I am trying to use the gradient fill under curves in matplotlib with datetime values on x-axis using this answer. I tried converting the datetime values to float to fix this, but that was not helpful.
The MWE and the error is given below. How to apply gradient fill under curves with datetime values on x-axis in matplotlib?
MWE
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
from datetime import datetime, timedelta
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
# x = np.linspace(0, 100, 100)
# y = np.sin(x) + x
x = np.arange(datetime(2010,7,1), datetime(2015,7,1), timedelta(hours=12)).astype(datetime)
y = np.linspace(0, 100, len(x))
fig, ax = plt.subplots()
ax.plot(x, y)
gradient_fill(x, y, ax=ax, zfunc=zfunc)
ax.grid()
fig.savefig("test.pdf")
plt.show()
Error
File "./gradient_fill_test.py", line 95, in <module>
gradient_fill(x, y, ax=ax, zfunc=zfunc)
File "./radient_fill_test.py", line 69, in gradient_fill
z = zfunc(x, y, fill_color=fill_color, alpha=alpha)
File "./gradient_fill_test.py", line 24, in zfunc
x = (x*scale).astype(int)
TypeError: unsupported operand type(s) for *: 'datetime.datetime' and 'int'
Try something like:
x = np.arange(datetime(2010,7,1), datetime(2015,7,1), timedelta(hours=12)).astype(datetime)
y = np.linspace(0, 100, len(x))
fig, ax = plt.subplots()
ax.plot(x, y)
gradient_fill(mdates.date2num(x), y, ax=ax, zfunc=zfunc)
I couldn't actually get that to work because your zfunc seems to have a bug in it, but if you fix that it will get you properly labeled date ticks.
Related
I have 2 functions, 'one function calling other' to plot some curves. What I want is to have legends in these functions that can be seen as one in the final plot, but I am unable to achieve that. Can someone help me with this? Code below (works fine without the label/legend lines).
import matplotlib.colors as mcolors
from matplotlib.patches import Polygon
def gradient_fill(x, y, fill_color=None, ax=None, label = None, **kwargs):
"""
Plot a line with a linear alpha gradient filled beneath it.
Parameters
----------
x, y : array-like
The data values of the line.
"""
if ax is None:
ax = plt.gca()
line, = ax.plot(x, y, **kwargs, color='black', lw = 1.5)
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*1.12],
origin='lower', zorder=zorder,alpha = 0.8, label = label)
ax.legend([str(label)])
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
def gaussian_kde(data,bandwidth = 20,x_range_st = -50,x_range_end = 1000, N = 1000, fill_color = None, label = None):
from sklearn.neighbors import KernelDensity
# instantiate and fit the KDE model
kde = KernelDensity(bandwidth=bandwidth, kernel='gaussian')
kde.fit(data[:, None])
x_d = np.linspace(x_range_st, x_range_end, N)
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
#plt.fill_between(x_d, np.exp(logprob), alpha=0.5)
gradient_fill(x_d,np.exp(logprob), fill_color = fill_color, label = label)
plt.gca().invert_yaxis()
Example of the code:
gaussian_kde(data1, label = 'Apple')
gaussian_kde(data2, label = 'Banana', fill_color = 'red')
gaussian_kde(data3, label = 'Mango', fill_color = 'green')
I want to add label directly in the gaussian_kde function so that it appears in the final figure. The error I get with the code TypeError: zip argument #2 must support iteration. The code works fine, but only displays the last label. Can someone provide some guidance on how to get all the labels as also defined in the figure?
I happened to see a beautiful graph on this page which is shown below:
Is it possible to get such color gradients in matplotlib?
There have been a handful of previous answers to similar questions (e.g. https://stackoverflow.com/a/22081678/325565), but they recommend a sub-optimal approach.
Most of the previous answers recommend plotting a white polygon over a pcolormesh fill. This is less than ideal for two reasons:
The background of the axes can't be transparent, as there's a filled polygon overlying it
pcolormesh is fairly slow to draw and isn't smoothly interpolated.
It's a touch more work, but there's a method that draws much faster and gives a better visual result: Set the clip path of an image plotted with imshow.
As an example:
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()
Please note Joe Kington deserves the lion's share of the credit here; my sole contribution is zfunc.
His method opens to door to many gradient/blur/drop-shadow
effects. For example, to make the lines have an evenly blurred underside, you
could use PIL to build an alpha layer which is 1 near the line and 0 near the bottom edge.
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(list(map(tuple, xy)), 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()
yields
I've tried something :
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')
.. and the result:
Of course the gradient could go down to 0 by changing the range of feel_between function.
I'm using some sample code I got from:
https://stackoverflow.com/a/29331211/9469766
I'm attempting to save as pdf but whenever I do the gradient colors are formatted incorrectly. But if I simply show instead of saving or save the file as .png instead, the gradients appear correctly. Which leads me to believe that its the .pdf format that's the issue. Are there specific parameters that I need to set when saving as pdf?
Expected Image Output:
Actual Image Output
My code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Polygon
np.random.seed(1977)
import os
def main():
home_dir = os.path.expanduser('~/Desktop/test2.pdf')
x, y = generate_data(100)
for _ in range(5):
gradient_fill(*generate_data(100))
# plt.show()
plt.savefig(home_dir, rasterized = True, dpi=300)
plt.close()
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):
"""
"""
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()
The problem is the same as in https://github.com/matplotlib/matplotlib/issues/7151.
The solution is to use the "image.composite_image" rc parameter and set it to False.
plt.rcParams["image.composite_image"] =False
I've currently constructed a plot using rectangle Patches to display a sequence of positions.
EDIT: Code used to generate this (built off of the RLPy library)-
def visualize_trajectory(self, trajectory=[[0,0,0,0], [0.1,0.1,0,0]]):
domain_fig = plt.figure()
for i, s in enumerate(trajectory):
x, y, speed, heading = s[:4]
car_xmin = x - self.REAR_WHEEL_RELATIVE_LOC
car_ymin = y - self.CAR_WIDTH / 2.
car_fig = matplotlib.patches.Rectangle(
[car_xmin,
car_ymin],
self.CAR_LENGTH,
self.CAR_WIDTH,
alpha=(0.8 * i) / len(trajectory) )
rotation = Affine2D().rotate_deg_around(
x, y, heading * 180 / np.pi) + plt.gca().transData
car_fig.set_transform(rotation)
plt.gca().add_patch(car_fig)
Is there any way to overlay each of these patches with images? Ideally, there would be a car image instead of a rectangle at each of the positions.
I've played around with AnnotationBbox and TransformedBbox, but both seem to be inflexible when dealing with rotations.
Take a look at
demo_affine_image
from the matplotlib gallery. It shows how
to rotate an image.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import matplotlib.cbook as cbook
def get_image():
fn = cbook.get_sample_data("necked_tensile_specimen.png")
arr = plt.imread(fn)
# make background transparent
# you won't have to do this if your car image already has a transparent background
mask = (arr == (1,1,1,1)).all(axis=-1)
arr[mask] = 0
return arr
def imshow_affine(ax, z, *args, **kwargs):
im = ax.imshow(z, *args, **kwargs)
x1, x2, y1, y2 = im.get_extent()
im._image_skew_coordinate = (x2, y1)
return im
N = 7
x = np.linspace(0, 1, N)
y = x**1.1
heading = np.linspace(10, 90, N)
trajectory = list(zip(x, y, heading))
width, height = 0.3, 0.3
car = get_image()
fig, ax = plt.subplots()
for i, t in enumerate(trajectory, start=1):
xi, yi, deg = t
im = imshow_affine(ax, car, interpolation='none',
extent=[0, width, 0, height], clip_on=True,
alpha=0.8*i/len(trajectory))
center_x, center_y = width//2, height//2
im_trans = (mtransforms.Affine2D()
.rotate_deg_around(center_x, center_y, deg)
.translate(xi, yi)
+ ax.transData)
im.set_transform(im_trans)
ax.set_xlim(-0.5, 1.5)
ax.set_ylim(-0.5, 1.7)
plt.show()
I happened to see a beautiful graph on this page which is shown below:
Is it possible to get such color gradients in matplotlib?
There have been a handful of previous answers to similar questions (e.g. https://stackoverflow.com/a/22081678/325565), but they recommend a sub-optimal approach.
Most of the previous answers recommend plotting a white polygon over a pcolormesh fill. This is less than ideal for two reasons:
The background of the axes can't be transparent, as there's a filled polygon overlying it
pcolormesh is fairly slow to draw and isn't smoothly interpolated.
It's a touch more work, but there's a method that draws much faster and gives a better visual result: Set the clip path of an image plotted with imshow.
As an example:
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()
Please note Joe Kington deserves the lion's share of the credit here; my sole contribution is zfunc.
His method opens to door to many gradient/blur/drop-shadow
effects. For example, to make the lines have an evenly blurred underside, you
could use PIL to build an alpha layer which is 1 near the line and 0 near the bottom edge.
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(list(map(tuple, xy)), 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()
yields
I've tried something :
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')
.. and the result:
Of course the gradient could go down to 0 by changing the range of feel_between function.