

In [6]:

    
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns

import sklearn.mixture
import scipy.stats as ss
import seaborn as sns

np.random.seed(12345678)

df = pd.read_csv('../output.csv')

create weighted. Throw out bins with unmasked<=.5*nvox



In [7]:

    
nvox = 64*64*48
df['weighted'] = df['synapses']/df['unmasked']*nvox

dfthr = df[df['unmasked']>nvox*0.5] # Thresholded data frame

1) 2D exploratory graphs with weighted data



In [8]:

    
sumXY = pd.pivot_table(df, index='cy', columns='cx', values='synapses', aggfunc=np.sum)
sumXZ = pd.pivot_table(dfthr, index='cz', columns='cx', values='synapses', aggfunc=np.sum)
sumYZ = pd.pivot_table(dfthr, index='cz', columns='cy', values='synapses', aggfunc=np.sum)

plt.figure()
sns.heatmap(sumXY, xticklabels=20, yticklabels=10, cbar_kws={'label': 'Synapses'});
plt.title('Number of Synapses at X-Y coordinates');

plt.figure()
sns.heatmap(sumXZ, xticklabels=20, yticklabels=2, cbar_kws={'label': 'Synapses'});
plt.title('Number of Synapses at X-Z coordinates');

plt.figure()
sns.heatmap(sumYZ, xticklabels=10, yticklabels=2, cbar_kws={'label': 'Synapses'});
plt.title('Number of Synapses at Y-Z coordinates');