diff --git a/README.md b/README.md index 3dbf7ef..7d0ba3f 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ # ATMS 523 Module-3 -Code, notebooks, and homework for ATMS 523 Module 3. +Hello. In this repository is my submission for the module 3 homework, `homework_assignment_3.ipynb`. In that notebook, you will find my code which addresses the assignment instructions spelled out in `HW.md`. + +The first part of the assignment contains 2 functions. The first one being `select_station` which allows you to select a weather station from the GHCN-d network based on a city of your choosing. This function will display a dropdown menu of stations with the city in their name, from which you select a station and it will return that station's ID and name. This function is set to only include stations that still reported temperature data in 2025. The other function, `get_station_data`, uses the station ID from the previous function as input and grabs the daily min and max temperature data for the station's period of record. From that, it will return a dataframe containing the all time min and max temperatures for each calendar day of the year, as well as the 30 year average (1991-2020) min and max temperatures for each calendar day. It will also return dataframes for the daily min and max temperature for the entire period of record. These two functions must be run in separate cells, as Jupyter Notebook does not allow you to pause a cell to wait for user input using widgets. + +The second part of the assignment involves plotting the data we gathered for the given station in part 1. The function `plot_station_data_year` takes in a year of your choosing and plots a time series of the actual daily min/max temperatures for the station, as well as the all time records and 30 year average. You can also choose to smooth the data using a 30-day window, which is smoothed using `scipy.signal.savgol_filter`. The plot was created using the Bokeh library, and allows you to interact with the plot itself if you choose. + +Bokeh library: https://docs.bokeh.org/en/latest/ diff --git a/homework_assignment_3.ipynb b/homework_assignment_3.ipynb new file mode 100644 index 0000000..ab75c07 --- /dev/null +++ b/homework_assignment_3.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "82cd5496", + "metadata": {}, + "source": [ + "## Homework Assingment 3\n", + "## ATMS523\n", + "## Author: Domenic Brooks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b78addc9", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "c15e4e44", + "metadata": {}, + "source": [ + "# Part 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bef4aecf", + "metadata": {}, + "outputs": [], + "source": [ + "# Station selection function\n", + "def select_station(city):\n", + " \"\"\" \n", + " This function returns the GHCN-d station ID based on the city/town\n", + " you input and allows you to select the specfiic station from\n", + " a dropdown menu.\n", + "\n", + " Inputs:\n", + " city (str): City/town name you want station data from \n", + " (MUST BE IN ALL CAPITAL LETTERS). \n", + "\n", + " Outputs:\n", + " selected_id (dict): Station ID and name based on dropdown menu selection.\n", + " \"\"\"\n", + " \n", + " # Read in available stations\n", + " stn_ids = pd.read_fwf('http://noaa-ghcn-pds.s3.amazonaws.com/ghcnd-stations.txt', \n", + " header=None, infer_nrows=1000)\n", + " stn_ids.columns = ['ID','LAT','LON','ELEV','UKN','NAME','GSN','WBAN']\n", + "\n", + " # Read in station inventory file (gives years available for each variable)\n", + " periods = pd.read_fwf('http://noaa-ghcn-pds.s3.amazonaws.com/ghcnd-inventory.txt', \n", + " header=None, infer_nrows=1000)\n", + " periods.columns = ['ID','LAT','LON','ELEM','TiMIN','TiMAX']\n", + "\n", + " # Merge the inventory and station dfs based on ID\n", + " merged_stns = pd.merge(stn_ids,periods,how='left',left_on='ID',right_on='ID')\n", + "\n", + " # Grab stations where TMAX data is availble up to 2025\n", + " # TiMIN is the earliest year available\n", + " # TiMAX is the most recent year available\n", + " merged_stns = merged_stns[(merged_stns['ELEM'] == 'TMAX') & \n", + " (merged_stns['TiMAX'] == 2025)]\n", + "\n", + " # Select staions with city in name\n", + " merged_stns = merged_stns[merged_stns['NAME'].str.contains(city, regex=False)]\n", + "\n", + " if merged_stns.empty:\n", + " print(f\"No stations found for city: {city}\")\n", + " return None\n", + "\n", + " # Create dropdown options (include empty option first)\n", + " # Including the empty option allows callback to \n", + " # work properly when selection is made.\n", + " options = {'-- Select a Station --': None}\n", + " for _, row in merged_stns.iterrows():\n", + " label = f\"{row['NAME']} ({row['ID']})\"\n", + " options[label] = row['ID']\n", + "\n", + " dropdown = widgets.Dropdown(\n", + " options=options,\n", + " value=None, # Ensures no pre-selection\n", + " description='Station:',\n", + " style={'description_width': 'initial'},\n", + " layout=widgets.Layout(width='70%')\n", + " )\n", + "\n", + " output = widgets.Output()\n", + " \n", + " # Store both ID and name\n", + " selected_station = {'id': None, 'name': None}\n", + "\n", + "\t\n", + " def on_select(change):\n", + " \"\"\"\n", + " Function to return station info based on selection.\n", + " \"\"\"\n", + " if change.new: # Only trigger when a station is chosen\n", + " stn = merged_stns[merged_stns['ID'] == change.new].iloc[0]\n", + " selected_station['id'] = stn['ID']\n", + " selected_station['name'] = stn['NAME']\n", + "\t\t\t# Print station metadata\n", + " with output:\n", + " clear_output()\n", + " print(\"Selected Station:\")\n", + " print(stn[['ID', \n", + " 'NAME', \n", + " 'LAT_x', \n", + " 'LON_x', \n", + " 'ELEV', \n", + " 'TiMIN', \n", + " 'TiMAX']])\n", + "\n", + "\t# Display dropdown menu\n", + " dropdown.observe(on_select, names='value')\n", + " display(dropdown, output)\n", + "\n", + " print(\"Please choose a station from the dropdown above.\")\n", + " return selected_station\n", + "\n", + "# Station data retrieval funciton\n", + "def get_station_data(station_id):\n", + " \"\"\"\n", + " This function grabs the data from the selected station and returns\n", + " a dataframe with the record min and max temp from each calendar day\n", + " over the period of record, as well as the mean min and max daily\n", + " temps for the 1991-2020 period.\n", + "\n", + " Inputs:\n", + " station_id (str): Station ID of selected station, default is the one you\n", + " selected using the previous function.\n", + "\n", + " Outputs:\n", + " df_records (pd.DataFrame): The dataframe containing average_max_temp,\n", + " average_min_temp, record_max_temp, and record_min_temp. \n", + "\n", + " df_tmin (pd.DataFrame): Daily min temps for the period of record.\n", + "\n", + " df_tmax (pd.DataFrame): Daily max temps for the period of record.\n", + " \"\"\"\n", + "\n", + " # Read in actual data for selected station from AWS\n", + " df = pd.read_csv(\n", + " f\"s3://noaa-ghcn-pds/csv/by_station/{station_id}.csv\",\n", + " storage_options={\"anon\": True}, # passed to `s3fs.S3FileSystem`\n", + " dtype={'Q_FLAG': 'object', 'M_FLAG': 'object'},\n", + " parse_dates=['DATE']\n", + " ).set_index('DATE')\n", + "\n", + " # Grab max and min temps (in tenths of degrees C)\n", + " df_tmax = df.loc[df['ELEMENT'] == 'TMAX']\n", + " df_tmin = df.loc[df['ELEMENT'] == 'TMIN']\n", + "\n", + " # Convert GHCN temps from tenths of °C to °C\n", + " df_tmin[\"TMIN\"] = df_tmin[\"DATA_VALUE\"] / 10\n", + " df_tmax[\"TMAX\"] = df_tmax[\"DATA_VALUE\"] / 10\n", + "\n", + " # Add month-day identifier\n", + " df_tmin[\"MONTH_DAY\"] = df_tmin.index.strftime(\"%m-%d\")\n", + " df_tmax[\"MONTH_DAY\"] = df_tmax.index.strftime(\"%m-%d\")\n", + "\n", + " # Group by month-day and find all time max/min\n", + " df_tmax_daily = (\n", + " df_tmax.groupby(['MONTH_DAY'], as_index=True)\n", + " .agg(record_max_temp=('TMAX','max'))\n", + " )\n", + "\n", + " df_tmin_daily = (\n", + " df_tmin.groupby(['MONTH_DAY'], as_index=True)\n", + " .agg(record_min_temp=('TMIN','min'))\n", + " )\n", + "\n", + " # Now get 1991-2020 averages\n", + " df_91_20_max = df_tmax[(df_tmax.index >= pd.to_datetime('1991-01-01')) & \n", + " (df_tmax.index <= pd.to_datetime('2020-01-01'))]\n", + " df_91_20_min = df_tmin[(df_tmin.index >= pd.to_datetime('1991-01-01')) & \n", + " (df_tmin.index <= pd.to_datetime('2020-01-01'))]\n", + "\n", + " # Group by month-day and find means\n", + " df_tmax_daily_mean = (\n", + " df_91_20_max.groupby(['MONTH_DAY'], as_index=True)\n", + " .agg(average_max_temp=('TMAX','mean'))\n", + " )\n", + "\n", + " df_tmin_daily_mean = (\n", + " df_91_20_min.groupby(['MONTH_DAY'], as_index=True)\n", + " .agg(average_min_temp=('TMIN','mean'))\n", + " )\n", + "\n", + " # Merge all dataframes\n", + " df_records = pd.concat([df_tmax_daily_mean, \n", + " df_tmin_daily_mean, \n", + " df_tmax_daily, \n", + " df_tmin_daily], \n", + " axis=1)\n", + "\n", + " # Drop unecessary columns\n", + " df_tmax.drop(['ID',\n", + " 'ELEMENT', \n", + " 'DATA_VALUE',\t\n", + " 'M_FLAG', \n", + " 'Q_FLAG', \n", + " 'S_FLAG', \n", + " 'OBS_TIME'],\n", + " axis=1, inplace=True)\n", + " \n", + " df_tmin.drop(['ID',\n", + " 'ELEMENT', \n", + " 'DATA_VALUE',\t\n", + " 'M_FLAG', \n", + " 'Q_FLAG', \n", + " 'S_FLAG', \n", + " 'OBS_TIME'], \n", + " axis=1, inplace=True)\n", + "\n", + " return df_records, df_tmin, df_tmax\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9150e346", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b80e7bddcdbb4ccea678c466b8c85994", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Station:', layout=Layout(width='70%'), options={'-- Select a Station --': None, 'NEW BOS…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c1baf601aca64a8b87329812ab5267a5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please choose a station from the dropdown above.\n" + ] + } + ], + "source": [ + "# Need to run seperately from the other function\n", + "# because Jupyter Notebook cells cannot pause to wait for \n", + "# user input for the callback.\n", + "city = 'BOSTON'\n", + "s = select_station(city)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "16c0489c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
average_max_tempaverage_min_temprecord_max_temprecord_min_temp
MONTH_DAY
01-0111.460000-2.20666721.7-18.9
01-0211.741379-1.80000026.1-15.0
01-039.786207-1.43793120.0-14.4
01-049.793103-2.35172423.3-13.3
01-0511.155172-2.51379323.3-15.6
...............
12-278.975862-3.17931024.4-17.8
12-2810.775862-2.93448323.9-11.1
12-2910.213793-2.40689724.4-12.2
12-3010.639286-1.22758625.0-11.1
12-3111.025000-2.43928624.4-20.0
\n", + "

366 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " average_max_temp average_min_temp record_max_temp \\\n", + "MONTH_DAY \n", + "01-01 11.460000 -2.206667 21.7 \n", + "01-02 11.741379 -1.800000 26.1 \n", + "01-03 9.786207 -1.437931 20.0 \n", + "01-04 9.793103 -2.351724 23.3 \n", + "01-05 11.155172 -2.513793 23.3 \n", + "... ... ... ... \n", + "12-27 8.975862 -3.179310 24.4 \n", + "12-28 10.775862 -2.934483 23.9 \n", + "12-29 10.213793 -2.406897 24.4 \n", + "12-30 10.639286 -1.227586 25.0 \n", + "12-31 11.025000 -2.439286 24.4 \n", + "\n", + " record_min_temp \n", + "MONTH_DAY \n", + "01-01 -18.9 \n", + "01-02 -15.0 \n", + "01-03 -14.4 \n", + "01-04 -13.3 \n", + "01-05 -15.6 \n", + "... ... \n", + "12-27 -17.8 \n", + "12-28 -11.1 \n", + "12-29 -12.2 \n", + "12-30 -11.1 \n", + "12-31 -20.0 \n", + "\n", + "[366 rows x 4 columns]" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Can use output from the selection function,\n", + "# or manually input the station ID.\n", + "df_records, df_tmin, df_tmax = get_station_data(s['id'])\n", + "df_records" + ] + }, + { + "cell_type": "markdown", + "id": "76f2cd5a", + "metadata": {}, + "source": [ + "# Part 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aaf4528f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(\"b3f5855f-f7ab-4bb6-93ca-db47dc753917\");\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.6.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {display_loaded(error);throw error;\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 const cell = $(document.getElementById(\"b3f5855f-f7ab-4bb6-93ca-db47dc753917\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\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));", + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"b8315dbb-0dc4-42a8-b637-7baa59261d64\":{\"version\":\"3.6.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p7125\",\"attributes\":{\"width\":1200,\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p7204\",\"attributes\":{\"range_padding\":0.0}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p7127\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p7134\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p7135\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p7132\",\"attributes\":{\"text\":\"Daily Temperature Record for 2024 for Station USC00447925 (S BOSTON)\\n(Using 1991-2020 Average)\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p7179\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p7170\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p7171\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p7172\"},\"data\":{\"type\":\"map\",\"entries\":[[\"DATE\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"MONTH_DAY\",{\"type\":\"ndarray\",\"array\":[\"01-01\",\"01-02\",\"01-03\",\"01-04\",\"01-05\",\"01-06\",\"01-07\",\"01-08\",\"01-09\",\"01-10\",\"01-11\",\"01-12\",\"01-13\",\"01-14\",\"01-15\",\"01-16\",\"01-17\",\"01-18\",\"01-19\",\"01-20\",\"01-21\",\"01-22\",\"01-23\",\"01-24\",\"01-25\",\"01-26\",\"01-27\",\"01-28\",\"01-29\",\"01-30\",\"01-31\",\"02-01\",\"02-02\",\"02-03\",\"02-04\",\"02-05\",\"02-06\",\"02-07\",\"02-08\",\"02-09\",\"02-10\",\"02-11\",\"02-12\",\"02-13\",\"02-14\",\"02-15\",\"02-16\",\"02-17\",\"02-18\",\"02-19\",\"02-20\",\"02-21\",\"02-22\",\"02-23\",\"02-24\",\"02-25\",\"02-26\",\"02-27\",\"02-28\",\"02-29\",\"03-01\",\"03-02\",\"03-03\",\"03-04\",\"03-05\",\"03-06\",\"03-07\",\"03-08\",\"03-09\",\"03-10\",\"03-11\",\"03-12\",\"03-13\",\"03-14\",\"03-15\",\"03-16\",\"03-17\",\"03-18\",\"03-19\",\"03-20\",\"03-21\",\"03-22\",\"03-23\",\"03-24\",\"03-25\",\"03-26\",\"03-27\",\"03-28\",\"03-29\",\"03-30\",\"03-31\",\"04-01\",\"04-02\",\"04-03\",\"04-04\",\"04-05\",\"04-06\",\"04-07\",\"04-08\",\"04-09\",\"04-10\",\"04-11\",\"04-12\",\"04-13\",\"04-14\",\"04-15\",\"04-16\",\"04-17\",\"04-18\",\"04-19\",\"04-20\",\"04-21\",\"04-22\",\"04-23\",\"04-24\",\"04-25\",\"04-26\",\"04-27\",\"04-28\",\"04-29\",\"04-30\",\"05-01\",\"05-02\",\"05-03\",\"05-04\",\"05-05\",\"05-06\",\"05-07\",\"05-08\",\"05-09\",\"05-10\",\"05-11\",\"05-12\",\"05-13\",\"05-14\",\"05-15\",\"05-16\",\"05-17\",\"05-18\",\"05-19\",\"05-20\",\"05-21\",\"05-22\",\"05-23\",\"05-24\",\"05-25\",\"05-26\",\"05-27\",\"05-28\",\"05-29\",\"05-30\",\"05-31\",\"06-01\",\"06-02\",\"06-03\",\"06-04\",\"06-05\",\"06-06\",\"06-07\",\"06-08\",\"06-09\",\"06-10\",\"06-11\",\"06-12\",\"06-13\",\"06-14\",\"06-15\",\"06-16\",\"06-17\",\"06-18\",\"06-19\",\"06-20\",\"06-21\",\"06-22\",\"06-23\",\"06-24\",\"06-25\",\"06-26\",\"06-27\",\"06-28\",\"06-29\",\"06-30\",\"07-01\",\"07-02\",\"07-03\",\"07-04\",\"07-05\",\"07-06\",\"07-07\",\"07-08\",\"07-09\",\"07-10\",\"07-11\",\"07-12\",\"07-13\",\"07-14\",\"07-15\",\"07-16\",\"07-17\",\"07-18\",\"07-19\",\"07-20\",\"07-21\",\"07-22\",\"07-23\",\"07-24\",\"07-25\",\"07-26\",\"07-27\",\"07-28\",\"07-29\",\"07-30\",\"07-31\",\"08-01\",\"08-02\",\"08-03\",\"08-04\",\"08-05\",\"08-06\",\"08-07\",\"08-08\",\"08-09\",\"08-10\",\"08-11\",\"08-12\",\"08-13\",\"08-14\",\"08-15\",\"08-16\",\"08-17\",\"08-18\",\"08-19\",\"08-20\",\"08-21\",\"08-22\",\"08-23\",\"08-24\",\"08-25\",\"08-26\",\"08-27\",\"08-28\",\"08-29\",\"08-30\",\"08-31\",\"09-01\",\"09-02\",\"09-03\",\"09-04\",\"09-05\",\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\",\"09-23\",\"09-24\",\"09-25\",\"09-26\",\"09-27\",\"09-28\",\"09-29\",\"09-30\",\"10-01\",\"10-02\",\"10-03\",\"10-04\",\"10-05\",\"10-06\",\"10-07\",\"10-08\",\"10-09\",\"10-10\",\"10-11\",\"10-12\",\"10-13\",\"10-14\",\"10-15\",\"10-16\",\"10-17\",\"10-18\",\"10-19\",\"10-20\",\"10-21\",\"10-22\",\"10-23\",\"10-24\",\"10-25\",\"10-26\",\"10-27\",\"10-28\",\"10-29\",\"10-30\",\"10-31\",\"11-01\",\"11-02\",\"11-03\",\"11-04\",\"11-05\",\"11-06\",\"11-07\",\"11-08\",\"11-09\",\"11-10\",\"11-11\",\"11-12\",\"11-13\",\"11-14\",\"11-15\",\"11-16\",\"11-17\",\"11-18\",\"11-19\",\"11-20\",\"11-21\",\"11-22\",\"11-23\",\"11-24\",\"11-25\",\"11-26\",\"11-27\",\"11-28\",\"11-29\",\"11-30\",\"12-01\",\"12-02\",\"12-03\",\"12-04\",\"12-05\",\"12-06\",\"12-07\",\"12-08\",\"12-09\",\"12-10\",\"12-11\",\"12-12\",\"12-13\",\"12-14\",\"12-15\",\"12-16\",\"12-17\",\"12-18\",\"12-19\",\"12-20\",\"12-21\",\"12-22\",\"12-23\",\"12-24\",\"12-25\",\"12-26\",\"12-27\",\"12-28\",\"12-29\",\"12-30\",\"12-31\"],\"shape\":[366],\"dtype\":\"object\",\"order\":\"little\"}],[\"TMAX\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"TMIN\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"average_max_temp\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"average_min_temp\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"record_max_temp\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"record_min_temp\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"left\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}],[\"right\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[366],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p7180\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p7181\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7176\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"record_min_temp\"},\"top\":{\"type\":\"field\",\"field\":\"record_max_temp\"},\"line_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"fill_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"hatch_color\":{\"type\":\"value\",\"value\":\"#f4a582\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7177\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"record_min_temp\"},\"top\":{\"type\":\"field\",\"field\":\"record_max_temp\"},\"line_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7178\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"record_min_temp\"},\"top\":{\"type\":\"field\",\"field\":\"record_max_temp\"},\"line_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"#f4a582\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p7190\",\"attributes\":{\"data_source\":{\"id\":\"p7170\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p7191\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p7192\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7187\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"average_min_temp\"},\"top\":{\"type\":\"field\",\"field\":\"average_max_temp\"},\"line_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"fill_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"hatch_color\":{\"type\":\"value\",\"value\":\"#4393c3\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7188\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"average_min_temp\"},\"top\":{\"type\":\"field\",\"field\":\"average_max_temp\"},\"line_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7189\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"average_min_temp\"},\"top\":{\"type\":\"field\",\"field\":\"average_max_temp\"},\"line_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"#4393c3\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p7200\",\"attributes\":{\"data_source\":{\"id\":\"p7170\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p7201\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p7202\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7197\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"TMIN\"},\"top\":{\"type\":\"field\",\"field\":\"TMAX\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.5},\"fill_color\":{\"type\":\"value\",\"value\":\"#053061\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.5},\"hatch_color\":{\"type\":\"value\",\"value\":\"#053061\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.5}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7198\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"TMIN\"},\"top\":{\"type\":\"field\",\"field\":\"TMAX\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#053061\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"#053061\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p7199\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"field\",\"field\":\"TMIN\"},\"top\":{\"type\":\"field\",\"field\":\"TMAX\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#053061\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"#053061\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p7133\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p7158\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p7159\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p7160\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p7161\",\"attributes\":{\"syncable\":false,\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"handles\":{\"type\":\"object\",\"name\":\"BoxInteractionHandles\",\"id\":\"p7167\",\"attributes\":{\"all\":{\"type\":\"object\",\"name\":\"AreaVisuals\",\"id\":\"p7166\",\"attributes\":{\"fill_color\":\"white\",\"hover_fill_color\":\"lightgray\"}}}}}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p7168\"},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p7169\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":[[\"Date\",\"@left{%F}\"],[\"Record Max (\\u00b0C)\",\"@record_max_temp\"],[\"Record Min (\\u00b0C)\",\"@record_min_temp\"],[\"Average Max (\\u00b0C)\",\"@average_max_temp\"],[\"Average Min (\\u00b0C)\",\"@average_min_temp\"],[\"Actual Max (\\u00b0C)\",\"@TMAX\"],[\"Actual Min (\\u00b0C)\",\"@TMIN\"]],\"formatters\":{\"type\":\"map\",\"entries\":[[\"@left\",\"datetime\"]]}}}]}},\"toolbar_location\":\"above\",\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p7153\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p7154\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p7155\"},\"axis_label\":\"Temperature (C)\",\"axis_label_text_font_style\":\"bold\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p7156\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"DatetimeAxis\",\"id\":\"p7136\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"DatetimeTicker\",\"id\":\"p7137\",\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p7138\",\"attributes\":{\"num_minor_ticks\":0,\"mantissas\":[1,2,5],\"max_interval\":500.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p7139\",\"attributes\":{\"num_minor_ticks\":0,\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"min_interval\":1000.0,\"max_interval\":1800000.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p7140\",\"attributes\":{\"num_minor_ticks\":0,\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"min_interval\":3600000.0,\"max_interval\":43200000.0}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p7141\",\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p7142\",\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p7143\",\"attributes\":{\"days\":[1,8,15,22]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p7144\",\"attributes\":{\"days\":[1,15]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p7145\",\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p7146\",\"attributes\":{\"months\":[0,2,4,6,8,10]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p7147\",\"attributes\":{\"months\":[0,4,8]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p7148\",\"attributes\":{\"months\":[0,6]}},{\"type\":\"object\",\"name\":\"YearsTicker\",\"id\":\"p7149\"}]}},\"formatter\":{\"type\":\"object\",\"name\":\"DatetimeTickFormatter\",\"id\":\"p7150\"},\"axis_label\":\"Day of Year\",\"axis_label_text_font_style\":\"bold\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p7151\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p7152\",\"attributes\":{\"axis\":{\"id\":\"p7136\"},\"grid_line_alpha\":0.8}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p7157\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p7153\"},\"grid_line_alpha\":0.8}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p7182\",\"attributes\":{\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p7183\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Record\"},\"renderers\":[{\"id\":\"p7179\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p7193\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Average\"},\"renderers\":[{\"id\":\"p7190\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p7203\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Actual\"},\"renderers\":[{\"id\":\"p7200\"}]}}]}}]}}]}};\n const render_items = [{\"docid\":\"b8315dbb-0dc4-42a8-b637-7baa59261d64\",\"roots\":{\"p7125\":\"a5c4726e-8085-419e-bf82-1982b21f4b04\"},\"root_ids\":[\"p7125\"]}];\n void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p7125" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.signal import savgol_filter\n", + "from bokeh.models import ColumnDataSource, DataRange1d, HoverTool\n", + "from bokeh.palettes import RdBu11\n", + "from bokeh.plotting import figure, show\n", + "from bokeh.io import output_notebook\n", + "output_notebook()\n", + "\n", + "# Plot (using Bokeh library)\n", + "def plot_station_data_year(year, smoothed=False):\n", + "\t\"\"\"\n", + "\tCreate plot of daily temperature record for selected station\n", + "\tand a given year.\n", + "\n", + "\tInputs:\n", + "\t\tyear (int): Year you want to grab data from, must be valid year within \n", + "\t\tperiod of record for the given station.\n", + "\n", + "\t\tsmoothed (bool): Set to True if you want to apply 30-day smoothing window.\n", + "\t\"\"\"\n", + "\t# Select data from given year\n", + "\tdf_max_year = df_tmax[(df_tmax.index >= pd.to_datetime(f'{year}-01-01')) &\n", + "\t\t\t\t\t (df_tmax.index <= pd.to_datetime(f'{year}-12-31'))]\n", + "\tdf_min_year = df_tmin[(df_tmin.index >= pd.to_datetime(f'{year}-01-01')) & \n", + "\t\t\t\t\t (df_tmin.index <= pd.to_datetime(f'{year}-12-31'))]\n", + "\n", + "\t# Handle leap years\n", + "\tif year % 4 != 0:\n", + "\t\tdf_records2 = df_records.drop('02-29')\n", + "\telse:\n", + "\t\tdf_records2 = df_records\n", + "\n", + "\t# Get datetime index\n", + "\tdates = df_max_year.index\n", + "\n", + "\t# Reset indices for merge\n", + "\tdf_max_year = df_max_year.set_index('MONTH_DAY')\n", + "\tdf_min_year = df_min_year.set_index('MONTH_DAY')\n", + "\n", + "\t# Merge with df_records\n", + "\tmerged = pd.concat([df_max_year, df_min_year, df_records2], axis=1)\n", + "\tmerged = merged.reset_index()\n", + "\n", + "\t# Set index as the dates\n", + "\tdf_year = merged.set_index(dates)\n", + "\n", + "\t# Smooth lines if desired\n", + "\tif smoothed==True:\n", + "\t\tdf_year.drop('MONTH_DAY', axis=1, inplace=True)\n", + "\t\tfor key in df_year.columns:\n", + "\t\t\twindow, order = 30, 3 # Window of n days, 3rd degree polynomial\n", + "\t\t\tdf_year[key] = savgol_filter(df_year[key], window, order)\n", + "\n", + "\t# Plotting \n", + "\tplot = figure(x_axis_type=\"datetime\",\n", + "\t\t\t width=1200,\n", + "\t\t\t\t tools='pan,wheel_zoom,box_zoom,reset',\n", + "\t\t\t\t toolbar_location='above')\n", + "\n", + "\t# Configure hover tool to show data properly\n", + "\thover = HoverTool(\n", + "\t\ttooltips=[\n", + "\t\t\t(\"Date\", \"@left{%F}\"), # Formatted datetime\n", + "\t\t\t(\"Record Max (°C)\", \"@record_max_temp\"),\n", + "\t\t\t(\"Record Min (°C)\", \"@record_min_temp\"),\n", + "\t\t\t(\"Average Max (°C)\", \"@average_max_temp\"),\n", + "\t\t\t(\"Average Min (°C)\", \"@average_min_temp\"),\n", + "\t\t\t(\"Actual Max (°C)\", \"@TMAX\"),\n", + "\t\t\t(\"Actual Min (°C)\", \"@TMIN\"),\n", + "\t\t],\n", + "\t\tformatters={\n", + "\t\t\t\"@left\": \"datetime\" # Ensure date formatting\n", + "\t\t},\n", + "\t\tmode=\"mouse\" # Show data only when mouse intersects data point\n", + "\t)\n", + "\tplot.add_tools(hover)\n", + "\n", + "\t# Change title if smootheed\n", + "\tif smoothed==True:\n", + "\t\tplot.title.text = (f'Daily Temperature Record for {year} for Station' \n", + "\t\t\t\t\t \t f' {s[\"id\"]} ({s[\"name\"]})'\n", + "\t\t\t\t\t f'\\n(Using 1991-2020 Average)'\n", + "\t\t\t\t\t f'\\nSmoothed Over {window} Day Window')\n", + "\telse:\n", + "\t\tplot.title.text = (f'Daily Temperature Record for {year} for Station'\n", + "\t\t\t\t\t f' {s[\"id\"]} ({s[\"name\"]})'\n", + "\t\t\t\t\t f'\\n(Using 1991-2020 Average)')\n", + "\t\t\n", + "\t# For Bokeh, you need to create a ColumnDataSource (CDS) from the dataframe,\n", + "\t# with the names of the columns in the CDS correspond to those in the df.\n", + "\t# Also need to define the left and right edges of each data point to plot with quad.\n", + "\tdf_year[\"left\"] = df_year.index\n", + "\tdf_year[\"right\"] = df_year.index + pd.Timedelta(days=1)\n", + "\tsource = ColumnDataSource(df_year)\n", + "\n", + "\tplot.quad(top='record_max_temp', \n", + "\t\t \t bottom='record_min_temp', \n", + "\t\t\t left='left', \n", + "\t\t \t right='right', \n", + "\t\t\t color=RdBu11[7], \n", + "\t\t\t source=source, \n", + "\t\t\t legend_label=\"Record\") # Records\n", + "\n", + "\tplot.quad(top='average_max_temp', \n", + "\t\t bottom='average_min_temp', \n", + "\t\t\t left='left', right='right',\n", + "\t\t\t color=RdBu11[2], source=source, \n", + "\t\t\t legend_label=\"Average\") # Averages\n", + "\t\n", + "\tplot.quad(top='TMAX', \n", + "\t\t bottom='TMIN', \n", + "\t\t\t left='left', \n", + "\t\t\t right='right',\n", + "\t\t\t color=RdBu11[0], \n", + "\t\t\t source=source, \n", + "\t\t\t alpha=0.5, \n", + "\t\t\t line_color=\"black\", \n", + "\t\t\t legend_label=\"Actual\") # Actual\n", + "\n", + "\t# Plot attributes\n", + "\tplot.xaxis.axis_label = 'Day of Year'\n", + "\tplot.yaxis.axis_label = \"Temperature (C)\"\n", + "\tplot.axis.axis_label_text_font_style = \"bold\"\n", + "\tplot.x_range = DataRange1d(range_padding=0.0)\n", + "\tplot.grid.grid_line_alpha = 0.8\n", + "\n", + "\tshow(plot) # Show plot\n", + "\n", + "year = 2024\n", + "smoothed=False\n", + "plot_station_data_year(year, smoothed)" + ] + }, + { + "attachments": { + "image.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "4d82699b", + "metadata": {}, + "source": [ + "#### Screenshot of plot if does not load in github properly\n", + "![image.png](attachment:image.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}