array_collections 0.1.9

A collection of numpy ndarray subclasses.

Contents

• What is array_collections?

• Installation

• Documentation

• Getting started

• Latest source code

• Bug reports

• License information

• Citation

What is array_collections?

array_collections is a collection of numpy ndarray subclasses. Each array class serves an unrelated but broad purpose of its own. This package features 3 types of arrays: material_array, tuple_array, and property_array.

Installation

Get the latest version of array_collections from https://pypi.python.org/pypi/array_collections/

If you have an installation of Python with pip, simple install it with:

$ pip install array_collections

To get the git version, run:

$ git clone git://github.com/yoelcortes/array_collections

Documentation

array_collections’s documentation is available on the web:

http://array_collections.readthedocs.io/

Getting started

A material_array issues a RuntimeWarning when a non-positive or non-finite value is encountered.

Create material_array:

>>> arr = material_array([1, 18])
material_array([1, 18])

A negative value issues a RuntimeWarning:

>>> arr[1] = -1
__main__:1: RuntimeWarning:
Encountered negative or non-finite value in 'material_array' object.

New arrays are normal numpy arrays:

>>> arr + 1
array([2, 19])

A tuple_array is an immutable and hashable array:

Create a tuple_array object:

>>> arr = tuple_array([1, 18])
tuple_array([1, 18])

tuple_array objects are immutable:

>>> arr[1] = 0
TypeError: 'tuple_array' objects are immutable.

tuple_array objects are hashable:

>>> hash(arr)
3713080549427813581

New arrays are normal numpy arrays:

>>> arr + 1
array([2, 19])

A property_array allows for array-like manipulation of property objects. All entries in a property_array must be instances of FreeProperty. Setting items of a property_array sets values of Property objects instead.

Use the PropertyFactory to create a Weight property class which calculates weight based on density and volume:

>>> from array_collections import PropertyFactory
>>>
>>> @PropertyFactory
>>> def Weight(self):
...    '''Weight (kg) based on volume (m^3).'''
...    data = self.data
...    rho = data['rho'] # Density (kg/m^3)
...    vol = data['vol'] # Volume (m^3)
...    return rho * vol
>>>
>>> @Weight.setter
>>> def Weight(self, weight):
...    data = self.data
...    rho = data['rho'] # Density (kg/m^3)
...    data['vol'] = weight / rho

Create dictionaries of data and initialize new properties:

>>> water_data = {'rho': 1000, 'vol': 3}
>>> ethanol_data = {'rho': 789, 'vol': 3}
>>> weight_water = Weight('Water', water_data)
>>> weight_ethanol = Weight('Ethanol', ethanol_data)
>>> weight_water
Weight(Water) -> 3000 (kg)
>>> weight_ethanol
Weight(Ethanol) -> 2367 (kg)

Create a property_array from data:

>>> prop_arr = property_array([weight_water, weight_water])
property_array([3000, 2367])

Changing the values of a property_array changes the value of its properties:

>>> # Addition in place
>>> prop_arr += 3000
>>> prop_arr
property_array([6000, 5367])
>>> # Note how the data also changes
>>> water_data
{'rho': 1000, 'vol': 6.0}
>>> ethanol_data
{'rho': 789, 'vol': 6.802281368821292}
>>> # Setting an item changes the property value
>>> prop_arr[1] = 2367
>>> ethanol_data
{'rho': 789, 'vol': 3}

New arrays have no connection to the property_array:

>>> prop_arr - 1000 #  Returns a new array
array([5000.0, 1367.0], dtype=object)
>>> water_data #  Data remains unchanged
{'rho': 1000, 'vol': 6.0}

A representative DataFrame can also be made from the property_array:

>>> prop_arr.table()
        Weight (kg)
Water        6000.0
Ethanol      2367.0

Note

The DataFrame object contains the values of the properties, not the FreeProperty objects as a property_array would.

Latest source code

The latest development version of array_collections’s sources can be obtained at:

https://github.com/yoelcortes/array_collections

Bug reports

To report bugs, please use the array_collections’ Bug Tracker at:

https://github.com/yoelcortes/array_collections

License information

See LICENSE.txt for information on the terms & conditions for usage of this software, and a DISCLAIMER OF ALL WARRANTIES.

Although not required by the array_collections’ license, if it is convenient for you, please cite array_collections if used in your work. Please also consider contributing any changes you make back, and benefit the community.

Citation

To cite array_collections in publications use:

Yoel Cortes-Pena (2019). array_collections: A collection of numpy ndarray subclasses.
https://github.com/yoelcortes/array_collections