c_map.set_alpha(1, 0.8)
# Create the figure
plt.close('all')
figure = plt.figure()
axes = figure.add_subplot(111)
if for_paper:
observation = r"AIA {:s}".format(mc[0].measurement._repr_latex_())
title = "wave progress map\n{:s}".format(observation)
image_file_type = 'eps'
else:
title = "{:s} ({:s})".format(observation_date, wave_name)
image_file_type = 'png'
ret = c_map.plot(axes=axes, title=title)
c_map.draw_limb()
c_map.draw_grid()
# Set up the color bar
nticks = 6
timestamps_index = np.linspace(1, len(timestamps)-1, nticks, dtype=np.int).tolist()
cbar_tick_labels = []
for index in timestamps_index:
wpm_time = timestamps[index].strftime("%H:%M:%S")
cbar_tick_labels.append(wpm_time)
cbar = figure.colorbar(ret[1], ticks=timestamps_index)
cbar.ax.set_yticklabels(cbar_tick_labels)
cbar.set_label('time (UT) ({:s})'.format(observation_date))
cbar.set_clim(vmin=1, vmax=len(timestamps))
# Show the figure
plt.savefig(img_filepath + '_wave_progress_map.{:s}'.format(image_file_type))
import matplotlib.pyplot as plt
from sunpy.map import Map
import sunpy.data.sample as s
import numpy as np
aia = Map(s.AIA_171_IMAGE)
fig, axs = plt.subplots(1, 2)
aia.plot(axes=axs[0])
aia.draw_grid()
r = [11.52, 10.42, 6.14, 3.64, 2.75]
e = [10, 20, 30, 40, 50]
pixel_size = 3.4
number_of_pixels = 160
center = np.array([461, 303])
line_color = 'w'
rect = plt.Rectangle(center - pixel_size * number_of_pixels / 2.,
pixel_size * number_of_pixels,
pixel_size * number_of_pixels,
fill=False,
color=line_color)
ax[0].add_artist(rect)
rect = plt.Rectangle(center - pixel_size / 2.,
pixel_size,
pixel_size,
fill=False,
import matplotlib.pyplot as plt
from sunpy.map import Map
import sunpy.data.sample as s
import numpy as np
aia = Map(s.AIA_171_IMAGE)
fig, axs = plt.subplots(1,2)
aia.plot(axes=axs[0])
aia.draw_grid()
r = [11.52, 10.42, 6.14, 3.64, 2.75]
e = [10, 20, 30, 40, 50]
pixel_size = 3.4
number_of_pixels = 160
center = np.array([461, 303])
line_color = 'w'
rect = plt.Rectangle(center - pixel_size * number_of_pixels/2., pixel_size * number_of_pixels, pixel_size * number_of_pixels, fill=False, color=line_color)
ax[0].add_artist(rect)
rect = plt.Rectangle(center - pixel_size/2., pixel_size, pixel_size, fill=False, color=line_color)
ax[0].add_artist(rect)
for radius, energy in zip(r,e):
circle = plt.Circle(center, radius=radius*60, fill=False, label=str(energy), color=line_color)
ax[0].add_artist(circle)
plt.colorbar()
plt.legend()
c_map = Map(sun_image, fp_map, best_long_score_map, composite=True)
# Create the figure
plt.close('all')
figure = plt.figure()
axes = figure.add_subplot(111)
if for_paper:
observation = r"AIA {:s}".format(mc[0].measurement._repr_latex_())
title = "wave fit map\n{:s}".format(observation)
image_file_type = 'png'
else:
title = "{:s} ({:s})".format(observation_date, wave_name)
image_file_type = 'png'
ret = c_map.plot(axes=axes, title=title)
c_map.draw_limb(color='c')
c_map.draw_grid(color='c')
# Add a small circle to indicate the estimated epicenter of the wave
ip = SkyCoord(transform_hpc2hg_parameters['epi_lon'],
transform_hpc2hg_parameters['epi_lat'],
frame='heliographic_stonyhurst').transform_to(sun_image.coordinate_frame)
ccc = Circle((ip.Tx.value, ip.Ty.value), radius=50, edgecolor='w', fill=True, facecolor='c', zorder=1000)
axes.add_patch(ccc)
# Set up the color bar
nticks = 6
timestamps_index = np.linspace(1, len(timestamps)-1, nticks, dtype=np.int).tolist()
cbar_tick_labels = []
for index in timestamps_index:
wpm_time = timestamps[index].strftime("%H:%M:%S")
cbar_tick_labels.append(wpm_time)
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
