在3D散点图中添加一个点数的配色栏

发布于 2025-01-21 05:18:28 字数 438 浏览 3 评论 0原文

我试图在3D图中绘制某个区域的地震，以颜色显示事件的时间/日期。但是，数据点是准确的，但是，当我绘制颜色栏仅显示0.0至1.0时。如何获得颜色栏以将日期显示为标签？

我的日期变量是“ yyyy-mm-dd”格式中的日期列表。

%matplotlib notebook
from mpl_toolkits.mplot3d import axes3d

fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(projection='3d')

ax.scatter(longitude, latitude, -depth, c=mdates.date2num(dates), cmap='magma')
fig.colorbar(mpl.cm.ScalarMappable(cmap='magma'))
plt.show()

原文

I am attempting to plot earthquakes of a certain region in a 3D plot with the color as a way to show time/date of the event. The data points are accurately colored, however, when I plot the color bar it only displays 0.0 to 1.0. How do I get the color bar to show the dates as its label?

My dates variable is a list of dates in "YYYY-MM-DD" format.

%matplotlib notebook
from mpl_toolkits.mplot3d import axes3d

fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(projection='3d')

ax.scatter(longitude, latitude, -depth, c=mdates.date2num(dates), cmap='magma')
fig.colorbar(mpl.cm.ScalarMappable(cmap='magma'))
plt.show()

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夜巴黎 2025-01-28 05:18:28

由于您没有提供数据，因此我必须随机生成它。

from mpl_toolkits.mplot3d import axes3d
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import Normalize
import numpy as np

# generate random data and dates
longitude = np.random.uniform(0, 1, 100)
latitude = np.random.uniform(0, 1, 100)
depth = np.random.uniform(0, 1, 100)

dates = []
for i in range(1, 13):
    for j in range(1, 25):
        if j < 10: n = "0" + str(j)
        else: n = j
        if i < 10: m = "0" + str(i)
        else: n = i
        dates.append("2022-{}-{}".format(m, n))

fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(projection='3d')

colors = mdates.date2num(dates[:100])
ax.scatter(longitude, latitude, -depth, c=colors, cmap='magma')
norm = Normalize(vmin=colors.min(), vmax=colors.max())

# choose a proper number of ticks to be shown on the colorbar
N = 5
ticks = np.linspace(colors.min(), colors.max(), N)
# convert the tick back to date format
labels = [mdates.num2date(t).strftime("%Y-%m-%d") for t in ticks]
cb = fig.colorbar(
    mpl.cm.ScalarMappable(norm=norm, cmap='magma'),
    ticks=ticks,
    ax=ax,
    label="Date"
)
cb.ax.set_yticklabels(labels)
plt.show()

Since you didn't provide the data, I had to randomly generate it.

from mpl_toolkits.mplot3d import axes3d
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import Normalize
import numpy as np

# generate random data and dates
longitude = np.random.uniform(0, 1, 100)
latitude = np.random.uniform(0, 1, 100)
depth = np.random.uniform(0, 1, 100)

dates = []
for i in range(1, 13):
    for j in range(1, 25):
        if j < 10: n = "0" + str(j)
        else: n = j
        if i < 10: m = "0" + str(i)
        else: n = i
        dates.append("2022-{}-{}".format(m, n))

fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(projection='3d')

colors = mdates.date2num(dates[:100])
ax.scatter(longitude, latitude, -depth, c=colors, cmap='magma')
norm = Normalize(vmin=colors.min(), vmax=colors.max())

# choose a proper number of ticks to be shown on the colorbar
N = 5
ticks = np.linspace(colors.min(), colors.max(), N)
# convert the tick back to date format
labels = [mdates.num2date(t).strftime("%Y-%m-%d") for t in ticks]
cb = fig.colorbar(
    mpl.cm.ScalarMappable(norm=norm, cmap='magma'),
    ticks=ticks,
    ax=ax,
    label="Date"
)
cb.ax.set_yticklabels(labels)
plt.show()