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atbdnoise.py
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atbdnoise.py
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"""
This is similar to atbdfigs.py but focusing on noise removal. Use a different set of images to emphasize the noise.
"""
import matplotlib.pyplot as plt
import numpy as np
import sunpy.map
import sunpy.visualization.colormaps as cm
from astropy.io import fits
from CIMP import Snapshot as snap
from CIMP import Enhance
from sunpy.net import attrs as a
#------------------------------------------------------------------------------
"""
This replicates the process() method in the Animate class but with a little more freedom to specify the details of the processing
"""
def process(snap, background = 'ratio', point = 'none', \
detail = 'none', noise = 'none', equalize = False, \
downsample = False, clip = None, rmin = 0.0, rmax = None, \
params = None, rescale_output = False):
if background == 'subtract':
snap.mask_background(rmin = rmin, rmax = rmax, nonzero = False)
snap.subtract_background(rescale=False)
elif background == 'ratio':
snap.mask_background(rmin = rmin, rmax = rmax, nonzero = True)
snap.background_ratio(rescale=False)
else:
snap.mask_background(rmin = rmin, rmax = rmax, nonzero = False)
snap.mask_annulus(rmin = rmin, rmax = rmax)
if downsample:
snap.downsample()
snap.enhance(clip = clip, point = point, detail = detail, noise_filter = noise, \
equalize = equalize, params = params)
# hit it with another mask after processing
snap.mask_annulus(rmin=rmin, rmax = rmax, missingval = np.nanmin(snap.data))
if rescale_output:
snap.rescale()
return
#------------------------------------------------------------------------------
# parameters common to all images
# L0.5 LASCO data
instrument = a.Instrument.lasco
detector = a.Detector.c3
dir='/home/mark.miesch/data/lasco_monthly/c3/2012_04/'
bgfile = dir+'background.fts'
file1 = dir+'15/32296620.fts'
file2 = file1
rmin = 0.16
rmax = 1.0
params1 = None
params2 = None
rescale1 = False
rescale2 = False
outdir = '/home/mark.miesch/Products/image_processing/figs/'
#------------------------------------------------------------------------------
# choose the images you want to compare
fig = 7
x2file = None
if fig == 1:
outfile = 'pf_lasco_2012.png'
cmap = plt.get_cmap('stereocor2')
title1 = 'Median bright point filter'
background1 = 'ratio'
downsample1 = True
clip1 = None
point1 = 'median'
detail1 = 'None'
noise1 = 'None'
equalize1 = False
scale1 = (1.0, 1.3)
title2 = 'OMR'
background2 = 'ratio'
downsample2 = True
clip2 = None
point2 = 'omr'
detail2 = 'None'
noise2 = 'None'
equalize2 = False
scale2 = (1.0, 1.3)
elif fig == 2:
outfile = 'lasco_norm.png'
cmap = plt.get_cmap('stereocor2')
file1 = dir+'15/32296618.fts'
title1 = file1.split('/')[-1]
background1 = 'ratio'
downsample1 = True
clip1 = None
point1 = 'None'
detail1 = 'None'
noise1 = 'None'
equalize1 = False
scale1 = (0.056, 0.074)
file2 = dir+'15/32296619.fts'
title2 = file2.split('/')[-1]
background2 = 'ratio'
downsample2 = True
clip2 = None
point2 = 'None'
detail2 = 'None'
noise2 = 'None'
equalize2 = False
scale2 = (0.056, 0.074)
elif fig == 3:
outfile = 'lasco_noise_mgn.png'
cmap = plt.get_cmap('soholasco2')
file1 = dir+'15/32296635.fts'
title1 = 'base image'
background1 = 'ratio'
downsample1 = False
clip1 = None
point1 = 'None'
detail1 = 'None'
noise1 = 'None'
equalize1 = False
scale1 = (1.0, 1.2)
file2 = dir+'15/32296635.fts'
title2 = "OMR/MGN enhanced"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.4)
point2 = 'omr'
detail2 = 'mgn'
params2 = (0.8,1.5)
noise2 = 'None'
equalize2 = False
scale2 = (0.2,1.0)
elif fig == 4:
outfile = 'lasco_noise_fnrgf.png'
cmap = plt.get_cmap('stereocor2')
file1 = dir+'15/32296635.fts'
title1 = 'base image'
background1 = 'ratio'
downsample1 = False
clip1 = None
point1 = 'None'
detail1 = 'None'
noise1 = 'None'
equalize1 = False
scale1 = (1.0, 1.3)
file2 = dir+'15/32296635.fts'
title2 = "FNRGF enhanced"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.3)
point2 = 'None'
detail2 = 'fnrgf'
noise2 = 'None'
equalize2 = False
scale2 = (0.3666,0.371)
elif fig == 5:
outfile = 'lasco_noise_mgn_omr.png'
cmap = plt.get_cmap('stereocor2')
file1 = dir+'15/32296635.fts'
title1 = 'MGN enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.3)
point1 = 'None'
detail1 = 'mgn'
params1 = (0.8,1.2)
noise1 = 'None'
equalize1 = False
scale1 = (0.2, 1.0)
file2 = dir+'15/32296635.fts'
title2 = "MGN enhanced with OMR"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.3)
point2 = 'omr'
detail2 = 'mgn'
params2 = (0.7,1.0)
noise2 = 'None'
equalize2 = False
scale2 = (0.3,1.0)
elif fig == 6:
outfile = 'lasco_noise_fnrgf.png'
cmap = plt.get_cmap('stereocor2')
file1 = dir+'15/32296635.fts'
title1 = 'FNRGF enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.3)
point1 = 'None'
detail1 = 'fnrgf'
noise1 = 'None'
equalize1 = False
rescale1 = True
scale1 = (0.1,1.0)
file2 = dir+'15/32296635.fts'
title2 = "FNRGF enhanced with OMR"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.3)
point2 = 'None'
detail2 = 'fnrgf'
noise2 = 'omr'
equalize2 = False
rescale2 = True
scale2 = (0.5,1.0)
elif fig == 7:
outfile = 'lasco_noise_bregman.png'
cmap = plt.get_cmap('soholasco2')
#cmap = plt.get_cmap('stereocor2')
file1 = dir+'15/32296635.fts'
title1 = 'OMR/MGN enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.4)
point1 = 'omr'
detail1 = 'mgn'
params1 = (0.8,1.5)
noise1 = 'none'
equalize1 = False
scale1 = (0.2,1.0)
file2 = dir+'15/32296635.fts'
title2 = "with TV-Bregman noise filter"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.4)
point2 = 'omr'
detail2 = 'mgn'
params2 = (0.8,1.2)
noise2 = 'bregman'
equalize2 = False
scale2 = (0.1,1.0)
elif fig == 8:
outfile = 'lasco_noise_omr.png'
cmap = plt.get_cmap('soholasco2')
#cmap = plt.get_cmap('stereocor2')
file1 = dir+'15/32296635.fts'
title1 = 'OMR/MGN enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.4)
point1 = 'omr'
detail1 = 'mgn'
params1 = (0.8,1.5)
noise1 = 'none'
equalize1 = False
scale1 = (0.2,1.0)
file2 = dir+'15/32296635.fts'
title2 = "with OMR noise filter"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.4)
point2 = 'omr'
detail2 = 'mgn'
params2 = (0.8,1.2)
noise2 = 'omr'
equalize2 = False
scale2 = (0.2,1.0)
elif fig == 9:
outfile = 'lasco_noise_tv.png'
cmap = plt.get_cmap('soholasco2')
file1 = dir+'15/32296635.fts'
title1 = 'OMR/MGN enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.4)
point1 = 'omr'
detail1 = 'mgn'
params1 = (0.8,1.5)
noise1 = 'none'
equalize1 = False
scale1 = (0.2,1.0)
file2 = dir+'15/32296635.fts'
title2 = "with TV noise filter"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.4)
point2 = 'omr'
detail2 = 'mgn'
params2 = (0.8,1.2)
noise2 = 'tv'
equalize2 = False
scale2 = (0.1,1.0)
elif fig == 10:
# The goal with this figure is to compare the
# final product, after OMR, MGN, and NG, to the
# original unprocessed image
outfile = 'lasco_ng_pipeline.png'
title1 = 'Base Image'
file1 = dir+'15/32296635.fts'
cmap1 = plt.get_cmap('soholasco2')
background1 = 'ratio'
downsample1 = False
clip1 = None
point1 = 'None'
detail1 = 'None'
noise1 = 'None'
equalize1 = False
scale1 = (1.0, 1.2)
title2 = 'Processed image (OMR/MGN/NG)'
cmap2 = plt.get_cmap('soholasco2')
x2file = '/home/mark.miesch/data/lasco_monthly/c3/2012_04_ng/ng_hybrid_fig7.fts'
scale2 = (0.2, 1.0)
elif fig == 11:
outfile = 'lasco_noise_atrous.png'
cmap = plt.get_cmap('soholasco2')
file1 = dir+'15/32296635.fts'
title1 = 'OMR/MGN enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.4)
point1 = 'omr'
detail1 = 'mgn'
params1 = (0.8,1.5)
noise1 = 'none'
equalize1 = False
scale1 = (0.2,1.0)
file2 = dir+'15/32296635.fts'
title2 = "with a trous noise filter"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.4)
point2 = 'omr'
detail2 = 'mgn'
noise2 = 'atrous'
equalize2 = False
scale2 = (0.2,1.0)
elif fig == 12:
outfile = 'lasco_noise_hard.png'
cmap = plt.get_cmap('soholasco2')
file1 = dir+'15/32296635.fts'
title1 = 'OMR/MGN enhanced'
background1 = 'ratio'
downsample1 = True
clip1 = (1.0,1.4)
point1 = 'omr'
detail1 = 'mgn'
params1 = (0.8,1.5)
noise1 = 'none'
equalize1 = False
scale1 = (0.2,1.0)
file2 = dir+'15/32296635.fts'
title2 = "with hard wavelet filter"
background2 = 'ratio'
downsample2 = True
clip2 = (1.0,1.4)
point2 = 'omr'
detail2 = 'mgn'
noise2 = 'wavelet'
equalize2 = False
scale2 = (0.2,1.0)
else:
print("pick a valid figure number")
exit()
#------------------------------------------------------------------------------
# select color map
try:
cmap1 = cmap
cmap2 = cmap
except:
pass
#------------------------------------------------------------------------------
# First image
x1 = snap.snapshot(file = file1, bgfile = bgfile, instrument = instrument, \
detector = detector, normalize = True)
process(x1, background = background1, point = point1, detail = detail1, \
noise = noise1, equalize = equalize1, downsample = downsample1, \
clip = clip1, rmin = rmin, rmax = rmax, params = params1, \
rescale_output = rescale1)
print(f"file1 exposure time {x1.header['EXPTIME']}")
data1 = x1.data
#------------------------------------------------------------------------------
# second image
if x2file is None:
x2 = snap.snapshot(file = file2, bgfile = bgfile, instrument = instrument, \
detector = detector, normalize = True)
process(x2, background = background2, point = point2, detail = detail2, \
noise = noise2, equalize = equalize2, downsample = downsample2, \
clip = clip2, rmin = rmin, rmax = rmax, params = params2, \
rescale_output = rescale2)
print(f"file2 exposure time {x2.header['EXPTIME']}")
data2 = x2.data
else:
hdu = fits.open(x2file)[0]
data2 = hdu.data
#------------------------------------------------------------------------------
print(f"x1 minmax: {np.min(data1)} {np.max(data1)}")
print(f"x2 minmax: {np.min(data2)} {np.max(data2)}")
print(f"x1 res: {data1.shape[0]} {data1.shape[1]}")
print(f"x2 res: {data2.shape[0]} {data2.shape[1]}")
print(f"x1 time {x1.time}")
#------------------------------------------------------------------------------
# plot
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,6))
ax[0].imshow(data1,cmap=cmap1,vmin=scale1[0],vmax=scale1[1], \
origin='lower')
ax[0].axis('off')
ax[0].set_title(title1)
ax[1].imshow(data2,cmap=cmap2,vmin=scale2[0],vmax=scale2[1], \
origin='lower')
ax[1].axis('off')
ax[1].set_title(title2)
fig.tight_layout(pad=1,rect=(0.01,0.01,.99,.98))
# label
label = True
if label:
plt.annotate("(a)", (0.05,0.87), xycoords = 'figure fraction', color='white', \
fontsize = 'x-large', fontweight = 'semibold')
plt.annotate("(b)", (0.54,0.87), xycoords = 'figure fraction', color='white', \
fontsize = 'x-large', fontweight = 'semibold')
plt.savefig(outdir+outfile)
plt.show()