def test_store_expr(expr, outputdir): with pytest.warns(DeprecationWarning): store_expr(str(outputdir / "ELEY_1_SUBTRACT_DEPRECATION.expr"), expr) for obj in [*test_numbers, test_dict, *test_lists]: with pytest.warns(DeprecationWarning): with pytest.raises(TypeError): store_expr(obj, expr) for obj in [*test_numbers, test_string, test_dict, *test_lists]: with pytest.warns(DeprecationWarning): with pytest.raises(TypeError): store_expr(test_string, obj)
print("\t -> Executing peak post-procesing and quantification...")
# ignore TMS ions and use same mass range for all experiments
for peak in peak_list:
peak.crop_mass(50,540)
peak.null_mass(73)
peak.null_mass(147)
# find peak areas
area = peak_sum_area(im, peak)
peak.area = area
area_dict = peak_top_ion_areas(im, peak)
peak.set_ion_areas(area_dict)
# create an experiment
expr = Experiment(expr_code, peak_list)
# use same retention time range for all experiments
lo_rt_limit = "6.5m"
hi_rt_limit = "21m"
print(f"\t -> Selecting retention time range between '{lo_rt_limit}' and '{hi_rt_limit}'")
expr.sele_rt_range([lo_rt_limit, hi_rt_limit])
# store processed data as experiment object
output_file = "output/" + expr_code + ".expr"
print(f"\t -> Saving the result as '{output_file}'")
store_expr(output_file, expr)
def store(self, filename=None):
"""
Save the Project to a file
:param filename:
:type filename:
:return:
:rtype:
"""
if filename:
self.filename.value = filename
self.date_modified.value = time_now()
if any((self.expr is None, self.tic is None, self.peak_list is None,
self.intensity_matrix is None, self.gcms_data is None)):
raise ValueError("Must call 'Experiment.run()' before 'store()'")
# Write experiment, tic and peak list to temporary directory
with tempfile.TemporaryDirectory() as tmp:
self.gcms_data.dump(os.path.join(tmp, "gcms_data.dat"))
self.intensity_matrix.dump(
os.path.join(tmp, "intensity_matrix.dat"))
self.tic.write(os.path.join(tmp, "tic.dat"), formatting=False)
store_peaks(self.peak_list, os.path.join(tmp, "peaks.dat"), 3)
store_expr(os.path.join(tmp, "experiment.expr"), self.expr)
with tarfile.open(self.filename.value,
mode="w") as experiment_file:
# # Add the method files
# for method in self._method_files:
# experiment_file.add(method)
experiment_data = {
"name": str(self.name),
"user": str(self.user),
"device": str(self.device),
"date_created": float(self.date_created),
"date_modified": float(self.date_modified),
"description": str(self.description),
"version": "1.0.0",
"method": str(self.method),
"original_filename": str(self.original_filename),
"original_filetype": int(self.original_filetype),
"identification_performed": self.identification_performed,
"ident_audit_record": None,
}
if self.identification_performed:
experiment_data["ident_audit_record"] = dict(
self.ident_audit_record)
store_peaks(self.ident_peaks,
os.path.join(tmp, "ident_peaks.dat"), 3)
experiment_file.add(os.path.join(tmp, "ident_peaks.dat"),
arcname="ident_peaks.dat")
# Add the info file to the archive
info_json = json.dumps(experiment_data,
indent=4).encode("utf-8")
tarinfo = tarfile.TarInfo('info.json')
tarinfo.size = len(info_json)
experiment_file.addfile(tarinfo=tarinfo,
fileobj=BytesIO(info_json))
# Add the method to the archive
experiment_file.add(self.method.value,
arcname=filename_only(self.method.value))
# Add the experiment, tic, intrnsity_matrix, gcms_data and peak list
experiment_file.add(os.path.join(tmp, "experiment.expr"),
arcname="experiment.expr")
experiment_file.add(os.path.join(tmp, "tic.dat"),
arcname="tic.dat")
experiment_file.add(os.path.join(tmp, "peaks.dat"),
arcname="peaks.dat")
experiment_file.add(os.path.join(tmp, "gcms_data.dat"),
arcname="gcms_data.dat")
experiment_file.add(os.path.join(tmp, "intensity_matrix.dat"),
arcname="intensity_matrix.dat")
return self.filename
# do peak detection on pre-trimmed data
# get the list of Peak objects
pl = BillerBiemann(im, points, scans)
# trim by relative intensity
apl = rel_threshold(pl, r)
# trim by threshold
peak_list = num_ions_threshold(apl, n, t)
print("Number of Peaks found:", len(peak_list))
# ignore TMS ions and set mass range
for peak in peak_list:
peak.crop_mass(50,540)
peak.null_mass(73)
peak.null_mass(147)
# find area
area = peak_sum_area(im, peak)
peak.area = area
# create an experiment
expr = Experiment("a0806_077", peak_list)
# set time range for all experiments
expr.sele_rt_range(["6.5m", "21m"])
store_expr("output/a0806_077.expr", expr)
def import_processing(jcamp_file, spectrum_csv_file, report_csv_file, combined_csv_file, bb_points = 9, bb_scans = 2, noise_thresh = 2, target_range = (0,120), tophat_struct="1.5m", nistpath = "../MSSEARCH", base_peak_filter = ['73'], ExprDir = "."):
global nist_path
nist_path = nistpath
# Parameters
base_peak_filter = [int(x) for x in base_peak_filter]
target_range = tuple(target_range)
sample_name = os.path.splitext(os.path.basename(jcamp_file))[0]
number_of_peaks = 80
data = JCAMP_reader(jcamp_file)
# list of all retention times, in seconds
times = data.get_time_list()
# get Total Ion Chromatogram
tic = data.get_tic()
# RT Range, time step, no. scans, min, max, mean and median m/z
data.info()
#data.write("output/data") # save output
# Mass Binning
im = build_intensity_matrix_i(data) # covnert to intensity matrix
#im.get_size() #number of scans, number of bins
masses = im.get_mass_list() # list of mass bins
print(" Minimum m/z bin: {}".format(im.get_min_mass()))
print(" Maximum m/z bin: {}".format(im.get_max_mass()))
# Write Binned Mass Spectra to OpenChrom-like CSV file
ms = im.get_ms_at_index(0) # first mass spectrum
spectrum_csv = open(spectrum_csv_file, 'w')
spectrum_csv.write('RT(milliseconds);RT(minutes) - NOT USED BY IMPORT;RI;')
spectrum_csv.write(';'.join(str(mz) for mz in ms.mass_list))
spectrum_csv.write("\n")
for scan in range(len(times)):
spectrum_csv.write("{};{};{};".format(int(times[scan]*1000),rounders((times[scan]/60),"0.0000000000"),0))
ms = im.get_ms_at_index(scan)
spectrum_csv.write(';'.join(str(intensity) for intensity in ms.mass_spec))
spectrum_csv.write('\n')
spectrum_csv.close()
## Data filtering
# Note that Turbomass does not use smoothing for qualitative method.
# Top-hat baseline Correction seems to bring down noise,
# retaning shapes, but keeps points on actual peaks
#dump_object(im, "output/im.dump") # un-processed output
n_scan, n_mz = im.get_size()
for ii in range(n_mz):
#print("\rWorking on IC#", ii+1, ' ',end='')
ic = im.get_ic_at_index(ii)
ic_smooth = savitzky_golay(ic)
ic_bc = tophat(ic_smooth, struct=tophat_struct)
im.set_ic_at_index(ii, ic_bc)
#dump_object(im, "output/im-proc.dump") # processed output
# Peak Detection based on Biller and Biemann, 1974, with a window
# of n points, and combining y scans if they apex next to each other
peak_list = BillerBiemann(im, points=bb_points, scans=bb_scans)
print(" Number of peaks identified before filtering: {}".format(len(peak_list)))
# Filtering peak lists with automatic noise filtering
noise_level = window_analyzer(tic)
peak_list = num_ions_threshold(peak_list, noise_thresh, noise_level)
# why use 2 for number of ions above threshold?
print(" Number of peaks identified: {}".format(len(peak_list)))
# Peak Areas
peak_area_list = []
filtered_peak_list = []
for peak in peak_list:
apex_mass_list = peak.get_mass_spectrum().mass_list
apex_mass_spec = peak.get_mass_spectrum().mass_spec
base_peak_intensity = max(apex_mass_spec)
base_peak_index = [index for index, intensity in enumerate(apex_mass_spec) if intensity == base_peak_intensity][0]
base_peak_mass = apex_mass_list[base_peak_index]
#print(base_peak_mass)
if base_peak_mass in base_peak_filter:
continue # skip the peak if the base peak is at e.g. m/z 73, i.e. septum bleed
area = peak_sum_area(im, peak)
peak.set_area(area)
peak_area_list.append(area)
filtered_peak_list.append(peak)
# Save the TIC and Peak List
tic.write(os.path.join(ExprDir,"{}_tic.dat".format(sample_name)),formatting=False)
store_peaks(filtered_peak_list,os.path.join(ExprDir,"{}_peaks.dat".format(sample_name)))
# from https://stackoverflow.com/questions/16878715/how-to-find-the-index-of-n-largest-elements-in-a-list-or-np-array-python?lq=1
top_peaks = sorted(range(len(peak_area_list)), key=lambda x: peak_area_list[x])
# Write to turbomass-like CSV file
report_csv = open(report_csv_file, "w")
# Write to GunShotMatch Combine-like CSV file
combine_csv = open(combined_csv_file, "w")
combine_csv.write(sample_name)
combine_csv.write("\n")
report_csv.write("#;RT;Scan;Height;Area\n")
combine_csv.write("Retention Time;Peak Area;;Lib;Match;R Match;Name;CAS Number;Scan\n")
report_buffer = []
for index in top_peaks:
# Peak Number (1-80)
peak_number = top_peaks.index(index)+1
# Retention time (minutes, 3dp)
RT = rounders(filtered_peak_list[index].get_rt()/60,"0.000")
if not target_range[0] < RT <= target_range[1]:
continue # skip the peak if it is outside the desired range
# scan number, not that we really nead it as the peak object has the spectrum
Scan = data.get_index_at_time(filtered_peak_list[index].get_rt())+1
# the binned mass spectrum
filtered_peak_list[index].get_mass_spectrum()
# TIC intensity, as proxy for Peak height, which should be from baseline
Height = '{:,}'.format(rounders(tic.get_intensity_at_index(data.get_index_at_time(filtered_peak_list[index].get_rt())),"0"))
# Peak area, originally in "intensity seconds", so dividing by 60 to
# get "intensity minutes" like turbomass uses
Area = '{:,}'.format(rounders(filtered_peak_list[index].get_area()/60,"0.0"))
#report_csv.write("{};{};{};{};{};{}\n".format(peak_number, RT, Scan, Height, Area,bounds))
report_buffer.append([peak_number, RT, Scan, Height, Area])
report_buffer = report_buffer[::-1] # Reverse list order
# List of peaks already added to report
existing_peaks = []
filtered_report_buffer = []
for row in report_buffer:
filtered_report_buffer.append(row)
filtered_report_buffer = filtered_report_buffer[:number_of_peaks]
filtered_report_buffer.sort(key=operator.itemgetter(2))
for row in filtered_report_buffer:
index = filtered_report_buffer.index(row)
report_csv.write(";".join([str(i) for i in row]))
ms = im.get_ms_at_index(row[2]-1)
create_msp("{}_{}".format(sample_name,row[1]),ms.mass_list, ms.mass_spec)
matches_dict = nist_ms_comparison("{}_{}".format(sample_name,row[1]),ms.mass_list, ms.mass_spec)
combine_csv.write("{};{};Page {} of 80;;;;;;{}\n".format(row[1],row[4],index+1,row[2]))
for hit in range(1,6):
report_csv.write(str(matches_dict["Hit{}".format(hit)]))
report_csv.write(";")
combine_csv.write(";;{};{};{};{};{};{};\n".format(hit,
matches_dict["Hit{}".format(hit)]["Lib"],
matches_dict["Hit{}".format(hit)]["MF"],
matches_dict["Hit{}".format(hit)]["RMF"],
matches_dict["Hit{}".format(hit)]["Name"],
matches_dict["Hit{}".format(hit)]["CAS"],
))
report_csv.write("\n")
time.sleep(2)
report_csv.close()
combine_csv.close()
# Create an experiment
expr = Experiment(sample_name, filtered_peak_list)
expr.sele_rt_range(["{}m".format(target_range[0]),"{}m".format(target_range[1])])
store_expr(os.path.join(ExprDir,"{}.expr".format(sample_name)), expr)
return 0
def quantitative_processing(self, jcamp_file, log_stdout=True):
"""
Import JCAMP-DX Files
:param jcamp_file:
:type jcamp_file:
:param log_stdout:
:type log_stdout:
:return:
:rtype:
"""
# Determine the name of the sample from the filename
sample_name = os.path.splitext(os.path.basename(jcamp_file))[0]
# Log Stdout to File
if log_stdout:
sys.stdout = open(os.path.join(self.config.log_dir, sample_name + ".log"), "w")
# Load data using JCAMP_reader
data = JCAMP_reader(jcamp_file)
# list of all retention times, in seconds
times = data.get_time_list()
# get Total Ion Chromatogram
tic = data.get_tic()
# RT Range, time step, no. scans, min, max, mean and median m/z
data.info()
# Build "intensity matrix" by binning data with integer bins and a
# window of -0.3 to +0.7, the same as NIST uses
im = build_intensity_matrix_i(data)
# Show the m/z of the maximum and minimum bins
print(" Minimum m/z bin: {}".format(im.get_min_mass()))
print(" Maximum m/z bin: {}".format(im.get_max_mass()))
# Crop masses
min_mass, max_mass, *_ = self.config.mass_range
if min_mass < im.get_min_mass():
min_mass = im.get_min_mass()
if max_mass > im.get_max_mass():
max_mass = im.get_max_mass()
im.crop_mass(min_mass, max_mass)
# Perform Data filtering
n_scan, n_mz = im.get_size()
# Iterate over each IC in the intensity matrix
for ii in range(n_mz):
# print("\rWorking on IC#", ii+1, ' ',end='')
ic = im.get_ic_at_index(ii)
# Perform Savitzky-Golay smoothing.
# Note that Turbomass does not use smoothing for qualitative method.
ic_smooth = savitzky_golay(ic)
# Perform Tophat baseline correction
# Top-hat baseline Correction seems to bring down noise,
# retaining shapes, but keeps points on actual peaks
ic_bc = tophat(ic_smooth, struct=self.config.tophat_struct)
# Set the IC in the intensity matrix to the filtered one
im.set_ic_at_index(ii, ic_bc)
# Peak Detection based on Biller and Biemann (1974), with a window
# of <points>, and combining <scans> if they apex next to each other
peak_list = BillerBiemann(im, points=self.config.bb_points, scans=self.config.bb_scans)
print(" Number of peaks identified before filtering: {}".format(len(peak_list)))
# Filtering peak lists with automatic noise filtering
noise_level = window_analyzer(tic)
# should we also do rel_threshold() here?
# https://pymassspec.readthedocs.io/en/master/pyms/BillerBiemann.html#pyms.BillerBiemann.rel_threshold
peak_list = num_ions_threshold(peak_list, self.config.noise_thresh, noise_level)
filtered_peak_list = []
for peak in peak_list:
# Get mass and intensity lists for the mass spectrum at the apex of the peak
apex_mass_list = peak.mass_spectrum.mass_list
apex_mass_spec = peak.mass_spectrum.mass_spec
# Determine the intensity of the base peak in the mass spectrum
base_peak_intensity = max(apex_mass_spec)
# Determine the index of the base peak in the mass spectrum
base_peak_index = [
index for index, intensity in enumerate(apex_mass_spec)
if intensity == base_peak_intensity][0]
# Finally, determine the mass of the base peak
base_peak_mass = apex_mass_list[base_peak_index]
# skip the peak if the base peak is at e.g. m/z 73, i.e. septum bleed
if base_peak_mass in self.config.base_peak_filter:
continue
area = peak_sum_area(im, peak)
peak.set_area(area)
filtered_peak_list.append(peak)
print(" Number of peaks identified: {}".format(len(filtered_peak_list)))
# Save the TIC and Peak List
tic.write(os.path.join(self.config.expr_dir, "{}_tic.dat".format(sample_name)), formatting=False)
store_peaks(filtered_peak_list, os.path.join(self.config.expr_dir, "{}_peaks.dat".format(sample_name)))
# Create an experiment
expr = Experiment(sample_name, filtered_peak_list)
expr.sele_rt_range(["{}m".format(self.config.target_range[0]), "{}m".format(self.config.target_range[1])])
store_expr(os.path.join(self.config.expr_dir, "{}.expr".format(sample_name)), expr)