/
mode_strength_plot.py
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/
mode_strength_plot.py
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#############################################
## ##
## Imports .csv files of FFT's, finds peak ##
## values, and histogram strength of peak ##
## modes. ##
## ##
#############################################
import csv
import numpy as np
import Qfunction as Qfunc
import cv2
import peak_finder as pf
##--------------------------------------------------------------------##
## Returns a numpy array of the complex numbers associated with the ##
## selected mode. In practice, return will be appended to an external ##
## array with mode values from other .csv files ##
##--------------------------------------------------------------------##
#import *.csv into python memory
def complexcsv(filename, mode = 0, binsize = 10):
temp = np.genfromtxt(filename, skip_header = 1, delimiter = ',').astype(np.complex)
return temp[...,mode] + temp[...,mode+1]*1j
#takes mode-filtered complex fft data (e.g. from openyml()) and returns a histogram
def plotMode(modearray, mode, binsize = 10):
qfig = Qfunc.qfuncimage(modearray[2*mode:2*mode+2], binsize)
return qfig
#takes an unfiltered fft array and returns the peak indices (modes)
def findPeakModes(fftarray, smoothing = 7, finderwin = 20):
absdata = absfft(fftarray)
datashape = np.shape(absdata)
smoothing = 2*smoothing + 1 #smoothing must be an odd number
temparray = np.average(absdata, axis = 0)
print np.shape(temparray)
smoothdata = pf._smooth(temparray, window_len = smoothing)
return pf.peakdetect(smoothdata, lookahead = finderwin)[0]
#opens raw yml file and returns matrix with fft values for specified region
def openyml(filename, mode = -1):
if mode == -1: #for mode set to -1, return image data in a numpy array instead of fft data
imagedata = np.array([])
imagedata = cv2.cv.Load(filename, name = 'image')
return imagedata
else: #for a valid mode number, find the matching column and return subsequent values
realfft = cv2.cv.Load(filename, name = 'fft-real')
imagfft = cv2.cv.Load(filename, name = 'fft-imag')
modeindex = mode
#--------------------
#the code here is for if only select modes were recorded in the array and thus the mode index and mode are not the same
# modeindex = np.argwhere(realfft[0, :] == mode)[0,0] #finds the array index of the desired mode in the imported file
# if modeindex != np.argwhere(imagfft[0, :] == mode)[0,0] #should be same as modeindexreal, but you never know....
# return -1 #returns error for modeindex mismatch
#--------------------
#initialize and set first row of output array
fftdata = np.array([[realfft[0, modeindex],imagfft[0, modeindex]]])
datashape = np.shape(realfft)
for i in range(1, datashape[0]): #places real/imag pairs in successive rows
fftdata = np.append(fftdata, [[realfft[i, modeindex], imagfft[i, modeindex]]], axis = 0)
return fftdata
#creates an array with absolute fft values from a complex array of any even-width shape
def absfft(fftarray):
fftshape = np.shape(fftarray)
output = np.zeros([fftshape[0], fftshape[1]/2]) #initialize array of proper size
for col in range (0, fftshape[1]/2):
for row in range (0, fftshape[0]):
absval = np.sqrt(fftarray[row, 2*col]**2 + fftarray[row, 2*col+1]**2) #find abs of
output[row, col] = absval
return output