

In [1]:

    
drive_path = 'c:/'
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
%matplotlib
from scipy.stats import ks_2samp
from scipy.stats import anderson_ksamp
from scipy.stats import kruskal
from scipy.stats import variation
from scipy import signal as sps
import seaborn as sns
import glob
import re









    



Using matplotlib backend: Qt4Agg



In [7]:

    
complete=pd.DataFrame([])
date='160620_3'
os.chdir('C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\%s'%date)
for filename in glob.glob('*dt.txt'):
    f=pd.read_csv(filename,nrows=175)
    df=f[[col for col in f.columns if 'G PMT' in col]]
    complete=pd.concat([complete,df],axis=1)
#     peak=[]
#     for col in df.columns:
#         a=df[col]
#         firsta=1;
#         firstb=24;
#         #Figures out if there is a min or max and sees if it passes threshold (3SD)
#         if np.absolute(min(a[26:80]))>np.absolute(max(a[26:80])) and np.absolute(min(a[26:80]))>=3*np.std(df[col][firsta:firstb]):
#             b=min(a[26:80])
#             peak.append(b)
#         elif np.absolute(max(a[26:80]))>np.absolute(min(a[26:80]))and np.absolute(max(a[26:80]))>=3*np.std(df[col][firsta:firstb]):
#             b=max(a[26:80])
#             peak.append(b)
#         else:
#             b=0
#             peak.append(b)
#         peaks=pd.DataFrame(peak).T
#     peaks.columns=df.columns
#     peaks=pd.concat([pd.DataFrame({'Trial':[int(filename.split('dt')[0])]}),peaks],axis=1)
#     peakdf=peakdf.append(peaks,ignore_index=True)

PLOT TRACES



In [26]:

    
sns.set(palette="muted",color_codes=True);
sns.set_context("poster",font_scale=1.3);
plt.figure(figsize=(8,7))
plt.plot(complete);
sns.despine()
plt.ylabel('DF/F');
plt.title('Traces from one imaging session');
plt.xlabel('Frame');
plt.tight_layout()



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Separate by group



In [25]:

    
filename='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullpeak.csv'
comp=pd.read_csv(filename)
comp_sorted=comp.reindex_axis(comp.mean().sort_values().index, axis=1)
comp_labels=pd.DataFrame(comp.Mouse)
comp_group=pd.DataFrame(comp.Group)
tmp=[comp_group,comp_labels,comp_sorted]
composite_full=pd.concat(tmp,axis=1)
cfull=pd.melt(composite_full,['Group','Mouse'],var_name="Odor")
composite_full.head()



In [39]:

    
df=composite_full
# df.columns=['Group','Mouse',1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]



In [40]:

    
for x in df.index:
    plt.plot(df.iloc[x,2:]);



In [50]:

    
for x in df[df.Group=='Control'].index:
    plt.plot(df.iloc[x,2:]);
    plt.xlabel('Odor')
    plt.ylabel('Peak DF/F')
    plt.title('Control')



In [52]:

    
for x in df[df.Group=='Hexanal'].index:
    plt.plot(df.iloc[x,2:]);
    plt.xlabel('Odor')
    plt.ylabel('Peak DF/F')
    plt.title('Hexanal')



In [53]:

    
for x in df[df.Group=='Mint'].index:
    plt.plot(df.iloc[x,2:]);
    plt.xlabel('Odor')
    plt.ylabel('Peak DF/F')
    plt.title('Mint')



In [54]:

    
rs = np.random.RandomState(4)
pos = rs.randint(-1, 2, (20, 5)).cumsum(axis=1)
pos -= pos[:, 0, np.newaxis]
step = np.tile(range(5), 20)
walk = np.repeat(range(20), 5)
df = pd.DataFrame(np.c_[pos.flat, step, walk],
                  columns=["position", "step", "walk"])



In [ ]:

	Group	Mouse	MS01	THA	Blank	MS10	AP	MS05	IAA05	PA	IAA01	IAA10	Hexanal10	Hexanone	Hexanal01	Hexanal05	EB
0	Control	160321_1	0.015671	0.067358	NaN	0.150474	0.036738	0.169456	0.034294	0.037389	0.188597	0.099708	0.069948	0.093353	0.062999	0.124211	-0.061840
1	Control	160321_1	-0.034636	0.095266	NaN	0.074001	0.073576	0.139423	0.001814	-0.002271	0.188666	0.041676	0.036447	-0.026087	0.014205	0.101018	0.056893
2	Control	160321_1	-0.044179	0.146676	NaN	0.260675	0.058214	0.095906	0.127803	0.221412	0.477065	0.287615	0.263380	0.022945	0.129928	0.293199	0.153817
3	Control	160321_1	0.146939	0.094015	NaN	0.071141	0.110235	0.157300	0.104363	0.051386	0.248613	0.034975	0.037260	-0.057530	0.104142	0.198974	0.082859
4	Control	160321_1	0.268299	0.069755	NaN	0.220858	0.116399	0.233311	0.240154	0.203381	0.393188	0.309290	0.145721	-0.147715	0.315627	0.538062	0.694684