def preprocess(self):
matplotlib.use("Agg")
gs = GenomicSubset(self.params.region)
A = SnpSubset(self.refpanel, bedtool=gs.bedtool)
W = A.expanded_by(self.params.ld_window / 1000.0)
R = BlockDiag.ld_matrix(self.refpanel, W.irs.ranges(), 300, band_units="SNPs")
pickle.dump(R, self.R_file(mode="wb"), 2)
# R.plot(A.irs, filename=self.R_plotfilename())
RA = R.zero_outside_irs(A.irs)
pickle.dump(RA, self.RA_file(mode="wb"), 2)
def preprocess(self):
matplotlib.use('Agg')
gs = GenomicSubset(self.params.region)
A = SnpSubset(self.refpanel, bedtool=gs.bedtool)
W = A.expanded_by(self.params.ld_window / 1000.)
R = BlockDiag.ld_matrix(self.refpanel,
W.irs.ranges(),
300,
band_units='SNPs')
pickle.dump(R, self.R_file(mode='wb'), 2)
# R.plot(A.irs, filename=self.R_plotfilename())
RA = R.zero_outside_irs(A.irs)
pickle.dump(RA, self.RA_file(mode='wb'), 2)
if __name__ == '__main__':
from primitives import Dataset, GenomicSubset, SnpSubset
import copy
from time import time
import argparse
np.random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--M', type=int, required=True, help='the number of SNPs to use')
parser.add_argument('-check_dense', action='store_true', default=False)
args = parser.parse_args()
d = Dataset('GERA', forced_M=args.M)
indivs = d.random_indivs(200)
tiny_gs = GenomicSubset('50')
tiny_ss = SnpSubset(d, bedtool=tiny_gs.bedtool)
tiny_buffered_ss = tiny_ss.expanded_by(0.01)
t0 = time()
R = BlockDiag.ld_matrix(d, tiny_buffered_ss.irs.ranges(), 0.01, indivs=indivs) # 1 cM bandwidth
R = R.add_ridge(0.05, renormalize=True)
print('trace of renormalized R should be close to M (with noise due to sample vs pop LD',
R.trace(), tiny_buffered_ss.num_snps(),
R.trace() == tiny_buffered_ss.num_snps())
print('computing R took', time() - t0)
print('shape of R is:', R.shape())
RA = R.copy()
RA.zero_outside_irs(tiny_ss.irs)
b = BlockDiag.from_big1darray(np.random.randn(d.M), R.ranges())
# check inverse computation
