notebooks/ndc_workflows/ncar_benchmark_ap40.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13e3b0d8-3589-4125-834d-7d5a555f272c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import aiohttp\n",
    "import time\n",
    "from contextlib import contextmanager\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import intake\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc2d2f0e-6f1d-4b0f-99b2-847ccdf34f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec.implementations.http as fshttp\n",
    "from pelicanfs.core import PelicanFileSystem, PelicanMap, OSDFFileSystem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6938a354-27c0-4272-a74c-b917fe41206d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dask \n",
    "# from dask_jobqueue import PBSCluster\n",
    "# from dask.distributed import Client\n",
    "# from dask.distributed import performance_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f7dbe03-c57f-4741-a702-93469df8ba4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "init_year0  = '1991'\n",
    "init_year1  = '2020'\n",
    "final_year0 = '2071'\n",
    "final_year1 = '2100'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87a99384-cc65-413d-b277-1d591062ba96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# File paths\n",
    "rda_url       =  'https://data.rda.ucar.edu/'\n",
    "database_num  = 'd345001'\n",
    "cam6_dart_url = rda_url + database_num\n",
    "#\n",
    "https_catalog = cam6_dart_url + '/catalogs/https/'+ database_num +'-https-zarr.json'\n",
    "osdf_catalog  = cam6_dart_url + '/catalogs/osdf/'+ database_num +'-osdf-zarr.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab91f90-d1a1-457b-92f1-82e9490cc111",
   "metadata": {},
   "source": [
    "### Open intake catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d8fdcfe-51ec-48a4-a134-281acad7da6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['https://data.rda.ucar.edu/d345001/hourly6/HR.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/hourly6/TSA.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/hourly6/EFLX_LH_TOT.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/hourly6/ER.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/VS.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/PS.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/Q.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/US.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/CLDICE.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/T.zarr',\n",
       "       'https://data.rda.ucar.edu/d345001/weekly/CLDLIQ.zarr'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_https_test = intake.open_esm_datastore(https_catalog)\n",
    "df_https_test.df['path'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da40942c-5281-42a3-bac1-1ef7b9233fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['osdf:///ncar/rda/d345001/hourly6/HR.zarr',\n",
       "       'osdf:///ncar/rda/d345001/hourly6/TSA.zarr',\n",
       "       'osdf:///ncar/rda/d345001/hourly6/EFLX_LH_TOT.zarr',\n",
       "       'osdf:///ncar/rda/d345001/hourly6/ER.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/VS.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/PS.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/Q.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/US.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/CLDICE.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/T.zarr',\n",
       "       'osdf:///ncar/rda/d345001/weekly/CLDLIQ.zarr'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_osdf_test = intake.open_esm_datastore(osdf_catalog)\n",
    "df_osdf_test.df['path'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4201bc1-7dce-404f-80ac-60b884d63bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_var = 'PS'\n",
    "col_subset_https = df_https_test.search(variable=data_var)\n",
    "col_subset_osdf  = df_osdf_test.search(variable=data_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73e137c0-987a-4141-8683-c42341bc8ce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'variable.frequency.component.vertical_levels'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='1' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1/1 00:02&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset dictionary keys:\n",
      " dict_keys(['PS.weekly.atm.1'])\n"
     ]
    }
   ],
   "source": [
    "dsets_https = col_subset_https.to_dataset_dict(zarr_kwargs={\"consolidated\": True})\n",
    "#\n",
    "print(f\"\\nDataset dictionary keys:\\n {dsets_https.keys()}\")\n",
    "# Load the first dataset and display a summary.\n",
    "dataset_key = list(dsets_https.keys())[0]\n",
    "#\n",
    "ds_https = dsets_https[dataset_key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51239e04-deaa-44bb-bda0-5066a3e5c2c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'variable.frequency.component.vertical_levels'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='1' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1/1 00:00&lt;00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsets_osdf  = col_subset_osdf.to_dataset_dict()\n",
    "#\n",
    "ds_osdf0     = dsets_osdf[dataset_key]\n",
    "ds_https0    = dsets_https[dataset_key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82348d84-24ef-40a8-9658-3fa7c13d4201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;PS&#x27; (member_id: 80, time: 471, lat: 192, lon: 288)&gt;\n",
       "dask.array&lt;open_dataset-PS, shape=(80, 471, 192, 288), dtype=float64, chunksize=(80, 80, 32, 32), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float32 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 9 ... 73 74 75 76 77 78 79 80\n",
       "  * time       (time) datetime64[ns] 2011-01-03 2011-01-10 ... 2019-12-30\n",
       "Attributes:\n",
       "    long_name:  Surface pressure\n",
       "    units:      Pa</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'PS'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 80</li><li><span class='xr-has-index'>time</span>: 471</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a1a8cf64-17fe-4632-8e7f-745d461d4035' class='xr-array-in' type='checkbox' checked><label for='section-a1a8cf64-17fe-4632-8e7f-745d461d4035' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(80, 80, 32, 32), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 15.52 GiB </td>\n",
       "                        <td> 50.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (80, 471, 192, 288) </td>\n",
       "                        <td> (80, 80, 32, 32) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 324 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"415\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"41\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"41\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 41.29078054287256,0.0 41.29078054287256,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"20.645390\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
       "  <text x=\"61.290781\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,61.290781,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"111\" y1=\"0\" x2=\"181\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"111\" y1=\"8\" x2=\"181\" y2=\"78\" />\n",
       "  <line x1=\"111\" y1=\"16\" x2=\"181\" y2=\"86\" />\n",
       "  <line x1=\"111\" y1=\"24\" x2=\"181\" y2=\"95\" />\n",
       "  <line x1=\"111\" y1=\"32\" x2=\"181\" y2=\"103\" />\n",
       "  <line x1=\"111\" y1=\"40\" x2=\"181\" y2=\"111\" />\n",
       "  <line x1=\"111\" y1=\"48\" x2=\"181\" y2=\"119\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"111\" y1=\"0\" x2=\"111\" y2=\"48\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"122\" y1=\"11\" x2=\"122\" y2=\"60\" />\n",
       "  <line x1=\"134\" y1=\"23\" x2=\"134\" y2=\"72\" />\n",
       "  <line x1=\"146\" y1=\"35\" x2=\"146\" y2=\"84\" />\n",
       "  <line x1=\"158\" y1=\"47\" x2=\"158\" y2=\"96\" />\n",
       "  <line x1=\"170\" y1=\"59\" x2=\"170\" y2=\"108\" />\n",
       "  <line x1=\"181\" y1=\"70\" x2=\"181\" y2=\"119\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"111.0,0.0 181.58823529411765,70.58823529411765 181.58823529411765,119.50543274634697 111.0,48.91719745222931\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"111\" y1=\"0\" x2=\"184\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"122\" y1=\"11\" x2=\"196\" y2=\"11\" />\n",
       "  <line x1=\"134\" y1=\"23\" x2=\"208\" y2=\"23\" />\n",
       "  <line x1=\"146\" y1=\"35\" x2=\"220\" y2=\"35\" />\n",
       "  <line x1=\"158\" y1=\"47\" x2=\"232\" y2=\"47\" />\n",
       "  <line x1=\"170\" y1=\"59\" x2=\"244\" y2=\"59\" />\n",
       "  <line x1=\"181\" y1=\"70\" x2=\"254\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"111\" y1=\"0\" x2=\"181\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"119\" y1=\"0\" x2=\"189\" y2=\"70\" />\n",
       "  <line x1=\"127\" y1=\"0\" x2=\"197\" y2=\"70\" />\n",
       "  <line x1=\"135\" y1=\"0\" x2=\"206\" y2=\"70\" />\n",
       "  <line x1=\"143\" y1=\"0\" x2=\"214\" y2=\"70\" />\n",
       "  <line x1=\"151\" y1=\"0\" x2=\"222\" y2=\"70\" />\n",
       "  <line x1=\"159\" y1=\"0\" x2=\"230\" y2=\"70\" />\n",
       "  <line x1=\"168\" y1=\"0\" x2=\"238\" y2=\"70\" />\n",
       "  <line x1=\"176\" y1=\"0\" x2=\"246\" y2=\"70\" />\n",
       "  <line x1=\"184\" y1=\"0\" x2=\"254\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"111.0,0.0 184.37579617834393,0.0 254.96403147246158,70.58823529411765 181.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"181\" y1=\"70\" x2=\"254\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"181\" y1=\"78\" x2=\"254\" y2=\"78\" />\n",
       "  <line x1=\"181\" y1=\"86\" x2=\"254\" y2=\"86\" />\n",
       "  <line x1=\"181\" y1=\"95\" x2=\"254\" y2=\"95\" />\n",
       "  <line x1=\"181\" y1=\"103\" x2=\"254\" y2=\"103\" />\n",
       "  <line x1=\"181\" y1=\"111\" x2=\"254\" y2=\"111\" />\n",
       "  <line x1=\"181\" y1=\"119\" x2=\"254\" y2=\"119\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"181\" y1=\"70\" x2=\"181\" y2=\"119\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"189\" y1=\"70\" x2=\"189\" y2=\"119\" />\n",
       "  <line x1=\"197\" y1=\"70\" x2=\"197\" y2=\"119\" />\n",
       "  <line x1=\"206\" y1=\"70\" x2=\"206\" y2=\"119\" />\n",
       "  <line x1=\"214\" y1=\"70\" x2=\"214\" y2=\"119\" />\n",
       "  <line x1=\"222\" y1=\"70\" x2=\"222\" y2=\"119\" />\n",
       "  <line x1=\"230\" y1=\"70\" x2=\"230\" y2=\"119\" />\n",
       "  <line x1=\"238\" y1=\"70\" x2=\"238\" y2=\"119\" />\n",
       "  <line x1=\"246\" y1=\"70\" x2=\"246\" y2=\"119\" />\n",
       "  <line x1=\"254\" y1=\"70\" x2=\"254\" y2=\"119\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"181.58823529411765,70.58823529411765 254.9640314724616,70.58823529411765 254.9640314724616,119.50543274634697 181.58823529411765,119.50543274634697\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"218.276133\" y=\"139.505433\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"274.964031\" y=\"95.046834\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,274.964031,95.046834)\">192</text>\n",
       "  <text x=\"136.294118\" y=\"104.211315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,136.294118,104.211315)\">471</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-274dc079-fcf1-4675-ad97-d3edcf096aaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-274dc079-fcf1-4675-ad97-d3edcf096aaf' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-0e3cebe6-2842-4362-ac87-3ac20049fc3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e3cebe6-2842-4362-ac87-3ac20049fc3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95c9b88-bfde-4ced-a6cf-062ea9bc890f' class='xr-var-data-in' type='checkbox'><label for='data-b95c9b88-bfde-4ced-a6cf-062ea9bc890f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057594, -88.11518 , -87.172775, -86.23037 , -85.28796 ,\n",
       "       -84.34555 , -83.403145, -82.46073 , -81.518326, -80.57591 , -79.63351 ,\n",
       "       -78.6911  , -77.74869 , -76.80628 , -75.86388 , -74.92146 , -73.97906 ,\n",
       "       -73.03665 , -72.09424 , -71.15183 , -70.20943 , -69.26701 , -68.32461 ,\n",
       "       -67.3822  , -66.43979 , -65.49738 , -64.55498 , -63.612564, -62.67016 ,\n",
       "       -61.72775 , -60.78534 , -59.842934, -58.900524, -57.958115, -57.015705,\n",
       "       -56.0733  , -55.13089 , -54.18848 , -53.246075, -52.303665, -51.361256,\n",
       "       -50.41885 , -49.47644 , -48.53403 , -47.59162 , -46.649216, -45.706806,\n",
       "       -44.764397, -43.82199 , -42.87958 , -41.937172, -40.994766, -40.052357,\n",
       "       -39.109947, -38.167538, -37.225132, -36.282722, -35.340313, -34.397907,\n",
       "       -33.455498, -32.51309 , -31.57068 , -30.628273, -29.685863, -28.743456,\n",
       "       -27.801046, -26.858639, -25.916231, -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.20419 , -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.607329, -13.664922, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664922,  14.607329,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.20419 ,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.916231,  26.858639,  27.801046,\n",
       "        28.743456,  29.685863,  30.628273,  31.57068 ,  32.51309 ,  33.455498,\n",
       "        34.397907,  35.340313,  36.282722,  37.225132,  38.167538,  39.109947,\n",
       "        40.052357,  40.994766,  41.937172,  42.87958 ,  43.82199 ,  44.764397,\n",
       "        45.706806,  46.649216,  47.59162 ,  48.53403 ,  49.47644 ,  50.41885 ,\n",
       "        51.361256,  52.303665,  53.246075,  54.18848 ,  55.13089 ,  56.0733  ,\n",
       "        57.015705,  57.958115,  58.900524,  59.842934,  60.78534 ,  61.72775 ,\n",
       "        62.67016 ,  63.612564,  64.55498 ,  65.49738 ,  66.43979 ,  67.3822  ,\n",
       "        68.32461 ,  69.26701 ,  70.20943 ,  71.15183 ,  72.09424 ,  73.03665 ,\n",
       "        73.97906 ,  74.92146 ,  75.86388 ,  76.80628 ,  77.74869 ,  78.6911  ,\n",
       "        79.63351 ,  80.57591 ,  81.518326,  82.46073 ,  83.403145,  84.34555 ,\n",
       "        85.28796 ,  86.23037 ,  87.172775,  88.11518 ,  89.057594,  90.      ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-3aab8c05-7355-43b4-acb7-62a45fed47e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3aab8c05-7355-43b4-acb7-62a45fed47e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cbbbb0c-a5f9-4281-bcc4-926f069d99a1' class='xr-var-data-in' type='checkbox'><label for='data-5cbbbb0c-a5f9-4281-bcc4-926f069d99a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 75 76 77 78 79 80</div><input id='attrs-3e37687c-801c-48de-be77-3a2e83f001d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e37687c-801c-48de-be77-3a2e83f001d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d401344c-824f-47f8-831f-1103cf162faa' class='xr-var-data-in' type='checkbox'><label for='data-d401344c-824f-47f8-831f-1103cf162faa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2011-01-03 ... 2019-12-30</div><input id='attrs-08ed7c52-de9a-4abe-ba55-bbdac9582cd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08ed7c52-de9a-4abe-ba55-bbdac9582cd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fa274b4-e091-4188-ad83-0d7ea738b01b' class='xr-var-data-in' type='checkbox'><label for='data-7fa274b4-e091-4188-ad83-0d7ea738b01b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2011-01-03T00:00:00.000000000&#x27;, &#x27;2011-01-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-01-17T00:00:00.000000000&#x27;, ..., &#x27;2019-12-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-23T00:00:00.000000000&#x27;, &#x27;2019-12-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06a89d76-7ce5-4eef-ac24-7c5952e53ff2' class='xr-section-summary-in' type='checkbox'  ><label for='section-06a89d76-7ce5-4eef-ac24-7c5952e53ff2' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4f65e514-f42c-46e5-822a-b8249375aa86' class='xr-index-data-in' type='checkbox'/><label for='index-4f65e514-f42c-46e5-822a-b8249375aa86' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -90.0,  -89.0575942993164, -88.11518096923828,\n",
       "       -87.17277526855469,  -86.2303695678711, -85.28795623779297,\n",
       "       -84.34555053710938, -83.40314483642578, -82.46073150634766,\n",
       "       -81.51832580566406,\n",
       "       ...\n",
       "        81.51832580566406,  82.46073150634766,  83.40314483642578,\n",
       "        84.34555053710938,  85.28795623779297,   86.2303695678711,\n",
       "        87.17277526855469,  88.11518096923828,   89.0575942993164,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e20e493-91a2-4486-9027-70a8cbfda454' class='xr-index-data-in' type='checkbox'/><label for='index-8e20e493-91a2-4486-9027-70a8cbfda454' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c17d925-dc7f-4f2b-91ec-86b2453fae28' class='xr-index-data-in' type='checkbox'/><label for='index-2c17d925-dc7f-4f2b-91ec-86b2453fae28' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f1afa92-8742-4f44-ba6f-0abe19c2f822' class='xr-index-data-in' type='checkbox'/><label for='index-8f1afa92-8742-4f44-ba6f-0abe19c2f822' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2011-01-03 00:00:00&#x27;, &#x27;2011-01-10 00:00:00&#x27;,\n",
       "               &#x27;2011-01-17 00:00:00&#x27;, &#x27;2011-01-24 00:00:00&#x27;,\n",
       "               &#x27;2011-01-31 00:00:00&#x27;, &#x27;2011-02-07 00:00:00&#x27;,\n",
       "               &#x27;2011-02-14 00:00:00&#x27;, &#x27;2011-02-21 00:00:00&#x27;,\n",
       "               &#x27;2011-02-28 00:00:00&#x27;, &#x27;2011-03-07 00:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2019-10-28 00:00:00&#x27;, &#x27;2019-11-04 00:00:00&#x27;,\n",
       "               &#x27;2019-11-11 00:00:00&#x27;, &#x27;2019-11-18 00:00:00&#x27;,\n",
       "               &#x27;2019-11-25 00:00:00&#x27;, &#x27;2019-12-02 00:00:00&#x27;,\n",
       "               &#x27;2019-12-09 00:00:00&#x27;, &#x27;2019-12-16 00:00:00&#x27;,\n",
       "               &#x27;2019-12-23 00:00:00&#x27;, &#x27;2019-12-30 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=471, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-019cdb5f-7ddf-4d42-917f-410a26130ce3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-019cdb5f-7ddf-4d42-917f-410a26130ce3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'PS' (member_id: 80, time: 471, lat: 192, lon: 288)>\n",
       "dask.array<open_dataset-PS, shape=(80, 471, 192, 288), dtype=float64, chunksize=(80, 80, 32, 32), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float32 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 9 ... 73 74 75 76 77 78 79 80\n",
       "  * time       (time) datetime64[ns] 2011-01-03 2011-01-10 ... 2019-12-30\n",
       "Attributes:\n",
       "    long_name:  Surface pressure\n",
       "    units:      Pa"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_osdf  = ds_osdf0.PS\n",
    "ds_https = ds_https0.PS\n",
    "ds_https"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8106a36f-0d1b-4c73-89af-2af680fbbae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.81 s, sys: 5.02 s, total: 8.83 s\n",
      "Wall time: 6.56 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f68f038f350>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "ds_osdf.isel(member_id=0,time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4c31a0-a88c-4389-84ed-58f4a5f8b95a",
   "metadata": {},
   "source": [
    "## Data Access Speed tests\n",
    "- We will now test how long it takes to access data (via OSDF and https-only prrotocols) for various sizes using the above array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8482cce9-5877-4862-9e63-fabb62141265",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Prepare data subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "acd41ee1-e80f-44a4-903a-642dab40ab78",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_1Kb  = ds_osdf.isel(lat=0,lon=0,member_id=0).isel(time=np.arange(130))\n",
    "ds_https_1Kb = ds_https.isel(lat=0,lon=0,member_id=0).isel(time=np.arange(130))\n",
    "#ds_https_1Kb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a50058f-5f75-43df-bf46-56cb2702e8a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_1Mb  = ds_osdf.isel(time=0).isel(member_id =1+ np.arange(3))\n",
    "ds_https_1Mb = ds_https.isel(time=0).isel(member_id =1+ np.arange(3))\n",
    "# ds_osdf_1Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "120ea4e6-f684-420f-bd96-cbd7bf0bacf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_10Mb  = ds_osdf.isel(member_id =4).isel(time=np.arange(24))\n",
    "ds_https_10Mb = ds_https.isel(member_id =4).isel(time=np.arange(24))\n",
    "# ds_osdf_10Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de39f88b-6450-4252-8288-3c847edb6550",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_100Mb  = ds_osdf.isel(member_id =5).isel(time=np.arange(238))\n",
    "ds_https_100Mb = ds_https.isel(member_id =5).isel(time=np.arange(238))\n",
    "#ds_osdf_100Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "deed76ef-7ad9-48f0-8b66-c3fb0e766d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_200Mb  = ds_osdf.isel(member_id = 6)\n",
    "ds_https_200Mb = ds_https.isel(member_id =6)\n",
    "#ds_https_200Mb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eaf7f93b-56be-44c5-a250-ce2ed69d7ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_400Mb  = ds_osdf.isel(member_id = 7 +np.arange(2))\n",
    "ds_https_400Mb = ds_https.isel(member_id =7 + np.arange(2))\n",
    "# ds_https_400Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23baea99-fce1-44a4-86b0-7c778c69ed91",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_600Mb  = ds_osdf.isel(member_id  = 10 +np.arange(3))\n",
    "ds_https_600Mb = ds_https.isel(member_id = 10 + np.arange(3))\n",
    "# ds_https_600Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82ef215b-493f-4609-be72-453e9d1b2ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_800Mb  = ds_osdf.isel(member_id  = 14 +np.arange(4))\n",
    "ds_https_800Mb = ds_https.isel(member_id = 14 + np.arange(4))\n",
    "# ds_https_800Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11212e1b-ae45-4ec1-a925-b0dbe5f573a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_1Gb  = ds_osdf.isel(member_id  = 19 + np.arange(6)).isel(time = np.arange(410))\n",
    "ds_https_1Gb = ds_https.isel(member_id = 19 + np.arange(6)).isel(time = np.arange(410))\n",
    "# ds_osdf_1Gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09f9f06a-ee24-4d54-9f50-556ed9f80e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds_osdf_10Gb  = ds_osdf.isel(member_id  = 12 + np.arange(52))\n",
    "# ds_https_10Gb = ds_https.isel(member_id = 12 + np.arange(52))\n",
    "# ds_osdf_10Gb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e146f2-5028-4374-bfa8-f4b57f0969b2",
   "metadata": {},
   "source": [
    "### Access data and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64c00a59-4b3b-4ed0-a71f-6da4a1b69562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define file path for CSV\n",
    "csv_file_path = \"ncar_benchmark_ap40_nodask.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fcbfa66b-9687-4c68-9a2e-670ac59ee3e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_osdf_list  = [ds_osdf_1Mb,ds_osdf_10Mb,ds_osdf_100Mb,ds_osdf_200Mb,ds_osdf_400Mb,\n",
    "                 ds_osdf_600Mb,ds_osdf_800Mb,ds_osdf_1Gb]\n",
    "ds_https_list = [ds_https_1Mb,ds_https_10Mb,ds_https_100Mb,ds_https_200Mb,ds_https_400Mb,\n",
    "                 ds_https_600Mb,ds_https_800Mb,ds_https_1Gb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac8d2df0-7a17-4836-80c9-41a0b7e8780b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of data access calls\n",
    "num_calls = 8  # Modify this as needed\n",
    "n_workers = 4  # Set this to your preferred number of workers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "617daeb9-54f2-481a-bb6a-69528e709da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DiagnosticTimer class to keep track of runtimes\n",
    "class DiagnosticTimer:\n",
    "    def __init__(self):\n",
    "        self.diagnostics = []\n",
    "\n",
    "    @contextmanager\n",
    "    def time(self, **kwargs):\n",
    "        tic = time.time()\n",
    "        yield\n",
    "        toc = time.time()\n",
    "        kwargs[\"runtime\"] = toc - tic\n",
    "        self.diagnostics.append(kwargs)\n",
    "\n",
    "    def dataframe(self):\n",
    "        return pd.DataFrame(self.diagnostics)\n",
    "\n",
    "# Initialize the DiagnosticTimer\n",
    "diag_timer = DiagnosticTimer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d918ddc3-3820-4008-92ee-6c9c9a8cc122",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to check existing CSV file and determine missing runs\n",
    "def load_existing_results():\n",
    "    if os.path.exists(csv_file_path):\n",
    "        # Load existing CSV into DataFrame\n",
    "        existing_df = pd.read_csv(csv_file_path)\n",
    "    else:\n",
    "        # Create an empty DataFrame if the file does not exist\n",
    "        existing_df = pd.DataFrame(columns=[\"dataset_size\", \"protocol\", \"call_number\", \"runtime\", \"MBps\"])\n",
    "    return existing_df\n",
    "\n",
    "def filter_missing_runs(datasets, protocol_name, existing_df):\n",
    "    # Convert dataset sizes to MB for checking, using a list of tuples\n",
    "    dataset_sizes_mb = [(dataset, dataset.nbytes / (1024 ** 2)) for dataset in datasets]\n",
    "\n",
    "    # Identify missing dataset sizes and calls\n",
    "    filtered_datasets = []\n",
    "    for dataset, dataset_size_mb in dataset_sizes_mb:\n",
    "        for call_num in range(1, num_calls + 1):\n",
    "            # Check if this dataset size and call number combination already exists\n",
    "            if not ((existing_df[\"dataset_size\"] == dataset_size_mb) &\n",
    "                    (existing_df[\"protocol\"] == protocol_name) &\n",
    "                    (existing_df[\"call_number\"] == call_num)).any():\n",
    "                filtered_datasets.append((dataset, dataset_size_mb, call_num))\n",
    "    \n",
    "    return filtered_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "92e6684b-36e3-4137-abce-1278d28bc4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark_protocol(datasets, protocol_name, cluster=None):\n",
    "    existing_df = load_existing_results()  # Load existing results as a checkpoint\n",
    "\n",
    "    # Filter for missing runs based on existing results\n",
    "    missing_runs = filter_missing_runs(datasets, protocol_name, existing_df)\n",
    "    diag_timer = DiagnosticTimer()  # Initialize the diagnostic timer\n",
    "\n",
    "    # Process each dataset and call\n",
    "    for (dataset, dataset_size_mb, call_num) in missing_runs:\n",
    "        # Restart the Dask cluster if provided\n",
    "        if cluster is not None:\n",
    "            cluster.scale(0)  # Scale down to release worker memory\n",
    "            cluster.scale(n_workers)  # Scale up to required number of workers\n",
    "            client.wait_for_workers(n_workers)  # Wait for workers to be ready\n",
    "\n",
    "        # Inform the start of processing for this dataset and call\n",
    "        print(f\"Starting processing of dataset for protocol '{protocol_name}' (Size: {dataset_size_mb} MB) in call {call_num}\")\n",
    "\n",
    "        # Only count the time for loading dataset into memory\n",
    "        dataset_copy = dataset.copy()\n",
    "        with diag_timer.time(dataset_size=dataset_size_mb, protocol=protocol_name, call_number=call_num):\n",
    "            dataset_copy.load()  # Load the dataset into memory\n",
    "\n",
    "        # Convert the single call result to a DataFrame and add MBps column\n",
    "        call_result_df = diag_timer.dataframe().iloc[[-1]].copy()  # Get the latest diagnostic entry\n",
    "        call_result_df[\"MBps\"] = call_result_df[\"dataset_size\"] / call_result_df[\"runtime\"]\n",
    "\n",
    "        # Append this call's result to CSV\n",
    "        call_result_df.to_csv(csv_file_path, mode='a', header=not os.path.exists(csv_file_path), index=False)\n",
    "        print(f\"Appended results for protocol '{protocol_name}', call {call_num} to '{csv_file_path}'\")\n",
    "\n",
    "        # Print statement after finishing each call\n",
    "        print(f\"Finished processing dataset for protocol '{protocol_name}' in call {call_num}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1163c884-aff6-42b0-ad96-d7c117629b8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 596.109375 MB) in call 7\n",
      "Appended results for protocol 'HTTPS-only', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 7\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 596.109375 MB) in call 8\n",
      "Appended results for protocol 'HTTPS-only', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 8\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 1\n",
      "Appended results for protocol 'HTTPS-only', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 1\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 2\n",
      "Appended results for protocol 'HTTPS-only', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 2\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 3\n",
      "Appended results for protocol 'HTTPS-only', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 3\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 4\n",
      "Appended results for protocol 'HTTPS-only', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 4\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 5\n",
      "Appended results for protocol 'HTTPS-only', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 5\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 6\n",
      "Appended results for protocol 'HTTPS-only', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 6\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 7\n",
      "Appended results for protocol 'HTTPS-only', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 7\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 794.8125 MB) in call 8\n",
      "Appended results for protocol 'HTTPS-only', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 8\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 1\n",
      "Appended results for protocol 'HTTPS-only', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 1\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 2\n",
      "Appended results for protocol 'HTTPS-only', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 2\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 3\n",
      "Appended results for protocol 'HTTPS-only', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 3\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 4\n",
      "Appended results for protocol 'HTTPS-only', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 4\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 5\n",
      "Appended results for protocol 'HTTPS-only', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 5\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 6\n",
      "Appended results for protocol 'HTTPS-only', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 6\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 7\n",
      "Appended results for protocol 'HTTPS-only', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 7\n",
      "Starting processing of dataset for protocol 'HTTPS-only' (Size: 1037.8125 MB) in call 8\n",
      "Appended results for protocol 'HTTPS-only', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'HTTPS-only' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1.265625 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 10.125 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 100.40625 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 198.703125 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 397.40625 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 596.109375 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 794.8125 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 1\n",
      "Appended results for protocol 'OSDF-director', call 1 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 1\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 2\n",
      "Appended results for protocol 'OSDF-director', call 2 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 2\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 3\n",
      "Appended results for protocol 'OSDF-director', call 3 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 3\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 4\n",
      "Appended results for protocol 'OSDF-director', call 4 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 4\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 5\n",
      "Appended results for protocol 'OSDF-director', call 5 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 5\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 6\n",
      "Appended results for protocol 'OSDF-director', call 6 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 6\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 7\n",
      "Appended results for protocol 'OSDF-director', call 7 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 7\n",
      "Starting processing of dataset for protocol 'OSDF-director' (Size: 1037.8125 MB) in call 8\n",
      "Appended results for protocol 'OSDF-director', call 8 to 'ncar_benchmark_ap40_nodask.csv'\n",
      "Finished processing dataset for protocol 'OSDF-director' in call 8\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'dataset_size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m df_diagnostics \u001b[38;5;241m=\u001b[39m diag_timer\u001b[38;5;241m.\u001b[39mdataframe()\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Calculate MB/s for each run\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m df_diagnostics[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMBps\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df_diagnostics[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdataset_size\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m df_diagnostics[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mruntime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     10\u001b[0m df_diagnostics\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/pandas/core/indexes/range.py:417\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m--> 417\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m    419\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'dataset_size'"
     ]
    }
   ],
   "source": [
    "# Run benchmark for each protocol\n",
    "benchmark_protocol(ds_https_list, \"HTTPS-only\",cluster=None)\n",
    "benchmark_protocol(ds_osdf_list, \"OSDF-director\",cluster=None)\n",
    "\n",
    "# Convert diagnostics to a DataFrame for analysis\n",
    "df_diagnostics = diag_timer.dataframe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38946b10-57fa-416b-b7fc-abf2e68fd86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset_size</th>\n",
       "      <th>protocol</th>\n",
       "      <th>call_number</th>\n",
       "      <th>runtime</th>\n",
       "      <th>MBps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.265625</td>\n",
       "      <td>HTTPS-only</td>\n",
       "      <td>1</td>\n",
       "      <td>8.100158</td>\n",
       "      <td>0.156247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.265625</td>\n",
       "      <td>HTTPS-only</td>\n",
       "      <td>2</td>\n",
       "      <td>7.754515</td>\n",
       "      <td>0.163211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.265625</td>\n",
       "      <td>HTTPS-only</td>\n",
       "      <td>3</td>\n",
       "      <td>9.427972</td>\n",
       "      <td>0.134241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.265625</td>\n",
       "      <td>HTTPS-only</td>\n",
       "      <td>4</td>\n",
       "      <td>7.436454</td>\n",
       "      <td>0.170192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.265625</td>\n",
       "      <td>HTTPS-only</td>\n",
       "      <td>5</td>\n",
       "      <td>7.641388</td>\n",
       "      <td>0.165628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1037.812500</td>\n",
       "      <td>OSDF-director</td>\n",
       "      <td>4</td>\n",
       "      <td>29.810817</td>\n",
       "      <td>34.813286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1037.812500</td>\n",
       "      <td>OSDF-director</td>\n",
       "      <td>5</td>\n",
       "      <td>29.030086</td>\n",
       "      <td>35.749550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1037.812500</td>\n",
       "      <td>OSDF-director</td>\n",
       "      <td>6</td>\n",
       "      <td>28.728536</td>\n",
       "      <td>36.124796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1037.812500</td>\n",
       "      <td>OSDF-director</td>\n",
       "      <td>7</td>\n",
       "      <td>29.487627</td>\n",
       "      <td>35.194847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1037.812500</td>\n",
       "      <td>OSDF-director</td>\n",
       "      <td>8</td>\n",
       "      <td>30.149770</td>\n",
       "      <td>34.421904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     dataset_size       protocol  call_number    runtime       MBps\n",
       "0        1.265625     HTTPS-only            1   8.100158   0.156247\n",
       "1        1.265625     HTTPS-only            2   7.754515   0.163211\n",
       "2        1.265625     HTTPS-only            3   9.427972   0.134241\n",
       "3        1.265625     HTTPS-only            4   7.436454   0.170192\n",
       "4        1.265625     HTTPS-only            5   7.641388   0.165628\n",
       "..            ...            ...          ...        ...        ...\n",
       "123   1037.812500  OSDF-director            4  29.810817  34.813286\n",
       "124   1037.812500  OSDF-director            5  29.030086  35.749550\n",
       "125   1037.812500  OSDF-director            6  28.728536  36.124796\n",
       "126   1037.812500  OSDF-director            7  29.487627  35.194847\n",
       "127   1037.812500  OSDF-director            8  30.149770  34.421904\n",
       "\n",
       "[128 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate MB/s for each run\n",
    "# df_diagnostics['MBps'] = df_diagnostics['dataset_size'] / df_diagnostics['runtime']\n",
    "df_diagnostics = pd.read_csv(csv_file_path)\n",
    "df_diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52ff7472-ea85-4990-bd61-2c56921bc16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_diagnostics = diag_timer.dataframe()\n",
    "# df_diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09bebc16-c66b-4d02-ac00-8620c09cd0ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting MBps vs data size for each protocol and call type\n",
    "# Define different alpha values for each protocol\n",
    "alpha_values = {\"HTTPS-only\": 0.8, \"OSDF-director\": 0.6}  # Adjust transparency as needed\n",
    "marker_style = {\"HTTPS-only\": \"o\", \"OSDF-director\": \"x\"}  # Define different markers for each protocol\n",
    "edgecolors   = {\"HTTPS-only\": \"black\", \"OSDF-director\": None}\n",
    "#\n",
    "# Define markers for each protocol\n",
    "protocol_markers = {\n",
    "    \"HTTPS-only\": 'o',  # Circle marker\n",
    "    \"OSDF-director\": 'x'  # Cross marker\n",
    "}\n",
    "\n",
    "# Define alpha values for different access types\n",
    "alpha_first_access = 0.8\n",
    "alpha_subsequent_access = 0.5\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for protocol in [\"HTTPS-only\", \"OSDF-director\"]:\n",
    "    # First access (call_number == 1)\n",
    "    first_access = df_diagnostics[(df_diagnostics['protocol'] == protocol) & (df_diagnostics['call_number'] == 1)]\n",
    "    ax.scatter(first_access['dataset_size'], first_access['MBps'], \n",
    "               label=f\"{protocol} - First Access\",\n",
    "               marker=protocol_markers[protocol],\n",
    "               alpha=alpha_first_access,\n",
    "               edgecolor=edgecolors[protocol])\n",
    "\n",
    "    # Subsequent access (call_number > 1)\n",
    "    subsequent_access = df_diagnostics[(df_diagnostics['protocol'] == protocol) & (df_diagnostics['call_number'] > 1)]\n",
    "    subsequent_access_avg = subsequent_access.groupby('dataset_size')['MBps'].mean()\n",
    "\n",
    "    # Add a slight jitter to dataset size to avoid overlapping\n",
    "    jitter = 0.1 * subsequent_access_avg.index.to_series().std()  # Adjust jitter dynamically\n",
    "    jittered_index = subsequent_access_avg.index + np.random.uniform(-jitter, jitter, size=len(subsequent_access_avg.index))\n",
    "\n",
    "    ax.scatter(jittered_index, subsequent_access_avg.values,\n",
    "               label=f\"{protocol} - Subsequent Access (Avg)\",\n",
    "               marker=protocol_markers[protocol],\n",
    "               alpha=alpha_subsequent_access,\n",
    "               edgecolor=edgecolors[protocol])\n",
    "\n",
    "# Customize plot appearance\n",
    "ax.set_xlabel(\"Data Size (MB)\")\n",
    "ax.set_ylabel(\"Data Access Speed (MBps)\")\n",
    "ax.set_title(\"NCAR --> AP40, Dask: No, 8 requests\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06dc676e-dce9-4854-8eef-847f28d0e240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert dataset size to categorical to control the order in the plot\n",
    "#df_diagnostics['dataset_size'] = df_diagnostics['dataset_size'].astype(\"category\")\n",
    "df_diagnostics['dataset_size'] = df_diagnostics['dataset_size'].round(0).astype(int).astype(\"category\") \n",
    "\n",
    "# Set the order for dataset sizes to appear in ascending order\n",
    "size_order = sorted(df_diagnostics['dataset_size'].unique())\n",
    "\n",
    "# Create the box plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(\n",
    "    data=df_diagnostics, \n",
    "    x=\"dataset_size\", \n",
    "    y=\"MBps\", \n",
    "    hue=\"protocol\", \n",
    "    order=size_order\n",
    ")\n",
    "\n",
    "# Customize plot appearance\n",
    "plt.xlabel(\"Data Size (MB)\")\n",
    "plt.ylabel(\"Data Access Speed (MBps)\")\n",
    "# plt.yscale('log')\n",
    "plt.title(\"NCAR --> AP40, Dask: No, 8 requests\")\n",
    "plt.legend(title=\"Protocol\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55385195-23f4-4653-a437-03ed17c3ad85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert dataset size to categorical to control the order in the plot\n",
    "#df_diagnostics['dataset_size'] = df_diagnostics['dataset_size'].astype(\"category\")\n",
    "df_diagnostics['dataset_size'] = df_diagnostics['dataset_size'].round(0).astype(int).astype(\"category\") \n",
    "\n",
    "# Set the order for dataset sizes to appear in ascending order\n",
    "size_order = sorted(df_diagnostics['dataset_size'].unique())\n",
    "\n",
    "# Create the box plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(\n",
    "    data=df_diagnostics, \n",
    "    x=\"dataset_size\", \n",
    "    y=\"MBps\", \n",
    "    hue=\"protocol\", \n",
    "    order=size_order,\n",
    "    whis = [5,95]\n",
    ")\n",
    "\n",
    "# Customize plot appearance\n",
    "plt.xlabel(\"Data Size (MB)\")\n",
    "plt.ylabel(\"Data Access Speed (MBps)\")\n",
    "# plt.yscale('log')\n",
    "plt.title(\"NCAR --> AP40, Dask: No, 8 requests\")\n",
    "plt.legend(title=\"Protocol\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c10b087b-5e77-44d5-ae1b-7d905a9107fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python:osdf",
   "language": "python",
   "name": "osdf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}