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two-ray-to-three-gpp-ch-calibration.py File Reference

Go to the source code of this file.

Classes

class  two-ray-to-three-gpp-ch-calibration.FtrParams
 FtrParams class. More...
 

Namespaces

namespace  two
 -ray-to-three-gpp-ch-calibration
 
namespace  two-ray-to-three-gpp-ch-calibration
 

Functions

str two-ray-to-three-gpp-ch-calibration.append_ftr_params_to_cpp_string (str text, FtrParams params)
 
float two-ray-to-three-gpp-ch-calibration.compute_anderson_darling_measure (list ref_ecdf, list target_ecdf)
 Computes the Anderson-Darling measure for the specified reference and targets distributions.
 
np.ndarray two-ray-to-three-gpp-ch-calibration.compute_ecdf_value (list ecdf, float data_points)
 Given an ECDF and data points belonging to its domain, returns their associated EDCF value.
 
 two-ray-to-three-gpp-ch-calibration.compute_ftr_mean (FtrParams params)
 Computes the mean of the FTR fading model, given a specific set of parameters.
 
 two-ray-to-three-gpp-ch-calibration.compute_ftr_th_mean (FtrParams params)
 Computes the mean of the FTR fading model using the formula reported in the corresponding paper, given a specific set of parameters.
 
str two-ray-to-three-gpp-ch-calibration.fit_ftr_to_reference (pd.DataFrame ref_data, tuple ref_params_combo, int num_params, int num_refinements)
 Estimate the FTR parameters yielding the closest ECDF to the reference one.
 
 two-ray-to-three-gpp-ch-calibration.get_ftr_ecdf (FtrParams params, int n_samples, db=False)
 Returns the ECDF for the FTR fading model, for a given parameter grid.
 
float two-ray-to-three-gpp-ch-calibration.get_sigma_from_k (float k)
 Computes the value for the FTR parameter sigma, given k, yielding a unit-mean fading process.
 
 two-ray-to-three-gpp-ch-calibration.print_cplusplus_map_from_fit_results (pd.DataFrame fit, str out_fname)
 
 two-ray-to-three-gpp-ch-calibration.tqdm_joblib (tqdm_object)
 

Variables

float two-ray-to-three-gpp-ch-calibration.ad_meas = compute_anderson_darling_measure(np.sort(ref_data["gain"]), ftr_ecdf)
 
list two-ray-to-three-gpp-ch-calibration.ad_measures = []
 
 two-ray-to-three-gpp-ch-calibration.args = parser.parse_args()
 
 two-ray-to-three-gpp-ch-calibration.avg_mean = np.mean(mean_list)
 
 two-ray-to-three-gpp-ch-calibration.bbox_inches
 
 two-ray-to-three-gpp-ch-calibration.c_plus_plus_out_fname = args.c_plus_plus_out_fname
 
 two-ray-to-three-gpp-ch-calibration.data
 
tuple two-ray-to-three-gpp-ch-calibration.data_query
 
 two-ray-to-three-gpp-ch-calibration.default
 
 two-ray-to-three-gpp-ch-calibration.delta
 
 two-ray-to-three-gpp-ch-calibration.df = pd.read_csv(ref_data_fname, sep="\t")
 Data pre-processing ##.
 
 two-ray-to-three-gpp-ch-calibration.dpi
 
 two-ray-to-three-gpp-ch-calibration.encoding
 
 two-ray-to-three-gpp-ch-calibration.epsilon = float(args.epsilon)
 
 two-ray-to-three-gpp-ch-calibration.exist_ok
 
 two-ray-to-three-gpp-ch-calibration.figs_folder = args.figs_folder
 
 two-ray-to-three-gpp-ch-calibration.fit = pd.read_csv(fit_out_fname, delimiter="\t")
 
 two-ray-to-three-gpp-ch-calibration.fit_ftr_to_threegpp = bool(args.fit_ftr_to_threegpp)
 
 two-ray-to-three-gpp-ch-calibration.fit_line = fit.query(data_query)
 
 two-ray-to-three-gpp-ch-calibration.fit_out_fname = args.fit_out_fname
 
 two-ray-to-three-gpp-ch-calibration.frequencies = np.sort(list(set(df["fc"])))
 
 two-ray-to-three-gpp-ch-calibration.ftr_ecdf = get_ftr_ecdf(params, len(ref_data), db=True)
 
 two-ray-to-three-gpp-ch-calibration.help
 
 two-ray-to-three-gpp-ch-calibration.is_los = set(df["cond"])
 
 two-ray-to-three-gpp-ch-calibration.k
 
 two-ray-to-three-gpp-ch-calibration.label
 
 two-ray-to-three-gpp-ch-calibration.m
 
list two-ray-to-three-gpp-ch-calibration.mean_list = []
 
list two-ray-to-three-gpp-ch-calibration.mean_th_list = []
 
 two-ray-to-three-gpp-ch-calibration.num
 
 two-ray-to-three-gpp-ch-calibration.num_refinements = int(args.num_refinements)
 
 two-ray-to-three-gpp-ch-calibration.num_search_grid_params = int(args.num_search_grid_params)
 
 two-ray-to-three-gpp-ch-calibration.output_ns3_table = bool(args.output_ns3_table)
 
 two-ray-to-three-gpp-ch-calibration.params = FtrParams()
 Fit Fluctuating Two Ray model to the 3GPP TR 38.901 using the Anderson-Darling goodness-of-fit ##.
 
 two-ray-to-three-gpp-ch-calibration.parents
 
 two-ray-to-three-gpp-ch-calibration.parser = argp.ArgumentParser(formatter_class=argp.ArgumentDefaultsHelpFormatter)
 
 two-ray-to-three-gpp-ch-calibration.plot_fit_results = bool(args.plot_fit_results)
 
 two-ray-to-three-gpp-ch-calibration.preliminary_fit_test = bool(args.preliminary_fit_test)
 
 two-ray-to-three-gpp-ch-calibration.rc
 
 two-ray-to-three-gpp-ch-calibration.ref_data = df.query(data_query)
 
 two-ray-to-three-gpp-ch-calibration.ref_data_fname = args.ref_data_fname
 
 two-ray-to-three-gpp-ch-calibration.res
 
 two-ray-to-three-gpp-ch-calibration.scenarios = set(df["scen"])
 
float two-ray-to-three-gpp-ch-calibration.sigma = get_sigma_from_k(k)
 
 two-ray-to-three-gpp-ch-calibration.start
 
 two-ray-to-three-gpp-ch-calibration.stop
 
 two-ray-to-three-gpp-ch-calibration.True
 
 two-ray-to-three-gpp-ch-calibration.x