femto.helpers module¶

grouped(iterable, n)¶

Group an iterable in sub-groups of n elements.

The returned iterable have len(iterable)//n tuples containing n elements. If the number of elements of the input iterable are not a multiple of n, the remaining elements will not be returned.

s -> (s0,s1,s2,...sn-1), (sn,sn+1,sn+2,...s2n-1), (s2n,s2n+1,s2n+2,...s3n-1), ...

Parameters:

iterable (iterable) – Iterable to group in subset.
n (int) – Size of the sub-groups.

Returns:

Grouped iterable in n-sized subsets.

Return type:

iterable

pairwise()¶

Group an iterable in sub-groups of n elements.

The returned iterable have len(iterable)//n tuples containing n elements. If the number of elements of the input iterable are not a multiple of n, the remaining elements will not be returned.

s -> (s0,s1,s2,...sn-1), (sn,sn+1,sn+2,...s2n-1), (s2n,s2n+1,s2n+2,...s3n-1), ...

Parameters:

iterable (iterable) – Iterable to group in subset.
n (int) – Size of the sub-groups.

Returns:

Grouped iterable in n-sized subsets.

Return type:

iterable

swap(array, swap_pos)¶

Swaps elements.

Swap elements of an array given as input.

Parameters:

array (list, numpy.ndarray) – Input array.
swap_pos (list[tuple[int, int]]) – List of tuple containing indexes of the element of the array to swap.

Returns:

Swapped array.

Return type:

list, numpy.ndarray

listcast(x)¶

Cast to list.

Cast any input object to a list. If x is a Python dictionary, the output will be the list of all the dict-keys.

Code example: >>> d = {‘a’: 1, ‘b’: 2, ‘c’: 3} >>> e = listcast(d) >>> e >>> [‘a’, ‘b’, ‘c’]

Parameters:: x (Any) – Input element to be casted.
Returns:: x input casted to list.
Return type:: list

nest_level(lst)¶

Nest level.

Compute the neseting level of a list.

Parameters:: lst (list[Any] | Any) – Input list with a certain nested level.
Returns:: Number of nested lists, a flattened list has nest_level of 1.
Return type:: int

collapse(iterable)¶

Collapse Generator.

Flatten an iterable with multiple levels of nesting (e.g., a list of lists of tuples) into non-iterable types.

>>> iterable = [(1, 2), ([3, 4], [[5], [6]])]
>>> list(collapse(iterable))
[1, 2, 3, 4, 5, 6]

Binary and text strings are not considered iterable and will not be collapsed.

Parameters:: iterable (Iterable[Any]) – Input list with an arbitrary nesting level.
Yields:: Iterable[Any] – Flattened version of input iterable.
Return type:: Iterable[Any]

flatten(iterable)¶

Flatten list.

Flatten an arbitrarily nested list. Returns a new list, the original list is unchanged.

>>> flatten([1, 2, 3, [4], [], [[[[[[[[[5]]]]]]]]]])
[1, 2, 3, 4, 5]
>>> flatten([[1, 2], 3]
[1, 2, 3]

Parameters:: iterable (list) – Input list with an arbitrary nesting level.
Returns:: List with the same elements of the input list but a single nesting level.
Return type:: list

sign()¶

Sign iterator.

A generator that cycles through +1 and -1.

Code example: >>> s = sign() >>> next(s) 1 >>> next(s) -1 >>> next(s) 1 >>> next(s) -1 …

Returns:: Iterator cycling through +1 and -1.
Return type:: Iterator

remove_repeated_coordinates(array)¶

Remove repeated coordinates.

Parameters:: array (numpy.ndarray) – Coordinate array.
Returns:: Returns an array of the same dimensions as the input one in which repeated points (redundant movements) are substituted with NaN values.
Return type:: numpy.ndarray

unique_filter(arrays)¶

Remove duplicate subsequent points.

Filtering adjacent identical points from a list of arrays. The function is different from other unique functions such as numpy’s unique function. Indeed, unique return (a sorted list of) the unique elements of the whole array. For example:

>>> x = np.array([1, 2, 3, 3, 3, 4, 3, 3])
>>> np.unique(x)
np.array([1, 2, 3, 4])
>>> unique_filter([x])
np.array([1, 2, 3, 4, 3])

Duplicates can be selected by creating a boolean index mask as follows:

make a row-wise diff (numpy.diff)
compute absolute value of all elements in order to work only with positive numbers
make a column-wise sum (numpy.diff)
mask is converted to boolean values

In this way consecutive duplicates correspond to a 0 value in the latter array. Converting this array to boolean (all non-zero values are True) the index mask can be retrieved. The first element is set to True by default since it is lost by the diff operation.

unique_filter works also with multiple arrays. If the input list contains several elements, the arrays are stacked together to form a [length_array, length_list] matrix. Each row of this matrix represents the coordinates of a point in a space with length_list dimensions. Finally, filtering operation is applied to the newly-constructed matrix to filter all the identical adjacent points. For example:

>>> x = np.array([1, 2, 3, 3, 3, 4, 3, 3])
>>> y = np.array([0, 1, 0, 0, 1, 1, 0, 1])
>>> unique_filter([x, y])
np.array([1, 2, 3, 3, 4, 3, 3])

Parameters:: arrays (list[numpy.ndarray]) – List of arrays to filter.
Returns:: Modified numpy matrix without adjacent duplicates.
Return type:: numpy.ndarray

split_mask(arr, mask)¶

Split maks.

Splits an array into sub-arrays based on a mask. The function return the list of sub-arrays correspoding to True values.

Parameters:

arr (numpy.ndarray) – Input array.
maks (numpy.ndarray[bool]) – Boolean array used as mask to split the input array.

Returns:

List of arrays associated to True (1) values of mask.

Return type:

list[numpy.ndarray]

pad_infinite(iterable, padding=None)¶

Return type:: Iterator[Any]

pad(iterable, size, padding=None)¶

Return type:: Iterator[Any]

almost_equal(polygon, other, tol=1e-6)¶

Compute equality between polygons using the shapely builtin function symmetric_difference to build a new polygon. The more similar the two input polygons, the smaller the are of the new computed polygon.

Parameters:

polygon (Polygon) – shaperly Polygon object
other (Polygon) – second shapely Polygon object
tol (float) – tolerance controlling the similarity

Returns:

boolean value True if the polygon are almost equal, False otherwise

Return type:

bool

normalize_polygon(poly)¶

Normalize polygon.

The function standardize the input polygon. It set a given orientation and set a definite starting point for the inner and outer rings of the polygon.

Parameters:: poly (geometry.Polygon) – Input Polygon object.
Returns:: New Polygon object constructed with the new ordered sequence of points.
Return type:: geometry.Polygon