#!/usr/bin/env python3
# 2024 (c) Micha Johannes Birklbauer
# https://github.com/michabirklbauer/
# micha.birklbauer@gmail.com
from __future__ import annotations
import pandas as pd
from ..data import check_input
from .util import modifications_to_str
from typing import Optional
from typing import List
from typing import Dict
from typing import Any
def __cc(input_list: Optional[List[Any]], sep: str = ";") -> str | None:
r"""Concatenates list elements to a string using the defined seperator.
Parameters
----------
input_list : list, or None
The list to concatenate.
sep : str, default = ";"
The seperator to use for concatentation.
Returns
-------
str, or None
The concatenated string of the list (or ``None`` if no list was provided).
"""
s = ""
if input_list is None:
return None
for i in input_list:
s += str(i).strip() + sep
return s.rstrip(sep)
def __crosslinks_to_dataframe(data: List[Dict[str, Any]]) -> pd.DataFrame:
r"""Returns a pandas DataFrame of the given crosslinks.
Parameters
----------
data : list
A list of crosslinks as created by ``data.create_crosslink()``.
Returns
-------
pandas.DataFrame
The pandas DataFrame created from the list of input crosslinks.
Raises
------
TypeError
If the list does not contain crosslinks.
ValueError
If the list does not contain any objects.
Notes
-----
This function should not be called directly, it is called from ``to_dataframe()``.
"""
## columns
completeness = list()
alpha_peptide = list()
alpha_peptide_crosslink_position = list()
alpha_proteins = list()
alpha_proteins_crosslink_positions = list()
alpha_decoy = list()
beta_peptide = list()
beta_peptide_crosslink_position = list()
beta_proteins = list()
beta_proteins_crosslink_positions = list()
beta_decoy = list()
crosslink_type = list()
score = list()
## assign values
for crosslink in data:
completeness.append(crosslink["completeness"])
alpha_peptide.append(crosslink["alpha_peptide"])
alpha_peptide_crosslink_position.append(
crosslink["alpha_peptide_crosslink_position"]
)
alpha_proteins.append(__cc(crosslink["alpha_proteins"]))
alpha_proteins_crosslink_positions.append(
__cc(crosslink["alpha_proteins_crosslink_positions"])
)
alpha_decoy.append(crosslink["alpha_decoy"])
beta_peptide.append(crosslink["beta_peptide"])
beta_peptide_crosslink_position.append(
crosslink["beta_peptide_crosslink_position"]
)
beta_proteins.append(__cc(crosslink["beta_proteins"]))
beta_proteins_crosslink_positions.append(
__cc(crosslink["beta_proteins_crosslink_positions"])
)
beta_decoy.append(crosslink["beta_decoy"])
crosslink_type.append(crosslink["crosslink_type"])
score.append(crosslink["score"])
return pd.DataFrame(
{
"Completeness": completeness,
"Alpha Peptide": alpha_peptide,
"Alpha Peptide Crosslink Position": alpha_peptide_crosslink_position,
"Alpha Proteins": alpha_proteins,
"Alpha Proteins Crosslink Positions": alpha_proteins_crosslink_positions,
"Alpha Decoy": alpha_decoy,
"Beta Peptide": beta_peptide,
"Beta Peptide Crosslink Position": beta_peptide_crosslink_position,
"Beta Proteins": beta_proteins,
"Beta Proteins Crosslink Positions": beta_proteins_crosslink_positions,
"Beta Decoy": beta_decoy,
"Crosslink Type": crosslink_type,
"Crosslink Score": score,
}
)
def __csms_to_dataframe(data: List[Dict[str, Any]]) -> pd.DataFrame:
r"""Returns a pandas DataFrame of the given crosslink-spectrum-matches.
Parameters
----------
data : list
A list of crosslink-spectrum-matches as created by ``data.create_csm()``.
Returns
-------
pandas.DataFrame
The pandas DataFrame created from the list of input crosslink-spectrum-matches.
Raises
------
TypeError
If the list does not contain crosslink-spectrum-matches.
ValueError
If the list does not contain any objects.
Notes
-----
This function should not be called directly, it is called from ``to_dataframe()``.
"""
## columns
completeness = list()
alpha_peptide = list()
alpha_modifications = list()
alpha_peptide_crosslink_position = list()
alpha_proteins = list()
alpha_proteins_crosslink_positions = list()
alpha_proteins_peptide_positions = list()
alpha_score = list()
alpha_decoy = list()
beta_peptide = list()
beta_modifications = list()
beta_peptide_crosslink_position = list()
beta_proteins = list()
beta_proteins_crosslink_positions = list()
beta_proteins_peptide_positions = list()
beta_score = list()
beta_decoy = list()
crosslink_type = list()
score = list()
spectrum_file = list()
scan_nr = list()
charge = list()
retention_time = list()
ion_mobility = list()
## assign values
for csm in data:
completeness.append(csm["completeness"])
alpha_peptide.append(csm["alpha_peptide"])
alpha_modifications.append(modifications_to_str(csm["alpha_modifications"]))
alpha_peptide_crosslink_position.append(csm["alpha_peptide_crosslink_position"])
alpha_proteins.append(__cc(csm["alpha_proteins"]))
alpha_proteins_crosslink_positions.append(
__cc(csm["alpha_proteins_crosslink_positions"])
)
alpha_proteins_peptide_positions.append(
__cc(csm["alpha_proteins_peptide_positions"])
)
alpha_score.append(csm["alpha_score"])
alpha_decoy.append(csm["alpha_decoy"])
beta_peptide.append(csm["beta_peptide"])
beta_modifications.append(modifications_to_str(csm["beta_modifications"]))
beta_peptide_crosslink_position.append(csm["beta_peptide_crosslink_position"])
beta_proteins.append(__cc(csm["beta_proteins"]))
beta_proteins_crosslink_positions.append(
__cc(csm["beta_proteins_crosslink_positions"])
)
beta_proteins_peptide_positions.append(
__cc(csm["beta_proteins_peptide_positions"])
)
beta_score.append(csm["beta_score"])
beta_decoy.append(csm["beta_decoy"])
crosslink_type.append(csm["crosslink_type"])
score.append(csm["score"])
spectrum_file.append(csm["spectrum_file"])
scan_nr.append(csm["scan_nr"])
charge.append(csm["charge"])
retention_time.append(csm["retention_time"])
ion_mobility.append(csm["ion_mobility"])
return pd.DataFrame(
{
"Completeness": completeness,
"Alpha Peptide": alpha_peptide,
"Alpha Peptide Modifications": alpha_modifications,
"Alpha Peptide Crosslink Position": alpha_peptide_crosslink_position,
"Alpha Proteins": alpha_proteins,
"Alpha Proteins Crosslink Positions": alpha_proteins_crosslink_positions,
"Alpha Proteins Peptide Positions": alpha_proteins_peptide_positions,
"Alpha Score": alpha_score,
"Alpha Decoy": alpha_decoy,
"Beta Peptide": beta_peptide,
"Beta Peptide Modifications": beta_modifications,
"Beta Peptide Crosslink Position": beta_peptide_crosslink_position,
"Beta Proteins": beta_proteins,
"Beta Proteins Crosslink Positions": beta_proteins_crosslink_positions,
"Beta Proteins Peptide Positions": beta_proteins_peptide_positions,
"Beta Score": beta_score,
"Beta Decoy": beta_decoy,
"Crosslink Type": crosslink_type,
"CSM Score": score,
"Spectrum File": spectrum_file,
"Scan Nr": scan_nr,
"Precursor Charge": charge,
"Retention Time": retention_time,
"Ion Mobility": ion_mobility,
}
)
[docs]
def to_dataframe(data: List[Dict[str, Any]]) -> pd.DataFrame:
r"""Returns a pandas DataFrame of the given crosslinks or crosslink-spectrum-matches.
Parameters
----------
data : list
A list of crosslinks or crosslink-spectrum-matches as created by ``data.create_crosslink()`` or ``data.create_csm()``.
Returns
-------
pandas.DataFrame
The pandas DataFrame created from the list of input crosslinks or crosslink-spectrum-matches.
A full specification of the returned DataFrame can be found in the
`docs <https://github.com/hgb-bin-proteomics/pyXLMS/blob/master/docs/format.md>`_.
Raises
------
TypeError
If the list does not contain crosslinks or crosslink-spectrum-matches.
ValueError
If the list does not contain any objects.
Examples
--------
>>> from pyXLMS.transform import to_dataframe
>>> # assume that crosslinks is a list of crosslinks created by data.create_crosslink()
>>> crosslink_dataframe = to_dataframe(crosslinks)
>>> # assume csms is a list of crosslink-spectrum-matches created by data.create_csm()
>>> csm_dataframe = to_dataframe(csms)
"""
## input checks
check_input(data, "data", list, dict)
## function calls
if len(data) > 0:
if "data_type" in data[0] and data[0]["data_type"] == "crosslink":
return __crosslinks_to_dataframe(data)
elif (
"data_type" in data[0]
and data[0]["data_type"] == "crosslink-spectrum-match"
):
return __csms_to_dataframe(data)
else:
raise TypeError("The given data object is not supported!")
else:
raise ValueError("Parameter data has to be at least of length one!")
