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kwikteam/phy
|
phy/cluster/clustering.py
|
_extend_spikes
|
def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids,
assume_unique=True)
return extended_spike_ids
|
python
|
def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids,
assume_unique=True)
return extended_spike_ids
|
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Return all spikes belonging to the clusters containing the specified
spikes.
|
[
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"the",
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"containing",
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"specified",
"spikes",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L29-L41
|
train
|
kwikteam/phy
|
phy/cluster/clustering.py
|
Clustering.reset
|
def reset(self):
"""Reset the clustering to the original clustering.
All changes are lost.
"""
self._undo_stack.clear()
self._spike_clusters = self._spike_clusters_base
self._new_cluster_id = self._new_cluster_id_0
|
python
|
def reset(self):
"""Reset the clustering to the original clustering.
All changes are lost.
"""
self._undo_stack.clear()
self._spike_clusters = self._spike_clusters_base
self._new_cluster_id = self._new_cluster_id_0
|
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"=",
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] |
Reset the clustering to the original clustering.
All changes are lost.
|
[
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"clustering",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L178-L186
|
train
|
kwikteam/phy
|
phy/cluster/clustering.py
|
Clustering._do_assign
|
def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids,
old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up
|
python
|
def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids,
old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up
|
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Make spike-cluster assignments after the spike selection has
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|
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"has",
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"to",
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"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L258-L296
|
train
|
kwikteam/phy
|
phy/cluster/clustering.py
|
Clustering.merge
|
def merge(self, cluster_ids, to=None):
"""Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
"""
if not _is_array_like(cluster_ids):
raise ValueError("The first argument should be a list or "
"an array.")
cluster_ids = sorted(cluster_ids)
if not set(cluster_ids) <= set(self.cluster_ids):
raise ValueError("Some clusters do not exist.")
# Find the new cluster number.
if to is None:
to = self.new_cluster_id()
if to < self.new_cluster_id():
raise ValueError("The new cluster numbers should be higher than "
"{0}.".format(self.new_cluster_id()))
# NOTE: we could have called self.assign() here, but we don't.
# We circumvent self.assign() for performance reasons.
# assign() is a relatively costly operation, whereas merging is a much
# cheaper operation.
# Find all spikes in the specified clusters.
spike_ids = _spikes_in_clusters(self.spike_clusters, cluster_ids)
up = self._do_merge(spike_ids, cluster_ids, to)
undo_state = self.emit('request_undo_state', up)
# Add to stack.
self._undo_stack.add((spike_ids, [to], undo_state))
self.emit('cluster', up)
return up
|
python
|
def merge(self, cluster_ids, to=None):
"""Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
"""
if not _is_array_like(cluster_ids):
raise ValueError("The first argument should be a list or "
"an array.")
cluster_ids = sorted(cluster_ids)
if not set(cluster_ids) <= set(self.cluster_ids):
raise ValueError("Some clusters do not exist.")
# Find the new cluster number.
if to is None:
to = self.new_cluster_id()
if to < self.new_cluster_id():
raise ValueError("The new cluster numbers should be higher than "
"{0}.".format(self.new_cluster_id()))
# NOTE: we could have called self.assign() here, but we don't.
# We circumvent self.assign() for performance reasons.
# assign() is a relatively costly operation, whereas merging is a much
# cheaper operation.
# Find all spikes in the specified clusters.
spike_ids = _spikes_in_clusters(self.spike_clusters, cluster_ids)
up = self._do_merge(spike_ids, cluster_ids, to)
undo_state = self.emit('request_undo_state', up)
# Add to stack.
self._undo_stack.add((spike_ids, [to], undo_state))
self.emit('cluster', up)
return up
|
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Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
|
[
"Merge",
"several",
"clusters",
"to",
"a",
"new",
"cluster",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L320-L368
|
train
|
kwikteam/phy
|
phy/cluster/clustering.py
|
Clustering.assign
|
def assign(self, spike_ids, spike_clusters_rel=0):
"""Make new spike cluster assignments.
Parameters
----------
spike_ids : array-like
List of spike ids.
spike_clusters_rel : array-like
Relative cluster ids of the spikes in `spike_ids`. This
must have the same size as `spike_ids`.
Returns
-------
up : UpdateInfo instance
Note
----
`spike_clusters_rel` contain *relative* cluster indices. Their values
don't matter: what matters is whether two give spikes
should end up in the same cluster or not. Adding a constant number
to all elements in `spike_clusters_rel` results in exactly the same
operation.
The final cluster ids are automatically generated by the `Clustering`
class. This is because we must ensure that all modified clusters
get brand new ids. The whole library is based on the assumption that
cluster ids are unique and "disposable". Changing a cluster always
results in a new cluster id being assigned.
If a spike is assigned to a new cluster, then all other spikes
belonging to the same cluster are assigned to a brand new cluster,
even if they were not changed explicitely by the `assign()` method.
In other words, the list of spikes affected by an `assign()` is almost
always a strict superset of the `spike_ids` parameter. The only case
where this is not true is when whole clusters change: this is called
a merge. It is implemented in a separate `merge()` method because it
is logically much simpler, and faster to execute.
"""
assert not isinstance(spike_ids, slice)
# Ensure `spike_clusters_rel` is an array-like.
if not hasattr(spike_clusters_rel, '__len__'):
spike_clusters_rel = spike_clusters_rel * np.ones(len(spike_ids),
dtype=np.int64)
spike_ids = _as_array(spike_ids)
if len(spike_ids) == 0:
return UpdateInfo()
assert len(spike_ids) == len(spike_clusters_rel)
assert spike_ids.min() >= 0
assert spike_ids.max() < self._n_spikes, "Some spikes don't exist."
# Normalize the spike-cluster assignment such that
# there are only new or dead clusters, not modified clusters.
# This implies that spikes not explicitly selected, but that
# belong to clusters affected by the operation, will be assigned
# to brand new clusters.
spike_ids, cluster_ids = _extend_assignment(spike_ids,
self._spike_clusters,
spike_clusters_rel,
self.new_cluster_id(),
)
up = self._do_assign(spike_ids, cluster_ids)
undo_state = self.emit('request_undo_state', up)
# Add the assignment to the undo stack.
self._undo_stack.add((spike_ids, cluster_ids, undo_state))
self.emit('cluster', up)
return up
|
python
|
def assign(self, spike_ids, spike_clusters_rel=0):
"""Make new spike cluster assignments.
Parameters
----------
spike_ids : array-like
List of spike ids.
spike_clusters_rel : array-like
Relative cluster ids of the spikes in `spike_ids`. This
must have the same size as `spike_ids`.
Returns
-------
up : UpdateInfo instance
Note
----
`spike_clusters_rel` contain *relative* cluster indices. Their values
don't matter: what matters is whether two give spikes
should end up in the same cluster or not. Adding a constant number
to all elements in `spike_clusters_rel` results in exactly the same
operation.
The final cluster ids are automatically generated by the `Clustering`
class. This is because we must ensure that all modified clusters
get brand new ids. The whole library is based on the assumption that
cluster ids are unique and "disposable". Changing a cluster always
results in a new cluster id being assigned.
If a spike is assigned to a new cluster, then all other spikes
belonging to the same cluster are assigned to a brand new cluster,
even if they were not changed explicitely by the `assign()` method.
In other words, the list of spikes affected by an `assign()` is almost
always a strict superset of the `spike_ids` parameter. The only case
where this is not true is when whole clusters change: this is called
a merge. It is implemented in a separate `merge()` method because it
is logically much simpler, and faster to execute.
"""
assert not isinstance(spike_ids, slice)
# Ensure `spike_clusters_rel` is an array-like.
if not hasattr(spike_clusters_rel, '__len__'):
spike_clusters_rel = spike_clusters_rel * np.ones(len(spike_ids),
dtype=np.int64)
spike_ids = _as_array(spike_ids)
if len(spike_ids) == 0:
return UpdateInfo()
assert len(spike_ids) == len(spike_clusters_rel)
assert spike_ids.min() >= 0
assert spike_ids.max() < self._n_spikes, "Some spikes don't exist."
# Normalize the spike-cluster assignment such that
# there are only new or dead clusters, not modified clusters.
# This implies that spikes not explicitly selected, but that
# belong to clusters affected by the operation, will be assigned
# to brand new clusters.
spike_ids, cluster_ids = _extend_assignment(spike_ids,
self._spike_clusters,
spike_clusters_rel,
self.new_cluster_id(),
)
up = self._do_assign(spike_ids, cluster_ids)
undo_state = self.emit('request_undo_state', up)
# Add the assignment to the undo stack.
self._undo_stack.add((spike_ids, cluster_ids, undo_state))
self.emit('cluster', up)
return up
|
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".",
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",",
"\"Some spikes don't exist.\"",
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"# there are only new or dead clusters, not modified clusters.",
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"# belong to clusters affected by the operation, will be assigned",
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",",
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Make new spike cluster assignments.
Parameters
----------
spike_ids : array-like
List of spike ids.
spike_clusters_rel : array-like
Relative cluster ids of the spikes in `spike_ids`. This
must have the same size as `spike_ids`.
Returns
-------
up : UpdateInfo instance
Note
----
`spike_clusters_rel` contain *relative* cluster indices. Their values
don't matter: what matters is whether two give spikes
should end up in the same cluster or not. Adding a constant number
to all elements in `spike_clusters_rel` results in exactly the same
operation.
The final cluster ids are automatically generated by the `Clustering`
class. This is because we must ensure that all modified clusters
get brand new ids. The whole library is based on the assumption that
cluster ids are unique and "disposable". Changing a cluster always
results in a new cluster id being assigned.
If a spike is assigned to a new cluster, then all other spikes
belonging to the same cluster are assigned to a brand new cluster,
even if they were not changed explicitely by the `assign()` method.
In other words, the list of spikes affected by an `assign()` is almost
always a strict superset of the `spike_ids` parameter. The only case
where this is not true is when whole clusters change: this is called
a merge. It is implemented in a separate `merge()` method because it
is logically much simpler, and faster to execute.
|
[
"Make",
"new",
"spike",
"cluster",
"assignments",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L370-L446
|
train
|
kwikteam/phy
|
phy/cluster/clustering.py
|
Clustering.undo
|
def undo(self):
"""Undo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
_, _, undo_state = self._undo_stack.back()
# Retrieve the initial spike_cluster structure.
spike_clusters_new = self._spike_clusters_base.copy()
# Loop over the history (except the last item because we undo).
for spike_ids, cluster_ids, _ in self._undo_stack:
# We update the spike clusters accordingly.
if spike_ids is not None:
spike_clusters_new[spike_ids] = cluster_ids
# What are the spikes affected by the last changes?
changed = np.nonzero(self._spike_clusters !=
spike_clusters_new)[0]
clusters_changed = spike_clusters_new[changed]
up = self._do_assign(changed, clusters_changed)
up.history = 'undo'
# Add the undo_state object from the undone object.
up.undo_state = undo_state
self.emit('cluster', up)
return up
|
python
|
def undo(self):
"""Undo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
_, _, undo_state = self._undo_stack.back()
# Retrieve the initial spike_cluster structure.
spike_clusters_new = self._spike_clusters_base.copy()
# Loop over the history (except the last item because we undo).
for spike_ids, cluster_ids, _ in self._undo_stack:
# We update the spike clusters accordingly.
if spike_ids is not None:
spike_clusters_new[spike_ids] = cluster_ids
# What are the spikes affected by the last changes?
changed = np.nonzero(self._spike_clusters !=
spike_clusters_new)[0]
clusters_changed = spike_clusters_new[changed]
up = self._do_assign(changed, clusters_changed)
up.history = 'undo'
# Add the undo_state object from the undone object.
up.undo_state = undo_state
self.emit('cluster', up)
return up
|
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"emit",
"(",
"'cluster'",
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Undo the last cluster assignment operation.
Returns
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up : UpdateInfo instance of the changes done by this operation.
|
[
"Undo",
"the",
"last",
"cluster",
"assignment",
"operation",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L477-L508
|
train
|
kwikteam/phy
|
phy/cluster/clustering.py
|
Clustering.redo
|
def redo(self):
"""Redo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
# Go forward in the stack, and retrieve the new assignment.
item = self._undo_stack.forward()
if item is None:
# No redo has been performed: abort.
return
# NOTE: the undo_state object is only returned when undoing.
# It represents data associated to the state
# *before* the action. What might be more useful would be the
# undo_state object of the next item in the list (if it exists).
spike_ids, cluster_ids, undo_state = item
assert spike_ids is not None
# We apply the new assignment.
up = self._do_assign(spike_ids, cluster_ids)
up.history = 'redo'
self.emit('cluster', up)
return up
|
python
|
def redo(self):
"""Redo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
"""
# Go forward in the stack, and retrieve the new assignment.
item = self._undo_stack.forward()
if item is None:
# No redo has been performed: abort.
return
# NOTE: the undo_state object is only returned when undoing.
# It represents data associated to the state
# *before* the action. What might be more useful would be the
# undo_state object of the next item in the list (if it exists).
spike_ids, cluster_ids, undo_state = item
assert spike_ids is not None
# We apply the new assignment.
up = self._do_assign(spike_ids, cluster_ids)
up.history = 'redo'
self.emit('cluster', up)
return up
|
[
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"redo",
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"emit",
"(",
"'cluster'",
",",
"up",
")",
"return",
"up"
] |
Redo the last cluster assignment operation.
Returns
-------
up : UpdateInfo instance of the changes done by this operation.
|
[
"Redo",
"the",
"last",
"cluster",
"assignment",
"operation",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/clustering.py#L510-L537
|
train
|
kwikteam/phy
|
phy/stats/ccg.py
|
_increment
|
def _increment(arr, indices):
"""Increment some indices in a 1D vector of non-negative integers.
Repeated indices are taken into account."""
arr = _as_array(arr)
indices = _as_array(indices)
bbins = np.bincount(indices)
arr[:len(bbins)] += bbins
return arr
|
python
|
def _increment(arr, indices):
"""Increment some indices in a 1D vector of non-negative integers.
Repeated indices are taken into account."""
arr = _as_array(arr)
indices = _as_array(indices)
bbins = np.bincount(indices)
arr[:len(bbins)] += bbins
return arr
|
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"_as_array",
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"_as_array",
"(",
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")",
"arr",
"[",
":",
"len",
"(",
"bbins",
")",
"]",
"+=",
"bbins",
"return",
"arr"
] |
Increment some indices in a 1D vector of non-negative integers.
Repeated indices are taken into account.
|
[
"Increment",
"some",
"indices",
"in",
"a",
"1D",
"vector",
"of",
"non",
"-",
"negative",
"integers",
".",
"Repeated",
"indices",
"are",
"taken",
"into",
"account",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/stats/ccg.py#L19-L26
|
train
|
kwikteam/phy
|
phy/stats/ccg.py
|
_symmetrize_correlograms
|
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))
return np.dstack((sym, correlograms))
|
python
|
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))
return np.dstack((sym, correlograms))
|
[
"def",
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"(",
"(",
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] |
Return the symmetrized version of the CCG arrays.
|
[
"Return",
"the",
"symmetrized",
"version",
"of",
"the",
"CCG",
"arrays",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/stats/ccg.py#L39-L54
|
train
|
kwikteam/phy
|
phy/stats/ccg.py
|
correlograms
|
def correlograms(spike_times,
spike_clusters,
cluster_ids=None,
sample_rate=1.,
bin_size=None,
window_size=None,
symmetrize=True,
):
"""Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
"""
assert sample_rate > 0.
assert np.all(np.diff(spike_times) >= 0), ("The spike times must be "
"increasing.")
# Get the spike samples.
spike_times = np.asarray(spike_times, dtype=np.float64)
spike_samples = (spike_times * sample_rate).astype(np.int64)
spike_clusters = _as_array(spike_clusters)
assert spike_samples.ndim == 1
assert spike_samples.shape == spike_clusters.shape
# Find `binsize`.
bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds
binsize = int(sample_rate * bin_size) # in samples
assert binsize >= 1
# Find `winsize_bins`.
window_size = np.clip(window_size, 1e-5, 1e5) # in seconds
winsize_bins = 2 * int(.5 * window_size / bin_size) + 1
assert winsize_bins >= 1
assert winsize_bins % 2 == 1
# Take the cluster oder into account.
if cluster_ids is None:
clusters = _unique(spike_clusters)
else:
clusters = _as_array(cluster_ids)
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
# At a given shift, the mask precises which spikes have matching spikes
# within the correlogram time window.
mask = np.ones_like(spike_samples, dtype=np.bool)
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():
# Number of time samples between spike i and spike i+shift.
spike_diff = _diff_shifted(spike_samples, shift)
# Binarize the delays between spike i and spike i+shift.
spike_diff_b = spike_diff // binsize
# 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]
# # Update the masks given the clusters to update.
# m0 = np.in1d(spike_clusters[:-shift], clusters)
# m = m & m0
# d = spike_diff_b[m]
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
# Remove ACG peaks.
correlograms[np.arange(n_clusters),
np.arange(n_clusters),
0] = 0
if symmetrize:
return _symmetrize_correlograms(correlograms)
else:
return correlograms
|
python
|
def correlograms(spike_times,
spike_clusters,
cluster_ids=None,
sample_rate=1.,
bin_size=None,
window_size=None,
symmetrize=True,
):
"""Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
"""
assert sample_rate > 0.
assert np.all(np.diff(spike_times) >= 0), ("The spike times must be "
"increasing.")
# Get the spike samples.
spike_times = np.asarray(spike_times, dtype=np.float64)
spike_samples = (spike_times * sample_rate).astype(np.int64)
spike_clusters = _as_array(spike_clusters)
assert spike_samples.ndim == 1
assert spike_samples.shape == spike_clusters.shape
# Find `binsize`.
bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds
binsize = int(sample_rate * bin_size) # in samples
assert binsize >= 1
# Find `winsize_bins`.
window_size = np.clip(window_size, 1e-5, 1e5) # in seconds
winsize_bins = 2 * int(.5 * window_size / bin_size) + 1
assert winsize_bins >= 1
assert winsize_bins % 2 == 1
# Take the cluster oder into account.
if cluster_ids is None:
clusters = _unique(spike_clusters)
else:
clusters = _as_array(cluster_ids)
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
# At a given shift, the mask precises which spikes have matching spikes
# within the correlogram time window.
mask = np.ones_like(spike_samples, dtype=np.bool)
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():
# Number of time samples between spike i and spike i+shift.
spike_diff = _diff_shifted(spike_samples, shift)
# Binarize the delays between spike i and spike i+shift.
spike_diff_b = spike_diff // binsize
# 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]
# # Update the masks given the clusters to update.
# m0 = np.in1d(spike_clusters[:-shift], clusters)
# m = m & m0
# d = spike_diff_b[m]
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
# Remove ACG peaks.
correlograms[np.arange(n_clusters),
np.arange(n_clusters),
0] = 0
if symmetrize:
return _symmetrize_correlograms(correlograms)
else:
return correlograms
|
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"(",
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")",
"else",
":",
"return",
"correlograms"
] |
Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
|
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"-",
"correlograms",
"among",
"the",
"clusters",
"appearing",
"in",
"spike_clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/stats/ccg.py#L57-L177
|
train
|
kwikteam/phy
|
phy/cluster/views/correlogram.py
|
CorrelogramView.set_bin_window
|
def set_bin_window(self, bin_size=None, window_size=None):
"""Set the bin and window sizes."""
bin_size = bin_size or self.bin_size
window_size = window_size or self.window_size
assert 1e-6 < bin_size < 1e3
assert 1e-6 < window_size < 1e3
assert bin_size < window_size
self.bin_size = bin_size
self.window_size = window_size
# Set the status message.
b, w = self.bin_size * 1000, self.window_size * 1000
self.set_status('Bin: {:.1f} ms. Window: {:.1f} ms.'.format(b, w))
|
python
|
def set_bin_window(self, bin_size=None, window_size=None):
"""Set the bin and window sizes."""
bin_size = bin_size or self.bin_size
window_size = window_size or self.window_size
assert 1e-6 < bin_size < 1e3
assert 1e-6 < window_size < 1e3
assert bin_size < window_size
self.bin_size = bin_size
self.window_size = window_size
# Set the status message.
b, w = self.bin_size * 1000, self.window_size * 1000
self.set_status('Bin: {:.1f} ms. Window: {:.1f} ms.'.format(b, w))
|
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"'Bin: {:.1f} ms. Window: {:.1f} ms.'",
".",
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Set the bin and window sizes.
|
[
"Set",
"the",
"bin",
"and",
"window",
"sizes",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/views/correlogram.py#L56-L67
|
train
|
kwikteam/phy
|
phy/io/datasets.py
|
_md5
|
def _md5(path, blocksize=2 ** 20):
"""Compute the checksum of a file."""
m = hashlib.md5()
with open(path, 'rb') as f:
while True:
buf = f.read(blocksize)
if not buf:
break
m.update(buf)
return m.hexdigest()
|
python
|
def _md5(path, blocksize=2 ** 20):
"""Compute the checksum of a file."""
m = hashlib.md5()
with open(path, 'rb') as f:
while True:
buf = f.read(blocksize)
if not buf:
break
m.update(buf)
return m.hexdigest()
|
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Compute the checksum of a file.
|
[
"Compute",
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"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/datasets.py#L66-L75
|
train
|
kwikteam/phy
|
phy/io/datasets.py
|
download_file
|
def download_file(url, output_path):
"""Download a binary file from an URL.
The checksum will be downloaded from `URL + .md5`. If this download
succeeds, the file's MD5 will be compared to the expected checksum.
Parameters
----------
url : str
The file's URL.
output_path : str
The path where the file is to be saved.
"""
output_path = op.realpath(output_path)
assert output_path is not None
if op.exists(output_path):
checked = _check_md5_of_url(output_path, url)
if checked is False:
logger.debug("The file `%s` already exists "
"but is invalid: redownloading.", output_path)
elif checked is True:
logger.debug("The file `%s` already exists: skipping.",
output_path)
return output_path
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
logger.debug("The checksum doesn't match: retrying the download.")
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
raise RuntimeError("The checksum of the downloaded file "
"doesn't match the provided checksum.")
return
|
python
|
def download_file(url, output_path):
"""Download a binary file from an URL.
The checksum will be downloaded from `URL + .md5`. If this download
succeeds, the file's MD5 will be compared to the expected checksum.
Parameters
----------
url : str
The file's URL.
output_path : str
The path where the file is to be saved.
"""
output_path = op.realpath(output_path)
assert output_path is not None
if op.exists(output_path):
checked = _check_md5_of_url(output_path, url)
if checked is False:
logger.debug("The file `%s` already exists "
"but is invalid: redownloading.", output_path)
elif checked is True:
logger.debug("The file `%s` already exists: skipping.",
output_path)
return output_path
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
logger.debug("The checksum doesn't match: retrying the download.")
r = _download(url, stream=True)
_save_stream(r, output_path)
if _check_md5_of_url(output_path, url) is False:
raise RuntimeError("The checksum of the downloaded file "
"doesn't match the provided checksum.")
return
|
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"raise",
"RuntimeError",
"(",
"\"The checksum of the downloaded file \"",
"\"doesn't match the provided checksum.\"",
")",
"return"
] |
Download a binary file from an URL.
The checksum will be downloaded from `URL + .md5`. If this download
succeeds, the file's MD5 will be compared to the expected checksum.
Parameters
----------
url : str
The file's URL.
output_path : str
The path where the file is to be saved.
|
[
"Download",
"a",
"binary",
"file",
"from",
"an",
"URL",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/datasets.py#L103-L138
|
train
|
kwikteam/phy
|
phy/plot/plot.py
|
_make_class
|
def _make_class(cls, **kwargs):
"""Return a custom Visual class with given parameters."""
kwargs = {k: (v if v is not None else getattr(cls, k, None))
for k, v in kwargs.items()}
# The class name contains a hash of the custom parameters.
name = cls.__name__ + '_' + _hash(kwargs)
if name not in _CLASSES:
logger.log(5, "Create class %s %s.", name, kwargs)
cls = type(name, (cls,), kwargs)
_CLASSES[name] = cls
return _CLASSES[name]
|
python
|
def _make_class(cls, **kwargs):
"""Return a custom Visual class with given parameters."""
kwargs = {k: (v if v is not None else getattr(cls, k, None))
for k, v in kwargs.items()}
# The class name contains a hash of the custom parameters.
name = cls.__name__ + '_' + _hash(kwargs)
if name not in _CLASSES:
logger.log(5, "Create class %s %s.", name, kwargs)
cls = type(name, (cls,), kwargs)
_CLASSES[name] = cls
return _CLASSES[name]
|
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Return a custom Visual class with given parameters.
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[
"Return",
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"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/plot.py#L46-L56
|
train
|
kwikteam/phy
|
phy/plot/plot.py
|
View._add_item
|
def _add_item(self, cls, *args, **kwargs):
"""Add a plot item."""
box_index = kwargs.pop('box_index', self._default_box_index)
data = cls.validate(*args, **kwargs)
n = cls.vertex_count(**data)
if not isinstance(box_index, np.ndarray):
k = len(self._default_box_index)
box_index = _get_array(box_index, (n, k))
data['box_index'] = box_index
if cls not in self._items:
self._items[cls] = []
self._items[cls].append(data)
return data
|
python
|
def _add_item(self, cls, *args, **kwargs):
"""Add a plot item."""
box_index = kwargs.pop('box_index', self._default_box_index)
data = cls.validate(*args, **kwargs)
n = cls.vertex_count(**data)
if not isinstance(box_index, np.ndarray):
k = len(self._default_box_index)
box_index = _get_array(box_index, (n, k))
data['box_index'] = box_index
if cls not in self._items:
self._items[cls] = []
self._items[cls].append(data)
return data
|
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Add a plot item.
|
[
"Add",
"a",
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"item",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/plot.py#L118-L133
|
train
|
kwikteam/phy
|
phy/plot/plot.py
|
View.scatter
|
def scatter(self, *args, **kwargs):
"""Add a scatter plot."""
cls = _make_class(ScatterVisual,
_default_marker=kwargs.pop('marker', None),
)
return self._add_item(cls, *args, **kwargs)
|
python
|
def scatter(self, *args, **kwargs):
"""Add a scatter plot."""
cls = _make_class(ScatterVisual,
_default_marker=kwargs.pop('marker', None),
)
return self._add_item(cls, *args, **kwargs)
|
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Add a scatter plot.
|
[
"Add",
"a",
"scatter",
"plot",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/plot.py#L153-L158
|
train
|
kwikteam/phy
|
phy/plot/plot.py
|
View.build
|
def build(self):
"""Build all added items.
Visuals are created, added, and built. The `set_data()` methods can
be called afterwards.
"""
for cls, data_list in self._items.items():
# Some variables are not concatenated. They are specified
# in `allow_list`.
data = _accumulate(data_list, cls.allow_list)
box_index = data.pop('box_index')
visual = cls()
self.add_visual(visual)
visual.set_data(**data)
# NOTE: visual.program.__contains__ is implemented in vispy master
# so we can replace this with `if 'a_box_index' in visual.program`
# after the next VisPy release.
if 'a_box_index' in visual.program._code_variables:
visual.program['a_box_index'] = box_index.astype(np.float32)
# TODO: refactor this when there is the possibility to update existing
# visuals without recreating the whole scene.
if self.lasso:
self.lasso.create_visual()
self.update()
|
python
|
def build(self):
"""Build all added items.
Visuals are created, added, and built. The `set_data()` methods can
be called afterwards.
"""
for cls, data_list in self._items.items():
# Some variables are not concatenated. They are specified
# in `allow_list`.
data = _accumulate(data_list, cls.allow_list)
box_index = data.pop('box_index')
visual = cls()
self.add_visual(visual)
visual.set_data(**data)
# NOTE: visual.program.__contains__ is implemented in vispy master
# so we can replace this with `if 'a_box_index' in visual.program`
# after the next VisPy release.
if 'a_box_index' in visual.program._code_variables:
visual.program['a_box_index'] = box_index.astype(np.float32)
# TODO: refactor this when there is the possibility to update existing
# visuals without recreating the whole scene.
if self.lasso:
self.lasso.create_visual()
self.update()
|
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[
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7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/plot.py#L176-L200
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_range_from_slice
|
def _range_from_slice(myslice, start=None, stop=None, step=None, length=None):
"""Convert a slice to an array of integers."""
assert isinstance(myslice, slice)
# Find 'step'.
step = myslice.step if myslice.step is not None else step
if step is None:
step = 1
# Find 'start'.
start = myslice.start if myslice.start is not None else start
if start is None:
start = 0
# Find 'stop' as a function of length if 'stop' is unspecified.
stop = myslice.stop if myslice.stop is not None else stop
if length is not None:
stop_inferred = floor(start + step * length)
if stop is not None and stop < stop_inferred:
raise ValueError("'stop' ({stop}) and ".format(stop=stop) +
"'length' ({length}) ".format(length=length) +
"are not compatible.")
stop = stop_inferred
if stop is None and length is None:
raise ValueError("'stop' and 'length' cannot be both unspecified.")
myrange = np.arange(start, stop, step)
# Check the length if it was specified.
if length is not None:
assert len(myrange) == length
return myrange
|
python
|
def _range_from_slice(myslice, start=None, stop=None, step=None, length=None):
"""Convert a slice to an array of integers."""
assert isinstance(myslice, slice)
# Find 'step'.
step = myslice.step if myslice.step is not None else step
if step is None:
step = 1
# Find 'start'.
start = myslice.start if myslice.start is not None else start
if start is None:
start = 0
# Find 'stop' as a function of length if 'stop' is unspecified.
stop = myslice.stop if myslice.stop is not None else stop
if length is not None:
stop_inferred = floor(start + step * length)
if stop is not None and stop < stop_inferred:
raise ValueError("'stop' ({stop}) and ".format(stop=stop) +
"'length' ({length}) ".format(length=length) +
"are not compatible.")
stop = stop_inferred
if stop is None and length is None:
raise ValueError("'stop' and 'length' cannot be both unspecified.")
myrange = np.arange(start, stop, step)
# Check the length if it was specified.
if length is not None:
assert len(myrange) == length
return myrange
|
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|
[
"Convert",
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"slice",
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"array",
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"integers",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L28-L54
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_index_of
|
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]
|
python
|
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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Replace scalars in an array by their indices in a lookup table.
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|
[
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"indices",
"in",
"a",
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"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L99-L121
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_pad
|
def _pad(arr, n, dir='right'):
"""Pad an array with zeros along the first axis.
Parameters
----------
n : int
Size of the returned array in the first axis.
dir : str
Direction of the padding. Must be one 'left' or 'right'.
"""
assert dir in ('left', 'right')
if n < 0:
raise ValueError("'n' must be positive: {0}.".format(n))
elif n == 0:
return np.zeros((0,) + arr.shape[1:], dtype=arr.dtype)
n_arr = arr.shape[0]
shape = (n,) + arr.shape[1:]
if n_arr == n:
assert arr.shape == shape
return arr
elif n_arr < n:
out = np.zeros(shape, dtype=arr.dtype)
if dir == 'left':
out[-n_arr:, ...] = arr
elif dir == 'right':
out[:n_arr, ...] = arr
assert out.shape == shape
return out
else:
if dir == 'left':
out = arr[-n:, ...]
elif dir == 'right':
out = arr[:n, ...]
assert out.shape == shape
return out
|
python
|
def _pad(arr, n, dir='right'):
"""Pad an array with zeros along the first axis.
Parameters
----------
n : int
Size of the returned array in the first axis.
dir : str
Direction of the padding. Must be one 'left' or 'right'.
"""
assert dir in ('left', 'right')
if n < 0:
raise ValueError("'n' must be positive: {0}.".format(n))
elif n == 0:
return np.zeros((0,) + arr.shape[1:], dtype=arr.dtype)
n_arr = arr.shape[0]
shape = (n,) + arr.shape[1:]
if n_arr == n:
assert arr.shape == shape
return arr
elif n_arr < n:
out = np.zeros(shape, dtype=arr.dtype)
if dir == 'left':
out[-n_arr:, ...] = arr
elif dir == 'right':
out[:n_arr, ...] = arr
assert out.shape == shape
return out
else:
if dir == 'left':
out = arr[-n:, ...]
elif dir == 'right':
out = arr[:n, ...]
assert out.shape == shape
return out
|
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Pad an array with zeros along the first axis.
Parameters
----------
n : int
Size of the returned array in the first axis.
dir : str
Direction of the padding. Must be one 'left' or 'right'.
|
[
"Pad",
"an",
"array",
"with",
"zeros",
"along",
"the",
"first",
"axis",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L124-L160
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_in_polygon
|
def _in_polygon(points, polygon):
"""Return the points that are inside a polygon."""
from matplotlib.path import Path
points = _as_array(points)
polygon = _as_array(polygon)
assert points.ndim == 2
assert polygon.ndim == 2
if len(polygon):
polygon = np.vstack((polygon, polygon[0]))
path = Path(polygon, closed=True)
return path.contains_points(points)
|
python
|
def _in_polygon(points, polygon):
"""Return the points that are inside a polygon."""
from matplotlib.path import Path
points = _as_array(points)
polygon = _as_array(polygon)
assert points.ndim == 2
assert polygon.ndim == 2
if len(polygon):
polygon = np.vstack((polygon, polygon[0]))
path = Path(polygon, closed=True)
return path.contains_points(points)
|
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Return the points that are inside a polygon.
|
[
"Return",
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"points",
"that",
"are",
"inside",
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"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L183-L193
|
train
|
kwikteam/phy
|
phy/io/array.py
|
read_array
|
def read_array(path, mmap_mode=None):
"""Read a .npy array."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.load(path, mmap_mode=mmap_mode)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.")
|
python
|
def read_array(path, mmap_mode=None):
"""Read a .npy array."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.load(path, mmap_mode=mmap_mode)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.")
|
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Read a .npy array.
|
[
"Read",
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"array",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L219-L225
|
train
|
kwikteam/phy
|
phy/io/array.py
|
write_array
|
def write_array(path, arr):
"""Write an array to a .npy file."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.save(path, arr)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.")
|
python
|
def write_array(path, arr):
"""Write an array to a .npy file."""
file_ext = op.splitext(path)[1]
if file_ext == '.npy':
return np.save(path, arr)
raise NotImplementedError("The file extension `{}` ".format(file_ext) +
"is not currently supported.")
|
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Write an array to a .npy file.
|
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"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L228-L234
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_concatenate_virtual_arrays
|
def _concatenate_virtual_arrays(arrs, cols=None, scaling=None):
"""Return a virtual concatenate of several NumPy arrays."""
return None if not len(arrs) else ConcatenatedArrays(arrs, cols,
scaling=scaling)
|
python
|
def _concatenate_virtual_arrays(arrs, cols=None, scaling=None):
"""Return a virtual concatenate of several NumPy arrays."""
return None if not len(arrs) else ConcatenatedArrays(arrs, cols,
scaling=scaling)
|
[
"def",
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"arrs",
",",
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"=",
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"=",
"None",
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":",
"return",
"None",
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")",
"else",
"ConcatenatedArrays",
"(",
"arrs",
",",
"cols",
",",
"scaling",
"=",
"scaling",
")"
] |
Return a virtual concatenate of several NumPy arrays.
|
[
"Return",
"a",
"virtual",
"concatenate",
"of",
"several",
"NumPy",
"arrays",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L359-L362
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_excerpt_step
|
def _excerpt_step(n_samples, n_excerpts=None, excerpt_size=None):
"""Compute the step of an excerpt set as a function of the number
of excerpts or their sizes."""
assert n_excerpts >= 2
step = max((n_samples - excerpt_size) // (n_excerpts - 1),
excerpt_size)
return step
|
python
|
def _excerpt_step(n_samples, n_excerpts=None, excerpt_size=None):
"""Compute the step of an excerpt set as a function of the number
of excerpts or their sizes."""
assert n_excerpts >= 2
step = max((n_samples - excerpt_size) // (n_excerpts - 1),
excerpt_size)
return step
|
[
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"//",
"(",
"n_excerpts",
"-",
"1",
")",
",",
"excerpt_size",
")",
"return",
"step"
] |
Compute the step of an excerpt set as a function of the number
of excerpts or their sizes.
|
[
"Compute",
"the",
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"of",
"an",
"excerpt",
"set",
"as",
"a",
"function",
"of",
"the",
"number",
"of",
"excerpts",
"or",
"their",
"sizes",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L369-L375
|
train
|
kwikteam/phy
|
phy/io/array.py
|
chunk_bounds
|
def chunk_bounds(n_samples, chunk_size, overlap=0):
"""Return chunk bounds.
Chunks have the form:
[ overlap/2 | chunk_size-overlap | overlap/2 ]
s_start keep_start keep_end s_end
Except for the first and last chunks which do not have a left/right
overlap.
This generator yields (s_start, s_end, keep_start, keep_end).
"""
s_start = 0
s_end = chunk_size
keep_start = s_start
keep_end = s_end - overlap // 2
yield s_start, s_end, keep_start, keep_end
while s_end - overlap + chunk_size < n_samples:
s_start = s_end - overlap
s_end = s_start + chunk_size
keep_start = keep_end
keep_end = s_end - overlap // 2
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end
s_start = s_end - overlap
s_end = n_samples
keep_start = keep_end
keep_end = s_end
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end
|
python
|
def chunk_bounds(n_samples, chunk_size, overlap=0):
"""Return chunk bounds.
Chunks have the form:
[ overlap/2 | chunk_size-overlap | overlap/2 ]
s_start keep_start keep_end s_end
Except for the first and last chunks which do not have a left/right
overlap.
This generator yields (s_start, s_end, keep_start, keep_end).
"""
s_start = 0
s_end = chunk_size
keep_start = s_start
keep_end = s_end - overlap // 2
yield s_start, s_end, keep_start, keep_end
while s_end - overlap + chunk_size < n_samples:
s_start = s_end - overlap
s_end = s_start + chunk_size
keep_start = keep_end
keep_end = s_end - overlap // 2
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end
s_start = s_end - overlap
s_end = n_samples
keep_start = keep_end
keep_end = s_end
if s_start < s_end:
yield s_start, s_end, keep_start, keep_end
|
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"-",
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"s_end",
":",
"yield",
"s_start",
",",
"s_end",
",",
"keep_start",
",",
"keep_end"
] |
Return chunk bounds.
Chunks have the form:
[ overlap/2 | chunk_size-overlap | overlap/2 ]
s_start keep_start keep_end s_end
Except for the first and last chunks which do not have a left/right
overlap.
This generator yields (s_start, s_end, keep_start, keep_end).
|
[
"Return",
"chunk",
"bounds",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L378-L411
|
train
|
kwikteam/phy
|
phy/io/array.py
|
data_chunk
|
def data_chunk(data, chunk, with_overlap=False):
"""Get a data chunk."""
assert isinstance(chunk, tuple)
if len(chunk) == 2:
i, j = chunk
elif len(chunk) == 4:
if with_overlap:
i, j = chunk[:2]
else:
i, j = chunk[2:]
else:
raise ValueError("'chunk' should have 2 or 4 elements, "
"not {0:d}".format(len(chunk)))
return data[i:j, ...]
|
python
|
def data_chunk(data, chunk, with_overlap=False):
"""Get a data chunk."""
assert isinstance(chunk, tuple)
if len(chunk) == 2:
i, j = chunk
elif len(chunk) == 4:
if with_overlap:
i, j = chunk[:2]
else:
i, j = chunk[2:]
else:
raise ValueError("'chunk' should have 2 or 4 elements, "
"not {0:d}".format(len(chunk)))
return data[i:j, ...]
|
[
"def",
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"False",
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"(",
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")",
")",
")",
"return",
"data",
"[",
"i",
":",
"j",
",",
"...",
"]"
] |
Get a data chunk.
|
[
"Get",
"a",
"data",
"chunk",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L428-L441
|
train
|
kwikteam/phy
|
phy/io/array.py
|
_spikes_in_clusters
|
def _spikes_in_clusters(spike_clusters, clusters):
"""Return the ids of all spikes belonging to the specified clusters."""
if len(spike_clusters) == 0 or len(clusters) == 0:
return np.array([], dtype=np.int)
return np.nonzero(np.in1d(spike_clusters, clusters))[0]
|
python
|
def _spikes_in_clusters(spike_clusters, clusters):
"""Return the ids of all spikes belonging to the specified clusters."""
if len(spike_clusters) == 0 or len(clusters) == 0:
return np.array([], dtype=np.int)
return np.nonzero(np.in1d(spike_clusters, clusters))[0]
|
[
"def",
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"(",
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",",
"clusters",
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":",
"if",
"len",
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"spike_clusters",
")",
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"0",
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"np",
".",
"in1d",
"(",
"spike_clusters",
",",
"clusters",
")",
")",
"[",
"0",
"]"
] |
Return the ids of all spikes belonging to the specified clusters.
|
[
"Return",
"the",
"ids",
"of",
"all",
"spikes",
"belonging",
"to",
"the",
"specified",
"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L465-L469
|
train
|
kwikteam/phy
|
phy/io/array.py
|
grouped_mean
|
def grouped_mean(arr, spike_clusters):
"""Compute the mean of a spike-dependent quantity for every cluster.
The two arguments should be 1D array with `n_spikes` elements.
The output is a 1D array with `n_clusters` elements. The clusters are
sorted in increasing order.
"""
arr = np.asarray(arr)
spike_clusters = np.asarray(spike_clusters)
assert arr.ndim == 1
assert arr.shape[0] == len(spike_clusters)
cluster_ids = _unique(spike_clusters)
spike_clusters_rel = _index_of(spike_clusters, cluster_ids)
spike_counts = np.bincount(spike_clusters_rel)
assert len(spike_counts) == len(cluster_ids)
t = np.zeros(len(cluster_ids))
# Compute the sum with possible repetitions.
np.add.at(t, spike_clusters_rel, arr)
return t / spike_counts
|
python
|
def grouped_mean(arr, spike_clusters):
"""Compute the mean of a spike-dependent quantity for every cluster.
The two arguments should be 1D array with `n_spikes` elements.
The output is a 1D array with `n_clusters` elements. The clusters are
sorted in increasing order.
"""
arr = np.asarray(arr)
spike_clusters = np.asarray(spike_clusters)
assert arr.ndim == 1
assert arr.shape[0] == len(spike_clusters)
cluster_ids = _unique(spike_clusters)
spike_clusters_rel = _index_of(spike_clusters, cluster_ids)
spike_counts = np.bincount(spike_clusters_rel)
assert len(spike_counts) == len(cluster_ids)
t = np.zeros(len(cluster_ids))
# Compute the sum with possible repetitions.
np.add.at(t, spike_clusters_rel, arr)
return t / spike_counts
|
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"t",
",",
"spike_clusters_rel",
",",
"arr",
")",
"return",
"t",
"/",
"spike_counts"
] |
Compute the mean of a spike-dependent quantity for every cluster.
The two arguments should be 1D array with `n_spikes` elements.
The output is a 1D array with `n_clusters` elements. The clusters are
sorted in increasing order.
|
[
"Compute",
"the",
"mean",
"of",
"a",
"spike",
"-",
"dependent",
"quantity",
"for",
"every",
"cluster",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L506-L526
|
train
|
kwikteam/phy
|
phy/io/array.py
|
regular_subset
|
def regular_subset(spikes, n_spikes_max=None, offset=0):
"""Prune the current selection to get at most n_spikes_max spikes."""
assert spikes is not None
# Nothing to do if the selection already satisfies n_spikes_max.
if n_spikes_max is None or len(spikes) <= n_spikes_max: # pragma: no cover
return spikes
step = math.ceil(np.clip(1. / n_spikes_max * len(spikes),
1, len(spikes)))
step = int(step)
# Note: randomly-changing selections are confusing...
my_spikes = spikes[offset::step][:n_spikes_max]
assert len(my_spikes) <= len(spikes)
assert len(my_spikes) <= n_spikes_max
return my_spikes
|
python
|
def regular_subset(spikes, n_spikes_max=None, offset=0):
"""Prune the current selection to get at most n_spikes_max spikes."""
assert spikes is not None
# Nothing to do if the selection already satisfies n_spikes_max.
if n_spikes_max is None or len(spikes) <= n_spikes_max: # pragma: no cover
return spikes
step = math.ceil(np.clip(1. / n_spikes_max * len(spikes),
1, len(spikes)))
step = int(step)
# Note: randomly-changing selections are confusing...
my_spikes = spikes[offset::step][:n_spikes_max]
assert len(my_spikes) <= len(spikes)
assert len(my_spikes) <= n_spikes_max
return my_spikes
|
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"(",
"my_spikes",
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"<=",
"n_spikes_max",
"return",
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] |
Prune the current selection to get at most n_spikes_max spikes.
|
[
"Prune",
"the",
"current",
"selection",
"to",
"get",
"at",
"most",
"n_spikes_max",
"spikes",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L529-L542
|
train
|
kwikteam/phy
|
phy/io/array.py
|
select_spikes
|
def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None,
batch_size=None,
subset=None,
):
"""Return a selection of spikes belonging to the specified clusters."""
subset = subset or 'regular'
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spike_ids = spikes_per_cluster(cluster)
if subset == 'regular':
# Regular subselection.
if batch_size is None or len(spike_ids) <= max(batch_size, n):
spike_ids = regular_subset(spike_ids, n_spikes_max=n)
else:
# Batch selections of spikes.
spike_ids = get_excerpts(spike_ids,
n // batch_size,
batch_size)
elif subset == 'random' and len(spike_ids) > n:
# Random subselection.
spike_ids = np.random.choice(spike_ids, n, replace=False)
spike_ids = np.unique(spike_ids)
selection[cluster] = spike_ids
return _flatten_per_cluster(selection)
|
python
|
def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None,
batch_size=None,
subset=None,
):
"""Return a selection of spikes belonging to the specified clusters."""
subset = subset or 'regular'
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spike_ids = spikes_per_cluster(cluster)
if subset == 'regular':
# Regular subselection.
if batch_size is None or len(spike_ids) <= max(batch_size, n):
spike_ids = regular_subset(spike_ids, n_spikes_max=n)
else:
# Batch selections of spikes.
spike_ids = get_excerpts(spike_ids,
n // batch_size,
batch_size)
elif subset == 'random' and len(spike_ids) > n:
# Random subselection.
spike_ids = np.random.choice(spike_ids, n, replace=False)
spike_ids = np.unique(spike_ids)
selection[cluster] = spike_ids
return _flatten_per_cluster(selection)
|
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")"
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Return a selection of spikes belonging to the specified clusters.
|
[
"Return",
"a",
"selection",
"of",
"spikes",
"belonging",
"to",
"the",
"specified",
"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L545-L582
|
train
|
kwikteam/phy
|
phy/io/array.py
|
ConcatenatedArrays._get_recording
|
def _get_recording(self, index):
"""Return the recording that contains a given index."""
assert index >= 0
recs = np.nonzero((index - self.offsets[:-1]) >= 0)[0]
if len(recs) == 0: # pragma: no cover
# If the index is greater than the total size,
# return the last recording.
return len(self.arrs) - 1
# Return the last recording such that the index is greater than
# its offset.
return recs[-1]
|
python
|
def _get_recording(self, index):
"""Return the recording that contains a given index."""
assert index >= 0
recs = np.nonzero((index - self.offsets[:-1]) >= 0)[0]
if len(recs) == 0: # pragma: no cover
# If the index is greater than the total size,
# return the last recording.
return len(self.arrs) - 1
# Return the last recording such that the index is greater than
# its offset.
return recs[-1]
|
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"# its offset.",
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"[",
"-",
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Return the recording that contains a given index.
|
[
"Return",
"the",
"recording",
"that",
"contains",
"a",
"given",
"index",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/io/array.py#L297-L307
|
train
|
kwikteam/phy
|
phy/traces/filter.py
|
bandpass_filter
|
def bandpass_filter(rate=None, low=None, high=None, order=None):
"""Butterworth bandpass filter."""
assert low < high
assert order >= 1
return signal.butter(order,
(low / (rate / 2.), high / (rate / 2.)),
'pass')
|
python
|
def bandpass_filter(rate=None, low=None, high=None, order=None):
"""Butterworth bandpass filter."""
assert low < high
assert order >= 1
return signal.butter(order,
(low / (rate / 2.), high / (rate / 2.)),
'pass')
|
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"rate",
"/",
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")",
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"'pass'",
")"
] |
Butterworth bandpass filter.
|
[
"Butterworth",
"bandpass",
"filter",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/traces/filter.py#L19-L25
|
train
|
kwikteam/phy
|
phy/traces/filter.py
|
apply_filter
|
def apply_filter(x, filter=None, axis=0):
"""Apply a filter to an array."""
x = _as_array(x)
if x.shape[axis] == 0:
return x
b, a = filter
return signal.filtfilt(b, a, x, axis=axis)
|
python
|
def apply_filter(x, filter=None, axis=0):
"""Apply a filter to an array."""
x = _as_array(x)
if x.shape[axis] == 0:
return x
b, a = filter
return signal.filtfilt(b, a, x, axis=axis)
|
[
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"None",
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"filtfilt",
"(",
"b",
",",
"a",
",",
"x",
",",
"axis",
"=",
"axis",
")"
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Apply a filter to an array.
|
[
"Apply",
"a",
"filter",
"to",
"an",
"array",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/traces/filter.py#L28-L34
|
train
|
kwikteam/phy
|
phy/traces/filter.py
|
Whitening.fit
|
def fit(self, x, fudge=1e-18):
"""Compute the whitening matrix.
Parameters
----------
x : array
An `(n_samples, n_channels)` array.
"""
assert x.ndim == 2
ns, nc = x.shape
x_cov = np.cov(x, rowvar=0)
assert x_cov.shape == (nc, nc)
d, v = np.linalg.eigh(x_cov)
d = np.diag(1. / np.sqrt(d + fudge))
# This is equivalent, but seems much slower...
# w = np.einsum('il,lk,jk->ij', v, d, v)
w = np.dot(np.dot(v, d), v.T)
self._matrix = w
return w
|
python
|
def fit(self, x, fudge=1e-18):
"""Compute the whitening matrix.
Parameters
----------
x : array
An `(n_samples, n_channels)` array.
"""
assert x.ndim == 2
ns, nc = x.shape
x_cov = np.cov(x, rowvar=0)
assert x_cov.shape == (nc, nc)
d, v = np.linalg.eigh(x_cov)
d = np.diag(1. / np.sqrt(d + fudge))
# This is equivalent, but seems much slower...
# w = np.einsum('il,lk,jk->ij', v, d, v)
w = np.dot(np.dot(v, d), v.T)
self._matrix = w
return w
|
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Compute the whitening matrix.
Parameters
----------
x : array
An `(n_samples, n_channels)` array.
|
[
"Compute",
"the",
"whitening",
"matrix",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/traces/filter.py#L72-L92
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
History.current_item
|
def current_item(self):
"""Return the current element."""
if self._history and self._index >= 0:
self._check_index()
return self._history[self._index]
|
python
|
def current_item(self):
"""Return the current element."""
if self._history and self._index >= 0:
self._check_index()
return self._history[self._index]
|
[
"def",
"current_item",
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"self",
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"if",
"self",
".",
"_history",
"and",
"self",
".",
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"0",
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".",
"_check_index",
"(",
")",
"return",
"self",
".",
"_history",
"[",
"self",
".",
"_index",
"]"
] |
Return the current element.
|
[
"Return",
"the",
"current",
"element",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L28-L32
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
History._check_index
|
def _check_index(self):
"""Check that the index is without the bounds of _history."""
assert 0 <= self._index <= len(self._history) - 1
# There should always be the base item at least.
assert len(self._history) >= 1
|
python
|
def _check_index(self):
"""Check that the index is without the bounds of _history."""
assert 0 <= self._index <= len(self._history) - 1
# There should always be the base item at least.
assert len(self._history) >= 1
|
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"def",
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"1",
"# There should always be the base item at least.",
"assert",
"len",
"(",
"self",
".",
"_history",
")",
">=",
"1"
] |
Check that the index is without the bounds of _history.
|
[
"Check",
"that",
"the",
"index",
"is",
"without",
"the",
"bounds",
"of",
"_history",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L39-L43
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
History.iter
|
def iter(self, start=0, end=None):
"""Iterate through successive history items.
Parameters
----------
end : int
Index of the last item to loop through + 1.
start : int
Initial index for the loop (0 by default).
"""
if end is None:
end = self._index + 1
elif end == 0:
raise StopIteration()
if start >= end:
raise StopIteration()
# Check arguments.
assert 0 <= end <= len(self._history)
assert 0 <= start <= end - 1
for i in range(start, end):
yield self._history[i]
|
python
|
def iter(self, start=0, end=None):
"""Iterate through successive history items.
Parameters
----------
end : int
Index of the last item to loop through + 1.
start : int
Initial index for the loop (0 by default).
"""
if end is None:
end = self._index + 1
elif end == 0:
raise StopIteration()
if start >= end:
raise StopIteration()
# Check arguments.
assert 0 <= end <= len(self._history)
assert 0 <= start <= end - 1
for i in range(start, end):
yield self._history[i]
|
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",",
"end",
")",
":",
"yield",
"self",
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"_history",
"[",
"i",
"]"
] |
Iterate through successive history items.
Parameters
----------
end : int
Index of the last item to loop through + 1.
start : int
Initial index for the loop (0 by default).
|
[
"Iterate",
"through",
"successive",
"history",
"items",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L51-L73
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
History.add
|
def add(self, item):
"""Add an item in the history."""
self._check_index()
# Possibly truncate the history up to the current point.
self._history = self._history[:self._index + 1]
# Append the item
self._history.append(item)
# Increment the index.
self._index += 1
self._check_index()
# Check that the current element is what was provided to the function.
assert id(self.current_item) == id(item)
|
python
|
def add(self, item):
"""Add an item in the history."""
self._check_index()
# Possibly truncate the history up to the current point.
self._history = self._history[:self._index + 1]
# Append the item
self._history.append(item)
# Increment the index.
self._index += 1
self._check_index()
# Check that the current element is what was provided to the function.
assert id(self.current_item) == id(item)
|
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"assert",
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"(",
"self",
".",
"current_item",
")",
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"(",
"item",
")"
] |
Add an item in the history.
|
[
"Add",
"an",
"item",
"in",
"the",
"history",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L81-L92
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
History.back
|
def back(self):
"""Go back in history if possible.
Return the undone item.
"""
if self._index <= 0:
return None
undone = self.current_item
self._index -= 1
self._check_index()
return undone
|
python
|
def back(self):
"""Go back in history if possible.
Return the undone item.
"""
if self._index <= 0:
return None
undone = self.current_item
self._index -= 1
self._check_index()
return undone
|
[
"def",
"back",
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"self",
")",
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"if",
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"<=",
"0",
":",
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"=",
"self",
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"current_item",
"self",
".",
"_index",
"-=",
"1",
"self",
".",
"_check_index",
"(",
")",
"return",
"undone"
] |
Go back in history if possible.
Return the undone item.
|
[
"Go",
"back",
"in",
"history",
"if",
"possible",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L94-L105
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
History.forward
|
def forward(self):
"""Go forward in history if possible.
Return the current item after going forward.
"""
if self._index >= len(self._history) - 1:
return None
self._index += 1
self._check_index()
return self.current_item
|
python
|
def forward(self):
"""Go forward in history if possible.
Return the current item after going forward.
"""
if self._index >= len(self._history) - 1:
return None
self._index += 1
self._check_index()
return self.current_item
|
[
"def",
"forward",
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"self",
")",
":",
"if",
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".",
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">=",
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"(",
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"-",
"1",
":",
"return",
"None",
"self",
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"_index",
"+=",
"1",
"self",
".",
"_check_index",
"(",
")",
"return",
"self",
".",
"current_item"
] |
Go forward in history if possible.
Return the current item after going forward.
|
[
"Go",
"forward",
"in",
"history",
"if",
"possible",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L110-L120
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
GlobalHistory.add_to_current_action
|
def add_to_current_action(self, controller):
"""Add a controller to the current action."""
item = self.current_item
self._history[self._index] = item + (controller,)
|
python
|
def add_to_current_action(self, controller):
"""Add a controller to the current action."""
item = self.current_item
self._history[self._index] = item + (controller,)
|
[
"def",
"add_to_current_action",
"(",
"self",
",",
"controller",
")",
":",
"item",
"=",
"self",
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"current_item",
"self",
".",
"_history",
"[",
"self",
".",
"_index",
"]",
"=",
"item",
"+",
"(",
"controller",
",",
")"
] |
Add a controller to the current action.
|
[
"Add",
"a",
"controller",
"to",
"the",
"current",
"action",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L137-L140
|
train
|
kwikteam/phy
|
phy/cluster/_history.py
|
GlobalHistory.redo
|
def redo(self):
"""Redo the last action.
This will call `redo()` on all controllers involved in this action.
"""
controllers = self.forward()
if controllers is None:
ups = ()
else:
ups = tuple([controller.redo() for
controller in controllers])
if self.process_ups is not None:
return self.process_ups(ups)
else:
return ups
|
python
|
def redo(self):
"""Redo the last action.
This will call `redo()` on all controllers involved in this action.
"""
controllers = self.forward()
if controllers is None:
ups = ()
else:
ups = tuple([controller.redo() for
controller in controllers])
if self.process_ups is not None:
return self.process_ups(ups)
else:
return ups
|
[
"def",
"redo",
"(",
"self",
")",
":",
"controllers",
"=",
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"forward",
"(",
")",
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"(",
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":",
"return",
"self",
".",
"process_ups",
"(",
"ups",
")",
"else",
":",
"return",
"ups"
] |
Redo the last action.
This will call `redo()` on all controllers involved in this action.
|
[
"Redo",
"the",
"last",
"action",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_history.py#L159-L174
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
_insert_glsl
|
def _insert_glsl(vertex, fragment, to_insert):
"""Insert snippets in a shader.
to_insert is a dict `{(shader_type, location): snippet}`.
Snippets can contain `{{ var }}` placeholders for the transformed variable
name.
"""
# Find the place where to insert the GLSL snippet.
# This is "gl_Position = transform(data_var_name);" where
# data_var_name is typically an attribute.
vs_regex = re.compile(r'gl_Position = transform\(([\S]+)\);')
r = vs_regex.search(vertex)
if not r:
logger.debug("The vertex shader doesn't contain the transform "
"placeholder: skipping the transform chain "
"GLSL insertion.")
return vertex, fragment
assert r
logger.log(5, "Found transform placeholder in vertex code: `%s`",
r.group(0))
# Find the GLSL variable with the data (should be a `vec2`).
var = r.group(1)
assert var and var in vertex
# Headers.
vertex = to_insert['vert', 'header'] + '\n\n' + vertex
fragment = to_insert['frag', 'header'] + '\n\n' + fragment
# Get the pre and post transforms.
vs_insert = to_insert['vert', 'before_transforms']
vs_insert += to_insert['vert', 'transforms']
vs_insert += to_insert['vert', 'after_transforms']
# Insert the GLSL snippet in the vertex shader.
vertex = vs_regex.sub(indent(vs_insert), vertex)
# Now, we make the replacements in the fragment shader.
fs_regex = re.compile(r'(void main\(\)\s*\{)')
# NOTE: we add the `void main(){` that was removed by the regex.
fs_insert = '\\1\n' + to_insert['frag', 'before_transforms']
fragment = fs_regex.sub(indent(fs_insert), fragment)
# Replace the transformed variable placeholder by its name.
vertex = vertex.replace('{{ var }}', var)
return vertex, fragment
|
python
|
def _insert_glsl(vertex, fragment, to_insert):
"""Insert snippets in a shader.
to_insert is a dict `{(shader_type, location): snippet}`.
Snippets can contain `{{ var }}` placeholders for the transformed variable
name.
"""
# Find the place where to insert the GLSL snippet.
# This is "gl_Position = transform(data_var_name);" where
# data_var_name is typically an attribute.
vs_regex = re.compile(r'gl_Position = transform\(([\S]+)\);')
r = vs_regex.search(vertex)
if not r:
logger.debug("The vertex shader doesn't contain the transform "
"placeholder: skipping the transform chain "
"GLSL insertion.")
return vertex, fragment
assert r
logger.log(5, "Found transform placeholder in vertex code: `%s`",
r.group(0))
# Find the GLSL variable with the data (should be a `vec2`).
var = r.group(1)
assert var and var in vertex
# Headers.
vertex = to_insert['vert', 'header'] + '\n\n' + vertex
fragment = to_insert['frag', 'header'] + '\n\n' + fragment
# Get the pre and post transforms.
vs_insert = to_insert['vert', 'before_transforms']
vs_insert += to_insert['vert', 'transforms']
vs_insert += to_insert['vert', 'after_transforms']
# Insert the GLSL snippet in the vertex shader.
vertex = vs_regex.sub(indent(vs_insert), vertex)
# Now, we make the replacements in the fragment shader.
fs_regex = re.compile(r'(void main\(\)\s*\{)')
# NOTE: we add the `void main(){` that was removed by the regex.
fs_insert = '\\1\n' + to_insert['frag', 'before_transforms']
fragment = fs_regex.sub(indent(fs_insert), fragment)
# Replace the transformed variable placeholder by its name.
vertex = vertex.replace('{{ var }}', var)
return vertex, fragment
|
[
"def",
"_insert_glsl",
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"r'(void main\\(\\)\\s*\\{)'",
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"# NOTE: we add the `void main(){` that was removed by the regex.",
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"(",
"'{{ var }}'",
",",
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"return",
"vertex",
",",
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] |
Insert snippets in a shader.
to_insert is a dict `{(shader_type, location): snippet}`.
Snippets can contain `{{ var }}` placeholders for the transformed variable
name.
|
[
"Insert",
"snippets",
"in",
"a",
"shader",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L117-L165
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
BaseVisual.on_draw
|
def on_draw(self):
"""Draw the visual."""
# Skip the drawing if the program hasn't been built yet.
# The program is built by the interact.
if self.program:
# Draw the program.
self.program.draw(self.gl_primitive_type)
else: # pragma: no cover
logger.debug("Skipping drawing visual `%s` because the program "
"has not been built yet.", self)
|
python
|
def on_draw(self):
"""Draw the visual."""
# Skip the drawing if the program hasn't been built yet.
# The program is built by the interact.
if self.program:
# Draw the program.
self.program.draw(self.gl_primitive_type)
else: # pragma: no cover
logger.debug("Skipping drawing visual `%s` because the program "
"has not been built yet.", self)
|
[
"def",
"on_draw",
"(",
"self",
")",
":",
"# Skip the drawing if the program hasn't been built yet.",
"# The program is built by the interact.",
"if",
"self",
".",
"program",
":",
"# Draw the program.",
"self",
".",
"program",
".",
"draw",
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"self",
".",
"gl_primitive_type",
")",
"else",
":",
"# pragma: no cover",
"logger",
".",
"debug",
"(",
"\"Skipping drawing visual `%s` because the program \"",
"\"has not been built yet.\"",
",",
"self",
")"
] |
Draw the visual.
|
[
"Draw",
"the",
"visual",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L67-L76
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
GLSLInserter.add_transform_chain
|
def add_transform_chain(self, tc):
"""Insert the GLSL snippets of a transform chain."""
# Generate the transforms snippet.
for t in tc.gpu_transforms:
if isinstance(t, Clip):
# Set the varying value in the vertex shader.
self.insert_vert('v_temp_pos_tr = temp_pos_tr;')
continue
self.insert_vert(t.glsl('temp_pos_tr'))
# Clipping.
clip = tc.get('Clip')
if clip:
self.insert_frag(clip.glsl('v_temp_pos_tr'), 'before_transforms')
|
python
|
def add_transform_chain(self, tc):
"""Insert the GLSL snippets of a transform chain."""
# Generate the transforms snippet.
for t in tc.gpu_transforms:
if isinstance(t, Clip):
# Set the varying value in the vertex shader.
self.insert_vert('v_temp_pos_tr = temp_pos_tr;')
continue
self.insert_vert(t.glsl('temp_pos_tr'))
# Clipping.
clip = tc.get('Clip')
if clip:
self.insert_frag(clip.glsl('v_temp_pos_tr'), 'before_transforms')
|
[
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"self",
",",
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"# Generate the transforms snippet.",
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"if",
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"t",
",",
"Clip",
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"# Set the varying value in the vertex shader.",
"self",
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"continue",
"self",
".",
"insert_vert",
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".",
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"(",
"clip",
".",
"glsl",
"(",
"'v_temp_pos_tr'",
")",
",",
"'before_transforms'",
")"
] |
Insert the GLSL snippets of a transform chain.
|
[
"Insert",
"the",
"GLSL",
"snippets",
"of",
"a",
"transform",
"chain",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L207-L219
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
GLSLInserter.insert_into_shaders
|
def insert_into_shaders(self, vertex, fragment):
"""Apply the insertions to shader code."""
to_insert = defaultdict(str)
to_insert.update({key: '\n'.join(self._to_insert[key]) + '\n'
for key in self._to_insert})
return _insert_glsl(vertex, fragment, to_insert)
|
python
|
def insert_into_shaders(self, vertex, fragment):
"""Apply the insertions to shader code."""
to_insert = defaultdict(str)
to_insert.update({key: '\n'.join(self._to_insert[key]) + '\n'
for key in self._to_insert})
return _insert_glsl(vertex, fragment, to_insert)
|
[
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"+",
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"_to_insert",
"}",
")",
"return",
"_insert_glsl",
"(",
"vertex",
",",
"fragment",
",",
"to_insert",
")"
] |
Apply the insertions to shader code.
|
[
"Apply",
"the",
"insertions",
"to",
"shader",
"code",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L221-L226
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
BaseCanvas.add_visual
|
def add_visual(self, visual):
"""Add a visual to the canvas, and build its program by the same
occasion.
We can't build the visual's program before, because we need the canvas'
transforms first.
"""
# Retrieve the visual's GLSL inserter.
inserter = visual.inserter
# Add the visual's transforms.
inserter.add_transform_chain(visual.transforms)
# Then, add the canvas' transforms.
canvas_transforms = visual.canvas_transforms_filter(self.transforms)
inserter.add_transform_chain(canvas_transforms)
# Also, add the canvas' inserter.
inserter += self.inserter
# Now, we insert the transforms GLSL into the shaders.
vs, fs = visual.vertex_shader, visual.fragment_shader
vs, fs = inserter.insert_into_shaders(vs, fs)
# Finally, we create the visual's program.
visual.program = gloo.Program(vs, fs)
logger.log(5, "Vertex shader: %s", vs)
logger.log(5, "Fragment shader: %s", fs)
# Initialize the size.
visual.on_resize(self.size)
# Register the visual in the list of visuals in the canvas.
self.visuals.append(visual)
self.events.visual_added(visual=visual)
|
python
|
def add_visual(self, visual):
"""Add a visual to the canvas, and build its program by the same
occasion.
We can't build the visual's program before, because we need the canvas'
transforms first.
"""
# Retrieve the visual's GLSL inserter.
inserter = visual.inserter
# Add the visual's transforms.
inserter.add_transform_chain(visual.transforms)
# Then, add the canvas' transforms.
canvas_transforms = visual.canvas_transforms_filter(self.transforms)
inserter.add_transform_chain(canvas_transforms)
# Also, add the canvas' inserter.
inserter += self.inserter
# Now, we insert the transforms GLSL into the shaders.
vs, fs = visual.vertex_shader, visual.fragment_shader
vs, fs = inserter.insert_into_shaders(vs, fs)
# Finally, we create the visual's program.
visual.program = gloo.Program(vs, fs)
logger.log(5, "Vertex shader: %s", vs)
logger.log(5, "Fragment shader: %s", fs)
# Initialize the size.
visual.on_resize(self.size)
# Register the visual in the list of visuals in the canvas.
self.visuals.append(visual)
self.events.visual_added(visual=visual)
|
[
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"inserter",
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"canvas_transforms",
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",",
"fs",
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"# Initialize the size.",
"visual",
".",
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"size",
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"# Register the visual in the list of visuals in the canvas.",
"self",
".",
"visuals",
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"append",
"(",
"visual",
")",
"self",
".",
"events",
".",
"visual_added",
"(",
"visual",
"=",
"visual",
")"
] |
Add a visual to the canvas, and build its program by the same
occasion.
We can't build the visual's program before, because we need the canvas'
transforms first.
|
[
"Add",
"a",
"visual",
"to",
"the",
"canvas",
"and",
"build",
"its",
"program",
"by",
"the",
"same",
"occasion",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L258-L286
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
BaseCanvas.on_resize
|
def on_resize(self, event):
"""Resize the OpenGL context."""
self.context.set_viewport(0, 0, event.size[0], event.size[1])
for visual in self.visuals:
visual.on_resize(event.size)
self.update()
|
python
|
def on_resize(self, event):
"""Resize the OpenGL context."""
self.context.set_viewport(0, 0, event.size[0], event.size[1])
for visual in self.visuals:
visual.on_resize(event.size)
self.update()
|
[
"def",
"on_resize",
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"self",
",",
"event",
")",
":",
"self",
".",
"context",
".",
"set_viewport",
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"0",
",",
"event",
".",
"size",
"[",
"0",
"]",
",",
"event",
".",
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"[",
"1",
"]",
")",
"for",
"visual",
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".",
"visuals",
":",
"visual",
".",
"on_resize",
"(",
"event",
".",
"size",
")",
"self",
".",
"update",
"(",
")"
] |
Resize the OpenGL context.
|
[
"Resize",
"the",
"OpenGL",
"context",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L288-L293
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
BaseCanvas.on_draw
|
def on_draw(self, e):
"""Draw all visuals."""
gloo.clear()
for visual in self.visuals:
logger.log(5, "Draw visual `%s`.", visual)
visual.on_draw()
|
python
|
def on_draw(self, e):
"""Draw all visuals."""
gloo.clear()
for visual in self.visuals:
logger.log(5, "Draw visual `%s`.", visual)
visual.on_draw()
|
[
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"on_draw",
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"self",
",",
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"gloo",
".",
"clear",
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"visual",
"in",
"self",
".",
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"(",
"5",
",",
"\"Draw visual `%s`.\"",
",",
"visual",
")",
"visual",
".",
"on_draw",
"(",
")"
] |
Draw all visuals.
|
[
"Draw",
"all",
"visuals",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L295-L300
|
train
|
kwikteam/phy
|
phy/plot/base.py
|
BaseInteract.update
|
def update(self):
"""Update all visuals in the attached canvas."""
if not self.canvas:
return
for visual in self.canvas.visuals:
self.update_program(visual.program)
self.canvas.update()
|
python
|
def update(self):
"""Update all visuals in the attached canvas."""
if not self.canvas:
return
for visual in self.canvas.visuals:
self.update_program(visual.program)
self.canvas.update()
|
[
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"update_program",
"(",
"visual",
".",
"program",
")",
"self",
".",
"canvas",
".",
"update",
"(",
")"
] |
Update all visuals in the attached canvas.
|
[
"Update",
"all",
"visuals",
"in",
"the",
"attached",
"canvas",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/base.py#L324-L330
|
train
|
kwikteam/phy
|
phy/utils/cli.py
|
_add_log_file
|
def _add_log_file(filename):
"""Create a `phy.log` log file with DEBUG level in the
current directory."""
handler = logging.FileHandler(filename)
handler.setLevel(logging.DEBUG)
formatter = _Formatter(fmt=_logger_fmt,
datefmt='%Y-%m-%d %H:%M:%S')
handler.setFormatter(formatter)
logging.getLogger().addHandler(handler)
|
python
|
def _add_log_file(filename):
"""Create a `phy.log` log file with DEBUG level in the
current directory."""
handler = logging.FileHandler(filename)
handler.setLevel(logging.DEBUG)
formatter = _Formatter(fmt=_logger_fmt,
datefmt='%Y-%m-%d %H:%M:%S')
handler.setFormatter(formatter)
logging.getLogger().addHandler(handler)
|
[
"def",
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"=",
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"(",
"formatter",
")",
"logging",
".",
"getLogger",
"(",
")",
".",
"addHandler",
"(",
"handler",
")"
] |
Create a `phy.log` log file with DEBUG level in the
current directory.
|
[
"Create",
"a",
"phy",
".",
"log",
"log",
"file",
"with",
"DEBUG",
"level",
"in",
"the",
"current",
"directory",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/cli.py#L46-L55
|
train
|
kwikteam/phy
|
phy/utils/cli.py
|
_run_cmd
|
def _run_cmd(cmd, ctx, glob, loc): # pragma: no cover
"""Run a command with optionally a debugger, IPython, or profiling."""
if PDB:
_enable_pdb()
if IPYTHON:
from IPython import start_ipython
args_ipy = ['-i', '--gui=qt']
ns = glob.copy()
ns.update(loc)
return start_ipython(args_ipy, user_ns=ns)
# Profiling. The builtin `profile` is added in __init__.
prof = __builtins__.get('profile', None)
if prof:
prof = __builtins__['profile']
return _profile(prof, cmd, glob, loc)
return exec_(cmd, glob, loc)
|
python
|
def _run_cmd(cmd, ctx, glob, loc): # pragma: no cover
"""Run a command with optionally a debugger, IPython, or profiling."""
if PDB:
_enable_pdb()
if IPYTHON:
from IPython import start_ipython
args_ipy = ['-i', '--gui=qt']
ns = glob.copy()
ns.update(loc)
return start_ipython(args_ipy, user_ns=ns)
# Profiling. The builtin `profile` is added in __init__.
prof = __builtins__.get('profile', None)
if prof:
prof = __builtins__['profile']
return _profile(prof, cmd, glob, loc)
return exec_(cmd, glob, loc)
|
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Run a command with optionally a debugger, IPython, or profiling.
|
[
"Run",
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"command",
"with",
"optionally",
"a",
"debugger",
"IPython",
"or",
"profiling",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/cli.py#L58-L73
|
train
|
kwikteam/phy
|
phy/utils/cli.py
|
load_cli_plugins
|
def load_cli_plugins(cli, config_dir=None):
"""Load all plugins and attach them to a CLI object."""
from .config import load_master_config
config = load_master_config(config_dir=config_dir)
plugins = discover_plugins(config.Plugins.dirs)
for plugin in plugins:
if not hasattr(plugin, 'attach_to_cli'): # pragma: no cover
continue
logger.debug("Attach plugin `%s` to CLI.", _fullname(plugin))
# NOTE: plugin is a class, so we need to instantiate it.
try:
plugin().attach_to_cli(cli)
except Exception as e: # pragma: no cover
logger.error("Error when loading plugin `%s`: %s", plugin, e)
|
python
|
def load_cli_plugins(cli, config_dir=None):
"""Load all plugins and attach them to a CLI object."""
from .config import load_master_config
config = load_master_config(config_dir=config_dir)
plugins = discover_plugins(config.Plugins.dirs)
for plugin in plugins:
if not hasattr(plugin, 'attach_to_cli'): # pragma: no cover
continue
logger.debug("Attach plugin `%s` to CLI.", _fullname(plugin))
# NOTE: plugin is a class, so we need to instantiate it.
try:
plugin().attach_to_cli(cli)
except Exception as e: # pragma: no cover
logger.error("Error when loading plugin `%s`: %s", plugin, e)
|
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"\"Error when loading plugin `%s`: %s\"",
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"plugin",
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] |
Load all plugins and attach them to a CLI object.
|
[
"Load",
"all",
"plugins",
"and",
"attach",
"them",
"to",
"a",
"CLI",
"object",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/cli.py#L94-L109
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.get_mouse_pos
|
def get_mouse_pos(self, pos):
"""Return the mouse coordinates in NDC, taking panzoom into account."""
position = np.asarray(self._normalize(pos))
zoom = np.asarray(self._zoom_aspect())
pan = np.asarray(self.pan)
mouse_pos = ((position / zoom) - pan)
return mouse_pos
|
python
|
def get_mouse_pos(self, pos):
"""Return the mouse coordinates in NDC, taking panzoom into account."""
position = np.asarray(self._normalize(pos))
zoom = np.asarray(self._zoom_aspect())
pan = np.asarray(self.pan)
mouse_pos = ((position / zoom) - pan)
return mouse_pos
|
[
"def",
"get_mouse_pos",
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"self",
",",
"pos",
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":",
"position",
"=",
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".",
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"(",
"(",
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"/",
"zoom",
")",
"-",
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")",
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] |
Return the mouse coordinates in NDC, taking panzoom into account.
|
[
"Return",
"the",
"mouse",
"coordinates",
"in",
"NDC",
"taking",
"panzoom",
"into",
"account",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L228-L234
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.pan
|
def pan(self, value):
"""Pan translation."""
assert len(value) == 2
self._pan[:] = value
self._constrain_pan()
self.update()
|
python
|
def pan(self, value):
"""Pan translation."""
assert len(value) == 2
self._pan[:] = value
self._constrain_pan()
self.update()
|
[
"def",
"pan",
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"self",
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"]",
"=",
"value",
"self",
".",
"_constrain_pan",
"(",
")",
"self",
".",
"update",
"(",
")"
] |
Pan translation.
|
[
"Pan",
"translation",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L245-L250
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.zoom
|
def zoom(self, value):
"""Zoom level."""
if isinstance(value, (int, float)):
value = (value, value)
assert len(value) == 2
self._zoom = np.clip(value, self._zmin, self._zmax)
# Constrain bounding box.
self._constrain_pan()
self._constrain_zoom()
self.update()
|
python
|
def zoom(self, value):
"""Zoom level."""
if isinstance(value, (int, float)):
value = (value, value)
assert len(value) == 2
self._zoom = np.clip(value, self._zmin, self._zmax)
# Constrain bounding box.
self._constrain_pan()
self._constrain_zoom()
self.update()
|
[
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"self",
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"_constrain_zoom",
"(",
")",
"self",
".",
"update",
"(",
")"
] |
Zoom level.
|
[
"Zoom",
"level",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L258-L269
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.pan_delta
|
def pan_delta(self, d):
"""Pan the view by a given amount."""
dx, dy = d
pan_x, pan_y = self.pan
zoom_x, zoom_y = self._zoom_aspect(self._zoom)
self.pan = (pan_x + dx / zoom_x, pan_y + dy / zoom_y)
self.update()
|
python
|
def pan_delta(self, d):
"""Pan the view by a given amount."""
dx, dy = d
pan_x, pan_y = self.pan
zoom_x, zoom_y = self._zoom_aspect(self._zoom)
self.pan = (pan_x + dx / zoom_x, pan_y + dy / zoom_y)
self.update()
|
[
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] |
Pan the view by a given amount.
|
[
"Pan",
"the",
"view",
"by",
"a",
"given",
"amount",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L271-L279
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.zoom_delta
|
def zoom_delta(self, d, p=(0., 0.), c=1.):
"""Zoom the view by a given amount."""
dx, dy = d
x0, y0 = p
pan_x, pan_y = self._pan
zoom_x, zoom_y = self._zoom
zoom_x_new, zoom_y_new = (zoom_x * math.exp(c * self._zoom_coeff * dx),
zoom_y * math.exp(c * self._zoom_coeff * dy))
zoom_x_new = max(min(zoom_x_new, self._zmax), self._zmin)
zoom_y_new = max(min(zoom_y_new, self._zmax), self._zmin)
self.zoom = zoom_x_new, zoom_y_new
if self._zoom_to_pointer:
zoom_x, zoom_y = self._zoom_aspect((zoom_x,
zoom_y))
zoom_x_new, zoom_y_new = self._zoom_aspect((zoom_x_new,
zoom_y_new))
self.pan = (pan_x - x0 * (1. / zoom_x - 1. / zoom_x_new),
pan_y - y0 * (1. / zoom_y - 1. / zoom_y_new))
self.update()
|
python
|
def zoom_delta(self, d, p=(0., 0.), c=1.):
"""Zoom the view by a given amount."""
dx, dy = d
x0, y0 = p
pan_x, pan_y = self._pan
zoom_x, zoom_y = self._zoom
zoom_x_new, zoom_y_new = (zoom_x * math.exp(c * self._zoom_coeff * dx),
zoom_y * math.exp(c * self._zoom_coeff * dy))
zoom_x_new = max(min(zoom_x_new, self._zmax), self._zmin)
zoom_y_new = max(min(zoom_y_new, self._zmax), self._zmin)
self.zoom = zoom_x_new, zoom_y_new
if self._zoom_to_pointer:
zoom_x, zoom_y = self._zoom_aspect((zoom_x,
zoom_y))
zoom_x_new, zoom_y_new = self._zoom_aspect((zoom_x_new,
zoom_y_new))
self.pan = (pan_x - x0 * (1. / zoom_x - 1. / zoom_x_new),
pan_y - y0 * (1. / zoom_y - 1. / zoom_y_new))
self.update()
|
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] |
Zoom the view by a given amount.
|
[
"Zoom",
"the",
"view",
"by",
"a",
"given",
"amount",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L281-L305
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.set_range
|
def set_range(self, bounds, keep_aspect=False):
"""Zoom to fit a box."""
# a * (v0 + t) = -1
# a * (v1 + t) = +1
# =>
# a * (v1 - v0) = 2
bounds = np.asarray(bounds, dtype=np.float64)
v0 = bounds[:2]
v1 = bounds[2:]
pan = -.5 * (v0 + v1)
zoom = 2. / (v1 - v0)
if keep_aspect:
zoom = zoom.min() * np.ones(2)
self.set_pan_zoom(pan=pan, zoom=zoom)
|
python
|
def set_range(self, bounds, keep_aspect=False):
"""Zoom to fit a box."""
# a * (v0 + t) = -1
# a * (v1 + t) = +1
# =>
# a * (v1 - v0) = 2
bounds = np.asarray(bounds, dtype=np.float64)
v0 = bounds[:2]
v1 = bounds[2:]
pan = -.5 * (v0 + v1)
zoom = 2. / (v1 - v0)
if keep_aspect:
zoom = zoom.min() * np.ones(2)
self.set_pan_zoom(pan=pan, zoom=zoom)
|
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"pan",
",",
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"zoom",
")"
] |
Zoom to fit a box.
|
[
"Zoom",
"to",
"fit",
"a",
"box",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L317-L330
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.get_range
|
def get_range(self):
"""Return the bounds currently visible."""
p, z = np.asarray(self.pan), np.asarray(self.zoom)
x0, y0 = -1. / z - p
x1, y1 = +1. / z - p
return (x0, y0, x1, y1)
|
python
|
def get_range(self):
"""Return the bounds currently visible."""
p, z = np.asarray(self.pan), np.asarray(self.zoom)
x0, y0 = -1. / z - p
x1, y1 = +1. / z - p
return (x0, y0, x1, y1)
|
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] |
Return the bounds currently visible.
|
[
"Return",
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"visible",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L332-L337
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.on_mouse_move
|
def on_mouse_move(self, event):
"""Pan and zoom with the mouse."""
if event.modifiers:
return
if event.is_dragging:
x0, y0 = self._normalize(event.press_event.pos)
x1, y1 = self._normalize(event.last_event.pos)
x, y = self._normalize(event.pos)
dx, dy = x - x1, y - y1
if event.button == 1:
self.pan_delta((dx, dy))
elif event.button == 2:
c = np.sqrt(self.size[0]) * .03
self.zoom_delta((dx, dy), (x0, y0), c=c)
|
python
|
def on_mouse_move(self, event):
"""Pan and zoom with the mouse."""
if event.modifiers:
return
if event.is_dragging:
x0, y0 = self._normalize(event.press_event.pos)
x1, y1 = self._normalize(event.last_event.pos)
x, y = self._normalize(event.pos)
dx, dy = x - x1, y - y1
if event.button == 1:
self.pan_delta((dx, dy))
elif event.button == 2:
c = np.sqrt(self.size[0]) * .03
self.zoom_delta((dx, dy), (x0, y0), c=c)
|
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] |
Pan and zoom with the mouse.
|
[
"Pan",
"and",
"zoom",
"with",
"the",
"mouse",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L386-L399
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.on_mouse_wheel
|
def on_mouse_wheel(self, event):
"""Zoom with the mouse wheel."""
# NOTE: not called on OS X because of touchpad
if event.modifiers:
return
dx = np.sign(event.delta[1]) * self._wheel_coeff
# Zoom toward the mouse pointer.
x0, y0 = self._normalize(event.pos)
self.zoom_delta((dx, dx), (x0, y0))
|
python
|
def on_mouse_wheel(self, event):
"""Zoom with the mouse wheel."""
# NOTE: not called on OS X because of touchpad
if event.modifiers:
return
dx = np.sign(event.delta[1]) * self._wheel_coeff
# Zoom toward the mouse pointer.
x0, y0 = self._normalize(event.pos)
self.zoom_delta((dx, dx), (x0, y0))
|
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"(",
"(",
"dx",
",",
"dx",
")",
",",
"(",
"x0",
",",
"y0",
")",
")"
] |
Zoom with the mouse wheel.
|
[
"Zoom",
"with",
"the",
"mouse",
"wheel",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L425-L433
|
train
|
kwikteam/phy
|
phy/plot/panzoom.py
|
PanZoom.on_key_press
|
def on_key_press(self, event):
"""Pan and zoom with the keyboard."""
# Zooming with the keyboard.
key = event.key
if event.modifiers:
return
# Pan.
if self.enable_keyboard_pan and key in self._arrows:
self._pan_keyboard(key)
# Zoom.
if key in self._pm:
self._zoom_keyboard(key)
# Reset with 'R'.
if key == 'R':
self.reset()
|
python
|
def on_key_press(self, event):
"""Pan and zoom with the keyboard."""
# Zooming with the keyboard.
key = event.key
if event.modifiers:
return
# Pan.
if self.enable_keyboard_pan and key in self._arrows:
self._pan_keyboard(key)
# Zoom.
if key in self._pm:
self._zoom_keyboard(key)
# Reset with 'R'.
if key == 'R':
self.reset()
|
[
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"# Zooming with the keyboard.",
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"# Reset with 'R'.",
"if",
"key",
"==",
"'R'",
":",
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"reset",
"(",
")"
] |
Pan and zoom with the keyboard.
|
[
"Pan",
"and",
"zoom",
"with",
"the",
"keyboard",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/panzoom.py#L435-L452
|
train
|
kwikteam/phy
|
tools/api.py
|
_replace_docstring_header
|
def _replace_docstring_header(paragraph):
"""Process NumPy-like function docstrings."""
# Replace Markdown headers in docstrings with light headers in bold.
paragraph = re.sub(_docstring_header_pattern,
r'*\1*',
paragraph,
)
paragraph = re.sub(_docstring_parameters_pattern,
r'\n* `\1` (\2)\n',
paragraph,
)
return paragraph
|
python
|
def _replace_docstring_header(paragraph):
"""Process NumPy-like function docstrings."""
# Replace Markdown headers in docstrings with light headers in bold.
paragraph = re.sub(_docstring_header_pattern,
r'*\1*',
paragraph,
)
paragraph = re.sub(_docstring_parameters_pattern,
r'\n* `\1` (\2)\n',
paragraph,
)
return paragraph
|
[
"def",
"_replace_docstring_header",
"(",
"paragraph",
")",
":",
"# Replace Markdown headers in docstrings with light headers in bold.",
"paragraph",
"=",
"re",
".",
"sub",
"(",
"_docstring_header_pattern",
",",
"r'*\\1*'",
",",
"paragraph",
",",
")",
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"(",
"_docstring_parameters_pattern",
",",
"r'\\n* `\\1` (\\2)\\n'",
",",
"paragraph",
",",
")",
"return",
"paragraph"
] |
Process NumPy-like function docstrings.
|
[
"Process",
"NumPy",
"-",
"like",
"function",
"docstrings",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/tools/api.py#L47-L61
|
train
|
kwikteam/phy
|
tools/api.py
|
_iter_vars
|
def _iter_vars(mod):
"""Iterate through a list of variables define in a module's
public namespace."""
vars = sorted(var for var in dir(mod) if _is_public(var))
for var in vars:
yield getattr(mod, var)
|
python
|
def _iter_vars(mod):
"""Iterate through a list of variables define in a module's
public namespace."""
vars = sorted(var for var in dir(mod) if _is_public(var))
for var in vars:
yield getattr(mod, var)
|
[
"def",
"_iter_vars",
"(",
"mod",
")",
":",
"vars",
"=",
"sorted",
"(",
"var",
"for",
"var",
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"mod",
")",
"if",
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"(",
"var",
")",
")",
"for",
"var",
"in",
"vars",
":",
"yield",
"getattr",
"(",
"mod",
",",
"var",
")"
] |
Iterate through a list of variables define in a module's
public namespace.
|
[
"Iterate",
"through",
"a",
"list",
"of",
"variables",
"define",
"in",
"a",
"module",
"s",
"public",
"namespace",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/tools/api.py#L145-L150
|
train
|
kwikteam/phy
|
tools/api.py
|
_function_header
|
def _function_header(subpackage, func):
"""Generate the docstring of a function."""
args = inspect.formatargspec(*inspect.getfullargspec(func))
return "{name}{args}".format(name=_full_name(subpackage, func),
args=args,
)
|
python
|
def _function_header(subpackage, func):
"""Generate the docstring of a function."""
args = inspect.formatargspec(*inspect.getfullargspec(func))
return "{name}{args}".format(name=_full_name(subpackage, func),
args=args,
)
|
[
"def",
"_function_header",
"(",
"subpackage",
",",
"func",
")",
":",
"args",
"=",
"inspect",
".",
"formatargspec",
"(",
"*",
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".",
"getfullargspec",
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"func",
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")",
"return",
"\"{name}{args}\"",
".",
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"(",
"name",
"=",
"_full_name",
"(",
"subpackage",
",",
"func",
")",
",",
"args",
"=",
"args",
",",
")"
] |
Generate the docstring of a function.
|
[
"Generate",
"the",
"docstring",
"of",
"a",
"function",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/tools/api.py#L185-L190
|
train
|
kwikteam/phy
|
tools/api.py
|
_doc_method
|
def _doc_method(klass, func):
"""Generate the docstring of a method."""
argspec = inspect.getfullargspec(func)
# Remove first 'self' argument.
if argspec.args and argspec.args[0] == 'self':
del argspec.args[0]
args = inspect.formatargspec(*argspec)
header = "{klass}.{name}{args}".format(klass=klass.__name__,
name=_name(func),
args=args,
)
docstring = _doc(func)
return _concat(header, docstring)
|
python
|
def _doc_method(klass, func):
"""Generate the docstring of a method."""
argspec = inspect.getfullargspec(func)
# Remove first 'self' argument.
if argspec.args and argspec.args[0] == 'self':
del argspec.args[0]
args = inspect.formatargspec(*argspec)
header = "{klass}.{name}{args}".format(klass=klass.__name__,
name=_name(func),
args=args,
)
docstring = _doc(func)
return _concat(header, docstring)
|
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"def",
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"(",
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",",
"func",
")",
":",
"argspec",
"=",
"inspect",
".",
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"(",
"func",
")",
"# Remove first 'self' argument.",
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"argspec",
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"args",
"[",
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"==",
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"docstring",
"=",
"_doc",
"(",
"func",
")",
"return",
"_concat",
"(",
"header",
",",
"docstring",
")"
] |
Generate the docstring of a method.
|
[
"Generate",
"the",
"docstring",
"of",
"a",
"method",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/tools/api.py#L199-L211
|
train
|
kwikteam/phy
|
tools/api.py
|
_doc_property
|
def _doc_property(klass, prop):
"""Generate the docstring of a property."""
header = "{klass}.{name}".format(klass=klass.__name__,
name=_name(prop),
)
docstring = _doc(prop)
return _concat(header, docstring)
|
python
|
def _doc_property(klass, prop):
"""Generate the docstring of a property."""
header = "{klass}.{name}".format(klass=klass.__name__,
name=_name(prop),
)
docstring = _doc(prop)
return _concat(header, docstring)
|
[
"def",
"_doc_property",
"(",
"klass",
",",
"prop",
")",
":",
"header",
"=",
"\"{klass}.{name}\"",
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"(",
"klass",
"=",
"klass",
".",
"__name__",
",",
"name",
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"prop",
")",
",",
")",
"docstring",
"=",
"_doc",
"(",
"prop",
")",
"return",
"_concat",
"(",
"header",
",",
"docstring",
")"
] |
Generate the docstring of a property.
|
[
"Generate",
"the",
"docstring",
"of",
"a",
"property",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/tools/api.py#L214-L220
|
train
|
kwikteam/phy
|
tools/api.py
|
_generate_paragraphs
|
def _generate_paragraphs(package, subpackages):
"""Generate the paragraphs of the API documentation."""
# API doc of each module.
for subpackage in _iter_subpackages(package, subpackages):
subpackage_name = subpackage.__name__
yield "## {}".format(subpackage_name)
# Subpackage documentation.
yield _doc(_import_module(subpackage_name))
# List of top-level functions in the subpackage.
for func in _iter_functions(subpackage):
yield '##### ' + _doc_function(subpackage, func)
# All public classes.
for klass in _iter_classes(subpackage):
# Class documentation.
yield "### {}".format(_full_name(subpackage, klass))
yield _doc(klass)
yield "#### Methods"
for method in _iter_methods(klass, package):
yield '##### ' + _doc_method(klass, method)
yield "#### Properties"
for prop in _iter_properties(klass, package):
yield '##### ' + _doc_property(klass, prop)
|
python
|
def _generate_paragraphs(package, subpackages):
"""Generate the paragraphs of the API documentation."""
# API doc of each module.
for subpackage in _iter_subpackages(package, subpackages):
subpackage_name = subpackage.__name__
yield "## {}".format(subpackage_name)
# Subpackage documentation.
yield _doc(_import_module(subpackage_name))
# List of top-level functions in the subpackage.
for func in _iter_functions(subpackage):
yield '##### ' + _doc_function(subpackage, func)
# All public classes.
for klass in _iter_classes(subpackage):
# Class documentation.
yield "### {}".format(_full_name(subpackage, klass))
yield _doc(klass)
yield "#### Methods"
for method in _iter_methods(klass, package):
yield '##### ' + _doc_method(klass, method)
yield "#### Properties"
for prop in _iter_properties(klass, package):
yield '##### ' + _doc_property(klass, prop)
|
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Generate the paragraphs of the API documentation.
|
[
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] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/tools/api.py#L260-L289
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor._add_field_column
|
def _add_field_column(self, field): # pragma: no cover
"""Add a column for a given label field."""
@self.add_column(name=field)
def get_my_label(cluster_id):
return self.cluster_meta.get(field, cluster_id)
|
python
|
def _add_field_column(self, field): # pragma: no cover
"""Add a column for a given label field."""
@self.add_column(name=field)
def get_my_label(cluster_id):
return self.cluster_meta.get(field, cluster_id)
|
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Add a column for a given label field.
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[
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"field",
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7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L234-L238
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor._emit_select
|
def _emit_select(self, cluster_ids, **kwargs):
"""Choose spikes from the specified clusters and emit the
`select` event on the GUI."""
# Remove non-existing clusters from the selection.
cluster_ids = self._keep_existing_clusters(cluster_ids)
logger.debug("Select cluster(s): %s.",
', '.join(map(str, cluster_ids)))
self.emit('select', cluster_ids, **kwargs)
|
python
|
def _emit_select(self, cluster_ids, **kwargs):
"""Choose spikes from the specified clusters and emit the
`select` event on the GUI."""
# Remove non-existing clusters from the selection.
cluster_ids = self._keep_existing_clusters(cluster_ids)
logger.debug("Select cluster(s): %s.",
', '.join(map(str, cluster_ids)))
self.emit('select', cluster_ids, **kwargs)
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Choose spikes from the specified clusters and emit the
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7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L336-L343
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor._update_cluster_view
|
def _update_cluster_view(self):
"""Initialize the cluster view with cluster data."""
logger.log(5, "Update the cluster view.")
cluster_ids = [int(c) for c in self.clustering.cluster_ids]
self.cluster_view.set_rows(cluster_ids)
|
python
|
def _update_cluster_view(self):
"""Initialize the cluster view with cluster data."""
logger.log(5, "Update the cluster view.")
cluster_ids = [int(c) for c in self.clustering.cluster_ids]
self.cluster_view.set_rows(cluster_ids)
|
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Initialize the cluster view with cluster data.
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[
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7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L379-L383
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor._update_similarity_view
|
def _update_similarity_view(self):
"""Update the similarity view with matches for the specified
clusters."""
if not self.similarity:
return
selection = self.cluster_view.selected
if not len(selection):
return
cluster_id = selection[0]
cluster_ids = self.clustering.cluster_ids
self._best = cluster_id
logger.log(5, "Update the similarity view.")
# This is a list of pairs (closest_cluster, similarity).
similarities = self.similarity(cluster_id)
# We save the similarity values wrt the currently-selected clusters.
# Note that we keep the order of the output of the self.similary()
# function.
clusters_sim = OrderedDict([(int(cl), s) for (cl, s) in similarities])
# List of similar clusters, remove non-existing ones.
clusters = [c for c in clusters_sim.keys()
if c in cluster_ids]
# The similarity view will use these values.
self._current_similarity_values = clusters_sim
# Set the rows of the similarity view.
# TODO: instead of the self._current_similarity_values hack,
# give the possibility to specify the values here (?).
self.similarity_view.set_rows([c for c in clusters
if c not in selection])
|
python
|
def _update_similarity_view(self):
"""Update the similarity view with matches for the specified
clusters."""
if not self.similarity:
return
selection = self.cluster_view.selected
if not len(selection):
return
cluster_id = selection[0]
cluster_ids = self.clustering.cluster_ids
self._best = cluster_id
logger.log(5, "Update the similarity view.")
# This is a list of pairs (closest_cluster, similarity).
similarities = self.similarity(cluster_id)
# We save the similarity values wrt the currently-selected clusters.
# Note that we keep the order of the output of the self.similary()
# function.
clusters_sim = OrderedDict([(int(cl), s) for (cl, s) in similarities])
# List of similar clusters, remove non-existing ones.
clusters = [c for c in clusters_sim.keys()
if c in cluster_ids]
# The similarity view will use these values.
self._current_similarity_values = clusters_sim
# Set the rows of the similarity view.
# TODO: instead of the self._current_similarity_values hack,
# give the possibility to specify the values here (?).
self.similarity_view.set_rows([c for c in clusters
if c not in selection])
|
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Update the similarity view with matches for the specified
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[
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7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L385-L412
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.on_cluster
|
def on_cluster(self, up):
"""Update the cluster views after clustering actions."""
similar = self.similarity_view.selected
# Reinitialize the cluster view if clusters have changed.
if up.added:
self._update_cluster_view()
# Select all new clusters in view 1.
if up.history == 'undo':
# Select the clusters that were selected before the undone
# action.
clusters_0, clusters_1 = up.undo_state[0]['selection']
# Select rows in the tables.
self.cluster_view.select(clusters_0, up=up)
self.similarity_view.select(clusters_1, up=up)
elif up.added:
if up.description == 'assign':
# NOTE: we change the order such that the last selected
# cluster (with a new color) is the split cluster.
added = list(up.added[1:]) + [up.added[0]]
else:
added = up.added
# Select the new clusters in the cluster view.
self.cluster_view.select(added, up=up)
if similar:
self.similarity_view.next()
elif up.metadata_changed:
# Select next in similarity view if all moved are in that view.
if set(up.metadata_changed) <= set(similar):
next_cluster = self.similarity_view.get_next_id()
self._update_similarity_view()
if next_cluster is not None:
# Select the cluster in the similarity view.
self.similarity_view.select([next_cluster])
# Otherwise, select next in cluster view.
else:
self._update_cluster_view()
# Determine if there is a next cluster set from a
# previous clustering action.
cluster = up.metadata_changed[0]
next_cluster = self.cluster_meta.get('next_cluster', cluster)
logger.debug("Get next_cluster for %d: %s.",
cluster, next_cluster)
# If there is not, fallback on the next cluster in the list.
if next_cluster is None:
self.cluster_view.select([cluster], do_emit=False)
self.cluster_view.next()
else:
self.cluster_view.select([next_cluster])
|
python
|
def on_cluster(self, up):
"""Update the cluster views after clustering actions."""
similar = self.similarity_view.selected
# Reinitialize the cluster view if clusters have changed.
if up.added:
self._update_cluster_view()
# Select all new clusters in view 1.
if up.history == 'undo':
# Select the clusters that were selected before the undone
# action.
clusters_0, clusters_1 = up.undo_state[0]['selection']
# Select rows in the tables.
self.cluster_view.select(clusters_0, up=up)
self.similarity_view.select(clusters_1, up=up)
elif up.added:
if up.description == 'assign':
# NOTE: we change the order such that the last selected
# cluster (with a new color) is the split cluster.
added = list(up.added[1:]) + [up.added[0]]
else:
added = up.added
# Select the new clusters in the cluster view.
self.cluster_view.select(added, up=up)
if similar:
self.similarity_view.next()
elif up.metadata_changed:
# Select next in similarity view if all moved are in that view.
if set(up.metadata_changed) <= set(similar):
next_cluster = self.similarity_view.get_next_id()
self._update_similarity_view()
if next_cluster is not None:
# Select the cluster in the similarity view.
self.similarity_view.select([next_cluster])
# Otherwise, select next in cluster view.
else:
self._update_cluster_view()
# Determine if there is a next cluster set from a
# previous clustering action.
cluster = up.metadata_changed[0]
next_cluster = self.cluster_meta.get('next_cluster', cluster)
logger.debug("Get next_cluster for %d: %s.",
cluster, next_cluster)
# If there is not, fallback on the next cluster in the list.
if next_cluster is None:
self.cluster_view.select([cluster], do_emit=False)
self.cluster_view.next()
else:
self.cluster_view.select([next_cluster])
|
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Update the cluster views after clustering actions.
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[
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"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L438-L488
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.select
|
def select(self, *cluster_ids):
"""Select a list of clusters."""
# HACK: allow for `select(1, 2, 3)` in addition to `select([1, 2, 3])`
# This makes it more convenient to select multiple clusters with
# the snippet: `:c 1 2 3` instead of `:c 1,2,3`.
if cluster_ids and isinstance(cluster_ids[0], (tuple, list)):
cluster_ids = list(cluster_ids[0]) + list(cluster_ids[1:])
# Remove non-existing clusters from the selection.
cluster_ids = self._keep_existing_clusters(cluster_ids)
# Update the cluster view selection.
self.cluster_view.select(cluster_ids)
|
python
|
def select(self, *cluster_ids):
"""Select a list of clusters."""
# HACK: allow for `select(1, 2, 3)` in addition to `select([1, 2, 3])`
# This makes it more convenient to select multiple clusters with
# the snippet: `:c 1 2 3` instead of `:c 1,2,3`.
if cluster_ids and isinstance(cluster_ids[0], (tuple, list)):
cluster_ids = list(cluster_ids[0]) + list(cluster_ids[1:])
# Remove non-existing clusters from the selection.
cluster_ids = self._keep_existing_clusters(cluster_ids)
# Update the cluster view selection.
self.cluster_view.select(cluster_ids)
|
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Select a list of clusters.
|
[
"Select",
"a",
"list",
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"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L556-L566
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.merge
|
def merge(self, cluster_ids=None, to=None):
"""Merge the selected clusters."""
if cluster_ids is None:
cluster_ids = self.selected
if len(cluster_ids or []) <= 1:
return
self.clustering.merge(cluster_ids, to=to)
self._global_history.action(self.clustering)
|
python
|
def merge(self, cluster_ids=None, to=None):
"""Merge the selected clusters."""
if cluster_ids is None:
cluster_ids = self.selected
if len(cluster_ids or []) <= 1:
return
self.clustering.merge(cluster_ids, to=to)
self._global_history.action(self.clustering)
|
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] |
Merge the selected clusters.
|
[
"Merge",
"the",
"selected",
"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L575-L582
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.split
|
def split(self, spike_ids=None, spike_clusters_rel=0):
"""Split the selected spikes."""
if spike_ids is None:
spike_ids = self.emit('request_split', single=True)
spike_ids = np.asarray(spike_ids, dtype=np.int64)
assert spike_ids.dtype == np.int64
assert spike_ids.ndim == 1
if len(spike_ids) == 0:
msg = ("You first need to select spikes in the feature "
"view with a few Ctrl+Click around the spikes "
"that you want to split.")
self.emit('error', msg)
return
self.clustering.split(spike_ids,
spike_clusters_rel=spike_clusters_rel)
self._global_history.action(self.clustering)
|
python
|
def split(self, spike_ids=None, spike_clusters_rel=0):
"""Split the selected spikes."""
if spike_ids is None:
spike_ids = self.emit('request_split', single=True)
spike_ids = np.asarray(spike_ids, dtype=np.int64)
assert spike_ids.dtype == np.int64
assert spike_ids.ndim == 1
if len(spike_ids) == 0:
msg = ("You first need to select spikes in the feature "
"view with a few Ctrl+Click around the spikes "
"that you want to split.")
self.emit('error', msg)
return
self.clustering.split(spike_ids,
spike_clusters_rel=spike_clusters_rel)
self._global_history.action(self.clustering)
|
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"(",
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"clustering",
")"
] |
Split the selected spikes.
|
[
"Split",
"the",
"selected",
"spikes",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L584-L599
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.get_labels
|
def get_labels(self, field):
"""Return the labels of all clusters, for a given field."""
return {c: self.cluster_meta.get(field, c)
for c in self.clustering.cluster_ids}
|
python
|
def get_labels(self, field):
"""Return the labels of all clusters, for a given field."""
return {c: self.cluster_meta.get(field, c)
for c in self.clustering.cluster_ids}
|
[
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",",
"c",
")",
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"c",
"in",
"self",
".",
"clustering",
".",
"cluster_ids",
"}"
] |
Return the labels of all clusters, for a given field.
|
[
"Return",
"the",
"labels",
"of",
"all",
"clusters",
"for",
"a",
"given",
"field",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L610-L613
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.label
|
def label(self, name, value, cluster_ids=None):
"""Assign a label to clusters.
Example: `quality 3`
"""
if cluster_ids is None:
cluster_ids = self.cluster_view.selected
if not hasattr(cluster_ids, '__len__'):
cluster_ids = [cluster_ids]
if len(cluster_ids) == 0:
return
self.cluster_meta.set(name, cluster_ids, value)
self._global_history.action(self.cluster_meta)
|
python
|
def label(self, name, value, cluster_ids=None):
"""Assign a label to clusters.
Example: `quality 3`
"""
if cluster_ids is None:
cluster_ids = self.cluster_view.selected
if not hasattr(cluster_ids, '__len__'):
cluster_ids = [cluster_ids]
if len(cluster_ids) == 0:
return
self.cluster_meta.set(name, cluster_ids, value)
self._global_history.action(self.cluster_meta)
|
[
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"self",
".",
"_global_history",
".",
"action",
"(",
"self",
".",
"cluster_meta",
")"
] |
Assign a label to clusters.
Example: `quality 3`
|
[
"Assign",
"a",
"label",
"to",
"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L615-L628
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.move
|
def move(self, group, cluster_ids=None):
"""Assign a group to some clusters.
Example: `good`
"""
if isinstance(cluster_ids, string_types):
logger.warn("The list of clusters should be a list of integers, "
"not a string.")
return
self.label('group', group, cluster_ids=cluster_ids)
|
python
|
def move(self, group, cluster_ids=None):
"""Assign a group to some clusters.
Example: `good`
"""
if isinstance(cluster_ids, string_types):
logger.warn("The list of clusters should be a list of integers, "
"not a string.")
return
self.label('group', group, cluster_ids=cluster_ids)
|
[
"def",
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"label",
"(",
"'group'",
",",
"group",
",",
"cluster_ids",
"=",
"cluster_ids",
")"
] |
Assign a group to some clusters.
Example: `good`
|
[
"Assign",
"a",
"group",
"to",
"some",
"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L630-L640
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.next
|
def next(self):
"""Select the next cluster."""
if not self.selected:
self.cluster_view.next()
else:
self.similarity_view.next()
|
python
|
def next(self):
"""Select the next cluster."""
if not self.selected:
self.cluster_view.next()
else:
self.similarity_view.next()
|
[
"def",
"next",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"selected",
":",
"self",
".",
"cluster_view",
".",
"next",
"(",
")",
"else",
":",
"self",
".",
"similarity_view",
".",
"next",
"(",
")"
] |
Select the next cluster.
|
[
"Select",
"the",
"next",
"cluster",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L670-L675
|
train
|
kwikteam/phy
|
phy/cluster/supervisor.py
|
Supervisor.save
|
def save(self):
"""Save the manual clustering back to disk."""
spike_clusters = self.clustering.spike_clusters
groups = {c: self.cluster_meta.get('group', c) or 'unsorted'
for c in self.clustering.cluster_ids}
# List of tuples (field_name, dictionary).
labels = [(field, self.get_labels(field))
for field in self.cluster_meta.fields
if field not in ('next_cluster')]
# TODO: add option in add_field to declare a field unsavable.
self.emit('request_save', spike_clusters, groups, *labels)
# Cache the spikes_per_cluster array.
self._save_spikes_per_cluster()
|
python
|
def save(self):
"""Save the manual clustering back to disk."""
spike_clusters = self.clustering.spike_clusters
groups = {c: self.cluster_meta.get('group', c) or 'unsorted'
for c in self.clustering.cluster_ids}
# List of tuples (field_name, dictionary).
labels = [(field, self.get_labels(field))
for field in self.cluster_meta.fields
if field not in ('next_cluster')]
# TODO: add option in add_field to declare a field unsavable.
self.emit('request_save', spike_clusters, groups, *labels)
# Cache the spikes_per_cluster array.
self._save_spikes_per_cluster()
|
[
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",",
"*",
"labels",
")",
"# Cache the spikes_per_cluster array.",
"self",
".",
"_save_spikes_per_cluster",
"(",
")"
] |
Save the manual clustering back to disk.
|
[
"Save",
"the",
"manual",
"clustering",
"back",
"to",
"disk",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/supervisor.py#L692-L704
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
create_cluster_meta
|
def create_cluster_meta(cluster_groups):
"""Return a ClusterMeta instance with cluster group support."""
meta = ClusterMeta()
meta.add_field('group')
cluster_groups = cluster_groups or {}
data = {c: {'group': v} for c, v in cluster_groups.items()}
meta.from_dict(data)
return meta
|
python
|
def create_cluster_meta(cluster_groups):
"""Return a ClusterMeta instance with cluster group support."""
meta = ClusterMeta()
meta.add_field('group')
cluster_groups = cluster_groups or {}
data = {c: {'group': v} for c, v in cluster_groups.items()}
meta.from_dict(data)
return meta
|
[
"def",
"create_cluster_meta",
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"cluster_groups",
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"=",
"ClusterMeta",
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"(",
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"}",
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".",
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"(",
"data",
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"return",
"meta"
] |
Return a ClusterMeta instance with cluster group support.
|
[
"Return",
"a",
"ClusterMeta",
"instance",
"with",
"cluster",
"group",
"support",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L35-L44
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
ClusterMeta.add_field
|
def add_field(self, name, default_value=None):
"""Add a field with an optional default value."""
self._fields[name] = default_value
def func(cluster):
return self.get(name, cluster)
setattr(self, name, func)
|
python
|
def add_field(self, name, default_value=None):
"""Add a field with an optional default value."""
self._fields[name] = default_value
def func(cluster):
return self.get(name, cluster)
setattr(self, name, func)
|
[
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",",
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Add a field with an optional default value.
|
[
"Add",
"a",
"field",
"with",
"an",
"optional",
"default",
"value",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L116-L123
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
ClusterMeta.set
|
def set(self, field, clusters, value, add_to_stack=True):
"""Set the value of one of several clusters."""
# Add the field if it doesn't exist.
if field not in self._fields:
self.add_field(field)
assert field in self._fields
clusters = _as_list(clusters)
for cluster in clusters:
if cluster not in self._data:
self._data[cluster] = {}
self._data[cluster][field] = value
up = UpdateInfo(description='metadata_' + field,
metadata_changed=clusters,
metadata_value=value,
)
undo_state = self.emit('request_undo_state', up)
if add_to_stack:
self._undo_stack.add((clusters, field, value, up, undo_state))
self.emit('cluster', up)
return up
|
python
|
def set(self, field, clusters, value, add_to_stack=True):
"""Set the value of one of several clusters."""
# Add the field if it doesn't exist.
if field not in self._fields:
self.add_field(field)
assert field in self._fields
clusters = _as_list(clusters)
for cluster in clusters:
if cluster not in self._data:
self._data[cluster] = {}
self._data[cluster][field] = value
up = UpdateInfo(description='metadata_' + field,
metadata_changed=clusters,
metadata_value=value,
)
undo_state = self.emit('request_undo_state', up)
if add_to_stack:
self._undo_stack.add((clusters, field, value, up, undo_state))
self.emit('cluster', up)
return up
|
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] |
Set the value of one of several clusters.
|
[
"Set",
"the",
"value",
"of",
"one",
"of",
"several",
"clusters",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L140-L163
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
ClusterMeta.get
|
def get(self, field, cluster):
"""Retrieve the value of one cluster."""
if _is_list(cluster):
return [self.get(field, c) for c in cluster]
assert field in self._fields
default = self._fields[field]
return self._data.get(cluster, {}).get(field, default)
|
python
|
def get(self, field, cluster):
"""Retrieve the value of one cluster."""
if _is_list(cluster):
return [self.get(field, c) for c in cluster]
assert field in self._fields
default = self._fields[field]
return self._data.get(cluster, {}).get(field, default)
|
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",",
"{",
"}",
")",
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"get",
"(",
"field",
",",
"default",
")"
] |
Retrieve the value of one cluster.
|
[
"Retrieve",
"the",
"value",
"of",
"one",
"cluster",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L165-L171
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
ClusterMeta.set_from_descendants
|
def set_from_descendants(self, descendants):
"""Update metadata of some clusters given the metadata of their
ascendants."""
for field in self.fields:
# This gives a set of metadata values of all the parents
# of any new cluster.
candidates = defaultdict(set)
for old, new in descendants:
candidates[new].add(self.get(field, old))
# Loop over all new clusters.
for new, vals in candidates.items():
vals = list(vals)
default = self._fields[field]
# If all the parents have the same value, assign it to
# the new cluster if it is not the default.
if len(vals) == 1 and vals[0] != default:
self.set(field, new, vals[0])
|
python
|
def set_from_descendants(self, descendants):
"""Update metadata of some clusters given the metadata of their
ascendants."""
for field in self.fields:
# This gives a set of metadata values of all the parents
# of any new cluster.
candidates = defaultdict(set)
for old, new in descendants:
candidates[new].add(self.get(field, old))
# Loop over all new clusters.
for new, vals in candidates.items():
vals = list(vals)
default = self._fields[field]
# If all the parents have the same value, assign it to
# the new cluster if it is not the default.
if len(vals) == 1 and vals[0] != default:
self.set(field, new, vals[0])
|
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7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L173-L191
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
ClusterMeta.undo
|
def undo(self):
"""Undo the last metadata change.
Returns
-------
up : UpdateInfo instance
"""
args = self._undo_stack.back()
if args is None:
return
self._data = deepcopy(self._data_base)
for clusters, field, value, up, undo_state in self._undo_stack:
if clusters is not None:
self.set(field, clusters, value, add_to_stack=False)
# Return the UpdateInfo instance of the undo action.
up, undo_state = args[-2:]
up.history = 'undo'
up.undo_state = undo_state
self.emit('cluster', up)
return up
|
python
|
def undo(self):
"""Undo the last metadata change.
Returns
-------
up : UpdateInfo instance
"""
args = self._undo_stack.back()
if args is None:
return
self._data = deepcopy(self._data_base)
for clusters, field, value, up, undo_state in self._undo_stack:
if clusters is not None:
self.set(field, clusters, value, add_to_stack=False)
# Return the UpdateInfo instance of the undo action.
up, undo_state = args[-2:]
up.history = 'undo'
up.undo_state = undo_state
self.emit('cluster', up)
return up
|
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Undo the last metadata change.
Returns
-------
up : UpdateInfo instance
|
[
"Undo",
"the",
"last",
"metadata",
"change",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L194-L217
|
train
|
kwikteam/phy
|
phy/cluster/_utils.py
|
ClusterMeta.redo
|
def redo(self):
"""Redo the next metadata change.
Returns
-------
up : UpdateInfo instance
"""
args = self._undo_stack.forward()
if args is None:
return
clusters, field, value, up, undo_state = args
self.set(field, clusters, value, add_to_stack=False)
# Return the UpdateInfo instance of the redo action.
up.history = 'redo'
self.emit('cluster', up)
return up
|
python
|
def redo(self):
"""Redo the next metadata change.
Returns
-------
up : UpdateInfo instance
"""
args = self._undo_stack.forward()
if args is None:
return
clusters, field, value, up, undo_state = args
self.set(field, clusters, value, add_to_stack=False)
# Return the UpdateInfo instance of the redo action.
up.history = 'redo'
self.emit('cluster', up)
return up
|
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Redo the next metadata change.
Returns
-------
up : UpdateInfo instance
|
[
"Redo",
"the",
"next",
"metadata",
"change",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/cluster/_utils.py#L219-L237
|
train
|
kwikteam/phy
|
phy/plot/utils.py
|
_get_boxes
|
def _get_boxes(pos, size=None, margin=0, keep_aspect_ratio=True):
"""Generate non-overlapping boxes in NDC from a set of positions."""
# Get x, y.
pos = np.asarray(pos, dtype=np.float64)
x, y = pos.T
x = x[:, np.newaxis]
y = y[:, np.newaxis]
w, h = size if size is not None else _get_box_size(x, y, margin=margin)
x0, y0 = x - w, y - h
x1, y1 = x + w, y + h
# Renormalize the whole thing by keeping the aspect ratio.
x0min, y0min, x1max, y1max = x0.min(), y0.min(), x1.max(), y1.max()
if not keep_aspect_ratio:
b = (x0min, y0min, x1max, y1max)
else:
dx = x1max - x0min
dy = y1max - y0min
if dx > dy:
b = (x0min, (y1max + y0min) / 2. - dx / 2.,
x1max, (y1max + y0min) / 2. + dx / 2.)
else:
b = ((x1max + x0min) / 2. - dy / 2., y0min,
(x1max + x0min) / 2. + dy / 2., y1max)
r = Range(from_bounds=b,
to_bounds=(-1, -1, 1, 1))
return np.c_[r.apply(np.c_[x0, y0]), r.apply(np.c_[x1, y1])]
|
python
|
def _get_boxes(pos, size=None, margin=0, keep_aspect_ratio=True):
"""Generate non-overlapping boxes in NDC from a set of positions."""
# Get x, y.
pos = np.asarray(pos, dtype=np.float64)
x, y = pos.T
x = x[:, np.newaxis]
y = y[:, np.newaxis]
w, h = size if size is not None else _get_box_size(x, y, margin=margin)
x0, y0 = x - w, y - h
x1, y1 = x + w, y + h
# Renormalize the whole thing by keeping the aspect ratio.
x0min, y0min, x1max, y1max = x0.min(), y0.min(), x1.max(), y1.max()
if not keep_aspect_ratio:
b = (x0min, y0min, x1max, y1max)
else:
dx = x1max - x0min
dy = y1max - y0min
if dx > dy:
b = (x0min, (y1max + y0min) / 2. - dx / 2.,
x1max, (y1max + y0min) / 2. + dx / 2.)
else:
b = ((x1max + x0min) / 2. - dy / 2., y0min,
(x1max + x0min) / 2. + dy / 2., y1max)
r = Range(from_bounds=b,
to_bounds=(-1, -1, 1, 1))
return np.c_[r.apply(np.c_[x0, y0]), r.apply(np.c_[x1, y1])]
|
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Generate non-overlapping boxes in NDC from a set of positions.
|
[
"Generate",
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"overlapping",
"boxes",
"in",
"NDC",
"from",
"a",
"set",
"of",
"positions",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/utils.py#L76-L105
|
train
|
kwikteam/phy
|
phy/plot/utils.py
|
_get_texture
|
def _get_texture(arr, default, n_items, from_bounds):
"""Prepare data to be uploaded as a texture.
The from_bounds must be specified.
"""
if not hasattr(default, '__len__'): # pragma: no cover
default = [default]
n_cols = len(default)
if arr is None: # pragma: no cover
arr = np.tile(default, (n_items, 1))
assert arr.shape == (n_items, n_cols)
# Convert to 3D texture.
arr = arr[np.newaxis, ...].astype(np.float64)
assert arr.shape == (1, n_items, n_cols)
# NOTE: we need to cast the texture to [0., 1.] (float texture).
# This is easy as soon as we assume that the signal bounds are in
# [-1, 1].
assert len(from_bounds) == 2
m, M = map(float, from_bounds)
assert np.all(arr >= m)
assert np.all(arr <= M)
arr = (arr - m) / (M - m)
assert np.all(arr >= 0)
assert np.all(arr <= 1.)
return arr
|
python
|
def _get_texture(arr, default, n_items, from_bounds):
"""Prepare data to be uploaded as a texture.
The from_bounds must be specified.
"""
if not hasattr(default, '__len__'): # pragma: no cover
default = [default]
n_cols = len(default)
if arr is None: # pragma: no cover
arr = np.tile(default, (n_items, 1))
assert arr.shape == (n_items, n_cols)
# Convert to 3D texture.
arr = arr[np.newaxis, ...].astype(np.float64)
assert arr.shape == (1, n_items, n_cols)
# NOTE: we need to cast the texture to [0., 1.] (float texture).
# This is easy as soon as we assume that the signal bounds are in
# [-1, 1].
assert len(from_bounds) == 2
m, M = map(float, from_bounds)
assert np.all(arr >= m)
assert np.all(arr <= M)
arr = (arr - m) / (M - m)
assert np.all(arr >= 0)
assert np.all(arr <= 1.)
return arr
|
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Prepare data to be uploaded as a texture.
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|
[
"Prepare",
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"to",
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"as",
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"texture",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/utils.py#L122-L147
|
train
|
kwikteam/phy
|
phy/plot/utils.py
|
_get_array
|
def _get_array(val, shape, default=None, dtype=np.float64):
"""Ensure an object is an array with the specified shape."""
assert val is not None or default is not None
if hasattr(val, '__len__') and len(val) == 0: # pragma: no cover
val = None
# Do nothing if the array is already correct.
if (isinstance(val, np.ndarray) and
val.shape == shape and
val.dtype == dtype):
return val
out = np.zeros(shape, dtype=dtype)
# This solves `ValueError: could not broadcast input array from shape (n)
# into shape (n, 1)`.
if val is not None and isinstance(val, np.ndarray):
if val.size == out.size:
val = val.reshape(out.shape)
out.flat[:] = val if val is not None else default
assert out.shape == shape
return out
|
python
|
def _get_array(val, shape, default=None, dtype=np.float64):
"""Ensure an object is an array with the specified shape."""
assert val is not None or default is not None
if hasattr(val, '__len__') and len(val) == 0: # pragma: no cover
val = None
# Do nothing if the array is already correct.
if (isinstance(val, np.ndarray) and
val.shape == shape and
val.dtype == dtype):
return val
out = np.zeros(shape, dtype=dtype)
# This solves `ValueError: could not broadcast input array from shape (n)
# into shape (n, 1)`.
if val is not None and isinstance(val, np.ndarray):
if val.size == out.size:
val = val.reshape(out.shape)
out.flat[:] = val if val is not None else default
assert out.shape == shape
return out
|
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"val",
"if",
"val",
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"not",
"None",
"else",
"default",
"assert",
"out",
".",
"shape",
"==",
"shape",
"return",
"out"
] |
Ensure an object is an array with the specified shape.
|
[
"Ensure",
"an",
"object",
"is",
"an",
"array",
"with",
"the",
"specified",
"shape",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/utils.py#L150-L168
|
train
|
kwikteam/phy
|
phy/plot/utils.py
|
_get_index
|
def _get_index(n_items, item_size, n):
"""Prepare an index attribute for GPU uploading."""
index = np.arange(n_items)
index = np.repeat(index, item_size)
index = index.astype(np.float64)
assert index.shape == (n,)
return index
|
python
|
def _get_index(n_items, item_size, n):
"""Prepare an index attribute for GPU uploading."""
index = np.arange(n_items)
index = np.repeat(index, item_size)
index = index.astype(np.float64)
assert index.shape == (n,)
return index
|
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"index",
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"shape",
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Prepare an index attribute for GPU uploading.
|
[
"Prepare",
"an",
"index",
"attribute",
"for",
"GPU",
"uploading",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/utils.py#L225-L231
|
train
|
kwikteam/phy
|
phy/plot/utils.py
|
_load_shader
|
def _load_shader(filename):
"""Load a shader file."""
curdir = op.dirname(op.realpath(__file__))
glsl_path = op.join(curdir, 'glsl')
path = op.join(glsl_path, filename)
with open(path, 'r') as f:
return f.read()
|
python
|
def _load_shader(filename):
"""Load a shader file."""
curdir = op.dirname(op.realpath(__file__))
glsl_path = op.join(curdir, 'glsl')
path = op.join(glsl_path, filename)
with open(path, 'r') as f:
return f.read()
|
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"read",
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] |
Load a shader file.
|
[
"Load",
"a",
"shader",
"file",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/plot/utils.py#L242-L248
|
train
|
kwikteam/phy
|
phy/utils/_color.py
|
_random_color
|
def _random_color(h_range=(0., 1.),
s_range=(.5, 1.),
v_range=(.5, 1.),
):
"""Generate a random RGB color."""
h, s, v = uniform(*h_range), uniform(*s_range), uniform(*v_range)
r, g, b = hsv_to_rgb(np.array([[[h, s, v]]])).flat
return r, g, b
|
python
|
def _random_color(h_range=(0., 1.),
s_range=(.5, 1.),
v_range=(.5, 1.),
):
"""Generate a random RGB color."""
h, s, v = uniform(*h_range), uniform(*s_range), uniform(*v_range)
r, g, b = hsv_to_rgb(np.array([[[h, s, v]]])).flat
return r, g, b
|
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"return",
"r",
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"g",
",",
"b"
] |
Generate a random RGB color.
|
[
"Generate",
"a",
"random",
"RGB",
"color",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/_color.py#L18-L25
|
train
|
kwikteam/phy
|
phy/utils/_color.py
|
_is_bright
|
def _is_bright(rgb):
"""Return whether a RGB color is bright or not."""
r, g, b = rgb
gray = 0.299 * r + 0.587 * g + 0.114 * b
return gray >= .5
|
python
|
def _is_bright(rgb):
"""Return whether a RGB color is bright or not."""
r, g, b = rgb
gray = 0.299 * r + 0.587 * g + 0.114 * b
return gray >= .5
|
[
"def",
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"rgb",
")",
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"r",
",",
"g",
",",
"b",
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"rgb",
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"0.299",
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"+",
"0.587",
"*",
"g",
"+",
"0.114",
"*",
"b",
"return",
"gray",
">=",
".5"
] |
Return whether a RGB color is bright or not.
|
[
"Return",
"whether",
"a",
"RGB",
"color",
"is",
"bright",
"or",
"not",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/_color.py#L28-L32
|
train
|
kwikteam/phy
|
phy/utils/_types.py
|
_bunchify
|
def _bunchify(b):
"""Ensure all dict elements are Bunch."""
assert isinstance(b, dict)
b = Bunch(b)
for k in b:
if isinstance(b[k], dict):
b[k] = Bunch(b[k])
return b
|
python
|
def _bunchify(b):
"""Ensure all dict elements are Bunch."""
assert isinstance(b, dict)
b = Bunch(b)
for k in b:
if isinstance(b[k], dict):
b[k] = Bunch(b[k])
return b
|
[
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"b",
",",
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"b",
"[",
"k",
"]",
"=",
"Bunch",
"(",
"b",
"[",
"k",
"]",
")",
"return",
"b"
] |
Ensure all dict elements are Bunch.
|
[
"Ensure",
"all",
"dict",
"elements",
"are",
"Bunch",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/_types.py#L34-L41
|
train
|
kwikteam/phy
|
phy/utils/_types.py
|
_as_list
|
def _as_list(obj):
"""Ensure an object is a list."""
if obj is None:
return None
elif isinstance(obj, string_types):
return [obj]
elif isinstance(obj, tuple):
return list(obj)
elif not hasattr(obj, '__len__'):
return [obj]
else:
return obj
|
python
|
def _as_list(obj):
"""Ensure an object is a list."""
if obj is None:
return None
elif isinstance(obj, string_types):
return [obj]
elif isinstance(obj, tuple):
return list(obj)
elif not hasattr(obj, '__len__'):
return [obj]
else:
return obj
|
[
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"None",
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"hasattr",
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"obj",
",",
"'__len__'",
")",
":",
"return",
"[",
"obj",
"]",
"else",
":",
"return",
"obj"
] |
Ensure an object is a list.
|
[
"Ensure",
"an",
"object",
"is",
"a",
"list",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/_types.py#L67-L78
|
train
|
kwikteam/phy
|
phy/utils/_types.py
|
_as_array
|
def _as_array(arr, dtype=None):
"""Convert an object to a numerical NumPy array.
Avoid a copy if possible.
"""
if arr is None:
return None
if isinstance(arr, np.ndarray) and dtype is None:
return arr
if isinstance(arr, integer_types + (float,)):
arr = [arr]
out = np.asarray(arr)
if dtype is not None:
if out.dtype != dtype:
out = out.astype(dtype)
if out.dtype not in _ACCEPTED_ARRAY_DTYPES:
raise ValueError("'arr' seems to have an invalid dtype: "
"{0:s}".format(str(out.dtype)))
return out
|
python
|
def _as_array(arr, dtype=None):
"""Convert an object to a numerical NumPy array.
Avoid a copy if possible.
"""
if arr is None:
return None
if isinstance(arr, np.ndarray) and dtype is None:
return arr
if isinstance(arr, integer_types + (float,)):
arr = [arr]
out = np.asarray(arr)
if dtype is not None:
if out.dtype != dtype:
out = out.astype(dtype)
if out.dtype not in _ACCEPTED_ARRAY_DTYPES:
raise ValueError("'arr' seems to have an invalid dtype: "
"{0:s}".format(str(out.dtype)))
return out
|
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"dtype",
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"_ACCEPTED_ARRAY_DTYPES",
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"format",
"(",
"str",
"(",
"out",
".",
"dtype",
")",
")",
")",
"return",
"out"
] |
Convert an object to a numerical NumPy array.
Avoid a copy if possible.
|
[
"Convert",
"an",
"object",
"to",
"a",
"numerical",
"NumPy",
"array",
"."
] |
7e9313dc364304b7d2bd03b92938347343703003
|
https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/utils/_types.py#L85-L104
|
train
|
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