df = pd.merge(merged_map, wave_map, on="SNP", how="left").dropna()
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,
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)
