Source code for pymatgen.util.plotting

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
Utilities for generating nicer plots.
"""
import math
import numpy as np

from pymatgen.core.periodic_table import Element


__author__ = "Shyue Ping Ong"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__date__ = "Mar 13, 2012"


[docs]def pretty_plot(width=8, height=None, plt=None, dpi=None, color_cycle=("qualitative", "Set1_9")): """ Provides a publication quality plot, with nice defaults for font sizes etc. Args: width (float): Width of plot in inches. Defaults to 8in. height (float): Height of plot in inches. Defaults to width * golden ratio. plt (matplotlib.pyplot): If plt is supplied, changes will be made to an existing plot. Otherwise, a new plot will be created. dpi (int): Sets dot per inch for figure. Defaults to 300. color_cycle (tuple): Set the color cycle for new plots to one of the color sets in palettable. Defaults to a qualitative Set1_9. Returns: Matplotlib plot object with properly sized fonts. """ ticksize = int(width * 2.5) golden_ratio = (math.sqrt(5) - 1) / 2 if not height: height = int(width * golden_ratio) if plt is None: import matplotlib.pyplot as plt import importlib mod = importlib.import_module("palettable.colorbrewer.%s" % color_cycle[0]) colors = getattr(mod, color_cycle[1]).mpl_colors from cycler import cycler plt.figure(figsize=(width, height), facecolor="w", dpi=dpi) ax = plt.gca() ax.set_prop_cycle(cycler('color', colors)) else: fig = plt.gcf() fig.set_size_inches(width, height) plt.xticks(fontsize=ticksize) plt.yticks(fontsize=ticksize) ax = plt.gca() ax.set_title(ax.get_title(), size=width * 4) labelsize = int(width * 3) ax.set_xlabel(ax.get_xlabel(), size=labelsize) ax.set_ylabel(ax.get_ylabel(), size=labelsize) return plt
[docs]def pretty_plot_two_axis(x, y1, y2, xlabel=None, y1label=None, y2label=None, width=8, height=None, dpi=300, **plot_kwargs): """ Variant of pretty_plot that does a dual axis plot. Adapted from matplotlib examples. Makes it easier to create plots with different axes. Args: x (np.ndarray/list): Data for x-axis. y1 (dict/np.ndarray/list): Data for y1 axis (left). If a dict, it will be interpreted as a {label: sequence}. y2 (dict/np.ndarray/list): Data for y2 axis (right). If a dict, it will be interpreted as a {label: sequence}. xlabel (str): If not None, this will be the label for the x-axis. y1label (str): If not None, this will be the label for the y1-axis. y2label (str): If not None, this will be the label for the y2-axis. width (float): Width of plot in inches. Defaults to 8in. height (float): Height of plot in inches. Defaults to width * golden ratio. dpi (int): Sets dot per inch for figure. Defaults to 300. plot_kwargs: Passthrough kwargs to matplotlib's plot method. E.g., linewidth, etc. Returns: matplotlib.pyplot """ import palettable.colorbrewer.diverging colors = palettable.colorbrewer.diverging.RdYlBu_4.mpl_colors c1 = colors[0] c2 = colors[-1] golden_ratio = (math.sqrt(5) - 1) / 2 if not height: height = int(width * golden_ratio) import matplotlib.pyplot as plt width = 12 labelsize = int(width * 3) ticksize = int(width * 2.5) styles = ["-", "--", "-.", "."] fig, ax1 = plt.subplots() fig.set_size_inches((width, height)) if dpi: fig.set_dpi(dpi) if isinstance(y1, dict): for i, (k, v) in enumerate(y1.items()): ax1.plot(x, v, c=c1, marker='s', ls=styles[i % len(styles)], label=k, **plot_kwargs) ax1.legend(fontsize=labelsize) else: ax1.plot(x, y1, c=c1, marker='s', ls='-', **plot_kwargs) if xlabel: ax1.set_xlabel(xlabel, fontsize=labelsize) if y1label: # Make the y-axis label, ticks and tick labels match the line color. ax1.set_ylabel(y1label, color=c1, fontsize=labelsize) ax1.tick_params('x', labelsize=ticksize) ax1.tick_params('y', colors=c1, labelsize=ticksize) ax2 = ax1.twinx() if isinstance(y2, dict): for i, (k, v) in enumerate(y2.items()): ax2.plot(x, v, c=c2, marker='o', ls=styles[i % len(styles)], label=k) ax2.legend(fontsize=labelsize) else: ax2.plot(x, y2, c=c2, marker='o', ls='-') if y2label: # Make the y-axis label, ticks and tick labels match the line color. ax2.set_ylabel(y2label, color=c2, fontsize=labelsize) ax2.tick_params('y', colors=c2, labelsize=ticksize) return plt
[docs]def pretty_polyfit_plot(x, y, deg=1, xlabel=None, ylabel=None, **kwargs): r""" Convenience method to plot data with trend lines based on polynomial fit. Args: x: Sequence of x data. y: Sequence of y data. deg (int): Degree of polynomial. Defaults to 1. xlabel (str): Label for x-axis. ylabel (str): Label for y-axis. \\*\\*kwargs: Keyword args passed to pretty_plot. Returns: matplotlib.pyplot object. """ plt = pretty_plot(**kwargs) pp = np.polyfit(x, y, deg) xp = np.linspace(min(x), max(x), 200) plt.plot(xp, np.polyval(pp, xp), 'k--', x, y, 'o') if xlabel: plt.xlabel(xlabel) if ylabel: plt.ylabel(ylabel) return plt
[docs]def periodic_table_heatmap(elemental_data, cbar_label="", cbar_label_size=14, show_plot=False, cmap="YlOrRd", cmap_range=None, blank_color="grey", value_format=None, max_row=9): """ A static method that generates a heat map overlayed on a periodic table. Args: elemental_data (dict): A dictionary with the element as a key and a value assigned to it, e.g. surface energy and frequency, etc. Elements missing in the elemental_data will be grey by default in the final table elemental_data={"Fe": 4.2, "O": 5.0}. cbar_label (string): Label of the colorbar. Default is "". cbar_label_size (float): Font size for the colorbar label. Default is 14. cmap_range (tuple): Minimum and maximum value of the colormap scale. If None, the colormap will autotmatically scale to the range of the data. show_plot (bool): Whether to show the heatmap. Default is False. value_format (str): Formatting string to show values. If None, no value is shown. Example: "%.4f" shows float to four decimals. cmap (string): Color scheme of the heatmap. Default is 'YlOrRd'. Refer to the matplotlib documentation for other options. blank_color (string): Color assigned for the missing elements in elemental_data. Default is "grey". max_row (integer): Maximum number of rows of the periodic table to be shown. Default is 9, which means the periodic table heat map covers the first 9 rows of elements. """ # Convert primitive_elemental data in the form of numpy array for plotting. if cmap_range is not None: max_val = cmap_range[1] min_val = cmap_range[0] else: max_val = max(elemental_data.values()) min_val = min(elemental_data.values()) max_row = min(max_row, 9) if max_row <= 0: raise ValueError("The input argument 'max_row' must be positive!") value_table = np.empty((max_row, 18)) * np.nan blank_value = min_val - 0.01 for el in Element: if el.row > max_row: continue value = elemental_data.get(el.symbol, blank_value) value_table[el.row - 1, el.group - 1] = value # Initialize the plt object import matplotlib.pyplot as plt fig, ax = plt.subplots() plt.gcf().set_size_inches(12, 8) # We set nan type values to masked values (ie blank spaces) data_mask = np.ma.masked_invalid(value_table.tolist()) heatmap = ax.pcolor(data_mask, cmap=cmap, edgecolors='w', linewidths=1, vmin=min_val - 0.001, vmax=max_val + 0.001) cbar = fig.colorbar(heatmap) # Grey out missing elements in input data cbar.cmap.set_under(blank_color) # Set the colorbar label and tick marks cbar.set_label(cbar_label, rotation=270, labelpad=25, size=cbar_label_size) cbar.ax.tick_params(labelsize=cbar_label_size) # Refine and make the table look nice ax.axis('off') ax.invert_yaxis() # Label each block with corresponding element and value for i, row in enumerate(value_table): for j, el in enumerate(row): if not np.isnan(el): symbol = Element.from_row_and_group(i + 1, j + 1).symbol plt.text(j + 0.5, i + 0.25, symbol, horizontalalignment='center', verticalalignment='center', fontsize=14) if el != blank_value and value_format is not None: plt.text(j + 0.5, i + 0.5, value_format % el, horizontalalignment='center', verticalalignment='center', fontsize=10) plt.tight_layout() if show_plot: plt.show() return plt
[docs]def format_formula(formula): """ Converts str of chemical formula into latex format for labelling purposes Args: formula (str): Chemical formula """ formatted_formula = "" number_format = "" for i, s in enumerate(formula): if s.isdigit(): if not number_format: number_format = "_{" number_format += s if i == len(formula) - 1: number_format += "}" formatted_formula += number_format else: if number_format: number_format += "}" formatted_formula += number_format number_format = "" formatted_formula += s return r"$%s$" % (formatted_formula)
[docs]def van_arkel_triangle(list_of_materials, annotate=True): """ A static method that generates a binary van Arkel-Ketelaar triangle to quantify the ionic, metallic and covalent character of a compound by plotting the electronegativity difference (y) vs average (x). See: A.E. van Arkel, Molecules and Crystals in Inorganic Chemistry, Interscience, New York (1956) and J.A.A Ketelaar, Chemical Constitution (2nd edn.), An Introduction to the Theory of the Chemical Bond, Elsevier, New York (1958) Args: list_of_materials (list): A list of computed entries of binary materials or a list of lists containing two elements (str). annotate (bool): Whether or not to lable the points on the triangle with reduced formula (if list of entries) or pair of elements (if list of list of str). """ # F-Fr has the largest X difference. We set this # as our top corner of the triangle (most ionic) pt1 = np.array([(Element("F").X + Element("Fr").X) / 2, abs(Element("F").X - Element("Fr").X)]) # Cs-Fr has the lowest average X. We set this as our # bottom left corner of the triangle (most metallic) pt2 = np.array([(Element("Cs").X + Element("Fr").X) / 2, abs(Element("Cs").X - Element("Fr").X)]) # O-F has the highest average X. We set this as our # bottom right corner of the triangle (most covalent) pt3 = np.array([(Element("O").X + Element("F").X) / 2, abs(Element("O").X - Element("F").X)]) # get the parameters for the lines of the triangle d = np.array(pt1) - np.array(pt2) slope1 = d[1] / d[0] b1 = pt1[1] - slope1 * pt1[0] d = pt3 - pt1 slope2 = d[1] / d[0] b2 = pt3[1] - slope2 * pt3[0] # Initialize the plt object import matplotlib.pyplot as plt # set labels and appropriate limits for plot plt.xlim(pt2[0] - 0.45, -b2 / slope2 + 0.45) plt.ylim(-0.45, pt1[1] + 0.45) plt.annotate("Ionic", xy=[pt1[0] - 0.3, pt1[1] + 0.05], fontsize=20) plt.annotate("Covalent", xy=[-b2 / slope2 - 0.65, -0.4], fontsize=20) plt.annotate("Metallic", xy=[pt2[0] - 0.4, -0.4], fontsize=20) plt.xlabel(r"$\frac{\chi_{A}+\chi_{B}}{2}$", fontsize=25) plt.ylabel(r"$|\chi_{A}-\chi_{B}|$", fontsize=25) # Set the lines of the triangle chi_list = [el.X for el in Element] plt.plot([min(chi_list), pt1[0]], [slope1 * min(chi_list) + b1, pt1[1]], 'k-', linewidth=3) plt.plot([pt1[0], -b2 / slope2], [pt1[1], 0], 'k-', linewidth=3) plt.plot([min(chi_list), -b2 / slope2], [0, 0], 'k-', linewidth=3) plt.xticks(fontsize=15) plt.yticks(fontsize=15) # Shade with appropriate colors corresponding to ionic, metallci and covalent ax = plt.gca() # ionic filling ax.fill_between([min(chi_list), pt1[0]], [slope1 * min(chi_list) + b1, pt1[1]], facecolor=[1, 1, 0], zorder=-5, edgecolor=[1, 1, 0]) ax.fill_between([pt1[0], -b2 / slope2], [pt1[1], slope2 * min(chi_list) - b1], facecolor=[1, 1, 0], zorder=-5, edgecolor=[1, 1, 0]) # metal filling XPt = Element("Pt").X ax.fill_between([min(chi_list), (XPt + min(chi_list)) / 2], [0, slope1 * (XPt + min(chi_list)) / 2 + b1], facecolor=[1, 0, 0], zorder=-3, alpha=0.8) ax.fill_between([(XPt + min(chi_list)) / 2, XPt], [slope1 * ((XPt + min(chi_list)) / 2) + b1, 0], facecolor=[1, 0, 0], zorder=-3, alpha=0.8) # covalent filling ax.fill_between([(XPt + min(chi_list)) / 2, ((XPt + min(chi_list)) / 2 + -b2 / slope2) / 2], [0, slope2 * (((XPt + min(chi_list)) / 2 + -b2 / slope2) / 2) + b2], facecolor=[0, 1, 0], zorder=-4, alpha=0.8) ax.fill_between([((XPt + min(chi_list)) / 2 + -b2 / slope2) / 2, -b2 / slope2], [slope2 * (((XPt + min(chi_list)) / 2 + -b2 / slope2) / 2) + b2, 0], facecolor=[0, 1, 0], zorder=-4, alpha=0.8) # Label the triangle with datapoints for entry in list_of_materials: if type(entry).__name__ not in ['ComputedEntry', 'ComputedStructureEntry']: X_pair = [Element(el).X for el in entry] formatted_formula = "%s-%s" % tuple(entry) else: X_pair = [Element(el).X for el in entry.composition.as_dict().keys()] formatted_formula = format_formula(entry.composition.reduced_formula) plt.scatter(np.mean(X_pair), abs(X_pair[0] - X_pair[1]), c='b', s=100) if annotate: plt.annotate(formatted_formula, fontsize=15, xy=[np.mean(X_pair) + 0.005, abs(X_pair[0] - X_pair[1])]) plt.tight_layout() return plt
[docs]def get_ax_fig_plt(ax=None, **kwargs): """ Helper function used in plot functions supporting an optional Axes argument. If ax is None, we build the `matplotlib` figure and create the Axes else we return the current active figure. Args: kwargs: keyword arguments are passed to plt.figure if ax is not None. Returns: ax: :class:`Axes` object figure: matplotlib figure plt: matplotlib pyplot module. """ import matplotlib.pyplot as plt if ax is None: fig = plt.figure(**kwargs) ax = fig.add_subplot(1, 1, 1) else: fig = plt.gcf() return ax, fig, plt
[docs]def get_ax3d_fig_plt(ax=None, **kwargs): """ Helper function used in plot functions supporting an optional Axes3D argument. If ax is None, we build the `matplotlib` figure and create the Axes3D else we return the current active figure. Args: kwargs: keyword arguments are passed to plt.figure if ax is not None. Returns: ax: :class:`Axes` object figure: matplotlib figure plt: matplotlib pyplot module. """ import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import axes3d if ax is None: fig = plt.figure(**kwargs) ax = axes3d.Axes3D(fig) else: fig = plt.gcf() return ax, fig, plt
[docs]def get_axarray_fig_plt(ax_array, nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw): """ Helper function used in plot functions that accept an optional array of Axes as argument. If ax_array is None, we build the `matplotlib` figure and create the array of Axes by calling plt.subplots else we return the current active figure. Returns: ax: Array of :class:`Axes` objects figure: matplotlib figure plt: matplotlib pyplot module. """ import matplotlib.pyplot as plt if ax_array is None: fig, ax_array = plt.subplots(nrows=nrows, ncols=ncols, sharex=sharex, sharey=sharey, squeeze=squeeze, subplot_kw=subplot_kw, gridspec_kw=gridspec_kw, **fig_kw) else: fig = plt.gcf() ax_array = np.reshape(np.array(ax_array), (nrows, ncols)) if squeeze: if ax_array.size == 1: ax_array = ax_array[0] elif any(s == 1 for s in ax_array.shape): ax_array = ax_array.ravel() return ax_array, fig, plt
[docs]def add_fig_kwargs(func): """ Decorator that adds keyword arguments for functions returning matplotlib figures. The function should return either a matplotlib figure or None to signal some sort of error/unexpected event. See doc string below for the list of supported options. """ from functools import wraps @wraps(func) def wrapper(*args, **kwargs): # pop the kwds used by the decorator. title = kwargs.pop("title", None) size_kwargs = kwargs.pop("size_kwargs", None) show = kwargs.pop("show", True) savefig = kwargs.pop("savefig", None) tight_layout = kwargs.pop("tight_layout", False) ax_grid = kwargs.pop("ax_grid", None) ax_annotate = kwargs.pop("ax_annotate", None) fig_close = kwargs.pop("fig_close", False) # Call func and return immediately if None is returned. fig = func(*args, **kwargs) if fig is None: return fig # Operate on matplotlib figure. if title is not None: fig.suptitle(title) if size_kwargs is not None: fig.set_size_inches(size_kwargs.pop("w"), size_kwargs.pop("h"), **size_kwargs) if ax_grid is not None: for ax in fig.axes: ax.grid(bool(ax_grid)) if ax_annotate: from string import ascii_letters tags = ascii_letters if len(fig.axes) > len(tags): tags = (1 + len(ascii_letters) // len(fig.axes)) * ascii_letters for ax, tag in zip(fig.axes, tags): ax.annotate("(%s)" % tag, xy=(0.05, 0.95), xycoords="axes fraction") if tight_layout: try: fig.tight_layout() except Exception as exc: # For some unknown reason, this problem shows up only on travis. # https://stackoverflow.com/questions/22708888/valueerror-when-using-matplotlib-tight-layout print("Ignoring Exception raised by fig.tight_layout\n", str(exc)) if savefig: fig.savefig(savefig) import matplotlib.pyplot as plt if show: plt.show() if fig_close: plt.close(fig=fig) return fig # Add docstring to the decorated method. s = "\n\n" + """\ Keyword arguments controlling the display of the figure: ================ ==================================================== kwargs Meaning ================ ==================================================== title Title of the plot (Default: None). show True to show the figure (default: True). savefig "abc.png" or "abc.eps" to save the figure to a file. size_kwargs Dictionary with options passed to fig.set_size_inches e.g. size_kwargs=dict(w=3, h=4) tight_layout True to call fig.tight_layout (default: False) ax_grid True (False) to add (remove) grid from all axes in fig. Default: None i.e. fig is left unchanged. ax_annotate Add labels to subplots e.g. (a), (b). Default: False fig_close Close figure. Default: False. ================ ==================================================== """ if wrapper.__doc__ is not None: # Add s at the end of the docstring. wrapper.__doc__ += "\n" + s else: # Use s wrapper.__doc__ = s return wrapper