{ "cells": [ { "cell_type": "markdown", "id": "749df4dc", "metadata": {}, "source": [ "# Wheat demo scene \n", "\n", "Notebook creator: Hannah Weiser & Sina Zumstein, 2022\n", "\n", "This demo scene uses a 3D model of several wheat ears, which will be scanned by mobile laser scanning (MLS). We will use the command-line access of HELIOS++ to run the simulation, and use Python just for displaying the input XMLs and the resulting point cloud." ] }, { "cell_type": "code", "execution_count": 1, "id": "36c544f4", "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "from pathlib import Path\n", "from IPython.display import Code\n", "\n", "current_folder = globals()['_dh'][0]\n", "helios_path = str(Path(current_folder).parent)\n", "sys.path.append(helios_path) # add helios-plusplus directory to PATH\n", "import pyhelios\n", "\n", "from pyhelios.util.xmldisplayer import display_xml, find_playback_dir" ] }, { "cell_type": "markdown", "id": "6c330acc", "metadata": {}, "source": [ "# Survey\n", "Lets's have a look at the XML files in the simulation. First, we investigate the **survey** XML file, `mls_wheat_demo.xml`:" ] }, { "cell_type": "code", "execution_count": 2, "id": "185f9b23", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<document>\n",
       "    <survey name="mls_arbaro_wheat" scene="data/scenes/demo/arbaro_wheat_field.xml#arbaro_wheat" platform="data/platforms.xml#tractor" scanner="data/scanners_tls.xml#riegl_vz400">\n",
       "\t\t\n",
       "\t\t<leg>\n",
       "\t\t\t<platformSettings x="-10" y="-10" z="0" onGround="true" movePerSec_m="3" />\n",
       "\t\t\t<scannerSettings active="true" pulseFreq_hz="100000" scanAngle_deg="20" scanFreq_hz="50" headRotatePerSec_deg="0.00" headRotateStart_deg="0.00" headRotateStop_deg="0.00" trajectoryTimeInterval_s="0.05" />\n",
       "\t\t</leg>\n",
       "\t\t\n",
       "\t\t<leg>\n",
       "\t\t\t<platformSettings x="10" y="-10" z="0" onGround="true" movePerSec_m="3" />\n",
       "\t\t\t<scannerSettings active="true" pulseFreq_hz="100000" scanAngle_deg="20" scanFreq_hz="50" headRotatePerSec_deg="0.00" headRotateStart_deg="0.00" headRotateStop_deg="0.00" trajectoryTimeInterval_s="0.05" />\n",
       "\t\t</leg>\n",
       "\t\t\n",
       "\t\t<leg>\n",
       "\t\t\t<platformSettings x="10" y="10" z="0" onGround="true" movePerSec_m="3" />\n",
       "\t\t\t<scannerSettings active="true" pulseFreq_hz="100000" scanAngle_deg="20" scanFreq_hz="50" headRotatePerSec_deg="0.00" headRotateStart_deg="0.00" headRotateStop_deg="0.00" trajectoryTimeInterval_s="0.05" />\n",
       "\t\t</leg>\n",
       "\t\t\n",
       "\t\t<leg>\n",
       "\t\t\t<platformSettings x="-10" y="10" z="0" onGround="true" movePerSec_m="3" />\n",
       "\t\t\t<scannerSettings active="true" pulseFreq_hz="100000" scanAngle_deg="20" scanFreq_hz="50" headRotatePerSec_deg="0.00" headRotateStart_deg="0.00" headRotateStop_deg="0.00" trajectoryTimeInterval_s="0.05" />\n",
       "\t\t</leg>\n",
       "\t\t\n",
       "\t\t<leg>\n",
       "\t\t\t<platformSettings x="-10" y="-10" z="0" onGround="true" movePerSec_m="3" />\n",
       "\t\t\t<scannerSettings active="false" pulseFreq_hz="100000" scanAngle_deg="20" scanFreq_hz="50" headRotatePerSec_deg="0.00" headRotateStart_deg="0.00" headRotateStop_deg="0.00" trajectoryTimeInterval_s="0.05" />\n",
       "\t\t</leg>\n",
       "    </survey>\n",
       "</document>\n",
       "
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`leg`elements, that define the waypoints of the vehicle around the to be scanned object. Here, only `x` and `y` values are important `z` remains at 0. In total there are four lines the vehicle will drive along. The `movePerSec_m` parameter indicates the speed between these waypoints. Furthermore, we see that the `tractor` platform in `data/platforms.xml` is referenced, so let's have a look at that next:" ] }, { "cell_type": "markdown", "id": "f8f5d973", "metadata": {}, "source": [ "# Platform" ] }, { "cell_type": "code", "execution_count": 3, "id": "1cdd92dd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
<platform id="tractor" name="Tractor" type="groundvehicle" drag="0.005">\n",
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       "\t\t<attitudeYNoise\n",
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       "\t\t<attitudeZNoise\n",
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       "\t\t\ttype="NORMAL" mean="0.0" stdev="0.001"/>-->\n",
       "\t</platform>\n",
       "\n",
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       "\t\t<part>\n",
       "            <filter type="objloader">\n",
       "                <param type="string" key="filepath" value="data/sceneparts/arbaro/wheat.obj" />\n",
       "            </filter>\n",
       "            <filter type="rotate">\n",
       "                <param type="rotation" key="rotation">\n",
       "                    <rot axis="x" angle_deg="90" />\n",
       "                </param>\n",
       "            </filter>\n",
       "            <filter type="translate">\n",
       "                <param type="vec3" key="offset" value="-1.4;-1.4;0" />\n",
       "            </filter>\n",
       "\t\t\t<filter type="scale">\n",
       "                <param type="double" key="scale" value="2" />\n",
       "            </filter>\n",
       "        </part>\n",
       "\t\t<part>\n",
       "            <filter type="objloader">\n",
       "                <param type="string" key="filepath" value="data/sceneparts/arbaro/wheat.obj" />\n",
       "            </filter>\n",
       "            <filter type="rotate">\n",
       "                <param type="rotation" key="rotation">\n",
       "                    <rot axis="x" angle_deg="90" />\n",
       "                </param>\n",
       "            </filter>\n",
       "            <filter type="translate">\n",
       "                <param type="vec3" key="offset" value="-1.4;-2.8;0" />\n",
       "            </filter>\n",
       "\t\t\t<filter type="scale">\n",
       "                <param type="double" key="scale" value="2" />\n",
       "            </filter>\n",
       "        </part>\n",
       "\t\t\n",
       "    </scene>\n",
       "
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To load it we use the `objloader` filter and give the the relative path to the file in the `filepath`parameter. \n", "The same object (the wheat plant) is placed in the scene 15 times. Using the `translate` filter, it is positioned in three columns and five rows to form a wheat field." ] }, { "cell_type": "markdown", "id": "1dda1ef8", "metadata": {}, "source": [ "# Executing the Simulation \n", "Next, we will run the simulation. In Jupyter Notebooks, we can run external commands with the !command syntax, but you can also just run it from the command line." ] }, { "cell_type": "code", "execution_count": 6, "id": "7f219fa5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HELIOS++ VERSION 1.2.0\n", "\n", "CWD: \"H:\\helios4pyhelios\\helios\"\n", "seed: AUTO\n", "surveyPath: \"data/surveys/demo/mls_wheat_demo.xml\"\n", "assetsPath: \"assets/\"\n", "outputPath: \"output/\"\n", "writeWaveform: 0\n", "calcEchowidth: 0\n", "fullWaveNoise: 0\n", "splitByChannel: 0\n", "parallelization: 1\n", "njobs: 0\n", "chunkSize: 32\n", "warehouseFactor: 4\n", "platformNoiseDisabled: 0\n", "legNoiseDisabled: 0\n", "rebuildScene: 0\n", "lasOutput: 0\n", "las10: 0\n", "fixedIncidenceAngle: 0\n", "gpsStartTime: \n", "kdtType: 4\n", "kdtJobs: 0\n", "kdtGeomJobs: 0\n", "sahLossNodes: 32\n", "\n", "xmlDocFilename: mls_wheat_demo.xml\n", "xmlDocFilePath: data/surveys/demo\n", "xmlDocFilename: scanners_tls.xml\n", "xmlDocFilePath: data\n", "Using default value for attribute 'averagePower_w' : 4\n", "Using default value for attribute 'beamQualityFactor' : 1\n", "Using default value for attribute 'opticalEfficiency' : 0.99\n", "Using default value for attribute 'receiverDiameter_m' : 0.15\n", "Using default value for attribute 'atmosphericVisibility_km' : 23\n", "Using default value for attribute 'wavelength_nm' : 1064\n", "Scanner: riegl_vz400\n", "Device[0]: riegl_vz400\n", "\tAverage Power: 4 W\n", "\tBeam Divergence: 0.3 mrad\n", "\tWavelength: 1064 nm\n", "\tVisibility: 23 km\n", "\n", "Using default value for attribute 'maxNOR' : 0\n", "Using default value for attribute 'rangeMax_m' : 1.79769e+308\n", "Using default value for attribute 'binSize_ns' : 0.25\n", "Using default value for attribute 'winSize_ns' : 1.25\n", "Using default value for attribute 'maxFullwaveRange_ns' : 0\n", "Number of subsampling rays (riegl_vz400): 19\n", "xmlDocFilename: platforms.xml\n", "xmlDocFilePath: data\n", "Number of subsampling rays (riegl_vz400): 19\n", "Using default value for attribute 'numRuns' : 1\n", "Using default value for attribute 'simSpeed' : 1\n", "Using default value for attribute 'stripId' : NULL_STRIP_ID\n", "Using platform default value for attribute 'stopAndTurn' : 1\n", "Using platform default value for attribute 'smoothTurn' : 0\n", "Using platform default value for attribute 'slowdownEnabled' : 1\n", "Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n", "Using default value for attribute 'stripId' : NULL_STRIP_ID\n", "Using platform default value for attribute 'stopAndTurn' : 1\n", "Using platform default value for attribute 'smoothTurn' : 0\n", "Using platform default value for attribute 'slowdownEnabled' : 1\n", "Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n", "Using default value for attribute 'stripId' : NULL_STRIP_ID\n", "Using platform default value for attribute 'stopAndTurn' : 1\n", "Using platform default value for attribute 'smoothTurn' : 0\n", "Using platform default value for attribute 'slowdownEnabled' : 1\n", "Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n", "Using default value for attribute 'stripId' : NULL_STRIP_ID\n", "Using platform default value for attribute 'stopAndTurn' : 1\n", "Using platform default value for attribute 'smoothTurn' : 0\n", "Using platform default value for attribute 'slowdownEnabled' : 1\n", "Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n", "Using default value for attribute 'stripId' : NULL_STRIP_ID\n", "Using platform default value for attribute 'stopAndTurn' : 1\n", "Using platform default value for attribute 'smoothTurn' : 0\n", "Using platform default value for attribute 'slowdownEnabled' : 1\n", "Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n", "Loading Scene...\n", "xmlDocFilename: arbaro_wheat_field.xml\n", "xmlDocFilePath: data/scenes/demo\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/basic/groundplane/groundplane.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "Failed to read 'up'-axis from scene XML file.\n", "Assuming 'z' axis points upwards for scene part \"data/sceneparts/arbaro/wheat.obj\".\n", "Set up axis explicitly to silence this warning.\n", "C++ Exception: boost::bad_get: failed value get using boost::get\n", "17 sceneparts loaded in 10.664s\n", "\n", "CRS bounding box (by vertices): Min: dvec3(-100.000000, -100.000000, -0.001500), Max: dvec3(100.000000, 100.000000, 1.187800)\n", "Shift: dvec3(-100.000000, -100.000000, -0.001500)\n", "# vertices to translate: 3629142\n", "Actual bounding box (by vertices): Min: dvec3(0.000000, 0.000000, 0.000000), Max: dvec3(200.000000, 200.000000, 1.189300)\n", "Writing serial scene wrapper object to data/scenes/demo/arbaro_wheat_field.scene ...\n", "Building KD-Grove... \n", "KDTree (num. primitives 1209714) :\n", "\tMax. # primitives in leaf: 264\n", "\tMin. # primitives in leaf: 1\n", "\tMax. depth reached: 45\n", "\tKDTree axis-aligned surface area: 80951.4\n", "\tInterior nodes: 1110989\n", "\tLeaf nodes: 855568\n", "\tTotal tree cost: 6.11116\n", "KDGrove stats:\n", "\tNumber of trees: 1\n", "\tNumber of static trees: 1\n", "\tNumber of dynamic trees: 0\n", "\tStatistics (min, max, total, mean, stdev):\n", "\t\tBuilding time: (2.7320, 2.7320, 2.7320, 2.7320, 0.0000)\n", "\t\tTree primitives: (1209714, 1209714, 1209714, 1209714.0000, 0.0000)\n", "\t\tMax primitives in leaf: (264, 264, 264, 264.0000, 0.0000)\n", "\t\tMin primitives in leaf: (1, 1, 1, 1.0000, 0.0000)\n", "\t\tMaximum depth: (45, 45, 45, 45.0000, 0.0000)\n", "\t\tAxis-aligned surface area: (80951.4400, 80951.4400, 80951.4400, 80951.4400, 0.0000)\n", "\t\tNumber of interior nodes: (1110989, 1110989, 1110989, 1110989.0000, 0.0000)\n", "\t\tNumber of leaf nodes: (855568, 855568, 855568, 855568.0000, 0.0000)\n", "\t\tTree cost: (6.1112, 6.1112, 6.1112, 6.1112, 0.0000)\n", "\n", "KDG built in 2.74s\n", "Scene loaded in 26.015s\n", "Reading Spectral Library...\n", "Using default value for attribute 'seed' : AUTO\n", "Output directory: \"output/\\mls_arbaro_wheat\\2023-02-27_09-30-03\\\"\n", "Simulation: Scanner changed!\n", "Starting leg 0\n", "\n", "Pulse frequency set to 100000\n", "Scan angle set to 20\n", "Applying settings for PolygonMirrorBeamDeflector...\n", "Vertical angle min/max -nan(ind)/-nan(ind)\n", " -- verticalAngleMin not set, using the value of -20 degrees\n", " -- verticalAngleMax not set, using the value of 20 degrees\n", "Leg0 waypoints:\n", "\tOrigin: (90, 90, 0.0015)\n", "\tTarget: (110, 90, 0.0015)\n", "\tNext: (110, 110, 0.0015)\n", "\n", "Iterative mode was used for manual leg initialization because default one failed for MovingPlatform\n", "Running simulation...\n", "Clearing point cloud: \"output/\\mls_arbaro_wheat\\2023-02-27_09-30-03\\leg000_points.xyz\"\n", "Survey 0.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 0.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 0.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 1.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 1.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 1.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 2.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 2.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 2.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 3.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 3.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 3.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 4.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 4.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 4.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 5.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 5.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 5.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 6.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 6.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 6.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 7.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 7.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 7.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 8.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 8.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 8.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 9.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 9.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 9.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 10.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 10.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 10.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 11.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 11.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 11.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 48.00%\tElapsed 00 00:00:00 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Remaining 00 00:00:00\n", "Survey 14.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 14.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 14.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 15.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 15.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 15.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 16.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 16.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 16.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 17.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 17.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 17.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 18.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 18.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 18.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 19.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 19.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 19.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 20.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 20.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 20.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 21.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 21.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 21.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 22.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 22.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 22.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 23.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 23.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 23.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 24.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 24.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 24.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg1/4 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Waypoint reached!\n", "Starting leg 1\n", "\n", "Pulse frequency set to 100000\n", "Scan angle set to 20\n", "Applying settings for PolygonMirrorBeamDeflector...\n", "Vertical angle min/max -nan(ind)/-nan(ind)\n", " -- verticalAngleMin not set, using the value of -20 degrees\n", " -- verticalAngleMax not set, using the value of 20 degrees\n", "Leg1 waypoints:\n", "\tOrigin: (110, 90, 0.0015)\n", "\tTarget: (110, 110, 0.0015)\n", "\tNext: (90, 110, 0.0015)\n", "\n", "Clearing point cloud: \"output/\\mls_arbaro_wheat\\2023-02-27_09-30-03\\leg001_points.xyz\"\n", "Turn mode 1\n", "Survey 25.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 25.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Turn mode 2\n", "Survey 25.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 26.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 26.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 26.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 27.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 27.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 27.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 28.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 28.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 28.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 29.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 29.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 29.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 30.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 30.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 30.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 31.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 31.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 31.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 32.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 32.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 32.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 33.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 33.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 33.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 34.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 34.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 34.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 35.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 35.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 35.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 36.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 36.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 36.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 37.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 37.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 37.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 38.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 38.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 38.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 39.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 39.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 39.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 40.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 40.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 40.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 41.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 41.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 41.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 42.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 42.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 42.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 43.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 43.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 43.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 44.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 44.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 44.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 45.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 45.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 45.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 46.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 46.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 46.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 47.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 47.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 47.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 48.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 48.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 48.75%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 49.25%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 49.50%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Leg2/4 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 49.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Leg2/4 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Waypoint reached!\n", "Starting leg 2\n", "\n", "Pulse frequency set to 100000\n", "Scan angle set to 20\n", "Applying settings for PolygonMirrorBeamDeflector...\n", "Vertical angle min/max -nan(ind)/-nan(ind)\n", " -- verticalAngleMin not set, using the value of -20 degrees\n", " -- verticalAngleMax not set, using the value of 20 degrees\n", "Leg2 waypoints:\n", "\tOrigin: (110, 110, 0.0015)\n", "\tTarget: (90, 110, 0.0015)\n", "\tNext: (90, 90, 0.0015)\n", "\n", "Clearing point cloud: \"output/\\mls_arbaro_wheat\\2023-02-27_09-30-03\\leg002_points.xyz\"\n", "Turn mode 1\n", "Survey 50.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 50.50%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Turn mode 2\n", "Survey 50.75%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 51.00%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 51.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 51.50%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 51.75%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 52.00%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 52.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 52.50%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 52.75%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 53.00%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 53.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 53.50%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 53.75%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 54.00%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 54.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 54.50%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 54.75%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 55.00%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 55.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 55.50%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 55.75%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 56.00%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 56.25%\tElapsed 00 00:00:01 Remaining 00 00:00:01\n", "Leg3/4 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 56.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 56.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 57.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 57.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 57.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 57.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 58.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 58.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 58.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 58.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 59.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 59.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 59.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 59.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 60.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 60.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 60.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 60.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 61.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 61.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 61.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 61.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 62.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 62.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 62.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 62.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 63.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 63.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 63.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 63.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 64.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 64.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 64.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 64.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 65.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 65.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 65.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 65.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 66.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 66.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 66.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 66.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 67.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 67.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 67.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 67.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 68.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 68.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 68.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 68.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 69.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 69.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 69.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 69.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 70.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 70.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 70.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 70.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 71.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 71.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 71.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 71.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 72.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 72.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 72.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 72.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 73.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 73.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 73.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 73.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 74.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 74.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 74.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 74.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg3/4 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Waypoint reached!\n", "Starting leg 3\n", "\n", "Pulse frequency set to 100000\n", "Scan angle set to 20\n", "Applying settings for PolygonMirrorBeamDeflector...\n", "Vertical angle min/max -nan(ind)/-nan(ind)\n", " -- verticalAngleMin not set, using the value of -20 degrees\n", " -- verticalAngleMax not set, using the value of 20 degrees\n", "Leg3 waypoints:\n", "\tOrigin: (90, 110, 0.0015)\n", "\tTarget: (90, 90, 0.0015)\n", "\tNext: (90, 90, 0.0015)\n", "\n", "Clearing point cloud: \"output/\\mls_arbaro_wheat\\2023-02-27_09-30-03\\leg003_points.xyz\"\n", "Turn mode 1\n", "Survey 75.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 75.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Turn mode 2\n", "Survey 75.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 76.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 76.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 76.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 76.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 77.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 77.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 77.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 77.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 78.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 78.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 78.50%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 78.75%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 79.00%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:01\n", "Survey 79.25%\tElapsed 00 00:00:01 Remaining 00 00:00:00\n", "Leg4/4 17.00%\tElapsed 00 00:00:00 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Remaining 00 00:00:00\n", "Survey 99.50%\tElapsed 00 00:00:02 Remaining 00 00:00:00\n", "Leg4/4 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Survey 99.75%\tElapsed 00 00:00:02 Remaining 00 00:00:00\n", "Leg4/4 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n", "Waypoint reached!\n", "Starting leg 4\n", "\n", "Pulse frequency set to 100000\n", "Scan angle set to 20\n", "Applying settings for PolygonMirrorBeamDeflector...\n", "Vertical angle min/max -nan(ind)/-nan(ind)\n", " -- verticalAngleMin not set, using the value of -20 degrees\n", " -- verticalAngleMax not set, using the value of 20 degrees\n", "Waypoint reached!\n", "Elapsed simulation steps = 3404028\n", "Elapsed virtual time = 34.0403 sec.\n", "Main thread simulation loop finished in 2.20997 sec.\n", "Waiting for completion of pulse computation tasks...\n", "Pulse computation tasks finished in 2.21026 sec.\n", "Total simulation time: 0:00:2\n", "\n" ] } ], "source": [ "!\"run/helios\" data/surveys/demo/mls_wheat_demo.xml" ] }, { "cell_type": "markdown", "id": "d0a29675", "metadata": {}, "source": [ "## The results\n", "Now we can display a 3D plot" ] }, { "cell_type": "code", "execution_count": 7, "id": "08136de6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading points from H:\\helios4pyhelios\\helios\\output\\mls_arbaro_wheat\\2023-02-27_09-30-03\n" ] } ], "source": [ "#%matplotlib widget\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "output_path = find_playback_dir('data/surveys/demo/mls_wheat_demo.xml')\n", "\n", "print('Loading points from', output_path)\n", "\n", "strip_1 = np.loadtxt(Path(output_path) / 'leg000_points.xyz')\n", "strip_2 = np.loadtxt(Path(output_path) / 'leg001_points.xyz')\n", "strip_3 = np.loadtxt(Path(output_path) / 'leg002_points.xyz')\n", "strip_4 = np.loadtxt(Path(output_path) / 'leg003_points.xyz')\n", "traj_1 = np.loadtxt(Path(output_path) / 'leg000_trajectory.txt')\n", "traj_2 = np.loadtxt(Path(output_path) / 'leg001_trajectory.txt')\n", "traj_3 = np.loadtxt(Path(output_path) / 'leg002_trajectory.txt')\n", "traj_4 = np.loadtxt(Path(output_path) / 'leg003_trajectory.txt')\n", "traj = np.vstack((traj_1[:, :3], traj_2[:, :3], traj_3[:, :3], traj_4[:, :3]))" ] }, { "cell_type": "code", "execution_count": 8, "id": "e9137c8d", "metadata": {}, "outputs": [], "source": [ "def set_axes_equal(ax):\n", " '''Make axes of 3D plot have equal scale so that spheres appear as spheres,\n", " cubes as cubes, etc.. This is one possible solution to Matplotlib's\n", " ax.set_aspect('equal') and ax.axis('equal') not working for 3D.\n", "\n", " Input\n", " ax: a matplotlib axis, e.g., as output from plt.gca().\n", " '''\n", "\n", " x_limits = ax.get_xlim3d()\n", " y_limits = ax.get_ylim3d()\n", " z_limits = ax.get_zlim3d()\n", "\n", " x_range = abs(x_limits[1] - x_limits[0])\n", " x_middle = np.mean(x_limits)\n", " y_range = abs(y_limits[1] - y_limits[0])\n", " y_middle = np.mean(y_limits)\n", " z_range = abs(z_limits[1] - z_limits[0])\n", " z_middle = np.mean(z_limits)\n", "\n", " # The plot bounding box is a sphere in the sense of the infinity\n", " # norm, hence I call half the max range the plot radius.\n", " plot_radius = 0.5*max([x_range, y_range, z_range])\n", "\n", " ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius]) \n", " ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])\n", " ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])" ] }, { "cell_type": "code", "execution_count": 9, "id": "cc2b4279", "metadata": {}, "outputs": [], "source": [ "def extract_by_bb(arr, b_box):\n", " assert len(b_box) == 6\n", " x_min, y_min, z_min, x_max, y_max, z_max = b_box\n", " subset = arr[(arr[:, 0] > x_min) & \n", " (arr[:, 0] < x_max) & \n", " (arr[:, 1] > y_min) & \n", " (arr[:, 1] < y_max) & \n", " (arr[:, 2] > z_min) & \n", " (arr[:, 2] < z_max)]\n", " \n", " return subset" ] }, { "cell_type": "code", "execution_count": 10, "id": "90e1cd97", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Matplotlib figure.\n", "fig = plt.figure(figsize=(12,8))\n", "# Axes3d axis onto mpl figure.\n", "ax = fig.add_subplot(projection='3d')\n", "\n", "# create scene subset\n", "bbox = [-5, -5, 0, 5, 5, 1.5]\n", "\n", "strip_1_sub = extract_by_bb(strip_1, bbox)\n", "strip_2_sub = extract_by_bb(strip_2, bbox)\n", "strip_3_sub = extract_by_bb(strip_3, bbox)\n", "strip_4_sub = extract_by_bb(strip_4, bbox)\n", "\n", "# Scatter plot of points (coloured by height).\n", "sc = ax.scatter(strip_1_sub[:, 0], strip_1_sub[:, 1], strip_1_sub[:, 2], c=strip_1_sub[:, 2], cmap=\"RdYlBu_r\", s=0.02, label='scene')\n", "sc = ax.scatter(strip_2_sub[:, 0], strip_2_sub[:, 1], strip_2_sub[:, 2], c=strip_2_sub[:, 2], cmap=\"RdYlBu_r\", s=0.02, label='scene')\n", "sc = ax.scatter(strip_3_sub[:, 0], strip_3_sub[:, 1], strip_3_sub[:, 2], c=strip_3_sub[:, 2], cmap=\"RdYlBu_r\", s=0.02, label='scene')\n", "sc = ax.scatter(strip_4_sub[:, 0], strip_4_sub[:, 1], strip_4_sub[:, 2], c=strip_4_sub[:, 2], cmap=\"RdYlBu_r\", s=0.02, label='scene')\n", "\n", "# Plot of trajectory.\n", "ax.plot(traj[:,0], traj[:,1], traj[:,2], c = 'black', label = 'scanner trajectory')\n", "\n", "cax = plt.axes([0.85, 0.2, 0.025, 0.55])\n", "\n", "cbar = plt.colorbar(sc, cax=cax)\n", "\n", "cbar.set_label(\"Height ($Z$)\")\n", "\n", "# Add axis labels.\n", "ax.set_xlabel('$X$')\n", "ax.set_ylabel('$Y$')\n", "ax.set_zlabel('$Z$')\n", "\n", "\n", "set_axes_equal(ax) \n", "\n", "# Set title.\n", "ax.set_title(label='Point cloud and trajectory of scanner',fontsize=15)\n", "# Set subtitle.\n", "\n", "\n", "# Display results\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }