Source code for pymatgen.core.spectrum

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.

"""
This module defines classes to represent any type of spectrum, essentially any
x y value pairs.
"""

import numpy as np
from scipy.ndimage.filters import gaussian_filter1d

from monty.json import MSONable

from pymatgen.util.coord import get_linear_interpolated_value

__author__ = "Chen Zheng"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "2.0"
__maintainer__ = "Chen Zheng"
__email__ = "chz022@ucsd.edu"
__date__ = "Aug 9, 2017"


[docs]class Spectrum(MSONable): """ Base class for any type of xas, essentially just x, y values. Examples include XRD patterns, XANES, EXAFS, NMR, DOS, etc. Implements basic tools like application of smearing, normalization, addition multiplication, etc. Subclasses should extend this object and ensure that super is called with ALL args and kwargs. That ensures subsequent things like add and mult work properly. """ XLABEL = "x" YLABEL = "y" def __init__(self, x, y, *args, **kwargs): r""" Args: x (ndarray): A ndarray of N values. y (ndarray): A ndarray of N x k values. The first dimension must be the same as that of x. Each of the k values are interpreted as *args: All subclasses should provide args other than x and y when calling super, e.g., super().__init__( x, y, arg1, arg2, kwarg1=val1, ..). This guarantees the +, -, *, etc. operators work properly. **kwargs: Same as that for *args. """ self.x = np.array(x) self.y = np.array(y) self.ydim = self.y.shape if self.x.shape[0] != self.ydim[0]: raise ValueError("x and y values have different first dimension!") self._args = args self._kwargs = kwargs def __getattr__(self, item): if item == self.XLABEL.lower(): return self.x if item == self.YLABEL.lower(): return self.y raise AttributeError("Invalid attribute name %s" % str(item)) def __len__(self): return self.ydim[0]
[docs] def normalize(self, mode="max", value=1): """ Normalize the spectrum with respect to the sum of intensity Args: mode (str): Normalization mode. Supported modes are "max" (set the max y value to value, e.g., in XRD patterns), "sum" (set the sum of y to a value, i.e., like a probability density). value (float): Value to normalize to. Defaults to 1. """ if mode.lower() == "sum": factor = np.sum(self.y, axis=0) elif mode.lower() == "max": factor = np.max(self.y, axis=0) else: raise ValueError("Unsupported normalization mode %s!" % mode) self.y /= factor / value
[docs] def smear(self, sigma): """ Apply Gaussian smearing to spectrum y value. Args: sigma: Std dev for Gaussian smear function """ diff = [self.x[i + 1] - self.x[i] for i in range(len(self.x) - 1)] avg_x_per_step = np.sum(diff) / len(diff) if len(self.ydim) == 1: self.y = gaussian_filter1d(self.y, sigma / avg_x_per_step) else: self.y = np.array([ gaussian_filter1d(self.y[:, k], sigma / avg_x_per_step) for k in range(self.ydim[1])]).T
[docs] def get_interpolated_value(self, x): """ Returns an interpolated y value for a particular x value. Args: x: x value to return the y value for Returns: Value of y at x """ if len(self.ydim) == 1: return get_linear_interpolated_value(self.x, self.y, x) return [get_linear_interpolated_value(self.x, self.y[:, k], x) for k in range(self.ydim[1])]
[docs] def copy(self): """ Returns: Copy of Spectrum object. """ return self.__class__(self.x, self.y, *self._args, **self._kwargs)
def __add__(self, other): """ Add two Spectrum object together. Checks that x scales are the same. Otherwise, a ValueError is thrown. Args: other: Another Spectrum object Returns: Sum of the two Spectrum objects """ if not all(np.equal(self.x, other.x)): raise ValueError("X axis values are not compatible!") return self.__class__(self.x, self.y + other.y, *self._args, **self._kwargs) def __sub__(self, other): """ Substract one Spectrum object from another. Checks that x scales are the same. Otherwise, a ValueError is thrown Args: other: Another Spectrum object Returns: Substraction of the two Spectrum objects """ if not all(np.equal(self.x, other.x)): raise ValueError("X axis values are not compatible!") return self.__class__(self.x, self.y - other.y, *self._args, **self._kwargs) def __mul__(self, other): """ Scale the Spectrum's y values Args: other: scalar, The scale amount Returns: Spectrum object with y values scaled """ return self.__class__(self.x, other * self.y, *self._args, **self._kwargs) __rmul__ = __mul__ def __truediv__(self, other): """ True division of y Args: other: The divisor Returns: Spectrum object with y values divided """ return self.__class__(self.x, self.y.__truediv__(other), *self._args, **self._kwargs) def __floordiv__(self, other): """ True division of y Args: other: The divisor Returns: Spectrum object with y values divided """ return self.__class__(self.x, self.y.__floordiv__(other), *self._args, **self._kwargs) __div__ = __truediv__ def __str__(self): """ Returns a string containing values and labels of spectrum object for plotting. """ return "\n".join([self.__class__.__name__, "%s: %s" % (self.XLABEL, self.x), "%s: %s" % (self.YLABEL, self.y)]) def __repr__(self): """ Returns a printable representation of the class """ return self.__str__()