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Compare mean, median, COV, std





In [ ]:



    






    











In [17]:



    


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 [18]:



    


comp=pd.read_csv('C:\Users\Annie\Documents\Data\Ca_Imaging\GoodFiles\\fullpeak.csv')
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")
# cfull['Mouse'] = cfull['Mouse'].apply(lambda x: x.split('_')[0])
# composite_full['Mouse']=composite_full['Mouse'].apply(lambda x:x.split('_')[0])



    











In [4]:



    


cfull



    


















    
Out[4]:










  
    

      
      Group
      Mouse
      Odor
      value
    

  
  
    

      0
      Control
      160321_1
      MS01
      0.015671
    

    

      1
      Control
      160321_1
      MS01
      -0.034636
    

    

      2
      Control
      160321_1
      MS01
      -0.044179
    

    

      3
      Control
      160321_1
      MS01
      0.146939
    

    

      4
      Control
      160321_1
      MS01
      0.268299
    

    

      5
      Control
      160321_1
      MS01
      0.111727
    

    

      6
      Control
      160321_1
      MS01
      0.182874
    

    

      7
      Control
      160321_1
      MS01
      0.086948
    

    

      8
      Control
      160321_1
      MS01
      0.106260
    

    

      9
      Control
      160321_1
      MS01
      0.154366
    

    

      10
      Control
      160321_1
      MS01
      0.020829
    

    

      11
      Control
      160321_1
      MS01
      -0.093950
    

    

      12
      Control
      160321_1
      MS01
      0.091192
    

    

      13
      Control
      160321_1
      MS01
      0.369279
    

    

      14
      Control
      160321_1
      MS01
      0.378874
    

    

      15
      Control
      160321_1
      MS01
      0.080862
    

    

      16
      Control
      160321_1
      MS01
      0.117418
    

    

      17
      Control
      160321_1
      MS01
      0.065225
    

    

      18
      Control
      160321_2
      MS01
      0.108320
    

    

      19
      Control
      160321_2
      MS01
      0.179008
    

    

      20
      Control
      160321_2
      MS01
      0.116572
    

    

      21
      Control
      160321_2
      MS01
      0.191554
    

    

      22
      Control
      160321_2
      MS01
      0.203699
    

    

      23
      Control
      160321_2
      MS01
      0.073796
    

    

      24
      Control
      160321_2
      MS01
      0.117774
    

    

      25
      Control
      160321_2
      MS01
      0.146185
    

    

      26
      Control
      160321_2
      MS01
      0.114494
    

    

      27
      Control
      160321_2
      MS01
      0.215789
    

    

      28
      Control
      160321_2
      MS01
      0.188919
    

    

      29
      Control
      160321_2
      MS01
      0.157538
    

    

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

    

      12915
      Hexanal
      160626_1
      EB
      0.322258
    

    

      12916
      Hexanal
      160626_1
      EB
      0.910795
    

    

      12917
      Hexanal
      160626_1
      EB
      1.243407
    

    

      12918
      Hexanal
      160626_1
      EB
      1.029620
    

    

      12919
      Hexanal
      160626_2
      EB
      2.129344
    

    

      12920
      Hexanal
      160626_2
      EB
      2.526371
    

    

      12921
      Hexanal
      160626_2
      EB
      2.787493
    

    

      12922
      Hexanal
      160626_2
      EB
      2.872082
    

    

      12923
      Hexanal
      160626_2
      EB
      2.623944
    

    

      12924
      Hexanal
      160626_2
      EB
      3.141432
    

    

      12925
      Hexanal
      160626_2
      EB
      3.826471
    

    

      12926
      Hexanal
      160626_2
      EB
      3.415339
    

    

      12927
      Hexanal
      160626_2
      EB
      4.020523
    

    

      12928
      Hexanal
      160626_2
      EB
      3.575100
    

    

      12929
      Hexanal
      160626_2
      EB
      3.256687
    

    

      12930
      Hexanal
      160626_2
      EB
      2.497531
    

    

      12931
      Hexanal
      160626_2
      EB
      3.091349
    

    

      12932
      Hexanal
      160626_2
      EB
      2.897091
    

    

      12933
      Hexanal
      160626_2
      EB
      3.002847
    

    

      12934
      Hexanal
      160626_2
      EB
      1.644408
    

    

      12935
      Hexanal
      160626_2
      EB
      2.301010
    

    

      12936
      Hexanal
      160626_2
      EB
      3.717106
    

    

      12937
      Hexanal
      160626_2
      EB
      3.135329
    

    

      12938
      Hexanal
      160626_2
      EB
      2.555852
    

    

      12939
      Hexanal
      160626_2
      EB
      2.778626
    

    

      12940
      Hexanal
      160626_2
      EB
      3.687546
    

    

      12941
      Hexanal
      160626_2
      EB
      4.049244
    

    

      12942
      Hexanal
      160626_2
      EB
      4.306552
    

    

      12943
      Hexanal
      160626_2
      EB
      3.434703
    

    

      12944
      Hexanal
      160626_2
      EB
      3.634535
    

  



12945 rows × 4 columns



















In [19]:



    


colors={'160321':'r', '160421':'r', '160502':'r', '160503':'r', '160420':'r',
       '160517':'b', '160525':'b', '160620':'b', '160621':'b', '160622':'b', '160626':'b',
       '160330':'g', '160328':'g', '160428':'g', '160429':'g', '160325':'g', '160401':'g'}



    











In [20]:



    


cctrl=cfull[cfull.Mouse.isin(['160321','160421','160502','160503','160420'])]
hexfull=cfull[cfull.Mouse.isin(['160517', '160525', '160620', '160621', '160622', '160626'])]
msfull=cfull[cfull.Mouse.isin(['160330', '160328', '160428', '160429', '160325','160401'])]



    











In [21]:



    


cfc=composite_full[composite_full.Mouse.isin(['160321','160421','160502','160503','160420'])]
cfh=composite_full[composite_full.Mouse.isin(['160517', '160525', '160620', '160621', '160622', '160626'])]
cfm=composite_full[composite_full.Mouse.isin(['160330', '160328', '160428', '160429', '160325','160401'])]



    











In [22]:



    


odorlist=['THA', 'MS01', 'Blank', 'AP', 'MS10', 'MS05',
       'IAA05', 'IAA01', 'PA', 'IAA10', 'Hexanone', 'Hexanal10',
       'Hexanal01', 'Hexanal05', 'EB']

Calculate zscores for each group





In [23]:



    


from scipy.stats import zscore
from scipy.stats import nanmean
from scipy.stats import nanstd
# zscore(composite_full[composite_full.Mouse==x][y])



    











In [34]:



    


zscores=pd.DataFrame([])

for x in composite_full.Mouse.unique():
    tmp=pd.DataFrame([])
    for y in odorlist:
        v=(composite_full[composite_full.Mouse==x][y])
        a=pd.DataFrame((v - nanmean(v)) / nanstd(v))
        a=a.reset_index(drop=True)
        a.columns=[y]
        tmp=pd.concat([tmp,a],axis=1)
    b=pd.DataFrame(composite_full[composite_full.Mouse==x]['Mouse'])
    b=b.reset_index(drop=True)
    c=pd.DataFrame(composite_full[composite_full.Mouse==x]['Group'])
    c=c.reset_index(drop=True)
    tmp=pd.concat([c,tmp],axis=1)
    zscores=zscores.append(tmp)



    











In [32]:



    


zscores[zscores.Group=='Mint']['AP']



    


















    
Out[32]:








0    -0.615288
1     0.903824
2     0.715327
3    -1.364441
4    -1.014849
5    -1.326433
6    -0.714424
7    -0.058275
8     0.822923
9    -0.748909
10   -1.358461
11   -0.122966
12   -0.382172
13    0.369332
14    1.730979
15         NaN
16   -0.899317
17    0.232029
18   -0.729886
19    2.044908
20    0.773271
21    0.702630
22    1.040198
0    -0.027423
1    -0.195629
2     1.050387
3    -1.297867
4     0.834173
5    -0.224580
6    -1.110993
        ...   
13    0.341740
14   -1.451162
15    0.336718
16    0.021488
0    -2.021330
1    -0.668554
2     0.405857
3     0.000229
4    -0.622962
5     0.396640
6     0.838872
7    -0.105340
8    -0.289195
9    -0.188033
10   -0.228417
11    0.395511
12   -0.224052
13   -0.775834
14    0.358138
15   -0.195243
16   -0.224691
17    1.810836
18    2.944039
19   -0.423262
20    0.773096
21   -1.210640
22   -0.461908
23    1.340388
24   -1.216957
25   -0.407187
Name: AP, dtype: float64

















In [42]:



    


sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
sns.distplot(zscores[zscores.Group=='Control']['IAA10'].dropna(),color='r');
sns.distplot(zscores[zscores.Group=='Mint']['IAA10'].dropna(),color='g');
sns.distplot(zscores[zscores.Group=='Hexanal']['IAA10'].dropna(),color='b');



    











In [27]:



    


zmelt=pd.melt(zscores,"Group",var_name="Odor")



    











In [24]:



    


groupcolors={'Control':'r','Mint':'g','Hexanal':'b'}



    











In [30]:



    


#Plot everything by group
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=zmelt)
ax.legend_.remove()
sns.despine()
plt.ylabel('Zscore', fontsize=48);
plt.title('Zscore', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [319]:



    


sns.distplot(zscores[zscores.Group=='Control']['Hexanal01'], hist=False, color="r", kde_kws={"shade": True});
sns.distplot(zscores[zscores.Group=='Mint']['Hexanal01'], hist=False, color="g", kde_kws={"shade": True});
sns.distplot(zscores[zscores.Group=='Hexanal']['Hexanal01'], hist=False, color="b", kde_kws={"shade": True});



    











In [11]:



    


means=pd.DataFrame([])
for x in composite_full.Mouse.unique():
    tmp=pd.DataFrame([])
    for y in odorlist:
        a=composite_full[composite_full.Mouse==x][y].mean()
        m=pd.DataFrame({y:[a]})
        tmp=pd.concat([tmp,m],axis=1)
    b=pd.DataFrame({'Mouse':[x]})
    c=pd.DataFrame({'Group':[composite_full[composite_full.Mouse==x]['Group'].iloc[0]]})
#     c=c.reset_index(drop=True)
    tmp=pd.concat([c,tmp],axis=1)
    means=means.append(tmp)



    











In [12]:



    


meanmelt=pd.melt(means,"Group",var_name="Odor")



    











In [15]:



    


#Plot everything by group
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=meanmelt)
ax.legend_.remove()
sns.despine()
plt.ylabel('Mean', fontsize=48);
plt.title('Mean', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [25]:



    


medians=pd.DataFrame([])
for x in composite_full.Mouse.unique():
    tmp=pd.DataFrame([])
    for y in odorlist:
        a=composite_full[composite_full.Mouse==x][y].median()
        m=pd.DataFrame({y:[a]})
        tmp=pd.concat([tmp,m],axis=1)
    b=pd.DataFrame({'Mouse':[x]})
    c=pd.DataFrame({'Group':[composite_full[composite_full.Mouse==x]['Group'].iloc[0]]})
#     c=c.reset_index(drop=True)
    tmp=pd.concat([c,tmp],axis=1)
    medians=medians.append(tmp)



    











In [26]:



    


medianmelt=pd.melt(medians,"Group",var_name="Odor")



    











In [27]:



    


#Plot everything by group
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=medianmelt)
ax.legend_.remove()
sns.despine()
plt.ylabel('Median', fontsize=48);
plt.title('Median', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [ ]:



    






    











In [275]:



    


stds=pd.DataFrame([])
for x in composite_full.Mouse.unique():
    tmp=pd.DataFrame([])
    for y in odorlist:
        a=composite_full[composite_full.Mouse==x][y].std()
        m=pd.DataFrame({y:[a]})
        tmp=pd.concat([tmp,m],axis=1)
    b=pd.DataFrame({'Mouse':[x]})
    c=pd.DataFrame({'Group':[composite_full[composite_full.Mouse==x]['Group'].iloc[0]]})
#     c=c.reset_index(drop=True)
    tmp=pd.concat([c,tmp],axis=1)
    stds=stds.append(tmp)



    











In [276]:



    


stdmelt=pd.melt(stds,"Group",var_name="Odor")



    











In [277]:



    


#Plot everything by group
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot(x='Odor',y='value',hue='Group',palette=groupcolors,data=stdmelt)
ax.legend_.remove()
sns.despine()
plt.ylabel('STD', fontsize=48);
plt.title('STD', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [ ]:

Plot everything by mouse





In [330]:



    


#Plot everything by mouse
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot(x='Odor',y='value',hue='Mouse',palette=colors,data=cfull)
ax.legend_.remove()
sns.despine()
plt.ylabel('Integral', fontsize=48);
plt.title('By Mouse', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-330-fc5374bcb9e2> in <module>()
      3 sns.set_context("talk", font_scale=1.8);
      4 plt.figure(figsize=(45, 20));
----> 5 ax=sns.boxplot(x='Odor',y='value',hue='Mouse',palette=colors,data=cfull)
      6 ax.legend_.remove()
      7 sns.despine()

C:\Users\Annie\Anaconda2\lib\site-packages\seaborn\categorical.pyc in boxplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, fliersize, linewidth, whis, notch, ax, **kwargs)
   2173     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,
   2174                           orient, color, palette, saturation,
-> 2175                           width, fliersize, linewidth)
   2176 
   2177     if ax is None:

C:\Users\Annie\Anaconda2\lib\site-packages\seaborn\categorical.pyc in __init__(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, fliersize, linewidth)
    424                  width, fliersize, linewidth):
    425 
--> 426         self.establish_variables(x, y, hue, data, orient, order, hue_order)
    427         self.establish_colors(color, palette, saturation)
    428 

C:\Users\Annie\Anaconda2\lib\site-packages\seaborn\categorical.pyc in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
    152 
    153             # Figure out the plotting orientation
--> 154             orient = self.infer_orient(x, y, orient)
    155 
    156             # Option 2a:

C:\Users\Annie\Anaconda2\lib\site-packages\seaborn\categorical.pyc in infer_orient(self, x, y, orient)
    347         elif is_not_numeric(y):
    348             if is_not_numeric(x):
--> 349                 raise ValueError(no_numeric)
    350             else:
    351                 return "h"

ValueError: Neither the `x` nor `y` variable appears to be numeric.
















In [99]:



    


#Plot everything by mouse
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.swarmplot(x='Odor',y='value',hue='Mouse',palette=colors,data=cfull)
ax.legend_.remove()
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('By Mouse', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [351]:



    


#Plot control by mouse
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot('Odor','value',hue='Mouse',data=cctrl)
ax.legend_.remove()
sns.despine()
plt.ylabel('Peak', fontsize=48);
plt.title('By Mouse - Control', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [352]:



    


#Plot everything by mouse - Hexanal
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot('Odor','value',hue='Mouse',data=hexfull)
ax.legend_.remove()
sns.despine()
plt.ylabel('Peak', fontsize=48);
plt.title('By Mouse - Hexanal', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [353]:



    


#Plot everything by mouse - Mint
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.boxplot('Odor','value',hue='Mouse',data=msfull)
ax.legend_.remove()
sns.despine()
plt.ylabel('Peak', fontsize=48);
plt.title('By Mouse - Mint', fontsize=55);
plt.xlabel('Odor', fontsize=48);

Means





In [9]:



    


cdf=[]
for x in cfc.Mouse.unique():
    cdf.append(cfc[cfc.Mouse=='%s'%x][odorlist].mean(0,skipna=True))
cmeandf=pd.concat([pd.DataFrame(cfc.Mouse.unique()),pd.DataFrame(cdf)],axis=1)
cmeandf.columns=cfc.columns
cmeanmelt=pd.melt(cmeandf,"Mouse",var_name="Odor")

mdf=[]
for x in cfm.Mouse.unique():
    mdf.append(cfm[cfm.Mouse=='%s'%x][odorlist].mean(0,skipna=True))
mmeandf=pd.concat([pd.DataFrame(cfm.Mouse.unique()),pd.DataFrame(mdf)],axis=1)
mmeandf.columns=cfm.columns

hdf=[]
for x in cfh.Mouse.unique():
    hdf.append(cfh[cfh.Mouse=='%s'%x][odorlist].mean(0,skipna=True))
hmeandf=pd.concat([pd.DataFrame(cfh.Mouse.unique()),pd.DataFrame(hdf)],axis=1)
hmeandf.columns=cfh.columns
full_mean=pd.melt(pd.concat([cmeandf,mmeandf,hmeandf],ignore_index=True),'Mouse',var_name='Odor')



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-fd2cc96061dc> in <module>()
      3     cdf.append(cfc[cfc.Mouse=='%s'%x][odorlist].mean(0,skipna=True))
      4 cmeandf=pd.concat([pd.DataFrame(cfc.Mouse.unique()),pd.DataFrame(cdf)],axis=1)
----> 5 cmeandf.columns=cfc.columns
      6 cmeanmelt=pd.melt(cmeandf,"Mouse",var_name="Odor")
      7 

C:\Users\Annie\Anaconda2\lib\site-packages\pandas\core\generic.pyc in __setattr__(self, name, value)
   2683         try:
   2684             object.__getattribute__(self, name)
-> 2685             return object.__setattr__(self, name, value)
   2686         except AttributeError:
   2687             pass

pandas\src\properties.pyx in pandas.lib.AxisProperty.__set__ (pandas\lib.c:44748)()

C:\Users\Annie\Anaconda2\lib\site-packages\pandas\core\generic.pyc in _set_axis(self, axis, labels)
    426 
    427     def _set_axis(self, axis, labels):
--> 428         self._data.set_axis(axis, labels)
    429         self._clear_item_cache()
    430 

C:\Users\Annie\Anaconda2\lib\site-packages\pandas\core\internals.pyc in set_axis(self, axis, new_labels)
   2633             raise ValueError('Length mismatch: Expected axis has %d elements, '
   2634                              'new values have %d elements' %
-> 2635                              (old_len, new_len))
   2636 
   2637         self.axes[axis] = new_labels

ValueError: Length mismatch: Expected axis has 1 elements, new values have 17 elements
















In [10]:



    


full_mean



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-10-5befe0f2a882> in <module>()
----> 1 full_mean

NameError: name 'full_mean' is not defined
















In [279]:



    


sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.pointplot('Odor','value',hue='Mouse',palette=colors,data=full_mean)
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('Mean, By Mouse', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [ ]:



    






    











In [178]:



    






    











In [181]:



    






    


















    
Out[181]:










  
    

      
      Mouse
      Mean
    

  
  
    

      0
      160517
      0.202578
    

    

      1
      160517
      0.174359
    

    

      2
      160517
      0.282034
    

    

      3
      160517
      0.074394
    

    

      4
      160517
      0.084072
    

    

      5
      160517
      0.132455
    

    

      6
      160517
      0.026838
    

    

      7
      160517
      0.152396
    

    

      8
      160517
      0.107700
    

    

      9
      160517
      0.129484
    

    

      10
      160517
      0.199730
    

    

      11
      160517
      0.205829
    

    

      12
      160517
      0.204539
    

    

      13
      160517
      0.276089
    

    

      14
      160517
      0.263289
    

    

      15
      160517
      0.152796
    

    

      16
      160517
      0.014208
    

    

      17
      160517
      0.143471
    

    

      18
      160517
      0.356650
    

    

      19
      160517
      0.043189
    

    

      20
      160517
      0.225102
    

    

      21
      160525
      0.122464
    

    

      22
      160525
      0.157107
    

    

      23
      160525
      0.105191
    

    

      24
      160525
      0.245960
    

    

      25
      160525
      0.168111
    

    

      26
      160525
      0.172553
    

    

      27
      160525
      0.147849
    

    

      28
      160525
      0.134845
    

    

      29
      160525
      0.319380
    

    

      ...
      ...
      ...
    

    

      339
      160626
      0.168338
    

    

      340
      160626
      0.472677
    

    

      341
      160626
      0.639406
    

    

      342
      160626
      0.502097
    

    

      343
      160626
      1.377059
    

    

      344
      160626
      1.024115
    

    

      345
      160626
      1.655063
    

    

      346
      160626
      1.042272
    

    

      347
      160626
      1.119279
    

    

      348
      160626
      1.199992
    

    

      349
      160626
      1.760510
    

    

      350
      160626
      1.765566
    

    

      351
      160626
      1.847226
    

    

      352
      160626
      1.373974
    

    

      353
      160626
      1.352353
    

    

      354
      160626
      0.969210
    

    

      355
      160626
      1.134219
    

    

      356
      160626
      1.109174
    

    

      357
      160626
      1.178214
    

    

      358
      160626
      1.100854
    

    

      359
      160626
      0.929194
    

    

      360
      160626
      1.291236
    

    

      361
      160626
      1.594152
    

    

      362
      160626
      1.627045
    

    

      363
      160626
      1.318785
    

    

      364
      160626
      1.899250
    

    

      365
      160626
      2.046839
    

    

      366
      160626
      2.029860
    

    

      367
      160626
      1.711172
    

    

      368
      160626
      1.899989
    

  



369 rows × 2 columns



















In [ ]:

Medians





In [16]:



    


cdf=[]
for x in cfc.Mouse.unique():
    cdf.append(cfc[cfc.Mouse=='%s'%x][odorlist].median(0,skipna=True))
cmeddf=pd.concat([pd.DataFrame(cfc.Mouse.unique()),pd.DataFrame(cdf)],axis=1)
cmeddf.columns=cfc.columns
cmedmelt=pd.melt(cmeandf,"Mouse",var_name="Odor")

mdf=[]
for x in cfm.Mouse.unique():
    mdf.append(cfm[cfm.Mouse=='%s'%x][odorlist].median(0,skipna=True))
mmeddf=pd.concat([pd.DataFrame(cfm.Mouse.unique()),pd.DataFrame(mdf)],axis=1)
mmeddf.columns=cfm.columns

hdf=[]
for x in cfh.Mouse.unique():
    hdf.append(cfh[cfh.Mouse=='%s'%x][odorlist].median(0,skipna=True))
hmeddf=pd.concat([pd.DataFrame(cfh.Mouse.unique()),pd.DataFrame(hdf)],axis=1)
hmeddf.columns=cfh.columns
full_med=pd.melt(pd.concat([cmeddf,mmeddf,hmeddf],ignore_index=True),'Mouse',var_name='Odor')



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-16-ac5f11699f06> in <module>()
      3     cdf.append(cfc[cfc.Mouse=='%s'%x][odorlist].median(0,skipna=True))
      4 cmeddf=pd.concat([pd.DataFrame(cfc.Mouse.unique()),pd.DataFrame(cdf)],axis=1)
----> 5 cmeddf.columns=cfc.columns
      6 cmedmelt=pd.melt(cmeandf,"Mouse",var_name="Odor")
      7 

C:\Users\Annie\Anaconda2\lib\site-packages\pandas\core\generic.pyc in __setattr__(self, name, value)
   2683         try:
   2684             object.__getattribute__(self, name)
-> 2685             return object.__setattr__(self, name, value)
   2686         except AttributeError:
   2687             pass

pandas\src\properties.pyx in pandas.lib.AxisProperty.__set__ (pandas\lib.c:44748)()

C:\Users\Annie\Anaconda2\lib\site-packages\pandas\core\generic.pyc in _set_axis(self, axis, labels)
    426 
    427     def _set_axis(self, axis, labels):
--> 428         self._data.set_axis(axis, labels)
    429         self._clear_item_cache()
    430 

C:\Users\Annie\Anaconda2\lib\site-packages\pandas\core\internals.pyc in set_axis(self, axis, new_labels)
   2633             raise ValueError('Length mismatch: Expected axis has %d elements, '
   2634                              'new values have %d elements' %
-> 2635                              (old_len, new_len))
   2636 
   2637         self.axes[axis] = new_labels

ValueError: Length mismatch: Expected axis has 1 elements, new values have 17 elements
















In [281]:



    


sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.pointplot('Odor','value',hue='Mouse',palette=colors,data=full_med)
sns.despine()
plt.ylabel('Peak DF/F', fontsize=48);
plt.title('Median, By Mouse', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [289]:



    






    


















    
Out[289]:










  
    

      
      Mouse
      Odor
      value
    

  
  
    

      0
      160321
      THA
      0.137798
    

    

      1
      160421
      THA
      0.096810
    

    

      2
      160502
      THA
      0.108563
    

    

      3
      160503
      THA
      0.176428
    

    

      4
      160420
      THA
      0.150987
    

    

      5
      160310
      THA
      1.261965
    

    

      6
      160330
      THA
      0.069513
    

    

      7
      160328
      THA
      0.390481
    

    

      8
      160428
      THA
      0.221489
    

    

      9
      160429
      THA
      0.146081
    

    

      10
      160325
      THA
      0.295490
    

    

      11
      160401
      THA
      0.192857
    

    

      12
      160517
      THA
      0.146055
    

    

      13
      160525
      THA
      0.101379
    

    

      14
      160620
      THA
      0.511665
    

    

      15
      160621
      THA
      0.199847
    

    

      16
      160622
      THA
      0.366532
    

    

      17
      160626
      THA
      0.653575
    

    

      18
      160321
      MS 0.01
      0.126235
    

    

      19
      160421
      MS 0.01
      0.078415
    

    

      20
      160502
      MS 0.01
      0.114379
    

    

      21
      160503
      MS 0.01
      0.219756
    

    

      22
      160420
      MS 0.01
      0.130730
    

    

      23
      160310
      MS 0.01
      1.429755
    

    

      24
      160330
      MS 0.01
      0.061813
    

    

      25
      160328
      MS 0.01
      0.407542
    

    

      26
      160428
      MS 0.01
      0.177892
    

    

      27
      160429
      MS 0.01
      0.088924
    

    

      28
      160325
      MS 0.01
      0.249406
    

    

      29
      160401
      MS 0.01
      0.109788
    

    

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

    

      240
      160330
      Hexanal 0.05
      0.185113
    

    

      241
      160328
      Hexanal 0.05
      0.810908
    

    

      242
      160428
      Hexanal 0.05
      0.197639
    

    

      243
      160429
      Hexanal 0.05
      0.347239
    

    

      244
      160325
      Hexanal 0.05
      0.454709
    

    

      245
      160401
      Hexanal 0.05
      0.423419
    

    

      246
      160517
      Hexanal 0.05
      0.452034
    

    

      247
      160525
      Hexanal 0.05
      0.417041
    

    

      248
      160620
      Hexanal 0.05
      1.271598
    

    

      249
      160621
      Hexanal 0.05
      0.264920
    

    

      250
      160622
      Hexanal 0.05
      0.620433
    

    

      251
      160626
      Hexanal 0.05
      1.605382
    

    

      252
      160321
      EB
      0.182283
    

    

      253
      160421
      EB
      0.272477
    

    

      254
      160502
      EB
      0.221886
    

    

      255
      160503
      EB
      0.301049
    

    

      256
      160420
      EB
      0.254961
    

    

      257
      160310
      EB
      2.046201
    

    

      258
      160330
      EB
      0.071947
    

    

      259
      160328
      EB
      0.943080
    

    

      260
      160428
      EB
      0.245750
    

    

      261
      160429
      EB
      0.299182
    

    

      262
      160325
      EB
      0.398232
    

    

      263
      160401
      EB
      0.280932
    

    

      264
      160517
      EB
      0.216629
    

    

      265
      160525
      EB
      0.222401
    

    

      266
      160620
      EB
      1.819728
    

    

      267
      160621
      EB
      0.150275
    

    

      268
      160622
      EB
      0.790037
    

    

      269
      160626
      EB
      2.568513
    

  



270 rows × 3 columns



















In [ ]:



    






    











In [ ]:

STD and COV





In [282]:



    


#Individual Animal COV
cdf=[]
for x in cfc.Mouse.unique():
    cdf.append(cfc[cfc.Mouse=='%s'%x][odorlist].std(0,skipna=True))
cstddf=pd.concat([pd.DataFrame(cfc.Mouse.unique()),pd.DataFrame(cdf)],axis=1)
cstddf.columns=cfc.columns
cstdmelt=pd.melt(cmeandf,"Mouse",var_name="Odor")

mdf=[]
for x in cfm.Mouse.unique():
    mdf.append(cfm[cfm.Mouse=='%s'%x][odorlist].std(0,skipna=True))
mstddf=pd.concat([pd.DataFrame(cfm.Mouse.unique()),pd.DataFrame(mdf)],axis=1)
mstddf.columns=cfm.columns

hdf=[]
for x in cfh.Mouse.unique():
    hdf.append(cfh[cfh.Mouse=='%s'%x][odorlist].std(0,skipna=True))
hstddf=pd.concat([pd.DataFrame(cfh.Mouse.unique()),pd.DataFrame(hdf)],axis=1)
hstddf.columns=cfh.columns
full_std=pd.melt(pd.concat([cstddf,mstddf,hstddf],ignore_index=True),'Mouse',var_name='Odor')
full_cov=pd.concat([full_std.Mouse,full_std.Odor,full_std.value.div(full_mean.value,axis=0)],axis=1)



    











In [283]:



    


#Get Averages
c_ave=pd.DataFrame(cstddf[odorlist].div(cmeandf[odorlist]).mean())
c_ave.columns=['Control']
m_ave=pd.DataFrame(mstddf[odorlist].div(mmeandf[odorlist]).mean())
m_ave.columns=['Mint']
h_ave=pd.DataFrame(hstddf[odorlist].div(hmeandf[odorlist]).mean())
h_ave.columns=['Hexanal']

full_ave=pd.concat([c_ave,m_ave,h_ave],axis=1)
full_ave=full_ave.reset_index()
full_ave.columns=['Odor','Control','Mint','Hexanal']

avemelt=pd.melt(full_ave,'Odor')



    











In [284]:



    


#PLOT STUFF
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.pointplot('Odor','value',hue='Mouse',palette=colors,data=full_cov)
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV, by Mouse', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [ ]:



    






    











In [37]:



    


#Get Composite COV
cfc_std=pd.DataFrame(cfc[odorlist].std(0,skipna=True))
cfc_mean=pd.DataFrame(cfc[odorlist].mean(0,skipna=True))
cfc_cov=cfc_std.div(cfc_mean)

mfc_std=pd.DataFrame(cfm[odorlist].std(0,skipna=True))
mfc_mean=pd.DataFrame(cfm[odorlist].mean(0,skipna=True))
mfc_cov=mfc_std.div(mfc_mean)

hfc_std=pd.DataFrame(cfh[odorlist].std(0,skipna=True))
hfc_mean=pd.DataFrame(cfh[odorlist].mean(0,skipna=True))
hfc_cov=hfc_std.div(hfc_mean)

cfc_cov.columns=['Control']
mfc_cov.columns=['Mint']
hfc_cov.columns=['Hexanal']

full=pd.concat([cfc_cov,mfc_cov,hfc_cov],axis=1)
full=full.reset_index()
full.columns=['Odor','Control','Mint','Hexanal']

fullmelt=pd.melt(full,'Odor')
fullmelt.head()
# cstdall.columns=cfc.columns

# mdf.append(cfm[cfm.Mouse=='%s'%x][odorlist].std(0,skipna=True))
# mstddf=pd.concat([pd.DataFrame(cfm.Mouse.unique()),pd.DataFrame(mdf)],axis=1)
# mstddf.columns=cfm.columns

# hdf.append(cfh[cfh.Mouse=='%s'%x][odorlist].std(0,skipna=True))
# hstddf=pd.concat([pd.DataFrame(cfh.Mouse.unique()),pd.DataFrame(hdf)],axis=1)
# hstddf.columns=cfh.columns
# full_std=pd.melt(pd.concat([cstddf,mstddf,hstddf],ignore_index=True),'Mouse',var_name='Odor')
# full_cov=pd.concat([full_std.Mouse,full_std.Odor,full_std.value.div(full_mean.value,axis=0)],axis=1)



    


















    
Out[37]:










  
    

      
      Odor
      variable
      value
    

  
  
    

      0
      THA
      Control
      0.744257
    

    

      1
      MS 0.01
      Control
      0.828195
    

    

      2
      BLANK
      Control
      0.868441
    

    

      3
      AP
      Control
      0.820212
    

    

      4
      MS 0.1
      Control
      0.793777
    

  




















In [382]:



    






    











In [44]:



    


#PLOT STUFF
sns.set(style="white", palette="muted", color_codes=True);
sns.set_context("talk", font_scale=1.8);
plt.figure(figsize=(45, 20));
ax=sns.pointplot('Odor','value',hue='variable',palette={'Control':'r','Hexanal':'b','Mint':'g'},data=fullmelt)
sns.despine()
plt.ylabel('COV', fontsize=48);
plt.title('COV, Complete', fontsize=55);
plt.xlabel('Odor', fontsize=48);



    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:

Content source: liu-annie/CaI

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