acsmnode/workflow_data_integration.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Data integration workflow of experimental campaign\n",
    "\n",
    "In this notebook, we will go through a our data integration workflow. This involves the following steps:\n",
    "\n",
    "1. Specify data integration file through YAML configuration file.\n",
    "2. Create an integrated HDF5 file of experimental campaign from configuration file.\n",
    "3. Display the created HDF5 file using a treemap\n",
    "\n",
    "## Import libraries and modules\n",
    "\n",
    "* Excecute (or Run) the Cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "# Set up project root directory\n",
    "root_dir = os.path.abspath(os.curdir)\n",
    "sys.path.append(root_dir)\n",
    "sys.path.append(os.path.join(root_dir,'dima'))\n",
    "#sys.path.append(os.path.join(root_dir,'dima','instruments'))\n",
    "#sys.path.append(os.path.join(root_dir,'dima','src'))\n",
    "#sys.path.append(os.path.join(root_dir,'dima','utils'))\n",
    "\n",
    "import dima.src.hdf5_vis as hdf5_vis\n",
    "import dima.pipelines.data_integration as dilib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Specify data integration task through YAML configuration file\n",
    "\n",
    "* Create your configuration file (i.e., *.yaml file) adhering to the example yaml file in the input folder.\n",
    "* Set up input directory and output directory paths and Excecute Cell.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "yaml_config_file_path = 'dima_config.yaml'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create an integrated HDF5 file of experimental campaign.\n",
    "\n",
    "* Excecute Cell. Here we run the function `integrate_data_sources` with input argument as the previously specified YAML config file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "hdf5_file_path = dilib.integrate_data_sources(yaml_config_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display integrated HDF5 file using a treemap\n",
    "\n",
    "* Excecute Cell. A visual representation in html format of the integrated file should be displayed and stored in the output directory folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if isinstance(hdf5_file_path ,list):\n",
    "    for path_item in hdf5_file_path :\n",
    "        hdf5_vis.display_group_hierarchy_on_a_treemap(path_item)\n",
    "else:\n",
    "    hdf5_vis.display_group_hierarchy_on_a_treemap(hdf5_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dima.pipelines.metadata_revision as metadata\n",
    "\n",
    "import dima.src.hdf5_ops as h5de\n",
    "\n",
    "channels1 = ['Chl_11000','NH4_11000','SO4_11000','NO3_11000','Org_11000']\n",
    "channels2 = ['FilamentEmission_mA','VaporizerTemp_C','FlowRate_mb','ABsamp']\n",
    "\n",
    "target_channels = {'location':'ACSM_TOFWARE/ACSM_JFJ_2024_JantoFeb_timeseries.txt/data_table',\n",
    "                   'names': ','.join(['t_start_Buf','Chl_11000','NH4_11000','SO4_11000','NO3_11000','Org_11000'])\n",
    "                   }\n",
    "diagnostic_channels = {'location':'ACSM_TOFWARE/ACSM_JFJ_2024_JantoFeb_meta.txt/data_table',\n",
    "                       'names': ','.join(['t_base','FilamentEmission_mA','VaporizerTemp_C','FlowRate_mb','ABsamp'])}\n",
    "\n",
    "DataOpsAPI = h5de.HDF5DataOpsManager(hdf5_file_path[0])\n",
    "\n",
    "DataOpsAPI.load_file_obj()\n",
    "DataOpsAPI.append_metadata('/ACSM_TOFWARE/',{'target_channels' : target_channels, 'diagnostic_channels' : diagnostic_channels})\n",
    "\n",
    "DataOpsAPI.reformat_datetime_column('ACSM_TOFWARE/ACSM_JFJ_2024_JantoFeb_timeseries.txt/data_table','t_start_Buf',src_format='%d.%m.%Y %H:%M:%S.%f')\n",
    "DataOpsAPI.reformat_datetime_column('ACSM_TOFWARE/ACSM_JFJ_2024_JantoFeb_meta.txt/data_table','t_base',src_format='%d.%m.%Y %H:%M:%S')\n",
    "\n",
    "DataOpsAPI.unload_file_obj()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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