diff --git a/knoten/bundle.py b/knoten/bundle.py
index 789b4e510bf430ba29169da2d0b20b775b10d51e..ed64bdc82a3c1a1df7aa880ccffd956fb3c12d42 100644
--- a/knoten/bundle.py
+++ b/knoten/bundle.py
@@ -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
diff --git a/tests/test_bundle.py b/tests/test_bundle.py
index 657015c3bb2d1c2a0f54f47a8b47efa16c97e802..7f8c02cb9b2b83559b2fa9b86b6caee4203fb96f 100644
--- a/tests/test_bundle.py
+++ b/tests/test_bundle.py
@@ -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])