Commit cfc0972b authored by Lauren Adoram-Kershner's avatar Lauren Adoram-Kershner Committed by GitHub
Browse files

Adding bundle logic helper functions (#73) 



* added to bundle adjust nb, up to setting up bundle iteration, known issues

* adding compute_residuals + test and generate_sensors

* Updated some docs

Co-authored-by: default avatarJesse Mapel <jam826@nau.edu>
parent ec482a89

knoten/bundle.py

+77 −1
Original line number Diff line number Diff line
@@ -3,7 +3,51 @@ import pandas as pd 
import pvl

import os

import csmapi



from pysis import isis

from ale.drivers import loads

from collections import OrderedDict

from knoten.csm import create_csm



def generate_sensors(cubes, directory=None, clean=False):

    """

    Generate a set of USGSCSM sensor models from a list of ISIS cube files



    Parameters

    ----------

    cubes     : str

              Directory/filename of a file containing ISIS cube file paths

    directory : str

              Output directory to save resulting json files. Defaults to the

              same directory as cube list file

    clean     : flag

              Option to delete json file outputs



    Returns

    -------

    sensors   : dictionary

              Dictionary mapping ISIS serial numbers to USGSCSM sensor models

    """

    if directory is None:

        directory = os.path.dirname(cubes)



    isd_files = []

    sensors = {}

    for line in open(cubes):

        basename = os.path.splitext(os.path.basename(line.strip()))[0]

        isd = os.path.join(directory, basename+'.json')

        isd_files.append(isd)

        with open(isd, 'w+') as f:

            f.write(loads(line.strip(), formatter='usgscsm'))



        sn = isis.getsn(from_=line.strip()).strip().decode('utf-8')

        sensors[sn] = create_csm(isd)



    if clean:

        for isd in isd_files:

            os.remove(isd)



    return sensors



def closest_approach(points, direction):

    """
@@ -203,4 +247,36 @@ def compute_jacobian(network, sensors, parameters): 
        jacobian[2*i : 2*i+2, image_column : image_column+len(params)] = compute_sensor_partials(sensor, params, ground_pt)

        jacobian[2*i : 2*i+2, point_column : point_column+3] = compute_ground_partials(sensor, ground_pt)



    return jacobian
 
    return jacobian, coefficient_columns



def compute_residuals(network, sensors):

    """

    Compute the error in the observations by taking the difference between the

    ground points groundToImage projections and measure values.



    Parameters

    ----------

    network : DataFrame

              The control network as a dataframe generated by plio

    sensors : dict

             A dictionary that maps ISIS serial numbers to CSM sensors



    Returns

    -------

    V : np.array

       The control network dataframe with updated ground points

    """

    num_meas = len(network)

    V = np.zeros((num_meas, 2))



    for i in range(num_meas):

        row = network.iloc[i]

        serial = row["serialnumber"]

        ground_pt = row[["adjustedX", "adjustedY", "adjustedZ"]].values

        ground_pt = csmapi.EcefCoord(ground_pt[0], ground_pt[1], ground_pt[2])

        sensor = sensors[serial]

        img_coord = sensor.groundToImage(ground_pt)

        V[i,:] = [row['line'] - img_coord.line, row['sample'] - img_coord.samp]



    V = V.reshape(num_meas*2)

    return V

tests/test_bundle.py

+15 −1
Original line number Diff line number Diff line
@@ -119,7 +119,7 @@ def test_compute_jacobian(control_network, sensors): 
    ground_partials = [-(i+1) * np.ones((2, 3)) for i in range(9)]

    with mock.patch('knoten.bundle.compute_sensor_partials', side_effect=sensor_partials) as sensor_par_mock, \

         mock.patch('knoten.bundle.compute_ground_partials', side_effect=ground_partials) as ground_par_mock:

        J = bundle.compute_jacobian(control_network, sensors, parameters)

        J, coefficient_columns = bundle.compute_jacobian(control_network, sensors, parameters)



    expected_J = [

    [1, 1, 0, 0, 0, 0, 0, 0, -1, -1, -1, 0, 0, 0, 0, 0, 0],
@@ -141,3 +141,17 @@ def test_compute_jacobian(control_network, sensors): 
    [0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 0, 0, -9, -9, -9],

    [0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 0, 0, -9, -9, -9]]

    np.testing.assert_array_equal(J, expected_J)



def test_compute_residuals(control_network, sensors):

    # sensor.groundToImage.side_effect = [csmapi.ImageCoord(-0.1, 7.8), csmapi.ImageCoord(0.7, 6.6), csmapi.ImageCoord(1.5, 5.4),

    #                                     csmapi.ImageCoord(2.3, 4.2), csmapi.ImageCoord(3.1, 4.9), csmapi.ImageCoord(5.8, 3.7),

    #                                     csmapi.ImageCoord(6.6, 2.5), csmapi.ImageCoord(7.4, 1.3), csmapi.ImageCoord(8.2, 0.1)]



    sensors['a'].groundToImage.side_effect = [csmapi.ImageCoord(-0.1, 7.8), csmapi.ImageCoord(5.8, 3.7)]

    sensors['b'].groundToImage.side_effect = [csmapi.ImageCoord(0.7, 6.6), csmapi.ImageCoord(3.1, 4.9), csmapi.ImageCoord(6.6, 2.5)]

    sensors['c'].groundToImage.side_effect = [csmapi.ImageCoord(1.5, 5.4), csmapi.ImageCoord(7.4, 1.3)]

    sensors['d'].groundToImage.side_effect = [csmapi.ImageCoord(2.3, 4.2), csmapi.ImageCoord(8.2, 0.1)]



    V = bundle.compute_residuals(control_network, sensors)

    assert V.shape == (18,)

    np.testing.assert_allclose(V, [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1])