Source code for gammapy.visualization.heatmap

import numpy as np
import matplotlib.pyplot as plt

# taken from the matploltlib documentation

__all__ = [

[docs]def plot_heatmap( data, row_labels, col_labels, ax=None, cbar_kw=None, cbarlabel="", **kwargs ): """ Create a heatmap from a numpy array and two lists of labels. Parameters ---------- data A 2D numpy array of shape (N, M). row_labels A list or array of length N with the labels for the rows. col_labels A list or array of length M with the labels for the columns. ax A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If not provided, use current axes or create a new one. Optional. cbar_kw A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional. cbarlabel The label for the colorbar. Optional. **kwargs All other arguments are forwarded to `imshow`. """ if ax is None: ax = plt.gca() if cbar_kw is None: cbar_kw = {} # Plot the heatmap im = ax.imshow(data, **kwargs) # Create colorbar cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw), rotation=-90, va="bottom") # We want to show all ticks... ax.set_xticks(np.arange(data.shape[1])) ax.set_yticks(np.arange(data.shape[0])) # ... and label them with the respective list entries. ax.set_xticklabels(col_labels) ax.set_yticklabels(row_labels) # Let the horizontal axes labeling appear on top. ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False) # Rotate the tick labels and set their alignment. plt.setp(ax.get_xticklabels(), rotation=-30, ha="right", rotation_mode="anchor") # Turn spines off and create white grid. for edge, spine in ax.spines.items(): spine.set_visible(False) ax.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True) ax.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True) ax.grid(which="minor", color="w", linestyle="-", linewidth=1.5) ax.tick_params(which="minor", bottom=False, left=False) return im, cbar
[docs]def annotate_heatmap( im, data=None, valfmt="{x:.2f}", textcolors=("black", "white"), threshold=None, **textkw, ): """ A function to annotate a heatmap. Parameters ---------- im The AxesImage to be labeled. data Data used to annotate. If None, the image's data is used. Optional. valfmt The format of the annotations inside the heatmap. This should either use the string format method, e.g. "$ {x:.2f}", or be a `matplotlib.ticker.Formatter`. Optional. textcolors A list or array of two color specifications. The first is used for values below a threshold, the second for those above. Optional. threshold Value in data units according to which the colors from textcolors are applied. If None (the default) uses the middle of the colormap as separation. Optional. **kwargs All other arguments are forwarded to each call to `text` used to create the text labels. """ import matplotlib if not isinstance(data, (list, np.ndarray)): data = im.get_array() # Normalize the threshold to the images color range. if threshold is not None: threshold = im.norm(threshold) else: threshold = im.norm(data.max()) / 2.0 # Set default alignment to center, but allow it to be # overwritten by textkw. kw = dict(horizontalalignment="center", verticalalignment="center") kw.update(textkw) # Get the formatter in case a string is supplied if isinstance(valfmt, str): valfmt = matplotlib.ticker.StrMethodFormatter(valfmt) # Loop over the data and create a `Text` for each "pixel". # Change the text's color depending on the data. texts = [] for i in range(data.shape[0]): for j in range(data.shape[1]): kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)]) text = im.axes.text(j, i, valfmt(data[i, j], None), **kw) texts.append(text) return texts