/v2P78wMf\\n+AA/+MEPAPiv//ovXvOa1+xY+RYWFnj/+9/P7/3e7/HLv/zLwO7pv+1k20199w//8A/85V/+JQBJ\\nkiCE4LWvfe2u6DvYXr7f+Z3f2dH9t2OqGq1HOz/zzDN47/nCF77AsWPHbnezXhJlWfLJT36Ss2fP\\nIoTgYx/7GN1ul8985jNUVcU999zDI488glLqdjf1hjh9+jS/+7u/y6OPPsqJEye2lefRRx/l61//\\nOt57PvjBD/L2t7/9djf7utks3xNPPMHDDz9MEATMzMzw8MMP02w2d6R8jzzyCP/0T//EPffcs/G3\\nP/iDP+CRRx7Z8f23nWwPPfQQX/7yl3dF343HYz75yU+ysLCAMYbf+q3f4tixY7vm3dtOvv379+/o\\nd2/HKN+ampqamprdwo5xO9fU1NTU1OwWauVbU1NTU1PzCvOKhAvPz9/a7TLdboPl5fEtPeedwm6W\\nDXa3fLtZNqjl28nsZtngzpVvdvbKkdY70vLVemcFIN0Iu1k22N3y7WbZoJZvJ7ObZYOdKd+OVL41\\nNTU1NTU7mVr51tTU1NS8JKyzPL7wQ86PLtzupuwYauVbU1NTU/OSyG1BbnJOD87e7qbsGGrlW1NT\\nU1PzkjDuYp79cXXnBT7didTKt6ampqbmJVFtUr7LxeptbMnOoVa+NTU1NTUvCeOqjf8PyjuzitCd\\nRq18a2pqampeEpst38KWt7ElO4da+dbU1NTUvCTWlW+oQipX4by7zS2686mVb01NTU3NS2Ld7dwI\\nGuD9Fku4Zntq5VtTU1NT85KonEFKRaJjAMra9XxNauVbU1NzR1LkFVVlb3czaq6DyhkCqQllCNya\\ndV87HOLN7rWgd43y9d5jTb3OUFOzWzj9wgoXzvRvdzNqroH3HmMrAhkQqonyfamWr6sqRk8+TnH6\\n1K1o4h3JrlG+ywtjjj8zj6lnyjU1Ox7vPc65O2ZC7b1nmFV47293U+441hNsaKmJVADcAsvXWvDg\\nq9ryveNZd0+ZbV7WsbFkplbKNTU7Be/9xJtlX9n3tl8OtrXaziyMePzEIheWs1e0PTuBjUjnW2j5\\nXpzk7N7Jzq5Rvt5NOsm5yzvr5CDjxWH+SjeppqbmJvEOXihPcCZ/5XIFG2d5Zuk5fjD/xGVbZZb6\\nBQD90ZWViitLyvPntljHS/2ccb57rTeAai3SWUuNFBIt9cbfbpq1+7+bPQ27Rvm6tU7y2yhf6/3G\\n9zU1NXc+xlkKV5C7/GUZgBfzkmdXR1vGhcpeVBjnLqnOs36cEFc+ZzU/R37yJHY4AKCsLM+cXuEH\\nxxduuH3V/DzDH3wf/wpb/tfL5j4p7GRiEq1ZvUJI/DUs1rmspF9eZVKy/nN3Zyw7vBzsGuW7rnS3\\ne1Edu9l5UVOz+6jWolyddzh769/e06OCsXHk9uLgvnlv6sol+YnXxxexpn2rpUVsttUFvaEo185Z\\n3sB69dy5PoPVi945M+jjsgxXFNsePzaWwVXWQ0/2T7GYLV/39W+E04Oz/GDhyQ3vQLk2aVl3OUsh\\nrmrsOO85Ny6Yy6/iml7//S42mnaP8vVXdjt7D9v8uaam5g7De0+Vz1OZSWUch8O9jNbPZkPW2IvK\\nzLitFuf6+CGFwFUl2bPPMvrB99nuoPWxqLpO5Wsqy+pyxvkzmxT+usxXUD7Pro453r+o/K11LM4N\\nscZhnWV+vMBCtnhd179RMpNR2XLjHq0HV21YvoirWr7egyntVbeR+drtvHNwV7B8i9E5rBlf0w1y\\nM0wCQnavW6Rmexbnh4wG21skdwrOux2Z4s9Wq5TjCxSjEwB4/LYT6pfC5jFi86k3l8WzfqtiWP+N\\nlOCr7dcz/ZrC9GuWYLlNkOewMry4PMdo+UlsNZq0wXnsaIwvq8vOdb2W32hQsLQwYjQsNvr95er/\\ndat2w/J1JQhBIIONY66mNK13LL+4ytyJq1jm7uqWb5FXPPvkBUbDO/s9vBq7RvmuP2ebX1TvHWW+\\niMkXXxa388KFIS88u3jLB4eaOxdrHEvzI86eWrnqcc4WG7N3ax3mFY62f2LxKf534Yev6DVvhPGo\\nZLzNwLmuuIyZWFPOO+wtdjsPhiX5WgCVWxsZnC023KcAztlLlPSmNlxpwu0djoJ89BzjvE9RXt7n\\ny4VhMJ6nsoZ8cALvDNZYynNnqZYurg1bO6QSS7hrpGl0l3j8nPPY9efOvzzP3Lohs658C1sSqXDD\\nJX+tNd/KXHmJ8OJFrq58lxcnnpG5s4MbavudhL7al1VV8alPfYozZ85QliUf/vCHuffee/nEJz6B\\nEIL77ruPz372s0h5+3W42y7a2buNR+Dl8F5UpcU5h7MOKdWtv0DNHcd2W9kuxTtD1n+WIJombOzn\\n+NPzANz34N5b2hZrHWVhSBrhZd8VZqJcKlsRqOCy728368kzjt4fbfm7Z81qW3NpejzGGOByGW/6\\n2nMDhosjolaI82DKPsXwRXI7aYuSGusM1lu0mAyRvjKoxXPY5j24aGvUVVlZKuuQzuMx9G3I3MqY\\nanz58Gq9R7gCvzb02mqIrdausWbtDirDqWqME4aGKbi09zZPBLwHxMUkQ2deWGbfsebata78rC4P\\nClqNAK1ufOzebFk77zC2IglbG99LxMbEczvs2kT0ajaLv4bylXLSBzvZ8Lnqnf/Hf/xHpqam+NrX\\nvsZf//Vf8/DDD/PHf/zHPPTQQ3zta1/De8+//du/vVJtvSrrnbW5z713W/ruVq8fbFxzF69L1Gzl\\nepYZvKsmCQIusVpu9XNy/vQqp19YZnzJ9pfNgUOD6uZrq3rvuXC2z3CTi92UqxSj0zhbkA9PXibj\\n9Z7XGHvZvfTeMx4uYpzBbBpUzS2O+C3N2rjgJ4rMVpOJQL4WoLS+dmm9w+UZp547xalnT+IX5nAr\\nS3BJysP/fnae/z2+iHUW8AyNBDyL40m/CGBhfsjChSHO5rDJIvU47LqFvCbzhaxkwXoqBMO84qmT\\ny+TlZpf4Jouci+NeVVnywvD8kwtkq/aKbudhVvH0qeWb3rO8YfniNvbzrgdbwSQo7WpPulnr96u+\\nDRtj6/YyrBt8l8YDPLP8HP/f+f+52pnvGK6qfN/xjnfw0Y9+FJi8GEopnnjiCd70pjcB8LM/+7N8\\n+9vffvlbeR1sv+brtnTwrVaRF5XvLa5CBN0AACAASURBVD7xHU5lK04Nzm5ZI/tR4XoyLm0Ei7DV\\n3Xyr4wPWlW4+3roGuXnLzEo+JN/G5Z0Zu6HgFueG27rRR8OS/krGuU3fFcNTmGKFbPVZbDmgyucu\\n+11lLFlx5Wdj/T6sJ9JYZ2HhDP/v0xf47xeXmC+rjffK3qKAq+OrJzk7PE+15foX+2vNG0qkorXr\\nWoqz53j+yZNQVWSFxRc53my/5uvsZF+FwyNchlsLKDo1N+Lx5xZYXc5wdk3hqYS1RmxYgusY5/De\\n4oFT80NWRgUnzl10r5bjDLe27nxxadRj7cT6NcYyXrAU4+3bWRkH/vq8OFvkyzNcnm+xfIu1Zy3a\\nrHwnpvgVJ5vrgWiTe+853b/Af5/74dbj1/9/Bcv2Slu++sVgo213Old1O6dpCsBwOOQjH/kIDz30\\nEF/60pc2fPtpmjIYXNvn3u020PrWumVnZ1tbPl84NZm9djrJxnemlDgTkThI2zHTMy20vMpGvRtk\\ndTEjUJpeL93W9XezNKcCTq6c4Z7eYcI70GV4bjDHuOijmzPMpq1rHl9ZR7DJvXVp390KbJZhi5Jw\\nqnPLz70Z4QXj/kTpbSfH7GyLMncMRYwOY1SUYOOcyhnG1TRnzq/ylh/bT3AL3oeF7gBrPK1WvKUt\\ny5mjVU0G95PDPvh9/MR0G71mLYxKw/GFPt1AcV+3yfzZAQpJqxkTJ5uCZiy0W8mGXFWV0WxFyE0j\\nX5ymNNpb78O/fPckxjn+77ccBSbK5OTqmIOthFgrsnG5cd5eL90YG85eyJFasjxyjGPDQGv2hQHN\\nVrRFPldVDJ8/TnrkMCpJ+M7j5+ikEQ8c7bEyKFhYzTh2sAPeMeqfImnux0tNNc6oyAiTPcSVJUkC\\nOt0GaRlTFSXnB0Na7YR97S6mn5OkAS5QpI2AYV4RhppGIJhqR4zb8cZ9abcmUcpmVHJ2tEQ4GxJy\\ngXbYpNU8hFzMUFrTasXYuES5gKnpWXS5QKPdQJSOJAlQccD0dJOwrNBaEEhF0NDEcUIrDTfuwdwT\\nT+BUSOfY3fSmmzQChSksi/EIhaD0jpWhhEIxPZMihdxoK0CGYM5aZkN53e9iVfRZevJxpAhoH2uT\\nG8lUN8EDLZuwd3qK2ebkXBdcA3LDzGxzEvlsDDK4+FyN1uQFmJ5p8R+nvkd/WPLTrwpoRpPnIncZ\\ng3aMjCKmt2mjyQ3juWV0q7khw+xsi9Z48vupXkKkb92Y/HJwVeULcO7cOX77t3+b3/iN3+Dd7343\\nX/7ylze+G41GtNvta15keXn80lp5CbOzLebnLyp95zz9wWRG6YUnTC6up6z2C7JM4lTOvOpvDEC3\\ngpWVMWVhmJ8f3DLlOzvb4vkzZzg1OIPIA3px95ac91ayMOozGGQsiAGMry53ZizPrI453IzpRsFl\\nfXerGD/zNHZlheZP/h/EyxiDsDA32HjWLpVjXTZT9ClGOVILsuxpnlt6Ai+aLPVDojjg+MklemuD\\nt7MFQgYIceNtHo1LysJQecuSN8xEIUoKFrJlBmvbUJaGOW0x4LxURGsToFPDnEFRMejnJMOKlZXJ\\n+3nyhUV6M5MJd55VLM4NGY9KpJTMzw+Yr5Y5c75if+RYqASHY09U5YyKrffh+KllstLyhqNdtFIs\\n5RWnRjnZIGdvEjEaFBv38ML5PmE0eV+L0ZCiqDBOoaqSvFKMreep43OcXSw4PNtkuJoT989Tzc/z\\n9OkXmH3gjcwvDjm/sEI7gZPnRiz2c0I8gRhRDM8R9h2lTjfuSTXIODM35NQwRz9QcJceYk2OxTMa\\nlgx8wWCQ8djzLxDMDRmPS6pxTiYrBourLHUSlpeGKDWkePF5+oOJlZWfWWJ10dLqDgh9C5utsOKm\\nGWclq6VhNQ3waoQpK5b7FVGZk5shS/OGLKsQFv7ne6d4cXkEtqLwjnKYU1UZtjIbz9t/n+izkkQ8\\nMJszLyQNrVhcGLC6MocxEYU3lHnFaOS5MLeKlnrLe3f83Cp5VvHs+VWO9dKrPmPjKiNWEdnqDymG\\n82jfY3lVY2zFPAM8nkE/Y0VkqGxy/v5qxqDImJvrs3D8NC8+e5rX/18/RtiZ6IrFxQFZNrGYL8z3\\nGfRzxoXh/NlVbL5Kd7pBtTgg7+fI0OK2GS/mTp5n+fiLBLOzzM93N+Rb7+PzwTKNoIF1jnOLY/Z2\\nk1sy4b1Rrja5uaryXVhY4P3vfz9/+Id/yFve8hYAHnzwQb773e/y5je/mW9961v81E/91K1t7XUy\\nrAyBlERKbslqtTUY4WV2O7uXx+28sY5zh/qz7Ybbafv2zY3nuTCe58HeqyjdekTky+sG8mU5cSNa\\ng5DXPxHy1lKcepFw3z5knFzz+M2uOu/9hhdoyznX1/S8o8xWET7HiyaZKYgIUGvel0lg1nMEUY+w\\nsf+627yOUhLjPc+NcqxyPNhN2d+IN6J2m2ELyBlVfayfeASM8yyvbWlRQpCNL64Xj4cFvZmU0bDg\\n7IsXXc1iLaDn1JlFFlYMjVnBoLRkgWI9XGpxbkiShkSJXnObelazijHVxl7adTe3MQ7hzmOt4fjx\\nPqN8kdccvBtj8zUXsAJvqaoxXo1YXobCvxq7klPmhr0iw/mS09Uy1eACEHG+PMUzywOUmQS1Oe/x\\n2I3+yNcC0Na3B/bHBYgB/332LGcafR5I9nHhXEXcFaimoj+w2IU5ovwMp2xI0wh84HFlgSkrFvOK\\nUKzgFy8AsxO5qhHCVzhbUZYW0Nh1ma1fKxax7o6XF9uz/kw5T1FU2Mqi5EYc9tpxa4c4hxWTsc1a\\nt/EOmmqIrfp438Q6NXHpuq3u18l6/SmqtXu0XTbAzYyrMU8uPk0narHfu421XlOsUJQDbGv/hhxq\\n04RXCMn8as7TxTLzz5zHGc+Fp57n0Jt/HFsNqTYlB3F+XUJYWR1TrjrCUBGuu9PdRff15nfNrZ3D\\nX6H4RrUWsHd6bsS5pRHDrOLVh+8sQ+aq0+2/+Iu/oN/v82d/9me8973v5b3vfS8PPfQQX/nKV/jV\\nX/1Vqqri7W9/+yvV1g2c9xzvZ5wdFxuf19n6QF2ifG+xLnu5Aq7cNZTb7Wa9fVcKhhiUIwpTULnq\\n4prUy9ymjbqfNxicU5w6RXnhAvmJExt/y7NqyzYY7x1VvoAp+xjj8KbCO3vFSMt15etxVFXFuvR5\\nNclgtD4gTwKzPM7dXBJ67z3nrOFcUTKfl6ysrbOur8WnwWSwGVXDjSCdzNpJ0hljMM4yGk6uHQSK\\nbFzhnKMstt5Dax22GnBqOef8WJCVY0y5Sl5UeG+ZP99n/vyAlaUxRWk3gh5PjwqWiorjgzHO+03K\\n1yL8kHy4wtzJ/0WsPM/Sk9+i7C8wiYOaRDhb7/B2hCUns4bBas7ywogqKzA4hFRkZYnzjqEZspyv\\nbFzDWr8pGMxtpEB8cXCak8WzuMGQcnWZC8uLDPOC0/0VisJiMtBScWHecv7CBRbcOSpvcaaYTO4q\\nQ55leO8p8or+8sWANmcAMVlm6Q8rEJJAKYbliFE5uQd5YVhcdQzzjX0YW9Z8S+vW1kLtutrd6GuY\\nJOTwTBRrXpqN92t9a5S1luFqgSk9+MlEucirSd+MToGHcs1T4dcivZ3ZPu/9evKMlXx1LXvXWkDY\\ncJVTY0ueL2+MBWKTKhEIxnnF8jBHJhMPj88zzGiFfPACRbaK83ayOu79xpi9HohVDscXI2e95/jT\\n85x4ZmuKTrf+nnu/7dq19ZO+z0uDdZ5nlkYsXC2j1m3gqpbvpz/9aT796U9f9ve//du/fdkadD1Y\\nN5mDXRzENlu7l1i+m9fwb3E7Xq4MaBtKnTsraGBueczcSkazd3EbyHY4v76V4GJO7RudoDx7eoUo\\nUBzee31rUn4tO5G/wf20pr+WVWjTrPrC2T6mchx79SyDcsjxxR9yOAgIpaYYJvDccxDGuFftRW3n\\nyfKTQeX46mlGWQFMZu/r2382ntt1C3lTNqXKGSpb0QiubYU756mA0nkC5ze8DOsRqFLGhCoiM0NW\\nipIzo5xOGOCtpXzxBVS7w9CmRFgi6clNzmj5OUw5sYySRogxlmFWMRzO8cKyxfqA0hhMBWfPFdjx\\nkOHQUeaGtBWRl3bN6oRhaWgIz+pwEdXs0Qomw42pzFrEsyNQY7wFI0f8/+y9WZMlV3al953B3e8U\\nQ05IjEUWRRalLtIoPelRT/rnsn6QdcvaWCOBKow5xHhHn860tx78RmQkkEUU2KhSw0znBci4cd09\\n3P2cddbea69dygyRqRQnpEDAUYpy1YHsXvH3yyfTPQoJO1OMEfJ4TZbCIR64GkYW8nP6MfHrL9b8\\ny8/umK8wHqMBRcsE6qr0Q6b7omA6y+IxtHGgyQVrHAqIEbIYjCZsEUQrsipfXt6SNTMXSDESbMQ4\\nsEUwQD8ILw6Rn39kqFymTx3OTCz4ZhO53Wa+fH3B//nLzOP5GyDJ6zW6WCC1wzKitv6OqDPnhBzf\\n1TGkB3W+gogyDoUYlaR6z3w3VyNbM7BcCMZYRO8iL0JovwZg+fifpvtThBTL27l/lH3qmR3neysG\\nUPb9jsf1FFFx5iHznRitoth2R3P5Ek4/IXd78NPzvx0P1K4iSr5fR0op0PWML19gKsE1Da+HK9bV\\ngifV07ff/fwGnL8tWIM3G9AiiigYawhFiJeX2FmDPzv/E7Pqrze+N+f7P+K428W/Kzz7p+p8v/17\\nf+6QGLH1u8OY8g7w/zGG8Jdjvutxw0m1emftp6SIrf50yPbz15OozQx5WtC6V8jsHOvertW8C0vr\\nA3j+IX9JTIXb/bQb/3PAV1XvQbekntxuqJcfYsz353jk6M/7MORcshwXM+Gz28+JueeyKJ8snlLC\\nOFn/jcM7n3uWzGe7r7i9WTPY/lhGouQCbRjZbQbSeyty2KDHgKzqmzDcN4eXbMYt/+uzf8J9T+34\\n3fuXOIJvET69+Zw/vHjBbFbxeGGY+QUxBC4OO764uOWXn3yCiWsWEom/+y1p9oTFzFLSyHD2mK8P\\ncKY9WQ1X1cBFe4W0FWEtKBaMoUuGVIQaZX09krKliPKHrscNI4chYRaemIUo17weDszqiqGqEJmR\\nhtek0KJqpw2agJoyLZQkjCRs6NhbyyiWrqvAK3EmWCDHjJ8lZrJBg0VKZszCLga25cDlLjDqgU8e\\nP+VRU1AVQg7c7Ae6kJFcMIC0EZMccZm5rRJXoWPHyPt9phsGNBdOLHhGKBCKIeSG20OkrpS5U/aH\\nwL/1f+DkfMlHR7XzfhC8jvzhqz1/8+QlNve0ZcHFdiTOJlZsvXCzjzx+NIVNVQoyDqT1mvakY3F6\\nYMZj/H3Y+cgOY0GO+8R2O7CuW84/eYQWmdhwiGzNDodHtZ42G0Vw3iICzkF5AL7fHpubnvXVgY8e\\nG/L5HTwo+9gx4y4EPH2/T5HTMoIKpX9FsR/h/AKDoRxTD3bosCke00LTHE2lcNevOZWMyPQur/eB\\nx3GkhMD4xRe4Z0/pl5nrfP1d8L1jviL3zPfh+n5nfXk3R6wByYXxyynCdfq//3+TLn04fprge2eo\\ncR9yeTf4qspb3PGHQlm6uWb44x9Z/vKfcKvVdz7/S4Wd3zDfH/e4fer5fPslz5fP+eTkw7c+ixcX\\njF99yepf/uV7c59qlBy2ZF8T+5fMTv7urc/f5ITl+1zi3jna4Ye1I9MHdZeh+wa7aDCupp7/aVML\\nFSFvHtrbvbnAu1KY26uOzdcJf14o8+m55DEiV1fYpqE9BKoxc3I2u//uLuy5vGrZXCdyXTgvPeoc\\nxUGIkY5Au1tzZve4anqnVAv77bQJCEdnrCiRuZ2eg+QB42Z8vvuKVbXgab1ENaOiFBFCiVTbnkOt\\nXO9eUIrQd5Eoij9a/n1+8YpXr19xWjk+nq1p4g1jH9Bo8W6BitJ1HUNcYSXQRuGPF7fcjBfkXcO+\\nPAFqvMn0445eM149uzExXFySvGF54nhxa7gdHSezGTe3V8zOblEaDkPLy+srLm4yH+cD7e6Kvl9R\\n2Yy52SOPFZ0/R2RSw3pnprxxWND2Dih0TWKlhlIyhoyKsDsM9Nmx38MhDdjmFduD5WTp2RxuWJYp\\ndz0Wy/XNBZ1uqdIMi2KkoGopSQlFKerpg9IFIVxekPeWfTknWyGPA01T0A5CqYmN8GSlhBhBDMOY\\n2LU91kdEPKd1IauhDytUYRwTL/sRV5lp0yUFGSPtfuTm4oDpAkb1aJ71hlxICoSbF/gP3wemMq67\\nTdvhomPnasqHZ+Scjwz1wKLqCNYi5RRR4XZck9zILxoBLHeGYfKOFE1KhbLf0W42lH98//7nuRzD\\n3YBFKEAvSikRoxnNB0rc4fwCxdzPdyNviISWDNWbphkKjCUiCt2YeXU4sEKoU8YBuT0g8xnvGlIe\\nMt83m/37633AfI2Z8sV5+B+rF/NPEnzzt0DvIQNJsfDZby/5+G8f4R5Ar+oPhzIJR4u7EL4Dvm+H\\nt3/ggb/vvHfM/keuVbvL4eR39NqUMB7/G78XfEspqEREPW9b0x+PpW/C0vKtKMWfMw4P6hNTFipv\\nkRK4XV/wxXXDL//uCcvZA+b+wBA/5pF2aHlev11yJCoYDN0hEMbMKmwIr15O16nKVzcDH7wXWTZv\\npsQ4JnKb0WFH92xFbgr7m5ZaLHUIXP3xNVqET/75bxi6yNOnK15sLnlxsQUteAuz6x1Ujt2zjwAz\\n2SUe77+UwH43MJtVXK/3GAPXbsfizBNLZu6hpI7x8AXFzdmMezbjhtPFCVICm1jzRfiCtqyoS0PX\\nunvrQ0UJuVAdxWe7dvIR3u/3qE/sbw6MveJOplKftm1pu8Dy/SUxJ3ajsAnCtk+4oebgBSrLwu2g\\ntFTGsNHEIcyxXtnrmv1uwW1n6PIcv81sh5EPZwFoGOLALm04+9fPeP0BVGUxvd8pUaLSdwU59WxG\\nx5gj82Qw84o2rcgBfKNctwOrZqToBAA5ZKLJ7EJH6AwmWy77SLupsBl2jwIfzSGEWzRbnIQpahYH\\nGs6xWhB1bDc1c1HiyRLEELMiMn0Wi2M/es6lI/oF2idcUxPSQB4GEgesrRj7xC6NLMxUHyxWqF3i\\noo1kUYwWOjKVWMiZaveaXvf829c3DP1TfBdYAEkUw1SHK0DpOmScEV5fwD99TEkFayPOeJLUU8e2\\nY+g1pAyasSXSFIOKkkvh5fiKWeNJpcZ7T4qF7SZwvvqu5Gdi4UI5AiuWSZegUwRHJvcYSme5vVY+\\nWCUmTyt7H8G5F6KiaDlGGUTv5+lDsEw53+d9RQUt6d5OVEqZEPodC6zku5SC3jdoeEtcdrwWkWmr\\n4qyhDG9X3eTtlrzd0vzsZ3/RCok/NX6S4HvPfI//fpfw5faq49l7b5gX/Ad6+t6FZd4RnnmbYf/I\\nzJfvhlF+jBGPi/63O7YAD7qofD/g5yKI5COcvgt83zDf+7DzD2G+43Sdm0Pgi9c7fvHJI3LY8PmL\\nG4p7zMvrjl988iZnow/Uk/vU02pgmQ40qlwPN5zWJ/xfrz9l7iqWF6cMMfOfqmtAMAtot8pVH7n6\\nZsM/fPCmdC5HYWGuUUZiq2y10G4FW2a8Z3vSZo2mzGe/baiWhsfrGZ9dfTEBbN9hxYEIgiMXDxQE\\nIR+vN4VI38apSYMVFMO+7XGzxf0GScq0KToMN9xZLOaUKaXQ5T0pKtpBagp9iCwMGHE8qZ6TUuBU\\nd7Rlyyv16DHM1w6JPY4xCY2CqSx/CJlrrRletNRPHIcI602ijZ5aC1s8lsmXtysVp/7AZZ7EZH7e\\nQ4aQhVTcFFIcEj4eiANYHUgOzvIO70d0KCSdYTThJDDqnDh6uuuB6CuURBGDmkdkndiTeDi4lpu0\\nZmWX2BxIKRGKYZCAZI9xmXYUUhRSypTxmtswZ6EGxkiJAyNKUzKNKlYzRWbkrBxaS7U0oIZ+SEiy\\nCNAVQyhxcqJyFZ1tWEkmakvb7gnZYxfnkEDFIEAq4N0EuCEr0dQ4CmOOSBdwhx1aPF3bsagtQzfw\\nuNmhYkg5YVQwRcFFaj9iZw1JnyIlcr1es6xumNmKl73nul8TNzOGi0tEZAI+N4mt8uEwbZS/tZnv\\nh2kT3u0D6xZmleGu4KgUARGKHMHPAiippAlMdRLsaTJIUYaQ0NpijUXvQ73HeXmMLsBk/PJl94L3\\nZ4/J+W7NUELOU+oBRcoE1pJlYrSSp2u+z2sr1hrCmLhZB5ZGQZUU32hM7ufu8bzlCL7GGMp4FFEe\\nT99/+ntQsPMZ9fM3LP+vNX6S4Jvvw85vixHuhwpWbzi000S4//EPPM+9U9E7wPdt5vsj53zv2eKP\\ny3zjA9HJt8ebjix/DvhOgKDopNj91rjP+epD5vvd0aWeXdjz4epheEvojmHn291IUzn+4eNzVBKp\\nKMYUKv/2LlUfMN9SCjghDTdc1Zd8c3jN3C04JMOhi+y/XDP0gU8+Dvj3E18MlxBrMn/DtovMDyOn\\nOolGNtueYRhwZiD7hjFaJA7Ux9OXsQMpvOxeIm6kv7gihIyJgaIQj+rjPhsOZQqdqgopBXLckVON\\nfvE1eXkKzzJxfUEYLzhUH5Kevn1fhxLB1Rxiy69/9YJq8ISPzpFiJ2aSC0PKeCeY9hE7Z3jv5BVS\\nDvT7LV0+wYZIKJGLPiN4didzdL5gVjeEUhhRulxgE+i0oqblkRsIzMiSGNaFXhIzL8ytAR1wRM7W\\nn3NonjLMGnI2ky3jEEj5wPZ2RNUweouvG2Q2R9OAseDciEXQE0M/wHZ3Szh5imsKIhaRNaXArJyR\\npaaVlsDAbtaRb/eUKtH2jiEnbAkghSEoTsGZxG6f2Nk9H5+esb44IWznDPOAK0KfEuNE6zBiSa6m\\ntOCXge6wn+5rKQzJUnTaUNhSuD15RLN7jbHKiKc3DhcE8hQyzr4iG3/kiIq3QhgiTVHaMbHIgWUW\\ncuzpa6WxEZdb7KwDWxOlUF0faLoW91ENZvIxrtjQbn7PdvMVIQt1JYzS08XCxee/Jo8jJSn3xo4W\\nNKR7ZglTrnUoA0UWTAikfH4xvaP/x7HSTYpMZUwakGOYWFS4bW9YDfCkfjTN6X5Ab3f073XUz55P\\n4HvHNh8QnjskvtQty7Hh+vcXlPR3GDyqhpACRzI9HTeXYzhcp2uZqP39egKGro3EUJjXYM3EfHPb\\nUsKbsPJd2Fl0msyKko/M11bVlKo6Xuf44iXV02eYd6on/3LjJwm+5Vug+23ma/SKnEZevBhpzQmr\\nxzMmxekPPNF9wvJd4Pvg/39kUfJfqpdlOpa0vBN8HwgY3jXyA1BOOYA55nEeiIXuxp0p/vT58fjv\\n+FtetJe87nY44/imfcn/8vgXbDZT7eLsThlblFyE0u0JF2vs8wYDvP5my9mjOfWiJj9o8Va0YCVS\\n4pb14WuKVrR5BLWEXSQHi6TIzaAMuxdcf/k162pBWRiacUFJc+LLF6DKrjqn5ESc1bSlYdlXkBPe\\nWURhPNyQjbJb7rGl4o+bDW5fqIMyusyQ3RRGtArFUlASiZTiVO+4y2iIvN63YA/k239jb7b48RkX\\n6wPvL5+jmslZ6XPElQ3XhytWX73imTzi4jzTR4fPCbWwz5ZdSixiR9DAezlztRdu0pxYAm6+olRC\\nKMrBrSAn+nrGZnbGqC/ACeu+YMYeY5SVbVm4lp0qY98xRkvymSELAUdlEqNcEvPIarhlPVshGKwF\\nshKw6CBcpIaljyxrx76c4k2mAYwdUYFgarQq5GAo9FiTKGIRGcklEWVFSkq1EJZ1jxFDapS2F7II\\nRQyfND1llvi8E4wKNifGQciq/LeLgSdugS1u2ggIBC2IODCKVUdWg8cQemU4bEEsmhIHc8qMPT4X\\n3O0VTSp8/eE5T3aRJyWyyB3L8QXd/B8QtRxqRc2AGLCuxnsIHCjOc2ITcx/xRhmGzNhAp5lkW7KO\\nGHOMKGVHrmv8cVPZ1AMpCTlFDC2ZGhKEWDjcttw86ZEylVfdzV+rimQhhHQ3AdmGPZIz/fgR0PBt\\nOjKEzLaNwAV7+5LzCJUt7HNNEri6nTM2BV2C3e2heMqhgyeZ0nawPDY7eHXBbLNF3/8bzHFdCZo4\\naROmGFJ7wC5mGDFc3lQc9iNijuVSJSNyVxM9sWxzXD8mVg9SpjaTxkDlIPaB9X/9f2hDgg+mdFSW\\nPK1pmxvs6WM0C+W4TqgIeb9jvx141Y/Ex45/vvoDp+//4p11+3+p8dME3/ucwvGhfAsJjbZksdNE\\ny7DibeXtnzv+PTb4p8qbfozxJuf74x03xcyrLw64c6Fyfzrs/C6WD2+Db84JazqkmqMysbk7h6aH\\neRdF3ijS33HMqyGzCY7fbnasnLIbW16voXKOT95b8usvbilFCElI1zfs+z37TWZ1esp5meG85TYn\\nTBd5DygYckkYGQmvbxieL3nt38NLQfsBuWmJB4sYw+tQ8GEyTBj6jq66xQ83LNeRp+MNp7okaiY7\\ngxhHFuGz2PEBEWvm5AxyfUnvAvrkA4x4uv0NzcZzUjxDk0lake7uZzEM/Zqqu2LfPIPnkPsDWWE/\\nNOxfvMD7nmIjh67jqxef0+yX9Hqg6iK7uuXspKDjAChj7NitFWbvQ8kM0THOejQM7MsTlhpYtAO7\\nZMhmjuHAWDVApg9KykJzGJFPTsjJstjdsjl7bwrZ+oGT25c0zxNN6IgniWiWNH3D6C1DcNzUnjon\\nuuxYiicL0PZINcPbCQRGHN3+hOShGy3W1KzmC2yzQ0QJGiip5pDnzE1LNo6dLFjSsUiKy5NfcZZA\\nKQ2YSCHTm4aZh311iliDAWbMoOgkxNIaTyYXyAkkj4R2wPqCUUcphT4UKplN5YjGIALJWeYoV+vM\\nLtSoqXAlc15akIyOwqLaobuIO85NJ5mVn4whjBaqVUtISzAZ54RkMgVwpjDzA02VML6Qk5DUUErB\\nIFgrBAptCKy84lPE3Y50Z6fUUpOzEFJgk2tKgSbnyce5RPYSMNlz03mMlik5YQ2z3Zr1r75gtIJ5\\nPLIpHYuZZ9+OCBXe3amiIA6XVYWV1QAAIABJREFUfPb1jq9v4X3bIgZyuiW6JZtUcxNPmcWIp8Pm\\nHekIitmC9j1pM5CbNcsnv4TbLc3ugD5X0CnVMqYMWgHurQqIIUw5XrVTpE9LhmOZlHIMmeu0ktyR\\nrFKmvLSScUZpNxvGR0yVCMceUFkLw2ef4i++oeTEfHsDqwqWDdu+UL+6nkSJywW5RIZ+w6oMOL94\\n5/r3lxg/TfB9WFrEAzm5NUiZQgtTaPoB4Op/ACTfNAn+7kd/QcGV3gPWj0epb687YkiUq4L74IaS\\n2nu1LTwA3T9B4/ODnqoxHqj9+EBgkTF3nWAe5JPfZr7fPeZYCoJy3Q1UK8N+GClS88Gz1TG0PAlG\\nYioM7ciehJbMethzXs+IqoQslJR5iWfA0uREvV4jqqRDj5wql1eRfD2AGBJKmyO/6UaeygEXOkRP\\nKSnSD1t+s/6MExL/4v8T/Rig9qhmuqEnmTnBZHor5CBU7QGaAOUJp2QOcUfMT5BSUUtDEYNrMmIs\\nYxmxcSQNlpcXO/7xgxoZD/zK/y1JI6VOdNTkbiCFS7xdcnHzGZf7OY/nHafmAvPljqaqJmVy3GCH\\nGcYp53bPNp+iCiHpBEIzwzfJk0tH42u0cmS15JjYbaAmUUtiH4Vt9zmntmNIgCkcwoFxteRxbLHW\\nYkwhyog3hoXzpGRpU8bbBmuF0cxRMn9rrvh8dkLIntpk5nVi8JaihgpQa4lmBtbf1976YghqseKI\\ntsEbRWyNaqbOgdCDasaaQJJA1oJHGJqaYM5Y2YAfhXSwYARvIiIzPAnnMksfaYtiY2AfDaN3GFVi\\nDXW22GIpziBiEGuQJNxcB0J2rGziLG151q8JYlHnODMtURRrPTpRMgwF7+D95Z5623O5rOFYRpVK\\noRhHrKpptVVwlWKKJZe7GV4AYYfjQiP/gFLFHo0O2XV8yscQE/vfb/nmUFGXwtKVqTzJGcxiyW3q\\nCdkTi+fJHKwxxPGS6xee9eMFogPGG9wTKChGAK+MJeKB0L1k93JNuJozPklYE5DSEOSU2CdSsKRh\\nJJIYUkvODXM1U05VjxroON5XmEwqZyXEkTG2tKlQxgZ51WObBTqbmHfKbxpESJlEWWo86zyDMk6q\\nZlXuDDmkJIZuB5oR06J4XsQ5y+xYohxiR5bMeXPKsJlKI+36GhNGzOEa/v7n/GGTOWmvOQXKbI7m\\niJ89/6sCL/wEwVdU+d3nt/Ql03hL18Z7FjqB7zHkmYW3M40/RG9795U7NviOMO1Dsv2jM98fv85X\\ndQrFGF+Icc+w/4LVk39+cNJ/P+f7Vti5JGr/IPwvCR60YXtzzjdZ65v9wEezmtt+wyGOnNQrQskc\\n+sAQhLPaU8WRVzeJDx8vsMbidYMEGMMjLvuOjCBlZD20/Mw95l+/2eLPGh4j7LFYlL4bGKPHZTgs\\nlZKVnBMpg6plV6BPwjwJrzvPM/HUxRFKIeU1yz6gRHb5ApVb9AMh0xHsQCjPiKrcOkdjhfed4H3B\\njYFGI5KmfJmKRXrFuUhtoI2W0Qe8TiH0XIR233OIyq5u0JmhyT2XqUbTCY9aiC9H1t3vcScrNOzI\\njxPh0GGxhFY4326pZytOzh8zLwNB5wzaUHDgrkimJpeKMVdY48DXmKK0N4UQLFmEGR2tKDJklsZS\\nCqgTgnUYgWhr9n01NYjAgysY4xmDhZXF4Kh1OJafZM7rwOP5wOXeE6Xws0cHvhwfgVWcEwYNvF/3\\nGAsHDN3O87Q29E1g8IVyMNgMLleoKk6VflhRqeNpFdhqwqKoFOyQGffK7EzwRsh4MFDVgW5sqZdr\\nHAZVQ+Mn9hWKJUXDjMKHfouvF2ykAmNRA+INdoyYongD86bHaE8lmSQV1A4vIzEl6kVEyaxcpi7K\\ncBiZp0ypLE2K5LrBWoWjxaX3fio2NWC9oTAZeIgKrom4WcFGxZpCVlBx6GgYizJIoargMESywBgM\\nz+uJEUYtfJZuKFhGLKqWoQBNQGY9Nu0wscaUGllGQpjmkBFHysLFuGWWO9orZTwsCKGibTtWS8gl\\nEMrUGIEo5DKZd5RSsBzVyblgdXK4UqaNeMEeAXMCSyRxet3CYoHEAY17LuKMpnGcLf1x+TmuFTkz\\niGMsNf24oApTbvZuI//Zr//Al59dYyRPvjgSUZ3RF1ioctleYt0kAPtVes2CFQ53vJ43w+QCxiHW\\nHrUpf33f558c+KYkbNtAEmEU4eubkcXRjUUVzF17MFGMCs4dY/j6HxBc/ZlqZymB0O2p5+9jvscY\\n4c8Z32ff+NY1aiH2F1Tz97D2u8YZdyVWmxgRVe5ux7cbbd9vMP5U2PmBhVu+c5O6Y+j6Juf6MOw8\\n1fkqIQaG9Wf8ev+U059nDvuR/+29f+GqX7MdEzOWpCRcvr6mHx7z4qbjpLrkPL8gHxp2ry2H1BFt\\nZrQ9N9sVl/s1Vs/4sPqQR/VIh7KghxSQZAk586oNaD2Z0mdRoqsYa4FZzbxpKX3hIAueBIuJguQB\\nH5WysFxWhUcbMEWI8wayp77tGI2nX1VU1TkfpxfUTcHs9xgKaeZY7JRcCRKE6CxlsYAyR4Nl4Roi\\ngVgSN5cDr/IjBqBxU4lcKQ7UMNtmxugYTp7ywdVL7JlnsTtASIzlMUNxNNoBjspZTBK0KJGaRiB6\\nwcnArB3o4ooyNxgP9nrkcMi4ylOrUBCGMWF9IHkPRcGD2AoZDZaCDQlXambNJLQbacjZ07Qj8WzB\\nsigmgkexuZDKVDcbtbAJNV2o0QbUCyYFaj+QimPvTrH+EbdLGPs4+UcDRsDJFEq2ZRLYYAVroEIQ\\nOz2Trq+RpIzB0DgzmSgYy+PlwKOmxfuASIWoYWaFAUEM+FjAFE5MJoihxpGtIxtDkwwywLPlHp1l\\n1rMdwogrhcyMYCzdPCJWp5aAmjHmCDJ9P0VVzVTHa1CcmTZjzsKilin3j2CrSdmeUbIpNE0ku4wB\\nKjJZJ/AyClIspWRWVabbNGTrGWYLvimJkpTRFGxoKWVOkoCnplUluwM3T1Y82Z5gUsTQIFpoS0Lk\\nHKtCVwZmKlRhw9evE1E+QbSQdFJNZylITjgyNk9/qwBaLC4VqkFRqbEy1Q+jSvvr/0Y5HCY2rJNy\\n2kjEpwwhkjJkkxmlIFFYpoiEcVpJZDJa6UUnKqx39RRT2HkcM1+/2E9q67t1ygMBxgJRE0ih7Tzd\\n9Y6PSyGazKIYZnqDwb7p/yuFjEGsBVFyKnzTjrw3r+8bkPylx08OfK2dgE9EMKKsNwOhqzg9nx0Z\\n6DGQUwQoGHuXd3x3f8lYIpWt3p1o/3fyoKpKkEDQQDMYrvtb3vt4ST3/77ctexN2/v7tQhpvyGGD\\nlIH56d9P3zsWscfLC+LlJeu//595MY6MWFZ39+fbwH5vefhu8H3YA1XuwPdeDZ4nH11j7sVch9Tx\\n25vf8Kw8YxgXmHLAGM++rScLvhyRcWCxaUlnc/abkfrrS5qzxL7M+MPX37DYXNFfKbfVHrs4gK0g\\n1ez6TJaESOBk1tCdttwaSNozF4vLQnGBPBrSmMkxkYph0IriDBUGP3ikeIb0MZo2NCWSVHm2HdBk\\nuHzvMSYLp84i1uBDRnvLvjqdJq6vGZuGTI9vW8ZBqGcVbkw8XVxwUxxjWTKmij54jBgwDmMEM/To\\n717izRm8t6QVQ44OcRkrQHDI+RIJlnY5Z7PccdqHyRBCKry34BRjZ1gL1uqxPlRxxlIZxUpPGhdU\\nXWK+jzibWOuK5BrUFupSqF1mwZpuNOzNAisFRDHzCh063Jhog6NUPcU7lptIYyCnJc9ubrn5+QwT\\nCnkONldo6Th01QSglWfMDjDUXjFqcFIQETb5hHa2pCRwoyJEnEZyXqEWvDn2gy0G1OFtoakT0STu\\n9nlxmFElBY3E7HBGKWJxJZPKHOpIHpUwNmipUTWozxgVRAyumtaE2heyqagn1IcsrHyimQXaMGcW\\nLGK6KR99NtBUmaHOnPuplEhKDQirfEBrEDXYXAGTytZXhY+aHqHCmYKLiYqMVpCsUIziKzBWiL7G\\n1mAkYgpkW6FqoGSi21PParw5I5uGvbXEPDKbFZpS2ItgMxSbKU4oFNQ4ilpcDJiqJmkmlWkjqknQ\\nRmiKoRJh0MiaAZw5KjUUcqBxN5xi2IrlUBWyNZCVJmR8MOgQMCIY9Ygk4u01eQzT87urxWVSLZco\\nxOQY/PTTIjAetsgYMCVT/DlfB8NKhUrv9DygZQLfT79cc9FmHiHYO0/pI9dJOHoSmgv7nWMlA8kK\\nNYIlYihYO/LlZcducKyaTMahtqAuso+ZEBILb9/qTfyXHD858EWV9M3XyHKJOz1DytSlRPWuoPrI\\nfIugRrhMN+Rwxofz974DZUMe+c3t7/nZyUe8t3j23VPd75KUknusmwEGY6YC9i/C5wAseUxFpu8C\\n9fdb8n7v+EGCq7vffWCOfviv/wUA/+gRmhLrfqRIoWDua9wmf9vC1Zjps/D0Tu38Dteb3eWW7U0L\\nxiOS39hqYo7AmxgOfyS/WrMvc9qVMOqIbwM36y1JR7wNhJNX7A7PeW/ZcD3sePL5C+IYufRndBpx\\nJlPZyE1c091sedL3HLqaYT9yOissKsOYGlwunJqBGGfsXq+5bmDvajKZZVEeu0w9z9Sp0PXKMGay\\nV4bsiOo4ico8KiKepI6gCxZxT/EDWeeghn3J6FnPonqGtZHzurD1FcEtMClQThe85gNmQ4JDJpuC\\nCQusERrbMTcVOZ8yqtDbzJgCNnkWwDwEcrGYuaHu9vjTimSEnYBTx2kFg3P0jWFQjzWW0SWwJxM4\\nFVAcxUyiQmvBSMGJUkyF0YApI7Kffr8eR4KfUTUWrJtsBkV5vOhZnn/BV/vnhDJnGQpGleygNy07\\nMr2pmLUV1Jmnmw2xLlzNz+h6y2IfoEwvVPKegiOGCpKhNoV07K7TpISTQqVCyoaWE15vTphJYJET\\ngzqe2EKVI0YN3lqyUUpyaPE8Pu15Mu8J0SDY6f1TsBTEdhgMRs6x2RKlIWsF1hAMkGrG5NBZR/KF\\nEmtMAjHCahF4vuj4eveYztfUXoiUKfrgIOcam0ewjtmpQZyB4sg+chJHFkXIuWJWwemsIxtQ9ZNS\\n+ugpbJtCbcy9SNSkoylF7RENZBWcKxQ17OaPiJXF715PdpY6I5cKpGBsQZ0SnEUTJJspVctIxhfw\\n0dOmBl8X+tkK41qqEUxqmElPbAr1MKJuErFpmWwtJSXs0fXqkBNzKYgRTBaSTOBXkSHPUHt83HnK\\nGasokuS4ZngKO0QzRSbm6DWTdVqHDdNmKpdCdtNGUZnK0vS4jIUx0heLQ/E6be7TDsIS9EzYHwLG\\nQk7KHTzq3XrmK7oQp5SEsRAHSg3FCEEVK4I6w5gnT/GDvWVfKtRZsIV0LJ+0f0W181/f1uO/cxgR\\nZAzoYerMUfSuNk2xzrK+ORxVgIowPfwgd0zt7WONeQTV+3Zj3xl6x6Jbxv3nhO4Fv779Hb+7/fTe\\nLQremFeU/N2ym//IuGOUf5bD1bdeFlVl1wm7Tt7YLopQVHAolXWIClkK113PV4eRfcxcC/yemvAO\\n8L38L//K/le/IYZCKRGvkWXpoWREEiqJXUj8/tcX/N//+Qt+83mmi8purElFuN4FutFwELjq9gjK\\nzfoCN0RWN1uasGFkJGvCeDflf+JIdobdyQmtBWszq2bEEnAm82g+cjIbOZSBLvVghBaI2KkLjctU\\nFgZT0zUD0Q2ICrYI89gzHwfccYEc7ZJSVSz7QtETzpsVHzcJ45RilNpkKlVWJ8fF31YUdexLw275\\nFLUNSR8Bhc2jHQdNaIYgFrkaMPstY8zkoeCHkX70k7jKCqo3BBOxVlAnzHCIn2OyosbT1RV5mDOm\\nOYPOsTqBbzYzBDftpXzBSsApFGvwUjCxwJBQ6aBkqjEw94HaZWam0MyE2QqMH6lXc86eTBsHZ4Q4\\n33My31FmA/u6YmyeU/QRVUnUJqPWcboQfi4vaOrJyOZklZDZjFwMtg80+44iSh0jsziwrDO1JvY6\\nEFgzhkxxHtdMm6B5m3AizPqIwYBRcrJYnYRUqgVvFJcLTRuoSsL6DEYJ2RAHj8meIpbRQNCKVszE\\nuISJ0VUCzuHTnHZ3xmoWUB+Yn4ykiil1YKZ4k7eGUgxWlHpRo3OHdRUuPuGJPqXxYI0Q+4ZBlsxd\\npKComKnM5xg11ZLIOGZNwXrDiW/JFowHUYexU3h6TMd5bJQkTIBlwZXJR91iMDiyb5gZ5dFi4Pn5\\n1FGpoaKIm1oNYnHDiC2KiAUcJhWqEHEp4caIlEjWadOcdaTPnsPQEAMcssGOCR0K+7IEDILBiKWo\\nJaqFAikb+uzJSTElkeMWyRGVSB8dKXm039NKYL3IJKsczEBCWCwis2py/hpSwlSTN7yWMgH8VP2N\\nuRNrJqWEHvoWg5CS3jtXiTEkZ8jGcdCMpMlRyw09RZRBB7bplmsrJAzZOUSVkgMJoRytMO9Sae7/\\nLzX608PVFRiDxESOHaSBwopSlHphWJ3UxDge+0AKiptqvvS7gqsoCZFEOoJnLFPv0bumA3dCLikD\\njooYNoQsBAJfxK8A6FW4kMjfGiX/wI46d0OO57/L2cqDsHN8/Qr/+Am2ad795QdWjp9+veHR3LHv\\np++fp8yQFT02CVAmzcenr9eUc8uqe8JFrvjk8ZIricCMXYaHxow5biF3SJr8hxenicYErPM4HRGJ\\n/Oc/bMhWuC4NYxxwMXJzYznZ9Wxbw7YTdo3n548j++sbPh1bno0WN0R8idhuINkVkke0JFIWTDWj\\nPzulXTwjPIFRNmwTbPo5zGdk01HZRDO/ZLAGKwu6HBAdOWHqslIck5tSY5F0jeRzEKUuCdRijFKc\\npzKCNCv81mOtMpsJM2OpT2HpR5wGMpCP5RnJgKphoMG7CjVnzNSQbODp5sDukeH5SUO1F8wQaSjI\\nIziJW06HHX2zYkuNuAGDZdQ9P185Wus4PX9McR7tlIUJdA5sl9nojMP5c0wMnGwzkRWjXbIQxZjA\\n6SiM4xOEjLMT+GZVxHVYCTRjxWo4cCMrcBaD4k8VsUrlIe4NKVusK8yMsJodGNMMqedEW+OcgAq1\\nyTiEpjqW2jSG2Fmch+zmSAt1GsmlpmwDdZ3wxuG9I2fhevRABBup62Zq3YtBy7QImjylh4wqvghz\\nI8e8asEbQx0SRS1NCgxNTa+WT6+ecrab0cxnDMuIsRWXSWj8GtMEDv0JJ40HF1FfUWePL4ZBC74e\\noKqx7Ypn9Z7bxYD6AlapJOGLkF1NMY5iFJwnzt6nqyrUFeLhlEp7VBN9NsxnFdv5Izx71Cjq1nSs\\nmPslLkwMt9h6Al/jiPWS4gL9HfhiKALBWzIjdTb4rFTGksQjOOYuUlUZJdOEgqdBtEGNUqxQ1RkS\\nlFiRksUEsKkwq0eMWpwpmFPhverACS1BpghDSgZJhZKUQ7XEFGhMYlRDKg7UoWIgQ8wehxJGg4Y9\\nJRs6PVCrY9ud4seBrJ9y9WgyP7Ei1OowNjFrMs+WLfvbmsFbGvykds4F5yAXSymFoZoj9QyDYi9e\\nUt9EcnUCSWiMogYG4OA9wVgqDPt0Qs6OWDJ5Nkf7lt5ZtlVNsRXndnLuUqu4KhGZUiK5ZOrj+vjX\\nGj858AVImtBhJHUL6jExNzMub1vW64HT5TecxzlqaoSC6tGST8rkclKEdkicrxrGsCUOl0RrGPOH\\n/Hb9b5Sw5h9PPuT0/BcgQq+Rr4bX/H35nybV6HF0aSppOlaeUdB7v9GHQ1VpU6bNwvvz+p255WH7\\nb8Cbtl53zDd2Pb963fHxGHn+87995724E0rlomwOB8bOkjHUqqw3kau9UJ7G+xxvLoWSDK9e3PDJ\\n2SXRrQjNgWxHnNTsH+S3t+2Idl8Sx0u64ZQcC5sQ0OxYFMeJt+zbyIvPL7FSEDWE+Qx3dUPqYdN7\\nurZQLJQA4y5ib/fcdDuanaHSQpZI2QS0mSNWuBkS/SJz4iuyr1B1GFe4jqd8fXjKgKdJnvHkjJN8\\nzftuwJmaTkaSHkAzowpOK4oaEh1IgpQmZanaKU9khX7mickiwdIEi5YZk4+74orhtKowvcHPEtk6\\nqqVirKGESR17qiMLa1mfPcbt9tS01DkzK5NyOBbPTVnyPF3iSsQ3iUCFS5GhFpxYnC1gE9Yrq7pg\\nwmQE0cwTRhSL0lRKTg2p8lgTUDUUqXDeY0Q47Q/YeELeXPP60QJHnlybjBJVMDEwqwI+OSwLsrEU\\nb5Ha4o0h28moouCgKKfdiJcy5dXMCSoW1KBSWLjIk6Yn5BkzO10fFtQZUnL4UqhFCFZpJOHUYZzH\\neogjWCcMboGbC44MXjBqKXJ0YheLzcJQhIaCN5MYy2jEWUsxFqcGj2C9MIjHKFjN9MOS0cP5SaIp\\nFeoM6iOtLdTFT6IuEWZ14cSMYKc87zJ3/DL+nhiWnJ11ZGuYj4UPWkOYOTqzZDQzjK7JzQowJL9i\\nLIWuecRZHsnWU3JiN3+MuIreLVnQ46z+v+y9Sa9uW1am94wx51xrfcXe+xS3ihtJGCWJZCHZTaBF\\nN34Cf4MeNJBo0qEXTZpI/As6yEoZp9NpBDgoIoh7I+IW556zq69aa81iDDfmdy9gkcbYEDJSrsaR\\ntrS/81VrzznHGO/7vNQwUV346P4z9ocjywe3hCmwHwwnkYctpxr7LFoMESNL/zyCGKkWJiJN+nK9\\n2y5IaFjrm2GtYBb7OMyFpg1FePkIae2HMKYTK4GL72G/YRMKLY7IevrmsF9NSDWzNoUWiBVO64y/\\ng/m0xzfOlBfC3AlU7nRNwFr48eMtcYrEcGatii4XpvnI7mbluUx88e++w/b8KUl7BnGTPlk2hBIi\\nOIzxzHZ7YsiRSU+oKPc3r7p8rWa8GdUbZYFp6K/3M7ZUFaIIX142PBLYHM6sq7GkiaBnCgFDeQ7K\\nTvtoxdwIsX4Tf1msb77xv7Wd/+tXs4JJo64HltPCvDj+5ke0v/7PPJQvKK3yXA/XzMr2DaSqV7/w\\npz98w5/89Y94+3Th/s07np6F83Lkr59+RKmZNR/52+dPuirOjS/sGXHjfn7k71ldv2lhd6tfT/yo\\n/0jLNl8+4wdf/pA//+q5o/v+L9c/JnD6Whi2lExBOJV/+LilNR6v5JqvvbYlH/E285N55rNhwBxy\\nrmSE09sLJVcOpbF+HXG3VubS03MOX4drUzjmR3JZKbXx159+wfc/eeB4XjnMhSDG43mlnZ3Hmpgd\\nTvdn6lJJn39FWAslQs3vOM1Kzkq7hglIKcyfH5FWsSXDw8wlwef5Pey+sj4LSw28XfeUc8G0I/9C\\ncFoYeKgf0nyi2UBwoDmHdWTNEUHIeUWzXeUVjYPc4Rqpntk+R/ZPXRCiONoKIpnnW6ekirmQLn1h\\nGzaN2pyddfGSiyMLqBvD4Ayj4aqIOikaKUFjh/rcPYqmRHN8CAw0TJSncMvL8sywOVJjn5u5VmIr\\n3GQhRGMNlUkbXAGBKlfBToMwKHVMlDHSktKsMbNDQiCVldGc21gZzku3f9WKt5FzG8inoW+IySkS\\nkdYIbkjrCtV4BcprFEx6RRwwFk/QRoJvoSpaK9aMu9vCLiw8y0RFQBpxFBBlaQOxZkIrtNhneu5C\\nnRoeHcS4eV4ZluucmLcQCiAU77hBbY33vviMtC4IcJMWQjOiVwatNBGqy1Vo48wl4ALigSC9bZgE\\nxhpJ7b1ugUmVuQUw2FhXHVftB49oyn4t7IcLIV96y1KMqpnTt9+nfrjHBlhTxHTHcvsKNUgt0K5k\\nrOWs1KLcPD7RCNd5aAdsqAqqI2KN/fEZzIilMOwdu7thmrrwa/EREKI6liKXtOsVmhixNqjhGljv\\nRDJIA02YX5kG101DERoVGtxejmzWE7EU9g8L5zqyxi0jmd2wYDVha6TGmVUzdRnwVmk4MbQ+Y39c\\nCY+ZD+/vSdKwdUM9vIa25ewj98uG0zmQXVnnxPG44RJGTts9X+mA0JhvPqamkSwbmvT7O66VmAvF\\nnTUlSlQkHonMFC2Upmg09mOm7reQj+BnbK00g8u5cvBIQVhUMTGej8Lwrnfe5jSwaqKoY9fnLLbw\\n0+FT5njqcJNorF55kPrNGPFnOfP9N1f5mgMxdMh2KSAjx+OPyTje9ghGbs6oTm6V4o3qxqfPbzgv\\nZ96++YqH55nD/Ur+9Cvm9wNrhvD+E6+j8DJteSoXHpcHBjPmYrw7C+XGGVIFiWzSlpP3OXE3FTQM\\npdVKPRwIu903nFCrM58+znyej/z7Fzv2L/7hR+6W/+H7+3uVZ22V5In2tz8g3+4YPuoM5L966lX3\\nLgZaq3zy5sBOn1lWx9rr63/slFxYq3arz7FSJuecM5hzFGfHEWXhUgKTG6pHzDcclgOBG5J9xbvD\\ngbH0gPRBIX7yKZubI7a/I48D//tpy1Gcmic2c8F3TmsX5rUyEDosnQI0ypsTm7sDbbthq411G3m+\\nGWB9IjztOMbX+KiMLMQELUekwvFRKPOAmmJEsII1Zy2JZd5yUy+wHNgwc5HEPOxxfUmUwCCZzVIZ\\nnxuHcentzHLBQ2GZZjjvQJWYlRWHqVIuiWljNE0cdMNZBswrkUQIzj41mIzBDEzwEEAyjmIaaCos\\nltDW25cLO94rhWBCbs45RLDAOCc2UhnWzLJxkk4UF1wE6XsojqJJsTBgCENZySu8G15xizCsK9EC\\njYAXZ3k44x7YcJ3tN0GtkZJx8Mi0zoheGFrpFCGJV3VrvFZaTq2B1baU8gJRwYpcVbAQvLFUJYty\\nittuCYpgKiwMRK9M7cRX+wE9RRAjTBkZ5RpFF1FThpy5y59hdz9PlAEXYU0b7C6x2sKNHeAmEc5b\\n1twPCyE6awoc9QXvL0fOS2JWvSIIHQ296r67FERu0LuE2hmVbjXCYXClT3e7cFJM0FIxcYItiCYS\\nhbyJxGVGzBiHGdj2WbwqsVx46fcc2k0PXzgNlGJ87IZ7P8DgRqEgEkgIQ2kUGRhsRczxKVGCMm8j\\nL/XE6pHodM/yONCsP4+LKXHqAAAgAElEQVTghNaweQRVgsC4zFRpzBrAlGa9iorq3KTGt+/OzI8N\\nIZKHiOrEmpXgI80j21DYeuWJDatv0HahaSGsjcFWNhtns8lUD+hcwBPiwmQrtW1pCsmERmfnX0pi\\nWSJ6vGFMlafbO16/vWfJCdncA/1wpmZdsGWOFmOfFwbNHPU1OkXsspJr481XG14/Ky+/fSHlmfm8\\nMh8WlphYU6TOlechcpvr9XuEtTYsRKBQh0DdTIidKOpdgOU9qKHpwpMoaRW+PRRme6aKcKwrr+jp\\nRz+r699c5asaCYPizZBWWFsh10KxSrWM14KfHmkPDzRbKfdnDl8+8+bpgb98+0M+P3/FZYbz4xNf\\n+IV8qhzmTGuVmk+8SH1B+enDp1zuH/n80libsqzw49MTxStbD/hVpKWA0RN+2vnE5fv/B+8++atv\\nTlL3q/HJc+ar50c+eXfgf/n+G85zZv6bv6Edj1j7O7HX0/rM//zT/8KPDj9mbf01tXWlns588Rd/\\nxsOhE1tO58yXb080Nx6Pma/uV55+8I51mXl4WjitXWB1PBbuT2ce1yfmtzN2gVwLbXFyG1iaodSe\\nTtJWzqdCbc5lXflP3/+cx6cfs718QXs4ovOZky3k9S2mF9wqzxfjnSeeR2dcvmI6PxGl2wRiauwn\\np9oV8Vd6i7B6o2YQadQIw/bMxhYuMnPCkOBMUglqVE3MWSjWBVSixnUNxa5pYzhIrbwvBz5OfWFb\\nZQMoKk4MGSRcF/0+O6z6zDqd2KgioVd8eZxgk6gJrAheFKLwBS9oBsW2LLJF5dqtbBUPobcJY+My\\nbZlvP8KCQlCObKAZGy2Mr5TXrxaaj1z8fU664SA73sprqgX2dqK19drm7TP/Kt69wg009NatiKE0\\n5mnLcrODUFFfcRoizrEkljlSli1ehabWW7IYqNA8IMV4OjdK6wk+WEAsIl4ZYmOI3Zta4kSJN5gp\\nrQpiTtWAhI6N8GCsw4CJdJiEdIJWCI0kBULlvE3M44RuuiL7vJFeQcbE7eXEh4+PSLE+zwwb1jQi\\nqh2AsBZeDDP7uGBz94saylv5gKZw0A2XNTLPY78fQqRGYSqFu/nM3jIiirZEUMihIA5jG0GgophG\\npueF+wfh8wfAjRhgNzWOsVvKgmeWFHE1JARUAml7AARa11q49rypoop1TiLNBReDEInekJq5XBLr\\n8vc8zA5NIy7CbCPu/SjvLhQT8AFQYu3flbn2yv7re0UDilJr6PjKYIyxUuYNHw8FBdabDZcXL1nb\\nCxLdwx1ojHlBRHlYbzguqSMuvYve6GnHnYJVBehz96kWzAJqEBXs2tU6Lxsus/G4bHlYJ2oMuEda\\nDbSrGr67x5Rl2FGbdeiNCXJyVAKVGfFKpbGIEFumZEWXmfTwFdnh3f4FZQqcR8W3ylsdqA4mhVNZ\\nqDHR0kTajfzC7om6njDJ+NVE3gVmFZpQNGAu5NbtYRnHrf43wdX/3RVV+Xf7wF81Y50beV5ILTD7\\n119g5qCZzeWCrjPvAS8ODzx93vjy4xvWPBHdeWj35JDBlGiK/PhTzusjn/z3/x0v0pb1iy94/uwr\\nLruJ8VYotTIvT2Q7Y5fK0+GZOEz4embeKU0nbD5TRPnpmx/w4r0dU3vJf/l85rB24dfb80KKE4cv\\n37J7uKc83LP5H3/hm/f2N49/y5En5rrwt0+fEtdX1HeBn8/w8Pkbzn/+fX71W++z/s0bnu4+4t3T\\nA8sXb+Bw5t1nMwdfeJ4XJof7XeK8wkmOHPKBwT/Ezs6iC63AnCeej4GXwdkNDzyfjcOTsjwPjJsf\\n4jmh5RH94h2nNWF+4X79ihrucQvk08zwxQU2L2jm7PORgYLHVxASslVCAa+grjiNrMLkQvg6f1O7\\nx3DxwMUiFpXXsbA9H9FNoiJXF0Pj9q6wLk6yoeMBW69gejC5QilIuM4P00BpgeMhMLdGJFCJDL6y\\nkWc25ci639NsQNWpMTLfjLQQGeNCyoHTMXF8b8PqgdwKykBDiAJ5NSpw7wN7z4hWnl58jPkjZXtD\\n8RM/bS/Z24F9XHiUDdWUZBuKDyRZEDnjeounwuKKV2G4Hiwq/Z98cZ5kw7jNyJhQ74e8S5gow4Ck\\nM94q69AVnoeWcARrkSaOixHMCcG/sWTE6ohEqgeMGRFoPrOPJ5YhEpIwpcITiZpuSXQ8ZnBnDBWI\\n4Iaj1wrdiXTARfGARkO0dZ9ncEpKJOvtvCDGFURFaLVXq9VJ0WkhdesCgSWs0BpShHoBiuFFeVdv\\nWVoixspg9Jl4HVA3agzUyZnyhUEyhYqXLSqRKB1dGGpk0wJnNy63t0xjZLo8oHOjjEJoFZUdRiRL\\nBL+mU+1Hiu+hNsSVqA3EkdoRih4FXY0l7vAr60mK9xOiBl4ez7yYjyyrELJcD0SOXcEU98Nrlrhl\\nZ7kTvMSpHpCrD1W94M17hyZUEguZDYrgVWllJOxG/LJAgFN4xW41hrT2ufcgTNvCqJUmgcFXYmtY\\nFLLDgOACQY1gjUUG4jASmqHeyQniRmyNRmS/bYQQWeyONh84XuDigZdjZS1OsjNlN7JMe3z7HfL4\\nsresiSi58xNC7b7qE/BxpMXEoJH96dAdLaKY9bCF6JmLg9VK1IXBMh+9e8tp+3McDPb6FQ/rhEkA\\nTYRQqE2YVyP6NTbTYXDn/hRZSiBOM9VDF6QZNFek/cMu5L/29W9u8wWQIXBewYfK4oHctlQxcjuT\\nW6OFTNUr+7Qq6xrxWrj55Jn7cWQXnFJzFydUePuUKXbh569e2SCKXc4sVrHcgE6ZEZypnclZWbMS\\n10cel3fk4UPW9YH/4QdvWD/8NjktHJaV/+mHP+D+4S2X8470MnLKlR+1mY80fpOfafZ3lW95fMfD\\n+SeUccDYsa4rYdxyOsGNAmb89Z/8b9Q88JyU//Uvv+TVX/6Asmy5uPPJqZDzpSuFUapBjn2uXL2i\\nJbCeehsu18AlK/vm2OmMHTM/evgA31X+7CcLv/Tux6gc+PPL+5wJfMtn7p9P3KK9XTlXHuuOKivB\\nwMQpzWglMNoLfsHvOdePqA4DTnCjoqTitLTiqsxtg4YLVYcuHJHA0DJTWwmXlXPcYt5IXhGFaZeJ\\n8wY1ZymRCXBRKAOUM56UYiOuILni1SlLYG1KCjCWShoXop256E0HOIiRY2RqoKPwvH1NvI3sPXMK\\ngZobUz1T0q6DGtxZCqwFNCixOeHW8RjQBdQ6ei+2rpbdpcyjQLGAhD57GlmRYWFtd6g1jvtXSHK+\\n1XoE3ZIDPkdCPUCYOK6JNAaQQg4TdYhocHChrU6ZFLvOVwkGRalZUK0kb5gJFaW5E0vP7z2NL7ho\\nVy4f1iOsDbSCj6TB2DtcAmw0wxX12EKfOULv+LQYib5Q3FGRnsEbhecXO8AZ8oK6EpshgR5en3pl\\nGLxjAyUbm1Q4hQjVIUC1wFiNfIR1iYg2WhXmsEEMzJXs4eodVaL1ihFpRArRM80rPgs3+cxJIt4S\\nKU+dhKWOjBkNhcKAWO5dkpYx2XFmA2YMCbIGfjK9YltHmkKoikrvUGjpVieNgAjzcIO5XEMXpCf6\\nSCQECBlq20H7u9i7fj9d349GrHWfsxidCCWOqRID3O6OFO9zdvMZqYmQutms6o7QOoykK8ed5okk\\n/XA1bmGr3YpZJbJt3bLjDqsJQfqBwYZCjmfeqyNNNiRxaEausVOt3Bgmxwic93ewCFICMR5JdsPt\\nduZ8bqT0juVuQ8nKcfw2LhCD4R5o6gzW+mYeDM8NN8HTHZM8QwsEc/JmABYMqFF4M8wcx8YQPkK2\\nzuKJNSjqcFlHntuOJg1K7XoJIFqh0e13WqC6cjhsWeYtw+2RelVZt9xYhn5Q/Vle/yY330rl/tWe\\nFFf8GKkeKbdvyeacyzVsQftckKshX3B8dXbHM/uQedr0llwrkMvC4g1xOP/wc+5PK62uvH6CvFmI\\nr15wPK9oOLER4eH+SCsFlXfI5JBPbJ4r5zxTP/2cS1x4M33G4R7qcUVsoNnEl/M97o9s37xmHI27\\nrZDnE2CsZtyf3rKUAxbvqJ655MhEZWnOjTf0cuHxYSYryOEd8tPvcz72m+1+SFwuQskL46Sc84Dm\\nTNPSlcgY0Z5o64UkfZ2tqYtXLvNIaYVtWsjReLs8cM+Zj756ppWXlJiw/IjPR6hOaJUgFU+RtQmh\\nld4WM4M6ENLIIE88pevNHRpSjdqU6bIiIXHcv2TVDcHAY7w6+wKxNII0XuZHQtuxm3eMrXGOL9ml\\nwpQasjrFlWCKm7CuCTXFK5glXJRYK3tbeLAJ84j5wJiN+vqOh7t/D/kCKC9iw0LgcYbtIH1WL5Bf\\nDNwflEueSXUlv2iIwbBciItSBsWum+nNeMHShiUbGpT74WMWttz6PdOmMQTjUgYk9so0hYYl8DrQ\\nWkIJtCC0VvtCXY3mTuACvultyiFjNJoIw+RMQ6W6UGajvVaqKLgRkiGLos0ILGzbwsWMXLVHtAWn\\nR8g4uaUeXnAaKJZRDFsMv4OXsVJjb4967qHqa0woC7SuEM0+YLGjJc2EtQRsDOTdhvq8kq6+4dB3\\nGAKGxUg05/ZSGNbAMHd4zev9zNJWZntJkcRNW5mKcB4iXg0z8NiJWUYPYxAazYQovbSp0i0tSZ1G\\nQWpDgX08o/lDzARCwHToHloq1hQV8BRofl0OrXtM05SZ4w0UcPlagKfMJWBUqA1PUMeEReE8BVSN\\nhF+rNiM27ZjI3DfkRW87QOJKwBKMNoSuF0AB5XY9s1wiumm4KGGQTscKRkJRrLONVa4t1SszGiek\\ngJpTJDEgmAh5u8HTAkOjTQqn69pnYMHwJqiDx8b2srAxJbcK9I1Rvj7YuZOGv6vYRQLboRFjZc+M\\nNmNkZbWKR8PmQrU9QsIssNbEFIDSuxYxOIu+YjgLOTWCdUZ3GTacQ6a0htFYJDJHp0nFxWjNOqe+\\nnjmsAyYjw+VCOBViqQQ6BGSQwsEgCExAa8rIirVbYjAyvaIvTXhz7J7ln+X1/2jz/dM//VN+7/d+\\njz/4gz/g008/5bd+67cQEX7xF3+R3/md30H1Zzc6bla4Xz9h2mzITfGSqUPpqr8M62TUojxUIbaK\\nWOszmJjI1ZBs1OTkteGaMAmsy4xL4/Zl5fLpl9wXJ2wrIkaYdjT25FKJSyZb5t15oVnCbj7mlDJ7\\nntluKj+92eOfO9kTy5+9Yf8CbtOFr3JivCjj6QdcXn3Iu/zMH31W2L33yHRvbDcv2L3/irezkUOA\\nVpirkWtm5ZEgmdvHzzkd7sl1S4iJ7eERORWkGVWdoye8Nqw41VeeKWxzZt05Je24iRdevTxzmFce\\n/CNiS8S8Yj5wZoduCuPZWMg0zxxk4WWGtTl5s2Lpiai9KjEKsRQkOR99+TnpXFB3mjpNuujoyAvW\\nYcRTN2LVIREXh6icueXiN1iB7W4k+MCuGqsrxZ1IQ6wy2BObBttaOb/ZEr7VuB0qx7XDLh7ngamu\\nhFQx26Bp7RARYCiF4pVgA26QPbIv99y8eMm+Rd74yE2svBgiD6PgC+QKWzLxWODFnuqBzbxwW46s\\nd07WrqWmBGToJN4mSqyNSQrVnTLsueiOi2zxOPBw9z6L71mWgI1b2mMmqLOGW5puONst2pwa3ye3\\nFQlgcmWUG4g6LooVpQ1OUNDkiBprDYxLjz1s13anRu9ABu8q2hYyc5w63rFYDxFAWRchF6EiqDdU\\nnAokc3JRUnL2m0K9dDV/EGdOI2YXpFXUhOqJaKDSOOZAscgUMiFm7mNAhsSkjdinxARxCkLbJOw8\\nEUsg5oXQMkTYtIVZwTUwVHiZlU9fv8966QIsp7cIq/UNLZLxq+caQKhEb0Q3hrAgpbfe9+KMZtTx\\nBVkE04Ug1wq8dF70Yb/lVekCSgFw7981kSgG0rno7oLXLe5npDZsgDYIgnLebphEgYp7B/ZLC1ds\\np3QLURwJ2q1qasZdfUL2hnPDqsYQDaHPjMUNk0CIhlB7QIUDbkjrucC23eLXzOhqoaM8zalEctrS\\nUiRcN+lMZCxL9x+b4OqYgjUh1IGND4Q40VQpPvM83zLVipDAYU16Dd4YcYSAcVsXiihTqNCcnRqL\\nV5ZrZyAsM556glE8F9KmE8SUnu5UfCTkSvBHotc+ZnIoCXIemSyTIjz5SJTa7+tSOj2LJxqJ6jv2\\nz0fqCqP2A40RejCHXzteNdJc+bmvvqQ9F/TjgVwGkMTslebCT54v/IdS2aefTU36T+6av//7v89v\\n//Zvs669Pfq7v/u7/MZv/AZ/+Id/iLvzR3/0R//qL/LvX82UYzY0GElWZFxpQ0GuHbcgldyEtc2c\\nGgzaCOq0aeDBbjn4ljg0fvHVjHDDMmxBNkStSM5oaUjtubBVeqaoq5Ot8cBrnuotT2siHxvydKIQ\\naLLnng/Iuz33ac9TDTycZ2yrtN1A8zMfvPtLds9v2Dw/cimVZ595+5PPOX3+zE/+9J7//J8uzG8E\\nPzm7H99j1WhecBqH7cpzyJxLJq+P5KcHynPmXF+RfeJYnAsDXhqYsQalhEwdZuJx4e6rNyRZ2T0d\\n2brznmZ0iEgz2lI528Bp84rmcC7Gca7Y6cRTS/idMWwaUTPDWjnIjs8uH1Cv5v9AZTPPNA2cx0Ab\\nE+fbW6wlkhY8dlFTpCJWu1JDA8UDOQce1juqRe42jRi8ewzdmE4LZV1oWokuyAV8bQxSuduVvkGJ\\ndQpYhWDKVCLDlcbTudwFtUqrYBrYTJkoFVDa1NtqqopFRWvhhT0w1HviesRopCWzuZwQc4baF/pS\\ne0Re/Jp+RuhqYOntdQmw1kgtoK1w2eyZpx01BrKBR0WTIDbRxKiWUOmim6bhGjDggGGt88tdlFIT\\nOURe5K+4Wx5ZC7xZAj8ZP2RNgSqKuKNaUAnIFaoSWmc/G9LnfCoc9zPZK5qN9RAY14bIlYpuxnkJ\\nmAmboaECwQoahJIiTa2rVa0DI6S1bvsqI0tLDNEZY+4bSOiWqXD1DE9WGV1JJkxkzCFcjJfP7xja\\nhVDnvriqsq+gVUGFGpRVRvxqGclNcRW2wwkPPaTAAbWVRJ9bam2EtGJuTFReLIXt3AVFngLRK2PO\\nNE0ctxtaCBQCrqHPB9vckYR98orjxNDD2zfthkbvYog7xL6xlnFD45qQZEKxDaF2q5C4Eyi0EHrl\\nKu0aAuOkkqHZtYDt1hhzoTnUEAmhHzZaCGi8Zie3SgA8DeCQYuXjuzNbq8SrGvE4dVZ2J0H1GepY\\nC8kbHmIncl2/eEEQBeEGk0i1zNPQPcSl9XSlz4mcPbNIZ2XvysyGxlgXgtYexBGVfOhBIsGNmIXp\\ntDKsK0PODK0fRNS7et1EiLUwrvOV/e1QlTXdsoy3rL4lhYqrYdJV5GKNWhWbM2EN6NJhNqk07uqB\\n7TqTPVB9YLHeGNCr17f7B4SYoYlQtFIGOKfufY//f1I7f+c73+F73/veNz//xV/8Bb/8y78MwK/9\\n2q/xH//jf/zXe3X/yJWCYnVDRIhm2KigxkDP+TSHV/vMJTZaaBD6vNBj5GF8QRm31Fd3uA3EKYIo\\nJp1gcykVqYY28NJ49pF3yytKdao51YTjcosBaV2Ynp8J85FQYYh9DrTcRL7cjEgonBfjcIncjca2\\nncGd6XRhYeHUnhnLpYdqm1DbymSFjz9Z2P7knvTY8Whu3TKw1MZle8dlf8diK+28Ys046Q1vx5fM\\naUPw7jlYdlsutz1C4XY5Mx3OTIeZo+wZ5ki1Ls/30jg+G8ux4RVmesyYLIm1CWcCaSPcDDDoC4IG\\njrajtsgh38A6EX3Di2llUWFJjqviquCJeopEhY/uZrZhpaI86PvUMOEi1HJF1V2EQY1pMFprWK29\\nqvCO6hN3YnX8UtiuB7ayELcwhdoFVsUIYaaWhlr3YBtG1Erwhjtsb4ybuy4gAbAwdMVmiDQJ2OCE\\nXVfU2nCh2ZFgtZN6hCt3uHu/AV7pmd3QOhtauuq3pOEa1RbAekzZuQx4DKxBMXF8FESFzdArDwRe\\nbhKb0PoioV3FP52eekV63XyrBaonNlRiy13lGZ0claZCQUGcKIUSrKtX6Z+d0XGNuGMBLARMGq05\\nyzyQ1tQRe+i1DRdQQERJoXW1LlA0dWCDO65dfVtXIRfl1EaaJgTYxgximDpBjcHKtXIUJg9EhHpo\\nlOboyRnayr480BRcGi0OBIV1miD25Jm1pT5tNqgIOUSiNtJUibF/x2qNAEyWEW8ECtUDD9P72G5k\\nnJyJFYKyn0+kWpjjhuN20+fxJoDwwfLMe/nE9hIwehCHSKcfTUNjswcPV4uPG3UXKUPEhkDOSl4a\\nqydWfUVsjf16uHLiG2XojxFx1PsIZ/flPS+++IJ4OfXxGIqZsqQN9/v3e1vbhToM/ZDk4XpvgEZl\\njI1t6jPir0c4CByH2/6Zaccw9vcHExkJykr8xneLC77bM9+8R04jpW3IYctZbxF3RIwlJ+YZivTD\\nWV9v+oaWQiExd7+4BSQrWhraArGs1MUwH0Fij9xsgo2JenuLpch2LsRWmdMN+fUdebOjMrDULYoR\\nI2SLWINgjUyAZcdUBjazE0tgY5koDbxxqf0eri1cWQ/SBVkCcYzsWHGuFjMCNRjBhCn87KIF/8nN\\n97vf/W7Porxe7v4NpWm323G8MpZ/VpeIcDoLQRLYDosJ0cqYKrvkjCjvHl+wDrUDzmnMactp9wG2\\n2VM3PQKrtoE76cpIk8Axvc+b8D5g/XS4VubrTLK0Ru0cPNacoPaT/6UNxHcP3LV3yBURGbaVNUV4\\ntePkE2tJ3A7CfuzYu5BhKRcII00S3jp/tRZjPBvkxE4crTORlWxvaWuh1hfUeMt6+x7LKJzywJfc\\nMWvg8npPHiZ2nqnD0LnWrZ+aY4DBCmLCgRc83H6Ls28pplykcPKKGVzmQLluWpIj6s4aIzUN6H6g\\n6MCQHcLAXG94JwlZM14DAePLccs6DAypR6OVMLJkZ0qZcVJuhh4bdt5+SBlvrr7KbmsoWdkPK4M2\\nWrVrK/EqUsJJ0timwuWS8PPM4ynRpP/diwvnS+BSI+aRF/PK7SWj7RozmBeiG5tY2W+6DcalWx80\\nRGpIXZSitW+8OeMRpsM7tvM9YBRRtK1XoEqvhjaWCQImkVk6ICKhNCK1KcGhxImmnTw0p4kSIzI6\\n+ICo4gQ8BSSObIKhsVc+wQrTfKC5XlNSe4WNBUSUavpNlQS9yqpXf+lohSAd1qHXGSgiTK0TiSxA\\nU6XGwKntMRGS9kqpeBdFiQv1uhFFMaQ2Gop6okJnF2vs8XCLc3/ZYNb9vtWEpJVXtyfifkZTI9HT\\nhERDV0gDoV0Tx7JhpqwWaerdQhMi63bDPG5xVexqUxHth4puNxOC1W/mnSaGtt7aFgxplVgqRTqH\\ne/NKmabGdmoEGkNbUS88D7tuQ5U+oxUHaV3Nrd7nm1F6C1+kx5QOgyPSBTzJKzpkLvstEhW3yrpY\\nV3e7simXztq23l0oSUG7qrkUZVhXWlHuLgfAu7o4KNWVFlJXkaeJ1QKmPS1I3AnerVNBhVG7e+B0\\nGjjaBvFCjpGmgRAcyJyunxsOsRWyJLJHRmsMuXWYTFJMYZ5uMUtoqWTpyNtXLzO3o1El0hxkubKQ\\nh66UTppJyXANvP/+gfd2z4TsWA2QL6ThpyyydsuPK95g3d/2Rb11GWJojWW47evHlAhrIS+KxsY0\\nOuYDZCi+4+DjN/dSihciGR0Cceyxh0sJmCuDCI9z6nN6F0zh9bcKd3cLXgQLRlRnY4WH+XOe15/d\\nfvbPbm7//fnu+Xzm9vb2n3zMy5dbYvyXOVEsa6GclLATIhBC5MOXmZstfGo71iUj5sxr5EXq7aLj\\ndINWZTQI2qjZOcgWYkPmngRrtVs4IsaogSYTcxgZGjzbgsmGwQesht4+NeOhjjw+bPi59z7nVXvL\\nJ+U7iDc+lBMiNzQJiAl5FeKLDea3qCd0LQymjOOWVvrvCM6mzF35GAeaLKxaKa3hxVhs02O76O2S\\n1cfenhz6EjyMA002iIJVozQow9gFIMG5FLg/J8ZNp8EIxiLtOl+CfHZyP1bz3qDE9/8Dx+eJEpQx\\nVJa0gbky7gRJjdYmqmY21kPKmwhpNzFtLpxlR0nG+hTYT5kpKdshgyY0RcIAdqldnSwjqczsRmNP\\nT0wKUvEM5RpwnbzPuoTE43Ek33YBSLh6Dc+XwNPuA2Rn3dbUAstJmaeAWlfadluL4UtjlELwHck3\\nCBMoTEOnU2GZIMaQV4YWuLDjmS3ny4BuakdRiLPxzIqRNXWwhFVEhKyJ/kphTS8QdSQ0WhuIavjk\\nLGzQIeIE6jASv/YBXcU8anZtqcs30Wl98y1cxsQaI1rWntFrTnOleLfymEFE2MnKTXxLM0VkR/QO\\ngqkx4qpU+ZrPu7KNjbN3xbRcq+VskRT6BlesA+gHCbSmlJBY0pZSYSlCDUKKjaDCpfQD081knPKK\\ntw0BEKt4GHHvm1qyiiWj5r54N03U5EhuSOhRerNvCAGG2A8YIl0PcKqBGJ3n50bYwGWT2ZoympG0\\nh2dQOvqyXe1QQYwqCaESaEy+4lqZp9yTfjY35EtmdOuK6nDlSamjdvWqONeMcLjbFi6hjxa2nlnD\\nhGO4Xi1UBsHCVfkMX8eTNlGqJZCRvAine2VKStHYRyjXX38edrSbhKmQx4HztGFRBXLHuUtFrnar\\n5IUWB07bO2YdEXXKVmiXyKgraOZpHfGzE19CyJWiQs3GTV3Z5ExTZUzeuwiqVJ+Y5pVFRlwXpl3h\\no5vG3zwH3Jx2bqQ96KDsUkFipsR+n8xpZbpcCMML2u2ebfuCpJm4XRE1vEUOryJlFGRxaoXAyNAK\\nljq7X6zw+vwG9YC8rEwBrAUKnV/dB/O9G1KmL9lMI9N2QtxYc2SxwMuhcTs678JLDu8KQmB/agzx\\nch3t9EOYB2GSmfK849WrLa+2N/8ie9U/df2zN99f+qVf4k/+5E/4lV/5Ff74j/+YX/3VX/0nH/P4\\nePl/9eL+setyOpVt2nsAACAASURBVDPOK7JR1I39trc2T8O3GcqG03LPRWqP4mqVKhNVDcmZyEhI\\ntbdfQv9DbkuhhV1X/5ripsiLPct2wQ4JM5AMy3Yh2YgVJ9VrJigw1EK12NuMBpYDPx9mvpIb5tSt\\nM9UCZ5m4maCs3tvU58zNB30e9vYzZ9LMrh04lj1VJ5LAXrvh/MYrpInz3ftcknP35T2NSPSZopHC\\nxDLeoeOeYcnM1sirUMeAaiOl0H9G0QqxdTtDE0FDZNGJOhu5BW6jsxl7aHcNAzVG8DMH2bI2uBsN\\nTYkjN+TlLVuvXGSH6IRpQryhapxLBK+8nLqla9CKTj1v0Vw5K0zJCBg3llm4YRgM1ZVcujq0jqGH\\nqNc+d4sqLK49zBwnXjNciwWaJtQWPCRww0pEBuNmLFzcaa3HvCUqY1lI8QXKhpi6b3PUTK0KyfDk\\nyNo5zmojS0loMNYZtv2pmcRI1jAGmgljvVDSBrahq7Bx6phIsaKiuAegERKc08i+AgR8SmDG4cr2\\nNVcGd0RaP2BcK/zmPUruwkgJkY0fqFkQSx1EjxDotiwDNtLYhTMH24MIQXtFWFPEry3PUpUQYJcK\\nZx/7OKI3qSmWGNxJ1ri0/p1tQq8m/HoAL0VotSuw0+jICrYKpRk3SXExymIMOKlWUhyoRUnWW81t\\n65DBpc+T63BiOCw03VN9IOvYm+cqmHaOtdYVlw3PJXBTnVQd31Y8KLH04IdBV4alEqjkMRL7V0Zp\\nyiYKL+PKThdKtR5vN+0gGEUjMRvrWQg3EHq2Trfl0A9DQQoavI87gCoFqtNCn7Ob5iuwQpAmBDNM\\nBaxeFc2wtKG3nHOjrtrnnnYV2Lng2n3ppl0FvQxbLDhf992tQtBeeSrO6JmzjVfdQKOEQLKZhZdo\\n6M9a/ZoNbIZX5zJXXDpuFTcspG63il2E5TWideG02WPrmeBL7zQNkbgufMCRtp1AAtFgv860s+Fp\\notjA4IWy3wClI1nNiUEw3VKSUsYeBDH5gkYlEAmXQtHYRxRlJbZMY0stEJPj7JnTHrt/5vXlyHr7\\nbUwbwed+aNQNOQvLtSGyaiDsb4nLSA4Dw/xEqh3J2ojoaGxDpcUCBFotPH6x0m7/5arf99//r2/k\\n/2yZ8m/+5m/yve99j1//9V+nlMJ3v/vd/08v7p97eRRmH64pGWdUHLERLDHgnOdET5cUbNnSZNvt\\nCO3YSTyMRC+As+fMt28uTHZkOSoPxz1f2Xu0MbFsUxc6SEMqVBrrqoSmhGadYIMz1JVWlXNRDmsg\\nZyNtupAgSOAmdmrU7BNob7F6i5jA23zLMf+f1L1ZjyRZkqX3idxFVW1x91gya+lqNquHJDD/e57m\\n5/CJIEBOz3R1dWXG4puZmurdhA9imU2QAEEMMIUpAxKZDxHpHuZhKvceOec7B4IKCyu577QRWPcj\\n29uENCUjTNsZiwfGObPlhRId7i5UqkW6ZCQKddqQ1EgBpyPtg203XtIDK4k+hNHsDlL3ikH6oJpR\\nCFgQnoLRJTshCcFGxkqih0AKQhJl3A1CjewggYfM8RgYRJoNJK68jR0JKyF0WvXbx+HoUZGOQkze\\nIGKBshy59YXUNzYq319gX4XWo5dh9+AnfPH9pd0dqaeeSZogOvnH45UundUupAGnCFPsdAK1+++b\\nx+5mD1VihL/7YeNp3qhdsa6UFOkjuoGvz8gIhFG9K7Q3h0jEwXz5TmgNeiPXHcS/txCd4NVjuMdJ\\nFFNDYiVndyQPCURt1BhpTai1USQgwYvC1ewOt/A85BChrIHalY4xhcJxubiyUpPL+DqYtTl1a7hE\\nerZ3UnCe8beno39P94d47b4LPqaG9oDuyWM2uGMfQFtFGdSuSPDhURvsNVK3CzZWQDAVYu48zjtf\\ndmEfyjlCTp04jNM+SEFYFuOoldANJvj58Jk1zvQgtKgkuWIqPD/9gRqzewgQkEwWv1EiPrAVI46B\\naaeLob0TMdQ6cauE1tn03oImyt4ihjJrQ3vl60tz85j8GwWsXAd1V7oqdjdcJTF0FI6vN7T5zXbr\\nkzuFx07doU+uQFkowLgfcO/DNwiMzhCha6NYAwzdCmqNfeQ7f9gH7BClEPnl8XxX1u97Xs9t0/yW\\nHK2TqF6tet9hqsG0F8D/Tozh7uah6ntxE6iD2JsfykycV26Qmq9ltBvRBts0IwgxdkbI5EPg0DYO\\nuqLD13HzXpjKPXlhwrR7vlYyTHajRYEu5MnBLE0US94gNo2bL2tyYtwPeZirVMKgzDM3y6SamfYJ\\nrZVj3TgBEiI9VFK/sYzCQFgtozJIHd7HCWLkcSqYCvO2+YoievlM1QRqRAXE2NaK2F8vufP/6+b7\\nhz/8gf/4H/8jAH/84x/5D//hP/w3/ab+P18dt6f3qzvj1NhKJg1zlJkpGhrYgT4iIsuvPELtHWSw\\n1CtbPvD7+BP/Mh6YpFJbwFLie3rkHw7f6T2zppkPtwtxmzBLVCbSDdJo1JBQdXeePAuv8cDVlEUr\\nb/vCPkU0dsreqDWx7plDDuwlMI8bNUaqKOtNeDhtjHjjrSlvs8F+pJbK9T3z2CrZOnueECnIPvl/\\ntx0pnWqRphlJFY0Fs8aH58a1JN57INkTcShvIQOV4+1CnY5Eq6RWGCkTD1duPRMbHOncRnb8nN1D\\n+3cn5DQNthE9/jAiuwQewlf6fCS3hTUEejdEroxxIuSCIqw33Gw2KxcyTRtBM1gllIl6iO6G7e88\\n68TWGm0EzAI5OChFMDLBzXMCUxyk1NFTgmBk9l8lu6Z+OJo7RDMCnWZK7ZlQhUQjSWMXfxgkq6gY\\no8GRSGGCWpmksxEQu6sstxdCGP69S4deObc3Ihsc3VE9xOXFrI1BQlRp5kqLijAn6GUBM5ZU6SHT\\nbg2JhaInhkSm8ULuisVMGiDqeLx03+11JwuiOhg98HqdOGNE6UzXQr8bpLQbCyuf5meu+wnTe4E5\\nSmTQGZj676ML2l3mVzNKEyQNZK93/rNQ0oHX8YjhO2FrV3fm4ib2qINUOmszvpSZP8wXllgYqzKC\\n+zVOh055Ce44TZ00wf++/ZYfx5/4HjPjQ0KK0ltmGjtzW1lFMQkkGRgd7lESxdxJLEZVzwM/2Iqq\\nx1nEjC3MbBKoq3o4xgT6oJfKrjPaKiH5z3eIkO/d33tMMEXqCATrJO3YTdnmTBuRJIEwvTJaY7v9\\nlj1caI+RuPn+XE0JHVI3ShCGeWZ3iFEujb5txDf3R1zTwth3fimYN1WKZIb7z+E+lL13r9Obu5T1\\nHssLo9PHYMQBZEKbiHTm6wsfeeXb8Xgf6gEbjdiM2HeOp3Z3Vyd6SEi3e3+w773FBhahhXDft/sA\\nfTjtpOGHRMPZ2gqsmvjp8Q8IN47lFR3GLJUalKUKMQT3HYgiEgl0DmFnR9Ck3Mhu9mDQwo3tYeP2\\neKCejkx8Y+6dUFbmxfh0HrxMYLoRys6Esgu8x4lpXEjDeA0L09j5YX/lO2eexgU5DvYpklukRueN\\nR1wZq7X9+o7/NV5/c2znOhrz1ImteP2T+L6kdaXtMBdIfeVQlF2OLkftxV1yoRC3C0EqaftOLU7u\\nURn3xLkyUmLSQuHInIR5DuR4YKof/eYzNo7XC3lR6ofKOiujB6TepSOBW4t0C2jauMyNjrKuRy5v\\ngdYVlYSFRE6eIRyxMyTwp/aBb2FiSEBGou9KvETCMGpIvD4P6nX3D0PYGSHRSF41FjdEjbkZf9++\\n8GjvMISeZupppkwRwwj9QuyVSOGHUfjd/oUf7WfSoXA8GBNGtUToFTGPkzQiGoUcjWBK0Ilog31E\\n8lQZFEYymiZag6xXRodpqYzDjXB459v5d4zDAfQOhAgLhSdCE0ZOvvMKcBvQh1FFXNJCXY7t94iA\\nBMQC15jZzgEJRhNBxXOvBuzSqdXhDlHcBQtGW2aCQZJBprrRBpygJcJ1zRxvM0c7Im3mVO57Q9N7\\nlnTciz286Lw1QWluQLEBpvThfN8QPIb1S2YzTffhGxVLM+XpRFrwpqG13K83A+7ubIbRwgFLAcs+\\nFKcwKGbstWEMQhgOq+96N710fth2zvVGGF44bveIhbY7YcrcSCXj3psbxj076thDujd0dVl4vSyM\\nl52mXmi/bok/9T+4shALopU+gusQwTPJWPW92/Cve548VzrZBgotuRO8huneLTvYG/x5tTvN6BcO\\ncCDvN3K5+ezRQJJOuh9uTA3RQByGqtHxfOtCceLaL+1CIlz7zOjGKEYcjdQLrQ1KVCg37i4gWnBL\\nswEtKi1H3otjTJ9SIR9Wrrs7brtFRnB3uIkxgjB0EEciDCEMIRZXMGQowzpDB6YeJYvvK9PYsNAo\\nmhitclhXpJtX7Gngsic3zOErZ7k7um0YfXg+O4rH6dqAFrrDQ/ZIFOOBV5bxwrhzsXeJXNvBjWJ1\\nJfXuQCL+jXKl1gijYxbudXudMgVG8Ns+QGKjR7+9j5gow+9wl/hEF3dW72lCY+cwF9DM2j4yLPlB\\nyQJoYNkHj63xGK7M0mmHk0c+aRAa2+nBEwFDHbcprtnN0YElOt+I7cIPf75yak4O20PAonlRTFyw\\ntTOXG2ftVDvwz3/393x/OFNGJr434rXxsFa0Gq0Xevqv62T/r3n9zQ1fu12Z80YaO3m4uarLzG0P\\nvL1FLtfI8XJmjw8UJuKohG1nDEFTdXu8RYoZ//qqBOsEgUVWEoORAhszjzqYwiBlSFE4tIjuN37U\\nr3zO78gk9NTZ54k2FC2eZUzZeNlOGMIUCiOB9MgYiev9bBuzMGKGFmgxsltyI0psXHpmEFgs8Pkt\\ncW4zbx9+5HZYaH0wh430oIwPT14GYMYvlhxtneVa+LBsHNNGss75BC0GwsElpxSMeXvlcb8wWSDV\\nxqlemWLh88dGZyEgULrvtLqyt+SLR4O+QRqdc6gY8LocuerCu57ZSmDshWAdNSXHDul6R08KGu8d\\nUEN/lXdsDEbKWHU84V6MJsqX/ECzA73PrOsvcYHBdHedm0QvG1Blz5nXceCtzny7JN5aQKQRbfjP\\nu99JS5OxlBUUYi8uBZoSR2XdJ/bdncSqg5tmB1PY/cMoAhjVQHLHUHoXlO5emuEKQWvuT35Yn8mj\\nMbXKGJCOxg8fbxAVXSLnZaPlmdoiYRN0wLYJ+824lcTP44M3BR2PDPXhG3FTlQ3Pr4r4Q8iG7w3n\\nUPn4KPxu/s40Km0oLUSG+PAVM/epiLlHAe/EDSZ8SJ0TStoL0jpS3dEae6PJvUiCezmDCCnsBGn0\\nFl01UD+QMLobjgS2FsjSCbqTbbsbvSLXMfFy+pHVPvmKQwYixf0Vw9BgdKI7e++O+CHKHI1Fm8ex\\ngps/U++EthLXjdiN9eHMXx5+T0XR7uahof5nZ2s8rs+ct59ozbzGbrti24XY3tH4b2XqL2Xi1gLW\\nBq+bryCelpsfYJpQeuZfHx75dp5opbCmjpXvQEBHBLsbqxC0KtI7Lfit2lnJlRReXQELkSGN+Xaj\\nD+GWZ0Ly93rabwTzyFy4/8zH8CBZ6g4wCc2r+fzzikNgzJiXb4ju2Kj+ubNAIyAMYvOd6rhDXH4R\\nQVXwQogB3WbA0YsjDJ63E6+3TBw7Fgaq/vfpume2d+GlnkBcW2kSWcbV0aA6cwmPFF3YeuAtPCAt\\ncmxX1BqpDsIIvKbfk5KhNC8CmSaQCg2aJDcUmqBzYARljoW/tzeeEqToJTF9gKQGGpiDYrUTW0Oj\\ncXn6yHX6kefHz6w6s94mlleYNmO5Kmad//zy03+jyfX/fv3NDd8eTyQqeV/9tiEGMbHtgdaEOTRi\\ni3SbYMC5PNN7pA4lTB4KdyHS4ek6Bg+1kiWhc2KXiZd9YQylSaCXRmHmcLlx/vaODh+AP1jhdyGR\\nE2wSvVmEBufBFgK9Kd/XQKgNNY9gXFvGbPB4XPlweuVTuLn7j87TfCU9Dmrwk/bpUlgYbOlMQXle\\nGoNODh7qPzwaJee7mWMgFPLrhafySrbGyTamuZOPgiRDtbPMlZrDr85YLLCVieOu/PbaySas/YxV\\nIdbOe/UdY2mRss6UPtHrvctWPTO6TTPP6ROtwnT7Gd2/Qh/E4ejKMswpNGPD0mCeBme53zZwhKFF\\nhdr458sEbVAJvMwHN6iIcJOFvTu+chZl9InQD1gILsn2BGWiNpcV1+/CKIU0OjZcRg3RNel8h6dP\\nfSXt77QuHNc3YnODU2meoXyfT9R5Ilq7Dz8B6zQGmqFLoIyI3oPFdTisoA7/tYnGh7cvHPYL1hsq\\njUl937jEiomw9sUdx8WYbzfKc6d8M/pV2WRiNfF9slWWdiPq4POHN1JsmPmNL3JX6gRSHiTgNN28\\nXF5nSpp9bdDt19iRqoNIxhik2JGh5CE8ACaB8P5KMJiuV1Q7FgJTWYmtMizR+uSRpA69z+46l0HV\\nG0P8/c0Des1ogCCdaVyJNriWyLoKQxKtn1AJdG3AjtkgjZ0YB+k8OXax+94VIKuTnJSB29+NH+w7\\nn7cLU3Pq1tvHz6x5oYRMaOW+5xzEbSOtG3pbie3KGJ0Wg7uX+4VDuJICnG/fMeDbfuDnywEdUHug\\nNs+Vf8oXDOG2Z75OP3ANJ9LtnXH7C2+3wrUm2vABNCwQFCLOgx56A4Su7tfIVJjVzV6hIb3y2ma+\\n90emWdy5XQsfty/MdUPVPHJlhnTlsbzyaf2OjOHxQtO7guEKkIbqt/Ix0GEMMa7SMNlIxcjNc+T7\\n40eMSO43UKHmxjpFpAdkBG75gS0sVLIztUdjqCJ39WiLB/5z/geu05kh0KuDb071GWXQu1C2yLfv\\nB768Zfq8EGsg1J3ShWW/EbqxdzeYTd335qKgoWBtIASSegZak2DT3Si57ZwnQaNx2l9gffPO7DAR\\n90Habl4QoYalQdNIzZmvj5+4yUykQY+kMpj4QO7zX2uU/e0N3/MhsqYLdbg0JJNRLLLviSCdJT2T\\n5IaJENrmEsgBtlCZ84UjNwJC3B4Z/YyAI/LqTIyRpjPvdeG2Z641QEpUi+i28XDxfJ5MlYf8wsNc\\neIyw6YG3qvSg7BKYzoUpQHgX9Pvk3NThp9I5DXLsfPzwwlE3D6/PnevR2JYz43DEAoRuWPYBJPvq\\nJeni/NMsoCeFFJxjbI00bsxb5BwrIsYPaePp0NnSwmqRasr5sELI2DDqLfJzfWBsiaU1fjtWDkXY\\nu+PeUi0OXRDvPcWg7epwAB2kcKNnuMYj2x4JoxJHoW2D5cvOtFXH6V0VGuzV24OOc+ehXpjHCjaw\\nFD3bWoW0LUwlMDTyJpOXn4jSQsKGsY/OiIM8Egx3wCL3Ww1u1gjNJeYqyvPXlfU1oDRyHOislPPE\\n+8PJc5jXZ37//F+Y9hvT9UZaV65bpDejig/ROJqXJdxvjYIxq7DHmed5IYUdM3i5TlgXbnsAEzKV\\noC7hsq0s/WfmuhFlcGw3pIe7/KbEvXMug9Q6qRSKKZoiPXj3zbm/oXSHiuDmIe63oCD2K14xpUHE\\n/5FutBJ80OI3SuHO/1VnFw8xpslh6KXoPXOuxHLj4ec/cVjfIQg9CY/Txof53vpiwjIC05YYIxIl\\nYGpU3blMr6BGrsWbfRhECrEbH7ZvPF1/QrY3EOhjwUQYUgltY7q8Mm2bt+sEoffgTOfhXzNrJ1hD\\nGHQdvJ0X1s+fiPmJuVVEE/HsCkRJM+9PnxBgHYp+X5HtjevLzstzZT9FLASX1KOBCsf1nziUl1/t\\niOyTx9MYXHdffzxElzcHSrgplJkh5qujrly3BVMhiIM++i732JGXchhwmzLrQahTdPVKhP/ywyNf\\nz5E9J7CEhsw57KwlYwZl9bXAGGAMZ5mvjXy7+dGk3+Xp8UtkrBOSy7RSupPeRBkN/vLcSPtO3vzP\\nogZz3XnY/8zIlSA3elwJWyO+vXsJSjwx7pnf7Tq4mSKyMSRxnZ3HXabM8zWxv3fkVpj66hjIFqAr\\nrRv7gyIo07UwutC0IVeniZWu1CaEUUnaUOB6bO6+HkoMRggBotAWZ3unbac1ZX+MVIXJOjUqNiZC\\nhWmtpFo49XfC/uIlD0PZbOI2eTLf7kjaI4mP56f/ZrPr//n6mxu+bQzWfOWf+4H3W8KCsN3cDXtI\\nFVGhqWcCZRi1TSjuWl60saTq2csxXPKyiGq+uyA979Z7pjXPLHZJ2ICpbiztbnhZIMgGGEcVigR+\\nDkdeW2RtgbhEVAaxGNN2YK5+z1PwYgCS/zu/cz5/J502nsPEPn1AUmB7ONKy5/UOcYd2gzF4kCuH\\nWDxOkQYjKwQ4T4Xf9BuPaechVyzA8/6BsiVuY2a7XmnthbIVRjVKnEhn8exoD2QTEsXzjN577xg+\\nnE6jqTBOERl3zE8QximRsnLjSC0g5mXytMxUhCyDKIGVR0JtyNsbX24B/XZz6euOo+s5A4bsE1P7\\nRN4nTJSdxPPbxPev2QsMbLC3xuHtnWnshN7Il41cPZM7rSs9R8Y5YosfhOLo6O3Cx+XC348/83n9\\niaqZEAeqoFUwu7n0XYzUzd+TXwhzaqg2FKdb3f2sBGCLE+s0EdIGOvjylvnL15m9efYwqUMVWixU\\naeS28enrF077O6dyYa/R94VAS5FpKcyteIeqDVpKtBhp2vjFgDlT3bUqgWaOjQzq194cGlNuxGgk\\nG7TdMZDR/4qgfaCIS+76C1hfSMlNdW5qA0XJ2lhaJ7WdocZIgWjGKa6oOYR/GoFYHZgQAijxLmu7\\niWuqhfR+AXXqGf1uErv3OEOnjkjViqaK6j3C1+779Q5355JLqQhRxW8x3d/b54+fGQEikUnBTgt9\\n9iztbTo4OvFexNQtYSqss/LzaeH2lBFpSCiMLEQCE5FkylJeOX7/M3l/dWn/7iB+bsK6CturUt8U\\nujG6UubmO/a2EGt2kEmMbg67mwDjmDBzOEgX4XY09seEBRixs0iB0VxRUCHoIPXKVYQv32ds9xuv\\nmasYNgSx/m+91r25S9nk1+Ebo1sJ7L3fzVH+vo8gSNjR1rBsZPGfl2fKG3N/Y67/TOqD6d2jc02y\\n36pH4+2q/MvLkT55nKyFxNYmNgKq8OPP/wfnL39GME63Z/payL0QaYz8gdyNadv4l+8H/tPWeX17\\nw366UucF2zseihyYKCUVrz6sAdQ4nRstCVuOZAq5dmrI2CRogqMKVSfGUGKvLKvHQp/6V7rtRN1R\\noOyJPSq3w8xIyr5E5iB8mP56XUN/c8O3W6PVI5sKdZiXMl8HcR8cxBnPu86EXnhfM//59QzDjRFL\\nr0wBJLkbkn6ANgOCameOK0OV5+uRr1tkaPQT8DDfG4xBatBT5v7IIiajdYc+dBRRZSQlVJc9Ux2c\\nzOlKh8lYa8KIpJ5poyLTRpsfeU6/9+gLAtIY04KGzJNe+fybHXv/ifn6lc/5lU/H3SMEOSDZTStz\\nM6IpMboEKhooz5H3P3eO6cqBZ4xOL3BMDUk3FiloNkYw320WI/TqexU1MGVqO5krMTZUO4WImXEa\\nV3IythHdZNVXkkW0B6wZ0RpBAr3A/PVfHYV5vRDfK10Ch+PKca6MGO7Q/8ZIkaQRI4EFqinFEjaa\\n54et0cPMuV75+PoVHb7PSaUiozNT+RjeeZrfmeYbx7E5JStGTuVKqI2uiVO88SGvnD3cjZnvoaME\\niiWG+G6OFLxGTw2lk3RD6CxtYzQh0HheImV2Du+tuDFEzfPEKh0LA3TwoQwO205+X72S8d1P9nLn\\nPUsIjjVFaVG5PJxokhgGVZRjLJzt4oYuCQxtxN5JBjIGOTWmyW+2dK9wCxhZBhAdzOABEJbig9Jo\\nPuy00cpK3HZqmNAReFwbYVRgUJNngpHCb47vnKKbmrQLpSfqSNSRCXcut6kQB9AymYASsQZRYJtP\\nbGn2qoWw0ZedZV4RUYIJY90Jt8bTy36/3Q8OWjinzvH9jdA9mypDCZaJyZgsMeYfqMuMyqD936oP\\nR4B82zEiPQaS3NykJe7kDcFrFR0uMiHiDtjUAolCEs/tTq3x8fWfeK2Jt+uRagnBfRBbMo6tE6+f\\nmIobz7r6QUMwGI3C0QE7w58VQ71nWaMPvaVvFMGfKVKJ4kkJizuCkq/FeeEvL8x1Rwi/ljPY8K8B\\nShidFgdhVNowahP660AsMBQilR4U1ULQlTp5LV8yjyTV4IUVg+4O/eL77q6J1Bq5bFSJ7HHiVY3A\\nG60Lu2RC8Dz+cb2QWqfsw2/dL1fmcSOdIiozh7rCgOlWWeqFFDb0dqHKgL24kWx0RohYrMCNehN4\\nu92//0AxIbeN3AaS9D4bzNuTRqJoYCo3UoOqyu1p5uV8It1XPrfmn6HrPUkxIrz2n/jp7etfbZb9\\nzQ1fs4hsZ5oqKo33N6+Vy9K9EWbO9JBJW6EYbNH3JKk2HsqNj2nn8bwxVIio5xrHxhKvZO2IGrfq\\ntCidZm7xCGaEcbsXOyd0HP0DMSbiHQmI7lhojoYLRrKdmUZqG8vt2TmsvdG6MgiMtjBM2GWixYlS\\nDSWQWkNphNnzJGF0eqhMDLJ1jtdnfvP9P5G0EpIwkpBGRWoiEhHpbE28u3W+kR+/ErWT8w5q/DBv\\nHKYKsTFCRQKQ/cMqZWDBAew3PWItIHRECyMH9NQIqXksAYjBozhDjRQ7/Xig2UIh3su3DWlGWDea\\nZhZztN+eOkveOU/j14KEWC8kacxmmBxhDN4Pg+s8sy4Rmyv5nkE97yvHsWKqqLgLucZAGsYcqw/d\\nU2BLC1/mD9BhqoXWJyxEssKkhTkYoze+fg18HWd3e44BVgChHieuDw+/3kT+7vgTv1t+5inujlOU\\nyl9a4Ms1kdgcViABGKh0ctrd0NSMzwN6EEJp/OvLB2j3G6ENl/ZX/IamCQYMM7oKRudWlTnsjBGc\\niRsNDbvvzWSQSyNKv9PNoA3j0292Pj+aE7mCVz5m6agqQYyH9I1wfOGXq6FshXm70jTzupxIHeay\\n+V6w7ezNs89TbjzlG6EOwoiYRF7rERvO0rX7iiKJYPnEUTL74QCWqKczSPCctxgEGDF67lQDCWVq\\nnfy60a6viCWdOQAAIABJREFUBDNkLti68mH9Sv7+RmAQpRF1cOgwL8JD8tuihcD/UP9ED4EWFaOx\\nXL8Rt0oj07KnIyKujC2HxjIlcnBFax9yN3/5Pj3NhU8KP95gaXcQwzKxLwt1PuBVe/ByPnJZFpZw\\n5CkOXweZS6W3q/Cv28JlPqD6QBonMPVdrBjSB3MpBOk8Px7uxQt3hG9/JVnlCBzazulffmJ6fya3\\nCr/Q3/qNppvHEE2heY/01DsWoNXAYXV1ZIhykM6nwwahEqnUKTuEpXp8659umVtp3GTGkkA3ovnN\\nPzfhuL0j4j6oUjJv75XSvONbxA+auQ3abfD8rfPzl8hhrQQK22nBlpnQOv0KuVSeLisKDHtmXL9j\\noaB3dCbi75Pwr3x8/j+xb+9sqzCG0faZeSukphBcDi9a6aawJ1pQpFbuogtkAx1MsYH5qrLHxpct\\nc3mbqLuw1eGH5b/S629u+EaB1haaBlLwXcFAmKWjmjCd2MMHUqv0IXefTOfxunPsg7wEpiehLAdg\\nJgzjd4fvcBSYzAueiQTuLs6Q7o7ZgJKZwiCWyHwzYu9MKgwbLK2RLKBTowdny4ZmxHFlWt94fPn6\\nb004TfzhOwJ7dAhI+LJxer3y2FeWg3J8KqS5IePC+hY4W+WsO/tFaHskjUpUN19lq3zadk7BB/fa\\n4TUV6rRic6VLoyfx/bFFVKGmcQ/2Q2xXDrJh1RxIQOB7+y26BaIU6h3juD08wuNMmCNBOpmdJpGX\\neeH1+MTt48RlmdlNsA7SQJo/jOshcVDPC7alYMHZx2EkN73IxrFuCE6rGla98zYF9pjuOUPHa0Tp\\nnPRGPyckChaEpopU471ORCqWI//08Xdc9MBf/nJifTa+lkdayNTbhBbl5WXw9eed64iMJUPQu5nE\\ngQ8qQp8Cv5TJK8JZ3bBXWyD1gJbI+S0ydf/+TOQOgCl8jC/YGIgpSRpbnHjimSk5i5be0eL/v3Dt\\ngGM6dTSgMiTw9bZwK36jqd2VhJ68TzlcIff6awRFbp2yBwjwEG48pkqISmBn4Run/R3Vwlxe+Xj7\\nRi6ZMGaOWnjY3u67YWVLfl+dq3F4/wbrdzcUNmdRBxpxNacoBOF8iD4ocXYueELgfOxEFTRnbsdH\\nLCcG/v60IPQYAXEpW5Vk4l3RbSfzzGl6932sdXo0ypSIIqgOpvqdpVaHkqgxpU4IwrIGQnOqlLbi\\n7l0CJc/I1DwfLMrC4JCU334SQvLnRAwDCR3EqwjD3DgEYRlw3CsxnHhIA4lA9PcKYPDImj6ibcJs\\nEO+givxy5eX2yF4z2+wgnB7dr0Cwe7GG0fpgPUbapH7wGo3Ud0bZeCyvHBj0qtgu5NbuNL2E4ReL\\nQMO0ImSk3dnWQ7EoDDNEvDykB2XSlWkemCo9+2qj5Im3twP/6/W31FZ5bwI6M2KidaFchPamYK4g\\n5gBJdyKRftvIb1fi2xVTCNzXLUMoJrSiLKV6FPCYkGCsKVMt0FKl5wrD6AFHXurAxAsyh6gXSsTZ\\nm+nMeCuJyypcbOH258xzf4KeSUMpobKpMWqmaUbva5whRrZKCo2ozaN5BkMHdRrUNthKJG9HTsvx\\nrzbL/uaGryLUIWRJ/HZ+5SFvLKkx9UJuBdFHtrjQ6oDifNXalFOvHHPg9niixUDX4UadYsRhWPAH\\n3CE1h7KPhVQD9EjoHe3qWUMd9BAIHS7fI70uzCOxDON8gpyMpr6XWm4bobwyJHC2C/9++hMz1aXD\\n7nGBC49sFez7FawTmxCqS5cSG1f1EurTsvM5vZO2K/vVm3FiaBD8pLtgzGy+t+2R2P0h2WNgO3gP\\np92znCJGjQ0LAiJMvVC+FrTeqTIxU5hJPTLJhppLpC1muk6oesQm204b3kTUaoJbp+6DtU+OtKtO\\nu0kCM0cO9eTyWxSGNVp0VKT0wcTGcbs4BSzHO+RQ2SVTYkCy4x6Fziw3zuENzYH9OFMOmToXCJ1R\\nT8xjI7bCh9s7dC/o/ueHf8dz/h2iwss288//W+TyzVOOI8FIAaKyh8D7vIAEggii405Mg7nD3Pym\\nUBHyUPK+8NAGc7jjSO7WYz/WVKwruYHUu/mqw2FyGpA1P4wNApf1kcpCno2DbsRw8UxuWuhdfj14\\nXFsGdSxnqJGAI0oTfqJHfBAmCqkP9JTQRXl/PCJNiPZCqIV/+DKYthMB5SCV433nJ0MgKnLvXFWB\\nZOKEK41EjeQlckBRUY5ZCPM9d4mSTZlMOGTD46rKdDjSD0dkiVQ8T/p+WLg8fCK22XOxlggEcivM\\n6zuZjRg6COTZ6OzEvhEwVANzhj8eAjl4VOo3hxu//bABB+JeyWMw1+ZtWVGY54m4CEEVldmxounM\\n7fCZESOWImnyRNk9CU1ajIdaOV1fCW2geSIuiQNOMwvNf52MGd2eCBJIdALCx29/Ia0rIoEwhL5E\\nRhL6dEBFkajovTHKGKgtxP3h/owzst2QXJgRBplaAk8vF0LvztZukTcNrNOZWOHjBcKI6F7I729U\\n3Lw1eicF52c3C7S9UzelS0RkB4GWErf5zBoeuUnkezrSe0IChBgI14oWQIRUKmmLhNDREQgqpP2C\\nmHDYXji0KyNNbGGiSiKOSu6DGAqahD4Zz0+fCKJoL+SWUXGWeFOndYh1pugM7tw+oHwA/CCxjcqa\\nvOzkp8Nv719j8HBLpD5+ZSS0IGhvmAntTsZboptvdm2Uu/xsGmDsHrSaA5H8V5xlf2MvzYkPpysf\\ntWEhsUyNOTVSK4RRGRIpacKsEq0ygtGuhdS7t1+FQcfoUbgchE0SL8cjKQxiNA7ZWGbY4xFpJ97r\\nROhure8SCcE4nApz2lm/wvt6ciNIgGnx/GmL6lWAe2Mer0gCrHPsV576G73CaMK7PfCtHLi9d2LL\\njDD5h6GJZ+9MeY4L1xA5TVciRt2N62v0vci9oWaycscYwrQJ5xE575lPMnNKygiGJXcfK6DBGGkn\\nxE6MnRQ6k7nkKji4YXR3OecxCEM9FqFKQ0l0olWPVu0XtBekB3pxNvE6ksdBiCy2U6cnziUShrKd\\nZgQwG3RTNlEO+Y2DNLJ0+hwhQFNlaGB/aoxppR6S7zAZnPKVdCiEe6vRcW5Mywp9J77u3MKReb+S\\nhstg63Lg6/F3lOS5WpmFfXW+8TCjJ0CE6zHx/TGzh0wIRg+ZnN3IkoJx6oHQ3ZC1tsQYytRnxpJI\\nByWo51sRo6tSNFCGkXtn6QWtjfUSeXvzvt/Yx70uMHJrBxAhx8FBruT8SswvSILNIj0LNgLvW8SA\\nIIbaDFPgIIVFdn/44jGyqRWCCT0E1nxk5AUxYdRO0ECKxtP+haAVa+6ejdJJzYsPkPvNXx2T2EZn\\nmeDQjaNM/Oa3n/n9IXI4ZSKD2SKLuIQczNGqGtWJWOHI5eEPvIcPrG1iThty2PDkspC68rE+oar0\\nFNGwcDud2NqRb9uRkYWigdgHZ618OlU+nq58PHRy8ErQGIVz7tQwE2NgqjtZAh9FOefAcRH+8PBb\\nPslngmbyfGbJDxyWHzicf+AfH/49EheCeFZbxJiOgfl0Yimb1xqeT6RD5GFfmd9uhLX64J5mUkiI\\nKL9n5XNaiboRxl0xyQdm27BeXeUQocXMLxa+HgYSDO0Zkteg3sIVCxHVmUv121jo92pHyeiIWBCG\\nDLrCXIx0u0Ic5PUbOgQajO6nCb1VTAL9CvvNJeuWfWf93hd+qo/YCJSmfKv5fvMETeb716EIQpYr\\nGnZUINd75js2Qho+NMPgcn5gPS3UnECGH+BCdeefCJbgyx8fsVmYR6SEyBr1XhgBul84Xr8TX14R\\n3IdxGxkY2GiYFpL6bt/LUPyw+HHtBD0TyoK1wmVJtGVimxRC5awbWhuXFmnWQJxhbtXrN3UC5b+j\\nSsH/3l77euU4Liy2cdUjrQnEysI3QusOPUjqNJy+03Qj3DbGDkOhhV+6T51q9PPyiRJm8mQMGazx\\nQHs6M50TLQp7CtQQsOxM46QdzY0smwf83wutCCGr19UR79b4ThW5o+5gNKcuJeuMIry+zjy3z5gF\\n5M0YLKBCEGPblG1PXPfApSysMphjpeTEbr5fqTWSdZCs0XK8+3DBihteNFQkVJIGEpURlKEVJGAj\\nIMmIy06cKkSYwoVZbi63jUbpgZ4E5gPTiMx0pjhgCEnKHXQh1Cao7hxuLxzqG1mLm7IC0CKxnGnn\\nJ8rD4HYKXOcH+n5mrZHanNubsnGwATphy0ySSI0JUWEcv1Efv7A+RZcSj5FZB+fFHDXZA8MCKRTm\\ncQGEoQ8kmzh14fjYydHD9mns1NfGtd5YF+fpouaYSXP+MamzDcVCoJN4nN7Jc/ecsLli6LAI/5B+\\n6sIP6UqaI1F9beGHC3cCb/2Fx/aFSTqRxmtf2J87sRaiQs6JOpRG8AiQDExv7CMw953z5RvvGLd6\\nh4sMIRE4lMQf5zc+H3b+ML8QraEmpJQJySXyYOLKxzoz4gdSABQ0R3pK/KZ9RfsGWyNOmYXIsSh/\\n+MuJERYgMOlMGpEYV/7ug/IQYNGF//F/+T3Hv/890zJzQni8VY7mRDAEckpYnGjxCNOPRJ05vr/x\\nD+ONfzy/k+bqMqZAxEjTgTwviEZkPlOmxFYeubUjIXhVY+q+Kz4vnahKDsIUE9v5kSGBss+81gfm\\nqHxqGzplLvqBfjqhx8Tpj/+OKQSiKiFmHnRiijP/09P/zNP0A59/+Acaj2wlESXAlPj4j79Hz2eH\\nzUxHHiZXAyQIH85nphRZpsQhHvi0ZA46+LFdOcp3ohq3T5/YT7+hnw+spwP+9kQsqFdRdqWkQQqR\\nYJmRLuzzOxe7Uclc6mfKmFD190mA2DqKMnTQU6WC4y71A5Mc+On4gRGVXCNln6hBCLfC6fnKaURi\\nn7k8Hnh79KKTFn4BkSRH9t4rKkMwdOrOspfECIEp7qRcnIr27tniQadHwSSwiDAHYw4dk4lYnSgn\\nB1cxXOVaiXHlgKDbxNYnSlZs3JMGMtheN95HRBT2obyOAzUVkMbMzZ+9ozAsMFrAhvqzf17R8k74\\n/gw0dJmYQqfK4DAqUtRhQEDvwq1OyLqRthuTQv/rAa7+9obvNM982jcCwtfyA/Wn75zef2Iaq8c0\\nxn0no8I8HJBh2UsCKv6XbIsBiYL2ziggpiypMUKj3B+uIoC4kaMnD237Xqjxus1sdmTkhLZGmEAO\\nkLUgODrteroy8zM9OzQj1Mq+GfM9l/oeHlwifF7Q+JHysPClPrIXJyddS+S2ZqoINRRelpk/HR+d\\nTyMwXd9IVlnaO12cUmNA2QO3EZjjjfnhgkgkq8vq+2L0ECEYB5mIpx073mg5EfLgQ9pdEtv7ryUF\\n5EBKytQHj6fOjw+NMw26OjwgFJb0yskuPISVJVaagQXFLJFVuS4f+NNvfuD94cT+9JHPhx+p5ZGX\\nLRDFsPnIWH6kHw4cTj+S1I1s8//V3pvF6pZVhb+/2a9+ra/f/Wmr6lANBYh4b+xi/hKMAYmJJhoT\\nHiAGSIwSg9IoUVIVg9E3Y4wmPBkTqfDgm0F9MCQq5n+5ILdAGrGo/nS7/9rV3oe1T1v7FFVFccp9\\n/t/vae+vWd8ca8w1x5hzjjmGEyAaDgVs2xmzTUncUZitFNv1qJVgUXjIBmRUI/0ZphqTT0Pkooto\\nBCZqq6m0p06OEm9Mc3wzQegK5VVMZeu8CCGQAiaNpvLalJpGNMyEYY5j1ti2+o4zWNUnnEVYIfBF\\nSSMNVjUoKajrNlcwAvR4xubONghFLizzwpIe7JHs7tGdzxCNYF4rqka3xlfmbZ1bIXEV+DVUZbuv\\nWRbq6FiPIJ4Z4sTw0JmCpBuBFkghUb5PpAUGjayatj/WGXWc4W+lpAOH7VqmwsMsCoqrFYf7miIK\\nkUdBfWiPUrbOiZIalCGWc84YD99GmH6P9c2UTt9hNYR+hGcszrRJ8QVgtcFzHqPMw1MGKx3zucMv\\nAlQNrqnRtFnPjK/IvC6hDum6dVACUZVILQmcJsQjnzUUCw9qi4di2GR0hMM4B8ZitEbWhoUMQQg8\\nK+mFgqBjSeuSJg7wOxk28tmgw9D1SJTCKo1nLErUbKWraGFQ3hw/3KZRktC3yJURVjgWUUISgPFr\\n8BqMUXhaEFqLpxxKtrV/g0rQR9MLSqwTGM9jmGyCTpCLqn2uhKCy7TnmwoAvLbryWCxy6lm7jOvV\\nCis9hO9jnEAKQTiV+PMAIRSNrDF2j8pTeL5Du4TM6+FqDysEloRFHpA3bbrKWMBa5RhWbepPJRdc\\nMwG+KPHrBtu0fa1u2nKGWjUcKo+D/ohJEjELJY1uaI62rikNQhS0YfcKJ4Fao+qSqWepTI0f7iOC\\ndi+aokY1FYIaoyoaWR0Z3PZ8c20bSltyOYuZGx95tDW263XY6y+o4l08qfDEnIa2EluJpq4lYSlR\\nRYS0GqwPrsDzpnSUQeFQdYxoAq5ViKyLmrLQlAuIJtv4RlJUy4CrO2KMJfBG1BgCLC/21nlxZRW5\\nWBwtjZQo2XqGRhTtgKQLGquYYailpqStKaurnDmmLabsL6j8OQtPMQ2aNlqonCORbbYmado9Ai3Q\\nGIwyEPmYskbphjJw+KJs608KyTxUzCOfZ05lTII209BCOrRW2KYNo68biabBixuMatp8sbRJJxa5\\nawO9hEDrQ54aK64eNkihQII3ndGtLuG7RZubGkGVa/aKiAOryIJtRFMjhUKJ9mDUREtqr6LyGzQW\\nJ0Bqh1Aezgs5FyecX4xxVZuOTso2eb8Uoo14VBI/aIOOAmqk1HjxgsAUpN4BVAsanbeJ7Ku2wLwz\\nirAfYGVNYSydbkIQuesPoNYNSInqDggDg408tIxJzIhBtIKU6qjyiKbjCXwjMb7GCkiLQ5JZgSdL\\nglijuhVaLzClZawDai0oTI2Wsi2HJhqavGJ6mBOaOS6cIUNN7aCijb5VUqCE5HQ/INEF4zyioE09\\nuTv2KJ1lUlus8aBWFLVsjYXx6IQaKQVWtrNfKyRBrXE1CC0plKPKwc0XdA73iZ3FmPZ8rRASz81R\\nbt46UlLhi4YEgZSK7blPVRnqUqMLQVYbtFVEiUc3CeiaFJdaso5P4luk1Lirhmw7YF0NiIKEaH2D\\n4XpIqUMOYp+mcNRXGspOl9J6iLrCNHmbcUweOSNSkGQ5K50S1+kwrCo2mxznLH5g0FUNWlIFHtoo\\nkBVC1ChtqBqNEOCUwimDr0JCEyKiBE8oMlESGDArI8LGsFV3MG09O2Rd46cF3dMzNtIJUd6w2zmH\\nCDuE8WnWZAcjJFpLhDbYXoaJMnAB0hqcblCqZrWekgYKFftkziBObeJtnGHTxnQp6bsUozXrpzfp\\ndI8Mt7as+RnDqENHw+DcCHP/mxn1B2yGBhc2FE3rZKWxJowcwyTCUwobWIRzOGGwR2dG0zTkkTQk\\nrVsHGSHR2kIomVpH6SocGmpNUWuQMa62rDQeQeIIOwHWQuk5MDGNTtAIKiUQukCGAr15FqEMgWc5\\nHSYEWiBLh6otWkkwFk8JrBDtZCPPKRfQHB7gDsbE+Zz+eIKhwUPS1B555TPPPcgTbN6gypzD1DDt\\n+kjZruQ0nkFRMY00VlmUsjSlAyWZ9jVXNruEXgOmYTFrZ74NYKUkPSqLqCuHLVJSMYJwwqVRxMzp\\ndvyTbVEXYwVromCdMWtCEJdFu+pXaupSUeERRB064qgCWiVRqmhn3QJEFYBKaBrT/i+ASmKnlqox\\nOAVaK8q7aHzv3oni14m6KCloKxYFQUaYt7O2Sn0FUZWgKpSs2kLy1ZzscA/Dgjz22sGv8UEVOFMz\\nDzTMFGXdJgpodJtkX0lDqUp0mdOUFaZpqyFZnZMTUeRdpNzH6IaZ8TBxwdh5ePoKUhhk2SBKzbiX\\nshgUcEVRyphaSazWJHXBtGzQssFrZlyaeW1uUilY4AjqGWVl8NWCDhd5Pi+ZNxEeBhVYRK2xi0tU\\noaIfT9nf6yBkQZ4rDkWHF2Wf8/nzBPUEr5owUbTXziNebFLqRc3I5lgalLFtUIj1jhwbhxs35GWF\\nLDVWirYeq5B0TMXlIsGXM2ZRwuTAkamcSBZIOSUQmktBTL7wYL/ESyrS2GPqFKGp8bRhYxATjqdM\\ndxW6bJMoqLqNdrVSts6JM/SGGSuR4WD3RYryAKEVSlk4KlBeNZJ1cQBuhbEzuCimmyhmVyrcXDC1\\niiv9FL85QFEQ+gqdO1Q5R8ocH80kVkw9gzIlLBq0arMWIg1ro3WG7jm+tm+pBUwPHLX0uHpgUauO\\nrucxfVGwqAx78SpRCqH1mC0kiz3LXq6Z2QghJ1AYFouAMT7zpsRXOcJ3hFsbeDs5i902BaZxoJVg\\nt5NhGkgFxBiGzjHyZ9hKMN3J6HgFQXyAkA3Kefgmp6gFxgi0ipFmTq5zGp2hFyGBdkRJxlhfZFpY\\nsl6X6GxB+bUdfEpY69F8e4yiTWmok5p6YlAlLMIILQQi6xE9cJrxxa9gBG1mLq3bs8pFhYzCVuZy\\nShQqtNGEKsOpCT2dcSgO8ZVBZTFM9oh1w0JC7hYoGaCsw9QKfRRUF4aObnyKMILy6nfwCgVBgNgI\\niXsJ/tVdPJ3TpeZyZMGF2FkM88t0ZxKtIFdtxq4wc1zwfB7pxXx3L6aqKjJKosUY3QmZ+Jv0fMu8\\nDACBFobYepQuIDLwYF/ypo0Nnp/MSPIpWRTx1I6mwXJqGFPHAXHYxUY12Y7Dyz2K+RRpJTpUxKOI\\nVNecWutS72qk1By4AxJVsBsVaASe8mmURimNMRa9kGhPEA5T8t0GtyMhlzRTSYEHZXtMTwJGGkLr\\nk0qLKwtSv12SptH0g4DSVCyMxRdtWccmV6hcki8UijGeW8FWmqTY44poqxgljWOWh6hcEJiSuoFG\\nVFQWfCepJg1uUuA1cHmQYUxElGpMXnBoMvbcHC+I6VmPxNX851y11aCamkYrnJIMZ5qmKMmbgKoa\\nIJOAvDQs1AKzT5vaTEisqwl9SYRAlYZVX5LLikWgqOc125dDtjoKPzWoyzWiBH+h2ZoLRoNtct+y\\nq7ocbgfsNA4ZFgDIwuKVijz3Kac1OlfkeXnXbNmJM77CGPxOhFceEsYJ/UlFo3fBE+2+V1hjbQMr\\nfdzFF9qE7KGPHgpKkx8lxZD4BnLRRtQWdZuZxTRtcEyUl+3+h/bI5wXCiKOUig21FCxUB6kvEdQN\\neeFQvZIq0sznAZXyEUWJspZBr8dleYC0B6RNgAs6mK4luTLnoJYYCgorkaVql5FKQZWmLA4FppHY\\nckFDhfUFVk6IrGIRnyZSW5j9f0BOJ8zSFF8KZmMfPR4jiwZdzdmfGqbUyP4cJxyV0ngmZXscsREX\\nGGHQwYKVwOF2QyKvQBuH6w/QB7v4dU0UCTYRFEFbfq4nFYfSYnOP3UKwkS64VBuc8BBTjVYC36ZI\\n6RF7mq2dy8jVYeuBqhohBUZrzg06PPeMQZatw+EtQOrWOzdSkiYe50cJGfCNA78N4BICo31krTEs\\nqJiyOexRZSGLpo9wE6JmQRGXpPsFReOYMCGbz/HqA1SQslAV/v6UppTMhgk2dezEDe6wYhov0LWP\\nm0YY5RP3fFayPnvP7BN4gqnxCPfn4CRxV7C7Z9szxlIQrSaEesrIRLxY1NTNnN2mRngegaqpnKWq\\nAy7lHVK5g28kg45Pvj4k9w545oUrmBpM5BNFDdPSIeYNiZH4o3U6mceo+m/ymaEbJRhTUAwDKC5h\\nrMP4FltPacIMqQ12PGB/7pC9FF2DkJJ+r0eaddi+8l+c6qwgVMXuf38P31Zk/fM8+8Jz2EtPQZxS\\nWEVJQBmPGKc+3cWCMyJrg/q0AqGQ1uFMO3yoqkB6HmZWMtqtmTUZs84GkdaczlL0XFGICmUk+1HG\\n9gQCBTUDUi+gkiVydAorA5rJHtHVi21Ec2+Dg+J5qkqwoRQHoaE2IdZTdHsOMynoNjU6DZFK0pcp\\nh7ZkmL/A7o5GaoOQDikUVmuMlJxeidsMYnPa/UA/IAvbfL7eUapHhWTgxchkhJRtJZ/ABtzvd5jt\\n79PtjOGZKaiQ1UHExXyKUZpOJFgzCYvdQ/JtDf0VVBJifU1y5j5WD6bsPb3PijVEvRHfu/pttg9f\\nwKsskUhY2ehz4Bak2rCSTujNFXUU00xzlDaYWnJgfCjbNpYSKgQ+DmMdEQrX1G0KRmlQ1qOTeFyc\\nj0k6IetU6MUYoTRNZa+nZc26IczGGC3JdENlA1aF5ioxCxZIBb5tWAjIu3EbZAnIWhLmBfuqwq5I\\nup0A+b0pz+nTLHoHPHpqxMN7JeJyTioLpsogdQEOfGFwShHIOdLB3FRYM0M0kJU588qjcJpGQhBX\\ndKUm2bXMPE2oIopQtvWQ9RTrDIt8SORlJLNn2Z0ZNIp+EKObHUpXsDMx7B9E1H5JUiumtaWqFLqZ\\nUGifg7qh43yEvnuLwSfP+AowTmK0oNPrEkcNU9VQDFdQM0uWhShVU9MnqAXCRWThNl6wS4mj0g1V\\nacA0GC0ohUJXlsg6KuszkhleqZgVBt+3XN6+iLMNjNso0tI6ytrD8x1nzJwmMXzL5MzwGbsYvxEU\\n1vDQhQuEoWB/u8S5MXE+JrCOKLbMLk8J5gWhmDIeDWGsiC0ILVhgsYv2WE2oLM4LmfoTKHMMEm36\\naC9ELBIENZEU4BkCJ1ELxbgURLZETAR1Y5j7Gm0MugzxIoGKBSuZo94LUFpzYf0s2m5TXd1GKMNo\\npcP3dmuiuqL2LL6uEaYN1R9pWDQGNRMEKiRQXZqtkPL5b9Fc3cNJh1YeRWVYWVlh9MA5ZnWNyS8i\\nxQES6AWWFSvwlERVc1wVUXWG+JOCfG2DbC5ZzEpCq7FVTWACDiqD1aC1RaoMJwtG6TlU30dWlnGZ\\noRZzZoWmWG/ARnSmHvH+RRLZUMm2sk/oJEOvYWeq2eslrJx/kGb2LPpwziUdYiuLsZJhLHjAm1At\\nApwniWrB+V6f8rtfI+1W7NmAzHfMtUJJRXczJZ5BWmtiT1K6Bq9SDNa7NJOEZnuMUAqbRpTTBn96\\nFTZ6118fAAAfh0lEQVRPo5ViZTNj9rUaXVWsoHjAKj7XGKqmZC2JOTtK+JY1BEVEL5RodY6duiIZ\\nKcJJgHElOvNptnNk3MFzAXJ6AGKC0BpVFIDAjzw2V/ucGq2zu9jnmf2LNIEiqhRJEFMGXRajnNSH\\nS6tDwsmcpr9BrQTxpQXhcNBuM1w4g3ObSOdwbfoCgroiNxqvhjBPmIoR/V7EA512D1xUNV1qFlpz\\nGEQAGN/yYLZCEDguH87RnofrrWIOc8odQRMnWOvoVj0qsU3gF5S+oXYpfrZF2HyHYjLGYdBSEGjN\\nijV06gAv6EIzQboIIdXRSYB2UHXRKQCq8uuoOEGIG9GtgR+2Ob+FwlMW34/w4jMASN0aaO169OIF\\nWkr8wLKaRkz2DaKUbXSzGeCZQ+aqoElHdAZbxKmPNIbVOCA8b0mUwvN6bO+/SOqHNIeGRiZ0k5ha\\nO0KtyWRDPwiYO8u0muP7EexLChfQzDRISWM9apETiwykwVcSIR1ldp68cswOas5EDQuj8YYh/l6O\\nDFOMLNlsFM83BbrukCUZkxfGyP6AZFCjPYG3KzCeoxAVWWiwXk4tQ/a0IR8G6AOLbMCvLamvkUFE\\nP+pj/W2U0GjpSCOPleEpxgcz1pXgsm7QuUQoS6oDUM9CI4jjht66x3YjkSIgyg8wwYIq7vPfZkZd\\nS4ZpzNphH9losq1zlHu76L2rNMpgEGSRpTtIcf/5DJt2htRd4ixlGgw4cFPy0hAFkB+l55W1Ba1A\\nFkijyXtbhIMBRt+9aOcTZ3wpS0azfXa0RVmPRDUUuSR3MaKxaF8TN+1RkM59b2ZFSUZS82IBi2rc\\nFn6WGqsXRGkPVRkCmeDrhnTtPqTpsNMtKEWP7cs7jAuJMR5lniOEoFQWYT3iToCaHlAUBucqAjWm\\nrAIskIQN92+sUlPzzPwQafbwVcPZrZCJFMytY5OKOIu5vHKGy1d26PgVNq/YyxUiiSAv8T1JqD32\\n7ILZ0SGf0O8jpSAVHjPrI63Dmh75wRQhJkRZhBeUNLtgpERYSyo148LhZISMIU0sO/vtwXXfC6jd\\njKaTE249wqzJWLsy47Ax+GbOWidiLvcRRhBtvInmRR95+Xl6Zc3ayhYvdgY821SMnt2jMl12kjVW\\nKnh4s0fiGZ6+eIC2HrI8wBcFZ9IIU86InYcSM3wt0WFMc/4UaQ0HL455YCPlLff1+e43L9MPOxzQ\\nppg0ziBLTXLuUcKsQz55gYFqGAURk72SZwrodUImzpKMh+zwLCkJ08ojD32iCZwKUuZiwSwZsBas\\n8aMrF3j6yv+LEAml1dR5RRLUKCko6wKtLco4/DAhGI647A6wLmxrpna76NhwbmWF8f5F4jIg9Tz2\\nxruoZItsfRPzwjbVMGa/rwlzg+n3qIuAJkras6aRJeylZFnIA51nMNKykgW8cHWbbmhJ0ghmFc4I\\nOqEh7gzgcIbfDVjfWqXc+w+s9dD3Z3S8hlCEzC9P2+hYLY8C5wQuaGdwSiqcsjRao4OULPcxzz9D\\nfzbBdiP0ow/RXL5M4yzSSITxkN0UmWUIMUMYg067AAxGPTa+d5HN+zfQfsiXXthB+CGB7yGth1Fz\\ntEvR4ZB6vMA/u466PIfRBtkoJpy0Bq3TCTmsKnxrwfc4PLtJGA8wE0GqM5LBKttXrhA1DYEz3JdF\\nTA8dBaC048FuiqccbEsKGRJf+FEu7zVIX2KVIgfc0f6rEOKW4UTIG0OgC0O2sh5aHVz/rFAWKc2N\\nzwuFZxWjjsELfU4nW8zrMRdnYwRtgJrxRowuDLE/8qN862B2/bupNaT2xrXesvowxa5gPpiiZBcv\\ndOTC4rQiNdDzQvLKp+6C3lNMopgoi7ki1qgWFZHcwRhFWPmMaY8GChp0kjLe26es5xib02kkzijk\\nUXENm3bxC83mvOGqjZHWoAz4nqHndTgQ+21gnXSYak4vERxWoKUh0o4JNdqLKP2GTlXTeFv0ulto\\ndYAbDVAv+nTLjLevP4TTDvmOd+B99f+hURJLjm48znVWqMPvIPYWCClZWfW5sLnOt763x/5T29TS\\n54EL93FQPk8xkZzyhjTqv5FS4vdH2MmULLYsipyR9Rj0M1wUYISjqRq2OgYpwfMzGrVgZdWhAsf4\\nKlTVDjk5mbXsSUllJYm1bBpLeFv/+GFy4oxvjUQPBu2ZXSEInEbnmlmYUIWWLAioSo+wDlBSMhxG\\nnJMp+1dnLGZTIqXQuoRQks8tjW+pG43AEtgYrSQzJ7FBiFzULKYrBMEVqskOviiZCo2NHIMz97P4\\n3ncwjWGle5588jRetk488UmyDsNggK89vr13lb3VGaUtCFfXKfcvoXvr2MkO3qnzdPsJ60bTyTZ5\\n5ttX8co5yfkOdlKzVoypDgU70jL1KrpRyChJ2bkyxilNqrpk/S3K7D7+8/KXcaFh7cyA7YM9nsxW\\nUKuWMR6RH9PNM6amwMoZ63GPRTTGUqOUREYJdTFFd7roicatrzG+tM8gLumeWWd+MKWhxoWdtlKK\\n8xGLGUnkkfuO7bBHFiQcuIgg8FgLHN1eCHkbNS2VQJSQMedCP+bq5YpTgw22q4sMhw6rQqzT2KrB\\neoZ+J0Cr9pxpx+vi5YZqUdFLIx7o+Pihu2XGom2KFAKBoOtnXNh4AL/q8h25wFy+2GbzCQbIyWX8\\nlVXK/Qov6OIZR5oO2HrHT3LlqW32ix2MABcGwAKpfKSqEdLikgybDxCqrRWqpKSfhQQdn7V4hExX\\nILR0n76Kqs+g4xRpNd2VLS5VgtodYPYWSKXxETTG4bQCo3jT/3oLa5GjenIfmppzZwf4sqRjfMI4\\n4gE1pxrnlFXA2dWEqWtzSRvjgU1RRY5wHloVmGRA7l2ks9pBZx12dybkWuHF/vX7FWgf7Xn00xHh\\n/qzNLuU5wtPn8NOQvT1F2pTsNBDqAKVqjFFoGyH1AiHaWWTU6/COd/4Y0hgW4xmdqiIsA9aCmGCw\\nTuDbduYJhA89zHxWIK/koBTamfYoTF5y9lTGLK9QrkDOBLXniKzHWi9DKYmaauq9y+ROMwqP8i+r\\n1pkQynA+a2ezB9UM5xlsJ6bz0HkuPvs0g55iJfIYrnSPH1DEjWVGZRyDh88y//b3brx9W0yqEO3Z\\n1mEnwAbtPZVHlb8UDaJu9wxVFCLlyy9hZmnG2958gacOnyEWEatJj93nnwYgSU7jW0Uzj/ECgffI\\nm5n8Z431Y1QTga7oiJRuNWPk+ewPAq5eaah9jWc1XqpI0i7hxR3muUAahe4PWezso6KQoCiIihmF\\nqhHaoNKQOCh46+ZZDi89y9WVU1y+OOP+rYhTvYInn54jhaNjAqbVjCiMqOUCr9thZTSkiDxUfsDq\\n+goX9jRWSjzd6sj0+iQPvIn0v/6drKjJPMFg1OXK6Q2qgwVWCIyn8OOMYbzG1HuRUXfI1n2b7FyS\\nXC0LPJcgwx6z+T5RnHBFddBijA0Em6OM9a2M3PNZ7RnKqsYaRZVHhIGPp2oqL8MzHk0z5LmmS43A\\nPL+L1grRaJLIwwmBMcuZ7x1RWuJ1I+y07eTGSFSj0DqiCgydYEhPdZk2bS5dpyRCWhKvT1UvoJkj\\nRIWJ4HCsEdZQ55aKHnG0SqQVu/UMIQTONwTWISKL/0BKtafIZx0So3DxkMq7ipgJOmkPFTo87fHQ\\nmQeue9dVXeEpRXdzxH0PjvCikMNJjY27LM5voLohj57vo3YnALwQHOJLzaCb4joNKy8+zV7dJex6\\nlAZW+xdwY4U2Cq+TEOuceGOTUqVs3d/FXlT014fosubJvKIKHQbDyvAsRSR5anebVZdxX/cM33vu\\nIk4apNKofopMPKR2SNGm6RNC0MkkShsQbUUVqRxWL5htniU42MFfXaWPYNfvox/5UYzUyH2JkgKt\\nBEHmsZnHTPM5B1cEndBrByqpOT30mDoNnkfoMopGEliFTmrS5GhG1AuIx4JevsZ0cYmzWQ+ji6MB\\n8EbXVTZpZx1CUshWD53QY3j6FFWds3pqnafGimZ7Dx1rXHQfCwpSF7d9yBl8bcjNgHOnIs5GGub/\\njXZdwjRjXM2wfkpy4WGuXjxkZGLmM42OPHrdAHlUgT00Cl8r1s8OeOagnT0NN84jg4r6yreZTXJK\\nZQiUIjcWzyjOZuH1/nJAew40TULKUYrcFUjPo+P5bE8s2rTJFrQUVFWDEgIZnaKZPk83WKMfBGgp\\niX/k7cgn/z8ANt7+ILK/grE33S+peGT1UQ5fyFlMnwGjYXNIri3WKgLPEBY5TmtmxhKvGoarMWGa\\nMc1vLbkmTTuTU56lax02CAjDkCT0uZ1r92nVrHJfZ42wF1BVDc7T+J6haRz3Z2dRcsJ61CGM2sEb\\nv0e8skq9WCCtPfrdto9IdSMjUZLd+M1OP4RSEoQW49k7Life7MQJqVFZjP/ohfb4S/vibV9Qbd3f\\nwDAatf1HCtE6sVS44ZD86acwoxVeySTKHckRBJLIuyFL6BJckDHPp+37YcDGqM9F7aNUxGRvQSwD\\nhirFasO5YULUtTx7+RCjFT917kcQQnA4/9/s7bfpV20cI86cQc338MKM/rg1vqUUiNEKcjMlHaV0\\nN0+RHC64OLnIg1sevj7AixK8rEe/Y9neLvG7HXS/xEhBmMXsIbACQmP48R/b5MYNbIn8BO0FdGcH\\nJOsZURKxHYdHZVHbrTFlYjwTkNqE0PgYYzjTW8Xu7QCw9ZM/QzMdY8OYRiokEdoPiCMfLwkphUMr\\nSd00+KsjCiydUY+d8r+omgoKQc/32MHR8Q2npmOawmfmdXjk0Q3ObWbf12F6PTlxxhdgdS3kO9tz\\nstARBobupD0jOO+EhHFIJCXb4zG9TsBG7GPFGVw1weW7WAryxmJUzam1Dk/lFRBw/uyQXtx6x2ra\\nKmDUC5nIiLy4AnMJmUesMraGCdbvYx4e0JQlTJ6BEtSRV3wNJRUdq8FqvDjF2AjjdfGCiChVbJ3r\\noW5S9n2rKWpe8db1EWVdUueHqDed4uLOf5CXc7RQOKtIOz6j/haqaVB+BI2hcWdQ3nfRcczWIymn\\n/+si22bCfp2TeglpbClRPLraxRrD6egUvncePxlRVdPWwEpL08xQWjJYSRitVghlUSYBaqQ01PUc\\ntME7dQqhNQGwFXl46VmslNT/dZW8rNBKopRkZTVBTnOcTgnsrQOyrwwzIQi0Y78AZxRrZ27MUPqj\\nmKtOcij76NQQhiHke20iBnlj0JTSIKRBCUkuTZtdSQrObnZh88cAePFbV6hUq5u3vekt7B3M6GZt\\nOj/faYaZz/2JpRu3DsJk3h7/sS5Bufa4htaOzWQT4/V5eq9hYQX6pgCNwGguZCGTYnpDRu2zOYTA\\nDsh8KLojMp3zfO1htLylv4QPPUxTlahmD52kBHEH3ekQ1wXFdB0vbO9NoBVStEfAMCFhej83Z6QV\\n9sYgbrMU7RtuRxiDtBadZZg4IRqt4SnJAnA2QJRTfBuzEALndHuM6GWQUuDW1tDV+LqBfMlvHoma\\n6uy646PNze8LVsIOVkXE9tbfE1rBom03gLIhqk7RpnPsb2kT4fttn0bcue3iJuMqj/qUDmPqcn69\\nTbd+vn3GHzoV46L2rvecwRtGjJzFGIXtZEjXOg4dp4nNnYfZa89/09RHM+g2QYs7ciquOU0u8jFa\\n000jRG7orVjiuUbsiNYRlQp91L9v7lfRo29l4+x5PF9z5WrNYSOIvQjf1gyfe5Z0vMvzUpLL9vnT\\nRw5SN/F4549sIqsdJpNDkhWH8UPu663TCUa88Pwe/oMxaaRJ1jvs7e63Z3yFvMXRu0ZkfDbCB6iH\\nu8SdHto4OMqnUGufeHABKQ2pF1MEXSLTxgb0fceubOUxSQxJ22+aI71JKQkffZRwlDG9fEAWWYqy\\nRgcBVaPRWrEVrPPswXNoT+OMQdYFzike/b/fwv17h4x1yMYwvqOOflicPOPbVAghePv9KS7MqOuG\\nnSsTrDKc6iVcLCs2Ep+zWXDLg3M6iYiKjJ3JIVI4KuWzGic89/wBZ1cc0dFsC+Bs4jMpKoa+pa49\\nXtirkaZGScX50/eR+m3HEEd7qtW4PRsmb/eSb8JIi1QOL17nYHxIkgXo27zxR850GWYhk1mORsED\\nF6jyMeF8xOHkctu21YSyrpGLfeoqR0qLsYZOPyY+93/hjgbabF8RNyVBLHHGcjoNGIUe0dEg+tAD\\np2iatnqKljHa3Nr5ktjDT862BcCrnIYGITX5UT5ia27I2nE3RlBr5HXjew0lZGuMbzKYXnyKdZMw\\nQWOVz36RY+VLpwpKwDBa5Uy8gRMleb7XDja3DajSJuj5LkiLli/t1m86lVEl9+FkjU1D4vSGuZJC\\ncH4jvVVf/oCqGCPLo4he2ZZp9NMLR+dfr9DthaxtvbT4tqfa2fcg6F9/rZdt0k3XEUJydW8GL+zj\\nbjNoKjoKRjoYI6TE761e33dMuqtHThBsRT7NUaTqcVybjQKoMDr2M0JKgjc9BEohrcXtT1nU7TVX\\n1vqUlwP6KzF7z+9dn7G+HNeeNfPAQ4SnjjeIxmq6g5AgfPn8uV3vGGdBHe3ZXrPWSqEIrs+Ab0eZ\\nCC85T7nYuWV2ew0brFIsriL1jX4gpEZqH21T8vLisde97vTd1Jc7ztzyDFwzvNDq6uXwlMcwGND1\\n23vWlohv8I/kDELLuQtDpBSUb3qQyPcxl6ZoLdFXNQspEEohpEQfFX6++dkTWhN1OgwGMcLsE6ce\\nYWRpqgq7skp8+jQvLASjwHFfFtzSNmcVxULdtAjQBhiOwiHZVofD/Tm9YYQQgrNJgJw0x95rgCx2\\nvO30Fo0ekLiozRNuNEoLpJIYd/TbeUliE4S82SmSL1lFuDb8BEYijW3vgdakaUBTVkw8C7MarSX2\\naM8+NAFbZ3s8e3kfrQQuCHFByEuf4LvDiTO+QmqCaJ3i2j6LEgxWYqaTnG7k0bvDWo9TktSL2JmA\\nkgYhBKkXcmZNMoxjjHdjoIzNDW+14zmeR2ClwLcJwyR9ybXr5vsbX6tuDfiQ6kY7h76laRpCZwgi\\nx2SW39RuhxSSrXSD8+lZnFU4FLU9Q5UfoF3bdQYrtxrPfuJz9WDGI+vD6w+jsbfvX730Xg06PlXd\\nMOz4KCWvD/ICAUKxOYx45vIhg+z4QSVwpvU8b5Lv2n1Rt+yvxXRMTAdYVDUHRUVyzAzBV4pCGzIX\\n0uZ3rFA2BQTapSjdGhfrenjelEOhsOqlg3vgGVgdHtvm47D+CPwRQ7/NchYfGYxr92y4GiOkwB1j\\nKJRUPNx/00tevzbL6iYeW2VNPzvecIzCIZ52BObGPbbB6m3XenmDaFdXEULeMoi9pJ3hDcPj6Rtn\\nHNPQkT7Qvne6SkiCl8p4O0IIRmsJ2qiXbVtvcLwz8H1R7aAujvqIdB5CSlRw5yo014KpjjMIxuth\\nvN6tnxcSPzkHQDG/QtO8NOGCVB42WEWZ12emJIRgK9m4/r9Vkqqpb3Egrzk/Ommdr9UjR3F/EaMO\\n2yx24ugoFYC9wyqFUpIoPtor15rO//rZ9vPfvoJWEv+Ypfl2pk8bMX7TfXSeuaXvp55PwQrKHK9f\\nKQSd2AE3HJPOaIPD1e+BNEh9pFevfV93b6yAWadeYnzXEk0dSYbxrWOG9APqyQQvCZjMJxinUbKd\\niCUuwXmGtcjxP4HXZHzruuYP//AP+da3voW1lscff5xTp0693m27I1445HB6eP3/rBuQdYOX+UaL\\n7w+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pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 289,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"plotly.tools.set_credentials_file(username='jxljiang221', api_key='RgXxTYxCam4vBca8DHDq')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/home/xiaoliangjiang/work'\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 274,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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LogNumberInstitutionProgramProgramTypeProjectTitleProjectTypeAwardDateInstAddr1InstAddr2InstAddr3...FIPSStateFIPSCountyCensusTractCensusBlockFIPSMCDCodeFIPSPlaceCodeCBSACodeMetroDivisionCodeDescriptionLevel
0IA-00-00-0001-00Museum of the AleutiansConservation Assessment Prog.IANaNNaN3/30/2000P.O. Box 648NaNNaN...2.016.0200.02014.01615.080770.0NaNNaNNaN1.0
1IA-00-00-0002-00Depot Museum, Inc.Conservation Assessment Prog.IANaNNaN3/30/2000P.O. Box 681420NaNNaN...1.049.0961000.02013.091206.027616.0NaNNaNNaN1.0
2IA-00-00-0003-00National Voting Rights Museum and InstituteConservation Assessment Prog.IANaNNaN3/30/20006 Highway 80 EastNaNNaN...1.047.0957200.01005.092883.0NaN42820.0NaNNaN1.0
3IA-00-00-0004-00Bob Jones MuseumConservation Assessment Prog.IANaNNaN3/30/2000P.O. Box 613NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0
4IA-00-00-0005-00Coronado Museum of History and ArtConservation Assessment Prog.IANaNNaN3/30/20001100 Orange AvenueNaNNaN...6.073.010900.01021.092780.016378.041740.0NaNNaN1.0
\\n\",\n \"

5 rows × 44 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" LogNumber Institution \\\\\\n\",\n \"0 IA-00-00-0001-00 Museum of the Aleutians \\n\",\n \"1 IA-00-00-0002-00 Depot Museum, Inc. \\n\",\n \"2 IA-00-00-0003-00 National Voting Rights Museum and Institute \\n\",\n \"3 IA-00-00-0004-00 Bob Jones Museum \\n\",\n \"4 IA-00-00-0005-00 Coronado Museum of History and Art \\n\",\n \"\\n\",\n \" Program ProgramType ProjectTitle ProjectType \\\\\\n\",\n \"0 Conservation Assessment Prog. IA NaN NaN \\n\",\n \"1 Conservation Assessment Prog. IA NaN NaN \\n\",\n \"2 Conservation Assessment Prog. IA NaN NaN \\n\",\n \"3 Conservation Assessment Prog. IA NaN NaN \\n\",\n \"4 Conservation Assessment Prog. IA NaN NaN \\n\",\n \"\\n\",\n \" AwardDate InstAddr1 InstAddr2 InstAddr3 ... FIPSState \\\\\\n\",\n \"0 3/30/2000 P.O. Box 648 NaN NaN ... 2.0 \\n\",\n \"1 3/30/2000 P.O. Box 681420 NaN NaN ... 1.0 \\n\",\n \"2 3/30/2000 6 Highway 80 East NaN NaN ... 1.0 \\n\",\n \"3 3/30/2000 P.O. Box 613 NaN NaN ... NaN \\n\",\n \"4 3/30/2000 1100 Orange Avenue NaN NaN ... 6.0 \\n\",\n \"\\n\",\n \" FIPSCounty CensusTract CensusBlock FIPSMCDCode FIPSPlaceCode CBSACode \\\\\\n\",\n \"0 16.0 200.0 2014.0 1615.0 80770.0 NaN \\n\",\n \"1 49.0 961000.0 2013.0 91206.0 27616.0 NaN \\n\",\n \"2 47.0 957200.0 1005.0 92883.0 NaN 42820.0 \\n\",\n \"3 NaN NaN NaN NaN NaN NaN \\n\",\n \"4 73.0 10900.0 1021.0 92780.0 16378.0 41740.0 \\n\",\n \"\\n\",\n \" MetroDivisionCode Description Level \\n\",\n \"0 NaN NaN 1.0 \\n\",\n \"1 NaN NaN 1.0 \\n\",\n \"2 NaN NaN 1.0 \\n\",\n \"3 NaN NaN 1.0 \\n\",\n \"4 NaN NaN 1.0 \\n\",\n \"\\n\",\n \"[5 rows x 44 columns]\"\n ]\n },\n \"execution_count\": 274,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2 = pd.read_csv('DiscGrants96to13-2017_04_10_19_27_08.csv',encoding='iso-8859-1',sep='\\\\t')\\n\",\n \"df2.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 282,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df3=df2[(df2[\\\"Longitude\\\"]>-95)&(df2[\\\"Latitude\\\"]<24)]\\n\",\n \"#df2=df2[(((df2[\\\"Longitude\\\"]>-95)&(df2[\\\"Latitude\\\"]<24))==False)]\\n\",\n \"df2=df2[((df2[\\\"InstState\\\"]==\\\"GU\\\")|(df2[\\\"InstState\\\"]==\\\"VI\\\")|(df2[\\\"InstState\\\"]==\\\"PR\\\")|(df2[\\\"InstState\\\"]==\\\"MP\\\")|(df2[\\\"InstState\\\"]==\\\"FM\\\")|(df2[\\\"InstState\\\"]==\\\"MH\\\")|(df2[\\\"InstState\\\"]==\\\"AS\\\"))==False]\\n\",\n \"#VI,GU,PR,MP,FM,MH\\n\",\n \"# since some points (Saint Thomas, U.S. Virgin Islands) could not be shown on the US map, so I tried to remove them by using the codes above.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 283,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#df2.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 284,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#df2.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 285,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df2['Text'] = df2['Institution'] + '
' + df2['Program'] + '
' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\\n\",\n \"#colors = [\\\"#ffcccc\\\",\\\"#ffddcc\\\",\\\"#ffeecc\\\",\\\"#ffffcc\\\",\\\"#eeffcc\\\",\\\"#ddffcc\\\"]\\n\",\n \"colors = [\\\"e0e0e0\\\",\\\"#66b2ff\\\",\\\"#66ff66\\\",\\\"#ffff66\\\",\\\"#ffb266\\\",\\\"#ff6666\\\"]\\n\",\n \"institutions = []\\n\",\n \"scale = 6000\\n\",\n \"\\n\",\n \"for i in range(len(limits)):\\n\",\n \" subdf2=df2[((df2['AwardTotal']/1e3)limits[i][0])]\\n\",\n \" institution = dict(\\n\",\n \" type = 'scattergeo',\\n\",\n \" locationmode = 'USA-states',\\n\",\n \" lon = subdf2['Longitude'],\\n\",\n \" lat = subdf2['Latitude'],\\n\",\n \" text = subdf2['Text'],\\n\",\n \" marker = dict(\\n\",\n \" size = subdf2['AwardTotal']/scale,\\n\",\n \" color = colors[i],\\n\",\n \" line = dict(width=0.5, color='rgb(40,40,40)'),\\n\",\n \" sizemode = 'area'\\n\",\n \" ),\\n\",\n \" name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )\\n\",\n \" institutions.append(institution)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 286,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 286,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"layout = dict(\\n\",\n \" title = 'Administrative Discretionary Grants
(Click legend to toggle traces)',\\n\",\n \" showlegend = True,\\n\",\n \" geo = dict(\\n\",\n \" scope='usa',\\n\",\n \" projection=dict( type='albers usa' ),\\n\",\n \" showland = True,\\n\",\n \" landcolor = 'rgb(217, 217, 217)',\\n\",\n \" subunitwidth=1,\\n\",\n \" countrywidth=1,\\n\",\n \" subunitcolor=\\\"rgb(255, 255, 255)\\\",\\n\",\n \" countrycolor=\\\"rgb(255, 255, 255)\\\"\\n\",\n \" ),\\n\",\n \" updatemenus=list([\\n\",\n \" dict(\\n\",\n \" x=-0.05,\\n\",\n \" y=1,\\n\",\n \" yanchor='top',\\n\",\n \" buttons=list([\\n\",\n \" dict(\\n\",\n \" args=['visible', [True, True, True, True, True, True]],\\n\",\n \" label='All',\\n\",\n \" method='restyle'\\n\",\n \" ),\\n\",\n \" dict(\\n\",\n \" args=['visible', [True, False, False, False, False, False]],\\n\",\n \" label='0-10 thousand dollar',\\n\",\n \" method='restyle'\\n\",\n \" ),\\n\",\n \" dict(\\n\",\n \" args=['visible', [False, True, False, False, False, False]],\\n\",\n \" label='10-100 thousand dollar',\\n\",\n \" method='restyle'\\n\",\n \" ),\\n\",\n \" dict(\\n\",\n \" args=['visible', [False, False, True, False, False, False]],\\n\",\n \" label='100-200 thousand dollar',\\n\",\n \" method='restyle'\\n\",\n \" ),\\n\",\n \" dict(\\n\",\n \" args=['visible', [False, False, False, True, False, False]],\\n\",\n \" label='200-500 thousand dollar',\\n\",\n \" method='restyle'\\n\",\n \" ),\\n\",\n \" dict(\\n\",\n \" args=['visible', [False, False, False, False, True, False]],\\n\",\n \" label='500-1000 thousand dollar',\\n\",\n \" method='restyle'\\n\",\n \" ),\\n\",\n \" dict(\\n\",\n \" args=['visible', [False, False, False, False, False, True]],\\n\",\n \" label='1000+ thousand dollar',\\n\",\n \" method='restyle'\\n\",\n \" )\\n\",\n \" ]),\\n\",\n \" )\\n\",\n \" ]),\\n\",\n \" )\\n\",\n \"\\n\",\n \"fig = dict( data=institutions, layout=layout )\\n\",\n \"py.iplot( fig, validate=False, filename='q2testworldmap' )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 270,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 270,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2['Text'] = df2['Institution'] + '
' + df2['Program'] + '
' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\\n\",\n \"#colors = [\\\"#ffcccc\\\",\\\"#ffddcc\\\",\\\"#ffeecc\\\",\\\"#ffffcc\\\",\\\"#eeffcc\\\",\\\"#ddffcc\\\"]\\n\",\n \"colors = [\\\"#ff6666\\\",\\\"#ffb266\\\",\\\"#ffff66\\\",\\\"#b2ff66\\\",\\\"#66ff66\\\",\\\"#66ffb2\\\",\\\"#66ffff\\\",\\\"#66b2ff\\\",\\\"#6666ff\\\",\\\"#ff66ff\\\",\\\"#b266ff\\\",\\\"#ff66b2\\\",\\\"#000000\\\",\\\"#404040\\\",\\\"#808080\\\",\\\"#c0c0c0\\\",\\\"#ffffff\\\"]\\n\",\n \"pt=[\\\"IL\\\",\\\"ST\\\",\\\"ML\\\",\\\"RE\\\",\\\"IC\\\",\\\"IM\\\",\\\"IS\\\",\\\"MN\\\",\\\"LE\\\",\\\"MP\\\",\\\"MH\\\",\\\"LT\\\",\\\"LI\\\",\\\"IG\\\",\\\"MA\\\",\\\"IA\\\",\\\"IG\\\"]\\n\",\n \"institutions = []\\n\",\n \"scale = 5000\\n\",\n \"\\n\",\n \"for i in range(len(colors)):\\n\",\n \" subdf2=df2[df2[\\\"ProgramType\\\"]==pt[16-i]]\\n\",\n \" institution = dict(\\n\",\n \" type = 'scattergeo',\\n\",\n \" locationmode = 'USA-states',\\n\",\n \" lon = subdf2['Longitude'],\\n\",\n \" lat = subdf2['Latitude'],\\n\",\n \" text = subdf2['Text'],\\n\",\n \" marker = dict(\\n\",\n \" size = subdf2['AwardTotal']/scale,\\n\",\n \" color = colors[16-i],\\n\",\n \" line = dict(width=0.5, color='rgb(40,40,40)'),\\n\",\n \" sizemode = 'area'\\n\",\n \" ),\\n\",\n \" name =pt[i])\\n\",\n \" institutions.append(institution)\\n\",\n \"layout = dict(\\n\",\n \" title = 'Administrative Discretionary Grants
(Click legend to toggle traces)',\\n\",\n \" showlegend = True,\\n\",\n \" geo = dict(\\n\",\n \" scope='usa',\\n\",\n \" projection=dict( type='albers usa' ),\\n\",\n \" showland = True,\\n\",\n \" landcolor = 'rgb(217, 217, 217)',\\n\",\n \" subunitwidth=1,\\n\",\n \" countrywidth=1,\\n\",\n \" subunitcolor=\\\"rgb(255, 255, 255)\\\",\\n\",\n \" countrycolor=\\\"rgb(255, 255, 255)\\\"\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"fig = dict( data=institutions, layout=layout )\\n\",\n \"py.iplot( fig, validate=False, filename='q2testworldmap' )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 271,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 271,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2['Text'] = df2['Institution'] + '
' + df2['Program'] + '
' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\\n\",\n \"#colors = [\\\"#ffcccc\\\",\\\"#ffddcc\\\",\\\"#ffeecc\\\",\\\"#ffffcc\\\",\\\"#eeffcc\\\",\\\"#ddffcc\\\"]\\n\",\n \"colors = [\\\"#ff6666\\\",\\\"#ffb266\\\",\\\"#ffff66\\\",\\\"#b2ff66\\\",\\\"#66ff66\\\",\\\"#66ffb2\\\",\\\"#66ffff\\\",\\\"#66b2ff\\\",\\\"#6666ff\\\",\\\"#ff66ff\\\",\\\"#b266ff\\\",\\\"#ff66b2\\\",\\\"#000000\\\",\\\"#404040\\\",\\\"#808080\\\",\\\"#c0c0c0\\\",\\\"#ffffff\\\"]\\n\",\n \"pt=[\\\"IL\\\",\\\"ST\\\",\\\"ML\\\",\\\"RE\\\",\\\"IC\\\",\\\"IM\\\",\\\"IS\\\",\\\"MN\\\",\\\"LE\\\",\\\"MP\\\",\\\"MH\\\",\\\"LT\\\",\\\"LI\\\",\\\"IG\\\",\\\"MA\\\",\\\"IA\\\",\\\"IG\\\"]\\n\",\n \"institutions = []\\n\",\n \"scale = 6000\\n\",\n \"\\n\",\n \"for i in range(len(colors)):\\n\",\n \" subdf2=df2[df2[\\\"ProgramType\\\"]==pt[i]]\\n\",\n \" institution = dict(\\n\",\n \" type = 'scattergeo',\\n\",\n \" locationmode = 'USA-states',\\n\",\n \" lon = subdf2['Longitude'],\\n\",\n \" lat = subdf2['Latitude'],\\n\",\n \" text = subdf2['Text'],\\n\",\n \" marker = dict(\\n\",\n \" size = subdf2['AwardTotal']/scale,\\n\",\n \" color = colors[16-i],\\n\",\n \" line = dict(width=0.5, color='rgb(40,40,40)'),\\n\",\n \" sizemode = 'area'\\n\",\n \" ),\\n\",\n \" name =pt[i])\\n\",\n \" institutions.append(institution)\\n\",\n \"layout = dict(\\n\",\n \" title = 'Administrative Discretionary Grants
(Click legend to toggle traces)',\\n\",\n \" showlegend = True,\\n\",\n \" geo = dict(\\n\",\n \" scope='usa',\\n\",\n \" projection=dict( type='albers usa' ),\\n\",\n \" showland = True,\\n\",\n \" landcolor = 'rgb(217, 217, 217)',\\n\",\n \" subunitwidth=1,\\n\",\n \" countrywidth=1,\\n\",\n \" subunitcolor=\\\"rgb(255, 255, 255)\\\",\\n\",\n \" countrycolor=\\\"rgb(255, 255, 255)\\\"\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"fig = dict( data=institutions, layout=layout )\\n\",\n \"py.iplot( fig, validate=False, filename='q2testworldmap' )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 272,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~xjiang36/0 or inside your plot.ly account where it is named 'q2testworldmap'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 272,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2['Text'] = df2['Institution'] + '
' + df2['Program'] + '
' + 'Program Type: ' + df2['ProgramType'] + '
'+ 'Total Award: ' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\\n\",\n \"#colors = [\\\"#ffcccc\\\",\\\"#ffddcc\\\",\\\"#ffeecc\\\",\\\"#ffffcc\\\",\\\"#eeffcc\\\",\\\"#ddffcc\\\"]\\n\",\n \"colors = [\\\"e0e0e0\\\",\\\"#66b2ff\\\",\\\"#66ff66\\\",\\\"#ffff66\\\",\\\"#ffb266\\\",\\\"#ff6666\\\"]\\n\",\n \"institutions = []\\n\",\n \"scale = 6000\\n\",\n \"\\n\",\n \"for i in range(len(limits)):\\n\",\n \" subdf2=df2[((df2['AwardTotal']/1e3)limits[i][0])]\\n\",\n \" subdf2=subdf2[1:50]\\n\",\n \" institution = dict(\\n\",\n \" type = 'scattergeo',\\n\",\n \" locationmode = 'USA-states',\\n\",\n \" lon = subdf2['Longitude'],\\n\",\n \" lat = subdf2['Latitude'],\\n\",\n \" text = subdf2['Text'],\\n\",\n \" marker = dict(\\n\",\n \" size = subdf2['AwardTotal']/scale,\\n\",\n \" color = colors[i],\\n\",\n \" line = dict(width=0.5, color='rgb(40,40,40)'),\\n\",\n \" sizemode = 'area'\\n\",\n \" ),\\n\",\n \" name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )\\n\",\n \" institutions.append(institution)\\n\",\n \"layout = dict(\\n\",\n \" title = 'Temp Scatter Plot of Top 50 for Administrative Discretionary Grants
(Click legend to toggle traces)',\\n\",\n \" showlegend = True,\\n\",\n \" geo = dict(\\n\",\n \" scope='usa',\\n\",\n \" projection=dict( type='albers usa' ),\\n\",\n \" showland = True,\\n\",\n \" landcolor = 'rgb(217, 217, 217)',\\n\",\n \" subunitwidth=1,\\n\",\n \" countrywidth=1,\\n\",\n \" subunitcolor=\\\"rgb(255, 255, 255)\\\",\\n\",\n \" countrycolor=\\\"rgb(255, 255, 255)\\\"\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"fig = dict( data=institutions, layout=layout )\\n\",\n \"py.iplot( fig, validate=False, filename='q2testworldmap' )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 290,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 290,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2['Text'] = df2['Institution'] + '
' + df2['Program'] + '
' + df2['ProgramType'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'\\n\",\n \"limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\\n\",\n \"#colors = [\\\"#ffcccc\\\",\\\"#ffddcc\\\",\\\"#ffeecc\\\",\\\"#ffffcc\\\",\\\"#eeffcc\\\",\\\"#ddffcc\\\"]\\n\",\n \"colors = [\\\"e0e0e0\\\",\\\"#66b2ff\\\",\\\"#66ff66\\\",\\\"#ffff66\\\",\\\"#ffb266\\\",\\\"#ff6666\\\"]\\n\",\n \"institutions = []\\n\",\n \"scale = 6000\\n\",\n \"\\n\",\n \"\\n\",\n \"for i in range(len(limits)):\\n\",\n \" subdf2=df2[((df2['AwardTotal']/1e3)limits[i][0])]\\n\",\n \" institution = dict(\\n\",\n \" type = 'scattergeo',\\n\",\n \" locationmode = 'ISO-3',\\n\",\n \" lon = subdf2['Longitude'],\\n\",\n \" lat = subdf2['Latitude'],\\n\",\n \" text = subdf2['Text'],\\n\",\n \" marker = dict(\\n\",\n \" size = subdf2['AwardTotal']/scale,\\n\",\n \" color = colors[i],\\n\",\n \" line = dict(width=0.5, color='rgb(40,40,40)'),\\n\",\n \" sizemode = 'area'\\n\",\n \" ),\\n\",\n \" name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )\\n\",\n \" institutions.append(institution)\\n\",\n \"layout = dict(\\n\",\n \" title = 'Administrative Discretionary Grants
(Click legend to toggle traces)',\\n\",\n \" showlegend = True,\\n\",\n \" geo = dict(\\n\",\n \" scope='usa',\\n\",\n \" projection=dict( type='albers usa' ),\\n\",\n \" showland = True,\\n\",\n \" landcolor = 'rgb(217, 217, 217)',\\n\",\n \" subunitwidth=1,\\n\",\n \" countrywidth=1,\\n\",\n \" subunitcolor=\\\"rgb(255, 255, 255)\\\",\\n\",\n \" countrycolor=\\\"rgb(255, 255, 255)\\\"\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"fig = dict( data=institutions, layout=layout )\\n\",\n \"py.iplot( fig, validate=False, filename='q2testworldmap' )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":252,"cells":{"repo_name":{"kind":"string","value":"baumanab/noaa_requests"},"path":{"kind":"string","value":"NOAA_sandbox.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"26222"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from pandas.io import json\\n\",\n \"import requests\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"import string\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"NOAA_Token_Here= 'enter as string'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Play with some basic functions adapted from [tide data functions](https://github.com/baumanab/seattle_tides)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Query Builder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def query_builder(start_dt, end_dt, station, offset= 1):\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"Function accepts: a start and end datetime string in the form 'YYYYMMDD mm:ss'\\n\",\n \" which are <= 1 year apart, a station ID, and an offset. \\n\",\n \" Function assembles a query parameters/arguments dict and returns an API query and the \\n\",\n \" query dictionary (query_dict). The relevant base URL is the NCDC endpoint \\n\",\n \" 'http://www.ncdc.noaa.gov/cdo-web/api/v2/data?'.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" import urllib\\n\",\n \" \\n\",\n \" # API endpoint\\n\",\n \" base_url= 'http://www.ncdc.noaa.gov/cdo-web/api/v2/data?'\\n\",\n \"\\n\",\n \" # dict of NOAA query parameters/arguments\\n\",\n \"\\n\",\n \" query_dict = dict(startdate= start_dt, enddate= end_dt, stationid= station,\\n\",\n \" offset= offset, datasetid= 'GHCND', limit= 1000)\\n\",\n \"\\n\",\n \" # encode arguments\\n\",\n \"\\n\",\n \" encoded_args = urllib.urlencode(query_dict)\\n\",\n \" \\n\",\n \" # query\\n\",\n \" query = base_url + encoded_args\\n\",\n \" \\n\",\n \" # decode url % (reconvert reserved characters to utf8 string)\\n\",\n \" query= urllib.unquote(query)\\n\",\n \"\\n\",\n \" # create and return query from base url and encoded arguments\\n\",\n \" return query, query_dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2015-01-01&offset=1&limit=1000&datasetid=GHCND\\n\"\n ]\n }\n ],\n \"source\": [\n \"query_1, query_dict= query_builder('2014-01-01', '2015-01-01', station= 'GHCND:USW00023174')\\n\",\n \"print(query_1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2015-01-01&offset=1001&limit=1000&datasetid=GHCND\\n\"\n ]\n }\n ],\n \"source\": [\n \"query_2, query_dict= query_builder('2014-01-01', '2015-01-01', station= 'GHCND:USW00023174', offset= 1001)\\n\",\n \"print(query_2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Offset Generator\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def offsetter(response):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Function accepts a restful query response (JSON)\\n\",\n \" Function returns a dictionary of offsets to pull the entire query set\\n\",\n \" where the set is limited to 1000 records per query. Function also \\n\",\n \" returns a record count for use in validation.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # get repeats and repeat range\\n\",\n \" import math\\n\",\n \" count= response['metadata']['resultset']['count']\\n\",\n \" repeats= math.ceil(count/1000.)\\n\",\n \" repeat_range= range(int(repeats))\\n\",\n \" \\n\",\n \" # get offsets dictionary\\n\",\n \" \\n\",\n \" offset= 1\\n\",\n \" offsets= [1]\\n\",\n \" for item in repeat_range[1:]:\\n\",\n \" offset += 1000\\n\",\n \" offsets.append(offset)\\n\",\n \" \\n\",\n \" \\n\",\n \" # zip up the results and convert to dictionary\\n\",\n \" offset_dict= dict(zip(repeat_range[1:], offsets[1:])) # the first call has been done already to get meta\\n\",\n \" \\n\",\n \" return offset_dict, count # for quality control \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Query Generator\\n\",\n \"\\n\",\n \"#### TODO\\n\",\n \"- refactor with a decorator\\n\",\n \"- make key an attribute that can be hidden\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def execute_query(query):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Function accepts an NOAA query for daily summaries for a specfic location\\n\",\n \" and executes the query.\\n\",\n \" Function returns a response (JSON)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" url = query\\n\",\n \" # replace token with token provided by NOAA. Enter token as string\\n\",\n \" headers = {'token': NOAA_Token_Here} # https://www.ncdc.noaa.gov/cdo-web/token\\n\",\n \" response = requests.get(url, headers = headers)\\n\",\n \" response = response.json()\\n\",\n \" \\n\",\n \" return response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'query_1' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mworking_1\\u001b[0m\\u001b[1;33m=\\u001b[0m \\u001b[0mexecute_query\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mquery_1\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;34m'results'\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 2\\u001b[0m \\u001b[0mworking_2\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mexecute_query\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mquery_2\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;34m'results'\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'query_1' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"working_1= execute_query(query_1)['results']\\n\",\n \"working_2 = execute_query(query_2)['results']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Extract Results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def extract_results(response):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Function accepts a NOAA query response (JSON) return the results\\n\",\n \" key values as well as the number of records (for use in validation).\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" data= response['results']\\n\",\n \" # for quality control to verify retrieval of all rows\\n\",\n \" length= len(data)\\n\",\n \" \\n\",\n \" return data, length\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def collator(results):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Functions accepts the results key of an NOAA query response (JSON)\\n\",\n \" and returns a tidy data set in PANDAS, where each record is an \\n\",\n \" observation about a day.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" df= pd.DataFrame(results) \\n\",\n \" df= df.drop(['attributes','station'], axis=1)\\n\",\n \" df= df.pivot(index= 'date',columns= 'datatype', values= 'value').reset_index()\\n\",\n \" \\n\",\n \" return df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_ncdc(start_dt, end_dt, station):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Function accepts a start date (MM-DD-YYY) an end date (MM-DD-YYYY)\\n\",\n \" and a NOAA station ID. Date limit is 1 year.\\n\",\n \" Function returns a tidy dataset in a PANDAS DataFrame where\\n\",\n \" each row represents an observation about a day, a record count\\n\",\n \" and a query parameters dictionary.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" \\n\",\n \" # count for verifying retrieval of all rows\\n\",\n \" record_count= 0\\n\",\n \" # initial query\\n\",\n \" query, query_dict= query_builder(start_dt, end_dt, station)\\n\",\n \" response= execute_query(query)\\n\",\n \" \\n\",\n \" # extract results and count \\n\",\n \" results, length= extract_results(response)\\n\",\n \" record_count += length\\n\",\n \" \\n\",\n \" # get offsets for remaining queries\\n\",\n \" off_d, count= offsetter(response)\\n\",\n \" \\n\",\n \" # execute remaining queries and operations\\n\",\n \" for offset in off_d:\\n\",\n \" query, _= query_builder(start_dt, end_dt, station, off_d[offset])\\n\",\n \" print(query)\\n\",\n \" response= execute_query(query)\\n\",\n \" next_results, next_length= extract_results(response)\\n\",\n \" \\n\",\n \" record_count += next_length\\n\",\n \" \\n\",\n \" # concat results lists\\n\",\n \" results += next_results\\n\",\n \" \\n\",\n \" assert record_count == count, 'record count != count'\\n\",\n \" \\n\",\n \" collated_data= collator(results)\\n\",\n \" \\n\",\n \" return collated_data, record_count, query_dict\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=1001&limit=1000&datasetid=GHCND\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=2001&limit=1000&datasetid=GHCND\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=3001&limit=1000&datasetid=GHCND\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-01-01&stationid=GHCND:USW00023174&enddate=2014-12-31&offset=4001&limit=1000&datasetid=GHCND\\n\"\n ]\n }\n ],\n \"source\": [\n \"test, qc, params = get_ncdc('2014-01-01', '2014-12-31', station= 'GHCND:USW00023174')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 2014-01-01T00:00:00\\n\",\n \"1 2014-01-02T00:00:00\\n\",\n \"2 2014-01-03T00:00:00\\n\",\n \"3 2014-01-04T00:00:00\\n\",\n \"4 2014-01-05T00:00:00\\n\",\n \"Name: date, dtype: object\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test.date.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"360 2014-12-27T00:00:00\\n\",\n \"361 2014-12-28T00:00:00\\n\",\n \"362 2014-12-29T00:00:00\\n\",\n \"363 2014-12-30T00:00:00\\n\",\n \"364 2014-12-31T00:00:00\\n\",\n \"Name: date, dtype: object\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test.date.tail()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 365 entries, 0 to 364\\n\",\n \"Data columns (total 16 columns):\\n\",\n \"date 365 non-null object\\n\",\n \"AWND 365 non-null float64\\n\",\n \"PRCP 365 non-null float64\\n\",\n \"SNOW 357 non-null float64\\n\",\n \"SNWD 360 non-null float64\\n\",\n \"TAVG 365 non-null float64\\n\",\n \"TMAX 365 non-null float64\\n\",\n \"TMIN 365 non-null float64\\n\",\n \"WDF2 365 non-null float64\\n\",\n \"WDF5 354 non-null float64\\n\",\n \"WSF2 365 non-null float64\\n\",\n \"WSF5 354 non-null float64\\n\",\n \"WT01 94 non-null float64\\n\",\n \"WT02 5 non-null float64\\n\",\n \"WT03 1 non-null float64\\n\",\n \"WT08 37 non-null float64\\n\",\n \"dtypes: float64(15), object(1)\\n\",\n \"memory usage: 45.7+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"test.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
datatypedateAWNDPRCPSNOWSNWDTAVGTMAXTMINWDF2WDF5WSF2WSF5WT01WT02WT03WT08
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \"Empty DataFrame\\n\",\n \"Columns: [date, AWND, PRCP, SNOW, SNWD, TAVG, TMAX, TMIN, WDF2, WDF5, WSF2, WSF5, WT01, WT02, WT03, WT08]\\n\",\n \"Index: []\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test[test.date.isnull()]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y1, qc, params = get_ncdc('2014-05-03', '2015-05-02', station= 'GHCND:USW00023174')\\n\",\n \"y2, qc, params = get_ncdc('2015-05-03', '2016-05-02', station= 'GHCND:USW00023174')\\n\",\n \"y3, qc, params = get_ncdc('2016-05-03', '2017-05-02', station= 'GHCND:USW00023174')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y1.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"years= pd.concat([y1, y2, y3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"years.date.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"years.date.tail()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"years.to_csv('LAX_3years.csv', index= False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### CSV Generator\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def gen_csv(df, query_dict):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Arguments: PANDAS DataFrame, a query parameters dictionary\\n\",\n \" Returns: A CSV of the df with dropped index and named by dict params\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # extract params\\n\",\n \" station= query_dict['stationid']\\n\",\n \" start= query_dict['startdate']\\n\",\n \" end= query_dict['enddate']\\n\",\n \" \\n\",\n \" # using os.path in case of future expansion to other directories\\n\",\n \" path= os.path.join(station + '_' + start + '_' + end + '.' + 'csv')\\n\",\n \" \\n\",\n \" # remove problem characters (will add more in future)\\n\",\n \" exclude_chars= ':'\\n\",\n \" path= path.replace(exclude_chars, \\\"_\\\")\\n\",\n \" \\n\",\n \" # export to csv\\n\",\n \" \\n\",\n \" my_csv= df.to_csv(path, index= False)\\n\",\n \" \\n\",\n \" return my_csv, path\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"stuff, path= gen_csv(test, query_dict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'GHCND_USW00023174_2014-01-01_2015-01-01.csv'\"\n ]\n },\n \"execution_count\": 86,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"path\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" Volume in drive C has no label.\\n\",\n \" Volume Serial Number is CE97-BE73\\n\",\n \"\\n\",\n \" Directory of C:\\\\Users\\\\Andrew\\\\Documents\\\\noaa_requests\\n\",\n \"\\n\",\n \"05/12/2017 12:35 AM 30,420 GHCND_USW00023174_2014-01-01_2015-01-01.csv\\n\",\n \" 1 File(s) 30,420 bytes\\n\",\n \" 0 Dir(s) 685,094,088,704 bytes free\\n\"\n ]\n }\n ],\n \"source\": [\n \"ls *csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Check dt format('DD-MM-YYYY', and whether dates span <= 1 year from a current or past date\\n\",\n \"If dates exceed one year, NCDC query returns a null object\\n\",\n \"Need a token take a token, have a token, keep it to yourself @ https://www.ncdc.noaa.gov/cdo-web/token\\n\",\n \"start_dt: -f\\n\",\n \" end_dt: C:\\\\Users\\\\vhim98198\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-9420aae1-29a1-4c51-ae89-41b7fd679e89.json\\n\"\n ]\n }\n ],\n \"source\": [\n \"#!/usr/bin/env python\\n\",\n \"# coding: utf-8\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"Python code for querying NOAA daily summary weather and returnig a CSV per year\\n\",\n \"for a specfic station. Code is intended to be executed from CLI.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"# set path to tools library and import\\n\",\n \"sys.path.append(r'noaa_weather_tools')\\n\",\n \"import noaa_weather_tools\\n\",\n \"\\n\",\n \"NOAA_Token_Here= 'enter token as string'\\n\",\n \"\\n\",\n \"print(\\\"Check dt format('DD-MM-YYYY', and whether dates span <= 1 year from a current or past date\\\")\\n\",\n \"print(\\\"If dates exceed one year, NCDC query returns a null object\\\")\\n\",\n \"print(\\\"Need a token take a token, have a token, keep it to yourself @ https://www.ncdc.noaa.gov/cdo-web/token\\\")\\n\",\n \"print('start_dt: {}\\\\n end_dt: {}'.format(sys.argv[1], sys.argv[2]))\\n\",\n \"\\n\",\n \"\\n\",\n \"def noaa_dailysum_weather_processor(start_dt, end_dt, station):\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"Function accepts a station ID, and beginning/end datetime as strings with date format as\\n\",\n \" 'MM-DD-YYYY' which span <= 1 year from a current or past date, passing them to the query_builder function. \\n\",\n \" Function creates a .csv file of NOAA (NCDC) Daily Summary data for a specific station.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" print(15 * '.' + \\\"reticulating splines\\\" + 5* '.' + \\\"getting records\\\") \\n\",\n \" df, record_count, query_parameters= noaa_weather_tools.get_ncdc(start_dt, end_dt, station)\\n\",\n \" \\n\",\n \" print(15* '.' + \\\"exporting to csv\\\")\\n\",\n \" my_csv, my_path= noaa_weather_tools.gen_csv(df, query_parameters)\\n\",\n \" \\n\",\n \" print(\\\"spines reticulated\\\")\\n\",\n \" return my_csv\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"...............reticulating splines.....getting records\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=1001&limit=1000&datasetid=GHCND\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=2001&limit=1000&datasetid=GHCND\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=3001&limit=1000&datasetid=GHCND\\n\",\n \"http://www.ncdc.noaa.gov/cdo-web/api/v2/data?startdate=2014-05-03&stationid=GHCND:USW00023174&enddate=2015-05-02&offset=4001&limit=1000&datasetid=GHCND\\n\",\n \"...............exporting to csv\\n\",\n \"spines reticulated\\n\"\n ]\n }\n ],\n \"source\": [\n \"noaa_dailysum_weather_processor('2014-05-03', '2015-05-02', station= 'GHCND:USW00023174')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" Volume in drive C is Acer\\n\",\n \" Volume Serial Number is 3829-CAE6\\n\",\n \"\\n\",\n \" Directory of C:\\\\Users\\\\vhim98198\\\\Documents\\\\noaa_requests\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"File Not Found\\n\"\n ]\n }\n ],\n \"source\": [\n \"ls *csv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discarded Functions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"```python\\n\",\n \"def collator(response):\\n\",\n \" \\n\",\n \" data= pd.DataFrame(response['results'])\\n\",\n \" # for quality control to verify retrieval of all rows\\n\",\n \" length= len(data)\\n\",\n \" \\n\",\n \" data= data.drop(['attributes','station'], axis=1)\\n\",\n \" data= data.pivot(index= 'date',columns= 'datatype', values= 'value').reset_index()\\n\",\n \" \\n\",\n \" return data, length\\n\",\n \"\\n\",\n \"def get_ncdc(start_dt, end_dt, station):\\n\",\n \" \\n\",\n \" \\n\",\n \" # count for verifying retrieval of all rows\\n\",\n \" row_count= 0\\n\",\n \" # initial query\\n\",\n \" query, query_dict= query_builder(start_dt, end_dt, station)\\n\",\n \" response= execute_query(query)\\n\",\n \" \\n\",\n \" # collate and count \\n\",\n \" collated_data, length= collator(response)\\n\",\n \" row_count += length\\n\",\n \" \\n\",\n \" # get offsets for remaining queries\\n\",\n \" off_d, count= offsetter(response)\\n\",\n \" \\n\",\n \" # execute remaining queries and operations\\n\",\n \" for offset in off_d:\\n\",\n \" query, _= query_builder(start_dt, end_dt, station, off_d[offset])\\n\",\n \" print(query)\\n\",\n \" response= execute_query(query)\\n\",\n \" next_data, next_length= collator(response)\\n\",\n \" \\n\",\n \" row_count += next_length\\n\",\n \" \\n\",\n \" # stack DataFrames\\n\",\n \" collated_data= pd.concat([collated_data, next_data])\\n\",\n \" \\n\",\n \" assert row_count == count, 'row count != count'\\n\",\n \" \\n\",\n \" return collated_data, row_count\\n\",\n \"``` \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":253,"cells":{"repo_name":{"kind":"string","value":"peterwittek/ipython-notebooks"},"path":{"kind":"string","value":"Parameteric and Bilevel Polynomial Optimization Problems.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"77812"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Relaxations of parametric and bilevel polynomial optimization problems\\n\",\n \"========================================\\n\",\n \"Suppose we are interested in finding the global optimum of the following constrained polynomial optimization problem:\\n\",\n \"\\n\",\n \"$$ \\\\min_{x\\\\in\\\\mathbb{R}^n}f(x)$$\\n\",\n \"such that\\n\",\n \"$$ g_i(x) \\\\geq 0, i=1,\\\\ldots,r$$\\n\",\n \"\\n\",\n \"Here $f$ and $g_i$ are polynomials in $x$. We can think of the constraints as a semialgebraic set $\\\\mathbf{K}=\\\\{x\\\\in\\\\mathbb{R}^n: g_i(x) \\\\geq 0, i=1,\\\\ldots,r\\\\}$. [Lasserre's method](http://dx.doi.org/10.1137/S1052623400366802) gives a series of semidefinite programming (SDP) relaxation of increasing size that approximate this optimum through the moments of $x$.\\n\",\n \"\\n\",\n \"A [manuscript](http://arxiv.org/abs/1506.02099) appeared on arXiv a few months ago that considers bilevel polynomial optimization problems, that is, when a set of variables is subject to a lower level optimization. The method heavily relies on the [joint+marginal approach](http://dx.doi.org/10.1137/090759240) to SDP relaxations of parametric polynomial optimization problems. Here we discuss both methods and how they can be implemented with [Ncpol2sdpa](https://ncpol2sdpa.readthedocs.io/)>=1.10 in Python.\\n\",\n \"\\n\",\n \"Constraints on moments\\n\",\n \"------------------------------\\n\",\n \"\\n\",\n \"Let us consider the plainest form of the SDP relaxation first with the the following trivial polynomial optimization problem:\\n\",\n \"\\n\",\n \"$$ \\\\min_{x\\\\in \\\\mathbb{R}}2x^2$$\\n\",\n \"\\n\",\n \"Let us denote the linear mapping from monomials to $\\\\mathbb{R}$ by $L_x$. Then, for instance, $L_x(2x^2)= 2y_2$. If there is a representing Borel measure $\\\\mu$ on the semialgebraic set $\\\\mathbf{K}$ of the constraints (in this case, there are no constraints), then we can write $y_\\\\alpha = \\\\int_\\\\mathbf{K} x^\\\\alpha \\\\mathrm{d}\\\\mu$. The level-1 relaxation will be\\n\",\n \"\\n\",\n \"$$ \\\\min_{y}2y_{2}$$\\n\",\n \"such that\\n\",\n \"$$\\\\left[ \\\\begin{array}{cc}1 & y_{1} \\\\\\\\y_{1} & y_{2}\\\\end{array} \\\\right] \\\\succeq{}0.$$\\n\",\n \"\\n\",\n \"We import some functions first that we will use later:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"from math import sqrt\\n\",\n \"from sympy import integrate, N\\n\",\n \"from ncpol2sdpa import SdpRelaxation, generate_variables, flatten\\n\",\n \"%matplotlib inline \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Not surprisingly, solving the example gives a very accurate result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.5990354545454662e-08 -1.5990354545454556e-08\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = generate_variables('x')[0]\\n\",\n \"sdp = SdpRelaxation([x])\\n\",\n \"sdp.get_relaxation(1, objective=x**2)\\n\",\n \"sdp.solve()\\n\",\n \"print(sdp.primal, sdp.dual)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that even in this formulation, there is an implicit constraint on a moment: the top left element of the moment matrix is 1. Given a representing measure, this means that $\\\\int_\\\\mathbf{K} \\\\mathrm{d}\\\\mu=1$. It is actually because of this that a $\\\\lambda$ dual variable appears in the dual formulation:\\n\",\n \"$$max_{\\\\lambda, \\\\sigma_0} \\\\lambda$$\\n\",\n \"such that\\n\",\n \"$$2x^2 - \\\\lambda = \\\\sigma_0\\\\\\\\\\n\",\n \"\\\\sigma_0\\\\in \\\\Sigma{[x]}, \\\\mathrm{deg}\\\\sigma_0\\\\leq 2.$$\\n\",\n \"\\n\",\n \"In fact, we can move $\\\\lambda$ to the right-hand side, where the sum-of-squares (SOS) decomposition is, $\\\\lambda$ being a trivial SOS multiplied by the constraint $\\\\int_\\\\mathbf{K} \\\\mathrm{d}\\\\mu$, that is, by 1.\\n\",\n \"\\n\",\n \"We normally think of adding some $g_i(x)$ polynomial constraints that define a semialgebraic set, and then constructing matching localizing matrices. We can, however, impose more constraints on the moments. For instance, we can add a constraint that $\\\\int_\\\\mathbf{K} x\\\\mathrm{d}\\\\mu = 1$. All of these constraints will have a constant instead of an SOS polynomial in the dual. To ensure the moments are not substituted out while generating the SDP, we enter them as pairs of moment inequalities. Solving this problem gives the correct result again:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.0000006249546196 0.9999997167810939\\n\"\n ]\n }\n ],\n \"source\": [\n \"moments = [x-1, 1-x]\\n\",\n \"sdp = SdpRelaxation([x])\\n\",\n \"sdp.get_relaxation(1, objective=x**2, momentinequalities=moments)\\n\",\n \"sdp.solve()\\n\",\n \"print(sdp.primal, sdp.dual)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The dual changed, slightly. Let $\\\\gamma_\\\\beta=\\\\int_\\\\mathbf{K} x^\\\\beta\\\\mathrm{d}\\\\mu$ for $\\\\beta=0, 1$. Then the dual reads as\\n\",\n \"\\n\",\n \"$$max_{\\\\lambda_\\\\beta, \\\\sigma_0} \\\\sum_{\\\\beta=0}^1\\\\lambda_\\\\beta \\\\gamma_\\\\beta$$\\n\",\n \"such that\\n\",\n \"$$2x^2 - \\\\sum_{\\\\beta=0}^1\\\\lambda_\\\\beta x^\\\\beta = \\\\sigma_0\\\\\\\\\\n\",\n \"\\\\sigma_0\\\\in \\\\Sigma{[x]}, \\\\mathrm{deg}\\\\sigma_0\\\\leq 2.$$\\n\",\n \"\\n\",\n \"Indeed, if we extract the coefficients, we will see that $x$ gets a $\\\\lambda_1=2$ (note that equalities are replaced by pairs of inequalities):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1.0, 2.0] [0.9999997167810939, 2.00000000000000]\\n\"\n ]\n }\n ],\n \"source\": [\n \"coeffs = [-sdp.extract_dual_value(0, range(1))]\\n\",\n \"coeffs += [sdp.y_mat[2*i+1][0][0] - sdp.y_mat[2*i+2][0][0]\\n\",\n \" for i in range(len(moments)//2)]\\n\",\n \"sigma_i = sdp.get_sos_decomposition()\\n\",\n \"print(coeffs, [sdp.dual, sigma_i[1]-sigma_i[2]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Moment constraints play a crucial role in the joint+marginal approach of the SDP relaxation of polynomial optimization problems, and hence also indirectly in the bilevel polynomial optimization problems.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Joint+marginal approach\\n\",\n \"-------------------------------\\n\",\n \"In a parametric polynomial optimization problem, we can separate two sets of variables, and one set acts as a parameter to the problem. More formally, we would like to find the following function:\\n\",\n \"\\n\",\n \"$$ J(x) = \\\\inf_{y\\\\in\\\\mathbb{R}^m}\\\\{f(x,y): h_j(y)\\\\geq 0, j=1,\\\\ldots,r\\\\},$$\\n\",\n \"\\n\",\n \"where $x\\\\in\\\\mathbf{X}=\\\\{x\\\\in \\\\mathbb{R}^n: h_k(x)\\\\geq 0, k=r+1,\\\\ldots,t\\\\}$. This [can be relaxed](http://dx.doi.org/10.1137/090759240) as an SDP, and we can extract an approximation $J_k(x)$ at level-$k$ from the dual solution. The primal form reads as\\n\",\n \"\\n\",\n \"$$ \\\\mathrm{inf}_z L_z(f)$$\\n\",\n \"such that\\n\",\n \"$$ M_k(z)\\\\succeq 0,\\\\\\\\\\n\",\n \"M_{k-v_j}(h_j z)\\\\succeq 0, j=1,\\\\ldots,t\\\\\\\\\\n\",\n \"L_z(x^\\\\beta) = \\\\gamma_\\\\beta, \\\\forall\\\\beta\\\\in\\\\mathbb{N}_k^n.$$\\n\",\n \"\\n\",\n \"Notice that the localizing matrices also address the polynomial constraints that define the semialgebraic set $\\\\mathbf{X}$. If the positivity constraints are fulfilled, then we have a finite Borel representing measure $\\\\mu$ on $\\\\mathbf{K}=\\\\{h_j(y)\\\\geq 0, j=1,\\\\ldots,r\\\\}$ such that $z_{\\\\alpha\\\\beta}=\\\\int_\\\\mathbf{K} x^\\\\alpha y^\\\\beta\\\\mathrm{d}\\\\mu$. \\n\",\n \"\\n\",\n \"The part that is different from the regular Lasserre hierachy is the last line, where $\\\\gamma_\\\\beta=\\\\int_\\\\mathbf{X} x^\\\\beta\\\\mathrm{d}\\\\varphi(x)$. This establishes a connection between the moments of $x$ on $\\\\mathbf{K}$ in measure $\\\\mu$ and the moments of $x$ on $\\\\mathbf{X}$ in measure $\\\\varphi$. This $\\\\varphi$ measure is a Borel probability measure on $\\\\mathbf{X}$ with a positive density with respect to the Lebesgue measure on $\\\\mathbb{R}^n$. In other words, the marginal of one measure must match the other on $\\\\mathbf{X}$.\\n\",\n \"\\n\",\n \"The dual of the primal form of the SDP with these moment constraints is\\n\",\n \"\\n\",\n \"$$ \\\\mathrm{sup}_{p, \\\\sigma_i} \\\\int_\\\\mathbf{X} p \\\\mathrm{d}\\\\varphi$$\\n\",\n \"such that\\n\",\n \"$$ f - p = \\\\sigma_0 + \\\\sum_{j=1}^t \\\\sigma_j h_j\\\\\\n\",\n \"p\\\\in\\\\mathbb{R}[x], \\\\sigma_j\\\\in\\\\Sigma[x, y], j=0,\\\\ldots,t\\\\\\\\\\n\",\n \"\\\\mathrm{deg} p\\\\leq 2k, \\\\mathrm{deg} \\\\sigma_0\\\\leq 2k, \\\\mathrm{deg}\\\\sigma_j h_j \\\\leq 2k, j=1,\\\\ldots, t.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The polynomial $p=\\\\sum_{\\\\beta=0}^{2k} \\\\lambda_\\\\beta x^\\\\beta$ is the approximation $J_k(x)$. Below we reproduce the three examples of the paper.\\n\",\n \"\\n\",\n \"*Example 1*\\n\",\n \"\\n\",\n \"Let $\\\\mathbf{X}=[0,1]$, $\\\\mathbf{K}=\\\\{(x,y): 1-x^2-y^2\\\\geq 0; x,y\\\\in\\\\mathbf{X}\\\\}$, and $f(x,y)= - xy^2$. We know that $J(x) = -1(1-x^2)x$. First we declare $J(x)$, a helper function to define a polynomial, and we set up the symbolic variables $x$ and $y$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def J(x):\\n\",\n \" return -(1-x**2)*x\\n\",\n \"\\n\",\n \"\\n\",\n \"def Jk(x, coeffs):\\n\",\n \" return sum(ci*x**i for i, ci in enumerate(coeffs))\\n\",\n \"\\n\",\n \"x = generate_variables('x')[0]\\n\",\n \"y = generate_variables('y')[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, we define the level of the relaxation and the moment constraints:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"level = 4\\n\",\n \"gamma = [integrate(x**i, (x, 0, 1)) for i in range(1, 2*level+1)]\\n\",\n \"marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*level+1)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally we define the objective function and the constraints that define the semialgebraic sets, and we generate and solve the relaxation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-0.25003374095299485 -0.2500337409529949 optimal\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = -x*y**2\\n\",\n \"inequalities = [1.0-x**2-y**2, 1-x, x, 1-y, y]\\n\",\n \"\\n\",\n \"sdp = SdpRelaxation([x, y], verbose=0)\\n\",\n \"sdp.get_relaxation(level, objective=f, momentinequalities=marginals,\\n\",\n \" inequalities=inequalities)\\n\",\n \"sdp.solve()\\n\",\n \"print(sdp.primal, sdp.dual, sdp.status)\\n\",\n \"coeffs = [sdp.extract_dual_value(0, range(len(inequalities)+1))]\\n\",\n \"coeffs += [sdp.y_mat[len(inequalities)+1+2*i][0][0] - sdp.y_mat[len(inequalities)+1+2*i+1][0][0]\\n\",\n \" for i in range(len(marginals)//2)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To check the correctness of the approximation, we plot the optimal and the approximated functions over the domain.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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HTICW8jISGBhIQEn7P49CiriKwD4lQ1UUSqA8tUtWEBba8G+qhq2xzr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\\njDEmDysOxhhj8vh/LJ94blT5nQUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_domain = [i/100. for i in range(100)]\\n\",\n \"plt.plot(x_domain, [J(xi) for xi in x_domain], linewidth=2.5)\\n\",\n \"plt.plot(x_domain, [Jk(xi, coeffs) for xi in x_domain], linewidth=2.5)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*Example 2*\\n\",\n \"\\n\",\n \"The set $\\\\mathbf{X}=[0,1]$ remains the same. Let $\\\\mathbf{K}=\\\\{(x,y): 1-y_1^2-y_2^2\\\\geq 0\\\\}$, and $f(x,y) = xy_1 + (1-x)y_2$. Now the optimal $J(x)$ will be $-\\\\sqrt{x^2+(1-x)^2}$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-0.8116610109835365 -0.8116610109835365 optimal\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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fD7euezuiINa1/NnEfXQpJjJP/pJ7rTe9YMi1Op3NLioP7lfGo61aJb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\\n/A9Iqo/3zGpE6QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def J(x):\\n\",\n \" return -sqrt(x**2+(1-x)**2)\\n\",\n \"\\n\",\n \"x = generate_variables('x')[0]\\n\",\n \"y = generate_variables('y', 2)\\n\",\n \"\\n\",\n \"f = x*y[0] + (1-x)*y[1]\\n\",\n \"\\n\",\n \"gamma = [integrate(x**i, (x, 0, 1)) for i in range(1, 2*level+1)]\\n\",\n \"marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*level+1)])\\n\",\n \"inequalities = [1-y[0]**2-y[1]**2, x, 1-x]\\n\",\n \"sdp = SdpRelaxation(flatten([x, y]))\\n\",\n \"sdp.get_relaxation(level, objective=f, momentinequalities=marginals,\\n\",\n \" inequalities=inequalities)\\n\",\n \"sdp.solve()\\n\",\n \"print(sdp.primal, sdp.dual, sdp.status)\\n\",\n \"coeffs = [sdp.extract_dual_value(0, range(len(inequalities)+1))]\\n\",\n \"coeffs += [sdp.y_mat[len(inequalities)+1+2*i][0][0] - sdp.y_mat[len(inequalities)+1+2*i+1][0][0]\\n\",\n \" for i in range(len(marginals)//2)]\\n\",\n \"plt.plot(x_domain, [J(xi) for xi in x_domain], linewidth=2.5)\\n\",\n \"plt.plot(x_domain, [Jk(xi, coeffs) for xi in x_domain], linewidth=2.5)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*Example 3*\\n\",\n \"\\n\",\n \"Note that this is Example 4 in the paper. The set $\\\\mathbf{X}=[0,1]$ remains the same, whereas $\\\\mathbf{K}=\\\\{(x,y): xy_1^2+y_2^2-x= 0, y_1^2+xy_2^2-x= 0\\\\}$, and $f(x,y) = (1-2x)(y_1+y_2)$. The optimal $J(x)$ is $-2|1-2x|\\\\sqrt{x/(1+x)}$. We enter the equalities as pairs of inequalities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-0.535187880633569 -0.535188348622651 near_optimal\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/pwittek/.local/lib/python3.5/site-packages/mosek/__init__.py:5463: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\\n\",\n \" if barxj_ != None:\\n\",\n \"/home/pwittek/.local/lib/python3.5/site-packages/mosek/__init__.py:5498: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\\n\",\n \" if barsj_ != None:\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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NgySR3ZPeJTuFiIyEira6hCmrNulL/wySQgCEu6LqFEcmPKOkf79e8P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\\nEgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def J(x):\\n\",\n \" return -2*abs(1-2*x)*sqrt(x/(1+x))\\n\",\n \"\\n\",\n \"x = generate_variables('x')[0]\\n\",\n \"y = generate_variables('y', 2)\\n\",\n \"f = (1-2*x)*(y[0] + y[1])\\n\",\n \"\\n\",\n \"gamma = [integrate(x**i, (x, 0, 1)) for i in range(1, 2*level+1)]\\n\",\n \"marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*level+1)])\\n\",\n \"\\n\",\n \"inequalities = [x*y[0]**2 + y[1]**2 - x, - x*y[0]**2 - y[1]**2 + x,\\n\",\n \" y[0]**2 + x*y[1]**2 - x, - y[0]**2 - x*y[1]**2 + x,\\n\",\n \" 1-x, x]\\n\",\n \"sdp = SdpRelaxation(flatten([x, y]))\\n\",\n \"sdp.get_relaxation(level, objective=f, momentinequalities=marginals,\\n\",\n \" inequalities=inequalities)\\n\",\n \"sdp.solve(solver=\\\"mosek\\\")\\n\",\n \"print(sdp.primal, sdp.dual, sdp.status)\\n\",\n \"coeffs = [sdp.extract_dual_value(0, range(len(inequalities)+1))]\\n\",\n \"coeffs += [sdp.y_mat[len(inequalities)+1+2*i][0][0] - sdp.y_mat[len(inequalities)+1+2*i+1][0][0]\\n\",\n \" for i in range(len(marginals)//2)]\\n\",\n \"plt.plot(x_domain, [J(xi) for xi in x_domain], linewidth=2.5)\\n\",\n \"plt.plot(x_domain, [Jk(xi, coeffs) for xi in x_domain], linewidth=2.5)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Bilevel problem of nonconvex lower level\\n\",\n \"------------------------------------------------------\\n\",\n \"We define the bilevel problem as follows:\\n\",\n \"$$ \\\\min_{x\\\\in\\\\mathbb{R}^n, y\\\\in\\\\mathbb{R}^m}f(x,y)$$\\n\",\n \"such that\\n\",\n \"$$g_i(x, y) \\\\geq 0, i=1,\\\\ldots,s,\\\\\\\\\\n\",\n \"y\\\\in Y(x)=\\\\mathrm{argmin}_{w\\\\in\\\\mathbb{R}^m}\\\\{G(x,w): h_j(w)\\\\geq 0, j=1,...,r\\\\}.$$\\n\",\n \"\\n\",\n \"The more interesting case is when the when the lower level problem's objective function $G(x,y)$ is nonconvex. We consider the $\\\\epsilon$-approximation of this case:\\n\",\n \"\\n\",\n \"$$ \\\\min_{x\\\\in\\\\mathbb{R}^n, y\\\\in\\\\mathbb{R}^m}f(x,y)$$\\n\",\n \"such that\\n\",\n \"$$g_i(x, y) \\\\geq 0, i=1,\\\\ldots,s,\\\\\\\\\\n\",\n \"h_j(y) \\\\geq 0, j=1,\\\\dots, r,\\\\\\\\\\n\",\n \"G(x,y) - \\\\mathrm{min}_{w\\\\in\\\\mathbb{R}^m}\\\\{G(x,w): h_j(w)\\\\geq 0, j=1,...,r\\\\}\\\\leq \\\\epsilon.$$\\n\",\n \"\\n\",\n \"This approximation will give an [increasing lower bound](http://arxiv.org/abs/1506.02099)) on the original problem. The min function on the right of $G(x,y)$ is essentially a parametric polynomial optimization problem, that is, our task is to find $J(x)$. We have to ensure that the parameter set is compact, so we add a set of constraints on the coordinates of $x$: $\\\\{M^2-x_l^2\\\\geq 0, l=1,\\\\ldots,n\\\\}$ for some $M>0$.\\n\",\n \"\\n\",\n \"The idea is that we relax this as an SDP at some level $k$ and fixed $\\\\epsilon$ to obtain the following single-level polynomial optimization problem:\\n\",\n \"$$ \\\\min_{x\\\\in\\\\mathbb{R}^n, y\\\\in\\\\mathbb{R}^m}f(x,y)$$\\n\",\n \"such that\\n\",\n \"$$g_i(x, y) \\\\geq 0, i=1,\\\\ldots,s,\\\\\\\\\\n\",\n \"h_j(y) \\\\geq 0, j=1,\\\\dots, r,\\\\\\\\\\n\",\n \"G(x,y) - J_k(x)\\\\leq \\\\epsilon.$$\\n\",\n \"\\n\",\n \"Then we relax this is an another SDP at level $k$.\\n\",\n \"\\n\",\n \"Consider a test problem defined as follows:\\n\",\n \"$$ \\\\min_{(x,y)\\\\in\\\\mathbb{R}^2} x+y$$\\n\",\n \"such that\\n\",\n \"$$x\\\\in[-1,1], \\\\\\\\\\n\",\n \"y\\\\in \\\\mathrm{argmin}_{w\\\\in\\\\mathbb{R}^m}\\\\{\\\\frac{xy^2}{2}-\\\\frac{y^3}{3}, y\\\\in[-1,1]\\\\}$$.\\n\",\n \"\\n\",\n \"This is clearly a bilevel problem. We set up the necessary variables and constraints, requesting a level-3 relaxation, and also fixing $\\\\epsilon$ and a choice of $M$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"x = generate_variables('x')[0]\\n\",\n \"y = generate_variables('y')[0]\\n\",\n \"\\n\",\n \"f = x + y\\n\",\n \"g = [x <= 1.0, x >= -1.0]\\n\",\n \"G = x*y**2/2.0 - y**3/3.0\\n\",\n \"h = [y <= 1.0, y >= -1.0]\\n\",\n \"epsilon = 0.001\\n\",\n \"M = 1.0\\n\",\n \"level = 3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We define the relaxation of the parametric polynomial optimization problem that returns an approximation of $J(x)$ from the dual:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def lower_level(k, G, h, M):\\n\",\n \" gamma = [integrate(x**i, (x, -M, M))/(2*M) for i in range(1, 2*k+1)]\\n\",\n \" marginals = flatten([[x**i-N(gamma[i-1]), N(gamma[i-1])-x**i] for i in range(1, 2*k+1)])\\n\",\n \" inequalities = h + [x**2 <= M**2]\\n\",\n \" lowRelaxation = SdpRelaxation([x, y])\\n\",\n \" lowRelaxation.get_relaxation(k, objective=G,\\n\",\n \" momentinequalities=marginals,\\n\",\n \" inequalities=inequalities)\\n\",\n \" lowRelaxation.solve()\\n\",\n \" print(\\\"Low-level:\\\", lowRelaxation.primal, lowRelaxation.dual, lowRelaxation.status)\\n\",\n \" coeffs = []\\n\",\n \" for i in range(len(marginals)//2):\\n\",\n \" coeffs.append(lowRelaxation.y_mat[len(inequalities)+1+2*i][0][0] -\\n\",\n \" lowRelaxation.y_mat[len(inequalities)+1+2*i+1][0][0])\\n\",\n \" blocks = [i for i in range(len(inequalities)+1)]\\n\",\n \" constant = lowRelaxation.extract_dual_value(0, blocks)\\n\",\n \" return constant + sum(ci*x**(i+1) for i, ci in enumerate(coeffs))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we put it all together:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Low-level: -0.3506176102365808 -0.3506180235695789 unknown\\n\",\n \"High-level: -0.0027997294093268232 unknown\\n\",\n \"Optimal x and y: -1.0 0.9972\\n\"\n ]\n }\n ],\n \"source\": [\n \"Jk = lower_level(level, G, h, M)\\n\",\n \"inequalities = g + h + [G - Jk <= epsilon]\\n\",\n \"highRelaxation = SdpRelaxation([x, y], verbose=0)\\n\",\n \"highRelaxation.get_relaxation(level, objective=f,\\n\",\n \" inequalities=inequalities)\\n\",\n \"highRelaxation.solve()\\n\",\n \"print(\\\"High-level:\\\", highRelaxation.primal, highRelaxation.status)\\n\",\n \"print(\\\"Optimal x and y:\\\", highRelaxation[x], highRelaxation[y])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"These values are close to the analytical solution.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":254,"cells":{"repo_name":{"kind":"string","value":"nilbody/h2o-3"},"path":{"kind":"string","value":"h2o-docs/src/booklets/v2_2015/source/DeepLearning_Vignette.ipynb"},"copies":{"kind":"string","value":"15"},"size":{"kind":"string","value":"4853"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#---------------------------------------------------------------------\\n\",\n \"#\\n\",\n \"# Include and run all the Python code snippets from the H2O Deep Learning Vignette.\\n\",\n \"#\\n\",\n \"# The snippets are broken out into separate files so the exact same\\n\",\n \"# piece of code both shows up in the document and is run by this\\n\",\n \"# script.\\n\",\n \"#\\n\",\n \"# Currently these scripts replicate the GLM ipynb version, but are not finished.\",\n \"#\\n\",\n \"#---------------------------------------------------------------------\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import h2o\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"print(os.getcwd())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.init()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_gaussian_example.py\\\")\\n\",\n \"\\n\",\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_binomial_example.py\\\")\\n\",\n \"\\n\",\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_poisson_example.py\\\")\\n\",\n \"\\n\",\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_gamma_example.py\\\")\\n\",\n \"\\n\",\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_tweedie_example.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/coerce_column_to_factor.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_cross_validation.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_stopping_criteria.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_grid_search_over_alpha.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Not supported right now.\\n\",\n \"#\\n\",\n \"# h2o.remove_all()\\n\",\n \"# execfile(\\\"deeplearning/deeplearning_grid_search_over_lambda.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_model_output_10.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_model_output_20.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_model_output_30.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_model_output_40.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_accessors.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_confusion_matrix.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_scoring_history.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_binomial_predictions_with_response.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_binomial_predictions_without_response.py\\\")\\n\",\n \"execfile(\\\"deeplearning/deeplearning_recalculate_predict.py\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"h2o.remove_all()\\n\",\n \"execfile(\\\"deeplearning/deeplearning_download_pojo.py\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":255,"cells":{"repo_name":{"kind":"string","value":"izaid/dynd-python"},"path":{"kind":"string","value":"docs/notebooks/SciPy2013Intro.ipynb"},"copies":{"kind":"string","value":"8"},"size":{"kind":"string","value":"16177"},"content":{"kind":"string","value":"{\n \"metadata\": {\n \"name\": \"\",\n \"signature\": \"sha256:59d5d6f4be6a184d13faea3e888d526f35876a3a49d4feb01c62a7e6e4682589\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n {\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"import sys, dynd\\n\",\n \"print('Python %s' % sys.version)\\n\",\n \"print('DyND Python Bindings %s' % dynd.__version__)\\n\",\n \"print('LibDyND %s' % dynd.__libdynd_version__)\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \"Python 3.3.3 |Continuum Analytics, Inc.| (default, Dec 3 2013, 11:56:40) [MSC v.1600 64 bit (AMD64)]\\n\",\n \"DyND Python Bindings 0.6.5.post024.g5f07540\\n\",\n \"LibDyND 0.6.5.post150.g8e9d756\\n\"\n ]\n }\n ],\n \"prompt_number\": 1\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Standard dynd import: The `nd` namespace is for array operations, and the `ndt` namespace is for types. We'll import numpy as well to show some comparisons.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"from dynd import nd, ndt\\n\",\n \"import numpy as np\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 2\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Basic usage is quite similar to numpy, the data type is deduced from python types when converting.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"nd.array([1,2,3,4,5]) # dynd\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 3,\n \"text\": [\n \"nd.array([1, 2, 3, 4, 5],\\n\",\n \" type=\\\"5 * int32\\\")\"\n ]\n }\n ],\n \"prompt_number\": 3\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"np.array([1,2,3,4,5]) # numpy\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 4,\n \"text\": [\n \"array([1, 2, 3, 4, 5])\"\n ]\n }\n ],\n \"prompt_number\": 4\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"nd.array([[1, 1.5], [-1.5, 1]]) # dynd\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 5,\n \"text\": [\n \"nd.array([ [ 1, 1.5], [-1.5, 1]],\\n\",\n \" type=\\\"2 * 2 * float64\\\")\"\n ]\n }\n ],\n \"prompt_number\": 5\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"np.array([[1, 1.5], [-1.5, 1]]) # numpy\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 6,\n \"text\": [\n \"array([[ 1. , 1.5],\\n\",\n \" [-1.5, 1. ]])\"\n ]\n }\n ],\n \"prompt_number\": 6\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see some differences with numpy at this level already, such as the array dimensionality being included in the type.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The default string is variable-length in dynd. In numpy, you either choose a maximum size, or use object arrays with lower performance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"nd.array([\\\"this\\\", \\\"is\\\", \\\"a\\\", \\\"test\\\"]) # dynd\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 7,\n \"text\": [\n \"nd.array([\\\"this\\\", \\\"is\\\", \\\"a\\\", \\\"test\\\"],\\n\",\n \" type=\\\"4 * string\\\")\"\n ]\n }\n ],\n \"prompt_number\": 7\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"np.array([\\\"this\\\", \\\"is\\\", \\\"a\\\", \\\"test\\\"]) # numpy\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 8,\n \"text\": [\n \"array(['this', 'is', 'a', 'test'], \\n\",\n \" dtype='\\n\"\n ]\n }\n ],\n \"source\": [\n \"ho = pd.read_csv('../data/head_nb1.csv', parse_dates=['date'], index_col='date', squeeze=True)\\n\",\n \"print('The data type of the oseries is:', type(ho))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The variable `ho` is now a `pandas Series` object. To see the first five lines, type `ho.head()`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"date\\n\",\n \"1985-11-14 27.61\\n\",\n \"1985-11-28 27.73\\n\",\n \"1985-12-14 27.91\\n\",\n \"1985-12-28 28.13\\n\",\n \"1986-01-13 28.32\\n\",\n \"Name: head, dtype: float64\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ho.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The series can be plotted as follows\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ho.plot(style='.', figsize=(12, 4))\\n\",\n \"plt.ylabel('Head [m]');\\n\",\n \"plt.xlabel('Time [years]');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Load the stresses\\n\",\n \"The head variation shown above is believed to be caused by two stresses: rainfall and evaporation. Measured rainfall is stored in the file `rain_nb1.csv` and measured potential evaporation is stored in the file `evap_nb1.csv`. \\n\",\n \"The rainfall and potential evaporation are loaded and plotted.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The data type of the rain series is: \\n\",\n \"The data type of the evap series is \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rain = pd.read_csv('../data/rain_nb1.csv', parse_dates=['date'], index_col='date', squeeze=True)\\n\",\n \"print('The data type of the rain series is:', type(rain))\\n\",\n \"\\n\",\n \"evap = pd.read_csv('../data/evap_nb1.csv', parse_dates=['date'], index_col='date', squeeze=True)\\n\",\n \"print('The data type of the evap series is', type(evap))\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12, 4))\\n\",\n \"rain.plot(label='rain')\\n\",\n \"evap.plot(label='evap')\\n\",\n \"plt.xlabel('Time [years]')\\n\",\n \"plt.ylabel('Rainfall/Evaporation (m/d)')\\n\",\n \"plt.legend(loc='best');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Recharge\\n\",\n \"As a first simple model, the recharge is approximated as the measured rainfall minus the measured potential evaporation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"recharge = rain - evap\\n\",\n \"plt.figure(figsize=(12, 4))\\n\",\n \"recharge.plot()\\n\",\n \"plt.xlabel('Time [years]')\\n\",\n \"plt.ylabel('Recharge (m/d)');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### First time series model\\n\",\n \"Once the time series are read from the data files, a time series model can be constructed by going through the following three steps:\\n\",\n \"\\n\",\n \"1. Creat a `Model` object by passing it the observed head series. Store your model in a variable so that you can use it later on. \\n\",\n \"2. Add the stresses that are expected to cause the observed head variation to the model. In this example, this is only the recharge series. For each stess, a `StressModel` object needs to be created. Each `StressModel` object needs three input arguments: the time series of the stress, the response function that is used to simulate the effect of the stress, and a name. In addition, it is recommended to specified the `kind` of series, which is used to perform a number of checks on the series and fix problems when needed. This checking and fixing of problems (for example, what to substitute for a missing value) depends on the kind of series. In this case, the time series of the stress is stored in the variable `recharge`, the Gamma function is used to simulate the response, the series will be called `'recharge'`, and the kind is `prec` which stands for precipitation. One of the other keyword arguments of the `StressModel` class is `up`, which means that a positive stress results in an increase (up) of the head. The default value is `True`, which we use in this case as a positive recharge will result in the heads going up. Each `StressModel` object needs to be stored in a variable, after which it can be added to the model. \\n\",\n \"3. When everything is added, the model can be solved. The default option is to minimize the sum of the squares of the errors between the observed and modeled heads. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fit report head Fit Statistics\\n\",\n \"==================================================\\n\",\n \"nfev 10 EVP 92.02\\n\",\n \"nobs 518 R2 0.92\\n\",\n \"noise True RMSE 0.13\\n\",\n \"tmin 1985-11-14 00:00:00 AIC -2592.26\\n\",\n \"tmax 2010-01-01 00:00:00 BIC -2571.01\\n\",\n \"freq D Obj 1.70\\n\",\n \"warmup 3650 days 00:00:00 ___ \\n\",\n \"solver LeastSquares Interp. No\\n\",\n \"\\n\",\n \"Parameters (5 optimized)\\n\",\n \"==================================================\\n\",\n \" optimal stderr initial vary\\n\",\n \"recharge_A 749.010453 ±4.79% 215.674528 True\\n\",\n \"recharge_n 1.049137 ±1.53% 1.000000 True\\n\",\n \"recharge_a 134.483793 ±6.73% 10.000000 True\\n\",\n \"constant_d 27.547665 ±0.07% 27.900078 True\\n\",\n \"noise_alpha 58.973443 ±12.37% 15.000000 True\\n\"\n ]\n }\n ],\n \"source\": [\n \"ml = ps.Model(ho)\\n\",\n \"sm1 = ps.StressModel(recharge, ps.Gamma, name='recharge', settings='prec')\\n\",\n \"ml.add_stressmodel(sm1)\\n\",\n \"ml.solve(tmin='1985', tmax='2010')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `solve` function has a number of default options that can be specified with keyword arguments. One of these options is that by default a fit report is printed to the screen. The fit report includes a summary of the fitting procedure, the optimal values obtained by the fitting routine, and some basic statistics. The model contains five parameters: the parameters $A$, $n$, and $a$ of the Gamma function used as the response function for the recharge, the parameter $d$, which is a constant base level, and the parameter $\\\\alpha$ of the noise model, which will be explained a little later on in this notebook.\\n\",\n \"The results of the model are plotted below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ml.plot(figsize=(12, 4));\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fit report head Fit Statistics\\n\",\n \"==================================================\\n\",\n \"nfev 10 EVP 92.02\\n\",\n \"nobs 518 R2 0.92\\n\",\n \"noise True RMSE 0.13\\n\",\n \"tmin 1985-11-14 00:00:00 AIC -2592.26\\n\",\n \"tmax 2010-01-01 00:00:00 BIC -2571.01\\n\",\n \"freq D Obj 1.70\\n\",\n \"warmup 3650 days 00:00:00 ___ \\n\",\n \"solver LeastSquares Interp. No\\n\",\n \"\\n\",\n \"Parameters (5 optimized)\\n\",\n \"==================================================\\n\",\n \" optimal stderr initial vary\\n\",\n \"recharge_A 749.010453 ±4.79% 215.674528 True\\n\",\n \"recharge_n 1.049137 ±1.53% 1.000000 True\\n\",\n \"recharge_a 134.483793 ±6.73% 10.000000 True\\n\",\n \"constant_d 27.547665 ±0.07% 27.900078 True\\n\",\n \"noise_alpha 58.973443 ±12.37% 15.000000 True\\n\"\n ]\n }\n ],\n \"source\": [\n \"ml = ps.Model(ho)\\n\",\n \"sm1 = ps.StressModel(recharge, ps.Gamma, name='recharge', settings='prec')\\n\",\n \"ml.add_stressmodel(sm1)\\n\",\n \"ml.solve(tmin='1985', tmax='2010', solver=ps.LeastSquares)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fit report head Fit Statistics\\n\",\n \"===================================================\\n\",\n \"nfev 23 EVP 91.52\\n\",\n \"nobs 518 R2 0.92\\n\",\n \"noise True RMSE 0.13\\n\",\n \"tmin 1985-11-14 00:00:00 AIC -2584.62\\n\",\n \"tmax 2010-01-01 00:00:00 BIC -2567.62\\n\",\n \"freq D Obj 1.74\\n\",\n \"warmup 3650 days 00:00:00 ___ \\n\",\n \"solver LeastSquares Interp. No\\n\",\n \"\\n\",\n \"Parameters (4 optimized)\\n\",\n \"===================================================\\n\",\n \" optimal stderr initial vary\\n\",\n \"recharge_A 776.738694 ±5.08% 215.674528 True\\n\",\n \"recharge_n 1.000000 ±nan% 1.000000 False\\n\",\n \"recharge_a 153.740242 ±5.46% 10.000000 True\\n\",\n \"constant_d 27.534802 ±0.08% 27.900078 True\\n\",\n \"noise_alpha 64.122503 ±12.81% 15.000000 True\\n\"\n ]\n }\n ],\n \"source\": [\n \"ml = ps.Model(ho)\\n\",\n \"sm1 = ps.StressModel(recharge, ps.Gamma, name='recharge', settings='prec')\\n\",\n \"ml.add_stressmodel(sm1)\\n\",\n \"ml.set_parameter('recharge_n', vary=False)\\n\",\n \"ml.solve(tmin='1985', tmax='2010', solver=ps.LeastSquares)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"tags\": [\n \"nbsphinx-thumbnail\"\n ]\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ml.plot(figsize=(10, 4));\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n },\n \"widgets\": {\n \"state\": {},\n \"version\": \"1.1.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":258,"cells":{"repo_name":{"kind":"string","value":"Sz593/coursera_ml_notes"},"path":{"kind":"string","value":"Jupyter Notebooks/LaTeX Notes Sandboxes/Coursera ML Notes Tex 2 - Logistic Regression.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"350317"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from scipy import stats, constants\\n\",\n \"from scipy.special import comb, perm, factorial, expit\\n\",\n \"import statsmodels.api as sm\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True True\\n\"\n ]\n }\n ],\n \"source\": [\n \"fp_list_master = ['C:', 'Users', 'szahn', 'Dropbox', 'Statistics & Machine Learning', 'coursera_ml_notes']\\n\",\n \"fp = os.sep.join(fp_list_master)\\n\",\n \"fp_fig = fp + os.sep + 'LaTeX Notes' + os.sep + 'Figures'\\n\",\n \"print(os.path.isdir(fp), os.path.isdir(fp_fig))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YsXe7sXAACAgOQosC1ZskSNGzfWww8/LMuyKtxuxowZOuecc/Tmm296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\\nAACA4QhsAAAAhiOwAQAAGO7/AUxTPaFPN0GeAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list = [1, 2, 3, 4, 7, 8, 9, 10]\\n\",\n \"y_list = [0, 0, 0, 0, 1, 1, 1, 1]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" \\n\",\n \" fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \" plt.plot(x_list, y_list, 'rD', markersize=16)\\n\",\n \" plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks([0, 1], fontsize=24)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(-0.10, 1.10)\\n\",\n \" plt.xlim(-0.1, 11)\\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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pwsDRwoHT1qrYeGmmGteXN7+gK8Ect6AAAqLD5euuOO0mGta1fp008J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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list = [1, 2, 3, 4, 7, 8, 9, 10]\\n\",\n \"y_list = [0, 0, 0, 0, 1, 1, 1, 1]\\n\",\n \"\\n\",\n \"x_list_reg = list(range(11))\\n\",\n \"regline = lambda x: (1/6) * (x - 1) - 0.25\\n\",\n \"y_list_reg = [regline(x) for x in x_list_reg]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" \\n\",\n \" fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \" plt.plot(x_list, y_list, 'rD', markersize=16)\\n\",\n \" plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks([0, 1], fontsize=24)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(-0.10, 1.10)\\n\",\n \" plt.xlim(-0.1, 11)\\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor_linreg1.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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xV2DvWGslZV3Ho/pd0dsyGN5dAvX+Ps/t8Q1GMgIgbeBw+/QNg5OgMA\\nNi6Z1eyGTWHvgOp63npQ3/KmyLl0HrtW/xtO7t6IeXwuvNt30swKPZGw8VbDRkR0m5xcYMwjdkg6\\noj3T3cVFxIbVVRgew7eXUMP4DJuFcXLzQu/7H0NNtRL7f/5CZ71nuyBUV5Sj8PoVnXXX008CADx8\\n/37p/J23MVtTxtG9cHBxR8zjc+Eb0kXTrNVUK7WedWsq74BOKCvKQ0VJoc66nMvn9a7zwrE/ARG4\\ne9IsBPUYqBXhcVPzc9XvqhnvhBBZntR0Af2H2es0a+381dj3WyWbNWoSNmwWKHzgaPh0DEN+1gWd\\nmZCd+8RChIjETV9pRYCU5F/Hsd9/hI2NHMFRgzTLZTI5RJVxLtPL5bZQVStRVV6qWSaq1UjctBI1\\nf90iVTejls59h0IURSRt+Vbr+bf05N0Nvquz0ToVtgCA8mLtRvBaagoyju79q84avT5bZnPronft\\n83JEZN4Y20GGwluiZuzOVzPdbuDDz+OXD16A6o7GoXN0DK6cSsLlEwexaelstA/vheqqClw+dQjV\\nlRUYMOEZrStGju5eKM7Lxp7vP0S70J7oHN16r38K6X0vTu3+BVs+ehmBkX0hqtTIPHcUxbnX4ODs\\nhsrSYlSVF8PBxaNpnxc1GOnJu5Fx5A8UXr8Mv07dUJyXjaunD8PeyQ1VZcUQhPqDeG93+886qOcg\\nnNy9GQc2fInr6Sfh4OqJguxLyDp3FPbObqgsKUJlWYk+PwI4unlBhIjjO35EfmYGeo6cbNbvRiWy\\nZoztIEPiFTYz1tA7J91926Nb7MQ6t4l9Yj76jX8atvYOSD0Uj6unD8MnKByjnnsHYX/lpNWKHvM4\\n3H074FLKAWQk72m0nroqurW07lpv36P3fY+h56gpkAkynNv/Gy6fTISLty9GPPs2ug+dBAC4euZI\\nnfvetlDL0BkL0X3YJFSVl+Ls/jiU5F/HPVNfhl/nW/Ebt0eb1Ff/ncfyaheEEc+8Be/2nXD51CGc\\nT/wdlSU30Wv0VDz46meAICDz7JF6979z+e23nYOjBiG4xyCU5N/A2f1xKC3MraciIpIqxnZQaxBE\\nC56ilpmZidjYWGz7bhH8feueFShVe07m45Siv6nLMGtlN/OgsHfUvKbqdts+X4D8zAw89u+fTVBZ\\ny1QrqzDK7QjCAr1NXYrZej/4SJ3L37z5qpErIUvD2A5qMTfdcxbAK2xkwU4kbMTaBY8gO+OU1vIb\\nF8/hxsUz8Psr5JaIyBDKy4EJ02x1mjW5XMS3y6vw5nw2a6Q/PsNGFqtz36E4n7gD8Sv/iY7d+sPR\\n3Qsl+Tdw+WQibO2dEN2M11wRETWkodiOjWuqMGwIZ4JSy7BhI4vlHRCCMS9+gJSd63Et/SQqS4tg\\n7+SKkKh70GP4Q1qTK4iI9JWaLmDURDudmaDt/NWI+5kzQckw2LBJlI2Mf8ANwatdEGIen2vqMgxK\\nVVMNB1s+zUAkBfsTZXhgsh0KCrXvdUZGqBG3vgoB7fh3ORkG/9aXKG8noLpK/zR+smAVBWjjXvdD\\nqURkPBu22CB2rG6zNvReFf78rZLNGhkUGzaJCu3gCXnxZVOXQRLUVp4PR4e639lKRK2PsR1kCmzY\\nJEomkyHEqRCqmmpTl0ISUl1RjHCPysY3JKJWoVIBL8xTYM5rthBF7WbtrflKrFquhIJZ19QK2LBJ\\n2MgenvAtPqR5LRNZN2X5TUTKTiL6Li9Tl0JklRjbQabESQcSJpPJMKmfN1IyDiOjyA7XKxxQJdro\\n/KuOLJeNIMJOVoMAx3KEtVHjrvZtTF0SkVVibAeZGhs2iZPJZOjZuQ16mroQMhEBgO1f/xGRKTC2\\ng6SAt0SJiIjqsT9Rhv7D7HWatcgINRLj2ayR8bBhIyIiqgNjO0hK2LARERHdhrEdJEVs2IiIiP7C\\n2A6SKk46ICIiwq3YjikzbbElTvvUKJeL+OpTJaY/qjJRZURs2IiIiBjbQZLHho2IiKwaYzvIHPAZ\\nNiIislqM7SBzwYaNiIisEmM7yJywYSMiIqvC2A4yR2zYiIjIajC2g8wVJx0QEZFVYGwHmTM2bERE\\nZPEY20Hmjg0bERFZNMZ2kCXgM2xERGSxGNtBloINGxERWSTGdpAlYcNGREQWhbEdZInYsBERkcVg\\nbAdZKk46ICIii8DYDrJkbNiIiMjsMbaDLB0bNiIiMmuM7SBrwGfYiIjIbNUX29GtC2M7yLKwYSMi\\nIrNUX2zHsCGM7SDLw4aNiIjMSmOxHdt+roKrq4mKI2olbNiIiMhsMLaDrBUnHRARkVlgbAdZMzZs\\nREQkeYztIGvHho2IiCSNsR1EfIaNiIgkjLEdRLewYSMiIklibAfR39iwERGRpDC2g0gXGzYiIpIM\\nlQqYPbfu2I4351UztoOsFicdEBGRJDC2g6h+bNiIiMjkGNtB1DA2bEREZFKM7SBqHJ9hIyIik2Fs\\nB1HTsGEjIiKTYGwHUdOxYSMiIqNibAdR87FhIyIio2kotuOt+UrGdhDVg5MOiIjIKBjbQaQ/NmxE\\nRNTqGNtB1DJs2IiIqFUxtoOo5fgMGxERtZr6YjsiIxjbQdQcbNiIiKhVrP+l7tiOofcytoOouZp1\\nS/Tq1as4c+YMFAoFevToAU9Pz9aqi4iIzFRtbMcrr9vqrJs+pQYrP+VMUKLmalLDVlJSgoULF2Ln\\nzp2aZTY2NnjkkUcwd+5c2Nrq/qEkIiLro1IBL85XYNlXuh3ZW/OVeGNeDQShjh2JqEGNNmxKpRKP\\nP/44zp49C1EU4e3tDUdHR1y5cgU//PAD0tLS8J///Af29vbGqJeIiCSKsR1ErafRZ9j++9//4syZ\\nM2jTpg2+++477Nu3Dzt27MDq1avh6emJpKQkzJo1C0ql0hj1EhGRBOXkAkPG2Ok0ay4uIuLWV7FZ\\nI2qhRhu2bdu2QRAEfPTRR+jXr59med++fbFmzRp4enri4MGDePrpp5GZmdmqxRIRkfSkpgvoP8xe\\nJ2Otnb8a+36rZMYakQE02rBduHABfn5+6N27t866kJAQfPfdd/Dy8kJiYiJGjBiBMWPG4B//+Idm\\n3/DwcERERBi+ciIiMjnGdhAZR6MNW3V1NRwdHetd37lzZ2zatAmjRo2CIAhIS0vD8ePHNetFUYQo\\n8g8sEZGlYWwHkfE0OunAz88Ply5dQnZ2Nvz8/Orcpm3btvj4449RXl6OS5cuoaqqSrN8yZIlhq2Y\\niIhMirEdRMbX6BW2e+65BzU1NXjppZeQnZ3d4LaOjo6IiIhAz549AQDOzs548MEH8eCDDxqmWiIi\\nMimVCpg9V1Fns/bWfCVWLWezRtQaGm3YnnrqKXh4eCAlJQWjR4/GrFmzcOrUKWPURkREElJeDkyY\\nZquTsSaXi/h2eRXenM+MNaLW0mjDVhvn0aFDB1RUVGD37t24fv26MWojIiKJYGwHkWk16U0HoaGh\\n2LZtGxISEnDgwAF06tSptesiIiKJSE0XMGqinc5M0Hb+asT9zJmgRMbQ5HeJyuVyjBgxAiNGjGjN\\neoiISEL2J8rwwGTdmaCREWrEra/iTFAiI2n0lmhdHnvsMbz77rtN2nb27NkYPny4PochIiITYmwH\\nkXQ0+Qrb7ZKSkqBSNe15hfPnz/OZNyIiM8LYDiLpabRhu3DhAj799NM6l7/wwgv17ieKIrKzs3H5\\n8uV689uIiEhaVCrgxfkKnZmgwK3YjjfmcSYokSk02rAFBwejqKgIiYmJmmWCIKCwsBC///57kw4y\\nefJk/SskIiKjKC8Hpsy01ZkJKpeL+OpTJWeCEplQk26Jvv3229i6davm98uWLYO/vz/Gjx9f7z6C\\nIMDJyQmhoaHo379/yyslIqJWk5MLjHnETucF7i4uIjauqeIL3IlMrEkNW2BgIP7v//5P8/tly5bB\\nz89PaxkREZmn82kCRk9ibAeRlOk16eDcuXOGroOIiEyAsR1E5kGvWA8iIjJ/jO0gMh96XWEDgBMn\\nTmD58uU4fvw4ysrKGoz5EAQBZ86c0fdQRERkQIztIDI/ejVsp06dwrRp06BUKiGKjf8LrCnbEBFR\\n62NsB5F50qth+/LLL1FVVYVOnTrh6aefRlBQEOzt7Q1dGxERGRBjO4jMl14NW3JyMuzs7PDdd9/B\\n29vb0DUREZGBMbaDyLzp1bBVVlYiJCSEzRoRkRlgbAeR+dNrlmiHDh2Qk5Nj6FqIiMjA9ifKMGC4\\nvU6zFhmhRmI8mzUic6FXw/bAAw8gLy8P27dvN3Q9RERkIIztILIcet0SnTFjBg4dOoSFCxciKysL\\ngwcPho+PDxQNzAN3cHDQu0giImo6xnYQWR69GrZx48ZBpVKhvLwcH3zwAT744IMGt2cOGxGRcTC2\\ng8gy6dWwpaWlaX7NHDYiImlgbAeR5dKrYUtISDB0HURE1AKM7SCybHo1bO3atTN0HUREpCfGdhBZ\\nPqO8/L28vNwYhyEisjqM7SCyDnq//L2mpgY7d+5Eeno6KisroVZrX25XqVSoqqpCTk4OkpOTkZSU\\n1OJiiYjob+t/scG0Z2xRVaUb27FhdRXc3ExUGBEZnF4NW2lpKaZOnYrz5883uq0oihA4JYmIyGAY\\n20FkffS6Jfrtt9/i3LlzEAQB/fr1Q2xsLERRRFhYGEaPHo1evXrBxubWg6/R0dGIi4szaNFERNZK\\npQJmz1XU2ay9NV+JVcvZrBFZIr1niQqCgI8//hgjRoyASqVC37594e3tjQ8//BAAkJGRgaeeegrH\\njh1DVVWVQYsmIrJGjO0gsl56XWG7evUqvLy8MGLECACAjY0NIiIicOzYMc02ISEhWLJkCWpqavDt\\nt98aploiIiuVkwsMGWOn06y5uIiIW1/FZo3IwunVsFVVVcHPz09rWVBQEMrKynD16lXNsr59+6Jt\\n27ZITk5uWZVERFbsfJqA/sPsdTLW2vmrse+3SmasEVkBvRo2d3d3FBcXay1r3749AODChQtay9u2\\nbYvc3Fw9yyMism6M7SAiQM+GLSIiAleuXNF6RVVwcDBEUdS6LapWq5Gdnc0XvxMR6WH9LzaIHWuH\\ngkLd2I4/f6tEQDs2a0TWQq+G7b777oMoinjiiSewfv16qNVq9O7dGw4ODlizZg2Sk5NRVlaGTz75\\nBPn5+ejYsaOByyYislyiCHz4uRwPTbfTyVibPqUGceuZsUZkbfRq2MaMGYOBAwciLy8Pb7/9NkRR\\nhKurKx566CGUl5dj2rRp6N27N7766isIgoApU6YYum4iIovE2A4iqotesR4ymQwrVqzAunXrcOjQ\\nIU3m2pw5c5CXl4e4uDiIogiZTIZHH30UY8eONWjRRESWiLEdRFQfQRRFgz8EcePGDVy7dg2BgYHw\\n9PQ09Mc3WWZmJmJjY7Htu0Xw9zVdHUQkLe8HH6lz+Zs3XzVyJX/LyQXGPGKnMxPUxUXExjVVnAlK\\nZC3cHOtcrPe7RBvi4+MDHx+f1vhoIiKLk5ouYNREO52ZoO381Yj7mTNBiaiFDZtKpUJGRgZKS0uh\\nVqvR0MW66OjolhyKiMgi7U+U4YHJujNBIyPUiFtfxZmgRASgBQ3b6tWrsXz5cpSUlDS6rSAIOHPm\\njL6HIiKySOt/scG0Z2x1ZoIOvVeFDas5E5SI/qZXwxYXF4clS5Zofu/g4AA7OzuDFUVEZMlEEfho\\nmbzOmaDTp9Rg5aecCUpE2vRq2NauXQvgVh7b3Llz+bwaEVETqVTAi/MVWPaVbkf21nwl3phXA0Go\\nY0cismp6NWznzp2Du7s73nvvPSj4z0AioiZhbAcR6UvvHDZ/f382a0RETcTYDiJqCb3edBAaGorL\\nly+jpqbG0PUQEVmc1HQB/YfZ6zRr7fzV2PdbJZs1ImqUXg3b9OnTUVZWhi+++MLQ9RARWZT9iTL0\\nH2avk7EWGaFGYjwz1oioafS6JdqnTx889thj+PLLL3H69GkMHjwYPj4+Dd4iveeee/QukojIHG3Y\\nYoOpTzO2g4haTq+GrX///gAAURSxd+9e7N27t8HtmcNGRNZEFIGPl8vxyusKiKJ2s8bYDiLSh14N\\nm5+fn6HrICKyCIztIKLWoFfDtmvXLkPXQURk9hjbQUStpVVe/k5EZG0Y20FErYkNGxFRC6WmCxg1\\n0U5nJmg7fzXifuZMUCJqOb0attjY2KYfQC6HnZ0d2rRpg/DwcIwfPx7BwcH6HJaISHL2J8rwwGQ7\\nFBRqP5jL38tyAAAgAElEQVQWGaFG3PoqBLRjs0ZELadXw5aVldXsfVJTU3HgwAGsXbsWb7/9NsaN\\nG6fPoYmIJIOxHURkLHo1bAkJCXj33Xexa9cuREZG4pFHHkFERAScnJxQVlaG1NRUbNiwAYcPH0Zk\\nZCSmT5+O4uJi7N27F7t378brr7+O0NBQhIeHG3o8REStjrEdRGRser3p4NChQ9i9ezcmTJiAn3/+\\nGRMmTEB4eDg6dOiA8PBwjB07FmvXrsXjjz+OU6dOQRAETJ48GV9++SVefvllVFdXY+3atYYeCxFR\\nq1OpgNlzFZjzmq1Os/bWfCVWLWezRkSGp1fD9v3338PJyQmvv/46hAYChebMmQMXFxd8++23mmVP\\nPPEEXF1dkZSUpM+hiYhMprwcmDDNVidjTS4X8e3yKrw5nxlrRNQ69LolmpGRgbvuugv29vYNbmdr\\na4vAwECkpaVplikUCgQEBCAjI0OfQxMRmQRjO4jIlPRq2Nzc3JCVlQW1Wg2ZrP6LdGq1GllZWbCz\\ns9NaXllZCRcXF30OTURkdIztICJT0+uWaI8ePVBYWIgvvviiwe3+85//oKCgAD179tQsy8rKwuXL\\nlxEQEKDPoYmIjGp/ogz9h9nrNGuREWokxrNZIyLj0OsK2zPPPINdu3Zh+fLlSE1NxUMPPYTQ0FA4\\nODhoZolu2rQJ27dvh42NDZ555hkAwJ49e/Dhhx9CrVZj7NixBh0IEZGhMbaDiKRCr4atS5cuWLp0\\nKRYuXIgdO3YgPj5eZxtRFGFvb49//vOfmitsn332GdLS0hAWFoYJEya0rHIiolbC2A4ikhq9X001\\nevRoREVF4euvv8aePXuQmZmpWefj44OYmBjMmDED7du31ywPDQ3F/fffj8mTJ8PW1rZllRMRtQKV\\nCnhpgQKfr9TtyN6ar8Qb8zgTlIiMTxBF0SAPYCiVSty8eROOjo5wdnY2xEe2WGZmJmJjY7Htu0Xw\\n9/U0dTlEJBHvBx+pc/mr117FlJm22BKn/W9ZuVzEV58qMf1RlTHKIyJr5uZY52KDvfzd1tYWbdu2\\nNdTHEREZ3ZAxjO0gImlqtGFbunQpBEHAzJkz4eHhoVnWHIIg4NVXX9WvQiIiI7mzWWNsBxFJRaMN\\n26pVqyAIAiZOnKhp2GqXNYUoimzYiMjsREaoEbe+CgHt2KwRkek12rCNGzcOgiBoBd3WLiMiskSM\\n7SAiqWm0YXvvvfeatIyIyBIwtoOIpEivNx0QEZkrVQMTPd+ar8Sq5WzWiEh6Gr3CVlFRYZADOTg4\\nGORziIj0VV4hwxMvRqE36o71eHN+jZErIiJqmkYbtqioqBYfRBAEnDlzpsWfQ0Skr5w8W0x6KhrJ\\nKR7obepiiIiaqdGGzRC5ugbK5iUi0kvaBSc8OKMPLl5xMnUpRER6abRhS0hIMEYdRESt4mCyBx56\\nOhoFN/k6PCIyX402bO3atTNGHUREBrc5zg9PvtwDVUqbxjcmIpIwo8wSzcrKMsZhiIgAAKIIfPZ1\\nEKY9H6XTrE2dcNVEVRER6U/vd4kWFRVhw4YNSE9PR2VlJdRq7ffsqVQqVFVVIScnB+np6Th9+nSL\\niyUiaoxKBcx7pwu+XB2ks27RC+exYHYaPthogsKIiFpAr4YtLy8PEydOxI0bNzQTCgRB0JpcUPsm\\nBFEUIZcb7B3zRET1qo3t+DXeV2u5XK7GssUnMG1ipokqIyJqGb06qa+//hrXr1+Ho6MjRo8eDQcH\\nB6xduxa9e/dGr169cP36dezZswdFRUXo168fvvjiC0PXTUSk5fbYjtu5OFfjh+VHEDsoz0SVERG1\\nnF4N2969eyEIAlauXInevW8lGv36668QBAEvvfQSACA/Px9PPvkkDh06hNOnTyM6OtpwVRMR3aa+\\n2A5/3wps+iYJkeElJqqMiMgw9Jp0kJ2dDV9fX02zBgARERE4efKk5lk2Ly8vLFmyBKIoYu3atYap\\nlojoDgeTPRAz8W6dZq1LaDH2bNzPZo2ILIJeDZtKpYK3t7fWso4dO6KqqgpXrlzRLAsPD0dAQABS\\nUlJaViURUR02x/nhvqn9dDLWhtydi/ifDqCdX6WJKiMiMiy9GjZPT0/k5+drLWvfvj0AIC0tTWu5\\nm5sbCgoK9CyPiEhXY7Edm1clwc2V7wUlIsuhV8MWGRmJ7OxsHD58WLMsJCQEoigiKSlJs0ypVCIz\\nMxOurq4tr5SICLdiO155uwsWLO4CURS01i164TxWLE2BQsHX4RGRZdGrYRs/fjxEUcQzzzyDjz/+\\nGDU1Nejduzfc3Nywbt06bNmyBampqXjjjTdQVFSE4OBgQ9dNRFaovEKGKbN6Y8Ua7Yw1uVyNFUuP\\nY+ELaRCEenYmIjJjejVsQ4YMwYQJE1BeXo5Vq1bBxsYGDg4OmD59OmpqajB//nyMHTsWW7ZsgSAI\\nmDlzpqHrJiIrk5Nni1FT+utkrLk4V2PTN0nMWCMii6Z3ou27776L2NhYHDx4UBOS++yzz6KyshJr\\n1qxBRUUFXF1dMWvWLNxzzz0GK5iIrA9jO4jI2rXoFQQxMTGIiYnR/L42h+35559HQUEBvLy8YGPD\\nly4Tkf4OJnvgoaejdWaCdgktxuZVSZwJSkRWodGG7dq1a3p/+I0bNzS/9vf31/tziMg6bY7zw5Mv\\n99CZCTrk7lz8sPwIZ4ISkdVotGGLiYnR3PLUlyAIOHPmTIs+g4ishygCn38ThIVLInRmgk6dcBXL\\nFp/gTFAisipNviV6+4vdm6sl+xKRdVGpgLn/6qIzExS4FduxYDZnghKR9Wm0YZPJZFCr1RAEAeHh\\n4Rg5ciSGDx+Otm3bGqM+IrIi5RUyPPFilM5MULlcjWWLT3AmKBFZrUYbtn379iE+Ph6///47kpKS\\ncPbsWXzyySeIiopi80ZEBpOTZ4tJT0UjOcVDa7mLczV+WH4EsYPyTFQZEZHpNdqweXp64uGHH8bD\\nDz+MmzdvIj4+Htu3b8ehQ4eQnJyMxYsXo2fPnprmzcfHxxh1E5EFYWwHEVHDmhXr4e7ujkmTJmHS\\npEkoLi7Gzp07sX37dhw8eBBHjhzBkiVL0KNHD4wcORIjRoxg80ZEjWJsBxFR4/TOYXN1dcX48eMx\\nfvx4lJaWYufOnfj999+xf/9+HD16FO+99x66deuGkSNHYuTIkfD19W38Q4nIqjC2g4ioaVoUnFvL\\n2dkZ48aNw7hx41BWVoY9e/Zgx44d2Lt3L1JSUrB06VLGehCRBmM7iIiaxyAN2+1yc3ORnZ2NnJwc\\nKJVKRnoQkRbGdhARNZ9BGrZz585hx44diI+PR3p6OoBb2Wtt2rTBsGHDMHz4cEMchojMHGM7iIj0\\no3fDdvToUcTHxyM+Ph5ZWVkAbjVp/v7+GDZsGEaMGIGoqCiDFUpE5o2xHURE+mtyw6ZSqXDo0CHs\\n2LEDCQkJyMvL09zuDAwMxPDhwzF8+HBERka2WrFEZJ4Y20FE1DKNNmwJCQmIj4/H7t27UVxcrGnS\\nOnXqpGnSwsLCWr1QIjJPjO0gImq5Rhu25557DoIgQBRFREREaJq04OBgY9RHRGZsU5wfZjK2g4io\\nxZp8S1QulyM7OxurV6/G6tWrm3UQQRBw4MCBZhdHROZJFIHPvgnGwsUROusY20FE1HxNathEUURN\\nTQ0KCwv1OojAOfpEVoOxHUREhtdow7ZmzRpj1EFEFoCxHUREraPRhq1Pnz7GqIOIzBxjO4iIWo/B\\n33RARNaHsR1ERK2LDRsRtQhjO4iIWp/M1AUQkfnaFOeH+6b202nWhtydi/ifDrBZIyIyEDZsRNRs\\nogh8+nUwpv1fL52MtakTrmLzqiRmrBERGRBviRJRszC2g4jI+NiwEVGTMbaDiMg02LARUZMwtoOI\\nyHTYsBFRoxjbQURkWmzYiKhBjO0gIjI9zhIlonoxtoOISBrYsBGRjoZiOx5lbAcRkdHxligRaWFs\\nBxGR9LBhIyINxnYQEUkTGzYiAsDYDiIiKWPDRkSM7SAikjg2bERWjrEdRETSx1miRFaMsR1EROaB\\nDRuRFWootmMqYzuIiCSHt0SJrAxjO4iIzA8bNiIrwtgOIiLzxIaNyEowtoOIyHyxYSOyAoztICIy\\nb2zYiCwcYzuIiMwfZ4kSWbDNjO0gIrIIbNiILJAoAp99HYRpz0cxtoOIyALwliiRhWFsBxGR5WHD\\nRmRBGNtBRGSZ2LARWQjGdhARWS42bEQWgLEdRESWjQ0bkZljbAcRkeVjw0ZkxjbH+eHJl3vozAQd\\ncncuflh+hDNBiSRKpVKhsrISarXa1KVYJEEQYGdnB4VCYepSDIYNG5EZEkXg82+CsHBJBERRe8rn\\n1AlXsWzxCSgUoomqI6K6VFdXY9+pi7hYDORUKqCWO0AUmK7VGgRRhFCTDw+FEh1d1RgQGgBXF6fG\\nd5QwNmxEZkalAua90wVfrmZsB5G5qK6uxpo/zuKmR0/IXGWwczV1RdahAsAZUUTaoTOYFu0HdzcX\\nU5ekN7b2RGakvEKGKbN66zRrcrkaK5Yex8IX2KwRSdEvh9JuNWsynnaNTRAE1Hh1wU+Hr5q6lBbh\\nFTYiM8HYDiLzVFNTg8sV9rBxYrNmSgUKX2Tn5sGvjbepS9EL/9dDZAbSLjghZuLdOs2av28F4n86\\nwGaNSMJOZ1yF6NrR1GVYPXtXH5y4XGDqMvTGK2xEEsfYDiLzVlhRA7mtnanLIADlKvO9TmW+lRNZ\\ngc1xfrhvaj+dZm3I3bmI/+kAmzUiM6BqZMJ28tYvsOLZSKx4NhJH41Y2uO2+Hxdrti0pyG52LSue\\njcSGdyZpfn/+4BaseDYSJ3d93+zPkpLyojycO/BLo9upzDhFhQ0bkQSJIvDZ10GY9nyUTsba1AlX\\nsXlVEjPWiCyMAAEXj+1scJuLxxMg/PV/huAVEIre989C26DuBvk8U6goKcC6N+7D5ZTdpi6lVfGW\\nKJHEMLaDyDo5uHkj7+o5lBRkw8XTT2f99YzjKLuZA1s7J1RXlRvkmN7tw+DdPswgn2UqNcpKg/08\\npIxX2IgkhLEdRNZJgICg7jEAUO9VtgtH42Hn4ALfzlHGLE3yRNE6QsJ5hY1IIhjbQWTd2oX1Rfrh\\nOFw8loBusdN01l84Fo/A7kOgrCjRWXf+4BacP/AL8jPPo1pZAXsnd7QL7YPosc/D1Tug3mOeP7gF\\nu1e/hrsfmofImKma5VnnDiF525fIv3oeMrkCwT1j0XXIo/j5nw+i9/2z0Pv+fwAAtnw4HaUF1zH2\\nldVI3PghMs8cQE11FdoEdkH0A8/B/65oreMVXEvHse1fIzs1GeUl+ZDL7eDZrjO6DX0MwVHDNNsd\\n3rocR7atwCNv/Q/nD25BWtI2VBTnw7VtB0QOmYKIwQ9p1S9AwMWUXVjxbCSGPP4OQvuPbd4P3wyw\\nYSOSgLQLTnhwRh9cvKL96hR/3wps+iYJkeG6f0ETkWWR2cgR2H0I0g9tQ0VJARxcPDXrblw8ibKC\\n6wjpNRxn923U2u/ghveRsnMNvAPCEDrgQQgCcC01GWmH45CdcQyT3/4VNgrbOw+ncefzcBeOxmPn\\nV69C4eCE4F7DIVfYIf3wb8g8m6izrQAB1VXl2PLB41DYOiB0wFiUF+UhPXk7tn32LCYu+hkefiGa\\nMWz98AnY2NohuOcw2Lt4oDjnCi6m7EL8yjkY+dwyBEYO1nyuAAEJq+ajtCAbQVHDIJPZIO3Qr/jz\\nv+9AkNkgfOAEeAWEolvMNJzYtRYevkHoFD3a7G/x1ocNG5GJMbaDiGoF9xyG1MT/4VLKLoQPnKhZ\\nfuHoDigcnBEQMUCrYSu7mYMTCd/D/65ojHnpGwi3PTMRt2wWrp7ah+z0IwgI71/vMUX8fUuxWlmB\\nP9e9A1sHZ4xfsA6ubdoDAHqMmKE1u/R2laWF8OsUhWFPfwiZ7NYkKQ//Tji85XOkJm5F3wdfBAAk\\nb10OtajCxHk/wN2no2b/jCM7EP/VHKQnbdM0bLV1VZUV4eG3t8LeyQ0A0Cl6NH55fxrO7d+E8IET\\n4N0+DHaOrjixay3cfYLQ675nG/0Zmys+w0ZkQoztIKLbtY8YAIWdIy4cS9BafvHYTnTsPgQ2Ngqt\\n5TYKO8TOWIK7H5qn1awBgH/n3gBuzaJsqqun9qGipABdh0zRNGsA4Ozhi25DH9Nq7moJENBt6GOa\\nZg0AOnQdBBEiSvKvaZZ1G/oYYmf8W6tZu1VnrzrrFCAg7O7xmmYNAHxDesDOwUXrc60Fr7ARmYAo\\nAp9/E4SFSyIgitp/yU6dcBXLFp+AQmEdD9IS0d9sFLYIjByMi8cSoKwsg629E3KvnEFxXibufni+\\nzvb2Tm7oFD0aoiii4Fo6CrMvoDgvE/mZ55F19iAAQFSrmnz83MunIUBAm45dddb5hvSsd787mzBb\\nh1svWVfVKDXL2kcMAACUF+chP/M8inOvovD6RVxPP/ZXnbohaW4+gTrLFA7OqK4sa3wwFoYNG5GR\\nMbaDiBoS1HMY0pO34/KJP9C5z2hcOLIDtva3bofW5cLReBza/AmKcq9AgACFnSO8AyPg1T4MWWcT\\nmzWLsrL0JgDA0U33fZtO7m3r3c9Grn2XQHO177ZjlxZkY9+PS3D5xJ6/tpHBzScQvp2ikHf1bJ1X\\n7+78XOCvZ+6sZGbo7diwERlReYUMT7wYhV/jfbWWy+VqLFt8AtMmZpqoMiKSig6RgyBX2OPisZ3o\\n3Gc0Lh5LQGC3e3RuhwLAjYsnEP/VK3D28MWwpz5Am8Aumlmhx35fhayzic06tq3DrYlPyopSnXXK\\nSt1lzRH3+SzcvH4RUaOfRsceMfD06wQbhS0qivNxdt+GFn22NWDDRmQkjO0goqZQ2DqgfcQAXD29\\nDzmXTuFmziX0mzCnzm0zDm8HRBGDpryODl0Haq0rzM5o9rG9O3SBCBE5l06hXWgfrXU3Lpxo9ufV\\nys88j4LsdIREDUf0A/+nta6gtk59r5pZyS0JTjogMoK0C06ImXi3TrPm71uB+J8OsFkjIi1BUcNQ\\nrazA/p+WwNbOCe273F3ndjYKW4gQUV6s/XdI5tlEpB+OAwCoVdVNPm7HHkNg7+iGU7t+QHFelmZ5\\naeF1pOz4Vu9XYtko7ADoTiyoLCtC4sYP/6pTv9ftyWxuXXtSNWOc5ohX2IhaGWM7iKgxdz6/1bHb\\nvbCxUeDGxRPoHD0aNnLd26EAENJ7JFJ2rsaf//0XrqUehpObN/IzU3H1zAE4OHugoqQAlaVFTa5D\\nYeuAgZMXIWHVfGxc/BCCeg6FTCbDhWMJmlZNkDX/Wo9b20C07RiJ7LQj2PL+4/Dt1AMVpTdx6fgu\\nqGqUUNg6oLLsptY+dT3TVhd7Z3fYyG1x7fxhHFj/PoJ6xsKvk+W9DYJX2IhaEWM7iKgp7rxyZevg\\njHZhfSFAQHCvEXXscGt77/ZhuO/5FWgT2AWXUnbj7J8bUVGcjz5jn8ek1zdCEGS4cvpP7eMIuuG3\\nt+sUPQojZ30Gd5+OSD8chwvHEtApehQGTl4EESLktg511lLnmP5aJwgCRs36HKEDxqIkPwsnd/8X\\n19OOokPkYExY+DMCwvuj6MZlFOdlau9f7w/s73U2NgoMmvwa7JxcceaPn3DtfFL9+5kxQbTgl3Bl\\nZmYiNjYW275bBH9fz8Z3IDIQxnZI2/vBR+pc/ubNV41cCVmDhGNpOIEIU5fRJMrKMlRXltU5I/Tc\\n/s3Ys/YNDHvqQ4T0Gm6C6lqufeVZTOzfydRlNMzNsc7FvMJGZGAqFfDqP7tgweIuOs3aohfOY8XS\\nFDZrRCRJRTcuYe38WOxZ87rW8hplJU7vWQeZjdwibzeaAz7DRmRA5RUyzHgpClt3MLaDiMyPd4cI\\n+HTshvMHtqA4LwttO0aiRlmByyf3ojT/GvqMe6HOjDZqfWzYiAyEsR1EZO4EQcB9L67EifjVyDi6\\n49ZVNbkCXu3uwoCJryCo51BTl2i12LARGUDaBSc8OKMPLl5x0lru71uBTd8kITK8xESVEZGp2QhA\\nEyc8SoKtvRN6j5mF3mNmmboUg5OZcWQbn2EjaqGDyR6ImXi3TrPWJbQYezbuZ7NGZOVc7GRQ1Vh2\\nRpi5cJDrvq/UXLBhI2oBxnYQUWMiQ9pDVXjJ1GVYvcrSfET4u5q6DL2xYSPSgygCn30dhGnPR6FK\\naaO1buqEq9i8KglurvqldhORZbG1tUV7+/JmvYSdDM+1Mgsd/HxMXYbe2LARNRNjO4ioucb1DoJD\\n/gk2baaSn4rxPX0gmPF7RznpgKgZyitkeOLFKPwaz9gOImo6R0cHPH53EHadOoXLxTa4KXODoHCC\\nILNpfGdqNlGthrqmAs41hQh0UWNAzzbw8fJofEcJY8NG1ESM7SCilnB0dMD9fcIgiiJKSkpQXFaC\\nahWvuLUGGxng4mgPd7cQs76qdjs2bERNwNgOIjIUQRDg6uoKV1fzfQCejI8NG1EjDiZ74KGno3Vm\\ngnYJLcbmVUmcCUpERK2Okw6IGsDYDiIikgI2bER1YGwHERFJCW+JEt1BpQLmvdMFX64O0lm36IXz\\nWDA7DRbyDCsREZkJyTZsxcXF+Pzzz5GQkICcnBx4enpi0KBBeO655+Dv72/q8kxG8cl/AQDVL04x\\ncSWGI6UxGSq2Q0pjMhRLHBMRkbmQZMNWXFyMhx9+GBcvXoSzszPCwsJw9epVbNy4EfHx8fj+++9x\\n1113mbpMo1N88l8oPlun+b0lnDilNCZDxXZIaUyGYoljIiIyJ5J8hu21117DxYsXce+992Lv3r3Y\\nsGED/vzzT4wfPx7FxcV4+eWXrS4t+s4TpuKzdZorHuZKSmNKu+CEmIl36zRr/r4ViP/pgN7NGr8n\\nIiIyBMk1bBcuXEB8fDycnJywdOlSODo6Arj1LrZ33nkHISEhyMjIQHx8vIkrNZ47T5ia5WZ84pTS\\nmA4meyBm4t06GWtdQouxZ+P+JmesSWlMhmKJYyIiMkeSa9j+97//QRRFDBkyRCdUUCaTYfz48RBF\\nEXFxcSaq0LjqO2Fq1pvhiVNKYzJUbIeUxmQoljgmIiJzJbmG7cSJExAEAT179qxzfffu3QEAycnJ\\nxizLJBo7YWq2M6MTp1TGZMjYDqmMyZAscUxEROZMcpMOLl++DAAICAioc327du0AAPn5+aioqICD\\ng4PRajOmpp4wNdv/ta2UHwaXypgMGdshlTEZkiWOiYjI3EmuYSsoKAAAeHh41Lnezc1N8+vCwkKL\\nbNiae8LU7CfhE6dUxmSo2A5AOmMyJEscExGRJZBcw1ZVVQUAsLOzq3O9vb295teVlZb3WiB9T5ia\\n/SV44pTKmAwV2wFIZ0yGZIljIiKyFJJr2GQyGVQqVb3r1Wq15teChcXNt/SEqfkcCZ04pTKmtAtO\\neHBGH52ZoP6+Fdj0TVKTZ4IC0hmTIVnimIiILInkJh3UxnjUXmm7k1Kp1Pz69qtt5s5QJ0zN50ng\\nYXCpjKm+2I6uYc2L7QCkMyZDssQxERFZGsk1bO7u7gCAoqKiOtffvHlT82tPT0+j1ETmq77YjpiB\\nzYvtICIiMiXJNWzBwcEAgKysrDrXX7t2DQDQpk2bep9zM0fVL05B9ezJhvu82ZNNflvKlGNqLLZj\\n0zdJcHVpWmyHVg38nhr/PAmMiYjI0kiuYevatStEUURKSkqd648fPw7g7zw2S2KoE6eUTpimGJNK\\nBbz6zy5YsLgLRFH7OcdFL5zHiqUpUCj0f7UZv6cGPkdCYyIisiSSa9iGDRsGANi5cyeKi4u11qnV\\namzevBmCIGDs2LGmKK/VtfTEKcUTpjHHVF4hw5RZvXUy1uRyNVYsPY6FLzQ9Y63Bmvg96e4vwTER\\nEVkKyTVsoaGhuPfee1FSUoLnn39e88yaUqnEokWLkJGRgeDgYAwdOtTElbYefU+cUj5hGmNMOXm2\\nGDWlv07GmotzNTZ9k9SsjLUm1cbv6e/9JDwmIiJLILlYDwB4++23MWXKFCQlJWHIkCEIDg5GZmYm\\nioqK4ObmhmXLlpm6xFZXe/Jr6uw9czhhtuaYDBnb0Rz8nsxjTERE5k5yV9gAwMfHB5s2bcK0adPg\\n6emJ1NRUyOVyjBkzBuvXr0dQkO4rhSxRU692mNMJszXGZMjYDn3wezKPMRERmTNJXmEDbr2CauHC\\nhVi4cKGpSzGpxq52mOMJ05Bj2hznhydf7qEzEzRmYC5+WH5Er5mg+uD3RERErUmSV9hIW31XO8z5\\nhNnSMbVWbEdL8HsiIqLWItkrbKTtzqsdlnDC1HdMKhUw919dsGKN7q3xRS+cx4LZhpkJqg9+T0RE\\n1BrYsJmR20+SlnLCbO6YyitkeOLFKJ2ZoHK5GssWnzD4TFB98HsiIiJDY8NmZizxZNmc2I5JT0Uj\\nOcVDa7mLczX++8URxAzMa43y9GLN3xMRERkeGzYyC6aK7SAiIpICNmwkeQeTPfDQ09E6L3DvGlaM\\nTd8k8QXuRERk8ThLlCRtc5wf7pvaT6dZixmYi/ifDrBZIyIiq8CGjSRJirEdREREpsJboiQ5Uo7t\\nICIiMgU2bCQp5hDbQUREZGxs2EgyzCm2g4iIyJjYsJEkMLaDiIiofmzYyOQY20FERNQwzhIlk2Js\\nBxERUePYsJFJMLaDiIio6XhLlIxOpQLmvdMFX65mbAcREVFTsGEjo2JsBxERUfOxYSOjYWwHERGR\\nftiwkVEwtoOIiEh/Ft2wqVQqAMCN3JsmrsS6HT3phucWdMTN4kLI5YWa5XcFl+I/76fAy6MK166b\\nsECyOqXy0jqXZ167ZuRKiIjuUGIPX19fyOXaLZogiqJoopJaXXJyMh599FFTl0FERETUZAkJCQgI\\nCNBaZtENW2VlJU6dOoU2bdrAxsam8R2IiIiITMzqrrARERERWQIG5xIRERFJHBs2IiIiIomz6Fmi\\nRNT6li1bhmXLljV7v127dsHf378VKjIvf/zxB7Zs2YLjx48jPz8ftra2aNu2Lfr27YsJEyagS5cu\\nOnLrKOAAAAoWSURBVPts3rwZCxYsQNeuXbFhwwYTVE1ExsaGjYhaxM/PD7169dJZfurUKSiVSgQG\\nBsLLy0trnSAIsLOzM1aJkqRSqTBnzhxs374dgiDA19cXYWFhKC4uRlZWFtatW4d169bhiSeewNy5\\nc3X2FwQBAt/hRmQ1OOmAiFpFTEwMsrOzsWTJEowbN87U5UjOBx98gK+//hohISH48MMPERYWplmn\\nVCqxZs0afPTRRxBFEa+99ppWRFFpaSlyc3Nhb28PPz8/U5RPREbGZ9iIiIysoqICP/zwAwRBwCef\\nfKLVrAGAra0tZs6ciX/84x8QRRErVqzQWu/s7IygoCA2a0RWhA0bEZGRXbp0CRUVFbC1tUXnzp3r\\n3W7SpEkAgPz8fGRnZxurPCKSIDZsRGRSMTExCAsLwx9//FHn+r59+yIsLAyHDx/WLNu8eTPCwsKw\\nZMkS5Ofn44033sCgQYPQvXt33Hffffj+++812/7444944IEH0L17d/Tv3x+vvvoqcnNz6zzW5cuX\\n8cYbbyA2NhaRkZHo27cvZsyYge3bt+tsm5WVhbCwMIwZMwYZGRl4+OGH0a1bNwwaNAg//PBDg2Ou\\nDcRUKpVITEysdztfX1/88ssvSEhIgK+vr874J06cqFm2YMEChIWFNfrfnRNElEolvvvuO0yYMAFR\\nUVHo2bMnxo8fj1WrVkGpVDY4DiIyHk46ICKTa+jh+foerhcEAVlZWRg3bhwKCwvRqVMnyGQyXLhw\\nAe+++y7Ky8tx8eJFbN68GW3btkVwcDBSU1OxdetWnD17Flu2bNF6A8rOnTvxyiuvoKqqCo6OjggL\\nC0NBQQEOHjyIAwcO4P7778f777+vU0tpaSmefPJJlJSUoFOnTrh48SJCQkIaHG9wcDB8fHxw48YN\\nPPfcc3j88ccxZswYBAUF6Wx75+3S+nTs2LHOyR8AUFRUhPT0dAiCoDUzt6ioCDNnzsTJkydhY2OD\\ngIAAODg4IDU1FUuXLsW2bduwatUquLm5NakGImo9bNiIyCyJooidO3eic+fOWLdunea9e6+//jrW\\nr1+PTz75BAqFAp988glGjhwJAEhJScGjjz6KjIwM7N27F0OGDAFw6xblnDlzoFQqMW3aNMyZM0cz\\ni3Xfvn14+eWXsW3bNnTo0AGzZ8/WquP69esIDAzE5s2b4eHhgeLiYri6ujZYu42NDV5//XXMnj0b\\n5eXl+OKLL/DFF1/A398fffr0Qd++fTFw4EC0adOmyT+PZ555Bs8884zO8toxCYKAUaNGYfz48Zp1\\n8+bNw8mTJ9GrVy/8+9//1vwMb9y4gVdeeQWHDx/GokWL9IptISLD4i1RIjJbgiDgX//6l9ZLkmfO\\nnAngVkM3ffp0TbMGAN27d0d0dDQA4OzZs5rlK1euRFVVFQYPHoyFCxdqRY4MHDgQixcvhiiK+Pbb\\nb1FUVKRTx5NPPgkPDw8AaLRZqzV06FB8/fXX8Pf311xFvHbtGn755RcsWLAA99xzD2bMmIEzZ840\\n4yeia9GiRUhJScFdd92FxYsXa5afOnUKe/bsgaenJ5YvX671M/Tx8cGnn34KR0dHJCQk4Pz58y2q\\ngYhajg0bEZktFxcX9OjRQ2vZ7bf8BgwYoLNPbSZcWVmZZtnevXshCAImT55c53GGDh0Kf39/VFZW\\n1vnM2Z01NNWAAQMQHx+Pr776Co888ggCAwM1zZsoijhw4AAmTpyIn376Sa/PX7lyJbZu3Qp3d3cs\\nX74c9vb2mnUJCQkAgP79+8Pd3V1nX09PT/Tv3x/ArZ8PEZkWb4kSkdmq65ahQqHQ/NrT07Pe9bUR\\nlKWlpcjLy4MgCAgPD6/3WOHh4cjOzsalS5eaVEdTyWQyDBw4EAMHDgRw63bkgf9v715C4e3iOIB/\\nn0YyTFPkbxYTCisbl3LZyCxIxiUWNjZukdmIGKWsRCkRYTM1G5eURlkQjVIGU7KQKJcyIpfE5DY0\\nJM+7eBuvyzPzel2ft76f1TTnzHPmzOrbmd85x27H9PQ0bDYbHh8f0dLSgsTERJ87Sl+bnZ1Fd3c3\\nFAoFurq6XqygAcDOzg4AYHl5GcXFxZLPODg4gCiK2N3d/eDsiOirMLAR0f+WUqn02f6emwCer7QF\\nBQV57RcYGPimv8dX3tqg0WhQWFiIwsJCLC0tobq6Gm63GxaLBU1NTe96xvb2NoxGI0RRRENDg+RK\\no8vlAgCcnp563TUL/P0bevoS0e9hYCMiWfB26Yrb7f7WcZ+HtJubG6hUKsl+19fXAP4Jbp9RX1+P\\n1dVVGI1GZGVlee2XkpKCoqIiDAwMYG9v713PPj8/h8FgwO3tLfR6PcrLyyX7KZVKCIKAxsZGlJWV\\nfWgeRPRzWMNGRL/q+Zlkr11fX397YFOpVAgNDQUAnwX+nk0KERERnx7z5uYGh4eHXs+ee87z3aTq\\nzF57eHhATU3N0xlxzzcZvBYZGQlRFOFwOLz22djYwObmpuSqIhH9LAY2IvpVnl2VUnVSnsL475ae\\nng5RFDEyMiLZPjMzg5OTE/j5+SE1NfXT4+n1eoiiiMnJSayvr3vt9/j4CKvVCkEQnmrcfGlpacHy\\n8jKCg4PR39/v869anU4HALBarbi4uHjT7nK5UFJSgoKCAsmDg4noZzGwEdGvSkhIgCiKGBwcfLHa\\nY7fb0d7e/q46tM+qqKhAQEAA5ufn0dbW9mJVz2azobm5GYIgoLS0VHIjw3+Vk5OD+Ph43N3doays\\nDENDQ2/qxHZ2dmAwGLC+vo7Y2FhkZ2f7fObAwABGR0fh7++P3t7eF7tlpSQnJyMpKQmXl5eoqqrC\\n/v7+U9vJyQkMBgOurq6g0WiQl5f38ckS0ZdgDRsR/arS0lJMTEzA6XQiPz8fMTExcLlcODg4QGJi\\nIpRKJex2+5eO+bpeLioqCh0dHTAajRgaGsLY2Biio6PhdDpxdHQEQRCg1+tRW1v7JeMrFAqYTCbU\\n1dXBbrejtbUV7e3tCA8Ph0qlwtnZGY6PjyEIAuLi4tDX1/fiVobXnE7nU7gNDQ2F2WxGf38/7u/v\\n38z1z58/6OnpAQB0dnaioqICa2tryMrKenFbxMPDA9RqNUwmE/z9/b9k3kT0cQxsRPRt3rM6ptVq\\nYbFY0NfXh4WFBTgcDmi1WtTU1KCystJrSPJ2ZdV7xpb6bGZmJsbHx2E2m7G4uIitrS2o1Wqkp6ej\\nqKgIGRkZH56jFLVaDbPZjLm5OVitVqysrMDpdOLw8BAhISHQ6XTIyclBbm6u16u5PO+73e6nYHZ8\\nfOzzonitVvv0OiwsDBaLBcPDw5iamoLD4cD9/T00Gg3S0tJQWVn5ryt1RPQzBNHb1iwiIiIikgXW\\nsBERERHJHAMbERERkcwxsBERERHJHAMbERERkcwxsBERERHJHAMbERERkcwxsBERERHJHAMbERER\\nkcwxsBERERHJHAMbERERkcwxsBERERHJ3F9EigQoVrdSRgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list = [1, 2, 3, 4, 7, 8, 9, 10]\\n\",\n \"y_list = [0, 0, 0, 0, 1, 1, 1, 1]\\n\",\n \"\\n\",\n \"x_list_reg = list(range(11))\\n\",\n \"regline = lambda x: (1/6) * (x - 1) - 0.25\\n\",\n \"y_list_reg = [regline(x) for x in x_list_reg]\\n\",\n \"\\n\",\n \"textstr1 = 'Not malignant'\\n\",\n \"textstr2 = 'Malignant'\\n\",\n \"props = dict(boxstyle='round', facecolor='dodgerblue', alpha=0.5)\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" \\n\",\n \" fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \" plt.plot(x_list, y_list, 'rD', markersize=16)\\n\",\n \" plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks([0, 1], fontsize=24)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(-0.10, 1.10)\\n\",\n \" plt.xlim(0, 10.5)\\n\",\n \" \\n\",\n \" plt.axvline(x=5, color='purple', linewidth=6)\\n\",\n \" plt.axvspan(0, 5, color='wheat')\\n\",\n \" plt.axvspan(5, 11, color='lavenderblush')\\n\",\n \" \\n\",\n \" ax.text(1, 0.95, textstr1, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" ax.text(8, 0.15, textstr2, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor_linreg1_threshold.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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cH9lv4/kU+GEtkMzgdKSsDApgDdx05H8vXzOBP5Cxq2aq+33tHJpcJB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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list = [1, 2, 3, 4, 7, 8, 9, 10, 15]\\n\",\n \"y_list = [0, 0, 0, 0, 1, 1, 1, 1, 1]\\n\",\n \"\\n\",\n \"x_list_reg = list(range(16))\\n\",\n \"regline = lambda x: (1/10) * x - 0.25\\n\",\n \"y_list_reg = [regline(x) for x in x_list_reg]\\n\",\n \"\\n\",\n \"\\n\",\n \"textstr1 = 'Not malignant'\\n\",\n \"textstr2 = 'Malignant'\\n\",\n \"props = dict(boxstyle='round', facecolor='dodgerblue', alpha=0.5)\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" \\n\",\n \" fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \" plt.plot(x_list, y_list, 'rD', markersize=16)\\n\",\n \" plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks([0, 1], fontsize=24)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(-0.10, 1.10)\\n\",\n \" plt.xlim(0, 16)\\n\",\n \" \\n\",\n \" plt.axvline(x=7.5, color='purple', linewidth=6)\\n\",\n \" plt.axvspan(0, 7.5, color='wheat')\\n\",\n \" plt.axvspan(7.5, 16, color='lavenderblush')\\n\",\n \" \\n\",\n \" ax.text(1, 0.95, textstr1, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" ax.text(13, 0.15, textstr2, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg1_maltumor_linreg1_newpoint.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 140,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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aSk9tprwcKfvutPUtgsVZKS2ksvBVtLlaSwWaokJbWXXoKMDPjqV8NO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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list = np.linspace(-15, 15, 150)\\n\",\n \"y_list = expit(x_list)\\n\",\n \"\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" \\n\",\n \" fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \" plt.plot(x_list, y_list, 'b')\\n\",\n \" plt.xlim(-15, 15)\\n\",\n \" plt.ylim(-0.05, 1.05)\\n\",\n \" \\n\",\n \" ax.spines['left'].set_position('zero')\\n\",\n \" ax.spines['right'].set_color('none')\\n\",\n \" ax.spines['bottom'].set_position('zero')\\n\",\n \" ax.spines['top'].set_color('none')\\n\",\n \" ax.spines['left'].set_smart_bounds(True)\\n\",\n \" ax.spines['bottom'].set_smart_bounds(True)\\n\",\n \" ax.xaxis.set_ticks_position('bottom')\\n\",\n \" ax.yaxis.set_ticks_position('left')\\n\",\n \" plt.yticks([0, 0.5, 1], fontsize=18)\\n\",\n \" plt.xticks(fontsize=18)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg2_sigmoid_func_plot.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXMAAAF/CAYAAAC7eCnpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAGu5JREFUeJzt3X9oVff9x/HXVXe9SmwYUuskqy0bU1KXNpKkrDDrkq51\\n7bQOqqWbv5LQ0D9WsLARa3UaRGMYbMJW9ocL+VantToYOCjiSImT1dKIdineNY5axbTNuqFGr1mS\\nqef7x+Fm0+TmnnvvOd5z3/f5AMHlnPPJ50XJa29PTk4ijuM4AgAUtEn53gAAIHeUOQAYQJkDgAGU\\nOQAYQJkDgAGUOQAY4LnMT5w4oRdeeEELFy7UokWLtGPHDg0ODga5NwCARxEvz5mfOHFCjY2N+uY3\\nv6lnn31W/f39euONN7RgwQLt27fvbuwTADCBKV5O+vnPf645c+Zo7969ikajkqTZs2dr27ZtOn78\\nuL797W8HukkAwMTS3mYZGRnRzJkztXLlytEil6Samho5jqPe3t5ANwgASC/tZB6NRrV79+4xH4/H\\n45KkOXPm+L8rAEBGPN1m+V+fffaZ3nvvPbW1tWnevHl64okngtgXACADGZX5wMCAamtrFYlEFIvF\\ntGnTpttuvQAA8sPT0yxJV69e1V/+8hf95z//0d69exWPx7Vr1y5997vfDXKPAIA0Mirz/zU8PKzv\\nf//7unnzpt55551xz7lx44b6+/s1e/ZsTZmS8R0dAIBHWf8E6NSpU7V48WJ9/vnnunLlyrjn9Pf3\\nq66uTv39/VlvEACQXtoyP3funGpra/Xmm2+OOZZIJBSJRLhvDgB5lrbM586dq0QioQMHDujGjRuj\\nH//000919OhR1dTUaPr06YFuEgAwsbQ3sidPnqxNmzapublZq1at0tKlS3X58mXt379fU6ZM0ebN\\nm+/GPgEAE/D0Xclly5aN/vBQW1ubpk2bpscee0zr16/X3Llzg94jACANz4+YLFmyREuWLAlyLwCA\\nLPE+cwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMo\\ncwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAw\\ngDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIHAAMocwAwgDIH\\nAAMocwAwgDIHAAMoc6TUdb5LXee78r2NwFjOZzmbZD9fNqbkewMIr61dWyVJXeu68rqPoFjOZzmb\\nZD9fNpjMMa6u8106duGYjl04ZnICspzPcjbJfr5sUeYYV3LyufPvVljOZzmbZD9ftihzjJGcfJKs\\nTUCW81nOJtnPlwvKHGOMN+1YmoAs57OcTbKfLxeUOW5z5+STZGUCspzPcjbJfr5cUea4zURTjoUJ\\nyHI+y9kk+/lyRZljVKrJJ6nQJyDL+Sxnk+zn8wNljlFepptCnoAs57OcTbKfzw+UOSSln3ySCnUC\\nspzPcjbJfj6/UOaQlNlUU4gTkOV8lrNJ9vP5hTKH58knqdAmIMv5LGeT7OfzE2WOrKaZQpqALOez\\nnE2yn89PlHmRy3TySSqUCchyPsvZJPv5/EaZF7lcpphCmIAs57OcTbKfz2+UeRHLdvJJCvsEZDmf\\n5WyS/XxBoMyLmB/TS5gnIMv5LGeT7OcLAmVepHKdfJLCOgFZzmc5m2Q/X1Ao8yLl59QSxgnIcj7L\\n2ST7+YIScRzHCWrxvr4+1dXVqbOzU2VlZUF9GgAoekzmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4A\\nBlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDm\\nAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAAZQ4ABlDmAGAA\\nZQ4ABlDmAGAAZQ4ABlDmAGDAlHxvoBANDEjd3VJPj5RISCUlUkWFVF0tlZbme3fwqut8lyRp8QOL\\n87qPIFjOJtnPlw3PZX78+HH95je/UTweVyQS0SOPPKL169fr4YcfDnJ/oRKPSzt3SgcPSsPDY4/H\\nYtKKFdKGDVJ5+d3fHzKztWurJKlrXVde9xEEy9kk+/my4ek2y/vvv6+mpiYlEgm98sorevnll3Xx\\n4kWtWrVKH374YdB7zDvHkXbskCorpb17xy9ySRoaco9XVrrnO87d3Se86zrfpWMXjunYhWOjU54V\\nlrNJ9vNly1OZ79ixQ1/5ylf0+9//XmvXrlVDQ4PeeustTZ8+Xbt27Qp6j3nlOFJjo/Taa9LIiLdr\\nRkbc8xsbKfSwSk52d/7dAsvZJPv5spW2zK9evaqzZ8/q6aefVjQaHf34zJkzVV1drVOnTgW6wXxr\\nbZU6OrK7tqPDvR7hkpzskixNeJazSfbz5SJtmZeUlOjIkSNau3btmGOXL1/WlCl2v4caj0stLbmt\\n0dLiroPwGG+aszLhWc4m2c+Xi7RlPmnSJN1///269957b/v4Rx99pFOnTmnhwoWBbS7fdu70fmsl\\nlZERqa3Nn/0gd3dOdkkWJjzL2ST7+XKV1XPmg4ODam5uViQS0Ysvvuj3nkJhYMB9asUPBw+66yH/\\nJpriCn3Cs5xNsp8vVxmX+dDQkF566SWdPXtWTU1NqqqqCmJfedfdnfqplUwNDUknT/qzFrKXarJL\\nKuQJz3I2yX4+P2RU5teuXVN9fb26u7v13HPPaf369UHtK+96esK9HjLnZXor1AnPcjbJfj4/eC7z\\nS5cuafXq1frggw/0/PPPa9u2bUHuK+8SiXCvh8ykm+ySCnHCs5xNsp/PL57K/Pr162poaFBvb6/W\\nrVunrVu3Bryt/CspCfd6yEwmU1uhTXiWs0n28/nFU5m3tLSot7dXa9euVXNzc9B7CoWKinCvB++8\\nTnZJhTThWc4m2c/np7QPiX/88cc6fPiwSktLNW/ePB0+fHjMOcuWLQtkc/lUXe2+a2VoKPe1YjHJ\\n6PeJC0I209rWrq0F8d4Py9kk+/n8lLbMu7u7FYlEdPXqVW3cuHHccyyWeWmp+9KsvXtzX2vlSt6m\\nmC+ZTnZJyQkvzG/ls5xNsp/PbxHHCe7tIX19faqrq1NnZ6fKysqC+jSBicfdl2bl8oND0ah0+jRv\\nUcyXxf+3OKtCkKTH5z4e6gnPcjbJfj6/8cspJlBeLm3ZktsaW7ZQ5PmS7WSXFOb7r5azSfbzBYEy\\nT+PVV6X6+uyura93r0d++PFkQ1ifjrCcTbKfLwiUeRqRiNTeLm3f7t4y8SIadc9vb3evx92X62SX\\nFMYJz3I2yX6+oFDmHkQi0saN7r3vNWvcp1PGE4u5x0+fds+nyPPHz6ksbBOe5WyS/XxB4RugWRgY\\ncN+1cufvAK2q4qkVAPlh92XkASotlerq3D8AEAbcZgEAAyhzADCAMgcAAyhzADCAMgcAAyhzADCA\\nMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcA\\nAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhz\\nADCAMgcAA6bkewOFaGBA6u6WenqkREIqKZEqKqTqaqm0NN+7y531fMWg63yXJGnxA4vzuo+gWM+X\\nDco8A/G4tHOndPCgNDw89ngsJq1YIW3YIJWX3/395cp6vmKytWurJKlrXVde9xEU6/mywW0WDxxH\\n2rFDqqyU9u4dv+gkaWjIPV5Z6Z7vOHd3n9mynq/YdJ3v0rELx3TswrHRCdYS6/myRZmn4ThSY6P0\\n2mvSyIi3a0ZG3PMbG8NfeNbzFaPk1Hrn362wni9blHkara1SR0d213Z0uNeHmfV8xSY5tSZZm16t\\n58sFZT6BeFxqacltjZYWd50wsp6vGI03qVqaXq3nywVlPoGdO73fekhlZERqa/NnP36znq/Y3Dm1\\nJlmZXq3nyxVlnsLAgPtUhx8OHnTXCxPr+YrRRBOqhenVer5cUeYpdHenfqojU0ND0smT/qzlF+v5\\nik2qqTWp0KdX6/n8QJmn0NMT7vVyZT1fsfEymRby9Go9nx8o8xQSiXCvlyvr+YpJuqk1qVCnV+v5\\n/EKZp1BSEu71cmU9XzHJZCItxOnVej6/UOYpVFSEe71cWc9XLLxOrUmFNr1az+cnyjyF6mr3XSR+\\niMWkqip/1vKL9XzFIptJtJCmV+v5/ESZp1Ba6r5Uyg8rV4bvbYPW8xWDTKfWpEKZXq3n8xtlPoEN\\nG6RoNLc1olGpudmf/fjNej7rcplAC2F6tZ7Pb5T5BMrLpS1bcltjy5bwvi7Wej7Lsp1ak8I+vVrP\\nFwTKPI1XX5Xq67O7tr7evT7MrOezyo/JM8zTq/V8QaDM04hEpPZ2aft277ckolH3/PZ29/ows57P\\nolyn1qSwTq/W8wWFMvcgEpE2bpROn5bWrEn9FEgs5h4/fdo9v1CKzno+a/ycOMM4vVrPF5SI4wT3\\n6wX6+vpUV1enzs5OlZWVBfVp7rqBAfddJHf+jsyqKhtPdVjPB1hEmQOAAdxmAQADKHMAMIAyBwAD\\nKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMA\\nMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAy\\nBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMGBKvjdQiAYGpO5uqadHSiSkkhKpokKqrpZKS/O9u9yR\\nD2HXdb5LkrT4gcV53UeYZFXmmzdv1oULF7Rnzx6/9xNq8bi0c6d08KA0PDz2eCwmrVghbdgglZff\\n/f3linyFna+YbO3aKknqWteV132ESca3WQ4dOqRDhw4FsZfQchxpxw6pslLau3f8IpCkoSH3eGWl\\ne77j3N19Zot8rkLNV2y6znfp2IVjOnbh2OiEjgzK/NatW/r1r3+tn/3sZ4pEIkHuKVQcR2pslF57\\nTRoZ8XbNyIh7fmNj+AuBfGMVUr5ilJzK7/x7sfNU5iMjI1q+fLlef/11LV++XLNmzQp6X6HR2ip1\\ndGR3bUeHe32YkS+1QshXbJJTeRLT+X95KvPh4WENDg5q165dam1t1eTJk4PeVyjE41JLS25rtLS4\\n64QR+dILc75iNN4kznTu8lTmM2bM0NGjR/XUU08FvZ9Q2bnT+z/NUxkZkdra/NmP38iXXpjzFZs7\\np/IkpnOX53vmkyYV1yPpAwPuUw9+OHjQXS9MyOddGPMVo4kmcKZzfmgope7u1E89ZGpoSDp50p+1\\n/EI+78KYr9ikmsqTmM4p85R6esK9Xq7Il9/1kBkvk3exT+eUeQqJRLjXyxX58rsevEs3lScV+3RO\\nmadQUhLu9XJFvvyuB+8ymbiLeTqnzFOoqAj3erkiX37Xgzdep/KkYp7OKfMUqqvdd3X4IRaTqqr8\\nWcsv5PMujPmKRTaTdrFO51mXufUf6S8tdV+65IeVK8P3Nj7yeRfGfMUg06k8qVin84jjBPf2ib6+\\nPtXV1amzs1NlZWVBfZrAxOPuS5dy+cGTaFQ6fTqcb+EjX3phzmfd4v9bnFWZS9Ljcx8vujcqcptl\\nAuXl0pYtua2xZUt4i4B86YU5n2XZTuVJxTidU+ZpvPqqVF+f3bX19e71YUa+1Aohn1V+3Pcutnvn\\nlHkakYjU3i5t3+7+k9uLaNQ9v73dvT7MyDdWIeWzKNepPKnYpnPK3INIRNq40b13umZN6qckYjH3\\n+OnT7vmFUgTkcxVqPmv8nKiLaTrnG6BZGBhw39Vx5++QrKqy8dQD+YDCQ5kDgAHcZgEAAyhzADCA\\nMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcA\\nAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhz\\nADCAMgcAAyhzADCAMgcAAyhzADCAMgcAAyhzADBgSr43UIgGBqTubqmnR0okpJISqaJCqq6WSkvz\\nvbvcka9wWc6GNJwAXbx40fnGN77hXLx4MchPc9ecOeM4q1c7ztSpjiON/ROLucfPnMn3TrNDvsLN\\nZzkbvKHMPbh1y3G2b3ecaHT8L5Q7/0Sj7vm3buV7596Qr3DzWc6GzFDmady65Tj19d6+UO78U18f\\n/i8a8hVuPsvZkDm+AZpGa6vU0ZHdtR0d7vVhRr7Uwp7PcjZkLuI4jhPU4n19faqrq1NnZ6fKysqC\\n+jSBicelykppZCT7NaJR6fRpqbzcv335hXzphTWf5WzIDpP5BHbuzO2LRXKvb2vzZz9+I196Yc1n\\nORuyw2SewsCAdN990vBw7mvFYlJ/f7geDSOfd2HLZzkbssdknkJ3tz9fLJI0NCSdPOnPWn4hn3dh\\ny2c5G7JHmafQ0xPu9XJFvvyulwvL2ZA9yjyFRCLc6+WKfPldLxeWsyF7lHkKJSXhXi9X5Mvvermw\\nnA3Zo8xTqKgI93q5Il9+18uF5WzIHmWeQnW1+51+P8RiUlWVP2v5hXzehS2f5WzIHmWeQmmptGKF\\nP2utXBm+R7/I513Y8lnOhuxR5hPYsMH9KblcRKNSc7M/+/Eb+dILaz7L2ZAdynwC5eXSli25rbFl\\nS3h/XJp86YU1n+VsyFKQb/HirYnhfzMd+Qo3n+VsyByTeRqRiNTeLm3f7v2ftdGoe357u3t9mJFv\\nrELJZzkbshDk/1NYmMz/15kzjrNmjftbW8abdmIx93ih/jYX8hVuPsvZ4A0v2srCwID7Pos7f89i\\nVZWNJwPIV7gsZ8PEKHMAMIB75gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBg\\nAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUO\\nAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ5gBgAGUOAAZQ\\n5gBggOcy7+vr049//GM9+uijevTRR9Xc3KxLly4FuTcAgEdTvJx05coVrVmzRjdu3FBTU5Nu3Lih\\n3/72tzp79qwOHTqkKVM8LQMACIinFu7o6NAXX3yhP/7xj3rwwQclSRUVFaqvr9cf/vAHrVixItBN\\nAgAm5uk2y9tvv62amprRIpekb33rW3rwwQf19ttvB7Y5AIA3acv86tWrunjxoh566KExx8rLy3Xm\\nzJlANgYA8C5tmf/jH/+QJN13331jjs2aNUvXrl1TIpHwf2cAAM/Slvn169clSbFYbMyxqVOnSpL+\\n/e9/+7wtAEAm0pa54ziSpEgkkvKciY4BAIKX9mmW6dOnS5KGhobGHBseHpYklZSUjHvtzZs3JUn9\\n/f1ZbxAAitXs2bM9P/qd9qw5c+ZIkv75z3+OOfbFF1/onnvuGfcWzP9e86Mf/cjTZgAA/9XZ2amy\\nsjJP56Yt8xkzZqisrEzxeHzMsXg8rgULFqS8dsGCBdq3b5/uvfdeTZ482dOGAACu2bNnez7X0/z+\\n5JNPas+ePfrkk09GnzV/99139cknn+jFF19MeV0sFlNVVZXnzQAAshNxkt/hnMClS5e0dOlSTZ48\\nWQ0NDRoaGlJ7e7seeOAB7d+/X1/60pfuxl4BACl4KnNJOn/+vFpbW9Xd3a1p06bp8ccf109/+lN9\\n+ctfDnqPAIA0PJc5ACC8eJ85ABgQWJnz/nM7Nm/erDVr1uR7G/Do+PHj+uEPf6hHHnlElZWVqq+v\\n11//+td8bwsenThxQi+88IIWLlyoRYsWaceOHRocHEx7XSBlnnz/eU9Pj5qamtTQ0KB33nlHjY2N\\nunHjRhCfEgE5dOiQDh06lO9twKP3339fTU1NSiQSeuWVV/Tyyy/r4sWLWrVqlT788MN8bw9pnDhx\\nQo2Njbp165Z+8pOfaPny5XrrrbcmfGpwlBOAX/ziF85DDz3knDt3bvRj7777rjNv3jzn4MGDQXxK\\n+OzmzZvOr371K2f+/PnO/PnzndWrV+d7S/Dg2Wefdb7zne84w8PDox/717/+5dTU1DgNDQ153Bm8\\n+MEPfuDU1dXd9t9v3759zvz5850///nPE14byGTO+88L28jIiJYvX67XX39dy5cv16xZs/K9JXhw\\n9epVnT17Vk8//bSi0ejox2fOnKnq6mqdOnUqj7tDOiMjI5o5c6ZWrlx523+/mpoaOY6j3t7eCa/3\\n/fe9Jd9/vmTJkjHHysvLdfz4cb8/JXw2PDyswcFB7dq1S0899ZRqa2vzvSV4UFJSoiNHjmjatGlj\\njl2+fJlf7xhy0WhUu3fvHvPx5E/fJ1+tkorv/3W9vv881cu5kH8zZszQ0aNHNWkSDzsVkkmTJun+\\n++8f8/GPPvpIp06d0qJFi/KwK2Trs88+03vvvae2tjbNmzdPTzzxxITn+17mXt9/TpmHG0Vuw+Dg\\noJqbmxWJRLx9Ew2hMDAwoNraWkUiEcViMW3atOm2Wy/j8f0r1uH950AoDA0N6aWXXtLZs2fV1NTE\\ne5IKSCQS0S9/+Uu1tbXp61//utatW6c//elPE17je5nn8v5zAP64du2a6uvr1d3dreeee07r16/P\\n95aQgXvuuUff+973tGzZMv3ud7/TnDlz1NraOuE1vpd5Lu8/B5C7S5cuafXq1frggw/0/PPPa9u2\\nbfneEnIwdepULV68WJ9//rmuXLmS8jzfyzyX958DyM3169fV0NCg3t5erVu3Tlu3bs33luDRuXPn\\nVFtbqzfffHPMsUQioUgkMuF980C+y/Xkk0+Ovu88Kfm/n3nmmSA+JQBJLS0t6u3t1dq1a9Xc3Jzv\\n7SADc+fOVSKR0IEDB277SflPP/1UR48eVU1Nzeht7PEE8tZE3n9uS21trcrKyrRnz558bwUT+Pjj\\nj/XMM8+otLRUGzZsGPe3ey1btiwPO4NXhw8fVnNzsx5++GEtXbpUly9f1v79+3Xz5k3t379fX/va\\n11JeG9grcHn/uR21tbX66le/qjfeeCPfW8EEDhw4oJaWlgnP+dvf/naXdoNsHTlyRLt379bf//53\\nTZs2TY899pjWr1+vuXPnTngd7zMHAAP4yRAAMIAyBwADKHMAMIAyBwADKHMAMIAyBwADKHMAMIAy\\nBwADKHMAMIAyBwAD/h81AvlXCDKD5AAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list1 = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1.5, 1.5, 2]\\n\",\n \"y_list1 = [0.5, 1, 1.5, 2, 0.5, 1, 1.5, 0.5, 1, 0.5]\\n\",\n \"\\n\",\n \"x_list2 = [2.5, 2.5, 2.5, 2.5, 2, 1.5, 1, 2, 1.5, 2]\\n\",\n \"y_list2 = [1, 1.5, 2, 2.5, 2.5, 2.5, 2.5, 2, 2, 1.5]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x_list1, y_list1, 'bo', markersize=20)\\n\",\n \" plt.plot(x_list2, y_list2, 'g^', markersize=20)\\n\",\n \" \\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" #plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" #plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.xticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.ylim(0, 3)\\n\",\n \" plt.xlim(0, 3)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_noline.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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+bMwTPPPGNlLstlZMh9vnipWt6xTHMyTn4q51c5u1sZKu7Lly9j4cKF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\\nhsVNRKQYFjcRkWL+P/U8Xrl4JwG6AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list1 = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1.5, 1.5, 2]\\n\",\n \"y_list1 = [0.5, 1, 1.5, 2, 0.5, 1, 1.5, 0.5, 1, 0.5]\\n\",\n \"\\n\",\n \"x_list2 = [2.5, 2.5, 2.5, 2.5, 2, 1.5, 1, 2, 1.5, 2]\\n\",\n \"y_list2 = [1, 1.5, 2, 2.5, 2.5, 2.5, 2.5, 2, 2, 1.5]\\n\",\n \"\\n\",\n \"x_list3 = np.linspace(0, 3, 50)\\n\",\n \"y_list3 = x_list3[::-1]\\n\",\n \"\\n\",\n \"textstr1 = 'y = 0'\\n\",\n \"textstr2 = 'y = 1'\\n\",\n \"props = dict(boxstyle='round', facecolor='purple', alpha=0.5)\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x_list1, y_list1, 'bo', markersize=20)\\n\",\n \" plt.plot(x_list2, y_list2, 'g^', markersize=20)\\n\",\n \" plt.plot(x_list3, y_list3, '-', color='purple', linewidth=4)\\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" #plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" #plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.xticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.ylim(0, 3.5)\\n\",\n \" plt.xlim(0, 3.5)\\n\",\n \" \\n\",\n \" ax.text(0.1, 0.3, textstr1, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" ax.text(2.3, 3, textstr2, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_withline.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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sNsHqEQnJlvAQIuYErQ9ZpCyFHXIV15Bc8wu9Cq3aba1IOKBSoZUyT2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\\npBCDLhGRQv8PwCZObmfK/eQAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\\n\",\n \"y_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\\n\",\n \"\\n\",\n \"x_partial_1 = np.random.uniform(-1, 1, 25)\\n\",\n \"y_partial_1 = np.random.uniform(2, 3, 12)\\n\",\n \"y_partial_2 = np.random.uniform(-3, -2, 13)\\n\",\n \"x_partial_2 = np.random.uniform(2, 3, 13)\\n\",\n \"x_partial_3 = np.random.uniform(-3, -2, 12)\\n\",\n \"y_partial_3 = np.random.uniform(-1, 1, 25)\\n\",\n \"x_partial_4 = np.random.uniform(1, 2, 12)\\n\",\n \"y_partial_4 = np.random.uniform(1, 2, 12)\\n\",\n \"x_partial_5 = np.random.uniform(-2, -1, 12)\\n\",\n \"y_partial_5 = np.random.uniform(1, 2, 12)\\n\",\n \"x_partial_6 = np.random.uniform(1, 2, 12)\\n\",\n \"y_partial_6 = np.random.uniform(-2, -1, 12)\\n\",\n \"x_partial_7 = np.random.uniform(-2, -1, 12)\\n\",\n \"y_partial_7 = np.random.uniform(-2, -1, 12)\\n\",\n \"\\n\",\n \"x_list2 = np.concatenate([x_partial_1, x_partial_2, x_partial_3, x_partial_4, x_partial_5, x_partial_6, x_partial_7], 0)\\n\",\n \"y_list2 = np.concatenate([y_partial_1, y_partial_2, y_partial_3, y_partial_4, y_partial_5, y_partial_6, y_partial_7], 0)\\n\",\n \"\\n\",\n \"textstr1 = 'y = 0'\\n\",\n \"textstr2 = 'y = 1'\\n\",\n \"props = dict(boxstyle='round', facecolor='purple', alpha=0.5)\\n\",\n \"circle = plt.Circle((0, 0), 1.3, color='purple', fill=False, linewidth=4)\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x_list1, y_list1, 'b.')\\n\",\n \" plt.plot(x_list2, y_list2, 'rd')\\n\",\n \" #plt.plot(x_list3, y_list3, '-', color='purple', linewidth=4)\\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" #plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" #plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks(fontsize=18)\\n\",\n \" plt.xticks(fontsize=18)\\n\",\n \" plt.ylim(-3.2, 3.2)\\n\",\n \" plt.xlim(-3.2, 3.2)\\n\",\n \" \\n\",\n \" #ax.add_artist(circle)\\n\",\n \" \\n\",\n \" #ax.text(0.1, 0.3, textstr1, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" #ax.text(2.3, 3, textstr2, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" \\n\",\n \" ax.spines['left'].set_position('zero')\\n\",\n \" ax.spines['right'].set_color('none')\\n\",\n \" ax.spines['bottom'].set_position('zero')\\n\",\n \" ax.spines['top'].set_color('none')\\n\",\n \" #ax.spines['left'].set_smart_bounds(True)\\n\",\n \" #ax.spines['bottom'].set_smart_bounds(True)\\n\",\n \" ax.xaxis.set_ticks_position('bottom')\\n\",\n \" ax.yaxis.set_ticks_position('left')\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_nonlinear_nocirc.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YPMTFkFt+zzMryS+gr2r/MsuINuH4R5387DPe/egytuvEIVwVWbHlf1\\nwO0v345Hyh7B8N8OhyXU/Xs8/vlxvHL1K9i3dh8k+zFydQWjnsu6hjxdrdD5SnHzz834z8L/4MDG\\nAx5f731Db4x/Zjz63txXZctYtOyne670HAqfKMThrYc9vn7luCuRtTFLmtcbaIjI2+d1lMcd7JCn\\nqwWBtGlUwYtxeLeeBLdbcjdM2T4FD/73Qc0EV2u6J3fH5PcnY843c9B3tPvPoGJnhXSv11EKvGqV\\ntJi8t57L1FtXN5CnqwVS42sKL5S1t7Ujf0k+dj+72+01S4gFNzx6A8YuH+u3cCEQvvvuOzz77LN4\\n8803fb5PL5MjGIZB8bpi7Hh0B1psLW6vJ2UmYdLmSepUtvnzZiklTBeQp2skFJwc0VzXjHcmvuNR\\ncLsP7o4H//cgJqycoKjgbtiwAU888QRaHcn7BsBisWDEQyPw8PcP48oMd8/xaN5RbLxhI34q+0l5\\nY/xNnwjUizYSOo5rk+hqAf+iF1JyqeDkiPNHz2PDyA0o+3cZZ78lxIJRi0bhoQMPoff1ynuTffv2\\nxerVqxU/jxJ06dcF03dMxx1r70CENYLz2tnDZ7H+uvUoLyjXyDoXZF5o1SWOReq5c4Ft27S2xg1l\\nRVfHdxtNkRJfU2icdtnnZVh/3XqcLz3P2R+bGItZX89S3Lt1ZcKECQgNNUjTcQ+4er0JaQmc15ov\\nNGPzrzdj70t7pWc3+EPKzdyMOByS2lpgzhx5s4NkQDnRVSolyizoYHbVN6u/wdu3v41LP1/i7O91\\nfS/M3TcXfW7oo4ldRqdLvy6Y9dUspN6fytnPtDH494J/4+N5H6O9rV3+E9NimfsT4YUL7M9BRygn\\nukabXKu2Vy42viazF/P1yq/x2e8/A9PO9bqumX4NHvjiAcQmxEo+dqCYYW03PDock96ahIycDICX\\n1rt/3X58NOMjtNsVEF4d3Mw1xZOT9/LLuhriqozoGm1yrVZeuZj4mkxeDMMw+GL5FyhYUsDZbwmx\\nYMKzE3DnpjtVCyd4wywFFhaLBTcuuRFTtk9BRCw3zvv9299j65StaGtpk/ekwbRYJpT2dllCcXKh\\njOgqFH9UDKN45TJ4MV8+9SV2Ze/i7AuLDsP9efdj1MJRmgter169sGXLFk1tkJvkzGTM3j0bcb3j\\nOPt/+OAHfPB/H8jv8QbDYpk3Vq0CQkTKmspPuZS9YCSvPEAv5r///C++ePILzr4IawSmfjYVg24f\\nJJORhCcuH3I5HvjyAbcqtR8++AEfzfxImRhvMNK/P/D733P3+QrFafCUq4zoGmkV1WheuUQvpnhd\\nMfIX5XP2RcZFYtp/pqHf6H4yGUf4ouuVXfHAlw/gsoGXcfZ///b3+GT+J6aIZeuClSuBLi43N1+h\\nOA2ecpURXVpF1RU/fvEjPv3dp5x94Z3CMfWzqZShoDKd+3TGjJ0z3Dze/ev245v/941GVpmMqChg\\nwwagZ0/foTiNnnKVKwM2SsmhyRuBXKi4gPXp6zlzwcKiwjD1s6noN6afdoYFiF7KgKVyoeIC/nXz\\nv3Cx6mLHPkuoBdM+n4b+GTL05iX8tznVqN2lcjFdo6yimtgrv1R/CVuytrgNYpz09iRDC64Z6Hpl\\nV0z991ROVgPTxuD9ye/jp+MqlAwbhUAWuXS6oEgNbwDjeOUiYNoZvHf3ezjy0RHO/jF/G4PRfx2t\\njVEyItbTtdnYX+2QIYDVqoKBAinNK8WW32zh9CrukdIDs3fP1mb8u55Q+u9So6dcyl4AjOOVi2DX\\n33a5CW7K5BTc/MTNGlmkHTYbkJ4OjBzJ/m8TNh1eFZIzkzFuxTjOvrOHz2LbtG20sKb0IpdGT7nk\\n6ZqQyt2VeP3G1znVZvFD4zHr61mI6BTh45PGQYynW1gIjHPRtT17gOuvl8cOOTxohmGw7f+24dCW\\nQ5z9d7xyB0bMGyGDlQZELS9Ug6dc8nRNRmtTK7bP2s4R3JgeMWxVlEkEVww2GzBvnnM7KYn9W5br\\n2HJ40BaLBVkbs9ya5Ox4bAfqfqyTwVIDolYqpwZPuSS6JqPwyUK3jmGTNk9C5ys6a2SRtpSUAEdd\\nhhe/8AK7T44QQ0kJO1AXYP8vKZF+rPCYcNy79V5OW8gWWwty5+QGX5ghLw84fVq986m84EaiayIq\\nd1diz/N7OPuGzx2OAbcM0Mgi7RkyBBg8mP06KQn405/8e6Y2G7B3r39hdj324MGBe9Bd+nXBhH9O\\n4OyrKKhA8avFgR3YSDgqxE6fBiJdFhL1XGAlEhJdk+AprBDXJw63PHuLhlYJQ6jIScFqBYqK2Dju\\n2rVOr9ebZ+oaMkhNZVuyCjl2UZE8WRFpD6W5TaAIqjCDY/Hs5En2l+DARKmcJLom4cunvnQLK2Rt\\nyNJ92pEamQVWK7twlp7u3zN1DRmcOMHaJdQmOW4eFosFWRuy3MIMH88LgmEA/CyFvXuB3r1N16aS\\nRNcEXDx10bBhBTnjov4Q4pkOGQL0dRnwe+KEd5tcbxhpaew/OW4ensIMxz8/jvJ8HYz7URJPi2cJ\\nCaZK5QRIdE3BruW7YG+2d2xbE6y6Diu4eoRyx0X94fB6vYUCrFZWlB3C68sm1xvG0aP+QxdiSHso\\nzW28e/6S/OBbVOvZU5dVZYFAomtwzpWew4HXDnD2jV42WrdhBX44AZA/Lhoo8fHAoUP+beIv0iUl\\nsV/LcfOwWCxu3m5NcQ0Obz0c2IH1jJG6EwaA8USXhl1yKHyiEEyb0/u5bNBlGPbgMA0t8o2ncII/\\n71MLhNjkGq4oLmb/yXnz6JXeC1fdfRVn386lO9HWKvO0Cb1g4j4orhhLdGnYJYdTRafcPJ9xT41D\\naLiyE3Ud4YHaWvELR2qHE5TGVZyVuHmMWzEOllDnNI+fjv2Eb1//Vr4T6I0gmPFmLNE1ylgdlSj8\\nayFnOyEtASn3pCh6TtfwQN++4heOvC1meVv5VzKdzAh0T+7u9uSya/ku+Wer6QUT9kHhYxzRDaTh\\nsAlDEudKz+H458c5+zJyMmAJUXbGmWt4oKWF/V/swhHfI/SWNqbnRjVqMnrZaM6w0Prqevyw7QcN\\nLVIYnbZklAvjiK7UWmyThiT2rd3H2e59Q28MmKB8iphreCDil1TSQMME3tLG1Ewn0zNxveIwbA7X\\n2y1aU6SRNTrCoM6UMUQ3kFpsE4YkWhpa3OJ66b9LV+XcruGBEyfkWTjyFuc1SvxXjRBI+nzu7/fk\\nVydx+nsV+xPoDQM7U/oX3UBqsY006VcEh945hEs/X+rYjukeo2gsly8qjvBAfLw8C0fe4rxKlNnK\\njVohkB5X9UC/sf04+/a9ss/je4MCAztT+hfdQGqxjTbpVwAMw6BoNffRcticYQiLDPPyCenYbGwv\\nWqmVVmI8QG8r/1Yr6+HK1RlMKEJtVzMEwvd2D755EJcuXvLybhNjcGdK36IbJLXYYji19xRqv3Xp\\nwmIBRjwkf6Nrhwc3bpywSiu+SMnlAWqxmCbmnGqGQJJ/kwxrgvOu1GJrwcHNB5U7oV4xuDOlb9EN\\ntBbbhBUuJe9xVS/pjiS3cd6ynMfFg3PgTVQ8iZRcHqAWi2lizqlmCCQ0PBRpv03j2vpekK4uGhh9\\ni64nxNRim6zChWEYlOaWcvZdO/NaRc7FL3HdudO7qHgSKbk8QH/H+fZb356oJw/cX9hArO1qVtTx\\nf98nvz6JxvONyp9YTxjcmdK36MrxwzVRhcu5H87hwvELHduhEaEY8Gtl0sT4Ja5jxwrrQeAQKbk8\\nQG/HafxFZ+66y3sIgO+B19YKCxvoeQGv65VdcfnVl3dsM20Myj4r09AiDTC4M6Vv0ZXjh2uiChe+\\nl9tvbD9ExirX2EaoB+cr+0Cu7Ab+cVxH8HgLAfA98E8+ERc20Fs/CAdJmUmcbf51ERQY2JnSt+gC\\n8vxwTVLhcjTvKGc7OStZI0vckSJSgeS3JrnojrcQAN8Dv+MO6SEPPZUj83/vZf8uM29ZsDcM7EzJ\\nn2ckN44frsViuB+unDScaUDl7krOPr7HYyQcj/5HjrACKPYxPiaG/f/DD4ExYzx/1uGBu45I52+r\\nYavc9ErvhU49O6HhdAMAoKW+BT/u+lGVikRdkZmptQWS0L+nC5jGUw2E4zuOAy79q+OHxaNzH+NO\\n+JUrK2HoUP/tF109cCl5v3orR7aEWNxuuEEX1zUwxhBdAtVF1ZxtpRbQ1EKrEl8peb96LEce+OuB\\nnO3qfdVe3knoDRJdg1BTXMPZThyRqJEl4vEUD9UqQ0CK16rHbAb+77/2QC1nEjShX0h0DUB7Wztq\\nDqgrunItHPkaaa5FhgDfa+3bV9j3qbdshs59OyP6suiO7RZbC84fPe/jE4ReINE1AOePnkdrQ2vH\\ndnS3aHS+Qrl4rpylt/yR5tdfL23iBB9Hnm6jyLoAV6+1sJDNPzZiv16LxYKEtATOvupiCjEYARJd\\nA+AWWkhLhMWiXLNyOReOhgwB+vRxbp88yQqvWKFz9bxtNiAri92flSVeLB1e64kT+logEwtfdPnX\\nCaFPSHQNAH+RhP/HJjdyLhxZrcDatdx9J0+y/wsVOpvN2eksLY31VI//MjTj+HHpYqnHBTIxJKZx\\nQ0y0mGYMSHQNwPlSbqwufli8oufztHAUSIz35pudxQwDB4ofVV5U5KxAO3oUaGoCBvySvDFggHSx\\n1OMCmRgShnNvvvzrhAgQhSZT6L84gkB9TT1nW4muYnwcj+BA4MUBVivbv8FRlAAIK1BwdCtrauLu\\nj44GcnPZ9O3c3MDE0vX7NBpxfeI42w1nG9DW2qb4NOigoLkZmDuXLcqqqJC1MIs8XQNQX80V3diE\\nWFXPL0eMV+yoctfFvAULgPBwdn9EBHDVVc6KNMf/wUhoeChierj8ABh0VKkRAfLUU+y0mtpaYMUK\\nWQ9Noqtz2lrb0HjWZYneAnTq2UlVG7SIfboKfVkZ0PpL8kZLC7sARrDEJnJvwPynIkICCk+mINHV\\nObZabhC1U49Oqj8+ahH7dBX6gQOdk4cjItjcWoKF/9TDfyoiJDB3LmC3O7ftdmDOHNkOT6Krc2w1\\nXNHlezZqoXZxgKvQr1vHeriA9p6unrqNAYA1kfsL4V8vhAQOHRK2TyLGFV2DzrwXC/9x0XVGlplg\\nGAbLli3DlClTMGPGDFRWVnYIfXq6PlK7tJjX5g/ydBXA0wWWmirb4Y0pugaeeS+WlvoWznZ012gv\\n7zQ2+fn5aGlpwZYtW7Bw4ULk5OR0vKaX1C69dRsDwCkFBthyYCJANmwAQl1CeGFh7D6ZMKboGnjm\\nvVja7e2c7ZBwY/7K/FFcXIybbroJAHDttdfiEO9xTqnwhphwgR6LKULCuNcD/3ohJNC/Pzs8wcHi\\nxbKOA/Kap2u321Hr2p1EL1RWAs8+y959AOCf/wRuuYVba2oias/VwhbmVIQ6ex2qqqpUt6OxkS1M\\nSEqSnqbl6xinT5/GpUuXON9bZWWl13Jnx7XJv0bF2NnYyJYRHz/OFlnk5vr/zPbtzuPX1bH/tOR8\\n43nO9XH24llNrg/TMWsWsHGj82sJP9P4+HiEhblLrIVhGI/94KqqqpCRkSH6RARBEARQUFCA3r17\\nu+33Krq69XTnzAEKCrj7MjJkjbnoiZJ3S5C/JL9je8h9QzD+mfGq2vDtt+zUXQcffshObJDzGF99\\n9RV2796NRYsW4fDhw9i8eTOefvppr8erra3F1KlT8dZbbyE+Pl6SnVI8Xb1xcPNBFP61sGP7mmnX\\nYOzfx2poEeHAm6frNbwQFhbmUaU1Z80aNpjmWECLigJeeQXQo60ycK7bOVjtzkBml9Auiv9eHOW3\\njjLdLl3YXFlHGbC3mWS+8HeMKVOm4MiRI3j00UcBADk5OYK+z/j4+I73SbFz3z7xM9P0RFV0Fef6\\n6B7bXZ9/t0QHxuu94JgKvHw5u22wmfdi4S+cKT311VufBSkDHV3xdwyLxYLljt+pRKTYqUTvBf5N\\nS0naW3kLrWHmXGg1E8b8DRl45r1Y+ClijedEdu0Wibe0KDmyB9QosPB2DiWLGvi9fr1NylCChrPc\\nXgv8FDJCfxhTdA08814s/GIIpZPfxaZF6a1CyxNKFjXwj11UxJ2UMXKksj8bWzX34GYtnjETxhRd\\nIGjGsrtVHCnc0ERMIYJQMdNamJUsauAfG+D2hjhxQtkiCv71oHYHOt1hgEpV44pukBDTIwaWUGeu\\navOFZti67WO5AAAf/ElEQVSb7T4+ETi+wgCuAipEzPRQOqtkUQP/2Onp7A3LIbxKF1HopTeHLjBI\\npaosotvU1IT58+dj2rRpePDBB3HmzBk5DqspNpsN8+bNw/Tp0zFlyhR8++23mtgREhoCa09eiCEA\\nb3fHjh1YuHChpM/yBbRvX/9iJtTLFOoNO3o0PPLIIwCAmhr/c8GULCP2dOz4eLY/ys6dbLKNEL77\\n7jtMnz5d9Pn54SY9hBfsdjsWLVqEqVOn4t5778XOnTvVObFClart7e14/PHHcf/992Pq1KkoKysL\\n6HiyiO57772H1NRUbN68GZmZmVi/fr0ch9WU119/HaNGjcKbb76JnJwc/O1vf9PMFr73IrWT1IoV\\nK/DCCy9ItoMvoCdO+BczIV6mGG/Y0aPh5ZdfBgCsEahqSi7ieTv2/PnAuHH+v6cNGzbgiSeeQGtr\\nq/c3eaC1qRXNdU6PzhJqQace6vZa9kRubi66du2Kt956C+vXr8ff//535U+qYA/cnTt3wmKx4J13\\n3sGCBQvw/PPPB3Q8WUR35syZePjhhwEA1dXV6NxZufHgajFr1ixMmTIFAHvnjoyM1MwWvuj+dPwn\\nSccZPnw4srOzJdvhSUD9iZkQL1NMzNW1RwMAHHUMT/sFpeLHYo8r5nvq27cvVq9eLdqmC+UXONux\\nCbGwhCg3JVoot912GxYsWACA9RI9FQhIwle8lh9SaG4G/vCHwI+dl4fxzc0dN45Tp04FrG+ifxpb\\nt27Fpk2bOPtycnKQmpqKmTNn4tixY3jttdcCMkptfH1PZ8+exaJFi7B06VKNrAN6pPZAaW5px3ZN\\ncQ2unX6t1/d7+35uu+02fPPNN5LtkJqv6y8X1iHmR46wPQ0aG1lx8xxTtiE21nkTCg0NRXt7O0JC\\nQgKe5eYNKcd1/Z78xXUnTJiAU6dOibaLP3L98tTLRR9DCaKj2bQ1m82GBQsW4E9/+lPgB3XEay0W\\nYPx4ebOWvB3bZX/I+PFYkp2N/Px8vPTSS4Gdj5GZ48ePM+PHj5f7sJpw5MgRZuLEicxXX32lqR2H\\nPzjMZCO7499rN74m+Vh79+5l/vznP8tonTzU1zPMzp0Mk5TEMADDDB7M7uOTk5PDfPbZZ0xlZSWT\\nlJTE/OpXv+p4bc8e9rOOf3v2yGOb1OPW17Pv9fR98KmqqmLuu+8+UXZ9+odPOddFwdICUZ9Xkurq\\nambSpEnMtm3b5DngsmXOX0B2tvvrx48zTFSU8z1RUQxTXh7YsT3sP3fuHDN27FimqalJ8rciS3hh\\n3bp12L59OwAgJiYGoaE6mEYaYOpIWVkZ/vjHP+LZZ5/FjTfeKKNh4klI447arjlQg/Y2c7Xws1rZ\\nvgeOaIG3R/Lhw4dj165dHdv9+/fv+FqJLAWbjfW8xY6NB8THkRnPbVC8wvd0+deJVpw7dw6zZ8/G\\nY489hrtcm2FIRUi8ll8oJbRS1duxefvtK1YAFRWIjIxESEgIQkKkS6cswZa7774bixcvxtatW8Ew\\nDKcBtSbI8Cjy/PPPo6WlBStWrADDMIiLi5MUd5ODzld0RnS3aDSdZ2eRtza04nzpefRI6aGJPYAy\\npa7+HsltNiAubgKAoo7sBcdaAiBPuTL/fI6wQlISm42Qni78uGJ/Rt7aWHqiva0dtQe45W6JaYmC\\nP68kr776Ki5evIg1a9Zg9erVsFgs2LBhAyIcg+7E4i1em5fHfd+SJcAbb7B/90IrVX3Fgl32h7W2\\nYv9NN+EfN96IpUuXSv9eAPnDC7rA36OIAXnzljc5j5LfvvGtZrbU17OP/65hADGP0v6O7ek4/HOW\\nlrLhhcrKSsHHEEsg4QpPPyM5OVNyhnM9/KP7P5j29nZ5T6IXJk7k/iIAdp8ncnMZJi8v8GOLOadI\\nzFccofD4ZK3gPzqe2it+4UUu+CvzRUXyFUB4eyTnn5OXtNCBHMUYjkwFIXnI3lB6tM+pb7i//4S0\\nBFGesqHgl/tHRQHeFrPEVqp6O7aYc4rEWKIrJE4baOqITul1fS/O9rFPj4mOAcoFP3YKKD87jH9O\\nR4yVT6Bi5yraY8cChYXSiiqGDHHamJQkf1XasU+Ocbb514epkBqvDeTYCp7TOKJrkBI/peif0R+h\\nEc4FyrqKOpwtOauJLfzcWzWm9fLP6a3ZeKCLaZ4KQHwthmnRV8J+yY6yf3OropLu8HIXMgtKdhb0\\nJ7Qyn9M4oiu0xE/BxwItibBG4MoM7p22NK/Uy7uVxzUMoFSZLV/QhGQDiLWFfw4xou0rlFFS4gyB\\nHD0qr/d/YtcJztRfa7wViSP0sYimGEp2FvR2bIXOaQzRFROnVfJRRGOSs5I520dzvQQ2NUDuMtva\\nWrYfrZTYrFBbPImmGNH2FNt2oGSTHddCGQBIykzSRSWa4ijZWdDbsRU4pzFEV2yc1qRNzpMmch8h\\nq/ZWwVar40a2ErHZWCE8cYLdVipO7Ek09+5lt4WItmvcFgAeeMDZtFwp759hGDfR5d+MCX1jDNEV\\ni0mbnMf1jkPCcJcsBkbbEINSlJQ4BRdgswiUiBO7eqNJScC8ed49a0+xW6sVWLvWuX3yJLdpuRJN\\ndmoP1OJi5cWO7bDoMLewE6FvjCG6UuK0Jm1ynpTF9XYPbDygkSXK4SqGffuy3mIgwuVtscvVG127\\n1ns1nK/YraPFpQOlm5bv37ifsz1gwgCER4crd0JCdowhuiaO04rl6v+7mrN9au8pVBdXa2SN/Diq\\nuBypWocOseIoNUPAX96uwxv1lYHhKw3NalWvafml+ks4+MZBzr6rp17t5d2EXjGG6AKmjdOKpdug\\nbhhwywDOvn2v7NPIGnnh58g6xCuQYgehebu+YrD+FsUcTcuVaJLuysHNB92yFgbfNViZkxGKYRzR\\nNWmcVgoj5o/gbH//9vdoutCkkTXisdlYT7aw0D3Nii+QgRY7iMki8BaDFbIopvSkY4ZhsG8N9+Y6\\n/LfDERqug+ZShCiMI7qAaeO0Ykm6IwlxfeI6tu1Ndny36TsNLRKOzQakpbETFcaNY7/2lSMbaOqV\\nXFkEaoyP98XJr0/izCHnGCxLqAVpc9O0MYYICGOJLgEACAkLwYh5XG+3aHWRIdo9uhYNANzCAU8C\\nKYdoShVMracYu/LNy9zm84N/MxhxveO8vJvQMyS6BmXY7GEICXf++n4q+wkH3zzo4xPq4E+ohgwB\\nBg50bkdEcFf/PQmkFl6mHqYYO6j9rhaHtx7m7OOHmAjjQKJrUKw9rbhm6jWcfYVPFio+nt0XQoTK\\namXD8g5aWrg5uXpB6S5hYij4SwHg0tvo8qsvx5XjgjN7xwyQ6PIJcOKEmoxeNprTBOdi5UUUvVLk\\n4xPKIkSobDbAdWSWEh245EDJMl4x/LjrR5R9xm1uk5GTYd42jkEAia4rButk1qVfF4x4mPuY+dWK\\nr9D8sza2CxGqoiJuTHftWm7YIJA4qpwxWKXKeMXAMAwKlhRw9l1x0xUYdPsg9Y0hZINE1xWhncx0\\nxE1Lb0JErHN0SNP5Jux+brdi5/MlbP6EymZjS20dJCWxYQjX16XGUQP9rLeKNS0zFkpzS1G1p4qz\\nb/wz48nLNTjKiq6BHtWNOnGiU49OGPXoKM6+3c/tRt2PdbKfS2jM1ptQ8TMX+F5uIHFUqZ/19j1p\\nnblgb7Yjf3E+Z19yVjL6jOqjjUGEbCgnugZ7VDfyxImRfxqJmB7Ort6tja3InZMr+2SJQBeX+vbl\\nlsu6erlAYHFU/mf79hUmmp6+Jz1kLhQ+WYjzpec7ti0hFox7epz6hhCyo5zoGvBR3ahExkZi3FPc\\nP8iKggoUryuW9TxSRdFRgTZ6NJup0Lcvu221cj3KQOKorp8tLGTLiIWIpqfvyVefXDWo2lPlFiIa\\nNmcYLh9yubqGEIpgYZQYtFVezl69Ds8xKgo4fFjfTWqMaLMLTDuDNye8iYqdzpBIhDUCDx96GF36\\ndpHtPDYbK0JNTUB0tP+R5K5jzF3Zs4f9cTteGzxYnNBWVVUhIyMDBQUF6N27N+e1vXtZwXU91/XX\\n+7bRdVS6o2rOEQpJSgKKi9WJ7dqb7Xh12Ks4d+Rcx764PnGYf2g+IuMilTeAUBxlPF0jPqobvJOZ\\nJcSCrI1ZiLA6F9VabC3Im5Mne5hh3jzgjjvcy3g94eo1OvDmUcqVCyt25I6r4ALufXLlHrfji8In\\nCzmCCwBZG7JIcE0EZS+4YvBOZl36dcGEf07g7CvPL8e+tfJ1IfNVxusJfqPwnTudHq2UcIUjHNHY\\n6P09QsMUrrHb1FTn1AdAnWGbfCr/V+kWVhg+d7hbVznC2FB4gU9eHmCxGLaxjqcwQ2hEKGZ+MRN9\\nbgh85VvKo7cnb1LIa56O4whHJCdXgWE8hxeEwg9D9O3r7N8r1DYx9vuivroe60asg63G+dhAYQUe\\nBv/bdKCM6AJAdjawfLnz62XLFDkN4U7dj3VYk7oGrQ2tHfs69eyE3+77rSxNUhxxXcB/TFdOXEUy\\nLKwK/fsHJro2G+vhupYh+4v/8j8vNSbtSmtTK/41+l+oLuI2o5/2+TTych00NwMpKazolpQYur2r\\ncuEFgz+qG5ku/boga0MWZ1/D6QZsuXMLWptavXxKOFYrmx0wdqy6hQOu4YgBMmhRoFMf5IhJMwyD\\nj3/7sZvgjnpsFAmuKybKhlJOdKnpuKakTknFjY/fyNlXU1yD3Nny5++qhWusNjdXnmMGMvVBjv4M\\nu5/bjYObud3hBt42EBk5GeIPZlYMWrjkDWUX0qjpuKaM+/s4JGVyB1keeucQvs75WiOLAsdR8RYT\\n4/+9Yo8p1msPtD/D0U+OYseiHZx93ZK74e537kZIKK1xd2DEbCgfmOc3a6SSY5WwhFgwafMk9Ejp\\nwdm/c+lOTbuRmQmpgv3jFz/i/cnvc1o2RnWJwv259yOqMz0ZmhlziK7RSo5VJDIuElNypyCqK/cP\\n+dP5n+LA6+Yb324EKv9Xibcnvg17k7P3sSXEgnvevQfdkrppaJlO4Ycoo6KAl17Szp4AMYfomijI\\nrgSXDbgM9227D6GR3CGGubNzSXhV5uR/T+Kt297iZJYAwK0v3UoLZ94weOESH/2KrtBwgcmC7ErR\\nb0w/3PfhfZwRP2CA3AdzdRVq2LFjBxYuXKi1GYpQUViBzb/ejEsXL3H2j//HeFz3u+s0ssogmCgb\\nSp+iKyZcYLIgu5IMum0QJr83GZZQbj/WT+d/ii9XfKl5VsOKFSvwwgsvaGqDUvzw4Q94+/a33Tzc\\nMcvH4FeP/UojqwyEibKh9Cm6FC5wIvMC4eA7B2Py+5O5Hi+AwicKse3/tqG1MfA8XqkMHz4c2dnZ\\nmp1fCZh2Brv+tgvvTXrPbX7d2L+PxegnR2tkmQExSTaUchVpUhFbQmzkkmN/CKnCkVgaeeyzYx6F\\nICEtAVM+mqLoeO+tW7di06ZNnH05OTlITU3FN998g3fffRfPPfecz2P46jKmF1oaWrD9ge1uk3wB\\nYMKzEzBq4SgPnyLMjv48XbHhApMF2Tn48/gDyNoYdNsgTP1sKqIvi+bsrymuwboR61C5uzIQy31y\\nzz33IC8vj/MvNTVVsfNpQd2JOrz2q9fcBDckLAQT100kwQ1i9Ce6UjBRkL0DIQuEAYZh+o3phznf\\nzHHL42043YBNYzah6JUizeO8RqTs8zKsT1+P09+d5uyP6R6D6fnTkTY3TSPLCD2gP9GVkpNnoiB7\\nB/48fpmyNi4bcBlm757tVrnW1tKGT+d/ijcnvIm6E/LPWzMjzT83I3duLt669S00nuX2nux5TU/M\\nLZqLfqP7aWMcoRv0J7pSwwUmCbILIi8PmDJFtqyNyLhITPloiluvBoAd+/NK6ivY9+o+Vbze6667\\nzm88V4+UfV6GV1JfwYEN7nnPg+8ajAf/+yC69JNvggdhXPS3kAaYqo2bZLwtECYksD+b06fdO3lP\\nnMgKcgAc2nIIeXPz0GJrcXvtyowrkbUxS9bxP1LRy0Ja88/N+M+j//EotpYQC27+680Y/eRoWEJ0\\nNDbdJH1pjUqY1gZ4xBEusFiMKbhyXNQOj9/Rk9jh8WdnO8MIYWGA/ZfsA5lKI1OnpKL3yN7InZ3L\\naYQOsF7vmpQ1GPnnkRj16Kig7hHQ1tqGAxsPYNfyXbDVus8r6pbcDXf+6070HqmzzArH4qvFAowf\\nb8y/L4OjT0/XyDi89MZGYM0aYNIk/5/xJtJ8j7+6muv9uoquzI3iGYZB8avF2PHYDo9eb3S3aNz0\\n+E1In5+OsCj1791aebpMO4OS90tQ+EQhfir7ye11S4gFNyy8AWOWj0F4dLhqdgmGhgtoDomu3Lhe\\n1F27skLpy5vwF0pxFeTMTPdCiehoNuSgUBim7sc6j16vg7g+cRizfAyunXGtqu0I1RZdhmFQvqMc\\nBX8pQM3+Go/v0a1368BbyOrQIfFPZhSikAyJrpzwL2qAfZR78UXvnxHjeXgS3fR04MknFb34GYbB\\n/vX7UfjXQjScafD4nssGXoYR80dg6ANDEd012uN75EQt0bU323F462EUrS5C1Z4qj+8JiwrDyD+P\\nxM1P3KxP79aBp+vn9tuBH34Qt35Cay4BQaIrJ54u6pAQoKzMcwaGwarvLtVfwp4X9uB/z/4PLfXu\\nIQcACIsOQ+r9qUifn47EtETFbFFadC9UXEDxq8U4sPEAGs95Hj1sCbVg2OxhGP3kaMT1Uq6CTzY8\\nXZ9JSc4po0LDDRSiCAgSXTnxdFED3rMKPL3fXwaCDi74hrMN+Orpr7BvzT60tbR5fV+v63ph2Oxh\\nSM5KhjVe3mFqSojupYuXUPZ5Gb7b9B2OfXqM02CcT8rkFIx7apyx+t/yb9qRkQDDAC2/3ECF3MTN\\nXHavEiS6clJeDgwaBLS3c/fLKbo6erSrO1GHXdm7cPCtg2hvbff53l7X90JyVjKSs5LRY0gPWCyB\\npVDJJbo/n/wZpXmlOJp7FBWFFX6/j4G3DsTYv49F4gjlvHhJCI2xut60Xb1cB/6uPynXLMGBRFdu\\nFizgpm758gSkeg06W8SwnbbhwMYD2Ld2Hy5WXvT7/i79umDQHYPQ67peSEhLQPfB3UUvwkkRXYZh\\n8POJn1G9rxrV+6px/PPjqP221u/norpEYeisoRgxb4Q+PVsxN2LX9yYnA599xn2dRFdxSHTlprmZ\\nzSao+6V01l8IQAfhArlob2vHsU+OoWhNEY5/flzw58I7hSN+aDwS0hKQmJaIrgO6IjYhFtYEq9eF\\nKV+i29baBlutDbYaG+pO1KFmfw1qitl/TT81CbYrflg80n+XjqvvvxrhMTpeIBN7DTlu2ikp4m/6\\nFF4IGBJdJfjgA+B3vwM6dRLneZhoJfj8sfM49M4hlOaWoqbYc4qVEKK6RMGaYEVsYiw69eiEkPAQ\\nhISHoK61Dk/vfRqLhy5Gl7AuaPqpCfXV9aivqXfreyCGrgO6Ivk3yRhy7xD0uq5XwGEQxREqgt6e\\njqTc9E3kKGgBia5SiAkB6CxcIDcXT13E0Y+P4mjuUZQXlKPtkvfFN6HYwmzI7Z+LrPIsWO0BLNJZ\\ngD6j+iA5KxlJmUnoPri7/oXWFSGP+75u7FJu+iZ1FNRCn2XAZiAzU5n3GpC4XnEY8dAIjHhoBFps\\nLajYWYGqvVUdj/zeUrKUIMIagfhhv4QyRiRiwIQB6HR5J9XOrwmuHehWruR6plJK7o1epq8x5OkS\\nmsIwDC5WXkR1cTVqimtw5tAZNkxQXQ9brQ1Mm+fL06enawE69eiE2EQ2LtwtuRsSRyQiMS0R3ZK6\\n6av5TKD88AMbXnD8GfPDC/zwQ0QEsHo1MGeONvYS5OkS2mKxWND5is7ofEVnXHXXVZzXmHYGDWcb\\nYKuxob6mHk0/NaHd3o52eztOnz+N3I25mPDsBMR3j0eENQKxibGITYhFp56dEBoe6uWMJuPdd52C\\nC7i3QuX3ZW5pYfdNm0ZeqkaQ6BK6xRJigbWnFdaeVsQPjee8VlVVBWwEUu5O0e2MNMXhN7K3WID7\\n7vP/ucZGbpjB5GsKekN/TcwJghAG34tlGOCxx7jv8TZNxRHnDWDOHiENEl2CMDPe5gY6Jo0EOGeP\\nEA+JLkEYFaHzBJcsAWJi3Pc3NMgyZ48QB4kuQRgVofMEo6LY9qLh4dx958/LNmePEA6JLkEYgbw8\\nzx3sHELrLYzgYO5c4PHHndsLF7ItRwnVoewFMdAqL6EFvuaaiSlUWLIEeOMN9r2A+2BTmebsEb4h\\n0RUKDfQjtMJXRRkgvKLRIdCnTwOPPOL+urfwBCErFF4QiqdVXm+PfFqgJ1sI+eBnFQS62JWZCWzf\\n7p4eFhPjOzxByAaJrhA8XfhHjrCe75w5wLZt2tkGUK6lmeH/TpVa7BoyhJ7eVIJEVwieLvysLNbj\\nOH2aFV4txY5yLQkxeEo1e/dd7ewJMkh0pXLcpUn3hQtsPEwL5H78JPSF0FxcMQhNNSMUgURXCPwL\\nPyTEfQ7ayy9rI3ZqPX4S2tC/P3Dnnc5tuQRSaKoZITskukLgX5gDB7q/p73dXez++lfgySeVtY0w\\nN83NwO7dQFgYK5JyCaQjk8FbbwZCMUh0heLqGXz0Eevt+qKujn3Uz8lxzktTAiUePwn98MwzwIkT\\ngN0OjBwpr0BmZlLOuQaQ6ArF1TO46irg9793f91V7O68k/1DsduBu+5Szi6Kz5kXfrz+ww/1F6+n\\nVEXR0OQIqfia+vvFF8DYsdz379oF3HyzcrYE2cwqKSPYDYfex50H4XUnB+TpSiUqCtiwAejZ093b\\n9NRIevJkZW2h+ByhNpSqKAnydAPFUz+Gnj2BM2e477v8cjanl5CFoPB0hY5X1wI926ZzyNMNFE+L\\nEZ4Szd9/Xx17CPOg53g9pSpKhkRXCcaMAUaP5m4rFc8lzA3l05oO6jKmFB99BPTowX794Yfa2kIY\\nFzGtG9Vk1SogP58bXqBURUGQ6CpFly6sl2KxsF8ThFSEtm5UE4fnvXw5u62n0IfOoYU0wpAExUKa\\n3qGUMUmQp0sQhDT0GvrQOSS6BEFIR4+hD51D2QsEQRAqQqJLEAShIiS6BEEQKkKiSxAEoSK0kEbo\\nApvNhkcffRQNDQ1obW3FkiVLMHToUK3NIgjZIdEldMHrr7+OUaNGYcaMGaioqMDChQuxTespywSh\\nACS6hC6YNWsWIiIiAAB2ux2RkZEaW0QQykCiS6jO1q1bsWnTJs6+nJwcpKam4uzZs1i0aBGWLl2q\\nkXUEoSwkuoTq3HPPPbjnnnvc9peWluLRRx/F4sWLMWLECA0sIwjlIdElAsNTE3cJlJWV4Y9//CNe\\nfPFFJCcny2QcQegPEl1COs3NbDNriwUYPz6g+vvnn38eLS0tWLFiBRiGQVxcHFavXi2jsQShD0h0\\nCek4ZmQB7Iwsx2BOCaxZs0YmowhC31BxBCGevDx2KKfrMEJXASYIwivk6RLicIQUTp/2PCNLL+PB\\nCUKnkOgS4lDTo5VpkY4g9ASJLiGc8nJuSMEVuWdkybhIpwvoBkL8AsV0CeHwx267IveMLIdH7Uvo\\njYLjBuLr50cEDSS6RGDExIgfD56XB3z8sffX+UJr9EU6M91AiIAh0SWEs2oV9zHfMSOLv98XQrw+\\n/mvNzcB99/kWar1ithsIETAkuoRw+B7tkiXAnDni4pRSvb6SEmM+nnu6gfzhD9rZQ2gOiS4hDkfs\\nVmxIARDu9fE959BQoLGRHs8JU0CiG8z4i616QkpIwYFQr48v6BaL82ujPZ57CsnImeVBGA4S3WAl\\nkBX1zEzlU58cHnVMDGC3O/cb7fHcU0hGziwPwnCQ6AYrga6oS/GSxXh9Do96yBDxtumNQEIyhOmw\\nMAzDaG0EoTLl5ayYOTzcqCjg8GHhHlhzM5CSwj72l5SICzNkZwPLlzu/9tckx4utVeHhyMjIQEFB\\nAXr37i38/FpBxRHEL5CnG4wEuqIeiJcs1uszy+O5GiEZwhCQ6BLiCDTvVMpCHD2eEyaCei8EI6tW\\nAfn53Ed2oSvq3rxkMd3FMjOFv9dh36pV7OO50XswEEEPebrBiBEf2enxnDAJJLrBitRHdso7JYiA\\nINENVqQWORjRSyYIHUEpY4R4AkkZk4mqqipjpYwRxC/QQhohHlrYIgjJkOgS0hCbgUAQBACK6QYX\\nUkp3CYKQFfJ0gwWzzRwjCINCnm6wQCNjnJDHT2gIiW4wQCNjnNCQSEJjSHSDARoZ40QJj588Z0IE\\nJLpE8KCEx0+eMyESEt1ggEp3WZTw+ClWToiERDcYoNJdZaBYOSEBEt1ggXrSyu/xU6yckACJbrAQ\\nyBRfs0AeP6EDqOENYUgkN7yRs1lPoLPmiKCEPF0iuJDT4yfPmZAAebqE/hAwOVc3rR110OaSMBbU\\ne4HQF0brEUFtLgmRUHiB0BdGzHul+W2ECEh0CXUQUipLea9EEECiSyiP0FJZynslggASXUJ5jBgy\\nIAiFINEllEVMyIB6RBBBAIkuoSxiQgaU90oEASS6hL6gHhGEyaE8XUJZVq0C8vO5pbK+QgaU90qY\\nHPJ0CWURGDJoamrC/PnzMW3aNDz44Yc4c911KhpJEOpBoksoj4CQwXvvvYfU1FRs3rwZmZmZWL9+\\nvcpGEoQ6UHiBUB4BIYOZM2fC0QakuroanTt3VtNCglANEl1CHTIzO77cunUrNm3axHk5JycHqamp\\nmDlzJo4dO4bXXntNbQsJQhWoyxihO8rLy/HQQw9hx44dXt+jmy5jBCESiukSumDdunXYvn07ACAm\\nJgahoaEaW0QQykDhBUIX3H333Vi8eDG2bt0KhmGQk5OjtUkEoQgkuoQu6NatGzZs2KC1GQShOBRe\\nIAiCUBESXYIgCBUh0SUIglAREl2CIAgVIdElCIJQERJdgiAIFSHRJQiCUBESXYIgCBWh3guEIbHb\\n7aitrUV8fDzCwqjGhzAOJLoEQRAqQuEFgiAIFSHRJQiCUBESXYIgCBUh0SUIglCR/w+SZ+7waBQU\\ncQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\\n\",\n \"y_list1 = np.random.uniform(low=-0.8, high=0.8, size=100)\\n\",\n \"\\n\",\n \"x_partial_1 = np.random.uniform(-1, 1, 25)\\n\",\n \"y_partial_1 = np.random.uniform(2, 3, 12)\\n\",\n \"y_partial_2 = np.random.uniform(-3, -2, 13)\\n\",\n \"x_partial_2 = np.random.uniform(2, 3, 13)\\n\",\n \"x_partial_3 = np.random.uniform(-3, -2, 12)\\n\",\n \"y_partial_3 = np.random.uniform(-1, 1, 25)\\n\",\n \"x_partial_4 = np.random.uniform(1, 2, 12)\\n\",\n \"y_partial_4 = np.random.uniform(1, 2, 12)\\n\",\n \"x_partial_5 = np.random.uniform(-2, -1, 12)\\n\",\n \"y_partial_5 = np.random.uniform(1, 2, 12)\\n\",\n \"x_partial_6 = np.random.uniform(1, 2, 12)\\n\",\n \"y_partial_6 = np.random.uniform(-2, -1, 12)\\n\",\n \"x_partial_7 = np.random.uniform(-2, -1, 12)\\n\",\n \"y_partial_7 = np.random.uniform(-2, -1, 12)\\n\",\n \"\\n\",\n \"x_list2 = np.concatenate([x_partial_1, x_partial_2, x_partial_3, x_partial_4, x_partial_5, x_partial_6, x_partial_7], 0)\\n\",\n \"y_list2 = np.concatenate([y_partial_1, y_partial_2, y_partial_3, y_partial_4, y_partial_5, y_partial_6, y_partial_7], 0)\\n\",\n \"\\n\",\n \"textstr1 = 'y = 0'\\n\",\n \"textstr2 = 'y = 1'\\n\",\n \"props = dict(boxstyle='round', facecolor='purple', alpha=0.5)\\n\",\n \"circle = plt.Circle((0, 0), 1.3, color='purple', fill=False, linewidth=4)\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x_list1, y_list1, 'b.')\\n\",\n \" plt.plot(x_list2, y_list2, 'rd')\\n\",\n \" #plt.plot(x_list3, y_list3, '-', color='purple', linewidth=4)\\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" #plt.xlabel(\\\"Tumor Size\\\", fontsize=24)\\n\",\n \" #plt.ylabel('Malignant?', fontsize=24)\\n\",\n \" plt.yticks(fontsize=18)\\n\",\n \" plt.xticks(fontsize=18)\\n\",\n \" plt.ylim(-3.2, 3.2)\\n\",\n \" plt.xlim(-3.2, 3.2)\\n\",\n \" \\n\",\n \" ax.add_artist(circle)\\n\",\n \" \\n\",\n \" #ax.text(0.1, 0.3, textstr1, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" #ax.text(2.3, 3, textstr2, fontsize=20, verticalalignment='top', bbox=props)\\n\",\n \" \\n\",\n \" ax.spines['left'].set_position('zero')\\n\",\n \" ax.spines['right'].set_color('none')\\n\",\n \" ax.spines['bottom'].set_position('zero')\\n\",\n \" ax.spines['top'].set_color('none')\\n\",\n \" #ax.spines['left'].set_smart_bounds(True)\\n\",\n \" #ax.spines['bottom'].set_smart_bounds(True)\\n\",\n \" ax.xaxis.set_ticks_position('bottom')\\n\",\n \" ax.yaxis.set_ticks_position('left')\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg3_decision_bndy_nonlinear.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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QNrbVSHfx8DrrjI8a8DU3sfmoiIdLS3oBKXC6bm9X/UsT9NVCciMkg0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\\niAjw/wGPEiiH27/84QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"nonconvex = lambda x: x**2 + 10*np.sin(2*x)\\n\",\n \"x = np.arange(-10, 10, 0.1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(6, 4))\\n\",\n \"plt.plot(x, nonconvex(x))\\n\",\n \"plt.xlabel(r'$\\\\theta$', fontsize=20)\\n\",\n \"plt.yticks([0, 50, 100], fontsize=14)\\n\",\n \"plt.xticks([-10, 0, 10], fontsize=14)\\n\",\n \"plt.title('Non-Convex Curve', fontsize=20)\\n\",\n \"\\n\",\n \"plt.savefig(fp_fig + os.sep + 'logreg_eg4_sample_nonconvex_curve.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 151,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def cost_function(hypothesis_function, y):\\n\",\n \" if y == 1:\\n\",\n \" return -1 * np.log(hypothesis_function)\\n\",\n \" elif y == 0:\\n\",\n \" return -1 * np.log(1 - hypothesis_function)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 152,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"x = np.arange(0, 1, 0.05)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 163,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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5LQM49FeeIvD/uitEKBBetPoIZPtSMiHWJJ6KFJD/visah+uHy1Cou/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\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 4))\\n\",\n \" plt.plot(x, cost_function(x, 1))\\n\",\n \"\\n\",\n \" ax.spines['left'].set_position('zero')\\n\",\n \" ax.spines['right'].set_color('none')\\n\",\n \" ax.spines['bottom'].set_position('zero')\\n\",\n \" ax.spines['top'].set_color('none')\\n\",\n \" ax.xaxis.set_ticks_position('bottom')\\n\",\n \" ax.yaxis.set_ticks_position('left')\\n\",\n \"\\n\",\n \" plt.yticks(fontsize=14)\\n\",\n \" plt.xticks(fontsize=14)\\n\",\n \" plt.ylim(ymin=-0.5)\\n\",\n \" plt.xlabel(r'$h_\\\\theta (x)$', fontsize=20)\\n\",\n \"\\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg5_cost_func_y1.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 165,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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8T1inr8zyR/PMbBcneFIUFEvZ6quRVv7TyL/KvViH9gCH7/SKCxS+ox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\\nEQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 4))\\n\",\n \" plt.plot(x, cost_function(x, 0))\\n\",\n \"\\n\",\n \" ax.spines['left'].set_position('zero')\\n\",\n \" ax.spines['right'].set_color('none')\\n\",\n \" ax.spines['bottom'].set_position('zero')\\n\",\n \" ax.spines['top'].set_color('none')\\n\",\n \" ax.xaxis.set_ticks_position('bottom')\\n\",\n \" ax.yaxis.set_ticks_position('left')\\n\",\n \"\\n\",\n \"\\n\",\n \" plt.yticks(fontsize=14)\\n\",\n \" plt.xticks(fontsize=14)\\n\",\n \" plt.ylim(ymin=-0.5)\\n\",\n \" plt.xlabel(r'$h_\\\\theta (x)$', fontsize=20)\\n\",\n \"\\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg5_cost_func_y0.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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V0tff65NecC0PoICSyxe7ezzweg9RASWKKqytnnA9B6CAks0aGDs88H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\\nWIO52Y43AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [0.6, 1, 0.6, 0.9]\\n\",\n \"y1 = [0.7, 0.6, 0.9, 1.1]\\n\",\n \"\\n\",\n \"x2 = [2, 1.9, 2.4, 2.2]\\n\",\n \"y2 = [2.2, 1.7, 2.1, 1.8]\\n\",\n \"\\n\",\n \"x3 = [0.75, 0.5, 0.9]\\n\",\n \"y3 = [2.1, 2.5, 2.3]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'bo', markersize=20)\\n\",\n \" plt.plot(x2, y2, 'g^', markersize=20)\\n\",\n \" plt.plot(x3, y3, 'rs', markersize=20)\\n\",\n \" \\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" plt.xlabel(\\\"$x_1$\\\", fontsize=28)\\n\",\n \" plt.ylabel('$x_2$', fontsize=28)\\n\",\n \" plt.yticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.xticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.ylim(0, 3)\\n\",\n \" plt.xlim(0, 3)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg6_multiclass_eg_data.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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UJOUZG0ZIm0datUUVH3fY9HGj9emjVLio8P/nxApOHWFkKG1ystXiwN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\\nQgIAMEJIAABG/g+kQSmKwn3uCQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [0.7, 0.9, 0.8, 1.1]\\n\",\n \"y1 = [0.4, 0.5, 0.7, 0.9]\\n\",\n \"\\n\",\n \"x2 = [2.2, 2.1, 2.4, 2.6]\\n\",\n \"y2 = [2.2, 1.7, 2.1, 1.8]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'bo', markersize=20)\\n\",\n \" plt.plot(x2, y2, 'g^', markersize=20)\\n\",\n \" \\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" plt.xlabel(\\\"$x_1$\\\", fontsize=28)\\n\",\n \" plt.ylabel('$x_2$', fontsize=28)\\n\",\n \" plt.yticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.xticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.ylim(0, 3)\\n\",\n \" plt.xlim(0, 3)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg6_binary_eg_data.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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fm65pprTH4kAMBh+LAVAMBIq7u2ysvL9eqrr+rKlSt66qmn+CUNAO1c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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [0.6, 1, 0.6, 0.9]\\n\",\n \"y1 = [0.7, 0.6, 0.9, 1.1]\\n\",\n \"\\n\",\n \"x2 = [2, 1.9, 2.4, 2.2]\\n\",\n \"y2 = [2.2, 1.7, 2.1, 1.8]\\n\",\n \"\\n\",\n \"x3 = [0.75, 0.5, 0.9]\\n\",\n \"y3 = [2.1, 2.5, 2.3]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'bo', markersize=20)\\n\",\n \" plt.plot(x2, y2, 'kd', markersize=20)\\n\",\n \" plt.plot(x3, y3, 'kd', markersize=20)\\n\",\n \" \\n\",\n \" \\n\",\n \" #plt.plot(x_list_reg, y_list_reg, 'b-', linewidth=4)\\n\",\n \" plt.xlabel(\\\"$x_1$\\\", fontsize=28)\\n\",\n \" plt.ylabel('$x_2$', fontsize=28)\\n\",\n \" plt.yticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.xticks([0, 1, 2, 3], fontsize=18)\\n\",\n \" plt.ylim(0, 3)\\n\",\n \" plt.xlim(0, 3)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg6_multiclass_onevall_step1.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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iB0ApoNLSkpkST94Ac/8On46667TpJ05syZQN4ODkGHKiC0Agr8Nm3a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\\nAA5B4AOAQ/w/nxoUdsu7QpkAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [1, 1.5, 2, 3.2, 4.5, 6]\\n\",\n \"y1 = [1, 3.5, 5, 5.5, 6, 6.2]\\n\",\n \"\\n\",\n \"x2 = np.linspace(0, 7, 50)\\n\",\n \"fx = lambda x:0.9 * x + 1.5\\n\",\n \"y2 = [fx(x) for x in x2]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'rd', markersize=16)\\n\",\n \" plt.plot(x2, y2, 'b-')\\n\",\n \" \\n\",\n \" plt.xlabel(\\\"Size \\\\n $\\\\\\\\theta_0 + \\\\\\\\theta_1 x$\\\", fontsize=28)\\n\",\n \" plt.ylabel('Price', fontsize=28)\\n\",\n \" plt.yticks([])\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(0, 7)\\n\",\n \" plt.xlim(0, 7)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_linreg.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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iB0ApoNLSkpkST94Ac/8On46667TpJ05syZQN4ODkGHKiC0Agr8Nm3a\\nyOVyyeVy+XT8+fPnJUkJCQmBvB0cgA5VQOgFNKWT+nVp43vvvefT8Z4buqmURKIB5eXSHXdYYd+7\\nt7RjB2EPhEJAgT9w4ECZpqnFixc3O01z7NgxLV26VIZhqH///gENErHr0g5VublUzwKhElDgjxo1\\nSpdddpmKior0s5/9TFlZWTp27FjN/osXL+rYsWNavny5/vmf/1llZWXq2LGj7rvvvqANHPZ3+LDU\\nqxcdqoBwMUzTNAP5wYKCAo0bN06VlZUNLsn0ME1TrVu31tKlS32+wj9x4oQGDRqkvLw8XXXVVYEM\\nD1EuP9+axvniC6ug6rHHWGMPhFrAlbY33nij1q1bp/T0dJmm2ehXWlqa/ud//ofpHNTIyZEyMiio\\nAsKtRUXq3/3ud7Vy5Up9/PHH+tvf/qbjx4/r7NmzateunZKTk5Wenq4fsq4OdaxcKT3wAB2qgEgI\\nylNJrr32Wl177bVe21wuFw9LQw1PQdX06dJll1mPNu7bN9KjApylRS0OP/30U82ZM0fPPfdcvX1v\\nvvmm0tPTNWfOnJpCLQRJVpb1ZRNutzRlihX2KSnS9u2EPRAJAQf+5s2bddddd2ndunUqKCiot//4\\n8eOqqKjQunXrdOedd2rPnj0tGii+VlYmTZpkfZWXR3o0zaqqku67z6qe7d5devddqmeBSAn4aZlT\\np07VhQsX1LZtW6WlpdU7JiMjQyNHjlRCQoLKy8v1b//2bzp16lSLB+x4M2dKRUXW18yZkR5Nkyio\\nAqJLQIH/xz/+US6XSykpKdqwYYPmzJlT75iePXvq0UcfVVZWlpKTk3XmzJmaTlgI0I4d1gPhPTIz\\npZ07IzeeJlBQBUSfFj0tc9q0abrmmmuaPDY1NVW//vWvZZqm/vrXvwbydpCs7t3jx1t3Pz1M06pY\\n8vGZRuFy+LB1RU9BFRBdAgr84uJiSdZafF+kp6dLkgoLCwN5O0jSvHnSoUP1tx86JD39dPjH0wg6\\nVAHRK6DA79ChgyT5/LTMVq2st2nNv/zAHDzYdKg39mEQZps3W9M4p05RUAVEoxY9LfPtt9/26fid\\nX88zd+WOnf9M05rKaerD1eWy5k4Ce0pGUKxaZd2gpUMVEL0CCvzBgwfLNE09//zzXg9Na0hRUZEW\\nLVokwzA0YMCAQN7O2Xy9Mbtzp/cN3TDydKjq0MG6yh8+PCLDANCMgB6edubMGQ0dOlSlpaVq3769\\n7r//fg0YMEDXXHON2rdvr8rKSh0/flzbtm3TypUrVVZWpqSkJGVnZ6tTp07Nvj4PT/taYaGUlub7\\nevvERGtqJzk5tOP6Gh2qAHsJy9My27dvr8zMTN10000+vTaB/7V77pE2bPD/Z15/PTTjqcPlkkaP\\nru1QlZ0t0d8GiG4telrmm2++qZ/+9Kdq3bp1g0/KlKT+/fvrtdde8znsEf0qKuoXVBH2QPQL+Aq/\\nrrNnz6qgoEDFxcU6c+aM2rdvr+TkZN1www264oor/H49rvC/VlRkXT77OqWTlGSt6AnhlE5JiTRk\\niLRnj1VQtWYNa+wBuwjKOsmOHTtyQzYUkpOl+fOliRN9O37+/JCG/ZEj0uDB1hr7ceOkZctYYw/Y\\nSYuelokwmDDBqmRqTt++0kMPhWwYBQXW9A0FVYB9NftPduHChZKkTp06acyYMV7bAjF58uSAf9aR\\nDENasUL68Y8bX4sfHy8tXx6yKifPUsvKSqugijX2gD01G/jLly+XYRhKTU2tCXzPtkAQ+AHo3l2a\\nNcsqXW3II49Yx4TAqlXSmDF0qAJigU9TOg3d122qj21TXwhQY6GelmZ9GISAp6CqY0fraZeEPWBv\\nzV7hf/TRRz5tQ4jFx1tTO/361T5CwTCsqZwgt5KkoAqITQHdtN23b58qKiqCPRY0p08f7xuzvt7Q\\n9YPLRYcqIFYFFPhz585Vv3799MYbbwR7PGjOM89YSy9TUqxlmEFEhyogtgW0sO7TTz9VVVWVuofo\\nRiGakJgoLVlS+32QlJRIt99uNS2hoAqITQEFvmeFzje+8Y2gDgY+CvLd08OHpdtus9bYjx8vLV3K\\nGnsgFgU0pZORkSHTNPXqq68GezwIs/z82g5Vc+daT2Mm7IHYFNA/7Tlz5ujEiRPKzMzUp59+qttu\\nu03du3dXp06dFN/MipHm9iN8cnKkESMoqAKcIqDAHz9+vFwul0zT1FtvvaW33nrLp58zDEMHDx4M\\n5C0RZBRUAc4TUOD/3//9X833FFPZi2laBVXTp0uXXSZt2mQ9hgdA7Aso8CdNmhTscSAMKKgCnI3A\\nd4iqKmsKx9OhKieHNfaA0/gd+Pv371dhYaHi4uLUrVs3devWLRTjQhCVl1tPu8zLswqqNm2SOneO\\n9KgAhJvPgb9+/XotXrxYn3/+udf26667TtOnT1dfJoKjEgVVADx8Woe/YMECzZ49W59//nm9p1/+\\n/e9/1/jx47V27dpQjxV+OnLEuqLfu9fqULV+PWEPOFmzV/j79u3TihUrJElt27bVHXfcobS0NBmG\\noX379umtt96Sy+XSk08+qb59+yo5hC324LuCAqv37BdfWB2qHnssZP1RANhEs4G/fv16SVJycrJe\\neuklXX311TX7Ro4cqTFjxui+++7TuXPntG7dOv3Hf/xH6EYLn1BQBaAhzU7p7NmzR4ZhaOrUqV5h\\n7/GDH/xAY8eOlWmaKigoCMkg4btVq6ShQ6XqaqugirAH4NFs4JeUlEiSfvKTnzR6TP/+/SVJx44d\\nC9KwEIi6Haq2bKF6FoC3Zqd0KisrJUkdOnRo9Jgrr7xSkmiKEiEUVAHwRbOBf/HiRRmGobi4uEaP\\nadu2rSTJ5XIFb2TwicsljR5dW1CVnS2lpkZ6VACiEQ/CtbHycuvmbG4uBVUAmhfQ8/AReSUl0oAB\\nVtjfeac1Z0/YA2gKgW9Dhw97F1S9/rqUkBDpUQGIdj4HvkHVTlQoKKjtUDVnjvTii3SoAuAbn6Ni\\n7NixatWq4c8Ht9td8/2oUaMafQ3DMPTyyy/7MTzURUEVgJbwOfDff//9Jvd7fgPIz89vcL9pmvyW\\n0AJ0qALQUs0GPs/GiSzTlBYskKZNszpUbdwo9esX6VEBsKNmA3/r1q3hGAcaQEEVgGDidl+UokMV\\ngGAj8KMQHaoAhALr8KOMp6AqL8/qUJWbS9gDCA4CP4rULagaP54OVQCCi8CPEnULqubOlTIzKagC\\nEFxEShSoW1CVmSk99FCkRwQgFhH4EVa3oGr9eunuuyM9IgCxiimdCKrboSo3l7AHEFpc4UeA221V\\nzi5cSEEVgPAh8MPs0g5VFFQBCBcCP4wqKqyCKjpUAYgE5vDDpG6HKgqqAEQCgR8GR45YV/R79lgd\\nqiioAhAJBH6IFRRYYU+HKgCRRvSEEB2qAEQTAj9E6FAFINowpRNkpuldULVlC2EPIDpwhR9EdKgC\\nEM0I/CChQxWAaEfgBwEdqgDYAXP4LVRcTIcqAPZA4LdA3Q5VFFQBiHYEfoA8HaqOHbM6VFFQBSDa\\nEVEBoKAKgB0R+H5auVJ64AEKqgDYD1M6PjJN6be/lUaNqu1QRdgDsBOu8H1AQRWAWEDgN4OCKgCx\\ngsBvAgVVAGIJc/iNoKAKQKwh8BtAQRWAWETgX6JuQRUdqgDEEqKsjs2brTl7CqoAxCIC/2t0qAIQ\\n65jSER2qADiDo6/wKagC4CSODXyXSxo9uragKjtbSk2N9KgAIHQcGfgVFdbN2dxcqVcv6Y03WGMP\\nIPY5bg6/pMQqqMrNpaAKgLM4KvCPHLEKqvbskcaOtQqqEhIiPSoACA/HBH5BgRX2R49aBVXLl1NQ\\nBcBZHBF5noKq8+cpqALgXDEf+JcWVA0fHukRAUBkxPSUzqUFVYQ9ACeLySt8CqoAoL6YC/xLC6ro\\nUAUAlpgK/PJyacQIa209HaoAwFvMzOGXlEgZGRRUAUBjYiLwPR2q9uyhQxUANMb2ge/pUHX0qDR3\\nLh2qAKAxto5GOlQBgO9sG/h0qAIA/9hySqduQVVuLmEPAL6w1RW+2y1NmyYtXEhBFQD4yzaBT0EV\\nALSMLQK/bkFVnz7Sxo2ssQcAf0X9HP6lHaq2bCHsASAQUR34n3xiFVTt3UtBFQC0VFQH/s9+Vtuh\\nioIqAGiZqI7QL7+koAoAgiWqA3/ZMunBByM9CgCIDVE9pTN4cKRHAACxI6oDHwAQPAQ+ADgEgQ8A\\nDkHgA4BDEPgA4BAEPgA4BIEPAA5B4AOAQxD4AOAQBD4AOASBDwAOQeCHQlaW9QUAUSSqn5ZpS2Vl\\n0qRJ1veDBkmJiZEdDwB8jSv8YJs5Uyoqsr5mzoz0aACgBoEfTDt2WK25PDIzpZ07IzceAKiDwA8W\\nl0saP14yzdptpmk143W5IjcuAPgagR8s8+ZJhw7V337okPT00+EfDwBcgsAPhoMHmw71xj4MACCM\\nCPyWMk1rKqepaRuXy5raqTvdAwBhRuC3lK83Znfu9L6hCwBhRuC3RGGhf0svZ8ywlmsCQAQQ+C0x\\naZJUXu778eXltUVZABBmBD4AOASB3xJLl/r36ISkJGnJktCNBwCaQOC3RHKyNH++78fPn2/9DABE\\nAIHfUhMmSH36NH9c377SQw+FfjwA0AgCv6UMQ1qxQoqPb/yY+Hhp+XLrWACIEAI/GLp3l2bNanz/\\nI49YxwBABBH4wdJYqKelNf1hAABhQuAHS3y8NbVTd9rGMKypnKamewAgTAj8YOrTx/vGrK83dAEg\\nDAj8YHvmGWvpZUqKf0s2ASDE6GkbbImJtcVV9LMFEEUI/FC4555IjwAA6mFKBwAcgsAHAIcg8AHA\\nIQh8AHAIAh8AHILABwCHIPABwCEIfABwCAIfAByCwAcAhyDwAcAhCHwAcAgCHwAcgsAHAIcg8AHA\\nIQh8AHAIAh8AHILABwCHIPABwCEIfABwCAIfAByCwAcAhyDwAcAhCHwAcAgCHwAcgsAHAIcg8AHA\\nIQh8AHAIAh8AHILABwCHIPABwCEIfABwCAIfAByCwAcAhyDwAcAhWkd6AA356quvJEnFxcURHgkA\\n2M+VV16p1q3rx3tUBv4XX3whSRo5cmSERwIA9pOXl6errrqq3nbDNE0zAuNp0oULF7R//35985vf\\nVFxcXKSHAwC20tgVflQGPgAg+LhpCwAOQeADgENE5U1bIBBut1vZ2dnKy8vThx9+qFOnTsntdqtT\\np076zne+oz59+ujuu+/W5Zdf3uDPL1myREuWLJFhGPrggw8UHx8f5jMAQovAR0w4cuSIfv3rX+vw\\n4cMyDMNrX3FxsYqLi7Vr1y4tXbpUU6ZMYQUYHInAh+2dOnVKY8aM0RdffKHOnTtr3Lhx6tWrl668\\n8krFxcXp888/1+7du7VixQoVFRXpqaeeUps2bfSLX/zC63WSkpJ09dVXS5JatWK2E7GHVTqwvXnz\\n5umVV15RYmKisrKylJKS0uBxp0+f1vDhw1VcXKzExERt3bpVHTt2DPNogcjhMga2t3XrVhmGodtu\\nu63RsJekzp07a9q0aZKkiooKbdu2LVxDBKICUzqwPU9ldlVVVbPH9unTR9///veVlJRU76ZsQzdt\\nCwsLNWjQIJ/H8o//+I965ZVX6m3ftWuX1q5dq7179+r06dPq0KGDvve97+nOO+/U8OHDKTBEWBD4\\nsL2uXbvqyJEjysnJ0ciRI3X99dc3euxll12m//3f//Xr9S+9CdyUhIQEr/+/ePGiHnnkEW3atMnr\\ndcrKypSfn6/du3frz3/+szIzM3XFFVf4NS7AXwQ+bG/EiBF65plndOHCBd17773KyMjQbbfdpl69\\neqlz584teu2UlBTt2bOn0f1VVVV68MEHdeDAASUmJmrmzJle+x999NGasB8xYoT+9V//VV27dtWX\\nX36pnJwcvfDCC9q/f78eeughrVmzRm3atGnReIGmEPiwvV/+8pfKz8/XX//6V3311VfasmWLtmzZ\\nIknq1q2bfvKTn+imm25S7969A/oAaN++faP75s6dqwMHDiguLk4LFizQNddcU7Nv9+7d2rBhgwzD\\n0IwZMzR69OiafYmJiRo/frxuuOEGjRo1SgcPHtSrr76qUaNG+T0+wFfctIXttWrVSsuWLdOMGTOU\\nlJQkwzBqvo4ePap169Zp6tSp6tu3r8aOHatDhw4F5X2XL1+ujRs3yjAMTZkyRf369fPav3r1aklS\\ncnKyV9ha5KKeAAALHklEQVTXlZ6erp/+9KcyTVNr164NyriAxhD4iBmjR4/W9u3btWzZMv3iF79Q\\namqqV/ibpqkdO3ZoxIgR+v3vf9+i98rNzdWiRYtkGIaGDRumBx54oN4x+fn5MgxDPXr00Pnz5xv9\\n+tGPfiRJ+vjjj1VWVtaicQFNYUoHMaVNmzbKyMhQRkaGJKsoa/fu3dq1a5dyc3NVVlYmt9utBQsW\\nKCUlRbfffrvf7/HRRx/VLO+8/vrr9eSTT9Y75ty5cyotLZVhGNq8ebM2b97s02ufPHlSSUlJfo8J\\n8AVX+IhpV1xxhYYMGaKnnnpKb7/9tsaNG1ezb8mSJX6/XmlpqSZOnKjKykp16dJFS5YsafCZO2fP\\nnq35vu5vGc191f05INi4woetvfXWW9q/f7/atm2rX/3qV00e265dO02ePFmfffaZsrOzdfToUZ09\\ne9bnaluXy6WJEyeqqKhIbdu21fPPP69vfvObDR5b90bvuHHjNHnyZN9PCggRrvBha9nZ2frDH/6g\\nP/zhD3K5XD79THp6es33vhRrefzXf/2XPvjgAxmGoSeeeKLJ9f6JiYk1HySFhYU+vwcQSgQ+bO3G\\nG2+UZF19v/baaz79zGeffSbJelhaY49KvlRmZmbNevoxY8borrvu8mlsnhvFTX2wTJ8+XTfffLN+\\n/vOf6/z58z6NBwgEgQ9bu+uuu5SUlCTTNPXss8/q7bffbvL4Dz/8UGvXrpVhGD4/InnLli367//+\\nbxmGof79+9fcsG2O52mc5eXlevrppxs8Jj8/X2+++abKysrUqVOnepW6QDAxhw9bS0xM1O9+9ztN\\nmDBBVVVVmjBhgvr376+hQ4eqZ8+e6ty5syorK3X06FFt3rxZ69ev18WLF9WjRw+NHTu22df/6KOP\\nNH36dElS9+7dtWjRIknShQsX5Ha7G/yZdu3aqVWrVho4cKAGDhyorVu3as2aNSouLtaDDz6o6667\\nTmVlZcrNzdWyZcv01VdfqX379jXvA4QKj0dGTMjPz9fcuXN17NgxSVJjf60Nw9Ctt96qJ554Qp06\\ndfLa19DD02bNmqWsrCxJ1o1Yt9vd7Lz/ypUra+4TnD9/XtOnT1deXl6D4zIMQx07dtSiRYvUt29f\\n/08c8ANX+IgJ6enp2rRpk3Jzc7V9+3bt27dPp0+fVllZmRISEtSlSxelp6dryJAhNfP+DfEsj7x0\\nm2Rd1df9/8Z+vq6EhAQtWbJE27Zt0+uvv64PPvhApaWliouLU2pqqvr376/777+/0dU+QDBxhQ8A\\nDsFNWwBwCAIfAByCwAcAhyDwAcAhCHwAcAgCHwAcgsAHAIcg8AHAIQh8AHAIAh8AHILABwCH4OFp\\niGoul0u5ubk6efKkbrjhBv3DP/xDpIcUdE44R0QHrvARtd5//30NGzZMH3/8sZKTkzV16lT98Y9/\\njPSwgsoJ54jowRU+olJBQYHGjx+vxYsX1zwn/uzZs3riiSc0dOhQdenSJcIjbDknnCOiC1f4iDqn\\nT5/W5MmTNWTIEK+mID169NDFixf1zjvvhHU8X331VdBfM9rOEc5A4CPqzJo1S6dOndKECRO8tnfs\\n2FGSdWUcTvPmzdOf/vSnoL5mtJ0jnIHAR1TZtm2b3nnnHfXu3VtXXXWV175z585Jkj7//POwjun8\\n+fM6e/Zs0F4vGs8RzkDgI6osXLhQhmHoX/7lX+rt84SgJxTtygnniOhE4CNqvPfee/roo4/0jW98\\nQxkZGfX2//3vf5dUO+1hR044R0QvVukgaqxbt06GYeiWW25RXFxcvf2HDh2SJF122WXhHlrQhOMc\\nq6ur9eKLL2r79u2qrq5W586dNXv2bKWmpuovf/mLXn75ZbVt21bf+ta3NGvWLHXu3Dng94K9cIWP\\nqOB2u2tWpgwcOLDBY/bu3SvDMHTdddeFc2hBE45zrK6u1sSJE9WlSxetWbNGr732mjp37qz77rtP\\nb775pnJzc7V69Wo9/PDD2rVrl5599tmAzwf2wxU+osKBAwd09uxZxcXFadWqVVqzZo3X/srKSp08\\neVKGYSgtLS1Co2yZcJzjkiVLNHDgQP385z+v2XbLLbdow4YNeuqpp5Sbm6u4uDg9++yzOn36tNxu\\nd4vOCfZC4CMq7N69W5KUlpam1atX19v/8ssv68MPP1SbNm2Unp5es/3jjz/WokWL1LVrV128eFEu\\nl0vTp0+PyjnwQM/R48SJE3r22We1ePHiBl+/oqJCu3bt0tq1a722nzp1SpI0ePBgdejQQZI0ZMgQ\\nfetb39KvfvWrFp0T7IUpHUSF/fv3yzAM/fjHP25w/65duyRJN998sxISEiRZATd69GjdfffdmjFj\\nhmbPnq327dvr3//938M2bn8Eco4eO3fu1KhRo3TmzJlGX/+TTz7RL3/5y3rbDxw4IMMwdNNNN9Vs\\nGzdunDIzM+stC0Vs4wofUaGwsFCS9MMf/rDevsrKSv3tb3+TYRi66667aravWLFCcXFxuvXWW2u2\\njRw5Uv/0T/+kHTt2eFWwNubAgQN69NFHZZpmg/tN09TJkyfVpk0b5eXlNXpM27Zt9cILLzR5AzSQ\\nc9y3b5+ef/55paSkKD4+vslz6dmzp3r27Flvu+c3i4Z+a4CzEPiICmVlZZKk73//+/X25eTk6MKF\\nC7r88ss1ePBgr+2XXi2npqaqU6dOys7O9inwe/Tooddff73JY2bNmqWUlBRNmjTJl1NpVCDneP31\\n12vFihWSpPvvv9/v9zx+/LhOnjypq6++WldccUWAI0esYEoHUSUlJaXetqysLBmGoVGjRql1a+sa\\n5dy5c/r000/17W9/u97x3/72t3XgwIGQjzVQvp5jMLz33nuSpBtvvDForwn7IvARFTp16iSpfsFR\\nYWGhdu/erZSUFI0ePbpme1FRUYPHe7aVlpaGbrAB8vccg2H37t0yDKPBwF+wYEFQ3wvRj8BHVOjW\\nrZskqaqqymv7iy++qFatWuk3v/mN2rZtW7Pd82ybNm3a1HutNm3aqKKiIoSjDYy/5+ivv/zlLxo1\\nalTNVb0kvfvuu5KsqaG6Dh8+rJMnTwb8XrAnAh9R4ZZbbpEkrxDat2+fXn/9dT388MNeK0wk1VSp\\nGoZR77VcLpeqq6tDONrA+HuO/qisrNTMmTOVn5+vt99+W5L0zjvv1Pym4/ntwmP+/Pl64IEHAn4/\\n2BOBj6hw66236rvf/a7eeOMNSdbNxsmTJ2v48OEN3iy9NMDqqqysrLesMRr4e47+ME1ThmGoR48e\\nGjNmjIqLi7Vs2TL97ne/U1xcnLZt2yZJunDhgubMmaMBAwbYtoANgWOVDqJC69atlZmZqdmzZ9c8\\nRdLTIKQhl19+uQzDUHl5eb19lZWVSk5ODul4A+HvOfojISFBmZmZWrp0qaZMmaJ27dpp3rx5uvba\\na9WuXTstXrxYf/7zn9W6dWvde++9uv3221v8nrAfAh9RIyUlRS+99JJPxyYkJKh79+715qFN01Rh\\nYWFQQjQU/DlHf/Xq1Uu9evWqt71///7q379/SN4T9sKUDmyrX79+2rdvn9e2Dz74QBcuXPBay95S\\nV199tVJTU4P2ekCkEPiwrXvvvVelpaXKzc2t2bZ69Wr16tUrqFe0EyZM0LBhw4L2eoFyuVy6cOFC\\npIcBGzPMxmrKARs4cOCAFi9erG7duunLL7+U2+3W7NmzlZiYGOmhBcVnn32mxx9/XCdOnNBnn30m\\nSeratau6du2qxx57TF27do3wCGEnBD4AOARTOgDgEAQ+ADgEgQ8ADkHgA4BDEPgA4BAEPgA4BIEP\\nAA5B4AOAQ/w/nxoUdsu7QpkAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [1, 1.5, 2, 3.2, 4.5, 6]\\n\",\n \"y1 = [1, 3.5, 5, 5.5, 6, 6.2]\\n\",\n \"\\n\",\n \"x2 = np.linspace(0, 7, 50)\\n\",\n \"fx = lambda x:0.9 * x + 1.5\\n\",\n \"y2 = [fx(x) for x in x2]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'rd', markersize=16)\\n\",\n \" plt.plot(x2, y2, 'b-')\\n\",\n \" \\n\",\n \" plt.xlabel(\\\"Size \\\\n $\\\\\\\\theta_0 + \\\\\\\\theta_1 x$\\\", fontsize=28)\\n\",\n \" plt.ylabel('Price', fontsize=28)\\n\",\n \" plt.yticks([])\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(0, 7)\\n\",\n \" plt.xlim(0, 7)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_linreg.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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PwKZpm00J49wMqVwHPPiTZ8IrI9Br6WBQaaNh/8jBniNRZKTRWdflxd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\\nkUYw8ImINIKBT0SkEQx8IiKN+D+sy4l+4RQSEgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [1, 1.5, 2, 3.2, 4.5, 6]\\n\",\n \"y1 = [1, 3.5, 5, 5.5, 6, 6.2]\\n\",\n \"\\n\",\n \"x2 = np.linspace(0, 6, 50)\\n\",\n \"fx = lambda x: -0.3545*x**2 + 3.2983*x - 1.1147\\n\",\n \"y2 = [fx(x) for x in x2]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'rd', markersize=16)\\n\",\n \" plt.plot(x2, y2, 'b-')\\n\",\n \" \\n\",\n \" plt.xlabel(\\\"Size \\\\n $\\\\\\\\theta_0 + \\\\\\\\theta_1 x + \\\\\\\\theta_2 x^2$\\\", fontsize=28)\\n\",\n \" plt.ylabel('Price', fontsize=28)\\n\",\n \" plt.yticks([])\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(0, 7)\\n\",\n \" plt.xlim(0, 7)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_quadreg.pdf')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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6NGQVQUREfDyJFOVyPiahqTkHwZPRp+/tnsjfSPfzhdjZciIsyWtOn/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\\n3U6wbD//rRM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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = [1, 1.5, 2, 3.2, 4.5, 6]\\n\",\n \"y1 = [1, 3.5, 5, 5.5, 6, 6.2]\\n\",\n \"\\n\",\n \"x2 = np.linspace(0, 6, 50)\\n\",\n \"fx = lambda x: -0.0394*x**5 + 0.5618*x**4 - 2.5949*x**3 + 3.356*x**2 + 4.8908*x - 5.1743\\n\",\n \"y2 = [fx(x) for x in x2]\\n\",\n \"\\n\",\n \"with sns.axes_style('white'):\\n\",\n \" fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n \" plt.plot(x1, y1, 'rd', markersize=16)\\n\",\n \" plt.plot(x2, y2, 'b-')\\n\",\n \" \\n\",\n \" plt.xlabel(\\\"Size \\\\n $\\\\\\\\theta_0 + \\\\\\\\theta_1 x + \\\\\\\\theta_2 x^2 + \\\\\\\\theta_3x^2 + \\\\\\\\theta_4x^4$\\\", fontsize=28)\\n\",\n \" plt.ylabel('Price', fontsize=28)\\n\",\n \" plt.yticks([])\\n\",\n \" plt.xticks([])\\n\",\n \" plt.ylim(0, 7)\\n\",\n \" plt.xlim(0, 7)\\n\",\n \" \\n\",\n \" \\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" \\n\",\n \" plt.savefig(fp_fig + os.sep + 'logreg_eg7_housing_data_quadreg_overfit.pdf')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":259,"cells":{"repo_name":{"kind":"string","value":"kimkipyo/dss_git_kkp"},"path":{"kind":"string","value":"Python 복습/07일차.목_크롤링, 정규표현식/7일차_4T_크롤링(직방_json_api).ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"675787"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n 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단지명으로 찾기

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지하철역으로 찾기

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지역으로 찾기

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보증금

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월세

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방구조

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지도의 표시 영역내에 등록된 방이 아직 없습니다.

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\\\\r\\\\n · 지도를 좀 더 움직여 다른 곳을 탐색해보세요.
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안심중개사 이 지역 VIP 방

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안심중개사 이 지역 추천 방

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안심중개사 이 지역 일반 방

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이 지역 최신 방

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이 지역 일반 방

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\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t회사소개 |\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t이용약관 |\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t개인정보 취급방침 |\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t위치기반 서비스 이용약관 |\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t중개사 사이트 바로가기\\\\r\\\\n

\\\\r\\\\n \\\\r\\\\n
\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t상호 : (주)직방 &nbsp;|&nbsp; 대표 : 안성우
\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t주소 : 서울특별시 종로구 청계천로 85 (관철동 10-2) 삼일빌딩 13층 (우:110-748) &nbsp;|&nbsp; 팩스 : 02-568-4908
\\\\r\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t사업자등록번호: 120-87-61559 &nbsp;|&nbsp; 통신판매업 신고번호 : 제2015-서울종로-0834호
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지하철역으로 찾기

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지역으로 찾기

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지도의 표시 영역내에 등록된 방이 아직 없습니다.

\\n\",\n \"

\\n\",\n \" · 지도를 좀 더 움직여 다른 곳을 탐색해보세요.
\\n\",\n \" · 이 지역의 입주가능한 방을 알고 계시다면 직방에 알려주세요.\\n\",\n \"

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안심중개사 이 지역 VIP 방

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\\n\",\n \"\\t\\t\\t\\t\\t사업자등록번호: 120-87-61559  |  통신판매업 신고번호 : 제2015-서울종로-0834호
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\\\"https://api.zigbang.com/v1/items?detail=true&item_ids=5624018&item_ids=5608602&item_ids=5631474&item_ids=5642023&item_ids=5630702&item_ids=5644599&item_ids=5444342&item_ids=5644627&item_ids=5463078&item_ids=5555431&item_ids=5595091&item_ids=5567541&item_ids=5563197&item_ids=5519658&item_ids=5596519&item_ids=5647242&item_ids=5626793&item_ids=5512589&item_ids=5646209&item_ids=5490317&item_ids=5533865&item_ids=5610946&item_ids=5519928&item_ids=5655575&item_ids=5629638&item_ids=4774173&item_ids=5590314&item_ids=5526171&item_ids=5586149&item_ids=5589916&item_ids=5615990&item_ids=5169178&item_ids=5644492&item_ids=5608525&item_ids=5602646&item_ids=5608282&item_ids=5600902&item_ids=5441521&item_ids=5630922&item_ids=5644123&item_ids=5634530&item_ids=5652932&item_ids=5605314&item_ids=5641189&item_ids=5623803&item_ids=5641154&item_ids=5619885&item_ids=5508463&item_ids=5423901&item_ids=5642175&item_ids=5537230&item_ids=5644685&item_ids=5628997&item_ids=5595814&item_ids=5646707&item_ids=5593179&item_ids=5608753&item_ids=5600860&item_ids=5512559&item_ids=5594247\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"response = requests.get(BASE_URL)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"room_information = json.loads(response.text)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 서초구 잠원동',\\n\",\n \" 'address2': '11-10',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 173-21번지 ',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'phil1600@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-9830',\\n\",\n \" 'agent_name': '큰길공인중개사(박상엽)',\\n\",\n \" 'agent_phone': '02-512-0132',\\n\",\n \" 'bjd_code': '1165010600',\\n\",\n \" 'bonbun_code': '11',\\n\",\n \" 'bubun_code': '10',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 500,\\n\",\n \" 'description': '▶가까운 지하철역 : 신사역\\\\n\\\\n▶편의시설 : 병원, 공원, 은행, 편의점, 음식점, 커피숍 등 \\\\n\\\\n▶옵션 : 에어컨, 싱크대, 가스레인지, 냉장고, 세탁기, 신발장\\\\n\\\\n▶보증금 조절 가능여부는 확인해야 함!!(궁금하신 사항은 전화로!)\\\\n\\\\n▶주차 가능여부도 확인해야 함!\\\\n\\\\n\\\\n큰 원룸방을 원하시는 분들에게 적합한 방!! 여성분들이 선호하는 방!!\\\\n이 외에도 원룸, 투룸 등 가격대에 비해 퀄리티 높은 위주로 다양한 매물 보유중입니다~ 연락주세요:)\\\\n전화 부재시 문자, 카톡으로 남겨 두시면 확인 후 바로 답변 해드릴게요!!(카톡 아이디 : fraukenzo)',\\n\",\n \" 'description_og': '▶가까운 지하철역 : 신사역\\\\n\\\\n▶편의시설 : 병원, 공원, 은행, 편의점, 음식점, 커피숍 등 \\\\n\\\\n▶옵션 : 에어컨, 싱크대, 가스레인지, 냉장고, 세탁기, 신발장\\\\n\\\\n▶보증금 조절 가능여부는 확인해야 함!!(궁금하신 사항은 전화로!)\\\\n\\\\n▶주차 가능여부도 확인해야 함!\\\\n\\\\n\\\\n큰 원룸방을 원하시는 분들에게 적합한 방!! 여성분들이 선호하는 방!!\\\\n이 외에도 원룸, 투룸 등 가격대에 비해 퀄리티 높은 위주로 다양한 매물 보유중입니다~ 연락주세요:)\\\\n전화 부재시 문자, 카톡으로 남겨 두시면 확인 후 바로 답변 해드릴게요!!(카톡 아이디 : fraukenzo)',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5624018,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/2892099a86c36abe1a6c5ae8d97e42c45ae84003.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/ab8362087f4d44058aefe18bbe0bec7e849e9d11.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/ac89ec83fa879c7fa44556f78e7cea9c6817887f.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/730544d545ae1e866e534c01c6fb3f4fbc884b07.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/d3adbb3b22595f22cc167fc47e1c8f35a413f1eb.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/411bdc31a1afeb906e69ee95b4fa6aad381a9a62.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5624018/3246a76c14a9dee97a7387785167440d25285405.JPG'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '서초구',\\n\",\n \" 'local3': '잠원동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '협의입주가능',\\n\",\n \" 'near_subways': '신사역(3호선), 잠원역(3호선), 논현역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-5917-2526',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1545912/5493dd3ca4b72770b1681844b85c0471b53b7fb6.jpg',\\n\",\n \" 'random_location': '37.5172016054739,127.018038583093',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 55,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 12.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '39.67',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '●잠원동●신사역 5번 출구에서 5분거리인 원룸방!!',\\n\",\n \" 'updated_at': '4일 전',\\n\",\n \" 'user_email': 'namdoongcom@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-8321',\\n\",\n \" 'user_name': '중개보조원(김나윤)',\\n\",\n \" 'user_no': 1545912,\\n\",\n \" 'user_phone': '0507-1281-8321',\\n\",\n \" 'view_count': 63},\\n\",\n \" 'title': '서울시 서초구 잠원동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '65-13',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8, 2층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'click3090@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7491',\\n\",\n \" 'agent_name': '건우공인중개사(김상모)',\\n\",\n \" 'agent_phone': '010-8456-3090',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '65',\\n\",\n \" 'bubun_code': '13',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 140,\\n\",\n \" 'description': '[ 교통/위치 ] - ●\\\\n\\\\nㅇ 을지병원 뒷편에 위치한 풀옵션 원룸원거실 입니다.\\\\n\\\\nㅇ 학동역, 압구정역, 신사역 7~8분거리에 위치해 있습니다.\\\\n\\\\n\\\\n[ 인테리어/특징 ] - ●\\\\n\\\\nㅇ 거실에 티비, 쇼파, 테이블이 배치 되고도 넓직함을 자랑합니다.\\\\n\\\\nㅇ 신축건물로 깔끔한 화이트 톤에 포인트 아트월이 있습니다.\\\\n\\\\n\\\\n[ 주차/편의시설 ] - ●\\\\n\\\\nㅇ 필로티 구조로1층에 주차공간이 있으며 주차 1대 가능합니다.\\\\n\\\\nㅇ 인근에 편의점, 세탁소, 커피숍 등 다양한 편의시설이 있습니다.\\\\n\\\\n------------------------------------------------------------------------★\\\\n\\\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\\\n\\\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\\\n\\\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\\\n\\\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\\n\",\n \" 'description_og': '[ 교통/위치 ] - ●\\\\n\\\\nㅇ 을지병원 뒷편에 위치한 풀옵션 원룸원거실 입니다.\\\\n\\\\nㅇ 학동역, 압구정역, 신사역 7~8분거리에 위치해 있습니다.\\\\n\\\\n\\\\n[ 인테리어/특징 ] - ●\\\\n\\\\nㅇ 거실에 티비, 쇼파, 테이블이 배치 되고도 넓직함을 자랑합니다.\\\\n\\\\nㅇ 신축건물로 깔끔한 화이트 톤에 포인트 아트월이 있습니다.\\\\n\\\\n\\\\n[ 주차/편의시설 ] - ●\\\\n\\\\nㅇ 필로티 구조로1층에 주차공간이 있으며 주차 1대 가능합니다.\\\\n\\\\nㅇ 인근에 편의점, 세탁소, 커피숍 등 다양한 편의시설이 있습니다.\\\\n\\\\n------------------------------------------------------------------------★\\\\n\\\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\\\n\\\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\\\n\\\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\\\n\\\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5608602,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/7e93c257b58d2e1dca6c24aee51271c610966416.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/724ba546e9c5584b6c55e5b45339284553efe8cb.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/45e8bfbe773f9b0c6ea362ffd18c1b3ef5b76722.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/2c5ac87fda79ab4ef466af6948d3f89aa9572706.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/0ced7220dc53fba39a15c606096e7905c4c88140.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/3950271fa98333c5865220e1afa2872550237828.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/46464f3363836628080f086b3639716dbc488292.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/795087a338358ac44550adbd43df860fe02076aa.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/03d47f53d6af934b2d871bc9d1a2db22c6f21273.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/89f6b6b88d77591ec6dbf2e36d5bbde2bb11abd9.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608602/d9986c8033000ba8343db57b9b8629b5e610030a.JPG'}],\\n\",\n \" 'images_count': 11,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '하시입주가능',\\n\",\n \" 'near_subways': '학동역(7호선), 압구정역(3호선), 강남구청역(7호선,분당선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-4622-8144',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/50423.jpg',\\n\",\n \" 'random_location': '37.519445951968,127.030540130409',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 140,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '➰1룸+1거실➰을지병원 뒷편 깔끔한 최고옵션의 원룸원거실~ ★',\\n\",\n \" 'updated_at': '5일 전',\\n\",\n \" 'user_email': 'cpd2108@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-5783',\\n\",\n \" 'user_name': '중개보조원(최영수)',\\n\",\n \" 'user_no': 50423,\\n\",\n \" 'user_phone': '0507-1281-5783',\\n\",\n \" 'view_count': 125},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '517-21',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 압구정로 2길 62, 1층 ',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'hasunja@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-8981',\\n\",\n \" 'agent_name': '청록공인중개사(하순자)',\\n\",\n \" 'agent_phone': '02-3443-6500',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '517',\\n\",\n \" 'bubun_code': '21',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 2000,\\n\",\n \" 'description': '원룸가격에 투룸을!!\\\\n\\\\n \\\\n\\\\n신사역에서 5분거리!! 완전 가깝고 주변에 편의시설이좋은 현대그린마트와 강남시장의 투룸있어요~\\\\n\\\\n주택가라서 저녁에는 조용합니다~~\\\\n\\\\n1000/65 2000/60 실평수 11평의 투룸입니다!!\\\\n\\\\n이외에도 좋은물건 갖고 있으니 더궁금하신 사항은 연락주세요~\\\\n\\\\n청록공인 3443-6500\\\\n',\\n\",\n \" 'description_og': '원룸가격에 투룸을!!\\\\n\\\\n \\\\n\\\\n신사역에서 5분거리!! 완전 가깝고 주변에 편의시설이좋은 현대그린마트와 강남시장의 투룸있어요~\\\\n\\\\n주택가라서 저녁에는 조용합니다~~\\\\n\\\\n1000/65 2000/60 실평수 11평의 투룸입니다!!\\\\n\\\\n이외에도 좋은물건 갖고 있으니 더궁금하신 사항은 연락주세요~\\\\n\\\\n청록공인 3443-6500\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5631474,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/96dc0d04fbe477151a5a3d11842b0adfa057d3cc.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/90333385c5ecd58c92f41d29b4b51f338a61ba98.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/261d031fd11f2f10fcaed274c3e3f78eb3e35dd4.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/58ff05d01c1e4b4063cb0c9f1368196539cab824.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/a21497ff72fafd29b3f54bcc6b10b3ec35f564b7.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/886dd1c3bce988c613c4340f50ffdf65dc137cb6.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5631474/a938de9c6e6a5379e7523ef4fa82b21c8eb38a0d.jpg'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '계약후10일이내',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 압구정역(3호선)',\\n\",\n \" 'options': '에어컨, 가스레인지',\\n\",\n \" 'original_user_phone': '010-6271-1877',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5196143196011,127.020800893177',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 60,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 11.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '36.36',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '원룸방가격에 투룸을!! +_+ 클릭클릭!!',\\n\",\n \" 'updated_at': '3일 전',\\n\",\n \" 'user_email': 'huneloves@gmail.net',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-0534',\\n\",\n \" 'user_name': '중개보조원(서재훈)',\\n\",\n \" 'user_no': 935113,\\n\",\n \" 'user_phone': '0507-1280-0534',\\n\",\n \" 'view_count': 74},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '73-24',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'artsoup@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6420',\\n\",\n \" 'agent_name': '즐겨찾기공인중개사(김영한)',\\n\",\n \" 'agent_phone': '02-540-0124',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '73',\\n\",\n \" 'bubun_code': '24',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '큰길이라 밤길도 문제 없이 안전하구요 \\\\n\\\\n\\\\n남향이라 밝고 쾌적합니다 \\\\n\\\\n베란다가 있어 용이하구요\\\\n\\\\n\\\\n\\\\n거실은 보시다시피 소파 티뷔 맞벽구로 좋습니다 \\\\n\\\\n\\\\n언제든지 보실수 있습니다 늦은시간이나 일찍오실분들은 미리 연락 부탁드리구요^^\\\\n\\\\n\\\\n언제나 다시 찾고 싶은 부동산과 중개인이 되도록 좋은집으로 보답하겠습니다 ^^',\\n\",\n \" 'description_og': '큰길이라 밤길도 문제 없이 안전하구요 \\\\n\\\\n\\\\n남향이라 밝고 쾌적합니다 \\\\n\\\\n베란다가 있어 용이하구요\\\\n\\\\n\\\\n\\\\n거실은 보시다시피 소파 티뷔 맞벽구로 좋습니다 \\\\n\\\\n\\\\n언제든지 보실수 있습니다 늦은시간이나 일찍오실분들은 미리 연락 부탁드리구요^^\\\\n\\\\n\\\\n언제나 다시 찾고 싶은 부동산과 중개인이 되도록 좋은집으로 보답하겠습니다 ^^',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5642023,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/9f50f52c2ca6b1956c2ea18d468e437040cb7cbf.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/3136e93d1d10f3bba0d586bc7a0e15faab157514.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/17174f15e10242041b29b59f6a057951870ff509.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/7cc20089b3848afc372e524f59e4551c40880cae.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/69d127f3147de39536b38e1f28316f2a00e1a6e9.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/b360f12422d05e84e4c7394b40f175d2f514d7fe.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/84a8fd729546107df72ab99ad1f7f28af8b0664a.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/cc1565164fe1d7b1296e0d7fd8c1c1b98af84b30.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642023/b314d52a48c022ebc6640149807230f22229c26c.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시',\\n\",\n \" 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\\n\",\n \" 'options': '-',\\n\",\n \" 'original_user_phone': '010-2446-3484',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5183542989367,127.031831461043',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 115,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '거실 맞벽구조 제대로 나옵니다 채광 통풍 잘되는 구조구요 집 좋으네요',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'cjpark12@hanmail.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6676',\\n\",\n \" 'user_name': '중개보조원(박충진)',\\n\",\n \" 'user_no': 810168,\\n\",\n \" 'user_phone': '0507-1280-6676',\\n\",\n \" 'view_count': 44},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '67-17',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 선릉로116길 24',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'pudu0719__@daum.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-7300',\\n\",\n \" 'agent_name': '삼성하우스공인중개사(윤미경)',\\n\",\n \" 'agent_phone': '02-549-7654',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '67',\\n\",\n \" 'bubun_code': '17',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 10000,\\n\",\n \" 'description': '층당 1호실씩 있는 집합건물로, 4층 단독으로 1세대입니다. \\\\n\\\\n주인분이 거주하고 있는 집으로,\\\\n집을 아주 잘 꾸며 놓으셨습니다.\\\\n\\\\n빨간벽돌 건물이지만, 내부는 으리으리합니다~\\\\n\\\\n안방 붙박이장은 물론 방 하나를 붙박이장으로 만들어놓았습니다.\\\\n아무리 많은 옷도 소화 가능합니다.\\\\n거실 화장실 호텔식으로 리모델링했습니다. 욕조도 깨끗\\\\n거실 바닥 고급원목으로 깔았습니다.\\\\n각 창문마다 블러인드와 커튼 달아놓으셨구요\\\\n\\\\n옵션도 많습니다.\\\\n양문형냉장고 2개 김치냉장고 드럼세탁기 오븐 식기세척기 붙박이장\\\\n에어콘은 3대~~~\\\\n다 쓰시면 됩니다. 상태 좋습니다.\\\\n\\\\n주인분은 근처 단독주택으로 이사 가는겁니다.\\\\n전혀 문제 없는 집입니다.\\\\n\\\\n거실특대사이즈 (개인적으로 영화관 느낌으로 꾸며도 좋을 듯요^^)\\\\n거실주방분리형 \\\\n\\\\n실평 35평 정확히 나옵니다. \\\\n넓직합니다.\\\\n\\\\n통베란다 있습니다.\\\\n\\\\n주차 지정 1대입니다. 빼줄일 없구요\\\\n\\\\n외관이 빨간벽돌,\\\\n엘리베이터 없는 4층 \\\\n이 두가지가 나올법한 얘기인데, 감안해도 너무 싸게 잘 나온 방입니다.\\\\n\\\\n정말이지 에이스입니다\\\\n광복절 이전에 분명 나갈 매물입니다.\\\\n서두르세요~^^',\\n\",\n \" 'description_og': '층당 1호실씩 있는 집합건물로, 4층 단독으로 1세대입니다. \\\\n\\\\n주인분이 거주하고 있는 집으로,\\\\n집을 아주 잘 꾸며 놓으셨습니다.\\\\n\\\\n빨간벽돌 건물이지만, 내부는 으리으리합니다~\\\\n\\\\n안방 붙박이장은 물론 방 하나를 붙박이장으로 만들어놓았습니다.\\\\n아무리 많은 옷도 소화 가능합니다.\\\\n거실 화장실 호텔식으로 리모델링했습니다. 욕조도 깨끗\\\\n거실 바닥 고급원목으로 깔았습니다.\\\\n각 창문마다 블러인드와 커튼 달아놓으셨구요\\\\n\\\\n옵션도 많습니다.\\\\n양문형냉장고 2개 김치냉장고 드럼세탁기 오븐 식기세척기 붙박이장\\\\n에어콘은 3대~~~\\\\n다 쓰시면 됩니다. 상태 좋습니다.\\\\n\\\\n주인분은 근처 단독주택으로 이사 가는겁니다.\\\\n전혀 문제 없는 집입니다.\\\\n\\\\n거실특대사이즈 (개인적으로 영화관 느낌으로 꾸며도 좋을 듯요^^)\\\\n거실주방분리형 \\\\n\\\\n실평 35평 정확히 나옵니다. \\\\n넓직합니다.\\\\n\\\\n통베란다 있습니다.\\\\n\\\\n주차 지정 1대입니다. 빼줄일 없구요\\\\n\\\\n외관이 빨간벽돌,\\\\n엘리베이터 없는 4층 \\\\n이 두가지가 나올법한 얘기인데, 감안해도 너무 싸게 잘 나온 방입니다.\\\\n\\\\n정말이지 에이스입니다\\\\n광복절 이전에 분명 나갈 매물입니다.\\\\n서두르세요~^^',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5630702,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/ce2c653600249cf2a80c3980f9cd17a72070d2dd.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/c6a77ed65ec7787af73fd287e67fd43c06cd49b1.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/117bac6ab9220c00003ef6b1dc1a70450f27ddbc.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/a4472a028a18053c84f594c8653b35c5b4928ebe.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/09b032dca3127941ddde98ce958055d315b59b1c.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/b7292ae2be5ff0b2534b4492557316533daa547f.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/e5b5d16e00b2827e40e20322c4e29642fb1ef133.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/b1a9b88fd2a7153cfc861e0a671e491b681fb69f.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/a5bae87ef58104b582aa028fe9bd73e6d8fda853.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/795f77a5bcfadd476e8db49ec78eb07c8dcf7b1a.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/64c389b49e0ce4a9a1ab456c68bc749757982853.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/65d04f96871fea3daaf54cd8e7f5bcad073be0d5.JPG'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5630702/5bcbdc310ca29866fbab0550f7b3c582385c4da5.JPG'}],\\n\",\n \" 'images_count': 13,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '없음',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '협의',\\n\",\n \" 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9045-6470',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1616272/8e717c0ec7333cf0c29d53ce0cf1c3d9371cf8f7.jpg',\\n\",\n \" 'random_location': '37.5185473925834,127.03087936438',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 190,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '쓰리룸+',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 35.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '115.70',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '완전에이스 4R 포룸+욕실2개 아파트형구조 주인거주중인집 실평 넓고 채광좋고 옵션굿',\\n\",\n \" 'updated_at': '3일 전',\\n\",\n \" 'user_email': 'minguk6470@hanmail.net',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-6803',\\n\",\n \" 'user_name': '중개보조원(송원환)',\\n\",\n \" 'user_no': 1616272,\\n\",\n \" 'user_phone': '0507-1281-6803',\\n\",\n \" 'view_count': 98},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '523-34',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'snws@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6934',\\n\",\n \" 'agent_name': '나무공인중개사(조민영)',\\n\",\n \" 'agent_phone': '02-557-1888',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '523',\\n\",\n \" 'bubun_code': '34',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 100,\\n\",\n \" 'description': ' ㅇ 위치 및 역세권 정보\\\\n \\\\n &#186; 신사동 가로수길 바로 옆에 위치한 풀옵션 투룸입니다.\\\\n \\\\n &#186; 신사역 3출구 도보 5분거리, 대로변 버스정류장 도보 3분거리\\\\n \\\\n \\\\n ㅇ 구조 및 옵션 \\\\n \\\\n &#186; 신사동에 귀한 투룸이고 가격에 비해 싸게 나와 인기가 매우 많습니다.\\\\n \\\\n &#186; 올 화이트 톤으로 깔끔하게 리모델링해서 정말 깔끔합니다.\\\\n\\\\n &#186; 벽걸이 티비있으며, 3인용 쇼파, 앉아 안락하게 볼수있습니다.\\\\n \\\\n &#186; 남향이라 햇빛도 잘들어오고 겨울에 춥지 않게 사용할수 있습니다. \\\\n \\\\n \\\\n ㅇ 주차 및 편의시설\\\\n\\\\n &#186; 주차 1대 협의\\\\n \\\\n &#186; 대로변 주변으로 세탁소, 커피숍, 카센터 PC방등 편의시설이 다양합니다.\\\\n',\\n\",\n \" 'description_og': ' ㅇ 위치 및 역세권 정보\\\\n \\\\n &amp;#186; 신사동 가로수길 바로 옆에 위치한 풀옵션 투룸입니다.\\\\n \\\\n &amp;#186; 신사역 3출구 도보 5분거리, 대로변 버스정류장 도보 3분거리\\\\n \\\\n \\\\n ㅇ 구조 및 옵션 \\\\n \\\\n &amp;#186; 신사동에 귀한 투룸이고 가격에 비해 싸게 나와 인기가 매우 많습니다.\\\\n \\\\n &amp;#186; 올 화이트 톤으로 깔끔하게 리모델링해서 정말 깔끔합니다.\\\\n\\\\n &amp;#186; 벽걸이 티비있으며, 3인용 쇼파, 앉아 안락하게 볼수있습니다.\\\\n \\\\n &amp;#186; 남향이라 햇빛도 잘들어오고 겨울에 춥지 않게 사용할수 있습니다. \\\\n \\\\n \\\\n ㅇ 주차 및 편의시설\\\\n\\\\n &amp;#186; 주차 1대 협의\\\\n \\\\n &amp;#186; 대로변 주변으로 세탁소, 커피숍, 카센터 PC방등 편의시설이 다양합니다.\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5644599,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/ddd39acae052ff14c88b3ff435f03bca389ade6f.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/3c5be037f1430da3950664cd88ab3edac03ebd2c.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/cce2097def2340e37a9fd83e517790933d4a2f5d.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/6e05c2145a64c1b8d38b990911196c682e05923b.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/5689199f6538d58d50475e3077d3453085a25a47.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/948a832feb2d9b157d00a7da624e6eb5cbb3612c.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/32753d5d5d4abb0346c7cf4334e46feedbf8aa9d.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/73094f8cf2e1fb9256c78a3ed1c56c9557c46c78.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/89c3534164847dd4fc776c8e5974b92cbf875286.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/0fce439ae36e22973399cafc1e8567bae67d26a4.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/50bb1320cbbc0d1b5516188804420ab4ed2deeee.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644599/08c238f4937dc13a48b965f7d247f5c662abed82.JPG'}],\\n\",\n \" 'images_count': 12,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '신사역(3호선), 압구정역(3호선), 논현역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-7589-0888',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\\n\",\n \" 'random_location': '37.5214737984472,127.020739525254',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 100,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 16.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '52.89',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '❌신사동❌ 리모델링❌ 귀한 투룸',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'zldzkfn@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-2240',\\n\",\n \" 'user_name': '중개보조원(김동현)',\\n\",\n \" 'user_no': 1000906,\\n\",\n \" 'user_phone': '0507-1281-2240',\\n\",\n \" 'view_count': 55},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '550-2',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 625-10 커피한잔부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'handstar1018@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6659',\\n\",\n \" 'agent_name': '커피한잔공인중개사(손별)',\\n\",\n \" 'agent_phone': '010-9226-9082',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '550',\\n\",\n \" 'bubun_code': '2',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 3000,\\n\",\n \" 'description': ' # 100% 실매물 100% 실사진 # \\\\n차별화된 서비스 전문적으로 책임있게 구해드립니다\\\\n친절한 미소로 불편한점 없도록 모시겠습니다~^^\\\\n ★매물담당자 번호로 연락주세요~★\\\\n\\\\n▶(신사동)가로수길 압구정역 도보5분거리에 위치합니다.\\\\n\\\\n▶방2개 욕실1개 거실맞벽 베란다2개 실16평 \\\\n\\\\n▶주차는 1대 무료 가능합니다.\\\\n\\\\n▶올수리해서 연식대비 아주 깔끔합니다.\\\\n\\\\n▶에어컨,세탁기,냉장고,싱크대,가스렌지,붙박이장,신발장 풀옵션 입니다.\\\\n\\\\n▶입주는 현 세입자와 협의(빠른입주 가능합니다.)\\\\n\\\\n▶언제든지 볼 수 있습니다.\\\\n\\\\n▶자세한사항은 부담없이 24시간 연락주시면 친절히 상담하겠습니다~^^ \\\\n\\\\n------------------------------------------------------------------\\\\n\\\\n카카오톡 ID happyhouse9 입니다. 카카오톡 상담도 환영입니다~^^',\\n\",\n \" 'description_og': ' # 100% 실매물 100% 실사진 # \\\\n차별화된 서비스 전문적으로 책임있게 구해드립니다\\\\n친절한 미소로 불편한점 없도록 모시겠습니다~^^\\\\n ★매물담당자 번호로 연락주세요~★\\\\n\\\\n▶(신사동)가로수길 압구정역 도보5분거리에 위치합니다.\\\\n\\\\n▶방2개 욕실1개 거실맞벽 베란다2개 실16평 \\\\n\\\\n▶주차는 1대 무료 가능합니다.\\\\n\\\\n▶올수리해서 연식대비 아주 깔끔합니다.\\\\n\\\\n▶에어컨,세탁기,냉장고,싱크대,가스렌지,붙박이장,신발장 풀옵션 입니다.\\\\n\\\\n▶입주는 현 세입자와 협의(빠른입주 가능합니다.)\\\\n\\\\n▶언제든지 볼 수 있습니다.\\\\n\\\\n▶자세한사항은 부담없이 24시간 연락주시면 친절히 상담하겠습니다~^^ \\\\n\\\\n------------------------------------------------------------------\\\\n\\\\n카카오톡 ID happyhouse9 입니다. 카카오톡 상담도 환영입니다~^^',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5444342,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/7a9402b64f3f4cf9a5a0100a3f98fbded37f4de8.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/3de536ca9179af4526a9e0cfde1cafb4d48e6697.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/797475f20508f4ec4eb5de78a39d0089c0c26ab1.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/1992a24baed044c631da8bb16f58db13f2f9a56a.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/46bac53d7f362877fbc6e1109cfd6932f828eab0.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/490c40ed27679b2c002da41b806492ff0e8dadf8.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/e34ed1842ee196e414e2ab54b29987e86aec676f.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/ec9b76b586193cc9a235804026554f9054b33582.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/b9755ce670fc53a0fdf8b7033ac7da6d3f9a6187.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/7d235ca27dd02ce5442752c03963c56b4a9c86c0.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/077ff671571f89b9d6e0c0d69635abe209d794ed.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5444342/278d9a27cf51c41f25fda0c3c04ae611ea055dd2.JPG'}],\\n\",\n \" 'images_count': 12,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '7만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '협의입주(빠른입주가능)',\\n\",\n \" 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-4551-0717',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5235003949622,127.023868325345',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 100,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 18.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '59.50',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '♣♧실매물 리모델링 깔끔한2룸 거실맞벽 베란다2개 풀옵션 A급 추천매물♣♧',\\n\",\n \" 'updated_at': '20일 전',\\n\",\n \" 'user_email': 'raymond4860@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-8195',\\n\",\n \" 'user_name': '중개보조원(김세원)',\\n\",\n \" 'user_no': 14897,\\n\",\n \" 'user_phone': '0507-1280-8195',\\n\",\n \" 'view_count': 269},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '13-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'snws@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6934',\\n\",\n \" 'agent_name': '나무공인중개사(조민영)',\\n\",\n \" 'agent_phone': '02-557-1888',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 120,\\n\",\n \" 'description': '- 신사역 도보 약 4분거리에 위치\\\\n\\\\n- 복층형 구조로 공간활용성 좋구요.\\\\n\\\\n- 신축 대리석바닥에 새집기 모두다 새것입니다.\\\\n\\\\n- 1층과 복층 모두 TV가 있어 두분이 지내시기에도 정말 좋구요.\\\\n\\\\n- 신축이라서 내부 컨디션은 최상입니다.\\\\n\\\\n- 주차 가능\\\\n\\\\n- 신사역 인근으로 편의시설 많습니다.',\\n\",\n \" 'description_og': '- 신사역 도보 약 4분거리에 위치\\\\n\\\\n- 복층형 구조로 공간활용성 좋구요.\\\\n\\\\n- 신축 대리석바닥에 새집기 모두다 새것입니다.\\\\n\\\\n- 1층과 복층 모두 TV가 있어 두분이 지내시기에도 정말 좋구요.\\\\n\\\\n- 신축이라서 내부 컨디션은 최상입니다.\\\\n\\\\n- 주차 가능\\\\n\\\\n- 신사역 인근으로 편의시설 많습니다.',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5644627,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/2e5caee1cff5f0d3083edbe3d86f85fe004b552d.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/071f87053c55fdc1951853e37c30f80d20054e31.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/b45746ea5487fa2c55a45e372be5f6ade503ec4e.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/0b83b907ef36dfebba3a72257397a79066f12a3d.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/1a22eec51b6d04f05a60ad6dafebac2ddb16aec9.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/65f9d165a16d5c284dd3df1a577efcf86f74ac33.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/9c67401edbb79ea27d90f91a8fc812a8b48e352c.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/59a3876ee8776c6f3783e1e60b436d04f5afe172.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/2cc9abca313eed915dae0b783c385aa055447c31.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/b022e378bcf694a46c4f897ba3f63526038d511a.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/d3c557b78b34c65bab433da17609d61ce4788a16.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644627/c4e51b2d8881f18406174736987132110ae52810.JPG'}],\\n\",\n \" 'images_count': 12,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '수도, 인터넷, TV',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-7589-0888',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\\n\",\n \" 'random_location': '37.5165587310278,127.023506978132',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 120,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(복층형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 11.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '36.36',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '❌신사역❌ 인근에 위치한 귀한 복층 풀옵션',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'zldzkfn@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-2240',\\n\",\n \" 'user_name': '중개보조원(김동현)',\\n\",\n \" 'user_no': 1000906,\\n\",\n \" 'user_phone': '0507-1281-2240',\\n\",\n \" 'view_count': 38},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '21-6',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 삼성동41번지 101호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'scvjcs@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7189',\\n\",\n \" 'agent_name': '한빛공인중개사(정철수)',\\n\",\n \" 'agent_phone': '02-515-9639',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '21',\\n\",\n \" 'bubun_code': '6',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '*강남대로변 바로 이면이며 방사이즈가 큰편이며\\\\n 앞에 막힌건물이 없어 확트여 채광,통풍좋으면 구조가 아주 좋은 방입니다\\\\n*올 수리한 깔끔 하며 조용한 학동공원 주변\\\\n*버스정 류장1분거리이며 신사.논현역 역세권 입니다\\\\n*항상고객을 먼저 생각하는 부동산 입니다 ',\\n\",\n \" 'description_og': '*강남대로변 바로 이면이며 방사이즈가 큰편이며\\\\n 앞에 막힌건물이 없어 확트여 채광,통풍좋으면 구조가 아주 좋은 방입니다\\\\n*올 수리한 깔끔 하며 조용한 학동공원 주변\\\\n*버스정 류장1분거리이며 신사.논현역 역세권 입니다\\\\n*항상고객을 먼저 생각하는 부동산 입니다 ',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5463078,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/42ea80191ee094d0f5663c560a10c12c3d910eb7.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/0be593035eaf1f818d672066c2daa654420dd23f.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/9b292b55e0cbdfec900ffa1815dcc0d9c7ed1850.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/50c57776991e48443626c5e0aa83ad7912a1984c.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/a7b34e645bf54c02120e01ae49a8da7af4575166.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/62299be69f6c2eba5c8726ca4cf99920d71f01ac.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5463078/9911ca0107113e7bfbf62526d7393f5e461a96b6.JPG'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '날짜협의',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\\n\",\n \" 'options': '-',\\n\",\n \" 'original_user_phone': '010-8983-8111',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5147530100533,127.021129970585',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 60,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '🌺🌺신사초역세권 올 수리 원룸 풀옵션 월세🌺🌺',\\n\",\n \" 'updated_at': '19일 전',\\n\",\n \" 'user_email': 'scvjcs@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-2906',\\n\",\n \" 'user_name': '중개보조원(홍성호)',\\n\",\n \" 'user_no': 930206,\\n\",\n \" 'user_phone': '0507-1281-2906',\\n\",\n \" 'view_count': 480},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '78-3',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 173-21번지 ',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'phil1600@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-9830',\\n\",\n \" 'agent_name': '큰길공인중개사(박상엽)',\\n\",\n \" 'agent_phone': '02-512-0132',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '78',\\n\",\n \" 'bubun_code': '3',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '【매물 정보】\\\\n \\\\n○ 위치:학동역 10번 출구 도보 5분 거리\\\\n\\\\n○ 금액:보증금1000/월세50 보증금조정가능,허위매물X\\\\n\\\\n○ 입주: 즉시입주\\\\n\\\\n○ 층수:102호\\\\n\\\\n○ 크기:실10평\\\\n\\\\n○ 옵션:에어컨,싱크대\\\\n \\\\n○ 비고: YO!! 어차피 지금은 성수기 !\\\\n\\\\n #왜모르세요? #강남 #오피스텔 #원룸 #\\\\n#행운의여보세요 #좋아요',\\n\",\n \" 'description_og': '【매물 정보】\\\\n \\\\n○ 위치:학동역 10번 출구 도보 5분 거리\\\\n\\\\n○ 금액:보증금1000/월세50 보증금조정가능,허위매물X\\\\n\\\\n○ 입주: 즉시입주\\\\n\\\\n○ 층수:102호\\\\n\\\\n○ 크기:실10평\\\\n\\\\n○ 옵션:에어컨,싱크대\\\\n \\\\n○ 비고: YO!! 어차피 지금은 성수기 !\\\\n\\\\n #왜모르세요? #강남 #오피스텔 #원룸 #\\\\n#행운의여보세요 #좋아요',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '1층',\\n\",\n \" 'floor_all': '3층',\\n\",\n \" 'id': 5555431,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5555431/fb65977caf7ad41c5db8c79776d61ca89164c93f.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5555431/6989cc014e3f5c233dcceb45596c3f5d9135d25b.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5555431/aa5bd1149df4ed15a97b5a028b7819997da6319d.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5555431/8bc0197de73098490b000ceb625b263a8475d510.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5555431/1f3985b0d6e642f2c1853965a5fb48f0a730cd88.JPG'}],\\n\",\n \" 'images_count': 5,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 신사역(3호선)',\\n\",\n \" 'options': '에어컨, 싱크대',\\n\",\n \" 'original_user_phone': '010-5820-0678',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1545912/5493dd3ca4b72770b1681844b85c0471b53b7fb6.jpg',\\n\",\n \" 'random_location': '37.516657741281,127.032024827408',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 50,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '#논현동#초A급매물#히트다히트잉~♥',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'cjw4060@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-1909',\\n\",\n \" 'user_name': '중개보조원(최정웅)',\\n\",\n \" 'user_no': 1545912,\\n\",\n \" 'user_phone': '0507-1281-1909',\\n\",\n \" 'view_count': 486},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '566-11',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 신사동 610-2 1층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'suny1209@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7187',\\n\",\n \" 'agent_name': '열린공인중개사(강광수)',\\n\",\n \" 'agent_phone': '02-511-0222',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '566',\\n\",\n \" 'bubun_code': '11',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 8000,\\n\",\n \" 'description': '분리형원룸 넓은방과 부엌이 넓어요\\\\n\\\\n전체 건물 융자없어요\\\\n\\\\n반지층 이지만 채광 좋아요\\\\n\\\\n실매물 실사진 연락주셔요',\\n\",\n \" 'description_og': '분리형원룸 넓은방과 부엌이 넓어요\\\\n\\\\n전체 건물 융자없어요\\\\n\\\\n반지층 이지만 채광 좋아요\\\\n\\\\n실매물 실사진 연락주셔요',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '반지하',\\n\",\n \" 'floor_all': '3층',\\n\",\n \" 'id': 5595091,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/a5a9c066c428e7af1775cc184c9d9fd5191492bc.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/1023bb9e37455db5aec4d7eeee6c6d60cdaea4d0.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/14f79f9fda963146dd6a6c2c2e32a31893341825.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/720017b963b2bb82206a3167a6c5dacb638338ef.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/f61188ab5379bc448e6e7bf0ec68ec30b3344e12.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/dcd5a5e6395d1550c317b9395e742a914ddd3a82.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595091/918ee6b6508c76f9d6c8ecc11bac9fe810512725.jpg'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '7만원',\\n\",\n \" 'manage_cost_inc': '수도',\\n\",\n \" 'movein_date': '협의',\\n\",\n \" 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9134-5291',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/676867/5ab3eaadbeb532e1a1a991f14cb8804cbcbf04ac.jpg',\\n\",\n \" 'random_location': '37.5223675424642,127.026164124225',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '분리형원룸 베란다 있어요 ^^',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'suny1209@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-7187',\\n\",\n \" 'user_name': '대표공인중개사(강광수)',\\n\",\n \" 'user_no': 676867,\\n\",\n \" 'user_phone': '0507-1280-7187',\\n\",\n \" 'view_count': 291},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '39-6',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 삼성동41번지 101호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'scvjcs@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7189',\\n\",\n \" 'agent_name': '한빛공인중개사(정철수)',\\n\",\n \" 'agent_phone': '02-515-9639',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '39',\\n\",\n \" 'bubun_code': '6',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 3000,\\n\",\n \" 'description': '*풀옵션 방 신축첫입니다\\\\n*건물수려하며 내부 고급스러운 자재 사용\\\\n*테라스 있는 방구하기 쉬지 않아용 얼른서두르세요\\\\n*주변 조용하며 치안좋고 살기 좋은 동네 학동공원 주변입니다\\\\n*헉동역도보3분 논현역도보5분 이내거리입니다',\\n\",\n \" 'description_og': '*풀옵션 방 신축첫입니다\\\\n*건물수려하며 내부 고급스러운 자재 사용\\\\n*테라스 있는 방구하기 쉬지 않아용 얼른서두르세요\\\\n*주변 조용하며 치안좋고 살기 좋은 동네 학동공원 주변입니다\\\\n*헉동역도보3분 논현역도보5분 이내거리입니다',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '5층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5567541,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/84ce51f62a6676284c931d1dae426c3bef2427b4.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/e50799a7d98e71110d0041db77d644db107d0d71.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/bd70eb23e6ae2358a87ba7b468ed2a261bcda3d6.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/055e6bad8055f3c974ecb0a8569750f74fbb035d.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/2a6073947a5c1bfe9137bcf51356ffd67764af35.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/5705a3cf2f207e720f07102766ca119a949bd4bc.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5567541/a889490ee7ad86eaa3fe6b8f4bdff53b2b08936b.JPG'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시',\\n\",\n \" 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\\n\",\n \" 'options': '-',\\n\",\n \" 'original_user_phone': '010-8983-8111',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5139452609109,127.027338094298',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 100,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 12.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '39.67',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '**학동역 테라스있는 신축투룸월세**',\\n\",\n \" 'updated_at': '10일 전',\\n\",\n \" 'user_email': 'scvjcs@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-2906',\\n\",\n \" 'user_name': '중개보조원(홍성호)',\\n\",\n \" 'user_no': 930206,\\n\",\n \" 'user_phone': '0507-1281-2906',\\n\",\n \" 'view_count': 627},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '73-5',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'artsoup@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6420',\\n\",\n \" 'agent_name': '즐겨찾기공인중개사(김영한)',\\n\",\n \" 'agent_phone': '02-540-0124',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '73',\\n\",\n \" 'bubun_code': '5',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 2000,\\n\",\n \" 'description': '- 조용한 동네에 위치하고 있어 주거환경 좋은 방이에요\\\\n\\\\n- 분리형 스타일이라서 집에서 요리해도 옷에 음식냄새 걱정없는 추천드릴 방~\\\\n\\\\n- 현재 공실로 바로 입주 가능해요~\\\\n\\\\n- 주인분이 거주하는 집이라서 집 관리도 잘되고 보안 방범도 좋답니다.\\\\n\\\\n- 빨리 계약되는 집이니 서두르셔서 오세요~^^\\\\n',\\n\",\n \" 'description_og': '- 조용한 동네에 위치하고 있어 주거환경 좋은 방이에요\\\\n\\\\n- 분리형 스타일이라서 집에서 요리해도 옷에 음식냄새 걱정없는 추천드릴 방~\\\\n\\\\n- 현재 공실로 바로 입주 가능해요~\\\\n\\\\n- 주인분이 거주하는 집이라서 집 관리도 잘되고 보안 방범도 좋답니다.\\\\n\\\\n- 빨리 계약되는 집이니 서두르셔서 오세요~^^\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5563197,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/6efcb7ccb86acd4bb2f4826e5eb69030655836b8.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/58d592c99f82d47d0047010a70de94ee980dba02.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/eb3d8d9d66886d0e76ce171f304271a5825a29dd.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/10306fde9c537b35afd8e4013da2a265658d7fdb.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/52c66efbf59f8f584a9033b2bcc68ca8487d498f.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/30fbac752bfe2ae199d450c85c5a99671b9a14b3.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5563197/3329ed644b833099969aa665c9e33a737b1f7f7a.JPG'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 압구정역(3호선)',\\n\",\n \" 'options': '에어컨, 신발장',\\n\",\n \" 'original_user_phone': '010-2446-3484',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5181632633649,127.03131882067',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 50,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 9.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '29.75',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '@@ 채광좋고 구조좋은 지상층 분리형 방 @@',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'cjpark12@hanmail.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6676',\\n\",\n \" 'user_name': '중개보조원(박충진)',\\n\",\n \" 'user_no': 810168,\\n\",\n \" 'user_phone': '0507-1280-6676',\\n\",\n \" 'view_count': 293},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '593-31월담빌딩 5층 503호',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 217-46',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'mhj06100610@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-4362',\\n\",\n \" 'agent_name': '한영공인중개사(모형진)',\\n\",\n \" 'agent_phone': '010-4737-9178',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 12000,\\n\",\n \" 'description': '안녕하세요 신사동에 위치하고 있고 급하게 나온 매물입니다.\\\\n\\\\n일단 현재 여성 세입자가 살고 계신데 방이 너무 깨끗합니다. 일단 실평수 10평정도 인데 그것보다 더큰평수같구요\\\\n혼자살기에 부담 스러울 정도의 사이즈라고 생각합니다\\\\n\\\\n에어컨 , 신발장, 가스레인지등 옵션이 있구요 주차는 달에 10만원 입니다 강남치고는 저렴한 가격입니다.\\\\n\\\\n걸어서 5분거리에 바로 압구정역이 있구요 근처 여건이 너무 좋습니다.\\\\n1층에 어린이집이 있어서 근처에 혐오시설은 없습니다.\\\\n\\\\n현재 살고계신 세입자분이 침대, 냉장고, 세탁기등 싸게 양도한다고 하니 이점 참고하시면 될것 같습니다.\\\\n\\\\n너무좋은 조건으로 나온 매물이라 금방 계약되오니 바로 연락주세요',\\n\",\n \" 'description_og': '안녕하세요 신사동에 위치하고 있고 급하게 나온 매물입니다.\\\\n\\\\n일단 현재 여성 세입자가 살고 계신데 방이 너무 깨끗합니다. 일단 실평수 10평정도 인데 그것보다 더큰평수같구요\\\\n혼자살기에 부담 스러울 정도의 사이즈라고 생각합니다\\\\n\\\\n에어컨 , 신발장, 가스레인지등 옵션이 있구요 주차는 달에 10만원 입니다 강남치고는 저렴한 가격입니다.\\\\n\\\\n걸어서 5분거리에 바로 압구정역이 있구요 근처 여건이 너무 좋습니다.\\\\n1층에 어린이집이 있어서 근처에 혐오시설은 없습니다.\\\\n\\\\n현재 살고계신 세입자분이 침대, 냉장고, 세탁기등 싸게 양도한다고 하니 이점 참고하시면 될것 같습니다.\\\\n\\\\n너무좋은 조건으로 나온 매물이라 금방 계약되오니 바로 연락주세요',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '5층',\\n\",\n \" 'floor_all': '8층',\\n\",\n \" 'id': 5519658,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/2442c3ddaa932579aa91304f339967f7c048f9a9.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/5d753ccb675500cfbae35196db1b4ae9654b47e3.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/3a298ff43553ae1da6fe98eb947ca7f8fc89d4c4.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/1abad812840849156d83541485e3b1539d56fc45.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/19312ecee618b611c2887c25d5e6063aef12eba3.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/23247798952314a8fe2876163a6833554e8de611.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/0a3fba9335d4624efed09b6db0d33952467fc7fb.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/c0f66c34defc89d543345b37c92d95d5dad9fe35.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/8ac0da44c1b8620fb3a1def606ae4042d428294c.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/05e10c4a1bbb3b6bbc7483ca8fa4b563cbfdeb1f.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/2c4a5c4a7f0fd6567efbc81e23f39f7ed562180b.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/188ed5c55c5873194f82f34d1445be358af25f86.JPG'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519658/dc08efa074306bf5553f93e70a9b61272cd538ee.JPG'}],\\n\",\n \" 'images_count': 13,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '수도',\\n\",\n \" 'movein_date': '8월22일 이후',\\n\",\n \" 'near_subways': '압구정역(3호선), 학동역(7호선), 압구정로데오역(분당선)',\\n\",\n \" 'options': '에어컨, 가스레인지, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-6554-3473',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5219997788444,127.029659203959',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 15,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.1,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.39',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '신사동 가격대비 컨디션 좋은 원룸 방 싸이즈 큽니다.',\\n\",\n \" 'updated_at': '13일 전',\\n\",\n \" 'user_email': 'secretk1107@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-0918',\\n\",\n \" 'user_name': '중개보조원(박민우)',\\n\",\n \" 'user_no': 112775,\\n\",\n \" 'user_phone': '0507-1281-0918',\\n\",\n \" 'view_count': 462},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '13-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 639-14',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 5000,\\n\",\n \" 'description': '▶ 신사역 인근에 있는 원룸원거실입니다!\\\\n\\\\n▶ 대리석 바닥에 고급스러운 인테리어!\\\\n\\\\n▶ 엘레베이터 있는 6층입니다!\\\\n\\\\n▶ 초저렴가격!\\\\n\\\\n▶ 깔끔함과 채광은 걱정 노노!!\\\\n\\\\n▶ 주차 가능!\\\\n\\\\n',\\n\",\n \" 'description_og': '▶ 신사역 인근에 있는 원룸원거실입니다!\\\\n\\\\n▶ 대리석 바닥에 고급스러운 인테리어!\\\\n\\\\n▶ 엘레베이터 있는 6층입니다!\\\\n\\\\n▶ 초저렴가격!\\\\n\\\\n▶ 깔끔함과 채광은 걱정 노노!!\\\\n\\\\n▶ 주차 가능!\\\\n\\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '6층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5596519,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/89002b6da9787c031ed7401b9370b31ad0437ca4.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/6f78348d00eff1c3721155ddfd83e2877472a5ce.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/ee9387392e949005f58f580dbcc659f647465427.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/5bcf2a196b78a0435b10d904433b468c7718e28a.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/bbd3dfd92a61b07781b26110c6e931406c4f119d.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/8ee05f69eddc4c7e0b579e5dcb89007b4d180709.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/48ce51ef61993e55479bbded48c08590f1504994.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/56e2c6b03676ba2fcf65b5b392185704a35aff5c.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/edf5ae991637a5b15f54b26dcc54f57639c9ef3f.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/835f9faf0782c9d87c42bcb2e6e099b17b96b496.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/c29a8683acecb672552e8e65ff7a370e3dbd5e00.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5596519/a0769beb950844cc834ea04c2716b9f74c55a5f1.JPG'}],\\n\",\n \" 'images_count': 12,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '협의입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-2854-7104',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5160723904131,127.022841125068',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 90,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '★고급스러운원룸원거실!일단클릭!',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'mose004@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-8838',\\n\",\n \" 'user_name': '중개보조원(백진수)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1281-8838',\\n\",\n \" 'view_count': 147},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '596-1',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 신사동 610-2 1층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'suny1209@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7187',\\n\",\n \" 'agent_name': '열린공인중개사(강광수)',\\n\",\n \" 'agent_phone': '02-511-0222',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '596',\\n\",\n \" 'bubun_code': '1',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '신축원룸 주차 및 보안 완벽합니다\\\\n\\\\n신축으로 옵션 잘되어 있어요\\\\n\\\\n입주하시 가능 합니다\\\\n\\\\n실매물 실사진 연락주셔요',\\n\",\n \" 'description_og': '신축원룸 주차 및 보안 완벽합니다\\\\n\\\\n신축으로 옵션 잘되어 있어요\\\\n\\\\n입주하시 가능 합니다\\\\n\\\\n실매물 실사진 연락주셔요',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5647242,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/c48d2474f1365c7300f121ded8b3b5edf370794c.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/dbe158476640fd8408a22e9d68d035ef95579dc1.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/8367950e3374372fbb5d3a365093c5b6058ba3d6.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/1d6c9ff33f4f9e939d5a80369c39d7e947825043.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/63d3ee12b3d0dbdf72ceb1a13f4cead8a510ea57.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/fd4e4e701336e1c3a3f5f5325f87c2144e7e90fc.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/22e89d35056a66261a21553ce7f325f96834de28.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/b394d0bc87a19a13b841b67f505226d1793629e1.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/ab7936407993faeefaa6ea65c75e327e6c967f1d.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/c3d726aae503e00b65c78baa5fa02e6a6dfdb55f.jpg'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5647242/d0eb53d9b65e25dd03d0421d787d0173f1384784.jpg'}],\\n\",\n \" 'images_count': 11,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시',\\n\",\n \" 'near_subways': '압구정역(3호선), 학동역(7호선), 압구정로데오역(분당선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 책상, 침대, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9134-5291',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/676867/5ab3eaadbeb532e1a1a991f14cb8804cbcbf04ac.jpg',\\n\",\n \" 'random_location': '37.522970287609,127.029636722075',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 70,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 8.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '26.45',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '신축원룸 도시형주택 풀옵션 좋아요',\\n\",\n \" 'updated_at': '어제',\\n\",\n \" 'user_email': 'suny1209@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-7187',\\n\",\n \" 'user_name': '대표공인중개사(강광수)',\\n\",\n \" 'user_no': 676867,\\n\",\n \" 'user_phone': '0507-1280-7187',\\n\",\n \" 'view_count': 16},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 서초구 잠원동',\\n\",\n \" 'address2': '10-43',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 서초구 동산로 46 , 1층 (양재동)',\\n\",\n \" 'agent_comment': '안녕하세요 서초 강남 송파지역 원룸/오피스텔/전세빌라/매매 를 전문으로하고있는 언남부동산 염영규 실장입니다.\\\\n저희는 허위매물따위는 절대 하지않고 고객께서 쓸데없는 헛걸음 하시는걸 제일싫어하므로 \\\\n전화나 카톡으로 사전에 꼼꼼히 체크하여 안좋은방이나 원하지 않는방을 보여주는일은 절대없습니다.\\\\n믿고 맡기시면 됩니다. 감사합니다~!',\\n\",\n \" 'agent_email': 'lwh2136@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '서초구',\\n\",\n \" 'agent_mobile': '0507-1280-7497',\\n\",\n \" 'agent_name': '언남공인중개사(이원희)',\\n\",\n \" 'agent_phone': '02-573-8600',\\n\",\n \" 'bjd_code': '1165010600',\\n\",\n \" 'bonbun_code': '10',\\n\",\n \" 'bubun_code': '43',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\\\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\\\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\\\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, \\\\n 위치만 말씀해주시면 픽업서비스도 해드립니다. \\\\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) ※인증된 대형 부동산 !! \\\\n★ 보증금 / 임대기간 (조정가능) \\\\n\\\\n\\\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\\\n\\\\n\\\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 염실장 입니다^^ \\\\n저희는 300% 실매물에 실사진으로만 운영합니다. \\\\n강남 전지역(강남구,서초구,송파구) 원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\\\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House\\\\n★ 구경오세요!!! 직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\\\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\\\n직방내 모든매물 상담가능합니다.^^ \\\\n\\\\n\\\\n■ KAKAO Talk : yyk0410 ■',\\n\",\n \" 'description_og': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\\\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\\\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\\\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, \\\\n 위치만 말씀해주시면 픽업서비스도 해드립니다. \\\\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) ※인증된 대형 부동산 !! \\\\n★ 보증금 / 임대기간 (조정가능) \\\\n\\\\n\\\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\\\n\\\\n\\\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 염실장 입니다^^ \\\\n저희는 300% 실매물에 실사진으로만 운영합니다. \\\\n강남 전지역(강남구,서초구,송파구) 원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\\\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House\\\\n★ 구경오세요!!! 직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\\\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\\\n직방내 모든매물 상담가능합니다.^^ \\\\n\\\\n\\\\n■ KAKAO Talk : yyk0410 ■',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5626793,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 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{'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5626793/e2aada999b08f40da6dab0def8eeb793c0d2011c.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5626793/fb2ea3a53fc24a729a3fa6e17482f133e2aa1667.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5626793/48bb98304dde6182e25d39c65d070ea1cc2eda25.JPG'}],\\n\",\n \" 'images_count': 10,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '서초구',\\n\",\n \" 'local3': '잠원동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시',\\n\",\n \" 'near_subways': '신사역(3호선), 잠원역(3호선), 논현역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 책상, 책장, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-5139-2669',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5161220416603,127.017151886331',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 80,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 9.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '29.75',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '■■신축■첫입주■화이트톤■풀옵션■신사역3분■■',\\n\",\n \" 'updated_at': '4일 전',\\n\",\n \" 'user_email': 'jsyyk0410@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-6502',\\n\",\n \" 'user_name': '중개보조원(염영규)',\\n\",\n \" 'user_no': 1129550,\\n\",\n \" 'user_phone': '0507-1281-6502',\\n\",\n \" 'view_count': 67},\\n\",\n \" 'title': '서울시 서초구 잠원동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '569-14',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '569',\\n\",\n \" 'bubun_code': '14',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 65,\\n\",\n \" 'description': '* 압구정역에서 5분거리에 위치한 깔끔한고 예쁘게 꾸며진 방입니다.\\\\n\\\\n* 화이트침구류와 쇼파가 셋팅되어있고 벽걸이TV까지 편리함을 더했습니다.\\\\n\\\\n* 컬러벽지로 도보해서 단조로운 원룸을 하나밖에 없는 나만의 원룸으로 꾸몄어요\\\\n\\\\n* 압구정역근처 몇없는 매물중에 저렴하면서 가장 깔끔하게 나오는 원룸이에요~!!\\\\n\\\\n* 주택가에 위치해서 시끄럽지 않고 조용해서 좋아요\\\\n\\\\n* 지하철 3호선을 5분거리로 이용하실수 있고 동호대교로 강북으로의 이동도 편리해요',\\n\",\n \" 'description_og': '* 압구정역에서 5분거리에 위치한 깔끔한고 예쁘게 꾸며진 방입니다.\\\\n\\\\n* 화이트침구류와 쇼파가 셋팅되어있고 벽걸이TV까지 편리함을 더했습니다.\\\\n\\\\n* 컬러벽지로 도보해서 단조로운 원룸을 하나밖에 없는 나만의 원룸으로 꾸몄어요\\\\n\\\\n* 압구정역근처 몇없는 매물중에 저렴하면서 가장 깔끔하게 나오는 원룸이에요~!!\\\\n\\\\n* 주택가에 위치해서 시끄럽지 않고 조용해서 좋아요\\\\n\\\\n* 지하철 3호선을 5분거리로 이용하실수 있고 동호대교로 강북으로의 이동도 편리해요',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5512589,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/39a6dc638c0f1c038b332f7476c2ce9b52bebd1b.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/f29c7b1cf674c94e3d4cb87297f5d994069b8b84.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/023b497c7d4da2e5047264f2b5b0003b0c539aff.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/ab25517fe2f22bf0e436e07b3c65ca37d0a633ca.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/f7f9a8608356a67d64fe0c30f8ce4b4132f28b68.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/2ba1dc72eecff3ca931be7764a22c91f39c39056.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512589/bb9958412ad51b038b49fa53bd61de04936932ad.jpg'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '6만원',\\n\",\n \" 'manage_cost_inc': 'TV',\\n\",\n \" 'movein_date': '즉시 가능',\\n\",\n \" 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '싱크대, 가스레인지, 에어컨, 침대, 냉장고, 전자레인지, 옷장, 세탁기, 신발장',\\n\",\n \" 'original_user_phone': '010-6406-9464',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5234972734648,127.027030530475',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 65,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 7.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '23.14',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕ 압구정역 가까운 깔끔하고 아주 예쁜방!!',\\n\",\n \" 'updated_at': '14일 전',\\n\",\n \" 'user_email': 'jinyong0223@hanmail.net',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-8860',\\n\",\n \" 'user_name': '중개보조원(박진용)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': '0507-1281-8860',\\n\",\n \" 'view_count': 449},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '29-7 토미하우스 플러스 2층 205호 ',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 173-21번지 ',\\n\",\n \" 'agent_comment': '제가 직접 발로 뛰며 찍은 200% 실가격 실사진입니다 \\\\n절대 양심은 팔지 않습니다 말도 안되는 거짓매물에 현혹 되지 마시길 바랍니다\\\\n[24시 문의♥카톡ID : mcmax456] \\\\n',\\n\",\n \" 'agent_email': 'phil1600@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-9830',\\n\",\n \" 'agent_name': '큰길공인중개사(박상엽)',\\n\",\n \" 'agent_phone': '02-512-0132',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 2000,\\n\",\n \" 'description': '◇방 소 개◇\\\\n\\\\n① 분리형 구조 풀옵션 원룸 채광좋고 통풍이 잘되어 있습니다\\\\n② 관리가 잘된 신축 건물이라 실내가 전체적으로 깨끗합니다 \\\\n③ 독특한 구조로 만든 원룸입니다 드레스룸 따로 설치되있습니다\\\\n④ 호텔보다 더 좋은 실내 인테리어로 고급스러움을 자랑합니다\\\\n⑤ 베란다가 상당히 넓어 수납공간에 용이합니다\\\\n⑥ 로비룸이 따로있어 간단한 음료등등 마실수있는 공간배치\\\\n⑦ 현관 입구도 보안키가 있어 보안에 신경을 많이 쓴 건물입니다\\\\n⑧ 보증금 조절 가능합니다 언제든지 문의주세요 \\\\n⑨ 고민하는 순간 계약 완료!! 서두르세요 \\\\n\\\\n\\\\n◇관 리 비◇\\\\n\\\\n⊙ 10만원 (자세한건 임대인과 협의)\\\\n\\\\n\\\\n◇가 까 운 역◇\\\\n\\\\n⊙ 학동역 도보 5분거리\\\\n\\\\n\\\\n◇주 차 시 설◇\\\\n\\\\n⊙ 주차 1인 1대 가능합니다(주차시스템 잘되어있습니다)\\\\n\\\\n\\\\n◇편 의 시 설◇\\\\n\\\\n① 건물 주변이 대부분 주택가 지역이라 조용하고 아늑합니다\\\\n② 주변 편의시설 (병원,약국,우체국,편의점,각종음식점등)\\\\n③ 주변 초등학교 공원등 다양한 편의시설이 있습니다\\\\n\\\\n\\\\n◇한 마 디◇ \\\\n\\\\n⊙ 가격대비 좋은 원룸! 언제 빠질지 모릅니다 관심 있으신분들은 빠른 문의주세요\\\\n⊙ 이 외에도 원룸 투룸등 S급 A급 위주로 다양한 매물 보유중! 언제든지 문의주세요 \\\\n⊙ 전화 부재시 문자&카톡 문의주시면 무조건~무조건~ 친절상담 해드려요^^♥ \\\\n⊙ 200% 실사진 실가격 실매물입니다 믿고 보셔도 됩니다\\\\n⊙ 절대 양심은 팔지 않겠습니다 말도 안되는 거짓 매물에 현혹 되지마시길 바랍니다 \\\\n\\\\n[24시 문의♥카톡ID : mcmax456] \\\\n\\\\n\\\\n♠어떠한 조건이라도 내집처럼 마련해 드리겠습니다♠ \\\\n♠200% 실매물 실가격으로 보답하겠습니다♠\\\\n',\\n\",\n \" 'description_og': '◇방 소 개◇\\\\n\\\\n① 분리형 구조 풀옵션 원룸 채광좋고 통풍이 잘되어 있습니다\\\\n② 관리가 잘된 신축 건물이라 실내가 전체적으로 깨끗합니다 \\\\n③ 독특한 구조로 만든 원룸입니다 드레스룸 따로 설치되있습니다\\\\n④ 호텔보다 더 좋은 실내 인테리어로 고급스러움을 자랑합니다\\\\n⑤ 베란다가 상당히 넓어 수납공간에 용이합니다\\\\n⑥ 로비룸이 따로있어 간단한 음료등등 마실수있는 공간배치\\\\n⑦ 현관 입구도 보안키가 있어 보안에 신경을 많이 쓴 건물입니다\\\\n⑧ 보증금 조절 가능합니다 언제든지 문의주세요 \\\\n⑨ 고민하는 순간 계약 완료!! 서두르세요 \\\\n\\\\n\\\\n◇관 리 비◇\\\\n\\\\n⊙ 10만원 (자세한건 임대인과 협의)\\\\n\\\\n\\\\n◇가 까 운 역◇\\\\n\\\\n⊙ 학동역 도보 5분거리\\\\n\\\\n\\\\n◇주 차 시 설◇\\\\n\\\\n⊙ 주차 1인 1대 가능합니다(주차시스템 잘되어있습니다)\\\\n\\\\n\\\\n◇편 의 시 설◇\\\\n\\\\n① 건물 주변이 대부분 주택가 지역이라 조용하고 아늑합니다\\\\n② 주변 편의시설 (병원,약국,우체국,편의점,각종음식점등)\\\\n③ 주변 초등학교 공원등 다양한 편의시설이 있습니다\\\\n\\\\n\\\\n◇한 마 디◇ \\\\n\\\\n⊙ 가격대비 좋은 원룸! 언제 빠질지 모릅니다 관심 있으신분들은 빠른 문의주세요\\\\n⊙ 이 외에도 원룸 투룸등 S급 A급 위주로 다양한 매물 보유중! 언제든지 문의주세요 \\\\n⊙ 전화 부재시 문자&amp;카톡 문의주시면 무조건~무조건~ 친절상담 해드려요^^♥ \\\\n⊙ 200% 실사진 실가격 실매물입니다 믿고 보셔도 됩니다\\\\n⊙ 절대 양심은 팔지 않겠습니다 말도 안되는 거짓 매물에 현혹 되지마시길 바랍니다 \\\\n\\\\n[24시 문의♥카톡ID : mcmax456] \\\\n\\\\n\\\\n♠어떠한 조건이라도 내집처럼 마련해 드리겠습니다♠ \\\\n♠200% 실매물 실가격으로 보답하겠습니다♠\\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '3층',\\n\",\n \" 'id': 5646209,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/d9488a9ef5187e2ebf0711c5e775db07da4a142f.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/203875babde2e2b0672edc290a4b89cbe9bba81c.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/3b1af2a289a04c6ba1e690b20112572641c8012d.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/c21fec301bc2ebdf5ca7089bdea9159a9d1a24d0.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/1e186e3750ac48cd45bece9a5629169fc3605df7.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/f5d07ba79dd4749e2bf006e81842fb6e2c2ca44e.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/d9529f582df48a5c8d2a665170c300cb5e28ec9b.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/df0e06bb404cd23c989ee556c3d17f3a87441136.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/95ced47c78cf30d7ab1744b320792c2c5f6cc5c7.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/39b5e89a155ea4b23d1be4aeddadf1c3577bcd31.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/63bf0e4f6f040c0519d1008a44e2909763869096.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/84d58f2c99805ad4eb1fc53cf9a5bb2300574126.JPG'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/bd72e4727696bb64fb252d99e2e211e66fb37d99.JPG'},\\n\",\n \" {'count': 14,\\n\",\n \" 'index': 13,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/6a04465972c312a635a5e8999f47d2a6efdce80b.JPG'},\\n\",\n \" {'count': 15,\\n\",\n \" 'index': 14,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/3e4f6acc4ef8bca36d43657b36e5b9430da56118.JPG'},\\n\",\n \" {'count': 16,\\n\",\n \" 'index': 15,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/76d64e527e99059b8cfc843aca1e7f707f44ebde.JPG'},\\n\",\n \" {'count': 17,\\n\",\n \" 'index': 16,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/3d0ba2567e040dafeaade5ca5afdd0b93afac233.JPG'},\\n\",\n \" {'count': 18,\\n\",\n \" 'index': 17,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/286e52fe9459e23abf13a4c6ba561452e8806f71.JPG'},\\n\",\n \" {'count': 19,\\n\",\n \" 'index': 18,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646209/da4b569fc3555032168d62b08264b106b5f49b08.JPG'}],\\n\",\n \" 'images_count': 19,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-7378-4343',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1545912/5493dd3ca4b72770b1681844b85c0471b53b7fb6.jpg',\\n\",\n \" 'random_location': '37.5156004701152,127.028433010821',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 140,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 13.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '42.98',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '♥논현동♥ ⓢ급 매물 오픈형 원룸',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'cda486@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-9333',\\n\",\n \" 'user_name': '중개보조원(최동안)',\\n\",\n \" 'user_no': 1545912,\\n\",\n \" 'user_phone': '0507-1281-9333',\\n\",\n \" 'view_count': 26},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '39-17',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 서초구 반포동 718-7 나라부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'nara5090@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '서초구',\\n\",\n \" 'agent_mobile': '0507-1281-3636',\\n\",\n \" 'agent_name': '나라부동산공인중개사(강덕귀)',\\n\",\n \" 'agent_phone': '02-537-5030',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '39',\\n\",\n \" 'bubun_code': '17',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '◈ 지하철 7호선 학동역 도보 5분이내거리!!\\\\n\\\\n◈ 조용한곳에 위치하고 방범 정말 잘되어있어요~\\\\n\\\\n◈ 풀옵이라 근처 직장인들에겐 최고의 방은듯싶네요 몸만오시면되요!! (TV 넣어주신다고 합니다)\\\\n\\\\n◈ 대로변 인접한 위치라 편의시설도 다양하게 갖춰져 있고 생활하기 너무 편하실듯해요^^\\\\n\\\\n◈ 도배 및 수리 전부 완료된 상태이니 언제든지 확인하실수있습니다!!\\\\n\\\\n◈ 깔끔하고 좋은 매물 원하시면 부담없이 문의주시기 바랍니다~ \\\\n\\\\n◈ 강남 전지역 픽업서비스 가능!! \\\\n\\\\n◈ 항상 정직하고 성실하게 상담해드리겠습니다^^',\\n\",\n \" 'description_og': '◈ 지하철 7호선 학동역 도보 5분이내거리!!\\\\n\\\\n◈ 조용한곳에 위치하고 방범 정말 잘되어있어요~\\\\n\\\\n◈ 풀옵이라 근처 직장인들에겐 최고의 방은듯싶네요 몸만오시면되요!! (TV 넣어주신다고 합니다)\\\\n\\\\n◈ 대로변 인접한 위치라 편의시설도 다양하게 갖춰져 있고 생활하기 너무 편하실듯해요^^\\\\n\\\\n◈ 도배 및 수리 전부 완료된 상태이니 언제든지 확인하실수있습니다!!\\\\n\\\\n◈ 깔끔하고 좋은 매물 원하시면 부담없이 문의주시기 바랍니다~ \\\\n\\\\n◈ 강남 전지역 픽업서비스 가능!! \\\\n\\\\n◈ 항상 정직하고 성실하게 상담해드리겠습니다^^',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '1층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5490317,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/a564bf86c22080479e52385ac9b17d06a98821d6.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/6e355df52950f7da832473421d45cf7edd41c7ad.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/1b118e25a92d612cd00220142e45d1d7a3aeb889.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/aad0225317806f2f23a66a22a236ceb050080f68.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/05045184c48bda4df836add6c98096c09b19fa06.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/5e7f25181dda71f1037404a0165b42f30d58e703.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/418d3e472a09c4de5aa367dd4a274e336cc2fce8.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/a564bf86c22080479e52385ac9b17d06a98821d6.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/6e355df52950f7da832473421d45cf7edd41c7ad.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/1b118e25a92d612cd00220142e45d1d7a3aeb889.jpg'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/aad0225317806f2f23a66a22a236ceb050080f68.jpg'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/05045184c48bda4df836add6c98096c09b19fa06.jpg'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/5e7f25181dda71f1037404a0165b42f30d58e703.jpg'},\\n\",\n \" {'count': 14,\\n\",\n \" 'index': 13,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5490317/418d3e472a09c4de5aa367dd4a274e336cc2fce8.jpg'}],\\n\",\n \" 'images_count': 14,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '6만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-4965-5090',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1449116/fbd3ca43efd6afc373ea56edebf352ac528862ec.jpg',\\n\",\n \" 'random_location': '37.5142397489325,127.027435065049',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 60,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 7.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '23.14',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '★☆실매물☆★ 논현동 역세권 깔끔한 풀옵션 원룸#',\\n\",\n \" 'updated_at': '14일 전',\\n\",\n \" 'user_email': 'nara5090@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-2205',\\n\",\n \" 'user_name': '중개보조원(최승우)',\\n\",\n \" 'user_no': 1449116,\\n\",\n \" 'user_phone': '0507-1281-2205',\\n\",\n \" 'view_count': 111},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '75',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 639-14',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '75',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 5000,\\n\",\n \" 'description': '*대리석건물에 데코타일 건물내외관 럭셔리한 집입니다!\\\\n\\\\n*실20평에 12자 화이트붙박이장 잘되어잇고 리모컨 일렬주차100% 주차걱정NO!\\\\n\\\\n*거실완벽히넓게잘빠져잇고 쌍둥이방둘다크고 통베란다2개!\\\\n\\\\n*저는 가격대비 평범한물건은 아예 광고를하지않고 상위2%내 최상급물건만 취급하고있습니다.\\\\n\\\\n*기타 자세한 문의는 문자나 연락주시면 상세설명해드리겟습니다.길게광고올리지않고 여기까지만 . 감사합니다',\\n\",\n \" 'description_og': '*대리석건물에 데코타일 건물내외관 럭셔리한 집입니다!\\\\n\\\\n*실20평에 12자 화이트붙박이장 잘되어잇고 리모컨 일렬주차100% 주차걱정NO!\\\\n\\\\n*거실완벽히넓게잘빠져잇고 쌍둥이방둘다크고 통베란다2개!\\\\n\\\\n*저는 가격대비 평범한물건은 아예 광고를하지않고 상위2%내 최상급물건만 취급하고있습니다.\\\\n\\\\n*기타 자세한 문의는 문자나 연락주시면 상세설명해드리겟습니다.길게광고올리지않고 여기까지만 . 감사합니다',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5533865,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/7efdb475b03162bfddcbe63ea2abd0a8c99b1f23.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/3fe2431def4dc3200c3de5aac6b8f297623083cd.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/6648a00fe0fecd270b299d7116bddc2f97e10eae.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/19060e78ae2537ebb18fa6df00fd8202769be612.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/81de283e1ae7fc75e2ef8a39f3dd880d7e9f75f9.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/e53170a92a3a5265b1ba6e71028a3f09e2bf4eaa.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/24d448ba7e0b7fc311baf0cc632d72b73ea128d3.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/2b23a8fa55a40c8f0f0ee7101c580508339fe64c.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/57cd37838d8a9a5533fcda50dd8b8f84d7d4c8f9.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5533865/93c26c46425705720989f4b200f8099f3cdb462d.JPG'}],\\n\",\n \" 'images_count': 10,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 신사역(3호선)',\\n\",\n \" 'options': '에어컨, 세탁기, 가스레인지, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-3897-6929',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5171573873475,127.030370626783',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 160,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 20.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '66.12',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '*럭셔리 화이트톤 투룸의거실대.붙박이짱.주차짱!',\\n\",\n \" 'updated_at': '13일 전',\\n\",\n \" 'user_email': 'jmk_78@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6925',\\n\",\n \" 'user_name': '중개보조원(김명섭)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1280-6925',\\n\",\n \" 'view_count': 257},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '518',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 서초구 반포동 718-7 나라부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'nara5090@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '서초구',\\n\",\n \" 'agent_mobile': '0507-1281-3636',\\n\",\n \" 'agent_name': '나라부동산공인중개사(강덕귀)',\\n\",\n \" 'agent_phone': '02-537-5030',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '518',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '▶ 가로수길 라인 / 신사역 도보5-10분 역세권 위치 최고 !!\\\\n\\\\n▶ 도배/장판/싱크대 상태 매우 깔끔하여 즉시입주 가능합니다.\\\\n\\\\n▶ 신사역세권 깔끔한 원룸원거실 느낌 분리형 원룸이며 화장실역시 깔끔합니다.\\\\n\\\\n▶ 방을 보시면 사이즈도 딱 좋고 깔끔하다고 느낄수 있는 그런 방입니다. \\\\n\\\\n▶ 주차는 1대 개별 확인하셔야 합니다.\\\\n\\\\n▶ 근처 24시 편의점 , 음식점 등등 다양한 편의시설이 갖추어져있습니다.\\\\n\\\\n▶ 위 사진은 광고 게시중인 담당자가 직접 촬영한 실제 사진입니다. \\\\n\\\\n▶ 항상 정직하고 밝고 친절하게 양심껏 상담해드리겠습니다. ',\\n\",\n \" 'description_og': '▶ 가로수길 라인 / 신사역 도보5-10분 역세권 위치 최고 !!\\\\n\\\\n▶ 도배/장판/싱크대 상태 매우 깔끔하여 즉시입주 가능합니다.\\\\n\\\\n▶ 신사역세권 깔끔한 원룸원거실 느낌 분리형 원룸이며 화장실역시 깔끔합니다.\\\\n\\\\n▶ 방을 보시면 사이즈도 딱 좋고 깔끔하다고 느낄수 있는 그런 방입니다. \\\\n\\\\n▶ 주차는 1대 개별 확인하셔야 합니다.\\\\n\\\\n▶ 근처 24시 편의점 , 음식점 등등 다양한 편의시설이 갖추어져있습니다.\\\\n\\\\n▶ 위 사진은 광고 게시중인 담당자가 직접 촬영한 실제 사진입니다. \\\\n\\\\n▶ 항상 정직하고 밝고 친절하게 양심껏 상담해드리겠습니다. ',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '반지하',\\n\",\n \" 'floor_all': '3층',\\n\",\n \" 'id': 5610946,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/a7d189678034434391383f885c4f4314ba67a721.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/eff8b06ece3be93f9bdc974d7a6247572be5e548.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/a0f6a40504e0d65d0a7656c0beedaf6e43aedf9e.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/8739de640fc724b7b00bbdab1fedc90982b8cb8c.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/87b64c478b3f3a8254475aa7475ef736f4223473.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/dccb58496ab5dbe9f68d9c2081faab9c0c5ab992.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/42429c91e8e25717a1d9f0cf3ae169327b30454b.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/012f65d8a7cb13ee09e062fa495e4b9174b9117b.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5610946/75d2393e94a0da45a2be991ded17720f826edd2a.jpg'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 압구정역(3호선)',\\n\",\n \" 'options': '에어컨, 싱크대',\\n\",\n \" 'original_user_phone': '010-5714-5090',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1449116/fbd3ca43efd6afc373ea56edebf352ac528862ec.jpg',\\n\",\n \" 'random_location': '37.5192588547188,127.021094948653',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 50,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '▶실매물◀가로수길 원룸원거실 느낌 분리형#',\\n\",\n \" 'updated_at': '5일 전',\\n\",\n \" 'user_email': 'nara5090@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-1949',\\n\",\n \" 'user_name': '중개보조원(최진우)',\\n\",\n \" 'user_no': 1449116,\\n\",\n \" 'user_phone': '0507-1281-1949',\\n\",\n \" 'view_count': 72},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '559-11 1층 101호',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 217-46',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'mhj06100610@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-4362',\\n\",\n \" 'agent_name': '한영공인중개사(모형진)',\\n\",\n \" 'agent_phone': '010-4737-9178',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 8000,\\n\",\n \" 'description': '안녕하세요 신사동에 저렴하게 8000전세로 나온 매물입니다.\\\\n\\\\n전세종말시대가 온 시점에서 나온 매물입니다. 현재 방 컨디션은 깔금하고 혼자살기에 나쁘지 않습니다.\\\\n\\\\n8000 전세 찾기 힘듭니다. 바로 연락주세요',\\n\",\n \" 'description_og': '안녕하세요 신사동에 저렴하게 8000전세로 나온 매물입니다.\\\\n\\\\n전세종말시대가 온 시점에서 나온 매물입니다. 현재 방 컨디션은 깔금하고 혼자살기에 나쁘지 않습니다.\\\\n\\\\n8000 전세 찾기 힘듭니다. 바로 연락주세요',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '1층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5519928,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/df775983a18eb09e486b3bda61e6eb7b92a7eae5.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/fc084fbf5bdf2ca1cad0f4dab98eff17a0ad4ece.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/c4dcb9e232789930f1c2e3b4039a228256f1d32e.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/208d13732f35608755ddf454d327144611da6cb3.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/969dac4cf1c536e4228870ee26bcdd93eb71946c.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/21234c7e7aab71c6bf34b73782e1b355f2d0a6a1.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/fba7d9ec542af9a0afc35eb1c8cc96f9ff98c48a.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5519928/98cbc7fcc1cd1b30ce6f4770bf5af5a34fca048f.JPG'}],\\n\",\n \" 'images_count': 8,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '8만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시 가능',\\n\",\n \" 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 세탁기, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-6554-3473',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5198473281445,127.026119028607',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 5.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '16.53',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '전세 멸종시대에 컨디션 좋은 8000전세입니다',\\n\",\n \" 'updated_at': '13일 전',\\n\",\n \" 'user_email': 'secretk1107@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-0918',\\n\",\n \" 'user_name': '중개보조원(박민우)',\\n\",\n \" 'user_no': 112775,\\n\",\n \" 'user_phone': '0507-1281-0918',\\n\",\n \" 'view_count': 1679},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '27-9',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼로34길 14 1층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'lee2002top@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-7165',\\n\",\n \" 'agent_name': '늘파란공인중개사(이기복)',\\n\",\n \" 'agent_phone': '02-557-8249',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '27',\\n\",\n \" 'bubun_code': '9',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 35000,\\n\",\n \" 'description': '★ 실매물/ 실사진 매물없으면 교통비지급해드립니다.\\\\n\\\\n★ 3호선 신사역 역세권에 위치했습니다.\\\\n\\\\n★ 지하 주차장으로 넉넉한 주차대수 편리한 주차시설입니다.\\\\n\\\\n★ 내부 수리 넓은 통 베란다 입니다.\\\\n\\\\n★ 학동공원 인근으로 애완견과 산책하시기 최고입니다.\\\\n\\\\n★ 거실과 주방 분리구조로 신혼 부부 강추합니다.\\\\n\\\\n★ 궁금하신사항 24시간 친절상담해드립니다...',\\n\",\n \" 'description_og': '★ 실매물/ 실사진 매물없으면 교통비지급해드립니다.\\\\n\\\\n★ 3호선 신사역 역세권에 위치했습니다.\\\\n\\\\n★ 지하 주차장으로 넉넉한 주차대수 편리한 주차시설입니다.\\\\n\\\\n★ 내부 수리 넓은 통 베란다 입니다.\\\\n\\\\n★ 학동공원 인근으로 애완견과 산책하시기 최고입니다.\\\\n\\\\n★ 거실과 주방 분리구조로 신혼 부부 강추합니다.\\\\n\\\\n★ 궁금하신사항 24시간 친절상담해드립니다...',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5655575,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/8d1ccb5767e9216c56222d27f1d9ef076f3a1766.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/2eb3aeb05a3cc0eb05e8aa97b7372640bcdcdd63.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/b6724bd3b6f3075e3d23d06a68c247a6eb0726a3.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/a33ba2dd3a3fc51e264cb5d91ba30943e570b29a.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/cf2fc64b261aeb6778dcf3b64483d9692eea748c.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/2c182cbd732a31d92547d22a7df3a5bf5d26f096.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/27e3548f4674b8adf66c323fd460f8a8e2e3294c.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5655575/d3648f0400bc88b34923d10db526f306dd717648.JPG'}],\\n\",\n \" 'images_count': 8,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '3만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '협의가능',\\n\",\n \" 'near_subways': '신사역(3호선), 학동역(7호선), 논현역(7호선)',\\n\",\n \" 'options': '신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9924-3243',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5170005176222,127.026435313614',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 18.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '59.50',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '✔실매물/실사진✔지하주차장✔넓은 베란다✔신혼부부강추✔',\\n\",\n \" 'updated_at': '2시간 전',\\n\",\n \" 'user_email': 'lee2002top@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-7165',\\n\",\n \" 'user_name': '대표공인중개사(이기복)',\\n\",\n \" 'user_no': 1351140,\\n\",\n \" 'user_phone': '0507-1281-7165',\\n\",\n \" 'view_count': 3},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '588-16',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 735-11 111호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'gugu1966@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-8210',\\n\",\n \" 'agent_name': '강남사무실공인중개사(김지영)',\\n\",\n \" 'agent_phone': '02-508-4982',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '588',\\n\",\n \" 'bubun_code': '16',\\n\",\n \" 'building': {'address': '서울시 강남구 신사동 588-16',\\n\",\n \" 'address2': '588-16',\\n\",\n \" 'building_id': 18799,\\n\",\n \" 'count': 3,\\n\",\n \" 'elevator': '1',\\n\",\n \" 'established': '2015.02',\\n\",\n \" 'floor': '16층',\\n\",\n \" 'lat': 37.5211,\\n\",\n \" 'lng': 127.0314,\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'name': '현대썬앤빌',\\n\",\n \" 'rooms': '120세대'},\\n\",\n \" 'building_type': '오피스텔·도생',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 130,\\n\",\n \" 'description': '신사동 도산공원사거리 인근에 위치한 신축오피스텔 입니다\\\\n\\\\n\\\\n현재 사진에는 침대 티비가 없지만 모두다 셋팅되어 있는 상태입니다.\\\\n\\\\n\\\\n인근에 오피스텔이 없기때문에 위치적 메리트가 꽤 큰편입니다\\\\n\\\\n\\\\n관리비에 일반관리비 공과금 모두 포함입니다~\\\\n\\\\n\\\\n주차는 기계식으로 월 13만원 별도 입니다.',\\n\",\n \" 'description_og': '신사동 도산공원사거리 인근에 위치한 신축오피스텔 입니다\\\\n\\\\n\\\\n현재 사진에는 침대 티비가 없지만 모두다 셋팅되어 있는 상태입니다.\\\\n\\\\n\\\\n인근에 오피스텔이 없기때문에 위치적 메리트가 꽤 큰편입니다\\\\n\\\\n\\\\n관리비에 일반관리비 공과금 모두 포함입니다~\\\\n\\\\n\\\\n주차는 기계식으로 월 13만원 별도 입니다.',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '고층/16층',\\n\",\n \" 'floor_all': '16층',\\n\",\n \" 'id': 5629638,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5629638/1e8a0ec047021d745a235a9df4f791d2cac25846.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5629638/1367dfc4824b59e609f3e447f71208f2eb681971.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5629638/e360d92facdfdc187e1a2184ef8e9de57d878219.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5629638/a752a0aea3d0d9c7bdbc3d23be934f5e8b4b98e4.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5629638/6f296c2dbfbcecc5890f0f1a62178e87fc0718d7.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5629638/85ac24bd8715b5dc5e4dbc5b2f5f90d60fd51a9d.JPG'}],\\n\",\n \" 'images_count': 6,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '20만원',\\n\",\n \" 'manage_cost_inc': '전기세, 가스, 수도, 인터넷, TV',\\n\",\n \" 'movein_date': '즉시',\\n\",\n \" 'near_subways': '압구정역(3호선), 학동역(7호선), 강남구청역(7호선,분당선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 책상, 책장, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-6833-0203',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5208684000066,127.031113228511',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 130,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '02',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 8.0,\\n\",\n \" 'size_contract': 18.0,\\n\",\n \" 'size_m2': '26.45',\\n\",\n \" 'size_m2_contract': '59.50',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '신사동 도산대로바로앞 풀옵션 오피스텔',\\n\",\n \" 'updated_at': '3일 전',\\n\",\n \" 'user_email': 'jch198412@gmail.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-8722',\\n\",\n \" 'user_name': '중개보조원(전진현)',\\n\",\n \" 'user_no': 578453,\\n\",\n \" 'user_phone': '0507-1280-8722',\\n\",\n \" 'view_count': 43},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '566-27',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'artsoup@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6420',\\n\",\n \" 'agent_name': '즐겨찾기공인중개사(김영한)',\\n\",\n \" 'agent_phone': '02-540-0124',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '566',\\n\",\n \" 'bubun_code': '27',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 5000,\\n\",\n \" 'description': '- 압구정역에서 도보거리 5분내에 위치한 신축 첫입주 투룸\\\\n\\\\n- 엘리베이터 운행되는 집이라서 계단 소음없구요~\\\\n\\\\n- 1층 필로티 주차장으로 주차도 편리하답니다.~\\\\n\\\\n- 대로변에서 가까운 위치라서 보안방범도 좋고 임대인분도 좋아서 더 좋아요~^^\\\\n\\\\n- 거실 맞벽구조라서 TV소파공간 나오는 구조에요~\\\\n',\\n\",\n \" 'description_og': '- 압구정역에서 도보거리 5분내에 위치한 신축 첫입주 투룸\\\\n\\\\n- 엘리베이터 운행되는 집이라서 계단 소음없구요~\\\\n\\\\n- 1층 필로티 주차장으로 주차도 편리하답니다.~\\\\n\\\\n- 대로변에서 가까운 위치라서 보안방범도 좋고 임대인분도 좋아서 더 좋아요~^^\\\\n\\\\n- 거실 맞벽구조라서 TV소파공간 나오는 구조에요~\\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 4774173,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/00a8613c8b1e9ad8d1d960925429ba62a027b511.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/9343942e4266eb575e569c4659fdb42ffc67e3e4.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/e23c913d789e043d893b1bdbc2334c622a889ebd.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/25d57162cc1c262a2814cd6973e33e72e6f88aa3.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/7b04259806cf2a1e536e884c6e2cfb5c72920f09.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/34c46f05a2e1bc8e7432be2fcdadba819b33d4d5.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/4774173/bc1a69d65f65bcdc4fe9d5ba4b3aa809f2ce4051.JPG'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-2446-3484',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5230577630193,127.026885771612',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 100,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 14.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '46.28',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '@@ 추천~~압구정역세권에 위치한 신축 첫입주 투룸 @@',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'cjpark12@hanmail.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6676',\\n\",\n \" 'user_name': '중개보조원(박충진)',\\n\",\n \" 'user_no': 810168,\\n\",\n \" 'user_phone': '0507-1280-6676',\\n\",\n \" 'view_count': 660},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 서초구 잠원동',\\n\",\n \" 'address2': '23-19',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'snws@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6934',\\n\",\n \" 'agent_name': '나무공인중개사(조민영)',\\n\",\n \" 'agent_phone': '02-557-1888',\\n\",\n \" 'bjd_code': '1165010600',\\n\",\n \" 'bonbun_code': '23',\\n\",\n \" 'bubun_code': '19',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 75,\\n\",\n \" 'description': '잠원동 신사역에서 3분거리 대로변 이면에 위치하고 있습니다.\\\\n\\\\n이번에 리모델링 완공하고 아직 잉크도 안말랐습니다.\\\\n\\\\n채광, 통풍, 환기가 우수해서 쾌적합니다.\\\\n\\\\n화이트톤 넉넉한 수납장\\\\n\\\\n베란다에 히노끼 느낌 넓은 테라스가~~~!! 운치가 더해집니다.\\\\n\\\\n',\\n\",\n \" 'description_og': '잠원동 신사역에서 3분거리 대로변 이면에 위치하고 있습니다.\\\\n\\\\n이번에 리모델링 완공하고 아직 잉크도 안말랐습니다.\\\\n\\\\n채광, 통풍, 환기가 우수해서 쾌적합니다.\\\\n\\\\n화이트톤 넉넉한 수납장\\\\n\\\\n베란다에 히노끼 느낌 넓은 테라스가~~~!! 운치가 더해집니다.\\\\n\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5590314,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/39e44c290adcfff1a892ad8e0439e22646c8020a.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/fc6eb1918e07057ec5c7e42a91e5019f1ca8ad73.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/560d71ac35e638d855704a56bfab4799603f05ab.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/8f31dbdeaa61e959d460832a7eb6d12b2923c0b5.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/a6940a91451835534bb42571acfdeaa0b3c049bc.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/3f813bcea0e21ca3bbd9dd636ff8c5890a3dcc1d.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/c735635b68a24772c7115bdde869f3bd12adaf31.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/402ca112ee93a654fe1febfc84097c2e447a2045.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5590314/aa77c2790b4d1fc8b18c1b60d9c42f0c52dbfce3.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '서초구',\\n\",\n \" 'local3': '잠원동',\\n\",\n \" 'manage_cost': '7만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 잠원역(3호선), 논현역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 책상, 책장, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-7540-8818',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\\n\",\n \" 'random_location': '37.5145613320559,127.017498428909',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 75,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '잠원동 신사역3분거리 리모델링완공',\\n\",\n \" 'updated_at': '7일 전',\\n\",\n \" 'user_email': 'snws@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6934',\\n\",\n \" 'user_name': '대표공인중개사(조민영)',\\n\",\n \" 'user_no': 1000906,\\n\",\n \" 'user_phone': '0507-1280-6934',\\n\",\n \" 'view_count': 63},\\n\",\n \" 'title': '서울시 서초구 잠원동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '523-34',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8, 2층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'click3090@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7491',\\n\",\n \" 'agent_name': '건우공인중개사(김상모)',\\n\",\n \" 'agent_phone': '010-8456-3090',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '523',\\n\",\n \" 'bubun_code': '34',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 105,\\n\",\n \" 'description': '[ 교통/위치 ] - ●\\\\n\\\\nㅇ 신사동 신구초등학교 인근에 위치한 분리형 원룸 풀옵션입니다.\\\\n\\\\nㅇ 신사역 6번출구 도보 7~8분 거리에 위치해 있습니다.\\\\n\\\\n\\\\n[ 인테리어/특징 ] - ●\\\\n\\\\nㅇ 독립된 방 하나와 주방, 미닫이문으로 분리된 거실이 있습니다.\\\\n\\\\nㅇ 리모델링 완료된 매물이라 아주 깔끔합니다.\\\\n\\\\n\\\\n[ 주차/편의시설 ] - ●\\\\n\\\\nㅇ 주차는 선착순 협의주차 가능합니다.\\\\n\\\\nㅇ 가로수길 인근이라 다양한 문화시설과 편의시설이 많습니다.\\\\n\\\\n------------------------------------------------------------------------★\\\\n\\\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\\\n\\\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\\\n\\\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\\\n\\\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\\n\",\n \" 'description_og': '[ 교통/위치 ] - ●\\\\n\\\\nㅇ 신사동 신구초등학교 인근에 위치한 분리형 원룸 풀옵션입니다.\\\\n\\\\nㅇ 신사역 6번출구 도보 7~8분 거리에 위치해 있습니다.\\\\n\\\\n\\\\n[ 인테리어/특징 ] - ●\\\\n\\\\nㅇ 독립된 방 하나와 주방, 미닫이문으로 분리된 거실이 있습니다.\\\\n\\\\nㅇ 리모델링 완료된 매물이라 아주 깔끔합니다.\\\\n\\\\n\\\\n[ 주차/편의시설 ] - ●\\\\n\\\\nㅇ 주차는 선착순 협의주차 가능합니다.\\\\n\\\\nㅇ 가로수길 인근이라 다양한 문화시설과 편의시설이 많습니다.\\\\n\\\\n------------------------------------------------------------------------★\\\\n\\\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\\\n\\\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\\\n\\\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\\\n\\\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5526171,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/b4fe3663a8736797060e70ac96bdb1f7ebbea921.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/24ee016b4209aff3de1da7eeed4a86572a5f536e.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/dcd56df6ab6c6d3b11afdb813638e737973bc4fe.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/f006be80cf72c05123bab32a7d10688deeb49ac0.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/a27c3b0159f330fb88967217e945dd2a1795b730.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/7ef6dc9093947253d0256555cdccf45c337ad262.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/1cc4d9668f70b270af0fda7f27514db064a053ba.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/45c6c4a45bfa17d847b90b145c5984bdc2930f22.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/7369514f5eaaa79fbfbe5b6c270b98811df5a98e.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5526171/b4007a83e2f8c2e5a3c3ef789f08fae5e6ceefb0.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 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옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9468-2025',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/50423.jpg',\\n\",\n \" 'random_location': '37.5219478315751,127.020065937533',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 105,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '➰2룸 또는 1룸+거실➰ 신사동 리모델링 완료된 원룸+주방+거실',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'skw1018@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-0922',\\n\",\n \" 'user_name': '중개보조원(김동빈)',\\n\",\n \" 'user_no': 50423,\\n\",\n \" 'user_phone': '0507-1281-0922',\\n\",\n \" 'view_count': 76},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '552-11',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '552',\\n\",\n \" 'bubun_code': '11',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 70,\\n\",\n \" 'description': '압구정역 4번출구 도보 5분거리이고, 현대백화점과 신사동 가로수길이 가까워서 \\\\n\\\\n애완견 키우면 안된다고 하시네요~^^*\\\\n\\\\n관리비에 케이블 포함 신사동 초특가 특aaaaa급 스페셜 원룸!! \\\\n\\\\n',\\n\",\n \" 'description_og': '압구정역 4번출구 도보 5분거리이고, 현대백화점과 신사동 가로수길이 가까워서 \\\\n\\\\n애완견 키우면 안된다고 하시네요~^^*\\\\n\\\\n관리비에 케이블 포함 신사동 초특가 특aaaaa급 스페셜 원룸!! \\\\n\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5586149,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5586149/04b1445290c68e45a26365c1f763a99e3ecc3685.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5586149/92b24571f08af59a688d9aae9edf38ddef93c577.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5586149/551523980854acea574e5507b76add8db251e3db.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5586149/148f3a78de6b9f4d0bd921d5c1d7ebfe0869c954.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 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'images_count': 11,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': 'TV',\\n\",\n \" 'movein_date': '입주 협의',\\n\",\n \" 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '싱크대, 가스레인지, 에어컨, 침대, 냉장고, 전자레인지, 옷장, 세탁기, 신발장',\\n\",\n \" 'original_user_phone': '010-8886-5877',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5223640629791,127.024459214668',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 70,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕신사동 진짜 귀한 단기 원룸★★ 특aaaa급',\\n\",\n \" 'updated_at': '7일 전',\\n\",\n \" 'user_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6920',\\n\",\n \" 'user_name': '대표공인중개사(손석진)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': '0507-1280-6920',\\n\",\n \" 'view_count': 295},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '518-18',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로26길 30 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'jsgongin@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-4624',\\n\",\n \" 'agent_name': '정성공인중개사(정문식)',\\n\",\n \" 'agent_phone': '010-4776-8102',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '518',\\n\",\n \" 'bubun_code': '18',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 200,\\n\",\n \" 'description': '♠ 내부구조 및 인테리어 ♠ \\\\n\\\\n▷ 내외관 관리가 잘된 풀옵션 분리형~~ \\\\n\\\\n▷ 흔하지 않은 단기 입대 가로수길 ~~~\\\\n\\\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요? \\\\n\\\\n▷ 신축 첫 입주 비품 모두 첫 번째 사용^^ \\\\n\\\\n▷ 보안 좋고 채광도 굿~~!\\\\n\\\\n♠ 대중교통 및 위치 ♠ \\\\n\\\\n▷ 신사역 번 도보5분 거리에 있습니다. \\\\n\\\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다. \\\\n\\\\n♠ 주차 및 편의시설 ♠ \\\\n\\\\n▷ 주차는 1대 가능합니다. \\\\n\\\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다. \\\\n\\\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★ \\\\n\\\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다. \\\\n\\\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\\\n\\\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\\\n\\\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\\n\",\n \" 'description_og': '♠ 내부구조 및 인테리어 ♠ \\\\n\\\\n▷ 내외관 관리가 잘된 풀옵션 분리형~~ \\\\n\\\\n▷ 흔하지 않은 단기 입대 가로수길 ~~~\\\\n\\\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요? \\\\n\\\\n▷ 신축 첫 입주 비품 모두 첫 번째 사용^^ \\\\n\\\\n▷ 보안 좋고 채광도 굿~~!\\\\n\\\\n♠ 대중교통 및 위치 ♠ \\\\n\\\\n▷ 신사역 번 도보5분 거리에 있습니다. \\\\n\\\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다. \\\\n\\\\n♠ 주차 및 편의시설 ♠ \\\\n\\\\n▷ 주차는 1대 가능합니다. \\\\n\\\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다. \\\\n\\\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★ \\\\n\\\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다. \\\\n\\\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\\\n\\\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\\\n\\\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5589916,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/c4e5191c3e9e49aea48a24dd22eb2b97b6ec9c35.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/f9d88c2f083c9e3ef8c3b0744c2a94a9b2c990d3.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/070a227cc06d2340486d8cdc377b967046ab83ed.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/9ed73a48d9ff71b682e42922977462695d4a8daa.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/dbfbe7d8de97f5ffc8f32354c47a943110b50fa5.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/a3b6ad57b654568e92faac240c872bc2215daa28.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/427396b654d2f7baf57f51880402113fde63df0f.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/22cf589130722c5e187ce86fdfb20984ba7e68d4.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/cd2665e1d782793a7d1206888e0d73d515c2b55a.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/a90bef0c562e766e249e93b8d69c2ab3b54d406c.jpg'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/e44eb0bcd5eb1a4e79f622b6889c45aef346f214.jpg'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/94c83e679ee42189b02a525f29b2e01bebf28396.jpg'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5589916/351c4c6f4bbccf0220d9c60c065e925b1025dcbf.jpg'}],\\n\",\n \" 'images_count': 13,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷',\\n\",\n \" 'movein_date': '즉시 가능',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 압구정역(3호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-7193-9904',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/431435.jpg',\\n\",\n \" 'random_location': '37.5191962672748,127.021849608438',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 170,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 16.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '52.89',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '☺☺ ~~가로수길 분리형☺☺ 원룸 원거실 가로수길 도보 2분 ☺☺',\\n\",\n \" 'updated_at': '7일 전',\\n\",\n \" 'user_email': 'arnani810403@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-5648',\\n\",\n \" 'user_name': '중개보조원(권혁준)',\\n\",\n \" 'user_no': 431435,\\n\",\n \" 'user_phone': '0507-1280-5648',\\n\",\n \" 'view_count': 94},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '38-6',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 서초구 잠원동 13-1 아이미서초빌딩 2층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'woopers@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '서초구',\\n\",\n \" 'agent_mobile': '0507-1281-0426',\\n\",\n \" 'agent_name': '오렌지하우스공인중개사(김승식)',\\n\",\n \" 'agent_phone': '02-512-4243',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '38',\\n\",\n \" 'bubun_code': '6',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 28000,\\n\",\n \" 'description': '★ 전세자급대출가능, 신축 첫입주, 사이즈 완전 큰 거실+투룸입니다^^\\\\n\\\\n★ 도보로 학동역5분, 논현역10분거리로 교통이 편리하고 학동공원이 가까이 있어 운동하기 좋아요^^\\\\n\\\\n★ 몸만 들어 오세요^^\\\\n\\\\n★ 직접 보세요^^\\\\n\\\\n',\\n\",\n \" 'description_og': '★ 전세자급대출가능, 신축 첫입주, 사이즈 완전 큰 거실+투룸입니다^^\\\\n\\\\n★ 도보로 학동역5분, 논현역10분거리로 교통이 편리하고 학동공원이 가까이 있어 운동하기 좋아요^^\\\\n\\\\n★ 몸만 들어 오세요^^\\\\n\\\\n★ 직접 보세요^^\\\\n\\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5615990,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/15e6dd167ac118a23def8eccad0264661a83a43d.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/2166a7af929fba04893434d496ba15d9e30411df.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/a3a8683b8f844b61b2257172ef57ad56d4a4290d.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/25d56d0767d2916d1d6cc54344b3ecc0f207a29a.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/225b156d616dc283f38f5ad266fcbc3c66ef56a5.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/5e2e128089d5f47e725a2c38806b60939a04e008.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/ad13b5e2a57199e8784b6e7b19f098634f80fc70.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/da20ef2348c3ede0da60cd2ba11ed2ffd64fe61a.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5615990/99aa8a5c83550a278af4961e304cf9bb4ba3d186.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '7만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '학동역(7호선), 논현역(7호선), 신사역(3호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 싱크대',\\n\",\n \" 'original_user_phone': '010-2247-1355',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5144702520298,127.02801180459',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '◈◈전세자금대출가능!!◈신축첫입주사이즈완~전큰거실+투룸전세!!◈도보학동역5분,논현역10분거리!!강추◈◈',\\n\",\n \" 'updated_at': '5일 전',\\n\",\n \" 'user_email': 'pysrose66@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-7038',\\n\",\n \" 'user_name': '중개보조원(박윤숙)',\\n\",\n \" 'user_no': 327679,\\n\",\n \" 'user_phone': '0507-1281-7038',\\n\",\n \" 'view_count': 120},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '13-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 85,\\n\",\n \" 'description': 'ㅇ 논현동 학동공원 블럭에 위치한 신축 복층형 풀옵션입니다.\\\\n\\\\nㅇ 신사역 1번 출구 도보 5분 거리 초역세권에 위치해 있습니다.\\\\n\\\\n\\\\n[ 인테리어/특징 ] - ●\\\\n\\\\nㅇ 2015년 8월 신축 매물로 내외부 깔끔함에 아기자기한 복층형구조.\\\\n\\\\nㅇ 노출 콘크리트와 대리석 바닥의 감각적인 내부 인테리어^^.\\\\n\\\\n\\\\n[ 주차/편의시설 ] - ●\\\\n\\\\nㅇ 1층에 주차장이 있으며 1대 주차 가능합니다.\\\\n\\\\nㅇ 길건너 가로수길이 있으며 대로변과 가까워 접근성 또한 용이합니다.\\\\n\\\\n------------------------------------------------------------------------★\\\\n\\\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\\\n\\\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\\\n\\\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\\\n\\\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\\n\",\n \" 'description_og': 'ㅇ 논현동 학동공원 블럭에 위치한 신축 복층형 풀옵션입니다.\\\\n\\\\nㅇ 신사역 1번 출구 도보 5분 거리 초역세권에 위치해 있습니다.\\\\n\\\\n\\\\n[ 인테리어/특징 ] - ●\\\\n\\\\nㅇ 2015년 8월 신축 매물로 내외부 깔끔함에 아기자기한 복층형구조.\\\\n\\\\nㅇ 노출 콘크리트와 대리석 바닥의 감각적인 내부 인테리어^^.\\\\n\\\\n\\\\n[ 주차/편의시설 ] - ●\\\\n\\\\nㅇ 1층에 주차장이 있으며 1대 주차 가능합니다.\\\\n\\\\nㅇ 길건너 가로수길이 있으며 대로변과 가까워 접근성 또한 용이합니다.\\\\n\\\\n------------------------------------------------------------------------★\\\\n\\\\nㅇ 10년 경력의 강남 풀옵션 단기임대 전문 부동산입니다.\\\\n\\\\nㅇ 본 광고의 모든 사진을 100% 직접 촬영 하였습니다.\\\\n\\\\nㅇ 3개월 기준 가격이며 1~2개월은 별도로 상담 바랍니다.\\\\n\\\\nㅇ 연중무휴 24시간~ 친절하게 상담해 드립니다.',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '5층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5169178,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/e141653fa319f2abb0a7d4c0f94eda0547515e82.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/0f8b7245a93db0ad51b55350e12acc432739ec8d.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/15bd79b47b869994443e7307c8cd9f4225705a8d.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/83782790d1949a89594b1b4e957205d226f33d22.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/f018fd67037b1f00d9d9ec7103448087596328e9.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/474e0dd079de0fe1b72dbf079a5936aa1f1f0c12.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/a6b344a32d5061e7f6374dd9c6b0cc4d02d400db.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/c78e7e16c53318707669051a0b42aa336cb0eaba.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5169178/c0a30bbdbe7af8ea4a3770cf252493d32c84e45c.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시 입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-3727-0204',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5165492895236,127.02284824381',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 85,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 8.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '26.45',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕ 신사역 2분거리*비티지스타일 신축*',\\n\",\n \" 'updated_at': '14일 전',\\n\",\n \" 'user_email': 'yegwang6412@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-7324',\\n\",\n \" 'user_name': '중개보조원(한승용)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': '0507-1281-7324',\\n\",\n \" 'view_count': 318},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '553-21',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 657-271층 101호',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'snws@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6934',\\n\",\n \" 'agent_name': '나무공인중개사(조민영)',\\n\",\n \" 'agent_phone': '02-557-1888',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '553',\\n\",\n \" 'bubun_code': '21',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 70,\\n\",\n \" 'description': '\\\\n ㅇ 위치 및 역세권 정보\\\\n \\\\n º 신사동 가로수길 인근에 위치한 풀옵션 원룸입니다.\\\\n\\\\n º 압구정역 도보 5분거리, 대로변 버스정류장 2분거리\\\\n \\\\n \\\\n ㅇ 구조 및 옵션 \\\\n \\\\n º 원룸 오픈형 구조로 되어있으며, 집기 모두 풀옵션 들어가 있습니다\\\\n\\\\n º 침대, 세탁기, TV, 냉장고 등 풀옵션으로 몸만 들어오시면 됩니다. \\\\n\\\\n º 화장실 샤워부스 설치되어 있어 깔끔합니다.\\\\n\\\\n \\\\n ㅇ 주차 및 편의시설\\\\n\\\\n º 지하주차장이 있어 주차 편리하게 가능\\\\n \\\\n º 가로수길 도보로 3분거리에 있어 편의시설 정말 많습니다.\\\\n',\\n\",\n \" 'description_og': '\\\\n ㅇ 위치 및 역세권 정보\\\\n \\\\n &#186; 신사동 가로수길 인근에 위치한 풀옵션 원룸입니다.\\\\n\\\\n &#186; 압구정역 도보 5분거리, 대로변 버스정류장 2분거리\\\\n \\\\n \\\\n ㅇ 구조 및 옵션 \\\\n \\\\n &#186; 원룸 오픈형 구조로 되어있으며, 집기 모두 풀옵션 들어가 있습니다\\\\n\\\\n &#186; 침대, 세탁기, TV, 냉장고 등 풀옵션으로 몸만 들어오시면 됩니다. \\\\n\\\\n &#186; 화장실 샤워부스 설치되어 있어 깔끔합니다.\\\\n\\\\n \\\\n ㅇ 주차 및 편의시설\\\\n\\\\n &#186; 지하주차장이 있어 주차 편리하게 가능\\\\n \\\\n &#186; 가로수길 도보로 3분거리에 있어 편의시설 정말 많습니다.\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5644492,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/46a1b6e4e5de1ede94c36fbe6c5409c83b221ac6.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/20dd3e49acd7d381701d448b8b03f3acc4b7a6d9.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/28fa4865fbb2a78c35ddc5eaa8511839897d26b1.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/85b83a9c7d3b690a730b63cbdbf51ff1bae6515d.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/a60be564885a1ae1ac480e73f9d82861939d8809.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/0787c765d915ff6a4c8eb4a11b8c7d7798758818.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/17bc1e468d5fdd1f5a7c17022417368104b98ea4.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/3754b3704e1ebbf57f9cd99edede53310f2b5da6.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/a44edcf34143d97b5b4e96dbcddaf08f27a9345c.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644492/500e4bd1ef1ec0dd3308d1b2e203832f4cb12cf8.JPG'}],\\n\",\n \" 'images_count': 10,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '7만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '압구정역(3호선), 신사역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-7540-8818',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/1000906.jpg',\\n\",\n \" 'random_location': '37.5217678573152,127.024994157203',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 65,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '❌압구정역',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'snws@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6934',\\n\",\n \" 'user_name': '대표공인중개사(조민영)',\\n\",\n \" 'user_no': 1000906,\\n\",\n \" 'user_phone': '0507-1280-6934',\\n\",\n \" 'view_count': 31},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '86-10',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '86',\\n\",\n \" 'bubun_code': '10',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 72,\\n\",\n \" 'description': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\\\n\\\\n ㅁ 7호선 지하철 역세권 단기임대 사이즈가 넓습니다.\\\\n\\\\n ㅁ 저렴한 가격에 역세권 단기임대 매물로 채광이 아주 우수합니다.\\\\n\\\\n ㅁ 대로변 바로 이면에 있어서 늦은시간 다니기도 무섭지 않구요.\\\\n\\\\n ㅁ 사이즈가 다른 매물에 비해 넓어서 두분이서 지내셔도 넉넉합니다.\\\\n\\\\n ㅁ 내부 시설상태 아주 깔끔하여 바로 입주도 가능하십니다.\\\\n\\\\n ㅁ 주차는 1대 가능하십니다.\\\\n\\\\n \\\\n\\\\n\\\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &quot; 주인을 때려서라도 맞춰드립니다 \\\\n\\\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\\\n',\\n\",\n \" 'description_og': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\\\n\\\\n ㅁ 7호선 지하철 역세권 단기임대 사이즈가 넓습니다.\\\\n\\\\n ㅁ 저렴한 가격에 역세권 단기임대 매물로 채광이 아주 우수합니다.\\\\n\\\\n ㅁ 대로변 바로 이면에 있어서 늦은시간 다니기도 무섭지 않구요.\\\\n\\\\n ㅁ 사이즈가 다른 매물에 비해 넓어서 두분이서 지내셔도 넉넉합니다.\\\\n\\\\n ㅁ 내부 시설상태 아주 깔끔하여 바로 입주도 가능하십니다.\\\\n\\\\n ㅁ 주차는 1대 가능하십니다.\\\\n\\\\n \\\\n\\\\n\\\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &amp;quot; 주인을 때려서라도 맞춰드립니다 \\\\n\\\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5608525,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/f5f55eef67addca608ec74d3ef7cd4fb6787ba5d.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/dffa018fc455417967aec2edaa7f9f1e94ea0f0d.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/acc6bf98d40055e7d48d1809be7884e9482ef022.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/8adff982c3d88bacba8dabd3ccc089ac7f9beb9c.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/b4c06c8590db51b7124cbb05d3ca2bf2302c4450.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/50cb32d429bd5f58d5f2ffcb72fa360d36806e9e.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/9e4190dedfd988d0cac267eb64e91b6c106d405f.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608525/037adc95c91f5727c483d5c696f112cc5cf248e1.JPG'}],\\n\",\n \" 'images_count': 8,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '7만원',\\n\",\n \" 'manage_cost_inc': 'TV',\\n\",\n \" 'movein_date': '즉시 입주',\\n\",\n \" 'near_subways': '학동역(7호선), 강남구청역(7호선,분당선), 언주역(9호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-2068-9980',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5156337365346,127.030938222767',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 72,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕ ☆7호선 학동역 역세권 단기임대',\\n\",\n \" 'updated_at': '5일 전',\\n\",\n \" 'user_email': 'promising14@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-8604',\\n\",\n \" 'user_name': '중개보조원(박규백)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': '0507-1281-8604',\\n\",\n \" 'view_count': 76},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 서초구 잠원동',\\n\",\n \" 'address2': '10-43 4층 402호',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 서초구 동산로 46 , 1층 (양재동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'lwh2136@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '서초구',\\n\",\n \" 'agent_mobile': '0507-1280-7497',\\n\",\n \" 'agent_name': '언남공인중개사(이원희)',\\n\",\n \" 'agent_phone': '02-573-8600',\\n\",\n \" 'bjd_code': '1165010600',\\n\",\n \" 'bonbun_code': '',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\\\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\\\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\\\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, 위치만 말씀해주시면 픽업서비스도 해드립니다. \\\\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) \\\\n※인증된 대형 부동산 !! \\\\n★ 보증금 / 임대기간 (조정가능) \\\\n\\\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\\\n\\\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 장실장 입니다^^ \\\\n저희는 300% 실매물에 실사진으로만 운영합니다. 강남 전지역(강남구,서초구,송파구) \\\\n원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\\\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House★ 구경오세요!!! \\\\n직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\\\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\\\n직방내 모든매물 상담가능합니다.^^ \\\\n\\\\n■ KAKAO Talk : jangyong1109 ■ ',\\n\",\n \" 'description_og': '★ TV에 나온방!! 옆에 조금 떨어져 있는 방★ \\\\n★ 월세는 무조건 깍는다(호두깍기 인형에 버금가는 월세깍기 인형) \\\\n★ 저렴한 ACE(침대아님) 매물만 소개해드려요 \\\\n★ 총알 픽업 서비스(치일수도 있어요) ※부동산으로 오시면 더욱 좋지만, 위치만 말씀해주시면 픽업서비스도 해드립니다. \\\\n★ 한국공제손해보험가입 2억!!(보증금걱정無/국가안전보장) \\\\n※인증된 대형 부동산 !! \\\\n★ 보증금 / 임대기간 (조정가능) \\\\n\\\\n■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ \\\\n\\\\n특A급 매물을 꽈악~!!말보단 행동을 좋아하는 장실장 입니다^^ \\\\n저희는 300% 실매물에 실사진으로만 운영합니다. 강남 전지역(강남구,서초구,송파구) \\\\n원룸,투룸,오피스텔 전문 고객님들께 친절은 기본!! \\\\n안전한 부동산거래를 원칙으로 센스있는 젊은 공인중개사에게 초특급 House★ 구경오세요!!! \\\\n직장인과 학생여러분들을 위해 365일 연중무휴(엄마,아빠생신빼고)!! \\\\n24시간 전화.카톡 상담가능합니다.망설이지 마시고 바로 연락주세요!! \\\\n직방내 모든매물 상담가능합니다.^^ \\\\n\\\\n■ KAKAO Talk : jangyong1109 ■ ',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5602646,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5602646/1edff41a208a93d7f11f5d70239420ed4549c84f.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 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{'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5602646/48bb98304dde6182e25d39c65d070ea1cc2eda25.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5602646/ee1d71ac533db971eaa3f16cefad475369c43720.JPG'}],\\n\",\n \" 'images_count': 10,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '서초구',\\n\",\n \" 'local3': '잠원동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 잠원역(3호선), 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1129550,\\n\",\n \" 'user_phone': '0507-1281-6246',\\n\",\n \" 'view_count': 120},\\n\",\n \" 'title': '서울시 서초구 잠원동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '568-5',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 도산대로25길 15',\\n\",\n \" 'agent_comment': '신사동 한솔부동산은 허위매물 근절과 더불어 실매물만 기재할것을 약속드립니다. 감사합니다.\\\\n\\\\n카카오톡 ID sanga8949입니다. 24시간 카톡상담도 환영합니다.',\\n\",\n \" 'agent_email': 'chulwan@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-0864',\\n\",\n \" 'agent_name': '한솔공인중개사(박철완)',\\n\",\n \" 'agent_phone': '02-517-2799',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '568',\\n\",\n \" 'bubun_code': '5',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 10000,\\n\",\n \" 'description': '가로수길인근 방3 욕실1 통베란다 주차\\\\n\\\\n1. 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'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 도산대로38길 11 1층(논현동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'sm7784@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-4119',\\n\",\n \" 'agent_name': '청학공인중개사(이석문)',\\n\",\n \" 'agent_phone': '02-514-1028',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '595',\\n\",\n \" 'bubun_code': '8',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 53000,\\n\",\n \" 'description': '◈ 압구정역세권 귀한전세 (융자무)\\\\n◈ 급 고급 올수리 32평 빌라\\\\n◈ 편리한지상주차장 \\\\n◈ 방3개+화장실2개+넓은거실+주방\\\\n◈ 급급급 !! 가격,크기대비 최고의 매물입니다.... ^^ 8282서두르세요 \\\\n\\\\n☆ 강남,서초 전지역 고급빌라만을 고집하여 .. \\\\n☆ 10년 이상의 노하우와 \\\\n☆ 2만개이상의 데이터로 \\\\n★ 항상 고객님의 입장에서 생각하고 고민하여 .. \\\\n★ 원하시는 평수,가격,스타일을 말해주시면 \\\\n★ 최고의 매물만을 소개하겠습니다. \\\\n\\\\n☞ 청학공인중개사 (24시 친절상담^^) \\\\n☎ 514-0400 / 010-3232-3782 (대표 이석문)',\\n\",\n \" 'description_og': '◈ 압구정역세권 귀한전세 (융자무)\\\\n◈ 급 고급 올수리 32평 빌라\\\\n◈ 편리한지상주차장 \\\\n◈ 방3개+화장실2개+넓은거실+주방\\\\n◈ 급급급 !! 가격,크기대비 최고의 매물입니다.... ^^ 8282서두르세요 \\\\n\\\\n☆ 강남,서초 전지역 고급빌라만을 고집하여 .. \\\\n☆ 10년 이상의 노하우와 \\\\n☆ 2만개이상의 데이터로 \\\\n★ 항상 고객님의 입장에서 생각하고 고민하여 .. \\\\n★ 원하시는 평수,가격,스타일을 말해주시면 \\\\n★ 최고의 매물만을 소개하겠습니다. \\\\n\\\\n☞ 청학공인중개사 (24시 친절상담^^) \\\\n☎ 514-0400 / 010-3232-3782 (대표 이석문)',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '3층',\\n\",\n \" 'id': 5600902,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600902/4d3b1f88a056e1c3b7f96be5b6ab8ecb32ad46c3.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600902/cf8a66ce1c88cd63ae0975a8e7890936c4e2b29f.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600902/54c0cd82c36fdea790c8842b35157a130541b8e7.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600902/c4d82e8f659c5851c04d3144e732f3ba437615fa.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600902/f56c7218ad3e4975ea0abd7796257a5beb5ea45a.jpg'}],\\n\",\n \" 'images_count': 5,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '없음',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시가능',\\n\",\n \" 'near_subways': '압구정역(3호선), 학동역(7호선), 압구정로데오역(분당선)',\\n\",\n \" 'options': '-',\\n\",\n \" 'original_user_phone': '010-3232-3782',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5228383782411,127.03047191494',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '쓰리룸+',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 26.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '85.95',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '◈◈ 급 고급올수리!융자무 넓은쓰리룸화장실2넓은거실주방 급!급!급! ◈◈',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'sm7784@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-4119',\\n\",\n \" 'user_name': '대표공인중개사(이석문)',\\n\",\n \" 'user_no': 1066592,\\n\",\n \" 'user_phone': '0507-1280-4119',\\n\",\n \" 'view_count': 129},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '67-11',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 696-11번지 지하 1층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'te2797@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-7013',\\n\",\n \" 'agent_name': '수호부동산중개(정연진)',\\n\",\n \" 'agent_phone': '02-555-2249',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '67',\\n\",\n \" 'bubun_code': '11',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 2000,\\n\",\n \" 'description': '을지병원 사거리 을지병원 뒷편에 있는 투룸입니다.\\\\n\\\\n집 정말 넓어요!!!!\\\\n\\\\n이번에 새로 다시 수리 했습니다.\\\\n\\\\n집 정말 싸이즈 잘 나왔어요~~~\\\\n\\\\n이집 진짜 실가격 실매물입니다.\\\\n\\\\n근데... 주차는 안되요... ㅠㅠ\\\\n\\\\n주차안되는거 말고는 이가격에 이집 어마어마하게 훌륭한 집입니다.\\\\n\\\\n에이스예여~~~\\\\n\\\\n빨리 보러오세요~~',\\n\",\n \" 'description_og': '을지병원 사거리 을지병원 뒷편에 있는 투룸입니다.\\\\n\\\\n집 정말 넓어요!!!!\\\\n\\\\n이번에 새로 다시 수리 했습니다.\\\\n\\\\n집 정말 싸이즈 잘 나왔어요~~~\\\\n\\\\n이집 진짜 실가격 실매물입니다.\\\\n\\\\n근데... 주차는 안되요... ㅠㅠ\\\\n\\\\n주차안되는거 말고는 이가격에 이집 어마어마하게 훌륭한 집입니다.\\\\n\\\\n에이스예여~~~\\\\n\\\\n빨리 보러오세요~~',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5441521,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/bea37d4f0c78e3d7dd663c8c3a0d1658d3d47b09.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/86cf50950aa3d814dd93e9d1053e7febbf518ab0.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/e9eecc204225b35bf1a1f81728bbecd11f385a2c.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 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{'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/a7ea4df1b10b326d06d808b4baf32eacaa74575f.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/7241243dec95477bf3537d07df1814da83f175a0.JPG'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/c683e5b845a22e768fed832c74491b15580b574d.JPG'},\\n\",\n \" {'count': 14,\\n\",\n \" 'index': 13,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/cef7c2701fc8a36fbee2ce9e5b81f61e58ddc58e.JPG'},\\n\",\n \" {'count': 15,\\n\",\n \" 'index': 14,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/64bfbfe7342f21c07fbf0281a8b2a0765701a068.JPG'},\\n\",\n \" {'count': 16,\\n\",\n \" 'index': 15,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5441521/3efcebb271ebc55e4f4d44cbd0f495e65968cec3.JPG'},\\n\",\n \" {'count': 17,\\n\",\n \" 'index': 16,\\n\",\n \" 'url': 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ㅎ\\\\n\\\\n- 위로 올라가는 복층 보다 덜 불편하신게 장점이죠 ^^\\\\n\\\\n- 정말 저렴하게 최저가 보장해드리니 마지막 기회 얼른 잡으세요',\\n\",\n \" 'description_og': '- 전속관리물건 T3하우스 3단복층 타입!\\\\n\\\\n- 마지막 최저가! 단한번의 찬스~!\\\\n\\\\n- 신사역 도보5분+가로수길 도보3분!\\\\n\\\\n- 내부 빈티지톤 컨셉으로 느낌있게 꾸며져있어요\\\\n\\\\n- 현관 입장하시고 거실은 밑에 있다는거!! 재밌는 구조 입니다 ㅎ\\\\n\\\\n- 위로 올라가는 복층 보다 덜 불편하신게 장점이죠 ^^\\\\n\\\\n- 정말 저렴하게 최저가 보장해드리니 마지막 기회 얼른 잡으세요',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5644123,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644123/fa773300028110b6dc38dfed8c96d758255a13da.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644123/8aa37298175dba2f040e0f1236bf656e6cfdfbc0.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644123/f641d076c981318ca6424bf7b72d8e21eae3d125.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 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TV',\\n\",\n \" 'movein_date': '즉시',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-8240-5676',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5165686342578,127.022841908394',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 110,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(복층형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 8.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '26.45',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '★마지막최저가! 주차 100%일렬1대!럭셔리복층 ★',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'jmk_78@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-7437',\\n\",\n \" 'user_name': '중개보조원(강상환)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1280-7437',\\n\",\n \" 'view_count': 88},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 서초구 잠원동',\\n\",\n \" 'address2': '12-4',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 도산대로25길 15',\\n\",\n \" 'agent_comment': '▼신사동 한솔부동산은 허위매물없는 정직한 중개와 믿을 만한 매물을 약속드리겠습니다. \\\\n\\\\n▼보시는 사진 그대로 똑같은 매물로 보여드립니다\\\\n\\\\n▼픽업도 가능하니 언제든지 연락주세요. 감사합니다.&amp;quot;\\\\n\\\\n카카오톡 ID sanga8949입니다. 24시간 카톡상담도 환영합니다.',\\n\",\n \" 'agent_email': 'chulwan@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-0864',\\n\",\n \" 'agent_name': '한솔공인중개사(박철완)',\\n\",\n \" 'agent_phone': '02-517-2799',\\n\",\n \" 'bjd_code': '1165010600',\\n\",\n \" 'bonbun_code': '12',\\n\",\n \" 'bubun_code': '4',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 15000,\\n\",\n \" 'description': '[매물지역] 서울 서초구 잠원동 3호선 신사역\\\\n\\\\n[건물층수] 총5층 건물 중 해당층 4층 \\\\n\\\\n[월세금액] 월세 1.5억/30, 1억/70, 월세금액 조절가능. \\\\n\\\\n[월관리비] 월80,000원\\\\n\\\\n[옵션내역] 12자붙박이장,씽크대 신발장등 \\\\n\\\\n[교통편] 3호선 신사역 도보 3분이내. \\\\n\\\\n[매물특징] - 번호키, 엘리베이터, CCTV설치, 지하 주차장. 바닥데코타일시공 깔끔함',\\n\",\n \" 'description_og': '[매물지역] 서울 서초구 잠원동 3호선 신사역\\\\n\\\\n[건물층수] 총5층 건물 중 해당층 4층 \\\\n\\\\n[월세금액] 월세 1.5억/30, 1억/70, 월세금액 조절가능. \\\\n\\\\n[월관리비] 월80,000원\\\\n\\\\n[옵션내역] 12자붙박이장,씽크대 신발장등 \\\\n\\\\n[교통편] 3호선 신사역 도보 3분이내. \\\\n\\\\n[매물특징] - 번호키, 엘리베이터, CCTV설치, 지하 주차장. 바닥데코타일시공 깔끔함',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5634530,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/c10317ff2b094481c0e15cd461fe155c52e35563.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/d3ce87c3652880db9d6fb4e3b52c0e6bb312f24b.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/dd9d2cc7fdee1e3ee36e70ceea5dbac8447530d3.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/d5a08e1005a0e133fde2dea4540732a5df236da1.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/3d0bc9dd59d857a6b53d40bc1f3d411e665139ae.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/23effaf2ca723fc23b070163c1aa349e22186a6a.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/48aab933eee0ca8f6efa670c687337b4b0783ab2.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/10da17c89fa2f44569a8c0b50af26894ec0afe77.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/fecabf2e21d78aef89ae18bbc33b737b0b6ed693.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/5bf3d319eb8674609b2fa994c2547d8150488930.jpg'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/8bd653010f432b93aa8a92c9a358bf6cc1990c51.jpg'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/7dfa7b38b21e7143300dfc337aa44eb6f4f40394.jpg'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/5f3a7f4fe5d203b03cce0488ed8f2897d4f5e392.jpg'},\\n\",\n \" {'count': 14,\\n\",\n \" 'index': 13,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/a833dc798bacb7700151af68db5c52eafc44fbeb.jpg'},\\n\",\n \" {'count': 15,\\n\",\n \" 'index': 14,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/bcf275020c3aaf2b87d38fb629039df9ed7dbbd1.jpg'},\\n\",\n \" {'count': 16,\\n\",\n \" 'index': 15,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/e3bcea19f59cfbc34f5c051c1d33bd91bd7339be.jpg'},\\n\",\n \" {'count': 17,\\n\",\n \" 'index': 16,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5634530/011ff07d6b9b8799c4a1afce1f2f034e3cfba2d8.jpg'}],\\n\",\n \" 'images_count': 17,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '서초구',\\n\",\n \" 'local3': '잠원동',\\n\",\n \" 'manage_cost': '8만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\\n\",\n \" 'options': '옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9498-3916',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5172222368081,127.018857805201',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 30,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 17.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '56.20',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '신사역도보3분 방2 욕실1 엘리베이터 지하주차장',\\n\",\n \" 'updated_at': '3일 전',\\n\",\n \" 'user_email': 'sipp1@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1282-0165',\\n\",\n \" 'user_name': '소속공인중개사(장승일)',\\n\",\n \" 'user_no': 1531083,\\n\",\n \" 'user_phone': '0507-1282-0165',\\n\",\n \" 'view_count': 104},\\n\",\n \" 'title': '서울시 서초구 잠원동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '609-5',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '609',\\n\",\n \" 'bubun_code': '5',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 70,\\n\",\n \" 'description': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\\\n\\\\n ㅁ 압구정역 3번출구 인근에 있는 너무나 귀한 단기임대 매물입니다.\\\\n\\\\n ㅁ 지역 특성상 단기임대 매물이 귀해서 이쪽으로 보시면 이거 꼭 보셔야하는 매물입니다.\\\\n\\\\n ㅁ 지하철역과 가깝고 압구정 로데오와 가깝구요 큰 대로변 바로 이면에 있습니다.\\\\n\\\\n ㅁ 늦은시간 다니시기 무섭지 않은 곳이구요.\\\\n\\\\n ㅁ 시세대비 저렴하게 나온 매물이라 보시는분들마다 좋아하십니다.\\\\n\\\\n ㅁ 주차는 1대 가능하십니다\\\\n\\\\n \\\\n\\\\n # 경력 9년에 강남 최고의 베테랑 중개인이 \\\" 주인을 때려서라도 맞춰드립니다 \\\\n\\\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.',\\n\",\n \" 'description_og': '안녕하세요 100% 실사진 , 실가격 부동산 입니다\\\\n\\\\n ㅁ 압구정역 3번출구 인근에 있는 너무나 귀한 단기임대 매물입니다.\\\\n\\\\n ㅁ 지역 특성상 단기임대 매물이 귀해서 이쪽으로 보시면 이거 꼭 보셔야하는 매물입니다.\\\\n\\\\n ㅁ 지하철역과 가깝고 압구정 로데오와 가깝구요 큰 대로변 바로 이면에 있습니다.\\\\n\\\\n ㅁ 늦은시간 다니시기 무섭지 않은 곳이구요.\\\\n\\\\n ㅁ 시세대비 저렴하게 나온 매물이라 보시는분들마다 좋아하십니다.\\\\n\\\\n ㅁ 주차는 1대 가능하십니다\\\\n\\\\n \\\\n\\\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &quot; 주인을 때려서라도 맞춰드립니다 \\\\n\\\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5652932,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/32b38deea2d8f6b30e0b5c1791d1aeaf0cffa25e.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/98440ef63fb07e0669c099916d919ade75dab7f5.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/1a748b1ac151f71f48e1b585f6ead9d4398f4528.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/ff14b3b25171200bb0c4ef22bb80380bea8a77f2.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/4821498727e4ddf9b14712c1786b7bf61f51d401.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/76f2896bc71be55300c08a829278862a702e4f3c.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5652932/b835a15eccb56782899e24e6eff240831c888e83.jpg'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '6만원',\\n\",\n \" 'manage_cost_inc': 'TV',\\n\",\n \" 'movein_date': '즉시 입주',\\n\",\n \" 'near_subways': '압구정역(3호선), 압구정로데오역(분당선), 학동역(7호선)',\\n\",\n \" 'options': '싱크대, 에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장',\\n\",\n \" 'original_user_phone': '010-3727-0204',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5262690640023,127.030077677758',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 70,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 9.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '29.75',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕정말 귀한 신사동 단기임대 오픈형~!!!',\\n\",\n \" 'updated_at': '5시간 전',\\n\",\n \" 'user_email': 'yegwang6412@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-7324',\\n\",\n \" 'user_name': '중개보조원(한승용)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': 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',\\n\",\n \" 'description_og': '★ 지정주차!! 컨디션 특AA+급 완~전 큰거실+쓰리룸 반전세입니다(보증금조정가능)^^ \\\\n\\\\n★ 도보로 학동역 10분거리로 주변 편의시설과 교통이 편리해요^^ \\\\n\\\\n★ 현세입자 완전 깔끔한 분으로 집이 새집같아요\\\\n\\\\n★ 직접 보세요^^ ',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5605314,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5605314/ae61e0b0d102267ab1ecc58caf03750a2c095667.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5605314/0967f4401cc4cd793bc8bc6ca954f30ba44c4600.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5605314/991bef6fb54810a2d9064a39500e28c508cc58a7.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5605314/a1d97aed7f35b4f697c81d11f1039ab2bf6614fd.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 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'agent_comment': '',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 110,\\n\",\n \" 'description': '☆ 신사역 5분 \\\\n☆ 가로수길 3분 \\\\n☆ 복층구조라 분리형 느낌 \\\\n☆ 빈티지 대리석 인테리어로 이국적인느낌 \\\\n☆ 엘리베이터있구요 \\\\n☆ 신축 건물이라 내외관 아주깔끔해욧',\\n\",\n \" 'description_og': '☆ 신사역 5분 \\\\n☆ 가로수길 3분 \\\\n☆ 복층구조라 분리형 느낌 \\\\n☆ 빈티지 대리석 인테리어로 이국적인느낌 \\\\n☆ 엘리베이터있구요 \\\\n☆ 신축 건물이라 내외관 아주깔끔해욧',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5641189,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/f641d076c981318ca6424bf7b72d8e21eae3d125.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/479ee2025dde62bc0c02b55332c65a7417360c9e.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/37eaef757da7575b5f97ffd21283a22fd8891799.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/bdf801ed360ab2d94f3136b3f26919b4c43fa45a.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/4c1b2bf0da5f3c5e84e1851475cc0813aedb9d20.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/98d426e5290802c1c88a7b260add50ef38c8f68a.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/0f47d74e4138c58eb361d28d207c330fdf9a962e.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/4d73cff6cd94bd4527bc93fa75f312970e79127d.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/9180fcf332955d42881fbd5bed260f3ff151cdaf.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641189/6c95dc9923708c60b2c685e53e43570e473cf942.JPG'}],\\n\",\n \" 'images_count': 10,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9014-9280',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5165445422669,127.022843068526',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 110,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(복층형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '동일매물최저가보장',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'jmk_78@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6157',\\n\",\n \" 'user_name': '중개보조원(김하근)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1280-6157',\\n\",\n \" 'view_count': 52},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '73-7',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로26길 30 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'jsgongin@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-4624',\\n\",\n \" 'agent_name': '정성공인중개사(정문식)',\\n\",\n \" 'agent_phone': '010-4776-8102',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '73',\\n\",\n \" 'bubun_code': '7',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 165,\\n\",\n \" 'description': '♠ 내부구조 및 인테리어 ♠\\\\n\\\\n▷ 내외관 관리가 잘된 풀옵션 원룸 원거실~~\\\\n\\\\n▷ 전체적인 화이트톤의 아늑한 분위기가 물씬 풍기네요 ~\\\\n\\\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요?\\\\n\\\\n▷ 넓은 원룸 원거실~! 두분이서 살기에 딱 좋으며~\\\\n\\\\n▷ 좋은방은 금방금방 나가요~서두르셔야 할거에요~\\\\n\\\\n♠ 대중교통 및 위치 ♠\\\\n\\\\n▷ 강남을지병원 사거리 도보3분 거리에 있습니다.\\\\n\\\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다.\\\\n\\\\n♠ 주차 및 편의시설 ♠\\\\n\\\\n▷ 주차는 1대 가능합니다.\\\\n\\\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다.\\\\n\\\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★\\\\n\\\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다.\\\\n\\\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\\\n\\\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\\\n\\\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\\n\",\n \" 'description_og': '♠ 내부구조 및 인테리어 ♠\\\\n\\\\n▷ 내외관 관리가 잘된 풀옵션 원룸 원거실~~\\\\n\\\\n▷ 전체적인 화이트톤의 아늑한 분위기가 물씬 풍기네요 ~\\\\n\\\\n▷ 직장인 또는 자취하시는 분들이 원하는 곳이 이런곳 아닌가요?\\\\n\\\\n▷ 넓은 원룸 원거실~! 두분이서 살기에 딱 좋으며~\\\\n\\\\n▷ 좋은방은 금방금방 나가요~서두르셔야 할거에요~\\\\n\\\\n♠ 대중교통 및 위치 ♠\\\\n\\\\n▷ 강남을지병원 사거리 도보3분 거리에 있습니다.\\\\n\\\\n▷ 대로변에 인접해 있어 대중교통이 편리합니다.\\\\n\\\\n♠ 주차 및 편의시설 ♠\\\\n\\\\n▷ 주차는 1대 가능합니다.\\\\n\\\\n▷ 주변에 음식점,편의점,커피숍,세탁소,은행,병원등이 있어 편리합니다.\\\\n\\\\n★★ 저희 부동산은 100% 실매물,실사진,실시간,실가격의 광고 등록합니다. ★★\\\\n\\\\n1. 본광고의 모든사진은 직접촬영 했으며,못올린 실사진이 훨씬 더 많습니다.\\\\n\\\\n2. 1년 연중무휴 24시간 상담가능 언제든 전화주시면 정성껏 상담해 드리겠습니다. \\\\n\\\\n3. 항상 고객님 입장에서 생각하고 고객님이 원하는 부분을 최대한 맞춰드리겠습니다. \\\\n\\\\n4. 가격대비 최상의 물건만 센스있게 뽑아서 젋은 감각으로~',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5623803,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5623803/0076c9cdec0549be670ea070e0a0d73a2e1a14ec.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5623803/365efa15c0825371430644286d321babe721ef92.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5623803/86b480859e484f1fdfdf6158c674612ea2d6610d.JPG'},\\n\",\n \" 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\" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '13-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 639-14',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 115,\\n\",\n \" 'description': '☆ 신사역 5분\\\\n\\\\n☆ 가로수길 3분\\\\n\\\\n☆ 복층구조라 분리형 느낌\\\\n\\\\n☆ 빈티지 대리석 인테리어로 이국적인느낌\\\\n\\\\n☆ 엘리베이터있구요\\\\n\\\\n☆ 신축 건물이라 내외관 아주깔끔해욧',\\n\",\n \" 'description_og': '☆ 신사역 5분\\\\n\\\\n☆ 가로수길 3분\\\\n\\\\n☆ 복층구조라 분리형 느낌\\\\n\\\\n☆ 빈티지 대리석 인테리어로 이국적인느낌\\\\n\\\\n☆ 엘리베이터있구요\\\\n\\\\n☆ 신축 건물이라 내외관 아주깔끔해욧',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5641154,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641154/0f47d74e4138c58eb361d28d207c330fdf9a962e.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641154/f641d076c981318ca6424bf7b72d8e21eae3d125.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641154/479ee2025dde62bc0c02b55332c65a7417360c9e.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641154/37eaef757da7575b5f97ffd21283a22fd8891799.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5641154/bdf801ed360ab2d94f3136b3f26919b4c43fa45a.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 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'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 가스레인지, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9014-9280',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5160556284199,127.023502323327',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 115,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(복층형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '가로수길 걸어서 가요~',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'jmk_78@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6157',\\n\",\n \" 'user_name': '중개보조원(김하근)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1280-6157',\\n\",\n \" 'view_count': 100},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '68-17',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 639-14',\\n\",\n \" 'agent_comment': '★ 저는 100% 실매물 실가격 직접찍은 실사진으로만 광고합니다. \\\\n연락주시면 믿음에 보답해 드리겠습니다!!★\\\\n\\\\n○ 이광고를 보고 계시다면 아직 방계약전입니다!! 서둘러 연락주세요!!!\\\\n\\\\n*24시 전화문자상담 환영~ 성실히 중개해드리겠습니다.\\\\n',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '68',\\n\",\n \" 'bubun_code': '17',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 3000,\\n\",\n \" 'description': '★ 언북중, 서울세관 뒷편 을지병원사거리 도보5분!!\\\\n\\\\n★ 거실 5~6인용쇼파와 티비 배치가능한 ㅎㄷㄷ한 크기!!\\\\n\\\\n★ 안방 12자 고급붙박이장!!! 슈퍼킹싸이즈 여유있게 들어갑니다!\\\\n\\\\n★ 작은방 일반 투룸 안방싸이즈!!! 슈퍼킹 싸이즈 작은방에도 들어갑니다^^\\\\n\\\\n★ 베란다또한 굿~!!!!\\\\n\\\\n★ 광고그대로 강남에서 손꼽히는 탑클래스 고급형 빌라입니다!!!!!',\\n\",\n \" 'description_og': '★ 언북중, 서울세관 뒷편 을지병원사거리 도보5분!!\\\\n\\\\n★ 거실 5~6인용쇼파와 티비 배치가능한 ㅎㄷㄷ한 크기!!\\\\n\\\\n★ 안방 12자 고급붙박이장!!! 슈퍼킹싸이즈 여유있게 들어갑니다!\\\\n\\\\n★ 작은방 일반 투룸 안방싸이즈!!! 슈퍼킹 싸이즈 작은방에도 들어갑니다^^\\\\n\\\\n★ 베란다또한 굿~!!!!\\\\n\\\\n★ 광고그대로 강남에서 손꼽히는 탑클래스 고급형 빌라입니다!!!!!',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 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가능합니다.^^*\\\\n\\\\n- 주변에 은행, 편의점,약국, 음식점 등이 있어 아주 편리한 곳 입니당^^\\\\n\\\\n\\\\n◈ 내집 구하는 마음으로 주인을 적으로 생각합니다. ^^*\\\\n\\\\n◈ 차별화된 시스템으로 항상 업데이트합니다.\\\\n\\\\n◈ 100% 실매물.실사진.실가격의 광고등록으로 원칙이며 사훈입니다.\\\\n\\\\n◈ 강남지역 누구나 인정하는 중개사님들이 세입자들의 입장에서 안내해드립니다 \\\\n\\\\n◈ 강남,서초,송파 픽업걱정 No!! 전화연결24시 항상 Ok!! \\\\n',\\n\",\n \" 'description_og': '❄【내부구조 및 인테리어】❄\\\\n\\\\n- 신사역 도보3분거리 신축복층원룸입니다\\\\n\\\\n- 노출콘크리트 디자인으로 인테리어 최강~!!\\\\n\\\\n- 빌트인냉장고.드럼세탁기.lcdtv,원목가구장. !!\\\\n\\\\n- 화장실 넓고 깨끗한주방시설에 바닥은 대리석시공~~\\\\n\\\\n- 채광.환기~~굿!!!\\\\n\\\\n\\\\n【대중교통 및 위치】\\\\n\\\\n- 신사역 도보3분 거리에 위치해 있습니다~^^\\\\n\\\\n- 대로변과 가까워 안전하고, 전철역,버스정류장 아주 가깝습니다!!\\\\n\\\\n\\\\n【 편의시설 및 주차】 \\\\n\\\\n- 주차는 가능합니다.^^*\\\\n\\\\n- 주변에 은행, 편의점,약국, 음식점 등이 있어 아주 편리한 곳 입니당^^\\\\n\\\\n\\\\n◈ 내집 구하는 마음으로 주인을 적으로 생각합니다. ^^*\\\\n\\\\n◈ 차별화된 시스템으로 항상 업데이트합니다.\\\\n\\\\n◈ 100% 실매물.실사진.실가격의 광고등록으로 원칙이며 사훈입니다.\\\\n\\\\n◈ 강남지역 누구나 인정하는 중개사님들이 세입자들의 입장에서 안내해드립니다 \\\\n\\\\n◈ 강남,서초,송파 픽업걱정 No!! 전화연결24시 항상 Ok!! \\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 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'http://z1.zigbang.com/items/5508463/0d01322027dbc67355aa863176ce60e5bc80c430.JPG'}],\\n\",\n \" 'images_count': 19,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-6836-4428',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5165591244404,127.022864324202',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 110,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(복층형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 10.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '33.06',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '❄신축1룸+복층+엘리베이터❄',\\n\",\n \" 'updated_at': '14일 전',\\n\",\n \" 'user_email': 'global704@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-7314',\\n\",\n \" 'user_name': '중개보조원(이문섭)',\\n\",\n \" 'user_no': 1316477,\\n\",\n \" 'user_phone': '0507-1281-7314',\\n\",\n \" 'view_count': 209},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '559-5',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 신사동 610-2 1층',\\n\",\n \" 'agent_comment': '신사동 열린부동산 입니다 \\\\n\\\\n최선을 다하는 중개사가 되도록 노력할게요',\\n\",\n \" 'agent_email': 'suny1209@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-7187',\\n\",\n \" 'agent_name': '열린공인중개사(강광수)',\\n\",\n \" 'agent_phone': '02-511-0222',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '559',\\n\",\n \" 'bubun_code': '5',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 24000,\\n\",\n \" 'description': '2년전 싱크대 및 욕실 도배 장판 올수리한 연립주택 입니다\\\\n\\\\n1.5층 정남향 손볼거 전혀 없어요 \\\\n\\\\n융자 전혀 없고 주차 협의 1대 가능 합니다\\\\n\\\\n임차인 있어 사진을 제대로 못찍어 네요\\\\n\\\\n실제와서 보시면 더욱 마음에 들거에요\\\\n\\\\n거실 베란다 있고 안방에도 베란다 있어요\\\\n\\\\n방3,욕실1개 룸1개는 옷장 및 서립장으로 사용하면 좋을것 같아요\\\\n\\\\n입주는 9월20일 고정 입니다\\\\n\\\\n실매물 실사진 연락주셔요',\\n\",\n \" 'description_og': '2년전 싱크대 및 욕실 도배 장판 올수리한 연립주택 입니다\\\\n\\\\n1.5층 정남향 손볼거 전혀 없어요 \\\\n\\\\n융자 전혀 없고 주차 협의 1대 가능 합니다\\\\n\\\\n임차인 있어 사진을 제대로 못찍어 네요\\\\n\\\\n실제와서 보시면 더욱 마음에 들거에요\\\\n\\\\n거실 베란다 있고 안방에도 베란다 있어요\\\\n\\\\n방3,욕실1개 룸1개는 옷장 및 서립장으로 사용하면 좋을것 같아요\\\\n\\\\n입주는 9월20일 고정 입니다\\\\n\\\\n실매물 실사진 연락주셔요',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '1층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5423901,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/c3661e422467266774a5849bdbe787d00936ae1b.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/c2e78440323b51a6ddcec75d7a73c0908c49fa78.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/b01196980ecb227bebed4d5c522dfccd08087b7a.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/6017859bc1e0885aaa59b45f35da656420eb059b.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/1ffd92170a01771757f5a910794b946715487b9c.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/fa854b5fd2aea8dff3f72ee3e1f0da126f3f162a.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/19f5c6f16c06f9e98936bb6aa0185f0dfad2d8b8.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/7cd7946f291489204fedd53214fb62beb71d691b.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/07d08315bd67c7a61dfac243c14a11111d2280af.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5423901/5183962fcdd2949b7e966bcf14a58e8ea698923b.jpg'}],\\n\",\n \" 'images_count': 10,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '2만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '협의',\\n\",\n \" 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-9134-5291',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/676867/5ab3eaadbeb532e1a1a991f14cb8804cbcbf04ac.jpg',\\n\",\n \" 'random_location': '37.5206003116355,127.025412540907',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '쓰리룸+',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 17.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '56.20',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '2년전 올수리 한 투룸 정남향 입니다',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'suny1209@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-7187',\\n\",\n \" 'user_name': '대표공인중개사(강광수)',\\n\",\n \" 'user_no': 676867,\\n\",\n \" 'user_phone': '0507-1280-7187',\\n\",\n \" 'view_count': 605},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '16-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 서초구 잠원동 13-1 아이미서초빌딩 2층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'woopers@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '서초구',\\n\",\n \" 'agent_mobile': '0507-1281-0426',\\n\",\n \" 'agent_name': '오렌지하우스공인중개사(김승식)',\\n\",\n \" 'agent_phone': '02-512-4243',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '16',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '♥신사역 3분이내 도보권 으로 교통이 편리한 곳에 위치한 저렴한 투룸 입니다.\\\\n\\\\n♥실면적 15p, 직접 발품을 팔아 엄선하여 광고등록된 매물로, 시세평수대비 저렴 합니다.\\\\n\\\\n♥주거밀집 많은 논현동에 위치하며 강남구에서 보안이 가장 띄어난 동네로 치안에도 안전합니다.\\\\n\\\\n♥인근에 세탁소, 편의점, 마트, 은행 등 편의시설이 잘 되어있어 편하게 생활하실 수 있습니다.\\\\n\\\\n♥투룸 및 넓은 小거실 구조로 공간활용도가 높습니다.\\\\n\\\\n직접 현장에서 DSLR카메라로 찍어온 실매물 입니다~*^^*',\\n\",\n \" 'description_og': '♥신사역 3분이내 도보권 으로 교통이 편리한 곳에 위치한 저렴한 투룸 입니다.\\\\n\\\\n♥실면적 15p, 직접 발품을 팔아 엄선하여 광고등록된 매물로, 시세평수대비 저렴 합니다.\\\\n\\\\n♥주거밀집 많은 논현동에 위치하며 강남구에서 보안이 가장 띄어난 동네로 치안에도 안전합니다.\\\\n\\\\n♥인근에 세탁소, 편의점, 마트, 은행 등 편의시설이 잘 되어있어 편하게 생활하실 수 있습니다.\\\\n\\\\n♥투룸 및 넓은 小거실 구조로 공간활용도가 높습니다.\\\\n\\\\n직접 현장에서 DSLR카메라로 찍어온 실매물 입니다~*^^*',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '반지하',\\n\",\n \" 'floor_all': '3층',\\n\",\n \" 'id': 5642175,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/bf727ee9c01381d56fccdd486d7305621e607308.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/a9c20f69c90027e4c85cb12dd3547e7f8d6af131.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/8622a0b45645a2782bb044d6f0de2f62f0d0534c.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/434a9e2b1905b91ac929f7270b73a221f1b34a96.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/db4e65ee83793661c06506b9469901c8272b29b7.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/b9b3e7ac2014a1d5b0316023c96c517273c18cd0.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5642175/9af64291a1e5cc1c34920a631a55a2921b8578f4.JPG'}],\\n\",\n \" 'images_count': 7,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '수도',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\\n\",\n \" 'options': '에어컨, 가스레인지, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-8886-0847',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.51497885071,127.021464593274',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 55,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '💖💗이가격에💖투룸💟실매물이라니💖💟👰👨💜💙💚💙💜💙💚💙💜💖💚💙💜',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': 'wogudckacl@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-4233',\\n\",\n \" 'user_name': '중개보조원(배재형)',\\n\",\n \" 'user_no': 327679,\\n\",\n \" 'user_phone': '0507-1281-4233',\\n\",\n \" 'view_count': 50},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '13-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 639-14',\\n\",\n \" 'agent_comment': '★ 저는 100% 실매물 실가격 직접찍은 실사진으로만 광고합니다. \\\\n연락주시면 믿음에 보답해 드리겠습니다!!★\\\\n\\\\n*24시 전화문자상담환영~ 성실히 중개해 드립니다.',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 100,\\n\",\n \" 'description': '★ 보안, 주차, 생활여건, 방컨디션 모든게 최상급입니다.\\\\n\\\\n★ 긴말 않겠습니다. 이가격에서 가장 럭셔리입니다!!!!\\\\n\\\\n★ 다른매물 보여드리지도 않습니다^^\\\\n\\\\n',\\n\",\n \" 'description_og': '★ 보안, 주차, 생활여건, 방컨디션 모든게 최상급입니다.\\\\n\\\\n★ 긴말 않겠습니다. 이가격에서 가장 럭셔리입니다!!!!\\\\n\\\\n★ 다른매물 보여드리지도 않습니다^^\\\\n\\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '5층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5537230,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/9c5676594d81c8ab656b7c91d30059ac512fe6fd.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/dd10d960a84d33352ccad12904eabb83a10aac6a.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/d3ff921d7029078f773a0decc0716575cb15bd76.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/884607bdf4f95307ab6b1b93a170e78c1424471c.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/35e8052efab37807c689c7afb4812259861ff241.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/46a63ba6ca9d2773b3b9ae4f667e6c0899bc1ea5.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/94e979cae103fabe3274030c8050474603317f14.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/854e67b42c4259550804e514f90137064afc259f.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5537230/61a2ef3137910f5043ee02d87d2b18cccb4d950e.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션',\\n\",\n \" 'original_user_phone': '010-4130-9632',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5160789051649,127.022838649819',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 100,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 7.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '23.14',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '★ 초특가!!! 신시역1번출구 도보5분 오픈형 원룸!!!',\\n\",\n \" 'updated_at': '4일 전',\\n\",\n \" 'user_email': 'jmk_78@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-8205',\\n\",\n \" 'user_name': '중개보조원(조관혁)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1280-8205',\\n\",\n \" 'view_count': 437},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '16-38',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 664-25',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'extravvip@gmail.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-5172',\\n\",\n \" 'agent_name': '퍼스트공인중개사(성민규)',\\n\",\n \" 'agent_phone': '02-508-8966',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '16',\\n\",\n \" 'bubun_code': '38',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 2000,\\n\",\n \" 'description': '신사역 도보 1분거이에 위치한 최고에 신축 풀옵션입니다~~여성분만 계약 가능하실것 같아요~\\\\n\\\\n보증금은 1000/65 2000/60 3000/55\\\\n\\\\n관리비 5만원(인터넷,케이블 포함) 깍은 금액입니다!!(살짝 변동 될 수도 있습니다)\\\\n\\\\n주차비 별도로 있지만 안하시는분으로 구한다고 하세요(방문주차는 좋습니다 주차장이 좋아요)\\\\n\\\\n옵션은 에어컨 드럼세탁기 냉장고 전기인덕션 붙박이장(커서 많이 들어갑니다) 책상 책장\\\\n\\\\n혼자 사시기 여기보다 좋은곳은 없는것 같네요~~^^신축이여서 깨끗 깔끔하구요~~\\\\n\\\\n6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋습니다~~\\\\n\\\\n입주는 빠르시면 좋으시구요~~계약하실분 오시면 일주일 안으로 방 구할 예정입니다~~^^\\\\n\\\\n가성비 좋은 매물만 올려 계약이 빠르게 되니 서둘러주세요~~!!\\\\n\\\\n안녕하세요^^ 반갑습니다!! \\\\n\\\\n현재 100% 계약 가능한 매물만 제가 직접가서 찍은 사진입니다.\\\\n\\\\n◆ 1년 365일 24시간 언제든 카톡,문자,전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n◆ 강남 어디든 전화주시면 젭싸게 차량으로 픽업해드리고 가시는 곳 까지 편하고 안전하게 모셔다 드립니다.\\\\n (점심시간,퇴근시간 때도 모시러가구요 방보시고 다시 모셔다드립니다.)\\\\n◆ 내 집을 구한다는 마음가짐으로 최선을 다해 손님이 원하는 깨끗한 주거생활 책임지겠습니다.\\\\n \\\\n◆ 시간낭비 하시지 않도록 확실하게 중개합니다^^\\\\n\\\\n최대한 세입자 입장에 서서 미소를 짓게 만드는 가격에 \\\\n계약을 성사 시켜드리겠습니다. 감사합니다!\\\\n\\\\n:+:세입자의, 세입자에 의한, 세입자를 위한 매물:+:\\\\n\\\\n-------------------------------------------정성과 최선을 다하겠습니다-------------\\\\n\\\\n직장인과 학생여러분들을 위해 \\\\n24시간 카톡 상담가능합니다~^ㅡ^*\\\\n카카오톡ID:2222dragon\\\\n\\\\n어떤 중개사를 만나는가에 따라 내가 사는집이 달라집니다~!~!\\\\n\\\\n혼자 살기 이보다 좋은곳은 없습니다!! 신축이여서 깨끗 깔끔하구요~~6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋아요~~\\\\n\\\\n입주는 빠르면 좋으시구요 계약하시는분 오시면 바로 1주일안으로 방 구할 예정입니다~',\\n\",\n \" 'description_og': '신사역 도보 1분거이에 위치한 최고에 신축 풀옵션입니다~~여성분만 계약 가능하실것 같아요~\\\\n\\\\n보증금은 1000/65 2000/60 3000/55\\\\n\\\\n관리비 5만원(인터넷,케이블 포함) 깍은 금액입니다!!(살짝 변동 될 수도 있습니다)\\\\n\\\\n주차비 별도로 있지만 안하시는분으로 구한다고 하세요(방문주차는 좋습니다 주차장이 좋아요)\\\\n\\\\n옵션은 에어컨 드럼세탁기 냉장고 전기인덕션 붙박이장(커서 많이 들어갑니다) 책상 책장\\\\n\\\\n혼자 사시기 여기보다 좋은곳은 없는것 같네요~~^^신축이여서 깨끗 깔끔하구요~~\\\\n\\\\n6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋습니다~~\\\\n\\\\n입주는 빠르시면 좋으시구요~~계약하실분 오시면 일주일 안으로 방 구할 예정입니다~~^^\\\\n\\\\n가성비 좋은 매물만 올려 계약이 빠르게 되니 서둘러주세요~~!!\\\\n\\\\n안녕하세요^^ 반갑습니다!! \\\\n\\\\n현재 100% 계약 가능한 매물만 제가 직접가서 찍은 사진입니다.\\\\n\\\\n◆ 1년 365일 24시간 언제든 카톡,문자,전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n◆ 강남 어디든 전화주시면 젭싸게 차량으로 픽업해드리고 가시는 곳 까지 편하고 안전하게 모셔다 드립니다.\\\\n (점심시간,퇴근시간 때도 모시러가구요 방보시고 다시 모셔다드립니다.)\\\\n◆ 내 집을 구한다는 마음가짐으로 최선을 다해 손님이 원하는 깨끗한 주거생활 책임지겠습니다.\\\\n \\\\n◆ 시간낭비 하시지 않도록 확실하게 중개합니다^^\\\\n\\\\n최대한 세입자 입장에 서서 미소를 짓게 만드는 가격에 \\\\n계약을 성사 시켜드리겠습니다. 감사합니다!\\\\n\\\\n:+:세입자의, 세입자에 의한, 세입자를 위한 매물:+:\\\\n\\\\n-------------------------------------------정성과 최선을 다하겠습니다-------------\\\\n\\\\n직장인과 학생여러분들을 위해 \\\\n24시간 카톡 상담가능합니다~^ㅡ^*\\\\n카카오톡ID:2222dragon\\\\n\\\\n어떤 중개사를 만나는가에 따라 내가 사는집이 달라집니다~!~!\\\\n\\\\n혼자 살기 이보다 좋은곳은 없습니다!! 신축이여서 깨끗 깔끔하구요~~6층은 엘리베이터에 비밀번호가 따로 있어 보안이 더 좋아요~~\\\\n\\\\n입주는 빠르면 좋으시구요 계약하시는분 오시면 바로 1주일안으로 방 구할 예정입니다~',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '6층',\\n\",\n \" 'floor_all': '7층',\\n\",\n \" 'id': 5644685,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/59dcd1b47381fb2867105a5034dd39a35dbdbfa8.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/e98138f62800645cea547ee3cd8304aa44ebd0fe.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/84e5e60321ee07552bde05c918828de6033bc4fe.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/e7a730aff63377f34e24cd14c7d218654e936f22.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/8e003d560b9cb85968ca63cba6ebc7deb550a9c7.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/c7295caf049ce512c1733f33a74ef7f8bceb15c1.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/213065ef705f43e17e1c094d71d5a09064e56b37.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/70e0c7e6176ec9c0b27ac2948bd8b1b89f2ab09b.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5644685/36fd8a2970998551e5655fe46264f01df89f1050.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시입주가능',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 전자레인지, 책상, 책장, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-3158-9915',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5147493154384,127.020664946803',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 60,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 7.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '23.14',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '◆■절대 허위아님!! 신사역 도보1분 신축 풀옵션◆■',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': '2222dragon@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6667',\\n\",\n \" 'user_name': '중개보조원(이용)',\\n\",\n \" 'user_no': 158186,\\n\",\n \" 'user_phone': '0507-1280-6667',\\n\",\n \" 'view_count': 28},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '545-5',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 논현동 73-24번지 1층 즐겨찾기부동산',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'artsoup@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6420',\\n\",\n \" 'agent_name': '즐겨찾기공인중개사(김영한)',\\n\",\n \" 'agent_phone': '02-540-0124',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '545',\\n\",\n \" 'bubun_code': '5',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 25000,\\n\",\n \" 'description': '복도식이며 거실 창문과 복도 창문 열면 맞바람 통풍 잘되구요\\\\n\\\\n\\\\n\\\\n채광 좋습니다 \\\\n\\\\n\\\\n\\\\n주차가 불편해서 저렴하게 나왔습니다 잠깐 잠깐 대는건 괜찮구요!\\\\n\\\\n\\\\n 저녁 8시부터 다음날 오전10까지는 가능하나 낮에는 안됩니다 \\\\n\\\\n\\\\n\\\\n가로수길 다들 아시니 따로 말씀 드리지는 않겠지만 골목 들어가지 않아 밤길 귀가도 걱정 없습니다\\\\n\\\\n\\\\n\\\\n반전세도 가능합니다 22000-20 가능 \\\\n\\\\n\\\\n미리연락 부탁드리며 비번 있으니 언제든지 보러 오세요~~~\\\\n\\\\n\\\\n\\\\n',\\n\",\n \" 'description_og': '복도식이며 거실 창문과 복도 창문 열면 맞바람 통풍 잘되구요\\\\n\\\\n\\\\n\\\\n채광 좋습니다 \\\\n\\\\n\\\\n\\\\n주차가 불편해서 저렴하게 나왔습니다 잠깐 잠깐 대는건 괜찮구요!\\\\n\\\\n\\\\n 저녁 8시부터 다음날 오전10까지는 가능하나 낮에는 안됩니다 \\\\n\\\\n\\\\n\\\\n가로수길 다들 아시니 따로 말씀 드리지는 않겠지만 골목 들어가지 않아 밤길 귀가도 걱정 없습니다\\\\n\\\\n\\\\n\\\\n반전세도 가능합니다 22000-20 가능 \\\\n\\\\n\\\\n미리연락 부탁드리며 비번 있으니 언제든지 보러 오세요~~~\\\\n\\\\n\\\\n\\\\n',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '3층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5628997,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/0521923f47d27792e7d489253abb20e699232153.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/5054d2f65972460d2b05e29f8abe84b98c8a91ab.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/821b76570776985a82d84a878dcc90d617acd192.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/d759061df9f0b7b6c4072c3567adc328edf7bd72.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/37adb268efce848259d0ab4c0bdf2b311f1a0a6f.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/47d5d00b42faf11d8161013c30f83ae4deeddf60.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/4a2b95f48bc0f0aed70623f2ecd778b3ca03689b.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5628997/3ba2d66bfca172942b82287d4299ade55f2d5c9a.jpg'}],\\n\",\n \" 'images_count': 8,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': True,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '수도',\\n\",\n \" 'movein_date': '즉시또는협의',\\n\",\n \" 'near_subways': '신사역(3호선), 압구정역(3호선), 학동역(7호선)',\\n\",\n \" 'options': '-',\\n\",\n \" 'original_user_phone': '010-2446-3484',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5209045544202,127.022701987347',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 0,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '전세자금대출가능 가로수길 에 위치한 거실 넓게 빠진 투룸으로 신혼부부나 방보다 넓은 거실 찾는분들에게 딱입니다 가로수길이라도 창문이 반대오 나와 조용합니다',\\n\",\n \" 'updated_at': '4일 전',\\n\",\n \" 'user_email': 'cjpark12@hanmail.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-6676',\\n\",\n \" 'user_name': '중개보조원(박충진)',\\n\",\n \" 'user_no': 810168,\\n\",\n \" 'user_phone': '0507-1280-6676',\\n\",\n \" 'view_count': 152},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '13-20',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 역삼동 639-14',\\n\",\n \" 'agent_comment': '★ 저는 100% 실매물 실가격 직접찍은 실사진으로만 광고합니다. \\\\n연락주시면 믿음에 보답해 드리겠습니다!!★\\\\n\\\\n○ 이광고를 보고 계시다면 아직 방계약전입니다!! 서둘러 연락주세요!!!\\\\n\\\\n*24시 전화문자상담 환영~ 성실히 중개해드리겠습니다.\\\\n',\\n\",\n \" 'agent_email': 'jmk_78@nate.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-5901',\\n\",\n \" 'agent_name': '우정공인중개사(김성훈)',\\n\",\n \" 'agent_phone': '02-566-2004',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '13',\\n\",\n \" 'bubun_code': '20',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 5000,\\n\",\n \" 'description': '★ 강남 하이클래스 인테리어\\\\n\\\\n★ 빈티지스타일 대리석 인테리어!!\\\\n\\\\n★ 거실 완벽한 맞벽구조 티비쇼파 배치가능!!\\\\n\\\\n★ 침실싸이즈 굿~~ 슈퍼킹싸이즈도 무난하게 소화가능합니다.\\\\n\\\\n★ 신사역 1번출구 도보 3분거리 초역세권!! \\\\n\\\\n★ 이동네에 유일무이한 스타일입니다!! 서둘러 연락주세요.',\\n\",\n \" 'description_og': '★ 강남 하이클래스 인테리어\\\\n\\\\n★ 빈티지스타일 대리석 인테리어!!\\\\n\\\\n★ 거실 완벽한 맞벽구조 티비쇼파 배치가능!!\\\\n\\\\n★ 침실싸이즈 굿~~ 슈퍼킹싸이즈도 무난하게 소화가능합니다.\\\\n\\\\n★ 신사역 1번출구 도보 3분거리 초역세권!! \\\\n\\\\n★ 이동네에 유일무이한 스타일입니다!! 서둘러 연락주세요.',\\n\",\n \" 'elevator': '있음',\\n\",\n \" 'floor': '6층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5595814,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/6f78348d00eff1c3721155ddfd83e2877472a5ce.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/ee9387392e949005f58f580dbcc659f647465427.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/bbd3dfd92a61b07781b26110c6e931406c4f119d.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/8ee05f69eddc4c7e0b579e5dcb89007b4d180709.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/5bcf2a196b78a0435b10d904433b468c7718e28a.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/89002b6da9787c031ed7401b9370b31ad0437ca4.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/835f9faf0782c9d87c42bcb2e6e099b17b96b496.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/edf5ae991637a5b15f54b26dcc54f57639c9ef3f.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/5f7528cc9a780e59299d80ff1d40678b5610c0fc.JPG'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/88ff21c41e4a1d594c80f9370523b2b41829e39b.JPG'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/56e2c6b03676ba2fcf65b5b392185704a35aff5c.JPG'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/1bb1bd9ad4ed317a4ba324e95afc0bdfc9de31de.JPG'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5595814/c29a8683acecb672552e8e65ff7a370e3dbd5e00.JPG'}],\\n\",\n \" 'images_count': 13,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '9월초 즉시가능',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 학동역(7호선)',\\n\",\n \" 'options': '에어컨, 냉장고, 세탁기, 인덕션, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-4130-9632',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/252306.jpg',\\n\",\n \" 'random_location': '37.5165376313724,127.022892016915',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 90,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(분리형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 14.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '46.28',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '★전속관리★꿈의 원룸원거실★',\\n\",\n \" 'updated_at': '4일 전',\\n\",\n \" 'user_email': 'jmk_78@nate.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-8205',\\n\",\n \" 'user_name': '중개보조원(조관혁)',\\n\",\n \" 'user_no': 252306,\\n\",\n \" 'user_phone': '0507-1280-8205',\\n\",\n \" 'view_count': 130},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '17',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 삼성동 33-18 1층',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'newmanchoi@naver.com',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1281-3522',\\n\",\n \" 'agent_name': '신화공인중개사(최재호)',\\n\",\n \" 'agent_phone': '02-3444-6444',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '17',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '안녕하세요 !! 신화부동산입니다!!\\\\n\\\\n오늘보실매물은 논현동 학동공원에 위치한 \\\\n\\\\n초저렴한 반지하 투룸인데요!!\\\\n\\\\n원룸가격이라고해도 정말 저렴한 가격인데!!\\\\n\\\\n거기다가 투룸이라니!!\\\\n\\\\n반지하지만 반지하느낌 거의 없죠!!\\\\n\\\\n구조가 잘나와있고 세탁실처럼 사용할 수 있는 베란다가 깨알같이 있구요!!\\\\n\\\\n짐많으신 독신자분들, 단촐하게 2인거주하실분들 정말 강추드립니다!!',\\n\",\n \" 'description_og': '안녕하세요 !! 신화부동산입니다!!\\\\n\\\\n오늘보실매물은 논현동 학동공원에 위치한 \\\\n\\\\n초저렴한 반지하 투룸인데요!!\\\\n\\\\n원룸가격이라고해도 정말 저렴한 가격인데!!\\\\n\\\\n거기다가 투룸이라니!!\\\\n\\\\n반지하지만 반지하느낌 거의 없죠!!\\\\n\\\\n구조가 잘나와있고 세탁실처럼 사용할 수 있는 베란다가 깨알같이 있구요!!\\\\n\\\\n짐많으신 독신자분들, 단촐하게 2인거주하실분들 정말 강추드립니다!!',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '반지하',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5646707,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/15c32ce913e5d2a96c3ce9a0ef33f1f5174b0b7d.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/49c44e57c2af461433d3331ea28c07decc0e1dc1.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/4607ae7e027af35a1feafb03da602bbb099b3785.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/16ddf74931e81f0ddda039ffd143ba8e04faedbd.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/bfb2495f28e9fecb4f2ce5a34a6c7069d3715c46.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/3ca25d450dd5c330f1a677691f669cdff6693376.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/4b880e5a19d070b59045501de30eb457aa46cc56.JPG'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/7fe9f6a10a6cebf441b6702deead0a754943b088.JPG'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5646707/57286a4a1c6a218e86c4e5421ffbbaa035c5ad0c.JPG'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '5만원',\\n\",\n \" 'manage_cost_inc': '-',\\n\",\n \" 'movein_date': '즉시입주',\\n\",\n \" 'near_subways': '신사역(3호선), 논현역(7호선), 잠원역(3호선)',\\n\",\n \" 'options': '에어컨, 가스레인지',\\n\",\n \" 'original_user_phone': '010-6455-6755',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/47524/c10638eb8b2d6d7482c794fd5ba1a3259cae7f35.jpg',\\n\",\n \" 'random_location': '37.5150775103776,127.020536263529',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 55,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 15.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '49.59',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '힛트다 힛트!! 초저렴 원룸가격에 ★★★투룸!!!★★★',\\n\",\n \" 'updated_at': '2일 전',\\n\",\n \" 'user_email': '4everace@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-0865',\\n\",\n \" 'user_name': '중개보조원(서진원)',\\n\",\n \" 'user_no': 47524,\\n\",\n \" 'user_phone': '0507-1281-0865',\\n\",\n \" 'view_count': 30},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '569-14',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '569',\\n\",\n \" 'bubun_code': '14',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 45,\\n\",\n \" 'description': '* 압구정역 도보 3분거리 초저렴 풀옵션원룸 입니다.\\\\n\\\\n* 화이트톤으로 채광 좋으며 수납공간도 잘 되어 있습니다.\\\\n\\\\n* 대로변이라 주변 편의시설 좋으며, 대중교통 이용이 아주 편리합니다.\\\\n\\\\n* 풀옵션 단기임대가 귀한지역이라 빠른계약이 예상됩니다.\\\\n',\\n\",\n \" 'description_og': '* 압구정역 도보 3분거리 초저렴 풀옵션원룸 입니다.\\\\n\\\\n* 화이트톤으로 채광 좋으며 수납공간도 잘 되어 있습니다.\\\\n\\\\n* 대로변이라 주변 편의시설 좋으며, 대중교통 이용이 아주 편리합니다.\\\\n\\\\n* 풀옵션 단기임대가 귀한지역이라 빠른계약이 예상됩니다.\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '5층',\\n\",\n \" 'floor_all': '5층',\\n\",\n \" 'id': 5593179,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/4c598bedad7684dc5da75ce1b04ef5a52d93f532.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/cf2e4d60521c012ebd529f071a00965839894f63.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/f1da6c1ba7eb43ccdff0bf016abd634f86d0c2a6.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/4fb5b857bb5741bf71dc1dd4c31f87b8a3a90f66.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/bff5255b4ad79c07249ad563c26e091adbb2780d.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/0d0da34c31570332c0a3d65f545e9b8b01ee7403.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/214b096b46a951d9400d9d190338034739fd7750.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5593179/a292386f9e3fcf86ccf8cac1d51f27ee3d2aec27.jpg'}],\\n\",\n \" 'images_count': 8,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': 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{'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608753/47f4819ffd709c6f970cffedf3b46b13fb0126f3.jpg'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608753/e191cbff2ea442ddeb7b2ca8d8f81475962761a7.jpg'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608753/debf7eef851d62a98fc73e8556b7bc6fbf1dc1d2.jpg'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608753/39b5357647350bca4500a0f8311ec3eb8ee0f85c.jpg'},\\n\",\n \" {'count': 14,\\n\",\n \" 'index': 13,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5608753/4ffdc3e142abd914b674014bdd86e90c53d607d8.jpg'}],\\n\",\n \" 'images_count': 14,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 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'agent_phone': '02-511-0123',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '554',\\n\",\n \" 'bubun_code': '',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 1000,\\n\",\n \" 'description': '▒ 100% 실매물, 직접 촬영한 사진으로만 광고합니다.\\\\n\\\\n▒ 24시시간 상담가능합니다. 편하게 문의주세요!!\\\\n\\\\n▒ 인테리어 및 특징\\\\n\\\\n ☞ 건물 내외관 화이트톤으로 리모델링 했습니다!!\\\\n\\\\n ☞ TV/침대까지 있는 풀옵션 원룸\\\\n\\\\n ☞ 집기상태 매우 양호하고 몸만 오셔도 될정도입니다.\\\\n\\\\n ☞ 주변 쇼핑, 커피숍, 미용실 등 편의시설 多多多\\\\n\\\\n ☞ 신사역과, 버스노선 다수있어 대중교통이용 편리합니다.\\\\n\\\\n▒ 행복한 하루되세요~♬♬♬',\\n\",\n \" 'description_og': '▒ 100% 실매물, 직접 촬영한 사진으로만 광고합니다.\\\\n\\\\n▒ 24시시간 상담가능합니다. 편하게 문의주세요!!\\\\n\\\\n▒ 인테리어 및 특징\\\\n\\\\n ☞ 건물 내외관 화이트톤으로 리모델링 했습니다!!\\\\n\\\\n ☞ TV/침대까지 있는 풀옵션 원룸\\\\n\\\\n ☞ 집기상태 매우 양호하고 몸만 오셔도 될정도입니다.\\\\n\\\\n ☞ 주변 쇼핑, 커피숍, 미용실 등 편의시설 多多多\\\\n\\\\n ☞ 신사역과, 버스노선 다수있어 대중교통이용 편리합니다.\\\\n\\\\n▒ 행복한 하루되세요~♬♬♬',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '4층',\\n\",\n \" 'floor_all': '6층',\\n\",\n \" 'id': 5600860,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600860/cf16282782159f07f02a16f5b1768288cc557426.JPG'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600860/39bd6e19b2c4fbfdbf2172c303b35a9060f6997f.JPG'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600860/fe8d21bc264efb4f8b9105845558ee6ae9e93957.JPG'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600860/eabadad82dd4efaa39a32a27b25676653c6ad865.JPG'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600860/d28d52a401d241edff29c8dd410d9904be1ac4f0.JPG'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5600860/98ab6c678bdbebbd2666fe2a6fdc9c7ebe13b612.JPG'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 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인덕션, 책장, 침대, 옷장, 신발장, 싱크대',\\n\",\n \" 'original_user_phone': '010-3487-2672',\\n\",\n \" 'parking': '불가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': None,\\n\",\n \" 'random_location': '37.5213095416224,127.023417962265',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 68,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 8.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '26.45',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '◆◆리모델링 풀옵션 원룸/가로수길 접근성 우수◆◆',\\n\",\n \" 'updated_at': '6일 전',\\n\",\n \" 'user_email': 'todayroommk@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1280-0273',\\n\",\n \" 'user_name': '중개보조원(지명구)',\\n\",\n \" 'user_no': 449807,\\n\",\n \" 'user_phone': '0507-1280-0273',\\n\",\n \" 'view_count': 44},\\n\",\n \" 'title': '서울시 강남구 신사동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 논현동',\\n\",\n \" 'address2': '9-16',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010800',\\n\",\n \" 'bonbun_code': '9',\\n\",\n \" 'bubun_code': '16',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 80,\\n\",\n \" 'description': '\\\\n ㅁ 도산대로 안쪽에 위치한 신축 풀옵션 입니다\\\\n \\\\n ㅁ 신축 건물로 아주 깨끗하여 여성분들에게 인기 좋아요\\\\n\\\\n ㅁ 벽걸이TV, 트럼세탁기등 옵션또한 최상으로 셋팅되었습니다\\\\n\\\\n ㅁ 화이트톤 싱크대로 셋팅되었고 붙박이장도 아주 좋아요\\\\n\\\\n ㅁ 샤워부스 셋팅되었고 도로변이라 밤에도 안전하고 좋아요\\\\n\\\\n ㅁ 주차는 1대 편리하게 가능합니다 \\\\n \\\\n \\\\n\\\\n # 저희 홈페이지는 100% 실사진/실가격/실매물 싸이트입니다\\\\n\\\\n # 경력 9년에 강남 최고의 베테랑 중개인이 \\\" 주인을 때려서라도 맞춰드립니다 \\\\n\\\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\\\n',\\n\",\n \" 'description_og': '\\\\n ㅁ 도산대로 안쪽에 위치한 신축 풀옵션 입니다\\\\n \\\\n ㅁ 신축 건물로 아주 깨끗하여 여성분들에게 인기 좋아요\\\\n\\\\n ㅁ 벽걸이TV, 트럼세탁기등 옵션또한 최상으로 셋팅되었습니다\\\\n\\\\n ㅁ 화이트톤 싱크대로 셋팅되었고 붙박이장도 아주 좋아요\\\\n\\\\n ㅁ 샤워부스 셋팅되었고 도로변이라 밤에도 안전하고 좋아요\\\\n\\\\n ㅁ 주차는 1대 편리하게 가능합니다 \\\\n \\\\n \\\\n\\\\n # 저희 홈페이지는 100% 실사진/실가격/실매물 싸이트입니다\\\\n\\\\n # 경력 9년에 강남 최고의 베테랑 중개인이 &quot; 주인을 때려서라도 맞춰드립니다 \\\\n\\\\n # 1년 365일 24시간 언제든 전화주시면 성심껏 상담해드리겠습니다.\\\\n\\\\n # 강남 서초 송파 어디든 전화주시면 젭싸게 모시러 가겠습니다.\\\\n',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5512559,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/c46b9f86b327f93ea63c4b87374a8768d799933c.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/9645fdaa5be01998146d2438363658121121b6c6.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/1e7bea4ac610fe8d5806c2c50fbcca7f31597442.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/047fd27ac909433714485ea60adcc6a047689bf4.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/ab2015e135c271e01b24e00092bf29818fadfac5.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/7b18bcd2626ba764f7ff6d73976f773f7a76337f.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/047293348ff585e706106a56baf2ae78124b45c3.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/261a0e6e596b832cdc42a9331b4e099e973b3c41.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5512559/8f8120a46019b214b49a7f7dfc74330cab46d3b7.jpg'}],\\n\",\n \" 'images_count': 9,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '논현동',\\n\",\n \" 'manage_cost': '6만원',\\n\",\n \" 'manage_cost_inc': 'TV',\\n\",\n \" 'movein_date': '즉시 입주',\\n\",\n \" 'near_subways': '학동역(7호선), 신사역(3호선), 논현역(7호선)',\\n\",\n \" 'options': '싱크대, 에어컨, 냉장고, 세탁기, 가스레인지, 전자레인지, 침대, 옷장, 신발장',\\n\",\n \" 'original_user_phone': '010-6406-9464',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5175282814183,127.027387119575',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 80,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '원룸(오픈형)',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 9.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '29.75',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕을지병원 사거리부근 모던한 원룸',\\n\",\n \" 'updated_at': '14일 전',\\n\",\n \" 'user_email': 'jinyong0223@hanmail.net',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-8860',\\n\",\n \" 'user_name': '중개보조원(박진용)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': '0507-1281-8860',\\n\",\n \" 'view_count': 444},\\n\",\n \" 'title': '서울시 강남구 논현동'},\\n\",\n \" {'header': False,\\n\",\n \" 'header_height': 0,\\n\",\n \" 'item': {'address1': '서울시 강남구 신사동',\\n\",\n \" 'address2': '523-34',\\n\",\n \" 'address3': None,\\n\",\n \" 'agent_address1': '서울특별시 강남구 봉은사로54길 8 1층(역삼동)',\\n\",\n \" 'agent_comment': '',\\n\",\n \" 'agent_email': 'rjsdn110926@hanmail.net',\\n\",\n \" 'agent_local1': '서울시',\\n\",\n \" 'agent_local2': '강남구',\\n\",\n \" 'agent_mobile': '0507-1280-6920',\\n\",\n \" 'agent_name': '도원공인중개사(손석진)',\\n\",\n \" 'agent_phone': '010-8886-5877',\\n\",\n \" 'bjd_code': '1168010700',\\n\",\n \" 'bonbun_code': '523',\\n\",\n \" 'bubun_code': '34',\\n\",\n \" 'building': None,\\n\",\n \" 'building_type': '다세대건물',\\n\",\n \" 'contract': '서울특별시',\\n\",\n \" 'deposit': 100,\\n\",\n \" 'description': '★ 신사역 7분거리 가로수길 3분으로 최고의 위치!!\\\\n\\\\n★ 최고급 옵션으로 깔끔하게 셋팅 완료!!\\\\n\\\\n★정말 귀한 위치로 서두르셔야 합니다!!\\\\n\\\\n★ 투룸 구조로 2~3명이서도 충분히 쓰실수 있는 사이즈!!\\\\n\\\\n★ 즉시입주 가능하며 언제든 보실수 있습니다!!',\\n\",\n \" 'description_og': '★ 신사역 7분거리 가로수길 3분으로 최고의 위치!!\\\\n\\\\n★ 최고급 옵션으로 깔끔하게 셋팅 완료!!\\\\n\\\\n★정말 귀한 위치로 서두르셔야 합니다!!\\\\n\\\\n★ 투룸 구조로 2~3명이서도 충분히 쓰실수 있는 사이즈!!\\\\n\\\\n★ 즉시입주 가능하며 언제든 보실수 있습니다!!',\\n\",\n \" 'elevator': '없음',\\n\",\n \" 'floor': '2층',\\n\",\n \" 'floor_all': '4층',\\n\",\n \" 'id': 5594247,\\n\",\n \" 'images': [{'count': 1,\\n\",\n \" 'index': 0,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/8109ca56c6e73cdff48d70b6d805b1843a85a20b.jpg'},\\n\",\n \" {'count': 2,\\n\",\n \" 'index': 1,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/f47987d3494ecbf11debf874ee8409b77b887dc1.jpg'},\\n\",\n \" {'count': 3,\\n\",\n \" 'index': 2,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/69356c9e345f8e0cfabc2f2298f111146dcd5eb4.jpg'},\\n\",\n \" {'count': 4,\\n\",\n \" 'index': 3,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/2ad48bbb1bac4af1a2e82e2b1d9ac19decb9af06.jpg'},\\n\",\n \" {'count': 5,\\n\",\n \" 'index': 4,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/3c0c89159dd16d87ecd1513d580cc2fc81b5882e.jpg'},\\n\",\n \" {'count': 6,\\n\",\n \" 'index': 5,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/15d45441766f1d59760a382b1e22832d88cb2934.jpg'},\\n\",\n \" {'count': 7,\\n\",\n \" 'index': 6,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/98970fd542fc9b06260a6d9ecd66fcb4b5b68cfa.jpg'},\\n\",\n \" {'count': 8,\\n\",\n \" 'index': 7,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/428cb51d51d0f19ab2b18690bd61afb1aa6f0c15.jpg'},\\n\",\n \" {'count': 9,\\n\",\n \" 'index': 8,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/0baa4c98f45609c835f3c10d1fa3c1b2efd15665.jpg'},\\n\",\n \" {'count': 10,\\n\",\n \" 'index': 9,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/c3bbdb769a626d5fd616cc16651bae83cef55071.jpg'},\\n\",\n \" {'count': 11,\\n\",\n \" 'index': 10,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/913cc63fe2a1ddd3f097ce69c2f1f2b6cc0ee5be.jpg'},\\n\",\n \" {'count': 12,\\n\",\n \" 'index': 11,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/0279c31f0561b0b93ecab3f64a10593e36031e2c.jpg'},\\n\",\n \" {'count': 13,\\n\",\n \" 'index': 12,\\n\",\n \" 'url': 'http://z1.zigbang.com/items/5594247/a8120211213b5b8477c6817272312f9f8d799756.jpg'}],\\n\",\n \" 'images_count': 13,\\n\",\n \" 'images_thumbnail': '',\\n\",\n \" 'is_deposit_only': False,\\n\",\n \" 'is_direct': False,\\n\",\n \" 'is_owner': None,\\n\",\n \" 'is_premium': True,\\n\",\n \" 'is_premium2': True,\\n\",\n \" 'is_realestate': True,\\n\",\n \" 'is_room': False,\\n\",\n \" 'is_status_close': False,\\n\",\n \" 'is_status_open': True,\\n\",\n \" 'is_type_room': False,\\n\",\n \" 'is_zzim': False,\\n\",\n \" 'loan_text': '확인필요',\\n\",\n \" 'local1': '서울시',\\n\",\n \" 'local2': '강남구',\\n\",\n \" 'local3': '신사동',\\n\",\n \" 'manage_cost': '10만원',\\n\",\n \" 'manage_cost_inc': '인터넷, TV',\\n\",\n \" 'movein_date': '즉시 가능',\\n\",\n \" 'near_subways': '신사역(3호선), 압구정역(3호선), 논현역(7호선)',\\n\",\n \" 'options': '싱크대, 가스레인지, 에어컨, 침대, 냉장고, 전자레인지, 옷장, 세탁기, 신발장',\\n\",\n \" 'original_user_phone': '010-2068-9980',\\n\",\n \" 'parking': '가능',\\n\",\n \" 'pets_text': '확인필요',\\n\",\n \" 'profile_url': 'http://api.zigbang.com/ic/profile/68880_640.jpg',\\n\",\n \" 'random_location': '37.5214826313499,127.02071888967',\\n\",\n \" 'read_updated_at': '1/1/0001 12:00:00 AM',\\n\",\n \" 'rent': 100,\\n\",\n \" 'room_direction_text': '확인필요',\\n\",\n \" 'room_gubun_code': '01',\\n\",\n \" 'room_type': '투룸',\\n\",\n \" 'secret_memo': None,\\n\",\n \" 'size': 14.0,\\n\",\n \" 'size_contract': 0.0,\\n\",\n \" 'size_m2': '46.28',\\n\",\n \" 'size_m2_contract': '-',\\n\",\n \" 'status': '광고중',\\n\",\n \" 'title': '⭕ 투룸 ⭕ 가로수길옆 리모델링완료 마지막호실!!',\\n\",\n \" 'updated_at': '7일 전',\\n\",\n \" 'user_email': 'promising14@naver.com',\\n\",\n \" 'user_has_no_penalty': True,\\n\",\n \" 'user_has_penalty': False,\\n\",\n \" 'user_mobile': '0507-1281-8604',\\n\",\n \" 'user_name': '중개보조원(박규백)',\\n\",\n \" 'user_no': 68880,\\n\",\n \" 'user_phone': '0507-1281-8604',\\n\",\n \" 'view_count': 256},\\n\",\n \" 'title': '서울시 강남구 신사동'}]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"room_information[\\\"items\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'서울특별시 강남구 논현동 173-21번지 '\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#http://m.cafe.naver.com/joonggonara\\n\",\n \"room_information[\\\"items\\\"][0][\\\"item\\\"][\\\"agent_address1\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('서울특별시 은평구 진흥로 27', 11500, 0)\\n\",\n \"('전라남도 목포시 상동 1021-7', 200, 30)\\n\",\n \"('서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)', 5000, 20)\\n\",\n \"('경기도 오산시 원동 788-5번지 1층', 4300, 0)\\n\",\n \"('경기도 성남시 중원구 금광동 4352번지', 300, 30)\\n\",\n \"('충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)', 500, 53)\\n\",\n \"('대구광역시 달서구 야외음악당로39길 54 114동105호(두류동, 삼정그린빌상가)', 300, 28)\\n\",\n \"('대구광역시 동구 율하동로23길 84 (율하동)', 300, 27)\\n\",\n \"('서울특별시 관악구 남부순환로 1795', 1000, 45)\\n\",\n \"('서울특별시 관악구 신림동 1409-13', 3000, 30)\\n\",\n \"('광주광역시 광산구 신창동 1229-3', 4000, 0)\\n\",\n \"('서울특별시 강동구 성내동 64-13번지 브라운스톤 지하3층', 3000, 130)\\n\",\n \"('경기도 오산시 원동 787-3', 1000, 60)\\n\",\n \"('서울특별시 강북구 도봉로89길 12 1층', 7000, 50)\\n\",\n \"('경기도 화성시 향남읍 하길리 1456-1', 500, 35)\\n\",\n \"('서울특별시 관악구 관악로15길 26 (봉천동)', 300, 30)\\n\",\n \"('서울특별시 서초구 논현로11길 6-7, 1층 102호', 2000, 60)\\n\",\n \"('경기도 성남시 중원구 성남동 제일로 35번길27', 12000, 0)\\n\",\n \"('서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)', 4000, 50)\\n\",\n \"('충청남도 천안시 동남구 청수12로 18(청당동 850) 황금부동산', 1000, 60)\\n\",\n \"('서울특별시 강북구 도봉로89길 12 1층', 1000, 40)\\n\",\n \"('인천광역시 서구 검단1동 930-4 대인부동산랜드 공인중개사사무소', 300, 36)\\n\",\n \"('충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)', 200, 30)\\n\",\n \"('광주광역시 광산구 신창동 1229-3', 2000, 0)\\n\",\n \"('부산광역시 수영구 광안동 100-26', 1000, 35)\\n\",\n \"('경기도 안산시 상록구 이화로 44', 200, 33)\\n\",\n \"('경기도 안양시 만안구 병목안로 14, 104(안양동,우남리치빌)', 2000, 30)\\n\",\n \"('서울특별시 송파구 석촌동 282-2 서광빌딩 1층', 55, 55)\\n\",\n \"('충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)', 500, 27)\\n\",\n \"('서울특별시 송파구 방이동 69-6', 10000, 50)\\n\"\n ]\n }\n ],\n \"source\": [\n \"for room_id in range(4461545, 4461545+100):\\n\",\n \" BASE_URL = \\\"https://api.zigbang.com/v1/items?detail=true&item_ids=\\\" + str(room_id)\\n\",\n \" response = requests.get(BASE_URL)\\n\",\n \" room_inforation = json.loads(response.text)\\n\",\n \" \\n\",\n \" try:\\n\",\n \" address = room_inforation[\\\"items\\\"][0][\\\"item\\\"][\\\"agent_address1\\\"]\\n\",\n \" deposit = room_inforation[\\\"items\\\"][0][\\\"item\\\"][\\\"deposit\\\"]\\n\",\n \" rent = room_inforation[\\\"items\\\"][0][\\\"item\\\"][\\\"rent\\\"]\\n\",\n \" print((address, deposit, rent))\\n\",\n \" df.loc[len(df)] = [address, int(deposit), int(rent)]\\n\",\n \" except:\\n\",\n \" pass\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
addressdepositrent
0서울특별시 은평구 진흥로 2711500.00.0
1전라남도 목포시 상동 1021-7200.030.0
2서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)5000.020.0
3경기도 오산시 원동 788-5번지 1층4300.00.0
4경기도 성남시 중원구 금광동 4352번지300.030.0
5충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)500.053.0
6대구광역시 달서구 야외음악당로39길 54 114동105호(두류동, 삼정그린빌상가)300.028.0
7대구광역시 동구 율하동로23길 84 (율하동)300.027.0
8서울특별시 관악구 남부순환로 17951000.045.0
9서울특별시 관악구 신림동 1409-133000.030.0
10광주광역시 광산구 신창동 1229-34000.00.0
11서울특별시 강동구 성내동 64-13번지 브라운스톤 지하3층3000.0130.0
12경기도 오산시 원동 787-31000.060.0
13서울특별시 강북구 도봉로89길 12 1층7000.050.0
14경기도 화성시 향남읍 하길리 1456-1500.035.0
15서울특별시 관악구 관악로15길 26 (봉천동)300.030.0
16서울특별시 서초구 논현로11길 6-7, 1층 102호2000.060.0
17경기도 성남시 중원구 성남동 제일로 35번길2712000.00.0
18서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층)4000.050.0
19충청남도 천안시 동남구 청수12로 18(청당동 850) 황금부동산1000.060.0
20서울특별시 강북구 도봉로89길 12 1층1000.040.0
21인천광역시 서구 검단1동 930-4 대인부동산랜드 공인중개사사무소300.036.0
22충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)200.030.0
23광주광역시 광산구 신창동 1229-32000.00.0
24부산광역시 수영구 광안동 100-261000.035.0
25경기도 안산시 상록구 이화로 44200.033.0
26경기도 안양시 만안구 병목안로 14, 104(안양동,우남리치빌)2000.030.0
27서울특별시 송파구 석촌동 282-2 서광빌딩 1층55.055.0
28충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌)500.027.0
29서울특별시 송파구 방이동 69-610000.050.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" address deposit rent\\n\",\n \"0 서울특별시 은평구 진흥로 27 11500.0 0.0\\n\",\n \"1 전라남도 목포시 상동 1021-7 200.0 30.0\\n\",\n \"2 서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층) 5000.0 20.0\\n\",\n \"3 경기도 오산시 원동 788-5번지 1층 4300.0 0.0\\n\",\n \"4 경기도 성남시 중원구 금광동 4352번지 300.0 30.0\\n\",\n \"5 충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌) 500.0 53.0\\n\",\n \"6 대구광역시 달서구 야외음악당로39길 54 114동105호(두류동, 삼정그린빌상가) 300.0 28.0\\n\",\n \"7 대구광역시 동구 율하동로23길 84 (율하동) 300.0 27.0\\n\",\n \"8 서울특별시 관악구 남부순환로 1795 1000.0 45.0\\n\",\n \"9 서울특별시 관악구 신림동 1409-13 3000.0 30.0\\n\",\n \"10 광주광역시 광산구 신창동 1229-3 4000.0 0.0\\n\",\n \"11 서울특별시 강동구 성내동 64-13번지 브라운스톤 지하3층 3000.0 130.0\\n\",\n \"12 경기도 오산시 원동 787-3 1000.0 60.0\\n\",\n \"13 서울특별시 강북구 도봉로89길 12 1층 7000.0 50.0\\n\",\n \"14 경기도 화성시 향남읍 하길리 1456-1 500.0 35.0\\n\",\n \"15 서울특별시 관악구 관악로15길 26 (봉천동) 300.0 30.0\\n\",\n \"16 서울특별시 서초구 논현로11길 6-7, 1층 102호 2000.0 60.0\\n\",\n \"17 경기도 성남시 중원구 성남동 제일로 35번길27 12000.0 0.0\\n\",\n \"18 서울특별시 관악구 신림로 353, 1층 102호(신림동, 1층) 4000.0 50.0\\n\",\n \"19 충청남도 천안시 동남구 청수12로 18(청당동 850) 황금부동산 1000.0 60.0\\n\",\n \"20 서울특별시 강북구 도봉로89길 12 1층 1000.0 40.0\\n\",\n \"21 인천광역시 서구 검단1동 930-4 대인부동산랜드 공인중개사사무소 300.0 36.0\\n\",\n \"22 충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌) 200.0 30.0\\n\",\n \"23 광주광역시 광산구 신창동 1229-3 2000.0 0.0\\n\",\n \"24 부산광역시 수영구 광안동 100-26 1000.0 35.0\\n\",\n \"25 경기도 안산시 상록구 이화로 44 200.0 33.0\\n\",\n \"26 경기도 안양시 만안구 병목안로 14, 104(안양동,우남리치빌) 2000.0 30.0\\n\",\n \"27 서울특별시 송파구 석촌동 282-2 서광빌딩 1층 55.0 55.0\\n\",\n \"28 충청남도 천안시 서북구 두정상가5길 14, 104호(두정동, 석성빌) 500.0 27.0\\n\",\n \"29 서울특별시 송파구 방이동 69-6 10000.0 50.0\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"deposit 2615.166667\\n\",\n \"rent 35.800000\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.mean()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":260,"cells":{"repo_name":{"kind":"string","value":"UCSBarchlab/PyRTL"},"path":{"kind":"string","value":"ipynb-examples/introduction-to-hardware.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"10106"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Introduction to Hardware Design\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This code works through the hardware design process with the the\\n\",\n \"audience of software developers more in mind. We start with the simple\\n\",\n \"problem of designing a fibonacci sequence calculator (http://oeis.org/A000045).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pyrtl\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### A normal old python function to return the Nth fibonacci number.\\n\",\n \"\\n\",\n \"Interative implementation of fibonacci, just iteratively adds a and b to\\n\",\n \"calculate the nth number in the sequence.\\n\",\n \"```>> [software_fibonacci(x) for x in range(10)]\\n\",\n \"[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def software_fibonacci(n):\\n\",\n \" a, b = 0, 1\\n\",\n \" for i in range(n):\\n\",\n \" a, b = b, a + b\\n\",\n \" return a\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Attempt 1\\n\",\n \"\\n\",\n \"Let's convert this into some hardware that computes the same thing. Our first go will be to just replace the 0 and 1 with WireVectors to see\\n\",\n \"what happens.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def attempt1_hardware_fibonacci(n, bitwidth):\\n\",\n \" a = pyrtl.Const(0)\\n\",\n \" b = pyrtl.Const(1)\\n\",\n \" for i in range(n):\\n\",\n \" a, b = b, a + b\\n\",\n \" return a\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The above looks really nice does not really represent a hardware implementation\\n\",\n \"of fibonacci.\\n\",\n \"\\n\",\n \"Let's reason through the code, line by line, to figure out what it would actually build.\\n\",\n \"\\n\",\n \"* **```a = pyrtl.Const(0)```**\\n\",\n \"> This makes a wirevector of ```bitwidth=1``` that is driven by a zero. Thus **```a```** is a wirevector. Seems good.\\n\",\n \"\\n\",\n \"* **```b = pyrtl.Const(1)```** \\n\",\n \"> Just like above, ```b``` is a wirevector driven by 1\\n\",\n \"\\n\",\n \"* **```for i in range(n):```**\\n\",\n \"> Okay, here is where things start to go off the rails a bit. This says to perform the following code 'n' times, but the value 'n' is passed as an input and is not something that is evaluated in the hardware, it is evaluated when you run the PyRTL program which generates (or more specifically elaborates) the hardware. Thus the hardware we are building will have The value of 'n' built into the hardware and won't actually be a run-time parameter. Loops are really useful for building large repetitive hardware structures, but they CAN'T be used to represent hardware that should do a computation iteratively. Instead we are going to need to use some registers to build a state machine.\\n\",\n \"* **```a, b = b, a + b```**\\n\",\n \"> Let's break this apart. In the first cycle **```b```** is ```Const(1)``` and ```(a + b)``` builds an adder with a ```(Const(0))``` and ```b (Const(1)``` as inputs. Thus ```(b, a + b)``` in the first iteration is: ```( Const(1), result_of_adding( Const(0), Const(1) )``` At the end of the first iteration **```a```** and **```b```** refer to those two constant values. In each following iteration more adders are built and the names **```a```** and **```b```** are bound to larger and larger trees of adders but all the inputs are constants!\\n\",\n \"* **```return a```** \\n\",\n \"> The final thing that is returned then is the last output from this tree of adders which all have ```Consts``` as inputs. Thus this hardware is hard-wired to find only and exactly the value of fibonacci of the value N specified at design time! Probably not what you are intending.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Attempt 2\\n\",\n \"\\n\",\n \"Let's try a different approach. Let's specify two registers (\\\"a\\\" and \\\"b\\\") and then we can **update those values** as we iteratively compute fibonacci of N **cycle by cycle.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def attempt2_hardware_fibonacci(n, bitwidth):\\n\",\n \" a = pyrtl.Register(bitwidth, 'a')\\n\",\n \" b = pyrtl.Register(bitwidth, 'b')\\n\",\n \"\\n\",\n \" a.next <<= b\\n\",\n \" b.next <<= a + b\\n\",\n \"\\n\",\n \" return a\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is looking much better. \\n\",\n \"\\n\",\n \"Two registers, **```a```** and **```b```** store the values from which we\\n\",\n \"can compute the series. \\n\",\n \"\\n\",\n \"The line ```a.next <<= b``` means that the value of a in the next\\n\",\n \"cycle should be simply be **```b```** from the current cycle.\\n\",\n \"\\n\",\n \"The line ```b.next <<= a + b``` says\\n\",\n \"to build an adder, with inputs of **```a```** and **```b```** from the current cycle and assign the value\\n\",\n \"to **```b```** in the next cycle.\\n\",\n \"\\n\",\n \"### A visual representation of the hardware built is as such:\\n\",\n \"```\\n\",\n \"      +-----+      +---------+\\n\",\n \"      |     |      |         |\\n\",\n \"  +===V==+  |  +===V==+      |\\n\",\n \"  |      |  |  |      |      |\\n\",\n \"  |   a  |  |  |   b  |      |\\n\",\n \"  |      |  |  |      |      |\\n\",\n \"  +===V==+  |  +==V===+      |\\n\",\n \"      |     |     |          |\\n\",\n \"      |     +-----+          |\\n\",\n \"      |           |          |\\n\",\n \"  +===V===========V==+       |\\n\",\n \"   \\\\      adder     /        |\\n\",\n \"   +==============+        |\\n\",\n \"             |               |\\n\",\n \"           +---------------+\\n\",\n \"```\\n\",\n \"\\n\",\n \"\\n\",\n \"Note that in the picture the register **```a```** and **```b```** each have a wirevector which is\\n\",\n \"the current value (shown flowing out of the bottom of the register) and an *input*\\n\",\n \"which is giving the value that should be the value of the register in the following\\n\",\n \"cycle (shown flowing into the top of the register) which are **```a```** and **```a.next```** respectively.\\n\",\n \"\\n\",\n \"When we say **```return a```** what we are returning is a reference to the register **```a```** in\\n\",\n \"the picture above.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Attempt 3\\n\",\n \"\\n\",\n \"Of course one problem is that we don't know when we are done! **How do we know we\\n\",\n \"reached the \\\"nth\\\" number in the sequence?** Well, we need to **add a register** to\\n\",\n \"count up and see if we are done.\\n\",\n \"\\n\",\n \"This is very similliar to the example before, except that now we have a register \\\"i\\\"\\n\",\n \"which keeps track of the iteration that we are on (i.next <<= i + 1).\\n\",\n \"\\n\",\n \"The **function now returns two values**, a reference to the register \\\"a\\\" and a reference to a single\\n\",\n \"bit that tells us if we are done. That bit is calculated by comparing \\\"i\\\" to the\\n\",\n \"to a wirevector \\\"n\\\" that is passed in to see if they are the same. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def attempt3_hardware_fibonacci(n, bitwidth):\\n\",\n \" a = pyrtl.Register(bitwidth, 'a')\\n\",\n \" b = pyrtl.Register(bitwidth, 'b')\\n\",\n \" i = pyrtl.Register(bitwidth, 'i')\\n\",\n \"\\n\",\n \" i.next <<= i + 1\\n\",\n \" a.next <<= b\\n\",\n \" b.next <<= a + b\\n\",\n \"\\n\",\n \" return a, i == n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Attempt 4\\n\",\n \"\\n\",\n \"This is now far enough along that we can **simulate the design** and see what happens...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def attempt4_hardware_fibonacci(n, req, bitwidth):\\n\",\n \" a = pyrtl.Register(bitwidth, 'a')\\n\",\n \" b = pyrtl.Register(bitwidth, 'b')\\n\",\n \" i = pyrtl.Register(bitwidth, 'i')\\n\",\n \" local_n = pyrtl.Register(bitwidth, 'local_n')\\n\",\n \" done = pyrtl.WireVector(bitwidth=1, name='done')\\n\",\n \"\\n\",\n \" with pyrtl.conditional_assignment:\\n\",\n \" with req:\\n\",\n \" local_n.next |= n\\n\",\n \" i.next |= 0\\n\",\n \" a.next |= 0\\n\",\n \" b.next |= 1\\n\",\n \" with pyrtl.otherwise:\\n\",\n \" i.next |= i + 1\\n\",\n \" a.next |= b\\n\",\n \" b.next |= a + b\\n\",\n \" done <<= i == local_n\\n\",\n \" return a, done\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": 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times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"loading nmi score progress\\n\",\n \"loading running time progress\\n\",\n \"\\n\",\n \"starting ghsom for: 99/1/embedded_network_10.gml\\n\",\n \"running time of algorithm: 9.88899993896\\n\",\n \"saved nmi score for network embedded_network_10.gml: [ 0.]\\n\",\n \"\\n\",\n \"writing nmi scores and running times to file\\n\",\n \"\\n\",\n \"DONE\\n\",\n \"OVERALL NMI SCORES\\n\",\n \"[[ 0.54952507 0.56748932 0.62117634 0.8689669 0.90786313 0.61891829]\\n\",\n \" [ 0.49040801 0.52003907 0.55385055 0.68342899 0.75099239 0.20930235]\\n\",\n \" [ 0.74189169 0.7927954 0.91430549 0.77401103 0.51242947 0.30388959]\\n\",\n \" [ 0.86975907 0.90847255 0.94223418 0.76437275 0.49650535 0.26940284]\\n\",\n \" [ 0.59114813 0.6314837 0.6413286 0.79549181 0.89866097 0.44922438]\\n\",\n \" [ 0.77742183 0.79794609 0.81828777 0.81413612 0.77374776 0.04583205]\\n\",\n \" [ 0.56573641 0.65302357 0.66161511 0.76606045 0.85220675 0.2584614 ]\\n\",\n \" [ 0.67196792 0.70070307 0.76196421 0.78278838 0.90585361 0.54734927]\\n\",\n \" [ 0.71442567 0.73101836 0.73740175 0.83227564 0.90810815 0.17938423]\\n\",\n \" [ 0.39265984 0.41185519 0.42299292 0.44501771 0.46549164 0.21294819]\\n\",\n \" [ 0.51729484 0.52877433 0.57226428 0.68004549 0.85490593 0.34548417]\\n\",\n \" [ 0.69599862 0.73971083 0.75569862 0.88054738 0.907541 0.53787565]\\n\",\n \" [ 0.66178272 0.68701664 0.71348357 0.7359724 0.86818984 0.63783515]\\n\",\n \" [ 0.66916629 0.70154855 0.72716467 0.80823427 0.89660667 0.54878722]\\n\",\n \" [ 0.6766697 0.74272099 0.80111974 0.85289248 0.89200511 0.53935135]\\n\",\n \" [ 0.43036159 0.42974014 0.41008955 0.45686715 0.46698894 0.32971662]\\n\",\n \" [ 0.46224051 0.5239402 0.55973181 0.55407724 0.47730452 0.22830577]\\n\",\n \" [ 0.43790651 0.44167666 0.46434119 0.45471284 0.45866686 0.19605661]\\n\",\n \" [ 0.50409803 0.52188909 0.59000313 0.60940083 0.64128171 0.56599709]\\n\",\n \" [ 0.58249127 0.60712079 0.67087225 0.76685493 0.88429845 0.70463111]\\n\",\n \" [ 0.73512153 0.80380315 0.79303377 0.86058764 0.76931769 0.14101088]\\n\",\n \" [ 0.52432465 0.52685064 0.56583541 0.69664756 0.85627336 0.42335175]\\n\",\n \" [ 0.8685156 0.88487633 0.89449922 0.92202488 0.53305698 0.18598665]\\n\",\n \" [ 0.43143541 0.45036984 0.49538811 0.65719984 0.72592287 0.38357852]\\n\",\n \" [ 0.6727648 0.70089606 0.72449522 0.78389157 0.89693662 0.54845905]\\n\",\n \" [ 0.86578841 0.88629416 0.94215701 0.81481251 0.48257458 0.13512311]\\n\",\n \" [ 0.71510114 0.72506788 0.74158547 0.78940905 0.86478929 0.66856926]\\n\",\n \" [ 0.4788749 0.51388749 0.54773472 0.67065553 0.82326338 0.25808119]\\n\",\n \" [ 0.60175011 0.68068056 0.6955397 0.76101609 0.89916926 0.35968311]\\n\",\n \" [ 0.67026661 0.71764841 0.72965867 0.85628396 0.89688288 0.44804714]\\n\",\n \" [ 0.43706169 0.47310536 0.50828678 0.6094256 0.78458666 0.60977995]\\n\",\n \" [ 0.68472119 0.67462261 0.71686832 0.72909237 0.84626626 0.4092297 ]\\n\",\n \" [ 0.7558466 0.77093929 0.80673139 0.8535845 0.71086114 0.55441457]\\n\",\n \" [ 0.79408313 0.79904343 0.85742581 0.8428316 0.62844791 0.34590136]\\n\",\n \" [ 0.42912769 0.48695157 0.50954179 0.49759907 0.55888392 0.48890269]\\n\",\n \" [ 0.61534255 0.6520512 0.67686749 0.78657334 0.88408489 0.45062018]\\n\",\n \" [ 0.61220301 0.64474238 0.67866419 0.76588344 0.89397077 0.72583468]\\n\",\n \" [ 0.70427041 0.69100481 0.72923623 0.81265725 0.89599084 0.358284 ]\\n\",\n \" [ 0.65237501 0.67344766 0.67759013 0.75710735 0.89223168 0.62566046]\\n\",\n \" [ 0.72913538 0.75581506 0.76308175 0.80277497 0.90785414 0.44840138]\\n\",\n \" [ 0.64361903 0.67975856 0.71380516 0.76328574 0.89303927 0.71472595]\\n\",\n \" [ 0.66093933 0.70151879 0.73460762 0.78296666 0.8947692 0.64730623]\\n\",\n \" [ 0.46689171 0.4659103 0.50331961 0.66440175 0.68762023 0.297966 ]\\n\",\n \" [ 0.56740454 0.61620314 0.64066982 0.80461263 0.90698445 0.52779473]\\n\",\n \" [ 0.69789711 0.73175138 0.77443267 0.78267117 0.8968744 0.44861129]\\n\",\n \" [ 0.6842159 0.70155859 0.76323417 0.8116452 0.86406129 0.45815237]\\n\",\n \" [ 0.56614448 0.57753163 0.62302067 0.64928133 0.69363532 0.42002466]\\n\",\n \" [ 0.75317611 0.7136595 0.64840915 0.60801226 0.47886036 0.18344557]\\n\",\n \" [ 0.8625339 0.86324523 0.84857976 0.74212945 0.50580999 0.12299423]\\n\",\n \" [ 0.70722337 0.75093061 0.77465986 0.78292685 0.85728533 0.36928518]\\n\",\n \" [ 0.76084206 0.79685743 0.80715672 0.82722595 0.89766646 0.28980628]\\n\",\n \" [ 0.83731322 0.85010262 0.87957149 0.80641364 0.44375914 0.31934683]\\n\",\n \" [ 0.79876243 0.80649439 0.82271547 0.83520206 0.69007944 0.34115843]\\n\",\n \" [ 0.8248597 0.78721335 0.83300509 0.77654594 0.6020407 0.27704426]\\n\",\n \" [ 0.6399694 0.72260669 0.78404524 0.75524357 0.91796948 0.5486662 ]\\n\",\n \" [ 0.73435074 0.75213578 0.79013807 0.79614173 0.78898045 0.24545142]\\n\",\n \" [ 0.85685709 0.8578449 0.82091753 0.79542476 0.63863615 0.29615394]\\n\",\n \" [ 0.64136285 0.68659274 0.67674475 0.720359 0.74281426 0.42472824]\\n\",\n \" [ 0.8084302 0.81164128 0.85075465 0.80842885 0.59248826 0.2541637 ]\\n\",\n \" [ 0.80267374 0.7492153 0.70632592 0.65755461 0.47720788 0.24925608]\\n\",\n \" [ 0.84590507 0.84649262 0.87274696 0.88658659 0.71091225 0.14746158]\\n\",\n \" [ 0.76976603 0.78235066 0.75965799 0.7498199 0.59059672 0.2916559 ]\\n\",\n \" [ 0.60038 0.61252167 0.63584 0.65715981 0.74072097 0.42108186]\\n\",\n \" [ 0.62638449 0.6530487 0.7319694 0.71705946 0.88892576 0.2801512 ]\\n\",\n \" [ 0.61641814 0.67391002 0.71501202 0.72437325 0.81318462 0.44912298]\\n\",\n \" [ 0.60791793 0.63956886 0.71967724 0.7022293 0.89563493 0.44790805]\\n\",\n \" [ 0.8181593 0.80948791 0.8485548 0.85773862 0.75089196 0.34042436]\\n\",\n \" [ 0.59856681 0.63139099 0.71782715 0.69740854 0.87191473 0.37839586]\\n\",\n \" [ 0.72637538 0.74250726 0.77264405 0.77401104 0.80115311 0.31120285]\\n\",\n \" [ 0.77670652 0.74522905 0.70089113 0.57646704 0.47504081 0.17607441]\\n\",\n \" [ 0.79174193 0.7555183 0.70000471 0.63272893 0.47746115 0.14575981]\\n\",\n \" [ 0.8053393 0.7499825 0.73614648 0.64512311 0.48459734 0.09722981]\\n\",\n \" [ 0.82625864 0.85067941 0.79787576 0.67037047 0.49684078 0.25735227]\\n\",\n \" [ 0.66688711 0.66666619 0.75028918 0.73468054 0.72370009 0.47098527]\\n\",\n \" [ 0.59908463 0.57598744 0.51710369 0.45597234 0.37104299 0.1679981 ]\\n\",\n \" [ 0.7711235 0.76912794 0.68395538 0.56620825 0.45498247 0.20605832]\\n\",\n \" [ 0.73409469 0.72750921 0.69103044 0.58755294 0.47810317 0.24788601]\\n\",\n \" [ 0.76725277 0.79915449 0.80136854 0.82909858 0.74453964 0.33343368]\\n\",\n \" [ 0.69782823 0.67502995 0.65677351 0.59980177 0.40928058 0.10149589]\\n\",\n \" [ 0.65011018 0.61048631 0.72456986 0.70863219 0.87596625 0.60786757]\\n\",\n \" [ 0.61088287 0.57505597 0.53767897 0.48511584 0.35918304 0.11345659]\\n\",\n \" [ 0.58917786 0.605467 0.7250835 0.70696132 0.91650798 0.26839953]\\n\",\n \" [ 0.69831313 0.6162117 0.61035977 0.4793488 0.3452575 0.13722418]\\n\",\n \" [ 0.81486034 0.80294798 0.7418346 0.6811155 0.4451345 0.13145958]\\n\",\n \" [ 0.77144422 0.77953852 0.80482346 0.8117083 0.67209845 0.41409052]\\n\",\n \" [ 0.81174832 0.79939028 0.80285814 0.69078983 0.55825099 0.16391824]\\n\",\n \" [ 0.58982492 0.54904596 0.53509968 0.4945495 0.36657169 0.11542566]\\n\",\n \" [ 0.8288504 0.8221208 0.8578931 0.7863148 0.51187383 0.12900125]\\n\",\n \" [ 0.66945223 0.67920425 0.70096102 0.71667352 0.78389768 0.52914696]\\n\",\n \" [ 0.71434567 0.70863833 0.76450924 0.76982958 0.82680669 0.38021429]\\n\",\n \" [ 0.78990647 0.8146741 0.82189723 0.8168652 0.6989619 0.20465515]\\n\",\n \" [ 0.68857028 0.67125901 0.65039418 0.55529524 0.41534553 0.21349388]\\n\",\n \" [ 0.83280633 0.84535158 0.8656412 0.832823 0.6544874 0.12947269]\\n\",\n \" [ 0.7805793 0.79853232 0.81739112 0.82611417 0.71671765 0.17729986]\\n\",\n \" [ 0.69434991 0.67756319 0.63619565 0.5451263 0.39579527 0.15423876]\\n\",\n \" [ 0.76373613 0.76655354 0.82576943 0.84426464 0.64264467 0.21106216]\\n\",\n \" [ 0.7559443 0.77042208 0.81552976 0.82898259 0.79994685 0.34616979]\\n\",\n \" [ 0.79544854 0.77184386 0.73012865 0.66283832 0.54984685 0.09519321]\\n\",\n \" [ 0.71889643 0.66853765 0.65314659 0.59153232 0.38862889 0.22591419]\\n\",\n \" [ 0.61416811 0.66571283 0.72491661 0.72400041 0.89553419 0.54820093]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import division\\n\",\n \"\\n\",\n \"import os\\n\",\n \"from shutil import copyfile\\n\",\n \"import subprocess\\n\",\n \"from save_embedded_graph27 import main_binary as embed_main\\n\",\n \"from spearmint_ghsom import main as ghsom_main\\n\",\n \"import numpy as np\\n\",\n \"import pickle\\n\",\n \"from time import time\\n\",\n \"import networkx as nx\\n\",\n \"\\n\",\n \"def save_obj(obj, name):\\n\",\n \" with open(name + '.pkl', 'wb') as f:\\n\",\n \" pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\\n\",\n \"\\n\",\n \"def load_obj(name):\\n\",\n \" with open(name + '.pkl', 'rb') as f:\\n\",\n \" return pickle.load(f)\\n\",\n \" \\n\",\n \"def graph_measures(gml_filename):\\n\",\n \" \\n\",\n \" G = nx.read_gml(gml_filename)\\n\",\n \" \\n\",\n \" density = nx.density(G)\\n\",\n \" \\n\",\n \" arr = np.array([val for key, val in nx.degree_centrality(G).iteritems()])\\n\",\n \" arr = np.sort(arr)[::-1]\\n\",\n \" \\n\",\n \" k = len(arr)\\n\",\n \"\\n\",\n \" k1 = k\\n\",\n \"\\n\",\n \" sum = np.sum(arr)\\n\",\n \" \\n\",\n \" var = 1\\n\",\n \"\\n\",\n \" while var > 0.95:\\n\",\n \"\\n\",\n \" k -= 1\\n\",\n \"\\n\",\n \" var = np.sum(arr[:k]) / sum \\n\",\n \" \\n\",\n \" centrality = k / k1\\n\",\n \" \\n\",\n \" assortativity = nx.degree_assortativity_coefficient(G)\\n\",\n \" \\n\",\n \" connectivity = nx.node_connectivity(G)\\n\",\n \" \\n\",\n \" return np.array([density, centrality, assortativity, connectivity])\\n\",\n \"\\n\",\n \"#root dir\\n\",\n \"os.chdir(\\\"C:\\\\Miniconda3\\\\Jupyter\\\\GHSOM_simplex_dsd\\\")\\n\",\n \"\\n\",\n \"#save directory\\n\",\n \"dir = os.path.abspath(\\\"parameter_tests_density\\\")\\n\",\n \"\\n\",\n \"#number of networks to generate\\n\",\n \"num_networks = 100\\n\",\n \"\\n\",\n \"#number of times to repeat\\n\",\n \"num_repeats = 10\\n\",\n \"\\n\",\n \"#number of nodes in the graph\\n\",\n \"N = 64\\n\",\n \"\\n\",\n \"#make save directory\\n\",\n \"if not os.path.isdir(dir):\\n\",\n \" os.mkdir(dir)\\n\",\n \"\\n\",\n \"#change to dir\\n\",\n \"os.chdir(dir) \\n\",\n \"\\n\",\n \"#network file names -- output of network generator\\n\",\n \"network = \\\"network.dat\\\"\\n\",\n \"first_level = \\\"community.dat\\\"\\n\",\n \"\\n\",\n \"#community labels\\n\",\n \"labels = 'firstlevelcommunity'\\n\",\n \"\\n\",\n \"#mixing factors\\n\",\n \"mu = 0.3\\n\",\n \"\\n\",\n \"parameter_settings = [0.5, 0.6, 0.7, 0.8, 0.9, 1]\\n\",\n \"\\n\",\n \"# densities = np.zeros(num_networks)\\n\",\n \"overall_nmi_scores = np.zeros((num_networks, len(parameter_settings)))\\n\",\n \"\\n\",\n \"MAX_EDGES = 2017\\n\",\n \"MIN_DENSITY = 0.05\\n\",\n \"MAX_DENSITY = 0.2\\n\",\n \"\\n\",\n \"for i in range(num_networks):\\n\",\n \" \\n\",\n \" #create directory\\n\",\n \" dir_string = os.path.join(dir, str(i))\\n\",\n \" if not os.path.isdir(dir_string):\\n\",\n \" os.mkdir(dir_string)\\n\",\n \" \\n\",\n \" #change working directory \\n\",\n \" os.chdir(dir_string)\\n\",\n \" \\n\",\n \" #make benchmark parameter file\\n\",\n \" filename = \\\"benchmark_flags_{}.dat\\\".format(i)\\n\",\n \"\\n\",\n \" if os.path.isfile('density.txt'):\\n\",\n \" \\n\",\n \" density = np.genfromtxt('density.txt')\\n\",\n \" \\n\",\n \" else:\\n\",\n \" \\n\",\n \" #number of edges\\n\",\n \" num_edges = np.random.randint(MAX_EDGES * MIN_DENSITY, 2017 * 0.8)\\n\",\n \"\\n\",\n \" #number of communities\\n\",\n \" num_communities = np.random.randint(1, 5)\\n\",\n \"\\n\",\n \" #number of nodes in micro community\\n\",\n \" minc = 1\\n\",\n \" maxc = np.random.randint(16, 30)\\n\",\n \"\\n\",\n \" #average number of edges\\n\",\n \" k = float(num_edges) / N\\n\",\n \"\\n\",\n \" #max number of edges\\n\",\n \" maxk = 2 * k\\n\",\n \"\\n\",\n \" ##calculate density\\n\",\n \" density = 2 * float(num_edges) / (N * (N-1))\\n\",\n \" with open('density.txt','w') as f:\\n\",\n \" f.write('{}\\\\n'.format(density))\\n\",\n \"\\n\",\n \" if not os.path.isfile(filename):\\n\",\n \" print 'density: {}'.format(density)\\n\",\n \" print '-N {} -k {} -maxk {} -minc {} -maxc {} -mu {}'.format(N, k, maxk, minc, maxc, mu)\\n\",\n \" with open(filename,\\\"w\\\") as f:\\n\",\n \" f.write(\\\"-N {} -k {} -maxk {} -minc {} -maxc {} -mu {}\\\".format(N, k, maxk, minc, maxc, mu))\\n\",\n \" print 'written flag file: {}'.format(filename)\\n\",\n \" \\n\",\n \"# if not os.path.isfile('data.pkl'):\\n\",\n \"# data = np.zeros((len(parameter_settings), 4, num_repeats))\\n\",\n \"# else:\\n\",\n \"# data = load_obj('data')\\n\",\n \"\\n\",\n \" data = np.zeros((len(parameter_settings), 4, num_repeats))\\n\",\n \" \\n\",\n \" for j in range(len(parameter_settings)):\\n\",\n \" \\n\",\n \" #setting fo e_sg\\n\",\n \" p = parameter_settings[j]\\n\",\n \" \\n\",\n \" #ghsom parameters\\n\",\n \" params = {'w': 0.0001,\\n\",\n \" 'eta': 0.0001,\\n\",\n \" 'sigma': 1,\\n\",\n \" 'e_sg': p,\\n\",\n \" 'e_en': 0.8}\\n\",\n \" \\n\",\n \" #create directory\\n\",\n \" dir_string_p = os.path.join(dir_string, str(p))\\n\",\n \" if not os.path.isdir(dir_string_p):\\n\",\n \" os.mkdir(dir_string_p)\\n\",\n \" \\n\",\n \" #change working directory \\n\",\n \" os.chdir(dir_string_p)\\n\",\n \" \\n\",\n \"# if os.path.isfile('nmi_scores.csv') and :\\n\",\n \"# print 'already completed {}/{}, loading scores and continuing'.format(i, p)\\n\",\n \"# nmi_scores = np.genfromtxt('nmi_scores.csv', delimiter=',')\\n\",\n \"# overall_nmi_scores[i,j] = np.mean(nmi_scores, axis=0)\\n\",\n \"# continue\\n\",\n \" \\n\",\n \" #record NMI scores\\n\",\n \" if not os.path.isfile('nmi_scores.pkl'):\\n\",\n \" print 'creating new nmi scores array'\\n\",\n \" nmi_scores = np.zeros(num_repeats)\\n\",\n \" else:\\n\",\n \" print 'loading nmi score progress'\\n\",\n \" nmi_scores = load_obj('nmi_scores')\\n\",\n \"\\n\",\n \" #record running times\\n\",\n \" if not os.path.isfile('running_times.pkl'):\\n\",\n \" print 'creating new running time array'\\n\",\n \" running_times = np.zeros(num_repeats)\\n\",\n \" else:\\n\",\n \" print 'loading running time progress'\\n\",\n \" running_times = load_obj('running_times')\\n\",\n \"\\n\",\n \" print\\n\",\n \" \\n\",\n \" #copy executable\\n\",\n \" ex = \\\"benchmark.exe\\\" \\n\",\n \" if not os.path.isfile(ex):\\n\",\n \"\\n\",\n \" source = \\\"C:\\\\\\\\Users\\\\\\\\davem\\\\\\\\Documents\\\\\\\\PhD\\\\\\\\Benchmark Graph Generators\\\\\\\\binary_networks\\\\\\\\benchmark.exe\\\"\\n\",\n \" copyfile(source, ex)\\n\",\n \"\\n\",\n \" #copy flag file\\n\",\n \" if not os.path.isfile(filename):\\n\",\n \" \\n\",\n \" source = os.path.join(dir_string, filename)\\n\",\n \" copyfile(source, filename)\\n\",\n \" \\n\",\n \" print 'copied flag file {} to {}'.format(filename, os.getcwd())\\n\",\n \"\\n\",\n \" \\n\",\n \" #cmd strings\\n\",\n \" change_dir_cmd = \\\"cd {}\\\".format(dir_string_p)\\n\",\n \" generate_network_cmd = \\\"benchmark -f {}\\\".format(filename)\\n\",\n \"\\n\",\n \" #output of cmd\\n\",\n \" output_file = open(\\\"cmd_output.out\\\", 'w')\\n\",\n \"\\n\",\n \" for r in range(1, num_repeats+1):\\n\",\n \"\\n\",\n \" network_rename = \\\"{}_{}\\\".format(r,network)\\n\",\n \" first_level_rename = \\\"{}_{}\\\".format(r,first_level)\\n\",\n \" gml_filename = 'embedded_network_{}.gml'.format(r)\\n\",\n \" \\n\",\n \" #generate network and rename\\n\",\n \" if not os.path.isfile(network_rename):\\n\",\n \"\\n\",\n \" process = subprocess.Popen(change_dir_cmd + \\\" && \\\" + generate_network_cmd, \\n\",\n \" stdout=output_file, \\n\",\n \" stderr=output_file, \\n\",\n \" shell=True)\\n\",\n \" process.wait()\\n\",\n \"\\n\",\n \" print 'generated graph {}'.format(r)\\n\",\n \"\\n\",\n \" os.rename(network, network_rename)\\n\",\n \" os.rename(first_level, first_level_rename)\\n\",\n \"\\n\",\n \" print 'renamed graph {}'.format(r)\\n\",\n \"\\n\",\n \" #embed into gml file\\n\",\n \" if not os.path.isfile(gml_filename):\\n\",\n \"\\n\",\n \" ##embed graph\\n\",\n \" embed_main(network_rename, first_level_rename, gml_filename)\\n\",\n \"\\n\",\n \" print 'embedded graph {} as {} in {}'.format(r, gml_filename, os.getcwd())\\n\",\n \" \\n\",\n \" ##graph measures\\n\",\n \"# data[j, :, r-1] = graph_measures(gml_filename)\\n\",\n \"# print 'graph measures of network {}: {}'.format(i, data[j, :, r-1])\\n\",\n \"\\n\",\n \" ##score for this network\\n\",\n \"# if not np.all(nmi_scores[r-1]):\\n\",\n \" if nmi_scores[r-1] == 0:\\n\",\n \"\\n\",\n \" start_time = time()\\n\",\n \"\\n\",\n \" print 'starting ghsom for: {}/{}/{}'.format(i, p, gml_filename)\\n\",\n \" nmi_score, communities_detected = ghsom_main(params, gml_filename, labels, 1000)\\n\",\n \" nmi_scores[r-1] = nmi_score\\n\",\n \"\\n\",\n \" running_time = time() - start_time\\n\",\n \" print 'running time of algorithm: {}'.format(running_time)\\n\",\n \" running_times[r-1] = running_time\\n\",\n \"\\n\",\n \" #save\\n\",\n \" save_obj(nmi_scores, 'nmi_scores')\\n\",\n \" save_obj(running_times, 'running_times')\\n\",\n \"\\n\",\n \" print 'saved nmi score for network {}: {}'.format(gml_filename, nmi_score)\\n\",\n \" print\\n\",\n \"\\n\",\n \" ##output nmi scores to csv file\\n\",\n \" print 'writing nmi scores and running times to file'\\n\",\n \" np.savetxt('nmi_scores.csv',nmi_scores,delimiter=',')\\n\",\n \" np.savetxt('running_times.csv',running_times,delimiter=',')\\n\",\n \" print\\n\",\n \" \\n\",\n \" #odd to overall list\\n\",\n \" overall_nmi_scores[i,j] = np.mean(nmi_scores, axis=0)\\n\",\n \" \\n\",\n \" ##save data\\n\",\n \" os.chdir(dir_string)\\n\",\n \" save_obj(data, 'data')\\n\",\n \" \\n\",\n \"print 'DONE'\\n\",\n \"\\n\",\n \"print 'OVERALL NMI SCORES'\\n\",\n \"print overall_nmi_scores\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"data=[ 0.1577381 0.90625 -0.14080981 5.7 ]\\n\",\n \"data=[ 0.075 0.9125 -0.06205974 2.9 ]\\n\",\n 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0.18060516 0.9078125 -0.13328277 6.9 ]\\n\",\n \"data=[ 0.1030754 0.909375 -0.08411983 3.6 ]\\n\",\n \"data=[ 0.18769841 0.90625 -0.18277893 7. ]\\n\",\n \"data=[ 0.0719246 0.915625 -0.03287666 2.6 ]\\n\",\n \"data=[ 0.20892857 0.90625 -0.15655046 7.5 ]\\n\",\n \"data=[ 0.18784722 0.90625 -0.07179581 7. ]\\n\",\n \"data=[ 0.21463294 0.90625 -0.13646402 7.7 ]\\n\",\n \"data=[ 0.10034722 0.9109375 -0.05672818 3.7 ]\\n\",\n \"data=[ 0.12366071 0.915625 -0.07777758 4.9 ]\\n\",\n \"data=[ 0.15223214 0.9078125 -0.10447013 5.9 ]\\n\",\n \"data=[ 0.07207341 0.9171875 -0.0298948 2.9 ]\\n\",\n \"data=[ 0.21215278 0.90625 -0.1375781 7.4 ]\\n\",\n \"data=[ 0.21274802 0.90625 -0.12180549 7.8 ]\\n\",\n \"data=[ 0.13258929 0.90625 -0.15305189 4.9 ]\\n\",\n \"data=[ 0.0437004 0.921875 -0.04133827 1.5 ]\\n\",\n \"data=[ 0.20302579 0.909375 -0.14663542 7.6 ]\\n\",\n \"data=[ 0.20525794 0.90625 -0.16923193 7.6 ]\\n\",\n \"data=[ 0.2093254 0.90625 -0.15477288 7.4 ]\\n\",\n \"data=[ 0.23581349 0.90625 -0.09938762 8.4 ]\\n\",\n \"data=[ 0.20972222 0.90625 -0.16281232 7.8 ]\\n\",\n \"data=[ 0.29077381 0.90625 -0.04759838 10.3 ]\\n\",\n \"data=[ 0.2312996 0.9078125 -0.12512121 8.6 ]\\n\",\n \"data=[ 0.07777778 0.9078125 -0.05700868 3. ]\\n\",\n \"data=[ 0.12594246 0.9109375 -0.09827548 4.7 ]\\n\",\n \"data=[ 0.28462302 0.9078125 -0.13154085 10.5 ]\\n\",\n \"data=[ 0.23784722 0.90625 -0.14933069 8.5 ]\\n\",\n \"data=[ 0.0750496 0.9140625 -0.03613751 2.8 ]\\n\",\n \"data=[ 0.10362103 0.909375 0.17922952 2.3 ]\\n\",\n \"data=[ 0.18154762 0.9078125 -0.04515814 6.9 ]\\n\",\n \"data=[ 0.25798611 0.9078125 -0.10510886 9.7 ]\\n\",\n \"data=[ 0.26949405 0.90625 -0.18622398 9.7 ]\\n\",\n \"data=[ 0.18467262 0.90625 -0.01075829 6.9 ]\\n\",\n \"data=[ 0.26185516 0.90625 -0.05688427 9.9 ]\\n\",\n \"data=[ 0.2109623 0.90625 0.04574404 8. ]\\n\",\n \"data=[ 0.37713294 0.9109375 -0.13048315 14.3 ]\\n\",\n \"data=[ 0.31026786 0.9078125 -0.0445929 11.9 ]\\n\",\n \"data=[ 0.2046627 0.909375 -0.02733698 8. ]\\n\",\n \"data=[ 0.28467262 0.9078125 -0.11060863 10.5 ]\\n\",\n \"data=[ 0.21051587 0.90625 -0.04447615 8. ]\\n\",\n \"data=[ 0.12361111 0.915625 0.1011259 5. ]\\n\",\n \"data=[ 0.23660714 0.90625 0.05042981 9. ]\\n\",\n \"data=[ 0.18154762 0.9078125 0.00981767 7. ]\\n\",\n \"data=[ 0.28501984 0.90625 -0.12098013 10.3 ]\\n\",\n \"data=[ 0.28318452 0.9078125 -0.0871737 10.9 ]\\n\",\n \"data=[ 0.33988095 0.90625 -0.19125458 12.3 ]\\n\",\n \"data=[ 0.3750496 0.9109375 -0.12145297 14.7 ]\\n\",\n \"data=[ 0.23650794 0.90625 -0.07546205 9. ]\\n\",\n \"data=[ 0.33328373 0.909375 -0.10597067 12.3 ]\\n\",\n \"data=[ 0.26170635 0.9078125 -0.09666098 9.5 ]\\n\",\n \"data=[ 0.13000992 0.9078125 0.0440029 5. ]\\n\",\n \"data=[ 0.12678571 0.9109375 0.07177607 5. ]\\n\",\n \"data=[ 0.15297619 0.909375 0.10798277 6. ]\\n\",\n \"data=[ 0.16185516 0.90625 0.02650372 6. ]\\n\",\n \"data=[ 0.28864087 0.90625 -0.07852581 10.6 ]\\n\",\n \"data=[ 0.07202381 0.91875 0.044311 2.9 ]\\n\",\n \"data=[ 0.1062004 0.90625 0.11925376 3.8 ]\\n\",\n \"data=[ 0.12678571 0.909375 0.10321317 4.9 ]\\n\",\n \"data=[ 0.28998016 0.9078125 -0.06676234 10.6 ]\\n\",\n \"data=[ 0.12683532 0.9109375 0.01839583 5. ]\\n\",\n \"data=[ 0.35401786 0.9109375 -0.07634657 13.6 ]\\n\",\n \"data=[ 0.07772817 0.909375 -0.01026146 2.9 ]\\n\",\n \"data=[ 0.35491071 0.9109375 -0.13573923 13.4 ]\\n\",\n \"data=[ 0.09791667 0.9171875 0.02145155 4. ]\\n\",\n \"data=[ 0.15297619 0.909375 0.10603514 6. ]\\n\",\n \"data=[ 0.28685516 0.9078125 -0.05271674 10.6 ]\\n\",\n \"data=[ 0.18501984 0.90625 -0.0133152 6.9 ]\\n\",\n \"data=[ 0.07197421 0.91875 -0.04122683 2.8 ]\\n\",\n \"data=[ 2.10615079e-01 9.06250000e-01 -7.83400279e-03 7.90000000e+00]\\n\",\n \"data=[ 0.30828373 0.9078125 -0.09316521 11.4 ]\\n\",\n \"data=[ 0.31205357 0.90625 -0.04147372 11.6 ]\\n\",\n \"data=[ 0.2391369 0.90625 -0.03231725 8.9 ]\\n\",\n \"data=[ 0.09454365 0.91875 0.0361221 3.9 ]\\n\",\n \"data=[ 0.2078373 0.9078125 0.06683772 8. ]\\n\",\n \"data=[ 0.26121032 0.9078125 -0.01568238 9.9 ]\\n\",\n \"data=[ 0.09791667 0.9171875 0.02145155 4. ]\\n\",\n \"data=[ 0.2421131 0.90625 -0.06022925 9. ]\\n\",\n \"data=[ 0.26418651 0.90625 -0.043744 10. ]\\n\",\n \"data=[ 0.15297619 0.909375 0.04801643 6. ]\\n\",\n \"data=[ 0.10034722 0.9109375 0.10093667 3.8 ]\\n\",\n \"data=[ 0.32698413 0.9109375 -0.13074014 12.1 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"data = np.zeros((num_networks, 4))\\n\",\n \"best_settings = np.zeros(num_networks)\\n\",\n \"\\n\",\n \"for i in range(len(overall_nmi_scores)):\\n\",\n \" \\n\",\n \" scores = overall_nmi_scores[i]\\n\",\n \" idx = np.argsort(scores)[::-1]\\n\",\n \" \\n\",\n \" best_settings[i] = parameter_settings[idx[0]]\\n\",\n \" \\n\",\n \" os.chdir('C:\\\\Miniconda3\\\\Jupyter\\\\GHSOM_simplex_dsd\\\\parameter_tests_density\\\\{}\\\\{}'.format(i, best_settings[i]))\\n\",\n \" \\n\",\n \" measures = np.zeros((num_repeats, 4))\\n\",\n \" \\n\",\n \" for r in range(num_repeats):\\n\",\n \" \\n\",\n \" gml = 'embedded_network_{}.gml'.format(r+1)\\n\",\n \" \\n\",\n \" measures[r] = graph_measures(gml)\\n\",\n \" data[i] = np.mean(measures, axis=0)\\n\",\n \" print 'data={}'.format(data[i])\\n\",\n \" \\n\",\n \"# print densities[i]\\n\",\n \"# print best_settings[i]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.1577381 0.90625 -0.14080981 5.7 ]\\n\",\n \"0.9\\n\",\n \"\\n\",\n \"[ 0.075 0.9125 -0.06205974 2.9 ]\\n\",\n \"0.9\\n\",\n \"\\n\",\n \"[ 0.12688492 0.9109375 -0.14340794 5. ]\\n\",\n \"0.7\\n\",\n \"\\n\",\n \"[ 0.18159722 0.9078125 -0.20372251 7. ]\\n\",\n \"0.7\\n\",\n \"\\n\",\n \"[ 0.12668651 0.9109375 -0.06859236 4.7 ]\\n\",\n \"0.9\\n\",\n \"\\n\",\n \"[ 0.17802579 0.9078125 -0.16647651 6.8 ]\\n\",\n \"0.7\\n\",\n \"\\n\",\n \"[ 0.12633929 0.9109375 -0.0606638 4.9 ]\\n\",\n \"0.9\\n\",\n 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9.06250000e-01 -7.83400279e-03 7.90000000e+00]\\n\",\n \"0.7\\n\",\n \"\\n\",\n \"[ 0.30828373 0.9078125 -0.09316521 11.4 ]\\n\",\n \"0.9\\n\",\n \"\\n\",\n \"[ 0.31205357 0.90625 -0.04147372 11.6 ]\\n\",\n \"0.9\\n\",\n \"\\n\",\n \"[ 0.2391369 0.90625 -0.03231725 8.9 ]\\n\",\n \"0.7\\n\",\n \"\\n\",\n \"[ 0.09454365 0.91875 0.0361221 3.9 ]\\n\",\n \"0.5\\n\",\n \"\\n\",\n \"[ 0.2078373 0.9078125 0.06683772 8. ]\\n\",\n \"0.7\\n\",\n \"\\n\",\n \"[ 0.26121032 0.9078125 -0.01568238 9.9 ]\\n\",\n \"0.8\\n\",\n \"\\n\",\n \"[ 0.09791667 0.9171875 0.02145155 4. ]\\n\",\n \"0.5\\n\",\n \"\\n\",\n \"[ 0.2421131 0.90625 -0.06022925 9. ]\\n\",\n \"0.8\\n\",\n \"\\n\",\n \"[ 0.26418651 0.90625 -0.043744 10. ]\\n\",\n \"0.8\\n\",\n \"\\n\",\n \"[ 0.15297619 0.909375 0.04801643 6. ]\\n\",\n \"0.5\\n\",\n \"\\n\",\n \"[ 0.10034722 0.9109375 0.10093667 3.8 ]\\n\",\n \"0.5\\n\",\n \"\\n\",\n \"[ 0.32698413 0.9109375 -0.13074014 12.1 ]\\n\",\n \"0.9\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"os.chdir('C:\\\\Miniconda3\\\\Jupyter\\\\GHSOM_simplex_dsd\\\\parameter_tests_density')\\n\",\n \"\\n\",\n \"save_obj(data, 'data')\\n\",\n \"\\n\",\n \"for i in range(num_networks):\\n\",\n \" print data[i]\\n\",\n \" print best_settings[i]\\n\",\n \" print \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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C/ys5d3cvroEX3a73wcz74ajPsk7+nJHEasCaO/DcSkt4jI0exomvQW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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.plot(data[:,2], best_settings, 'x')\\n\",\n \"plt.xlabel('density')\\n\",\n \"plt.ylabel('best setting')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0.00000000e+00 8.93502361e-01 8.94734085e-01 -2.77555756e-06\\n\",\n \" 8.93502361e-01 8.94734085e-01 0.00000000e+00 0.00000000e+00\\n\",\n \" -2.77555756e-06 -2.77555756e-06]\\n\"\n ]\n }\n ],\n \"source\": [\n \"os.chdir(\\\"C:\\\\\\\\Miniconda3\\\\\\\\Jupyter\\\\\\\\GHSOM_simplex_dsd\\\\\\\\parameter_tests_density\\\\\\\\0\\\\\\\\1\\\")\\n\",\n \"\\n\",\n \"nmi_scores = load_obj('nmi_scores')\\n\",\n \"\\n\",\n \"print nmi_scores\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:py27]\",\n \"language\": \"python\",\n \"name\": \"conda-env-py27-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"gpl-2.0"}}},{"rowIdx":262,"cells":{"repo_name":{"kind":"string","value":"amygdala/tensorflow-workshop"},"path":{"kind":"string","value":"workshop_sections/high_level_APIs/mnist_eager_keras-debug.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"14437"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Using Keras with TensorFlow eager mode, on the 'Fashion MNIST' dataset\\n\",\n \"\\n\",\n \"In this notebook, we'll use [TensorFlow's new eager execution mode](https://www.tensorflow.org/programmers_guide/eager).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n },\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"id\": \"WZfwoVGAa9jd\",\n \"outputId\": \"cb516ba7-baa3-41c8-e01a-91f58e23bee6\"\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"# The TF version should be >= 1.7\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n }\n },\n \"colab_type\": \"code\",\n \"id\": \"zX-y0FVXrd_l\"\n },\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten\\n\",\n \"from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Enable eager execution for this program. Eager execution makes TensorFlow evaluate operations immediately, returning concrete values instead of creating a [computational graph](https://www.tensorflow.org/programmers_guide/graphs) that is executed later. If you are used to a REPL or the `python` interactive console, you'll feel at home.\\n\",\n \"\\n\",\n \"Once eager execution is enabled, it *cannot* be disabled within the same program. See the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager) for more details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n }\n },\n \"colab_type\": \"code\",\n \"id\": \"xU-Wf-vVa-t8\"\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow.contrib.eager as tfe\\n\",\n \"tfe.enable_eager_execution()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the 'Fashion MNIST' dataset, this time via the Keras `datasets` module.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n },\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 68\n },\n \"id\": \"54FtfeNQa0_n\",\n \"outputId\": \"0ef457e2-b3aa-4a45-c684-2b204cfa3abf\"\n },\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.python.keras._impl.keras.datasets import fashion_mnist\\n\",\n \"\\n\",\n \"(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\\n\",\n \"\\n\",\n \"y_train = tf.cast(y_train, tf.int32)\\n\",\n \"y_test = tf.cast(y_test, tf.int32)\\n\",\n \"\\n\",\n \"x_train = x_train.reshape(60000, 784)\\n\",\n \"x_test = x_test.reshape(10000, 784)\\n\",\n \"x_train = x_train.astype('float32')\\n\",\n \"x_test = x_test.astype('float32')\\n\",\n \"x_train /= 255\\n\",\n \"x_test /= 255\\n\",\n \"\\n\",\n \"# convert class vectors to binary class matrices... e.g. based on\\n\",\n \"# https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py\\n\",\n \"y_train = tf.keras.utils.to_categorical(y_train, 10)\\n\",\n \"y_test = tf.keras.utils.to_categorical(y_test, 10)\\n\",\n \"\\n\",\n \"print(x_train.shape[0], 'train samples')\\n\",\n \"print(x_test.shape[0], 'test samples')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Define a CNN model using Keras.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n },\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 306\n },\n \"id\": \"BNe8xG1Pr0P1\",\n \"outputId\": \"54b4a975-1d09-4600-de63-8c8a8fff1fb5\"\n },\n \"outputs\": [],\n \"source\": [\n \"num_classes = 10\\n\",\n \"\\n\",\n \"model = tf.keras.Sequential()\\n\",\n \"model.add(Dense(512, activation='relu', input_shape=(784,)))\\n\",\n \"model.add(Dropout(0.2))\\n\",\n \"model.add(Dense(512, activation='relu'))\\n\",\n \"model.add(Dropout(0.2))\\n\",\n \"model.add(Dense(num_classes))\\n\",\n \"\\n\",\n \"model.summary()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"TensorFlow's [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) handles many common cases for feeding data into a model. This is a high-level API for reading data and transforming it into a form used for training. See the [Datasets Quick Start guide](https://www.tensorflow.org/get_started/datasets_quickstart) for more information.\\n\",\n \"Datasets work with TensorFlow Eager mode.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n }\n },\n \"colab_type\": \"code\",\n \"id\": \"CMajrImWbAvZ\"\n },\n \"outputs\": [],\n \"source\": [\n \"BATCH_SIZE = 128\\n\",\n \"SHUFFLE_SIZE = 1000\\n\",\n \"\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) \\\\\\n\",\n \" .shuffle(SHUFFLE_SIZE) \\\\\\n\",\n \" .batch(BATCH_SIZE)\\n\",\n \"\\n\",\n \"test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)) \\\\\\n\",\n \" .batch(BATCH_SIZE)\\n\",\n \"\\n\",\n \"# View a single example entry from a batch\\n\",\n \"features, labels = tfe.Iterator(test_dataset).next()\\n\",\n \"# print(\\\"example feature:\\\", features[0])\\n\",\n \"print(\\\"example label:\\\", labels[0])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Define the loss and gradient function\\n\",\n \"\\n\",\n \"Both training and evaluation stages need to calculate the model's *[loss](https://developers.google.com/machine-learning/crash-course/glossary#loss)*. This measures how off a model's predictions are from the desired label, in other words, how bad the model is performing. We want to minimize, or optimize, this value.\\n\",\n \"\\n\",\n \"Our model will calculate its loss using the [tf.losses.sparse_softmax_cross_entropy](https://www.tensorflow.org/api_docs/python/tf/losses/sparse_softmax_cross_entropy) function which takes the model's prediction and the desired label. The returned loss value is progressively larger as the prediction gets worse.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n }\n },\n \"colab_type\": \"code\",\n \"id\": \"VxmgSrb9118w\"\n },\n \"outputs\": [],\n \"source\": [\n \"def loss(model, x, y):\\n\",\n \" y_ = model(x)\\n\",\n \" # import pdb; pdb.set_trace()\\n\",\n \" return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)\\n\",\n \"\\n\",\n \"\\n\",\n \"def grad(model, inputs, targets):\\n\",\n \" with tfe.GradientTape() as tape:\\n\",\n \" loss_value = loss(model, inputs, targets)\\n\",\n \" return tape.gradient(loss_value, model.variables)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create an optimizer\\n\",\n \"\\n\",\n \"An *[optimizer](https://developers.google.com/machine-learning/crash-course/glossary#optimizer)* applies the computed gradients to the model's variables to minimize the `loss` function. You can think of a curved surface (see Figure 3) and we want to find its lowest point by walking around. The gradients point in the direction of steepest ascent—so we'll travel the opposite way and move down the hill. By iteratively calculating the loss and gradient for each batch, we'll adjust the model during training. Gradually, the model will find the best combination of weights and bias to minimize loss. And the lower the loss, the better the model's predictions.\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\\"Optimization\\n\",\n \"
\\n\",\n \" Figure 3. Optimization algorthims visualized over time in 3D space. (Source: Stanford class CS231n, MIT License)
&nbsp;\\n\",\n \"
\\n\",\n \"\\n\",\n \"TensorFlow has many [optimization algorithms](https://www.tensorflow.org/api_guides/python/train) available for training. This model uses the [tf.train.GradientDescentOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer) that implements the *[stochastic gradient descent](https://developers.google.com/machine-learning/crash-course/glossary#gradient_descent)* (SGD) algorithm. The `learning_rate` sets the step size to take for each iteration down the hill. This is a *hyperparameter* that you'll commonly adjust to achieve better results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n }\n },\n \"colab_type\": \"code\",\n \"id\": \"i1PcGU6n18zz\"\n },\n \"outputs\": [],\n \"source\": [\n \"optimizer = tf.train.AdamOptimizer()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we'll run a training loop, applying the gradients tape as defined above.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n },\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 187\n },\n \"id\": \"zNfMvIgK24qL\",\n \"outputId\": \"d1edcb3d-1d59-4508-ec54-661656eb262c\"\n },\n \"outputs\": [],\n \"source\": [\n \"## Note: Rerunning this cell uses the same model variables\\n\",\n \"\\n\",\n \"# keep results for plotting\\n\",\n \"train_loss_results = []\\n\",\n \"train_accuracy_results = []\\n\",\n \"\\n\",\n \"num_epochs = 10\\n\",\n \"\\n\",\n \"for epoch in range(num_epochs):\\n\",\n \" epoch_loss_avg = tfe.metrics.Mean()\\n\",\n \" epoch_accuracy = tfe.metrics.Accuracy()\\n\",\n \"\\n\",\n \" # Training loop - using batches of BATCH_SIZE\\n\",\n \" for images, labels in tfe.Iterator(train_dataset):\\n\",\n \" # Optimize the model\\n\",\n \" grads = grad(model, images, labels)\\n\",\n \" optimizer.apply_gradients(zip(grads, model.variables),\\n\",\n \" global_step=tf.train.get_or_create_global_step())\\n\",\n \"\\n\",\n \" # Track progress\\n\",\n \" epoch_loss_avg(loss(model, images, labels)) # add current batch loss\\n\",\n \" # compare predicted label to actual label\\n\",\n \" epoch_accuracy(tf.argmax(model(images), axis=1, output_type=tf.int32), labels)\\n\",\n \"\\n\",\n \" # end epoch\\n\",\n \" train_loss_results.append(epoch_loss_avg.result())\\n\",\n \" train_accuracy_results.append(epoch_accuracy.result())\\n\",\n \" \\n\",\n \"# if epoch % 50 == 0:\\n\",\n \" print(\\\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\\\".format(epoch,\\n\",\n \" epoch_loss_avg.result(),\\n\",\n \" epoch_accuracy.result()))\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can plot the training loss and accuracy.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n },\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 558\n },\n \"id\": \"bsULxWzUaziF\",\n \"outputId\": \"0b7e642a-6853-4292-aeb4-81975c814688\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Skip this cell if you don't have matplotlib installed\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\\n\",\n \"fig.suptitle('Training Metrics')\\n\",\n \"\\n\",\n \"axes[0].set_ylabel(\\\"Loss\\\", fontsize=14)\\n\",\n \"axes[0].plot(train_loss_results)\\n\",\n \"\\n\",\n \"axes[1].set_ylabel(\\\"Accuracy\\\", fontsize=14)\\n\",\n \"axes[1].set_xlabel(\\\"Epoch\\\", fontsize=14)\\n\",\n \"axes[1].plot(train_accuracy_results)\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's look at how our model does with the test set:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n },\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"id\": \"KOW1QNKud8nh\",\n \"outputId\": \"7107ff56-7512-4126-edb1-772f0c2743c2\"\n },\n \"outputs\": [],\n \"source\": [\n \"test_accuracy = tfe.metrics.Accuracy()\\n\",\n \"\\n\",\n \"for (x, y) in tfe.Iterator(test_dataset):\\n\",\n \" prediction = tf.argmax(model(x), axis=1, output_type=tf.int32)\\n\",\n \" test_accuracy(prediction, y)\\n\",\n \"\\n\",\n \"print(\\\"Test set accuracy: {:.3%}\\\".format(test_accuracy.result()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"tcAPS4vVe8Lg\"\n },\n \"source\": [\n \"
\\n\",\n \"Copyright 2018 Google Inc. All Rights Reserved.\\n\",\n \"\\n\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n\",\n \"you may not use this file except in compliance with the License.\\n\",\n \"You may obtain a copy of the License at\\n\",\n \"\\n\",\n \" http://www.apache.org/licenses/LICENSE-2.0\\n\",\n \"\\n\",\n \"Unless required by applicable law or agreed to in writing, software\\n\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n\",\n \"See the License for the specific language governing permissions and\\n\",\n \"limitations under the License.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"autoexec\": {\n \"startup\": false,\n \"wait_interval\": 0\n }\n },\n \"colab_type\": \"code\",\n \"id\": \"ruV-pM0Le9G7\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":263,"cells":{"repo_name":{"kind":"string","value":"LucaCanali/Miscellaneous"},"path":{"kind":"string","value":"Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"14926"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# IPython magic functions for Pyspark\\n\",\n \"# Examples of shortcuts for executing SQL in Spark\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"#\\n\",\n \"# IPython magic functions to use with Pyspark and Spark SQL\\n\",\n \"# The following code is intended as examples of shorcuts to simplify the use of SQL in pyspark\\n\",\n \"# The defined functions are:\\n\",\n \"#\\n\",\n \"# %sql - return a Spark DataFrame for lazy evaluation of the SQL\\n\",\n \"# %sql_show - run the SQL statement and show max_show_lines (50) lines\\n\",\n \"# %sql_display - run the SQL statement and display the results using a HTML table\\n\",\n \"# - this is implemented passing via Pandas and displays up to max_show_lines (50)\\n\",\n \"# %sql_explain - display the execution plan of the SQL statement\\n\",\n \"#\\n\",\n \"# Use: % for line magic or %% for cell magic.\\n\",\n \"#\\n\",\n \"# Author: Luca.Canali@cern.ch\\n\",\n \"# September 2016\\n\",\n \"#\\n\",\n \"\\n\",\n \"from IPython.core.magic import register_line_cell_magic\\n\",\n \"\\n\",\n \"# Configuration parameters\\n\",\n \"max_show_lines = 50 # Limit on the number of lines to show with %sql_show and %sql_display\\n\",\n \"detailed_explain = True # Set to False if you want to see only the physical plan when running explain\\n\",\n \"\\n\",\n \"\\n\",\n \"@register_line_cell_magic\\n\",\n \"def sql(line, cell=None):\\n\",\n \" \\\"Return a Spark DataFrame for lazy evaluation of the sql. Use: %sql or %%sql\\\"\\n\",\n \" val = cell if cell is not None else line \\n\",\n \" return spark.sql(val)\\n\",\n \"\\n\",\n \"@register_line_cell_magic\\n\",\n \"def sql_show(line, cell=None):\\n\",\n \" \\\"Execute sql and show the first max_show_lines lines. Use: %sql_show or %%sql_show\\\"\\n\",\n \" val = cell if cell is not None else line \\n\",\n \" return spark.sql(val).show(max_show_lines) \\n\",\n \"\\n\",\n \"@register_line_cell_magic\\n\",\n \"def sql_display(line, cell=None):\\n\",\n \" \\\"\\\"\\\"Execute sql and convert results to Pandas DataFrame for pretty display or further processing.\\n\",\n \" Use: %sql_display or %%sql_display\\\"\\\"\\\"\\n\",\n \" val = cell if cell is not None else line \\n\",\n \" return spark.sql(val).limit(max_show_lines).toPandas() \\n\",\n \"\\n\",\n \"@register_line_cell_magic\\n\",\n \"def sql_explain(line, cell=None):\\n\",\n \" \\\"Display the execution plan of the sql. Use: %sql_explain or %%sql_explain\\\"\\n\",\n \" val = cell if cell is not None else line \\n\",\n \" return spark.sql(val).explain(detailed_explain)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Define test tables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Define test data and register it as tables \\n\",\n \"# This is a classic example of employee and department relational tables\\n\",\n \"# Test data will be used in the examples later in this notebook\\n\",\n \"\\n\",\n \"from pyspark.sql import Row\\n\",\n \"\\n\",\n \"Employee = Row(\\\"id\\\", \\\"name\\\", \\\"email\\\", \\\"manager_id\\\", \\\"dep_id\\\")\\n\",\n \"df_emp = sqlContext.createDataFrame([\\n\",\n \" Employee(1234, 'John', 'john@mail.com', 1236, 10),\\n\",\n \" Employee(1235, 'Mike', 'mike@mail.com', 1237, 10),\\n\",\n \" Employee(1236, 'Pat', 'pat@mail.com', 1237, 20),\\n\",\n \" Employee(1237, 'Claire', 'claire@mail.com', None, 20),\\n\",\n \" Employee(1238, 'Jim', 'jim@mail.com', 1236, 30)\\n\",\n \" ])\\n\",\n \"\\n\",\n \"df_emp.registerTempTable(\\\"employee\\\")\\n\",\n \"\\n\",\n \"Department = Row(\\\"dep_id\\\", \\\"dep_name\\\")\\n\",\n \"df_dep = sqlContext.createDataFrame([\\n\",\n \" Department(10, 'Engineering'),\\n\",\n \" Department(20, 'Head Quarter'),\\n\",\n \" Department(30, 'Human resources')\\n\",\n \" ])\\n\",\n \"\\n\",\n \"df_dep.registerTempTable(\\\"department\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Examples of how to use %SQL magic functions with Spark\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Use %sql to run SQL and return a DataFrame, lazy evaluation \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DataFrame[id: bigint, name: string, email: string, manager_id: bigint, dep_id: bigint]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Example of line magic, a shortcut to run SQL in pyspark\\n\",\n \"# Pyspark has lazy evaluation, so the query is not executed in this exmaple\\n\",\n \"\\n\",\n \"df = %sql select * from employee\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Use %sql_show to run SQL and show the top lines of the result set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"+----+------+---------------+----------+------+\\n\",\n \"| id| name| email|manager_id|dep_id|\\n\",\n \"+----+------+---------------+----------+------+\\n\",\n \"|1234| John| john@mail.com| 1236| 10|\\n\",\n \"|1235| Mike| mike@mail.com| 1237| 10|\\n\",\n \"|1236| Pat| pat@mail.com| 1237| 20|\\n\",\n \"|1237|Claire|claire@mail.com| null| 20|\\n\",\n \"|1238| Jim| jim@mail.com| 1236| 30|\\n\",\n \"+----+------+---------------+----------+------+\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Example of line magic, the SQL is executed and the result is displayed\\n\",\n \"# the maximum number of displayed lines is configurable (max_show_lines)\\n\",\n \"\\n\",\n \"%sql_show select * from employee\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example of cell magic to run SQL spanning multiple lines\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"+----+------+---------------+----------+---------------+\\n\",\n \"| id| name| email|manager_id| dep_name|\\n\",\n \"+----+------+---------------+----------+---------------+\\n\",\n \"|1234| John| john@mail.com| 1236| Engineering|\\n\",\n \"|1235| Mike| mike@mail.com| 1237| Engineering|\\n\",\n \"|1238| Jim| jim@mail.com| 1236|Human resources|\\n\",\n \"|1236| Pat| pat@mail.com| 1237| Head Quarter|\\n\",\n \"|1237|Claire|claire@mail.com| null| Head Quarter|\\n\",\n \"+----+------+---------------+----------+---------------+\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%sql_show \\n\",\n \"select emp.id, emp.name, emp.email, emp.manager_id, dep.dep_name \\n\",\n \"from employee emp, department dep \\n\",\n \"where emp.dep_id=dep.dep_id\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Use %sql_display to run SQL and display the results as a HTML table\\n\",\n \"Example of cell magic that runs SQL and then transforms it to Pandas. This will display the output as a HTML table in Jupyter notebooks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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idnameemailmanager_namedep_name
01234Johnjohn@mail.comPatEngineering
11235Mikemike@mail.comClaireEngineering
21238Jimjim@mail.comPatHuman resources
31237Claireclaire@mail.comNoneHead Quarter
41236Patpat@mail.comClaireHead Quarter
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id name email manager_name dep_name\\n\",\n \"0 1234 John john@mail.com Pat Engineering\\n\",\n \"1 1235 Mike mike@mail.com Claire Engineering\\n\",\n \"2 1238 Jim jim@mail.com Pat Human resources\\n\",\n \"3 1237 Claire claire@mail.com None Head Quarter\\n\",\n \"4 1236 Pat pat@mail.com Claire Head Quarter\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%sql_display \\n\",\n \"select emp.id, emp.name, emp.email, emp2.name as manager_name, dep.dep_name \\n\",\n \"from employee emp \\n\",\n \" left outer join employee emp2 on emp2.id=emp.manager_id\\n\",\n \" join department dep on emp.dep_id=dep.dep_id\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Use %sql_explain to display the execution plan\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"== Parsed Logical Plan ==\\n\",\n \"'Project ['emp.id, 'emp.name, 'emp.email, 'emp2.name AS manager_name#68, 'dep.dep_name]\\n\",\n \"+- 'Join Inner, ('emp.dep_id = 'dep.dep_id)\\n\",\n \" :- 'Join LeftOuter, ('emp2.id = 'emp.manager_id)\\n\",\n \" : :- 'UnresolvedRelation `employee`, emp\\n\",\n \" : +- 'UnresolvedRelation `employee`, emp2\\n\",\n \" +- 'UnresolvedRelation `department`, dep\\n\",\n \"\\n\",\n \"== Analyzed Logical Plan ==\\n\",\n \"id: bigint, name: string, email: string, manager_name: string, dep_name: string\\n\",\n \"Project [id#0L, name#1, email#2, name#80 AS manager_name#68, dep_name#13]\\n\",\n \"+- Join Inner, (dep_id#4L = dep_id#12L)\\n\",\n \" :- Join LeftOuter, (id#79L = manager_id#3L)\\n\",\n \" : :- SubqueryAlias emp\\n\",\n \" : : +- SubqueryAlias employee\\n\",\n \" : : +- LogicalRDD [id#0L, name#1, email#2, manager_id#3L, dep_id#4L]\\n\",\n \" : +- SubqueryAlias emp2\\n\",\n \" : +- SubqueryAlias employee\\n\",\n \" : +- LogicalRDD [id#79L, name#80, email#81, manager_id#82L, dep_id#83L]\\n\",\n \" +- SubqueryAlias dep\\n\",\n \" +- SubqueryAlias department\\n\",\n \" +- LogicalRDD [dep_id#12L, dep_name#13]\\n\",\n \"\\n\",\n \"== Optimized Logical Plan ==\\n\",\n \"Project [id#0L, name#1, email#2, name#80 AS manager_name#68, dep_name#13]\\n\",\n \"+- Join Inner, (dep_id#4L = dep_id#12L)\\n\",\n \" :- Project [id#0L, name#1, email#2, dep_id#4L, name#80]\\n\",\n \" : +- Join LeftOuter, (id#79L = manager_id#3L)\\n\",\n \" : :- Filter isnotnull(dep_id#4L)\\n\",\n \" : : +- LogicalRDD [id#0L, name#1, email#2, manager_id#3L, dep_id#4L]\\n\",\n \" : +- Project [id#79L, name#80]\\n\",\n \" : +- LogicalRDD [id#79L, name#80, email#81, manager_id#82L, dep_id#83L]\\n\",\n \" +- Filter isnotnull(dep_id#12L)\\n\",\n \" +- LogicalRDD [dep_id#12L, dep_name#13]\\n\",\n \"\\n\",\n \"== Physical Plan ==\\n\",\n \"*Project [id#0L, name#1, email#2, name#80 AS manager_name#68, dep_name#13]\\n\",\n \"+- *SortMergeJoin [dep_id#4L], [dep_id#12L], Inner\\n\",\n \" :- *Sort [dep_id#4L ASC], false, 0\\n\",\n \" : +- Exchange hashpartitioning(dep_id#4L, 200)\\n\",\n \" : +- *Project [id#0L, name#1, email#2, dep_id#4L, name#80]\\n\",\n \" : +- SortMergeJoin [manager_id#3L], [id#79L], LeftOuter\\n\",\n \" : :- *Sort [manager_id#3L ASC], false, 0\\n\",\n \" : : +- Exchange hashpartitioning(manager_id#3L, 200)\\n\",\n \" : : +- *Filter isnotnull(dep_id#4L)\\n\",\n \" : : +- Scan ExistingRDD[id#0L,name#1,email#2,manager_id#3L,dep_id#4L]\\n\",\n \" : +- *Sort [id#79L ASC], false, 0\\n\",\n \" : +- Exchange hashpartitioning(id#79L, 200)\\n\",\n \" : +- *Project [id#79L, name#80]\\n\",\n \" : +- Scan ExistingRDD[id#79L,name#80,email#81,manager_id#82L,dep_id#83L]\\n\",\n \" +- *Sort [dep_id#12L ASC], false, 0\\n\",\n \" +- Exchange hashpartitioning(dep_id#12L, 200)\\n\",\n \" +- *Filter isnotnull(dep_id#12L)\\n\",\n \" +- Scan ExistingRDD[dep_id#12L,dep_name#13]\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%sql_explain\\n\",\n \"select emp.id, emp.name, emp.email, emp2.name as manager_name, dep.dep_name \\n\",\n \"from employee emp \\n\",\n \" left outer join employee emp2 on emp2.id=emp.manager_id\\n\",\n \" join department dep on emp.dep_id=dep.dep_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [Root]\",\n \"language\": \"python\",\n \"name\": \"Python [Root]\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":264,"cells":{"repo_name":{"kind":"string","value":"aitatanit/metatlas"},"path":{"kind":"string","value":"4notebooks/old/examplenotebooks/MetAtlas_005_Find_Data_in_Grouped_Files_For_Given_Atlas.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"3608986"},"content":{"kind":"null"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":265,"cells":{"repo_name":{"kind":"string","value":"adrn/TwoFace"},"path":{"kind":"string","value":"notebooks/figures/HighK-multimodal.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"16764"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"from os import path\\n\",\n \"\\n\",\n \"# Third-party\\n\",\n \"from astropy.io import fits\\n\",\n \"from astropy.stats import median_absolute_deviation\\n\",\n \"from astropy.table import QTable, Table, join\\n\",\n \"from astropy.time import Time\\n\",\n \"import astropy.units as u\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib.gridspec import GridSpec\\n\",\n \"import numpy as np\\n\",\n \"%matplotlib inline\\n\",\n \"import h5py\\n\",\n \"import pandas as pd\\n\",\n \"from sqlalchemy import func\\n\",\n \"import tqdm\\n\",\n \"from sklearn.cluster import KMeans\\n\",\n \"\\n\",\n \"from thejoker import JokerSamples\\n\",\n \"\\n\",\n \"from twoface.config import TWOFACE_CACHE_PATH\\n\",\n \"from twoface.samples_analysis import unimodal_P, MAP_sample\\n\",\n \"from twoface.db import (db_connect, AllStar, AllVisit, AllVisitToAllStar, NessRG,\\n\",\n \" StarResult, Status, JokerRun)\\n\",\n \"from twoface.plot import plot_two_panel, plot_phase_fold, _RV_LBL\\n\",\n \"from twoface.mass import get_m2_min, mf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Session, _ = db_connect(path.join(TWOFACE_CACHE_PATH, 'apogee.sqlite'))\\n\",\n \"session = Session()\\n\",\n \"\\n\",\n \"plot_path = '../../paper/1-catalog/figures/'\\n\",\n \"table_path = '../../paper/1-catalog/tables/'\\n\",\n \"\\n\",\n \"samples_file = path.join(TWOFACE_CACHE_PATH, 'apogee-jitter.hdf5')\\n\",\n \"mcmc_samples_file = path.join(TWOFACE_CACHE_PATH, 'apogee-jitter-mcmc.hdf5')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"run = session.query(JokerRun).limit(1).one()\\n\",\n \"joker_pars = run.get_joker_params()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"high_K_stars = session.query(AllStar).join(StarResult).filter(StarResult.status_id.in_([1, 4]))\\\\\\n\",\n \" .filter(StarResult.high_K).all()\\n\",\n \"print(len(high_K_stars))\\n\",\n \"print(session.query(AllStar).join(StarResult).filter(StarResult.high_K).count())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Remove ones that are already identified as unimodal:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"unimodal = QTable.read(path.join(table_path, 'highK-unimodal.fits'))\\n\",\n \"mask = np.logical_not(np.isin(np.array([s.apogee_id for s in high_K_stars], dtype='U20'), \\n\",\n \" unimodal['APOGEE_ID']))\\n\",\n \"high_K_stars = np.array(high_K_stars)[mask]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Make the catalog:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def is_n_modal(data, samples, n_clusters=2):\\n\",\n \" clf = KMeans(n_clusters=n_clusters)\\n\",\n \" \\n\",\n \" ecc = samples['e'].value\\n\",\n \" lnP = np.log(samples['P'].value).reshape(-1, 1)\\n\",\n \" y = clf.fit_predict(lnP)\\n\",\n \"\\n\",\n \" data = star.apogeervdata()\\n\",\n \" \\n\",\n \" unimodals = []\\n\",\n \" means = []\\n\",\n \" for j in np.unique(y):\\n\",\n \" sub_samples = samples[y==j]\\n\",\n \" if len(sub_samples) == 1:\\n\",\n \" unimodals.append(True)\\n\",\n \" means.append(sub_samples)\\n\",\n \" else:\\n\",\n \" unimodals.append(unimodal_P(sub_samples, data))\\n\",\n \" means.append(MAP_sample(data, sub_samples, joker_pars))\\n\",\n \" \\n\",\n \" return all(unimodals), means\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bimodal = []\\n\",\n \"MAP_samples = []\\n\",\n \"nsamples = []\\n\",\n \"\\n\",\n \"n = 0\\n\",\n \"with h5py.File(samples_file, 'r') as f:\\n\",\n \" for star in tqdm.tqdm(high_K_stars):\\n\",\n \" samples = JokerSamples.from_hdf5(f[star.apogee_id])\\n\",\n \" data = star.apogeervdata()\\n\",\n \" \\n\",\n \" if len(samples) > 1:\\n\",\n \" is_bimodal, MAP = is_n_modal(data, samples, n_clusters=2)\\n\",\n \" bimodal.append(is_bimodal)\\n\",\n \" MAP_samples.append(MAP)\\n\",\n \" \\n\",\n \" else:\\n\",\n \" bimodal.append(False)\\n\",\n \" \\n\",\n \" nsamples.append(len(samples))\\n\",\n \"\\n\",\n \"nsamples = np.array(nsamples)\\n\",\n \"bimodal = np.array(bimodal)\\n\",\n \"MAP_samples = np.array(MAP_samples)\\n\",\n \"bimodal.sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bimodal.sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Most of these only have a few samples:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.hist(nsamples[bimodal], bins='auto');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bi_stars = np.array(high_K_stars)[bimodal]\\n\",\n \"bi_MAP_samples = np.array(MAP_samples)[bimodal]\\n\",\n \"assert len(bi_MAP_samples) == len(bi_stars)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"catalog = {'APOGEE_ID':[], 'P':[], 'e':[], 'K':[]}\\n\",\n \"\\n\",\n \"for samples, star in zip(bi_MAP_samples, bi_stars):\\n\",\n \" for s in samples:\\n\",\n \" catalog['APOGEE_ID'].append(star.apogee_id)\\n\",\n \" catalog['P'].append(s['P'])\\n\",\n \" catalog['e'].append(s['e'])\\n\",\n \" catalog['K'].append(s['K'])\\n\",\n \" \\n\",\n \"catalog['P'] = u.Quantity(catalog['P'])\\n\",\n \"catalog['K'] = u.Quantity(catalog['K'])\\n\",\n \"catalog = Table(catalog)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Add Ness masses:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ness_tbl = Table.read('../../data/NessRG.fits')\\n\",\n \"ness_tbl.rename_column('2MASS', 'APOGEE_ID')\\n\",\n \"ness_tbl = ness_tbl[np.isin(ness_tbl['APOGEE_ID'], catalog['APOGEE_ID'])]\\n\",\n \"\\n\",\n \"# trim the duplicates...\\n\",\n \"_, unq_idx = np.unique(ness_tbl['APOGEE_ID'], return_index=True)\\n\",\n \"ness_tbl = ness_tbl[unq_idx]\\n\",\n \"\\n\",\n \"tbl_with_ness = join(catalog, ness_tbl, keys='APOGEE_ID', join_type='outer')\\n\",\n \"assert len(tbl_with_ness) == len(catalog)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"np.isfinite(tbl_with_ness['lnM']).sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rnd = np.random.RandomState(seed=42)\\n\",\n \"N = rnd.normal\\n\",\n \"\\n\",\n \"m1 = np.full(len(tbl_with_ness), np.nan) * u.Msun\\n\",\n \"m2_min = np.full(len(tbl_with_ness), np.nan) * u.Msun\\n\",\n \"\\n\",\n \"for i, row in tqdm.tqdm(enumerate(tbl_with_ness)):\\n\",\n \" if tbl_with_ness['lnM'].mask[i]:\\n\",\n \" continue\\n\",\n \" \\n\",\n \" m1_ = np.exp(row['lnM']) * u.Msun\\n\",\n \" mass_func = mf(P=row['P'] * catalog['P'].unit, \\n\",\n \" K=row['K'] * catalog['K'].unit, \\n\",\n \" e=row['e'])\\n\",\n \" \\n\",\n \" m1[i] = m1_\\n\",\n \" m2_min[i] = get_m2_min(m1_, mass_func)\\n\",\n \" \\n\",\n \"tbl_with_ness['M1'] = m1\\n\",\n \"tbl_with_ness['M2_min'] = m2_min\\n\",\n \"\\n\",\n \"tbl_with_ness['M1'].mask = np.isnan(tbl_with_ness['M1'])\\n\",\n \"tbl_with_ness['M2_min'].mask = np.isnan(tbl_with_ness['M1'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Add APOGEE DR14 info:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"allstar_tbl = fits.getdata('/Users/adrian/data/APOGEE_DR14/allStar-l31c.2.fits')\\n\",\n \"allstar_tbl = allstar_tbl[np.isin(allstar_tbl['APOGEE_ID'], tbl_with_ness['APOGEE_ID'])]\\n\",\n \"\\n\",\n \"# trim the duplicates...\\n\",\n \"_, unq_idx = np.unique(allstar_tbl['APOGEE_ID'], return_index=True)\\n\",\n \"allstar_tbl = allstar_tbl[unq_idx]\\n\",\n \"assert len(allstar_tbl) == len(tbl_with_ness)//2\\n\",\n \"\\n\",\n \"allstar_tbl = Table(allstar_tbl)\\n\",\n \"allstar_tbl.rename_column('K', 'KS')\\n\",\n \"allstar_tbl.rename_column('K_ERR', 'KS_ERR')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"output_catalog = join(tbl_with_ness[catalog.colnames + ['M1', 'M2_min']], allstar_tbl, keys='APOGEE_ID')\\n\",\n \"output_catalog[:1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## By-eye vetting: \\n\",\n \"\\n\",\n \"Plot all of the stars, see what orbits look like bad (2) or questionable (1) fits:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# with h5py.File(samples_file, 'r') as f:\\n\",\n \"# for star in tqdm.tqdm(np.array(high_K_stars)[bimodal]):\\n\",\n \"# samples = JokerSamples.from_hdf5(f[star.apogee_id])\\n\",\n \"# data = star.apogeervdata()\\n\",\n \" \\n\",\n \"# fig = plot_two_panel(data, samples, \\n\",\n \"# plot_data_orbits_kw=dict(highlight_P_extrema=False))\\n\",\n \"# fig.savefig('../../plots/bimodal/{0}.png'.format(star.apogee_id), dpi=200)\\n\",\n \"# plt.close(fig)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# bimodal:\\n\",\n \"suspect = ['2M09490802+3649393', '2M09494588+3711256', \\n\",\n \" '2M10244885+1336456', '2M13412997+2836185',\\n\",\n \" '2M15125534+6748381']\\n\",\n \"\\n\",\n \"check = ['2M18525864-0031500']\\n\",\n \"\\n\",\n \"\\n\",\n \"clean_flag = np.zeros(len(catalog), dtype=int)\\n\",\n \"clean_flag[np.isin(output_catalog['APOGEE_ID'], check)] = 1\\n\",\n \"clean_flag[np.isin(output_catalog['APOGEE_ID'], suspect)] = 2\\n\",\n \"output_catalog['clean_flag'] = clean_flag\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"np.isin(output_catalog['APOGEE_ID'], suspect).sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"output_catalog.write(path.join(table_path, 'highK-bimodal.fits'), overwrite=True)\\n\",\n \"# output_catalog[:4].write('../../paper/1-catalog/tables/bimodal-top.tex', overwrite=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"--- \\n\",\n \"\\n\",\n \"# Make paper figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"catalog = Table.read(path.join(table_path, 'highK-bimodal.fits'))\\n\",\n \"len(catalog)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"np.random.seed(111)\\n\",\n \"\\n\",\n \"rc = {\\n\",\n \" 'axes.labelsize': 18,\\n\",\n \" 'xtick.labelsize': 14,\\n\",\n \" 'ytick.labelsize': 14\\n\",\n \"}\\n\",\n \" \\n\",\n \"# rand_subset = np.random.choice(catalog['APOGEE_ID'].astype('U20'), \\n\",\n \"# size=4, \\n\",\n \"# replace=False)\\n\",\n \"rand_subset = ['2M18041328-2958182',\\n\",\n \" '2M19114515-0725486',\\n\",\n \" '2M20184780+2023122',\\n\",\n \" '2M22030551+6844336']\\n\",\n \" \\n\",\n \"with mpl.rc_context(rc):\\n\",\n \" gs = GridSpec(4, 3)\\n\",\n \" fig = plt.figure(figsize=(8., 9.5))\\n\",\n \" for j, apogee_id in enumerate(rand_subset):\\n\",\n \" ax1 = fig.add_subplot(gs[j, :2])\\n\",\n \" ax2 = fig.add_subplot(gs[j, 2])\\n\",\n \"\\n\",\n \" if j == 0:\\n\",\n \" ax1.set_title('High-$K$, bimodal', fontsize=20)\\n\",\n \" \\n\",\n \" star = AllStar.get_apogee_id(session, apogee_id)\\n\",\n \" data = star.apogeervdata()\\n\",\n \"\\n\",\n \" with h5py.File(samples_file, 'r') as f:\\n\",\n \" samples = JokerSamples.from_hdf5(f[star.apogee_id])\\n\",\n \"\\n\",\n \" fig = plot_two_panel(data, samples, axes=[ax1, ax2], tight=False,\\n\",\n \" plot_data_orbits_kw=dict(n_times=16384, \\n\",\n \" n_orbits=128,\\n\",\n \" highlight_P_extrema=False,\\n\",\n \" xlim_choice='data',\\n\",\n \" relative_to_t0=True,\\n\",\n \" plot_kwargs=dict(linewidth=0.2,\\n\",\n \" rasterized=True)))\\n\",\n \"\\n\",\n \" xlim = ax1.get_xlim()\\n\",\n \" ylim = ax1.get_ylim()\\n\",\n \"\\n\",\n \" ax1.text(xlim[0] + (xlim[1]-xlim[0])/20,\\n\",\n \" ylim[1] - (ylim[1]-ylim[0])/20,\\n\",\n \" star.apogee_id, \\n\",\n \" fontsize=15, va='top', ha='left')\\n\",\n \"\\n\",\n \" ax1.set_xlabel('')\\n\",\n \" ax2.set_xlabel('')\\n\",\n \" \\n\",\n \" logP = np.log10(samples['P'].to(u.day).value)\\n\",\n \" span = np.ptp(logP)\\n\",\n \"# ax2.set_xlim(10**(logP.min()-0.75),\\n\",\n \"# 10**(logP.max()+0.75))\\n\",\n \"# ax2.set_xlim(10**(logP.min()-0.5*span),\\n\",\n \"# 10**(logP.max()+0.5*span))\\n\",\n \"\\n\",\n \" ax1.set_xlabel(r'${\\\\rm BMJD} - t_0$ [day]')\\n\",\n \" ax2.set_xlabel('period, $P$ [day]')\\n\",\n \"\\n\",\n \" fig.tight_layout()\\n\",\n \" fig.subplots_adjust(left=0.125, right=0.95, hspace=0.2, wspace=0.4)\\n\",\n \" \\n\",\n \" fig.savefig(path.join(plot_path, 'highK-bimodal.pdf'), dpi=250)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"(old idea)\\n\",\n \"\\n\",\n \"#### Stars with samples that have small dispersion, or PTP lnP:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"stats = []\\n\",\n \"with h5py.File(samples_file, 'r') as f:\\n\",\n \" for star in tqdm.tqdm(high_K_stars):\\n\",\n \" logP = np.log10(f[star.apogee_id]['P'][:])\\n\",\n \" stats.append([np.ptp(logP), np.std(logP)])\\n\",\n \"stats = np.array(stats)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots(1, 1, figsize=(6,6))\\n\",\n \"ax.scatter(stats[:, 0], 3*stats[:, 1], alpha=0.25, linewidth=0)\\n\",\n \"ax.set_xlim(-0.02, 5)\\n\",\n \"ax.set_ylim(-0.02, 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"((stats[:, 0] < 1) | bimodal).sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"star = np.array(high_K_stars)[(stats[:, 0] < 1) & np.logical_not(bimodal)][11]\\n\",\n \"\\n\",\n \"data = star.apogeervdata()\\n\",\n \"with h5py.File(samples_file, 'r') as f:\\n\",\n \" samples = JokerSamples.from_hdf5(f[star.apogee_id])\\n\",\n \" \\n\",\n \"_ = plot_two_panel(data, samples, plot_data_orbits_kw=dict(highlight_P_extrema=False))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:twoface]\",\n \"language\": \"python\",\n \"name\": \"conda-env-twoface-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":266,"cells":{"repo_name":{"kind":"string","value":"pramitchoudhary/Experiments"},"path":{"kind":"string","value":"notebook_gallery/other_experiments/build-models/model-selection-and-tuning/current-solutions/TPOT/TPOT-demo.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"15275"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from IPython import display\\n\",\n \"URL = \\\"https://github.com/rhiever/tpot\\\"\\n\",\n \"display.IFrame(URL, 1000, 1000)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_datascience\": {}\n },\n \"source\": [\n \"TPOT uses a genetic algorithm (implemented with DEAP library) to pick an optimal pipeline for a regression task.\\n\",\n \"\\n\",\n \"What is a pipeline?\\n\",\n \"\\n\",\n \"Pipeline is composed of preprocessors:\\n\",\n \"* take polynomial transformations of features\\n\",\n \"* \\n\",\n \"\\n\",\n \"\\n\",\n \"TPOTBase is key class\\n\",\n \"\\n\",\n \"parameters:\\n\",\n \"\\n\",\n \"population_size: int (default: 100)\\n\",\n \" The number of pipelines in the genetic algorithm population. Must\\n\",\n \" be > 0.The more pipelines in the population, the slower TPOT will\\n\",\n \" run, but it's also more likely to find better pipelines.\\n\",\n \"* generations: int (default: 100)\\n\",\n \" The number of generations to run pipeline optimization for. Must\\n\",\n \" be > 0. The more generations you give TPOT to run, the longer it\\n\",\n \" takes, but it's also more likely to find better pipelines.\\n\",\n \"* mutation_rate: float (default: 0.9)\\n\",\n \" The mutation rate for the genetic programming algorithm in the range\\n\",\n \" [0.0, 1.0]. This tells the genetic programming algorithm how many\\n\",\n \" pipelines to apply random changes to every generation. We don't\\n\",\n \" recommend that you tweak this parameter unless you know what you're\\n\",\n \" doing.\\n\",\n \"* crossover_rate: float (default: 0.05)\\n\",\n \" The crossover rate for the genetic programming algorithm in the\\n\",\n \" range [0.0, 1.0]. This tells the genetic programming algorithm how\\n\",\n \" many pipelines to \\\"breed\\\" every generation. We don't recommend that\\n\",\n \" you tweak this parameter unless you know what you're doing.\\n\",\n \"* scoring: function or str\\n\",\n \" Function used to evaluate the quality of a given pipeline for the\\n\",\n \" problem. By default, balanced class accuracy is used for\\n\",\n \" classification problems, mean squared error for regression problems.\\n\",\n \" TPOT assumes that this scoring function should be maximized, i.e.,\\n\",\n \" higher is better.\\n\",\n \" Offers the same options as sklearn.cross_validation.cross_val_score:\\n\",\n \" ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1',\\n\",\n \" 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted',\\n\",\n \" 'precision', 'precision_macro', 'precision_micro', 'precision_samples',\\n\",\n \" 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro',\\n\",\n \" 'recall_samples', 'recall_weighted', 'roc_auc']\\n\",\n \"* num_cv_folds: int (default: 3)\\n\",\n \" The number of folds to evaluate each pipeline over in k-fold\\n\",\n \" cross-validation during the TPOT pipeline optimization process\\n\",\n \"* max_time_mins: int (default: None)\\n\",\n \" How many minutes TPOT has to optimize the pipeline. If not None,\\n\",\n \" this setting will override the `generations` parameter.\\n\",\n \"\\n\",\n \"TPOTClassifier and TPOTRegressor inherit parent class TPOTBase, with modifications of the scoring function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting deap\\n\",\n \" Downloading deap-1.0.2.post2.tar.gz (852kB)\\n\",\n \"\\u001b[K 100% |################################| 856kB 722kB/s \\n\",\n \"\\u001b[?25hCollecting update-checker\\n\",\n \" Downloading update_checker-0.12-py2.py3-none-any.whl\\n\",\n \"Collecting tqdm\\n\",\n \" Downloading tqdm-4.8.4-py2.py3-none-any.whl\\n\",\n \"Requirement already satisfied (use --upgrade to upgrade): requests>=2.3.0 in /usr/local/lib/python2.7/dist-packages (from update-checker)\\n\",\n \"Building wheels for collected packages: deap\\n\",\n \" Running setup.py bdist_wheel for deap\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/c9/9c/cd/d52106f0148e675df35718c0efff2ecf03cc86d5bdcfb91db5\\n\",\n \"Successfully built deap\\n\",\n \"Installing collected packages: deap, update-checker, tqdm\\n\",\n \"Successfully installed deap-1.0.2 tqdm-4.8.4 update-checker-0.12\\n\",\n \"\\u001b[33mYou are using pip version 7.1.2, however version 8.1.2 is available.\\n\",\n \"You should consider upgrading via the 'pip install --upgrade pip' command.\\u001b[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"!sudo pip install deap update_checker tqdm xgboost tpot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\\n\",\n \" warnings.warn(self.msg_depr % (key, alt_key))\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd \\n\",\n \"import numpy as np\\n\",\n \"import psycopg2 \\n\",\n \"import os\\n\",\n \"import json\\n\",\n \"from tpot import TPOTClassifier\\n\",\n \"from sklearn.metrics import classification_report\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"conn = psycopg2.connect(\\n\",\n \" user = os.environ['REDSHIFT_USER']\\n\",\n \" ,password = os.environ['REDSHIFT_PASS'] \\n\",\n \" ,port = os.environ['REDSHIFT_PORT']\\n\",\n \" ,host = os.environ['REDSHIFT_HOST']\\n\",\n \" ,database = 'tradesy'\\n\",\n \")\\n\",\n \"query = \\\"\\\"\\\"\\n\",\n \" select \\n\",\n \" purchase_dummy\\n\",\n \" ,shipping_price_ratio\\n\",\n \" ,asking_price\\n\",\n \" ,price_level\\n\",\n \" ,brand_score\\n\",\n \" ,brand_size\\n\",\n \" ,a_over_b\\n\",\n \" ,favorite_count\\n\",\n \" ,has_blurb\\n\",\n \" ,has_image\\n\",\n \" ,seasonal_component\\n\",\n \" ,description_length\\n\",\n \" ,product_category_accessories\\n\",\n \" ,product_category_shoes\\n\",\n \" ,product_category_bags\\n\",\n \" ,product_category_tops\\n\",\n \" ,product_category_dresses\\n\",\n \" ,product_category_weddings\\n\",\n \" ,product_category_bottoms\\n\",\n \" ,product_category_outerwear\\n\",\n \" ,product_category_jeans\\n\",\n \" ,product_category_activewear\\n\",\n \" ,product_category_suiting\\n\",\n \" ,product_category_swim\\n\",\n \" \\n\",\n \" from saleability_model_v2\\n\",\n \" \\n\",\n \" limit 50000\\n\",\n \" \\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"df = pd.read_sql(query, conn)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"target = 'purchase_dummy'\\n\",\n \"domain = filter(lambda x: x != target, df.columns.values)\\n\",\n \"df = df.astype(float)\\n\",\n \"\\n\",\n \"y_all = df[target].values\\n\",\n \"X_all = df[domain].values\\n\",\n \"\\n\",\n \"idx_all = np.random.RandomState(1).permutation(len(y_all))\\n\",\n \"idx_train = idx_all[:int(.8 * len(y_all))]\\n\",\n \"idx_test = idx_all[int(.8 * len(y_all)):]\\n\",\n \"\\n\",\n \"# TRAIN AND TEST DATA\\n\",\n \"X_train = X_all[idx_train]\\n\",\n \"y_train = y_all[idx_train]\\n\",\n \"X_test = X_all[idx_test]\\n\",\n \"y_test = y_all[idx_test]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_datascience\": {}\n },\n \"source\": [\n \"### Sklearn model: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\\n\",\n \" max_depth=None, max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\\n\",\n \" oob_score=False, random_state=None, verbose=0,\\n\",\n \" warm_start=False)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"sklearn_model = RandomForestClassifier()\\n\",\n \"sklearn_model.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0.0 0.86 0.96 0.91 8260\\n\",\n \" 1.0 0.60 0.27 0.37 1740\\n\",\n \"\\n\",\n \"avg / total 0.82 0.84 0.82 10000\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"sklearn_predictions = sklearn_model.predict(X_test)\\n\",\n \"print classification_report(y_test, sklearn_predictions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_datascience\": {}\n },\n \"source\": [\n \"### TPOT Classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"GP Progress: 90%|█████████ | 18/20 [09:47<01:56, 58.40s/pipeline]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Generation 1 - Current best internal CV score: 0.647821506914\\n\",\n \"Generation 2 - Current best internal CV score: 0.647821506914\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r\",\n \" \\r\",\n \"\\r\",\n \" \\r\",\n \"GP Progress: 90%|█████████ | 18/20 [00:00<01:56, 58.40s/pipeline]\\r\",\n \"GP Progress: 70%|███████ | 21/30 [10:09<06:28, 43.13s/pipeline]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"GP closed prematurely - will use current best pipeline\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r\",\n \" \"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Best pipeline: XGBClassifier(input_matrix, 32, 6, 0.48999999999999999, 27.0)\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r\"\n ]\n }\n ],\n \"source\": [\n \"tpot_model = TPOTClassifier(generations=3, population_size=10, verbosity=2, max_time_mins=10)\\n\",\n \"tpot_model.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0.0 0.88 0.93 0.90 8260\\n\",\n \" 1.0 0.54 0.39 0.45 1740\\n\",\n \"\\n\",\n \"avg / total 0.82 0.84 0.82 10000\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"tpot_predictions = tpot_model.predict(X_test)\\n\",\n \"print classification_report(y_test, tpot_predictions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_datascience\": {}\n },\n \"source\": [\n \"### Export Pseudo Pipeline Code\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tpot_model.export('optimal-saleability-model.py')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"import numpy as np\\r\\n\",\n \"\\r\\n\",\n \"from sklearn.cross_validation import train_test_split\\r\\n\",\n \"from sklearn.ensemble import VotingClassifier\\r\\n\",\n \"from sklearn.pipeline import make_pipeline, make_union\\r\\n\",\n \"from sklearn.preprocessing import FunctionTransformer\\r\\n\",\n \"from xgboost import XGBClassifier\\r\\n\",\n \"\\r\\n\",\n \"# NOTE: Make sure that the class is labeled 'class' in the data file\\r\\n\",\n \"tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)\\r\\n\",\n \"features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)\\r\\n\",\n \"training_features, testing_features, training_classes, testing_classes = \\\\\\r\\n\",\n \" train_test_split(features, tpot_data['class'], random_state=42)\\r\\n\",\n \"\\r\\n\",\n \"exported_pipeline = make_pipeline(\\r\\n\",\n \" XGBClassifier(learning_rate=0.49, max_depth=10, min_child_weight=6, n_estimators=500, subsample=1.0)\\r\\n\",\n \")\\r\\n\",\n \"\\r\\n\",\n \"exported_pipeline.fit(training_features, training_classes)\\r\\n\",\n \"results = exported_pipeline.predict(testing_features)\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!cat optimal-saleability-model.py\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_datascience\": {},\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_datascience\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"unlicense"}}},{"rowIdx":267,"cells":{"repo_name":{"kind":"string","value":"camigord/Self-Driving-Car-Nanodegree"},"path":{"kind":"string","value":"Camera_Calibration/camera_calibration.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"1691676"},"content":{"kind":"null"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":268,"cells":{"repo_name":{"kind":"string","value":"efoley/deep-learning"},"path":{"kind":"string","value":"transfer-learning/Transfer_Learning.ipynb"},"copies":{"kind":"string","value":"3"},"size":{"kind":"string","value":"23475"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"# Transfer Learning\\n\",\n \"\\n\",\n \"Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebook, you'll be using [VGGNet](https://arxiv.org/pdf/1409.1556.pdf) trained on the [ImageNet dataset](http://www.image-net.org/) as a feature extractor. Below is a diagram of the VGGNet architecture.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"VGGNet is great because it's simple and has great performance, coming in second in the ImageNet competition. The idea here is that we keep all the convolutional layers, but replace the final fully connected layers with our own classifier. This way we can use VGGNet as a feature extractor for our images then easily train a simple classifier on top of that. What we'll do is take the first fully connected layer with 4096 units, including thresholding with ReLUs. We can use those values as a code for each image, then build a classifier on top of those codes.\\n\",\n \"\\n\",\n \"You can read more about transfer learning from [the CS231n course notes](http://cs231n.github.io/transfer-learning/#tf).\\n\",\n \"\\n\",\n \"## Pretrained VGGNet\\n\",\n \"\\n\",\n \"We'll be using a pretrained network from https://github.com/machrisaa/tensorflow-vgg. Make sure to clone this repository to the directory you're working from. You'll also want to rename it so it has an underscore instead of a dash.\\n\",\n \"\\n\",\n \"```\\n\",\n \"git clone https://github.com/machrisaa/tensorflow-vgg.git tensorflow_vgg\\n\",\n \"```\\n\",\n \"\\n\",\n \"This is a really nice implementation of VGGNet, quite easy to work with. The network has already been trained and the parameters are available from this link. **You'll need to clone the repo into the folder containing this notebook.** Then download the parameter file using the next cell.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from urllib.request import urlretrieve\\n\",\n \"from os.path import isfile, isdir\\n\",\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"vgg_dir = 'tensorflow_vgg/'\\n\",\n \"# Make sure vgg exists\\n\",\n \"if not isdir(vgg_dir):\\n\",\n \" raise Exception(\\\"VGG directory doesn't exist!\\\")\\n\",\n \"\\n\",\n \"class DLProgress(tqdm):\\n\",\n \" last_block = 0\\n\",\n \"\\n\",\n \" def hook(self, block_num=1, block_size=1, total_size=None):\\n\",\n \" self.total = total_size\\n\",\n \" self.update((block_num - self.last_block) * block_size)\\n\",\n \" self.last_block = block_num\\n\",\n \"\\n\",\n \"if not isfile(vgg_dir + \\\"vgg16.npy\\\"):\\n\",\n \" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:\\n\",\n \" urlretrieve(\\n\",\n \" 'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',\\n\",\n \" vgg_dir + 'vgg16.npy',\\n\",\n \" pbar.hook)\\n\",\n \"else:\\n\",\n \" print(\\\"Parameter file already exists!\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Flower power\\n\",\n \"\\n\",\n \"Here we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the [TensorFlow inception tutorial](https://www.tensorflow.org/tutorials/image_retraining).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import tarfile\\n\",\n \"\\n\",\n \"dataset_folder_path = 'flower_photos'\\n\",\n \"\\n\",\n \"class DLProgress(tqdm):\\n\",\n \" last_block = 0\\n\",\n \"\\n\",\n \" def hook(self, block_num=1, block_size=1, total_size=None):\\n\",\n \" self.total = total_size\\n\",\n \" self.update((block_num - self.last_block) * block_size)\\n\",\n \" self.last_block = block_num\\n\",\n \"\\n\",\n \"if not isfile('flower_photos.tar.gz'):\\n\",\n \" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:\\n\",\n \" urlretrieve(\\n\",\n \" 'http://download.tensorflow.org/example_images/flower_photos.tgz',\\n\",\n \" 'flower_photos.tar.gz',\\n\",\n \" pbar.hook)\\n\",\n \"\\n\",\n \"if not isdir(dataset_folder_path):\\n\",\n \" with tarfile.open('flower_photos.tar.gz') as tar:\\n\",\n \" tar.extractall()\\n\",\n \" tar.close()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## ConvNet Codes\\n\",\n \"\\n\",\n \"Below, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.\\n\",\n \"\\n\",\n \"Here we're using the `vgg16` module from `tensorflow_vgg`. The network takes images of size $224 \\\\times 224 \\\\times 3$ as input. Then it has 5 sets of convolutional layers. The network implemented here has this structure (copied from [the source code](https://github.com/machrisaa/tensorflow-vgg/blob/master/vgg16.py)):\\n\",\n \"\\n\",\n \"```\\n\",\n \"self.conv1_1 = self.conv_layer(bgr, \\\"conv1_1\\\")\\n\",\n \"self.conv1_2 = self.conv_layer(self.conv1_1, \\\"conv1_2\\\")\\n\",\n \"self.pool1 = self.max_pool(self.conv1_2, 'pool1')\\n\",\n \"\\n\",\n \"self.conv2_1 = self.conv_layer(self.pool1, \\\"conv2_1\\\")\\n\",\n \"self.conv2_2 = self.conv_layer(self.conv2_1, \\\"conv2_2\\\")\\n\",\n \"self.pool2 = self.max_pool(self.conv2_2, 'pool2')\\n\",\n \"\\n\",\n \"self.conv3_1 = self.conv_layer(self.pool2, \\\"conv3_1\\\")\\n\",\n \"self.conv3_2 = self.conv_layer(self.conv3_1, \\\"conv3_2\\\")\\n\",\n \"self.conv3_3 = self.conv_layer(self.conv3_2, \\\"conv3_3\\\")\\n\",\n \"self.pool3 = self.max_pool(self.conv3_3, 'pool3')\\n\",\n \"\\n\",\n \"self.conv4_1 = self.conv_layer(self.pool3, \\\"conv4_1\\\")\\n\",\n \"self.conv4_2 = self.conv_layer(self.conv4_1, \\\"conv4_2\\\")\\n\",\n \"self.conv4_3 = self.conv_layer(self.conv4_2, \\\"conv4_3\\\")\\n\",\n \"self.pool4 = self.max_pool(self.conv4_3, 'pool4')\\n\",\n \"\\n\",\n \"self.conv5_1 = self.conv_layer(self.pool4, \\\"conv5_1\\\")\\n\",\n \"self.conv5_2 = self.conv_layer(self.conv5_1, \\\"conv5_2\\\")\\n\",\n \"self.conv5_3 = self.conv_layer(self.conv5_2, \\\"conv5_3\\\")\\n\",\n \"self.pool5 = self.max_pool(self.conv5_3, 'pool5')\\n\",\n \"\\n\",\n \"self.fc6 = self.fc_layer(self.pool5, \\\"fc6\\\")\\n\",\n \"self.relu6 = tf.nn.relu(self.fc6)\\n\",\n \"```\\n\",\n \"\\n\",\n \"So what we want are the values of the first fully connected layer, after being ReLUd (`self.relu6`). To build the network, we use\\n\",\n \"\\n\",\n \"```\\n\",\n \"with tf.Session() as sess:\\n\",\n \" vgg = vgg16.Vgg16()\\n\",\n \" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\\n\",\n \" with tf.name_scope(\\\"content_vgg\\\"):\\n\",\n \" vgg.build(input_)\\n\",\n \"```\\n\",\n \"\\n\",\n \"This creates the `vgg` object, then builds the graph with `vgg.build(input_)`. Then to get the values from the layer,\\n\",\n \"\\n\",\n \"```\\n\",\n \"feed_dict = {input_: images}\\n\",\n \"codes = sess.run(vgg.relu6, feed_dict=feed_dict)\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"from tensorflow_vgg import vgg16\\n\",\n \"from tensorflow_vgg import utils\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data_dir = 'flower_photos/'\\n\",\n \"contents = os.listdir(data_dir)\\n\",\n \"classes = [each for each in contents if os.path.isdir(data_dir + each)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Below I'm running images through the VGG network in batches.\\n\",\n \"\\n\",\n \"> **Exercise:** Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Set the batch size higher if you can fit in in your GPU memory\\n\",\n \"batch_size = 10\\n\",\n \"codes_list = []\\n\",\n \"labels = []\\n\",\n \"batch = []\\n\",\n \"\\n\",\n \"codes = None\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" \\n\",\n \" # TODO: Build the vgg network here\\n\",\n \"\\n\",\n \" for each in classes:\\n\",\n \" print(\\\"Starting {} images\\\".format(each))\\n\",\n \" class_path = data_dir + each\\n\",\n \" files = os.listdir(class_path)\\n\",\n \" for ii, file in enumerate(files, 1):\\n\",\n \" # Add images to the current batch\\n\",\n \" # utils.load_image crops the input images for us, from the center\\n\",\n \" img = utils.load_image(os.path.join(class_path, file))\\n\",\n \" batch.append(img.reshape((1, 224, 224, 3)))\\n\",\n \" labels.append(each)\\n\",\n \" \\n\",\n \" # Running the batch through the network to get the codes\\n\",\n \" if ii % batch_size == 0 or ii == len(files):\\n\",\n \" \\n\",\n \" # Image batch to pass to VGG network\\n\",\n \" images = np.concatenate(batch)\\n\",\n \" \\n\",\n \" # TODO: Get the values from the relu6 layer of the VGG network\\n\",\n \" codes_batch = \\n\",\n \" \\n\",\n \" # Here I'm building an array of the codes\\n\",\n \" if codes is None:\\n\",\n \" codes = codes_batch\\n\",\n \" else:\\n\",\n \" codes = np.concatenate((codes, codes_batch))\\n\",\n \" \\n\",\n \" # Reset to start building the next batch\\n\",\n \" batch = []\\n\",\n \" print('{} images processed'.format(ii))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# write codes to file\\n\",\n \"with open('codes', 'w') as f:\\n\",\n \" codes.tofile(f)\\n\",\n \" \\n\",\n \"# write labels to file\\n\",\n \"import csv\\n\",\n \"with open('labels', 'w') as f:\\n\",\n \" writer = csv.writer(f, delimiter='\\\\n')\\n\",\n \" writer.writerow(labels)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Building the Classifier\\n\",\n \"\\n\",\n \"Now that we have codes for all the images, we can build a simple classifier on top of them. The codes behave just like normal input into a simple neural network. Below I'm going to have you do most of the work.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# read codes and labels from file\\n\",\n \"import csv\\n\",\n \"\\n\",\n \"with open('labels') as f:\\n\",\n \" reader = csv.reader(f, delimiter='\\\\n')\\n\",\n \" labels = np.array([each for each in reader]).squeeze()\\n\",\n \"with open('codes') as f:\\n\",\n \" codes = np.fromfile(f, dtype=np.float32)\\n\",\n \" codes = codes.reshape((len(labels), -1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Data prep\\n\",\n \"\\n\",\n \"As usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!\\n\",\n \"\\n\",\n \"> **Exercise:** From scikit-learn, use [LabelBinarizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) to create one-hot encoded vectors from the labels. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"labels_vecs = # Your one-hot encoded labels array here\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typically, you'll also want to make sure that each smaller set has the same the distribution of classes as it is for the whole data set. The easiest way to accomplish both these goals is to use [`StratifiedShuffleSplit`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) from scikit-learn.\\n\",\n \"\\n\",\n \"You can create the splitter like so:\\n\",\n \"```\\n\",\n \"ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\\n\",\n \"```\\n\",\n \"Then split the data with \\n\",\n \"```\\n\",\n \"splitter = ss.split(x, y)\\n\",\n \"```\\n\",\n \"\\n\",\n \"`ss.split` returns a generator of indices. You can pass the indices into the arrays to get the split sets. The fact that it's a generator means you either need to iterate over it, or use `next(splitter)` to get the indices. Be sure to read the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) and the [user guide](http://scikit-learn.org/stable/modules/cross_validation.html#random-permutations-cross-validation-a-k-a-shuffle-split).\\n\",\n \"\\n\",\n \"> **Exercise:** Use StratifiedShuffleSplit to split the codes and labels into training, validation, and test sets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"train_x, train_y = \\n\",\n \"val_x, val_y = \\n\",\n \"test_x, test_y = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"print(\\\"Train shapes (x, y):\\\", train_x.shape, train_y.shape)\\n\",\n \"print(\\\"Validation shapes (x, y):\\\", val_x.shape, val_y.shape)\\n\",\n \"print(\\\"Test shapes (x, y):\\\", test_x.shape, test_y.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"If you did it right, you should see these sizes for the training sets:\\n\",\n \"\\n\",\n \"```\\n\",\n \"Train shapes (x, y): (2936, 4096) (2936, 5)\\n\",\n \"Validation shapes (x, y): (367, 4096) (367, 5)\\n\",\n \"Test shapes (x, y): (367, 4096) (367, 5)\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Classifier layers\\n\",\n \"\\n\",\n \"Once you have the convolutional codes, you just need to build a classfier from some fully connected layers. You use the codes as the inputs and the image labels as targets. Otherwise the classifier is a typical neural network.\\n\",\n \"\\n\",\n \"> **Exercise:** With the codes and labels loaded, build the classifier. Consider the codes as your inputs, each of them are 4096D vectors. You'll want to use a hidden layer and an output layer as your classifier. Remember that the output layer needs to have one unit for each class and a softmax activation function. Use the cross entropy to calculate the cost.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])\\n\",\n \"labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])\\n\",\n \"\\n\",\n \"# TODO: Classifier layers and operations\\n\",\n \"\\n\",\n \"logits = # output layer logits\\n\",\n \"cost = # cross entropy loss\\n\",\n \"\\n\",\n \"optimizer = # training optimizer\\n\",\n \"\\n\",\n \"# Operations for validation/test accuracy\\n\",\n \"predicted = tf.nn.softmax(logits)\\n\",\n \"correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))\\n\",\n \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Batches!\\n\",\n \"\\n\",\n \"Here is just a simple way to do batches. I've written it so that it includes all the data. Sometimes you'll throw out some data at the end to make sure you have full batches. Here I just extend the last batch to include the remaining data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_batches(x, y, n_batches=10):\\n\",\n \" \\\"\\\"\\\" Return a generator that yields batches from arrays x and y. \\\"\\\"\\\"\\n\",\n \" batch_size = len(x)//n_batches\\n\",\n \" \\n\",\n \" for ii in range(0, n_batches*batch_size, batch_size):\\n\",\n \" # If we're not on the last batch, grab data with size batch_size\\n\",\n \" if ii != (n_batches-1)*batch_size:\\n\",\n \" X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size] \\n\",\n \" # On the last batch, grab the rest of the data\\n\",\n \" else:\\n\",\n \" X, Y = x[ii:], y[ii:]\\n\",\n \" # I love generators\\n\",\n \" yield X, Y\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Training\\n\",\n \"\\n\",\n \"Here, we'll train the network.\\n\",\n \"\\n\",\n \"> **Exercise:** So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. Use the `get_batches` function I wrote before to get your batches like `for x, y in get_batches(train_x, train_y)`. Or write your own!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"saver = tf.train.Saver()\\n\",\n \"with tf.Session() as sess:\\n\",\n \" \\n\",\n \" # TODO: Your training code here\\n\",\n \" saver.save(sess, \\\"checkpoints/flowers.ckpt\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Testing\\n\",\n \"\\n\",\n \"Below you see the test accuracy. You can also see the predictions returned for images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with tf.Session() as sess:\\n\",\n \" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\\n\",\n \" \\n\",\n \" feed = {inputs_: test_x,\\n\",\n \" labels_: test_y}\\n\",\n \" test_acc = sess.run(accuracy, feed_dict=feed)\\n\",\n \" print(\\\"Test accuracy: {:.4f}\\\".format(test_acc))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from scipy.ndimage import imread\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Below, feel free to choose images and see how the trained classifier predicts the flowers in them.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'\\n\",\n \"test_img = imread(test_img_path)\\n\",\n \"plt.imshow(test_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Run this cell if you don't have a vgg graph built\\n\",\n \"if 'vgg' in globals():\\n\",\n \" print('\\\"vgg\\\" object already exists. Will not create again.')\\n\",\n \"else:\\n\",\n \" #create vgg\\n\",\n \" with tf.Session() as sess:\\n\",\n \" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\\n\",\n \" vgg = vgg16.Vgg16()\\n\",\n \" vgg.build(input_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with tf.Session() as sess:\\n\",\n \" img = utils.load_image(test_img_path)\\n\",\n \" img = img.reshape((1, 224, 224, 3))\\n\",\n \"\\n\",\n \" feed_dict = {input_: img}\\n\",\n \" code = sess.run(vgg.relu6, feed_dict=feed_dict)\\n\",\n \" \\n\",\n \"saver = tf.train.Saver()\\n\",\n \"with tf.Session() as sess:\\n\",\n \" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\\n\",\n \" \\n\",\n \" feed = {inputs_: code}\\n\",\n \" prediction = sess.run(predicted, feed_dict=feed).squeeze()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plt.imshow(test_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plt.barh(np.arange(5), prediction)\\n\",\n \"_ = plt.yticks(np.arange(5), lb.classes_)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n },\n \"widgets\": {\n \"state\": {},\n \"version\": \"1.1.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":269,"cells":{"repo_name":{"kind":"string","value":"Am3ra/CS"},"path":{"kind":"string","value":"CS4/Metodos/Generación de números aleatorios.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"1174"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alan Macedo Esparza\\n\",\n \"A01366288\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[4 3 2 1 2 1 1 2 4 3 2 3 4 2 1 2 3 4 2 4 2 1 4 4 4 2 2 4 1 2 4 4 3 4 4 2 1\\n\",\n \" 4 1 4 2 2 1 2 4 4 1 4 2 4 4 4 1 4 4 4 1 4 3 4 1 3 3 1 1 2 1 4 2 2 2 1 3 2\\n\",\n \" 3 4 1 3 1 4 4 1 4 1 3 1 1 1 4 4 4 1 4 4 4 4 1 2 4 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"a = np.random.choice([1,2,3,4], 100, p=[0.3,0.2,0.1,0.4])\\n\",\n \"print(a)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3.7.7 64-bit\",\n \"language\": \"python\",\n \"name\": \"python37764bit6381e19668b04d93b1afa4267a4f1b24\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":270,"cells":{"repo_name":{"kind":"string","value":"giacomov/astromodels"},"path":{"kind":"string","value":"examples/EBL_attenuation_example.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"35446"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# EBL Attenuation of a Spectral Model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook we create a simple *astromodels* spectrum and then apply EBL attenuation, as a function of redshift and energy.\\n\",\n \"\\n\",\n \"The `EBLattenuation` class in astromodels relies directly on the *ebltable* package by Manuel Meyer, see [ebltable](https://github.com/me-manu/ebltable) .\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Configuration read from /home/rlauer/.threeML/threeML_config.yml\\n\"\n ]\n }\n ],\n \"source\": [\n \"from threeML import *\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define power law spectrum\\n\",\n \"sourceName = 'Mrk421'\\n\",\n \"spectrum = Powerlaw()\\n\",\n \"#put it into a PS model, in this context primarily in order to establish units\\n\",\n \"source1 = PointSource(sourceName, ra=166.11, dec=38.21, spectral_shape=spectrum)\\n\",\n \"#and set parameters:\\n\",\n \"spectrum.piv = 1. * u.TeV\\n\",\n \"spectrum.K = 1.e-11 / (u.TeV * u.cm**2 * u.s)\\n\",\n \"spectrum.index = -2.2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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  • description: (Powerlaw{1} * EBLattenuation{2})
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      • value: 1.0
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\\n\"\n ],\n \"text/plain\": [\n \" * description: (Powerlaw{1} * EBLattenuation{2})\\n\",\n \" * formula: (no latex formula available)\\n\",\n \" * parameters: \\n\",\n \" * K_1: \\n\",\n \" * value: 1e-20\\n\",\n \" * desc: Normalization (differential flux at the pivot value)\\n\",\n \" * min_value: 1e-30\\n\",\n \" * max_value: 1000.0\\n\",\n \" * unit: cm-2 keV-1 s-1\\n\",\n \" * is_normalization: True\\n\",\n \" * delta: 0.1\\n\",\n \" * free: True\\n\",\n \" * piv_1: \\n\",\n \" * value: 1000000000.0\\n\",\n \" * desc: Pivot value\\n\",\n \" * min_value: None\\n\",\n \" * max_value: None\\n\",\n \" * unit: keV\\n\",\n \" * is_normalization: False\\n\",\n \" * delta: 0.1\\n\",\n \" * free: False\\n\",\n \" * index_1: \\n\",\n \" * value: -2.2\\n\",\n \" * desc: Photon index\\n\",\n \" * min_value: -10.0\\n\",\n \" * max_value: 10.0\\n\",\n \" * unit: \\n\",\n \" * is_normalization: False\\n\",\n \" * delta: 0.2\\n\",\n \" * free: True\\n\",\n \" * redshift_2: \\n\",\n \" * value: 1.0\\n\",\n \" * desc: redshift of the source\\n\",\n \" * min_value: None\\n\",\n \" * max_value: None\\n\",\n \" * unit: \\n\",\n \" * is_normalization: False\\n\",\n \" * delta: 0.1\\n\",\n \" * free: False\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#define attenuated spectrum:\\n\",\n \"ebl = EBLattenuation()\\n\",\n \"spectrumEBL = spectrum * ebl\\n\",\n \"source2 = PointSource(sourceName, ra=166.11, dec=38.21, spectral_shape=spectrumEBL)\\n\",\n \"#show new parameter names:\\n\",\n \"spectrumEBL.display()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#set redshift:\\n\",\n \"spectrumEBL.redshift_2 = 0.031*u.dimensionless_unscaled\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Currently, the 3ML implementation selects the `Dominguez` model for optical depth by default.\\n\",\n \"In the next cell, we also define EBL attenuation for a different model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"#new EBL with different model:\\n\",\n \"ebl2 = EBLattenuation()\\n\",\n \"ebl2.set_ebl_model('gilmore')\\n\",\n \"spectrumEBL_Gil = spectrum*ebl2\\n\",\n \"spectrumEBL_Gil.redshift_2 = 0.031*u.dimensionless_unscaled\\n\",\n \"source3 = PointSource(sourceName, ra=166.11, dec=38.21, spectral_shape=spectrumEBL_Gil)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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XdcuHCBXbt2YWNjg4+Pz13xFqe489iwYUOx31Vx38kbb7xBVFQUy5cv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\\nM6sqB4+ZmVWVg8fMzKrqfwEcDxXvpLp7zAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#use matplotlib to plot both spectra:\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"energies = np.logspace(-1.,2.,100)*u.TeV\\n\",\n \"#factor 1e9 to convert to TeV^-1\\n\",\n \"plt.loglog(energies,spectrum(energies)*1e9,label=\\\"intrinsic\\\")\\n\",\n \"plt.loglog(energies,spectrumEBL(energies)*1e9,label=\\\"with Dominguez EBL attenuation\\\")\\n\",\n \"plt.loglog(energies,spectrumEBL_Gil(energies)*1e9,label=\\\"with Gilmore EBL attenuation\\\")\\n\",\n \"plt.legend(loc=1)\\n\",\n \"\\n\",\n \"plt.xlim(0.2,100.)\\n\",\n \"plt.ylim(1e-19,1e-9)\\n\",\n \"plt.xlabel(\\\"Energy [TeV]\\\")\\n\",\n \"plt.ylabel(r\\\"Flux (ph cm$^{-2}$ s$^{-1}$ TeV$^{-1}$)\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":271,"cells":{"repo_name":{"kind":"string","value":"parrt/msan501"},"path":{"kind":"string","value":"notes/sound.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"2087627"},"content":{"kind":"null"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":272,"cells":{"repo_name":{"kind":"string","value":"jmhsi/justin_tinker"},"path":{"kind":"string","value":"data_science/courses/cs231/simple_net_spiral_data.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"6985"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"N = 100 # number of points per class\\n\",\n \"D = 2 # dimensionality\\n\",\n \"K = 3 # number of classes\\n\",\n \"X = np.zeros((N*K,D)) # data matrix (each row = single example)\\n\",\n \"y = np.zeros(N*K, dtype='uint8') # class labels\\n\",\n \"for j in range(K):\\n\",\n \" ix = range(N*j,N*(j+1))\\n\",\n \" r = np.linspace(0.0,1,N) # radius\\n\",\n \" t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\\n\",\n \" X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\\n\",\n \" y[ix] = j\\n\",\n \"# lets visualize the data:\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Softmax Linear Classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Train a Linear Classifier\\n\",\n \"\\n\",\n \"# initialize parameters randomly\\n\",\n \"W = 0.01 * np.random.randn(D,K)\\n\",\n \"b = np.zeros((1,K))\\n\",\n \"\\n\",\n \"# some hyperparameters\\n\",\n \"step_size = 1e-0\\n\",\n \"reg = 1e-3 # regularization strength\\n\",\n \"\\n\",\n \"# gradient descent loop\\n\",\n \"num_examples = X.shape[0]\\n\",\n \"for i in range(200):\\n\",\n \" \\n\",\n \" # evaluate class scores, [N x K]\\n\",\n \" scores = np.dot(X, W) + b \\n\",\n \" \\n\",\n \" # compute the class probabilities\\n\",\n \" exp_scores = np.exp(scores)\\n\",\n \" probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]\\n\",\n \" \\n\",\n \" # compute the loss: average cross-entropy loss and regularization\\n\",\n \" corect_logprobs = -np.log(probs[range(num_examples),y])\\n\",\n \" data_loss = np.sum(corect_logprobs)/num_examples\\n\",\n \" reg_loss = 0.5*reg*np.sum(W*W)\\n\",\n \" loss = data_loss + reg_loss\\n\",\n \" if i % 10 == 0:\\n\",\n \" print( \\\"iteration %d: loss %f\\\" % (i, loss))\\n\",\n \" \\n\",\n \" # compute the gradient on scores\\n\",\n \" dscores = probs\\n\",\n \" dscores[range(num_examples),y] -= 1\\n\",\n \" dscores /= num_examples\\n\",\n \" \\n\",\n \" # backpropate the gradient to the parameters (W,b)\\n\",\n \" dW = np.dot(X.T, dscores)\\n\",\n \" db = np.sum(dscores, axis=0, keepdims=True)\\n\",\n \" \\n\",\n \" dW += reg*W # regularization gradient\\n\",\n \" \\n\",\n \" # perform a parameter update\\n\",\n \" W += -step_size * dW\\n\",\n \" b += -step_size * db\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# evaluate training set accuracy\\n\",\n \"scores = np.dot(X, W) + b\\n\",\n \"predicted_class = np.argmax(scores, axis=1)\\n\",\n \"print( 'training accuracy: %.2f' % (np.mean(predicted_class == y)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Training a Neural Network\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pdb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# initialize parameters randomly\\n\",\n \"h = 100 # size of hidden layer\\n\",\n \"W = 0.01 * np.random.randn(D,h)\\n\",\n \"b = np.zeros((1,h))\\n\",\n \"W2 = 0.01 * np.random.randn(h,K)\\n\",\n \"b2 = np.zeros((1,K))\\n\",\n \"\\n\",\n \"# some hyperparameters\\n\",\n \"step_size = 1e-0\\n\",\n \"reg = 1e-3 # regularization strength\\n\",\n \"\\n\",\n \"# gradient descent loop\\n\",\n \"num_examples = X.shape[0]\\n\",\n \"for i in range(10000):\\n\",\n \" \\n\",\n \" # evaluate class scores, [N x K]\\n\",\n \" hidden_layer = np.maximum(0, np.dot(X, W) + b) # note, ReLU activation\\n\",\n \" scores = np.dot(hidden_layer, W2) + b2\\n\",\n \" \\n\",\n \" # compute the class probabilities\\n\",\n \" exp_scores = np.exp(scores)\\n\",\n \" probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) # [N x K]\\n\",\n \" \\n\",\n \" # compute the loss: average cross-entropy loss and regularization\\n\",\n \" corect_logprobs = -np.log(probs[range(num_examples),y])\\n\",\n \" data_loss = np.sum(corect_logprobs)/num_examples\\n\",\n \" reg_loss = 0.5*reg*np.sum(W*W) + 0.5*reg*np.sum(W2*W2)\\n\",\n \" loss = data_loss + reg_loss\\n\",\n \" if i % 1000 == 0:\\n\",\n \" print(\\\"iteration %d: loss %f\\\" % (i, loss))\\n\",\n \" \\n\",\n \" # compute the gradient on scores\\n\",\n \" dscores = probs\\n\",\n \" dscores[range(num_examples),y] -= 1\\n\",\n \" dscores /= num_examples\\n\",\n \" \\n\",\n \" # backpropate the gradient to the parameters\\n\",\n \" # first backprop into parameters W2 and b2\\n\",\n \" dW2 = np.dot(hidden_layer.T, dscores)\\n\",\n \" db2 = np.sum(dscores, axis=0, keepdims=True)\\n\",\n \" # next backprop into hidden layer\\n\",\n \" dhidden = np.dot(dscores, W2.T)\\n\",\n \" # backprop the ReLU non-linearity\\n\",\n \" dhidden[hidden_layer <= 0] = 0\\n\",\n \" # finally into W,b\\n\",\n \" dW = np.dot(X.T, dhidden)\\n\",\n \" db = np.sum(dhidden, axis=0, keepdims=True)\\n\",\n \" \\n\",\n \" # add regularization gradient contribution\\n\",\n \" dW2 += reg * W2\\n\",\n \" dW += reg * W\\n\",\n \" \\n\",\n \" # perform a parameter update\\n\",\n \" W += -step_size * dW\\n\",\n \" b += -step_size * db\\n\",\n \" W2 += -step_size * dW2\\n\",\n \" b2 += -step_size * db2\\n\",\n \" pdb.set_trace()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# evaluate training set accuracy\\n\",\n \"hidden_layer = np.maximum(0, np.dot(X, W) + b)\\n\",\n \"scores = np.dot(hidden_layer, W2) + b2\\n\",\n \"predicted_class = np.argmax(scores, axis=1)\\n\",\n \"print( 'training accuracy: %.2f' % (np.mean(predicted_class == y)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n },\n \"varInspector\": {\n \"cols\": {\n \"lenName\": 16,\n \"lenType\": 16,\n \"lenVar\": 40\n },\n \"kernels_config\": {\n \"python\": {\n \"delete_cmd_postfix\": \"\",\n \"delete_cmd_prefix\": \"del \",\n \"library\": \"var_list.py\",\n \"varRefreshCmd\": \"print(var_dic_list())\"\n },\n \"r\": {\n \"delete_cmd_postfix\": \") \",\n \"delete_cmd_prefix\": \"rm(\",\n \"library\": \"var_list.r\",\n \"varRefreshCmd\": \"cat(var_dic_list()) \"\n }\n },\n \"types_to_exclude\": [\n \"module\",\n \"function\",\n \"builtin_function_or_method\",\n \"instance\",\n \"_Feature\"\n ],\n \"window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":273,"cells":{"repo_name":{"kind":"string","value":"Myllyenko/incubator-toree"},"path":{"kind":"string","value":"etc/examples/notebooks/meetup-streaming-toree.ipynb"},"copies":{"kind":"string","value":"8"},"size":{"kind":"string","value":"29770"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Streaming Meetups Dashboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The purpose of this notebook is to give an all-in-one demo of streaming data from the [meetup.com RSVP API](http://www.meetup.com/meetup_api/docs/stream/2/rsvps/#websockets), through a local [Spark Streaming job](http://spark.apache.org/streaming/), and into [declarative widgets](https://github.com/jupyter-incubator/declarativewidgets) in a dashboard layout. You can peek at the meetup.com stream [here](http://stream.meetup.com/2/rsvps).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This example is a Scala adaptation of [this](https://github.com/jupyter-incubator/dashboards/blob/master/etc/notebooks/stream_demo/meetup-streaming.ipynb) notebook from [jupyter_dashboards](https://github.com/jupyter-incubator/dashboards).\\n\",\n \"\\n\",\n \"On your first visit to this notebook, we recommend that you execute one cell at a time as you read along. Later, if you just want to see the demo, select *Cell > Run All* from the menu bar. Once you've run all of the cells, select *View > View Dashboard* and then click on the **Stream** toggle to start the data stream.\\n\",\n \"\\n\",\n \"**Table of Contents**\\n\",\n \"\\n\",\n \"1. [Initialize DeclarativeWidgets](#Initialize-DeclarativeWidgets) \\n\",\n \"2. [Define the Spark Streaming Job](#Streaming-Meetups-Scala) create stream context, custom receiver, filter by topic, top topics, venue metadata\\n\",\n \"3. [Create a Dashboard Interface with Widgets](#Streaming-Meetups-Dashboard)charts, interactive controls, globe\\n\",\n \"4. [Arrange the Dashboard Layout](#Arrange-the-Dashboard-Layout-Top)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"

Note

\\n\",\n \"\\n\",\n \"

We've condensed all of the demo logic into a single notebook for educational purposes. If you want to turn this into a scalable, multi-tenant dashboard, you'll want to separate the stream processing portions from the dashboard view. That way, multiple dashboard instances can pull from the same processed data stream instead of recomputing it.

\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Initialize DeclarativeWidgetsTop\\n\",\n \"\\n\",\n \"In Toree, declarativewidgets need to be initialized by adding the JAR with the scala implementation and calling `initWidgets`. This is must take place very close to the top of the notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%%html\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"

Note The output of the cell above is the URL to use below.\\n\",\n \"

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%AddJar http://localhost:8888/nbextensions/urth_widgets/urth-widgets.jar\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import urth.widgets._\\n\",\n \"initWidgets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Define the Spark Streaming ApplicationTop\\n\",\n \"\\n\",\n \"With the frontend widgest in mind, we'll now setup our Spark Streaming job to fulfill their data requirements. In this section, we'll define a set of functions that act on a `SparkStreamingContext` or `RDDs` from that context.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Install external dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%AddDeps eu.piotrbuda scalawebsocket_2.10 0.1.1 --transitive\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Custom WebSocker Receiver\\n\",\n \"\\n\",\n \"We create here a custom receiver that can connect to a WebSocket. That is how we will stream data out of the Meetup API.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import scalawebsocket.WebSocket\\n\",\n \"import org.apache.spark.storage.StorageLevel \\n\",\n \"import org.apache.spark.streaming.receiver.Receiver\\n\",\n \"\\n\",\n \"class WebSocketReceiver(url: String, storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER) extends Receiver[play.api.libs.json.JsValue](storageLevel) {\\n\",\n \" @volatile private var webSocket: WebSocket = _\\n\",\n \"\\n\",\n \" def onStart() {\\n\",\n \" try{\\n\",\n \" val newWebSocket = WebSocket().open(url).onTextMessage({ msg: String => parseJson(msg) })\\n\",\n \" setWebSocket(newWebSocket)\\n\",\n \" } catch {\\n\",\n \" case e: Exception => restart(\\\"Error starting WebSocket stream\\\", e)\\n\",\n \" }\\n\",\n \" }\\n\",\n \"\\n\",\n \" def onStop() {\\n\",\n \" setWebSocket(null)\\n\",\n \" }\\n\",\n \"\\n\",\n \" private def setWebSocket(newWebSocket: WebSocket) = synchronized {\\n\",\n \" if (webSocket != null) {\\n\",\n \" webSocket.shutdown()\\n\",\n \" }\\n\",\n \" webSocket = newWebSocket\\n\",\n \" }\\n\",\n \"\\n\",\n \" private def parseJson(jsonStr: String): Unit = {\\n\",\n \" val json: play.api.libs.json.JsValue = play.api.libs.json.Json.parse(jsonStr)\\n\",\n \" store(json)\\n\",\n \" }\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Spark Meetup Application\\n\",\n \"\\n\",\n \"We then put all our functionality into an `object` that is marked as `Serializable`. This is a great technique to avoid serialization problems when interacting with Spark. Also not that anything that we do not want to serialize, such as the `StreamingContext` and the `SQLContext` should be marked `@transient`.\\n\",\n \"\\n\",\n \"The rest of the call are methods for starting and stopping the streaming application as well as functions that define the streaming flow.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"case class TopicCount(topic: String, count: Int)\\n\",\n \"\\n\",\n \"object MeetupApp extends Serializable { \\n\",\n \" import play.api.libs.json._\\n\",\n \" import org.apache.spark.storage.StorageLevel \\n\",\n \" import org.apache.spark.Logging\\n\",\n \" import org.apache.spark.streaming._\\n\",\n \" import org.apache.spark.sql.functions._\\n\",\n \" import org.apache.spark.streaming.dstream.DStream\\n\",\n \" import org.apache.spark.rdd.RDD\\n\",\n \" import urth.widgets.WidgetChannels.channel\\n\",\n \" import org.apache.spark.sql.SQLContext\\n\",\n \" \\n\",\n \" @transient var ssc:StreamingContext = _\\n\",\n \" @transient val sqlContext:SQLContext = new SQLContext(sc)\\n\",\n \" \\n\",\n \" import sqlContext.implicits._\\n\",\n \" \\n\",\n \" var topic_filter = \\\"\\\"\\n\",\n \" \\n\",\n \" //Resetting the values on the data channels\\n\",\n \" channel(\\\"status\\\").set(\\\"streaming\\\", false)\\n\",\n \" channel(\\\"stats\\\").set(\\\"topics\\\", Map())\\n\",\n \" channel(\\\"stats\\\").set(\\\"venues\\\", List())\\n\",\n \"\\n\",\n \" def create_streaming_context(sample_rate: Int): StreamingContext = {\\n\",\n \" return new StreamingContext(sc, Seconds(sample_rate))\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Creates a websocket client that pumps events into a ring buffer queue. Creates\\n\",\n \" a SparkStreamContext that reads from the queue. Creates the events, topics, and\\n\",\n \" venues DStreams, setting the widget channel publishing functions to iterate over\\n\",\n \" RDDs in each. Starts the stream processing.\\n\",\n \" */\\n\",\n \" def start_stream():Unit = {\\n\",\n \" ssc = create_streaming_context(5)\\n\",\n \" ssc.checkpoint(\\\"/tmp/meetups.checkpoint\\\")\\n\",\n \" val events = get_events(ssc, sample_event)\\n\",\n \" get_topics(events, get_topic_counts)\\n\",\n \" get_venues(events, aggregate_venues)\\n\",\n \" ssc.start()\\n\",\n \" \\n\",\n \" channel(\\\"status\\\").set(\\\"streaming\\\", true)\\n\",\n \" }\\n\",\n \"\\n\",\n \" /**\\n\",\n \" Shuts down the websocket, stops the streaming context, and cleans up the file ring.\\n\",\n \" */\\n\",\n \" def shutdown_stream():Unit = {\\n\",\n \" ssc.stop(false)\\n\",\n \" channel(\\\"status\\\").set(\\\"streaming\\\", false) \\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Parses the events from the queue. Retains only those events that have at\\n\",\n \" least one topic exactly matching the current topic_filter. Sends event\\n\",\n \" RDDs to the for_each function. Returns the event DStream.\\n\",\n \" */\\n\",\n \" def get_events(ssc: StreamingContext, for_each: (RDD[play.api.libs.json.JsValue]) => Unit): org.apache.spark.streaming.dstream.DStream[play.api.libs.json.JsValue] = {\\n\",\n \"\\n\",\n \" val all_events = ssc.receiverStream( new WebSocketReceiver(\\\"ws://stream.meetup.com/2/rsvps\\\"))\\n\",\n \"\\n\",\n \" // Filter set of event\\n\",\n \" val events = all_events.filter(retain_event)\\n\",\n \"\\n\",\n \" // Send event data to a widget channel. This will be covered below.\\n\",\n \" events.foreachRDD(for_each)\\n\",\n \"\\n\",\n \" return events\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Returns true if the user defined topic filter is blank or if at least one\\n\",\n \" group topic in the event exactly matches the user topic filter string.\\n\",\n \" */\\n\",\n \" def retain_event(event: play.api.libs.json.JsValue):Boolean = {\\n\",\n \" val topics = (event \\\\ \\\"group\\\" \\\\ \\\"group_topics\\\").as[play.api.libs.json.JsArray].value\\n\",\n \" val isEmpty = topic_filter.trim == \\\"\\\"\\n\",\n \" val containsTopic = topics.map(topic => topic_filter.\\n\",\n \" equals((topic\\\\\\\"urlkey\\\").as[play.api.libs.json.JsString].value)\\n\",\n \" ).reduce( (a,b) => a || b)\\n\",\n \"\\n\",\n \" isEmpty || containsTopic \\n\",\n \" }\\n\",\n \" \\n\",\n \" /*\\n\",\n \" Takes an RDD from the event DStream. Takes one event from the RDD.\\n\",\n \" Substitutes a placeholder photo if the member who RSVPed does not\\n\",\n \" have one. Publishes the event metadata on the meetup channel.\\n\",\n \" */\\n\",\n \" def sample_event(rdd: org.apache.spark.rdd.RDD[play.api.libs.json.JsValue]):Unit = {\\n\",\n \" \\n\",\n \" try {\\n\",\n \" val event = rdd.take(1)(0)\\n\",\n \"\\n\",\n \" // use a fallback photo for those members without one\\n\",\n \" val default_event: play.api.libs.json.JsObject = play.api.libs.json.Json.parse(\\\"\\\"\\\"{\\n\",\n \" \\\"member\\\" : {\\n\",\n \" \\\"photo\\\" : \\\"http://photos4.meetupstatic.com/img/noPhoto_50.png\\\"\\n\",\n \" }\\n\",\n \" }\\n\",\n \" \\\"\\\"\\\").as[play.api.libs.json.JsObject]\\n\",\n \" val fixed_event = default_event ++ (event).as[play.api.libs.json.JsObject] \\n\",\n \"\\n\",\n \" channel(\\\"meetups\\\").set(\\\"meetup\\\", fixed_event.value)\\n\",\n \" } catch {\\n\",\n \" case _ => print(\\\"No data to sample\\\")\\n\",\n \" }\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Pulls group topics from meetup events. Counts each one once and updates\\n\",\n \" the global topic counts seen since stream start. Sends topic count RDDs\\n\",\n \" to the for_each function. Returns nothing new.\\n\",\n \" */ \\n\",\n \" def get_topics(events:DStream[JsValue], for_each: (RDD[((String,String),Int)]) => Unit) = {\\n\",\n \" //Extract the group topic url keys and \\\"namespace\\\" them with the current topic filter\\n\",\n \" val topics = events.\\n\",\n \" flatMap( (event: JsValue) => {\\n\",\n \" (event \\\\ \\\"group\\\" \\\\ \\\"group_topics\\\").as[JsArray].value\\n\",\n \" }).map((topic: JsValue) => {\\n\",\n \" val filter = if(topic_filter.equals(\\\"\\\")) {\\n\",\n \" \\\"*\\\"\\n\",\n \" } else { \\n\",\n \" topic_filter \\n\",\n \" }\\n\",\n \" ((filter, (topic \\\\ \\\"urlkey\\\").as[JsString].value), 1) \\n\",\n \" })\\n\",\n \"\\n\",\n \" val topic_counts = topics.updateStateByKey(update_topic_counts)\\n\",\n \"\\n\",\n \" // Send topic data to a widget channel. This will be covered below.\\n\",\n \" topic_counts.foreachRDD(for_each)\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Sums the number of times a topic has been seen in the current sampling\\n\",\n \" window. Then adds that to the number of times the topic has been\\n\",\n \" seen in the past. Returns the new sum.\\n\",\n \" */\\n\",\n \" def update_topic_counts(new_values: Seq[Int], last_sum: Option[Int]): Option[Int] = {\\n\",\n \" return Some((new_values :+ 0).reduce(_+_) + last_sum.getOrElse(0))\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Takes an RDD from the topic DStream. Takes the top 25 topics by occurrence\\n\",\n \" and publishes them in a pandas DataFrame on the counts channel.\\n\",\n \" */\\n\",\n \" def get_topic_counts(rdd: RDD[((String,String),Int)]){\\n\",\n \" //counts = rdd.takeOrdered(25, key=lambda x: -x[1])\\n\",\n \" val filterStr = if (topic_filter.equals(\\\"\\\")) \\\"*\\\" else topic_filter\\n\",\n \" \\n\",\n \" /*\\n\",\n \" keep only those matching current filter\\n\",\n \" and sort in descending order, taking top 25\\n\",\n \" */\\n\",\n \" val countDF = rdd.filter((x:((String,String),Int)) => filterStr.equals(x._1._1)).\\n\",\n \" map(tuple => TopicCount(tuple._1._2, tuple._2)).toDF().\\n\",\n \" sort($\\\"count\\\".desc).limit(25)\\n\",\n \" \\n\",\n \" channel(\\\"stats\\\").set(\\\"topics\\\", countDF)\\n\",\n \"\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Pulls venu metadata from meetup events if it exists. Sends venue \\n\",\n \" dictionaries RDDs to the for_each function. Returns nothing new.\\n\",\n \" */\\n\",\n \" def get_venues(events:DStream[JsValue], calculate: (DStream[JsValue]) => Unit):Unit = {\\n\",\n \" \\n\",\n \" val venues = events.\\n\",\n \" filter(hasVenue).\\n\",\n \" map((event:JsValue) => {event \\\\ \\\"venue\\\"})\\n\",\n \"\\n\",\n \" calculate(venues)\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Checks if there is a venue in the JSON object\\n\",\n \" */\\n\",\n \" def hasVenue(event:JsValue):Boolean = {\\n\",\n \" (event \\\\ \\\"venue\\\") match {\\n\",\n \" case e:play.api.libs.json.JsUndefined => false\\n\",\n \" case _ => true\\n\",\n \" }\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Aggregating the venues by lat/lon\\n\",\n \" */\\n\",\n \" def aggregate_venues(venues:DStream[JsValue]): Unit = {\\n\",\n \" //build a (lat, long, mag) tuple\\n\",\n \" val venue_loc = venues.map((venue:JsValue) => {\\n\",\n \" ( \\n\",\n \" (venue \\\\ \\\"lat\\\").asOpt[Int].getOrElse(0),\\n\",\n \" (venue \\\\ \\\"lon\\\").asOpt[Int].getOrElse(0)\\n\",\n \" )\\n\",\n \" }).\\n\",\n \" filter((latLon:(Int,Int)) => {latLon != (0,0)}).\\n\",\n \" map((latLon:(Int,Int)) => {\\n\",\n \" ((\\\"\\\"+latLon._1+\\\",\\\"+latLon._2), (latLon._1, latLon._2, 1))\\n\",\n \" })\\n\",\n \" \\n\",\n \" val venue_loc_counts = venue_loc.updateStateByKey(update_venue_loc_counts)\\n\",\n \" \\n\",\n \" venue_loc_counts.map{\\n\",\n \" _._2\\n\",\n \" }.foreachRDD((rdd:RDD[(Int,Int,Int)]) => {\\n\",\n \" val venue_data = rdd.collect()\\n\",\n \" \\n\",\n \" if( !venue_data.isEmpty ){\\n\",\n \" val total = venue_data.reduce((a,b)=> (0, 0, a._3+b._3))._3 * 1.0\\n\",\n \" \\n\",\n \" val venue_data_list= venue_data.map( (x:(Int,Int,Int)) => List(x._1,x._2,x._3/total)).toList\\n\",\n \" \\n\",\n \" channel(\\\"stats\\\").set(\\\"venues\\\", venue_data_list)\\n\",\n \" }\\n\",\n \" })\\n\",\n \" }\\n\",\n \" \\n\",\n \" /**\\n\",\n \" Function to count the lat, lon\\n\",\n \" */\\n\",\n \" def update_venue_loc_counts(new_values: Seq[(Int,Int,Int)], last_sum: Option[(Int,Int,Int)]): Option[(Int,Int,Int)] = {\\n\",\n \" if( new_values.isEmpty )\\n\",\n \" last_sum\\n\",\n \" else{\\n\",\n \" val partial = new_values.reduce((a,b)=> (a._1, a._2, a._3+b._3))\\n\",\n \" Some((partial._1, partial._2, partial._3 + last_sum.getOrElse((0,0,0))._3))\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create a Dashboard Interface with Widgets Top\\n\",\n \"\\n\",\n \"Now that we have a streaming job defined, we can create a series of interactive areas that can control and display the data is being produced.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Control to start and stop the Stream\\n\",\n \"\\n\",\n \"Here we will use a switch widget and tie it to a function that can start and stop the stream job.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"val start_stream = () => {\\n\",\n \" MeetupApp.start_stream()\\n\",\n \"}:Unit\\n\",\n \"\\n\",\n \"val shutdown_stream = () => {\\n\",\n \" MeetupApp.shutdown_stream()\\n\",\n \"}:Unit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%%html\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Topic Bar Chart\\n\",\n \"\\n\",\n \"Here we insert a `` to show the top 25 meetup topics by occurrence in the stream. Take note of the `