Source code for

import inspect
import os
import subprocess
from itertools import cycle

import numpy as np

from scm.plams.interfaces.adfsuite.scmjob import SCMJob, SCMResults
from import Units

__all__ = ["CRSResults", "CRSJob"]

[docs]class CRSResults(SCMResults): """A |SCMResults| subclass for accessing results of |CRSJob|.""" _kfext = ".crskf" _rename_map = {"CRSKF": "$JN.crskf"} @property def section(self) -> str: try: # Return the cached value if possible return self._section except AttributeError: try: self._section = except AttributeError: self._section = self.job.settings.input.t.upper() return self._section
[docs] def get_energy(self, energy_type: str = "deltag", compound_idx: int = 0, unit: str = "kcal/mol") -> float: """Returns the solute solvation energy from an Activity Coefficients calculation.""" E = self.readkf(self.section, energy_type)[compound_idx] return Units.convert(E, "kcal/mol", unit)
[docs] def get_activity_coefficient(self, compound_idx: int = 0) -> float: """Return the solute activity coefficient from an Activity Coefficients calculation.""" return self.readkf(self.section, "gamma")[compound_idx]
[docs] def get_sigma_profile(self, subsection: str = "profil", as_df: bool = False) -> dict: r"""Grab all sigma profiles, returning a dictionary of Numpy Arrays. Values of :math:`\sigma` are stored under the ``"σ (e/A**2)"`` key. Results can be returned as a Pandas DataFrame by settings *as_df* to ``True``. The returned results can be plotted by passing them to the :meth:`CRSResults.plot` method. .. note:: *as_df* = ``True`` requires the Pandas_ package. Plotting requires the `matplotlib <>`__ package. .. _Pandas: """ args = (subsection, "σ (e/A**2)", "chdval") try: return self._get_array_dict("SIGMAPROFILE", *args, as_df=as_df) except KeyError: return self._get_array_dict("PURESIGMAPROFILE", *args, as_df=as_df)
[docs] def get_sigma_potential(self, subsection: str = "mu", unit: str = "kcal/mol", as_df: bool = False) -> dict: r"""Grab all sigma profiles, expressed in *unit*, and return a dictionary of Numpy Arrays. Values of :math:`\sigma` are stored under the ``"σ (e/A**2)"`` key. Results can be returned as a Pandas DataFrame by settings *as_df* to ``True``. The returned results can be plotted by passing them to the :meth:`CRSResults.plot` method. .. note:: *as_df* = ``True`` requires the Pandas_ package. Plotting requires the `matplotlib <>`__ package. .. _Pandas: """ args = (subsection, "σ (e/A**2)", "chdval") try: return self._get_array_dict("SIGMAPOTENTIAL", *args, unit=unit, as_df=as_df) except KeyError: return self._get_array_dict("PURESIGMAPOTENTIAL", *args, unit=unit, as_df=as_df)
[docs] def get_prop_names(self, section=None) -> list: r"""Read the section of the .crskf file and return a list of the properties that were calculated. The section argument can be supplied to look at previously-calculated results. If no section name is supplied, the function defaults to using the most recent property that was calculated.""" if section is None: section = self.section try: return self._kf.get_skeleton()[section] except KeyError: raise KeyError("Cannot find section name: " + str(section))
[docs] def get_results(self, section=None) -> dict: r"""Read the section from the most recent calculation type and return the result as a dictionary.""" if section is None: section = self.section output = getattr(self, "_prop_dict", False) if output and output["section"] == section: return output props = self.get_prop_names() try: props.remove("ncomp") props.remove("nitems") except ValueError: raise ValueError("Results object is missing or incomplete.") # first get the two ranges for the indices ncomp = self.readkf(section, "ncomp") nitems = self.readkf(section, "nitems") try: nstruct = self.readkf(section, "nstruct") except: nstruct = ncomp np_dict = {"section": section} np_dict["ncomp"] = ncomp for prop in props: tmp = self.readkf(section, prop) if (prop == "filename") or (prop == "name"): chunk_length = len(tmp) // ncomp np_dict[prop] = [tmp[i : i + chunk_length].strip() for i in range(0, len(tmp), chunk_length)] continue if prop == "struct names": chunk_length = len(tmp) // nstruct np_dict[prop] = [tmp[i : i + chunk_length].strip() for i in range(0, len(tmp), chunk_length)] continue if not isinstance(tmp, list): np_dict[prop] = tmp else: np_dict[prop] = np.array(tmp) if len(tmp) == ncomp * nitems: np_dict[prop].shape = (ncomp, nitems) setattr(self, "_prop_dict", np_dict) return np_dict
[docs] def get_multispecies_dist(self): """ This function returns multispecies distribution for each (compound,structure) pair. The format is a list with indices corresponding to compound indices. Each item in the list is a dictionary with a structure name : list pair, where the structure name corresponds to a structure the compound can be exist as and the list is the distribution of that compound in that structure over the number of points (mole fractions, temperatures, pressures). """ res = self.get_results() property_name = res["property"].rstrip() if(property_name=="LOGP"): nPhase = 2 else: nPhase = 1 ncomp = self.readkf(self.section, "ncomp") struct_names = res["struct names"] num_points = self.readkf(self.section, "nitems") valid_structs = [[] for _ in range(ncomp)] comp_dist = res["comp distribution"].flatten() for i in range(len(struct_names)): for j in range(ncomp): if res["valid structs"][i * ncomp + j]: valid_structs[j].append(struct_names[i]) compositions = [{vs: [] for vs in valid_structs[i]} for i in range(ncomp)] idx = 0 for i in range(ncomp): for nfrac in range(num_points): for k in range(nPhase): for j in range(len(valid_structs[i])): compositions[i][valid_structs[i][j]].append(comp_dist[idx]) idx += 1 return compositions
[docs] def plot(self, *arrays: "np.ndarray", x_axis: str = None, plot_fig: bool = True, x_label=None, y_label=None): """Plot, show and return a series of COSMO-RS results as a matplotlib Figure instance. Accepts the output of, *e.g.*, :meth:`CRSResults.get_sigma_profile`: A dictionary of Numpy arrays or a Pandas DataFrame. Returns a matplotlib Figure_ instance which can be further modified to the users liking. Automatic plotting of the resulting figure can be disabled with the *plot_fig* argument. .. note:: This method requires the `matplotlib <>`__ package. .. note:: The name of the dictionary/DataFrame key containing the index (*i.e.* the x-axis) can, and should, be manually specified in *x_axis* if a custom *x_axis* is passed to :meth:`CRSResults._get_array_dict`. This argument can be ignored otherwise. .. _Figure: """ # noqa def get_x_axis(array, x_axis): """Find and return the index and its name.""" if x_axis is None: return np.arange(array.shape[1]) if isinstance(x_axis, str): ret = self._prop_dict[x_axis] else: ret = np.array(x_axis, copy=False) ret = ret.ravel() # Flatten it return ret[: array.shape[1]] # Check if matplotlib is installed try: import matplotlib matplotlib.use("TkAgg") if plot_fig else matplotlib.use("Agg") import matplotlib.pyplot as plt except ImportError: method = self.__class__.__name__ + ".plot" raise ImportError("{}: this method requires the 'matplotlib' package".format(method)) self.get_results() # Create a dictionary of 1d arrays array_dict = {} for array in arrays: name = None if isinstance(array, str): # Array refers to a section in the kf file name = array array = self._prop_dict[array] # Ensure it's a 2D array array = np.array(array, ndmin=2, dtype=float, copy=False) # Fill the array dict with 1d arrays base_key = "" if name is None else name + " " iterator = enumerate(array, 1) if array.shape[0] != 1 else zip(cycle(" "), array) for i, array_1d in iterator: key = f"{base_key}{i}" array_dict[key] = array_1d # Retrieve the index and its name index = get_x_axis(array, x_axis) # print ("INDEX::::", index) if x_label is None: if isinstance(x_axis, str): x_label = x_axis else: x_label = "" if y_label is None: y_label = "" # Assign various series to the plot fig, ax = plt.subplots() for k, v in array_dict.items(): ax.plot(index, v, label=k) # Add the legend and x-label ax.legend() ax.set_xlabel(x_label) ax.set_ylabel(y_label) # Show and return if plot_fig: return fig
[docs] def _get_array_dict( self, section: str, subsection: str, x_axis: str, index_subsection: str, unit: str = "kcal/mol", as_df: bool = False, ) -> dict: """Create dictionary or DataFrame containing all values in *section*/*subsection*. Takes the following arguments: * The *section*/*subsection* of the desired quantity. * The desired name of the index (*x_axis*). * The name of subsection containing the index (*index_subsection*). * The *unit* of the output quanty (ignore this keyword if not applicable). * If the result should be returned as Pandas DataFrame (*as_df*). """ ret = self._construct_array_dict(section, subsection, unit) # Create the index index = self.readarray(section, index_subsection, dtype=float) if section in ("BINMIXCOEF", "COMPOSITIONLINE", "TERNARYMIX"): ncomponent = 3 if section == "TERNARYMIX" else 2 index.shape = ncomponent, len(index) // ncomponent iterator = np.nditer(index.astype(str), flags=["external_loop"], order="F") ret[x_axis] = np.array([" / ".join(i for i in item) for item in iterator]) else: ret[x_axis] = index # Return a dictionary of arrays or a DataFrame if not as_df: return ret else: return self._dict_to_df(ret, section, x_axis)
[docs] def _construct_array_dict(self, section: str, subsection: str, unit: str = "kcal/mol") -> dict: """Construct dictionary containing all values in *section*/*subsection*.""" # Use filenames as keys _filenames = self.readkf(section, "filename").split() filenames = [_filenames] if not isinstance(_filenames, list) else _filenames # Grab the keys and the number of items per key keys = [os.path.basename(key) for key in filenames] + ["Total"] nitems = self.readkf(section, "nitems") # Use sigma profiles/potentials as values ratio = Units.conversion_ratio("kcal/mol", unit) values = ratio * self.readarray(section, subsection, dtype=float) values.shape = len(values) // nitems, nitems ret = dict(zip(keys, values)) try: ret["Total"] = self.readarray(section, subsection + "tot", dtype=float) except KeyError: pass return ret
[docs] @staticmethod def _dict_to_df(array_dict: dict, section: str, x_axis: str): """Attempt to convert a dictionary into a DataFrame.""" try: import pandas as pd except ImportError: method = inspect.stack()[2][3] raise ImportError("{}: as_df=True requires the 'pandas' package".format(method)) index = pd.Index(array_dict.pop(x_axis), name=x_axis) df = pd.DataFrame(array_dict, index=index) = section.lower() return df
[docs]class CRSJob(SCMJob): """A |SCMJob| subclass intended for running COSMO-RS jobs.""" _command = "crs" _result_type = CRSResults _subblock_end = "end"
[docs] def __init__(self, **kwargs) -> None: """Initialize a :class:`CRSJob` instance.""" super().__init__(**kwargs) self.settings.ignore_molecule = True
@staticmethod def database() -> str: database_path = os.path.join(os.environ["SCM_PKG_ADFCRSDIR"], "ADFCRS-2018") if not os.path.isdir(database_path): raise FileNotFoundError("The ADFCRS-2018 database does not seem to be installed") return database_path @staticmethod def coskf_from_database(name: str) -> str: if not name.endswith(".coskf"): name += ".coskf" return os.path.join(CRSJob.database(), name)
[docs] @staticmethod def cos_to_coskf(filename: str) -> str: """Convert a .cos file into a .coskf file with the :code:`$AMSBIN/cosmo2kf` command. Returns the filename of the new .coskf file. """ filename_out = filename + "kf" try: amsbin = os.environ["AMSBIN"] except KeyError: raise EnvironmentError( "cos_to_coskf: Failed to load 'cosmo2kf' from '$AMSBIN/'; " "the 'AMSBIN' environment variable has not been set" ) args = [os.path.join(amsbin, "cosmo2kf"), filename, filename_out] return filename_out