In [216]:

    

# Set-up default environment
%run '../ipython_startup.py'


    

    




Importing commonly used libraries: os, sys, numpy as np, scipy as sp, pandas as pd, matplotlib as mp, matplotlib.pyplot as plt, datetime as dt
Creating project level variables: MCLAB = /home/jfear/mclab, PROJ = /home/jfear/mclab/cegs_ase_paper, TODAY = 20150721
Adding ['scripts/mclib_Python', 'scripts/ase_Python'] to PYTHONPATH
The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [217]:

    

# Additional Imports
import sas7bdat


    

In [218]:

    

# Import clean Stack data from SAS (See Makefile_ase_summarize.sas for filtering details)
with sas7bdat.SAS7BDAT(os.path.join(PROJ, 'sas_data/clean_ase_stack.sas7bdat')) as F:
    dfClean = F.to_data_frame()
dfClean.head()


    

    




[clean_ase_stack.sas7bdat] header length 65536 != 8192
[clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] header length 65536 != 8192
[clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] header length 65536 != 8192
WARNING:/home/jfear/mclab/cegs_ase_paper/sas_data/clean_ase_stack.sas7bdat:[clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] [clean_ase_stack.sas7bdat] header length 65536 != 8192






    
Out[218]:






  

    

      

      
flag_AI_combined
      
flag_AI_qsim
      
flag_all_AI
      
flag_q4_AI
      
flag_q5_AI
      
flag_q6_AI
      
fusion_id
      
line
      
mating_status
      
mean_apn
      
q4_mean_theta
      
q5_mean_theta
      
q6_mean_theta
      
qsim_line
      
qsim_mean_theta
      
sum_both
      
sum_line
      
sum_tester
      
sum_total
    
  
  

    

      
0
      
0
      
0
      
0
      
1
      
0
      
1
      
F10005_SI
      
r101
      
M
      
29.523922
      
0.573
      
0.475
      
0.379
      
0.5
      
0.476
      
1274
      
155
      
133
      
1562
    
    

      
1
      
1
      
1
      
1
      
1
      
1
      
1
      
F10136_SI
      
r101
      
M
      
94.156800
      
0.862
      
0.812
      
0.747
      
0.5
      
0.812
      
1526
      
61
      
252
      
1839
    
    

      
2
      
1
      
1
      
1
      
1
      
1
      
1
      
F10268_SI
      
r101
      
M
      
67.062201
      
0.746
      
0.667
      
0.574
      
0.5
      
0.664
      
658
      
76
      
142
      
876
    
    

      
3
      
1
      
1
      
1
      
1
      
1
      
1
      
F10317_SI
      
r101
      
M
      
32.648139
      
0.157
      
0.110
      
0.077
      
0.5
      
0.112
      
1909
      
230
      
27
      
2166
    
    

      
4
      
0
      
0
      
0
      
1
      
0
      
0
      
F10806_SI
      
r101
      
M
      
40.588235
      
0.679
      
0.596
      
0.510
      
0.5
      
0.597
      
969
      
28
      
38
      
1035
    
  

In [219]:

    

# Pivot q5 mean theta to compare M and V
q5sbs = dfClean.pivot_table(values='q5_mean_theta', index=['fusion_id', 'line'], columns='mating_status')
q5sbs.rename(columns={'M': 'q5_M', 'V': 'q5_V'}, inplace=True)

# Pivot Flag AI Combined to compare M and V
AIsbs = dfClean.pivot_table(values='flag_AI_combined', index=['fusion_id', 'line'], columns='mating_status')
AIsbs.rename(columns={'M': 'AI_M', 'V': 'AI_V'}, inplace=True)

# Merge them together
merged = q5sbs.join(AIsbs)
merged.head()


    

    
Out[219]:






  

    

      

      
mating_status
      
q5_M
      
q5_V
      
AI_M
      
AI_V
    
    

      
fusion_id
      
line
      

      

      

      

    
  
  

    

      
F10001_SI
      
r324
      
0.507
      
0.490
      
0
      
0
    
    

      
r365
      
0.646
      
0.542
      
0
      
0
    
    

      
r373
      
0.589
      
0.553
      
0
      
1
    
    

      
r427
      
0.500
      
0.483
      
0
      
0
    
    

      
r491
      
0.401
      
0.501
      
0
      
0
    
  

In [220]:

    

# Group by line
grp = merged.groupby(level='line')
grp.ngroups


    

    
Out[220]:





49

In [221]:

    

# Panel Plot each line 
fig, axes = plt.subplots(7, 7, figsize=(8.5, 8.5), sharex=True, sharey=True)
axes = np.ravel(axes)
fig.suptitle(r"Comparison of Mean $\theta$'s" + '\nMated vs Virgin (q=0.5)', fontsize=18, x=0.45)
fig.text(0.45, -0.03, 'Mated', ha='center', fontsize=15)
fig.text(-0.03, 0.45, 'Virgin', va='center', rotation='vertical', fontsize=15)

cnt = 0
for line, df in grp:
    ax = axes[cnt]
    df.plot(kind='scatter', x='q5_M', y='q5_V', s=1, ax=ax)
    ax.set_title(line, fontsize=10)
    ax.plot([0, 1], [0, 1], transform=ax.transAxes, c="r", alpha=0.6)
    
    # Hide x and y axis labels
    ax.xaxis.set_ticklabels([]); ax.set_xlabel('')
    ax.yaxis.set_ticklabels([]); ax.set_ylabel('')
    
    cnt += 1   

plt.tight_layout(rect=[0, 0, .90, .90])
fig.savefig(os.path.join(PROJ, 'manuscript/figures/supp_mated_virgin_mean_theta.png'), bbox_inches='tight', dpi=300)


    

In [222]:

    

# Plot of two lines r350 and w47
subset = merged.loc[(slice(None), ['r350', 'w47']), :]

# Group by line
grp = subset.groupby(level='line')
grp.ngroups


    

    
Out[222]:





2

In [223]:

    

# Panel Plot each line 
fig, axes = plt.subplots(1, 2, figsize=(8.5, 4), dpi=300, sharex=True, sharey=True)
axes = np.ravel(axes)
fig.suptitle(r"Comparison of Mean $\theta$'s" + '\nMated vs Virgin (q=0.5)', fontsize=18, x=0.415)
fig.text(0.415, -0.03, 'Mated', ha='center', fontsize=15)
fig.text(-0.03, 0.415, 'Virgin', va='center', rotation='vertical', fontsize=15)

cnt = 0
for line, df in grp:
    ax = axes[cnt]
    
    noAI = (AIsbs['AI_M'] == 0) & (AIsbs['AI_V'] == 0)
    mAI = (AIsbs['AI_M'] == 1) & (AIsbs['AI_V'] == 0)
    vAI = (AIsbs['AI_M'] == 0) & (AIsbs['AI_V'] == 1)
    bAI = (AIsbs['AI_M'] == 1) & (AIsbs['AI_V'] == 1)
        
    # Set colors: black -> no AI, yellow -> AI in mated, cyan -> AI in virgin, red -> AI in both mated and virgin    
    df[noAI].plot(kind='scatter', x='q5_M', y='q5_V', s=15, ax=ax, c='k', label='No AI')
    df[mAI].plot(kind='scatter', x='q5_M', y='q5_V', s=15, ax=ax, c='y', label='AI Mated')
    df[vAI].plot(kind='scatter', x='q5_M', y='q5_V', s=15, ax=ax, c='c', label='AI Virgin')
    df[bAI].plot(kind='scatter', x='q5_M', y='q5_V', s=15, ax=ax, c='r', label='AI Mated and Virgin')
    
    if cnt == 1:
        handles, labels = ax.get_legend_handles_labels()
        plt.legend(handles, labels, bbox_to_anchor=(1, 1.03), loc=2, scatterpoints=1)
    else:
        ax.legend().remove()
        

    # Set options
    ax.set_title(line, fontsize=12)
    ax.plot([0, 1], [0, 1], transform=ax.transAxes, c="r", alpha=0.6)
    
    # Hide x and y axis labels
    ax.xaxis.set_ticklabels([]); ax.set_xlabel('')
    ax.yaxis.set_ticklabels([]); ax.set_ylabel('')
    
    cnt += 1   

plt.tight_layout(rect=[0, 0, .83, .83])
fig.savefig(os.path.join(PROJ, 'manuscript/figures/mated_virgin_mean_theta.png'), bbox_inches='tight', dpi=300)


    

In [ ]: