python - 使用 Matplotlib 绘制散点图矩阵的刻度属性

Question

我正在尝试根据 Joe Kington 编写的代码绘制散点图矩阵:Is there a function to make scatterplot matrices in matplotlib?

有些人已经帮助过我:再次感谢你(尤其是 J.K.)。

我遇到了最后一个问题:我无法旋转某些数字重叠的轴的刻度线(左下角):

我想尝试让它们垂直，但我做不到....这是我的代码:

import itertools
import numpy as np
import pylab as plot
import scipy
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import axis
import math
from matplotlib import rc
import os
import platform


def main():
    FigSize=8.89
    FontSize=8
    np.random.seed(1977)
    numvars, numdata = 4, 10
    data = 10 * np.random.random((numvars, numdata))
    fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'], FigSize, FontSize,
        linestyle='none', marker='o', color='black', mfc='none', markersize=3,)
    fig.suptitle('Simple Scatterplot Matrix')
    plt.savefig('Plots/ScatterplotMatrix/ScatterplotMatrix2.pdf',format='pdf', dpi=1000, transparent=True, bbox_inches='tight')
    plt.show()


def scatterplot_matrix(data, names, FigSize, FontSize, **kwargs):
    """Plots a scatterplot matrix of subplots.  Each row of "data" is plotted
    against other rows, resulting in a nrows by nrows grid of subplots with the
    diagonal subplots labeled with "names".  Additional keyword arguments are
    passed on to matplotlib's "plot" command. Returns the matplotlib figure
    object containg the subplot grid."""

    legend=['(kPa)','\%','\%','\%']
    numvars, numdata = data.shape
    fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(FigSize/2.54,FigSize/2.54))
    fig.subplots_adjust(hspace=0.05, wspace=0.05)

    sub_labelx_top=[2,4]
    sub_labelx_bottom=[13,15]
    sub_labely_left=[5,13]
    sub_labely_right=[4,12]

    for i, ax in enumerate(axes.flat, start=1):
        # Hide all ticks and labels
        ax.xaxis.set_visible(False)
        ax.yaxis.set_visible(False)
        ax.xaxis.set_major_locator(MaxNLocator(prune='both',nbins=4))
        ax.yaxis.set_major_locator(MaxNLocator(prune='both',nbins=4)) #http://matplotlib.org/api/ticker_api.html#matplotlib.ticker.MaxNLocator


        # Set up ticks only on one side for the "edge" subplots...
        if ax.is_first_col():
            ax.yaxis.set_ticks_position('left')
            ax.tick_params(direction='out')
            ax.yaxis.set_tick_params(labelsize=0.75*FontSize)
            if i in sub_labely_left:
        ax.yaxis.set_label_position('left')
            ax.set_ylabel('(\%)',fontsize=0.75*FontSize)

        if ax.is_last_col():
            ax.yaxis.set_ticks_position('right')
            ax.tick_params(direction='out')
            ax.yaxis.set_tick_params(labelsize=0.75*FontSize)
            if i in sub_labely_right:
                ax.yaxis.set_label_position('right')
                if i==4:
                ax.set_ylabel('(kPa)',fontsize=0.75*FontSize)
                else:
                ax.set_ylabel('(\%)',fontsize=0.75*FontSize)

        if ax.is_first_row():
            ax.xaxis.set_ticks_position('top')
            ax.tick_params(direction='out')
            ax.xaxis.set_tick_params(labelsize=0.75*FontSize)
            if i in sub_labelx_top:
                ax.xaxis.set_label_position('top')
                ax.set_xlabel('(\%)',fontsize=0.75*FontSize)

        if ax.is_last_row():
            ax.xaxis.set_ticks_position('bottom')
            ax.tick_params(direction='out')
            ax.xaxis.set_tick_params(labelsize=0.75*FontSize)

            if i in sub_labelx_bottom:
                ax.xaxis.set_label_position('bottom')

                if i==13:
                ax.set_xlabel('(kPa)',fontsize=0.75*FontSize)
                else:
                ax.set_xlabel('(\%)',fontsize=0.75*FontSize)

             # Plot the data.
    for i, j in zip(*np.triu_indices_from(axes, k=1)):
        for x, y in [(i,j), (j,i)]:
            axes[x,y].plot(data[y], data[x], **kwargs)   



    # Label the diagonal subplots...
    for i, label in enumerate(names):
        axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
            ha='center', va='center',fontsize=FontSize)

    # Turn on the proper x or y axes ticks.
    for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
        axes[j,i].xaxis.set_visible(True)
        axes[i,j].yaxis.set_visible(True)

    return fig

main()

我的第二个问题更多的是为了“好玩”:我怎样才能使子图完美正方形？

我向 Joe Kington 道歉；我知道我的代码不如他的优雅......我几周前才开始。如果您有任何改进我的建议，例如让它更有活力，我很感兴趣。

Answer 1

您可以使用 setp 旋转 xtick 标签。

from matplotlib.artist import setp

然后在为子图调用的顶行和左列设置 x 刻度位置后:

setp(ax.get_xticklabels(), rotation=90)

要使子图的大小相等，您可以fig.subplots_adjust 将所有子图的面积设置为正方形。像这样:

gridSize = 0.6
leftBound = 0.5 - gridSize/2
bottomBound = 0.1
rightBound = leftBound + gridSize
topBound = bottomBound + gridSize
fig.subplots_adjust(hspace=0.05, wspace=0.05, left=leftBound,
                        bottom=bottomBound, right=rightBound, top=topBound)

如果图形大小不是正方形，则需要相应地更改网格的形状。或者，您可以使用 fig.add_axes 单独添加每个子图轴。这将允许您直接设置大小，但您还必须设置位置。

不要使用 bbox_inches='tight' 来保存图形，否则这些设置会导致标题丢失。您可以这样保存:

plt.savefig('ScatterplotMatrix.pdf',format='pdf', dpi=1000, transparent=True)

结果图如下所示: