tsdownsample 0.1.4.1
Time series downsampling in rust
License: MIT
Requires Python: >=3.8
Keywords: time series, downsampling, rust, data science, visualization

# tsdownsample

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<!-- TODO: codecov -->

Extremely fast **time series downsampling 📈** for visualization, written in Rust.

## Features ✨

- **Fast**: written in rust with PyO3 bindings
  - leverages optimized [argminmax](https://github.com/jvdd/argminmax) - which is SIMD accelerated with runtime feature detection
  - scales linearly with the number of data points
  <!-- TODO check if it scales sublinearly -->
  - multithreaded with Rayon (in Rust)
    <details>
      <summary><i>Why we do not use Python multiprocessing</i></summary>
      Citing the <a href="https://pyo3.rs/v0.17.3/parallelism.html">PyO3 docs on parallelism</a>:<br>
      <blockquote>
          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.
      </blockquote>
      In Rust - which is a compiled language - there is no GIL, so CPU-bound tasks can be parallelized (with <a href="https://github.com/rayon-rs/rayon">Rayon</a>) with little to no overhead.
    </details>
- **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`
    <details>
      <summary><i>!! 🚀 <code>f16</code> <a href="https://github.com/jvdd/argminmax">argminmax</a> is 200-300x faster than numpy</i></summary>
      In contrast with all other data types above, <code>f16</code> is *not* hardware supported (i.e., no instructions for f16) by most modern CPUs!! <br>
      🐌 Programming languages facilitate support for this datatype by either (i) upcasting to <u>f32</u> or (ii) using a software implementation. <br>
      💡 As for argminmax, only comparisons are needed - and thus no arithmetic operations - creating a <u>symmetrical ordinal mapping from <code>f16</code> to <code>i16</code></u> is sufficient. This mapping allows to use the hardware supported scalar and SIMD <code>i16</code> instructions - while not producing any memory overhead 🎉 <br>
      <i>More details are described in <a href="https://github.com/jvdd/argminmax/pull/1">argminmax PR #1</a>.</i>
    </details>
- **Easy to use**: simple & flexible API

## Install

```bash
pip install tsdownsample
```

## Usage

```python
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 optional
- `x` and `y` are both positional arguments
- `n_out` is a mandatory keyword argument that defines the number of output values<sup>*</sup>
- `**kwargs` are optional keyword arguments *(see [table below](#downsampling-algorithms-📈))*:
  - `parallel`: whether to use multi-threading (default: `False`)  
     ❗ The max number of threads can be configured with the `TSDOWNSAMPLE_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.

<sup>\*</sup><i>When there are gaps in the time series, fewer than `n_out` indices may be returned.</i>

### 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**](https://dl.acm.org/doi/pdf/10.14778/2732951.2732953) value in each bin | `parallel` |
| `LTTBDownsampler` | performs the [**Largest Triangle Three Buckets**](https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf) 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`<sup>*</sup> |

<sup>*</sup><i>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</i>

### Handling NaNs

This library supports two `NaN`-policies:

1. Omit `NaN`s (`NaN`s are ignored during downsampling).
2. 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;

1. `x`-data is (non-strictly) monotonic increasing (i.e., sorted)
2. no `NaN`s in `x`-data

---

<p align="center">
👤 <i>Jeroen Van Der Donckt</i>
</p>