基于【matplotlib】【imshow】【cmap】绘制【numpy.ndarray】二维数组的“二维码”

来源：互联网发布：cd刻录软件编辑：程序博客网时间：2024/06/01 08:18

基于 matplotlib 画 numpy.ndarray 二维数组的二维图/灰度图：

全文参考：http://matplotlib.org/examples/color/colormaps_reference.html

import numpy as npimport pandas as pdimport matplotlib as mplimport matplotlib.pyplot as plttrain_data_source = pd.read_csv('df_recon_train', header=0, encoding='utf-8')train_data = train_data_source.drop(['uid', 'y'], axis=1)

#以train_data的第一行数据为例，画二维数组的二维图row_ins = train_data[0:1].values[0]row_ins_rect = row_ins[::-1].reshape(45, 46)fig = plt.figure()ax1 = fig.add_subplot(1,2,1)ax1.imshow(row_ins_rect, cmap=plt.cm.gray_r)ax2 = fig.add_subplot(1,2,2)ax2.imshow(row_ins_rect, cmap=plt.cm.hot_r)plt.show()

implot = plt.imshow(row_ins_rect, cmap="summer")#选一个漂亮的颜色plt.show()

import numpy as npimport matplotlib.pyplot as plt# Have colormaps separated into categories:# http://matplotlib.org/examples/color/colormaps_reference.htmlcmaps = [('Perceptually Uniform Sequential',                            ['viridis', 'inferno', 'plasma', 'magma']),         ('Sequential',     ['Blues', 'BuGn', 'BuPu',                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),         ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',                             'copper', 'gist_heat', 'gray', 'hot',                             'pink', 'spring', 'summer', 'winter']),         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',                             'seismic']),         ('Qualitative',    ['Accent', 'Dark2', 'Paired', 'Pastel1',                             'Pastel2', 'Set1', 'Set2', 'Set3']),         ('Miscellaneous',  ['gist_earth', 'terrain', 'ocean', 'gist_stern',                             'brg', 'CMRmap', 'cubehelix',                             'gnuplot', 'gnuplot2', 'gist_ncar',                             'nipy_spectral', 'jet', 'rainbow',                             'gist_rainbow', 'hsv', 'flag', 'prism'])]nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)gradient = np.linspace(0, 1, 256)gradient = np.vstack((gradient, gradient))def plot_color_gradients(cmap_category, cmap_list):    fig, axes = plt.subplots(nrows=nrows)    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)    axes[0].set_title(cmap_category + ' colormaps', fontsize=14)    for ax, name in zip(axes, cmap_list):        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))        pos = list(ax.get_position().bounds)        x_text = pos[0] - 0.01        y_text = pos[1] + pos[3]/2.        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)    # Turn off *all* ticks & spines, not just the ones with colormaps.    for ax in axes:        ax.set_axis_off()for cmap_category, cmap_list in cmaps:    plot_color_gradients(cmap_category, cmap_list)plt.show()

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