6.1. Matplotlib Examples¶

6.1.1. Examples¶

import matplotlib.pyplot as plt
import numpy as np

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()  # doctest: +SKIP

import matplotlib.pyplot as plt
import numpy as np

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--')
plt.plot(t, t**2, 'bs')
plt.plot(t, t**3, 'g^')

plt.show()  # doctest: +SKIP


6.1.3. Scales¶

Code 6.61. Scales
import numpy as np
import matplotlib.pyplot as plt

# Create some mock data
t = np.arange(0.01, 10.0, 0.01)
data1 = np.exp(t)
data2 = np.sin(2 * np.pi * t)

fig, ax1 = plt.subplots()

color = 'tab:red'
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp', color=color)
ax1.plot(t, data1, color=color)
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

color = 'tab:blue'
ax2.set_ylabel('sin', color=color)  # we already handled the x-label with ax1
ax2.plot(t, data2, color=color)
ax2.tick_params(axis='y', labelcolor=color)

fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.show()


6.1.4. Grid¶

Code 6.62. Grid
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()

# Major ticks every 20, minor ticks every 5
major_ticks = np.arange(0, 101, 20)
minor_ticks = np.arange(0, 101, 5)

ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor=True)
ax.set_yticks(major_ticks)
ax.set_yticks(minor_ticks, minor=True)

# And a corresponding grid
ax.grid(which='both')

# Or if you want different settings for the grids:
ax.grid(which='minor', alpha=0.2)
ax.grid(which='major', alpha=0.5)

plt.show()


6.1.5. Legend using pre-defined labels¶

Code 6.63. Legend using pre-defined labels
import numpy as np
import matplotlib.pyplot as plt

# Make some fake data.
a = b = np.arange(0, 3, .02)
c = np.exp(a)
d = c[::-1]

# Create plots with pre-defined labels.
fig, ax = plt.subplots()
ax.plot(a, c, 'k--', label='Model length')
ax.plot(a, d, 'k:', label='Data length')
ax.plot(a, c + d, 'k', label='Total message length')

legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')

# Put a nicer background color on the legend.
# noinspection SpellCheckingInspection
legend.get_frame().set_facecolor('#00FFCC')

plt.show()


import numpy as np

import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.spines import Spine
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection

"""Create a radar chart with num_vars axes.

This function creates a RadarAxes projection and registers it.

Parameters
----------
num_vars : int
Number of variables for radar chart.
frame : {'circle' | 'polygon'}
Shape of frame surrounding axes.

"""
# calculate evenly-spaced axis angles
theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False)

def draw_poly_patch(self):
# rotate theta such that the first axis is at the top
vertices = unit_poly_vertices(theta + np.pi / 2)
return plt.Polygon(vertices, closed=True, edgecolor='k')

def draw_circle_patch(self):
# unit circle centered on (0.5, 0.5)
return plt.Circle((0.5, 0.5), 0.5)

patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}
if frame not in patch_dict:
raise ValueError('unknown value for frame: %s' % frame)

# use 1 line segment to connect specified points
RESOLUTION = 1
# define draw_frame method
draw_patch = patch_dict[frame]

def __init__(self, *args, **kwargs):
# rotate plot such that the first axis is at the top
self.set_theta_zero_location('N')

def fill(self, *args, **kwargs):
"""Override fill so that line is closed by default"""
closed = kwargs.pop('closed', True)

def plot(self, *args, **kwargs):
"""Override plot so that line is closed by default"""
for line in lines:
self._close_line(line)

def _close_line(self, line):
x, y = line.get_data()
if x[0] != x[-1]:
x = np.concatenate((x, [x[0]]))
y = np.concatenate((y, [y[0]]))
line.set_data(x, y)

def set_vertices_labels(self, labels):
self.set_thetagrids(np.degrees(theta), labels)

def _gen_axes_patch(self):
return self.draw_patch()

def _gen_axes_spines(self):
if frame == 'circle':
return PolarAxes._gen_axes_spines(self)
# The following is a hack to get the spines (i.e. the axes frame)
# to draw correctly for a polygon frame.

# spine_type must be 'left', 'right', 'top', 'bottom', or circle.
spine_type = 'circle'
vertices = unit_poly_vertices(theta + np.pi / 2)
# close off polygon by repeating first vertex
vertices.append(vertices[0])
path = Path(vertices)

spine = Spine(self, spine_type, path)
spine.set_transform(self.transAxes)
return {'polar': spine}

return theta

def unit_poly_vertices(theta):
"""Return vertices of polygon for subplot axes.

This polygon is circumscribed by a unit circle centered at (0.5, 0.5)
"""
x0, y0, r = [0.5] * 3
vertices = [(r * np.cos(t) + x0, r * np.sin(t) + y0) for t in theta]
return vertices

def example_data():
# The following data is from the Denver Aerosol Sources and Health study.
# See  doi:10.1016/j.atmosenv.2008.12.017
#
# The data are pollution source profile estimates for five modeled
# pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical
# species. The radar charts are experimented with here to see if we can
# nicely visualize how the modeled source profiles change across four
# scenarios:
#  1) No gas-phase species present, just seven particulate counts on
#     Sulfate
#     Nitrate
#     Elemental Carbon (EC)
#     Organic Carbon fraction 1 (OC)
#     Organic Carbon fraction 2 (OC2)
#     Organic Carbon fraction 3 (OC3)
#     Pyrolized Organic Carbon (OP)
#  2)Inclusion of gas-phase specie carbon monoxide (CO)
#  3)Inclusion of gas-phase specie ozone (O3).
#  4)Inclusion of both gas-phase species is present...
data = [
['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
('Basecase', [
[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
[0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],
[0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],
[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),
('With CO', [
[0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],
[0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],
[0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],
[0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],
[0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),
('With O3', [
[0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],
[0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],
[0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],
[0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],
[0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),
('CO & O3', [
[0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],
[0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],
[0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],
[0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],
[0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])
]
return data

if __name__ == '__main__':
N = 9

data = example_data()
spoke_labels = data.pop(0)

fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,

colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes
for ax, (title, case_data) in zip(axes.flatten(), data):
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_vertices_labels(spoke_labels)

# add legend relative to top-left plot
ax = axes[0, 0]
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
legend = ax.legend(labels, loc=(0.9, .95),
labelspacing=0.1, fontsize='small')

fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
horizontalalignment='center', color='black', weight='bold',
size='large')

plt.show()