csindexchange.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb  5 01:35:23 2018

@author: ycan
"""

import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import iofuncs as iof
import plotfuncs as plf
import analysis_scripts as asc


def csindexchange(exp_name, onoffcutoff=.5, qualcutoff=9):
    """
    Plots the change in center surround indexes in different light
    levels. Also classifies based on ON-OFF index from the onoffsteps
    stimulus at the matching light level.
    """

    # For now there are only three experiments with the
    # different light levels and the indices of stimuli
    # are different. To automate it will be tricky and
    # ROI is just not enough to justify; so they are
    # hard coded.
    if '20180124' in exp_name or '20180207' in exp_name:
        stripeflicker = [6, 12, 17]
        onoffs = [3, 8, 14]
    elif '20180118' in exp_name:
        stripeflicker = [7, 14, 19]
        onoffs = [3, 10, 16]

    exp_dir = iof.exp_dir_fixer(exp_name)
    exp_name = os.path.split(exp_dir)[-1]
    clusternr = asc.read_spikesheet(exp_name)[0].shape[0]

    # Collect all CS indices, on-off indices and quality scores
    csinds = np.zeros((3, clusternr))
    quals = np.zeros((3, clusternr))

    onoffinds = np.zeros((3, clusternr))
    for i, stim in enumerate(onoffs):
        onoffinds[i, :] = iof.load(exp_name, stim)['onoffbias']

    for i, stim in enumerate(stripeflicker):
        data = iof.load(exp_name, stim)
        quals[i, :] = data['quals']
        csinds[i, :] = data['cs_inds']

    csinds_f = np.copy(csinds)
    quals_f = np.copy(quals)
    onoffbias_f = np.copy(onoffinds)

    # Filter them according to the quality cutoff value
    # and set excluded ones to NaN
    for j in range(quals.shape[1]):
        if not np.all(quals[:, j] > qualcutoff):
            quals_f[:, j] = np.nan
            csinds_f[:, j] = np.nan
            onoffbias_f[:, j] = np.nan

    # Define the color for each point depending on each cell's ON-OFF index
    # by appending the color name in an array.
    colors = []
    for j in range(onoffbias_f.shape[1]):
        if np.all(onoffbias_f[:, j] > onoffcutoff):
            # If it stays ON througout
            colors.append('blue')
        elif np.all(onoffbias_f[:, j] < -onoffcutoff):
            # If it stays OFF throughout
            colors.append('red')
        elif (np.all(onoffcutoff > onoffbias_f[:, j]) and
              np.all(onoffbias_f[:, j] > -onoffcutoff)):
            # If it's ON-OFF throughout
            colors.append('black')
        else:
            colors.append('white')

    scatterkwargs = {'c':colors, 'alpha':.6, 'linewidths':0}

    colorcategories = ['blue', 'red', 'black']
    colorlabels = ['ON', 'OFF', 'ON-OFF']

    # Create an array for all the colors to use with plt.legend()
    patches = []
    for color, label in zip(colorcategories, colorlabels):
        patches.append(mpatches.Patch(color=color, label=label))

    x = [np.nanmin(csinds_f), np.nanmax(csinds_f)]

    plt.figure(figsize=(12, 6))
    ax1 = plt.subplot(121)
    plt.legend(handles=patches, fontsize='small')
    plt.scatter(csinds_f[0, :], csinds_f[1, :], **scatterkwargs)
    plt.plot(x, x, 'r--', alpha=.5)
    plt.xlabel('Low 1')
    plt.ylabel('High')

    ax1.set_aspect('equal')
    plf.spineless(ax1)

    ax2 = plt.subplot(122)
    plt.scatter(csinds_f[0, :], csinds_f[2, :], **scatterkwargs)
    plt.plot(x, x, 'r--', alpha=.5)
    plt.xlabel('Low 1')
    plt.ylabel('Low 2')
    ax2.set_aspect('equal')
    plf.spineless(ax2)

    plt.suptitle(f'Center-Surround Index Change\n{exp_name}')
    plt.text(.8, -0.1, f'qualcutoff:{qualcutoff} onoffcutoff:{onoffcutoff}',
             fontsize='small', transform=ax2.transAxes)
    plotsave = os.path.join(exp_dir, 'data_analysis', 'csinds')
    plt.savefig(plotsave+'.svg', format='svg', bbox_inches='tight')
    plt.savefig(plotsave+'.pdf', format='pdf', bbox_inches='tight')
    plt.show()
    plt.close()