{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access SOOP CO2 data in Parquet\n",
    "\n",
    "A jupyter notebook to show how to access and plot SOOP CO2 data available as a [Parquet](https://parquet.apache.org) dataset on S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = \"vessel_co2_delayed_qc\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install/Update packages and Load common functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only run once, then restart session if needed\n",
    "!pip install uv\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "def is_colab():\n",
    "    try:\n",
    "        import google.colab\n",
    "        return True\n",
    "    except ImportError:\n",
    "        return False\n",
    "\n",
    "# Get the current directory of the notebook\n",
    "current_dir = os.getcwd()\n",
    "\n",
    "# Check if requirements.txt exists in the current directory\n",
    "local_requirements = os.path.join(current_dir, 'requirements.txt')\n",
    "if os.path.exists(local_requirements):\n",
    "    requirements_path = local_requirements\n",
    "else:\n",
    "    # Fall back to the online requirements.txt file\n",
    "    requirements_path = 'https://raw.githubusercontent.com/aodn/aodn_cloud_optimised/main/notebooks/requirements.txt'\n",
    "\n",
    "# Install packages using uv and the determined requirements file\n",
    "if is_colab():\n",
    "    os.system(f'uv pip install --system -r {requirements_path}')\n",
    "else:\n",
    "    os.system('uv venv')\n",
    "    os.system(f'uv pip install -r {requirements_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import os\n",
    "if not os.path.exists('parquet_queries.py'):\n",
    "  print('Downloading parquet_queries.py')\n",
    "  url = 'https://raw.githubusercontent.com/aodn/aodn_cloud_optimised/main/aodn_cloud_optimised/lib/ParquetDataQuery.py'\n",
    "  response = requests.get(url)\n",
    "  with open('parquet_queries.py', 'w') as f:\n",
    "      f.write(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lbesnard/miniforge3/envs/AodnCloudOptimised/lib/python3.12/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    }
   ],
   "source": [
    "from parquet_queries import create_time_filter, create_bbox_filter, query_unique_value, plot_spatial_extent, \\\n",
    "    get_temporal_extent, get_schema_metadata\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow.dataset as pds\n",
    "import pyarrow as pa\n",
    "import pandas as pd\n",
    "import pyarrow.compute as pc\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import LineCollection\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Location of the parquet dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET_OPTIMISED_DEFAULT=\"aodn-cloud-optimised\"\n",
    "dname = f\"s3://anonymous@{BUCKET_OPTIMISED_DEFAULT}/{dataset_name}.parquet/\"\n",
    "parquet_ds = pq.ParquetDataset(dname,partitioning='hive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get partition keys\n",
    "Partitioning in Parquet involves organising data files based on the values of one or more columns, known as partition keys. When data is written to Parquet files with partitioning enabled, the files are physically stored in a directory structure that reflects the partition keys. This directory structure makes it easier to retrieve and process specific subsets of data based on the partition keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timestamp: int32\n",
      "polygon: string\n",
      "platform_code: string\n"
     ]
    }
   ],
   "source": [
    "dataset = pds.dataset(dname, format=\"parquet\", partitioning=\"hive\")\n",
    "\n",
    "partition_keys = dataset.partitioning.schema\n",
    "print(partition_keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List unique partition values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZMFR', 'VLMJ']\n",
      "CPU times: user 19.8 ms, sys: 0 ns, total: 19.8 ms\n",
      "Wall time: 18.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "unique_partition_value = query_unique_value(parquet_ds, 'platform_code')\n",
    "print(list(unique_partition_value)[0:2])  # showing a subset only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise Spatial Extent of the dataset\n",
    "In this section, we're plotting the polygons where data exists. This helps then with creating a bounding box where there is data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spatial_extent(parquet_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Temporal Extent of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similary to the spatial extent, we're retrieving the minimum and maximum timestamp partition values of the dataset. This is not necessarely accurately representative of the TIME values, as the timestamp partition can be yearly/monthly... but is here to give an idea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2008, 1, 1, 11, 0), datetime.datetime(2024, 4, 1, 11, 0))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_temporal_extent(parquet_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Metadata\n",
    "\n",
    "For all parquet dataset, we create a sidecar file in the root of the dataset named **_common_matadata**. This contains the variable attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'TIME': {'type': 'timestamp[ns]',\n",
       "  'standard_name': 'time',\n",
       "  'long_name': 'analysis_time',\n",
       "  'axis': 'T',\n",
       "  'valid_min': 0.0,\n",
       "  'valid_max': 999999.0,\n",
       "  'ancillary_variables': 'TIME_quality_control'},\n",
       " 'TIME_quality_control': {'type': 'float',\n",
       "  'standard_name': 'time status_flag',\n",
       "  'long_name': 'Quality Control flag for time',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'LATITUDE': {'type': 'double',\n",
       "  'standard_name': 'latitude',\n",
       "  'long_name': 'latitude',\n",
       "  'units': 'degrees_north',\n",
       "  'axis': 'Y',\n",
       "  'valid_min': -90.0,\n",
       "  'valid_max': 90.0,\n",
       "  'reference_datum': 'geographical coordinates, WGS84 projection',\n",
       "  'ancillary_variables': 'LATITUDE_quality_control'},\n",
       " 'LATITUDE_quality_control': {'type': 'float',\n",
       "  'standard_name': 'latitude status_flag',\n",
       "  'long_name': 'Quality Control flag for latitude',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'LONGITUDE': {'type': 'double',\n",
       "  'standard_name': 'longitude',\n",
       "  'long_name': 'longitude',\n",
       "  'units': 'degrees_east',\n",
       "  'axis': 'X',\n",
       "  'valid_min': -180.0,\n",
       "  'valid_max': 180.0,\n",
       "  'reference_datum': 'geographical coordinates, WGS84 projection',\n",
       "  'ancillary_variables': 'LONGITUDE_quality_control'},\n",
       " 'LONGITUDE_quality_control': {'type': 'float',\n",
       "  'standard_name': 'longitude status_flag',\n",
       "  'long_name': 'Quality Control flag for longitude',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'TEMP': {'type': 'double',\n",
       "  'standard_name': 'sea_surface_temperature',\n",
       "  'long_name': 'sea surface temperature',\n",
       "  'units': 'degree_Celsius',\n",
       "  'valid_min': -2.0,\n",
       "  'valid_max': 40.0,\n",
       "  'ancillary_variables': 'TEMP_quality_control'},\n",
       " 'TEMP_quality_control': {'type': 'float',\n",
       "  'standard_name': 'sea_surface_temperature status_flag',\n",
       "  'long_name': 'Quality Control flag for sea_surface_temperature',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'TEMP_2': {'type': 'double',\n",
       "  'long_name': 'equilibrator water temperature',\n",
       "  'units': 'degree_Celsius',\n",
       "  'valid_min': -2.0,\n",
       "  'valid_max': 40.0,\n",
       "  'ancillary_variables': 'TEMP_2_quality_control'},\n",
       " 'TEMP_2_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for sea_surface_temperature',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'PSAL': {'type': 'double',\n",
       "  'standard_name': 'sea_surface_salinity',\n",
       "  'long_name': 'sea surface salinity',\n",
       "  'units': '1e-3',\n",
       "  'valid_min': 0.0,\n",
       "  'valid_max': 42.0,\n",
       "  'ancillary_variables': 'PSAL_quality_control'},\n",
       " 'PSAL_quality_control': {'type': 'float',\n",
       "  'standard_name': 'sea_surface_salinity status_flag',\n",
       "  'long_name': 'Quality Control flag for sea_surface_salinity',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'WSPD': {'type': 'double',\n",
       "  'standard_name': 'wind_speed',\n",
       "  'long_name': 'wind speed',\n",
       "  'units': 'm s-1',\n",
       "  'ancillary_variables': 'WSPD_quality_control'},\n",
       " 'WSPD_quality_control': {'type': 'float',\n",
       "  'standard_name': 'wind_speed status_flag',\n",
       "  'long_name': 'Quality Control flag for wind speed',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'WDIR': {'type': 'double',\n",
       "  'long_name': 'wind direction',\n",
       "  'units': 'degree',\n",
       "  'ancillary_variables': 'WDIR_quality_control',\n",
       "  'comment': 'true wind direction where 0 is North and 90 is East'},\n",
       " 'WDIR_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for wind direction',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'Press_Equil': {'type': 'double',\n",
       "  'long_name': 'equilibrator head space pressure',\n",
       "  'units': 'hPa',\n",
       "  'ancillary_variables': 'Press_Equil_quality_control'},\n",
       " 'Press_Equil_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for equilibrator head space pressure',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'Press_ATM': {'type': 'double',\n",
       "  'long_name': 'barometric pressure',\n",
       "  'units': 'hPa',\n",
       "  'ancillary_variables': 'Press_ATM_quality_control'},\n",
       " 'Press_ATM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for barometric pressure',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'xCO2EQ_PPM': {'type': 'double',\n",
       "  'long_name': 'mole fraction of CO2 in the equilibrator head space (dry)',\n",
       "  'units': '1e-6',\n",
       "  'ancillary_variables': 'xCO2EQ_PPM_quality_control',\n",
       "  'comment': 'the unit 1e-6 is also called parts per million (ppm)'},\n",
       " 'xCO2EQ_PPM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for xCO2EQ_PPM',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'xCO2ATM_PPM': {'type': 'double',\n",
       "  'long_name': 'mole fraction of CO2 in the atmosphere (dry) measured every 4 hours after standard runs',\n",
       "  'units': '1e-6',\n",
       "  'ancillary_variables': 'xCO2ATM_PPM_quality_control',\n",
       "  'comment': 'the unit 1e-6 is also called parts per million (ppm)'},\n",
       " 'xCO2ATM_PPM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for xCO2ATM_PPM',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'xCO2ATM_PPM_INTERPOLATED': {'type': 'double',\n",
       "  'long_name': 'mole fraction of CO2 in the atmosphere (dry) measured every 4 hours after standard runs and values linearly interpolated to the times shown',\n",
       "  'units': '1e-6',\n",
       "  'ancillary_variables': 'xCO2ATM_PPM_INTERPOLATED_quality_control',\n",
       "  'comment': 'the unit 1e-6 is also called parts per million (ppm)'},\n",
       " 'xCO2ATM_PPM_INTERPOLATED_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for xCO2ATM_PPM_INTERPOLATED',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'fCO2SW_UATM': {'type': 'double',\n",
       "  'long_name': 'fugacity of carbon dioxide at surface water salinity and temperature',\n",
       "  'units': 'microatmospheres',\n",
       "  'ancillary_variables': 'fCO2SW_UATM_quality_control'},\n",
       " 'fCO2SW_UATM_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for fCO2SW_UATM',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'fCO2ATM_UATM_INTERPOLATED': {'type': 'double',\n",
       "  'long_name': 'fugacity of CO2 in the atmosphere',\n",
       "  'units': 'microatmospheres',\n",
       "  'ancillary_variables': 'fCO2ATM_UATM_INTERPOLATED_quality_control'},\n",
       " 'fCO2ATM_UATM_INTERPOLATED_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for fCO2ATM_UATM_INTERPOLATED',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'DfCO2': {'type': 'double',\n",
       "  'long_name': 'Difference between fCO2SW and fCO2ATM',\n",
       "  'units': 'microatmospheres',\n",
       "  'ancillary_variables': 'DfCO2_quality_control'},\n",
       " 'DfCO2_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for DfCO2',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'LICORflow': {'type': 'double',\n",
       "  'long_name': 'Gas flow through infrared gas analyser',\n",
       "  'units': 'ml min-1',\n",
       "  'ancillary_variables': 'LICORflow_quality_control'},\n",
       " 'LICORflow_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for LICORflow',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'H2OFLOW': {'type': 'double',\n",
       "  'long_name': 'water flow to equilibrator',\n",
       "  'units': 'L min-1',\n",
       "  'ancillary_variables': 'H2OFLOW_quality_control'},\n",
       " 'H2OFLOW_quality_control': {'type': 'float',\n",
       "  'long_name': 'Quality Control flag for H2OFLOW',\n",
       "  'quality_control_conventions': 'WOCE quality control procedure',\n",
       "  'valid_min': 2,\n",
       "  'valid_max': 4,\n",
       "  'flag_values': [2, 3, 4],\n",
       "  'flag_meanings': 'good questionable bad',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005',\n",
       "  'ancillary_variables': 'SUBFLAG'},\n",
       " 'SUBFLAG': {'type': 'float',\n",
       "  'long_name': 'secondary flags, only for questionable measurements, WOCE flag 3 (Pierrot et Al 2009)',\n",
       "  'valid_min': 1,\n",
       "  'valid_max': 10,\n",
       "  'flag_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "  'flag_meanings': 'Outside_of_standard_range Questionable_or_interpolated_SST Questionable_EQU_temperature Anomalous_EQU_temperature-SST_+or-1degC Questionable_sea-surface_salinity Questionable_pressure Low_EQU_gas_flow Questionable_air_value Interpolated_standard Other_see_metadata',\n",
       "  'references': 'Pierrot,D. et al. 2009, Recommendations for Autonomous Underway pCO2 Measuring Systems and Data Reduction Routines, Deep-Sea Research II, doi:10.1016/j.dsr2.2008.12.005'},\n",
       " 'TYPE': {'type': 'string',\n",
       "  'long_name': 'measurement type (equilibrator, standard or atmosphere)',\n",
       "  'units': 'categorical'},\n",
       " 'timestamp': {'type': 'int64'},\n",
       " 'polygon': {'type': 'string'},\n",
       " 'platform_code': {'type': 'string'},\n",
       " 'cruise_id': {'type': 'string'},\n",
       " 'vessel_name': {'type': 'string'},\n",
       " 'filename': {'type': 'string'},\n",
       " 'dataset_metadata': {'metadata_uuid': '63db5801-cc19-40ef-83b3-85ccba884cf7',\n",
       "  'title': 'Upper Ocean Thermal Data collected using XBT (expendable bathythermographs)',\n",
       "  'principal_investigator': 'Cowley, Rebecca',\n",
       "  'principal_investigator_email': 'rebecca.cowley@csiro.au',\n",
       "  'featureType': 'profile'}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parquet_meta = pa.parquet.read_schema(os.path.join(dname + '_common_metadata'))  # parquet metadata\n",
    "metadata = get_schema_metadata(dname)  # schema metadata\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Query and Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a TIME and BoundingBox filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_time = create_time_filter(parquet_ds, date_start='2020-12-23 10:14:00', date_end='2024-01-01 07:50:00')\n",
    "filter_geo = create_bbox_filter(parquet_ds, lat_min=-34, lat_max=-32, lon_min=150, lon_max=155)\n",
    "\n",
    "\n",
    "filter = filter_geo & filter_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10199 entries, 0 to 10198\n",
      "Data columns (total 44 columns):\n",
      " #   Column                                     Non-Null Count  Dtype         \n",
      "---  ------                                     --------------  -----         \n",
      " 0   TIME                                       10199 non-null  datetime64[ns]\n",
      " 1   TIME_quality_control                       10199 non-null  float32       \n",
      " 2   LATITUDE                                   10199 non-null  float64       \n",
      " 3   LATITUDE_quality_control                   10199 non-null  float32       \n",
      " 4   LONGITUDE                                  10199 non-null  float64       \n",
      " 5   LONGITUDE_quality_control                  10199 non-null  float32       \n",
      " 6   TEMP                                       10199 non-null  float64       \n",
      " 7   TEMP_quality_control                       10199 non-null  float32       \n",
      " 8   TEMP_2                                     10196 non-null  float64       \n",
      " 9   TEMP_2_quality_control                     10199 non-null  float32       \n",
      " 10  PSAL                                       10199 non-null  float64       \n",
      " 11  PSAL_quality_control                       10199 non-null  float32       \n",
      " 12  WSPD                                       9592 non-null   float64       \n",
      " 13  WSPD_quality_control                       10199 non-null  float32       \n",
      " 14  WDIR                                       10157 non-null  float64       \n",
      " 15  WDIR_quality_control                       10199 non-null  float32       \n",
      " 16  Press_Equil                                10199 non-null  float64       \n",
      " 17  Press_Equil_quality_control                10199 non-null  float32       \n",
      " 18  Press_ATM                                  10199 non-null  float64       \n",
      " 19  Press_ATM_quality_control                  10199 non-null  float32       \n",
      " 20  xCO2EQ_PPM                                 9931 non-null   float64       \n",
      " 21  xCO2EQ_PPM_quality_control                 10199 non-null  float32       \n",
      " 22  xCO2ATM_PPM                                268 non-null    float64       \n",
      " 23  xCO2ATM_PPM_quality_control                10199 non-null  float32       \n",
      " 24  xCO2ATM_PPM_INTERPOLATED                   10199 non-null  float64       \n",
      " 25  xCO2ATM_PPM_INTERPOLATED_quality_control   10199 non-null  float32       \n",
      " 26  fCO2SW_UATM                                9931 non-null   float64       \n",
      " 27  fCO2SW_UATM_quality_control                10199 non-null  float32       \n",
      " 28  fCO2ATM_UATM_INTERPOLATED                  10199 non-null  float64       \n",
      " 29  fCO2ATM_UATM_INTERPOLATED_quality_control  10199 non-null  float32       \n",
      " 30  DfCO2                                      9931 non-null   float64       \n",
      " 31  DfCO2_quality_control                      10199 non-null  float32       \n",
      " 32  LICORflow                                  10199 non-null  float64       \n",
      " 33  LICORflow_quality_control                  10199 non-null  float32       \n",
      " 34  H2OFLOW                                    10199 non-null  float64       \n",
      " 35  H2OFLOW_quality_control                    10199 non-null  float32       \n",
      " 36  SUBFLAG                                    0 non-null      float32       \n",
      " 37  TYPE                                       10199 non-null  object        \n",
      " 38  cruise_id                                  10199 non-null  object        \n",
      " 39  vessel_name                                10199 non-null  object        \n",
      " 40  filename                                   10199 non-null  object        \n",
      " 41  timestamp                                  10199 non-null  category      \n",
      " 42  polygon                                    10199 non-null  category      \n",
      " 43  platform_code                              10199 non-null  category      \n",
      "dtypes: category(3), datetime64[ns](1), float32(19), float64(17), object(4)\n",
      "memory usage: 2.5+ MB\n",
      "CPU times: user 415 ms, sys: 54.1 ms, total: 469 ms\n",
      "Wall time: 3.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# using pandas instead of pyarrow so that filters can directly be applied to the data, and not just the partition\n",
    "df = pd.read_parquet(dname, engine='pyarrow',filters=filter)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>TIME</th>\n",
       "      <th>TIME_quality_control</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LATITUDE_quality_control</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LONGITUDE_quality_control</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TEMP_quality_control</th>\n",
       "      <th>TEMP_2</th>\n",
       "      <th>TEMP_2_quality_control</th>\n",
       "      <th>...</th>\n",
       "      <th>H2OFLOW</th>\n",
       "      <th>H2OFLOW_quality_control</th>\n",
       "      <th>SUBFLAG</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>cruise_id</th>\n",
       "      <th>vessel_name</th>\n",
       "      <th>filename</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>polygon</th>\n",
       "      <th>platform_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-10 18:05:01.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.999871</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.395629</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.435</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-10 18:06:23.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.996769</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.399301</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.429</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-10 18:07:41.999999744</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.993654</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.402956</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.438</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.67</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-10 18:09:03.999999744</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.990543</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.406601</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.428</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-10 18:10:25.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.987426</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.410262</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.432</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2021_V03</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc</td>\n",
       "      <td>1619827200</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10194</th>\n",
       "      <td>2023-11-01 16:37:56.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.852700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.471700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.172</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10195</th>\n",
       "      <td>2023-11-01 16:39:14.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.850300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.470400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.164</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10196</th>\n",
       "      <td>2023-11-01 16:40:34.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.847900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.469200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.147</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10197</th>\n",
       "      <td>2023-11-01 16:41:54.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.845800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.468100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10198</th>\n",
       "      <td>2023-11-01 16:43:14.000000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-33.843700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.467100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.110</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EQU</td>\n",
       "      <td>IN2023_V06</td>\n",
       "      <td>RV Investigator</td>\n",
       "      <td>IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc</td>\n",
       "      <td>1698796800</td>\n",
       "      <td>0103000000010000000500000000000000002062400000...</td>\n",
       "      <td>VLMJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10199 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               TIME  TIME_quality_control   LATITUDE  \\\n",
       "0     2021-05-10 18:05:01.000000000                   2.0 -33.999871   \n",
       "1     2021-05-10 18:06:23.000000000                   2.0 -33.996769   \n",
       "2     2021-05-10 18:07:41.999999744                   2.0 -33.993654   \n",
       "3     2021-05-10 18:09:03.999999744                   2.0 -33.990543   \n",
       "4     2021-05-10 18:10:25.000000000                   2.0 -33.987426   \n",
       "...                             ...                   ...        ...   \n",
       "10194 2023-11-01 16:37:56.000000000                   2.0 -33.852700   \n",
       "10195 2023-11-01 16:39:14.000000000                   2.0 -33.850300   \n",
       "10196 2023-11-01 16:40:34.000000000                   2.0 -33.847900   \n",
       "10197 2023-11-01 16:41:54.000000000                   2.0 -33.845800   \n",
       "10198 2023-11-01 16:43:14.000000000                   2.0 -33.843700   \n",
       "\n",
       "       LATITUDE_quality_control   LONGITUDE  LONGITUDE_quality_control  \\\n",
       "0                           2.0  151.395629                        2.0   \n",
       "1                           2.0  151.399301                        2.0   \n",
       "2                           2.0  151.402956                        2.0   \n",
       "3                           2.0  151.406601                        2.0   \n",
       "4                           2.0  151.410262                        2.0   \n",
       "...                         ...         ...                        ...   \n",
       "10194                       2.0  151.471700                        2.0   \n",
       "10195                       2.0  151.470400                        2.0   \n",
       "10196                       2.0  151.469200                        2.0   \n",
       "10197                       2.0  151.468100                        2.0   \n",
       "10198                       2.0  151.467100                        2.0   \n",
       "\n",
       "         TEMP  TEMP_quality_control  TEMP_2  TEMP_2_quality_control  ...  \\\n",
       "0      20.435                   2.0   20.66                     2.0  ...   \n",
       "1      20.429                   2.0   20.66                     2.0  ...   \n",
       "2      20.438                   2.0   20.67                     2.0  ...   \n",
       "3      20.428                   2.0   20.66                     2.0  ...   \n",
       "4      20.432                   2.0   20.66                     2.0  ...   \n",
       "...       ...                   ...     ...                     ...  ...   \n",
       "10194  20.172                   2.0   20.45                     2.0  ...   \n",
       "10195  20.164                   2.0   20.45                     2.0  ...   \n",
       "10196  20.147                   2.0   20.43                     2.0  ...   \n",
       "10197  20.129                   2.0   20.42                     2.0  ...   \n",
       "10198  20.110                   2.0   20.40                     2.0  ...   \n",
       "\n",
       "       H2OFLOW  H2OFLOW_quality_control  SUBFLAG  TYPE   cruise_id  \\\n",
       "0         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "1         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "2         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "3         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "4         2.09                      2.0      NaN   EQU  IN2021_V03   \n",
       "...        ...                      ...      ...   ...         ...   \n",
       "10194     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10195     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10196     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10197     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "10198     2.12                      2.0      NaN   EQU  IN2023_V06   \n",
       "\n",
       "           vessel_name                                         filename  \\\n",
       "0      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "1      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "2      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "3      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "4      RV Investigator  IMOS_SOOP-CO2_GST_20210508T033536Z_VLMJ_FV01.nc   \n",
       "...                ...                                              ...   \n",
       "10194  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10195  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10196  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10197  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "10198  RV Investigator  IMOS_SOOP-CO2_GST_20231009T002809Z_VLMJ_FV01.nc   \n",
       "\n",
       "        timestamp                                            polygon  \\\n",
       "0      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "1      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "2      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "3      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "4      1619827200  0103000000010000000500000000000000002062400000...   \n",
       "...           ...                                                ...   \n",
       "10194  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10195  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10196  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10197  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "10198  1698796800  0103000000010000000500000000000000002062400000...   \n",
       "\n",
       "       platform_code  \n",
       "0               VLMJ  \n",
       "1               VLMJ  \n",
       "2               VLMJ  \n",
       "3               VLMJ  \n",
       "4               VLMJ  \n",
       "...              ...  \n",
       "10194           VLMJ  \n",
       "10195           VLMJ  \n",
       "10196           VLMJ  \n",
       "10197           VLMJ  \n",
       "10198           VLMJ  \n",
       "\n",
       "[10199 rows x 44 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sorted = df.sort_values('TIME')\n",
    "\n",
    "# Create a list of segments\n",
    "points = np.array([df_sorted['LONGITUDE'], df_sorted['LATITUDE']]).T.reshape(-1, 1, 2)\n",
    "segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "\n",
    "# Create a LineCollection with segments colored by temperature\n",
    "norm = plt.Normalize(df_sorted['TEMP'].min(), df_sorted['TEMP'].max())\n",
    "lc = LineCollection(segments, cmap='RdYlBu_r', norm=norm)\n",
    "lc.set_array(df_sorted['TEMP'])\n",
    "lc.set_linewidth(2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.add_collection(lc)\n",
    "ax.autoscale()\n",
    "ax.set_xlabel(metadata['LONGITUDE']['standard_name'])\n",
    "ax.set_ylabel(metadata['LATITUDE']['standard_name'])\n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Adding color bar\n",
    "cbar = plt.colorbar(lc, ax=ax)\n",
    "cbar.set_label('Temperature')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a TIME and scalar/number filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_time = create_time_filter(parquet_ds, date_start='2020-01-31 10:14:00', date_end='2022-02-01 07:50:00')\n",
    "\n",
    "expr_1 = pc.field('platform_code') == pa.scalar(\"VLMJ\")\n",
    "filter = expr_1 & filter_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244944 entries, 0 to 244943\n",
      "Data columns (total 44 columns):\n",
      " #   Column                                     Non-Null Count   Dtype         \n",
      "---  ------                                     --------------   -----         \n",
      " 0   TIME                                       244944 non-null  datetime64[ns]\n",
      " 1   TIME_quality_control                       244944 non-null  float32       \n",
      " 2   LATITUDE                                   244944 non-null  float64       \n",
      " 3   LATITUDE_quality_control                   244944 non-null  float32       \n",
      " 4   LONGITUDE                                  244944 non-null  float64       \n",
      " 5   LONGITUDE_quality_control                  244944 non-null  float32       \n",
      " 6   TEMP                                       244680 non-null  float64       \n",
      " 7   TEMP_quality_control                       244944 non-null  float32       \n",
      " 8   TEMP_2                                     244780 non-null  float64       \n",
      " 9   TEMP_2_quality_control                     244944 non-null  float32       \n",
      " 10  PSAL                                       244944 non-null  float64       \n",
      " 11  PSAL_quality_control                       244944 non-null  float32       \n",
      " 12  WSPD                                       222686 non-null  float64       \n",
      " 13  WSPD_quality_control                       244944 non-null  float32       \n",
      " 14  WDIR                                       237361 non-null  float64       \n",
      " 15  WDIR_quality_control                       244944 non-null  float32       \n",
      " 16  Press_Equil                                244944 non-null  float64       \n",
      " 17  Press_Equil_quality_control                244944 non-null  float32       \n",
      " 18  Press_ATM                                  241825 non-null  float64       \n",
      " 19  Press_ATM_quality_control                  244944 non-null  float32       \n",
      " 20  xCO2EQ_PPM                                 236906 non-null  float64       \n",
      " 21  xCO2EQ_PPM_quality_control                 244944 non-null  float32       \n",
      " 22  xCO2ATM_PPM                                8038 non-null    float64       \n",
      " 23  xCO2ATM_PPM_quality_control                244944 non-null  float32       \n",
      " 24  xCO2ATM_PPM_INTERPOLATED                   244944 non-null  float64       \n",
      " 25  xCO2ATM_PPM_INTERPOLATED_quality_control   244944 non-null  float32       \n",
      " 26  fCO2SW_UATM                                236647 non-null  float64       \n",
      " 27  fCO2SW_UATM_quality_control                244944 non-null  float32       \n",
      " 28  fCO2ATM_UATM_INTERPOLATED                  241561 non-null  float64       \n",
      " 29  fCO2ATM_UATM_INTERPOLATED_quality_control  244944 non-null  float32       \n",
      " 30  DfCO2                                      233528 non-null  float64       \n",
      " 31  DfCO2_quality_control                      244944 non-null  float32       \n",
      " 32  LICORflow                                  244944 non-null  float64       \n",
      " 33  LICORflow_quality_control                  244944 non-null  float32       \n",
      " 34  H2OFLOW                                    244944 non-null  float64       \n",
      " 35  H2OFLOW_quality_control                    244944 non-null  float32       \n",
      " 36  SUBFLAG                                    0 non-null       float32       \n",
      " 37  TYPE                                       244944 non-null  object        \n",
      " 38  cruise_id                                  244944 non-null  object        \n",
      " 39  vessel_name                                244944 non-null  object        \n",
      " 40  filename                                   244944 non-null  object        \n",
      " 41  timestamp                                  244944 non-null  category      \n",
      " 42  polygon                                    244944 non-null  category      \n",
      " 43  platform_code                              244944 non-null  category      \n",
      "dtypes: category(3), datetime64[ns](1), float32(19), float64(17), object(4)\n",
      "memory usage: 60.0+ MB\n",
      "CPU times: user 1.73 s, sys: 305 ms, total: 2.03 s\n",
      "Wall time: 8.04 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# using pandas instead of pyarrow so that filters can directly be applied to the data, and not just the partition\n",
    "df = pd.read_parquet(dname, engine='pyarrow',filters=filter)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sorted = df.sort_values('TIME')\n",
    "\n",
    "# Create a list of segments\n",
    "points = np.array([df_sorted['LONGITUDE'], df_sorted['LATITUDE']]).T.reshape(-1, 1, 2)\n",
    "segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "\n",
    "# Create a LineCollection with segments colored by temperature\n",
    "norm = plt.Normalize(df_sorted['TEMP'].min(), df_sorted['TEMP'].max())\n",
    "lc = LineCollection(segments, cmap='RdYlBu_r', norm=norm)\n",
    "lc.set_array(df_sorted['TEMP'])\n",
    "lc.set_linewidth(2)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.add_collection(lc)\n",
    "ax.autoscale()\n",
    "ax.set_xlabel(metadata['LONGITUDE']['standard_name'])\n",
    "ax.set_ylabel(metadata['LATITUDE']['standard_name'])\n",
    "ax.invert_yaxis()\n",
    "\n",
    "# Adding color bar\n",
    "cbar = plt.colorbar(lc, ax=ax)\n",
    "cbar.set_label('Temperature')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}