def load(cls, filename):
"""
Load an Experiment from file
:param filename:
:type filename:
"""
experiment_data = load_info_json(filename)
expr = cls(**experiment_data, filename=filename)
expr.gcms_data = pickle.load(
get_file_from_archive(expr.filename.Path, "gcms_data.dat"))
expr.expr = pickle.load(
get_file_from_archive(expr.filename.Path, "experiment.expr"))
expr.peak_list = pickle.load(
get_file_from_archive(expr.filename.Path, "peaks.dat"))
expr.intensity_matrix = pickle.load(
get_file_from_archive(expr.filename.Path, "intensity_matrix.dat"))
if expr.identification_performed:
expr.ident_peaks = pickle.load(
get_file_from_archive(expr.filename.Path, "ident_peaks.dat"))
expr.tic = expr.tic_data[1]
expr.get_info_from_gcms_data()
return expr
def ammo_file(self):
"""
Gets Ammunition Details from the Project tarfile and convert to BytesIO
"""
return BytesIO(
get_file_from_archive(self.filename.value,
filename_only(
self.ammo_details.value)).read())
def load_consolidate_results(self):
if self.consolidate_performed:
archive = tarfile.open(self.filename.value, mode="r")
raw_consolidated_peaks = json.load(
get_file_from_archive(archive, "consolidate.json"))
archive.close()
self.consolidated_peaks = []
for peak in raw_consolidated_peaks:
self.consolidated_peaks.append(
ConsolidatedPeak.from_dict(peak))
def load_alignment_data(self):
if self.alignment_performed:
from pyms.Spectrum import MassSpectrum
self.rt_alignment = pandas.read_json(
get_file_from_archive(self.filename.Path, 'alignment_rt.json'))
# To make sure that columns of dataframe are in the same order as the experiment name list
if self.rt_alignment.columns.tolist() != self.experiment_name_list:
self.rt_alignment = self.rt_alignment[
self.experiment_name_list]
self.area_alignment = pandas.read_json(
get_file_from_archive(self.filename.Path,
'alignment_area.json'))
# To make sure that columns of dataframe are in the same order as the experiment name list
if self.area_alignment.columns.tolist(
) != self.experiment_name_list:
self.area_alignment = self.area_alignment[
self.experiment_name_list]
raw_ms_alignment = json.load(
get_file_from_archive(self.filename.Path, 'alignment_ms.json'))
ordered_ms_alignment = {}
for expr, peaks in raw_ms_alignment.items():
ordered_ms_alignment[expr] = []
for peak_idx in range(len(peaks)):
peak_idx = str(peak_idx)
if peaks[peak_idx]:
peaks[peak_idx] = MassSpectrum.from_dict(
peaks[peak_idx])
ordered_ms_alignment[expr].append(peaks[peak_idx])
self.ms_alignment = pandas.DataFrame(data=ordered_ms_alignment)
def _load_experiments(self):
"""
Load the experiments from file
"""
for filename in self.experiment_file_list:
# self._experiment_objects.append(Experiment.load(filename))
# Get Experiment tarfile from the Project tarfile and convert to BytesIO
expr_tarfile = BytesIO(
get_file_from_archive(str(self.filename.value),
filename_only(filename)).read())
# Load the Experiment
expr = Experiment.Experiment.load(expr_tarfile)
# Add the Experiment to the list of Experiment objects
self._experiment_objects.append(expr)
def tic_data(self):
"""
Returns the TIC stored in the tic.dat file
:return:
:rtype:
"""
intensity_list = []
time_list = []
for row in get_file_from_archive(
self.filename.Path,
"tic.dat").read().decode("utf-8").split("\n"):
row = list(filter(None, row.split(" ")))
if len(row) == 0:
break
intensity_list.append(float(row[1]))
time_list.append(float(row[0]))
intensity_array = numpy.array(intensity_list)
tic = IonChromatogram(intensity_array, time_list)
return intensity_array, tic
def export_method(self, output_filename):
with open(output_filename, 'w') as f:
f.write(
get_file_from_archive(
self.filename.Path,
self.method.filename).read().decode("utf-8"))