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* env file for conda, more TimeSeries docs, misc * bincount2D import fix * WIP: xcorr and acorr functions * Sig change for bb.processing.sync to take times/vals first * Create raster_cluster_ordered.py * first pass on plotting psths * Added conda env files for core packages and complete dev including jupyter etc * Modified env.yml files to contain only minimal requirements. Other sub-dependencies will be resolved by conda * env file for conda, more TimeSeries docs, misc * bincount2D import fix * WIP: xcorr and acorr functions * Create raster_cluster_ordered.py * first pass on plotting psths * Added conda env files for core packages and complete dev including jupyter etc * Modified env.yml files to contain only minimal requirements. Other sub-dependencies will be resolved by conda * renamed conda env * minimalized env .yml for cross-platform use * typo fix * flake * modelbox first commit * cleanup filenames * readme * oops * Added plot module to brainbox. Currently empty. * Flakify and make unittest pass
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,190 @@ | ||
| name: brainboxdev | ||
| channels: | ||
| - defaults | ||
| dependencies: | ||
| - _libgcc_mutex=0.1=main | ||
| - _tflow_select=2.3.0=mkl | ||
| - absl-py=0.7.1=py37_0 | ||
| - alabaster=0.7.12=py37_0 | ||
| - asn1crypto=0.24.0=py37_0 | ||
| - astor=0.7.1=py37_0 | ||
| - astroid=2.2.5=py37_0 | ||
| - attrs=19.1.0=py37_1 | ||
| - babel=2.7.0=py_0 | ||
| - backcall=0.1.0=py37_0 | ||
| - blas=1.0=mkl | ||
| - bleach=3.1.0=py37_0 | ||
| - bzip2=1.0.8=h7b6447c_0 | ||
| - c-ares=1.15.0=h7b6447c_1 | ||
| - ca-certificates=2019.5.15=0 | ||
| - certifi=2019.6.16=py37_0 | ||
| - cffi=1.12.2=py37h2e261b9_1 | ||
| - chardet=3.0.4=py37_1 | ||
| - cloudpickle=1.2.1=py_0 | ||
| - conda-package-handling=1.3.11=py37_0 | ||
| - cryptography=2.6.1=py37h1ba5d50_0 | ||
| - cycler=0.10.0=py37_0 | ||
| - dbus=1.13.6=h746ee38_0 | ||
| - decorator=4.4.0=py37_1 | ||
| - defusedxml=0.6.0=py_0 | ||
| - docutils=0.14=py37_0 | ||
| - entrypoints=0.3=py37_0 | ||
| - expat=2.2.6=he6710b0_0 | ||
| - fontconfig=2.13.0=h9420a91_0 | ||
| - freetype=2.9.1=h8a8886c_1 | ||
| - gast=0.2.2=py37_0 | ||
| - glib=2.56.2=hd408876_0 | ||
| - gmp=6.1.2=h6c8ec71_1 | ||
| - google-pasta=0.1.7=py_0 | ||
| - grpcio=1.16.1=py37hf8bcb03_1 | ||
| - gst-plugins-base=1.14.0=hbbd80ab_1 | ||
| - gstreamer=1.14.0=hb453b48_1 | ||
| - h5py=2.9.0=py37h7918eee_0 | ||
| - hdf5=1.10.4=hb1b8bf9_0 | ||
| - icu=58.2=h9c2bf20_1 | ||
| - idna=2.8=py37_0 | ||
| - imagesize=1.1.0=py37_0 | ||
| - intel-openmp=2019.4=243 | ||
| - ipykernel=5.1.1=py37h39e3cac_0 | ||
| - ipython=7.6.1=py37h39e3cac_0 | ||
| - ipython_genutils=0.2.0=py37_0 | ||
| - ipywidgets=7.5.0=py_0 | ||
| - isort=4.3.21=py37_0 | ||
| - jedi=0.13.3=py37_0 | ||
| - jeepney=0.4=py37_0 | ||
| - jinja2=2.10.1=py37_0 | ||
| - joblib=0.13.2=py37_0 | ||
| - jpeg=9b=h024ee3a_2 | ||
| - jsonschema=3.0.1=py37_0 | ||
| - jupyter=1.0.0=py37_7 | ||
| - jupyter_client=5.3.1=py_0 | ||
| - jupyter_console=6.0.0=py37_0 | ||
| - jupyter_core=4.5.0=py_0 | ||
| - keras-applications=1.0.8=py_0 | ||
| - keras-preprocessing=1.1.0=py_1 | ||
| - keyring=18.0.0=py37_0 | ||
| - kiwisolver=1.1.0=py37he6710b0_0 | ||
| - lazy-object-proxy=1.4.1=py37h7b6447c_0 | ||
| - libarchive=3.3.3=h5d8350f_5 | ||
| - libedit=3.1.20181209=hc058e9b_0 | ||
| - libffi=3.2.1=hd88cf55_4 | ||
| - libgcc-ng=8.2.0=hdf63c60_1 | ||
| - libgfortran-ng=7.3.0=hdf63c60_0 | ||
| - libpng=1.6.37=hbc83047_0 | ||
| - libprotobuf=3.8.0=hd408876_0 | ||
| - libsodium=1.0.16=h1bed415_0 | ||
| - libstdcxx-ng=8.2.0=hdf63c60_1 | ||
| - libuuid=1.0.3=h1bed415_2 | ||
| - libxcb=1.13=h1bed415_1 | ||
| - libxml2=2.9.9=hea5a465_1 | ||
| - lz4-c=1.8.1.2=h14c3975_0 | ||
| - lzo=2.10=h49e0be7_2 | ||
| - markdown=3.1.1=py37_0 | ||
| - markupsafe=1.1.1=py37h7b6447c_0 | ||
| - matplotlib=3.1.0=py37h5429711_0 | ||
| - mccabe=0.6.1=py37_1 | ||
| - mistune=0.8.4=py37h7b6447c_0 | ||
| - mkl=2019.4=243 | ||
| - mkl-service=2.0.2=py37h7b6447c_0 | ||
| - mkl_fft=1.0.12=py37ha843d7b_0 | ||
| - mkl_random=1.0.2=py37hd81dba3_0 | ||
| - nbconvert=5.5.0=py_0 | ||
| - nbformat=4.4.0=py37_0 | ||
| - ncurses=6.1=he6710b0_1 | ||
| - notebook=5.7.8=py37_0 | ||
| - numpy=1.16.4=py37h7e9f1db_0 | ||
| - numpy-base=1.16.4=py37hde5b4d6_0 | ||
| - numpydoc=0.9.1=py_0 | ||
| - openssl=1.1.1c=h7b6447c_1 | ||
| - packaging=19.0=py37_0 | ||
| - pandas=0.24.2=py37he6710b0_0 | ||
| - pandoc=2.2.3.2=0 | ||
| - pandocfilters=1.4.2=py37_1 | ||
| - parso=0.5.0=py_0 | ||
| - patsy=0.5.1=py37_0 | ||
| - pcre=8.43=he6710b0_0 | ||
| - pexpect=4.7.0=py37_0 | ||
| - pickleshare=0.7.5=py37_0 | ||
| - pip=19.0.3=py37_0 | ||
| - prometheus_client=0.7.1=py_0 | ||
| - prompt_toolkit=2.0.9=py37_0 | ||
| - protobuf=3.8.0=py37he6710b0_0 | ||
| - psutil=5.6.3=py37h7b6447c_0 | ||
| - ptyprocess=0.6.0=py37_0 | ||
| - pycodestyle=2.5.0=py37_0 | ||
| - pycosat=0.6.3=py37h14c3975_0 | ||
| - pycparser=2.19=py37_0 | ||
| - pyflakes=2.1.1=py37_0 | ||
| - pygments=2.4.2=py_0 | ||
| - pylint=2.3.1=py37_0 | ||
| - pyopenssl=19.0.0=py37_0 | ||
| - pyparsing=2.4.0=py_0 | ||
| - pyqt=5.9.2=py37h05f1152_2 | ||
| - pyrsistent=0.14.11=py37h7b6447c_0 | ||
| - pysocks=1.6.8=py37_0 | ||
| - python=3.7.3=h0371630_0 | ||
| - python-dateutil=2.8.0=py37_0 | ||
| - python-libarchive-c=2.8=py37_10 | ||
| - pytz=2019.1=py_0 | ||
| - pyzmq=18.0.0=py37he6710b0_0 | ||
| - qt=5.9.7=h5867ecd_1 | ||
| - qtawesome=0.5.7=py37_1 | ||
| - qtconsole=4.5.1=py_0 | ||
| - qtpy=1.8.0=py_0 | ||
| - readline=7.0=h7b6447c_5 | ||
| - requests=2.22.0=py37_0 | ||
| - rope=0.14.0=py_0 | ||
| - ruamel_yaml=0.15.46=py37h14c3975_0 | ||
| - scikit-learn=0.21.2=py37hd81dba3_0 | ||
| - seaborn=0.9.0=py37_0 | ||
| - secretstorage=3.1.1=py37_0 | ||
| - send2trash=1.5.0=py37_0 | ||
| - setuptools=41.0.0=py37_0 | ||
| - sip=4.19.8=py37hf484d3e_0 | ||
| - six=1.12.0=py37_0 | ||
| - snowballstemmer=1.9.0=py_0 | ||
| - sphinx=2.1.2=py_0 | ||
| - sphinxcontrib-applehelp=1.0.1=py_0 | ||
| - sphinxcontrib-devhelp=1.0.1=py_0 | ||
| - sphinxcontrib-htmlhelp=1.0.2=py_0 | ||
| - sphinxcontrib-jsmath=1.0.1=py_0 | ||
| - sphinxcontrib-qthelp=1.0.2=py_0 | ||
| - sphinxcontrib-serializinghtml=1.1.3=py_0 | ||
| - spyder=3.3.6=py37_0 | ||
| - spyder-kernels=0.5.1=py37_0 | ||
| - sqlite=3.27.2=h7b6447c_0 | ||
| - statsmodels=0.10.0=py37hdd07704_0 | ||
| - tensorboard=1.14.0=py37hf484d3e_0 | ||
| - tensorflow=1.14.0=mkl_py37h45c423b_0 | ||
| - tensorflow-base=1.14.0=mkl_py37h7ce6ba3_0 | ||
| - tensorflow-estimator=1.14.0=py_0 | ||
| - termcolor=1.1.0=py37_1 | ||
| - terminado=0.8.2=py37_0 | ||
| - testpath=0.4.2=py37_0 | ||
| - tk=8.6.8=hbc83047_0 | ||
| - tornado=6.0.3=py37h7b6447c_0 | ||
| - tqdm=4.32.1=py_0 | ||
| - traitlets=4.3.2=py37_0 | ||
| - urllib3=1.24.1=py37_0 | ||
| - wcwidth=0.1.7=py37_0 | ||
| - webencodings=0.5.1=py37_1 | ||
| - werkzeug=0.15.4=py_0 | ||
| - wheel=0.33.1=py37_0 | ||
| - widgetsnbextension=3.5.0=py37_0 | ||
| - wrapt=1.11.2=py37h7b6447c_0 | ||
| - wurlitzer=1.0.2=py37_0 | ||
| - xz=5.2.4=h14c3975_4 | ||
| - yaml=0.1.7=had09818_2 | ||
| - zeromq=4.3.1=he6710b0_3 | ||
| - zlib=1.2.11=h7b6447c_3 | ||
| - zstd=1.3.7=h0b5b093_0 | ||
| - pip: | ||
| - autopep8==1.4.4 | ||
| - colorlog==4.0.2 | ||
| - dataclasses==0.6 | ||
| - flake8==3.7.8 | ||
| - globus-sdk==1.8.0 | ||
| - ibllib==1.0.6 | ||
| - pyjwt==1.7.1 | ||
| - scipy==1.3.0 | ||
|
|
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1 @@ | ||
| from .population import * |
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| ''' | ||
| Population functions. | ||
| Code from https://github.com/cortex-lab/phylib/blob/master/phylib/stats/ccg.py by C. Rossant. | ||
| ''' | ||
|
|
||
| import numpy as np | ||
|
|
||
|
|
||
| def _index_of(arr, lookup): | ||
| """Replace scalars in an array by their indices in a lookup table. | ||
| Implicitely assume that: | ||
| * All elements of arr and lookup are non-negative integers. | ||
| * All elements or arr belong to lookup. | ||
| This is not checked for performance reasons. | ||
| """ | ||
| # Equivalent of np.digitize(arr, lookup) - 1, but much faster. | ||
| # TODO: assertions to disable in production for performance reasons. | ||
| # TODO: np.searchsorted(lookup, arr) is faster on small arrays with large | ||
| # values | ||
| lookup = np.asarray(lookup, dtype=np.int32) | ||
| m = (lookup.max() if len(lookup) else 0) + 1 | ||
| tmp = np.zeros(m + 1, dtype=np.int) | ||
| # Ensure that -1 values are kept. | ||
| tmp[-1] = -1 | ||
| if len(lookup): | ||
| tmp[lookup] = np.arange(len(lookup)) | ||
| return tmp[arr] | ||
|
|
||
|
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||
| def _increment(arr, indices): | ||
| """Increment some indices in a 1D vector of non-negative integers. | ||
| Repeated indices are taken into account.""" | ||
| bbins = np.bincount(indices) | ||
| arr[:len(bbins)] += bbins | ||
| return arr | ||
|
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| def _diff_shifted(arr, steps=1): | ||
| return arr[steps:] - arr[:len(arr) - steps] | ||
|
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| def _create_correlograms_array(n_clusters, winsize_bins): | ||
| return np.zeros((n_clusters, n_clusters, winsize_bins // 2 + 1), dtype=np.int32) | ||
|
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|
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| def _symmetrize_correlograms(correlograms): | ||
| """Return the symmetrized version of the CCG arrays.""" | ||
|
|
||
| n_clusters, _, n_bins = correlograms.shape | ||
| assert n_clusters == _ | ||
|
|
||
| # We symmetrize c[i, j, 0]. | ||
| # This is necessary because the algorithm in correlograms() | ||
| # is sensitive to the order of identical spikes. | ||
| correlograms[..., 0] = np.maximum( | ||
| correlograms[..., 0], correlograms[..., 0].T) | ||
|
|
||
| sym = correlograms[..., 1:][..., ::-1] | ||
| sym = np.transpose(sym, (1, 0, 2)) | ||
|
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| return np.dstack((sym, correlograms)) | ||
|
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| def xcorr(spike_times, spike_clusters, bin_size=None, window_size=None): | ||
| """Compute all pairwise cross-correlograms among the clusters appearing in `spike_clusters`. | ||
| Parameters | ||
| ---------- | ||
| :param spike_times: Spike times in seconds. | ||
| :type spike_times: array-like | ||
| :param spike_clusters: Spike-cluster mapping. | ||
| :type spike_clusters: array-like | ||
| :param bin_size: Size of the bin, in seconds. | ||
| :type bin_size: float | ||
| :param window_size: Size of the window, in seconds. | ||
| :type window_size: float | ||
| Returns an `(n_clusters, n_clusters, winsize_samples)` array with all pairwise | ||
| cross-correlograms. | ||
| """ | ||
| assert np.all(np.diff(spike_times) >= 0), ("The spike times must be increasing.") | ||
| assert spike_times.ndim == 1 | ||
| assert spike_times.shape == spike_clusters.shape | ||
|
|
||
| # Find `binsize`. | ||
| bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds | ||
|
|
||
| # Find `winsize_bins`. | ||
| window_size = np.clip(window_size, 1e-5, 1e5) # in seconds | ||
| winsize_bins = 2 * int(.5 * window_size / bin_size) + 1 | ||
|
|
||
| # Take the cluster order into account. | ||
| clusters = np.unique(spike_clusters) | ||
| n_clusters = len(clusters) | ||
|
|
||
| # Like spike_clusters, but with 0..n_clusters-1 indices. | ||
| spike_clusters_i = _index_of(spike_clusters, clusters) | ||
|
|
||
| # Shift between the two copies of the spike trains. | ||
| shift = 1 | ||
|
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||
| # At a given shift, the mask precises which spikes have matching spikes | ||
| # within the correlogram time window. | ||
| mask = np.ones_like(spike_times, dtype=np.bool) | ||
|
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||
| correlograms = _create_correlograms_array(n_clusters, winsize_bins) | ||
|
|
||
| # The loop continues as long as there is at least one spike with | ||
| # a matching spike. | ||
| while mask[:-shift].any(): | ||
| # Interval between spike i and spike i+shift. | ||
| spike_diff = _diff_shifted(spike_times, shift) | ||
|
|
||
| # Binarize the delays between spike i and spike i+shift. | ||
| spike_diff_b = np.round(spike_diff / bin_size).astype(np.int64) | ||
|
|
||
| # Spikes with no matching spikes are masked. | ||
| mask[:-shift][spike_diff_b > (winsize_bins / 2)] = False | ||
|
|
||
| # Cache the masked spike delays. | ||
| m = mask[:-shift].copy() | ||
| d = spike_diff_b[m] | ||
|
|
||
| # Find the indices in the raveled correlograms array that need | ||
| # to be incremented, taking into account the spike clusters. | ||
| indices = np.ravel_multi_index( | ||
| (spike_clusters_i[:-shift][m], spike_clusters_i[+shift:][m], d), correlograms.shape) | ||
|
|
||
| # Increment the matching spikes in the correlograms array. | ||
| _increment(correlograms.ravel(), indices) | ||
|
|
||
| shift += 1 | ||
|
|
||
| return _symmetrize_correlograms(correlograms) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1 @@ | ||
| from .singlecell import * |
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