def make_nan(wavespaths, chunk, i):
chunkfam = h.read_fam(wp0.filepaths[i])
chunkmap = h.read_map(wavespaths.filepaths[0])
chunkngt = h.read_ngt(wavespaths.filepaths[0])
chunkinfo = h.read_info(wavespaths.infopaths[0])
chunkinfo['info'] = "NA"
chunkinfo['freq'] = "NA"
chunkinfo['a1'] = "NA"
chunkinfo['a2'] = "NA"
dosage = pd.read_csv(wavespaths.filepaths[0] + '.gz', sep=' ', compression='gzip', usecols=['SNP', 'A1', 'A2'])
for j in range(len(chunkfam.famID)):
dosage[chunkfam.famID[j]] = pd.Series("NA", index=dosage.index)
dosage[chunkfam.indID[j]] = pd.Series("NA", index=dosage.index)
h.write_fam('temp/' + mf + '/'+ chunk + '_' + str(i), chunkfam)
h.write_map('temp/' + mf + '/' + chunk + '_' + str(i), chunkmap)
h.write_ngt('temp/' + mf + '/' + chunk + '_' + str(i), chunkngt)
h.write_info('temp/' + mf + '/' + chunk + '_' + str(i), chunkinfo)
dosage.to_csv('temp/' + mf + '/' + chunk + '_' + str(i) + '.gz', sep=' ', index=False, compression='gzip')
df["previ"] = df.i.shift()
df2 = df[~(df.previ + 1 == df.i)]
if not pd.Index(df.i).is_monotonic:
return False
return True
s = h.parse()
s.wavepaths = h.replace(s)
wave_maps = h.read_wave_maps(s.wavepaths)
wave_fams = h.read_wave_fams(s.wavepaths)
merged_map = h.read_map(s.mergepath)
merged_fam = h.read_fam(s.mergepath)
include_inds = h.read_include_inds(s.indlist)
if include_inds:
merged_fam = merged_fam[merged_fam["indID"].isin(include_inds)]
wave_inds, wave_snps = h.read_wave_dosages(s.wavepaths.filepaths)
merged_inds, merged_snps = h.read_dosage(s.mergepath)
merged_info = h.read_info(s.mergepath)
checks = [
original__inds_in_dosage_and_fam_are_identical,
original__variants_in_dosage_and_map_are_identical,
original__variants_are_sorted_by_position,
original__indIDs_are_unique,
original__each_variant_has_just_one_position,
import pandas as pd
import helper as h
s = h.parse()
s.wavepaths = h.replace(s)
wave_fams = h.read_wave_fams(s.wavepaths, usecols=["famID", "indID"])
for i, fam in enumerate(wave_fams):
fam["wave"] = i + 1
merged_fam = h.read_fam(s.mergepath, usecols=["famID", "indID"])
waves = pd.concat(wave_fams)
merged_fam = pd.merge(merged_fam, waves, how='left', on=['famID', 'indID'])
merged_fam.to_csv(s.checkfolder + "covar.txt", sep=" ", header=False, index=False)