icepyx.Variables.parse_var_list

static Variables.parse_var_list(varlist, tiered=True, tiered_vars=False)

Parse a list of path strings into tiered lists and names of variables

Parameters:

varlistlist of strings

List of full variable paths to be parsed.

tieredboolean, default True

Whether to return the paths (sans variable name) as a nested list of component strings (e.g. [[‘orbit_info’, ‘ancillary_data’, ‘gt1l’],[‘none’,’none’,’land_ice_segments’]]) or a single list of path strings (e.g. [‘orbit_info’,’ancillary_data’,’gt1l/land_ice_segments’])

tiered_varsboolean, default False

Whether or not to append a list of the variable names to the nested list of component strings (e.g. [[‘orbit_info’, ‘ancillary_data’, ‘gt1l’],[‘none’,’none’,’land_ice_segments’],

[‘sc_orient’,’atlas_sdp_gps_epoch’,’h_li’]]))

Examples

>>> reg_a = ipx.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'], version='1') 
>>> var_dict, paths = reg_a.order_vars.parse_var_list(reg_a.order_vars.avail()) 
>>> var_dict 
{'atlas_sdp_gps_epoch': ['ancillary_data/atlas_sdp_gps_epoch'],
.
.
.
'latitude': ['gt1l/land_ice_segments/latitude',
'gt1r/land_ice_segments/latitude',
'gt2l/land_ice_segments/latitude',
'gt2r/land_ice_segments/latitude',
'gt3l/land_ice_segments/latitude',
'gt3r/land_ice_segments/latitude'],
.
.
.
}
>>> var_dict.keys() 
dict_keys(['atlas_sdp_gps_epoch', 'control', 'data_end_utc', 'data_start_utc',
'end_cycle', 'end_delta_time', 'end_geoseg', 'end_gpssow', 'end_gpsweek',
'end_orbit', 'end_region', 'end_rgt', 'granule_end_utc', 'granule_start_utc',
'qa_at_interval', 'release', 'start_cycle', 'start_delta_time', 'start_geoseg',
'start_gpssow', 'start_gpsweek', 'start_orbit', 'start_region', 'start_rgt',
'version', 'dt_hist', 'fit_maxiter', 'fpb_maxiter', 'maxiter', 'max_res_ids',
'min_dist', 'min_gain_th', 'min_n_pe', 'min_n_sel', 'min_signal_conf', 'n_hist',
'nhist_bins', 'n_sigmas', 'proc_interval', 'qs_lim_bsc', 'qs_lim_hrs', 'qs_lim_hsigma',
'qs_lim_msw', 'qs_lim_snr', 'qs_lim_sss', 'rbin_width', 'sigma_beam', 'sigma_tx',
't_dead', 'atl06_quality_summary', 'delta_time', 'h_li', 'h_li_sigma', 'latitude',
'longitude', 'segment_id', 'sigma_geo_h', 'fpb_mean_corr', 'fpb_mean_corr_sigma',
'fpb_med_corr', 'fpb_med_corr_sigma', 'fpb_n_corr', 'med_r_fit', 'tx_mean_corr',
tx_med_corr', 'dem_flag', 'dem_h', 'geoid_h', 'dh_fit_dx', 'dh_fit_dx_sigma', '
dh_fit_dy', 'h_expected_rms', 'h_mean', 'h_rms_misfit', 'h_robust_sprd',
'n_fit_photons', 'n_seg_pulses', 'sigma_h_mean', 'signal_selection_source',
'signal_selection_source_status', 'snr', 'snr_significance', 'w_surface_window_final',
ckgrd', 'bsnow_conf', 'bsnow_h', 'bsnow_od', 'cloud_flg_asr', 'cloud_flg_atm', 'dac',
'e_bckgrd', 'layer_flag', 'msw_flag', 'neutat_delay_total', 'r_eff', 'solar_azimuth',
'solar_elevation', 'tide_earth', 'tide_equilibrium', 'tide_load', 'tide_ocean',
'tide_pole', 'ref_azimuth', 'ref_coelv', 'seg_azimuth', 'sigma_geo_at', 'sigma_geo_r',
igma_geo_xt', 'x_atc', 'y_atc', 'bckgrd_per_m', 'bin_top_h', 'count', 'ds_segment_id',
'lat_mean', 'lon_mean', 'pulse_count', 'segment_id_list', 'x_atc_mean', 'record_number',
'reference_pt_lat', 'reference_pt_lon', 'signal_selection_status_all',
'signal_selection_status_backup', 'signal_selection_status_confident', 'crossing_time',
'cycle_number', 'lan', 'orbit_number', 'rgt', 'sc_orient', 'sc_orient_time',
'qa_granule_fail_reason', 'qa_granule_pass_fail', 'signal_selection_source_fraction_0',
'signal_selection_source_fraction_1', 'signal_selection_source_fraction_2',
'signal_selection_source_fraction_3'])
>>> import numpy 
>>> numpy.unique(paths) 
array(['ancillary_data', 'bias_correction', 'dem', 'fit_statistics',
'geophysical', 'ground_track', 'gt1l', 'gt1r', 'gt2l', 'gt2r',
'gt3l', 'gt3r', 'land_ice', 'land_ice_segments', 'none',
'orbit_info', 'quality_assessment', 'residual_histogram',
'segment_quality', 'signal_selection_status'], dtype='<U23')