{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":102335,"databundleVersionId":12518947,"sourceType":"competition"}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## **🔴 import**","metadata":{}},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:02:32.874587Z","iopub.execute_input":"2025-07-31T04:02:32.875135Z","iopub.status.idle":"2025-07-31T04:02:34.325492Z","shell.execute_reply.started":"2025-07-31T04:02:32.875093Z","shell.execute_reply":"2025-07-31T04:02:34.324471Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **🔴 EDA**","metadata":{}},{"cell_type":"markdown","source":"### **🟠 Load the datasets**","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv(\"/kaggle/input/cmi-detect-behavior-with-sensor-data/train.csv\")\ntrain_dem_df = pd.read_csv(\"/kaggle/input/cmi-detect-behavior-with-sensor-data/train_demographics.csv\")\ntest_df = pd.read_csv(\"/kaggle/input/cmi-detect-behavior-with-sensor-data/test.csv\")\ntest_dem_df = pd.read_csv(\"/kaggle/input/cmi-detect-behavior-with-sensor-data/test_demographics.csv\")\nprint(f\"Train rows: {len(train_df):,}  Test rows: {len(test_df)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:02:34.326526Z","iopub.execute_input":"2025-07-31T04:02:34.327009Z","iopub.status.idle":"2025-07-31T04:03:09.668596Z","shell.execute_reply.started":"2025-07-31T04:02:34.326975Z","shell.execute_reply":"2025-07-31T04:03:09.667720Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 Overview of the Demographics**","metadata":{}},{"cell_type":"code","source":"train_dem_df.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.670654Z","iopub.execute_input":"2025-07-31T04:03:09.670900Z","iopub.status.idle":"2025-07-31T04:03:09.708268Z","shell.execute_reply.started":"2025-07-31T04:03:09.670879Z","shell.execute_reply":"2025-07-31T04:03:09.707335Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df.isnull().sum().sum(), train_dem_df.duplicated().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.709201Z","iopub.execute_input":"2025-07-31T04:03:09.709521Z","iopub.status.idle":"2025-07-31T04:03:09.733433Z","shell.execute_reply.started":"2025-07-31T04:03:09.709486Z","shell.execute_reply":"2025-07-31T04:03:09.732625Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.734676Z","iopub.execute_input":"2025-07-31T04:03:09.734908Z","iopub.status.idle":"2025-07-31T04:03:09.769420Z","shell.execute_reply.started":"2025-07-31T04:03:09.734889Z","shell.execute_reply":"2025-07-31T04:03:09.768629Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"These tabular files contain demographic and physical characteristics of the participants.\n\n* subject\n* adult_child: Indicates whether the participant is a child (0) or an adult (1). Adults are defined as individuals aged 18 years or older.\n* age: Participant's age in years at time of data collection.\n* sex: Participants sex assigned at birth, 0= female, 1 = male.\n* handedness: Dominant hand used by the participant, 0 = left-handed, 1 = right-handed.\n* height_cm: Height of the participant in centimeters.\n* shoulder_to_wrist_cm: Distance from shoulder to wrist in centimeters.\n* elbow_to_wrist_cm: Distance from elbow to wrist in centimeters.","metadata":{}},{"cell_type":"code","source":"train_dem_df.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.770345Z","iopub.execute_input":"2025-07-31T04:03:09.770675Z","iopub.status.idle":"2025-07-31T04:03:09.777016Z","shell.execute_reply.started":"2025-07-31T04:03:09.770651Z","shell.execute_reply":"2025-07-31T04:03:09.776092Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('unique subject values:')\nprint(train_dem_df['subject'].unique()), print(f\"number of unique subject values: {len(train_dem_df['subject'].unique())}\")\nprint()\nprint('unique adult_child values:')\nprint(train_dem_df['adult_child'].unique()), print(f\"number of unique adult_child values: {len(train_dem_df['adult_child'].unique())}\")\nprint()\nprint('unique age values:')\nprint(train_dem_df['age'].unique()), print(f\"number of unique age values: {len(train_dem_df['age'].unique())}\")\nprint()\nprint('unique sex values:')\nprint(train_dem_df['sex'].unique()), print(f\"number of unique sex values: {len(train_dem_df['sex'].unique())}\")\nprint()\nprint('unique handedness values:')\nprint(train_dem_df['handedness'].unique()), print(f\"number of unique handedness values: {len(train_dem_df['handedness'].unique())}\")\nprint()\nprint('unique height_cm values:')\nprint(train_dem_df['height_cm'].unique()), print(f\"number of unique height_cm values: {len(train_dem_df['height_cm'].unique())}\")\nprint()\nprint('unique shoulder_to_wrist_cm values:')\nprint(train_dem_df['shoulder_to_wrist_cm'].unique()), print(f\"number of unique shoulder_to_wrist_cm values: {len(train_dem_df['shoulder_to_wrist_cm'].unique())}\")\nprint()\nprint('unique elbow_to_wrist_cm values:')\nprint(train_dem_df['elbow_to_wrist_cm'].unique()), print(f\"number of unique elbow_to_wrist_cm values: {len(train_dem_df['elbow_to_wrist_cm'].unique())}\");","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.778003Z","iopub.execute_input":"2025-07-31T04:03:09.778389Z","iopub.status.idle":"2025-07-31T04:03:09.802907Z","shell.execute_reply.started":"2025-07-31T04:03:09.778360Z","shell.execute_reply":"2025-07-31T04:03:09.802095Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.803815Z","iopub.execute_input":"2025-07-31T04:03:09.804163Z","iopub.status.idle":"2025-07-31T04:03:09.835807Z","shell.execute_reply.started":"2025-07-31T04:03:09.804139Z","shell.execute_reply":"2025-07-31T04:03:09.834815Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cat_cols = ['sex', 'adult_child', 'handedness']\n\nfig, axes = plt.subplots(1, 3, figsize=(12, 4))\naxes = axes.flatten()\n\nfor i, col in enumerate(cat_cols):\n    counts = train_dem_df[col].value_counts().sort_index()\n\n    if col == 'sex':\n        labels = ['female' if i == 0 else 'male' for i in counts.index]\n    elif col == 'adult_child':\n        labels = ['child' if i == 0 else 'adult' for i in counts.index]\n    elif col == 'handedness':\n        labels = ['left-handed' if i == 0 else 'right-handed' for i in counts.index]\n\n    plot_df = counts.reset_index()\n    plot_df.columns = [col, 'count']\n    plot_df[col] = labels\n\n    sns.barplot(data=plot_df, x=col, y='count', palette='Set2', ax=axes[i])\n    axes[i].set_title(f'Count of {col}')\n    axes[i].set_xlabel(col)\n    axes[i].set_ylabel('Count')\n    axes[i].grid(True, axis='y')\n\n# fig.delaxes(axes[3])\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:09.838512Z","iopub.execute_input":"2025-07-31T04:03:09.838758Z","iopub.status.idle":"2025-07-31T04:03:10.549685Z","shell.execute_reply.started":"2025-07-31T04:03:09.838738Z","shell.execute_reply":"2025-07-31T04:03:10.548735Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"numeric_cols = ['height_cm', 'age', 'shoulder_to_wrist_cm', 'elbow_to_wrist_cm']\n\nfig, axes = plt.subplots(2, 2, figsize=(12, 6))\naxes = axes.flatten()\n\nfor i, col in enumerate(numeric_cols):\n    sns.histplot(train_dem_df[col], bins=20, kde=True, color='skyblue', ax=axes[i])\n    axes[i].set_title(f'Distribution of {col}')\n    axes[i].set_ylabel('Frequency')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:10.550476Z","iopub.execute_input":"2025-07-31T04:03:10.550696Z","iopub.status.idle":"2025-07-31T04:03:11.525834Z","shell.execute_reply.started":"2025-07-31T04:03:10.550678Z","shell.execute_reply":"2025-07-31T04:03:11.524993Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\nsns.boxplot(data=train_dem_df, x='sex', y='height_cm', ax=axes[0])\naxes[0].set_title('Height by Sex')\naxes[0].set_xticks([0, 1])\naxes[0].set_xticklabels(['female', 'male'])\n\nsns.boxplot(data=train_dem_df, x='adult_child', y='age', ax=axes[1])\naxes[1].set_title('Age by Adult/Child')\naxes[1].set_xticks([0, 1])\naxes[1].set_xticklabels(['child', 'adult'])\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:11.527002Z","iopub.execute_input":"2025-07-31T04:03:11.527360Z","iopub.status.idle":"2025-07-31T04:03:11.840561Z","shell.execute_reply.started":"2025-07-31T04:03:11.527331Z","shell.execute_reply":"2025-07-31T04:03:11.839452Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ax = sns.scatterplot(data=train_dem_df, x='age', y='height_cm', hue='sex')\nplt.title('Age vs Height')\n\nhandles, labels = ax.get_legend_handles_labels()\nnew_labels = ['female', 'male']\nax.legend(handles=handles, labels=new_labels, title='sex')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:11.841530Z","iopub.execute_input":"2025-07-31T04:03:11.841843Z","iopub.status.idle":"2025-07-31T04:03:12.082946Z","shell.execute_reply.started":"2025-07-31T04:03:11.841811Z","shell.execute_reply":"2025-07-31T04:03:12.081861Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟡 Outliers**","metadata":{}},{"cell_type":"code","source":"numeric_cols = ['height_cm', 'age', 'shoulder_to_wrist_cm', 'elbow_to_wrist_cm']\n\nfig, axes = plt.subplots(2, 2, figsize=(12, 6))\naxes = axes.flatten()\n\nfor i, col in enumerate(numeric_cols):\n    sns.boxplot(x=train_dem_df[col], ax=axes[i])\n    axes[i].set_title(f'Boxplot of {col}')\n    \nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.084327Z","iopub.execute_input":"2025-07-31T04:03:12.084829Z","iopub.status.idle":"2025-07-31T04:03:12.556083Z","shell.execute_reply.started":"2025-07-31T04:03:12.084789Z","shell.execute_reply":"2025-07-31T04:03:12.555114Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df.groupby('sex')['height_cm'].mean()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.557108Z","iopub.execute_input":"2025-07-31T04:03:12.557428Z","iopub.status.idle":"2025-07-31T04:03:12.566586Z","shell.execute_reply.started":"2025-07-31T04:03:12.557406Z","shell.execute_reply":"2025-07-31T04:03:12.565861Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df.groupby('sex')['age'].mean()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.567711Z","iopub.execute_input":"2025-07-31T04:03:12.568062Z","iopub.status.idle":"2025-07-31T04:03:12.585612Z","shell.execute_reply.started":"2025-07-31T04:03:12.568006Z","shell.execute_reply":"2025-07-31T04:03:12.584762Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df.groupby('adult_child')['height_cm'].mean()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.586677Z","iopub.execute_input":"2025-07-31T04:03:12.587716Z","iopub.status.idle":"2025-07-31T04:03:12.603706Z","shell.execute_reply.started":"2025-07-31T04:03:12.587687Z","shell.execute_reply":"2025-07-31T04:03:12.602807Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dem_df.groupby('adult_child')['age'].mean()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.604922Z","iopub.execute_input":"2025-07-31T04:03:12.605283Z","iopub.status.idle":"2025-07-31T04:03:12.627777Z","shell.execute_reply.started":"2025-07-31T04:03:12.605255Z","shell.execute_reply":"2025-07-31T04:03:12.627036Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"corr = train_dem_df.corr(numeric_only=True)\nsns.heatmap(corr, annot=True, cmap='coolwarm')\nplt.title('Correlation Matrix')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.628746Z","iopub.execute_input":"2025-07-31T04:03:12.629013Z","iopub.status.idle":"2025-07-31T04:03:12.962939Z","shell.execute_reply.started":"2025-07-31T04:03:12.628990Z","shell.execute_reply":"2025-07-31T04:03:12.962122Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 Overview of the train.csv**","metadata":{}},{"cell_type":"code","source":"train_df.info()\ntrain_df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:12.964242Z","iopub.execute_input":"2025-07-31T04:03:12.964551Z","iopub.status.idle":"2025-07-31T04:03:21.285650Z","shell.execute_reply.started":"2025-07-31T04:03:12.964524Z","shell.execute_reply":"2025-07-31T04:03:21.284769Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_temp = train_df[['row_id', 'sequence_type', 'sequence_id', 'sequence_counter',\n                       'subject', 'orientation', 'behavior', 'phase', 'gesture']]\ntrain_temp","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:21.286457Z","iopub.execute_input":"2025-07-31T04:03:21.286688Z","iopub.status.idle":"2025-07-31T04:03:21.341247Z","shell.execute_reply.started":"2025-07-31T04:03:21.286670Z","shell.execute_reply":"2025-07-31T04:03:21.340332Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_temp.isna().any().any()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:21.342135Z","iopub.execute_input":"2025-07-31T04:03:21.342384Z","iopub.status.idle":"2025-07-31T04:03:21.571657Z","shell.execute_reply.started":"2025-07-31T04:03:21.342365Z","shell.execute_reply":"2025-07-31T04:03:21.570881Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:21.572617Z","iopub.execute_input":"2025-07-31T04:03:21.572896Z","iopub.status.idle":"2025-07-31T04:03:21.610765Z","shell.execute_reply.started":"2025-07-31T04:03:21.572874Z","shell.execute_reply":"2025-07-31T04:03:21.609605Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"🔰 Columns overview:\n* **row_id**: Unique row identifier.\n\n* **sequence_id**: An ID for the batch of sensor data. Each sequence includes one Transition, one Pause, and one Gesture.\n\n* **sequence_type**: Indicates if the gesture is a target or non-target type. (Train only)\n\n* **sequence_counter**: A counter of the row within each sequence.\n\n* **subject**: Unique identifier for the subject who provided the data.\n\n* **gesture**: The target column. Description of the sequence Gesture. (Train only)\n\n* **orientation**: Description of the subject's orientation during the sequence. (Train only)\n\n* **behavior**: Description of the subject's behavior during the current phase of the sequence.\n\n* **acc_[x/y/z]**: Measures linear acceleration along three axes in meters per second squared from the IMU sensor.\n\n* **rot_[w/x/y/z]**: Orientation data combining information from the IMU's gyroscope, accelerometer, and magnetometer to describe the device's orientation in 3D space.\n\n* **thm_[1-5]**: Five thermopile sensors on the watch recording temperature in degrees Celsius. Each index corresponds to the sensor's position (see Overview tab photo).\n\n* **tof_[1-5]_v[0-63]**: Five time-of-flight sensors on the watch that measure distance.\n\n  * Columns are named like tof_1_v0 for the 0th pixel of the first sensor, up to tof_5_v63 for the 63rd pixel of the fifth sensor.\n  * Data is collected row-wise in an 8×8 grid (top-left to bottom-right).\n  * Each sensor has 64 pixels.\n  * A value of -1 indicates no sensor response (no reflection).\n  * Units are uncalibrated sensor values ranging from 0 to 254.","metadata":{}},{"cell_type":"code","source":"print(train_df.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:21.611841Z","iopub.execute_input":"2025-07-31T04:03:21.612203Z","iopub.status.idle":"2025-07-31T04:03:21.625696Z","shell.execute_reply.started":"2025-07-31T04:03:21.612152Z","shell.execute_reply":"2025-07-31T04:03:21.624739Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('unique row_id values:')\nprint(train_df['row_id'].unique()), print(f\"number of unique row_id values: {len(train_df['row_id'].unique())}\")\nprint()\nprint('unique sequence_type values:')\nprint(train_df['sequence_type'].unique()), print(f\"number of unique sequence_type values: {len(train_df['sequence_type'].unique())}\")\nprint()\nprint('unique sequence_id values:')\nprint(train_df['sequence_id'].unique()), print(f\"number of unique sequence_id values: {len(train_df['sequence_id'].unique())}\")\nprint()\nprint('unique orientation values:')\nprint(train_df['orientation'].unique()), print(f\"number of unique orientation values: {len(train_df['orientation'].unique())}\")\nprint()\nprint('unique behavior values:')\nprint(train_df['behavior'].unique()), print(f\"number of unique behavior values: {len(train_df['behavior'].unique())}\")\nprint()\nprint('unique phase values:')\nprint(train_df['phase'].unique()), print(f\"number of unique phase values: {len(train_df['phase'].unique())}\")\nprint()\nprint('unique gesture values:')\nprint(train_df['gesture'].unique()), print(f\"number of unique gesture values: {len(train_df['gesture'].unique())}\")\nprint()\nprint('unique subject values:')\nprint(train_df['subject'].unique()), print(f\"number of unique subject values: {len(train_df['subject'].unique())}\")\nprint()\nprint('unique sequence_counter values:')\nprint(train_df['sequence_counter'].unique()), print(f\"number of unique sequence_counter values: {len(train_df['sequence_counter'].unique())}\");","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:21.626693Z","iopub.execute_input":"2025-07-31T04:03:21.627077Z","iopub.status.idle":"2025-07-31T04:03:22.461345Z","shell.execute_reply.started":"2025-07-31T04:03:21.627047Z","shell.execute_reply":"2025-07-31T04:03:22.460410Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df_unique_pair = train_df.drop_duplicates(subset=['sequence_id', 'gesture', 'orientation'], keep='first')\ntrain_df_unique_pair","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:22.462204Z","iopub.execute_input":"2025-07-31T04:03:22.462500Z","iopub.status.idle":"2025-07-31T04:03:22.684606Z","shell.execute_reply.started":"2025-07-31T04:03:22.462472Z","shell.execute_reply":"2025-07-31T04:03:22.683725Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\ntrain_df_unique_with_subject = train_df.drop_duplicates(subset=['gesture', 'orientation', 'subject'], keep='first')\ntrain_df_unique_with_subject","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:22.685582Z","iopub.execute_input":"2025-07-31T04:03:22.685893Z","iopub.status.idle":"2025-07-31T04:03:22.894223Z","shell.execute_reply.started":"2025-07-31T04:03:22.685857Z","shell.execute_reply":"2025-07-31T04:03:22.893410Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Number of Unique Sequence_id per Orientation**","metadata":{}},{"cell_type":"code","source":"# Count the number of unique sequence_id values for each orientation\ngesture_to_seq_count = train_df.groupby('orientation')['sequence_id'].nunique().sort_values(ascending=False)\nprint(gesture_to_seq_count)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:22.901621Z","iopub.execute_input":"2025-07-31T04:03:22.901893Z","iopub.status.idle":"2025-07-31T04:03:23.008240Z","shell.execute_reply.started":"2025-07-31T04:03:22.901874Z","shell.execute_reply":"2025-07-31T04:03:23.007105Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Distribution of Sequence Types in Full vs. Unique Datasets**","metadata":{}},{"cell_type":"code","source":"# Count sequence_type in both DataFrames\ntype_counts_full = train_df['sequence_type'].value_counts()\ntype_counts_unique = train_df_unique_pair['sequence_type'].value_counts()\n\n# Create side-by-side bar plots (independent Y axes)\nfig, axes = plt.subplots(1, 2, figsize=(10, 4))  # removed sharey=True\n\n# Plot for full dataset\ntype_counts_full.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[0])\naxes[0].set_title('All Data')\naxes[0].set_xlabel('Sequence Type')\naxes[0].set_ylabel('Count')\naxes[0].set_xticklabels(type_counts_full.index, rotation=0)\n\n# Plot for unique dataset\ntype_counts_unique.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[1])\naxes[1].set_title('Unique Sequence_id-Gesture-Orientation')\naxes[1].set_xlabel('Sequence Type')\naxes[1].set_ylabel('Count')\naxes[1].set_xticklabels(type_counts_unique.index, rotation=0)\n\n# Adjust layout\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:23.009315Z","iopub.execute_input":"2025-07-31T04:03:23.009585Z","iopub.status.idle":"2025-07-31T04:03:23.566061Z","shell.execute_reply.started":"2025-07-31T04:03:23.009563Z","shell.execute_reply":"2025-07-31T04:03:23.565003Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Phase Distribution Across All Data and Unique Data Subsets**","metadata":{}},{"cell_type":"code","source":"# Count sequence_type in both DataFrames\ntype_counts_full = train_df['phase'].value_counts()\ntype_counts_unique = train_df_unique_pair['phase'].value_counts()\ntrain_df_unique_with_phase = train_df.drop_duplicates(subset=['sequence_id', 'gesture', 'orientation', 'phase'], keep='first')\ntype_counts_unique_with_phase = train_df_unique_with_phase['phase'].value_counts()\n\n# Create side-by-side bar plots (independent Y axes)\nfig, axes = plt.subplots(1, 3, figsize=(14, 7))  # removed sharey=True\n\n# Plot for full dataset\ntype_counts_full.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[0])\naxes[0].set_title('All Data')\naxes[0].set_xlabel('Phase')\naxes[0].set_ylabel('Count')\naxes[0].set_xticklabels(type_counts_full.index, rotation=90)\n\n# Plot for unique dataset\ntype_counts_unique.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[1])\naxes[1].set_title('Unique Sequence_id-Gesture-Orientation')\naxes[1].set_xlabel('Phase')\naxes[1].set_ylabel('Count')\naxes[1].set_xticklabels(type_counts_unique.index, rotation=90)\n\n# Plot for unique dataset\ntype_counts_unique_with_phase.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[2])\naxes[2].set_title('Unique Sequence_id-Gesture-Orientation-Phase')\naxes[2].set_xlabel('Phase')\naxes[2].set_ylabel('Count')\naxes[2].set_xticklabels(type_counts_unique_with_phase.index, rotation=90)\n\n# Adjust layout\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:23.567186Z","iopub.execute_input":"2025-07-31T04:03:23.567521Z","iopub.status.idle":"2025-07-31T04:03:24.504699Z","shell.execute_reply.started":"2025-07-31T04:03:23.567490Z","shell.execute_reply":"2025-07-31T04:03:24.503721Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Gesture Distribution Across All Data and Unique Data Subsets**","metadata":{}},{"cell_type":"code","source":"# Count the number of unique sequence_id values for each gesture\nsequence_id_counts = train_df.groupby('gesture')['sequence_id'].nunique().sort_values(ascending=False)\nprint(sequence_id_counts)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:24.505725Z","iopub.execute_input":"2025-07-31T04:03:24.506110Z","iopub.status.idle":"2025-07-31T04:03:24.615409Z","shell.execute_reply.started":"2025-07-31T04:03:24.506080Z","shell.execute_reply":"2025-07-31T04:03:24.614327Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Count sequence_type in both DataFrames\ntype_counts_full = train_df['gesture'].value_counts()\ntype_counts_unique = train_df_unique_pair['gesture'].value_counts()\ntype_counts_unique_with_subject = train_df_unique_with_subject['gesture'].value_counts()\n\n# Create side-by-side bar plots (independent Y axes)\nfig, axes = plt.subplots(1, 3, figsize=(14, 7))  # removed sharey=True\n\n# Plot for full dataset\ntype_counts_full.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[0])\naxes[0].set_title('All Data')\naxes[0].set_xlabel('Gesture')\naxes[0].set_ylabel('Count')\naxes[0].set_xticklabels(type_counts_full.index, rotation=90)\n\n# Plot for unique dataset\ntype_counts_unique.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[1])\naxes[1].set_title('Unique Sequence_id-Gesture-Orientation')\naxes[1].set_xlabel('Gesture')\naxes[1].set_ylabel('Count')\naxes[1].set_xticklabels(type_counts_unique.index, rotation=90)\n\n# Plot for unique dataset\ntype_counts_unique_with_subject.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[2])\naxes[2].set_title('Unique Subject-Gesture-Orientation')\naxes[2].set_xlabel('Gesture')\naxes[2].set_ylabel('Count')\naxes[2].set_xticklabels(type_counts_unique_with_subject.index, rotation=90)\n\n# Adjust layout\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:24.616353Z","iopub.execute_input":"2025-07-31T04:03:24.616612Z","iopub.status.idle":"2025-07-31T04:03:25.432567Z","shell.execute_reply.started":"2025-07-31T04:03:24.616588Z","shell.execute_reply":"2025-07-31T04:03:25.431502Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Behavior Distribution in Full and Unique Sequence Data**","metadata":{}},{"cell_type":"code","source":"# Count sequence_type in both DataFrames\ntype_counts_full = train_df['behavior'].value_counts()\ntype_counts_unique = train_df_unique_pair['behavior'].value_counts()\n\n# Create side-by-side bar plots (independent Y axes)\nfig, axes = plt.subplots(1, 2, figsize=(14, 7))  # removed sharey=True\n\n# Plot for full dataset\ntype_counts_full.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[0])\naxes[0].set_title('All Data')\naxes[0].set_xlabel('Behavior')\naxes[0].set_ylabel('Count')\naxes[0].set_xticklabels(type_counts_full.index, rotation=90)\n\n# Plot for unique dataset\ntype_counts_unique.plot(kind='bar', color=['skyblue', 'salmon'], ax=axes[1])\naxes[1].set_title('Unique Sequence_id-Gesture-Orientation')\naxes[1].set_xlabel('Behavior')\naxes[1].set_ylabel('Count')\naxes[1].set_xticklabels(type_counts_unique.index, rotation=90)\n\n# Adjust layout\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:25.433955Z","iopub.execute_input":"2025-07-31T04:03:25.434372Z","iopub.status.idle":"2025-07-31T04:03:25.860083Z","shell.execute_reply.started":"2025-07-31T04:03:25.434335Z","shell.execute_reply":"2025-07-31T04:03:25.859071Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gesture_counts = train_df.groupby('behavior')['sequence_id'].nunique().sort_values(ascending=False)\n\nplt.figure(figsize=(16, 8))\nsns.barplot(x=gesture_counts.index, y=gesture_counts.values, width=0.5,  color='salmon')\nplt.title('Unique Behavior-Sequence_id')\nplt.xlabel('Behavior')\nplt.ylabel('Count')\nplt.xticks(rotation=90)\nplt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:25.861182Z","iopub.execute_input":"2025-07-31T04:03:25.861452Z","iopub.status.idle":"2025-07-31T04:03:26.186927Z","shell.execute_reply.started":"2025-07-31T04:03:25.861429Z","shell.execute_reply":"2025-07-31T04:03:26.185923Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Gesture Distribution per Subject**","metadata":{}},{"cell_type":"code","source":"gesture_counts = train_df.groupby('subject')['gesture'].nunique().sort_values(ascending=False)\n\nplt.figure(figsize=(16, 6))\nsns.barplot(x=gesture_counts.index, y=gesture_counts.values, width=0.5)\nplt.title('Number of Unique Gesture per Subject')\nplt.xlabel('Subject')\nplt.ylabel('Unique Gesture')\nplt.xticks(rotation=90)\nplt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:26.188019Z","iopub.execute_input":"2025-07-31T04:03:26.188370Z","iopub.status.idle":"2025-07-31T04:03:27.372676Z","shell.execute_reply.started":"2025-07-31T04:03:26.188334Z","shell.execute_reply":"2025-07-31T04:03:27.371397Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gesture_counts = train_df.groupby('subject')['gesture'].nunique()\n\nsubjects_with_less_than_18 = gesture_counts[gesture_counts < 18]\n\nprint(\"Subjects with fewer than 18 unique gestures:\")\nprint(subjects_with_less_than_18)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:27.374126Z","iopub.execute_input":"2025-07-31T04:03:27.375151Z","iopub.status.idle":"2025-07-31T04:03:27.466930Z","shell.execute_reply.started":"2025-07-31T04:03:27.375094Z","shell.execute_reply":"2025-07-31T04:03:27.465891Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"🔍 All 81 subjects performed exactly 18 unique gestures, indicating a well-balanced and consistent dataset across individuals.","metadata":{}},{"cell_type":"markdown","source":"#### **🟡 Number of Unique Orientations Per Subject**","metadata":{}},{"cell_type":"code","source":"gesture_counts = train_df.groupby('subject')['orientation'].nunique().sort_values(ascending=False)\n\nplt.figure(figsize=(16, 5))\nsns.barplot(x=gesture_counts.index, y=gesture_counts.values, width=0.4)\nplt.title('Number of Unique Orientation per Subject')\nplt.xlabel('Subject')\nplt.ylabel('Unique Orientation')\nplt.xticks(rotation=90)\nplt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:27.468330Z","iopub.execute_input":"2025-07-31T04:03:27.468687Z","iopub.status.idle":"2025-07-31T04:03:28.809922Z","shell.execute_reply.started":"2025-07-31T04:03:27.468662Z","shell.execute_reply":"2025-07-31T04:03:28.808942Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df[train_df['subject'] == 'SUBJ_053217']['orientation'].unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:28.811288Z","iopub.execute_input":"2025-07-31T04:03:28.811655Z","iopub.status.idle":"2025-07-31T04:03:28.897599Z","shell.execute_reply.started":"2025-07-31T04:03:28.811624Z","shell.execute_reply":"2025-07-31T04:03:28.896347Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"🔍 All 81 subjects performed under 4 different orientations, except subject SUBJ_053217, who participated in only 2 orientations: \"Seated Straight\" and \"Seated Lean Non Dom - FACE DOWN\".","metadata":{}},{"cell_type":"markdown","source":"#### **🟡 Number of Unique Sequences per Subject**","metadata":{}},{"cell_type":"code","source":"sequence_id_counts = train_df.groupby('subject')['sequence_id'].nunique().sort_values(ascending=False)\nprint(sequence_id_counts)\n\nplt.figure(figsize=(16, 4))\nsns.barplot(x=sequence_id_counts.index, y=sequence_id_counts.values, width=0.5)\nplt.title('Number of Unique sequence_id per Subject')\nplt.xlabel('Subject')\nplt.ylabel('Unique sequence_id Count')\nplt.xticks(rotation=90)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:28.899247Z","iopub.execute_input":"2025-07-31T04:03:28.899675Z","iopub.status.idle":"2025-07-31T04:03:30.396426Z","shell.execute_reply.started":"2025-07-31T04:03:28.899633Z","shell.execute_reply":"2025-07-31T04:03:30.395494Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(train_df[train_df['subject'] == 'SUBJ_036405']['gesture'].unique())\nprint()\nprint(train_df[train_df['subject'] == 'SUBJ_036405']['orientation'].unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:30.397534Z","iopub.execute_input":"2025-07-31T04:03:30.397832Z","iopub.status.idle":"2025-07-31T04:03:30.515777Z","shell.execute_reply.started":"2025-07-31T04:03:30.397798Z","shell.execute_reply":"2025-07-31T04:03:30.514626Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"4*18","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:30.516790Z","iopub.execute_input":"2025-07-31T04:03:30.517082Z","iopub.status.idle":"2025-07-31T04:03:30.523231Z","shell.execute_reply.started":"2025-07-31T04:03:30.517056Z","shell.execute_reply":"2025-07-31T04:03:30.522053Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Keep only one row per sequence_id (e.g., the first occurrence)\nunique_sequences_df = train_df.drop_duplicates(subset='sequence_id')\n\n# Filter for the two specific subjects\nsubset_df = unique_sequences_df[unique_sequences_df['subject'].isin(['SUBJ_053217', 'SUBJ_036405'])]\n\n# Count the number of times each gesture was performed by each subject\ngesture_counts = subset_df.groupby(['gesture', 'subject']).size().unstack(fill_value=0)\n\nprint(gesture_counts)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:30.524530Z","iopub.execute_input":"2025-07-31T04:03:30.524806Z","iopub.status.idle":"2025-07-31T04:03:30.634507Z","shell.execute_reply.started":"2025-07-31T04:03:30.524781Z","shell.execute_reply":"2025-07-31T04:03:30.633349Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Keep only one row per sequence_id (e.g., the first occurrence)\nunique_sequences_df = train_df.drop_duplicates(subset='sequence_id')\n\n# Filter for the two specific subjects\nsubset_df = unique_sequences_df[unique_sequences_df['subject'].isin(['SUBJ_053217', 'SUBJ_036405', 'SUBJ_011323', 'SUBJ_016552'])]\n\n# Count the number of times each orientation was performed by each subject\norientation_counts = subset_df.groupby(['orientation', 'subject']).size().unstack(fill_value=0)\n\nprint(orientation_counts)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:30.635710Z","iopub.execute_input":"2025-07-31T04:03:30.636130Z","iopub.status.idle":"2025-07-31T04:03:30.739375Z","shell.execute_reply.started":"2025-07-31T04:03:30.636095Z","shell.execute_reply":"2025-07-31T04:03:30.738422Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Step 1: Unique sequences only (one per sequence_id)\nunique_sequences_df = train_df.drop_duplicates(subset='sequence_id')\n\n# Step 2: Subjects to check\nsubjects_to_check = ['SUBJ_016552', 'SUBJ_038023', 'SUBJ_053217', 'SUBJ_036405']\n\n# Step 3: All possible gesture–orientation combinations in the dataset\nall_gestures = train_df['gesture'].unique()\nall_orientations = train_df['orientation'].unique()\n\nfrom itertools import product\n\n# Create full combination set\nfull_combinations = set(product(all_gestures, all_orientations))\n\n# Step 4: Check missing combinations for each subject\nfor subject in subjects_to_check:\n    subject_df = unique_sequences_df[unique_sequences_df['subject'] == subject]\n    subject_combinations = set(zip(subject_df['gesture'], subject_df['orientation']))\n\n    missing = full_combinations - subject_combinations\n\n    print(f\"\\n🔍 Missing gesture-orientation combinations for {subject} ({len(missing)} missing):\")\n    for gesture, orientation in sorted(missing):\n        print(f\" - {gesture} / {orientation}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:30.740639Z","iopub.execute_input":"2025-07-31T04:03:30.741001Z","iopub.status.idle":"2025-07-31T04:03:30.908285Z","shell.execute_reply.started":"2025-07-31T04:03:30.740967Z","shell.execute_reply":"2025-07-31T04:03:30.907127Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"⚠️ While each subject can potentially have 72 unique gesture–orientation combinations (18 gestures × 4 orientations), there are 21 gesture–orientation pairs that are naturally unlikely or impossible (e.g., \"Drink from bottle/cup\" while \"Lie on Back\"). Therefore, most subjects have around 51 valid combinations, which reflects realistic activity patterns rather than missing data.\n\nHowever, some subjects show notably fewer valid combinations:\n\n * SUBJ_016552 with 23 missing\n\n * SUBJ_038023 with 24 missing\n\n * SUBJ_053217 with 45 missing\n\nThis variability suggests differences in how subjects performed or recorded certain gestures and orientations.\n\n","metadata":{}},{"cell_type":"markdown","source":"#### **🟡 Histogram of Unique sequence_counter Counts within Each sequence_id**","metadata":{}},{"cell_type":"code","source":"# dedup_df = train_df.drop_duplicates(subset=['sequence_id', 'sequence_counter'])\n\ncounter_dist = train_df.groupby('sequence_id')['sequence_counter'].nunique()\n\nsns.histplot(counter_dist, bins=50, color='steelblue')\nplt.title('Histogram of Number of sequence_counter per sequence_id')\nplt.xlabel('Number of sequence_counter')\nplt.ylabel('Frequency')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:30.909492Z","iopub.execute_input":"2025-07-31T04:03:30.910645Z","iopub.status.idle":"2025-07-31T04:03:31.301716Z","shell.execute_reply.started":"2025-07-31T04:03:30.910615Z","shell.execute_reply":"2025-07-31T04:03:31.300734Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **🟡 Number of Unique sequence_counter Values per Phase**","metadata":{}},{"cell_type":"code","source":"sequence_counter_counts = train_df.groupby('phase')['sequence_counter'].nunique().sort_values(ascending=False)\n\nsns.barplot(x=sequence_counter_counts.index, y=sequence_counter_counts.values, width=0.5)\nplt.title('Number of Unique sequence_counter per Phase')\nplt.xlabel('Phase')\nplt.ylabel('Unique sequence_counter')\nplt.xticks(rotation=0)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:31.302904Z","iopub.execute_input":"2025-07-31T04:03:31.303302Z","iopub.status.idle":"2025-07-31T04:03:31.534079Z","shell.execute_reply.started":"2025-07-31T04:03:31.303263Z","shell.execute_reply":"2025-07-31T04:03:31.532832Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 IMU & THM & TOF Sensor**","metadata":{}},{"cell_type":"code","source":"print(train_df.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:31.535532Z","iopub.execute_input":"2025-07-31T04:03:31.535971Z","iopub.status.idle":"2025-07-31T04:03:31.543173Z","shell.execute_reply.started":"2025-07-31T04:03:31.535931Z","shell.execute_reply":"2025-07-31T04:03:31.541885Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"columns_to_drop = ['row_id', 'sequence_type', 'sequence_id', 'sequence_counter',\n                   'subject', 'orientation', 'behavior', 'phase', 'gesture']\n\ntrain_temp = train_df.drop(columns=columns_to_drop)\ntrain_temp","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:31.544239Z","iopub.execute_input":"2025-07-31T04:03:31.544985Z","iopub.status.idle":"2025-07-31T04:03:32.346485Z","shell.execute_reply.started":"2025-07-31T04:03:31.544960Z","shell.execute_reply":"2025-07-31T04:03:32.345495Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_temp.isna().any().any()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:32.347509Z","iopub.execute_input":"2025-07-31T04:03:32.347781Z","iopub.status.idle":"2025-07-31T04:03:32.855184Z","shell.execute_reply.started":"2025-07-31T04:03:32.347748Z","shell.execute_reply":"2025-07-31T04:03:32.854013Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_temp.isna().sum().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:32.856337Z","iopub.execute_input":"2025-07-31T04:03:32.856706Z","iopub.status.idle":"2025-07-31T04:03:33.485367Z","shell.execute_reply.started":"2025-07-31T04:03:32.856674Z","shell.execute_reply":"2025-07-31T04:03:33.484389Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **⚪ IMU_Accelerometer**","metadata":{}},{"cell_type":"code","source":"train_acc = train_df[['acc_x', 'acc_y', 'acc_z']]\ntrain_acc","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:33.486472Z","iopub.execute_input":"2025-07-31T04:03:33.486731Z","iopub.status.idle":"2025-07-31T04:03:33.502436Z","shell.execute_reply.started":"2025-07-31T04:03:33.486710Z","shell.execute_reply":"2025-07-31T04:03:33.501249Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_acc.isna().any().any()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:33.503431Z","iopub.execute_input":"2025-07-31T04:03:33.503747Z","iopub.status.idle":"2025-07-31T04:03:33.520258Z","shell.execute_reply.started":"2025-07-31T04:03:33.503719Z","shell.execute_reply":"2025-07-31T04:03:33.519153Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Choose a random sequence\nrandom_seq_id = np.random.choice(train_df['sequence_id'].unique())\ntrain_temp = train_df[train_df['sequence_id'] == random_seq_id].copy()\ntrain_temp = train_temp.sort_values('sequence_counter').reset_index(drop=True)\n# Get handedness\nhandedness = train_dem_df[train_dem_df['subject']== train_temp['subject'].iloc[0]]['handedness'].iloc[0]\nlabel_handedness = 'Left-handed' if handedness == 0 else 'Right-handed'\n\n# Acceleration Magnitude\ntrain_temp['acc_mag'] = np.sqrt(train_temp['acc_x']**2 + train_temp['acc_y']**2 + train_temp['acc_z']**2)\n\nbehavior_colors = {\n    'Relaxes and moves hand to target location': 'lightgray',\n    'Moves hand to target location': 'lightgray',\n    'Hand at target location': 'gold',\n    'Performs gesture': 'lightcoral'\n}\n\nplt.figure(figsize=(15, 7))\nplt.plot(train_temp['sequence_counter'], train_temp['acc_mag'], label='Acceleration Magnitude', color='black', linewidth=2.5)\nplt.plot(train_temp['sequence_counter'], train_temp['acc_x'], label='acc_x', color='blue')\nplt.plot(train_temp['sequence_counter'], train_temp['acc_y'], label='acc_y', color='orange')\nplt.plot(train_temp['sequence_counter'], train_temp['acc_z'], label='acc_z', color='green')\n\n\nbehavior_series = train_temp['behavior']\ncounter_series = train_temp['sequence_counter']\nchange_points = (behavior_series != behavior_series.shift()).cumsum()\ngrouped = list(train_temp.groupby(change_points))\n\nused_labels = set()\nfor i, (_, group) in enumerate(grouped):\n    behavior = group['behavior'].iloc[0]\n    color = behavior_colors.get(behavior, 'white')\n    t0 = group['sequence_counter'].iloc[0]\n\n    if i < len(grouped) - 1:\n        t1 = grouped[i + 1][1]['sequence_counter'].iloc[0]\n    else:\n        t1 = group['sequence_counter'].iloc[-1]\n\n    label_arg = behavior if behavior not in used_labels else None\n    plt.axvspan(t0, t1, color=color, alpha=0.3, label=label_arg)\n    used_labels.add(behavior)\n\nsubject_name = train_temp['subject'].iloc[0]\ngesture_name = train_temp['gesture'].iloc[0]\nseq_type = train_temp['sequence_type'].iloc[0]\n\nplt.title(\nf\"$\\\\bf{{Subject:}}${subject_name}   $\\\\bf{{Sequence:}}${random_seq_id}   $\\\\bf{{Gesture:}}${gesture_name}   $\\\\bf{{Seq-type:}}${seq_type}   $\\\\bf{{Handedness:}}${label_handedness}\"\n)\n# plt.title(\n# f\"Subject: $\\\\bf{{{subject_name}}}$     Sequence: $\\\\bf{{{random_seq_id}}}$     Gesture: $\\\\bf{{{gesture_name}}}$   ({seq_type}) - ({label_handedness})\"\n# )\nplt.xlabel('Time Step')\nplt.ylabel('Acceleration')\nplt.legend(loc='upper right')\nplt.grid(True)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:33.521462Z","iopub.execute_input":"2025-07-31T04:03:33.521741Z","iopub.status.idle":"2025-07-31T04:03:34.101521Z","shell.execute_reply.started":"2025-07-31T04:03:33.521707Z","shell.execute_reply":"2025-07-31T04:03:34.100516Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **⚪ IMU_Orientation**","metadata":{}},{"cell_type":"code","source":"train_rot = train_df[['rot_x', 'rot_y', 'rot_z', 'rot_w']]\ntrain_rot","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:34.103137Z","iopub.execute_input":"2025-07-31T04:03:34.103511Z","iopub.status.idle":"2025-07-31T04:03:34.125278Z","shell.execute_reply.started":"2025-07-31T04:03:34.103478Z","shell.execute_reply":"2025-07-31T04:03:34.124368Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_rot.isna().any().any()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:34.126332Z","iopub.execute_input":"2025-07-31T04:03:34.126668Z","iopub.status.idle":"2025-07-31T04:03:34.139546Z","shell.execute_reply.started":"2025-07-31T04:03:34.126636Z","shell.execute_reply":"2025-07-31T04:03:34.138527Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_rot.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:34.140634Z","iopub.execute_input":"2025-07-31T04:03:34.141002Z","iopub.status.idle":"2025-07-31T04:03:34.163696Z","shell.execute_reply.started":"2025-07-31T04:03:34.140969Z","shell.execute_reply":"2025-07-31T04:03:34.162766Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"random_seq_id = np.random.choice(train_df['sequence_id'].unique())\ntrain_temp = train_df[train_df['sequence_id'] == random_seq_id].copy()\ntrain_temp = train_temp.sort_values('sequence_counter').reset_index(drop=True)\n\n# Label handedness\nhandedness = train_dem_df[train_dem_df['subject']== train_temp['subject'].iloc[0]]['handedness'].iloc[0]\nlabel_handedness = 'Left-handed' if handedness == 0 else 'Right-handed'\n\n# Rotation Angle (rad)\ntrain_temp['rot_w_clipped'] = train_temp['rot_w'].clip(-1, 1)\ntrain_temp['rot_angle'] = 2 * np.arccos(train_temp['rot_w_clipped'])\n\nbehavior_colors = {\n    'Relaxes and moves hand to target location': 'lightgray',\n    'Moves hand to target location': 'lightgray',\n    'Hand at target location': 'gold',\n    'Performs gesture': 'lightcoral'\n}\n\nplt.figure(figsize=(15, 7))\nplt.plot(train_temp['sequence_counter'], train_temp['rot_angle'], label='Rotation Angle (rad)', color='black', linewidth=1.5)\nplt.plot(train_temp['sequence_counter'], train_temp['rot_x'], label='rot_x', color='blue')\nplt.plot(train_temp['sequence_counter'], train_temp['rot_y'], label='rot_y', color='orange')\nplt.plot(train_temp['sequence_counter'], train_temp['rot_z'], label='rot_z', color='green')\nplt.plot(train_temp['sequence_counter'], train_temp['rot_w'], label='rot_w', color='purple')\n\n\nbehavior_series = train_temp['behavior']\ncounter_series = train_temp['sequence_counter']\nchange_points = (behavior_series != behavior_series.shift()).cumsum()\ngrouped = list(train_temp.groupby(change_points))\n\nused_labels = set()\nfor i, (_, group) in enumerate(grouped):\n    behavior = group['behavior'].iloc[0]\n    color = behavior_colors.get(behavior, 'white')\n    t0 = group['sequence_counter'].iloc[0]\n\n    if i < len(grouped) - 1:\n        t1 = grouped[i + 1][1]['sequence_counter'].iloc[0]\n    else:\n        t1 = group['sequence_counter'].iloc[-1]\n\n    label_arg = behavior if behavior not in used_labels else None\n    plt.axvspan(t0, t1, color=color, alpha=0.3, label=label_arg)\n    used_labels.add(behavior)\n\nsubject_name = train_temp['subject'].iloc[0]\ngesture_name = train_temp['gesture'].iloc[0]\nseq_type = train_temp['sequence_type'].iloc[0]\n\nplt.title(\nf\"$\\\\bf{{Subject:}}${subject_name}   $\\\\bf{{Sequence:}}${random_seq_id}   $\\\\bf{{Gesture:}}${gesture_name}   $\\\\bf{{Seq-type:}}${seq_type}   $\\\\bf{{Handedness:}}${label_handedness}\"\n)\n# plt.title(\n# f\"Subject: $\\\\bf{{{subject_name}}}$     Sequence: $\\\\bf{{{random_seq_id}}}$     Gesture: $\\\\bf{{{gesture_name}}}$   ({seq_type}) - ({label_handedness})\"\n# )\nplt.xlabel('Time Step')\nplt.ylabel('Orientation data')\nplt.legend(loc='upper right')\nplt.grid(True)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:34.164935Z","iopub.execute_input":"2025-07-31T04:03:34.165318Z","iopub.status.idle":"2025-07-31T04:03:34.834567Z","shell.execute_reply.started":"2025-07-31T04:03:34.165289Z","shell.execute_reply":"2025-07-31T04:03:34.833501Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from scipy.spatial.transform import Rotation as R\n\nrandom_seq_id = np.random.choice(train_df['sequence_id'].unique())\ntrain_temp = train_df[train_df['sequence_id'] == random_seq_id].copy()\ntrain_temp = train_temp.sort_values('sequence_counter').reset_index(drop=True)\n\n# Label handedness\nhandedness = train_dem_df[train_dem_df['subject']== train_temp['subject'].iloc[0]]['handedness'].iloc[0]\nlabel_handedness = 'Left-handed' if handedness == 0 else 'Right-handed'\n\n# Convert quaternion (rot_x, rot_y, rot_z, rot_w) to Euler angles: roll, pitch, yaw\nquats = train_temp[['rot_x', 'rot_y', 'rot_z', 'rot_w']].values\nr = R.from_quat(quats)  # Format: (x, y, z, w)\neuler_angles = r.as_euler('xyz', degrees=True)  # Convert to degrees: roll (x), pitch (y), yaw (z)\n\n# Add Euler angles to DataFrame\ntrain_temp['roll'] = euler_angles[:, 0]\ntrain_temp['pitch'] = euler_angles[:, 1]\ntrain_temp['yaw'] = euler_angles[:, 2]\n\nbehavior_colors = {\n    'Relaxes and moves hand to target location': 'lightgray',\n    'Moves hand to target location': 'lightgray',\n    'Hand at target location': 'gold',\n    'Performs gesture': 'lightcoral'\n}\n\n# Plot roll, pitch, yaw over time\nplt.figure(figsize=(15, 7))\nplt.plot(train_temp['sequence_counter'], train_temp['roll'], label='Roll (°)', color='red')\nplt.plot(train_temp['sequence_counter'], train_temp['pitch'], label='Pitch (°)', color='blue')\nplt.plot(train_temp['sequence_counter'], train_temp['yaw'], label='Yaw (°)', color='green')\n\nbehavior_series = train_temp['behavior']\ncounter_series = train_temp['sequence_counter']\nchange_points = (behavior_series != behavior_series.shift()).cumsum()\ngrouped = list(train_temp.groupby(change_points))\n\nused_labels = set()\nfor i, (_, group) in enumerate(grouped):\n    behavior = group['behavior'].iloc[0]\n    color = behavior_colors.get(behavior, 'white')\n    t0 = group['sequence_counter'].iloc[0]\n\n    if i < len(grouped) - 1:\n        t1 = grouped[i + 1][1]['sequence_counter'].iloc[0]\n    else:\n        t1 = group['sequence_counter'].iloc[-1]\n\n    label_arg = behavior if behavior not in used_labels else None\n    plt.axvspan(t0, t1, color=color, alpha=0.3, label=label_arg)\n    used_labels.add(behavior)\n\nsubject_name = train_temp['subject'].iloc[0]\ngesture_name = train_temp['gesture'].iloc[0]\nseq_type = train_temp['sequence_type'].iloc[0]\n\nplt.title(\nf\"$\\\\bf{{Subject:}}${subject_name}   $\\\\bf{{Sequence:}}${random_seq_id}   $\\\\bf{{Gesture:}}${gesture_name}   $\\\\bf{{Seq-type:}}${seq_type}   $\\\\bf{{Handedness:}}${label_handedness}\"\n)\nplt.xlabel('Time Step')\nplt.ylabel('Orientation data')\nplt.legend(loc='upper right')\nplt.grid(True)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:34.835656Z","iopub.execute_input":"2025-07-31T04:03:34.835978Z","iopub.status.idle":"2025-07-31T04:03:35.533709Z","shell.execute_reply.started":"2025-07-31T04:03:34.835944Z","shell.execute_reply":"2025-07-31T04:03:35.532452Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"🔄 Difference Between Roll/Pitch/Yaw and Quaternion Representations\n🎯 Roll, Pitch, and Yaw are Euler angles used to describe the orientation of an object in 3D space with sequential rotations:\n\n🔁 Roll → rotation around the x-axis\n\n🔃 Pitch → rotation around the y-axis\n\n🔄 Yaw → rotation around the z-axis\n\nThey are intuitive and widely used in fields like 🛫 aerospace, 🤖 robotics, and 🎮 animation.\n⚠️ However, Euler angles suffer from gimbal lock, where the loss of one rotational degree of freedom can occur due to axis alignment.\n\n🧮 Quaternions, on the other hand, use four values (x, y, z, w) to represent orientation as a rotation around an arbitrary axis in 3D space:\n\n✨ No gimbal lock\n\n⚡ More efficient for calculations\n\n🔁 Smooth interpolation (e.g., SLERP)\n\n👉 While quaternions are ideal for computation and internal systems, Euler angles like roll/pitch/yaw are preferred for visualization and interpretation.\n📊 In this plot, we converted quaternions to roll, pitch, and yaw for a clearer understanding of how rotation evolves over time.","metadata":{}},{"cell_type":"markdown","source":"#### **⚪ Thermopile**","metadata":{}},{"cell_type":"code","source":"train_thm = train_df[['thm_1', 'thm_2', 'thm_3', 'thm_4', 'thm_5']]\ntrain_thm","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:35.534763Z","iopub.execute_input":"2025-07-31T04:03:35.535213Z","iopub.status.idle":"2025-07-31T04:03:35.559867Z","shell.execute_reply.started":"2025-07-31T04:03:35.535186Z","shell.execute_reply":"2025-07-31T04:03:35.558989Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_thm.isna().any().any()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:35.561073Z","iopub.execute_input":"2025-07-31T04:03:35.561384Z","iopub.status.idle":"2025-07-31T04:03:35.575213Z","shell.execute_reply.started":"2025-07-31T04:03:35.561351Z","shell.execute_reply":"2025-07-31T04:03:35.574115Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_thm.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:35.576148Z","iopub.execute_input":"2025-07-31T04:03:35.576487Z","iopub.status.idle":"2025-07-31T04:03:35.596586Z","shell.execute_reply.started":"2025-07-31T04:03:35.576452Z","shell.execute_reply":"2025-07-31T04:03:35.595820Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"random_seq_id = np.random.choice(train_df['sequence_id'].unique())\ntrain_temp = train_df[train_df['sequence_id'] == random_seq_id].copy()\nhandedness = train_dem_df[train_dem_df['subject']== train_temp['subject'].iloc[0]]['handedness'].iloc[0]\nlabel_handedness = 'Left-handed' if handedness == 0 else 'Right-handed'\n# train_temp = train_temp.sort_values('sequence_counter').reset_index(drop=True)\n\n# Gather thermopile columns and compute their per-timestamp mean\nthm_cols = ['thm_1', 'thm_2', 'thm_3', 'thm_4', 'thm_5']\ntrain_temp['thm_mean'] = train_temp[thm_cols].mean(axis=1)\n\nbehavior_colors = {\n    'Relaxes and moves hand to target location': 'lightgray',\n    'Moves hand to target location': 'lightgray',\n    'Hand at target location': 'gold',\n    'Performs gesture': 'lightcoral'\n}\n\nplt.figure(figsize=(15, 7))\nplt.plot(train_temp['sequence_counter'], train_temp['thm_mean'], label='Average Temperature', color='black', linewidth=2.5)\nplt.plot(train_temp['sequence_counter'], train_temp['thm_1'], label='thm_1', color='blue')\nplt.plot(train_temp['sequence_counter'], train_temp['thm_2'], label='thm_2', color='orange')\nplt.plot(train_temp['sequence_counter'], train_temp['thm_3'], label='thm_3', color='green')\nplt.plot(train_temp['sequence_counter'], train_temp['thm_4'], label='thm_4', color='purple')\nplt.plot(train_temp['sequence_counter'], train_temp['thm_5'], label='thm_5', color='red')\n\n\nbehavior_series = train_temp['behavior']\ncounter_series = train_temp['sequence_counter']\nchange_points = (behavior_series != behavior_series.shift()).cumsum()\ngrouped = list(train_temp.groupby(change_points))\n\nused_labels = set()\nfor i, (_, group) in enumerate(grouped):\n    behavior = group['behavior'].iloc[0]\n    color = behavior_colors.get(behavior, 'white')\n    t0 = group['sequence_counter'].iloc[0]\n\n    if i < len(grouped) - 1:\n        t1 = grouped[i + 1][1]['sequence_counter'].iloc[0]\n    else:\n        t1 = group['sequence_counter'].iloc[-1]\n\n    label_arg = behavior if behavior not in used_labels else None\n    plt.axvspan(t0, t1, color=color, alpha=0.3, label=label_arg)\n    used_labels.add(behavior)\n\nsubject_name = train_temp['subject'].iloc[0]\ngesture_name = train_temp['gesture'].iloc[0]\nseq_type = train_temp['sequence_type'].iloc[0]\n\nplt.title(\nf\"$\\\\bf{{Subject:}}${subject_name}   $\\\\bf{{Sequence:}}${random_seq_id}   $\\\\bf{{Gesture:}}${gesture_name}   $\\\\bf{{Seq-type:}}${seq_type}   $\\\\bf{{Handedness:}}${label_handedness}\"\n)\n# plt.title(\n# f\"Subject: $\\\\bf{{{subject_name}}}$     Sequence: $\\\\bf{{{random_seq_id}}}$     Gesture: $\\\\bf{{{gesture_name}}}$   ({seq_type}) - ({label_handedness})\"\n# )\n\nplt.xlabel('Time Step')\nplt.ylabel('Temperature (°C)')\nplt.legend(loc='upper right')\nplt.grid(True)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:35.597527Z","iopub.execute_input":"2025-07-31T04:03:35.597780Z","iopub.status.idle":"2025-07-31T04:03:36.124119Z","shell.execute_reply.started":"2025-07-31T04:03:35.597759Z","shell.execute_reply":"2025-07-31T04:03:36.123091Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### **⚪ Time-Of-Flight**","metadata":{}},{"cell_type":"code","source":"columns_TOF = [f'tof_{i}_v{j}' for i in range(1, 6) for j in range(64)]\ntrain_TOF = train_df[columns_TOF]\ntrain_TOF","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:36.125098Z","iopub.execute_input":"2025-07-31T04:03:36.125377Z","iopub.status.idle":"2025-07-31T04:03:36.788454Z","shell.execute_reply.started":"2025-07-31T04:03:36.125356Z","shell.execute_reply":"2025-07-31T04:03:36.787591Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_thm.isna().any().any()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:36.789371Z","iopub.execute_input":"2025-07-31T04:03:36.789706Z","iopub.status.idle":"2025-07-31T04:03:36.804280Z","shell.execute_reply.started":"2025-07-31T04:03:36.789677Z","shell.execute_reply":"2025-07-31T04:03:36.803314Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_thm.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:36.805130Z","iopub.execute_input":"2025-07-31T04:03:36.805379Z","iopub.status.idle":"2025-07-31T04:03:36.824811Z","shell.execute_reply.started":"2025-07-31T04:03:36.805350Z","shell.execute_reply":"2025-07-31T04:03:36.823864Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"random_seq_id = np.random.choice(train_df['sequence_id'].unique())\ntrain_temp = train_df[train_df['sequence_id'] == random_seq_id].copy()\ntrain_temp = train_temp.sort_values('sequence_counter').reset_index(drop=True)\nhandedness = train_dem_df[train_dem_df['subject']== train_temp['subject'].iloc[0]]['handedness'].iloc[0]\nlabel_handedness = 'Left-handed' if handedness == 0 else 'Right-handed'\n\n\n# Identify the 64 columns for each ToF sensor and compute their per-timestamp mean\nfor i_sensor in range(1, 6):\n    pixel_cols = [f'tof_{i_sensor}_v{pix}' for pix in range(64)]\n    train_temp[f'tof_{i_sensor}_mean'] = train_temp[pixel_cols].replace(-1, np.nan).mean(axis=1)\n\nbehavior_colors = {\n    'Relaxes and moves hand to target location': 'lightgray',\n    'Moves hand to target location': 'lightgray',\n    'Hand at target location': 'gold',\n    'Performs gesture': 'lightcoral'\n}\n\nplt.figure(figsize=(15, 7))\nplt.plot(train_temp['sequence_counter'], train_temp['tof_1_mean'], label='tof_1_mean', color='blue')\nplt.plot(train_temp['sequence_counter'], train_temp['tof_2_mean'], label='tof_2_mean', color='orange')\nplt.plot(train_temp['sequence_counter'], train_temp['tof_3_mean'], label='tof_3_mean', color='green')\nplt.plot(train_temp['sequence_counter'], train_temp['tof_4_mean'], label='tof_4_mean', color='purple')\nplt.plot(train_temp['sequence_counter'], train_temp['tof_5_mean'], label='tof_5_mean', color='red')\n\n\nbehavior_series = train_temp['behavior']\ncounter_series = train_temp['sequence_counter']\nchange_points = (behavior_series != behavior_series.shift()).cumsum()\ngrouped = list(train_temp.groupby(change_points))\n\nused_labels = set()\nfor i, (_, group) in enumerate(grouped):\n    behavior = group['behavior'].iloc[0]\n    color = behavior_colors.get(behavior, 'white')\n    t0 = group['sequence_counter'].iloc[0]\n\n    if i < len(grouped) - 1:\n        t1 = grouped[i + 1][1]['sequence_counter'].iloc[0]\n    else:\n        t1 = group['sequence_counter'].iloc[-1]\n\n    label_arg = behavior if behavior not in used_labels else None\n    plt.axvspan(t0, t1, color=color, alpha=0.3, label=label_arg)\n    used_labels.add(behavior)\n\nsubject_name = train_temp['subject'].iloc[0]\ngesture_name = train_temp['gesture'].iloc[0]\nseq_type = train_temp['sequence_type'].iloc[0]\n\nplt.title(\nf\"$\\\\bf{{Subject:}}$ {subject_name}    $\\\\bf{{Sequence:}}$ {random_seq_id}    $\\\\bf{{Gesture:}}$ {gesture_name}     ({seq_type}) - ({label_handedness})\"\n)\n# plt.title(\n# f\"Subject: $\\\\bf{{{subject_name}}}$     Sequence: $\\\\bf{{{random_seq_id}}}$     Gesture: $\\\\bf{{{gesture_name}}}$   ({seq_type}) - ({label_handedness})\"\n# )\n\nplt.xlabel('Time Step')\nplt.ylabel('Mean ToF Distance')\nplt.legend(loc='upper right')\nplt.grid(True)\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:36.826186Z","iopub.execute_input":"2025-07-31T04:03:36.826530Z","iopub.status.idle":"2025-07-31T04:03:37.295946Z","shell.execute_reply.started":"2025-07-31T04:03:36.826500Z","shell.execute_reply":"2025-07-31T04:03:37.294858Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import imageio.v2 as imageio  # for saving GIF\n\n# Choose ToF sensor number (1 to 5)\ntof_sensor_num = 5  # Change to desired sensor number\n\n# Extract the 64 sensor values from the selected ToF sensor\nsensor_cols = [f'tof_{tof_sensor_num}_v{i}' for i in range(64)]\ntof_sensor_data = train_temp[sensor_cols].replace(-1, np.nan).values  # Replace -1 with NaN for better visualization\n\n# Determine the number of frames available and limit to a maximum (e.g., 100)\nnum_frames = min(100, len(tof_sensor_data))  # Prevent index out of bounds\n\n# Create a temporary directory to store individual frame images\nos.makedirs(\"temp_frames\", exist_ok=True)\nfilenames = []\n\n# Generate a plot for each frame and save it as an image\nfor i in range(num_frames):\n    frame = tof_sensor_data[i].reshape(8, 8)\n    \n    fig, ax = plt.subplots(figsize=(3, 3))\n    im = ax.imshow(frame, cmap='inferno', vmin=0, vmax=np.nanmax(tof_sensor_data), interpolation='nearest')\n    ax.set_title(f'ToF Sensor {tof_sensor_num} - Frame {i}')\n    ax.axis('off')\n    \n    filename = f\"temp_frames/frame_{i:03d}.png\"\n    plt.savefig(filename, bbox_inches='tight', pad_inches=0)\n    filenames.append(filename)\n    plt.close()\n\n# Combine all saved images into a single animated GIF\ngif_filename = f'tof_sensor_{tof_sensor_num}_frames.gif'\nwith imageio.get_writer(gif_filename, mode='I', duration=0.1) as writer:\n    for filename in filenames:\n        image = imageio.imread(filename)\n        writer.append_data(image)\n\nprint(f\"✅ GIF saved as: {gif_filename}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:37.297377Z","iopub.execute_input":"2025-07-31T04:03:37.297655Z","iopub.status.idle":"2025-07-31T04:03:42.407813Z","shell.execute_reply.started":"2025-07-31T04:03:37.297633Z","shell.execute_reply":"2025-07-31T04:03:42.406791Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![tof_sensor_1_frames.gif](attachment:ecfee1a4-b7bd-4cf3-8f1a-78691c29dfcc.gif)  ![tof_sensor_2_frames.gif](attachment:adc8a618-3b01-4532-95f0-4dd55529dbe0.gif)  ![tof_sensor_3_frames (2).gif](attachment:24299f2b-7c06-4d81-ad43-16117037a96d.gif)  ![tof_sensor_4_frames.gif](attachment:2ba7249e-8866-48af-ba79-4b1604bc741d.gif) ![tof_sensor_5_frames.gif](attachment:1ef85035-e920-4211-947b-d0c8fbda0302.gif)\n\n\n🎥 This script visualizes data from all five ToF sensors in a sequence, displaying each time frame as an 8x8 heatmap 🌡️ for each sensor.\nEach frame from each sensor is saved as an image, and then combined into an animated GIF 🎞️.\nThis animation provides a clear and dynamic way to observe how the readings from all sensors change over time ⏳, helping in better analysis and understanding of the sensor data 📊.","metadata":{},"attachments":{"88329cdf-8cc1-432a-846c-1a3b93e41df7.gif":{"image/gif":"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rRAN/CocGOyMQmBjGJF0yYE6dYRChOUIpUzCISvaXEgmkxi1Z8IBa/6MQwLnCMZGSiGQ+IxjQacY0DbKMbiQjH/8lxjkKs4/7uiMctdquLBOvjG7kYRYQJsoqEvKIhDzlEPd6Pj3h05PwgOUdJvo+SbrTk+jCZRk2ej5Nk9CT7FsnIJyZSjKQspSjFB8ovrpJ+BytlHk95xlQy8pX4s+Uhccm9VmqRlwCMpSz9yC1ADmyYxNyWMQUWgGY685nQjKY0A5DDgUzzmthsZjUFks1uRnObHfCmOLW5zXGKE5zm9CY605nNdbLzmu58pzTjKU9o0rOezrwnPqlZ/8592rOf/swnQAPKz2oSVKAGPWhBc6jQhdqwofrEZ0TrOVF5VvSdF2VnRtO5UXN2dJwfPedAAxrShqpTeCZV6PNSetCVspSkKH2pP10qU4nGtKYUvSlOMarTnXK0pz4FKVCDelLfEVWjQz0qNmmq1KLqrqlCNSpUnWq7qVJVdlbtJlOzOs+kcpWcUv1qV8Mq1n+StawIfSpazarWtaa1qm59K1bjCta2xnWray3pUQnA17769a+ADazJ6NrMwBr2sH4dLGERy1jBloywAWisZPuqWLpOdrKVjetlJZtZt262sZ1d62cZG1q0jhaxpS3raQ+bWrGu1rCt/eprHUsyyMfOFrCx5ept/5rbrO42sY9d7G/52lurDpe4wbXscYs71eMSgLlQdS50myrd5Gp2udb1LHZrK9zhTlep1eWucr2bXdFud2S2Pa/I0kte8V63vejt7m+/u1f1hoy98y2vae0LMvzulr5EDW98x5tf92oXvuuV73/1q1r+fsy/twVwUAWcYAIv2MDmRfB9FRxhBrvWwR6D8Gwl7FMKb9jCHcbwfjXcXw6P2MOyBXHHRPxaEu/UxC1G8YtV3GAWP9jFNYaxbmX8r4AAACH5BAEAAB4ALAAAAQDXAPwAhP7+/kwMam0XbTAKXDsJZPiKC1sRbu9tIQICAhoaGunp6aamprq6ujo6OpeXl2hoaCcnJ0lJSdra2nh4eIiIiMfHx1ZWVgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj/AD0IHEiwoAcECBMqNEhQIUKGDSFKnEixIkUEFi86zMix40QECwZWeAABQQILEjx6WMByQQSQLSm2ZEnRgQcLCFKq3NnRgkCbGHkKnBlSqNGPIAdaSPDAAYUEOY0GrRjBIoIKR7OqXJB0Z1WtYAtKwNjVQwUAHgAAGOtT6NSBbwd+vTixwkuMWOFKwBlX4EOGGIt6GAs3r8QKCGx6hYh1atW+AlMGNntQIkbFEiP4LDtQLYAGDaTKvfyy4NyPgg2+vQr3JtCBhB1gbg1UMeGMjXWqPG1QsQWfDnBCBFr0de/SErlKTi3QcwLeHqe+xal7o+WhBnnPnR6xN+egYyEX/0y40u1Cv7NVNywa3nBiiMIrcm4OgGv66K1FvmeoGyJ5gYgxJ51hlXngwH4E8cZVZOJJdB9POjW4UlR+feQgaxcxl9ZYoYnmAXRvQTeRBLVN6NBfBf602k0awvbebbhJSJGIBu3lEIGmdXfQifMNhmCPCkCQgAJHBQVigiqNVdWC1w10YH9+0TiWTTBy1JZqKGbHEWYSnibdlRP5JMGY9kHZQAJQ7iQdQYhhRmOMD1boJIXjCaQgeDIyJKJhadpZEVeGHTiRl3CBidSJBDVwFVpF5icQdXJVhCNib+poIJ3dHZganh0xuZh8BHZpKaaMzVSZYC8xoFZWjpEWV6UFyf+2o3s/+Smniv09eNlBtkmYl6x5DqqmsI5WaJOhlgn2AAIREOWhpZFSVN1NBFaw2U2WPuknRiiph1mVlgU7o0WNFauluRVGEOd4gil6IqNhxSvvvPTWa++9+Oar77789uvvvwAHLPDABBds8MEIJ6zwwgw37PDDEEcs8cQUV2zxxRhnrPHGHHfs8ccghyzyyCSXbPLJKKes8sost+zyyzDHLPPMNNds880456zzzjz37PPPQAct9NBEF2300UgnrfTSTDft9NNQRy311FRXbTXFBWSt9dZcd+11AVc3/PXYZGcdNsNlp9312Qur7bbZbCf8tttxyz132nUjfDfeeRv/vHfZffv999iBFzw44YUPfPjXiSu++NqNB/w45JH/OznXlQN8+daZW7453J3z+znooes7Otil73t66qqPzrrprr+O7+qy30t77fXejvu8uu8eb+++gwV88FkNT7xRxrPu2fLMN+/88sfH+/z01K8aPVjVZ9/89dhr7z283Bv1vffhZzW+9uUfdX726Yu/PvXtC/U+/PHvNP/09dt/v/P5q7Q///3ryP+2F0CODJB5BTTgAT2TwIwskIENrMgDrRfBiUwQfBVkyAUzaMEJclAiG/ygBj0oQoOEsIQEOSEK6fPAFaaQhC5MCwxdqMIV1vBiBsihDnfIwx760AAPm+AB/4ZIxCIa8YhIPADHfsjEJuYwiA9MohSnOMQlOvGKPITiAqnIxSNaEYtg1OIBu0jGKm4MjGgU4wDLSMYvotGJavwfG7voxjcyMY77myMX62hHH+LxfnqkIh/7mEWHCTGQUhwkIXX4x/khMpFnXOQPG/m+RyZRkZKk5PosiURMLlKT5+OkFyMpyR6CcnyiNKInCXnK76WyiKvsYyu990oixtKOs9ReLc2osVL60ZBR3OUt35jL7O1SiaT0JSOBuUVhJlOZQGTmGJ3ZS2gus2GHrOUw0yjNNVIzY9a8JsOy+cpthrGbcvwmxsL5RHTmUZ04ZGcxq3dMc2JxntSr58YCwP/PfvrznwANaABiKBCBGvSg/CSoBxDKUIAqtKEQTShBIwrRh1KUoRa96EEzqlGBcrSjDp0oSD0q0pGGNIYmDehHUzrQkrJUoih9aT9XmlKamtSmI8UpSHXaUZ5q1KcXBSpFhRpRolbUpS81akOVilGkspSpMj2q76JK1ZkGr6pVJR5WqarVrcq0q1596lXDKtapkrWmYz1rTtOq1p6yta1BfStciyrXuS61rnZFKFjzKtXd8XWoeP2rSgMr2H/utbAGPSxiB2vWxSaWsI5VrGP9KdnJwtSvlmUsZjNrWMgutrKWBe1kiUeA0pr2tKhNrWoJwFmACuC1sI2tbGdL25z/rfa2uC1ta/9J2976Nra2za1wUbtbf/72uLXF2XCXq9vi8hO50IVtcJkrXOc+N7rQnS51cWvdAGA3u8rdbm67+13kale8qiVveX97XvQS17rrZW943Zte+Ma3t+2lb3Ode1/8zle/p1Vvf2WbX/0KeMDS/S+A91tcBBNYwQs+sIMLTF8JI5jC7rXwgDGMXg33l8Pi9fB9QbxdEceXxNQ18XpRzFwVl5fFy3Xxd2E8XBljl8bVta+DX4vj8ep4xz3m7o8nDGEA2zi6Qb7tkcF7swE4+clQjrKUpzyAJZsXZ1TOspadbOXj5mzLYJZyl+Xb5DCbucpDvjCWzxzmMfv2kstsBrOb/VvmOGt5zsmts52pjOfZwnnPfE7zhtcM6Cn3+cF6LjSUDw1cQit60YL+sKMfzeVIj3jSlGZ0ghOdaUufGNOP1jSPQa1oUQvgz5SuNH93PGpOh9rTKyZ1oU2N6lTTWtaAvrWrSw3rF+N6z7q2WaohvWpW17rTxQbyr+0c7JoN+8nNptmzVd1gVp8aZwEBACH5BAEAAB4ALAAAAQDXAPwAhP7+/o8kZ3IZbbY0VqgsXlANbFgQbcc+TAICAhoaGunp6bm5uZeXl6enpzo6OicnJ0hISGhoaNra2oiIiHh4eMfHx1VVVQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj/AD0IHEiwoMGCCw4qXMiwocOHECNKnEixosEGAyVYGOgg4cQGIBtA8BASI8OSJhtutMgS4sqWBFHCnLkw5QIIEzwwcKCTpsORPoP6xJhyIlChSAWmBEAQwIMHSQ8ebTnV4EuCHhsWjbqwqsKbELdmpQjBQsKtBCEk8HnUq1eFaAWOVXiVa9K3WD1YWFn3YFyDeAeaPGtQgYQJCPpWnKtYItqqeBtfHGo37FeGkvNujYCgswUFlbEuGG3xb8WqGOc2JD1zNGuHpqMSJihhgcjPNAML1A17poXYMFVD5D0wc0HiMVeTNAigOQSeM3kjl7rcg2vXELNOp2i8q0TWwncv/zR7XfjVv80BMEAgIbSH7aETorbOEi92hcTjhn8/3md6nQhU0FpBdcGXVlAJZQWcZfU5JNx+BvoVkmACKfCfBw4gwNZAbgUHmHvuRSiXdw91R19KFuDEwAQPIJBTiNxZVdx+A4ElkIkgHjhRgiT+9NBsJKnlQQIQLMBUjkgmqeSSTDbp5JNQRinllFRWaeWVWGap5ZZcdunll2CGKeaYZJZppkIBpKnmmmy26eabcMap5pl0SiTnnXjm+WadfDqk55+AxtnnoGgGauihhCZK0KGMAqroo41GiuejikpqKZyUJnrppmxmSiinoAbg6aChcjpqn6VueiqfqV66ap2tWv/6Kp2xSjrrmbVGequZuTa6a5m9MvormcEiOqyYxRp6LLLJOrosmM06+6yX0f457ZfV6nkttdlOui2X3Xr7rZbh3jnuluXKeS656WK6LpbtuvuulfG+OcC9+Oar7778DqBlegAHLPDAAC9Zr5v9Jqzwvf8S7PDDRyZ5cJsLV7xvwxBnXLCSE7Np8ccMZ6nxyM0Z3LGaIH+MMckQm3xyAClbvDLLDrt8cswVz0zzwDZ3jPPCOu8ccM8T/6xw0EJfKPHLMBvdL9JJR4wk0007fbHIUfPMMdNW8wt10kQf3PXVWGat9dIvj63v10KHXa/a+bK9s9vxwo2v3DTT3a7dIZf/bfbQW6fNN94s650u3/5i/bfSU3M9uOKLS50j1YgTTrLh5VYO+eKYh6u535FLDiLlj4Meeefdfn5l6CUHfnPpq7OOeraqW8m66O6RbrflI89ebe1V3r6kAMQXb/zxyCcvAKjAz1uR8tBHTzzzsDtvkfTYI0/97ta3lP3303PafPcRgf/99nCTz5L52aOvtvrXsy+9+2PD/7z80dPftf0U4Z+/+NXjn0P8Bz39WU2AEiGg8gzoNASWT4HaAyD3HDhACB6PgUaj4EMseEEJpk+DDeGg8TD4MxCGUITh29T4TCgQFKbwUitkoQuX58H3sVAhMyQhzm6IQxfqMGY8PEgO/2tYvyAWZIgqDGAQkQhDJfKQiZaKoQmhKCkpgpCKkbKiBrHYKC1SkIuM8qIDwXgoMSKQjIYigBrXyMY2uvGNBDiAHOdIxzra8Y7COxMaAwXHPvpRjXcMpCDpmEcz7RFQf0ykGwfJSDzKTo8+5JQiJwnIRlpSjoUs0yH/RMlJXvKSmSTTJvXUSUV+0pKhHNMo81TKRJ6ykakU0yrx1Mo/vpKRsQzTLO9USz/ecpC5BNMu5dTLPv5SkMH80jDjVEw4HjOQyfTSMuHUzDc+05Gho9M031TNRV6zjtHs0jbd1M02fhOcjzRkJDdVTjaek5Dp1OQ6L9XONb5zjuHk0jjbVP/PSt4zn1vaJ5v6Gcd7HgCgWhLomghq0IPGU5TztBRDDYrQLClUTRP950NVGVFJZfSdFcXSRdP00XOG9EojDUBJv3lSK6V0pddsaZVe2s+GypRKNK2nTTcqy45GCqbPvOmUctpOJx6LqOU06rCQ2k2l/oqp1XTqrqDaTKneiqrFtOqssNpLrb6Kq7X06qrA2kqxnoqspTTrqNDaSbV6iq2UdGum4DpJuVKKroq066Pwmki9Koqvf/RrogDrR8ESirB9NOygEAtHxfaJsW90LJ8g60bJ1omybbSsNn3aKIJqFpIoBJVnJ7gtzLLxs+oMrST7iVp5qpadrCXttUy7xtb/QvS19IztB781w5TalqO9FeEPQXau4OI2irKdlnGFS8QDjmu5HByuyp4LXQhKV2bUrS4Br5uz7GoXf9wFmne/y77wHm285AWfeRNW3PTKb71PQ697sQdfr8l3vv9L4m5Li1/1NreB9+1v8upLNv4KmL7/zWCADzzCBJdwwQx+IXL3O9sI57eJFFauhQvo4B1COMIEXtuHGRziuI34wCW+24kFnOK+GXjDEdSvDXkL4wF3GIgr7m+LE0fjGndQxkXssY+Lt+NzGeDISE6ykpfMZAOklKfLarKUp3zkJ2dzXFTO8pKtfDosa/nLTuYso6B8LDB/mcuc87KZqYzmvxl5/81sFvOhyDwsOMf5uJKi86/sPOU2m+3NfGayn7MG6EAredBRK7ShkYxosKl50YyWs6H0vCtIH1rSgaL0rSyd5Ea37dGW9vTcQA1pUeeN1Is2deFQbWhVX47VgXZ172DNZ1lrTNGlxjSgND0rTkcaz5Hi9at8XWVd/0nYqyJ2mIHdKGSfStm2zhiuU21sPTl7VNCudp6u7alsM3vMV/5WAcZN7nKb+9zoLsCQj8ftTKX73fAe97qN125Kxfve55538er9KHz7W976FgC/FfVvfwdc4OHeVsHxffCBJ2rh9254wq8F8XhLvMvirvi7L57mjGsc3Rx387g+nu6Q/3nkJG3Pd8AdTqiUq1zfLB+Uy81tckKjfOYAh/nEp4Vzctc80TfH+c8d7XGhr3znz+p5zucd8z4pXd1Hx7jClT70Txd95lUf9dVdnvVTbz3lXV/110ke9leP/eNln/XZNZ72Wwcd61Hv+NR73nZpjysgACH5BAEAACAALAAAAQDnAPwAhP7+/nAYbYwjaKouXdZKPl0SboAfa6AqYsxBSAICAhoaGunp6SgoKKioqLq6ujs7O5eXl2hoaNvb20hISHh4eIiIiMfHx1ZWVgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj/AEEIHEiwoMGCDg4qXAiCgcAHDCNKnBgRYkOKGDNq3FiwwUAAIAFUSOCQI8UGKD2CSKlS4QOWLRleACHBpM2JMwmW5PiS5c2fEVuGXJCAJACgSJMqXUrQY0ymUDHWFPgUxIUJDxgcjUpxJ1eLBCXkRGjRq86WUy9yZZgVBNiEAs1mlQCRgcq6cA2aVXgh4VMLCehqXUt4pcKEZQ0+GDuwJliwO8HSbLjX5eSkkhNeyMnYrU7DQD1K8FuQQQQAWbcWJphX4My0GR0+NVvZ9cmBYGHfVipZIkSVulsb7HwYtEAICRagHrw64wMH0B3o3h3VY8LphxHffB7dcdDLvdUm/yUtcIGCCiBTN1+ruypGixdaMpCcFrtE4dvNinVZm3pj1sZFEBlz63GVlkrRRcdQWgl5NV9Y/21EnE1tlQYCdALBVSFOCWI4HAMShAgcTQlAEKJgEixQIGH9RWQffoZdt1R373nlnoYtFjShYgs5UNSPRYEQwYrCvaYUAzkWNF1CebWUm0bu5QcgQRDhmJF9HaU0kEcLwMSAAg1guaJSwoWn5EMKRQahRkk6119bVnK0I0LGhZQegWPGxZF3tuk4UF8LXkjQBdh5JeZAJbWp0Ya4DcTkQ4qaRB4Idi6X56WYZqrpppx26umnoIYq6qiklmrqqaimquqqrLbq6quwxv8q66y01ppUALjmquuuvPbq66/A5irAsMQWa+yxyCar7LLD2ppnsNBGK62vzFZr7bXIOjvmtNx2Cyy24IarrLYremvuuQGIq+665BaI7rvTrisvuO2uB++9wc6rb7X1Nofvv73uK3Cy/a4G8MHCDqwwsQUXhjDCC0fcMGEPHxzxwhOvVTHAFyucMVcb/9vxwB9HFTK+IwtcMlQn35vyvisz1TK8L+sb81Izv1vzvDcrlTO6O8vb860/mxs0u0MDVbTRR4ubtNJLd9u000/fFLXUU9NbtU1Xc5u11ltz1HW8X18btkljS1u22WdvlHa0a1vbtttv5xs3s3NrVLfdd4//mzdGe3/bt99/TxT4r4MTXnhEh1ObOLIDRC755JRXbvkAKzce8OPHXu7555FnrvmunHcO+umUiz56wqUTi/rroZe8uq6tFwv766rPXrvrt5+e++q7D9u777LPjmvwAgwP+u+jI6/858xr7vzzl0ff+PTUV2794dhnP/n2gXfvfewfG3988OOnXrzx4o8P/t7te/9+3fFnP//b9VN/f9r5P7//2P1T3v+6FsDhDfBqBezdAaOWwNstcGkNhN0DixZB3K1Pd+hLn+Qm+LMKoo6DOfMg8cpnPhEu74LAy6AGMYfC5qlQgyCcmQmh10LpvTB9MWzZDD2Xw5PtsHo1vN4N/90XRO4NUX5FDN8R7ZdE+C1Rf02k3xP9F0X8TVGAVeTfFQ2YRQBuUYFdJOAXHRhGBI5RgmVk4BktSEL2rfGDaYTgG0eYMfOla44nbCMGd7dC8tWxhHikoR5TyMc+9jBkP7TcITeWSO3FkYKB5OEjOxhJIA7ShYVc4SIr1kj1XdKGmdQgAUZJylKa8pSoTKUqV0lKBLjylbCMpSxniQA7dnJyrMylLneZSlr68peutGUlLcfLYhpzlcBMpiyFGcr0HfOZ0CSAMqcZTEA2c3zRzCYvqTlNZtaujwPQpjhZyU1lerN14BynOlFZzmSes3TpXKc8R9lOYL6Tc/Gc5zrr+f/Lez4un/ocJz996c/EATSg2hwoLQs6uIMiNJoKnSVD++bQhz4zosu05jf7aFFxYjSWE71bRTtazI/CMqRxGylJd2nSV6J0bSpdaS5bWk03XtN7MoUmTWupUXRyNKfH3OlLyxZToPaSpkP9WlGNekqh9hSeP2UqS5H6VHxGVaozpapNN7pCrE61pUnN2lK9Kk2t7pGroiQrOc1KSLQ6U63IZCsm3YpNuKrSqVtt3eIMV9XH7VUiYZ3aXxnX18QNliGBbdphF5LYoy1WIY0N2mMPEtmdTdYgla3ZZQuS2ZdtliCdTdlnBxLakY1WIKXt2GlBkNqLrba1EjstbDEm28L/Du61tu0bbvNaut2eVa+15S3nfNtW4I52th4L7m97q9ziMve4ub0bcedq3M8il2TNpe5zrWs+A3j3u+ANr3jHawDaQtd45E2ver1rXu6id73wDW97N2vH+NqXvck97+zua9/5Xra+/IWvfycL4ACrd8CPLbCByYvgxSp4weJt8GEfDGHwSniwFK4wfrGr39VpeLwX/muGNRzivY64wiVe3IkhnOLCrXjBLf7biw0c47zNOMA1ntuN+Zvjtu34vj0+24/7m1/37vfD8i0yfbuLZAsr+b9MbvKGVZZdzUn5u0EO25Djm+WtbVnATyZwlKXc5ap9eb1lfpodEXaANrv5/81wjrOcp7vmd8n5znh+M53rfK48+3nOVeazuf5MaDfvWdDdKnShD43oaSma0IxudLQe/edISzpYlPazpS/9q0znedOc7pWn8QzqUO9q1HcutalzhWpAd3jV0Gp1nFUNa1nDmdartrWeAw1rX+na0LzuNa9+3WZcm5rYBzB2qJGtbE4zO9jC1tWzXx1tURO72ZeetpGr7etrQ5vb2l4ytzvtbWqPm9Xl3va5pZ1uca/71O2+bAHmTe962/ve+C6AHVe7lHz7+9/z3je/kwLwgt9b4AMHisEXHnDzJRwpDF84wh9uk4gbfOIU54jFC47xjGtk4wDvuMcxAvJ/i3zkE3cpub9PjvKIqDzfLG/5Ql6O75jL/CA0P7jDb07ynNfb5jwniM9/vvOgS2To9Aa60UGA9IYbb+lHb7rSjd50fRcd6gqp+tSDrvWrY90gXX/617Muda+PfSBhn93ZcV52sa9d6G1X+9vhjvSt8zztq5s73Ydu95sHBAAh+QQBAAAgACwAAAEA5wD8AIT+/v5sF22LImmoLV28N1PCO09aEW2AH2sAAAJXD20aGhq6urrp6emnp6eXl5c6OjpISEhoaGgnJyfa2tp4eHiIiIjHx8dWVlYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI/wBBCBxIsCAIBAgTKjRIUCFChgMlIADxAKLFixgzMqyIQILGiw4nfhxJ0iKCBgMXhLRQkmSDlyhPwrz4AObLjwgmtNzJU2DHnjVt9hyKESFKgSoj2GRAdKTIpj6hSh3Z4OTUq0QnTLSK9CSArwCwgjT4c+dTkwgqNtT5sOADhB7JSjgKQqtAiRrPNn3g8W3BsgP5Tng7VyBhiIAzcgWhsgHYsGIRNxyoFyddsmrxUlZ7cKBWzpw1+xWoVULcjG91SuUsOTBBiZcrU75YlS1dlQkhsIzM8KnenJQXmvR4GcTpu5Nbk6U7Wqvsvw+Lk3Ro+Pns6kedJ1cOkq6FCw4WVP9QgGA37+2/CbJWjfbs4qjwr3fOWJW09Y1D11O8X7t6weP33UcZXWDZB8F5f5nUk2oiVRWSbwn6BwJdEqwnkl3TXcVXQWwJR9FxZAUXknQY+kQgZCAAcIGAU1nHIkYY1qfgWv89VSFBGJbYVEIkbfjfdj4q9+JdExRZn06PgUUBAigiCOF2Q8UV4F/sQUmQjDpKVSVGQU6YnmEgRjgfag9OlORXKzZ53pOUbdmSjWHK55ObYy7W3JACybgaiGeN9uFYbWok1EQoAcDAY1pBoCZv7snJU1vxUWQlcJIyJNpTWRYVJ1CbfvhUlyFamRddEEBQgQNLUopgpD51epFOHAH/OmmVF8qFI4sSJbanQX5+6qpseB5ElwMPkKfABXSuquyyzDbr7LPQRivttNRWa+212Gar7bbcduvtt+CGK+645JZr7rnopqvuus0a4O678MYr77wGBGDvvfjmq+++/Pbr778A8yvAwAQXbPDBCAvAbkn0NuywuwFHLPHEFP+b8MUYD7wwSQ93LG/FIIcsssAZl2zwxiN5rDLEI7fs8sQmx6wxyhqtrPLLOOdMsswl01yzzR3rLLTQPJvsc0ZABz300i4X3fPRFyX9MNNUi+x0xlBHLXXDVXdN8dUYZ23R1lx7bTbAYF8sNkRk03v22/2mnfDaDLU9L9x45ys3wnQb/2T3x3kHvvfBfRf0d7yBCz54wYUTdDi8iee9OOONC/T4u5HjPTnBlVt+eb2Zv735zJV/DnroZo+ucOemo3626p2D0LrrXsPO+ue01z567LPnTrXtpePu+++733758MRvzrvwyA8NfOO9N6/z84VHLz3O1Pdt/fVNFx/88dznnD3dCZRv/vnop69+AuGLP/oA8Mcv//z01z8AuOvnr3/57WP/vv0ADOD9vrW/Aqavfy9TnQAXSD/8GfCB7ENgyxTIwAoO0FsQfKAEJ/g/Cy7QgRnc3wZHRkEPChCEIczfCK3WQRMCEIUpVN8KQ1ZCF9oPhjFE3wxBVkMbNpCAOVzfDv8r1kMfyg+HQeTfEGHWQiMeEYhJ1OESJVZEJ16wW1E84BQjVkUnIjGJW+RiE614RW5lUYphtNgYrfjFIKYRbWv0IhTPGME3xi2ORmxjDu3ory7mcY5n5OMdN0fG+ekxhoLc2eQK+UQM0lGJidQbHn14yBRGUl9+pCQgs3hJSRKSkfCrZAg7ia9M2lCUGSTlvUzpQlRCUJX2YqUJXalBWMrSg7Q0ICwDcEsL5rKAu+xlBcG1ywAc4JjITKYyl8lMYTIQKwSIpjSnSc1qWpMAxWSmNreZTGd+8CrXDKc4o5lNbppzmd48ITjHyU5qlvOc8DxAOgMIzXbaE5u7jGc85/n/wnXek53v1Cc3+XlDf/5TnAEVqDYJWr96HhSh+VToQCd5SoM+1JoJlagyGfrDqVwUorDU6DY5akiLfnSaGRXpMUnaSKmc9JopVSlL4+fQl6I0oirdKEVbaVKbxlSkMw1lT1/6U40GtYxNsWk1iyrRo9ZUqfgMaU51+klQPlWpTFWoU4d60qwKdKsehao0vapPsLpUrOTE6VRXutNZcvWjZN1nW3H51ovGFZ5mhQpa0yrVtbK1qoy8qk/Vuta8JnWvdz2nYYmy16iq0q/IXOxQGptYc0q2J5Ql7FQvy5PM2lJ1myuAaEdL2tKa9rSoTa1qR+tZVYI2tKuNrWxne9rW/5LytZOjrW53q1rbdhK3i+OtcIdbAN9eEriDI65yaWvcSCJ3b8uN7mqbm8jnyk262K0tYoNpXbBl97us3e5nu+s08IKXuoIk79XM+1308lG95WUvdt1rR/gWTb7zFa9r7Ssz/EqXvm/kb3/9u1wAp1HAMSNwgfV7WwSXTMHKNXAYHfxgCA9XwlukcMYsfGEG/1bDF+OwcDE8RRCHWMS7JfESTZwwFKfYw8dl8cFcrFsVD1HGM6bxbG28QxwbTMc7hrFzfUwwIMuWxzMkcpGNPF0hV1fJAmByk9FaTChHWcqpRfIKIXvMxoK3TGAOM6SkUlluevm7Yk5zSLBS5m2eOf+7ao7zmKHSZm2+GbtyjjObNZvTO0s3z2rec1/96ufoAjrNgn4slwu93EOLOdGk5PIBGK1cR4cZ0p2UNKWJa2kwY/qSmt7rlzv9oE9HMtRoHTWpqXOVOjNz08Nd9ZpbzWeVwlq4smb1VFy9zFvzNtceInOtRerr3QKbR7Qe9FqLrdtjz7kpvFYms2nr7GBpJNrJnPZsq23qRKJarKoGdrcF+W2ohjvX4+ZjuZV6blmn247rtmm7V/3uN8b7pfMmdb3TeO+T5rvT+w5jvz/6b0sHfIsDv2jBHX3wKSb8oQs/dMOX+PCDRhzQEx9ixf958TxnvMfKi12y9zs5kY+8wSX/N7mwx7s4la+c5C13ObS5m3KZD6XKIbd5T3Bec53vhOcx9/nPaR50oZcE6IMz+tBZnnSlH53oTXf6SJC+N6mThOpys/rUoV51rV+b61n3ekawnjaxjx3sZTf7RcgONrWvHe1tdztE2H41uc8d7nW3u0Ho7jS97x3vffc7QfheNMEPHvCFN7xACM8zxS8e8Y13fDFBiVS9T96qkt8l5R0PgssHNvOw3DzoVSl6xXu+kJw/PRlTr3nMm771n3996F1veNWzcfSkLH3tYY963HdS94K3vRxlT3raB5/3q/f9JYHvd+H/kfi5N37zkX976P9e+pan/vB3P/vYc7/43j9+2fd7b/3lY9/uztek4rfHwZxzHirsJ6H33t+U+LPQ/fQfiv1pOP/892T/PNR//rcTAEhEAjiADMM8/oN/CJiA4OM+DNiAKaOACXSAEvgzD7iAPXeBE5iBFRiBHIgRBfg1FhiCY0OB3QOCJsg2KNh+G7iCWuOBKfiCMMiCMuiCRVeDJ3iD8qeCOjgQI8hEPviDstOCPUiDRAiERnh/SJiERciDTJiDTqiEUMh/Q/iDQUhFJZiEWShGV6iDXRgw4zOFYQhHX1iDZahGZwiDadhHW0iEbThITZiEAQEAIfkEAQAAIQAsAAABAOcA/ACE/v7+bxhtiyJoWhFtwjtOujZUqi5cVQ9toSthgB9rAgICGhoaurq66enpp6enl5eXaGhoJycnOjo6SEhIeHh42traiIiIx8fHVlZWAAAAAAAAAAAAAAAAAAAAAAAAAAAACP8AQwgcSLBgCAUIEyo0SFAhQoYNIUqcSLEiRQUWLzrMyLHjRIwDAQC4MGGBgggPPKocCHKlxZYuY3aEKbOmTYkPQzJQIMHCAwoUbr4UKpAm0aMMjSJdyhEjTAALMDCtaFRpRqsNqxZVmtOg1qJDmdLEerAgVrJeCT5QUGHqxYhgVZKFSRdu3KQs7V51ixenWb8f74bAoIBBBIQQ+P4VXJblQsAez0Ic65hqV5cbGafNW5nz5LACDyuA4ACCAqmK71LGfPlrY89oYad+G9hz389UCR5OHAKA6bapJdds6TCh7dfIIcderDgz6OKX9cI8fGHgBQUOZguXSbz2cc12l1//jW5T/GvzzENMYDuwgoKUwY+jzw0ZfPLws2emp30fJ3SBFBQ20E7ZxcfYfLfxZ193+OnH12qg9UefQNehBgBhDWjXIHd14fbdbR3uld+HHtrnXW+mYfAAYUFpuN9Kc20mX30mZjXieU0lOGFvAFhgUgQW3CjkkEQWaeSRSCap5JJMNunkk1BGKeWUVFZp5ZVYZqnlllx26eWXYIaJZABklmnmmWimqeaabLbp5ptwsikmkXHWaeedeOYp55xC6unnn4AGymefgRZq6KFqDnojoow2Kqii+Tkq6aR1QhoppZhmiqals2nqqaacpvbpqJOGqhipqDJqKl+ptlroqm65/yqrn7BONeutd9bKFK68wqnrUr0Gu+evRAlr7KbEFnvssgEkqyyzxjorFLTHSnsTtdFaWxO2wmq7Lbe9eisTuOGK6xK5vJp7Lrq3qrsSu+266xG8s8o7L72u2tsRvvnqesC/AAcs8MAEH8BvnQIkrPDCDDfssAAIRCzxxBRXbDECUxas8cb/HhznwyCHnPDFJJccccYcpyywx3CK7HLDJsdcMcoq18zymy/nPLLMPGMsZc1A3+ymzjn3zDPNQHMsdJtEv2y0zEgnrfHSbDbt8tMxRy01wVSvabXIWJus9dYrd53m1yGHXfLYZANs9tloP6w2yWy3bfDbZ8Yt99wW1//dNt556w0z3zP/bDfXgJcp+OCET+w32YkrvvjCjVP8+NaRkzk55ZVLfLnUmQewucKde2744WVHPvrOpX+edOirQ1y6z1GiPjDsq89OO5S2p5547Lq7HnTmwM8uvM3E52786b3frbryrTPfO+6jBy+97dRvbn3tzXecfPXLc9999pNvz3v3zv8OfefHq0z+4uY/GXqzRMVuwP3456///vwbgFQBAAygAAdIwAIWYH5HsV//FshA/x3FgBCMIAARWL/VNfCC+/ufBDc4QAoKRYEYDKEGOUhCD94EhCG84AhJuEET2gSFKWTgClkYQRfWBIYx7N8MaWhAG8oEhznM4AP/eShBH8YEiEHM3w6J2MHQJdCCSVzgEpkYQCO6BIlRdCBRqAhBK64Ei1GcIhe9qBIwJlGMVCSjR8wYRDQyUY0dYWMO3UhEOHJEjjGkIw/tmBE8plCPNOSjRfwowiFykYCCrAghMQhIFiaSIotUoSEPKcBHTiSSDWxkCZ1YwdFlUYeTpOQEOflBKH5Sf5rkoCUlgkkZhlKUq4RIK6X4SkrGkiGzBOUWRVlJUp7QlKfEXypb6MsXAjOYWhQKL3uZuSd6EpnCrOUhB0DNalrzmtjM5gCYFbvVEeCb4AynOMdJTgIs85wD1KY610lNbnZzc+WMpzy/ic56FoCd+MSmO9+5/7h5+nOc9kRnPgfazmXxc3L/TCg9A7pMgg50nweNm0ITytCGOhSfEI3o1yb6z4ry8qIYNahGJcrReXpUlCBlZ0ZHSrSSmvSk00ypOlfK0py5VJ4wjalMs0nTmrrspvHMKRd3qs2e+jRkQC2nUKlIVJ6K9Kg6Syo5l8rEpurzqVB9mVQBSlUeWvWaRs1qw7Yqzq569avVDKtYF0bWcJqVhmhNK1bXCrK2gvOtLIxrQY9F15/a1Zx45aBetznXvo71r4EVrF7V2te/AjaxERwsY+nqWMhKULKFNSxbEWtZCGKWr5p9WGU7a8DPGiu0ouUsaQloWmGh1mGjXW06F5tZ1P/GVrYBbG2wXntYu+J2tnGd7Fpv+1vd9oq3DCMubue3OpE497nQja50ReLY6Vr3utT9K3NHh93uRre63g0vABy73c2JN7zgPS92yfs+vam3u+l973TZ+73Jyfe68b0vdOn7PPPqV7r5/W927Vpe+wp4v389MIIJ3N64Kfi5ARYwf9Xn3wePN8EWvjCD67u4DGu4rR6eMOCam+EI/1fEeCOxhU2sXxS/TcUPZvF9XWw2GCtYxvKlcddsfGAcv1fHVOOxhDG8Yu02GG0hJnKMjcxhwSXZrk9ua4E7XGIl35jJ/TVwkaFc5Q1nmcpbBnGXpXzkr0WZrGfe6pSdPGY0t1n/zWW2WpqlOuekrtm9b6Zznu0c56bVGah/vumdHbxnQBda0H0mWqBduuiSDhrJh2Z0pB0dOmjeT18cmZ+lk4npiWja0p22yKehGeqKjBqZpabIqYOZak9XGtSthsiqTxlrWb+a1LU2yKw/mWtd3xrVvSbIrrMYbGH/mtXFFsiww5jsECz7jM1+dhujfWxaUztzm262s6vN62tHLtveThy4ky3tOYYbcOMudrnzeG68pTvY6/5ju9/27l7Hu5Dk5jax542r2GVY28LV2b/5fSt/WxjgtSX0gxEOWu4enOCzMvjCIS4riSuY4acVsn4x7lqN35fju/W4fEF+XJG/l+S8/7L4gVHeb5Orl+UFd/l5YR5xmYuX5hW3eXhx7iqVC5jnrfL5f4GeKqFvnOI917l3iY4qo38c6UFXeneZTiqnjxzqRZc6dqk+KqufHOtN1/p1uf4pr78c7FUXu3XJ7imzz1zbR0GUh+EeJbkPnO5OsvvD8d4kvU+c70zy+8UBH/hDzZ3wSxL8yhGvJMX/nPFJcvzQIT8mw9+d8kWS/NExn3nL753zQ9L800Efes//nfSLMv3gUZ96Qx2e9SMS/dVhfynXX572p1L94nEvKt0/nve5t/3ngW8r30+e+LEy/uaRvyvlj575wHL+7KGPFNl/nfrVl/71sf+sQr2e+9PS/gTZqR8QACH5BAEAAB4ALAAAAQDnAPwAhP7+/m4YbY4jaLo2U1wRbYAfa6suXME6UAICAhoaGunp6aamprq6upeXlzo6OmhoaCcnJ0lJSdvb23h4eIiIiMfHx1ZWVgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj/AD0IHEiwoMGDCBMqXMiwocOHECNKnMhwwUALCDIi8JBRAcWKC0JG8BAy5MKSJh1K+MiyZUSUFl3KNBjTQwWYCCDM3Dlyp0+XFmtG7Plz5kqBQgEAEFgBAYWiLYmylFrwKMWaVqEapJqQK0GrQqMOrKl0qYcHCLJq3TqQq9eDYVuqbTiXLsW3LOMexIvUw1GyZQEkcLA2Yl2Hcanybaj3sEK9hWkKdNwSsFIGCBqYjfyRckLIE6li5ZzQc1/TPi17wKhAKem2CxdXLQgaImirqHnafSibYNy5qhFEKPta4OLes0fWRlj3Le7iHpBDlK5QgvWYR8suQLCAOPTY3ymL/x6Yu+Diw7KXs11bNsJG799nG79L0XTjl1OL8kWNcqxApQogYIEH8L1GlFv6VVcfdNQ51CBjBAHQAAIMFBjfXgyVt5tUpmno0oMMISddWIRZeOGJKKao4oostujiizDGKOOMNNZo44045qjjjjz26OOPQAYp5JBE6kjAkUgmqeSSTDbp5JNIBiDllFRWaeWVWGappZQFdOnll2CGKWYBRTYE5Zloptnklmy26eaVY8YpZ5dlMqTmnXg++eaefGY5559h1rlQnoQWSkCfiCYK6KJ0CpqQoZCqmeikezK6qKOPRqqpnpR2uqWlgGKK0KakMunpqViC+qeoB5XqapSoxv86papzsmrQq6/KqiutctpaEK6u6iorr3H6ShCwpQobK7FjGjsQsqQqiyqzYjorELSbSnsqtYFai62m2nrKLZjWevBtpOF2Ou6X5Z4LabqUrutlu+4WCu+k8jbqbL323otovmR6y2+e/v6bL70D31lwnwAjnHCaC/PZsMAPQxzxmxPvW7HFF7eZsbEbc9zxpwdTHDKnI5Msr8Mnr5myyuuy3PKSL8M8rswzJ1mzlh/7mrOTO/tZssbJBg2vAEgnrfTSTDft9NNQJ11jrkanG/XVWGft9NTBVh2u1mCHDTXXRXstrdhopy0A2dGafbbacGfNdrZuKxv33VHPDW7dwuL/7XfTeqPLt65/Fy41jVQPHqvhhgf+ruKLM/6344ZCHrnkeFPer+WeYu635oRyfqrnmSPeteiUkn436ASjnrrqcLOOp+uvw5627ArTnqjtauMuqe6I8n676WUDv6fwaPsusvFuIi+28mgyz6fzYUN/pvTHU6+19VBi/6b22xPftvdsgi+3+HSTv6X5WHOPsvpZsn+1+0DDr6X8eaO/t/1Y4j+2/oLj35X897QaCTBLPjGcARbIwAY68IEQNMAAJkjBClrwghjMoAY3SMEDYimBhYugCEe4QA6a8IQozKAHrwTCv5HwhQ9MoQxnuMEVWqmFfoOhDktIwx760IZVwiHe/3aoQx8acYZApJIQ70ZEGB7xiSdM4pSWGLcmvhCKWNSgFKVERbhZkYRZDKMFtxiALqrtiyMUoxoHQEYzpg2NIlyjGNu4EwXCEYJyDCMdZ2LHOzowj1nco0z66EcGAhKLgnQJIQspwUM+MZEtWWQhHfnILboRbYxsICWPCEmWSNKPmzRiJz/yyTuG8oeWrGMIM8nDUyIxlXxcJStdScNRUqSUcKTlK6V4SbGxspW6jCIsBynLTAYzhbacCC7ReEwUJlMiy/xiM4XJS1W68JfTNOEzIxJNK2aTg9uESDeb+M0aDlORxWRkObW4RQI2TY0HiKc850nPetrzAGokozuZBv/Pe/rzn/icYzv3qbR+AvSg88znQAmKNIMiFKEKlSJDk+bQhwI0okmcaEPFaNGHYhSIGhVARTt6z4/aMKQjJWk9TbpClHJUpf9kqQddGkaYxlSgEtVoSm0aT5kekKZZ5GlJcZpRnb5UqPT0qQCBikWkrpSoIDVqTZ2aUKieVKpBpao8lco/pkJRq1u1akux2lSwBlSPC2XoTnnKVft59YlmPWsg00rQtdq0rfB76xHjilf16dWIfBXrTMn6VbP2lXx/9WFg0ZrTidoVpof1XmJ7uNi5NlatR9VqZC8W0p+o0bOCNVpnffJZ0oY2aKPdSWlVe9qdpXYmq4Vta2v2Wpn/xNa2s31ZbV1yW97mNmW7bUlvhfvbkQWXJcNFbnE7dtyPJNe5y+WsRkEbRupatm7NpchztRvdiGV3ItsFb3cX9l2JhNe84y1YeSNyXvam11/rhUh75fvee8X3IfPFb32PNl3TVte/13XbfR2SXwLv12r9Ze1/FRxgsw24IQWG8IG/lmDZLtjCDfbagxkSYQ5PWFsbXkiHRfzht03UulhEMRQVF2KFjNjFJbZbhXF7YRpnuGotTsiLdRzjvs3YtzUG8o1F+2PiBtnIQxbgL8v1EzIumck7cTIroRzlLT6Zyi6RciaxLBMtM5LLWbbylMHMEi8XksxlFvOW0UwRM/uR/81tVvOX4SwRN9+RznWW85nxDBE7w5HPfdbzmwHtED+jkdCFFvSdEc0QQ3+R0Y1W9J8hrRBHW5HSlZb0oTGNEEs3kdOd1vSjQW0QTxOR1KUW9aVRTRBT75DVrVb1p2EtEFfrkNa1lvWpcc3iwuG6yYNr3K+rzDdhD7vLwfb1sZFdbGUvuyW9ntyzoZ1saU/7I9H+3LWxXW1tb3si2S7dt/PcbGuPO9Dl9va5HxLu1a0b3dh19rsb0u64zZvd3Rb3vRdS79jtO9Lp1ve/Qx1wdw88If3u3cERnm+DLzzVBbf3ww+S8OFNvCAVT97FMd5wiW98IBl/3sdB3nF/j9wDIX2v3slRXnKFn1xxKy8KzGPuk5nTfCY2v3mY+aZznA+u58yuG9B3LvShp5nnRuc20pMO7p8zvelLfzq83SZ1che96viOOtYBfvWtc53qXqe308PO77GTneFaPzvEu672tYO97RQ3O9xjnfa5s7zuc8+53XONd7jrfe9/t3tAAAAh+QQBAAAiACwAAAEA5wD8AIT+/v5uGG23NFaMImheEm5VD22AH2upLl6jK2HLQEnFPU0CAgIaGhrp6em6urqnp6eXl5c6OjpnZ2cnJydJSUl4eHiIiIja2trHx8dWVlYAAAAAAAAAAAAAAAAAAAAAAAAI/wBFCBxIsKDBgwgTGpSgsKHDhxALMoxIsaLFig8GAriQgYGICRYAALj48IHJjCJOokyocqXDCyRjQpwoc2DLmjgXClzZgAEDCxAYUsjpkCbRo0gdZnSZtGlEmDsHWlgAFUCGBQ2cSkRq1CJUEV0T0lz5VWvFsAe/MtVpcWUFrAIBvDVLMCxaimvPEiwLkW/fpneV5swIdaWDBRQwNHiwoALdgYEF+p2ZV2FkhHknN6ysNSxKzQIvK3Q5dcECEY4fh4ZMEXRgl6LxDoQZuylogoQv1jbo8gEFCA8kLICgGqxWvpxlI/x6u3hamZHX+l3JOKtI4Vkf776o1mvr2c7Db/+ffeHCZ4ERIogQCYCxA9U0x6tuXpCsU/osa8pXOEG9yJQLvKddQ/gdJ5hBBdoUHkkTMYSfSgqKQAFV/12VHV12xcTQfs/Jh1+DCzK4FUYDYbDATxBMyKFeRyX40GRloUXfiiGuxlpbBGEw1AIg1ejjj0AGKeSQRBZp5JFIJqnkkkw26eSTUEYp5ZRUVmnllVhmqeWWXBJVwJdghinmmGQWQMCZaKap5ppsEsBkAHDGKeecdNYZQJcPlannnl+26eefZ75p56CE3olnQ3wmOiagjK4paKGQynkooopWamajmLq5ZKScwjmpQpZWmimmj3Za6KcJharoqI2WauqgqCL/pGqirDLq6qt1xnrQrHzWCuituM6pq0G87unrn8AGG+ewBRWr57F+JqusocwK5GyZ0LYprbLVDnQtmdmyuW2w3Vr7rZjhOrrptLmWey666aY5Lq7livBumPHKuy67wrp7b5/5Brovv8v6+2/AAitJcL/d/gtwwPO+Wq/Dl0I88MITO4ywpgovXHDDGiMcsakZHyzyxQSXfO/GI3eq8rsso8zvy+fG3LHH1FZLsc1J4uypwSuffLPHNH/LM5I+58zszkL37HO9R+s6wNRUV2311VgPgMDWXHft9ddgI9Bk1LFmbfbZU4et9tpbj930sGjHfTXbdH/ttsXVyq132nX3/y02k2Sjurfefvd9d77lDi534XUfHm/iiqPNON2Opwt55GZPznbl4V6OOdaar815tp5/bnXoao8ObemmU4162Kofy3rrWr9uN+BvS0376bZ7Hbuvs7feu++4483s7rwP33bxiHeLfNXKc/17rcGbHv3ySwb+6fOuXz89q9V/fv3f2edeNve1R//9qOFjPv76mbYf+fvMP+48+vSXbzzc+Htfv+X3417+lKS9SaEvfcqDH6kC+LwBJqmAhzqgA5EEQTxJ0H/1ktm0DniADnrwgyAMoQgPIIASmvCEKEyhClfIwhaasF4+4+AIZ0hDErrwhjjMoQphiDMZ1vCHH9ShEP+H2EIeesyHQAQiEZfIRAEYcWFITGINm0hFHT6RYFGU4gyryEUXXpFfWdSiCLtIxh2WK4boE+MPy8jGF56xh2lUIw3b2MYvsiuMcvQgHdloxw3GMY9j3CMZ+6gsPAJSkIN84xH/CEgQIrKLhAyWIfP4SC5GEleTlGMlq3jJV2VSjZukYidN9UkxhrKJo+xUKbV4SiamklOrlGIrl/jKSMUyibMkYi0hdUsl5lKIuyxUL9f4SysqEoqMbKQei5nDYBJqmFNkJg6dOShozlGaN6Smnay5RWx68ZhYTKYybehNFmqzTtwcYTmLCE4wilOZ6zSn/A44OAXY8574zKc+96n/gHiusF705B4/B0pQe/rTjAwMaOsKylB9HjSFAFXo7hpKUYM+9IQRlehCK9rQi2J0nho9G0c76tESZjSkmBspQ0tqUpCiFGsqLShLnejSl1otpgSd6UltujecDlSnNeXp1HzKT6AmVKhxI+o+jZo3pPZUqflk6vGcqjeoRpWlO6Wq2ayKT6nyT6to4+o9vao7sIpUrP3EalCFita0ljSrZr0pWsl6vrhmra10FZxd7zpXtR51r0Pt61vXylO8+rWpgJWrWPO6vcQqlquMNaBjq2bYwf4VsJX1KFwxK1jNtgyW3EtKGxNA2tKa9rSoTW0C6vhZW4YWKaNVrWxnu1o+/7aWl689Smxpy1vTslaDfnyeaNnY2+KS9rdDQ6ZwYUtc4/YWuU6D43J121zn0ha6SEPjdImyW+vKFrtHShr6hltG717XtsAtZG65W13zpha8RhLvenPSXfeeFr5Fku926dte+/oWvckN535xUl//HhfA0V3kgGtSYAPjl0j6RR55yWhg1D54SBHe3YS7WOH7Iji70pUwc8vb4dJeWEgZpt2GuVhiE384vNoVMXVJ3OITBynFrVtxFVt84DI2Ccem0zEVeVxbH99WmPMlcH8rbGMgAflzQm4ikZv8oydjLspMnPKL4xtjDY+Ywjymso+sHDksL1HLRk6vJJPM4CU7eP/L+e2yir/M4TDDGcJyzjGdWWznNAfYnQuWSYP9K2YtsefQiE60otmTwUYTZdGQjvRIHE3pmEj60oiutKYtgulOT3rToHaIpzEd6lIrZNSXNrWqDYJqSa/61XFpNaRh/WpZz5rWqrb1onGda10nmtem9vWvgR1qYWea2KA29qGRnWxl/4fZmnb2s6FNaWl/mtqNtja2qy3tbTta297OILjDXa5xk7ta5j73sNKt7lixu92feje8DyXveXep3vbeEr7zHaID8ruxbP53lvwtcAuOt+BdIjjCt6TwhQ/84A5/eMAjLqWGU7xKFr/4lDKu8ShxvONP+jjImyTykS+p5CbZTxLKU36klbO8SC5/+ZBiLvMg0bzmP7o5zmuk850vqOc+dw7Qg66aoROdLkY/ulaSrvSmML3pSHn60Q1A9apb/epYz7oBoF4krXv961TnOpHATnasi31IZU972M8eJLWnne1tdzvZ4Q4kuc+d7j6yO9jxnne9e53vNfL73wG/IMFrnfCFN7zZEe8cxS+e8apx/NUhXxzJW53ykbf82jFvFs1vnvNO8fzWQa8V0ZO+9J4/fehTr/qkmL71SHk97Iki+9njpPa2lwnuc0+S3fPeIr7/PUWCD/mAAAAh+QQBAAAgACwAAAEA5wD8AIT+/v5tF22MImi4NVXGPUxZEG2pLV2AH2ujK2ECAgIaGhrp6emnp6e6uronJyeXl5c6OjpISEhoaGja2tp4eHiIiIjHx8dWVlYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI/wBBCBxIsKDBgwgTGpSgsKHDhxALMoxIsaLFiwMtREgAIoIFjBYjgBxJsqTAiSZTqpRo0EICByAqwJywMiFKhyJr6tyJk6fPCAoWCFyQ4AIAnwNvNsypUOlBpk0hOkWKEOrBiVZBTq16sOhRgVl53hTJMGvYnVupgjwLIq1Jtg29Aph7IcFHqm5HTrUKNa/avycx+n3owMHcuQoSMFCLcvBDx465RgVMuW3JB0UnTLgAQjHjgXA7Vh49MvRFq5EfAqCQoDUECiAW4wW9FKNpircr58bNUuXcBRZowqY5O7DC3X+RP1ROuybqlXMJQlDwFWlk5mBJ69b53HLJwwIZJP+oUN3nWMtmHTK8rl6s8bXceyclGaHCAwkJIpQ3HxH7W5uCqeTfcfJ5J5pFEwD1EnnaNejggxBGKOGEFFZo4YUYZqjhhhx26OGHIIYo4ogklmjiiSimqGKJBbTo4oswxihjAQHUaOONOOao44489ujjj0AG2eOKD81o5JEtCqnkkkw26eSQRDaE5JQxPmnllVhmGaWUVHZJY5Zghinmjlsq5GWXY6apppZlInQmlWvGKaeSbbr5JpJz5qmnjnUedCeeewaqZ58G/XmkoIjKSWhBhhqZ6KNpLkpQozNCaimYkg5EqYyXdmplpgJtWqWnpC4JKgiiwljqqkGemuqLrMb/CmWmr7oo6605ulrrl7j2qmutvQb766vB+grqrrwWG+uwqSp7K7OiOisrtJtKu+yxu1rLKrWUarsqt416Wyq4hopLKrl/muspuneq2ym7b7pr6koG1Gvvvfjmq68BGyIrL5307ivwwPxq6O+/rQZM8ML39pstwkDWxPDE9ToMLMQ/SkwxwxYTi7GPGm9McMfNfjxrSiIvTHK0JvMYcsr7rlxty2QqDLO+MndLM58234xvzuHunGvPPtsLdLlC4/hy0UYb/HDSNi7NdMEZHgx1jVIzfXS6V2NNtNZOX9x11kVv3W7XAZDts9nxoq32zWyfiXbaX5cdtsdj17323SXn/63S1D/zzbLfKAPedNVPX/02zHF7OffiKTeOptt6wy34zISbZPjhGFqteOWMX65z5iVtXrHoQZNOkulUYzj3mJCLPMDstNdu++2456777rS/LmbsG/Mu/PDE5+57mMBTXPzyzO9+PKagp9z89NQP8Dybf5te/fbFX49l8hNzLz7v3l8JPsPjp497+Z9GL7v68M/O/pPnLxx//PM7WT/B98Off5P7G1j/1Pc/JgVQYANMXwHnlb3NJXB8CwRYAw33QPFFUEgH3FcFuXfBhE0QcBvcXgcj5r7ghZB6I8xYCZV3wumlEGQrDF8Lm/fCk2lOezNkXg1dFkP05XB5O6zZB/+n9kMgBnFoQ2RaEbt3RKX10H5LHF4TnZjEokVRilOM2hP5d0XyZdFrVfRZF734xQzqa4zO+yLdwngzNOrudStBgBznSMc62vGOCCCAHvfIxz768Y8EcKPxHqcSPBrykHIEpCIXqUdBro+QKUGkJO3IyEr60ZG3g2MhJ8nJPFryk4HEZO00GclOThKUnxTlKCFpElOeEpWVVGXvWFkSV0oSlrGUpfVoSRJbIhKXjNTlLilXSl/iEZiLFCYpW2nMYyITkMrk5Uia6cxnXlKXy6wlNSlpzWvKMpu93GYdu+lNVYJzmuKkIzn7GE1iMjOdiVznHtupOpDAc47ynCc2pWn/z3t6Mp/0/Fwx4ZnPRu7Tndq8Z0FD+U1+YsSf/5RnQKEWR38udKJJq6hCC4pRoWmUoBw9aD0fatGQNhSh4dwoQEUq0HeCdKUnHelFIHpRllJ0kyqVqE1ZdYCe+vSnQA2qUA+goYXWlIC9GqpSl9rTohoVpgpMKlOnClSnPnWd+JMqVbdq1at2M6u42qpYu+rVZ4L1VmLlaobKSs6zyiqtVCUrW3Hp1ljBdapynSsq68rTuy41r3pNpf+06lehAjawuURqWAs71MMiNpmDXSxjg+rYx0IzsmidLGXXatm9Yvatmq0qZzsrWMVmNrQ+rSxp+cjXVaH2p6pdrUFNC9rX/xJ1tLKFLG3tatvbYii3iY2qZF8bW9m2tlS99e2FgBvMz/LWtsVd7XFJldzokna6nqoubplbTggSFrXW7Sx2O6Xd33L3srvtK3S3e159pte1vQ2vZcd7qfIut73dteB3Qyvfx9LXUgIIsIAHTOACG1gAp9JQsA7M4AYHOMEZWrCDJ0xgCLuuVxTO8IMtbCEJa3jCHO4whj8M4hBTyMMkZrCJTzziFKt4xRJCsYsLDOMYt3jGNK4xhGSMYwHreMc37rGPf+wgHguZyEUOspARjGTtGLnHTXayko8c5dE8GcdVtvKUoZxlylx5xl328paxHOa/fNnFZTbzmMGcZqqcOf/FbXbzmtEcZ5+8mcR1tvOc4Zznndz5w332857xHOia/FnDhTa0sRJ9YVwxOsKLfnSFhCVpETu60pOONKYjROlN2/jSnua0pkPdoE6TOsmgPnWpR63qypi61VpONaxdzepZq+XVtlazrHN961rzmie4/rWedy1sYPu62CsJNrIVTexlJ/vYzi6JsqNtkmlTmyTWvjZIsq3ti3C72xUZ05IrDG6ViHvcQy53tcWE7nSrG9vsbjeT3w3vMMl73vTedrzbne96g+ne/R7JufkdcIwMHN0FN/i+EZ5wixx83A13+MIhHnGKPHzJFbf4xDGecYhcnModd8jHuRzyhoyczCVBV8jJ2ZzyhKyczi1HyMv5HPODzJzQNTfIzQGdc51vHOQ9H8jOER10ggw9w0U3+s9JnvSjUzjpQl86ypsudZYnPSAAIfkEAQAAIAAsAAABANcA/ACE/v7+bhhtiyJpuDVVWhFtpy1exz5LoythgR9rAgICGhoaOjo6ubm56enpJycnl5eXSEhIZ2dnd3d3iIiIpaWl2trax8fHVVVVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACP8AQQgcSLCgwYIMDipcGIGghIUQI0qcWLAhxYsYM15ckKBjAhAQCAKQoADEgoQZKaikIHAlS4guXyp8COKCxps3L1ikibOly55ADy5Q4BIlCAAXEkh4sACEhZ4Wg0qdCpSlTKpYcS5wAKBr14EWEkwQCMBBU5xRBfK8mRYjTZsg0hoFsTbq1axSG86NeHdvW4VbvX5V+3Hg2AZoDUZde/HuwL0OBdpsyFitwcp4geqtCddmZZ6ONXIEkUCBBAADITggmBAyxah74c6MGCH07Kiy+VK1rRGzYoO257bNXXGCypC5Qw6sAOJBYoF/DTKY7npg9JYEF0tlafQ66+oZqYP/L8h7NnSKgwFESPAUhAMIgpmPZRvRt8by9gleKB90/FTiGHkHgmNekVWBWAK9F19zzy2UH0YviTedZRAx8KBUABZ04UQTguBfdgNdIKFrgnmllECqFdhagwJmpiFGrnGXGXVVHfShdSFOVCIA8qEmQWECTZAAYvQJFJuL5km0Inbb0YWkhzZeZFGLA+2YVHthzVfWWUWCSKGUcX1ppGVr2UeZg89RieSN53mZoYemPTBBU1EhpRRT7EH1IlBrASibiGIipF9FDTr55GVLTpQWlRVc4EBHCzyAGlkjKZDASYdmqummnHbq6aeghirqqKSWauqpqKaq6qqsturqq7DG/yrrrLTWauutuOZaEAG89urrr8AGS0AAxBZr7LHIJqvsssw266yyAkQr7bTUVmutALoOJOy23PL67Lfghitus9eWa2602QrU7brAjuvuu/BCe+681KYLArv4ehvvvvyGS++/6KabL779FmywvADPa+/A7B7ssMMJ07sww90+bHG/ESssMMXcXuwxvBmfOzHHwn5ssrghmzsyye2e7LKzKZe7Msu+vmzzsjFfOzPN+t7sc7E5W7szzz8XHUDQ1Q5Ns9E/I13vxjzXzPTNTk+rNMtTU111wNlG/WvWNm/Nta5eSw32yWJjC3XZZ7uc9tUkt4222HBzLLfJb6/t9d0f5//dddk98/2w32QDPqzgFhOeq+GHIw4x3XpH7fjgkP8N+OSPb103xZgfrDiujHdu8Oe3hi46xmIXoPrqrLfu+usFgGr66fumDfvtuKsuu+G082t77sC3vvvlvcf7e/DID8928SCnjnzyn87O/LjHP5+78ntP72711t+OveTaU+9897h/T3T4KI9PPuzmL42+v+qv73r7WL8PLvfys05/3PZ/i3/+uose7/r3rP8BcH92IyDM4gfAAHpKegpEWNUaOD8BEi+COGNgAxHIOQxmcGsUFJ4Fl+fBZBkwfxxkWAkl6LQQ6m+E2VvhsU4ovxQOTIbIouH6bJgvHM5QgweEIfj/fEgsHZKPhwQjYhGBiEIhnk+JRuweEhumxKMxsYZOdB8Ur7jDLNZviyB0oQM7BUEcRtF6oKoisRDAxja68Y1wjGPaBkDHOtrxjnjM4wDSqMY4+vGPbpyjHgdJyD1+So0BAKQi5Si2QjoSj3ys4iIn2UZBPvKSkVQiJSlpyUs6MpNE3OQkO+lJQoLSh6JcJClLqcdT4jCVilwlKyF5yD7C8o+ynKUdXSnDW+KykbocJC9X6Es/5jKYhvQUIovJyK0hs5W1lCQz33jMYA6zhNOkJjCfecdrejCbgdwmN+voTQyCs5LiHGcyO7XMcyKgmrosZwTd+c50jlOeCqQnPGeJ/08C6tOe3Oxn//7pTHWSM5qadOc+WSlQ+xG0agY9qDJtec6FlrKh73uo0yJKR4yiT6NI4+g6OdXOigL0mR4NH0iDJtKUam+lOWspQonIRfIZ4KY4zalOd8pTA7h0ejXtXk+HStSb/pR5QbVeUZe606MWL6nPY6pUjTpTH0IVeVOVqlN7d9XgZZWpW6VdV4H31aWG9XRjzV1Zi3pW0aUVd2slals799bbxXWoc8VcXWF3157mdXJ7fV1fefpXxwXWdYNtalXNGEYxJlanhUXcYVv32JxGVnCTZV1lcXpZvmV2dZul6kSr+FnVhdani5VhaQtw2s7ebbWtTe0KYRta1//KjbabtW3bcFtZ3Z6Nt4/1LdiAm1jhZo24gzXu1JDbV+Uyjbl3da7RKkeV0+7outjF7kQQSd2pWDe74M3udtXYXal8N7zoFcx4Sau5rJw3veldLxidhpf3wje88qVpe7Fi3/uKVyLc3W91Q+vf+AKYvAL2LoELjN8Ds7dq9V0wg/8bkQBD2L0SnvB182vVBJs3wxouEYcZe2H+gjjEBXLwfJEW4c2ieMMq1m+JB+ziF4s4xh2esYJrbOMUVxjBOv4wj3s8qR8/mL4YHnKPR6xaDwelvy9m8mydDBQoo1jKJSzvk08cZRyTGMkmVrKNsexBLVeZy1f2cpODvGUxd9n/yCsOWosrS2QfQ8TCYKYxnetc5DsDOc873nOdyYxBM/fEyiEmdAQNjRNEa1jRCmT0TRw9YUhP96TWtFdW9IrpeGoaK5wuqEE/DWrAdpqfpJ5KqCEa0VSr2tSiVqerpbLqjbZ61kCpdUhvjWuc6JqlvO61Rn4d02ALGyPEjhlHj32TZKds2czOiLNDBu1oX2TaGau2tS1dNItictsUwXbEtA1uPxv21Awtt5p3i+6LqhvOmG23J98Nb8/K+9v0Voi4E0bufBNk3wDrt78FAvB/CXzgBafXwf2d8HktPN8NP9fD6R1xc0383RUv18XVnfFrbbzcmDuAyEdO8pKb/OQH/xh4pkKO8pa7POUqfxLLX05zksdc5pOruc5FfnMkzXznL++5i34O9JYLPTNEL/rJj46XpCu95EzfdM6fbvSoU8XpVOe51V/tuKyjfOtcR5zXlw72oGA962U3+9THbvO09+TsVHf729fOdq3Lfdh0r/vdm513tu8d712v+8j/Lu2+j53wyDa81xF/bcWjnfHc/pngBw/5dZ9t8nav/ELg/nTN1/tumIe55/Xt+LiPnvSBn/zpUS92zK/+IJlj8+ulPjpJz17ttafy7cOOOt3vHvcFI93vae052w8f8ME3/vETn3vZL9/Xxff98wvffEBPf+7VZ/H1iZ99OW8f+L13/l/3wx198Y/f8ttT/vlhX37rr5/83c/Z+/ke/5jNH/nhd//9zZ186e+/ILGnf//HfvWXMgMIf/1nfgdIcO2nfQvIf/nngA/IehHofRNIgb6jfv8XgBJ4gQDYgBbogQUREAAh+QQBAAAgACwAAAEA1wD8AIT+/v5uGG24NVWPI2dbEW3EPE6BH2unLV+kLGABAQEaGhqYmJjp6ek6Ojq5ublnZ2cnJydISEilpaWIiIh4eHja2trIyMhVVVUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI/wBBCBxIsCCIBAgTKjRIUCFChg0FUoBIsaLFixATYLToUOPGjyALOixIIcLBBQBCGvSosiKFBQJZtpwJcgEFmTRz6lRZIYGCCAlQpqSJU2cCCzuThiyqtKlTAAwAWAgKYOhMmROZbtQqEOlDpBI9PlypcaLIlQOPWpwK06nXsxRZvs0Iom3IqiDYVrWqUqZGmAnMguR6sCHYmwIBC/5rU+TLk2k/znWauKBig49ZAma4Oe5DwSDw6sV7NXLEtApBwwWJmK7BBThZjq2Y0GjqmHYZ+m2oFndcjDJFUyXdUjZBtldnT0UL4ibYmImDAjf9MXdSwoWp61Y9EPZz2mCFC//luzQu9+qQD3b02Dr6aY5tsRdcTlm3wu+0UXfcOlD8XqLmGaUdSTLBRlJ2GcXXVELnhWSXfNRBmJFZ/hHXl3bIJZUhZgVqxZVxGirVW10SIlgic6GlNBp5gw14Yl7zQdgeia4Z+N6LgdVn2HsX7VZceFWNltNul7W0gE0IPVdWYwPNaKNE1sWU2YMVgXUkgjq6Nl1EUWKmIEELTPBAAhdMMMGQBGVFk1kefQcWTk7G9ltuEH6W5XGFqckfc12iVldBCnRUwZ2EFmrooYgmquiijDbq6KOQRirppJRWaumlmGaq6aacdurpp6CGKuqopB5KwKmopqrqqqwSUGpLBsT/KuustNZqqwGvDtTqrryemmtItwYrbKy/gtDrsasW+9GwzNZaLLLQ+qosRs1WS+yv0UI7LbXWNvtstsdue1G33mILbq/iWkQus9+eu2u6Fa07bLvusgovRfIKS2+9qt4LUb7B7ssvqv4yBPCtAg/sasEFHWxrwgMz3LDDtELMr8QEUVyxuQqnivFAGs9qcb0fCxSyrCO7WzIIJ1+ba8f9ltwyrhzDvPDHM6d87so51wwzzy3rDC7QJwudLdEhGx0t0horra3MQfvcMdMUO40s1Q5bHS7URUutMNYHa41uyQOUbfbZaKet9gAItO3223DHLTcCjNpMMNlr560323P3/+033YvaLe3Hexee9t+Ix1234Csb7njZiUfe9uJ2N/644ZJHTrnNll++d+aJb/4z3p7rDTriok9Neulrn/536l+vzvrhrvcNe8Syz3527bYHznjuukPOu9y3Xwx88MMT73vlx+uevOLLc9787M/DXTzJ07Ne/dvXq5x96du73f3O33se/uTRj0548LSHP/7Q5V9+PuCKCn4zxuy3v/37R8f/+Pz8W5r/HAfA9KluffkzWwHr9zsEJpBv7jNg7ByYwAUmyn6de6AFEYXBAWLufAF8GgXzt0FTNRB/DxReBBnIPE0F4IUwjKEMZ0jDFJbtADjMoQ53yMMeHiCEV9sUDf+HSMQY2nAAPkyiEnEIxK25sIhQnOERl0hFHjZxbE+MohYDMMUqevGHEsRdFrcIxS5+kYpX5BWnyKhFM55RiWl8lxDZWEYbvhGNYTTeGOlYQzveEY55xN4e+ShDN/7RioH03iAJCUNDHlKHcWzVGhnZxxQ+0oeRtNccKVlIP15yh5lM1iY52UhPfjKHoYzZIinpyE+m0mOjJCUXTXlKMLJQeqtkZCsv+cq75ZKQu3xkLweXKVkakZanHOb9MGXMUlqylqhMJPl+ycdgHlKZk2ymNf+IzViScpt37CY16QjON4qzmM2c5TOhacsLnpCZ6SznGc8JT20i05XShN842Sj/zy/S81LpVOcD2cnEfPZvn2TspxdX1qgj2lAAEI2oRCdK0YoKgKGMcmgKLcrRjkIUo4vS6AM9SlKKglRRIk1gSVf60ZMiKqX5Y+lKXfpSmAZPpiWl6aFselOcelSnhuKp7nz6U6ASSqizI2pHjXpUpJZOqRxl6p2c+lSoVlSqWaKq56x6VazWR6uX46pJvUoZsD5OrBMla1nNaji0SlStTmFrW93aUrgmRa6Fo2td7aoTvO5Nrxfl6078qjfACnawhF2bYQ+bk8QqVq+Mbaxj07bYyLZkspSFrGUvi9mzVXazIOmsZzUL2tCKtmyfLS1GTota0qp2taxN7WsrYkPK/xTgtrjNrW53y9ve+va3uCVrbZ0C3OIa97i9FW4KbYvc5jrXt8p9IHOfS13qRjeB062udo973fxld7vgha5Xh9uU8JpXvFglr1LOy17ddpd9322ved8bvPjKF7z01Z1976vd/M5uv/y17niXS9wAz3fA0i2wgfGLYOwqeMH9bbB3HwxhAaeXwOWtcIQvnOAMa9jCUlVvUj5cXf+yDsAk/q2JS4fiFCdXwvClsIuLu2LPtXjG7oVxfWWMY/SGGMPr7bFxa3y5Gwu5AER+nJGFnGTHLbnHTTbck3FcrIAGYC9YzrKWt/yf2TLKylwOs5hZ5GVDgXnMaMZymRd15jSnef/Nimqzm8cM50TJec5hrjOi7oznLev5UHzuc5b/bOaAClrMhC5UoA9toUTXZ9GMdvSdIH1oSWeJ0oK2tI4w3WdNP9rQjPazp53CaTyPmtSgDvWgT62UUs+Z1a1Otaq7DGuduNrNtd7Jrd+c65zsGs299rWsZ03mYG/k13Q2dkuQjWhlq4TZeXZ2SKDNZWlPe9iztjZIqC1qbcMWyN6uKbjDHVQRk7tQRzx3otKt7p2au906Yje8mzruea+13vZuirzz/dV38/uu/v53XwMucJrsu+AAxzfCZ3LwhUtW4Q4PScMjzlmIU3wjE7+4aS2ucYtkvOPf7jDIJU7wkVPk4yZtP3nJU24QlLO85St/+UBcTm4ry/zYAb05Rmyuc4vwvOcU+TnQGSL0oRek6EYfCNKTDoKlJ93pRof60KUOdKr33Oo6x/rNtS5zrr/c6ywHe8rFbnKyj9zsIEd7x9WucbZf3O0Uh3vE5e5wuo86IAAh+QQBAAAgACwAAAEA5wD8AIT+/v5tF222NFZaEG2OI2dVDm2BH2ugKmLBOlACAgIaGho6Ojrp6em5ubloaGiXl5cnJyekpKRJSUna2tp4eHiIiIjHx8dVVVUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI/wBBCBxIsCCIBAgTKjRIUCFChgIdHIRIsaLFixYTgJCIEaJDjR1DiizoUMJACw4gJFBwYcLIkRFiRpCQQGYEizZvVnwgEOTLnyMl8vQJNCfQozhlErygwMGDCgoSuETa0QHRiiYvJrBAtevLCDWBZvVK9qIFACAAAJiQ4ELZilYNXhU4lmJcihZoauQ6ECRCjg3nStSos2dfvngT8PxZlyDXBBxNzm0I4uZjhoNBLAaqFsCCBW/t+qTplGbBxh4Ly22IGOSDu4YfbO6r+eDmyRAvi7U7uzbuoTqHMnxgmmJCip0VoA4d0edVog4ranSgmu7pvoBJDg9LuyfuwISPRv/v2Zsg7IOFEbZenN0w5sozJ6sFW575RpDnLSh+OfihQP2qwbYVZZrtdx1BYGEnUn1IfVcZdNKZR9ADA1pkFWICrZUAaPYZdBdqRC23k20PfuTchBBWJt1iDhqk31EiMuQQhgf2xJGJ3GVkEAMQKMBAhx6CBCJBMUpnUoLGoShjjAa2KNJxEBVZ0G1YNXSjeAYtoMBUQJqHH1H6bSalRWEmORCFHlmH4IkjLUcjQ2OChRiaFNUFmXs/XbXAVmh16WV3gIIwJo36jTnRmbi5luOhjGKEJGMYVYgnnFY2B5SkNDWglp9/0gUZcVeNKZFTQ0nqmpqN0slRfRoNRSVeG8n/dt5Lhq5WZ6XYMTiljQNZJYFRnM7a6EC1uochX3MRRaegw0Y00Wwt9teVobo1SySuzmokga4HtUrQnh/1yem45JZr7rnopqvuuuy26+678MYr77z01mvvvfjmq+++/Pbr778AByzwwAQzVMDBCCes8MIMFzDAwxBHLPHEFA8QwMUYZ6zxxhx37PHHFxe8bsMkl3xwxSin/DDILLfsMsciq2vyzAurbPPEL+ess8cxp0vzzyffLLTFOxdddM/oAv3z0EIb7XTOSJ+rNM1M3/z01SxHbe7UM1dtM9Zgd6x1uVyb7LXKYaed8djkll3y2SmrLTfb47pNMtwoy6023Zza/90w3hXrnTbffvrNMOAUCx424V0aXjPiEisONuNAOq4w5JFLfjXlHVqeMOYRa74558x5jjDoEIv+NOmlm+4w6kSrfjTrb7n+OuqyG0177a7DHnvuUO9Olu2+A7+z8MP3DrvxOiPvFfHLM/+y811Bj7v0LlNPlfWgY5+99kdxj7n3LYMfvvLXk/+x+UCJD7n6ILP/k/uIw7++/CPRD7j9POMvUvH8U50BBkjAAhrwgAg0QGcWyMAGOnCB+AJgADWXwApacIAPzKAGN3UvCU5QcRcM4QE3SMIGRjB6HwShCFeowBK6UFz18mAK5cbCFb7QhSdM3wxpWMMQ3rCEOezeDv/11kMf/nCDQRzfEHlYxAoeEYkdROESw9ZECz5Rg0l83xTTVkUnXvGBWazfFqnYRQR+EYxR1OEYn1ZGM57RhGkU4hqv1sYRvpGBYdzfHOlYxwLeEY9xVOIendZHP/6xM3nE2yDZWEgMHpKD9pLhInPWSEc+MpFwm6TRKtnCSwZSi5rUGScfCckYSjGULhslKTF5NlSKspKkhCG9JOnKj6nSk5E8ZS1tCctVflKMu2TZLQ/JSq8Fs2XD/GMxq3ZMYfYSl6ZUYzM3lsw7LpNp0+RlI2N5zaFl02PVfCO+bPfNjhHgnOhMpzrXyc52uvOd6IxlLMlZzo3B8574zGc75Un/SnrWM2P6DKhA38nPR/rznxcbqEIXSoCCHvKgCGWoRPXp0D9C9J8TzSg8K3rHi9ZToyBlJ0ff6NFyhvSk8RzpF0v6TZSiVKUrdR1CAerSkML0iizNZk1tetMj5nSaOwVpT30q05kmNKgZHeoPf9pMpCZVqS9k6jGdOlGoRrWoRqWqRK3qQqkGU6sM5WoJvbpLsC5UrCQkay3NqlC0blCtrmTrQN2qQbiiUq4CpWsG7RpKvAZUrw/kqyb9SlHANlCwkyRsPg17WKzOVLH4ZCwDEbtIyN5Tsguk7CAtu1HMqkWze+QsQT0LANDOUbTuJC0AjCo4lArgtbCNrWxnS9va/9r2trjNrW1ZqzfX6va3wA2ucHHLW7n5drjITa5yaVtctR13udCNLnCbm7bnSve62JUtdcNm3ex6V7rbBVt3v0ve5IYXa+Mtr3qne96npXe98L1te9170vja97fzddp778tfAeTXaPvtr33/W7QACxi+BN6ZgQ+s3gTrbMEMJq+DcwbhCHt3wi+rsIWxi2GXaXjD4O0wyz4MYuiKeMT1LfF9TwwyEqvYvCz2mItfPNwYyzjFNF6vjc2J4xyXd8ccm7GP8QtkjQl5yLktspF7jOTsKpmmIW0yeWlZzgNY+cpYzrKWt3wAKX+Xyt/kspjHbGUvexfM2SSzmrVs5uyief+aa45zmdt83Tc3U85xpnOddVlPPK9Zz9K18zH9rGZAR1fQwSQ0mQ0NXUTvUtFjZvRyHV1LSItZ0sqltCstzWVMJ1fTqOT0lj2NXFCHUtRsJrVwTa1JVGdZ1avmc5VdfWVYB5fVk6R1rW39W1wvUtdz5nVufT1IYHdZ2MOWdZiBjexkS7PPzG72bYm9R2NLe9rKTnO0r01bas/R2tzudrbhvO1wx9bbawS3uc897juXe90CQPcY1Q3veLd70O9et7y3SG94PxljoUGAwAdO8IIb/OAIwNe/Q/YWhDv84QJX+MIDDvGKF1zi/6a4xTeO8SdrfOMV77iSPw7yh4u8yCT/LznCTw7klKvc4C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"},"2ba7249e-8866-48af-ba79-4b1604bc741d.gif":{"image/gif":"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Y+tUnPgoLznggV5kCXGdBn+vOhGgFoQ125UGUatKAJTWhFkznRZkL0oRLt6Co3isuL0jOjCCXpLUX6yo/6M6QsVSk3TfpNlN5TprVkqStd2k+YihSntKRpTW06TqDOUqet5Ck7fdpRo8pSqN4kalGdilRWKrWdOanqSJ0K1W5KVZxOTYBWoXnVaTJ1omHtKjC/Gs6wjlWdZZXmWRuaVrXWk63GdOtY4yrXrI61rnblJV7zStW98jVNcw0oYAN70MEOU69aPSxi/arVxTLWseQsbGQlW03KVtWygcXsYzVbVc52FidvBa1dRatQ0iLVtBH1LFJVq1bW/0IAsqWFbWL3Sduu2ha3r9WtbHXaW6j+1rU6hW1kUPtXrjJ2l8c16luVu9t3Fleo0QXqdIXL3Mo697nBZC1wk8tdmKT2u8/NLk63a9rqpvO6NFWvTNnLWfeiE74mla9K6StZ+2ITvxfVL0n5e1j/whO9lxUvcllK3eHGFMGhVbB0DdvenHjRchHIsIY3zOEOezgCDAixiEdM4hKbmAETSLGKV8ziFrs4Ucu5cOU+TOMaZ/jEOM5xiF3M4x6vGMa9kTHlbEzkDuv4yCX2sZJfDGTJCFkBRY7yjZFMZRQv+copbrKThSzlKFeZyljGspbR8uQuF/nLSA7zlcd8lTKb2f/GaD6ympfMZqS4+c00jrOO56zkOh/lznj2sJ5zzGcf+xkogA40hweN40L3+NA/SbSiNczoEzuax5DuiaQnDeJKJ/nSLc40TzY9aU9/GtQ/FjVOSK1oU5MY1SxW9aq5zOlFu1rEsE61rF3C6kDfGte5zvKueU3rWlP611YO9rCJLWNjbxjZyc71slvSazxDO9jCnnZIqv3ma2Nb2yvhtpm9rWxwg0TcXSa3tM2NEXRLWd2wZne7i+1seKNa3hdxt5eRje0J4Nsi+j4zv7/9b4kEnMj2BnXBDU5vYyf80guPCAImTvGKW/ziGEcAtB/A8Y57/OMgD/kDJEDykpv85Cj/T3nEIZLxlrt84hsXucxnPvKU2/zmJV/5Q17O84vHnOZA9zjOh65ynSek50iHObKDznSOE/3pOTc6QpKO9J83neZQh7rUp051nlv96jLP+tO3fpCue33pYMe62IdOdoOY/eVfTzvI1872thPk7S6Pu9yFTveb2/3ueM+43vfu9L7b/O8DCbzg0U74uRu+6IhXPMYHT/jHQ/7vkvc54xvPd8ubHPECybzFKb93z58c9C4QfcVJL3fTfx70qqc469Pu+qhHPvYa3zznC1971OM+97/e/cdrT3Lf4372YCe+BIwfe+RfXfnMV73zmw592B9f97uv/u2bj33Oax/z1w++//A7/n27/376TC9/28/f/carn+zsF//4a95763Nf/uN//9bjf+v58971qOcAAjiABFiABniADmAACriADNiADviABvB74LUAAYiAFniBCQiBGriBEYh7E1iBGBiCBMiBJOiAEgheICiCIliCLKiAJ/hcKaiCGNiCLPiCjBWDMmiBNFiCNhhYOJiDB7iDJNiDdvWDQFiAQsiBRKhWRniEA5iEG7iEXdWETpiBUPiAUghVVOiEVwiBWShUW3iEXYiFHoiCoFeFOjiGDfiFNBWGQKiGa1iGMHiGaBiEcLiAbGhSbpiDd4iHcniDdFiHSNiHHRh7HxiIgviEhJiHF7WHMv9IiIWoeoeIeIk4iH3IiAbliCoIiZhYUJq4gov4hz6IiJXIiaJYhKSYiKZoiGZIiZWoiJd4ikyYioK4ipLYin/3irB4h51IT58YgrYoepOYi7pohbwoi1MIesSCP5C4L2+VacvoPs2oL88IadHYPtMoL9V4aNc4PtkYL9voZ90oPt+YLuFYZ+OIPeWILufIZul4PesILu04Zu/YPPH4LfOoZfXIPPdoLfnYZPsoPP1YLf8IZAEZPAO5LAUJYwdZOwmpLAuZKA1JOw+ZLBGJKBM5OxXpKxd5KBlpOhvZKx1pKB9ZOiHJKyNZKCVJOie5KylJKCs5Oi3ZKi85KDEZOTP/ySo1KSg3CTk5uSo7GSg9+Tg/qSpBCShD6ThFmSpH+SdJ2ThLiSpN6SdPyThReSpT2SdVuThXaSpZySdbqThdWSpfuSdhmThjSSplqSdniThpOQBrmSdtKQBvGZeiNpeEGEZ/g3p42Yd66Td8OSzM6Jd/GZjCMph3+JcHYJjBgphwqJiMCSyOqYaQqYyCKY2EqZeRWSuTOYaViXh9mZiFaZmHiZmiqZmk2Zim+ZijCZqXiY2ZGUabSSud2YWf+XehyZqo6ZqlCZunKZupKZmrSZmtiZuv6Y2xaUSzOSu1eYW3aXe5SZy7aZy9iZy/qZzByZnD6ZnFCZ3HSY7JSUPLNCkrzQmFz9l20cmd0+md1Qme1yme2Umb22mb3Yme36mO4alB4xkr5ZmE50l26Umf68lmAQEAIfkEAQAAKQAsAAABAOcA/ACE/v7+41owkiVmqC1etTNX9oMP+pUGxj1M0UZD+MozgB9r3lM38HAe2k47+7MY8uRdAQEBGxsburq6SEhIpqam6Ojol5eX3NzciIiIaGhoJycneHh4ODg4ycnJVlZWAAAACP8AUwgcSLCgwYMICUZImHAhw4cQI0pcSEEiRIcWM2qMAKGjwAwaQyakQLJiipImD1JEiTDCQgsYPIicSVNgSpEob9bcmSICB5Y8g06MiEGoUaInjyqdGWECgKdPlwZ1WHQgRqkDZW60WRBDVYZade68WtRCT4FmrR4Um/VqS6dQAWDlmVYhUrYEvSL0ULeghb4E0wJOoXctQbxMz/bFIFhtBK8UH3/tmhYvx44RLMidO/OrVoFuGw5EDNEDVYRmST+8qtqi2dAMYQ8cLHECBgkWOKTYwFloWtktB05uLfCrWdqst44+Cry43Yd1J08uGDUFgAkQKvRWrjKkhZdJXRv/jOBBdXToh5lnbO48I1SBACRAIL5dbfCaFacnpB3x/N70/AVmkWzIQaTfQ3E9dQEEAdbXH2iJzSYRf5Mll5eD3V0o2kMHMpRgfOFhaGBWzznYIFohGtShTSVVRV9EbgF2FXuE2fdQBQlykJ2I4hVEI4R/BXfiYDC5ZFBjwmX0Ioyo6UfjV0bCtJ8GG8CkQY08MoklhAP29FlbXu7nY5hjpgDYioYZ1RxjJSY0mWlnsdXBBJf9tFmWeOap55589unnn4AGKuighBZq6KGIJqrooow26uijkEYq6aSUVmrppZjyRMCmnHbq6aegEhDAqKSWauqpqKaq6qqkZhppqLDG/7opq7TWamuqrkIq666f3urrr6vm+iivxM4K7LHICutoscQi6+yvyjbKLK/PVltrtIxOu6u13KqK7aLaytrtuKZ+q2i4sZKrbgDmJoourOuS2y6i74Ya77jzHlovqPd2m6+h+/bar7X/FhqwpwMTXPCgB3eacLULM9ywsQ8nG3GgE1NcMbAXY5zxxhZ37GfGooLMscgjf2wytCj3SfLKLLe858sw3yoznzTXfO3NeuasM6s896zyz0AHjafPRONqdJZIJ33q0kwP7TSqUPPY9NSkIqD11lx37fXXCCwg9thkl2322QuQPMDabLft9ttwxy333GuTzMDdeOet9958M/+gFNiAB6412oQXLrbadCeu+OJv293345D7fZTglHtt+OVmI8745pzD7XjkoOf9d+Wkh4356Wln3PnqrH8eeuijl0456qdrzvrtirv+euSxyx447Zjbjvvwceu+++O9+w428JcLT/zzbBt/PN/JK28584U7D/3z0k+vd/XWc4199qpvb/4A3XuPN/jhDz4+2tqff3v66ktuVPvLv392/PKvTr/67Guf/vZXvv4R73/eC2D4Bpi5AhoQdwicngKtx8Cy8e+BjIvg8SaovAqS7YIYzF3G6oe8yeHveh5M3cRCCMERkpB6Jjyh+FKowoaxcH4ufOH3YihD96UQhDeUmwb/d8dB39GwhgcLov9yqMP18bCHRwSiEhvHxCbaTyg9nOEPHThFEU7MiqJ7ogyjyMUu0m2Iryui7Mi4QjMuDo2wE+MJ2WhDN3qxYWB04v2y6EMPStGOcASdGktHxyTaMXGB5J0c8VfIgB0SkVVs4iBJ18h9PfKMkdThJCtXyXpdcm6JhJxS1FWAUprylKhMpSpXycpWmtIAsIylLGdJy1ra8pa4hKW6DkVKV/ryl8BUZS6HScxi1nKXhuplMJfJzFUa85nQvCUyC6XMZlqzmdHMpjanSahqXvObv9SmOJ/JzUF5E5zodOY415nLcgrqnOmM5yvZSc9jyiuZ5JKnPlFZ/89+ytKdgYLnPtHpz4ICFFACHeg3C+rPg/4poQq1JkP76VA/QTSizJxoPSvap4tiNJgapSdH+eTRj4YzpOsc6Z5KalJXojSl96RmPlsKzpeOU6V6YilN1WnTbOI0TzrdaSp7us2YdnOmQsUmUaP5UzwFNanzXCo5jWpOpEIVpFKdKr7wOa6rZjSrxmxqlp56VbCGlarvtKpXfWnWYoqVR2SFaluJ+VYRxTWpcx1mXTF0V6HmtZ1oDaha19rKv+Jyrw7q604NK83AAmqgDoisZCdL2cpa1gEJyKxmN8vZzno2AQ8IrWhHS9rSmrZqEIHsZVfLWsx+9rWwBa1pZ0tb0f+i9iGqba1uJxvb3na2tsA97W0Tktvd7ta3yM1scJdr2+EipLjGbW1ykctc5jr3ufuMrnGn69vqLve6B4Gudi/L3d56N7jgNYh4x1vZ8sb2vMBNb0HWy17euve18K2tfAlC3/pG9r74ze9s9zuQ/voXwJ8V8IAJnAID1xfBnlWwcAnsYPZC+LcSJi2DG5xd/5L3wpvNsIYZXOHxgjjEIm4uhTvs4faeWLkpDu2GS6zdF8M4xjNmcYvt++IYy5jEOt7xf23s4wfkWJ9CpqyNZYtjICM5yZJdcpGPLE8oR5nIPqZyPK085B5n2clV5rKUv7ziJ1t5zE0uc5jPjOU075f/xtFFc4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"}}},{"cell_type":"markdown","source":"## **🔴 Preprocessing**","metadata":{}},{"cell_type":"code","source":"train_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:42.409168Z","iopub.execute_input":"2025-07-31T04:03:42.409914Z","iopub.status.idle":"2025-07-31T04:03:42.682176Z","shell.execute_reply.started":"2025-07-31T04:03:42.409882Z","shell.execute_reply":"2025-07-31T04:03:42.681221Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.iloc[:, 8:].isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:42.683057Z","iopub.execute_input":"2025-07-31T04:03:42.683382Z","iopub.status.idle":"2025-07-31T04:03:43.979980Z","shell.execute_reply.started":"2025-07-31T04:03:42.683348Z","shell.execute_reply":"2025-07-31T04:03:43.979208Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:43.980816Z","iopub.execute_input":"2025-07-31T04:03:43.981095Z","iopub.status.idle":"2025-07-31T04:03:52.378383Z","shell.execute_reply.started":"2025-07-31T04:03:43.981076Z","shell.execute_reply":"2025-07-31T04:03:52.377296Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df[train_df['tof_5_v1'] > -1].tof_5_v1.median()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:52.379200Z","iopub.execute_input":"2025-07-31T04:03:52.379484Z","iopub.status.idle":"2025-07-31T04:03:52.790134Z","shell.execute_reply.started":"2025-07-31T04:03:52.379462Z","shell.execute_reply":"2025-07-31T04:03:52.789207Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define feature groups\nacc_cols = [col for col in train_df.columns if col.startswith(\"acc_\")]\nrot_cols = [col for col in train_df.columns if col.startswith(\"rot_\")]\nthm_cols = [col for col in train_df.columns if col.startswith(\"thm_\")]\ntof_cols = [f\"tof_{i}_v{0}\" for i in range(1, 6)]\n\n\ndef plot_combined_box_violin(df, columns, title):\n    data = [df[col].dropna().values for col in columns]\n\n    fig, ax = plt.subplots(figsize=(20, 10))\n\n    # Horizontal boxplot on top of violin plot\n    ax.boxplot(data, notch=False, patch_artist=True, widths=0.1, vert=False)\n\n    # Horizontal violin plot\n    ax.violinplot(data, showmeans=False, showmedians=False, showextrema=False, vert=False)\n\n    # Axis settings\n    ax.set_title(title)\n    ax.set_yticks(range(1, len(columns) + 1))\n    ax.set_yticklabels(columns)\n    ax.grid(True, axis='x')  # Grid on x-axis since horizontal plot\n\n    plt.tight_layout()\n    plt.show()\n\n\n# Run plots\nplot_combined_box_violin(train_df, acc_cols, \"Accelerometer Sensors\")\nplot_combined_box_violin(train_df, rot_cols, \"Rotation Sensors\")\nplot_combined_box_violin(train_df, thm_cols, \"Thermal Sensors\")\nplot_combined_box_violin(train_df, tof_cols, \"ToF Sensors (first pixel per group)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:03:52.791299Z","iopub.execute_input":"2025-07-31T04:03:52.791645Z","iopub.status.idle":"2025-07-31T04:04:38.433632Z","shell.execute_reply.started":"2025-07-31T04:03:52.791600Z","shell.execute_reply":"2025-07-31T04:04:38.432632Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 Outliers**","metadata":{}},{"cell_type":"code","source":"# Find outliers in thm_1\noutliers_thm1 = train_df[train_df['thm_1'] < 10]\ndisplay(outliers_thm1[thm_cols])\n\n# Find outliers in thm_3\noutliers_thm3 = train_df[train_df['thm_3'] == 0]\ndisplay(outliers_thm3[thm_cols])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:38.434590Z","iopub.execute_input":"2025-07-31T04:04:38.434902Z","iopub.status.idle":"2025-07-31T04:04:38.474369Z","shell.execute_reply.started":"2025-07-31T04:04:38.434869Z","shell.execute_reply":"2025-07-31T04:04:38.473316Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"thm_target = 'thm_3'\n\n# Identify all sequence_ids where thm_3 is zero in at least one row\nsequences_with_zero_thm3 = train_df[train_df[thm_target] == 0]['sequence_id'].unique()\n\n# Create a dataframe only for those sequences\nsubset = train_df[train_df['sequence_id'].isin(sequences_with_zero_thm3)]\n\n# For each sequence, check if *all* thm_3 values are zero or just some\nsequence_zero_stats = subset.groupby('sequence_id')[thm_target].agg(\n    total_frames='count',\n    zero_count=lambda x: (x == 0).sum()\n)\n\n# Step 4: Add a column showing if the entire sequence is zero or partial\nsequence_zero_stats['zero_type'] = sequence_zero_stats.apply(\n    lambda row: 'All Zero' if row['total_frames'] == row['zero_count'] else 'Partial Zero',\n    axis=1\n)\n\n# Display results\nprint(sequence_zero_stats['zero_type'].value_counts())  # Count how many are \"All Zero\" vs \"Partial Zero\"\ndisplay(sequence_zero_stats)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:38.475369Z","iopub.execute_input":"2025-07-31T04:04:38.475655Z","iopub.status.idle":"2025-07-31T04:04:38.547560Z","shell.execute_reply.started":"2025-07-31T04:04:38.475632Z","shell.execute_reply":"2025-07-31T04:04:38.546734Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"thm_target = 'thm_1'\n\n# Identify all sequence_ids where thm_3 is zero in at least one row\nsequences_with_zero_thm3 = train_df[train_df[thm_target] < 10]['sequence_id'].unique()\n\n# Create a dataframe only for those sequences\nsubset = train_df[train_df['sequence_id'].isin(sequences_with_zero_thm3)]\n\n# For each sequence, check if *all* thm_3 values are zero or just some\nsequence_zero_stats = subset.groupby('sequence_id')[thm_target].agg(\n    total_frames='count',\n    zero_count=lambda x: (x < 10).sum()\n)\n\n# Step 4: Add a column showing if the entire sequence is zero or partial\nsequence_zero_stats['zero_type'] = sequence_zero_stats.apply(\n    lambda row: 'All Zero' if row['total_frames'] == row['zero_count'] else 'Partial Zero',\n    axis=1\n)\n\n# Display results\nprint(sequence_zero_stats['zero_type'].value_counts())  # Count how many are \"All Zero\" vs \"Partial Zero\"\ndisplay(sequence_zero_stats)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:38.548515Z","iopub.execute_input":"2025-07-31T04:04:38.548790Z","iopub.status.idle":"2025-07-31T04:04:38.610205Z","shell.execute_reply.started":"2025-07-31T04:04:38.548759Z","shell.execute_reply":"2025-07-31T04:04:38.609327Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def impute_low_thermal_values(df, thm_columns, threshold=10.0):\n    # Create a mask of low values\n    low_mask = df[thm_columns] < threshold\n\n    # For each row, replace low values with the mean of other valid (>= threshold) thermal values\n    for col in thm_columns:\n        low_rows = low_mask[col]\n\n        # Compute row-wise means excluding the low value itself\n        row_means = df.loc[low_rows, thm_columns].mask(low_mask, np.nan).mean(axis=1)\n\n        # Impute\n        df.loc[low_rows, col] = row_means\n\n    return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:38.611248Z","iopub.execute_input":"2025-07-31T04:04:38.611576Z","iopub.status.idle":"2025-07-31T04:04:38.617066Z","shell.execute_reply.started":"2025-07-31T04:04:38.611548Z","shell.execute_reply":"2025-07-31T04:04:38.616336Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = impute_low_thermal_values(train_df, thm_cols, threshold=10.0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:38.618004Z","iopub.execute_input":"2025-07-31T04:04:38.618340Z","iopub.status.idle":"2025-07-31T04:04:38.716125Z","shell.execute_reply.started":"2025-07-31T04:04:38.618304Z","shell.execute_reply":"2025-07-31T04:04:38.715046Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plot_combined_box_violin(train_df, thm_cols, \"Thermal Sensors\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:38.717364Z","iopub.execute_input":"2025-07-31T04:04:38.717636Z","iopub.status.idle":"2025-07-31T04:04:53.042276Z","shell.execute_reply.started":"2025-07-31T04:04:38.717614Z","shell.execute_reply":"2025-07-31T04:04:53.041287Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 Missing Values**","metadata":{}},{"cell_type":"code","source":"train_df.iloc[:, 9:].isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:53.043300Z","iopub.execute_input":"2025-07-31T04:04:53.043620Z","iopub.status.idle":"2025-07-31T04:04:54.129286Z","shell.execute_reply.started":"2025-07-31T04:04:53.043597Z","shell.execute_reply":"2025-07-31T04:04:54.128370Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Total number of rows in the dataset\ntotal_rows = len(train_df)\n\n# Define sensor groups (using one column per ToF sensor)\nsensor_groups = {\n    \"Accelerometer\": [\"acc_x\", \"acc_y\", \"acc_z\"],\n    \"Rotation\": [\"rot_w\", \"rot_x\", \"rot_y\", \"rot_z\"],\n    \"Thermal thm_1\": [\"thm_1\"],\n    \"Thermal thm_2\": [\"thm_2\"],\n    \"Thermal thm_3\": [\"thm_3\"],\n    \"Thermal thm_4\": [\"thm_4\"],\n    \"Thermal thm_5\": [\"thm_5\"],\n    \"ToF 1 (64 cols)\": [\"tof_1_v0\"],\n    \"ToF 2 (64 cols)\": [\"tof_2_v0\"],\n    \"ToF 3 (64 cols)\": [\"tof_3_v0\"],\n    \"ToF 4 (64 cols)\": [\"tof_4_v0\"],\n    \"ToF 5 (64 cols)\": [\"tof_5_v0\"],\n}\n\n# Initialize list to collect results\nmissing_data = []\n\n# Loop over each sensor group and calculate missing values\nfor sensor_name, columns in sensor_groups.items():\n    # Count missing values from the representative column(s)\n    total_missing = train_df[columns].isna().sum().sum()\n    # Compute missing percentage\n    missing_percentage = (total_missing / total_rows) * 100\n    # Append result\n    missing_data.append({\n        \"Sensor Type\": sensor_name,\n        \"Missing Count\": int(total_missing),\n        \"Missing Percentage\": f\"{missing_percentage:.2f}%\"\n    })\n\n# Create summary DataFrame\ndf_missing_summary = pd.DataFrame(missing_data)\n\n# Display result\ndf_missing_summary","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:54.130520Z","iopub.execute_input":"2025-07-31T04:04:54.130862Z","iopub.status.idle":"2025-07-31T04:04:54.207133Z","shell.execute_reply.started":"2025-07-31T04:04:54.130833Z","shell.execute_reply":"2025-07-31T04:04:54.206284Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Sensor groups\nacc_cols = [\"acc_x\", \"acc_y\", \"acc_z\"]\nrot_cols = [\"rot_x\", \"rot_y\", \"rot_z\", \"rot_w\"]\ntof_sensors = [f\"tof_{i}_\" for i in range(1, 6)]\nthm_sensors = [f\"thm_{i}\" for i in range(1, 6)]\n\n# Result storage\nsensor_results = []\n\n\n# Function to analyze missingness for a sensor group\ndef analyze_sensor_missingness(df, feature_cols, sensor_name):\n    nan_stats = (\n        df.groupby(\"sequence_id\")[feature_cols]\n        .apply(lambda x: x.isna().mean())\n        .reset_index()\n    )\n\n    nan_stats[\"max_nan_ratio\"] = nan_stats[feature_cols].max(axis=1)\n    nan_stats[\"min_nan_ratio\"] = nan_stats[feature_cols].min(axis=1)\n\n    sequences_with_nan = nan_stats[nan_stats[\"max_nan_ratio\"] > 0]\n    fully_missing = nan_stats[nan_stats[\"max_nan_ratio\"] == 1.0]\n    partially_missing = nan_stats[\n        (nan_stats[\"max_nan_ratio\"] > 0) & (nan_stats[\"max_nan_ratio\"] < 1.0)\n    ]\n    nan_stats[\"nan_overlap\"] = nan_stats[\"max_nan_ratio\"] == nan_stats[\"min_nan_ratio\"]\n    overlapping = nan_stats[\n        (nan_stats[\"max_nan_ratio\"] > 0) & (nan_stats[\"nan_overlap\"] == True)\n    ]\n\n    sensor_results.append({\n        \"Sensor\": sensor_name,\n        \"Any Missing\": len(sequences_with_nan),\n        \"Fully Missing\": len(fully_missing),\n        \"Partially Missing\": len(partially_missing),\n        \"Overlap Missing\": len(overlapping),\n        \"Independent Missing\": len(sequences_with_nan) - len(overlapping)\n    })\n\n\n# Analyze accelerometer\nanalyze_sensor_missingness(train_df, acc_cols, \"acc\")\n\n# Analyze rotation\nanalyze_sensor_missingness(train_df, rot_cols, \"rot\")\n\n# Analyze thermal sensors\nfor thm in thm_sensors:\n    analyze_sensor_missingness(train_df, [thm], thm)\n\n# Analyze ToF sensors\nfor i, base in enumerate(tof_sensors, 1):\n    tof_cols = [f\"{base}v{j}\" for j in range(64)]\n    analyze_sensor_missingness(train_df, tof_cols, f\"tof_{i}\")\n\n# Create and display result table\ndf_sensor_missing = pd.DataFrame(sensor_results)\ndf_sensor_missing","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:04:54.208118Z","iopub.execute_input":"2025-07-31T04:04:54.208424Z","iopub.status.idle":"2025-07-31T04:05:25.467915Z","shell.execute_reply.started":"2025-07-31T04:04:54.208401Z","shell.execute_reply":"2025-07-31T04:05:25.467223Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define sensor groups\nacc_cols = [\"acc_x\", \"acc_y\", \"acc_z\"]\nrot_cols = [\"rot_x\", \"rot_y\", \"rot_z\", \"rot_w\"]\nthm_sensors = [f\"thm_{i}\" for i in range(1, 6)]\ntof_sensors = [f\"tof_{i}_v{j}\" for i in range(1, 6) for j in range(64)]\n\n# Organize sensor groups into a dictionary\nsensor_groups = {\n    \"acc\": acc_cols,\n    \"rot\": rot_cols,\n    \"thm\": thm_sensors,\n    \"tof\": tof_sensors\n}\n\n# Compute NaN ratio per column for each sequence\nnan_ratio_per_seq = train_df.groupby(\"sequence_id\").apply(lambda x: x.isna().mean())\n\n# For each sequence, count how many columns (sensors) in each group have missing values\nsequence_missing_counts = pd.DataFrame(index=nan_ratio_per_seq.index)\n\nfor group_name, group_cols in sensor_groups.items():\n    group_nan = nan_ratio_per_seq[group_cols]\n    # Count how many columns in the group have any NaNs (ratio > 0)\n    sequence_missing_counts[f\"{group_name}_missing_sensors\"] = (group_nan > 0).sum(axis=1)\n\n# Summarize: For each group, how many sequences have N sensors missing (N = 1, 2, ..., 320)\nsummary = {}\nfor group in [\"thm\", \"tof\"]:\n    counts = sequence_missing_counts[f\"{group}_missing_sensors\"].value_counts().sort_index()\n    summary[group] = counts\n\n# Convert summary into a DataFrame\nsummary_df = pd.DataFrame(summary).fillna(0).astype(int)\nsummary_df.index.name = \"Num sensors with NaN\"\nsummary_df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:05:25.475915Z","iopub.execute_input":"2025-07-31T04:05:25.476257Z","iopub.status.idle":"2025-07-31T04:05:32.690280Z","shell.execute_reply.started":"2025-07-31T04:05:25.476232Z","shell.execute_reply":"2025-07-31T04:05:32.689191Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Copy the sequence-level missing sensor counts\ncombo_df = sequence_missing_counts.copy()\n\n# Convert counts to boolean: True if any sensor in the group is missing\ncombo_df_binary = combo_df > 0\n\n# Analyze sensor group combinations with missing values\ncombo_df_binary[\"thm_and_rot\"] = combo_df_binary[\"thm_missing_sensors\"] & combo_df_binary[\"rot_missing_sensors\"]\ncombo_df_binary[\"thm_and_tof\"] = combo_df_binary[\"thm_missing_sensors\"] & combo_df_binary[\"tof_missing_sensors\"]\ncombo_df_binary[\"rot_and_tof\"] = combo_df_binary[\"rot_missing_sensors\"] & combo_df_binary[\"tof_missing_sensors\"]\n\n# All three sensor groups missing\ncombo_df_binary[\"all_three\"] = (\n    combo_df_binary[\"thm_missing_sensors\"]\n    & combo_df_binary[\"rot_missing_sensors\"]\n    & combo_df_binary[\"tof_missing_sensors\"]\n)\n\n# Count how many sequences fall into each combination\ncombo_stats = combo_df_binary[[\"thm_and_rot\", \"thm_and_tof\", \"rot_and_tof\", \"all_three\"]].sum()\ncombo_stats","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:05:32.691541Z","iopub.execute_input":"2025-07-31T04:05:32.691819Z","iopub.status.idle":"2025-07-31T04:05:32.706092Z","shell.execute_reply.started":"2025-07-31T04:05:32.691782Z","shell.execute_reply":"2025-07-31T04:05:32.705267Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Get all ToF columns (those that start with 'tof_')\ntof_columns = [col for col in train_df.columns if col.startswith(\"tof_\")]\n\n# Total number of rows\ntotal_rows = len(train_df)\n\n# Create list to store results\ntof_neg1_stats = []\n\n# Loop through each ToF column and count -1 values\nfor col in tof_columns:\n    count_neg1 = (train_df[col] == -1).sum()\n    percent_neg1 = (count_neg1 / total_rows) * 100\n    tof_neg1_stats.append({\n        \"ToF Column\": col,\n        \"Count of -1\": int(count_neg1),\n        \"Percentage of -1\": f\"{percent_neg1:.2f}%\"\n    })\n\n# Convert list to DataFrame\ndf_neg1_summary = pd.DataFrame(tof_neg1_stats)\n\n# Display summary\ndf_neg1_summary","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:05:32.707112Z","iopub.execute_input":"2025-07-31T04:05:32.707375Z","iopub.status.idle":"2025-07-31T04:05:33.241250Z","shell.execute_reply.started":"2025-07-31T04:05:32.707345Z","shell.execute_reply":"2025-07-31T04:05:33.240348Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:06:32.274641Z","iopub.execute_input":"2025-07-31T04:06:32.275116Z","iopub.status.idle":"2025-07-31T04:06:40.454404Z","shell.execute_reply.started":"2025-07-31T04:06:32.275079Z","shell.execute_reply":"2025-07-31T04:06:40.453557Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.base import BaseEstimator, TransformerMixin\n\nclass SensorImputer(BaseEstimator, TransformerMixin):\n    def __init__(self, method='mean'):\n        self.method = method  # 'mean' or 'median'\n        self.acc_columns = []\n        self.rot_columns = []\n        self.thm_columns = []\n        self.tof_columns = []\n        self.column_stats = {}\n\n    def fit(self, X, y=None):\n        # Identify sensor columns\n        self.acc_columns = [col for col in X.columns if col.startswith(\"acc_\")]\n        self.rot_columns = [col for col in X.columns if col.startswith(\"rot_\")]\n        self.thm_columns = [col for col in X.columns if col.startswith(\"thm_\")]\n        self.tof_columns = [col for col in X.columns if col.startswith(\"tof_\")]\n\n        # Calculate statistics for acc, rot, and thm columns\n        sensor_cols = self.acc_columns + self.rot_columns + self.thm_columns\n        if self.method == 'mean':\n            self.column_stats.update({col: X[col].mean() for col in sensor_cols})\n        elif self.method == 'median':\n            self.column_stats.update({col: X[col].median() for col in sensor_cols})\n        else:\n            raise ValueError(\"Method must be either 'mean' or 'median'\")\n\n        # Calculate statistics for tof columns (ignoring -1 values)\n        for col in self.tof_columns:\n            valid_values = X[col][X[col] != -1]\n            if self.method == 'mean':\n                self.column_stats[col] = valid_values.mean()\n            elif self.method == 'median':\n                self.column_stats[col] = valid_values.median()\n\n        return self\n\n    def transform(self, X):\n        X = X.copy()\n\n        # Fill missing values in acc, rot, and thm columns using precomputed stats\n        for col in self.acc_columns + self.rot_columns + self.thm_columns:\n            X[col] = X[col].fillna(self.column_stats[col])\n\n        # Replace -1 with NaN in tof columns, then fill with precomputed stats\n        for col in self.tof_columns:\n            X[col] = X[col].replace(-1, np.nan)\n            X[col] = X[col].fillna(self.column_stats[col])  # 255\n\n        return X","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:06:42.810564Z","iopub.execute_input":"2025-07-31T04:06:42.810902Z","iopub.status.idle":"2025-07-31T04:06:42.949609Z","shell.execute_reply.started":"2025-07-31T04:06:42.810874Z","shell.execute_reply":"2025-07-31T04:06:42.948794Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"imputer = SensorImputer(method='median')\nimputer","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:06:52.538618Z","iopub.execute_input":"2025-07-31T04:06:52.538960Z","iopub.status.idle":"2025-07-31T04:06:52.551425Z","shell.execute_reply.started":"2025-07-31T04:06:52.538932Z","shell.execute_reply":"2025-07-31T04:06:52.550546Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_train_imputed = imputer.fit_transform(train_df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:07:00.837693Z","iopub.execute_input":"2025-07-31T04:07:00.838070Z","iopub.status.idle":"2025-07-31T04:07:07.143384Z","shell.execute_reply.started":"2025-07-31T04:07:00.838041Z","shell.execute_reply":"2025-07-31T04:07:07.142424Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_train_imputed.isna().sum().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:17:36.929587Z","iopub.execute_input":"2025-07-31T04:17:36.930397Z","iopub.status.idle":"2025-07-31T04:17:37.935940Z","shell.execute_reply.started":"2025-07-31T04:17:36.930368Z","shell.execute_reply":"2025-07-31T04:17:37.935016Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plot_combined_box_violin(train_df, acc_cols, \"Accelerometer Sensors\")\n\nplot_combined_box_violin(train_df, rot_cols, \"Rotation Sensors\")\n\nplot_combined_box_violin(train_df, thm_cols, \"Thermal Sensors\")\n\ntof_cols = [f\"tof_{i}_v{0}\" for i in range(1, 6)]\nplot_combined_box_violin(train_df, tof_cols, \"ToF Sensors (first pixel per group)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:17:55.319694Z","iopub.execute_input":"2025-07-31T04:17:55.320102Z","iopub.status.idle":"2025-07-31T04:18:36.977087Z","shell.execute_reply.started":"2025-07-31T04:17:55.320014Z","shell.execute_reply":"2025-07-31T04:18:36.976214Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 Normalization**","metadata":{}},{"cell_type":"code","source":"train_df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:26:27.452724Z","iopub.execute_input":"2025-07-31T04:26:27.453177Z","iopub.status.idle":"2025-07-31T04:26:35.667358Z","shell.execute_reply.started":"2025-07-31T04:26:27.453142Z","shell.execute_reply":"2025-07-31T04:26:35.666450Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\n\n# Select sensor columns (excluding rotation)\nfeature_cols = [col for col in train_df.columns if col.startswith((\"acc_\", \"thm_\", \"tof_\"))]\n\n# Initialize scaler\nscaler = StandardScaler()\n\n# Apply Z-score normalization\ntrain_df[feature_cols] = scaler.fit_transform(train_df[feature_cols])\n\ntrain_df.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:26:51.509234Z","iopub.execute_input":"2025-07-31T04:26:51.509550Z","iopub.status.idle":"2025-07-31T04:27:12.618865Z","shell.execute_reply.started":"2025-07-31T04:26:51.509528Z","shell.execute_reply":"2025-07-31T04:27:12.617753Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_train_imputed.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:30:22.001250Z","iopub.execute_input":"2025-07-31T04:30:22.001603Z","iopub.status.idle":"2025-07-31T04:30:32.006469Z","shell.execute_reply.started":"2025-07-31T04:30:22.001581Z","shell.execute_reply":"2025-07-31T04:30:32.005573Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# from sklearn.preprocessing import StandardScaler\n\n# Select sensor columns (excluding rotation)\nfeature_cols = [col for col in train_df.columns if col.startswith((\"acc_\", \"thm_\", \"tof_\"))]\n\n# Initialize scaler\nscaler = StandardScaler()\n\n# Apply Z-score normalization\ndf_train_imputed[feature_cols] = scaler.fit_transform(df_train_imputed[feature_cols])\n\ndf_train_imputed.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:30:37.640010Z","iopub.execute_input":"2025-07-31T04:30:37.640385Z","iopub.status.idle":"2025-07-31T04:30:51.934131Z","shell.execute_reply.started":"2025-07-31T04:30:37.640363Z","shell.execute_reply":"2025-07-31T04:30:51.933142Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_train_imputed.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:30:51.935539Z","iopub.execute_input":"2025-07-31T04:30:51.935875Z","iopub.status.idle":"2025-07-31T04:30:51.960383Z","shell.execute_reply.started":"2025-07-31T04:30:51.935845Z","shell.execute_reply":"2025-07-31T04:30:51.959570Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **🟠 Label Encoding**","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\nle = LabelEncoder()\ndf_train_imputed['gesture'] = le.fit_transform(df_train_imputed['gesture'])\ndf_train_imputed.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:05:33.403886Z","iopub.status.idle":"2025-07-31T04:05:33.404248Z","shell.execute_reply.started":"2025-07-31T04:05:33.404107Z","shell.execute_reply":"2025-07-31T04:05:33.404124Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **🔴 Save Preprocessed Dataset**","metadata":{}},{"cell_type":"code","source":"# df_train_imputed.to_csv(\"train-preprocessed.csv\", index=False)\ndf_train_imputed.sample(100).to_csv(\"train-preprocessed.csv\", index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T04:05:33.405422Z","iopub.status.idle":"2025-07-31T04:05:33.405694Z","shell.execute_reply.started":"2025-07-31T04:05:33.405568Z","shell.execute_reply":"2025-07-31T04:05:33.405581Z"}},"outputs":[],"execution_count":null}]}