# # 创建带注释的热度图

## # 一种简单的分类热度图

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()


## # 使用辅助函数的编码风格

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


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()


## # 一些更复杂的热度图示例

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()


## # 参考

matplotlib.axes.Axes.imshow
matplotlib.pyplot.imshow
matplotlib.figure.Figure.colorbar
matplotlib.pyplot.colorbar