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Edit and run 





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 inline
import seaborn as sns
from scipy.stats import zscore
from scipy.stats import nanmean
from scipy.stats import nanstd



    











In [2]:



    


filename='C:\Users\Annie\Documents\Data\Ca_Imaging\Analysis\\Odor_Panel\\Odor_Trials.csv'
trials=pd.read_csv(filename)

Figure 1: Graph individual traces





In [95]:



    


cell=5
trial=39
filename='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\160328_1\\%sdt.txt'%trial
comp=pd.read_csv(filename,index_col=0)
ncomp=pd.DataFrame(comp[1:150]['G PMT (%s)'%cell])
sns.set(style="white")
sns.set_context("paper",font_scale=3);
f,ax=plt.subplots()
ax.plot(ncomp,linewidth=4.0)
sns.despine(left=True,bottom=True)
plt.ylim(-0.15, 1);
plt.xticks(np.arange(0, 160, 25.0))
ax.set_yticks([]);
ax.set_xticks([]);
plt.tight_layout();
f.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\SampleTraces\HighRes\cell%s_%s.pdf"%(cell,trial))

Figure 2: Baseline and Peak, Integral





In [3]:



    


x='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullbaseline_WL.csv'
baseline=pd.read_csv(x)
y='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullintegral_WL.csv'
integral=pd.read_csv(y)
z='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullpeak_WL.csv'
peak=pd.read_csv(z)
s='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullsuccess_WI.csv'
success=pd.read_csv(s)



    











In [4]:



    


melt_base=pd.melt(baseline,('Mouse','Group'),var_name='Odor')
melt_int=pd.melt(integral,('Mouse','Group'),var_name='Odor')
melt_success=pd.melt(success,('Mouse','Group'),var_name='Odor')
melt_peak=pd.melt(peak,('Mouse','Group'),var_name='Odor')



    











In [5]:



    


#THE ENTIRE DATAFRAME
completedf=pd.concat([melt_base[['Mouse','Group','Odor','value']],melt_int['value'],melt_peak['value'],melt_success['value']],axis=1)
completedf.columns=['Mouse','Group','Odor','Baseline','Integral','Peak','Success']



    











In [150]:



    


completedf.tail()



    


















    
Out[150]:










  
    

      
      Mouse
      Group
      Odor
      Baseline
      Integral
      Peak
      Success
    

  
  
    

      13105
      G PMT (22)_160626_2
      Hexanal
      Hexanal05
      773.446970
      NaN
      NaN
      NaN
    

    

      13106
      G PMT (23)_160626_2
      Hexanal
      Hexanal05
      1403.306348
      NaN
      NaN
      NaN
    

    

      13107
      G PMT (24)_160626_2
      Hexanal
      Hexanal05
      1208.390758
      NaN
      NaN
      NaN
    

    

      13108
      G PMT (25)_160626_2
      Hexanal
      Hexanal05
      1190.651894
      NaN
      NaN
      NaN
    

    

      13109
      G PMT (26)_160626_2
      Hexanal
      Hexanal05
      836.463303
      NaN
      NaN
      NaN
    

  




















In [101]:



    


#GRAPH THE INTERACTIONS
sns.set(style='white',palette="muted", color_codes=True);
sns.set_context("paper",font_scale=2);
sns.jointplot('Peak','Integral',completedf);
sns.despine(left=True,bottom=True);
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\Correlations\Integral_Peak.pdf")



    


















    
























In [102]:



    


sns.set(style='white');
sns.set_context("paper",font_scale=2);
x=melt_peak[melt_peak.Group=='Control'].value.dropna();
y=melt_peak[melt_peak.Group=='Hexanal'].value.dropna();
z=melt_peak[melt_peak.Group=='Mint'].value.dropna();
sns.distplot(x,hist=False,color=sns.xkcd_rgb["black"],kde_kws={'lw':3,'label':'Control'});
sns.distplot(y,hist=False,color=sns.xkcd_rgb["azure"],kde_kws={'lw':3,'label':'Hexanal'});
sns.distplot(z,hist=False,color=sns.xkcd_rgb["grey"],kde_kws={'lw':3,'label':'Mint'});
sns.despine();
plt.tight_layout();
plt.xlabel('Peak');
plt.ylabel('Density Function');
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\Correlations\KDE_Peak.pdf")



    


















    
























In [103]:



    


x=completedf.Peak.dropna()
sns.set(style='white',palette="muted", color_codes=True);
sns.set_context("paper",font_scale=2);
sns.distplot(x,kde=False);
sns.despine(left=True,bottom=True);
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\Correlations\Hist_Peak.pdf")

Figure 3: Habituation





In [104]:



    


h='C:\Users\Annie\Documents\Data\Ca_Imaging\HabituationFiles\NormalizedHabituation.csv'
habituation=pd.read_csv(h)



    











In [105]:



    


h=pd.melt(habituation,var_name='Time',value_name='Normalized Change');
sns.set(style='white',palette="muted", color_codes=True);
sns.set_context("paper",font_scale=2);
sns.pointplot(x='Time',y='Normalized Change',data=h);
plt.ylabel('Normalized Change')
sns.despine();
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\Habituation.pdf")

Prep Peaks Dataframe





In [19]:



    


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



    











In [20]:



    


groupcolors={'Control':sns.xkcd_rgb["black"],'Mint':sns.xkcd_rgb["grey"],'Hexanal':sns.xkcd_rgb["azure"]};



    











In [21]:



    


#Make separate excitatory and inhibitory dataframes
mindf=pd.DataFrame([])
maxdf=pd.DataFrame([])
zdf=pd.DataFrame([])
for x in cfull.index:
    if cfull.value[x]>0:
        a=pd.DataFrame(cfull.iloc[x]).T
        maxdf=maxdf.append(a,ignore_index=True)
    elif cfull.value[x]<0:
        a=pd.DataFrame(cfull.iloc[x]).T
        mindf=mindf.append(a,ignore_index=True)
    else:
        a=pd.DataFrame(cfull.iloc[x]).T
        zdf=zdf.append(a,ignore_index=True)



    











In [6]:



    


one_mindf=mindf[['Group','Odor','value']][mindf.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]
conc_mindf=mindf[['Group','Odor','value']][mindf.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]
conc_mindf=conc_mindf.sort_values(by='Odor')

one_maxdf=maxdf[['Group','Odor','value']][maxdf.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]
conc_maxdf=maxdf[['Group','Odor','value']][maxdf.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]

one_mindf=one_mindf.replace(to_replace='MS01',value='MS')
one_mindf=one_mindf.replace(to_replace='Hexanal01',value='Hexanal')
one_mindf=one_mindf.replace(to_replace='IAA01',value='IAA')

one_maxdf=one_maxdf.replace(to_replace='MS01',value='MS')
one_maxdf=one_maxdf.replace(to_replace='Hexanal01',value='Hexanal')
one_maxdf=one_maxdf.replace(to_replace='IAA01',value='IAA')

sorter=('MS01','MS05','MS10','IAA01','IAA05','IAA10','Hexanal01','Hexanal05','Hexanal10')
conc_maxdf.Odor=conc_maxdf.Odor.astype('category')
conc_maxdf.Odor.cat.set_categories(sorter,inplace=True)
conc_maxdf=conc_maxdf.sort_values(['Odor'])

conc_mindf.Odor=conc_mindf.Odor.astype('category')
conc_mindf.Odor.cat.set_categories(sorter,inplace=True)
conc_mindf=conc_mindf.sort_values(['Odor'])

conc_mindf=conc_mindf.replace(to_replace='MS01',value='MS 1%')
conc_mindf=conc_mindf.replace(to_replace='MS05',value='MS 5%')
conc_mindf=conc_mindf.replace(to_replace='MS10',value='MS 10%')
conc_mindf=conc_mindf.replace(to_replace='IAA01',value='IAA 1%')
conc_mindf=conc_mindf.replace(to_replace='IAA05',value='IAA 5%')
conc_mindf=conc_mindf.replace(to_replace='IAA10',value='IAA 10%')
conc_mindf=conc_mindf.replace(to_replace='Hexanal01',value='Hexanal 1%')
conc_mindf=conc_mindf.replace(to_replace='Hexanal05',value='Hexanal 5%')
conc_mindf=conc_mindf.replace(to_replace='Hexanal10',value='Hexanal 10%')

conc_maxdf=conc_maxdf.replace(to_replace='MS01',value='MS 1%')
conc_maxdf=conc_maxdf.replace(to_replace='MS05',value='MS 5%')
conc_maxdf=conc_maxdf.replace(to_replace='MS10',value='MS 10%')
conc_maxdf=conc_maxdf.replace(to_replace='IAA01',value='IAA 1%')
conc_maxdf=conc_maxdf.replace(to_replace='IAA05',value='IAA 5%')
conc_maxdf=conc_maxdf.replace(to_replace='IAA10',value='IAA 10%')
conc_maxdf=conc_maxdf.replace(to_replace='Hexanal01',value='Hexanal 1%')
conc_maxdf=conc_maxdf.replace(to_replace='Hexanal05',value='Hexanal 5%')
conc_maxdf=conc_maxdf.replace(to_replace='Hexanal10',value='Hexanal 10%')



    











In [7]:



    


cat=('Control','Mint','Hexanal')

Figure 4-5: Amplitude of excitatory responses/BOXPLOTS





In [115]:



    


#1% CONCENTRATION
cat=('Control','Mint','Hexanal')
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(7,5));
sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=one_maxdf);
sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=one_mindf);
# ax.legend_.remove()
sns.despine()
plt.ylabel('DF/F', fontsize=12);
plt.title('Peak DF/F, 1%', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(-1,5)
plt.tight_layout()
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Box\\box.pdf")



    


















    
























In [116]:



    


MS_conc_maxdf=conc_maxdf[conc_maxdf.Odor.isin(['MS 1%','MS 5%','MS 10%'])]
hex_conc_maxdf=conc_maxdf[conc_maxdf.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]
IAA_conc_maxdf=conc_maxdf[conc_maxdf.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]

MS_conc_mindf=conc_mindf[conc_mindf.Odor.isin(['MS 1%','MS 5%','MS 10%'])]
hex_conc_mindf=conc_mindf[conc_mindf.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]
IAA_conc_mindf=conc_mindf[conc_mindf.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]



    











In [117]:



    


conc_maxdf



    


















    
Out[117]:










  
    

      
      Group
      Odor
      value
    

  
  
    

      0
      Control
      MS 1%
      0.015671
    

    

      512
      Hexanal
      MS 1%
      0.216320
    

    

      513
      Hexanal
      MS 1%
      0.200734
    

    

      514
      Hexanal
      MS 1%
      0.126636
    

    

      515
      Hexanal
      MS 1%
      0.146380
    

    

      516
      Hexanal
      MS 1%
      0.209309
    

    

      517
      Hexanal
      MS 1%
      0.299622
    

    

      518
      Hexanal
      MS 1%
      0.148053
    

    

      519
      Hexanal
      MS 1%
      0.086919
    

    

      520
      Hexanal
      MS 1%
      0.024138
    

    

      511
      Hexanal
      MS 1%
      0.097680
    

    

      521
      Hexanal
      MS 1%
      0.202925
    

    

      523
      Hexanal
      MS 1%
      0.184702
    

    

      524
      Hexanal
      MS 1%
      0.051542
    

    

      525
      Hexanal
      MS 1%
      0.044623
    

    

      526
      Hexanal
      MS 1%
      0.133760
    

    

      527
      Hexanal
      MS 1%
      0.030670
    

    

      528
      Hexanal
      MS 1%
      0.118389
    

    

      529
      Hexanal
      MS 1%
      0.055969
    

    

      530
      Hexanal
      MS 1%
      0.340597
    

    

      531
      Hexanal
      MS 1%
      0.130009
    

    

      522
      Hexanal
      MS 1%
      0.270302
    

    

      532
      Hexanal
      MS 1%
      0.280088
    

    

      510
      Hexanal
      MS 1%
      0.153363
    

    

      508
      Hexanal
      MS 1%
      0.118162
    

    

      488
      Hexanal
      MS 1%
      0.331770
    

    

      489
      Hexanal
      MS 1%
      0.157791
    

    

      490
      Hexanal
      MS 1%
      0.417632
    

    

      491
      Hexanal
      MS 1%
      0.033887
    

    

      492
      Hexanal
      MS 1%
      0.194419
    

    

      ...
      ...
      ...
      ...
    

    

      8040
      Hexanal
      Hexanal 10%
      0.351417
    

    

      8039
      Hexanal
      Hexanal 10%
      0.294391
    

    

      8049
      Hexanal
      Hexanal 10%
      0.760009
    

    

      8037
      Hexanal
      Hexanal 10%
      0.291506
    

    

      8062
      Hexanal
      Hexanal 10%
      0.154878
    

    

      8064
      Hexanal
      Hexanal 10%
      0.152494
    

    

      8086
      Hexanal
      Hexanal 10%
      0.218386
    

    

      8085
      Hexanal
      Hexanal 10%
      0.124959
    

    

      8084
      Hexanal
      Hexanal 10%
      0.081698
    

    

      8083
      Hexanal
      Hexanal 10%
      0.098549
    

    

      8082
      Hexanal
      Hexanal 10%
      0.368336
    

    

      8081
      Hexanal
      Hexanal 10%
      0.129421
    

    

      8080
      Hexanal
      Hexanal 10%
      0.770505
    

    

      8079
      Hexanal
      Hexanal 10%
      0.323544
    

    

      8078
      Hexanal
      Hexanal 10%
      0.086767
    

    

      8077
      Hexanal
      Hexanal 10%
      0.134787
    

    

      8063
      Hexanal
      Hexanal 10%
      0.179797
    

    

      8076
      Hexanal
      Hexanal 10%
      0.603242
    

    

      8074
      Hexanal
      Hexanal 10%
      0.429644
    

    

      8073
      Hexanal
      Hexanal 10%
      0.281141
    

    

      8072
      Hexanal
      Hexanal 10%
      0.149075
    

    

      8071
      Hexanal
      Hexanal 10%
      0.220699
    

    

      8070
      Hexanal
      Hexanal 10%
      0.130865
    

    

      8069
      Hexanal
      Hexanal 10%
      0.019291
    

    

      8068
      Hexanal
      Hexanal 10%
      0.288933
    

    

      8067
      Hexanal
      Hexanal 10%
      0.223579
    

    

      8066
      Hexanal
      Hexanal 10%
      0.031014
    

    

      8065
      Hexanal
      Hexanal 10%
      0.114366
    

    

      8075
      Hexanal
      Hexanal 10%
      0.579442
    

    

      7960
      Control
      Hexanal 10%
      0.189300
    

  



7150 rows × 3 columns



















In [120]:



    


#Multiple Concentration
cat=('Control','Mint','Hexanal')
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(3.5,3));
sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=hex_conc_mindf);
ax=sns.boxplot(x='Odor',y='value',hue_order=cat,hue='Group',palette=groupcolors,data=hex_conc_maxdf);
ax.legend_.remove()
sns.despine()
plt.ylabel('DF/F', fontsize=12);
plt.title('Peak DF/F, 1%', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(-1,4)
plt.tight_layout()
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Box\\hex_conc.pdf")

Figure 4-5: MEDIAN

1%





In [132]:



    


#MEDIANS for 1%
medians=pd.DataFrame([])
df=one_maxdf
for x in df.Group.unique():
    tmp=pd.DataFrame([])
    for y in df.Odor.unique():
        a=df[(df.Group==x)&(df.Odor==y)]['value'].dropna().median()
        m=pd.DataFrame({y:[a]})
        tmp=pd.concat([tmp,m],axis=1)
    b=pd.DataFrame({'Group':[x]})
#     c=pd.DataFrame({'Group':[df[df.Mouse==x]['Group'].iloc[0]]})
#     c=c.reset_index(drop=True)
    tmp=pd.concat([b,tmp],axis=1)
    medians=medians.append(tmp)
maxmedianmelt=pd.melt(medians,"Group",var_name="Odor")



    











In [134]:



    


#Plot everything by group
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(7,5));
ax=sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=maxmedianmelt)
# sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=minmedianmelt)
ax.legend_.remove()
sns.despine()
plt.ylabel('DF/F', fontsize=12);
plt.title('Median Peak', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(-0.2,0.6)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Median\\Median_one.pdf")

Concentrations





In [129]:



    


#MEDIANS
medians=pd.DataFrame([])
# df=MS_conc_maxdf
# df=hex_conc_maxdf
df=IAA_conc_maxdf
for x in df.Group.unique():
    tmp=pd.DataFrame([])
    for y in df.Odor.unique():
        a=df[(df.Group==x)&(df.Odor==y)]['value'].dropna().median()
        m=pd.DataFrame({y:[a]})
        tmp=pd.concat([tmp,m],axis=1)
    b=pd.DataFrame({'Group':[x]})
#     c=pd.DataFrame({'Group':[df[df.Mouse==x]['Group'].iloc[0]]})
#     c=c.reset_index(drop=True)
    tmp=pd.concat([b,tmp],axis=1)
    medians=medians.append(tmp)
maxmedianmelt=pd.melt(medians,"Group",var_name="Odor")



    











In [130]:



    


#Plot everything by group
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(3.5,3));
ax=sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=maxmedianmelt)
# sns.pointplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=minmedianmelt)
ax.legend_.remove()
sns.despine()
plt.ylabel('DF/F', fontsize=12);
plt.title('Median Peak', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(0,0.6)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Median\\Median_IAA.pdf")

Figure 6: Number of excitatory events





In [3]:



    


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

maxdf=pd.DataFrame([])
zdf=pd.DataFrame([])
for x in cfull.index:
    if cfull.value[x]>0:
        a=pd.DataFrame(cfull.iloc[x]).T
        maxdf=maxdf.append(a,ignore_index=True)
    else:
        a=pd.DataFrame(cfull.iloc[x]).T
        zdf=zdf.append(a,ignore_index=True)
zdf=zdf.dropna()



    











In [4]:



    


ratio=pd.DataFrame([])
for x in maxdf.Group.unique():
    for y in maxdf.Odor.unique():
        a=float(len(maxdf[(maxdf.Group==x) & (maxdf.Odor==y)]['value']))
        b=float(len(zdf[(zdf.Group==x) & (zdf.Odor==y)]['value']))
        E=a/(a+b)
        ratio=ratio.append(pd.DataFrame({'Group':[x],'Odor':[y],'Ratio':[E]}))



    











In [5]:



    


one_ratio=ratio[['Group','Odor','Ratio']][ratio.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]
conc_ratio=ratio[['Group','Odor','Ratio']][ratio.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]
conc_ratio=conc_ratio.sort_values(by='Odor')

one_ratio=one_ratio.replace(to_replace='MS01',value='MS')
one_ratio=one_ratio.replace(to_replace='Hexanal01',value='Hexanal')
one_ratio=one_ratio.replace(to_replace='IAA01',value='IAA')

sorter=('MS01','MS05','MS10','IAA01','IAA05','IAA10','Hexanal01','Hexanal05','Hexanal10')
conc_ratio.Odor=conc_ratio.Odor.astype('category')
conc_ratio.Odor.cat.set_categories(sorter,inplace=True)
conc_ratio=conc_ratio.sort_values(['Odor'])

conc_ratio=conc_ratio.replace(to_replace='MS01',value='MS 1%')
conc_ratio=conc_ratio.replace(to_replace='MS05',value='MS 5%')
conc_ratio=conc_ratio.replace(to_replace='MS10',value='MS 10%')
conc_ratio=conc_ratio.replace(to_replace='IAA01',value='IAA 1%')
conc_ratio=conc_ratio.replace(to_replace='IAA05',value='IAA 5%')
conc_ratio=conc_ratio.replace(to_replace='IAA10',value='IAA 10%')
conc_ratio=conc_ratio.replace(to_replace='Hexanal01',value='Hexanal 1%')
conc_ratio=conc_ratio.replace(to_replace='Hexanal05',value='Hexanal 5%')
conc_ratio=conc_ratio.replace(to_replace='Hexanal10',value='Hexanal 10%')



    











In [6]:



    


MS_conc_ratio=conc_ratio[conc_ratio.Odor.isin(['MS 1%','MS 5%','MS 10%'])]
hex_conc_ratio=conc_ratio[conc_ratio.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]
IAA_conc_ratio=conc_ratio[conc_ratio.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]



    











In [8]:



    


one_ratio.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Ratio_One.csv')



    











In [9]:



    


MS_conc_ratio.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Ratio_MS.csv')
hex_conc_ratio.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Ratio_hex.csv')
IAA_conc_ratio.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Ratio_IAA.csv')



    











In [65]:



    


# 1%
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(7,5));
ax=sns.pointplot(x='Odor',y='Ratio',hue='Group',palette=groupcolors,data=one_ratio)
# ax.legend_.remove()
sns.despine()
plt.ylabel('Ratio', fontsize=12);
plt.title('Excitatory Responses', fontsize=12);
plt.xlabel('Odor', fontsize=12);
# plt.ylim(-0.2,0.6)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Excitatory\\Excitatory_One.pdf")



    


















    
























In [82]:



    


#Different concentrations
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(3.5,3));
ax=sns.pointplot(x='Odor',y='Ratio',hue='Group',palette=groupcolors,data=IAA_conc_ratio)
ax.legend_.remove()
sns.despine()
plt.ylabel('Ratio', fontsize=12);
plt.title('Excitatory Responses', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(0.8,1)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Excitatory\\Excitatory_IAA.pdf")

Number of odorants responded to





In [14]:



    


composite_full=composite_full.drop('Blank',1)



    











In [15]:



    


EI=pd.DataFrame([])
for x in composite_full.index:
    a=0
    b=0
    for value in composite_full.iloc[x][2:]:
        if value>0:
            a=a+1
        elif value<0:
            b=b+1
    c=pd.DataFrame(composite_full.iloc[x][['Group','Mouse']]).T
    c=c.reset_index(drop=True)
    d=pd.concat([c,pd.DataFrame({'E':[a]}),pd.DataFrame({'I':[b]})],axis=1)
    EI=EI.append(d)



    











In [16]:



    


EI.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Odor_Number.csv')



    











In [18]:



    


groupcolors={'Control':sns.xkcd_rgb["black"],'Mint':sns.xkcd_rgb["grey"],'Hexanal':sns.xkcd_rgb["azure"]};



    











In [19]:



    


sns.set(style="white")
sns.set_context('paper')
plt.figure(figsize=(3,3))
sns.barplot(x='Group', y="E", data=EI,
            label="E", palette=groupcolors);
sns.despine(left=True, bottom=True)
plt.ylabel('Odor Number', fontsize=12);
plt.title('Responses', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.xticks(fontsize=11);
plt.yticks(fontsize=11);
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Excitatory\\Odors.pdf")

Figure 7: Reliability





In [21]:



    


z='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullpeak_WL.csv'
peak=pd.read_csv(z)
s='C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullsuccess_WI.csv'
success=pd.read_csv(s)
melt_success=pd.melt(success,('Mouse','Group'),var_name='Odor')
melt_peak=pd.melt(peak,('Mouse','Group'),var_name='Odor')
rdf=pd.concat([melt_peak[['Mouse','Group','Odor','value']],melt_success['value']],axis=1)
rdf.columns=['Mouse','Group','Odor','Peak','Success']



    











In [22]:



    


rdf.tail()



    


















    
Out[22]:










  
    

      
      Mouse
      Group
      Odor
      Peak
      Success
    

  
  
    

      12940
      G PMT (22)_160626_2
      Hexanal
      Hexanal05
      2.629784
      1.0
    

    

      12941
      G PMT (23)_160626_2
      Hexanal
      Hexanal05
      2.228448
      1.0
    

    

      12942
      G PMT (24)_160626_2
      Hexanal
      Hexanal05
      2.581529
      1.0
    

    

      12943
      G PMT (25)_160626_2
      Hexanal
      Hexanal05
      1.906673
      1.0
    

    

      12944
      G PMT (26)_160626_2
      Hexanal
      Hexanal05
      2.273161
      1.0
    

  




















In [23]:



    


smalldf=pd.DataFrame([])
xdf=pd.DataFrame([])
for x in rdf.Mouse.unique():
    for y in rdf.Odor.unique():
        b=rdf[(rdf.Mouse==x)&(rdf.Odor==y)].Peak.values[0]
        if b>0:
            c=rdf[(rdf.Mouse==x)&(rdf.Odor==y)]
            smalldf=smalldf.append(c,ignore_index=True)
        else:
            a=rdf[(rdf.Mouse==x)&(rdf.Odor==y)]
            xdf=xdf.append(a,ignore_index=True)
xdf=xdf.dropna()



    











In [24]:



    


one_r=smalldf[['Group','Odor','Success']][smalldf.Odor.isin(['MS01','Hexanal01','IAA01','THA','AP','PA','Hexanone','EB'])]
conc_r=smalldf[['Group','Odor','Success']][smalldf.Odor.isin(['MS01','MS05','MS10','Hexanal01','Hexanal05','Hexanal10','IAA01','IAA05','IAA10'])]
conc_r=conc_r.sort_values(by='Odor')

sorting=('MS01','THA','AP','PA','IAA01','Hexanone','Hexanal01','EB')
one_r.Odor=one_r.Odor.astype('category')
one_r.Odor.cat.set_categories(sorting,inplace=True)
one_r=one_r.sort_values(['Odor'])

one_r=one_r.replace(to_replace='MS01',value='MS')
one_r=one_r.replace(to_replace='Hexanal01',value='Hexanal')
one_r=one_r.replace(to_replace='IAA01',value='IAA')

sorter=('MS01','MS05','MS10','IAA01','IAA05','IAA10','Hexanal01','Hexanal05','Hexanal10')
conc_r.Odor=conc_r.Odor.astype('category')
conc_r.Odor.cat.set_categories(sorter,inplace=True)
conc_r=conc_r.sort_values(['Odor'])

conc_r=conc_r.replace(to_replace='MS01',value='MS 1%')
conc_r=conc_r.replace(to_replace='MS05',value='MS 5%')
conc_r=conc_r.replace(to_replace='MS10',value='MS 10%')
conc_r=conc_r.replace(to_replace='IAA01',value='IAA 1%')
conc_r=conc_r.replace(to_replace='IAA05',value='IAA 5%')
conc_r=conc_r.replace(to_replace='IAA10',value='IAA 10%')
conc_r=conc_r.replace(to_replace='Hexanal01',value='Hexanal 1%')
conc_r=conc_r.replace(to_replace='Hexanal05',value='Hexanal 5%')
conc_r=conc_r.replace(to_replace='Hexanal10',value='Hexanal 10%')



    











In [25]:



    


groupcolors={'Control':sns.xkcd_rgb["black"],'Mint':sns.xkcd_rgb["grey"],'Hexanal':sns.xkcd_rgb["azure"]};



    











In [26]:



    


# 1%
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(7,5));
ax=sns.pointplot(x='Odor',y='Success',hue='Group',palette=groupcolors,data=one_r)
# ax.legend_.remove()
sns.despine()
plt.ylabel('Ratio', fontsize=12);
plt.title('Successful Trials', fontsize=12);
plt.xlabel('Odor', fontsize=12);
# plt.ylim(-0.2,0.6)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Excitatory\\Successes_One.pdf")



    


















    
























In [28]:



    


MS_conc_r=conc_r[conc_r.Odor.isin(['MS 1%','MS 5%','MS 10%'])]
hex_conc_r=conc_r[conc_r.Odor.isin(['Hexanal 1%','Hexanal 5%','Hexanal 10%'])]
IAA_conc_r=conc_r[conc_r.Odor.isin(['IAA 1%','IAA 5%','IAA 10%'])]



    











In [74]:



    


# 1%
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(3.5,3));
ax=sns.pointplot(x='Odor',y='Success',hue='Group',palette=groupcolors,data=MS_conc_r)
ax.legend_.remove()
sns.despine()
plt.ylabel('Ratio', fontsize=12);
plt.title('Successful Trials', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(0.65,1)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Excitatory\\Successes_MS.pdf")



    


















    
























In [86]:



    


#boxplots
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(3.5,3));
ax=sns.barplot(x='Group',y='Success',palette=groupcolors,data=smalldf)
# ax.legend_.remove()
sns.despine()
plt.ylabel('Ratio', fontsize=12);
plt.title('Successful Trials', fontsize=12);
plt.xlabel('Odor', fontsize=12);
plt.ylim(0.5,1)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Excitatory\\Successes_Summary.pdf")



    


















    
























In [32]:



    


# smalldf.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Success.csv')



    











In [85]:



    


# mint=one_r[one_r.Group=='Mint']
# hexanal=one_r[one_r.Group=='Hexanal']
# control=one_r[one_r.Group=='Control']



    











In [90]:



    


# MS_conc_r.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Success_MS.csv')
hex_conc_r.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Success_hex.csv')
# IAA_conc_r.to_csv('C:\Users\Annie\Desktop\Plasticity\Stats\\Success_IAA.csv')

Make some ranks





In [6]:



    


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



    











In [19]:



    


composite_full=composite_full.drop('Blank',axis=1)
labels=composite_full.iloc[:,0:2]
df=composite_full.iloc[:,2:].rank(axis=1)
rankdf=pd.concat((labels,df),axis=1)



    











In [38]:



    


rankdf=rankdf.rename(index=str,columns={'MS01':'MS 1%','MS05':'MS 5%','MS10':'MS 10%','IAA01':'IAA 1%','IAA05':'IAA 5%','IAA10':'IAA 10%','Hexanal01':'Hexanal 1%','Hexanal05':'Hexanal 5%','Hexanal10':'Hexanal 10%'})



    











In [83]:



    


allrank=pd.melt(rankdf,('Mouse','Group'),var_name='Odor',value_name='Rank')



    











In [92]:



    


control=rankdf[rankdf.Group=='Control'].iloc[:,2:]
mint=rankdf[rankdf.Group=='Mint'].iloc[:,2:]
hexanal=rankdf[rankdf.Group=='Hexanal'].iloc[:,2:]



    











In [54]:



    


rankone=rankdf[['Group','Mouse','MS 1%','THA','AP','PA','IAA 1%','Hexanone','Hexanal 1%','EB']]
rankone=pd.melt(rankone,('Mouse','Group'),var_name='Odor',value_name='Rank')



    











In [59]:



    


rankms=rankdf[['Group','Mouse','MS 1%','MS 5%','MS 10%']]
rankms=pd.melt(rankms,('Mouse','Group'),var_name='Odor',value_name='Rank')
rankhex=rankdf[['Group','Mouse','Hexanal 1%','Hexanal 5%','Hexanal 10%']]
rankhex=pd.melt(rankhex,('Mouse','Group'),var_name='Odor',value_name='Rank')
rankiaa=rankdf[['Group','Mouse','IAA 1%','IAA 5%','IAA 10%']]
rankiaa=pd.melt(rankiaa,('Mouse','Group'),var_name='Odor',value_name='Rank')



    











In [82]:



    


rankone.to_csv('C:\Users\Annie\Desktop\Plasticity\Figures\\Ranks\\Ranks_One.csv')
rankms.to_csv('C:\Users\Annie\Desktop\Plasticity\Figures\\Ranks\\Ranks_ms.csv')
rankhex.to_csv('C:\Users\Annie\Desktop\Plasticity\Figures\\Ranks\\Ranks_hex.csv')
rankiaa.to_csv('C:\Users\Annie\Desktop\Plasticity\Figures\\Ranks\\Ranks_iaa.csv')



    











In [60]:



    


groupcolors={'Control':sns.xkcd_rgb["black"],'Mint':sns.xkcd_rgb["grey"],'Hexanal':sns.xkcd_rgb["azure"]};



    











In [80]:



    


# 1%
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(7,5));
ax=sns.pointplot(x='Odor',y='Rank',hue='Group',palette=groupcolors,data=rankone)
# ax.legend_.remove()
sns.despine()
plt.ylabel('Rank', fontsize=12);
plt.title('Odor Ranking', fontsize=12);
plt.xlabel('Odor', fontsize=12);
# plt.ylim(-0.2,0.6)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Ranks\\Ranks_One.pdf")



    


















    
























In [79]:



    


sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("paper", font_scale=1.3);
plt.figure(figsize=(3.5,3));
ax=sns.pointplot(x='Odor',y='Rank',hue='Group',palette=groupcolors,data=rankhex)
ax.legend_.remove()
sns.despine()
plt.ylabel('Rank', fontsize=12);
plt.title('Odor Ranking', fontsize=12);
plt.xlabel('Odor', fontsize=12);
# plt.ylim(0,7)
plt.tight_layout();
plt.savefig("C:\Users\Annie\Desktop\Plasticity\Figures\\Ranks\\Ranks_hex.pdf")



    


















    
























In [4]:



    






    











In [5]:



    






    


















    
Out[5]:










  
    

      
      Group
      Mouse
      Odor
      value
    

  
  
    

      0
      Control
      160321_1
      MS01
      0.0156712
    

    

      1
      Control
      160321_1
      MS01
      0.146939
    

    

      2
      Control
      160321_1
      MS01
      0.268299
    

    

      3
      Control
      160321_1
      MS01
      0.111727
    

    

      4
      Control
      160321_1
      MS01
      0.182874
    

    

      5
      Control
      160321_1
      MS01
      0.086948
    

    

      6
      Control
      160321_1
      MS01
      0.10626
    

    

      7
      Control
      160321_1
      MS01
      0.154366
    

    

      8
      Control
      160321_1
      MS01
      0.0208285
    

    

      9
      Control
      160321_1
      MS01
      0.0911923
    

    

      10
      Control
      160321_1
      MS01
      0.369279
    

    

      11
      Control
      160321_1
      MS01
      0.378874
    

    

      12
      Control
      160321_1
      MS01
      0.0808621
    

    

      13
      Control
      160321_1
      MS01
      0.117418
    

    

      14
      Control
      160321_1
      MS01
      0.0652251
    

    

      15
      Control
      160321_2
      MS01
      0.10832
    

    

      16
      Control
      160321_2
      MS01
      0.179008
    

    

      17
      Control
      160321_2
      MS01
      0.116572
    

    

      18
      Control
      160321_2
      MS01
      0.191554
    

    

      19
      Control
      160321_2
      MS01
      0.203699
    

    

      20
      Control
      160321_2
      MS01
      0.073796
    

    

      21
      Control
      160321_2
      MS01
      0.117774
    

    

      22
      Control
      160321_2
      MS01
      0.146185
    

    

      23
      Control
      160321_2
      MS01
      0.114494
    

    

      24
      Control
      160321_2
      MS01
      0.215789
    

    

      25
      Control
      160321_2
      MS01
      0.188919
    

    

      26
      Control
      160321_2
      MS01
      0.157538
    

    

      27
      Control
      160321_2
      MS01
      0.0952496
    

    

      28
      Control
      160321_2
      MS01
      0.290834
    

    

      29
      Control
      160321_2
      MS01
      0.143763
    

    

      ...
      ...
      ...
      ...
      ...
    

    

      11576
      Hexanal
      160626_1
      EB
      0.322258
    

    

      11577
      Hexanal
      160626_1
      EB
      0.910795
    

    

      11578
      Hexanal
      160626_1
      EB
      1.24341
    

    

      11579
      Hexanal
      160626_1
      EB
      1.02962
    

    

      11580
      Hexanal
      160626_2
      EB
      2.12934
    

    

      11581
      Hexanal
      160626_2
      EB
      2.52637
    

    

      11582
      Hexanal
      160626_2
      EB
      2.78749
    

    

      11583
      Hexanal
      160626_2
      EB
      2.87208
    

    

      11584
      Hexanal
      160626_2
      EB
      2.62394
    

    

      11585
      Hexanal
      160626_2
      EB
      3.14143
    

    

      11586
      Hexanal
      160626_2
      EB
      3.82647
    

    

      11587
      Hexanal
      160626_2
      EB
      3.41534
    

    

      11588
      Hexanal
      160626_2
      EB
      4.02052
    

    

      11589
      Hexanal
      160626_2
      EB
      3.5751
    

    

      11590
      Hexanal
      160626_2
      EB
      3.25669
    

    

      11591
      Hexanal
      160626_2
      EB
      2.49753
    

    

      11592
      Hexanal
      160626_2
      EB
      3.09135
    

    

      11593
      Hexanal
      160626_2
      EB
      2.89709
    

    

      11594
      Hexanal
      160626_2
      EB
      3.00285
    

    

      11595
      Hexanal
      160626_2
      EB
      1.64441
    

    

      11596
      Hexanal
      160626_2
      EB
      2.30101
    

    

      11597
      Hexanal
      160626_2
      EB
      3.71711
    

    

      11598
      Hexanal
      160626_2
      EB
      3.13533
    

    

      11599
      Hexanal
      160626_2
      EB
      2.55585
    

    

      11600
      Hexanal
      160626_2
      EB
      2.77863
    

    

      11601
      Hexanal
      160626_2
      EB
      3.68755
    

    

      11602
      Hexanal
      160626_2
      EB
      4.04924
    

    

      11603
      Hexanal
      160626_2
      EB
      4.30655
    

    

      11604
      Hexanal
      160626_2
      EB
      3.4347
    

    

      11605
      Hexanal
      160626_2
      EB
      3.63454
    

  



11606 rows × 4 columns



















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In [ ]:

Content source: liu-annie/CaI

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