def returnRaw(folder, azfn=None, elfn=None, wl=558, timelim=[]):
t1 = datetime.now()
data = read_asi.load(folder,
azfn=azfn,
elfn=elfn,
wavelenreq=wl,
treq=timelim)
print('Data loaded in {}'.format(datetime.now() - t1))
return data
def main():
p = ArgumentParser(description="register/plot other camera FOV onto Themis FOV")
p.add_argument("themiscal", help="ASI calibration")
p.add_argument("dasccal", help="DASC cal", nargs=3)
p.add_argument("-o", "--ofn", help="write output plot")
P = p.parse_args()
# %% load data
themis = ta.loadcal(P.themiscal)
dasc = dio.load(*P.dasccal)
# %% merge FOV
# paint HiST field of view onto Themis
themis, dasc = taf.mergefov(themis, dasc, projalt=110e3, method="perimeter")
# %% plot joint az/el contours
tap.jointazel(themis, P.ofn, "Themis Gakona overlaid on Poker Flat ASI")
show()
def main():
p = ArgumentParser(
description="register/plot other camera FOV onto Themis FOV")
p.add_argument("themiscal", help="ASI calibration")
p.add_argument("dasccal", help="DASC cal", nargs=3)
p.add_argument("-o", "--ofn", help="write output plot")
p.add_argument("-v", "--verbose", action="store_true")
P = p.parse_args()
# %% load az/el data
themis = ta.loadcal(P.themiscal)
if themis.site == "gako":
themis.attrs["Baz"] = 21.028
themis.attrs["Bel"] = 75.816
themis.attrs["Bepoch"] = "2010-01-01"
themis.attrs["verbose"] = False
themis.attrs["srpts"] = np.logspace(5, 6.9, 40)
themis["cutcol"] = np.arange(50, themis.x.size - 75, 1, dtype=int)
dasc = dio.load(*P.dasccal)
if "lat" not in dasc.attrs:
dasc.attrs["lat"] = 65.126
dasc.attrs["lon"] = -147.479
dasc.attrs["alt_m"] = 200.0
dasc.attrs["Baz"] = 21.145 # Baz = declination
dasc.attrs["Bel"] = 77.464 # Bel = inclination
dasc.attrs["Bepoch"] = "2010-01-01"
dasc.attrs["verbose"] = False
# np.linspace(10**4.6,10**6.8,40) #np.logspace(4.6, 6.4, 40)
dasc.attrs["srpts"] = np.logspace(4.6, 7.5, 40)
dasc["cutcol"] = np.arange(75, dasc.x.size - 100, 1, dtype=int)
# %% extract common 2-D slice (plane)
# paint HiST field of view onto Themis
themis, dasc = taf.mergefov(themis, dasc, projalt=110e3, method="MZslice")
themis = taf.line2plane(themis)
dasc = taf.line2plane(dasc)
if show is None:
return
# %% plot slice pixel mask
tap.jointazel(themis, P.ofn,
"Themis Gakona slice toward DASC and magnetic zenith")
tap.jointazel(
dasc, P.ofn,
"DASC Poker Flat slice toward Themis Gakona and magnetic zenith")
show()
@author: Raymond
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import dascutils.io as dio
import pymap3d as pm
from pathlib import Path
PC = ccrs.PlateCarree()
ST = ccrs.Stereographic()
MR = ccrs.Mercator()
R = Path('~/code/dascutils/').expanduser()
dat = dio.load(R / 'tests', R / 'cal/PKR_DASC_20110112')
img = dat[428].isel(time=1).values
# %% coord conv.
alt = 100000
srange = alt / np.sin(np.radians(dat.el.values))
lat, lon, alt = pm.aer2geodetic(dat.az.values, dat.el.values, srange, dat.lat,
dat.lon, 0)
mask = np.isfinite(lat)
top = None
for i, m in enumerate(mask):
good = m.nonzero()[0]
if good.size == 0:
continue
elif top is None:
def returnASpolar(folder,
azfn=None,
elfn=None,
wl=558,
timelim=[],
Nim=512,
asi=False,
el_filter=None):
# Read in the data utliizing DASCutils
print('Reading the data')
t1 = datetime.now()
data = read_asi.load(folder,
azfn=azfn,
elfn=elfn,
wavelenreq=wl,
treq=timelim)
print('Data loaded in {}'.format(datetime.now() - t1))
# Get time vector as datetime
T = data.time.values.astype(datetime)
print('Data reducted from {0} to {1}'.format(T.shape[0], T.shape[0]))
# Get Az and El grids
az = data.az[1]
el = data.el[1]
# Camera position
lat0 = data.lat
lon0 = data.lon
alt0 = data.alt_m
# Reshape image calibration if needed
im_test = data[wl][0].values
if im_test.shape[0] != el.shape[0]:
el = interpolateCoordinate(el, N=im_test.shape[0])
az = interpolateCoordinate(az, N=im_test.shape[0])
# Elivation filter/mask
if el_filter is not None and isinstance(el_filter, int):
el = np.where(el >= el_filter, el, np.nan)
az = np.where(el >= el_filter, az, np.nan)
# Image size
if Nim is None or (not isinstance(Nim, int)):
Nim = az.shape[0]
# Prepare a polar projection to cartesian
rel = 90 - el
x = rel * np.cos(np.deg2rad(az))
y = rel * np.sin(np.deg2rad(az))
# Mask NaNs
# mask = np.ma.masked_invalid(x)
# x = x[~mask.mask]
# y = y[~mask.mask]
# Make an empty image array
imae = np.nan * np.ones((T.shape[0], Nim, Nim))
# Interpolation grid
# Input grid
# X,Y = np.mgrid[np.nanmin(x):np.nanmax(x):x.shape[0]*1j,
# np.nanmin(y):np.nanmax(y):y.shape[0]*1j]
# xy=np.zeros([X.shape[0] * x.shape[1],2])
# xy[:,0]=X.flatten()
# xy[:,1]=Y.flatten()
# Output grid
# uv=np.zeros([Nim*Nim,2])
# Xi, Yi = np.mgrid[-80:80:Nim*1j,
# -80:80:Nim*1j]
# uv[:,0]=Yi.flatten()
# uv[:,1]=Xi.flatten()
# vtx, wts = interp_weights(xy, uv)
for i in range(T.shape[0]):
print('Processing-interpolating {}/{}'.format(i + 1, T.shape[0]))
# Read a raw image
im = np.rot90(data[wl][i].values, -1)
#Interpolate Polar
xgrid, ygrid, Zim = interpolatePolar(x, y, im, Nim)
# Zim=interpolate(im.flatten(), vtx, wts)
Zim = Zim.reshape(Nim, Nim)
# Assign to array
imae[i, :, :] = Zim
if asi:
return T, xgrid, ygrid, imae, [lon0, lat0, alt0]
else:
return T, xgrid, ygrid, imae
def returnASLatLonAlt(folder,
azfn=None,
elfn=None,
wl=558,
timelim=[],
alt=130,
Nim=512,
el_filter=None,
asi=False,
verbose=False):
# Mapping altitude to meters
mapping_alt = alt * 1e3
# Read in the data utliizing DASCutils
print('Reading the PKR asi images')
t1 = datetime.now()
data = read_asi.load(folder,
azfn=azfn,
elfn=elfn,
wavelenreq=wl,
treq=timelim,
verbose=verbose)
print('Data loaded in {}'.format(datetime.now() - t1))
# Get time vector as datetime
T = data.time.values.astype(datetime)
# Get Az and El grids
az = data.az[1]
el = data.el[1]
# Reshape image calibration if needed
im_test = data[wl][0].values
if im_test.shape[0] != el.shape[0]:
el = interpolateCoordinate(el, N=im_test.shape[0])
az = interpolateCoordinate(az, N=im_test.shape[0])
# Elivation filter/mask
if el_filter is not None and isinstance(el_filter, int):
el = np.where(el >= el_filter, el, np.nan)
az = np.where(el >= el_filter, az, np.nan)
# Image size
if Nim is None or (not isinstance(Nim, int)):
Nim = az.shape[0]
########################## Convert into WSG84 #############################
# Map to altitude
r = mapping_alt / np.sin(np.deg2rad(el))
# Convert to WSG
lat0 = data.lat
lon0 = data.lon
alt0 = data.alt_m
lat, lon, alt = aer2geodetic(az, el, r, lat0, lon0, alt0)
# Make an empty image array
imlla = np.nan * np.ones((T.shape[0], Nim, Nim))
# c = 0
for i in range(T.shape[0]):
print('Processing-interpolating {}/{}'.format(i + 1, T.shape[0]))
# Read a raw image
im = data[wl][i].values
#Interpolate Lat Lon to preset Alt
xgrid, ygrid, Zim = interpolateAS(lon, lat, im, N=Nim)
# Assign to an array
imlla[i, :, :] = Zim
# c += 1
if asi:
return T, xgrid, ygrid, imlla, [lon0, lat0, alt0]
else:
return T, xgrid, ygrid, imlla