paramcheckup 1.0.0

Check whether the parameters received by a function are correct

paramcheckup

This package has a collection of functions that check whether the parameter received by a function is of a certain type, returning True if the input is as expected or raising an error that indicates what the problem is.

Install

pip install paramcheckup

Example 1

import numpy as np
from scipy import stats

Assume a function t_test() that applies one sample Student's t test to compare means (two sided). This function receives three parameters, which are x_data, mu and alpha.

def t_test(x_data, mu, alpha):
    tcalc = (x_data.mean() - mu)*np.sqrt(x_data.size)/(x_data.std(ddof=1))
    t_critical = stats.t.ppf(1-alpha/2, x_data.size - 1)
    p_value = (1 - stats.t.cdf(np.abs(tcalc), x_data.size - 1))*2
    if p_value < alpha:
        conclusion = "Reject H0"
    else:
        conclusion = "Fail to reject H0"
    return tcalc, t_critical, p_value, conclusion

The t_test function strongly depends on the x_data parameter being a one-dimensional NumpyArray. The types.is_numpy(value, param_name, func_name) function can checks whether this is True:

from paramcheckup import types
def t_test(x_data, mu, alpha):
    types.is_numpy(x_data, "x_data", "t_test")
    
    tcalc = (x_data.mean() - mu)*np.sqrt(x_data)/(x_data.std(ddof=1))
    t_critical = stats.t.ppf(1-alpha/2, x_data.size - 1)
    p_value = (1 - stats.t.cdf(np.abs(tcalc), x_data.size - 1))*2
    if p_value < alpha:
        conclusion = "Reject H0"
    else:
        conclusion = "Fail to reject H0"
    return tcalc, t_critical, p_value, conclusion

If the user passes a NumpyArray as input for x_data, the result of types.is_numpy function will be True and the calculation will be performed.

x = np.array([1.24, 1.3, 1.11])
result = t_test(x, 3, 0.05)
print(result)
(-31.80244895786038, 4.302652729911275, 0.0009872686643235262, 'Reject H0')

However, if you use a list instead of NumpyArray, an TypeError will be raised indicating what the error is:

x = [1.24, 1.3, 1.11]
result = t_test(x, 3, 0.05)
The parameter 'x_data' in function 't_test' must be of type *numpy.ndarray*, but its type is *list*.

The Traceback error is also displayed:

Traceback (most recent call last):
  File "...\main.py", line 21, in <module>
    result = t_test(x, 3, 0.05)
  File "...\main.py", line 8, in t_test
    types.is_numpy(x_data, "x_data", "t_test")
  File "...\venv\lib\site-packages\paramcheckup\types.py", line 436, in is_numpy
    raise TypeError("NotNumPyError")
TypeError: NotNumPyError

In future releases, the Traceback will be optional

Example 2

The alpha parameter indicates the level of significance that should be adopted for the test. It is a value that varies between 0 and 1. To limit the range of values, you can use the numbers.is_between_a_and_b() function:

from paramcheckup import types, numbers

def t_test(x_data, mu, alpha):
    types.is_numpy(x_data, "x_data", "t_test")
    numbers.is_between_a_and_b(alpha, 0, 1, "alpha", "t_test", inclusive=False)
    tcalc = (x_data.mean() - mu)*np.sqrt(x_data.size)/(x_data.std(ddof=1))
    t_critical = stats.t.ppf(1-alpha/2, x_data.size - 1)
    p_value = (1 - stats.t.cdf(np.abs(tcalc), x_data.size - 1))*2
    if p_value < alpha:
        conclusion = "Reject H0"
    else:
        conclusion = "Fail to reject H0"
    return tcalc, t_critical, p_value, conclusion


x = np.array([1.24, 1.3, 1.11])
alpha = 1.05
result = t_test(x, 3, alpha)
The value of parameter 'alpha' in function 't_test' must be within the range of 0 < value < 1, but it is '1.05'.

Traceback ommited

Note that the inclusive=False parameter causes the limits to be open, which makes sense for the significance level.

License

• BSD 3-Clause License