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"""
============
Contour Demo
============
Illustrate simple contour plotting, contours on an image with
a colorbar for the contours, and labelled contours.
See also the :doc:`contour image example
</gallery/images_contours_and_fields/contour_image>`.
"""
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
# %%
# Create a simple contour plot with labels using default colors. The inline
# argument to clabel will control whether the labels are draw over the line
# segments of the contour, removing the lines beneath the label.
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z)
ax.clabel(CS, fontsize=10)
ax.set_title('Simplest default with labels')
# %%
# Contour labels can be placed manually by providing list of positions (in data
# coordinate). See :doc:`/gallery/event_handling/ginput_manual_clabel_sgskip`
# for interactive placement.
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z)
manual_locations = [
(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]
ax.clabel(CS, fontsize=10, manual=manual_locations)
ax.set_title('labels at selected locations')
# %%
# You can force all the contours to be the same color.
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z, 6, colors='k') # Negative contours default to dashed.
ax.clabel(CS, fontsize=9)
ax.set_title('Single color - negative contours dashed')
# %%
# You can set negative contours to be solid instead of dashed:
plt.rcParams['contour.negative_linestyle'] = 'solid'
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z, 6, colors='k') # Negative contours default to dashed.
ax.clabel(CS, fontsize=9)
ax.set_title('Single color - negative contours solid')
# %%
# And you can manually specify the colors of the contour
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z, 6,
linewidths=np.arange(.5, 4, .5),
colors=('r', 'green', 'blue', (1, 1, 0), '#afeeee', '0.5'),
)
ax.clabel(CS, fontsize=9)
ax.set_title('Crazy lines')
# %%
# Or you can use a colormap to specify the colors; the default
# colormap will be used for the contour lines
fig, ax = plt.subplots()
im = ax.imshow(Z, interpolation='bilinear', origin='lower',
cmap=cm.gray, extent=(-3, 3, -2, 2))
levels = np.arange(-1.2, 1.6, 0.2)
CS = ax.contour(Z, levels, origin='lower', cmap='flag', extend='both',
linewidths=2, extent=(-3, 3, -2, 2))
# Thicken the zero contour.
lws = np.resize(CS.get_linewidth(), len(levels))
lws[6] = 4
CS.set_linewidth(lws)
ax.clabel(CS, levels[1::2], # label every second level
fmt='%1.1f', fontsize=14)
# make a colorbar for the contour lines
CB = fig.colorbar(CS, shrink=0.8)
ax.set_title('Lines with colorbar')
# We can still add a colorbar for the image, too.
CBI = fig.colorbar(im, orientation='horizontal', shrink=0.8)
# This makes the original colorbar look a bit out of place,
# so let's improve its position.
l, b, w, h = ax.get_position().bounds
ll, bb, ww, hh = CB.ax.get_position().bounds
CB.ax.set_position([ll, b + 0.1*h, ww, h*0.8])
plt.show()
# %%
#
# .. admonition:: References
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
#
# - `matplotlib.axes.Axes.contour` / `matplotlib.pyplot.contour`
# - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`
# - `matplotlib.axes.Axes.clabel` / `matplotlib.pyplot.clabel`
# - `matplotlib.axes.Axes.get_position`
# - `matplotlib.axes.Axes.set_position`
|
