Customize your baredSC plots
============================

.. contents:: 
    :local:

When ``--figure`` is used. baredSC generates plots and output text tables.
To use baredSC results in your publication, you may want to customize your plots.
Here is an example of python script you can use to customize your plots.

baredSC_1d
----------

We assume you ran the :doc:`tutorial_sim_default_1d`.

We will use matplotlib to display the pdf with the error bar:

.. ipython::    

    @savefig custom_1d.png
    In [1]: import numpy as np
       ...: import pandas as pd
       ...: import matplotlib.pyplot as plt
       ...: # Get the pdf
       ...: pdf = pd.read_csv("../example/first_example_1d_1-3gauss_pdf.txt",
       ...:                   sep="\t")
       ...: x = pdf['x'].to_numpy()
       ...: # We assume x is equally spaced
       ...: dx = x[1] - x[0]
       ...: xmin = x[0] - dx / 2
       ...: xmax = x[-1] + dx / 2
       ...: # Plot PDF with 1 sigma
       ...: plt.figure()
       ...: plt.fill_between(x, pdf['low'].to_numpy(), pdf['high'].to_numpy(),
       ...:                  color='g', alpha=0.3, rasterized=True)
       ...: # Mean
       ...: plt.plot(x, pdf['mean'].to_numpy(), 'g', lw=2, rasterized=True)
       ...: plt.plot(x, pdf['median'].to_numpy(), 'k--', lw=2, rasterized=True)
       ...: plt.xlim(xmin, xmax)
       ...: plt.ylim(0, )
       ...: plt.xlabel('log(1 + 10000 * expression)')
       ...: plt.ylabel('PDF')
       ...: plt.title('My title')


baredSC_2d
----------

We assume you ran the :doc:`tutorial_sim_default_2d`.

We will use matplotlib to display the mean pdf in 2d:

.. ipython::    

    @savefig custom_2d.png
    In [1]: import numpy as np
       ...: import matplotlib.pyplot as plt
       ...: # Get the pdf
       ...: pdf = np.loadtxt("../example/first_example_2d_cellgroup1_1-3gauss_pdf2d.txt",
       ...:                  delimiter="\t", dtype='S30')
       ...: x = pdf[0, 1:].astype('float')
       ...: y = pdf[1:, 0].astype('float')
       ...: # We assume x and y is equally spaced
       ...: dx = x[1] - x[0]
       ...: xmin = x[0] - dx / 2
       ...: xmax = x[-1] + dx / 2
       ...: x_borders = np.linspace(xmin, xmax, len(x) + 1)
       ...: dy = y[1] - y[0]
       ...: ymin = y[0] - dy / 2
       ...: ymax = y[-1] + dy / 2
       ...: y_borders = np.linspace(ymin, ymax, len(y) + 1)
       ...: plt.figure()
       ...: plt.pcolormesh(x_borders, y_borders, pdf[1:,1:].astype('float'),
       ...:                shading='flat', rasterized=True, cmap='viridis')
       ...: plt.xlabel('log(1 + 10000 * genex)')
       ...: plt.ylabel('log(1 + 10000 * geney)')
       ...: plt.title('My title')
       ...: