for item in test:
if item.endswith(".1"):
os.remove(os.path.join(fits_dir, item))
Xdata_dir = workdir + 'Xdata/'
if not os.path.exists(Xdata_dir):
os.mkdir(Xdata_dir)
print("Directory " + Xdata_dir + "does not exist. Creating...")
fits_filenames = os.listdir(fits_dir)
resizing = [256]
for resize in resizing:
resize_dir = Xdata_dir + str(resize)
if os.path.exists(
resize_dir
): #delete any resizing directories matching the new resizes
shutil.rmtree(resize_dir)
os.makedirs(resize_dir) #creates new resize directories
for filename in fits_filenames: #iterates over fits files and converts to a numpy array
hmi_map = Map(fits_dir + filename)
rotateddata90 = hmi_map.rotate(angle=90 * u.deg, order=0)
rotateddata180 = rotateddata90.rotate(angle=90 * u.deg, order=0)
data = rotateddata180.data
data[np.where(np.isnan(data))] = 0.0 # replacing nans with 0s
print('saving ' + filename + ' in sizes' + str(resizing))
for resize in resizing: #resizes and saves numpy array data into given resizes
resized_image = np.array(
Image.fromarray(data).resize((resize, resize), Image.LANCZOS))
np.save(Xdata_dir + str(resize) + '/' + filename[:26] + '_' +
str(resize), resized_image) #saves series,time,and resize
def prepData(files, base_dir, prefix, custom_keywords={}, plot=False):
diffs = {'center_x': [], 'center_y': [], 'radius': [], 'scale': []}
os.makedirs(os.path.join(base_dir, 'level1'), exist_ok=True)
os.makedirs(os.path.join(base_dir, 'level1_5'), exist_ok=True)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for file in tqdm(files):
try:
# load existing file
hdul = fits.open(file)
hdu = hdul[0]
hdu.verify('fix')
d, h = hdu.data, hdu.header
# set custom keywords
h.update(custom_keywords)
# evaluate center and radius
imsave("demo.jpg", d)
myCmd = os.popen(
'/home/rja/PythonProjects/SpringProject/spring/limbcenter/sunlimb demo.jpg'
).read()
center_x, center_y, radius, d_radius = map(
float, myCmd.splitlines())
if "EXPTIME" in h:
h['EXP_TIME'] = h['EXPTIME']
del h['EXPTIME']
if 'TIME-OBS' in h:
obs_date = datetime.strptime(
h['DATE-OBS'] + 'T' + h['TIME-OBS'],
"%m/%d/1%yT%H:%M:%S")
h['DATE-OBS'] = obs_date.isoformat()
del h["TIME-OBS"]
if 'TIME' in h:
obs_date = datetime.strptime(
h['DATE-OBS'] + 'T' + h['TIME'], "%d/%m/%YT%H:%M:%S")
h['DATE-OBS'] = obs_date.isoformat()
del h["TIME"]
obs_time = parse(h["DATE-OBS"])
rsun = angular_radius(obs_time)
b0_angle = sun.B0(obs_time)
l0 = sun.L0(obs_time)
p_angle = sun.P(obs_time)
filename = "%s_%s_fi_%s.fits" % (
prefix, h["OBS_TYPE"].lower(),
obs_time.strftime("%Y%m%d_%H%M%S"))
# prepare existing header information
if "ANGLE" not in h:
h["ANGLE"] = p_angle.value
scale = rsun / (radius * u.pix)
coord = SkyCoord(0 * u.arcsec,
0 * u.arcsec,
obstime=obs_time,
observer='earth',
frame=frames.Helioprojective)
# create WCS header info
header = header_helper.make_fitswcs_header(
d,
coord,
rotation_angle=h["ANGLE"] * u.deg,
reference_pixel=u.Quantity([center_x, center_y] * u.pixel),
scale=u.Quantity([scale, scale]),
instrument=h["INSTRUME"],
telescope=h["TELESCOP"],
observatory=h["OBSVTRY"],
exposure=h["EXP_TIME"] * u.ms,
wavelength=h["WAVELNTH"] * u.angstrom)
header["KEYCOMMENTS"] = {
"EXPTIME": "[s] exposure time in seconds",
"DATE": "file creation date (YYYY-MM-DDThh:mm:ss UT)",
"DATE-OBS": "date of observation",
"WAVELNTH": "[Angstrom] wavelength",
"BANDPASS": "******",
"WAVEMIN": "[Angstrom] minimum wavelength",
"WAVEMAX": "[Angstrom] maximum wavelength",
"BZERO": "offset data range to that of unsigned short",
"CDELT1": "[arcsec/pix]",
"CDELT2": "[arcsec/pix]",
"SOLAR_R": "[pix]",
"DSUN_OBS": "[m]",
"RSUN_REF": "[m]",
"RSUN_ARC": "[%s]" % rsun.unit,
"ANGLE": "[deg]",
"SOLAR_P": "[%s]" % p_angle.unit,
"SOLAR_L0": "[%s]" % l0.unit,
"SOLAR_B0": "[%s]" % b0_angle.unit,
'SIMPLE': 'file does conform to FITS standard',
'BITPIX': 'number of bits per data pixel',
'CUNIT1': '[arcsec]',
'CUNIT2': '[arcsec]',
'CRVAL1': 'coordinate system value at reference pixel',
'CRVAL2': 'coordinate system value at reference pixel',
'CTYPE1': 'name of the coordinate axis',
'CTYPE2': 'name of the coordinate axis',
'INSTRUME': 'name of instrument',
'TELESCOP': 'name of telescope',
'OBSVTRY': 'name of observatory',
}
# set constants and default values
header["FILENAME"] = filename
header["DATE"] = datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
header["SOLAR_R"] = radius
header["RSUN_ARC"] = rsun.value
header["SOLAR_P"] = p_angle.value
header["SOLAR_L0"] = l0.value
header["SOLAR_B0"] = b0_angle.value
header["DATAMIN"] = np.min(d)
header["DATAMEAN"] = np.mean(d)
header["DATAMAX"] = np.max(d)
# copy existing keys
for key, value in h.items():
if key not in header:
header[key] = value
# copy comments
for key, value in zip(list(h.keys()), list(h.comments)):
if key not in header["KEYCOMMENTS"]:
header["KEYCOMMENTS"][key] = value
# LEVEL 1
s_map = Map(d.astype(np.float32), header)
level1_path = os.path.join(base_dir, 'level1', filename)
h.add_history("unified FITS header")
s_map.meta["HISTORY"] = h["HISTORY"]
s_map.meta["LVL_NUM"] = "1.0"
s_map = Map(s_map.data.astype(np.float32), s_map.meta)
s_map.save(level1_path, overwrite=True)
# LEVEL 1.5
scale = s_map.scale[0].value
s_map = padScale(s_map)
s_map = s_map.rotate(
recenter=True,
scale=scale,
missing=s_map.min(),
)
center = np.floor(s_map.meta['crpix1'])
range_side = (center + np.array([-1, 1]) * 2048 / 2) * u.pix
s_map = s_map.submap(
u.Quantity([range_side[0], range_side[0]]),
u.Quantity([range_side[1], range_side[1]]))
level1_5_path = os.path.join(base_dir, 'level1_5', filename)
h.add_history("recentered and derotated")
s_map.meta["HISTORY"] = h["HISTORY"]
s_map.meta["LVL_NUM"] = "1.5"
s_map = Map(s_map.data.astype(np.float32), s_map.meta)
s_map.save(level1_5_path, overwrite=True)
if plot:
s_map.plot()
s_map.draw_grid()
plt.savefig(level1_5_path.replace(".fits", ".jpg"))
plt.close()
# check header
hdul = fits.open(level1_5_path)
hdu = hdul[0]
hdu.verify('exception')
# evaluate difference
if 'center_x' in h and not isinstance(h["center_x"], str):
diffs['center_x'].append(
np.abs(h['center_x'] - header['crpix1']))
if 'center_y' in h and not isinstance(h["center_y"], str):
diffs['center_y'].append(
np.abs(h['center_y'] - header['crpix2']))
if 'SOLAR_R' in h and not isinstance(h["SOLAR_R"], str):
diffs['radius'].append(
np.abs(h['SOLAR_R'] - header['SOLAR_R']))
if 'cdelt1' in h and not isinstance(h["cdelt1"], str):
diffs['scale'].append(
np.abs(h['cdelt1'] - header['cdelt1']))
except Exception as ex:
print("INVALID FILE", file)
print("ERROR:", ex)
return diffs
# Read fits files and make maps
file_list = []
file_list.append(glob.glob(path+'*.i*.fits'))
file_list=sorted(file_list)
Mfiles=Map(file_list)
for mf in Mfiles: # changing 'CRDER1' and 'CRDER2' keywords to avoid annoying warnings
mf.meta['CRDER1'] = 0.0
mf.meta['CRDER2'] = 0.0
# Máscara:
imask = 10
diff = Mfiles[imask+1].data-Mfiles[imask].data
Mdiff = Map(np.nan_to_num(np.abs(diff)),Mfiles[imask].meta)
Mdiffrot = Mdiff.rotate(angle=Mdiff.meta['crota2'] * u.deg)
mask = Mdiffrot.data > 10
Gdiff = ndimage.gaussian_filter(Mdiffrot.data,40)
Gmask = Gdiff > Gdiff.max()*0.5
labels, n = ndimage.label(Gdiff*Gmask)
flaremask = (labels==3)
# Creating arrays containing time-serie data
Int_Inc = []
tiempos = []
for i in range(20):
diff = Mfiles[i+1].data-Mfiles[i].data
Mdiff = Map(np.nan_to_num(np.abs(diff)),Mfiles[i].meta)
Mdiffrot = Mdiff.rotate(angle=Mdiff.meta['crota2'] * u.deg)
Int_Inc.append((Mdiffrot.data*flaremask).sum())
tiempos.append(datetime.datetime.strptime(Mdiffrot.meta["date-obs"],'%Y-%m-%dT%H:%M:%S.%f'))
def scale_rotate(amap, target_scale=0.504273, target_factor=0):
"""
Parameters
----------
amap
Returns
-------
"""
scalex = amap.meta['cdelt1']
scaley = amap.meta['cdelt2']
# Set ratios to 1 if no scalin is done
if target_factor == 0:
ratio_plate = 1
ratio_dist = 1
new_shape = amap.data.shape[0]
else:
ratio_plate = target_factor * target_scale / scalex
logger.info(np.round(scalex / target_scale) / scalex * target_scale)
ratio_dist = amap.meta['dsun_obs'] / amap.meta['dsun_ref']
logger.info(ratio_dist)
# Pad image, if necessary
new_shape = int(4096 / target_factor)
# Reform map to new size if original shape is too small
if new_shape > amap.data.shape[0]:
new_fov = np.zeros((new_shape, new_shape)) * np.nan
new_meta = amap.meta
new_meta['crpix1'] = new_meta[
'crpix1'] - amap.data.shape[0] / 2 + new_fov.shape[0] / 2
new_meta['crpix2'] = new_meta[
'crpix2'] - amap.data.shape[1] / 2 + new_fov.shape[1] / 2
# Identify the indices for appending the map original FoV
i1 = int(new_fov.shape[0] / 2 - amap.data.shape[0] / 2)
i2 = int(new_fov.shape[0] / 2 + amap.data.shape[0] / 2)
# Insert original image in new field of view
new_fov[i1:i2, i1:i2] = amap.data[:, :]
# Assemble Sunpy map
amap = Map(new_fov, new_meta)
# Rotate solar north up rescale
rot_map = amap.rotate(scale=ratio_dist / ratio_plate, recenter=True)
if target_factor != 0:
rot_map.meta['cdelt1'] = target_factor * target_scale
rot_map.meta['cdelt2'] = target_factor * target_scale
rot_map.meta['rsun_obs'] = rot_map.meta['rsun_obs'] * ratio_dist
rot_map.meta['dsun_obs'] = rot_map.meta['dsun_ref']
# # Crop the image to desired shape
sz_x_diff = (rot_map.data.shape[0] - new_shape) // 2
sz_y_diff = (rot_map.data.shape[0] - new_shape) // 2
rot_map.meta['crpix1'] = rot_map.meta['crpix1'] - sz_x_diff
rot_map.meta['crpix2'] = rot_map.meta['crpix2'] - sz_y_diff
rot_map = Map(
rot_map.data[sz_x_diff:sz_x_diff + new_shape,
sz_y_diff:sz_y_diff + new_shape].copy(), rot_map.meta)
print(rot_map.data.shape)
return rot_map
