docs/notebooks/customized_swath.ipynb

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   "source": [
    "# Customized swath analyiss and essential data structure"
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   "source": [
    "## Customized swath area\n",
    "\n",
    "PyOSP provides several geo-parameter based swath analyses, including elevation, slope, and topographic position index (TPI). However, in real applications, these options may not be sufficient for user to accurately delineate the desired topgraphic object. For example, the intended object may possess roughness>0.5, or curvture<3, among other possibilities that not being covered by PyOSP. In order to deal with this issue, we provide a tip here that allows users to perform customized swath analysis. \n",
    "\n",
    "We use the same synthetic landscape that being used in the section [Fixed-width curvilinear swath profile](./fix_width_curv.ipynb). \n",
    "\n",
    "Here, let's suppose PyOSP does not have elevation-based characterization. We use raster analysis in other software to exact the range of elevation>10, and incoporate the resulted DEM into swath profile analysis.\n",
    "\n",
    "### Step 1. Raster calculation in GIS software\n",
    "\n",
    "<img alt=\"homo_circle\" src=\"https://i.imgur.com/y1fvgik.png[/img]\" height=\"250\"/>\n",
    "\n",
    "As shown above, we used raster calculation to extract the portion of DEM having that elevation>10. \n",
    "\n",
    "### Step 2. Use fixed-width swath method to generate the swath object\n",
    "\n",
    "Now, we use fixed-width swath method to generate the swath object bounded by new DEM. As illustrated previously, PyOSP keeps detecting the boundary of DEM to truncate the analysis at the data boundary. Thus, we can set swath width beyond the possible length of profile line, so the swath area will be determined by the DEM range rather than fixed-width.\n",
    "\n",
    "**Note, PyOSP would assume no data area has cell value less then -1e+20. Make sure the raster file satisfy this criteria before the analysis.**\n"
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     "text": [
      "Processing: [#########################] 71 of 71 lineSteps"
     ]
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   ],
   "source": [
    "import pyosp\n",
    "\n",
    "baseline = pyosp.datasets.get_path(\"homo_baseline.shp\") # the path to baseline shapefile\n",
    "raster = pyosp.datasets.get_path(\"homo_elev10.tif\")  # the path to raster file\n",
    "\n",
    "orig = pyosp.Orig_curv(baseline, raster, width=1000,\n",
    "                       line_stepsize=3, cross_stepsize=None)"
   ]
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   "source": [
    "### Step 3. Swath results.\n",
    "\n",
    "Now we can verify the results by swath profile plot, or exported polygon or polylines."
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "orig.profile_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc020086b80>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pyosp import read_shape\n",
    "\n",
    "# read the baseline shape\n",
    "line_shape = read_shape(baseline)\n",
    "lx, ly = line_shape.xy\n",
    "\n",
    "# Plot the swath polygon\n",
    "fig, ax = plt.subplots()\n",
    "swath_polygon = orig.out_polygon()\n",
    "px, py = swath_polygon.exterior.xy\n",
    "ax.plot(px, py)\n",
    "ax.plot(lx, ly, color='C3', label=\"Baseline\")\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "ax.set_title(\"Swath polygon\")\n",
    "ax.legend()\n",
    "\n",
    "# Plot the swath profile lines\n",
    "fig, ax = plt.subplots()\n",
    "swath_polylines = orig.out_polylines()\n",
    "for line in swath_polylines:\n",
    "    x, y = line.xy\n",
    "    ax.plot(x, y, color='C1')\n",
    "\n",
    "ax.plot(lx, ly, color='C3', label=\"Baseline\")\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "ax.set_title(\"Swath profile lines\")\n",
    "ax.legend()"
   ]
  },
  {
   "source": [
    "Notice that all elevation data is above 10, and swath area is bounded by DEM data range. It demonstrates that PyOSP offers users the option to perform customized swath analysis (i.e., roughness, aspect, refief, structure, etc.). This flexibility provides the potential to incorporate any raster classification technique into the swath analysis beyond the default methods in PyOSP."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## PyOSP data structure\n",
    "\n",
    "Now, let's take a look at structure of most essential data in PyOSP.\n",
    "\n",
    "(1) Distance\n",
    "\n",
    "Attibute *distance* presents an array of baseline points with increasing distance from the starting point. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "orig.distance"
   ],
   "cell_type": "code",
   "metadata": {
    "tags": []
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([  0.,   3.,   6.,   9.,  12.,  15.,  18.,  21.,  24.,  27.,  30.,\n",
       "        33.,  36.,  39.,  42.,  45.,  48.,  51.,  54.,  57.,  60.,  63.,\n",
       "        66.,  69.,  72.,  75.,  78.,  81.,  84.,  87.,  90.,  93.,  96.,\n",
       "        99., 102., 105., 108., 111., 114., 117., 120., 123., 126., 129.,\n",
       "       132., 135., 138., 141., 144., 147., 150., 153., 156., 159., 162.,\n",
       "       165., 168., 171., 174., 177., 180., 183., 186., 189., 192., 195.,\n",
       "       198., 201., 204., 207., 210.])"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "source": [
    "(2) Lines\n",
    "\n",
    "Lines restore coordinates of points along each profile line, from left hand side to the right. Let's examine a profile line with index of 9 (distance is 27 from the starting point)."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[[147.66393878283108, 30.266284945875174],\n",
       " [148.2435671297984, 30.817674988759467],\n",
       " [148.8231954767657, 31.369065031643764],\n",
       " [149.402823823733, 31.920455074528054],\n",
       " [149.98245217070033, 32.47184511741235],\n",
       " [150.56208051766765, 33.02323516029664],\n",
       " [151.14170886463495, 33.574625203180936],\n",
       " [151.72133721160225, 34.12601524606523],\n",
       " [152.30096555856957, 34.67740528894952],\n",
       " [152.88059390553687, 35.228795331833815],\n",
       " [153.4602222525042, 35.78018537471811],\n",
       " [154.0398505994715, 36.3315754176024],\n",
       " [154.61947894643882, 36.882965460486695],\n",
       " [155.19910729340612, 37.43435550337099],\n",
       " [155.77873564037344, 37.98574554625528],\n",
       " [156.35836398734074, 38.537135589139574],\n",
       " [156.93799233430806, 39.088525632023874],\n",
       " [157.51762068127536, 39.63991567490817],\n",
       " [158.0972490282427, 40.19130571779246],\n",
       " [158.67687737520998, 40.74269576067675],\n",
       " [159.2565057221773, 41.294085803561046],\n",
       " [159.8361340691446, 41.84547584644534],\n",
       " [160.41576241611193, 42.39686588932963],\n",
       " [160.99539076307923, 42.948255932213925],\n",
       " [161.57501911004655, 43.49964597509822],\n",
       " (162.15464745701385, 44.05103601798251),\n",
       " [162.73427580398115, 44.602426060866804],\n",
       " [163.31390415094847, 45.1538161037511],\n",
       " [163.89353249791577, 45.70520614663539],\n",
       " [164.4731608448831, 46.25659618951968],\n",
       " [165.0527891918504, 46.80798623240398],\n",
       " [165.63241753881772, 47.35937627528827],\n",
       " [166.21204588578502, 47.91076631817256],\n",
       " [166.79167423275234, 48.462156361056856],\n",
       " [167.37130257971964, 49.01354640394115],\n",
       " [167.95093092668696, 49.56493644682545],\n",
       " [168.53055927365426, 50.11632648970974],\n",
       " [169.1101876206216, 50.667716532594035],\n",
       " [169.68981596758888, 51.21910657547833],\n",
       " [170.2694443145562, 51.77049661836262],\n",
       " [170.8490726615235, 52.321886661246914],\n",
       " [171.42870100849083, 52.87327670413121],\n",
       " [172.00832935545813, 53.4246667470155],\n",
       " [172.58795770242546, 53.97605678989979],\n",
       " [173.16758604939275, 54.527446832784086],\n",
       " [173.74721439636005, 55.07883687566838],\n",
       " [174.32684274332738, 55.63022691855267],\n",
       " [174.9064710902947, 56.18161696143697],\n",
       " [175.486099437262, 56.73300700432126],\n",
       " [176.0657277842293, 57.28439704720556],\n",
       " [176.64535613119662, 57.83578709008985],\n",
       " [177.22498447816392, 58.387177132974145],\n",
       " [177.80461282513124, 58.93856717585844],\n",
       " [178.38424117209854, 59.48995721874273],\n",
       " [178.96386951906587, 60.041347261627024],\n",
       " [179.54349786603316, 60.59273730451132],\n",
       " [180.1231262130005, 61.14412734739561]]"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "orig.lines[9]"
   ]
  },
  {
   "source": [
    "(3) data\n",
    "\n",
    "Attibute *data* provides raster values along each profile line. For example, values along the same profile line as presented above. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[10.366577,\n",
       " 10.775647,\n",
       " 10.985182,\n",
       " 11.849683,\n",
       " 12.324719,\n",
       " 12.434934,\n",
       " 13.207694,\n",
       " 14.339156,\n",
       " 14.339156,\n",
       " 15.918287,\n",
       " 17.868557,\n",
       " 18.705082,\n",
       " 20.37459,\n",
       " 22.935509,\n",
       " 23.32269,\n",
       " 25.494387,\n",
       " 27.356518,\n",
       " 28.160154,\n",
       " 27.377426,\n",
       " 28.819553,\n",
       " 27.965193,\n",
       " 26.129932,\n",
       " 26.043837,\n",
       " 26.854143,\n",
       " 26.317019,\n",
       " 26.637743,\n",
       " 27.222136,\n",
       " 26.62463,\n",
       " 26.012062,\n",
       " 24.822708,\n",
       " 25.181057,\n",
       " 23.875961,\n",
       " 22.917784,\n",
       " 23.280067,\n",
       " 21.87542,\n",
       " 20.613186,\n",
       " 19.87687,\n",
       " 19.87687,\n",
       " 19.525146,\n",
       " 19.106987,\n",
       " 17.9117,\n",
       " 18.822304,\n",
       " 19.342886,\n",
       " 18.516066,\n",
       " 19.936953,\n",
       " 20.327038,\n",
       " 20.0634,\n",
       " 18.608377,\n",
       " 19.296824,\n",
       " 17.993258,\n",
       " 16.700563,\n",
       " 15.849781,\n",
       " 16.132235,\n",
       " 14.963792,\n",
       " 13.446796,\n",
       " 13.099882,\n",
       " 11.64522]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "orig.dat[9]"
   ]
  },
  {
   "source": [
    "## Summary\n",
    "\n",
    "We introduced herein using customized option to perform swath analysis beyond default methods in PyOSP. The afforded flexibility provides the potential to incorporate any raster classification technique into swath analysis. In addition, we walked through essential data attributes of PyOSP swath object, users should be able to further analysis, plot, or conduct other post-processings by making use of [numpy](https://numpy.org/), [Matplotlib](https://matplotlib.org/), [SciPy](https://www.scipy.org/), among other Python packages.   "
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}