Tools that contain the Simulator Tool Topic : Graphics

(These tools produce or manipulate graphs.)
No.ToolDescription
1. Data thief-like python script that converts paper figures to NEURON vector dat files Retrieve (approximate) values from graphs! By first using inkscape (available for free from inkscape.org)(or equivalent) to prepare a file created by clicking on traces on a graph, this python script, traces2vecs.py, will create vector dat files suitable for reading into NEURON. See readme.txt for detailed usage instructions.
2. MFP For use with the NQS tool available in SimToolDB<p> Organizes conductance and tree information from ModelDB models into a relational database for data-mining.<p>
3. PANDORA Neural Analysis Toolbox <p>PANDORA is a Matlab Toolbox that makes database management accessible from your electrophysiology project. <p>PANDORA works by extracting user-defined characteristics from raw neural data (e.g., voltage traces) and creating numerical database tables from them. These tables can then be subjected to further analyses, such as invariant effects, statistical, correlation, and principal components. Publication-ready plots can be produced with an embedded plotting system. <p> PANDORA's features are: <ul> <li>Works offline within Matlab; <li>requires no external software; <li>is object oriented and allows easy extensions; <li>can easily tie with existing Matlab scripts; <li>can query a database as in SQL. </ul> <p>See <a href="http://software.incf.org/software/pandora">the PANDORA website</a> for finding documentation and the latest version of the toolbox.
4. SimTracker - Parallel NEURON network model simulation management & analysis The SimTracker tool streamlines the entire modeling process, from code writing/versioning and simulation design to execution of simulations on a variety of machines (local, remote supercomputer, NSG) to organization and analysis of simulation results. It also provides tools to: - characterize model components (cells, synapses, ion channels) in experimentalist-friendly ways - read in experimental data and fit model components using that data (in conjunction with NEURON's Multiple Run Fitter) - Explore the parameter space of the model - Share model specifications and results online in an interactive manner, as an alternative to placing all the figures/tables into a supplemental PDF for a publication, although LaTeX code for a PDF can also be generated.