np.set_printoptions(linewidth=200, precision=5, suppress=True) import pandas as pd; pd.options.display.max_rows = 20; pd.options.display.expand_frame_repr = False import os; import popgen.Util as utl import pylab as plt import popgen.Plots as pplt import popgen.Estimate as est # reload(dta) import popgen.hypoxia.Utils as hutl a=hutl.load()['L'] d=a.xs('D',level='READ',axis=1) reload(pplt) print d dd=d.groupby(level=[0,1],axis=1).apply(lambda xx: utl.scanGenome(xx,f=lambda x:x.max(),winSize=500000,step=500000).iloc[:,0]) pplt.Manhattan(dd) # plt.savefig(utl.home+'L.coverage.png', format='png', dpi=100) L17=hutl.loadscores('L',17).max(1).rename('L17') L=hutl.loadscores('L',180).max(1) C=hutl.loadscores('C',180).max(1) H=hutl.loadscores('H',180).max(1) all=pd.concat([L,C,H],1);all.columns=['L','C','H'] # H=L.apply(lambda x: x.idxmax(),1).rename('h')
''' Copyleft Nov 13, 2016 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: [email protected] ''' import numpy as np; np.set_printoptions(linewidth=200, precision=5, suppress=True) import pandas as pd; pd.options.display.max_rows = 20; pd.options.display.expand_frame_repr = False import seaborn as sns import pylab as plt; import matplotlib as mpl import os; import popgen.Util as utl import popgen.hypoxia.Utils as hutl # utl.createAnnotation(vcf='/home/arya/storage/Data/Dmelanogaster/Hypoxia/pops/all.vcf') hutl.scanCMH() hutl.scanSFSAll() hutl.saveAnnotationUCSC()