是否可以在 matplotlib 中獲得曲線下的顏色漸變？

Question

我碰巧在這個頁面上看到一個漂亮的圖表，如下所示：

是否有可能在 matplotlib 中獲得這樣的顏色漸變？

Answer 1

之前有一些類似問題的答案（例如https://stackoverflow.com/a/22081678/325565 ），但他們推薦了一種次優方法。

以前的大多數答案都建議在pcolormesh填充上繪制一個白色多邊形。 這不太理想，原因有兩個：

1. 軸的背景不能是透明的，因為上面有一個填充的多邊形
2. pcolormesh繪制速度相當慢，並且不能順利插入。

這是更多的工作，但有一種方法可以更快地繪制並提供更好的視覺效果：設置使用imshow繪制的圖像的剪輯路徑。

舉個例子：

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

請注意， Joe Kington 在此應得的大部分功勞； 我唯一的貢獻是zfunc 。 他的方法為許多漸變/模糊/陰影效果打開了大門。 例如，要使線條具有均勻模糊的底面，您可以使用 PIL 構建一個 alpha 圖層，該圖層在線條附近為 1，在底部邊緣附近為 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()

產量

Answer 3

我試過一些東西：

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')

.. 結果：

當然，通過改變feel_between函數的范圍，梯度可以降到0。

Answer 4

只是重新打開這個線程，所以只是重新打開這個線程，我使用了絕大多數 joe 的代碼，但是我一生都無法弄清楚如何在 X 軸上使用 0 而不是這個向上傾斜的限制來進行着色。

理想情況下，在數據的腹部，它應該是弱顏色，在大量的正負數處是極端顏色，我希望陰影開始/停止的線為水平（0）而不是我得到這個奇怪的傾斜軸我只是不明白如何糾正。

我有一個以前的圖表，您可以看到（非漸變）我試圖實現的目標。 和紅色漸變圖表我搞砸了。

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Polygon

# Variables
AUM = df['#AHD_AUM'].head(104)
MM = df['#AHD_Managed_Money_Net'].head(104)
PRICE = df['#AHD_Price'].head(104)
DATES = df['DATES'].head(104)

# Date Friendly Variables for Plot
List_AUM = df['#AHD_AUM'].head(104).to_list()
List_MM = df['#AHD_Managed_Money_Net'].head(104).to_list()
List_DATES = df['DATES'].head(104).to_list()
X = 0 * df['#AHD_AUM'].head(104)


# Make a date list changing dates with numbers to avoid the issue with the plot 
interpreting dates
for i in range(len(df['DATES'].head(104))):
count = i
df['count'][i] = 120 - i


# X and Y data variables changed to arrays as when i had these set as dates 
matplotlib hates it    
x = df['count'].head(104).to_numpy()
y = df['#AHD_Managed_Money_Net'].head(104).to_numpy()

#DD = AUM.to_numpy()
#MMM = MM.to_numpy()

def main():
for _ in range(len(DD)):
    gradient_fill(x,y)
plt.show()


def gradient_fill(x,y, fill_color=None, ax=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

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]]) ### i dont 
need this so i have just commented it out
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()

以前的版本測試一個我搞砸了