def eventplot():
""" copied from http://matplotlib.org/xkcd/examples/pylab_examples/eventplot_demo.html"""
import numpy as np
from smartplotlib import subplot
np.random.seed(0)
# create random data
data1 = np.random.random([6, 50])
# set different colors for each set of positions
colors1 = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 1, 0],
[1, 0, 1],
[0, 1, 1]])
# set different line properties for each set of positions
# note that some overlap
lineoffsets1 = np.array([-15, -3, 1, 1.5, 6, 10])
linelengths1 = [5, 2, 1, 1, 3, 1.5]
ax1, ax2 = subplot.iteraxes(2,1, title=["horizontal eventplot", "vertical eventplot"],
orientation=["horizontal", "vertical"],
figure="way 1"
)
# clear the figure
ax1.fclear()
ax1.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
linelengths=linelengths1)
ax1.axes()
ax2.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,
linelengths=linelengths1)
ax2.axes()
ax1.go("show", "draw")
######
# another way to do the same
axs = subplot(figure="way 2")
##
# data are both the same, the only things that will change
# is the orienatation and title
axs.eventplot.update(positions=data1, colors=colors1, lineoffsets=lineoffsets1,
linelengths=linelengths1)
for a in axs.iteraxes(2,1,
title=["horizontal eventplot", "vertical eventplot"],
orientation=["horizontal", "vertical"]):
a.go("axes","eventplot")
axs.go("show", "draw")
return axs
def histogram():
import numpy as np
from smartplotlib import dataplot, subplot, alias
data1 = np.random.normal(size=(1000,))
data2 = np.random.normal(scale=0.8, size=(1000,))
dp = dataplot(data1, figure="histograms")
## clear the figure
dp.go("fclear")
# make a copy of dp.histogram
h = dp.histogram.derive(bins=20,
rwidth=0.8,
align="mid")
# set default for errorbar and turn of label so it does not
# a appear twice
h.errorbar.update(color="k", linestyle="none", label=None)
###
# histogram version does not accept several set of data
# but one can still stack by calling back the histogram plot
h1 = h(axes=221,
density=False,
ylabel="Stacked Histogram",
label="some data"
)
h1.go("axes", "bar", "errorbar")
###
# make a second histogram on the same figure
# calling histogram again assure that the same bins are used for
# both, because they are now set in h1 as an array
h1(data2, stacked=True, color="green", label="more data").bar()
h1.legend()
h2 = h(density=True,
ylabel="Fitted Density",
axes=222
)
h2.go("axes", "bar", "errorbar")
##
# fit the distribution an plot
# label=True will generate a label with fit result
fit = dp.distribfit(label=True)
fit.plot(); fit.derive(min=-fit["scale"]+fit["loc"],
max=+fit["scale"]+fit["loc"],
label=None,
npoints=2, color="red"
).plot()
for s in dp.stat.iter(fstat=["-std","+std"]):
s().axvline(color="red", linestyle=":")
h2.legend() # should print fir result
####
# make a second density histogram
#h2.histogram(data2, stacked=True, color="green").bar()
####
# Age pyramid like histogram
h3 = h(
ylabel="+- Histogram",
axes=223
)
h3.go("axes", "bar", "errorbar")
####
# make a opposite histogram, since stacked is false one must
# put the counter to 0
h3(data2, stacked=False, count=0, amplitude=-1, color="green").bar()
####
# Side by side histogram
# since we are going to make to histogram, put the rwidth to 0.4
# and since align="mid" (="center") shift the histograms by -0.2
h4 = h(
ylabel="Side by Side Histogram",
axes=224, rwidth=0.4, align="mid",
roffset=-0.2
)
h4.go("axes", "bar", "errorbar")
####
# make a opposite histogram, since stacked is false one must
# put the counter to 0
h4(data2, stacked=False, color="green").bar()
h4.go("show", "draw")
return h1, h2, h3, h4
a = subplot(figure="matplotlib.hist", go=["fclear","axes"],
sharey=h, sharex=h, axes=211
)
a.hist([data1,data2], bins=bins, color=["blue", "green"],
stacked=True, align=align)
a2 = subplot(figure="matplotlib.hist",
sharey=h2, sharex=h2,
axes=212)
a2.hist([data1,data2], bins=bins, color=["blue", "green"],
stacked=False, align=align)
a.go("show", "draw")
return h
