Lower_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a051ac8-c990-4d79-ad47-bcc9ace28e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cmocean.cm as cmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b72c5c8-377d-4c1e-89f4-a58530f9d021",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65ebbc16-bc49-4c77-b912-c9d712a772c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('1_std_events_dens_spg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "508ebe6d-d113-4635-8f3d-0644f6596468",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 1850 to all 'Values' to adjust\n",
    "df['Adjusted_Values'] = df['Values'] + 1850\n",
    "\n",
    "# Define bins from 1850 to 2010 in steps of 5\n",
    "bins = range(1850, 2015, 5)\n",
    "labels = [f\"{i} - {i+4}\" for i in bins[:-1]]\n",
    "\n",
    "# Creating the bins in the DataFrame\n",
    "df['Value_bins'] = pd.cut(df['Adjusted_Values'], bins=bins, labels=labels, include_lowest=True, right=False)\n",
    "\n",
    "# Filter data based on Condition and count occurrences\n",
    "value_counts_above = df[df['Condition'] == 'Above']['Value_bins'].value_counts().sort_index()\n",
    "value_counts_below = df[df['Condition'] == 'Below']['Value_bins'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c979d264-aa40-4275-a35c-677f0724146c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = cmo.tarn(np.linspace(0.3, 0.9, 2))\n",
    "\n",
    "# Plot configuration\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "value_counts_above.plot(kind='bar', ax=ax, color=colors[1], width=0.4)\n",
    "(-value_counts_below).plot(kind='bar', ax=ax, color=colors[0], alpha=0.8, width=0.4)\n",
    "\n",
    "# Adjust title and labels\n",
    "ax.set_title('Number of 1-std events in 5 year bins', fontsize=18)\n",
    "ax.set_xlabel('Adjusted Value Ranges')\n",
    "ax.set_ylabel('Counts')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
    "\n",
    "# Removing spines (frame of the plot)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Enhance display with grid\n",
    "ax.grid(True, axis='y', linestyle='--', linewidth=0.5)\n",
    "\n",
    "#plt.show()\n",
    "plt.savefig(os.path.expanduser('~/phase1_CONDA/results/') + 'Occurences_incomplete.png', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bacd4146-c85c-4d61-8d52-d11ab9d49ad4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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