| Name | paramcheckup JSON |
| Version |
1.0.3
JSON |
| download |
| home_page | None |
| Summary | Check whether the parameters received are correct |
| upload_time | 2024-07-24 01:53:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | BSD 3-Clause License Copyright (c) 2023, paramcheckup Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
type
check
warnings
|
| VCS |
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|
| requirements |
No requirements were recorded.
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<img src="https://raw.githubusercontent.com/puzzle-in-a-mug/paramcheckup/main/docs/_static/logo.png" align="right" />
# paramcheckup
<img src="https://img.shields.io/badge/License-BSD%203--Clause-blue.svg">
This package has a collection of functions that check whether the parameter received has 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
```python
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``.
```python
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 ``x_data`` is in fact a ``NumpyArray`` (``True``):
```python
from paramcheckup import types
def t_test(x_data, mu, alpha):
types.is_numpy(
value=x_data,
param_name="x_data",
kind="function",
kind_name="t_test",
stacklevel=4,
error=True,
)
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
```
If the user passes a ``NumpyArray`` as input for ``x_data``, the result of ``types.is_numpy`` will be ``True`` and the calculation will be performed as expected:
```python
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:
```python
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*.
UserWarning at line 28: The parameter `x_data` in function `t_test` must be of type `numpy.ndarray`, but its type is `list`.
```
The UserWarning informs the line ***where*** the error occurred, ***which parameter*** is wrong and ***how*** this parameter should be to be correct. By default, the error traceback is also showed to the user:
```
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
```
However, it is possible to silence the traceback through the error parameter:
```python
types.is_numpy(
value=x_data,
param_name="x_data",
kind="function",
kind_name="t_test",
stacklevel=4,
error=False, # <------
)
```
> Note that the function also requires the array to have a single dimension. This could be checked using the ``paramcheckup.numpy_arrays.n_dimensions function()``.
## 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:
```python
from paramcheckup import types, numbers
def t_test(x_data, mu, alpha):
types.is_numpy(
value=x_data,
param_name="x_data",
kind="function",
kind_name="t_test",
stacklevel=4,
error=False,
)
numbers.is_between_a_and_b(
number=alpha,
lower=0,
upper=1,
param_name="alpha",
kind="function",
kind_name="t_test",
inclusive=False,
stacklevel=4,
error=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)
UserWarning at line 39: The value of `alpha` in function `t_test` must be within the range of `0 < alpha < 1`, but it is `1.05`.
```
> Note that the ``inclusive=False`` parameter causes the limits to be open, which makes sense for the significance level. If ``inclusive=True``, we would have obtained the following error:
```python
UserWarning at line 39: The value of `alpha` in function `t_test` must be within the range of `0 <= alpha <= 1`, but it is `1.05`.
```
> Note that ``alpha`` must be of numeric type. This could be checked using function ``paramcheckup.numbers.is_float_or_int()`` ***before*** checking whether the parameter is within a numerical range.
## License
- [BSD 3-Clause License](https://github.com/puzzle-in-a-mug/paramcheckup/blob/main/LICENSE)
Raw data
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"description": "<img src=\"https://raw.githubusercontent.com/puzzle-in-a-mug/paramcheckup/main/docs/_static/logo.png\" align=\"right\" />\r\n\r\n# paramcheckup\r\n\r\n<img src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\">\r\n\r\nThis package has a collection of functions that check whether the parameter received has a certain type, returning ``True`` if the input is as expected or ``raising an error`` that indicates what the problem is.\r\n\r\n\r\n\r\n## Install\r\n\r\n```\r\npip install paramcheckup\r\n```\r\n\r\n\r\n\r\n## Example 1\r\n\r\n\r\n```python\r\nimport numpy as np\r\nfrom scipy import stats\r\n```\r\n\r\nAssume 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``.\r\n\r\n```python\r\ndef t_test(x_data, mu, alpha):\r\n tcalc = (x_data.mean() - mu)*np.sqrt(x_data.size)/(x_data.std(ddof=1))\r\n t_critical = stats.t.ppf(1-alpha/2, x_data.size - 1)\r\n p_value = (1 - stats.t.cdf(np.abs(tcalc), x_data.size - 1))*2\r\n if p_value < alpha:\r\n conclusion = \"Reject H0\"\r\n else:\r\n conclusion = \"Fail to reject H0\"\r\n return tcalc, t_critical, p_value, conclusion\r\n```\r\n\r\nThe ``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 ``x_data`` is in fact a ``NumpyArray`` (``True``):\r\n\r\n\r\n\r\n```python\r\nfrom paramcheckup import types\r\ndef t_test(x_data, mu, alpha):\r\n types.is_numpy(\r\n value=x_data,\r\n param_name=\"x_data\",\r\n kind=\"function\",\r\n kind_name=\"t_test\",\r\n stacklevel=4,\r\n error=True,\r\n )\r\n \r\n tcalc = (x_data.mean() - mu)*np.sqrt(x_data.size)/(x_data.std(ddof=1))\r\n t_critical = stats.t.ppf(1-alpha/2, x_data.size - 1)\r\n p_value = (1 - stats.t.cdf(np.abs(tcalc), x_data.size - 1))*2\r\n if p_value < alpha:\r\n conclusion = \"Reject H0\"\r\n else:\r\n conclusion = \"Fail to reject H0\"\r\n return tcalc, t_critical, p_value, conclusion\r\n```\r\n\r\nIf the user passes a ``NumpyArray`` as input for ``x_data``, the result of ``types.is_numpy`` will be ``True`` and the calculation will be performed as expected:\r\n\r\n```python\r\nx = np.array([1.24, 1.3, 1.11])\r\nresult = t_test(x, 3, 0.05)\r\nprint(result)\r\n(-31.80244895786038, 4.302652729911275, 0.0009872686643235262, 'Reject H0')\r\n```\r\n\r\nHowever, if you use a ``list`` instead of ``NumpyArray``, an ``TypeError`` will be raised indicating what the error is:\r\n\r\n```python\r\nx = [1.24, 1.3, 1.11]\r\nresult = t_test(x, 3, 0.05)\r\nThe parameter 'x_data' in function 't_test' must be of type *numpy.ndarray*, but its type is *list*.\r\nUserWarning at line 28: The parameter `x_data` in function `t_test` must be of type `numpy.ndarray`, but its type is `list`.\r\n```\r\n\r\nThe UserWarning informs the line ***where*** the error occurred, ***which parameter*** is wrong and ***how*** this parameter should be to be correct. By default, the error traceback is also showed to the user:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"...\\main.py\", line 21, in <module>\r\n result = t_test(x, 3, 0.05)\r\n File \"...\\main.py\", line 8, in t_test\r\n types.is_numpy(x_data, \"x_data\", \"t_test\")\r\n File \"...\\venv\\lib\\site-packages\\paramcheckup\\types.py\", line 436, in is_numpy\r\n raise TypeError(\"NotNumPyError\")\r\nTypeError: NotNumPyError\r\n```\r\n\r\nHowever, it is possible to silence the traceback through the error parameter:\r\n\r\n```python\r\ntypes.is_numpy(\r\n value=x_data,\r\n param_name=\"x_data\",\r\n kind=\"function\",\r\n kind_name=\"t_test\",\r\n stacklevel=4,\r\n error=False, # <------\r\n)\r\n```\r\n\r\n> Note that the function also requires the array to have a single dimension. This could be checked using the ``paramcheckup.numpy_arrays.n_dimensions function()``. \r\n\r\n## Example 2\r\n\r\nThe ``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:\r\n\r\n```python\r\nfrom paramcheckup import types, numbers\r\n\r\ndef t_test(x_data, mu, alpha):\r\n types.is_numpy(\r\n value=x_data,\r\n param_name=\"x_data\",\r\n kind=\"function\",\r\n kind_name=\"t_test\",\r\n stacklevel=4,\r\n error=False,\r\n )\r\n\r\n numbers.is_between_a_and_b(\r\n number=alpha,\r\n lower=0,\r\n upper=1,\r\n param_name=\"alpha\",\r\n kind=\"function\",\r\n kind_name=\"t_test\",\r\n inclusive=False,\r\n stacklevel=4,\r\n error=False,\r\n )\r\n\r\n tcalc = (x_data.mean() - mu)*np.sqrt(x_data.size)/(x_data.std(ddof=1))\r\n t_critical = stats.t.ppf(1-alpha/2, x_data.size - 1)\r\n p_value = (1 - stats.t.cdf(np.abs(tcalc), x_data.size - 1))*2\r\n if p_value < alpha:\r\n conclusion = \"Reject H0\"\r\n else:\r\n conclusion = \"Fail to reject H0\"\r\n return tcalc, t_critical, p_value, conclusion\r\n\r\n\r\nx = np.array([1.24, 1.3, 1.11])\r\nalpha = 1.05\r\nresult = t_test(x, 3, alpha)\r\nUserWarning at line 39: The value of `alpha` in function `t_test` must be within the range of `0 < alpha < 1`, but it is `1.05`.\r\n```\r\n\r\n\r\n> Note that the ``inclusive=False`` parameter causes the limits to be open, which makes sense for the significance level. If ``inclusive=True``, we would have obtained the following error:\r\n\r\n```python\r\nUserWarning at line 39: The value of `alpha` in function `t_test` must be within the range of `0 <= alpha <= 1`, but it is `1.05`.\r\n```\r\n\r\n> Note that ``alpha`` must be of numeric type. This could be checked using function ``paramcheckup.numbers.is_float_or_int()`` ***before*** checking whether the parameter is within a numerical range.\r\n\r\n\r\n## License\r\n\r\n- [BSD 3-Clause License](https://github.com/puzzle-in-a-mug/paramcheckup/blob/main/LICENSE)\r\n\r\n\r\n\r\n\r\n",
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