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#!/usr/bin/env python
#######################################
import sys
import os
import os.path
import time
#import re
import logging
# ------------------------ logging levels ------------------------------------
logging.basicConfig(level= logging.DEBUG,
format='%(module)s.%(funcName)s: %(levelname)-7s: %(message)s')
config_log = logging.INFO
# ----------------------------------------------------------------------------
#from optparse import OptionParser
from math import factorial
import numpy
from scipy import interpolate #optimize, signal
#scipy.signal.savgol_filter(x, window_length, polyorder, deriv=0, delta=1.0, axis=-1, mode='interp', cval=0.0)[source]
#from matplotlib import pylab
# ----------------------- updated funcs --------------------
def poly_fit(spectrum, user_params):
start_ix, end_ix = spectrum[0].searchsorted([user_params['from'],
user_params['to']])
if end_ix <= start_ix:
print (start_ix, end_ix)
sys.tracebacklimit = -1
raise Exception('Invalid energy bounds for fit')
fit_coeffs = numpy.polyfit(spectrum[0][start_ix:end_ix],
spectrum[1][start_ix:end_ix],
user_params['deg'])
return numpy.array([spectrum[0], numpy.polyval(fit_coeffs, spectrum[0])]), fit_coeffs
def centered_polynomial(x, *args):
# tupo
res = []
for i in range(len(x)):
res.append(numpy.dot(numpy.array([x[i]**(n+1) for n in range(len(args))]), args))
return numpy.array(res)
def poly_calc(x, *args):
#return pylab.polyval(args, x)
return numpy.polyval(args, x)
def equalstep_interp(x, y, step):
'''
ddk=numpy.diff(x)
#print 'init steps:' + str(ddk) # debug
if not(numpy.allclose(ddk[0],ddk)):
'''
if 1==1:
logging.debug(' x[0]= '+ str(x[0]) +', x[-1]= '+ str(x[-1]) +'; '+
str(len(x)) + ' points')
lin_y = interpolate.interp1d(x, y, 'linear', bounds_error=False, fill_value=0.)
x = numpy.arange(x[0], x[-1] + step, step)
y = lin_y(x)
logging.debug('interp_x[0]= '+ str(x[0]) +', interp_x[-1]= '+ str(x[-1]) +'; '+
str(len(x)) + ' points')
return x, y
# -------------------- funcs on stand by -------------------
def derivative_3points(curve):
f_diffs = curve[1,2:] - curve[1, :-2]
f_diffs = numpy.concatenate(([curve[1,1] - curve[1,0]],
f_diffs,
[curve[1,-1] - curve[1,-2]]))
x_diffs = curve[0,2:] - curve[0,:-2]
x_diffs = numpy.concatenate( ([curve[0,1] - curve[0,0]],
x_diffs,
[curve[0,-1] - curve[0,-2]]) )
return f_diffs/x_diffs
def derivative_5points(curve):
f_diffs = curve[1,4:] - curve[1, :-4]
f_diffs = numpy.concatenate(([curve[1,1] - curve[1,0]],
[curve[1,2] - curve[1,0]],
f_diffs,
[curve[1,-1] - curve[1,-3]],
[curve[1,-1] - curve[1,-2]]))
x_diffs = curve[0,4:] - curve[0,:-4]
x_diffs = numpy.concatenate( ([curve[0,1] - curve[0,0]],
[curve[0,2] - curve[0,0]],
x_diffs,
[curve[0,-1] - curve[0,-3]],
[curve[0,-1] - curve[0,-2]]) )
return f_diffs/x_diffs
def derivative_vals(curve, points_nb=5):
if points_nb == 5:
return derivative_5points(curve)
else:
return derivative_3points(curve)
def max_deriv_pos(spectrum, deriv_over=3):
'''
deriv_over how many points: 3 or 5 are available
'''
if deriv_over == 5:
deriv_vals = derivative_5points(spectrum)
else:
deriv_vals = derivative_3points(spectrum)
edge_indx = numpy.argmax(deriv_vals) # - index
return spectrum[0][edge_indx]
#http://stackoverflow.com/questions/22988882/how-to-smooth-a-curve-in-python
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
import numpy as np
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError:
raise ValueError("window_size and order have to be of type int")
if window_size % 2 != 1 or window_size < 1:
raise TypeError("window_size size must be a positive odd number")
if window_size < order + 2:
raise TypeError("window_size is too small for the polynomials order")
order_range = range(order+1)
half_window = (window_size -1) // 2
# precompute coefficients
b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
#print b
#print m
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
y = np.concatenate((firstvals, y, lastvals))
return np.convolve( m[::-1], y, mode='valid')
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