{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":87793,"databundleVersionId":11403143,"sourceType":"competition"}],"dockerImageVersionId":30918,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef visualize_62d_rna(data_62d, labels=None):\n    \"\"\"\n    Visualizes a 62D RNA molecule representation.\n\n    Args:\n        data_62d: A numpy array of shape (n_points, 62), where n_points is the number of RNA components.\n        labels: (Optional) A numpy array of shape (n_points,) representing cluster labels for coloring.\n    \"\"\"\n\n    if data_62d.shape[1] != 62:\n        raise ValueError(\"Input data must have 62 dimensions.\")\n\n    # Reduce dimensionality for visualization (e.g., using PCA or t-SNE)\n    # For simplicity, we'll use the first 3 dimensions for 3D plotting.\n    if data_62d.shape[0] > 0:\n        x = data_62d[:, 0]\n        y = data_62d[:, 1]\n        z = data_62d[:, 2]\n\n        fig = plt.figure()\n        ax = fig.add_subplot(111, projection='3d')\n\n        if labels is not None:\n            unique_labels = np.unique(labels)\n            for label in unique_labels:\n                indices = np.where(labels == label)[0]\n                ax.scatter(x[indices], y[indices], z[indices], label=f'Cluster {label}')\n            ax.legend()\n        else:\n            ax.scatter(x, y, z)\n\n        ax.set_xlabel('Dimension 1')\n        ax.set_ylabel('Dimension 2')\n        ax.set_zlabel('Dimension 3')\n        plt.title('62D RNA Molecule Visualization (Simplified)')\n        plt.show()\n    else:\n        print(\"Empty data provided.\")\n\n# Example usage (replace with your actual 62D data):\n# Generate some random 62D data for demonstration.\nnp.random.seed(42)\nn_points = 100\ndata_62d = np.random.rand(n_points, 62)\n\n# Generate random labels for demonstration.\nlabels = np.random.randint(0, 3, n_points)  # 3 clusters\n\nvisualize_62d_rna(data_62d, labels) #with labels\nvisualize_62d_rna(data_62d) #without labels\n\n# Ultrasound Wave Lighting Machine Code (Conceptual)\n\ndef ultrasound_lighting(data_62d, labels=None):\n    \"\"\"\n    Conceptual function to control ultrasound wave lighting based on 62D RNA data.\n\n    This is a highly simplified example. In a real-world scenario, you would need:\n    - A specific hardware interface for the ultrasound lighting machine.\n    - A mapping between the 62D data/labels and the desired lighting patterns.\n    - Precise control over ultrasound frequencies and intensities.\n\n    Args:\n        data_62d: 62D RNA molecule data.\n        labels: Cluster labels (optional).\n    \"\"\"\n\n    if labels is not None:\n        unique_labels = np.unique(labels)\n        for label in unique_labels:\n            indices = np.where(labels == label)[0]\n\n            # Example: Control lighting based on cluster labels.\n            # Replace with actual hardware control commands.\n            print(f\"Lighting for cluster {label}:\")\n            # Example: calculate average of the data points within the cluster.\n            cluster_data = data_62d[indices,:]\n            cluster_average = np.average(cluster_data, axis=0)\n\n            #example of using the average to control some light parameter.\n            light_intensity = np.linalg.norm(cluster_average)\n            print(f\"  Light intensity: {light_intensity}\")\n            # ... other lighting control commands based on cluster_average ...\n\n    else:\n        # Example: Control lighting based on overall data.\n        print(\"Lighting based on overall RNA data:\")\n        #calculate average of all data points.\n        all_average = np.average(data_62d, axis=0)\n        light_intensity = np.linalg.norm(all_average)\n        print(f\"  Light intensity: {light_intensity}\")\n        # ... other lighting control commands based on all_average ...\n\n# Example usage (conceptual):\nultrasound_lighting(data_62d, labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-19T01:03:07.623923Z","iopub.execute_input":"2025-03-19T01:03:07.624249Z","iopub.status.idle":"2025-03-19T01:03:08.245543Z","shell.execute_reply.started":"2025-03-19T01:03:07.624224Z","shell.execute_reply":"2025-03-19T01:03:08.244392Z"}},"outputs":[],"execution_count":null}]}