Numpy functions¶
This section of the manual discusses those functions that were adapted
from numpy
.
argmax¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html
See numpy.max.
argmin¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html
See numpy.max.
argsort¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
Similarly to sort, argsort
takes a positional, and a
keyword argument, and returns an unsigned short index array of type
ndarray
with the same dimensions as the input, or, if axis=None
,
as a row vector with length equal to the number of elements in the input
(i.e., the flattened array). The indices in the output sort the input in
ascending order. The routine in argsort
is the same as in sort
,
therefore, the comments on computational expenses (time and RAM) also
apply. In particular, since no copy of the original data is required,
virtually no RAM beyond the output array is used.
Since the underlying container of the output array is of type
uint16_t
, neither of the output dimensions should be larger than
65535. If that happens to be the case, the function will bail out with a
ValueError
.
# code to be run in micropython
from ulab import numpy as np
a = np.array([[1, 12, 3, 0], [5, 3, 4, 1], [9, 11, 1, 8], [7, 10, 0, 1]], dtype=np.float)
print('\na:\n', a)
b = np.argsort(a, axis=0)
print('\na sorted along vertical axis:\n', b)
c = np.argsort(a, axis=1)
print('\na sorted along horizontal axis:\n', c)
c = np.argsort(a, axis=None)
print('\nflattened a sorted:\n', c)
a:
array([[1.0, 12.0, 3.0, 0.0],
[5.0, 3.0, 4.0, 1.0],
[9.0, 11.0, 1.0, 8.0],
[7.0, 10.0, 0.0, 1.0]], dtype=float64)
a sorted along vertical axis:
array([[0, 1, 3, 0],
[1, 3, 2, 1],
[3, 2, 0, 3],
[2, 0, 1, 2]], dtype=uint16)
a sorted along horizontal axis:
array([[3, 0, 2, 1],
[3, 1, 2, 0],
[2, 3, 0, 1],
[2, 3, 0, 1]], dtype=uint16)
Traceback (most recent call last):
File "/dev/shm/micropython.py", line 12, in <module>
NotImplementedError: argsort is not implemented for flattened arrays
Since during the sorting, only the indices are shuffled, argsort
does not modify the input array, as one can verify this by the following
example:
# code to be run in micropython
from ulab import numpy as np
a = np.array([0, 5, 1, 3, 2, 4], dtype=np.uint8)
print('\na:\n', a)
b = np.argsort(a, axis=0)
print('\nsorting indices:\n', b)
print('\nthe original array:\n', a)
a:
array([0, 5, 1, 3, 2, 4], dtype=uint8)
sorting indices:
array([0, 2, 4, 3, 5, 1], dtype=uint16)
the original array:
array([0, 5, 1, 3, 2, 4], dtype=uint8)
clip¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.clip.html
Clips an array, i.e., values that are outside of an interval are clipped
to the interval edges. The function is equivalent to
maximum(a_min, minimum(a, a_max))
broadcasting takes place exactly
as in minimum. If the arrays are of different dtype
,
the output is upcast as in Binary operators.
# code to be run in micropython
from ulab import numpy as np
a = np.array(range(9), dtype=np.uint8)
print('a:\t\t', a)
print('clipped:\t', np.clip(a, 3, 7))
b = 3 * np.ones(len(a), dtype=np.float)
print('\na:\t\t', a)
print('b:\t\t', b)
print('clipped:\t', np.clip(a, b, 7))
a: array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)
clipped: array([3, 3, 3, 3, 4, 5, 6, 7, 7], dtype=uint8)
a: array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)
b: array([3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0], dtype=float64)
clipped: array([3.0, 3.0, 3.0, 3.0, 4.0, 5.0, 6.0, 7.0, 7.0], dtype=float64)
convolve¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html
Returns the discrete, linear convolution of two one-dimensional arrays.
Only the full
mode is supported, and the mode
named parameter is
not accepted. Note that all other modes can be had by slicing a full
result.
# code to be run in micropython
from ulab import numpy as np
x = np.array((1, 2, 3))
y = np.array((1, 10, 100, 1000))
print(np.convolve(x, y))
array([1.0, 12.0, 123.0, 1230.0, 2300.0, 3000.0], dtype=float64)
diff¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.diff.html
The diff
function returns the numerical derivative of the forward
scheme, or more accurately, the differences of an ndarray
along a
given axis. The order of derivative can be stipulated with the n
keyword argument, which should be between 0, and 9. Default is 1. If
higher order derivatives are required, they can be gotten by repeated
calls to the function. The axis
keyword argument should be -1 (last
axis, in ulab
equivalent to the second axis, and this also happens
to be the default value), 0, or 1.
Beyond the output array, the function requires only a couple of bytes of
extra RAM for the differentiation stencil. (The stencil is an int8
array, one byte longer than n
. This also explains, why the highest
order is 9: the coefficients of a ninth-order stencil all fit in signed
bytes, while 10 would require int16
.) Note that as usual in
numerical differentiation (and also in numpy
), the length of the
respective axis will be reduced by n
after the operation. If n
is larger than, or equal to the length of the axis, an empty array will
be returned.
WARNING: the diff
function does not implement the prepend
and append
keywords that can be found in numpy
.
# code to be run in micropython
from ulab import numpy as np
a = np.array(range(9), dtype=np.uint8)
a[3] = 10
print('a:\n', a)
print('\nfirst derivative:\n', np.diff(a, n=1))
print('\nsecond derivative:\n', np.diff(a, n=2))
c = np.array([[1, 2, 3, 4], [4, 3, 2, 1], [1, 4, 9, 16], [0, 0, 0, 0]])
print('\nc:\n', c)
print('\nfirst derivative, first axis:\n', np.diff(c, axis=0))
print('\nfirst derivative, second axis:\n', np.diff(c, axis=1))
a:
array([0, 1, 2, 10, 4, 5, 6, 7, 8], dtype=uint8)
first derivative:
array([1, 1, 8, 250, 1, 1, 1, 1], dtype=uint8)
second derivative:
array([0, 249, 14, 249, 0, 0, 0], dtype=uint8)
c:
array([[1.0, 2.0, 3.0, 4.0],
[4.0, 3.0, 2.0, 1.0],
[1.0, 4.0, 9.0, 16.0],
[0.0, 0.0, 0.0, 0.0]], dtype=float64)
first derivative, first axis:
array([[3.0, 1.0, -1.0, -3.0],
[-3.0, 1.0, 7.0, 15.0],
[-1.0, -4.0, -9.0, -16.0]], dtype=float64)
first derivative, second axis:
array([[1.0, 1.0, 1.0],
[-1.0, -1.0, -1.0],
[3.0, 5.0, 7.0],
[0.0, 0.0, 0.0]], dtype=float64)
equal¶
numpy
:
https://numpy.org/doc/stable/reference/generated/numpy.equal.html
numpy
:
https://numpy.org/doc/stable/reference/generated/numpy.not_equal.html
In micropython
, equality of arrays or scalars can be established by
utilising the ==
, !=
, <
, >
, <=
, or =>
binary
operators. In circuitpython
, ==
and !=
will produce
unexpected results. In order to avoid this discrepancy, and to maintain
compatibility with numpy
, ulab
implements the equal
and
not_equal
operators that return the same results, irrespective of
the python
implementation.
These two functions take two ndarray
s, or scalars as their
arguments. No keyword arguments are implemented.
# code to be run in micropython
from ulab import numpy as np
a = np.array(range(9))
b = np.zeros(9)
print('a: ', a)
print('b: ', b)
print('\na == b: ', np.equal(a, b))
print('a != b: ', np.not_equal(a, b))
# comparison with scalars
print('a == 2: ', np.equal(a, 2))
a: array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float64)
b: array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=float64)
a == b: array([True, False, False, False, False, False, False, False, False], dtype=bool)
a != b: array([False, True, True, True, True, True, True, True, True], dtype=bool)
a == 2: array([False, False, True, False, False, False, False, False, False], dtype=bool)
flip¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html
The flip
function takes one positional, an ndarray
, and one
keyword argument, axis = None
, and reverses the order of elements
along the given axis. If the keyword argument is None
, the matrix’
entries are flipped along all axes. flip
returns a new copy of the
array.
# code to be run in micropython
from ulab import numpy as np
a = np.array([1, 2, 3, 4, 5])
print("a: \t", a)
print("a flipped:\t", np.flip(a))
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.uint8)
print("\na flipped horizontally\n", np.flip(a, axis=1))
print("\na flipped vertically\n", np.flip(a, axis=0))
print("\na flipped horizontally+vertically\n", np.flip(a))
a: array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=float64)
a flipped: array([5.0, 4.0, 3.0, 2.0, 1.0], dtype=float64)
a flipped horizontally
array([[3, 2, 1],
[6, 5, 4],
[9, 8, 7]], dtype=uint8)
a flipped vertically
array([[7, 8, 9],
[4, 5, 6],
[1, 2, 3]], dtype=uint8)
a flipped horizontally+vertically
array([9, 8, 7, 6, 5, 4, 3, 2, 1], dtype=uint8)
interp¶
numpy
: https://docs.scipy.org/doc/numpy/numpy.interp
The interp
function returns the linearly interpolated values of a
one-dimensional numerical array. It requires three positional
arguments,x
, at which the interpolated values are evaluated,
xp
, the array of the independent data variable, and fp
, the
array of the dependent values of the data. xp
must be a
monotonically increasing sequence of numbers.
Two keyword arguments, left
, and right
can also be supplied;
these determine the return values, if x < xp[0]
, and x > xp[-1]
,
respectively. If these arguments are not supplied, left
, and
right
default to fp[0]
, and fp[-1]
, respectively.
# code to be run in micropython
from ulab import numpy as np
x = np.array([1, 2, 3, 4, 5]) - 0.2
xp = np.array([1, 2, 3, 4])
fp = np.array([1, 2, 3, 5])
print(x)
print(np.interp(x, xp, fp))
print(np.interp(x, xp, fp, left=0.0))
print(np.interp(x, xp, fp, right=10.0))
array([0.8, 1.8, 2.8, 3.8, 4.8], dtype=float64)
array([1.0, 1.8, 2.8, 4.6, 5.0], dtype=float64)
array([0.0, 1.8, 2.8, 4.6, 5.0], dtype=float64)
array([1.0, 1.8, 2.8, 4.6, 10.0], dtype=float64)
mean¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html
If the axis keyword is not specified, it assumes the default value of
None
, and returns the result of the computation for the flattened
array. Otherwise, the calculation is along the given axis.
# code to be run in micropython
from ulab import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print('a: \n', a)
print('mean, flat: ', np.mean(a))
print('mean, horizontal: ', np.mean(a, axis=1))
print('mean, vertical: ', np.mean(a, axis=0))
a:
array([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]], dtype=float64)
mean, flat: 5.0
mean, horizontal: array([2.0, 5.0, 8.0], dtype=float64)
mean, vertical: array([4.0, 5.0, 6.0], dtype=float64)
max¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.max.html
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.min.html
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html
WARNING: Difference to numpy
: the out
keyword argument is
not implemented.
These functions follow the same pattern, and work with generic
iterables, and ndarray
s. min
, and max
return the minimum
or maximum of a sequence. If the input array is two-dimensional, the
axis
keyword argument can be supplied, in which case the
minimum/maximum along the given axis will be returned. If axis=None
(this is also the default value), the minimum/maximum of the flattened
array will be determined.
argmin/argmax
return the position (index) of the minimum/maximum in
the sequence.
# code to be run in micropython
from ulab import numpy as np
a = np.array([1, 2, 0, 1, 10])
print('a:', a)
print('min of a:', np.min(a))
print('argmin of a:', np.argmin(a))
b = np.array([[1, 2, 0], [1, 10, -1]])
print('\nb:\n', b)
print('min of b (flattened):', np.min(b))
print('min of b (axis=0):', np.min(b, axis=0))
print('min of b (axis=1):', np.min(b, axis=1))
a: array([1.0, 2.0, 0.0, 1.0, 10.0], dtype=float64)
min of a: 0.0
argmin of a: 2
b:
array([[1.0, 2.0, 0.0],
[1.0, 10.0, -1.0]], dtype=float64)
min of b (flattened): -1.0
min of b (axis=0): array([1.0, 2.0, -1.0], dtype=float64)
min of b (axis=1): array([0.0, -1.0], dtype=float64)
median¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.median.html
The function computes the median along the specified axis, and returns
the median of the array elements. If the axis
keyword argument is
None
, the arrays is flattened first. The dtype
of the results is
always float.
# code to be run in micropython
from ulab import numpy as np
a = np.array(range(12), dtype=np.int8).reshape((3, 4))
print('a:\n', a)
print('\nmedian of the flattened array: ', np.median(a))
print('\nmedian along the vertical axis: ', np.median(a, axis=0))
print('\nmedian along the horizontal axis: ', np.median(a, axis=1))
a:
array([[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]], dtype=int8)
median of the flattened array: 5.5
median along the vertical axis: array([4.0, 5.0, 6.0, 7.0], dtype=float64)
median along the horizontal axis: array([1.5, 5.5, 9.5], dtype=float64)
min¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.min.html
See numpy.max.
minimum¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.minimum.html
See numpy.maximum
maximum¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.maximum.html
Returns the maximum of two arrays, or two scalars, or an array, and a
scalar. If the arrays are of different dtype
, the output is upcast
as in Binary operators. If both inputs are
scalars, a scalar is returned. Only positional arguments are
implemented.
# code to be run in micropython
from ulab import numpy as np
a = np.array([1, 2, 3, 4, 5], dtype=np.uint8)
b = np.array([5, 4, 3, 2, 1], dtype=np.float)
print('minimum of a, and b:')
print(np.minimum(a, b))
print('\nmaximum of a, and b:')
print(np.maximum(a, b))
print('\nmaximum of 1, and 5.5:')
print(np.maximum(1, 5.5))
minimum of a, and b:
array([1.0, 2.0, 3.0, 2.0, 1.0], dtype=float64)
maximum of a, and b:
array([5.0, 4.0, 3.0, 4.0, 5.0], dtype=float64)
maximum of 1, and 5.5:
5.5
not_equal¶
See numpy.equal.
polyfit¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html
polyfit takes two, or three arguments. The last one is the degree of the
polynomial that will be fitted, the last but one is an array or iterable
with the y
(dependent) values, and the first one, an array or
iterable with the x
(independent) values, can be dropped. If that is
the case, x
will be generated in the function as range(len(y))
.
If the lengths of x
, and y
are not the same, the function raises
a ValueError
.
# code to be run in micropython
from ulab import numpy as np
x = np.array([0, 1, 2, 3, 4, 5, 6])
y = np.array([9, 4, 1, 0, 1, 4, 9])
print('independent values:\t', x)
print('dependent values:\t', y)
print('fitted values:\t\t', np.polyfit(x, y, 2))
# the same with missing x
print('\ndependent values:\t', y)
print('fitted values:\t\t', np.polyfit(y, 2))
independent values: array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float64)
dependent values: array([9.0, 4.0, 1.0, 0.0, 1.0, 4.0, 9.0], dtype=float64)
fitted values: array([1.0, -6.0, 9.000000000000004], dtype=float64)
dependent values: array([9.0, 4.0, 1.0, 0.0, 1.0, 4.0, 9.0], dtype=float64)
fitted values: array([1.0, -6.0, 9.000000000000004], dtype=float64)
Execution time¶
polyfit
is based on the inversion of a matrix (there is more on the
background in https://en.wikipedia.org/wiki/Polynomial_regression), and
it requires the intermediate storage of 2*N*(deg+1)
floats, where
N
is the number of entries in the input array, and deg
is the
fit’s degree. The additional computation costs of the matrix inversion
discussed in linalg.inv also apply. The example from above
needs around 150 microseconds to return:
# code to be run in micropython
from ulab import numpy as np
@timeit
def time_polyfit(x, y, n):
return np.polyfit(x, y, n)
x = np.array([0, 1, 2, 3, 4, 5, 6])
y = np.array([9, 4, 1, 0, 1, 4, 9])
time_polyfit(x, y, 2)
execution time: 153 us
polyval¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyval.html
polyval
takes two arguments, both arrays or generic micropython
iterables returning scalars.
# code to be run in micropython
from ulab import numpy as np
p = [1, 1, 1, 0]
x = [0, 1, 2, 3, 4]
print('coefficients: ', p)
print('independent values: ', x)
print('\nvalues of p(x): ', np.polyval(p, x))
# the same works with one-dimensional ndarrays
a = np.array(x)
print('\nndarray (a): ', a)
print('value of p(a): ', np.polyval(p, a))
coefficients: [1, 1, 1, 0]
independent values: [0, 1, 2, 3, 4]
values of p(x): array([0.0, 3.0, 14.0, 39.0, 84.0], dtype=float64)
ndarray (a): array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=float64)
value of p(a): array([0.0, 3.0, 14.0, 39.0, 84.0], dtype=float64)
roll¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.roll.html
The roll function shifts the content of a vector by the positions given
as the second argument. If the axis
keyword is supplied, the shift
is applied to the given axis.
# code to be run in micropython
from ulab import numpy as np
a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print("a:\t\t\t", a)
a = np.roll(a, 2)
print("a rolled to the left:\t", a)
# this should be the original vector
a = np.roll(a, -2)
print("a rolled to the right:\t", a)
a: array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float64)
a rolled to the left: array([7.0, 8.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float64)
a rolled to the right: array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float64)
Rolling works with matrices, too. If the axis
keyword is 0, the
matrix is rolled along its vertical axis, otherwise, horizontally.
Horizontal rolls are faster, because they require fewer steps, and
larger memory chunks are copied, however, they also require more RAM:
basically the whole row must be stored internally. Most expensive are
the None
keyword values, because with axis = None
, the array is
flattened first, hence the row’s length is the size of the whole matrix.
Vertical rolls require two internal copies of single columns.
# code to be run in micropython
from ulab import numpy as np
a = np.array(range(12)).reshape((3, 4))
print("a:\n", a)
a = np.roll(a, 2, axis=0)
print("\na rolled up:\n", a)
a = np.array(range(12)).reshape((3, 4))
print("a:\n", a)
a = np.roll(a, -1, axis=1)
print("\na rolled to the left:\n", a)
a = np.array(range(12)).reshape((3, 4))
print("a:\n", a)
a = np.roll(a, 1, axis=None)
print("\na rolled with None:\n", a)
a:
array([[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0]], dtype=float64)
a rolled up:
array([[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0],
[0.0, 1.0, 2.0, 3.0]], dtype=float64)
a:
array([[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0]], dtype=float64)
a rolled to the left:
array([[1.0, 2.0, 3.0, 0.0],
[5.0, 6.0, 7.0, 4.0],
[9.0, 10.0, 11.0, 8.0]], dtype=float64)
a:
array([[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0]], dtype=float64)
a rolled with None:
array([[11.0, 0.0, 1.0, 2.0],
[3.0, 4.0, 5.0, 6.0],
[7.0, 8.0, 9.0, 10.0]], dtype=float64)
sort¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.sort.html
The sort function takes an ndarray, and sorts its elements in ascending
order along the specified axis using a heap sort algorithm. As opposed
to the .sort()
method discussed earlier, this function creates a
copy of its input before sorting, and at the end, returns this copy.
Sorting takes place in place, without auxiliary storage. The axis
keyword argument takes on the possible values of -1 (the last axis, in
ulab
equivalent to the second axis, and this also happens to be the
default value), 0, 1, or None
. The first three cases are identical
to those in diff, while the last one flattens the array
before sorting.
If descending order is required, the result can simply be flip
ped,
see flip.
WARNING: numpy
defines the kind
, and order
keyword
arguments that are not implemented here. The function in ulab
always
uses heap sort, and since ulab
does not have the concept of data
fields, the order
keyword argument would have no meaning.
# code to be run in micropython
from ulab import numpy as np
a = np.array([[1, 12, 3, 0], [5, 3, 4, 1], [9, 11, 1, 8], [7, 10, 0, 1]], dtype=np.float)
print('\na:\n', a)
b = np.sort(a, axis=0)
print('\na sorted along vertical axis:\n', b)
c = np.sort(a, axis=1)
print('\na sorted along horizontal axis:\n', c)
c = np.sort(a, axis=None)
print('\nflattened a sorted:\n', c)
a:
array([[1.0, 12.0, 3.0, 0.0],
[5.0, 3.0, 4.0, 1.0],
[9.0, 11.0, 1.0, 8.0],
[7.0, 10.0, 0.0, 1.0]], dtype=float64)
a sorted along vertical axis:
array([[1.0, 3.0, 0.0, 0.0],
[5.0, 10.0, 1.0, 1.0],
[7.0, 11.0, 3.0, 1.0],
[9.0, 12.0, 4.0, 8.0]], dtype=float64)
a sorted along horizontal axis:
array([[0.0, 1.0, 3.0, 12.0],
[1.0, 3.0, 4.0, 5.0],
[1.0, 8.0, 9.0, 11.0],
[0.0, 1.0, 7.0, 10.0]], dtype=float64)
flattened a sorted:
array([0.0, 0.0, 1.0, ..., 10.0, 11.0, 12.0], dtype=float64)
Heap sort requires \(\sim N\log N\) operations, and notably, the worst case costs only 20% more time than the average. In order to get an order-of-magnitude estimate, we will take the sine of 1000 uniformly spaced numbers between 0, and two pi, and sort them:
# code to be run in micropython
import ulab as np
from ulab import vector
from ulab import numerical
@timeit
def sort_time(array):
return numerical.sort(array)
b = vector.sin(np.linspace(0, 6.28, num=1000))
print('b: ', b)
sort_time(b)
print('\nb sorted:\n', b)
std¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.std.html
If the axis keyword is not specified, it assumes the default value of
None
, and returns the result of the computation for the flattened
array. Otherwise, the calculation is along the given axis.
# code to be run in micropython
from ulab import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print('a: \n', a)
print('sum, flat array: ', np.std(a))
print('std, vertical: ', np.std(a, axis=0))
print('std, horizonal: ', np.std(a, axis=1))
a:
array([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]], dtype=float64)
sum, flat array: 2.581988897471611
std, vertical: array([2.449489742783178, 2.449489742783178, 2.449489742783178], dtype=float64)
std, horizonal: array([0.8164965809277261, 0.8164965809277261, 0.8164965809277261], dtype=float64)
sum¶
numpy
:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html
If the axis keyword is not specified, it assumes the default value of
None
, and returns the result of the computation for the flattened
array. Otherwise, the calculation is along the given axis.
# code to be run in micropython
from ulab import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print('a: \n', a)
print('sum, flat array: ', np.sum(a))
print('sum, horizontal: ', np.sum(a, axis=1))
print('std, vertical: ', np.sum(a, axis=0))
a:
array([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]], dtype=float64)
sum, flat array: 45.0
sum, horizontal: array([6.0, 15.0, 24.0], dtype=float64)
std, vertical: array([12.0, 15.0, 18.0], dtype=float64)
trapz¶
numpy
:
https://numpy.org/doc/stable/reference/generated/numpy.trapz.html
The function takes one or two one-dimensional ndarray
s, and
integrates the dependent values (y
) using the trapezoidal rule. If
the independent variable (x
) is given, that is taken as the sample
points corresponding to y
.
# code to be run in micropython
from ulab import numpy as np
x = np.linspace(0, 9, num=10)
y = x*x
print('x: ', x)
print('y: ', y)
print('============================')
print('integral of y: ', np.trapz(y))
print('integral of y at x: ', np.trapz(y, x=x))
x: array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype=float64)
y: array([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, 36.0, 49.0, 64.0, 81.0], dtype=float64)
============================
integral of y: 244.5
integral of y at x: 244.5