Source code for pyart.retrieve.gate_id

Functions that retrieve height of the gates and the profile interpolated
onto the radar gates.


    from netCDF4 import DatetimeGregorian, num2date
except ImportError:
    from cftime import DatetimeGregorian, num2date

import numpy as np
from scipy import interpolate

from ..config import get_field_name, get_fillvalue, get_metadata
from ..core.transforms import antenna_to_cartesian

[docs]def map_profile_to_gates( profile, heights, radar, toa=None, profile_field=None, height_field=None ): """ Given a profile of a variable map it to the gates of radar assuming 4/3Re. Parameters ---------- profile : array Profile array to map. heights : array Monotonically increasing heights in meters with same shape as profile. radar : Radar Radar to map to. toa : float, optional Top of atmosphere, where to use profile up to. If None check for mask and use lowest element, if no mask uses whole profile. height_field : str, optional Name to use for height field metadata. None will use the default field name from the Py-ART configuration file. profile_field : str, optional Name to use for interpolate profile field metadata. None will use the default field name from the Py-ART configuration file. Returns ------- height_dict, profile_dict : dict Field dictionaries containing the height of the gates and the profile interpolated onto the radar gates. """ # retrieve the Z coordinates of the radar gates rg, azg = np.meshgrid(radar.range["data"], radar.azimuth["data"]) rg, eleg = np.meshgrid(radar.range["data"], radar.elevation["data"]) _, _, z = antenna_to_cartesian(rg / 1000.0, azg, eleg) # Check that z is not a MaskedArray if isinstance(z, z = z.filled(np.NaN) # find toa is not provided if toa is None: ismasked = np.where([0] if len(ismasked) == 0: toa = None else: toa = ismasked.min() # interpolate f_interp = interpolate.interp1d( heights[:toa], profile[:toa], bounds_error=False, fill_value=get_fillvalue() ) fld = + radar.altitude["data"][0]), get_fillvalue()) # populate field dictionaries if height_field is None: height_field = get_field_name("height") height_dict = get_metadata(height_field) height_dict["data"] = z + radar.altitude["data"][0] if profile_field is None: profile_field = get_field_name("interpolated_profile") profile_dict = get_metadata(profile_field) profile_dict["data"] = fld return height_dict, profile_dict
[docs]def fetch_radar_time_profile( sonde_dset, radar, time_key="time", height_key="height", nvars=None ): """ Extract the correct profile from a interpolated sonde. This is an ARM specific method which extract the correct profile out of netCDF Variables from a Interpolated Sonde VAP for the volume start time of a radar object. Parameters ---------- sonde_dset : Dataset Interpolate sonde Dataset. radar : Radar Radar object from which the nearest profile will be found. time_key : string, optional Key to find a CF startard time variable. height_key : string, optional Key to find profile height data. nvars : list, optional NetCDF variable to generated profiles for. If None (the default) all variables with dimension of time, height will be found in ncvars. Returns ------- return_dic : dict Profiles at the start time of the radar. """ ncvars = sonde_dset.variables if nvars is None: time_height_shape = (len(ncvars[time_key]), len(ncvars[height_key])) nvars = [k for k, v in ncvars.items() if v.shape == time_height_shape] radar_start = num2date(radar.time["data"][0], radar.time["units"]) radar_day_start = DatetimeGregorian( radar_start.year, radar_start.month, ) seconds_since_start_of_day = (radar_start - radar_day_start).seconds time_index = abs(ncvars[time_key][:] - seconds_since_start_of_day).argmin() return_dic = {key: ncvars[key][time_index, :] for key in nvars} return_dic[height_key] = ncvars[height_key][:] return return_dic