Update Jupyter notebooks and dependencies

analysis/Dockerfile

-FROM jupyter/base-notebook:python-3.8
+FROM jupyter/base-notebook:python-3.10
 
 COPY . /home/jovyan
 

analysis/README.md

@@ -19,9 +19,8 @@ In general, the Theodolite Analysis Jupyter notebooks should be runnable by any
 we provide introductions for running notebooks with Docker and with Visual Studio Code. These intoduction may also be
 a good starting point for using another service.
 
-For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is
-required (e.g., in a virtual environment). **Please note that Python 3.9 seems not to be working as not all our
-dependencies are ported to Python 3.9 yet.**
+For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.10 is
+required (e.g., in a virtual environment).
 
 ### Running with Docker
 
@@ -39,7 +38,7 @@ docker run --rm -p 8888:8888 -v "$PWD/../results":/home/jovyan/results -v "$PWD/
 ### Running with Visual Studio Code
 
 The [Visual Studio Code Documentation](https://code.visualstudio.com/docs/python/jupyter-support) shows to run Jupyter
-notebooks with Visual Studio Code. For our notebooks, Python 3.7 or newer is required (e.g., in a virtual environment).
+notebooks with Visual Studio Code. For our notebooks, Python 3.10 or newer is required (e.g., in a virtual environment).
 Moreover, they require some Python libraries, which can be installed by:
 
 ```sh

analysis/lag-trend-graph.ipynb

@@ -46,8 +46,8 @@
     "#input.plot(kind='line',x='timestamp',y='value',color='red')\n",
     "#plt.show()\n",
     "\n",
-    "X = regress.iloc[:, 4].values.reshape(-1, 1)  # values converts it into a numpy array\n",
-    "Y = regress.iloc[:, 3].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
+    "X = regress.iloc[:, 3].values.reshape(-1, 1)  # values converts it into a numpy array\n",
+    "Y = regress.iloc[:, 2].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
     "linear_regressor = LinearRegression()  # create object for the class\n",
     "linear_regressor.fit(X, Y)  # perform linear regression\n",
     "Y_pred = linear_regressor.predict(X)  # make predictions"
@@ -84,8 +84,8 @@
     "#filename = f\"exp{exp_id}_{benchmark}_{dim_value}_{instances}\"\n",
     "\n",
     "\n",
-    "t_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 4].values\n",
-    "y_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 3].values\n",
+    "t_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 3].values\n",
+    "y_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 2].values\n",
     "\n",
     "plt.figure()\n",
     "#plt.figure(figsize=(4, 3))\n",
@@ -109,26 +109,26 @@
   }
  ],
  "metadata": {
+  "file_extension": ".py",
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('.venv': venv)",
+   "name": "python37064bitvenvvenv21b61136d7f443749f2918b47e00d223"
+  },
   "language_info": {
-   "name": "python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
+   "name": "python",
    "version": "3.7.0-final"
   },
-  "orig_nbformat": 2,
-  "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "npconvert_exporter": "python",
+  "orig_nbformat": 2,
   "pygments_lexer": "ipython3",
-  "version": 3,
-  "kernelspec": {
-   "name": "python37064bitvenvvenv21b61136d7f443749f2918b47e00d223",
-   "display_name": "Python 3.7.0 64-bit ('.venv': venv)"
-  }
+  "version": 3
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}

analysis/requirements.txt

-jupyter==1.0.0
-matplotlib==3.2.0
-pandas==1.0.1
+jupyter==1.1.1
+matplotlib==3.10.0
+pandas==2.2.3
 scikit-learn==1.0.2
-numpy==1.23.1
+numpy==1.26.4