def main():
usage = """
%prog [options] [inputFileGlob [outputFile]]
"""
parser = OptionParser(usage=usage, version="%prog 1.0")
parser.add_option("-w",
"--windowsize",
dest="windowsize",
type="int",
default=None,
help="window size to write to different files")
parser.add_option("-o",
"--outputdir",
dest="outputdir",
type="str",
default=None,
help="output dir")
(options, args) = parser.parse_args()
file_name_list = args
assert options.windowsize is not None
assert options.outputdir is not None
if not os.path.exists(options.outputdir):
os.makedirs(options.outputdir)
# full_file_name_list = [l.strip() for l in file_name_list]
meta_data, populations, regions = simons_meta_data.get_meta_data()
# window_iter = genome_window_iter(*file_name_list, window_size=options.windowsize, chunk_size=options.chunksize)
window_iter = genome_window_iter(*file_name_list,
window_size=options.windowsize)
for window in window_iter:
# names, starts, ends, _ = list(zip(*window))
names, starts, ends, seqs = list(zip(*window))
assert names[1:] == names[:-1]
assert starts[1:] == starts[:-1]
assert ends[1:] == ends[:-1]
outfile = os.path.join(
options.outputdir,
"{}-{:09d}-{:09d}.fa".format(names[0], starts[0], ends[0]))
with open(outfile, 'w') as f:
for (name, start, end,
seq), file_name in zip(window, file_name_list):
print(">{}\n{}\n".format(file_base_name(file_name), seq),
file=f)
sys.path.insert(0, script_dir + '/../notebooks')
import analysis_globals
parser = argparse.ArgumentParser()
parser.add_argument("--dist-dir", dest="dist_dir", type=Path)
parser.add_argument("--meta-data-dir", dest="meta_data_dir", type=Path)
parser.add_argument("--out-file", dest="out_file", type=Path)
parser.add_argument("--dist-twice-out-file", dest="dist_twice_out_file", type=Path)
parser.add_argument("--include-ust-ishim", dest="include_ust_ishim", action='store_true', default=False)
# parser.add_argument("--result-dir", dest="result_dir", type=Path)
# parser.add_argument("--result-file-prefix", dest="result_file_prefix", type=str, default='dist_data')
args = parser.parse_args()
# easy loading of meta data in a consistent manner across code
individuals, populations, regions = simons_meta_data.get_meta_data(
meta_data_dir=args.meta_data_dir,
include_ust_ishim=args.include_ust_ishim)
def optimize_data_frame(df, down_int='integer'):
# down_int can be 'unsigned'
converted_df = pandas.DataFrame()
floats_optim = (df
.select_dtypes(include=['float'])
.apply(pandas.to_numeric,downcast='float')
)
converted_df[floats_optim.columns] = floats_optim
ints_optim = (df
from pandas import DataFrame, Series
import simons_meta_data
script_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, script_dir + '/../notebooks')
import analysis_globals
parser = argparse.ArgumentParser()
parser.add_argument("dist_file", type=Path)
parser.add_argument("dist_twice_file", type=Path)
args = parser.parse_args()
dist_data = pandas.read_hdf(args.dist_file)
individuals, populations, regions = simons_meta_data.get_meta_data(
meta_data_dir=analysis_globals.meta_data_dir)
# dict for swapping columns
swap_dict = dict()
for colname in dist_data.columns.values:
if colname.endswith('_1'):
swap_dict[colname] = colname[:-2] + '_2'
if colname.endswith('_2'):
swap_dict[colname] = colname[:-2] + '_1'
cols = ['start', 'end', 'indiv_1', 'indiv_2', 'dist']
dist_data_twice = (pandas.concat([
dist_data[cols], dist_data[cols].rename(columns=swap_dict)
]).sort_values(['indiv_1', 'start']).reset_index(drop=True))
import simons_meta_data
individuals, populations, regions = simons_meta_data.get_meta_data()
f = open('samples.ind', 'w')
print('Chimp', file=f)
for indiv in individuals:
chromotype = individuals[indiv]['Genetic sex assignment']
sex = chromotype == 'XY' and 'M' or 'F'
pop = individuals[indiv]['Population ID']
print(indiv, sex, pop, file=f)
import gc
import simons_meta_data
from hg19_chrom_sizes import hg19_chrom_sizes as chromosome_lengths
parser = argparse.ArgumentParser()
parser.add_argument("--dist-dir", dest="dist_dir", type=Path)
parser.add_argument("--meta-data-dir", dest="meta_data_dir", type=Path)
parser.add_argument("--out-file", dest="out_file", type=Path)
# parser.add_argument("--result-dir", dest="result_dir", type=Path)
# parser.add_argument("--result-file-prefix", dest="result_file_prefix", type=str, default='dist_data')
args = parser.parse_args()
# easy loading of meta data in a consistent manner across code
individuals, populations, regions = simons_meta_data.get_meta_data(meta_data_dir=args.meta_data_dir)
def optimize_data_frame(df, down_int='integer'):
# down_int can be 'unsigned'
converted_df = pandas.DataFrame()
floats_optim = (df
.select_dtypes(include=['float'])
.apply(pandas.to_numeric,downcast='float')
)
converted_df[floats_optim.columns] = floats_optim
ints_optim = (df
.select_dtypes(include=['int'])
.apply(pandas.to_numeric,downcast=down_int)