def run(self):
print("Quantitative Processing in Progress...")
# TODO: Include data etc. in experiment file
self.update_pbar()
if self.filetype == ID_Format_jcamp:
# Load data using JCAMP_reader
from pyms.GCMS.IO.JCAMP import JCAMP_reader
data = JCAMP_reader(self.properties["Original Filename"])
elif self.filetype == ID_Format_mzML:
# Load data using JCAMP_reader
from pyms.GCMS.IO.MZML import MZML_reader
data = MZML_reader(self.properties["Original Filename"])
elif self.filetype == ID_Format_ANDI:
# Load data using JCAMP_reader
from pyms.GCMS.IO.ANDI import ANDI_reader
data = ANDI_reader(self.properties["Original Filename"])
else:
# Unknown Format
return
# TODO: Waters RAW, Thermo RAW, Agilent .d
self.update_pbar()
method = Method.Method(self.properties["Method"])
self.update_pbar()
# 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)
self.update_pbar()
# 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, *_ = method.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)
self.update_pbar()
# 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)
if method.enable_sav_gol:
# Perform Savitzky-Golay smoothing.
# Note that Turbomass does not use smoothing for qualitative method.
ic = savitzky_golay(ic)
if method.enable_tophat:
# Perform Tophat baseline correction
# Top-hat baseline Correction seems to bring down noise,
# retaining shapes, but keeps points on actual peaks
ic = tophat(ic, struct=method.tophat_struct)
# Set the IC in the intensity matrix to the filtered one
im.set_ic_at_index(ii, ic)
self.update_pbar()
# 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=method.bb_points,
scans=method.bb_scans)
self.update_pbar()
print(" Number of peaks identified before filtering: {}".format(
len(peak_list)))
if method.enable_noise_filter:
# 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, method.noise_thresh,
noise_level)
self.update_pbar()
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 method.base_peak_filter:
continue
area = peak_sum_area(im, peak)
peak.set_area(area)
filtered_peak_list.append(peak)
self.update_pbar()
print(" Number of peaks identified: {}".format(
len(filtered_peak_list)))
# Create an experiment
self.expr = Experiment(self.sample_name, filtered_peak_list)
self.expr.sele_rt_range([
"{}m".format(method.target_range[0]),
"{}m".format(method.target_range[1])
])
self.update_pbar()
current_time = time_now()
# The date and time the experiment was created
self.properties["Date Created"] = current_time
# The date and time the experiment was last modified
self.properties["Date Modified"] = current_time
if self.pbar:
self.pbar.Update(self.pbar.Range)
self.tic = tic
self.filtered_peak_list = filtered_peak_list
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
data = JCAMP_reader(jcamp_file)
im = build_intensity_matrix(data)
# ## Retention time range
#
# A basic operation on the GC-MS data is to select a specific time range for
# processing. In PyMassSpec, any data outside the chosen time range is discarded.
# The |trim()| method operates on the raw data, so any subsequent processing only
# refers to the trimmed data.
#
# The data can be trimmed to specific scans:
# In[3]:
data.trim(1000, 2000)
data.info()
# or specific retention times (in ``seconds`` or ``minutes``):
# In[4]:
data.trim("700s", "15m")
data.info()
# ## Mass Spectrum range and entries
#
# An |IntensityMatrix| object has a set mass range and interval that is derived
# from the data at the time of building the intensity matrix. The range of mass
# values can be cropped. This is done, primarily, to ensure that the range of
# masses used are consistent when comparing samples.
#
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)