# Rolling Quantiles for NumPy
## Hyper-efficient and composable filters.
* Simple, clean, intuitive interface.
* Supports streaming data or bulk processing.
* Python 3 bindings for a compact library written in pure C.
### A Quick Tour
```python
import numpy as np
import rolling_quantiles as rq
pipe = rq.Pipeline( # rq.Pipeline is the only stateful object
# declare a cascade of filters by a sequence of immutable description objects
rq.LowPass(window=200, portion=100, subsample_rate=2),
# the above takes a median (100 out of 200) of the most recent 200 points
# and then spits out every other one
rq.HighPass(window=10, portion=3, subsample_rate=1))
# that subsampled rolling median is then fed into this filter that takes a
# 30% quantile on a window of size 10, and subtracts it from its raw input
# the pipeline exposes a set of read-only attributes that describe it
pipe.lag # = 60.0, the effective number of time units that the real-time output
# is delayed from the input
pipe.stride # = 2, how many inputs it takes to produce an output
# (>1 due to subsampling)
input = np.random.randn(1000)
output = pipe.feed(input) # the core, singular exposed method
# every other output will be a NaN to demarcate unready values
subsampled_output = output[1::pipe.stride]
```
See the [Github repository](https://github.com/marmarelis/rolling-quantiles) for more details.
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