def quick_et(r_list: Volume_T) -> Radial:
r"""
Calculate echo tops
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
echo tops
"""
r_data, d, a, elev = _extract(r_list)
i = r_list[0]
et = echo_top(
r_data.astype(np.double), d.astype(np.double), elev.astype(np.double), 0.0
)
l2_obj = Radial(
np.ma.array(et, mask=(et < 2)),
i.drange,
0,
i.reso,
i.code,
i.name,
i.scantime,
"ET",
i.stp["lon"],
i.stp["lat"],
**i.scan_info
)
lon, lat = get_coordinate(d[0], a[:, 0], 0, i.stp["lon"], i.stp["lat"])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def quick_vild(r_list: RList) -> Radial:
r'''
Calculate vertically integrated liquid density
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
vertically integrated liquid density
'''
r_data, d, a, elev = _extract(r_list)
i = r_list[0]
vil = vert_integrated_liquid(r_data.astype(np.double),
d.astype(np.double),
elev.astype(np.double),
density=True)
vild = np.ma.masked_invalid(vil)
l2_obj = Radial(np.ma.array(vild, mask=(vild <= 0.1)), i.drange, 0, i.reso,
i.code, i.name, i.scantime, 'VILD', i.stp['lon'],
i.stp['lat'], **i.scan_info)
lon, lat = get_coordinate(d[0], a[:, 0], 0, i.stp['lon'], i.stp['lat'])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def quick_vil(r_list: Volume_T) -> Radial:
r"""
Calculate vertically integrated liquid
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
vertically integrated liquid
"""
r_data, d, a, elev = _extract(r_list)
i = r_list[0]
vil = vert_integrated_liquid(
r_data.astype(np.double), d.astype(np.double), elev.astype(np.double)
)
l2_obj = Radial(
np.ma.array(vil, mask=(vil <= 0)),
i.drange,
0,
i.reso,
i.code,
i.name,
i.scantime,
"VIL",
i.stp["lon"],
i.stp["lat"],
**i.scan_info
)
lon, lat = get_coordinate(d[0], a[:, 0], 0, i.stp["lon"], i.stp["lat"])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def max_potential_gust(r_list: Volume_T) -> Radial:
r"""
Estimate maximum potential descending gust by Stewart's formula
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
Wind gust
"""
r_data, d, a, elev = _extract(r_list)
i = r_list[0]
g = potential_maximum_gust_from_reflectivity(r_data, d, elev)
l2_obj = Radial(
np.ma.array(g, mask=(g <= 0)),
i.drange,
0,
i.reso,
i.code,
i.name,
i.scantime,
"GUST",
i.stp["lon"],
i.stp["lat"],
**i.scan_info
)
lon, lat = get_coordinate(d[0], a.T[0], 0, i.stp["lon"], i.stp["lat"])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def get_data(self, tilt:int, drange:Number_T, dtype:str) -> Radial:
r'''
Get radar data
Parameters
----------
tilt: int
index of elevation angle
drange: float
radius of data
dtype: str
type of product (REF, VEL, etc.)
Returns
-------
r_obj: cinrad.datastruct.Radial
'''
task = getattr(self, 'task_name', None)
reso = self.Rreso if dtype == 'REF' else self.Vreso
ret = self.get_raw(tilt, drange, dtype)
shape = ret[0].shape[1] if isinstance(ret, tuple) else ret.shape[1]
r_obj = Radial(ret, int(np.round(shape * reso)), self.elev, reso, self.code, self.name, self.scantime, dtype,
self.stationlon, self.stationlat, nyquist_velocity=self.nyquist_v[tilt], task=task)
x, y, z, d, a = self.projection(reso)
r_obj.add_geoc(x, y, z)
r_obj.add_polarc(d, a)
if self.radartype == 'CC':
r_obj.a_reso = 512
return r_obj
def get_data(self, tilt, drange, dtype):
r'''
Get radar raw data
Parameters
----------
tilt: int
index of elevation angle
drange: float
radius of data
dtype: str
type of product (REF, VEL, etc.)
Returns
-------
r_obj: cinrad.datastruct.Radial
'''
rf_flag = False
self.tilt = tilt
reso = self.Rreso if dtype == 'REF' else self.Vreso
dmax = np.round(self.data[tilt][dtype][0].shape[0] * reso)
if dmax < drange:
warnings.warn('Requested data range exceed max range in this tilt')
self.drange = drange
self.elev = self.el[tilt]
try:
data = np.ma.array(self.data[tilt][dtype])
except KeyError:
raise RadarDecodeError('Invalid product name')
length = data.shape[1] * reso
cut = data.T[:int(np.round(drange / reso))]
shape_diff = np.round(drange / reso) - cut.shape[0]
append = np.zeros(
(int(np.round(shape_diff)), cut.shape[1])) * np.ma.masked
if dtype == 'VEL':
try:
rf = self.data[tilt]['RF']
except KeyError:
pass
else:
rf_flag = True
rf = rf.T[:int(np.round(drange / reso))]
rf = np.ma.vstack([rf, append])
#r = np.ma.array(cut, mask=np.isnan(cut))
r = np.ma.vstack([cut, append])
if rf_flag:
r.mask = np.logical_or(r.mask, ~rf.mask)
ret = (r.T, rf.T)
else:
ret = r.T
r_obj = Radial(ret, int(np.round(r.shape[0] * reso)), self.elev, reso,
self.code, self.name, self.scantime, dtype,
self.stationlon, self.stationlat)
x, y, z, d, a = self.projection(reso)
r_obj.add_geoc(x, y, z)
r_obj.add_polarc(d, a)
if self.radartype == 'CC':
r_obj.a_reso = 512
return r_obj
def get_data(
self, tilt: int, drange: Number_T, dtype: str
) -> Union[Radial, Slice_]:
r"""
Get radar data
Parameters
----------
tilt: int
index of elevation angle
drange: float
radius of data
dtype: str
type of product (REF, VEL, etc.)
Returns
-------
r_obj: cinrad.datastruct.Radial
"""
reso = self.scan_config[tilt].dop_reso / 1000
ret = self.get_raw(tilt, drange, dtype)
if self.scan_type == "PPI":
shape = ret[0].shape[1] if isinstance(ret, tuple) else ret.shape[1]
r_obj = Radial(
ret,
int(shape * reso),
self.elev,
reso,
self.code,
self.name,
self.scantime,
dtype,
self.stationlon,
self.stationlat,
nyquist_velocity=self.scan_config[tilt].nyquist_spd,
task=self.task_name,
)
x, y, z, d, a = self.projection(reso)
r_obj.add_geoc(x, y, z)
r_obj.add_polarc(d, a)
return r_obj
else:
# Manual projection
shape = ret[0].shape[1] if isinstance(ret, tuple) else ret.shape[1]
dist = np.linspace(reso, self.drange, ret.shape[1])
d, e = np.meshgrid(dist, self.aux[tilt]["elevation"])
h = height(d, e, 0)
rhi = Slice_(
ret,
d,
h,
self.scantime,
self.code,
self.name,
dtype,
azimuth=self.aux[tilt]["azimuth"][0],
)
return rhi
def get_data(self, tilt, drange, dtype):
r'''
Get radar raw data
Parameters
----------
tilt: int
index of elevation angle
drange: float
radius of data
dtype: str
type of product (REF, VEL, etc.)
Returns
-------
r_obj: cinrad.datastruct.Radial
'''
self.tilt = tilt
self.drange = drange
self.elev = self.el[tilt]
try:
raw = np.array(self.data[tilt][dtype])
except KeyError:
raise RadarDecodeError('Invalid product name')
if raw.size == 0:
warnings.warn('Empty data', RuntimeWarning)
data = np.ma.array(raw, mask=(raw <= 5))
reso = self.scan_config[tilt].dop_reso / 1000
cut = data[:, :int(drange / reso)]
shape_diff = np.round(drange / reso) - cut.shape[1]
append = np.zeros((cut.shape[0], int(shape_diff))) * np.ma.masked
if dtype == 'VEL':
rf = np.ma.array(cut.data, mask=(cut.data != 1))
rf = np.ma.hstack([rf, append])
cut = np.ma.hstack([cut, append])
scale, offset = self.aux[tilt][dtype]
r = (cut - offset) / scale
if dtype == 'VEL':
ret = (r, rf)
else:
ret = r
r_obj = Radial(ret, int(r.shape[1] * reso), self.elev, reso, self.code,
self.name, self.scantime, dtype, self.stationlon,
self.stationlat)
x, y, z, d, a = self.projection(reso)
r_obj.add_geoc(x, y, z)
r_obj.add_polarc(d, a)
return r_obj
def max_potential_gust(r_list: RList) -> Radial:
r'''
Estimate maximum potential descending gust by Stewart's formula
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
Wind gust
'''
r_data, d, a, elev = _extract(r_list)
i = r_list[0]
g = potential_maximum_gust_from_reflectivity(r_data, d, elev)
l2_obj = Radial(np.ma.array(g, mask=(g <= 0)), i.drange, 0, 1, i.code,
i.name, i.scantime, 'GUST', i.stp['lon'], i.stp['lat'])
lon, lat = get_coordinate(d[0], a.T[0], 0, i.stp['lon'], i.stp['lat'])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def quick_vil(Rlist):
r'''
Calculate vertically integrated liquid
Paramters
---------
Rlist: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
vertically integrated liquid
'''
r_data, d, a, elev = _extract(Rlist)
i = Rlist[0]
vil = vert_integrated_liquid(r_data, d, elev)
l2_obj = Radial(np.ma.array(vil, mask=(vil <= 0)), i.drange, 0, 1, i.code,
i.name, i.scantime, 'VIL', i.stp['lon'], i.stp['lat'])
lon, lat = get_coordinate(d[0], a.T[0], 0, i.stp['lon'], i.stp['lat'])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def quick_et(Rlist):
r'''
Calculate echo tops
Paramters
---------
Rlist: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
echo tops
'''
r_data, d, a, elev = _extract(Rlist)
i = Rlist[0]
et = echo_top(r_data, d, elev, 0)
l2_obj = Radial(et, i.drange, 0, 1, i.code, i.name, i.scantime, 'ET',
i.stp['lon'], i.stp['lat'])
lon, lat = get_coordinate(d[0], a.T[0], 0, i.stp['lon'], i.stp['lat'])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def quick_et(r_list:RList) -> Radial:
r'''
Calculate echo tops
Paramters
---------
r_list: list of cinrad.datastruct.Radial
Returns
-------
l2_obj: cinrad.datastruct.Grid
echo tops
'''
r_data, d, a, elev = _extract(r_list)
i = r_list[0]
et = echo_top(r_data.astype(np.double), d.astype(np.double), elev.astype(np.double), 0.)
l2_obj = Radial(np.ma.array(et, mask=(et < 2)), i.drange, 0, 1, i.code, i.name, i.scantime, 'ET',
i.stp['lon'], i.stp['lat'])
lon, lat = get_coordinate(d[0], a[:, 0], 0, i.stp['lon'], i.stp['lat'])
l2_obj.add_geoc(lon, lat, np.zeros(lon.shape))
return l2_obj
def get_data(self, tilt, drange, dtype):
r'''
Get radar raw data
Parameters
----------
tilt: int
index of elevation angle
drange: float
radius of data
dtype: str
type of product (REF, VEL, etc.)
Returns
-------
r_obj: cinrad.datastruct.Radial
'''
self.tilt = tilt
self.drange = drange
self.elev = self.el[tilt]
try:
data = np.array(self.data[tilt][dtype])
except KeyError:
raise RadarDecodeError('Invalid product name')
if data.size == 0:
raise RadarDecodeError(
'Current elevation does not contain this data.')
reso = self.scanconfig[tilt]['radial_reso']
length = data.shape[1] * reso
cut = data[:, :int(drange / reso)]
r = np.ma.array(cut, mask=np.isnan(cut))
r_obj = Radial(r, int(r.shape[0] * reso), self.elev, reso, self.code,
self.name, self.scantime, dtype, self.stationlon,
self.stationlat)
x, y, z, d, a = self.projection(reso)
r_obj.add_geoc(x, y, z)
r_obj.add_polarc(d, a)
return r_obj
from metpy.io.nexrad import Level3File
from tkinter import filedialog
from metpy.plots import colortables
from cinrad.datastruct import Radial
from cinrad.projection import get_coordinate
from cinrad.visualize import ppi
name = filedialog.askopenfilename()
f = Level3File(name)
# Pull the data out of the file object
datadict = f.sym_block[0][0]
# Turn into an array, then mask
data = np.array(datadict['data']).astype(float)
data[data == 0] = np.nan
rf = np.ma.array(data, mask=(data != 1))
# Grab azimuths and calculate a range based on number of gates
az = np.deg2rad(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
elev = f.metadata['el_angle']
slon, slat = f.lon, f.lat
scantime = f.metadata['msg_time']
lon, lat = get_coordinate(rng, az, elev, slon, slat)
threshold = f.thresholds
data = data * threshold[1] / 10 + threshold[0] / 10
v_obj = Radial([data, rf], int(f.max_range), elev, f.ij_to_km, f.siteID,
f.siteID, scantime.strftime('%Y%m%d%H%M%S'), 'VEL', slon, slat)
v_obj.add_geoc(lon, lat, np.zeros(lon.shape))
norm2, v_cmap = colortables.get_with_range('NWS8bitVel', -64, 64)
fig = ppi.PPI(v_obj, coastline=True, norm=norm2, cmap=v_cmap, nlabel=17)
fig()
def get_data(self, tilt: int, drange: number_type, dtype: str) -> Radial:
r'''
Get radar raw data
Parameters
----------
tilt: int
index of elevation angle
drange: float
radius of data
dtype: str
type of product (REF, VEL, etc.)
Returns
-------
r_obj: cinrad.datastruct.Radial
'''
self.tilt = tilt if self.scan_type == 'PPI' else 0
self.drange = drange
if self.scan_type == 'RHI':
max_range = self.scan_config[0].max_range1 / 1000
if drange > max_range:
drange = max_range
self.elev = self.el[tilt]
try:
raw = np.array(self.data[tilt][dtype])
except KeyError:
raise RadarDecodeError('Invalid product name')
if raw.size == 0:
warnings.warn('Empty data', RuntimeWarning)
data = np.ma.array(raw, mask=(raw <= 5))
reso = self.scan_config[tilt].dop_reso / 1000
cut = data[:, :int(drange / reso)]
shape_diff = np.round(drange / reso) - cut.shape[1]
append = np.zeros((cut.shape[0], int(shape_diff))) * np.ma.masked
if dtype == 'VEL':
rf = np.ma.array(cut.data, mask=(cut.data != 1))
rf = np.ma.hstack([rf, append])
cut = np.ma.hstack([cut, append])
scale, offset = self.aux[tilt][dtype]
r = (cut - offset) / scale
if dtype in ['VEL', 'SW']:
ret = (r, rf)
else:
ret = r
if self.scan_type == 'PPI':
r_obj = Radial(ret,
int(r.shape[1] * reso),
self.elev,
reso,
self.code,
self.name,
self.scantime,
dtype,
self.stationlon,
self.stationlat,
nyquist_velocity=self.scan_config[tilt].nyquist_spd)
x, y, z, d, a = self.projection(reso)
r_obj.add_geoc(x, y, z)
r_obj.add_polarc(d, a)
return r_obj
else:
# Manual projection
dist = np.linspace(reso, self.drange, cut.shape[1])
d, e = np.meshgrid(dist, self.aux[tilt]['elevation'])
h = height(d, e, 0)
rhi = Slice_(ret,
d,
h,
self.scantime,
self.code,
self.name,
dtype,
azimuth=self.aux[tilt]['azimuth'][0])
return rhi