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phmath.py
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phmath.py
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#------------------------------- phmath library----------------------
from numpy import *
from scipy import *
from scipy.optimize import leastsq
from scipy import fft as sfft
#-------------------------- Least square fitting routines--------------
def find_frequency(x,y): # estimate frequency, x y are numpy arrays
mid = (max(y)+min(y))/2
zc = []
for k in range(1,len(y)):
if (y[k-1] < mid) and (y[k]>mid): # crossing middle level
zc.append(x[k])
if len(zc) < 2:
return 1.0/(x[-1]-x[0])
sum = 0.0
np = len(zc)
for k in range(1,np):
sum = sum + (zc[k] - zc[k-1])
per = sum / (np-1)
return 1.0/per
#--Damped sinusoid -------
def dsine_erf(p,y,x):
return y - p[0] * sin(2*pi*p[1]*x+p[2]) * exp(-p[4]*x) + p[3]
def dsine_eval(x,p):
return p[0] * sin(2*pi*p[1]*x+p[2]) * exp(-p[4]*x) - p[3]
def fit_dsine(data):
size = len(data)
x = zeros(size, float)
y = zeros(size, float)
for k in range(size):
x[k] = data[k][0]
y[k] = data[k][1]
amp = (max(y)-min(y))/2
freq = find_frequency(x,y)
par = [amp, freq, 0.0, 0.0, 1.0] # Amp, freq, phase , offset, damping
plsq = leastsq(dsine_erf, par,args=(y,x))
# print 'Fit result = ', plsq
y2 = dsine_eval(x, plsq[0])
ep = [ plsq[0][0], -plsq[0][4], plsq[0][3] ]
y3 = exp_eval(x,ep)
for k in range(size):
data[k].append(y2[k])
data[k].append(y3[k])
return data,plsq[0]
#--------------------------------------------------------------------
def sine_erf(p,y,x):
return y - p[0] * sin(2*pi*p[1]*x+p[2])+p[3]
def sine_eval(x,p):
return p[0] * sin(2*pi*p[1]*x+p[2])-p[3]
def fit_sine(data):
size = len(data)
x = zeros(size, float)
y = zeros(size, float)
for k in range(size):
x[k] = data[k][0]
y[k] = data[k][1]
amp = (max(y)-min(y))/2
freq = find_frequency(x,y)
par = [amp, freq, 0.0, 0.0] # Amp, freq, phase , offset
plsq = leastsq(sine_erf, par,args=(y,x))
y2 = sine_eval(x, plsq[0])
for k in range(size):
data[k].append(y2[k])
return data,plsq[0]
#-----------------Exponential decay fit------------------------------------
def exp_erf(p,y,x):
return y - p[0] * exp(p[1]*x) + p[2]
def exp_eval(x,p):
return p[0] * exp(p[1]*x) -p[2]
def fit_exp(data):
size = len(data)
x = zeros(size, float)
y = zeros(size, float)
for k in range(size):
x[k] = data[k][0]
y[k] = data[k][1]
maxy = max(y)
halfmaxy = maxy / 2.0
halftime = 1.0
for k in range(size):
if abs(y[k] - halfmaxy) < halfmaxy/100:
halftime = x[k]
break
par = [maxy, -halftime,0] # Amp, decay, offset
plsq = leastsq(exp_erf, par,args=(y,x))
y2 = exp_eval(x, plsq[0])
for k in range(size):
data[k].append(y2[k])
return data,plsq[0]
#--------------------------------------------------------------------
def gauss_erf(p,y,x):#height, mean, sigma
return y - p[0] * exp(-(x-p[1])**2 /(2.0 * p[2]**2))
def gauss_eval(x,p):
return p[0] * exp(-(x-p[1])**2 /(2.0 * p[2]**2))
def fit_gauss(data):
size = len(data)
x = zeros(size, float)
y = zeros(size, float)
for k in range(size):
x[k] = data[k][0]
y[k] = data[k][1]
maxy = max(y)
halfmaxy = maxy / 2.0
for k in range(size):
if abs(y[k] - maxy) < maxy/100:
mean = x[k]
break
for k in range(size):
if abs(y[k] - halfmaxy) < halfmaxy/10:
halfmaxima = x[k]
break
sigma = mean - halfmaxima
par = [maxy, halfmaxima, sigma] # Amplitude, mean, sigma
plsq = leastsq(gauss_erf, par,args=(y,x))
y2 = gauss_eval(x, plsq[0])
for k in range(size):
data[k].append(y2[k])
return data,plsq[0]
#-------------Fast Fourier Transform Routines ---by Kishore A.------
def fft(data):
"""
Returns the Fourier transform of the signal represented by the samples
in 'data' which is obtained by a read_block() call.
Usage example:
x = p.read_block(200,10,1)
y = p.fft(x)
"""
np = len(data)
delay = data[1][0] - data[0][0] # in microseconds
v = []
for i in range(np):
v.append(data[i][1])
ft = sfft(v)
#corrections in the frequency axis
fmax = 1/(delay*1.0e-6)
incf = fmax/np
freq = []
for i in range(np/2):
freq.append(i*incf - fmax/2)
for i in range(np/2,np):
freq.append((i-np/2)*incf)
ft_freq = []
for i in range(np/2):
x = [freq[i], abs(ft[i+np/2])/np]
ft_freq.append(x)
for i in range(np/2,np):
x = [freq[i], abs(ft[i-np/2])/np]
ft_freq.append(x)
return ft_freq
def plot_fft(data):
"""
Plots the Fourier transform of the signal represented by the samples in
'data'. Calls self.fft() for obtaining the Fourier Transform
Usage example:
x = p.read_block(200,10,1)
p.plot_fft(x)
"""
ft_freq = self.fft(data)
np = len(data)
delay = data[1][0] - data[0][0]
fmax = 1/(delay*1.0e-6)
y = 0.0
for i in range(np):
if data[i][1] > y:
y = data[i][1]
if self.root == None:
self.window(400,300,None)
self.remove_lines()
self.set_scale(-fmax/2, 0, fmax/2, y*1.1)
self.line(ft_freq)
def freq_comp(data):
"""
Displays the frequency components with the greatest
spectral density. Calls self.fft() for obtaining the Fourier transform
Usage example:
x = p.read_block(200,10,1)
p.freq_comp(x) # Only prints the components
"""
ft_freq = self.fft(data)
np = len(data)
delay = data[1][0] - data[0][0]
peaks = []
for n in range(1,np-1):
a = ft_freq[n-1][1]
b = ft_freq[n][1]
c = ft_freq[n+1][1]
if (b>50) & (b>a) & (b>c):
peaks.append([ft_freq[n][1],ft_freq[n][0]])
peaks.sort()
peaks.reverse()
print 'Dominant frequency components are:'
for i in range(len(peaks)):
print '%6.3f Hz, %5.3f mV'%(peaks[i][1],peaks[i][0])