From 997849c57f9ee9dec82997da4f98dfc458f5ba38 Mon Sep 17 00:00:00 2001 From: haeussma <83341109+haeussma@users.noreply.github.com> Date: Mon, 9 Sep 2024 22:57:23 +0200 Subject: [PATCH] optimized suboptimal code --- chromatopy/ioutils/enzymeml.py | 136 +- chromatopy/model.py | 31 +- chromatopy/readers/asm.py | 196 + chromatopy/readers/chromeleon.py | 11 +- chromatopy/tools/analyzer.py | 818 +- chromatopy/tools/fit_chromatogram.py | 1 + chromatopy/tools/molecule.py | 131 +- chromatopy/tools/peak_utils.py | 258 +- chromatopy/tools/utility.py | 52 + pyproject.toml | 1 + specifications/chromatography.md | 28 +- .../data/asm/N6BENZYLADOONB_RNCOMT_TIME0.json | 20383 ++++++++++++++++ .../data/asm/N6BENZYLADOONB_RNCOMT_TIME1.json | 1 + .../data/asm/N6BENZYLADOONB_RNCOMT_TIME2.json | 1 + .../data/asm/N6BENZYLADOONB_RNCOMT_TIME3.json | 1 + 15 files changed, 21564 insertions(+), 485 deletions(-) create mode 100644 chromatopy/readers/asm.py create mode 100644 tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME0.json create mode 100644 tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME1.json create mode 100644 tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME2.json create mode 100644 tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME3.json diff --git a/chromatopy/ioutils/enzymeml.py b/chromatopy/ioutils/enzymeml.py index 93cba8a..a555281 100644 --- a/chromatopy/ioutils/enzymeml.py +++ b/chromatopy/ioutils/enzymeml.py @@ -1,3 +1,4 @@ +from calipytion.tools.calibrator import Calibrator from pyenzyme import ( DataTypes, EnzymeMLDocument, @@ -9,7 +10,7 @@ from pyenzyme import UnitDefinition as EnzymeMLUnitDefinition from chromatopy.model import Measurement -from chromatopy.model import UnitDefinition as ChromatopyUnitDefinition +from chromatopy.model import UnitDefinition as UnitDefinition from chromatopy.tools.internal_standard import InternalStandard from chromatopy.tools.molecule import Molecule @@ -37,9 +38,12 @@ def create_enzymeml( doc = EnzymeMLDocument(name=name) + # check if concentration unit is defined for each molecule + measurement_data_instances = {} # add EnzymeML SmallMolecules for molecule in molecules: + _check_molecule_conc_unit_and_init_conc(molecule) doc.small_molecules.append( SmallMolecule( id=molecule.id, @@ -52,20 +56,18 @@ def create_enzymeml( measurement_data_instances[molecule.id] = MeasurementData( species_id=molecule.id, initial=molecule.init_conc, - data_unit=EnzymeMLUnitDefinition(**molecule.conc_unit.model_dump()), + data_unit=EnzymeMLUnitDefinition(**molecule.conc_unit.model_dump()), # type: ignore data_type=DataTypes.PEAK_AREA, ) + from devtools import pprint + # add Proteins for protein in proteins: - protein_data = { - "id": protein.id, - "name": protein.name, - } - if protein.uniprot_id: - protein_data["ld_id"] = ( - f"https://www.uniprot.org/uniprot/{protein.uniprot_id}" - ) + protein_data = protein.model_dump() + pprint(protein_data) + protein_data.pop("conc_unit") + protein_data.pop("init_conc") doc.proteins.append(Protein(**protein_data)) @@ -73,7 +75,7 @@ def create_enzymeml( measurement_data_instances[molecule.id] = MeasurementData( species_id=molecule.id, initial=molecule.init_conc, - data_unit=EnzymeMLUnitDefinition(**molecule.conc_unit.model_dump()), + data_unit=EnzymeMLUnitDefinition(**protein.conc_unit.model_dump()), data_type=DataTypes.PEAK_AREA, ) @@ -129,7 +131,7 @@ def add_measurements_to_enzymeml( mol.id: MeasurementData( species_id=mol.id, initial=mol.init_conc, - data_unit=EnzymeMLUnitDefinition(**mol.conc_unit.model_dump()), + data_unit=EnzymeMLUnitDefinition(**mol.conc_unit.model_dump()), # type: ignore data_type=DataTypes.PEAK_AREA, ) for mol in molecules @@ -193,6 +195,7 @@ def measurements_to_measurmentdata( """ all_moecules = {molecule.id: molecule for molecule in molecules} measured_once = measured_molecule_dict(list(all_moecules.keys()), measurements) + has_external_standard = any([molecule.standard for molecule in molecules]) # setup internal standard if it exists if calculate_concentration and internal_standard: @@ -200,19 +203,39 @@ def measurements_to_measurmentdata( for molecule_id in measured_once: if internal_standard.id == molecule_id: continue + assert internal_standard.init_conc, f""" + The initial concentration of the internal standard molecule {internal_standard.name} + needs to be defined. Please specify the `init_conc` attribute of the molecule. + """ + assert internal_standard.conc_unit, f""" + The concentration unit of the internal standard molecule {internal_standard.name} + needs to be defined. Please specify the `conc_unit` attribute of the molecule. + """ + + assert all_moecules[molecule_id].init_conc, f""" + The initial concentration of the molecule {all_moecules[molecule_id].name} + needs to be defined. Please specify the `init_conc` attribute of the molecule. + """ + internal_std_dict[molecule_id] = InternalStandard( molecule_id=molecule_id, standard_molecule_id=internal_standard.id, - molecule_init_conc=all_moecules[molecule_id].init_conc, + molecule_init_conc=all_moecules[molecule_id].init_conc, # type: ignore standard_init_conc=internal_standard.init_conc, molecule_conc_unit=internal_standard.conc_unit, ) + if calculate_concentration and has_external_standard: + calibrators: dict[str, Calibrator] = {} + for molecule in molecules: + if molecule.standard: + calibrators[molecule.id] = Calibrator.from_standard(molecule.standard) + for meas_idx, measurement in enumerate(measurements): if calculate_concentration: # set t0 signals for each internal standard if the measurement is at t=0 if internal_standard: - if measurement.reaction_time == 0: + if meas_idx == 0: for internal_std in internal_std_dict.values(): internal_std._set_t0_signals(measurement) @@ -237,42 +260,56 @@ def measurements_to_measurmentdata( if peak: break - if internal_standard: - for chrom in measurement.chromatograms: - internal_std_peak = next( - ( - peak - for peak in chrom.peaks - if peak.molecule_id - == internal_std_dict[molecule_id].standard_molecule_id - ), - None, - ) - if internal_std_peak: - break + if internal_standard and peak: + internal_std_peak = next( + ( + peak + for peak in chrom.peaks + if peak.molecule_id + == internal_std_dict[molecule_id].standard_molecule_id + ), + None, + ) + if internal_std_peak: + break assert internal_std_peak is not None, f""" No peak for the internal standard molecule {internal_std_dict[molecule_id]} was assigned in measurement {meas_idx}. """ - # use internal standard to calculate concentration - if ( - peak - and calculate_concentration - and internal_standard - and internal_std_peak - ): - calibrator = internal_std_dict[molecule_id] + internal_calibrator = internal_std_dict[molecule_id] measurement_data_instances[molecule_id].data.append( - calibrator.calculate_conc(peak.area, internal_std_peak.area) + internal_calibrator.calculate_conc( + peak.area, internal_std_peak.area + ) ) measurement_data_instances[ molecule_id ].data_type = DataTypes.CONCENTRATION - # use peak area as data - elif peak: + elif has_external_standard and peak: + try: + print(f"calculating concentration for molecule {molecule_id}") + print( + f"is not extrapolate: {peak.area < calibrators[molecule_id].standard.result.calibration_range.signal_upper}" + ) + print(calibrators[molecule_id].molecule_id) + measurement_data_instances[molecule_id].data.append( + calibrators[molecule_id].calculate_concentrations( + model=calibrators[molecule_id].standard.result, + signals=[peak.area], + )[0] + ) + measurement_data_instances[ + molecule_id + ].data_type = DataTypes.CONCENTRATION + + except KeyError: + print(f"no calibrator for molecule {molecule_id}") + continue + + elif peak and not calculate_concentration: measurement_data_instances[molecule_id].data.append(peak.area) measurement_data_instances[molecule_id].data_type = DataTypes.PEAK_AREA @@ -292,7 +329,7 @@ def measurements_to_measurmentdata( def extract_measurement_conditions( measurements: list[Measurement], -) -> tuple[float, float, ChromatopyUnitDefinition, ChromatopyUnitDefinition]: +) -> tuple[float, float, UnitDefinition, UnitDefinition]: """Asserts and extracts the measurement conditions from a list of Measurement instances. Args: @@ -368,3 +405,24 @@ def measured_molecule_dict( measured_dict[peak.molecule_id] = True return measured_dict + + +def _check_molecule_conc_unit_and_init_conc(molecule: Molecule): + if molecule.init_conc is None: + raise ValueError(f""" + No initial concentration is defined for molecule {molecule.name}. + Please specify the initial concentration of the molecule. + """) + + elif molecule.conc_unit: + return + + elif molecule.conc_unit is None and molecule.standard: + molecule.conc_unit = molecule.standard.samples[0].conc_unit + return + + else: + raise ValueError(f""" + No concentration unit is defined for molecule {molecule.name}. + Please specify the concentration unit or define a standard for the molecule. + """) diff --git a/chromatopy/model.py b/chromatopy/model.py index b3d161f..1a25113 100644 --- a/chromatopy/model.py +++ b/chromatopy/model.py @@ -80,10 +80,10 @@ class Measurement(BaseModel): id: str reaction_time: float time_unit: UnitDefinition + temperature: float + temperature_unit: UnitDefinition + ph: float chromatograms: list[Chromatogram] = Field(default_factory=list) - temperature: Optional[float] = Field(default=None) - temperature_unit: Optional[UnitDefinition] = Field(default=None) - ph: Optional[float] = Field(default=None) sample_name: Optional[str] = Field(default=None) timestamp: Optional[str] = Field(default=None) injection_volume: Optional[float] = Field(default=None) @@ -325,13 +325,15 @@ def add_to_peaks( area: float, molecule_id: Optional[str] = None, type: Optional[str] = None, - peak_start: Optional[float] = None, - peak_end: Optional[float] = None, width: Optional[float] = None, - height: Optional[float] = None, + amplitude: Optional[float] = None, + max_signal: Optional[float] = None, + skew: Optional[float] = None, percent_area: Optional[float] = None, tailing_factor: Optional[float] = None, separation_factor: Optional[float] = None, + peak_start: Optional[float] = None, + peak_end: Optional[float] = None, **kwargs, ): params = { @@ -339,13 +341,15 @@ def add_to_peaks( "area": area, "molecule_id": molecule_id, "type": type, - "peak_start": peak_start, - "peak_end": peak_end, "width": width, - "height": height, + "amplitude": amplitude, + "max_signal": max_signal, + "skew": skew, "percent_area": percent_area, "tailing_factor": tailing_factor, "separation_factor": separation_factor, + "peak_start": peak_start, + "peak_end": peak_end, } if "id" in kwargs: @@ -365,13 +369,15 @@ class Peak(BaseModel): area: float molecule_id: Optional[str] = Field(default=None) type: Optional[str] = Field(default=None) - peak_start: Optional[float] = Field(default=None) - peak_end: Optional[float] = Field(default=None) width: Optional[float] = Field(default=None) - height: Optional[float] = Field(default=None) + amplitude: Optional[float] = Field(default=None) + max_signal: Optional[float] = Field(default=None) + skew: Optional[float] = Field(default=None) percent_area: Optional[float] = Field(default=None) tailing_factor: Optional[float] = Field(default=None) separation_factor: Optional[float] = Field(default=None) + peak_start: Optional[float] = Field(default=None) + peak_end: Optional[float] = Field(default=None) # JSON-LD fields ld_id: str = Field( @@ -460,7 +466,6 @@ def add_type_term( class UnitDefinition(BaseModel): model_config: ConfigDict = ConfigDict( # type: ignore validate_assigment=True, - use_enum_values=True, ) # type: ignore id: Optional[str] = Field(default=None) diff --git a/chromatopy/readers/asm.py b/chromatopy/readers/asm.py new file mode 100644 index 0000000..5f3716c --- /dev/null +++ b/chromatopy/readers/asm.py @@ -0,0 +1,196 @@ +import json +from pathlib import Path + +from loguru import logger + +from chromatopy.model import Chromatogram, Measurement, Peak, UnitDefinition +from chromatopy.readers.abstractreader import AbstractReader + + +class ASMReader(AbstractReader): + def __init__( + self, + dirpath: str, + reaction_times: list[float], + time_unit: UnitDefinition, + ph: float, + temperature: float, + temperature_unit: UnitDefinition, + ): + super().__init__( + dirpath, + reaction_times, + time_unit, + ph, + temperature, + temperature_unit, + ) + self._get_file_paths() + + def read(self) -> list[Measurement]: + """Reads the chromatographic data from the specified files. + + Returns: + list[Measurement]: A list of Measurement objects representing the chromatographic data. + """ + + measurements = [] + for file, reaction_time in zip(self.file_paths, self.reaction_times): + content = self._read_asm_file(file) + measurement = self._map_measurement(content, reaction_time, file) + measurements.append(measurement) + + return measurements + + def _get_file_paths(self): + """Collects the file paths from the directory.""" + + files = [] + directory = Path(self.dirpath) + + # check if directory exists + assert directory.exists(), f"Directory '{self.dirpath}' does not exist." + assert directory.is_dir(), f"'{self.dirpath}' is not a directory." + assert any( + directory.rglob("*.json") + ), f"No .json files found in '{self.dirpath}'." + + for file_path in directory.iterdir(): + if file_path.name.startswith(".") or not file_path.name.endswith(".json"): + continue + + files.append(str(file_path.absolute())) + + assert ( + len(files) == len(self.reaction_times) + ), f"Number of files ({len(files)}) does not match the number of reaction times ({len(self.reaction_times)})." + + self.file_paths = sorted(files) + + def _read_asm_file(self, file_path: str) -> dict: + with open(file_path, "r") as file: + content = json.load(file) + + return content + + def _map_measurement( + self, + content: dict, + reaction_time: float, + path: str, + ): + doc = content["liquid chromatography aggregate document"][ + "liquid chromatography document" + ] + if len(doc) > 1: + logger.warning( + f"More than one chromatogram found in file '{path}'. Using the first chromatogram only." + ) + + sample_document = doc[0]["sample document"] + name = sample_document.get("written name") + sample_id = sample_document.get("sample identifier") + if not sample_id and name: + sample_id = name + + meas_document = doc[0]["measurement document"] + peak_list = meas_document["peak list"]["peak"] + signal = meas_document["chromatogram data cube"]["data"]["measures"][0] + time = meas_document["chromatogram data cube"]["data"]["dimensions"][0] + time_unit = meas_document["chromatogram data cube"]["cube-structure"][ + "dimensions" + ][0]["unit"] + + if time_unit == "s": + # to min + time = [t / 60 for t in time] + elif time_unit == "min": + pass + else: + raise ValueError(f"Unit '{time_unit}' not recognized") + + peaks = [self.map_peaks(peak) for peak in peak_list] + + chrom = Chromatogram( + peaks=peaks, + signals=signal, + times=time, + ) + + return Measurement( + id=sample_id, + sample_name=name, + reaction_time=reaction_time, + time_unit=self.time_unit, + temperature=self.temperature, + temperature_unit=self.temperature_unit, + ph=self.ph, + chromatograms=[chrom], + ) + + def map_peaks(self, peak_dict: dict) -> Peak: + area = peak_dict["peak area"] + if area["unit"] == "mAU.s": + area["value"] *= 60 + elif area["unit"] == "mAU.min": + pass + else: + raise ValueError(f"Unit '{area['unit']}' not recognized") + + width = peak_dict["peak width at half height"] + if width["unit"] == "s": + width["value"] /= 60 + elif width["unit"] == "min": + pass + else: + raise ValueError(f"Unit '{width['unit']}' not recognized") + + retention_time = peak_dict["retention time"] + if retention_time["unit"] == "s": + retention_time["value"] /= 60 + elif retention_time["unit"] == "min": + pass + else: + raise ValueError(f"Unit '{retention_time['unit']}' not recognized") + + peak_start = peak_dict["peak start"] + if peak_start["unit"] == "s": + peak_start["value"] /= 60 + elif peak_start["unit"] == "min": + pass + else: + raise ValueError(f"Unit '{peak_start['unit']}' not recognized") + + peak_end = peak_dict["peak end"] + if peak_end["unit"] == "s": + peak_end["value"] /= 60 + elif peak_end["unit"] == "min": + pass + else: + raise ValueError(f"Unit '{peak_end['unit']}' not recognized") + + return Peak( + retention_time=peak_dict["retention time"]["value"], + area=peak_dict["peak area"]["value"], + amplitude=peak_dict["peak height"]["value"], + width=peak_dict["peak width at half height"]["value"], + skew=peak_dict["chromatographic peak asymmetry factor"], + percent_area=peak_dict["relative peak area"]["value"], + peak_start=peak_dict["peak start"]["value"], + peak_end=peak_dict["peak end"]["value"], + ) + + +if __name__ == "__main__": + from chromatopy.units import C, minute + + reader = ASMReader( + dirpath="tests/test_readers/data/asm", + reaction_times=[0, 10, 20, 30], + time_unit=minute, + ph=7.4, + temperature=37, + temperature_unit=C, + ) + measurements = reader.read() + print(measurements) diff --git a/chromatopy/readers/chromeleon.py b/chromatopy/readers/chromeleon.py index c148e4e..b301ca3 100644 --- a/chromatopy/readers/chromeleon.py +++ b/chromatopy/readers/chromeleon.py @@ -79,10 +79,8 @@ def _map_measurement( # reaction_time, unit = self._extract_reaction_time(file_name) - print(content["Sample Information"][2][1]) - return Measurement( - id=content["Sample Information"][2][1], + id=content["Sample Information"][5][1], chromatograms=[chromatogram], injection_volume=float( content["Sample Information"][13][1].replace(",", ".") @@ -93,6 +91,9 @@ def _map_measurement( ), reaction_time=reaction_time, time_unit=time_unit, + ph=self.ph, + temperature=self.temperature, + temperature_unit=self.temperature_unit, ) def _extract_reaction_time(self, file_name: str) -> tuple[float, UnitDefinition]: @@ -139,8 +140,8 @@ def _get_file_paths(self): directory.rglob("*.txt") ), f"No .txt files found in '{self.dirpath}'." - for file_path in directory.rglob("*.txt"): - if file_path.name.startswith("."): + for file_path in directory.iterdir(): + if file_path.name.startswith(".") or not file_path.name.endswith(".txt"): continue files.append(str(file_path.absolute())) diff --git a/chromatopy/tools/analyzer.py b/chromatopy/tools/analyzer.py index f66d67c..22262a6 100644 --- a/chromatopy/tools/analyzer.py +++ b/chromatopy/tools/analyzer.py @@ -1,3 +1,7 @@ +from __future__ import annotations + +import copy +import multiprocessing as mp import time import warnings from collections import defaultdict @@ -5,15 +9,15 @@ import numpy as np import plotly.colors as pc import plotly.graph_objects as go +import scipy +import scipy.stats from calipytion.model import Standard from calipytion.tools import Calibrator from calipytion.tools.utility import pubchem_request_molecule_name -from joblib import Parallel, delayed -from lmfit.models import GaussianModel from loguru import logger from pydantic import BaseModel, Field -from pyenzyme import EnzymeMLDocument, Protein -from rich.progress import Progress, TaskID +from pyenzyme import EnzymeMLDocument +from rich.progress import Progress from chromatopy.model import ( Chromatogram, @@ -22,7 +26,7 @@ SignalType, UnitDefinition, ) -from chromatopy.tools.molecule import Molecule +from chromatopy.tools.molecule import Molecule, Protein from chromatopy.tools.peak_utils import SpectrumProcessor from chromatopy.tools.utility import _resolve_chromatogram from chromatopy.units import C @@ -63,32 +67,79 @@ def add_molecule_from_standard( molecule = Molecule.from_standard(standard, init_conc, conc_unit) - self.add_molecule(**molecule.model_dump()) + self.define_molecule(**molecule.model_dump()) def add_molecule( + self, + molecule: Molecule, + init_conc: float | None = None, + conc_unit: UnitDefinition | None = None, + retention_tolerance: float | None = None, + ) -> None: + """Adds a molecule to the list of species. Allowing to update the initial concentration, + concentration unit and retention time tolerance. + + Args: + molecule (Molecule): The molecule object to be added. + retention_tolerance (float | None, optional): Retention time tolerance for peak annotation + in minutes. Defaults to None. + """ + + new_mol = copy.deepcopy(molecule) + + if init_conc: + new_mol.init_conc = init_conc + + if conc_unit: + new_mol.conc_unit = conc_unit + + if retention_tolerance: + new_mol.retention_tolerance = retention_tolerance + + if self._update_molecule(molecule): + self.molecules.append(new_mol) + + self._register_peaks( + molecule, molecule.retention_tolerance, molecule.wavelength + ) + + def define_molecule( self, id: str, pubchem_cid: int, - init_conc: float, - conc_unit: UnitDefinition, retention_time: float, retention_tolerance: float = 0.2, + init_conc: float | None = None, + conc_unit: UnitDefinition | None = None, name: str | None = None, wavelength: float | None = None, - ): - """Adds a molecule to the list of species. + ) -> Molecule: + """Defines and adds a molecule to the list of molecules. Args: id (str): Internal identifier of the molecule such as `s0` or `asd45`. pubchem_cid (int): PubChem CID of the molecule. - init_conc (float): Initial concentration of the molecule at reaction start. - conc_unit (UnitDefinition): Unit of the concentration. retention_time (float): Retention time tolerance for peak annotation in minutes. + retention_tolerance (float, optional): Retention time tolerance for peak annotation in minutes. Defaults to 0.2. + init_conc (float | None): Initial concentration of the molecule. Defaults to None. + conc_unit (UnitDefinition | None): Unit of the concentration. Defaults to None. name (str | None, optional): Name of the molecule. If not provided, the name is retrieved from the PubChem database. Defaults to None. wavelength (float | None, optional): Wavelength of the detector on which the molecule was detected. Defaults to None. + + Returns: + Molecule: The molecule object that was added to the list of species. """ + if conc_unit: + assert ( + init_conc is not None + ), "Initial concentration must be provided if concentration unit is given." + if init_conc: + assert ( + conc_unit + ), "Concentration unit must be provided if initial concentration is given." + if name is None: name = pubchem_request_molecule_name(pubchem_cid) @@ -106,6 +157,8 @@ def add_molecule( self._register_peaks(molecule, retention_tolerance, wavelength) + return molecule + def define_internal_standard( self, id: str, @@ -114,9 +167,23 @@ def define_internal_standard( init_conc: float, conc_unit: UnitDefinition, retention_time: float, - retention_tolerance: float = 0.2, + retention_tolerance: float = 0.1, wavelength: float | None = None, ): + """Defines an molecule as the internal standard for concentration calculation. + + Args: + id (str): Internal identifier of the molecule such as `s0` or `asd45`. + pubchem_cid (int): PubChem CID of the molecule. + name (str): Name of the internal standard molecule. + init_conc (float): Initial concentration of the internal standard. + conc_unit (UnitDefinition): Unit of the concentration. + retention_time (float): Retention time of the internal standard in minutes. + retention_tolerance (float, optional): Retention time tolerance for + peak annotation in minutes. Defaults to 0.1. + wavelength (float | None, optional): Wavelength of the detector on + which the molecule was detected. Defaults to None. + """ self.internal_standard = Molecule( id=id, pubchem_cid=pubchem_cid, @@ -132,12 +199,32 @@ def define_internal_standard( wavelength=wavelength, ) + def get_peaks(self, molecule_id: str): + peaks = [ + peak + for meas in self.measurements + for chrom in meas.chromatograms + for peak in chrom.peaks + if peak.molecule_id == molecule_id + ] + + if not peaks: + raise ValueError(f"No peaks found for molecule {molecule_id}.") + return peaks + def _register_peaks( self, molecule: Molecule, ret_tolerance: float, wavelength: float | None, ): + """Registers the peaks of a molecule based on the retention time tolerance and wavelength. + + Args: + molecule (Molecule): The molecule for which the peaks should be registered. + ret_tolerance (float): Retention time tolerance for peak annotation in minutes. + wavelength (float | None): Wavelength of the detector on which the molecule was detected. + """ for meas in self.measurements: for peak in _resolve_chromatogram(meas.chromatograms, wavelength).peaks: if ( @@ -150,15 +237,16 @@ def _register_peaks( f"{molecule.id} assigned as molecule ID for peak at {peak.retention_time}." ) - def add_protein( + def define_protein( self, id: str, name: str, - uniprot_id: str | None = None, + init_conc: float, + conc_unit: UnitDefinition, sequence: str | None = None, - ecnumber: str | None = None, organism: str | None = None, organism_tax_id: int | None = None, + constant: bool | None = True, ): """Adds a protein to the list of proteins or updates an existing protein based on the pubchem_cid of the molecule. @@ -166,39 +254,65 @@ def add_protein( Args: id (str): Internal identifier of the protein such as `p0` or `asd45`. name (str): Name of the protein. - uniprot_id (str | None, optional): UniProt ID of the protein. Defaults to None. - sequence (str | None, optional): Amino acid sequence of the protein. Defaults to None. - ecnumber (str | None, optional): EC number of the protein. Defaults to None. - organism (str | None, optional): Name of the organism the protein belongs to. Defaults to None. - organism_tax_id (int | None, optional): NCBI Taxonomy ID of the organism. Defaults to None. + init_conc (float): Initial concentration of the protein. + conc_unit (UnitDefinition): Unit of the concentration. + sequence (str, optional): Amino acid sequence of the protein. Defaults to None. + organism (str, optional): Name of the organism. Defaults to None. + organism_tax_id (int, optional): NCBI taxonomy ID of the organism. Defaults to None. + constant (bool, optional): Boolean indicating whether the protein concentration is constant. Defaults to True. """ protein = Protein( id=id, name=name, - uniprot_id=uniprot_id, + init_conc=init_conc, + conc_unit=conc_unit, sequence=sequence, - ecnumber=ecnumber, organism=organism, organism_tax_id=organism_tax_id, + constant=constant, ) if not self._update_protein(protein): self.proteins.append(protein) + def add_protein( + self, + protein: Protein, + init_conc: float | None = None, + conc_unit: UnitDefinition | None = None, + ) -> None: + """Adds a protein to the list of proteins or updates an existing protein + based on the pubchem_cid of the molecule. + + Args: + protein (Protein): The protein object to be added. + """ + + nu_prot = copy.deepcopy(protein) + + if init_conc: + nu_prot.init_conc = init_conc + + if conc_unit: + nu_prot.conc_unit = conc_unit + + if not self._update_protein(protein): + self.proteins.append(protein) + @classmethod - def read_shimadzu( + def read_asm( cls, - id: str, path: str, reaction_times: list[float], time_unit: UnitDefinition, ph: float, temperature: float, + id: str | None = None, temperature_unit: UnitDefinition = C, ): - from chromatopy.readers.shimadzu import ShimadzuReader + from chromatopy.readers.asm import ASMReader - measurements = ShimadzuReader( + measurements = ASMReader( dirpath=path, reaction_times=reaction_times, time_unit=time_unit, @@ -207,10 +321,13 @@ def read_shimadzu( temperature_unit=temperature_unit, ).read() + if id is None: + id = path + return cls(id=id, measurements=measurements) @classmethod - def read_agilent_csv( + def read_shimadzu( cls, id: str, path: str, @@ -220,32 +337,16 @@ def read_agilent_csv( temperature: float, temperature_unit: UnitDefinition = C, ): - """Reads peak data from Agilent CSV files from a directory containing *.D directories. - - - Args: - path (str): _description_ - reaction_times (list[float]): _description_ - time_unit (UnitDefinition): _description_ - ph (float): _description_ - temperature (float): _description_ - temperature_unit (UnitDefinition, optional): _description_. Defaults to C. - - Returns: - _type_: _description_ - """ - from chromatopy.readers.agilent_csv import ( - assamble_measurements_from_agilent_csv, - ) + from chromatopy.readers.shimadzu import ShimadzuReader - measurements = assamble_measurements_from_agilent_csv( - path=path, + measurements = ShimadzuReader( + dirpath=path, reaction_times=reaction_times, time_unit=time_unit, ph=ph, temperature=temperature, temperature_unit=temperature_unit, - ) + ).read() return cls(id=id, measurements=measurements) @@ -259,6 +360,19 @@ def read_agilent( temperature: float, temperature_unit: UnitDefinition = C, ): + """Reads chromatographic data from an Agilent *.csv or *.txt file. + + Args: + path (str): _description_ + reaction_times (list[float]): _description_ + time_unit (UnitDefinition): _description_ + ph (float): _description_ + temperature (float): _description_ + temperature_unit (UnitDefinition, optional): _description_. Defaults to C. + + Returns: + _type_: _description_ + """ from chromatopy.readers.agilent_csv import ( assamble_measurements_from_agilent_csv, ) @@ -285,6 +399,43 @@ def read_agilent( return cls(id=path, measurements=measurements) + @classmethod + def read_chromeleon( + cls, + path: str, + reaction_times: list[float], + time_unit: UnitDefinition, + ph: float, + temperature: float, + temperature_unit: UnitDefinition = C, + ) -> ChromAnalyzer: + """Reads chromatographic data from a Chromeleon *.txt file. + + Args: + path (str): Path to directory containing the Chromeleon *.txt files. + reaction_times (list[float]): List of reaction times for each measurement. + time_unit (UnitDefinition): Unit of the time values. + ph (float): pH value of the measurement. + temperature (float): Temperature of the measurement. + temperature_unit (UnitDefinition, optional): Unit of the temperature value. + Defaults to C. + + Returns: + ChromAnalyzer: ChromAnalyzer object containing the measurements. + """ + from chromatopy.readers.chromeleon import ChromeleonReader + + measurements = ChromeleonReader( + dirpath=path, + reaction_times=reaction_times, + time_unit=time_unit, + ph=ph, + temperature=temperature, + temperature_unit=temperature_unit, + ).read() + + return cls(id=path, measurements=measurements) + def create_enzymeml( self, name: str, @@ -336,227 +487,270 @@ def add_to_enzymeml( calculate_concentration=calculate_concentration, ) - # @classmethod - # def read_csv( - # cls, - # experiment_id: str, - # path: str, - # wavelength: float, - # detector: str, - # time_unit: str = min, - # **kwargs, - # ): - # detectors = [det.value for det in SignalType] - # assert detector in detectors, ( - # f"Detector '{detector}' not found. Available detectors are" f" {detectors}." - # ) - - # df = pd.read_csv(path, **kwargs) - # col_names = df.columns - - # if not len(col_names) == 2: - # raise ValueError( - # f"Expected two columns in the csv file, found {len(col_names)}.", - # "The first column should contain the time values and the second column" - # " should contain the signal values.", - # ) - - # measurement = Measurement(id=path) - # measurement.add_to_chromatograms( - # times=df[col_names[0]].values.tolist(), - # signals=df[col_names[1]].values.tolist(), - # wavelength=wavelength, - # time_unit=time_unit, - # type=detector, - # ) - - # return cls(id=experiment_id, measurements=[measurement]) + def get_molecule(self, molecule_id: str) -> Molecule: + for molecule in self.molecules: + if molecule.id == molecule_id: + return molecule - @classmethod - def read_chromeleon( - cls, - path: str, - reaction_times: list[float], - time_unit: UnitDefinition, - ph: float, - temperature: float, - temperature_unit: UnitDefinition = C, - ): - from chromatopy.readers.chromeleon import ChromeleonReader + if self.internal_standard: + if self.internal_standard.id == molecule_id: + return self.internal_standard - measurements = ChromeleonReader( - dirpath=path, - reaction_times=reaction_times, - time_unit=time_unit, - ph=ph, - temperature=temperature, - temperature_unit=temperature_unit, - ).read() + raise ValueError(f"Molecule with ID {molecule_id} not found.") - return cls(id=path, measurements=measurements) + @staticmethod + def process_task(processor: SpectrumProcessor, **kwargs): + try: + return processor.silent_fit(**kwargs) + except KeyError as e: + if "retention_time" in str(e): + # check if retention time is in kwargs and halve it + if "retention_time" in kwargs: + kwargs["retention_time"] /= 2 + return processor.silent_fit(**kwargs) + else: + raise e - def find_peaks(self): - print( - f"🔍 Processing signal and integrating peaks for {len(self.measurements)} chromatograms." - ) + def process_chromatograms( + self, + prominence: float = 0.03, + min_retention_time: float | None = None, + max_retention_time: float | None = None, + **hplc_py_kwargs, + ): + """Processes the chromatograms using the HPLC-Py library. - # get all chromatograms and create spectrum processors: - processors = [ - SpectrumProcessor( - time=chrom.times, - raw_data=chrom.signals, - silent=True, - ) - for meas in self.measurements - for chrom in meas.chromatograms - ] + Args: + prominence: The prominence of the peaks to be detected. Defaults to 0.03. + hplc_py_kwargs: Keyword arguments to be passed to the `fit_peaks` method of the + `HPLC-Py library `_. + """ + hplc_py_kwargs["prominence"] = prominence + hplc_py_kwargs["approx_peak_width"] = 0.6 + processors = [] - def process_task( - processor: SpectrumProcessor, progress: Progress, task_id: TaskID - ): - processor.run_pripeline() - progress.update(task_id, advance=1) - time.sleep(0.1) - return processor + for meas in self.measurements: + for chrom in meas.chromatograms: + if min_retention_time is not None: + # get index of first retention time greater than min_retention_time + idx_min = int(np.argmax(np.array(chrom.times) > min_retention_time)) + times = chrom.times[idx_min:] + signals = chrom.signals[idx_min:] + else: + times = chrom.times + signals = chrom.signals + idx_min = 0 + + if max_retention_time is not None: + # filter out retention times greater than max_retention_time + idx_max = int(np.argmax(np.array(times) > max_retention_time)) + times = times[:idx_max] + signals = signals[:idx_max] + else: + idx_max = len(chrom.times) + + processors.append( + SpectrumProcessor( + time=times, + raw_data=signals, + silent=True, + ) + ) with Progress() as progress: task = progress.add_task( "Processing chromatograms...", total=len(self.measurements) ) - results = Parallel(n_jobs=-1, backend="threading")( - delayed(process_task)(processor, progress, task) - for processor in processors - ) + with mp.Pool(processes=mp.cpu_count()) as pool: + result_objects = [ + pool.apply_async( + self.process_task, args=(processor,), kwds=hplc_py_kwargs + ) + for processor in processors + ] + + results = [] + for result in result_objects: + processor_result = result.get() # Retrieve the actual result + results.append(processor_result) + progress.update(task, advance=1) + time.sleep(0.1) + progress.update(task, refresh=True) processor_idx = 0 + no_peaks = False for meas in self.measurements: for chrom in meas.chromatograms: - chrom.processed_signal = results[processor_idx].baseline_corrected_data chrom.peaks = [] - for peak in results[processor_idx].peak_params: - chrom.add_to_peaks( - retention_time=peak.center, - area=peak.area, - width=peak.width, - height=peak.amplitude, + if not hasattr(results[processor_idx], "peaks"): + no_peaks = True + logger.warning( + f"No peaks found in chromatogram {meas.id} at {chrom.wavelength} nm." ) + processor_idx += 1 + continue + # pad the processed signal with zeros to match the length of the raw signal accounting for the cropping of the retention time + nans_laft = idx_min + nans_right = len(chrom.signals) - len(times) + + chrom.processed_signal = np.concatenate( + [ + np.full(nans_laft, np.nan), + results[processor_idx].processed_signal, + np.full(nans_right, np.nan), + ] + ).tolist() + + chrom.peaks = results[processor_idx].peaks processor_idx += 1 - def _handel_detector(self, detector: SignalType): - """ - Handles the detector selection for the given SignalType. - If only one detector is found in a measurement, it is selected. - - Args: - detector (SignalType): The type of detector to handle. - - Returns: - SignalType: The selected detector. - - Raises: - ValueError: If data from multiple detectors is found and no specific detector is specified. - - """ - detectors = list( - set( - [ - chomatogram.type - for measurement in self.measurements - for chomatogram in measurement.chromatograms - ] + for molecule in self.molecules: + self._register_peaks( + molecule, molecule.retention_tolerance, molecule.wavelength ) - ) - - if detector in detectors: - return detector - if all(detector == detectors[0] for detector in detectors): - return detectors[0] - raise ValueError( - "Data from multiple detectors found. Please specify detector." - f" {list(set(detectors))}" - ) + if no_peaks: + print( + "No peaks found in one of the chromatograms, try to reduce the `prominence` in the `hplc_py_kwargs` of the `process_chromatograms` method." + ) - def plot_peaks_fitted_spectrum(self): + def plot_peaks_fitted_spectrum( + self, assigned_only: bool = False, dark_mode: bool = False + ): # make plotly figure for each chromatogram whereas ech chromatogram contains multiple traces and each comatogram is mapped to one slider from plotly.express.colors import sample_colorscale - from chromatopy.tools.utility import generate_visibility + from chromatopy.tools.utility import generate_gaussian_data, generate_visibility + + if dark_mode: + theme = "plotly_dark" + signal_color = "white" + else: + theme = "plotly_white" + signal_color = "black" fig = go.Figure() - for idx, meas in enumerate(self.measurements): + for meas in self.measurements: for chrom in meas.chromatograms: - fig.add_trace( - go.Scatter( - visible=False, - x=chrom.times, - y=chrom.signals, - mode="lines", - name="Unprocessed", - hovertext=f"{meas.id}", - line=dict( - color="black", # Line color - dash="dot", # Line style ('dash' for dashed line) - width=2, # Line width - ), - ) - ) - - fig.add_trace( - go.Scatter( - visible=False, - x=chrom.times, - y=chrom.processed_signal, - mode="lines", - name="Processed", - hovertext=f"{meas.id}", - line=dict( - color="red", # Line color - dash="dot", # Line style ('dash' for dashed line) - width=2, # Line width - ), - ) - ) - # model peaks as gaussians color_map = sample_colorscale("viridis", len(chrom.peaks)) + if chrom.peaks: + peaks_exist = True + for color, peak in zip(color_map, chrom.peaks): + if assigned_only and not peak.molecule_id: + continue + if peak.amplitude is None or peak.width is None: + continue + if peak.peak_start and peak.peak_end: + x_arr, data = generate_gaussian_data( + amplitude=peak.amplitude, + center=peak.retention_time, + half_height_diameter=peak.width, + start=peak.peak_start, + end=peak.peak_end, + ) + else: + x_start = peak.retention_time - 3 * peak.width + x_end = peak.retention_time + 3 * peak.width + x_arr = np.linspace(x_start, x_end, 100) + data = ( + scipy.stats.skewnorm.pdf( + x_arr, + peak.skew if peak.skew else 0, + loc=peak.retention_time, + scale=peak.width, + ) + * peak.amplitude + ) + + custom1 = [round(peak.area)] * len(x_arr) + custom2 = [round(peak.retention_time, 2)] * len(x_arr) + if peak.molecule_id: + peak_name = self.get_molecule(peak.molecule_id).name + else: + peak_name = f"Peak {peak.retention_time:.2f}" + customdata = np.stack((custom1, custom2), axis=-1) + fig.add_trace( + go.Scatter( + visible=False, + x=x_arr, + y=data, + mode="lines", + name=peak_name, + customdata=customdata, + hovertemplate="Area: %{customdata[0]}
" + + "Center: %{customdata[1]}
" + + "", + hovertext=f"{meas.id}", + line=dict( + color=color, + width=1, + ), + fill="tozeroy", + fillcolor=color, + ) + ) + else: + peaks_exist = False - for peak in chrom.peaks: - gaussian = GaussianModel() - x_start = peak.retention_time - 5 * peak.width - x_end = peak.retention_time + 5 * peak.width - x_arr = np.linspace(x_start, x_end, 100) + if chrom.times and chrom.signals: + signal_exist = True fig.add_trace( go.Scatter( visible=False, - x=x_arr, - y=gaussian.eval( - x=x_arr, - amplitude=peak.height, - center=peak.retention_time, - sigma=peak.width, + x=chrom.times, + y=chrom.signals, + mode="lines", + name="Signal", + hovertext=f"{meas.id}", + line=dict( + color=signal_color, + dash="solid", + width=1, ), + ) + ) + else: + signal_exist = False + + if chrom.processed_signal and chrom.times: + processed_signal_exist = True + fig.add_trace( + go.Scatter( + visible=False, + x=chrom.times, + y=chrom.processed_signal, mode="lines", - name=f"Peak {peak.retention_time}", + name="Processed Signal", hovertext=f"{meas.id}", line=dict( - color=color_map.pop(0), + color="red", + dash="dot", width=2, ), ) ) + else: + processed_signal_exist = False - # get data for peak by solving the gaussian model from lmfit + if peak.peak_start: + logger.info( + "Peaks are visualized as Gaussians. Visual deviations between the signal and the peaks may occur." + ) - fig.data[0].visible = True - fig.data[1].visible = True n_peaks_in_first_chrom = len(self.measurements[0].chromatograms[0].peaks) - for i in range(2, 2 + n_peaks_in_first_chrom): - fig.data[i].visible = True - # define slider for each chromatogram + if signal_exist and not processed_signal_exist: + fig.data[n_peaks_in_first_chrom].visible = True + elif signal_exist and processed_signal_exist: + fig.data[n_peaks_in_first_chrom].visible = True + fig.data[n_peaks_in_first_chrom + 1].visible = True + + if peaks_exist: + for i in range(n_peaks_in_first_chrom): + fig.data[i].visible = True steps = [] for meas in self.measurements: @@ -580,10 +774,97 @@ def plot_peaks_fitted_spectrum(self): } ] - fig.update_layout(sliders=sliders) + fig.update_layout( + sliders=sliders, + xaxis_title="Retention time (min)", + yaxis_title="Intensity", + template=theme, + ) fig.show() + def add_standard( + self, + molecule: Molecule, + concs: list[float], + conc_unit: UnitDefinition, + wavelength: float | None = None, + visualize: bool = True, + ): + assert any( + [molecule in [mol for mol in self.molecules]] + ), "Molecule not found in molecules of analyzer." + + # check if all measurements only contain one chromatogram + if all([len(meas.chromatograms) == 1 for meas in self.measurements]): + chroms = [ + chrom for meas in self.measurements for chrom in meas.chromatograms + ] + else: + assert ( + wavelength is not None + ), "Multiple chromatograms found for each measurment, wavelength needs to be provided." + + chroms = self._get_chromatograms_by_wavelegnth(wavelength) + + assert ( + len(chroms) > 0 + ), "No chromatograms found at the specified wavelength." + + peak_areas = [ + peak.area for chrom in chroms for peak in chrom.peaks if peak.molecule_id + ] + + assert ( + len(peak_areas) == len(concs) + ), f"Number of {molecule.name} peak areas {len(peak_areas)} and concentrations {len(concs)} do not match." + + assert all( + meas.ph == self.measurements[0].ph for meas in self.measurements + ), "All measurements need to have the same pH value." + ph = self.measurements[0].ph + + assert all( + meas.temperature == self.measurements[0].temperature + for meas in self.measurements + ), "All measurements need to have the same temperature value." + temperature = self.measurements[0].temperature + + assert all( + meas.temperature_unit.name == self.measurements[0].temperature_unit.name + for meas in self.measurements + ), "All measurements need to have the same temperature unit." + temperature_unit = self.measurements[0].temperature_unit + + molecule.create_standard( + areas=peak_areas, + concs=concs, + conc_unit=conc_unit, + ph=ph, + temperature=temperature, + temp_unit=temperature_unit, + visualize=visualize, + ) + print(f"Standard for {molecule.name} added.") + + def _get_chromatograms_by_wavelegnth(self, wavelength: float) -> list[Chromatogram]: + """Returns a list of chromatograms at a specified wavelength. + + Args: + wavelength (float): The wavelength of the detector. + + Returns: + list[Chromatogram]: A list of chromatograms at the specified wavelength. + """ + + chroms = [] + for meas in self.measurements: + for chrom in meas.chromatograms: + if chrom.wavelength == wavelength: + chroms.append(chrom) + + return chroms + def _get_peaks_by_retention_time( self, retention_time: float, @@ -696,7 +977,7 @@ def _apply_calibrators(self): def _update_molecule(self, molecule) -> bool: """Updates the molecule if it already exists in the list of species.""" for idx, mol in enumerate(self.molecules): - if mol.pubchem_cid == molecule.pubchem_cid: + if mol.id == molecule.id: self.molecules[idx] = molecule return True @@ -772,17 +1053,6 @@ def visualize_peaks(self): fig.show() - def get_molecule(self, molecule_id: str) -> Molecule: - for molecule in self.molecules: - if molecule.id == molecule_id: - return molecule - - if self.internal_standard: - if self.internal_standard.id == molecule_id: - return self.internal_standard - - raise ValueError(f"Molecule with ID {molecule_id} not found.") - def visualize(self) -> go.Figure: """ Plot the chromatogram. @@ -997,21 +1267,77 @@ def apply_standards(self, tolerance: float = 1): # ana.visualize() - from chromatopy.tools.analyzer import ChromAnalyzer - from chromatopy.units import min as min_ + # from chromatopy.tools.analyzer import ChromAnalyzer + # from chromatopy.units import min as min_ - path = "/Users/max/Documents/jan-niklas/MjNK" + # path = "/Users/max/Documents/jan-niklas/MjNK" - reaction_times = [0.0] * 18 + # reaction_times = [0.0] * 18 - ana = ChromAnalyzer.read_chromeleon( - path=path, - reaction_times=reaction_times, - time_unit=min_, - ph=7.4, - temperature=25.0, + # ana = ChromAnalyzer.read_chromeleon( + # path=path, + # reaction_times=reaction_times, + # time_unit=min_, + # ph=7.4, + # temperature=25.0, + # ) + # ana.measurements = ana.measurements + + # ana.process_chromatograms( + # min_retention_time=5, max_retention_time=25, prominence=0.008 + # ) + + # print(ana.measurements[0].chromatograms[0].peaks[0]) + # ana.plot_peaks_fitted_spectrum() + + ###### + + from chromatopy.units import hour, mM + + path = "/Users/max/Documents/GitHub/chromatopy/tests/test_readers/data/asm" + + analyzer = ChromAnalyzer.read_asm( + path, + reaction_times=[0, 0.5, 2, 6], + time_unit=hour, + ph=7, + temperature=25, ) - ana.find_peaks() + n1_triphosphate = analyzer.define_molecule( + pubchem_cid=127255957, + id="n1_triphosphate", + name="N6 Benzyl ATP", + retention_time=13.94, + init_conc=2.5, + conc_unit=mM, + ) + + y_Hcy = analyzer.define_molecule( + pubchem_cid=-1, + id="y_Hcy", + name="y Hcy", + retention_time=15.68, + init_conc=0.5, + conc_unit=mM, + ) + + DHBAL = analyzer.define_molecule( + pubchem_cid=8768, + id="DHBAL", + name="DHBAL", + retention_time=22.97, + init_conc=0.3, + conc_unit=mM, + ) + + DHBAL_O3 = analyzer.define_molecule( + pubchem_cid=-1, + id="DHBAL_O3", + name="DHBAL O3", + retention_time=23.21, + init_conc=0, + conc_unit=mM, + ) - print(ana.measurements[0].chromatograms[0].peaks[0]) + analyzer.plot_peaks_fitted_spectrum() diff --git a/chromatopy/tools/fit_chromatogram.py b/chromatopy/tools/fit_chromatogram.py index 68bb511..a02e1ba 100644 --- a/chromatopy/tools/fit_chromatogram.py +++ b/chromatopy/tools/fit_chromatogram.py @@ -14,6 +14,7 @@ def __init__(self, signals: list[float], times: list[float]): self.times = times self.prcessed_signal: list[float] = [] self.peaks: list[Peak] = [] + self._remove_nan() def fit( self, diff --git a/chromatopy/tools/molecule.py b/chromatopy/tools/molecule.py index 31a96b7..c3faecd 100644 --- a/chromatopy/tools/molecule.py +++ b/chromatopy/tools/molecule.py @@ -1,7 +1,11 @@ from calipytion.model import Standard +from calipytion.model import UnitDefinition as CalUnit +from calipytion.tools.calibrator import Calibrator +from calipytion.units import C +from loguru import logger from pydantic import BaseModel, ConfigDict, Field -from chromatopy.model import UnitDefinition as ChromatopyUnitDefinition +from chromatopy.model import UnitDefinition class Molecule(BaseModel): @@ -10,11 +14,21 @@ class Molecule(BaseModel): use_enum_values=True, ) # type: ignore - id: str = Field(description="ID of the molecule") - pubchem_cid: int = Field(description="PubChem CID of the molecule") - name: str = Field(description="Name of the molecule") - init_conc: float = Field(description="Initial concentration of the molecule at t=0") - conc_unit: ChromatopyUnitDefinition = Field(description="Unit of the concentration") + id: str = Field( + description="ID of the molecule", + ) + pubchem_cid: int = Field( + description="PubChem CID of the molecule", + ) + name: str = Field( + description="Name of the molecule", + ) + init_conc: float | None = Field( + description="Initial concentration of the molecule at t=0", default=None + ) + conc_unit: UnitDefinition | None = Field( + description="Unit of the concentration", default=None + ) retention_time: float = Field( description="Retention time of the molecule in minutes" ) @@ -24,6 +38,9 @@ class Molecule(BaseModel): standard: Standard | None = Field( description="Standard instance associated with the molecule", default=None ) + retention_tolerance: float = Field( + description="Tolerance for the retention time of the molecule", default=0.2 + ) # @model_validator(mode="before") # @classmethod @@ -46,7 +63,7 @@ class Molecule(BaseModel): @classmethod def from_standard( - cls, standard: Standard, init_conc: float, conc_unit: ChromatopyUnitDefinition + cls, standard: Standard, init_conc: float, conc_unit: UnitDefinition ): """Creates a Molecule instance from a Standard instance.""" @@ -65,6 +82,106 @@ def from_standard( standard=standard, ) + def create_standard( + self, + areas: list[float], + concs: list[float], + conc_unit: UnitDefinition, + ph: float, + temperature: float, + temp_unit: UnitDefinition = C, + visualize: bool = True, + ) -> Standard: + """Creates a linear standard from the molecule's calibration data.""" + + calibrator = Calibrator( + molecule_id=self.id, + pubchem_cid=self.pubchem_cid, + molecule_name=self.name, + wavelength=self.wavelength, + concentrations=concs, + conc_unit=CalUnit(**conc_unit.model_dump()), + signals=areas, + ) + calibrator.models = [] + model = calibrator.add_model( + name="linear", + signal_law=f"{self.id} * a", + ) + + calibrator.fit_models() + model.calibration_range.conc_lower = 0.0 + model.calibration_range.signal_lower = 0.0 + + if visualize: + calibrator.visualize() + + standard = calibrator.create_standard( + model=model, + ph=ph, + temperature=temperature, + temp_unit=CalUnit(**temp_unit.model_dump()), + ) + + # check if the `conc` attribute of the molecule is defined and if, it must have the same baseunit names as the calibration unit + if self.conc_unit: + try: + for idx, unit in enumerate(self.conc_unit.base_units): + assert ( + unit.kind == conc_unit.base_units[idx].kind + and unit.exponent == conc_unit.base_units[idx].exponent + ), """ + Units dont match. + """ + except AssertionError: + logger.warning( + f"The concentration unit of the molecule {self.name} does not match the calibration unit defined in its standard. Conc unit of the molecule was set to {conc_unit}." + ) + self.conc_unit = conc_unit + self.init_conc = None + else: + self.conc_unit = conc_unit + + self.standard = standard + + return standard + + +class Protein(BaseModel): + model_config: ConfigDict = ConfigDict( # type: ignore + validate_assigment=True, + use_enum_values=True, + ) # type: ignore + + id: str = Field( + description="ID of the Protein", + ) + name: str = Field( + description="Name of the protein", + ) + init_conc: float = Field( + description="Initial concentration of the protein at t=0", + ) + conc_unit: UnitDefinition = Field( + description="Unit of the concentration", + ) + sequence: str | None = Field( + description="Amino acid sequence of the protein", + default=None, + ) + organism: str | None = Field( + description="Organism from which the protein originates", + default=None, + ) + organism_tax_id: str | None = Field( + description="Taxonomic ID of the organism", + default=None, + ) + constant: bool = Field( + description="Boolean indicating whether the protein concentration is constant", + default=True, + ) + if __name__ == "__main__": from calipytion.model import Standard diff --git a/chromatopy/tools/peak_utils.py b/chromatopy/tools/peak_utils.py index b9d803e..62c10f7 100644 --- a/chromatopy/tools/peak_utils.py +++ b/chromatopy/tools/peak_utils.py @@ -1,38 +1,25 @@ from __future__ import annotations -import numpy as np -from lmfit.models import GaussianModel -from matplotlib import pyplot as plt -from pybaselines import Baseline -from pydantic import BaseModel -from rich import print -from scipy.ndimage import uniform_filter1d -from scipy.signal import find_peaks +import os +import sys -# from chromatopy.model import UnitDefinition +import numpy as np +import pandas as pd +# from scipy.ndimage import uniform_filter1d +# from scipy.signal import find_peaks +from hplc.quant import Chromatogram as hplcChromatogram -class PeakFitResult(BaseModel): - peak_index: int - amplitude: float - center: float - width: float - mode: str - area: float | None = None # You can calculate and store the area if needed +# from lmfit.models import GaussianModel +from loguru import logger - def calculate_area(self, model: str = "gaussian"): - """Calculates the area under the peak based on the model.""" - models = ("gaussian", "lorentzian") +# from pybaselines import Baseline +from pydantic import BaseModel - if model == "gaussian": - # For Gaussian: Area = Amplitude * Width * sqrt(2*pi) - self.area = self.amplitude * self.width * np.sqrt(2 * np.pi) - elif model == "lorentzian": - # For Lorentzian: Area = Amplitude * Width * pi - self.area = self.amplitude * self.width * np.pi +from chromatopy.model import Peak - else: - raise ValueError(f"Model {model} not supported. Choose from {models}.") +logger.remove() +logger.add(sys.stderr, level="INFO") class SpectrumProcessor(BaseModel): @@ -40,155 +27,104 @@ class SpectrumProcessor(BaseModel): raw_data: list[float] smoothed_data: list[float] = [] baseline: list[float] = [] - baseline_corrected_data: list[float] = [] + processed_signal: list[float] = [] peak_indices: list[int] = [] - peak_params: list[PeakFitResult] = [] + peaks: list[Peak] = [] silent: bool = False - # time_unit: UnitDefinition - - def smooth_data(self, window: int = 11) -> list[float]: - """Smooths the raw data using a uniform filter. + smooth_window: int = 11 + baseline_half_window: int | None = None + peak_prominence: float = 4 + min_peak_height: float | None = None - Args: - window (int, optional): The size of the window. Defaults to 11. - """ + def model_post_init(self, __context: sys.Any) -> None: self._remove_nan() - filtered = uniform_filter1d(self.raw_data, size=window) - if not self.silent: - print("Data smoothed.") - return filtered - - # private method that checks the data for nan values adn removes them, deletes the corresponding time values def _remove_nan(self) -> None: """Removes NaN values from the data and the corresponding time values.""" self.raw_data = [d for d in self.raw_data if not np.isnan(d)] self.time = [t for t, d in zip(self.time, self.raw_data) if not np.isnan(d)] - def correct_baseline(self) -> None: - # smooth the data - self.smoothed_data = self.smooth_data(window=11) - - baseliner = Baseline(self.smoothed_data) - - self.baseline = baseliner.mor(self.smoothed_data)[0] - self.baseline_corrected_data = self.smoothed_data - self.baseline - - if not self.silent: - print("Baseline corrected.") - - def search_peaks(self): - peaks, _ = find_peaks(self.baseline_corrected_data, height=None, prominence=4) - self.peak_indices = peaks - - if not self.silent: - print(f"Found {len(peaks)} peaks.") - - def fit_multiple_gaussians(self): + def silent_fit(self, **hplc_py_kwargs) -> hplcChromatogram: + """Wrapper function to suppress the output of the hplc-py Chromatogram.fit_peaks() method.""" + + # Save the original stdout and stderr + original_stdout = sys.stdout + original_stderr = sys.stderr + + # Redirect stdout and stderr to /dev/null + sys.stdout = open(os.devnull, "w") + sys.stderr = open(os.devnull, "w") + + try: + # Call the function that prints the unwanted output + return self.fit(**hplc_py_kwargs) + finally: + # Restore the original stdout and stderr + sys.stdout = original_stdout + sys.stderr = original_stderr + + def fit( + self, + **hplc_py_kwargs, + ) -> SpectrumProcessor: """ - Fits multiple Gaussians to the baseline-corrected data using the peaks found. + Fit the chromatogram peaks using the `hplc-py` `Chromatogram` class. + + Parameters: + visualize (bool, optional): Whether to visualize the fitted peaks. Defaults to False. + **hplc_py_kwargs: Additional keyword arguments to pass to the hplc.quant.Chromatogram.fit_peaks() method. Returns: - dict: A dictionary containing the fit results and individual component fits. + hplcChromatogram: The fitted chromatogram. """ - # Check if peak_indices is empty - if self.peak_indices is None or len(self.peak_indices) == 0: - raise ValueError("No peaks found. Run search_peaks() first.") - - # Create a combined model by summing Gaussian models for each peak - mod = None - pars = None - for i, peak in enumerate(self.peak_indices, start=1): - prefix = f"g{i}_" - gauss = GaussianModel(prefix=prefix) - center = self.time[peak] - amplitude = self.baseline_corrected_data[peak] - sigma = ( - self.time[min(len(self.time) - 1, peak + 10)] - - self.time[max(0, peak - 10)] - ) / 2.355 # FWHM estimate - - if mod is None: - mod = gauss - else: - mod += gauss - - # Update parameters with initial guesses - if pars is None: - pars = gauss.make_params( - center=center, sigma=sigma, amplitude=amplitude + # if not isinstance(molecules, list): + # molecules = [molecules] + fitter = hplcChromatogram(file=self.to_dataframe()) + try: + fitter.fit_peaks(**hplc_py_kwargs) + except KeyError as e: + if "retention_time" in str(e): + logger.info( + "No peaks found in the chromatogram. halving the prominence and trying again." ) - else: - pars.update( - gauss.make_params(center=center, sigma=sigma, amplitude=amplitude) + hplc_py_kwargs["prominence"] /= 2 + self.fit(**hplc_py_kwargs) + + self.processed_signal = np.sum(fitter.unmixed_chromatograms, axis=1) + + peaks = [] + for record in fitter.peaks.to_dict(orient="records"): + peaks.append( + Peak( + retention_time=record["retention_time"], + area=record["area"], + amplitude=record["amplitude"], + skew=record["skew"], + width=record["scale"], + # max_signal=record["signal_maximum"], ) - - # Fit the combined model to the data - out = mod.fit(self.baseline_corrected_data, pars, x=np.array(self.time)) - - # Extract the peak parameters - self.peak_params = [ - PeakFitResult( - peak_index=i, - amplitude=out.params[f"g{i}_amplitude"].value, - center=out.params[f"g{i}_center"].value, - width=out.params[f"g{i}_sigma"].value, - mode="gaussian", - ) - for i in range(1, len(self.peak_indices) + 1) - ] - - if not self.silent: - print("Fitted Gaussians to peaks.") - - def run_pripeline(self): - self.correct_baseline() - self.search_peaks() - self.fit_multiple_gaussians() - self.calculate_peak_areas() - - def plot(self): - plt.plot(self.time, self.raw_data, label="Raw Data") - plt.plot(self.time, self.baseline, "--", label="Baseline") - plt.plot(self.time, self.baseline_corrected_data, label="Corrected") - # highlight peaks - plt.plot( - [self.time[i] for i in self.peak_indices], - [self.baseline_corrected_data[i] for i in self.peak_indices], - "x", - label="Peaks", - ) - # plot the fitted Gaussians - for peak in self.peak_params: - gaussian = GaussianModel() - peak_start = max(0, int(peak.center - 3 * peak.width)) - peak_end = min(len(self.time) - 1, int(peak.center + 3 * peak.width)) - x_arr = np.array(self.time[peak_start:peak_end]) - plt.plot( - x_arr, - gaussian.eval( - x=x_arr, - amplitude=peak.amplitude, - center=peak.center, - sigma=peak.width, - ), - ":", - label=f"Peak {peak.peak_index}", ) - plt.legend() - plt.show() - def calculate_peak_areas(self): - for peak in self.peak_params: - peak.calculate_area() + if len(peaks) > 0: + peaks = sorted(peaks, key=lambda x: x.retention_time) - if not self.silent: - print("Calculated peak areas.") + self.peaks = peaks + return self -if __name__ == "__main__": - import matplotlib.pyplot as plt + def to_dataframe(self) -> pd.DataFrame: + """ + Returns the chromatogram as a pandas DataFrame with the columns 'time' and 'signal' + """ + return pd.DataFrame( + { + "time": self.time, + "signal": self.raw_data, + } + ).dropna() + +if __name__ == "__main__": from chromatopy.tools.analyzer import ChromAnalyzer from chromatopy.units import min as min_ @@ -204,15 +140,9 @@ def calculate_peak_areas(self): temperature=25.0, ) - chrom = ana.measurements[2].chromatograms[0] - # Check if chrom.times is not empty, contains only floats, and has the expected length + ana.visualize() - fitter = SpectrumProcessor(time=chrom.times, raw_data=chrom.signals) - fitter.correct_baseline() - fitter.search_peaks() - fitter.fit_multiple_gaussians() - fitter.calculate_peak_areas() - print(fitter.peak_params[0]) - fitter.plot() + chrom = ana.measurements[0].chromatograms[0] - print(fitter.baseline_corrected_data) + ana.process_chromatograms(prominence=0.035) + ana.plot_peaks_fitted_spectrum(chrom) diff --git a/chromatopy/tools/utility.py b/chromatopy/tools/utility.py index 9d9671f..c066b9b 100644 --- a/chromatopy/tools/utility.py +++ b/chromatopy/tools/utility.py @@ -1,7 +1,14 @@ +import sys + +import numpy as np import plotly.graph_objects as go +from loguru import logger from chromatopy.model import Chromatogram +logger.remove() +logger.add(sys.stderr, level="INFO") + def _resolve_chromatogram( chromatograms: list[Chromatogram], wavelength: float | None @@ -24,6 +31,21 @@ def _resolve_chromatogram( raise ValueError("No chromatogram found.") +def pick_peak( + chromatograms: list[Chromatogram], retention_time: float, tolerance: float +): + current_retention = retention_time + peaks = [] + + for chrom in chromatograms: + for peak in chrom.peaks: + if abs(peak.retention_time - current_retention) < tolerance: + peaks.append(peak) + current_retention = peak.retention_time + else: + pass + + def generate_visibility(hover_text: str, fig: go.Figure) -> list[bool]: visibility = [] for trace in fig.data: @@ -32,3 +54,33 @@ def generate_visibility(hover_text: str, fig: go.Figure) -> list[bool]: else: visibility.append(False) return visibility + + +def generate_gaussian_data( + amplitude, center, half_height_diameter, start, end, num_points=100 +): + """ + Generate x and y data for a Gaussian curve. + + Parameters: + - amplitude: The peak height of the Gaussian. + - center: The position of the center of the peak. + - half_height_diameter: The full width at half maximum (FWHM) of the peak. + - start: The starting x-value. + - end: The ending x-value. + - num_points: Number of points to generate (default is 100). + + Returns: + - x_values: Array of x-values. + - y_values: Array of y-values corresponding to the Gaussian curve. + """ + # Calculate sigma from the half-height diameter (FWHM) + sigma = half_height_diameter / (2 * np.sqrt(2 * np.log(2))) + + # Generate x values + x_values = np.linspace(start, end, num_points) + + # Generate y values using the Gaussian function + y_values = amplitude * np.exp(-((x_values - center) ** 2) / (2 * sigma**2)) + + return x_values, y_values diff --git a/pyproject.toml b/pyproject.toml index e3bbfeb..c7daccb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,7 @@ joblib = "^1.4.2" rich = "^13.7.1" loguru = "^0.7.2" pybaselines = "^1.1.0" +hplc-py = "^0.2.7" [tool.poetry.group.dev.dependencies] mkdocs-material = "^9.5.12" diff --git a/specifications/chromatography.md b/specifications/chromatography.md index b0c6db0..43eeabd 100644 --- a/specifications/chromatography.md +++ b/specifications/chromatography.md @@ -21,13 +21,13 @@ prefix: "chromatopy" - __chromatograms__ - Type: Chromatogram[] - Description: Measured chromatogram and peaks. -- temperature +- __temperature__ - Type: float - Description: Temperature of the measurement. -- temperature_unit +- __temperature_unit__ - Type: UnitDefinition - Description: Unit of temperature. -- ph +- __ph__ - Type: float - Description: pH of the measurement. - sample_name @@ -81,18 +81,18 @@ prefix: "chromatopy" - type - Type: string - Description: Type of peak (baseline-baseline / baseline-valley / ...) -- peak_start +- width - Type: float - - Description: Start retention time of the peak -- peak_end + - Description: Width of the peak at half height. +- amplitude - Type: float - - Description: End retention time of the peak. -- width + - Description: Amplitude of the peak. +- max_signal - Type: float - - Description: Width of the peak. -- height + - Description: Maximum signal of the peak. +- skew - Type: float - - Description: Height of the peak. + - Description: Skew of the peak. - percent_area - Type: float - Description: Percent area of the peak. @@ -102,6 +102,12 @@ prefix: "chromatopy" - separation_factor - Type: float - Description: Separation factor of the peak. +- peak_start + - Type: float + - Description: Start time of the peak. +- peak_end + - Type: float + - Description: End time of the peak. ### SignalType diff --git a/tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME0.json b/tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME0.json new file mode 100644 index 0000000..13da53f --- /dev/null +++ b/tests/test_readers/data/asm/N6BENZYLADOONB_RNCOMT_TIME0.json @@ -0,0 +1,20383 @@ +{ + "$asm.manifest": "http://purl.allotrope.org/manifests/liquid-chromatography/REC/2021/12/liquid-chromatography.manifest", + "liquid chromatography aggregate document": { + "liquid chromatography document": [ + { + "sample document": { + "sample identifier": "", + "written name": "N6BENZYLADOONB_RNCOMT_TIME0", + "description": "" + }, + "measurement document": { + "peak list": { + "peak": [ + { + "chromatographic peak asymmetry factor": null, + "identifier": "f3a29aeb-fc9e-4cd8-afeb-8fb71a8a97c9", + "peak start": { + "value": 81.962, + "unit": "s" + }, + "peak selectivity (chromatography)": null, + "capacity factor (chromatography)": null, + "written name": null, + "relative peak height": { + "value": 0.5829141139984131, + "unit": "%" + }, + "peak end": { + "value": 131.362, + "unit": "s" + }, + "peak height": { + "value": 2524529.25, + "unit": "mAU" + }, + "chromatographic peak resolution": null, + "retention time": { + "value": 86.562, + "unit": "s" + }, + "number of theoretical plates (chromatography)": null, + "relative peak area": { + "value": 0.17565702059550836, + "unit": "%" + }, + "peak area": { + "value": 1.4624784109333748E8, + "unit": "mAU.s" + }, + "peak width at half height": { + "value": 4.362, + "unit": "s" + } + }, + { + "chromatographic peak asymmetry factor": null, + "identifier": "94fd3a44-1a56-4121-a77e-e0a9b5bc5b84", + "peak start": { + "value": 584.362, + "unit": "s" + }, + "peak selectivity (chromatography)": null, + "capacity factor (chromatography)": null, + "written name": null, + "relative peak height": { + "value": 0.29972779750823975, + "unit": "%" + }, + "peak end": { + "value": 627.562, + "unit": "s" + }, + "peak height": { + "value": 1298084.25, + "unit": "mAU" + }, + "chromatographic peak resolution": null, + "retention time": { + "value": 590.362, + "unit": "s" + }, + "number of theoretical plates (chromatography)": null, + "relative peak area": { + "value": 0.12338656620679228, + "unit": "%" + }, + "peak area": { + "value": 1.0272870885824955E8, + "unit": "mAU.s" + }, + "peak width at half height": { + "value": 6.277, + "unit": "s" + } + }, + { + "chromatographic peak asymmetry factor": null, + "identifier": "69ff7fc5-85ef-4082-be90-37b6ac078523", + "peak start": { + "value": 705.562, + "unit": "s" + }, + "peak selectivity (chromatography)": null, + "capacity factor (chromatography)": null, + "written name": null, + "relative peak height": { + "value": 0.9850224256515503, + "unit": "%" + }, + "peak end": { + "value": 746.162, + "unit": "s" + }, + "peak height": { + "value": 4266011.0, + "unit": "mAU" + }, + "chromatographic peak resolution": null, + "retention time": { + "value": 713.362, + "unit": "s" + }, + "number of theoretical plates (chromatography)": null, + "relative peak area": { + "value": 0.4623866607371821, + "unit": "%" + }, + "peak area": { + "value": 3.849720930818265E8, + "unit": "mAU.s" + }, + "peak width at half height": { + "value": 6.938, + "unit": "s" + } + }, + { + "chromatographic peak asymmetry factor": null, + "identifier": "74a2f1d8-27e8-4db2-a609-2094e3e711ed", + "peak start": { + "value": 746.562, + "unit": "s" + }, + "peak selectivity (chromatography)": null, + "capacity factor (chromatography)": null, + "written name": null, + "relative peak height": { + "value": 3.3102829456329346, + "unit": "%" + }, + "peak end": { + "value": 800.762, + "unit": "s" + }, + "peak height": { + "value": 1.4336428E7, + "unit": "mAU" + }, + "chromatographic peak resolution": null, + "retention time": { + "value": 757.762, + "unit": "s" + }, + "number of theoretical plates (chromatography)": null, + "relative peak area": { + "value": 2.6463026076171414, + "unit": "%" + }, + "peak area": { + "value": 2.203248363951657E9, + "unit": "mAU.s" + }, + "peak width at half height": { + "value": 9.825, + "unit": "s" + } + }, + { + "chromatographic peak asymmetry factor": null, + "identifier": "4f1ad768-0ab7-4392-9cac-ab867195d9ce", + "peak start": { + "value": 812.362, + "unit": "s" + }, + "peak selectivity (chromatography)": null, + "capacity factor (chromatography)": null, + "written name": null, + "relative peak height": { + "value": 0.18106503784656525, + "unit": "%" + }, + "peak end": { + "value": 823.762, + "unit": "s" + }, + "peak height": { + "value": 784170.4375, + "unit": "mAU" + }, + "chromatographic peak resolution": null, + "retention time": { + "value": 816.562, + "unit": "s" + }, + "number of 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