是否有可能在matplotlib中获得曲线下的颜色渐变？

Question

我偶然在这个页面上看到了一个漂亮的图表,如下所示:

是否有可能在matplotlib中获得这样的颜色渐变？

Answer 1

以前对类似问题有一些答案(例如/sf/answers/1545717491/),但他们建议采用次优方法.

大多数先前的答案建议在pcolormesh填充上绘制白色多边形.由于两个原因,这不太理想:

1. 轴的背景不能是透明的,因为它上面有一个填充的多边形
2. pcolormesh 绘制相当慢,并且不能平滑插值.

这是一个更多的工作,但有一种方法可以更快地绘制并提供更好的视觉效果:设置用图表绘制的图像的剪辑路径imshow.

举个例子:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Polygon
np.random.seed(1977)

def main():
    for _ in range(5):
        gradient_fill(*generate_data(100))
    plt.show()

def generate_data(num):
    x = np.linspace(0, 100, num)
    y = np.random.normal(0, 1, num).cumsum()
    return x, y

def gradient_fill(x, y, fill_color=None, ax=None, **kwargs):
    """
    Plot a line with a linear alpha gradient filled beneath it.

    Parameters
    ----------
    x, y : array-like
        The data values of the line.
    fill_color : a matplotlib color specifier (string, tuple) or None
        The color for the fill. If None, the color of the line will be used.
    ax : a matplotlib Axes instance
        The axes to plot on. If None, the current pyplot axes will be used.
    Additional arguments are passed on to matplotlib's ``plot`` function.

    Returns
    -------
    line : a Line2D instance
        The line plotted.
    im : an AxesImage instance
        The transparent gradient clipped to just the area beneath the curve.
    """
    if ax is None:
        ax = plt.gca()

    line, = ax.plot(x, y, **kwargs)
    if fill_color is None:
        fill_color = line.get_color()

    zorder = line.get_zorder()
    alpha = line.get_alpha()
    alpha = 1.0 if alpha is None else alpha

    z = np.empty((100, 1, 4), dtype=float)
    rgb = mcolors.colorConverter.to_rgb(fill_color)
    z[:,:,:3] = rgb
    z[:,:,-1] = np.linspace(0, alpha, 100)[:,None]

    xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()
    im = ax.imshow(z, aspect='auto', extent=[xmin, xmax, ymin, ymax],
                   origin='lower', zorder=zorder)

    xy = np.column_stack([x, y])
    xy = np.vstack([[xmin, ymin], xy, [xmax, ymin], [xmin, ymin]])
    clip_path = Polygon(xy, facecolor='none', edgecolor='none', closed=True)
    ax.add_patch(clip_path)
    im.set_clip_path(clip_path)

    ax.autoscale(True)
    return line, im

main()

Answer 2

请注意,Joe Kington在这里应得到最大的赞誉 ; 我唯一的贡献是zfunc. 他的方法打开了许多渐变/模糊/阴影效果.例如,要使线条具有均匀模糊的底面,可以使用PIL构建一个α线图层,该图层在线条附近为1,在底线边缘附近为0.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.patches as patches
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFilter

np.random.seed(1977)
def demo_blur_underside():
    for _ in range(5):
        # gradient_fill(*generate_data(100), zfunc=None) # original
        gradient_fill(*generate_data(100), zfunc=zfunc)
    plt.show()

def generate_data(num):
    x = np.linspace(0, 100, num)
    y = np.random.normal(0, 1, num).cumsum()
    return x, y

def zfunc(x, y, fill_color='k', alpha=1.0):
    scale = 10
    x = (x*scale).astype(int)
    y = (y*scale).astype(int)
    xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()

    w, h = xmax-xmin, ymax-ymin
    z = np.empty((h, w, 4), dtype=float)
    rgb = mcolors.colorConverter.to_rgb(fill_color)
    z[:,:,:3] = rgb

    # Build a z-alpha array which is 1 near the line and 0 at the bottom.
    img = Image.new('L', (w, h), 0)  
    draw = ImageDraw.Draw(img)
    xy = (np.column_stack([x, y]))
    xy -= xmin, ymin
    # Draw a blurred line using PIL
    draw.line(map(tuple, xy.tolist()), fill=255, width=15)
    img = img.filter(ImageFilter.GaussianBlur(radius=100))
    # Convert the PIL image to an array
    zalpha = np.asarray(img).astype(float) 
    zalpha *= alpha/zalpha.max()
    # make the alphas melt to zero at the bottom
    n = zalpha.shape[0] // 4
    zalpha[:n] *= np.linspace(0, 1, n)[:, None]
    z[:,:,-1] = zalpha
    return z

def gradient_fill(x, y, fill_color=None, ax=None, zfunc=None, **kwargs):
    if ax is None:
        ax = plt.gca()

    line, = ax.plot(x, y, **kwargs)
    if fill_color is None:
        fill_color = line.get_color()

    zorder = line.get_zorder()
    alpha = line.get_alpha()
    alpha = 1.0 if alpha is None else alpha

    if zfunc is None:
        h, w = 100, 1
        z = np.empty((h, w, 4), dtype=float)
        rgb = mcolors.colorConverter.to_rgb(fill_color)
        z[:,:,:3] = rgb
        z[:,:,-1] = np.linspace(0, alpha, h)[:,None]
    else:
        z = zfunc(x, y, fill_color=fill_color, alpha=alpha)
    xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()
    im = ax.imshow(z, aspect='auto', extent=[xmin, xmax, ymin, ymax],
                   origin='lower', zorder=zorder)

    xy = np.column_stack([x, y])
    xy = np.vstack([[xmin, ymin], xy, [xmax, ymin], [xmin, ymin]])
    clip_path = patches.Polygon(xy, facecolor='none', edgecolor='none', closed=True)
    ax.add_patch(clip_path)
    im.set_clip_path(clip_path)
    ax.autoscale(True)
    return line, im

demo_blur_underside()

产量

Answer 3

我试过一些东西：

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()

xData = range(100)
yData = range(100)
plt.plot(xData, yData)

NbData = len(xData)
MaxBL = [[MaxBL] * NbData for MaxBL in range(100)]
Max = [np.asarray(MaxBL[x]) for x in range(100)]

for x in range (50, 100):
  plt.fill_between(xData, Max[x], yData, where=yData >Max[x], facecolor='red', alpha=0.02)

for x in range (0, 50):
  plt.fill_between(xData, yData, Max[x], where=yData <Max[x], facecolor='green', alpha=0.02)

plt.fill_between([], [], [], facecolor='red', label="x > 50")
plt.fill_between([], [], [], facecolor='green', label="x < 50")

plt.legend(loc=4, fontsize=12)
plt.show()
fig.savefig('graph.png')

.. 结果：

当然，通过改变feel_between函数的范围，梯度可以降到 0 。