maplotlib 标准颜色、风格样式和水印

"""
========================
Visualizing named colors
========================
Simple plot example with the named colors and its visual representation.
"""
from __future__ import division
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors

colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
# Sort colors by hue, saturation, value and name.
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
                for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
n = len(sorted_names)
ncols = 4
nrows = n // ncols + 1
fig, ax = plt.subplots(figsize=(8, 5))
# Get height and width
X, Y = fig.get_dpi() * fig.get_size_inches()
h = Y / (nrows + 1)
w = X / ncols
for i, name in enumerate(sorted_names):
    col = i % ncols
    row = i // ncols
    y = Y - (row * h) - h
    xi_line = w * (col + 0.05)
    xf_line = w * (col + 0.25)
    xi_text = w * (col + 0.3)
    ax.text(xi_text, y, name, fontsize=(h * 0.8),
            horizontalalignment='left',
            verticalalignment='center')
    ax.hlines(y + h * 0.1, xi_line, xf_line,
              color=colors[name], linewidth=(h * 0.6))
ax.set_xlim(0, X)
ax.set_ylim(0, Y)
ax.set_axis_off()
fig.subplots_adjust(left=0, right=1,
                    top=1, bottom=0,
                    hspace=0, wspace=0)
plt.show()

from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(0, 10)
print (plt.style.available)
#with plt.style.context('Solarize_Light2'):
with plt.style.context('classic'):#经典样式
    plt.plot(x, np.sin(x) + x + np.random.randn(50))
    plt.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
    plt.plot(x, np.sin(x) + 3 * x + np.random.randn(50))
    plt.plot(x, np.sin(x) + 4 + np.random.randn(50))
    plt.plot(x, np.sin(x) + 5 * x + np.random.randn(50))
    plt.plot(x, np.sin(x) + 6 * x + np.random.randn(50))
    plt.plot(x, np.sin(x) + 7 * x + np.random.randn(50))
    plt.plot(x, np.sin(x) + 8 * x + np.random.randn(50))
    # Number of accent colors in the color scheme
    plt.title('8 Random Lines - Line')
    plt.xlabel('x label', fontsize=14)
    plt.ylabel('y label', fontsize=14)
plt.show()

"""
======================
Style sheets reference
======================
This script demonstrates the different available style sheets on a
common set of example plots: scatter plot, image, bar graph, patches,
line plot and histogram,
"""
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)

def plot_scatter(ax, prng, nb_samples=100):
    """Scatter plot.
    """
    for mu, sigma, marker in [(-.5, 0.75, 'o'), (0.75, 1., 's')]:
        x, y = prng.normal(loc=mu, scale=sigma, size=(2, nb_samples))
        ax.plot(x, y, ls='none', marker=marker)
    ax.set_xlabel('X-label')
    return ax

def plot_colored_sinusoidal_lines(ax):
    """Plot sinusoidal lines with colors following the style color cycle.
    """
    L = 2 * np.pi
    x = np.linspace(0, L)
    nb_colors = len(plt.rcParams['axes.prop_cycle'])
    shift = np.linspace(0, L, nb_colors, endpoint=False)
    for s in shift:
        ax.plot(x, np.sin(x + s), '-')
    ax.set_xlim([x[0], x[-1]])
    return ax

def plot_bar_graphs(ax, prng, min_value=5, max_value=25, nb_samples=5):
    """Plot two bar graphs side by side, with letters as x-tick labels.
    """
    x = np.arange(nb_samples)
    ya, yb = prng.randint(min_value, max_value, size=(2, nb_samples))
    width = 0.25
    ax.bar(x, ya, width)
    ax.bar(x + width, yb, width, color='C2')
    ax.set_xticks(x + width)
    ax.set_xticklabels(['a', 'b', 'c', 'd', 'e'])
    return ax

def plot_colored_circles(ax, prng, nb_samples=15):
    """Plot circle patches.
    NB: draws a fixed amount of samples, rather than using the length of
    the color cycle, because different styles may have different numbers
    of colors.
    """
    for sty_dict, j in zip(plt.rcParams['axes.prop_cycle'], range(nb_samples)):
        ax.add_patch(plt.Circle(prng.normal(scale=3, size=2),
                                radius=1.0, color=sty_dict['color']))
    # Force the limits to be the same across the styles (because different
    # styles may have different numbers of available colors).
    ax.set_xlim([-4, 8])
    ax.set_ylim([-5, 6])
    ax.set_aspect('equal', adjustable='box')  # to plot circles as circles
    return ax

def plot_image_and_patch(ax, prng, size=(20, 20)):
    """Plot an image with random values and superimpose a circular patch.
    """
    values = prng.random_sample(size=size)
    ax.imshow(values, interpolation='none')
    c = plt.Circle((5, 5), radius=5, label='patch')
    ax.add_patch(c)
    # Remove ticks
    ax.set_xticks([])
    ax.set_yticks([])

def plot_histograms(ax, prng, nb_samples=10000):
    """Plot 4 histograms and a text annotation.
    """
    params = ((10, 10), (4, 12), (50, 12), (6, 55))
    for a, b in params:
        values = prng.beta(a, b, size=nb_samples)
        ax.hist(values, histtype="stepfilled", bins=30, alpha=0.8, density=True)
    # Add a small annotation.
    ax.annotate('Annotation', xy=(0.25, 4.25), xycoords='data',
                xytext=(0.9, 0.9), textcoords='axes fraction',
                va="top", ha="right",
                bbox=dict(boxstyle="round", alpha=0.2),
                arrowprops=dict(
                          arrowstyle="->",
                          connectionstyle="angle,angleA=-95,angleB=35,rad=10"),
                )
    return ax

def plot_figure(style_label=""):
    """Setup and plot the demonstration figure with a given style.
    """
    # Use a dedicated RandomState instance to draw the same "random" values
    # across the different figures.
    prng = np.random.RandomState(96917002)
    # Tweak the figure size to be better suited for a row of numerous plots:
    # double the width and halve the height. NB: use relative changes because
    # some styles may have a figure size different from the default one.
    (fig_width, fig_height) = plt.rcParams['figure.figsize']
    fig_size = [fig_width * 2, fig_height / 2]
    fig, axes = plt.subplots(ncols=6, nrows=1, num=style_label,
                             figsize=fig_size, squeeze=True)
    axes[0].set_ylabel(style_label)
    plot_scatter(axes[0], prng)
    plot_image_and_patch(axes[1], prng)
    plot_bar_graphs(axes[2], prng)
    plot_colored_circles(axes[3], prng)
    plot_colored_sinusoidal_lines(axes[4])
    plot_histograms(axes[5], prng)
    fig.tight_layout()
    return fig

if __name__ == "__main__":
    # Setup a list of all available styles, in alphabetical order but
    # the `default` and `classic` ones, which will be forced resp. in
    # first and second position.
    style_list = ['default', 'classic'] + sorted(
        style for style in plt.style.available if style != 'classic')
    #plt.style.availabl 返回可用样式列表！！！！！
    # Plot a demonstration figure for every available style sheet.
    for style_label in style_list:
        with plt.style.context(style_label):
            fig = plot_figure(style_label=style_label)
    plt.show()

"""
===============
Watermark image
===============
Use a PNG file as a watermark
"""
from __future__ import print_function
import numpy as np
import matplotlib.cbook as cbook
import matplotlib.image as image
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)

datafile = cbook.get_sample_data('logo2.png', asfileobj=False)
print('loading %s' % datafile)
im = image.imread(datafile)
im[:, :, -1] = 0.5  # set the alpha channel
fig, ax = plt.subplots()
ax.plot(np.random.rand(20), '-o', ms=20, lw=2, alpha=0.7, mfc='orange')
ax.grid()
fig.figimage(im, 10, 10, zorder=3)
plt.show()

"""
==============
Text watermark
==============
Use a Text as a watermark
"""
import numpy as np
#import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)

fig, ax = plt.subplots()
ax.plot(np.random.rand(20), '-o', ms=20, lw=2, alpha=0.7, mfc='magenta')
ax.grid()
# position bottom right
fig.text(0.95, 0.05, 'Property of MPL',
         fontsize=50, color='gray',
         ha='right', va='bottom', alpha=0.5)
plt.show()