def test_two(self):
'''
Lock in results on arbitrary data -- because meaningful runs take too long to run.
'''
fn = "two.txt"
logging.info(fn)
tmpOutfile = self.file_name(fn)
snpreader = self.snpreader_whole[:10, :]
spatial_coor = [[i, -i] for i in xrange(snpreader.iid_count)]
alpha_list = alpha_list_big = [
int(v) for v in np.logspace(2, np.log10(4000), 2)
]
dataframe = heritability_spatial_correction(snpreader,
spatial_coor,
snpreader.iid,
alpha_list,
2,
self.pheno_whole,
jackknife_count=2,
permute_plus_count=1,
permute_times_count=1,
just_testing=False)
dataframe.to_csv(tmpOutfile, sep="\t", index=False)
referenceOutfile = TestFeatureSelection.reference_file(
"heritability_spatial_correction/" + fn)
out, msg = ut.compare_files(tmpOutfile, referenceOutfile, tolerance)
self.assertTrue(
out,
"msg='{0}', ref='{1}', tmp='{2}'".format(msg, referenceOutfile,
tmpOutfile))
def test_two(self):
'''
Lock in results on arbitrary data -- because meaningful runs take too long to run.
'''
fn = "two.txt"
logging.info(fn)
tmpOutfile = self.file_name(fn)
snpreader = self.snpreader_whole[:10,:]
spatial_coor = [[i,-i] for i in xrange(snpreader.iid_count)]
alpha_list = alpha_list_big=[int(v) for v in np.logspace(2,np.log10(4000), 2)]
dataframe = heritability_spatial_correction(snpreader,spatial_coor,snpreader.iid,alpha_list,self.pheno_whole,jackknife_count=2,permute_plus_count=1,permute_times_count=1,just_testing=False)
dataframe.to_csv(tmpOutfile,sep="\t",index=False)
referenceOutfile = TestFeatureSelection.reference_file("heritability_spatial_correction/"+fn)
out,msg=ut.compare_files(tmpOutfile, referenceOutfile, tolerance)
self.assertTrue(out, "msg='{0}', ref='{1}', tmp='{2}'".format(msg, referenceOutfile, tmpOutfile))