Add new demo for acsm pipeline and clearout the other notebooks

notebooks/demo_flaggingapp_manager.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,8 @@
     "\n",
     "import data_flagging_utils as utils\n",
     "\n",
-    "DataOpsObj = utils.FlaggingAppDataManager('data_products/collection_acsm_campaign_NoraN_2024-01-01_2024-02-29.h5')\n",
+    "path_to_file = 'data_products/collection_JFJ_2024_acsm_campaign_NoraN_2024-01-01_2024-02-29.h5'\n",
+    "DataOpsObj = utils.FlaggingAppDataManager(path_to_file)\n",
     "\n"
    ]
   },
@@ -25,14 +26,105 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path_to_append_dir = 'data_products/collection_acsm_campaign_NoraN_2024-01-01_2024-02-29'\n",
+    "import sys\n",
+    "import os\n",
+    "import pandas as pd\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",
+    "\n",
+    "\n",
+    "from pipelines.steps.apply_calibration_factors import apply_calibration_factors\n",
+    "\n",
+    "import dima.src.hdf5_ops as dataOps\n",
+    "\n",
+    "file_path = \"data/collection_JFJ_2024_LeilaS_2025-02-03_2025-02-03.h5\"\n",
+    "dataOpsManager = dataOps.HDF5DataOpsManager(file_path)\n",
+    "\n",
+    "\n",
+    "dataOpsManager.load_file_obj()\n",
+    "dataOpsManager.extract_and_load_dataset_metadata()\n",
+    "print(dataOpsManager.dataset_metadata_df.head())\n",
+    "#dataOpsManager.unload_file_obj\n",
+    "\n",
+    "#dataOpsManager.unload_file_obj()\n",
+    "dataset_name = \"ACSM_TOFWARE/2024/ACSM_JFJ_2024_timeseries.txt/data_table\"\n",
+    "data_table = dataOpsManager.extract_dataset_as_dataframe(dataset_name)\n",
+    "datetime_var, datetime_format = dataOpsManager.infer_datetime_variable(dataset_name)\n",
+    "\n",
+    "#data_table['t_start_Buf'] = data_table['t_start_Buf'].apply(lambda x : x.decode())\n",
+    "dataOpsManager.unload_file_obj()\n",
+    "\n",
+    "a, b = apply_calibration_factors(data_table, datetime_var,'pipelines/params/calibration_factors.yaml')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "apply_calib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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",
+    "\n",
+    "import dima.src.hdf5_ops as data_ops\n",
+    "\n",
+    "path_to_file = 'data/collection_JFJ_2024_NoraN_2024-01-01_2024-02-29.h5'\n",
+    "DataOpsObj = data_ops.HDF5DataOpsManager(path_to_file)\n",
+    "\n",
+    "DataOpsObj.load_file_obj()\n",
+    "data_table = DataOpsObj.extract_dataset_as_dataframe('/ACSM_TOFWARE/2024/ACSM_JFJ_2024_JantoFeb_timeseries.txt/data_table')\n",
+    "DataOpsObj.unload_file_obj()\n",
+    "\n",
+    "data_table.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_append_dir = 'data_products/collection_JFJ_2024_acsm_campaign_NoraN_2024-01-01_2024-02-29'\n",
     "DataOpsObj.load_file_obj()\n",
     "DataOpsObj.transfer_flags()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +185,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "multiphase_chemistry_env",
+   "display_name": "flaggingapp_env",
    "language": "python",
    "name": "python3"
   },
@@ -107,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,