{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hdf5_lib as h5lib\n",
    "import os\n",
    "\n",
    "# define input file directory\n",
    "\n",
    "input_file_path = './input_files\\\\BeamTimeMetaData.h5'\n",
    "output_dir_path = './output_files'\n",
    "if not os.path.exists(output_dir_path):\n",
    "    os.makedirs(output_dir_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the above specified input_file_path as a dataframe. \n",
    "\n",
    "Since we know this file was created from a Thorsten Table's format, we can use h5lib.read_mtable_as_dataframe() to read it.\n",
    "\n",
    "Then, we rename the 'name' column as 'filename', as this is the column's name use to idenfify files in subsequent functions.\n",
    "Also, we augment the dataframe with a few categorical columns to be used as grouping variables when creating the hdf5 file's group hierarchy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read BeamTimeMetaData.h5, containing Thorsten's Matlab Table\n",
    "input_data_df = h5lib.read_mtable_as_dataframe(input_file_path)\n",
    "\n",
    "# Preprocess Thorsten's input_data dataframe so that i can be used to create a newer .h5 file\n",
    "# under certain grouping specificiations.\n",
    "input_data_df = input_data_df.rename(columns = {'name':'filename'})\n",
    "input_data_df = h5lib.augment_with_filenumber(input_data_df)\n",
    "input_data_df = h5lib.augment_with_filetype(input_data_df)\n",
    "input_data_df = h5lib.split_sample_col_into_sample_and_data_quality_cols(input_data_df)\n",
    "input_data_df['lastModifiedDatestr'] = input_data_df['lastModifiedDatestr'].astype('datetime64[s]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a hdf5 file with a 3-level group hierarchy based on the input_data and three grouping functions. Then\n",
    "we visualize the group hierarchy of the created file as a treemap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define grouping functions to be passed into create_hdf5_file function. These can also be set\n",
    "# as strings refering to categorical columns in input_data_df.\n",
    "\n",
    "test_grouping_funcs = True\n",
    "if test_grouping_funcs:\n",
    "    group_by_sample = lambda x : h5lib.group_by_df_column(x,'sample')\n",
    "    group_by_type = lambda x : h5lib.group_by_df_column(x,'filetype')\n",
    "    group_by_filenumber = lambda x : h5lib.group_by_df_column(x,'filenumber')\n",
    "else:\n",
    "    group_by_sample = 'sample'\n",
    "    group_by_type = 'filetype'\n",
    "    group_by_filenumber = 'filenumber'\n",
    "\n",
    "output_filename = 'test.h5'\n",
    "\n",
    "ofilepath = os.path.join(output_dir_path,output_filename)\n",
    "\n",
    "h5lib.create_hdf5_file(ofilepath,\n",
    "                       input_data_df, 'top-down', \n",
    "                       group_by_funcs = [group_by_sample, group_by_type, group_by_filenumber]\n",
    "                       )\n",
    "\n",
    "annotation_dict = {'Campaign name': 'SLS-Campaign-2023',\n",
    "                    'Users':'Thorsten, Luca, Zoe',\n",
    "                    'Startdate': str(input_data_df['lastModifiedDatestr'].min()),\n",
    "                    'Enddate': str(input_data_df['lastModifiedDatestr'].max())\n",
    "                    }\n",
    "h5lib.annotate_root_dir(ofilepath,annotation_dict)\n",
    "\n",
    "h5lib.display_group_hierarchy_on_a_treemap(ofilepath)\n",
    "\n",
    "print(':)')\n"
   ]
  }
 ],
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