TDA logo

Topological Data Analysis

Persistent homology beyond H0 and H1
A Python and Rust implementation built for performance

Pepy total downloads

TDA computes Vietoris–Rips persistent homology from point clouds or precomputed distance matrices. The performance-critical implementation is written in Rust and is available through both Python and Rust APIs.

[!CAUTION] TDA is in an early stage of development. APIs may change between releases.

Installation

TDA requires Python 3.11 or newer. Pre-built wheels for common macOS, Windows, and Linux platforms are published on PyPI:

python -m pip install tda

Compiling from source

Building from source requires Rust/Cargo and maturin:

git clone https://github.com/antonio-leitao/topological-data-analysis.git
cd topological-data-analysis

python3 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
python -m pip install maturin numpy

cd crates/python
maturin develop --release

Python usage

import numpy as np
import tda

# Point cloud: an (n, d) float32 array.
points = np.random.rand(200, 3).astype(np.float32)
barcode = tda.persistent_homology(points, max_dim=2)

# barcode[d] is a (k_d, 2) array of [birth, death] intervals.
print(barcode[0])  # H0
print(barcode[1])  # H1
print(barcode[2])  # H2

Precomputed distance matrices are also supported:

distances = np.asarray(my_distance_matrix, dtype=np.float32)
barcode = tda.persistent_homology(
    distances,
    max_dim=1,
    distance_matrix=True,
)

Use filtration_size to inspect the size of the truncated filtration built with the same options:

size = tda.filtration_size(points, max_dim=2, peel=True)
print(size)

Parameters

Both persistent_homology and filtration_size accept the following parameters:

Parameter Type Default Description
data np.ndarray — Two-dimensional float32 array. Shape (n, d) for a point cloud or (n, n) for a distance matrix. Inputs are copied into row-major internal storage.
max_dim int 1 Highest homology dimension to compute. Capped at 4.
threshold float | None None Maximum filtration value. None uses the enclosing radius; an explicit value is capped by that radius.
distance_matrix bool False Interpret data as a square distance matrix. Symmetry, zero diagonal, and non-negativity are assumed rather than validated.
quotient bool False Use the smaller quotient-cover filtration. This is an approximation with a log(3) interleaving guarantee, not the exact Vietoris–Rips barcode.
peel bool False Apply an exact strong-collapse reduction before computing the result.
parallel bool True Enable parallel preprocessing, sorting, and candidate assembly for sufficiently large inputs.

persistent_homology returns a list of length max_dim + 1. Entry barcode[d] is a NumPy array of shape (k_d, 2) whose rows are [birth, death] intervals. A death value of inf marks an essential feature.

Rust usage

The Rust crate is published as tda_core:

[dependencies]
tda_core = "0.3"

For a point cloud, pass a flat row-major (n, d) slice. The ambient dimension is inferred from the slice length and n:

use tda_core::{persistent_homology, Error};

fn main() -> Result<(), Error> {
    let points: Vec<f32> = vec![
        0.0, 0.0,
        1.0, 0.0,
        0.0, 1.0,
        1.0, 1.0,
    ];

    let barcode = persistent_homology(
        &points,
        4,     // number of points
        1,     // max_dim
        None,  // threshold
        false, // distance_matrix
        false, // quotient
        false, // peel
        true,  // parallel
    )?;

    for (dim, intervals) in barcode.intervals.iter().enumerate() {
        for interval in intervals {
            println!("H{dim}: [{}, {})", interval.birth, interval.death);
        }
    }

    Ok(())
}

Distance matrices use the same function with distance_matrix = true:

use tda_core::{persistent_homology, Error};

fn main() -> Result<(), Error> {
    let distances: Vec<f32> = vec![
        0.0, 1.0, 2.0,
        1.0, 0.0, 1.5,
        2.0, 1.5, 0.0,
    ];

    let barcode = persistent_homology(
        &distances,
        3,
        1,
        None,
        true,  // distance_matrix
        false, // quotient
        false, // peel
        true,  // parallel
    )?;

    println!("{:?}", barcode.intervals);
    Ok(())
}

Invalid inputs return tda_core::Error, including shape mismatches, invalid thresholds, too few or too many points, and unsupported dimensions.

License

TDA is distributed under the MIT License.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for leitao from gravatar.com leitao

Credits

Author: António Leitão

Requires

Python >=3.11

Tags

topology homology tda persistent-homology ripser

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

tda-0.3.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (470.6 kB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

tda-0.3.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (454.1 kB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

tda-0.3.0-cp315-cp315t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (465.9 kB view details)

Uploaded CPython 3.15tmanylinux: glibc 2.17+ x86-64

tda-0.3.0-cp315-cp315-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (466.4 kB view details)

Uploaded CPython 3.15manylinux: glibc 2.17+ x86-64

tda-0.3.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (465.8 kB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.17+ x86-64

tda-0.3.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (447.7 kB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.17+ ARM64

tda-0.3.0-cp314-cp314-win_amd64.whl (289.5 kB view details)

Uploaded CPython 3.14Windows x86-64

tda-0.3.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (466.1 kB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

tda-0.3.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (449.9 kB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ ARM64

tda-0.3.0-cp314-cp314-macosx_11_0_arm64.whl (401.6 kB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

tda-0.3.0-cp314-cp314-macosx_10_12_x86_64.whl (416.0 kB view details)

Uploaded CPython 3.14macOS 10.12+ x86-64

tda-0.3.0-cp313-cp313-win_amd64.whl (289.4 kB view details)

Uploaded CPython 3.13Windows x86-64

tda-0.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (466.1 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

tda-0.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (449.4 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

tda-0.3.0-cp313-cp313-macosx_11_0_arm64.whl (401.3 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

tda-0.3.0-cp313-cp313-macosx_10_12_x86_64.whl (415.6 kB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

tda-0.3.0-cp312-cp312-win_amd64.whl (289.7 kB view details)

Uploaded CPython 3.12Windows x86-64

tda-0.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (466.8 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

tda-0.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (449.5 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

tda-0.3.0-cp312-cp312-macosx_11_0_arm64.whl (402.1 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

tda-0.3.0-cp312-cp312-macosx_10_12_x86_64.whl (416.3 kB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

tda-0.3.0-cp311-cp311-win_amd64.whl (292.0 kB view details)

Uploaded CPython 3.11Windows x86-64

tda-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (468.5 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

tda-0.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (453.3 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

tda-0.3.0-cp311-cp311-macosx_11_0_arm64.whl (403.9 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

tda-0.3.0-cp311-cp311-macosx_10_12_x86_64.whl (418.0 kB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

File details

Details for the file tda-0.3.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0e4e0903735076874e9776fa09e8af0ad38819031d0f185cfc159e6e64d2c876
MD5 85a8701cd2f1aeef410d6201445ac391
BLAKE2b-256 dce43f4ffc2f446b10b62291972304b71692c88ff02d935ed71ac56a9bbae37c

See more details on using hashes here.

File details

Details for the file tda-0.3.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 14ebf03a1ecfd09d6d97170e688046b7266ce1d617992ac6a7c97df34d143a3a
MD5 823412be9a8e204d1311d5eb736be700
BLAKE2b-256 238abc84a423d841d8d83a53f586ec2d0df8cf1c855016f62182a2780acbb02e

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp315-cp315t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp315-cp315t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8ad44ac06f3a874c6347c77fe8cb12e2ec3fca032ef9a986c6e1baa0d3a41c59
MD5 6c8116a77f5006180573feecc9e2ffa0
BLAKE2b-256 f67752c6d635afab588cc53ce37ac861318b51208ada1364fda3a64f79ec18a2

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp315-cp315-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp315-cp315-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 81fd5b4076ef4cab4784ac19c344318d59f277b37d086ec97bf88ade804cfea5
MD5 ecfc21e418a5318d3f8bc06c6a235e38
BLAKE2b-256 268dfba338ca4d3709f9fabe9081dde4d9a80aed7475f39f18f0b95c30a8702b

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 10f6ce0a6ab1c9f9fd257ab1dd35a20f26871df86670aaae48409bf01dd9f8ef
MD5 25db9e3ddd8e8abfe7b96bab3bedc08d
BLAKE2b-256 4a1ccbf72cea25c5e6fb669c973854d4c6ab85b3cfeee0054fa29acef0bf27cb

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 9b068195e0935fa24d2214f71b3f906ee50a6c615f55be38cebba7bc995aebac
MD5 acc34c0de497ce5ef47b96562a09bbac
BLAKE2b-256 2cd49b27f41d3d46cfb381fb5c8b75a5229d92316b1ddaba7d7c5fd8a6642644

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: tda-0.3.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 289.5 kB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: maturin/1.14.1

File hashes

Hashes for tda-0.3.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 18f82f639eac5fd2a7fd55b2f3a6868fa5c66b0fe4b00d4f1061e13bc8c39119
MD5 3bf812eadf12f59868975b0bb0f0e5af
BLAKE2b-256 3e53ec5f8f155fbac58bb1b6bb0fd151f1832196ca6ed56cbeee992bd0684b6e

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8073c9c74af3045eeae959c06afd0801b2c9f1aaf9cb7e97cf921427f86717ad
MD5 0ffc0ad7df0c2455fdc33f208471111d
BLAKE2b-256 5cc69f9230da48a718bc471fdac19502d1f493a906a099672d860661cdcb258f

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1cd0f9ea8312b9c6e943a9d7c7bfe5712bdd3d6dc38d7eb26152af415c789e98
MD5 c8e184302774bc50ea85afe01055b9c4
BLAKE2b-256 ac9b3325be71eb3b7758dc512e84152c90626cab05077a553086ecbaf4f8fd58

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9e993491100bcd34df880e93a6ca5053d191e3dae4fb44d0ea3a47d3a96ae29e
MD5 f01c996393717e06d2ddac8d6261248e
BLAKE2b-256 aabad6f643f080c6d11b0b2f577dc6fd880efa8314ad4f66e974b37678e486fd

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp314-cp314-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp314-cp314-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 b95fb98f274a4702fa71d6033ace18c7fedb12e4332bb9d98bd132f74cf02082
MD5 7fa7cc8bec0b2450f182e4decc92026f
BLAKE2b-256 c812a0887a35c58fdf69dd6854176455131216b0ff759602037a3bbe75e77174

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: tda-0.3.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 289.4 kB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: maturin/1.14.1

File hashes

Hashes for tda-0.3.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 5a4c8c5e259d3a73d6db62758b399cc91873a849d45eb104f3f063f3290555a7
MD5 93aaefddb1243c5d3c9413c0ce217f0d
BLAKE2b-256 e04cba9b186e9dbdbb64151018b0e1ba473daf1c4757481e4a31a51ed0c05fd7

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 965c8cb958a9340da0372a4660fa3c4bbc968f89bdd844d6d0d8216474dfe93a
MD5 af624dbb151448d2d77afcc3a5a60085
BLAKE2b-256 e84fa0e75495c320a7641260c76ca29c5f7b2b16c7d646e2a986472cc7a829eb

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d088cea298daa9e877f54e00a890eb843f9c35ce42e708f4b4db446c4c42cff9
MD5 6ae989e86b8b61eed00d6bde6cf3627c
BLAKE2b-256 094e74ee7fb127d4062c7ab50e839e9beb64c3c8c4a280be584286c081672b73

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 eba27824519a8eee093deac49d29c6886657ed5390a20901116907e1f723bf0a
MD5 404dab3503bb4b66b9af6b9d5dc8073a
BLAKE2b-256 3e79025d1476f5302f54f4065cf4094a0792c1f70bc6c70062781f9f6c11f8d1

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 419c474842595c5b393d12fbabcd7391a9b5cc7eb264f9f046b3057ca600fbb3
MD5 0c5c2545509a54b24a5cb14ca49687f6
BLAKE2b-256 68ed15a4cd4fcd8bc3a12719b1ecaa9cd8f5008b88f02637ef083fcca72fafec

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: tda-0.3.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 289.7 kB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: maturin/1.14.1

File hashes

Hashes for tda-0.3.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 693502e2c7f5969856a1ea7c30bee429732fcc8ebdcfea8e01d05d74a6ccba3b
MD5 24bcdf62e9903224d2af91074a17797f
BLAKE2b-256 bb2737d74bfb9c9535bd1bc6b631b8969b252eb76f7507c404798d6039608073

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 404ef918da892e36405560f4aaf1c1a9aa995e092f08f1e8eea9af89bd531b15
MD5 756a7885f0f8dcfbc88f59e2d20b649f
BLAKE2b-256 1f04a2b4ac30880de4433ab6993457e7bf1c46abbe8abe90cc0e8f5c4417d662

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5875fef000b23b4b2faaf30a4671d275667da91c5793763a912fccec9b096c76
MD5 abfbf27a238b99233f81a64812903f06
BLAKE2b-256 bd94b380e17fbc2205ed280ebde9b0d2539dfba24410408f8fae082ab26adacc

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 096bd2217216d0cb2f3ce478143caa6a34e5526abe06440a159d7d433596a0ce
MD5 f33f78b2d764e0e01dbfc67a8b09cede
BLAKE2b-256 a8eef1ff90d01af3e9813732bcc2dd9fe5acaeb2bc0121b9bc0720e50ed58da1

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 3cb89ebddb890b9889e892c79d95e6c4df3c60f3a9ffcd2e06d770439681ca2c
MD5 95b43defe877a2e88a83fbc71592197b
BLAKE2b-256 21c980052947145136ad5c378189142d1053607648162e860a48c5b7e69a9847

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: tda-0.3.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 292.0 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: maturin/1.14.1

File hashes

Hashes for tda-0.3.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 3a82c38876047ef6a66cb43d3ea64cb53abe8833a6908bc47b658e29b8e6f22c
MD5 19234b0c2f9b2d9499b3a1e0464521f4
BLAKE2b-256 bf24b5218457b939ff1cef15743a8d4d81c1f9412004261cfe766065483e3b20

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6b7eaa0ea9710489bdcdab13afad8bb69973bc4fe0feb47adbedd484411aa583
MD5 b692666ed11bf26b1cc4821003053b41
BLAKE2b-256 fd21021f6e2d99050c8f2b6c0e10f5cac7ed0c36d8724ef620e86b67cb4e4ac6

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f8809406deec9dd13e54da2f1385dbc12d5718484e470e11701e294a438f38e6
MD5 e335e2c0cf9b642e4873ec76ca245aed
BLAKE2b-256 fd83c190688590722067dcfd1f8a32dada6ddf820676ec53812366028b6802b6

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 69b8fa736b326321203d81601de731ad12031e590b18f5338d63c8c169021436
MD5 342943c77dc015237b5ef3ca850f6fbc
BLAKE2b-256 01be11312b461f67a0c049a32ba53c2716635bad96555abdfbcb93268bab171a

See more details on using hashes here.

File details

Details for the file tda-0.3.0-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for tda-0.3.0-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 87047a216f7e62fe6dc683591748e16cd08ca9502253d91107b8e9e40363c8f8
MD5 d573fbabc2890499fe9a2951ee96de15
BLAKE2b-256 23c6ac376b3c55c3b120fc691a9309cf77fcfb5e79e38b90bdcd549cae87cda3

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.3.0 This release

26 files

0.2.1

31 files

0.1.0

74 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page