Объедините две тепловые карты в морском заливе

Question

У меня проблема с двумя тепловыми картами в seaborn и matplotlib.

Моя проблема в том, что мне нужно объединить две тепловые карты, чтобы показать основные различия.У меня есть некоторые точки данных (x, y), некоторые точки для красной тепловой карты, некоторые для синей.Цель состоит в их комбинации, и она должна выглядеть следующим образом.

Тепловая карта цели:

Теперь у меня есть этодве карты:

Карта 1:

Карта 2:

Мой код выглядит следующим образом.

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

import pandas as pd
data=pd.read_csv('data.csv')
data.head(10)

red = data.loc[data.type == "red"]
blue = data.loc[data.type == "blue"]

ax = sns.kdeplot(blue.x_pos, blue.y_pos,
                  cmap="Blues", n_levels=15, shade=True, shade_lowest=False, legend=False)

ax = sns.kdeplot(red.x_pos, red.y_pos,
                  cmap="Reds", n_levels=15, shade=True, shade_lowest=False, legend=False)

plt.ylim(-300, 300)
plt.xlim(-300, 300)


ax.set_frame_on(False)
ax.set_yticklabels([])
ax.set_xticklabels([])

plt.gca().axes.get_yaxis().set_visible(False)
plt.gca().axes.get_xaxis().set_visible(False)

plt.show()

CSV FILE

x_pos,y_pos,type
108,-204,red
108,-168,red
108,-228,red
108,-192,red
108,-144,red
108,108,red
108,180,red
108,192,red
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108,132,red
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108,156,blue
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108,-12,red
108,168,blue
108,216,blue
108,252,blue
96,-156,red
96,-36,red
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96,-72,red
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96,144,red
96,204,red
96,-240,red
96,-228,red
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96,-168,red
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96,-12,red
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72,156,blue
72,12,blue

Существует ли какой-нибудь способ, как это сделать в морской части?Спасибо

Answer 1

Хорошо, это работает, оно явно скопировано / переназначено с здесь .

Поместите ваши данные в 'data.csv'

import numpy as np
import pandas as pd
# import seaborn as sns # No longer used
import matplotlib.pyplot as plt
from scipy import stats

df = pd.read_csv('data.csv')
red_df = df[df['type']=='red']
blue_df = df[df['type']=='blue']

def compute_KDE_from_pos(x_pos,y_pos):

    xmin = x_pos.min()
    xmax = x_pos.max()
    ymin = y_pos.min()
    ymax = y_pos.max()

    X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
    positions = np.vstack([X.ravel(), Y.ravel()])
    values = np.vstack([x_pos, y_pos])
    kernel = stats.gaussian_kde(values)
    Z = np.reshape(kernel(positions).T, X.shape)

    return Z

Z_red = compute_KDE_from_pos(red_df.x_pos,red_df.y_pos)
Z_blue = compute_KDE_from_pos(blue_df.x_pos,blue_df.y_pos)
Z_diff = Z_red-Z_blue

fig, ax = plt.subplots()
ax.imshow(np.rot90(Z_diff), cmap='RdBu',
           extent=[df.x_pos.min(), df.x_pos.max(), df.y_pos.min(), df.y_pos.max()])
ax.plot(red_df.x_pos, red_df.y_pos, 'r.', markersize=2)
ax.plot(blue_df.x_pos, blue_df.y_pos, 'b.', markersize=2)
ax.set_xlim([df.x_pos.min(), df.x_pos.max()])
ax.set_ylim([df.y_pos.min(), df.y_pos.max()])
plt.show()

Вывод:

РЕДАКТИРОВАТЬ:

Я написал более чистую версию кода.Вы все еще можете проверить, соответствует ли используемая карта цветов тому, что вы ожидаете, или вы должны инвертировать красные оттенки синего.В любом случае, этот код отвечает на вопрос.