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Adds on-the-fly enabling of function argument casting
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,31 +1,107 @@ | ||
| import functools | ||
| import inspect | ||
| import sys | ||
| import types | ||
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| import numpy as np | ||
| import pandas as pd | ||
| import logging | ||
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| logger = logging.getLogger(__name__) | ||
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| CAST_TYPES = [list, pd.Series] | ||
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| def cast_array_to_original_type(arg, arg_type): | ||
| """Cast array to another array-like type | ||
| def type_casting(func): | ||
| """Type casting | ||
| This decorator casts input arguments of types [list, pandas.Series] to numpy.ndarray | ||
| so the algorithms accept these input arguments. | ||
| As a bonus, the decorator casts the return value of the algorithm to the type of the first | ||
| array-like input argument. | ||
| Parameters | ||
| ---------- | ||
| arg: array-like {list, ndarray, pd.Series} | ||
| arg_type: type | ||
| module_or_func : [types.ModuleType, types.FunctionType] | ||
| Module or function that has to be type casted. Can be None. | ||
| Returns | ||
| ------- | ||
| casted : arg_type array-like | ||
| function | ||
| Decorated function. | ||
| """ | ||
| @functools.wraps(func) | ||
| def func_wrapper(*args, **kwargs): | ||
| output_type = None | ||
| new_args = [] | ||
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| for arg in args: | ||
| input_type = type(arg) | ||
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| if input_type in CAST_TYPES: | ||
| new_args.append(np.asarray(arg)) | ||
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| if output_type is None: | ||
| # Type of first array-like argument is used for output casting | ||
| output_type = input_type | ||
| else: | ||
| new_args.append(arg) | ||
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| # There is no use-case for type casting kwargs now. | ||
| # If there is, this can be uncommented and tests can be added. | ||
| # new_kwargs = dict() | ||
| # for key, value in kwargs.items(): | ||
| # input_type = type(value) | ||
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| # if input_type in CAST_TYPES: | ||
| # new_kwargs[key] = np.asarray(value) | ||
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| # if output_type is None: | ||
| # # Type of first array-like argument is used for output casting | ||
| # output_type = input_type | ||
| # else: | ||
| # new_kwargs[key] = value | ||
| # | ||
| # output = func(*new_args, **new_kwargs) | ||
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| output = func(*new_args) | ||
| if output_type is not None and isinstance(output, np.ndarray): | ||
| return output_type(output) | ||
| else: | ||
| return output | ||
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| if arg_type == list: | ||
| return list(arg) | ||
| return func_wrapper | ||
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| def enable_type_casting(module_or_func=None): | ||
| """Enable type casting | ||
| This method enables casting of input arguments to numpy.ndarray so the algorithms accept | ||
| array-like input arguments of types list and pandas.Series. | ||
| As a bonus, the return value of the algorithm is casted to the type of the first array-like input argument. | ||
| Parameters | ||
| ---------- | ||
| module_or_func : [types.ModuleType, types.FunctionType] | ||
| Module or function that has to be type casted. Can be None. | ||
| Returns | ||
| ------- | ||
| function | ||
| Decorated function. | ||
| """ | ||
| if module_or_func is None: | ||
| # Because sys.modules changes during this operation we cannot loop over sys.modules directly | ||
| key_values = [(key, value) for key, value in sys.modules.items()] | ||
| for key, value in key_values: | ||
| # @TODO this if statement might not cover all cases (or too much cases) | ||
| if key.startswith('sweat.hrm') or key.startswith('sweat.pdm') or key.startswith('sweat.metrics'): | ||
| enable_type_casting(module_or_func=value) | ||
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| elif arg_type == np.ndarray: | ||
| return np.array(arg) | ||
| elif isinstance(module_or_func, types.ModuleType): | ||
| for name, obj in [(name, obj) for name, obj in inspect.getmembers(module_or_func)]: | ||
| if inspect.isfunction(obj) and inspect.getmodule(obj).__package__ == module_or_func.__package__: | ||
| func = getattr(module_or_func, name) | ||
| setattr(module_or_func, name, type_casting(func)) | ||
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| elif arg_type == pd.Series: | ||
| return pd.Series(arg) | ||
| elif isinstance(module_or_func, types.FunctionType): | ||
| return type_casting(module_or_func) | ||
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| else: | ||
| raise ValueError("arg_type must be list, ndarray or pd.Series") | ||
| raise ValueError('enable_type_casting takes arguments of types [ModuleType, FunctionType]') |
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