matplotlib · jklymak · Apr 12, 2018 · Apr 10, 2018 · jklymak · Apr 10, 2018
diff --git a/examples/images_contours_and_fields/image_annotated_heatmap.py b/examples/images_contours_and_fields/image_annotated_heatmap.py
@@ -0,0 +1,312 @@
+"""
+===========================
+Creating annotated heatmaps
+===========================
+
+It is often desirable to show data which depends on two independent
+variables as a color coded image plot. This is often referred to as a
+heatmap. If the data is categorical, this would be called a categorical
+heatmap.
+Matplotlib's :meth:`imshow <matplotlib.axes.Axes.imshow>` function makes
+production of such plots particularly easy.
+
+The following examples show how to create a heatmap with annotations.
+We will start with an easy example and expand it to be usable as a
+universal function.
+"""
+
+
+##############################################################################
+#
+# A simple categorical heatmap
+# ----------------------------
+#
+# We may start by defining some data. What we need is a 2D list or array
+# which defines the data to color code. We then also need two lists or arrays
+# of categories; of course the number of elements in those lists
+# need to match the data along the respective axes.
+# The heatmap itself is an :meth:`imshow <matplotlib.axes.Axes.imshow>` plot
+# with the labels set to the categories we have.
+# Note that it is important to set both, the tick locations
+# (:meth:`set_xticks<matplotlib.axes.Axes.set_xticks>`) as well as the
+# tick labels (:meth:`set_xticklabels<matplotlib.axes.Axes.set_xticklabels>`),
+# otherwise they would become out of sync. The locations are just
+# the ascending integer numbers, while the ticklabels are the labels to show.
+# Finally we can label the data itself by creating a
+# :class:`~matplotlib.text.Text` within each cell showing the value of
+# that cell.
+
+
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+# sphinx_gallery_thumbnail_number = 2
+
+vegetables = ["cucumber", "tomato", "lettuce", "asparagus",
+              "potato", "wheat", "barley"]
+farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening",
+           "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]
+
+harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
+                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
+                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
+                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
+                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
+                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
+                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])
+
+
+fig, ax = plt.subplots()
+im = ax.imshow(harvest)
+
+# We want to show all ticks...
+ax.set_xticks(np.arange(len(farmers)))
+ax.set_yticks(np.arange(len(vegetables)))
+# ... and label them with the respective list entries
+ax.set_xticklabels(farmers)
+ax.set_yticklabels(vegetables)
+
+# Rotate the tick labels and set their alignment.
+plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+         rotation_mode="anchor")
+
+# Loop over data dimensions and create text annotations.
+for i in range(len(vegetables)):
+    for j in range(len(farmers)):
+        text = ax.text(j, i, harvest[i, j],
+                       ha="center", va="center", color="w")
+
+ax.set_title("Harvest of local farmers (in tons/year)")
+fig.tight_layout()
+plt.show()
+
+
+#############################################################################
+# Using the helper function code style
+# ------------------------------------
+#
+# As discussed in the :ref:`Coding styles <coding_styles>`
+# one might want to reuse such code to create some kind of heatmap
+# for different input data and/or on different axes.
+# We create a function that takes the data and the row and column labels as
+# input, and allows arguments that are used to customize the plot
+#
+# Here, in addition to the above we also want to create a colorbar and
+# position the labels above of the heatmap instead of below it.
+# The annotations shall get different colors depending on a threshold
+# for better contrast against the pixel color.
+# Finally, we turn the surrounding axes spines off and create
+# a grid of white lines to separate the cells.
+
+
+def heatmap(data, row_labels, col_labels, ax=None,
+            cbar_kw={}, cbarlabel="", **kwargs):
+    """
+    Create a heatmap from a numpy array and two lists of labels.
+
+    Arguments:
+        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
+    Optional arguments:
+        ax         : A matplotlib.axes.Axes instance to which the heatmap
+                     is plotted. If not provided, use current axes or
+                     create a new one.
+        cbar_kw    : A dictionary with arguments to
+                     :meth:`matplotlib.Figure.colorbar`.
+        cbarlabel  : The label for the colorbar
+    All other arguments are directly passed on to the imshow call.
+    """
+
+    if not ax:
+        ax = plt.gca()
+
+    # Plot the heatmap
+    im = ax.imshow(data, **kwargs)
+
+    # Create colorbar
+    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
+    cbar.ax.set_ylabel(cbarlabel, 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)-.5, minor=True)
+    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
+    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
+    ax.tick_params(which="minor", bottom=False, left=False)
+
+    return im, cbar
+
+
+def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
+                     textcolors=["black", "white"],
+                     threshold=None, **textkw):
+    """
+    A function to annotate a heatmap.
+
+    Arguments:
+        im         : The AxesImage to be labeled.
+    Optional arguments:
+        data       : Data used to annotate. If None, the image's data is used.
+        valfmt     : The format of the annotations inside the heatmap.
+                     This should either use the string format method, e.g.
+                     "$ {x:.2f}", or be a :class:`matplotlib.ticker.Formatter`.
+        textcolors : A list or array of two color specifications. The first is
+                     used for values below a threshold, the second for those
+                     above.
+        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.
+
+    Further arguments are passed on to the created text labels.
+    """
+
+    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.
+
+    # 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[im.norm(data[i, j]) > threshold])
+            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
+            texts.append(text)
+
+    return texts
+
+
+##########################################################################
+# The above now allows us to keep the actual plot creation pretty compact.
+#
+
+fig, ax = plt.subplots()
+
+im, cbar = heatmap(harvest, vegetables, farmers, ax=ax,
+                   cmap="YlGn", cbarlabel="harvest [t/year]")
+texts = annotate_heatmap(im, valfmt="{x:.1f} t")
+
+fig.tight_layout()
+plt.show()
+
+
+#############################################################################
+# Some more complex heatmap examples
+# ----------------------------------
+#
+# In the following we show the versitality of the previously created
+# functions by applying it in different cases and using different arguments.
+#
+
+np.random.seed(19680801)
+
+fig, ((ax, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6))
+
+# Replicate the above example with a different font size and colormap.
+
+im, _ = heatmap(harvest, vegetables, farmers, ax=ax,
+                cmap="Wistia", cbarlabel="harvest [t/year]")
+annotate_heatmap(im, valfmt="{x:.1f}", size=7)
+
+# Create some new data, give further arguments to imshow (vmin),
+# use an integer format on the annotations and provide some colors.
+
+data = np.random.randint(2, 100, size=(7, 7))
+y = ["Book {}".format(i) for i in range(1, 8)]
+x = ["Store {}".format(i) for i in list("ABCDEFG")]
+im, _ = heatmap(data, y, x, ax=ax2, vmin=0,
+                cmap="magma_r", cbarlabel="weekly sold copies")
+annotate_heatmap(im, valfmt="{x:d}", size=7, threshold=20,
+                 textcolors=["red", "white"])
+
+# Sometimes even the data itself is categorical. Here we use a
+# :class:`matplotlib.colors.BoundaryNorm` to get the data into classes
+# and use this to colorize the plot, but also to obtain the class
+# labels from an array of classes.
+
+data = np.random.randn(6, 6)
+y = ["Prod. {}".format(i) for i in range(10, 70, 10)]
+x = ["Cycle {}".format(i) for i in range(1, 7)]
+
+qrates = np.array(list("ABCDEFG"))
+norm = matplotlib.colors.BoundaryNorm(np.linspace(-3.5, 3.5, 8), 7)
+fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: qrates[::-1][norm(x)])
+
+im, _ = heatmap(data, y, x, ax=ax3,
+                cmap=plt.get_cmap("PiYG", 7), norm=norm,
+                cbar_kw=dict(ticks=np.arange(-3, 4), format=fmt),
+                cbarlabel="Quality Rating")
+
+annotate_heatmap(im, valfmt=fmt, size=9, fontweight="bold", threshold=-1,
+                 textcolors=["red", "black"])
+
+# We can nicely plot a correlation matrix. Since this is bound by -1 and 1,
+# we use those as vmin and vmax. We may also remove leading zeros and hide
+# the diagonal elements (which are all 1) by using a
+# :class:`matplotlib.ticker.FuncFormatter`.
+
+corr_matrix = np.corrcoef(np.random.rand(6, 5))
+im, _ = heatmap(corr_matrix, vegetables, vegetables, ax=ax4,
+                cmap="PuOr", vmin=-1, vmax=1,
+                cbarlabel="correlation coeff.")
+
+
+def func(x, pos):
+    return "{:.2f}".format(x).replace("0.", ".").replace("1.00", "")
+
+annotate_heatmap(im, valfmt=matplotlib.ticker.FuncFormatter(func), size=7)
+
+
+plt.tight_layout()
+plt.show()
+
+
+#############################################################################
+#
+# ------------
+#
+# References
+# """"""""""
+#
+# The usage of the following functions and methods is shown in this example:
+
+
+matplotlib.axes.Axes.imshow
+matplotlib.pyplot.imshow
+matplotlib.figure.Figure.colorbar
+matplotlib.pyplot.colorbar