poisson_class2.ipynb

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   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "from scipy.stats import poisson\n",
    "from scipy.signal import savgol_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure\n",
    "from bokeh.models import Span\n",
    "\n",
    "from bokeh.io import show\n",
    "from bokeh.io import output_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_poisson_for_day(date, cases, loc = 2):\n",
    "    \"\"\"\n",
    "    Calculate when the infections occurred for a single day\n",
    "    \"\"\"\n",
    "    final = []\n",
    "    for i in range(14):\n",
    "        p = poisson.pmf(i, 5, loc = loc)\n",
    "        final.append((date, p * cases))\n",
    "        date -= datetime.timedelta(days = 1)\n",
    "    return final\n",
    "\n",
    "def get_poisson_for_all_days(dates, cases):\n",
    "    temp_dict = {}\n",
    "    for i in range(len(cases)):\n",
    "        p_data =  get_poisson_for_day(dates[i], cases[i])\n",
    "        data = {}\n",
    "        for i in p_data:\n",
    "            data[i[0]] = i[1]\n",
    "        for key in data.keys():\n",
    "            if not temp_dict.get(key):\n",
    "                temp_dict[key] = data[key]\n",
    "            else:\n",
    "                temp_dict[key] += data[key]\n",
    "    final = []\n",
    "    for key in sorted(temp_dict.keys()):\n",
    "        final.append((key, temp_dict[key]))\n",
    "    return final\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "def get_lin_vals(cases):\n",
    "    x = range(len(cases))\n",
    "    X = list(zip(*[x]))\n",
    "    xm = sm.add_constant(X)\n",
    "    model = sm.OLS(cases, xm) \n",
    "    result = model.fit()\n",
    "    predictions = result.get_prediction(xm)\n",
    "    print(result.pvalues[1])\n",
    "\n",
    "    pass"
   ]
  },
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   "execution_count": 6,
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\": \"ufR9RFnRs6lniiaFvtJziE0YeidtAgBRH6ux2oUItHw5WTvE1zuk9uzhUU/FJXDp\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\": \"8QM/PGWBT+IssZuRcDcjzwIh1mkOmJSoNMmyYDZbCfXJg3Ap1lEvdVgFuSAwhb/J\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\": \"Jm8cH3Rg0P6UeZhVY5cLy1WzKajUT9KImCY+76hEqrcJt59/d8GPvFHjCkYgnSIn\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\": \"Ozhzj+SI7ywm74aOI/UajcWz+C0NjsPunEVyVIrxzYkB+jA+2tUw8x5xJCbVtK5I\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"a5431982-4ddf-4f34-b792-0dae537a8f80\" data-root-id=\"1226\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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   "source": [
    "DF = pd.read_csv('data/states.csv')\n",
    "DF['date'] = pd.to_datetime(DF['date'])\n",
    "\n",
    "wash = DF[DF['state'] == 'Washington']\n",
    "cases = wash['cases'].tolist()\n",
    "cases_smooth = savgol_filter(cases, 9, 2)\n",
    "dates = wash['date'].tolist()\n",
    "poisson_data = get_poisson_for_all_days(dates, cases)[0:-14]\n",
    "p = figure(x_axis_type = 'datetime')\n",
    "#p.line(x = dates, y = cases)\n",
    "p.line(x = [x[0] for x in poisson_data], y = [x[1] for x in poisson_data],\n",
    "      color = 'red', legend_label = 'poisson')\n",
    "p.line(x = dates, y = cases_smooth, color = 'green')\n",
    "mask_date_start = datetime.datetime(2020, 6, 26)\n",
    "mask_start = Span(location=mask_date_start,\n",
    "                            dimension='height', line_color='red',\n",
    "                            line_dash='dashed', line_width=3)\n",
    "labor_span = Span(location=datetime.datetime(2020, 9, 7),\n",
    "                            dimension='height', line_color='red',\n",
    "                            line_dash='dashed', line_width=3)\n",
    "p.add_layout(mask_start)\n",
    "p.add_layout(labor_span)\n",
    "\n",
    "\n",
    "show(p)\n"
   ]
  }
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