def test_profile_info(self):
""" Test the ability of mpathic.profile_info to compute mutation rates based on total count values
"""
print '\nIn test_profile_info...'
file_names = glob.glob(self.input_dir + 'dataset_*.txt')
for err in [True, False]:
for file_name in file_names:
print '\t%s, err=%s =' % (file_name, str(err)),
description = file_name.split('_')[-1].split('.')[0]
executable = lambda: \
profile_info.main(io.load_dataset(file_name),err=err)
# If good, then profile_info.main should produce a valid df
if '_good' in file_name:
try:
df = executable()
qc.validate_profile_info(df)
out_file = self.output_dir+\
'profile_info_%s_err_%s.txt'%(description,str(err))
io.write(df, out_file)
io.load_profile_info(out_file)
print 'good.'
except:
print 'bad (ERROR).'
raise
# If bad, then profile_info.main should raise SortSeqError
elif '_bad' in file_name:
try:
self.assertRaises(SortSeqError, executable)
print 'badtype.'
except:
print 'good (ERROR).'
raise
# There are no other options
else:
raise SortSeqError('Unrecognized class of file_name.')
def test_profile_info(self):
""" Test the ability of mpathic.profile_info to compute mutation rates based on total count values
"""
print '\nIn test_profile_info...'
file_names = glob.glob(self.input_dir+'dataset_*.txt')
for err in [True,False]:
for file_name in file_names:
print '\t%s, err=%s ='%(file_name,str(err)),
description = file_name.split('_')[-1].split('.')[0]
executable = lambda: \
profile_info.main(io.load_dataset(file_name),err=err)
# If good, then profile_info.main should produce a valid df
if '_good' in file_name:
try:
df = executable()
qc.validate_profile_info(df)
out_file = self.output_dir+\
'profile_info_%s_err_%s.txt'%(description,str(err))
io.write(df,out_file)
io.load_profile_info(out_file)
print 'good.'
except:
print 'bad (ERROR).'
raise
# If bad, then profile_info.main should raise SortSeqError
elif '_bad' in file_name:
try:
self.assertRaises(SortSeqError,executable)
print 'badtype.'
except:
print 'good (ERROR).'
raise
# There are no other options
else:
raise SortSeqError('Unrecognized class of file_name.')
def main(dataset_df, err=False, method="naive", pseudocount=1.0, start=0, end=None):
"""
Computes the mutual information (in bits), at each position, between the character and the bin number.
Arguments:
dataset_df (pd.DataFrame): A dataframe containing a valid dataset.
start (int): An integer specifying the sequence start position
end (int): An integer specifying the sequence end position
method (str): Which method to use to estimate mutual information
Returns:
info_df (pd.DataFrame): A dataframe containing results.
"""
# Validate dataset_df
qc.validate_dataset(dataset_df)
# Get number of bins
bin_cols = [c for c in dataset_df.columns if qc.is_col_type(c, "ct_")]
if not len(bin_cols) >= 2:
raise SortSeqError("Information profile requires at least 2 bins.")
bins = [int(c.split("_")[1]) for c in bin_cols]
num_bins = len(bins)
# Get number of characters
seq_cols = [c for c in dataset_df.columns if qc.is_col_type(c, "seqs")]
if not len(seq_cols) == 1:
raise SortSeqError("Must be only one seq column.")
seq_col = seq_cols[0]
seqtype = qc.colname_to_seqtype_dict[seq_col]
alphabet = qc.seqtype_to_alphabet_dict[seqtype]
ct_cols = ["ct_" + a for a in alphabet]
num_chars = len(alphabet)
# Get sequence length and check start, end numbers
num_pos = len(dataset_df[seq_col][0])
if not (0 <= start < num_pos):
raise SortSeqError("Invalid start==%d, num_pos==%d" % (start, num_pos))
if end is None:
end = num_pos
elif end > num_pos:
raise SortSeqError("Invalid end==%d, num_pos==%d" % (end, num_pos))
elif end <= start:
raise SortSeqError("Invalid: start==%d >= end==%d" % (start, end))
# Record positions in new dataframe
counts_df = profile_ct.main(dataset_df)
info_df = counts_df.loc[start : (end - 1), ["pos"]].copy() # rows from start:end
info_df["info"] = 0.0
if err:
info_df["info_err"] = 0.0
# Fill in 3D array of counts
ct_3d_array = np.zeros([end - start, num_chars, num_bins])
for i, bin_num in enumerate(bins):
# Compute counts
counts_df = profile_ct.main(dataset_df, bin=bin_num)
# Fill in counts table
ct_3d_array[:, :, i] = counts_df.loc[start : (end - 1), ct_cols].astype(float)
# Compute mutual information for each position
for i in range(end - start): # i only from start:end
# Get 2D counts
nxy = ct_3d_array[i, :, :]
assert len(nxy.shape) == 2
# Compute mutual informaiton
if err:
mi, mi_err = info.estimate_mutualinfo(nxy, err=True, method=method, pseudocount=pseudocount)
info_df.loc[i + start, "info"] = mi
info_df.loc[i + start, "info_err"] = mi_err
else:
mi = info.estimate_mutualinfo(nxy, err=False, method=method, pseudocount=pseudocount)
info_df.loc[i + start, "info"] = mi
# Validate info dataframe
info_df = qc.validate_profile_info(info_df, fix=True)
return info_df
