# Source code for pyart.map.grid_mapper

```
"""
Utilities for mapping radar objects to Cartesian grids.
"""
import warnings
import netCDF4
import numpy as np
import scipy.spatial
from ..config import get_fillvalue, get_metadata
from ..core.transforms import geographic_to_cartesian
from ..core.grid import Grid
from ..core.radar import Radar
from ..filters import GateFilter, moment_based_gate_filter
from ..io.common import make_time_unit_str
from ._load_nn_field_data import _load_nn_field_data
from .ckdtree import cKDTree
from .gates_to_grid import map_gates_to_grid
[docs]def grid_from_radars(radars, grid_shape, grid_limits,
gridding_algo='map_gates_to_grid', copy_field_dtypes=True,
**kwargs):
"""
Map one or more radars to a Cartesian grid returning a Grid object.
Additional arguments are passed to :py:func:`map_to_grid` or
:py:func:`map_gates_to_grid`.
Parameters
----------
radars : Radar or tuple of Radar objects.
Radar objects which will be mapped to the Cartesian grid.
grid_shape : 3-tuple of floats
Number of points in the grid (z, y, x).
grid_limits : 3-tuple of 2-tuples
Minimum and maximum grid location (inclusive) in meters for the
z, y, x coordinates.
gridding_algo : 'map_to_grid' or 'map_gates_to_grid'
Algorithm to use for gridding. 'map_to_grid' finds all gates within
a radius of influence for each grid point, 'map_gates_to_grid' maps
each radar gate onto the grid using a radius of influence and is
typically significantly faster.
copy_field_dtypes : bool
Whether or not to maintain the original dtypes found in the radar
fields, which will then be used in the grid fields.
Returns
-------
grid : Grid
A :py:class:`pyart.io.Grid` object containing the gridded radar
data.
See Also
--------
map_to_grid : Map to grid and return a dictionary of radar fields.
map_gates_to_grid : Map each gate onto a grid returning a dictionary of
radar fields.
References
----------
Barnes S., 1964: A Technique for Maximizing Details in Numerical Weather
Map Analysis. Journal of Applied Meteorology and Climatology, 3(4),
396-409.
Cressman G., 1959: An operational objective analysis system. Monthly
Weather Review, 87(10), 367-374.
Pauley, P. M. and X. Wu, 1990: The theoretical, discrete, and actual
response of the Barnes objective analysis scheme for one- and
two-dimensional fields. Monthly Weather Review, 118, 1145-1164
"""
# 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')
# map the radar(s) to a cartesian grid
if gridding_algo == 'map_to_grid':
grids = map_to_grid(radars, grid_shape, grid_limits, **kwargs)
elif gridding_algo == 'map_gates_to_grid':
grids = map_gates_to_grid(radars, grid_shape, grid_limits, **kwargs)
else:
raise ValueError('invalid gridding_algo')
# create and populate the field dictionary
fields = {}
first_radar = radars[0]
for field in grids.keys():
if field == 'ROI':
fields['ROI'] = {
'data': grids['ROI'],
'standard_name': 'radius_of_influence',
'long_name': 'Radius of influence for mapping',
'units': 'm',
'least_significant_digit': 1,
'_FillValue': get_fillvalue()}
else:
fields[field] = {'data': grids[field]}
# copy the metadata from the radar to the grid
for key in first_radar.fields[field].keys():
if key == 'data':
continue
fields[field][key] = first_radar.fields[field][key]
# time dictionaries
time = get_metadata('grid_time')
time['data'] = np.array([first_radar.time['data'][0]])
time['units'] = first_radar.time['units']
# grid coordinate dictionaries
nz, ny, nx = grid_shape
(z0, z1), (y0, y1), (x0, x1) = grid_limits
x = get_metadata('x')
x['data'] = np.linspace(x0, x1, nx)
y = get_metadata('y')
y['data'] = np.linspace(y0, y1, ny)
z = get_metadata('z')
z['data'] = np.linspace(z0, z1, nz)
# grid origin location dictionaries
origin_latitude = get_metadata('origin_latitude')
origin_longitude = get_metadata('origin_longitude')
if 'grid_origin' in kwargs:
origin_latitude['data'] = np.array([kwargs['grid_origin'][0]])
origin_longitude['data'] = np.array([kwargs['grid_origin'][1]])
else:
origin_latitude['data'] = first_radar.latitude['data']
origin_longitude['data'] = first_radar.longitude['data']
origin_altitude = get_metadata('origin_altitude')
if 'grid_origin_alt' in kwargs:
origin_altitude['data'] = np.array([kwargs['grid_origin_alt']])
else:
origin_altitude['data'] = first_radar.altitude['data']
# metadata dictionary
metadata = dict(first_radar.metadata)
# create radar_ dictionaries
radar_latitude = get_metadata('radar_latitude')
radar_latitude['data'] = np.array(
[r.latitude['data'][0] for r in radars])
radar_longitude = get_metadata('radar_longitude')
radar_longitude['data'] = np.array(
[radar.longitude['data'][0] for radar in radars])
radar_altitude = get_metadata('radar_altitude')
radar_altitude['data'] = np.array(
[radar.altitude['data'][0] for radar in radars])
radar_time = get_metadata('radar_time')
times, units = _unify_times_for_radars(radars)
radar_time['units'] = units
radar_time['data'] = times
radar_name = get_metadata('radar_name')
name_key = 'instrument_name'
names = [radar.metadata[name_key] if name_key in radar.metadata else ''
for radar in radars]
radar_name['data'] = np.array(names)
projection = kwargs.pop('grid_projection', None)
# Copies radar field dtypes to grid field dtypes if True.
if copy_field_dtypes:
for field in fields.keys():
if field == 'ROI':
continue
dtype = first_radar.fields[field]['data'].dtype
fields[
field]['data'] = fields[field]['data'].astype(dtype)
return Grid(
time, fields, metadata,
origin_latitude, origin_longitude, origin_altitude, x, y, z,
radar_latitude=radar_latitude, radar_longitude=radar_longitude,
radar_altitude=radar_altitude, radar_name=radar_name,
radar_time=radar_time, projection=projection)
def _unify_times_for_radars(radars):
""" Return unified start times and units for a number of radars. """
dates = [netCDF4.num2date(radar.time['data'][0], radar.time['units'])
for radar in radars]
units = make_time_unit_str(min(dates))
times = netCDF4.date2num(dates, units)
return times, units
class NNLocator:
"""
Nearest neighbor locator.
Class for finding the neighbors of a points within a given distance.
Parameters
----------
data : array_like, (n_sample, n_dimensions)
Locations of points to be indexed. Note that if data is a
C-contiguous array of dtype float64 the data will not be copied.
Othersize and internal copy will be made.
leafsize : int
The number of points at which the algorithm switches over to
brute-force. This can significantly impact the speed of the
contruction and query of the tree.
algorithm : 'kd_tree', optional.
Algorithm used to compute the nearest neigbors. 'kd_tree' uses a
k-d tree.
"""
def __init__(self, data, leafsize=10, algorithm='kd_tree'):
""" initalize. """
self._algorithm = algorithm
if algorithm == 'kd_tree':
self.tree = cKDTree(data, leafsize=leafsize)
else:
raise ValueError('invalid algorithm')
def find_neighbors_and_dists(self, q, r):
"""
Find all neighbors and distances within a given distance.
Parameters
----------
q : n_dimensional tuple
Point to query
r : float
Distance within which neighbors are returned.
Returns
-------
ind : array of intergers
Indices of the neighbors.
dist : array of floats
Distances to the neighbors.
"""
if self._algorithm == 'kd_tree':
ind = self.tree.query_ball_point(q, r)
if len(ind) == 0:
return ind, 0
dist = scipy.spatial.minkowski_distance(q, self.tree.data[ind])
return ind, dist
[docs]def map_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,
copy_field_data=True, algorithm='kd_tree', leafsize=10.,
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, **kwargs):
"""
Map 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. The field value for a grid
point is found by interpolating from the collected points within a given
radius of influence and weighting these nearby points according to their
distance from the grid points. Collected points are filtered
according to a number of criteria so that undesired points are not
included in the interpolation.
Parameters
----------
radars : Radar or tuple of Radar objects.
Radar objects which will be mapped to the Cartesian grid.
grid_shape : 3-tuple of floats
Number of points in the grid (z, y, x).
grid_limits : 3-tuple of 2-tuples
Minimum and maximum grid location (inclusive) in meters for the
z, y, x coordinates.
grid_origin : (float, float) or None
Latitude and longitude of grid origin. None sets the origin
to the location of the first radar.
grid_origin_alt: float or None
Altitude of grid origin, in meters. None sets the origin
to the location of the first radar.
grid_projection : dict
Projection parameters defining the map projection used to transform the
locations of the radar gates in geographic coordinate to Cartesian
coodinates. None will use the default dictionary which uses a native
azimutal equidistance projection. See :py:func:`pyart.core.Grid` for
additional details on this parameter. The geographic coordinates of
the radar gates are calculated using the projection defined for each
radar. No transformation is used if a grid_origin and grid_origin_alt
are None and a single radar is specified.
fields : list or None
List of fields within the radar objects which will be mapped to
the cartesian grid. None, the default, will map the fields which are
present in all the radar objects.
gatefilters : GateFilter, tuple of GateFilter objects, optional
Specify what gates from each radar will be included in the
interpolation onto the grid. Only gates specified in each gatefilters
will be included in the mapping to the grid. A single GateFilter can
be used if a single Radar is being mapped. A value of False for a
specific element or the entire parameter will apply no filtering of
gates for a specific radar or all radars (the default).
Similarily a value of None will create a GateFilter from the
radar moments using any additional arguments by passing them to
:py:func:`moment_based_gate_filter`.
roi_func : str or function
Radius of influence function. A functions 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
:py:func:`example_roi_func_constant`,
:py:func:`example_roi_func_dist`, and
:py:func:`example_roi_func_dist_beam`.
Alternatively the 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.
The parameters which control these functions are listed in the
`Other Parameters` section below.
map_roi : bool
True to include a radius of influence field in the returned
dictionary under the 'ROI' key. This is the value of roi_func at all
grid points.
weighting_function : 'Barnes' or 'Barnes2' or 'Cressman' or 'Nearest'
Functions used to weight nearby collected points when interpolating a
grid point.
toa : float
Top of atmosphere in meters. Collected points above this height are
not included in the interpolation.
Other Parameters
----------------
constant_roi : float
Radius of influence parameter for the built in 'constant' function.
This parameter is the constant radius in meter for all grid points.
This parameter is used when `roi_func` is `constant` or constant_roi
is not None. If constant_roi is not None, the constant roi_func is
used automatically.
z_factor, xy_factor, min_radius : float
Radius of influence parameters for the built in 'dist' function.
The parameter correspond to the radius size increase, in meters,
per meter increase in the z-dimension from the nearest radar,
the same foreach meteter in the xy-distance from the nearest radar,
and the minimum radius of influence in meters. These parameters are
only used when `roi_func` is 'dist'.
h_factor, nb, bsp, min_radius : float
Radius of influence parameters for the built in 'dist_beam' function.
The parameter correspond to the height scaling, virtual beam width,
virtual beam spacing, and minimum radius of influence. These
parameters are only used when `roi_func` is 'dist_mean'.
copy_field_data : bool
True to copy the data within the radar fields for faster gridding,
the dtype for all fields in the grid will be float64. False will not
copy the data which preserves the dtype of the fields in the grid,
may use less memory but results in significantly slower gridding
times. When False gates which are masked in a particular field but
are not masked in the `refl_field` field will still be included in
the interpolation. This can be prevented by setting this parameter
to True or by gridding each field individually setting the
`refl_field` parameter and the `fields` parameter to the field in
question. It is recommended to set this parameter to True.
algorithm : 'kd_tree'.
Algorithms to use for finding the nearest neighbors. 'kd_tree' is the
only valid option.
leafsize : int
Leaf size passed to the neighbor lookup tree. This can affect the
speed of the construction and query, as well as the memory required
to store the tree. The optimal value depends on the nature of the
problem. This value should only effect the speed of the gridding,
not the results.
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 grid and return a Grid object.
"""
# 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')
skip_transform = False
if len(radars) == 1 and grid_origin_alt is None and grid_origin is None:
skip_transform = True
# parse the gatefilters argument
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')
# check the parameters
if weighting_function.upper() not in [
'CRESSMAN', 'BARNES2', 'BARNES', 'NEAREST']:
raise ValueError('unknown weighting_function')
if algorithm not in ['kd_tree']:
raise ValueError('unknown algorithm: %s' % algorithm)
badval = get_fillvalue()
# parse the grid_projection
if grid_projection is None:
grid_projection = {
'proj': 'pyart_aeqd', '_include_lon_0_lat_0': True}
# find the grid origin if not given
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
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'])
# fields which should be mapped, None for fields which are in all radars
if fields is None:
fields = set(radars[0].fields.keys())
for radar in radars[1:]:
fields = fields.intersection(radar.fields.keys())
fields = list(fields)
nfields = len(fields)
# determine the number of gates (collected points) in each radar
nradars = len(radars)
ngates_per_radar = [r.fields[fields[0]]['data'].size for r in radars]
total_gates = np.sum(ngates_per_radar)
gate_offset = np.cumsum([0] + ngates_per_radar)
# create arrays to hold the gate locations and indicators if the gate
# should be included in the interpolation.
gate_locations = np.ma.empty((total_gates, 3), dtype=np.float64)
include_gate = np.ones((total_gates), dtype=np.bool_)
offsets = [] # offsets from the grid origin, in meters, for each radar
# create a field lookup tables
if copy_field_data:
# copy_field_data == True, lookups are performed on a 2D copy of
# all of the field data in all radar objects, this can be a
# large array. These lookup are fast as the dtype is know.
field_data = np.ma.empty((total_gates, nfields), dtype=np.float64)
else:
# copy_field_data == False, lookups are performed on a 2D object
# array pointing to the radar fields themselved, no copies are made.
# A table mapping filtered gates to raw gates is created later.
# Since the dtype is not not know this method is slow.
field_data_objs = np.empty((nfields, nradars), dtype='object')
# We also need to know how many gates from each radar are included
# in the NNLocator, the filtered_gates_per_radar list records this
filtered_gates_per_radar = []
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
# loop over the radars finding gate locations, field data, and offset
for iradar, (radar, gatefilter) in enumerate(zip(radars, gatefilters)):
# calculate radar offset from the origin
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)))
# calculate cartesian locations of gates
if skip_transform:
xg_loc = radar.gate_x['data']
yg_loc = radar.gate_y['data']
else:
xg_loc, yg_loc = geographic_to_cartesian(
radar.gate_longitude['data'], radar.gate_latitude['data'],
projparams)
zg_loc = radar.gate_altitude['data'] - grid_origin_alt
# add gate locations to gate_locations array
start, end = gate_offset[iradar], gate_offset[iradar + 1]
gate_locations[start:end, 0] = zg_loc.flatten()
gate_locations[start:end, 1] = yg_loc.flatten()
gate_locations[start:end, 2] = xg_loc.flatten()
del xg_loc, yg_loc
# determine which gates should be included in the interpolation
gflags = zg_loc < toa # include only those below toa
if gatefilter is not False:
# excluded gates marked by the gatefilter
if gatefilter is None:
gatefilter = moment_based_gate_filter(radar, **kwargs)
gflags = np.logical_and(gflags, gatefilter.gate_included)
include_gate[start:end] = gflags.flatten()
if not copy_field_data:
# record the number of gates from the current radar which
# are included in the interpolation.
filtered_gates_per_radar.append(gflags.sum())
del gflags, zg_loc
# copy/store references to field data for lookup
for ifield, field in enumerate(fields):
flat_field_data = radar.fields[field]['data'].ravel()
if copy_field_data:
field_data[start:end, ifield] = flat_field_data
else:
field_data_objs[ifield, iradar] = flat_field_data
del flat_field_data
# build field data lookup tables
if copy_field_data:
# copy_field_data == True we filtered the field data in the
# same manner as we will filter the gate locations.
filtered_field_data = field_data[include_gate]
else:
# copy_field_data == True, build a lookup table which maps from
# filtered gate number to (radar number, radar gate number)
# the radar number is given as the quotent of the lookup table
# value divided by total_gates, the remainer gives the index of
# the gate in the flat field data array.
# initalize the lookup table with values from 0 ... total gates
lookup = np.where(include_gate)[0]
# number of filtered gates before a given radar
filtered_gate_offset = np.cumsum([0] + filtered_gates_per_radar)
# for radars 1 to N-1 add total_gates to the lookup table and
# subtract the number of gates in all ealier radars.
for i in range(1, nradars):
l_start = filtered_gate_offset[i]
l_end = filtered_gate_offset[i + 1]
gates_before = gate_offset[i]
lookup[l_start:l_end] += (total_gates * i - gates_before)
# populate the nearest neighbor locator with the filtered gate locations
nnlocator = NNLocator(gate_locations[include_gate], algorithm=algorithm,
leafsize=leafsize)
# unpack the grid parameters
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.
else:
z_step = (z_stop - z_start) / (nz - 1.)
if ny == 1:
y_step = 0.
else:
y_step = (y_stop - y_start) / (ny - 1.)
if nx == 1:
x_step = 0.
else:
x_step = (x_stop - x_start) / (nx - 1.)
if not hasattr(roi_func, '__call__'):
if constant_roi is not None:
roi_func = 'constant'
else:
constant_roi = 500.0
if roi_func == 'constant':
roi_func = _gen_roi_func_constant(constant_roi)
elif roi_func == 'dist':
roi_func = _gen_roi_func_dist(
z_factor, xy_factor, min_radius, offsets)
elif roi_func == 'dist_beam':
roi_func = _gen_roi_func_dist_beam(
h_factor, nb, bsp, min_radius, offsets)
else:
raise ValueError('unknown roi_func: %s' % roi_func)
# create array to hold interpolated grid data and roi if requested
grid_data = np.ma.empty((nz, ny, nx, nfields), dtype=np.float64)
grid_data.set_fill_value(badval)
if map_roi:
roi = np.empty((nz, ny, nx), dtype=np.float64)
# interpolate field values for each point in the grid
for iz, iy, ix in np.ndindex(nz, ny, nx):
# calculate the grid point
x = x_start + x_step * ix
y = y_start + y_step * iy
z = z_start + z_step * iz
r = roi_func(z, y, x)
if map_roi:
roi[iz, iy, ix] = r
# find neighbors and distances
ind, dist = nnlocator.find_neighbors_and_dists((z, y, x), r)
if len(ind) == 0:
# when there are no neighbors, mark the grid point as bad
grid_data[iz, iy, ix] = np.ma.masked
grid_data.data[iz, iy, ix] = badval
continue
# find the field values for all neighbors
if copy_field_data:
# copy_field_data == True, a slice will get the field data.
nn_field_data = filtered_field_data[ind]
else:
# copy_field_data == False, use the lookup table to find the
# radar numbers and gate numbers for the neighbors. Then
# use the _load_nn_field_data function to load this data from
# the field data object array. This is done in Cython for speed.
r_nums, e_nums = divmod(lookup[ind], total_gates)
npoints = r_nums.size
r_nums = r_nums.astype(np.intc)
e_nums = e_nums.astype(np.intc)
nn_field_data = np.empty((npoints, nfields), np.float64)
_load_nn_field_data(field_data_objs, nfields, npoints, r_nums,
e_nums, nn_field_data)
# preforms weighting of neighbors.
dist2 = dist * dist
r2 = r * r
if weighting_function.upper() == 'NEAREST':
value = nn_field_data[np.argmin(dist2)]
else:
if weighting_function.upper() == 'CRESSMAN':
weights = (r2 - dist2) / (r2 + dist2)
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)
weights = np.exp(-dist2 / (2.0 * r2)) + 1e-5
elif weighting_function.upper() == 'BARNES2':
weights = np.exp(-dist2 / (r2/4)) + 1e-5
value = np.ma.average(nn_field_data, weights=weights, axis=0)
grid_data[iz, iy, ix] = value
# create and return the grid dictionary
grids = dict([(f, grid_data[..., i]) for i, f in enumerate(fields)])
if map_roi:
grids['ROI'] = roi
return grids
# Radius of Influence (RoI) functions
[docs]def example_roi_func_constant(zg, yg, xg):
"""
Example RoI function which returns a constant radius.
Parameters
----------
zg, yg, xg : float
Distance from the grid center in meters for the x, y and z axes.
Returns
-------
roi : float
Radius of influence in meters
"""
# RoI function parameters
constant = 500. # constant 500 meter RoI
return constant
def _gen_roi_func_constant(constant_roi):
"""
Return a RoI function which returns a constant radius.
See :py:func:`map_to_grid` for a description of the parameters.
"""
def roi(zg, yg, xg):
""" constant radius of influence function. """
return constant_roi
return roi
[docs]def example_roi_func_dist(zg, yg, xg):
"""
Example RoI function which returns a radius which grows with distance.
Parameters
----------
zg, yg, xg : float
Distance from the grid center in meters for the x, y and z axes.
Returns
-------
roi : float
"""
# RoI function parameters
z_factor = 0.05 # increase in radius per meter increase in z dim
xy_factor = 0.02 # increase in radius per meter increase in xy dim
min_radius = 500. # minimum radius
offsets = ((0, 0, 0), ) # z, y, x offset of grid in meters from radar(s)
offsets = np.array(offsets)
zg_off = offsets[:, 0]
yg_off = offsets[:, 1]
xg_off = offsets[:, 2]
r = np.maximum(z_factor * (zg - zg_off) +
xy_factor * np.sqrt((xg - xg_off)**2 + (yg - yg_off)**2),
min_radius)
return min(r)
def _gen_roi_func_dist(z_factor, xy_factor, min_radius, offsets):
"""
Return a RoI function whose radius grows with distance.
See :py:func:`map_to_grid` for a description of the parameters.
"""
offsets = np.array(offsets)
zg_off = offsets[:, 0]
yg_off = offsets[:, 1]
xg_off = offsets[:, 2]
def roi(zg, yg, xg):
""" dist radius of influence function. """
r = np.maximum(
z_factor * (zg - zg_off) +
xy_factor * np.sqrt((xg - xg_off)**2 + (yg - yg_off)**2),
min_radius)
return min(r)
return roi
[docs]def example_roi_func_dist_beam(zg, yg, xg):
"""
Example RoI function which returns a radius which grows with distance
and whose parameters are based on virtual beam size.
Parameters
----------
zg, yg, xg : float
Distance from the grid center in meters for the x, y and z axes.
Returns
-------
roi : float
"""
# RoI function parameters
h_factor = 1.0 # height scaling
nb = 1.5 # virtual beam width
bsp = 1.0 # virtual beam spacing
min_radius = 500. # minimum radius in meters
offsets = ((0, 0, 0), ) # z, y, x offset of grid in meters from radar(s)
offsets = np.array(offsets)
zg_off = offsets[:, 0]
yg_off = offsets[:, 1]
xg_off = offsets[:, 2]
r = np.maximum(
h_factor * ((zg - zg_off) / 20.0) +
np.sqrt((yg - yg_off)**2 + (xg - xg_off)**2) *
np.tan(nb * bsp * np.pi / 180.0), min_radius)
return min(r)
def _gen_roi_func_dist_beam(h_factor, nb, bsp, min_radius, offsets):
"""
Return a RoI function whose radius which grows with distance
and whose parameters are based on virtual beam size.
See :py:func:`map_to_grid` for a description of the parameters.
"""
offsets = np.array(offsets)
zg_off = offsets[:, 0]
yg_off = offsets[:, 1]
xg_off = offsets[:, 2]
def roi(zg, yg, xg):
""" dist_beam radius of influence function. """
r = np.maximum(
h_factor * ((zg - zg_off) / 20.0) +
np.sqrt((yg - yg_off)**2 + (xg - xg_off)**2) *
np.tan(nb * bsp * np.pi / 180.0), min_radius)
return min(r)
return roi
```