examples/phoreal.ipynb

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   "source": [
    "## PhoREAL / SlideRule Example\n",
    "\n",
    "Demonstrate running the PhoREAL algorithm in SlideRule to produce canopy metrics over the Grand Mesa, Colorado region."
   ]
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   "source": [
    "#### Imports"
   ]
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # suppress warnings"
   ]
  },
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import geopandas\n",
    "import logging\n",
    "import sliderule\n",
    "from sliderule import icesat2"
   ]
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    "#### Initialize Client\n",
    "* Organization currently set to \"utexas\"; if you want to be a member of the utexas SlideRule organization, make a request through the SlideRule provisioning system (https://ps.slideruleearth.io); otherwise, remove the organization parameter to default to the public SlideRule cluster.\n",
    "* Notebook only processes one granule, so one desired_node is sufficient"
   ]
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   "source": [
    "icesat2.init(\"slideruleearth.io\", verbose=True, loglevel=logging.INFO)"
   ]
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    "#### Processing parameters\n",
    "* 100m segments stepped every 100m\n",
    "* Subsetted to the Grand Mesa region\n",
    "* Time range is one day, Nov 14, 2019\n",
    "* Only processing ground, canopy, and top of canopy photons\n",
    "* Request the \"h_dif_ref\" variable as an ancillary field to be included in the results\n",
    "* Running PhoREAL algorithm using a binsize of 1m, and geolocating each segment at the center of the segment\n",
    "* Sending reconstructed waveforms along with metrics (for diagnostics and demonstration purposes only)"
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    "parms = {\n",
    "    \"poly\": sliderule.toregion('grandmesa.geojson')['poly'],\n",
    "    \"t0\": '2019-11-14T00:00:00Z',\n",
    "    \"t1\": '2019-11-15T00:00:00Z',\n",
    "    \"srt\": icesat2.SRT_LAND,\n",
    "    \"len\": 100,\n",
    "    \"res\": 100,\n",
    "    \"pass_invalid\": True, \n",
    "    \"atl08_class\": [\"atl08_ground\", \"atl08_canopy\", \"atl08_top_of_canopy\"],\n",
    "    \"atl08_fields\": [\"h_dif_ref\"],\n",
    "    \"phoreal\": {\"binsize\": 1.0, \"geoloc\": \"center\", \"use_abs_h\": False, \"send_waveform\": True}\n",
    "}"
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   "source": [
    "#### Make Atl08 Request"
   ]
  },
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   "source": [
    "atl08 = icesat2.atl08p(parms, keep_id=True)"
   ]
  },
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   "source": [
    "#### Print Resulting GeoDataFrame"
   ]
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   "source": [
    "atl08"
   ]
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   "source": [
    "#### Plot Canopy Height"
   ]
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   "source": [
    "canopy_gt1l = atl08[atl08['gt'] == icesat2.GT1L]\n",
    "canopy_gt1l.plot.scatter(x='x_atc', y='h_canopy')"
   ]
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   "id": "d3665d73-2745-4ba6-b209-5cf7a6abe693",
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   "source": [
    "#### Plot Landcover"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "atl08.plot('landcover')"
   ]
  },
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   "source": [
    "#### Create and Plot 75th percentile Across All Ground Tracks"
   ]
  },
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   "source": [
    "atl08['75'] = atl08.apply(lambda row : row[\"canopy_h_metrics\"][icesat2.P['75']], axis = 1)\n",
    "atl08.plot.scatter(x='x_atc', y='75')"
   ]
  },
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   "id": "22804320-47c7-4965-a4d2-afc855cfd1b9",
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   "source": [
    "#### Create Sample Waveform Plots"
   ]
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   "source": [
    "num_plots = 5\n",
    "waveform_index = [96, 97, 98, 100, 101]\n",
    "fig,ax = plt.subplots(num=1, ncols=num_plots, sharey=True, figsize=(12, 6))\n",
    "for x in range(num_plots):\n",
    "    ax[x].plot([x for x in range(len(canopy_gt1l['waveform'][waveform_index[x]]))], canopy_gt1l['waveform'][waveform_index[x]], zorder=1, linewidth=1.0, color='mediumseagreen')\n",
    "plt.show()"
   ]
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   "source": [
    "#### Make Atl06 Request\n",
    "* Below we run an ATL06-SR processing request on the same source data using the same parameters.  Because the `keep_id` argument is set to true here and above when we made the ATL08 request, we can merge the resulting dataframes and have a single table of both elevation data using the customized ATL06-SR algorithm, and vegatation data using the PhoREAL algorithm."
   ]
  },
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   "execution_count": null,
   "id": "ce21732e-df05-4f20-8797-202f4c0f4b74",
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   "source": [
    "atl06 = icesat2.atl06p(parms, keep_id=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd03f4ab-163a-49e3-b810-2f7d3bb37ffc",
   "metadata": {
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   },
   "source": [
    "#### Merge Atl06 and Atl08 GeoDataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba76368-4a10-4be9-8237-f26d7b1b996e",
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   "outputs": [],
   "source": [
    "gdf = geopandas.pd.merge(atl08, atl06, on='extent_id', how='left', suffixes=('.atl08','.atl06')).set_axis(atl08.index)\n",
    "gdf"
   ]
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