🎉 first pypi deploy

Author: jvdd

.github/workflows/ci-tsdownsample.yml

@@ -0,0 +1,41 @@
+name: CI tsdownsample
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+defaults:
+  run:
+    shell: bash
+
+jobs:
+  Check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+
+      - name: Install Rust toolchain
+        uses: actions-rs/toolchain@v1
+        with:
+          profile: minimal
+          toolchain: nightly
+          components: clippy, rustfmt
+      - name: Setup Rust
+        run: |
+          rustup update nightly --no-self-update
+          rustup default nightly
+
+      - name: Rust toolchain info
+        run: |
+          cargo --version --verbose
+          rustc --version
+          cargo clippy --version
+          cargo fmt --version
+        
+      - name: check
+        run: cargo check --verbose
+      - name: formatting check
+        run: cargo fmt --all -- --check

README.md

@@ -1,2 +1,51 @@
 # tsdownsample
-Time series downsampling algorithms for visualization
+
+[![PyPI Latest Release](https://img.shields.io/pypi/v/tsdownsample.svg)](https://pypi.org/project/tsdownsample/)
+[![support-version](https://img.shields.io/pypi/pyversions/tsdownsample)](https://img.shields.io/pypi/pyversions/tsdownsample)
+[![Downloads](https://pepy.tech/badge/tsdownsample)](https://pepy.tech/project/tsdownsample)
+<!-- [![Testing](https://github.com/predict-idlab/tsflex/actions/workflows/test.yml/badge.svg)](https://github.com/predict-idlab/tsflex/actions/workflows/test.yml) -->
+
+**📈 Time series downsampling** algorithms for visualization
+
+## 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
+  - scales multi-threaded with rayon (rust)
+* **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 `f16`, `f32`, `f64`, `i16`, `i32`, `i64`, `u16`, `u32`, `u64`  
+    *!! 🚀 `f16` [argminmax](https://github.com/jvdd/argminmax) is 200-300x faster than numpy*
+* **Easy to use**: simple API
+
+## Install
+
+> ❗🚨❗ This package is currently under development - no stable release yet ❗🚨❗
+
+
+```bash
+pip install tsdownsample
+```
+
+## Usage
+
+```python
+import tsdownsample as tsds
+import pandas as pd; import numpy as np
+
+# Create a time series
+y = np.random.randn(10_000_000)
+s = pd.Series(y)
+
+# Downsample to 1000 points
+s_ds = tsds.minmaxlttb(s, n_out=1000)
+```
+
+---
+
+<p align="center">
+👤 <i>Jeroen Van Der Donckt</i>
+</p>

poetry.lock

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "tsdownsample"
+version = "0.1.0a0"
+description = "Time series downsampling in rust"
+authors = ["Jeroen Van Der Donckt"]
+readme = "README.md"
+license = "MIT"
+repository = "https://github.com/predict-idlab/tsdownsample"
+keywords = ["time series", "downsampling", "rust", "data science", "visualization"]
+
+[tool.poetry.dependencies]
+python = "^3.7.1"
+numpy = ">=1.21"
+pandas = ">=1.3"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

pyproject.toml

@@ -1,7 +1,6 @@
 extern crate downsample_rs;
 extern crate paste;
 
-
 // TODO
 // - support bool
 //      => issue: does not have to_primitive (necessary for lttb)
@@ -39,30 +38,29 @@ macro_rules! _create_pyfunc_without_x {
         }
         // Add the function to the module
         $mod.add_wrapped(wrap_pyfunction!($name))?;
-    }; 
-    // ($name:ident, $resample_mod:ident, $resample_fn:ident, $type:ty, $mod:ident, $cast_type:ty) => {
-    //     // Create the Python function
-    //     #[pyfunction]
-    //     fn $name<'py>(
-    //         py: Python<'py>,
-    //         y: PyReadonlyArray1<$type>,
-    //         n_out: usize,
-    //     ) -> &'py PyArray1<usize> {
-    //         let y = y.as_array().mapv(|v| v as $cast_type);
-    //         let sampled_indices = $resample_mod::$resample_fn(y.view(), n_out);
-    //         sampled_indices.into_pyarray(py)
-    //     }
-    //     // Add the function to the module
-    //     $mod.add_wrapped(wrap_pyfunction!($name))?;
-    // };
+    }; // ($name:ident, $resample_mod:ident, $resample_fn:ident, $type:ty, $mod:ident, $cast_type:ty) => {
+       //     // Create the Python function
+       //     #[pyfunction]
+       //     fn $name<'py>(
+       //         py: Python<'py>,
+       //         y: PyReadonlyArray1<$type>,
+       //         n_out: usize,
+       //     ) -> &'py PyArray1<usize> {
+       //         let y = y.as_array().mapv(|v| v as $cast_type);
+       //         let sampled_indices = $resample_mod::$resample_fn(y.view(), n_out);
+       //         sampled_indices.into_pyarray(py)
+       //     }
+       //     // Add the function to the module
+       //     $mod.add_wrapped(wrap_pyfunction!($name))?;
+       // };
 }
 
 macro_rules! _create_pyfuncs_without_x {
     ($resample_mod:ident, $resample_fn:ident, $mod:ident, $($t:ty)*) => {
         $(
             paste! {
                 _create_pyfunc_without_x!([<downsample_ $t>], $resample_mod, $resample_fn, $t, $mod);
-            }    
+            }
         )*
     };
 }
@@ -99,7 +97,7 @@ macro_rules! _create_pyfuncs_with_x {
         $(
             paste! {
                 _create_pyfunc_with_x!([<downsample_ $tx _ $ty>], $resample_mod, $resample_fn, $tx, $ty, $mod);
-            }    
+            }
         )*
     };
 }

src/lib.rs

@@ -0,0 +1 @@
+import tsdownample_rs as _tsdownsample_rs