load result values using ODM2API - select values with bokeh and add annotations.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>1) Load all of the needed python packages to run this notebook. These include ODM2API, ipython widgets for user interaction, bokeh for interactive plots. ui_utils </p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import datetime\n",
    "import getpass\n",
    "import utilities.ui_utils as ui_utils\n",
    "from bokeh.plotting import  figure, show\n",
    "from IPython.core.display import HTML\n",
    "from ipywidgets import Layout, HBox\n",
    "from IPython.display import display\n",
    "from odm2api.ODMconnection import dbconnection\n",
    "import odm2api.ODM2.services.readService as odm2\n",
    "from odm2api.ODM2.models import *\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.models import ColumnDataSource"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) The code below retreives the ODM2 login prompt from ui_utils python code which should be in your utilities folder. This will log directly into SQL.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#print(\"Enter your ODM2 username\") \n",
    "container = ui_utils.ODM2LoginPrompt()\n",
    "container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Next we prompt a user for their password. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter your password: \n",
      "········\n"
     ]
    }
   ],
   "source": [
    "print(\"enter your password: \")\n",
    "p = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> 4) Here we look into the login prompt container. to retrieve the database connection information entered by the user. </p>\n",
    "<p> 5) On the second line, I'm using the ODM2API read service for retrieving result values this should help make retrieving \n",
    "them agnostic to the result type. </p>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "session_factory = dbconnection.createConnection('postgresql', container.children[1].value, \n",
    "                                                container.children[2].value, \n",
    "                                                container.children[0].value, p) \n",
    "\n",
    "read = odm2.ReadODM2(session_factory)\n",
    "DBSession = session_factory.getSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Next we retreive another UI element for selecting observation actions. Which we will use to filter results. Otherwise the list of results maybe too long. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) Now we get a UI element for results, for which we would like to display values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    }
   ],
   "source": [
    "HTML('<style>select, option, dropdown-menu, li, a { width: 50%; !important;}</style>')\n",
    "actionWidget, awidget = ui_utils.actionWidgetNotPulse(DBSession)\n",
    "#box = HBox([awidget])\n",
    "#box.layout.display = 'flex'\n",
    "#box.layout.align_items = 'stretch'\n",
    "display(awidget) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16647\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#featureaction = 1700\n",
    "actionid = actionWidget.value\n",
    "print(actionid)\n",
    "resultWidget, rwidget = ui_utils.resultWidget(read, actionid)\n",
    "display(rwidget)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8) Now we create a UI element for entering the dates we would like to filter the time series on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "startdate_enddate_container = ui_utils.startDateEndDateContainer()\n",
    "startdate_enddate_container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9) Next we read more information about the result, pull the results values for the time frame we specified and create a data dictionary our selection graph can display. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "10) Now we create a bokeh (graphing package) ColumnDataSource for our graph, create our plot (also using ui_utils) and display it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "selectedResult = read.getDetailedResultInfo(resultTypeCV = 'Time series coverage',resultID=resultWidget.value)\n",
    "#resultValues = read.getResultValues(resultid=resultWidget.value, starttime='2016-8-1', \n",
    "#                                                 endtime= '2016-8-30')\n",
    "resultValues = read.getResultValues(resultid=resultWidget.value, starttime=startdate_enddate_container.children[0].value, \n",
    "                                                 endtime= startdate_enddate_container.children[1].value)\n",
    "#print(startdate_enddate_container.children[0].value)\n",
    "#print(startdate_enddate_container.children[1].value)\n",
    "data = dict(x=resultValues.ValueDateTime, y= resultValues.DataValue, z=resultValues.ValueID)\n",
    "#print(data)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
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    "scrolled": false
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       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
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       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[14]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7f03ed856f50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = ColumnDataSource(data=data)\n",
    "plot = ui_utils.selectPlot(s1,selectedResult)\n",
    "output_notebook()\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11) The code below prints out the selected data points. This can be used for diagnostic purposes and is commented out here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# data = zip([s1.data['x'][i] for i in inds],\n",
    "#    [s1.data['y'][i] for i in inds],\n",
    "#          [s1.data['z'][i] for i in inds])\n",
    "# print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12) Next we prompt a user to enter an annotation and annotation code to prompt the user for. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annoatation : d, Annotation Code: d\n"
     ]
    }
   ],
   "source": [
    "\n",
    "annotationcontainer = ui_utils.annotationContainer()\n",
    "annotationcontainer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13) Using the below drop down list select the data quality code for these records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bad\n"
     ]
    }
   ],
   "source": [
    "dataqualityWidget, qdwidget =ui_utils.dataqualityWidget(DBSession)\n",
    "qdwidget\n",
    "#print(qdwidget)\n",
    "#os.write(1, str(qdwidget))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14) This creates the actual annotation database record and the associated result value annotations. This links each result value to the annotation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bad\n"
     ]
    }
   ],
   "source": [
    "print(dataqualityWidget.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time series result value annotation\n",
      "Miguel\n",
      "Lithological Influences on Contemporary and Long-Term Regolith Weathering at the Luquillo Critical Zone Observatory\n",
      "\n",
      "Buss, H.L., Lara Chapela M., Moore, O.W., Kurtz A.C., Schulz, M.S., White A.F.\n",
      "<odm2api.ODM2.models.Annotations object at 0x7f03ed866b90>\n",
      "<odm2api.ODM2.models.Annotations object at 0x7f03ed866b90>\n"
     ]
    }
   ],
   "source": [
    "annotation_type =  DBSession.query(CVAnnotationType).filter_by(Name=\"Time series result value annotation\").one() \n",
    "author =  DBSession.query(People).filter_by(PersonFirstName=\"Miguel\").one()\n",
    "citation =  DBSession.query(Citations).filter_by(CitationID=327).one() \n",
    "print(annotation_type.Name)\n",
    "print(author.PersonFirstName)\n",
    "print(citation.Title)\n",
    "tsrvavalueids = []\n",
    "annotation = Annotations(AnnotationTypeCV=annotation_type.Name,\n",
    "                           AnnotationCode = annotationcontainer.children[0].value,\n",
    "                           AnnotationText = annotationcontainer.children[1].value,\n",
    "                           AnnotationDateTime = datetime.datetime.now(),\n",
    "                           AnnotationUTCOffset = 0,\n",
    "                           AnnotationLink = 'test',\n",
    "                           AnnotatorID = 1,\n",
    "                           CitationID =327)\n",
    "print(annotation)\n",
    "DBSession.add(annotation)\n",
    "DBSession.commit()\n",
    "annotationid = annotation.AnnotationID\n",
    "print(annotation)\n",
    "for i in inds:\n",
    "    valueid = s1.data['z'][i]\n",
    "    tsrvAnnotation = TimeSeriesResultValueAnnotations(ValueID = valueid,\n",
    "                                                     AnnotationID =annotationid)\n",
    "    DBSession.add(tsrvAnnotation)\n",
    "    DBSession.commit()\n",
    "    tsrvavalueids.append(tsrvAnnotation.BridgeID)\n",
    "for i in inds:\n",
    "    valueid = s1.data['z'][i]\n",
    "    tsrv = DBSession.query(TimeSeriesResultValues).filter_by(ValueID=valueid).one()\n",
    "    tsrv.QualityCodeCV = dataqualityWidget.value\n",
    "DBSession.commit()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> 1) This will last block of code will delete the values back out of the database. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#print(tsrvavalueids)\n",
    "time_series_values_annotations = DBSession.query(TimeSeriesResultValueAnnotations).filter(\n",
    "        TimeSeriesResultValueAnnotations.BridgeID.in_(tsrvavalueids))\n",
    "for time_series_value_annotation in time_series_values_annotations:\n",
    "    DBSession.delete(time_series_value_annotation)\n",
    "DBSession.commit()\n",
    "\n",
    "DBSession.delete(annotation)\n",
    "DBSession.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21, 22, 23, 24, 25, 26, 27]\n"
     ]
    }
   ],
   "source": [
    "print(tsrvavalueids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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