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acsm-fairifier/notebooks/demo_data_integration.ipynb

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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": [
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"name": "stdout",
"output_type": "stream",
"text": [
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\python311.zip\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\DLLs\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\Lib\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\n",
"\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\Lib\\site-packages\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\Lib\\site-packages\\win32\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\Lib\\site-packages\\win32\\lib\n",
"c:\\Users\\florez_j\\.conda\\envs\\dash_multi_chem_env\\Lib\\site-packages\\Pythonwin\n",
"c:\\Users\\florez_j\\Documents\\GitLab\\acsmnode\n",
"c:\\Users\\florez_j\\Documents\\GitLab\\acsmnode\\dima\n"
]
}
],
"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",
"for item in sys.path:\n",
" print(item)\n",
"\n",
"\n",
"if project_path not in sys.path: # Avoid duplicate entries\n",
" sys.path.append(project_path)\n",
" print(project_path)\n",
"if dima_path not in sys.path:\n",
" sys.path.insert(0,dima_path)\n",
" print(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",
"\n",
"\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/input_files/data_integr_config_file_TBR.yaml' \n",
"yaml_config_file_path ='../campaignDescriptor.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": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[Start] Data integration :\n",
"Source: ..\\data\\collection_JFJ_2024_2025-03-14_2025-03-14\n",
"Destination: ..\\data\\collection_JFJ_2024_2025-03-14_2025-03-14.h5\n",
"\n",
"Starting data transfer from instFolder: /ACSM_TOFWARE/2024\n",
"Completed transfer for //ACSM_TOFWARE/2024/ACSM_JFJ_2024_meta.txt\n",
"Completed transfer for //ACSM_TOFWARE/2024/ACSM_JFJ_2024_timeseries.txt\n",
"Completed transfer for //ACSM_TOFWARE/2024/Org_data_valid.csv\n",
"Completed transfer for //ACSM_TOFWARE/2024/Org_err_valid.csv\n",
"Completed transfer for //ACSM_TOFWARE/2024/Org_mz_valid.csv\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\florez_j\\Documents\\GitLab\\acsmnode\\dima\\instruments\\readers\\acsm_tofware_reader.py:112: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" df = pd.read_csv(tmp_filename,\n",
"c:\\Users\\florez_j\\Documents\\GitLab\\acsmnode\\dima\\instruments\\readers\\acsm_tofware_reader.py:112: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" df = pd.read_csv(tmp_filename,\n"
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"name": "stdout",
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"text": [
"Completed transfer for //ACSM_TOFWARE/2024/Org_time_valid.csv\n",
"[====================================================================================================] 100.0% ...\n",
"Completed data transfer for instFolder: /ACSM_TOFWARE/2024\n",
"[End] Data integration\n"
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"source": [
"\n",
"hdf5_file_path = data_integration.run_pipeline(yaml_config_file_path)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"data": {
"text/plain": [
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},
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"cell_type": "markdown",
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"\n",
"* Excecute Cell. A visual representation in html format of the integrated file should be displayed and stored in the output directory folder"
]
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"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"width": 800
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"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": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "code",
"execution_count": null,
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "dash_multi_chem_env",
"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": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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