Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update reduction.py

Browse Source
This commit is contained in:
sugar_jo
2024-06-30 11:06:26 +08:00
parent b176e7a56e
commit a4e8f51c50
1 changed files with 1 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
package/lib/reduction.py
View File

@@ -1587,36 +1587,4 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
new_headers.append(new_header)
globals()['theta'] = globals()["theta"] - alpha
return new_data_array, new_error_array, new_data_mask, new_headers
def adaptive_binning(I_stokes, Q_stokes, U_stokes, Stokes_cov):
shape = I_stokes.shape
assert shape[0] == shape[1], "Only square images are supported"
assert shape[0] % 2 == 0, "Image size must be a power of 2"
n = int(np.log2(shape[0]))
bin_map = np.zeros(shape)
bin_num = 0
for level in range(n):
grid_size = 2**level
temp_I = I_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_Q = Q_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_U = U_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_cov = Stokes_cov.reshape(3, 3, int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(3).sum(4)
temp_bin_map = bin_map.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_P = (temp_Q**2 + temp_U**2)**0.5 / temp_I
temp_P_err = (1 / temp_I) * np.sqrt((temp_Q**2 * temp_cov[1,1,:,:] + temp_U**2 * temp_cov[2,2,:,:] + 2. * temp_Q * temp_U * temp_cov[1,2,:,:]) / (temp_Q**2 + temp_U**2) + \
((temp_Q / temp_I)**2 + (temp_U / temp_I)**2) * temp_cov[0,0,:,:] - \
2. * (temp_Q / temp_I) * temp_cov[0,1,:,:] - \
2. * (temp_U / temp_I) * temp_cov[0,2,:,:])
for i in range(int(shape[0]/grid_size)):
for j in range(int(shape[1]/grid_size)):
if (temp_P[i,j] / temp_P_err[i,j] > 3) and (temp_bin_map[i,j] == 0): # the default criterion is 3 sigma in P
bin_num += 1
bin_map[i*grid_size:(i+1)*grid_size,j*grid_size:(j+1)*grid_size] = bin_num
return bin_map, bin_num
return new_data_array, new_error_array, new_data_mask, new_headers