注意
转到结尾 下载完整的示例代码。
创建带标注的热图#
通常希望将依赖于两个自变量的数据显示为颜色编码的图像图。这通常称为热图。如果数据是分类的，则称为分类热图。
Matplotlib 的 imshow 函数使创建此类图变得特别容易。
以下示例展示了如何创建带标注的热图。我们将从一个简单的示例开始，并将其扩展为可作为通用函数使用。
一个简单的分类热图#
我们可以从定义一些数据开始。我们需要一个 2D 列表或数组来定义要颜色编码的数据。然后我们还需要两个分类列表或数组；当然，这些列表中的元素数量需要与沿相应轴的数据相匹配。热图本身是一个 imshow 图，其标签设置为我们拥有的分类。请注意，重要的是要同时设置刻度线位置 (set_xticks) 和刻度线标签 (set_xticklabels)，否则它们会不同步。位置只是升序的整数，而刻度线标签是要显示的标签。最后，我们可以通过在每个单元格中创建一个 Text 来标记数据本身，显示该单元格的值。
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
import matplotlib as mpl
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)
# Show all ticks and label them with the respective list entries
ax.set_xticks(np.arange(len(farmers)), labels=farmers)
ax.set_yticks(np.arange(len(vegetables)), labels=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()
使用辅助函数代码样式#
正如 代码样式 中所述，可能希望重用此类代码来为不同的输入数据和/或不同的轴创建某种热图。我们创建一个函数，该函数将数据以及行和列标签作为输入，并允许用于自定义绘图的参数
这里，除了上述内容之外，我们还希望创建一个颜色条，并将标签放置在热图的上方而不是下方。标注应根据阈值获得不同的颜色，以更好地与像素颜色形成对比。最后，我们将周围轴脊柱关闭，并创建一个白色线条网格来分隔单元格。
def 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 (M, N).
row_labels
A list or array of length M with the labels for the rows.
col_labels
A list or array of length N 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)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# Show all ticks and label them with the respective list entries.
ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)
ax.set_yticks(np.arange(data.shape[0]), labels=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.
ax.spines[:].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 pair of colors. 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
现在，以上内容使我们能够保持实际的绘图创建非常简洁。
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()
一些更复杂的热图示例#
在以下内容中，我们将通过在不同情况下应用它并使用不同的参数来展示先前创建的函数的多功能性。
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 = [f"Book {i}" for i in range(1, 8)]
x = [f"Store {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
# `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 = [f"Prod. {i}" for i in range(10, 70, 10)]
x = [f"Cycle {i}" for i in range(1, 7)]
qrates = 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=mpl.colormaps["PiYG"].resampled(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
# `matplotlib.ticker.FuncFormatter`.
corr_matrix = np.corrcoef(harvest)
im, _ = heatmap(corr_matrix, vegetables, vegetables, ax=ax4,
cmap="PuOr", vmin=-1, vmax=1,
cbarlabel="correlation coeff.")
def func(x, pos):
return f"{x:.2f}".replace("0.", ".").replace("1.00", "")
annotate_heatmap(im, valfmt=matplotlib.ticker.FuncFormatter(func), size=7)
plt.tight_layout()
plt.show()
参考资料
本示例中展示了以下函数、方法、类和模块的使用
脚本的总运行时间：（0 分钟 3.394 秒）