turboflow.utilities.numerics module

turboflow.utilities.numerics.check_lists_match(list1, list2)[source]

Check if two lists contain the exact same elements, regardless of their order.

Parameters:

list1list: The first list for comparison.
list2list: The second list for comparison.

Returns:

bool: Returns True if the lists contain the exact same elements, regardless of their order. Returns False otherwise.

Examples

>>> check_lists_match([1, 2, 3], [3, 2, 1])
True

>>> check_lists_match([1, 2, 3], [4, 5, 6])
False

turboflow.utilities.numerics.ensure_iterable(obj)[source]

Ensure that an object is iterable. If the object is already an iterable (except for strings, which are not treated as iterables in this context), it will be returned as is. If the object is not an iterable, or if it is a string, it will be placed into a list to make it iterable.

Parameters:

objany type: The object to be checked and possibly converted into an iterable.

Returns:

Iterable: The original object if it is an iterable (and not a string), or a new list containing the object if it was not iterable or was a string.

Examples

>>> ensure_iterable([1, 2, 3])
[1, 2, 3]
>>> ensure_iterable('abc')
['abc']
>>> ensure_iterable(42)
[42]
>>> ensure_iterable(np.array([1, 2, 3]))
array([1, 2, 3])

turboflow.utilities.numerics.extract_timestamp(filename)[source]

Extract the timestamp from the filename.

Parameters:

filenamestr: The filename containing the timestamp.

Returns:

str: The extracted timestamp.

turboflow.utilities.numerics.fill_array_with_increment(n)[source]

Fill an array of length n with values that sum to 1, where each value is different but has the same increment between neighboring values.

Parameters:

nint: Length of the array.

Returns:

arrayndarray: Array of length ‘n’ filled with values incrementing by a constant factor, resulting in a sum of 1.

turboflow.utilities.numerics.flatten_dataframe(df: DataFrame) → DataFrame[source]

Convert a DataFrame with multiple rows and columns into a single-row DataFrame with each column renamed to include the original row index as a suffix.

Parameters: - df (pd.DataFrame): The original DataFrame to be flattened.

Returns: - pd.DataFrame: A single-row DataFrame with (n×m) columns.

Example usage: >>> original_df = pd.DataFrame({‘A’: [1, 2], ‘B’: [3, 4]}) >>> flattened_df = flatten_dataframe(original_df) >>> print(flattened_df)

Note: Row indices start at 1 for the suffix in the column names.

turboflow.utilities.numerics.is_float(element: any) → bool[source]

Check if the given element can be converted to a float.

Parameters:

elementany: The element to be checked.

Returns:

bool: True if the element can be converted to a float, False otherwise.

turboflow.utilities.numerics.is_numeric(value)[source]

Check if a value is a numeric type, including both Python and NumPy numeric types.

This function checks if the given value is a numeric type (int, float, complex) in the Python standard library or NumPy, while explicitly excluding boolean types.

Parameters:

valueany type: The value to be checked for being a numeric type.

Returns:

bool: Returns True if the value is a numeric type (excluding booleans), otherwise False.

turboflow.utilities.numerics.isclose_significant_digits(a, b, significant_digits)[source]

Check if two floating-point numbers are close based on a specified number of significant digits.

Parameters:

afloat: The first number to compare.
bfloat: The second number to compare.
sig_digitsint: The number of significant digits to use for the comparison.

Returns:

bool: True if numbers are close up to the specified significant digits, False otherwise.