def map_hpc_to_hg_rotate(map, epi_lon=0, epi_lat=0, xbin=1, ybin=1):
"""Take a map (like an AIA map) and convert it from HPC to HG."""
# epi_lon = 0
# epi_lat = 90
# xbin = 1
# ybin = 1
x, y = sunpy.wcs.convert_pixel_to_data(
map.shape[1],
map.shape[0],
map.scale["x"],
map.scale["y"],
map.reference_pixel["x"],
map.reference_pixel["y"],
map.reference_coordinate["x"],
map.reference_coordinate["y"],
map.coordinate_system["x"],
)
hccx, hccy, hccz = wcs.convert_hpc_hcc_xyz(map.rsun_meters, map.dsun, map.units["x"], map.units["y"], x, y)
rot_hccz, rot_hccx, rot_hccy = euler_zyz((hccz, hccx, hccy), (0.0, epi_lat - 90.0, -epi_lon))
lon_map, lat_map = wcs.convert_hcc_hg(
map.rsun_meters, map.heliographic_latitude, map.heliographic_longitude, rot_hccx, rot_hccy, z=rot_hccz
)
lon_bin = xbin
lat_bin = ybin
lon_range = (np.nanmin(lon_map), np.nanmax(lon_map))
lat_range = (np.nanmin(lat_map), np.nanmax(lat_map))
lon = np.arange(lon_range[0], lon_range[1], lon_bin)
lat = np.arange(lat_range[0], lat_range[1], lat_bin)
newgrid = np.meshgrid(lon, lat)
ng_xyz = wcs.convert_hg_hcc_xyz(
map.rsun_meters, map.heliographic_latitude, map.heliographic_longitude, newgrid[0], newgrid[1]
)
ng_zp, ng_xp, ng_yp = euler_zyz((ng_xyz[2], ng_xyz[0], ng_xyz[1]), (epi_lon, 90.0 - epi_lat, 0.0))
points = np.vstack((lon_map.ravel(), lat_map.ravel())).T
values = np.array(map).ravel()
# get rid of all of the bad (nan) indices (i.e. those off of the sun)
index = np.isfinite(points[:, 0]) * np.isfinite(points[:, 1])
# points = np.vstack((points[index,0], points[index,1])).T
points = points[index]
values = values[index]
newdata = griddata(points, values, newgrid, method="linear")
newdata[ng_zp < 0] = np.nan
dict_header = {
"CDELT1": lon_bin,
"NAXIS1": len(lon),
"CRVAL1": lon.min(),
"CRPIX1": 1,
"CRPIX2": 1,
"CUNIT1": "deg",
"CTYPE1": "HG",
"CDELT2": lat_bin,
"NAXIS2": len(lat),
"CRVAL2": lat.min(),
"CUNIT2": "deg",
"CTYPE2": "HG",
}
header = sunpy.map.MapHeader(dict_header)
transformed_map = sunpy.make_map(newdata, header)
return transformed_map
def map_hpc_to_hg_rotate(map, epi_lon = 0, epi_lat = 90, lon_bin = 1, lat_bin = 1):
"""
Transform raw data in HPC coordinates to HG' coordinates
HG' = HG, except center at wave epicenter
"""
x, y = sunpy.wcs.convert_pixel_to_data(map.shape[1],
map.shape[0],
map.scale['x'],
map.scale['y'],
map.reference_pixel['x'],
map.reference_pixel['y'],
map.reference_coordinate['x'],
map.reference_coordinate['y'],
map.coordinate_system['x'])
hccx, hccy, hccz = wcs.convert_hpc_hcc_xyz(map.rsun_meters,
map.dsun, map.units['x'],
map.units['y'],
x, y)
rot_hccz, rot_hccx, rot_hccy = euler_zyz((hccz, hccx, hccy), (0., epi_lat-90., -epi_lon))
lon_map, lat_map = wcs.convert_hcc_hg(map.rsun_meters,
map.heliographic_latitude,
map.heliographic_longitude,
rot_hccx, rot_hccy, z = rot_hccz)
lon_range = (np.nanmin(lon_map), np.nanmax(lon_map))
lat_range = (np.nanmin(lat_map), np.nanmax(lat_map))
lon = np.arange(lon_range[0], lon_range[1], lon_bin)
lat = np.arange(lat_range[0], lat_range[1], lat_bin)
newgrid = np.meshgrid(lon, lat)
ng_xyz = wcs.convert_hg_hcc_xyz(map.rsun_meters,
map.heliographic_latitude,
map.heliographic_longitude,
newgrid[0], newgrid[1])
ng_zp, ng_xp, ng_yp = euler_zyz((ng_xyz[2], ng_xyz[0], ng_xyz[1]),
(epi_lon, 90.-epi_lat, 0.))
points = np.vstack((lon_map.ravel(), lat_map.ravel())).T
values = np.array(map).ravel()
# get rid of all of the bad (nan) indices (i.e. those off of the sun)
index = np.isfinite(points[:,0]) * np.isfinite(points[:,1])
#points = np.vstack((points[index,0], points[index,1])).T
points = points[index]
values = values[index]
newdata = griddata(points, values, newgrid, method="linear")
newdata[ng_zp < 0] = np.nan
dict_header = {
'CDELT1': lon_bin,
'NAXIS1': len(lon),
'CRVAL1': lon.min(),
'CRPIX1': 1,
'CRPIX2': 1,
'CUNIT1': "deg",
'CTYPE1': "HG",
'CDELT2': lat_bin,
'NAXIS2': len(lat),
'CRVAL2': lat.min(),
'CUNIT2': "deg",
'CTYPE2': "HG"
}
header = sunpy.map.MapHeader(dict_header)
transformed_map = sunpy.make_map(newdata, header)
transformed_map.name = map.name
transformed_map.date = map.date
return transformed_map
def map_hpc_to_hg_rotate(smap, epi_lon = 0, epi_lat = 0, xbin = 1, ybin = 1):
"""Take a map (like an AIA map) and convert it from HPC to HG."""
#import sunpy
#import util
#from sunpy import wcs
#import numpy as np
#from scipy.interpolate import griddata
from sim.wave2d.wave2d import euler_zyz
#from matplotlib import colors
# epi_lon = -10
# epi_lat = 0
#aia = sunpy.Map(sunpy.AIA_171_IMAGE).resample([500,500])
# tmap = util.map_hpc_to_hg(aia)
# tmap.show()
#map = aia
#x, y = wcs.convert_pixel_to_data(map.header)
x, y = wcs.convert_pixel_to_data(smap.shape[1],
smap.shape[0],
smap.scale['x'],
smap.scale['y'],
smap.reference_pixel['x'],
smap.reference_pixel['y'],
smap.reference_coordinate['x'],
smap.reference_coordinate['y'],
smap.coordinate_system['x'])
#hccx, hccy, hccz = wcs.convert_hpc_hcc_xyz(map.header, x, y)
hccx, hccy, hccz = wcs.convert_hpc_hcc_xyz(smap.rsun_meters,
smap.dsun,
smap.units['x'],
smap.units['y'],
x,
y)
# rot_hccz, rot_hccy, rot_hccx = euler_zyz((hccz, hccx, hccy), (epi_lon, 90.-epi_lat, 0.))
rot_hccz, rot_hccx, rot_hccy = euler_zyz((hccz, hccx, hccy), (0., epi_lat-90., -epi_lon))
# zpp, xpp, ypp = euler_zyz(zxy_p, (0., hglt_obs, total_seconds*rotation))
#lon_map, lat_map = wcs.convert_hcc_hg(map.header, rot_hccx, rot_hccy, z = rot_hccz)
lon_map, lat_map = wcs.convert_hcc_hg(smap.rsun_meters,
smap.heliographic_latitude,
smap.carrington_longitude,
rot_hccx, rot_hccy, z = rot_hccz)
lon_bin = xbin
lat_bin = ybin
lon_range = (np.nanmin(lon_map), np.nanmax(lon_map))
lat_range = (np.nanmin(lat_map), np.nanmax(lat_map))
lon = np.arange(lon_range[0], lon_range[1], lon_bin)
lat = np.arange(lat_range[0], lat_range[1], lat_bin)
newgrid = np.meshgrid(lon, lat)
#This extra conversion and rotation back are needed to determine where to
#mask out points that can't have corresponding data
#ng_xyz = wcs.convert_hg_hcc_xyz(map.header, newgrid[0], newgrid[1])
ng_xyz = wcs.convert_hg_hcc_xyz(smap.rsun_meters,
smap.heliographic_latitude,
smap.carrington_longitude,
newgrid[0], newgrid[1])
ng_zp, ng_xp, ng_yp = euler_zyz((ng_xyz[2], ng_xyz[0], ng_xyz[1]),
(epi_lon, 90.-epi_lat, 0.))
points = np.vstack((lon_map.ravel(), lat_map.ravel())).T
values = np.array(smap).ravel()
# get rid of all of the bad (nan) indices (i.e. those off of the sun)
index = np.isfinite(points[:,0]) * np.isfinite(points[:,1])
#points = np.vstack((points[index,0], points[index,1])).T
points = points[index]
values = values[index]
newdata = griddata(points, values, newgrid, method="cubic")
newdata[ng_zp < 0] = np.nan
header = smap._original_header.copy()
header['CDELT1'] = lon_bin
header['NAXIS1'] = len(lon)
header['CRVAL1'] = lon.min()
header['CRPIX1'] = 1
header['CRPIX2'] = 1
header['CUNIT1'] = "deg"
header['CTYPE1'] = "HG"
header['CDELT2'] = lat_bin
header['NAXIS2'] = len(lat)
header['CRVAL2'] = lat.min()
header['CUNIT2'] = "deg"
header['CTYPE2'] = "HG"
transformed_map = sunpy.map.BaseMap(newdata, header)
transformed_map.cmap = smap.cmap
transformed_map.name = smap.name
transformed_map.date = smap.date
transformed_map.center['x'] = wcs.get_center(smap.shape[1], smap.scale['x'], smap.reference_coordinate['x'],smap.reference_pixel['x'])
transformed_map.center['y'] = wcs.get_center(smap.shape[0], smap.scale['y'], smap.reference_coordinate['y'],smap.reference_pixel['y'])
#transformed_map.show()
return transformed_map