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utils.py
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utils.py
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import math
import matplotlib.pyplot as plt
import numpy as np
import pdffitx.modeling as md
import pdffitx.parsers as ps
from diffpy.srfit.fitbase import FitResults
from diffpy.srfit.fitbase.parameter import Parameter
from diffpy.srfit.fitbase.fitresults import initializeRecipe
from diffpy.srfit.fitbase.profile import Profile
import pdffitx.modeling.running as rn
from pyobjcryst.crystal import Crystal
from diffpy.srfit.fitbase.fithook import FitHook
import typing as tp
import xarray as xr
import scipy.optimize as opt
import pdfstream.visualization
import pathlib
import matplotlib.gridspec as gridspec
def load_data(filename: str, metadata: dict = None) -> Profile:
profile = Profile()
parser = md.MyParser()
parser.parseFile(filename, metadata)
profile.loadParsedData(parser)
return profile
def get_symbol(name: str) -> str:
"""A conventional rule to rename the parameter name to latex version."""
words = name.split("_")
if "scale" in words:
return "scale"
if "delta2" in words:
return r"$\delta_2$"
if "delta1" in words:
return r"$\delta_1$"
for word in ("a", "b", "c"):
if word in words:
return word
for word in ("alpha", "beta", "gamma"):
if word in words:
return rf"$\{word}$"
for word in (
'Uiso', 'U11', 'U12', 'U13', 'U21', 'U22', 'U23', 'U31', 'U32', 'U33',
'Biso', 'B11', 'B12', 'B13', 'B21', 'B22', 'B23', 'B31', 'B32', 'B33',
):
if word in words:
return rf"{word[0]}$_{{{word[1:]}}}$({words[1]})"
for word in ("x", "y", "z"):
if word in words:
return rf"{word}({words[1]})"
for word in ("psize", "psig", "sthick", "thickness", "radius"):
if word in words:
return rf"{word}"
return " ".join(words[1:])
def get_unit(name: str) -> str:
"""A conventional rule to get the unit."""
words = name.split("_")
if "scale" in words:
return ""
if "delta2" in words:
return r"Å$^2$"
if "delta1" in words:
return r"Å"
for word in ("a", "b", "c"):
if word in words:
return "Å"
for word in ("alpha", "beta", "gamma"):
if word in words:
return "deg"
for word in (
'Uiso', 'U11', 'U12', 'U13', 'U21', 'U22', 'U23', 'U31', 'U32', 'U33',
'Biso', 'B11', 'B12', 'B13', 'B21', 'B22', 'B23', 'B31', 'B32', 'B33',
):
if word in words:
return "Å$^2$"
for word in ("x", "y", "z"):
if word in words:
return "Å"
for word in ("psize", "psig", "sthick", "thickness", "radius"):
if word in words:
return "Å"
return ""
class ModelBase:
"""The template for the model class."""
def __init__(self, equation: str, structures: tp.Dict[str, Crystal], characteristics: tp.Dict[str, tp.Callable]):
self._equation = equation
self._structures = structures
self._characteristics = characteristics
self._recipe = self._create_recipe()
self._fit_result = FitResults(self._recipe, update=False)
self._verbose: int = 1
self._order: tp.List[tp.Union[str, tp.Iterable[str]]] = []
self._options: dict = {}
def parallel(self, ncpu: int):
fc = self.get_contribution()
for g in fc.generators.values():
g.parallel(ncpu)
def set_xrange(self, xmin: float = None, xmax: float = None, xstep: float = None) -> None:
profile = self.get_profile()
profile.setCalculationRange(xmin=xmin, xmax=xmax, dx=xstep)
def get_xrange(self) -> tp.List:
return self._xrange
def set_verbose(self, level: int) -> None:
self._verbose = level
def get_verbose(self) -> int:
return self._verbose
def set_options(self, **kwargs) -> None:
self._options = kwargs
def get_options(self) -> dict:
return self._options
def set_order(self, *order: tp.Union[str, tp.Iterable[str]]) -> None:
order = list(order)
self._check_order(order)
self._order = order
def _check_order(self, order: tp.Any) -> None:
tags = set(self._recipe._tagmanager.alltags())
if isinstance(order, str):
if not hasattr(self._recipe, order) and order not in tags:
raise ValueError("'{}' is not in the variable names.".format(order))
elif isinstance(order, tp.Iterable):
for x in order:
self._check_order(x)
else:
raise TypeError("'{}' is not allowed.".format(type(order)))
def get_order(self) -> tp.List[tp.Union[str, tp.Iterable[str]]]:
return self._order
def set_param(self, **kwargs) -> None:
for name, value in kwargs.items():
if not hasattr(self._recipe, name):
raise ValueError("There is no parameter called '{}'".format(name))
for name, value in kwargs.items():
var: Parameter = getattr(self._recipe, name)
var.setValue(value)
def _create_recipe(self) -> md.MyRecipe:
raise NotImplemented
def get_contribution(self) -> md.MyContribution:
return next(iter(self._recipe.contributions.values()))
def set_profile(self, profile: Profile) -> None:
fc: md.MyContribution = self.get_contribution()
fc.setProfile(profile)
def optimize(self) -> None:
md.optimize(self._recipe, self._order, validate=False, verbose=self._verbose, **self._options)
def update_result(self) -> None:
return self._fit_result.update()
def show(self) -> None:
self._recipe.show()
def get_result(self) -> dict:
fr = self._fit_result
dct = dict()
n = len(fr.varnames)
for i in range(n):
dct[fr.varnames[i]] = fr.varvals[i]
n = len(fr.fixednames)
for i in range(n):
dct[fr.fixednames[i]] = fr.fixedvals[i]
dct["rw"] = self._fit_result.rw
return dct
def get_profile(self) -> Profile:
fc = self.get_contribution()
return fc.profile
def save(self, filepath: str):
self._fit_result.saveResults(filepath)
def load(self, filepath: str):
initializeRecipe(self._recipe, filepath)
def export_result(self) -> xr.Dataset:
dct = self.get_result()
ds = xr.Dataset(dct)
for name in ds.variables:
ds[name].attrs["long_name"] = get_symbol(name)
ds[name].attrs["units"] = get_unit(name)
return ds
def export_fits(self) -> xr.Dataset:
profile = self.get_profile()
ds = xr.Dataset(
{"y": (["x"], profile.y), "ycalc": (["x"], profile.ycalc), "yobs": (["xobs"], profile.yobs)},
{"x": (["x"], profile.x), "xobs": (["xobs"], profile.xobs)}
)
ds["y"].attrs["standard_name"] = "G"
ds["y"].attrs["units"] = r"Å$^{-2}$"
ds["ycalc"].attrs["standard_name"] = "G"
ds["ycalc"].attrs["units"] = r"Å$^{-2}$"
ds["yobs"].attrs["standard_name"] = "G"
ds["yobs"].attrs["units"] = r"Å$^{-2}$"
ds["x"].attrs["standard_name"] = "r"
ds["x"].attrs["units"] = "Å"
ds["xobs"].attrs["standard_name"] = "r"
ds["xobs"].attrs["units"] = "Å"
return ds
def get_structures(self):
return self._structures
def export_in_files(self, directory: str, file_prefix: str) -> None:
directory = pathlib.Path(directory)
result = self.export_result()
path = directory.joinpath("{}_result.nc".format(file_prefix))
result.to_netcdf(path)
fits = self.export_fits()
path = directory.joinpath("{}_fits.nc".format(file_prefix))
fits.to_netcdf(path)
structures = self.get_structures()
for name, structure in structures.items():
path = directory.joinpath("{}_{}.cif".format(file_prefix, name))
with path.open("w") as f:
structure.CIFOutput(f)
def plot(self) -> None:
md.view_fits(self._recipe)
class MultiPhaseModel(ModelBase):
def _create_recipe(self) -> md.MyRecipe:
n = len(self._structures)
if n != 2:
raise ValueError("The model needs exactly two structures. Currently, it has {}".format(n))
pgs = []
for name, structure in self._structures.items():
pg = md.PDFGenerator(name)
pg.setStructure(structure, periodic=True)
pgs.append(pg)
fc = md.MyContribution(self.__class__.__name__)
fc.xname = "x"
for pg in pgs:
fc.addProfileGenerator(pg)
for name, sf in self._characteristics.items():
fc.registerFunction(sf, name)
fc.setEquation(self._equation)
fr = md.MyRecipe()
fr.clearFitHooks()
fr.addContribution(fc)
md.initialize(fr)
return fr
def plot_fits(fits: xr.Dataset, offset: float = 0., ax: plt.Axes = None, **kwargs) -> None:
if ax is None:
ax = plt.gca()
fits["yobs"].plot.line(ax=ax, marker="o", fillstyle="none", ls="none", **kwargs)
fits["ycalc"].plot.line(ax=ax, _labels=False, **kwargs)
diff = fits["y"] - fits["ycalc"]
shift = offset + fits["y"].min() - diff.max()
diff += shift
ax.axhline(shift, ls='--', alpha=0.5, color="black")
diff.plot.line(ax=ax, _labels=False, **kwargs)
ax.set_title("")
return
def plot_fits_along_dim(fits: xr.Dataset, dim: str, num_col: int = 4, offset: float = 0., figure_config: dict = None, grid_config: dict = None, plot_config: dict = None) -> tp.List[plt.Axes]:
if grid_config is None:
grid_config = {}
if plot_config is None:
plot_config = {}
if figure_config is None:
figure_config = {}
fig: plt.Figure = plt.figure(**figure_config)
n = len(fits[dim])
num_row = math.ceil(n / num_col)
grids = gridspec.GridSpec(num_row, num_col, figure=fig, **grid_config)
axes = []
for i, grid in zip(fits[dim], grids):
fit = fits.isel({dim: i})
ax = fig.add_subplot(grid)
axes.append(ax)
plot_fits(fit, offset, ax=ax, **plot_config)
return axes