To run the server, you must follow these instructions: At the very beginning, just clone the repo and create a “venv” for it and install the requirements via: pip install -r requirements.txt windows First, you have to install postgresql. The instructions are held in this link below:https://www.postgresqltutorial.com/install-postgresql/ Set “password” and “port” carefully, as you should know them further. Then, open pgadmin and create a database named “notebook_researcher”. After that, open file“quran_django/settings/production.py” and change these items: On line 88, set “Name” […]

This repository contains the set of artficats used for the ENGN8601/8602 research project. The thesis emphasizes on the following aspects: Evaluating and Analysing the performance of existing learning-based MVS networks on internet images or in a CPC scenario. Proposing a novel mask estimation module and depth estimation (with depth alignment) framework to estimate depth values of foreground objects. Fusing the depthmaps estimated by the proposed methodology to compute complete point clouds (including foreground objects) Recommended: python 3.7.7, cudatoolkit 10.2.* and […]

Challenge to solve The rise of new technologies in medical imaging, such as artificial intelligence, is an great opportunity to accelerate research and facilitate clinical improvements. This requires access to innovative services and computational capabilities, that the cloud can provide, as well as imaging and clinical data to support research studies. Historically, it has been difficult for researchers to access medical images collected during the course of clinical care. This sometimes consists of a one-time, manual process where a clinical […]

Our released data can be found at this link. Make sure the following steps are adopted to use our codes. How to Use Init the repo Before using the repo, please run the following command to init: # create the necessay folders python init.py # prepare the environment # if some package cannot be installed, just google and install it from other ways pip install -r requirements.txt train the model

Scitizen has been built from the ground up to give everyone the possibility to contribute to scientific research ✨. No Ph.D. required 🧑‍🎓. ✨Overview You want to make the world a better place for future generations🌱? From computing molecules interactions for biomedical and environmental research🧬, analyzing pictures took by astronomical space telescopes to find a new home for humanity🔭, piercing the secrets of quantum physics by improving the design of the Large Hadron Collider🧲, you can do it all from […]

REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main features: unified python wrapper for different ML libraries (wrappers follow extended scikit-learn interface) Sklearn TMVA XGBoost uBoost Theanets Pybrain Neurolab MatrixNet service(available to CERN) parallel training of classifiers on cluster classification/regression reports with plots interactive plots supported smart grid-search algorithms with parallel execution research versioning using git pluggable quality metrics for classification meta-algorithm design (aka ‘rep-lego’) REP is not trying to substitute scikit-learn, but extends […]

MIKE IO: input/output of MIKE files in python Read, write and manipulate dfs0, dfs1, dfs2, dfs3, dfsu and mesh files. Facilitates common data processing workflows for MIKE files. For res1d and xns11 files use the related package MIKE IO 1D Requirements Windows or Linux operating system Python x64 3.6, 3.7,3.8 or 3.9 (Windows) VC++ redistributables (already installed if you have MIKE) More info about dependencies Where can I get help? Installation From PyPI: pip install mikeio Or development version (main […]

pymbar Python implementation of the multistate Bennett acceptance ratio (MBAR) method for estimating expectations and free energy differences from equilibrium samples from multiple probability densities. Installation The easiest way to install the pymbar release is via conda: conda install -c conda-forge pymbar You can also install pymbar from the Python package index using pip: pip install pymbar The development version can be installed directly from github via pip: pip install git+https://github.com/choderalab/pymbar.git Usage Basic usage involves importing pymbar and constructing an […]