def get_stripped_extenstion_file_mapping(params):
all_mappings = get_mangled_file_mapping(params)
output_mapping = {}
for mangled_name in all_mappings:
output_mapping[ming_fileio_library.get_filename_without_extension(mangled_name)] = all_mappings[mangled_name]
return output_mapping
def create_bucket_from_clusterinfo(cluster_info_filename, param_filename, clusterinfosummary_filename, output_filename, metadata_mapping):
output_file = open(output_filename, "w")
line_counts, table_data = ming_fileio_library.parse_table_with_headers(cluster_info_filename)
param_object = ming_proteosafe_library.parse_xml_file(open(param_filename, "r"))
mangled_mapping = ming_proteosafe_library.get_mangled_file_mapping(param_object)
clusters_in_network = set()
for row in csv.DictReader(open(clusterinfosummary_filename), delimiter='\t'):
clusters_in_network.add(row["cluster index"])
cluster_index_to_file_map = {}
clusters_map = {}
all_files = {}
for i in range(line_counts):
cluster_number = table_data["#ClusterIdx"][i]
if not(cluster_number in clusters_in_network):
continue
if not (cluster_number in clusters_map):
clusters_map[cluster_number] = []
cluster_index_to_file_map[cluster_number] = {}
#Adding all file names to mapping
for mangled_name in mangled_mapping.keys():
cluster_index_to_file_map[cluster_number][mangled_name] = 0.0
#print table_data["#Filename"][i].split("/")[1]
mangled_filename_only = os.path.basename(table_data["#Filename"][i])
cluster_index_to_file_map[cluster_number][mangled_filename_only] += max(float(table_data["#PrecIntensity"][i]), 1.0)
spectrum_info = {"filename":table_data["#Filename"][i], "intensity": table_data["#PrecIntensity"][i]}
all_files[table_data["#Filename"][i]] = 1
clusters_map[cluster_number].append(spectrum_info)
output_header_list = []
output_header_list.append("#OTU ID")
for header in mangled_mapping.keys():
if header.find("spec") == -1:
continue
if os.path.basename(mangled_mapping[header]) in metadata_mapping:
output_header_list.append(metadata_mapping[os.path.basename(mangled_mapping[header])])
else:
output_header_list.append(ming_fileio_library.get_filename_without_extension(os.path.basename(mangled_mapping[header])))
output_file.write("\t".join(output_header_list) + "\n")
for cluster_idx in cluster_index_to_file_map:
line_output_list = []
line_output_list.append(str(cluster_idx))
#line_string = str(cluster_idx) + "\t"
for header in mangled_mapping.keys():
if header.find("spec") == -1:
continue
line_output_list.append(str(cluster_index_to_file_map[cluster_idx][header]))
#line_string += str(cluster_index_to_file_map[cluster_idx][header]) + "\t"
#print line_string
#output_file.write(line_string + "\n")
output_file.write("\t".join(line_output_list) + "\n")
output_file.close()
def simple_presence_of_merged_spectra_processing(input_integrals_filename, output_clusterinfo_filename, mangled_mapping):
extension_stripped_mangled_mapping = {}
for key in mangled_mapping:
without_ext = ming_fileio_library.get_filename_without_extension(key)
extension_stripped_mangled_mapping[without_ext] = mangled_mapping[key]
header_order = open(input_integrals_filename).readline().rstrip().split(",")[1:]
table_list = ming_fileio_library.parse_table_with_headers_object_list(input_integrals_filename, delimiter=",")
#Removing other header infroamtion
table_list = table_list[2:]
output_dict = defaultdict(list)
print("for zheng's sanity print the wholetable ----")
print(table_list)
for result_object in table_list:
try:
sample_name = result_object["RTS:"]
except:
sample_name = "unknown"
scan_number = 0
for header in header_order:
scan_number += 1
abundance = result_object[header]
output_dict["filename"].append( sample_name )
output_dict["abundance"].append( abundance )
output_dict["scan_number"].append( scan_number )
output_dict["RT"].append( header )
ming_fileio_library.write_dictionary_table_data(output_dict, output_clusterinfo_filename)
def create_bucket_from_clusterinfo(cluster_info_filename, param_filename, clusterinfosummary_filename, output_filename, metadata_mapping):
output_file = open(output_filename, "w")
line_counts, table_data = ming_fileio_library.parse_table_with_headers(cluster_info_filename)
param_object = ming_proteosafe_library.parse_xml_file(open(param_filename, "r"))
mangled_mapping = ming_proteosafe_library.get_mangled_file_mapping(param_object)
clusters_in_network = set()
for row in csv.DictReader(open(clusterinfosummary_filename), delimiter='\t'):
clusters_in_network.add(row["cluster index"])
cluster_index_to_file_map = {}
clusters_map = {}
all_files = {}
for i in range(line_counts):
cluster_number = table_data["#ClusterIdx"][i]
if not(cluster_number in clusters_in_network):
continue
if not (cluster_number in clusters_map):
clusters_map[cluster_number] = []
cluster_index_to_file_map[cluster_number] = {}
#Adding all file names to mapping
for mangled_name in mangled_mapping.keys():
cluster_index_to_file_map[cluster_number][mangled_name] = 0.0
#print table_data["#Filename"][i].split("/")[1]
mangled_filename_only = os.path.basename(table_data["#Filename"][i])
cluster_index_to_file_map[cluster_number][mangled_filename_only] += max(float(table_data["#PrecIntensity"][i]), 1.0)
spectrum_info = {"filename":table_data["#Filename"][i], "intensity": table_data["#PrecIntensity"][i]}
all_files[table_data["#Filename"][i]] = 1
clusters_map[cluster_number].append(spectrum_info)
output_header_list = []
output_header_list.append("#OTU ID")
for header in mangled_mapping.keys():
if header.find("spec") == -1:
continue
if os.path.basename(mangled_mapping[header]) in metadata_mapping:
output_header_list.append(metadata_mapping[os.path.basename(mangled_mapping[header])])
else:
output_header_list.append(ming_fileio_library.get_filename_without_extension(os.path.basename(mangled_mapping[header])))
output_file.write("\t".join(output_header_list) + "\n")
for cluster_idx in cluster_index_to_file_map:
line_output_list = []
line_output_list.append(str(cluster_idx))
#line_string = str(cluster_idx) + "\t"
for header in mangled_mapping.keys():
if header.find("spec") == -1:
continue
line_output_list.append(str(cluster_index_to_file_map[cluster_idx][header]))
#line_string += str(cluster_index_to_file_map[cluster_idx][header]) + "\t"
#print line_string
#output_file.write(line_string + "\n")
output_file.write("\t".join(line_output_list) + "\n")
output_file.close()
def get_stripped_extenstion_file_mapping(params):
all_mappings = get_mangled_file_mapping(params)
output_mapping = {}
for mangled_name in all_mappings:
output_mapping[ming_fileio_library.get_filename_without_extension(mangled_name)] = all_mappings[mangled_name]
return output_mapping
def resolve_metadata_filename_to_all_files(filename, dataset_files):
stripped_extension = ming_fileio_library.get_filename_without_extension(filename)
acceptable_filenames = ["f." + dataset_filename for dataset_filename in dataset_files if dataset_filename.find(stripped_extension) != -1]
if len(acceptable_filenames) != 1:
return None
return acceptable_filenames[0]
def main():
parser = argparse.ArgumentParser(description='Modifying script')
parser.add_argument('param_xml', help='metadata_folder')
parser.add_argument('metadata_folder', help='metadata_folder')
parser.add_argument('output_metadata_table', help='output_metadata_table')
parser.add_argument('output_view_emporer', help='output_metadata_table')
args = parser.parse_args()
param_object = ming_proteosafe_library.parse_xml_file(
open(args.param_xml, "r"))
"""Outputting html"""
from urllib.parse import urlencode, quote_plus
parameters_for_qiime = {
'biom':
'http://gnps.ucsd.edu/ProteoSAFe/DownloadResultFile?task=%s&block=main&file=biom_output/networking_quant.biom'
% (param_object["task"][0]),
'metadata':
'http://gnps.ucsd.edu/ProteoSAFe/DownloadResultFile?task=%s&block=main&file=metadata_for_qiime/metadata_for_qiime.txt'
% (param_object["task"][0])
}
output_html_file = open(args.output_view_emporer, "w")
output_html_file.write("<script>\n")
output_html_file.write(
'window.location.replace("https://mingwangbeta.ucsd.edu/emperor?%s")\n'
% urlencode(parameters_for_qiime))
output_html_file.write("</script>\n")
output_html_file.close()
reverse_file_mangling = ming_proteosafe_library.get_reverse_mangled_file_mapping(
param_object)
metadata_files_in_folder = ming_fileio_library.list_files_in_dir(
args.metadata_folder)
object_list = []
if len(metadata_files_in_folder) != 1:
for real_name in reverse_file_mangling:
mangled_name = reverse_file_mangling[real_name]
if mangled_name.find("spec") == -1:
continue
object_list.append({"filename": real_name})
#open(args.output_metadata_table, "w").write("NO OUTPUT")
#open(args.output_view_emporer, "w").write("Please Include Metadata File")
#exit(0)
else:
object_list = ming_fileio_library.parse_table_with_headers_object_list(
metadata_files_in_folder[0])
if len(object_list) == 0:
for real_name in reverse_file_mangling:
mangled_name = reverse_file_mangling[real_name]
if mangled_name.find("spec") == -1:
continue
object_list.append({"filename": real_name})
#open(args.output_metadata_table, "w").write("NO OUTPUT")
#open(args.output_view_emporer, "w").write("Please Include Non Empty Metadata File")
#exit(0)
#Writing headers
header_list = ["#SampleID", "BarcodeSequence", "LinkerPrimerSequence"]
for key in object_list[0]:
if not key in header_list:
header_list.append(key)
header_list.append("ATTRIBUTE_GNPSDefaultGroup")
for metadata_object in object_list:
if not "#SampleID" in metadata_object:
metadata_object[
"#SampleID"] = ming_fileio_library.get_filename_without_extension(
metadata_object["filename"])
if not "BarcodeSequence" in metadata_object:
metadata_object["BarcodeSequence"] = "GATACA"
if not "LinkerPrimerSequence" in metadata_object:
metadata_object["LinkerPrimerSequence"] = "GATACA"
mangled_name = reverse_file_mangling[metadata_object["filename"]]
if mangled_name.find("spec-") != -1:
metadata_object["ATTRIBUTE_GNPSDefaultGroup"] = "G1"
elif mangled_name.find("spectwo-") != -1:
metadata_object["ATTRIBUTE_GNPSDefaultGroup"] = "G2"
elif mangled_name.find("specthree-") != -1:
metadata_object["ATTRIBUTE_GNPSDefaultGroup"] = "G3"
elif mangled_name.find("specfour-") != -1:
metadata_object["ATTRIBUTE_GNPSDefaultGroup"] = "G4"
elif mangled_name.find("specfive-") != -1:
metadata_object["ATTRIBUTE_GNPSDefaultGroup"] = "G5"
elif mangled_name.find("specsix-") != -1:
metadata_object["ATTRIBUTE_GNPSDefaultGroup"] = "G6"
ming_fileio_library.write_list_dict_table_data(object_list,
args.output_metadata_table,
header_list)
def main():
input_paramxml = sys.argv[1]
input_tsv_filename = sys.argv[2]
intermediate_output_folder = sys.argv[3]
output_file_bins = int(sys.argv[4])
params_obj = ming_proteosafe_library.parse_xml_file(open(input_paramxml))
snr_threshold = get_snr_filter(params_obj)
#Filtering Criteria
minimum_explained_intensity = 0.0
min_number_of_peaks_within_1_percent_of_max = 0.0
min_signal_peaks = 0.0
min_number_of_annotated_ions = 0.0
max_kl_strict_score = 50
max_ppm_error = 100000000
try:
minimum_explained_intensity = float(
params_obj["min_explained_intensity"][0])
min_number_of_peaks_within_1_percent_of_max = float(
params_obj["min_number_of_peaks_within_1_percent_of_max"][0])
min_signal_peaks = float(params_obj["min_signal_peaks"][0])
min_number_of_annotated_ions = float(
params_obj["min_number_of_annotated_ions"][0])
max_kl_strict_score = float(params_obj["kl_strict_max"][0])
if max_kl_strict_score == 0:
max_kl_strict_score = 50
max_ppm_error = float(params_obj["max_ppm_error"][0])
except:
print("exception")
minimum_explained_intensity = 0.0
min_number_of_peaks_within_1_percent_of_max = 0.0
min_signal_peaks = 0.0
max_kl_strict_score = 50
#lets find the 1% variant point, and then the naive solution is to to take the top scoring one
psm_set = ming_psm_library.PSMset("")
psm_set.load_PSM_tsvfile(input_tsv_filename, load_extra_metadata=True)
filename_to_psm_dict = group_psms_by_filename(psm_set)
#All output files, we are going to bin them starting now
output_filename_prefix = os.path.join(
intermediate_output_folder,
ming_fileio_library.get_filename_without_extension(
os.path.basename(input_tsv_filename)) + "_partition_")
output_files = {}
output_files_number_spectra = {}
for i in range(output_file_bins):
output_filename = output_filename_prefix + str(i) + ".json"
output_file = open(output_filename, "w")
output_file.write("[")
output_files[i] = output_file
output_files_number_spectra[i] = 0
for filename in filename_to_psm_dict:
extracted_spectra = extract_psms_from_filename(
filename, filename_to_psm_dict[filename], snr_threshold,
minimum_explained_intensity, min_signal_peaks,
min_number_of_peaks_within_1_percent_of_max,
min_number_of_annotated_ions, max_ppm_error)
for spectrum in extracted_spectra:
hashed_index = int(
hashlib.sha1(
spectrum["annotation"].encode('utf-8')).hexdigest(),
16) % (output_file_bins)
if output_files_number_spectra[hashed_index] == 0:
output_files[hashed_index].write(json.dumps(spectrum) + "\n")
else:
output_files[hashed_index].write("," + json.dumps(spectrum) +
"\n")
output_files_number_spectra[hashed_index] += 1
for i in range(output_file_bins):
output_files[i].write("]")
output_files[i].close()
