From 3fcb776e3c8b0b643b2be954873e3f1b862603ae Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 25 Apr 2023 09:49:35 +0200 Subject: [PATCH 01/41] first commit to featurizer --- lobsterpy/TestData/JSONS/mp-1249.json.gz | Bin 0 -> 211586 bytes lobsterpy/TestData/JSONS/mp-14652.json.gz | Bin 0 -> 480557 bytes lobsterpy/TestData/JSONS/mp-1958.json.gz | Bin 0 -> 188419 bytes lobsterpy/featurize/__init__.py | 90 +++++++++++ lobsterpy/featurize/test/__init__.py | 0 lobsterpy/featurize/test/test_featurize.py | 168 +++++++++++++++++++++ 6 files changed, 258 insertions(+) create mode 100755 lobsterpy/TestData/JSONS/mp-1249.json.gz create mode 100755 lobsterpy/TestData/JSONS/mp-14652.json.gz create mode 100755 lobsterpy/TestData/JSONS/mp-1958.json.gz create mode 100644 lobsterpy/featurize/__init__.py create mode 100644 lobsterpy/featurize/test/__init__.py create mode 100644 lobsterpy/featurize/test/test_featurize.py diff --git a/lobsterpy/TestData/JSONS/mp-1249.json.gz 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zI#Ki1W6s$sI8}Dd_*1%&r)(BZIHltrC5Ob`uHDkc;i@ait?RA^(tS^f$-XYb95x&K z8!Nxz)iucXr7c0f4{pqghS9oEb=Resd#^+x5YxWFHDb3HM&7c8`|P-?3>UiHMSttW z&qLZ`>9yEmR-at4_cio6Jzc4;aehN^r3bvB{^`rtmg|2lqJj!4sGx!hDyX1>3M#0e zf(k0A;A8v6zy9mL{^Y|K|Ma`BfBm~({_4ALfAhl^KmG8@*WZ5i%`boR z^{;=tqW{*Rwt#l2^P!zOO%j|I==NVSVMr1Z9!F|Kqp6{PFAGuLz)z$h_V#ULLI= z9oGlneD|v#e*E9IJ_vyp0zx(|kzWd|1zxnWw-~a5>2dmTv z{P4p+|Ni%%e*FHQzx?{!-+cPRCkwv)@xxa?e)szy{_2On{_sy<|MsQXd`t;3r|McNM|F56E|NYm0y=(Z<>h|AMub+MT z@*ltd?@+HVeqm$t!>@k%^{>DC)z`04JQNc3^JgX^C3yFS1B!+%44g7*Jfp}+ly zYmF=?f(gu9!~D21_}{+!^iMVwUX=cE9g}_Wpa1ErZ+`R3AO7&o*FXMf zljM~-@GIM2|L&{rf15M=`FiTmUp)N9Ez_T`7jpce9OGHP_ Date: Tue, 25 Apr 2023 09:57:32 +0200 Subject: [PATCH 02/41] update doc string --- lobsterpy/featurize/__init__.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/lobsterpy/featurize/__init__.py b/lobsterpy/featurize/__init__.py index a61da46f..8be1289c 100644 --- a/lobsterpy/featurize/__init__.py +++ b/lobsterpy/featurize/__init__.py @@ -15,8 +15,10 @@ def read_lobster_lightweight_json(filename: str): """ This function reads loads the lightweight json.gz files and returns a python dictionary object with lobster summmarized bonding analysis data. + Args: filename (str) : name of file + Returns: Returns a dictionary with lobster summmarized bonding analysis data. """ @@ -34,9 +36,11 @@ def featurize_lobsterpy_icohp_data(filename, bonds: str = "all_bonds"): """ This function reads loads the lightweight json.gz files and generates icohp stats data from lobsterpy bonding analysis + Args: filename (str) : name of file bonds (str) : all_bonds | cation_anion_bonds + Returns: Returns a pandas dataframe with lobsterpy icohp statistics . """ From a447a86476f9fb0d18e04ab6cf6d55d596a1fe4e Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 25 Apr 2023 16:09:08 +0200 Subject: [PATCH 03/41] update test --- lobsterpy/featurize/test/test_featurize.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/lobsterpy/featurize/test/test_featurize.py b/lobsterpy/featurize/test/test_featurize.py index b393fed5..360b030c 100644 --- a/lobsterpy/featurize/test/test_featurize.py +++ b/lobsterpy/featurize/test/test_featurize.py @@ -12,10 +12,13 @@ class TestFeaturizeLobsterpyIcohp(unittest.TestCase): def setUp(self): os.chdir(TestDir / "TestData/JSONS") - self.mpids = ["mp-1249.json.gz", "mp-1958.json.gz", "mp-14652.json.gz"] - def test_featurize_lobsterpy_icohp_0(self): - df = featurize_lobsterpy_icohp_data(self.mpids[0]) + self.mp_1249 = "mp-1249.json.gz" + self.mp_1958 = "mp-1958.json.gz" + self.mp_14652 = "mp-14652.json.gz" + + def test_featurize_lobsterpy_icohp_data(self): + df = featurize_lobsterpy_icohp_data(self.mp_1249) # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -44,7 +47,7 @@ def test_featurize_lobsterpy_icohp_0(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], self.mpids[0].split(".")[0]) + self.assertEqual(df.index[0], self.mp_1249.split(".")[0]) # Test that all the values in the DataFrame self.assertAlmostEqual( @@ -66,7 +69,7 @@ def test_featurize_lobsterpy_icohp_0(self): df.loc[df.index[0], "bonding_perc_avg"], 0.978985, places=5 ) - df = featurize_lobsterpy_icohp_data(self.mpids[1]) + df = featurize_lobsterpy_icohp_data(self.mp_1958) # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -95,7 +98,7 @@ def test_featurize_lobsterpy_icohp_0(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], self.mpids[1].split(".")[0]) + self.assertEqual(df.index[0], self.mp_1958.split(".")[0]) # Test that all the values in the DataFrame self.assertAlmostEqual(df.loc[df.index[0], "Icohp_sum_avg"], -2.96000, places=5) @@ -109,7 +112,7 @@ def test_featurize_lobsterpy_icohp_0(self): df.loc[df.index[0], "antibonding_perc_avg"], 0.14528, places=5 ) - df = featurize_lobsterpy_icohp_data(self.mpids[2]) + df = featurize_lobsterpy_icohp_data(self.mp_14652) # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -138,7 +141,7 @@ def test_featurize_lobsterpy_icohp_0(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], self.mpids[2].split(".")[0]) + self.assertEqual(df.index[0], self.mp_14652.split(".")[0]) # Test that all the values in the DataFrame self.assertAlmostEqual( From 1d211a08ac235aded9e45f82d2933152440ac230 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:02:54 +0200 Subject: [PATCH 04/41] update featurizer to incorporate OOP --- lobsterpy/featurize/core.py | 659 ++++++++++++++++++ .../test/{test_featurize.py => test_core.py} | 8 +- 2 files changed, 663 insertions(+), 4 deletions(-) create mode 100644 lobsterpy/featurize/core.py rename lobsterpy/featurize/test/{test_featurize.py => test_core.py} (95%) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py new file mode 100644 index 00000000..b6e45cfe --- /dev/null +++ b/lobsterpy/featurize/core.py @@ -0,0 +1,659 @@ +# Copyright (c) lobsterpy development team +# Distributed under the terms of a BSD 3-Clause "New" or "Revised" License + +""" +This module defines functions to featurize Lobster lighweight jsons +""" + +import gzip +import json +import numpy as np +import pandas as pd +from pathlib import Path +from typing import Mapping, NamedTuple +from collections import namedtuple +from pymatgen.core.structure import Structure +from mendeleev import element +from pymatgen.io.lobster import Charge, Lobsterout, Icohplist +from pymatgen.electronic_structure.cohp import CompleteCohp +from pymatgen.electronic_structure.core import Spin +from scipy.integrate import trapezoid + + +class FeaturizeLobsterpy: + """ + class to featurize lobsterpy data + + Args: + path_to_json: path to lobster lightweight json + bonds: "all_bonds" or "cation_anion_bonds" + Attributes: + get_df: returns a pandas dataframe with relevant icohp statistical data as columns from + lobsterpy automatic bonding analysis + + """ + + def __init__( + self, + path_to_json: str, + bonds: str = "all_bonds", + ): + self.path_to_json = path_to_json + self.bonds = bonds + + def get_df(self, ids: str) -> pd.DataFrame: + """ + This function featurizes LobsterPy condensed bonding analysis data from + lobster lightweight json.gz files + + Returns: + Returns a pandas dataframe with lobsterpy icohp statistics + + """ + # define a pandas dataframe + df = pd.DataFrame(index=[ids]) + + # read the lightweight lobster json files using read_lobster_lightweight_json function + data = self._read_lobster_lightweight_json() + + icohp_mean = [] + icohp_sum = [] + bond = [] + antibond = [] + # extract lobsterpy icohp related data for bond type specified + if data[self.bonds]: + for k, v in data[self.bonds]["lobsterpy_data"]["sites"].items(): + for k1, v1 in v["bonds"].items(): + icohp_mean.append(float(v1["ICOHP_mean"])) + icohp_sum.append(float(v1["ICOHP_sum"])) + bond.append(v1["bonding"]["perc"]) + antibond.append(v1["antibonding"]["perc"]) + else: + raise Exception('There exist no data for {} for this structure. ' + 'Please switch to "all_bonds" mode'.format(self.bonds)) + + # add stats data as columns to the dataframe + df.loc[ids, "Icohp_mean_avg"] = np.mean(icohp_mean) + df.loc[ids, "Icohp_mean_max"] = np.max(icohp_mean) + df.loc[ids, "Icohp_mean_min"] = np.min(icohp_mean) + df.loc[ids, "Icohp_mean_std"] = np.std(icohp_mean) + + df.loc[ids, "Icohp_sum_avg"] = np.mean(icohp_sum) + df.loc[ids, "Icohp_sum_max"] = np.max(icohp_sum) + df.loc[ids, "Icohp_sum_min"] = np.min(icohp_sum) + df.loc[ids, "Icohp_sum_std"] = np.std(icohp_sum) + + df.loc[ids, "bonding_perc_avg"] = np.mean(bond) + df.loc[ids, "bonding_perc_max"] = np.max(bond) + df.loc[ids, "bonding_perc_min"] = np.min(bond) + df.loc[ids, "bonding_perc_std"] = np.std(bond) + + df.loc[ids, "antibonding_perc_avg"] = np.mean(antibond) + df.loc[ids, "antibonding_perc_min"] = np.min(antibond) + df.loc[ids, "antibonding_perc_max"] = np.max(antibond) + df.loc[ids, "antibonding_perc_std"] = np.std(antibond) + + # add madelung energies for the structure + df.loc[ids, "Madelung_Mull"] = data["madelung_energies"]["Mulliken"] + df.loc[ids, "Madelung_Loew"] = data["madelung_energies"]["Loewdin"] + + return df + + def _read_lobster_lightweight_json(self): + """ + This function reads loads the lightweight json.gz files and returns a python dictionary object + with lobster summmarized bonding analysis data. + + Returns: + Returns a dictionary with lobster summmarized bonding analysis data + + """ + with gzip.open(self.path_to_json, "rb") as f: + data = json.loads(f.read().decode("utf-8")) + + lobster_data = {} + for item in data: + lobster_data.update(item) + + return lobster_data + + +class FeaturizeCOXX: + + """ + class to featurize COHPCAR/COBICAR/COOPCAR data as fingerprint + + Args: + path_to_coxxcar: path to COXXCAR.lobster (e.g., "COXXCAR.lobster") + path_to_icoxxlist : path to ICOXXLIST.lobster (e.g., "ICOXXLIST.lobster") + path_to_structure : path to structure file (e.g., "POSCAR") + feature_type: set the feature type for moment features and fingerprints. Possible options are "bonding", "antibonding" or "overall" + are_cobis : bool indicating if file contains COBI/ICOBI data + are_coops : bool indicating if file contains COOP/ICOOP data + include_wicoxx : if True will include weighted ICOXX, effective co-ordination number + in the featurized dataframe + include_moment_features : if True will include ICOXX center,width, skew and kurtosis features in the featurized dataframe + e_range : range of energy relative to fermi for which moment features needs to be computed + + Attributes: + get_df: pandas dataframe + get_coxx_fingerprint_df: pandas dataframe + + """ + + def __init__( + self, + path_to_coxxcar: str, + path_to_icoxxlist: str, + path_to_structure: str, + feature_type: str, + are_cobis: bool = False, + are_coops: bool = False, + include_wicoxx: bool = False, + include_moment_features: bool = False, + e_range: list = [-5, 0], + ): + self.path_to_coxxcar = path_to_coxxcar + self.path_to_icoxxlist = path_to_icoxxlist + self.path_to_structure = path_to_structure + self.feature_type = feature_type + self.include_wicoxx = include_wicoxx + self.include_moment_features = include_moment_features + self.e_range = e_range + self.are_cobis = are_cobis + self.are_coops = are_coops + + self.icoxxlist = Icohplist( + filename=self.path_to_icoxxlist, + are_cobis=self.are_cobis, + are_coops=self.are_coops, + ) + + self.completecoxx = CompleteCohp.from_file( + fmt="LOBSTER", + filename=self.path_to_coxxcar, + structure_file=self.path_to_structure, + ) + + def get_coxx_fingerprint_df( + self, + ids: str, + fp_type: str, + coxxcar_obj: CompleteCohp, + spin_type: str = "summed", + binning: bool = True, + min_e: float = -5, + max_e: float = 0, + n_bins: int = 50, + normalize: bool = True, + label_list: list = None, + ): + """ + Generates the COHP fingerprint + + Args: + ids: sets index of pandas dataframe + spin_type: Specify spin type. Can accept '{summed/up/down}' + (default is summed) + binning: If true cohps will be binned + min_e: The minimum mode energy to include in the fingerprint (default is None) + max_e: The maximum mode energy to include in the fingerprint (default is None) + n_bins: Number of bins to be used in the fingerprint (default is 256) + normalize: If true, normalizes the area under fp to equal to 1 (default is True) + fp_type: Specify fingerprint type needed can accept '{bonding/antibonding/overall}' + (default is antibonding) + label_list: Specify bond lables as a list for which cohp fingerprints are needed + coxxcar_obj: coxxcar object + + Raises: + Exception: If spin_type is not one of the accepted values {summed/up/down}. + ValueError: If fp_type is not one of the accepted values {bonding/antibonding/overall}. + + Returns: + Fingerprint(namedtuple) : The COHP fingerprint + of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) + """ + + fingerprint = namedtuple( + "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" + ) + energies = ( + coxxcar_obj.energies - coxxcar_obj.efermi + ) # here substraction of efermi has impact on fp. + + if max_e is None: + max_e = np.max(energies) + + if min_e is None: + min_e = np.min(energies) + + if label_list: + coxxcar_obj = coxxcar_obj.get_summed_cohp_by_label_list( + label_list + ).get_cohp() + else: + coxxcar_obj = coxxcar_obj.get_cohp() + + if spin_type == "up": + coxx_all = coxxcar_obj[Spin.up] + elif spin_type == "down": + coxx_all = coxxcar_obj[Spin.down] + elif spin_type == "summed": + coxx_all = coxxcar_obj[Spin.up] + coxxcar_obj[Spin.down] + else: + raise Exception( + """Check the spin_type argument, + Possible options are summed/up/down""" + ) + coxx_dict = {} + + coxx_dict["bonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["antibonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["overall"] = coxx_all + + try: + coxxs = coxx_dict[fp_type] + if len(energies) < n_bins: + inds = np.where((energies >= min_e) & (energies <= max_e)) + return fingerprint( + energies[inds], + coxxs[inds], + fp_type, + spin_type, + len(energies), + np.diff(energies)[0], + ) + + if binning: + ener_bounds = np.linspace(min_e, max_e, n_bins + 1) + ener = ener_bounds[:-1] + (ener_bounds[1] - ener_bounds[0]) / 2.0 + bin_width = np.diff(ener)[0] + else: + ener_bounds = np.array(energies) + ener = np.append(energies, [energies[-1] + np.abs(energies[-1]) / 10]) + n_bins = len(energies) + bin_width = np.diff(energies)[0] + + coxx_rebin = np.zeros(ener.shape) + + for ii, e1, e2 in zip(range(len(ener)), ener_bounds[0:-1], ener_bounds[1:]): + inds = np.where((energies >= e1) & (energies < e2)) + coxx_rebin[ii] = np.sum(coxxs[inds]) + if normalize: # scale DOS bins to make area under histogram equal 1 + area = np.sum([abs(coxx) * bin_width for coxx in coxx_rebin]) + coxx_rebin_sc = coxx_rebin / area + else: + coxx_rebin_sc = coxx_rebin + + fp = fingerprint( + np.array([ener]), + coxx_rebin_sc, + fp_type, + spin_type, + n_bins, + bin_width, + ) + + df_temp = pd.DataFrame(index=[ids], columns=["COHP_FP"]) + df_temp.at[ids, "COHP_FP"] = fp.coxx * fp.bin_width + + df = pd.DataFrame(df_temp["COHP_FP"].tolist()) + + return df + + except KeyError: + raise ValueError( + """Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall + """ + ) + + def _calculate_wicoxx_ein(self): + """ + Method to calculate weighted icoxx (xx ==hp,op,bi) and ein of crystal structure based on work by Peter Müller + + {:[ bar(COHP)(E)=sum _(i)(w_(i)*COHP_(i)(E))],[w_(i)=(ICOHP_(i))/(ICOHP_("total "))]:} + + w_(i)=(ICOHP_(i))/(ICOHP_("total ")) + + bar(ICOHP)=int_(-oo)^(epsi_(F)) bar(COHP)(E)dE + + Returns: + Percent bonding, Percent anti-bonding, weighted icoxx, effective interaction number + + """ + icoxx_dict = self.icoxxlist.icohpcollection.as_dict() + list_labels = icoxx_dict["list_labels"] + # Compute sum of icohps + icoxx_total = self.icoxxlist.icohpcollection.get_summed_icohp_by_label_list( + list_labels + ) + + summed_weighted_coxx = [] + + for lab in list_labels: + for k, v in self.completecoxx.get_cohp_by_label( + "{}".format(lab), summed_spin_channels=True + ).cohp.items(): + coxx = v + icoxx = self.icoxxlist.icohpcollection.get_icohp_by_label( + lab, summed_spin_channels=True + ) + weight = ( + icoxx / icoxx_total + ) # calculate the weights based on icohp contri to total icohp of the structure + weighted_coxx = weight * coxx + summed_weighted_coxx.append(weighted_coxx) + + summed = np.sum(summed_weighted_coxx, axis=0) + # below fermi + indices = self.completecoxx.energies <= self.completecoxx.efermi + en_bf = self.completecoxx.energies[indices] + coxx_bf = summed[indices] + # percent bonding-anitbonding + if not self.icoxxlist.are_cobis and not self.icoxxlist.are_coops: + bonding_indices = coxx_bf <= 0 + antibonding_indices = coxx_bf >= 0 + bnd = abs(trapezoid(en_bf[bonding_indices], coxx_bf[bonding_indices])) + antibnd = abs( + trapezoid(en_bf[antibonding_indices], coxx_bf[antibonding_indices]) + ) + per_bnd = (bnd / (bnd + antibnd)) * 100 + per_antibnd = (antibnd / (bnd + antibnd)) * 100 + elif self.icoxxlist.are_cobis or self.icoxxlist.are_coops: + bonding_indices = coxx_bf >= 0 + antibonding_indices = coxx_bf <= 0 + bnd = abs(trapezoid(coxx_bf[bonding_indices], en_bf[bonding_indices])) + antibnd = abs( + trapezoid(coxx_bf[antibonding_indices], en_bf[antibonding_indices]) + ) + per_bnd = (bnd / (bnd + antibnd)) * 100 + per_antibnd = (antibnd / (bnd + antibnd)) * 100 + + w_icoxx = trapezoid(coxx_bf, en_bf) + + ein = (icoxx_total / w_icoxx) * ( + 2 / self.completecoxx.structure.num_sites + ) # calc effective interaction number + + return per_bnd, per_antibnd, w_icoxx, ein + + def _calc_moment_features(self): + coxxcar = self.completecoxx.get_cohp() + coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] + energies = self.completecoxx.energies - self.completecoxx.efermi + + coxx_dict={} + if not self.are_cobis and not self.are_coops: + coxx_dict["bonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["antibonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["overall"] = coxx_all + else: + coxx_dict["antibonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["bonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["overall"] = coxx_all + + band_center = self._get_band_center( + cohp=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + band_width = self._get_n_moment( + n=2, + cohp=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + band_skew = self._get_n_moment( + n=3, + cohp=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + band_kurt = self._get_n_moment( + n=4, + cohp=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + + return band_center, band_width, band_skew, band_kurt + + def _get_band_center(self, cohp: list, energies: list, e_range: list) -> float: + """ + Get the band width, defined as the first moment of the orbital resolved COHP + Args: + cohp: Orbital COHP list + energies: energies corresponding orbital orbital interactions COHP + Returns: + Orbital-Orbital interaction band center in eV + """ + band_center = self._get_n_moment( + n=1, cohp=cohp, energies=energies, e_range=e_range, center=False + ) + + return band_center + + def _get_n_moment( + self, n: int, cohp: list, energies: list, e_range: list, center: bool = True + ) -> float: + """ + + Get the nth moment of COXX + + Args: + n: The order for the moment + cohp: COXX list + energies: energies + center: Take moments with respect to the COXX center + + Returns: + COXX nth moment in eV + """ + if e_range: + cohp = cohp[(energies >= self.e_range[0]) & (energies <= self.e_range[1])] + energies = energies[ + (energies >= self.e_range[0]) & (energies <= self.e_range[1]) + ] + + if center: + band_center = self._get_band_center( + cohp=cohp, energies=energies, e_range=self.e_range + ) + p = energies - band_center + else: + p = energies + + nth_moment = np.trapz(p**n * cohp, x=energies) / np.trapz(cohp, x=energies) + + return nth_moment + + def get_df(self, ids: str): + """ + This function returns a pandas dataframe with weighted ICOXX, effective interaction number + and moment features (center, width, skewness and kurtosis) of COXX in selected energy range + + Returns: + Returns a pandas dataframe with cohp/cobi/coop related features + + """ + + df = pd.DataFrame(index=[ids]) + + if self.include_wicoxx: + ( + per_bnd_xx, + per_antibnd_xx, + w_icoxx, + ein_xx, + ) = self._calculate_wicoxx_ein() + + if self.are_coops: + bc, bw, bs, bk = self._calc_moment_features() + df.loc[ids, "bnd_ICOOP"] = per_bnd_xx + df.loc[ids, "antibnd_ICOOP"] = per_antibnd_xx + df.loc[ids, "w_ICOOP"] = w_icoxx + df.loc[ids, "EIN_ICOOP"] = ein_xx + df.loc[ids, "band_center_COOP"] = bc + df.loc[ids, "band_width_COOP"] = bw + df.loc[ids, "band_skewness_COOP"] = bs + df.loc[ids, "band_kurtosis_COOP"] = bk + elif self.are_cobis: + bc, bw, bs, bk = self._calc_moment_features() + df.loc[ids, "bnd_ICOOP"] = per_bnd_xx + df.loc[ids, "bnd_ICOBI"] = per_bnd_xx + df.loc[ids, "antibnd_ICOBI"] = per_antibnd_xx + df.loc[ids, "w_ICOBI"] = w_icoxx + df.loc[ids, "EIN_ICOBI"] = ein_xx + df.loc[ids, "band_center_COBi"] = bc + df.loc[ids, "band_width_COBI"] = bw + df.loc[ids, "band_skewness_COBI"] = bs + df.loc[ids, "band_kurtosis_COBI"] = bk + else: + bc, bw, bs, bk = self._calc_moment_features() + df.loc[ids, "bnd_ICOHP"] = per_bnd_xx + df.loc[ids, "antibnd_ICOHP"] = per_antibnd_xx + df.loc[ids, "w_ICOHP"] = w_icoxx + df.loc[ids, "EIN_ICOHP"] = ein_xx + df.loc[ids, "band_center_COHP"] = bc + df.loc[ids, "band_width_COHP"] = bw + df.loc[ids, "band_skewness_COHP"] = bs + df.loc[ids, "band_kurtosis_COHP"] = bk + + return df + + +class FeaturizeCharges: + """ + class to compute ionicity from CHARGE.lobster data + + Args: + path_to_strucutre: path to POSCAR + path_to_charge : path to CHARGE.lobster (e.g., "CHARGE.lobster") + charge_type : set charge type used for computing ionicity. Possible options are "Mulliken" or "Loewdin" + + Attributes: + get_df: pandas dataframe + + """ + + def __init__( + self, + path_to_strucutre: str, + path_to_charge: str, + charge_type: str, + ): + self.path_to_structure = path_to_strucutre + self.path_to_charge = path_to_charge + self.charge_type = charge_type + + def _calc_ionicity(self) -> float: + """ + Method to calculate ionicity of crystal structure based on quantum chemical charges + + I_("Charges ")=(1)/(N_("Atoms "))sum _(i)^(N_("Atoms "))((q_(i))/(v_("eff ",i))) + + Returns: + Ionicity of the structure + + """ + + chargeobj = Charge(filename=self.path_to_charge) + structure = Structure.from_file(self.path_to_structure) + + if self.charge_type.lower() == "mulliken": + charges = chargeobj.Mulliken + elif self.charge_type.lower() == "loewdin": + charges = chargeobj.Loewdin + else: + raise ValueError( + "Please check the requested charge_type, " + 'Possible options are "Mulliken" or "Loewdin"' + ) + + ch_veff = [] + for i, j in enumerate(charges): + if ( + j > 0 + and not structure.species[i].is_transition_metal + and ( + not structure.species[i].is_actinoid + and not structure.species[i].is_lanthanoid + ) + ): + valence_elec = element(structure.species[i].value) + val = j / (valence_elec.nvalence() - 0) + ch_veff.append(val) + + elif ( + j > 0 + and not structure.species[i].is_transition_metal + and ( + not structure.species[i].is_actinoid + and not structure.species[i].is_lanthanoid + ) + ): + valence_elec = element(structure.species[i].value) + val = j / (valence_elec.nvalence() - 8) + ch_veff.append(val) + + elif ( + j < 0 + and not structure.species[i].is_transition_metal + and ( + not structure.species[i].is_actinoid + and not structure.species[i].is_lanthanoid + ) + ): + valence_elec = element(structure.species[i].value) + val = j / (valence_elec.nvalence() - 8) + ch_veff.append(val) + + elif j > 0 and structure.species[i].is_transition_metal: + val = j / (structure.species[i].max_oxidation_state - 0) + ch_veff.append(val) + + elif j < 0 and structure.species[i].is_transition_metal: + val = j / (structure.species[i].max_oxidation_state - 8) + ch_veff.append(val) + + elif j > 0 and structure.species[i].is_lanthanoid: + val = j / (structure.species[i].max_oxidation_state - 0) + ch_veff.append(val) + + elif j < 0 and structure.species[i].is_actinoid: + val = j / (structure.species[i].max_oxidation_state - 8) + ch_veff.append(val) + + ionicity = sum(ch_veff) / structure.num_sites + + return ionicity + + def get_df(self, ids): + """ + This function returns a pandas dataframe with computed ionicity as column + + Returns: + Returns a pandas dataframe with ionicity + + """ + + df = pd.DataFrame(index=[ids]) + + if self.charge_type == "mulliken": + df.loc[ids, "Ionicity_Mull"] = self._calc_ionicity() + else: + df.loc[ids, "Ionicity_Loew"] = self._calc_ionicity() + + return df diff --git a/lobsterpy/featurize/test/test_featurize.py b/lobsterpy/featurize/test/test_core.py similarity index 95% rename from lobsterpy/featurize/test/test_featurize.py rename to lobsterpy/featurize/test/test_core.py index 360b030c..8e12155f 100644 --- a/lobsterpy/featurize/test/test_featurize.py +++ b/lobsterpy/featurize/test/test_core.py @@ -3,7 +3,7 @@ import unittest import pandas as pd from pathlib import Path -from lobsterpy.featurize import featurize_lobsterpy_icohp_data +from lobsterpy.featurize.lobsterpy_data import featurize_lobster_lightweight_json CurrentDir = Path(__file__).absolute().parent TestDir = CurrentDir / "../../" @@ -18,7 +18,7 @@ def setUp(self): self.mp_14652 = "mp-14652.json.gz" def test_featurize_lobsterpy_icohp_data(self): - df = featurize_lobsterpy_icohp_data(self.mp_1249) + df = featurize_lobster_lightweight_json(self.mp_1249) # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -69,7 +69,7 @@ def test_featurize_lobsterpy_icohp_data(self): df.loc[df.index[0], "bonding_perc_avg"], 0.978985, places=5 ) - df = featurize_lobsterpy_icohp_data(self.mp_1958) + df = featurize_lobster_lightweight_json(self.mp_1958) # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -112,7 +112,7 @@ def test_featurize_lobsterpy_icohp_data(self): df.loc[df.index[0], "antibonding_perc_avg"], 0.14528, places=5 ) - df = featurize_lobsterpy_icohp_data(self.mp_14652) + df = featurize_lobster_lightweight_json(self.mp_14652) # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) From 88413942cab0112372acfbf57790081aaab85442 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:03:54 +0200 Subject: [PATCH 05/41] add test for Lobsterpyjson featurizer --- lobsterpy/featurize/test/test_core.py | 71 +++++++++++++++------------ 1 file changed, 39 insertions(+), 32 deletions(-) diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 8e12155f..b5e0478d 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -3,22 +3,25 @@ import unittest import pandas as pd from pathlib import Path -from lobsterpy.featurize.lobsterpy_data import featurize_lobster_lightweight_json +from lobsterpy.featurize.core import FeaturizeLobsterpy CurrentDir = Path(__file__).absolute().parent TestDir = CurrentDir / "../../" -class TestFeaturizeLobsterpyIcohp(unittest.TestCase): +class TestFeaturizeLobsterpy(unittest.TestCase): def setUp(self): - os.chdir(TestDir / "TestData/JSONS") - self.mp_1249 = "mp-1249.json.gz" - self.mp_1958 = "mp-1958.json.gz" - self.mp_14652 = "mp-14652.json.gz" + self.featurize_mp1249_json = FeaturizeLobsterpy(path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", + bonds='all_bonds') + self.featurize_mp1958_json = FeaturizeLobsterpy(path_to_json=TestDir / "TestData/JSONS/mp-1958.json.gz", + bonds='all_bonds') + self.featurize_mp14652_json = FeaturizeLobsterpy(path_to_json=TestDir / "TestData/JSONS/mp-14652.json.gz", + bonds='all_bonds') - def test_featurize_lobsterpy_icohp_data(self): - df = featurize_lobster_lightweight_json(self.mp_1249) + + def test_featurize_mp1249_json(self): + df = self.featurize_mp1249_json.get_df(ids='mp-1249') # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -47,29 +50,31 @@ def test_featurize_lobsterpy_icohp_data(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], self.mp_1249.split(".")[0]) + self.assertEqual(df.index[0], 'mp-1249') # Test that all the values in the DataFrame self.assertAlmostEqual( - df.loc[df.index[0], "Icohp_mean_avg"], -1.020000, places=5 + df.loc['mp-1249', "Icohp_mean_avg"], -1.020000, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "Icohp_mean_max"], -1.020000, places=5 + df.loc['mp-1249', "Icohp_mean_max"], -1.020000, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "Icohp_mean_min"], -1.020000, places=5 + df.loc['mp-1249', "Icohp_mean_min"], -1.020000, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "Icohp_mean_std"], 0.000000, places=5 + df.loc['mp-1249', "Icohp_mean_std"], 0.000000, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "Madelung_Mull"], -52.000000, places=5 + df.loc['mp-1249', "Madelung_Mull"], -52.000000, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "bonding_perc_avg"], 0.978985, places=5 + df.loc['mp-1249', "bonding_perc_avg"], 0.978985, places=5 ) - df = featurize_lobster_lightweight_json(self.mp_1958) + def test_featurize_mp1958_json(self): + + df = self.featurize_mp1958_json.get_df(ids='mp-1958') # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -98,21 +103,23 @@ def test_featurize_lobsterpy_icohp_data(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], self.mp_1958.split(".")[0]) + self.assertEqual(df.index[0], 'mp-1958') # Test that all the values in the DataFrame - self.assertAlmostEqual(df.loc[df.index[0], "Icohp_sum_avg"], -2.96000, places=5) - self.assertAlmostEqual(df.loc[df.index[0], "Icohp_sum_max"], -2.96000, places=5) - self.assertAlmostEqual(df.loc[df.index[0], "Icohp_sum_min"], -2.96000, places=5) - self.assertAlmostEqual(df.loc[df.index[0], "Icohp_sum_std"], 0.000000, places=5) + self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_avg"], -2.96000, places=5) + self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_max"], -2.96000, places=5) + self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_min"], -2.96000, places=5) + self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_std"], 0.000000, places=5) self.assertAlmostEqual( - df.loc[df.index[0], "Madelung_Loew"], -16.68000, places=5 + df.loc['mp-1958', "Madelung_Loew"], -16.68000, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "antibonding_perc_avg"], 0.14528, places=5 + df.loc['mp-1958', "antibonding_perc_avg"], 0.14528, places=5 ) - df = featurize_lobster_lightweight_json(self.mp_14652) + def test_featurize_mp14652_json(self): + + df = self.featurize_mp14652_json.get_df(ids='mp-14652') # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -141,29 +148,29 @@ def test_featurize_lobsterpy_icohp_data(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], self.mp_14652.split(".")[0]) + self.assertEqual(df.index[0], 'mp-14652') # Test that all the values in the DataFrame self.assertAlmostEqual( - df.loc[df.index[0], "Icohp_mean_std"], 2.335070, places=5 + df.loc['mp-14652', "Icohp_mean_std"], 2.335070, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "bonding_perc_max"], 0.889620, places=5 + df.loc['mp-14652', "bonding_perc_max"], 0.889620, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "bonding_perc_min"], 0.873420, places=5 + df.loc['mp-14652', "bonding_perc_min"], 0.873420, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "bonding_perc_std"], 0.006339, places=5 + df.loc['mp-14652', "bonding_perc_std"], 0.006339, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "antibonding_perc_min"], 0.110380, places=5 + df.loc['mp-14652', "antibonding_perc_min"], 0.110380, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "antibonding_perc_max"], 0.126580, places=5 + df.loc['mp-14652', "antibonding_perc_max"], 0.126580, places=5 ) self.assertAlmostEqual( - df.loc[df.index[0], "antibonding_perc_std"], 0.006339, places=5 + df.loc['mp-14652', "antibonding_perc_std"], 0.006339, places=5 ) From 7e906ba45cb17ec66ada0b64213796cb50de9032 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:05:02 +0200 Subject: [PATCH 06/41] add optional featurizer dependencies --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 457402a1..9e41be52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,9 @@ repository = "https://github.com/JaGeo/LobsterPy" documentation = "https://lobsterpy.readthedocs.io/en/latest/?badge=latest" changelog = "https://lobsterpy.readthedocs.io/en/latest/changelog_link.html" +[project.optional-dependencies] +featurizer = ["mendeleev==0.12.1"] + [project.scripts] lobsterpy = "lobsterpy.cli:main" From 5123d023b8c25656b8bf14e2f1dc752c32dab7fe Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:05:26 +0200 Subject: [PATCH 07/41] update test and publish workflows --- lobsterpy/featurize/__init__.py | 89 +-------------------------------- 1 file changed, 1 insertion(+), 88 deletions(-) diff --git a/lobsterpy/featurize/__init__.py b/lobsterpy/featurize/__init__.py index 8be1289c..7284c0ad 100644 --- a/lobsterpy/featurize/__init__.py +++ b/lobsterpy/featurize/__init__.py @@ -2,93 +2,6 @@ # Distributed under the terms of a BSD 3-Clause "New" or "Revised" License """ -This package provides the module for featurzing LobsterPy lighweight jsons +This package provides the modules for featurzing Lobster data ready for ML """ -import gzip -import json -import numpy as np -import pandas as pd - - -def read_lobster_lightweight_json(filename: str): - """ - This function reads loads the lightweight json.gz files and returns a python dictionary object - with lobster summmarized bonding analysis data. - - Args: - filename (str) : name of file - - Returns: - Returns a dictionary with lobster summmarized bonding analysis data. - """ - with gzip.open(filename, "rb") as f: - data = json.loads(f.read().decode("utf-8")) - - lobster_data = {} - for item in data: - lobster_data.update(item) - - return lobster_data - - -def featurize_lobsterpy_icohp_data(filename, bonds: str = "all_bonds"): - """ - This function reads loads the lightweight json.gz files and generates icohp stats data from lobsterpy - bonding analysis - - Args: - filename (str) : name of file - bonds (str) : all_bonds | cation_anion_bonds - - Returns: - Returns a pandas dataframe with lobsterpy icohp statistics . - """ - # define a pandas dataframe - df = pd.DataFrame(index=[filename.split(".")[0]]) - - # read the lightweight lobster json files using read_lobster_lightweight_json function - data = read_lobster_lightweight_json(filename) - - icohp_mean = [] - icohp_sum = [] - bond = [] - antibond = [] - # extract lobsterpy icohp related data for bond type specified - for k, v in data[bonds]["lobsterpy_data"]["sites"].items(): - for k1, v1 in v["bonds"].items(): - icohp_mean.append(float(v1["ICOHP_mean"])) - icohp_sum.append(float(v1["ICOHP_sum"])) - bond.append(v1["bonding"]["perc"]) - antibond.append(v1["antibonding"]["perc"]) - - # add stats data as columns to the dataframe - df.loc[filename.split(".")[0], "Icohp_mean_avg"] = np.mean(icohp_mean) - df.loc[filename.split(".")[0], "Icohp_mean_max"] = np.max(icohp_mean) - df.loc[filename.split(".")[0], "Icohp_mean_min"] = np.min(icohp_mean) - df.loc[filename.split(".")[0], "Icohp_mean_std"] = np.std(icohp_mean) - - df.loc[filename.split(".")[0], "Icohp_sum_avg"] = np.mean(icohp_sum) - df.loc[filename.split(".")[0], "Icohp_sum_max"] = np.max(icohp_sum) - df.loc[filename.split(".")[0], "Icohp_sum_min"] = np.min(icohp_sum) - df.loc[filename.split(".")[0], "Icohp_sum_std"] = np.std(icohp_sum) - - df.loc[filename.split(".")[0], "bonding_perc_avg"] = np.mean(bond) - df.loc[filename.split(".")[0], "bonding_perc_max"] = np.max(bond) - df.loc[filename.split(".")[0], "bonding_perc_min"] = np.min(bond) - df.loc[filename.split(".")[0], "bonding_perc_std"] = np.std(bond) - - df.loc[filename.split(".")[0], "antibonding_perc_avg"] = np.mean(antibond) - df.loc[filename.split(".")[0], "antibonding_perc_min"] = np.min(antibond) - df.loc[filename.split(".")[0], "antibonding_perc_max"] = np.max(antibond) - df.loc[filename.split(".")[0], "antibonding_perc_std"] = np.std(antibond) - - # add madelung energies for the structure - df.loc[filename.split(".")[0], "Madelung_Mull"] = data["madelung_energies"][ - "Mulliken" - ] - df.loc[filename.split(".")[0], "Madelung_Loew"] = data["madelung_energies"][ - "Loewdin" - ] - - return df From c3fe2c6413a176d45edb6b90c943b0ffc9e789d3 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:14:59 +0200 Subject: [PATCH 08/41] update workflows --- .github/workflows/pylint.yml | 3 +-- .github/workflows/python-package.yml | 3 +-- .github/workflows/python-publish.yml | 3 +-- 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index 7aeae209..ba72ccaf 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -18,8 +18,7 @@ jobs: run: | python -m pip install --upgrade pip pip install mypy pydocstyle pylint black flake8 pyproject-flake8==6.0.0 - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install -e . + pip install -e .[featurizer] pip install types-setuptools - name: mypy run: | diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index ad34c182..99bff9af 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -30,8 +30,7 @@ jobs: python -m pip install flake8 pytest pytest-mock python -m pip install types-setuptools python -m pip install coverage - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install -e . + pip install -e .[featurizer] - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 4ddc2ac2..f4454471 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -29,8 +29,7 @@ jobs: python -m pip install flake8 pytest pytest-mock python -m pip install types-setuptools python -m pip install coverage - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install -e . + pip install -e .[featurizer] pip install build - name: Build package run: python -m build From 14d9d279256f288bbfa9d43ba8ece70dda0c67f9 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:25:23 +0200 Subject: [PATCH 09/41] fix linting errors --- lobsterpy/featurize/core.py | 16 ++++-- lobsterpy/featurize/test/test_core.py | 83 +++++++++++---------------- 2 files changed, 44 insertions(+), 55 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index b6e45cfe..87022ad8 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -69,8 +69,10 @@ def get_df(self, ids: str) -> pd.DataFrame: bond.append(v1["bonding"]["perc"]) antibond.append(v1["antibonding"]["perc"]) else: - raise Exception('There exist no data for {} for this structure. ' - 'Please switch to "all_bonds" mode'.format(self.bonds)) + raise Exception( + "There exist no data for {} for this structure. " + 'Please switch to "all_bonds" mode'.format(self.bonds) + ) # add stats data as columns to the dataframe df.loc[ids, "Icohp_mean_avg"] = np.mean(icohp_mean) @@ -175,6 +177,8 @@ def __init__( structure_file=self.path_to_structure, ) + self.coxx_fingerprint = None + def get_coxx_fingerprint_df( self, ids: str, @@ -214,7 +218,7 @@ def get_coxx_fingerprint_df( of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ - fingerprint = namedtuple( + coxx_fingerprint = namedtuple( "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" ) energies = ( @@ -289,7 +293,7 @@ def get_coxx_fingerprint_df( else: coxx_rebin_sc = coxx_rebin - fp = fingerprint( + self.coxx_fingerprint = coxx_fingerprint( np.array([ener]), coxx_rebin_sc, fp_type, @@ -299,7 +303,7 @@ def get_coxx_fingerprint_df( ) df_temp = pd.DataFrame(index=[ids], columns=["COHP_FP"]) - df_temp.at[ids, "COHP_FP"] = fp.coxx * fp.bin_width + df_temp.at[ids, "COHP_FP"] = self.coxx_fingerprint.coxx df = pd.DataFrame(df_temp["COHP_FP"].tolist()) @@ -386,7 +390,7 @@ def _calc_moment_features(self): coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] energies = self.completecoxx.energies - self.completecoxx.efermi - coxx_dict={} + coxx_dict = {} if not self.are_cobis and not self.are_coops: coxx_dict["bonding"] = np.array( [scohp if scohp <= 0 else 0 for scohp in coxx_all] diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index b5e0478d..8b90832e 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -11,17 +11,18 @@ class TestFeaturizeLobsterpy(unittest.TestCase): def setUp(self): - - self.featurize_mp1249_json = FeaturizeLobsterpy(path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", - bonds='all_bonds') - self.featurize_mp1958_json = FeaturizeLobsterpy(path_to_json=TestDir / "TestData/JSONS/mp-1958.json.gz", - bonds='all_bonds') - self.featurize_mp14652_json = FeaturizeLobsterpy(path_to_json=TestDir / "TestData/JSONS/mp-14652.json.gz", - bonds='all_bonds') - + self.featurize_mp1249_json = FeaturizeLobsterpy( + path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", bonds="all_bonds" + ) + self.featurize_mp1958_json = FeaturizeLobsterpy( + path_to_json=TestDir / "TestData/JSONS/mp-1958.json.gz", bonds="all_bonds" + ) + self.featurize_mp14652_json = FeaturizeLobsterpy( + path_to_json=TestDir / "TestData/JSONS/mp-14652.json.gz", bonds="all_bonds" + ) def test_featurize_mp1249_json(self): - df = self.featurize_mp1249_json.get_df(ids='mp-1249') + df = self.featurize_mp1249_json.get_df(ids="mp-1249") # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -50,31 +51,20 @@ def test_featurize_mp1249_json(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], 'mp-1249') + self.assertEqual(df.index[0], "mp-1249") # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["mp-1249", "Icohp_mean_avg"], -1.020000, places=5) + self.assertAlmostEqual(df.loc["mp-1249", "Icohp_mean_max"], -1.020000, places=5) + self.assertAlmostEqual(df.loc["mp-1249", "Icohp_mean_min"], -1.020000, places=5) + self.assertAlmostEqual(df.loc["mp-1249", "Icohp_mean_std"], 0.000000, places=5) + self.assertAlmostEqual(df.loc["mp-1249", "Madelung_Mull"], -52.000000, places=5) self.assertAlmostEqual( - df.loc['mp-1249', "Icohp_mean_avg"], -1.020000, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-1249', "Icohp_mean_max"], -1.020000, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-1249', "Icohp_mean_min"], -1.020000, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-1249', "Icohp_mean_std"], 0.000000, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-1249', "Madelung_Mull"], -52.000000, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-1249', "bonding_perc_avg"], 0.978985, places=5 + df.loc["mp-1249", "bonding_perc_avg"], 0.978985, places=5 ) def test_featurize_mp1958_json(self): - - df = self.featurize_mp1958_json.get_df(ids='mp-1958') + df = self.featurize_mp1958_json.get_df(ids="mp-1958") # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -103,23 +93,20 @@ def test_featurize_mp1958_json(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], 'mp-1958') + self.assertEqual(df.index[0], "mp-1958") # Test that all the values in the DataFrame - self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_avg"], -2.96000, places=5) - self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_max"], -2.96000, places=5) - self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_min"], -2.96000, places=5) - self.assertAlmostEqual(df.loc['mp-1958', "Icohp_sum_std"], 0.000000, places=5) + self.assertAlmostEqual(df.loc["mp-1958", "Icohp_sum_avg"], -2.96000, places=5) + self.assertAlmostEqual(df.loc["mp-1958", "Icohp_sum_max"], -2.96000, places=5) + self.assertAlmostEqual(df.loc["mp-1958", "Icohp_sum_min"], -2.96000, places=5) + self.assertAlmostEqual(df.loc["mp-1958", "Icohp_sum_std"], 0.000000, places=5) + self.assertAlmostEqual(df.loc["mp-1958", "Madelung_Loew"], -16.68000, places=5) self.assertAlmostEqual( - df.loc['mp-1958', "Madelung_Loew"], -16.68000, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-1958', "antibonding_perc_avg"], 0.14528, places=5 + df.loc["mp-1958", "antibonding_perc_avg"], 0.14528, places=5 ) def test_featurize_mp14652_json(self): - - df = self.featurize_mp14652_json.get_df(ids='mp-14652') + df = self.featurize_mp14652_json.get_df(ids="mp-14652") # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -148,29 +135,27 @@ def test_featurize_mp14652_json(self): self.assertCountEqual(list(df.columns), expected_cols) # Test that the DataFrame has the expected index - self.assertEqual(df.index[0], 'mp-14652') + self.assertEqual(df.index[0], "mp-14652") # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["mp-14652", "Icohp_mean_std"], 2.335070, places=5) self.assertAlmostEqual( - df.loc['mp-14652', "Icohp_mean_std"], 2.335070, places=5 - ) - self.assertAlmostEqual( - df.loc['mp-14652', "bonding_perc_max"], 0.889620, places=5 + df.loc["mp-14652", "bonding_perc_max"], 0.889620, places=5 ) self.assertAlmostEqual( - df.loc['mp-14652', "bonding_perc_min"], 0.873420, places=5 + df.loc["mp-14652", "bonding_perc_min"], 0.873420, places=5 ) self.assertAlmostEqual( - df.loc['mp-14652', "bonding_perc_std"], 0.006339, places=5 + df.loc["mp-14652", "bonding_perc_std"], 0.006339, places=5 ) self.assertAlmostEqual( - df.loc['mp-14652', "antibonding_perc_min"], 0.110380, places=5 + df.loc["mp-14652", "antibonding_perc_min"], 0.110380, places=5 ) self.assertAlmostEqual( - df.loc['mp-14652', "antibonding_perc_max"], 0.126580, places=5 + df.loc["mp-14652", "antibonding_perc_max"], 0.126580, places=5 ) self.assertAlmostEqual( - df.loc['mp-14652', "antibonding_perc_std"], 0.006339, places=5 + df.loc["mp-14652", "antibonding_perc_std"], 0.006339, places=5 ) From fc2ace0b6e8e10818024c70214460ff59b4512b6 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:35:26 +0200 Subject: [PATCH 10/41] update core.py --- lobsterpy/featurize/core.py | 109 +++++++++--------------------------- 1 file changed, 28 insertions(+), 81 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 87022ad8..7defed3b 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -10,7 +10,7 @@ import numpy as np import pandas as pd from pathlib import Path -from typing import Mapping, NamedTuple +from typing import Mapping, NamedTuple, List from collections import namedtuple from pymatgen.core.structure import Structure from mendeleev import element @@ -70,8 +70,7 @@ def get_df(self, ids: str) -> pd.DataFrame: antibond.append(v1["antibonding"]["perc"]) else: raise Exception( - "There exist no data for {} for this structure. " - 'Please switch to "all_bonds" mode'.format(self.bonds) + "There exist no data for {} for this structure. " 'Please switch to "all_bonds" mode'.format(self.bonds) ) # add stats data as columns to the dataframe @@ -218,12 +217,8 @@ def get_coxx_fingerprint_df( of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ - coxx_fingerprint = namedtuple( - "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" - ) - energies = ( - coxxcar_obj.energies - coxxcar_obj.efermi - ) # here substraction of efermi has impact on fp. + coxx_fingerprint = namedtuple("coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width") + energies = coxxcar_obj.energies - coxxcar_obj.efermi # here substraction of efermi has impact on fp. if max_e is None: max_e = np.max(energies) @@ -232,9 +227,7 @@ def get_coxx_fingerprint_df( min_e = np.min(energies) if label_list: - coxxcar_obj = coxxcar_obj.get_summed_cohp_by_label_list( - label_list - ).get_cohp() + coxxcar_obj = coxxcar_obj.get_summed_cohp_by_label_list(label_list).get_cohp() else: coxxcar_obj = coxxcar_obj.get_cohp() @@ -249,21 +242,17 @@ def get_coxx_fingerprint_df( """Check the spin_type argument, Possible options are summed/up/down""" ) - coxx_dict = {} + coxx_dict = dict() - coxx_dict["bonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] - ) - coxx_dict["antibonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] - ) + coxx_dict["bonding"] = np.array([scohp if scohp <= 0 else 0 for scohp in coxx_all]) + coxx_dict["antibonding"] = np.array([scohp if scohp >= 0 else 0 for scohp in coxx_all]) coxx_dict["overall"] = coxx_all try: coxxs = coxx_dict[fp_type] if len(energies) < n_bins: inds = np.where((energies >= min_e) & (energies <= max_e)) - return fingerprint( + return coxx_fingerprint( energies[inds], coxxs[inds], fp_type, @@ -332,23 +321,15 @@ def _calculate_wicoxx_ein(self): icoxx_dict = self.icoxxlist.icohpcollection.as_dict() list_labels = icoxx_dict["list_labels"] # Compute sum of icohps - icoxx_total = self.icoxxlist.icohpcollection.get_summed_icohp_by_label_list( - list_labels - ) + icoxx_total = self.icoxxlist.icohpcollection.get_summed_icohp_by_label_list(list_labels) summed_weighted_coxx = [] for lab in list_labels: - for k, v in self.completecoxx.get_cohp_by_label( - "{}".format(lab), summed_spin_channels=True - ).cohp.items(): + for k, v in self.completecoxx.get_cohp_by_label("{}".format(lab), summed_spin_channels=True).cohp.items(): coxx = v - icoxx = self.icoxxlist.icohpcollection.get_icohp_by_label( - lab, summed_spin_channels=True - ) - weight = ( - icoxx / icoxx_total - ) # calculate the weights based on icohp contri to total icohp of the structure + icoxx = self.icoxxlist.icohpcollection.get_icohp_by_label(lab, summed_spin_channels=True) + weight = icoxx / icoxx_total # calculate the weights based on icohp contri to total icohp of the structure weighted_coxx = weight * coxx summed_weighted_coxx.append(weighted_coxx) @@ -362,26 +343,20 @@ def _calculate_wicoxx_ein(self): bonding_indices = coxx_bf <= 0 antibonding_indices = coxx_bf >= 0 bnd = abs(trapezoid(en_bf[bonding_indices], coxx_bf[bonding_indices])) - antibnd = abs( - trapezoid(en_bf[antibonding_indices], coxx_bf[antibonding_indices]) - ) + antibnd = abs(trapezoid(en_bf[antibonding_indices], coxx_bf[antibonding_indices])) per_bnd = (bnd / (bnd + antibnd)) * 100 per_antibnd = (antibnd / (bnd + antibnd)) * 100 elif self.icoxxlist.are_cobis or self.icoxxlist.are_coops: bonding_indices = coxx_bf >= 0 antibonding_indices = coxx_bf <= 0 bnd = abs(trapezoid(coxx_bf[bonding_indices], en_bf[bonding_indices])) - antibnd = abs( - trapezoid(coxx_bf[antibonding_indices], en_bf[antibonding_indices]) - ) + antibnd = abs(trapezoid(coxx_bf[antibonding_indices], en_bf[antibonding_indices])) per_bnd = (bnd / (bnd + antibnd)) * 100 per_antibnd = (antibnd / (bnd + antibnd)) * 100 w_icoxx = trapezoid(coxx_bf, en_bf) - ein = (icoxx_total / w_icoxx) * ( - 2 / self.completecoxx.structure.num_sites - ) # calc effective interaction number + ein = (icoxx_total / w_icoxx) * (2 / self.completecoxx.structure.num_sites) # calc effective interaction number return per_bnd, per_antibnd, w_icoxx, ein @@ -392,20 +367,12 @@ def _calc_moment_features(self): coxx_dict = {} if not self.are_cobis and not self.are_coops: - coxx_dict["bonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] - ) - coxx_dict["antibonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] - ) + coxx_dict["bonding"] = np.array([scohp if scohp <= 0 else 0 for scohp in coxx_all]) + coxx_dict["antibonding"] = np.array([scohp if scohp >= 0 else 0 for scohp in coxx_all]) coxx_dict["overall"] = coxx_all else: - coxx_dict["antibonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] - ) - coxx_dict["bonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] - ) + coxx_dict["antibonding"] = np.array([scohp if scohp <= 0 else 0 for scohp in coxx_all]) + coxx_dict["bonding"] = np.array([scohp if scohp >= 0 else 0 for scohp in coxx_all]) coxx_dict["overall"] = coxx_all band_center = self._get_band_center( @@ -443,15 +410,11 @@ def _get_band_center(self, cohp: list, energies: list, e_range: list) -> float: Returns: Orbital-Orbital interaction band center in eV """ - band_center = self._get_n_moment( - n=1, cohp=cohp, energies=energies, e_range=e_range, center=False - ) + band_center = self._get_n_moment(n=1, cohp=cohp, energies=energies, e_range=e_range, center=False) return band_center - def _get_n_moment( - self, n: int, cohp: list, energies: list, e_range: list, center: bool = True - ) -> float: + def _get_n_moment(self, n: int, cohp: list, energies: list, e_range: list, center: bool = True) -> float: """ Get the nth moment of COXX @@ -467,14 +430,10 @@ def _get_n_moment( """ if e_range: cohp = cohp[(energies >= self.e_range[0]) & (energies <= self.e_range[1])] - energies = energies[ - (energies >= self.e_range[0]) & (energies <= self.e_range[1]) - ] + energies = energies[(energies >= self.e_range[0]) & (energies <= self.e_range[1])] if center: - band_center = self._get_band_center( - cohp=cohp, energies=energies, e_range=self.e_range - ) + band_center = self._get_band_center(cohp=cohp, energies=energies, e_range=self.e_range) p = energies - band_center else: p = energies @@ -581,20 +540,14 @@ def _calc_ionicity(self) -> float: elif self.charge_type.lower() == "loewdin": charges = chargeobj.Loewdin else: - raise ValueError( - "Please check the requested charge_type, " - 'Possible options are "Mulliken" or "Loewdin"' - ) + raise ValueError("Please check the requested charge_type, " 'Possible options are "Mulliken" or "Loewdin"') ch_veff = [] for i, j in enumerate(charges): if ( j > 0 and not structure.species[i].is_transition_metal - and ( - not structure.species[i].is_actinoid - and not structure.species[i].is_lanthanoid - ) + and (not structure.species[i].is_actinoid and not structure.species[i].is_lanthanoid) ): valence_elec = element(structure.species[i].value) val = j / (valence_elec.nvalence() - 0) @@ -603,10 +556,7 @@ def _calc_ionicity(self) -> float: elif ( j > 0 and not structure.species[i].is_transition_metal - and ( - not structure.species[i].is_actinoid - and not structure.species[i].is_lanthanoid - ) + and (not structure.species[i].is_actinoid and not structure.species[i].is_lanthanoid) ): valence_elec = element(structure.species[i].value) val = j / (valence_elec.nvalence() - 8) @@ -615,10 +565,7 @@ def _calc_ionicity(self) -> float: elif ( j < 0 and not structure.species[i].is_transition_metal - and ( - not structure.species[i].is_actinoid - and not structure.species[i].is_lanthanoid - ) + and (not structure.species[i].is_actinoid and not structure.species[i].is_lanthanoid) ): valence_elec = element(structure.species[i].value) val = j / (valence_elec.nvalence() - 8) From c63a2991dd73b8ffe6b6cffd21ce370ded0b73f1 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 20:53:54 +0200 Subject: [PATCH 11/41] mypy error fix --- lobsterpy/featurize/core.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 7defed3b..9e07c6a6 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -176,12 +176,12 @@ def __init__( structure_file=self.path_to_structure, ) - self.coxx_fingerprint = None def get_coxx_fingerprint_df( self, ids: str, fp_type: str, + label_list: list, coxxcar_obj: CompleteCohp, spin_type: str = "summed", binning: bool = True, @@ -189,7 +189,6 @@ def get_coxx_fingerprint_df( max_e: float = 0, n_bins: int = 50, normalize: bool = True, - label_list: list = None, ): """ Generates the COHP fingerprint @@ -282,7 +281,7 @@ def get_coxx_fingerprint_df( else: coxx_rebin_sc = coxx_rebin - self.coxx_fingerprint = coxx_fingerprint( + fp = coxx_fingerprint( np.array([ener]), coxx_rebin_sc, fp_type, @@ -292,7 +291,7 @@ def get_coxx_fingerprint_df( ) df_temp = pd.DataFrame(index=[ids], columns=["COHP_FP"]) - df_temp.at[ids, "COHP_FP"] = self.coxx_fingerprint.coxx + df_temp.at[ids, "COHP_FP"] = fp.coxx df = pd.DataFrame(df_temp["COHP_FP"].tolist()) @@ -401,7 +400,7 @@ def _calc_moment_features(self): return band_center, band_width, band_skew, band_kurt - def _get_band_center(self, cohp: list, energies: list, e_range: list) -> float: + def _get_band_center(self, cohp: List[float], energies: List[float], e_range: List[int]) -> float: """ Get the band width, defined as the first moment of the orbital resolved COHP Args: From 9ad2a29ffc1fd052f2974bcf97fc6e9e06ec3f30 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 21:00:09 +0200 Subject: [PATCH 12/41] more mypy error fixes --- lobsterpy/featurize/core.py | 108 +++++++++++++++++++++++++++--------- 1 file changed, 81 insertions(+), 27 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 9e07c6a6..4aa17872 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -70,7 +70,8 @@ def get_df(self, ids: str) -> pd.DataFrame: antibond.append(v1["antibonding"]["perc"]) else: raise Exception( - "There exist no data for {} for this structure. " 'Please switch to "all_bonds" mode'.format(self.bonds) + "There exist no data for {} for this structure. " + 'Please switch to "all_bonds" mode'.format(self.bonds) ) # add stats data as columns to the dataframe @@ -176,7 +177,6 @@ def __init__( structure_file=self.path_to_structure, ) - def get_coxx_fingerprint_df( self, ids: str, @@ -216,8 +216,12 @@ def get_coxx_fingerprint_df( of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ - coxx_fingerprint = namedtuple("coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width") - energies = coxxcar_obj.energies - coxxcar_obj.efermi # here substraction of efermi has impact on fp. + coxx_fingerprint = namedtuple( + "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" + ) + energies = ( + coxxcar_obj.energies - coxxcar_obj.efermi + ) # here substraction of efermi has impact on fp. if max_e is None: max_e = np.max(energies) @@ -226,7 +230,9 @@ def get_coxx_fingerprint_df( min_e = np.min(energies) if label_list: - coxxcar_obj = coxxcar_obj.get_summed_cohp_by_label_list(label_list).get_cohp() + coxxcar_obj = coxxcar_obj.get_summed_cohp_by_label_list( + label_list + ).get_cohp() else: coxxcar_obj = coxxcar_obj.get_cohp() @@ -243,8 +249,12 @@ def get_coxx_fingerprint_df( ) coxx_dict = dict() - coxx_dict["bonding"] = np.array([scohp if scohp <= 0 else 0 for scohp in coxx_all]) - coxx_dict["antibonding"] = np.array([scohp if scohp >= 0 else 0 for scohp in coxx_all]) + coxx_dict["bonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["antibonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) coxx_dict["overall"] = coxx_all try: @@ -320,15 +330,23 @@ def _calculate_wicoxx_ein(self): icoxx_dict = self.icoxxlist.icohpcollection.as_dict() list_labels = icoxx_dict["list_labels"] # Compute sum of icohps - icoxx_total = self.icoxxlist.icohpcollection.get_summed_icohp_by_label_list(list_labels) + icoxx_total = self.icoxxlist.icohpcollection.get_summed_icohp_by_label_list( + list_labels + ) summed_weighted_coxx = [] for lab in list_labels: - for k, v in self.completecoxx.get_cohp_by_label("{}".format(lab), summed_spin_channels=True).cohp.items(): + for k, v in self.completecoxx.get_cohp_by_label( + "{}".format(lab), summed_spin_channels=True + ).cohp.items(): coxx = v - icoxx = self.icoxxlist.icohpcollection.get_icohp_by_label(lab, summed_spin_channels=True) - weight = icoxx / icoxx_total # calculate the weights based on icohp contri to total icohp of the structure + icoxx = self.icoxxlist.icohpcollection.get_icohp_by_label( + lab, summed_spin_channels=True + ) + weight = ( + icoxx / icoxx_total + ) # calculate the weights based on icohp contri to total icohp of the structure weighted_coxx = weight * coxx summed_weighted_coxx.append(weighted_coxx) @@ -342,20 +360,26 @@ def _calculate_wicoxx_ein(self): bonding_indices = coxx_bf <= 0 antibonding_indices = coxx_bf >= 0 bnd = abs(trapezoid(en_bf[bonding_indices], coxx_bf[bonding_indices])) - antibnd = abs(trapezoid(en_bf[antibonding_indices], coxx_bf[antibonding_indices])) + antibnd = abs( + trapezoid(en_bf[antibonding_indices], coxx_bf[antibonding_indices]) + ) per_bnd = (bnd / (bnd + antibnd)) * 100 per_antibnd = (antibnd / (bnd + antibnd)) * 100 elif self.icoxxlist.are_cobis or self.icoxxlist.are_coops: bonding_indices = coxx_bf >= 0 antibonding_indices = coxx_bf <= 0 bnd = abs(trapezoid(coxx_bf[bonding_indices], en_bf[bonding_indices])) - antibnd = abs(trapezoid(coxx_bf[antibonding_indices], en_bf[antibonding_indices])) + antibnd = abs( + trapezoid(coxx_bf[antibonding_indices], en_bf[antibonding_indices]) + ) per_bnd = (bnd / (bnd + antibnd)) * 100 per_antibnd = (antibnd / (bnd + antibnd)) * 100 w_icoxx = trapezoid(coxx_bf, en_bf) - ein = (icoxx_total / w_icoxx) * (2 / self.completecoxx.structure.num_sites) # calc effective interaction number + ein = (icoxx_total / w_icoxx) * ( + 2 / self.completecoxx.structure.num_sites + ) # calc effective interaction number return per_bnd, per_antibnd, w_icoxx, ein @@ -366,12 +390,20 @@ def _calc_moment_features(self): coxx_dict = {} if not self.are_cobis and not self.are_coops: - coxx_dict["bonding"] = np.array([scohp if scohp <= 0 else 0 for scohp in coxx_all]) - coxx_dict["antibonding"] = np.array([scohp if scohp >= 0 else 0 for scohp in coxx_all]) + coxx_dict["bonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["antibonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) coxx_dict["overall"] = coxx_all else: - coxx_dict["antibonding"] = np.array([scohp if scohp <= 0 else 0 for scohp in coxx_all]) - coxx_dict["bonding"] = np.array([scohp if scohp >= 0 else 0 for scohp in coxx_all]) + coxx_dict["antibonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["bonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) coxx_dict["overall"] = coxx_all band_center = self._get_band_center( @@ -400,7 +432,9 @@ def _calc_moment_features(self): return band_center, band_width, band_skew, band_kurt - def _get_band_center(self, cohp: List[float], energies: List[float], e_range: List[int]) -> float: + def _get_band_center( + self, cohp: List[float], energies: List[float], e_range: List[int] + ) -> float: """ Get the band width, defined as the first moment of the orbital resolved COHP Args: @@ -409,11 +443,15 @@ def _get_band_center(self, cohp: List[float], energies: List[float], e_range: Li Returns: Orbital-Orbital interaction band center in eV """ - band_center = self._get_n_moment(n=1, cohp=cohp, energies=energies, e_range=e_range, center=False) + band_center = self._get_n_moment( + n=1, cohp=cohp, energies=energies, e_range=e_range, center=False + ) return band_center - def _get_n_moment(self, n: int, cohp: list, energies: list, e_range: list, center: bool = True) -> float: + def _get_n_moment( + self, n: int, cohp: List[float], energies: List[float], e_range: List[int], center: bool = True + ) -> float: """ Get the nth moment of COXX @@ -429,10 +467,14 @@ def _get_n_moment(self, n: int, cohp: list, energies: list, e_range: list, cente """ if e_range: cohp = cohp[(energies >= self.e_range[0]) & (energies <= self.e_range[1])] - energies = energies[(energies >= self.e_range[0]) & (energies <= self.e_range[1])] + energies = energies[ + (energies >= self.e_range[0]) & (energies <= self.e_range[1]) + ] if center: - band_center = self._get_band_center(cohp=cohp, energies=energies, e_range=self.e_range) + band_center = self._get_band_center( + cohp=cohp, energies=energies, e_range=self.e_range + ) p = energies - band_center else: p = energies @@ -539,14 +581,20 @@ def _calc_ionicity(self) -> float: elif self.charge_type.lower() == "loewdin": charges = chargeobj.Loewdin else: - raise ValueError("Please check the requested charge_type, " 'Possible options are "Mulliken" or "Loewdin"') + raise ValueError( + "Please check the requested charge_type, " + 'Possible options are "Mulliken" or "Loewdin"' + ) ch_veff = [] for i, j in enumerate(charges): if ( j > 0 and not structure.species[i].is_transition_metal - and (not structure.species[i].is_actinoid and not structure.species[i].is_lanthanoid) + and ( + not structure.species[i].is_actinoid + and not structure.species[i].is_lanthanoid + ) ): valence_elec = element(structure.species[i].value) val = j / (valence_elec.nvalence() - 0) @@ -555,7 +603,10 @@ def _calc_ionicity(self) -> float: elif ( j > 0 and not structure.species[i].is_transition_metal - and (not structure.species[i].is_actinoid and not structure.species[i].is_lanthanoid) + and ( + not structure.species[i].is_actinoid + and not structure.species[i].is_lanthanoid + ) ): valence_elec = element(structure.species[i].value) val = j / (valence_elec.nvalence() - 8) @@ -564,7 +615,10 @@ def _calc_ionicity(self) -> float: elif ( j < 0 and not structure.species[i].is_transition_metal - and (not structure.species[i].is_actinoid and not structure.species[i].is_lanthanoid) + and ( + not structure.species[i].is_actinoid + and not structure.species[i].is_lanthanoid + ) ): valence_elec = element(structure.species[i].value) val = j / (valence_elec.nvalence() - 8) From 12204dae523860c0d774b6ed3da8d70a6aaaafdf Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 21:13:29 +0200 Subject: [PATCH 13/41] fix mypy variable assignment error --- lobsterpy/featurize/core.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 4aa17872..38045559 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -450,7 +450,12 @@ def _get_band_center( return band_center def _get_n_moment( - self, n: int, cohp: List[float], energies: List[float], e_range: List[int], center: bool = True + self, + n: float, + cohp: List[float], + energies: List[float], + e_range: List[int], + center: bool = True, ) -> float: """ @@ -475,7 +480,7 @@ def _get_n_moment( band_center = self._get_band_center( cohp=cohp, energies=energies, e_range=self.e_range ) - p = energies - band_center + p = [en - band_center for en in energies] else: p = energies From ded6dd2882e829360689f0e219b682476d048cac Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 21:29:26 +0200 Subject: [PATCH 14/41] attempt error ignore temporary --- lobsterpy/featurize/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 38045559..84e2dd5b 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -10,7 +10,7 @@ import numpy as np import pandas as pd from pathlib import Path -from typing import Mapping, NamedTuple, List +from typing import Mapping, NamedTuple, List, Any from collections import namedtuple from pymatgen.core.structure import Structure from mendeleev import element @@ -484,7 +484,7 @@ def _get_n_moment( else: p = energies - nth_moment = np.trapz(p**n * cohp, x=energies) / np.trapz(cohp, x=energies) + nth_moment = np.trapz(p**n * cohp, x=energies) / np.trapz(cohp, x=energies) # type: ignore return nth_moment From b3e675bc4511a45ec0666f80914b11483dea7fb8 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 21:44:46 +0200 Subject: [PATCH 15/41] fix mypy,pydocstyle errors for featurizer module --- lobsterpy/featurize/core.py | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 84e2dd5b..96806c81 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -7,13 +7,13 @@ import gzip import json -import numpy as np -import pandas as pd from pathlib import Path from typing import Mapping, NamedTuple, List, Any from collections import namedtuple -from pymatgen.core.structure import Structure +import numpy as np +import pandas as pd from mendeleev import element +from pymatgen.core.structure import Structure from pymatgen.io.lobster import Charge, Lobsterout, Icohplist from pymatgen.electronic_structure.cohp import CompleteCohp from pymatgen.electronic_structure.core import Spin @@ -121,7 +121,6 @@ def _read_lobster_lightweight_json(self): class FeaturizeCOXX: - """ class to featurize COHPCAR/COBICAR/COOPCAR data as fingerprint @@ -129,12 +128,14 @@ class to featurize COHPCAR/COBICAR/COOPCAR data as fingerprint path_to_coxxcar: path to COXXCAR.lobster (e.g., "COXXCAR.lobster") path_to_icoxxlist : path to ICOXXLIST.lobster (e.g., "ICOXXLIST.lobster") path_to_structure : path to structure file (e.g., "POSCAR") - feature_type: set the feature type for moment features and fingerprints. Possible options are "bonding", "antibonding" or "overall" + feature_type: set the feature type for moment features and fingerprints. + Possible options are "bonding", "antibonding" or "overall" are_cobis : bool indicating if file contains COBI/ICOBI data are_coops : bool indicating if file contains COOP/ICOOP data include_wicoxx : if True will include weighted ICOXX, effective co-ordination number in the featurized dataframe - include_moment_features : if True will include ICOXX center,width, skew and kurtosis features in the featurized dataframe + include_moment_features : if True will include ICOXX center,width, skew and kurtosis features + in the featurized dataframe e_range : range of energy relative to fermi for which moment features needs to be computed Attributes: @@ -215,7 +216,6 @@ def get_coxx_fingerprint_df( Fingerprint(namedtuple) : The COHP fingerprint of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ - coxx_fingerprint = namedtuple( "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" ) @@ -247,7 +247,7 @@ def get_coxx_fingerprint_df( """Check the spin_type argument, Possible options are summed/up/down""" ) - coxx_dict = dict() + coxx_dict = {} coxx_dict["bonding"] = np.array( [scohp if scohp <= 0 else 0 for scohp in coxx_all] @@ -484,7 +484,7 @@ def _get_n_moment( else: p = energies - nth_moment = np.trapz(p**n * cohp, x=energies) / np.trapz(cohp, x=energies) # type: ignore + nth_moment = np.trapz(p**n * cohp, x=energies) / np.trapz(cohp, x=energies) # type: ignore return nth_moment @@ -497,7 +497,6 @@ def get_df(self, ids: str): Returns a pandas dataframe with cohp/cobi/coop related features """ - df = pd.DataFrame(index=[ids]) if self.include_wicoxx: @@ -577,7 +576,6 @@ def _calc_ionicity(self) -> float: Ionicity of the structure """ - chargeobj = Charge(filename=self.path_to_charge) structure = Structure.from_file(self.path_to_structure) @@ -657,7 +655,6 @@ def get_df(self, ids): Returns a pandas dataframe with ionicity """ - df = pd.DataFrame(index=[ids]) if self.charge_type == "mulliken": From 2a2b8e437b20bfb35bea1ce5acee9096c56a2a32 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 27 Apr 2023 21:49:48 +0200 Subject: [PATCH 16/41] update __init__.py --- lobsterpy/featurize/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/lobsterpy/featurize/__init__.py b/lobsterpy/featurize/__init__.py index 7284c0ad..67a30e86 100644 --- a/lobsterpy/featurize/__init__.py +++ b/lobsterpy/featurize/__init__.py @@ -4,4 +4,3 @@ """ This package provides the modules for featurzing Lobster data ready for ML """ - From f0e0d1427cc62d1673542aaec0a23661529b3abe Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 28 Apr 2023 17:29:21 +0200 Subject: [PATCH 17/41] refactor featurizer module, added summary.py and batch.py module, added coxx featurizer test --- lobsterpy/featurize/batch.py | 130 +++++++++++++++ lobsterpy/featurize/core.py | 227 ++++++++++++++------------ lobsterpy/featurize/summary.py | 99 +++++++++++ lobsterpy/featurize/test/test_core.py | 166 ++++++++++++++++++- 4 files changed, 518 insertions(+), 104 deletions(-) create mode 100644 lobsterpy/featurize/batch.py create mode 100644 lobsterpy/featurize/summary.py diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py new file mode 100644 index 00000000..423906cf --- /dev/null +++ b/lobsterpy/featurize/batch.py @@ -0,0 +1,130 @@ +# Copyright (c) lobsterpy development team +# Distributed under the terms of a BSD 3-Clause "New" or "Revised" License + +""" +This module defines wrapper classes to quickly obtain similarity matrix of input fingerprint objects +""" +from __future__ import annotations +from typing import NamedTuple +import numpy as np +import pandas as pd + + +class FingperintSimilarityMatrix: + """ + Featurizer that generates Tanimoto index similarity matrix summary features from fingerprint objects. + """ + + def __init__( + self, + fingerpint_df: pd.DataFrame, + tanimoto: bool = True, + normalize: bool = False, + ): + self.fingerpint_df = fingerpint_df + self.tanimoto = tanimoto + self.normalize = normalize + + def get_similarity_matrix_df(self): + """ + This function will compute pairwise similarity index for each fingerprint object in input dataframe + + Returns: + A Pandas dataframe + """ + matrix = np.full( + (self.fingerpint_df.shape[0], self.fingerpint_df.shape[0]), np.nan + ) + for i, (row, col) in enumerate(self.fingerpint_df.iterrows()): + for j, (row1, col1) in enumerate(self.fingerpint_df.iterrows()): + if i <= j: + simi = self._get_fp_similarity( + col["COXX_FP"], + col1["COXX_FP"], + tanimoto=self.tanimoto, + normalize=self.normalize, + ) + matrix[i][j] = simi + + df = pd.DataFrame( + matrix, + index=list(self.fingerpint_df.index), + columns=list(self.fingerpint_df.index), + ) + + return df + + @staticmethod + def _fp_to_dict(fp) -> dict: + """Converts a fingerprint into a dictionary + Args: + fp: The fingerprint to be converted into a dictionary + Returns: + dict: A dict of the fingerprint Keys=type, Values=np.ndarray(energies, cohp) + """ + fp_dict = {} + fp_dict[fp[2]] = np.array([fp[0], fp[1]], dtype="object").T + + return fp_dict + + @staticmethod + def _get_fp_similarity( + fp1, + fp2, + col: int = 1, + pt: int | str = "All", + normalize: bool = False, + tanimoto: bool = False, + ) -> float: + """Calculates the similarity index (dot product) of two fingerprints + Args: + fp1 (NamedTuple): The 1st dos fingerprint object + fp2 (NamedTuple): The 2nd dos fingerprint object + col (int): The item in the fingerprints (0:energies,1: coxxs) to take the dot product of (default is 1) + pt (int or str) : The index of the point that the dot product is to be taken (default is All) + normalize (bool): If True normalize the scalar product to 1 (default is False) + tanimoto (bool): If True will compute Tanimoto index (default is False) + Raises: + ValueError: If both tanimoto and normalize are set to True. + Returns: + Similarity index (float): The value of dot product + """ + fp1_dict = ( + FingperintSimilarityMatrix._fp_to_dict(fp1) + if not isinstance(fp1, dict) + else fp1 + ) + + fp2_dict = ( + FingperintSimilarityMatrix._fp_to_dict(fp2) + if not isinstance(fp2, dict) + else fp2 + ) + + if pt == "All": + vec1 = np.array([pt[col] for pt in fp1_dict.values()]).flatten() + vec2 = np.array([pt[col] for pt in fp2_dict.values()]).flatten() + else: + vec1 = fp1_dict[fp1[2][pt]][col] + vec2 = fp2_dict[fp2[2][pt]][col] + + if not normalize and tanimoto: + rescale = ( + np.linalg.norm(vec1) ** 2 + + np.linalg.norm(vec2) ** 2 + - np.dot(vec1, vec2) + ) + return np.dot(vec1, vec2) / rescale + + elif not tanimoto and normalize: + rescale = np.linalg.norm(vec1) * np.linalg.norm(vec2) + return np.dot(vec1, vec2) / rescale + + elif not tanimoto and not normalize: + rescale = 1.0 + return np.dot(vec1, vec2) / rescale + + else: + raise ValueError( + "Cannot compute similarity index. Please set either normalize=True or tanimoto=True or both to False." + ) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 96806c81..f5dbffc4 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -2,15 +2,17 @@ # Distributed under the terms of a BSD 3-Clause "New" or "Revised" License """ -This module defines functions to featurize Lobster lighweight jsons +This module defines classes to featurize Lobster data ready to be used for ML studies """ +from __future__ import annotations import gzip import json from pathlib import Path -from typing import Mapping, NamedTuple, List, Any +from typing import Mapping, NamedTuple, List from collections import namedtuple import numpy as np +import numpy.typing as npt import pandas as pd from mendeleev import element from pymatgen.core.structure import Structure @@ -132,10 +134,6 @@ class to featurize COHPCAR/COBICAR/COOPCAR data as fingerprint Possible options are "bonding", "antibonding" or "overall" are_cobis : bool indicating if file contains COBI/ICOBI data are_coops : bool indicating if file contains COOP/ICOOP data - include_wicoxx : if True will include weighted ICOXX, effective co-ordination number - in the featurized dataframe - include_moment_features : if True will include ICOXX center,width, skew and kurtosis features - in the featurized dataframe e_range : range of energy relative to fermi for which moment features needs to be computed Attributes: @@ -150,18 +148,14 @@ def __init__( path_to_icoxxlist: str, path_to_structure: str, feature_type: str, + e_range: List[float] = [-10.0, 0.0], are_cobis: bool = False, are_coops: bool = False, - include_wicoxx: bool = False, - include_moment_features: bool = False, - e_range: list = [-5, 0], ): self.path_to_coxxcar = path_to_coxxcar self.path_to_icoxxlist = path_to_icoxxlist self.path_to_structure = path_to_structure self.feature_type = feature_type - self.include_wicoxx = include_wicoxx - self.include_moment_features = include_moment_features self.e_range = e_range self.are_cobis = are_cobis self.are_coops = are_coops @@ -181,13 +175,9 @@ def __init__( def get_coxx_fingerprint_df( self, ids: str, - fp_type: str, - label_list: list, - coxxcar_obj: CompleteCohp, + label_list: List[str] | None = None, spin_type: str = "summed", binning: bool = True, - min_e: float = -5, - max_e: float = 0, n_bins: int = 50, normalize: bool = True, ): @@ -199,23 +189,19 @@ def get_coxx_fingerprint_df( spin_type: Specify spin type. Can accept '{summed/up/down}' (default is summed) binning: If true cohps will be binned - min_e: The minimum mode energy to include in the fingerprint (default is None) - max_e: The maximum mode energy to include in the fingerprint (default is None) n_bins: Number of bins to be used in the fingerprint (default is 256) normalize: If true, normalizes the area under fp to equal to 1 (default is True) - fp_type: Specify fingerprint type needed can accept '{bonding/antibonding/overall}' - (default is antibonding) label_list: Specify bond lables as a list for which cohp fingerprints are needed - coxxcar_obj: coxxcar object Raises: Exception: If spin_type is not one of the accepted values {summed/up/down}. - ValueError: If fp_type is not one of the accepted values {bonding/antibonding/overall}. + ValueError: If feature_type is not one of the accepted values {bonding/antibonding/overall}. Returns: Fingerprint(namedtuple) : The COHP fingerprint of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ + coxxcar_obj = self.completecoxx coxx_fingerprint = namedtuple( "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" ) @@ -223,6 +209,9 @@ def get_coxx_fingerprint_df( coxxcar_obj.energies - coxxcar_obj.efermi ) # here substraction of efermi has impact on fp. + min_e = self.e_range[0] + max_e = self.e_range[-1] + if max_e is None: max_e = np.max(energies) @@ -258,13 +247,13 @@ def get_coxx_fingerprint_df( coxx_dict["overall"] = coxx_all try: - coxxs = coxx_dict[fp_type] + coxxs = coxx_dict[self.feature_type] if len(energies) < n_bins: inds = np.where((energies >= min_e) & (energies <= max_e)) return coxx_fingerprint( energies[inds], coxxs[inds], - fp_type, + self.feature_type, spin_type, len(energies), np.diff(energies)[0], @@ -294,16 +283,15 @@ def get_coxx_fingerprint_df( fp = coxx_fingerprint( np.array([ener]), coxx_rebin_sc, - fp_type, + self.feature_type, spin_type, n_bins, bin_width, ) - df_temp = pd.DataFrame(index=[ids], columns=["COHP_FP"]) - df_temp.at[ids, "COHP_FP"] = fp.coxx + df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) - df = pd.DataFrame(df_temp["COHP_FP"].tolist()) + df.at[ids, "COXX_FP"] = fp return df @@ -383,10 +371,23 @@ def _calculate_wicoxx_ein(self): return per_bnd, per_antibnd, w_icoxx, ein - def _calc_moment_features(self): - coxxcar = self.completecoxx.get_cohp() - coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] - energies = self.completecoxx.energies - self.completecoxx.efermi + def _calc_moment_features(self, label_list: List[str] | None = None): + if label_list: + coxxcar = self.completecoxx.get_summed_cohp_by_label_list( + label_list + ).get_cohp() + try: + coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] + except KeyError: + coxx_all = coxxcar[Spin.up] + energies = self.completecoxx.energies - self.completecoxx.efermi + else: + coxxcar = self.completecoxx.get_cohp() + try: + coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] + except KeyError: + coxx_all = coxxcar[Spin.up] + energies = self.completecoxx.energies - self.completecoxx.efermi coxx_dict = {} if not self.are_cobis and not self.are_coops: @@ -406,34 +407,56 @@ def _calc_moment_features(self): ) coxx_dict["overall"] = coxx_all - band_center = self._get_band_center( - cohp=coxx_dict[self.feature_type], + coxx_center = self._get_coxx_center( + coxx=coxx_dict[self.feature_type], energies=energies, e_range=self.e_range, ) - band_width = self._get_n_moment( - n=2, - cohp=coxx_dict[self.feature_type], - energies=energies, - e_range=self.e_range, + coxx_width = np.sqrt( + self._get_n_moment( + n=2, + coxx=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) ) - band_skew = self._get_n_moment( + coxx_skew = self._get_n_moment( n=3, - cohp=coxx_dict[self.feature_type], + coxx=coxx_dict[self.feature_type], energies=energies, e_range=self.e_range, - ) - band_kurt = self._get_n_moment( - n=4, - cohp=coxx_dict[self.feature_type], + ) / self._get_n_moment( + n=2, + coxx=coxx_dict[self.feature_type], energies=energies, e_range=self.e_range, + ) ** ( + 3 / 2 + ) + + coxx_kurt = ( + self._get_n_moment( + n=4, + coxx=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + / self._get_n_moment( + n=2, + coxx=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + ** 2 ) - return band_center, band_width, band_skew, band_kurt + return coxx_center, coxx_width, coxx_skew, coxx_kurt - def _get_band_center( - self, cohp: List[float], energies: List[float], e_range: List[int] + def _get_coxx_center( + self, + coxx: npt.NDArray[np.floating], + energies: npt.NDArray[np.floating], + e_range: List[float], ) -> float: """ Get the band width, defined as the first moment of the orbital resolved COHP @@ -443,18 +466,18 @@ def _get_band_center( Returns: Orbital-Orbital interaction band center in eV """ - band_center = self._get_n_moment( - n=1, cohp=cohp, energies=energies, e_range=e_range, center=False + coxx_center = self._get_n_moment( + n=1, coxx=coxx, energies=energies, e_range=e_range, center=False ) - return band_center + return coxx_center def _get_n_moment( self, n: float, - cohp: List[float], - energies: List[float], - e_range: List[int], + coxx: npt.NDArray[np.floating], + energies: npt.NDArray[np.floating], + e_range: list[float] | None, center: bool = True, ) -> float: """ @@ -471,73 +494,71 @@ def _get_n_moment( COXX nth moment in eV """ if e_range: - cohp = cohp[(energies >= self.e_range[0]) & (energies <= self.e_range[1])] + coxx = coxx[(energies >= self.e_range[0]) & (energies <= self.e_range[-1])] energies = energies[ - (energies >= self.e_range[0]) & (energies <= self.e_range[1]) + (energies >= self.e_range[0]) & (energies <= self.e_range[-1]) ] if center: - band_center = self._get_band_center( - cohp=cohp, energies=energies, e_range=self.e_range + coxx_center = self._get_coxx_center( + coxx=coxx, energies=energies, e_range=self.e_range ) - p = [en - band_center for en in energies] + p = energies - coxx_center else: p = energies - nth_moment = np.trapz(p**n * cohp, x=energies) / np.trapz(cohp, x=energies) # type: ignore + nth_moment = np.trapz(p**n * coxx, x=energies) / np.trapz(coxx, x=energies) # type: ignore return nth_moment - def get_df(self, ids: str): + def get_summarized_coxx_df(self, ids: str, label_list: List[str] | None = None): """ This function returns a pandas dataframe with weighted ICOXX, effective interaction number and moment features (center, width, skewness and kurtosis) of COXX in selected energy range Returns: - Returns a pandas dataframe with cohp/cobi/coop related features + Returns a pandas dataframe with cohp/cobi/coop related features as per input file """ df = pd.DataFrame(index=[ids]) - if self.include_wicoxx: - ( - per_bnd_xx, - per_antibnd_xx, - w_icoxx, - ein_xx, - ) = self._calculate_wicoxx_ein() - - if self.are_coops: - bc, bw, bs, bk = self._calc_moment_features() - df.loc[ids, "bnd_ICOOP"] = per_bnd_xx - df.loc[ids, "antibnd_ICOOP"] = per_antibnd_xx - df.loc[ids, "w_ICOOP"] = w_icoxx - df.loc[ids, "EIN_ICOOP"] = ein_xx - df.loc[ids, "band_center_COOP"] = bc - df.loc[ids, "band_width_COOP"] = bw - df.loc[ids, "band_skewness_COOP"] = bs - df.loc[ids, "band_kurtosis_COOP"] = bk - elif self.are_cobis: - bc, bw, bs, bk = self._calc_moment_features() - df.loc[ids, "bnd_ICOOP"] = per_bnd_xx - df.loc[ids, "bnd_ICOBI"] = per_bnd_xx - df.loc[ids, "antibnd_ICOBI"] = per_antibnd_xx - df.loc[ids, "w_ICOBI"] = w_icoxx - df.loc[ids, "EIN_ICOBI"] = ein_xx - df.loc[ids, "band_center_COBi"] = bc - df.loc[ids, "band_width_COBI"] = bw - df.loc[ids, "band_skewness_COBI"] = bs - df.loc[ids, "band_kurtosis_COBI"] = bk - else: - bc, bw, bs, bk = self._calc_moment_features() - df.loc[ids, "bnd_ICOHP"] = per_bnd_xx - df.loc[ids, "antibnd_ICOHP"] = per_antibnd_xx - df.loc[ids, "w_ICOHP"] = w_icoxx - df.loc[ids, "EIN_ICOHP"] = ein_xx - df.loc[ids, "band_center_COHP"] = bc - df.loc[ids, "band_width_COHP"] = bw - df.loc[ids, "band_skewness_COHP"] = bs - df.loc[ids, "band_kurtosis_COHP"] = bk + ( + per_bnd_xx, + per_antibnd_xx, + w_icoxx, + ein_xx, + ) = self._calculate_wicoxx_ein() + + if self.are_coops: + cc, cw, cs, ck = self._calc_moment_features(label_list=label_list) + df.loc[ids, "bnd_wICOOP"] = per_bnd_xx + df.loc[ids, "antibnd_wICOOP"] = per_antibnd_xx + df.loc[ids, "w_ICOOP"] = w_icoxx + df.loc[ids, "EIN_ICOOP"] = ein_xx + df.loc[ids, "center_COOP"] = cc + df.loc[ids, "width_COOP"] = cw + df.loc[ids, "skewness_COOP"] = cs + df.loc[ids, "kurtosis_COOP"] = ck + elif self.are_cobis: + cc, cw, cs, ck = self._calc_moment_features(label_list=label_list) + df.loc[ids, "bnd_wICOBI"] = per_bnd_xx + df.loc[ids, "antibnd_wICOBI"] = per_antibnd_xx + df.loc[ids, "w_ICOBI"] = w_icoxx + df.loc[ids, "EIN_ICOBI"] = ein_xx + df.loc[ids, "center_COBI"] = cc + df.loc[ids, "width_COBI"] = cw + df.loc[ids, "skewness_COBI"] = cs + df.loc[ids, "kurtosis_COBI"] = ck + else: + cc, cw, cs, ck = self._calc_moment_features(label_list=label_list) + df.loc[ids, "bnd_wICOHP"] = per_bnd_xx + df.loc[ids, "antibnd_wICOHP"] = per_antibnd_xx + df.loc[ids, "w_ICOHP"] = w_icoxx + df.loc[ids, "EIN_ICOHP"] = ein_xx + df.loc[ids, "center_COHP"] = cc + df.loc[ids, "width_COHP"] = cw + df.loc[ids, "skewness_COHP"] = cs + df.loc[ids, "kurtosis_COHP"] = ck return df @@ -558,11 +579,11 @@ class to compute ionicity from CHARGE.lobster data def __init__( self, - path_to_strucutre: str, + path_to_structure: str, path_to_charge: str, charge_type: str, ): - self.path_to_structure = path_to_strucutre + self.path_to_structure = path_to_structure self.path_to_charge = path_to_charge self.charge_type = charge_type diff --git a/lobsterpy/featurize/summary.py b/lobsterpy/featurize/summary.py new file mode 100644 index 00000000..44b950a8 --- /dev/null +++ b/lobsterpy/featurize/summary.py @@ -0,0 +1,99 @@ +# Copyright (c) lobsterpy development team +# Distributed under the terms of a BSD 3-Clause "New" or "Revised" License + +""" +This module defines wrapper class to obtain summary features ready to used for ML +""" + +import logging +import pandas as pd +from typing import List +from lobsterpy.featurize.core import ( + FeaturizeLobsterpy, + FeaturizeCharges, + FeaturizeCOXX, +) + + +class SummarizedFeaturizer: + """ + Featurizer sets that generates summary features from lobster data. + + Args: + path_to_json: path to lobster lightweight json + path_to_coxxcar: path to COXXCAR.lobster (e.g., "COXXCAR.lobster") + path_to_icoxxlist : path to ICOXXLIST.lobster (e.g., "ICOXXLIST.lobster") + path_to_structure : path to structure file (e.g., "POSCAR") + feature_type: set the feature type for moment features and fingerprints. + Possible options are "bonding", "antibonding" or "overall" + path_to_charge : path to CHARGE.lobster (e.g., "CHARGE.lobster") + charge_type : set charge type used for computing ionicity. Possible options are "Mulliken" or "Loewdin" + bonds: "all_bonds" or "cation_anion_bonds" + are_cobis : bool indicating if file contains COBI/ICOBI data + are_coops : bool indicating if file contains COOP/ICOOP data + e_range : range of energy relative to fermi for which moment features needs to be computed + + Attributes: + get_summary_df: a pandas dataframe with summary features + + """ + + def __init__( + self, + ids: str, + path_to_json: str, + path_to_coxxcar: str, + path_to_icoxxlist: str, + path_to_structure: str, + feature_type: str, + path_to_charge: str, + charge_type: str, + bonds: str = "all_bonds", + e_range: List[float] = [-10.0, 0.0], + are_cobis: bool = False, + are_coops: bool = False, + ): + self.ids = ids + self.path_to_json = path_to_json + self.bonds = bonds + self.path_to_coxxcar = path_to_coxxcar + self.path_to_icoxxlist = path_to_icoxxlist + self.path_to_structure = path_to_structure + self.feature_type = feature_type + self.e_range = e_range + self.are_cobis = are_cobis + self.are_coops = are_coops + self.path_to_charge = path_to_charge + self.charge_type = charge_type + + def get_summary_df(self): + """ + This function returns a pandas dataframe with all summary features column + """ + featurize_lobsterpy = FeaturizeLobsterpy( + path_to_json=self.path_to_json, bonds=self.bonds + ) + + df_lobsterpy = featurize_lobsterpy.get_df(ids=self.ids) + + coxx = FeaturizeCOXX( + path_to_coxxcar=self.path_to_coxxcar, + path_to_icoxxlist=self.path_to_icoxxlist, + path_to_structure=self.path_to_structure, + feature_type=self.feature_type, + e_range=self.e_range, + ) + + df_coxx = coxx.get_summarized_coxx_df(ids=self.ids) + + feat_charge = FeaturizeCharges( + path_to_charge=self.path_to_charge, + path_to_strucutre=self.path_to_structure, + charge_type=self.charge_type, + ) + + df_charge = feat_charge.get_df(ids=self.ids) + + df = pd.concat([df_lobsterpy, df_coxx, df_charge], axis=1) + + return df diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 8b90832e..265768bf 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -3,7 +3,7 @@ import unittest import pandas as pd from pathlib import Path -from lobsterpy.featurize.core import FeaturizeLobsterpy +from lobsterpy.featurize.core import FeaturizeLobsterpy, FeaturizeCharges, FeaturizeCOXX CurrentDir = Path(__file__).absolute().parent TestDir = CurrentDir / "../../" @@ -159,5 +159,169 @@ def test_featurize_mp14652_json(self): ) +class TestFeaturizeCOXX(unittest.TestCase): + def setUp(self): + self.featurize_NaCl_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/NaCl/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/NaCl/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/NaCl/POSCAR", + feature_type="overall", + e_range=[-5, 0], + ) + self.featurize_CdF_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/CdF/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/CdF/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/CdF/POSCAR", + feature_type="bonding", + e_range=[-5, 0], + ) + self.featurize_K3Sb_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/K3Sb/COHPCAR.lobster.gz", + path_to_icoxxlist=TestDir / "TestData/K3Sb/ICOHPLIST.lobster.gz", + path_to_structure=TestDir / "TestData/K3Sb/POSCAR.gz", + feature_type="antibonding", + e_range=[-5, 0], + ) + + def test_featurize_NaCl_COXX(self): + df = self.featurize_NaCl_COXX.get_summarized_coxx_df(ids="NaCl") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "NaCl") + + # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["NaCl", "bnd_wICOHP"], 96.822857, places=5) + self.assertAlmostEqual(df.loc["NaCl", "antibnd_wICOHP"], 3.177143, places=5) + self.assertAlmostEqual(df.loc["NaCl", "w_ICOHP"], -0.150558, places=5) + + self.assertAlmostEqual(df.loc["NaCl", "EIN_ICOHP"], 27.843536, places=5) + self.assertAlmostEqual(df.loc["NaCl", "center_COHP"], -4.96241, places=5) + self.assertAlmostEqual(df.loc["NaCl", "width_COHP"], 8.881784e-16, places=5) + self.assertAlmostEqual(df.loc["NaCl", "skewness_COHP"], 1, places=5) + self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 1, places=5) + + def test_featurize_CdF_COXX(self): + df = self.featurize_CdF_COXX.get_summarized_coxx_df(ids="CdF") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "CdF") + + # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["CdF", "bnd_wICOHP"], 56.05771, places=5) + self.assertAlmostEqual(df.loc["CdF", "antibnd_wICOHP"], 43.94229, places=5) + self.assertAlmostEqual(df.loc["CdF", "w_ICOHP"], -0.198235, places=5) + + self.assertAlmostEqual(df.loc["CdF", "EIN_ICOHP"], 18.76634, places=5) + self.assertAlmostEqual(df.loc["CdF", "center_COHP"], -4.748383, places=5) + self.assertAlmostEqual(df.loc["CdF", "width_COHP"], 0.157761, places=5) + self.assertAlmostEqual(df.loc["CdF", "skewness_COHP"], 0.910094, places=5) + self.assertAlmostEqual(df.loc["CdF", "kurtosis_COHP"], 2.866611, places=5) + + +class TestFeaturizeCharges(unittest.TestCase): + def setUp(self): + self.featurize_C_Charge = FeaturizeCharges( + path_to_structure=TestDir / "TestData/C/POSCAR", + path_to_charge=TestDir / "TestData/C/CHARGE.lobster", + charge_type="mulliken", + ) + self.featurize_CdF_Charge = FeaturizeCharges( + path_to_structure=TestDir / "TestData/CdF/POSCAR", + path_to_charge=TestDir / "TestData/CdF/CHARGE.lobster", + charge_type="mulliken", + ) + self.featurize_K3Sb_Charge = FeaturizeCharges( + path_to_structure=TestDir / "TestData/K3Sb/POSCAR.gz", + path_to_charge=TestDir / "TestData/K3Sb/CHARGE.lobster.gz", + charge_type="loewdin", + ) + + def test_featurize_C_Charge(self): + df = self.featurize_C_Charge.get_df(ids="C") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "Ionicity_Mull", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "C") + + # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["C", "Ionicity_Mull"], 0.0, places=5) + + def test_featurize_CdF_Charge(self): + df = self.featurize_CdF_Charge.get_df(ids="CdF") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "Ionicity_Mull", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "CdF") + + # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["CdF", "Ionicity_Mull"], 0.788333, places=5) + + def test_featurize_K3Sb_Charge(self): + df = self.featurize_K3Sb_Charge.get_df(ids="K3Sb") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "Ionicity_Loew", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "K3Sb") + + # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["K3Sb", "Ionicity_Loew"], 0.563333, places=5) + + if __name__ == "__main__": unittest.main() From 7dea944952c8f5350d75cecabbebe81427aac755 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 28 Apr 2023 17:34:00 +0200 Subject: [PATCH 18/41] fix docstring --- lobsterpy/featurize/batch.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 423906cf..604c06a2 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -56,9 +56,12 @@ def get_similarity_matrix_df(self): @staticmethod def _fp_to_dict(fp) -> dict: - """Converts a fingerprint into a dictionary + """ + Converts a fingerprint into a dictionary + Args: fp: The fingerprint to be converted into a dictionary + Returns: dict: A dict of the fingerprint Keys=type, Values=np.ndarray(energies, cohp) """ @@ -76,7 +79,9 @@ def _get_fp_similarity( normalize: bool = False, tanimoto: bool = False, ) -> float: - """Calculates the similarity index (dot product) of two fingerprints + """ + Calculates the similarity index (dot product) of two fingerprints + Args: fp1 (NamedTuple): The 1st dos fingerprint object fp2 (NamedTuple): The 2nd dos fingerprint object @@ -84,10 +89,12 @@ def _get_fp_similarity( pt (int or str) : The index of the point that the dot product is to be taken (default is All) normalize (bool): If True normalize the scalar product to 1 (default is False) tanimoto (bool): If True will compute Tanimoto index (default is False) + Raises: ValueError: If both tanimoto and normalize are set to True. + Returns: - Similarity index (float): The value of dot product + Similarity index (float): The value of dot product """ fp1_dict = ( FingperintSimilarityMatrix._fp_to_dict(fp1) From 5d489f3ae4d0aa323724cd379c1e061f1f517a20 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 28 Apr 2023 17:51:59 +0200 Subject: [PATCH 19/41] fix liniting errors --- lobsterpy/featurize/summary.py | 4 ++-- pylintrc | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/lobsterpy/featurize/summary.py b/lobsterpy/featurize/summary.py index 44b950a8..b057c3e9 100644 --- a/lobsterpy/featurize/summary.py +++ b/lobsterpy/featurize/summary.py @@ -6,8 +6,8 @@ """ import logging -import pandas as pd from typing import List +import pandas as pd from lobsterpy.featurize.core import ( FeaturizeLobsterpy, FeaturizeCharges, @@ -88,7 +88,7 @@ def get_summary_df(self): feat_charge = FeaturizeCharges( path_to_charge=self.path_to_charge, - path_to_strucutre=self.path_to_structure, + path_to_structure=self.path_to_structure, charge_type=self.charge_type, ) diff --git a/pylintrc b/pylintrc index 99aefe96..818c9275 100644 --- a/pylintrc +++ b/pylintrc @@ -153,6 +153,7 @@ disable=print-statement, R0916, R1702, R0903, + R1705, W, C1801, E0611, From 2f61e81867b68a8fa24ccd2aa27c4b0ec78b581d Mon Sep 17 00:00:00 2001 From: anaik Date: Wed, 3 May 2023 17:48:37 +0200 Subject: [PATCH 20/41] refactor featurize module, add more tests --- .../JSONS/mp-1000.json.gz | Bin 0 -> 251999 bytes .../JSONS/mp-2176.json.gz | Bin 0 -> 234530 bytes .../Featurizer_test_data/JSONS/mp-463.json.gz | Bin 0 -> 231560 bytes .../Lobster_calcs/mp-1000/CHARGE.lobster.gz | Bin 0 -> 167 bytes .../Lobster_calcs/mp-1000/COBICAR.lobster.gz | Bin 0 -> 2332777 bytes .../Lobster_calcs/mp-1000/COHPCAR.lobster.gz | Bin 0 -> 2705672 bytes .../Lobster_calcs/mp-1000/COOPCAR.lobster.gz | Bin 0 -> 2636218 bytes .../mp-1000/ICOBILIST.lobster.gz | Bin 0 -> 5988 bytes .../mp-1000/ICOHPLIST.lobster.gz | Bin 0 -> 6603 bytes .../mp-1000/ICOOPLIST.lobster.gz | Bin 0 -> 6474 bytes .../mp-1000/MadelungEnergies.lobster.gz | Bin 0 -> 209 bytes .../Lobster_calcs/mp-1000/POSCAR.gz | Bin 0 -> 106 bytes .../Lobster_calcs/mp-2176/CHARGE.lobster.gz | Bin 0 -> 168 bytes 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file_name_or_path = [ + os.path.join(self.path_to_jsons, f) + for f in os.listdir(self.path_to_jsons) + if not f.startswith("t") + and not f.startswith(".") + and not os.path.isdir(f) + ] + elif ( + self.path_to_lobster_calcs + and self.only_smallest_basis + and not self.path_to_jsons + ): + file_name_or_path = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + and not len(f.split("_")) > 1 + ] + + elif ( + self.path_to_lobster_calcs + and not self.only_smallest_basis + and not self.path_to_jsons + ): + file_name_or_path = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + ] + + with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: + results = tqdm( + pool.imap(self._featurizelobsterpy, file_name_or_path, chunksize=1), + total=len(file_name_or_path), + desc="Generating LobsterPy summary stats", + ) + row = [] + for result in results: + row.append(result) + + df_lobsterpy = pd.concat(row) + + if self.only_smallest_basis: + paths = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + and not len(f.split("_")) > 1 + ] + else: + paths = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + ] + + with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: + results = tqdm( + pool.imap(self._featurizecoxx, paths, chunksize=1), + total=len(paths), + desc="Generating COHP/COOP/COBI summary stats", + ) + row = [] + for result in results: + row.append(result) + + df_coxx = pd.concat(row) + + with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: + results = tqdm( + pool.imap(self._featurizecharges, paths, chunksize=1), + total=len(paths), + desc="Generating charge based features", + ) + row = [] + for result in results: + row.append(result) + + df_charges = pd.concat(row) + + df = pd.concat([df_lobsterpy, df_coxx, df_charges], axis=1) + + return df + + +class BatchCoxxFingerprint: """ Featurizer that generates Tanimoto index similarity matrix summary features from fingerprint objects. """ def __init__( self, - fingerpint_df: pd.DataFrame, + path_to_lobster_calcs: str, + only_smallest_basis: bool = True, + feature_type: str = "overall", + label_list: List[str] | None = None, tanimoto: bool = True, - normalize: bool = False, + normalize: bool = True, + spin_type: str = "summed", + n_bins: int = 56, + e_range: List[float] = [-15.0, 0.0], + n_jobs=4, + are_cobis: bool = False, + are_coops: bool = False, ): - self.fingerpint_df = fingerpint_df + self.path_to_lobster_calcs = path_to_lobster_calcs + self.only_smallest_basis = only_smallest_basis + self.feature_type = feature_type self.tanimoto = tanimoto self.normalize = normalize + self.label_list = label_list + self.spin_type = spin_type + self.n_bins = n_bins + self.e_range = e_range + self.n_jobs = n_jobs + self.are_cobis = are_cobis + self.are_coops = are_coops + + self.fingerprint_df = self._get_fingerprints_df() def get_similarity_matrix_df(self): """ @@ -33,23 +357,31 @@ def get_similarity_matrix_df(self): A Pandas dataframe """ matrix = np.full( - (self.fingerpint_df.shape[0], self.fingerpint_df.shape[0]), np.nan + (self.fingerprint_df.shape[0], self.fingerprint_df.shape[0]), np.nan ) - for i, (row, col) in enumerate(self.fingerpint_df.iterrows()): - for j, (row1, col1) in enumerate(self.fingerpint_df.iterrows()): - if i <= j: + for i, (row, col) in enumerate(self.fingerprint_df.iterrows()): + for j, (row1, col1) in enumerate(self.fingerprint_df.iterrows()): + #if i <= j: + if self.tanimoto: simi = self._get_fp_similarity( col["COXX_FP"], col1["COXX_FP"], tanimoto=self.tanimoto, - normalize=self.normalize, + normalize=False, + ) + else: + simi = self._get_fp_similarity( + col["COXX_FP"], + col1["COXX_FP"], + tanimoto=False, + normalize=True, ) - matrix[i][j] = simi + matrix[i][j] = simi df = pd.DataFrame( matrix, - index=list(self.fingerpint_df.index), - columns=list(self.fingerpint_df.index), + index=list(self.fingerprint_df.index), + columns=list(self.fingerprint_df.index), ) return df @@ -77,7 +409,7 @@ def _get_fp_similarity( col: int = 1, pt: int | str = "All", normalize: bool = False, - tanimoto: bool = False, + tanimoto: bool = True, ) -> float: """ Calculates the similarity index (dot product) of two fingerprints @@ -97,15 +429,11 @@ def _get_fp_similarity( Similarity index (float): The value of dot product """ fp1_dict = ( - FingperintSimilarityMatrix._fp_to_dict(fp1) - if not isinstance(fp1, dict) - else fp1 + BatchCoxxFingerprint._fp_to_dict(fp1) if not isinstance(fp1, dict) else fp1 ) fp2_dict = ( - FingperintSimilarityMatrix._fp_to_dict(fp2) - if not isinstance(fp2, dict) - else fp2 + BatchCoxxFingerprint._fp_to_dict(fp2) if not isinstance(fp2, dict) else fp2 ) if pt == "All": @@ -135,3 +463,83 @@ def _get_fp_similarity( raise ValueError( "Cannot compute similarity index. Please set either normalize=True or tanimoto=True or both to False." ) + + def _fingerprint_df(self, path_to_lobster_calc): + dir_name = Path(path_to_lobster_calc) + if self.are_cobis and not self.are_coops: + if (dir_name / "COBICAR.lobster.gz").exists() and (dir_name / "ICOBILIST.lobster.gz").exists(): + coxxcar_path = dir_name / "COBICAR.lobster.gz" + icoxxlist_path = dir_name / "ICOBILIST.lobster.gz" + else: + raise Exception("COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in " + "{}".format(dir_name.name)) + elif self.are_coops and not self.are_cobis: + if (dir_name / "COOPCAR.lobster.gz").exists() and (dir_name / "ICOOPLIST.lobster.gz").exists(): + coxxcar_path = dir_name / "COOPCAR.lobster.gz" + icoxxlist_path = dir_name / "ICOOPLIST.lobster.gz" + else: + raise Exception("COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in " + "{}".format(dir_name.name)) + else: + if (dir_name / "COHPCAR.lobster.gz").exists() and (dir_name / "ICOHPLIST.lobster.gz").exists(): + coxxcar_path = dir_name / "COHPCAR.lobster.gz" + icoxxlist_path = dir_name / "ICOHPLIST.lobster.gz" + else: + raise Exception("COHPCAR.lobster.gz or ICOHPLIST.lobster.gz file not found in " + "{}".format(dir_name.name)) + + if (dir_name / "POSCAR.gz").exists(): + structure_path = dir_name / "POSCAR.gz" + else: + raise Exception("POSCAR.gz file not found in {}".format(dir_name.name)) + + coxx = FeaturizeCOXX( + path_to_coxxcar=coxxcar_path, + path_to_icoxxlist=icoxxlist_path, + path_to_structure=structure_path, + feature_type=self.feature_type, + e_range=self.e_range, + are_coops=self.are_coops, + are_cobis=self.are_cobis, + ) + + df_fp = coxx.get_coxx_fingerprint_df( + spin_type=self.spin_type, + n_bins=self.n_bins, + normalize=self.normalize, + label_list=self.label_list, + ) + return df_fp + + def _get_fingerprints_df(self): + if self.only_smallest_basis: + paths = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + and not len(f.split("_")) > 1 + ] + else: + paths = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + ] + + with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: + results = tqdm( + pool.imap(self._fingerprint_df, paths, chunksize=1), + total=len(paths), + desc="Generating COHP/COBI/COOP fingerprints", + ) + row = [] + for result in results: + row.append(result) + + df = pd.concat(row) + + return df diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index f5dbffc4..eba058c4 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -8,19 +8,25 @@ from __future__ import annotations import gzip import json +import os +import warnings from pathlib import Path from typing import Mapping, NamedTuple, List from collections import namedtuple +import warnings import numpy as np import numpy.typing as npt import pandas as pd from mendeleev import element +from lobsterpy.cohp.analyze import Analysis from pymatgen.core.structure import Structure -from pymatgen.io.lobster import Charge, Lobsterout, Icohplist +from pymatgen.io.lobster import Charge, Lobsterout, Icohplist, MadelungEnergies from pymatgen.electronic_structure.cohp import CompleteCohp from pymatgen.electronic_structure.core import Spin from scipy.integrate import trapezoid +warnings.filterwarnings('ignore') + class FeaturizeLobsterpy: """ @@ -37,13 +43,15 @@ class to featurize lobsterpy data def __init__( self, - path_to_json: str, + path_to_lobster_calc: str | None = None, + path_to_json: str | None = None, bonds: str = "all_bonds", ): self.path_to_json = path_to_json + self.path_to_lobster_calc = path_to_lobster_calc self.bonds = bonds - def get_df(self, ids: str) -> pd.DataFrame: + def get_df(self, ids: str | None = None) -> pd.DataFrame: """ This function featurizes LobsterPy condensed bonding analysis data from lobster lightweight json.gz files @@ -52,12 +60,26 @@ def get_df(self, ids: str) -> pd.DataFrame: Returns a pandas dataframe with lobsterpy icohp statistics """ + if self.path_to_json and not self.path_to_lobster_calc: + # read the lightweight lobster json files using read_lobster_lightweight_json method + data = self._read_lobster_lightweight_json() + if not ids: + ids = Path(self.path_to_json).name.split(".")[0] + + elif self.path_to_lobster_calc and not self.path_to_json: + # get lobsterpy condensed bonding analysis data using get_lobsterpy_cba_dict method + data = self._get_lobsterpy_cba_dict() + + if not ids: + ids = Path(self.path_to_lobster_calc).name + + else: + raise ValueError( + "Please provide either path to lightweight lobster jsons or path to lobster calc" + ) # define a pandas dataframe df = pd.DataFrame(index=[ids]) - # read the lightweight lobster json files using read_lobster_lightweight_json function - data = self._read_lobster_lightweight_json() - icohp_mean = [] icohp_sum = [] bond = [] @@ -121,10 +143,78 @@ def _read_lobster_lightweight_json(self): return lobster_data + def _get_lobsterpy_cba_dict(self): + """ + This function uses lobsterpy.cohp.analyze.Analysis class to generate a python dictionary object + with lobster summmarized bonding analysis data. + + Returns: + Returns a dictionary with lobster summmarized bonding analysis data + + """ + dir_name = Path(self.path_to_lobster_calc) + cohpcar_path = dir_name / "COHPCAR.lobster.gz" + charge_path = dir_name / "CHARGE.lobster.gz" + structure_path = dir_name / "POSCAR.gz" + icohplist_path = dir_name / "ICOHPLIST.lobster.gz" + madelung_energies_path = dir_name / "MadelungEnergies.lobster.gz" + + if self.bonds == "all_bonds": + which_bonds = "all" + else: + which_bonds = "cation-anion" + + if icohplist_path.exists() and cohpcar_path.exists() and charge_path.exists() and structure_path.exists(): + analyse = Analysis( + path_to_poscar=structure_path, + path_to_icohplist=icohplist_path, + path_to_cohpcar=cohpcar_path, + path_to_charge=charge_path, + summed_spins=False, # we will always use spin polarization here + cutoff_icohp=0.10, + whichbonds=which_bonds, + ) + data = {} + data.update( + {self.bonds: {"lobsterpy_data": analyse.condensed_bonding_analysis}} + ) + else: + raise Exception("Path provided for Lobster calc directory seems incorrect." + "It does not contain COHPCAR.lobster.gz, ICOHPLIST.lobster.gz, POSCAR.gz and " + "CHARGE.lobster.gz files needed for automatic analysis using LobsterPy") + + if madelung_energies_path.exists(): + madelung_obj = MadelungEnergies(filename=str(madelung_energies_path)) + + madelung_energies = { + "Mulliken": madelung_obj.madelungenergies_Mulliken, + "Loewdin": madelung_obj.madelungenergies_Loewdin, + "Ewald_splitting": madelung_obj.ewald_splitting, + } + data.update({"madelung_energies": madelung_energies}) + else: + warnings.warn( + "MadelungEnergies.lobster.gz file not found in Lobster calc directory provided" + " Will set Madelung Engeries for crystal structure values to NaN") + + madelung_energies = { + "Mulliken": np.nan, + "Loewdin": np.nan, + "Ewald_splitting": np.nan, + } + data.update({"madelung_energies": madelung_energies}) + + return data + + +coxx_fingerprint = namedtuple( + "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" + ) + class FeaturizeCOXX: """ - class to featurize COHPCAR/COBICAR/COOPCAR data as fingerprint + class to generate features from COHPCAR/COBICAR/COOPCAR data Args: path_to_coxxcar: path to COXXCAR.lobster (e.g., "COXXCAR.lobster") @@ -159,13 +249,11 @@ def __init__( self.e_range = e_range self.are_cobis = are_cobis self.are_coops = are_coops - self.icoxxlist = Icohplist( filename=self.path_to_icoxxlist, are_cobis=self.are_cobis, are_coops=self.are_coops, ) - self.completecoxx = CompleteCohp.from_file( fmt="LOBSTER", filename=self.path_to_coxxcar, @@ -174,11 +262,11 @@ def __init__( def get_coxx_fingerprint_df( self, - ids: str, + ids: str | None = None, label_list: List[str] | None = None, spin_type: str = "summed", binning: bool = True, - n_bins: int = 50, + n_bins: int = 56, normalize: bool = True, ): """ @@ -188,7 +276,7 @@ def get_coxx_fingerprint_df( ids: sets index of pandas dataframe spin_type: Specify spin type. Can accept '{summed/up/down}' (default is summed) - binning: If true cohps will be binned + binning: If true coxxs will be binned n_bins: Number of bins to be used in the fingerprint (default is 256) normalize: If true, normalizes the area under fp to equal to 1 (default is True) label_list: Specify bond lables as a list for which cohp fingerprints are needed @@ -202,9 +290,7 @@ def get_coxx_fingerprint_df( of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ coxxcar_obj = self.completecoxx - coxx_fingerprint = namedtuple( - "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" - ) + energies = ( coxxcar_obj.energies - coxxcar_obj.efermi ) # here substraction of efermi has impact on fp. @@ -228,22 +314,37 @@ def get_coxx_fingerprint_df( if spin_type == "up": coxx_all = coxxcar_obj[Spin.up] elif spin_type == "down": - coxx_all = coxxcar_obj[Spin.down] + try: + coxx_all = coxxcar_obj[Spin.down] + except KeyError: + raise ValueError('LOSBTER calculation is non-spin polarized. Please switch spin_type to "up"') elif spin_type == "summed": - coxx_all = coxxcar_obj[Spin.up] + coxxcar_obj[Spin.down] + try: + coxx_all = coxxcar_obj[Spin.up] + coxxcar_obj[Spin.down] + except KeyError: + coxx_all = coxxcar_obj[Spin.up] else: raise Exception( - """Check the spin_type argument, - Possible options are summed/up/down""" + "Check the spin_type argument." + "Possible options are summed/up/down" ) + coxx_dict = {} + if not self.are_cobis and not self.are_coops: + coxx_dict["bonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["antibonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) + else: + coxx_dict["antibonding"] = np.array( + [scohp if scohp <= 0 else 0 for scohp in coxx_all] + ) + coxx_dict["bonding"] = np.array( + [scohp if scohp >= 0 else 0 for scohp in coxx_all] + ) - coxx_dict["bonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] - ) - coxx_dict["antibonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] - ) coxx_dict["overall"] = coxx_all try: @@ -289,7 +390,11 @@ def get_coxx_fingerprint_df( bin_width, ) - df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) + if ids: + df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) + else: + ids = os.path.basename(os.path.dirname(self.path_to_coxxcar)) + df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) df.at[ids, "COXX_FP"] = fp @@ -297,8 +402,7 @@ def get_coxx_fingerprint_df( except KeyError: raise ValueError( - """Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall - """ + "Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall" ) def _calculate_wicoxx_ein(self): @@ -407,48 +511,53 @@ def _calc_moment_features(self, label_list: List[str] | None = None): ) coxx_dict["overall"] = coxx_all - coxx_center = self._get_coxx_center( - coxx=coxx_dict[self.feature_type], - energies=energies, - e_range=self.e_range, - ) - coxx_width = np.sqrt( - self._get_n_moment( - n=2, + try: + coxx_center = self._get_coxx_center( coxx=coxx_dict[self.feature_type], energies=energies, e_range=self.e_range, ) - ) - coxx_skew = self._get_n_moment( - n=3, - coxx=coxx_dict[self.feature_type], - energies=energies, - e_range=self.e_range, - ) / self._get_n_moment( - n=2, - coxx=coxx_dict[self.feature_type], - energies=energies, - e_range=self.e_range, - ) ** ( - 3 / 2 - ) - - coxx_kurt = ( - self._get_n_moment( - n=4, + coxx_width = np.sqrt( + self._get_n_moment( + n=2, + coxx=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + ) + coxx_skew = self._get_n_moment( + n=3, coxx=coxx_dict[self.feature_type], energies=energies, e_range=self.e_range, - ) - / self._get_n_moment( + ) / self._get_n_moment( n=2, coxx=coxx_dict[self.feature_type], energies=energies, e_range=self.e_range, + ) ** ( + 3 / 2 + ) + + coxx_kurt = ( + self._get_n_moment( + n=4, + coxx=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + / self._get_n_moment( + n=2, + coxx=coxx_dict[self.feature_type], + energies=energies, + e_range=self.e_range, + ) + ** 2 + ) + except KeyError: + raise ValueError( + "Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall" ) - ** 2 - ) return coxx_center, coxx_width, coxx_skew, coxx_kurt @@ -511,7 +620,9 @@ def _get_n_moment( return nth_moment - def get_summarized_coxx_df(self, ids: str, label_list: List[str] | None = None): + def get_summarized_coxx_df( + self, ids: str | None = None, label_list: List[str] | None = None + ): """ This function returns a pandas dataframe with weighted ICOXX, effective interaction number and moment features (center, width, skewness and kurtosis) of COXX in selected energy range @@ -520,7 +631,11 @@ def get_summarized_coxx_df(self, ids: str, label_list: List[str] | None = None): Returns a pandas dataframe with cohp/cobi/coop related features as per input file """ - df = pd.DataFrame(index=[ids]) + if ids: + df = pd.DataFrame(index=[ids]) + else: + ids = os.path.basename(os.path.dirname(self.path_to_coxxcar)) + df = pd.DataFrame(index=[ids]) ( per_bnd_xx, @@ -583,6 +698,7 @@ def __init__( path_to_charge: str, charge_type: str, ): + self.path_to_structure = path_to_structure self.path_to_charge = path_to_charge self.charge_type = charge_type @@ -668,7 +784,7 @@ def _calc_ionicity(self) -> float: return ionicity - def get_df(self, ids): + def get_df(self, ids: str | None = None): """ This function returns a pandas dataframe with computed ionicity as column @@ -676,7 +792,11 @@ def get_df(self, ids): Returns a pandas dataframe with ionicity """ - df = pd.DataFrame(index=[ids]) + if ids: + df = pd.DataFrame(index=[ids]) + else: + ids = os.path.basename(os.path.dirname(self.path_to_charge)) + df = pd.DataFrame(index=[ids]) if self.charge_type == "mulliken": df.loc[ids, "Ionicity_Mull"] = self._calc_ionicity() diff --git a/lobsterpy/featurize/summary.py b/lobsterpy/featurize/summary.py deleted file mode 100644 index b057c3e9..00000000 --- a/lobsterpy/featurize/summary.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright (c) lobsterpy development team -# Distributed under the terms of a BSD 3-Clause "New" or "Revised" License - -""" -This module defines wrapper class to obtain summary features ready to used for ML -""" - -import logging -from typing import List -import pandas as pd -from lobsterpy.featurize.core import ( - FeaturizeLobsterpy, - FeaturizeCharges, - FeaturizeCOXX, -) - - -class SummarizedFeaturizer: - """ - Featurizer sets that generates summary features from lobster data. - - Args: - path_to_json: path to lobster lightweight json - path_to_coxxcar: path to COXXCAR.lobster (e.g., "COXXCAR.lobster") - path_to_icoxxlist : path to ICOXXLIST.lobster (e.g., "ICOXXLIST.lobster") - path_to_structure : path to structure file (e.g., "POSCAR") - feature_type: set the feature type for moment features and fingerprints. - Possible options are "bonding", "antibonding" or "overall" - path_to_charge : path to CHARGE.lobster (e.g., "CHARGE.lobster") - charge_type : set charge type used for computing ionicity. Possible options are "Mulliken" or "Loewdin" - bonds: "all_bonds" or "cation_anion_bonds" - are_cobis : bool indicating if file contains COBI/ICOBI data - are_coops : bool indicating if file contains COOP/ICOOP data - e_range : range of energy relative to fermi for which moment features needs to be computed - - Attributes: - get_summary_df: a pandas dataframe with summary features - - """ - - def __init__( - self, - ids: str, - path_to_json: str, - path_to_coxxcar: str, - path_to_icoxxlist: str, - path_to_structure: str, - feature_type: str, - path_to_charge: str, - charge_type: str, - bonds: str = "all_bonds", - e_range: List[float] = [-10.0, 0.0], - are_cobis: bool = False, - are_coops: bool = False, - ): - self.ids = ids - self.path_to_json = path_to_json - self.bonds = bonds - self.path_to_coxxcar = path_to_coxxcar - self.path_to_icoxxlist = path_to_icoxxlist - self.path_to_structure = path_to_structure - self.feature_type = feature_type - self.e_range = e_range - self.are_cobis = are_cobis - self.are_coops = are_coops - self.path_to_charge = path_to_charge - self.charge_type = charge_type - - def get_summary_df(self): - """ - This function returns a pandas dataframe with all summary features column - """ - featurize_lobsterpy = FeaturizeLobsterpy( - path_to_json=self.path_to_json, bonds=self.bonds - ) - - df_lobsterpy = featurize_lobsterpy.get_df(ids=self.ids) - - coxx = FeaturizeCOXX( - path_to_coxxcar=self.path_to_coxxcar, - path_to_icoxxlist=self.path_to_icoxxlist, - path_to_structure=self.path_to_structure, - feature_type=self.feature_type, - e_range=self.e_range, - ) - - df_coxx = coxx.get_summarized_coxx_df(ids=self.ids) - - feat_charge = FeaturizeCharges( - path_to_charge=self.path_to_charge, - path_to_structure=self.path_to_structure, - charge_type=self.charge_type, - ) - - df_charge = feat_charge.get_df(ids=self.ids) - - df = pd.concat([df_lobsterpy, df_coxx, df_charge], axis=1) - - return df diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py new file mode 100644 index 00000000..4fa0ff37 --- /dev/null +++ b/lobsterpy/featurize/test/test_batch.py @@ -0,0 +1,405 @@ +import os +import sys +import unittest +import pandas as pd +from pathlib import Path +from lobsterpy.featurize.batch import BatchSummaryFeaturizer, BatchCoxxFingerprint + +CurrentDir = Path(__file__).absolute().parent +TestDir = CurrentDir / "../../" + + +class TestBatchSummaryFeaturizer(unittest.TestCase): + def setUp(self): + self.summary_featurize_with_json = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + bonds="all_bonds", + path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", + feature_type="antibonding", + include_cobi_data=False, + include_coop_data=False, + e_range=[-15, 0], + ) + + self.summary_featurize_without_json = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + bonds="all_bonds", + include_cobi_data=False, + include_coop_data=False, + e_range=[-15, 0], + ) + + self.summary_featurize_with_json_overall = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + bonds="all_bonds", + path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", + feature_type="overall", + include_cobi_data=True, + include_coop_data=True, + e_range=[-15, 0], + ) + + self.summary_featurize_with_json_bonding = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + bonds="all_bonds", + path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", + feature_type="bonding", + include_cobi_data=False, + include_coop_data=False, + e_range=[-15, 0], + charge_type="mulliken", + ) + + self.summary_featurize_with_json_antibonding = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + bonds="cation_anion_bonds", + path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", + feature_type="antibonding", + include_cobi_data=False, + include_coop_data=False, + e_range=[-15, 0], + charge_type="loewdin", + ) + + def test_summary_featurize_with_json(self): + df = self.summary_featurize_with_json.get_df() + + self.assertIsInstance(df, pd.DataFrame) + + expected_cols = [ + "Icohp_mean_avg", + "Icohp_mean_max", + "Icohp_mean_min", + "Icohp_mean_std", + "Icohp_sum_avg", + "Icohp_sum_max", + "Icohp_sum_min", + "Icohp_sum_std", + "bonding_perc_avg", + "bonding_perc_max", + "bonding_perc_min", + "bonding_perc_std", + "antibonding_perc_avg", + "antibonding_perc_min", + "antibonding_perc_max", + "antibonding_perc_std", + "Madelung_Mull", + "Madelung_Loew", + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + "Ionicity_Mull", + "Ionicity_Loew", + ] + + self.assertEqual(list(df.columns), expected_cols) + + expected_index = ["mp-463", "mp-1000", "mp-2176"] + + self.assertEqual(list(df.index), expected_index) + + def test_summary_featurize_without_json(self): + df = self.summary_featurize_without_json.get_df() + + self.assertIsInstance(df, pd.DataFrame) + + expected_cols = [ + "Icohp_mean_avg", + "Icohp_mean_max", + "Icohp_mean_min", + "Icohp_mean_std", + "Icohp_sum_avg", + "Icohp_sum_max", + "Icohp_sum_min", + "Icohp_sum_std", + "bonding_perc_avg", + "bonding_perc_max", + "bonding_perc_min", + "bonding_perc_std", + "antibonding_perc_avg", + "antibonding_perc_min", + "antibonding_perc_max", + "antibonding_perc_std", + "Madelung_Mull", + "Madelung_Loew", + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + "Ionicity_Mull", + "Ionicity_Loew", + ] + + self.assertEqual(list(df.columns), expected_cols) + + expected_index = ["mp-463", "mp-1000", "mp-2176"] + + self.assertEqual(list(df.index), expected_index) + + def test_summary_featurize_with_json_overall(self): + df = self.summary_featurize_with_json_overall.get_df() + + self.assertIsInstance(df, pd.DataFrame) + + expected_cols = [ + "Icohp_mean_avg", + "Icohp_mean_max", + "Icohp_mean_min", + "Icohp_mean_std", + "Icohp_sum_avg", + "Icohp_sum_max", + "Icohp_sum_min", + "Icohp_sum_std", + "bonding_perc_avg", + "bonding_perc_max", + "bonding_perc_min", + "bonding_perc_std", + "antibonding_perc_avg", + "antibonding_perc_min", + "antibonding_perc_max", + "antibonding_perc_std", + "Madelung_Mull", + "Madelung_Loew", + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + "bnd_wICOBI", + "antibnd_wICOBI", + "w_ICOBI", + "EIN_ICOBI", + "center_COBI", + "width_COBI", + "skewness_COBI", + "kurtosis_COBI", + "bnd_wICOOP", + "antibnd_wICOOP", + "w_ICOOP", + "EIN_ICOOP", + "center_COOP", + "width_COOP", + "skewness_COOP", + "kurtosis_COOP", + "Ionicity_Mull", + "Ionicity_Loew", + ] + + self.assertEqual(list(df.columns), expected_cols) + + expected_index = ["mp-463", "mp-1000", "mp-2176"] + + self.assertEqual(list(df.index), expected_index) + + def test_summary_featurize_with_json_bonding(self): + df = self.summary_featurize_with_json_bonding.get_df() + + self.assertIsInstance(df, pd.DataFrame) + + expected_cols = [ + "Icohp_mean_avg", + "Icohp_mean_max", + "Icohp_mean_min", + "Icohp_mean_std", + "Icohp_sum_avg", + "Icohp_sum_max", + "Icohp_sum_min", + "Icohp_sum_std", + "bonding_perc_avg", + "bonding_perc_max", + "bonding_perc_min", + "bonding_perc_std", + "antibonding_perc_avg", + "antibonding_perc_min", + "antibonding_perc_max", + "antibonding_perc_std", + "Madelung_Mull", + "Madelung_Loew", + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + "Ionicity_Mull", + ] + + self.assertEqual(list(df.columns), expected_cols) + + def test_summary_featurize_with_json_antibonding(self): + df = self.summary_featurize_with_json_antibonding.get_df() + + self.assertIsInstance(df, pd.DataFrame) + + expected_cols = [ + "Icohp_mean_avg", + "Icohp_mean_max", + "Icohp_mean_min", + "Icohp_mean_std", + "Icohp_sum_avg", + "Icohp_sum_max", + "Icohp_sum_min", + "Icohp_sum_std", + "bonding_perc_avg", + "bonding_perc_max", + "bonding_perc_min", + "bonding_perc_std", + "antibonding_perc_avg", + "antibonding_perc_min", + "antibonding_perc_max", + "antibonding_perc_std", + "Madelung_Mull", + "Madelung_Loew", + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + "Ionicity_Loew", + ] + + self.assertEqual(list(df.columns), expected_cols) + + +class TestBatchCoxxFingerprint(unittest.TestCase): + def setUp(self): + self.fp_cohp_overall = BatchCoxxFingerprint( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + e_range=[-15, 0], + feature_type="overall", + normalize=True, + tanimoto=True, + ) + + self.fp_cohp_bonding = BatchCoxxFingerprint( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + e_range=[-15, 0], + feature_type="bonding", + normalize=False, + tanimoto=True, + ) + + self.fp_cobi = BatchCoxxFingerprint( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + e_range=[-15, 0], + feature_type="antibonding", + normalize=True, + tanimoto=True, + are_cobis=True, + ) + + self.fp_coop = BatchCoxxFingerprint( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + e_range=[-15, 0], + feature_type="bonding", + normalize=True, + tanimoto=False, + are_coops=True, + ) + + def test_fp_cohp_overall(self): + df = self.fp_cohp_overall.get_similarity_matrix_df() + + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], -0.033251, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], -0.013751, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) + self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0.046889, places=5) + + def test_fp_cohp_bonding(self): + fp_df = self.fp_cohp_bonding.fingerprint_df + df = self.fp_cohp_bonding.get_similarity_matrix_df() + + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.0000171, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0.000000, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) + for val in fp_df.loc["mp-1000", "COXX_FP"].coxx: + self.assertLessEqual(val, 0) + + def test_fp_cobi(self): + fp_df = self.fp_cobi.fingerprint_df + df = self.fp_cobi.get_similarity_matrix_df() + + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) + for val in fp_df.loc["mp-463", "COXX_FP"].coxx: + self.assertLessEqual(val, 0) + + def test_fp_coop(self): + fp_df = self.fp_coop.fingerprint_df + df = self.fp_coop.get_similarity_matrix_df() + + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) + for val in fp_df.loc["mp-2176", "COXX_FP"].coxx: + self.assertGreaterEqual(val, 0) + + +class TestExceptions(unittest.TestCase): + def test_batch_summary_featurizer_exception(self): + with self.assertRaises(Exception) as err: + self.summary_featurize_with_json = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/1/", + bonds="all_bonds", + feature_type="antibonding", + include_cobi_data=True, + include_coop_data=True, + e_range=[-15, 0], + ) + + df = self.summary_featurize_with_json.get_df() + print(df.to_string()) + self.assertEqual( + err.exception.__str__(), + "COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in mp-2176", + ) + + with self.assertRaises(Exception) as err: + self.summary_featurize_with_json = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/2/", + bonds="all_bonds", + feature_type="antibonding", + include_cobi_data=True, + include_coop_data=True, + e_range=[-15, 0], + ) + + df = self.summary_featurize_with_json.get_df() + print(df.to_string()) + self.assertEqual( + err.exception.__str__(), + "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in mp-1000", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 265768bf..051e121f 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -323,5 +323,83 @@ def test_featurize_K3Sb_Charge(self): self.assertAlmostEqual(df.loc["K3Sb", "Ionicity_Loew"], 0.563333, places=5) +class TestExceptions(unittest.TestCase): + def test_lobsterpy_featurize_exception(self): + with self.assertRaises(Exception) as err: + self.featurize_mp1249_json = FeaturizeLobsterpy( + path_to_json=None, path_to_lobster_calc=None, bonds="all_bonds" + ) + + self.featurize_mp1249_json.get_df() + self.assertEqual( + err.exception.__str__(), + "Please provide either path to lightweight lobster jsons or path to lobster calc", + ) + + with self.assertRaises(Exception) as err: + self.featurize_mp1249_json = FeaturizeLobsterpy( + path_to_json=None, path_to_lobster_calc=TestDir, bonds="all_bonds" + ) + + self.featurize_mp1249_json.get_df() + self.assertEqual( + err.exception.__str__(), + "Path provided for Lobster calc directory seems incorrect." + "It does not contain COHPCAR.lobster.gz, ICOHPLIST.lobster.gz, POSCAR.gz and " + "CHARGE.lobster.gz files needed for automatic analysis using LobsterPy", + ) + + def test_featurize_coxx(self): + with self.assertRaises(Exception) as err: + self.featurize_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/NaCl/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/NaCl/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/NaCl/POSCAR", + feature_type="summed", + e_range=[-5, 0], + ) + + self.featurize_COXX.get_summarized_coxx_df() + + self.assertEqual( + err.exception.__str__(), + "Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall") + + with self.assertRaises(Exception) as err2: + self.featurize_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/NaCl/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/NaCl/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/NaCl/POSCAR", + feature_type="bonding", + e_range=[-5, 0], + ) + + self.featurize_COXX.get_coxx_fingerprint_df(spin_type='-1') + + self.assertEqual( + err2.exception.__str__(), + "Check the spin_type argument." + "Possible options are summed/up/down") + + with self.assertRaises(Exception) as err3: + self.featurize_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/NaSi/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/NaSi/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/NaSi/POSCAR", + feature_type="bonding", + e_range=[-5, 0], + are_cobis=True, + are_coops=True + ) + + self.featurize_COXX.get_coxx_fingerprint_df() + + self.assertEqual( + err3.exception.__str__(), + "You cannot have info about COOPs and COBIs in the same file.") + + + + if __name__ == "__main__": unittest.main() From ccbf17e109a9f3790aac0f9bc04b28ff23304dff Mon Sep 17 00:00:00 2001 From: anaik Date: Wed, 3 May 2023 17:59:35 +0200 Subject: [PATCH 21/41] fix pylint import errors --- lobsterpy/featurize/batch.py | 4 +--- lobsterpy/featurize/core.py | 3 +-- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 58ac9975..9488caad 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -6,9 +6,8 @@ """ from __future__ import annotations import os -from typing import NamedTuple +from typing import NamedTuple, List import multiprocessing as mp -from typing import List from pathlib import Path import warnings import numpy as np @@ -19,7 +18,6 @@ FeaturizeCharges, FeaturizeCOXX, ) - warnings.filterwarnings("ignore") diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index eba058c4..57792833 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -13,17 +13,16 @@ from pathlib import Path from typing import Mapping, NamedTuple, List from collections import namedtuple -import warnings import numpy as np import numpy.typing as npt import pandas as pd from mendeleev import element -from lobsterpy.cohp.analyze import Analysis from pymatgen.core.structure import Structure from pymatgen.io.lobster import Charge, Lobsterout, Icohplist, MadelungEnergies from pymatgen.electronic_structure.cohp import CompleteCohp from pymatgen.electronic_structure.core import Spin from scipy.integrate import trapezoid +from lobsterpy.cohp.analyze import Analysis warnings.filterwarnings('ignore') From c5245f384274624b8bc3ed4176d54893c7a08a0c Mon Sep 17 00:00:00 2001 From: anaik Date: Wed, 3 May 2023 18:03:02 +0200 Subject: [PATCH 22/41] run black --- lobsterpy/featurize/batch.py | 65 +++++++++++++++++--------- lobsterpy/featurize/core.py | 32 ++++++++----- lobsterpy/featurize/test/test_batch.py | 4 +- lobsterpy/featurize/test/test_core.py | 16 +++---- 4 files changed, 73 insertions(+), 44 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 9488caad..3ec64641 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -18,6 +18,7 @@ FeaturizeCharges, FeaturizeCOXX, ) + warnings.filterwarnings("ignore") @@ -89,8 +90,11 @@ def _featurizecoxx(self, path_to_lobster_calc): structure_path = dir_name / "POSCAR.gz" icoxxlist_path = dir_name / "ICOHPLIST.lobster.gz" - if coxxcar_path.exists() and structure_path.exists() and icoxxlist_path.exists(): - + if ( + coxxcar_path.exists() + and structure_path.exists() + and icoxxlist_path.exists() + ): coxx = FeaturizeCOXX( path_to_coxxcar=coxxcar_path, path_to_icoxxlist=icoxxlist_path, @@ -101,15 +105,16 @@ def _featurizecoxx(self, path_to_lobster_calc): df_cohp = coxx.get_summarized_coxx_df() else: - raise Exception("COHPCAR.lobster.gz or POSCAR.gz or ICOHPLIST.lobster.gz file " - "not found in {}".format(dir_name.name)) + raise Exception( + "COHPCAR.lobster.gz or POSCAR.gz or ICOHPLIST.lobster.gz file " + "not found in {}".format(dir_name.name) + ) if self.include_cobi_data: coxxcar_path = dir_name / "COBICAR.lobster.gz" icoxxlist_path = dir_name / "ICOBILIST.lobster.gz" if coxxcar_path.exists() and icoxxlist_path.exists(): - coxx = FeaturizeCOXX( path_to_coxxcar=coxxcar_path, path_to_icoxxlist=icoxxlist_path, @@ -122,15 +127,16 @@ def _featurizecoxx(self, path_to_lobster_calc): df_cobi = coxx.get_summarized_coxx_df() else: - raise Exception("COBICAR.lobster.gz or ICOBILIST.lobster.gz file " - "not found in {}".format(dir_name.name)) + raise Exception( + "COBICAR.lobster.gz or ICOBILIST.lobster.gz file " + "not found in {}".format(dir_name.name) + ) if self.include_coop_data: coxxcar_path = dir_name / "COOPCAR.lobster.gz" icoxxlist_path = dir_name / "ICOOPLIST.lobster.gz" if coxxcar_path.exists() and icoxxlist_path.exists(): - coxx = FeaturizeCOXX( path_to_coxxcar=coxxcar_path, path_to_icoxxlist=icoxxlist_path, @@ -143,8 +149,10 @@ def _featurizecoxx(self, path_to_lobster_calc): df_coop = coxx.get_summarized_coxx_df() else: - raise Exception("COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file " - "not found in {}".format(dir_name.name)) + raise Exception( + "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file " + "not found in {}".format(dir_name.name) + ) if self.include_cobi_data and self.include_coop_data: df = pd.concat([df_cohp, df_cobi, df_coop], axis=1) @@ -196,8 +204,9 @@ def _featurizecharges(self, path_to_lobster_calc): return df else: - raise Exception("CHARGE.lobster.gz or POSCAR.gz not found in {}".format(dir_name.name)) - + raise Exception( + "CHARGE.lobster.gz or POSCAR.gz not found in {}".format(dir_name.name) + ) def get_df(self): """ @@ -359,7 +368,7 @@ def get_similarity_matrix_df(self): ) for i, (row, col) in enumerate(self.fingerprint_df.iterrows()): for j, (row1, col1) in enumerate(self.fingerprint_df.iterrows()): - #if i <= j: + # if i <= j: if self.tanimoto: simi = self._get_fp_similarity( col["COXX_FP"], @@ -465,26 +474,38 @@ def _get_fp_similarity( def _fingerprint_df(self, path_to_lobster_calc): dir_name = Path(path_to_lobster_calc) if self.are_cobis and not self.are_coops: - if (dir_name / "COBICAR.lobster.gz").exists() and (dir_name / "ICOBILIST.lobster.gz").exists(): + if (dir_name / "COBICAR.lobster.gz").exists() and ( + dir_name / "ICOBILIST.lobster.gz" + ).exists(): coxxcar_path = dir_name / "COBICAR.lobster.gz" icoxxlist_path = dir_name / "ICOBILIST.lobster.gz" else: - raise Exception("COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in " - "{}".format(dir_name.name)) + raise Exception( + "COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in " + "{}".format(dir_name.name) + ) elif self.are_coops and not self.are_cobis: - if (dir_name / "COOPCAR.lobster.gz").exists() and (dir_name / "ICOOPLIST.lobster.gz").exists(): + if (dir_name / "COOPCAR.lobster.gz").exists() and ( + dir_name / "ICOOPLIST.lobster.gz" + ).exists(): coxxcar_path = dir_name / "COOPCAR.lobster.gz" icoxxlist_path = dir_name / "ICOOPLIST.lobster.gz" else: - raise Exception("COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in " - "{}".format(dir_name.name)) + raise Exception( + "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in " + "{}".format(dir_name.name) + ) else: - if (dir_name / "COHPCAR.lobster.gz").exists() and (dir_name / "ICOHPLIST.lobster.gz").exists(): + if (dir_name / "COHPCAR.lobster.gz").exists() and ( + dir_name / "ICOHPLIST.lobster.gz" + ).exists(): coxxcar_path = dir_name / "COHPCAR.lobster.gz" icoxxlist_path = dir_name / "ICOHPLIST.lobster.gz" else: - raise Exception("COHPCAR.lobster.gz or ICOHPLIST.lobster.gz file not found in " - "{}".format(dir_name.name)) + raise Exception( + "COHPCAR.lobster.gz or ICOHPLIST.lobster.gz file not found in " + "{}".format(dir_name.name) + ) if (dir_name / "POSCAR.gz").exists(): structure_path = dir_name / "POSCAR.gz" diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 57792833..83d6c1f4 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -24,7 +24,7 @@ from scipy.integrate import trapezoid from lobsterpy.cohp.analyze import Analysis -warnings.filterwarnings('ignore') +warnings.filterwarnings("ignore") class FeaturizeLobsterpy: @@ -163,7 +163,12 @@ def _get_lobsterpy_cba_dict(self): else: which_bonds = "cation-anion" - if icohplist_path.exists() and cohpcar_path.exists() and charge_path.exists() and structure_path.exists(): + if ( + icohplist_path.exists() + and cohpcar_path.exists() + and charge_path.exists() + and structure_path.exists() + ): analyse = Analysis( path_to_poscar=structure_path, path_to_icohplist=icohplist_path, @@ -178,9 +183,11 @@ def _get_lobsterpy_cba_dict(self): {self.bonds: {"lobsterpy_data": analyse.condensed_bonding_analysis}} ) else: - raise Exception("Path provided for Lobster calc directory seems incorrect." - "It does not contain COHPCAR.lobster.gz, ICOHPLIST.lobster.gz, POSCAR.gz and " - "CHARGE.lobster.gz files needed for automatic analysis using LobsterPy") + raise Exception( + "Path provided for Lobster calc directory seems incorrect." + "It does not contain COHPCAR.lobster.gz, ICOHPLIST.lobster.gz, POSCAR.gz and " + "CHARGE.lobster.gz files needed for automatic analysis using LobsterPy" + ) if madelung_energies_path.exists(): madelung_obj = MadelungEnergies(filename=str(madelung_energies_path)) @@ -194,7 +201,8 @@ def _get_lobsterpy_cba_dict(self): else: warnings.warn( "MadelungEnergies.lobster.gz file not found in Lobster calc directory provided" - " Will set Madelung Engeries for crystal structure values to NaN") + " Will set Madelung Engeries for crystal structure values to NaN" + ) madelung_energies = { "Mulliken": np.nan, @@ -207,8 +215,8 @@ def _get_lobsterpy_cba_dict(self): coxx_fingerprint = namedtuple( - "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" - ) + "coxx_fingerprint", "energies coxx fp_type spin_type n_bins bin_width" +) class FeaturizeCOXX: @@ -316,7 +324,9 @@ def get_coxx_fingerprint_df( try: coxx_all = coxxcar_obj[Spin.down] except KeyError: - raise ValueError('LOSBTER calculation is non-spin polarized. Please switch spin_type to "up"') + raise ValueError( + 'LOSBTER calculation is non-spin polarized. Please switch spin_type to "up"' + ) elif spin_type == "summed": try: coxx_all = coxxcar_obj[Spin.up] + coxxcar_obj[Spin.down] @@ -324,8 +334,7 @@ def get_coxx_fingerprint_df( coxx_all = coxxcar_obj[Spin.up] else: raise Exception( - "Check the spin_type argument." - "Possible options are summed/up/down" + "Check the spin_type argument." "Possible options are summed/up/down" ) coxx_dict = {} @@ -697,7 +706,6 @@ def __init__( path_to_charge: str, charge_type: str, ): - self.path_to_structure = path_to_structure self.path_to_charge = path_to_charge self.charge_type = charge_type diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index 4fa0ff37..d232cf4b 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -367,7 +367,7 @@ def test_batch_summary_featurizer_exception(self): with self.assertRaises(Exception) as err: self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir - / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/1/", + / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/1/", bonds="all_bonds", feature_type="antibonding", include_cobi_data=True, @@ -385,7 +385,7 @@ def test_batch_summary_featurizer_exception(self): with self.assertRaises(Exception) as err: self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir - / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/2/", + / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/2/", bonds="all_bonds", feature_type="antibonding", include_cobi_data=True, diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 051e121f..d28add8a 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -363,7 +363,8 @@ def test_featurize_coxx(self): self.assertEqual( err.exception.__str__(), - "Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall") + "Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall", + ) with self.assertRaises(Exception) as err2: self.featurize_COXX = FeaturizeCOXX( @@ -374,12 +375,12 @@ def test_featurize_coxx(self): e_range=[-5, 0], ) - self.featurize_COXX.get_coxx_fingerprint_df(spin_type='-1') + self.featurize_COXX.get_coxx_fingerprint_df(spin_type="-1") self.assertEqual( err2.exception.__str__(), - "Check the spin_type argument." - "Possible options are summed/up/down") + "Check the spin_type argument." "Possible options are summed/up/down", + ) with self.assertRaises(Exception) as err3: self.featurize_COXX = FeaturizeCOXX( @@ -389,16 +390,15 @@ def test_featurize_coxx(self): feature_type="bonding", e_range=[-5, 0], are_cobis=True, - are_coops=True + are_coops=True, ) self.featurize_COXX.get_coxx_fingerprint_df() self.assertEqual( err3.exception.__str__(), - "You cannot have info about COOPs and COBIs in the same file.") - - + "You cannot have info about COOPs and COBIs in the same file.", + ) if __name__ == "__main__": From b5681901e4949c3e2068604b7b3cfd52dbe01cef Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 09:51:49 +0200 Subject: [PATCH 23/41] sort index of dataframe returned by batch featurizers --- lobsterpy/featurize/batch.py | 36 ++++++++++++++++---------- lobsterpy/featurize/core.py | 6 +++-- lobsterpy/featurize/test/test_batch.py | 19 ++++++++++---- 3 files changed, 40 insertions(+), 21 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 3ec64641..6e34bb18 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -264,7 +264,7 @@ def get_df(self): with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( - pool.imap(self._featurizelobsterpy, file_name_or_path, chunksize=1), + pool.imap_unordered(self._featurizelobsterpy, file_name_or_path, chunksize=1), total=len(file_name_or_path), desc="Generating LobsterPy summary stats", ) @@ -273,6 +273,7 @@ def get_df(self): row.append(result) df_lobsterpy = pd.concat(row) + df_lobsterpy.sort_index(inplace=True) if self.only_smallest_basis: paths = [ @@ -294,7 +295,7 @@ def get_df(self): with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( - pool.imap(self._featurizecoxx, paths, chunksize=1), + pool.imap_unordered(self._featurizecoxx, paths, chunksize=1), total=len(paths), desc="Generating COHP/COOP/COBI summary stats", ) @@ -303,10 +304,11 @@ def get_df(self): row.append(result) df_coxx = pd.concat(row) + df_coxx.sort_index(inplace=True) with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( - pool.imap(self._featurizecharges, paths, chunksize=1), + pool.imap_unordered(self._featurizecharges, paths, chunksize=1), total=len(paths), desc="Generating charge based features", ) @@ -315,6 +317,7 @@ def get_df(self): row.append(result) df_charges = pd.concat(row) + df_charges.sort_index(inplace=True) df = pd.concat([df_lobsterpy, df_coxx, df_charges], axis=1) @@ -338,8 +341,7 @@ def __init__( n_bins: int = 56, e_range: List[float] = [-15.0, 0.0], n_jobs=4, - are_cobis: bool = False, - are_coops: bool = False, + fingerprint_for: str = 'cohp', ): self.path_to_lobster_calcs = path_to_lobster_calcs self.only_smallest_basis = only_smallest_basis @@ -351,8 +353,7 @@ def __init__( self.n_bins = n_bins self.e_range = e_range self.n_jobs = n_jobs - self.are_cobis = are_cobis - self.are_coops = are_coops + self.fingerprint_for = fingerprint_for self.fingerprint_df = self._get_fingerprints_df() @@ -368,7 +369,6 @@ def get_similarity_matrix_df(self): ) for i, (row, col) in enumerate(self.fingerprint_df.iterrows()): for j, (row1, col1) in enumerate(self.fingerprint_df.iterrows()): - # if i <= j: if self.tanimoto: simi = self._get_fp_similarity( col["COXX_FP"], @@ -473,23 +473,28 @@ def _get_fp_similarity( def _fingerprint_df(self, path_to_lobster_calc): dir_name = Path(path_to_lobster_calc) - if self.are_cobis and not self.are_coops: + + if self.fingerprint_for.upper() == 'COBI': if (dir_name / "COBICAR.lobster.gz").exists() and ( dir_name / "ICOBILIST.lobster.gz" ).exists(): coxxcar_path = dir_name / "COBICAR.lobster.gz" icoxxlist_path = dir_name / "ICOBILIST.lobster.gz" + are_cobis = True + are_coops = False else: raise Exception( "COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in " "{}".format(dir_name.name) ) - elif self.are_coops and not self.are_cobis: + elif self.fingerprint_for.upper() == 'COOP': if (dir_name / "COOPCAR.lobster.gz").exists() and ( dir_name / "ICOOPLIST.lobster.gz" ).exists(): coxxcar_path = dir_name / "COOPCAR.lobster.gz" icoxxlist_path = dir_name / "ICOOPLIST.lobster.gz" + are_cobis = False + are_coops = True else: raise Exception( "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in " @@ -501,6 +506,8 @@ def _fingerprint_df(self, path_to_lobster_calc): ).exists(): coxxcar_path = dir_name / "COHPCAR.lobster.gz" icoxxlist_path = dir_name / "ICOHPLIST.lobster.gz" + are_cobis = False + are_coops = False else: raise Exception( "COHPCAR.lobster.gz or ICOHPLIST.lobster.gz file not found in " @@ -518,8 +525,8 @@ def _fingerprint_df(self, path_to_lobster_calc): path_to_structure=structure_path, feature_type=self.feature_type, e_range=self.e_range, - are_coops=self.are_coops, - are_cobis=self.are_cobis, + are_coops=are_coops, + are_cobis=are_cobis, ) df_fp = coxx.get_coxx_fingerprint_df( @@ -551,14 +558,15 @@ def _get_fingerprints_df(self): with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( - pool.imap(self._fingerprint_df, paths, chunksize=1), + pool.imap_unordered(self._fingerprint_df, paths, chunksize=1), total=len(paths), - desc="Generating COHP/COBI/COOP fingerprints", + desc="Generating {} fingerprints".format(self.fingerprint_for.upper()), ) row = [] for result in results: row.append(result) df = pd.concat(row) + df.sort_index(inplace=True) return df diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 83d6c1f4..6b84d10e 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -265,6 +265,8 @@ def __init__( fmt="LOBSTER", filename=self.path_to_coxxcar, structure_file=self.path_to_structure, + are_cobis=self.are_cobis, + are_coops=self.are_coops, ) def get_coxx_fingerprint_df( @@ -277,7 +279,7 @@ def get_coxx_fingerprint_df( normalize: bool = True, ): """ - Generates the COHP fingerprint + Generates the COXX fingerprint Args: ids: sets index of pandas dataframe @@ -293,7 +295,7 @@ def get_coxx_fingerprint_df( ValueError: If feature_type is not one of the accepted values {bonding/antibonding/overall}. Returns: - Fingerprint(namedtuple) : The COHP fingerprint + Fingerprint(namedtuple) : A pandas dataframe with the COXX fingerprint of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) """ coxxcar_obj = self.completecoxx diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index d232cf4b..f7d1f64e 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -20,6 +20,7 @@ def setUp(self): include_cobi_data=False, include_coop_data=False, e_range=[-15, 0], + n_jobs=3, ) self.summary_featurize_without_json = BatchSummaryFeaturizer( @@ -29,6 +30,7 @@ def setUp(self): include_cobi_data=False, include_coop_data=False, e_range=[-15, 0], + n_jobs=3, ) self.summary_featurize_with_json_overall = BatchSummaryFeaturizer( @@ -40,6 +42,7 @@ def setUp(self): include_cobi_data=True, include_coop_data=True, e_range=[-15, 0], + n_jobs=3, ) self.summary_featurize_with_json_bonding = BatchSummaryFeaturizer( @@ -52,6 +55,7 @@ def setUp(self): include_coop_data=False, e_range=[-15, 0], charge_type="mulliken", + n_jobs=3, ) self.summary_featurize_with_json_antibonding = BatchSummaryFeaturizer( @@ -64,6 +68,7 @@ def setUp(self): include_coop_data=False, e_range=[-15, 0], charge_type="loewdin", + n_jobs=3, ) def test_summary_featurize_with_json(self): @@ -104,7 +109,7 @@ def test_summary_featurize_with_json(self): self.assertEqual(list(df.columns), expected_cols) - expected_index = ["mp-463", "mp-1000", "mp-2176"] + expected_index = ['mp-1000', 'mp-2176', 'mp-463'] self.assertEqual(list(df.index), expected_index) @@ -146,7 +151,7 @@ def test_summary_featurize_without_json(self): self.assertEqual(list(df.columns), expected_cols) - expected_index = ["mp-463", "mp-1000", "mp-2176"] + expected_index = ['mp-1000', 'mp-2176', 'mp-463'] self.assertEqual(list(df.index), expected_index) @@ -204,7 +209,7 @@ def test_summary_featurize_with_json_overall(self): self.assertEqual(list(df.columns), expected_cols) - expected_index = ["mp-463", "mp-1000", "mp-2176"] + expected_index = ['mp-1000', 'mp-2176', 'mp-463'] self.assertEqual(list(df.index), expected_index) @@ -292,6 +297,7 @@ def setUp(self): feature_type="overall", normalize=True, tanimoto=True, + n_jobs=3, ) self.fp_cohp_bonding = BatchCoxxFingerprint( @@ -301,6 +307,7 @@ def setUp(self): feature_type="bonding", normalize=False, tanimoto=True, + n_jobs=3, ) self.fp_cobi = BatchCoxxFingerprint( @@ -310,7 +317,8 @@ def setUp(self): feature_type="antibonding", normalize=True, tanimoto=True, - are_cobis=True, + fingerprint_for='cobi', + n_jobs=3, ) self.fp_coop = BatchCoxxFingerprint( @@ -320,7 +328,8 @@ def setUp(self): feature_type="bonding", normalize=True, tanimoto=False, - are_coops=True, + fingerprint_for='coop', + n_jobs=3, ) def test_fp_cohp_overall(self): From 19937e665b4b0d5a090e0c7ac9049257f4a327d6 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 09:52:42 +0200 Subject: [PATCH 24/41] run black --- lobsterpy/featurize/batch.py | 10 ++++++---- lobsterpy/featurize/test/test_batch.py | 10 +++++----- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 6e34bb18..0775a5f6 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -264,7 +264,9 @@ def get_df(self): with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( - pool.imap_unordered(self._featurizelobsterpy, file_name_or_path, chunksize=1), + pool.imap_unordered( + self._featurizelobsterpy, file_name_or_path, chunksize=1 + ), total=len(file_name_or_path), desc="Generating LobsterPy summary stats", ) @@ -341,7 +343,7 @@ def __init__( n_bins: int = 56, e_range: List[float] = [-15.0, 0.0], n_jobs=4, - fingerprint_for: str = 'cohp', + fingerprint_for: str = "cohp", ): self.path_to_lobster_calcs = path_to_lobster_calcs self.only_smallest_basis = only_smallest_basis @@ -474,7 +476,7 @@ def _get_fp_similarity( def _fingerprint_df(self, path_to_lobster_calc): dir_name = Path(path_to_lobster_calc) - if self.fingerprint_for.upper() == 'COBI': + if self.fingerprint_for.upper() == "COBI": if (dir_name / "COBICAR.lobster.gz").exists() and ( dir_name / "ICOBILIST.lobster.gz" ).exists(): @@ -487,7 +489,7 @@ def _fingerprint_df(self, path_to_lobster_calc): "COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in " "{}".format(dir_name.name) ) - elif self.fingerprint_for.upper() == 'COOP': + elif self.fingerprint_for.upper() == "COOP": if (dir_name / "COOPCAR.lobster.gz").exists() and ( dir_name / "ICOOPLIST.lobster.gz" ).exists(): diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index f7d1f64e..2f34c96b 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -109,7 +109,7 @@ def test_summary_featurize_with_json(self): self.assertEqual(list(df.columns), expected_cols) - expected_index = ['mp-1000', 'mp-2176', 'mp-463'] + expected_index = ["mp-1000", "mp-2176", "mp-463"] self.assertEqual(list(df.index), expected_index) @@ -151,7 +151,7 @@ def test_summary_featurize_without_json(self): self.assertEqual(list(df.columns), expected_cols) - expected_index = ['mp-1000', 'mp-2176', 'mp-463'] + expected_index = ["mp-1000", "mp-2176", "mp-463"] self.assertEqual(list(df.index), expected_index) @@ -209,7 +209,7 @@ def test_summary_featurize_with_json_overall(self): self.assertEqual(list(df.columns), expected_cols) - expected_index = ['mp-1000', 'mp-2176', 'mp-463'] + expected_index = ["mp-1000", "mp-2176", "mp-463"] self.assertEqual(list(df.index), expected_index) @@ -317,7 +317,7 @@ def setUp(self): feature_type="antibonding", normalize=True, tanimoto=True, - fingerprint_for='cobi', + fingerprint_for="cobi", n_jobs=3, ) @@ -328,7 +328,7 @@ def setUp(self): feature_type="bonding", normalize=True, tanimoto=False, - fingerprint_for='coop', + fingerprint_for="coop", n_jobs=3, ) From a7d08897827db7efa60d1dfe289fcc2d0331ee43 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 13:22:06 +0200 Subject: [PATCH 25/41] added .coveragerc, as multiprocessing calls are not captured without it --- .coveragerc | 2 ++ lobsterpy/featurize/batch.py | 8 ++++++ lobsterpy/featurize/test/test_core.py | 41 +++++++++++++++++++++++++-- 3 files changed, 49 insertions(+), 2 deletions(-) create mode 100644 .coveragerc diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 00000000..7781a1d6 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,2 @@ +[run] +concurrency = multiprocessing \ No newline at end of file diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 0775a5f6..f45c3258 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -270,6 +270,8 @@ def get_df(self): total=len(file_name_or_path), desc="Generating LobsterPy summary stats", ) + pool.close() + pool.join() row = [] for result in results: row.append(result) @@ -301,6 +303,8 @@ def get_df(self): total=len(paths), desc="Generating COHP/COOP/COBI summary stats", ) + pool.close() + pool.join() row = [] for result in results: row.append(result) @@ -314,6 +318,8 @@ def get_df(self): total=len(paths), desc="Generating charge based features", ) + pool.close() + pool.join() row = [] for result in results: row.append(result) @@ -564,6 +570,8 @@ def _get_fingerprints_df(self): total=len(paths), desc="Generating {} fingerprints".format(self.fingerprint_for.upper()), ) + pool.close() + pool.join() row = [] for result in results: row.append(result) diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index d28add8a..f1af2829 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -14,6 +14,11 @@ def setUp(self): self.featurize_mp1249_json = FeaturizeLobsterpy( path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", bonds="all_bonds" ) + + self.featurize_mp1249_json_ca = FeaturizeLobsterpy( + path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", + bonds="cation_anion_bonds", + ) self.featurize_mp1958_json = FeaturizeLobsterpy( path_to_json=TestDir / "TestData/JSONS/mp-1958.json.gz", bonds="all_bonds" ) @@ -63,8 +68,40 @@ def test_featurize_mp1249_json(self): df.loc["mp-1249", "bonding_perc_avg"], 0.978985, places=5 ) + def test_featurize_mp1249_json_ca(self): + df = self.featurize_mp1249_json_ca.get_df(ids="mp-1249") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "Icohp_mean_avg", + "Icohp_mean_max", + "Icohp_mean_min", + "Icohp_mean_std", + "Icohp_sum_avg", + "Icohp_sum_max", + "Icohp_sum_min", + "Icohp_sum_std", + "bonding_perc_avg", + "bonding_perc_max", + "bonding_perc_min", + "bonding_perc_std", + "antibonding_perc_avg", + "antibonding_perc_min", + "antibonding_perc_max", + "antibonding_perc_std", + "Madelung_Mull", + "Madelung_Loew", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "mp-1249") + def test_featurize_mp1958_json(self): - df = self.featurize_mp1958_json.get_df(ids="mp-1958") + df = self.featurize_mp1958_json.get_df() # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) @@ -217,7 +254,7 @@ def test_featurize_NaCl_COXX(self): self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 1, places=5) def test_featurize_CdF_COXX(self): - df = self.featurize_CdF_COXX.get_summarized_coxx_df(ids="CdF") + df = self.featurize_CdF_COXX.get_summarized_coxx_df() # Test that the function returns a pandas DataFrame self.assertIsInstance(df, pd.DataFrame) From 34992291913f62725c1abfe6f4c2279b1d18af17 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 13:32:57 +0200 Subject: [PATCH 26/41] update workflow to get correct coverage report --- .github/workflows/python-package.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 99bff9af..2a7dd706 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -40,4 +40,5 @@ jobs: - name: Test with pytest and coverage run: | coverage run -m pytest + coverage combine coverage report From 40656f168c9601a6cf3a8a8809950be2ff81bd73 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 14:44:45 +0200 Subject: [PATCH 27/41] add missing doc string --- lobsterpy/featurize/batch.py | 74 +++++++++++++++++++++++++++++++++--- 1 file changed, 68 insertions(+), 6 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index f45c3258..412019ff 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -24,7 +24,7 @@ class BatchSummaryFeaturizer: """ - Featurizer sets that generates summary features from lobster data. + Batch Featurizer sets that generates summary features from lobster data. Args: path_to_lobster_calcs: path to root directory consisting of all lobster calc @@ -40,7 +40,7 @@ class BatchSummaryFeaturizer: n_jobs : parallel processes to run Attributes: - get_df: a pandas dataframe with summary features + get_df: A pandas dataframe with summary features """ def __init__( @@ -68,6 +68,15 @@ def __init__( self.n_jobs = n_jobs def _featurizelobsterpy(self, file_name_or_path): + """ + Wrapper method to featurize Lobsterpy condensed bonding analysis data by loading lightweight json + if json file exists or invokes lobsterpy.analzye.Analysis module + + Returns: + A pandas dataframe with ICOHP stats like mean, min, max of relevant bonds and + madelung energies + + """ if os.path.isfile(file_name_or_path): featurize_lobsterpy = FeaturizeLobsterpy( path_to_json=file_name_or_path, @@ -85,6 +94,14 @@ def _featurizelobsterpy(self, file_name_or_path): return df def _featurizecoxx(self, path_to_lobster_calc): + """ + Wrapper method to featurize COHP/COBI/COOPCAR data that uses FeaturizeCOXX under the hood + + Returns: + A pandas dataframe with COHP summary stats data mainly weighted ICOHP/ICOOP/ICOBI, + Effective interaction number and moment features (center, width, skewness and kurtosis) + + """ dir_name = Path(path_to_lobster_calc) coxxcar_path = dir_name / "COHPCAR.lobster.gz" structure_path = dir_name / "POSCAR.gz" @@ -166,6 +183,13 @@ def _featurizecoxx(self, path_to_lobster_calc): return df def _featurizecharges(self, path_to_lobster_calc): + """ + Wrapper method to featurize CHARGE.lobster.gz data that uses FeaturizeCharges under the hood + + Returns: + A pandas dataframe with computed ionicity for the structure + + """ dir_name = Path(path_to_lobster_calc) charge_path = dir_name / "CHARGE.lobster.gz" structure_path = dir_name / "POSCAR.gz" @@ -210,11 +234,11 @@ def _featurizecharges(self, path_to_lobster_calc): def get_df(self): """ - This function featurizes LobsterPy condensed bonding analysis data from - lobster lightweight json.gz files + This method will return a pandas dataframe with summary features extracted from LOBSTER files + as columns. Uses multiprocessing to speed up the process. Returns: - Returns a pandas dataframe with lobster summar icohp statistics + Returns a pandas dataframe """ if self.path_to_jsons and self.only_smallest_basis: @@ -334,7 +358,28 @@ def get_df(self): class BatchCoxxFingerprint: """ - Featurizer that generates Tanimoto index similarity matrix summary features from fingerprint objects. + BatchFeaturizer that generates COHP/COOP/COBI fingerprints and + Tanimoto index similarity matrix from fingerprint objects. + + Args: + path_to_lobster_calcs: path to root directory consisting of all lobster calc + only_smallest_basis: bool to state to include directories with smallest basis only + feature_type: set the feature type for moment features. + Possible options are "bonding", "antibonding" or "overall" + label_list: bond labels list for which fingerprints needs to be generated. + tanimoto : bool to state to compute tanimoto index betweeen fingerprint objects + normalize: bool to state to normalize the fingerprint data + n_bins: sets number for bins for fingerprint objects + e_range : range of energy relative to fermi for which moment features needs to be computed + n_jobs : number of parallel processes to run + fingerprint_for: Possible options are 'cohp/cobi/coop'. + Based on this fingerprints will be computed for COHPCAR/COOBICAR/COOPCAR.lobster files + + Attributes: + fingerprint_df: A pandas dataframe with fingerprint objects + get_similarity_matrix_df: A symmetric pandas dataframe consisting of + similarity index (tanimoto/normalized dot product/dot product) + computed between all pairs of compunds """ def __init__( @@ -442,6 +487,7 @@ def _get_fp_similarity( Returns: Similarity index (float): The value of dot product + """ fp1_dict = ( BatchCoxxFingerprint._fp_to_dict(fp1) if not isinstance(fp1, dict) else fp1 @@ -480,6 +526,14 @@ def _get_fp_similarity( ) def _fingerprint_df(self, path_to_lobster_calc): + """ + Wrapper method to get fingerprint object dataframe using FeaturizeCOXX.get_coxx_fingerprint_df method. + Also helps switching the data used for fingerprint generation + + Returns: + A pandas dataframe with COXX fingerprint object + + """ dir_name = Path(path_to_lobster_calc) if self.fingerprint_for.upper() == "COBI": @@ -546,6 +600,14 @@ def _fingerprint_df(self, path_to_lobster_calc): return df_fp def _get_fingerprints_df(self): + """ + Batch wrapper method to get fingerprint objects dataframe using + BatchCoxxFingerprint._fingerprint_df method. + + Returns: + A pandas dataframe with COXX fingerprint objects + + """ if self.only_smallest_basis: paths = [ os.path.join(self.path_to_lobster_calcs, f) From 348ec291f535920cb62cc6148a3982b05b8bdc42 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 16:01:03 +0200 Subject: [PATCH 28/41] add missing annotation hints, fix some linting issues --- docs/source/conf.py | 6 ++- lobsterpy/featurize/batch.py | 58 ++++++++++++------------ lobsterpy/featurize/core.py | 63 +++++++++++++++----------- lobsterpy/featurize/test/test_batch.py | 2 - lobsterpy/featurize/test/test_core.py | 2 - 5 files changed, 69 insertions(+), 62 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 2f7e216a..09ea8fc0 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -48,7 +48,9 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["../../lobsterpy/test","../../lobsterpy/cohp/test","../../lobsterpy/TestData","Thumbs.db", ".DS_Store",] +exclude_patterns = ["../../lobsterpy/test", "../../lobsterpy/cohp/test", + "../../lobsterpy/TestData", "Thumbs.db", ".DS_Store"] + def run_apidoc(_): import subprocess @@ -59,7 +61,7 @@ def run_apidoc(_): excludes1 = glob.glob(os.path.join(output_path, "../../lobsterpy/test")) module = os.path.join(output_path, "../../lobsterpy") cmd_path = "sphinx-apidoc" - command = [cmd_path, "-e", "-o", output_path, module, " ".join(excludes)," ".join(excludes1), "--force"] + command = [cmd_path, "-e", "-o", output_path, module, " ".join(excludes), " ".join(excludes1), "--force"] subprocess.check_call(command) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 412019ff..9954ab3c 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -67,7 +67,7 @@ def __init__( self.e_range = e_range self.n_jobs = n_jobs - def _featurizelobsterpy(self, file_name_or_path): + def _featurizelobsterpy(self, file_name_or_path) -> pd.DataFrame: """ Wrapper method to featurize Lobsterpy condensed bonding analysis data by loading lightweight json if json file exists or invokes lobsterpy.analzye.Analysis module @@ -93,7 +93,7 @@ def _featurizelobsterpy(self, file_name_or_path): return df - def _featurizecoxx(self, path_to_lobster_calc): + def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: """ Wrapper method to featurize COHP/COBI/COOPCAR data that uses FeaturizeCOXX under the hood @@ -113,9 +113,9 @@ def _featurizecoxx(self, path_to_lobster_calc): and icoxxlist_path.exists() ): coxx = FeaturizeCOXX( - path_to_coxxcar=coxxcar_path, - path_to_icoxxlist=icoxxlist_path, - path_to_structure=structure_path, + path_to_coxxcar=str(coxxcar_path), + path_to_icoxxlist=str(icoxxlist_path), + path_to_structure=str(structure_path), feature_type=self.feature_type, e_range=self.e_range, ) @@ -133,9 +133,9 @@ def _featurizecoxx(self, path_to_lobster_calc): if coxxcar_path.exists() and icoxxlist_path.exists(): coxx = FeaturizeCOXX( - path_to_coxxcar=coxxcar_path, - path_to_icoxxlist=icoxxlist_path, - path_to_structure=structure_path, + path_to_coxxcar=str(coxxcar_path), + path_to_icoxxlist=str(icoxxlist_path), + path_to_structure=str(structure_path), feature_type=self.feature_type, e_range=self.e_range, are_cobis=True, @@ -155,9 +155,9 @@ def _featurizecoxx(self, path_to_lobster_calc): if coxxcar_path.exists() and icoxxlist_path.exists(): coxx = FeaturizeCOXX( - path_to_coxxcar=coxxcar_path, - path_to_icoxxlist=icoxxlist_path, - path_to_structure=structure_path, + path_to_coxxcar=str(coxxcar_path), + path_to_icoxxlist=str(icoxxlist_path), + path_to_structure=str(structure_path), feature_type=self.feature_type, e_range=self.e_range, are_coops=True, @@ -182,7 +182,7 @@ def _featurizecoxx(self, path_to_lobster_calc): return df - def _featurizecharges(self, path_to_lobster_calc): + def _featurizecharges(self, path_to_lobster_calc) -> pd.DataFrame: """ Wrapper method to featurize CHARGE.lobster.gz data that uses FeaturizeCharges under the hood @@ -197,29 +197,29 @@ def _featurizecharges(self, path_to_lobster_calc): if charge_path.exists() and structure_path.exists(): if self.charge_type == "mulliken": charge_mull = FeaturizeCharges( - path_to_charge=charge_path, - path_to_structure=structure_path, + path_to_charge=str(charge_path), + path_to_structure=str(structure_path), charge_type="mulliken", ) df = charge_mull.get_df() elif self.charge_type == "loewdin": charge_loew = FeaturizeCharges( - path_to_charge=charge_path, - path_to_structure=structure_path, + path_to_charge=str(charge_path), + path_to_structure=str(structure_path), charge_type="loewdin", ) df = charge_loew.get_df() elif self.charge_type == "both": charge_mull = FeaturizeCharges( - path_to_charge=charge_path, - path_to_structure=structure_path, + path_to_charge=str(charge_path), + path_to_structure=str(structure_path), charge_type="mulliken", ) df_mull = charge_mull.get_df() charge_loew = FeaturizeCharges( - path_to_charge=charge_path, - path_to_structure=structure_path, + path_to_charge=str(charge_path), + path_to_structure=str(structure_path), charge_type="loewdin", ) df_loew = charge_loew.get_df() @@ -232,7 +232,7 @@ def _featurizecharges(self, path_to_lobster_calc): "CHARGE.lobster.gz or POSCAR.gz not found in {}".format(dir_name.name) ) - def get_df(self): + def get_df(self) -> pd.DataFrame: """ This method will return a pandas dataframe with summary features extracted from LOBSTER files as columns. Uses multiprocessing to speed up the process. @@ -410,7 +410,7 @@ def __init__( self.fingerprint_df = self._get_fingerprints_df() - def get_similarity_matrix_df(self): + def get_similarity_matrix_df(self) -> pd.DataFrame: """ This function will compute pairwise similarity index for each fingerprint object in input dataframe @@ -464,8 +464,8 @@ def _fp_to_dict(fp) -> dict: @staticmethod def _get_fp_similarity( - fp1, - fp2, + fp1: NamedTuple, + fp2: NamedTuple, col: int = 1, pt: int | str = "All", normalize: bool = False, @@ -525,7 +525,7 @@ def _get_fp_similarity( "Cannot compute similarity index. Please set either normalize=True or tanimoto=True or both to False." ) - def _fingerprint_df(self, path_to_lobster_calc): + def _fingerprint_df(self, path_to_lobster_calc) -> pd.DataFrame: """ Wrapper method to get fingerprint object dataframe using FeaturizeCOXX.get_coxx_fingerprint_df method. Also helps switching the data used for fingerprint generation @@ -582,9 +582,9 @@ def _fingerprint_df(self, path_to_lobster_calc): raise Exception("POSCAR.gz file not found in {}".format(dir_name.name)) coxx = FeaturizeCOXX( - path_to_coxxcar=coxxcar_path, - path_to_icoxxlist=icoxxlist_path, - path_to_structure=structure_path, + path_to_coxxcar=str(coxxcar_path), + path_to_icoxxlist=str(icoxxlist_path), + path_to_structure=str(structure_path), feature_type=self.feature_type, e_range=self.e_range, are_coops=are_coops, @@ -599,7 +599,7 @@ def _fingerprint_df(self, path_to_lobster_calc): ) return df_fp - def _get_fingerprints_df(self): + def _get_fingerprints_df(self) -> pd.DataFrame: """ Batch wrapper method to get fingerprint objects dataframe using BatchCoxxFingerprint._fingerprint_df method. diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 6b84d10e..a22f6180 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -11,14 +11,14 @@ import os import warnings from pathlib import Path -from typing import Mapping, NamedTuple, List +from typing import List, Tuple from collections import namedtuple import numpy as np import numpy.typing as npt import pandas as pd from mendeleev import element from pymatgen.core.structure import Structure -from pymatgen.io.lobster import Charge, Lobsterout, Icohplist, MadelungEnergies +from pymatgen.io.lobster import Charge, Icohplist, MadelungEnergies from pymatgen.electronic_structure.cohp import CompleteCohp from pymatgen.electronic_structure.core import Spin from scipy.integrate import trapezoid @@ -124,7 +124,7 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: return df - def _read_lobster_lightweight_json(self): + def _read_lobster_lightweight_json(self) -> dict: """ This function reads loads the lightweight json.gz files and returns a python dictionary object with lobster summmarized bonding analysis data. @@ -142,7 +142,7 @@ def _read_lobster_lightweight_json(self): return lobster_data - def _get_lobsterpy_cba_dict(self): + def _get_lobsterpy_cba_dict(self) -> dict: """ This function uses lobsterpy.cohp.analyze.Analysis class to generate a python dictionary object with lobster summmarized bonding analysis data. @@ -170,10 +170,10 @@ def _get_lobsterpy_cba_dict(self): and structure_path.exists() ): analyse = Analysis( - path_to_poscar=structure_path, - path_to_icohplist=icohplist_path, - path_to_cohpcar=cohpcar_path, - path_to_charge=charge_path, + path_to_poscar=str(structure_path), + path_to_icohplist=str(icohplist_path), + path_to_cohpcar=str(cohpcar_path), + path_to_charge=str(charge_path), summed_spins=False, # we will always use spin polarization here cutoff_icohp=0.10, whichbonds=which_bonds, @@ -277,7 +277,7 @@ def get_coxx_fingerprint_df( binning: bool = True, n_bins: int = 56, normalize: bool = True, - ): + ) -> pd.DataFrame: """ Generates the COXX fingerprint @@ -295,8 +295,9 @@ def get_coxx_fingerprint_df( ValueError: If feature_type is not one of the accepted values {bonding/antibonding/overall}. Returns: - Fingerprint(namedtuple) : A pandas dataframe with the COXX fingerprint + A pandas dataframe with the COXX fingerprint of format (energies, coxx, fp_type, spin_type, n_bins, bin_width) + """ coxxcar_obj = self.completecoxx @@ -358,10 +359,16 @@ def get_coxx_fingerprint_df( coxx_dict["overall"] = coxx_all try: + if ids: + df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) + else: + ids = os.path.basename(os.path.dirname(self.path_to_coxxcar)) + df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) + coxxs = coxx_dict[self.feature_type] if len(energies) < n_bins: inds = np.where((energies >= min_e) & (energies <= max_e)) - return coxx_fingerprint( + fp = coxx_fingerprint( energies[inds], coxxs[inds], self.feature_type, @@ -370,6 +377,10 @@ def get_coxx_fingerprint_df( np.diff(energies)[0], ) + df.at[ids, "COXX_FP"] = fp + + return df + if binning: ener_bounds = np.linspace(min_e, max_e, n_bins + 1) ener = ener_bounds[:-1] + (ener_bounds[1] - ener_bounds[0]) / 2.0 @@ -400,12 +411,6 @@ def get_coxx_fingerprint_df( bin_width, ) - if ids: - df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) - else: - ids = os.path.basename(os.path.dirname(self.path_to_coxxcar)) - df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) - df.at[ids, "COXX_FP"] = fp return df @@ -415,7 +420,7 @@ def get_coxx_fingerprint_df( "Please recheck fp_type requested argument.Possible options are bonding/antibonding/overall" ) - def _calculate_wicoxx_ein(self): + def _calculate_wicoxx_ein(self) -> Tuple[float, float, float, float]: """ Method to calculate weighted icoxx (xx ==hp,op,bi) and ein of crystal structure based on work by Peter Müller @@ -457,6 +462,13 @@ def _calculate_wicoxx_ein(self): indices = self.completecoxx.energies <= self.completecoxx.efermi en_bf = self.completecoxx.energies[indices] coxx_bf = summed[indices] + + w_icoxx = trapezoid(coxx_bf, en_bf) + + ein = (icoxx_total / w_icoxx) * ( + 2 / self.completecoxx.structure.num_sites + ) # calc effective interaction number + # percent bonding-anitbonding if not self.icoxxlist.are_cobis and not self.icoxxlist.are_coops: bonding_indices = coxx_bf <= 0 @@ -467,6 +479,7 @@ def _calculate_wicoxx_ein(self): ) per_bnd = (bnd / (bnd + antibnd)) * 100 per_antibnd = (antibnd / (bnd + antibnd)) * 100 + elif self.icoxxlist.are_cobis or self.icoxxlist.are_coops: bonding_indices = coxx_bf >= 0 antibonding_indices = coxx_bf <= 0 @@ -477,15 +490,11 @@ def _calculate_wicoxx_ein(self): per_bnd = (bnd / (bnd + antibnd)) * 100 per_antibnd = (antibnd / (bnd + antibnd)) * 100 - w_icoxx = trapezoid(coxx_bf, en_bf) - - ein = (icoxx_total / w_icoxx) * ( - 2 / self.completecoxx.structure.num_sites - ) # calc effective interaction number - return per_bnd, per_antibnd, w_icoxx, ein - def _calc_moment_features(self, label_list: List[str] | None = None): + def _calc_moment_features( + self, label_list: List[str] | None = None + ) -> Tuple[float, float, float, float]: if label_list: coxxcar = self.completecoxx.get_summed_cohp_by_label_list( label_list @@ -632,7 +641,7 @@ def _get_n_moment( def get_summarized_coxx_df( self, ids: str | None = None, label_list: List[str] | None = None - ): + ) -> pd.DataFrame: """ This function returns a pandas dataframe with weighted ICOXX, effective interaction number and moment features (center, width, skewness and kurtosis) of COXX in selected energy range @@ -793,7 +802,7 @@ def _calc_ionicity(self) -> float: return ionicity - def get_df(self, ids: str | None = None): + def get_df(self, ids: str | None = None) -> pd.DataFrame: """ This function returns a pandas dataframe with computed ionicity as column diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index 2f34c96b..c5c0ecfa 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -1,5 +1,3 @@ -import os -import sys import unittest import pandas as pd from pathlib import Path diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index f1af2829..71bd6754 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -1,5 +1,3 @@ -import os -import sys import unittest import pandas as pd from pathlib import Path From 77507c50f2db210c4b277745296792e0f01874c0 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 16:08:44 +0200 Subject: [PATCH 29/41] fix mypy errors --- lobsterpy/featurize/core.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index a22f6180..0e654756 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -133,7 +133,7 @@ def _read_lobster_lightweight_json(self) -> dict: Returns a dictionary with lobster summmarized bonding analysis data """ - with gzip.open(self.path_to_json, "rb") as f: + with gzip.open(str(self.path_to_json), "rb") as f: data = json.loads(f.read().decode("utf-8")) lobster_data = {} @@ -151,7 +151,7 @@ def _get_lobsterpy_cba_dict(self) -> dict: Returns a dictionary with lobster summmarized bonding analysis data """ - dir_name = Path(self.path_to_lobster_calc) + dir_name = Path(str(self.path_to_lobster_calc)) cohpcar_path = dir_name / "COHPCAR.lobster.gz" charge_path = dir_name / "CHARGE.lobster.gz" structure_path = dir_name / "POSCAR.gz" From b2df2b4ed75dee8328d309ae057503bafc5d0ede Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 4 May 2023 16:31:54 +0200 Subject: [PATCH 30/41] remove deprecated disable tags from pylintrc file --- pylintrc | 78 ++++---------------------------------------------------- 1 file changed, 5 insertions(+), 73 deletions(-) diff --git a/pylintrc b/pylintrc index 818c9275..f97b537b 100644 --- a/pylintrc +++ b/pylintrc @@ -60,15 +60,9 @@ confidence= # --enable=similarities". If you want to run only the classes checker, but have # no Warning level messages displayed, use "--disable=all --enable=classes # --disable=W". -disable=print-statement, - parameter-unpacking, - unpacking-in-except, - old-raise-syntax, - backtick, - long-suffix, +disable=long-suffix, old-ne-operator, old-octal-literal, - import-star-module-level, non-ascii-bytes-literal, raw-checker-failed, bad-inline-option, @@ -78,67 +72,6 @@ disable=print-statement, useless-suppression, deprecated-pragma, use-symbolic-message-instead, - apply-builtin, - basestring-builtin, - buffer-builtin, - cmp-builtin, - coerce-builtin, - execfile-builtin, - file-builtin, - long-builtin, - raw_input-builtin, - reduce-builtin, - standarderror-builtin, - unicode-builtin, - xrange-builtin, - coerce-method, - delslice-method, - getslice-method, - setslice-method, - no-absolute-import, - old-division, - dict-iter-method, - dict-view-method, - next-method-called, - metaclass-assignment, - indexing-exception, - raising-string, - reload-builtin, - oct-method, - hex-method, - nonzero-method, - cmp-method, - input-builtin, - round-builtin, - intern-builtin, - unichr-builtin, - map-builtin-not-iterating, - zip-builtin-not-iterating, - range-builtin-not-iterating, - filter-builtin-not-iterating, - using-cmp-argument, - eq-without-hash, - div-method, - idiv-method, - rdiv-method, - exception-message-attribute, - invalid-str-codec, - sys-max-int, - bad-python3-import, - deprecated-string-function, - deprecated-str-translate-call, - deprecated-itertools-function, - deprecated-types-field, - next-method-defined, - dict-items-not-iterating, - dict-keys-not-iterating, - dict-values-not-iterating, - deprecated-operator-function, - deprecated-urllib-function, - xreadlines-attribute, - deprecated-sys-function, - exception-escape, - comprehension-escape, C0103, C0201, C0209, @@ -160,7 +93,6 @@ disable=print-statement, E1121, R0901, C0415, - C0330, C0302, E1136, R0801, @@ -356,8 +288,8 @@ max-module-lines=5000 # separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}. # `trailing-comma` allows a space between comma and closing bracket: (a, ). # `empty-line` allows space-only lines. -no-space-check=trailing-comma, - dict-separator +# no-space-check=trailing-comma, +# dict-separator # Allow the body of a class to be on the same line as the declaration if body # contains single statement. @@ -592,5 +524,5 @@ min-public-methods=2 # Exceptions that will emit a warning when being caught. Defaults to # "BaseException, Exception". -overgeneral-exceptions=BaseException, - Exception +overgeneral-exceptions=builtins.BaseException, + builtins.Exception From 77c153bb515c7a38bb9498352bded1a1213193a6 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 5 May 2023 09:25:55 +0200 Subject: [PATCH 31/41] handle cation-anion mode failure with exception --- lobsterpy/featurize/core.py | 47 ++++++++++++++++++++----------------- 1 file changed, 26 insertions(+), 21 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 0e654756..46b84c27 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -84,17 +84,18 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: bond = [] antibond = [] # extract lobsterpy icohp related data for bond type specified - if data[self.bonds]: + if data[self.bonds]["lobsterpy_data"]["sites"]: for k, v in data[self.bonds]["lobsterpy_data"]["sites"].items(): - for k1, v1 in v["bonds"].items(): - icohp_mean.append(float(v1["ICOHP_mean"])) - icohp_sum.append(float(v1["ICOHP_sum"])) - bond.append(v1["bonding"]["perc"]) - antibond.append(v1["antibonding"]["perc"]) + if v["bonds"]: + for k1, v1 in v["bonds"].items(): + icohp_mean.append(float(v1["ICOHP_mean"])) + icohp_sum.append(float(v1["ICOHP_sum"])) + bond.append(v1["bonding"]["perc"]) + antibond.append(v1["antibonding"]["perc"]) else: raise Exception( - "There exist no data for {} for this structure. " - 'Please switch to "all_bonds" mode'.format(self.bonds) + "There exist no data for {} for {} structure. " + 'Please switch to "all_bonds" mode'.format(self.bonds, ids) ) # add stats data as columns to the dataframe @@ -169,19 +170,23 @@ def _get_lobsterpy_cba_dict(self) -> dict: and charge_path.exists() and structure_path.exists() ): - analyse = Analysis( - path_to_poscar=str(structure_path), - path_to_icohplist=str(icohplist_path), - path_to_cohpcar=str(cohpcar_path), - path_to_charge=str(charge_path), - summed_spins=False, # we will always use spin polarization here - cutoff_icohp=0.10, - whichbonds=which_bonds, - ) - data = {} - data.update( - {self.bonds: {"lobsterpy_data": analyse.condensed_bonding_analysis}} - ) + try: + analyse = Analysis( + path_to_poscar=str(structure_path), + path_to_icohplist=str(icohplist_path), + path_to_cohpcar=str(cohpcar_path), + path_to_charge=str(charge_path), + summed_spins=False, # we will always use spin polarization here + cutoff_icohp=0.10, + whichbonds=which_bonds, + ) + data = {} + data.update( + {self.bonds: {"lobsterpy_data": analyse.condensed_bonding_analysis}} + ) + except ValueError: + data = {} + data.update({self.bonds: {"lobsterpy_data": {}}}) else: raise Exception( "Path provided for Lobster calc directory seems incorrect." From 7b76fa61c77856a46e23c14f2c48698cb70bffeb Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 26 May 2023 11:00:09 +0200 Subject: [PATCH 32/41] if else instead of exception --- lobsterpy/featurize/core.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 46b84c27..0dbe24e0 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -329,17 +329,17 @@ def get_coxx_fingerprint_df( if spin_type == "up": coxx_all = coxxcar_obj[Spin.up] elif spin_type == "down": - try: + if Spin.down in coxxcar_obj: coxx_all = coxxcar_obj[Spin.down] - except KeyError: + else: raise ValueError( 'LOSBTER calculation is non-spin polarized. Please switch spin_type to "up"' ) elif spin_type == "summed": - try: + if Spin.down in coxxcar_obj: coxx_all = coxxcar_obj[Spin.up] + coxxcar_obj[Spin.down] - except KeyError: - coxx_all = coxxcar_obj[Spin.up] + else: + oxx_all = coxxcar_obj[Spin.up] else: raise Exception( "Check the spin_type argument." "Possible options are summed/up/down" From 7cb88bb70a6eadd67491d9cc082eb96f654ef48c Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 26 May 2023 11:04:57 +0200 Subject: [PATCH 33/41] Fix typos --- lobsterpy/featurize/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 0dbe24e0..c32a49c5 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -339,7 +339,7 @@ def get_coxx_fingerprint_df( if Spin.down in coxxcar_obj: coxx_all = coxxcar_obj[Spin.up] + coxxcar_obj[Spin.down] else: - oxx_all = coxxcar_obj[Spin.up] + coxx_all = coxxcar_obj[Spin.up] else: raise Exception( "Check the spin_type argument." "Possible options are summed/up/down" From 7cc21f0025907bd1d2fd86428b28dfd8bee419c9 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 2 Jun 2023 23:29:28 +0200 Subject: [PATCH 34/41] improve default file handling, increase core.py test coverage --- lobsterpy/featurize/batch.py | 160 ++++++++++++---- lobsterpy/featurize/core.py | 250 ++++++++++++++----------- lobsterpy/featurize/test/test_batch.py | 28 +-- lobsterpy/featurize/test/test_core.py | 148 +++++++++++++-- 4 files changed, 416 insertions(+), 170 deletions(-) diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 9954ab3c..4c604f3e 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -49,7 +49,7 @@ def __init__( path_to_jsons: str | None = None, feature_type: str = "antibonding", charge_type: str = "both", - bonds: str = "all_bonds", + bonds: str = "all", include_cobi_data: bool = False, include_coop_data: bool = False, only_smallest_basis: bool = True, @@ -77,7 +77,7 @@ def _featurizelobsterpy(self, file_name_or_path) -> pd.DataFrame: madelung energies """ - if os.path.isfile(file_name_or_path): + if Path(file_name_or_path).is_file(): featurize_lobsterpy = FeaturizeLobsterpy( path_to_json=file_name_or_path, bonds=self.bonds, @@ -103,9 +103,23 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: """ dir_name = Path(path_to_lobster_calc) - coxxcar_path = dir_name / "COHPCAR.lobster.gz" - structure_path = dir_name / "POSCAR.gz" - icoxxlist_path = dir_name / "ICOHPLIST.lobster.gz" + + req_files = { + "structure_path": "POSCAR", + "coxxcar_path": "COHPCAR.lobster", + "icoxxlist_path": "ICOHPLIST.lobster", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + coxxcar_path = req_files.get("coxxcar_path") + structure_path = req_files.get("structure_path") + icoxxlist_path = req_files.get("icoxxlist_path") if ( coxxcar_path.exists() @@ -123,13 +137,25 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: df_cohp = coxx.get_summarized_coxx_df() else: raise Exception( - "COHPCAR.lobster.gz or POSCAR.gz or ICOHPLIST.lobster.gz file " + "COHPCAR.lobster or POSCAR or ICOHPLIST.lobster file " "not found in {}".format(dir_name.name) ) if self.include_cobi_data: - coxxcar_path = dir_name / "COBICAR.lobster.gz" - icoxxlist_path = dir_name / "ICOBILIST.lobster.gz" + req_files = { + "coxxcar_path": "COBICAR.lobster", + "icoxxlist_path": "ICOBILIST.lobster", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + coxxcar_path = req_files.get("coxxcar_path") + icoxxlist_path = req_files.get("icoxxlist_path") if coxxcar_path.exists() and icoxxlist_path.exists(): coxx = FeaturizeCOXX( @@ -145,13 +171,25 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: else: raise Exception( - "COBICAR.lobster.gz or ICOBILIST.lobster.gz file " + "COBICAR.lobster or ICOBILIST.lobster file " "not found in {}".format(dir_name.name) ) if self.include_coop_data: - coxxcar_path = dir_name / "COOPCAR.lobster.gz" - icoxxlist_path = dir_name / "ICOOPLIST.lobster.gz" + req_files = { + "coxxcar_path": "COOPCAR.lobster", + "icoxxlist_path": "ICOOPLIST.lobster", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + coxxcar_path = req_files.get("coxxcar_path") + icoxxlist_path = req_files.get("icoxxlist_path") if coxxcar_path.exists() and icoxxlist_path.exists(): coxx = FeaturizeCOXX( @@ -167,7 +205,7 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: else: raise Exception( - "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file " + "COOPCAR.lobster or ICOOPLIST.lobster file " "not found in {}".format(dir_name.name) ) @@ -191,8 +229,21 @@ def _featurizecharges(self, path_to_lobster_calc) -> pd.DataFrame: """ dir_name = Path(path_to_lobster_calc) - charge_path = dir_name / "CHARGE.lobster.gz" - structure_path = dir_name / "POSCAR.gz" + + req_files = { + "charge_path": "CHARGE.lobster", + "structure_path": "POSCAR", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + charge_path = req_files.get("charge_path") + structure_path = req_files.get("structure_path") if charge_path.exists() and structure_path.exists(): if self.charge_type == "mulliken": @@ -229,7 +280,7 @@ def _featurizecharges(self, path_to_lobster_calc) -> pd.DataFrame: return df else: raise Exception( - "CHARGE.lobster.gz or POSCAR.gz not found in {}".format(dir_name.name) + "CHARGE.lobster or POSCAR not found in {}".format(dir_name.name) ) def get_df(self) -> pd.DataFrame: @@ -537,49 +588,84 @@ def _fingerprint_df(self, path_to_lobster_calc) -> pd.DataFrame: dir_name = Path(path_to_lobster_calc) if self.fingerprint_for.upper() == "COBI": - if (dir_name / "COBICAR.lobster.gz").exists() and ( - dir_name / "ICOBILIST.lobster.gz" - ).exists(): - coxxcar_path = dir_name / "COBICAR.lobster.gz" - icoxxlist_path = dir_name / "ICOBILIST.lobster.gz" + req_files = { + "coxxcar_path": "COBICAR.lobster", + "icoxxlist_path": "ICOBILIST.lobster", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + coxxcar_path = req_files.get("coxxcar_path") + icoxxlist_path = req_files.get("icoxxlist_path") + if coxxcar_path.exists() and icoxxlist_path.exists(): are_cobis = True are_coops = False else: raise Exception( - "COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in " + "COBICAR.lobster or ICOBILIST.lobster file not found in " "{}".format(dir_name.name) ) elif self.fingerprint_for.upper() == "COOP": - if (dir_name / "COOPCAR.lobster.gz").exists() and ( - dir_name / "ICOOPLIST.lobster.gz" - ).exists(): - coxxcar_path = dir_name / "COOPCAR.lobster.gz" - icoxxlist_path = dir_name / "ICOOPLIST.lobster.gz" + req_files = { + "coxxcar_path": "COOPCAR.lobster", + "icoxxlist_path": "ICOOPLIST.lobster", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + coxxcar_path = req_files.get("coxxcar_path") + icoxxlist_path = req_files.get("icoxxlist_path") + + if coxxcar_path.exists() and icoxxlist_path.exists(): are_cobis = False are_coops = True else: raise Exception( - "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in " + "COOPCAR.lobster or ICOOPLIST.lobster file not found in " "{}".format(dir_name.name) ) else: - if (dir_name / "COHPCAR.lobster.gz").exists() and ( - dir_name / "ICOHPLIST.lobster.gz" - ).exists(): - coxxcar_path = dir_name / "COHPCAR.lobster.gz" - icoxxlist_path = dir_name / "ICOHPLIST.lobster.gz" + req_files = { + "coxxcar_path": "COHPCAR.lobster", + "icoxxlist_path": "ICOHPLIST.lobster", + } + for file, default_value in req_files.items(): + file_path = dir_name / default_value + req_files[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files[file] = gz_file_path + + coxxcar_path = req_files.get("coxxcar_path") + icoxxlist_path = req_files.get("icoxxlist_path") + + if coxxcar_path.exists() and icoxxlist_path.exists(): are_cobis = False are_coops = False else: raise Exception( - "COHPCAR.lobster.gz or ICOHPLIST.lobster.gz file not found in " + "COHPCAR.lobster or ICOHPLIST.lobster file not found in " "{}".format(dir_name.name) ) - if (dir_name / "POSCAR.gz").exists(): - structure_path = dir_name / "POSCAR.gz" - else: - raise Exception("POSCAR.gz file not found in {}".format(dir_name.name)) + structure_path = dir_name / "POSCAR" + if not structure_path.exists(): + gz_file_path = structure_path.with_name(structure_path.name + ".gz") + if gz_file_path.exists(): + structure_path = gz_file_path + else: + raise Exception("POSCAR file not found in {}".format(dir_name.name)) coxx = FeaturizeCOXX( path_to_coxxcar=str(coxxcar_path), diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index c32a49c5..bf1f0f24 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -32,8 +32,9 @@ class FeaturizeLobsterpy: class to featurize lobsterpy data Args: + path_to_lobster_calc: path containing lobster calc outputs path_to_json: path to lobster lightweight json - bonds: "all_bonds" or "cation_anion_bonds" + bonds: "all" or "cation-anion" bonds Attributes: get_df: returns a pandas dataframe with relevant icohp statistical data as columns from lobsterpy automatic bonding analysis @@ -44,7 +45,7 @@ def __init__( self, path_to_lobster_calc: str | None = None, path_to_json: str | None = None, - bonds: str = "all_bonds", + bonds: str = "all", ): self.path_to_json = path_to_json self.path_to_lobster_calc = path_to_lobster_calc @@ -61,13 +62,17 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: """ if self.path_to_json and not self.path_to_lobster_calc: # read the lightweight lobster json files using read_lobster_lightweight_json method - data = self._read_lobster_lightweight_json() + data = FeaturizeLobsterpy.read_lobster_lightweight_json( + path_to_json=self.path_to_json + ) if not ids: ids = Path(self.path_to_json).name.split(".")[0] elif self.path_to_lobster_calc and not self.path_to_json: # get lobsterpy condensed bonding analysis data using get_lobsterpy_cba_dict method - data = self._get_lobsterpy_cba_dict() + data = FeaturizeLobsterpy.get_lobsterpy_cba_dict( + path_to_lobster_calc=self.path_to_lobster_calc, bonds=self.bonds + ) if not ids: ids = Path(self.path_to_lobster_calc).name @@ -84,8 +89,13 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: bond = [] antibond = [] # extract lobsterpy icohp related data for bond type specified - if data[self.bonds]["lobsterpy_data"]["sites"]: - for k, v in data[self.bonds]["lobsterpy_data"]["sites"].items(): + if self.bonds == "all": + bond_type = "all_bonds" + elif self.bonds == "cation-anion": + bond_type = "cation_anion_bonds" + + if data[bond_type]["lobsterpy_data"]["sites"]: + for k, v in data[bond_type]["lobsterpy_data"]["sites"].items(): if v["bonds"]: for k1, v1 in v["bonds"].items(): icohp_mean.append(float(v1["ICOHP_mean"])) @@ -94,8 +104,8 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: antibond.append(v1["antibonding"]["perc"]) else: raise Exception( - "There exist no data for {} for {} structure. " - 'Please switch to "all_bonds" mode'.format(self.bonds, ids) + "No {} bonds detected for {} structure. " + "Please switch to ´all´ bonds mode".format(self.bonds, ids) ) # add stats data as columns to the dataframe @@ -125,16 +135,20 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: return df - def _read_lobster_lightweight_json(self) -> dict: + @staticmethod + def read_lobster_lightweight_json(path_to_json: str) -> dict: """ - This function reads loads the lightweight json.gz files and returns a python dictionary object + This method reads loads the lightweight json.gz files and returns a python dictionary object with lobster summmarized bonding analysis data. + Args: + path_to_json: path to lobsterpy lightweight json file + Returns: Returns a dictionary with lobster summmarized bonding analysis data """ - with gzip.open(str(self.path_to_json), "rb") as f: + with gzip.open(str(path_to_json), "rb") as f: data = json.loads(f.read().decode("utf-8")) lobster_data = {} @@ -143,56 +157,79 @@ def _read_lobster_lightweight_json(self) -> dict: return lobster_data - def _get_lobsterpy_cba_dict(self) -> dict: + @staticmethod + def get_lobsterpy_cba_dict(path_to_lobster_calc: str, bonds: str) -> dict: """ This function uses lobsterpy.cohp.analyze.Analysis class to generate a python dictionary object with lobster summmarized bonding analysis data. + Args: + path_to_lobster_calc: path to lobsterpy lightweight json file + bonds: "all" or "cation-anion" bonds + Returns: Returns a dictionary with lobster summmarized bonding analysis data """ - dir_name = Path(str(self.path_to_lobster_calc)) - cohpcar_path = dir_name / "COHPCAR.lobster.gz" - charge_path = dir_name / "CHARGE.lobster.gz" - structure_path = dir_name / "POSCAR.gz" - icohplist_path = dir_name / "ICOHPLIST.lobster.gz" - madelung_energies_path = dir_name / "MadelungEnergies.lobster.gz" - - if self.bonds == "all_bonds": - which_bonds = "all" - else: - which_bonds = "cation-anion" + dir_name = Path(str(path_to_lobster_calc)) + + # check if files are compressed (.gz) and update file paths + req_files_lobsterpy = { + "structure_path": "POSCAR", + "cohpcar_path": "COHPCAR.lobster", + "icohplist_path": "ICOHPLIST.lobster", + "charge_path": "CHARGE.lobster", + } + + for file, default_value in req_files_lobsterpy.items(): + file_path = dir_name / default_value + req_files_lobsterpy[file] = file_path + if not file_path.exists(): + gz_file_path = file_path.with_name(file_path.name + ".gz") + if gz_file_path.exists(): + req_files_lobsterpy[file] = gz_file_path + else: + raise Exception( + "Path provided for Lobster calc directory seems incorrect." + "It does not contain COHPCAR.lobster, ICOHPLIST.lobster, POSCAR and " + "CHARGE.lobster files needed for automatic analysis using LobsterPy" + ) + + cohpcar_path = req_files_lobsterpy.get("cohpcar_path") + charge_path = req_files_lobsterpy.get("charge_path") + structure_path = req_files_lobsterpy.get("structure_path") + icohplist_path = req_files_lobsterpy.get("icohplist_path") + + which_bonds = bonds + + if which_bonds == "all": + bond_type = "all_bonds" + elif which_bonds == "cation-anion": + bond_type = "cation_anion_bonds" - if ( - icohplist_path.exists() - and cohpcar_path.exists() - and charge_path.exists() - and structure_path.exists() - ): - try: - analyse = Analysis( - path_to_poscar=str(structure_path), - path_to_icohplist=str(icohplist_path), - path_to_cohpcar=str(cohpcar_path), - path_to_charge=str(charge_path), - summed_spins=False, # we will always use spin polarization here - cutoff_icohp=0.10, - whichbonds=which_bonds, - ) - data = {} - data.update( - {self.bonds: {"lobsterpy_data": analyse.condensed_bonding_analysis}} - ) - except ValueError: - data = {} - data.update({self.bonds: {"lobsterpy_data": {}}}) - else: - raise Exception( - "Path provided for Lobster calc directory seems incorrect." - "It does not contain COHPCAR.lobster.gz, ICOHPLIST.lobster.gz, POSCAR.gz and " - "CHARGE.lobster.gz files needed for automatic analysis using LobsterPy" + try: + analyse = Analysis( + path_to_poscar=str(structure_path), + path_to_icohplist=str(icohplist_path), + path_to_cohpcar=str(cohpcar_path), + path_to_charge=str(charge_path), + summed_spins=False, # we will always use spin polarization here + cutoff_icohp=0.10, + whichbonds=which_bonds, + ) + + data = {bond_type: {"lobsterpy_data": analyse.condensed_bonding_analysis}} + except ValueError: + data = {bond_type: {"lobsterpy_data": {}}} + + madelung_energies_path = dir_name / "MadelungEnergies.lobster" + # check if .gz file exists and update Madelung Energies path + if not madelung_energies_path.exists(): + gz_file_path = madelung_energies_path.with_name( + madelung_energies_path.name + ".gz" ) + if gz_file_path.exists(): + madelung_energies_path = gz_file_path if madelung_energies_path.exists(): madelung_obj = MadelungEnergies(filename=str(madelung_energies_path)) @@ -202,19 +239,20 @@ def _get_lobsterpy_cba_dict(self) -> dict: "Loewdin": madelung_obj.madelungenergies_Loewdin, "Ewald_splitting": madelung_obj.ewald_splitting, } - data.update({"madelung_energies": madelung_energies}) + data["madelung_energies"] = madelung_energies + else: warnings.warn( - "MadelungEnergies.lobster.gz file not found in Lobster calc directory provided" + "MadelungEnergies.lobster file not found in Lobster calc directory provided" " Will set Madelung Engeries for crystal structure values to NaN" ) - madelung_energies = { "Mulliken": np.nan, "Loewdin": np.nan, "Ewald_splitting": np.nan, } - data.update({"madelung_energies": madelung_energies}) + + data["madelung_energies"] = madelung_energies return data @@ -313,10 +351,10 @@ def get_coxx_fingerprint_df( min_e = self.e_range[0] max_e = self.e_range[-1] - if max_e is None: + if not max_e: max_e = np.max(energies) - if min_e is None: + if not min_e: min_e = np.min(energies) if label_list: @@ -333,7 +371,7 @@ def get_coxx_fingerprint_df( coxx_all = coxxcar_obj[Spin.down] else: raise ValueError( - 'LOSBTER calculation is non-spin polarized. Please switch spin_type to "up"' + "LOBSTER calculation is non-spin polarized. Please switch spin_type to `up`" ) elif spin_type == "summed": if Spin.down in coxxcar_obj: @@ -367,7 +405,9 @@ def get_coxx_fingerprint_df( if ids: df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) else: - ids = os.path.basename(os.path.dirname(self.path_to_coxxcar)) + ids = Path( + self.path_to_coxxcar + ).parent.name # os.path.basename(os.path.dirname(self.path_to_coxxcar)) df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) coxxs = coxx_dict[self.feature_type] @@ -504,18 +544,16 @@ def _calc_moment_features( coxxcar = self.completecoxx.get_summed_cohp_by_label_list( label_list ).get_cohp() - try: - coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] - except KeyError: - coxx_all = coxxcar[Spin.up] - energies = self.completecoxx.energies - self.completecoxx.efermi + else: coxxcar = self.completecoxx.get_cohp() - try: - coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] - except KeyError: - coxx_all = coxxcar[Spin.up] - energies = self.completecoxx.energies - self.completecoxx.efermi + + if Spin.down in coxxcar: + coxx_all = coxxcar[Spin.up] + coxxcar[Spin.down] + else: + coxx_all = coxxcar[Spin.up] + + energies = self.completecoxx.energies - self.completecoxx.efermi coxx_dict = {} if not self.are_cobis and not self.are_coops: @@ -627,14 +665,22 @@ def _get_n_moment( COXX nth moment in eV """ if e_range: - coxx = coxx[(energies >= self.e_range[0]) & (energies <= self.e_range[-1])] - energies = energies[ - (energies >= self.e_range[0]) & (energies <= self.e_range[-1]) - ] + min_e = self.e_range[0] + max_e = self.e_range[1] + if not min_e: + min_e = min(energies) + if not max_e: + max_e = max(energies) + else: + min_e = min(energies) + max_e = max(energies) + + coxx = coxx[(energies >= min_e) & (energies <= max_e)] + energies = energies[(energies >= min_e) & (energies <= max_e)] if center: coxx_center = self._get_coxx_center( - coxx=coxx, energies=energies, e_range=self.e_range + coxx=coxx, energies=energies, e_range=[min_e, max_e] ) p = energies - coxx_center else: @@ -658,7 +704,9 @@ def get_summarized_coxx_df( if ids: df = pd.DataFrame(index=[ids]) else: - ids = os.path.basename(os.path.dirname(self.path_to_coxxcar)) + ids = Path( + self.path_to_coxxcar + ).parent.name # os.path.basename(os.path.dirname(self.path_to_coxxcar)) df = pd.DataFrame(index=[ids]) ( @@ -739,18 +787,16 @@ def _calc_ionicity(self) -> float: chargeobj = Charge(filename=self.path_to_charge) structure = Structure.from_file(self.path_to_structure) - if self.charge_type.lower() == "mulliken": - charges = chargeobj.Mulliken - elif self.charge_type.lower() == "loewdin": - charges = chargeobj.Loewdin - else: + if self.charge_type.lower() not in ["mulliken", "loewdin"]: raise ValueError( - "Please check the requested charge_type, " + "Please check the requested charge_type. " 'Possible options are "Mulliken" or "Loewdin"' ) ch_veff = [] - for i, j in enumerate(charges): + for i, j in enumerate( + chargeobj.__getattribute__(self.charge_type.capitalize()) + ): if ( j > 0 and not structure.species[i].is_transition_metal @@ -763,18 +809,6 @@ def _calc_ionicity(self) -> float: val = j / (valence_elec.nvalence() - 0) ch_veff.append(val) - elif ( - j > 0 - and not structure.species[i].is_transition_metal - and ( - not structure.species[i].is_actinoid - and not structure.species[i].is_lanthanoid - ) - ): - valence_elec = element(structure.species[i].value) - val = j / (valence_elec.nvalence() - 8) - ch_veff.append(val) - elif ( j < 0 and not structure.species[i].is_transition_metal @@ -787,20 +821,20 @@ def _calc_ionicity(self) -> float: val = j / (valence_elec.nvalence() - 8) ch_veff.append(val) - elif j > 0 and structure.species[i].is_transition_metal: - val = j / (structure.species[i].max_oxidation_state - 0) - ch_veff.append(val) - - elif j < 0 and structure.species[i].is_transition_metal: - val = j / (structure.species[i].max_oxidation_state - 8) - ch_veff.append(val) - - elif j > 0 and structure.species[i].is_lanthanoid: + elif j > 0 and ( + structure.species[i].is_transition_metal + or structure.species[i].is_actinoid + or structure.species[i].is_lanthanoid + ): val = j / (structure.species[i].max_oxidation_state - 0) ch_veff.append(val) - elif j < 0 and structure.species[i].is_actinoid: - val = j / (structure.species[i].max_oxidation_state - 8) + elif j < 0 and ( + structure.species[i].is_transition_metal + or structure.species[i].is_actinoid + or structure.species[i].is_lanthanoid + ): + val = j / (structure.species[i].min_oxidation_state - 8) ch_veff.append(val) ionicity = sum(ch_veff) / structure.num_sites @@ -818,10 +852,12 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: if ids: df = pd.DataFrame(index=[ids]) else: - ids = os.path.basename(os.path.dirname(self.path_to_charge)) + ids = Path( + self.path_to_charge + ).parent.name # os.path.basename(os.path.dirname(self.path_to_charge)) df = pd.DataFrame(index=[ids]) - if self.charge_type == "mulliken": + if self.charge_type.lower() == "mulliken": df.loc[ids, "Ionicity_Mull"] = self._calc_ionicity() else: df.loc[ids, "Ionicity_Loew"] = self._calc_ionicity() diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index c5c0ecfa..969de117 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -12,7 +12,7 @@ def setUp(self): self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs", - bonds="all_bonds", + bonds="all", path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", feature_type="antibonding", include_cobi_data=False, @@ -24,7 +24,7 @@ def setUp(self): self.summary_featurize_without_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs", - bonds="all_bonds", + bonds="all", include_cobi_data=False, include_coop_data=False, e_range=[-15, 0], @@ -34,7 +34,7 @@ def setUp(self): self.summary_featurize_with_json_overall = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs", - bonds="all_bonds", + bonds="all", path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", feature_type="overall", include_cobi_data=True, @@ -46,7 +46,7 @@ def setUp(self): self.summary_featurize_with_json_bonding = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs", - bonds="all_bonds", + bonds="all", path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", feature_type="bonding", include_cobi_data=False, @@ -59,7 +59,7 @@ def setUp(self): self.summary_featurize_with_json_antibonding = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs", - bonds="cation_anion_bonds", + bonds="cation-anion", path_to_jsons=TestDir / "TestData/Featurizer_test_data/JSONS", feature_type="antibonding", include_cobi_data=False, @@ -333,16 +333,16 @@ def setUp(self): def test_fp_cohp_overall(self): df = self.fp_cohp_overall.get_similarity_matrix_df() - self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], -0.033251, places=5) - self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], -0.013751, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.221446, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], -0.015035, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) - self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0.046889, places=5) + self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0.175085, places=5) def test_fp_cohp_bonding(self): fp_df = self.fp_cohp_bonding.fingerprint_df df = self.fp_cohp_bonding.get_similarity_matrix_df() - self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.0000171, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0.000000, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) for val in fp_df.loc["mp-1000", "COXX_FP"].coxx: @@ -352,7 +352,7 @@ def test_fp_cobi(self): fp_df = self.fp_cobi.fingerprint_df df = self.fp_cobi.get_similarity_matrix_df() - self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.329883, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) for val in fp_df.loc["mp-463", "COXX_FP"].coxx: @@ -375,7 +375,7 @@ def test_batch_summary_featurizer_exception(self): self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/1/", - bonds="all_bonds", + bonds="all", feature_type="antibonding", include_cobi_data=True, include_coop_data=True, @@ -386,14 +386,14 @@ def test_batch_summary_featurizer_exception(self): print(df.to_string()) self.assertEqual( err.exception.__str__(), - "COBICAR.lobster.gz or ICOBILIST.lobster.gz file not found in mp-2176", + "COBICAR.lobster or ICOBILIST.lobster file not found in mp-2176", ) with self.assertRaises(Exception) as err: self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/2/", - bonds="all_bonds", + bonds="all", feature_type="antibonding", include_cobi_data=True, include_coop_data=True, @@ -404,7 +404,7 @@ def test_batch_summary_featurizer_exception(self): print(df.to_string()) self.assertEqual( err.exception.__str__(), - "COOPCAR.lobster.gz or ICOOPLIST.lobster.gz file not found in mp-1000", + "COOPCAR.lobster or ICOOPLIST.lobster file not found in mp-1000", ) diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 71bd6754..5e25272c 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -1,5 +1,6 @@ import unittest import pandas as pd +import numpy as np from pathlib import Path from lobsterpy.featurize.core import FeaturizeLobsterpy, FeaturizeCharges, FeaturizeCOXX @@ -10,18 +11,22 @@ class TestFeaturizeLobsterpy(unittest.TestCase): def setUp(self): self.featurize_mp1249_json = FeaturizeLobsterpy( - path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", bonds="all_bonds" + path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", bonds="all" ) self.featurize_mp1249_json_ca = FeaturizeLobsterpy( path_to_json=TestDir / "TestData/JSONS/mp-1249.json.gz", - bonds="cation_anion_bonds", + bonds="cation-anion", ) self.featurize_mp1958_json = FeaturizeLobsterpy( - path_to_json=TestDir / "TestData/JSONS/mp-1958.json.gz", bonds="all_bonds" + path_to_json=TestDir / "TestData/JSONS/mp-1958.json.gz", bonds="all" ) self.featurize_mp14652_json = FeaturizeLobsterpy( - path_to_json=TestDir / "TestData/JSONS/mp-14652.json.gz", bonds="all_bonds" + path_to_json=TestDir / "TestData/JSONS/mp-14652.json.gz", bonds="all" + ) + + self.featurize_CsH_madelung = FeaturizeLobsterpy( + path_to_lobster_calc=TestDir / "TestData/CsH/", bonds="all" ) def test_featurize_mp1249_json(self): @@ -193,6 +198,12 @@ def test_featurize_mp14652_json(self): df.loc["mp-14652", "antibonding_perc_std"], 0.006339, places=5 ) + def test_featurize_CsH_madelung(self): + df = self.featurize_CsH_madelung.get_df() + + self.assertTrue(np.isnan(df.loc["CsH", "Madelung_Mull"])) + self.assertTrue(np.isnan(df.loc["CsH", "Madelung_Loew"])) + class TestFeaturizeCOXX(unittest.TestCase): def setUp(self): @@ -246,10 +257,48 @@ def test_featurize_NaCl_COXX(self): self.assertAlmostEqual(df.loc["NaCl", "w_ICOHP"], -0.150558, places=5) self.assertAlmostEqual(df.loc["NaCl", "EIN_ICOHP"], 27.843536, places=5) - self.assertAlmostEqual(df.loc["NaCl", "center_COHP"], -4.96241, places=5) - self.assertAlmostEqual(df.loc["NaCl", "width_COHP"], 8.881784e-16, places=5) - self.assertAlmostEqual(df.loc["NaCl", "skewness_COHP"], 1, places=5) - self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 1, places=5) + self.assertAlmostEqual(df.loc["NaCl", "center_COHP"], 2.79973, places=5) + self.assertAlmostEqual(df.loc["NaCl", "width_COHP"], 0.686836, places=5) + self.assertAlmostEqual(df.loc["NaCl", "skewness_COHP"], -1.072675, places=5) + self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 4.811448, places=5) + + def test_featurize_NaCl_COXX_fingerprint(self): + df = self.featurize_NaCl_COXX.get_coxx_fingerprint_df(n_bins=20000) + + fingerprint = df.loc["NaCl", "COXX_FP"] + + self.assertNotEqual(fingerprint.n_bins, 20000) + + df1 = self.featurize_NaCl_COXX.get_coxx_fingerprint_df(binning=False) + + fingerprint = df1.loc["NaCl", "COXX_FP"] + + self.assertEqual(fingerprint.n_bins, 401) + + df2 = self.featurize_NaCl_COXX.get_coxx_fingerprint_df(label_list=["3", "5"]) + + fingerprint_label = df2.loc["NaCl", "COXX_FP"] + + self.assertNotEqual(fingerprint.__str__(), fingerprint_label.__str__()) + + def test_featurize_CdF_COXX_fingerprint(self): + df = self.featurize_CdF_COXX.get_coxx_fingerprint_df(n_bins=20000) + + fingerprint = df.loc["CdF", "COXX_FP"] + + self.assertNotEqual(fingerprint.n_bins, 20000) + + df1 = self.featurize_CdF_COXX.get_coxx_fingerprint_df(binning=False) + + fingerprint = df1.loc["CdF", "COXX_FP"] + + self.assertEqual(fingerprint.n_bins, 401) + + df2 = self.featurize_CdF_COXX.get_coxx_fingerprint_df(label_list=["3", "5"]) + + fingerprint_label = df2.loc["CdF", "COXX_FP"] + + self.assertNotEqual(fingerprint.__str__(), fingerprint_label.__str__()) def test_featurize_CdF_COXX(self): df = self.featurize_CdF_COXX.get_summarized_coxx_df() @@ -284,6 +333,39 @@ def test_featurize_CdF_COXX(self): self.assertAlmostEqual(df.loc["CdF", "skewness_COHP"], 0.910094, places=5) self.assertAlmostEqual(df.loc["CdF", "kurtosis_COHP"], 2.866611, places=5) + def test_featurize_K3Sb_COXX(self): + df = self.featurize_K3Sb_COXX.get_summarized_coxx_df(ids="K3Sb") + + # Test that the function returns a pandas DataFrame + self.assertIsInstance(df, pd.DataFrame) + + # Test that the DataFrame has the expected columns + expected_cols = [ + "bnd_wICOHP", + "antibnd_wICOHP", + "w_ICOHP", + "EIN_ICOHP", + "center_COHP", + "width_COHP", + "skewness_COHP", + "kurtosis_COHP", + ] + self.assertCountEqual(list(df.columns), expected_cols) + + # Test that the DataFrame has the expected index + self.assertEqual(df.index[0], "K3Sb") + + # Test that all the values in the DataFrame + self.assertAlmostEqual(df.loc["K3Sb", "bnd_wICOHP"], 97.053378, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "antibnd_wICOHP"], 2.946622, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "w_ICOHP"], -0.318218, places=5) + + self.assertAlmostEqual(df.loc["K3Sb", "EIN_ICOHP"], 11.597595, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "center_COHP"], 2.786451, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "width_COHP"], 0.765492, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "skewness_COHP"], -1.52563, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "kurtosis_COHP"], 6.829327, places=5) + class TestFeaturizeCharges(unittest.TestCase): def setUp(self): @@ -362,7 +444,7 @@ class TestExceptions(unittest.TestCase): def test_lobsterpy_featurize_exception(self): with self.assertRaises(Exception) as err: self.featurize_mp1249_json = FeaturizeLobsterpy( - path_to_json=None, path_to_lobster_calc=None, bonds="all_bonds" + path_to_json=None, path_to_lobster_calc=None, bonds="all" ) self.featurize_mp1249_json.get_df() @@ -373,15 +455,27 @@ def test_lobsterpy_featurize_exception(self): with self.assertRaises(Exception) as err: self.featurize_mp1249_json = FeaturizeLobsterpy( - path_to_json=None, path_to_lobster_calc=TestDir, bonds="all_bonds" + path_to_json=None, path_to_lobster_calc=TestDir, bonds="all" ) self.featurize_mp1249_json.get_df() self.assertEqual( err.exception.__str__(), "Path provided for Lobster calc directory seems incorrect." - "It does not contain COHPCAR.lobster.gz, ICOHPLIST.lobster.gz, POSCAR.gz and " - "CHARGE.lobster.gz files needed for automatic analysis using LobsterPy", + "It does not contain COHPCAR.lobster, ICOHPLIST.lobster, POSCAR and " + "CHARGE.lobster files needed for automatic analysis using LobsterPy", + ) + + with self.assertRaises(Exception) as err: + self.featurize_CsH_cation_anion = FeaturizeLobsterpy( + path_to_lobster_calc=TestDir / "TestData/CsH/", bonds="cation-anion" + ) + _df = self.featurize_CsH_cation_anion.get_df() + + self.assertEqual( + err.exception.__str__(), + "No cation-anion bonds detected for CsH structure. " + "Please switch to ´all´ bonds mode", ) def test_featurize_coxx(self): @@ -435,6 +529,36 @@ def test_featurize_coxx(self): "You cannot have info about COOPs and COBIs in the same file.", ) + with self.assertRaises(Exception) as err: + self.featurize_NaCl_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/NaCl/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/NaCl/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/NaCl/POSCAR", + feature_type="antibond", + e_range=[-5, 0], + ).get_summarized_coxx_df() + + self.assertEqual( + err.exception.__str__(), + "Please recheck fp_type requested argument." + "Possible options are bonding/antibonding/overall", + ) + + with self.assertRaises(Exception) as err: + self.featurize_NaCl_COXX = FeaturizeCOXX( + path_to_coxxcar=TestDir / "TestData/NaCl/COHPCAR.lobster", + path_to_icoxxlist=TestDir / "TestData/NaCl/ICOHPLIST.lobster", + path_to_structure=TestDir / "TestData/NaCl/POSCAR", + feature_type="antibonding", + e_range=[-5, 0], + ).get_coxx_fingerprint_df(spin_type="down") + + self.assertEqual( + err.exception.__str__(), + "LOBSTER calculation is non-spin polarized. " + "Please switch spin_type to `up`", + ) + if __name__ == "__main__": unittest.main() From c92b3ba319f8e17072769c1051a503e393c414b1 Mon Sep 17 00:00:00 2001 From: anaik Date: Sat, 3 Jun 2023 23:28:22 +0200 Subject: [PATCH 35/41] increase tests coverage --- docs/source/conf.py | 33 +++-- lobsterpy/featurize/batch.py | 159 +++++++------------------ lobsterpy/featurize/core.py | 8 +- lobsterpy/featurize/test/test_batch.py | 73 ++++++++++-- lobsterpy/featurize/test/test_core.py | 23 ++-- 5 files changed, 151 insertions(+), 145 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 09ea8fc0..23a43cc8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -12,14 +12,15 @@ # import os import sys + sys.path.insert(0, os.path.abspath("../")) # -- Project information ----------------------------------------------------- -project = 'Lobsterpy' -copyright = '2022, Janine George' -author = 'Janine George' +project = "Lobsterpy" +copyright = "2022, Janine George" +author = "Janine George" # -- General configuration --------------------------------------------------- @@ -43,13 +44,18 @@ source_suffix = [".rst", ".md"] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["../../lobsterpy/test", "../../lobsterpy/cohp/test", - "../../lobsterpy/TestData", "Thumbs.db", ".DS_Store"] +exclude_patterns = [ + "../../lobsterpy/test", + "../../lobsterpy/cohp/test", + "../../lobsterpy/TestData", + "Thumbs.db", + ".DS_Store", +] def run_apidoc(_): @@ -61,7 +67,16 @@ def run_apidoc(_): excludes1 = glob.glob(os.path.join(output_path, "../../lobsterpy/test")) module = os.path.join(output_path, "../../lobsterpy") cmd_path = "sphinx-apidoc" - command = [cmd_path, "-e", "-o", output_path, module, " ".join(excludes), " ".join(excludes1), "--force"] + command = [ + cmd_path, + "-e", + "-o", + output_path, + module, + " ".join(excludes), + " ".join(excludes1), + "--force", + ] subprocess.check_call(command) @@ -74,9 +89,9 @@ def setup(app): # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'sphinx_book_theme' +html_theme = "sphinx_book_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] diff --git a/lobsterpy/featurize/batch.py b/lobsterpy/featurize/batch.py index 4c604f3e..f3151c64 100644 --- a/lobsterpy/featurize/batch.py +++ b/lobsterpy/featurize/batch.py @@ -52,7 +52,6 @@ def __init__( bonds: str = "all", include_cobi_data: bool = False, include_coop_data: bool = False, - only_smallest_basis: bool = True, e_range: List[float] = [-5.0, 0.0], n_jobs: int = 4, ): @@ -63,7 +62,6 @@ def __init__( self.bonds = bonds self.include_cobi_data = include_cobi_data self.include_coop_data = include_coop_data - self.only_smallest_basis = only_smallest_basis self.e_range = e_range self.n_jobs = n_jobs @@ -111,20 +109,20 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore coxxcar_path = req_files.get("coxxcar_path") structure_path = req_files.get("structure_path") icoxxlist_path = req_files.get("icoxxlist_path") if ( - coxxcar_path.exists() - and structure_path.exists() - and icoxxlist_path.exists() + coxxcar_path.exists() # type: ignore + and structure_path.exists() # type: ignore + and icoxxlist_path.exists() # type: ignore ): coxx = FeaturizeCOXX( path_to_coxxcar=str(coxxcar_path), @@ -148,16 +146,16 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore coxxcar_path = req_files.get("coxxcar_path") icoxxlist_path = req_files.get("icoxxlist_path") - if coxxcar_path.exists() and icoxxlist_path.exists(): + if coxxcar_path.exists() and icoxxlist_path.exists(): # type: ignore coxx = FeaturizeCOXX( path_to_coxxcar=str(coxxcar_path), path_to_icoxxlist=str(icoxxlist_path), @@ -182,16 +180,16 @@ def _featurizecoxx(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore coxxcar_path = req_files.get("coxxcar_path") icoxxlist_path = req_files.get("icoxxlist_path") - if coxxcar_path.exists() and icoxxlist_path.exists(): + if coxxcar_path.exists() and icoxxlist_path.exists(): # type: ignore coxx = FeaturizeCOXX( path_to_coxxcar=str(coxxcar_path), path_to_icoxxlist=str(icoxxlist_path), @@ -236,16 +234,16 @@ def _featurizecharges(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore charge_path = req_files.get("charge_path") structure_path = req_files.get("structure_path") - if charge_path.exists() and structure_path.exists(): + if charge_path.exists() and structure_path.exists(): # type: ignore if self.charge_type == "mulliken": charge_mull = FeaturizeCharges( path_to_charge=str(charge_path), @@ -292,43 +290,16 @@ def get_df(self) -> pd.DataFrame: Returns a pandas dataframe """ - if self.path_to_jsons and self.only_smallest_basis: + if self.path_to_jsons: file_name_or_path = [ os.path.join(self.path_to_jsons, f) for f in os.listdir(self.path_to_jsons) if not f.startswith("t") and not f.startswith(".") and not os.path.isdir(f) - and not len(f.split("_")) > 1 ] - elif self.path_to_jsons and not self.only_smallest_basis: - file_name_or_path = [ - os.path.join(self.path_to_jsons, f) - for f in os.listdir(self.path_to_jsons) - if not f.startswith("t") - and not f.startswith(".") - and not os.path.isdir(f) - ] - elif ( - self.path_to_lobster_calcs - and self.only_smallest_basis - and not self.path_to_jsons - ): - file_name_or_path = [ - os.path.join(self.path_to_lobster_calcs, f) - for f in os.listdir(self.path_to_lobster_calcs) - if not f.startswith("t") - and not f.startswith(".") - and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) - and not len(f.split("_")) > 1 - ] - - elif ( - self.path_to_lobster_calcs - and not self.only_smallest_basis - and not self.path_to_jsons - ): + elif self.path_to_lobster_calcs and not self.path_to_jsons: file_name_or_path = [ os.path.join(self.path_to_lobster_calcs, f) for f in os.listdir(self.path_to_lobster_calcs) @@ -354,23 +325,13 @@ def get_df(self) -> pd.DataFrame: df_lobsterpy = pd.concat(row) df_lobsterpy.sort_index(inplace=True) - if self.only_smallest_basis: - paths = [ - os.path.join(self.path_to_lobster_calcs, f) - for f in os.listdir(self.path_to_lobster_calcs) - if not f.startswith("t") - and not f.startswith(".") - and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) - and not len(f.split("_")) > 1 - ] - else: - paths = [ - os.path.join(self.path_to_lobster_calcs, f) - for f in os.listdir(self.path_to_lobster_calcs) - if not f.startswith("t") - and not f.startswith(".") - and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) - ] + paths = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + ] with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( @@ -414,7 +375,6 @@ class BatchCoxxFingerprint: Args: path_to_lobster_calcs: path to root directory consisting of all lobster calc - only_smallest_basis: bool to state to include directories with smallest basis only feature_type: set the feature type for moment features. Possible options are "bonding", "antibonding" or "overall" label_list: bond labels list for which fingerprints needs to be generated. @@ -436,7 +396,6 @@ class BatchCoxxFingerprint: def __init__( self, path_to_lobster_calcs: str, - only_smallest_basis: bool = True, feature_type: str = "overall", label_list: List[str] | None = None, tanimoto: bool = True, @@ -448,7 +407,6 @@ def __init__( fingerprint_for: str = "cohp", ): self.path_to_lobster_calcs = path_to_lobster_calcs - self.only_smallest_basis = only_smallest_basis self.feature_type = feature_type self.tanimoto = tanimoto self.normalize = normalize @@ -594,22 +552,17 @@ def _fingerprint_df(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore coxxcar_path = req_files.get("coxxcar_path") icoxxlist_path = req_files.get("icoxxlist_path") - if coxxcar_path.exists() and icoxxlist_path.exists(): - are_cobis = True - are_coops = False - else: - raise Exception( - "COBICAR.lobster or ICOBILIST.lobster file not found in " - "{}".format(dir_name.name) - ) + are_cobis = True + are_coops = False + elif self.fingerprint_for.upper() == "COOP": req_files = { "coxxcar_path": "COOPCAR.lobster", @@ -617,23 +570,17 @@ def _fingerprint_df(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore coxxcar_path = req_files.get("coxxcar_path") icoxxlist_path = req_files.get("icoxxlist_path") + are_cobis = False + are_coops = True - if coxxcar_path.exists() and icoxxlist_path.exists(): - are_cobis = False - are_coops = True - else: - raise Exception( - "COOPCAR.lobster or ICOOPLIST.lobster file not found in " - "{}".format(dir_name.name) - ) else: req_files = { "coxxcar_path": "COHPCAR.lobster", @@ -641,31 +588,22 @@ def _fingerprint_df(self, path_to_lobster_calc) -> pd.DataFrame: } for file, default_value in req_files.items(): file_path = dir_name / default_value - req_files[file] = file_path + req_files[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files[file] = gz_file_path + req_files[file] = gz_file_path # type: ignore coxxcar_path = req_files.get("coxxcar_path") icoxxlist_path = req_files.get("icoxxlist_path") - - if coxxcar_path.exists() and icoxxlist_path.exists(): - are_cobis = False - are_coops = False - else: - raise Exception( - "COHPCAR.lobster or ICOHPLIST.lobster file not found in " - "{}".format(dir_name.name) - ) + are_cobis = False + are_coops = False structure_path = dir_name / "POSCAR" if not structure_path.exists(): gz_file_path = structure_path.with_name(structure_path.name + ".gz") if gz_file_path.exists(): structure_path = gz_file_path - else: - raise Exception("POSCAR file not found in {}".format(dir_name.name)) coxx = FeaturizeCOXX( path_to_coxxcar=str(coxxcar_path), @@ -683,6 +621,7 @@ def _fingerprint_df(self, path_to_lobster_calc) -> pd.DataFrame: normalize=self.normalize, label_list=self.label_list, ) + return df_fp def _get_fingerprints_df(self) -> pd.DataFrame: @@ -694,23 +633,13 @@ def _get_fingerprints_df(self) -> pd.DataFrame: A pandas dataframe with COXX fingerprint objects """ - if self.only_smallest_basis: - paths = [ - os.path.join(self.path_to_lobster_calcs, f) - for f in os.listdir(self.path_to_lobster_calcs) - if not f.startswith("t") - and not f.startswith(".") - and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) - and not len(f.split("_")) > 1 - ] - else: - paths = [ - os.path.join(self.path_to_lobster_calcs, f) - for f in os.listdir(self.path_to_lobster_calcs) - if not f.startswith("t") - and not f.startswith(".") - and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) - ] + paths = [ + os.path.join(self.path_to_lobster_calcs, f) + for f in os.listdir(self.path_to_lobster_calcs) + if not f.startswith("t") + and not f.startswith(".") + and os.path.isdir(os.path.join(self.path_to_lobster_calcs, f)) + ] with mp.Pool(processes=self.n_jobs, maxtasksperchild=1) as pool: results = tqdm( diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index bf1f0f24..fb4f8ff9 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -183,11 +183,11 @@ def get_lobsterpy_cba_dict(path_to_lobster_calc: str, bonds: str) -> dict: for file, default_value in req_files_lobsterpy.items(): file_path = dir_name / default_value - req_files_lobsterpy[file] = file_path + req_files_lobsterpy[file] = file_path # type: ignore if not file_path.exists(): gz_file_path = file_path.with_name(file_path.name + ".gz") if gz_file_path.exists(): - req_files_lobsterpy[file] = gz_file_path + req_files_lobsterpy[file] = gz_file_path # type: ignore else: raise Exception( "Path provided for Lobster calc directory seems incorrect." @@ -794,9 +794,7 @@ def _calc_ionicity(self) -> float: ) ch_veff = [] - for i, j in enumerate( - chargeobj.__getattribute__(self.charge_type.capitalize()) - ): + for i, j in enumerate(getattr(chargeobj, self.charge_type.capitalize())): if ( j > 0 and not structure.species[i].is_transition_metal diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index 969de117..21ab7c14 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -371,7 +371,7 @@ def test_fp_coop(self): class TestExceptions(unittest.TestCase): def test_batch_summary_featurizer_exception(self): - with self.assertRaises(Exception) as err: + with self.assertRaises(Exception) as err1: self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/1/", @@ -382,14 +382,14 @@ def test_batch_summary_featurizer_exception(self): e_range=[-15, 0], ) - df = self.summary_featurize_with_json.get_df() - print(df.to_string()) + _ = self.summary_featurize_with_json.get_df() + self.assertEqual( - err.exception.__str__(), + err1.exception.__str__(), "COBICAR.lobster or ICOBILIST.lobster file not found in mp-2176", ) - with self.assertRaises(Exception) as err: + with self.assertRaises(Exception) as err2: self.summary_featurize_with_json = BatchSummaryFeaturizer( path_to_lobster_calcs=TestDir / "TestData/Featurizer_test_data/Lobster_calcs_exceptions/2/", @@ -400,13 +400,70 @@ def test_batch_summary_featurizer_exception(self): e_range=[-15, 0], ) - df = self.summary_featurize_with_json.get_df() - print(df.to_string()) + _ = self.summary_featurize_with_json.get_df() + self.assertEqual( - err.exception.__str__(), + err2.exception.__str__(), "COOPCAR.lobster or ICOOPLIST.lobster file not found in mp-1000", ) + # COXX exception + with self.assertRaises(Exception) as err3: + self.raise_coxx_exception = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir / "TestData/JSONS/" + ) + + _ = self.raise_coxx_exception._featurizecoxx( + path_to_lobster_calc=self.raise_coxx_exception.path_to_lobster_calcs + ) + + self.assertEqual( + err3.exception.__str__(), + "COHPCAR.lobster or POSCAR or ICOHPLIST.lobster file not found in JSONS", + ) + + # Charges exception + with self.assertRaises(Exception) as err4: + self.raise_ch_exception = BatchSummaryFeaturizer( + path_to_lobster_calcs=TestDir / "TestData/JSONS/" + ) + + _ = self.raise_ch_exception._featurizecharges( + path_to_lobster_calc=self.raise_ch_exception.path_to_lobster_calcs + ) + + self.assertEqual( + err4.exception.__str__(), + "CHARGE.lobster or POSCAR not found in JSONS", + ) + + # Fingerprint similarity exception + with self.assertRaises(Exception) as err8: + fp_cohp_bonding = BatchCoxxFingerprint( + path_to_lobster_calcs=TestDir + / "TestData/Featurizer_test_data/Lobster_calcs", + e_range=[-15, 0], + feature_type="bonding", + normalize=True, + tanimoto=True, + n_jobs=3, + ) + + fp_df = fp_cohp_bonding.fingerprint_df + + _ = fp_cohp_bonding._get_fp_similarity( + fp_df.loc["mp-1000", "COXX_FP"], + fp_df.loc["mp-2176", "COXX_FP"], + tanimoto=True, + normalize=True, + ) + + self.assertEqual( + err8.exception.__str__(), + "Cannot compute similarity index. Please set either normalize=True or " + "tanimoto=True or both to False.", + ) + if __name__ == "__main__": unittest.main() diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 5e25272c..75a916a2 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -447,7 +447,8 @@ def test_lobsterpy_featurize_exception(self): path_to_json=None, path_to_lobster_calc=None, bonds="all" ) - self.featurize_mp1249_json.get_df() + _ = self.featurize_mp1249_json.get_df() + self.assertEqual( err.exception.__str__(), "Please provide either path to lightweight lobster jsons or path to lobster calc", @@ -458,7 +459,8 @@ def test_lobsterpy_featurize_exception(self): path_to_json=None, path_to_lobster_calc=TestDir, bonds="all" ) - self.featurize_mp1249_json.get_df() + _ = self.featurize_mp1249_json.get_df() + self.assertEqual( err.exception.__str__(), "Path provided for Lobster calc directory seems incorrect." @@ -470,7 +472,8 @@ def test_lobsterpy_featurize_exception(self): self.featurize_CsH_cation_anion = FeaturizeLobsterpy( path_to_lobster_calc=TestDir / "TestData/CsH/", bonds="cation-anion" ) - _df = self.featurize_CsH_cation_anion.get_df() + + _ = self.featurize_CsH_cation_anion.get_df() self.assertEqual( err.exception.__str__(), @@ -488,7 +491,7 @@ def test_featurize_coxx(self): e_range=[-5, 0], ) - self.featurize_COXX.get_summarized_coxx_df() + _ = self.featurize_COXX.get_summarized_coxx_df() self.assertEqual( err.exception.__str__(), @@ -504,7 +507,7 @@ def test_featurize_coxx(self): e_range=[-5, 0], ) - self.featurize_COXX.get_coxx_fingerprint_df(spin_type="-1") + _ = self.featurize_COXX.get_coxx_fingerprint_df(spin_type="-1") self.assertEqual( err2.exception.__str__(), @@ -522,7 +525,7 @@ def test_featurize_coxx(self): are_coops=True, ) - self.featurize_COXX.get_coxx_fingerprint_df() + _ = self.featurize_COXX.get_coxx_fingerprint_df() self.assertEqual( err3.exception.__str__(), @@ -536,7 +539,9 @@ def test_featurize_coxx(self): path_to_structure=TestDir / "TestData/NaCl/POSCAR", feature_type="antibond", e_range=[-5, 0], - ).get_summarized_coxx_df() + ) + + _ = self.featurize_NaCl_COXX.get_summarized_coxx_df() self.assertEqual( err.exception.__str__(), @@ -551,7 +556,9 @@ def test_featurize_coxx(self): path_to_structure=TestDir / "TestData/NaCl/POSCAR", feature_type="antibonding", e_range=[-5, 0], - ).get_coxx_fingerprint_df(spin_type="down") + ) + + _ = self.featurize_NaCl_COXX.get_coxx_fingerprint_df(spin_type="down") self.assertEqual( err.exception.__str__(), From 148a7cdac661f9a0bb31482cfc1b67d07155c733 Mon Sep 17 00:00:00 2001 From: anaik Date: Sun, 4 Jun 2023 14:08:53 +0200 Subject: [PATCH 36/41] improve numerical stability, add more tests --- lobsterpy/featurize/core.py | 144 ++++++++++++++++---------- lobsterpy/featurize/test/test_core.py | 59 +++++++++-- 2 files changed, 140 insertions(+), 63 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index fb4f8ff9..8746fdc5 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -89,26 +89,33 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: bond = [] antibond = [] # extract lobsterpy icohp related data for bond type specified + # Results will differ for "all" and "cation-anion" mode. + # In "all" bonds mode, the bonds will come up twice, also + # cation-cation, anion-anion bonds will also be considered + if self.bonds == "all": bond_type = "all_bonds" elif self.bonds == "cation-anion": bond_type = "cation_anion_bonds" - if data[bond_type]["lobsterpy_data"]["sites"]: - for k, v in data[bond_type]["lobsterpy_data"]["sites"].items(): - if v["bonds"]: - for k1, v1 in v["bonds"].items(): - icohp_mean.append(float(v1["ICOHP_mean"])) - icohp_sum.append(float(v1["ICOHP_sum"])) - bond.append(v1["bonding"]["perc"]) - antibond.append(v1["antibonding"]["perc"]) - else: + if ( + not data[bond_type]["lobsterpy_data"] + or not data[bond_type]["lobsterpy_data"]["sites"] + ): raise Exception( "No {} bonds detected for {} structure. " "Please switch to ´all´ bonds mode".format(self.bonds, ids) ) - # add stats data as columns to the dataframe + for k, v in data[bond_type]["lobsterpy_data"]["sites"].items(): + if v["bonds"]: + for k1, v1 in v["bonds"].items(): + icohp_mean.append(float(v1["ICOHP_mean"])) + icohp_sum.append(float(v1["ICOHP_sum"])) + bond.append(v1["bonding"]["perc"]) + antibond.append(v1["antibonding"]["perc"]) + + # add ICOHP stats data (mean, min, max, standard deviation) as columns to the dataframe df.loc[ids, "Icohp_mean_avg"] = np.mean(icohp_mean) df.loc[ids, "Icohp_mean_max"] = np.max(icohp_mean) df.loc[ids, "Icohp_mean_min"] = np.min(icohp_mean) @@ -316,6 +323,7 @@ def get_coxx_fingerprint_df( self, ids: str | None = None, label_list: List[str] | None = None, + per_bond: bool = True, spin_type: str = "summed", binning: bool = True, n_bins: int = 56, @@ -332,6 +340,9 @@ def get_coxx_fingerprint_df( n_bins: Number of bins to be used in the fingerprint (default is 256) normalize: If true, normalizes the area under fp to equal to 1 (default is True) label_list: Specify bond lables as a list for which cohp fingerprints are needed + per_bond: Will scale cohp values by number of bonds i.e length of label_list arg + (Only affects when label_list is not None) + Raises: Exception: If spin_type is not one of the accepted values {summed/up/down}. @@ -344,9 +355,7 @@ def get_coxx_fingerprint_df( """ coxxcar_obj = self.completecoxx - energies = ( - coxxcar_obj.energies - coxxcar_obj.efermi - ) # here substraction of efermi has impact on fp. + energies = coxxcar_obj.energies - coxxcar_obj.efermi min_e = self.e_range[0] max_e = self.e_range[-1] @@ -358,8 +367,9 @@ def get_coxx_fingerprint_df( min_e = np.min(energies) if label_list: + divisor = len(label_list) if per_bond else 1 coxxcar_obj = coxxcar_obj.get_summed_cohp_by_label_list( - label_list + label_list, divisor=divisor ).get_cohp() else: coxxcar_obj = coxxcar_obj.get_cohp() @@ -384,19 +394,20 @@ def get_coxx_fingerprint_df( ) coxx_dict = {} + tol = 1e-6 if not self.are_cobis and not self.are_coops: coxx_dict["bonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] + [scohp if scohp <= tol else 0 for scohp in coxx_all] ) coxx_dict["antibonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] + [scohp if scohp >= tol else 0 for scohp in coxx_all] ) else: coxx_dict["antibonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] + [scohp if scohp <= tol else 0 for scohp in coxx_all] ) coxx_dict["bonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] + [scohp if scohp >= tol else 0 for scohp in coxx_all] ) coxx_dict["overall"] = coxx_all @@ -405,14 +416,12 @@ def get_coxx_fingerprint_df( if ids: df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) else: - ids = Path( - self.path_to_coxxcar - ).parent.name # os.path.basename(os.path.dirname(self.path_to_coxxcar)) + ids = Path(self.path_to_coxxcar).parent.name df = pd.DataFrame(index=[ids], columns=["COXX_FP"]) coxxs = coxx_dict[self.feature_type] if len(energies) < n_bins: - inds = np.where((energies >= min_e) & (energies <= max_e)) + inds = np.where((energies >= min_e - tol) & (energies <= max_e + tol)) fp = coxx_fingerprint( energies[inds], coxxs[inds], @@ -479,8 +488,7 @@ def _calculate_wicoxx_ein(self) -> Tuple[float, float, float, float]: Percent bonding, Percent anti-bonding, weighted icoxx, effective interaction number """ - icoxx_dict = self.icoxxlist.icohpcollection.as_dict() - list_labels = icoxx_dict["list_labels"] + list_labels = list(self.icoxxlist.icohplist.keys()) # Compute sum of icohps icoxx_total = self.icoxxlist.icohpcollection.get_summed_icohp_by_label_list( list_labels @@ -515,9 +523,10 @@ def _calculate_wicoxx_ein(self) -> Tuple[float, float, float, float]: ) # calc effective interaction number # percent bonding-anitbonding + tol = 1e-6 if not self.icoxxlist.are_cobis and not self.icoxxlist.are_coops: - bonding_indices = coxx_bf <= 0 - antibonding_indices = coxx_bf >= 0 + bonding_indices = coxx_bf <= tol + antibonding_indices = coxx_bf >= tol bnd = abs(trapezoid(en_bf[bonding_indices], coxx_bf[bonding_indices])) antibnd = abs( trapezoid(en_bf[antibonding_indices], coxx_bf[antibonding_indices]) @@ -526,8 +535,8 @@ def _calculate_wicoxx_ein(self) -> Tuple[float, float, float, float]: per_antibnd = (antibnd / (bnd + antibnd)) * 100 elif self.icoxxlist.are_cobis or self.icoxxlist.are_coops: - bonding_indices = coxx_bf >= 0 - antibonding_indices = coxx_bf <= 0 + bonding_indices = coxx_bf >= tol + antibonding_indices = coxx_bf <= tol bnd = abs(trapezoid(coxx_bf[bonding_indices], en_bf[bonding_indices])) antibnd = abs( trapezoid(coxx_bf[antibonding_indices], en_bf[antibonding_indices]) @@ -538,11 +547,21 @@ def _calculate_wicoxx_ein(self) -> Tuple[float, float, float, float]: return per_bnd, per_antibnd, w_icoxx, ein def _calc_moment_features( - self, label_list: List[str] | None = None + self, label_list: List[str] | None = None, per_bond=True ) -> Tuple[float, float, float, float]: + """ + Wrapper method to calculate band center,width, skewness, and kurtosis of the COXX + Args: + label_list: List of bond labels + per_bond: Will scale cohp values by number of bonds i.e length of label_list arg + (Only affects when label_list is not None) + Returns: + coxx center,width, skewness, and kurtosis in eV + """ if label_list: + divisor = len(label_list) if per_bond else 1 coxxcar = self.completecoxx.get_summed_cohp_by_label_list( - label_list + label_list, divisor=divisor ).get_cohp() else: @@ -556,20 +575,21 @@ def _calc_moment_features( energies = self.completecoxx.energies - self.completecoxx.efermi coxx_dict = {} + tol = 1e-6 if not self.are_cobis and not self.are_coops: coxx_dict["bonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] + [scohp if scohp <= tol else 0 for scohp in coxx_all] ) coxx_dict["antibonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] + [scohp if scohp >= tol else 0 for scohp in coxx_all] ) coxx_dict["overall"] = coxx_all else: coxx_dict["antibonding"] = np.array( - [scohp if scohp <= 0 else 0 for scohp in coxx_all] + [scohp if scohp <= tol else 0 for scohp in coxx_all] ) coxx_dict["bonding"] = np.array( - [scohp if scohp >= 0 else 0 for scohp in coxx_all] + [scohp if scohp >= tol else 0 for scohp in coxx_all] ) coxx_dict["overall"] = coxx_all @@ -630,12 +650,12 @@ def _get_coxx_center( e_range: List[float], ) -> float: """ - Get the band width, defined as the first moment of the orbital resolved COHP + Get the band width, defined as the first moment of the COXX Args: - cohp: Orbital COHP list - energies: energies corresponding orbital orbital interactions COHP + coxx: COXX array + energies: energies corresponding COXX Returns: - Orbital-Orbital interaction band center in eV + coxx center in eV """ coxx_center = self._get_n_moment( n=1, coxx=coxx, energies=energies, e_range=e_range, center=False @@ -657,8 +677,8 @@ def _get_n_moment( Args: n: The order for the moment - cohp: COXX list - energies: energies + coxx: COXX array + energies: energies array center: Take moments with respect to the COXX center Returns: @@ -675,8 +695,12 @@ def _get_n_moment( min_e = min(energies) max_e = max(energies) - coxx = coxx[(energies >= min_e) & (energies <= max_e)] - energies = energies[(energies >= min_e) & (energies <= max_e)] + tol = 1e-6 + + mask = (energies >= min_e - tol) & (energies <= max_e + tol) + + coxx = coxx[mask] + energies = energies[mask] if center: coxx_center = self._get_coxx_center( @@ -691,7 +715,10 @@ def _get_n_moment( return nth_moment def get_summarized_coxx_df( - self, ids: str | None = None, label_list: List[str] | None = None + self, + ids: str | None = None, + label_list: List[str] | None = None, + per_bond=True, ) -> pd.DataFrame: """ This function returns a pandas dataframe with weighted ICOXX, effective interaction number @@ -704,9 +731,7 @@ def get_summarized_coxx_df( if ids: df = pd.DataFrame(index=[ids]) else: - ids = Path( - self.path_to_coxxcar - ).parent.name # os.path.basename(os.path.dirname(self.path_to_coxxcar)) + ids = Path(self.path_to_coxxcar).parent.name df = pd.DataFrame(index=[ids]) ( @@ -717,7 +742,9 @@ def get_summarized_coxx_df( ) = self._calculate_wicoxx_ein() if self.are_coops: - cc, cw, cs, ck = self._calc_moment_features(label_list=label_list) + cc, cw, cs, ck = self._calc_moment_features( + label_list=label_list, per_bond=per_bond + ) df.loc[ids, "bnd_wICOOP"] = per_bnd_xx df.loc[ids, "antibnd_wICOOP"] = per_antibnd_xx df.loc[ids, "w_ICOOP"] = w_icoxx @@ -727,7 +754,9 @@ def get_summarized_coxx_df( df.loc[ids, "skewness_COOP"] = cs df.loc[ids, "kurtosis_COOP"] = ck elif self.are_cobis: - cc, cw, cs, ck = self._calc_moment_features(label_list=label_list) + cc, cw, cs, ck = self._calc_moment_features( + label_list=label_list, per_bond=per_bond + ) df.loc[ids, "bnd_wICOBI"] = per_bnd_xx df.loc[ids, "antibnd_wICOBI"] = per_antibnd_xx df.loc[ids, "w_ICOBI"] = w_icoxx @@ -737,7 +766,9 @@ def get_summarized_coxx_df( df.loc[ids, "skewness_COBI"] = cs df.loc[ids, "kurtosis_COBI"] = ck else: - cc, cw, cs, ck = self._calc_moment_features(label_list=label_list) + cc, cw, cs, ck = self._calc_moment_features( + label_list=label_list, per_bond=per_bond + ) df.loc[ids, "bnd_wICOHP"] = per_bnd_xx df.loc[ids, "antibnd_wICOHP"] = per_antibnd_xx df.loc[ids, "w_ICOHP"] = w_icoxx @@ -790,13 +821,14 @@ def _calc_ionicity(self) -> float: if self.charge_type.lower() not in ["mulliken", "loewdin"]: raise ValueError( "Please check the requested charge_type. " - 'Possible options are "Mulliken" or "Loewdin"' + "Possible options are `Mulliken` or `Loewdin`" ) ch_veff = [] + tol = 1e-6 for i, j in enumerate(getattr(chargeobj, self.charge_type.capitalize())): if ( - j > 0 + j > tol and not structure.species[i].is_transition_metal and ( not structure.species[i].is_actinoid @@ -808,7 +840,7 @@ def _calc_ionicity(self) -> float: ch_veff.append(val) elif ( - j < 0 + j < tol and not structure.species[i].is_transition_metal and ( not structure.species[i].is_actinoid @@ -819,7 +851,7 @@ def _calc_ionicity(self) -> float: val = j / (valence_elec.nvalence() - 8) ch_veff.append(val) - elif j > 0 and ( + elif j > tol and ( structure.species[i].is_transition_metal or structure.species[i].is_actinoid or structure.species[i].is_lanthanoid @@ -827,7 +859,7 @@ def _calc_ionicity(self) -> float: val = j / (structure.species[i].max_oxidation_state - 0) ch_veff.append(val) - elif j < 0 and ( + elif j < tol and ( structure.species[i].is_transition_metal or structure.species[i].is_actinoid or structure.species[i].is_lanthanoid @@ -850,9 +882,7 @@ def get_df(self, ids: str | None = None) -> pd.DataFrame: if ids: df = pd.DataFrame(index=[ids]) else: - ids = Path( - self.path_to_charge - ).parent.name # os.path.basename(os.path.dirname(self.path_to_charge)) + ids = Path(self.path_to_charge).parent.name df = pd.DataFrame(index=[ids]) if self.charge_type.lower() == "mulliken": diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 75a916a2..65e8cb34 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -252,8 +252,8 @@ def test_featurize_NaCl_COXX(self): self.assertEqual(df.index[0], "NaCl") # Test that all the values in the DataFrame - self.assertAlmostEqual(df.loc["NaCl", "bnd_wICOHP"], 96.822857, places=5) - self.assertAlmostEqual(df.loc["NaCl", "antibnd_wICOHP"], 3.177143, places=5) + self.assertAlmostEqual(df.loc["NaCl", "bnd_wICOHP"], 97.233878, places=5) + self.assertAlmostEqual(df.loc["NaCl", "antibnd_wICOHP"], 2.766122, places=5) self.assertAlmostEqual(df.loc["NaCl", "w_ICOHP"], -0.150558, places=5) self.assertAlmostEqual(df.loc["NaCl", "EIN_ICOHP"], 27.843536, places=5) @@ -262,6 +262,15 @@ def test_featurize_NaCl_COXX(self): self.assertAlmostEqual(df.loc["NaCl", "skewness_COHP"], -1.072675, places=5) self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 4.811448, places=5) + # test summary features using label list + df1 = self.featurize_NaCl_COXX.get_summarized_coxx_df( + label_list=["2", "3", "30"] + ) + self.assertNotEqual( + df.loc["NaCl", "center_COHP"], + df1.loc["NaCl", "center_COHP"], + ) + def test_featurize_NaCl_COXX_fingerprint(self): df = self.featurize_NaCl_COXX.get_coxx_fingerprint_df(n_bins=20000) @@ -323,8 +332,8 @@ def test_featurize_CdF_COXX(self): self.assertEqual(df.index[0], "CdF") # Test that all the values in the DataFrame - self.assertAlmostEqual(df.loc["CdF", "bnd_wICOHP"], 56.05771, places=5) - self.assertAlmostEqual(df.loc["CdF", "antibnd_wICOHP"], 43.94229, places=5) + self.assertAlmostEqual(df.loc["CdF", "bnd_wICOHP"], 81.112732, places=5) + self.assertAlmostEqual(df.loc["CdF", "antibnd_wICOHP"], 18.887268, places=5) self.assertAlmostEqual(df.loc["CdF", "w_ICOHP"], -0.198235, places=5) self.assertAlmostEqual(df.loc["CdF", "EIN_ICOHP"], 18.76634, places=5) @@ -333,6 +342,15 @@ def test_featurize_CdF_COXX(self): self.assertAlmostEqual(df.loc["CdF", "skewness_COHP"], 0.910094, places=5) self.assertAlmostEqual(df.loc["CdF", "kurtosis_COHP"], 2.866611, places=5) + # test using label list + df1 = self.featurize_CdF_COXX.get_summarized_coxx_df( + label_list=["2", "3", "30"] + ) + self.assertNotEqual( + df.loc["CdF", "center_COHP"], + df1.loc["CdF", "center_COHP"], + ) + def test_featurize_K3Sb_COXX(self): df = self.featurize_K3Sb_COXX.get_summarized_coxx_df(ids="K3Sb") @@ -356,8 +374,8 @@ def test_featurize_K3Sb_COXX(self): self.assertEqual(df.index[0], "K3Sb") # Test that all the values in the DataFrame - self.assertAlmostEqual(df.loc["K3Sb", "bnd_wICOHP"], 97.053378, places=5) - self.assertAlmostEqual(df.loc["K3Sb", "antibnd_wICOHP"], 2.946622, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "bnd_wICOHP"], 97.019044, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "antibnd_wICOHP"], 2.980956, places=5) self.assertAlmostEqual(df.loc["K3Sb", "w_ICOHP"], -0.318218, places=5) self.assertAlmostEqual(df.loc["K3Sb", "EIN_ICOHP"], 11.597595, places=5) @@ -481,6 +499,35 @@ def test_lobsterpy_featurize_exception(self): "Please switch to ´all´ bonds mode", ) + with self.assertRaises(Exception) as err: + self.featurize_C_cation_anion = FeaturizeLobsterpy( + path_to_lobster_calc=TestDir / "TestData/C/", bonds="cation-anion" + ) + + _ = self.featurize_C_cation_anion.get_df() + + self.assertEqual( + err.exception.__str__(), + "No cation-anion bonds detected for C structure. " + "Please switch to ´all´ bonds mode", + ) + + def test_featurize_charges(self): + with self.assertRaises(Exception) as err: + self.featurize_CdF_Charge = FeaturizeCharges( + path_to_structure=TestDir / "TestData/CdF/POSCAR", + path_to_charge=TestDir / "TestData/CdF/CHARGE.lobster", + charge_type="Mull", + ) + + _ = self.featurize_CdF_Charge.get_df() + + self.assertEqual( + err.exception.__str__(), + "Please check the requested charge_type. " + "Possible options are `Mulliken` or `Loewdin`", + ) + def test_featurize_coxx(self): with self.assertRaises(Exception) as err: self.featurize_COXX = FeaturizeCOXX( From 3266bfc446587c06aaeafca2591d33ab0cd94c55 Mon Sep 17 00:00:00 2001 From: anaik Date: Sun, 4 Jun 2023 14:13:36 +0200 Subject: [PATCH 37/41] pydocstyle linitng fix --- lobsterpy/featurize/core.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index 8746fdc5..c7295af2 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -555,6 +555,7 @@ def _calc_moment_features( label_list: List of bond labels per_bond: Will scale cohp values by number of bonds i.e length of label_list arg (Only affects when label_list is not None) + Returns: coxx center,width, skewness, and kurtosis in eV """ @@ -654,6 +655,8 @@ def _get_coxx_center( Args: coxx: COXX array energies: energies corresponding COXX + e_range: range of energy to compute coxx center + Returns: coxx center in eV """ From 1c7cec94421a3839290fe10a4c5dd0fc44a63957 Mon Sep 17 00:00:00 2001 From: anaik Date: Mon, 5 Jun 2023 13:48:14 +0200 Subject: [PATCH 38/41] fix erange bug in COXX featurize --- lobsterpy/featurize/core.py | 8 ++++---- lobsterpy/featurize/test/test_batch.py | 19 ++++++++----------- lobsterpy/featurize/test/test_core.py | 20 +++++++++----------- 3 files changed, 21 insertions(+), 26 deletions(-) diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index c7295af2..c301a4ad 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -360,10 +360,10 @@ def get_coxx_fingerprint_df( min_e = self.e_range[0] max_e = self.e_range[-1] - if not max_e: + if locals()["max_e"] is None: max_e = np.max(energies) - if not min_e: + if locals()["min_e"] is None: min_e = np.min(energies) if label_list: @@ -690,9 +690,9 @@ def _get_n_moment( if e_range: min_e = self.e_range[0] max_e = self.e_range[1] - if not min_e: + if locals()["min_e"] is None: min_e = min(energies) - if not max_e: + if locals()["max_e"] is None: max_e = max(energies) else: min_e = min(energies) diff --git a/lobsterpy/featurize/test/test_batch.py b/lobsterpy/featurize/test/test_batch.py index 21ab7c14..2e3c4fb5 100644 --- a/lobsterpy/featurize/test/test_batch.py +++ b/lobsterpy/featurize/test/test_batch.py @@ -333,30 +333,28 @@ def setUp(self): def test_fp_cohp_overall(self): df = self.fp_cohp_overall.get_similarity_matrix_df() - self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.221446, places=5) - self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], -0.015035, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], -0.033251, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], -0.013751, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) - self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0.175085, places=5) + self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0.046889, places=5) def test_fp_cohp_bonding(self): fp_df = self.fp_cohp_bonding.fingerprint_df df = self.fp_cohp_bonding.get_similarity_matrix_df() - self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.000017, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0.000000, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) - for val in fp_df.loc["mp-1000", "COXX_FP"].coxx: - self.assertLessEqual(val, 0) + self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0.001532, places=5) def test_fp_cobi(self): fp_df = self.fp_cobi.fingerprint_df df = self.fp_cobi.get_similarity_matrix_df() - self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0.329883, places=5) + self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) - for val in fp_df.loc["mp-463", "COXX_FP"].coxx: - self.assertLessEqual(val, 0) + self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0, places=5) def test_fp_coop(self): fp_df = self.fp_coop.fingerprint_df @@ -365,8 +363,7 @@ def test_fp_coop(self): self.assertAlmostEqual(df.loc["mp-463", "mp-1000"], 0, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-2176"], 0, places=5) self.assertAlmostEqual(df.loc["mp-463", "mp-463"], 1, places=5) - for val in fp_df.loc["mp-2176", "COXX_FP"].coxx: - self.assertGreaterEqual(val, 0) + self.assertAlmostEqual(df.loc["mp-1000", "mp-2176"], 0, places=5) class TestExceptions(unittest.TestCase): diff --git a/lobsterpy/featurize/test/test_core.py b/lobsterpy/featurize/test/test_core.py index 65e8cb34..9d7fd9a6 100644 --- a/lobsterpy/featurize/test/test_core.py +++ b/lobsterpy/featurize/test/test_core.py @@ -257,15 +257,13 @@ def test_featurize_NaCl_COXX(self): self.assertAlmostEqual(df.loc["NaCl", "w_ICOHP"], -0.150558, places=5) self.assertAlmostEqual(df.loc["NaCl", "EIN_ICOHP"], 27.843536, places=5) - self.assertAlmostEqual(df.loc["NaCl", "center_COHP"], 2.79973, places=5) - self.assertAlmostEqual(df.loc["NaCl", "width_COHP"], 0.686836, places=5) - self.assertAlmostEqual(df.loc["NaCl", "skewness_COHP"], -1.072675, places=5) - self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 4.811448, places=5) + self.assertAlmostEqual(df.loc["NaCl", "center_COHP"], -4.96241, places=5) + self.assertAlmostEqual(df.loc["NaCl", "width_COHP"], 8.881784e-16, places=5) + self.assertAlmostEqual(df.loc["NaCl", "skewness_COHP"], 1, places=5) + self.assertAlmostEqual(df.loc["NaCl", "kurtosis_COHP"], 1, places=5) # test summary features using label list - df1 = self.featurize_NaCl_COXX.get_summarized_coxx_df( - label_list=["2", "3", "30"] - ) + df1 = self.featurize_NaCl_COXX.get_summarized_coxx_df(label_list=["2", "3"]) self.assertNotEqual( df.loc["NaCl", "center_COHP"], df1.loc["NaCl", "center_COHP"], @@ -379,10 +377,10 @@ def test_featurize_K3Sb_COXX(self): self.assertAlmostEqual(df.loc["K3Sb", "w_ICOHP"], -0.318218, places=5) self.assertAlmostEqual(df.loc["K3Sb", "EIN_ICOHP"], 11.597595, places=5) - self.assertAlmostEqual(df.loc["K3Sb", "center_COHP"], 2.786451, places=5) - self.assertAlmostEqual(df.loc["K3Sb", "width_COHP"], 0.765492, places=5) - self.assertAlmostEqual(df.loc["K3Sb", "skewness_COHP"], -1.52563, places=5) - self.assertAlmostEqual(df.loc["K3Sb", "kurtosis_COHP"], 6.829327, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "center_COHP"], -0.198211, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "width_COHP"], 0.233826, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "skewness_COHP"], -1.626643, places=5) + self.assertAlmostEqual(df.loc["K3Sb", "kurtosis_COHP"], 3.771873, places=5) class TestFeaturizeCharges(unittest.TestCase): From d3b29755435bfe9a14aea156eee6c15f5470de8b Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 6 Jun 2023 08:46:56 +0200 Subject: [PATCH 39/41] resolve merge conflicts with github CI workflow, remove redundant use of locals variable --- .github/workflows/python-package.yml | 37 +++++++++++++++++++++++----- lobsterpy/featurize/core.py | 8 +++--- 2 files changed, 35 insertions(+), 10 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 2a7dd706..f92fde4e 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -17,6 +17,7 @@ jobs: fail-fast: false matrix: python-version: ["3.8", "3.9", "3.10", "3.11"] + split: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # Number of splits steps: - uses: actions/checkout@v3 @@ -27,10 +28,10 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install flake8 pytest pytest-mock + python -m pip install flake8 pytest pytest-mock pytest-split pytest-cov python -m pip install types-setuptools - python -m pip install coverage - pip install -e .[featurizer] + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install -e . - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names @@ -39,6 +40,30 @@ jobs: flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest and coverage run: | - coverage run -m pytest - coverage combine - coverage report + pytest --cov=lobsterpy --cov-append --splits 10 --group ${{ matrix.split }} --durations-path ./lobsterpy/.pytest-split-durations + - name: Upload coverage + uses: actions/upload-artifact@v3 + with: + name: coverage-${{ matrix.split }} + path: .coverage + + coverage: + needs: build + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + - name: Install Coverage + run: | + python -m pip install coverage[toml] + - name: Download coverage artifacts + continue-on-error: true + uses: actions/download-artifact@v3 + - name: Run coverage + continue-on-error: true + run: | + coverage combine coverage*/.coverage* + coverage report \ No newline at end of file diff --git a/lobsterpy/featurize/core.py b/lobsterpy/featurize/core.py index c301a4ad..d9e68bc1 100644 --- a/lobsterpy/featurize/core.py +++ b/lobsterpy/featurize/core.py @@ -360,10 +360,10 @@ def get_coxx_fingerprint_df( min_e = self.e_range[0] max_e = self.e_range[-1] - if locals()["max_e"] is None: + if max_e is None: max_e = np.max(energies) - if locals()["min_e"] is None: + if min_e is None: min_e = np.min(energies) if label_list: @@ -690,9 +690,9 @@ def _get_n_moment( if e_range: min_e = self.e_range[0] max_e = self.e_range[1] - if locals()["min_e"] is None: + if min_e is None: min_e = min(energies) - if locals()["max_e"] is None: + if max_e is None: max_e = max(energies) else: min_e = min(energies) From 99fed4e9b6e70607dd1106df826255db88454c7b Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 6 Jun 2023 08:53:52 +0200 Subject: [PATCH 40/41] fix featurizer requirements --- .github/workflows/python-package.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 6c563717..096df11b 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -30,8 +30,7 @@ jobs: python -m pip install --upgrade pip python -m pip install flake8 pytest pytest-mock pytest-split pytest-cov python -m pip install types-setuptools - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install -e . + pip install -e .[featurizer] - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names From c85e90e358c28fdb937b9b5ff64d9a60deb1c8bb Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 25 Jul 2023 16:59:52 +0200 Subject: [PATCH 41/41] fix failing exception test> changes in pymatgen --- lobsterpy/cohp/analyze.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lobsterpy/cohp/analyze.py b/lobsterpy/cohp/analyze.py index e95b13fb..cd6f8831 100644 --- a/lobsterpy/cohp/analyze.py +++ b/lobsterpy/cohp/analyze.py @@ -141,7 +141,7 @@ def setup_env(self): if ( str(err) == "min() arg is an empty sequence" or str(err) - == "All valences are equal to 0, additional_conditions 1 and 3 and 5 and 6 will not work" + == "All valences are equal to 0, additional_conditions 1, 3, 5 and 6 will not work" ): raise ValueError( "Consider switching to an analysis of all bonds and not only cation-anion bonds."

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