decimalpy - A Decimal based version of numpy

## decimalpy - long description

In financial calculations it is recommended to use Decimal as a numerical base. At the same time there is a need to have the same functionality as in numpy or R. So the goal of this package is to fill this gap, i.e. implementing Decimal based structures to ease eg. financial calculations.

In the creation of the finance package. it was decided to use Decimal based structures.

There were 2 reasons for this:

1. In finance decimals matters and when other financial systems use the IEEE standard 854-1987 the package finance need to do the same
2. For valuation purposes it is important that the financial calculations are the exact same as those performed in eg spreadsheets who use the IEEE standard 854-1987

After a while it was clear that there were a lot of code with a life or purpose of its own. And that was how the decimalpy package was born.

The Package decimalpy is inspired by numpy and eg the vector concept of The R package. The key difference from numpy is that in decimalpy the only number type is decimal.

The Package contains:

• An n-dimensional array of decimals, a decimalvector
• An n-dimensional array of decimals where the keys can be of a specific type and not just integers as in a decimalvector, a SortedKeysDecimalValuedDict
• A decorator decimalvector_function that converts a simpel function into a function that given a decimalvector as an argument returns a decimalvector of function values. This makes it fairly easy to extend the number of decimalvector functions. Also decimalvector functions makes it fairly easy to use other packages like eg matplotlib
• A set of decimalvector (typically financial) functions
• Meta functions (functions on functions) for numerical first (NumericalFirstOrder) and second (NumericalSecondOrder) order differention
• A meta function for finding the inverse value of a function

The package will be extended in order to support the needs in the package finance .

The decimal package is open source under the Python Software Foundation License

### How to install

Just run setup.py install command. Or in windows use the windows installer.

### Documentation, etc

Visit my homepage to see more on how to use and the research behind the code. It’s a blog like place on finance, math and scientific computing.

The planned development so far is:

Planned added content of version 0.2:
Implementation of matrix and more decimalbased functions
Planned added content of version 0.3:
Implementation af a statistical test package

## Project details

This version 0.101 0.1