Source code for pyart.aux_io.odim_h5

Routines for reading ODIM_H5 files.


import datetime

import numpy as np
    import h5py
    _H5PY_AVAILABLE = True
except ImportError:
    _H5PY_AVAILABLE = False

from ..config import FileMetadata, get_fillvalue
from import make_time_unit_str, _test_arguments
from ..core.radar import Radar
from ..exceptions import MissingOptionalDependency

    'TH': 'total_power_horizontal',        # uncorrected reflectivity, horizontal
    'TV': 'total_power_vertical',        # uncorrected reflectivity, vertical
    'DBZH': 'reflectivity_horizontal',     # corrected reflectivity, horizontal
    'DBZV': 'reflectivity_vertical',     # corrected reflectivity, vertical
    'ZDR': 'differential_reflectivity',     # differential reflectivity
    'RHOHV': 'cross_correlation_ratio',
    'LDR': 'linear_polarization_ratio',
    'PHIDP': 'differential_phase',
    'KDP': 'specific_differential_phase',
    'SQI': 'normalized_coherent_power',
    'SNR': 'signal_to_noise_ratio',
    'VRAD': 'velocity',  # radial velocity, marked for deprecation in ODIM HDF5 2.2
    'VRADH': 'velocity_horizontal',  # radial velocity, horizontal polarisation
    'VRADV': 'velocity_vertical',  # radial velocity, vertical polarisation
    'WRAD': 'spectrum_width',
    'QIND': 'quality_index',

[docs]def read_odim_h5(filename, field_names=None, additional_metadata=None, file_field_names=False, exclude_fields=None, include_fields=None, include_datasets=None, exclude_datasets=None, **kwargs): """ Read a ODIM_H5 file. Parameters ---------- filename : str Name of the ODIM_H5 file to read. field_names : dict, optional Dictionary mapping ODIM_H5 field names to radar field names. If a data type found in the file does not appear in this dictionary or has a value of None it will not be placed in the radar.fields dictionary. A value of None, the default, will use the mapping defined in the Py-ART configuration file. additional_metadata : dict of dicts, optional Dictionary of dictionaries to retrieve metadata from during this read. This metadata is not used during any successive file reads unless explicitly included. A value of None, the default, will not introduct any addition metadata and the file specific or default metadata as specified by the Py-ART configuration file will be used. file_field_names : bool, optional True to use the MDV data type names for the field names. If this case the field_names parameter is ignored. The field dictionary will likely only have a 'data' key, unless the fields are defined in `additional_metadata`. exclude_fields : list or None, optional List of fields to exclude from the radar object. This is applied after the `file_field_names` and `field_names` parameters. Set to None to include all fields specified by include_fields. include_fields : list or None, optional List of fields to include from the radar object. This is applied after the `file_field_names` and `field_names` parameters. Set to None to include all fields not specified by exclude_fields. include_datasets : list or None, optional List of datasets to include from the HDF5 file, given as ["dataset1", "dataset2", ...]. Set to None to include all datasets not specified by exclude_datasets. exclude_datasets : list or None, optional List of datasets to exclude from the HDF5 file, given as ["dataset1", "dataset2", ...]. Set to None to include all datasets specified by include_datasets. Returns ------- radar : Radar Radar object containing data from ODIM_H5 file. """ # TODO before moving to # * unit test # * add default field mapping, etc to default config # * auto-detect file type with function # * instrument parameters # * add additional checks for HOW attributes # * support for other objects (SCAN, XSEC) # check that h5py is available if not _H5PY_AVAILABLE: raise MissingOptionalDependency( "h5py is required to use read_odim_h5 but is not installed") # test for non empty kwargs _test_arguments(kwargs) # create metadata retrieval object if field_names is None: field_names = ODIM_H5_FIELD_NAMES filemetadata = FileMetadata('odim_h5', field_names, additional_metadata, file_field_names, exclude_fields, include_fields) # open the file with h5py.File(filename, 'r') as hfile: odim_object = _to_str(hfile['what'].attrs['object']) if odim_object not in ['PVOL', 'SCAN', 'ELEV', 'AZIM']: raise NotImplementedError( 'object: %s not implemented.' % (odim_object)) # determine the number of sweeps by the number of groups which # begin with dataset if include_datasets is not None: datasets = [k for k in hfile if k.startswith('dataset') and k in include_datasets] elif exclude_datasets is not None: datasets = [k for k in hfile if k.startswith('dataset') and k not in exclude_datasets] else: datasets = [k for k in hfile if k.startswith('dataset')] datasets.sort(key=lambda x: int(x[7:])) nsweeps = len(datasets) # latitude, longitude and altitude latitude = filemetadata('latitude') longitude = filemetadata('longitude') altitude = filemetadata('altitude') h_where = hfile['where'].attrs latitude['data'] = np.array([h_where['lat']], dtype='float64') longitude['data'] = np.array([h_where['lon']], dtype='float64') altitude['data'] = np.array([h_where['height']], dtype='float64') # metadata metadata = filemetadata('metadata') metadata['source'] = _to_str(hfile['what'].attrs['source']) metadata['original_container'] = 'odim_h5' metadata['odim_conventions'] = _to_str(hfile.attrs['Conventions']) h_what = hfile['what'].attrs metadata['version'] = _to_str(h_what['version']) metadata['source'] = _to_str(h_what['source']) try: ds1_how = hfile[datasets[0]]['how'].attrs except KeyError: # if no how group exists mock it with an empty dictionary ds1_how = {} if 'system' in ds1_how: metadata['system'] = ds1_how['system'] if 'software' in ds1_how: metadata['software'] = ds1_how['software'] if 'sw_version' in ds1_how: metadata['sw_version'] = ds1_how['sw_version'] # sweep_start_ray_index, sweep_end_ray_index sweep_start_ray_index = filemetadata('sweep_start_ray_index') sweep_end_ray_index = filemetadata('sweep_end_ray_index') if odim_object in ['AZIM', 'SCAN', 'PVOL']: rays_per_sweep = [ int(hfile[d]['where'].attrs['nrays']) for d in datasets] elif odim_object == 'ELEV': rays_per_sweep = [ int(hfile[d]['where'].attrs['angles'].size) for d in datasets] total_rays = sum(rays_per_sweep) ssri = np.cumsum(np.append([0], rays_per_sweep[:-1])).astype('int32') seri = np.cumsum(rays_per_sweep).astype('int32') - 1 sweep_start_ray_index['data'] = ssri sweep_end_ray_index['data'] = seri # sweep_number sweep_number = filemetadata('sweep_number') sweep_number['data'] = np.arange(nsweeps, dtype='int32') # sweep_mode sweep_mode = filemetadata('sweep_mode') sweep_mode['data'] = np.array(nsweeps * ['azimuth_surveillance']) # scan_type if odim_object == 'ELEV': scan_type = 'rhi' else: scan_type = 'ppi' # fixed_angle fixed_angle = filemetadata('fixed_angle') if odim_object == 'ELEV': sweep_el = [hfile[d]['where'].attrs['az_angle'] for d in datasets] else: sweep_el = [hfile[d]['where'].attrs['elangle'] for d in datasets] fixed_angle['data'] = np.array(sweep_el, dtype='float32') # elevation elevation = filemetadata('elevation') if 'elangles' in ds1_how: edata = np.empty(total_rays, dtype='float32') for d, start, stop in zip(datasets, ssri, seri): edata[start:stop+1] = hfile[d]['how'].attrs['elangles'][:] elevation['data'] = edata elif odim_object == 'ELEV': edata = np.empty(total_rays, dtype='float32') for d, start, stop in zip(datasets, ssri, seri): edata[start:stop+1] = hfile[d]['where'].attrs['angles'][:] elevation['data'] = edata else: elevation['data'] = np.repeat(sweep_el, rays_per_sweep) # range _range = filemetadata('range') if 'rstart' in hfile['dataset1/where'].attrs: # derive range from rstart and rscale attributes if available # check that the gate spacing is constant between sweeps rstart = [hfile[d]['where'].attrs['rstart'] for d in datasets] if any(rstart != rstart[0]): raise ValueError('range start changes between sweeps') rscale = [hfile[d]['where'].attrs['rscale'] for d in datasets] if any(rscale != rscale[0]): raise ValueError('range scale changes between sweeps') all_sweeps_nbins = [hfile[d]['where'].attrs['nbins'] for d in datasets] # check for max range off all sweeps max_nbins = max(all_sweeps_nbins) if isinstance(max_nbins, np.ndarray): max_nbins = max_nbins[0] else: max_nbins = max(all_sweeps_nbins) rscenter = 1e3 * rstart[0] + rscale[0] / 2 _range['data'] = np.arange(rscenter, rscenter + max_nbins * rscale[0], rscale[0], dtype='float32') _range['meters_to_center_of_first_gate'] = rstart[0] * 1000. _range['meters_between_gates'] = float(rscale[0]) else: # if not defined use range attribute which defines the maximum range # in km. There is no information on the starting location of the # range bins so we assume this to be 0. # This most often occurs in RHI files, which technically do not meet # the ODIM 2.2 specs. Section 7.4 requires that these files include # the where/rstart, where/rscale and where/nbins attributes. max_range = [hfile[d]['where'].attrs['range'] for d in datasets] if any(max_range != max_range[0]): raise ValueError('maximum range changes between sweeps') # nbins is required nbins = hfile['dataset1/data1/data'].shape[1] _range['data'] = np.linspace( 0, max_range[0] * 1000., nbins).astype('float32') _range['meters_to_center_of_first_gate'] = 0 _range['meters_between_gates'] = max_range[0] * 1000. / nbins # azimuth azimuth = filemetadata('azimuth') az_data = np.ones((total_rays, ), dtype='float32') for dset, start, stop in zip(datasets, ssri, seri): if odim_object == 'ELEV': # all azimuth angles are the sweep azimuth angle sweep_az = hfile[dset]['where'].attrs['az_angle'] elif odim_object == 'AZIM': # Sector azimuths are specified in the startaz and stopaz # attribute of dataset/where. # Assume that the azimuth angles do not pass through 0/360 deg. startaz = hfile[dset]['where'].attrs['startaz'] stopaz = hfile[dset]['where'].attrs['stopaz'] nrays = stop - start + 1 sweep_az = np.linspace(startaz, stopaz, nrays, endpoint=True) elif ('startazA' in ds1_how) and ('stopazA' in ds1_how): # average between start and stop azimuth angles startaz = hfile[dset]['how'].attrs['startazA'] stopaz = hfile[dset]['how'].attrs['stopazA'] sweep_az = np.angle( (np.exp(1.j*np.deg2rad(startaz)) + np.exp(1.j*np.deg2rad(stopaz))) / 2., deg=True) else: # according to section 5.1 the first ray points to the # northernmost direction and proceeds clockwise for a complete # 360 rotation. try: astart = hfile[dset]['how'].attrs['astart'] except KeyError: astart = 0.0 nrays = hfile[dset]['where'].attrs['nrays'] da = 360.0 / nrays sweep_az = np.arange(astart + da / 2., 360., da, dtype='float32') az_data[start:stop+1] = sweep_az azimuth['data'] = az_data # time _time = filemetadata('time') if ('startazT' in ds1_how) and ('stopazT' in ds1_how): # average between startazT and stopazT t_data = np.empty((total_rays, ), dtype='float32') for dset, start, stop in zip(datasets, ssri, seri): t_start = hfile[dset]['how'].attrs['startazT'] t_stop = hfile[dset]['how'].attrs['stopazT'] t_data[start:stop+1] = (t_start + t_stop) / 2 start_epoch = t_data.min() start_time = datetime.datetime.utcfromtimestamp(start_epoch) _time['units'] = make_time_unit_str(start_time) _time['data'] = t_data - start_epoch else: t_data = np.empty((total_rays, ), dtype='int32') # interpolate between each sweep starting and ending time for dset, start, stop in zip(datasets, ssri, seri): dset_what = hfile[dset]['what'].attrs start_str = _to_str( dset_what['startdate'] + dset_what['starttime']) end_str = _to_str(dset_what['enddate'] + dset_what['endtime']) start_dt = datetime.datetime.strptime(start_str, '%Y%m%d%H%M%S') end_dt = datetime.datetime.strptime(end_str, '%Y%m%d%H%M%S') time_delta = end_dt - start_dt delta_seconds = time_delta.seconds + time_delta.days * 3600 * 24 rays = stop - start + 1 sweep_start_epoch = ( start_dt - datetime.datetime(1970, 1, 1)).total_seconds() t_data[start:stop+1] = (sweep_start_epoch + np.linspace(0, delta_seconds, rays)) start_epoch = t_data.min() start_time = datetime.datetime.utcfromtimestamp(start_epoch) _time['units'] = make_time_unit_str(start_time) _time['data'] = (t_data - start_epoch).astype('float32') # fields fields = {} h_field_keys = [k for k in hfile['dataset1'] if k.startswith('data')] odim_fields = [hfile['dataset1'][d]['what'].attrs['quantity'] for d in h_field_keys] for odim_field, h_field_key in zip(odim_fields, h_field_keys): field_name = filemetadata.get_field_name(_to_str(odim_field)) if field_name is None: continue fdata =, max_nbins), dtype='float32') start = 0 # loop on the sweeps, copy data into correct location in data array for dset, rays_in_sweep in zip(datasets, rays_per_sweep): try: sweep_data = _get_odim_h5_sweep_data(hfile[dset][h_field_key]) except KeyError: sweep_data = np.zeros((rays_in_sweep, max_nbins)) + np.NaN sweep_nbins = sweep_data.shape[1] fdata[start:start + rays_in_sweep, :sweep_nbins] = sweep_data[:] # set data to NaN if its beyond the range of this sweep fdata[start:start + rays_in_sweep, sweep_nbins:max_nbins] = np.nan start += rays_in_sweep # create field dictionary field_dic = filemetadata(field_name) field_dic['data'] = fdata field_dic['_FillValue'] = get_fillvalue() fields[field_name] = field_dic # instrument_parameters instrument_parameters = None 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, instrument_parameters=instrument_parameters)
def _to_str(text): """ Convert bytes to str if necessary. """ if hasattr(text, 'decode'): return text.decode('utf-8') else: return text def _get_odim_h5_sweep_data(group): """ Get ODIM_H5 sweet data from an HDF5 group. """ # mask raw data what = group['what'] raw_data = group['data'][:] if 'nodata' in what.attrs: nodata = what.attrs.get('nodata') data =, nodata) else: data = if 'undetect' in what.attrs: undetect = what.attrs.get('undetect') data[data == undetect] = offset = 0.0 gain = 1.0 if 'offset' in what.attrs: offset = what.attrs.get('offset') if 'gain' in what.attrs: gain = what.attrs.get('gain') return data * gain + offset