"""
Functions for creating sample Radar and Grid objects.
"""
import numpy as np
import scipy
from ..config import get_metadata
from ..core.grid import Grid
from ..core.radar import Radar
from ..exceptions import MissingOptionalDependency
from .sample_files import _EXAMPLE_RAYS_FILE
try:
import xarray as xr
from ..core.radar_spectra import RadarSpectra
_XARRAY_AVAILABLE = True
except ImportError:
_XARRAY_AVAILABLE = False
[docs]def make_empty_ppi_radar(ngates, rays_per_sweep, nsweeps):
"""
Return an Radar object, representing a PPI scan.
Parameters
----------
ngates : int
Number of gates per ray.
rays_per_sweep : int
Number of rays in each PPI sweep.
nsweeps : int
Number of sweeps.
Returns
-------
radar : Radar
Radar object with no fields, other parameters are set to default
values.
"""
nrays = rays_per_sweep * nsweeps
time = get_metadata("time")
_range = get_metadata("range")
latitude = get_metadata("latitude")
longitude = get_metadata("longitude")
altitude = get_metadata("altitude")
sweep_number = get_metadata("sweep_number")
sweep_mode = get_metadata("sweep_mode")
fixed_angle = get_metadata("fixed_angle")
sweep_start_ray_index = get_metadata("sweep_start_ray_index")
sweep_end_ray_index = get_metadata("sweep_end_ray_index")
azimuth = get_metadata("azimuth")
elevation = get_metadata("elevation")
antenna_transition = get_metadata("antenna_transition")
fields = {}
scan_type = "ppi"
metadata = {"instrument_name": "fake_radar"}
time["data"] = np.arange(nrays, dtype="float64")
time["units"] = "seconds since 1989-01-01T00:00:01Z"
_range["data"] = np.linspace(0, 1000, ngates).astype("float32")
latitude["data"] = np.array([36.5], dtype="float64")
longitude["data"] = np.array([-97.5], dtype="float64")
altitude["data"] = np.array([200], dtype="float64")
sweep_number["data"] = np.arange(nsweeps, dtype="int32")
sweep_mode["data"] = np.array(["azimuth_surveillance"] * nsweeps)
fixed_angle["data"] = np.array([0.75] * nsweeps, dtype="float32")
sweep_start_ray_index["data"] = np.arange(0, nrays, rays_per_sweep, dtype="int32")
sweep_end_ray_index["data"] = np.arange(
rays_per_sweep - 1, nrays, rays_per_sweep, dtype="int32"
)
antenna_transition["data"] = np.zeros(nrays)
azimuth["data"] = np.arange(nrays, dtype="float32")
elevation["data"] = np.array([0.75] * nrays, dtype="float32")
return Radar(
time,
_range,
fields,
metadata,
scan_type,
latitude,
longitude,
altitude,
sweep_number,
sweep_mode,
fixed_angle,
sweep_start_ray_index,
sweep_end_ray_index,
azimuth,
elevation,
antenna_transition=antenna_transition,
instrument_parameters=None,
)
[docs]def make_empty_rhi_radar(ngates, rays_per_sweep, nsweeps):
"""
Return an Radar object, representing a RHI scan.
Parameters
----------
ngates : int
Number of gates per ray.
rays_per_sweep : int
Number of rays in each PPI sweep.
nsweeps : int
Number of sweeps.
Returns
-------
radar : Radar
Radar object with no fields, other parameters are set to default
values.
"""
radar = make_empty_ppi_radar(ngates, rays_per_sweep, nsweeps)
radar.scan_type = "rhi"
nrays = rays_per_sweep * nsweeps
radar.sweep_mode["data"] = np.array(["rhi"] * nsweeps)
radar.elevation["data"] = np.arange(nrays, dtype="float32")
radar.azimuth["data"] = np.array([0.75] * nrays, dtype="float32")
return radar
[docs]def make_target_radar():
"""
Return a PPI radar with a target like reflectivity field.
"""
radar = make_empty_ppi_radar(50, 360, 1)
fields = {"reflectivity": get_metadata("reflectivity")}
fdata = np.zeros((360, 50), dtype="float32")
fdata[:, 0:10] = 0.0
fdata[:, 10:20] = 10.0
fdata[:, 20:30] = 20.0
fdata[:, 30:40] = 30.0
fdata[:, 40:50] = 40.0
fields["reflectivity"]["data"] = fdata
radar.fields = fields
return radar
[docs]def make_velocity_aliased_radar(alias=True):
"""
Return a PPI radar with a target like reflectivity field.
Set alias to False to return a de-aliased radar.
"""
radar = make_empty_ppi_radar(50, 360, 1)
radar.range["meters_between_gates"] = 1.0
radar.range["meters_to_center_of_first_gate"] = 1.0
radar.instrument_parameters = {"nyquist_velocity": {"data": np.array([10.0] * 360)}}
fields = {
"reflectivity": get_metadata("reflectivity"),
"velocity": get_metadata("velocity"),
}
# fake reflectivity data, all zero reflectivity
fdata = np.zeros((360, 50), dtype="float32")
fields["reflectivity"]["data"] = fdata
# fake velocity data, all zeros except a wind burst on at ~13 degrees.
# burst is partially aliased.
vdata = np.zeros((360 * 1, 50), dtype="float32")
for i, idx in enumerate(range(13, -1, -1)):
vdata[i, idx : idx + i + 1] = np.arange(0.5, 0.5 + i * 1.0 + 0.001)
vdata[:14, 14:27] = vdata[:14, 12::-1] # left/right flip
vdata[14:27] = vdata[12::-1, :] # top/bottom flip
aliased = np.where(vdata > 10.0)
if alias:
vdata[aliased] += -20.0
fields["velocity"]["data"] = vdata
radar.fields = fields
return radar
[docs]def make_velocity_aliased_rhi_radar(alias=True):
"""
Return a RHI radar with a target like reflectivity field.
Set alias to False to return a de-aliased radar.
"""
radar = make_empty_rhi_radar(50, 180, 1)
radar.range["meters_between_gates"] = 1.0
radar.range["meters_to_center_of_first_gate"] = 1.0
radar.instrument_parameters = {"nyquist_velocity": {"data": np.array([10.0] * 360)}}
fields = {
"reflectivity": get_metadata("reflectivity"),
"velocity": get_metadata("velocity"),
}
# fake reflectivity data, all zero reflectivity
fdata = np.zeros((180, 50), dtype="float32")
fields["reflectivity"]["data"] = fdata
# fake velocity data, all zeros except a wind burst on at ~13 degrees.
# burst is partially aliased.
vdata = np.zeros((180 * 1, 50), dtype="float32")
for i, idx in enumerate(range(13, -1, -1)):
vdata[i, idx : idx + i + 1] = np.arange(0.5, 0.5 + i * 1.0 + 0.001)
vdata[:14, 14:27] = vdata[:14, 12::-1] # left/right flip
vdata[14:27] = vdata[12::-1, :] # top/bottom flip
aliased = np.where(vdata > 10.0)
if alias:
vdata[aliased] += -20.0
fields["velocity"]["data"] = vdata
radar.fields = fields
return radar
[docs]def make_single_ray_radar():
"""
Return a PPI radar with a single ray taken from a ARM C-SAPR Radar.
Radar object returned has 'reflectivity_horizontal',
'norm_coherent_power', 'copol_coeff', 'dp_phase_shift', 'diff_phase', and
'differential_reflectivity' fields with no metadata but a 'data' key.
This radar is used for unit tests in correct modules.
"""
radar = make_empty_ppi_radar(983, 1, 1)
radar.range["data"] = 117.8784 + np.arange(983) * 119.91698
f = np.load(_EXAMPLE_RAYS_FILE)
for field_name in f:
radar.fields[field_name] = {"data": f[field_name]}
f.close()
return radar
[docs]def make_empty_grid(grid_shape, grid_limits):
"""
Make an empty grid object without any fields or metadata.
Parameters
----------
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.
Returns
-------
grid : Grid
Empty Grid object, centered near the ARM SGP site (Oklahoma).
"""
time = get_metadata("grid_time")
time["data"] = np.array([0.0])
time["units"] = "seconds since 2000-01-01T00:00:00Z"
# 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)
origin_altitude = get_metadata("origin_altitude")
origin_altitude["data"] = np.array([300.0])
origin_latitude = get_metadata("origin_latitude")
origin_latitude["data"] = np.array([36.74])
origin_longitude = get_metadata("origin_longitude")
origin_longitude["data"] = np.array([-98.1])
fields = {}
metadata = {}
radar_latitude = get_metadata("radar_latitude")
radar_latitude["data"] = np.array([36.74])
radar_longitude = get_metadata("radar_longitude")
radar_longitude["data"] = np.array([-98.1])
radar_altitude = get_metadata("radar_altitude")
radar_altitude["data"] = np.array([300.0])
radar_time = get_metadata("radar_time")
radar_time["data"] = np.array([0.0])
radar_time["units"] = "seconds since 2000-01-01T00:00:00Z"
radar_name = get_metadata("radar_name")
radar_name["data"] = np.array(["ExampleRadar"])
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_time=radar_time,
radar_name=radar_name,
)
[docs]def make_target_grid():
"""
Make a sample Grid with a rectangular target.
"""
grid_shape = (2, 400, 320)
grid_limits = ((0, 500), (-400000, 400000), (-300000, 300000))
grid = make_empty_grid(grid_shape, grid_limits)
fdata = np.zeros((2, 400, 320), dtype="float32")
fdata[:, 50:-50, 40:-40] = 10.0
fdata[:, 100:-100, 80:-80] = 20.0
fdata[:, 150:-150, 120:-120] = 30.0
fdata[1] += 5
rdic = {"data": fdata, "long_name": "reflectivity", "units": "dBz"}
grid.fields = {"reflectivity": rdic}
return grid
[docs]def make_storm_grid():
"""
Make a sample Grid with a rectangular storm target.
"""
grid_shape = (2, 50, 40)
grid_limits = ((0, 500), (-400000, 400000), (-300000, 300000))
grid = make_empty_grid(grid_shape, grid_limits)
fdata = np.ma.zeros((2, 50, 40), dtype="float32")
fdata[:] = np.ma.masked
fdata[:, 5:-5, 4:-4] = 20.0
fdata[:, 10:-10, 8:-8] = 30.0
fdata[:, 15:-15, 12:-12] = 60.0
fdata[1] += 5
rdic = {"data": fdata, "long_name": "reflectivity", "units": "dBz"}
grid.fields = {"reflectivity": rdic}
return grid
[docs]def make_normal_storm(sigma, mu):
"""
Make a sample Grid with a gaussian storm target.
"""
test_grid = make_empty_grid([1, 101, 101], [(1, 1), (-50, 50), (-50, 50)])
x = test_grid.x["data"]
y = test_grid.y["data"]
z = test_grid.z["data"]
zg, yg, xg = np.meshgrid(z, y, x, indexing="ij")
r = np.sqrt((xg - mu[0]) ** 2 + (yg - mu[1]) ** 2)
term1 = 1.0 / (sigma * np.sqrt(2.0 * np.pi))
term2 = -1.0 * (r**2 / (2.0 * sigma**2))
data = term1 * np.exp(term2)
rdic = {"data": data, "long_name": "reflectivity", "units": "dBz"}
test_grid.fields.update({"reflectivity": rdic})
return test_grid
[docs]def make_gaussian_storm_grid(
min_value=5, max_value=45, grid_len=32, sigma=0.2, mu=0.0, masked_boundary=3
):
"""
Make a 1 km resolution grid with a Gaussian storm pattern at the center,
with two layers having the same data and masked boundaries.
Parameters
-----------
min_value : float
Minimum value of the storm intensity.
max_value : float
Maximum value of the storm intensity.
grid_len : int
Size of the grid (grid will be grid_len x grid_len).
sigma : float
Standard deviation of the Gaussian distribution.
mu : float
Mean of the Gaussian distribution.
masked_boundary : int
Number of pixels around the edge to be masked.
Returns
--------
A Py-ART grid with the Gaussian storm field added.
"""
# Create an empty Py-ART grid
grid_shape = (2, grid_len, grid_len)
grid_limits = (
(1000, 1000),
(-grid_len * 1000 / 2, grid_len * 1000 / 2),
(-grid_len * 1000 / 2, grid_len * 1000 / 2),
)
grid = make_empty_grid(grid_shape, grid_limits)
# Creating a grid with Gaussian distribution values
x, y = np.meshgrid(np.linspace(-1, 1, grid_len), np.linspace(-1, 1, grid_len))
d = np.sqrt(x * x + y * y)
gaussian = np.exp(-((d - mu) ** 2 / (2.0 * sigma**2)))
# Normalize and scale the Gaussian distribution
gaussian_normalized = (gaussian - np.min(gaussian)) / (
np.max(gaussian) - np.min(gaussian)
)
storm_intensity = gaussian_normalized * (max_value - min_value) + min_value
storm_intensity = np.stack([storm_intensity, storm_intensity])
# Apply thresholds for storm intensity and masking
mask = np.zeros_like(storm_intensity, dtype=bool)
mask[:, :masked_boundary, :] = True
mask[:, -masked_boundary:, :] = True
mask[:, :, :masked_boundary] = True
mask[:, :, -masked_boundary:] = True
storm_intensity = np.ma.array(storm_intensity, mask=mask)
# Prepare dictionary for Py-ART grid fields
rdic = {"data": storm_intensity, "long_name": "reflectivity", "units": "dBz"}
grid.fields = {"reflectivity": rdic}
return grid
[docs]def make_empty_spectra_radar(nrays, ngates, npulses_max):
"""
Return a Spectra Radar object.
Parameters
----------
nrays : int
Number of rays in the object.
ngates : int
Number of gates per ray.
npulses_max : int
Number of pulses in each gate.
Returns
-------
radar : RadarSpectra
Radar spectra object with an empty spectra field, other parameters are
set to default values.
"""
if not _XARRAY_AVAILABLE:
raise MissingOptionalDependency(
"Xarray is required to use make_empty_spectra_radar "
"but is not installed!"
)
time_dict = get_metadata("time")
_range_dict = get_metadata("range")
latitude_dict = get_metadata("latitude")
longitude_dict = get_metadata("longitude")
altitude_dict = get_metadata("altitude")
sweep_number_dict = get_metadata("sweep_number")
sweep_mode_dict = get_metadata("sweep_mode")
fixed_angle_dict = get_metadata("fixed_angle")
sweep_start_ray_index_dict = get_metadata("sweep_start_ray_index")
sweep_end_ray_index_dict = get_metadata("sweep_end_ray_index")
azimuth_dict = get_metadata("azimuth")
elevation_dict = get_metadata("elevation")
fields = {}
scan_type = "vpt"
c = 299792458
wavelength = c / 34.830 * 1e-9
metadata = {"instrument_name": "fake_spectra_radar", "wavelength": wavelength}
time_dict["units"] = "seconds since 1989-01-01T00:00:01Z"
time = xr.DataArray(np.arange(nrays, dtype="float32"), attrs=time_dict, dims="time")
_range = xr.DataArray(
np.linspace(0, 1000, ngates).astype("float32"), attrs=_range_dict, dims="range"
)
latitude = xr.DataArray(np.array([36.5], dtype="float64"), attrs=latitude_dict)
longitude = xr.DataArray(np.array([-97.5], dtype="float64"), attrs=longitude_dict)
altitude = xr.DataArray(np.array([200], dtype="float64"), attrs=altitude_dict)
sweep_number = xr.DataArray(np.arange(1, dtype="int32"), attrs=sweep_number_dict)
sweep_mode = xr.DataArray(np.array(["spectra"] * 1), attrs=sweep_mode_dict)
fixed_angle = xr.DataArray(
np.array([0.75] * 1, dtype="float32"), attrs=fixed_angle_dict
)
sweep_start_ray_index = xr.DataArray(
np.array([0], dtype="int32"), attrs=sweep_start_ray_index_dict
)
sweep_end_ray_index = xr.DataArray(
np.array([len(time.values) - 1], dtype="int32"), attrs=sweep_end_ray_index_dict
)
azimuth = xr.DataArray(
np.arange(nrays, dtype="float32"), attrs=azimuth_dict, dims="time"
)
elevation = xr.DataArray(
np.array([0.75] * nrays, dtype="float32"), attrs=elevation_dict, dims="time"
)
npulses_max = np.arange(npulses_max, dtype="float32")
velocity_bins = xr.DataArray(
np.linspace(-10.0, 10.0, len(npulses_max)), attrs={}, dims="npulses_max"
)
fields = np.zeros((len(time.values), len(_range.values), len(npulses_max)))
return RadarSpectra(
time,
_range,
fields,
metadata,
scan_type,
latitude,
longitude,
altitude,
sweep_number,
sweep_mode,
fixed_angle,
sweep_start_ray_index,
sweep_end_ray_index,
azimuth,
elevation,
npulses_max,
velocity_bins,
instrument_parameters=None,
)
[docs]def make_target_spectra_radar():
"""
Return a spectra radar with a target like spectra field.
"""
if not _XARRAY_AVAILABLE:
raise MissingOptionalDependency(
"Xarray is required to use make_target_spectra_radar "
"but is not installed!"
)
radar = make_empty_spectra_radar(10, 20, 50)
fdata = np.zeros((10, 20, 50), dtype="float32")
max_value = 10 ** (-10 / 10)
fdata[:, :, :] = 10 * np.log10(scipy.signal.windows.gaussian(50, std=7) * max_value)
radar.ds["spectra"].values = fdata
return radar