使用Matplotlib Python在后台不获取热图

Question

我已经尝试过了，并得到了如图所示的结果：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list("", ["red","grey","green"])
df = pd.read_csv('t.csv', header=0)

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax = ax1.twiny()
# Scatter plot of positive points, coloured blue (C0)
ax.scatter(np.argwhere(df['real'] > 0), df.loc[df['real'] > 0, 'real'], color='C2')
# Scatter plot of negative points, coloured red (C3)
ax.scatter(np.argwhere(df['real'] < 0), df.loc[df['real'] < 0, 'real'], color='C3')
# Scatter neutral values in grey (C7)
ax.scatter(np.argwhere(df['real'] == 0), df.loc[df['real'] == 0, 'real'], color='C7')

ax.set_ylim([df['real'].min(), df['real'].max()])
index = len(df.index)
ymin = df['prediction'].min()
ymax= df['prediction'].max()
ax1.imshow([np.arange(index),df['prediction']],cmap=cmap,
                        extent=(0,index-1,ymin, ymax), alpha=0.8)
plt.show()

图片：

我期待根据图放置颜色的输出。我的颜色是绿色，没有红色或灰色。

我想按值散布图像或轮廓。我该怎么做？参见下图，类似：

请让我知道我如何实现这一目标。我使用的数据在这里：t.csv
对于实时版本，请查看Tensorflow Playground

Answer 1

在这样的解决方案中，基本上需要执行2个任务：

• 绘制热图作为背景；
• 绘制散点数据；

输出：

源代码：

import numpy as np
import matplotlib.pyplot as plt

###
# Plot heatmap in the background
###

# Setting up input values
x = np.arange(-6.0, 6.0, 0.1)
y = np.arange(-6.0, 6.0, 0.1)
X, Y = np.meshgrid(x, y)

# plot heatmap colorspace in the background
fig, ax = plt.subplots(nrows=1)
im = ax.imshow(X, cmap=plt.cm.get_cmap('RdBu'), extent=(-6, 6, -6, 6), interpolation='bilinear')
cax = fig.add_axes([0.21, 0.95, 0.6, 0.03]) # [left, bottom, width, height]
fig.colorbar(im, cax=cax, orientation='horizontal')  # add colorbar at the top

###
# Plot data as scatter
###
# generate the points
num_samples = 150
theta = np.linspace(0, 2 * np.pi, num_samples)

# generate inner points
circle_r = 2
r = circle_r * np.random.rand(num_samples)
inner_x, inner_y = r * np.cos(theta), r * np.sin(theta)

# generate outter points
circle_r = 4
r = circle_r + np.random.rand(num_samples)
outter_x, outter_y = r * np.cos(theta), r * np.sin(theta)

# plot data
ax.scatter(inner_x, inner_y, s=30, marker='o', color='royalblue', edgecolors='white', linewidths=0.8)
ax.scatter(outter_x, outter_y, s=30, marker='o', color='crimson', edgecolors='white', linewidths=0.8)
ax.set_ylim([-6,6])
ax.set_xlim([-6,6])

plt.show()

为简单起见，我将颜色栏范围保持(-6, 6)为与数据范围相匹配。

我确定可以更改此代码以适合您的特定需求。祝好运！

Answer 2

这是一个可能的解决方案。

一些注意事项和问题：

• 数据文件中的“预测”值是什么？它们似乎与“真实”列中的值不相关。
• 为什么要创建第二个轴？图中底部X轴代表什么？我删除了第二个轴，并标记了其余的轴（索引轴和实数轴）。
• 当您对pandas DataFrame进行切片时，索引随之而来。您无需创建单独的索引（代码中的argwhere和arange（index））。我简化了代码的第一部分，在其中生成了散点图。

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list("", ["red","grey","green"])
df = pd.read_csv('t.csv', header=0)
print(df)

fig = plt.figure()
ax = fig.add_subplot(111)

# Data limits
xmin = 0
xmax = df.shape[0]
ymin = df['real'].min()
ymax = df['real'].max()

# Scatter plots
gt0 = df.loc[df['real'] > 0, 'real']
lt0 = df.loc[df['real'] < 0, 'real']
eq0 = df.loc[df['real'] == 0, 'real']
ax.scatter(gt0.index, gt0.values, edgecolor='white', color='C2')
ax.scatter(lt0.index, lt0.values, edgecolor='white', color='C3')
ax.scatter(eq0.index, eq0.values, edgecolor='white', color='C7')
ax.set_ylim((ymin, ymax))
ax.set_xlabel('index')
ax.set_ylabel('real')

# We want 0 to be in the middle of the colourbar, 
# because gray is defined as df['real'] == 0
if abs(ymax) > abs(ymin):
    lim = abs(ymax)
else:
    lim = abs(ymin)

# Create a gradient that runs from -lim to lim in N number of steps,
# where N is the number of colour steps in the cmap.
grad = np.arange(-lim, lim, 2*lim/cmap.N)

# Arrays plotted with imshow must be 2D arrays. In this case it will be
# 1 pixel wide and N pixels tall. Set the aspect ratio to auto so that
# each pixel is stretched out to the full width of the frame.
grad = np.expand_dims(grad, axis=1)
im = ax.imshow(grad, cmap=cmap, aspect='auto', alpha=1, origin='bottom',
               extent=(xmin, xmax, -lim, lim))
fig.colorbar(im, label='real')
plt.show()

得到以下结果：