def no_source(time: datetime, glat: float, glon: float, Nbins: int,
Talt: float, Thot: float) -> xarray.Dataset:
""" testing only, may give non-physical results"""
idate, utsec = glowdate(time)
ip = gi.get_indices([time - timedelta(days=1), time], 81)
cmd = [
str(EXE),
idate,
utsec,
str(glat),
str(glon),
str(ip["f107s"][1]),
str(ip["f107"][1]),
str(ip["f107"][0]),
str(ip["Ap"][1]),
"-nosource",
str(Nbins),
str(Talt),
str(Thot),
]
dat = subprocess.check_output(cmd,
timeout=15,
stderr=subprocess.DEVNULL,
universal_newlines=True)
return glowparse(dat, time, ip, glat, glon)
def no_precipitation(time: datetime, glat: float, glon: float,
Nbins: int) -> xarray.Dataset:
idate, utsec = glowdate(time)
ip = gi.get_indices([time - timedelta(days=1), time], 81)
cmd = [
str(EXE),
idate,
utsec,
str(glat),
str(glon),
str(ip["f107s"][1]),
str(ip["f107"][1]),
str(ip["f107"][0]),
str(ip["Ap"][1]),
"-noprecip",
str(Nbins),
]
dat = subprocess.check_output(cmd,
timeout=15,
stderr=subprocess.DEVNULL,
universal_newlines=True)
return glowparse(dat, time, ip, glat, glon)
def test_multi_past():
dates = pandas.date_range(datetime(2017, 12, 31, 23), datetime(2018, 1, 1, 2), freq="3H")
try:
dat = gi.get_indices(dates)
except ConnectionError as e:
pytest.skip(f"possible timeout error {e}")
assert (dat.index == [datetime(2017, 1, 1, 22, 30), datetime(2018, 1, 1, 1, 30)]).all
def test_farfuture():
t = date(2029, 12, 21)
dat = gi.get_indices(t, 81)
assert dat.shape == (1, 5)
assert dat.index[0] == datetime(2030, 1, 1, 2, 37, 40, 799998)
assert "Ap" in dat
assert "f107" in dat
assert "f107s" in dat
assert "Aps" in dat
def test_past_and_future():
dates = [datetime(2017, 1, 1), datetime(2030, 3, 1)]
try:
dat = gi.get_indices(dates)
except ConnectionError as e:
pytest.skip(f"possible timeout error {e}")
pasttime = datetime(2017, 1, 1)
if dat["resolution"][0] == "d":
pasttime += timedelta(hours=1, minutes=30)
assert (dat.index == [pasttime, datetime(2030, 3, 2, 22, 55, 11, 999997)]).all()
def test_list():
t = [date(2018, 1, 1), datetime(2018, 1, 2)]
try:
dat = gi.get_indices(t)
except ConnectionError as e:
pytest.skip(f"possible timeout error {e}")
assert dat.shape[0] == 2
assert dat.shape[1] in (3, 4)
assert (dat.index == [datetime(2018, 1, 1, 1, 30), datetime(2018, 1, 2, 1, 30)]).all
def test_past(dt, hour, f107, f107s, ap, aps, kp):
try:
dat = gi.get_indices(dt, 81)
except ConnectionError as e:
pytest.skip(f"possible timeout error {e}")
assert dat.shape[0] == 1
assert dat["f107"].iloc[0] == approx(f107, abs=0.1)
assert dat["f107s"].iloc[0] == approx(f107s, abs=0.1)
assert dat["Ap"].iloc[0] == ap
assert dat["Aps"].iloc[0] == approx(aps, abs=0.1)
if "Kp" in dat:
assert dat["Kp"].iloc[0] == approx(kp, abs=0.1)
def ebins(time: datetime, glat: float, glon: float, Ebins: np.ndarray,
Phitop: np.ndarray) -> xarray.Dataset:
idate, utsec = glowdate(time)
# %% Matlab compatible workaround (may change to use stdin in future)
Efn = Path(tempfile.mkstemp(".dat")[1])
with Efn.open("wb") as f:
Ebins.tofile(f)
Phitop.tofile(f)
tmpfile_size = Efn.stat().st_size
expected_size = (Ebins.size + Phitop.size) * 4
if tmpfile_size != expected_size:
raise OSError(f"{Efn} size {tmpfile_size} != {expected_size}")
ip = gi.get_indices([time - timedelta(days=1), time], 81)
cmd = [
str(EXE),
idate,
utsec,
str(glat),
str(glon),
str(ip["f107s"][1]),
str(ip["f107"][1]),
str(ip["f107"][0]),
str(ip["Ap"][1]),
"-e",
str(Ebins.size),
str(Efn),
]
ret = subprocess.run(cmd,
timeout=15,
universal_newlines=True,
stdout=subprocess.PIPE)
if ret.returncode:
raise RuntimeError(f"GLOW failed at {time}")
try:
Efn.unlink()
except PermissionError:
# Windows sometimes does this if something else is holding the file open.
# this is also why we don't use a tempfile context manager for this application.
pass
return glowparse(ret.stdout, time, ip, glat, glon)
def test_nearfuture():
t = datetime.today() + timedelta(days=3)
try:
dat = gi.get_indices(t)
except ConnectionError as e:
pytest.skip(f"possible timeout error {e}")
assert dat.shape[0] == 1
if t.hour >= 12:
assert dat.index[0] == datetime(t.year, t.month, t.day) + timedelta(days=1)
else:
assert dat.index[0] == datetime(t.year, t.month, t.day)
assert "Ap" in dat
assert "f107" in dat
def test_past(dt, hour, f107s, ap, aps, kp):
try:
dat = gi.get_indices(dt, 81)
except ConnectionError as e:
pytest.skip(f"possible timeout error {e}")
assert dat.shape[0] == 1
if dat["resolution"].iloc[0] == "m":
assert dat["f107"].iloc[0] == approx(73.5, abs=0.1)
assert dat["f107s"].iloc[0] == approx(76.4, abs=0.1)
assert dat["Ap"].iloc[0] == 9
assert dat["Aps"].iloc[0] == approx(9.66, abs=0.1)
elif dat["resolution"].iloc[0] == "d":
assert dat["f107"].iloc[0] == approx(76.4)
assert dat["f107s"].iloc[0] == approx(f107s, abs=0.1)
assert dat["Ap"].iloc[0] == ap
assert dat["Aps"].iloc[0] == approx(aps, abs=0.1)
assert dat["Kp"].iloc[0] == approx(kp, abs=0.1)
else:
raise ValueError(f"unknown resolution {dat.resolution}")
def rungtd1d(
time: datetime, altkm: float, glat: float, glon: float, indices: dict[str, typing.Any] = None
) -> xarray.Dataset:
"""
This is the "atomic" function looped by other functions
"""
time = todatetime(time)
# %% get solar parameters for date
if not indices:
indices = gi.get_indices(time, smoothdays=81).squeeze().to_dict()
assert isinstance(indices, dict)
# %% dimensions
altkm = np.atleast_1d(altkm)
if altkm.ndim != 1:
raise ValueError("altitude read incorrectly")
if not isinstance(glon, (int, float, np.int32, np.int64)):
raise TypeError("single longitude only")
if not isinstance(glat, (int, float, np.int32, np.int64)):
raise TypeError("single latitude only")
# %%
doy = time.strftime("%j")
altkm = np.atleast_1d(altkm)
# %%
dens = np.empty((altkm.size, len(species)))
temp = np.empty((altkm.size, len(ttypes)))
# %% build on run
exe_name = "msise00_driver"
if os.name == "nt":
exe_name += ".exe"
if not importlib.resources.is_resource(__package__, exe_name):
# check for CMake here to avoid "generator didn't stop after throw() higher level raise"
if not shutil.which("ctest"):
raise FileNotFoundError(
"""
CMake not available. try installing CMake like:
pip install cmake"""
)
with importlib.resources.path(__package__, "setup.cmake") as setup_file:
cmake(setup_file)
if not importlib.resources.is_resource(__package__, exe_name):
raise RuntimeError("could not build MSISE00 Fortran driver")
# check inputs for error, especially unavailable indices
if not np.isfinite(glat).all():
raise ValueError("glat is not finite.")
if not np.isfinite(glon).all():
raise ValueError("glon is not finite.")
f107s = indices["f107s"]
if not np.isfinite(f107s):
raise ValueError("f107s is not finite.")
f107s = indices["f107s"]
if not np.isfinite(f107s):
raise ValueError("f107s is not finite.")
f107 = indices["f107"]
if not np.isfinite(f107):
raise ValueError("f107 is not finite.")
Ap = indices["Ap"]
if not np.isfinite(Ap):
raise ValueError("Ap is not finite.")
with importlib.resources.path(__package__, exe_name) as exe:
for i, a in enumerate(altkm):
cmd = [
str(exe),
doy,
str(time.hour),
str(time.minute),
str(time.second),
str(glat),
str(glon),
str(f107s),
str(f107),
str(Ap),
str(a),
]
logging.info(" ".join(cmd))
ret = subprocess.check_output(cmd, text=True)
# different compilers throw in extra \n
raw = list(map(float, ret.split()))
if not len(raw) == 9 + 2:
raise ValueError(ret)
dens[i, :] = raw[:9]
temp[i, :] = raw[9:]
dsf = {
k: (("time", "alt_km", "lat", "lon"), v[None, :, None, None])
for (k, v) in zip(species, dens.T)
}
dsf.update(
{
"Tn": (("time", "alt_km", "lat", "lon"), temp[:, 1][None, :, None, None]),
"Texo": (("time", "alt_km", "lat", "lon"), temp[:, 0][None, :, None, None]),
}
)
atmos = xarray.Dataset(
dsf, # type: ignore
coords={"time": [time], "alt_km": altkm, "lat": [glat], "lon": [glon]},
attrs={
"species": species,
"f107s": indices["f107s"],
"f107": indices["f107"],
"Ap": indices["Ap"],
},
)
return atmos
from argparse import ArgumentParser
p = ArgumentParser()
p.add_argument(
"start_stop",
help="date or date range of observation yyyy-mm-dd (START, STOP)",
nargs="+")
a = p.parse_args()
start = parse(a.start_stop[0])
if len(a.start_stop) > 1:
end = parse(a.start_stop[1])
else:
end = start + timedelta(days=1)
dates = pandas.date_range(start, end, freq="3H")
inds = gi.get_indices(dates)
# %% plot
fig = figure()
ax = fig.gca()
inds.plot(ax=ax) # , marker='.'
ax.set_ylabel("index values")
ax.set_xlabel("time [UTC]")
ax.grid(True)
# fig.savefig('2015.png', bbox_inches='tight')
show()