baltrad/BALTRAD QC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BALTRAD Quality Control"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Import the file I/O module along with the main RAVE module containing useful constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pylab inline\n",
    "import _raveio, _rave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read an input ODIM_H5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rio = _raveio.open(\"data/201405190715_SUR.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a simple plotter for B-scans, elaborating the example from the I/O exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two color palettes, one used in GoogleMapsPlugin, and the other from RAVE\n",
    "from GmapColorMap import dbzh as dbzp\n",
    "from rave_win_colors import continuous_MS as vradp\n",
    "\n",
    "# Convert a 768-list palette to a matplotlib colorlist\n",
    "def make_colorlist(pal):\n",
    "    colorlist = []\n",
    "    for i in range(0, len(pal), 3):\n",
    "        colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])\n",
    "    return colorlist\n",
    "\n",
    "# Convert lists to colormaps\n",
    "dbzcl = make_colorlist(dbzp)\n",
    "vradcl = make_colorlist(vradp)\n",
    "\n",
    "# Then create a simple plotter\n",
    "import matplotlib.pyplot as plt\n",
    "#from types import StringType\n",
    "StringType = type('')\n",
    "def plot(data, colorlist=dbzcl, title=\"B-scan\"):\n",
    "    mini, maxi = data.shape.index(min(data.shape)), data.shape.index(max(data.shape))\n",
    "    figsize=(16,12) if mini == 0 else (12,8)\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    plt.title(title)\n",
    "    clist=colorlist if type(colorlist)==StringType else matplotlib.colors.ListedColormap(colorlist)\n",
    "    plt.imshow(data, cmap=clist, clim=(0,255))\n",
    "    plt.colorbar(shrink=float(data.shape[mini])/data.shape[maxi])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access the polar volume and plot VRAD data from the lowest scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pvol = rio.object\n",
    "plot(pvol.getScan(0).getParameter(\"VRADH\").getData(), vradcl, \"Original VRAD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealias the volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _dealias\n",
    "ret = _dealias.dealias(pvol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check whether the first scan's been dealiased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This first scan is dealiased: %s\" % str(_dealias.dealiased(pvol.getScan(0))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replot for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(pvol.getScan(0).getParameter(\"VRADH\").getData(), vradcl, \"Dealiased VRAD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shift gears - back to reflectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rio = _raveio.open(\"data/plrze_pvol_20120205T0430Z.h5\")\n",
    "pvol = rio.object\n",
    "plot(pvol.getScan(0).getParameter(\"DBZH\").getData(), title=\"Original DBZH\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the bRopo package's quality plugin to identify and remove non-precipitation echoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import odc_polarQC\n",
    "odc_polarQC.algorithm_ids = [\"ropo\"]\n",
    "\n",
    "pvol = odc_polarQC.QC(pvol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the resulting DBZH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(pvol.getScan(0).getParameter(\"DBZH\").getData(), title=\"DBZH after bRopo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topographical beam-blockage QC using the beamb package's quality plugin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "odc_polarQC.algorithm_ids = [\"beamb\"]\n",
    "before = time.time()\n",
    "pvol = odc_polarQC.QC(pvol)\n",
    "after = time.time()\n",
    "print(\"beamb runtime = %2.2f seconds\" % (after-before))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability of overshooting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "odc_polarQC.algorithm_ids = [\"rave-overshooting\"]\n",
    "pvol = odc_polarQC.QC(pvol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing and manging data quality fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scan = pvol.getScan(0)\n",
    "print(\"Scan contains %i quality fields\" % scan.getNumberOfQualityFields())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(scan.getNumberOfQualityFields()):\n",
    "    qf = scan.getQualityField(i)\n",
    "    print(\"Quality field %i has identifier %s\" % (i, qf.getAttribute(\"how/task\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot quality fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beam blockage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb = scan.getQualityFieldByHowTask(\"se.smhi.detector.beamblockage\")\n",
    "plot(bb.getData(), \"binary\", \"Quality indicator for beam blockage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability of non-precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb = scan.getQualityFieldByHowTask(\"fi.fmi.ropo.detector.classification\")\n",
    "plot(bb.getData(), \"binary\", \"Quality indicator for ropo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability of overshooting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb = scan.getQualityFieldByHowTask(\"se.smhi.detector.poo\")\n",
    "plot(bb.getData(), \"binary\", \"Quality indicator for PoO\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chaining algorithms - new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rio = _raveio.open(\"data/sekir.h5\")\n",
    "pvol = rio.object\n",
    "\n",
    "odc_polarQC.algorithm_ids = [\"ropo\", \"beamb\", \"radvol-att\", \"radvol-broad\", \"rave-overshooting\"]\n",
    "pvol = odc_polarQC.QC(pvol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scan = pvol.getScan(0)\n",
    "att = scan.getQualityField(2)\n",
    "plot(att.getData(), \"binary\", \"Attenuation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Total Quality\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "odc_polarQC.algorithm_ids = [\"qi-total\"]\n",
    "pvol = odc_polarQC.QC(pvol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qitot = scan.getQualityField(5)\n",
    "plot(qitot.getData(), \"binary\", \"Total quality index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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