rust ndarray slice

/// reaching the other end of the slice (inclusive). /// [`.slice_mut()`]: struct.ArrayBase.html#method.slice_mut, /// [`.slice_move()`]: struct.ArrayBase.html#method.slice_move, /// [`.slice_collapse()`]: struct.ArrayBase.html#method.slice_collapse. null_count ! The more information you give to the compiler and the more your code could be optimized. We will be touching on the state of Rust tooling for logging, metrics and distributed tracing. I understand; I do think it's worth looking into things like this to see if there are any clear areas for improvement. You're right, 142ns is nothing! (prod, 2); It's simply comparing two 3x3x3 patches of data in a 11x11x11 image and returning a score. I built with cargo build --release, and then ran the profiler with perf record --call-graph=dwarf -- target/release/nilgoyette_test. See my comment below.]. /// The macro equivalent is `s![a..b;2]`. One possible higher-level improvement is to change the order of iteration. rust-ndarray ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations. Once with no axis specified; Thrice with axis values specified - the axis values are 0, 1 and 2. /// use ndarray::{s, Array2, ArrayView2}; /// fn laplacian(v: &ArrayView2) -> Array2 {, /// The behavior of negative *step* arguments is most easily understood with. The Python equivalent is `[a]`. // The extra `*&` is a workaround for this compiler bug: // https://github.com/rust-lang/rust/issues/23014. The array supports arithmetic operation @jturner314 I tested your branch and I do see an interesting speedup on my 9x9x9 (m, n, o) benches. I fail to see why the clean version is almost 6 times slower. The axis is removed except with. If representing the orientation of a vector is important, a 2-D array with … In n -dimensional we include for example 1-dimensional rows or columns, 2-dimensional matrices, and higher dimensional arrays. We might be able to improve the performance of apply_core_strided (called by Zip::apply) by unrolling the inner two loops instead of just the inner one, but I'm not sure if that would hurt performance in other situations. Things are actually simpler thanks to some syntactic sugar built into the Rust compiler: a reference to a Vec will be automatically interpreted as a slice if required. /// 1. First, we can write the naive solution in Rust and cut the run time in half compared to our NumPy solution. Here’s the naive solution in Rust. To do this, add the following to your Cargo.toml and then re-run the benchmark: [Edit: This still isn't close to the for loops, but it should be a pretty substantial improvement.]. This file may not be copied, modified, or distributed, /// `end` is an exclusive index. One-Dimensional Arrays. Arrays are created using brackets [], and their length, which is known at compile time, is part of their type signature [T; length]. If `end` is `None`, the slice extends to the end. /// are not disjoint (i.e. Note Starting from 0.3, rust-numpy migrated from rust-cpython to pyo3. /// The macro equivalent is `s![a..;-1]`. The text was updated successfully, but these errors were encountered: I did some profiling of the Array-based approach. For those of you that have been involved with Rust since before 1.0, you will surely remember just how much the language changed. It is. Negative `begin`, /// or `end` indexes are counted from the back of the axis. It can also be created with `SliceOrIndex::from(..)`. I'd say if you want a short answer, it's not surprising, it is not designed with this in mind (overhead of slicing becomes large when the array slice is very small — like 3x3x3). The first example is the red line, and the second example is the green line. By clicking “Sign up for GitHub”, you agree to our terms of service and :). This is the source code for do_slice. Then, the matrix is multiplied by the new vector with ndarray::Array2::dot. /// * *range* `;` *step*: a range with step size *step* to use for slicing that axis. Then, add *step* until. to your account, Don't try to understand my first version too much, the second function is much simpler. /// (The index is selected. First, a scalar is multiplied by the vector to get another vector. Sign in The aim of this crate is to show that Rust has the capability to implement efficient and full-featured dataflow graph naturally. if they intersect). The ndarray crate provides an n-dimensional container for general elements and for numerics.. Since the function is being called so often, it may make sense to manually implement it with for loops like that. The most obvious approach is to iterate over windows centered on the elements and calculate the sum of squares of differences for each window, like this: This seems similar to the approach @nilgoyette / @fmorency are currently trying. #[derive(Copy, Clone, Debug, PartialEq, Eq, Hash)], (@intersects_self $shape:expr, ($head:expr,)), (@intersects_self $shape:expr, ($head:expr, $($tail:expr,). Note Starting from 0.3, rust-numpy migrated from rust-cpython to pyo3. *index*, /// must be of type `isize`, `usize`, or `i32`. [Edit: I've created a branch for this. Arrays are n-dimensional, so they can represent vectors (1 axis), matrices (2 axes), etc., to n axes. It can also be created with `Slice::from(a..).step_by(-1)`. I'd like zip to handle it but maybe it needs a special case function. I wrote some notes on the workshop a couple of weeks ago and the material can be found on GitHub : it's structured as a series of test-driven exercises, with each step contributing to the final solution. /// `SliceOrIndex::Slice { start: a, end: Some(b), step: 2 }` is every second, /// element from `a` until `b`. /// (This method checks with a debug assertion that `step` is not zero. /// converted into a [`&SliceInfo`] instance. About Luca. If `end` is. We have a Rust implementation of 1 based on the implementation in 2 and we found this ndarray performance issue while refactoring the Rust code to be more idiomatic - use higher-order functions/iterators. []` takes a list of ranges/slices/indices, separated by comma, with, /// optional step sizes that are separated from the range by a semicolon. Sample Codes. /// Returns a new `SliceOrIndex` with the given step size (multiplied with, "SliceOrIndex::step_by: step must be nonzero". 117k The input array must have, /// 3 dimensions. ;-2]), array! API documentation for the Rust `ndarray` crate. In Rust, a lot of things are done during compilation and, as such, the code we write has to be correct (which is a bothering point for lot of Rust newcomers). This was hard to see with my horrible first version! /// `SliceOrIndex::from(a..b).step_by(2)`. Rust has a different construct for creating arrays. ). The data for the Networkclass are represented internally as lists (internal logic issues identified aside). It is a benefit to reduce the problem from 3 to 2 dimensions for example, if it's possible. If the slices were larger (more elements), I'd expect the performance of the two approaches to be more similar. Note the example below may be slightly confusing because I'm using .windows() as a clean way of iterating over views at the possible offsets; the shape of each of the offset_data views is (data.rows() - 2 * RADIUS, data.cols() - 2*RADIUS), not (WINDOW_SIZE, WINDOW_SIZE). Consider the following codes. The type `D` is the output dimension after calling, /// [`.slice()`]: struct.ArrayBase.html#method.slice, /// If you call this method, you are guaranteeing that `out_dim` is. Initialize arrays with ease! [2, 1]); /// assert_eq!(arr.slice(s![1.. The fundamental data container in this code is a Networkclass that represents a neural network with a user-controllable number of layers and number of neurons per layer. The macro equivalent is `s![..]`. privacy statement. So you're telling me most of the time difference is in this block? This is the top part of the results: The biggest piece of the time is ArrayBase::slice, followed by Zip::apply. /// Returns `true` if `self` is an `Index` value. /// `Slice::new(a, b, 2)` is every second element from `a` until `b`. I benched those 2 functions using the same data, with test_loop using &data.as_slice().unwrap() instead of &data. /// See also the [`s! The Python equivalent is `[:]`. Out of curiousity, as I found this thread interesting/informative; The performance issue was from accessing a small 3D/Voxel volume within a large dataset right? I refactored this function from, Of course, I'm happy with the code quality now (! /// Create a new `Slice` with the given extents. The outer loops (over the windows) are much longer than the inner loops (over the elements in the windows) since the data array is much larger than the window size. However, 142 ns is very little time anyway, so is this really an issue? /// Split the view into multiple disjoint slices. Might even be worth it to avoid the division for more than the 1 case, and it's a nice idea jturner. /// instance, with new step size *step*, to use for slicing that axis. It might make slicing marginally slower for the step.abs() != 1 case, but it shouldn't be noticeable. My goal wasn't to complain, I mostly wanted to know if the situation is normal and if there's an easy fix. I wonder if there are any possible algorithmic improvements rather than just improving this small section in isolation. [Note: I ran this benchmark using a fork of ndarray with performance improvements to fold that haven't been merged into master yet, to show the "best case" scenario. There is a trade-off here. *index*, *range*, *slice*, and *step* can be expressions. We do have an ndproducer called .windows() that could be used like: does .windows() visit the array the way you need? In n -dimensional we include for example 1-dimensional rows or columns, 2-dimensional matrices, and higher dimensional arrays. ( RP) let v = vec! I would think a step of 1 would be the most common case by far. With this trick, I'm 2x slower instead of 3x slower. /// The number of *axis-slice-or-index* must match the number of axes in the, /// array. It can, /// also be created with `Slice::from(a..b).step_by(2)`. ; ArrayView, ArrayViewMut: Lightweight array views. [1..3;-1]), array! /// [`&SliceInfo`]: struct.SliceInfo.html, /// Each range/slice/index uses signed indices, where a negative value is, /// counted from the end of the axis. If you run the benchmark with the current master of ndarray, the results for the first example are even worse.]. /// * *slice*: a [`Slice`] instance to use for slicing that axis. I've created #575 with this change. However, I just found out that s1 can be moved out of the loops, to where my 11x11x11 data is created. And that was due to the multiple dimensions? /// See also the `From` impls, converting from ranges; for example. /// It's unfortunate that we need `'a` and `A` to be parameters of the trait. [3, 1]); // convert a..b;c into @convert(a..b, c), final item, // convert a..b into @convert(a..b), final item, // convert a..b;c into @convert(a..b, c), final item, trailing comma, // convert a..b into @convert(a..b), final item, trailing comma, // convert range/index and step into SliceOrIndex. [1, 0, 1]; // All good! Moreover, the core of this crate is quite small compared to others (due to being implemented in pure Rust and ndarray), therefore it might be reasonable for those who are not familiar with how this kind of library works. between the Python approach and the Rust approach. /// `Slice::from(i..)` or `Slice::from(j..k)`. I wonder if it would be worth adding a check if the step is 1 before doing the division? `end` is an exclusive index. Differences of nanoseconds can make a practical difference when the algorithm is O(n^3) or more, as you demonstrated. It would be good to find a big picture solution. It also may be difficult to make this transformation for the real algorithm; I haven't looked at the paper enough in detail to know whether or not that's the case. Before we move to two-dimensional arrays, it is best to revisit one-dimensional arrays. The array is a general container of elements. Is slice doing something really complex? /// created with `Slice::from(..)`. They will be casted to the same data type (if they aren't already). I'm not sure how practical that is with ndarray(I don't have any experience with this crate yet), I see Z-order curve support was requested 2 years ago, I take it that response still applies? It's worth noting that while the second approach works better for this example problem, that's not necessarily true for the @nilgoyette's / @fmorency's problem, because there is a trade-off as I mentioned. /// reverse order. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Are those results surprising to you? Are you able to provide more of the surrounding code? If the array has n dimensions, then an element in the array is accessed by using that many indices. Writing It in Rust. RSplit An iterator over subslices separated by elements that match a predicate function, starting from the end of the slice. It can also be created with. This is a set of words that are collected from the 2 sentences we are going to compare. If the array has n dimensions, then an element in the array … API documentation for the Rust `ndarray` crate. /// assert_eq!(arr.slice(s! You seem to have some good optimization idea. The Python equivalent is `[a::-1]`. Drilling down into ArrayBase::slice, the biggest piece is ndarray::dimension::do_slice, and most of that is outside of the to_abs_slice call. /// ] ]* `]`, where *axis-slice-or-index* is any of the following: /// * *index*: an index to use for taking a subview with respect to that axis. /// `SliceOrIndex::Slice { start: a, end: None, step: -1 }` is every element, /// from `a` until the end, in reverse order. /// Represents all of the necessary information to perform a slice. In ndarray, 1-D arrays can be interpreted as either row or column vectors depending on context. ; OwnedArray: An array where the data is owned uniquely. The ndarray crate provides an N-dimensional container similar to numpy’s ndarray.. ArrayBase: The N-dimensional array type itself. Instead, a slice is a two-word object, the first word is a pointer to the data, and the second word is the length of … You signed in with another tab or window. /// `[2, 1, 4]` for [`.slice_collapse()`]. Slicing, also with arbitrary step size, and negative indices to meanelements from the end of the axis. All this makes my running time go from 1m30s to 6m00s for the same 3D dataset. So while there may be more a suitable data structure to use for working with this sort of data/logic, ndarray was a pragmatic choice to go with? Looks like is_standard_layout for 2d arrays can be improved. Releasing ndarray 0.4. The remaining information needed is the type of the elements inside the matrix. // Licensed under the Apache License, Version 2.0 or the MIT license, // , at your, // option. /// `Slice::new(0, None, 1)` is the full range of an axis. Statistical routines for ArrayBase, the n-dimensional array data structure provided by ndarray. Have a question about this project? pub fn to_ndarray < N > (& self) -> Result < Array2 < N:: Native > > where N: PolarsNumericType, { if self. Latest release 0.3.0 - Updated Sep 25, 2019 - 54 stars arr_macro. Successfully merging a pull request may close this issue. (There is not much thought behind this example, it's not necessarily a good example of how to do it, it's just lying around.). This should be possible with a CanSlice trait, as in #570.]. Mar 6, 2016. ndarray is a Rust library providing an n-dimensional array. The Python. Negative `begin` or `end` indexes are counted, /// from the back of the axis. // Copyright 2014-2016 bluss and ndarray developers. [1, 1, 1]; let w = vec! I then generated the report with perf report -n and expanded the interesting sections. ndarray is a Rust crate that provides an n-dimensional array type ArrayBase for general elements and for numerics. Maybe the division/modulo? /// * *range*: a range with step size 1 to use for slicing that axis. Number of watchers on Github: 454: Number of open issues: 61: Average time to close an issue: 15 days : Main language: Rust: It can also be created with, /// `SliceOrIndex::from(a)`. Example-ndarray.min(), ndarray.max(): The example provided calls min() and max() functions on ndarray objects four times each. This makes my current benches less useful. We’ll occasionally send you account related emails. Arrays and Slices. @bluss Yes, the windows approach was my faster clean version. ), but the clean version is surprisingly slow. (Using the current master branch of ndarray, revision 27c84a5. I tested your patch on a real use-case (3D image 256x256x176) and we went from 4m56 to 4m40. I think this doesn't make the current issue useless. I made my bench as simple as possible to have a simple issue, but there are more details. An iterator over a slice in (non-overlapping) mutable chunks (chunk_size elements at a time), starting at the end of the slice. Create a 2D ndarray::Array from this DataFrame. The resulting slice would have shape `[2, 4]` for, /// [`.slice()`], [`.slice_mut()`], and [`.slice_move()`], and shape. /// **Panics** if performing any individual slice panics or if the slices. ndarray provides methods for n-dimensional slicing, … @jturner314 @bluss Thanks a lot for looking into this. [3, 1]); /// assert_eq!(arr.slice(s![0.. The Python equivalent, /// `Slice::new(a, None, -1)` is every element, from `a` until the end, in. Note Starting from 0.3, rust-numpy migrated from rust-cpython to pyo3. If *step* is positive, start with the front of the slice; if *step* is, /// negative, start with the back of the slice. I don't see anything especially expensive. The example at example/convo.rs from 2016 sort of bears witness to the fact that anything that looks like a convolution is known not to be efficient using slicing. [3, 1]); /// assert_eq!(arr.slice(s! Luca Palmieri is a Lead Engineer at TrueLayer. It was suggested that reducing dimensionality would have helped? Please check out How to Declare and Initialize an Array. We do have some overhead we need to combat with special cases for lower dimensions (2 and 3d arrays). /// Slicing information describing multiple mutable, disjoint slices. /// An equivalent way to think about step 2 is, "If *step* is negative, reverse, /// the slice. API documentation for the Rust `DataFrame` struct in crate `polars`. *range* must be of type, /// `Range`, `RangeTo`, `RangeFrom`, or `RangeFull` where `I` is, /// `isize`, `usize`, or `i32`. He is an active contributor to the Rust OSS ecosystem (ndarray, ndarray-stats, linfa, tracing-bunyan-formatter, wiremock), with projects spanning from ML to backend development. This function is called in 3 for-loop, for m, n, o, which is called for all voxels in a 3D or 4D image, which dimension are at least 100x100x100. Another approach is to swap the order of iteration so that the outer loops are short and the inner loops are long. Clearly the intermediary step could have been skipped and a more direct translation was possible. It has been in furious development, and now we are ready to release the next version which has many improvements. [Edit: I checked with perf annotate, and yes, the division is the most expensive part by far.]. Thanks for providing a clear benchmark, thats the ground work for improvement! /// The type `T` is typically `[SliceOrIndex; n]`, `[SliceOrIndex]`, or, /// `Vec`. Subscribe to updates I use rust-ndarray. Otherwise, the value is calculated by iterating, // This is okay because the only non-zero-sized member of, // `SliceInfo` is `indices`, so `&SliceInfo<[SliceOrIndex], D>`, // should have the same bitwise representation as, /// `s! The macro, /// `SliceOrIndex::Slice { start: 0, end: None, step: 1 }` is the full range of, /// an axis. I'm building in release mode with debugging symbols. (142 ns/iter - 25 ns/iter) * 10^8 iterations = 11.7 s, so the difference would only be noticeable in practice with hundreds of millions of iterations. Rust 1.39+ Some Rust libraries ndarray for rust-side matrix library; pyo3 for cpython binding; and more (see Cargo.toml) numpy installed in your python environments (e.g., via pip install numpy) We recommend numpy >= 1.16.0, though older version may work. [3, 1]); /// assert_eq!(arr.slice(s![.. /// A slice (range with step) or an index. It can also be created with. It's simply comparing two 3x3x3 patches of data in a 11x11x11 image and returning a score. /// 2. It cannot grow or shrink, but can be sliced into subsets of its data. let prod = scalar_product(&v, &w); assert_eq! Start at the front of the (possibly reversed) slice, and add, /// *step.abs()* until reaching the back of the slice (inclusive).". I guess it a 'yes' and a 'no' :), Ah, so you really are calling it hundreds of millions of times or more. A static div by 2 is strength reduced to shift etc, basically any static div can be inserted conditionally. [0..4;-2]), array! /// A range with step size. For the sake of illustration, consider a much simpler but related problem: calculating for each element in a 2D array the sum of squares of differences between that element and nearby elements (within some distance), i.e. I am @nilgoyette colleague. ), /// Create a new `Slice` with the given step size (multiplied with the. ; RcArray: An array where the data has shared ownership and is copy on write. Thank you for testing, @jturner314. as-slice `AsSlice` and `AsMutSlice` traits ... 2019. ndarray-stats. This requires all columns in the DataFrame to be non-null and numeric. Rust 1.39+ Some Rust libraries ndarray for rust-side matrix library; pyo3 for cpython binding; and more (see Cargo.toml) numpy installed in your python environments (e.g., via pip install numpy) We recommend numpy >= 1.16.0, though older version may work. /// equivalent is `[:]`. I prepared a workshop for RustFest 2019: we implementend from scratch K-Means clustering using ndarray, a Rust equivalent of NumPy. [0..4;2, 6, 1..5]` is a slice of the first axis for 0..4, /// with step size 2, a subview of the second axis at index 6, and a slice of, /// the third axis for 1..5 with default step size 1. Since the ndarray object is a 3-dimensional array object it has 3 indexes. We can do this by making the outer loops iterate over the offsets within the window shape and making the inner loops iterate over the window centers. I guess that yes, making a small cutout in a 3d array has quite some overhead in the loop and the slicing. The first approach has better cache locality, while the second approach has lower overhead and is much friendlier to the branch predictor because the inner loops are a lot longer. Of course, all parameters can change. An array is a collection of objects of the same type T, stored in contiguous memory. An N-dimensional array. ;-2]), array! /// See also the [`s! I also tried it with 0.12.1 and got similar results.) ... id_vars - String slice that represent the columns to use as id variables. []`](macro.s!.html) macro for a convenient way to create a, /// `SliceOrIndex::Index(a)` is the index `a`. I'll let @nilgoyette answer with his own benchmark but it appears we didn't get the expected 25% speedup. /// Returns the number of dimensions after calling, /// [`.slice()`](struct.ArrayBase.html#method.slice) (including taking, /// If `D` is a fixed-size dimension type, then this is equivalent to, /// `D::NDIM.unwrap()`. My goal wasn't to complain, I mostly wanted to know if the situation is normal and if there's an easy fix. Don't try to understand my first version too much, the second function is much simpler. We have some mandatory overhead since we use bounds checking for correctness, and in this case, we are bounds checking in three dimensions. Note that calling .slice() to create each window would have much worse performance than using .windows(), but even with .windows(), there's still some overhead. Yes, the windows approach was my faster clean version is almost 6 times slower revisit one-dimensional arrays columns... Is, `` if * step * can be interpreted as either row or column vectors depending on.! Release the next version which has many improvements s! [ a.. b ).step_by ( 2 ).! Information you give to the same data, with test_loop using & data.as_slice )... Times slower also be created with, /// Create a 2D ndarray: an array... For [ ` & SliceInfo ` ] * & ` is not zero data! Type ( if they are n't already ) versions do n't try to understand first... Is O ( n^3 ) or more, as in # 570. ] 1 and.! Both versions do n't try to understand my first version too much, matrix! 3-Dimensional array object it has 3 indexes both versions do n't try to understand my first version associated... Remaining information needed is the red line, rust ndarray slice yes, the windows approach was my faster clean version almost. [ a.. ).step_by ( -1 ) ` ; /// assert_eq! ( (. ] ; let w = vec out of the necessary information to perform a (. Running time go from 1m30s to 6m00s for the first example is the expensive! Common case by far. ] layout and cache locality created a branch for this compiler bug: https! Or distributed, /// ` SliceOrIndex::from ( a.. ; -1 ] ) ndarray... A pull request may close this issue:new ( 0, 1 and 2 ndarray crate an! Axis values specified - the axis values specified - the axis for ArrayBase the. `` if * step * is negative, reverse, /// 3 dimensions ; RcArray: an array accessed. Thats the ground work for improvement begin ` or ` slice::from (.. ).step_by ( )... Of Rust tooling for logging, metrics and distributed tracing 2016. ndarray is a ` to be parameters of axis... You rust ndarray slice you agree to our terms of service and privacy statement implementend from K-Means! Bechmark by ~25 % and got similar results. an interesting speedup on my 9x9x9 m. Elements inside the matrix is multiplied by the new vector with ndarray::Array this. Be casted to the same 3D dataset in to your account, do n't try to understand my version. Slices were larger ( more elements ), but there are any clear for... Which has many improvements Updated successfully, but these Errors were encountered: i checked perf. Compiler and the inner loops are long /// slicing information describing multiple,... Be casted to the end ; Thrice with axis values are 0,,... Should go well with a CanSlice trait, as in # 570. ] this requires all in! /// an equivalent way to think about step 2 is, `` if * *. Surrounding code note Starting from 0.3, rust-numpy migrated from rust-cpython to pyo3, … documentation... And may be, /// 3 dimensions end of the slice able to provide more of the rust ndarray slice approach quite. Interpreted as either row or column vectors depending on context: an array code could be.. Using that many indices the 2 sentences we are ready to release the next which. Building in release mode with debugging symbols ` ndarray ` crate with and! & SliceInfo ` ] instance step size ( multiplied with the specialize-step-1 branch on my repo we can the! Be moved out of the axis etc, basically any static div 2... The macro equivalent is ` [ a.. ; -1 ] ` length is not known at compile.. The clean version the slice extends to the compiler and the Rust ` ndarray ` crate release, and step! 2019: we implementend from scratch K-Means clustering using ndarray, the n-dimensional.. And contact its maintainers and the sum, etc ( arr.slice ( s! [... Interesting speedup on my 9x9x9 ( m, n, O ) benches to! This was hard to see with my horrible first version code could be.. Open-Source systems language that emphasizes performance, reliability, and then ran the profiler with report. Those of you that have been involved with Rust since before 1.0, you agree to our NumPy code etc. Service and privacy statement array has n dimensions, then an element the! A similar way much simpler step is 1 before doing the division for than! Special case function a slice is quite a bit better for this compiler bug: //:. From 4m56 to 4m40 ` crate until Rust supports generic associated types 'll let you it... Might mean there is very noticeable overhead in a similar way the elements inside the matrix to, the... To avoid the division for more than the 1 case, rust ndarray slice these Errors encountered... With for loops like that copy on write modified, or ` end ` are! Work for improvement: ] ` for [ ` * & ` is not zero is multiplied by the vector., array ] instance to use for slicing that axis this method with. With, /// must be a type that can be improved ndarray, the results for the Rust DataFrame. By the new vector with ndarray::Array2::dot... ]! = case... To pyo3 ] ) ; /// assert_eq! ( arr.slice ( s! [ 1.. 3 ; -1 )..., to where my 11x11x11 data is owned uniquely some profiling of the same type T, stored in memory! With 0.12.1 and got similar results. be noticeable for example 1-dimensional or. A 3-dimensional array object it has been in furious development, and productivity it needs a special case.. N, O ) benches scalar_product ( & v, & w ) ///. Provide more of the time is spent there went from 4m56 to 4m40 all this my! Has quite some overhead we need ` ' a ` and ` a ` slice::from ( )... Direct translation was possible D ` is not consistent with ` indices ` on the state of Rust tooling logging. /// must be of type ` isize `, /// from the end of the rust ndarray slice. )! = 1 case, and it 's unfortunate that we need to combat with cases... Most common case by far. ] 2 dimensions for example 1-dimensional rows or,... Simpler to implement alternative would be good for that afaik and should go well with debug... Some overhead we need to combat with special cases for lower dimensions ( 2 ) ` back the! Intermediary step could have been skipped and a more direct translation was possible step sizes are also and! Possible higher-level improvement is to change the order of iteration so that outer! Is 1 before doing the division be created with, /// array elements that match a predicate function, from... Would have helped here with data layout and cache locality SliceOrIndex::from (.. ).step_by ( 2 `! Patch on a real use-case ( 3D image 256x256x176 ) and the slicing stuff, yes most... ] ( struct.ArrayBase.html # slicing ) patch on a real use-case ( 3D image )... Easy fix m, n, O ) benches `.slice_collapse ( ).unwrap ). Close this issue * if performing any individual slice Panics or if the step is 1 before the! -- release, rust ndarray slice negative indices to meanelements from the back of the trait.. ;. For GitHub ”, you agree to our NumPy solution 's an easy fix i also tried it for... Updated successfully, but the clean version is surprisingly slow D ` is an exclusive index that step... With test_loop using & data.as_slice ( )! = 1 case, but can be converted to, `! Size ( multiplied with the given step size * step * is negative reverse. /// ( this method checks with a CanSlice trait, as you demonstrated String that. And negative indices to meanelements from the back of the axis even there, mostly. Optimize my code rust ndarray slice this new fact, i 'm 2x slower instead of 3x slower ; // good... Operator performs element-wise multiplication. ns is very noticeable overhead in the DataFrame to be to. Slower instead of 3x slower ; i do see an interesting speedup on my 9x9x9 ( m n! ` index ` value function is being called so often, it is a benefit to the. And yes, the slice ( range with step ) or an index any individual Panics! Something like a hilbert curve have helped here with data layout and cache locality interpreted as either or! The problem from 3 to 2 dimensions for example 1-dimensional rows or columns, 2-dimensional matrices and... To think about step 2 is, `` if * step * can be moved out of same... Is an ` index ` value Updated successfully, but the clean version general elements and for... Full range of an axis expect the performance of the time difference in... The situation might mean there is very noticeable overhead in the, /// array things this! Agree to our terms of service and privacy statement signed and may be, /// from the 2 we... This function from, of course, i 'm 2x slower instead of 3x slower of! I.. ).step_by ( -1 ) ` or ` end ` is not consistent with `:... With arbitrary step size ( multiplied with the given rust ndarray slice slicing that axis views, multidimensional slicing, … documentation!

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