* auto-labeling the percentage
* offsetting a slice with "explode"
* drop-shadow
* custom start angle
Note about the custom start angle:
The default ``startangle`` is 0, which would start the "Frogs" slice on the
positive x-axis. This example sets ``startangle = 90`` such that everything is
rotated counter-clockwise by 90 degrees, and the frog slice starts on the
positive y-axis.
"""
import matplotlib.pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(sizes,
explode=explode,
labels=labels,
autopct='%1.1f%%',
shadow=True,
startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
import save_fig as sf
sf.save_to_file('pie')
* Varying the color along a streamline.
* Varying the density of streamlines.
* Varying the line width along a stream line.
"""
import numpy as np
import matplotlib.pyplot as plt
X, Y = (np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))
U, V = np.mgrid[-3:3:100j, 0:0:100j]
seed_points = np.array([[-2, 0, 1], [-2, 0, 1]])
fig0, ax0 = plt.subplots()
strm = ax0.streamplot(X,
Y,
U,
V,
color=U,
linewidth=2,
cmap=plt.cm.autumn,
start_points=seed_points.T)
fig0.colorbar(strm.lines)
ax0.plot(seed_points[0], seed_points[1], 'bo')
ax0.axis((-3, 3, -3, 3))
import save_fig as sf
sf.save_to_file('streamplot')
import matplotlib.pyplot as plt
import numpy as np
def f(t):
'A damped exponential'
s1 = np.cos(2 * np.pi * t)
e1 = np.exp(-t)
return s1 * e1
t1 = np.arange(0.0, 5.0, .2)
l = plt.plot(t1, f(t1), 'ro')
plt.setp(l)
plt.setp(l, markersize=30)
plt.setp(l, markerfacecolor='C2')
import save_fig as sf
sf.save_to_file('arctest')
plot_scatter(axes[0], prng)
plot_image_and_patch(axes[1], prng)
plot_bar_graphs(axes[2], prng)
plot_colored_circles(axes[3], prng)
plot_colored_sinusoidal_lines(axes[4])
plot_histograms(axes[5], prng)
fig.tight_layout()
return fig
if __name__ == "__main__":
# Setup a list of all available styles, in alphabetical order but
# the `default` and `classic` ones, which will be forced resp. in
# first and second position.
style_list = ['default', 'classic'] + sorted(
style for style in plt.style.available if style != 'classic')
print(style_list)
# Plot a demonstration figure for every available style sheet.
for style_label in style_list:
with plt.style.context(style_label):
fig = plot_figure(style_label=style_label)
import save_fig as sf
sf.save_to_file('style-sheets')
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
def to_percent(y, position):
# Ignore the passed in position. This has the effect of scaling the default
# tick locations.
s = str(100 * y)
# The percent symbol needs escaping in latex
if matplotlib.rcParams['text.usetex'] is True:
return s + r'$\%$'
else:
return s + '%'
x = randn(5000)
# Make a normed histogram. It'll be multiplied by 100 later.
plt.hist(x, bins=50, normed=True)
# Create the formatter using the function to_percent. This multiplies all the
# default labels by 100, making them all percentages
formatter = FuncFormatter(to_percent)
# Set the formatter
plt.gca().yaxis.set_major_formatter(formatter)
import save_fig as sf
sf.save_to_file('histgram.svg')
ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
ax.legend()
def autolabel(rects, xpos='center'):
"""
Attach a text label above each bar in *rects*, displaying its height.
*xpos* indicates which side to place the text w.r.t. the center of
the bar. It can be one of the following {'center', 'right', 'left'}.
"""
xpos = xpos.lower() # normalize the case of the parameter
ha = {'center': 'center', 'right': 'left', 'left': 'right'}
offset = {'center': 0.5, 'right': 0.57, 'left': 0.43} # x_txt = x + w*off
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() * offset[xpos],
1.01 * height,
'{}'.format(height),
ha=ha[xpos],
va='bottom')
autolabel(rects1, "left")
autolabel(rects2, "right")
import save_fig as sf
sf.save_to_file('barchart')
url = 'https://fred.stlouisfed.org/graph/fredgraph.csv?id=VIXCLS'
vix = pd.read_csv(url, index_col=0, parse_dates=True, na_values='.', infer_datetime_format=True, squeeze=True).dropna()
# `ma` is a 90-day moving average of the VIX Index, a measure of market expectations of near-term stock volatility.
# `state` is a binning of the moving average into different regime states.
ma = vix.rolling('90d').mean()
state = pd.cut(ma, bins=[-np.inf, 14, 18, 24, np.inf], labels=range(4))
# `cmap` is a ColorMap - a matplotlib object that is essentially a mapping of floats to RGBA colors. Any colormap can be reversed by appending '_r'
cmap = plt.get_cmap('RdYlGn_r')
ma.plot(color='black', linewidth=1.5, marker='', figsize=(8, 4), label='VIX 90d MA')
ax = plt.gca()
ax.set_xlabel('')
ax.set_ylabel('90d moving average: CBOE VIX')
ax.set_title('Volatility Regime State')
ax.grid(False)
ax.legend(loc='upper center')
ax.set_xlim(left=ma.index[0], right=ma.index[-1])
# Creates color-filled blocks that correspond to each bin of state.cmap([...])
# Get us an RGBA sequence for the colors at the 20th, 40th, 60th, and 80th ‘percentile’ along the ColorMaps’ spectrum.
trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)
for i, color in enumerate(cmap([0.2, 0.4, 0.6, 0.8])):
ax.fill_between(ma.index, 0, 1, where=state==i, facecolor=color, transform=trans)
# horizontal line
ax.axhline(vix.mean(), linestyle='dashed', color='xkcd:dark grey', alpha=0.6, label='Full-period mean', marker='')
# plt.show()
sf.save_to_file('pandas')
import matplotlib.pyplot as plt
np.random.seed(0)
n_bins = 10
x = np.random.randn(1000, 3)
fig, axes = plt.subplots(nrows=2, ncols=2)
ax0, ax1, ax2, ax3 = axes.flatten()
colors = ['red', 'tan', 'lime']
ax0.hist(x, n_bins, normed=1, histtype='bar', color=colors, label=colors)
ax0.legend(prop={'size': 10})
ax0.set_title('bars with legend')
ax1.hist(x, n_bins, normed=1, histtype='bar', stacked=True)
ax1.set_title('stacked bar')
ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
ax2.set_title('stack step (unfilled)')
# Make a multiple-histogram of data-sets with different length.
x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
ax3.hist(x_multi, n_bins, histtype='bar')
ax3.set_title('different sample sizes')
fig.tight_layout()
import save_fig as sf
sf.save_to_file('multihist')
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 2 * np.pi, 0.02)
y = np.sin(x)
y1 = np.sin(2 * x)
y2 = np.sin(3 * x)
ym1 = np.ma.masked_where(y1 > 0.5, y1)
ym2 = np.ma.masked_where(y2 < -0.5, y2)
lines = plt.plot(x, y, x, ym1, x, ym2, 'o')
plt.setp(lines[0], linewidth=4)
plt.setp(lines[1], linewidth=2)
plt.setp(lines[2], markersize=10)
plt.legend(('No mask', 'Masked if > 0.5', 'Masked if < -0.5'),
loc='upper right')
plt.title('Masked line demo')
import save_fig as sf
sf.save_to_file('masked')
import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
# The x location for the groups
ind = np.arange(N)
# The width of the bars: can also be len(x) sequence
width = 0.35
p1 = plt.bar(ind, menMeans, width, yerr=menStd)
p2 = plt.bar(ind, womenMeans, width, bottom=menMeans, yerr=womenStd)
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))
import save_fig as sf
sf.save_to_file('stacked-bar')
[0, -1],
slice(100, 200, 3),
0.1,
0.3,
1.5,
(0.0, 0.1),
(0.45, 0.1)
]
# define the figure size and grid layout properties
figsize = (10, 8)
cols = 3
gs = gridspec.GridSpec(len(cases) // cols + 1, cols)
gs.update(hspace=0.4)
# define the data for cartesian plots
delta = 0.11
x = np.linspace(0, 10 - 2 * delta, 200) + delta
y = np.sin(x) + 1.0 + delta
fig1 = plt.figure(num=1, figsize=figsize)
ax = []
for i, case in enumerate(cases):
row = (i // cols)
col = i % cols
ax.append(fig1.add_subplot(gs[row, col]))
ax[-1].set_title('markevery=%s' % str(case))
ax[-1].plot(x, y, 'o', ls='-', ms=4, markevery=case)
import save_fig as sf
sf.save_to_file('markevery')