Question

我试图根据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了：再次感谢你（特别是J.K。）。

我遇到了最后一个问题：我无法旋转数字重叠的某个轴的刻度线（左下角）：

我想尝试让它们垂直但我不能这样做....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“有趣”：我如何才能使子图完全正方形？

我向乔金顿道歉;我知道我的代码不像他那么优雅......我刚开始几周前。如果你有任何改进我的建议，例如为了让它更有活力，我很有意思。

Answer 1

您可以使用xtick轮换setp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单独添加每个子绘图轴。这将允许您直接设置大小，但您还必须设置位置。

请勿使用bbox_inches='tight'保存图片，否则您将失去使用这些设置的标题。你可以像这样保存：

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

结果图如下所示：

scatter plot matrix

使用Matplotlib勾选散点图矩阵的属性

1 个答案: