def scanPop(pop,step=10000,winSize=50000):
print winSize,pop
path='{}results/'.format(dir)
pathcsv='{}csv/'.format(path);pathpng='{}plot/'.format(path);pathdf='{}pandas/'.format(path)
if not os.path.exists(pathcsv): os.makedirs(pathcsv)
if not os.path.exists(pathpng): os.makedirs(pathpng)
if not os.path.exists(pathdf): os.makedirs(pathdf)
AF=pd.read_pickle( '{}AlleleFrequencies/{}.df'.format(dir,pop))
fname='{}.win{:.0f}K.'.format(pop,winSize/1000,step/1000)
res=[]
n=getNumSamples(pop)
CHROMS=AF.index.get_level_values(0).unique()
def f(x): return Estimate.getEstimate(x=x, n=n, method='tajimaD', selectionPredictor=True),Estimate.getEstimate(x=x, n=n, method='H', selectionPredictor=True),Estimate.getEstimate(x=x, n=n, method='SFSelect', selectionPredictor=True, svm_model_sfselect=svm)
for CHROM in CHROMS:
df=utl.slidingWindow(AF.loc[CHROM],n,step=step,winSize=winSize,f=f)
df['CHROM']=CHROM
df.set_index('CHROM', append=True, inplace=True)
df.index=df.index.swaplevel(0, 1)
res.append(df)
print pop, CHROM,AF.loc[CHROM].shape[0]
df=pd.concat(res)
df.to_pickle(pathdf+fname+'df')
df.to_csv(pathcsv+fname+'csv')
pplt.Manhattan.David(df.reset_index(),fname=pathpng+fname+'png')
print pop,'Done!'
# exportIndividualNames()
# collectAFs()
# scanPop('SAS')
# map(scanPop,Populations)
# Pool(2).map(scanPop,Populations)
def plotTests(pop):
print pop
dspath='/home/arya/20130502/'+pop+'/'
df= pd.read_pickle(dspath+'all.Tests.df')
outpath=dspath+pop+'/'
if not os.path.exists(outpath): os.makedirs(outpath)
df.to_pickle(outpath+'test_scores.df')
CHROMS= sorted(df.index.get_level_values(0).unique())
for CHROM in CHROMS:
fig=plt.figure(figsize=(20,10))
for i,test in enumerate(['tajimaD','H','SFSelect']):
x=df.loc[CHROM][test]
plt.subplot(3,1,i+1)
x.plot(style='o',grid=True)
outliers=utl.findOutliers(x)
if len(outliers):
outliers.plot(style='ro',grid=True)
plt.ylabel(test)
plt.suptitle('Chromoseome {}'.format(CHROM))
plt.savefig('{}{}.png'.format( outpath,CHROM) )
