def peakInf_split_func(my_map_area, peakInf_leaf_current, contour_bin,
area_pix_sum_min):
filt = guass.fspecial_gauss(20, 10)
my_map_filt = my_map_area
my_map_log2 = np.log(my_map_filt) / np.log(2)
peakInf_split_num = 1
peakInf_split = []
peakInf_split.append(peakInf_leaf_current)
am_sort = []
peakInf_split_temp = {}
area = peakInf_leaf_current['area']
area_pix_sum = area.sum()
if area_pix_sum > area_pix_sum_min:
for Z_thresh in range(contour_bin[0], contour_bin[-1] + 1):
Z_bigger = (my_map_log2 >= Z_thresh)
print Z_bigger
[L, M] = pybwlabel.bwlabel1(Z_bigger, 8)
am_init_var = []
if M > 1:
peakInf_split_num = M
for L_labelIdx in range(M):
area = (L == L_labelIdx)
#my_map_area_temp = np.dot(my_map_area,area)
my_map_area_temp = my_map_area * area
am_init_var[L_labelIdx] = my_map_area_temp.max()
b = sorted(am_init_var)
sort_idx = []
for i in b:
sort_idx.append(am_init_var.index(i))
for peakIdx in range(M):
L_labelIdx = sort_idx(peakIdx)
area = (L == L_labelIdx)
#my_map_area_temp = np.dot(my_map_area,area)
my_map_area_temp = my_map_area * area
am = my_map_area_temp.max()
# TODO
row, col = my_map_area_temp.shape
for i in range(row):
for j in range(col):
if my_map_area_temp[i][j] == am:
r, c = i, j
#[r c] = find(my_map_area_temp == am);
#r,c = 857,354
peakInf_split_temp['centerPos'] = [r, c]
peakInf_split_temp['am'] = my_map_area_temp[r][c]
peakInf_split_temp['area'] = area
peakInf_split.append(peakInf_split_temp)
am_sort[peakIdx] = am
return peakInf_split
def peakInf_split_func(my_map_area,peakInf_leaf_current,contour_bin, area_pix_sum_min):
filt = guass.fspecial_gauss(20,10)
my_map_filt = my_map_area
my_map_log2 = np.log(my_map_filt)/np.log(2)
peakInf_split_num = 1
peakInf_split = []
peakInf_split.append(peakInf_leaf_current)
am_sort = []
peakInf_split_temp = {}
area = peakInf_leaf_current['area']
area_pix_sum = area.sum()
if area_pix_sum > area_pix_sum_min:
for Z_thresh in range(contour_bin[0],contour_bin[-1]+1):
Z_bigger =(my_map_log2 >= Z_thresh)
print Z_bigger
[L,M]=pybwlabel.bwlabel1(Z_bigger,8)
am_init_var = []
if M>1:
peakInf_split_num = M
for L_labelIdx in range(M):
area = (L == L_labelIdx)
#my_map_area_temp = np.dot(my_map_area,area)
my_map_area_temp = my_map_area*area
am_init_var[L_labelIdx] = my_map_area_temp.max()
b = sorted(am_init_var)
sort_idx = []
for i in b:
sort_idx.append(am_init_var.index(i))
for peakIdx in range(M):
L_labelIdx = sort_idx(peakIdx)
area = (L ==L_labelIdx)
#my_map_area_temp = np.dot(my_map_area,area)
my_map_area_temp = my_map_area * area
am = my_map_area_temp.max()
# TODO
row, col = my_map_area_temp.shape
for i in range(row):
for j in range(col):
if my_map_area_temp[i][j] == am:
r, c = i, j
#[r c] = find(my_map_area_temp == am);
#r,c = 857,354
peakInf_split_temp['centerPos'] = [r,c]
peakInf_split_temp['am'] = my_map_area_temp[r][c]
peakInf_split_temp['area'] = area
peakInf_split.append(peakInf_split_temp)
am_sort[peakIdx] = am
return peakInf_split
print my_units.u_limit
print my_units.v_limit
print my_units.xinmap
print my_units.yinmap
print my_units.binwid
map_org = maplot.maplot(uv_data_residual,my_units,domap)
#Setting the filtering intensity based on the beam
map_re = np.real(map_org)
#Gussian filtering
hsize = 20
sigma = 10
filt = guass.fspecial_gauss(hsize,sigma)
#The filt is differ with Matlab, 0.00152093 vs 0.0014
if all_class.debug:
print '-'*80 + 'filt is'
print filt
print '-'*80
#map_filt = np.convolve(map_re,filt,'same')
map_filt = sgnl.convolve2d(map_re,filt,'same')
plt.contour(map_filt)
plt.title('Convolution')
plt.savefig('convolution.png')
map_positive = map_filt - map_filt.min()
map_normal = map_positive/map_positive.max()
print my_units.u_limit
print my_units.v_limit
print my_units.xinmap
print my_units.yinmap
print my_units.binwid
map_org = maplot.maplot(uv_data_residual, my_units, domap)
#Setting the filtering intensity based on the beam
map_re = np.real(map_org)
#Gussian filtering
hsize = 20
sigma = 10
filt = guass.fspecial_gauss(hsize, sigma)
#The filt is differ with Matlab, 0.00152093 vs 0.0014
if all_class.debug:
print '-' * 80 + 'filt is'
print filt
print '-' * 80
#map_filt = np.convolve(map_re,filt,'same')
map_filt = sgnl.convolve2d(map_re, filt, 'same')
plt.contour(map_filt)
plt.title('Convolution')
plt.savefig('convolution.png')
map_positive = map_filt - map_filt.min()
map_normal = map_positive / map_positive.max()
my_map = map_normal
