Merge branch 'main' of https://github.com/peak-solution/data_management_learning_path

Author: StefanRR

data_management_learning_path/ods/compare_measurement.ipynb

@@ -269,7 +269,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -283,7 +283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.6"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

data_management_learning_path/ods/crash_analysis.ipynb

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,8 +48,7 @@
     "import scipy.signal as signal\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from odsbox.con_i import ConI\n",
-    "from odsbox.submatrix_to_pandas import submatrix_to_pandas\n"
+    "from odsbox.con_i import ConI\n"
    ]
   },
   {
@@ -134,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,10 +155,7 @@
     "submatrix_id = sms[\"SubMatrix.Id\"].iloc[0]\n",
     "\n",
     "# We need the data as a pandas DataFrame\n",
-    "df = submatrix_to_pandas(con_i, submatrix_id)\n",
-    "\n",
-    "# The time channel is the index of the DataFrame\n",
-    "df.set_index(\"00TIRS000001TI00\", inplace=True)\n",
+    "df =  con_i.bulk.data_read(submatrix_id)\n",
     "\n",
     "# Extract the head acceleration channels\n",
     "a_x = df[\"24HEADCG0000ACXP\"];\n",

data_management_learning_path/ods/kaggl_electric_motor_temperature_example.ipynb

@@ -44,8 +44,7 @@
     "except:\n",
     "  !pip install odsbox \n",
     "  \n",
-    "from odsbox.con_i import ConI\n",
-    "from odsbox.submatrix_to_pandas import submatrix_to_pandas"
+    "from odsbox.con_i import ConI"
    ]
   },
   {
@@ -218,7 +217,7 @@
     "\n",
     "Until now, we haven't loaded any mass data, yet. \n",
     "\n",
-    "In our data model, the individual profiles are not contained in a big single dataset. They are stored profile by profile in so called submatrices (we looked at them before a bit). So we go and fetch the first profile returned as DataFrame by using `submatrix_to_pandas`. "
+    "In our data model, the individual profiles are not contained in a big single dataset. They are stored profile by profile in so called submatrices (we looked at them before a bit). So we go and fetch the first profile returned as DataFrame by using ` con_i.bulk.data_read(...)`. "
    ]
   },
   {
@@ -560,11 +559,8 @@
     "\n",
     "print(profile_name)\n",
     "\n",
-    "dm_df = submatrix_to_pandas(con_i, dm_id)\n",
-    "if \"Time\" in dm_df.columns:\n",
-    "    dm_df.set_index(\"Time\", inplace=True)\n",
-    "elif \"time\" in dm_df.columns:\n",
-    "    dm_df.set_index(\"time\", inplace=True)\n",
+    "dm_df =  con_i.bulk.data_read(dm_id)\n",
+    "\n",
     "dm_df.head()"
    ]
   },
@@ -636,11 +632,7 @@
     }
    ],
    "source": [
-    "dm_df = submatrix_to_pandas(con_i, dm_id)\n",
-    "if \"Time\" in dm_df.columns:\n",
-    "    dm_df.set_index(\"Time\", inplace=True)\n",
-    "elif \"time\" in dm_df.columns:\n",
-    "    dm_df.set_index(\"time\", inplace=True)\n",
+    "dm_df = con_i.bulk.data_read(dm_id)\n",
     "\n",
     "coi = target_features\n",
     "fig, ax = plt.subplots()\n",
@@ -797,7 +789,7 @@
     "\n",
     "\n",
     "# normalize\n",
-    "dm_df = submatrix_to_pandas(con_i, dm_id)\n",
+    "dm_df = con_i.bulk.data_read(dm_id, set_independent_as_index=False)\n",
     "\n",
     "dm_df = dm_df / dm_df.abs().max(axis=0)\n",
     "transformed = PCA().fit_transform(dm_df.drop(target_features, axis=1))\n",
@@ -861,7 +853,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -875,7 +867,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.6"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

data_management_learning_path/ods/load_measurement.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,8 +42,7 @@
     "except:\n",
     "  !pip install odsbox \n",
     "\n",
-    "from odsbox.con_i import ConI\n",
-    "from odsbox.submatrix_to_pandas import submatrix_to_pandas"
+    "from odsbox.con_i import ConI"
    ]
   },
   {
@@ -142,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -307,12 +306,7 @@
     }
    ],
    "source": [
-    "df = submatrix_to_pandas(con_i, submatrix_id)\n",
-    "\n",
-    "if \"Time\" in df.columns:\n",
-    "    df.set_index(\"Time\", inplace=True)\n",
-    "elif \"time\" in df.columns:\n",
-    "    df.set_index(\"time\", inplace=True)\n",
+    "df =  con_i.bulk.data_read(submatrix_id)\n",
     "\n",
     "df.head()"
    ]

data_management_learning_path/ods/playground.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -62,7 +62,6 @@
     "  !pip install -U odsbox\n",
     "\n",
     "from odsbox.con_i import ConI\n",
-    "from odsbox.submatrix_to_pandas import submatrix_to_pandas\n",
     "\n",
     "con_i = ConI(url='https://docker.peak-solution.de:10032/api', auth=('Demo','mdm'))"
    ]
@@ -81,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -256,12 +255,7 @@
     ")\n",
     "\n",
     "# get the bulk data of first submatrix\n",
-    "df = submatrix_to_pandas(con_i, submatrices.iloc[0,1])\n",
-    "\n",
-    "if \"Time\" in df.columns:\n",
-    "    df.set_index(\"Time\", inplace=True)\n",
-    "elif \"time\" in df.columns:\n",
-    "    df.set_index(\"time\", inplace=True)\n",
+    "df =  con_i.bulk.data_read(submatrices.iloc[0,1])\n",
     "\n",
     "df.head()"
    ]

data_management_learning_path/ods/powerbi.ipynb

@@ -47,13 +47,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
     "from odsbox.con_i import ConI\n",
-    "from odsbox.submatrix_to_pandas import submatrix_to_pandas\n",
     "\n",
     "with ConI(url='https://docker.peak-solution.de:10032/api', auth=('Demo','mdm'), verify_certificate=True) as con_i:\n",
     "    # find submatrix (bulk root) of a certain measurement\n",
@@ -66,7 +65,7 @@
     "    )\n",
     "\n",
     "    # get the bulk data of first submatrix\n",
-    "    df = submatrix_to_pandas(con_i, submatrices.iloc[0,1])\n"
+    "    df =  con_i.bulk.data_read(submatrices.iloc[0,1])\n"
    ]
   },
   {
@@ -406,12 +405,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from odsbox.con_i import ConI\n",
-    "from odsbox.submatrix_to_pandas import submatrix_to_pandas\n",
     "\n",
     "with ConI(url='https://docker.peak-solution.de:10032/api', auth=('Demo','mdm'), verify_certificate=True) as con_i:\n",
     "\n",

data_management_learning_path/overview.ipynb

@@ -42,6 +42,9 @@
     "* [📓 Using Notebooks](/notebooking/overview.md)\n",
     "<br>*Data Management Learning Path* is based on [Jupyter Notebooks](https://jupyter.org/) such that you can interact and explore the content \"live\". You can find some hints and ideas how you can use **Notebooks** in this chapter.\n",
     "\n",
+    "* [🔎 PeakTDM FileFocus](https://github.com/peak-solution/filefocus?tab=readme-ov-file#readme)\n",
+    "<br>*PeakTDM FileFocus* is an out‑of‑the‑box product experience for ASAM ODS and EXD‑API ecosystem, featuring integrated file indexing and AI‑ready data transformation — ideal for ODSBox, EXD‑API plugins, and the Data Management Learning Path\n",
+    "\n",
     "### Big Data Connectors ###\n",
     "\n",
     "* [📓 Apache Spark](/spark/overview.ipynb)\n",