def write_intensities_stream(self, file_name: PathLike):
"""
Loop over all scans and, for each scan, write the intensities to the
given file, one intensity per line.
Intensities from different scans are joined without any delimiters.
:param file_name: Output file name.
:authors: Vladimir Likic, Dominic Davis-Foster (pathlib support)
""" # noqa: D400
if not is_path(file_name):
raise TypeError(
"'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
# n = len(self._scan_list)
print(" -> Writing scans to a file")
fp = file_name.open('w', encoding="UTF-8")
for scan in self._scan_list:
intensities = scan.intensity_list
for i in intensities:
fp.write(f"{i:8.4f}\n")
fp.close()
def write(self,
file_name: PathLike,
minutes: bool = False,
formatting: bool = True):
"""
Writes the ion chromatogram to the specified file.
:param file_name: The name of the output file
:param minutes: A boolean value indicating whether to write time in minutes
:param formatting: Whether to format the numbers in the output.
:authors: Lewis Lee, Vladimir Likic, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError(
"'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
with file_name.open('w', encoding="UTF-8") as fp:
time_list = copy.deepcopy(self._time_list)
if minutes:
for ii in range(len(time_list)):
time_list[ii] = time_list[ii] / 60.0
for ii in range(len(time_list)):
if formatting:
fp.write(
f"{time_list[ii]:8.4f} {self._intensity_array[ii]:#.6e}\n"
)
else:
fp.write(f"{time_list[ii]} {self._intensity_array[ii]}\n")
def file2matrix(file_name):
"""
Convert a .csv file to a numpy array
:param file_name: Filename (.csv) to convert (area.csv, area_ci.csv)
:type file_name: str or pathlib.Path
:return: Data matrix
:rtype: :class:`numpy.array`
:author: Jairus Bowne
:author: Sean O'Callaghan
:author: Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
with file_name.open() as fp:
reader = csv.reader(fp, delimiter=",", quotechar='"')
matrix = []
for row in reader:
newrow = []
for each in row:
try:
each = float(each)
except:
pass
newrow.append(each)
matrix.append(newrow)
return numpy.array(matrix)
def read_expr_list(file_name):
"""
Reads the set of experiment files and returns a list of :class:`pyms.Experiment.Experiment` objects
:param file_name: The name of the file which lists experiment dump file names, one file per line
:type file_name: str or pathlib.Path
:return: A list of Experiment instances
:rtype: list of pyms.Experiment.Experiment
:author: Vladimir Likic
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
fp = file_name.open()
exprfiles = fp.readlines()
fp.close()
expr_list = []
for exprfile in exprfiles:
exprfile = exprfile.strip()
expr = load_expr(exprfile)
expr_list.append(expr)
return expr_list
def load_peaks(file_name: Union[str, pathlib.Path]) -> Peak:
"""
Loads the peak_list stored with 'store_peaks'
:param file_name: File name of peak list
:type file_name: str or os.PathLike
:return: The list of Peak objects
:rtype: :class:`list` of :class:`pyms.Peak.Class.Peak`
:author: Andrew Isaac
:author: Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
fp = file_name.open('rb')
peak_list = pickle.load(fp)
fp.close()
if not is_sequence(peak_list):
raise IOError("The selected file is not a List")
if not len(peak_list) > 0 or not isinstance(peak_list[0], Peak):
raise IOError("The selected file is not a list of Peak objects")
return peak_list
def store_peaks(peak_list: Peak,
file_name: Union[str, pathlib.Path],
protocol=1):
"""
Store the list of peak objects
:param peak_list: A list of peak objects
:type peak_list: list of :class:`pyms.Peaks.Class.Peak`
:param file_name: File name to store peak list
:type file_name: str or os.PathLike
:param protocol:
:type protocol:
:author: Andrew Isaac
:author: Dominic Davis-Foster (type assertions and pathlib support)
"""
if not is_peak_list(peak_list):
raise TypeError("'peak_list' must be a list of Peak objects")
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
fp = file_name.open('wb')
pickle.dump(peak_list, fp, protocol)
fp.close()
def load_expr(file_name):
"""
Loads an experiment saved with :meth:`pyms.Experiment.store_expr`
:param file_name: Experiment file name
:type file_name: str or pathlib.Path
:return: The loaded experiment
:rtype: pyms.Experiment.Experiment
:author: Vladimir Likic
:author: Andrew Isaac
:author: Dominic Davis-Foster (type assertions and pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
fp = file_name.open('rb')
expr = pickle.load(fp)
fp.close()
if not isinstance(expr, Experiment):
raise IOError("The loaded file is not an experiment file")
return expr
def from_jcamp(cls, file_name):
"""
Create a MassSpectrum from a JCAMP-DX file
:param file_name: Path of the file to read
:type file_name: str or os.PathLike
:return: MassSpectrum
:rtype: :class:`pyms.Spectrum.MassSpectrum`
:authors: Qiao Wang, Andrew Isaac, Vladimir Likic, David Kainer, Dominic Davis-Foster
"""
if not is_path(file_name):
raise TypeError(
"'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
print(f" -> Reading JCAMP file '{file_name}'")
lines_list = file_name.open('r')
xydata = []
last_tag = None
for line in lines_list:
if line.strip():
if line.startswith("##"):
# key word or information
fields = line.split('=', 1)
current_tag = fields[0] = fields[0].lstrip("##").upper()
last_tag = fields[0]
if current_tag.upper().startswith("END"):
break
else:
if last_tag in xydata_tags:
line_sub = re.split(r",| ", line.strip())
for item in line_sub:
if not len(item.strip()) == 0:
xydata.append(float(item.strip()))
# By this point we should have all of the xydata
if len(xydata) % 2 == 1:
# TODO: This means the data is not in x, y pairs
# Make a better error message
raise ValueError("data not in pair !")
mass_list = []
intensity_list = []
for i in range(len(xydata) // 2):
mass_list.append(xydata[i * 2])
intensity_list.append(xydata[i * 2 + 1])
return cls(mass_list, intensity_list)
def export_leco_csv(self, file_name):
"""
Exports data in LECO CSV format
:param file_name: The name of the output file
:type file_name: str or pathlib.Path
:authors: Andrew Isaac, Vladimir Likic, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError(
"'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
if not file_name.parent.is_dir():
file_name.parent.mkdir(parents=True)
mass_list = self._mass_list
time_list = self._time_list
vals = self._intensity_array
fp = file_name.open("w")
# Format is text header with:
# "Scan","Time",...
# and the rest is "TIC" or m/z as text, i.e. "50","51"...
# The following lines are:
# scan_number,time,value,value,...
# scan_number is an int, rest seem to be fixed format floats.
# The format is 0.000000e+000
# write header
fp.write("\"Scan\",\"Time\"")
for ii in mass_list:
if isinstance(ii, Number):
fp.write(f",\"{int(ii):d}\"")
else:
raise TypeError("mass list datum not a number")
fp.write("\r\n") # windows CR/LF
# write lines
for ii, time_ in enumerate(time_list):
fp.write(f"{ii},{time_:#.6e}")
for jj in range(len(vals[ii])):
if isinstance(vals[ii][jj], Number):
fp.write(f",{vals[ii][jj]:#.6e}")
else:
raise TypeError("datum not a number")
fp.write("\r\n")
fp.close()
def dump_object(obj: Any, file_name: PathLike):
"""
Dumps an object to a file through :func:`pickle.dump`.
:param obj: Object to be dumped
:param file_name: Name of the file for the object dump
:authors: Vladimir Likic, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
_pickle_dump_path(prepare_filepath(file_name), obj)
def write_filled_csv(
sample_list: List[Sample],
area_file: PathLike,
filled_area_file: PathLike,
):
r"""
Creates a new ``area_ci.csv`` file, replacing NAs with values from the sample_list objects where possible.
:param sample_list:
:param area_file: The file ``'area_ci.csv'`` from PyMassSpec output.
:param filled_area_file: the new output file which has ``'NA'``\s values replaced.
:authors: Jairus Bowne, Sean O'Callaghan, Dominic Davis-Foster
"""
if not is_path(filled_area_file):
raise TypeError(
"'filled_area_file' must be a string or a pathlib.Path object")
filled_area_file = prepare_filepath(filled_area_file)
df = file2dataframe(area_file)
uid_list: List[str] = df["UID"]
rt_list: List[float] = []
for uid in uid_list:
rt = uid.split('-')[-1]
rt_list.append(float(rt))
for sample_name in df.columns[3:]:
for sample in sample_list:
if sample_name in sample.name:
rt_area_dict = sample.rt_areas
break
else:
raise ValueError(
f"Sample {sample_name!r} not found in sample_list.")
for i, part in enumerate(df[sample_name]):
if part == "NA":
try:
df[sample_name][i] = rt_area_dict[rt_list[i]]
except KeyError:
pass
df.to_csv(filled_area_file, index=False, na_rep="NA")
def export_ascii(
self,
root_name: PathLike,
fmt: AsciiFiletypes = AsciiFiletypes.ASCII_DAT,
):
"""
Exports the intensity matrix, retention time vector, and m/z vector to the ascii format.
By default, export_ascii("NAME") will create NAME.im.dat, NAME.rt.dat,
and NAME.mz.dat where these are the intensity matrix, retention time
vector, and m/z vector in tab delimited format.
If ``format`` == ``<AsciiFiletypes.ASCII_CSV>``, the files will be in the CSV format, named
NAME.im.csv, NAME.rt.csv, and NAME.mz.csv.
:param root_name: Root name for the output files
:param fmt: Format of the output file, either ``<AsciiFiletypes.ASCII_DAT>`` or ``<AsciiFiletypes.ASCII_CSV>``
:authors: Milica Ng, Andrew Isaac, Vladimir Likic, Dominic Davis-Foster (pathlib support)
"""
if not is_path(root_name):
raise TypeError("'root_name' must be a string or a pathlib.Path object")
root_name = prepare_filepath(root_name, mkdirs=True)
fmt = AsciiFiletypes(fmt)
if fmt is AsciiFiletypes.ASCII_DAT:
separator = ' '
extension = "dat"
elif fmt is AsciiFiletypes.ASCII_CSV:
separator = ','
extension = "csv"
# export 2D matrix of intensities
vals = self._intensity_array
save_data(f"{root_name}.im.{extension}", vals, sep=separator)
# export 1D vector of m/z's, corresponding to rows of
# the intensity matrix
mass_list = self._mass_list
save_data(f"{root_name}.mz.{extension}", mass_list, sep=separator)
# export 1D vector of retention times, corresponding to
# columns of the intensity matrix
time_list = self._time_list
save_data(f"{root_name}.rt.{extension}", time_list, sep=separator)
def file_lines(file_name, strip=False):
"""
Returns lines from a file, as a list
:param file_name: Name of a file
:type file_name: str or os.PathLike
:param strip: If True, lines are pre-processed. Newline characters are
removed, leading and trailing whitespaces are removed, and lines
starting with '#' are discarded
:type strip: bool, optional
:return: A list of lines
:rtype: list
:author: Vladimir Likic
:author: Dominic Davis-Foster (pathlib support)
"""
from pyms.Utils.Utils import is_path
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
with file_name.open() as fp:
lines = fp.readlines()
if strip:
# strip leading and trailing whitespaces
lines_filtered = []
for line in lines:
line = line.strip()
lines_filtered.append(line)
# discard comments
lines_to_discard = []
for line in lines_filtered:
# remove empty lines and comments
if len(line) == 0 or line[0] == "#":
lines_to_discard.append(line)
for line in lines_to_discard:
lines_filtered.remove(line)
lines = lines_filtered
return lines
def load_object(file_name: PathLike) -> object:
"""
Loads an object previously dumped with :func:`~.dump_object`.
:param file_name: Name of the object dump file.
:return: Object contained in the file.
:authors: Vladimir Likic, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
with file_name.open("wb") as fp:
return pickle.load(fp)
def store(self, file_name):
"""
stores an experiment to a file
:param file_name: The name of the file
:type file_name: str or os.PathLike
:author: Vladimir Likic, Andrew Isaac, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
fp = file_name.open('wb')
pickle.dump(self, fp, 1)
fp.close()
def dump_object(obj: Any, file_name: Union[str, pathlib.Path]):
"""
Dumps an object to a file through pickle.dump()
:param obj: Object to be dumped
:type obj: any
:param file_name: Name of the file for the object dump
:type file_name: str or os.PathLike
:author: Vladimir Likic
:author: Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
with file_name.open('wb') as fp:
pickle.dump(obj, fp)
def load_object(file_name: Union[str, pathlib.Path]):
"""
Loads an object previously dumped with dump_object()
:param file_name: Name of the object dump file
:type file_name: str or os.PathLike
:return: Object contained in the file 'file_name'
:rtype: An instance of an arbitrary class
:author: Vladimir Likic
:author: Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
with file_name.open('wb') as fp:
return pickle.load(fp)
def load_expr(file_name: PathLike) -> Experiment:
"""
Loads an experiment saved with :meth:`pyms.Experiment.Experiment.dump`.
:param file_name: Experiment file name.
:return: The loaded experiment.
:author: Vladimir Likic, Andrew Isaac, Dominic Davis-Foster (type assertions and pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
expr = _pickle_load_path(file_name)
if not isinstance(expr, Experiment):
raise TypeError("The loaded file is not an experiment file")
return expr
def dump(self, file_name: Union[str, pathlib.Path], protocol: int = 3):
"""
Dumps an object to a file through :func:`pickle.dump()`
:param file_name: Filename to save the dump as
:type file_name: str or os.PathLike
:param protocol: The pickle protocol to use. Default ``3``
:type protocol: int, optional
:authors: Vladimir Likic, Dominic Davis-Foster (pathlib and pickle protocol support)
"""
if not is_path(file_name):
raise TypeError(
"'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
fp = file_name.open('wb')
pickle.dump(self, fp, protocol=protocol)
fp.close()
def file_lines(file_name: PathLike, strip: bool = False) -> List[str]:
"""
Returns lines from a file, as a list.
:param file_name: Name of a file
:param strip: If True, lines are pre-processed. Newline characters are
removed, leading and trailing whitespaces are removed, and lines
starting with '#' are discarded
:return: A list of lines
:authors: Vladimir Likic, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
with file_name.open(encoding="UTF-8") as fp:
lines = fp.readlines()
if strip:
# strip leading and trailing whitespaces
lines_filtered = []
for line in lines:
line = line.strip()
lines_filtered.append(line)
# discard comments
lines_to_discard = []
for line in lines_filtered:
# remove empty lines and comments
if len(line) == 0 or line[0] == '#':
lines_to_discard.append(line)
for line in lines_to_discard:
lines_filtered.remove(line)
lines = lines_filtered
return lines
def dump(self, file_name, protocol=3):
"""
Dumps an object to a file through pickle.dump()
:param file_name: Name of the file for the object dump
:type file_name: str or pathlib.Path
:param protocol: The pickle protocol to use. Default 3
:type protocol: int, optional
:author: Vladimir Likic
:author: Dominic Davis-Foster (pathlib and pickle protocol support)
"""
if not is_path(file_name):
raise TypeError(
"'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
fp = file_name.open('wb')
pickle.dump(self, fp, protocol=protocol)
fp.close()
def file2dataframe(file_name: PathLike) -> pandas.DataFrame:
"""
Convert a .csv file to a pandas DataFrame.
:param file_name: CSV file to read.
:authors: Jairus Bowne, Sean O'Callaghan, Dominic Davis-Foster (pathlib support)
.. versionadded:: 2.3.0
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
return pandas.read_csv(
file_name,
delimiter=',',
quotechar='"',
header=0,
)
def write_transposed_output(
alignment: Alignment,
file_name: PathLike,
minutes: bool = True,
):
"""
:param alignment: :class:`pyms.DPA.Alignment.Alignment` object to write to file
:param file_name: The name of the file
:param minutes:
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
wb = Workbook()
ws1 = wb.create_sheet(title="Aligned RT")
ws2 = wb.create_sheet(title="Aligned Area")
ws1["A1"] = "Peak"
ws1["A2"] = "RTavg"
ws2["A1"] = "Peak"
ws2["A2"] = "RTavg"
style_outlier = PatternFill(fill_type="solid", fgColor="FFAE19", bgColor="FFAE19")
# write column with sample IDs
for i, item in enumerate(alignment.expr_code):
ws1.cell(column=1, row=i + 3, value=f"{item}")
ws2.cell(column=1, row=i + 3, value=f"{item}")
# for each alignment position write alignment's peak and area
for peak_idx in range(len(alignment.peakpos[0])): # loop through peak lists
new_peak_list = [] # this will contain a list of tuples of form (peak, col, row), but only non-NA peaks
for align_idx in range(len(alignment.peakpos)): # loops through samples
peak = alignment.peakpos[align_idx][peak_idx]
cell_col = 2 + peak_idx
cell_row = 3 + align_idx
if peak is not None:
if minutes:
rt = peak.rt / 60.0
else:
rt = peak.rt
area = peak.area
# these are the col,row coords of the peak in the output matrix
new_peak_list.append((peak, cell_col, cell_row))
# write the RT into the cell in the excel file
currcell1 = ws1.cell(column=cell_col, row=cell_row, value=round(rt, 3))
ws2.cell(column=cell_col, row=cell_row, value=round(area, 3)) # type: ignore
# get the mini-mass spec for this peak, and divide the ion intensities by 1000 to shorten them
ia = peak.ion_areas
ia.update((mass, int(intensity / 1000)) for mass, intensity in ia.items())
sorted_ia = sorted(ia.items(), key=operator.itemgetter(1), reverse=True)
# write the peak area and mass spec into the comment for the cell
comment = Comment(f"Area: {area:.0f} | MassSpec: {sorted_ia}", "dave")
currcell1.comment = comment
else:
# rt = 'NA'
# area = 'NA'
currcell1 = ws1.cell(column=cell_col, row=cell_row, value="NA")
ws2.cell(column=cell_col, row=cell_row, value="NA")
comment = Comment("Area: NA", "dave")
currcell1.comment = comment
# this method will create the compo peak, and also mark outlier peaks with a bool is_outlier
compo_peak = composite_peak(list(p[0] for p in new_peak_list))
if compo_peak is not None:
ws1.cell(column=2 + peak_idx, row=1, value=f'"{compo_peak.UID}"')
ws1.cell(column=2 + peak_idx, row=2, value=f"{float(compo_peak.rt / 60):.3f}")
ws2.cell(column=2 + peak_idx, row=1, value=f'"{compo_peak.UID}"')
ws2.cell(column=2 + peak_idx, row=2, value=f"{float(compo_peak.rt / 60):.3f}")
# highlight outlier cells in the current peak list
for p in new_peak_list:
if p[0].is_outlier:
# ws[ get_column_letter(p[1]) + str(p[2]) ].style = style_outlier
ws1.cell(column=p[1], row=p[2]).fill = style_outlier
ws2.cell(column=p[1], row=p[2]).fill = style_outlier
wb.save(file_name)
def write_excel(
alignment: Alignment,
file_name: PathLike,
minutes: bool = True,
):
"""
Writes the alignment to an excel file, with colouring showing possible mis-alignments.
:param alignment: :class:`pyms.DPA.Alignment.Alignment` object to write to file.
:param file_name: The name for the retention time alignment file.
:param minutes: Whether to save retention times in minutes.
If :py:obj:`False`, retention time will be saved in seconds.
:author: David Kainer
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
wb = Workbook()
ws = wb.active
ws.title = "Aligned RT"
# create header row
ws["A1"] = "UID"
ws["B1"] = "RTavg"
for i, item in enumerate(alignment.expr_code):
currcell = ws.cell(row=1, column=i + 3, value=f"{item}")
comment = Comment("sample " + str(i), "dave")
currcell.comment = comment
# for each alignment position write alignment's peak and area
for peak_idx in range(len(alignment.peakpos[0])): # loop through peak lists (rows)
new_peak_list = []
for align_idx in range(len(alignment.peakpos)): # loops through samples (columns)
peak = alignment.peakpos[align_idx][peak_idx]
if peak is not None:
if minutes:
rt = peak.rt / 60.0
else:
rt = peak.rt
area = peak.area
new_peak_list.append(peak)
# write the RT into the cell in the excel file
currcell = ws.cell(row=2 + peak_idx, column=3 + align_idx, value=round(rt, 3))
# get the mini-mass spec for this peak, and divide the ion intensities by 1000 to shorten them
ia = peak.ion_areas
ia.update((mass, int(intensity / 1000)) for mass, intensity in ia.items())
sorted_ia = sorted(ia.items(), key=operator.itemgetter(1), reverse=True)
# write the peak area and mass spec into the comment for the cell
comment = Comment(f"Area: {area:.0f} | MassSpec: {sorted_ia}", "dave")
# currcell.number_format
currcell.comment = comment
else:
# rt = 'NA'
# area = 'NA'
currcell = ws.cell(row=2 + peak_idx, column=3 + align_idx, value="NA")
comment = Comment("Area: NA", "dave")
# currcell.number_format
currcell.comment = comment
compo_peak = composite_peak(new_peak_list)
if compo_peak is not None:
peak_UID = compo_peak.UID
peak_UID_string = f'"{peak_UID}"'
ws.cell(row=2 + peak_idx, column=1, value=peak_UID_string)
ws.cell(row=2 + peak_idx, column=2, value=f"{float(compo_peak.rt / 60):.3f}")
# colour the cells in each row based on their RT percentile for that row
i = 0
for row in ws.rows:
i += 1
cell_range = ("{0}" + str(i) + ":{1}" + str(i)).format(get_column_letter(3), get_column_letter(len(row)))
ws.conditional_formatting.add(
cell_range,
ColorScaleRule(
start_type="percentile",
start_value=1,
start_color="E5FFCC",
mid_type="percentile",
mid_value=50,
mid_color="FFFFFF",
end_type="percentile",
end_value=99,
end_color="FFE5CC"
),
)
wb.save(file_name)
def write_ion_areas_csv(self,
ms_file_name: Union[str, pathlib.Path],
minutes: bool = True):
"""
Write Ion Areas to CSV File
:param ms_file_name: The name of the file
:type ms_file_name: str, PathLike
:param minutes:
:type minutes: bool
:author: David Kainer
:author: Dominic Davis-Foster (pathlib support)
"""
if not is_path(ms_file_name):
raise TypeError(
"'ms_file_name' must be a string or a PathLike object")
ms_file_name = prepare_filepath(ms_file_name)
with ms_file_name.open("w") as fp1:
# create header
header = ['"UID"', '"RTavg"']
for item in self.expr_code:
header.append(f'"{item}"')
# write headers
fp1.write("|".join(header) + "\n")
for peak_idx in range(len(self.peakpos[0])):
ias = []
new_peak_list = []
for align_idx in range(len(self.peakpos)):
peak = self.peakpos[align_idx][peak_idx]
if peak is not None:
ia = peak.ion_areas
ia.update((mass, math.floor(intensity))
for mass, intensity in ia.items())
sorted_ia = sorted(ia.items(),
key=operator.itemgetter(1),
reverse=True)
ias.append(sorted_ia)
new_peak_list.append(peak)
compo_peak = composite_peak(new_peak_list)
# write to ms file
fp1.write(compo_peak.UID)
if minutes:
fp1.write(f"|{compo_peak.rt/60:.3f}")
else:
fp1.write(f"|{compo_peak.rt:.3f}")
for ia in ias:
if ia is None:
fp1.write("|NA")
else:
fp1.write(f"|{ia}")
fp1.write("\n")
def write_common_ion_csv(self,
area_file_name: Union[str, pathlib.Path],
top_ion_list: List,
minutes: bool = True):
"""
Writes the alignment to CSV files
This function writes two files: one containing the alignment of peak
retention times and the other containing the alignment of peak areas.
:param area_file_name: The name for the areas alignment file
:type area_file_name: str or os.PathLike
:param top_ion_list: A list of the highest intensity common ion along the aligned peaks
:type top_ion_list: ~collections.abc.Sequence
:param minutes: An optional indicator whether to save retention times
in minutes. If False, retention time will be saved in seconds
:type minutes: bool, optional
:author: Woon Wai Keen
:author: Andrew Isaac
:author: Sean O'Callaghan
:author: Vladimir Likic
:author: Dominic Davis-Foster (pathlib support)
"""
# TODO: minutes currently does nothing
if not is_path(area_file_name):
raise TypeError(
"'area_file_name' must be a string or a PathLike object")
if not is_sequence_of(top_ion_list, Number):
raise TypeError("'top_ion_list' must be a Sequence of Numbers")
area_file_name = prepare_filepath(area_file_name)
with area_file_name.open("w") as fp:
# create header
header = ['"UID"', '"RTavg"', '"Quant Ion"']
for item in self.expr_code:
header.append(f'"{item}"')
# write headers
fp.write(",".join(header) + "\n")
rtsums = []
rtcounts = []
# The following two arrays will become list of lists
# such that:
# areas = [ [align1_peak1, align2_peak1, .....,alignn_peak1]
# [align1_peak2, ................................]
# .............................................
# [align1_peakm,....................,alignn_peakm] ]
areas: List[List] = []
new_peak_lists: List[List[Peak]] = []
for peak_list in self.peakpos:
index = 0
for peak in peak_list:
# one the first iteration, populate the lists
if len(areas) < len(peak_list):
areas.append([])
new_peak_lists.append([])
rtsums.append(0)
rtcounts.append(0)
if peak is not None:
rt = peak.rt
# get the area of the common ion for the peak
# an area of 'na' shows that while the peak was
# aligned, the common ion was not present
area = peak.get_ion_area(top_ion_list[index])
areas[index].append(area)
new_peak_lists[index].append(peak)
# The following code to the else statement is
# just for calculating the average rt
rtsums[index] += rt
rtcounts[index] += 1
else:
areas[index].append(None)
index += 1
out_strings = []
index = 0
# now write the strings for the file
for area_list in areas:
# write initial info:
# peak unique id, peak average rt
compo_peak = composite_peak(new_peak_lists[index])
peak_UID = compo_peak.UID
peak_UID_string = f'"{peak_UID}"'
rt_avg = rtsums[index] / rtcounts[index]
out_strings.append(
f"{peak_UID_string},{rt_avg / 60:.3f},{top_ion_list[index]:f}"
)
for area in area_list:
if area is not None:
out_strings[index] += f",{area:.4f}"
else:
out_strings[index] += ",NA"
index += 1
# now write the file
# print("length of areas[0]", len(areas[0]))
# print("length of areas", len(areas))
# print("length of out_strings", len(out_strings))
for row in out_strings:
fp.write(row + "\n")
def save_data(file_name, data, format_str="%.6f", prepend="", sep=" ", compressed=False):
"""
Saves a list of numbers or a list of lists of numbers to a file with specific formatting
:param file_name: Name of a file
:type file_name: str or os.PathLike
:param data: A list of numbers, or a list of lists
:type data: list
:param format_str: A format string for individual entries
:type format_str: str
:param prepend: A string, printed before each row
:type prepend: str
:param sep: A string, printed after each number
:type sep: str
:param compressed: A boolean. If True, the output will be gzipped
:type compressed: bool
:author: Vladimir Likic
:author: Dominic Davis-Foster (pathlib support)
"""
from pyms.Utils.Utils import is_path
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
if not isinstance(data, _list_types):
raise TypeError("'data' must be a list")
if not isinstance(prepend, str):
raise TypeError("'prepend' must be a string")
if not isinstance(sep, str):
raise TypeError("'sep' must be a string")
with file_name.open("w") as fp:
# decide whether data is a vector or matrix
if isinstance(data[0], Number):
for item in data:
if not isinstance(item, Number):
raise TypeError("not all elements of the list are numbers")
data_is_matrix = 0
else:
for item in data:
if not isinstance(item, _list_types):
raise TypeError("not all elements of the list are lists")
data_is_matrix = 1
if data_is_matrix:
for x_value in data:
fp.write(prepend)
for jj, y_value in enumerate(x_value):
if isinstance(y_value, Number):
fp.write(format_str % y_value)
if jj < (len(x_value) - 1):
fp.write(sep)
else:
raise TypeError("'datum' must be a number")
fp.write("\n")
else:
for x_value in data:
fp.write(prepend)
fp.write(format_str % x_value)
fp.write("\n")
if compressed:
with file_name.open() as f_in:
with gzip.open(str(file_name) + '.gz', 'wb') as f_out:
f_out.writelines(f_in)
def JCAMP_reader(file_name: Union[str, os.PathLike]) -> GCMS_data:
"""
Generic reader for JCAMP DX files
:param file_name: Path of the file to read
:type file_name: str or os.PathLike
:return: GC-MS data object
:rtype: :class:`pyms.GCMS.Class.GCMS_data`
:authors: Qiao Wang, Andrew Isaac, Vladimir Likic, David Kainer, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
print(f" -> Reading JCAMP file '{file_name}'")
lines_list = file_name.open('r')
data = []
page_idx = 0
xydata_idx = 0
time_list = []
scan_list = []
header_info = {} # Dictionary containing header information
for line in lines_list:
if len(line.strip()) != 0:
# prefix = line.find('#')
# if prefix == 0:
if line.startswith("##"):
# key word or information
fields = line.split('=', 1)
fields[0] = fields[0].lstrip("##").upper()
fields[1] = fields[1].strip()
if "PAGE" in fields[0]:
if "T=" in fields[1]:
# PAGE contains retention time starting with T=
# FileConverter Pro style
time = float(fields[1].lstrip(
"T=")) # rt for the scan to be submitted
time_list.append(time)
page_idx = page_idx + 1
elif "RETENTION_TIME" in fields[0]:
# OpenChrom style
time = float(fields[1]) # rt for the scan to be submitted
# Check to make sure time is not already in the time list;
# Can happen when both ##PAGE and ##RETENTION_TIME are specified
if time_list[-1] != time:
time_list.append(time)
elif fields[0] in xydata_tags:
xydata_idx = xydata_idx + 1
elif fields[0] in header_info_fields:
if fields[1].isdigit():
header_info[fields[0]] = int(fields[1])
elif is_float(fields[1]):
header_info[fields[0]] = float(fields[1])
else:
header_info[fields[0]] = fields[1]
# elif prefix == -1:
else:
# Line doesn't start with ##
# data
if page_idx > 1 or xydata_idx > 1:
if len(data) % 2 == 1:
# TODO: This means the data is not in x, y pairs
# Make a better error message
raise ValueError("data not in pair !")
mass_list = []
intensity_list = []
for i in range(len(data) // 2):
mass_list.append(data[i * 2])
intensity_list.append(data[i * 2 + 1])
if len(mass_list) != len(intensity_list):
raise ValueError(
"len(mass_list) is not equal to len(intensity_list)"
)
scan_list.append(Scan(mass_list, intensity_list))
data = []
data_sub = line.strip().split(',')
for item in data_sub:
if not len(item.strip()) == 0:
data.append(float(item.strip()))
if page_idx > 1:
page_idx = 1
if xydata_idx > 1:
xydata_idx = 1
else:
data_sub = line.strip().split(',')
for item in data_sub:
if not len(item.strip()) == 0:
data.append(float(item.strip()))
if len(data) % 2 == 1:
# TODO: This means the data is not in x, y pairs
# Make a better error message
raise ValueError("data not in pair !")
# get last scan
mass = []
intensity = []
for i in range(len(data) // 2):
mass.append(data[i * 2])
intensity.append(data[i * 2 + 1])
if len(mass) != len(intensity):
raise ValueError("len(mass) is not equal to len(intensity)")
scan_list.append(Scan(mass, intensity))
# sanity check
time_len = len(time_list)
scan_len = len(scan_list)
if time_len != scan_len:
print(time_list)
print(scan_list)
raise ValueError(
f"Number of time points ({time_len}) does not equal the number of scans ({scan_len})"
)
data = GCMS_data(time_list, scan_list)
return data
def import_leco_csv(file_name):
"""
Imports data in LECO CSV format
:param file_name: Path of the file to read
:type file_name: str or pathlib.Path
:return: Data as an IntensityMatrix
:rtype: pyms.IntensityMatrix.IntensityMatrix
:authors: Andrew Isaac, Dominic Davis-Foster (pathlib support)
"""
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name, mkdirs=False)
lines_list = file_name.open('r')
data = []
time_list = []
mass_list = []
# Format is text header with:
# "Scan","Time",...
# and the rest is "TIC" or m/z as text, i.e. "50","51"...
# The following lines are:
# scan_number,time,value,value,...
# scan_number is an int, rest seem to be fixed format floats.
# The format is 0.000000e+000
num_mass = 0
FIRST = True
HEADER = True
data_col = -1
time_col = -1
# get each line
for line in lines_list:
cols = -1
data_row = []
if len(line.strip()) > 0:
data_list = line.strip().split(',')
# get each value in line
for item in data_list:
item = item.strip()
item = item.strip('\'"') # remove quotes (in header)
# Get header
if HEADER:
cols += 1
if len(item) > 0:
if item.lower().find("time") > -1:
time_col = cols
try:
value = float(item)
# find 1st col with number as header
if FIRST and value > 1: # assume >1 mass
data_col = cols
# assume time col is previous col
if time_col < 0:
time_col = cols - 1
FIRST = False
mass_list.append(value)
num_mass += 1
except ValueError:
pass
# Get rest
else:
cols += 1
if len(item) > 0:
try:
value = float(item)
if cols == time_col:
time_list.append(value)
elif cols >= data_col:
data_row.append(value)
except ValueError:
pass
# check row length
if not HEADER:
if len(data_row) == num_mass:
data.append(data_row)
else:
warn("ignoring row")
HEADER = False
# check col lengths
if len(time_list) != len(data):
warn("number of data rows and time list length differ")
return IntensityMatrix(time_list, mass_list, data)
def write_mass_hunter_csv(
alignment: Alignment,
file_name: PathLike,
top_ion_list: List[int],
): # , peak_list_name):
"""
Creates a csv file with UID, common and qualifying ions and their
ratios for mass hunter interpretation.
:param alignment: alignment object to write to file
:param file_name: name of the output file.
:param top_ion_list: a list of the common ions for each peak in the
averaged peak list for the alignment.
""" # noqa: D400
if not is_path(file_name):
raise TypeError("'file_name' must be a string or a PathLike object")
file_name = prepare_filepath(file_name)
fp = file_name.open('w', encoding="UTF-8")
if top_ion_list is None:
raise ValueError("List of common ions must be supplied")
# write headers
fp.write(
'"UID","Common Ion","Qual Ion 1","ratio QI1/CI","Qual Ion 2",'
'"ratio QI2/CI","l window delta","r window delta"\n'
)
rtsums: List[float] = []
rtcounts = []
# The following two arrays will become list of lists
# such that:
# areas = [ [align1_peak1, align2_peak1, .....,alignn_peak1]
# [align1_peak2, ................................]
# .............................................
# [align1_peakm,....................,alignn_peakm] ]
areas = [] # type: ignore
new_peak_lists = [] # type: ignore
rtmax = []
rtmin = []
for peak_list in alignment.peakpos:
index = 0
for peak in peak_list:
# on the first iteration, populate the lists
if len(areas) < len(peak_list):
areas.append([])
new_peak_lists.append([])
rtsums.append(0)
rtcounts.append(0)
rtmax.append(0.0)
rtmin.append(0.0)
if peak is not None:
rt = peak.rt
# get the area of the common ion for the peak
# an area of 'na' shows that while the peak was
# aligned, the common ion was not present
area = peak.get_ion_area(top_ion_list[index])
areas[index].append(area)
new_peak_lists[index].append(peak)
# The following code to the else statement is
# just for calculating the average rt
rtsums[index] += rt
rtcounts[index] += 1
# quick workaround for weird problem when
# attempting to set rtmin to max time above
if rtmin[index] == 0.0:
rtmin[index] = 5400.0
if rt > rtmax[index]:
rtmax[index] = rt
if rt < rtmin[index]:
rtmin[index] = rt
else:
areas[index].append(None)
index += 1
out_strings = []
compo_peaks = []
index = 0
# now write the strings for the file
for area_list in areas:
# write initial info:
# peak unique id, peak average rt
compo_peak = composite_peak(new_peak_lists[index])
if compo_peak is None:
continue
compo_peaks.append(compo_peak)
peak_UID = compo_peak.UID
peak_UID_string = f'"{peak_UID}"'
# calculate the time from the leftmost peak to the average
l_window_delta = compo_peak.rt - rtmin[index]
# print("l_window", l_window_delta, "rt", compo_peak.rt, "rt_min", rtmin[index])
r_window_delta = rtmax[index] - compo_peak.rt
common_ion = top_ion_list[index]
qual_ion_1 = int(peak_UID_string.split('-')[0].strip('"'))
qual_ion_2 = int(peak_UID_string.split('-')[1])
if qual_ion_1 == common_ion:
qual_ion_1 = compo_peak.get_third_highest_mz()
elif qual_ion_2 == common_ion:
qual_ion_2 = compo_peak.get_third_highest_mz()
else:
pass
ci_intensity = compo_peak.get_int_of_ion(common_ion)
q1_intensity = compo_peak.get_int_of_ion(qual_ion_1)
q2_intensity = compo_peak.get_int_of_ion(qual_ion_2)
try:
q1_ci_ratio = float(q1_intensity) / float(ci_intensity)
except TypeError: # if no area available for that ion
q1_ci_ratio = 0.0
except ZeroDivisionError:
# shouldn't happen but does!!
q1_ci_ratio = 0.01
try:
q2_ci_ratio = float(q2_intensity) / float(ci_intensity)
except TypeError:
q2_ci_ratio = 0.0
except ZeroDivisionError:
# shouldn't happen, but does!!
q2_ci_ratio = 0.01
out_strings.append(
','.join([
peak_UID,
f"{common_ion}",
f"{qual_ion_1}",
f"{q1_ci_ratio * 100:.1f}",
f"{qual_ion_2}",
f"{q2_ci_ratio * 100:.1f}",
f"{(l_window_delta + 1.5) / 60:.2f}",
f"{(r_window_delta + 1.5) / 60:.2f}",
])
)
index += 1
# now write the file
# print("length of areas[0]", len(areas[0]))
# print("lenght of areas", len(areas))
# print("length of out_strings", len(out_strings))
for row in out_strings:
fp.write(f"{row}\n")
# dump_object(compo_peaks, peak_list_name)
fp.close()