Climate/09_global_trends_CO2.ipynb

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   "source": [
    "import calendar\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "matplotlib.use('agg')\n",
    "plt.style.use('seaborn-darkgrid')"
   ]
  },
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   "source": [
    "# Source: Global Monitoring Laboratory, NOAA\n",
    "\n",
    "# Global Monthly Mean CO2\n",
    "co2_mm_gl = \"https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.txt\"\n",
    "\n",
    "# Daily Global CO2\n",
    "co2_trend_gl = \"https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_trend_gl.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# Read the Global Monthly Mean CO2 file\n",
    "co2_mm_gl_df = pd.read_csv(co2_mm_gl, \n",
    "                         sep=r\"\\s+\", \n",
    "                         comment='#', \n",
    "                         names=[\"year\",\n",
    "                                \"month\", \n",
    "                                \"decimal\", \n",
    "                                \"average\", \n",
    "                                \"trend\"])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "co2_mm_gl_df[\"date_text\"] = co2_mm_gl_df.apply(lambda r: \"{} {}\".format(calendar.month_name[int(r[\"month\"])], \n",
    "                       int(r[\"year\"])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Get the most up-to-date Global Monthly Mean of CO2 reading\n",
    "last_month = co2_mm_gl_df.iloc[-1]\n",
    "\n",
    "# ...and compare it with that of the previous year\n",
    "prev_year = co2_mm_gl_df[co2_mm_gl_df[\"decimal\"] == last_month[\"decimal\"] - 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   "source": [
    "boxtext = \"\"\n",
    "boxtext += \"{}: {} ppm\\n\".format(last_month[\"date_text\"], \n",
    "                              last_month[\"average\"])\n",
    "boxtext += \"{}: {} ppm\".format(prev_year[\"date_text\"].item(), \n",
    "                            prev_year[\"average\"].item())"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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    "execution": {
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   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(co2_mm_gl_df[\"decimal\"], co2_mm_gl_df[\"average\"], c=\"tab:red\")\n",
    "ax.plot(co2_mm_gl_df[\"decimal\"], co2_mm_gl_df[\"trend\"], c=\"k\")\n",
    "min_year = min(co2_mm_gl_df[\"year\"])\n",
    "max_year = max(co2_mm_gl_df[\"year\"])\n",
    "ax.set_title(f\"Global Monthly Mean $CO_{2}$ ({min_year}-{max_year})\")\n",
    "ax.set_xlabel(\"Years\")\n",
    "ax.set_ylabel(\"ppm\")\n",
    "ax.text(0.95, 0.05, boxtext,\n",
    "        horizontalalignment='right', \n",
    "        color='white',\n",
    "        fontsize=14,\n",
    "        fontweight='bold',\n",
    "        verticalalignment='bottom',\n",
    "        bbox=dict(facecolor=\"green\", \n",
    "                  edgecolor=\"darkgreen\", \n",
    "                  boxstyle='round'),\n",
    "        transform=ax.transAxes)\n",
    "\n",
    "# Add last update date\n",
    "last_update = datetime.today().strftime(\"%d-%m-%Y %H:%M\")\n",
    "last_update_at = AnchoredText(f\"Last update: {last_update}\",\n",
    "                        borderpad=0., \n",
    "                        frameon=False, \n",
    "                        loc=3)\n",
    "ax.add_artist(last_update_at)\n",
    "ax.legend([\"Global Monthly Mean\", \"Global Trend\"])\n",
    "ax.grid(color=\"white\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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    "execution": {
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   "outputs": [],
   "source": [
    "# Read the Global daily CO2 file\n",
    "co2_trend_gl_df = pd.read_csv(co2_trend_gl, \n",
    "                        sep=r\"\\s+\", \n",
    "                        comment='#', \n",
    "                        names=[\"year\", \n",
    "                               \"month\", \n",
    "                               \"day\", \n",
    "                               \"cycle\", \n",
    "                               \"trend\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "# Create a Datetime column from year, month, day\n",
    "co2_trend_gl_df[\"date\"] = pd.to_datetime(co2_trend_gl_df[['year', \n",
    "                                              'month', \n",
    "                                              'day']])\n",
    "\n",
    "co2_trend_gl_df[\"date_str\"] = co2_trend_gl_df.apply(lambda r: \"{} {}\".format(calendar.month_name[int(r[\"month\"])], \n",
    "                       int(r[\"day\"])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# Get the last 5 days\n",
    "last_5days_co2 = co2_trend_gl_df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
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   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(co2_trend_gl_df[\"date\"], co2_trend_gl_df[\"cycle\"], c=\"tab:red\")\n",
    "ax.plot(co2_trend_gl_df[\"date\"], co2_trend_gl_df[\"trend\"], c=\"k\")\n",
    "min_year = min(co2_trend_gl_df[\"year\"])\n",
    "max_year = max(co2_trend_gl_df[\"year\"])\n",
    "ax.set_title(f\"Daily Global $CO_{2}$ ({min_year}-{max_year})\")\n",
    "ax.set_xlabel(\"Years\")\n",
    "ax.set_ylabel(\"ppm\")\n",
    "\n",
    "boxtext = \"\"\n",
    "for idx, row in last_5days_co2[::-1].iterrows():\n",
    "    if idx == last_5days_co2.index[0]:\n",
    "        boxtext += \"{}: {} ppm\".format(row[\"date_str\"], row[\"trend\"])\n",
    "    else:\n",
    "        boxtext += \"{}: {} ppm\\n\".format(row[\"date_str\"], row[\"trend\"])\n",
    "\n",
    "ax.text(0.95, 0.05, boxtext,\n",
    "        horizontalalignment='right', \n",
    "        color='white',\n",
    "        fontsize=14,\n",
    "        fontweight='bold',\n",
    "        verticalalignment='bottom',\n",
    "        bbox=dict(facecolor=\"green\", \n",
    "                  edgecolor=\"darkgreen\", \n",
    "                  boxstyle='round'),\n",
    "        transform=ax.transAxes)\n",
    "# Add last update date\n",
    "last_update = datetime.today().strftime(\"%d-%m-%Y %H:%M\")\n",
    "last_update_at = AnchoredText(f\"Last update: {last_update}\",\n",
    "                        borderpad=0., \n",
    "                        frameon=False, \n",
    "                        loc=3)\n",
    "ax.add_artist(last_update_at)\n",
    "ax.legend([\"Global Seasonal Cycle\", \"Global Trend\"])\n",
    "ax.grid(color=\"white\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"09_DailyGlobalCO2.jpg\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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