Classes
class	FtrParams
	FtrParams class. More...

Functions
str	append_ftr_params_to_cpp_string (str text, FtrParams params)

float	compute_anderson_darling_measure (list ref_ecdf, list target_ecdf)
	Computes the Anderson-Darling measure for the specified reference and targets distributions.

np.ndarray	compute_ecdf_value (list ecdf, float data_points)
	Given an ECDF and data points belonging to its domain, returns their associated EDCF value.

	compute_ftr_mean (FtrParams params)
	Computes the mean of the FTR fading model, given a specific set of parameters.

	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	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.

	get_ftr_ecdf (FtrParams params, int n_samples, db=False)
	Returns the ECDF for the FTR fading model, for a given parameter grid.

float	get_sigma_from_k (float k)
	Computes the value for the FTR parameter sigma, given k, yielding a unit-mean fading process.

	print_cplusplus_map_from_fit_results (pd.DataFrame fit, str out_fname)

	tqdm_joblib (tqdm_object)

Variables
float	ad_meas = compute_anderson_darling_measure(np.sort(ref_data["gain"]), ftr_ecdf)

list	ad_measures = []

	args = parser.parse_args()

	avg_mean = np.mean(mean_list)

	bbox_inches

	c_plus_plus_out_fname = args.c_plus_plus_out_fname

	data

tuple	data_query

	default

	delta

	df = pd.read_csv(ref_data_fname, sep="\t")
	Data pre-processing ##.

	dpi

	encoding

	epsilon = float(args.epsilon)

	exist_ok

	figs_folder = args.figs_folder

	fit = pd.read_csv(fit_out_fname, delimiter="\t")

	fit_ftr_to_threegpp = bool(args.fit_ftr_to_threegpp)

	fit_line = fit.query(data_query)

	fit_out_fname = args.fit_out_fname

	frequencies = np.sort(list(set(df["fc"])))

	ftr_ecdf = get_ftr_ecdf(params, len(ref_data), db=True)

	help

	is_los = set(df["cond"])

	k

	label

	m

list	mean_list = []

list	mean_th_list = []

	num

	num_refinements = int(args.num_refinements)

	num_search_grid_params = int(args.num_search_grid_params)

	output_ns3_table = bool(args.output_ns3_table)

	params = FtrParams()
	Fit Fluctuating Two Ray model to the 3GPP TR 38.901 using the Anderson-Darling goodness-of-fit ##.

	parents

	parser = argp.ArgumentParser(formatter_class=argp.ArgumentDefaultsHelpFormatter)

	plot_fit_results = bool(args.plot_fit_results)

	preliminary_fit_test = bool(args.preliminary_fit_test)

	rc

	ref_data = df.query(data_query)

	ref_data_fname = args.ref_data_fname

	res

	scenarios = set(df["scen"])

float	sigma = get_sigma_from_k(k)

	start

	stop

	True

	x

Function Documentation

◆ append_ftr_params_to_cpp_string()

str two-ray-to-three-gpp-ch-calibration.append_ftr_params_to_cpp_string	(	str	text,
		FtrParams	params )

Definition at line 423 of file two-ray-to-three-gpp-ch-calibration.py.

Referenced by print_cplusplus_map_from_fit_results().

Here is the caller graph for this function:

◆ compute_anderson_darling_measure()

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.

In particular, the Anderson-Darling measure is defined as: , where $S = \sum_{i=1}^N \frac{2i - 1}{N} \left[ ln F(Y_i) + ln F(Y_{N + 1 - i}) \right]$ .

See https://www.itl.nist.gov/div898/handbook/eda/section3/eda35e.htm for further details.

Parameters

ref_ecdf	The reference ECDF.
target_ecdf	The target ECDF we wish to match the reference distribution to.

Returns: The Anderson-Darling measure for the specified reference and targets distributions.

Definition at line 238 of file two-ray-to-three-gpp-ch-calibration.py.

References compute_ecdf_value().

Referenced by fit_ftr_to_reference().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ compute_ecdf_value()

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.

Parameters

ecdf	The ECDF, represented as a sorted list of samples.
data_points	A list of data points belonging to the same domain as the samples.

Returns: The ECDF value of the domain points of the specified ECDF

Definition at line 266 of file two-ray-to-three-gpp-ch-calibration.py.

Referenced by compute_anderson_darling_measure().

Here is the caller graph for this function:

◆ compute_ftr_mean()

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.

Parameters

params The FTR fading model parameters.

Definition at line 215 of file two-ray-to-three-gpp-ch-calibration.py.

◆ compute_ftr_th_mean()

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.

Parameters

params The FTR fading model parameters.

Definition at line 229 of file two-ray-to-three-gpp-ch-calibration.py.

◆ fit_ftr_to_reference()

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.

Uses a global search to estimate the FTR parameters yielding the best fit to the reference ECDF. Then, the search is refined by repeating the procedure in the neighborhood of the parameters identified with the global search. Such a neighborhood is determined as the interval whose center is the previous iteration best value, and the lower and upper bounds are the first lower and upper values which were previously considered, respectively.

Parameters

ref_data	The reference data, represented as a DataFrame of samples.
ref_params_combo	The specific combination of simulation parameters corresponding to the reference ECDF
num_params	The number of values of each parameter in the global and local search grids.
num_refinements	The number of local refinement search to be carried out after the global search.

Returns: An estimate of the FTR parameters yielding the closest ECDF to the reference one.

Definition at line 291 of file two-ray-to-three-gpp-ch-calibration.py.

References compute_anderson_darling_measure(), get_ftr_ecdf(), and get_sigma_from_k().

Here is the call graph for this function:

◆ get_ftr_ecdf()

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.

Parameters

params	The FTR parameters grid.
n_samples	The number of samples of the output ECDF
db	Whether to return the ECDF with the gain expressed in dB

Returns: The ECDF for the FTR fading model

Definition at line 165 of file two-ray-to-three-gpp-ch-calibration.py.

Referenced by fit_ftr_to_reference().

Here is the caller graph for this function:

◆ get_sigma_from_k()

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.

Parameters

k	The K parameter of the FTR fading model, which represents the ratio of the average power of the dominant components to the power of the remaining diffuse multipath.

Returns: The value for the FTR parameter sigma, given k, yielding a unit-mean fading process.

Definition at line 281 of file two-ray-to-three-gpp-ch-calibration.py.

Referenced by fit_ftr_to_reference().

Here is the caller graph for this function:

◆ print_cplusplus_map_from_fit_results()

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

Prints to a file the results of the FTR fit, as C++ code.

Args:
  fit (pd.DataFrame): A Pandas Dataframe holding the results of the FTR fit.
  out_fname (str): The name of the file to print the C++ code to.

Definition at line 430 of file two-ray-to-three-gpp-ch-calibration.py.

References append_ftr_params_to_cpp_string().

Here is the call graph for this function:

◆ tqdm_joblib()

two-ray-to-three-gpp-ch-calibration.tqdm_joblib ( tqdm_object )

Context manager to patch joblib to report into tqdm progress bar given as argument.
Taken from: https://stackoverflow.com/questions/24983493/tracking-progress-of-joblib-parallel-execution

Definition at line 100 of file two-ray-to-three-gpp-ch-calibration.py.

Variable Documentation

◆ ad_meas

float two-ray-to-three-gpp-ch-calibration.ad_meas = compute_anderson_darling_measure(np.sort(ref_data["gain"]), ftr_ecdf)

Definition at line 590 of file two-ray-to-three-gpp-ch-calibration.py.

◆ ad_measures

two-ray-to-three-gpp-ch-calibration.ad_measures = []

Definition at line 567 of file two-ray-to-three-gpp-ch-calibration.py.

◆ args

two-ray-to-three-gpp-ch-calibration.args = parser.parse_args()

Definition at line 74 of file two-ray-to-three-gpp-ch-calibration.py.

◆ avg_mean

two-ray-to-three-gpp-ch-calibration.avg_mean = np.mean(mean_list)

Definition at line 514 of file two-ray-to-three-gpp-ch-calibration.py.

◆ bbox_inches

two-ray-to-three-gpp-ch-calibration.bbox_inches

Definition at line 600 of file two-ray-to-three-gpp-ch-calibration.py.

◆ c_plus_plus_out_fname

two-ray-to-three-gpp-ch-calibration.c_plus_plus_out_fname = args.c_plus_plus_out_fname

Definition at line 84 of file two-ray-to-three-gpp-ch-calibration.py.

◆ data

two-ray-to-three-gpp-ch-calibration.data

Definition at line 593 of file two-ray-to-three-gpp-ch-calibration.py.

◆ data_query

tuple two-ray-to-three-gpp-ch-calibration.data_query

Initial value:

=  (
                "scen == @params_comb[0] and cond == @params_comb[1] and fc == @params_comb[2]"
            )

Definition at line 570 of file two-ray-to-three-gpp-ch-calibration.py.

◆ default

two-ray-to-three-gpp-ch-calibration.default

Definition at line 28 of file two-ray-to-three-gpp-ch-calibration.py.

◆ delta

two-ray-to-three-gpp-ch-calibration.delta

Definition at line 511 of file two-ray-to-three-gpp-ch-calibration.py.

◆ df

two-ray-to-three-gpp-ch-calibration.df = pd.read_csv(ref_data_fname, sep="\t")

Data pre-processing ##.

Definition at line 490 of file two-ray-to-three-gpp-ch-calibration.py.

◆ dpi

two-ray-to-three-gpp-ch-calibration.dpi

Definition at line 599 of file two-ray-to-three-gpp-ch-calibration.py.

◆ encoding

two-ray-to-three-gpp-ch-calibration.encoding

Definition at line 546 of file two-ray-to-three-gpp-ch-calibration.py.

◆ epsilon

two-ray-to-three-gpp-ch-calibration.epsilon = float(args.epsilon)

Definition at line 88 of file two-ray-to-three-gpp-ch-calibration.py.

◆ exist_ok

two-ray-to-three-gpp-ch-calibration.exist_ok

Definition at line 566 of file two-ray-to-three-gpp-ch-calibration.py.

◆ figs_folder

two-ray-to-three-gpp-ch-calibration.figs_folder = args.figs_folder

Definition at line 86 of file two-ray-to-three-gpp-ch-calibration.py.

◆ fit

two-ray-to-three-gpp-ch-calibration.fit = pd.read_csv(fit_out_fname, delimiter="\t")

Definition at line 553 of file two-ray-to-three-gpp-ch-calibration.py.

◆ fit_ftr_to_threegpp

two-ray-to-three-gpp-ch-calibration.fit_ftr_to_threegpp = bool(args.fit_ftr_to_threegpp)

Definition at line 92 of file two-ray-to-three-gpp-ch-calibration.py.

◆ fit_line

two-ray-to-three-gpp-ch-calibration.fit_line = fit.query(data_query)

Definition at line 578 of file two-ray-to-three-gpp-ch-calibration.py.

◆ fit_out_fname

two-ray-to-three-gpp-ch-calibration.fit_out_fname = args.fit_out_fname

Definition at line 82 of file two-ray-to-three-gpp-ch-calibration.py.

◆ frequencies

two-ray-to-three-gpp-ch-calibration.frequencies = np.sort(list(set(df["fc"])))

Definition at line 497 of file two-ray-to-three-gpp-ch-calibration.py.

◆ ftr_ecdf

two-ray-to-three-gpp-ch-calibration.ftr_ecdf = get_ftr_ecdf(params, len(ref_data), db=True)

Definition at line 587 of file two-ray-to-three-gpp-ch-calibration.py.

◆ help

two-ray-to-three-gpp-ch-calibration.help

Definition at line 29 of file two-ray-to-three-gpp-ch-calibration.py.

◆ is_los

two-ray-to-three-gpp-ch-calibration.is_los = set(df["cond"])

Definition at line 496 of file two-ray-to-three-gpp-ch-calibration.py.

◆ k

two-ray-to-three-gpp-ch-calibration.k

Definition at line 524 of file two-ray-to-three-gpp-ch-calibration.py.

◆ label

two-ray-to-three-gpp-ch-calibration.label

Definition at line 593 of file two-ray-to-three-gpp-ch-calibration.py.

◆ m

two-ray-to-three-gpp-ch-calibration.m

Definition at line 581 of file two-ray-to-three-gpp-ch-calibration.py.

◆ mean_list

list two-ray-to-three-gpp-ch-calibration.mean_list = []

Definition at line 508 of file two-ray-to-three-gpp-ch-calibration.py.

◆ mean_th_list

list two-ray-to-three-gpp-ch-calibration.mean_th_list = []

Definition at line 519 of file two-ray-to-three-gpp-ch-calibration.py.

◆ num

two-ray-to-three-gpp-ch-calibration.num

Definition at line 510 of file two-ray-to-three-gpp-ch-calibration.py.

◆ num_refinements

two-ray-to-three-gpp-ch-calibration.num_refinements = int(args.num_refinements)

Definition at line 78 of file two-ray-to-three-gpp-ch-calibration.py.

◆ num_search_grid_params

two-ray-to-three-gpp-ch-calibration.num_search_grid_params = int(args.num_search_grid_params)

Definition at line 76 of file two-ray-to-three-gpp-ch-calibration.py.

◆ output_ns3_table

two-ray-to-three-gpp-ch-calibration.output_ns3_table = bool(args.output_ns3_table)

Definition at line 94 of file two-ray-to-three-gpp-ch-calibration.py.

◆ params

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 ##.

Definition at line 504 of file two-ray-to-three-gpp-ch-calibration.py.

◆ parents

two-ray-to-three-gpp-ch-calibration.parents

Definition at line 566 of file two-ray-to-three-gpp-ch-calibration.py.

◆ parser

two-ray-to-three-gpp-ch-calibration.parser = argp.ArgumentParser(formatter_class=argp.ArgumentDefaultsHelpFormatter)

Definition at line 25 of file two-ray-to-three-gpp-ch-calibration.py.

◆ plot_fit_results

two-ray-to-three-gpp-ch-calibration.plot_fit_results = bool(args.plot_fit_results)

Definition at line 96 of file two-ray-to-three-gpp-ch-calibration.py.

◆ preliminary_fit_test

two-ray-to-three-gpp-ch-calibration.preliminary_fit_test = bool(args.preliminary_fit_test)

Definition at line 90 of file two-ray-to-three-gpp-ch-calibration.py.

◆ rc

two-ray-to-three-gpp-ch-calibration.rc

Definition at line 560 of file two-ray-to-three-gpp-ch-calibration.py.

◆ ref_data

two-ray-to-three-gpp-ch-calibration.ref_data = df.query(data_query)

Definition at line 575 of file two-ray-to-three-gpp-ch-calibration.py.

◆ ref_data_fname

two-ray-to-three-gpp-ch-calibration.ref_data_fname = args.ref_data_fname

Definition at line 80 of file two-ray-to-three-gpp-ch-calibration.py.

◆ res

two-ray-to-three-gpp-ch-calibration.res

Initial value:

=  joblib.Parallel(n_jobs=10)(
                joblib.delayed(fit_ftr_to_reference)(
                    df, params_comb, num_search_grid_params, num_refinements
                )
                for params_comb in product(scenarios, is_los, frequencies)
            )

Definition at line 539 of file two-ray-to-three-gpp-ch-calibration.py.

◆ scenarios

two-ray-to-three-gpp-ch-calibration.scenarios = set(df["scen"])

Definition at line 495 of file two-ray-to-three-gpp-ch-calibration.py.

◆ sigma

two-ray-to-three-gpp-ch-calibration.sigma = get_sigma_from_k(k)

Definition at line 522 of file two-ray-to-three-gpp-ch-calibration.py.

◆ start

two-ray-to-three-gpp-ch-calibration.start

Definition at line 510 of file two-ray-to-three-gpp-ch-calibration.py.

◆ stop

two-ray-to-three-gpp-ch-calibration.stop

Definition at line 510 of file two-ray-to-three-gpp-ch-calibration.py.

◆ True

two-ray-to-three-gpp-ch-calibration.True

Definition at line 566 of file two-ray-to-three-gpp-ch-calibration.py.

◆ x

two-ray-to-three-gpp-ch-calibration.x

Definition at line 593 of file two-ray-to-three-gpp-ch-calibration.py.

Classes

Functions

Variables

Function Documentation

◆ append_ftr_params_to_cpp_string()

◆ compute_anderson_darling_measure()

◆ compute_ecdf_value()

◆ compute_ftr_mean()

◆ compute_ftr_th_mean()

◆ fit_ftr_to_reference()

◆ get_ftr_ecdf()

◆ get_sigma_from_k()

◆ print_cplusplus_map_from_fit_results()

◆ tqdm_joblib()

Variable Documentation

◆ ad_meas

◆ ad_measures

◆ args

◆ avg_mean

◆ bbox_inches

◆ c_plus_plus_out_fname

◆ data

◆ data_query

◆ default

◆ delta

◆ df

◆ dpi

◆ encoding

◆ epsilon

◆ exist_ok

◆ figs_folder

◆ fit

◆ fit_ftr_to_threegpp

◆ fit_line

◆ fit_out_fname

◆ frequencies

◆ ftr_ecdf

◆ help

◆ is_los

◆ k

◆ label

◆ m

◆ mean_list

◆ mean_th_list

◆ num

◆ num_refinements

◆ num_search_grid_params

◆ output_ns3_table

◆ params

◆ parents

◆ parser

◆ plot_fit_results

◆ preliminary_fit_test

◆ rc

◆ ref_data

◆ ref_data_fname

◆ res

◆ scenarios

◆ sigma

◆ start

◆ stop

◆ True

◆ x