diff --git a/.gitignore b/.gitignore
index 01e97e591f0d4ad63503e72196f076aebfc67e38..474001df2483eb4ab48cbf8756393daa8a90b044 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,4 +116,5 @@ print.prt
*.cub
*.CUB
-.DS_Store
\ No newline at end of file
+.DS_Store
+default.profraw
diff --git a/examples/bundle_adjust.ipynb b/examples/bundle_adjust.ipynb
index fd53e123bee751181edbcb5a63d1e0da9d25bc8a..9375c2d0622f23baf8a0855ab784411b95dde7ff 100644
--- a/examples/bundle_adjust.ipynb
+++ b/examples/bundle_adjust.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -39,14 +39,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "cubes = 'data/bundle-adjust/cubelist.lis'\n",
- "sensors = generate_sensors(cubes, only_isis_spice=True, verbose=True)\n",
+ "cubes = 'data/cubes.lis'\n",
+ "sensors = generate_sensors(cubes)\n",
"\n",
- "network = 'data/bundle-adjust/controlnet.net'\n",
+ "network = 'data/hand_dense.net'\n",
"cnet = io_controlnetwork.from_isis(network)\n",
"cnet = compute_apriori_ground_points(cnet, sensors) # autoseed did not generate ground points, calculate and repopulate the data frame"
]
@@ -68,7 +68,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Image: MRO/CTX/1016816309:030\n",
+ "Image: MRO/CTX/1085197697:073\n",
+ " IT Pos. Bias | 0 | 0.0\n",
+ " CT Pos. Bias | 1 | 0.0\n",
+ " Rad Pos. Bias | 2 | 0.0\n",
+ " IT Vel. Bias | 3 | 0.0\n",
+ " CT Vel. Bias | 4 | 0.0\n",
+ " Rad Vel. Bias | 5 | 0.0\n",
+ " Omega Bias | 6 | 0.0\n",
+ " Phi Bias | 7 | 0.0\n",
+ " Kappa Bias | 8 | 0.0\n",
+ " Omega Rate | 9 | 0.0\n",
+ " Phi Rate | 10 | 0.0\n",
+ " Kappa Rate | 11 | 0.0\n",
+ " Omega Accl | 12 | 0.0\n",
+ " Phi Accl | 13 | 0.0\n",
+ " Kappa Accl | 14 | 0.0\n",
+ " Focal Bias | 15 | 0.0\n",
+ "Image: MRO/CTX/1096561308:045\n",
" IT Pos. Bias | 0 | 0.0\n",
" CT Pos. Bias | 1 | 0.0\n",
" Rad Pos. Bias | 2 | 0.0\n",
@@ -85,7 +102,7 @@
" Phi Accl | 13 | 0.0\n",
" Kappa Accl | 14 | 0.0\n",
" Focal Bias | 15 | 0.0\n",
- "Image: MRO/CTX/0884541979:200\n",
+ "Image: MRO/CTX/1136952576:186\n",
" IT Pos. Bias | 0 | 0.0\n",
" CT Pos. Bias | 1 | 0.0\n",
" Rad Pos. Bias | 2 | 0.0\n",
@@ -102,7 +119,7 @@
" Phi Accl | 13 | 0.0\n",
" Kappa Accl | 14 | 0.0\n",
" Focal Bias | 15 | 0.0\n",
- "Image: MRO/CTX/0891288961:044\n",
+ "Image: MRO/CTX/1157902986:250\n",
" IT Pos. Bias | 0 | 0.0\n",
" CT Pos. Bias | 1 | 0.0\n",
" Rad Pos. Bias | 2 | 0.0\n",
@@ -282,7 +299,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "(1365,)\n"
+ "(129,)\n"
]
}
],
@@ -307,13 +324,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.676667701883667\n"
+ "1.3577841505153914\n"
]
}
],
"source": [
"dof = W_observations.shape[0] - W_params.shape[0]\n",
- "updated_sigma0 = np.sqrt((V.dot(W_observations).dot(V) - dX.dot(J.T).dot(W_observations).dot(V))/dof)\n",
+ "VTPV = V.dot(W_observations).dot(V) - dX.dot(J.T).dot(W_observations).dot(V)\n",
+ "if (dof > 0):\n",
+ " updated_sigma0 = np.sqrt(VTPV/dof)\n",
+ "else:\n",
+ " updated_sigma0 = np.sqrt(VTPV)\n",
"print(updated_sigma0)"
]
},
@@ -333,20 +354,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Minimum redundancy: 0.9236070227160993\n",
- "Maximum redundancy: 0.9887066971286086\n"
+ "Minimum redundancy: 0.7502088509294791\n",
+ "Maximum redundancy: 0.9653389403009147\n"
]
},
{
"data": {
"text/plain": [
- "{'whiskers': [<matplotlib.lines.Line2D at 0x1b839c400>,\n",
- " <matplotlib.lines.Line2D at 0x1b839c6a0>],\n",
- " 'caps': [<matplotlib.lines.Line2D at 0x1b839c940>,\n",
- " <matplotlib.lines.Line2D at 0x1b839cbe0>],\n",
- " 'boxes': [<matplotlib.lines.Line2D at 0x1b837cfd0>],\n",
- " 'medians': [<matplotlib.lines.Line2D at 0x1b839ce80>],\n",
- " 'fliers': [<matplotlib.lines.Line2D at 0x1b83ab160>],\n",
+ "{'whiskers': [<matplotlib.lines.Line2D at 0x1ad6cea60>,\n",
+ " <matplotlib.lines.Line2D at 0x1ad6ced00>],\n",
+ " 'caps': [<matplotlib.lines.Line2D at 0x1ad6cefa0>,\n",
+ " <matplotlib.lines.Line2D at 0x1ad6ec280>],\n",
+ " 'boxes': [<matplotlib.lines.Line2D at 0x1ad6ce7c0>],\n",
+ " 'medians': [<matplotlib.lines.Line2D at 0x1ad6ec520>],\n",
+ " 'fliers': [<matplotlib.lines.Line2D at 0x1ad6ec7c0>],\n",
" 'means': []}"
]
},
@@ -356,7 +377,7 @@
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -398,38 +419,29 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "iteration 0: sigma0 = 2.5728422824613784\n",
- "\n",
- "corrections: mean = 0.653346079031689 min = -15.919884218441833 max = 10.327444239483894\n",
- "iteration 1: sigma0 = 0.9391071877144452\n",
- "\n",
- "corrections: mean = 0.007578983174291483 min = -0.6175605679370911 max = 2.362208151398094\n",
- "iteration 2: sigma0 = 0.9219212955967535\n",
+ "iteration 0: sigma0 = 4.0255575933213645\n",
"\n",
- "corrections: mean = 0.00047562127535878755 min = -6.720164033293535e-05 max = 0.34999571717706907\n",
- "iteration 3: sigma0 = 0.9219206529217796\n",
+ "corrections: mean = 0.0607625027080373 min = -15.894645144585613 max = 43.173076576185686\n",
+ "iteration 1: sigma0 = 1.3372395601830835\n",
"\n",
- "corrections: mean = -0.00043949558479830525 min = -0.35037971701721626 max = 0.0013604163271051927\n",
- "iteration 4: sigma0 = 0.9219208109877487\n",
+ "corrections: mean = -1.9487862295781398e-05 min = -0.002348815201592963 max = 0.0011802360614414334\n",
+ "iteration 2: sigma0 = 1.116733991093145\n",
"\n",
- "corrections: mean = 0.00044131364530114236 min = -5.7892344840921925e-06 max = 0.35036852412427094\n",
- "iteration 5: sigma0 = 0.9219206528104202\n",
+ "corrections: mean = -6.40338937723717e-10 min = -5.349525439150892e-08 max = 4.348174825756319e-08\n",
+ "iteration 3: sigma0 = 1.116733988965997\n",
"\n",
- "corrections: mean = -0.00044126496471851365 min = -0.35037040745737597 max = 1.4554057900294883e-05\n",
- "iteration 6: sigma0 = 0.9219208110024477\n",
+ "corrections: mean = -8.137793063550186e-11 min = -6.4754516024339534e-09 max = 9.929915597684175e-09\n",
+ "iteration 4: sigma0 = 1.1167339889935555\n",
"\n",
- "corrections: mean = 0.00044126488421801513 min = -5.783747480588058e-06 max = 0.3503829032363816\n",
- "iteration 7: sigma0 = 0.921920652818743\n",
- "\n",
- "corrections: mean = -0.0004411037049932667 min = -0.3503829421081344 max = 1.2348549615825461e-05\n",
- "iteration 8: sigma0 = 0.9219208109707007\n",
- "\n",
- "corrections: mean = 0.0004409523138932092 min = -0.00011674460187552498 max = 0.35038181894977977\n",
- "iteration 9: sigma0 = 0.9219206528067627\n",
- "\n",
- "corrections: mean = -0.0004411144651418224 min = -0.3503813294832801 max = 6.1212020049696e-06\n",
- "iteration 10: sigma0 = 0.9219208109789235\n",
- "\n"
+ "change in sigma0 of 2.7558400006455486e-11 converged!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/astamile/opt/anaconda3/envs/knoten/lib/python3.9/site-packages/plio/io/io_controlnetwork.py:356: UserWarning: The pointLog field is currently unsupported. Any pointLog data will not be saved.\n",
+ " warnings.warn('The pointLog field is currently unsupported. Any pointLog data will not be saved.')\n"
]
}
],
@@ -455,13 +467,13 @@
"sigma0 = compute_sigma0(V, dX, W_params, W_observations)\n",
"print(f'iteration {iteration}: sigma0 = {sigma0}\\n')\n",
"\n",
- "max_iterations = 10\n",
+ "max_iterations = 50\n",
"tol = 1e-10\n",
"total_correction = np.zeros(num_parameters)\n",
- "for i in range(max_iterations): \n",
+ "for i in range(max_iterations): \n",
" iteration += 1\n",
" old_sigma0 = sigma0\n",
- " \n",
+ "\n",
" J = compute_jacobian(cnet, sensors, parameters, column_dict) \n",
" N = J.T.dot(W_observations).dot(J) + W_params # calculate the normal equation\n",
" C = J.T.dot(W_observations).dot(V) - W_params.dot(total_correction)\n",
@@ -470,18 +482,23 @@
" print(f'corrections: mean = {dX.mean()} min = {dX.min()} max = {dX.max()}')\n",
" \n",
" update_parameters(sensors, parameters, cnet, dX, column_dict)\n",
- " \n",
" V = compute_residuals(cnet, sensors)\n",
" sigma0 = compute_sigma0(V, dX, W_params, W_observations)\n",
- " sigma0 = np.sqrt((V.dot(W_observations).dot(V) + dX.dot(W_params).dot(dX))/dof)\n",
" print(f'iteration {iteration}: sigma0 = {sigma0}\\n')\n",
- " \n",
" if (abs(sigma0 - old_sigma0) < tol):\n",
" print(f'change in sigma0 of {abs(sigma0 - old_sigma0)} converged!')\n",
+ " onet = io_controlnetwork.to_isis(cnet, 'data/py-adjusted.net', targetname=\"Mars\")\n",
" break\n",
" "
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/examples/data/F03_037064_1857_XN_05N218W.json b/examples/data/F03_037064_1857_XN_05N218W.json
deleted file mode 100644
index dd94e8b60a92d7702eee401f54471062de8339d3..0000000000000000000000000000000000000000
--- a/examples/data/F03_037064_1857_XN_05N218W.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/examples/data/bundle-adjust/G21_026491_2001_XN_20N286W.lev1.json b/examples/data/bundle-adjust/G21_026491_2001_XN_20N286W.lev1.json
deleted file mode 100644
index 069360071b8585493617cde852ba10fd28ba80aa..0000000000000000000000000000000000000000
--- a/examples/data/bundle-adjust/G21_026491_2001_XN_20N286W.lev1.json
+++ /dev/null
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diff --git a/examples/data/bundle-adjust/P15_006844_2009_XN_20N286W.lev1.json b/examples/data/bundle-adjust/P15_006844_2009_XN_20N286W.lev1.json
deleted file mode 100644
index 2e39f67c92ce5f7a5f8484bad09b6e9089c119fa..0000000000000000000000000000000000000000
--- a/examples/data/bundle-adjust/P15_006844_2009_XN_20N286W.lev1.json
+++ /dev/null
@@ -1,10428 +0,0 @@
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diff --git a/examples/data/bundle-adjust/P17_007846_2007_XN_20N286W.lev1.json b/examples/data/bundle-adjust/P17_007846_2007_XN_20N286W.lev1.json
deleted file mode 100644
index c21cd0a44c38d7e04dc28f590fdf8b2cc5e74df3..0000000000000000000000000000000000000000
--- a/examples/data/bundle-adjust/P17_007846_2007_XN_20N286W.lev1.json
+++ /dev/null
@@ -1,9919 +0,0 @@
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\ No newline at end of file
diff --git a/examples/data/bundle-adjust/controlnet.net b/examples/data/bundle-adjust/controlnet.net
deleted file mode 100644
index 928fc6f72640048a12346ad6e82be02f16e9428f..0000000000000000000000000000000000000000
Binary files a/examples/data/bundle-adjust/controlnet.net and /dev/null differ
diff --git a/examples/data/bundle-adjust/cubelist.lis b/examples/data/bundle-adjust/cubelist.lis
deleted file mode 100644
index 8192bb7df68157dc6102dc381ffbf76c29c0b347..0000000000000000000000000000000000000000
--- a/examples/data/bundle-adjust/cubelist.lis
+++ /dev/null
@@ -1,3 +0,0 @@
-data/bundle-adjust/G21_026491_2001_XN_20N286W.lev1.cub
-data/bundle-adjust/P15_006844_2009_XN_20N286W.lev1.cub
-data/bundle-adjust/P17_007846_2007_XN_20N286W.lev1.cub
\ No newline at end of file
diff --git a/knoten/bundle.py b/knoten/bundle.py
index ff82ae68d77a23f002dc1907c8c5d16772fb44aa..d59250afe73e279038ce3ec60fb33562ef001632 100644
--- a/knoten/bundle.py
+++ b/knoten/bundle.py
@@ -184,7 +184,7 @@ def compute_sensor_partials(sensor, parameters, ground_pt):
derivatives are in the same order as the parameter list passed in.
"""
partials = np.zeros((2, len(parameters)))
- csm_ground = csmapi.EcefCoord(ground_pt.iloc[0], ground_pt.iloc[1], ground_pt.iloc[2])
+ csm_ground = csmapi.EcefCoord(ground_pt[0], ground_pt[1], ground_pt[2])
for i in range(len(parameters)):
partials[:, i] = sensor.computeSensorPartials(parameters[i].index, csm_ground)
return partials
@@ -208,7 +208,7 @@ def compute_ground_partials(sensor, ground_pt):
partials and the second array is the sample partials. The partial
derivatives are in (x, y, z) order.
"""
- csm_ground = csmapi.EcefCoord(ground_pt.iloc[0], ground_pt.iloc[1], ground_pt.iloc[2])
+ csm_ground = csmapi.EcefCoord(ground_pt[0], ground_pt[1], ground_pt[2])
partials = np.array(sensor.computeGroundPartials(csm_ground))
return np.reshape(partials, (2, 3))
@@ -282,8 +282,8 @@ def compute_jacobian(network, sensors, parameters, coefficient_columns):
params = parameters[serial]
image_range = coefficient_columns[serial]
point_range = coefficient_columns[row["id"]]
- jacobian[2*i : 2*i+2, image_range[0] : image_range[1]] = compute_sensor_partials(sensor, params, ground_pt)
- jacobian[2*i : 2*i+2, point_range[0] : point_range[1]] = compute_ground_partials(sensor, ground_pt)
+ jacobian[2*i : 2*i+2, image_range[0] : image_range[1]] = compute_sensor_partials(sensor, params, ground_pt.tolist())
+ jacobian[2*i : 2*i+2, point_range[0] : point_range[1]] = compute_ground_partials(sensor, ground_pt.tolist())
return jacobian
@@ -399,7 +399,7 @@ def compute_residuals(network, sensors):
row = network.iloc[i]
serial = row["serialnumber"]
ground_pt = row[["adjustedX", "adjustedY", "adjustedZ"]].values
- ground_pt = csmapi.EcefCoord(ground_pt.iloc[0], ground_pt.iloc[1], ground_pt.iloc[2])
+ 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]
@@ -472,8 +472,15 @@ def compute_sigma0(V, dX, W_parameters, W_observations):
num_observations = W_observations.shape[0]
dof = num_observations - num_parameters
VTPV = V.dot(W_observations).dot(V) + dX.dot(W_parameters).dot(dX)
- sigma0 = np.sqrt(VTPV/dof)
- return sigma0
+
+ if (dof > 0):
+ sigma0 = VTPV/dof
+ elif (dof == 0):
+ sigma0 = VTPV
+ else:
+ raise ValueError(f"Computed degrees of freedom [{dof}] is invalid.")
+
+ return np.sqrt(sigma0)
def compute_sigma0_sparse(V, dX, W_sensors, W_points, W_observations, column_dict):
"""