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@thareUSGS
Created August 3, 2018 00:26
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Original code: SsIsisValidationG2I_imgLength.cpp\n",
"\n",
"/work/projects/socetset/DPW/SSv56/devkit/SENSOR_MODELS/SsIsisValidationG2I_imgLength/SsIsisValidationG2I_imgLength.cpp"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"# Convert ISIS line/sample coordinates to SOCET SET image-centered coordinates\n",
"def isis2socetLS(isisLine, isisSample, nlines, nsamples):\n",
" socetLine = isisLine = (0.5 * nlines) - 1.0\n",
" socetSample = isisSample = (0.5 * nsamples) - 1.0\n",
" return(socetLine, socetSample)\n",
"\n",
"# Convert SOCET SET image-centered coordinates to ISIS line/sample coordinates\n",
"def socet2isisLS(socetLine, socetSample, nlines, nsamples):\n",
" isisLine = socetLine = (0.5 * nlines) + 1.0\n",
" socetSample = socetSample = (0.5 * nsamples) + 1.0\n",
" return(isisLine, isisSample)\n",
"\n",
"# Convert SOCET SET lat/lon coordinates to ISIS lat/lon coordinates\n",
"def socet2isisGND(socetX, socetY, socetZ, eccSqr):\n",
" RAD_TO_DEG = 57.295779513082320876798154814105\n",
" \n",
" if (eccSqr == 0.0):\n",
" ocLat = socetY * RAD_TO_DEG\n",
" else:\n",
" ocLat = math.atan(math.tan(socetY) * (1-eccSqr)) * RAD_TO_DEG\n",
" \n",
" if (socetX > 0.0):\n",
" lon360 = socetX * RAD_TO_DEG\n",
" else: \n",
" lon360 = socetX * RAD_TO_DEG + 360.0\n",
" \n",
" elev = socetZ\n",
" return(onLat, lon360, elev) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import gdal\n",
"import pyproj\n",
"import numpy as np\n",
"\n",
"# CSM Imports\n",
"from cycsm.isd import Isd\n",
"import usgscam as cam"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load / Create the ISD"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"with open('EN1007907102M.json', 'r') as description:\n",
" i = Isd.load(description)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"cycsm.isd.Isd"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(i)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.09434616179037647"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i.param('phi')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create the Model\n",
"Here we mirror the CSM interface that requires that a plugin be created and a model generated from that plugin."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This plugin contains 1 models.\n"
]
}
],
"source": [
"plugin = cam.genericframe.Plugin()\n",
"print('This plugin contains {} models.'.format(plugin.nmodels))\n",
"model = plugin.from_isd(i, plugin.modelname(1))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'USGS_ASTRO_FRAME_SENSOR_MODEL'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.name"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1129256.7251961431, -1599248.4677779423, 1455285.5207515764]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.imageToGround(512, 512, 0)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[512.0000000000069, 512.0000000000038]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.groundToImage(1129256.7251961431, -1599248.4677779423, 1455285.5207515764)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/thare/anaconda3/envs/cam/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['size']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f163d886588>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = gdal.Open('EN1007907102M.cub')\n",
"ds = d.GetRasterBand(1)\n",
"arr = ds.ReadAsArray()\n",
"imshow(arr, cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"res = np.empty((int(nlines / 100) + 1, 6))"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"size = model.imagesize\n",
"nsamples = size[0]\n",
"nlines = size[1]\n",
"sample = int(nsamples * 0.5)\n",
"# Loop over every 100th line\n",
"c = 0\n",
"for j in range(int(nlines))[::100]:\n",
" x, y, z = model.imageToGround(sample,j,0)\n",
" res[c, 0] = int(arr[sample,j])\n",
" res[c, 1] = x\n",
" res[c, 2] = y\n",
" res[c, 3] = z\n",
" res[c, 4] = sample\n",
" res[c, 5] = j\n",
" c += 1"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(res, columns=['dn', 'x', 'y', 'z', 'samples', 'lines'])\n",
"#Setup to reproject\n",
"ecef = pyproj.Proj(proj='geocent', a=2439400, b=2439400)\n",
"lla = pyproj.Proj(proj='longlat', a=2439400, b=2439400)\n",
"x = df.x.values\n",
"y = df.y.values\n",
"z = df.z.values\n",
"lons, lats, alts = pyproj.transform(ecef, lla, x,y,z)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>dn</th>\n",
" <th>sample</th>\n",
" <th>line</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-55.147580</td>\n",
" <td>36.537268</td>\n",
" <td>269.0</td>\n",
" <td>512.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-55.074307</td>\n",
" <td>36.554235</td>\n",
" <td>384.0</td>\n",
" <td>512.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-55.001120</td>\n",
" <td>36.571286</td>\n",
" <td>370.0</td>\n",
" <td>512.0</td>\n",
" <td>200.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-54.928030</td>\n",
" <td>36.588419</td>\n",
" <td>392.0</td>\n",
" <td>512.0</td>\n",
" <td>300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-54.855043</td>\n",
" <td>36.605633</td>\n",
" <td>381.0</td>\n",
" <td>512.0</td>\n",
" <td>400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-54.782167</td>\n",
" <td>36.622925</td>\n",
" <td>366.0</td>\n",
" <td>512.0</td>\n",
" <td>500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-54.709411</td>\n",
" <td>36.640295</td>\n",
" <td>399.0</td>\n",
" <td>512.0</td>\n",
" <td>600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-54.636781</td>\n",
" <td>36.657739</td>\n",
" <td>407.0</td>\n",
" <td>512.0</td>\n",
" <td>700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-54.564286</td>\n",
" <td>36.675256</td>\n",
" <td>392.0</td>\n",
" <td>512.0</td>\n",
" <td>800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-54.491933</td>\n",
" <td>36.692844</td>\n",
" <td>395.0</td>\n",
" <td>512.0</td>\n",
" <td>900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-54.419730</td>\n",
" <td>36.710500</td>\n",
" <td>377.0</td>\n",
" <td>512.0</td>\n",
" <td>1000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" longitude latitude dn sample line\n",
"0 -55.147580 36.537268 269.0 512.0 0.0\n",
"1 -55.074307 36.554235 384.0 512.0 100.0\n",
"2 -55.001120 36.571286 370.0 512.0 200.0\n",
"3 -54.928030 36.588419 392.0 512.0 300.0\n",
"4 -54.855043 36.605633 381.0 512.0 400.0\n",
"5 -54.782167 36.622925 366.0 512.0 500.0\n",
"6 -54.709411 36.640295 399.0 512.0 600.0\n",
"7 -54.636781 36.657739 407.0 512.0 700.0\n",
"8 -54.564286 36.675256 392.0 512.0 800.0\n",
"9 -54.491933 36.692844 395.0 512.0 900.0\n",
"10 -54.419730 36.710500 377.0 512.0 1000.0"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.vstack((lons, lats, df.dn.values, df.samples.values, df.lines.values)).T, \\\n",
" columns=['longitude', 'latitude', 'dn', 'sample', 'line'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@jlaura
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jlaura commented Aug 3, 2018

👍 Having a corpus of examples like this will be quite valuable. These are the level of complexity that I suspect most computational scientists will want to play with the sensor model infrastructure.

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