def cohere():
"""
Compute the coherence of two signals
mpl_examples/pylab_examples/cohere_demo.py
"""
import numpy as np
from smartplotlib import xyplot, subplot, alias
# make a little extra space between the subplots
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t)) # white noise 1
nse2 = np.random.randn(len(t)) # white noise 2
r = np.exp(-t/0.05)
cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
# two signals with a coherent part and a random part
s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
ps1 = xyplot(t, s1, fmt='b-', y2= s2, fmt2="g-",
axes=211, xlabel="time", ylabel="s1 & s2")
ps1.go("fclear", "axes", "plot")
c = xyplot.cohere(s1, s2, 256, 1./dt,
ylabel="coherence", axes=212)
c.go("axes", "plot", "draw", "show")
return c
def annotates():
import numpy as np
from smartplotlib import xyplot, cycle
n=10
x = np.arange(n)
y = x*x
dy = ([10,-15]*(int(n/2)+1))[:n]
print (dy)
xy = xyplot(x, y, marker="*", dx=0.1, dy=dy,
arrowprops=dict(width=2), axes=211,
ylim=(-20,100), xlim=(-1,10)
)
xy.go("fclear", "axes", "plot", "annotates")
# or an other fancy way to cycle is to use the iter method
xy.plot(axes=212)
xy.axes(axes=212)
ans = [a() for a in xyplot.annotates.iter(len(x), x=x, y=y,
texts=list("abcdefghijklmnopqrstuvwxyz"),
dx=0.0,
dy=[10,-10], axes=212)
]
xy.go("show", "draw")
return xy
def psd():
import numpy as np
from smartplotlib import xyplot, alias
Fs = 150.0; # sampling rate
Ts = 1.0/Fs; # sampling interval
t = np.arange(0,1,Ts) # time vector
noises = np.zeros( (len(t),), dtype=float )
for a,f in zip([1e-2,2e-2,0.5e-2,1e-3],[20,40,60,80]):
noises += np.sin(2*np.pi*f*t)*a
ff = 5; # frequency of the signal
y = np.sin(2*np.pi*ff*t)+noises
xy = xyplot(t, y, xlabel="time", ylabel="Signal",
axes=211, hspace=0.4)
xy.go("fclear", "fset", "axes", "plot")
psd = xy.ydata.psd(axes=212, Fs=Fs)
psd.axvline([5, 20,40,60,80], color="red", linestyle="--")
###
# psd is plotted in 10*log10(psd) ('dB/Hz' or dB)
# one may want to plot the linear scale
# that easy :
# psd.plot(y=alias("psd"))
psd.go("axes", "plot")
xy.go("show", "draw")
return psd
def xbinedstat():
import numpy as np
from smartplotlib import xyplot
N=10000
x = np.linspace(0, 2, N)
yerr = np.random.rand(N)*np.exp(x)*np.sign(np.random.rand(N)-0.5)
y = 5+yerr
xy = xyplot(y, x, fmt="k+")
xy.go("fclear", "axes", "plot")
stat = xy.xdata.binedstat.derive(linestyle="-", direction="x")
## itertcall iter on iterable and call the instances
ps = [s.plot() for s in stat.itercall(fstat=["-std", "+std", "mean", "median"],
color=list("rrgb"))]
xy.go("show", "draw")
return ps
import numpy as np
from smartplotlib import xyplot
N=10000
x = np.linspace(0, 2, N)
yerr = np.random.rand(N)*np.exp(x)*np.sign(np.random.rand(N)-0.5)
y = 5+yerr
xy = xyplot(y, x, marker="+", color="k")
xy.go("fclear", "axes", "plot")
## itertcall iter on iterable and call the instances
s, m = xy.xbinedstat.itercall(fstat=["std","mean"],color=["red", "blue"])
s.plot(linestyle="-")
m.errorbar(linestyle="-", marker="None")
xy.go("show", "draw")
return xy
def histogram2d():
from matplotlib.colors import LogNorm
from smartplotlib import xyplot
import numpy as np
from pylab import randn
#normal distribution center at x=0 and y=5
x = randn(100000)
y = randn(100000)+5
xy = xyplot(x, y)
xy.fclear()
h = xy.histogram2d( bins=40, norm=LogNorm())
pc = h.pcolorfast()
h.colorbar(pc)
h.contour(colors="k", contours=[10,300,700,900])
h.go("show", "draw")
return h
def binedstat():
import numpy as np
from smartplotlib import xyplot
N=10000
x = np.linspace(0, 2, N)
yerr = np.random.rand(N)*np.exp(x)*np.sign(np.random.rand(N)-0.5)
y = 5+yerr
xy = xyplot(x, y, fmt="k+", axes=211)
xy.go("fclear", "axes", "plot")
stat = xy.ydata.binedstat.derive(linestyle="-")
sp = stat(fstat="+std", color="red")
sp.step()
## call sp.binedstat to enable the fill_between
## count = 0 is necessary here to have both lines sharing the same x
## otherwhis they will be shifted (for histogram purpose)
sm = sp.binedstat(fstat="-std", color="red", count=0)
sm.step()
## can fill between the last two calls
sm.fill_between(alpha=0.3)
m = stat(fstat="mean", color="b")
m.plot()
m.errorbar(linestyle="-", marker="None")
std = stat(axes=212, fstat="std", ylabel="Std",
linestyle="solid", label="bined std")
std.go("axes","fill")
##
# fit a polynome on the newly created standar deviation
# histogram. label=True mean that result of the fit is plotted
# as label
std.polyfit(dim=2, label=True).plot(fmt="r-")
std.legend(loc="upper left")
xy.go("show", "draw")
return stat
def spectrum():
""" example of magnitude_spectrum, angle_spectrum and phase_spectrum
with smartplotlib
"""
import numpy as np
from smartplotlib import xyplot, subplot
###
# make some data
Fs = 150.0; # sampling rate
Ts = 1.0/Fs; # sampling interval
t = np.arange(0,1,Ts) # time vector
ff = 5; # frequency of the signal
y = np.sin(2*np.pi*ff*t)
###
# make an new instance of xyplot with the data
xy = xyplot(t, y, xlabel="time", ylabel="Signal", axes=311)
###
###
# plot the original data
xy.go("fclear", "axes", "plot")
###
# xy.ydata is a dataplot from whish data is aliased to "y"
# a dataplot contain a collection of plot that can be obtain
# from a 1d data
m = xy.ydata.magnitude_spectrum(axes=312,
#
)
a = xy.ydata.angle_spectrum(axes=313,
sharex=311 # note the sharex
)
p = xy.ydata.phase_spectrum(axes=313, color="red")
a.go("axes", "plot")
p.plot() # p is on same axes than a no need to make axes
m.go("axes", "plot", "draw", "show")
return m
def specgram():
""" smartplotlib example of specgram """
from pylab import np, pi, sin, arange, logical_and, where, randn, cm
from smartplotlib import xyplot, subplot
dt = 0.0005
t = arange(0.0, 20.0, dt)
s1 = sin(2*pi*100*t)
s2 = 2*sin(2*pi*400*t)
# create a transient "chirp"
mask = where(logical_and(t>10, t<12), 1.0, 0.0)
s2 = s2 * mask
# add some noise into the mix
nse = 0.01*randn(len(t))
x = s1 + s2 + nse # the signal
NFFT = 1024 # the length of the windowing segments
Fs = int(1.0/dt) # the sampling frequency
# Pxx is the segments x freqs array of instantaneous power, freqs is
# the frequency vector, bins are the centers of the time bins in which
# the power is computed, and im is the matplotlib.image.AxesImage
# instance
xy = xyplot(t, x, axes=211, xlabel="t", ylabel="x")
###
# xy.ydata is a dataplot from whish data is aliased to "y"
# a dataplot contain a collection of plot that can be obtain
# from a 1d data
spec = xy.ydata.specgram(NFFT=NFFT, Fs=Fs, noverlap=900,
cmap=cm.gist_heat, axes=212)
xy.fclear()
xy.axes()
xy.plot()
# above is same as xy.go("fclear", "axes", "plot")
spec.go("axes", "imshow", "show", "axes", "draw")
return spec
def polyfit():
from smartplotlib import xyplot
import numpy as np
x = np.arange(100);
yerr = np.random.rand(100)*2.0*x
y = x*x + yerr
xyplot.fclear()
# make a new xyplot with my data
xy = xyplot(x,y, yerr=yerr, xlabel="t [s]")
# plot the data with errobar
xy.errorbar(linestyle="None", label="data")
# fit a polynome and plot it
# note that color and linestyle can also be in .plot
xy.polyfit(dim=2, color="red", linestyle="--", label=True).plot()
# fit a tangent to a xrange
# by default plot only for the given range
# so we need to make it a bit longer with xmin and xmax
# also xmin and xmax can be True, that mean, min and max of data
# not range
plf = xy.polyfit(dim=1, xrange=(55,65), color="green",
xmin=35, xmax=90,
linestyle="--", label="tangent @ 55<x<65").plot()
##
# other method can be done with the go method
# here. axes plot, set the axes labels etc ..., legend plot
# the legend, than show and draw are figure.show() and figure.canvas.draw()
xy.go("axes", "legend", "show", "draw")
return plf