def fit_wavefront(diffs, detection):
"""Fit the wavefront that has been detected by the hough transform.
Simplest case is to fit along the y-direction for some x or range of x."""
dims=diffs[0].shape
answers=[]
wavefront_maps=[]
for i in range (0, len(diffs)):
if (detection[i].max() == 0.0):
#if the 'detection' array is empty then skip this image
fit_map=sunpy.make_map(np.zeros(dims),diffs[0]._original_header)
print("Nothing detected in image " + str(i) + ". Skipping.")
answers.append([])
wavefront_maps.append(fit_map)
else:
#if the 'detection' array is not empty, then fit the wavefront in the image
img = diffs[i]
fit_map=np.zeros(dims)
#get the independent variable for the columns in the image
x=(np.linspace(0,dims[0],num=dims[0])*img.scale['y']) + img.yrange[0]
#use 'detection' to guess the centroid of the Gaussian fit function
guess_index=detection[i].argmax()
guess_index=np.unravel_index(guess_index,detection[i].shape)
guess_position=x[guess_index[0]]
print("Analysing wavefront in image " + str(i))
column_fits=[]
#for each column in image, fit along the y-direction a function to find wave parameters
for n in range (0,dims[1]):
#guess the amplitude of the Gaussian fit from the difference image
guess_amp=np.float(img[guess_index[0],n])
#put the guess input parameters into a vector
guess_params=[guess_amp,guess_position,5]
#get the current image column
y=img[:,n]
y=y.flatten()
#call Albert's fitting function
result = util.fitfunc(x,y,'Gaussian',guess_params)
#define a Gaussian function. Messy - clean this up later
gaussian = lambda p,x: p[0]/np.sqrt(2.*np.pi)/p[2]*np.exp(-((x-p[1])/p[2])**2/2.)
#Draw the Gaussian fit for the current column and save it in fit_map
#save the best-fit parameters in column_fits
#only want to store the successful fits, discard the others.
#result contains a pass/fail integer. Keep successes ( ==1).
if result[1] == 1:
#if we got a pass integer, perform some other checks to eliminate unphysical values
result=check_fit(result)
column_fits.append(result)
if result != []:
fit_column = gaussian(result[0],x)
else:
fit_column = np.zeros(len(x))
else:
#if the fit failed then save as zeros/null values
result=[]
column_fits.append(result)
fit_column = np.zeros(len(x))
#draw the Gaussian fit for the current column and save it in fit_map
#gaussian = lambda p,x: p[0]/np.sqrt(2.*np.pi)/p[2]*np.exp(-((x-p[1])/p[2])**2/2.)
#save the drawn column in fit_map
fit_map[:,n] = fit_column
#save the fit parameters for the image in 'answers' and the drawn map in 'wavefront_maps'
fit_map=sunpy.make_map(fit_map,diffs[0]._original_header)
answers.append(column_fits)
wavefront_maps.append(fit_map)
#now get the mean values of the fitted wavefront, averaged over all x
#average_fits=[]
#for ans in answers:
# cleaned_answers=[]
# for k in range(0,len(ans)):
# #ans[:,1] contains a pass/fail integer. Keep successes (==1), discard the rest
# if ans[k][1] == 1:
# tmp=ans[k][0]
# cleaned_answers.append(tmp)
# else:
# cleaned_answers.append([])
# #get the mean of each fit parameter for this image and store it
# #average_fits.append(np.mean(g,axis=0))
return answers, wavefront_maps
def fit_wavefront(diffs, detection):
"""Fit the wavefront that has been detected by the hough transform.
Simplest case is to fit along the y-direction for some x or range of x."""
dims = diffs[0].shape
answers = []
wavefront_maps = []
for i in range(0, len(diffs)):
if (detection[i].max() == 0.0):
#if the 'detection' array is empty then skip this image
fit_map = sunpy.map.Map(np.zeros(dims), diffs[0].meta)
print("Nothing detected in image " + str(i) + ". Skipping.")
answers.append([])
wavefront_maps.append(fit_map)
else:
#if the 'detection' array is not empty, then fit the wavefront in the image
img = diffs[i]
fit_map = np.zeros(dims)
#get the independent variable for the columns in the image
x = (np.linspace(0, dims[0], num=dims[0]) *
img.scale['y']) + img.yrange[0]
#use 'detection' to guess the centroid of the Gaussian fit function
guess_index = detection[i].argmax()
guess_index = np.unravel_index(guess_index, detection[i].shape)
guess_position = x[guess_index[0]]
print("Analysing wavefront in image " + str(i))
column_fits = []
#for each column in image, fit along the y-direction a function to find wave parameters
for n in range(0, dims[1]):
#guess the amplitude of the Gaussian fit from the difference image
guess_amp = np.float(img.data[guess_index[0], n])
#put the guess input parameters into a vector
guess_params = [guess_amp, guess_position, 5]
#get the current image column
y = img.data[:, n]
y = y.flatten()
#call Albert's fitting function
result = util.fitfunc(x, y, 'Gaussian', guess_params)
#define a Gaussian function. Messy - clean this up later
gaussian = lambda p, x: p[0] / np.sqrt(2. * np.pi) / p[
2] * np.exp(-((x - p[1]) / p[2])**2 / 2.)
#Draw the Gaussian fit for the current column and save it in fit_map
#save the best-fit parameters in column_fits
#only want to store the successful fits, discard the others.
#result contains a pass/fail integer. Keep successes ( ==1).
if result[1] == 1:
#if we got a pass integer, perform some other checks to eliminate unphysical values
result = check_fit(result)
column_fits.append(result)
if result != []:
fit_column = gaussian(result[0], x)
else:
fit_column = np.zeros(len(x))
else:
#if the fit failed then save as zeros/null values
result = []
column_fits.append(result)
fit_column = np.zeros(len(x))
#draw the Gaussian fit for the current column and save it in fit_map
#gaussian = lambda p,x: p[0]/np.sqrt(2.*np.pi)/p[2]*np.exp(-((x-p[1])/p[2])**2/2.)
#save the drawn column in fit_map
fit_map[:, n] = fit_column
#save the fit parameters for the image in 'answers' and the drawn map in 'wavefront_maps'
fit_map = sunpy.map.Map(fit_map, diffs[0].meta)
answers.append(column_fits)
wavefront_maps.append(fit_map)
#now get the mean values of the fitted wavefront, averaged over all x
#average_fits=[]
#for ans in answers:
# cleaned_answers=[]
# for k in range(0,len(ans)):
# #ans[:,1] contains a pass/fail integer. Keep successes (==1), discard the rest
# if ans[k][1] == 1:
# tmp=ans[k][0]
# cleaned_answers.append(tmp)
# else:
# cleaned_answers.append([])
# #get the mean of each fit parameter for this image and store it
# #average_fits.append(np.mean(g,axis=0))
return answers, wavefront_maps
wave_normalization = np.dot(wave_normalization_coeff, time_powers).ravel()
wave_peak = 90. - (p(time) + (90. - lat_min))
n0 = wave_normalization * width.clip(0, 360) / 360.
m0 = wave_peak
s0 = wave_thickness
n1 = []
m1 = []
s1 = []
for wave in wave_maps_raw:
yy = np.average(np.asarray(wave), axis=1)
xx = np.arange(wave.yrange[0], wave.yrange[1],
wave.scale['y']) + wave.scale['y'] / 2.
p, success = util.fitfunc(xx, yy, 'gaussian', [1, 90, 1])
if p[0] < 0.1:
p, success = util.fitfunc(xx, yy, 'gaussian', [1, 75, 1])
n1 += [p[0]]
m1 += [p[1]]
s1 += [p[2]]
n2 = []
m2 = []
s2 = []
for wave in new_wave_maps:
data = np.array(wave)
data[np.isnan(data)] = 0.
yy = np.average(data, axis=1)
xx = np.linspace(wave.yrange[0], wave.yrange[1],
wave_thickness = np.dot(wave_thickness_coeff, time_powers).ravel()
wave_normalization = np.dot(wave_normalization_coeff, time_powers).ravel()
wave_peak = 90.-(p(time)+(90.-lat_min))
n0 = wave_normalization*width.clip(0,360)/360.
m0 = wave_peak
s0 = wave_thickness
n1 = []
m1 = []
s1 = []
for wave in wave_maps_raw:
yy = np.average(np.asarray(wave), axis=1)
xx = np.arange(wave.yrange[0],wave.yrange[1],wave.scale['y'])+wave.scale['y']/2.
p, success = util.fitfunc(xx, yy, 'gaussian', [1, 90, 1])
if p[0] < 0.1:
p, success = util.fitfunc(xx, yy, 'gaussian', [1, 75, 1])
n1 += [p[0]]
m1 += [p[1]]
s1 += [p[2]]
n2 = []
m2 = []
s2 = []
for wave in new_wave_maps:
data = np.array(wave)
data[np.isnan(data)] = 0.
yy = np.average(data, axis=1)
xx = np.linspace(wave.yrange[0],wave.yrange[1],wave.shape[0])+wave.scale['y']/2.
