experimentNumpy.py

__author__ = 'vikram'
import sys
import os
from os import listdir
from os.path import isfile, join
import numpy as np

import matplotlib.pyplot as plt
import matplotlib
import matplotlib.lines as mlines

plt.close("all")

t = np.arange(0, 10, 0.01)

ax1 = plt.subplot(211)

ax1.plot(t, np.sin(2*np.pi*t))

ax2 = plt.subplot(212, sharex=ax1)

ax2.plot(t, np.sin(4*np.pi*t))

#plt.show()


# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000]/0.05)               # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)]*dt  # colored noise

# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Gaussian colored noise')

# this is an inset axes over the main axes
a = plt.axes([.65, .6, .2, .2], axisbg='y')
n, bins, patches = plt.hist(s, 400, normed=1)
plt.title('Probability')
plt.xticks([])
plt.yticks([])

# this is another inset axes over the main axes
a = plt.axes([0.2, 0.6, .2, .2], axisbg='y')
plt.plot(t[:len(r)], r)
plt.title('Impulse response')
plt.xlim(0, 0.2)
plt.xticks([])
plt.yticks([])

plt.show()