Redlining, Race-Exclusive Deed Restriction Language, and Neighborhood Racial Composition in Houston This report investigates the effects of systemic racism in Houston by examining recent racial composition and racial composition changes of Houston neighborhoods intersected with historial geographic/race-based lending restriction (redlining) and race-based deed restrictions. Though, this analysis was not formally peer-reviewed, it recieved a 100% grade for the course. Sources for data and graphics can be found in the HTML report. Example Images Houston Historical Redlining Grade with Modern City […]

STAC in Jupyter Notebooks Install pip install stac-nb Usage To use stac-nb in a project, start Jupyter Lab (jupyter lab), create a new notebook, then: from stac_nb import STAC_Query_UI Display the UI for a STAC API: ui = STAC_Query_UI(“https://earth-search.aws.element84.com/v0”) ui.display() After you have run the query, retrieve the results: ui.query_results GitHub https://github.com/darrenwiens/stac-nb    

notebookJS notebookJS enables the execution of custom JavaScript code in Python Notebooks (Jupyter Notebook and Google Colab). This Python library can be useful for implementing and reusing interactive Data Visualizations in the Notebook environment. notebookJS takes care of downloading and handling Javascript libraries and CSS stylesheets from the web. Furthermore, it supports bidirectional communication between Python and JavaScript. User interactions in HTML/JavaScript can trigger Python callbacks that process data on demand and send the results back to the front-end code. […]

pytorch-wavenet This is an implementation of the WaveNet architecture, as described in the original paper. pytorch-wavenet This is an implementation of the WaveNet architecture, as described in the original paper. Features Automatic creation of a dataset (training and validation/test set) from all sound files (.wav, .aiff, .mp3) in a directory Efficient multithreaded data loading Logging to TensorBoard (Training loss, validation loss, validation accuracy, parameter and gradient histograms, generated samples) Fast generation, as introduced here Requirements python 3 pytorch 0.3 numpy […]

CambridgeTemperatureNotebooks Time series and other models for Cambridge UK temperature forecasts in python If you like CambridgeTemperatureNotebooks, give it a star, or fork it and contribute! Summary of single step-ahead predictions using simple Long Short Term Memory model with 12 hours of lagged variables plus test time augmentation: These predictions are for separate test data from 2019. RMSE is 7.26 and MAE is 3.71. GitHub https://github.com/makeyourownmaker/CambridgeTemperatureNotebooks    

notebook-environments Manage python virtual environments on the working notebook server. Installation It is recommended to use this package together with virtualenv and virtualenvwrapper to work with python virtual environments more suitable. Make sure the installed python interpreters work without errors on the current operating system. To install this package as a standalone application with the command-line interface you are to run the following command: sudo sh -c “$(curl https://raw.githubusercontent.com/vladpunko/notebook-environments/master/install.sh)” Use the package manager pip to install notebook-environments without the command-line […]

News 2021-6-21 Support CVPR-2021 Download all CVPR-2021 papers in one click. Just set the local download directory in download_cvpr2021.py and run it! Don’t forget to have your chrome driver ready (i.e., corresponding version to your Chrome browser) 2021-6-20 Support continuation of downloading from where the program encounters interruption. (prevent re-downloading from scratch) Introduction Python code to crawl computer vision papers from top CV conferences. Currently it supports CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, SIGGRAPH. It leverages selenium, a website testing […]

UmlsBERT UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus General info This is the code that was used of the paper : UmlsBERT: Augmenting Contextual Embeddings with a Clinical Metathesaurus (NAACL 2021). In this work, we introduced UmlsBERT, a contextual embedding model capable of integrating domain knowledge during pre-training. It was trained on biomedical corpora and uses the Unified Medical Language System (UMLS) clinical metathesaurus in two ways: We proposed a new multi-label […]