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

add gaussian fitting for better background estimation

Browse Source
This commit is contained in:
Tibeuleu
2023-01-27 17:52:52 +01:00
parent c0083c97b6
commit 47c4dfd2a0
5 changed files with 197 additions and 44 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
src/lib/reduction.py
View File

@@ -53,7 +53,7 @@ log.setLevel('ERROR')
import warnings
from lib.deconvolve import deconvolve_im, gaussian_psf, gaussian2d, zeropad
from lib.convex_hull import image_hull, clean_ROI
from lib.background import bkg_hist, bkg_mini
from lib.background import bkg_fit, bkg_hist, bkg_mini
from lib.plots import plot_obs
from lib.cross_correlation import phase_cross_correlation
@@ -409,8 +409,8 @@ def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
def get_error(data_array, headers, error_array=None, data_mask=None,
sub_type=None, display=False, savename=None, plots_folder="",
return_background=False):
sub_type=None, subtract_error=True, display=False, savename=None,
plots_folder="", return_background=False):
"""
Look for sub-image of shape sub_shape that have the smallest integrated
flux (no source assumption) and define the background on the image by the
@@ -431,10 +431,11 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
If None, will be initialized with a full true mask.
Defaults to None.
sub_type : str or int or tuple, optional
If str, statistic rule to be used for the number of bins in counts/s.
If 'auto', look for optimal binning and fit intensity histogram with au gaussian.
If str or None, statistic rule to be used for the number of bins in counts/s.
If int, number of bins for the counts/s histogram.
If tuple, shape of the sub-image to look for. Must be odd.
Defaults to "Freedman-Diaconis".
Defaults to None.
display : boolean, optional
If True, data_array will be displayed with a rectangle around the
sub-image selected for background computation.
@@ -468,18 +469,26 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
# Crop out any null edges
if error_array is None:
error_array = np.zeros(data_array.shape)
n_data_array, n_error_array = deepcopy(data_array), deepcopy(error_array)
data, error = deepcopy(data_array), deepcopy(error_array)
if not data_mask is None:
data, error, mask = n_data_array, n_error_array, deepcopy(data_mask)
mask = deepcopy(data_mask)
else:
data, error, _ = crop_array(n_data_array, headers, n_error_array, step=5, null_val=0., inside=False)
mask = np.ones(data[0].shape, dtype=bool)
data_c, error_c, _ = crop_array(data, headers, error, step=5, null_val=0., inside=False)
mask_c = np.ones(data_c[0].shape, dtype=bool)
for i,(data_ci, error_ci) in enumerate(zip(data_c, error_c)):
data[i], error[i] = zeropad(data_ci, data[i].shape), zeropad(error_ci, error[i].shape)
mask = zeropad(mask_c, data[0].shape).astype(bool)
background = np.zeros((data.shape[0]))
if (sub_type is None) or (type(sub_type)==str):
n_data_array, n_error_array, headers, background = bkg_hist(data, error, mask, headers, sub_type=sub_type, display=display, savename=savename, plots_folder=plots_folder)
if (sub_type is None):
n_data_array, n_error_array, headers, background = bkg_hist(data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
elif type(sub_type)==str:
if sub_type.lower() in ['auto']:
n_data_array, n_error_array, headers, background = bkg_fit(data, error, mask, headers, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
else:
n_data_array, n_error_array, headers, background = bkg_hist(data, error, mask, headers, sub_type=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
elif type(sub_type)==tuple:
n_data_array, n_error_array, headers, background = bkg_mini(data, error, mask, headers, sub_shape=sub_type, display=display, savename=savename, plots_folder=plots_folder)
n_data_array, n_error_array, headers, background = bkg_mini(data, error, mask, headers, sub_shape=sub_type, subtract_error=subtract_error, display=display, savename=savename, plots_folder=plots_folder)
else:
print("Warning: Invalid subtype.")
@@ -674,7 +683,7 @@ def align_data(data_array, headers, error_array=None, background=None,
raise ValueError("All images in data_array must have same shape as\
ref_data")
if (error_array is None) or (background is None):
_, error_array, headers, background = get_error_hist(data_array, headers, return_background=True)
_, error_array, headers, background = get_error(data_array, headers, sub_type=(10,10), return_background=True)
# Crop out any null edges
#(ref_data must be cropped as well)