Universal functions

Standard mathematical functions can be calculated on any scalar, scalar-valued iterable (ranges, lists, tuples containing numbers), and on ndarrays without having to change the call signature. In all cases the functions return a new ndarray of typecode float (since these functions usually generate float values, anyway). The functions execute faster with ndarray arguments than with iterables, because the values of the input vector can be extracted faster.

At present, the following functions are supported:

acos, acosh, arctan2, around, asin, asinh, atan, arctan2, atanh, ceil, cos, degrees, exp, expm1, floor, log, log10, log2, radians, sin, sinh, sqrt, tan, tanh.

These functions are applied element-wise to the arguments, thus, e.g., the exponential of a matrix cannot be calculated in this way.

# code to be run in micropython

from ulab import numpy as np

a = range(9)
b = np.array(a)

# works with ranges, lists, tuples etc.
print('a:\t', a)
print('exp(a):\t', np.exp(a))

# with 1D arrays
print('\n=============\nb:\n', b)
print('exp(b):\n', np.exp(b))

# as well as with matrices
c = np.array(range(9)).reshape((3, 3))
print('\n=============\nc:\n', c)
print('exp(c):\n', np.exp(c))
a:   range(0, 9)
exp(a):      array([1.0, 2.718281828459045, 7.38905609893065, 20.08553692318767, 54.59815003314424, 148.4131591025766, 403.4287934927351, 1096.633158428459, 2980.957987041728], dtype=float64)

 array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float64)
 array([1.0, 2.718281828459045, 7.38905609893065, 20.08553692318767, 54.59815003314424, 148.4131591025766, 403.4287934927351, 1096.633158428459, 2980.957987041728], dtype=float64)

 array([[0.0, 1.0, 2.0],
       [3.0, 4.0, 5.0],
       [6.0, 7.0, 8.0]], dtype=float64)
 array([[1.0, 2.718281828459045, 7.38905609893065],
       [20.08553692318767, 54.59815003314424, 148.4131591025766],
       [403.4287934927351, 1096.633158428459, 2980.957987041728]], dtype=float64)

Computation expenses

The overhead for calculating with micropython iterables is quite significant: for the 1000 samples below, the difference is more than 800 microseconds, because internally the function has to create the ndarray for the output, has to fetch the iterable’s items of unknown type, and then convert them to floats. All these steps are skipped for ndarrays, because these pieces of information are already known.

Doing the same with list comprehension requires 30 times more time than with the ndarray, which would become even more, if we converted the resulting list to an ndarray.

# code to be run in micropython

from ulab import numpy as np
import math

a = [0]*1000
b = np.array(a)

def timed_vector(iterable):
    return np.exp(iterable)

def timed_list(iterable):
    return [math.exp(i) for i in iterable]

print('iterating over ndarray in ulab')

print('\niterating over list in ulab')

print('\niterating over list in python')
iterating over ndarray in ulab
execution time:  441  us

iterating over list in ulab
execution time:  1266  us

iterating over list in python
execution time:  11379  us


numpy: https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.arctan2.html

The two-argument inverse tangent function is also part of the vector sub-module. The function implements broadcasting as discussed in the section on ndarrays. Scalars (micropython integers or floats) are also allowed.

# code to be run in micropython

from ulab import numpy as np

a = np.array([1, 2.2, 33.33, 444.444])
print('a:\n', a)
print('\narctan2(a, 1.0)\n', np.arctan2(a, 1.0))
print('\narctan2(1.0, a)\n', np.arctan2(1.0, a))
print('\narctan2(a, a): \n', np.arctan2(a, a))
 array([1.0, 2.2, 33.33, 444.444], dtype=float64)

arctan2(a, 1.0)
 array([0.7853981633974483, 1.14416883366802, 1.5408023243361, 1.568546328341769], dtype=float64)

arctan2(1.0, a)
 array([0.7853981633974483, 0.426627493126876, 0.02999400245879636, 0.002249998453127392], dtype=float64)

arctan2(a, a):
 array([0.7853981633974483, 0.7853981633974483, 0.7853981633974483, 0.7853981633974483], dtype=float64)


numpy: https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.around.html

numpy’s around function can also be found in the vector sub-module. The function implements the decimals keyword argument with default value 0. The first argument must be an ndarray. If this is not the case, the function raises a TypeError exception. Note that numpy accepts general iterables. The out keyword argument known from numpy is not accepted. The function always returns an ndarray of type mp_float_t.

# code to be run in micropython

from ulab import numpy as np

a = np.array([1, 2.2, 33.33, 444.444])
print('a:\t\t', a)
print('\ndecimals = 0\t', np.around(a, decimals=0))
print('\ndecimals = 1\t', np.around(a, decimals=1))
print('\ndecimals = -1\t', np.around(a, decimals=-1))
a:           array([1.0, 2.2, 33.33, 444.444], dtype=float64)

decimals = 0         array([1.0, 2.0, 33.0, 444.0], dtype=float64)

decimals = 1         array([1.0, 2.2, 33.3, 444.4], dtype=float64)

decimals = -1        array([0.0, 0.0, 30.0, 440.0], dtype=float64)

Vectorising generic python functions

numpy: https://numpy.org/doc/stable/reference/generated/numpy.vectorize.html

The examples above use factory functions. In fact, they are nothing but the vectorised versions of the standard mathematical functions. User-defined python functions can also be vectorised by help of vectorize. This function takes a positional argument, namely, the python function that you want to vectorise, and a non-mandatory keyword argument, otypes, which determines the dtype of the output array. The otypes must be None (default), or any of the dtypes defined in ulab. With None, the output is automatically turned into a float array.

The return value of vectorize is a micropython object that can be called as a standard function, but which now accepts either a scalar, an ndarray, or a generic micropython iterable as its sole argument. Note that the function that is to be vectorised must have a single argument.

# code to be run in micropython

from ulab import numpy as np

def f(x):
    return x*x

vf = np.vectorize(f)

# calling with a scalar
print('{:20}'.format('f on a scalar: '), vf(44.0))

# calling with an ndarray
a = np.array([1, 2, 3, 4])
print('{:20}'.format('f on an ndarray: '), vf(a))

# calling with a list
print('{:20}'.format('f on a list: '), vf([2, 3, 4]))
f on a scalar:       array([1936.0], dtype=float64)
f on an ndarray:     array([1.0, 4.0, 9.0, 16.0], dtype=float64)
f on a list:         array([4.0, 9.0, 16.0], dtype=float64)

As mentioned, the dtype of the resulting ndarray can be specified via the otypes keyword. The value is bound to the function object that vectorize returns, therefore, if the same function is to be vectorised with different output types, then for each type a new function object must be created.

# code to be run in micropython

from ulab import numpy as np

l = [1, 2, 3, 4]
def f(x):
    return x*x

vf1 = np.vectorize(f, otypes=np.uint8)
vf2 = np.vectorize(f, otypes=np.float)

print('{:20}'.format('output is uint8: '), vf1(l))
print('{:20}'.format('output is float: '), vf2(l))
output is uint8:     array([1, 4, 9, 16], dtype=uint8)
output is float:     array([1.0, 4.0, 9.0, 16.0], dtype=float64)

The otypes keyword argument cannot be used for type coercion: if the function evaluates to a float, but otypes would dictate an integer type, an exception will be raised:

# code to be run in micropython

from ulab import numpy as np

int_list = [1, 2, 3, 4]
float_list = [1.0, 2.0, 3.0, 4.0]
def f(x):
    return x*x

vf = np.vectorize(f, otypes=np.uint8)

print('{:20}'.format('integer list: '), vf(int_list))
# this will raise a TypeError exception
integer list:        array([1, 4, 9, 16], dtype=uint8)

Traceback (most recent call last):
  File "/dev/shm/micropython.py", line 14, in <module>
TypeError: can't convert float to int


It should be pointed out that the vectorize function produces the pseudo-vectorised version of the python function that is fed into it, i.e., on the C level, the same python function is called, with the all-encompassing mp_obj_t type arguments, and all that happens is that the for loop in [f(i) for i in iterable] runs purely in C. Since type checking and type conversion in f() is expensive, the speed-up is not so spectacular as when iterating over an ndarray with a factory function: a gain of approximately 30% can be expected, when a native python type (e.g., list) is returned by the function, and this becomes around 50% (a factor of 2), if conversion to an ndarray is also counted.

The following code snippet calculates the square of a 1000 numbers with the vectorised function (which returns an ndarray), with list comprehension, and with list comprehension followed by conversion to an ndarray. For comparison, the execution time is measured also for the case, when the square is calculated entirely in ulab.

# code to be run in micropython

from ulab import numpy as np

def f(x):
    return x*x

vf = np.vectorize(f)

def timed_vectorised_square(iterable):
    return vf(iterable)

def timed_python_square(iterable):
    return [f(i) for i in iterable]

def timed_ndarray_square(iterable):
    return np.array([f(i) for i in iterable])

def timed_ulab_square(ndarray):
    return ndarray**2

print('vectorised function')
squares = timed_vectorised_square(range(1000))

print('\nlist comprehension')
squares = timed_python_square(range(1000))

print('\nlist comprehension + ndarray conversion')
squares = timed_ndarray_square(range(1000))

print('\nsquaring an ndarray entirely in ulab')
a = np.array(range(1000))
squares = timed_ulab_square(a)
vectorised function
execution time:  7237  us

list comprehension
execution time:  10248  us

list comprehension + ndarray conversion
execution time:  12562  us

squaring an ndarray entirely in ulab
execution time:  560  us

From the comparisons above, it is obvious that python functions should only be vectorised, when the same effect cannot be gotten in ulab only. However, although the time savings are not significant, there is still a good reason for caring about vectorised functions. Namely, user-defined python functions become universal, i.e., they can accept generic iterables as well as ndarrays as their arguments. A vectorised function is still a one-liner, resulting in transparent and elegant code.

A final comment on this subject: the f(x) that we defined is a generic python function. This means that it is not required that it just crunches some numbers. It has to return a number object, but it can still access the hardware in the meantime. So, e.g.,

led = pyb.LED(2)

def f(x):
    if x < 100:
    return x*x

is perfectly valid code.