pysprint.utils package

Submodules

pysprint.utils.decorators module

inplacify(method)

Decorator used to allow a class function to be called as inplace as well. It will invalidate the parent object to have only one reference to it.

pprint_disp(f)

Pretty print the dispersion results from returned arrays.

pysprint.utils.exceptions module

exception PySprintWarning

Bases: Warning

Base pysprint warning class.

exception InterpolationWarning

Bases: pysprint.utils.exceptions.PySprintWarning

This warning is raised when a function applies linear interpolation on the data.

exception FourierWarning

Bases: pysprint.utils.exceptions.PySprintWarning

This warning is raised when FFT is called first instead of IFFT. Later on it will be improved. For more details see help(pysprint.FFTMethod.calculate)

exception DatasetError

Bases: Exception

This error is raised when invalid type of data encountered when initializing a dataset or inherited object.

exception NotCalculatedException

Bases: ValueError

This error is raised when a function is not available yet because something needs to be calculated before.

pysprint.utils.info module

This code is mostly adapted from pandas/pandas/util/_print_versions.py. pandas is licensed under the BSD 3-Clause “New” or “Revised” License. See at: pandas.pydata.org

print_info()

Print all the relevant information about system and dependecies.

pysprint.utils.meta module

class MetaData(doc='', copy=True)

Bases: object

A class to store additional meta property. This can be set to any valid ~collections.abc.Mapping.

Parameters:
  • doc (str, optional) – Documentation for the attribute of the class. Default is "".
  • copy (bool, optional) – If True the the value is deepcopied before setting, otherwise it is saved as reference. Default is True.
__init__(doc='', copy=True)

Initialize self. See help(type(self)) for accurate signature.

pysprint.utils.misc module

pprint_math_or_default(s)
run_from_ipython()

Detect if code is run inside Jupyter or maybe Spyder.

measurement(array, confidence=0.95, silent=False)

Give the measurement results with condifence interval assuming the standard deviation is unknown.

Parameters:
  • array (ndarray) – The array containing the measured values
  • confidence (float, optional) – The desired confidence level. Must be _between 0 and 1.
  • silent (bool, optional) – Whether to print results immediately. Default is False.
Returns:

  • mean (float) – The mean of the given array
  • conf (tuple-like (interval)) – The confidence interval

Examples

>>> import numpy as np
>>> from pysprint.utils import measurement
>>> a = np.array([123.783, 121.846, 122.248, 125.139, 122.569])
>>> mean, interval = measurement(a, 0.99)
123.117000 ± 2.763022
>>> mean
123.117
>>> interval
(120.35397798230359, 125.88002201769642)

Note

The results are printed immediately, because people often don’t use it for further code. Of course, they are also returned if needed.

find_nearest(array, value)

Find the nearest element in array to value.

Parameters:
  • array (np.ndarray-like) – The array to search in.
  • value (float) – The value to search.
Returns:

  • value (float) – The closest value in array.
  • idx (int) – The index of the closest element.
pad_with_trailing_zeros(array, shape)

Pad an array with trailing zeros to be the desired shape