{"id":22726247,"url":"https://github.com/maskedsyntax/2-stage-opamp-analysis","last_synced_at":"2025-04-13T20:46:36.865Z","repository":{"id":193907148,"uuid":"489984703","full_name":"MaskedSyntax/2-stage-opamp-analysis","owner":"MaskedSyntax","description":"Comparative Analysis of Machine Learning Models for Aspect Ratio Estimation of a Two-Stage Operational Amplifier","archived":false,"fork":false,"pushed_at":"2024-11-22T17:47:52.000Z","size":20913,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-27T11:06:30.758Z","etag":null,"topics":["analysis","gaussian-regression","knn","machine-learning","numpy","opamp","opamps","pandas","streamlit","tensorflow"],"latest_commit_sha":null,"homepage":"https://opamp-analysis.streamlit.app/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MaskedSyntax.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-08T15:39:35.000Z","updated_at":"2025-02-05T08:33:23.000Z","dependencies_parsed_at":null,"dependency_job_id":"8e34d433-a34a-4221-a537-f9c7d98d1e5d","html_url":"https://github.com/MaskedSyntax/2-stage-opamp-analysis","commit_stats":null,"previous_names":["aftaab25/2-stage-opamp-analysis","maskedsyntax/2-stage-opamp-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MaskedSyntax%2F2-stage-opamp-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MaskedSyntax%2F2-stage-opamp-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MaskedSyntax%2F2-stage-opamp-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MaskedSyntax%2F2-stage-opamp-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MaskedSyntax","download_url":"https://codeload.github.com/MaskedSyntax/2-stage-opamp-analysis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248782275,"owners_count":21160716,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","gaussian-regression","knn","machine-learning","numpy","opamp","opamps","pandas","streamlit","tensorflow"],"created_at":"2024-12-10T16:16:16.188Z","updated_at":"2025-04-13T20:46:36.839Z","avatar_url":"https://github.com/MaskedSyntax.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!-- # 2-Stage-OpAmp-Analysis --\u003e\n\u003c!-- Comparative Analysis of Machine Learning Models for Aspect Ratio Estimation of a Two-Stage Operational Amplifier --\u003e\n\n# Aspect Ratio Estimation of a Two-Stage Operational Amplifier\n\nThis repository contains a Streamlit web application that estimates the aspect ratios of a two-stage operational amplifier using various machine learning models. The application allows users to input specific parameters and select a model to predict the aspect ratios.\n\n## Features\n\n- **Interactive UI**: User-friendly interface to input parameters and select models.\n- **Multiple Models**: Provides predictions using different regression models including Linear Regression, Gaussian Process Regression, SVR, Decision Tree, KNN, Random Forest, and a Neural Network.\n- **Visualization**: Displays predictions and aspect ratios for the selected model.\n\n## Screenshots\n\u003cimg src=\"screenshots/5.png\" alt=\"drawing\" width=\"200\"/\u003e\u0026emsp; \u0026emsp;\n\u003cimg src=\"screenshots/6.png\" alt=\"drawing\" width=\"200\"/\u003e\u0026emsp; \u0026emsp;\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.x\n- Streamlit\n- Keras\n- Scikit-learn\n- Numpy\n- Pandas\n- Matplotlib\n\n### Installation\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/Aftaab25/2-Stage-OpAmp-Analysis.git\n   cd 2-Stage-OpAmp-Analysis\n   ```\n\n2. Install the required packages:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. Ensure you have the dataset `2STAGEOPAMP_DATASET.csv` in the same directory.\n\n4. Ensure you have the trained models `model.h5` and `gaussian_model.pkl` in the same directory.\n\n### Running the App\n\nRun the Streamlit app using the following command:\n```bash\nstreamlit run main.py\n```\n\nThis will start the Streamlit server, and you can interact with the app in your web browser.\n\n## Usage\n\n1. **Input Features**:\n   - DC Gain\n   - Unity Gain Frequency (ft)\n   - 3-dB Frequency (f3)\n   - Common Mode Voltage (Vcm)\n   - Power Dissipation (Pdiss)\n\n2. **Select a Model**:\n   - Linear Regression Model\n   - Gaussian Regression Model\n   - SVR\n   - Decision Tree Regressor\n   - KNN\n   - Random Forest Regressor\n   - Neural Network (Best)\n\n3. **Get Predictions**: Click the 'Calculate' button to get the predicted aspect ratios for the given input features.\n\n## Code Overview\n\n### `main.py`\n\n- **Imports**: Necessary libraries including Streamlit, Numpy, Pandas, Scikit-learn, and Keras.\n- **Data Loading**: Loads the dataset `2STAGEOPAMP_DATASET.csv` and preprocesses it.\n- **Model Loading**: Loads the pre-trained models for prediction.\n- **Model Functions**: Defines functions for each machine learning model to predict aspect ratios.\n- **Streamlit UI**: Creates the sidebar and main panel for user input and model selection.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaskedsyntax%2F2-stage-opamp-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaskedsyntax%2F2-stage-opamp-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaskedsyntax%2F2-stage-opamp-analysis/lists"}