예제 #1
0
 def cluster(self,
             model=None,
             spike_ids=None,
             features=None,
             masks=None,
             ):
     # Get the features and masks.
     if model is not None:
         if features is None:
             features = PartialArray(model.features_masks, 0)
         if masks is None:
             masks = PartialArray(model.features_masks, 1)
     # Select some spikes if needed.
     if spike_ids is not None:
         features = features[spike_ids]
         masks = masks[spike_ids]
     # Convert the features and masks to the sparse structure used
     # by KK.
     data = sparsify_features_masks(features, masks)
     data = data.to_sparse_data()
     # Run KK.
     from klustakwik2 import KK
     num_starting_clusters = self._kwargs.pop('num_starting_clusters', 100)
     kk = KK(data, **self._kwargs)
     self.params = kk.all_params
     self.params['num_starting_clusters'] = num_starting_clusters
     kk.cluster_mask_starts(num_starting_clusters)
     spike_clusters = kk.clusters
     return spike_clusters
예제 #2
0
파일: klustakwik.py 프로젝트: CphInf/klusta
def klustakwik(model=None,
               spike_ids=None,
               spike_clusters=None,  # Initial spike clusters.
               features=None,
               masks=None,
               iter_callback=None,
               **kwargs
               ):
    """Run the clustering algorithm on the model, or on any features
    and masks.

    Return the `spike_clusters` assignements.

    """
    # Get the features and masks.
    if model is not None:
        if features is None:
            features = PartialArray(model.all_features_masks, 0)
        if masks is None:
            masks = PartialArray(model.all_features_masks, 1)
    # Select some spikes if needed.
    if spike_ids is not None:
        features = features[spike_ids]
        masks = masks[spike_ids]
    # Convert the features and masks to the sparse structure used
    # by KK.
    data = sparsify_features_masks(features, masks)
    data = data.to_sparse_data()

    # Instantiate KK.
    from klustakwik2 import KK, __version__
    kk = KK(data, **kwargs)

    @kk.register_callback
    def f(_):
        # Skip split iterations.
        if _.name != '':
            return
        if iter_callback:
            iter_callback(kk.clusters)

    # Launch KK.
    if spike_clusters is None:
        kk.cluster_mask_starts()
    else:
        kk.cluster_from(spike_clusters)
    spike_clusters = kk.clusters
    params = kk.params
    params['version'] = __version__
    return spike_clusters, params
예제 #3
0
파일: klustakwik.py 프로젝트: nsteinme/phy
    def cluster(
        self,
        model=None,
        spike_ids=None,
        features=None,
        masks=None,
    ):
        """Run the clustering algorithm on the model, or on any features
        and masks.

        Return the `spike_clusters` assignements.

        Emit the `iter` event at every KlustaKwik iteration.

        """
        # Get the features and masks.
        if model is not None:
            if features is None:
                features = PartialArray(model.features_masks, 0)
            if masks is None:
                masks = PartialArray(model.features_masks, 1)
        # Select some spikes if needed.
        if spike_ids is not None:
            features = features[spike_ids]
            masks = masks[spike_ids]
        # Convert the features and masks to the sparse structure used
        # by KK.
        data = sparsify_features_masks(features, masks)
        data = data.to_sparse_data()
        # Run KK.
        from klustakwik2 import KK
        kk = KK(data, **self._kwargs)

        @kk.register_callback
        def f(_):
            # Skip split iterations.
            if _.name != '':
                return
            self.emit('iter', kk.clusters)

        self.params = kk.all_params
        kk.cluster_mask_starts()
        spike_clusters = kk.clusters
        return spike_clusters
예제 #4
0
파일: klustakwik.py 프로젝트: Peichao/phy
    def cluster(self,
                model=None,
                spike_ids=None,
                features=None,
                masks=None,
                ):
        """Run the clustering algorithm on the model, or on any features
        and masks.

        Return the `spike_clusters` assignements.

        Emit the `iter` event at every KlustaKwik iteration.

        """
        # Get the features and masks.
        if model is not None:
            if features is None:
                features = PartialArray(model.features_masks, 0)
            if masks is None:
                masks = PartialArray(model.features_masks, 1)
        # Select some spikes if needed.
        if spike_ids is not None:
            features = features[spike_ids]
            masks = masks[spike_ids]
        # Convert the features and masks to the sparse structure used
        # by KK.
        data = sparsify_features_masks(features, masks)
        data = data.to_sparse_data()
        # Run KK.
        from klustakwik2 import KK
        kk = KK(data, **self._kwargs)

        @kk.register_callback
        def f(_):
            # Skip split iterations.
            if _.name != '':
                return
            self.emit('iter', kk.clusters)

        self.params = kk.all_params
        kk.cluster_mask_starts()
        spike_clusters = kk.clusters
        return spike_clusters
예제 #5
0
def klustakwik(
        model=None,
        spike_ids=None,
        spike_clusters=None,  # Initial spike clusters.
        features=None,
        masks=None,
        iter_callback=None,
        **kwargs):
    """Run the clustering algorithm on the model, or on any features
    and masks.

    Return the `spike_clusters` assignements.

    """
    # Get the features and masks.
    if model is not None:
        if features is None:
            features = PartialArray(model.all_features_masks, 0)
        if masks is None:
            masks = PartialArray(model.all_features_masks, 1)
    # Select some spikes if needed.
    if spike_ids is not None:
        features = features[spike_ids]
        masks = masks[spike_ids]
    # Convert the features and masks to the sparse structure used
    # by KK.
    data = sparsify_features_masks(features, masks)
    data = data.to_sparse_data()

    # Instantiate KK.
    from klustakwik2 import KK, __version__
    kk = KK(data, **kwargs)

    @kk.register_callback
    def f(_):
        # Skip split iterations.
        if _.name != '':
            return
        if iter_callback:
            iter_callback(kk.clusters)

    # Launch KK.
    if spike_clusters is None:
        kk.cluster_mask_starts()
    else:
        kk.cluster_from(spike_clusters)
    spike_clusters = kk.clusters
    params = kk.params
    params['version'] = __version__
    return spike_clusters, params