def main():
fig, axes = example_utils.setup_axes()
basic_bar(axes[0])
tornado(axes[1])
general(axes[2])
example_utils.title(fig, '"ax.bar(...)": Plot rectangles')
fig.savefig('bar_example.png', facecolor='none')
plt.show()
def main():
fig, axes = example_utils.setup_axes()
fill_example(axes[0])
fill_between_example(axes[1])
stackplot_example(axes[2])
example_utils.title(fig, 'fill/fill_between/stackplot: Filled polygons',
y=0.95)
fig.savefig('fill_example.png', facecolor='none')
plt.show()
def main():
fig, axes = example_utils.setup_axes()
fill_example(axes[0])
fill_between_example(axes[1])
stackplot_example(axes[2])
example_utils.title(fig,
'fill/fill_between/stackplot: Filled polygons',
y=0.95)
fig.savefig('fill_example.png', facecolor='none')
plt.show()
def main():
colors = ['cyan', 'red', 'blue', 'green', 'purple']
dists = generate_data()
fig, axes = example_utils.setup_axes()
hist(axes[0], dists, colors)
boxplot(axes[1], dists, colors)
violinplot(axes[2], dists, colors)
example_utils.title(fig, 'hist/boxplot/violinplot: Statistical plotting',
y=0.9)
fig.savefig('statistical_example.png', facecolor='none')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
import example_utils
# Generate data
n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
xi, yi = np.meshgrid(x, y)
z = (1 - xi / 2 + xi**5 + yi**3) * np.exp(-xi**2 - yi**2)
dy, dx = np.gradient(z)
mag = np.hypot(dx, dy)
fig, axes = example_utils.setup_axes()
# Use ax.arrow to plot a single arrow on the axes.
axes[0].arrow(0, 0, -0.5, 0.5, width=0.005, color='black')
axes[0].axis([-1, 1, -1, 1])
example_utils.label(axes[0], 'arrow(x, y, dx, dy)')
# Plot a regularly-sampled vector field with ax.quiver
ds = np.s_[::16, ::16] # Downsample our array a bit...
axes[1].quiver(xi[ds], yi[ds], dx[ds], dy[ds], z[ds], cmap='gist_earth',
width=0.01, scale=0.25, pivot='middle')
axes[1].axis('tight')
example_utils.label(axes[1], 'quiver(x, y, dx, dy)')
# Use ax.streamplot to show flowlines through our vector field
# We'll get fancy and vary their width and color
lw = 2 * (mag - mag.min()) / mag.ptp() + 0.2
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cbook import get_sample_data
import example_utils
# Set up our data...
z = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))
ny, nx = z.shape
y, x = np.mgrid[:ny, :nx]
y = (y - y.mean()) * (x + 10)**2
mask = (z > -0.1) & (z < 0.1)
z2 = np.ma.masked_where(mask, z)
fig, axes = example_utils.setup_axes()
# Either pcolor or pcolormesh would produce the same result here.
# pcolormesh is faster, however.
axes[0].pcolor(x, y, z, cmap='gist_earth')
example_utils.label(axes[0], 'either')
# The difference between the two will become clear as we turn on edges:
# pcolor will completely avoid drawing masked cells...
axes[1].pcolor(x, y, z2, cmap='gist_earth', edgecolor='black')
example_utils.label(axes[1], 'pcolor(x,y,z)')
# While pcolormesh will draw them as empty (but still present) cells.
axes[2].pcolormesh(x, y, z2, cmap='gist_earth', edgecolor='black', lw=0.5,
antialiased=True)
