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stringclasses 7
values | ground_truth
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46
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class | completion_type
stringclasses 6
values | non_dunder_count_intellij
int64 0
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int64 0
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55k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
561 | 19 | 1,735 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.
|
562 | 19 | 1,740 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
format_elapsed
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.
|
564 | 19 | 1,970 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
SOMA
| true |
class
| 34 | 35 | false | false |
[
"SOMA",
"soma",
"util",
"None",
"util_ann",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.
|
566 | 19 | 2,371 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
util
| true |
module
| 33 | 35 | false | false |
[
"soma",
"util",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.
|
567 | 19 | 2,376 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
get_start_stamp
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.
|
569 | 19 | 2,547 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
util_ann
| true |
module
| 33 | 35 | false | true |
[
"soma",
"util",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.
|
570 | 19 | 2,556 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
_decategoricalize
| true |
function
| 8 | 14 | true | true |
[
"describe_ann_file",
"_describe_ann_file_show_data",
"_describe_ann_file_show_summary",
"_describe_ann_file_show_types",
"_decategoricalize",
"_describe_ann_file_show_uns_data",
"_describe_ann_file_show_uns_summary",
"_describe_ann_file_show_uns_types"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "np",
"type": "module"
},
{
"name": "os",
"type": "module"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "util",
"type": "module"
},
{
"name": "_decategoricalize",
"type": "function"
},
{
"name": "_describe_ann_file_show_data",
"type": "function"
},
{
"name": "_describe_ann_file_show_summary",
"type": "function"
},
{
"name": "_describe_ann_file_show_types",
"type": "function"
},
{
"name": "_describe_ann_file_show_uns_data",
"type": "function"
},
{
"name": "_describe_ann_file_show_uns_summary",
"type": "function"
},
{
"name": "_describe_ann_file_show_uns_types",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann.
|
572 | 19 | 2,641 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
util
| true |
module
| 33 | 35 | false | false |
[
"soma",
"util",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.
|
573 | 19 | 2,646 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
format_elapsed
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.
|
576 | 19 | 2,762 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
util
| true |
module
| 33 | 35 | false | false |
[
"soma",
"util",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.
|
577 | 19 | 2,767 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
get_start_stamp
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.
|
582 | 19 | 3,002 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
common
|
from_matrix_and_dim_values
| true |
function
| 5 | 18 | false | true |
[
"data",
"uri",
"name",
"exists",
"from_matrix_and_dim_values",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrix_and_dim_values",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.
|
586 | 19 | 3,191 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
from_dataframe
| true |
function
| 18 | 24 | false | true |
[
"to_dataframe",
"uri",
"from_dataframe",
"df",
"name",
"attr_names",
"attr_names_to_types",
"attribute_filter",
"dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ids",
"keys",
"shape",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "attribute_filter",
"type": "function"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_name",
"type": "statement"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_dataframe",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ids",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "shape",
"type": "function"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_dataframe",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.
|
590 | 19 | 3,286 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
from_dataframe
| true |
function
| 18 | 24 | false | false |
[
"to_dataframe",
"uri",
"from_dataframe",
"df",
"name",
"attr_names",
"attr_names_to_types",
"attribute_filter",
"dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ids",
"keys",
"shape",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "attribute_filter",
"type": "function"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_name",
"type": "statement"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_dataframe",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ids",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "shape",
"type": "function"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_dataframe",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.
|
594 | 19 | 3,453 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
from_matrices_and_dim_values
| true |
function
| 7 | 20 | false | true |
[
"to_dict_of_csr",
"uri",
"from_matrices_and_dim_values",
"name",
"keys",
"dim_name",
"exists",
"__contains__",
"__getitem__",
"__init__",
"__iter__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "dim_name",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrices_and_dim_values",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_dict_of_csr",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.
|
598 | 19 | 3,562 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
from_matrices_and_dim_values
| true |
function
| 7 | 20 | false | false |
[
"to_dict_of_csr",
"uri",
"from_matrices_and_dim_values",
"name",
"keys",
"dim_name",
"exists",
"__contains__",
"__getitem__",
"__init__",
"__iter__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "dim_name",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrices_and_dim_values",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_dict_of_csr",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.
|
602 | 19 | 3,671 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
from_matrices_and_dim_values
| true |
function
| 8 | 21 | false | false |
[
"uri",
"from_matrices_and_dim_values",
"to_dict_of_csr",
"name",
"keys",
"col_dim_name",
"exists",
"row_dim_name",
"__contains__",
"__getitem__",
"__init__",
"__iter__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "col_dim_name",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrices_and_dim_values",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "row_dim_name",
"type": "statement"
},
{
"name": "to_dict_of_csr",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.
|
606 | 19 | 3,780 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
from_matrices_and_dim_values
| true |
function
| 8 | 21 | false | false |
[
"uri",
"from_matrices_and_dim_values",
"to_dict_of_csr",
"name",
"keys",
"col_dim_name",
"exists",
"row_dim_name",
"__contains__",
"__getitem__",
"__init__",
"__iter__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "col_dim_name",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrices_and_dim_values",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "row_dim_name",
"type": "statement"
},
{
"name": "to_dict_of_csr",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.
|
610 | 19 | 3,990 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
infile
|
from_anndata
| true |
function
| 9 | 22 | false | true |
[
"to_anndata_raw",
"var",
"X",
"from_anndata",
"uri",
"exists",
"name",
"varm",
"varp",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "exists",
"type": "function"
},
{
"name": "from_anndata",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_anndata_raw",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "var",
"type": "statement"
},
{
"name": "varm",
"type": "statement"
},
{
"name": "varp",
"type": "statement"
},
{
"name": "X",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.
|
614 | 19 | 4,163 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
from_anndata_uns
| true |
function
| 6 | 19 | false | true |
[
"to_dict_of_matrices",
"uri",
"from_anndata_uns",
"name",
"keys",
"exists",
"__contains__",
"__getitem__",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "exists",
"type": "function"
},
{
"name": "from_anndata_uns",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_dict_of_matrices",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.
|
618 | 19 | 4,287 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
util
| true |
module
| 33 | 35 | false | false |
[
"soma",
"util",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.
|
619 | 19 | 4,292 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
format_elapsed
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.
|
622 | 19 | 4,460 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
SOMA
| true |
class
| 34 | 35 | false | false |
[
"SOMA",
"util",
"soma",
"None",
"util_ann",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.
|
624 | 19 | 4,694 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"SOMAOptions",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.
|
625 | 19 | 4,699 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
get_start_stamp
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.
|
628 | 19 | 4,858 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"SOMAOptions",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.
|
629 | 19 | 4,863 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
get_start_stamp
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.
|
632 | 19 | 5,030 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.
|
633 | 19 | 5,035 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
format_elapsed
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.
|
636 | 19 | 5,166 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.
|
637 | 19 | 5,171 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
format_elapsed
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.
|
639 | 19 | 5,378 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
SOMA
| true |
class
| 34 | 35 | false | false |
[
"SOMA",
"util",
"soma",
"None",
"util_ann",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.
|
641 | 19 | 5,794 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"raw_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.
|
642 | 19 | 5,799 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
get_start_stamp
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.
|
645 | 19 | 5,892 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_dataframe
| true |
function
| 18 | 24 | false | true |
[
"to_dataframe",
"uri",
"from_dataframe",
"df",
"name",
"attr_names",
"attr_names_to_types",
"attribute_filter",
"dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ids",
"keys",
"shape",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "attribute_filter",
"type": "function"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_name",
"type": "statement"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_dataframe",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ids",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "shape",
"type": "function"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_dataframe",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.
|
647 | 19 | 5,929 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_dataframe
| true |
function
| 18 | 24 | false | false |
[
"to_dataframe",
"from_dataframe",
"uri",
"df",
"name",
"attr_names",
"attr_names_to_types",
"attribute_filter",
"dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ids",
"keys",
"shape",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "attribute_filter",
"type": "function"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_name",
"type": "statement"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_dataframe",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ids",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "shape",
"type": "function"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_dataframe",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.
|
649 | 19 | 5,964 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
data
| true |
statement
| 5 | 18 | false | true |
[
"uri",
"data",
"from_matrix_and_dim_values",
"name",
"exists",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrix_and_dim_values",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.
|
650 | 19 | 5,969 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_csr_matrix
| true |
function
| 20 | 28 | false | true |
[
"to_csr_matrix",
"uri",
"from_matrix_and_dim_values",
"df",
"name",
"attr_name",
"attr_names",
"attr_names_to_types",
"col_dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ingest_data_cols_chunked",
"ingest_data_dense_rows_chunked",
"ingest_data_rows_chunked",
"ingest_data_whole",
"row_dim_name",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_name",
"type": "statement"
},
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "col_dim_name",
"type": "statement"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrix_and_dim_values",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ingest_data_cols_chunked",
"type": "function"
},
{
"name": "ingest_data_dense_rows_chunked",
"type": "function"
},
{
"name": "ingest_data_rows_chunked",
"type": "function"
},
{
"name": "ingest_data_whole",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "row_dim_name",
"type": "statement"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_csr_matrix",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_create_empty_array",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_ingest_data",
"type": "function"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.
|
652 | 19 | 6,033 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_dict_of_csr
| true |
function
| 7 | 20 | false | true |
[
"from_matrices_and_dim_values",
"uri",
"to_dict_of_csr",
"name",
"keys",
"dim_name",
"exists",
"__contains__",
"__getitem__",
"__init__",
"__iter__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "dim_name",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrices_and_dim_values",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_dict_of_csr",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.
|
654 | 19 | 6,071 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_dict_of_csr
| true |
function
| 7 | 20 | false | false |
[
"from_matrices_and_dim_values",
"uri",
"to_dict_of_csr",
"name",
"keys",
"dim_name",
"exists",
"__contains__",
"__getitem__",
"__init__",
"__iter__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "dim_name",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrices_and_dim_values",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_dict_of_csr",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__iter__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.
|
656 | 19 | 6,314 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_anndata_raw
| true |
function
| 9 | 22 | false | true |
[
"var",
"X",
"from_anndata",
"varm",
"uri",
"exists",
"name",
"to_anndata_raw",
"varp",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "exists",
"type": "function"
},
{
"name": "from_anndata",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_anndata_raw",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "var",
"type": "statement"
},
{
"name": "varm",
"type": "statement"
},
{
"name": "varp",
"type": "statement"
},
{
"name": "X",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.
|
658 | 19 | 6,550 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_dict_of_matrices
| true |
function
| 6 | 19 | false | true |
[
"from_anndata_uns",
"uri",
"name",
"keys",
"to_dict_of_matrices",
"exists",
"__contains__",
"__getitem__",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "exists",
"type": "function"
},
{
"name": "from_anndata_uns",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_dict_of_matrices",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__contains__",
"type": "function"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__getitem__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.
|
660 | 19 | 6,953 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
util
| true |
module
| 33 | 35 | false | false |
[
"util",
"soma",
"SOMA",
"util_ann",
"raw_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.
|
661 | 19 | 6,958 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
format_elapsed
| true |
function
| 10 | 16 | false | false |
[
"format_elapsed",
"get_start_stamp",
"List",
"Optional",
"ETATracker",
"_find_csc_chunk_size",
"_find_csr_chunk_size",
"_get_sort_and_permutation",
"_to_tiledb_supported_array_type",
"_X_and_ids_to_coo"
] |
[
{
"name": "ad",
"type": "module"
},
{
"name": "ETATracker",
"type": "class"
},
{
"name": "format_elapsed",
"type": "function"
},
{
"name": "get_start_stamp",
"type": "function"
},
{
"name": "List",
"type": "class"
},
{
"name": "numpy",
"type": "module"
},
{
"name": "Optional",
"type": "class"
},
{
"name": "pd",
"type": "module"
},
{
"name": "scipy",
"type": "module"
},
{
"name": "tiledb",
"type": "module"
},
{
"name": "time",
"type": "module"
},
{
"name": "_find_csc_chunk_size",
"type": "function"
},
{
"name": "_find_csr_chunk_size",
"type": "function"
},
{
"name": "_get_sort_and_permutation",
"type": "function"
},
{
"name": "_to_tiledb_supported_array_type",
"type": "function"
},
{
"name": "_X_and_ids_to_coo",
"type": "function"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.
|
663 | 19 | 7,143 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
SOMA
| true |
class
| 34 | 35 | false | false |
[
"SOMA",
"util",
"soma",
"None",
"util_ann",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"soma_options",
"SOMACollection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.
|
665 | 19 | 7,259 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
Unknown
|
to_dataframe
| true |
function
| 18 | 24 | false | false |
[
"from_dataframe",
"to_dataframe",
"uri",
"df",
"name",
"attr_names",
"attr_names_to_types",
"attribute_filter",
"dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ids",
"keys",
"shape",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "attribute_filter",
"type": "function"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_name",
"type": "statement"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_dataframe",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ids",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "shape",
"type": "function"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_dataframe",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Extract only the raw parts as a new AnnData object.
"""
obs_df = soma.obs.
|
667 | 19 | 7,296 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
var
| true |
statement
| 9 | 22 | false | true |
[
"var",
"to_anndata_raw",
"X",
"uri",
"varm",
"exists",
"from_anndata",
"name",
"varp",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "exists",
"type": "function"
},
{
"name": "from_anndata",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_anndata_raw",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "var",
"type": "statement"
},
{
"name": "varm",
"type": "statement"
},
{
"name": "varp",
"type": "statement"
},
{
"name": "X",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Extract only the raw parts as a new AnnData object.
"""
obs_df = soma.obs.to_dataframe()
var_df = soma.raw.
|
668 | 19 | 7,300 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_dataframe
| true |
function
| 18 | 24 | false | false |
[
"from_dataframe",
"to_dataframe",
"uri",
"df",
"name",
"attr_names",
"attr_names_to_types",
"attribute_filter",
"dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ids",
"keys",
"shape",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "attribute_filter",
"type": "function"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_name",
"type": "statement"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_dataframe",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ids",
"type": "function"
},
{
"name": "keys",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "shape",
"type": "function"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_dataframe",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Extract only the raw parts as a new AnnData object.
"""
obs_df = soma.obs.to_dataframe()
var_df = soma.raw.var.
|
670 | 19 | 7,336 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
X
| true |
statement
| 9 | 22 | false | true |
[
"to_anndata_raw",
"var",
"X",
"uri",
"varm",
"exists",
"from_anndata",
"name",
"varp",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "exists",
"type": "function"
},
{
"name": "from_anndata",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "to_anndata_raw",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "var",
"type": "statement"
},
{
"name": "varm",
"type": "statement"
},
{
"name": "varp",
"type": "statement"
},
{
"name": "X",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Extract only the raw parts as a new AnnData object.
"""
obs_df = soma.obs.to_dataframe()
var_df = soma.raw.var.to_dataframe()
X_mat = soma.raw.
|
671 | 19 | 7,338 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
data
| true |
statement
| 5 | 18 | false | false |
[
"uri",
"data",
"from_matrix_and_dim_values",
"name",
"exists",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "data",
"type": "statement"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrix_and_dim_values",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_add_object",
"type": "function"
},
{
"name": "_create",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_get_member_names",
"type": "function"
},
{
"name": "_get_member_names_to_uris",
"type": "function"
},
{
"name": "_get_member_uris",
"type": "function"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_open_withlessly",
"type": "function"
},
{
"name": "_remove_object",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Extract only the raw parts as a new AnnData object.
"""
obs_df = soma.obs.to_dataframe()
var_df = soma.raw.var.to_dataframe()
X_mat = soma.raw.X.
|
672 | 19 | 7,343 |
single-cell-data__tiledb-soma
|
e64f89e6251f221bd91c49a997d7fb67de3ed349
|
apis/python/src/tiledbsc/io/anndata.py
|
inproject
|
to_csr_matrix
| true |
function
| 20 | 28 | false | false |
[
"to_csr_matrix",
"uri",
"from_matrix_and_dim_values",
"df",
"name",
"attr_name",
"attr_names",
"attr_names_to_types",
"col_dim_name",
"dim_names",
"dim_names_to_types",
"dim_select",
"exists",
"has_attr_name",
"ingest_data_cols_chunked",
"ingest_data_dense_rows_chunked",
"ingest_data_rows_chunked",
"ingest_data_whole",
"row_dim_name",
"tiledb_array_schema",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "attr_name",
"type": "statement"
},
{
"name": "attr_names",
"type": "function"
},
{
"name": "attr_names_to_types",
"type": "function"
},
{
"name": "col_dim_name",
"type": "statement"
},
{
"name": "df",
"type": "function"
},
{
"name": "dim_names",
"type": "function"
},
{
"name": "dim_names_to_types",
"type": "function"
},
{
"name": "dim_select",
"type": "function"
},
{
"name": "exists",
"type": "function"
},
{
"name": "from_matrix_and_dim_values",
"type": "function"
},
{
"name": "has_attr_name",
"type": "function"
},
{
"name": "ingest_data_cols_chunked",
"type": "function"
},
{
"name": "ingest_data_dense_rows_chunked",
"type": "function"
},
{
"name": "ingest_data_rows_chunked",
"type": "function"
},
{
"name": "ingest_data_whole",
"type": "function"
},
{
"name": "name",
"type": "statement"
},
{
"name": "row_dim_name",
"type": "statement"
},
{
"name": "tiledb_array_schema",
"type": "function"
},
{
"name": "to_csr_matrix",
"type": "function"
},
{
"name": "uri",
"type": "statement"
},
{
"name": "_create_empty_array",
"type": "function"
},
{
"name": "_ctx",
"type": "statement"
},
{
"name": "_indent",
"type": "statement"
},
{
"name": "_ingest_data",
"type": "function"
},
{
"name": "_object_type",
"type": "function"
},
{
"name": "_open",
"type": "function"
},
{
"name": "_soma_options",
"type": "statement"
},
{
"name": "_verbose",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import tiledbsc
import tiledbsc.util
import tiledbsc.util_ann
import anndata as ad
# ----------------------------------------------------------------
def from_h5ad(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads an .h5ad file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_h5ad {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = ad.read_h5ad(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_h5ad {input_path} -> {soma.uri}"
)
)
# ----------------------------------------------------------------
def from_10x(soma: tiledbsc.SOMA, input_path: str) -> None:
"""
Reads a 10X file and writes to a TileDB group structure.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.from_10x {input_path} -> {soma.uri}")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START READING {input_path}")
anndata = scanpy.read_10x_h5(input_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"{soma._indent}FINISH READING {input_path}"
)
)
from_anndata(soma, anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.from_10x {input_path} -> {soma.uri}"
)
)
return anndata
# ----------------------------------------------------------------
def from_anndata(soma: tiledbsc.SOMA, anndata: ad.AnnData) -> None:
"""
Top-level writer method for creating a TileDB group for a SOMA object.
"""
# Without _at least_ an index, there is nothing to indicate the dimension indices.
if anndata.obs.index.empty or anndata.var.index.empty:
raise NotImplementedError("Empty AnnData.obs or AnnData.var unsupported.")
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START DECATEGORICALIZING")
anndata.obs_names_make_unique()
anndata.var_names_make_unique()
anndata = tiledbsc.util_ann._decategoricalize(anndata)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH DECATEGORICALIZING")
)
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START WRITING {soma.uri}")
# Must be done first, to create the parent directory
soma._create()
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.X.from_matrix_and_dim_values(anndata.X, anndata.obs.index, anndata.var.index)
soma._add_object(soma.X)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obs.from_dataframe(dataframe=anndata.obs, extent=256)
soma._add_object(soma.obs)
soma.var.from_dataframe(dataframe=anndata.var, extent=2048)
soma._add_object(soma.var)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
soma.obsm.from_matrices_and_dim_values(anndata.obsm, anndata.obs_names)
soma._add_object(soma.obsm)
soma.varm.from_matrices_and_dim_values(anndata.varm, anndata.var_names)
soma._add_object(soma.varm)
soma.obsp.from_matrices_and_dim_values(anndata.obsp, anndata.obs_names)
soma._add_object(soma.obsp)
soma.varp.from_matrices_and_dim_values(anndata.varp, anndata.var_names)
soma._add_object(soma.varp)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.raw != None:
soma.raw.from_anndata(anndata)
soma._add_object(soma.raw)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if anndata.uns != None:
soma.uns.from_anndata_uns(anndata.uns)
soma._add_object(soma.uns)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s, f"{soma._indent}FINISH WRITING {soma.uri}")
)
# ----------------------------------------------------------------
def to_h5ad(soma: tiledbsc.SOMA, h5ad_path: str) -> None:
"""
Converts the soma group to anndata format and writes it to the specified .h5ad file.
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_h5ad {soma.uri} -> {h5ad_path}")
anndata = to_anndata(soma)
if soma._verbose:
s2 = tiledbsc.util.get_start_stamp()
print(f"{soma._indent}START write {h5ad_path}")
anndata.write_h5ad(h5ad_path)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(s2, f"{soma._indent}FINISH write {h5ad_path}")
)
if soma._verbose:
print(
tiledbsc.util.format_elapsed(
s, f"FINISH SOMA.to_h5ad {soma.uri} -> {h5ad_path}"
)
)
# ----------------------------------------------------------------
def to_anndata(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Converts the soma group to anndata. Choice of matrix formats is following
what we often see in input .h5ad files:
* X as scipy.sparse.csr_matrix
* obs,var as pandas.dataframe
* obsm,varm arrays as numpy.ndarray
* obsp,varp arrays as scipy.sparse.csr_matrix
As of 2022-05-05 this is an incomplete prototype.
"""
if soma._verbose:
s = tiledbsc.util.get_start_stamp()
print(f"START SOMA.to_anndata {soma.uri}")
obs_df = soma.obs.to_dataframe()
var_df = soma.var.to_dataframe()
X_mat = soma.X.data.to_csr_matrix(obs_df.index, var_df.index)
obsm = soma.obsm.to_dict_of_csr()
varm = soma.varm.to_dict_of_csr()
# TODO
print(" OBSP OUTGEST NOT WORKING YET")
# obsp = soma.obsp.to_dict_of_csr()
print(" VARP OUTGEST NOT WORKING YET")
# varp = soma.varp.to_dict_of_csr()
(raw_X, raw_var_df, raw_varm) = soma.raw.to_anndata_raw(obs_df.index)
anndata = ad.AnnData(X=X_mat, obs=obs_df, var=var_df, obsm=obsm, varm=varm)
raw = ad.Raw(
anndata,
X=raw_X,
var=raw_var_df,
varm=raw_varm,
)
uns = soma.uns.to_dict_of_matrices()
anndata = ad.AnnData(
X=anndata.X,
dtype=None if anndata.X is None else anndata.X.dtype, # some datasets have no X
obs=anndata.obs,
var=anndata.var,
obsm=anndata.obsm,
obsp=anndata.obsp,
varm=anndata.varm,
varp=anndata.varp,
raw=raw,
uns=uns,
)
if soma._verbose:
print(tiledbsc.util.format_elapsed(s, f"FINISH SOMA.to_anndata {soma.uri}"))
return anndata
# ----------------------------------------------------------------
def to_anndata_from_raw(soma: tiledbsc.SOMA) -> ad.AnnData:
"""
Extract only the raw parts as a new AnnData object.
"""
obs_df = soma.obs.to_dataframe()
var_df = soma.raw.var.to_dataframe()
X_mat = soma.raw.X.data.
|
673 | 20 | 577 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
inproject
|
SOMACollection
| true |
class
| 32 | 35 | false | true |
[
"SOMACollection",
"soma",
"SOMA",
"soma_options",
"soma_collection",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.
|
674 | 20 | 2,901 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.
|
675 | 20 | 3,094 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.
|
676 | 20 | 3,341 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.
|
677 | 20 | 3,599 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"soma",
"SOMA",
"None",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.
|
678 | 20 | 3,941 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.
|
679 | 20 | 4,393 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.obs.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"obs_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_var_id_counts(soco: t.
|
680 | 20 | 4,856 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.obs.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"obs_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_var_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.var.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"var_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_obs_column_unique_values(soco: t.
|
681 | 20 | 5,161 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.obs.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"obs_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_var_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.var.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"var_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_obs_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.obs.keys():
print(soma.obs.df()[col_name].unique())
# ----------------------------------------------------------------
def show_var_column_unique_values(soco: t.
|
682 | 20 | 5,458 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.obs.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"obs_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_var_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.var.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"var_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_obs_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.obs.keys():
print(soma.obs.df()[col_name].unique())
# ----------------------------------------------------------------
def show_var_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.var.keys():
print(soma.var.df()[col_name].unique())
# ----------------------------------------------------------------
def show_obs_value_counts(soco: t.
|
683 | 20 | 6,404 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
Unknown
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"show_single_cell_group",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"soma_collection",
"soma_options",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.obs.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"obs_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_var_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.var.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"var_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_obs_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.obs.keys():
print(soma.obs.df()[col_name].unique())
# ----------------------------------------------------------------
def show_var_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.var.keys():
print(soma.var.df()[col_name].unique())
# ----------------------------------------------------------------
def show_obs_value_counts(soco: t.SOMACollection, obs_labels: List[str]) -> None:
for obs_label in obs_labels:
counts = {}
for soma in soco:
print("...", soma.name)
# print("\n".join(sorted(soma.obs.attr_names())))
obs = soma.obs.df()
if not obs_label in obs:
continue
obs_label_values = sorted(list(set(obs[obs_label])))
for obs_label_value in obs_label_values:
if obs_label_value in counts:
counts[obs_label_value] += 1
else:
counts[obs_label_value] = 1
print(
"----------------------------------------------------------------",
obs_label,
)
for k, v in dict(sorted(counts.items(), key=lambda item: item[1])).items():
print(k, v)
# ----------------------------------------------------------------
def show_var_value_counts(soco: t.
|
684 | 20 | 7,351 |
single-cell-data__tiledb-soma
|
a367a8ce8cdffbcd4c14677da1eeeb14debc1876
|
apis/python/examples/pre-query.py
|
inproject
|
SOMACollection
| true |
class
| 33 | 35 | false | false |
[
"SOMACollection",
"None",
"soma",
"SOMA",
"soma_options",
"annotation_matrix",
"annotation_matrix_group",
"annotation_pairwise_matrix_group",
"AnnotationMatrix",
"AnnotationMatrixGroup",
"AnnotationPairwiseMatrixGroup",
"assay_matrix",
"assay_matrix_group",
"AssayMatrix",
"AssayMatrixGroup",
"describe_ann_file",
"raw_group",
"RawGroup",
"show_single_cell_group",
"soma_collection",
"SOMAOptions",
"tiledb_array",
"tiledb_group",
"tiledb_object",
"TileDBArray",
"TileDBGroup",
"TileDBObject",
"uns_array",
"uns_group",
"UnsArray",
"UnsGroup",
"util_ann",
"util_tiledb"
] |
[
{
"name": "annotation_dataframe",
"type": "module"
},
{
"name": "annotation_matrix",
"type": "module"
},
{
"name": "annotation_matrix_group",
"type": "module"
},
{
"name": "annotation_pairwise_matrix_group",
"type": "module"
},
{
"name": "AnnotationMatrix",
"type": "class"
},
{
"name": "AnnotationMatrixGroup",
"type": "class"
},
{
"name": "AnnotationPairwiseMatrixGroup",
"type": "class"
},
{
"name": "assay_matrix",
"type": "module"
},
{
"name": "assay_matrix_group",
"type": "module"
},
{
"name": "AssayMatrix",
"type": "class"
},
{
"name": "AssayMatrixGroup",
"type": "class"
},
{
"name": "describe_ann_file",
"type": "function"
},
{
"name": "io",
"type": "module"
},
{
"name": "raw_group",
"type": "module"
},
{
"name": "RawGroup",
"type": "class"
},
{
"name": "show_single_cell_group",
"type": "function"
},
{
"name": "SOMA",
"type": "class"
},
{
"name": "soma",
"type": "module"
},
{
"name": "soma_collection",
"type": "module"
},
{
"name": "soma_options",
"type": "module"
},
{
"name": "SOMACollection",
"type": "class"
},
{
"name": "SOMAOptions",
"type": "class"
},
{
"name": "tiledb_array",
"type": "module"
},
{
"name": "tiledb_group",
"type": "module"
},
{
"name": "tiledb_object",
"type": "module"
},
{
"name": "TileDBArray",
"type": "class"
},
{
"name": "TileDBGroup",
"type": "class"
},
{
"name": "TileDBObject",
"type": "class"
},
{
"name": "uns_array",
"type": "module"
},
{
"name": "uns_group",
"type": "module"
},
{
"name": "UnsArray",
"type": "class"
},
{
"name": "UnsGroup",
"type": "class"
},
{
"name": "util",
"type": "module"
},
{
"name": "util_ann",
"type": "module"
},
{
"name": "util_tiledb",
"type": "module"
},
{
"name": "__doc__",
"type": "instance"
},
{
"name": "__file__",
"type": "instance"
},
{
"name": "__name__",
"type": "instance"
},
{
"name": "__package__",
"type": "instance"
}
] |
#!/usr/bin/env python
# Invoke this with, for example,
#
# peek-soco ./soma-collection
#
# -- then you can inspect the SOMACollection object
import tiledb
import tiledbsc as t
import pandas
import sys, os
from typing import List, Dict
# ================================================================
def main():
if len(sys.argv) == 1:
soco_path = "soma-collection"
elif len(sys.argv) == 2:
soco_path = sys.argv[1]
else:
print(f"{sys.argv[0]}: need just one soma-collection path.", file=sys.stderr)
sys.exit(1)
soco = t.SOMACollection(soco_path)
# print()
# print("================================================================")
# print("NAMES AND URIS")
# print_names_and_uris(soco)
#
# print()
# print("================================================================")
# print("OBS NAMES")
# show_obs_names(soco)
#
# print()
# print("================================================================")
# print("VAR NAMES")
# show_var_names(soco)
#
# print()
# print("================================================================")
# print("SOMAS HAVING ALL THREE")
# show_somas_with_all_three(soco)
#
# print()
# print("================================================================")
# print("OBS_ID COUNTS")
# show_obs_id_counts(soco)
#
# print()
# print("================================================================")
# print("VAR_ID COUNTS")
# show_var_id_counts(soco)
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR CELL_TYPE")
# show_obs_column_unique_values(soco, "cell_type")
#
# print()
# print("================================================================")
# print("OBS UNIQUE VALUES FOR FEATURE_NAME")
# show_var_column_unique_values(soco, "feature_name")
#
# print()
# print("================================================================")
# print("OBS VALUE COUNTS FOR CELL_TYPE AND TISSUE")
# show_obs_value_counts(soco, ["cell_type", "tissue"])
#
# print()
# print("================================================================")
# print("VAR VALUE COUNTS FOR CELL_TYPE AND FEATURE_NAME")
# show_var_value_counts(soco, ["feature_name"])
print()
print("================================================================")
print("SHOW SOMAS HAVING")
show_somas_having(
soco,
{"cell_type": ["B cell", "T cell"], "tissue": ["blood", "lung"]},
{"feature_name": ["MT-CO3"]},
)
# ----------------------------------------------------------------
def print_names_and_uris(soco: t.SOMACollection) -> None:
for soma in soco:
print("%-40s %s" % (soma.name, soma.uri))
# ----------------------------------------------------------------
def show_obs_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.obs.keys():
print(" obs", attr_name)
# ----------------------------------------------------------------
def show_var_names(soco: t.SOMACollection) -> None:
for soma in soco:
print(soma.uri)
for attr_name in soma.var.keys():
print(" var", attr_name)
# ----------------------------------------------------------------
def show_somas_with_all_three(soco: t.SOMACollection) -> None:
for soma in soco:
if "cell_type" in soma.obs.attr_names():
if "tissue" in soma.obs.attr_names():
if "feature_name" in soma.var.attr_names():
print(soma.uri)
# ----------------------------------------------------------------
def show_obs_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.obs.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"obs_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_var_id_counts(soco: t.SOMACollection) -> None:
counts = {}
for soma in soco:
for oid in soma.var.ids():
if oid in counts:
counts[oid] += 1
else:
counts[oid] = 1
df = pandas.DataFrame.from_dict(
{"var_id": counts.keys(), "counts": counts.values()}
)
# print(df.head())
print(df)
# ----------------------------------------------------------------
def show_obs_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.obs.keys():
print(soma.obs.df()[col_name].unique())
# ----------------------------------------------------------------
def show_var_column_unique_values(soco: t.SOMACollection, col_name: str) -> None:
for soma in soco:
print()
print(soma.uri)
if col_name in soma.var.keys():
print(soma.var.df()[col_name].unique())
# ----------------------------------------------------------------
def show_obs_value_counts(soco: t.SOMACollection, obs_labels: List[str]) -> None:
for obs_label in obs_labels:
counts = {}
for soma in soco:
print("...", soma.name)
# print("\n".join(sorted(soma.obs.attr_names())))
obs = soma.obs.df()
if not obs_label in obs:
continue
obs_label_values = sorted(list(set(obs[obs_label])))
for obs_label_value in obs_label_values:
if obs_label_value in counts:
counts[obs_label_value] += 1
else:
counts[obs_label_value] = 1
print(
"----------------------------------------------------------------",
obs_label,
)
for k, v in dict(sorted(counts.items(), key=lambda item: item[1])).items():
print(k, v)
# ----------------------------------------------------------------
def show_var_value_counts(soco: t.SOMACollection, var_labels: List[str]) -> None:
for var_label in var_labels:
counts = {}
for soma in soco:
print("...", soma.name)
# print("\n".join(sorted(soma.var.attr_names())))
var = soma.var.df()
if not var_label in var:
continue
var_label_values = sorted(list(set(var[var_label])))
for var_label_value in var_label_values:
if var_label_value in counts:
counts[var_label_value] += 1
else:
counts[var_label_value] = 1
print(
"----------------------------------------------------------------",
var_label,
)
for k, v in dict(sorted(counts.items(), key=lambda item: item[1])).items():
print(k, v)
# ----------------------------------------------------------------
def show_somas_having(
soco: t.
|
685 | 25 | 1,416 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
Unknown
|
auth
| true |
function
| 11 | 11 | false | true |
[
"auth",
"support",
"hubs",
"spec",
"deploy_support",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.
|
686 | 25 | 2,299 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
Unknown
|
support
| true |
statement
| 11 | 11 | false | true |
[
"support",
"deploy_support",
"auth",
"hubs",
"spec",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.
|
687 | 25 | 2,329 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
inproject
|
auth
| true |
function
| 11 | 11 | false | false |
[
"auth",
"support",
"deploy_support",
"hubs",
"spec",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.support:
with cluster.
|
688 | 25 | 2,357 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
infile
|
deploy_support
| true |
function
| 11 | 11 | false | true |
[
"support",
"auth",
"hubs",
"spec",
"deploy_support",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.support:
with cluster.auth():
cluster.
|
689 | 25 | 3,037 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
Unknown
|
support
| true |
statement
| 11 | 11 | false | false |
[
"auth",
"hubs",
"support",
"deploy_support",
"spec",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.support:
with cluster.auth():
cluster.deploy_support()
def deploy_grafana_dashboards(cluster_name):
"""
Deploy grafana dashboards to a cluster that provide useful metrics
for operating a JupyterHub
Grafana dashboards and deployment mechanism in question are maintained in
this repo: https://github.com/jupyterhub/grafana-dashboards
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# If grafana support chart is not deployed, then there's nothing to do
if not cluster.
|
690 | 25 | 3,956 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
common
|
support
| true |
statement
| 11 | 11 | false | false |
[
"deploy_support",
"support",
"hubs",
"auth",
"config_path",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"ensure_docker_credhelpers",
"spec",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.support:
with cluster.auth():
cluster.deploy_support()
def deploy_grafana_dashboards(cluster_name):
"""
Deploy grafana dashboards to a cluster that provide useful metrics
for operating a JupyterHub
Grafana dashboards and deployment mechanism in question are maintained in
this repo: https://github.com/jupyterhub/grafana-dashboards
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# If grafana support chart is not deployed, then there's nothing to do
if not cluster.support:
print_colour(
"Support chart has not been deployed. Skipping Grafana dashboards deployment..."
)
return
grafana_token_file = (config_file_path.parent).joinpath(
"enc-grafana-token.secret.yaml"
)
# Read the cluster specific secret grafana token file
with get_decrypted_file(grafana_token_file) as decrypted_file_path:
with open(decrypted_file_path) as f:
config = yaml.load(f)
# Check GRAFANA_TOKEN exists in the secret config file before continuing
if "grafana_token" not in config.keys():
raise ValueError(
f"`grafana_token` not provided in secret file! Please add it and try again: {grafana_token_file}"
)
# FIXME: We assume grafana_url and uses_tls config will be defined in the first
# file listed under support.helm_chart_values_files.
support_values_file = cluster.
|
691 | 25 | 7,670 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
Unknown
|
auth
| true |
function
| 11 | 11 | false | false |
[
"auth",
"support",
"hubs",
"deploy_support",
"spec",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.support:
with cluster.auth():
cluster.deploy_support()
def deploy_grafana_dashboards(cluster_name):
"""
Deploy grafana dashboards to a cluster that provide useful metrics
for operating a JupyterHub
Grafana dashboards and deployment mechanism in question are maintained in
this repo: https://github.com/jupyterhub/grafana-dashboards
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# If grafana support chart is not deployed, then there's nothing to do
if not cluster.support:
print_colour(
"Support chart has not been deployed. Skipping Grafana dashboards deployment..."
)
return
grafana_token_file = (config_file_path.parent).joinpath(
"enc-grafana-token.secret.yaml"
)
# Read the cluster specific secret grafana token file
with get_decrypted_file(grafana_token_file) as decrypted_file_path:
with open(decrypted_file_path) as f:
config = yaml.load(f)
# Check GRAFANA_TOKEN exists in the secret config file before continuing
if "grafana_token" not in config.keys():
raise ValueError(
f"`grafana_token` not provided in secret file! Please add it and try again: {grafana_token_file}"
)
# FIXME: We assume grafana_url and uses_tls config will be defined in the first
# file listed under support.helm_chart_values_files.
support_values_file = cluster.support.get("helm_chart_values_files", [])[0]
with open(config_file_path.parent.joinpath(support_values_file)) as f:
support_values_config = yaml.load(f)
# Get the url where grafana is running from the support values file
grafana_url = (
support_values_config.get("grafana", {}).get("ingress", {}).get("hosts", {})
)
uses_tls = (
support_values_config.get("grafana", {}).get("ingress", {}).get("tls", {})
)
if not grafana_url:
print_colour(
"Couldn't find `config.grafana.ingress.hosts`. Skipping Grafana dashboards deployment..."
)
return
grafana_url = (
f"https://{grafana_url[0]}" if uses_tls else f"http://{grafana_url[0]}"
)
# Use the jupyterhub/grafana-dashboards deployer to deploy the dashboards to this cluster's grafana
print_colour("Cloning jupyterhub/grafana-dashboards...")
dashboards_dir = "grafana_dashboards"
subprocess.check_call(
[
"git",
"clone",
"https://github.com/jupyterhub/grafana-dashboards",
dashboards_dir,
]
)
# We need the existing env too for the deployer to be able to find jssonnet and grafonnet
deploy_env = os.environ.copy()
deploy_env.update({"GRAFANA_TOKEN": config["grafana_token"]})
try:
print_colour(f"Deploying grafana dashboards to {cluster_name}...")
subprocess.check_call(
["./deploy.py", grafana_url], env=deploy_env, cwd=dashboards_dir
)
print_colour(f"Done! Dashboards deployed to {grafana_url}.")
finally:
# Delete the directory where we cloned the repo.
# The deployer cannot call jsonnet to deploy the dashboards if using a temp directory here.
# Might be because opening more than once of a temp file is tried
# (https://docs.python.org/3.8/library/tempfile.html#tempfile.NamedTemporaryFile)
shutil.rmtree(dashboards_dir)
def deploy(cluster_name, hub_name, skip_hub_health_test, config_path):
"""
Deploy one or more hubs in a given cluster
"""
validate_cluster_config(cluster_name)
validate_hub_config(cluster_name, hub_name)
with get_decrypted_file(config_path) as decrypted_file_path:
with open(decrypted_file_path) as f:
config = yaml.load(f)
# All our hubs use Auth0 for Authentication. This lets us programmatically
# determine what auth provider each hub uses - GitHub, Google, etc. Without
# this, we'd have to manually generate credentials for each hub - and we
# don't want to do that. Auth0 domains are tied to a account, and
# this is our auth0 domain for the paid account that 2i2c has.
auth0 = config["auth0"]
k = KeyProvider(auth0["domain"], auth0["client_id"], auth0["client_secret"])
# Each hub needs a unique proxy.secretToken. However, we don't want
# to manually generate & save it. We also don't want it to change with
# each deploy - that causes a pod restart with downtime. So instead,
# we generate it based on a single secret key (`PROXY_SECRET_KEY`)
# combined with the name of each hub. This way, we get unique,
# cryptographically secure proxy.secretTokens without having to
# keep much state. We can rotate them by changing `PROXY_SECRET_KEY`.
# However, if `PROXY_SECRET_KEY` leaks, that means all the hub's
# proxy.secretTokens have leaked. So let's be careful with that!
SECRET_KEY = bytes.fromhex(config["secret_key"])
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
with cluster.
|
692 | 25 | 7,701 |
2i2c-org__infrastructure
|
a417fb77f4ccf22fae09e1bf79e3746eedc1db86
|
deployer/deploy_actions.py
|
Unknown
|
hubs
| true |
statement
| 11 | 11 | false | true |
[
"hubs",
"support",
"auth",
"deploy_support",
"spec",
"auth_aws",
"auth_azure",
"auth_gcp",
"auth_kubeconfig",
"config_path",
"ensure_docker_credhelpers",
"__init__",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "auth",
"type": "function"
},
{
"name": "auth_aws",
"type": "function"
},
{
"name": "auth_azure",
"type": "function"
},
{
"name": "auth_gcp",
"type": "function"
},
{
"name": "auth_kubeconfig",
"type": "function"
},
{
"name": "config_path",
"type": "statement"
},
{
"name": "deploy_support",
"type": "function"
},
{
"name": "ensure_docker_credhelpers",
"type": "function"
},
{
"name": "hubs",
"type": "statement"
},
{
"name": "spec",
"type": "statement"
},
{
"name": "support",
"type": "statement"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
"""
Actions available when deploying many JupyterHubs to many Kubernetes clusters
"""
import os
import shutil
import subprocess
from pathlib import Path
from ruamel.yaml import YAML
from auth import KeyProvider
from cluster import Cluster
from utils import print_colour
from file_acquisition import find_absolute_path_to_cluster_file, get_decrypted_file
from config_validation import (
validate_cluster_config,
validate_hub_config,
validate_support_config,
)
# Without `pure=True`, I get an exception about str / byte issues
yaml = YAML(typ="safe", pure=True)
helm_charts_dir = Path(__file__).parent.parent.joinpath("helm-charts")
def use_cluster_credentials(cluster_name):
"""
Quickly gain command-line access to a cluster by updating the current
kubeconfig file to include the deployer's access credentials for the named
cluster and mark it as the cluster to work against by default.
This function is to be used with the `use-cluster-credentials` CLI
command only - it is not used by the rest of the deployer codebase.
"""
validate_cluster_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# Cluster.auth() method has the context manager decorator so cannot call
# it like a normal function
with cluster.auth():
# This command will spawn a new shell with all the env vars (including
# KUBECONFIG) inherited, and once you quit that shell the python program
# will resume as usual.
# TODO: Figure out how to change the PS1 env var of the spawned shell
# to change the prompt to f"cluster-{cluster.spec['name']}". This will
# make it visually clear that the user is now operating in a different
# shell.
subprocess.check_call([os.environ["SHELL"], "-l"])
def deploy_support(cluster_name):
"""
Deploy support components to a cluster
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
if cluster.support:
with cluster.auth():
cluster.deploy_support()
def deploy_grafana_dashboards(cluster_name):
"""
Deploy grafana dashboards to a cluster that provide useful metrics
for operating a JupyterHub
Grafana dashboards and deployment mechanism in question are maintained in
this repo: https://github.com/jupyterhub/grafana-dashboards
"""
validate_cluster_config(cluster_name)
validate_support_config(cluster_name)
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
# If grafana support chart is not deployed, then there's nothing to do
if not cluster.support:
print_colour(
"Support chart has not been deployed. Skipping Grafana dashboards deployment..."
)
return
grafana_token_file = (config_file_path.parent).joinpath(
"enc-grafana-token.secret.yaml"
)
# Read the cluster specific secret grafana token file
with get_decrypted_file(grafana_token_file) as decrypted_file_path:
with open(decrypted_file_path) as f:
config = yaml.load(f)
# Check GRAFANA_TOKEN exists in the secret config file before continuing
if "grafana_token" not in config.keys():
raise ValueError(
f"`grafana_token` not provided in secret file! Please add it and try again: {grafana_token_file}"
)
# FIXME: We assume grafana_url and uses_tls config will be defined in the first
# file listed under support.helm_chart_values_files.
support_values_file = cluster.support.get("helm_chart_values_files", [])[0]
with open(config_file_path.parent.joinpath(support_values_file)) as f:
support_values_config = yaml.load(f)
# Get the url where grafana is running from the support values file
grafana_url = (
support_values_config.get("grafana", {}).get("ingress", {}).get("hosts", {})
)
uses_tls = (
support_values_config.get("grafana", {}).get("ingress", {}).get("tls", {})
)
if not grafana_url:
print_colour(
"Couldn't find `config.grafana.ingress.hosts`. Skipping Grafana dashboards deployment..."
)
return
grafana_url = (
f"https://{grafana_url[0]}" if uses_tls else f"http://{grafana_url[0]}"
)
# Use the jupyterhub/grafana-dashboards deployer to deploy the dashboards to this cluster's grafana
print_colour("Cloning jupyterhub/grafana-dashboards...")
dashboards_dir = "grafana_dashboards"
subprocess.check_call(
[
"git",
"clone",
"https://github.com/jupyterhub/grafana-dashboards",
dashboards_dir,
]
)
# We need the existing env too for the deployer to be able to find jssonnet and grafonnet
deploy_env = os.environ.copy()
deploy_env.update({"GRAFANA_TOKEN": config["grafana_token"]})
try:
print_colour(f"Deploying grafana dashboards to {cluster_name}...")
subprocess.check_call(
["./deploy.py", grafana_url], env=deploy_env, cwd=dashboards_dir
)
print_colour(f"Done! Dashboards deployed to {grafana_url}.")
finally:
# Delete the directory where we cloned the repo.
# The deployer cannot call jsonnet to deploy the dashboards if using a temp directory here.
# Might be because opening more than once of a temp file is tried
# (https://docs.python.org/3.8/library/tempfile.html#tempfile.NamedTemporaryFile)
shutil.rmtree(dashboards_dir)
def deploy(cluster_name, hub_name, skip_hub_health_test, config_path):
"""
Deploy one or more hubs in a given cluster
"""
validate_cluster_config(cluster_name)
validate_hub_config(cluster_name, hub_name)
with get_decrypted_file(config_path) as decrypted_file_path:
with open(decrypted_file_path) as f:
config = yaml.load(f)
# All our hubs use Auth0 for Authentication. This lets us programmatically
# determine what auth provider each hub uses - GitHub, Google, etc. Without
# this, we'd have to manually generate credentials for each hub - and we
# don't want to do that. Auth0 domains are tied to a account, and
# this is our auth0 domain for the paid account that 2i2c has.
auth0 = config["auth0"]
k = KeyProvider(auth0["domain"], auth0["client_id"], auth0["client_secret"])
# Each hub needs a unique proxy.secretToken. However, we don't want
# to manually generate & save it. We also don't want it to change with
# each deploy - that causes a pod restart with downtime. So instead,
# we generate it based on a single secret key (`PROXY_SECRET_KEY`)
# combined with the name of each hub. This way, we get unique,
# cryptographically secure proxy.secretTokens without having to
# keep much state. We can rotate them by changing `PROXY_SECRET_KEY`.
# However, if `PROXY_SECRET_KEY` leaks, that means all the hub's
# proxy.secretTokens have leaked. So let's be careful with that!
SECRET_KEY = bytes.fromhex(config["secret_key"])
config_file_path = find_absolute_path_to_cluster_file(cluster_name)
with open(config_file_path) as f:
cluster = Cluster(yaml.load(f), config_file_path.parent)
with cluster.auth():
hubs = cluster.
|
694 | 27 | 1,038 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_console_handler
| true |
statement
| 18 | 18 | false | true |
[
"typing_console_handler",
"json_logger",
"logger",
"typing_logger",
"chat_plugins",
"__init__",
"_log",
"console_handler",
"debug",
"double_check",
"error",
"file_handler",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.
|
695 | 27 | 1,097 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_console_handler
| true |
statement
| 18 | 18 | false | false |
[
"typing_console_handler",
"json_logger",
"logger",
"typing_logger",
"chat_plugins",
"__init__",
"_log",
"console_handler",
"debug",
"double_check",
"error",
"file_handler",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.
|
697 | 27 | 1,279 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
console_handler
| true |
statement
| 18 | 18 | false | true |
[
"console_handler",
"json_logger",
"logger",
"typing_console_handler",
"typing_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"file_handler",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.
|
698 | 27 | 1,332 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
console_handler
| true |
statement
| 18 | 18 | false | false |
[
"console_handler",
"json_logger",
"logger",
"typing_console_handler",
"typing_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"file_handler",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.
|
700 | 27 | 1,550 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
file_handler
| true |
statement
| 18 | 18 | false | true |
[
"console_handler",
"file_handler",
"json_logger",
"typing_console_handler",
"typing_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"logger",
"set_level",
"speak_mode",
"typewriter_log",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.
|
701 | 27 | 1,722 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
file_handler
| true |
statement
| 18 | 18 | false | false |
[
"console_handler",
"file_handler",
"json_logger",
"typing_console_handler",
"typing_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"logger",
"set_level",
"speak_mode",
"typewriter_log",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.
|
703 | 27 | 2,255 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_logger
| true |
statement
| 18 | 18 | false | true |
[
"typing_logger",
"console_handler",
"file_handler",
"json_logger",
"logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.
|
704 | 27 | 2,285 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_console_handler
| true |
statement
| 18 | 18 | false | false |
[
"file_handler",
"logger",
"console_handler",
"json_logger",
"typing_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.
|
705 | 27 | 2,322 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_logger
| true |
statement
| 18 | 18 | false | false |
[
"typing_logger",
"console_handler",
"file_handler",
"json_logger",
"logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.
|
706 | 27 | 2,352 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
file_handler
| true |
statement
| 18 | 18 | false | false |
[
"file_handler",
"logger",
"console_handler",
"json_logger",
"typing_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.
|
707 | 27 | 2,379 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_logger
| true |
statement
| 18 | 18 | false | false |
[
"typing_logger",
"logger",
"console_handler",
"file_handler",
"json_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.
|
708 | 27 | 2,432 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
typing_logger
| true |
statement
| 18 | 18 | false | false |
[
"typing_logger",
"logger",
"console_handler",
"file_handler",
"json_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.
|
710 | 27 | 2,534 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
logger
| true |
statement
| 18 | 18 | false | true |
[
"logger",
"typing_logger",
"console_handler",
"file_handler",
"json_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.
|
711 | 27 | 2,557 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
console_handler
| true |
statement
| 18 | 18 | false | false |
[
"file_handler",
"typing_logger",
"logger",
"json_logger",
"console_handler",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.
|
712 | 27 | 2,587 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
logger
| true |
statement
| 18 | 18 | false | false |
[
"logger",
"typing_logger",
"console_handler",
"file_handler",
"json_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.
|
713 | 27 | 2,610 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
file_handler
| true |
statement
| 18 | 18 | false | false |
[
"file_handler",
"typing_logger",
"console_handler",
"logger",
"json_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.
|
714 | 27 | 2,637 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
logger
| true |
statement
| 18 | 18 | false | false |
[
"logger",
"typing_logger",
"json_logger",
"console_handler",
"file_handler",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.
|
715 | 27 | 2,683 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
logger
| true |
statement
| 18 | 18 | false | false |
[
"logger",
"typing_logger",
"json_logger",
"console_handler",
"file_handler",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.
|
717 | 27 | 2,788 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
json_logger
| true |
statement
| 18 | 18 | false | true |
[
"json_logger",
"logger",
"typing_logger",
"console_handler",
"file_handler",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.
|
718 | 27 | 2,816 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
file_handler
| true |
statement
| 18 | 18 | false | false |
[
"logger",
"file_handler",
"typing_logger",
"console_handler",
"json_logger",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.
|
719 | 27 | 2,843 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
json_logger
| true |
statement
| 18 | 18 | false | false |
[
"json_logger",
"logger",
"typing_logger",
"console_handler",
"file_handler",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.
|
720 | 27 | 2,894 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
json_logger
| true |
statement
| 18 | 18 | false | false |
[
"json_logger",
"logger",
"typing_logger",
"console_handler",
"file_handler",
"__init__",
"_log",
"chat_plugins",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.
|
723 | 27 | 3,150 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
speak_mode
| true |
statement
| 18 | 18 | false | true |
[
"speak_mode",
"logger",
"json_logger",
"typing_logger",
"file_handler",
"__init__",
"_log",
"chat_plugins",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.
|
724 | 27 | 3,234 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
Unknown
|
chat_plugins
| true |
statement
| 18 | 18 | false | true |
[
"chat_plugins",
"logger",
"json_logger",
"typing_logger",
"file_handler",
"__init__",
"_log",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.
|
725 | 27 | 3,457 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
random
|
typing_logger
| true |
statement
| 18 | 18 | false | false |
[
"logger",
"json_logger",
"typing_logger",
"file_handler",
"chat_plugins",
"__init__",
"_log",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.chat_plugins:
plugin.report(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.
|
726 | 27 | 3,684 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
infile
|
_log
| true |
function
| 18 | 18 | true | true |
[
"logger",
"json_logger",
"typing_logger",
"_log",
"file_handler",
"__init__",
"chat_plugins",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.chat_plugins:
plugin.report(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
def debug(
self,
message,
title="",
title_color="",
):
self.
|
727 | 27 | 3,857 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
infile
|
_log
| true |
function
| 18 | 18 | true | false |
[
"logger",
"json_logger",
"typing_logger",
"_log",
"file_handler",
"__init__",
"chat_plugins",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.chat_plugins:
plugin.report(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
def debug(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.DEBUG)
def info(
self,
message,
title="",
title_color="",
):
self.
|
728 | 27 | 4,029 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
infile
|
_log
| true |
function
| 18 | 18 | true | false |
[
"logger",
"json_logger",
"typing_logger",
"_log",
"file_handler",
"__init__",
"chat_plugins",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.chat_plugins:
plugin.report(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
def debug(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.DEBUG)
def info(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.INFO)
def warn(
self,
message,
title="",
title_color="",
):
self.
|
729 | 27 | 4,131 |
eosphoros-ai__DB-GPT
|
f684a7f6f06920e0054501c253fe0048ed36d058
|
pilot/logs.py
|
infile
|
_log
| true |
function
| 18 | 18 | true | false |
[
"logger",
"json_logger",
"typing_logger",
"file_handler",
"_log",
"__init__",
"chat_plugins",
"console_handler",
"debug",
"double_check",
"error",
"get_log_directory",
"info",
"log_json",
"set_level",
"speak_mode",
"typewriter_log",
"typing_console_handler",
"warn",
"__annotations__",
"__class__",
"__delattr__",
"__dict__",
"__dir__",
"__eq__",
"__format__",
"__getattribute__",
"__getstate__",
"__hash__",
"__init_subclass__",
"__ne__",
"__new__",
"__reduce__",
"__reduce_ex__",
"__repr__",
"__setattr__",
"__sizeof__",
"__str__",
"__subclasshook__",
"__doc__",
"__module__"
] |
[
{
"name": "chat_plugins",
"type": "statement"
},
{
"name": "console_handler",
"type": "statement"
},
{
"name": "debug",
"type": "function"
},
{
"name": "double_check",
"type": "function"
},
{
"name": "error",
"type": "function"
},
{
"name": "file_handler",
"type": "statement"
},
{
"name": "get_log_directory",
"type": "function"
},
{
"name": "info",
"type": "function"
},
{
"name": "json_logger",
"type": "statement"
},
{
"name": "log_json",
"type": "function"
},
{
"name": "logger",
"type": "statement"
},
{
"name": "set_level",
"type": "function"
},
{
"name": "speak_mode",
"type": "statement"
},
{
"name": "typewriter_log",
"type": "function"
},
{
"name": "typing_console_handler",
"type": "statement"
},
{
"name": "typing_logger",
"type": "statement"
},
{
"name": "warn",
"type": "function"
},
{
"name": "_log",
"type": "function"
},
{
"name": "__annotations__",
"type": "statement"
},
{
"name": "__class__",
"type": "property"
},
{
"name": "__delattr__",
"type": "function"
},
{
"name": "__dict__",
"type": "statement"
},
{
"name": "__dir__",
"type": "function"
},
{
"name": "__doc__",
"type": "statement"
},
{
"name": "__eq__",
"type": "function"
},
{
"name": "__format__",
"type": "function"
},
{
"name": "__getattribute__",
"type": "function"
},
{
"name": "__hash__",
"type": "function"
},
{
"name": "__init__",
"type": "function"
},
{
"name": "__init_subclass__",
"type": "function"
},
{
"name": "__module__",
"type": "statement"
},
{
"name": "__ne__",
"type": "function"
},
{
"name": "__new__",
"type": "function"
},
{
"name": "__reduce__",
"type": "function"
},
{
"name": "__reduce_ex__",
"type": "function"
},
{
"name": "__repr__",
"type": "function"
},
{
"name": "__setattr__",
"type": "function"
},
{
"name": "__sizeof__",
"type": "function"
},
{
"name": "__slots__",
"type": "statement"
},
{
"name": "__str__",
"type": "function"
}
] |
import logging
import os
import random
import re
import time
from logging import LogRecord
from typing import Any
from colorama import Fore, Style
from pilot.log.json_handler import JsonFileHandler, JsonFormatter
from pilot.singleton import Singleton
from pilot.speech import say_text
class Logger(metaclass=Singleton):
"""
Logger that handle titles in different colors.
Outputs logs in console, activity.log, and errors.log
For console handler: simulates typing
"""
def __init__(self):
# create log directory if it doesn't exist
this_files_dir_path = os.path.dirname(__file__)
log_dir = os.path.join(this_files_dir_path, "../logs")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_file = "activity.log"
error_file = "error.log"
console_formatter = DbGptFormatter("%(title_color)s %(message)s")
# Create a handler for console which simulate typing
self.typing_console_handler = TypingConsoleHandler()
self.typing_console_handler.setLevel(logging.INFO)
self.typing_console_handler.setFormatter(console_formatter)
# Create a handler for console without typing simulation
self.console_handler = ConsoleHandler()
self.console_handler.setLevel(logging.DEBUG)
self.console_handler.setFormatter(console_formatter)
# Info handler in activity.log
self.file_handler = logging.FileHandler(
os.path.join(log_dir, log_file), "a", "utf-8"
)
self.file_handler.setLevel(logging.DEBUG)
info_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
)
self.file_handler.setFormatter(info_formatter)
# Error handler error.log
error_handler = logging.FileHandler(
os.path.join(log_dir, error_file), "a", "utf-8"
)
error_handler.setLevel(logging.ERROR)
error_formatter = DbGptFormatter(
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
" %(message_no_color)s"
)
error_handler.setFormatter(error_formatter)
self.typing_logger = logging.getLogger("TYPER")
self.typing_logger.addHandler(self.typing_console_handler)
self.typing_logger.addHandler(self.file_handler)
self.typing_logger.addHandler(error_handler)
self.typing_logger.setLevel(logging.DEBUG)
self.logger = logging.getLogger("LOGGER")
self.logger.addHandler(self.console_handler)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(error_handler)
self.logger.setLevel(logging.DEBUG)
self.json_logger = logging.getLogger("JSON_LOGGER")
self.json_logger.addHandler(self.file_handler)
self.json_logger.addHandler(error_handler)
self.json_logger.setLevel(logging.DEBUG)
self.speak_mode = False
self.chat_plugins = []
def typewriter_log(
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
):
if speak_text and self.speak_mode:
say_text(f"{title}. {content}")
for plugin in self.chat_plugins:
plugin.report(f"{title}. {content}")
if content:
if isinstance(content, list):
content = " ".join(content)
else:
content = ""
self.typing_logger.log(
level, content, extra={"title": title, "color": title_color}
)
def debug(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.DEBUG)
def info(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.INFO)
def warn(
self,
message,
title="",
title_color="",
):
self._log(title, title_color, message, logging.WARN)
def error(self, title, message=""):
self.
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