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aia_fits_to_zarr.py
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#Given:
# a folder --target to contain zarr files
# an integer --scale (a proper divisor of 1024) containing the target output size
#Converts the source fits files to target zarr files:
# -Rescaling the sun to a constant pixel size and correcting for invalid interpolation
# -Applying the degradation constant and accounting for exposure time
# -Then downsampling by mean
# -Save all fits header information to meta data in zarr
import os, pdb
import numpy as np
import sunpy.io
from tqdm import tqdm
import zarr
from sunpy.map import Map
import skimage.transform
import gcsfs
from numcodecs import Blosc, Delta
import warnings
import argparse
warnings.filterwarnings("ignore")
CHANNELS = [131,1600,1700,171,193,211,304,335,94,4500]
trgtAS = 976.0
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--target',dest='target',required=True)
parser.add_argument('--scale',dest='scale',required=True,type=int)
args = parser.parse_args()
return args
def loadAIADegrads(path):
#return wavelength -> (date -> degradation dictionary)
degrads = {}
for wl in CHANNELS:
degrads[wl] = getDegrad("%s/degrad_%d.csv" % (path,wl))
return degrads
def getDegrad(fn):
#map YYYY-MM-DD -> degradation parameter
lines = open(fn).read().strip().split("\n")
degrad = {}
for l in lines:
d, f = l.split(",")
degrad[d[1:11]] = float(f)
return degrad
if __name__ == "__main__":
args = parse_args()
degrads = loadAIADegrads("degrad/")
print(np.shape(degrads))
print(args)
target, scale = args.target, args.scale
if not os.path.exists(target):
os.mkdir(target)
divideFactor = np.int(1024 / scale)
#load file lists for AIA channels
filelist_131 = np.load('./filelists_2011/filelist_131.npy')
filelist_1600 = np.load('./filelists_2011/filelist_1600.npy')
filelist_1700 = np.load('./filelists_2011/filelist_1700.npy')
filelist_171 = np.load('./filelists_2011/filelist_171.npy')
filelist_193 = np.load('./filelists_2011/filelist_193.npy')
filelist_211 = np.load('./filelists_2011/filelist_211.npy')
filelist_304 = np.load('./filelists_2011/filelist_304.npy')
filelist_335 = np.load('./filelists_2011/filelist_335.npy')
filelist_94 = np.load('./filelists_2011/filelist_94.npy')
store = zarr.DirectoryStore(target+'sdomlv2_2011.zarr')
compressor = Blosc(cname='zstd', clevel=5, shuffle=Blosc.BITSHUFFLE)
root = zarr.group(store=store,overwrite=True)
year = root.create_group('2011')
aia131 = year.create_dataset('131A',shape=(np.shape(filelist_131)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia1600 = year.create_dataset('1600A',shape=(np.shape(filelist_1600)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia1700 = year.create_dataset('1700A',shape=(np.shape(filelist_1700)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia171 = year.create_dataset('171A',shape=(np.shape(filelist_171)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia193 = year.create_dataset('193A',shape=(np.shape(filelist_193)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia211 = year.create_dataset('211A',shape=(np.shape(filelist_211)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia304 = year.create_dataset('304A',shape=(np.shape(filelist_304)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia335 = year.create_dataset('335A',shape=(np.shape(filelist_335)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
aia94 = year.create_dataset('94A',shape=(np.shape(filelist_94)[0],scale,scale),chunks=(15,None,None),dtype='f4',compressor=compressor)
# Process 131A
Xd = Map(filelist_131[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_131)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia131[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia131.attrs[key.upper()]=vars()[key]
aia131.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia131.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia131.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia131.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia131.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia131.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia131.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia131.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia131.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia131.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia131.attrs['PIXLUNIT'] = list(pixlunit)
# Process 1600A
Xd = Map(filelist_1600[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_1600)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia1600[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia1600.attrs[key.upper()]=vars()[key]
aia1600.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia1600.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia1600.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia1600.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia1600.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia1600.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia1600.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia1600.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia1600.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia1600.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia1600.attrs['PIXLUNIT'] = list(pixlunit)
# Process 1700A
Xd = Map(filelist_1700[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_1700)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia1700[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia1700.attrs[key.upper()]=vars()[key]
aia1700.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia1700.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia1700.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia1700.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia1700.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia1700.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia1700.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia1700.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia1700.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia1700.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia1700.attrs['PIXLUNIT'] = list(pixlunit)
# Process 171A
Xd = Map(filelist_171[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_171)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia171[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia171.attrs[key.upper()]=vars()[key]
aia171.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia171.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia171.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia171.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia171.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia171.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia171.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia171.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia171.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia171.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia171.attrs['PIXLUNIT'] = list(pixlunit)
# Process 193A
Xd = Map(filelist_193[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_193)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia193[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia193.attrs[key.upper()]=vars()[key]
aia193.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia193.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia193.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia193.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia193.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia193.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia193.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia193.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia193.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia193.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia193.attrs['PIXLUNIT'] = list(pixlunit)
# Process 211A
Xd = Map(filelist_211[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_211)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia211[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia211.attrs[key.upper()]=vars()[key]
aia211.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia211.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia211.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia211.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia211.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia211.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia211.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia211.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia211.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia211.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia211.attrs['PIXLUNIT'] = list(pixlunit)
# Process 304A
Xd = Map(filelist_304[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_304)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia304[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia304.attrs[key.upper()]=vars()[key]
aia304.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia304.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia304.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia304.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia304.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia304.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia304.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia304.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia304.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia304.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia304.attrs['PIXLUNIT'] = list(pixlunit)
# Process 335A
Xd = Map(filelist_335[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_335)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia335[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia335.attrs[key.upper()]=vars()[key]
aia335.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia335.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia335.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia335.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia335.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia335.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia335.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia335.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia335.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia335.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia335.attrs['PIXLUNIT'] = list(pixlunit)
# Process 94A
Xd = Map(filelist_94[0])
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key] = []
deg_cor = []
pixlunit = []
for fn,file in tqdm(enumerate(filelist_94)):
Xd = Map(file)
fn2 = file[-26:].split("_")[0].replace("AIA","")
datestring = "%s-%s-%s" % (fn2[:4],fn2[4:6],fn2[6:8])
wavelength = int(file[-26:].split("_")[-1].replace(".fits",""))
correction = degrads[wavelength][datestring]
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
vars()[key].append(Xd.meta[key])
deg_cor.append(correction)
pixlunit.append('DN/s')
X = Xd.data
validMask = 1.0 * (X > 0)
X[np.where(X<=0.0)] = 0.0
expTime = max(Xd.meta['EXPTIME'],1e-2)
rad = Xd.meta['RSUN_OBS']
scale_factor = trgtAS/rad
t = (X.shape[0]/2.0)-scale_factor*(X.shape[0]/2.0)
XForm = skimage.transform.SimilarityTransform(scale=scale_factor,translation=(t,t))
Xr = skimage.transform.warp(X,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xm = skimage.transform.warp(validMask,XForm.inverse,preserve_range=True,mode='edge',output_shape=(X.shape[0],X.shape[0]))
Xr = np.divide(Xr,(Xm+1e-8))
Xr = Xr / (expTime*correction)
Xr = skimage.transform.downscale_local_mean(Xr,(divideFactor,divideFactor))
Xr = Xr.astype('float32')
aia94[fn,:,:]=Xr
for key in Xd.meta:
if key != 'keycomments' and key != 'simple':
aia94.attrs[key.upper()]=vars()[key]
aia94.attrs['NAXIS1'] = list(np.asarray(naxis1, dtype=np.float64) / divideFactor)
aia94.attrs['NAXIS2'] = list(np.asarray(naxis2, dtype=np.float64) / divideFactor)
aia94.attrs['CDELT1'] = list(np.asarray(cdelt1, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia94.attrs['CDELT2'] = list(np.asarray(cdelt2, dtype=np.float64) * divideFactor * rsun_obs / trgtAS)
aia94.attrs['R_SUN'] = list(np.asarray(r_sun, dtype=np.float64) / (4. * divideFactor) * trgtAS / rsun_obs)
aia94.attrs['X0_MP'] = list(np.asarray(x0_mp, dtype=np.float64) / (4. * divideFactor))
aia94.attrs['Y0_MP'] = list(np.asarray(y0_mp, dtype=np.float64) / (4. * divideFactor))
aia94.attrs['CRPIX1'] = list(np.asarray(crpix1, dtype=np.float64) / divideFactor)
aia94.attrs['CRPIX2'] = list(np.asarray(crpix2, dtype=np.float64) / divideFactor)
aia94.attrs['DEG_COR'] = list(np.asarray(deg_cor, dtype=np.float64))
aia94.attrs['PIXLUNIT'] = list(pixlunit)