{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating and Using Panel Data Using ArcGIS Defined Location Cubes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os as OS\n",
    "import arcpy as ARCPY\n",
    "import SSDataObject as SSDO\n",
    "import SSPanelObject as SSPO\n",
    "import SSPanel as PANEL\n",
    "ARCPY.overwriteOutput = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Per Capita Incomes Relative to National Average in California (1969 - 2010)\n",
    "\n",
    "- Create Defined Locations Cube from Repeating Shapes Feature Class\n",
    "- Run Mann-Kendall Trend Statistic\n",
    "- Save to NetCDF File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputFC = r'../data/CA_Counties_Panel.shp'\n",
    "outputCube = r'../data/CA_Panel.nc'\n",
    "fullFC = OS.path.abspath(inputFC)\n",
    "outputCube = OS.path.abspath(outputCube)\n",
    "fullPath, fcName = OS.path.split(fullFC)\n",
    "ssdo = SSDO.SSDataObject(inputFC)\n",
    "uniqueIDField = \"MYID\"\n",
    "timeField = \"YEAR\"\n",
    "analysisField = \"PCR\"\n",
    "panelObj = SSPO.SSPanelObject(inputFC)\n",
    "requireGeometry = panelObj.ssdo.shapeType.upper() == \"POLYGON\"\n",
    "panelObj.obtainData(uniqueIDField, \"YEAR\", \"1 Years\", fields = [analysisField],\n",
    "                    requireGeometry = requireGeometry)\n",
    "panelCube = PANEL.SSPanel(outputCube, panelObj = panelObj)\n",
    "varName = panelObj.fieldNames[0]\n",
    "panelCube.mannKendall(varName)\n",
    "panelCube.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open Panel Cube From NetCDF File for Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "panel = PANEL.SSPanel(outputCube)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Locations and Time Periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# locations = 58, # time periods = 42\n"
     ]
    }
   ],
   "source": [
    "print(\"# locations = {0}, # time periods = {1}\".format(panel.numLocations, panel.numTime))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['projection' 'time' 'x' 'y' 'lat' 'lon' 'poly_breaks' 'poly_coords'\n",
      " 'time_step_ID' 'location_ID' 'MYID' 'PCR' 'PCR_TREND_ZSCORE'\n",
      " 'PCR_TREND_PVALUE' 'PCR_TREND_BIN']\n"
     ]
    }
   ],
   "source": [
    "print(panel.obtainVariableList())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Mann-Kendall Trend Results in PANDAS Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PCR_TREND_PVALUE  PCR_TREND_ZSCORE\n",
      "158      1.552410e-03          3.164704\n",
      "159      6.646548e-01         -0.433496\n",
      "160      1.202954e-06         -4.855150\n",
      "161      1.539366e-08         -5.657117\n",
      "162      1.441587e-03         -3.186192\n"
     ]
    }
   ],
   "source": [
    "import pandas as PANDAS\n",
    "locations = panel.locationLabel[0]\n",
    "z = panel.obtainValues('PCR_TREND_ZSCORE')\n",
    "pv = panel.obtainValues('PCR_TREND_PVALUE')\n",
    "d = {'PCR_TREND_ZSCORE':z, 'PCR_TREND_PVALUE':pv}\n",
    "df = PANDAS.DataFrame(d, index = locations)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get 3D Analysis Variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 58)\n"
     ]
    }
   ],
   "source": [
    "data = panel.obtainValues(analysisField)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use PySAL to Analyze LISA Markov Transitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "import pysal as PYSAL\n",
    "w = PYSAL.open(r\"../data/queen.gal\").read()\n",
    "lm = PYSAL.LISA_Markov(data.T, w)\n",
    "print(lm.classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Transistion Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.91103789 0.02800659 0.00988468 0.05107084]\n",
      " [0.07037037 0.84074074 0.08518519 0.0037037 ]\n",
      " [0.00246711 0.01726974 0.95805921 0.02220395]\n",
      " [0.09122807 0.00701754 0.09122807 0.81052632]]\n"
     ]
    }
   ],
   "source": [
    "print(lm.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "panel.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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