Time series downsampling in rust
Project description
tsdownsample
Extremely fast time series downsampling 📈 for visualization, written in Rust.
Features ✨
- Fast: written in rust with PyO3 bindings
- leverages optimized argminmax - which is SIMD accelerated with runtime feature detection
- scales linearly with the number of data points
- multithreaded with Rayon (in Rust)
Why we do not use Python multiprocessing
Citing the PyO3 docs on parallelism:
CPython has the infamous Global Interpreter Lock, which prevents several threads from executing Python bytecode in parallel. This makes threading in Python a bad fit for CPU-bound tasks and often forces developers to accept the overhead of multiprocessing.
In Rust - which is a compiled language - there is no GIL, so CPU-bound tasks can be parallelized (with Rayon) with little to no overhead.
- Efficient: memory efficient
- works on views of the data (no copies)
- no intermediate data structures are created
- Flexible: works on any type of data
- supported datatypes are
- for
x
:f32
,f64
,i16
,i32
,i64
,u16
,u32
,u64
,datetime64
,timedelta64
- for
y
:f16
,f32
,f64
,i8
,i16
,i32
,i64
,u8
,u16
,u32
,u64
,datetime64
,timedelta64
,bool
!! 🚀
In contrast with all other data types above,f16
argminmax is 200-300x faster than numpyf16
is *not* hardware supported (i.e., no instructions for f16) by most modern CPUs!!
🐌 Programming languages facilitate support for this datatype by either (i) upcasting to f32 or (ii) using a software implementation.
💡 As for argminmax, only comparisons are needed - and thus no arithmetic operations - creating a symmetrical ordinal mapping fromf16
toi16
is sufficient. This mapping allows to use the hardware supported scalar and SIMDi16
instructions - while not producing any memory overhead 🎉
More details are described in argminmax PR #1. - for
- supported datatypes are
- Easy to use: simple & flexible API
Install
pip install tsdownsample
Usage
from tsdownsample import MinMaxLTTBDownsampler
import numpy as np
# Create a time series
y = np.random.randn(10_000_000)
x = np.arange(len(y))
# Downsample to 1000 points (assuming constant sampling rate)
s_ds = MinMaxLTTBDownsampler().downsample(y, n_out=1000)
# Select downsampled data
downsampled_y = y[s_ds]
# Downsample to 1000 points using the (possible irregularly spaced) x-data
s_ds = MinMaxLTTBDownsampler().downsample(x, y, n_out=1000)
# Select downsampled data
downsampled_x = x[s_ds]
downsampled_y = y[s_ds]
Downsampling algorithms & API
Downsampling API 📑
Each downsampling algorithm is implemented as a class that implements a downsample
method.
The signature of the downsample
method:
downsample([x], y, n_out, **kwargs) -> ndarray[uint64]
Arguments:
x
is optionalx
andy
are both positional argumentsn_out
is a mandatory keyword argument that defines the number of output values***kwargs
are optional keyword arguments (see table below):parallel
: whether to use multi-threading (default:False
)
❗ The max number of threads can be configured with theTSDOWNSAMPLE_MAX_THREADS
ENV var (e.g.os.environ["TSDOWNSAMPLE_MAX_THREADS"] = "4"
)- ...
Returns: a ndarray[uint64]
of indices that can be used to index the original data.
*When there are gaps in the time series, fewer than n_out
indices may be returned.
Downsampling algorithms 📈
The following downsampling algorithms (classes) are implemented:
Downsampler | Description | **kwargs |
---|---|---|
MinMaxDownsampler |
selects the min and max value in each bin | parallel |
M4Downsampler |
selects the min, max, first and last value in each bin | parallel |
LTTBDownsampler |
performs the Largest Triangle Three Buckets algorithm | parallel |
MinMaxLTTBDownsampler |
(new two-step algorithm 🎉) first selects n_out * minmax_ratio min and max values, then further reduces these to n_out values using the Largest Triangle Three Buckets algorithm |
parallel , minmax_ratio * |
*Default value for minmax_ratio
is 4, which is empirically proven to be a good default. More details here: https://arxiv.org/abs/2305.00332
Handling NaNs
This library supports two NaN
-policies:
- Omit
NaN
s (NaN
s are ignored during downsampling). - Return index of first
NaN
once there is at least one present in the bin of the considered data.
Omit NaN s |
Return NaN s |
---|---|
MinMaxDownsampler |
NaNMinMaxDownsampler |
M4Downsampler |
NaNM4Downsampler |
MinMaxLTTBDownsampler |
NaNMinMaxLTTBDownsampler |
LTTBDownsampler |
Note that NaNs are not supported for
x
-data.
Limitations & assumptions 🚨
Assumes;
x
-data is (non-strictly) monotonic increasing (i.e., sorted)- no
NaN
s inx
-data
👤 Jeroen Van Der Donckt
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