In [1]:
import sys
sys.path.append("../")
from Linearity import Neuron
import numpy as np
import scipy.stats as ss
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import findfont, FontProperties
# print(plt.style.available)
plt.style.use('seaborn-white')
matplotlib.rcParams['font.family']
print(findfont(FontProperties(family=['sans-serif'])))
In [2]:
figAspect = 1.
In [4]:
#fileList = glob.glob("/media/sahil/NCBS_Shares_BGStim/patch_data/**/c?/plots/*.pkl")
neuronList = []
with open("/media/sahil/NCBS_Shares_BGStim/patch_data/CPP_files.txt", 'r') as fp:
fileList = fp.read().splitlines()
for i,filename in enumerate(fileList):
fileList[i] += "plots/" + filename.split('/')[-2] + ".pkl"
try:
print ("Reading ", fileList[i])
neuronList.append(Neuron.load(fileList[i]))
except:
print ("Couldn't read", fileList[i])
print ("Read {} files.".format(len(fileList)))
In [5]:
def figure1(feature = 0):
''' Plotting to show sublinearity of CA3 inputs at CA1. '''
ratio = []
numNeurons = 0
numBins = 500
for neuron in neuronList:
for experiment in neuron.experiment:
if experiment == "Control":
numNeurons+=1
for numSq in neuron.experiment[experiment]:
if numSq > 1:
#if 5 in neuron.experiment[experiment]: # If one vs five required
coords = neuron.experiment[experiment][numSq].coordwise
for coord in coords:
try:
if feature in coords[coord].average_feature and coords[coord].expected_feature:
if not np.isnan(coords[coord].average_feature[feature]/coords[coord].expected_feature[feature]) and not np.isinf(coords[coord].average_feature[feature]/coords[coord].expected_feature[feature]):
ratio.append(coords[coord].average_feature[feature]/coords[coord].expected_feature[feature])
except:
print ("Couldn't append to ratio, perhaps feature not found")
ratio = np.array(ratio)
gt_one = ratio[np.where(ratio>1)]
frac_gt_one = round(float(len(gt_one))/len(ratio)*1e2,2)
#print [x for x in ratio]
f, ax = plt.subplots()
ax.hist(ratio, bins=numBins)
ax.set_title("Sublinearity of input summation")
ax.axvline(1.0, linestyle='dashed', linewidth=2)
ax.set_xlabel("O/E")
ax.set_ylabel("Frequency")
ax.annotate("{} \%".format(frac_gt_one), xy=(1, 100), xytext=(1.5, 100), textcoords='data', arrowprops=dict(connectionstyle="arc3", arrowstyle = "<-"))
plt.figaspect(figAspect) #Length/width
plt.show()
print ("Legend text: Number of cells: {}, number of points: {}, fraction of points greater than one: {}".format(numNeurons, len(ratio), frac_gt_one ))
In [6]:
figure1()
