plot_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import h5py\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal\n",
    "data_dir='Outputs'\n",
    "\n",
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "\n",
    "# make sure we're dealing with the most recent version of any code we're using\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clouds over land ice: What problems they cause, and what to do about it.\n",
    "This tutorial covers some of the reasons you might see weird results over ice when clouds start to blot out the surface signals.  The learning objectives I'd like to get to are:\n",
    "- Understanding how clouds affect laser-altimetry signals\n",
    "- Recognizing how these effects are manifest in the ATL06 product\n",
    "- Gaining familiarity with the ATL06 parameters that can identify cloudy returns\n",
    "This part of the tutorial will focus on clouds that cause gross errors in surface-height estimates.  \n",
    "\n",
    "Along the way, we'll:\n",
    "\n",
    "- develop a simple function for reading ICESat-2 data from hdf-5 files\n",
    "\n",
    "If time allows, I'll also present on subtler effects caused by forward scattering of laser light by thin clouds, which can lead to smaller elevation biases.  The objectives of this part of the tutorial will be:\n",
    "- Understanding how forward scattering of laser light by clouds can introduce biases in surface-height estimates\n",
    "- Tools for identifying returns affected by forward scattering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preliminary matters:  \n",
    "I've pulled the ICESat-2 data for the Byrd Glacier catchment, and it's stored on S3, so you can grab it with these commands:\n",
    "\n",
    "    aws s3 cp s3://pangeo-data-upload-oregon/icesat2/Clouds_and_filtering_tutorial/ATL06 /home/jovyan/ATL06 --recursive\n",
    "    aws s3 cp s3://pangeo-data-upload-oregon/icesat2/Clouds_and_filtering_tutorial/ATL03 /home/jovyan/ATL03 --recursive\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can extract an entry from a dictionary by putting it in square brackets after the dictionary name:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.  Introduction: Signals and signal finding.\n",
    "\n",
    "<img src=\"images/Four_photon_paths.jpg\"  width=600 height=600>\n",
    "\n",
    "The photons that we see in ATLAS data can come to the receiver in a few different ways.  \n",
    "--(A, B): The majority of photons come from the sun.   These may reflect off clouds or off the land surface, and they arrive with a uniform random distribution. ATLAS does a good job of filtering these out, but some make it through into the data.  \n",
    "--C: A few are ATLAS laser photons reflected off clouds.  These can be loosely clustered in space, or widely spread out like background photons.  \n",
    "--D, E: Some are ATLAS photons that reflect off the surface.  These are the ones that most of us are interested in.\n",
    "\n",
    "Our challenge is to collect and analyze D and E, without getting confused by A, B, or C.  Part of the challenge is that there are many photons from ATLAS that end up in category F: photons that are scattered away by clouds, and never seen again.\n",
    "\n",
    "There are three stages that determine whether a surface in a higher-level product like ATL06.\n",
    "1. The ATLAS on-board algorithm may or may not decide to telemeter a photon.  ATLAS doesn't have a lot of computing power, and it can't transmit every photon to the surface.  It uses a coarse DEM and some simple rules to decide whether to transmit large blocks of data to the surface.  If it can't find an obvious group of photons from the surface, it usually transmits the strongest block of photons it finds.  This often works well.\n",
    "2. ATL03 uses a surface-type-dependent algorithm to search for spatially-extensive clusters of photons, and marks them in the _signal_confidence_ flags.  This mostly works very well.\n",
    "3. ATL06 uses the ATL03 signal confidence flags, when they are available, to find the surface.  If this fails, ATL06 uses a backup signal finder to try to find the surface that ATL03 missed.  \n",
    "\n",
    "If we look at an ATL03 we will see all of the photons that ATLAS telemetered to the ground, and we will see the results of ATL03 ground finding.  If we look at an ATL06, we will see the heights of segments for which ATL06 decided that there were enough photons that it might have detected the ground. We will look at some data to try to understand how these processes worked.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 ATL06 signal processing\n",
    "\n",
    "<img src=\"images/ATL06_segment_model.png\"  width=600 height=600>\n",
    "\n",
    "Recall that ATL06 gives us surface heights based on the heights of collections of photons in a 40-m (horizontal) by w_surface_window (vertical) window.  It uses a variety of techniques to shrink the window to the smallest size that contains the surface without clipping off any signal photons.  \n",
    "\n",
    "\n",
    "There's a general philosophy that went into the design of ATL06:\n",
    "1. Use the best available information to identiy the surface\n",
    "2. If there's a chance that we've found a weak surface return, report it\n",
    "3. Provide enough parameters that users can decide which returns are worth using, and which are not\n",
    "\n",
    "When there are no thick clouds between ICESat-2 and the surface, finding the surface return and reporting its height is straightforward: ATL03 provides a tight cluster of high-confidence photons, and ATL06 calculates a weighted average of their heights.\n",
    "\n",
    "Once clouds start to block some of the laser light, the number of photons that return to ATLAS from each return becomes progressively smaller.  Unfortunately, even if there are no laser photons to measure, during daylight there is no shortage of other photons to track.  ATLAS does a very good job of filtering out almost all of these photons, but on a sunny day, over a white surface, the measured background rate can be as high as 12 MHz.  Converting to dimensions that we'll be seeing this is:\n",
    "$$ \n",
    "    \\frac{1.2\\times10^7 photons}{second} \\times \\frac{1 second}{1.5\\times10^8 m} = \\frac{1 photon}{12.5 m}\n",
    "$$\n",
    "This doesn't sound like a lot, but over a 10-meter-high window that's 40 m long (typical for the kind of windows you might use to look for the surface if you didn't know where to find it) we can expect to find 45 photons.\n",
    "\n",
    "Unlike surface-return photons, background photons are uniformally distributed in height, and any clustering of these photons will be due to random chance.  When the signal quality is marginal, ATL03 may flag photons only as low- and medium- confidence for a particular segment, or may flag no photons at all.  If ATL03 hasn't told ATL06 which photons are the surface, the algorithm uses a backup signal-finding strategy that initializes surface finding using the strongest cluster of photons available.  It then attempts to converge its surface window on a tight cluster of photons.  This occasionally works, but if there really is no signal, the size of the window generally remains large, and we can evaluate the results based on the signal-to-noise ratio (SNR) of whatever ends up inside the window.  Only those segments with at least 10 photons, for which the probability of converging to an SNR equal to the observed SNR or better for random-noise inputs is less thatn 5% are reported.  This cuts down on false positives considerably.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from readers.read_HDF5_ATL03 import read_HDF5_ATL03\n",
    "from readers.get_ATL03_x_atc import get_ATL03_x_atc\n",
    "\n",
    "# read the IS2 data with Tyler's ATL03 reader:\n",
    "ATL03_file=glob('/home/jovyan/crackup/Outputs/*.h5')\n",
    "IS2_atl03_mds, IS2_atl03_attrs, IS2_atl03_beams =read_HDF5_ATL03(ATL03_file[0])\n",
    "\n",
    "# add x_atc to the ATL03 data structure (this function adds to the LS2_ATL03_mds dictionary)\n",
    "get_ATL03_x_atc(IS2_atl03_mds, IS2_atl03_attrs, IS2_atl03_beams)\n",
    "\n",
    "#-- select the 1r beam from ATL03\n",
    "D3 = IS2_atl03_mds['gt1r']\n",
    "\n",
    "#-- create scatter plot of photon data (e.g., photon elevation vs x_atc)\n",
    "%matplotlib inline\n",
    "f1,ax = plt.subplots(num=1,figsize=(10,6))\n",
    "plt.plot(D3['heights']['x_atc'], D3['heights']['h_ph'],'k.',markersize=0.1)\n",
    "ax.set_xlabel('x_atc, m')\n",
    "ax.set_ylabel('h, m')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A couple of things to notice. First, the width of this plot is about 120 km, and its height(top to bottom) is about 2 km.  We're looking at large structures here.  Second, This is mostly a plot of background photon heights. The black shapes here represent the top and bottom of the ATLAS telemetry window, so it's showing what the simple electronics on board the satellite identified as potentially coming from the surface. You can see that the ATLAS signal finder has locked onto something on the left-hand side of the plot (up to around $2.904\\times 10^7 m$), then it loses track of the surface in the middle of the plot between 2.904 and $2.910\\times 10^7 m$ and reports its best guess, then maybe picks the surface up again around $2.910\\times 10^7 m$.  We can get a better idea of these by zooming in on a couple of regions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in greater\n",
      "  \n",
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in less\n",
      "  \n",
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in greater\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in less\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in greater\n",
      "  after removing the cwd from sys.path.\n",
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in less\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pick some subsets of the data.  These are logical arrays- what's marked as true in these arrays will get plotted, what's not will be ignored\n",
    "subset1=(D3['heights']['x_atc'] > 2.68e7) & ((D3['heights']['x_atc'] < 2.70e7) )\n",
    "subset2=(D3['heights']['x_atc'] > 2.72e7) & ((D3['heights']['x_atc'] < 2.75e7) )\n",
    "subset3=(D3['heights']['x_atc'] > 2.885e7) & ((D3['heights']['x_atc'] < 2.89e7) )\n",
    "\n",
    "f1,ax = plt.subplots(1, 3,figsize=(10,6))\n",
    "ax[0].plot(D3['heights']['x_atc'][subset1], D3['heights']['h_ph'][subset1],'k.',markersize=0.5)\n",
    "ax[0].set_xlabel('x_atc, m')\n",
    "ax[0].set_ylabel('height, m')\n",
    "ax[0].set_title('likely signal')\n",
    "plt.axes(ax[1])\n",
    "ax[1].plot(D3['heights']['x_atc'][subset2], D3['heights']['h_ph'][subset2],'k.',markersize=0.5)\n",
    "ax[1].set_xlabel('x_atc, m')\n",
    "ax[1].set_ylabel('height, m')\n",
    "ax[1].set_title('likely noise')\n",
    "plt.axes(ax[2])\n",
    "ax[2].plot(D3['heights']['x_atc'][subset3], D3['heights']['h_ph'][subset3],'k.',markersize=0.5)\n",
    "ax[2].set_xlabel('x_atc, m')\n",
    "ax[2].set_ylabel('height, m')\n",
    "ax[2].set_title('Also likely noise')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like our guess that the area on the left-hand side of the plot was signal was right, but the regions we picked from the middle and right-hand side of the plot only contain noise.  This is not a very good way of looking at data.  ATL03 has automatic signal finders that do a good job of identifying the surface.  Here's what they look like for this granule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6b9006fb92c44039561d8d046c524cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "f1,ax = plt.subplots(num=1,figsize=(10,6))\n",
    "\n",
    "\n",
    "#-- check confidence level associated with each photon event\n",
    "#-- -1: Events not associated with a specific surface type\n",
    "#--  0: noise\n",
    "#--  1: buffer but algorithm classifies as background\n",
    "#--  2: low\n",
    "#--  3: medium\n",
    "#--  4: high\n",
    "#-- Signal classification confidence for land ice\n",
    "#-- 0=Land; 1=Ocean; 2=SeaIce; 3=LandIce; 4=InlandWater\n",
    "ice_sig_conf = D3['heights']['signal_conf_ph'][:,3]\n",
    "#-- background and buffer photons\n",
    "bg, = np.nonzero((ice_sig_conf == 0) | (ice_sig_conf == 1))\n",
    "lc, = np.nonzero(ice_sig_conf == 2)\n",
    "mc, = np.nonzero(ice_sig_conf == 3)\n",
    "hc, = np.nonzero(ice_sig_conf == 4)\n",
    "#-- Photon event delta time and elevation (WGS84)\n",
    "ax.plot(D3['heights']['x_atc'][bg],D3['heights']['h_ph'][bg],marker='.',\n",
    "   markersize=0.1,lw=0,color='gray',label='Background')\n",
    "ax.plot(D3['heights']['x_atc'][lc],D3['heights']['h_ph'][lc],marker='.',\n",
    "   markersize=0.25,lw=0,color='darkorange',label='Low Confidence')\n",
    "ax.plot(D3['heights']['x_atc'][mc],D3['heights']['h_ph'][mc],marker='.',\n",
    "   markersize=0.25,lw=0,color='mediumseagreen',label='Medium Confidence')\n",
    "ax.plot(D3['heights']['x_atc'][hc],D3['heights']['h_ph'][hc],marker='.',\n",
    "    markersize=0.25,lw=0,color='darkorchid',label='High Confidence')\n",
    "#-- set title and labels\n",
    "ax.set_xlabel('along-track distance, m')\n",
    "ax.set_ylabel('Elevation above WGS84 Ellipsoid [m]')\n",
    "ax.set_title(\"ATL03 with signal finding\")\n",
    "ax.set_ylim(0,100)\n",
    "# ax.set_xlim(2.85e7,2.89e7)\n",
    "# ax.set_xlim(2.89e7,3.00e7)\n",
    "#-- create legend\n",
    "lgd = ax.legend(loc=3,frameon=False)\n",
    "lgd.get_frame().set_alpha(1.0)\n",
    "for line in lgd.get_lines():\n",
    "    line.set_linewidth(6)\n",
    "#-- adjust ticks\n",
    "ax.get_xaxis().set_tick_params(which='both',direction='in')\n",
    "ax.get_yaxis().set_tick_params(which='both',direction='in')\n",
    "#-- show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clusters of photons are great, but they don't tell you how high the surface is.  ATL06 is where we want to go for that.  Because the ATL06 product is simpler, we can write a fairly simple function that reads useful data into a dictionary.  The code is stored in readers/ATL06_to_dict.py, but we can also look at it in Jupyter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "\n",
    "def ATL06_to_dict(filename, dataset_dict):\n",
    "    \"\"\"\n",
    "        Read selected datasets from an ATL06 file\n",
    "\n",
    "        Input arguments:\n",
    "            filename: ATl06 file to read\n",
    "            dataset_dict: A dictinary describing the fields to be read\n",
    "                    keys give the group names to be read, \n",
    "                    entries are lists of datasets within the groups\n",
    "        Output argument:\n",
    "            D6: dictionary containing ATL06 data.  Each dataset in \n",
    "                dataset_dict has its own entry in D6.  Each dataset \n",
    "                in D6 contains a list of numpy arrays containing the \n",
    "                data\n",
    "    \"\"\"\n",
    "    \n",
    "    D6=[]\n",
    "    pairs=[1, 2, 3]\n",
    "    beams=['l','r']\n",
    "    # open the HDF5 file\n",
    "    with h5py.File(filename) as h5f:\n",
    "        # loop over beam pairs\n",
    "        for pair in pairs:\n",
    "            # loop over beams\n",
    "            for beam_ind, beam in enumerate(beams):\n",
    "                # check if a beam exists, if not, skip it\n",
    "                if '/gt%d%s/land_ice_segments' % (pair, beam) not in h5f:\n",
    "                    continue\n",
    "                # loop over the groups in the dataset dictionary\n",
    "                temp={}\n",
    "                for group in dataset_dict.keys():\n",
    "                    for dataset in dataset_dict[group]:\n",
    "                        DS='/gt%d%s/%s/%s' % (pair, beam, group, dataset)\n",
    "                        # since a dataset may not exist in a file, we're going to try to read it, and if it doesn't work, we'll move on to the next:\n",
    "                        try:\n",
    "                            temp[dataset]=np.array(h5f[DS])\n",
    "                            # some parameters have a _FillValue attribute.  If it exists, use it to identify bad values, and set them to np.NaN\n",
    "                            if '_FillValue' in h5f[DS].attrs:\n",
    "                                fill_value=h5f[DS].attrs['_FillValue']\n",
    "                                bad=temp[dataset]==fill_value\n",
    "                                temp[dataset]=np.float64(temp[dataset])\n",
    "                                temp[dataset][bad]==np.NaN\n",
    "                        except KeyError as e:\n",
    "                            pass\n",
    "                if len(temp) > 0:\n",
    "                    # it's sometimes convenient to have the beam and the pair as part of the output data structure: This is how we put them there.\n",
    "                    temp['pair']=np.zeros_like(temp['h_li'])+pair\n",
    "                    temp['beam']=np.zeros_like(temp['h_li'])+beam_ind\n",
    "                    temp['filename']=filename\n",
    "                    D6.append(temp)\n",
    "    return D6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function returns a list of dictionary objects, where each dictionary contains the variables in the ATL06 product.  To tell the reader which variables to read, we just provide a dictionary that tells it where to look in the file (which groups to read) and what variables to read from each group.  Were going to read:\n",
    "- /gtxx/land_ice_segments/h_li   : the land-ice height.\n",
    "- /gtxx/land_ice_segments/delta_time : the time of the segment.\n",
    "- /gtxx/land_ice_segments/ground_track/x_atc  : the along-track coordinate of the segment.\n",
    "\n",
    "We define the dictionary that tells the function what to read like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dict={'land_ice_segments':['h_li', 'delta_time','longitude','latitude'], 'land_ice_segments/ground_track':['x_atc']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and then we use the reader function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-7a5249cdf88f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# pick out gt1r:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mD6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD6_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# plot ATL03, with ATL06 on top:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "\n",
    "# read ATL06 into a dictionary (the ATL06 file has the same name as the ATL03 file, except for the product name)\n",
    "ATL06_file=ATL03_file[0].replace('ATL03', 'ATL06')\n",
    "D6_list=ATL06_to_dict(ATL06_file, dataset_dict)\n",
    "\n",
    "# pick out gt1r:\n",
    "D6 = D6_list[1]\n",
    "\n",
    "# plot ATL03, with ATL06 on top:\n",
    "f1,ax = plt.subplots(num=1,figsize=(10,6))\n",
    "ax.plot(D3['heights']['x_atc'], D3['heights']['h_ph'],'k.',markersize=0.1, label='ATL03')\n",
    "ax.plot(D6['x_atc'], D6['h_li'],'r.', markersize=2, label='ATL06')\n",
    "lgd = ax.legend(loc=3,frameon=False)\n",
    "\n",
    "ax.set_xlabel('x_atc, m')\n",
    "ax.set_ylabel('h, m')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we see here is that ATL06 found the surface in the same places that ATL03 did, but also reported heights in some of the noise-only areas. This is a consequence of attempting to find surfaces under marginal conditions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraction of bad segments = 0.043\n"
     ]
    }
   ],
   "source": [
    "num_bad_segments=np.sum((D6['x_atc'] > 2.904e7) & (D6['x_atc'] < 2.912e7) & np.isfinite(D6['h_li']))\n",
    "num_possible_segments=(2.912e7 - 2.904e7)/20\n",
    "F_bad=num_bad_segments/num_possible_segments\n",
    "print(\"fraction of bad segments = %3.3f\" % F_bad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The signal-to-noise threshold on ATL06 is supposed to let through about 5% of noise-only segments.  If we assume that everything beyond $2.094\\times 10^7 m$ is noise, we find that ATL06 found a (bad) height about 4% of the time.  We'll work on cleaning these up in the next sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Relating cloud effects to ATL06 data parameters\n",
    "\n",
    "Our problem comes when the ground return is not strong enough to trigger the signal finder, and we start to see triggers associated with:\n",
    "- Cloud tops\n",
    "- Random clusterings of background photons\n",
    "\n",
    "These should both be statistically distinct from surface returns because:\n",
    "- The returns are less intense than a high-quality surface return\n",
    "- The photons are more widely vertcally spread than those in surface returns\n",
    "- The surface window cannot converge on a small vertical window around the surface\n",
    "- Surface heights and slopes are not consistent between adjacent segments\n",
    "\n",
    "There are a few ATL06 parameters that help quantify these distinctions. \n",
    "In the /gtxx/land_ice_segments group:\n",
    "- h_li_sigma : the estimated error in the surface-height estimate\n",
    "In the /gtxx/land_ice_segments/fit_statistics groups:\n",
    "- n_fit_photons : The number of photons found in each segment\n",
    "- w_surface_window_final : The size of the converged surface window\n",
    "- h_rms_misft : The RMS misfit of photons in the surface window\n",
    "- h_robust_sprd : A percentile-based estimate of the spread of the photons, corrected for background\n",
    "- snr : the observed signal-to-noise ratio for the selected photons\n",
    "- snr_significance : The estimated probability that a random clustering of photons would produce the observed SNR\n",
    "- dh_fit_dx : the along-track segment slope\n",
    "And in /gtxx/land_ice_segments/geophysical:\n",
    "- r_eff : the effective reflectance of the surface\n",
    "\n",
    "There's one more parameter that puts a few of these ideas together, in /gtxx/land_ice_segments:\n",
    "- atl06_quality_summary : a combination of parameters (h_li_sigma, n_fit_photons/w_surface_window_final, and signal_selection_source).  Zero indicates a good segment, 1 indicates a possibly bad segment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Taking our picture of cloud effects to the ice sheet\n",
    "We're next going to look at data from Byrd Glacier, in Antarctica.  This is one of the big outlet glaciers dumping ice from the East Antarctic plateau into the Ross Ice Shelf.  Here's a shaded-relief map from the REMA.\n",
    "<img src=\"https://www.gislounge.com/wp-content/uploads/2018/09/mulock-glacier-dem.jpg\">\n",
    "\n",
    "The glacier flows west to east (the REMA image is south-end-up, so east is on the left).  The inland catchment of the glacier should have a smooth ice surface, but the glacier trunk and the area where it joins the ice shelf are heavily crevassed.\n",
    "\n",
    "The dataset on AWS contains a large number of granules that I found with a spatial search using an NSIDC data query.  We can use matplotlib's scatter() function to make a map of all the elevations from all of these files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 528 beam/pair combinations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f7a604dd780>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NOTE: From now on, we'll be working in matplotlib's widget mode, which lets us zoom in on our plots.  \n",
    "# This means that the figures won't be rendered in the notebook until you run them.\n",
    "# That means no more spoiler plots (for now...)\n",
    "%matplotlib widget\n",
    "\n",
    "\n",
    "data_dir='/home/jovyan/ATL06/Byrd_Glacier_rel001/'\n",
    "D6=[]\n",
    "pairs=[1, 2, 3]\n",
    "beams=['l','r']\n",
    "\n",
    "files=glob(data_dir+'/*.h5')\n",
    "for file in files:\n",
    "    this_name=os.path.basename(file)\n",
    "    D6 += ATL06_to_dict(file, dataset_dict)\n",
    "print(\"read %d beam/pair combinations\" % (len(D6)))\n",
    "\n",
    "# now plot the results:\n",
    "plt.figure();\n",
    "for Di in D6:\n",
    "    plt.scatter(Di['longitude'], Di['latitude'], c=Di['h_li'], vmin=0, vmax=2000, linewidth=0)\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot is opposite the orientation of the REMA map, but we can see the trunk of the glacier (dark blue, right-center) and we can see the East Antarctic plateau (bright yellow, at left).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a few granules as plots of height against along-track distance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ae6bca661b74a79acb70359b819c085",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "125b55f54f424d398078b4c09ce1024b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "135542935db64ac8a00b957c4bb4f85d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_dict={'land_ice_segments':['latitude','longitude','h_li'], 'land_ice_segments/ground_track':['x_atc']}\n",
    "\n",
    "%matplotlib widget\n",
    "for file in files[52:55]:\n",
    "    this_D6=ATL06_to_dict(file, dataset_dict)\n",
    "    plt.figure()\n",
    "    plt.plot(this_D6[1]['x_atc'], this_D6[1]['h_li'],'.')\n",
    "    plt.title(this_D6[1]['filename'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Participant exercise:\n",
    "\n",
    "Let's split up into groups for a few minutes, and have a look through these granules.  Try to find some examples with \n",
    "- Clouds\n",
    "- Really steep surface slopes\n",
    "- Crevasses\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here are some of the files that I picked out as interesting:\n",
    "\n",
    "example_file_indices=[3, 1, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf61c7d21d134ae1912736332a1a25e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first example\n",
    "D6=ATL06_to_dict(files[example_file_indices[0]], dataset_dict)\n",
    "markers=['.','+']\n",
    "plt.figure()\n",
    "for ind, seg in enumerate(D6[0:2]):     \n",
    "    plt.plot(seg['x_atc'], seg['h_li'],marker=markers[ind], color='k', linestyle='')\n",
    "    plt.title(seg['filename'])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is our old friend, with good quality on the left, and bad quality on the right.  The weak beam of pair 1 is plotted with points, the strong beam is plotted with plusses- clearly having four times fewer photons in the weak beam doesn't give better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72339705dd544b04bdd9c4ce9065b069",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D6=ATL06_to_dict(files[example_file_indices[1]], dataset_dict)\n",
    "markers=['.','+']\n",
    "plt.figure()\n",
    "for ind, seg in enumerate(D6[0:2]):     \n",
    "    plt.plot(seg['x_atc'], seg['h_li'],marker=markers[ind], color='k', linestyle='')\n",
    "    plt.title(seg['filename'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is more interesting.  The quality is generally high, but there are a few places where the surface disappears and there are outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d95648ae6e15408eba54419cfc067190",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D6=ATL06_to_dict(files[example_file_indices[2]], dataset_dict)\n",
    "markers=['.','+']\n",
    "plt.figure()\n",
    "for ind, seg in enumerate(D6[0:2]):     \n",
    "    plt.plot(seg['x_atc'], seg['h_li'],marker=markers[ind], color='k', linestyle='')\n",
    "    plt.title(seg['filename'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one worked well. ATL06 maintained a good lock on the surface across some very rugged terrain, with no significant false detections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Participant exercise:\n",
    "Look at the Byrd Glacier profiles.  \n",
    "- Use the matplotlib scatter function to plot the parameters as a function of x_atc and h_li, and see which parameters correspond to good returns, which correspond to clouds.\n",
    "- See which returns are lost if we use the parameter as a threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf6b0850861f4732b4d134fe23a860e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in less\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7a60096198>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example:  \n",
    "dataset_dict['/land_ice_segments/fit_statistics']=['h_rms_misfit']\n",
    "D6=ATL06_to_dict(files[example_file_indices[0]], dataset_dict)\n",
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.scatter(D6[0]['x_atc'], D6[0]['h_li'], c=D6[0]['h_rms_misfit'], linewidth=0); plt.colorbar()\n",
    "plt.subplot(122)\n",
    "good=np.where(D6[0]['h_rms_misfit'] < .5)[0]\n",
    "plt.plot(D6[0]['x_atc'][good], D6[0]['h_li'][good],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have picked out a set of filtering parameters you like, compare the results with the atl06_quality_summary. Do you have anything you like better?  Regenerate the plot from the top of this section where we mapped the heights for Byrd Glacier using plt.scatter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one more way of flagging bad data that we can try, that can help clean up the remaining bad segments.  Recall that the the ATL06 data model fits both the height and the surface slope of segments: \n",
    "\n",
    "<img src=\"images/dh_segment.png\"  width=500 height=450>\n",
    "\n",
    "Let's look at the segments including their slopes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seg_slope_plot(D6):\n",
    "    \"\"\"\n",
    "    Plot a sloping line for each ATL06 segment\n",
    "    \"\"\"\n",
    "    #define the heights of the segment endpoints.  Leave a row of NaNs so that the endpoints don't get joined\n",
    "    h_ep=np.zeros([3, D6['h_li'].size])+np.NaN\n",
    "    h_ep[0, :]=D6['h_li']-D6['dh_fit_dx']*20\n",
    "    h_ep[1, :]=D6['h_li']+D6['dh_fit_dx']*20\n",
    "    # define the x coordinates of the segment endpoints\n",
    "    x_ep=np.zeros([3,D6['h_li'].size])+np.NaN\n",
    "    x_ep[0, :]=D6['x_atc']-20\n",
    "    x_ep[1, :]=D6['x_atc']+20\n",
    "\n",
    "    plt.plot(x_ep.T.ravel(), h_ep.T.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33826ddc74fa4520ba2aefdf0527dcc3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to try this, we'll need to add dh_fit_dx to the data dictionary:\n",
    "dataset_dict['land_ice_segments/fit_statistics']=['dh_fit_dx']\n",
    "\n",
    "D6=ATL06_to_dict(files[example_file_indices[0]], dataset_dict)\n",
    "markers=['.','+']\n",
    "plt.figure()\n",
    "for ind, seg in enumerate(D6[0:2]):     \n",
    "    seg_slope_plot(seg)\n",
    "    plt.title(seg['filename'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we zoom in we can see that the \"smooth\" section is a little messy (maybe crevassed), but that the slopes in the cloudy section are really big.  We can use this to define a new filtering strategy that can delete some of the worst segments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seg_difference_filter(D6, tol=2):\n",
    "    \"\"\"\n",
    "    seg_difference_filter: Use elevations and slopes to find bad ATL06 segments\n",
    "    \n",
    "    \n",
    "    Inputs: \n",
    "        D6: a granule of ATL06 data, in dictionary format.  Must have entries:\n",
    "            x_atc, h_li, dh_fit_dx\n",
    "        tol: a tolerance.  Segments whose ends are different from their neighbors \n",
    "             by more than tol are marked as bad\n",
    "    Returns:\n",
    "        good: an array the same size as D6['h_li'].  True  entries indicate that \n",
    "            both ends of the segment are compatible with the segment's neighbors\n",
    "        delta_h_seg: an array the same size as D6['h_li'].  Gives the largest \n",
    "            endpoint difference for each segment    \n",
    "    \"\"\"\n",
    "    h_ep=np.zeros([2, D6['h_li'].size])+np.NaN\n",
    "    h_ep[0, :]=D6['h_li']-D6['dh_fit_dx']*20\n",
    "    h_ep[1, :]=D6['h_li']+D6['dh_fit_dx']*20\n",
    "    delta_h_seg=np.zeros_like(D6['h_li'])\n",
    "    delta_h_seg[1:]=np.abs(D6['h_li'][1:]-h_ep[1, :-1])\n",
    "    delta_h_seg[:-1]=np.maximum(delta_h_seg[:-1], D6['h_li'][:-1]-h_ep[0, 1:])\n",
    "    good=delta_h_seg < tol\n",
    "    return good, delta_h_seg\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Participant Exercise: \n",
    "Try out the seg_difference_filter.  Generate a finalized map of elevations for Byrd Glacier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f961180a8e834b3fb164a2275136ce33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in less\n"
     ]
    }
   ],
   "source": [
    "##Workspace\n",
    "# to try this, we'll need to add dh_fit_dx to the data dictionary:\n",
    "dataset_dict['land_ice_segments/fit_statistics']=['dh_fit_dx']\n",
    "\n",
    "D6=ATL06_to_dict(files[example_file_indices[0]], dataset_dict)\n",
    "markers=['.','+']\n",
    "plt.figure()\n",
    "for ind, seg in enumerate(D6[0:2]): \n",
    "    good, delta_h=seg_difference_filter(seg, tol=2)\n",
    "    seg_slope_plot(seg)\n",
    "    plt.plot(seg['x_atc'][good],seg['h_li'][good],'.')\n",
    "    plt.title(seg['filename'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of this part of the tutorial:  \n",
    "- Clouds tend to produce weaker-than-normal returns\n",
    "- We can identify low-quality surface returns with a variety of parameters.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Errors due to forward scattering of photons.\n",
    "\n",
    "A more insidious problem that clouds can cause comes about when an optically thin cloud lets photons through, but some of the photons scatter off ice grains in the forward (downward) direction and hit the surface before returning to the telescope.\n",
    "\n",
    "<img src=\"images/Forward_scattering.png\"  width=600 height=600>\n",
    "\n",
    "The path for a photon that reaches the surface without hitting the cloud is shown in blue, the path of a scattered photon is shown in red.  Since photons are delayed by a small but random amount whose distribution depends on the height and thickness of the cloud, the expected probability distibution of the scattered photon heights is skewed low (shown in schematic at left), and the mean (or median) photon height has a negative bias. \n",
    "\n",
    "When clouds are high above the ground, most scattered photons end up outside the telescope field of view, and don't do much to skew the return distribution.  It's when clouds are low that more scattered photons are detected, and the bias can become large.  For this reason, Yeukui Yang has put together a set of parameters aimed at flagging segments most strongly affected by forward scattering.  These are in the ATL06 _/gtxx/land_ice_segments/geophysical_ group:\n",
    "\n",
    "There are two cloud parameters:\n",
    "- cloud_flg_asr  : an estimate of the presence/ absence of clouds based on the apparent surface reflectance (low reflectance -> possible clouds)\n",
    "- cloud_flg_atm  : An estimate of the presence / absence of clouds based on backscatter above the surface.  This parameter only gives useful values at night, because during the daytime there is too much solar background to see clouds\n",
    "\n",
    "There are three blowing-snow parameters:\n",
    "- bsnow_h : An estimate of the height of the top of any blowing-snow layers present\n",
    "- bsnow od : An estimate of the optical depth of a blowing-snow layer (if present )\n",
    "- bsnow_conf : A confidence flag for the presence / absence of a blowing-snow layer.\n",
    "\n",
    "And there is a combined flag that summarizes what has been learned from the atmospheric channels:\n",
    "- msw_flag: \"Combined flag indicating the risks of severe multiple scattering. The multiple scattering warning flag (ATL09 parameter msw_flag) has values from -1 to 5 where zero means no multiple scattering and 5 the greatest. If no layers were detected, then msw_flag = 0. If blowing snow is detected and its estimated optical depth is greater than or equal to 0.5, then msw_flag = 5. If the blowing snow optical depth is less than 0.5, then msw_flag = 4. If no blowing snow is detected but there are cloud or aerosol layers detected, the msw_flag assumes values of 1 to 3 based on the height of the bottom of the lowest layer: < 1 km, msw_flag = 3; 1-3 km, msw_flag = 2; > 3km, msw_flag = 1. A value of -1 indicates that the signal to noise of the data was too low to reliably ascertain the presence of cloud or blowing snow. We expect values of -1 to occur only during daylight.\"\n",
    "\n",
    "The long block quote here comes from the product description in ATL06.\n",
    "\n",
    "The algorithms behind these parameters still being refined by the atmosphere team, so it's not certain that they produce good results.  As an exercise for the group, let's split into groups, and map the values for these parameters to see if any of them produce good-looking results over Byrd Glacier.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Participant Exercise: \n",
    "Investigate cloud-filtering parameters over Byrd Glacier.  User the Matplotlib _scatter_ function to map the distribution of the cloud and blowing-snow flags.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e0080d1543748d1b6c2660a97343432",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f7a5f47e4e0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## workspace\n",
    "dataset_dict['land_ice_segments/geophysical']=['msw_flag','bsnow_od','bsnow_conf']\n",
    "\n",
    "D6=ATL06_to_dict(files[example_file_indices[0]], dataset_dict)\n",
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.scatter(D6[1]['x_atc'], D6[1]['h_li'], c=D6[1]['msw_flag'], linewidth=0); plt.colorbar()\n",
    "## this isn't workign right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Directly observing the effects of forward scattering.\n",
    "\n",
    "ATL06 was designed with a scheme to allow us to directly see the effects of forward scattering, or any other process that modifies the shape of the return.  Remember the least-squares fitting scheme?  What do we do with the residuals to the best-fitting line?  Instead of throwing them away, we make a histogram from them, and store it in the _/gtxx/residual_histogram_ group.  Because the histograms for any one segment tend to be a little noisy, we combine groups of ten segments for each \"waveform\" in the group, which gives it an intrinsic horizontal resolution of 200 m.  I've picked out one file where _resurnsidual_histogram_ group shows some returns with scattering and some without.  Let's have a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "thefile='/home/jovyan/ATL06/Byrd_Glacier_rel001/processed_ATL06_20181017061148_02840111_001_01.h5'\n",
    "\n",
    "rh=ATL06_to_dict(thefile,{'/residual_histogram':['count','bckgrd_per_bin', 'dh','x_atc_mean'],\n",
    "                                    '/land_ice_segments':['h_li','atl06_quality_summary'],\n",
    "                                   '/land_ice_segments/ground_track':['x_atc']})\n",
    "# pick gt1r from the residual histogram:\n",
    "rh=rh[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The field we want in the residual histogram group is called _count_.  The along-track centers for each bin in the variable are in _x_atc_mean_, and the relative heights of the bins are in _dh_.  We'll use matplotlib's _imshow_ function to visualize the variable.  I've pre-selected a few profiles that are interesting: One early in the profile (column 25), two later on where the surface starts looking more affected by clouds:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bc3d83fb897411f99ededbb38ed9fa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "slices=[25, 170, 350]\n",
    "\n",
    "plt.figure(); \n",
    "plt.imshow(rh['count'].T, extent=[rh['x_atc_mean'][0], rh['x_atc_mean'][-1], rh['dh'][0], rh['dh'][-1]], aspect='auto', origin='upper')\n",
    "yl=plt.gca().get_ylim()\n",
    "for slice in slices:\n",
    "    plt.plot(np.zeros(2)+rh['x_atc_mean'][slice], yl,'w:')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there's a sharp return at the left-hand side of the plot, but the returns farther to the right are increasingly dim and spread out.  Let's plot profiles for the three columns I selected and see what they look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8a8248dabb94c88ae8d6ca71aa8bbe3",
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       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7a5f441780>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for this_slice in slices:\n",
    "    plt.plot(rh['count'][this_slice,:], rh['dh'], label=\"column %d\" % this_slice)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly column 25 is a lot sharper than the others, but given the statistical noise and variable amplitude in the profiles, it's hard to see what's going on.  Each profile is binned at 1 cm (check the spacing on the dh variable), which is finer than we need for this plot.  Let's try smoothing the profile a bit to make it nicer to look at, and normalizing each profile to its maximum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34e9a1ce9a984587a4f7eb7807778580",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7a5f3aa5f8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tK=np.arange(-20, 20)*0.01\n",
    "kernel=np.exp(-(tK**2)/2/(0.1**2))\n",
    "kernel=kernel/kernel.sum()\n",
    "kernel.shape=[1, kernel.size]\n",
    "Ps=scipy.signal.convolve2d(rh['count'], kernel,'same')\n",
    "plt.figure()\n",
    "for this_slice in slices:\n",
    "    plt.plot(Ps[this_slice,:]/Ps[this_slice,:].max(), rh['dh'], label=\"column %d\" % this_slice)\n",
    "plt.legend()\n",
    "                              "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's better.  The results are still a little messy, but we can see that column 25 has a pretty sharp peak, column 350 has a sharp peak with a significantly skewed tail on the back side, and column 170 is broad on both sides of zero. Looking at the height profile makes it fairly clear what's happening with column 170:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0294df9049da4af9ad893b260bbbbeb7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "good=rh['atl06_quality_summary']==0\n",
    "plt.figure()\n",
    "plt.plot(rh['x_atc'][good], rh['h_li'][good],'.')\n",
    "yl=plt.gca().get_ylim()\n",
    "for slice in slices:\n",
    "    plt.plot(np.ones(2)*rh['x_atc_mean'][slice], yl,'k:')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like column 170 is broad because of a large surface slope, but the other two profiles are from fairly flat ice.  The shape of the profie at column 350 looks like exactly what we would expect from a return due to forward scattering.  As of yet, nobody (to my knowledge) has tried to relate the cloud parameters to the shape of the surface return.  If there's time, take a minute or two to look at the cloud-clearing parameters for this granule.  If you have time to do this after the tutorial, let me know what you see!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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