Prep stuff



In [1]:

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









    



Using matplotlib backend: Qt4Agg



In [63]:

    
#composite.head()



In [64]:

    
#sns.boxplot(x="Odor", y="value", hue="Group", data=comp_tmp);



In [65]:

    
#sns.violinplot(x="Odor", y="value", hue="Group", data=comp_tmp);



In [66]:

    
#sns.barplot(x="Odor", y="value", hue="Group", data=comp_tmp);

Import/organize data



In [3]:

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

Rank the odors



In [3]:

    
composite_full.head()









    Out[3]:






  
    
      
      Group
      THA
      MS 0.01
      BLANK
      AP
      MS 0.1
      MS 0.05
      IAA 0.05
      IAA 0.01
      PA
      IAA 0.1
      Hexanone
      Hexanal 0.1
      Hexanal 0.01
      Hexanal 0.05
      EB
    
  
  
    
      0
      Control
      0.212200
      0.127301
      NaN
      0.139029
      0.096683
      0.099481
      0.098134
      0.265576
      0.165395
      0.216148
      0.345297
      0.131523
      0.157010
      0.131461
      0.193486
    
    
      1
      Control
      0.066122
      0.074838
      NaN
      0.127978
      0.052906
      0.119782
      0.108896
      0.168569
      0.101373
      0.171575
      0.268874
      0.146749
      0.140497
      0.136131
      0.201126
    
    
      2
      Control
      0.022259
      0.094999
      NaN
      0.065581
      0.027137
      0.029730
      0.028554
      0.146171
      0.055140
      0.159719
      0.198244
      0.101923
      0.127977
      0.116245
      0.156971
    
    
      3
      Control
      0.145203
      0.107146
      NaN
      0.057500
      0.152532
      0.158519
      0.179303
      0.302537
      0.125637
      0.259681
      0.350995
      0.279234
      0.190135
      0.233960
      0.274613
    
    
      4
      Control
      0.155862
      0.147167
      NaN
      0.153746
      0.125200
      0.159181
      0.230083
      0.375661
      0.192976
      0.325386
      0.438699
      0.357176
      0.299774
      0.339993
      0.285755



In [3]:

    
x=composite_full.rank(axis=1,numeric_only=float,na_option='keep',ascending=False)
comp_labels=pd.DataFrame(comp.Group)
tmp=[comp_labels,x]
comp_ranks=pd.concat(tmp,axis=1)
comp_ranks.head()









    Out[3]:






  
    
      
      Group
      THA
      MS 0.01
      BLANK
      AP
      MS 0.1
      MS 0.05
      IAA 0.05
      IAA 0.01
      PA
      IAA 0.1
      Hexanone
      Hexanal 0.1
      Hexanal 0.01
      Hexanal 0.05
      EB
    
  
  
    
      0
      Control
      4.0
      11.0
      NaN
      8.0
      14.0
      12.0
      13.0
      2.0
      6.0
      3.0
      1.0
      9.0
      7.0
      10.0
      5.0
    
    
      1
      Control
      13.0
      12.0
      NaN
      8.0
      14.0
      9.0
      10.0
      4.0
      11.0
      3.0
      1.0
      5.0
      6.0
      7.0
      2.0
    
    
      2
      Control
      14.0
      8.0
      NaN
      9.0
      13.0
      11.0
      12.0
      4.0
      10.0
      2.0
      1.0
      7.0
      5.0
      6.0
      3.0
    
    
      3
      Control
      11.0
      13.0
      NaN
      14.0
      10.0
      9.0
      8.0
      2.0
      12.0
      5.0
      1.0
      3.0
      7.0
      6.0
      4.0
    
    
      4
      Control
      11.0
      13.0
      NaN
      12.0
      14.0
      10.0
      8.0
      2.0
      9.0
      5.0
      1.0
      3.0
      6.0
      4.0
      7.0



In [4]:

    
ctrl_cr=comp_ranks[comp_ranks['Group']=='Control']
mint_cr=comp_ranks[comp_ranks['Group']=='Mint']
hex_cr=comp_ranks[comp_ranks['Group']=='Hexanal']



In [10]:

    
pd.DataFrame({'Group':['Control','Mint','Hexanal']})









    Out[10]:






  
    
      
      Group
    
  
  
    
      0
      Control
    
    
      1
      Mint
    
    
      2
      Hexanal



In [20]:

    
ctrlmr=pd.DataFrame(comp_ranks[comp_ranks['Group']=='Control'].mean(axis=0)).T
mintmr=pd.DataFrame(comp_ranks[comp_ranks['Group']=='Mint'].mean(axis=0)).T
hexmr=pd.DataFrame(comp_ranks[comp_ranks['Group']=='Hexanal'].mean(axis=0)).T
mintmr









    Out[20]:






  
    
      
      THA
      MS 0.01
      BLANK
      AP
      MS 0.1
      MS 0.05
      IAA 0.05
      IAA 0.01
      PA
      IAA 0.1
      Hexanone
      Hexanal 0.1
      Hexanal 0.01
      Hexanal 0.05
      EB
    
  
  
    
      0
      9.98694
      11.625
      8.68254
      10.794776
      9.296642
      9.910448
      8.175373
      8.891791
      8.975746
      5.929104
      5.076493
      3.953358
      5.01306
      4.009328
      4.847015



In [41]:

    
ranks=pd.concat([ctrlmr,mintmr,hexmr])
grouplabels=pd.DataFrame({'Group':['Control','Mint','Hexanal']})
ranks=ranks.reset_index(drop=True)
# tmp=[grouplabels,ranks]
finaldf=pd.concat([grouplabels,ranks],axis=1)
finaldf









    Out[41]:






  
    
      
      Group
      THA
      MS 0.01
      BLANK
      AP
      MS 0.1
      MS 0.05
      IAA 0.05
      IAA 0.01
      PA
      IAA 0.1
      Hexanone
      Hexanal 0.1
      Hexanal 0.01
      Hexanal 0.05
      EB
    
  
  
    
      0
      Control
      10.535714
      10.415414
      11.667647
      10.372180
      9.530075
      9.836466
      9.105263
      6.765038
      10.806391
      5.052632
      3.759398
      5.731203
      5.986842
      4.118421
      5.114662
    
    
      1
      Mint
      9.986940
      11.625000
      8.682540
      10.794776
      9.296642
      9.910448
      8.175373
      8.891791
      8.975746
      5.929104
      5.076493
      3.953358
      5.013060
      4.009328
      4.847015
    
    
      2
      Hexanal
      11.222222
      12.147696
      9.899425
      9.535230
      11.214092
      10.284553
      10.021680
      7.728997
      8.398374
      4.802168
      5.326558
      6.643631
      4.586721
      3.162602
      4.735772



In [44]:

    
finalmelt=pd.melt(finaldf,"Group",var_name="Odor")



In [42]:

    
kruskal(ctrl_cr.EB,mint_cr.EB,hex_cr.EB)









    Out[42]:





KruskalResult(statistic=11.08517822319291, pvalue=0.0039163737715180091)



In [46]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(55, 20));
sns.pointplot(x="Odor", y="value", hue="Group", data=finalmelt,
              palette={"Control": "r", "Mint": "g", 'Hexanal': 'b'});
sns.despine()
plt.ylabel('Rank', fontsize=48);
plt.title('Rank of odor response', fontsize=55);
plt.xlabel('Odor', fontsize=48);
plt.legend(loc=2, prop={'size': 48});

Mean, Median, COV

Calculate Mean



In [109]:

    
#Calculate means for each odor
Cctrl=composite_full[composite_full['Group']=='Control']
Cmean=Cctrl.mean()
M=composite_full[composite_full['Group']=='Mint']
Mmean=M.mean()
H=composite_full[composite_full['Group']=='Hexanal']
Hmean=H.mean()

CtrlMDFT=pd.DataFrame(Cmean).transpose()
MMDFT=pd.DataFrame(Mmean).transpose()
HMDFT=pd.DataFrame(Hmean).transpose()

#add group labels back
gnc = pd.DataFrame({'Group':['Control']})
gnm=pd.DataFrame({'Group':['Mint']})
gnh=pd.DataFrame({'Group':['Hexanal']})

Ctmp=[gnc,CtrlMDFT]
Mtmp=[gnm,MMDFT]
Htmp=[gnh,HMDFT]

CtrlMDF=pd.concat(Ctmp,axis=1)
MMDF=pd.concat(Mtmp,axis=1)
HMDF=pd.concat(Htmp,axis=1)

final=[CtrlMDF,MMDF,HMDF]
finalmean=pd.concat(final)
finalmeandf=pd.melt(finalmean,"Group",var_name="Odor")



In [157]:

    
#plot means
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot(x="Odor", y="value", hue="Group", data=finalmeandf,palette={"Control": "r", "Mint": "g",'Hexanal':'b'});
sns.despine()
plt.ylabel('DF/F', fontsize=48);
plt.title('Mean Peak DF/F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});

Calculate Medians



In [8]:

    
#Calculate medians for each odor
Cctrl=composite_full[composite_full['Group']=='Control']
Cmedian=Cctrl.median()
M=composite_full[composite_full['Group']=='Mint']
Mmedian=M.median()
H=composite_full[composite_full['Group']=='Hexanal']
Hmedian=H.median()

CtrlMedT=pd.DataFrame(Cmedian).transpose()
MMedT=pd.DataFrame(Mmedian).transpose()
HMedT=pd.DataFrame(Hmedian).transpose()

#add group labels back
gnc = pd.DataFrame({'Group':['Control']})
gnm=pd.DataFrame({'Group':['Mint']})
gnh=pd.DataFrame({'Group':['Hexanal']})

Ctmp=[gnc,CtrlMedT]
Mtmp=[gnm,MMedT]
Htmp=[gnh,HMedT]

CtrlMed=pd.concat(Ctmp,axis=1)
MMed=pd.concat(Mtmp,axis=1)
HMed=pd.concat(Htmp,axis=1)

finalmed=[CtrlMed,MMed,HMed]
finalmedian=pd.concat(finalmed)
finalmediandf=pd.melt(finalmedian,"Group",var_name="Odor")



In [9]:

    
#Plot medians
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot(x="Odor", y="value", hue="Group", data=finalmediandf,palette={"Control": "r", "Mint": "g",'Hexanal':'b'});
sns.despine()
plt.ylabel('DF/F', fontsize=48);
plt.title('Median Peak DF/F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});

Calculate and Plot Variance



In [10]:

    
# Calculate variance
CC=composite_full[composite_full['Group']=='Control']
MM=composite_full[composite_full['Group']=='Mint']
HH=composite_full[composite_full['Group']=='Hexanal']
del CC['Group']
del MM['Group']
del HH['Group']

CvarT=pd.DataFrame(variation(CC)).transpose()
MvarT=pd.DataFrame(variation(MM)).transpose()
HvarT=pd.DataFrame(variation(HH)).transpose()

#add group labels back
gnc = pd.DataFrame({'Group':['Control']})
gnm=pd.DataFrame({'Group':['Mint']})
gnh=pd.DataFrame({'Group':['Hexanal']})

Ctmp=[gnc,CvarT]
Mtmp=[gnm,MvarT]
Htmp=[gnh,HvarT]

CV=pd.concat(Ctmp,axis=1)
MV=pd.concat(Mtmp,axis=1)
HV=pd.concat(Htmp,axis=1)

gn=['Group']
gname = np.array(gn)
odornames=CC.columns.values
columnnames=np.append(gname,odornames)
CV.columns=columnnames
MV.columns=columnnames
HV.columns=columnnames

#Fix BLANK NaNs
CCNB=pd.DataFrame(CC['BLANK'])
CCNB=CCNB.dropna()
CCNBvar=variation(CCNB)
CCNBvars=np.asscalar(CCNBvar)
CV=CV.replace(CV.BLANK[0],CCNBvars)

MMNB=pd.DataFrame(MM['BLANK'])
MMNB=MMNB.dropna()
MMNBvar=variation(MMNB)
MMNBvars=np.asscalar(MMNBvar)
MV=MV.replace(MV.BLANK[0],MMNBvars)

HHNB=pd.DataFrame(HH['BLANK'])
HHNB=HHNB.dropna()
HHNBvar=variation(HHNB)
HHNBvars=np.asscalar(HHNBvar)
HV=HV.replace(HV.BLANK[0],HHNBvars)

complete=[CV,HV,MV]
finalvariance=pd.concat(complete)
finalvariancedf=pd.melt(finalvariance,"Group",var_name="Odor")



In [11]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot(x="Odor", y="value", hue="Group", data=finalvariancedf,palette={"Control": "r", "Mint": "g",'Hexanal':'b'});
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV Peak DF/F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});

Calculate and plot variance for each odor concentration



In [12]:

    
MSconcvar=finalvariance[['Group','MS 0.01','MS 0.05','MS 0.1']]
Hexconcvar=finalvariance[['Group','Hexanal 0.01','Hexanal 0.05','Hexanal 0.1']]
IAAconcvar=finalvariance[['Group','IAA 0.01','IAA 0.05','IAA 0.1']]

MSconcvardf=pd.melt(MSconcvar,"Group",var_name="Odor")
Hexconcvardf=pd.melt(Hexconcvar,'Group', var_name='Odor')
IAAconcvardf=pd.melt(IAAconcvar,'Group',var_name='Odor')



In [13]:

    
#Plot COV MS Concentration
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot(x="Odor", y="value", hue="Group", data=MSconcvardf,palette={"Control": "r", "Mint": "g",'Hexanal':'b'});
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV Peak DF/F - Methyl Salicylate',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});



In [211]:

    
# Plot COV Hexanal Concentration
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot(x="Odor", y="value", hue="Group", data=Hexconcvardf,palette={"Control": "r", "Mint": "g",'Hexanal':'b'});
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV Peak DF/F - Hexanal',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});



In [212]:

    
# Plot COV IAA Concentration
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot(x="Odor", y="value", hue="Group", data=IAAconcvardf,palette={"Control": "r", "Mint": "g",'Hexanal':'b'});
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV Peak DF/F - Isoamyl acetate',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});

Correlate Baseline with DF/F



In [3]:

    
#Get Baselines
bl=pd.read_csv('C:\Users\Annie\Documents\Data\Ca_Imaging\Analysis\Odor_Panel\Composite_Baseline_NoP.csv')
del bl['Mouse']
bl_sorted=bl.reindex_axis(bl.mean().sort_values().index, axis=1)
bl_labels=pd.DataFrame(bl.Group)
tmp=[bl_labels,bl_sorted]
bdf=pd.concat(tmp,axis=1)
bdfull=pd.melt(bdf,"Group",var_name="Odor")
bdfull=bdfull.dropna()



In [5]:

    
composite_cn=composite_full.columns.tolist()
bdf=bdf[composite_cn]
bdf.head()









    Out[5]:






  
    
      
      Group
      THA
      MS 0.01
      BLANK
      AP
      MS 0.1
      MS 0.05
      IAA 0.05
      IAA 0.01
      PA
      IAA 0.1
      Hexanone
      Hexanal 0.1
      Hexanal 0.01
      Hexanal 0.05
      EB
    
  
  
    
      0
      Control
      2191.580000
      2156.251500
      NaN
      2604.431208
      2342.740333
      2438.371125
      2430.727208
      1916.923958
      2174.460083
      2295.764583
      2202.500042
      2446.136083
      2515.360375
      2457.603625
      2406.386167
    
    
      1
      Control
      1733.676000
      1897.166667
      NaN
      1805.344875
      1946.681708
      1828.752875
      1864.458375
      1685.373583
      1748.060375
      1850.529417
      1865.847750
      1903.826250
      1841.318875
      1795.494250
      1929.843458
    
    
      2
      Control
      1567.613708
      1688.818208
      NaN
      1678.700042
      1694.186292
      1619.368000
      1643.305542
      1556.993000
      1536.563083
      1662.997500
      1504.910333
      1681.573792
      1648.710833
      1688.000667
      1663.836417
    
    
      3
      Control
      829.527875
      888.688458
      NaN
      865.015708
      856.108625
      861.587750
      857.491250
      835.064458
      822.373417
      847.159000
      853.859458
      857.543458
      871.575583
      853.299500
      843.551167
    
    
      4
      Control
      886.599167
      1019.396583
      NaN
      924.648000
      929.562417
      916.596208
      916.315333
      955.118500
      895.472083
      914.607042
      948.426042
      899.221875
      918.836292
      914.384250
      942.410958



In [56]:

    
#Turn dataframe into a single column
bdfmelt=pd.melt(bdf,"Group",var_name='Odor')
cfmelt=pd.melt(composite_full,'Group',var_name='Odor')



In [86]:

    
#Make a dataframe so you can plot the correlation
plotcorr=pd.concat([cfmelt,bdfmelt['value']],axis=1)
plotcorr.tail()









    Out[86]:






  
    
      
      Group
      Odor
      value
      value
    
  
  
    
      13540
      Mint
      EB
      0.479948
      810.413917
    
    
      13541
      Mint
      EB
      0.367013
      719.335792
    
    
      13542
      Mint
      EB
      0.238822
      880.315292
    
    
      13543
      Mint
      EB
      0.259046
      1241.160417
    
    
      13544
      Mint
      EB
      0.313997
      1080.826792



In [99]:

    
#Rename these columns to what they're supposed to be
plotcorr.columns=['Group','Odor','DFF','Baseline']
plotcorr.head()









    Out[99]:






  
    
      
      Group
      Odor
      DFF
      Baseline
    
  
  
    
      0
      Control
      THA
      0.212200
      2191.580000
    
    
      1
      Control
      THA
      0.066122
      1733.676000
    
    
      2
      Control
      THA
      0.022259
      1567.613708
    
    
      3
      Control
      THA
      0.145203
      829.527875
    
    
      4
      Control
      THA
      0.155862
      886.599167



In [108]:

    
#Plot Baseline vs. DFF as a scatterplot
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.4);
plotcorr.plot(x='DFF',y='Baseline',kind='scatter')
plt.ylabel('Baseline Intensity');
plt.title('Peak DF/F vs. Baseline');
plt.xlabel('Peak DF/F');



In [101]:

    
#Calculate the spearman coefficient between DFF and Baseline
#rho is the coefficient, pval is the pvalue
from scipy.stats import spearmanr
rho,pval=spearmanr(plotcorr['DFF'],plotcorr['Baseline'])
rho









    Out[101]:





-0.30211262511970655



In [42]:

    
#Corr based on treatment groups
CC=composite_full[composite_full['Group']=='Control']
MM=composite_full[composite_full['Group']=='Mint']
HH=composite_full[composite_full['Group']=='Hexanal']

CB=bdf[bdf['Group']=='Control']
MB=bdf[bdf['Group']=='Mint']
HB=bdf[bdf['Group']=='Hexanal']

CCcorr=CC.corr()
MMcorr=MM.corr()
HHcorr=HH.corr()
CBcorr=CB.corr()
MBcorr=MB.corr()
HBcorr=HB.corr()

Ctrlcorr=CC.corrwith(CB)
Mintcorr=MM.corrwith(MB)
Hexcorr=HH.corrwith(HB)

CtrlCdf=pd.DataFrame(Ctrlcorr)
CtrlCdf=CtrlCdf.transpose()
MintCdf=pd.DataFrame(Mintcorr)
MintCdf=MintCdf.transpose()
HexCdf=pd.DataFrame(Hexcorr)
HexCdf=HexCdf.transpose()
tmp=[CtrlCdf,MintCdf,HexCdf]

CompleteCorrDF=pd.concat(tmp)



In [ ]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=1.8);
plt.figure(figsize=(55,20));
sns.pointplot();
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV Peak DF/F - Methyl Salicylate',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});

Composite Graphs



In [317]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=2.2);
plt.figure(figsize=(45,20));
ax=sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=cfull);
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('Peak DF/F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});



In [14]:

    
# sns.set_context("talk",font_scale=1.9);
# sns.set(style="white", palette="muted", color_codes=True);
# plt.figure(figsize=(8, 6));
# violinplot=sns.violinplot(x="Odor", y="value", hue="Group", data=cfull);
# violinplot.set(ylabel='Peak DF/F');



In [318]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=2.2);
plt.figure(figsize=(45,20));
ax=sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=cfull);
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('Peak DF/F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':48});

Concentration based graphs

MS



In [5]:

    
MS_full=composite_full[['Group','MS 0.01','MS 0.05','MS 0.1']]
MSdf=pd.melt(MS_full,"Group",var_name="Odor")
MSdf.head()



In [325]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=4.3);
plt.figure(figsize=(30, 20));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MSdf);
sns.despine()
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [326]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=4.3);
plt.figure(figsize=(30, 20));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MSdf);
sns.despine()
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');

Histograms (MS)



In [4]:

    
MSctrl=MS_full[MS_full['Group'] == 'Control']
MSMS=MS_full[MS_full['Group'] == 'Mint']
MShex=MS_full[MS_full['Group'] == 'Hexanal']
MSctrl.tail()









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-eb304979e053> in <module>()
----> 1 MSctrl=MS_full[MS_full['Group'] == 'Control']
      2 MSMS=MS_full[MS_full['Group'] == 'Mint']
      3 MShex=MS_full[MS_full['Group'] == 'Hexanal']
      4 MSctrl.tail()

NameError: name 'MS_full' is not defined



In [366]:

    
# Control, MS Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30, 20), sharex=True)
# f.suptitle("Control, MS Concentration", fontsize=40)
sns.despine(left=True)

#data
d = MSctrl['MS 0.01']
e = MSctrl['MS 0.05']
f = MSctrl['MS 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="MS 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="MS 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="MS 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [368]:

    
# Hexanal, MS Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Hexanal, MS Concentration", fontsize=44)
sns.despine(left=True)

#data
d = MShex['MS 0.01']
e = MShex['MS 0.05']
f = MShex['MS 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="MS 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="MS 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="MS 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [369]:

    
# Mint, MS Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Mint, MS Concentration", fontsize=44)
sns.despine(left=True)

#data
d = MSMS['MS 0.01']
e = MSMS['MS 0.05']
f = MSMS['MS 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="MS 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="MS 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="MS 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()

Hexanal



In [23]:

    
H_full=composite_full[['Group','Hexanal 0.01','Hexanal 0.05','Hexanal 0.1']]
Hdf=pd.melt(H_full,"Group",var_name="Odor")



In [370]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=4.3);
plt.figure(figsize=(30,20));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=Hdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [328]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=4.3);
plt.figure(figsize=(30,20));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=Hdf);
sns.despine()
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');

Histograms (Hexanal)



In [178]:

    
Hctrl=H_full[H_full['Group'] == 'Control']
HMS=H_full[H_full['Group'] == 'Mint']
HH=H_full[H_full['Group'] == 'Hexanal']
HH.head()









    Out[178]:






  
    
      
      Group
      Hexanal 0.01
      Hexanal 0.05
      Hexanal 0.1
    
  
  
    
      266
      Hexanal
      0.346753
      0.300836
      0.290083
    
    
      267
      Hexanal
      0.269426
      0.349465
      0.236634
    
    
      268
      Hexanal
      0.359969
      0.422324
      0.416844
    
    
      269
      Hexanal
      0.136656
      0.135468
      0.124364
    
    
      270
      Hexanal
      0.188876
      0.200218
      0.136488



In [373]:

    
# Control, Hexanal Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Control, Hexanal Concentration", fontsize=44)
sns.despine(left=True)

#data
d = Hctrl['Hexanal 0.01']
e = Hctrl['Hexanal 0.05']
f = Hctrl['Hexanal 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Hexanal 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Hexanal 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [372]:

    
# Hexanal, Hexanal Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Hexanal, Hexanal Concentration", fontsize=44)
sns.despine(left=True)

#data
d = HH['Hexanal 0.01']
e = HH['Hexanal 0.05']
f = HH['Hexanal 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Hexanal 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Hexanal 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [374]:

    
# Mint, Hexanal Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Mint, Hexanal Concentration", fontsize=44)
sns.despine(left=True)

#data
d = HMS['Hexanal 0.01']
e = HMS['Hexanal 0.05']
f = HMS['Hexanal 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Hexanal 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Hexanal 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()

IAA



In [35]:

    
IAA_full=composite_full[['Group','IAA 0.01','IAA 0.05','IAA 0.1']]
IAAdf=pd.melt(MS_full,"Group",var_name="Odor")



In [329]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=4.3);
plt.figure(figsize=(30,20));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAAdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [331]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=4.3);
plt.figure(figsize=(30,20));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAAdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');

Histograms (IAA)



In [182]:

    
Ictrl=IAA_full[IAA_full['Group'] == 'Control']
IMS=IAA_full[IAA_full['Group'] == 'Mint']
IH=IAA_full[IAA_full['Group'] == 'Hexanal']
IH.head()



In [375]:

    
# Control, IAA Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Control, IAA Concentration", fontsize=44)
sns.despine(left=True)

#data
d = Ictrl['IAA 0.01']
e = Ictrl['IAA 0.05']
f = Ictrl['IAA 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="IAA 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="IAA 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="IAA 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [376]:

    
# Hexanal, IAA Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Hexanal, IAA Concentration", fontsize=44)
sns.despine(left=True)

#data
d = IH['IAA 0.01']
e = IH['IAA 0.05']
f = IH['IAA 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="IAA 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="IAA 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="IAA 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [377]:

    
# Mint, IAA Concentration
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Mint, IAA Concentration", fontsize=44)
sns.despine(left=True)

#data
d = IMS['IAA 0.01']
e = IMS['IAA 0.05']
f = IMS['IAA 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="IAA 0.01")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="IAA 0.05")
sns.distplot(f, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="IAA 0.1")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="b", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="r", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="b",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="r",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()

Stats

Methyl Salicylate



In [8]:

    
odor = 'MS'



In [9]:

    
xdf=composite_full[['Group', '%s 0.01' % odor, '%s 0.05' % odor, '%s 0.1' % odor]]



In [11]:

    
xctrl = xdf[xdf['Group'] == 'Control']
xMS = xdf[xdf['Group'] == 'Mint']
xHex = xdf[xdf['Group'] == 'Hexanal']



In [17]:

    
kruskal(xctrl['%s 0.01'%odor],xctrl['%s 0.05'%odor],xctrl['%s 0.1'%odor],nan_policy='omit')









    Out[17]:





KruskalResult(statistic=1.9725528869809088, pvalue=0.37296285696071418)



In [6]:

    
MSctrl=MS_full[MS_full['Group'] == 'Control']
MSMS=MS_full[MS_full['Group'] == 'Mint']
MShex=MS_full[MS_full['Group'] == 'Hexanal']
MSctrl.tail()



In [39]:

    
kruskal(MSctrl['MS 0.01'],MSctrl['MS 0.05'],MSctrl['MS 0.1'],nan_policy='omit')









    Out[39]:





KruskalResult(statistic=1.9725528869809088, pvalue=0.37296285696071418)



In [40]:

    
kruskal(MSMS['MS 0.01'],MSMS['MS 0.05'],MSMS['MS 0.1'],nan_policy='omit')









    Out[40]:





KruskalResult(statistic=13.539494458225329, pvalue=0.0011479847913647104)



In [41]:

    
kruskal(MShex['MS 0.01'],MShex['MS 0.05'],MShex['MS 0.1'],nan_policy='omit')









    Out[41]:





KruskalResult(statistic=5.7933408549275871, pvalue=0.055206729192718254)

Hexanal



In [43]:

    
Hctrl=H_full[H_full['Group'] == 'Control']
HMS=H_full[H_full['Group'] == 'Mint']
HH=H_full[H_full['Group'] == 'Hexanal']
HH.head()









    Out[43]:






  
    
      
      Group
      Hexanal 0.01
      Hexanal 0.05
      Hexanal 0.1
    
  
  
    
      266
      Hexanal
      0.346753
      0.300836
      0.290083
    
    
      267
      Hexanal
      0.269426
      0.349465
      0.236634
    
    
      268
      Hexanal
      0.359969
      0.422324
      0.416844
    
    
      269
      Hexanal
      0.136656
      0.135468
      0.124364
    
    
      270
      Hexanal
      0.188876
      0.200218
      0.136488



In [44]:

    
kruskal(Hctrl['Hexanal 0.01'],Hctrl['Hexanal 0.05'],Hctrl['Hexanal 0.1'],nan_policy='omit')









    Out[44]:





KruskalResult(statistic=4.4640795238193043, pvalue=0.1073093212807168)



In [45]:

    
kruskal(HMS['Hexanal 0.01'],HMS['Hexanal 0.05'],HMS['Hexanal 0.1'],nan_policy='omit')









    Out[45]:





KruskalResult(statistic=4.1404582218539545, pvalue=0.12615687447522853)



In [46]:

    
kruskal(HH['Hexanal 0.01'],HH['Hexanal 0.05'],HH['Hexanal 0.1'],nan_policy='omit')









    Out[46]:





KruskalResult(statistic=32.384359703646624, pvalue=9.2859350217332081e-08)

IAA



In [47]:

    
Ictrl=IAA_full[IAA_full['Group'] == 'Control']
IMS=IAA_full[IAA_full['Group'] == 'Mint']
IH=IAA_full[IAA_full['Group'] == 'Hexanal']
IH.head()



In [48]:

    
kruskal(Ictrl['IAA 0.01'],Ictrl['IAA 0.05'],Ictrl['IAA 0.1'],nan_policy='omit')









    Out[48]:





KruskalResult(statistic=19.499111604184485, pvalue=5.8320563850495121e-05)



In [49]:

    
kruskal(IMS['IAA 0.01'],IMS['IAA 0.05'],IMS['IAA 0.1'],nan_policy='omit')









    Out[49]:





KruskalResult(statistic=31.735812204406784, pvalue=1.2842688251749286e-07)



In [50]:

    
kruskal(IH['IAA 0.01'],IH['IAA 0.05'],IH['IAA 0.1'],nan_policy='omit')









    Out[50]:





KruskalResult(statistic=38.874224328353328, pvalue=3.6188405076274751e-09)

By odor

MS 0.01



In [18]:

    
MS001_full=composite_full[['Group','MS 0.01']]
MS001df=pd.melt(MS001_full,"Group",var_name="Odor")



In [403]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MS001df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [404]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MS001df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [19]:

    
MS001ctrl=MS001_full[MS001_full['Group'] == 'Control']
MS001MS=MS001_full[MS001_full['Group'] == 'Mint']
MS001H=MS001_full[MS001_full['Group'] == 'Hexanal']
MS001H.head()



In [406]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("MS 0.01", fontsize=44)
sns.despine(left=True)

#data
d = MS001ctrl['MS 0.01']
e = MS001MS['MS 0.01']
f = MS001H['MS 0.01']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [407]:

    
kruskal(MS001_full[MS001_full['Group'] == 'Control']['MS 0.01'],MS001_full[MS001_full['Group'] == 'Mint']['MS 0.01'],MS001_full[MS001_full['Group'] == 'Hexanal']['MS 0.01'],nan_policy='omit')









    Out[407]:





KruskalResult(statistic=17.779511707365906, pvalue=0.00013779329560080323)

MS 0.05

ZScore



In [4]:

    
MS005_full=composite_full[['Group','MS 0.05']]
MS005df=pd.melt(MS005_full,"Group",var_name="Odor")



In [5]:

    
cg=MS005_full[MS005_full['Group']=='Control']
mg=MS005_full[MS005_full['Group']=='Mint']
hg=MS005_full[MS005_full['Group']=='Hexanal']
hg.head()



In [6]:

    
from scipy.stats import zscore



In [66]:

    
test=zscore(MS005_full[MS005_full['Group']=='Control']['MS 0.05'])
test









    Out[66]:





array([ -4.72358425e-01,  -4.46490393e-01,  -5.61236468e-01,
        -3.97130501e-01,  -3.96288055e-01,  -5.14181064e-01,
        -3.24306103e-01,  -4.28847851e-01,  -5.59660105e-01,
        -3.40810491e-01,  -4.68637861e-01,  -3.98931745e-01,
        -3.98706274e-01,  -1.09868618e-01,  -1.62971554e-01,
        -3.22466210e-01,  -5.22677717e-01,  -6.53193976e-02,
        -5.30853493e-01,  -4.32004754e-01,  -4.86898362e-01,
        -3.91940616e-01,  -5.57231325e-01,  -5.72096693e-01,
        -2.38257167e-01,   7.04491918e-03,  -5.99119714e-01,
        -5.62094788e-01,  -5.03675898e-01,  -4.56007654e-01,
        -5.58363736e-01,  -4.63235366e-01,  -3.30763631e-01,
        -4.54647986e-01,  -4.34519649e-01,  -5.99119714e-01,
        -2.58499060e-01,  -5.37653922e-01,  -4.70551488e-01,
        -3.84941523e-01,  -5.59134386e-01,  -4.05563636e-01,
        -3.78009481e-01,  -4.40463491e-01,  -4.68281318e-01,
        -5.02582999e-01,  -5.99119714e-01,  -4.48175697e-01,
        -2.02115460e-01,  -4.28285404e-01,  -5.78672902e-01,
        -5.16457861e-01,  -4.75322291e-01,  -5.99119714e-01,
        -5.99119714e-01,  -5.70480014e-01,  -5.65609585e-01,
        -4.88995441e-01,  -3.01829472e-01,  -4.79499610e-01,
        -4.91558062e-01,  -3.57780324e-01,  -4.13317942e-01,
        -4.12992152e-01,  -4.72151054e-01,  -3.79527461e-01,
        -2.74540606e-01,  -4.46793133e-01,  -3.90992370e-01,
        -5.63410186e-01,  -4.63189522e-01,  -3.82663018e-01,
        -2.89453431e-01,  -5.61057928e-01,  -3.46895858e-01,
        -3.45594444e-01,  -5.59935053e-01,  -5.83482136e-01,
        -4.48650874e-01,  -5.61677393e-01,  -5.06327036e-01,
        -5.99119714e-01,  -5.33332228e-01,  -5.14577002e-01,
        -5.28979390e-01,  -4.76924031e-01,  -4.23313120e-01,
        -4.37735631e-01,  -5.08832862e-01,  -4.77355821e-01,
        -2.88660097e-01,  -5.99119714e-01,  -5.99119714e-01,
        -5.05606875e-01,  -2.50939561e-01,  -5.45646175e-01,
        -1.94568988e-01,  -5.49597931e-01,   3.28043362e-02,
        -5.90089260e-01,  -5.55595896e-01,  -4.81386707e-01,
        -5.13313297e-01,  -5.76667828e-01,  -4.23938032e-01,
        -5.02170021e-01,  -4.30826621e-01,  -4.24060016e-01,
        -5.37406157e-01,  -3.10534913e-01,  -2.48110992e-01,
        -5.75270738e-01,  -5.71153491e-01,  -4.09860231e-01,
        -4.85340744e-01,  -3.05531584e-01,  -5.05232642e-01,
        -5.56511540e-01,  -4.80915459e-01,  -3.60979633e-01,
        -2.71180652e-02,  -1.45280470e-01,  -3.80070907e-01,
        -2.36839923e-02,  -4.11951499e-01,  -2.51880008e-01,
        -2.08848881e-01,  -1.50292129e-01,  -5.99119714e-01,
        -1.58947502e-01,  -1.44338209e-01,  -5.17780902e-01,
         3.05543310e-01,   6.31008667e-02,  -5.70730654e-01,
        -3.91982078e-01,  -4.21149640e-01,  -5.36956075e-01,
        -5.06446127e-01,  -3.48994869e-01,  -3.46162826e-01,
        -4.81919516e-01,  -4.86074831e-01,  -5.60115927e-01,
        -5.99119714e-01,  -5.24290984e-01,  -5.68399395e-01,
        -5.64902073e-01,  -5.71553674e-01,  -5.57633603e-01,
        -5.27166710e-01,  -3.47712641e-02,  -5.65675277e-01,
        -5.99119714e-01,  -4.86238857e-01,  -5.76187241e-01,
        -2.39720509e-02,  -5.73427590e-01,  -4.81140399e-01,
        -5.45153610e-01,  -4.89834205e-01,  -1.14580234e-01,
        -1.42928701e-01,  -4.59175409e-01,  -4.69617447e-01,
        -4.69165318e-01,  -3.20500123e-01,  -4.37534690e-01,
        -4.35682143e-01,  -4.04455514e-01,  -2.29462937e-01,
        -2.84570420e-01,   7.83271820e-02,  -1.42086246e-02,
        -2.08060723e-01,  -1.82401580e-01,  -2.70860749e-01,
        -1.80887067e-01,  -2.58200265e-01,  -2.20080497e-01,
        -2.52718015e-01,   3.03384437e-02,  -2.61049282e-01,
         7.30330547e-01,   4.96091914e-01,   4.27011923e+00,
         3.72681636e+00,   2.13692009e+00,   2.16151125e+00,
         4.22736506e-01,   2.81953535e-01,  -3.14508047e-07,
         1.07972254e+00,   1.34787788e+00,   3.23360449e+00,
         3.41930485e-01,   2.25113653e+00,   9.04079417e-01,
         4.22500791e+00,   8.76699415e-01,   2.65481566e+00,
         3.58848725e+00,   1.41051083e+00,   2.74954378e+00,
         1.03776647e+00,   1.85671121e+00,   2.04418971e+00,
         2.09902836e+00,   8.33719273e-01,   1.16005473e+00,
         1.70582947e+00,   2.18230143e+00,   1.21978732e+00,
         1.32758303e+00,   1.46829925e+00,  -8.19127864e-02,
         4.02285587e+00,   1.18955407e+00,   4.20060282e-01,
         1.76572491e+00,   2.77535449e+00,   2.34862081e+00,
         1.77833940e+00,   3.25769183e+00,   2.58085289e+00,
         1.20379458e+00,   2.19114601e+00,   2.75998399e+00,
         4.01090091e+00,   1.56087422e+00,  -4.53046121e-01,
        -4.54955137e-01,  -4.19046366e-01,  -4.20186555e-01,
        -1.35090907e-01,  -4.08265600e-01,  -3.60110116e-01,
        -2.19897186e-01,  -4.22761581e-01,  -4.06705894e-01,
        -3.59449358e-01,  -3.60664442e-01,  -3.92821498e-01,
        -1.09936767e-01,  -1.13797726e-01,  -3.41526999e-01,
        -3.59463843e-01,  -2.89727327e-01,  -4.99386844e-01,
        -5.16343179e-01,  -4.88470321e-01,  -4.71003978e-01,
        -1.90003505e-01,  -5.20819707e-01,  -4.57486059e-01,
        -2.64745152e-01,  -4.77285685e-01,  -4.33617203e-01,
        -4.16985891e-01,  -4.30746055e-01,  -4.93522903e-01,
        -1.61284709e-01,  -1.52890102e-01,  -3.26701614e-01,
        -4.18583310e-01,  -3.53747319e-01])



In [67]:

    
cgz=zscore(cg['MS 0.05'])
mgz=zscore(mg['MS 0.05'])
hgz=zscore(hg['MS 0.05'])
cgz









    Out[67]:





array([ -4.72358425e-01,  -4.46490393e-01,  -5.61236468e-01,
        -3.97130501e-01,  -3.96288055e-01,  -5.14181064e-01,
        -3.24306103e-01,  -4.28847851e-01,  -5.59660105e-01,
        -3.40810491e-01,  -4.68637861e-01,  -3.98931745e-01,
        -3.98706274e-01,  -1.09868618e-01,  -1.62971554e-01,
        -3.22466210e-01,  -5.22677717e-01,  -6.53193976e-02,
        -5.30853493e-01,  -4.32004754e-01,  -4.86898362e-01,
        -3.91940616e-01,  -5.57231325e-01,  -5.72096693e-01,
        -2.38257167e-01,   7.04491918e-03,  -5.99119714e-01,
        -5.62094788e-01,  -5.03675898e-01,  -4.56007654e-01,
        -5.58363736e-01,  -4.63235366e-01,  -3.30763631e-01,
        -4.54647986e-01,  -4.34519649e-01,  -5.99119714e-01,
        -2.58499060e-01,  -5.37653922e-01,  -4.70551488e-01,
        -3.84941523e-01,  -5.59134386e-01,  -4.05563636e-01,
        -3.78009481e-01,  -4.40463491e-01,  -4.68281318e-01,
        -5.02582999e-01,  -5.99119714e-01,  -4.48175697e-01,
        -2.02115460e-01,  -4.28285404e-01,  -5.78672902e-01,
        -5.16457861e-01,  -4.75322291e-01,  -5.99119714e-01,
        -5.99119714e-01,  -5.70480014e-01,  -5.65609585e-01,
        -4.88995441e-01,  -3.01829472e-01,  -4.79499610e-01,
        -4.91558062e-01,  -3.57780324e-01,  -4.13317942e-01,
        -4.12992152e-01,  -4.72151054e-01,  -3.79527461e-01,
        -2.74540606e-01,  -4.46793133e-01,  -3.90992370e-01,
        -5.63410186e-01,  -4.63189522e-01,  -3.82663018e-01,
        -2.89453431e-01,  -5.61057928e-01,  -3.46895858e-01,
        -3.45594444e-01,  -5.59935053e-01,  -5.83482136e-01,
        -4.48650874e-01,  -5.61677393e-01,  -5.06327036e-01,
        -5.99119714e-01,  -5.33332228e-01,  -5.14577002e-01,
        -5.28979390e-01,  -4.76924031e-01,  -4.23313120e-01,
        -4.37735631e-01,  -5.08832862e-01,  -4.77355821e-01,
        -2.88660097e-01,  -5.99119714e-01,  -5.99119714e-01,
        -5.05606875e-01,  -2.50939561e-01,  -5.45646175e-01,
        -1.94568988e-01,  -5.49597931e-01,   3.28043362e-02,
        -5.90089260e-01,  -5.55595896e-01,  -4.81386707e-01,
        -5.13313297e-01,  -5.76667828e-01,  -4.23938032e-01,
        -5.02170021e-01,  -4.30826621e-01,  -4.24060016e-01,
        -5.37406157e-01,  -3.10534913e-01,  -2.48110992e-01,
        -5.75270738e-01,  -5.71153491e-01,  -4.09860231e-01,
        -4.85340744e-01,  -3.05531584e-01,  -5.05232642e-01,
        -5.56511540e-01,  -4.80915459e-01,  -3.60979633e-01,
        -2.71180652e-02,  -1.45280470e-01,  -3.80070907e-01,
        -2.36839923e-02,  -4.11951499e-01,  -2.51880008e-01,
        -2.08848881e-01,  -1.50292129e-01,  -5.99119714e-01,
        -1.58947502e-01,  -1.44338209e-01,  -5.17780902e-01,
         3.05543310e-01,   6.31008667e-02,  -5.70730654e-01,
        -3.91982078e-01,  -4.21149640e-01,  -5.36956075e-01,
        -5.06446127e-01,  -3.48994869e-01,  -3.46162826e-01,
        -4.81919516e-01,  -4.86074831e-01,  -5.60115927e-01,
        -5.99119714e-01,  -5.24290984e-01,  -5.68399395e-01,
        -5.64902073e-01,  -5.71553674e-01,  -5.57633603e-01,
        -5.27166710e-01,  -3.47712641e-02,  -5.65675277e-01,
        -5.99119714e-01,  -4.86238857e-01,  -5.76187241e-01,
        -2.39720509e-02,  -5.73427590e-01,  -4.81140399e-01,
        -5.45153610e-01,  -4.89834205e-01,  -1.14580234e-01,
        -1.42928701e-01,  -4.59175409e-01,  -4.69617447e-01,
        -4.69165318e-01,  -3.20500123e-01,  -4.37534690e-01,
        -4.35682143e-01,  -4.04455514e-01,  -2.29462937e-01,
        -2.84570420e-01,   7.83271820e-02,  -1.42086246e-02,
        -2.08060723e-01,  -1.82401580e-01,  -2.70860749e-01,
        -1.80887067e-01,  -2.58200265e-01,  -2.20080497e-01,
        -2.52718015e-01,   3.03384437e-02,  -2.61049282e-01,
         7.30330547e-01,   4.96091914e-01,   4.27011923e+00,
         3.72681636e+00,   2.13692009e+00,   2.16151125e+00,
         4.22736506e-01,   2.81953535e-01,  -3.14508047e-07,
         1.07972254e+00,   1.34787788e+00,   3.23360449e+00,
         3.41930485e-01,   2.25113653e+00,   9.04079417e-01,
         4.22500791e+00,   8.76699415e-01,   2.65481566e+00,
         3.58848725e+00,   1.41051083e+00,   2.74954378e+00,
         1.03776647e+00,   1.85671121e+00,   2.04418971e+00,
         2.09902836e+00,   8.33719273e-01,   1.16005473e+00,
         1.70582947e+00,   2.18230143e+00,   1.21978732e+00,
         1.32758303e+00,   1.46829925e+00,  -8.19127864e-02,
         4.02285587e+00,   1.18955407e+00,   4.20060282e-01,
         1.76572491e+00,   2.77535449e+00,   2.34862081e+00,
         1.77833940e+00,   3.25769183e+00,   2.58085289e+00,
         1.20379458e+00,   2.19114601e+00,   2.75998399e+00,
         4.01090091e+00,   1.56087422e+00,  -4.53046121e-01,
        -4.54955137e-01,  -4.19046366e-01,  -4.20186555e-01,
        -1.35090907e-01,  -4.08265600e-01,  -3.60110116e-01,
        -2.19897186e-01,  -4.22761581e-01,  -4.06705894e-01,
        -3.59449358e-01,  -3.60664442e-01,  -3.92821498e-01,
        -1.09936767e-01,  -1.13797726e-01,  -3.41526999e-01,
        -3.59463843e-01,  -2.89727327e-01,  -4.99386844e-01,
        -5.16343179e-01,  -4.88470321e-01,  -4.71003978e-01,
        -1.90003505e-01,  -5.20819707e-01,  -4.57486059e-01,
        -2.64745152e-01,  -4.77285685e-01,  -4.33617203e-01,
        -4.16985891e-01,  -4.30746055e-01,  -4.93522903e-01,
        -1.61284709e-01,  -1.52890102e-01,  -3.26701614e-01,
        -4.18583310e-01,  -3.53747319e-01])



In [63]:

    
sns.distplot(cgz)
sns.distplot(mgz)
sns.distplot(hgz)









    Out[63]:





<matplotlib.axes._subplots.AxesSubplot at 0xc7886d8>



In [75]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.distplot(cgz, color="r", kde_kws={"shade": True},hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,label="Control")
sns.distplot(mgz, color="g", kde_kws={"shade": True},hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,label="Mint")
sns.distplot(hgz, color="b", kde_kws={"shade": True},hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,label="Hexanal")
sns.despine();
plt.legend(loc='upper right');
plt.xlabel('Z-score');
plt.title('Z-scores for MS');

Normalize by kde peak



In [5]:

    
MS005_full=composite_full[['Group','MS 0.05']]
MS005df=pd.melt(MS005_full,"Group",var_name="Odor")
cg=MS005_full[MS005_full['Group']=='Control']
mg=MS005_full[MS005_full['Group']=='Mint']
hg=MS005_full[MS005_full['Group']=='Hexanal']



In [6]:

    
from scipy.stats import gaussian_kde



In [7]:

    
from scipy.signal import find_peaks_cwt



In [8]:

    
gkde=gaussian_kde(cg['MS 0.05'])



In [10]:

    
sns.distplot(cg['MS 0.05'])









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0xc1aff60>



In [15]:

    
x=np.arange(-1,5,0.01)
y=gkde.evaluate(y)



In [22]:

    
plt.plot(x,y)









    Out[22]:





[<matplotlib.lines.Line2D at 0xe9b3828>]



In [23]:

    
from scipy import signal



In [34]:

    
np.amax(y)









    Out[34]:





1.1579025390002664



In [410]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MS005df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [411]:

    
MS005ctrl=MS005_full[MS005_full['Group'] == 'Control']
MS005MS=MS005_full[MS005_full['Group'] == 'Mint']
MS005H=MS005_full[MS005_full['Group'] == 'Hexanal']
MS005H.head()



In [412]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("MS 0.05", fontsize=44)
sns.despine(left=True)

#data
d = MS005ctrl['MS 0.05']
e = MS005MS['MS 0.05']
f = MS005H['MS 0.05']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [413]:

    
kruskal(MS005_full[MS005_full['Group'] == 'Control']['MS 0.05'],MS005_full[MS005_full['Group'] == 'Mint']['MS 0.05'],MS005_full[MS005_full['Group'] == 'Hexanal']['MS 0.05'],nan_policy='omit')









    Out[413]:





KruskalResult(statistic=22.348493455193285, pvalue=1.4030925116132286e-05)

MS 0.1



In [414]:

    
MS10_full=composite_full[['Group','MS 0.1']]
MS10df=pd.melt(MS10_full,"Group",var_name="Odor")



In [415]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MS10df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [416]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=MS10df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [417]:

    
MS10ctrl=MS10_full[MS10_full['Group'] == 'Control']
MS10MS=MS10_full[MS10_full['Group'] == 'Mint']
MS10H=MS10_full[MS10_full['Group'] == 'Hexanal']
MS10H.head()



In [418]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("MS 0.1", fontsize=44)
sns.despine(left=True)

#data
d = MS10ctrl['MS 0.1']
e = MS10MS['MS 0.1']
f = MS10H['MS 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [126]:

    
kruskal(THA_full[THA_full['Group'] == 'Control']['THA'],THA_full[THA_full['Group'] == 'Mint']['THA'],THA_full[THA_full['Group'] == 'Hexanal']['THA'],nan_policy='omit')









    Out[126]:





KruskalResult(statistic=26.646950444965789, pvalue=1.6356419143785338e-06)

IAA 1%



In [419]:

    
IAA001_full=composite_full[['Group','IAA 0.01']]
IAA001df=pd.melt(IAA001_full,"Group",var_name="Odor")



In [420]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAA001df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [421]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAA001df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [422]:

    
IAA001ctrl=IAA001_full[IAA001_full['Group'] == 'Control']
IAA001MS=IAA001_full[IAA001_full['Group'] == 'Mint']
IAA001H=IAA001_full[IAA001_full['Group'] == 'Hexanal']
IAA001H.head()



In [423]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("IAA 0.01", fontsize=44)
sns.despine(left=True)

#data
d = IAA001ctrl['IAA 0.01']
e = IAA001MS['IAA 0.01']
f = IAA001H['IAA 0.01']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [424]:

    
kruskal(IAA001_full[IAA001_full['Group'] == 'Control']['IAA 0.01'],IAA001_full[IAA001_full['Group'] == 'Mint']['IAA 0.01'],IAA001_full[IAA001_full['Group'] == 'Hexanal']['IAA 0.01'],nan_policy='omit')









    Out[424]:





KruskalResult(statistic=22.979999688546172, pvalue=1.0231904325257964e-05)

IAA 0.05



In [425]:

    
IAA005_full=composite_full[['Group','IAA 0.05']]
IAA005df=pd.melt(IAA005_full,"Group",var_name="Odor")



In [426]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAA005df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [427]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAA005df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [428]:

    
IAA005ctrl=IAA005_full[IAA005_full['Group'] == 'Control']
IAA005MS=IAA005_full[IAA005_full['Group'] == 'Mint']
IAA005H=IAA005_full[IAA005_full['Group'] == 'Hexanal']
IAA005H.head()



In [429]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("IAA 0.05", fontsize=44)
sns.despine(left=True)

#data
d = IAA005ctrl['IAA 0.05']
e = IAA005MS['IAA 0.05']
f = IAA005H['IAA 0.05']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [430]:

    
kruskal(IAA005_full[IAA005_full['Group'] == 'Control']['IAA 0.05'],IAA005_full[IAA005_full['Group'] == 'Mint']['IAA 0.05'],IAA005_full[IAA005_full['Group'] == 'Hexanal']['IAA 0.05'],nan_policy='omit')









    Out[430]:





KruskalResult(statistic=7.2116318108394726, pvalue=0.027165271476384416)

IAA 0.1



In [437]:

    
IAA10_full=composite_full[['Group','IAA 0.1']]
IAA10df=pd.melt(IAA10_full,"Group",var_name="Odor")



In [438]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAA10df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [439]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=IAA10df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [440]:

    
IAA10ctrl=IAA10_full[IAA10_full['Group'] == 'Control']
IAA10MS=IAA10_full[IAA10_full['Group'] == 'Mint']
IAA10H=IAA10_full[IAA10_full['Group'] == 'Hexanal']
IAA10H.head()



In [441]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("IAA 0.1", fontsize=44)
sns.despine(left=True)

#data
d = IAA10ctrl['IAA 0.1']
e = IAA10MS['IAA 0.1']
f = IAA10H['IAA 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [436]:

    
kruskal(IAA10_full[IAA10_full['Group'] == 'Control']['IAA 0.1'],IAA10_full[IAA10_full['Group'] == 'Mint']['IAA 0.1'],IAA10_full[IAA10_full['Group'] == 'Hexanal']['IAA 0.1'],nan_policy='omit')









    Out[436]:





KruskalResult(statistic=30.073164294270121, pvalue=2.9491397092061267e-07)

Hexanal 1%



In [448]:

    
H001_full=composite_full[['Group','Hexanal 0.01']]
H001df=pd.melt(H001_full,"Group",var_name="Odor")



In [449]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=H001df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [450]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=H001df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [451]:

    
H001ctrl=H001_full[H001_full['Group'] == 'Control']
H001MS=H001_full[H001_full['Group'] == 'Mint']
H001H=H001_full[H001_full['Group'] == 'Hexanal']
H001H.head()









    Out[451]:






  
    
      
      Group
      Hexanal 0.01
    
  
  
    
      266
      Hexanal
      0.346753
    
    
      267
      Hexanal
      0.269426
    
    
      268
      Hexanal
      0.359969
    
    
      269
      Hexanal
      0.136656
    
    
      270
      Hexanal
      0.188876



In [452]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Hexanal 0.01", fontsize=44)
sns.despine(left=True)

#data
d = H001ctrl['Hexanal 0.01']
e = H001MS['Hexanal 0.01']
f = H001H['Hexanal 0.01']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [453]:

    
kruskal(H001_full[H001_full['Group'] == 'Control']['Hexanal 0.01'],H001_full[H001_full['Group'] == 'Mint']['Hexanal 0.01'],H001_full[H001_full['Group'] == 'Hexanal']['Hexanal 0.01'],nan_policy='omit')









    Out[453]:





KruskalResult(statistic=24.469118887167379, pvalue=4.8595756261645399e-06)

Hexanal 0.05



In [455]:

    
H005_full=composite_full[['Group','Hexanal 0.05']]
H005df=pd.melt(H005_full,"Group",var_name="Odor")



In [456]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=H005df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [457]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=H005df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [458]:

    
H005ctrl=H005_full[H005_full['Group'] == 'Control']
H005MS=H005_full[H005_full['Group'] == 'Mint']
H005H=H005_full[H005_full['Group'] == 'Hexanal']
H005H.head()









    Out[458]:






  
    
      
      Group
      Hexanal 0.05
    
  
  
    
      266
      Hexanal
      0.300836
    
    
      267
      Hexanal
      0.349465
    
    
      268
      Hexanal
      0.422324
    
    
      269
      Hexanal
      0.135468
    
    
      270
      Hexanal
      0.200218



In [460]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Hexanal 0.05", fontsize=44)
sns.despine(left=True)

#data
d = H005ctrl['Hexanal 0.05']
e = H005MS['Hexanal 0.05']
f = H005H['Hexanal 0.05']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [461]:

    
kruskal(H005_full[H005_full['Group'] == 'Control']['Hexanal 0.05'],H005_full[H005_full['Group'] == 'Mint']['Hexanal 0.05'],H005_full[H005_full['Group'] == 'Hexanal']['Hexanal 0.05'],nan_policy='omit')









    Out[461]:





KruskalResult(statistic=66.921585593621458, pvalue=2.9387478759540041e-15)

Hexanal 0.1



In [462]:

    
H10_full=composite_full[['Group','Hexanal 0.1']]
H10df=pd.melt(H10_full,"Group",var_name="Odor")



In [463]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=H10df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [464]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=H10df);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [465]:

    
H10ctrl=H10_full[H10_full['Group'] == 'Control']
H10MS=H10_full[H10_full['Group'] == 'Mint']
H10H=H10_full[H10_full['Group'] == 'Hexanal']
H10H.head()









    Out[465]:






  
    
      
      Group
      Hexanal 0.1
    
  
  
    
      266
      Hexanal
      0.290083
    
    
      267
      Hexanal
      0.236634
    
    
      268
      Hexanal
      0.416844
    
    
      269
      Hexanal
      0.124364
    
    
      270
      Hexanal
      0.136488



In [466]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("IAA 0.05", fontsize=44)
sns.despine(left=True)

#data
d = H10ctrl['Hexanal 0.1']
e = H10MS['Hexanal 0.1']
f = H10H['Hexanal 0.1']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [468]:

    
kruskal(H10_full[H10_full['Group'] == 'Control']['Hexanal 0.1'],H10_full[dH10_full['Group'] == 'Mint']['Hexanal 0.1'],H10_full[H10_full['Group'] == 'Hexanal']['Hexanal 0.1'],nan_policy='omit')









    Out[468]:





KruskalResult(statistic=15.839109543591839, pvalue=0.00036356415948386392)

THA



In [296]:

    
THA_full=composite_full[['Group','THA']]
THAdf=pd.melt(THA_full,"Group",var_name="Odor")



In [360]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=THAdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');



In [362]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=THAdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [341]:

    
THActrl=THA_full[THA_full['Group'] == 'Control']
THAMS=THA_full[THA_full['Group'] == 'Mint']
THAH=THA_full[THA_full['Group'] == 'Hexanal']
THAH.head()



In [357]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("2-hydroxyacetophenone", fontsize=44)
sns.despine(left=True)

#data
d = THActrl['THA']
e = THAMS['THA']
f = THAH['THA']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [126]:

    
kruskal(THA_full[THA_full['Group'] == 'Control']['THA'],THA_full[THA_full['Group'] == 'Mint']['THA'],THA_full[THA_full['Group'] == 'Hexanal']['THA'],nan_policy='omit')









    Out[126]:





KruskalResult(statistic=26.646950444965789, pvalue=1.6356419143785338e-06)

AP



In [264]:

    
AP_full=composite_full[['Group','AP']]
APdf=pd.melt(AP_full,"Group",var_name="Odor")



In [335]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=APdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [336]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=APdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [358]:

    
APctrl=AP_full[AP_full['Group'] == 'Control']
APMS=AP_full[AP_full['Group'] == 'Mint']
APH=AP_full[AP_full['Group'] == 'Hexanal']
APH.head()



In [359]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("2-hydroxyacetophenone", fontsize=44)
sns.despine(left=True)

#data
d = APctrl['AP']
e = APMS['AP']
f = APH['AP']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [131]:

    
kruskal(AP_full[AP_full['Group'] == 'Control']['AP'],AP_full[AP_full['Group'] == 'Mint']['AP'],AP_full[AP_full['Group'] == 'Hexanal']['AP'],nan_policy='omit')









    Out[131]:





KruskalResult(statistic=46.307698447912315, pvalue=8.7985488016197506e-11)

EB



In [332]:

    
EB_full=composite_full[['Group','EB']]
EBdf=pd.melt(EB_full,"Group",var_name="Odor")



In [337]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=EBdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [338]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=EBdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [363]:

    
EBctrl=EB_full[EB_full['Group'] == 'Control']
EBMS=EB_full[EB_full['Group'] == 'Mint']
EBH=EB_full[EB_full['Group'] == 'Hexanal']
EBH.head()



In [364]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("2-hydroxyacetophenone", fontsize=44)
sns.despine(left=True)

#data
d = EBctrl['EB']
e = EBMS['EB']
f = EBH['EB']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [135]:

    
kruskal(EB_full[EB_full['Group'] == 'Control']['EB'],EB_full[EB_full['Group'] == 'Mint']['EB'],EB_full[EB_full['Group'] == 'Hexanal']['EB'],nan_policy='omit')









    Out[135]:





KruskalResult(statistic=20.686023094405726, pvalue=3.2217152941460278e-05)

Hexanone



In [378]:

    
Hone_full=composite_full[['Group','Hexanone']]
Honedf=pd.melt(Hone_full,"Group",var_name="Odor")



In [379]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=Honedf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [380]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=Honedf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [381]:

    
Honectrl=Hone_full[Hone_full['Group'] == 'Control']
HoneMS=Hone_full[Hone_full['Group'] == 'Mint']
HoneH=Hone_full[Hone_full['Group'] == 'Hexanal']
HoneH.head()



In [382]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("Hexanone", fontsize=44)
sns.despine(left=True)

#data
d = Honectrl['Hexanone']
e = HoneMS['Hexanone']
f = HoneH['Hexanone']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [383]:

    
kruskal(Hone_full[Hone_full['Group'] == 'Control']['Hexanone'],Hone_full[Hone_full['Group'] == 'Mint']['Hexanone'],Hone_full[Hone_full['Group'] == 'Hexanal']['Hexanone'],nan_policy='omit')









    Out[383]:





KruskalResult(statistic=20.558063665415894, pvalue=3.4345765132367753e-05)

PA



In [384]:

    
PA_full=composite_full[['Group','PA']]
PAdf=pd.melt(PA_full,"Group",var_name="Odor")



In [385]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=PAdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [386]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=PAdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [387]:

    
PActrl=PA_full[PA_full['Group'] == 'Control']
PAMS=PA_full[PA_full['Group'] == 'Mint']
PAH=PA_full[PA_full['Group'] == 'Hexanal']
PAH.head()



In [388]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("2-hydroxyacetophenone", fontsize=44)
sns.despine(left=True)

#data
d = PActrl['PA']
e = PAMS['PA']
f = PAH['PA']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [389]:

    
kruskal(PA_full[PA_full['Group'] == 'Control']['PA'],PA_full[PA_full['Group'] == 'Mint']['PA'],PA_full[PA_full['Group'] == 'Hexanal']['PA'],nan_policy='omit')









    Out[389]:





KruskalResult(statistic=36.777881600341829, pvalue=1.032249027618262e-08)

Blank



In [391]:

    
B_full=composite_full[['Group','BLANK']]
Bdf=pd.melt(B_full,"Group",var_name="Odor")



In [392]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=Bdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [393]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=3);
plt.figure(figsize=(15,18));
sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=Bdf);
sns.despine();
plt.legend(loc='upper right');
plt.ylabel('Peak DF/F');
plt.title('Peak DF/F');
plt.xlabel('Odor');



In [398]:

    
Bctrl=B_full[B_full['Group'] == 'Control']
Bctrl=Bctrl.dropna()
BMS=B_full[B_full['Group'] == 'Mint']
BMS=BMS.dropna()
BH=B_full[B_full['Group'] == 'Hexanal']
BH=BH.dropna()
BH.head()



In [399]:

    
sns.set(style="white", palette="muted", color_codes=True)
sns.set_context("talk", font_scale=2)
# Set up the matplotlib figure
f, axes = plt.subplots(2, 2, figsize=(30,20), sharex=True)
# f.suptitle("2-hydroxyacetophenone", fontsize=44)
sns.despine(left=True)

#data
d = Bctrl['BLANK']
e = BMS['BLANK']
f = BH['BLANK']

# Plot a simple histogram with binsize determined automatically
sns.distplot(d, kde=False, color="r", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(e, kde=False, color="g", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])
sns.distplot(f, kde=False, color="b", hist_kws={"histtype":'step',"linewidth":3,"alpha":0.7},axlabel= False,ax=axes[0, 0])

# Plot a kernel density estimate and rug plot
sns.distplot(d, hist=False, rug=True, color="r", axlabel= False,ax=axes[0, 1],label="Control")
sns.distplot(e, hist=False, rug=True, color="g", axlabel= False,ax=axes[0, 1],label="Mint")
sns.distplot(f, hist=False, rug=True, color="b", axlabel= False,ax=axes[0, 1],label="Hexanal")

# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="r", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(e, hist=False, color="g", kde_kws={"shade": True}, axlabel=False,ax=axes[1, 0])
sns.distplot(f, hist=False, color="b", kde_kws={"shade": True}, axlabel= False,ax=axes[1, 0])

# Plot a historgram and kernel density estimate
sns.distplot(d, color="r",axlabel= False,ax=axes[1, 1])
sns.distplot(e, color="g",axlabel= False,ax=axes[1, 1])
sns.distplot(f, color="b",axlabel= False,ax=axes[1, 1])

plt.setp(axes, yticks=[])
plt.tight_layout()



In [400]:

    
kruskal(B_full[B_full['Group'] == 'Control']['BLANK'],B_full[B_full['Group'] == 'Mint']['BLANK'],B_full[B_full['Group'] == 'Hexanal']['BLANK'],nan_policy='omit')









    Out[400]:





KruskalResult(statistic=109.93241092905365, pvalue=1.3442507132896537e-24)

Baseline, by group



In [23]:

    
bl=pd.read_csv('C:\Users\Annie\Documents\Data\Ca_Imaging\Analysis\Odor_Panel\Composite_Baseline_NoP.csv')
del bl['Mouse']
bl_sorted=bl.reindex_axis(bl.mean().sort_values().index, axis=1)
bl_labels=pd.DataFrame(bl.Group)
tmp=[bl_labels,bl_sorted]
bdf=pd.concat(tmp,axis=1)
bdfull=pd.melt(bdf,"Group",var_name="Odor")
bdfull=bdfull.dropna()

Composite Graphs - Baseline



In [498]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=2.2);
plt.figure(figsize=(45,20));
ax=sns.barplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=bdfull);
sns.despine()
plt.ylabel('Intensity', fontsize=48);
plt.title('Baseline F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':30});



In [518]:

    
# sns.set_context("talk",font_scale=1.9);
# sns.set(style="white", palette="muted", color_codes=True);
# plt.figure(figsize=(8, 6));
# violinplot=sns.violinplot(x="Odor", y="value", hue="Group", data=bdfull);
# violinplot.set(ylabel='Intensity');



In [502]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=2.2);
plt.figure(figsize=(45,20));
ax=sns.boxplot(x="Odor", y="value", hue="Group",palette={"Control": "r", "Hexanal": "b","Mint":"g"}, data=bdfull);
sns.despine()
plt.ylabel('Intensity', fontsize=48);
plt.title('Baseline F',fontsize=55);
plt.xlabel('Odor',fontsize=48);
plt.legend(loc=2,prop={'size':30});

Odor, by Mouse



In [20]:

    
readms=pd.read_csv('C:\Users\Annie\Documents\Data\Ca_Imaging\Analysis\Odor_Panel\Composite_MaxDF_NoP.csv')
ms = readms
del ms['Group']
ms_sorted=ms.reindex_axis(ms.mean().sort_values().index, axis=1)
ms_labels=pd.DataFrame(ms.Mouse)
tmp=[ms_labels,ms_sorted]
msdf=pd.concat(tmp,axis=1)
msfull=pd.melt(msdf,"Mouse",var_name="Odor")
msfull=msfull.dropna()
msfull.head()



In [522]:

    
#dictionary of imaging session and labels
msgroups={'160321_3':'r', '160421_2':'r', '160502_1':'r', '160503_1':'r', '160420_1':'r',
       '160420_2':'r', '160420_3':'r', '160421_1':'r', '160421_3':'r', '160503_2':'r',
       '160310_2':'r', '160310_3':'r', '160321_1':'r', '160321_2':'r', '160517_2':'b',
       '160525_1':'b', '160620_1':'b', '160517_1':'b', '160517_3':'b', '160525_2':'b',
       '160525_3':'b', '160620_2':'b', '160620_3':'b', '160621_1':'b', '160621_2':'b',
       '160622_1':'b', '160622_2':'b', '160626_1':'b', '160626_2':'b', '160330_3':'g',
       '160328_1_B':'g', '160428_1_B':'g', '160429_1':'g', '160429_2':'g', '160325_1':'g',
       '160325_2':'g', '160325_4':'g', '160328_2':'g', '160328_3':'g', '160330_2':'g',
       '160401_1':'g'}

Composite Graphs - by session



In [529]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=2.2);
plt.figure(figsize=(45,20));
ax=sns.barplot(x="Odor", y="value", hue="Mouse",palette={'160321_3':'r', '160421_2':'r', '160502_1':'r', '160503_1':'r', '160420_1':'r',
       '160420_2':'r', '160420_3':'r', '160421_1':'r', '160421_3':'r', '160503_2':'r',
       '160310_2':'r', '160310_3':'r', '160321_1':'r', '160321_2':'r', '160517_2':'b',
       '160525_1':'b', '160620_1':'b', '160517_1':'b', '160517_3':'b', '160525_2':'b',
       '160525_3':'b', '160620_2':'b', '160620_3':'b', '160621_1':'b', '160621_2':'b',
       '160622_1':'b', '160622_2':'b', '160626_1':'b', '160626_2':'b', '160330_3':'g',
       '160328_1_B':'g', '160428_1_B':'g', '160429_1':'g', '160429_2':'g', '160325_1':'g',
       '160325_2':'g', '160325_4':'g', '160328_2':'g', '160328_3':'g', '160330_2':'g',
       '160401_1':'g'}, data=msfull);
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('Odor-evoked response, by session',fontsize=55);
plt.xlabel('Odor',fontsize=48);
ax.legend_.remove()



In [14]:

    
# sns.set(style="white", palette="muted", color_codes=True);
# sns.set_context("talk",font_scale=2.2);
# plt.figure(figsize=(45,20));
# ax=sns.violinplot(x="Odor", y="value", hue="Mouse",palette={'160321_3':'r', '160421_2':'r', '160502_1':'r', '160503_1':'r', '160420_1':'r',
#        '160420_2':'r', '160420_3':'r', '160421_1':'r', '160421_3':'r', '160503_2':'r',
#        '160310_2':'r', '160310_3':'r', '160321_1':'r', '160321_2':'r', '160517_2':'b',
#        '160525_1':'b', '160620_1':'b', '160517_1':'b', '160517_3':'b', '160525_2':'b',
#        '160525_3':'b', '160620_2':'b', '160620_3':'b', '160621_1':'b', '160621_2':'b',
#        '160622_1':'b', '160622_2':'b', '160626_1':'b', '160626_2':'b', '160330_3':'g',
#        '160328_1_B':'g', '160428_1_B':'g', '160429_1':'g', '160429_2':'g', '160325_1':'g',
#        '160325_2':'g', '160325_4':'g', '160328_2':'g', '160328_3':'g', '160330_2':'g',
#        '160401_1':'g'}, data=msfull);
# sns.despine()
# plt.ylabel('Peak DF/F', fontsize=48);
# plt.title('Odor-evoked response, by session',fontsize=55);
# plt.xlabel('Odor',fontsize=48);
# ax.legend_.remove()



In [531]:

    
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk",font_scale=2.2);
plt.figure(figsize=(55,40));
ax=sns.boxplot(x="Odor", y="value", hue="Mouse",palette={'160321_3':'r', '160421_2':'r', '160502_1':'r', '160503_1':'r', '160420_1':'r',
       '160420_2':'r', '160420_3':'r', '160421_1':'r', '160421_3':'r', '160503_2':'r',
       '160310_2':'r', '160310_3':'r', '160321_1':'r', '160321_2':'r', '160517_2':'b',
       '160525_1':'b', '160620_1':'b', '160517_1':'b', '160517_3':'b', '160525_2':'b',
       '160525_3':'b', '160620_2':'b', '160620_3':'b', '160621_1':'b', '160621_2':'b',
       '160622_1':'b', '160622_2':'b', '160626_1':'b', '160626_2':'b', '160330_3':'g',
       '160328_1_B':'g', '160428_1_B':'g', '160429_1':'g', '160429_2':'g', '160325_1':'g',
       '160325_2':'g', '160325_4':'g', '160328_2':'g', '160328_3':'g', '160330_2':'g',
       '160401_1':'g'}, data=msfull);
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('Odor-evoked response, by session',fontsize=55);
plt.xlabel('Odor',fontsize=48);
ax.legend_.remove()



In [ ]:

	Group	IAA 0.01	IAA 0.05	IAA 0.1
266	Hexanal	0.210982	0.082528	0.382490
267	Hexanal	0.207433	0.068120	0.402160
268	Hexanal	0.350660	0.130934	0.439151
269	Hexanal	0.099299	0.042892	0.090707
270	Hexanal	0.103156	0.026460	0.154604

	Group	MS 0.01	MS 0.05	MS 0.1
261	Control	0.297978	0.343609	0.404345
262	Control	0.375490	0.350197	0.420784
263	Control	0.047742	0.213791	0.271523
264	Control	0.159659	0.141683	0.151191
265	Control	0.184315	0.192566	0.260542

	Group	IAA 0.01	IAA 0.05	IAA 0.1
266	Hexanal	0.210982	0.082528	0.382490
267	Hexanal	0.207433	0.068120	0.402160
268	Hexanal	0.350660	0.130934	0.439151
269	Hexanal	0.099299	0.042892	0.090707
270	Hexanal	0.103156	0.026460	0.154604

	Group	THA	MS 0.01	BLANK	AP	MS 0.1	MS 0.05	IAA 0.05	IAA 0.01	PA	IAA 0.1	Hexanone	Hexanal 0.1	Hexanal 0.01	Hexanal 0.05	EB
0	Control	0.212200	0.127301	NaN	0.139029	0.096683	0.099481	0.098134	0.265576	0.165395	0.216148	0.345297	0.131523	0.157010	0.131461	0.193486
1	Control	0.066122	0.074838	NaN	0.127978	0.052906	0.119782	0.108896	0.168569	0.101373	0.171575	0.268874	0.146749	0.140497	0.136131	0.201126
2	Control	0.022259	0.094999	NaN	0.065581	0.027137	0.029730	0.028554	0.146171	0.055140	0.159719	0.198244	0.101923	0.127977	0.116245	0.156971
3	Control	0.145203	0.107146	NaN	0.057500	0.152532	0.158519	0.179303	0.302537	0.125637	0.259681	0.350995	0.279234	0.190135	0.233960	0.274613
4	Control	0.155862	0.147167	NaN	0.153746	0.125200	0.159181	0.230083	0.375661	0.192976	0.325386	0.438699	0.357176	0.299774	0.339993	0.285755

	Group	THA	MS 0.01	BLANK	AP	MS 0.1	MS 0.05	IAA 0.05	IAA 0.01	PA	IAA 0.1	Hexanone	Hexanal 0.1	Hexanal 0.01	Hexanal 0.05	EB
0	Control	4.0	11.0	NaN	8.0	14.0	12.0	13.0	2.0	6.0	3.0	1.0	9.0	7.0	10.0	5.0
1	Control	13.0	12.0	NaN	8.0	14.0	9.0	10.0	4.0	11.0	3.0	1.0	5.0	6.0	7.0	2.0
2	Control	14.0	8.0	NaN	9.0	13.0	11.0	12.0	4.0	10.0	2.0	1.0	7.0	5.0	6.0	3.0
3	Control	11.0	13.0	NaN	14.0	10.0	9.0	8.0	2.0	12.0	5.0	1.0	3.0	7.0	6.0	4.0
4	Control	11.0	13.0	NaN	12.0	14.0	10.0	8.0	2.0	9.0	5.0	1.0	3.0	6.0	4.0	7.0

	Group	THA	MS 0.01	BLANK	AP	MS 0.1	MS 0.05	IAA 0.05	IAA 0.01	PA	IAA 0.1	Hexanone	Hexanal 0.1	Hexanal 0.01	Hexanal 0.05	EB
0	Control	10.535714	10.415414	11.667647	10.372180	9.530075	9.836466	9.105263	6.765038	10.806391	5.052632	3.759398	5.731203	5.986842	4.118421	5.114662
1	Mint	9.986940	11.625000	8.682540	10.794776	9.296642	9.910448	8.175373	8.891791	8.975746	5.929104	5.076493	3.953358	5.013060	4.009328	4.847015
2	Hexanal	11.222222	12.147696	9.899425	9.535230	11.214092	10.284553	10.021680	7.728997	8.398374	4.802168	5.326558	6.643631	4.586721	3.162602	4.735772

	Group	THA	MS 0.01	BLANK	AP	MS 0.1	MS 0.05	IAA 0.05	IAA 0.01	PA	IAA 0.1	Hexanone	Hexanal 0.1	Hexanal 0.01	Hexanal 0.05	EB
0	Control	2191.580000	2156.251500	NaN	2604.431208	2342.740333	2438.371125	2430.727208	1916.923958	2174.460083	2295.764583	2202.500042	2446.136083	2515.360375	2457.603625	2406.386167
1	Control	1733.676000	1897.166667	NaN	1805.344875	1946.681708	1828.752875	1864.458375	1685.373583	1748.060375	1850.529417	1865.847750	1903.826250	1841.318875	1795.494250	1929.843458
2	Control	1567.613708	1688.818208	NaN	1678.700042	1694.186292	1619.368000	1643.305542	1556.993000	1536.563083	1662.997500	1504.910333	1681.573792	1648.710833	1688.000667	1663.836417
3	Control	829.527875	888.688458	NaN	865.015708	856.108625	861.587750	857.491250	835.064458	822.373417	847.159000	853.859458	857.543458	871.575583	853.299500	843.551167
4	Control	886.599167	1019.396583	NaN	924.648000	929.562417	916.596208	916.315333	955.118500	895.472083	914.607042	948.426042	899.221875	918.836292	914.384250	942.410958

	Group	Odor	value	value
13540	Mint	EB	0.479948	810.413917
13541	Mint	EB	0.367013	719.335792
13542	Mint	EB	0.238822	880.315292
13543	Mint	EB	0.259046	1241.160417
13544	Mint	EB	0.313997	1080.826792

	Group	Hexanal 0.01	Hexanal 0.05	Hexanal 0.1
266	Hexanal	0.346753	0.300836	0.290083
267	Hexanal	0.269426	0.349465	0.236634
268	Hexanal	0.359969	0.422324	0.416844
269	Hexanal	0.136656	0.135468	0.124364
270	Hexanal	0.188876	0.200218	0.136488

	Group	MS 0.01
266	Hexanal	0.161091
267	Hexanal	0.032355
268	Hexanal	0.260874
269	Hexanal	0.034293
270	Hexanal	0.042151

	Group	MS 0.05
266	Hexanal	0.096774
267	Hexanal	0.081605
268	Hexanal	0.158723
269	Hexanal	0.024024
270	Hexanal	0.014518

	Group	MS 0.1
266	Hexanal	0.149891
267	Hexanal	0.094942
268	Hexanal	0.250858
269	Hexanal	0.048251
270	Hexanal	0.080076

	Group	THA
266	Hexanal	0.032360
267	Hexanal	0.068896
268	Hexanal	0.141124
269	Hexanal	0.031333
270	Hexanal	0.029448

	Group	AP
266	Hexanal	0.150098
267	Hexanal	0.074321
268	Hexanal	0.180250
269	Hexanal	0.113269
270	Hexanal	0.028062

	Group	EB
266	Hexanal	0.209884
267	Hexanal	0.193009
268	Hexanal	0.335625
269	Hexanal	0.082340
270	Hexanal	0.023194

	Group	Hexanone
266	Hexanal	0.378703
267	Hexanal	0.330326
268	Hexanal	0.501147
269	Hexanal	0.073911
270	Hexanal	0.117332

	Group	PA
266	Hexanal	0.043617
267	Hexanal	0.032337
268	Hexanal	0.000000
269	Hexanal	0.004714
270	Hexanal	0.032430

	Group	BLANK
287	Hexanal	0.059379
288	Hexanal	0.027291
289	Hexanal	0.069221
290	Hexanal	0.121546
291	Hexanal	0.055183

	Mouse	Odor	value
0	160321_3	THA	0.212200
1	160321_3	THA	0.066122
2	160321_3	THA	0.022259
3	160321_3	THA	0.145203
4	160321_3	THA	0.155862