ResInsight is an open source, cross-platform 3D visualization and post processing tool for reservoir models and simulations. The system also constitutes a framework for further development and support for new data sources and visualization methods, e.g. additional solvers, seismic data, CSEM, geomechanics, and more. The user interface is tailored for efficient interpretation of reservoir simulation data with specialized visualizations of properties, faults and wells. It enables easy handling of a large number of realizations and calculation of statistics. To be […]

discretisedfield Marijan Beg1,2, Martin Lang1, Ryan A. Pepper1, Thomas Kluyver1,2, and Hans Fangohr1,2,3 1 Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom2 European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany3 Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany About discretisedfield is a Python package, integrated with Jupyter, providing: definition of finite-difference regions, meshes, lines, and fields, analysis of finite-difference fields, visualisation using matplotlib and k3d, and […]

textos-constituyentes propuestas electorales de los candidatos a constituyentes, Chile 2021 Programas descargados desde https://elecciones2021.servel.cl/informacion-general/ Metodología/Programación Inicialmente, bastaba con utilizar beautifulsoup + fitz en Python para descargar los archivos en formato PDF y extraer sus textos. Luego notamos que venían muchos vacíos, por corresponder a PDF escaneados (como el notable caso de una candidata del distrito 15 que subió su diploma). Para resolver este problema, recurrimos al servicio Transcribe de Amazon Cloud (con la librería boto3). GitHub https://github.com/sergiolucero/textos-constituyentes    

Stock-market-forecasting Forecasting directional movements of stock-prices for intraday trading using LSTM and random-foresthttps://arxiv.org/abs/2004.10178Pushpendu Ghosh, Ariel Neufeld, Jajati K Sahoo We design a highly profitable trading stratergy and employ random forests and LSTM networks (more precisely CuDNNLSTM) to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500, for intraday trading, from January 1993 till December 2018. Bibtex @article{ghosh2021forecasting, title={Forecasting directional movements of stock prices for intraday trading using LSTM and random forests}, author={Ghosh, Pushpendu and […]

PDBASER Are you tired of using visualization software, or full blown suites just to separate protein chains / ligands ? Are you tired of organizing the mess of molecules into separate folders ? PDBaser does this for you ! PDBaser reads raw .pdb and .ent files as downloaded from the pdb, extracts pure protein chains and heteroatoms (ligands and others) and removes water molecules, and then saves everything in a directory named as the original input filename. This tool is […]

Python-Practicum Python Practicum – prepare for your interview or just get a refresher. Data visualization using data on births from the state of North Carolina. Data manupulation is based on “txhousing.csv” GitHub https://github.com/trajceskijovan/Python-Practicum    

Service for visualization of high dimensional for hydrosphere DEPENDENCIES DEBUG_ENV = bool(os.getenv(“DEBUG_ENV”, False)) APP_PORT = int(os.getenv(“APP_PORT”, 5000)) GRPC_PORT = os.getenv(“GRPC_PORT”, 5001) GRPC_UI_ADDRESS = os.getenv(“GRPC_UI_ADDRESS”, “localhost:9090”) HS_CLUSTER_ADDRESS = os.getenv(“HTTP_UI_ADDRESS”, “http://localhost”) SECURE = os.getenv(“SECURE”, False) MONGO_URL = os.getenv(“MONGO_URL”, “mongodb”) MONGO_PORT = int(os.getenv(“MONGO_PORT”, 27017)) MONGO_AUTH_DB = os.getenv(“MONGO_AUTH_DB”, “admin”) MONGO_USER = os.getenv(“MONGO_USER”) MONGO_PASS = os.getenv(“MONGO_PASS”) AWS_STORAGE_ENDPOINT = os.getenv(‘AWS_STORAGE_ENDPOINT’, ”) AWS_REGION = os.getenv(‘AWS_REGION’, ”) HYDRO_VIS_BUCKET_NAME = os.getenv(‘AWS_BUCKET’, ‘hydro-vis’) Assumptions: Model must have in it’s contract ’embedding’ output If model returns class prediction and confidence these […]

Tokern Lineage Engine Tokern Lineage Engine is fast and easy to use application to collect, visualize and analyze column-level data lineage in databases, data warehouses and data lakes in AWS and GCP. Tokern Lineage helps you browse column-level data lineage Resources Demo of Tokern Lineage App Quick Start Install a demo of using Docker and Docker Compose Download the docker-compose file from Github repository. # in a new directory run wget https://raw.githubusercontent.com/tokern/data-lineage/master/install-manifests/docker-compose/catalog-demo.yml # or run curl https://raw.githubusercontent.com/tokern/data-lineage/master/install-manifests/docker-compose/catalog-demo.yml -o docker-compose.yml Run […]

PyMove PyMove is a Python library for processing and visualization of trajectories and other spatial-temporal data. Main Features PyMove proposes: A familiar and similar syntax to Pandas; Clear documentation; Extensibility, since you can implement your main data structure by manipulating other data structures such as Dask DataFrame, numpy arrays, etc., in addition to adding new modules; Flexibility, as the user can switch between different data structures; Operations for data preprocessing, pattern mining and data visualization. Creating a Virtual Environment It […]

matplotlib_for_papers Handout for the tutorial “Creating publication-quality figures with matplotlib” This repository contains the handout (and the source of the handout) for the tutorial “Creating publication-quality with Python and Matplotlib”, given at the Alife 2014 conference. Contributions are welcomed: feel free to clone and send pull requests. Examples of figures: Reference Tonelli, Paul, and Jean-Baptiste Mouret. “On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.” PloS one 8.11 (2013): e79138. Reference Clune*, Jeff, […]