pynn_brainscales.brainscales2.standardmodels.cells.Quantity

class pynn_brainscales.brainscales2.standardmodels.cells.Quantity(data, units='', dtype=None, copy=True)

Bases: numpy.ndarray

__init__(): Initialize self. See help(type(self)) for accurate signature.

Methods

`argmax`([axis, out, keepdims])	Return indices of the maximum values along the given axis.
`argmin`([axis, out, keepdims])	Return indices of the minimum values along the given axis.
`argsort`([axis, kind, order])	Returns the indices that would sort this array.
`astype`(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
`clip`([min, max, out])	Return an array whose values are limited to `[min, max]`.
`cumprod`([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
`cumsum`([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
`fill`(value)	Fill the array with a scalar value.
`max`([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
`mean`([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
`min`([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
`nanargmax`([axis, out])	Return the indices of the maximum values in the specified axis ignoring NaNs.
`nanargmin`([axis, out])	Return the indices of the minimum values in the specified axis ignoring NaNs.
`nanmax`([axis, out])	Return the maximum of an array or maximum along an axis, ignoring any NaNs.
`nanmean`([axis, dtype, out])	Compute the arithmetic mean along the specified axis, ignoring NaNs.
`nanmin`([axis, out])	Return minimum of an array or minimum along an axis, ignoring any NaNs.
`nanstd`([axis, dtype, out, ddof])	Compute the standard deviation along the specified axis, while ignoring NaNs.
`nansum`([axis, dtype, out])	Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
`nonzero`()	Return the indices of the elements that are non-zero.
`prod`([axis, dtype, out, keepdims, initial, …])	Return the product of the array elements over the given axis
`ptp`([axis, out, keepdims])	Peak to peak (maximum - minimum) value along a given axis.
`put`(indices, values[, mode])	Set `a.flat[n] = values[n]` for all n in indices.
`rescale`([units])	Return a copy of the quantity converted to the specified units.
`rescale_preferred`()	Return a copy of the quantity converted to the preferred units and scale.
`round`([decimals, out])	Return a with each element rounded to the given number of decimals.
`searchsorted`(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
`squeeze`([axis])	Remove axes of length one from a.
`std`([axis, dtype, out, ddof, keepdims, where])	Returns the standard deviation of the array elements along given axis.
`sum`([axis, dtype, out, keepdims, initial, where])	Return the sum of the array elements over the given axis.
`tolist`()	Return the array as an `a.ndim`-levels deep nested list of Python scalars.
`trace`([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
`var`([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.

Attributes

`dimensionality`
`imag`	The imaginary part of the array.
`magnitude`
`real`	The real part of the array.
`simplified`
`units`

argmax(axis=None, out=None, *, keepdims=False)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

numpy.argmax : equivalent function

argmin(axis=None, out=None, *, keepdims=False)

Return indices of the minimum values along the given axis.

Refer to numpy.argmin for detailed documentation.

numpy.argmin : equivalent function

argsort(axis=- 1, kind=None, order=None)

Returns the indices that would sort this array.

Refer to numpy.argsort for full documentation.

numpy.argsort : equivalent function

astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array, cast to a specified type.

dtypestr or dtype

Typecode or data-type to which the array is cast.

order{‘C’, ‘F’, ‘A’, ‘K’}, optional

Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.

‘no’ means the data types should not be cast at all.

‘equiv’ means only byte-order changes are allowed.

‘safe’ means only casts which can preserve values are allowed.

‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

‘unsafe’ means any data conversions may be done.

subokbool, optional

If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.

copybool, optional

By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.

arr_tndarray: Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not.

Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted.

ComplexWarning: When casting from complex to float or int. To avoid this, one should use a.real.astype(t).

>>> x = np.array([1, 2, 2.5])
>>> x
array([1. ,  2. ,  2.5])

>>> x.astype(int)
array([1, 2, 2])

Scalars are returned as scalar Quantity arrays.

clip(min=None, max=None, out=None, **kwargs)

Return an array whose values are limited to [min, max]. One of max or min must be given.

Refer to numpy.clip for full documentation.

numpy.clip : equivalent function

cumprod(axis=None, dtype=None, out=None)

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

numpy.cumprod : equivalent function

cumsum(axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

numpy.cumsum : equivalent function

property dimensionality

fill(value)

Fill the array with a scalar value.

valuescalar: All elements of a will be assigned this value.

>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])

property imag

The imaginary part of the array.

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0.        ,  0.70710678])
>>> x.imag.dtype
dtype('float64')

property magnitude

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

numpy.amax : equivalent function

mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

numpy.mean : equivalent function

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

numpy.amin : equivalent function

nanargmax(axis=None, out=None)

Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs.

aarray_like: Input data.
axisint, optional: Axis along which to operate. By default flattened input is used.
outarray, optional: If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.

New in version 1.22.0.
keepdimsbool, optional: If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array.

New in version 1.22.0.

index_arrayndarray: An array of indices or a single index value.

argmax, nanargmin

>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])

nanargmin(axis=None, out=None)

Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs.

aarray_like: Input data.
axisint, optional: Axis along which to operate. By default flattened input is used.
outarray, optional: If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.

New in version 1.22.0.
keepdimsbool, optional: If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array.

New in version 1.22.0.

index_arrayndarray: An array of indices or a single index value.

argmin, nanargmax

>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])

nanmax(axis=None, out=None)

Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and NaN is returned for that slice.

aarray_like

Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted.

axis{int, tuple of int, None}, optional

Axis or axes along which the maximum is computed. The default is to compute the maximum of the flattened array.

outndarray, optional

Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details.

New in version 1.8.0.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

If the value is anything but the default, then keepdims will be passed through to the max method of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

New in version 1.8.0.

initialscalar, optional

The minimum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

wherearray_like of bool, optional

Elements to compare for the maximum. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

nanmaxndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.

nanmin :: The minimum value of an array along a given axis, ignoring any NaNs.
amax :: The maximum value of an array along a given axis, propagating any NaNs.
fmax :: Element-wise maximum of two arrays, ignoring any NaNs.
maximum :: Element-wise maximum of two arrays, propagating any NaNs.
isnan :: Shows which elements are Not a Number (NaN).
isfinite:: Shows which elements are neither NaN nor infinity.

amin, fmin, minimum

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to np.max.

>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmax(a)
3.0
>>> np.nanmax(a, axis=0)
array([3.,  2.])
>>> np.nanmax(a, axis=1)
array([2.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmax([1, 2, np.nan, np.NINF])
2.0
>>> np.nanmax([1, 2, np.nan, np.inf])
inf

nanmean(axis=None, dtype=None, out=None)

Compute the arithmetic mean along the specified axis, ignoring NaNs.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

For all-NaN slices, NaN is returned and a RuntimeWarning is raised.

New in version 1.8.0.

aarray_like

Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.

axis{int, tuple of int, None}, optional

Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.

dtypedata-type, optional

Type to use in computing the mean. For integer inputs, the default is float64; for inexact inputs, it is the same as the input dtype.

outndarray, optional

Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

If the value is anything but the default, then keepdims will be passed through to the mean or sum methods of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

wherearray_like of bool, optional

Elements to include in the mean. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

mndarray, see dtype parameter above: If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned. Nan is returned for slices that contain only NaNs.

average : Weighted average mean : Arithmetic mean taken while not ignoring NaNs var, nanvar

The arithmetic mean is the sum of the non-NaN elements along the axis divided by the number of non-NaN elements.

Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32. Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.

>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanmean(a)
2.6666666666666665
>>> np.nanmean(a, axis=0)
array([2.,  4.])
>>> np.nanmean(a, axis=1)
array([1.,  3.5]) # may vary

nanmin(axis=None, out=None)

Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice.

aarray_like

Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.

axis{int, tuple of int, None}, optional

Axis or axes along which the minimum is computed. The default is to compute the minimum of the flattened array.

outndarray, optional

Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details.

New in version 1.8.0.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

If the value is anything but the default, then keepdims will be passed through to the min method of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

New in version 1.8.0.

initialscalar, optional

The maximum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

wherearray_like of bool, optional

Elements to compare for the minimum. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

nanminndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.

nanmax :: The maximum value of an array along a given axis, ignoring any NaNs.
amin :: The minimum value of an array along a given axis, propagating any NaNs.
fmin :: Element-wise minimum of two arrays, ignoring any NaNs.
minimum :: Element-wise minimum of two arrays, propagating any NaNs.
isnan :: Shows which elements are Not a Number (NaN).
isfinite:: Shows which elements are neither NaN nor infinity.

amax, fmax, maximum

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to np.min.

>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
array([1.,  2.])
>>> np.nanmin(a, axis=1)
array([1.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmin([1, 2, np.nan, np.inf])
1.0
>>> np.nanmin([1, 2, np.nan, np.NINF])
-inf

nanstd(axis=None, dtype=None, out=None, ddof=0)

Compute the standard deviation along the specified axis, while ignoring NaNs.

Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised.

New in version 1.8.0.

aarray_like

Calculate the standard deviation of the non-NaN values.

axis{int, tuple of int, None}, optional

Axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array.

dtypedtype, optional

Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type.

outndarray, optional

Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary.

ddofint, optional

Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of non-NaN elements. By default ddof is zero.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

If this value is anything but the default it is passed through as-is to the relevant functions of the sub-classes. If these functions do not have a keepdims kwarg, a RuntimeError will be raised.

wherearray_like of bool, optional

Elements to include in the standard deviation. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

standard_deviationndarray, see dtype parameter above.: If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.

var, mean, std nanvar, nanmean ufuncs-output-type

The standard deviation is the square root of the average of the squared deviations from the mean: std = sqrt(mean(abs(x - x.mean())**2)).

The average squared deviation is normally calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of the infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ddof=1, it will not be an unbiased estimate of the standard deviation per se.

Note that, for complex numbers, std takes the absolute value before squaring, so that the result is always real and nonnegative.

For floating-point input, the std is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the dtype keyword can alleviate this issue.

>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
array([1., 0.])
>>> np.nanstd(a, axis=1)
array([0.,  0.5]) # may vary

nansum(axis=None, dtype=None, out=None)

Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned.

aarray_like

Array containing numbers whose sum is desired. If a is not an array, a conversion is attempted.

axis{int, tuple of int, None}, optional

Axis or axes along which the sum is computed. The default is to compute the sum of the flattened array.

dtypedata-type, optional

The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of a is used. An exception is when a has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact.

New in version 1.8.0.

outndarray, optional

Alternate output array in which to place the result. The default is None. If provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. The casting of NaN to integer can yield unexpected results.

New in version 1.8.0.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

If the value is anything but the default, then keepdims will be passed through to the mean or sum methods of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised.

New in version 1.8.0.

initialscalar, optional

Starting value for the sum. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

wherearray_like of bool, optional

Elements to include in the sum. See ~numpy.ufunc.reduce for details.

New in version 1.22.0.

nansumndarray.: A new array holding the result is returned unless out is specified, in which it is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.

numpy.sum : Sum across array propagating NaNs. isnan : Show which elements are NaN. isfinite : Show which elements are not NaN or +/-inf.

If both positive and negative infinity are present, the sum will be Not A Number (NaN).

>>> np.nansum(1)
1
>>> np.nansum([1])
1
>>> np.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
array([2.,  1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
>>> from numpy.testing import suppress_warnings
>>> with suppress_warnings() as sup:
...     sup.filter(RuntimeWarning)
...     np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan

nonzero()

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

numpy.nonzero : equivalent function

prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

numpy.prod : equivalent function

ptp(axis=None, out=None, keepdims=False)

Peak to peak (maximum - minimum) value along a given axis.

Refer to numpy.ptp for full documentation.

numpy.ptp : equivalent function

put(indices, values, mode='raise')

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

numpy.put : equivalent function

performs the equivalent of ndarray.put() but enforces units values - must be an Quantity with the same units as self

property real

The real part of the array.

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')

numpy.real : equivalent function

rescale(units=None): Return a copy of the quantity converted to the specified units. If units is None, an attempt will be made to rescale the quantity to preferred units (see rescale_preferred).

rescale_preferred(): Return a copy of the quantity converted to the preferred units and scale. These will be identified from among the compatible units specified in the list PREFERRED in this module. For example, a voltage quantity might be converted to mV: ` import quantities as pq pq.quantity.PREFERRED = [pq.mV, pq.pA] old = 3.1415 * pq.V new = old.rescale_preferred() # `new` will be 3141.5 mV. `

round(decimals=0, out=None)

Return a with each element rounded to the given number of decimals.

Refer to numpy.around for full documentation.

numpy.around : equivalent function

searchsorted(v, side='left', sorter=None)

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

numpy.searchsorted : equivalent function

property simplified

squeeze(axis=None)

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

numpy.squeeze : equivalent function

std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

numpy.std : equivalent function

sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

numpy.sum : equivalent function

tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

none

yobject, or list of object, or list of list of object, or …: The possibly nested list of array elements.

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:

>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 2]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>

Additionally, for a 2D array, tolist applies recursively:

>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]

The base case for this recursion is a 0D array:

>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
  ...
TypeError: iteration over a 0-d array
>>> a.tolist()
1

trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

numpy.trace : equivalent function

property units

var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

numpy.var : equivalent function