{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    #btable, th, tr, td {border:none!important}\n",
       "</style>\n"
      ],
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    "%%html \n",
    "<style>\n",
    "    #btable, th, tr, td {border:none!important}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Python 3.11\n",
      "Adding xrange for backwards compatibility\n",
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning: pylab import has clobbered these variables: ['histogram', 'interactive']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  warn(\"pylab import has clobbered these variables: %s\"  % clobbered +\n"
     ]
    },
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EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='326fe354-9404-482b-92de-dc1254d9edfc'>\n",
       "  <div id=\"b4db91a5-364f-49f4-a688-b68eb606feae\" data-root-id=\"326fe354-9404-482b-92de-dc1254d9edfc\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"2ee702e3-99d9-4a1f-bfbc-8d29123e29d1\":{\"version\":\"3.2.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"326fe354-9404-482b-92de-dc1254d9edfc\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"5296d95f-35b4-4b3f-b085-1d94944aa015\",\"attributes\":{\"plot_id\":\"326fe354-9404-482b-92de-dc1254d9edfc\",\"comm_id\":\"cb264afdf14e4636b8de5c723afd23bf\",\"client_comm_id\":\"502907ec45d848a2927c51bd1e140c92\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"2ee702e3-99d9-4a1f-bfbc-8d29123e29d1\",\"roots\":{\"326fe354-9404-482b-92de-dc1254d9edfc\":\"b4db91a5-364f-49f4-a688-b68eb606feae\"},\"root_ids\":[\"326fe354-9404-482b-92de-dc1254d9edfc\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['vega'] !== undefined) && ( root['vega'] !== undefined) && ( root['vega'] !== undefined) && ( root['vega'] !== undefined) && ( root['vega'] !== undefined))\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "326fe354-9404-482b-92de-dc1254d9edfc"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function, division, generators\n",
    "import sys\n",
    "print(\"Running Python {0}.{1}\".format(  \n",
    "    sys.version_info[:2][0],sys.version_info[:2][1]))\n",
    "if sys.version_info[:2] > (3, 0):\n",
    "    print(\"Adding xrange for backwards compatibility\".format(\n",
    "            sys.version_info[:2][0],sys.version_info[:2][1]))\n",
    "    from past.builtins import xrange\n",
    "#from __future__ import print_function,division,generators\n",
    "%pylab inline\n",
    "from scipy.stats import pearsonr\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from scipy.stats import kendalltau\n",
    "import seaborn as sns\n",
    "from random import randrange\n",
    "sns.set(style=\"darkgrid\")  # Optionally change plotstyle ('whitegrid', 'ticks','darkgrid')\n",
    "import altair as alt\n",
    "# Import panel and vega datasets\n",
    "import panel as pn\n",
    "pn.extension() \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "monsoon = pd.read_csv('https://raw.githubusercontent.com/Koi4595/SI-649/main/Monsoon_data.csv', parse_dates=['Date'])\n",
    "monsoon.index = monsoon.Date\n",
    "# monsoon = monsoon.drop('Date', axis=1)\n",
    "\n",
    "olou = pd.read_csv('https://raw.githubusercontent.com/Koi4595/SI-649/main/Olou_counts.csv',parse_dates=['Date'])\n",
    "olou.index = olou.Date\n",
    "# olou = olou.drop('Date', axis=1) \n",
    "olou['Date'] = pd.to_datetime(olou['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-0268947355fe48ddac38141d9a4f6d6a.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-0268947355fe48ddac38141d9a4f6d6a.vega-embed details,\n",
       "  #altair-viz-0268947355fe48ddac38141d9a4f6d6a.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-0268947355fe48ddac38141d9a4f6d6a\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-0268947355fe48ddac38141d9a4f6d6a\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-0268947355fe48ddac38141d9a4f6d6a\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"vconcat\": [{\"data\": {\"name\": \"data-a9f95f118ddd5b77f40eff0702e54119\"}, \"mark\": {\"type\": \"line\", \"interpolate\": \"step-after\"}, \"encoding\": {\"x\": {\"field\": \"Date\", \"title\": null, \"type\": \"temporal\"}, \"y\": {\"field\": \"Precip\", \"title\": \"Precipitation (mm)\", \"type\": \"quantitative\"}}, \"height\": 200, \"title\": \"Monthly Precipitation\", \"width\": 600}, {\"data\": {\"name\": \"data-be25f3be6473487276c9aafff67d1d96\"}, \"mark\": {\"type\": \"point\", \"color\": \"green\"}, \"encoding\": {\"x\": {\"field\": \"Date\", \"title\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Counts\", \"title\": \"Olou NM Counts (scaled by 1/1000)\", \"type\": \"quantitative\"}}, \"height\": 200, \"title\": \"Olou NM Counts\", \"width\": 600}], \"resolve\": {\"scale\": {\"x\": \"shared\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.16.3.json\", \"datasets\": {\"data-a9f95f118ddd5b77f40eff0702e54119\": [{\"Date\": \"1964-01-01T00:00:00\", \"Precip\": 1.4, \"Year\": 1964}, {\"Date\": \"1964-02-01T00:00:00\", \"Precip\": 5.7, \"Year\": 1964}, {\"Date\": \"1964-03-01T00:00:00\", \"Precip\": 8.3, \"Year\": 1964}, {\"Date\": \"1964-04-01T00:00:00\", \"Precip\": 24.8, \"Year\": 1964}, {\"Date\": \"1964-05-01T00:00:00\", \"Precip\": 43.7, \"Year\": 1964}, {\"Date\": \"1964-06-01T00:00:00\", \"Precip\": 143.7, \"Year\": 1964}, {\"Date\": \"1964-07-01T00:00:00\", \"Precip\": 313.0, \"Year\": 1964}, {\"Date\": \"1964-08-01T00:00:00\", \"Precip\": 263.6, \"Year\": 1964}, {\"Date\": \"1964-09-01T00:00:00\", \"Precip\": 202.1, \"Year\": 1964}, {\"Date\": \"1964-10-01T00:00:00\", \"Precip\": 73.3, \"Year\": 1964}, {\"Date\": \"1964-11-01T00:00:00\", \"Precip\": 25.7, \"Year\": 1964}, {\"Date\": \"1964-12-01T00:00:00\", \"Precip\": 6.5, \"Year\": 1964}, {\"Date\": \"1965-01-01T00:00:00\", \"Precip\": 5.5, \"Year\": 1965}, {\"Date\": 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       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create the step chart for Monsoon data\n",
    "step_chart = alt.Chart(monsoon).mark_line(interpolate='step-after').encode(\n",
    "    x=alt.X('Date:T', title=None),  # 'T' specifies temporal (time-based) data\n",
    "    y=alt.Y('Precip:Q', title='Precipitation (mm)')  # 'Q' specifies quantitative data\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Monthly Precipitation'\n",
    ")\n",
    "\n",
    "# Create the point chart for Olou data\n",
    "point_chart = alt.Chart(olou).mark_point(color='green').encode(\n",
    "    x=alt.X('Date:T', title='Date'),\n",
    "    y=alt.Y('Counts:Q', title='Olou NM Counts (scaled by 1/1000)')  # Adjust the title as necessary\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Olou NM Counts'\n",
    ")\n",
    "\n",
    "# Combine the charts\n",
    "combined_chart = alt.vconcat(step_chart, point_chart).resolve_scale(x='shared')\n",
    "\n",
    "# To display the chart in a Jupyter notebook, just call the variable\n",
    "combined_chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highlight1 = alt.selection_point(\n",
    "    on='mouseover', \n",
    "    fields=['Date'], \n",
    "    nearest=True \n",
    ")\n",
    "\n",
    "highlight2 = alt.selection_point(\n",
    "    on='mouseover', \n",
    "    fields=['Date'], \n",
    "    nearest=True \n",
    ")\n",
    "\n",
    "\n",
    "step_chart = alt.Chart(monsoon).mark_line(interpolate='step-after').encode(\n",
    "    x=alt.X('Date:T', title=None),  # 'T' specifies temporal (time-based) data\n",
    "    y=alt.Y('Precip:Q', title='Precipitation (mm)'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Precip:Q', title='preciptation')],\n",
    "    opacity=alt.condition(highlight1, alt.value(1), alt.value(0.2))  \n",
    ").add_params(\n",
    "      highlight1\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Monthly Precipitation'\n",
    ")\n",
    "\n",
    "# Create the point chart for Olou data\n",
    "point_chart = alt.Chart(olou).mark_point(color='green').encode(\n",
    "    x=alt.X('Date:T', title='Date'),\n",
    "    y=alt.Y('Counts:Q', title='Olou NM Counts'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Counts:Q', title='Counts')],\n",
    "    opacity=alt.condition(highlight2, alt.value(1), alt.value(0.2)) \n",
    ").add_params(highlight2\n",
    "             ).properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Olou NM Counts'\n",
    ")\n",
    "\n",
    "\n",
    "combined_chart = alt.vconcat(step_chart, point_chart).resolve_scale(x='shared')\n",
    "\n",
    "combined_chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"vconcat\": [{\"data\": {\"name\": \"data-a0df3222ba85397d22229db1dfc36e04\"}, \"mark\": {\"type\": \"line\", \"interpolate\": \"step-after\"}, \"encoding\": {\"opacity\": {\"condition\": {\"param\": \"param_34\", \"value\": 1}, \"value\": 0.2}, \"tooltip\": [{\"field\": \"Date\", \"title\": \"date\", \"type\": \"temporal\"}, {\"field\": \"Precip\", \"title\": \"preciptation\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"Date\", \"title\": null, \"type\": \"temporal\"}, \"y\": {\"field\": \"Precip\", \"title\": \"Precipitation (mm)\", \"type\": \"quantitative\"}}, \"height\": 200, \"name\": \"view_39\", \"title\": \"Monthly Precipitation\", \"width\": 600}, {\"data\": {\"name\": \"data-be25f3be6473487276c9aafff67d1d96\"}, \"mark\": {\"type\": \"point\", \"color\": \"green\"}, \"encoding\": {\"opacity\": {\"condition\": {\"param\": \"param_35\", \"value\": 1}, \"value\": 0.2}, \"tooltip\": [{\"field\": \"Date\", \"title\": \"date\", \"type\": \"temporal\"}, {\"field\": \"Counts\", \"title\": \"Counts\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"Date\", \"title\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Counts\", \"title\": \"Olou NM Counts\", \"type\": \"quantitative\"}}, \"height\": 200, \"name\": \"view_40\", \"title\": \"Olou NM Counts\", \"width\": 600}], \"params\": [{\"name\": \"param_34\", \"select\": {\"type\": \"point\", \"fields\": [\"Date\"], \"nearest\": true, \"on\": \"mouseover\"}, \"views\": [\"view_39\"]}, {\"name\": \"param_35\", \"select\": {\"type\": \"point\", \"fields\": [\"Date\"], \"nearest\": true, \"on\": \"mouseover\"}, \"views\": [\"view_40\"]}], \"resolve\": {\"scale\": {\"x\": \"shared\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.16.3.json\", \"datasets\": {\"data-a0df3222ba85397d22229db1dfc36e04\": [{\"Date\": \"1964-01-01T00:00:00\", \"Precip\": 1.4}, {\"Date\": 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       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monsoon['Date'] = pd.to_datetime(monsoon['Date'])\n",
    "\n",
    "step_chart = alt.Chart(monsoon).mark_line(interpolate='step-after').encode(\n",
    "    x=alt.X('Date:T', title=None),  # 'T' specifies temporal (time-based) data\n",
    "    y=alt.Y('Precip:Q', title='Precipitation (mm)'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Precip:Q', title='preciptation')],\n",
    "    opacity=alt.condition(highlight1, alt.value(1), alt.value(0.2))  \n",
    ").add_params(\n",
    "      highlight1\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Monthly Precipitation'\n",
    ")\n",
    "\n",
    "# Create the point chart for Olou data\n",
    "point_chart = alt.Chart(olou).mark_point(color='green').encode(\n",
    "    x=alt.X('Date:T', title='Date'),\n",
    "    y=alt.Y('Counts:Q', title='Olou NM Counts'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Counts:Q', title='Counts')],\n",
    "    opacity=alt.condition(highlight2, alt.value(1), alt.value(0.2)) \n",
    ").add_params(highlight2\n",
    "             ).properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Olou NM Counts'\n",
    ")\n",
    "\n",
    "\n",
    "combined_chart = alt.vconcat(step_chart, point_chart).resolve_scale(x='shared')\n",
    "\n",
    "combined_chart\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_stderr(data):\n",
    "    \"\"\"Calculate uncertainty of a np array as Standard Error of the Mean\"\"\"\n",
    "    return np.nanstd(data)/np.sqrt(np.count_nonzero(data) - 1)\n",
    "\n",
    "climo = {}       # Produce a dic of monthly climatology using list comprehension\n",
    "climo['means'] = [np.mean(monsoon.Precip[monsoon.index.month == (mnth+1)])\n",
    "                  for mnth in xrange(12)]\n",
    "climo['error'] = [return_stderr(monsoon.Precip[monsoon.index.month == (mnth+1)].values) \n",
    "                    for mnth in xrange(12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16236\\2603195274.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(labels=['Jan','Mar','May','Jul','Sep','Nov'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -- Plot the climatology --\n",
    "my_climo = plt.figure()\n",
    "my_climo.set_size_inches(5,5)\n",
    "ax1 = my_climo.add_subplot(111)\n",
    "ax1.errorbar(x=range(12),y=climo['means'],yerr=climo['error'])\n",
    "ax1.set_title(r'Precipitation climatology')\n",
    "ax1.set_ylabel(r'Precipitation (mm)')\n",
    "ax1.set_xlabel(r'Month')\n",
    "ax1.set_xlim(0,11)\n",
    "ax1.set_xticklabels(labels=['Jan','Mar','May','Jul','Sep','Nov'])\n",
    "ax1.grid(True)\n",
    "plt.show(my_climo)\n",
    "my_climo.savefig('Monthly_climo.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate monthly 𝛿 precip. (anomaly with respect to seasonal climatology)\n",
    "delta = []\n",
    "for date in monsoon.Precip.index:\n",
    "    delta.append(monsoon.Precip[date] - climo['means'][date.month-1])\n",
    "dseries = pd.Series(delta, index=monsoon.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of June July August September data\n",
    "def lookup_index(yr):\n",
    "    return ((monsoon.index.year == yr) & (monsoon.index.month >= 6) \n",
    "           &(monsoon.index.month <= 9))\n",
    "jjas = {}\n",
    "jjas['means']=[np.mean(dseries[lookup_index(yr)]) for yr in xrange(1964,2012,1)]\n",
    "jjas['SEM']=[return_stderr(dseries[lookup_index(yr)])for yr in xrange(1964,2012,1)]\n",
    "jjas['sum']=[np.sum(dseries[lookup_index(yr)]) for yr in xrange(1964,2012,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16236\\2149228777.py:16: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(jjas['means'], ax = ax2)\n",
      "c:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot precipitation anomaly\n",
    "sns.set(style=\"darkgrid\")\n",
    "yrange = xrange(1964,2012,1)\n",
    "my_jjas = plt.figure()\n",
    "my_jjas.set_size_inches(10,5)\n",
    "ax1 = my_jjas.add_subplot(121)\n",
    "ax2 = my_jjas.add_subplot(122)\n",
    "\n",
    "ax1.errorbar(x=yrange,y=jjas['means'],yerr=jjas['SEM'],fmt='.',ms=10)\n",
    "ax1.set_xlim(min(yrange)-1,max(yrange)+1)\n",
    "ax1.set_title(r'Mean JJAS precipitation anomaly')\n",
    "ax1.set_ylabel(r'$\\delta$ precipitation (mm/month)')\n",
    "ax1.set_xlabel(r'Year')\n",
    "ax1.grid(True)\n",
    "\n",
    "sns.distplot(jjas['means'], ax = ax2)\n",
    "ax2.set_title(\"Distribution of JJAS anomalies\")\n",
    "ax2.set_xlabel(r\"$\\delta$ precipitation (mm/month)\")\n",
    "ax2.set_ylabel(\"Density\")\n",
    "\n",
    "plt.show(my_jjas)\n",
    "my_jjas.savefig('delta_precip_pop.pdf',dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-e2cd74ac5eb64a8297fc17577e2cdde0.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-e2cd74ac5eb64a8297fc17577e2cdde0.vega-embed details,\n",
       "  #altair-viz-e2cd74ac5eb64a8297fc17577e2cdde0.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-e2cd74ac5eb64a8297fc17577e2cdde0\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-e2cd74ac5eb64a8297fc17577e2cdde0\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-e2cd74ac5eb64a8297fc17577e2cdde0\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.16.3\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"hconcat\": [{\"layer\": [{\"mark\": {\"type\": \"errorbar\", \"extent\": \"ci\"}, \"encoding\": {\"x\": {\"axis\": {\"values\": [1960, 1970, 1980, 1990, 2000, 2010]}, \"field\": \"Year\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"Means\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}, \"yError\": {\"field\": \"SEM\"}}, \"title\": \"Mean JJAS precipitation anomaly\"}, {\"mark\": {\"type\": \"point\", \"filled\": true}, \"encoding\": {\"x\": {\"field\": \"Year\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"Means\", \"type\": \"quantitative\"}}}], \"height\": 400, \"width\": 400}, {\"mark\": {\"type\": \"area\"}, \"encoding\": {\"tooltip\": [{\"field\": \"Means\", \"type\": \"quantitative\"}, {\"field\": \"Density\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"Means\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Density\", \"type\": \"quantitative\"}}, \"height\": 400, \"title\": \"Distribution of JJAS anomalies\", \"transform\": [{\"density\": \"Means\", \"as\": [\"Means\", \"Density\"]}], \"width\": 400}], \"data\": {\"name\": \"data-c547e8ed6a19cdc19148e5c822136630\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.16.3.json\", \"datasets\": {\"data-c547e8ed6a19cdc19148e5c822136630\": [{\"Year\": 1964, \"Means\": 23.07291666666668, \"SEM\": 15.05001163174769, \"Sum\": 92.29166666666671}, {\"Year\": 1965, \"Means\": -30.22708333333332, \"SEM\": 13.657374996038122, \"Sum\": -120.90833333333327}, {\"Year\": 1966, \"Means\": -22.55208333333332, \"SEM\": 9.731417625640658, \"Sum\": -90.20833333333329}, {\"Year\": 1967, \"Means\": 7.4479166666666785, \"SEM\": 10.720052088605135, \"Sum\": 29.791666666666714}, {\"Year\": 1968, \"Means\": -18.902083333333316, \"SEM\": 15.740881491064622, \"Sum\": -75.60833333333326}, {\"Year\": 1969, \"Means\": 0.22291666666666998, \"SEM\": 16.329544717208012, \"Sum\": 0.8916666666666799}, {\"Year\": 1970, \"Means\": 27.397916666666674, \"SEM\": 22.513368006689486, \"Sum\": 109.5916666666667}, {\"Year\": 1971, \"Means\": 14.147916666666681, \"SEM\": 15.699343231279409, \"Sum\": 56.591666666666725}, {\"Year\": 1972, \"Means\": -44.32708333333333, \"SEM\": 11.848033831454753, \"Sum\": -177.3083333333333}, {\"Year\": 1973, \"Means\": 20.772916666666674, \"SEM\": 17.763928497697137, \"Sum\": 83.0916666666667}, {\"Year\": 1974, \"Means\": -20.552083333333314, \"SEM\": 13.973549024423358, \"Sum\": -82.20833333333326}, {\"Year\": 1975, \"Means\": 33.097916666666684, \"SEM\": 10.480305742156292, \"Sum\": 132.39166666666674}, {\"Year\": 1976, \"Means\": 6.62291666666669, \"SEM\": 14.464813729421202, \"Sum\": 26.49166666666676}, {\"Year\": 1977, \"Means\": 13.222916666666684, \"SEM\": 14.935088814383347, \"Sum\": 52.89166666666674}, {\"Year\": 1978, \"Means\": 19.772916666666674, \"SEM\": 9.337045547719324, \"Sum\": 79.0916666666667}, {\"Year\": 1979, \"Means\": -30.60208333333332, \"SEM\": 5.4838401593699375, \"Sum\": -122.40833333333327}, {\"Year\": 1980, \"Means\": 13.122916666666676, \"SEM\": 16.48444351179373, \"Sum\": 52.4916666666667}, {\"Year\": 1981, \"Means\": 5.497916666666676, \"SEM\": 14.615455198908318, \"Sum\": 21.991666666666703}, {\"Year\": 1982, \"Means\": -23.75208333333333, \"SEM\": 17.068819122213814, \"Sum\": -95.00833333333333}, {\"Year\": 1983, \"Means\": 31.37291666666667, \"SEM\": 24.163718270814538, \"Sum\": 125.49166666666667}, {\"Year\": 1984, \"Means\": 1.5979166666666842, \"SEM\": 8.603144780586971, \"Sum\": 6.391666666666737}, {\"Year\": 1985, \"Means\": -17.60208333333332, \"SEM\": 3.2171744166040823, \"Sum\": -70.40833333333327}, {\"Year\": 1986, \"Means\": -21.80208333333332, \"SEM\": 13.69145881782453, \"Sum\": -87.20833333333329}, {\"Year\": 1987, \"Means\": -33.27708333333332, \"SEM\": 11.307940430618103, \"Sum\": -133.1083333333333}, {\"Year\": 1988, \"Means\": 32.82291666666667, \"SEM\": 14.014964823524513, \"Sum\": 131.29166666666669}, {\"Year\": 1989, \"Means\": 9.097916666666677, \"SEM\": 8.203488594234807, \"Sum\": 36.39166666666671}, {\"Year\": 1990, \"Means\": 19.57291666666668, \"SEM\": 9.048703259102737, \"Sum\": 78.29166666666671}, {\"Year\": 1991, \"Means\": -11.227083333333322, \"SEM\": 12.358229494019435, \"Sum\": -44.90833333333329}, {\"Year\": 1992, \"Means\": -11.327083333333327, \"SEM\": 14.585554691930518, \"Sum\": -45.30833333333331}, {\"Year\": 1993, \"Means\": 9.122916666666669, \"SEM\": 18.112242716650293, \"Sum\": 36.491666666666674}, {\"Year\": 1994, \"Means\": 30.64791666666668, \"SEM\": 18.081774206340317, \"Sum\": 122.59166666666673}, {\"Year\": 1995, \"Means\": -7.477083333333319, \"SEM\": 9.732941065392692, \"Sum\": -29.908333333333275}, {\"Year\": 1996, \"Means\": 7.122916666666676, \"SEM\": 13.47097198523002, \"Sum\": 28.491666666666703}, {\"Year\": 1997, \"Means\": 10.322916666666686, \"SEM\": 5.857082854133553, \"Sum\": 41.29166666666674}, {\"Year\": 1998, \"Means\": 7.29791666666668, \"SEM\": 13.064001220664307, \"Sum\": 29.19166666666672}, {\"Year\": 1999, \"Means\": -2.0520833333333286, \"SEM\": 11.958167947946547, \"Sum\": -8.208333333333314}, {\"Year\": 2000, \"Means\": -12.977083333333319, \"SEM\": 13.487234297832506, \"Sum\": -51.908333333333275}, {\"Year\": 2001, \"Means\": -9.177083333333325, \"SEM\": 28.65601672529148, \"Sum\": -36.7083333333333}, {\"Year\": 2002, \"Means\": -42.05208333333333, \"SEM\": 35.33536063530203, \"Sum\": -168.20833333333331}, {\"Year\": 2003, \"Means\": 4.84791666666667, \"SEM\": 8.96879637683195, \"Sum\": 19.39166666666668}, {\"Year\": 2004, \"Means\": -21.352083333333326, \"SEM\": 10.560178218412307, \"Sum\": -85.4083333333333}, {\"Year\": 2005, \"Means\": 6.672916666666673, \"SEM\": 26.64638557111695, \"Sum\": 26.69166666666669}, {\"Year\": 2006, \"Means\": 9.947916666666664, \"SEM\": 7.748490182980211, \"Sum\": 39.79166666666666}, {\"Year\": 2007, \"Means\": 26.222916666666684, \"SEM\": 9.220644265521589, \"Sum\": 104.89166666666674}, {\"Year\": 2008, \"Means\": 14.222916666666677, \"SEM\": 14.733368782165753, \"Sum\": 56.89166666666671}, {\"Year\": 2009, \"Means\": -40.62708333333331, \"SEM\": 20.412860260253332, \"Sum\": -162.50833333333324}, {\"Year\": 2010, \"Means\": 3.8729166666666757, \"SEM\": 12.570976387614568, \"Sum\": 15.491666666666703}, {\"Year\": 2011, \"Means\": 12.697916666666679, \"SEM\": 14.445099711365666, \"Sum\": 50.791666666666714}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your previous code to calculate the jjas dictionary...\n",
    "\n",
    "# Import the required libraries\n",
    "import altair as alt\n",
    "import pandas as pd\n",
    "\n",
    "# Convert the 'jjas' dictionary to a pandas DataFrame\n",
    "jjas_df = pd.DataFrame({\n",
    "    'Year': range(1964, 2012),\n",
    "    'Means': jjas['means'],\n",
    "    'SEM': jjas['SEM'],\n",
    "    'Sum': jjas['sum']\n",
    "})\n",
    "\n",
    "# Make sure to enable the appropriate renderer\n",
    "alt.renderers.enable('default')\n",
    "\n",
    "# Create the error bar chart\n",
    "error_bars = alt.Chart(jjas_df).mark_errorbar(extent='ci').encode(\n",
    "    x=alt.X('Year:O', axis=alt.Axis(values=list(range(1960, 2011, 10)))),  # 每10年显示一次刻度\n",
    "    y=alt.Y('Means:Q', scale=alt.Scale(zero=False)),\n",
    "    yError='SEM:Q'\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=400,\n",
    "    title='Mean JJAS precipitation anomaly'\n",
    ")\n",
    "\n",
    "# Combine the error bars with point marks\n",
    "points = alt.Chart(jjas_df).mark_point(filled=True).encode(\n",
    "    x='Year:O',\n",
    "    y='Means:Q'\n",
    ")\n",
    "\n",
    "error_chart = error_bars + points  # Layering the points on top of the error bars\n",
    "\n",
    "error_chart\n",
    "histogram = alt.Chart(jjas_df).transform_density(\n",
    "    density='Means',\n",
    "    as_=['Means', 'Density']\n",
    ").mark_area().encode(\n",
    "    x=\"Means:Q\",\n",
    "    y='Density:Q',\n",
    "    tooltip=['Means:Q', 'Density:Q']\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=400,\n",
    "    title='Distribution of JJAS anomalies'\n",
    ")\n",
    "\n",
    "histogram\n",
    "\n",
    "chart2=alt.hconcat(error_chart,histogram)\n",
    "chart2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cframe(c_years):\n",
    "    '''\n",
    "    Function to take a list of composite years (c_years)\n",
    "    and create a numpy array (c_years, months) for analysis.\n",
    "    Also returns back a month-wise set of means, and SEM values.\n",
    "    '''\n",
    "    c_group = np.zeros((12,12),dtype=float) \n",
    "    for n, yr in enumerate(c_years):\n",
    "        tmp = olou.index.year == yr\n",
    "        for i in xrange(len(olou.Counts[tmp])):\n",
    "            c_group[n,i] = olou.Counts[tmp][i]\n",
    "    aaa = np.where(c_group == 0)\n",
    "    c_group[aaa] = np.nan\n",
    "    c_means = []\n",
    "    c_errors = []\n",
    "    for i in xrange(12):\n",
    "        c_means.append(np.nanmean(c_group[:,i]))   # per month, all years\n",
    "        c_errors.append(return_stderr(c_group[:,i]))\n",
    "    return c_group,c_means,c_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16236\\2318271557.py:11: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  c_group[n,i] = olou.Counts[tmp][i]\n"
     ]
    }
   ],
   "source": [
    "drought_years = [1965, 1966, 1968, 1972, 1974,1979,\n",
    "                 1982, 1986, 1987, 2002, 2004, 2009]\n",
    "\n",
    "flood_years = [1964, 1970, 1971, 1973, 1975, 1978, \n",
    "               1983, 1988, 1990, 1994, 2007, 2008]\n",
    "\n",
    "d_group,d_means,d_errors = make_cframe(drought_years)\n",
    "f_group,f_means,f_errors = make_cframe(flood_years)\n",
    "\n",
    "d_means = np.array(d_means) * 0.001\n",
    "f_means = np.array(f_means) * 0.001\n",
    "d_errors = np.array(d_errors) * 0.001    # Make the values smaller for plotting\n",
    "f_errors = np.array(f_errors) * 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16236\\2648658402.py:19: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xlabs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 748x354 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a composite plot of the BA15 data. Emphasise the May to September period \n",
    "# without removing the rest of the data. This is useful for people to see. \n",
    "# Use diffrent line properties to acheive this effect.\n",
    "\n",
    "def simBA15plot(ax, dataset, derr, col_key):\n",
    "    \"\"\"\n",
    "    Set the plot and properties of the figure sub-pannels.\n",
    "    \"\"\"\n",
    "    lthick=1.0\n",
    "    ax.plot(mrange[0:5], dataset[0:5], 'k--',lw=lthick)  # Jan to May\n",
    "    ax.plot(mrange[4:9], dataset[4:9], 'k-',lw=lthick)   # May to Sep\n",
    "    ax.plot(mrange[8:], dataset[8:], 'k--',lw=lthick)    # Sep to Dec\n",
    "    ax.fill_between(mrange[0:5],(dataset[0:5] - derr[0:5]),\n",
    "                    (dataset[0:5] + derr[0:5]), color=col_key, linewidth=0.1,alpha=0.15)\n",
    "    ax.fill_between(mrange[4:9], (dataset[4:9] - derr[4:9]), (dataset[4:9] + derr[4:9]),\n",
    "                    color=col_key, linewidth=0.1, alpha=0.3)\n",
    "    ax.fill_between(mrange[8:],(dataset[8:] - derr[8:]), (dataset[8:] + derr[8:]),\n",
    "                    color=col_key, linewidth=0.1, alpha=0.15)\n",
    "    ax.set_xticklabels(xlabs)\n",
    "    ax.set_xlim(0,11)\n",
    "    return\n",
    "\n",
    "mrange = arange(0,12)\n",
    "xlabs =['Jan','Mar','May','Jul','Sep','Nov']\n",
    "\n",
    "figBA15 = plt.figure()\n",
    "figBA15.set_size_inches(7.48,3.54)\n",
    "ax1 = figBA15.add_subplot(121)          \n",
    "ax2 = figBA15.add_subplot(122) \n",
    "simBA15plot(ax=ax1, dataset=d_means, derr=d_errors, col_key='r')\n",
    "simBA15plot(ax=ax2, dataset=f_means, derr=f_errors, col_key='b')\n",
    "ax1.set_ylabel(r\"Neutron counts (cnt./min.$\\times10^{3}$)\", fontsize=11)\n",
    "ax1.set_title('Drought sample')\n",
    "ax2.set_title('Flood sample')\n",
    "plt.show(figBA15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16236\\461619174.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xlabs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 748x354 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa65e6e1640e434897fc4c5c22e2c67a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Type:', options=('Drought', 'Flood'), value='Drought'), Checkbox(v…"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import widgets, interactive\n",
    "def simBA15plot(ax, dataset, derr, col_key):\n",
    "    \"\"\"\n",
    "    Set the plot and properties of the figure sub-pannels.\n",
    "    \"\"\"\n",
    "    lthick=1.0\n",
    "    ax.plot(mrange[0:5], dataset[0:5], 'k--',lw=lthick)  # Jan to May\n",
    "    ax.plot(mrange[4:9], dataset[4:9], 'k-',lw=lthick)   # May to Sep\n",
    "    ax.plot(mrange[8:], dataset[8:], 'k--',lw=lthick)    # Sep to Dec\n",
    "    ax.fill_between(mrange[0:5],(dataset[0:5] - derr[0:5]),\n",
    "                    (dataset[0:5] + derr[0:5]), color=col_key, linewidth=0.1,alpha=0.15)\n",
    "    ax.fill_between(mrange[4:9], (dataset[4:9] - derr[4:9]), (dataset[4:9] + derr[4:9]),\n",
    "                    color=col_key, linewidth=0.1, alpha=0.3)\n",
    "    ax.fill_between(mrange[8:],(dataset[8:] - derr[8:]), (dataset[8:] + derr[8:]),\n",
    "                    color=col_key, linewidth=0.1, alpha=0.15)\n",
    "    ax.set_xticklabels(xlabs)\n",
    "    ax.set_xlim(0,11)\n",
    "    return\n",
    "\n",
    "mrange = arange(0,12)\n",
    "xlabs =['Jan','Mar','May','Jul','Sep','Nov']\n",
    "\n",
    "figBA15 = plt.figure()\n",
    "figBA15.set_size_inches(7.48,3.54)\n",
    "ax1 = figBA15.add_subplot(121)          \n",
    "ax2 = figBA15.add_subplot(122) \n",
    "simBA15plot(ax=ax1, dataset=d_means, derr=d_errors, col_key='r')\n",
    "simBA15plot(ax=ax2, dataset=f_means, derr=f_errors, col_key='b')\n",
    "ax1.set_ylabel(r\"Neutron counts (cnt./min.$\\times10^{3}$)\", fontsize=11)\n",
    "ax1.set_title('Drought sample')\n",
    "ax2.set_title('Flood sample')\n",
    "plt.show(figBA15)\n",
    "\n",
    "def update_chart(data_set='Drought', show_error=True):\n",
    "    dataset = d_means if data_set == 'Drought' else f_means\n",
    "    derr = d_errors if data_set == 'Drought' else f_errors\n",
    "    col_key = 'r' if data_set == 'Drought' else 'b'\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(7.48, 3.54))\n",
    "    ax.plot(mrange, dataset, 'k-', lw=1.0)\n",
    "    if show_error:\n",
    "        ax.fill_between(mrange, dataset - derr, dataset + derr, color=col_key, alpha=0.3)\n",
    "    ax.set_xticks(np.arange(len(xlabs)))\n",
    "    ax.set_ylabel(r\"Neutron counts (cnt./min.$\\times10^{3}$)\", fontsize=11)\n",
    "    ax.set_xticklabels(xlabs)\n",
    "    ax.set_title(f'{data_set} sample')\n",
    "    plt.show()\n",
    "\n",
    "# 交互式控件\n",
    "data_set_dropdown = widgets.Dropdown(options=['Drought', 'Flood'], description='Type:')\n",
    "show_error_checkbox = widgets.Checkbox(value=True, description='Show Error')\n",
    "\n",
    "interactive_plot = interactive(update_chart, data_set=data_set_dropdown, show_error=show_error_checkbox)\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A Pearson's r test, gives linear regressions and two-tailed p-values of:\n",
      "Drought sample: r-value = -0.949, p-value = 0.014\n",
      "Flood sample: r-value = 0.001, p-value = 0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c47a46ddad614dffa57307790bc22cb2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Sample:', options=('Both', 'Drought', 'Flood'), value='Both'), Int…"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interactive, IntSlider, FloatSlider, Dropdown\n",
    "rval_d,pval_d = pearsonr(xrange(5),d_means[4:9])\n",
    "rval_f,pval_f = pearsonr(xrange(5),f_means[4:9])\n",
    "print(\"A Pearson's r test, gives linear regressions and two-tailed p-values of:\")\n",
    "print(\"Drought sample: r-value = {0:4.3f}, p-value = {1:4.3f}\".format(rval_d, pval_d))     \n",
    "print(\"Flood sample: r-value = {1:4.3f}, p-value = {1:4.3f}\".format(rval_f, pval_f))\n",
    "# Some more functions...\n",
    "def bootstrap_r(mean_list, error_list, iterations=1000):\n",
    "    \"\"\"\n",
    "    Bootstrap info. Error and means are set up to accept data from make_cframe()\n",
    "    which is why the offsets within the arrays have been hard-coded to the months\n",
    "    of key interest (monsoon period).\n",
    "    Guess a possible min and max (poss_min/max) values based on observed mean and \n",
    "    calculate SEM values. Assume an equal chance of any value in that range occurring.\n",
    "    For a specified number of realization (controlled by iterations) calculate the \n",
    "    r-value and p-value, of the linear correlation. Save them to a MC array.\n",
    "    \"\"\"\n",
    "    bs_rvals = []\n",
    "    bs_pvals = []\n",
    "    for itr in xrange(iterations):\n",
    "        poss_vals = []   # List for possible values of CR flux\n",
    "        for n in range(5):\n",
    "            #nb. Below factor preserves sig. figs. in change float > ints\n",
    "            poss_min = int((mean_list[4 + n] - error_list[4 + n]) * 100000) \n",
    "            poss_max = int((mean_list[4 + n] + error_list[4 + n]) * 100000)\n",
    "            poss_vals.append(randrange(poss_min,poss_max)/100)\n",
    "            #print(4+n,poss_min/100,poss_max/100,poss_vals[-1])\n",
    "        rv, pv = pearsonr([0,1,2,3,4],poss_vals) # rval, pval\n",
    "        bs_rvals.append(rv)\n",
    "        bs_pvals.append(pv)\n",
    "    bs_rvals = np.array(bs_rvals)\n",
    "    bs_pvals = np.array(bs_pvals)\n",
    "    return bs_rvals, bs_pvals\n",
    "\n",
    "\n",
    "def freedman_diaconis_bins(a):\n",
    "    \"\"\"Calculate number of hist bins using Freedman-Diaconis rule.\"\"\"\n",
    "    # From http://stats.stackexchange.com/questions/798/\n",
    "    a = np.asarray(a)\n",
    "    h = 2 * iqr(a) / (len(a) ** (1 / 3))\n",
    "    # fall back to sqrt(a) bins if iqr is 0\n",
    "    if h == 0:\n",
    "        return int(np.sqrt(a.size))\n",
    "    else:\n",
    "        return int(np.ceil((a.max() - a.min()) / h))\n",
    "    \n",
    "\n",
    "    \n",
    "def iqr(data):\n",
    "    \"\"\"Return Inter Quartile Range\"\"\"\n",
    "    q75, q25 = np.percentile(data, [75 ,25])\n",
    "    return q75 - q25\n",
    "\n",
    "def add_hist(data, col_key, axobj, mkstyle='o', \n",
    "             obsval=None, mylabel=None, bin_num=None):\n",
    "    \"\"\"\n",
    "    Custom Plotting function for histograms.\n",
    "    data - np.array of values, e.g. generated by bootstrap_r()\n",
    "    col_key - code for setting color\n",
    "    axobj - axis object to add the plot to\n",
    "    mylabel - a label for the plotting legend\n",
    "    obsval - the observed r-value for comparison (e.g. rval_d)\n",
    "    mkstyle - matplotlib marker style (default set to circle 'o')\n",
    "    \"\"\"\n",
    "    if not bin_num:\n",
    "        bin_num = freedman_diaconis_bins(data) #if no bins set, use FD spacing\n",
    "    hist, bin_edges = np.histogram(data, bins=bin_num, density=False)\n",
    "    norm_hist = hist / sum(hist) # Normalize the data to show density\n",
    "    axobj.bar(bin_edges[0:-1], norm_hist, width = bin_edges[1] - bin_edges[0], \n",
    "            color = col_key, edgecolor = col_key, alpha = 0.3, label=mylabel)\n",
    "    mylabel = None\n",
    "    if obsval:\n",
    "        lookup = np.where(abs(obsval - bin_edges[0:-1]) == \n",
    "                          min(abs(obsval - bin_edges[0:-1])))\n",
    "        axobj.vlines(obsval,0,norm_hist[lookup], linestyles='dashed',\n",
    "               lw=1.0, zorder=6, label=mylabel)\n",
    "        axobj.plot(obsval, norm_hist[lookup], color='k', marker=mkstyle, \n",
    "                   ms=5., zorder=7, label=mylabel)\n",
    "    if bin_num is not None:\n",
    "     bin_num = int(bin_num) \n",
    "\n",
    "rbs1, pbs1 = bootstrap_r(mean_list = d_means, error_list = d_errors)\n",
    "rbs2, pbs2 = bootstrap_r(mean_list = f_means, error_list = f_errors)\n",
    "\n",
    "#---- Create plot object 1 (possible r and p-vals)\n",
    "possible_r = plt.figure()\n",
    "possible_r.set_size_inches(10,5)\n",
    "ax1 = possible_r.add_subplot(121)\n",
    "ax2 = possible_r.add_subplot(122)\n",
    "\n",
    "# --- Pannel 1 (r-values)\n",
    "add_hist(data=rbs1, col_key='r', axobj=ax1, obsval=rval_d, \n",
    "         mylabel=\"Drought sample\", mkstyle='o')\n",
    "add_hist(data=rbs2, col_key='b', axobj=ax1, obsval=rval_f, \n",
    "         mylabel=\"Flood sample\", mkstyle='D')\n",
    "ax1.legend(loc=9, prop={'size':11}, numpoints=1, markerscale=5.,\n",
    "            frameon=True, fancybox=True)\n",
    "\n",
    "#ax1.set_xlim(-1,1)\n",
    "ax1.set_ylabel('Density')\n",
    "ax1.set_xlabel('$r$-values')\n",
    "ax1.set_title('Potential $r$-values from Bootstrap')\n",
    "\n",
    "# --- Pannel 2 (p-values)\n",
    "add_hist(data=pbs1, col_key='r', axobj=ax2, bin_num=25,\n",
    "        obsval=pval_d, mkstyle='o')\n",
    "add_hist(data=pbs2, col_key='b', axobj=ax2, bin_num=25,\n",
    "        obsval=pval_f, mkstyle='D')\n",
    "\n",
    "ax3 = ax2.twinx()\n",
    "sns.kdeplot(pbs1, cumulative=True, color='r', ax=ax3, \n",
    "            lw=1, alpha=0.3, zorder = 10 )\n",
    "sns.kdeplot(pbs2, cumulative=True, color='b', ax=ax3, \n",
    "            lw=1, alpha=0.3, zorder = 11)\n",
    "ax3.grid(False)\n",
    "ax3.set_ylabel(\"Cumulative density\")\n",
    "\n",
    "ax2.set_title(r'Potential $p$-values from Bootstrap')\n",
    "ax2.set_xlim(0,1)\n",
    "ax2.set_xlabel(r'$p$-value')\n",
    "plt.show(possible_r)\n",
    "possible_r.savefig('possible_rvals.pdf',dpi=300)\n",
    "\n",
    "def update_plots(sample_type, bin_num_r, bin_num_p):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    \n",
    "    # 第一个图表 - r-values\n",
    "    if sample_type == 'Drought' or sample_type == 'Both':\n",
    "        add_hist(data=rbs1, col_key='r', axobj=ax1, obsval=rval_d, \n",
    "                 mylabel=\"Drought sample\", mkstyle='o', bin_num=bin_num_r)\n",
    "    if sample_type == 'Flood' or sample_type == 'Both':\n",
    "        add_hist(data=rbs2, col_key='b', axobj=ax1, obsval=rval_f, \n",
    "                 mylabel=\"Flood sample\", mkstyle='D', bin_num=bin_num_r)\n",
    "    ax1.legend(loc='upper right')\n",
    "    ax1.set_ylabel('Density')\n",
    "    ax1.set_xlabel('$r$-values')\n",
    "    ax1.set_title('Potential $r$-values from Bootstrap')\n",
    "\n",
    "    # 第二个图表 - p-values\n",
    "    if sample_type == 'Drought' or sample_type == 'Both':\n",
    "        add_hist(data=pbs1, col_key='r', axobj=ax2, obsval=pval_d, mkstyle='o', bin_num=bin_num_p)\n",
    "    if sample_type == 'Flood' or sample_type == 'Both':\n",
    "        add_hist(data=pbs2, col_key='b', axobj=ax2, obsval=pval_f, mkstyle='D', bin_num=bin_num_p)\n",
    "\n",
    "    ax3 = ax2.twinx()\n",
    "    if sample_type == 'Drought' or sample_type == 'Both':\n",
    "        sns.kdeplot(pbs1, cumulative=True, color='r', ax=ax3, \n",
    "                    lw=1, alpha=0.3, zorder=10)\n",
    "    if sample_type == 'Flood' or sample_type == 'Both':\n",
    "        sns.kdeplot(pbs2, cumulative=True, color='b', ax=ax3, \n",
    "                    lw=1, alpha=0.3, zorder=11)\n",
    "    ax3.grid(False)\n",
    "    ax3.set_ylabel(\"Cumulative density\")\n",
    "    ax2.set_xlabel(r'$p$-value')\n",
    "    ax2.set_title(r'Potential $p$-values from Bootstrap')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "sample_dropdown = Dropdown(options=['Both', 'Drought', 'Flood'], value='Both', description='Sample:')\n",
    "bin_slider_r = IntSlider(value=30, min=10, max=100, step=1, description='r-value bins:')\n",
    "bin_slider_p = IntSlider(value=25, min=10, max=100, step=1, description='p-value bins:')\n",
    "\n",
    "interactive_plot = interactive(update_plots, sample_type=sample_dropdown, bin_num_r=bin_slider_r, bin_num_p=bin_slider_p)\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.16.3?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 已知的干旱和洪水年份\n",
    "drought_years = [1965, 1966, 1968, 1972, 1974, 1979, 1982, 1986, 1987, 2002, 2004, 2009]\n",
    "flood_years = [1964, 1970, 1971, 1973, 1975, 1978, 1983, 1988, 1990, 1994, 2007, 2008]\n",
    "\n",
    "# 创建包含这些年份的数据框\n",
    "drought_data = pd.DataFrame({'Year': drought_years})\n",
    "flood_data = pd.DataFrame({'Year': flood_years})\n",
    "\n",
    "# 将年份转换为日期时间，这里我们将它们设置为每年的第一天\n",
    "drought_data['Date'] = pd.to_datetime(drought_data['Year'], format='%Y')\n",
    "flood_data['Date'] = pd.to_datetime(flood_data['Year'], format='%Y')\n",
    "\n",
    "# 使用这些数据框创建规则标记\n",
    "drought_marks = alt.Chart(drought_data).mark_rule(color='red', size=2).encode(\n",
    "    x='Date:T'\n",
    ")\n",
    "\n",
    "flood_marks = alt.Chart(flood_data).mark_rule(color='blue', size=2).encode(\n",
    "    x='Date:T'\n",
    ")\n",
    "monsoon['Date'] = pd.to_datetime(monsoon['Date'])\n",
    "\n",
    "step_chart = alt.Chart(monsoon).mark_line(interpolate='step-after').encode(\n",
    "    x=alt.X('Date:T', title=None),  # 'T' specifies temporal (time-based) data\n",
    "    y=alt.Y('Precip:Q', title='Precipitation (mm)'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Precip:Q', title='preciptation')],\n",
    "    opacity=alt.condition(highlight1, alt.value(1), alt.value(0.2))  \n",
    ").add_params(\n",
    "      highlight1\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Monthly Precipitation'\n",
    ")\n",
    "\n",
    "# Create the point chart for Olou data\n",
    "point_chart = alt.Chart(olou).mark_point(color='green').encode(\n",
    "    x=alt.X('Date:T', title='Date'),\n",
    "    y=alt.Y('Counts:Q', title='Olou NM Counts'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Counts:Q', title='Counts')],\n",
    "    opacity=alt.condition(highlight2, alt.value(1), alt.value(0.2)) \n",
    ").add_params(highlight2\n",
    "             ).properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Olou NM Counts'\n",
    ")\n",
    "\n",
    "\n",
    "combined_chart = alt.vconcat(\n",
    "    step_chart + drought_marks + flood_marks,  # 添加干旱和洪水标记到降水图表\n",
    "    point_chart  # NM计数图表保持不变\n",
    ").resolve_scale(x='shared')\n",
    "\n",
    "combined_chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d659ed9f0f0742b0b7180f8cf5f08a7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Event Type:', index=2, options=('Drought', 'Flood', 'All'), value=…"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "import ipywidgets as widgets\n",
    "\n",
    "# 假设 monsoon 和 olou 是已经存在的 DataFrame\n",
    "\n",
    "# 已知的干旱和洪水年份\n",
    "drought_years = [1965, 1966, 1968, 1972, 1974, 1979, 1982, 1986, 1987, 2002, 2004, 2009]\n",
    "flood_years = [1964, 1970, 1971, 1973, 1975, 1978, 1983, 1988, 1990, 1994, 2007, 2008]\n",
    "\n",
    "# 创建包含这些年份的数据框\n",
    "drought_data = pd.DataFrame({'Year': drought_years, 'Event': 'Drought'})\n",
    "flood_data = pd.DataFrame({'Year': flood_years, 'Event': 'Flood'})\n",
    "\n",
    "# 合并干旱和洪水数据\n",
    "events_data = pd.concat([drought_data, flood_data], ignore_index=True)\n",
    "\n",
    "# 将年份转换为日期时间，这里我们将它们设置为每年的第一天\n",
    "events_data['Year'] = pd.to_datetime(events_data['Year'], format='%Y')\n",
    "\n",
    "monsoon['Date'] = pd.to_datetime(monsoon['Date'])\n",
    "\n",
    "step_chart = alt.Chart(monsoon).mark_line(interpolate='step-after').encode(\n",
    "    x=alt.X('Date:T', title=None),  # 'T' specifies temporal (time-based) data\n",
    "    y=alt.Y('Precip:Q', title='Precipitation (mm)'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Precip:Q', title='preciptation')],\n",
    "    opacity=alt.condition(highlight1, alt.value(1), alt.value(0.2))  \n",
    ").add_params(\n",
    "      highlight1\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Monthly Precipitation'\n",
    ")\n",
    "\n",
    "# Create the point chart for Olou data\n",
    "point_chart = alt.Chart(olou).mark_point(color='green').encode(\n",
    "    x=alt.X('Date:T', title='Date'),\n",
    "    y=alt.Y('Counts:Q', title='Olou NM Counts'),\n",
    "    tooltip=[alt.Tooltip('Date:T', title='date'), alt.Tooltip('Counts:Q', title='Counts')],\n",
    "    opacity=alt.condition(highlight2, alt.value(1), alt.value(0.2)) \n",
    ").add_params(highlight2\n",
    "             ).properties(\n",
    "    width=600,\n",
    "    height=200,\n",
    "    title='Olou NM Counts'\n",
    ")\n",
    "\n",
    "monsoon['Year'] = monsoon['Date'].dt.year  # 这假设 'Date' 列已经是 datetime 类型\n",
    "\n",
    "# 假设这里的 monsoon 和 olou 是已经存在的 DataFrame\n",
    "# 并且您有相应的 Altair 图表代码\n",
    "\n",
    "# 创建一个选择器 widget\n",
    "selector = widgets.Dropdown(\n",
    "    options=['Drought', 'Flood', 'All'],\n",
    "    value='All',\n",
    "    description='Event Type:',\n",
    ")\n",
    "\n",
    "# 定义一个更新图表的函数\n",
    "def update_chart(event_type):\n",
    "    # 根据选择过滤数据\n",
    "    if event_type == 'Drought':\n",
    "        filtered_data = monsoon[monsoon['Year'].isin(drought_years)]\n",
    "    elif event_type == 'Flood':\n",
    "        filtered_data = monsoon[monsoon['Year'].isin(flood_years)]\n",
    "    else:\n",
    "        filtered_data = monsoon\n",
    "\n",
    "    # 创建 Altair 图表\n",
    "    chart = alt.Chart(filtered_data).mark_line().encode(\n",
    "        x='Date:T',\n",
    "        y='Precip:Q',\n",
    "        tooltip=['Date', 'Precip']\n",
    "    ).properties(\n",
    "        width=600,\n",
    "        height=200\n",
    "    )\n",
    "    \n",
    "    # 显示图表\n",
    "    return chart.display()\n",
    "\n",
    "# 创建一个交互式 widget，将 selector 的值传递给 update_chart 函数\n",
    "interactive(update_chart, event_type=selector)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d820cd0cde214fc9a53aa79a952ce9ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Event Type:', options=('All', 'Drought', 'Flood'), value='All')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e669496b7174862896eba631c1874f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from IPython.display import clear_output  \n",
    "import pandas as pd\n",
    "\n",
    "# 这里是您提供的干旱和洪水年份列表\n",
    "drought_years = [1965, 1966, 1968, 1972, 1974, 1979, 1982, 1986, 1987, 2002, 2004, 2009]\n",
    "flood_years = [1964, 1970, 1971, 1973, 1975, 1978, 1983, 1988, 1990, 1994, 2007, 2008]\n",
    "\n",
    "\n",
    "out = widgets.Output()\n",
    "\n",
    "event_selector = widgets.Dropdown(\n",
    "    options=['All', 'Drought', 'Flood'],\n",
    "    value='All',  # 初始状态设置为“All”\n",
    "    description='Event Type:',\n",
    ")\n",
    "\n",
    "def plot_timeseries(event_type):\n",
    "    with out:\n",
    "        clear_output(wait=True)\n",
    "        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
    "        \n",
    "        if event_type == 'Drought':\n",
    "            monsoon_selected_years = monsoon[monsoon['Date'].dt.year.isin(drought_years)]\n",
    "            olou_selected_years = olou[olou['Date'].dt.year.isin(drought_years)]\n",
    "        elif event_type == 'Flood':\n",
    "            monsoon_selected_years = monsoon[monsoon['Date'].dt.year.isin(flood_years)]\n",
    "            olou_selected_years = olou[olou['Date'].dt.year.isin(flood_years)]\n",
    "        else:\n",
    "            monsoon_selected_years = monsoon\n",
    "            olou_selected_years = olou\n",
    "        \n",
    "\n",
    "        ax1.step(monsoon_selected_years['Date'], monsoon_selected_years['Precip'], where='mid', color='blue')\n",
    "        ax1.set_title('Monthly Precipitation for Selected Years')\n",
    "        ax1.set_ylabel('Precipitation (mm)')\n",
    "        ax1.grid(True)\n",
    "\n",
    "        ax2.plot(olou_selected_years['Date'], olou_selected_years['Counts']/1000, 'r.', ms=3.0)\n",
    "        ax2.set_ylabel('Olou NM Counts for Selected Years (cnt./min. x 10^3)')\n",
    "        ax2.set_xlabel('Date')\n",
    "        ax2.grid(True)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "def on_selector_change(change):\n",
    "    plot_timeseries(change.new)\n",
    "\n",
    "event_selector.observe(on_selector_change, names='value')\n",
    "\n",
    "out = widgets.Output()\n",
    "display(event_selector, out)\n",
    "\n",
    "plot_timeseries(event_selector.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
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1965, \"Means\": -30.22708333333332, \"SEM\": 13.657374996038122, \"Sum\": -120.90833333333327, \"Condition\": \"Drought\"}, {\"Year\": 1966, \"Means\": -22.55208333333332, \"SEM\": 9.731417625640658, \"Sum\": -90.20833333333329, \"Condition\": \"Drought\"}, {\"Year\": 1967, \"Means\": 7.4479166666666785, \"SEM\": 10.720052088605135, \"Sum\": 29.791666666666714, \"Condition\": \"Normal\"}, {\"Year\": 1968, \"Means\": -18.902083333333316, \"SEM\": 15.740881491064622, \"Sum\": -75.60833333333326, \"Condition\": \"Drought\"}, {\"Year\": 1969, \"Means\": 0.22291666666666998, \"SEM\": 16.329544717208012, \"Sum\": 0.8916666666666799, \"Condition\": \"Normal\"}, {\"Year\": 1970, \"Means\": 27.397916666666674, \"SEM\": 22.513368006689486, \"Sum\": 109.5916666666667, \"Condition\": \"Flood\"}, {\"Year\": 1971, \"Means\": 14.147916666666681, \"SEM\": 15.699343231279409, \"Sum\": 56.591666666666725, \"Condition\": \"Flood\"}, {\"Year\": 1972, \"Means\": -44.32708333333333, \"SEM\": 11.848033831454753, \"Sum\": -177.3083333333333, \"Condition\": \"Drought\"}, {\"Year\": 1973, \"Means\": 20.772916666666674, \"SEM\": 17.763928497697137, \"Sum\": 83.0916666666667, \"Condition\": \"Flood\"}, {\"Year\": 1974, \"Means\": -20.552083333333314, \"SEM\": 13.973549024423358, \"Sum\": -82.20833333333326, \"Condition\": \"Drought\"}, {\"Year\": 1975, \"Means\": 33.097916666666684, \"SEM\": 10.480305742156292, \"Sum\": 132.39166666666674, \"Condition\": \"Flood\"}, {\"Year\": 1976, \"Means\": 6.62291666666669, \"SEM\": 14.464813729421202, \"Sum\": 26.49166666666676, \"Condition\": \"Normal\"}, {\"Year\": 1977, \"Means\": 13.222916666666684, \"SEM\": 14.935088814383347, \"Sum\": 52.89166666666674, \"Condition\": \"Normal\"}, {\"Year\": 1978, \"Means\": 19.772916666666674, \"SEM\": 9.337045547719324, \"Sum\": 79.0916666666667, \"Condition\": \"Flood\"}, {\"Year\": 1979, \"Means\": -30.60208333333332, \"SEM\": 5.4838401593699375, \"Sum\": -122.40833333333327, \"Condition\": 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\"SEM\": 11.307940430618103, \"Sum\": -133.1083333333333, \"Condition\": \"Drought\"}, {\"Year\": 1988, \"Means\": 32.82291666666667, \"SEM\": 14.014964823524513, \"Sum\": 131.29166666666669, \"Condition\": \"Flood\"}, {\"Year\": 1989, \"Means\": 9.097916666666677, \"SEM\": 8.203488594234807, \"Sum\": 36.39166666666671, \"Condition\": \"Normal\"}, {\"Year\": 1990, \"Means\": 19.57291666666668, \"SEM\": 9.048703259102737, \"Sum\": 78.29166666666671, \"Condition\": \"Flood\"}, {\"Year\": 1991, \"Means\": -11.227083333333322, \"SEM\": 12.358229494019435, \"Sum\": -44.90833333333329, \"Condition\": \"Normal\"}, {\"Year\": 1992, \"Means\": -11.327083333333327, \"SEM\": 14.585554691930518, \"Sum\": -45.30833333333331, \"Condition\": \"Normal\"}, {\"Year\": 1993, \"Means\": 9.122916666666669, \"SEM\": 18.112242716650293, \"Sum\": 36.491666666666674, \"Condition\": \"Normal\"}, {\"Year\": 1994, \"Means\": 30.64791666666668, \"SEM\": 18.081774206340317, \"Sum\": 122.59166666666673, \"Condition\": \"Flood\"}, {\"Year\": 1995, \"Means\": -7.477083333333319, \"SEM\": 9.732941065392692, \"Sum\": -29.908333333333275, \"Condition\": \"Normal\"}, {\"Year\": 1996, \"Means\": 7.122916666666676, \"SEM\": 13.47097198523002, \"Sum\": 28.491666666666703, \"Condition\": \"Normal\"}, {\"Year\": 1997, \"Means\": 10.322916666666686, \"SEM\": 5.857082854133553, \"Sum\": 41.29166666666674, \"Condition\": \"Normal\"}, {\"Year\": 1998, \"Means\": 7.29791666666668, \"SEM\": 13.064001220664307, \"Sum\": 29.19166666666672, \"Condition\": \"Normal\"}, {\"Year\": 1999, \"Means\": -2.0520833333333286, \"SEM\": 11.958167947946547, \"Sum\": -8.208333333333314, \"Condition\": \"Normal\"}, {\"Year\": 2000, \"Means\": -12.977083333333319, \"SEM\": 13.487234297832506, \"Sum\": -51.908333333333275, \"Condition\": \"Normal\"}, {\"Year\": 2001, \"Means\": -9.177083333333325, \"SEM\": 28.65601672529148, \"Sum\": -36.7083333333333, \"Condition\": \"Normal\"}, {\"Year\": 2002, \"Means\": -42.05208333333333, \"SEM\": 35.33536063530203, \"Sum\": -168.20833333333331, \"Condition\": \"Drought\"}, {\"Year\": 2003, \"Means\": 4.84791666666667, \"SEM\": 8.96879637683195, \"Sum\": 19.39166666666668, \"Condition\": \"Normal\"}, {\"Year\": 2004, \"Means\": -21.352083333333326, \"SEM\": 10.560178218412307, \"Sum\": -85.4083333333333, \"Condition\": \"Drought\"}, {\"Year\": 2005, \"Means\": 6.672916666666673, \"SEM\": 26.64638557111695, \"Sum\": 26.69166666666669, \"Condition\": \"Normal\"}, {\"Year\": 2006, \"Means\": 9.947916666666664, \"SEM\": 7.748490182980211, \"Sum\": 39.79166666666666, \"Condition\": \"Normal\"}, {\"Year\": 2007, \"Means\": 26.222916666666684, \"SEM\": 9.220644265521589, \"Sum\": 104.89166666666674, \"Condition\": \"Flood\"}, {\"Year\": 2008, \"Means\": 14.222916666666677, \"SEM\": 14.733368782165753, \"Sum\": 56.89166666666671, \"Condition\": \"Flood\"}, {\"Year\": 2009, \"Means\": -40.62708333333331, \"SEM\": 20.412860260253332, \"Sum\": -162.50833333333324, \"Condition\": \"Drought\"}, {\"Year\": 2010, \"Means\": 3.8729166666666757, \"SEM\": 12.570976387614568, \"Sum\": 15.491666666666703, \"Condition\": \"Normal\"}, {\"Year\": 2011, \"Means\": 12.697916666666679, \"SEM\": 14.445099711365666, \"Sum\": 50.791666666666714, \"Condition\": \"Normal\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "import pandas as pd\n",
    "\n",
    "# Your previously defined jjas_df DataFrame, drought_years, and flood_years here\n",
    "drought_years = [1965, 1966, 1968, 1972, 1974, 1979, 1982, 1986, 1987, 2002, 2004, 2009]\n",
    "flood_years = [1964, 1970, 1971, 1973, 1975, 1978, 1983, 1988, 1990, 1994, 2007, 2008]\n",
    "\n",
    "color_scale = alt.Scale(\n",
    "    domain=['Drought', 'Flood', 'Normal'],\n",
    "    range=['darkred', 'lightblue', 'orange']\n",
    ")\n",
    "\n",
    "jjas_df = pd.DataFrame({\n",
    "    'Year': range(1964, 2012),\n",
    "    'Means': jjas['means'],\n",
    "    'SEM': jjas['SEM'],\n",
    "    'Sum': jjas['sum']\n",
    "})\n",
    "jjas_df['Condition'] = jjas_df['Year'].apply(lambda x: 'Drought' if x in drought_years else 'Flood' if x in flood_years else 'Normal')\n",
    "\n",
    "# Now, create the error bar chart and points with the color condition based on the 'Condition' column\n",
    "error_bars = alt.Chart(jjas_df).mark_errorbar(extent='ci').encode(\n",
    "    x=alt.X('Year:O', axis=alt.Axis(values=list(range(1960, 2011, 10)))),\n",
    "    y=alt.Y('Means:Q', scale=alt.Scale(zero=False)),\n",
    "    yError='SEM:Q',\n",
    "   color=alt.Color('Condition:N', scale=color_scale)\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=400,\n",
    "    title='Mean JJAS precipitation anomaly'\n",
    ")\n",
    "\n",
    "points = alt.Chart(jjas_df).mark_point(filled=True).encode(\n",
    "    x='Year:O',\n",
    "    y='Means:Q',\n",
    "    color=alt.Color('Condition', legend=alt.Legend(title='Condition'))  # Add a legend\n",
    ")\n",
    "\n",
    "# Combine the error bars with the points\n",
    "error_chart = (error_bars + points).interactive()\n",
    "\n",
    "# Create the histogram chart, independent of the conditions\n",
    "histogram = alt.Chart(jjas_df).transform_density(\n",
    "    density='Means',\n",
    "    as_=['Means', 'Density']\n",
    ").mark_area().encode(\n",
    "    x=\"Means:Q\",\n",
    "    y='Density:Q',\n",
    "    tooltip=['Means:Q', 'Density:Q']\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=400,\n",
    "    title='Distribution of JJAS anomalies'\n",
    ")\n",
    "\n",
    "# Horizontally concatenate the error chart with the histogram\n",
    "chart2 = alt.hconcat(error_chart, histogram).resolve_legend(color='independent')\n",
    "\n",
    "# Display the final concatenated chart\n",
    "chart2"
   ]
  }
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