{
    "info": {
        "author": "Alessio Buccino",
        "author_email": "alessiob@ifi.uio.no",
        "bugtrack_url": null,
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "[![Build Status](https://travis-ci.org/CINPLA/pyopenephys.svg?branch=master)](https://travis-ci.org/CINPLA/pyopenephys)\n[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)\n[![codecov](https://codecov.io/gh/CINPLA/py-open-ephys/branch/master/graph/badge.svg)](https://codecov.io/gh/CINPLA/py-open-ephys)\n\n# pyopenephys\nPython reader for [Open Ephys](www.open-ephys.org).\n\n## Installation\n\nIn order to install the pyopenephys package, open a terminal and run:\n\n`pip install pyopenephys`\n\nIf you want to install from sources and get the latest updates, clone the repo and install locally:\n\n```bash\ngit clone https://github.com/CINPLA/pyopenephys\ncd pyopenephys\npython setup.py install \n# use 'python setup.py develop' to install fixed bugs \n```\n\n## Basic Usage\n\nPyopenephys allows the user to load data recorded with [Open Ephys](www.open-ephys.org). Currently, only the **binary** (recommended) and **openephys** (support for this format will be dropped in future releases) are supported. \n\nThe first step is creating a `File` object. It only requires to pass the paht to the recording folder.\n\n```python\nimport pyopenephys\nfile = pyopenephys.File(\"path-to-recording-folder\") \n```\n\nThe file object contains the different experiments (corresponding to different settings files) and each experiment contains a set of recordings.\n\n```python\n# all experiments\nexperiments = file.experiments\nprint(len(experiments))\n\n# recordings of first experiment\nexperiment = experiments[0]\nrecordings = experiment.recordings\nprint(len(experiments))\n\n# access first recording\nrecording = recordings[0]\n```\n\nExperiments store some useful information: \n- `experiment.datetime` contains the starting date and time of the experiment creation\n- `experiment.sig_chain` is a dictionary containing the processors and nodes in the signal chain\n- `experiment.settings` is a dictionary with the parsed setting.xml file\n- `experiment.acquisition_system` contains the system used to input continuous data (e.g. 'Rhythm FPGA')\n\nRecordings contain the actual data: \n- `recording.duration` is the duration of the recording (in seconds)\n- `recording.sample_rate` is the sampling frequency (in Hz)\n- `recording.analog_signals` is list of `AnalogSignal` objects, which in turn have a `signal`, `times` (in s), and `channel_id` fields.\n- `recording.events` is list of `EventData` objects, which in turn have a `times` (in s), `channels`, `channel_states`, `full_words`, `processor`, `node_id`, and `metadata`  fields.\n- `recording.tracking` is list of `TrackingData` objects , which in turn have a `times` (in s), `x`, `y`, `width`, `height`, `channels`, and `metadata` fields. Tracking data are recorded with the `Tracking` plugin (https://github.com/CINPLA/tracking-plugin) and are save in **binary** format only (not in **openephys** format).\n- `recording.spiketrains` is list of `SpikeTrain` objects, which in turn have a `times`, `waveforms`, `electrode_indices`, `clusters` and `metadata` fields. Spiketrains are saved by the `Spike Viewer` sink in the Open Ephys GUI, in combination with either the `Spike Detector` and `Spike Viewer`.\n\n\nWith a few lines of code, the data and relevant information can be easily parsed and accessed:\n\n```python\nimport pyopenephys\nimport matplotlib.pylab as plt\n\nfile = pyopenephys.File(\"path-to-recording-folder\") \n# experiment 1 (0 in Python)\nexperiment = file.experiments[0]\n# recording 1 \nrecording = experiment.recordings[0]\n\nprint('Duration: ', recording.duration)\nprint('Sampling Rate: ', recording.sample_rate)\n\nanalog_signals = recording.analog_signals\nevents_data = recording.events\nspiketrains = recording.spiketrains\n# tracking_data are accessible only using binary format\ntracking_data = recording.tracking\n\n# plot analog signal of channel 4\nsignals = analog_signals[0]\nfig_an, ax_an = plt.subplots()\nax_an.plot(signals.times, signals.signal[3])\n\n# plot raster for spike trains\nfig_sp, ax_sp = plt.subplots()\nfor i_s, sp in enumerate(spiketrains):\n    ax_sp.plot(sp.times, i_s*np.ones(len(sp.times)), '|')\n\nplt.show()\n```",
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