# tsdownsample
[](https://pypi.org/project/tsdownsample/)
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[](https://pepy.tech/project/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>
Raw data
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"description": "# tsdownsample\n\n[](https://pypi.org/project/tsdownsample/)\n[](https://img.shields.io/pypi/pyversions/tsdownsample)\n[](https://pepy.tech/project/tsdownsample)\n[](https://github.com/predict-idlab/tsdownsample/actions/workflows/codeql.yml)\n[](https://github.com/predict-idlab/tsdownsample/actions/workflows/ci-downsample_rs.yml)\n[](https://github.com/predict-idlab/tsdownsample/actions/workflows/ci-tsdownsample.yml)\n\n<!-- TODO: codecov -->\n\nExtremely fast **time series downsampling \ud83d\udcc8** for visualization, written in Rust.\n\n## Features \u2728\n\n- **Fast**: written in rust with PyO3 bindings\n - leverages optimized [argminmax](https://github.com/jvdd/argminmax) - which is SIMD accelerated with runtime feature detection\n - scales linearly with the number of data points\n <!-- TODO check if it scales sublinearly -->\n - multithreaded with Rayon (in Rust)\n <details>\n <summary><i>Why we do not use Python multiprocessing</i></summary>\n Citing the <a href=\"https://pyo3.rs/v0.17.3/parallelism.html\">PyO3 docs on parallelism</a>:<br>\n <blockquote>\n 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.\n </blockquote>\n 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.\n </details>\n- **Efficient**: memory efficient\n - works on views of the data (no copies)\n - no intermediate data structures are created\n- **Flexible**: works on any type of data\n - supported datatypes are\n - for `x`: `f32`, `f64`, `i16`, `i32`, `i64`, `u16`, `u32`, `u64`, `datetime64`, `timedelta64`\n - for `y`: `f16`, `f32`, `f64`, `i8`, `i16`, `i32`, `i64`, `u8`, `u16`, `u32`, `u64`, `datetime64`, `timedelta64`, `bool`\n <details>\n <summary><i>!! \ud83d\ude80 <code>f16</code> <a href=\"https://github.com/jvdd/argminmax\">argminmax</a> is 200-300x faster than numpy</i></summary>\n 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>\n \ud83d\udc0c Programming languages facilitate support for this datatype by either (i) upcasting to <u>f32</u> or (ii) using a software implementation. <br>\n \ud83d\udca1 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 \ud83c\udf89 <br>\n <i>More details are described in <a href=\"https://github.com/jvdd/argminmax/pull/1\">argminmax PR #1</a>.</i>\n </details>\n- **Easy to use**: simple & flexible API\n\n## Install\n\n```bash\npip install tsdownsample\n```\n\n## Usage\n\n```python\nfrom tsdownsample import MinMaxLTTBDownsampler\nimport numpy as np\n\n# Create a time series\ny = np.random.randn(10_000_000)\nx = np.arange(len(y))\n\n# Downsample to 1000 points (assuming constant sampling rate)\ns_ds = MinMaxLTTBDownsampler().downsample(y, n_out=1000)\n\n# Select downsampled data\ndownsampled_y = y[s_ds]\n\n# Downsample to 1000 points using the (possible irregularly spaced) x-data\ns_ds = MinMaxLTTBDownsampler().downsample(x, y, n_out=1000)\n\n# Select downsampled data\ndownsampled_x = x[s_ds]\ndownsampled_y = y[s_ds]\n```\n\n## Downsampling algorithms & API\n\n### Downsampling API \ud83d\udcd1\n\nEach downsampling algorithm is implemented as a class that implements a `downsample` method.\nThe signature of the `downsample` method:\n\n```\ndownsample([x], y, n_out, **kwargs) -> ndarray[uint64]\n```\n\n**Arguments**:\n\n- `x` is optional\n- `x` and `y` are both positional arguments\n- `n_out` is a mandatory keyword argument that defines the number of output values<sup>*</sup>\n- `**kwargs` are optional keyword arguments *(see [table below](#downsampling-algorithms-\ud83d\udcc8))*:\n - `parallel`: whether to use multi-threading (default: `False`) \n \u2757 The max number of threads can be configured with the `TSDOWNSAMPLE_MAX_THREADS` ENV var (e.g. `os.environ[\"TSDOWNSAMPLE_MAX_THREADS\"] = \"4\"`)\n - ...\n\n**Returns**: a `ndarray[uint64]` of indices that can be used to index the original data.\n\n<sup>\\*</sup><i>When there are gaps in the time series, fewer than `n_out` indices may be returned.</i>\n\n### Downsampling algorithms \ud83d\udcc8\n\nThe following downsampling algorithms (classes) are implemented:\n\n| Downsampler | Description | `**kwargs` |\n| ---:| --- |--- |\n| `MinMaxDownsampler` | selects the **min and max** value in each bin | `parallel` |\n| `M4Downsampler` | selects the [**min, max, first and last**](https://dl.acm.org/doi/pdf/10.14778/2732951.2732953) value in each bin | `parallel` |\n| `LTTBDownsampler` | performs the [**Largest Triangle Three Buckets**](https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf) algorithm | `parallel` |\n| `MinMaxLTTBDownsampler` | (*new two-step algorithm \ud83c\udf89*) 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> |\n\n<sup>*</sup><i>Default value for `minmax_ratio` is 4, which is empirically proven to be a good default. 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