# firanka
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firanka is a Python library to perform calculations on particular kinds of
functions. These functions have a domain, which is a single continuous subset
of the real number line. These functions can have any values.
firanka allows you do define two classes of such functions or series.
First are the _DiscreteSeries_. _DiscreteSeries_ further divide the function
domain into slices (left-closed, right-open) that have constant values.
Manipulating _DiscreteSeries_ and performing calculations on them is cheap.
Then you have _FunctionSeries_. These are simply defined by user-supplied
Python callable.
Best part is, you can join series together (given a joining operator),
slice them and so on.
# Usage
## Series
Can be imported from _sai.series_. A generic abstract superclass for series -
`Series` can be imported for checking if given object is a series.
Series are immutable, but non-hashable.
Read the source code of the [base class](firanka/series/series.py#L11) to get
to know more about series operations.
### DiscreteSeries
To use a _DiscreteSeries_ you must give it a set of data to work with. These
will define intervals with given values, left-closed, right-open. as in:
```python
fs = DiscreteSeries([(0,1), (3, 4), (5, 6)])
fs[0.5] == 1
fs[3] == 4
fs[5] == 6
fs.domain == '<0;5>'
# fs[6] - NotInDomainError's
```
Datapoints given **must be already sorted**!. By default, the domain
will be both sides closed, from minimum to maximum given in data, but you can
specify a custom one:
```python
fs = DiscreteSeries([(0,1), (3, 4), (5, 6)], '(0; 8>')
# fs[0] - NotInDomainError's !
fs[6] == 6
```
Although you can't specify a domain where it would be impossible to compute the value.
(ie. starting at smaller than zero). Doing so will throw a _ValueError_.
Note that when using `join_discrete()` sometimes other series might get calls
from beyond their domain. This can be seen for example here:
```python
logs = FunctionSeries(math.log, '(0;5>')
dirs = DiscreteSeries([(0,1)], '<0;5>')
# Raises ValueError due to math.log being called with 0
dirs.join_discrete(logs, lambda x, y: x+y)
```
### FunctionSeries
Using _FunctionSeries_ is straightforward. Just give them a callable and
a domain:
```python
fs = FunctionSeries(lambda x: x**2, '<-2;2>')
```
### ModuloSeries
_ModuloSeries_ allow you to wrap a finite series in repetition.
```python
fs = ModuloSeries(someOtherSeries)
```
By definition, _ModuloSeries_ has the domain of all real numbers.
Note that someOtherSeries's domain length must be non-zero and finite. Otherwise
_ValueError_ will be thrown.
## LinearInterpolationSeries
These are discretes, but allow you to define an operator that will
take its neighbours into account and let you return a custom value.
By default, it will assumes that values can be added, subbed, multed and dived,
and will do classical linear interpolation.
They can either utilize an existing discrete series, or be created just as
any other discrete series would be.
## Ranges
Can be imported from _sai.ranges_.
Range would have been better called an **interval**. It is a continuous subset
of the real number line.
You can create Ranges as follows:
```python
Range(-5, 5, True, False) == Range('<-5;5)')
```
For more information [use the source](firanka/ranges.py#L33)
Range's are immutable and hashable. They can be sliced:
```python
Range('<-5;5>')[0:] == Range('<0;5>')
```
Slices work as a both-sides-closed range if both sides are shown!
You can check whether a range contains a point
```python
5 not in Range('<-1;5)')
```
Or you can check for strict inclusion
```python
Range('<-1;1>') in Range('<-2;2>')
```
## TimeProviders
**EXPERIMENTAL**
Can be imported from _sai.timeproviders_.