# SITCoM: SiRGraF Integrated Tool for Coronal dynaMics

Purvi Udhwani1,2, Arpit Kumar Shrivastav1,3,4, Ritesh Patel 5

###### Abstract

SiRGraF Integrated Tool for Coronal dynaMics (SITCoM) is based on Simple
Radial Gradient Filter (SiRGraF) used to filter the radial gradient in the
white-light coronagraph images and bring out dynamic structures. SITCoM has
been developed in Python and integrated with SunPy and can be installed by
users with the command pip install sitcom. This enables the user to pass the
white-light coronagraph data to the tool and generate radially filtered output
with an option to save in various formats as required. We have implemented the
functionality of tracking the transients such as coronal mass ejections
(CMEs), outflows, plasma blobs, etc., using height-time plots and deriving
their kinematics. In addition, SITCoM also supports oscillation and waves
studies such as for streamer waves. This is done by creating a distance-time
plot at a user-defined location (artificial slice) and fitting a sinusoidal
function to derive the properties of waves, such as time period, amplitude,
and damping time (if any). We provide the provision to manually or
automatically select the data points to be used for fitting. SITCoM is a tool
to analyze some properties of coronal dynamics quickly. We present an overview
of the SITCoM with the applications for deriving coronal dynamics’ kinematics
and oscillation properties. We discuss the limitations of this tool along with
prospects for future improvement.

## 1

##  2 Keywords:

Python, Software Package, Corona, Coronal Mass Ejection (CME), Solar
Oscillations, Coronal Dynamics, Instrumentation and Data Management

##  3 Introduction

The white-light solar corona is associated with large-scale eruptions, coronal
mass ejections (CMEs; Hundhausen et al., 1984; Gosling, 1993; Webb and Howard,
2012), reconnection-based plasmoids (e.g. Riley et al., 2007; Guo et al.,
2013; Patel et al., 2020; Lee et al., 2020), coronal inflows (e.g. Wang et
al., 1999; Sheeley and Wang, 2001, 2007, 2014; Hess and Wang, 2017), streamer
waves (Chen et al., 2010a; Feng et al., 2013a; Decraemer et al., 2020a). All
these phenomena are seen to exhibit a wide range of properties and are
primarily observed and studied with white-light coronagraph imagery of Large
Angle Spectrometric Coronagraph (LASCO; Brueckner et al., 1995) of Solar and
Heliospheric Observatory (SOHO) and Sun Earth Connection Coronal and
Heliospheric Imager (SECCHI; Howard et al., 2008) onboard Solar Terrestrial
Relations Observatory (STEREO). It has been recently emphasized that most of
the dynamics occur in the region from 1.5-6 R⊙ defined as the middle corona
(West et al., 2022). To capture such a wide variety of phenomena, it becomes
important to separate these structures from the background and, at the same
time, enhance their visibility in the coronagraph FOV of observation.

A couple of algorithms have been developed recently for white-light datasets
to separate out the dynamic structures and reduce coronal intensity gradient.
Normalizing radial gradient filter (NRGF; Morgan et al., 2006) and Simple
Radial Gradient Filter (SiRGraF; Patel et al., 2022; Patel, 2022) are two such
processing algorithms. Along similar lines, some algorithms have been used to
enhance the off-limb structures in extreme ultraviolet images (Masson et al.,
2014; Patel et al., 2021; Seaton et al., 2021, 2023). In the manual CME
detection catalog of NASA Community Data Analysis Workshop (CDAW; Yashiro et
al., 2004), CMEs can be tracked in their web interface by manually clicking
the points in the running difference images. Automated CME detection has been
performed with the aid of feature detection and image processing algorithms
such as Computer Aided CME Tracking (CACTus; Robbrecht and Berghmans, 2004;
Pant et al., 2016), Solar Eruptive Events Detection System (SEEDS; Olmedo et
al., 2008), Automatic Recognition of Transient Events and Marseille Inventory
from Synoptic maps (ARTEMIS; Boursier et al., 2009), Coronal Image Processing
(CORIMP; Byrne et al., 2012; Morgan et al., 2012), CMEs Identification in
Inner Solar Corona (CIISCO; Patel et al., 2021) and algorithms based on
machine learning (Qu et al., 2006; Zhang et al., 2017). These algorithms have
been mainly designed for and applied on large-scale transients, but not on
small-scale flows and blobs-like structures.

Streamers are large-scale quasi-static bright structures and are seen to be
stretched from the inner to outer corona in the eclipse images (Koutchmy and
Livshits, 1992). CMEs are observed to displace streamers sideways and generate
streamer oscillations (Chen et al., 2010b; Feng et al., 2013b; Kwon et al.,
2013). The statistical study on streamer waves suggested that they can be a
plausible candidate for coronal seismology (Decraemer et al., 2020b). In the
context of automatic detection of transverse oscillations in the solar corona,
Northumbria University Wave Tracking (NUWT; Morton et al., 2016) was developed
and extended by (Weberg et al., 2018). The use of NUWT can be advantageous in
carrying out statistical analysis of transverse waves. However, the NUWT code
is best applicable for decayless waves observed in coronal loops (Weberg et
al., 2018). Furthermore, the automatic identification and fitting of waves in
time-distance ($x-t$) diagrams can be affected by the complex and varying
backgrounds, as well as the presence of neighboring structures, which may
necessitate manual fitting procedures. Additionally, there is a lack of a tool
for quickly analyzing wave properties.

In recent times, Jhelioviewer (Müller et al., 2017), an open-source data
visualization tool was developed with basic image processing and integration
of different catalogs for quick analysis before proceeding to an in-depth one.
An automated data-mining-based web interface combined with feature detection
has been developed as a part of the Heliophysics Events Knowledgebase (HEK;
Hurlburt et al., 2012). Using the multi-instrument observations and tracking
CMEs, solar energetic particles (SEPs), co-rotating interaction regions
(CIRs), etc a community-led effort by European Space Agency’s Modelling and
Data Analysis Working Group (MADAWG) developed several tools, e.g. magnetic
connectivity tool (Rouillard et al., 2020). Another web-based interface to
track the CMEs, SEPs, and CIRs from the inner corona to the outer heliosphere
is the ‘Propagation Tool’ (Rouillard et al., 2017). Solar MAgnetic Connection
Haus (SolarMACH; Gieseler et al., 2023), another open-source tool was
developed recently for the visualization of magnetic configurations and
connections with various spacecraft and planetary bodies in the heliosphere.
To track the CMEs based on multi-viewpoint observations from STEREO and SOHO
coronagraphs, a web-based application called STEREO CME Analysis Tool
(StereoCAT; Millward et al., 2013) is hosted at NASA Community Coordinated
Modeling Center (https://ccmc.gsfc.nasa.gov/analysis/stereo/).

All these tools have been supported with basic image processing algorithms and
are helpful in quick analysis serving their purposes. Often the output of
these tools has also been part of in-depth scientific analysis. In this paper
we introduce an open-source tool, SiRGraF Integrated Tool for COronal dynaMics
(SITCoM), utilizing the SiRGraF algorithm and bringing out the dynamic
features of the solar corona. This tool is currently targeted for studying the
kinematics of CMEs, plasmoids, streamer blobs, outflows, inflows, etc., and
the analysis of streamer waves and similar oscillatory phenomena. This paper
is arranged as follows: in Section 4 we introduce and explain the architecture
of the tool. The application of SITCoM for two broad aspects with illustrative
examples is presented in Section 5 followed by a summary and discussions along
with the scope of future improvements in Section 6.

##  4 Software Design

sitcom is fully developed using Python and is temporarily made available as a
PyPi package111PyPi: https://pypi.org/project/sitcom/ and is mainly compiled
using PyQt and has other small dependencies. It can be installed using the
command
pip install sitcom.
The software can be run for further use by the command in the terminal
python3 -m sitcom
Further updates will be released that will make it more flexible for different
platforms. It is available on Github222GitHub:https://github.com/pu3/SITCoM as
well for a better look at the code and to raise issues if any.

Figure 1: a. An inclusive overview of the tools available in the SiRGraF part
of the software including the availability to browse the path or add the path
to data. b. The intensity scaling options and the respective output below as
applied on LASCO-C2 images. c.The toolbar menu options to save images and to
switch themes. On the bottom is the dialog box that appears when the ‘Minimum
Image’ is saved displaying the location of the saved file. d. On the Left, is
a screenshot of a paused frame of the movie, and on the Right below are the
available formats in which you may save the movie. The ‘Save movie’ button
appears when the ‘Movie’ button is clicked.

###  4.1 SiRGraF interface

The initial part of this Graphical User Interface (GUI) is shown in Figure 1
where the first step is the implementation of the SiRGraF algorithm. The
interface is designed in such a way that the only task of the user is to
provide the path containing FITS files in the given address bar by pasting a
copied path or use the browse option available. The path for LASCO/C2 images
for January 07, 2001 is shown for illustration. After providing the path of
the data, the start button is used to initialize the process of implementation
of SiRGraF and the status of which can be tracked in the status bar. Once the
process is completed, the right panel in the GUI shows the minimum intensity,
and uniform intensity images along with the average intensity profile and a
sample filtered image after pressing the respective buttons as shown in Figure
1a. By default, linear intensity scaling is used to display all the generated
image outputs.

The images filtered with SiRGraF can be visualized in an embedded matplotlib-
based player consisting of basic functionalities like Play, Pause, and Zoom in
the toolbar, this, among other options to assess these images, will help in
better evaluation of the data. This section also allows the user to perform
the basic intensity scaling like logarithmic, min-max, and gamma correction
(Gonzalez and Woods, 2008). Figure 1b shows some of the different intensity
scaling options available and the respective output when applied to SiRGRaF
processed LASCO/C2 images. Utilizing these options will help highlight certain
features against others which can be used in the subsequent analysis section.
In conjunction with that, the user can save individual elements of SiRGRaF
like the minimum and uniform images from the ‘File’ option of the menu bar
(Figure 1c). Additionally, the movie can be saved in ‘.mp4’, ‘.gif’ (video
formats), ‘.png’, ‘.fits’, and ‘.sav’ (saves each processed frame with the
file name with the timestamp from the movie. For example, if the file name is
‘cme’, it will save files with one of the names as ‘cme-_MMDDTHHMMDD.png’).
Along with this, an option to control the frame rate is also provided to save
video formats. It creates a hierarchical structure of folders to save these
files. For example, for a data file dated 6th July 2004 it will first create a
folder named ‘2004-07-06’ in the ‘/home/’ directory and sub-folders within
that ‘PNG’, ‘FITS’, and ‘SAV’ depending on the chosen format to save. The
movies will be saved in the ‘2004-07-06’ folder.

An optional tool available in the menubar is to switch the GUI theme to dark
as it has lately become a preference of users worldwide. Once the feature of
interest is identified, the user can proceed to the analysis section by using
the ‘Analysis’ button.

###  4.2 Analysis Interface

Figure 2: Mechanism of the analysis interface showing the steps required to
obtain distance-time plots, and consequently perform respective fits on them

An overview of how the analysis using sitcom is presented through a flow chart
in Figure 2. Upon clicking the ‘Analysis’ button, a user is presented with
options to estimate either the kinematic or oscillation properties of the
dynamic event. The flow chart illustrates the series of processes that are
executed when utilizing the application.

Figure 3 provides a snapshot of the GUI corresponding to the analysis
interface. Clicking the analysis button takes the user to another window that
is adjacent to the main interface. This section can also be accessed directly
after the “Start” button has been clicked i.e. This window contains two
provisions that can be used for kinematics analysis and carry out waves or
oscillation studies.

The kinematic analysis section allows the user to generate height-time plots
at various position angles and perform linear or higher-order fitting on
selected data points. The fittings provide information about the velocity and
acceleration of the feature, depending on the type of fitting used. The
“Retry” option is available to help obtain better-fit values if required.
Additionally, the option for linear fitting is selected by default and can be
updated through a drop-down menu. To perform a Linear fit, the user can first
use the toolbar zoom and then click two points (Section 5.1.1). As shown in
3b, for quadratic fitting the user is required to first drag an area to zoom
in and then click five points for fitting. Two retry options are available:
clicking the “Retry” button plots the original height-time graph, allowing the
user to zoom again on a different region by dragging a rectangle, while
clicking “Retry Fit” clears the currently fitted points, enabling the user to
perform the fit again for the already zoomed region. These options can be
availed as many times as needed. After the fit is satisfactory, it can be
confirmed by ‘Ok’ and will display a plot with velocity and/or acceleration
obtained from the fit.

Another option is to analyze oscillations by placing an artificial slit across
a feature, which generates a distance-time plot over the length of the slit.
The “Oscillation” tab on the interface allows users to choose an artificial
slit of their desired position by clicking two points in the movie frame. Once
the slit is selected, a dialogue box will pop up to confirm the selection. If
the user wants to re-select the slit, they can use the “Retry” option on the
pop-up box (Figure 3c). After selecting the slit, the user can produce
distance-time ($x-t$) maps by clicking on the option “Generate distance-time
plot”, with “Automatic fit” set as the default option. If there are any
oscillations in the $x-t$ map, the user can fit these oscillations by
selecting the region using drag crop around the oscillation (for automatic
fit) or toolbar zoom option (for manual fit), and then perform fit through a
series of steps. This fitting procedure is discussed in Section 5.2.1 with
examples.

Figure 3: a. An overview of the analysis interface showing the PA with a
dotted line and selected slit with a solid green line. b. Dialogue boxes that
pop up when performing a quadratic fit c. Option to select slit after the user
presses the “Oscillations” button. The fitting can be performed manually or
using automatic fit selection.

##  5 Data Used and Software Functionality

We acquired the level 0.5 data of the C2 coronagraph of LASCO from the Virtual
Solar Observatory (VSO) for 29th August, 2022. We converted the data into
Level 1 using the reduce_level_1.pro procedure available in the solar software
(SSW) to correct for the image rotation and calibration. Other LASCO/C2 data
presented in this paper were already level 1 corrected. Apart from these,
STEREO/COR-1 data has also been used. Level 0.5 polarized brightness triplets
(0∘, 120∘, 240∘) of COR-1 are acquired from VSO for the date of 1st August,
2010. These images are reduced to level 1 using the secchi_prep.pro procedure
of STEREO pipeline. The triplets are combined to obtain the total brightness
images which are also calibrated and rotation corrected in this step. The
level 1 data of LASCO/C2 and COR-1 are provided to the SITCoM analysis tool as
FITS files.

###  5.1 Kinematics of Transients

####  5.1.1 CME case

CMEs are identified as propagating white-light large-scale structures in the
coronagraph field of view (FOV) (Hundhausen et al., 1984). An application is
illustrated in Figure 4 with LASCO/C2 data-set of January 07, 2001, where a
CME occurred at the north-west limb of the Sun. This CME has also been shown
as a representative example for demonstrating NRGF by Morgan et al. (2006) and
for SiRGraF by Patel et al. (2022). It is known that CMEs after reaching
heights beyond $\sim$3 R⊙ attain a constant speed (Majumdar et al., 2020).
This height also lies in the middle corona within the LASCO field of view and
hence CMEs can be considered to have a linear profile in the height-time plot.
As shown in Figure 4, any position angle can be provided to generate the
height-time plot, for this case, we have used a PA of 298∘. Once it is
generated, the bright ridge corresponding to the CME can be zoomed in using
the toolbar zoom icon, and the leading edge can be fitted with a straight line
by clicking two points (after de-selecting the zoom icon). The interface
provides an option to retry and finalize the points for the fit, to confirm
it. Once confirmed, a plot with the value for the velocity of the CME will be
displayed. For the example presented here, the speed estimated here of 588 km
s-1. matches well with the value in CDAW catalog as 633 km s-1.

Most of the CMEs which are observed in STEREO/COR-1 are still undergoing an
acceleration phase. This is also seen in an example shown in Figure 5 where a
CME is observed in COR-1B on August 01, 2010, at the north-east limb. The
height-time plot is generated along the dotted green line shown in the
coronagraph image and is shown in the b) panel of the same Figure. The leading
edge of the CME is seen with a curved profile suggesting the presence of
acceleration. Using the available quadratic fit option, data points are
selected in the height-time plot. The points are confirmed for fit by visual
inspection. We found that this CME showed an acceleration of $\approx$156 m
s-2 in the COR-1B FOV, and an average speed of 249 km s-1.

Figure 4: a. Movie frame showing a dashed line at the PA of 298∘ b. Usage of
the zoom tool to crop the region for fitting on the height-time plot c. Linear
fit confirmed after clicking 2 points showing a velocity of 588 km s-1.
Figure 5: a. Movie frame showing a line at the PA of 75∘ b. Usage of the drag
rectangle to crop the region for fitting on the height-time plot c. Quadratic
fit confirmed after clicking 5 points showing the values of acceleration and
average velocity.

####  5.1.2 Plasma Blobs case

Plasma blobs arising due to magnetic re-connection have been considered as one
of the transient structures formed in the Sun’s corona. These may be
associated with CMEs often observed in coronagraph images post-CME rays (Webb
and Vourlidas, 2016), and the streamer blobs originating at the tip of
streamers. These blobs appear as small-scale discrete white-light structures
with nearly elliptical shapes in coronagraph images. Figure 6 shows one of the
many plasma blobs observed along the post-CME current sheet of September 10,
2017. The right panel of the Figure shows the distance-time plot generated for
the position angle of 262∘. The first ridge corresponds to the leading edge of
the CME followed by plasma blobs. It can be noticed in the distance-time plot
that the observed blobs show inclined ridges, hence, we used linear fitting to
characterize the speed. The velocity of the blob is displayed after confirming
the plot fit. The estimated speed of $\approx$1000 km s-1 agrees with the
speed distribution of plasma blobs observed in LASCO/C2 FOV for this data set
reported by Patel et al. (2020). The distance-time plot also reveals more
ridges following the one used for analysis. These correspond to the successive
blobs which can be approached in a similar way to measure their speeds.

Figure 6: a. Movie frame with the dotted line at the PA of 262∘ along the
plasma blob b. Usage of the zoom tool to crop the region for fitting on the
height-time plot c. Linear fit on the ridge of plasma blob, showing a velocity
of 1089 km s-1

####  5.1.3 Inflow case

Often material has been observed falling toward the Sun after a CME has passed
in the LASCO FOV. This in-falling material can be seen as a result of partial
eruption or dark inflows which are in the wake of CME. These structures have
been tracked manually in many studies in the past. The SITCoM interface has
been tested to track such features using LASCO/C2 images. An example of dark
inflow is shown in Figure 7 where these flows are observed on October 12,
2005, moving towards the Sun after the CME has passed in LASCO FOV. The dotted
line represents the position angle of 221∘ used to track the inflow. The
height-time plot is generated corresponding to this dotted line and is shown
in Figure 7b. The colormap of this plot is inverted for the visualization of
the ridge corresponding to the inflow. This ridge shows straight-line behavior
and therefore linear fitting was used to estimate its speed. The fitted curve
is shown in panel (c). The negative value of the measured speed indicates that
the structure is moving toward the Sun. In case curved ridges are observed for
the sunward moving material, as in the studies such as Tripathi et al. (2007);
Longcope et al. (2018), then quadratic fit can be used to measure the
kinematics of such flows.

Figure 7: a. The dark curve as seen in the movie frame along the PA of 221∘ is
the observed inflow in LASCO FOV b. Usage of the zoom tool to crop the region
of the dark ridge c. Linear fit performed on the inner edge, showing a
velocity of -52 km s-1, indicating in-flowing material.

###  5.2 Oscillations and Waves

####  5.2.1 Streamer Wave case

To demonstrate the analysis of oscillations in helmet streamers perturbed by
coronal mass ejections (CMEs), we present two case studies. In this section,
we outline the methodology used, involving the SiRGraF algorithm as described
in Section 4.1, and highlight the analysis tool employed for accurate data
interpretation. By choosing the “Play Movie” option in the interface (Figure
3a), users can access a movie displaying the SiRGraF processed images. This
feature assists in identifying the optimal placement for the artificial slit,
by clicking two points on the frame, corresponding to the desired location.
The interface allows for multiple retries, ensuring the selection of the most
appropriate slit placement.

####  5.2.2 Manual fitting

We use the set of LASCO/C2 level-1 images of August 29, 2022, between 15:12
and 23:48 UT for the illustration of the manual fitting case, where a CME was
observed near the western limb, perturbing the streamer as it propagated in
the radial direction. We selected the slit perpendicular to the streamer
(Figure 8a). The “Generate distance-time plot” button on the interface
collates the intensities at the slit position for every frame and produces an
$x-t$ map. While still under development, the features provided in manual
fitting are, to perform fit on the distance-time plot through a series of
following simple steps implemented in the current version.

  1. 1.

Click on the zoom icon and crop the region of interest

  2. 2.

Deselect the zoom icon and use the left-click button to add a point where the
mouse pointer is. The right-click button deletes the last added point.(Figure
9b)

  3. 3.

Once the points are ready for fit, click on the ‘Fit Points’ button (Figure
9c), which will open a window displaying the parameters of the current fit and
will fit the current points based on this initial guess. The profile of
selected points will be fitted using a decaying sinusoidal function of the
following form (Nakariakov et al., 1999) :

$y(x)=p_{0}+p_{1}\sin\left(\frac{2\pi
x}{p_{2}}+p_{3}\right)e^{\frac{-x}{p_{4}}}+p_{5}x,$ (1)

where $p_{0}$ is the mean position, $p_{1}$ is the initial amplitude of the
oscillation, $p_{2}$ is the period of oscillation, $p_{3}$ denotes the phase,
$p_{4}$ indicates the damping time and $p_{5}$ is the coefficient for the
linear trend.

  4. 4.

These parameters can be updated or changed to modify the fit according to the
needs of the user to attain a better fit.

  5. 5.

After that, the ‘Plot’ button can be clicked to generate the final curve used
for fit, and their respective parameter will be automatically saved into a
‘.csv’ file in the ‘/home/’ directory.

The user can click on the ‘Generate distance-time plot’ button as many times
as required to retry the fit from the beginning. For this particular case of
the streamer, we got the initial perturbation amplitude, period, and damping
time as 0.14 R⊙, 253 minutes, and 96 minutes, respectively, which are
consistent with previous streamer wave events depicted in Chen et al. (2010b);
Kwon et al. (2013); Decraemer et al. (2020b). The period and damping time
relation at different heliocentric distances can be used to check the wave
mode of the streamer wave (Ofman and Aschwanden, 2002).

Figure 8:  a. Selected slit on the movie frame from LASCO C2 data of 29 August
2022, perpendicular to the streamer. b. The required area to fit on the
generated distance-time plot is first zoomed on and then points are selected
for fit. c. “Fit Points” button performs a fit on the current points based on
the initial guess and pops open a window to modify parameters for this fit. d.
Next, this fit is plotted and parameters saved in a ‘.csv’ file as shown in
the dialogue box The movie is available in the online version

####  5.2.3 Automatic fitting

We use LASCO/C2 level 1 images of July 06, 2004 between 00:04 and 23:52 UT for
the illustration of the automatic fitting case. A streamer is perturbed by a
CME located near the south-west region in the LASCO image (see Figure 9). We
selected the slit perpendicular to the streamer and used the “Generate
distance-time plot” to produce a $x-t$ map. As shown in the Figure, after the
plot is generated, the user can select the constricted part where the
oscillation occurs by dragging a rectangle around that region. At every time
step (corresponding to columns in $x-t$ map), a Gaussian profile is fitted
across the slit length to find out the position of the oscillating streamer.
An initial automatic fit, based on equation 1, will be performed on that
region (Note: User may have to try for different rectangles around the
oscillation for better fit). This part can be retried many times by clicking
on the “Generate distance-time plot” button. After the fit is performed with
the initial guess, another window shows up with these parameters (similar to
manual fitting 5.2.1), which can be modified to improve the fit. The automatic
fit can be advantageous over manual fit as it can remove the human bias
created by selecting the points on the x-t map. We found the initial
amplitude, period, and damping time from the automatic fit as 0.2 R⊙, 95
minutes, and 88 minutes, respectively. Using a sample of 22 events, Decraemer
et al. (2020b) determined the average period of streamer waves as 239 minutes,
a similar order of magnitude values are obtained using manual and automatic
fit techniques in this work. The period determination could be further used to
calculate phase speed and an estimate of background solar wind speed
considering the wavelength is known (Decraemer et al., 2020b). The measurement
of phase speed could also be useful in determining the magnetic fields in the
streamers (Chen et al., 2011). In brief, SITCoM is a useful tool for quickly
analyzing the oscillation properties of streamer waves.

Figure 9:  a. Selected slit on the movie frame from LASCO C2 data of 06 July
2004, perpendicular to the streamer. b. Selected area on the generated
distance-time plot, showing oscillations is first zoomed on, and then the
automatic fitting is performed with an initial guess. c. After plotting the
final fit, the parameters of the fit will be saved in a ‘.csv’ file

##  6 Summary and Future Developments

We introduce a new tool, SITCoM, which is built upon the SiRGRaF algorithm for
processing white-light coronagraph images. The application enables rapid
analysis of coronal dynamics such as CMEs, plasma blobs, streamer waves, and
similar transients, observed in white-light coronagraph data. The output of
SITCoM can serve as preliminary analysis for in-depth scientific
investigations or can be integrated into larger studies using various
datasets. SITCoM is developed as an open-source application that features a
simple user interface based on PyQT and other minor dependencies in Python. It
can be installed using pip command in python.

The SITCoM graphical user interface (GUI) incorporates the SiRGRaF algorithm
in its initial stage, which proves to be a simple yet effective tool for
highlighting dynamic features in the corona. The dynamic features are enhanced
against the background throughout the coronagraph FOV. We have illustrated the
usage of SITCoM using LASCO and STEREO or white-light coronagraph data for
various test cases. Since SiRGRaF has also been implemented on Solar Orbiter
Metis data by Telloni et al. (2022), it can be used in the SITCoM as input
along with the above mentioned data sets. Along with these data sets, the
application of SiRGraF on the extreme ultraviolet (EUV) dataset will require
detailed analysis and testing including image processing to enhance the
structures observed in EUV passbands. The future updates of SITCoM will be
equipped with such image-enhancing features to carry out the analysis
presented here. Users have the capability to save the processed movie and
individual frames in various formats. Moreover, SITCoM offers different
intensity scaling, such as MinMax, Logarithmic, and Gamma, allowing users to
enhance specific features based on their preferences and requirements.

To facilitate kinematics analysis of any sunward or anti-sunward moving
structures in the data, SITCoM empowers users to generate height-time plots at
different position angles. These plots provide valuable insights into the
temporal evolution of the observed phenomena. The users can perform linear or
higher-order fitting on these plots to derive essential parameters such as
velocity or acceleration, depending on the type of fitting performed. Multiple
measurements can be made at the same position angle or over desired angles to
get an estimate of deviation in the measured kinematics throughout the large-
scale structures.

Users can also investigate various types of oscillations and waves within a
selected slit across a dynamic feature. By employing a decaying sine wave
fitting technique, users can automatically or manually fit the resultant
distance-time plot generated from the slit. The extracted parameters of the
fitted wave can be saved for further analysis, providing insights into the
oscillatory behavior of coronal features.

This GUI is not only a powerful tool for analyzing coronal dynamics but also
saves time typically spent on plotting and fitting. By streamlining the
analysis process, SITCoM provides useful results obtained with quick analysis.
The combination of advanced algorithms and user-friendly tools facilitates a
comprehensive understanding of the observed phenomena, enhancing our knowledge
in this field. In the future, we plan to develop SITCoM for different
platforms to make it more accessible to researchers worldwide. This will
enable more scientists to benefit from its capabilities and contribute to our
understanding of the Sun’s corona.

We plan to improve the SITCoM application with the addition of new features in
the future. These include the implementation of fitting multiple ridges
identified in the height-time plots for kinematics analysis. As a result,
multiple transients at the same position angle can be tracked and
characterized manually without having to generate the same x-t map repeatedly.
Implementation of curved slit selection will enable a user to select a curved
slit for the generation of x-t maps. Such a feature will be helpful to
characterize the CME deflection, and non-radial flows (Alzate et al., 2023) as
well as tracking the waves and oscillations at bigger regions of interest
using a single large slit or a wide slit. A provision to save the coordinates
corresponding to the slit location and the x-t map array in the form of FITS
or ‘.sav’ file will be useful to revisit an analysis later or import to any
other program when required.

These planned advancements will further enhance the capability of SITCoM and
will be a part of its future releases.

## Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

## Author Contributions

RP planned the development and overlooked the implementation of the tool. PU
and AKS developed the tool in Python and carried out tests on different
datasets. PU and AKS prepared the manuscript. All the authors took part in the
discussion of the results.

## Funding

A.K.S is supported by the Council of Scientific & Industrial Research (CSIR),
India, under file no. 09/079(2872)/2021-EMR-I.

## Acknowledgments

PU and AKS would like to thank ARIES for providing the computational
facilities. The SECCHI data used here were produced by an international
consortium of the Naval Research Laboratory (USA), Lockheed Martin Solar and
Astrophysics Lab (USA), NASA Goddard Space Flight Center (USA), Rutherford
Appleton Laboratory (UK), University of Birmingham (UK), Max-Planck-Institut
for Solar System Research (Germany), Centre Spatiale de Li$\grave{e}$ge
(Belgium), Institut d’Optique Th$\acute{e}$orique et Appliqu$\acute{e}$e
(France), Institut d’Astrophysique Spatiale (France). We also thank NASA for
making SOHO/LASCO data publicly available.

## Data Availability Statement

The STEREO/SECCHI and SOHO/LASCO data sets analyzed for this study can be
found in their respective data archives under the open data policy. The data
sets generated in this study can be made available upon request.

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