-
Notifications
You must be signed in to change notification settings - Fork 15
/
image_annotated_heatmap.py
196 lines (164 loc) · 6.77 KB
/
image_annotated_heatmap.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
"""
From:
https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html
Accessed:
2020-07-31
"""
"""
===========================
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
#############################################################################
# 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=None, col_labels=None, ax=None,
cbar_kw={}, 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 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.
if col_labels is not None:
ax.set_xticklabels(col_labels)
else:
ax.get_xaxis().set_ticks([])
if row_labels is not None:
ax.set_yticklabels(row_labels)
else:
ax.get_yaxis().set_ticks([])
# 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.
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.
"""
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[int(im.norm(data[i, j]) > threshold)])
text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
texts.append(text)
return texts