{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Survey\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"https://github.com/3dgeo-heidelberg/helios/blob/dev/h++.png?raw=true\")
\n",
"\n",
"This page will give an introduction on using `pyhelios` to access and modify surveys.\n",
"\n",
"`pyhelios` allows you to:\n",
"\n",
"- obtain scanning device characteristics\n",
"- calculate the length of a survey\n",
"- view and modify the scanner and platform settings at each leg"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import math\n",
"import pyhelios"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.chdir(\"..\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SimulationBuilder is building simulation ...\n",
"SimulationBuilder built simulation in 0.013959017000161111 seconds\n",
"xmlDocFilename: als_toyblocks.xml\n",
"xmlDocFilePath: data/surveys/toyblocks\n",
"xmlDocFilename: scanners_als.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",
"Scanner: riegl_vq-880g\n",
"Device[0]: riegl_vq-880g\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 'headRotatePerSecMax_deg' : 0\n",
"Number of subsampling rays (riegl_vq-880g): 19\n",
"xmlDocFilename: platforms.xml\n",
"xmlDocFilePath: data\n",
"Using default value for attribute 'y' : 0\n",
"Using default value for attribute 'x' : 0\n",
"Number of subsampling rays (riegl_vq-880g): 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 'x' : 0\n",
"Using platform default value for attribute 'y' : 0\n",
"Using platform default value for attribute 'onGround' : 0\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 default value for attribute 'name' : Unnamed platformSettings asset\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using default value for attribute 'name' : Unnamed scannerSettings asset\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Loading Scene...\n",
"Reading serial scene wrapper object from data/scenes/toyblocks/toyblocks_scene.scene ...\n",
"Building KD-Grove... \n",
"KDTree (num. primitives 1110) :\n",
"\tMax. # primitives in leaf: 34\n",
"\tMin. # primitives in leaf: 1\n",
"\tMax. depth reached: 29\n",
"\tKDTree axis-aligned surface area: 51657.7\n",
"\tInterior nodes: 3942\n",
"\tLeaf nodes: 3492\n",
"\tTotal tree cost: 6.50866\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: (0.0030, 0.0030, 0.0030, 0.0030, 0.0000)\n",
"\t\tTree primitives: (1110, 1110, 1110, 1110.0000, 0.0000)\n",
"\t\tMax primitives in leaf: (34, 34, 34, 34.0000, 0.0000)\n",
"\t\tMin primitives in leaf: (1, 1, 1, 1.0000, 0.0000)\n",
"\t\tMaximum depth: (29, 29, 29, 29.0000, 0.0000)\n",
"\t\tAxis-aligned surface area: (51657.7208, 51657.7208, 51657.7208, 51657.7208, 0.0000)\n",
"\t\tNumber of interior nodes: (3942, 3942, 3942, 3942.0000, 0.0000)\n",
"\t\tNumber of leaf nodes: (3492, 3492, 3492, 3492.0000, 0.0000)\n",
"\t\tTree cost: (6.5087, 6.5087, 6.5087, 6.5087, 0.0000)\n",
"\n",
"KDG built in 0.003s\n",
"Scene loaded in 0.01s\n",
"Reading Spectral Library...\n",
"Using default value for attribute 'seed' : AUTO\n"
]
}
],
"source": [
"pyhelios.loggingDefault()\n",
"# build simulation parameters\n",
"simBuilder = pyhelios.SimulationBuilder(\n",
" \"data/surveys/toyblocks/als_toyblocks.xml\", \"assets/\", \"output/\"\n",
")\n",
"simBuilder.setNumThreads(0)\n",
"simBuilder.setLasOutput(True)\n",
"simBuilder.setZipOutput(True)\n",
"\n",
"# build the survey\n",
"simB = simBuilder.build()"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Once we built a survey, we have numerous options to obtain and change the characteristics of all components of our simulation. Note that after the steps above, simB is a SimulationBuild object. To access the Simulation itself, we have to call simB.sim."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"toyblocks_als\n"
]
}
],
"source": [
"# obtain survey path and name\n",
"survey_path = simB.sim.getSurveyPath()\n",
"survey = simB.sim.getSurvey()\n",
"survey_name = survey.name\n",
"print(survey_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"We can also obtain the survey length, i.e. the distance through all waypoints.\n",
"If the survey has not been running yet, `survey.getLength()` will return 0.0.\n",
"We can calculate the length of a loaded survey of a simulation which was built but not started with `survey.calculateLength()`.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"survey.getLength()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"400.0\n"
]
}
],
"source": [
"survey.calculateLength()\n",
"print(survey.getLength())"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## The Scanner\n",
"\n",
"Let's have a look at the scanner we are using."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scanner: riegl_vq-880g\n",
"Device[0]: riegl_vq-880g\n",
"\tAverage Power: 4 W\n",
"\tBeam Divergence: 0.3 mrad\n",
"\tWavelength: 1064 nm\n",
"\tVisibility: 23 km\n",
"\n"
]
}
],
"source": [
"scanner = simB.sim.getScanner()\n",
"# print scanner characteristics\n",
"print(scanner.toString())"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"The scanner characteristics can also be accessed individually:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Device ID: riegl_vq-880g\n",
"\n",
"Average power: 4.0 W\n",
"Beam divergence: 0.0003 rad\n",
"Wavelength: 1064.0 nm\n",
"Scanner visibility: 23.0 km\n",
"\n"
]
}
],
"source": [
"print(\n",
" f\"\"\"\n",
"{'Device ID:' : <25}{scanner.deviceId : ^8}\n",
"\n",
"{'Average power:' : <25}{scanner.averagePower : <8} W\n",
"{'Beam divergence:' : <25}{scanner.beamDivergence : <8} rad\n",
"{'Wavelength:' : <25}{scanner.wavelength*1000000000 : <8} nm\n",
"{'Scanner visibility:' : <25}{scanner.visibility : <8} km\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"The scanner has also some more properties:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Number of subsampling rays: 19\n",
"Pulse length: 2.0 ns\n",
"Supported pulse frequencies: [150000, 300000, 600000, 900000] Hz\n",
"Maximum number of returns: unlimited\n",
"\n",
"\n"
]
}
],
"source": [
"if scanner.maxNOR == 0:\n",
" max_nor = \"unlimited\"\n",
"else:\n",
" max_nor = scanner.maxNOR\n",
"\n",
"\n",
"print(\n",
" f\"\"\"\n",
"{'Number of subsampling rays:' : <30}{scanner.numRays}\n",
"{'Pulse length:' : <30}{scanner.pulseLength_ns} ns\n",
"{'Supported pulse frequencies:' : <30}{list(scanner.getSupportedPulseFrequencies())} Hz\n",
"{'Maximum number of returns:': <30}{max_nor}\n",
"\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"We can also get information about the scanner head, e.g. the maximum rotation speed in case of TLS scanners.\n",
"\n",
"\n",
"To demonstrate this, let's load a TLS survey."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SimulationBuilder is building simulation ...\n",
"xmlDocFilename: tls_arbaro_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",
"No platform type specified. Using static platform.\n",
"Using default value for attribuSimulationBuilder built simulation in 1.4786146420001387 seconds\n",
"te 'winSize_ns' : 1.25\n",
"Using default value for attribute 'maxFullwaveRange_ns' : 0\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 'z' : 0\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 platform default value for attribute 'movePerSec_m' : 70\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0\n",
"Using default value for attribute 'name' : Unnamed scannerSettings asset\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 100000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 120\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 'z' : 0\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 platform default value for attribute 'movePerSec_m' : 70\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 100000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 120\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Loading Scene...\n",
"Reading serial scene wrapper object from data/scenes/demo/arbaro_demo.scene ...\n",
"Building KD-Grove... \n",
"KDTree (num. primitives 226860) :\n",
"\tMax. # primitives in leaf: 77\n",
"\tMin. # primitives in leaf: 1\n",
"\tMax. depth reached: 39\n",
"\tKDTree axis-aligned surface area: 91156.3\n",
"\tInterior nodes: 420895\n",
"\tLeaf nodes: 350025\n",
"\tTotal tree cost: 6.16344\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: (0.3850, 0.3850, 0.3850, 0.3850, 0.0000)\n",
"\t\tTree primitives: (226860, 226860, 226860, 226860.0000, 0.0000)\n",
"\t\tMax primitives in leaf: (77, 77, 77, 77.0000, 0.0000)\n",
"\t\tMin primitives in leaf: (1, 1, 1, 1.0000, 0.0000)\n",
"\t\tMaximum depth: (39, 39, 39, 39.0000, 0.0000)\n",
"\t\tAxis-aligned surface area: (91156.3160, 91156.3160, 91156.3160, 91156.3160, 0.0000)\n",
"\t\tNumber of interior nodes: (420895, 420895, 420895, 420895.0000, 0.0000)\n",
"\t\tNumber of leaf nodes: (350025, 350025, 350025, 350025.0000, 0.0000)\n",
"\t\tTree cost: (6.1634, 6.1634, 6.1634, 6.1634, 0.0000)\n",
"\n",
"KDG built in 0.387s\n",
"Scene loaded in 1.463s\n",
"Reading Spectral Library...\n",
"Using default value for attribute 'seed' : AUTO\n"
]
}
],
"source": [
"pyhelios.loggingDefault()\n",
"# build simulation parameters\n",
"simBuilder = pyhelios.SimulationBuilder(\n",
" \"data/surveys/demo/tls_arbaro_demo.xml\", \"assets/\", \"output/\"\n",
")\n",
"simBuilder.setNumThreads(0)\n",
"simBuilder.setLasOutput(True)\n",
"simBuilder.setZipOutput(True)\n",
"\n",
"# build the survey\n",
"simB = simBuilder.build()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
}
],
"source": [
"scanner = simB.sim.getScanner()\n",
"print(scanner.toString())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Max. rotation speed: 60 degrees per second\n",
"\n"
]
}
],
"source": [
"head = scanner.getScannerHead()\n",
"# get scanner rotation speed and range\n",
"print(\n",
" f\"\"\"\n",
"Max. rotation speed: {round(head.rotatePerSecMax * 180 / math.pi)} degrees per second\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"If we want to obtain information about the scanning mechanism, we have to get the beam deflector."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Scanner deflector type: POLYGON_MIRROR\n",
"Scan frequency range: 3.0 - 120.0 Hz\n",
"Scan angle range: 120° FOV\n",
"\n"
]
}
],
"source": [
"deflector = scanner.getBeamDeflector()\n",
"print(\n",
" f\"\"\"\n",
"{'Scanner deflector type:': <25}{deflector.getOpticsType() : <8}\n",
"{'Scan frequency range:' : <25}{deflector.scanFreqMin} - {deflector.scanFreqMax} Hz\n",
"{'Scan angle range:' : <25}{round(deflector.scanAngleMax * 180 / math.pi)}° FOV\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"From the beam detector, we get information about, e.g., the accuracy of the scanner."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy: 0.005 m\n",
"Minimum range: 1.5 m\n",
"Maximum range: 1.7976931348623157e+308 m\n",
"\n"
]
}
],
"source": [
"detector = scanner.getDetector()\n",
"print(\n",
" f\"\"\"\n",
"{'Accuracy:' : <20}{detector.accuracy} m\n",
"{'Minimum range:' : <20}{detector.rangeMin} m\n",
"{'Maximum range:': <20}{detector.rangeMax} m\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"We can also get the scanner full waveform settings.\n",
"Like many of the scanner settings, they can be overwritten in the `scannerSettings` of a leg."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full waveform settings for riegl_vz400\n",
"Bin size: 0.2 ns\n",
"Window size: 1.25 ns\n",
"Beam sample quality: 3\n",
"\n"
]
}
],
"source": [
"print(\n",
" f\"\"\"Full waveform settings for {scanner.deviceId}\n",
"{'Bin size:' : <25}{scanner.fwfSettings.binSize_ns} ns\n",
"{'Window size:' : <25}{scanner.fwfSettings.winSize_ns} ns\n",
"{'Beam sample quality:' : <25}{scanner.fwfSettings.beamSampleQuality}\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Legs\n",
"\n",
"Each leg of a survey has scanner settings and platform settings, (cf. survey XML file),\n",
"which can be accessed and changed with `pyhelios`.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Scanner is active: True\n",
"Pulse frequency: 100000 Hz\n",
"Scan angle: 100.0°\n",
"Minimum vertical angle: -40.0°\n",
"Maximum vertical angle: +60.0°\n",
"Scan frequency: 120.0 Hz\n",
"Beam divergence: 0.3 mrad\n",
"Trajectory time interval: 1.0 s\n",
"Start angle of head rotation: 100.0°\n",
"Start angle of head rotation: 225.0°\n",
"Rotation speed: 10.0° per s\n",
"\n"
]
}
],
"source": [
"# get the first leg\n",
"leg = simB.sim.getLeg(0)\n",
"\n",
"# scanner settings\n",
"print(\n",
" f\"\"\"\n",
"{'Scanner is active:' : <30}{leg.getScannerSettings().active}\n",
"{'Pulse frequency:' : <30}{leg.getScannerSettings().pulseFreq} Hz\n",
"{'Scan angle:' : <30}{leg.getScannerSettings().scanAngle * 180 / math.pi}°\n",
"{'Minimum vertical angle:' : <30}{leg.getScannerSettings().verticalAngleMin * 180 / math.pi:+.1f}°\n",
"{'Maximum vertical angle:' : <30}{round(leg.getScannerSettings().verticalAngleMax * 180 / math.pi):+.1f}°\n",
"{'Scan frequency:' : <30}{leg.getScannerSettings().scanFreq} Hz\n",
"{'Beam divergence:' : <30}{leg.getScannerSettings().beamDivAngle * 1000} mrad\n",
"{'Trajectory time interval:' : <30}{leg.getScannerSettings().trajectoryTimeInterval} s\n",
"{'Start angle of head rotation:' : <30}{leg.getScannerSettings().headRotateStart * 180 / math.pi}°\n",
"{'Start angle of head rotation:' : <30}{leg.getScannerSettings().headRotateStop * 180 / math.pi}°\n",
"{'Rotation speed:' : <30}{leg.getScannerSettings().headRotatePerSec * 180 / math.pi}° per s\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Scanner Settings and platform settings may be defined through a template.\n",
"For this, let's first switch back to the ALS toyblocks demo."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SimulationBuilder is building simulation ...\n",
"SimulationBuilder built simulation in 0.016869478999979037 seconds\n",
"xmlDocFilename: als_toyblocks.xml\n",
"xmlDocFilePath: data/surveys/toyblocks\n",
"xmlDocFilename: scanners_als.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",
"Scanner: riegl_vq-880g\n",
"Device[0]: riegl_vq-880g\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 'headRotatePerSecMax_deg' : 0\n",
"Number of subsampling rays (riegl_vq-880g): 19\n",
"xmlDocFilename: platforms.xml\n",
"xmlDocFilePath: data\n",
"Using default value for attribute 'y' : 0\n",
"Using default value for attribute 'x' : 0\n",
"Number of subsampling rays (riegl_vq-880g): 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 'x' : 0\n",
"Using platform default value for attribute 'y' : 0\n",
"Using platform default value for attribute 'onGround' : 0\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 default value for attribute 'name' : Unnamed platformSettings asset\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using default value for attribute 'name' : Unnamed scannerSettings asset\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'active' : 1\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Using default value for attribute 'stripId' : NULL_STRIP_ID\n",
"Using platform default value for attribute 'z' : 100\n",
"Using platform default value for attribute 'onGround' : 0\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 platform default value for attribute 'movePerSec_m' : 30\n",
"Using scanner default value for attribute 'pulseFreq_hz' : 300000\n",
"Using scanner default value for attribute 'scanFreq_hz' : 200\n",
"Using scanner default value for attribute 'beamDivergence_rad' : 0.003\n",
"Using scanner default value for attribute 'trajectoryTimeInterval_s' : 0.01\n",
"Loading Scene...\n",
"Reading serial scene wrapper object from data/scenes/toyblocks/toyblocks_scene.scene ...\n",
"Building KD-Grove... \n",
"KDTree (num. primitives 1110) :\n",
"\tMax. # primitives in leaf: 34\n",
"\tMin. # primitives in leaf: 1\n",
"\tMax. depth reached: 29\n",
"\tKDTree axis-aligned surface area: 51657.7\n",
"\tInterior nodes: 3921\n",
"\tLeaf nodes: 3475\n",
"\tTotal tree cost: 6.50882\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: (0.0040, 0.0040, 0.0040, 0.0040, 0.0000)\n",
"\t\tTree primitives: (1110, 1110, 1110, 1110.0000, 0.0000)\n",
"\t\tMax primitives in leaf: (34, 34, 34, 34.0000, 0.0000)\n",
"\t\tMin primitives in leaf: (1, 1, 1, 1.0000, 0.0000)\n",
"\t\tMaximum depth: (29, 29, 29, 29.0000, 0.0000)\n",
"\t\tAxis-aligned surface area: (51657.7208, 51657.7208, 51657.7208, 51657.7208, 0.0000)\n",
"\t\tNumber of interior nodes: (3921, 3921, 3921, 3921.0000, 0.0000)\n",
"\t\tNumber of leaf nodes: (3475, 3475, 3475, 3475.0000, 0.0000)\n",
"\t\tTree cost: (6.5088, 6.5088, 6.5088, 6.5088, 0.0000)\n",
"\n",
"KDG built in 0.004s\n",
"Scene loaded in 0.011s\n",
"Reading Spectral Library...\n",
"Using default value for attribute 'seed' : AUTO\n"
]
}
],
"source": [
"simBuilder = pyhelios.SimulationBuilder(\n",
" \"data/surveys/toyblocks/als_toyblocks.xml\", \"assets/\", \"output/\"\n",
")\n",
"simBuilder.setNumThreads(0)\n",
"simBuilder.setLasOutput(True)\n",
"simBuilder.setZipOutput(True)\n",
"simB = simBuilder.build()"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"The template can be accessed for a given `ScannerSettings` or `PlatformSettings` instance:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Scanner template name: scanner1\n",
" Pulse frequency: 300.0 kHz\n",
" \n"
]
}
],
"source": [
"leg = simB.sim.getLeg(0)\n",
"\n",
"ss = leg.getScannerSettings()\n",
"if ss.hasTemplate():\n",
" ss_tmpl = ss.getTemplate()\n",
" print(\n",
" f\"\"\"\n",
" Scanner template name: {ss_tmpl.id}\n",
" Pulse frequency: {ss_tmpl.pulseFreq/1000} kHz\n",
" \"\"\"\n",
" ) # Print the pulse frequency defined in the template"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Platform template name: platform1\n",
" Speed: 30.0 m/s\n",
" Altitude: 100.0 m\n",
" \n"
]
}
],
"source": [
"ps = leg.getPlatformSettings()\n",
"if ps.hasTemplate():\n",
" ps_tmpl = ps.getTemplate()\n",
" print(\n",
" f\"\"\"\n",
" {'Platform template name:' : <25}{ps_tmpl.id}\n",
" {'Speed:' : <15}{ps_tmpl.movePerSec} m/s\n",
" {'Altitude:' : <15}{ps_tmpl.z} m\n",
" \"\"\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"We can also change the template."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New altitude: 120.0 m\n"
]
}
],
"source": [
"ps_tmpl.z += 20\n",
"print(f\"New altitude: {ps_tmpl.z} m\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"We can also obtain the position at the current leg:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"On ground? False\n",
"Position: (20.0, 20.0, 100.233912)\n",
"\n"
]
}
],
"source": [
"print(\n",
" f\"\"\"\n",
"On ground? {leg.getPlatformSettings().onGround}\n",
"Position: ({leg.getPlatformSettings().x}, {leg.getPlatformSettings().y}, {leg.getPlatformSettings().z})\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"If we compare the position here to the position in the XML survey file, we notice that they do not match.\n",
"The difference is 50 in x direction and 70 in y direction.\n",
"\n",
"When loading a survey, **a shift is applied to the scene and to each leg**. We can obtain this shift:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shift = (-50.0,-70.0,-0.233912)\n"
]
}
],
"source": [
"scene = simB.sim.getScene()\n",
"shift = scene.getShift()\n",
"print(f\"Shift = ({shift.x},{shift.y},{shift.z})\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Using a for-loop, we can get the positions of all legs.\n",
"Note that we add the shift to obtain the true coordinates as specified in the XML-file:\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Leg 0\tposition = -30.0,-50.0,100.0\tactive = True\n",
"Leg 1\tposition = 70.0,-50.0,100.0\tactive = False\n",
"Leg 2\tposition = 70.0,0.0,100.0\tactive = True\n",
"Leg 3\tposition = -30.0,0.0,100.0\tactive = False\n",
"Leg 4\tposition = -30.0,50.0,100.0\tactive = True\n",
"Leg 5\tposition = 70.0,50.0,100.0\tactive = False\n"
]
}
],
"source": [
"for i in range(simB.sim.getNumLegs()):\n",
" leg = simB.sim.getLeg(i)\n",
" print(\n",
" f\"Leg {i}\\tposition = \"\n",
" f\"{leg.getPlatformSettings().x+shift.x},\"\n",
" f\"{leg.getPlatformSettings().y+shift.y},\"\n",
" f\"{leg.getPlatformSettings().z+shift.z}\\t\"\n",
" f\"active = {leg.getScannerSettings().active}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"We can also use a for-loop to create new legs.\n",
"Here an example, where we initiate a simulation with a survey with no legs (`data/surveys/default_survey.xml`) and\n",
"then create the legs with Python."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SimulationBuilder is building simulation ...\n",
"SimulationBuilder built simulation in 0.0359427469993534 seconds\n",
"xmlDocFilename: default_survey.xml\n",
"xmlDocFilePath: data/surveys\n",
"xmlDocFilename: scanners_als.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_vux-1uav\n",
"Device[0]: riegl_vux-1uav\n",
"\tAverage Power: 4 W\n",
"\tBeam Divergence: 0.5 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 'headRotatePerSecMax_deg' : 0\n",
"Number of subsampling rays (riegl_vux-1uav): 19\n",
"xmlDocFilename: platforms.xml\n",
"xmlDocFilePath: data\n",
"Number of subsampling rays (riegl_vux-1uav): 19\n",
"Using default value for attribute 'numRuns' : 1\n",
"Using default value for attribute 'simSpeed' : 1\n",
"Loading Scene...\n",
"xmlDocFilename: toyblocks_scene.xml\n",
"xmlDocFilePath: data/scenes/toyblocks\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/toyblocks/cube.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/toyblocks/cube.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/toyblocks/sphere.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/toyblocks/cylinder.obj\".\n",
"Set up axis explicitly to silence this warning.\n",
"C++ Exception: boost::bad_get: failed value get using boost::get\n",
"5 sceneparts loaded in 0.017s\n",
"\n",
"CRS bounding box (by vertices): Min: dvec3(-50.000000, -70.000000, -0.233912), Max: dvec3(90.000000, 70.000000, 22.012018)\n",
"Shift: dvec3(-50.000000, -70.000000, -0.233912)\n",
"# vertices to translate: 3330\n",
"Actual bounding box (by vertices): Min: dvec3(0.000000, 0.000000, 0.000000), Max: dvec3(140.000000, 140.000000, 22.245930)\n",
"Writing serial scene wrapper object to data/scenes/toyblocks/toyblocks_scene.scene ...\n",
"Building KD-Grove... \n",
"KDTree (num. primitives 1110) :\n",
"\tMax. # primitives in leaf: 34\n",
"\tMin. # primitives in leaf: 1\n",
"\tMax. depth reached: 29\n",
"\tKDTree axis-aligned surface area: 51657.7\n",
"\tInterior nodes: 3905\n",
"\tLeaf nodes: 3459\n",
"\tTotal tree cost: 6.50873\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: (0.0030, 0.0030, 0.0030, 0.0030, 0.0000)\n",
"\t\tTree primitives: (1110, 1110, 1110, 1110.0000, 0.0000)\n",
"\t\tMax primitives in leaf: (34, 34, 34, 34.0000, 0.0000)\n",
"\t\tMin primitives in leaf: (1, 1, 1, 1.0000, 0.0000)\n",
"\t\tMaximum depth: (29, 29, 29, 29.0000, 0.0000)\n",
"\t\tAxis-aligned surface area: (51657.7208, 51657.7208, 51657.7208, 51657.7208, 0.0000)\n",
"\t\tNumber of interior nodes: (3905, 3905, 3905, 3905.0000, 0.0000)\n",
"\t\tNumber of leaf nodes: (3459, 3459, 3459, 3459.0000, 0.0000)\n",
"\t\tTree cost: (6.5087, 6.5087, 6.5087, 6.5087, 0.0000)\n",
"\n",
"KDG built in 0.003s\n",
"Scene loaded in 0.032s\n",
"Reading Spectral Library...\n",
"Using default value for attribute 'seed' : AUTO\n"
]
}
],
"source": [
"pyhelios.loggingDefault()\n",
"default_survey_path = \"data/surveys/default_survey.xml\"\n",
"\n",
"# default survey with the toyblocks scene (missing platform and scanner definition and not containing any legs)\n",
"survey = \"\"\"\n",
"\n",
"\n",
" \n",
" \n",
"\n",
"\"\"\"\n",
"\n",
"with open(default_survey_path, \"w\") as f:\n",
" f.write(survey)\n",
"\n",
"simBuilder = pyhelios.SimulationBuilder(default_survey_path, \"assets/\", \"output/\")\n",
"simBuilder.setCallbackFrequency(10)\n",
"simBuilder.setLasOutput(True)\n",
"simBuilder.setZipOutput(True)\n",
"simBuilder.setRebuildScene(True)\n",
"\n",
"simB = simBuilder.build()\n",
"\n",
"waypoints = [\n",
" [100.0, -100.0],\n",
" [-100.0, -100.0],\n",
" [-100.0, -50.0],\n",
" [100.0, -50.0],\n",
" [100.0, 0.0],\n",
" [-100.0, 0.0],\n",
" [-100.0, 50.0],\n",
" [100.0, 50.0],\n",
" [100.0, 100.0],\n",
" [-100.0, 100.0],\n",
"]\n",
"altitude = 100\n",
"speed = 150\n",
"pulse_freq = 300_000\n",
"scan_freq = 200\n",
"scan_angle = 37.5 / 180 * math.pi # convert to rad\n",
"shift = simB.sim.getScene().getShift()\n",
"for j, wp in enumerate(waypoints):\n",
" leg = simB.sim.newLeg(j)\n",
" leg.serialId = j # assigning a serialId is important!\n",
" leg.getPlatformSettings().x = wp[0] - shift.x # don't forget to apply the shift!\n",
" leg.getPlatformSettings().y = wp[1] - shift.y\n",
" leg.getPlatformSettings().z = altitude - shift.z\n",
" leg.getPlatformSettings().movePerSec = speed\n",
" leg.getScannerSettings().pulseFreq = pulse_freq\n",
" leg.getScannerSettings().scanFreq = scan_freq\n",
" leg.getScannerSettings().scanAngle = scan_angle\n",
" leg.getScannerSettings().trajectoryTimeInterval = (\n",
" 0.05 # important to get a trajectory output\n",
" )\n",
" if j % 2 != 0:\n",
" leg.getScannerSettings().active = False"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Let's execute this survey!"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulation is started!\n",
"Simulation is running since 0 min and 0 sec. Please wait.Output directory: \"output//some_survey/2024-02-13_11-07-21/\"\n",
"Simulation: Scanner changed!\n",
"Starting leg 0\n",
"\n",
"Pulse frequency set to 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Leg0 waypoints:\n",
"\tOrigin: (150, -30, 100.234)\n",
"\tTarget: (-50, -30, 100.234)\n",
"\tNext: (-50, 20, 100.234)\n",
"\n",
"Clearing point cloud: \"output//some_survey/2024-02-13_11-07-21/leg000_points.laz\"\n",
"Survey 0.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 0.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 0.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 0.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 0.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 1.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 1.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 1.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 1.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 1.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 2.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 2.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 2.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 2.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 2.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 3.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 3.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 3.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 3.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 3.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 4.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 4.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 4.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 4.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 4.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 5.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 5.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 5.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 5.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 5.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 6.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 6.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 6.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 6.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 6.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 7.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 7.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 7.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 7.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 7.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 8.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 8.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 8.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 8.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 8.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 9.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 9.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 9.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 9.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 9.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 10.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 10.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 10.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 10.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 10.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 11.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 11.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 11.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 11.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 11.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 12.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 12.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 12.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 12.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 12.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 13.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 13.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 13.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 13.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 13.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 14.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 14.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 14.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 14.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 14.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 15.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 15.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 15.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 15.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 15.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg1/9 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 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Iterative mode was used for manual leg initialization because default one failed for MovingPlatform\n",
"Leg1 waypoints:\n",
"\tOrigin: (-50, -30, 100.234)\n",
"\tTarget: (-50, 20, 100.234)\n",
"\tNext: (150, 20, 100.234)\n",
"\n",
"Survey 16.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 16.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 17.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 18.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 19.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 20.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg2/9 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 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Iterative mode was used for manual leg initialization because default one failed for MovingPlatform\n",
"Leg2 waypoints:\n",
"\tOrigin: (-50, 20, 100.234)\n",
"\tTarget: (150, 20, 100.234)\n",
"\tNext: (150, 70, 100.234)\n",
"\n",
"Clearing point cloud: \"output//some_survey/2024-02-13_11-07-21/leg002_points.laz\"\n",
"Survey 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 21.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 21.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 21.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 21.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 21.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 22.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 22.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 22.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 22.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 22.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 23.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 23.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 23.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 23.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 23.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 24.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 24.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 24.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 24.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 24.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 25.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 25.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 25.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 25.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 25.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 26.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 26.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 26.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 26.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 26.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 27.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 27.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 27.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 27.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 27.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 28.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 28.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 28.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 28.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 28.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 29.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 29.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 29.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 29.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 29.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 30.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 30.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 30.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 30.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 30.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 31.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 31.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 31.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 31.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 31.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 32.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 32.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 32.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 32.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 32.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 33.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 33.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 33.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 33.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 33.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 34.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 34.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 34.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 34.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 34.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 35.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 35.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 35.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 35.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 35.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 36.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 36.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 36.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 36.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 36.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg3/9 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 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Leg3 waypoints:\n",
"\tOrigin: (150, 20, 100.234)\n",
"\tTarget: (150, 70, 100.234)\n",
"\tNext: (-50, 70, 100.234)\n",
"\n",
"Survey 37.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 37.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 38.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 39.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 40.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 41.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg4/9 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 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Leg4 waypoints:\n",
"\tOrigin: (150, 70, 100.234)\n",
"\tTarget: (-50, 70, 100.234)\n",
"\tNext: (-50, 120, 100.234)\n",
"\n",
"Clearing point cloud: \"output//some_survey/2024-02-13_11-07-21/leg004_points.laz\"\n",
"Survey 41.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 42.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 42.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 42.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 42.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 42.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 43.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 43.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 43.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 43.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 43.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 44.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 44.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 44.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 44.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 44.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 45.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 45.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 45.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 45.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 45.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 46.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 46.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 46.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 46.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 46.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 47.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 47.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 47.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 47.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 47.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 48.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 48.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 48.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 48.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 48.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 49.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 49.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 49.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 49.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 49.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 50.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 50.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 50.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 50.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 50.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 51.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 51.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 51.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 51.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 51.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 52.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 52.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 52.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 52.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 52.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 53.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 53.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 53.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 53.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 53.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 54.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 54.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 54.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 54.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 54.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 55.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 55.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 55.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 55.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 55.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 56.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 56.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 56.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 56.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 56.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 57.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 57.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 57.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 57.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 57.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg5/9 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Waypoint reached!\n",
"Starting leg 5\n",
"\n",
"Pulse frequency set to 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Iterative mode was used for manual leg initialization because default one failed for MovingPlatform\n",
"Leg5 waypoints:\n",
"\tOrigin: (-50, 70, 100.234)\n",
"\tTarget: (-50, 120, 100.234)\n",
"\tNext: (150, 120, 100.234)\n",
"\n",
"Survey 58.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 58.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 59.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 60.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 61.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg6/9 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Waypoint reached!\n",
"Starting leg 6\n",
"\n",
"Pulse frequency set to 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Iterative mode was used for manual leg initialization because default one failed for MovingPlatform\n",
"Leg6 waypoints:\n",
"\tOrigin: (-50, 120, 100.234)\n",
"\tTarget: (150, 120, 100.234)\n",
"\tNext: (150, 170, 100.234)\n",
"\n",
"Clearing point cloud: \"output//some_survey/2024-02-13_11-07-21/leg006_points.laz\"\n",
"Survey 62.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 62.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 63.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 63.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 63.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 63.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 63.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 64.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 64.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 64.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 64.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 64.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 65.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 65.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 65.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 65.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 65.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 66.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 66.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 66.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 66.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 66.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 67.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 67.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 67.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 67.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 67.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 68.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 68.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 68.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 68.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 68.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 69.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 69.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 69.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 69.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 69.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 70.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 70.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 70.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 70.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 70.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 71.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 71.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 71.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 71.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 71.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 72.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 72.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 72.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 72.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 72.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 73.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 73.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 73.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 73.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 73.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 74.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 74.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 74.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 74.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 74.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 75.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 75.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 75.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 75.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 75.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 76.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 76.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 76.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 76.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 76.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 77.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 77.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 77.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 77.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 77.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 78.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 78.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 78.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 78.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 78.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg7/9 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Waypoint reached!\n",
"Starting leg 7\n",
"\n",
"Pulse frequency set to 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Leg7 waypoints:\n",
"\tOrigin: (150, 120, 100.234)\n",
"\tTarget: (150, 170, 100.234)\n",
"\tNext: (-50, 170, 100.234)\n",
"\n",
"Survey 79.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 79.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 80.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 81.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.38%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.42%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.46%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.54%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.58%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.63%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.71%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.75%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.79%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.88%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.92%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 82.96%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.04%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.08%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.13%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.21%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.25%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.29%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg8/9 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Waypoint reached!\n",
"Starting leg 8\n",
"\n",
"Pulse frequency set to 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Leg8 waypoints:\n",
"\tOrigin: (150, 170, 100.234)\n",
"\tTarget: (-50, 170, 100.234)\n",
"\tNext: (-50, 170, 100.234)\n",
"\n",
"Clearing point cloud: \"output//some_survey/2024-02-13_11-07-21/leg008_points.laz\"\n",
"Survey 83.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 1.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 2.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 83.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 3.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 4.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 84.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 5.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 84.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 6.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 84.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 7.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 84.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 8.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 84.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 9.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 10.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 85.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 11.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 85.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 12.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 85.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 13.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 85.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 14.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 85.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 15.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 16.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 86.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 17.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 86.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 18.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 86.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 19.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 86.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 20.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 86.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 21.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 22.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 87.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 23.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 87.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 24.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 87.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 25.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 87.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 26.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 87.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 27.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 28.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 88.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 29.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 88.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 30.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 88.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 31.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 88.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 32.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 88.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 33.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 34.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 89.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 35.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 89.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 36.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 89.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 37.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 89.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 38.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 89.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 39.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 40.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 90.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 41.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 90.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 42.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 90.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 43.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 90.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 44.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 90.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 45.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 46.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 91.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 47.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 91.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 48.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 91.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 49.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 91.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 50.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 91.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 51.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 52.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 92.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 53.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 92.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 54.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 92.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 55.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 92.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 56.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 92.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 57.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 58.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 93.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 59.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 93.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 60.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 93.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 61.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 93.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 62.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 93.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 63.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 64.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 94.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 65.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 94.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 66.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 94.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 67.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 94.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 68.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 94.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 69.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 70.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 95.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 71.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 95.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 72.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 95.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 73.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 95.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 74.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 95.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 75.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 76.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 96.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 77.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 96.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 78.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 96.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 79.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 96.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 80.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 96.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 81.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 82.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 97.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 83.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 97.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 84.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 97.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 85.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 97.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 86.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 97.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 87.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 88.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 98.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 89.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 98.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 90.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 98.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 91.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 98.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 92.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 98.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 93.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 94.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 99.17%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 95.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 99.33%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 96.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 99.50%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 97.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 99.67%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 98.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Survey 99.83%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Leg9/9 99.00%\tElapsed 00 00:00:00 Remaining 00 00:00:00\n",
"Waypoint reached!\n",
"Starting leg 9\n",
"\n",
"Pulse frequency set to 300000\n",
"Scan angle set to 37.5\n",
"Applying settings for PolygonMirrorBeamDeflector...\n",
"Vertical angle min/max nan/nan\n",
" -- verticalAngleMin not set, using the value of -37.5 degrees\n",
" -- verticalAngleMax not set, using the value of 37.5 degrees\n",
"Waypoint reached!\n",
"Elapsed simulation steps = 2400016\n",
"Elapsed virtual time = 8.00005 sec.\n",
"Main thread simulation loop finished in 0.737226 sec.\n",
"Waiting for completion of pulse computation tasks...\n",
"Pulse computation tasks finished in 0.737332 sec.\n",
"\n",
"Simulation has finished.\n"
]
}
],
"source": [
"import time\n",
"\n",
"start_time = time.time()\n",
"simB.start()\n",
"\n",
"if simB.isStarted():\n",
" print(\"Simulation is started!\")\n",
"\n",
"while simB.isRunning():\n",
" duration = time.time() - start_time\n",
" mins = duration // 60\n",
" secs = duration % 60\n",
" print(\n",
" \"\\r\"\n",
" + \"Simulation is running since {} min and {} sec. Please wait.\".format(\n",
" int(mins), int(secs)\n",
" ),\n",
" end=\"\",\n",
" )\n",
" time.sleep(1)\n",
"\n",
"output = simB.join()\n",
"print(\"\\nSimulation has finished.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us also quickly visualize the output. We load the points into numpy arrays using the function `outputToNumpy` and then visualize the point cloud with matplotlib as a simple top view with points coloured by the `hitObjectId`."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"pc, trajectory = pyhelios.outputToNumpy(output)\n",
"\n",
"# Matplotlib figure.\n",
"fig = plt.figure(figsize=(5, 5))\n",
"# Axes3d axis onto mpl figure.\n",
"ax = fig.add_subplot()\n",
"\n",
"# Scatter plot of original and simulated points in different colors\n",
"ax.scatter(pc[:, 0], pc[:, 1], c=pc[:, 14], s=0.01)\n",
"\n",
"# Add axis labels.\n",
"ax.set_xlabel(\"$X$\")\n",
"ax.set_ylabel(\"$Y$\")\n",
"ax.axis(\"equal\")\n",
"\n",
"# Set title.\n",
"from textwrap import wrap\n",
"\n",
"title = ax.set_title(\n",
" \"\\n\".join(\n",
" wrap(\n",
" \"Top view of the simulated point cloud, coloured by \" + r\"$hitObjectId$\", 40\n",
" )\n",
" )\n",
")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": []
}
],
"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": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
}