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Extracellular Electrophysiology
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===============================
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At the Allen Institute for Brain Science we collect extracellular electrophysiology (ecephys) data using Neuropixels probes. This subpackage of the AllenSDK contains
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At the Allen Institute for Brain Science we collect **e**xtra**c**ellular **e**lectro**phys**iology (abbreviated as **ecephys**) data using [Neuropixels probes](https://www.nature.com/articles/nature24636). The primary data consists of spike times recorded from individual units, as well as continuous local field potential (LFP) signals recorded from individual electrodes. Each data point is spatially registered to a location along the Neuropixels probe shank and (in most cases) a specific 3D point in the Allen Mouse Common Coordinate Framework (CCFv3). These datasets are incredibly rich, and can be used to address a variety of scientific questions related to visual physiology, inter-area interactions, and state-dependent signal processing.
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This subpackage of the AllenSDK contains:
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- code for accessing and working with our ecephys data
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- code that we use for processing and analyzing these data in our pipelines
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- code for data pre-processing and [NWB file](https://www.nwb.org/how-to-use/) packaging
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- code for analyzing these data in our pipelines
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Python compatibility
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--------------------
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The code in this subpackage is guaranteed to be compatible with Python versions 3.6 and later. It is not compatible with Python 2.
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The code in this subpackage is guaranteed to be compatible with Python versions 3.6 and later. It is not compatible with Python 2.
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allensdk/brain_observatory/ecephys/ecephys_session.py

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'lfp_sampling_rate': 'probe_lfp_sampling_rate',
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'has_lfp_data': 'probe_has_lfp_data',
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'l_ratio': 'L_ratio',
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'p_value_rf': 'on_screen_rf',
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'on_screen_rf': 'p_value_rf',
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'pref_images_multi_ns': 'pref_image_multi_ns',
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})
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allensdk/brain_observatory/ecephys/stimulus_analysis/receptive_field_mapping.py

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"""
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return self.receptive_fields['spike_counts'].sel(unit_id=unit_id).data
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def _response_by_stimulus_position(self, dataset, presentations, row_key=None, column_key=None, unit_key='unit_id',
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time_key='time_relative_to_stimulus_onset', spike_count_key='spike_count'):
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""" Calculate the unit's response to different locations

allensdk/test/brain_observatory/ecephys/test_ecephys_session.py

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'local_index': [0, 0, 1],
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'peak_channel_id': [2, 1, 0],
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'quality': ['good', 'good', 'noise'],
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'snr': [0.1, 1.4, 10.0]
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'snr': [0.1, 1.4, 10.0],
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'on_screen_rf': [True, False, True],
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'p_value_rf': [0.001, 0.01, 0.05]
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}, index=pd.Index(name='unit_id', data=np.arange(3)[::-1]))
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assert 3 == session.num_units
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assert np.allclose([10, 22, 33], obtained['probe_vertical_position'])
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assert np.allclose([0, 1, 2], obtained.index.values)
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assert np.allclose([0.05, 0.01, 0.001], obtained['p_value_rf'].values)
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def test_presentationwise_spike_counts(spike_times_api):

doc_template/examples_root/examples/nb/ecephys_data_access.ipynb

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doc_template/examples_root/examples/nb/ecephys_lfp_analysis.ipynb

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doc_template/examples_root/examples/nb/ecephys_quality_metrics.ipynb

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doc_template/examples_root/examples/nb/ecephys_quickstart.ipynb

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"\n",
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"Contents\n",
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"-------------\n",
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"* <a href='#peristimulus time histograms'>peristimulus time histograms</a>\n",
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"* <a href='#image-decoding'>image decoding</a>"
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"* <a href='#peristimulus-time-histograms'>peristimulus time histograms</a>\n",
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"* <a href='#image-classification'>image classification</a>"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "py37",
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"display_name": "Python 3",
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"language": "python",
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"name": "py37"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.5"
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}
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},
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"nbformat": 4,

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