def test_findnearest():
indf, xf = sd.find_nearest([10, 15, 12, 20, 14, 33], [32, 12.01])
assert_allclose(indf, [5, 2])
assert_allclose(xf, [33., 12.])
indf, xf = sd.find_nearest(
(datetime.datetime(2012, 1, 1, 12), datetime.datetime(2012, 1, 1, 11)),
datetime.datetime(2012, 1, 1, 11, 30))
assert_equal(indf, 0)
assert_equal(xf, datetime.datetime(2012, 1, 1, 12))
def test_findnearest():
indf, xf = sd.find_nearest([10, 15, 12, 20, 14, 33], [32, 12.01])
assert (indf == [5, 2]).all()
assert xf == approx([33.0, 12.0])
indf, xf = sd.find_nearest(
(datetime.datetime(2012, 1, 1, 12), datetime.datetime(2012, 1, 1, 11)),
datetime.datetime(2012, 1, 1, 11, 30),
)
assert indf == 0
assert xf == datetime.datetime(2012, 1, 1, 12)
def optical(vidfn,calfn,treq,terror_cam):
"""
quick-n-dirty load of optical data to corroborate with other tle and ncdf data
"""
#data, rawFrameInd,finf = goRead(vidfn,xyPix=(512,512),xyBin=(1,1), ut1Req=T,kineticraw=1/fps,startUTC=tstart)
treq -= timedelta(seconds=terror_cam)
treq = forceutc(treq)
treq = treq.timestamp()
vidfn = Path(vidfn).expanduser()
if not vidfn.suffix == '.h5':
raise IOError('{} needs to be HST HDF5 file'.format(vidfn))
with h5py.File(str(vidfn),'r',libver='latest') as f:
tcam = f['ut1_unix']
i = find_nearest(tcam,treq)[0]
if i==0 or i==tcam.size-1:
logging.critical('requested time {} at or past edge of camera time {}'.format(datetime.utcfromtimestamp(treq),datetime.utcfromtimestamp(tcam[i])))
tcam = f['ut1_unix'][i]
img = f['rawimg'][i,...]
llacam = f['sensorloc'].value
#%% map pixels to sky
calfn = Path(calfn).expanduser()
with h5py.File(str(calfn),'r',libver='latest') as f:
az = f['/az'].value
el = f['/el'].value
return img, tcam, llacam, az,el
def runglowaurora(params: dict, z_km: np.ndarray = None) -> xarray.Dataset:
""" Runs Fortran GLOW program and collects results in friendly arrays with metadata. """
# %% (-2) check/process user inputs
assert isinstance(params['flux'], (float, int, np.ndarray))
assert isinstance(params['E0'], (float, int))
assert isinstance(params['t0'], (datetime, str))
assert isinstance(params['glat'], (float, int))
assert isinstance(params['glon'], (float, int))
# %% (-1) if no manual f10.7 and ap, autoload by date
if not 'f107a' in params or params['f107a'] is None:
if getApF107 is None:
raise ImportError(GRIDERR)
f107Ap = getApF107(params['t0'])
params['f107a'] = params['f107p'] = f107Ap['f107s'].item()
params['f107'] = f107Ap['f107'].item()
params['Ap'] = (f107Ap['Ap'].item(), ) * 7
# %% flux grid / date
eflux = np.atleast_1d(params['flux'])
yeardoy, utsec = datetime2yeardoy(params['t0'])[:2]
# %% (0) define altitude grid [km]
# FIXME: dynamic grid
z_km = np.array(
list(range(80, 110, 1)) + [110., 111.5, 113., 114.5] +
list(range(116, 136, 2)) +
[137., 140., 144., 148., 153., 158., 164., 170] + list(
chain(range(176, 204, 7), range(205, 253, 8), range(254, 299, 9),
range(300, 650, 10))))
if z_km is None:
if glowalt is not None:
z_km = glowalt()
else:
raise ImportError(GRIDERR)
# %% (1) setup flux at top of ionosphere
ener, dE = glowfort.egrid()
if eflux.size == 1:
logging.info(
'generating maxwellian input differential number flux spectrum')
# maxwellian input PhiTop at top of ionosphere
phitop = glowfort.maxt(eflux,
params['E0'],
ener,
dE,
itail=0,
fmono=0,
emono=0)
elif eflux.size > 1: # eigenprofile generation, one non-zero bin at a time
logging.info('running in eigenprofile mode')
# FIXME should we interpolate instead? Maybe not, as long as we're consistent ref. Semeter 2006
e0ind = find_nearest(ener, params['e0'])[0]
phitop = np.zeros_like(ener)
phitop[e0ind] = ener[e0ind] # value in glow grid closest to zett grid
else:
return ValueError(
'I do not understand your electron flux input. Should be scalar or vector'
)
phi = np.stack((ener, dE, phitop), 1) # Nalt x 3
assert phi.shape[1] == 3
# %% (2) msis,iri,glow model
glowfort.glowbasic(
yeardoy,
utsec,
params['glat'],
params['glon'] % 360,
params['f107a'],
params['f107'],
params['f107p'],
params['Ap'],
z_km,
pyphi=phi,
pyverbose=False,
)
# %% (3) collect outputs
lamb = [
3371, 4278, 5200, 5577, 6300, 7320, 10400, 3466, 7774, 8446, 3726,
'LBH', 1356, 1493, 1304
] # same for ZETA and ZCETA
ions = [
'nO+(2P)', 'nO+(2D)', 'nO+(4S)', 'nN+', 'nN2+', 'nO2+', 'nNO+', 'nO',
'nO2', 'nN2', 'nNO'
]
neut = ['O', 'O2', 'N2']
sim = xarray.Dataset()
# %% array of volume emission rates at each altitude; cm-3 s-1:
sim['zeta'] = xarray.DataArray(glowfort.cglow.zeta.T,
dims=['z_km', 'wavelength_nm'],
coords={
'z_km': z_km,
'wavelength_nm': lamb
})
# %% array of contributions to each v.e.r at each alt; cm-3 s-1 Nalt x Nwavelength x Nprocess
sim['zceta'] = xarray.DataArray(
glowfort.cglow.zceta.T, # See Glow.txt.
dims=['z_km', 'wavelength_nm', 'process'],
coords={
'z_km': z_km,
'wavelength_nm': lamb,
'process': range(glowfort.cglow.zceta.T.shape[2])
})
# %% electron impact ionization rates calculated by ETRANS; cm-3 s-1
sim['sion'] = xarray.DataArray(glowfort.cglow.sion.T,
dims=['z_km', 'neutral_species'],
coords={
'z_km': z_km,
'neutral_species': neut
})
# %% total photoionization rate at each altitude, cm-3 s-1
sim['tpi'] = xarray.DataArray(glowfort.cglow.tpi,
dims=['z_km'],
coords={'z_km': z_km})
# %% total electron impact ionization rate at each altitude, cm-3 s-1
sim['tei'] = xarray.DataArray(glowfort.cglow.tei,
dims=['z_km'],
coords={'z_km': z_km})
# %% PESPEC photoelectron production rate at energy, altitude; cm-3 s-1
sim['pespec'] = xarray.DataArray(glowfort.cglow.pespec.T,
dims=['z_km', 'eV'],
coords={
'z_km': z_km,
'eV': ener
})
# sim['photIon'] = xarray.DataArray(np.hstack((photI[:,None],ImpI[:,None],ecalc[:,None],ion)),
# dims=['z_km','type'],
# coords={'z_km':z_km,
# 'type':['photoIoniz','eImpactIoniz','ne']+products})
#
# sim['isr'] = xarray.DataArray(isr,
# dims=['z_km','param'],
# coords={'z_km':z_km,'param':['ne','Te','Ti']})
#
# sim['phitop'] = xarray.DataArray(phi[:,2],
# dims=['eV'],
# coords={'eV':phi[:,0]})
#
sim.attrs['sza'] = np.degrees(glowfort.cglow.sza)
#
# sim['tez'] = xarray.DataArray(glowfort.cglow.tez,
# dims=['z_km'], coords={'z_km':z_km})
# %% production and loss rates
# prate = prate.T; lrate=lrate.T #fortran to C ordering 2x170x20, only first 12 columns are used
#
# #column labels by inspection of fortran/gchem.f staring after "DO 150 I=1,JMAX" (thanks Stan!)
# sim['prates'] = xarray.DataArray(prate[1,:,:12], #columns 12:20 are identically zero
# dims=['z_km','reaction'],
# coords={'z_km':z_km,
# 'reaction':['O+(2P)','O+(2D)','O+(4S)','N+','N2+','O2+','NO+',
# 'N2(A)','N(2P)','N(2D)','O(1S)','O(1D)']}
# )
#
# sim['lrates'] = xarray.DataArray(lrate[1,:,:12], #columns 12:20 are identically zero
# dims=['z_km','reaction'],
# coords={'z_km':z_km,
# 'reaction':['O+(2P)','O+(2D)','O+(4S)','N+','N2+','O2+','NO+',
# 'N2(A)','N(2P)','N(2D)','O(1S)','O(1D)']}
# )
#
# sim['sion'] = xarray.DataArray(glowfort.cglow.sion,
# dims=['gas','z_km'],
# coords={'gas':['O','O2','N2'],
# 'z_km':z_km})
return sim
def main():
print(sd.find_nearest([10, 15, 12, 20, 14, 33], [32, 12.01]))
print(INCORRECTRESULT_using_bisect([10, 15, 12, 20, 14, 33], [32, 12.01]))
#!/usr/bin/env python3
import numpy as np
from bisect import bisect
import sciencedates as sd
def INCORRECTRESULT_using_bisect(x, X0): # pragma: no cover
X0 = np.atleast_1d(X0)
x.sort()
ind = [bisect(x, x0) for x0 in X0]
x = np.asanyarray(x)
return np.asanyarray(ind), x[ind]
print(sd.find_nearest([10, 15, 12, 20, 14, 33], [32, 12.01]))
print(INCORRECTRESULT_using_bisect([10, 15, 12, 20, 14, 33], [32, 12.01]))