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hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
test_change_with_copy_false(self): # Changing spike train also changes data, because it is a
(self): # Changing spike train also changes data, because it is a view # Data source
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
test_change_with_copy_false(self): # Changing spike train also changes data, because it is a
(self): # Changing spike train also changes data, because it is a view # Data source
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
test_change_with_copy_false(self): # Changing spike train also changes data, because it is a
(self): # Changing spike train also changes data, because it is a view # Data source
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
test_change_with_copy_false(self): # Changing spike train also changes data, because it is a
(self): # Changing spike train also changes data, because it is a view # Data source
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
test_change_with_copy_false(self): # Changing spike train also changes data, because it is a
(self): # Changing spike train also changes data, because it is a view # Data source
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train, keep sliced spike times result = self.train1[1:2] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[1:] assert_arrays_equal(self.train1
train, keep sliced spike times result = self.train1[:2] assert_arrays_equal(self.train1
train, keep sliced spike times # this is the typical time slice falling somewhere #
train, keep sliced spike times t_start = 0.00012 * pq.s t_stop = 0.0035 * pq.s
train, keep sliced spike times t_start = 0.01 * pq.ms t_stop = 70.0 * pq.ms
) # time_slice spike train, keep sliced spike times t_start = 0.01 * pq.ms t_stop
train, keep sliced spike times t_stop = 1 * pq.ms result = self.train1.time_slice(None
def test_change_with_copy_false(self): # Changing spike train also changes data, because it is
test_change_with_copy_false_and_fake_rescale(self): # Changing spike train also changes data
hits
train containing the spike trains to be evaluated.
binary : bool, optional If True, two spikes of a particular spike train falling in the same bin
train containing the spike trains to be evaluated.
binary : bool, optional If True, two spikes of a particular spike train falling in the same bin
count of spike train $i$, # $l$ is the number of bins used (i.e., length of $b_i$ or $b_j
Default: False binary : bool (optional) whether to binary spikes from the same spike train
("Spike train must be one dimensional") if not binned_st1.binsize == binned_st2.binsize:
recording time within `[-dt, +dt]` of any spike in train 1, TB is the same propotion for train 2
Returns np.nan if any spike train is empty. References ---------- Cutts, C.
in train 1 to see if there's a spike in train 2 within dt """ Nab = 0
hits
train containing the spike trains to be evaluated.
binary : bool, optional If True, two spikes of a particular spike train falling in the same bin
train containing the spike trains to be evaluated.
binary : bool, optional If True, two spikes of a particular spike train falling in the same bin
count of spike train $i$, # $l$ is the number of bins used (i.e., length of $b_i$ or $b_j
Default: False binary : bool (optional) whether to binary spikes from the same spike train
("Spike train must be one dimensional") if not binned_st1.binsize == binned_st2.binsize:
recording time within `[-dt, +dt]` of any spike in train 1, TB is the same propotion for train 2
Returns np.nan if any spike train is empty. References ---------- Cutts, C.
in train 1 to see if there's a spike in train 2 within dt """ Nab = 0
hits
., a spike train stored as Neo SpikeTrain object) into other representations useful to perform calculations
An example is the representation of a spike train as a sequence of 0-1 values (binned spike train).
train and provides methods to transform the binned spike train to a boolean matrix or a matrix with
A binned spike train represents the occurrence of spikes in a certain time frame.
A boolean matrix represents the binned spike train in a binary (True/False) manner.
of a spike in a spike train.
It counts the occurrence of the timing of a spike in its respective spike train.
trains and the columns represent the binned index position of a spike in a spike train.
The **Trues** in the columns represent the index position of the spike in the spike train
trains and the columns represents the binned index position of a spike in a spike train
hits
., a spike train stored as Neo SpikeTrain object) into other representations useful to perform calculations
An example is the representation of a spike train as a sequence of 0-1 values (binned spike train).
train and provides methods to transform the binned spike train to a boolean matrix or a matrix with
A binned spike train represents the occurrence of spikes in a certain time frame.
A boolean matrix represents the binned spike train in a binary (True/False) manner.
of a spike in a spike train.
It counts the occurrence of the timing of a spike in its respective spike train.
trains and the columns represent the binned index position of a spike in a spike train.
The **Trues** in the columns represent the index position of the spike in the spike train
trains and the columns represents the binned index position of a spike in a spike train
hits
., a spike train stored as Neo SpikeTrain object) into other representations useful to perform calculations
An example is the representation of a spike train as a sequence of 0-1 values (binned spike train).
train and provides methods to transform the binned spike train to a boolean matrix or a matrix with
A binned spike train represents the occurrence of spikes in a certain time frame.
A boolean matrix represents the binned spike train in a binary (True/False) manner.
of a spike in a spike train.
It counts the occurrence of the timing of a spike in its respective spike train.
trains and the columns represent the binned index position of a spike in a spike train.
The **Trues** in the columns represent the index position of the spike in the spike train
trains and the columns represents the binned index position of a spike in a spike train
hits
., a spike train stored as Neo SpikeTrain object) into other representations useful to perform calculations
An example is the representation of a spike train as a sequence of 0-1 values (binned spike train).
train and provides methods to transform the binned spike train to a boolean matrix or a matrix with
A binned spike train represents the occurrence of spikes in a certain time frame.
A boolean matrix represents the binned spike train in a binary (True/False) manner.
of a spike in a spike train.
It counts the occurrence of the timing of a spike in its respective spike train.
trains and the columns represent the binned index position of a spike in a spike train.
The **Trues** in the columns represent the index position of the spike in the spike train
trains and the columns represents the binned index position of a spike in a spike train
hits
., a spike train stored as Neo SpikeTrain object) into other representations useful to perform calculations
An example is the representation of a spike train as a sequence of 0-1 values (binned spike train).
train and provides methods to transform the binned spike train to a boolean matrix or a matrix with
A binned spike train represents the occurrence of spikes in a certain time frame.
A boolean matrix represents the binned spike train in a binary (True/False) manner.
of a spike in a spike train.
It counts the occurrence of the timing of a spike in its respective spike train.
trains and the columns represent the binned index position of a spike in a spike train.
The **Trues** in the columns represent the index position of the spike in the spike train
trains and the columns represents the binned index position of a spike in a spike train
hits
., a spike train stored as Neo SpikeTrain object) into other representations useful to perform calculations
An example is the representation of a spike train as a sequence of 0-1 values (binned spike train).
train and provides methods to transform the binned spike train to a boolean matrix or a matrix with
A binned spike train represents the occurrence of spikes in a certain time frame.
A boolean matrix represents the binned spike train in a binary (True/False) manner.
of a spike in a spike train.
It counts the occurrence of the timing of a spike in its respective spike train.
trains and the columns represent the binned index position of a spike in a spike train.
The **Trues** in the columns represent the index position of the spike in the spike train
trains and the columns represents the binned index position of a spike in a spike train