{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from media_processing_lib.image import image_read, image_resize\n",
    "from argparse import ArgumentParser\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm, trange\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "from multiprocessing import Pool, cpu_count\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = Path(\"neo_1month/data\")\n",
    "# dataset_path = Path(\"neo_1week/data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_figure_of_date_to_representations(date_to_representations: dict[str, list[str]], representations):\n",
    "    n_repr = {k: len(v) for k, v in date_to_representations.items()}\n",
    "    x = np.arange(len(n_repr))\n",
    "\n",
    "    xticks = [[], []]\n",
    "    for i, key in enumerate(n_repr.keys()):\n",
    "        if key[5:7] in (\"01\", \"07\") and (len(xticks[1]) == 0 or (len(xticks[1]) > 0 and xticks[1][-1] != key[0:7])):\n",
    "            xticks[0].append(i)\n",
    "            xticks[1].append(key[0:7])\n",
    "\n",
    "    # keys = list(n_repr.keys())\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    bar_list = plt.bar(x, n_repr.values(), width=1)\n",
    "    for bar, bar_n in zip(bar_list, n_repr.values()):\n",
    "        if bar_n < 5:\n",
    "            bar.set_color(\"red\")\n",
    "        elif bar_n < 15:\n",
    "            bar.set_color(\"yellow\")\n",
    "        elif bar_n < len(representations):\n",
    "            bar.set_color(\"blue\")\n",
    "        else:\n",
    "            bar.set_color(\"green\")\n",
    "    plt.xticks(xticks[0], xticks[1], rotation=90)\n",
    "    plt.ylabel(\"N representations\")\n",
    "    plt.title(f\"Red = <5, Yellow = <15, Blue = <{len(representations)}, Green == {len(representations)}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AOD', 'CHLORA', 'CLD_FR', 'CLD_RD', 'CLD_WP', 'COT', 'FIRE', 'LAI', 'LSTD_AN', 'LSTD', 'LSTN_AN', 'LSTN', 'NO2', 'OZONE', 'SNOWC', 'SST', 'WV', 'NDVI', 'CO_M']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assert dataset_path.exists(), dataset_path\n",
    "representations = [x.name for x in dataset_path.iterdir() if x.is_dir()]\n",
    "print(representations)\n",
    "data = {}\n",
    "date_to_representations = {}\n",
    "for representation in representations:\n",
    "    data[representation] = [f.stem for f in (dataset_path / representation).iterdir() if f.suffix == \".npz\"]\n",
    "    assert len(data[representation]) > 0, f\"No files found for representation '{representation}'\"\n",
    "    for item in data[representation]:\n",
    "        date_to_representations.setdefault(item, []).append(representation)\n",
    "date_to_representations = {k: v for k, v in sorted(date_to_representations.items(), key=lambda item: item[0])}\n",
    "make_figure_of_date_to_representations(date_to_representations, representations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    213\n",
       "17     26\n",
       "13     25\n",
       "18     14\n",
       "16      3\n",
       "12      2\n",
       "8       1\n",
       "10      1\n",
       "11      1\n",
       "15      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({k: len(v) for k, v in date_to_representations.items()}).value_counts()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nasa",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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