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June 16, 2020 14:16
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Diurnal Figures\n", | |
| "\n", | |
| "This notebook contains an example of how to build nice diurnal figures." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import matplotlib.dates as mdates\n", | |
| "from matplotlib.ticker import MultipleLocator\n", | |
| "import seaborn as sns\n", | |
| "\n", | |
| "sns.set(\"notebook\", \"ticks\", palette=\"colorblind\", font_scale=1.25)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Load data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "DatetimeIndex: 8574 entries, 2020-04-30 20:00:00+00:00 to 2020-05-30 19:55:00+00:00\n", | |
| "Data columns (total 8 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 co2 8543 non-null float64\n", | |
| " 1 co 8574 non-null float64\n", | |
| " 2 no 8574 non-null float64\n", | |
| " 3 no2 8574 non-null float64\n", | |
| " 4 o3 8574 non-null float64\n", | |
| " 5 pm1 8574 non-null float64\n", | |
| " 6 pm25 8574 non-null float64\n", | |
| " 7 pm10 8574 non-null float64\n", | |
| "dtypes: float64(8)\n", | |
| "memory usage: 602.9 KB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# read the csv (this is just a random **final** data file from any sensor on the website)\n", | |
| "df = pd.read_csv(\"data/SN105-Riseboro-4d.csv\", index_col=0)\n", | |
| "\n", | |
| "# convert the timestamp_local column to a datetime object\n", | |
| "df[\"timestamp_local\"] = df[\"timestamp_local\"].map(pd.to_datetime)\n", | |
| "\n", | |
| "# set the index\n", | |
| "df.set_index(\"timestamp_local\", inplace=True)\n", | |
| "\n", | |
| "# only keep a few columns of interest\n", | |
| "df = df[[\"co2\", \"co\", \"no\", \"no2\", \"o3\", \"pm1\", \"pm25\", \"pm10\"]]\n", | |
| "\n", | |
| "# resample the data to be on a 5min time base\n", | |
| "df = df.resample(\"5min\").mean().dropna(how='all')\n", | |
| "\n", | |
| "# output the info\n", | |
| "df.info()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Compute the Diurnal Statistics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "Int64Index: 288 entries, 0 to 287\n", | |
| "Data columns (total 64 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 (co2, count) 288 non-null float64\n", | |
| " 1 (co2, mean) 288 non-null float64\n", | |
| " 2 (co2, std) 288 non-null float64\n", | |
| " 3 (co2, min) 288 non-null float64\n", | |
| " 4 (co2, 25%) 288 non-null float64\n", | |
| " 5 (co2, 50%) 288 non-null float64\n", | |
| " 6 (co2, 75%) 288 non-null float64\n", | |
| " 7 (co2, max) 288 non-null float64\n", | |
| " 8 (co, count) 288 non-null float64\n", | |
| " 9 (co, mean) 288 non-null float64\n", | |
| " 10 (co, std) 288 non-null float64\n", | |
| " 11 (co, min) 288 non-null float64\n", | |
| " 12 (co, 25%) 288 non-null float64\n", | |
| " 13 (co, 50%) 288 non-null float64\n", | |
| " 14 (co, 75%) 288 non-null float64\n", | |
| " 15 (co, max) 288 non-null float64\n", | |
| " 16 (no, count) 288 non-null float64\n", | |
| " 17 (no, mean) 288 non-null float64\n", | |
| " 18 (no, std) 288 non-null float64\n", | |
| " 19 (no, min) 288 non-null float64\n", | |
| " 20 (no, 25%) 288 non-null float64\n", | |
| " 21 (no, 50%) 288 non-null float64\n", | |
| " 22 (no, 75%) 288 non-null float64\n", | |
| " 23 (no, max) 288 non-null float64\n", | |
| " 24 (no2, count) 288 non-null float64\n", | |
| " 25 (no2, mean) 288 non-null float64\n", | |
| " 26 (no2, std) 288 non-null float64\n", | |
| " 27 (no2, min) 288 non-null float64\n", | |
| " 28 (no2, 25%) 288 non-null float64\n", | |
| " 29 (no2, 50%) 288 non-null float64\n", | |
| " 30 (no2, 75%) 288 non-null float64\n", | |
| " 31 (no2, max) 288 non-null float64\n", | |
| " 32 (o3, count) 288 non-null float64\n", | |
| " 33 (o3, mean) 288 non-null float64\n", | |
| " 34 (o3, std) 288 non-null float64\n", | |
| " 35 (o3, min) 288 non-null float64\n", | |
| " 36 (o3, 25%) 288 non-null float64\n", | |
| " 37 (o3, 50%) 288 non-null float64\n", | |
| " 38 (o3, 75%) 288 non-null float64\n", | |
| " 39 (o3, max) 288 non-null float64\n", | |
| " 40 (pm1, count) 288 non-null float64\n", | |
| " 41 (pm1, mean) 288 non-null float64\n", | |
| " 42 (pm1, std) 288 non-null float64\n", | |
| " 43 (pm1, min) 288 non-null float64\n", | |
| " 44 (pm1, 25%) 288 non-null float64\n", | |
| " 45 (pm1, 50%) 288 non-null float64\n", | |
| " 46 (pm1, 75%) 288 non-null float64\n", | |
| " 47 (pm1, max) 288 non-null float64\n", | |
| " 48 (pm25, count) 288 non-null float64\n", | |
| " 49 (pm25, mean) 288 non-null float64\n", | |
| " 50 (pm25, std) 288 non-null float64\n", | |
| " 51 (pm25, min) 288 non-null float64\n", | |
| " 52 (pm25, 25%) 288 non-null float64\n", | |
| " 53 (pm25, 50%) 288 non-null float64\n", | |
| " 54 (pm25, 75%) 288 non-null float64\n", | |
| " 55 (pm25, max) 288 non-null float64\n", | |
| " 56 (pm10, count) 288 non-null float64\n", | |
| " 57 (pm10, mean) 288 non-null float64\n", | |
| " 58 (pm10, std) 288 non-null float64\n", | |
| " 59 (pm10, min) 288 non-null float64\n", | |
| " 60 (pm10, 25%) 288 non-null float64\n", | |
| " 61 (pm10, 50%) 288 non-null float64\n", | |
| " 62 (pm10, 75%) 288 non-null float64\n", | |
| " 63 (pm10, max) 288 non-null float64\n", | |
| "dtypes: float64(64)\n", | |
| "memory usage: 146.2 KB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "stats = df.groupby([df.index.hour, df.index.minute], as_index=False).describe()\n", | |
| "\n", | |
| "stats.info()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Build the Figure\n", | |
| "\n", | |
| "Here is an example of CO2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x432 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, ax = plt.subplots(1, figsize=(10, 6))\n", | |
| "\n", | |
| "# get the index\n", | |
| "idx = stats.index.values\n", | |
| "\n", | |
| "# create an artificial time series where the start date is arbitrary\n", | |
| "idx = pd.date_range(start=\"2020-01-01\", periods=len(idx), freq='5min')\n", | |
| "\n", | |
| "# choose the pollutant\n", | |
| "col = \"co2\"\n", | |
| "\n", | |
| "# plot the mean\n", | |
| "ax.plot(idx, stats[col][\"mean\"], lw=3)\n", | |
| "\n", | |
| "# plot the IQR as a shaded box\n", | |
| "ax.fill_between(idx, y1=stats[col][\"25%\"], y2=stats[col][\"75%\"], alpha=0.25, lw=2)\n", | |
| "\n", | |
| "# fix the tick marks\n", | |
| "ticks = ax.get_xticks()\n", | |
| "\n", | |
| "ax.set_xticks(np.linspace(ticks[0], ticks[-1], 5))\n", | |
| "ax.set_xlim(ticks[0], ticks[-1])\n", | |
| "\n", | |
| "# format the xticks\n", | |
| "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%I:%M\\n%p\"))\n", | |
| "ax.yaxis.set_minor_locator(MultipleLocator(100))\n", | |
| "\n", | |
| "# label the Y axis\n", | |
| "ax.set_ylabel(\"{}\".format(col.upper()))\n", | |
| "\n", | |
| "sns.despine()\n", | |
| "plt.tight_layout()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "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": "3.6.5" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } |
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