实例1
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
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()
实例2
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
def heatmap(data, row_labels, col_labels, ax=None,
cbar_kw={}, cbarlabel="", **kwargs):
if not ax:
ax = plt.gca()
# Plot the heatmap
im = ax.imshow(data, **kwargs,vmin=0, vmax=1)
# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
tick_locator = ticker.MaxNLocator(nbins=11)
cbar.locator = tick_locator
cbar.update_ticks()
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.
font1 = {'family': 'Times New Roman',
'weight': 'normal',
'size': 16}
font2 = {'family': 'Times New Roman',
'weight': 'normal',
'size': 16}
ax.set_xticklabels(col_labels)
ax.set_yticklabels(row_labels)
# modify this part and change the label direction
# Let the horizontal axes labeling appear on top.
ax.tick_params(top=False, bottom=True,
labeltop=False, labelbottom=True)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=0, ha="center",
rotation_mode="anchor")
# Turn spines off and create white grid.
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticklabels(np.arange(data.shape[1]+1)-.5, minor=True)
ax.set_yticklabels(np.arange(data.shape[0]+1)-.5, minor=True)
ax.grid(which="minor", color="w", linestyle='-', linewidth=0.5)
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
# input data ARI
datasets = ["Usoskin", "Pollen","Yan", "Zeisel","Mouse","PBMC"]
methods = ["Raw","DrImpute", "SAVER", "scImpute","MAGIC","CMF-Impute","scLRTC"]
results = np.array([[0.891,0.900,0.930,0.212,0.115,0.932,0.940],
[0.934,0.952,0.937,0.951,0.781,0.952,0.962],
[0.641,0.670,0.655,0.618,0.565,0.641,0.722],
[0.788,0.764,0.785,0.403,0.221,0.797,0.850],
[0.583,0.568,0.560,0.423,0.319,0.559,0.630],
[0.679,0.654,0.667,0.384,0.233,0.823,0.831]])
# #input data NMI
# datasets = ["Usoskin", "Pollen","Yan", "Zeisel"]
# methods = ["Raw","DrImpute", "SAVER", "scImpute","MAGIC","CMF-Impute","scLRTC"]
# results = np.array([[0.887,0.780,0.829,0.510,0.228,0.915,0.915],
# [0.949,0.952,0.967,0.924,0.881,0.956,0.971],
# [0.797,0.803,0.800,0.792,0.749,0.797,0.845],
# [0.743,0.698,0.744,0.572,0.363,0.734,0.764],
# [0.756,0.736,0.732,0.696,0.547,0.745,0.765],
# [0.744,0.735,0.743,0.589,0.407,0.792,0.808]])
fig, ax = plt.subplots(figsize=(11,7))
im, cbar = heatmap(results, datasets, methods, ax=ax,
cmap=plt.get_cmap("GnBu"))
texts = annotate_heatmap(im, valfmt="{x:.3f}",size = 16)
fig.tight_layout()
# sive ARI.pdf or NMI.pdf
plt.savefig('./heatmaps.png', dpi=600)
plt.show()
