Tibeuleu/FOC_Reduction
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change name convention and cleanup

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Tibeuleu
2023-02-09 15:28:21 +01:00
parent fc80bafc66
commit ce670f4a19
8 changed files with 31 additions and 21 deletions
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10
src/lib/reduction.py
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@@ -46,7 +46,7 @@ import matplotlib.dates as mdates
from matplotlib.patches import Rectangle
from matplotlib.colors import LogNorm
from scipy.ndimage import rotate as sc_rotate, shift as sc_shift
from scipy.signal import convolve2d
from scipy.signal import fftconvolve
from astropy.wcs import WCS
from astropy import log
log.setLevel('ERROR')
@@ -683,7 +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(data_array, headers, sub_type=(10,10), return_background=True)
_, error_array, headers, background = get_error(data_array, headers, return_background=True)
# Crop out any null edges
#(ref_data must be cropped as well)
@@ -866,8 +866,8 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
weights /= weights.sum()
kernel = gaussian2d(x, y, stdev)
kernel /= kernel.sum()
smoothed[i] = np.where(data_mask, convolve2d(image*weights,kernel,'same')/convolve2d(weights,kernel,'same'), image)
error[i] = np.where(data_mask, np.sqrt(convolve2d(image_error**2*weights**2,kernel**2,'same'))/convolve2d(weights,kernel,'same'), image_error)
smoothed[i] = np.where(data_mask, fftconvolve(image*weights,kernel,'same')/fftconvolve(weights,kernel,'same'), image)
error[i] = np.where(data_mask, np.sqrt(fftconvolve(image_error**2*weights**2,kernel**2,'same'))/fftconvolve(weights,kernel,'same'), image_error)
# Nan handling
error[i][np.logical_or(np.isnan(smoothed[i]*error[i]),1-data_mask)] = 0.
@@ -1007,7 +1007,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
list_head = headers60
elif header['filtnam1']=='POL120':
list_head = headers120
header['exptime'] = np.sum([head['exptime'] for head in list_head])#/len(list_head)
header['exptime'] = np.sum([head['exptime'] for head in list_head])
pol_headers = [headers0[0], headers60[0], headers120[0]]
# Get image shape