Neural_Machine_Learning_Python/Neural_Machine_Learning_Python_web at master · semework/Neural_Machine_Learning_Python · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 26 15:18:33 2017

@author: mulugetasemework
"""

import os
import glob
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


from sklearn import decomposition

path = "/Documents/DATA_current/ResponseCSVs"

#bring in and catenate spreadsheets
all_files = glob.glob(os.path.join(path, "*.csv"))


df_from_each_file = (pd.read_csv(f) for f in all_files)
df = pd.concat(df_from_each_file, ignore_index=True)
print("CSVs catenated")

#Header = list(df.columns.values)


#figures ----------------------------------------------------------------------
plt.figure(1)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
#f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
#figures ----------------------------------------------------------------------
ax1.hist(df.pMemoBase_ranksum , bins=10)
ax1.set_title('Mem vs baseMax ranksum p-values',fontsize=10)
ax1.set_xlabel('p-values',fontsize=9)
ax1.set_ylabel('Count',fontsize=9)
ax1.tick_params(axis='both', which='major', labelsize=7)
ax1.tick_params(axis='both', which='minor', labelsize=7)


ax2.scatter(df.basePSTH_PREMEAN2, df.pMemoBase_ranksum,
           s=30, color='g', marker='o', alpha=.3)
ax2.set_title('BaseRate vs Memory',fontsize=10)
ax2.set_xlabel('Base rate (spikes/sec)',fontsize=8)
ax2.set_ylabel('Mem p-values',fontsize=8)

slope, intercept = np.polyfit(df.basePSTH_PREMEAN2, df.pMemoBase_ranksum,1)
# Create a list of values in the best fit line
abline_values = [slope * i + intercept for i in df.basePSTH_PREMEAN2]
ax2.plot(df.basePSTH_PREMEAN2, abline_values, 'r')
ax2.legend(['polyfit','data'], loc=1,fontsize=7)
ax2.tick_params(axis='both', which='major', labelsize=7)
ax2.tick_params(axis='both', which='minor', labelsize=7)

ax3.scatter(df.pVisVis_MAX_ranksum, df.pMemoBase_ranksum,
           s=30, color='m', marker='o', alpha=.3)
ax3.set_title('visualPval vs Memory',fontsize=10)
ax3.set_xlabel('visPre vs postSaccadic (pValue)',fontsize=9)
ax3.set_ylabel('Mem p-values',fontsize=9)
slope, intercept = np.polyfit(df.pVisVis_MAX_ranksum, df.pMemoBase_ranksum,1)
# Create a list of values in the best fit line
abline_values = [slope * i + intercept for i in df.pVisVis_MAX_ranksum]
ax3.plot(df.pVisVis_MAX_ranksum, abline_values, 'r')
ax3.legend(['polyfit','data'], loc=1,fontsize=7)
ax3.tick_params(axis='both', which='major', labelsize=7)
ax3.tick_params(axis='both', which='minor', labelsize=7)


ax4.scatter(df.base_memMeanMEDIAN2, df.memPSTH_memMeanMEDIAN2,
           s=30, color='b', marker='o', alpha=.3)
ax4.set_title('base vs Memory Median',fontsize=10)
ax4.set_xlabel('Basline median',fontsize=8)
ax4.set_ylabel('Mem median',fontsize=8)
slope, intercept = np.polyfit(df.base_memMeanMEDIAN2, df.memPSTH_memMeanMEDIAN2,1)
# Create a list of values in the best fit line
abline_values = [slope * i + intercept for i in df.base_memMeanMEDIAN2]
ax4.plot(df.base_memMeanMEDIAN2, abline_values, 'r')
ax4.legend(['polyfit','data'], loc=4,fontsize=7)
ax4.tick_params(axis='both', which='major', labelsize=7)
ax4.tick_params(axis='both', which='minor', labelsize=7)


f.subplots_adjust(hspace=.9)
f.subplots_adjust(wspace=.9)

plt.show()

f.savefig('/Documents/DATA_current/ResponseCSVs/NeuralIndicators.pdf')


#END figures ----------------------------------------------------------------------

memClass = []
for d in range(len(df.base_memMeanMEDIAN2)):
  if df.pMemoBase_MED_ranksum2[d] < 0.05:
     memClass.append(0)
  elif ((df.pMemoBase_ranksum[d]  > 0.05) & (df.pMemoBase_ranksum[d] < 1)):
     memClass.append(1)
  else:
     memClass.append(2)


np.random.seed(5)

centers = [[1, 1], [-1, -1], [1, -1]]

X = pd.DataFrame({'median': df.base_memMeanMEDIAN2,'mean': df.basePSTH_PREMEAN2,
                  'visMax':df.pVisVis_MAX_ranksum,'max':df.baseMEMmaxRAW})
pca = decomposition.PCA(n_components=3)
pca.fit(X)
X = pca.transform(X)


y = memClass
categories = np.array(y)
colormap = np.array(['r', 'g', 'b'])
legendlabels= ('Good Memory', 'No Memory', 'Undecided')

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')


goM = ax.scatter(X[:, 0], X[:, 1], X[:, 2],s=200,  marker='o', color='r',  alpha=.3,label='Good Memory')
NogoM = ax.scatter(X[:, 0], X[:, 1], X[:, 2],s=200, marker='o', color='g',  alpha=.3,label='No Memory')
UngoM = ax.scatter(X[:, 0], X[:, 1], X[:, 2],s=200, marker='o',  color='b',  alpha=.3,label='Undecided')
ax.scatter(X[:, 0], X[:, 1], X[:, 2],s=200, alpha=.3,
c=colormap[categories])


ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])

ax.set_title( "PCA classification of memory responses \n based on baseline median, mean and max firing rates, \n and visual responses", fontsize=12)
ax.legend(loc=8,fontsize=8)
#ax.view_init(elev=5., azim=34)
fig.savefig('/Documents/DATA_current/ResponseCSVs/NeuralPCAMemory.pdf')