Time series
pynapple.core.time_series
Pynapple time series are containers specialized for neurophysiological time series.
They provides standardized time representation, plus various functions for manipulating times series with identical sampling frequency.
Multiple time series object are avaible depending on the shape of the data.
TsdTensor
: for data with of more than 2 dimensions, typically movies.TsdFrame
: for column-based data. It can be easily converted to a pandas.DataFrame. Columns can be labelled and selected similar to pandas.Tsd
: One-dimensional time series. It can be converted to a pandas.Series.Ts
: For timestamps data only.
Most of the same functions are available through all classes. Objects behaves like numpy.ndarray. Slicing can be done the same way for example
tsd[0:10]
returns the first 10 rows. Similarly, you can call any numpy functions like np.mean(tsd, 1)
.
BaseTsd
Bases: Base
, NDArrayOperatorsMixin
, ABC
Abstract base class for time series objects.
Implement most of the shared functions across concrete classes Tsd
, TsdFrame
, TsdTensor
Source code in pynapple/core/time_series.py
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|
__setattr__
Object is immutable
Source code in pynapple/core/base_class.py
__getitem__
abstractmethod
times
The time index of the object, returned as np.double in the desired time units.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
ndarray
|
the time indexes |
Source code in pynapple/core/base_class.py
start_time
The first time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
end_time
The last time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
restrict
Restricts a time series object to a set of time intervals delimited by an IntervalSet object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
iset |
IntervalSet
|
the IntervalSet object |
required |
Returns:
Type | Description |
---|---|
(Ts, Tsd, TsdFrame or TsdTensor)
|
Tsd object restricted to ep |
Examples:
The Ts object is restrict to the intervals defined by ep.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> ep = nap.IntervalSet(start=0, end=500, time_units='s')
>>> newts = ts.restrict(ep)
The time support of newts automatically inherit the epochs defined by ep.
Source code in pynapple/core/base_class.py
find_support
find the smallest (to a min_gap resolution) IntervalSet containing all the times in the Tsd
Parameters:
Name | Type | Description | Default |
---|---|---|---|
min_gap |
float or int
|
minimal interval between timestamps |
required |
time_units |
str
|
Time units of min gap |
's'
|
Returns:
Type | Description |
---|---|
IntervalSet
|
Description |
Source code in pynapple/core/base_class.py
get
Slice the time series from start
to end
such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
float or int
|
The start (or closest time point if |
required |
end |
float or int or None
|
The end |
None
|
Source code in pynapple/core/base_class.py
get_slice
Get a slice object from the time series data based on the start and end values such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
This function is equivalent of calling the get
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
int or float
|
The starting value for the slice. |
required |
end |
int or float
|
The ending value for the slice. Defaults to None. |
None
|
time_unit |
str
|
The time unit for the start and end values. Defaults to "s" (seconds). |
's'
|
Returns:
Name | Type | Description |
---|---|---|
slice |
slice
|
A slice determining the start and end indices, with unit step
Slicing the array will be equivalent to calling get: |
Raises:
Type | Description |
---|---|
ValueError
|
|
Examples:
>>> # slice over a range
>>> start, end = 1.2, 2.6
>>> print(ts.get_slice(start, end)) # returns `slice(2, 3, None)`
>>> start, end = 1., 2.
>>> print(ts.get_slice(start, end, mode="forward")) # returns `slice(1, 3, None)`
>>> # slice a single value
>>> start = 1.2
>>> print(ts.get_slice(start)) # returns `slice(1, 2, None)`
>>> start = 2.
>>> print(ts.get_slice(start)) # returns `slice(2, 3, None)`
Source code in pynapple/core/base_class.py
__setitem__
__getattr__
Allow numpy functions to be attached as attributes of Tsd objects
Source code in pynapple/core/time_series.py
as_array
data
to_numpy
Return the data as a numpy.ndarray.
Mostly useful for matplotlib plotting when calling plot(tsd)
.
copy
value_from
Replace the value with the closest value from Tsd/TsdFrame/TsdTensor argument
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
(Tsd, TsdFrame or TsdTensor)
|
The object holding the values to replace. |
required |
ep |
IntervalSet(optional)
|
The IntervalSet object to restrict the operation. If None, the time support of the tsd input object is used. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame or TsdTensor)
|
Object with the new values |
Examples:
In this example, the ts object will receive the closest values in time from tsd.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100))) # random times
>>> ts = nap.Ts(t=t, time_units='s')
>>> tsd = nap.Tsd(t=np.arange(0,1000), d=np.random.rand(1000), time_units='s')
>>> ep = nap.IntervalSet(start = 0, end = 500, time_units = 's')
The variable ts is a time series object containing only nan. The tsd object containing the values, for example the tracking data, and the epoch to restrict the operation.
newts has the same size of ts restrict to ep.
Source code in pynapple/core/time_series.py
count
Count occurences of events within bin_size or within a set of bins defined as an IntervalSet. You can call this function in multiple ways :
-
tsd.count(bin_size=1, time_units = 'ms') -> Count occurence of events within a 1 ms bin defined on the time support of the object.
-
tsd.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
-
tsd.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
-
tsd.count() -> Count occurent of events within each epoch of the time support.
bin_size should be seconds unless specified. If bin_size is used and no epochs is passed, the data will be binned based on the time support of the object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
None or float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
required |
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
required |
dtype |
Data type for the count. Default is np.int64. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
Tsd
|
A Tsd object indexed by the center of the bins. |
Examples:
This example shows how to count events within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> bincount = ts.count(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 100, end = 800, time_units = 's')
>>> bincount = ts.count(0.1, ep=ep)
And bincount automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
bin_average
Bin the data by averaging points within bin_size bin_size should be seconds unless specified. If no epochs is passed, the data will be binned based on the time support.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
None
|
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame, TsdTensor)
|
A Tsd object indexed by the center of the bins and holding the averaged data points. |
Examples:
This example shows how to bin data within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> tsd = nap.Tsd(t=np.arange(100), d=np.random.rand(100))
>>> bintsd = tsd.bin_average(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 10, end = 80, time_units = 's')
>>> bintsd = tsd.bin_average(0.1, ep=ep)
And bintsd automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
dropna
Drop every rows containing NaNs. By default, the time support is updated to start and end around the time points that are non NaNs. To change this behavior, you can set update_time_support=False.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update_time_support |
bool
|
|
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The time series without the NaNs |
Source code in pynapple/core/time_series.py
convolve
Return the discrete linear convolution of the time series with a one dimensional sequence.
A parameter ep can control the epochs for which the convolution will apply. Otherwise the convolution is made over the time support.
This function assume a constant sampling rate of the time series.
The only mode supported is full. The returned object is trimmed to match the size of the original object. The parameter trim controls which side the trimming operates. Default is 'both'.
See the numpy documentation here : https://numpy.org/doc/stable/reference/generated/numpy.convolve.html
Parameters:
Name | Type | Description | Default |
---|---|---|---|
array |
array - like
|
1-D or 2-D array with kernel(s) to be used for convolution. First dimension is assumed to be time. |
required |
ep |
None
|
The epochs to apply the convolution |
None
|
trim |
str
|
The side on which to trim the output of the convolution ('left', 'right', 'both' [default]) |
'both'
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The convolved time series |
Source code in pynapple/core/time_series.py
smooth
Smooth a time series with a gaussian kernel.
std
is the standard deviation of the gaussian kernel in units of time.
If only std
is passed, the function will compute the standard deviation and size in number
of time points automatically based on the sampling rate of the time series.
For example, if the time series tsd
has a sample rate of 100 Hz and std
is 50 ms,
the standard deviation will be converted to an integer through
tsd.rate * std = int(100 * 0.05) = 5
.
If windowsize
is None, the function will select a kernel size as 100 times
the std in number of time points. This behavior can be controlled with the
parameter size_factor
.
norm
set to True normalizes the gaussian kernel to sum to 1.
In the following example, a time series tsd
with a sampling rate of 100 Hz
is convolved with a gaussian kernel. The standard deviation is
0.05 second and the windowsize is 2 second. When instantiating the gaussian kernel
from scipy, it corresponds to parameters M = 200
and std=5
>>> tsd.smooth(std=0.05, windowsize=2, time_units='s', norm=False)
This line is equivalent to :
>>> from scipy.signal.windows import gaussian
>>> kernel = gaussian(M = 200, std=5)
>>> tsd.convolve(window)
It is generally a good idea to visualize the kernel before applying any convolution.
See the scipy documentation for the gaussian window
Parameters:
Name | Type | Description | Default |
---|---|---|---|
std |
Number
|
Standard deviation in units of time |
required |
windowsize |
Number
|
Size of the gaussian window in units of time. |
None
|
time_units |
str
|
The time units in which std and windowsize are specified ('us', 'ms', 's' [default]). |
's'
|
size_factor |
int
|
How long should be the kernel size as a function of the standard deviation. Default is 100. Bypassed if windowsize is used. |
100
|
norm |
bool
|
Whether to normalized the gaussian kernel or not. Default is |
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame, TsdTensor)
|
Time series convolved with a gaussian kernel |
Source code in pynapple/core/time_series.py
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|
interpolate
Wrapper of the numpy linear interpolation method. See numpy interpolate for an explanation of the parameters. The argument ts should be Ts, Tsd, TsdFrame, TsdTensor to ensure interpolating from sorted timestamps in the right unit,
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ts |
(Ts, Tsd, TsdFrame or TsdTensor)
|
The object holding the timestamps |
required |
ep |
IntervalSet
|
The epochs to use to interpolate. If None, the time support of Tsd is used. |
None
|
left |
None
|
Value to return for ts < tsd[0], default is tsd[0]. |
None
|
right |
None
|
Value to return for ts > tsd[-1], default is tsd[-1]. |
None
|
Source code in pynapple/core/time_series.py
TsdTensor
Bases: BaseTsd
TsdTensor
Attributes:
Name | Type | Description |
---|---|---|
rate |
float
|
Frequency of the time series (Hz) computed over the time support |
time_support |
IntervalSet
|
The time support of the time series |
Source code in pynapple/core/time_series.py
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|
__setattr__
Object is immutable
Source code in pynapple/core/base_class.py
__setitem__
times
The time index of the object, returned as np.double in the desired time units.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
ndarray
|
the time indexes |
Source code in pynapple/core/base_class.py
start_time
The first time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
end_time
The last time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
value_from
Replace the value with the closest value from Tsd/TsdFrame/TsdTensor argument
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
(Tsd, TsdFrame or TsdTensor)
|
The object holding the values to replace. |
required |
ep |
IntervalSet(optional)
|
The IntervalSet object to restrict the operation. If None, the time support of the tsd input object is used. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame or TsdTensor)
|
Object with the new values |
Examples:
In this example, the ts object will receive the closest values in time from tsd.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100))) # random times
>>> ts = nap.Ts(t=t, time_units='s')
>>> tsd = nap.Tsd(t=np.arange(0,1000), d=np.random.rand(1000), time_units='s')
>>> ep = nap.IntervalSet(start = 0, end = 500, time_units = 's')
The variable ts is a time series object containing only nan. The tsd object containing the values, for example the tracking data, and the epoch to restrict the operation.
newts has the same size of ts restrict to ep.
Source code in pynapple/core/time_series.py
count
Count occurences of events within bin_size or within a set of bins defined as an IntervalSet. You can call this function in multiple ways :
-
tsd.count(bin_size=1, time_units = 'ms') -> Count occurence of events within a 1 ms bin defined on the time support of the object.
-
tsd.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
-
tsd.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
-
tsd.count() -> Count occurent of events within each epoch of the time support.
bin_size should be seconds unless specified. If bin_size is used and no epochs is passed, the data will be binned based on the time support of the object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
None or float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
required |
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
required |
dtype |
Data type for the count. Default is np.int64. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
Tsd
|
A Tsd object indexed by the center of the bins. |
Examples:
This example shows how to count events within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> bincount = ts.count(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 100, end = 800, time_units = 's')
>>> bincount = ts.count(0.1, ep=ep)
And bincount automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
restrict
Restricts a time series object to a set of time intervals delimited by an IntervalSet object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
iset |
IntervalSet
|
the IntervalSet object |
required |
Returns:
Type | Description |
---|---|
(Ts, Tsd, TsdFrame or TsdTensor)
|
Tsd object restricted to ep |
Examples:
The Ts object is restrict to the intervals defined by ep.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> ep = nap.IntervalSet(start=0, end=500, time_units='s')
>>> newts = ts.restrict(ep)
The time support of newts automatically inherit the epochs defined by ep.
Source code in pynapple/core/base_class.py
copy
find_support
find the smallest (to a min_gap resolution) IntervalSet containing all the times in the Tsd
Parameters:
Name | Type | Description | Default |
---|---|---|---|
min_gap |
float or int
|
minimal interval between timestamps |
required |
time_units |
str
|
Time units of min gap |
's'
|
Returns:
Type | Description |
---|---|
IntervalSet
|
Description |
Source code in pynapple/core/base_class.py
get
Slice the time series from start
to end
such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
float or int
|
The start (or closest time point if |
required |
end |
float or int or None
|
The end |
None
|
Source code in pynapple/core/base_class.py
get_slice
Get a slice object from the time series data based on the start and end values such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
This function is equivalent of calling the get
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
int or float
|
The starting value for the slice. |
required |
end |
int or float
|
The ending value for the slice. Defaults to None. |
None
|
time_unit |
str
|
The time unit for the start and end values. Defaults to "s" (seconds). |
's'
|
Returns:
Name | Type | Description |
---|---|---|
slice |
slice
|
A slice determining the start and end indices, with unit step
Slicing the array will be equivalent to calling get: |
Raises:
Type | Description |
---|---|
ValueError
|
|
Examples:
>>> # slice over a range
>>> start, end = 1.2, 2.6
>>> print(ts.get_slice(start, end)) # returns `slice(2, 3, None)`
>>> start, end = 1., 2.
>>> print(ts.get_slice(start, end, mode="forward")) # returns `slice(1, 3, None)`
>>> # slice a single value
>>> start = 1.2
>>> print(ts.get_slice(start)) # returns `slice(1, 2, None)`
>>> start = 2.
>>> print(ts.get_slice(start)) # returns `slice(2, 3, None)`
Source code in pynapple/core/base_class.py
__getattr__
Allow numpy functions to be attached as attributes of Tsd objects
Source code in pynapple/core/time_series.py
as_array
data
to_numpy
Return the data as a numpy.ndarray.
Mostly useful for matplotlib plotting when calling plot(tsd)
.
bin_average
Bin the data by averaging points within bin_size bin_size should be seconds unless specified. If no epochs is passed, the data will be binned based on the time support.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
None
|
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame, TsdTensor)
|
A Tsd object indexed by the center of the bins and holding the averaged data points. |
Examples:
This example shows how to bin data within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> tsd = nap.Tsd(t=np.arange(100), d=np.random.rand(100))
>>> bintsd = tsd.bin_average(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 10, end = 80, time_units = 's')
>>> bintsd = tsd.bin_average(0.1, ep=ep)
And bintsd automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
dropna
Drop every rows containing NaNs. By default, the time support is updated to start and end around the time points that are non NaNs. To change this behavior, you can set update_time_support=False.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update_time_support |
bool
|
|
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The time series without the NaNs |
Source code in pynapple/core/time_series.py
convolve
Return the discrete linear convolution of the time series with a one dimensional sequence.
A parameter ep can control the epochs for which the convolution will apply. Otherwise the convolution is made over the time support.
This function assume a constant sampling rate of the time series.
The only mode supported is full. The returned object is trimmed to match the size of the original object. The parameter trim controls which side the trimming operates. Default is 'both'.
See the numpy documentation here : https://numpy.org/doc/stable/reference/generated/numpy.convolve.html
Parameters:
Name | Type | Description | Default |
---|---|---|---|
array |
array - like
|
1-D or 2-D array with kernel(s) to be used for convolution. First dimension is assumed to be time. |
required |
ep |
None
|
The epochs to apply the convolution |
None
|
trim |
str
|
The side on which to trim the output of the convolution ('left', 'right', 'both' [default]) |
'both'
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The convolved time series |
Source code in pynapple/core/time_series.py
smooth
Smooth a time series with a gaussian kernel.
std
is the standard deviation of the gaussian kernel in units of time.
If only std
is passed, the function will compute the standard deviation and size in number
of time points automatically based on the sampling rate of the time series.
For example, if the time series tsd
has a sample rate of 100 Hz and std
is 50 ms,
the standard deviation will be converted to an integer through
tsd.rate * std = int(100 * 0.05) = 5
.
If windowsize
is None, the function will select a kernel size as 100 times
the std in number of time points. This behavior can be controlled with the
parameter size_factor
.
norm
set to True normalizes the gaussian kernel to sum to 1.
In the following example, a time series tsd
with a sampling rate of 100 Hz
is convolved with a gaussian kernel. The standard deviation is
0.05 second and the windowsize is 2 second. When instantiating the gaussian kernel
from scipy, it corresponds to parameters M = 200
and std=5
>>> tsd.smooth(std=0.05, windowsize=2, time_units='s', norm=False)
This line is equivalent to :
>>> from scipy.signal.windows import gaussian
>>> kernel = gaussian(M = 200, std=5)
>>> tsd.convolve(window)
It is generally a good idea to visualize the kernel before applying any convolution.
See the scipy documentation for the gaussian window
Parameters:
Name | Type | Description | Default |
---|---|---|---|
std |
Number
|
Standard deviation in units of time |
required |
windowsize |
Number
|
Size of the gaussian window in units of time. |
None
|
time_units |
str
|
The time units in which std and windowsize are specified ('us', 'ms', 's' [default]). |
's'
|
size_factor |
int
|
How long should be the kernel size as a function of the standard deviation. Default is 100. Bypassed if windowsize is used. |
100
|
norm |
bool
|
Whether to normalized the gaussian kernel or not. Default is |
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame, TsdTensor)
|
Time series convolved with a gaussian kernel |
Source code in pynapple/core/time_series.py
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|
interpolate
Wrapper of the numpy linear interpolation method. See numpy interpolate for an explanation of the parameters. The argument ts should be Ts, Tsd, TsdFrame, TsdTensor to ensure interpolating from sorted timestamps in the right unit,
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ts |
(Ts, Tsd, TsdFrame or TsdTensor)
|
The object holding the timestamps |
required |
ep |
IntervalSet
|
The epochs to use to interpolate. If None, the time support of Tsd is used. |
None
|
left |
None
|
Value to return for ts < tsd[0], default is tsd[0]. |
None
|
right |
None
|
Value to return for ts > tsd[-1], default is tsd[-1]. |
None
|
Source code in pynapple/core/time_series.py
__init__
TsdTensor initializer
Parameters:
Name | Type | Description | Default |
---|---|---|---|
t |
ndarray
|
the time index t |
required |
d |
ndarray
|
The data |
required |
time_units |
str
|
The time units in which times are specified ('us', 'ms', 's' [default]). |
's'
|
time_support |
IntervalSet
|
The time support of the TsdFrame object |
None
|
load_array |
bool
|
Whether the data should be converted to a numpy (or jax) array. Useful when passing a memory map object like zarr.
Default is True. Does not apply if |
True
|
Source code in pynapple/core/time_series.py
save
Save TsdTensor object in npz format. The file will contain the timestamps, the data and the time support.
The main purpose of this function is to save small/medium sized time series objects. For example, you extracted several channels from your recording and filtered them. You can save the filtered channels as a npz to avoid reprocessing it.
You can load the object with nap.load_file
. Keys are 't', 'd', 'start', 'end', 'type'
and 'columns' for columns names.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The filename |
required |
Examples:
>>> import pynapple as nap
>>> import numpy as np
>>> tsdtensor = nap.TsdTensor(t=np.array([0., 1.]), d = np.zeros((2,3,4)))
>>> tsdtensor.save("my_path/my_tsdtensor.npz")
To load you file, you can use the nap.load_file
function :
Raises:
Type | Description |
---|---|
RuntimeError
|
If filename is not str, path does not exist or filename is a directory. |
Source code in pynapple/core/time_series.py
TsdFrame
Bases: BaseTsd
TsdFrame
Attributes:
Name | Type | Description |
---|---|---|
rate |
float
|
Frequency of the time series (Hz) computed over the time support |
time_support |
IntervalSet
|
The time support of the time series |
Source code in pynapple/core/time_series.py
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|
__setattr__
Object is immutable
Source code in pynapple/core/base_class.py
times
The time index of the object, returned as np.double in the desired time units.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
ndarray
|
the time indexes |
Source code in pynapple/core/base_class.py
start_time
The first time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
end_time
The last time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
value_from
Replace the value with the closest value from Tsd/TsdFrame/TsdTensor argument
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
(Tsd, TsdFrame or TsdTensor)
|
The object holding the values to replace. |
required |
ep |
IntervalSet(optional)
|
The IntervalSet object to restrict the operation. If None, the time support of the tsd input object is used. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame or TsdTensor)
|
Object with the new values |
Examples:
In this example, the ts object will receive the closest values in time from tsd.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100))) # random times
>>> ts = nap.Ts(t=t, time_units='s')
>>> tsd = nap.Tsd(t=np.arange(0,1000), d=np.random.rand(1000), time_units='s')
>>> ep = nap.IntervalSet(start = 0, end = 500, time_units = 's')
The variable ts is a time series object containing only nan. The tsd object containing the values, for example the tracking data, and the epoch to restrict the operation.
newts has the same size of ts restrict to ep.
Source code in pynapple/core/time_series.py
count
Count occurences of events within bin_size or within a set of bins defined as an IntervalSet. You can call this function in multiple ways :
-
tsd.count(bin_size=1, time_units = 'ms') -> Count occurence of events within a 1 ms bin defined on the time support of the object.
-
tsd.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
-
tsd.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
-
tsd.count() -> Count occurent of events within each epoch of the time support.
bin_size should be seconds unless specified. If bin_size is used and no epochs is passed, the data will be binned based on the time support of the object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
None or float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
required |
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
required |
dtype |
Data type for the count. Default is np.int64. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
Tsd
|
A Tsd object indexed by the center of the bins. |
Examples:
This example shows how to count events within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> bincount = ts.count(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 100, end = 800, time_units = 's')
>>> bincount = ts.count(0.1, ep=ep)
And bincount automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
restrict
Restricts a time series object to a set of time intervals delimited by an IntervalSet object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
iset |
IntervalSet
|
the IntervalSet object |
required |
Returns:
Type | Description |
---|---|
(Ts, Tsd, TsdFrame or TsdTensor)
|
Tsd object restricted to ep |
Examples:
The Ts object is restrict to the intervals defined by ep.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> ep = nap.IntervalSet(start=0, end=500, time_units='s')
>>> newts = ts.restrict(ep)
The time support of newts automatically inherit the epochs defined by ep.
Source code in pynapple/core/base_class.py
copy
find_support
find the smallest (to a min_gap resolution) IntervalSet containing all the times in the Tsd
Parameters:
Name | Type | Description | Default |
---|---|---|---|
min_gap |
float or int
|
minimal interval between timestamps |
required |
time_units |
str
|
Time units of min gap |
's'
|
Returns:
Type | Description |
---|---|
IntervalSet
|
Description |
Source code in pynapple/core/base_class.py
get
Slice the time series from start
to end
such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
float or int
|
The start (or closest time point if |
required |
end |
float or int or None
|
The end |
None
|
Source code in pynapple/core/base_class.py
get_slice
Get a slice object from the time series data based on the start and end values such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
This function is equivalent of calling the get
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
int or float
|
The starting value for the slice. |
required |
end |
int or float
|
The ending value for the slice. Defaults to None. |
None
|
time_unit |
str
|
The time unit for the start and end values. Defaults to "s" (seconds). |
's'
|
Returns:
Name | Type | Description |
---|---|---|
slice |
slice
|
A slice determining the start and end indices, with unit step
Slicing the array will be equivalent to calling get: |
Raises:
Type | Description |
---|---|
ValueError
|
|
Examples:
>>> # slice over a range
>>> start, end = 1.2, 2.6
>>> print(ts.get_slice(start, end)) # returns `slice(2, 3, None)`
>>> start, end = 1., 2.
>>> print(ts.get_slice(start, end, mode="forward")) # returns `slice(1, 3, None)`
>>> # slice a single value
>>> start = 1.2
>>> print(ts.get_slice(start)) # returns `slice(1, 2, None)`
>>> start = 2.
>>> print(ts.get_slice(start)) # returns `slice(2, 3, None)`
Source code in pynapple/core/base_class.py
__getattr__
Allow numpy functions to be attached as attributes of Tsd objects
Source code in pynapple/core/time_series.py
as_array
data
to_numpy
Return the data as a numpy.ndarray.
Mostly useful for matplotlib plotting when calling plot(tsd)
.
bin_average
Bin the data by averaging points within bin_size bin_size should be seconds unless specified. If no epochs is passed, the data will be binned based on the time support.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
None
|
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame, TsdTensor)
|
A Tsd object indexed by the center of the bins and holding the averaged data points. |
Examples:
This example shows how to bin data within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> tsd = nap.Tsd(t=np.arange(100), d=np.random.rand(100))
>>> bintsd = tsd.bin_average(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 10, end = 80, time_units = 's')
>>> bintsd = tsd.bin_average(0.1, ep=ep)
And bintsd automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
dropna
Drop every rows containing NaNs. By default, the time support is updated to start and end around the time points that are non NaNs. To change this behavior, you can set update_time_support=False.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update_time_support |
bool
|
|
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The time series without the NaNs |
Source code in pynapple/core/time_series.py
convolve
Return the discrete linear convolution of the time series with a one dimensional sequence.
A parameter ep can control the epochs for which the convolution will apply. Otherwise the convolution is made over the time support.
This function assume a constant sampling rate of the time series.
The only mode supported is full. The returned object is trimmed to match the size of the original object. The parameter trim controls which side the trimming operates. Default is 'both'.
See the numpy documentation here : https://numpy.org/doc/stable/reference/generated/numpy.convolve.html
Parameters:
Name | Type | Description | Default |
---|---|---|---|
array |
array - like
|
1-D or 2-D array with kernel(s) to be used for convolution. First dimension is assumed to be time. |
required |
ep |
None
|
The epochs to apply the convolution |
None
|
trim |
str
|
The side on which to trim the output of the convolution ('left', 'right', 'both' [default]) |
'both'
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The convolved time series |
Source code in pynapple/core/time_series.py
smooth
Smooth a time series with a gaussian kernel.
std
is the standard deviation of the gaussian kernel in units of time.
If only std
is passed, the function will compute the standard deviation and size in number
of time points automatically based on the sampling rate of the time series.
For example, if the time series tsd
has a sample rate of 100 Hz and std
is 50 ms,
the standard deviation will be converted to an integer through
tsd.rate * std = int(100 * 0.05) = 5
.
If windowsize
is None, the function will select a kernel size as 100 times
the std in number of time points. This behavior can be controlled with the
parameter size_factor
.
norm
set to True normalizes the gaussian kernel to sum to 1.
In the following example, a time series tsd
with a sampling rate of 100 Hz
is convolved with a gaussian kernel. The standard deviation is
0.05 second and the windowsize is 2 second. When instantiating the gaussian kernel
from scipy, it corresponds to parameters M = 200
and std=5
>>> tsd.smooth(std=0.05, windowsize=2, time_units='s', norm=False)
This line is equivalent to :
>>> from scipy.signal.windows import gaussian
>>> kernel = gaussian(M = 200, std=5)
>>> tsd.convolve(window)
It is generally a good idea to visualize the kernel before applying any convolution.
See the scipy documentation for the gaussian window
Parameters:
Name | Type | Description | Default |
---|---|---|---|
std |
Number
|
Standard deviation in units of time |
required |
windowsize |
Number
|
Size of the gaussian window in units of time. |
None
|
time_units |
str
|
The time units in which std and windowsize are specified ('us', 'ms', 's' [default]). |
's'
|
size_factor |
int
|
How long should be the kernel size as a function of the standard deviation. Default is 100. Bypassed if windowsize is used. |
100
|
norm |
bool
|
Whether to normalized the gaussian kernel or not. Default is |
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame, TsdTensor)
|
Time series convolved with a gaussian kernel |
Source code in pynapple/core/time_series.py
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|
interpolate
Wrapper of the numpy linear interpolation method. See numpy interpolate for an explanation of the parameters. The argument ts should be Ts, Tsd, TsdFrame, TsdTensor to ensure interpolating from sorted timestamps in the right unit,
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ts |
(Ts, Tsd, TsdFrame or TsdTensor)
|
The object holding the timestamps |
required |
ep |
IntervalSet
|
The epochs to use to interpolate. If None, the time support of Tsd is used. |
None
|
left |
None
|
Value to return for ts < tsd[0], default is tsd[0]. |
None
|
right |
None
|
Value to return for ts > tsd[-1], default is tsd[-1]. |
None
|
Source code in pynapple/core/time_series.py
__init__
TsdFrame initializer A pandas.DataFrame can be passed directly
Parameters:
Name | Type | Description | Default |
---|---|---|---|
t |
ndarray or DataFrame
|
the time index t, or a pandas.DataFrame (if d is None) |
required |
d |
ndarray
|
The data |
None
|
time_units |
str
|
The time units in which times are specified ('us', 'ms', 's' [default]). |
's'
|
time_support |
IntervalSet
|
The time support of the TsdFrame object |
None
|
columns |
iterables
|
Column names |
None
|
load_array |
bool
|
Whether the data should be converted to a numpy (or jax) array. Useful when passing a memory map object like zarr.
Default is True. Does not apply if |
True
|
Source code in pynapple/core/time_series.py
as_dataframe
Convert the TsdFrame object to a pandas.DataFrame object.
Returns:
Name | Type | Description |
---|---|---|
out |
DataFrame
|
_ |
Source code in pynapple/core/time_series.py
as_units
Returns a DataFrame with time expressed in the desired unit.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Type | Description |
---|---|
DataFrame
|
the series object with adjusted times |
Source code in pynapple/core/time_series.py
save
Save TsdFrame object in npz format. The file will contain the timestamps, the data and the time support.
The main purpose of this function is to save small/medium sized time series objects. For example, you extracted several channels from your recording and filtered them. You can save the filtered channels as a npz to avoid reprocessing it.
You can load the object with nap.load_file
. Keys are 't', 'd', 'start', 'end', 'type'
and 'columns' for columns names.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The filename |
required |
Examples:
>>> import pynapple as nap
>>> import numpy as np
>>> tsdframe = nap.TsdFrame(t=np.array([0., 1.]), d = np.array([[2, 3],[4,5]]), columns=['a', 'b'])
>>> tsdframe.save("my_path/my_tsdframe.npz")
To load you file, you can use the nap.load_file
function :
Raises:
Type | Description |
---|---|
RuntimeError
|
If filename is not str, path does not exist or filename is a directory. |
Source code in pynapple/core/time_series.py
Tsd
Bases: BaseTsd
A container around numpy.ndarray specialized for neurophysiology time series.
Tsd provides standardized time representation, plus various functions for manipulating times series.
Attributes:
Name | Type | Description |
---|---|---|
rate |
float
|
Frequency of the time series (Hz) computed over the time support |
time_support |
IntervalSet
|
The time support of the time series |
Source code in pynapple/core/time_series.py
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|
__setattr__
Object is immutable
Source code in pynapple/core/base_class.py
__setitem__
times
The time index of the object, returned as np.double in the desired time units.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
ndarray
|
the time indexes |
Source code in pynapple/core/base_class.py
start_time
The first time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
end_time
The last time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
value_from
Replace the value with the closest value from Tsd/TsdFrame/TsdTensor argument
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
(Tsd, TsdFrame or TsdTensor)
|
The object holding the values to replace. |
required |
ep |
IntervalSet(optional)
|
The IntervalSet object to restrict the operation. If None, the time support of the tsd input object is used. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame or TsdTensor)
|
Object with the new values |
Examples:
In this example, the ts object will receive the closest values in time from tsd.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100))) # random times
>>> ts = nap.Ts(t=t, time_units='s')
>>> tsd = nap.Tsd(t=np.arange(0,1000), d=np.random.rand(1000), time_units='s')
>>> ep = nap.IntervalSet(start = 0, end = 500, time_units = 's')
The variable ts is a time series object containing only nan. The tsd object containing the values, for example the tracking data, and the epoch to restrict the operation.
newts has the same size of ts restrict to ep.
Source code in pynapple/core/time_series.py
count
Count occurences of events within bin_size or within a set of bins defined as an IntervalSet. You can call this function in multiple ways :
-
tsd.count(bin_size=1, time_units = 'ms') -> Count occurence of events within a 1 ms bin defined on the time support of the object.
-
tsd.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
-
tsd.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
-
tsd.count() -> Count occurent of events within each epoch of the time support.
bin_size should be seconds unless specified. If bin_size is used and no epochs is passed, the data will be binned based on the time support of the object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
None or float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
required |
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
required |
dtype |
Data type for the count. Default is np.int64. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
Tsd
|
A Tsd object indexed by the center of the bins. |
Examples:
This example shows how to count events within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> bincount = ts.count(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 100, end = 800, time_units = 's')
>>> bincount = ts.count(0.1, ep=ep)
And bincount automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
restrict
Restricts a time series object to a set of time intervals delimited by an IntervalSet object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
iset |
IntervalSet
|
the IntervalSet object |
required |
Returns:
Type | Description |
---|---|
(Ts, Tsd, TsdFrame or TsdTensor)
|
Tsd object restricted to ep |
Examples:
The Ts object is restrict to the intervals defined by ep.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> ep = nap.IntervalSet(start=0, end=500, time_units='s')
>>> newts = ts.restrict(ep)
The time support of newts automatically inherit the epochs defined by ep.
Source code in pynapple/core/base_class.py
copy
find_support
find the smallest (to a min_gap resolution) IntervalSet containing all the times in the Tsd
Parameters:
Name | Type | Description | Default |
---|---|---|---|
min_gap |
float or int
|
minimal interval between timestamps |
required |
time_units |
str
|
Time units of min gap |
's'
|
Returns:
Type | Description |
---|---|
IntervalSet
|
Description |
Source code in pynapple/core/base_class.py
get
Slice the time series from start
to end
such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
float or int
|
The start (or closest time point if |
required |
end |
float or int or None
|
The end |
None
|
Source code in pynapple/core/base_class.py
get_slice
Get a slice object from the time series data based on the start and end values such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
This function is equivalent of calling the get
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
int or float
|
The starting value for the slice. |
required |
end |
int or float
|
The ending value for the slice. Defaults to None. |
None
|
time_unit |
str
|
The time unit for the start and end values. Defaults to "s" (seconds). |
's'
|
Returns:
Name | Type | Description |
---|---|---|
slice |
slice
|
A slice determining the start and end indices, with unit step
Slicing the array will be equivalent to calling get: |
Raises:
Type | Description |
---|---|
ValueError
|
|
Examples:
>>> # slice over a range
>>> start, end = 1.2, 2.6
>>> print(ts.get_slice(start, end)) # returns `slice(2, 3, None)`
>>> start, end = 1., 2.
>>> print(ts.get_slice(start, end, mode="forward")) # returns `slice(1, 3, None)`
>>> # slice a single value
>>> start = 1.2
>>> print(ts.get_slice(start)) # returns `slice(1, 2, None)`
>>> start = 2.
>>> print(ts.get_slice(start)) # returns `slice(2, 3, None)`
Source code in pynapple/core/base_class.py
__getattr__
Allow numpy functions to be attached as attributes of Tsd objects
Source code in pynapple/core/time_series.py
as_array
data
to_numpy
Return the data as a numpy.ndarray.
Mostly useful for matplotlib plotting when calling plot(tsd)
.
bin_average
Bin the data by averaging points within bin_size bin_size should be seconds unless specified. If no epochs is passed, the data will be binned based on the time support.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
None
|
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame, TsdTensor)
|
A Tsd object indexed by the center of the bins and holding the averaged data points. |
Examples:
This example shows how to bin data within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> tsd = nap.Tsd(t=np.arange(100), d=np.random.rand(100))
>>> bintsd = tsd.bin_average(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 10, end = 80, time_units = 's')
>>> bintsd = tsd.bin_average(0.1, ep=ep)
And bintsd automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
dropna
Drop every rows containing NaNs. By default, the time support is updated to start and end around the time points that are non NaNs. To change this behavior, you can set update_time_support=False.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update_time_support |
bool
|
|
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The time series without the NaNs |
Source code in pynapple/core/time_series.py
convolve
Return the discrete linear convolution of the time series with a one dimensional sequence.
A parameter ep can control the epochs for which the convolution will apply. Otherwise the convolution is made over the time support.
This function assume a constant sampling rate of the time series.
The only mode supported is full. The returned object is trimmed to match the size of the original object. The parameter trim controls which side the trimming operates. Default is 'both'.
See the numpy documentation here : https://numpy.org/doc/stable/reference/generated/numpy.convolve.html
Parameters:
Name | Type | Description | Default |
---|---|---|---|
array |
array - like
|
1-D or 2-D array with kernel(s) to be used for convolution. First dimension is assumed to be time. |
required |
ep |
None
|
The epochs to apply the convolution |
None
|
trim |
str
|
The side on which to trim the output of the convolution ('left', 'right', 'both' [default]) |
'both'
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame or TsdTensor)
|
The convolved time series |
Source code in pynapple/core/time_series.py
smooth
Smooth a time series with a gaussian kernel.
std
is the standard deviation of the gaussian kernel in units of time.
If only std
is passed, the function will compute the standard deviation and size in number
of time points automatically based on the sampling rate of the time series.
For example, if the time series tsd
has a sample rate of 100 Hz and std
is 50 ms,
the standard deviation will be converted to an integer through
tsd.rate * std = int(100 * 0.05) = 5
.
If windowsize
is None, the function will select a kernel size as 100 times
the std in number of time points. This behavior can be controlled with the
parameter size_factor
.
norm
set to True normalizes the gaussian kernel to sum to 1.
In the following example, a time series tsd
with a sampling rate of 100 Hz
is convolved with a gaussian kernel. The standard deviation is
0.05 second and the windowsize is 2 second. When instantiating the gaussian kernel
from scipy, it corresponds to parameters M = 200
and std=5
>>> tsd.smooth(std=0.05, windowsize=2, time_units='s', norm=False)
This line is equivalent to :
>>> from scipy.signal.windows import gaussian
>>> kernel = gaussian(M = 200, std=5)
>>> tsd.convolve(window)
It is generally a good idea to visualize the kernel before applying any convolution.
See the scipy documentation for the gaussian window
Parameters:
Name | Type | Description | Default |
---|---|---|---|
std |
Number
|
Standard deviation in units of time |
required |
windowsize |
Number
|
Size of the gaussian window in units of time. |
None
|
time_units |
str
|
The time units in which std and windowsize are specified ('us', 'ms', 's' [default]). |
's'
|
size_factor |
int
|
How long should be the kernel size as a function of the standard deviation. Default is 100. Bypassed if windowsize is used. |
100
|
norm |
bool
|
Whether to normalized the gaussian kernel or not. Default is |
True
|
Returns:
Type | Description |
---|---|
(Tsd, TsdFrame, TsdTensor)
|
Time series convolved with a gaussian kernel |
Source code in pynapple/core/time_series.py
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|
interpolate
Wrapper of the numpy linear interpolation method. See numpy interpolate for an explanation of the parameters. The argument ts should be Ts, Tsd, TsdFrame, TsdTensor to ensure interpolating from sorted timestamps in the right unit,
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ts |
(Ts, Tsd, TsdFrame or TsdTensor)
|
The object holding the timestamps |
required |
ep |
IntervalSet
|
The epochs to use to interpolate. If None, the time support of Tsd is used. |
None
|
left |
None
|
Value to return for ts < tsd[0], default is tsd[0]. |
None
|
right |
None
|
Value to return for ts > tsd[-1], default is tsd[-1]. |
None
|
Source code in pynapple/core/time_series.py
__init__
Tsd Initializer.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
t |
ndarray or Series
|
An object transformable in a time series, or a pandas.Series equivalent (if d is None) |
required |
d |
ndarray
|
The data of the time series |
None
|
time_units |
str
|
The time units in which times are specified ('us', 'ms', 's' [default]) |
's'
|
time_support |
IntervalSet
|
The time support of the tsd object |
None
|
load_array |
bool
|
Whether the data should be converted to a numpy (or jax) array. Useful when passing a memory map object like zarr.
Default is True. Does not apply if |
True
|
Source code in pynapple/core/time_series.py
as_series
Convert the Ts/Tsd object to a pandas.Series object.
Returns:
Name | Type | Description |
---|---|---|
out |
Series
|
_ |
Source code in pynapple/core/time_series.py
as_units
Returns a pandas Series with time expressed in the desired unit.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Type | Description |
---|---|
Series
|
the series object with adjusted times |
Source code in pynapple/core/time_series.py
threshold
Apply a threshold function to the tsd to return a new tsd with the time support being the epochs above/below/>=/<= the threshold
Parameters:
Name | Type | Description | Default |
---|---|---|---|
thr |
float
|
The threshold value |
required |
method |
str
|
The threshold method ("above"[default], "below", "aboveequal", "belowequal") |
'above'
|
Returns:
Name | Type | Description |
---|---|---|
out |
Tsd
|
All the time points below/ above/greater than equal to/less than equal to the threshold |
Raises:
Type | Description |
---|---|
ValueError
|
Raise an error if method is unknown. |
RuntimeError
|
Raise an error if thr is too high/low and no epochs is found. |
Examples:
This example finds all epoch above 0.5 within the tsd object.
>>> import pynapple as nap
>>> tsd = nap.Tsd(t=np.arange(100), d=np.random.rand(100))
>>> newtsd = tsd.threshold(0.5)
The epochs with the times above/below the threshold can be accessed through the time support:
>>> tsd = nap.Tsd(t=np.arange(100), d=np.arange(100), time_units='s')
>>> tsd.threshold(50).time_support
>>> start end
>>> 0 50.5 99.0
Source code in pynapple/core/time_series.py
to_tsgroup
Convert Tsd to a TsGroup by grouping timestamps with the same values. By default, the values are converted to integers.
Examples:
>>> import pynapple as nap
>>> import numpy as np
>>> tsd = nap.Tsd(t = np.array([0, 1, 2, 3]), d = np.array([0, 2, 0, 1]))
Time (s)
0.0 0
1.0 2
2.0 0
3.0 1
dtype: int64
The reverse operation can be done with the TsGroup.to_tsd function :
Returns:
Type | Description |
---|---|
TsGroup
|
Grouped timestamps |
Source code in pynapple/core/time_series.py
save
Save Tsd object in npz format. The file will contain the timestamps, the data and the time support.
The main purpose of this function is to save small/medium sized time series objects. For example, you extracted one channel from your recording and filtered it. You can save the filtered channel as a npz to avoid reprocessing it.
You can load the object with nap.load_file
. Keys are 't', 'd', 'start', 'end' and 'type'.
See the example below.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The filename |
required |
Examples:
>>> import pynapple as nap
>>> import numpy as np
>>> tsd = nap.Tsd(t=np.array([0., 1.]), d = np.array([2, 3]))
>>> tsd.save("my_path/my_tsd.npz")
To load you file, you can use the nap.load_file
function :
Raises:
Type | Description |
---|---|
RuntimeError
|
If filename is not str, path does not exist or filename is a directory. |
Source code in pynapple/core/time_series.py
Ts
Bases: Base
Timestamps only object for a time series with only time index,
Attributes:
Name | Type | Description |
---|---|---|
rate |
float
|
Frequency of the time series (Hz) computed over the time support |
time_support |
IntervalSet
|
The time support of the time series |
Source code in pynapple/core/time_series.py
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|
__setattr__
Object is immutable
Source code in pynapple/core/base_class.py
times
The time index of the object, returned as np.double in the desired time units.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
ndarray
|
the time indexes |
Source code in pynapple/core/base_class.py
start_time
The first time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
end_time
The last time index in the time series object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Name | Type | Description |
---|---|---|
out |
float64
|
_ |
Source code in pynapple/core/base_class.py
restrict
Restricts a time series object to a set of time intervals delimited by an IntervalSet object
Parameters:
Name | Type | Description | Default |
---|---|---|---|
iset |
IntervalSet
|
the IntervalSet object |
required |
Returns:
Type | Description |
---|---|
(Ts, Tsd, TsdFrame or TsdTensor)
|
Tsd object restricted to ep |
Examples:
The Ts object is restrict to the intervals defined by ep.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> ep = nap.IntervalSet(start=0, end=500, time_units='s')
>>> newts = ts.restrict(ep)
The time support of newts automatically inherit the epochs defined by ep.
Source code in pynapple/core/base_class.py
copy
find_support
find the smallest (to a min_gap resolution) IntervalSet containing all the times in the Tsd
Parameters:
Name | Type | Description | Default |
---|---|---|---|
min_gap |
float or int
|
minimal interval between timestamps |
required |
time_units |
str
|
Time units of min gap |
's'
|
Returns:
Type | Description |
---|---|
IntervalSet
|
Description |
Source code in pynapple/core/base_class.py
get
Slice the time series from start
to end
such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
float or int
|
The start (or closest time point if |
required |
end |
float or int or None
|
The end |
None
|
Source code in pynapple/core/base_class.py
get_slice
Get a slice object from the time series data based on the start and end values such that all the timestamps satisfy start<=t<=end
.
If end
is None, only the timepoint closest to start
is returned.
By default, the time support doesn't change. If you want to change the time support, use the restrict
function.
This function is equivalent of calling the get
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
start |
int or float
|
The starting value for the slice. |
required |
end |
int or float
|
The ending value for the slice. Defaults to None. |
None
|
time_unit |
str
|
The time unit for the start and end values. Defaults to "s" (seconds). |
's'
|
Returns:
Name | Type | Description |
---|---|---|
slice |
slice
|
A slice determining the start and end indices, with unit step
Slicing the array will be equivalent to calling get: |
Raises:
Type | Description |
---|---|
ValueError
|
|
Examples:
>>> # slice over a range
>>> start, end = 1.2, 2.6
>>> print(ts.get_slice(start, end)) # returns `slice(2, 3, None)`
>>> start, end = 1., 2.
>>> print(ts.get_slice(start, end, mode="forward")) # returns `slice(1, 3, None)`
>>> # slice a single value
>>> start = 1.2
>>> print(ts.get_slice(start)) # returns `slice(1, 2, None)`
>>> start = 2.
>>> print(ts.get_slice(start)) # returns `slice(2, 3, None)`
Source code in pynapple/core/base_class.py
__init__
Ts Initializer
Parameters:
Name | Type | Description | Default |
---|---|---|---|
t |
ndarray or Series
|
An object transformable in timestamps, or a pandas.Series equivalent (if d is None) |
required |
time_units |
str
|
The time units in which times are specified ('us', 'ms', 's' [default]) |
's'
|
time_support |
IntervalSet
|
The time support of the Ts object |
None
|
Source code in pynapple/core/time_series.py
as_series
Convert the Ts/Tsd object to a pandas.Series object.
Returns:
Name | Type | Description |
---|---|---|
out |
Series
|
_ |
as_units
Returns a pandas Series with time expressed in the desired unit.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
units |
str
|
('us', 'ms', 's' [default]) |
's'
|
Returns:
Type | Description |
---|---|
Series
|
the series object with adjusted times |
Source code in pynapple/core/time_series.py
value_from
Replace the value with the closest value from Tsd/TsdFrame/TsdTensor argument
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
(Tsd, TsdFrame or TsdTensor)
|
The object holding the values to replace. |
required |
ep |
IntervalSet(optional)
|
The IntervalSet object to restrict the operation. If None, the time support of the tsd input object is used. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
(Tsd, TsdFrame or TsdTensor)
|
Object with the new values |
Examples:
In this example, the ts object will receive the closest values in time from tsd.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100))) # random times
>>> ts = nap.Ts(t=t, time_units='s')
>>> tsd = nap.Tsd(t=np.arange(0,1000), d=np.random.rand(1000), time_units='s')
>>> ep = nap.IntervalSet(start = 0, end = 500, time_units = 's')
The variable ts is a time series object containing only nan. The tsd object containing the values, for example the tracking data, and the epoch to restrict the operation.
newts is the same size as ts restrict to ep.
Source code in pynapple/core/time_series.py
count
Count occurences of events within bin_size or within a set of bins defined as an IntervalSet. You can call this function in multiple ways :
-
tsd.count(bin_size=1, time_units = 'ms') -> Count occurence of events within a 1 ms bin defined on the time support of the object.
-
tsd.count(1, ep=my_epochs) -> Count occurent of events within a 1 second bin defined on the IntervalSet my_epochs.
-
tsd.count(ep=my_bins) -> Count occurent of events within each epoch of the intervalSet object my_bins
-
tsd.count() -> Count occurent of events within each epoch of the time support.
bin_size should be seconds unless specified. If bin_size is used and no epochs is passed, the data will be binned based on the time support of the object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
bin_size |
None or float
|
The bin size (default is second) |
required |
ep |
None or IntervalSet
|
IntervalSet to restrict the operation |
required |
time_units |
str
|
Time units of bin size ('us', 'ms', 's' [default]) |
required |
dtype |
Data type for the count. Default is np.int64. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
Tsd
|
A Tsd object indexed by the center of the bins. |
Examples:
This example shows how to count events within bins of 0.1 second.
>>> import pynapple as nap
>>> import numpy as np
>>> t = np.unique(np.sort(np.random.randint(0, 1000, 100)))
>>> ts = nap.Ts(t=t, time_units='s')
>>> bincount = ts.count(0.1)
An epoch can be specified:
>>> ep = nap.IntervalSet(start = 100, end = 800, time_units = 's')
>>> bincount = ts.count(0.1, ep=ep)
And bincount automatically inherit ep as time support:
Source code in pynapple/core/time_series.py
fillna
Similar to pandas fillna function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
value |
Number
|
Value for filling |
required |
Returns:
Type | Description |
---|---|
Tsd
|
|
Source code in pynapple/core/time_series.py
save
Save Ts object in npz format. The file will contain the timestamps and the time support.
The main purpose of this function is to save small/medium sized timestamps object.
You can load the object with nap.load_file
. Keys are 't', 'start' and 'end' and 'type'.
See the example below.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
filename |
str
|
The filename |
required |
Examples:
>>> import pynapple as nap
>>> import numpy as np
>>> ts = nap.Ts(t=np.array([0., 1., 1.5]))
>>> ts.save("my_path/my_ts.npz")
To load you file, you can use the nap.load_file
function :
Raises:
Type | Description |
---|---|
RuntimeError
|
If filename is not str, path does not exist or filename is a directory. |