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nmrProblem.py
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nmrProblem.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jan 19 09:55:03 2021.
@author: ERIC
"""
import os
import sys
from collections.abc import Iterable
import re
import json
import copy
import pandas as pd
# import openpyxl
import numpy as np
from numpy import pi, sin, cos, exp
from scipy import stats
from scipy import fftpack
import nmrglue as ng
import yaml
from pathlib import Path
import networkx as nx
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
from rdkit.Chem.rdMolDescriptors import CalcMolFormula
import PIL
from PIL import Image
from qtutils import warning_dialog
from excelheaders import excel_orig_df_columns, excel_df_columns
# import nmrmol
import expectedmolecule
# from cacheUtils import cache
# global JAVA_AVAILABLE
# global JAVA_COMMAND
global XYDIM
XYDIM = 800
global CARBONSEPARATION
global PROTONSEPARATION
CARBONSEPARATION = 0.007
PROTONSEPARATION = 0.007
# new_dataframes = {}
# return missing excelsheets names
def get_missing_sheets(excel_fname: str, qtstarted: bool) -> set:
# read in the excel file into a pandas dataframe
missing_sheets = set()
try:
excelsheets = pd.read_excel(excel_fname, sheet_name=None, index_col=0)
# create a set of sheets keys that are missing
missing_sheets = set(excel_orig_df_columns.keys()) - set(excelsheets.keys())
# determine if sheets present are empty
empty_sheets = {
key
for key in excelsheets.keys()
if excelsheets[key].empty or (excelsheets[key].shape[0] == 0)
}
# return the union of the two sets
missing_sheets = missing_sheets.union(empty_sheets)
# remove the sheets that start with the name sheet
missing_sheets = {
sheet for sheet in missing_sheets if not sheet.lower().startswith("sheet")
}
except PermissionError:
warning_dialog("Excel File Permission Error\n Please Close the Excel File and Try Again",
"Excel File Access Error",
qtstarted)
except FileNotFoundError:
warning_dialog("File Not Found",
"Excel Read Error",
qtstarted)
return missing_sheets
def read_in_cs_tables(
h1: str, c13: str, scale_factor=6
) -> "list[pd.DataFrame, pd.DataFrame]":
"""
Reads in json files for proton and chemical shift info and creates two
pandas dataframes, one for proton and the other for carbon.
Parameters.
-----------
h1 : str
DESCRIPTION.
filename of json file containing proton chemical shift information
c13 : str
DESCRIPTION.
filename of json file containing proton chemical shift information
scale_factor : TYPE, optional
DESCRIPTION. The default is 6.
Returns
-------
[pd.DataFrame, pd.DataFrame]
"""
h1_df = pd.read_json(h1)
c13_df = pd.read_json(c13)
# create mean and sigma based on min max chemical shifts
h1_df["meanCS"] = (h1_df.minCS + h1_df.maxCS) / 2
h1_df["sigmaCS"] = (h1_df.maxCS - h1_df.minCS) / scale_factor
c13_df["meanCS"] = (c13_df.minCS + c13_df.maxCS) / 2
c13_df["sigmaCS"] = (c13_df.maxCS - c13_df.minCS) / scale_factor
# create probability density functions for each chemical shitf group
for i in h1_df.index:
h1_df.loc[i, "norm"] = stats.norm(
loc=h1_df.loc[i, "meanCS"], scale=h1_df.loc[i, "sigmaCS"]
)
for i in c13_df.index:
c13_df.loc[i, "norm"] = stats.norm(
loc=c13_df.loc[i, "meanCS"], scale=c13_df.loc[i, "sigmaCS"]
)
return h1_df, c13_df
def parse_argv(my_argv=None, qtstarted=True):
"""parse command line returnsa dictionary of arguments"""
if my_argv is None:
my_argv = sys.argv
datadirectories = None
data_directory = None
smilefilenames = None
pngfilenames = None
excelfilenames = None
smiles_fn = None
png_fn = None
excel_fn = None
xy3_fn = None
# datadirectories = [d for d in my_argv[1:] if os.path.isdir(d)]
datadirectories = [d for d in my_argv[1:] if (Path(d)).is_dir()]
excelfilenames = [e for e in my_argv[1:] if e.endswith(".xlsx")]
pngfilenames = [s for s in my_argv[1:] if s.endswith(".png")]
smilefilenames = [
s for s in my_argv[1:] if (s.endswith(".smi") or s.endswith(".smiles"))
]
xy3jsonfilenames = [s for s in my_argv[1:] if s == "xy3.json"]
if len(my_argv) > 1:
data_directory = my_argv[1]
else:
data_directory = "."
# if not os.path.isdir(data_directory):
# data_directory = "."
if not Path(data_directory).is_dir():
data_directory = "."
# if len(datadirectories) > 0:
# data_directory = datadirectories[0]
if len(excelfilenames) > 0:
if os.path.exists(os.path.join(data_directory, excelfilenames[0])):
excel_fn = os.path.join(data_directory, excelfilenames[0])
else:
return {
"data_directory": None,
"excel_fn": None,
"smiles_fn": None,
"png_fn": None,
"xy3_fn": None,
}
else:
excelfilenames = [e for e in os.listdir(data_directory) if e.endswith(".xlsx")]
if len(excelfilenames) > 0:
excel_fn = os.path.join(data_directory, excelfilenames[0])
else:
# qt message box No excel file found in data directory
if len(my_argv) > 1:
warning_dialog(
f"No excel file found in data directory\n{data_directory}",
"Excel File Not Found",
qtstarted,
)
return {
"data_directory": os.getcwd(),
"excel_fn": None,
"smiles_fn": None,
"png_fn": None,
"xy3_fn": None,
}
if len(smilefilenames) > 0:
if os.path.exists(os.path.join(data_directory, smilefilenames[0])):
smiles_fn = os.path.join(data_directory, smilefilenames[0])
else:
smiles_fn = None
else:
smilefilenames = [
s
for s in os.listdir(data_directory)
if (s.endswith(".smi") or s.endswith(".smiles"))
]
if len(smilefilenames) > 0:
smiles_fn = os.path.join(data_directory, smilefilenames[0])
else:
smiles_fn = None
if len(pngfilenames) > 0:
if os.path.exists(os.path.join(data_directory, pngfilenames[0])):
png_fn = os.path.join(data_directory, pngfilenames[0])
else:
png_fn = None
else:
pngfilenames = [s for s in os.listdir(data_directory) if s.endswith(".png")]
if len(pngfilenames) > 0:
png_fn = os.path.join(data_directory, pngfilenames[0])
else:
png_fn = None
if len(xy3jsonfilenames) > 0:
if os.path.exists(os.path.join(data_directory, xy3jsonfilenames[0])):
xy3_fn = os.path.join(data_directory, xy3jsonfilenames[0])
else:
xy3_fn = None
else:
xy3jsonfilenames = [s for s in os.listdir(data_directory) if s == "xy3.json"]
if len(xy3jsonfilenames) > 0:
xy3_fn = os.path.join(data_directory, xy3jsonfilenames[0])
else:
xy3_fn = None
return {
"data_directory": data_directory,
"excel_fn": excel_fn,
"smiles_fn": smiles_fn,
"png_fn": png_fn,
"xy3_fn": xy3_fn,
}
def return_carbon_xy3_positions(
rdkit_molecule: expectedmolecule.expectedMolecule,
) -> dict:
"""Returns the xy3 positions of the carbon atoms in the molecule"""
xy3_positions = {}
if isinstance(rdkit_molecule, expectedmolecule.expectedMolecule):
xxx = rdkit_molecule.molprops_df.x.values
yyy = rdkit_molecule.molprops_df.y.values
idx = rdkit_molecule.molprops_df.index.values
for i, x, y in zip(idx, xxx, yyy):
xy3_positions[f"C{str(i + 1)}"] = np.asarray([x, y])
else:
d2d = Draw.rdMolDraw2D.MolDraw2DSVG(XYDIM, XYDIM)
d2d.DrawMolecule(rdkit_molecule)
d2d.FinishDrawing()
for atom in rdkit_molecule.GetAtoms():
if atom.GetSymbol() == "C":
idx = atom.GetIdx()
point = d2d.GetDrawCoords(idx)
xy3_positions[f"C{str(idx + 1)}"] = np.asarray(
[point.x / XYDIM, point.y / XYDIM]
)
return xy3_positions
def read_xy3_jsonfile(problemdata_info: dict) -> dict:
"""Reads the xy3 json file and returns the json object"""
xy3 = None
if isinstance(problemdata_info["xy3_fn"], str):
with open(problemdata_info["xy3_fn"], "r") as filepointer:
xy3 = json.load(filepointer)
for k, v in xy3.items():
xy3[k] = np.asarray(v)
return xy3
else:
return None
def create_hmbc_edges_dict(nmrproblem) -> dict:
"""Creates a dictionary with the edges of the HMBC problem"""
hmbc_edges = {}
hmbc = nmrproblem.hmbc
for i in hmbc.index:
ci, cj = hmbc.loc[i, ["f1C_i", "f2Cp_i"]]
if ci is None or cj is None:
continue
if ci in hmbc_edges:
hmbc_edges[ci].add(cj)
else:
hmbc_edges[ci] = {cj}
if cj in hmbc_edges:
hmbc_edges[cj].add(ci)
else:
hmbc_edges[cj] = {ci}
for c in nmrproblem.carbonAtoms:
if c not in hmbc_edges:
hmbc_edges[c] = set()
return hmbc_edges
def build_model(nmrproblem):
"""Builds the model"""
h1fn = r"csTables/h1_chemical_shift_table.jsn"
c13fn = r"csTables/c13_chemical_shift_table.jsn"
nmrproblem.H1df_orig, nmrproblem.C13df_orig = read_in_cs_tables(h1fn, c13fn)
nmrproblem.calcProbDistFunctions(nmrproblem.H1df_orig, nmrproblem.C13df_orig)
nmrproblem.identify1HC13peaks()
nmrproblem.udic[0]["df"] = nmrproblem.H1df
nmrproblem.udic[1]["df"] = nmrproblem.C13df
nmrproblem.createInfoDataframes()
nmrproblem.calculate1H13CSpectra1D()
nmrproblem.save1DspecInfotoUdic()
nmrproblem.create1H13Clabels(num_poss=3)
def build_molecule_graph_network(nmrproblem):
"""build a graph of the molecule based on the COSY connections
Args:
nmrproblem (nmrProblem): the NMRproblem
"""
df = nmrproblem.df
catoms = nmrproblem.carbonAtoms
# hsqc = nmrproblem.hsqc
c13 = nmrproblem.c13
# create graph of molecule
nmrproblem.nx_graph_molecule = nx.Graph()
nmrproblem.nx_graph_molecule.add_nodes_from(catoms)
nx_graph_molecule = nmrproblem.nx_graph_molecule
# add some node information
for n in catoms:
nx_graph_molecule.nodes[n]["c13ppm"] = str(df.loc[n, "ppm"])
nx_graph_molecule.nodes[n]["label"] = n
# attach H1 chemical shifts to graph
for n in nx_graph_molecule.nodes:
# get list of labels of protons attached to carbon
h1list = df.loc["hsqc", n]
# convert to proton ppm
nx_graph_molecule.nodes[n]["h1ppm"] = ",".join(
[str(nmrproblem.H1labelH1ppm[hi]) for hi in h1list]
)
# add links to graph from cosy data
for c in catoms:
for l in df.loc["cosy", c]:
if (not nx_graph_molecule.has_edge(c, l)) and (c != l):
nx_graph_molecule.add_edge(c, l)
# add node color to graph
for i, n in enumerate(catoms):
nx_graph_molecule.nodes[n]["node_color"] = nmrproblem.hmbc_edge_colors[
c13.loc[i + 1, "attached_protons"]
]
def build_xy3_representation_of_molecule(nmrproblem):
"""Builds the xy3 representation of the molecule"""
nx_graph_molecule = nmrproblem.nx_graph_molecule
# create coodinates that look more molecule like
atomicNumberfromSymbol = {
"H": 1,
"Li": 3,
"Be": 4,
"B": 5,
"C": 6,
"N": 7,
"O": 8,
"F": 9,
"Na": 11,
"Mg": 12,
"Al": 13,
"Si": 14,
"P": 15,
"S": 16,
"Cl": 17,
"K": 19,
"Ca": 20,
"Sc": 21,
"Ti": 22,
"V": 23,
"Cr": 24,
"Mn": 25,
"Fe": 26,
"Co": 27,
"Ni": 28,
"Cu": 29,
"Zn": 30,
"Ga": 31,
"Ge": 32,
"As": 33,
"Se": 34,
"Br": 35,
"Kr": 36,
"Rb": 37,
"Sr": 38,
"Y": 39,
"Zr": 40,
"Nb": 41,
"Mo": 42,
"Tc": 43,
"Ru": 44,
"Rh": 45,
"Pd": 46,
"Ag": 47,
"Cd": 48,
"In": 49,
"Sn": 50,
"Sb": 51,
"Te": 52,
"I": 53,
"Xe": 54,
"Cs": 55,
"Ba": 56,
"La": 57,
"Ce": 58,
"Pr": 59,
"Nd": 60,
"Pm": 61,
"Sm": 62,
"Eu": 63,
"Gd": 64,
"Tb": 65,
"Dy": 66,
"Ho": 67,
"Er": 68,
"Tm": 69,
"Yb": 70,
"Lu": 71,
"Hf": 72,
"Ta": 73,
"W": 74,
"Re": 75,
"Os": 76,
"Ir": 77,
"Pt": 78,
"Au": 79,
"Hg": 80,
"Tl": 81,
"Pb": 82,
"Bi": 83,
"Po": 84,
"At": 85,
"Rn": 86,
"Fr": 87,
"Ra": 88,
"Ac": 89,
"Th": 90,
"Pa": 91,
"U": 92,
"Np": 93,
"Pu": 94,
"Am": 95,
"Cm": 96,
"Bk": 97,
"Cf": 98,
"Es": 99,
"Fm": 100,
"Md": 101,
"No": 102,
"Lr": 103,
"Rf": 104,
"Db": 105,
"Sg": 106,
"Bh": 107,
"Hs": 108,
"Mt": 109,
"Ds": 110,
"Rg": 111,
"Cn": 112,
"Uut": 113,
"Fl": 114,
"Uup": 115,
"Lv": 116,
"Uus": 117,
"Uuo": 118,
}
for node in nx_graph_molecule.nodes:
nx_graph_molecule.nodes[node]["atomic_num"] = atomicNumberfromSymbol[
"".join([c for c in node if c.isalpha()])
]
nx_graph_molecule.nodes[node][
"chiral_tag"
] = rdkit.Chem.rdchem.ChiralType.CHI_UNSPECIFIED
nx_graph_molecule.nodes[node]["formal_charge"] = 0
nx_graph_molecule.nodes[node]["is_aromatic"] = False
nx_graph_molecule.nodes[node][
"hybridization"
] = rdkit.Chem.rdchem.HybridizationType.SP3
nx_graph_molecule.nodes[node]["num_explicit_hs"] = 0
for e in nx_graph_molecule.edges:
nx_graph_molecule.edges[e]["bond_type"] = rdkit.Chem.rdchem.BondType.SINGLE
mmm = nx_to_mol(nx_graph_molecule)
mmm.Compute2DCoords()
xy3 = return_carbon_xy3_positions(mmm)
nmrproblem.xy3 = xy3
def nx_to_mol(G: nx.Graph) -> rdkit.Chem.Mol:
"""convert a nx.Graph to a rdkit.Chem.Mol
Args:
G (nx.Graph): molecule netowrk graph derived from NMR data
Returns:
rdkit.Chem.Mol: rdkit chem Mol variable
"""
atomicNumberfromSymbol = {"H": 1, "C": 6, "O": 8, "N": 7}
mol = Chem.RWMol()
atomic_nums = nx.get_node_attributes(G, "atomic_num")
chiral_tags = nx.get_node_attributes(G, "chiral_tag")
formal_charges = nx.get_node_attributes(G, "formal_charge")
node_is_aromatics = nx.get_node_attributes(G, "is_aromatic")
node_hybridizations = nx.get_node_attributes(G, "hybridization")
num_explicit_hss = nx.get_node_attributes(G, "num_explicit_hs")
node_to_idx = {}
for node in G.nodes():
a = Chem.Atom(atomic_nums[node])
a.SetChiralTag(chiral_tags[node])
a.SetFormalCharge(formal_charges[node])
a.SetIsAromatic(node_is_aromatics[node])
a.SetHybridization(node_hybridizations[node])
a.SetNumExplicitHs(num_explicit_hss[node])
idx = mol.AddAtom(a)
node_to_idx[node] = idx
bond_types = nx.get_edge_attributes(G, "bond_type")
for edge in G.edges():
first, second = edge
ifirst = node_to_idx[first]
isecond = node_to_idx[second]
bond_type = bond_types[first, second]
mol.AddBond(ifirst, isecond, bond_type)
Chem.SanitizeMol(mol)
return mol
def create_hmbc_graph_fragments(nmrproblem, hmbc_edges: dict) -> dict:
"""create a graph of fragments from the hmbc_edges"""
hmbc_graphs = {}
# ntwk_labels = []
catoms = nmrproblem.carbonAtoms
# df = nmrproblem.df
xy3 = nmrproblem.xy3
# molecule = nmrproblem.molecule
# udic = nmrproblem.udic
# ret1 = None
# ret2 = None
lineCollections = []
for i, c in enumerate(catoms):
if c not in hmbc_edges.keys():
continue
# create hmbc graph for node c and add xy coodinates
hmbc_graphs[c] = {"graph": nx.Graph()}
hmbc_graphs[c]["xy"] = {k: xy3[k] for k in [c] + list(hmbc_edges[c]) if k}
hmbc_graphs[c]["colors"] = []
# add nodes to hmbc graph
hmbc_graphs[c]["graph"].add_nodes_from([c] + list(hmbc_edges[c]))
# add edges
for i, c1 in enumerate(hmbc_edges[c]):
if c1:
hmbc_graphs[c]["graph"].add_edge(c, c1)
hmbc_graphs[c]["colors"].append(nmrproblem.hmbc_edge_colors[i])
return hmbc_graphs
class NMRproblem:
"""NMR problem class"""
def __init__(
self,
problemdata_info: dict,
loadfromwhere=None,
H1LarmorFreq=None,
qtstarted=True,
java_available=False,
xy3_calc_method="xy3",
java_command=None,
expts_available=[],
):
"""initialize NMR problem class"""
self.excelsheets = {}
# set added columns to false
self.h1_df_numProtons_added = False
self.h1_df_integral_added = False
self.h1_df_jCouplingClass_added = False
self.h1_df_jCouplingVals_added = False
self.c13_from_hsqc = False
self.expts_available = expts_available
self.java_available = java_available
self.java_command = java_command
self.xy3_calc_method = xy3_calc_method
self.qtstarted = qtstarted
self.H1LarmorFreq = H1LarmorFreq
self.problemdata_info = problemdata_info
self.loadfromwhere = loadfromwhere
self.problemDirectoryPath = problemdata_info["data_directory"]
# self.problemDirectory = problemdata_info["data_directory"]
self.rootDirectory, self.problemDirectory = os.path.split(
problemdata_info["data_directory"]
)
# self.hmbc_edge_colors = (
# "#1f77b4",
# "#ff7f0e",
# "#2ca02c",
# "#9467bd",
# "#8c564b",
# "#e377c2",
# "#7f7f7f",
# "#bcbd22",
# "#17becf",
# "#1f77b4",
# "#ff7f0e",
# "#2ca02c",
# "#9467bd",
# "#8c564b",
# "#e377c2",
# "#7f7f7f",
# "#bcbd22",
# "#17becf",
# )
self.hmbc_edge_colors = ['#377eb8', '#ff7f00', '#4daf4a',
'#f781bf', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']
self.xmin = -0.1
self.ymin = -0.1
self.xmax = 1.1
self.ymax = 1.1
self.data_complete = False
self.pngFiles = []
self.jupyterFiles = []
self.fidFilesDirectories = []
self.yamlFiles = []
self.excelFiles = []
self.csvFiles = []
self.pklFiles = []
self.df = pd.DataFrame()
self.dfBackup = pd.DataFrame()
self.dfColumns = None
self.dfIndex = None
self.moleculePNGpanel = None
self.nx_graph_molecule = None
self.moleculeAtomsStr = ""
self.protonAtoms = []
self.carbonAtoms = []
self.elements = {}
self.iprobs = {}
self.molecule_defined = False
self.expected_molecule = None # rdkit molecule
self.png = None # image from png
self.smiles = None
self.smiles_defined = False
self.numProtonGroups = 0
self.C = 0
self.dbe = 0
# pandas dataframes
self.h1 = pd.DataFrame(columns=excel_df_columns["h1"])
self.c13 = pd.DataFrame(columns=excel_df_columns["c13"])
self.pureshift = pd.DataFrame(columns=excel_df_columns["pureshift"])
self.cosy = pd.DataFrame(columns=excel_df_columns["cosy"])
self.hsqc = pd.DataFrame(columns=excel_df_columns["hsqc"])
self.hmbc = pd.DataFrame(columns=excel_df_columns["hmbc"])
# graph coodinates of carbon skeleton molecule
self.xy3_from_json = False
self.predict_c13ppm = True
self.xy = None
self.xy3 = None
if isinstance(H1LarmorFreq, (int, float)):
obs = H1LarmorFreq
else:
obs = 400.0
self.udic = {
"ndim": 2,
0: {
"obs": obs,
"sw": 18 * obs,
"dw": 1.0 / (18 * obs),
"car": (((16.0 - (-2.0)) / 2) - 2.0) * obs,
"size": int(32 * 1024),
"label": "1H",
"complex": True,
"encoding": "direct",
"time": False,
"freq": True,
"lb": 0.5,
},
1: {
"obs": obs / 4,
"sw": 220 * obs / 4,
"dw": 1.0 / (220 * obs / 4),
"car": 220 * obs / 4.0 / 2.0,
"size": int(1024 * 128),
"label": "13C",
"complex": True,
"encoding": "direct",
"time": False,
"freq": True,
"lb": 0.5,
},
}
self.udic[0]["axis"] = ng.fileiobase.unit_conversion(
self.udic[0]["size"],
self.udic[0]["complex"],
self.udic[0]["sw"],
self.udic[0]["obs"],
self.udic[0]["car"],
)
self.udic[1]["axis"] = ng.fileiobase.unit_conversion(
self.udic[1]["size"],
self.udic[1]["complex"],
self.udic[1]["sw"],
self.udic[1]["obs"],
self.udic[1]["car"],
)
if isinstance(problemdata_info["excel_fn"], str):
loadfromwhere = "xlsx"
if isinstance(loadfromwhere, type(None)):
if self.init_class_from_yml(self.problemDirectory):
self.data_complete = True
else:
rtn_okay, msg = self.init_class_from_excel(self.problemDirectory)
if rtn_okay:
self.data_complete = True
else:
self.data_complete = False
return
else:
if loadfromwhere == "yml":
if self.init_class_from_yml(self.problemDirectory):
self.data_complete = True
else:
self.data_complete = False
return
elif loadfromwhere == "xlsx":
rtn_ok, msg = self.init_class_from_excel(
self.problemDirectory, problemdata_info["excel_fn"]
)
if rtn_ok:
self.data_complete = True
else:
self.data_complete = False
return
# set xlims of 13C and 1H 1D spectra to +/- 10% of biggest and smallest ppm
self.min_max_1D_ppm = []
ppm_min = self.h1.ppm.min() - (self.h1.ppm.max() - self.h1.ppm.min()) * 0.3
ppm_max = self.h1.ppm.max() + (self.h1.ppm.max() - self.h1.ppm.min()) * 0.3
self.min_max_1D_ppm.append((ppm_max, ppm_min))
ppm_min = self.c13.ppm.min() - (self.c13.ppm.max() - self.c13.ppm.min()) * 0.30
ppm_max = self.c13.ppm.max() + (self.c13.ppm.max() - self.c13.ppm.min()) * 0.30
self.min_max_1D_ppm.append((ppm_max, ppm_min))
self.attempt_to_assign_integrals_to_nmrproblem()
def attempt_to_assign_integrals_to_nmrproblem(self):
print(self.c13[["ppm", "numProtons", "attached_protons"]])
if self.c13_from_hsqc:
if "CH3CH" not in self.hsqc.columns:
self.add_CH2_CH3CH_to_hsqc_dataframes()
print("added CH2 and CH3CH to hsqc dataframes")
print(self.hsqc)
if "CH3CH" not in self.c13_df.columns:
self.add_CH0_CH1_CH2_CH3_CH3CH1_to_C13_df()
print("added CH0, CH1, CH2, CH3, CH3CH1 to c13_df")
print(self.c13_df.ppm.tolist())
print(self.hsqc.f1_ppm.tolist())
if "CH3CH" not in self.c13.columns:
self.add_CH0_CH1_CH2_CH3_CH3CH1_to_C13()
print("added CH0, CH1, CH2, CH3, CH3CH1 to c13")
print(self.c13)
print(self.c13[["ppm", "numProtons", "attached_protons"]])
if self.c13_from_hsqc and self.H1_data_missing:
ch3_df = self.expected_molecule.molprops_df[
self.expected_molecule.molprops_df.CH3
]
ch3ch1_df = self.expected_molecule.molprops_df[
self.expected_molecule.molprops_df.CH3CH1
]
ch1_df = self.expected_molecule.molprops_df[
self.expected_molecule.molprops_df.CH1
]
# update h1 integrals for CH2 groups
ch2_df = self.hsqc[self.hsqc_df.CH2]
# count frequency of f1_ppm in ch2_df
ch2_df_f1_ppm = ch2_df.f1_ppm.value_counts()
# update corresponding integral in h1 frame
for idx, f1_ppm, f2_ppm in zip(ch2_df.index, ch2_df.f1_ppm, ch2_df.f2_ppm):
if ch2_df_f1_ppm.loc[f1_ppm] == 1:
# set the integral of f2_ppm in self.h1 to 2
self.h1.loc[self.h1.ppm == f2_ppm, "integral"] = 2
self.h1.loc[self.h1.ppm == f2_ppm, "numProtons"] = 2
else:
# set the integral of f2_ppm in self.h1 to 1
self.h1.loc[self.h1.ppm == f2_ppm, "integral"] = 1
self.h1.loc[self.h1.ppm == f2_ppm, "numProtons"] = 1
# if no CH3 groups in expected molecule assign all CH3CH1 groups to CH1
if ch3_df.shape[0] == 0 and ch1_df.shape[0 > 0]:
print("no CH3 groups in expected molecule")
self.c13.loc[self.c13.CH3CH1.index, "CH1"] = True
self.c13.loc[self.c13.CH1.index, "numProtons"] = 1
self.c13.loc[self.c13.CH1.index, "attached_protons"] = 1
# if no CH1 groups in expected molecule assign all CH3CH1 groups to CH3
elif ch3_df.shape[0] > 0 and ch1_df.shape[0] == 0:
print("no CH1 groups in expected molecule")
self.c13.loc[self.c13CH3CH.index, "CH3"] = True
self.c13.loc[self.c13.CH3.index, "numProtons"] = 3
self.c13.loc[self.c13.CH3.index, "attached_protons"] = 3
print(self.hsqc.columns)
print(self.c13.columns)
# if the number of CH3CH1 groups in the expected molecule == the number in the hsqc dataframe
# split the CH3CH1 groups between CH1 and CH3 based on closest ppm match
if ch3ch1_df.shape[0] == self.hsqc[self.hsqc.CH3CH].shape[0]:
pass
# drop duplicate rows based on c13ppm
print("drop duplicates")
print(ch3_df.shape[0])
if ch3ch1_df.shape[0] > self.hsqc[self.hsqc.CH3CH].shape[0]:
ch3_df = ch3_df.drop_duplicates(subset=["ppm"])
print("drop duplicates")
print(ch3_df.shape[0])
# sort ch3_df by ppm, lowest to highest
ch3_df = ch3_df.sort_values(by=["ppm"])
ch3ch_df = self.c13[self.c13.CH3CH].copy()
# CH3 groups expected to be below 67 ppm
ch3ch_df_lessthan_67 = ch3ch_df[ch3ch_df.ppm < 67].copy()
print(f"ch3_df {ch3_df.shape[0]} >= ch3ch_df_lessthan_67 {ch3ch_df_lessthan_67.shape[0]} {ch3_df.shape[0] >= ch3ch_df_lessthan_67.shape[0]}")
if ch3_df.shape[0] >= ch3ch_df_lessthan_67.shape[0]:
self.c13.loc[ch3ch_df_lessthan_67.index, "numProtons"] = 3
print("CH3 groups expected to be below 67 ppm")
else:
for idx, ppm in zip(ch3_df.index, ch3_df.ppm):
# find the closest match in the c13 dataframe
# and update the numProtons column to 3
closest_match = ch3ch_df_lessthan_67.iloc[
(ch3ch_df_lessthan_67["ppm"] - ppm).abs().argsort()[:1]
]
print(f"closest_match {closest_match.ppm} to {ppm}, closest_match index {closest_match.index}")
self.c13.loc[closest_match.index, "numProtons"] = 3
ch3ch_df_lessthan_67.drop(closest_match.index, inplace=True)
self.c13["attached_protons"] = self.c13["numProtons"]
# update corresponding integral in h1 frame
# f1_i C13 index in hsqc
# f2_i H1 index in hsqc
# f2p_i H1 index based on C13 index in hsqc
# ch3_df = self.c13[self.c13.numProtons == 3]
# for idx, ppm in zip(ch3_df.index, ch3_df.ppm):
# # find the closest match in the hsqc dataframe
# closest_match = self.hsqc.iloc[
# (self.hsqc["f1_ppm"] - ppm).abs().argsort()[:1]
# ]
# # update the integral in the h1 dataframe
# self.h1.loc[closest_match.index, "integral"] = 3
# self.h1.loc[closest_match.index, "numProtons"] = 3
# for idx in self.c13[self.c13.numProtons == 3].index:
# # obtain the row in the hsqc dataframe based on f1_i
# hsqc_row = self.hsqc[self.hsqc.f1_i == idx]
# # update the integral in the hsqc dataframe
# self.hsqc.loc[hsqc_row.index, "integral"] = 3
# # update the numProtons in the hsqc dataframe
# self.hsqc.loc[hsqc_row.index, "numProtons"] = 3
# # update the integral in the h1 dataframe
# for f2_i in hsqc_row.f2_i.values:
# if f2_i in self.h1.index:
# self.h1.loc[f2_i, "integral"] = 3
# # update the numProtons in the h1 dataframe
# self.h1.loc[f2_i, "numProtons"] = 3
# else:
# print(f"index {f2_i} not in h1 dataframe")
elif self.c13_from_hsqc and self.H1_data_present:
self.c13["CH3"] = False
self.c13["CH1"] = False
self.c13["numProtons"] = 0
self.c13["attached_protons"] = 0
# use integrals from h1 to set CH3s in C13
for numprotons in [3, 2, 1]:
for hidx in self.h1.query("numProtons == @numprotons").index.values:
hppm = self.h1.loc[hidx, "ppm"]
cidx = self.hsqc.query("f2_ppm == @hppm").f1_i