Samples/jupyter/netcdf-tutorial1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2d372de9",
   "metadata": {},
   "source": [
    "# Tutorial: Convert NETCDF files to OpenVisus\n",
    "\n",
    "## Python Setup\n",
    "\n",
    "Install Python packages:\n",
    "Note: if you need the OpenVisus Viewer, replace the `OpenVisusNoGui` python package with full fledged `OpenVisus`.\n",
    "\n",
    "```\n",
    "python -m pip install --upgrade pip\n",
    "python -m pip install OpenVisusNoGui openvisuspy xmltodict numpy colorcet netCDF4\n",
    "```\n",
    "\n",
    "Also you may want to install some NETCDF tools to verify, for example, a file version:\n",
    "\n",
    "```\n",
    "sudo apt install netcdf-bin\n",
    "ncdump -k psl_day_CNRM-CM6-1-HR_historical_r1i1p1f2_gr_20000101-20141231.nc\n",
    "netCDF-4\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "01f19fc0",
   "metadata": {},
   "source": [
    "## Import OpenVisus and openvisuspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76dfab06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,numpy as np\n",
    "\n",
    "# debug mode, uncomment for internal/developer mode only\n",
    "# sys.path.append(r\"C:\\projects\\OpenVisus\\build\\RelWithDebInfo\")\n",
    "# sys.path.append(r\"C:\\projects\\openvisuspy\\src\")\n",
    "\n",
    "import OpenVisus as ov\n",
    "import openvisuspy as ovy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0c46f76c",
   "metadata": {},
   "source": [
    "## Load the NETCDF file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd8666c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "ds = nc.Dataset('psl_day_CNRM-CM6-1-HR_historical_r1i1p1f2_gr_20000101-20141231.nc')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "df733a28",
   "metadata": {},
   "source": [
    "## Inspect the NetCDF structure\n",
    "\n",
    "A NetCDF file has three basic parts: metadata, dimensions and variables. \n",
    "Variables contain both metadata and data. \n",
    "Print its structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "016705d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Dataset'>\n",
      "root group (NETCDF4 data model, file format HDF5):\n",
      "    Conventions: CF-1.7 CMIP-6.2\n",
      "    creation_date: 2019-08-17T11:05:53Z\n",
      "    description: CMIP6 historical\n",
      "    title: CNRM-CM6-1-HR model output prepared for CMIP6 and CMIP / historical simulation\n",
      "    activity_id: CMIP\n",
      "    contact: contact.cmip@meteo.fr\n",
      "    data_specs_version: 01.00.21\n",
      "    dr2xml_version: 1.16\n",
      "    experiment_id: historical\n",
      "    experiment: all-forcing simulation of the recent past\n",
      "    external_variables: areacella\n",
      "    forcing_index: 2\n",
      "    frequency: day\n",
      "    further_info_url: https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS.CNRM-CM6-1-HR.historical.none.r1i1p1f2\n",
      "    grid: data regridded to a 359 gaussian grid (360x720 latlon) from a native atmosphere T359l reduced gaussian grid\n",
      "    grid_label: gr\n",
      "    nominal_resolution: 50 km\n",
      "    initialization_index: 1\n",
      "    institution_id: CNRM-CERFACS\n",
      "    institution: CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France)\n",
      "    license: CMIP6 model data produced by CNRM-CERFACS is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at http://www.umr-cnrm.fr/cmip6/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.\n",
      "    mip_era: CMIP6\n",
      "    parent_experiment_id: p i C o n t r o l\n",
      "    parent_mip_era: CMIP6\n",
      "    parent_activity_id: C M I P\n",
      "    parent_source_id: CNRM-CM6-1-HR\n",
      "    parent_time_units: days since 1850-01-01 00:00:00\n",
      "    parent_variant_label: r1i1p1f2\n",
      "    branch_method: standard\n",
      "    branch_time_in_parent: 0.0\n",
      "    branch_time_in_child: 0.0\n",
      "    physics_index: 1\n",
      "    product: model-output\n",
      "    realization_index: 1\n",
      "    realm: atmos\n",
      "    references: http://www.umr-cnrm.fr/cmip6/references\n",
      "    source: CNRM-CM6-1-HR (2017):  aerosol: prescribed monthly fields computed by TACTIC_v2 scheme atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km) atmosChem: OZL_v2 land: Surfex 8.0c ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m) seaIce: Gelato 6.1\n",
      "    source_id: CNRM-CM6-1-HR\n",
      "    source_type: AOGCM\n",
      "    sub_experiment_id: none\n",
      "    sub_experiment: none\n",
      "    table_id: day\n",
      "    variable_id: psl\n",
      "    variant_label: r1i1p1f2\n",
      "    EXPID: historical_HR_s2\n",
      "    CMIP6_CV_version: cv=6.2.3.0-7-g2019642\n",
      "    dr2xml_md5sum: 45d4369d889ddfb8149d771d8625e9ec\n",
      "    xios_commit: 1442-shuffle\n",
      "    nemo_gelato_commit: 84a9e3f161dade7_8250e198106a168\n",
      "    arpege_minor_version: 6.3.3\n",
      "    history: none\n",
      "    tracking_id: hdl:21.14100/fe023ea1-4d28-4278-8e11-05dd290a1af1\n",
      "    dimensions(sizes): lat(360), lon(720), time(5479), axis_nbounds(2)\n",
      "    variables(dimensions): float64 lat(lat), float64 lon(lon), float64 time(time), float64 time_bounds(time, axis_nbounds), float32 psl(time, lat, lon)\n",
      "    groups: \n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4663caab",
   "metadata": {},
   "source": [
    "## Print the NetCDF **dimensions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60397bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'axis_nbounds': <class 'netCDF4._netCDF4.Dimension'>: name = 'axis_nbounds', size = 2,\n",
      " 'lat': <class 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 360,\n",
      " 'lon': <class 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 720,\n",
      " 'time': <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 5479}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "pprint(ds.dimensions)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cf79d3cb",
   "metadata": {},
   "source": [
    "## Print  the NetCDF **variables**\n",
    "\n",
    "Variables contain binary data, to be converted later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "953f2524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([<class 'netCDF4._netCDF4.Variable'>\n",
      "float64 lat(lat)\n",
      "    axis: Y\n",
      "    standard_name: latitude\n",
      "    long_name: Latitude\n",
      "    units: degrees_north\n",
      "unlimited dimensions: \n",
      "current shape = (360,)\n",
      "filling on, default _FillValue of 9.969209968386869e+36 used, <class 'netCDF4._netCDF4.Variable'>\n",
      "float64 lon(lon)\n",
      "    axis: X\n",
      "    standard_name: longitude\n",
      "    long_name: Longitude\n",
      "    units: degrees_east\n",
      "unlimited dimensions: \n",
      "current shape = (720,)\n",
      "filling on, default _FillValue of 9.969209968386869e+36 used, <class 'netCDF4._netCDF4.Variable'>\n",
      "float64 time(time)\n",
      "    axis: T\n",
      "    standard_name: time\n",
      "    long_name: Time axis\n",
      "    calendar: gregorian\n",
      "    units: days since 1850-01-01 00:00:00\n",
      "    time_origin: 1850-01-01 00:00:00\n",
      "    bounds: time_bounds\n",
      "unlimited dimensions: time\n",
      "current shape = (5479,)\n",
      "filling on, default _FillValue of 9.969209968386869e+36 used, <class 'netCDF4._netCDF4.Variable'>\n",
      "float64 time_bounds(time, axis_nbounds)\n",
      "unlimited dimensions: time\n",
      "current shape = (5479, 2)\n",
      "filling on, default _FillValue of 9.969209968386869e+36 used, <class 'netCDF4._netCDF4.Variable'>\n",
      "float32 psl(time, lat, lon)\n",
      "    long_name: Sea Level Pressure\n",
      "    units: Pa\n",
      "    online_operation: average\n",
      "    cell_methods: area: time: mean\n",
      "    interval_operation: 900 s\n",
      "    interval_write: 1 d\n",
      "    _FillValue: 1e+20\n",
      "    missing_value: 1e+20\n",
      "    coordinates: \n",
      "    description: Sea Level Pressure\n",
      "    history: none\n",
      "    cell_measures: area: areacella\n",
      "    standard_name: air_pressure_at_mean_sea_level\n",
      "unlimited dimensions: time\n",
      "current shape = (5479, 360, 720)\n",
      "filling on])\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "pprint(ds.variables)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d6d7ba6f",
   "metadata": {},
   "source": [
    "## Choose the variable to convert \n",
    "\n",
    "The variable we are interested in converting is `ps1` i.e. the *Sea Level Pressure*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c0d7cb70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "float32 psl(time, lat, lon)\n",
      "    long_name: Sea Level Pressure\n",
      "    units: Pa\n",
      "    online_operation: average\n",
      "    cell_methods: area: time: mean\n",
      "    interval_operation: 900 s\n",
      "    interval_write: 1 d\n",
      "    _FillValue: 1e+20\n",
      "    missing_value: 1e+20\n",
      "    coordinates: \n",
      "    description: Sea Level Pressure\n",
      "    history: none\n",
      "    cell_measures: area: areacella\n",
      "    standard_name: air_pressure_at_mean_sea_level\n",
      "unlimited dimensions: time\n",
      "current shape = (5479, 360, 720)\n",
      "filling on\n"
     ]
    }
   ],
   "source": [
    "var=ds.variables['psl']\n",
    "print(var)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c6952240",
   "metadata": {},
   "source": [
    "## Read the NetCDF binary  data in memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "189d17bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetCDF file loaded in 23.107910871505737 seconds dtype=float32 num_timesteps=5479 W=720 H=360 m=91981.7109375 M=108768.9765625\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t1 = time.time()\n",
    "data=var[:,:,:]\n",
    "num_timesteps,H,W=data.shape[0], data.shape[1], data.shape[2]\n",
    "m,M=np.min(data),np.max(data)\n",
    "read_sec=time.time()-t1\n",
    "print(f\"NetCDF file loaded in {read_sec} seconds dtype={data.dtype} num_timesteps={num_timesteps} W={W} H={H} m={m} M={M}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b1d561a9",
   "metadata": {},
   "source": [
    "## Create the OpenVisus file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f16dabe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<dataset url=\"test1/visus.idx\" cache_dir=\"\" typename=\"IdxDataset\">\n",
      "\t<idxfile>\n",
      "\t\t<version value=\"6\" />\n",
      "\t\t<bitmask value=\"V0010101010101010101\" />\n",
      "\t\t<box value=\"0 720 0 360\" />\n",
      "\t\t<bitsperblock value=\"16\" />\n",
      "\t\t<blocksperfile value=\"8\" />\n",
      "\t\t<block_interleaving value=\"0\" />\n",
      "\t\t<filename_template value=\"./visus/%04x.bin\" />\n",
      "\t\t<missing_blocks value=\"False\" />\n",
      "\t\t<arco value=\"0\" />\n",
      "\t\t<time_template value=\"time_%04d/\" />\n",
      "\t\t<field name=\"data\" description=\"\" index=\"\" default_compression=\"\" default_layout=\"\" default_value=\"0\" filter=\"\" dtype=\"float32\" />\n",
      "\t\t<timestep from=\"0\" to=\"5479\" step=\"1\" />\n",
      "\t</idxfile>\n",
      "</dataset>\n",
      "Dataset created\n"
     ]
    }
   ],
   "source": [
    "arco=\"modvisus\"\n",
    "\n",
    "idx_filename=\"tmp/netcdf/tutorial-1/visus.idx\"\n",
    "db=ov.CreateIdx(\n",
    "    url=idx_filename, \n",
    "    dims=[W,H], \n",
    "    fields=[ov.Field(\"data\",str(data.dtype),\"row_major\")], \n",
    "    compression=\"raw\", \n",
    "    time=[0,num_timesteps,\"time_%04d/\"], \n",
    "    arco=arco)\n",
    "\n",
    "print(db.getDatasetBody().toString())\n",
    "print(\"Dataset created\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0b3dfe6f",
   "metadata": {},
   "source": [
    "## Write uncompressed OpenVisus binary data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "822d9c09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote new timestep=5478 done in 41.81589412689209 seconds\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "for timestep in range(num_timesteps):\n",
    "    db.write(data[timestep,:,:],time=timestep)\n",
    "write_sec=time.time() - t1\n",
    "print(f\"Wrote new timestep={timestep} done in {write_sec} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2b5263d7",
   "metadata": {},
   "source": [
    "### Compress OpenVisus binary data \n",
    "\n",
    "NOTE that this step can be slow since it' doing a lot of random reading/writings. \n",
    "You may use `lz4` to speed up this step, but you will get bigger files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f40d6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed timestep=5478 done in 182.16820549964905 seconds\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "algorithm=\"zip\"\n",
    "db.compressDataset([algorithm])\n",
    "compress_sec=time.time()-t1\n",
    "print(f\"Compressed timestep={timestep} done in {compress_sec} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "08710a15",
   "metadata": {},
   "source": [
    "## Display a  Region of Interest\n",
    "\n",
    "Read data from OpenVisus at a certain timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f1b71e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=db.read(x=[0,W],y=[0,H],time=num_timesteps//2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e44ed6e0",
   "metadata": {},
   "source": [
    "Display the data using `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e663ad76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def ShowData(data):\n",
    "    ratio=float(data.shape[1])/data.shape[0]\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.imshow(data, origin='lower')\n",
    "    plt.show()\n",
    "        \n",
    "ShowData(data)"
   ]
  },
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    "## Final considerations\n",
    "\n",
    "We converted the binary data as a 2d Dataset `(lat,log)` with timestep considered as a special dimension. In fact you will see the data is internally organized in different folders depending on the timesteps (e.g. `visus\\time_0000\\0000.bin`, `visus\\time_0001\\0000.bin` etc.).  \n",
    "\n",
    "You may also convert the binary data as a full 3D dataset: `(time,lat,lon)`. This modality makes more sense if you need to access/display slices related to different timesteps, for example the evolution of the pressure at a certain `lon` for some time range. The variation to the code above are minimal."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "f01ae3b1",
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