diff --git a/.travis.yml b/.travis.yml
index 215d630ba66837e65f8f0065709e4c66f5e247a0..48515d3ed21720b47e09971b571d55e84a23c82f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -30,7 +30,7 @@ before_install:
   - conda env update -n test -f environment.yml
 
 script:
-  - pytest tests
+  - PYTHONPATH=. pytest tests
 
 after_success:
   - coveralls
diff --git a/environment.yml b/environment.yml
index 69a1ce3b0baa871773eb16f6191c4f53fba6f670..63297d753a74a42d7edc4c6e3def2c6368b0528a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,16 +2,26 @@ name: knoten
 channels:
   - conda-forge
   - usgs-astrogeology
+  - plotly
 dependencies:
-  - python >= 3
   - coveralls
   - csmapi
   - gdal
+  - holoviews
   - jinja2
+  - jupyter
+  - matplotlib
   - numpy
-  - pytest
+  - plio
+  - plotly
+  - plotly-orca 
+  - psutil 
+  - pvl
   - pyproj
+  - pysis
+  - pytest
+  - python>=3
   - requests
   - scipy
-  - matplotlib
   - shapely
+  - usgscsm
diff --git a/examples/cassini_isis_cmp.ipynb b/examples/cassini_isis_cmp.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b4d060b3c1c24a1cbb375201d541bb511a8b6de4
--- /dev/null
+++ b/examples/cassini_isis_cmp.ipynb
@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing a USGSCSM and ISIS camera for Cassini ISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvl\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import knoten\n",
+    "import csmapi\n",
+    "\n",
+    "os.environ['ISISROOT'] = '/usgs/pkgs/isis3.8.0_RC1/install'\n",
+    "from pysis import isis\n",
+    "from pysis.exceptions import ProcessError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make a CSM sensor model\n",
+    "Requires N1702360370_1.LBL and N1702360370_1.IMG in data directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fileName = 'data/N1702360370_1.LBL'\n",
+    "camera = knoten.csm.create_csm(fileName)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ingest the image and spiceinit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the output location of the resulting .cub\n",
+    "cub_loc = os.path.splitext(fileName)[0] + '.cub'\n",
+    "\n",
+    "try: \n",
+    "    isis.ciss2isis(from_=fileName, to=cub_loc)\n",
+    "except ProcessError as e:\n",
+    "    print(e.stderr)\n",
+    "\n",
+    "try:\n",
+    "    isis.spiceinit(from_=cub_loc, shape='ellipsoid')\n",
+    "except ProcessError as e:\n",
+    "    print(e.stderr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define a function that compares ISIS and USGSCSM pixels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_pixel(camera, cub, line, sample):\n",
+    "    \"\"\"Compares ISIS and USGSCSM pixel.\n",
+    "    \n",
+    "    Takes an image coordinate, projects it to a ground point using ISIS, then projects\n",
+    "    the result back into an image coordinate using USGSCSM and computes the difference\n",
+    "    between image coordinates.\n",
+    "    \"\"\"\n",
+    "    output = isis.campt(from_=cub, line=line, sample=sample)\n",
+    "    pvl_output = pvl.loads(output)\n",
+    "    bodyfixed = pvl_output['GroundPoint']['BodyFixedCoordinate']\n",
+    "    bodyfixed = np.asarray(bodyfixed.value) * 1000\n",
+    "    image_coord = camera.groundToImage(csmapi.EcefCoord(*bodyfixed))\n",
+    "    # (.5,.5) in CSM == (1,1) in ISIS, so we have to subtract (.5,.5) from the ISIS pixels\n",
+    "    line_diff = line - image_coord.line - .5\n",
+    "    sample_diff = sample - image_coord.samp - .5\n",
+    "    return line_diff, sample_diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get the total number of lines / samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isis_label = pvl.load(cub_loc)\n",
+    "n_samples = isis_label['IsisCube']['Core']['Dimensions']['Samples']\n",
+    "n_lines = isis_label['IsisCube']['Core']['Dimensions']['Lines']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare top left, top right, bottom left, bottom right, and center pixels using check_pixel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>line</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>line_diff</th>\n",
+       "      <th>sample_diff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.153039</td>\n",
+       "      <td>0.906085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1024.0</td>\n",
+       "      <td>0.142231</td>\n",
+       "      <td>0.326977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1024.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.425122</td>\n",
+       "      <td>0.915083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1024.0</td>\n",
+       "      <td>1024.0</td>\n",
+       "      <td>-0.435769</td>\n",
+       "      <td>0.335980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>512.0</td>\n",
+       "      <td>512.0</td>\n",
+       "      <td>-0.142021</td>\n",
+       "      <td>0.620648</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     line  sample  line_diff  sample_diff\n",
+       "0     1.0     1.0   0.153039     0.906085\n",
+       "1     1.0  1024.0   0.142231     0.326977\n",
+       "2  1024.0     1.0  -0.425122     0.915083\n",
+       "3  1024.0  1024.0  -0.435769     0.335980\n",
+       "4   512.0   512.0  -0.142021     0.620648"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pixels_dict = {'line' : [1,1,n_lines, n_lines, n_lines/2],\n",
+    "               'sample' : [1, n_samples, 1, n_samples, n_samples/2]}\n",
+    "\n",
+    "pixels_df = pd.DataFrame.from_dict(pixels_dict)\n",
+    "pixels_df['line_diff'] = np.NaN\n",
+    "pixels_df['sample_diff'] = np.NaN\n",
+    "\n",
+    "for idx, row in pixels_df.iterrows():\n",
+    "    pixels_df.iloc[idx]['line_diff'], pixels_df.iloc[idx]['sample_diff'] = check_pixel(camera, cub_loc, row['line'], row['sample'])\n",
+    "    \n",
+    "pixels_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "autocnetdev",
+   "language": "python",
+   "name": "autocnetdev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/data/ESP_016076_2175_REDmos_hijitreged.balance.noproj.8bit.json b/examples/data/ESP_016076_2175_REDmos_hijitreged.balance.noproj.8bit.json
new file mode 100644
index 0000000000000000000000000000000000000000..c2900511590178192b569d328716f8e3ca6b707d
--- /dev/null
+++ b/examples/data/ESP_016076_2175_REDmos_hijitreged.balance.noproj.8bit.json
@@ -0,0 +1 @@
+{"radii": {"semimajor": 3396.19, "semiminor": 3376.2, "unit": "km"}, "sensor_position": {"positions": [[2814687.586086525, 814334.0251060642, 2229042.0644764258], [2814676.399481947, 814328.255164384, 2229058.4959684447], [2814665.2127845893, 814322.4852069564, 2229074.927390149], [2814654.025994451, 814316.7152337814, 2229091.3587415377], [2814642.839111534, 814310.9452448592, 2229107.7900226107], [2814631.652135836, 814305.1752401895, 2229124.2212333675], [2814620.46506736, 814299.4052197727, 2229140.652373807], [2814609.277906106, 814293.6351836089, 2229157.083443929], [2814598.090652071, 814287.8651316979, 2229173.514443733], [2814586.903305257, 814282.0950640392, 2229189.9453732176], [2814575.7158656674, 814276.3249806343, 2229206.376232383], [2814564.5283332993, 814270.554881482, 2229222.807021229], [2814553.340708153, 814264.7847665828, 2229239.2377397544], [2814542.1529902294, 814259.0146359367, 2229255.6683879592], [2814530.965179528, 814253.2444895438, 2229272.0989658427], [2814519.7772760508, 814247.4743274042, 2229288.5294734035], [2814508.5892797965, 814241.7041495176, 2229304.9599106414], [2814497.4011907657, 814235.9339558844, 2229321.390277556], [2814486.213008959, 814230.1637465045, 2229337.8205741476], [2814475.024734375, 814224.3935213779, 2229354.250800414], [2814463.8363670167, 814218.6232805047, 2229370.6809563558], [2814452.6479068827, 814212.8530238852, 2229387.111041972], [2814441.459353973, 814207.082751519, 2229403.541057262], [2814430.2707082885, 814201.3124634062, 2229419.971002226], [2814419.081969829, 814195.5421595472, 2229436.4008768625], [2814407.8931385954, 814189.7718399417, 2229452.8306811703], [2814396.704214587, 814184.0015045902, 2229469.2604151503], [2814385.515197805, 814178.231153492, 2229485.6900788015], [2814374.3260882488, 814172.4607866476, 2229502.119672122], [2814363.1368859196, 814166.690404057, 2229518.549195113], [2814351.9475908168, 814160.9200057205, 2229534.9786477736], [2814340.7582029416, 814155.1495916379, 2229551.4080301034], [2814329.5687222937, 814149.379161809, 2229567.837342101], [2814318.379148873, 814143.6087162343, 2229584.266583767], [2814307.18948268, 814137.8382549137, 2229600.695755099], [2814295.9997237152, 814132.067777847, 2229617.1248560976], [2814284.8098719786, 814126.2972850346, 2229633.5538867624], [2814273.6199274706, 814120.5267764762, 2229649.9828470927], [2814262.4298901907, 814114.7562521722, 2229666.4117370876], [2814251.23976014, 814108.9857121223, 2229682.840556747], [2814240.0495373183, 814103.2151563268, 2229699.2693060697], [2814228.859221727, 814097.4445847857, 2229715.6979850554], [2814217.6688133646, 814091.6739974988, 2229732.1265937034], [2814206.478312232, 814085.9033944664, 2229748.5551320147], [2814195.28771833, 814080.1327756888, 2229764.9835999864], [2814184.0970316585, 814074.3621411653, 2229781.4119976177], [2814172.906252218, 814068.5914908967, 2229797.840324911], [2814161.715380008, 814062.8208248824, 2229814.268581863], [2814150.5244150297, 814057.0501431231, 2229830.6967684748], [2814139.333357281, 814051.279445618, 2229847.1248847446], [2814128.142206766, 814045.5087323681, 2229863.552930672], [2814116.9509634823, 814039.738003373, 2229879.980906258], [2814105.759627431, 814033.9672586325, 2229896.4088115], [2814094.568198612, 814028.1964981472, 2229912.8366463985], [2814083.3766770256, 814022.4257219168, 2229929.2644109526], [2814072.1850626725, 814016.6549299412, 2229945.692105162], [2814060.993355552, 814010.8841222206, 2229962.1197290258], [2814049.801555665, 814005.1132987553, 2229978.5472825435], [2814038.609663013, 813999.3424595449, 2229994.9747657147], [2814027.4176775934, 813993.5716045897, 2230011.402178539], [2814016.2255994077, 813987.8007338898, 2230027.8295210153], [2814005.033428457, 813982.0298474451, 2230044.2567931428], [2813993.8411647407, 813976.2589452555, 2230060.683994922], [2813982.648808261, 813970.4880273218, 2230077.111126352], [2813971.4563590134, 813964.7170936427, 2230093.538187431], [2813960.2638170044, 813958.9461442197, 2230109.965178161], [2813949.0711822286, 813953.1751790518, 2230126.392098538], [2813937.8784546894, 813947.4041981396, 2230142.8189485646], [2813926.6856343867, 813941.6332014829, 2230159.245728239], [2813915.49272132, 813935.8621890818, 2230175.67243756], [2813904.2997154905, 813930.0911609364, 2230192.099076527], [2813893.106616897, 813924.3201170466, 2230208.525645141], [2813881.9134255424, 813918.5490574129, 2230224.9521434004], [2813870.7201414234, 813912.7779820347, 2230241.3785713045], [2813859.526764543, 813907.0068909124, 2230257.8049288527], [2813848.333294899, 813901.2357840459, 2230274.2312160446], [2813837.1397324945, 813895.4646614355, 2230290.6574328803], [2813825.946077328, 813889.6935230809, 2230307.083579358], [2813814.7523294003, 813883.9223689823, 2230323.509655478], [2813803.558488712, 813878.1511991398, 2230339.935661239], [2813792.3645552616, 813872.3800135535, 2230356.3615966416], [2813781.1705290508, 813866.6088122231, 2230372.7874616836], [2813769.9764100807, 813860.8375951493, 2230389.2132563666], [2813758.7821983476, 813855.0663623312, 2230405.638980687], [2813747.5878938576, 813849.29511377, 2230422.064634648], [2813736.3934966065, 813843.5238494647, 2230438.4902182454], [2813725.199006596, 813837.7525694158, 2230454.9157314813], [2813714.0044238265, 813831.9812736234, 2230471.341174354], [2813702.8097482994, 813826.2099620876, 2230487.766546863], [2813691.614980012, 813820.4386348081, 2230504.1918490077], [2813680.420118967, 813814.6672917854, 2230520.6170807877], [2813669.2251651636, 813808.8959330191, 2230537.042242202], [2813658.0301186014, 813803.1245585096, 2230553.4673332507], [2813646.8349792827, 813797.3531682566, 2230569.8923539324], [2813635.639747206, 813791.5817622603, 2230586.3173042475], [2813624.4444223726, 813785.810340521, 2230602.7421841947], [2813613.2490047812, 813780.0389030385, 2230619.166993774], [2813602.0534944343, 813774.267449813, 2230635.591732985], [2813590.85789133, 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\ No newline at end of file
diff --git a/examples/data/FC21A0038582_15170161546F6F.json b/examples/data/FC21A0038582_15170161546F6F.json
index 0513c9278e3c883f503f834b0d275e01e7b6ed8c..500fc30e10546598bd5543ca8ac2939cc3df183f 100644
--- a/examples/data/FC21A0038582_15170161546F6F.json
+++ b/examples/data/FC21A0038582_15170161546F6F.json
@@ -1 +1 @@
-{"radii": {"semimajor": 482.1, "semiminor": 445.94, "unit": "km"}, "sensor_position": {"positions": [[258052.46050459438, 14328.334911364209, -4862503.790282428]], "velocities": [[-104.71448804480892, -85.03343718442122, -5.785560799662908]], "unit": "m"}, "sun_position": {"positions": [[360894607.08392787, 246450736.5225146, 30565692.14307168, 47393.587698748765]], "velocities": [[47393.587698748765, -69400.21547545145, 0.19342351842482625]], "unit": "m"}, "sensor_orientation": {"quaternions": [[0.001892487503056546, 0.02136785850877805, -0.27780847719201385, -0.9603969403918642]]}, "detector_sample_summing": 1, "detector_line_summing": 1, "focal_length_model": {"focal_length": 150.08}, "detector_center": {"line": 512.0, "sample": 512.0}, "starting_detector_line": 0, "starting_detector_sample": 0, "focal2pixel_lines": [0.0, 0.0, 71.40816909454442], "focal2pixel_samples": [0.0, 71.40816909454442, 0.0], "optical_distortion": {"dawnfc": {"coefficients": [9.2e-06]}}, "image_lines": 1024, "image_samples": 1024, "name_platform": "DAWN", "name_sensor": "FRAMING CAMERA 2", "reference_height": {"maxheight": 1000, "minheight": -1000, "unit": "m"}, "name_model": "USGS_ASTRO_FRAME_SENSOR_MODEL", "center_ephemeris_time": 488002614.62294483}
\ No newline at end of file
+{"radii": {"semimajor": 482.1, "semiminor": 445.94, "unit": "km"}, "sensor_position": {"positions": [[258052.46050459438, 14328.334911364209, -4862503.790282428]], "velocities": [[-104.71448804480892, -85.03343718442122, -5.785560799662908]], "unit": "m"}, "sun_position": {"positions": [[360894607.08392787, 246450736.5225146, 30565692.14307168, 47393.587698748765]], "velocities": [[47393.587698748765, -69400.21547545145, 0.19342351842482625]], "unit": "m"}, "sensor_orientation": {"quaternions": [[0.001892487503056546, 0.02136785850877805, -0.27780847719201385, -0.9603969403918642]]}, "detector_sample_summing": 1, "detector_line_summing": 1, "focal_length_model": {"focal_length": 150.08}, "detector_center": {"line": 512.0, "sample": 512.0}, "starting_detector_line": 0, "starting_detector_sample": 0, "focal2pixel_lines": [0.0, 0.0, 71.40816909454442], "focal2pixel_samples": [0.0, 71.40816909454442, 0.0], "optical_distortion": {"dawnfc": {"coefficients": [9.2e-06]}}, "image_lines": 1024, "image_samples": 1024, "name_platform": "DAWN", "name_sensor": "FRAMING CAMERA 2", "reference_height": {"maxheight": 1000, "minheight": -1000, "unit": "m"}, "name_model": "USGS_ASTRO_FRAME_SENSOR_MODEL", "center_ephemeris_time": 488002614.62294483}
diff --git a/examples/data/M1142142198RE.json b/examples/data/M1142142198RE.json
index 02e5c5401f4fc7f68963621eb900326e85cbee92..6097ff9c3c82a47d4d81926f47c7c1020b0867ad 100644
--- a/examples/data/M1142142198RE.json
+++ b/examples/data/M1142142198RE.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/examples/data/N1702360370_1.IMG b/examples/data/N1702360370_1.IMG
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diff --git a/examples/data/N1702360370_1.LBL b/examples/data/N1702360370_1.LBL
new file mode 100644
index 0000000000000000000000000000000000000000..6269edb68c147d3cd45a4f2ee584c532525b4582
--- /dev/null
+++ b/examples/data/N1702360370_1.LBL
@@ -0,0 +1,120 @@
+PDS_VERSION_ID = PDS3
+
+/* FILE CHARACTERISTICS */
+
+RECORD_TYPE = FIXED_LENGTH
+RECORD_BYTES = 1048
+FILE_RECORDS = 1028
+
+/* POINTERS TO DATA OBJECTS */
+
+^IMAGE_HEADER = ("N1702360370_1.IMG",1)
+^TELEMETRY_TABLE = ("N1702360370_1.IMG",4)
+^LINE_PREFIX_TABLE = ("N1702360370_1.IMG",5)
+^IMAGE = ("N1702360370_1.IMG",5)
+
+/* IDENTIFICATION DATA ELEMENTS */
+
+ANTIBLOOMING_STATE_FLAG = "OFF"
+BIAS_STRIP_MEAN = 8.850293
+CALIBRATION_LAMP_STATE_FLAG = "N/A"
+COMMAND_FILE_NAME = "trigger_3618_1.ioi"
+COMMAND_SEQUENCE_NUMBER = 3618
+DARK_STRIP_MEAN = 0.269221
+DATA_CONVERSION_TYPE = "TABLE"
+DATA_SET_ID = "CO-S-ISSNA/ISSWA-2-EDR-V1.0"
+DELAYED_READOUT_FLAG = "NO"
+DESCRIPTION = "Incomplete product finalized due to truncated lines."
+DETECTOR_TEMPERATURE = -89.243546 <DEGC>
+EARTH_RECEIVED_START_TIME = 2011-346T22:30:08.981
+EARTH_RECEIVED_STOP_TIME = 2011-346T22:30:49.765
+ELECTRONICS_BIAS = 112
+EXPECTED_MAXIMUM = (62.996498,69.454498)
+EXPECTED_PACKETS = 576
+EXPOSURE_DURATION = 4600.000000
+FILTER_NAME = ("CL1","UV3")
+FILTER_TEMPERATURE = 0.248629
+FLIGHT_SOFTWARE_VERSION_ID = "1.4"
+GAIN_MODE_ID = "29 ELECTRONS PER DN"
+IMAGE_MID_TIME = 2011-346T05:02:22.073
+IMAGE_NUMBER = "1702360370"
+IMAGE_OBSERVATION_TYPE = {"SCIENCE"}
+IMAGE_TIME = 2011-346T05:02:24.373
+INSTRUMENT_DATA_RATE = 182.783997
+INSTRUMENT_HOST_NAME = "CASSINI ORBITER"
+INSTRUMENT_ID = "ISSNA"
+INSTRUMENT_MODE_ID = "FULL"
+INSTRUMENT_NAME = "IMAGING SCIENCE SUBSYSTEM - NARROW ANGLE"
+INST_CMPRS_PARAM = ("N/A","N/A","N/A","N/A")
+INST_CMPRS_RATE = (5.333330,3.631307)
+INST_CMPRS_RATIO = 2.203063
+INST_CMPRS_TYPE = "LOSSLESS"
+LIGHT_FLOOD_STATE_FLAG = "ON"
+METHOD_DESC = "ISSPT2.7;Enceladus;ISS_158EN_ENCEL001_PRIME"
+MISSING_LINES = 31
+MISSING_PACKET_FLAG = "NO"
+MISSION_NAME = "CASSINI-HUYGENS"
+MISSION_PHASE_NAME = "EXTENDED-EXTENDED MISSION"
+OBSERVATION_ID = "ISS_158EN_ENCEL001_PRIME"
+OPTICS_TEMPERATURE = (0.627499,1.905708)
+ORDER_NUMBER = 16
+PARALLEL_CLOCK_VOLTAGE_INDEX = 9
+PREPARE_CYCLE_INDEX = 4
+PRODUCT_CREATION_TIME = 2011-346T15:34:07.000
+PRODUCT_ID = "1_N1702360370.120"
+PRODUCT_VERSION_TYPE = "FINAL"
+READOUT_CYCLE_INDEX = 6
+RECEIVED_PACKETS = 523
+SENSOR_HEAD_ELEC_TEMPERATURE = 1.633024
+SEQUENCE_ID = "S71"
+SEQUENCE_NUMBER = 16
+SEQUENCE_TITLE = "ISS_158EN_ENCEL001_PRIME"
+SHUTTER_MODE_ID = "NACONLY"
+SHUTTER_STATE_ID = "ENABLED"
+SOFTWARE_VERSION_ID = "ISS 11.00 05-24-2006"
+SPACECRAFT_CLOCK_CNT_PARTITION = 1
+SPACECRAFT_CLOCK_START_COUNT = "1702360365.220"
+SPACECRAFT_CLOCK_STOP_COUNT = "1702360370.120"
+START_TIME = 2011-346T05:02:19.773
+STOP_TIME = 2011-346T05:02:24.373
+TARGET_DESC = "Enceladus"
+TARGET_LIST = "N/A"
+TARGET_NAME = "ENCELADUS"
+TELEMETRY_FORMAT_ID = "S&ER3"
+VALID_MAXIMUM = (4095,4095)
+OBJECT = IMAGE_HEADER
+      INTERCHANGE_FORMAT = ASCII
+      HEADER_TYPE = VICAR2
+       BYTES = 3144
+      RECORDS = 1
+      ^DESCRIPTION = "../../label/vicar2.txt"
+END_OBJECT = IMAGE_HEADER
+OBJECT = TELEMETRY_TABLE
+      INTERCHANGE_FORMAT = BINARY
+      ROWS = 1
+      COLUMNS = 2
+      ROW_BYTES = 1048
+      ^STRUCTURE = "../../label/tlmtab.fmt"
+      OBJECT = COLUMN
+            NAME = NULL_PADDING
+            DATA_TYPE = MSB_UNSIGNED_INTEGER
+            START_BYTE = 61
+            BYTES = 987
+      END_OBJECT = COLUMN
+END_OBJECT = TELEMETRY_TABLE
+OBJECT = LINE_PREFIX_TABLE
+      INTERCHANGE_FORMAT = BINARY
+      ROWS = 1024
+      COLUMNS = 7
+      ROW_BYTES = 24
+      ROW_SUFFIX_BYTES = 1024
+      ^LINE_PREFIX_STRUCTURE = "../../label/prefix3.fmt"
+END_OBJECT = LINE_PREFIX_TABLE
+OBJECT = IMAGE
+      LINES = 1024
+      LINE_SAMPLES = 1024
+      SAMPLE_BITS = 8
+      SAMPLE_TYPE = SUN_INTEGER
+      LINE_PREFIX_BYTES = 24
+END_OBJECT = IMAGE
+END
diff --git a/knoten/utils.py b/knoten/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..47bc704747219209f19bb38144f5e670b5547329
--- /dev/null
+++ b/knoten/utils.py
@@ -0,0 +1,43 @@
+import pyproj
+
+def reproject(record, semi_major, semi_minor, source_proj, dest_proj, **kwargs):
+    """
+    Thin wrapper around PyProj's Transform() function to transform 1 or more three-dimensional
+    point from one coordinate system to another. If converting between Cartesian
+    body-centered body-fixed (BCBF) coordinates and Longitude/Latitude/Altitude coordinates,
+    the values input for semi-major and semi-minor axes determine whether latitudes are
+    planetographic or planetocentric and determine the shape of the datum for altitudes.
+    If semi_major == semi_minor, then latitudes are interpreted/created as planetocentric
+    and altitudes are interpreted/created as referenced to a spherical datum.
+    If semi_major != semi_minor, then latitudes are interpreted/created as planetographic
+    and altitudes are interpreted/created as referenced to an ellipsoidal datum.
+
+    Parameters
+    ----------
+    record : object
+          Pandas series object
+
+    semi_major : float
+              Radius from the center of the body to the equater
+
+    semi_minor : float
+              Radius from the pole to the center of mass
+
+    source_proj : str
+                      Pyproj string that defines a projection space ie. 'geocent'
+
+    dest_proj : str
+                   Pyproj string that defines a project space ie. 'latlon'
+
+    Returns
+    -------
+    : list
+    Transformed coordinates as y, x, z
+
+    """
+    source_pyproj = pyproj.Proj(proj = source_proj, a = semi_major, b = semi_minor)
+    dest_pyproj = pyproj.Proj(proj = dest_proj, a = semi_major, b = semi_minor)
+
+    y, x, z = pyproj.transform(source_pyproj, dest_pyproj, record[0], record[1], record[2], **kwargs)
+
+    return y, x, z
diff --git a/knoten/vis.py b/knoten/vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7cefff01eb040374e7f40013bd96358db836bb6
--- /dev/null
+++ b/knoten/vis.py
@@ -0,0 +1,429 @@
+import tempfile
+
+import json
+import pvl
+import pyproj
+import csmapi
+
+from knoten import csm
+
+from numbers import Number
+
+import numpy as np
+import pandas as pd
+
+from pysis import isis
+from pysis.exceptions import ProcessError
+
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import plotly.figure_factory as ff
+
+def reproject(record, semi_major, semi_minor, source_proj, dest_proj, **kwargs):
+    """
+    Thin wrapper around PyProj's Transform() function to transform 1 or more three-dimensional
+    point from one coordinate system to another. If converting between Cartesian
+    body-centered body-fixed (BCBF) coordinates and Longitude/Latitude/Altitude coordinates,
+    the values input for semi-major and semi-minor axes determine whether latitudes are
+    planetographic or planetocentric and determine the shape of the datum for altitudes.
+    If semi_major == semi_minor, then latitudes are interpreted/created as planetocentric
+    and altitudes are interpreted/created as referenced to a spherical datum.
+    If semi_major != semi_minor, then latitudes are interpreted/created as planetographic
+    and altitudes are interpreted/created as referenced to an ellipsoidal datum.
+
+    Parameters
+    ----------
+    record : object
+          Pandas series object
+
+    semi_major : float
+              Radius from the center of the body to the equater
+
+    semi_minor : float
+              Radius from the pole to the center of mass
+
+    source_proj : str
+                      Pyproj string that defines a projection space ie. 'geocent'
+
+    dest_proj : str
+                   Pyproj string that defines a project space ie. 'latlon'
+
+    Returns
+    -------
+    : list
+    Transformed coordinates as y, x, z
+
+    """
+    source_pyproj = pyproj.Proj(proj = source_proj, a = semi_major, b = semi_minor)
+    dest_pyproj = pyproj.Proj(proj = dest_proj, a = semi_major, b = semi_minor)
+
+    y, x, z = pyproj.transform(source_pyproj, dest_pyproj, record[0], record[1], record[2], **kwargs)
+
+    return y, x, z
+
+
+def point_info(cube_path, x, y, point_type, allow_outside=False):
+    """
+    Use Isis's campt to get image/ground point info from an image
+
+    Parameters
+    ----------
+    cube_path : str
+                path to the input cube
+
+    x : float
+        point in the x direction. Either a sample or a longitude value
+        depending on the point_type flag
+
+    y : float
+        point in the y direction. Either a line or a latitude value
+        depending on the point_type flag
+
+    point_type : str
+                 Options: {"image", "ground"}
+                 Pass "image" if  x,y are in image space (sample, line) or
+                 "ground" if in ground space (longitude, lattiude)
+
+    Returns
+    -------
+    : PvlObject
+      Pvl object containing campt returns
+    """
+    point_type = point_type.lower()
+
+    if point_type not in {"image", "ground"}:
+        raise Exception(f'{point_type} is not a valid point type, valid types are ["image", "ground"]')
+
+
+    if isinstance(x, Number) and isinstance(y, Number):
+        x, y = [x], [y]
+
+    with tempfile.NamedTemporaryFile("w+") as f:
+        # ISIS wants points in a file, so write to a temp file
+        if point_type == "ground":
+            # campt uses lat, lon for ground but sample, line for image.
+            # So swap x,y for ground-to-image calls
+            x,y = y,x
+        elif point_type == "image":
+            # convert to ISIS pixels
+            x = np.add(x, .5)
+            y = np.add(y, .5)
+
+        f.write("\n".join(["{}, {}".format(xval,yval) for xval,yval in zip(x, y)]))
+        f.flush()
+
+        with tempfile.NamedTemporaryFile("r+") as campt_output:
+            try:
+                isis.campt(from_=cube_path, coordlist=f.name, allowoutside=allow_outside, usecoordlist=True, coordtype=point_type, to=campt_output.name)
+            except ProcessError as e:
+                warn(f"CAMPT call failed, image: {cube_path}\n{e.stderr}")
+                return
+
+            pvlres = pvl.load(campt_output.name)
+
+        if len(x) > 1 and len(y) > 1:
+            for r in pvlres:
+                # convert all pixels to PLIO pixels from ISIS
+                r[1]["Sample"] -= .5
+                r[1]["Line"] -= .5
+        else:
+            pvlres["GroundPoint"]["Sample"] -= .5
+            pvlres["GroundPoint"]["Line"] -= .5
+    return pvlres
+
+
+def plot_diff(data, title='diff plot', colx='x', coly='y', coldx='diffx', coldy='diffy', colmag='magnitude', width=500, height=500):
+    import matplotlib.cm as cm
+    from matplotlib.colors import Normalize
+
+    fig = make_subplots(rows=2, cols=2, column_widths=[0.9, .1], row_width=[.9, .1],
+                        shared_xaxes=True, shared_yaxes=True, horizontal_spacing = 0.01, vertical_spacing = 0.01)
+
+    quiver_plot = ff.create_quiver(data[colx],
+                           data[coly],
+                           data[coldx],
+                           data[coldy],
+                           scale=1,
+                           line_color='#e3838e',
+                           name='offset direction',
+                           arrow_scale=0.1)
+
+    for i in range(len(quiver_plot.data)):
+        quiver_plot.data[i].xaxis='x1'
+        quiver_plot.data[i].yaxis='y1'
+
+    quiver_plot.layout.xaxis1.update({'anchor': 'y1'})
+    quiver_plot.layout.yaxis1.update({'anchor': 'x1', 'domain': [.55, 1]})
+
+    fig.add_trace(quiver_plot.data[0], row=2, col=1)
+
+    text = [f'{coldx}: {r[coldx]}<br>{coldy}: {r[coldy]}<br>{colmag}: {r[colmag]}' for i,r in data.iterrows()]
+    fig.add_trace(go.Scatter(x=data[colx], y=data[coly],
+                customdata=data,
+                mode='markers',
+                name=f'{colx},{coly}',
+                hovertext=text,
+                marker=dict(
+                        color=data[colmag],
+                        colorbar=dict(
+                            thickness = 5,
+                            outlinewidth = 0,
+                            ypad=0,
+                            title=f'{colmag}'
+                        ),
+                        colorscale="viridis",
+                )), row=2, col=1)
+
+    xavg = data.groupby(colx).apply(np.mean)
+    fig.add_trace(go.Scatter(x=xavg.index, y=xavg[colmag],
+        customdata=xavg,
+        name=f'{colx} mean error',
+        mode='lines+markers',
+        line_shape='spline',
+        line_color='seagreen',
+        marker=dict(
+                color=xavg[colmag],
+                colorscale="viridis",
+        )), row=1, col=1)
+
+    yavg = data.groupby(coly).apply(np.mean)
+    fig.add_trace(go.Scatter(x=yavg[colmag],y=yavg.index,
+        customdata=yavg,
+        name=f'{coly} mean error',
+        mode='lines+markers',
+        line_shape='spline',
+        line_color='purple',
+        marker=dict(
+                color=yavg[colmag],
+                colorscale="viridis",
+        )), row=2, col=2)
+
+    fig.update_layout(width=width, height=height, showlegend=True,legend_orientation="h", title_text=title)
+    fig.update_yaxes(autorange="reversed", title_text=coly, row=2, col=1)
+    fig.update_xaxes(title_text=colx, row=2, col=1)
+
+    fig.update_xaxes(title_text=f'mean error', row=2, col=2)
+
+    return fig
+
+
+def plot_diff_3d(data, title='3D diff plot', colx='x', coly='y', colz='z', coldx='diffx', coldy='diffy', coldz='diffz', colmag='magnitude', width=500, height=500):
+
+    text = [f'{coldx}: {r[coldx]}<br>{coldy}: {r[coldy]}<br>{coldz}: {r[coldz]}<br>{colmag}: {r[colmag]}' for i,r in data.iterrows()]
+
+    plot_data = {
+    "type": "scatter3d",
+    "mode": "markers",
+    "name": "original",
+    "text": text,
+    "x": data[colx],
+    "y": data[coly],
+    "z": data[colz],
+    "marker": { "colorscale": 'viridis',
+                "opacity": .8,
+                "size": 5,
+                "color": data[colmag],
+                "colorbar":{
+                            "thickness": 5,
+                            "outlinewidth": 0,
+                            "ypad": 0,
+                            "title":f'{colmag}'
+                }
+              }
+    }
+
+
+    layout = {
+        "title": title,
+        "width": width,
+        "height": height,
+        "scene": {
+            "aspectratio": {"x": 1, "y": 1, "z": 0.8},
+        }
+    }
+
+    fig = go.Figure(data=[plot_data], layout=layout)
+
+    return fig
+
+def plot_diff_3d_cone(data, title='3D diff plot', colx='x', coly='y', colz='z',
+                                                  colu='u', colv='v',colw='w',
+                                                  coldx='diffx', coldy='diffy', coldz='diffz',
+                                                  coldv = 'diffu', coldu='diffv', coldw='diffw',
+                                                  colxyz_mag='xyz_magnitude', coluvw_mag='uvw_magnitude',
+                                                  width=500, height=500):
+    text = [f'{coldx}: {r[coldx]}<br>\
+              {coldy}: {r[coldy]}<br>\
+              {coldz}: {r[coldz]}<br>\
+              {coldu}: {r[coldu]}<br>\
+              {coldv}: {r[coldv]}<br>\
+              {coldw}: {r[coldw]}<br>\
+              {colxyz_mag}: {r[colxyz_mag]}<br>\
+              {coluvw_mag}: {r[coluvw_mag]}' for i,r in data.iterrows()]
+
+    plot_data = {
+        "type": "cone",
+        "text":text,
+        "x": data[colx],
+        "y": data[coly],
+        "z": data[colz],
+        "u": data[colu],
+        "v": data[colv],
+        "w": data[colw],
+        "sizeref": 5,
+        "colorscale": 'viridis',
+        "colorbar":{
+                    "thickness": 5,
+                    "outlinewidth": 0,
+                    "ypad": 0,
+                    "title": f'mean({colxyz_mag},{coluvw_mag})'
+        }
+    }
+
+    layout = {
+        "title": title,
+        "width": width,
+        "height": height,
+        "scene": {
+            "aspectratio": {"x": 1, "y": 1, "z": 0.8},
+        }
+    }
+
+    fig = go.Figure(data=[plot_data], layout=layout)
+
+    return fig
+
+def reprojection_diff(isd, cube, nx=10, ny=50, width=500, height=500):
+    """
+    """
+
+    isdjson = json.load(open(isd))
+
+    nlines = isdjson['image_lines']
+    nsamples = isdjson['image_samples']
+
+    # generate meshgrid
+    xs, ys = np.mgrid[0:nsamples:nsamples/nx, 0:nlines:nlines/ny]
+    xs, ys = xs.flatten(), ys.flatten()
+
+    csmcam = csm.create_csm(isd)
+
+    # get data for isis image to ground, csm ground to image
+    isis_pts = point_info(cube, xs, ys, 'image')
+    isisgnds = np.asarray([np.asarray(g[1]['BodyFixedCoordinate'].value)*1000 for g in isis_pts])
+    csm_pts = np.asarray([[p.samp, p.line] for p in [csmcam.groundToImage(csmapi.EcefCoord(*bf)) for bf in isisgnds]])
+
+    isis2csm_diff = np.asarray([xs,ys]).T - csm_pts
+    isis2csm_diffmag = np.linalg.norm(isis2csm_diff, axis=1)
+    isis2csm_angles = np.arctan2(*isis2csm_diff.T[::-1])
+
+    isis2csm_data = np.asarray([csm_pts.T[0], csm_pts.T[1], xs, ys,  isis2csm_diff.T[0], isis2csm_diff.T[1], isis2csm_diffmag, isis2csm_angles]).T
+    isis2csm_data = pd.DataFrame(isis2csm_data, columns=['csm sample', 'csm line', 'isis sample','isis line', 'diff sample', 'diff line', 'magnitude', 'angles'])
+
+    isis2csm_plot = plot_diff(isis2csm_data, colx='isis sample', coly='isis line',
+                     coldx='diff sample', coldy='diff line',
+                     title="ISIS2Ground->CSM2Image", width=width, height=height)
+
+
+    # get data for csm image to ground, isis ground to image
+    csmgnds = np.asarray([[p.x, p.y, p.z] for p in [csmcam.imageToGround(csmapi.ImageCoord(y,x), 0) for x,y in zip(xs,ys)]])
+    csmlon, csmlat, _ = reproject(csmgnds.T, isdjson['radii']['semimajor'], isdjson['radii']['semimajor'], 'geocent', 'latlong')
+
+    isis_imgpts = point_info(cube, (csmlon+360)%360, csmlat, 'ground')
+    isis_imgpts = np.asarray([(p[1]['Sample'], p[1]['Line']) for p in isis_imgpts])
+
+    csm2isis_diff = np.asarray([xs,ys]).T - isis_imgpts
+    csm2isis_diffmag = np.linalg.norm(csm2isis_diff, axis=1)
+    csm2isis_angles = np.arctan2(*(csm2isis_diff/csm2isis_diffmag[:,np.newaxis]).T[::-1])
+    csm2isis_data = np.asarray([xs, ys, isis_imgpts.T[0], isis_imgpts.T[1], csm2isis_diff.T[0], csm2isis_diff.T[1], csm2isis_diffmag, csm2isis_angles]).T
+    csm2isis_data = pd.DataFrame(csm2isis_data, columns=['csm sample', 'csm line', 'isis sample','isis line', 'diff sample', 'diff line', 'magnitude', 'angles'])
+
+    csm2isis_plot = plot_diff(csm2isis_data, colx='csm sample', coly='csm line',
+                 coldx='diff sample', coldy='diff line',
+                 title="CSM2Ground->ISIS2Image", width=width, height=height)
+
+    # get data for footprint comparison
+    isis_lonlat = np.asarray([[p[1]['PositiveEast360Longitude'].value, p[1]['PlanetocentricLatitude'].value] for p in isis_pts])
+    csm_lonlat = np.asarray([(csmlon+360)%360, csmlat]).T
+
+    isiscsm_difflatlon = csm_lonlat - isis_lonlat
+    isiscsm_difflatlonmag = np.linalg.norm(isiscsm_difflatlon, axis=1)
+    isiscsm_angleslatlon = np.arctan2(*isiscsm_difflatlon.T[::-1])
+    isiscsm_latlondata = np.asarray([isis_lonlat.T[0], isis_lonlat.T[1], csm_lonlat.T[0], csm_lonlat.T[1], isiscsm_difflatlon.T[0], isiscsm_difflatlon.T[1], isiscsm_difflatlonmag, isiscsm_angleslatlon]).T
+    isiscsm_latlondata = pd.DataFrame(isiscsm_latlondata, columns=['isis lon', 'isis lat', 'csm lon','csm lat', 'diff lon', 'diff lat', 'magnitude', 'angles'])
+
+    isiscsm_latlonplot = plot_diff(isiscsm_latlondata, colx='isis lon', coly='isis lat',
+                 coldx='diff lon', coldy='diff lat',
+                 title="ISIS Lat/Lon vs CSM Lat/Lon", width=width, height=height)
+
+
+    isiscsm_diffbf = isisgnds - csmgnds
+    isiscsm_diffbfmag = np.linalg.norm(isiscsm_diffbf, axis=1)
+    isiscsm_anglesbf = np.arctan2(*isiscsm_diffbf.T[::-1])
+
+    isiscsm_bfdata = np.asarray([isisgnds.T[0], isisgnds.T[1], isisgnds.T[2], csmgnds.T[0], csmgnds.T[1], csmgnds.T[2], isiscsm_diffbf.T[0], isiscsm_diffbf.T[1], isiscsm_diffbf.T[2], isiscsm_diffbfmag, isiscsm_anglesbf]).T
+    isiscsm_bfdata = pd.DataFrame(isiscsm_bfdata, columns=['isisx', 'isisy', 'isisz', 'csmx','csmy', 'csmz', 'diffx', 'diffy', 'diffz', 'magnitude', 'angles'])
+    isiscsm_bfplot = plot_diff_3d(isiscsm_bfdata, colx='isisx', coly='isisy', colz='isisz',
+                                  title='ISIS Body-Fixed vs CSM Body-Fixed',  width=width, height=height)
+
+    return isis2csm_plot, csm2isis_plot, isiscsm_latlonplot, isiscsm_bfplot, isis2csm_data, csm2isis_data, isiscsm_latlondata, isiscsm_bfdata
+
+
+def external_orientation_diff(isd, cube, nx=4, ny=4, width=500, height=500):
+    csmcam = csm.create_csm(isd)
+    isdjson = json.load(open(isd))
+    nlines, nsamples = isdjson['image_lines'], isdjson['image_samples']
+
+    xs, ys = np.mgrid[0:nsamples:nsamples/nx, 0:nlines:nlines/ny]
+    xs, ys = xs.flatten(), ys.flatten()
+
+    isis_pts = point_info(cube, xs, ys, "image")
+    isis_lv_bf = np.asarray([p[1]['LookDirectionBodyFixed'] for p in isis_pts])
+    isis_pos_bf = np.asarray([p[1]['SpacecraftPosition'].value for p in isis_pts])*1000
+    isis_ephem_times = np.asarray([p[1]['EphemerisTime'].value for p in isis_pts])
+
+    csm_locus = [csmcam.imageToRemoteImagingLocus(csmapi.ImageCoord(y, x)) for x,y in zip(xs,ys)]
+    csm_lv_bf = np.asarray([[lv.direction.x, lv.direction.y, lv.direction.z] for lv in csm_locus])
+    csm_pos_bf = np.asarray([[lv.point.x, lv.point.y, lv.point.z] for lv in csm_locus])
+    csm_ephem_times = np.asarray([csmcam.getImageTime(csmapi.ImageCoord(y, x)) for x,y in zip(xs,ys)])
+    csm_ephem_times += isdjson['center_ephemeris_time']
+
+    csmisis_diff_pos = csm_pos_bf - isis_pos_bf
+    csmisis_diff_lv = csm_lv_bf - isis_lv_bf
+    csmisis_diff_ephem = csm_ephem_times - isis_ephem_times
+    csmisis_diff_pos_mag = np.linalg.norm(csmisis_diff_pos, axis=1)
+    csmisis_diff_lv_mag = np.linalg.norm(csmisis_diff_lv, axis=1)
+
+    csmisis_diff_pos_data = np.asarray([csm_pos_bf.T[0], csm_pos_bf.T[1], csm_pos_bf.T[2],
+                                      isis_pos_bf.T[0], isis_pos_bf.T[1], isis_pos_bf.T[2],
+                                      csmisis_diff_pos.T[0], csmisis_diff_pos.T[1], csmisis_diff_pos.T[2],
+                                      csmisis_diff_pos_mag])
+    csmisis_diff_pos_data = pd.DataFrame(csmisis_diff_pos_data.T, columns=['csm pos x', 'csm pos y', 'csm pos z',
+                                                                       'isis pos x', 'isis pos y', 'isis pos z',
+                                                                       'diffx', 'diffy', 'diffz',
+                                                                        'magnitude'])
+
+    csmisis_diff_pos_plot = plot_diff_3d(csmisis_diff_pos_data, colx='isis pos x', coly='isis pos y', colz='isis pos z',
+                                                title='ISIS CSM Position Difference')
+
+    csmisis_diff_lv_data = np.asarray([isis_pos_bf.T[0], isis_pos_bf.T[1], isis_pos_bf.T[2],
+                                      csm_lv_bf.T[0], csm_lv_bf.T[1], csm_lv_bf.T[2],
+                                      isis_lv_bf.T[0], isis_lv_bf.T[1], isis_lv_bf.T[2],
+                                      csmisis_diff_pos.T[0], csmisis_diff_pos.T[1], csmisis_diff_pos.T[2],
+                                      csmisis_diff_lv.T[0], csmisis_diff_lv.T[1], csmisis_diff_lv.T[2],
+                                      csmisis_diff_pos_mag, csmisis_diff_lv_mag, isis_ephem_times, csm_ephem_times, csmisis_diff_ephem])
+    csmisis_diff_lv_data = pd.DataFrame(csmisis_diff_lv_data.T, columns=['isis pos x', 'isis pos y', 'isis pos z',
+                                                                        'csm lv x', 'csm lv y', 'csm lv z',
+                                                                       'isis lv x', 'isis lv y', 'isis lv z',
+                                                                       'diffx', 'diffy', 'diffz',
+                                                                       'diffu', 'diffv', 'diffw',
+                                                                        'xyz_magnitude', 'uvw_magnitude', 'isis ephem time', 'csm ephem time', 'diff ephem'])
+
+    csmisis_diff_lv_plot = plot_diff_3d_cone(csmisis_diff_lv_data, colx='isis pos x', coly='isis pos y', colz='isis pos z',
+                                                                   colu='isis lv x', colv='isis lv y', colw='isis lv z',
+                                                                    title='ISIS CSM Position and Look Vector Difference', width=width, height=height)
+
+    csmisis_diff_ephem_plot = go.Figure(go.Scatter(x=np.linspace(0, nlines, ny), y=csmisis_diff_ephem, line_shape='spline')).update_layout(title='ISIS CSM Ephem Time Difference', width=width, height=height/2).update_xaxes(title_text='Line').update_yaxes(title_text='Time Delta Seconds')
+
+    return csmisis_diff_lv_plot, csmisis_diff_ephem_plot, csmisis_diff_lv_data
diff --git a/tests/test_reproject.py b/tests/test_reproject.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b479af862e41825ac822fbdaf1e307f8c40c47f
--- /dev/null
+++ b/tests/test_reproject.py
@@ -0,0 +1,10 @@
+from unittest import mock
+import pytest
+
+from knoten import utils
+
+def test_reproject():
+    with mock.patch('pyproj.transform', return_value=[1,1,1]) as mock_pyproj:
+        res = utils.reproject([1,1,1], 10, 10, 'geocent', 'latlon')
+        mock_pyproj.assert_called_once()
+        assert res == (1,1,1)