{ "info": { "author": "Caleb Geniesse", "author_email": "geniesse@stanford.edu", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Visualization" ], "description": "\n\n

\n\n\n\n

\n\n\n## **DyNeuSR Fire**\n\nA command line interface for [DyNeuSR](https://braindynamicslab.github.io/dyneusr/) based on the [Python Fire](https://github.com/google/python-fire) library. \n\n\n\n## **Usage**\n\n[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/).\n\nTo 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.\n\n\n### **_Basic Usage_** \n\nYou can run the entire pipeline from the command line:\n```bash\n$ dyneusr-fire load_example --size=500 - run_mapper --projection=PCA(2) --resolution=10 --gain=0.5 - visualize\n```\n\n\n### **_Interactive Mode_** \n\nTo run in interactive mode, you can run the following from the command line:\n```bash\n$ dyneusr-fire init -- --interactive\n```\n\nThis will open an IPython shell.\n```python\nFire is starting a Python REPL with the following objects:\nModules: fire, np, pd\nObjects: Bunch, Cover, DBSCAN, DyNeuGraph, DyNeuSR, HDBSCAN, KMeans, KeplerMapper, MinMaxScaler, PCA, StandardScaler, TSNE, UMAP, check_estimator, component, f, result, self, trace\n\nPython 3.7.2 | packaged by conda-forge | (default, Mar 19 2019, 20:46:22) \nType 'copyright', 'credits' or 'license' for more information\nIPython 7.3.0 -- An enhanced Interactive Python. Type '?' for help.\n\nIn [1]: \n```\n\nThen, you can step through the pipeline:\n```python\nIn [1]: pipeline = DyNeuSR()\n\nIn [2]: pipeline.load_data(X='trefoil.npy', y='trefoil-target.npy')\n\nIn [3]: pipeline.run_mapper(projection=PCA(2), resolution=10, gain=0.5, clusterer=DBSCAN())\n\nIn [4]: pipeline.visualize()\n\n```\n\nOr, run it all at once:\n```python\nIn [1]: DyNeuSR().load_example().run_mapper(projection=PCA(2), resolution=10, gain=0.5, clusterer=DBSCAN()).visualize()\n```\n\nNote, 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.\n\n\n\n\n## **Setup**\n\n### **_Dependencies_**\n\n#### [Python 3.6+](https://www.python.org/)\n\n#### Required Python Packages\n* [fire](https://github.com/google/python-fire)\n* [dyneusr](https://braindynamicslab.github.io/dyneusr)\n* [kmapper](kepler-mapper.scikit-tda.org)\n* [sklearn](https://scikit-learn.org/)\n* [umap-learn](https://github.com/lmcinnes/umap)\n* [hdbscan](https://github.com/scikit-learn-contrib/hdbscan)\n\n\n### **_Install with PIP_**\n\n_To install with pip:_\n```bash\npip install dyneusr-fire\n```\n\n_To install from source:_\n```bash\ngit clone https://github.com/braindynamicslab/dyneusr-fire.git\ncd dyneusr-fire\n\npip install -e .\n```\n\n\n## **Support**\n\nPlease 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.\n\n\n\n## **Citation**\n\n> 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\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://braindynamicslab.github.io/dyneusr-fire", "keywords": "brain dynamics,topology data analysis,neuroimaging,brain networks,mapper,visualization", "license": "BSD-3", "maintainer": "", "maintainer_email": "", "name": "dyneusr-fire", "package_url": "https://pypi.org/project/dyneusr-fire/", "platform": "", "project_url": "https://pypi.org/project/dyneusr-fire/", "project_urls": { "Homepage": "https://braindynamicslab.github.io/dyneusr-fire" }, "release_url": "https://pypi.org/project/dyneusr-fire/0.0.3/", "requires_dist": [ "fire", "dyneusr", "kmapper", "sklearn", "umap-learn", "hdbscan" ], "requires_python": ">=3.6", "summary": "A command line interface for DyNeuSR", "version": "0.0.3" }, "last_serial": 5359887, "releases": { "0.0.1": [ { "comment_text": "", "digests": { "md5": "fab351314fa8db9d8ef5f90a4746de4e", "sha256": "6ed884967dedf6cc7543b2784de9d33286bcdd039027f619176149a5ab42157e" }, "downloads": -1, "filename": "dyneusr_fire-0.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "fab351314fa8db9d8ef5f90a4746de4e", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 5555, "upload_time": "2019-06-04T21:36:42", "url": "https://files.pythonhosted.org/packages/b8/27/3b919bae09a3e9ada168684827e7d9453eb8488c07c4ca80741062f87814/dyneusr_fire-0.0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "c93f3d4df33bfefbedf5e79db186919f", "sha256": "ae433ab8a7122571ac562ce0d50ef6df6c3f72cf583c6a69fa805d9e3d6d95d5" }, "downloads": -1, "filename": "dyneusr-fire-0.0.1.tar.gz", "has_sig": false, "md5_digest": "c93f3d4df33bfefbedf5e79db186919f", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 4618, "upload_time": "2019-06-04T21:36:44", "url": "https://files.pythonhosted.org/packages/bb/c0/be09be27727f30e62d902d6a3d1298b305660f8d69b214b0c7cbf15d0a51/dyneusr-fire-0.0.1.tar.gz" } ], "0.0.3": [ { "comment_text": "", "digests": { "md5": "5f11deb56f38914f7226baa82fb999cb", "sha256": "c41804cf677f7402199388d69d2da64dac421a3c0889d1542b7689d9ba7a89e0" }, "downloads": -1, "filename": "dyneusr_fire-0.0.3-py3-none-any.whl", "has_sig": false, "md5_digest": "5f11deb56f38914f7226baa82fb999cb", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 5544, "upload_time": "2019-06-04T22:24:48", "url": "https://files.pythonhosted.org/packages/88/57/041aa3edb1f170b2ed948981deb0045d88f1aa7056dde2a6ea716fc68b63/dyneusr_fire-0.0.3-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "10c74eb18514caeff67922304fcd7a23", "sha256": "9a86925dde3dd94c00a29bd79bcdb1956c3da5c420abbaff901a00d510dc2406" }, "downloads": -1, "filename": "dyneusr-fire-0.0.3.tar.gz", "has_sig": false, "md5_digest": "10c74eb18514caeff67922304fcd7a23", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 4667, "upload_time": "2019-06-04T22:24:51", "url": "https://files.pythonhosted.org/packages/35/4c/e6e7f3097811b0be6f5f330ce0c14a0c04f4cca5e50b2f140854f7c18ecd/dyneusr-fire-0.0.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "5f11deb56f38914f7226baa82fb999cb", "sha256": "c41804cf677f7402199388d69d2da64dac421a3c0889d1542b7689d9ba7a89e0" }, "downloads": -1, "filename": "dyneusr_fire-0.0.3-py3-none-any.whl", "has_sig": false, "md5_digest": "5f11deb56f38914f7226baa82fb999cb", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 5544, "upload_time": "2019-06-04T22:24:48", "url": "https://files.pythonhosted.org/packages/88/57/041aa3edb1f170b2ed948981deb0045d88f1aa7056dde2a6ea716fc68b63/dyneusr_fire-0.0.3-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "10c74eb18514caeff67922304fcd7a23", "sha256": "9a86925dde3dd94c00a29bd79bcdb1956c3da5c420abbaff901a00d510dc2406" }, "downloads": -1, "filename": "dyneusr-fire-0.0.3.tar.gz", "has_sig": false, "md5_digest": "10c74eb18514caeff67922304fcd7a23", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 4667, "upload_time": "2019-06-04T22:24:51", "url": "https://files.pythonhosted.org/packages/35/4c/e6e7f3097811b0be6f5f330ce0c14a0c04f4cca5e50b2f140854f7c18ecd/dyneusr-fire-0.0.3.tar.gz" } ] }