"""
plot using a variety of cm vs inches conversions. The example shows
how default unit instrospection works (ax1), how various keywords can
be used to set the x and y units to override the defaults (ax2, ax3,
ax4) and how one can set the xlimits using scalars (ax3, current units
assumed) or units (conversions applied to get the numbers to current
units)
"""
from basic_units import cm, inch
from pylab import figure, show, nx
cms = cm *nx.arange(0, 10, 2)
bottom=0*cm
width=0.8*cm
fig = figure()
ax1 = fig.add_subplot(2,2,1)
ax1.bar(cms, cms, bottom=bottom)
ax2 = fig.add_subplot(2,2,2)
ax2.bar(cms, cms, bottom=bottom, width=width, xunits=cm, yunits=inch)
ax3 = fig.add_subplot(2,2,3)
ax3.bar(cms, cms, bottom=bottom, width=width, xunits=inch, yunits=cm)
ax3.set_xlim(3, 6) # scalars are interpreted in current units
ax4 = fig.add_subplot(2,2,4)
ax4.bar(cms, cms, bottom=bottom, width=width, xunits=inch, yunits=inch)
#fig.savefig('simple_conversion_plot.png')
"""
plot using a variety of cm vs inches conversions. The example shows
how default unit instrospection works (ax1), how various keywords can
be used to set the x and y units to override the defaults (ax2, ax3,
ax4) and how one can set the xlimits using scalars (ax3, current units
assumed) or units (conversions applied to get the numbers to current
units)
"""
from basic_units import cm, inch
from pylab import figure, show, nx
cms = cm *nx.arange(0, 10, 2)
fig = figure()
ax1 = fig.add_subplot(2,2,1)
ax1.plot(cms, cms)
ax2 = fig.add_subplot(2,2,2)
ax2.plot(cms, cms, xunits=cm, yunits=inch)
ax3 = fig.add_subplot(2,2,3)
ax3.plot(cms, cms, xunits=inch, yunits=cm)
ax3.set_xlim(3, 6) # scalars are interpreted in current units
ax4 = fig.add_subplot(2,2,4)
ax4.plot(cms, cms, xunits=inch, yunits=inch)
#fig.savefig('simple_conversion_plot.png')
ax4.set_xlim(3*cm, 6*cm) # cm are converted to inches
from pylab import figure, nx, show
# you can specify the xypoint and the xytext in different
# positions and coordinate systems, and optionally turn on a
# connecting line and mark the point with a marker. Annotations
# work on polar axes too. In the example below, the xy point is
# in native coordinates (xycoords defaults to 'data'). For a
# polar axes, this is in (theta, radius) space. The text in this
# example is placed in the fractional figure coordinate system.
# Text keyword args like horizontal and vertical alignment are
# respected
fig = figure()
ax = fig.add_subplot(111, polar=True)
r = nx.arange(0,1,0.001)
theta = 2*2*nx.pi*r
line, = ax.plot(theta, r, color='#ee8d18', lw=3)
ind = 800
thisr, thistheta = r[ind], theta[ind]
ax.plot([thistheta], [thisr], 'o')
ax.annotate('a polar annotation',
xy=(thistheta, thisr), # theta, radius
xytext=(0.05, 0.05), # fraction, fraction
textcoords='figure fraction',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='left',
verticalalignment='bottom',
)
fig.savefig('../figures/annotation_demo.png')
fig.savefig('../figures/annotation_demo.ps')
show()
from pylab import figure, show, nx
Ntests = 3
t = nx.arange(0.0, 1.0, 0.05)
s = nx.sin(2*nx.pi*t)
fig = figure()
ax = fig.add_subplot(Ntests, 1, 1)
N = 100
x, y = 0.9*nx.mlab.rand(2,N)
area = nx.pi*(10 * nx.mlab.rand(N))**2 # 0 to 10 point radiuses
ax.scatter(x,y,s=area, marker='^', c='r', label='scatter')
ax.legend()
ax = fig.add_subplot(Ntests, 1, 2)
ax.vlines(t, [0], nx.sin(2*nx.pi*t), label='vlines')
ax.legend()
ax = fig.add_subplot(Ntests, 1, 3)
ax.plot(t, s, 'b-', lw=2, label='a line')
ax.legend()
fig.savefig('legend_unit')
show()
Some examples of how to annotate various artists.
See matplotlib.text.Annotation for details
"""
from pylab import figure, show, nx
from matplotlib.patches import Rectangle, CirclePolygon, Ellipse
from matplotlib.text import Annotation
fig = figure()
ax = fig.add_subplot(111, autoscale_on=False, xlim=(-1, 5), ylim=(-1, 5))
rect = Rectangle((0.5, 0.5), 1, 3, alpha=0.3)
ax.add_patch(rect)
t = nx.arange(0.0, 5.0, 0.01)
s = nx.sin(2 * nx.pi * t)
line, = ax.plot(t, s, lw=3, color="purple")
a = Annotation(rect, "A: rect", loc=("outside right", "outside top"), color="blue")
ax.add_artist(a)
b = Annotation(rect, "B: rect", loc=("inside left", "inside top"), autopad=8, color="blue")
ax.add_artist(b)
c = Annotation(rect, "C: rect", loc=("center", "center"), color="blue")
ax.add_artist(c)
d = Annotation(ax, "D: axes", loc=("inside right", "inside bottom"), color="red")
ax.add_artist(d)
#coding: UTF-8 from pylab import figure, nx, show fig = figure() ax = fig.add_subplot(111) t = nx.arange(0.0,3.01,0.01) s = nx.sin(2*nx.pi*t) c = nx.sin(4*nx.pi*t) ax.fill(t, s, 'b', t, c, 'g', alpha=0.2) show()
#!/usr/bin/env python
from basic_units import cm, inch
from pylab import figure, show, nx
N = 5
menMeans = (150 * cm, 160 * cm, 146 * cm, 172 * cm, 155 * cm)
menStd = (20 * cm, 30 * cm, 32 * cm, 10 * cm, 20 * cm)
fig = figure()
ax = fig.add_subplot(111)
ind = nx.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
p1 = ax.bar(ind, menMeans, width, color='r', bottom=0 * cm, yerr=menStd)
womenMeans = (145 * cm, 149 * cm, 172 * cm, 165 * cm, 200 * cm)
womenStd = (30 * cm, 25 * cm, 20 * cm, 31 * cm, 22 * cm)
p2 = ax.bar(ind + width,
womenMeans,
width,
color='y',
bottom=0 * cm,
yerr=womenStd)
ax.set_title('Scores by group and gender')
ax.set_xticks(ind + width)
ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
ax.legend((p1[0], p2[0]), ('Men', 'Women'))
ax.yaxis.set_units(inch)
ax.autoscale_view()
from basic_units import radians, degrees, cos
from pylab import figure, show, nx
x = nx.arange(0, 15, 0.01) * radians
fig = figure()
fig.subplots_adjust(hspace=0.3)
ax = fig.add_subplot(211)
ax.plot(x, cos(x), xunits=radians)
ax = fig.add_subplot(212)
ax.plot(x, cos(x), xunits=degrees)
fig.savefig('../figures/radian_demo.png')
fig.savefig('../figures/radian_demo.ps')
show()
from pylab import figure, nx, show # unit area ellipse rx, ry = 3., 1. area = rx * ry * nx.pi theta = nx.arange(0, 2 * nx.pi + 0.01, 0.1) verts = zip(rx / area * nx.cos(theta), ry / area * nx.sin(theta)) x, y, s, c = nx.mlab.rand(4, 30) s *= 10**2. fig = figure() ax = fig.add_subplot(111) ax.scatter(x, y, s, c, marker=None, verts=verts) show()
from matplotlib.widgets import MultiCursor from pylab import figure, show, nx t = nx.arange(0.0, 2.0, 0.01) s1 = nx.sin(2*nx.pi*t) s2 = nx.sin(4*nx.pi*t) fig = figure() ax1 = fig.add_subplot(211) ax1.plot(t, s1) ax2 = fig.add_subplot(212, sharex=ax1) ax2.plot(t, s2) multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1) show()
from matplotlib.widgets import MultiCursor from pylab import figure, show, nx t = nx.arange(0.0, 2.0, 0.01) s1 = nx.sin(2 * nx.pi * t) s2 = nx.sin(4 * nx.pi * t) fig = figure() ax1 = fig.add_subplot(211) ax1.plot(t, s1) ax2 = fig.add_subplot(212, sharex=ax1) ax2.plot(t, s2) multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1) show()
from __future__ import print_function
from pylab import figure, show, nx
Ntests = 3
t = nx.arange(0.0, 1.0, 0.05)
s = nx.sin(2 * nx.pi * t)
# scatter creates a RegPolyCollection
fig = figure()
ax = fig.add_subplot(Ntests, 1, 1)
N = 100
x, y = 0.9 * nx.mlab.rand(2, N)
area = nx.pi * (10 * nx.mlab.rand(N))**2 # 0 to 10 point radiuses
ax.scatter(x, y, s=area, marker='^', c='r', label='scatter')
ax.legend()
# vlines creates a LineCollection
ax = fig.add_subplot(Ntests, 1, 2)
ax.vlines(t, [0], nx.sin(2 * nx.pi * t), label='vlines')
ax.legend()
# vlines creates a LineCollection
ax = fig.add_subplot(Ntests, 1, 3)
ax.plot(t, s, 'b-', lw=2, label='a line')
ax.legend()
fig.savefig('legend_unit')
show()
#!/usr/bin/env python
from basic_units import cm, inch
from pylab import figure, show,nx
N = 5
menMeans = (150*cm, 160*cm, 146*cm, 172*cm, 155*cm)
menStd = ( 20*cm, 30*cm, 32*cm, 10*cm, 20*cm)
fig = figure()
ax = fig.add_subplot(111)
ind = nx.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
p1 = ax.bar(ind, menMeans, width, color='r', bottom=0*cm, yerr=menStd)
womenMeans = (145*cm, 149*cm, 172*cm, 165*cm, 200*cm)
womenStd = (30*cm, 25*cm, 20*cm, 31*cm, 22*cm)
p2 = ax.bar(ind+width, womenMeans, width, color='y', bottom=0*cm, yerr=womenStd)
ax.set_title('Scores by group and gender')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )
ax.legend( (p1[0], p2[0]), ('Men', 'Women') )
ax.yaxis.set_units(inch)
ax.autoscale_view()
#savefig('barchart_demo')
show()
