Overview.ipynb

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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import cufflinks as cf\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "# Overview\n",
    "    analysis of pressure data from cell phones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Spatial grouping\n",
    "* Grid of 2.5 by 2.5 degree squares.\n",
    "* Each data point was assigned to a square.\n",
    "* Original data dates - May 2013 to February 2017\n",
    "* This analysis is for dates between May 2013 to September 2014 only"
   ]
  },
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   "source": [
    "df = pd.read_csv(\"data/pressure_count.csv.gz\", index_col=[0,1]).iloc[:,4:]\n",
    "\n",
    "# change df index from tuples of locations to strings for plotting. Save list of tuples\n",
    "tup_locs = df.index\n",
    "df.index = df.index.map(lambda tup: f\"{tup[0]},{tup[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## World map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "sum_loc = df.sum(axis=1)\n",
    "sum_loc.index = tup_locs\n",
    "sum_loc_df = sum_loc.reset_index()\n",
    "sum_loc_df.columns = [\"lat\", \"lng\", \"count\"]\n",
    "\n",
    "\n",
    "c_slider = widgets.FloatLogSlider(value=100, base=10, min=0, max=10, step=1, description=\"Min value\")"
   ]
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    "# layout for map plot\n",
    "grid_dict = dict(showgrid = True, gridcolor = \"black\", gridwidth=0.1, dtick=2.5)\n",
    "\n",
    "geo_dict = dict(projection_type=\"natural earth\",\n",
    "                showcountries=True, countrycolor=\"white\", showcoastlines=False, showrivers=True, showframe=False,\n",
    "                showland=True, landcolor=\"#c2c2c2\", showocean=True, oceancolor=\"#e6fcfc\",\n",
    "                showlakes=True, lakecolor=\"#3399FF\", lonaxis=grid_dict, lataxis=grid_dict)\n",
    "\n",
    "colorbar_dict = dict(thicknessmode=\"fraction\", thickness=0.01, xpad=0)\n",
    "coloraxis={\"colorbar\": colorbar_dict, \"colorscale\": \"jet\"}\n",
    "margin={\"l\":0, \"b\":0, \"t\":50}\n",
    "\n",
    "\n",
    "scatter_geo_layout = go.Layout(coloraxis=coloraxis, margin=margin, geo=geo_dict)"
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   "source": [
    "@interact\n",
    "\n",
    "def show_sum_loc_map(min_val=c_slider):\n",
    "    \n",
    "    sum_loc_filter = sum_loc_df[sum_loc_df[\"count\"] > min_val]\n",
    "    \n",
    "    fig = px.scatter_geo(sum_loc_filter, lat=\"lat\", lon=\"lng\", color=\"count\")\n",
    "    fig.update_layout(scatter_geo_layout, title=\"Count of pressure data points - world map\")\n",
    "    fig.show()"
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   "cell_type": "markdown",
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   "source": [
    "## Histogram"
   ]
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    "@interact\n",
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    "    \n",
    "    sum_loc[sum_loc > min_val].iplot(kind=\"bar\", title=\"Count of pressure data points - histogram\", \n",
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    "## Heatmap"
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    "@interact\n",
    "\n",
    "def show_sum_loc_date(log_scale=True, dropna=False, inverted_colorscale=False, \n",
    "                      colorscale=[\"rdylgn\", \"gnbu\", \"purples\", \"rdbu\"]):\n",
    "    \n",
    "    if log_scale:\n",
    "        df_to_plot = np.log10(df)\n",
    "    \n",
    "    else:\n",
    "        df_to_plot = df\n",
    "        \n",
    "    if dropna:\n",
    "        df_to_plot = df_to_plot.dropna()\n",
    "        \n",
    "    if inverted_colorscale:\n",
    "        cs = f\"-{colorscale}\"\n",
    "    else:\n",
    "        cs = colorscale\n",
    "    \n",
    "    df_to_plot.T.iplot(kind=\"heatmap\", colorscale=cs, \n",
    "                     title=\"Count of pressure data points - heatmap location vs month\",\n",
    "                     xTitle=\"Date\", yTitle=\"Location\")"
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   "source": [
    "# Monthly grouping"
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