def load_peptide_set(peptide_length=[8, 9, 10, 11], nrows=None):
peptide_lengths = int_or_seq(peptide_length)
lens = "_".join(str(n) for n in peptide_lengths)
cache_filename = \
"reference_peptide_set_" + lens + "_nrows_" + str(nrows) + ".pickle.gz"
def save_set(src_path, dst_path):
string_set = _generate_set(src_path, peptide_lengths, nrows)
with GzipFile(dst_path, 'w') as out_file:
out_file.write(pickle.dumps(string_set))
return string_set
def load_set(path):
result = None
with GzipFile(path, 'r') as in_file:
result = pickle.loads(in_file.read())
return result
return fetch_and_transform(
transformed_filename=cache_filename,
transformer=save_set,
loader=load_set,
source_filename=FASTA_FILENAME,
source_url=FULL_URL,
subdir=cache.subdir)
def load_peptide_counts(peptide_length=[8, 9, 10, 11], nrows=None):
"""
List of all reference peptides encoded in a reference human exome
"""
peptide_lengths = int_or_seq(peptide_length)
lens = "_".join(str(n) for n in peptide_lengths)
cache_filename = \
"reference_peptide_counts_" + lens + "_nrows_" + str(nrows) + ".csv"
def save_counts(src_path, dst_path):
counts = _generate_counts(src_path, peptide_lengths, nrows)
print("Saving %s" % dst_path)
counts.to_csv(dst_path)
return counts
return fetch_and_transform(transformed_filename=cache_filename,
transformer=save_counts,
loader=pd.read_csv,
source_filename=FASTA_FILENAME,
source_url=FULL_URL,
subdir=cache.subdir)
def load_peptide_counts(peptide_length=[8, 9, 10, 11], nrows=None):
"""
List of all reference peptides encoded in a reference human exome
"""
peptide_lengths = int_or_seq(peptide_length)
lens = "_".join(str(n) for n in peptide_lengths)
cache_filename = \
"reference_peptide_counts_" + lens + "_nrows_" + str(nrows) + ".csv"
def save_counts(src_path, dst_path):
counts = _generate_counts(src_path, peptide_lengths, nrows)
print("Saving %s" % dst_path)
counts.to_csv(dst_path)
return counts
return fetch_and_transform(
transformed_filename=cache_filename,
transformer=save_counts,
loader=pd.read_csv,
source_filename=FASTA_FILENAME,
source_url=FULL_URL,
subdir=cache.subdir)
def load_peptide_set(peptide_length=[8, 9, 10, 11], nrows=None):
peptide_lengths = int_or_seq(peptide_length)
lens = "_".join(str(n) for n in peptide_lengths)
cache_filename = \
"reference_peptide_set_" + lens + "_nrows_" + str(nrows) + ".pickle.gz"
def save_set(src_path, dst_path):
string_set = _generate_set(src_path, peptide_lengths, nrows)
with GzipFile(dst_path, 'w') as out_file:
out_file.write(cPickle.dumps(string_set))
return string_set
def load_set(path):
result = None
with GzipFile(path, 'r') as in_file:
result = cPickle.loads(in_file.read())
return result
return fetch_and_transform(transformed_filename=cache_filename,
transformer=save_set,
loader=load_set,
source_filename=FASTA_FILENAME,
source_url=FULL_URL,
subdir=cache.subdir)
