68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
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import numpy as np
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import pyfits
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import matplotlib.pyplot as plt
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import skimage
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from skimage.feature import blob_dog, blob_doh, blob_log, canny
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from skimage.color import rgb2gray
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from skimage.feature import corner_harris, corner_subpix, corner_peaks
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from skimage.segmentation import slic
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from skimage.filters import sobel
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from scipy.signal import convolve2d
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from scipy.ndimage import gaussian_filter
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from skimage import measure
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from scipy.optimize import curve_fit
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import matplotlib.ticker as mtick
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from scipy.signal import savgol_filter
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import scipy
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from scipy import signal
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from scipy.signal import argrelextrema
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import cv2
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## this function will get the values along each circle. We start by defining a range of angles, computing the
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#coordinates and then finding the values at the nearest pixel position.
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def get_line(star,theta,radius,x_c,y_c):
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#theta = np.linspace(0,2*np.pi,N_theta)
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x = x_c + radius*np.cos(theta)
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y = y_c + radius*np.sin(theta)
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x = np.round(x)
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y = np.round(y)
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x = x.astype(int)
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y = y.astype(int)
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I = star[y,x]
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return I,x,y
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## a function to compute the frequecy for a certain radius
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def get_radius(freq):
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N_p = 36
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r = N_p/(2*np.pi*freq)
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return r
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## a function to compute the radius for a certain frequency
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def get_freq(radius):
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N_p = 36
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freq = N_p/(2*np.pi*radius)
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return freq
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def sinusoidal(theta,a,b,c):
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N_p = 36
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y = a + b*np.sin(N_p*theta) + c*np.cos(N_p*theta)
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return y
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def fit_sinusoid(I,theta,p0):
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popt, pcov = curve_fit(sinusoidal,theta,I,p0)
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a = popt[0]
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b = popt[1]
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c = popt[2]
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modulation = np.sqrt(b**2 + c**2)/a
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return modulation, popt
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def Gaussian(x,a,x0,sigma):
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y = a*(1/(np.sqrt(2*np.pi)*sigma))*np.exp(-(x-x0)**2/(2*sigma**2))
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#y = np.exp(-2*(np.pi**2)*((x-x0)**2)*sigma**2)
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return y
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