def test_geographic_to_cartesian():
# Example taken from:
# Snyder, J.P. Map Projections A Working Manual, 1987, page 338.
R = 3.0
lat_0 = 40.0 # 40 degrees North latitude
lon_0 = -100. # 100 degrees West longitude
lat = -20.0 # 20 degrees S latitude
lon = 100.0 # 100.0 E longitude
x = -5.8311398
y = 5.5444634
with warnings.catch_warnings(): # invalid divide is handled by code
warnings.simplefilter("ignore", category=RuntimeWarning)
projparams = {
'proj': 'pyart_aeqd', 'lon_0': lon_0, 'lat_0': lat_0, 'R': R}
x, y = transforms.geographic_to_cartesian(lon, lat, projparams)
assert_almost_equal(x, -5.8311398, 7)
assert_almost_equal(y, 5.5444634, 7)
# Use the default R value
with warnings.catch_warnings(): # invalid divide is handled by code
warnings.simplefilter("ignore", category=RuntimeWarning)
projparams = {
'proj': 'pyart_aeqd', 'lon_0': lon_0, 'lat_0': lat_0}
x, y = transforms.geographic_to_cartesian(lon, lat, projparams)
x /= 6370997. / 3.
y /= 6370997. / 3.
assert_almost_equal(x, -5.8311398, 7)
assert_almost_equal(y, 5.5444634, 7)
def _find_offsets(radars, projparams, grid_origin_alt):
""" Find offset between radars and grid origin. """
# loop over the radars finding offsets from the origin
offsets = [] # offsets from the grid origin, in meters, for each radar
for radar in radars:
x_disp, y_disp = geographic_to_cartesian(
radar.longitude['data'], radar.latitude['data'], projparams)
try:
z_disp = float(radar.altitude['data']) - grid_origin_alt
offsets.append((z_disp, float(y_disp), float(x_disp)))
except TypeError:
z_disp = np.mean(radar.altitude['data']) - grid_origin_alt
offsets.append((z_disp, np.mean(y_disp), np.mean(x_disp)))
return offsets
def test_geographic_to_cartesian_pyproj():
# Example taken from:
# Snyder, J.P. Map Projections A Working Manual, 1987, page 338.
R = 3.0
lat_0 = 40.0 # 40 degrees North latitude
lon_0 = -100. # 100 degrees West longitude
lat = -20.0 # 20 degrees S latitude
lon = 100.0 # 100.0 E longitude
x = -5.8311398
y = 5.5444634
projparams = {'proj': 'aeqd', 'lon_0': lon_0, 'lat_0': lat_0, 'R': R}
x, y = transforms.geographic_to_cartesian(lon, lat, projparams)
assert_almost_equal(x, -5.8311398, 7)
assert_almost_equal(y, 5.5444634, 7)
def map_gates_to_grid(
radars, grid_shape, grid_limits, grid_origin=None,
grid_origin_alt=None, grid_projection=None,
fields=None, gatefilters=False, map_roi=True,
weighting_function='Barnes', toa=17000.0, roi_func='dist_beam',
constant_roi=None, z_factor=0.05, xy_factor=0.02, min_radius=500.0,
h_factor=1.0, nb=1.5, bsp=1.0, analysis_time=None, **kwargs):
"""
Map gates from one or more radars to a Cartesian grid.
Generate a Cartesian grid of points for the requested fields from the
collected points from one or more radars. For each radar gate that is not
filtered a radius of influence is calculated. The weighted field values
for that gate are added to all grid points within that radius. This
routine scaled linearly with the number of radar gates and the effective
grid size.
Parameters not defined below are identical to those in
:py:func:`map_to_grid`.
Parameters
----------
roi_func : str or RoIFunction
Radius of influence function. A function which takes an
z, y, x grid location, in meters, and returns a radius (in meters)
within which all collected points will be included in the weighting
for that grid points. Examples can be found in the
Typically following strings can use to specify a built in
radius of influence function:
* constant: constant radius of influence.
* dist: radius grows with the distance from each radar.
* dist_beam: radius grows with the distance from each radar
and parameter are based of virtual beam sizes.
A custom RoIFunction can be defined using the RoIFunction class
and defining a get_roi method which returns the radius. For efficient
mapping this class should be implemented in Cython.
Returns
-------
grids : dict
Dictionary of mapped fields. The keys of the dictionary are given by
parameter fields. Each elements is a `grid_size` float64 array
containing the interpolated grid for that field.
"""
# make a tuple if passed a radar object as the first argument
if isinstance(radars, Radar):
radars = (radars, )
skip_transform = False
if len(radars) == 1 and grid_origin_alt is None and grid_origin is None:
skip_transform = True
if grid_origin_alt is None:
try:
grid_origin_alt = float(radars[0].altitude['data'])
except TypeError:
grid_origin_alt = np.mean(radars[0].altitude['data'])
gatefilters = _parse_gatefilters(gatefilters, radars)
cy_weighting_function = _determine_cy_weighting_func(weighting_function)
projparams = _find_projparams(grid_origin, radars, grid_projection)
fields = _determine_fields(fields, radars)
grid_starts, grid_steps = _find_grid_params(grid_shape, grid_limits)
offsets = _find_offsets(radars, projparams, grid_origin_alt)
roi_func = _parse_roi_func(roi_func, constant_roi, z_factor, xy_factor,
min_radius, h_factor, nb, bsp, offsets)
# prepare grid storage arrays
nfields = len(fields)
grid_sum = np.zeros(grid_shape + (nfields, ), dtype=np.float32)
grid_wsum = np.zeros(grid_shape + (nfields, ), dtype=np.float32)
gatemapper = GateToGridMapper(
grid_shape, grid_starts, grid_steps, grid_sum, grid_wsum)
# project gates from each radar onto the grid
for radar, gatefilter in zip(radars, gatefilters):
# Copy the field data and masks.
# TODO method that does not copy field data into new array
shape = (radar.nrays, radar.ngates, nfields)
field_data = np.empty(shape, dtype='float32')
field_mask = np.empty(shape, dtype='uint8')
for i, field in enumerate(fields):
fdata = radar.fields[field]['data']
field_data[:, :, i] = np.ma.getdata(fdata)
field_mask[:, :, i] = np.ma.getmaskarray(fdata)
# find excluded gates from the gatefilter
if gatefilter is False:
gatefilter = GateFilter(radar) # include all gates
elif gatefilter is None:
gatefilter = moment_based_gate_filter(radar, **kwargs)
excluded_gates = gatefilter.gate_excluded.astype('uint8')
# calculate gate locations relative to the grid origin
if skip_transform:
# single radar, grid centered at radar location
gate_x = radar.gate_x['data']
gate_y = radar.gate_y['data']
else:
gate_x, gate_y = geographic_to_cartesian(
radar.gate_longitude['data'], radar.gate_latitude['data'],
projparams)
gate_z = radar.gate_altitude['data'] - grid_origin_alt
# Range and offset params (OpenMosiac addition)
gate_range = radar.range['data']
if analysis_time is None:
gate_timedelta = np.zeros(radar.nrays)
else:
gate_timedelta = np.array([
(t - analysis_time.replace(tzinfo=None)).total_seconds()
for t in num2date(radar.time['data'], radar.time['units'])
])
# map the gates onto the grid (modified with OpenMosaic addition)
gatemapper.map_gates_to_grid(
radar.ngates, radar.nrays, gate_z.astype('float32'),
gate_y.astype('float32'), gate_x.astype('float32'),
gate_range.astype('float32'), gate_timedelta.astype('float32'),
field_data, field_mask, excluded_gates,
toa, roi_func, cy_weighting_function)
# create and return the grid dictionary
mweight = np.ma.masked_equal(grid_wsum, 0)
msum = np.ma.masked_array(grid_sum, mweight.mask)
grids = dict(
[(f, msum[..., i] / mweight[..., i]) for i, f in enumerate(fields)])
if map_roi:
roi_array = np.empty(grid_shape, dtype=np.float32)
gatemapper.find_roi_for_grid(roi_array, roi_func)
grids['ROI'] = roi_array
return grids