I have two list as below:
latt=[42.0,41.978567980875397,41.96622693388357,41.963791391892457,...,41.972407378075879]
lont=[-66.706920989908909,-66.703116557977069,-66.707351643324543,...-66.718218142021925]
now I want to plot this as a line, separate each 10 of those 'latt' and 'lont' records as a period and give it a unique color. what should I do?
The usual way to set the line color in matplotlib is to specify it in the plot command. This can either be done by a string after the data, e.g. "r-" for a red line, or by explicitely stating the color argument.
Create x and y data points using numpy. Plot x and y data points, with color=red and linewidth=2. To shade an area parallel to X-axis, initialize two variables, y1 and y2. To add horizontal span across the axes, use axhspan() method with y1, y2, green as shade color,and alpha for transprency of the shade.
you have two option of using scatter command with multiple colour in a single call.
There are several different ways to do this. The "best" approach will depend mostly on how many line segments you want to plot.
If you're just going to be plotting a handful (e.g. 10) line segments, then just do something like:
import numpy as np
import matplotlib.pyplot as plt
def uniqueish_color():
"""There're better ways to generate unique colors, but this isn't awful."""
return plt.cm.gist_ncar(np.random.random())
xy = (np.random.random((10, 2)) - 0.5).cumsum(axis=0)
fig, ax = plt.subplots()
for start, stop in zip(xy[:-1], xy[1:]):
x, y = zip(start, stop)
ax.plot(x, y, color=uniqueish_color())
plt.show()
If you're plotting something with a million line segments, though, this will be terribly slow to draw. In that case, use a LineCollection
. E.g.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
xy = (np.random.random((1000, 2)) - 0.5).cumsum(axis=0)
# Reshape things so that we have a sequence of:
# [[(x0,y0),(x1,y1)],[(x0,y0),(x1,y1)],...]
xy = xy.reshape(-1, 1, 2)
segments = np.hstack([xy[:-1], xy[1:]])
fig, ax = plt.subplots()
coll = LineCollection(segments, cmap=plt.cm.gist_ncar)
coll.set_array(np.random.random(xy.shape[0]))
ax.add_collection(coll)
ax.autoscale_view()
plt.show()
For both of these cases, we're just drawing random colors from the "gist_ncar" coloramp. Have a look at the colormaps here (gist_ncar is about 2/3 of the way down): http://matplotlib.org/examples/color/colormaps_reference.html
Copied from this example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
x = np.linspace(0, 3 * np.pi, 500)
y = np.sin(x)
z = np.cos(0.5 * (x[:-1] + x[1:])) # first derivative
# Create a colormap for red, green and blue and a norm to color
# f' < -0.5 red, f' > 0.5 blue, and the rest green
cmap = ListedColormap(['r', 'g', 'b'])
norm = BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
# Create a set of line segments so that we can color them individually
# This creates the points as a N x 1 x 2 array so that we can stack points
# together easily to get the segments. The segments array for line collection
# needs to be numlines x points per line x 2 (x and y)
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
# Create the line collection object, setting the colormapping parameters.
# Have to set the actual values used for colormapping separately.
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(z)
lc.set_linewidth(3)
fig1 = plt.figure()
plt.gca().add_collection(lc)
plt.xlim(x.min(), x.max())
plt.ylim(-1.1, 1.1)
plt.show()
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