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        "description": "# About seplot\nseplot is a frontend for PyX to create plot from text files in command line or through a python interface.\nDeveloped by Serge Dmitrieff.\nhttps://www.biophysics.fr\n\n# Installation\n\n## Installing with pip3 (recommended)\n ```shell\n $ pip3 install seplot\n```\n\n## Required packages\nseplot requires PyX, Numpy, and sio_tools. Pandas is necessary to import csv/excel documents.\n\n```shell\n$ pip3 install pandas\n```\n\n# Usage\n\n## Basic usage\nseplot is meant to be used from command line or from a python script. The typical command line instruction to plot from a file data.txt would be :\n```shell\n$ seplot.py data.txt\n```\nBy omitting further instructions, it is implied that the data in data.txt is a set of vertical columns, and we plot column 1 as a function of column 0. seplot uses Python's zero-indexing convention (column 0 is the first column). This could also be written :\n```shell\n$ seplot.py data.txt mode=v x=0 y=1 out=plot.pdf\n```\n\nWhere **mode**  is **v** (vertical) for columns of data and **h** for rows (horizontal), and plot.pdf is the output file. Of course several files can be plotted with different colors :\n```shell\n$ seplot.py data_1.txt color=red data_2.txt color=blue\n```\nWe can also plot several columns from the same file, use columns for errorbars dx and dy, and plot a function :\n```shell\n$ seplot.py data_1.txt x=0 dx=1 y=2 dy=3 color=4 size=5 function='y(x)=x'\n```\nHere, we even used data to assign a size and color to the plot symbols ! Note that seplot can easily be used from inside a python script :\n```python\nimport seplot\nplot=seplot.Splotter(file='data.txt')\n# alternatively, with A an array containing the data\nplot.add_plot(data=A)\nplot.make_and_save()\n```\nThis readme focuses on the command-line interface, but all instructions can also be used equally easily through the python interface.\n\n### Histograms\n```shell\n$ seplot.py data.txt x=0 y=1 dy=2 and x=0 y=1 line=1\n```\nDoes a scatter plot of the second column as a function of the first, using the third column for error bars. Then does a line plot of the secund column as a function of the first.\n\n```shell\n$ seplot.py data.txt -hist x=10 y=0 data.txt -hist x='[0,1,2,3,4]' y=0\n```\nDoes a histogram of the first column (y=0) of data.txt, with 10 bins (x=10) and then with bins centered around 0,1,2,3,4.\n\n### Data manipulation and conditional expressions\n Using Python's *eval()* function, we can perform operations on the input data. Data read from the data file (eg. data.txt) is stored in a numpy array called *A*. We can apply any numpy function on *A* in *seplot* through a simple syntax :\n```shell\n$ seplot.py data.txt y='A[:,1]^2'\n```\nHere *A[:,1]* is the *second* column of *A*. We can use the same syntax for conditional expressions using the keyword **if** :\n```shell\n$ seplot.py data.txt y='A[:,1]^2' if='y>0'\n```\nWe can now combine several features :\n```shell\n$ seplot.py data.txt y='A[:,1]^2' if='y>0' color=blue\n\t\t   and if='y<0' color=red\n```\nWe used the **and** keyword to re-use the data from *data.txt* into another plot element (note that the shorthand  **andif=**... is also supported).\n\nWe can easily compute and plot complex functions of the input data :\n```shell\n$ seplot.py data.txt y='sqrt(1/(1+A[:,1]^2))/A[:,2]+sin(A[:,3])'\n```\nSimilarly, the **if** keyword can be used for any function of the input data :\n```shell\n$ seplot.py data.txt y='A[:,1]^2' if='sqrt(A[:,1])>10'\n```\nAdditionally, one can  select a sub-set of the data, both by *first* choosing a range of lines (resp. columns in horizontal mode), and *second* a conditional expression, e.g. :\n```shell\n$ seplot.py data.txt range='0:10' if='A[:,1]>0'\n```\nHere data from the first 10 lines (lines 0-9 according to Python's numbering convention) if the value of the second columns (*A[:,1]*) is larger than 0.\n\n### Styles and propagation\nseplot allows for a wide variety of symbol and line styles and attributes. Some have shorthands, but any style from PyX can be used. For instance let us plot the same data as red dots, a blue solid line, and a thick black dashed line.\n```shell\n$ seplot.py data.txt color=red style=o and color=blue style=_ and color=black style=-- line=4\n```\nOther symbols include \"+\" (vertical cross), \"x\" (cross), \">\" or \"<\" (triangle), but any of PyX's  **graph.style.symbol** can be used.\n\nWhen using color-by-value, any of PyX's gradients can be used, and some have shorthands :\n```shell\n$ seplot.py data.txt color=2 gradient=jet and gradient=gray\n```\n\nTo keep the same style between two files, and change style for another file, we can use the **-keep** and **-discard** keywords :\n```shell\n$ seplot.py data_0.txt color=red -keep 'data_1.txt' -discard 'data_2.txt'\n```\nNote than **-keep** and **-discard** keep or discard any option, including **y=**, **range=**, **if=** , etc.\n\n### Labels and titles\nOne of the main interest on using PyX as a backend is to have full *LaTeX* compatibility. Therefore we can happily write :\n```shell\n$ seplot.py data.txt xlabel='time ($s$)' ylabel='$v$ ($m s^{-1}$)'\n```\nseplot also can read directly the label from a text file using the keyword  **-autolabel**. For example for a file with a simple header  &#35; time position}:\n```shell\n$ cat data.txt\n\t# time position\n\t0 1\n\t1 2\n\t2 3\n\t3 4\n```\nWe can use the instruction :\n```shell\n$ seplot.py data.txt -autolabel\n```\nWhich will yield *xlabel=time* and  *ylabel=position*.\n\nWe can also specify the position of the graph legend, e.g. with *key=tl* for the top left :\n```shell\n$ seplot.py data.txt -autolabel key=tl\n```\n## Calling seplot from Python\nCalling seplot from a Python script offers many possibility, including appending progressively plots during analysis, etc.\n```python\nimport seplot\nplot=seplot.Splotter(key='tl')\nfor i,A in enumerate(list_of_data):\n\t\t# A is an element of list_of_data\n\t\t# i is its index\n\t\tplot.add_plot(data=A,title=i)\nplot.make_and_save()\n```\n\nGlobal options are passed when calling **seplot.Splotter** and local options are passed when calling **plot.add_plot**, following the same syntax as the command line. One exception, **if=** (from command line) becomes **cond=** to avoid confusion.\n```python\nimport seplot\nplot=seplot.Splotter(key='tl')\nplot.add_plot(file='data.txt',cond='A[:,0]>0')\nplot.make_and_save()\n```\n\n\n",
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