Commit 79317329 authored by amichaut's avatar amichaut
Browse files

minor additions to doc

parent 432a64b9
Pipeline #91151 passed with stages
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......@@ -44,6 +44,8 @@ You can specify that the position file was generated by `Trackmate <https://imag
If **Track Analyzer** is run through Galaxy (see below). The position file format has to strictly follow the default column names: x, y, (z), frame, track.
However, if the position file was generated by `Trackmate <https://imagej.net/plugins/trackmate/>`_, then the columns are automatically identified.
If `Trackmate <https://imagej.net/plugins/trackmate/>`_ was used to track data, the csv file that must be used is the one generated by clicking on the "spots" button.
If **Track Analyzer** is run using command lines (see below), the data directory must contain:
- a comma-separated csv file named positions.csv which column names are: x, y, (z), frame, track
......@@ -52,9 +54,6 @@ If **Track Analyzer** is run using command lines (see below), the data directory
- (optional) configuration files in a config directory
The default config files can be generated by running :code:`TA_config <path_to_directory>`. The config files are csv files that can be easily edited.
..
add a section descibing config and info files
====================
Running the pipeline
......@@ -76,7 +75,9 @@ Complete documentation about Galaxy is available `here <https://training.galaxyp
2. Choose your input files that were previously uploaded.
3. Enter the parameters necessary to your analysis.
4. Hit the execution button to launch the execution on Galaxy's cluster.
5. You can find in the history panel all the output of each analysis job. For each of the output elements, you can have a quick look (6), or save it (7). Note that when you display output plots, it is not very intuitive how to display again the main interface. The double arrow 'Run this job again' button (8) displayed on every log file, is then useful. If you press the 'Run this job again' button, the interface will be displayed with the exact same set of parameters as the corresponding job.
5. You can find in the history panel all the output of each analysis job. For each of the output elements, you can have a quick look (6), or save it (7). Note that when you display output plots, it is not very intuitive how to display again the main interface. The double arrow 'Run this job again' button (8) displayed on every log file, is then useful. If you press the 'Run this job again' button, the interface will be displayed with the exact same set of parameters as the corresponding job.
Once you excute the job, new boxes appear in history panel (5). There are one box for each type of outputs (csv files, image files, log file, config files etc.). While the job is running these boxes are yellow, once the job is done, they turn green. If there was an error during the execution, the boxes turn red.
Using a Jupyter notebook (recommended for advanced options)
===========================================================
......@@ -101,13 +102,16 @@ To launch a notebook:
Using command lines (only if you need to run it on a distant machine)
====================================================================
If you need to run **Track Analyzer** from a terminal without any graphical interface, it is possible, but you won't beneficiate from the interactive
modules. Data filtering and analysis parameters will need to be passed through config files (see examples). **Track Analyzer** comes with two commands:
If you need to run **Track Analyzer** from a terminal without any graphical interface, it is possible, but you won't beneficiate from the interactive modules. Data filtering and analysis parameters will need to be passed through config files (see examples). **Track Analyzer** comes with four commands:
- :code:`traj_analysis` which runs the trajectory analysis section (see below).
It takes as arguments: path to data directory (optional: use the flag -r or --refresh to refresh the database)
It takes as arguments: :code:`path_to_data_directory` (optional: use the flag :code:`-r` or :code:`--refresh` to refresh the database)
- :code:`map_analysis` which runs the map analysis section (see below).
It takes as arguments: path to data directory (optional: use the flag -r or --refresh to refresh the database)
It takes as arguments: :code:`path_to_data_directory` (optional: use the flag :code:`-r` or :code:`--refresh` to refresh the database)
- :code:`TA_config` which generates default config files. It takes as one argument: :code:`path_to_data_directory`
- :code:`TA_synthetic` which generates synthetic data. It takes as arguments: :code:`path_to_data_directory` (optional: use the flag :code:`-p` or :code:`--plot` to plot the positions as a tiff file).
For all these commands, you can show the help by adding the optional flag :code:`-h` or :code:`--help`.
==================
......@@ -200,6 +204,12 @@ Divergence (contraction and expansion) maps, and curl (rotation) maps can also b
.. image:: ../_static/screenshots/map_section.png
:align: center
Notes on running times
======================
Some analysis steps can be time intensive. In particular, the database pre-processing (splitting and interpolation of tracks, velocity and acceleration calculation, etc.) and the trajectory plotting are the most time-intensive steps. To give ideas about orders of magnitude, the pre-processing step takes about 1 minute to run for a 10\ :sup:`4`-tracked objects dataset on a standard laptop.
Comparator section
==================
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......@@ -39,4 +39,4 @@ welcome_message = """
print(welcome_message)
__version__ = "0.3"
\ No newline at end of file
__version__ = "0.4"
\ No newline at end of file
......@@ -728,6 +728,7 @@ def get_data(data_dir, df=None, refresh=False, split_traj=False, set_origin_=Fal
# rename track id and deal with gaps
print("renaming tracks...")
df = df[df['track'].notna()] # remove Nan and None tracks
df = tca.regularize_traj(df, dimensions, split_traj)
# scale data
......
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