I was trying to plot a figure with a combination of a 3d subplot and 3 2d ones. Why do they overlap each other?
Here are my codes:
fig = plt.figure(figsize=(10,10)) ax = fig.add_subplot(3, 2, 1, projection='3d') ax = plt.axes(projection='3d') ax.scatter3D(extents[0], extents[1], extents[2], color='yellow') ax = fig.add_subplot(3, 2, 2) ax = sns.distplot(extents[0], color='red') ax.set_title("Extent_0 Distribution") ax = fig.add_subplot(3, 2, 4) ax = sns.distplot(extents[1], color='blue') ax.set_title("Extent_1 Distribution") ax = fig.add_subplot(3, 2, 6) ax = sns.distplot(extents[2], color='green') ax.set_title("Extent_2 Distribution") plt.show()
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Answer
- In each group, an
ax
is created withax = fig.add_subplot(3, 2, 1, projection='3d')
, but then you reassign the variable withax = plt.axes(projection='3d')
; this does not plot toax
. - To plot to a specific axes, use the
ax
parameter in the plot methodsns.histplot(df['freq: 1x'], ax=ax)
- Also, upgrade seaborn to version 0.11, because
sns.distplot
is deprecated fordisplot
orhistplot
.
import pandas as pd import numpy as np # for sample data # sinusoidal sample data sample_length = range(1, 3+1) rads = np.arange(0, 2*np.pi, 0.01) data = np.array([np.sin(t*rads) for t in sample_length]) df = pd.DataFrame(data.T, index=pd.Series(rads.tolist(), name='radians'), columns=[f'freq: {i}x' for i in sample_length]) # plot the figures and correctly use the ax parameter fig = plt.figure(figsize=(10,10)) ax = fig.add_subplot(3, 2, 1, projection='3d') ax.scatter3D(df['freq: 1x'], df['freq: 2x'], df['freq: 3x'], color='green', s=5) ax = fig.add_subplot(3, 2, 2) sns.histplot(df['freq: 1x'], ax=ax) ax.set_title("Extent_0 Distribution") ax = fig.add_subplot(3, 2, 4) sns.histplot(df['freq: 2x'], ax=ax) ax.set_title("Extent_1 Distribution") ax = fig.add_subplot(3, 2, 6) sns.histplot(df['freq: 3x'], ax=ax) ax.set_title("Extent_2 Distribution") plt.tight_layout()
Using matplotlib gridspec
- Customizing Figure Layouts Using GridSpec and Other Functions
- Tight Layout guide
- The size of the 3D plot can be increased by changing the number of rows,
nrows
.gs1 = fig.add_gridspec(nrows=4, ncols=3)
fig = plt.figure(constrained_layout=False, figsize=(10, 10)) gs1 = fig.add_gridspec(nrows=3, ncols=3) ax1 = fig.add_subplot(gs1[:-1, :], projection='3d') ax1.scatter3D(df['freq: 1x'], df['freq: 2x'], df['freq: 3x'], color='green', s=10) ax2 = fig.add_subplot(gs1[-1, 0]) sns.histplot(df['freq: 1x'], kde=True, ax=ax2) ax2.set_title("Extent_0 Distribution") ax3 = fig.add_subplot(gs1[-1, 1]) sns.histplot(df['freq: 2x'], kde=True, ax=ax3) ax3.set_title("Extent_1 Distribution") ax4 = fig.add_subplot(gs1[-1, 2]) sns.histplot(df['freq: 3x'], kde=True, ax=ax4) ax4.set_title("Extent_2 Distribution") plt.tight_layout()