Metadata-Version: 1.1
Name: retinotopic-mapping
Version: 2.2.3
Summary: retinotopic mapping tools
Home-page: https://github.com/zhuangjun1981/retinotopic_mapping
Author: Jun Zhuang @ Allen Institute for Brain Science
Author-email: junz@alleninstitute.org
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: # retinotopic_mapping package
        
        by Jun Zhuang
        &copy; 2016 Allen Institute
        email: junz&lt;AT&gt;alleninstitute&lt;DOT&gt;org
        
        For a more thorough introduction and explanation of the module please
        see our [documentation](http://retinotopic-mapping.readthedocs.io/).
        If the online version of documentation looks incomplete. Please refer
        to the locally built html version in `/doc/build/html/` folder under
        `doc` branch.
        
        The retinotopic mapping package is a self-contained module
        for display visual stimuli in visual physiology experiments and
        for data analysis on the results of those experiments. This package is
        used to display visual stimulus and to analyze data for the study
        Zhuang et al., 2017 (7)
        
        The visual stimuli generation and display is implemented in the modules
        `MonitorSetup.py`, `StimulusRoutines.py` and `DisplayStimulus.py`.
        These modules allow you to display flashing circle, sparse noise,
        locally sparse noise, drifting grading circle, static grading circle
        and others with spherical correction. The method for spherical
        correction is the same as Marshel et al. 2011 (2). These stimulus
        routines are highly customizable and designed to give the user
        significant flexibility and control in creative experimental design.
        
        Please check the '\examples\visual_stimulation' folder for
        example script `example_stimulation.py` of visual stimulation.
        
        One specific analysis this package can perform is automated
        segmentation of the mouse visual cortex, which is implemented in
        `RetinotopicMapping.py` module.
        The experimental setup and analysis routine was
        modified from Garrett et al. 2014 (1), and closely follows
        the protocols and procedures documented in Juavinett et al. 2016
        (2).
        
        The analysis takes visual altitude and azimuth maps of mouse cortex
        as inputs, calculates the visual sign of each pixel and auto-segments
        the cortical surface into primary visual cortex and multiple higher
        visual cortices. Ideally, the visual altitude and azimuth maps can be
        generated by fourier analysis of population cortical responses to
        periodic sweeping checker board visual stimuli (3, 4).
        
        The package also provides some useful plotting functions to visualize
        the results.
        
        Please check the '\examples\signmap_analysis' folder for a [jupyter
        notebook](https://github.com/zhuangjun1981/retinotopic_mapping/blob/master/retinotopic_mapping/examples/signmap_analysis/retinotopic_mapping_example.ipynb)
        showing automated visual area segmentation of mouse cortex.
        
        ### Contributors:
        * Jun Zhuang @zhuangj
        * John Yearseley @yearsj
        * Derric Williams @derricw
        
        ### Level of support
        We are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.
        
        #### Language:
        
        1. python 2.7
        
        
        #### Install:
        ```
        cd <package_path>
        python setup.py install
        ```
        
        
        #### Dependencies:
        
        1. numpy, version 1.13.1 or later
        2. scipy, version 0.17.1 or later
        3. matplotlib, version 1.5.1 or later
        4. psychopy, version 1.85.2 or later
        5. pyglet, version 1.2.4 or later
        6. OpenCV-Python, version >= 2.4.8, <= 2.4.10
        7. scikit-image, version 0.12.3 or later
        8. tifffile, version >=0.7.0, <=0.10.0
        9. PIL, version 4.3.0 or later
        10. PyDAQmx, version 1.3.2 or later
           * requires National Instruments DAQmx driver, version 15.0 or later
        
        #### References:
        
        1. Garrett ME, Nauhaus I, Marshel JH, Callaway EM (2014) Topography and areal organization of mouse visual cortex. J Neurosci 34:12587-12600.
        
        2. Juavinett AL, Nauhaus I, Garrett ME, Zhuang J, Callaway EM (2017). Automated identification of mouse visual areas with intrinsic signal imaging. Nature Protocols. 12: 32-43.
        
        3. Kalatsky VA, Stryker MP (2003) New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron 38:529-545.
        
        4. Marshel JH, Kaye AP, Nauhaus I, Callaway EM (2012) Anterior-posterior direction opponency in the superficial mouse lateral geniculate nucleus. Neuron 76:713-720.
        
        5. Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW, Brady TJ, Rosen BR, Tootell RB (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268:889-893.
        
        6. Sereno MI, McDonald CT, Allman JM (1994) Analysis of retinotopic maps in extrastriate cortex. Cereb Cortex 4:601-620.
        
        7. Zhuang J, Ng L, Williams D, Valley M, Li Y, Garrett M, Waters J (2017) An extended retinotopic map of mouse cortex. eLife 6: e18372.
        
        
        #### Issues:
        
        1. Most image analysis parameters are defined as number of pixels, not microns.
        2. Works in windows, but not fully tested on Mac and Linux.
Platform: any
Classifier: Programming Language :: Python
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
