{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "# Set up project root directory\n",
    "\n",
    "\n",
    "notebook_dir = os.getcwd()  # Current working directory (assumes running from notebooks/)\n",
    "project_path = os.path.normpath(os.path.join(notebook_dir, \"..\"))  # Move up to project root\n",
    "dima_path = os.path.normpath(os.path.join(project_path, \"dima\"))  # Move up to project root\n",
    "\n",
    "if project_path not in sys.path:  # Avoid duplicate entries\n",
    "    sys.path.append(project_path)\n",
    "if dima_path not in sys.path:\n",
    "    print(dima_path, ':)')\n",
    "    #sys.path.insert(0,dima_path)\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.visualization.hdf5_vis as hdf5_vis\n",
    "#import dima.pipelines.data_integration as data_integration\n",
    "#import subprocess\n",
    "\n",
    "import pandas as pd\n",
    "for item in sys.path:\n",
    "    print(item)\n",
    "\n",
    "CAMPAIGN_DATA_FILE = \"../data/collection_JFJ_2024_2025-03-14_2025-03-14.h5\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dima.src.hdf5_ops as dataOps\n",
    "\n",
    "\n",
    "path_to_data_file = CAMPAIGN_DATA_FILE\n",
    "dataManager = dataOps.HDF5DataOpsManager(path_to_data_file)\n",
    "\n",
    "dataManager.load_file_obj()\n",
    "dataManager.extract_and_load_dataset_metadata()\n",
    "dataset_metadata_df = dataManager.dataset_metadata_df\n",
    "\n",
    "print(dataset_metadata_df.head(n=15))\n",
    "dataManager.unload_file_obj()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataManager.load_file_obj()\n",
    "# Specify diagnostic variables and the associated flags \n",
    "dataset_name = 'ACSM_TOFWARE/2024/ACSM_JFJ_2024_meta.txt/data_table'\n",
    "flags_dataset_name = 'ACSM_TOFWARE_flags/2024/ACSM_JFJ_2024_meta_flags.csv/data_table'\n",
    "b''.decode()\n",
    "dataset_df = dataManager.extract_dataset_as_dataframe(dataset_name)\n",
    "flags_df = dataManager.extract_dataset_as_dataframe(flags_dataset_name)\n",
    "flags_df['t_base'] = pd.to_datetime(flags_df['t_base'].apply(lambda x : x.decode(encoding=\"utf-8\")))\n",
    "\n",
    "dataManager.unload_file_obj()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pipelines.steps.visualize_datatable_vars as vis\n",
    "\n",
    "\n",
    "\n",
    "dataset_name = 'ACSM_TOFWARE/2024/ACSM_JFJ_2024_meta.txt/data_table'\n",
    "flags_dataset_name = 'ACSM_TOFWARE_flags/2024/ACSM_JFJ_2024_meta_flags.csv/data_table'\n",
    "\n",
    "diagnostic_variables = ['VaporizerTemp_C', 'HeaterBias_V', 'FlowRefWave', 'FlowRate_mb', 'FlowRate_ccs', 'FilamentEmission_mA', 'Detector_V',\n",
    "                        'AnalogInput06_V', 'ABRefWave', 'ABsamp', 'ABCorrFact']\n",
    "fig, ax = vis.visualize_table_variables(path_to_data_file, \n",
    "                                        dataset_name, \n",
    "                                        flags_dataset_name,\n",
    "                                        x_var  = 't_base',\n",
    "                                        y_vars = diagnostic_variables)\n",
    "\n",
    "\n"
   ]
  }
 ],
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