Search Results for "numpy python 2.7"

Open Drug Discovery Toolkit (ODDT) is modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc. ODDT is written in Python, and makes extensive use of Numpy/Scipy. You can use any supported toolkit united under common API (for reference see Pybel or Cinfony). All methods and software based on Pybel/Cinfony should be drop-in compatible with ODDT toolkits. In contrast to its predecessors, which were aimed to have minimalistic API, ODDT introduces extended methods and additional handles. ...

RELEASE NOTE: Get raxmlGUI 2.0 at the NEW PROJECT LOCATION: https://antonellilab.github.io/raxmlGUI/ raxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used software for phylogenetic inference using maximum likelihood. A userfriendly graphical front-end for phylogenetic analyses using RAxML (Stamatakis, 2006). Please cite: Silvestro, Michalak (2012) - raxmlGUI: a graphical front-end for RAxML. Organisms Diversity and Evolution 12, 335-337. DOI:...

pySBOL is a SWIG-Python wrapper around libSBOL, a module for reading, writing, and constructing genetic designs according to the standardized specifications of the Synthetic Biology Open Language (SBOL). This page provides binary installers for Windows operating system for Python 2.7 and 3.6. For installation instructions, visit: https://pysbol2.readthedocs.io/en/latest/installation.html For full documentation, visit: https://pysbol2.readthedocs.io/en/latest/index.html

This project contains Python scripts usable for a dual transcript-discovery approach that improves the delimitation of gene features from RNA-seq data in the chicken model. Documentation: http://bio.biologists.org/content/biolopen/suppl/2018/01/17/bio.028498.DC1/BIO028498supp.pdf Citation: Orgeur M., Martens M., Börno S. T., Timmermann B., Duprez D. and Stricker S. (2018). A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the...

...It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.

This is an automatic spike detection program which takes account of probe geometry and produces a .mask file to be used with the new masked version of KlustaKwik. We recommend you use Python 2.6 or 2.7, e.g. a free academic version can be obtained from Entthought Python. The input files for SpiKeDeteKt are: .dat (raw data file) .probe (probe file, described below - user constructed) parameters.py (optional - otherwise it uses defaultparameters.py) SpiKeDeteKt outputs the following files: .fet.n (feature file) .mask.n (needed for using the new (masked) KlustaKwik) .clu.n (a trivial clue file where everything is put into a single cluster) .fmask.n (trial - float masks instead of binary, we are using this for testing masked KlustaKwik) .spk.n (spike file) .upsk.n (unfiltered spike waveform) .res.n (list of spike times) .xml (an xml file with all the parameters that can subsequently be used by neuroscope or klusters) .fil (highpass filtered data) .h5 (