Commit 03d046cd authored by Jesse Mapel's avatar Jesse Mapel Committed by Jesse Mapel
Browse files

Fixed covariance clalculations

parent 592d0cf7

plio/utils/covariance.py

+9 −5
Original line number Diff line number Diff line 
import numpy as np

import math



def compute_covariance(lat, lon, rad, latsigma=10., lonsigma=10., radsigma=15., semimajor_axis=1737400.0):

def compute_covariance(lat, lon, rad, latsigma=10., lonsigma=10., radsigma=15., semimajor_axis=None):

    """

    Given geospatial coordinates, desired accuracy sigmas, and an equitorial radius, compute a 2x3

    sigma covariange matrix.
@@ -27,7 +27,8 @@ def compute_covariance(lat, lon, rad, latsigma=10., lonsigma=10., radsigma=15., 
               The desired radius accuracy in meters (Defualt: 15.0)



    semimajor_axis : float

                     The semi-major or equitorial radius in meters (Default: 1737400.0 - Moon)

                     The semi-major or equitorial radius in meters. If not entered,

                     the local radius will be used.



    Returns

    -------
@@ -38,11 +39,14 @@ def compute_covariance(lat, lon, rad, latsigma=10., lonsigma=10., radsigma=15., 
    lat = math.radians(lat)

    lon = math.radians(lon)



    if semimajor_axis is None:

        semimajor_axis = rad



    # SetSphericalSigmasDistance

    scaled_lat_sigma = latsigma / semimajor_axis



    # This is specific to each lon.

    scaled_lon_sigma = lonsigma * math.cos(lat) / semimajor_axis

    scaled_lon_sigma = lonsigma / (math.cos(lat) * semimajor_axis)



    # SetSphericalSigmas

    cov = np.eye(3,3)

plio/utils/tests/test_covariance.py

+4 −4
Original line number Diff line number Diff line
@@ -14,6 +14,6 @@ def test_compute_covariance(): 
    sigmarad = 25.0

    semimajor_rad = 1737400.0

    cov = covariance.compute_covariance(lat, lon, rad, sigmalat, sigmalon, sigmarad, semimajor_rad)

    expected =  np.array([[132.97888775695, -111.55453178747, -20.08405416233],

                          [93.588991760138, 16.848822261184, 623.27512692323]])

    expected =  np.array([[225.85120968, -0.84930178, -20.08405416233],

                          [225.55132129, 16.848822261184, 623.27512692323]])

    np.testing.assert_almost_equal(cov, expected)