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沒(méi)有使用 Matplotlib Python 在后臺(tái)獲取熱圖

Python

炎炎設(shè)計(jì) 2021-11-16 16:07:06

我試過(guò)這個(gè)并得到如圖所示的結(jié)果：import pandas as pdimport matplotlib.pyplot as pltimport numpy as npfrom matplotlib.colors import LinearSegmentedColormapcmap = 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()圖片：我期待一個(gè)輸出，其中根據(jù)圖放置顏色。我得到綠色，沒(méi)有紅色或灰色。我想讓圖像或輪廓按值傳播。我怎么能做到這一點(diǎn)？見(jiàn)下圖，類(lèi)似的東西：請(qǐng)讓我知道我如何實(shí)現(xiàn)這一目標(biāo)。我使用的數(shù)據(jù)在這里：t.csv對(duì)于實(shí)時(shí)版本，請(qǐng)查看Tensorflow Playground

查看完整描述

2 回答

德瑪西亞99

TA貢獻(xiàn)1770條經(jīng)驗(yàn) 獲得超3個(gè)贊

像這樣的解決方案基本上需要 2 個(gè)任務(wù)：

繪制熱圖作為背景；
繪制散點(diǎn)數(shù)據(jù)；

輸出：

源代碼：

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()

為了簡(jiǎn)單起見(jiàn)，我保留了顏色條范圍(-6, 6)以匹配數(shù)據(jù)范圍。

我確信可以更改此代碼以滿(mǎn)足您的特定需求。祝你好運(yùn)！

反對(duì) 回復(fù) 2021-11-16

阿晨1998

TA貢獻(xiàn)2037條經(jīng)驗(yàn) 獲得超6個(gè)贊

這是一個(gè)可能的解決方案。

一些注意事項(xiàng)和問(wèn)題：

您的數(shù)據(jù)文件中的“預(yù)測(cè)”值是多少？它們似乎與“真實(shí)”列中的值無(wú)關(guān)。
為什么要?jiǎng)?chuàng)建第二個(gè)軸？圖中底部 X 軸代表什么？我刪除了第二個(gè)軸并標(biāo)記了剩余的軸（索引和實(shí)數(shù)）。
當(dāng)您對(duì) Pandas DataFrame 進(jìn)行切片時(shí)，索引會(huì)隨之而來(lái)。您不需要?jiǎng)?chuàng)建單獨(dú)的索引（代碼中的 argwhere 和 arange(index)）。我簡(jiǎn)化了代碼的第一部分，其中生成了散點(diǎn)圖。

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()

這給出了以下結(jié)果：

反對(duì) 回復(fù) 2021-11-16

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沒(méi)有使用 Matplotlib Python 在后臺(tái)獲取熱圖

沒(méi)有使用 Matplotlib Python 在后臺(tái)獲取熱圖

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