import numpy as np
import spiceypy
from spiceypy.utils import support_types as spiceytypes
import astropy.time as time
import astropy.units as u
import astropy.coordinates as astrocoords
import sunpy
import sunpy.coordinates as suncoords
import sunpy.sun.constants
# Mapping from SPICE frame name to (frame, frame kwargs)
spice_astropy_frame_mapping = {
'IAU_SUN': (suncoords.HeliographicCarrington, {
'observer':
suncoords.HeliographicStonyhurst(0 * u.deg, 0 * u.deg,
sunpy.sun.constants.radius)
}),
}
def furnish(kernel):
"""
Furnish SPICE with a kernel.
Parameters
----------
fname : `SPKKernel` or list of `SPKKernel`
SPICE kernel(s) to load.
See also
--------
def overplot_aia_coords(aiaimg, pch_obj):
plt.rcParams.update({'font.size': 16})
aia = Map(aiaimg)
if aia.fits_header['lvl_num'] == 1:
aia2 = update_pointing(aia)
aia = register(aia2)
aia_north = pch_obj['AIA171'][0]
#aia_north = pch_obj['AIA171'][0].resample('0.5D').median()
n_lat = aia_north[np.abs(
pd.to_datetime(aia_north.index).date - aia.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].N_lower_lat.values * u.rad
n_lon = aia_north[np.abs(
pd.to_datetime(aia_north.index).date - aia.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].N_mean_lon.values * u.rad
times = aia_north[np.abs(
pd.to_datetime(aia_north.index).date -
aia.date.to_datetime().date()) <= datetime.timedelta(days=8.25)].index
n_pts = coordinates.HeliographicStonyhurst(n_lon, n_lat, obstime=times)
n_pts_hpc = n_pts.transform_to(aia.coordinate_frame)
#fov = 800 * u.arcsec
#mid_pt = n_pts_hpc[int(np.floor((len(n_pts_hpc)-1)/2))]
#bottom_left = SkyCoord(mid_pt.Tx - fov/2, mid_pt.Ty - fov/2, frame=aia.coordinate_frame)
#smap = aia.submap(bottom_left, width=fov, height=fov)
#ax = plt.subplot(projection=smap)
#smap.plot()
#smap.draw_limb()
#ax.grid(False)
#ax.plot_coord(n_pts_hpc, 'x', color='deepskyblue', label='North PCH')
#plt.legend()
#plt.show()
#------------------------------------
#aia_south = pch_obj['AIA171'][1].resample('0.5D').median()
aia_south = pch_obj['AIA171'][1]
s_lat = aia_south[np.abs(
pd.to_datetime(aia_south.index).date - aia.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].S_lower_lat.values * u.rad
s_lon = aia_south[np.abs(
pd.to_datetime(aia_south.index).date - aia.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].S_mean_lon.values * u.rad
times = aia_south[np.abs(
pd.to_datetime(aia_south.index).date -
aia.date.to_datetime().date()) <= datetime.timedelta(days=8.25)].index
s_pts = coordinates.HeliographicStonyhurst(s_lon, s_lat, obstime=times)
s_pts_hpc = s_pts.transform_to(aia.coordinate_frame)
# fov = 800 * u.arcsec
# mid_pt = s_pts_hpc[int(np.floor((len(s_pts_hpc)-1)/2))]
# bottom_left = SkyCoord(mid_pt.Tx - fov/2, mid_pt.Ty - fov/2, frame=aia.coordinate_frame)
# smap = aia.submap(bottom_left, width=fov, height=fov)
smap = aia
n_zoom_coords = SkyCoord(Tx=(-200, 200) * u.arcsec,
Ty=(850, 1050) * u.arcsec,
frame=smap.coordinate_frame)
s_zoom_coords = SkyCoord(Tx=(-500, 500) * u.arcsec,
Ty=(-1050, -850) * u.arcsec,
frame=smap.coordinate_frame)
n_zoom = smap.submap(n_zoom_coords)
n_zoom.data[0, 0] = smap.min()
n_zoom.data[-1, -1] = smap.max()
s_zoom = smap.submap(s_zoom_coords)
s_zoom.data[0, 0] = smap.min()
s_zoom.data[-1, -1] = smap.max()
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(projection=smap)
smap.plot()
ax.grid(False)
ax.plot_coord(n_pts_hpc, 'x', color='cornflowerblue', label='North PCH')
ax.plot_coord(s_pts_hpc, 'x', color='rebeccapurple', label='South PCH')
smap.draw_quadrangle(n_zoom_coords,
edgecolor="white",
linestyle="-",
linewidth=2)
smap.draw_quadrangle(s_zoom_coords,
edgecolor="white",
linestyle="-",
linewidth=2)
plt.legend()
plt.savefig('/Users/mskirk/Desktop/SWAP Paper Plots/PCH_AIA171_' +
str(aia.date.to_datetime().date()) + '.png')
fig2 = plt.figure(figsize=(4, 2), frameon=False)
ax2 = plt.subplot(projection=n_zoom)
n_zoom.plot()
ax2.grid(False)
ax2.set_axis_off()
ax2.set_title('')
ax2.plot_coord(n_pts_hpc, 'x', color='cornflowerblue', label='North PCH')
plt.savefig('/Users/mskirk/Desktop/SWAP Paper Plots/PCH_AIA171N_' +
str(aia.date.to_datetime().date()) + '.png')
fig3 = plt.figure(figsize=(10, 2), frameon=False)
ax2 = plt.subplot(projection=s_zoom)
s_zoom.plot()
ax2.grid(False)
ax2.set_axis_off()
ax2.set_title('')
ax2.plot_coord(s_pts_hpc, 'x', color='rebeccapurple', label='South PCH')
plt.savefig('/Users/mskirk/Desktop/SWAP Paper Plots/PCH_AIA171S_' +
str(aia.date.to_datetime().date()) + '.png')
def overplot_swap_coords(swapimg, pch_obj):
plt.rcParams.update({'font.size': 16})
swap = Map(swapimg)
swap_north = pch_obj['SWAP174'][0]
#swap_north = pch_obj['SWAP174'][0].resample('0.5D').median()
n_lat = swap_north[np.abs(
pd.to_datetime(swap_north.index).date - swap.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].N_lower_lat.values * u.rad
n_lon = swap_north[np.abs(
pd.to_datetime(swap_north.index).date - swap.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].N_mean_lon.values * u.rad
times = swap_north[np.abs(
pd.to_datetime(swap_north.index).date -
swap.date.to_datetime().date()) <= datetime.timedelta(days=8.25)].index
n_pts = coordinates.HeliographicStonyhurst(n_lon, n_lat, obstime=times)
n_pts_hpc = n_pts.transform_to(swap.coordinate_frame)
#fov = 800 * u.arcsec
#mid_pt = n_pts_hpc[int(np.floor((len(n_pts_hpc)-1)/2))]
#bottom_left = SkyCoord(mid_pt.Tx - fov/2, mid_pt.Ty - fov/2, frame=swap.coordinate_frame)
#smap = swap.submap(bottom_left, width=fov, height=fov)
#ax = plt.subplot(projection=smap)
#smap.plot()
#smap.draw_limb()
#ax.grid(False)
#ax.plot_coord(n_pts_hpc, 'x', color='deepskyblue', label='North PCH')
#plt.legend()
#plt.show()
#------------------------------------
#swap_south = pch_obj['SWAP174'][1].resample('0.5D').median()
swap_south = pch_obj['SWAP174'][1]
s_lat = swap_south[np.abs(
pd.to_datetime(swap_south.index).date - swap.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].S_lower_lat.values * u.rad
s_lon = swap_south[np.abs(
pd.to_datetime(swap_south.index).date - swap.date.to_datetime().date()
) <= datetime.timedelta(days=8.25)].S_mean_lon.values * u.rad
times = swap_south[np.abs(
pd.to_datetime(swap_south.index).date -
swap.date.to_datetime().date()) <= datetime.timedelta(days=8.25)].index
s_pts = coordinates.HeliographicStonyhurst(s_lon, s_lat, obstime=times)
s_pts_hpc = s_pts.transform_to(swap.coordinate_frame)
fov = 2454 * u.arcsec
mid_pt = s_pts_hpc[int(np.floor((len(s_pts_hpc) - 1) / 2))]
#bottom_left = SkyCoord(mid_pt.Tx - fov/2, mid_pt.Ty - fov/2, frame=swap.coordinate_frame)
bottom_left = SkyCoord(-fov / 2, -fov / 2, frame=swap.coordinate_frame)
smap = swap.submap(bottom_left, width=fov, height=fov)
n_zoom_coords = SkyCoord(Tx=(-200, 200) * u.arcsec,
Ty=(850, 1050) * u.arcsec,
frame=smap.coordinate_frame)
s_zoom_coords = SkyCoord(Tx=(-500, 500) * u.arcsec,
Ty=(-1050, -850) * u.arcsec,
frame=smap.coordinate_frame)
#Contrast Adjustment
p2, p99 = np.percentile(smap.data, (2, 99))
img_rescale = exposure.rescale_intensity(smap.data, in_range=(p2, p99))
smap.data[:] = img_rescale
n_zoom = smap.submap(n_zoom_coords)
n_zoom.data[0, 0] = smap.min()
n_zoom.data[-1, -1] = smap.max()
s_zoom = smap.submap(s_zoom_coords)
s_zoom.data[0, 0] = smap.min()
s_zoom.data[-1, -1] = smap.max()
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(projection=smap)
smap.plot()
ax.grid(False)
ax.plot_coord(n_pts_hpc, 'x', color='cornflowerblue', label='North PCH')
ax.plot_coord(s_pts_hpc, 'x', color='rebeccapurple', label='South PCH')
smap.draw_quadrangle(n_zoom_coords,
edgecolor="white",
linestyle="-",
linewidth=2)
smap.draw_quadrangle(s_zoom_coords,
edgecolor="white",
linestyle="-",
linewidth=2)
plt.legend()
plt.savefig('/Users/mskirk/Desktop/SWAP Paper Plots/PCH_SWAP174_' +
str(swap.date.to_datetime().date()) + '.png')
fig2 = plt.figure(figsize=(4, 2), frameon=False)
ax2 = plt.subplot(projection=n_zoom)
n_zoom.plot()
ax2.grid(False)
ax2.set_axis_off()
ax2.set_title('')
ax2.plot_coord(n_pts_hpc, 'x', color='cornflowerblue', label='North PCH')
plt.savefig('/Users/mskirk/Desktop/SWAP Paper Plots/PCH_SWAP174N_' +
str(swap.date.to_datetime().date()) + '.png')
fig3 = plt.figure(figsize=(10, 2), frameon=False)
ax2 = plt.subplot(projection=s_zoom)
s_zoom.plot()
ax2.grid(False)
ax2.set_axis_off()
ax2.set_title('')
ax2.plot_coord(s_pts_hpc, 'x', color='rebeccapurple', label='South PCH')
plt.savefig('/Users/mskirk/Desktop/SWAP Paper Plots/PCH_SWAP174S_' +
str(swap.date.to_datetime().date()) + '.png')
def find_carr_coords(cme_root, craft, c1cme, eucme):
""" Function to calculate carrington longitude and latitude of events in
eucme and add these to the dataframe. These are used to create plots of
each event.
"""
# Find the euvi fits files
if craft == 'A':
fn_tag = '*eua.fts'
elif craft == 'B':
fn_tag = '*eub.fts'
# Calculate Carrington Longitudes and Latitudes of source locations
eucme['carrington_longitude'] = pd.Series(np.zeros(len(eucme)) * np.NaN,
index=eucme.index)
eucme['carrington_latitude'] = pd.Series(np.zeros(len(eucme)) * np.NaN,
index=eucme.index)
for i in range(len(eucme)):
print i
cme_root_new = r'/home/sq112614/SS/Images/EUVI/ST' + craft
cme_root2 = cme_root_new + '/cme_' + '{0:02d}'.format(i + 1)
euvi_files = find_files(cme_root2, fn_tag)
print len(euvi_files)
if len(euvi_files) > 0:
print '**********'
print cme_root2
print euvi_files
# Files exist, load in the subpy map to work out carrington coordinates
euvi = smap.Map(euvi_files[0])
# Convert the 256x256 coordinates into correctly scaled coordinates
xn = eucme['l'][i] + eucme['w'][i] / 2
yn = eucme['b'][i] + eucme['h'][i] / 2
xn = (xn / 256) * euvi.dimensions[0]
yn = euvi.dimensions[1] - (yn / 256) * euvi.dimensions[1]
# Get HPC and HPR coords
lon, lat = HPC_LONLAT(euvi)
el, pa = HPC_to_HPR(lon.to('deg').value, lat.to('deg').value)
# Get Carrington coords:
cf = euvi.coordinate_frame
hpc = spc.Helioprojective(Tx=lon,
Ty=lat,
D0=cf.D0,
B0=cf.B0,
L0=cf.L0,
representation=cf.representation)
hcc = spc.Heliocentric(D0=cf.D0, B0=cf.B0, L0=cf.L0)
hcc = spc.hpc_to_hcc(hpc, hcc)
hgs = spc.HeliographicStonyhurst(dateobs=cf.dateobs)
hgc = spc.HeliographicCarrington(dateobs=cf.dateobs)
hgs = spc.hcc_to_hgs(hcc, hgs)
hgc = spc.hgs_to_hgc(hgs, hgc)
# Try looking up carrington coordinates
idy = np.int(np.round(yn.value))
idx = np.int(np.round(xn.value))
hgc_lon = hgc.lon[idy, idx]
hgc_lat = hgc.lat[idy, idx]
# If returned NaNs extrapolate to limb
if np.logical_or(np.isnan(hgc_lon), np.isnan(hgc_lat)):
print('Limb Extrapolation')
# Get elongation of Limb
el_limb = np.arctan(0.99 * euvi.rsun_meters /
euvi.dsun).to('deg')
# Get position angle of point
ppa = pa[idy, idx]
# Find pixel best matching this elongation and pa
pa_diff = np.abs(pa - ppa)
el_diff = np.abs(el - el_limb)
diff = np.sqrt(pa_diff**2 + el_diff**2)
idy, idx = np.unravel_index(np.argmin(diff), diff.shape)
hgc_lon = hgc.lon[idy, idx]
hgc_lat = hgc.lat[idy, idx]
# Make sure hgc lon runs between 0 and 360, rather than -180, 180
if hgc_lon.value < 0:
hgc_lon = 360 * u.deg + hgc_lon
euvi.peek()
plt.contour(pa.value, levels=[c1cme['pa'][i]], colors=['r'])
plt.plot(xn.value, yn.value, 'yo', markersize=12)
plt.plot(idx, idy, 'y*', markersize=12)
plt.contour(hgc.lon.value, colors=['white'])
plt.contour(hgc.lat.value, colors=['m'])
plt.draw()
name = cme_root2 + r'\euvi_eruption_id.png'
plt.savefig(name)
plt.close('all')
# Mark out the coords for chucking in the array
eucme.loc[eucme.index == i, 'carrington_longitude'] = hgc_lon.value
eucme.loc[eucme.index == i, 'carrington_latitude'] = hgc_lat.value
return eucme