Metadata-Version: 2.1
Name: dyneusr-fire
Version: 0.0.3
Summary: A command line interface for DyNeuSR
Home-page: https://braindynamicslab.github.io/dyneusr-fire
Author: Caleb Geniesse
Author-email: geniesse@stanford.edu
License: BSD-3
Description: 
        
        <p align="center">
        <a href="https://braindynamicslab.github.io/dyneusr/">
        <img src="https://raw.githubusercontent.com/braindynamicslab/dyneusr/master/docs/assets/logo.png" height="250">
        </a>
        </p>
        
        
        ## **DyNeuSR Fire**
        
        A command line interface for [DyNeuSR](https://braindynamicslab.github.io/dyneusr/) based on the [Python Fire](https://github.com/google/python-fire) library. 
        
        
        
        ## **Usage**
        
        [DyNeuSR Fire](https://braindynamicslab.github.io/dyneusr-fire/) provides a command line interface for [DyNeuSR](https://braindynamicslab.github.io/dyneusr/). It wraps `kmapper` and `dyneusr` into a single pipeline, and uses the [Python Fire](https://github.com/google/python-fire) library to automatically generate a simple command line interface that accepts several important options and allows users to customize this pipeline. For more information about DyNeuSR, check out the [docs](https://braindynamicslab.github.io/dyneusr/).
        
        To get started, check out the [examples](https://github.com/braindynamicslab/dyneusr-fire/tree/master/examples/), or try running one of the commands below on your own data.
        
        
        ### **_Basic Usage_** 
        
        You can run the entire pipeline from the command line:
        ```bash
        $ dyneusr-fire load_example --size=500 - run_mapper --projection=PCA(2) --resolution=10 --gain=0.5 - visualize
        ```
        
        
        ### **_Interactive Mode_** 
        
        To run in interactive mode, you can run the following from the command line:
        ```bash
        $ dyneusr-fire init -- --interactive
        ```
        
        This will open an IPython shell.
        ```python
        Fire is starting a Python REPL with the following objects:
        Modules: fire, np, pd
        Objects: Bunch, Cover, DBSCAN, DyNeuGraph, DyNeuSR, HDBSCAN, KMeans, KeplerMapper, MinMaxScaler, PCA, StandardScaler, TSNE, UMAP, check_estimator, component, f, result, self, trace
        
        Python 3.7.2 | packaged by conda-forge | (default, Mar 19 2019, 20:46:22) 
        Type 'copyright', 'credits' or 'license' for more information
        IPython 7.3.0 -- An enhanced Interactive Python. Type '?' for help.
        
        In [1]:                                                               
        ```
        
        Then, you can step through the pipeline:
        ```python
        In [1]: pipeline = DyNeuSR()
        
        In [2]: pipeline.load_data(X='trefoil.npy', y='trefoil-target.npy')
        
        In [3]: pipeline.run_mapper(projection=PCA(2), resolution=10, gain=0.5, clusterer=DBSCAN())
        
        In [4]: pipeline.visualize()
        
        ```
        
        Or, run it all at once:
        ```python
        In [1]: DyNeuSR().load_example().run_mapper(projection=PCA(2), resolution=10, gain=0.5, clusterer=DBSCAN()).visualize()
        ```
        
        Note, in the examples above, `load_example` is used for demo purposes only. You can replace `load_example` with `load_data` and load your own data by passing the file names of your data and target labels to the `X` and `y` arguments, respectively.
        
        
        
        
        ## **Setup**
        
        ### **_Dependencies_**
        
        #### [Python 3.6+](https://www.python.org/)
        
        #### Required Python Packages
        * [fire](https://github.com/google/python-fire)
        * [dyneusr](https://braindynamicslab.github.io/dyneusr)
        * [kmapper](kepler-mapper.scikit-tda.org)
        * [sklearn](https://scikit-learn.org/)
        * [umap-learn](https://github.com/lmcinnes/umap)
        * [hdbscan](https://github.com/scikit-learn-contrib/hdbscan)
        
        
        ### **_Install with PIP_**
        
        _To install with pip:_
        ```bash
        pip install dyneusr-fire
        ```
        
        _To install from source:_
        ```bash
        git clone https://github.com/braindynamicslab/dyneusr-fire.git
        cd dyneusr-fire
        
        pip install -e .
        ```
        
        
        ## **Support**
        
        Please feel free to [report](https://github.com/braindynamicslab/dyneusr-fire/issues/new) any issues, [request](https://github.com/braindynamicslab/dyneusr-fire/issues/new) new features, or [propose](https://github.com/braindynamicslab/dyneusr-fire/compare) improvements. You can also contact Caleb Geniesse at geniesse [at] stanford [dot] edu.
        
        
        
        ## **Citation**
        
        > Geniesse, C., Sporns, O., Petri, G., & Saggar, M. (2019). [Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis](https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00093). *Network Neuroscience*. Advance publication. doi:10.1162/netn_a_00093
        
Keywords: brain dynamics,topology data analysis,neuroimaging,brain networks,mapper,visualization
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
