In [1]:
import numpy as np
import pandas as pd
from scipy.stats import ranksums
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.io import loadmat
%matplotlib inline
matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Arial']})
matplotlib.rcParams.update({'mathtext.default': 'regular'})
matplotlib.rcParams.update({'font.size': 14})
sns.set_style("whitegrid")
In [2]:
BC_ids=np.loadtxt('data/BC_IDs_new').astype(int)
all_contacts=pd.read_pickle('data/cone_contact_data')
tip_contacts=np.loadtxt('data/CBCX_OFF_CBC_contact_comparison.csv',delimiter=',')[:,:4].astype(int)
tip_contacts=pd.DataFrame(tip_contacts,columns=['cell','cone','type','contact_id'])
In [3]:
for i in range(tip_contacts.shape[0]):
tip_contacts.loc[i,'area']=all_contacts[all_contacts['contact_id']==tip_contacts.ix[i,'contact_id']]['area'].item()
In [4]:
tip_contacts=tip_contacts[tip_contacts['area']>0].reset_index().drop('index',axis=1)
In [6]:
print('CBCX contact area (median):',np.median(tip_contacts[tip_contacts['type']==66]['area'])/1e6)
print('OFF-CBC contact area (median):',np.median(tip_contacts[(tip_contacts['type']<65)]['area'])/1e6)
In [7]:
ranksums(tip_contacts[tip_contacts['type']==66]['area'],tip_contacts[(tip_contacts['type']<65)]['area'])
Out[7]:
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