def __init__(self, fields: Volume_T, max_dist: Number_T = 0.1):
# Process data type
if len(set([i.dtype for i in fields])) > 1:
raise RadarCalculationError("All input data should have same data type")
self.dtype = fields[0].dtype
# Process time
t_arr = np.array([time.mktime(i.scantime.timetuple()) for i in fields])
if (t_arr.max() - t_arr.min()) / 60 > 10:
raise RadarCalculationError(
"Time difference of input data should not exceed 10 minutes"
)
mean_time = t_arr.mean()
mean_dtime = datetime.datetime(*time.localtime(int(mean_time))[:6])
time_increment = 10
time_rest = mean_dtime.minute % time_increment
if time_rest > time_increment / 2:
mean_dtime += datetime.timedelta(minutes=(time_increment - time_rest))
else:
mean_dtime -= datetime.timedelta(minutes=time_rest)
self.scan_time = mean_dtime
self.lon_ravel = np.hstack([i.lon.ravel() for i in fields])
self.lat_ravel = np.hstack([i.lat.ravel() for i in fields])
self.data_ravel = np.ma.hstack([i.data.ravel() for i in fields])
self.dist_ravel = np.hstack(
[np.broadcast_to(i.dist, i.lon.shape).ravel() for i in fields]
)
self.tree = KDTree(np.dstack((self.lon_ravel, self.lat_ravel))[0])
self.md = max_dist
def __init__(self, r_list: RList):
if not is_uniform(r_list):
raise RadarCalculationError(
'All input radials must have the same data type')
self.rl = r_list
self.el = [i.elev for i in r_list]
self.x, self.y, self.h, self.r = self._geocoor()
def get_section(self, start_polar=None, end_polar=None, start_cart=None, end_cart=None,
spacing=100):
r'''
Get cross-section data from input points
Parameters
----------
start_polar: list or tuple
polar coordinates of start point i.e.(distance, azimuth)
end_polar: list or tuple
polar coordinates of end point i.e.(distance, azimuth)
start_cart: list or tuple
geographic coordinates of start point i.e.(longitude, latitude)
end_cart: list or tuple
geographic coordinates of end point i.e.(longitude, latitude)
Returns
-------
sl: cinrad.datastruct._Slice
'''
if start_polar and end_polar:
stlat = self.rl[0].stp['lat']
stlon = self.rl[0].stp['lon']
stp = np.round_(get_coordinate(start_polar[0], start_polar[1] * deg2rad, 0, stlon, stlat), 2)
enp = np.round_(get_coordinate(end_polar[0], end_polar[1] * deg2rad, 0, stlon, stlat), 2)
elif start_cart and end_cart:
stp = start_cart
enp = end_cart
else:
raise RadarCalculationError('Invalid input')
return self._get_section(stp, enp, spacing)
def __init__(self, fields: Volume_T, max_dist: Number_T = 0.1):
# Process data type
self.dtype = get_dtype(fields[0])
# Process time
t_arr = np.array(
[time.mktime(i.scan_time.timetuple()) for i in fields])
if (t_arr.max() - t_arr.min()) / 60 > 10:
raise RadarCalculationError(
"Time difference of input data should not exceed 10 minutes")
mean_time = t_arr.mean()
mean_dtime = datetime.datetime(*time.localtime(int(mean_time))[:6])
time_increment = 10
time_rest = mean_dtime.minute % time_increment
if time_rest > time_increment / 2:
mean_dtime += datetime.timedelta(minutes=(time_increment -
time_rest))
else:
mean_dtime -= datetime.timedelta(minutes=time_rest)
self.scan_time = mean_dtime
self.lon_ravel = np.hstack(
[i["longitude"].values.ravel() for i in fields])
self.lat_ravel = np.hstack(
[i["latitude"].values.ravel() for i in fields])
self.data_ravel = np.ma.hstack(
[i[self.dtype].values.ravel() for i in fields])
self.dist_ravel = np.hstack([
np.broadcast_to(i["distance"], i["longitude"].shape).ravel()
for i in fields
])
self.tree = KDTree(np.dstack((self.lon_ravel, self.lat_ravel))[0])
self.md = max_dist
self.attr = fields[0].attrs.copy()
def __init__(self, r_list: Volume_T):
if not is_uniform(r_list):
raise RadarCalculationError(
'All input radials must have the same data type')
el = [i.elev for i in r_list]
if len(el) != len(set(el)):
self.rl = list()
el_list = list()
for data in r_list:
if data.elev not in el_list:
self.rl.append(data)
el_list.append(data.elev)
else:
self.rl = r_list
self.x, self.y, self.h, self.r = self._geocoor()
def get_section(
self,
start_polar: Optional[Tuple[float, float]] = None,
end_polar: Optional[Tuple[float, float]] = None,
start_cart: Optional[Tuple[float, float]] = None,
end_cart: Optional[Tuple[float, float]] = None,
spacing: int = 500,
) -> Slice_:
r"""
Get cross-section data from input points
Parameters
----------
start_polar: list or tuple
polar coordinates of start point i.e.(distance, azimuth)
end_polar: list or tuple
polar coordinates of end point i.e.(distance, azimuth)
start_cart: list or tuple
geographic coordinates of start point i.e.(longitude, latitude)
end_cart: list or tuple
geographic coordinates of end point i.e.(longitude, latitude)
Returns
-------
sl: cinrad.datastruct.Slice_
"""
if start_polar and end_polar:
stlat = self.rl[0].stp["lat"]
stlon = self.rl[0].stp["lon"]
stp = np.round_(
get_coordinate(
start_polar[0], start_polar[1] * deg2rad, 0, stlon, stlat
),
2,
)
enp = np.round_(
get_coordinate(end_polar[0], end_polar[1] * deg2rad, 0, stlon, stlat), 2
)
elif start_cart and end_cart:
stp = start_cart
enp = end_cart
else:
raise RadarCalculationError("Invalid input")
return self._get_section(stp, enp, spacing)
def get_section(
self,
start_polar: Optional[Tuple[float, float]] = None,
end_polar: Optional[Tuple[float, float]] = None,
start_cart: Optional[Tuple[float, float]] = None,
end_cart: Optional[Tuple[float, float]] = None,
spacing: int = 500,
) -> Dataset:
r"""
Get cross-section data from input points
Args:
start_polar (tuple): polar coordinates of start point i.e.(distance, azimuth)
end_polar (tuple): polar coordinates of end point i.e.(distance, azimuth)
start_cart (tuple): geographic coordinates of start point i.e.(longitude, latitude)
end_cart (tuple): geographic coordinates of end point i.e.(longitude, latitude)
Returns:
xarray.Dataset: Cross-section data
"""
if start_polar and end_polar:
stlat = self.rl[0].stp["lat"]
stlon = self.rl[0].stp["lon"]
stp = np.round_(
get_coordinate(start_polar[0], start_polar[1] * deg2rad, 0,
stlon, stlat),
2,
)
enp = np.round_(
get_coordinate(end_polar[0], end_polar[1] * deg2rad, 0, stlon,
stlat), 2)
elif start_cart and end_cart:
stp = start_cart
enp = end_cart
else:
raise RadarCalculationError("Invalid input")
return self._get_section(stp, enp, spacing)
def _check_time(self):
d_list = [_nearest_ten_minute(i.scantime) for i in self.data_list]
d_list_stamp = [time.mktime(i.timetuple()) for i in d_list]
if np.average(d_list_stamp) != d_list_stamp[0]:
raise RadarCalculationError(
'Input radar data have inconsistent time')
