Recommendation ITU-R P.1144

This Recommendation provides a guide to the Recommendations of Radiocommunication Study Group 3 which contain propagation prediction methods. It advises users on the most appropriate methods for particular applications as well as the limits, required input information, and output for each of these methods.

Title PDF Approved in
Recommendation ITU-R P.1144 [PDF] 2019-08
Guide to the application of the propagation methods of Radiocommunication Study Group 3
Latest Recommendation   Date
Recommendation ITU-R P.1144-10 [PDF] 08/2019

Introduction

Recommendation ITU-R P.1144 provides . In particular, the recommendation describes how to perform bi-linear and bi-cubic interpolation over the geophysical maps included in other recommendations.

Module description

Interpolation methods for the geophysical properties used to compute propagation effects. These methods are based on those in Recommendation ITU-R P.1144-7.

References

[1] Guide to the application of the propagation methods of Radiocommunication Study Group 3: https://www.itu.int/rec/R-REC-P.1144/en

itur.models.itu1144.is_regular_grid(lats_o, lons_o)[source]

Determinere whether the grids in lats_o and lons_o are both regular grids or not.

A grid is regular if the difference (column-wise or row-wise) between consecutive values is constant across the grid.

Parameters:
  • lats_o (numpy.ndarray) – Grid of latitude coordinates
  • lons_o (numpy.ndarray) – Grid of longitude coordinates
Returns:

is_regular
Return type:

boolean
itur.models.itu1144.nearest_2D_interpolator(lats_o, lons_o, values)[source]

Produces a 2D interpolator function using the nearest value interpolation method. If the grids are regular grids, uses the scipy.interpolate.RegularGridInterpolator, otherwise, scipy.intepolate.griddata

Values can be interpolated from the returned function as follows:

f = nearest_2D_interpolator(lat_origin, lon_origin, values_origin)
interp_values = f(lat_interp, lon_interp)
Parameters:
  • lats_o (numpy.ndarray) – Latitude coordinates of the values usde by the interpolator
  • lons_o (numpy.ndarray) – Longitude coordinates of the values usde by the interpolator
  • values (numpy.ndarray) – Values usde by the interpolator
Returns:

interpolator – Nearest neighbour interpolator function
Return type:

function
itur.models.itu1144.bilinear_2D_interpolator(lats_o, lons_o, values)[source]

Produces a 2D interpolator function using the bilinear interpolation method. If the grids are regular grids, uses the scipy.interpolate.RegularGridInterpolator, otherwise, scipy.intepolate.griddata

Values can be interpolated from the returned function as follows:

f = nearest_2D_interpolator(lat_origin, lon_origin, values_origin)
interp_values = f(lat_interp, lon_interp)
Parameters:
  • lats_o (numpy.ndarray) – Latitude coordinates of the values usde by the interpolator
  • lons_o (numpy.ndarray) – Longitude coordinates of the values usde by the interpolator
  • values (numpy.ndarray) – Values usde by the interpolator
Returns:

interpolator – Bilinear interpolator function
Return type:

function
itur.models.itu1144.bicubic_2D_interpolator(lats_o, lons_o, values)[source]

Produces a 2D interpolator function using the bicubic interpolation method. Uses the scipy.intepolate.griddata method.

Values can be interpolated from the returned function as follows:

f = nearest_2D_interpolator(lat_origin, lon_origin, values_origin)
interp_values = f(lat_interp, lon_interp)
Parameters:
  • lats_o (numpy.ndarray) – Latitude coordinates of the values usde by the interpolator
  • lons_o (numpy.ndarray) – Longitude coordinates of the values usde by the interpolator
  • values (numpy.ndarray) – Values usde by the interpolator
Returns:

interpolator – Bicubic interpolator function
Return type:

function