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.
def	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.
def	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.
def	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.
def	two-ray-to-three-gpp-ch-calibration.print_cplusplus_map_from_fit_results (pd.DataFrame fit, str out_fname)
def	two-ray-to-three-gpp-ch-calibration.tqdm_joblib (tqdm_object)

Variables
float	two-ray-to-three-gpp-ch-calibration.ad_meas
list	two-ray-to-three-gpp-ch-calibration.ad_measures = []
argp	two-ray-to-three-gpp-ch-calibration.args = parser.parse_args()
np	two-ray-to-three-gpp-ch-calibration.avg_mean = np.mean(mean_list)
	two-ray-to-three-gpp-ch-calibration.bbox_inches
argp	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
str	two-ray-to-three-gpp-ch-calibration.data_query = 'scen == @params_comb[0] and cond == @params_comb[1] and fc == @params_comb[2]'
	two-ray-to-three-gpp-ch-calibration.default
	two-ray-to-three-gpp-ch-calibration.delta
pd	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
float	two-ray-to-three-gpp-ch-calibration.epsilon = float(args.epsilon)
	two-ray-to-three-gpp-ch-calibration.exist_ok
argp	two-ray-to-three-gpp-ch-calibration.figs_folder = args.figs_folder
pd	two-ray-to-three-gpp-ch-calibration.fit = pd.read_csv(fit_out_fname, delimiter='\t')
bool	two-ray-to-three-gpp-ch-calibration.fit_ftr_to_threegpp = bool(args.fit_ftr_to_threegpp)
pd	two-ray-to-three-gpp-ch-calibration.fit_line = fit.query(data_query)
argp	two-ray-to-three-gpp-ch-calibration.fit_out_fname = args.fit_out_fname
np	two-ray-to-three-gpp-ch-calibration.frequencies = np.sort(list(set(df['fc'])))
def	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
set	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
int	two-ray-to-three-gpp-ch-calibration.num_refinements = int(args.num_refinements)
int	two-ray-to-three-gpp-ch-calibration.num_search_grid_params = int(args.num_search_grid_params)
bool	two-ray-to-three-gpp-ch-calibration.output_ns3_table = bool(args.output_ns3_table)
FtrParams	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
argp	two-ray-to-three-gpp-ch-calibration.parser = argp.ArgumentParser(formatter_class=argp.ArgumentDefaultsHelpFormatter)
bool	two-ray-to-three-gpp-ch-calibration.plot_fit_results = bool(args.plot_fit_results)
bool	two-ray-to-three-gpp-ch-calibration.preliminary_fit_test = bool(args.preliminary_fit_test)
	two-ray-to-three-gpp-ch-calibration.rc
pd	two-ray-to-three-gpp-ch-calibration.ref_data = df.query(data_query)
argp	two-ray-to-three-gpp-ch-calibration.ref_data_fname = args.ref_data_fname
joblib	two-ray-to-three-gpp-ch-calibration.res
set	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