# Source code for pyart.map.gates_to_grid

```
"""
Generate a Cartesian grid by mapping from radar gates onto the grid.
"""
import gc
import warnings
import numpy as np
from ..core.radar import Radar
from ..core.transforms import geographic_to_cartesian
from ..filters import GateFilter, moment_based_gate_filter
from ._gate_to_grid_map import (
ConstantRoI,
DistBeamRoI,
DistRoI,
GateToGridMapper,
RoIFunction,
)
[docs]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="Barnes2",
toa=17000.0,
roi_func="dist_beam",
constant_roi=None,
z_factor=0.05,
xy_factor=0.02,
min_radius=250.0,
h_factor=(1.0, 1.0, 1.0),
nb=1.0,
bsp=1.0,
dist_factor=(1.0, 1.0, 1.0),
**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.
See Also
--------
grid_from_radars : Map to a grid and return a Grid object
map_to_grid : Create grid by finding the radius of influence around each
grid point.
"""
# make a tuple if passed a radar object as the first argument
if isinstance(radars, Radar):
radars = (radars,)
if len(radars) == 0:
raise ValueError("Length of radars tuple cannot be zero")
# set min_radius depending on whether processing ARM radars
try:
if "platform_id" in radars[0].metadata.keys():
if np.any(
[
x in radars[0].metadata["platform_id"].lower()
for x in ["sacr", "sapr"]
]
):
min_radius = 100.0
except AttributeError:
pass
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"])
# convert input h_factor and dist_factor from scalar, tuple, or list to array
if isinstance(h_factor, (tuple, list)):
h_factor = np.array(h_factor, dtype="float32")
elif isinstance(h_factor, float):
h_factor = np.full(3, h_factor, dtype="float32")
if isinstance(dist_factor, (tuple, list)):
dist_factor = np.array(dist_factor, dtype="float32")
elif isinstance(dist_factor, float):
dist_factor = np.full(3, dist_factor, dtype="float32")
gatefilters = _parse_gatefilters(gatefilters, radars)
cy_weighting_function = _detemine_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
if nfields == 0:
raise ValueError("There are 0 fields in the radar object to interpolate!")
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
# map the gates onto the grid
gatemapper.map_gates_to_grid(
radar.ngates,
radar.nrays,
gate_z.astype("float32"),
gate_y.astype("float32"),
gate_x.astype("float32"),
field_data,
field_mask,
excluded_gates,
roi_func,
cy_weighting_function,
dist_factor,
)
# create and return the grid dictionary
mweight = np.ma.masked_equal(grid_wsum, 0)
msum = np.ma.masked_array(grid_sum, mweight.mask)
grids = {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
gc.collect()
return grids
def _detemine_cy_weighting_func(weighting_function):
"""Determine cython weight function value."""
if weighting_function.upper() == "BARNES2":
cy_weighting_function = 3
elif weighting_function.upper() == "NEAREST":
cy_weighting_function = 2
elif weighting_function.upper() == "CRESSMAN":
cy_weighting_function = 1
elif weighting_function.upper() == "BARNES":
warnings.warn(
"Barnes weighting function is deprecated."
" Please use Barnes 2 to be consistent with"
" Pauley and Wu 1990. Default will be switched"
" to Barnes2 on June 1st.",
DeprecationWarning,
)
cy_weighting_function = 0
else:
raise ValueError("unknown weighting_function")
return cy_weighting_function
def _find_projparams(grid_origin, radars, grid_projection):
"""Determine the projection parameter."""
# parse grid_origin
if grid_origin is None:
try:
lat = float(radars[0].latitude["data"])
lon = float(radars[0].longitude["data"])
except TypeError:
lat = np.mean(radars[0].latitude["data"])
lon = np.mean(radars[0].longitude["data"])
grid_origin = (lat, lon)
grid_origin_lat, grid_origin_lon = grid_origin
# parse grid_projection
if grid_projection is None:
grid_projection = {"proj": "pyart_aeqd", "_include_lon_0_lat_0": True}
projparams = grid_projection.copy()
if projparams.pop("_include_lon_0_lat_0", False):
projparams["lon_0"] = grid_origin_lon
projparams["lat_0"] = grid_origin_lat
return projparams
def _parse_gatefilters(gatefilters, radars):
"""Parse the gatefilters parameter."""
if isinstance(gatefilters, GateFilter):
gatefilters = (gatefilters,) # make tuple if single filter passed
if gatefilters is False:
gatefilters = (False,) * len(radars)
if gatefilters is None:
gatefilters = (None,) * len(radars)
if len(gatefilters) != len(radars):
raise ValueError("Length of gatefilters must match length of radars")
return gatefilters
def _determine_fields(fields, radars):
"""Determine which field should be mapped to the grid."""
if fields is None:
fields = set(radars[0].fields.keys())
for radar in radars[1:]:
fields = fields.intersection(radar.fields.keys())
fields = list(fields)
return fields
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 _find_grid_params(grid_shape, grid_limits):
"""Find the starting points and step size of the grid."""
nz, ny, nx = grid_shape
zr, yr, xr = grid_limits
z_start, z_stop = zr
y_start, y_stop = yr
x_start, x_stop = xr
if nz == 1:
z_step = 0.0
else:
z_step = (z_stop - z_start) / (nz - 1.0)
if ny == 1:
y_step = 0.0
else:
y_step = (y_stop - y_start) / (ny - 1.0)
if nx == 1:
x_step = 0.0
else:
x_step = (x_stop - x_start) / (nx - 1.0)
grid_starts = (z_start, y_start, x_start)
grid_steps = (z_step, y_step, x_step)
return grid_starts, grid_steps
def _parse_roi_func(
roi_func, constant_roi, z_factor, xy_factor, min_radius, h_factor, nb, bsp, offsets
):
"""Return the Radius of influence object."""
if not isinstance(roi_func, RoIFunction):
if constant_roi is not None:
roi_func = "constant"
else:
constant_roi = 500.0
if roi_func == "constant":
roi_func = ConstantRoI(constant_roi)
elif roi_func == "dist":
roi_func = DistRoI(z_factor, xy_factor, min_radius, offsets)
elif roi_func == "dist_beam":
roi_func = DistBeamRoI(h_factor, nb, bsp, min_radius, offsets)
else:
raise ValueError(f"unknown roi_func: {roi_func}")
return roi_func
```