{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: All output_file() calls have been replaced with output_notebook() so that plots will display inline.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Basics of Bokeh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your First Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models.tools import LassoSelectTool, CrosshairTool, HoverTool\n",
    "output_notebook()\n",
    "\n",
    "x = [1, 3, 5, 7]\n",
    "y = [2, 4, 6, 8]\n",
    "\n",
    "p = figure()\n",
    "\n",
    "p.circle(x, y, size=10, color='red',  legend='circle')\n",
    "p.line(x, y, color='blue', legend='line')\n",
    "p.triangle(y, x, color='yellow', size=10, legend='triangle')\n",
    "\n",
    "p.legend.click_policy='hide'\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bokeh and Pandas: Exploring the WWII THOR Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Bokeh ColumnDataSource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.models.tools import HoverTool\n",
    "output_notebook()\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "sample = df.sample(50)\n",
    "source = ColumnDataSource(sample)\n",
    "\n",
    "p = figure()\n",
    "p.circle(x='TOTAL_TONS', y='AC_ATTACKING', \n",
    "         source=source, \n",
    "         size=10, color='green')\n",
    "\n",
    "p.title.text = 'Attacking Aircraft and Munitions Dropped'\n",
    "p.xaxis.axis_label = 'Tons of Munitions Dropped'\n",
    "p.yaxis.axis_label = 'Number of Attacking Aircraft'\n",
    "\n",
    "hover = HoverTool()\n",
    "hover.tooltips=[\n",
    "    ('Attack Date', '@MSNDATE'),\n",
    "    ('Attacking Aircraft', '@AC_ATTACKING'),\n",
    "    ('Tons of Munitions', '@TOTAL_TONS'),\n",
    "    ('Type of Aircraft', '@AIRCRAFT_NAME')\n",
    "]\n",
    "\n",
    "p.add_tools(hover)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Data and Bar Charts: Munitions Dropped by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.models.tools import HoverTool\n",
    "\n",
    "from bokeh.palettes import Spectral5\n",
    "from bokeh.transform import factor_cmap\n",
    "output_notebook()\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "grouped = df.groupby('COUNTRY_FLYING_MISSION')['TOTAL_TONS', 'TONS_HE', 'TONS_IC', 'TONS_FRAG'].sum()\n",
    "grouped = grouped / 1000\n",
    "\n",
    "source = ColumnDataSource(grouped)\n",
    "countries = source.data['COUNTRY_FLYING_MISSION'].tolist()\n",
    "p = figure(x_range=countries)\n",
    "\n",
    "color_map = factor_cmap(field_name='COUNTRY_FLYING_MISSION', \n",
    "                    palette=Spectral5, factors=countries)\n",
    "\n",
    "p.vbar(x='COUNTRY_FLYING_MISSION', top='TOTAL_TONS', source=source, width=0.70, color=color_map)\n",
    "\n",
    "p.title.text ='Munitions Dropped by Allied Country'\n",
    "p.xaxis.axis_label = 'Country'\n",
    "p.yaxis.axis_label = 'Kilotons of Munitions'\n",
    "\n",
    "hover = HoverTool()\n",
    "hover.tooltips = [\n",
    "    (\"Totals\", \"@TONS_HE High Explosive / @TONS_IC Incendiary / @TONS_FRAG \tFragmentation\")]\n",
    "\n",
    "hover.mode = 'vline'\n",
    "\n",
    "p.add_tools(hover)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacked Bar Charts and Sub-sampling Data: Types of Munitions Dropped by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.palettes import Spectral3\n",
    "output_notebook()\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "filter = df['COUNTRY_FLYING_MISSION'].isin(('USA','GREAT BRITAIN'))\n",
    "df = df[filter]\n",
    "\n",
    "grouped = df.groupby('COUNTRY_FLYING_MISSION')['TONS_IC', 'TONS_FRAG', 'TONS_HE'].sum()\n",
    "grouped = grouped / 1000\n",
    "\n",
    "source = ColumnDataSource(grouped)\n",
    "countries = source.data['COUNTRY_FLYING_MISSION'].tolist()\n",
    "p = figure(x_range=countries)\n",
    "\n",
    "p.vbar_stack(stackers=['TONS_HE', 'TONS_FRAG', 'TONS_IC'], \n",
    "             x='COUNTRY_FLYING_MISSION', source=source, \n",
    "             legend = ['High Explosive', 'Fragmentation', 'Incendiary'],\n",
    "             width=0.5, color=Spectral3)\n",
    "\n",
    "p.title.text ='Types of Munitions Dropped by Allied Country'\n",
    "p.legend.location = 'top_left'\n",
    "\n",
    "p.xaxis.axis_label = 'Country'\n",
    "p.xgrid.grid_line_color = None\t#remove the x grid lines\n",
    "\n",
    "p.yaxis.axis_label = 'Kilotons of Munitions'\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-Series, Annotations, and Multiple Plots: Bombing Operations over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.palettes import Spectral3\n",
    "output_notebook()\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "#make sure MSNDATE is a datetime format\n",
    "df['MSNDATE'] = pd.to_datetime(df['MSNDATE'], format='%m/%d/%Y')\n",
    "\n",
    "grouped = df.groupby('MSNDATE')['TOTAL_TONS', 'TONS_IC', 'TONS_FRAG'].sum()\n",
    "grouped = grouped/1000\n",
    "\n",
    "source = ColumnDataSource(grouped)\n",
    "\n",
    "p = figure(x_axis_type='datetime')\n",
    "\n",
    "p.line(x='MSNDATE', y='TOTAL_TONS', line_width=2, source=source, legend='All Munitions')\n",
    "p.line(x='MSNDATE', y='TONS_FRAG', line_width=2, source=source, color=Spectral3[1], legend='Fragmentation')\n",
    "p.line(x='MSNDATE', y='TONS_IC', line_width=2, source=source, color=Spectral3[2], legend='Incendiary')\n",
    "\n",
    "p.yaxis.axis_label = 'Kilotons of Munitions Dropped'\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling Time-Series Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.palettes import Spectral3\n",
    "output_notebook()\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "#make sure MSNDATE is a datetime format\n",
    "df['MSNDATE'] = pd.to_datetime(df['MSNDATE'], format='%m/%d/%Y')\n",
    "\n",
    "grouped = df.groupby(pd.Grouper(key='MSNDATE', freq='M'))['TOTAL_TONS', 'TONS_IC', 'TONS_FRAG'].sum()\n",
    "grouped = grouped/1000\n",
    "\n",
    "source = ColumnDataSource(grouped)\n",
    "\n",
    "p = figure(x_axis_type='datetime')\n",
    "\n",
    "p.line(x='MSNDATE', y='TOTAL_TONS', line_width=2, source=source, legend='All Munitions')\n",
    "p.line(x='MSNDATE', y='TONS_FRAG', line_width=2, source=source, color=Spectral3[1], legend='Fragmentation')\n",
    "p.line(x='MSNDATE', y='TONS_IC', line_width=2, source=source, color=Spectral3[2], legend='Incendiary')\n",
    "\n",
    "p.yaxis.axis_label = 'Kilotons of Munitions Dropped'\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating Trends in Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from datetime import datetime\n",
    "from bokeh.palettes import Spectral3\n",
    "output_file('eto_operations.html')\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "#filter for the European Theater of Operations\n",
    "filter = df['THEATER']=='ETO'\n",
    "df = df[filter]\n",
    "\n",
    "df['MSNDATE'] = pd.to_datetime(df['MSNDATE'], format='%m/%d/%Y')\n",
    "group = df.groupby(pd.Grouper(key='MSNDATE', freq='M'))['TOTAL_TONS', 'TONS_IC', 'TONS_FRAG'].sum()\n",
    "group = group / 1000\n",
    "\n",
    "source = ColumnDataSource(group)\n",
    "\n",
    "p = figure(x_axis_type=\"datetime\")\n",
    "\n",
    "p.line(x='MSNDATE', y='TOTAL_TONS', line_width=2, source=source, legend='All Munitions')\n",
    "p.line(x='MSNDATE', y='TONS_FRAG', line_width=2, source=source, color=Spectral3[1], legend='Fragmentation')\n",
    "p.line(x='MSNDATE', y='TONS_IC', line_width=2, source=source, color=Spectral3[2], legend='Incendiary')\n",
    "\n",
    "p.title.text = 'European Theater of Operations'\n",
    "\n",
    "p.yaxis.axis_label = 'Kilotons of Munitions Dropped'\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.models import BoxAnnotation, Label\n",
    "from datetime import datetime\n",
    "from bokeh.palettes import Spectral3\n",
    "output_notebook()\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "\n",
    "#filter for the European Theater of Operations\n",
    "filter = df['THEATER']=='ETO'\n",
    "df = df[filter]\n",
    "\n",
    "df['MSNDATE'] = pd.to_datetime(df['MSNDATE'], format='%m/%d/%Y')\n",
    "group = df.groupby(pd.Grouper(key='MSNDATE', freq='M'))['TOTAL_TONS', 'TONS_IC', 'TONS_FRAG'].sum()\n",
    "group = group / 1000\n",
    "\n",
    "source = ColumnDataSource(group)\n",
    "\n",
    "p = figure(x_axis_type=\"datetime\")\n",
    "\n",
    "p.line(x='MSNDATE', y='TOTAL_TONS', line_width=2, source=source, legend='All Munitions')\n",
    "p.line(x='MSNDATE', y='TONS_FRAG', line_width=2, source=source, color=Spectral3[1], legend='Fragmentation')\n",
    "p.line(x='MSNDATE', y='TONS_IC', line_width=2, source=source, color=Spectral3[2], legend='Incendiary')\n",
    "\n",
    "p.title.text = 'European Theater of Operations'\n",
    "\n",
    "p.yaxis.axis_label = 'Kilotons of Munitions Dropped'\n",
    "\n",
    "box_left = pd.to_datetime('6-6-1944')\n",
    "box_right = pd.to_datetime('16-12-1944')\n",
    "\n",
    "box = BoxAnnotation(left=box_left, right=box_right,\n",
    "                    line_width=1, line_color='black', line_dash='dashed',\n",
    "                    fill_alpha=0.2, fill_color='orange')\n",
    "\n",
    "p.add_layout(box)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Data: Mapping Target Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bokeh.plotting import figure, output_notebook, show\n",
    "from bokeh.models import ColumnDataSource, Range1d\n",
    "from bokeh.layouts import layout\n",
    "from bokeh.palettes import Spectral3\n",
    "from bokeh.tile_providers import CARTODBPOSITRON\n",
    "from pyproj import Proj, transform\n",
    "from bokeh.models.tools import HoverTool\n",
    "\n",
    "def LongLat_to_EN(long, lat):\n",
    "    try:\n",
    "      easting, northing = transform(\n",
    "        Proj(init='epsg:4326'), Proj(init='epsg:3857'), long, lat)\n",
    "      return easting, northing\n",
    "    except:\n",
    "      return None, None\n",
    "\n",
    "df = pd.read_csv('thor_wwii.csv')\n",
    "#convert all lat/long to webmercator and store in new column\n",
    "df['E'], df['N'] = zip(*df.apply(lambda x: LongLat_to_EN(x['TGT_LONGITUDE'], x['TGT_LATITUDE']), axis=1))\n",
    "\n",
    "grouped = df.groupby(['E', 'N'])['TONS_FRAG', 'TONS_IC'].sum().reset_index()\n",
    "\n",
    "filter = grouped['TONS_FRAG']!=0\n",
    "grouped = grouped[filter]\n",
    "\n",
    "source = ColumnDataSource(grouped)\n",
    "\n",
    "left = -2150000\n",
    "right = 18000000\n",
    "bottom = -5300000\n",
    "top = 11000000\n",
    "\n",
    "p = figure(x_range=Range1d(left, right), y_range=Range1d(bottom, top))\n",
    "p.add_tile(CARTODBPOSITRON)\n",
    "\n",
    "p.circle(x='E', y='N', source=source, line_color='grey', fill_color=Spectral3[1])\n",
    "\n",
    "p.axis.visible = False\n",
    "\n",
    "hover = HoverTool(tooltips=[\n",
    "    (\"Fragmentation Bombs\", \"@TONS_FRAG tons\")\n",
    "])\n",
    "\n",
    "p.add_tools(hover)\n",
    "\n",
    "\n",
    "output_notebook()\n",
    "show(p)"
   ]
  }
 ],
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