generate_train_val_timesteps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "start_time = '2010-01-01'\n",
    "end_time = '2011-01-01'\n",
    "\n",
    "time_range = pd.date_range(start=start_time, end=end_time, freq='3H')\n",
    "time_list = time_range.tolist()\n",
    "\n",
    "# print(time_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_list = [d.to_pydatetime().strftime('%Y%m%d.%H%M%S') for d in time_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
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    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_list = sorted(time_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20100101.000000'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_times = sorted(used_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1026"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times_not_used = set(time_list) - set(used_times)\n",
    "len(times_not_used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1895"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(used_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From Anna Timesplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake\n",
    "import xarray as xr\n",
    "import yaml\n",
    "import numpy as np\n",
    "import copy\n",
    "import os\n",
    "import vcm\n",
    "from toolz import partition_all\n",
    "from loaders.mappers import open_nudge_to_fine, open_zarr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 60 train times drawn from 2 week block, from which first/last two timesteps are excluded\n",
    "# 5 test times drawn from 1 week block following three week training block\n",
    "# repeat x 13\n",
    "\n",
    "STEPS_PER_WEEK = 56 # 3 hr sampling\n",
    "TRAIN_BLOCK_SIZE= 2 * STEPS_PER_WEEK\n",
    "TEST_BLOCK_SIZE = 1 * STEPS_PER_WEEK\n",
    "PAD = 2  # exclude this number of samples from the ends of the train block\n",
    "\n",
    "\n",
    "\n",
    "nudged_run_path = \"gs://vcm-ml-experiments/n2f-pire-sfc-updates/2022-01-21/nudged-run/fv3gfs_run/\"\n",
    "rad_fluxes_path = \"gs://vcm-ml-experiments/n2f-pire-sfc-updates/data/rad-train-test-data-time-centered-with-toa.zarr\"\n",
    "\n",
    "def split_times(times):\n",
    "    train, test = [], []\n",
    "\n",
    "    i = 0\n",
    "\n",
    "    while i < len(times) :\n",
    "        train_block = times[i + PAD: i + TRAIN_BLOCK_SIZE - PAD]\n",
    "        test_block = times[i + TRAIN_BLOCK_SIZE: i + TRAIN_BLOCK_SIZE+ TEST_BLOCK_SIZE]\n",
    "        \n",
    "        train += train_block\n",
    "        test += test_block\n",
    "        i += TRAIN_BLOCK_SIZE+ TEST_BLOCK_SIZE\n",
    "        \n",
    "        \n",
    "    np.random.seed(0)\n",
    "    np.random.shuffle(train)\n",
    "    np.random.shuffle(test) \n",
    "    return train, test   \n",
    "\n",
    "\n",
    "def sort_within_batches(times, timesteps_per_batch):\n",
    "    # Within batches of randomly sampled times, sort the timesteps for faster loading if shuffle_timesteps=false.\n",
    "    # Samples within each batch of timesteps will be shuffled by tf dataset during training.\n",
    "    batches = list(partition_all(timesteps_per_batch, times))\n",
    "    sorted_batches = [sorted(batch) for batch in batches]\n",
    "    flattened_sorted_batches = []\n",
    "    for batch in sorted_batches:\n",
    "        flattened_sorted_batches += batch\n",
    "    return flattened_sorted_batches\n",
    "\n",
    "\n",
    "\n",
    "def fill_timesteps(timesteps, template_path, output_path):\n",
    "    with open(template_path, \"r\") as f:\n",
    "        template_config = yaml.safe_load(f)\n",
    "    config_ = copy.copy(template_config)\n",
    "    config_[\"timesteps\"] = timesteps\n",
    "    with open(output_path, \"w\") as f:\n",
    "        yaml.dump(config_, f, indent=4)    \n",
    "    print(f\"Filled timesteps saved in {output_path} using base configuration from {template_path}.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_times_tq, test_times_tq = split_times(time_list)\n",
    "\n",
    "#t_full_rad = [vcm.encode_time(t) for t in ds_rad.time.values]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filled timesteps saved in train.yaml using base configuration from train-template.yaml.\n"
     ]
    }
   ],
   "source": [
    "fill_timesteps(train_times_tq, \"train-template.yaml\", \"train.yaml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1899"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_times_tq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "952"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_times_tq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1861"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(used_times) & set(train_times_tq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(test_times_tq) & set(used_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for_diags = np.random.choice(test_times_tq, 200, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       " '20100116.150000',\n",
       " '20100304.150000',\n",
       " '20100527.060000',\n",
       " '20100904.030000',\n",
       " '20101201.000000',\n",
       " '20100723.120000',\n",
       " '20100705.210000',\n",
       " '20101015.000000',\n",
       " '20100207.150000',\n",
       " '20100617.120000',\n",
       " '20100206.180000',\n",
       " '20101220.150000',\n",
       " '20100322.120000',\n",
       " '20100304.000000',\n",
       " '20100615.210000',\n",
       " '20101015.120000',\n",
       " '20101016.150000',\n",
       " '20100906.150000',\n",
       " '20100301.150000',\n",
       " '20100707.120000',\n",
       " '20100728.210000',\n",
       " '20100725.210000',\n",
       " '20100726.000000',\n",
       " '20100527.090000',\n",
       " '20100929.060000',\n",
       " '20100302.090000',\n",
       " '20100904.120000',\n",
       " '20100227.000000',\n",
       " '20100321.090000',\n",
       " '20100707.090000',\n",
       " '20100412.150000',\n",
       " '20100211.000000',\n",
       " '20100302.030000',\n",
       " '20100227.030000',\n",
       " '20100723.210000',\n",
       " '20100924.180000',\n",
       " '20100503.030000',\n",
       " '20101106.150000',\n",
       " '20100525.000000',\n",
       " '20100724.180000',\n",
       " '20101218.150000',\n",
       " '20100320.210000',\n",
       " '20100906.120000',\n",
       " '20100928.210000',\n",
       " '20100411.150000',\n",
       " '20100614.180000',\n",
       " '20100707.210000',\n",
       " '20100521.150000',\n",
       " '20100301.000000',\n",
       " '20101020.120000',\n",
       " '20100815.180000',\n",
       " '20100526.090000',\n",
       " '20100611.030000',\n",
       " '20100320.060000',\n",
       " '20101217.150000',\n",
       " '20100924.120000',\n",
       " '20100818.120000',\n",
       " '20101130.060000',\n",
       " '20101129.180000',\n",
       " '20100726.090000',\n",
       " '20100615.180000',\n",
       " '20100704.000000',\n",
       " '20100926.180000',\n",
       " '20101016.120000',\n",
       " '20101020.090000',\n",
       " '20100502.150000',\n",
       " '20100209.150000',\n",
       " '20100209.000000',\n",
       " '20100303.120000',\n",
       " '20100818.210000',\n",
       " '20101218.000000',\n",
       " '20100616.060000',\n",
       " '20100321.150000',\n",
       " '20100322.060000',\n",
       " '20100412.030000',\n",
       " '20100819.180000',\n",
       " '20100119.210000',\n",
       " '20100814.120000',\n",
       " '20100411.060000',\n",
       " '20100929.120000',\n",
       " '20100907.120000',\n",
       " '20100319.180000',\n",
       " '20101109.120000',\n",
       " '20101221.060000',\n",
       " '20101219.210000',\n",
       " '20100506.060000',\n",
       " '20100412.060000',\n",
       " '20101219.060000',\n",
       " '20101222.060000',\n",
       " '20100228.210000',\n",
       " '20100729.030000',\n",
       " '20100413.150000',\n",
       " '20100208.030000',\n",
       " '20101201.060000',\n",
       " '20100121.060000',\n",
       " '20101015.060000',\n",
       " '20100430.210000',\n",
       " '20101017.210000',\n",
       " '20100119.060000',\n",
       " '20101202.150000',\n",
       " '20100503.150000',\n",
       " '20100929.180000',\n",
       " '20100210.150000',\n",
       " '20101218.210000',\n",
       " '20100926.150000',\n",
       " '20100414.180000',\n",
       " '20100227.180000',\n",
       " '20100430.060000',\n",
       " '20100815.090000',\n",
       " '20100930.060000',\n",
       " '20100303.030000',\n",
       " '20100924.090000',\n",
       " '20100211.120000',\n",
       " '20100410.000000',\n",
       " '20100704.060000',\n",
       " '20101021.090000',\n",
       " '20100413.000000',\n",
       " '20100924.150000',\n",
       " '20100818.090000']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for_diags.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'timesteps:':\n",
      "- '20101219.180000'\n",
      "- '20100905.120000'\n",
      "- '20101202.030000'\n",
      "- '20100410.120000'\n",
      "- '20100506.000000'\n",
      "- '20100703.150000'\n",
      "- '20100814.060000'\n",
      "- '20101109.030000'\n",
      "- '20100116.180000'\n",
      "- '20100207.120000'\n",
      "- '20100813.120000'\n",
      "- '20100506.210000'\n",
      "- '20100813.180000'\n",
      "- '20100723.180000'\n",
      "- '20100908.030000'\n",
      "- '20100614.000000'\n",
      "- '20100206.030000'\n",
      "- '20100205.000000'\n",
      "- '20101127.150000'\n",
      "- '20100501.030000'\n",
      "- '20101222.090000'\n",
      "- '20100226.210000'\n",
      "- '20100727.000000'\n",
      "- '20100704.030000'\n",
      "- '20101128.030000'\n",
      "- '20101111.210000'\n",
      "- '20100415.150000'\n",
      "- '20100121.120000'\n",
      "- '20100708.180000'\n",
      "- '20101202.210000'\n",
      "- '20100304.030000'\n",
      "- '20100818.000000'\n",
      "- '20100526.150000'\n",
      "- '20100906.210000'\n",
      "- '20100324.030000'\n",
      "- '20100522.060000'\n",
      "- '20100414.090000'\n",
      "- '20100523.150000'\n",
      "- '20100702.120000'\n",
      "- '20100814.210000'\n",
      "- '20100211.090000'\n",
      "- '20100521.210000'\n",
      "- '20101201.150000'\n",
      "- '20101017.030000'\n",
      "- '20100725.180000'\n",
      "- '20100504.180000'\n",
      "- '20101127.060000'\n",
      "- '20100615.150000'\n",
      "- '20101130.000000'\n",
      "- '20100708.000000'\n",
      "- '20100928.120000'\n",
      "- '20100813.150000'\n",
      "- '20101126.120000'\n",
      "- '20101105.030000'\n",
      "- '20100410.150000'\n",
      "- '20100415.210000'\n",
      "- '20100118.210000'\n",
      "- '20100506.030000'\n",
      "- '20100928.000000'\n",
      "- '20100727.180000'\n",
      "- '20100729.000000'\n",
      "- '20100207.090000'\n",
      "- '20100930.210000'\n",
      "- '20100527.180000'\n",
      "- '20101221.000000'\n",
      "- '20100409.000000'\n",
      "- '20100727.150000'\n",
      "- '20100414.030000'\n",
      "- '20100301.030000'\n",
      "- '20101020.150000'\n",
      "- '20101126.150000'\n",
      "- '20100614.030000'\n",
      "- '20100303.210000'\n",
      "- '20101202.060000'\n",
      "- '20101017.150000'\n",
      "- '20101222.150000'\n",
      "- '20100526.000000'\n",
      "- '20100813.030000'\n",
      "- '20101105.180000'\n",
      "- '20100728.180000'\n",
      "- '20101019.180000'\n",
      "- '20100116.150000'\n",
      "- '20100304.150000'\n",
      "- '20100527.060000'\n",
      "- '20100904.030000'\n",
      "- '20101201.000000'\n",
      "- '20100723.120000'\n",
      "- '20100705.210000'\n",
      "- '20101015.000000'\n",
      "- '20100207.150000'\n",
      "- '20100617.120000'\n",
      "- '20100206.180000'\n",
      "- '20101220.150000'\n",
      "- '20100322.120000'\n",
      "- '20100304.000000'\n",
      "- '20100615.210000'\n",
      "- '20101015.120000'\n",
      "- '20101016.150000'\n",
      "- '20100906.150000'\n",
      "- '20100301.150000'\n",
      "- '20100707.120000'\n",
      "- '20100728.210000'\n",
      "- '20100725.210000'\n",
      "- '20100726.000000'\n",
      "- '20100527.090000'\n",
      "- '20100929.060000'\n",
      "- '20100302.090000'\n",
      "- '20100904.120000'\n",
      "- '20100227.000000'\n",
      "- '20100321.090000'\n",
      "- '20100707.090000'\n",
      "- '20100412.150000'\n",
      "- '20100211.000000'\n",
      "- '20100302.030000'\n",
      "- '20100227.030000'\n",
      "- '20100723.210000'\n",
      "- '20100924.180000'\n",
      "- '20100503.030000'\n",
      "- '20101106.150000'\n",
      "- '20100525.000000'\n",
      "- '20100724.180000'\n",
      "- '20101218.150000'\n",
      "- '20100320.210000'\n",
      "- '20100906.120000'\n",
      "- '20100928.210000'\n",
      "- '20100411.150000'\n",
      "- '20100614.180000'\n",
      "- '20100707.210000'\n",
      "- '20100521.150000'\n",
      "- '20100301.000000'\n",
      "- '20101020.120000'\n",
      "- '20100815.180000'\n",
      "- '20100526.090000'\n",
      "- '20100611.030000'\n",
      "- '20100320.060000'\n",
      "- '20101217.150000'\n",
      "- '20100924.120000'\n",
      "- '20100818.120000'\n",
      "- '20101130.060000'\n",
      "- '20101129.180000'\n",
      "- '20100726.090000'\n",
      "- '20100615.180000'\n",
      "- '20100704.000000'\n",
      "- '20100926.180000'\n",
      "- '20101016.120000'\n",
      "- '20101020.090000'\n",
      "- '20100502.150000'\n",
      "- '20100209.150000'\n",
      "- '20100209.000000'\n",
      "- '20100303.120000'\n",
      "- '20100818.210000'\n",
      "- '20101218.000000'\n",
      "- '20100616.060000'\n",
      "- '20100321.150000'\n",
      "- '20100322.060000'\n",
      "- '20100412.030000'\n",
      "- '20100819.180000'\n",
      "- '20100119.210000'\n",
      "- '20100814.120000'\n",
      "- '20100411.060000'\n",
      "- '20100929.120000'\n",
      "- '20100907.120000'\n",
      "- '20100319.180000'\n",
      "- '20101109.120000'\n",
      "- '20101221.060000'\n",
      "- '20101219.210000'\n",
      "- '20100506.060000'\n",
      "- '20100412.060000'\n",
      "- '20101219.060000'\n",
      "- '20101222.060000'\n",
      "- '20100228.210000'\n",
      "- '20100729.030000'\n",
      "- '20100413.150000'\n",
      "- '20100208.030000'\n",
      "- '20101201.060000'\n",
      "- '20100121.060000'\n",
      "- '20101015.060000'\n",
      "- '20100430.210000'\n",
      "- '20101017.210000'\n",
      "- '20100119.060000'\n",
      "- '20101202.150000'\n",
      "- '20100503.150000'\n",
      "- '20100929.180000'\n",
      "- '20100210.150000'\n",
      "- '20101218.210000'\n",
      "- '20100926.150000'\n",
      "- '20100414.180000'\n",
      "- '20100227.180000'\n",
      "- '20100430.060000'\n",
      "- '20100815.090000'\n",
      "- '20100930.060000'\n",
      "- '20100303.030000'\n",
      "- '20100924.090000'\n",
      "- '20100211.120000'\n",
      "- '20100410.000000'\n",
      "- '20100704.060000'\n",
      "- '20101021.090000'\n",
      "- '20100413.000000'\n",
      "- '20100924.150000'\n",
      "- '20100818.090000'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(yaml.dump({\"timesteps:\": for_diags.tolist()}))"
   ]
  },
  {
   "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": "3.8.16"
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 },
 "nbformat": 4,
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