mhw_climatology.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27ba5874-6884-4a94-a83a-317981fba5f7",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxr\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msci\u001b[39;00m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/__init__.py:46\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# let init-time option registration happen\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig_init\u001b[39;00m  \u001b[38;5;66;03m# pyright: ignore[reportUnusedImport] # noqa: F401\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;66;03m# dtype\u001b[39;00m\n\u001b[1;32m     48\u001b[0m     ArrowDtype,\n\u001b[1;32m     49\u001b[0m     Int8Dtype,\n\u001b[1;32m     50\u001b[0m     Int16Dtype,\n\u001b[1;32m     51\u001b[0m     Int32Dtype,\n\u001b[1;32m     52\u001b[0m     Int64Dtype,\n\u001b[1;32m     53\u001b[0m     UInt8Dtype,\n\u001b[1;32m     54\u001b[0m     UInt16Dtype,\n\u001b[1;32m     55\u001b[0m     UInt32Dtype,\n\u001b[1;32m     56\u001b[0m     UInt64Dtype,\n\u001b[1;32m     57\u001b[0m     Float32Dtype,\n\u001b[1;32m     58\u001b[0m     Float64Dtype,\n\u001b[1;32m     59\u001b[0m     CategoricalDtype,\n\u001b[1;32m     60\u001b[0m     PeriodDtype,\n\u001b[1;32m     61\u001b[0m     IntervalDtype,\n\u001b[1;32m     62\u001b[0m     DatetimeTZDtype,\n\u001b[1;32m     63\u001b[0m     StringDtype,\n\u001b[1;32m     64\u001b[0m     BooleanDtype,\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;66;03m# missing\u001b[39;00m\n\u001b[1;32m     66\u001b[0m     NA,\n\u001b[1;32m     67\u001b[0m     isna,\n\u001b[1;32m     68\u001b[0m     isnull,\n\u001b[1;32m     69\u001b[0m     notna,\n\u001b[1;32m     70\u001b[0m     notnull,\n\u001b[1;32m     71\u001b[0m     \u001b[38;5;66;03m# indexes\u001b[39;00m\n\u001b[1;32m     72\u001b[0m     Index,\n\u001b[1;32m     73\u001b[0m     CategoricalIndex,\n\u001b[1;32m     74\u001b[0m     RangeIndex,\n\u001b[1;32m     75\u001b[0m     MultiIndex,\n\u001b[1;32m     76\u001b[0m     IntervalIndex,\n\u001b[1;32m     77\u001b[0m     TimedeltaIndex,\n\u001b[1;32m     78\u001b[0m     DatetimeIndex,\n\u001b[1;32m     79\u001b[0m     PeriodIndex,\n\u001b[1;32m     80\u001b[0m     IndexSlice,\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;66;03m# tseries\u001b[39;00m\n\u001b[1;32m     82\u001b[0m     NaT,\n\u001b[1;32m     83\u001b[0m     Period,\n\u001b[1;32m     84\u001b[0m     period_range,\n\u001b[1;32m     85\u001b[0m     Timedelta,\n\u001b[1;32m     86\u001b[0m     timedelta_range,\n\u001b[1;32m     87\u001b[0m     Timestamp,\n\u001b[1;32m     88\u001b[0m     date_range,\n\u001b[1;32m     89\u001b[0m     bdate_range,\n\u001b[1;32m     90\u001b[0m     Interval,\n\u001b[1;32m     91\u001b[0m     interval_range,\n\u001b[1;32m     92\u001b[0m     DateOffset,\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;66;03m# conversion\u001b[39;00m\n\u001b[1;32m     94\u001b[0m     to_numeric,\n\u001b[1;32m     95\u001b[0m     to_datetime,\n\u001b[1;32m     96\u001b[0m     to_timedelta,\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;66;03m# misc\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     Flags,\n\u001b[1;32m     99\u001b[0m     Grouper,\n\u001b[1;32m    100\u001b[0m     factorize,\n\u001b[1;32m    101\u001b[0m     unique,\n\u001b[1;32m    102\u001b[0m     value_counts,\n\u001b[1;32m    103\u001b[0m     NamedAgg,\n\u001b[1;32m    104\u001b[0m     array,\n\u001b[1;32m    105\u001b[0m     Categorical,\n\u001b[1;32m    106\u001b[0m     set_eng_float_format,\n\u001b[1;32m    107\u001b[0m     Series,\n\u001b[1;32m    108\u001b[0m     DataFrame,\n\u001b[1;32m    109\u001b[0m )\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparseDtype\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtseries\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m infer_freq\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/api.py:47\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstruction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflags\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Flags\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     48\u001b[0m     Grouper,\n\u001b[1;32m     49\u001b[0m     NamedAgg,\n\u001b[1;32m     50\u001b[0m )\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     52\u001b[0m     CategoricalIndex,\n\u001b[1;32m     53\u001b[0m     DatetimeIndex,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     59\u001b[0m     TimedeltaIndex,\n\u001b[1;32m     60\u001b[0m )\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatetimes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     62\u001b[0m     bdate_range,\n\u001b[1;32m     63\u001b[0m     date_range,\n\u001b[1;32m     64\u001b[0m )\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/groupby/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m      2\u001b[0m     DataFrameGroupBy,\n\u001b[1;32m      3\u001b[0m     NamedAgg,\n\u001b[1;32m      4\u001b[0m     SeriesGroupBy,\n\u001b[1;32m      5\u001b[0m )\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GroupBy\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgrouper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Grouper\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/groupby/generic.py:67\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     60\u001b[0m     GroupByApply,\n\u001b[1;32m     61\u001b[0m     maybe_mangle_lambdas,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     64\u001b[0m     warn_alias_replacement,\n\u001b[1;32m     65\u001b[0m )\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcom\u001b[39;00m\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mframe\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataFrame\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     69\u001b[0m     base,\n\u001b[1;32m     70\u001b[0m     ops,\n\u001b[1;32m     71\u001b[0m )\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     73\u001b[0m     GroupBy,\n\u001b[1;32m     74\u001b[0m     GroupByPlot,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     78\u001b[0m     _transform_template,\n\u001b[1;32m     79\u001b[0m )\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/frame.py:142\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marrays\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msparse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparseFrameAccessor\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstruction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    137\u001b[0m     ensure_wrapped_if_datetimelike,\n\u001b[1;32m    138\u001b[0m     extract_array,\n\u001b[1;32m    139\u001b[0m     sanitize_array,\n\u001b[1;32m    140\u001b[0m     sanitize_masked_array,\n\u001b[1;32m    141\u001b[0m )\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    143\u001b[0m     NDFrame,\n\u001b[1;32m    144\u001b[0m     make_doc,\n\u001b[1;32m    145\u001b[0m )\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_key_length\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    148\u001b[0m     DatetimeIndex,\n\u001b[1;32m    149\u001b[0m     Index,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    153\u001b[0m     ensure_index_from_sequences,\n\u001b[1;32m    154\u001b[0m )\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/generic.py:146\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minference\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    138\u001b[0m     is_hashable,\n\u001b[1;32m    139\u001b[0m     is_nested_list_like,\n\u001b[1;32m    140\u001b[0m )\n\u001b[1;32m    141\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmissing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    142\u001b[0m     isna,\n\u001b[1;32m    143\u001b[0m     notna,\n\u001b[1;32m    144\u001b[0m )\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    147\u001b[0m     algorithms \u001b[38;5;28;01mas\u001b[39;00m algos,\n\u001b[1;32m    148\u001b[0m     arraylike,\n\u001b[1;32m    149\u001b[0m     common,\n\u001b[1;32m    150\u001b[0m     indexing,\n\u001b[1;32m    151\u001b[0m     missing,\n\u001b[1;32m    152\u001b[0m     nanops,\n\u001b[1;32m    153\u001b[0m     sample,\n\u001b[1;32m    154\u001b[0m )\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray_algos\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreplace\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m should_use_regex\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marrays\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExtensionArray\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/indexing.py:70\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstruction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     61\u001b[0m     array \u001b[38;5;28;01mas\u001b[39;00m pd_array,\n\u001b[1;32m     62\u001b[0m     extract_array,\n\u001b[1;32m     63\u001b[0m )\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     65\u001b[0m     check_array_indexer,\n\u001b[1;32m     66\u001b[0m     is_list_like_indexer,\n\u001b[1;32m     67\u001b[0m     is_scalar_indexer,\n\u001b[1;32m     68\u001b[0m     length_of_indexer,\n\u001b[1;32m     69\u001b[0m )\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     71\u001b[0m     Index,\n\u001b[1;32m     72\u001b[0m     MultiIndex,\n\u001b[1;32m     73\u001b[0m )\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m     76\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mabc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     77\u001b[0m         Hashable,\n\u001b[1;32m     78\u001b[0m         Sequence,\n\u001b[1;32m     79\u001b[0m     )\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-23.10/lib/python3.10/site-packages/pandas/core/indexes/api.py:27\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01malgorithms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_sort\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     21\u001b[0m     Index,\n\u001b[1;32m     22\u001b[0m     _new_Index,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     25\u001b[0m     get_unanimous_names,\n\u001b[1;32m     26\u001b[0m )\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcategory\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CategoricalIndex\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatetimes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DatetimeIndex\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mindexes\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IntervalIndex\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:1006\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:688\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap_external>:879\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap_external>:975\u001b[0m, in \u001b[0;36mget_code\u001b[0;34m(self, fullname)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap_external>:1074\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(self, path)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import scipy as sci\n",
    "\n",
    "### plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "### flox for GroupBy Reductions\n",
    "import flox.xarray\n",
    "\n",
    "import dask.array as da\n",
    "from dask.distributed import Client\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c09f8580-f353-4b4d-b331-8da75cdaba8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(threads_per_worker=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ed9c2f2-c111-48c5-bfc8-251741b0ebb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocesser to drop unwanted variables\n",
    "def drop_stuff(ds, coords_to_drop,vars_to_drop):\n",
    "    \"\"\"\n",
    "    Preprocessor function to drop specified coordinates and variables from a dataset loaded via xr.open_mfdataset\n",
    "\n",
    "    Parameters:\n",
    "        ds (xarray.Dataset): The dataset from which coordinates & variables are to be dropped.\n",
    "        coords_to_drop (list of str): List of coordinate names to drop.\n",
    "        vars_to_drop(list of str): List of variable names to drop\n",
    "\n",
    "    Returns:\n",
    "        xarray.Dataset: Dataset with specified coordinates and variables dropped.\n",
    "    \"\"\"\n",
    "    # Drop coordinates if they are in the dataset\n",
    "    ds = ds.drop_vars(coords_to_drop, errors='ignore')\n",
    "    ds = ds.drop_vars(vars_to_drop, errors='ignore')\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f8bb84-5596-46d2-8b86-169587a7b042",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_chunks(data_array):\n",
    "    ''' \n",
    "    Print the chunk sizes for each dimension in an Xarray dataset\n",
    "    \n",
    "    Parameters: \n",
    "    data_array (xarray.Daraset): The dataset from which to print chunks\n",
    "    '''\n",
    "    # Get chunk size\n",
    "    chunks = data_array.chunks\n",
    "    # Get dimension names\n",
    "    dim_names = data_array.dims\n",
    "    \n",
    "    readable_chunks = {dim: chunks[i] for i, dim in enumerate(dim_names)}\n",
    "    \n",
    "    # Print chunk sizes for each dimension\n",
    "    for dim, sizes in readable_chunks.items():\n",
    "        print(f\"{dim} chunks: {sizes}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8194a244-9e5d-4bdd-9724-16324207f5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "# Define coordinates and variables to drop\n",
    "coords_to_drop =['st_edges_ocean','nv']\n",
    "vars_to_drop =['Time_bounds','average_DT','average_T1','average_T2']\n",
    "\n",
    "dir = '/g/data/fp2/OFAM3/jra55_historical.1/surface/'\n",
    "\n",
    "# Load the datasets with preprocessing\n",
    "sst = xr.open_mfdataset(\n",
    "    dir + 'ocean_temp_sfc_*.nc', \n",
    "    parallel=True, \n",
    "    preprocess = lambda x: drop_stuff(x, \n",
    "                                         coords_to_drop, \n",
    "                                         vars_to_drop)).squeeze() #combine='by_coords' is default\n",
    "\n",
    "# Rename Time to time\n",
    "sst = sst.rename({'Time':'time'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed7007b7-dc84-4093-8dbb-eb7eec56cf40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate daily climatology and convert to single precision\n",
    "def monthly_climatology(ds, time_dim):\n",
    "    \"\"\"\n",
    "    Calculate the monthly climatology for a given dataset.\n",
    "\n",
    "    This function takes an xarray dataset and computes the monthly climatology\n",
    "    by averaging data for each month over all years. The function adds a 'month'\n",
    "    dimension to the dataset based on the provided time dimension.\n",
    "\n",
    "    Parameters:\n",
    "        ds (xarray.Dataset): The input dataset containing the time dimension.\n",
    "        time_dim (str): The name of the time dimension in the dataset.\n",
    "\n",
    "    Returns\n",
    "        xarray.Dataset: Dataset with the mean values for each month across all years. \n",
    "        The data type of returned values is float32.\n",
    "    \"\"\"\n",
    "    ds['doy'] = ds[time_dim].dt.dayofyear\n",
    "    climatology_cohorts = flox.xarray.xarray_reduce(\n",
    "        ds,\n",
    "        'doy',\n",
    "        func='mean',\n",
    "        method='cohorts',\n",
    "    )\n",
    "    return climatology_cohorts.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09cafa39-c488-4a65-9766-650bda82061a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process and save climatology data\n",
    "def process_climatology(ds, time_dim, variable):\n",
    "    data = getattr(ds, variable) \n",
    "    clim = monthly_climatology(data, time_dim).persist()\n",
    "\n",
    "    file_path = '/g/data/xv83/users/ep5799/Heatwaves/Australian_SST_monthly_climatology.nc'\n",
    "    \n",
    "    # Save as netCDF\n",
    "    clim.to_netcdf(file_path, compute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3238181e-0daa-49b5-97b6-22ac6c6eea40",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "process_climatology(sst_southern, 'time', 'temp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ab69f8e-f952-4d56-bf12-593778a8f9d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold90.to_netcdf('/g/data/ia39/ncra/ocean/peacey/mhw/Australian_SST_monthly_MHWthreshold.nc', mode='w', compute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b0364b7-3e0a-44c8-b8bf-eeda6a0ff6be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3c3fc1d-0d62-43db-9842-b79db72d3781",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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