def __init__(self, obj):
def lonlat(lon, lat):
from pyproj import Geod
geo = Geod(ellps='WGS84')
x = [np.min(lon), np.max(lon), np.min(lat), np.max(lat)]
return {
'lon_0': .5 * np.sum(x[:2]),
'lat_0': .5 * np.sum(x[2:]),
'width': geo.inv(x[0], x[3], x[1], x[3])[2],
'height': geo.inv(x[0], x[2], x[0], x[3])[2],
'lat_1': -10,
'lat_2': -40
}
def attshw(file):
h, w = file.variables['XLONG_M'].shape[-2:]
return atts(file, w, h)
# this doesn't work if one of the calls to try_list succeeds but returns the wrong
# object for the init call to Basemap
super().__init__(projection='lcc',
**hh.try_list(
obj, lambda x: lonlat(x['lon'], x['lat']),
lambda x: x.to_dict(), lambda x: hh.try_list(
x,
atts,
attshw,
lambda x: lonlat(hh.g2d(x.variables['XLONG']),
hh.g2d(x.variables['XLAT'])),
), lambda x: lonlat(*x), lambda x: lonlat(
(lambda a, b: b, a)(*x[1].latlons()))))
try:
self._lonlat = hh.lonlat(obj)
except:
pass
def project(cls, ds, stations):
"""Project coordinates from a netCDF file and station longitude / latitude data first, then call :meth:`.compute`.
:param ds: Dataset containing the coordinate variables and projection paramters
:type ds: :class:`~xarray.Dataset`
:param stations: DataFrame containing the locations (longitude / latitude) for which the surrounding 4 points should be determined (as returned from :meth:`.CEAZA.Downloader.get_stations`)
:type stations: :class:`~pandas.DataFrame`
:returns: see :class:`.Squares` for description of returned DataArray
:rtype: :class:`~xarray.DataArray`
"""
from helpers import g2d
from pyproj import Proj
pr = Proj(**proj_params(ds))
x, y = pr(g2d(ds.XLONG), g2d(ds.XLAT))
k, l = pr(*stations.loc[:, ('lon', 'lat')].as_matrix().T)
return cls.compute(x, y, k, l)
def xr_interp(v,
proj_params,
stations,
time=True,
method='linear',
return_type='pandas',
dt=-4):
p = Proj(**proj_params)
ij = p(*stations.loc[:, ('lon', 'lat')].as_matrix().T)
lon, lat, tv = hh.coord_names(v, 'lon', 'lat', 'time')
xy = p(hh.g2d(v.coords[lon]), hh.g2d(v.coords[lat]))
x = np.array(v).squeeze()
if time:
t = v[tv] + np.timedelta64(dt, 'h')
df = grid_interp(xy, x, ij, stations.index, t, method=method)
else:
df = grid_interp(xy, hh.g2d(x), ij, stations.index, method=method)
return df if return_type == 'pandas' else xr.DataArray(df)
def interp_nc(nc,
var,
stations,
time=True,
tz=False,
method='linear',
map=None):
m = mp.basemap(nc) if map is None else map
xy = m.xy()
ij = m(*hh.lonlat(stations))
x = nc.variables[var][:].squeeze()
if time:
t = pd.DatetimeIndex(hh.get_time(nc))
if tz:
t = t.tz_localize('UTC').tz_convert(hh.CEAZAMetTZ())
else:
t -= np.timedelta64(4, 'h')
return sp.grid_interp(xy, x, ij, stations.index, t, method=method)
else:
return sp.grid_interp(xy, hh.g2d(x), ij, stations.index, method=method)