utils

Miscellaneous utility functions that may be of use throughout PolarRoute

case_from_angle(start, end)

Determine the direction of travel between two points in the same cell and return the associated case

Parameters:

Name	Type	Description	Default
start	list	the coordinates of the start point within the cell	required
end	list	the coordinates of the end point within the cell	required

Returns:

Name	Type	Description
case	int	the case to use to select variable values from a list

convert_decimal_days(decimal_days, mins=False)

Convert decimal days to more readable Days, Hours and (optionally) Minutes Args: decimal_days (float): Number of days as a decimal mins (bool): Determines whether to return minutes or decimal hours Returns: new_time (str): The time in the new format

extract_geojson_routes(mesh)

Extract routes in a precomputed mesh in GEOJSON format

Parameters:

Name	Type	Description	Default
mesh	dict	Precomputed mesh JSON with routes embedded	required

Returns:

Name	Type	Description
list		List of all routes found in mesh. If no routes found, returns empty list

fftind(size)

Creates a numpy array of shifted Fourier coordinates.

Parameters:

Name	Type	Description	Default
size	int	The size of the coordinate array to create	required

Returns:

Type	Description
	np.array: Numpy array of shifted Fourier coordinates (k_x, k_y). Has shape (2, size, size), with: array[0,:,:] = k_x components array[1,:,:] = k_y components

gaussian_random_field(size, alpha)

Creates a gaussian random field with normal (circular) distribution Code from https://github.com/bsciolla/gaussian-random-fields/blob/master/gaussian_random_fields.py

Parameters:

Name	Type	Description	Default
size	int	Default = 512; The number of datapoints created per axis in the GRF	required
alpha	float	Default = 3.0; The power of the power-law momentum distribution	required

Returns:

Type	Description
	np.array: 2D Array of datapoints, shape (size, size)

gpx_route_import(f_name)

Function to import a route in gpx format and convert it to geojson format

Parameters:

Name	Type	Description	Default
f_name		Filename of gpx route file	required

Returns:

Name	Type	Description
geojson		Route in geojson format

json_str(input_json)

Normalise input for classes and functions that take in json, return dict whether a dict or path is given as input. Args: input_json (dict or str):

Returns:

Type	Description
	output_json (dict)

pandas_dataframe_str(input_df)

Normalise input for classes and functions that take in pandas dataframes, return a df whether a df or path is given as input. Args: input_df (pd.DataFrame or str):

Returns:

Type	Description
	output_df (pd.DataFrame)

round_to_sigfig(x, sigfig=5)

Rounds numbers to some number of significant figures

Parameters:

Name	Type	Description	Default
x	float or array	Value(s) to round to sig figs	required
sigfig	int	Number of significant figures desired	5

Returns:

Type	Description
	np.array: Values rounded to the desired number of significant figures

setup_logging(func, log_format='[%(asctime)-17s :%(levelname)-8s] - %(message)s')

Wraps a CLI endpoint and sets up logging for it

This is probably not the smoothest implementation, but it's an educational one for people who aren't aware of decorators and how they're implemented. In addition, it supports a nice pattern for CLI endpoints

TODO: start handling level configuration from logging yaml config

:param func: :param log_format: :return:

to_chart_track_csv(route)

Output a route in Chart Track csv format

unit_speed(val, unit)

Converting speed from m/s to the given units

Parameters:

Name	Type	Description	Default
val	float	input speed in m/s	required
unit	str	the unit to convert to	required

Returns: val (float) - output speed in unit type 'unit'

unit_time(val, unit)

Converting time from seconds to the given units

Parameters:

Name	Type	Description	Default
val	float	input time in s	required
unit	str	the unit to convert to	required

Returns: val (float) - output time in the converted units