seplot 1.1.7

A front-end for Python PyX

# About seplot
seplot is a frontend for PyX to create plot from text files in command line or through a python interface.
Developed by Serge Dmitrieff.
https://www.biophysics.fr

# Installation

## Required packages
seplot requires PyX, Numpy, and sio_tools. Pandas is necessary to import csv/excel documents.

```shell
$ pip3 install pandas
```

## With pip3, online
```shell
$ pip3 install seplot
```

## With pip3, from github

```shell
$ git clone https://github.com/SergeDmi/Python-Tools.git python_tools
$ cd python_tools
$ pip3 install .
```

# Usage

## Basic usage
seplot 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 :
```shell
$ seplot.py data.txt
```
By 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 :
```shell
$ seplot.py data.txt mode=v x=0 y=1 out=plot.pdf
```

Where **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 :
```shell
$ seplot.py data_1.txt color=red data_2.txt color=blue
```
We can also plot several columns from the same file, use columns for errorbars dx and dy, and plot a function :
```shell
$ seplot.py data_1.txt x=0 dx=1 y=2 dy=3 color=4 size=5 function='y(x)=x'
```
Here, 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 :
```python
import seplot
plot=seplot.Splotter(file='data.txt')
# alternatively, with A an array containing the data
plot.add_plot(data=A)
plot.make_and_save()
```
This readme focuses on the command-line interface, but all instructions can also be used equally easily through the python interface.


### Data manipulation and conditional expressions
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 :
```shell
$ seplot.py data.txt y='A[:,1]^2'
```
Here *A[:,1]* is the *second* column of *A*. We can use the same syntax for conditional expressions using the keyword **if** :
```shell
$ seplot.py data.txt y='A[:,1]^2' if='y>0'
```
We can now combine several features :
```shell
$ seplot.py data.txt y='A[:,1]^2' if='y>0' color=blue
and if='y<0' color=red
```
We used the **and** keyword to re-use the data from *data.txt* into another plot element (note that the shorthand **andif=**... is also supported).

We can easily compute and plot complex functions of the input data :
```shell
$ seplot.py data.txt y='sqrt(1/(1+A[:,1]^2))/A[:,2]+sin(A[:,3])'
```
Similarly, the **if** keyword can be used for any function of the input data :
```shell
$ seplot.py data.txt y='A[:,1]^2' if='sqrt(A[:,1])>10'
```
Additionally, 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. :
```shell
$ seplot.py data.txt range='0:10' if='A[:,1]>0'
```
Here 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.

### Styles and propagation
seplot 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.
```shell
$ seplot.py data.txt color=red style=o and color=blue style=_ and color=black style=-- line=4
```
Other symbols include "+" (vertical cross), "x" (cross), ">" or "<" (triangle), but any of PyX's **graph.style.symbol** can be used.

When using color-by-value, any of PyX's gradients can be used, and some have shorthands :
```shell
$ seplot.py data.txt color=2 gradient=jet and gradient=gray
```

To keep the same style between two files, and change style for another file, we can use the **-keep** and **-discard** keywords :
```shell
$ seplot.py data_0.txt color=red -keep 'data_1.txt' -discard 'data_2.txt'
```
Note than **-keep** and **-discard** keep or discard any option, including **y=**, **range=**, **if=** , etc.

### Labels and titles
One of the main interest on using PyX as a backend is to have full *LaTeX* compatibility. Therefore we can happily write :
```shell
$ seplot.py data.txt xlabel='time ($s$)' ylabel='$v$ ($m s^{-1}$)'
```
seplot 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}:
```shell
$ cat data.txt
# time position
0 1
1 2
2 3
3 4
```
We can use the instruction :
```shell
$ seplot.py data.txt -autolabel
```
Which will yield *xlabel}=time* and *ylabel=position*.

We can also specify the position of the graph legend, e.g. with *key=tl* for the top left :
```shell
$ seplot.py data.txt -autolabel key=tl
```
## Calling seplot from Python
Calling seplot from a Python script offers many possibility, including appending progressively plots during analysis, etc.
```python
import seplot
plot=seplot.Splotter(key='tl')
for i,A in enumerate(list_of_data):
# A is an element of list_of_data
# i is its index
plot.add_plot(data=A,title=i)
plot.make_and_save()
```

Global 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.
```python
import seplot
plot=seplot.Splotter(key='tl')
plot.add_plot(file='data.txt',cond='A[:,0]>0')
plot.make_and_save()
```