Skip to main content

This package provides "rough path" tools for analysing vector time series.

Project description

esig

The Python package esig provides a toolset (previously called sigtools) for transforming vector time series in stream space to signatures in effect space. It is based on the libalgebra C++ library.

build

Installation

esig can be installed from a wheel using pip in most cases. The wheels contain all of the dependencies and thus make it easy to use the package. For example, on Python 3.8, you can install esig using the following console command:

python3.8 -m pip install esig

(You may need to tweak this command based on your platform, Python version, and preferences.)

esig can be compiled from source, but this is not advised. More information can be found in the documentation.

Basic usage

esig provides a collection of basic functions for computing the signature of a data stream in the form of a Numpy array. The stream2sig function computes the signature of a data stream up to a specific depth. For example, we can create a very simple data stream and compute its signature as follows.

import numpy as np
import esig

stream = np.array([
    [1.0, 1.0],
    [3.0, 4.0],
    [5.0, 2.0],
    [8.0, 6.0]
])
depth = 2

sig = esig.stream2sig(stream, depth) # compute the signature
print(sig) # prints "[1.0, 7.0, 5.0, 24.5, 19.0, 16.0, 12.5]"

The signature is returned as a flat Numpy array that contains the terms of the signature - which is fundamentally a higher dimensional tensor - in degree order. This first element is always 1.0, which corresponds to the empty tensor key. In this case the dimension is 2 (specified by the number of columns in the stream array), and so the next two elements are the signature elements corresponding to the words (1) and (2). These are the depth 1 words. The final 4 elements are the depth 2 words (1,1), (1,2), (2,1), and (2,2). esig provides the sigkeys function to generate these labels for you based on the parameters of the data.

width = 2
sig_keys = esig.sigkeys(width, depth)
print(sig_keys) # prints " () (1) (2) (1,1) (1,2) (2,1) (2,2)"

To compute the log signature of a data stream you use the stream2logsig function. This works in a similar manner to the stream2sig function in that it takes a Numpy array (the data) and a depth and returns a flat Numpy array containing the elements of the log signature in degree order.

log_sig = esig.stream2logsig(stream, depth)
print(log_sig) # prints "[7.  5.  1.5]"

Here the first two elements are the depth 1 Lie elements (corresponding to the letters 1 and 2) and the third element is the coefficient of the Hall basis element [1,2]. Again, esig provides a utility function logsigkeys for getting the keys that correspond to the coefficients in order for the log signature.

log_sig_keys = esig.logsigkeys(width, depth)
print(log_sig_keys) # prints " 1 2 [1,2]"

There are two additional utility functions for computing the size of a signature or logsignature with a specified dimension and depth: sigdim and logsigdim. These functions return an integer that is the dimension of the Numpy array returned from the stream2sig or stream2logsig functions, respectively.

esig also provides another function recombine, which performs a reduction of a measure defined on a large ensemble in a way so that the resulting measure has the same total mass, but is supported on a (relatively) small subset of the original ensemble. In particuar, the expected value over the ensemble with respect to the new measure agrees with that of the original measure.

Using alternative computation backends

esig uses libalgebra as a backend for computing signatures and log signatures by default. However, the computation backend can be changed to instead use an alternative library for computing signatures and log signatures. This is achieved by using the set_backend function in esig and providing the name of the backed that you wish to use. For example, we can switch to using the iisignature package as a backend by first installing the iisignature package and then using the command

import esig
esig.set_backend("iisignature")

To make it easier to install and use iisignature as a backend, it is offered as an optional extra when installing esig:

python3.8 -m pip install esig[iisignature]

You can also define your own backend for performing calculations by creating a class derived from esig.backends.BackendBase, implementing the methods describe_path (log_signature) and signature and related methods.

##Changelog 0.9.8.3 Hotfix 2 - Fixed the range of configurations available. - Fixed MANIFEST.in, now reflects the updated project structure.

0.9.8.1 Hotfix - Fixed problem caused by reading data in from F-contiugous array - Fixed issue with readme on PyPI.

0.9.8 - Added support for Python3.10. - Updated build system to use cibuildwheel in Github actions. - Updated libalgebra to benefit from improvements to performance - Deprecated tosig submodule. The functions formerly from this module are available via the top-level esig module. The tosig module will be removed in version 1.0. - Removed builds on 32-bit Linux for Python 3.8+. There is no Numpy wheels on this architecture for these versions of Python.

0.9.0 - Switched Linux support to manylinux2010 - Support for 32-bit platforms

0.8.2 - Added builds for Python3.9 - Updated README

0.8.1 - Minor fixes to build routine

0.8.0 - Added Python interface in the package root to computation backends - Added iisignature backend to allow computation of signatures using the iisignature package - Added support for computing depth 1 signatures and logsignatures - Added new unit tests. - Made some changes to the build script - Added Python 3.9 build target

0.7.4 - Fixed message displayed for Python versions below 3.8 when importing esig

0.7.3 - Added recombine function

Project details


Download files

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

Source Distribution

esig-0.9.8.3.tar.gz (154.3 kB view details)

Uploaded Source

Built Distributions

esig-0.9.8.3-cp310-cp310-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.10 Windows x86-64

esig-0.9.8.3-cp310-cp310-win32.whl (2.8 MB view details)

Uploaded CPython 3.10 Windows x86

esig-0.9.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

esig-0.9.8.3-cp310-cp310-macosx_10_11_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.10 macOS 10.11+ x86-64

esig-0.9.8.3-cp39-cp39-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.9 Windows x86-64

esig-0.9.8.3-cp39-cp39-win32.whl (2.8 MB view details)

Uploaded CPython 3.9 Windows x86

esig-0.9.8.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

esig-0.9.8.3-cp39-cp39-macosx_10_11_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.9 macOS 10.11+ x86-64

esig-0.9.8.3-cp38-cp38-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.8 Windows x86-64

esig-0.9.8.3-cp38-cp38-win32.whl (2.8 MB view details)

Uploaded CPython 3.8 Windows x86

esig-0.9.8.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

esig-0.9.8.3-cp38-cp38-macosx_10_11_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.8 macOS 10.11+ x86-64

esig-0.9.8.3-cp37-cp37m-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.7m Windows x86-64

esig-0.9.8.3-cp37-cp37m-win32.whl (2.8 MB view details)

Uploaded CPython 3.7m Windows x86

esig-0.9.8.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

esig-0.9.8.3-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl (4.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ i686

esig-0.9.8.3-cp37-cp37m-macosx_10_11_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.7m macOS 10.11+ x86-64

esig-0.9.8.3-cp36-cp36m-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.6m Windows x86-64

esig-0.9.8.3-cp36-cp36m-win32.whl (2.8 MB view details)

Uploaded CPython 3.6m Windows x86

esig-0.9.8.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64

esig-0.9.8.3-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (4.3 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ i686

esig-0.9.8.3-cp36-cp36m-macosx_10_11_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.6m macOS 10.11+ x86-64

File details

Details for the file esig-0.9.8.3.tar.gz.

File metadata

  • Download URL: esig-0.9.8.3.tar.gz
  • Upload date:
  • Size: 154.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3.tar.gz
Algorithm Hash digest
SHA256 04665a252ae9352c1930705814398354f64eb606a6fc457287963914007c7b5a
MD5 9b086e9723c1ed907dc3f1651e325a41
BLAKE2b-256 dbf45d1e08b57af7eb627bf17bc87002911878199cd5d85810c388a9ae63f4e0

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c3c6e0da930abf702a3bb1d73f70d06743288e76cdf76a82a712f53cdaa32775
MD5 e44533e08041885e37eea7d49ec6c507
BLAKE2b-256 66d6e1fc519f73c5d67e5748ecfe85b065f3e4c56f35ff717ae8b286d8aa5625

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp310-cp310-win32.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp310-cp310-win32.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 f6bb20c0aedb9840ebf707c66996b4fb0762b9170dd5343bdcc0ed33be4900fe
MD5 605f4bf8f1137bf6dc80dbde3fc1d0ac
BLAKE2b-256 3337ba6a55dbf32ef353c2b255aef876eb458c202d07325509fc4f3f36d867e6

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 89b867f0b846615dc5eea6ce1a049396f246ca3f84ba8b7c0953d2aa97d23103
MD5 22a113f0475b324636cca6f0643b36b1
BLAKE2b-256 8aaf2039a91a56200c6b4744bf9dd0729d15c740a1ece1783cfee8a7951bcee5

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp310-cp310-macosx_10_11_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp310-cp310-macosx_10_11_x86_64.whl
Algorithm Hash digest
SHA256 a9553c4ae50005fa9b9270e43e712d441f2422ca33ae5d059e936b5f90a0e899
MD5 99d45821f86016eafa06bccf6aa383b3
BLAKE2b-256 644a51eed49f2454ca191d92933e9777a00a068bc2ea17cd0ad3b6f207815ceb

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 954bddb8060c29cc7b58f8f61ed657357e76a5562bad43045415eda378f5845a
MD5 ca4db4bc9ac4489025c27ca88490a354
BLAKE2b-256 093c516a40b7763ba536454ab3ef029083740796f235ef8565649c775208bded

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp39-cp39-win32.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp39-cp39-win32.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 b2c71589bebfc3215bce51b9bb440857a9acd56761e54c8dcac869179ed04c02
MD5 07d5d51ade17c0f8b2f5ef299151697f
BLAKE2b-256 f06c158ea60849b54319d17d7ba941d732835931355ed66a130a367776e3a208

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6b9697ca023d2b9296177e67cba90c5000a2c9fb7a9253c64cfcdf069d98f3f4
MD5 54e9851b1ed57d264061b3bdf08458f8
BLAKE2b-256 6232b8a01468b221108c0068cf8de9a8387ed238c26408004d3cdd86f2411c88

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp39-cp39-macosx_10_11_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp39-cp39-macosx_10_11_x86_64.whl
Algorithm Hash digest
SHA256 d4082d18f0e480c0e55693338abedb3371a8459afbf9e1c45ca0fb021e65948c
MD5 41f338ba38869f19204ea530817e618e
BLAKE2b-256 99b48d86d35f53adaae8818dfdc58961c09d80a744b796ccf9a74136fb1a5a75

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 6dae11d3c2bb29ea680e85909087cb29d5079b82fc6329190abe4e783097cd10
MD5 c112050f94a198d36638724049007afb
BLAKE2b-256 c394976ff4231267498676dacb9dfe921421734e4fd8139d9d14197caf4656ad

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp38-cp38-win32.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp38-cp38-win32.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 89741bafb0e7788154453e4f8915dd579f6277fb123454a2b2985ad8200efcac
MD5 67ddf614c06b3410c5af03c2e32834c6
BLAKE2b-256 e98c39c1e72dce3196d5f42e1108bbcd86577ab507ff02031f2d4433ad494e65

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0e93fb73cc69fccf3178863035af769ada990aa97fb01ab0ed450c8e9e118a65
MD5 e3adac3d2c8259f7e995c538750b29cf
BLAKE2b-256 340967565de4f1da820067e9bc71de3d1f0c8c2a489e78fa8a741e5516efb16f

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp38-cp38-macosx_10_11_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp38-cp38-macosx_10_11_x86_64.whl
Algorithm Hash digest
SHA256 20e8402f39c3104db67f2e2c080c945a094673f82a2661e7f945bfec0e00d800
MD5 de23158ab1af0937f737c68340d8c290
BLAKE2b-256 0af24002156fe65cbf7ff82df2f4669313d5e6688a3e8063d19e8ecb34d35d3a

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 81c803abc00ea1183764187d25cd7f8f099512a1a9d9ec57dc7d8cb7b2e8ffc1
MD5 d10fe683b01e9a3acebf43fc4ced8df5
BLAKE2b-256 ba34223b90394ca7eef21aa6117f94625881b72814a39b36d5db88bad8e61e44

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp37-cp37m-win32.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 a747e8a6da42841d813e1ea4b5bfd0e9cd598c254e9e5976d4351721c8dd321c
MD5 43224cbcb291280eece5428d369b2e41
BLAKE2b-256 c4c49afb3a9e59a41fa2404867b892a135594a055363f183814477a98b717760

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 cf3c6b89294dfc9bacc486db25fe4e89b8085eb4987dc3d442883f52fae4a081
MD5 09b058ce2ff6260a8dcca76344fd4134
BLAKE2b-256 c041b6ffb0a56fcd0348d968ad611f73f1cb807fa26475d8c86ad3a50b6e00d0

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 7ee859e50f1e87d17a05c8432d6adae158013cf22e5d8f516c7506af435eacc0
MD5 106a925fa07946eb773ba915dc2bad62
BLAKE2b-256 c60021fe2101b422965582921fe2097bd3a737f900a7147d9827b09f11d46144

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp37-cp37m-macosx_10_11_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp37-cp37m-macosx_10_11_x86_64.whl
Algorithm Hash digest
SHA256 05cf7864c5ac59aa8f70f0d25589dabae28a896c31d3341c7108a113ff852772
MD5 69d99269372891ac74e2e0110516acc3
BLAKE2b-256 dee402defd59d98466b6f5fb4542d5b2bb3bfc78550708efc6a283208cbd347f

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 facf7c23561311177ba74e83b8f271d1155cc0a63dac1864a77da560d41ae3f0
MD5 8b0f2a34433096ad395fec5a29cc15d8
BLAKE2b-256 da1678a8ccf3248f90ce892d3bad045de312ea603aafe4d2f2f282e362db16f7

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp36-cp36m-win32.whl.

File metadata

  • Download URL: esig-0.9.8.3-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for esig-0.9.8.3-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 8844e46e8cf6cafcf6bc67f20e8f1bff130c9ecc2fa7ee6be7e5e13027b0048c
MD5 2a835fb647b00b5dc51839183f500f65
BLAKE2b-256 22b359e42d9672c7a7a6047810cedc314ed0b327d308fc2dd2006f9723937da9

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0011f1d70675334c730613721306b726f57f1faa18408d66d10fc8c7a6aaa0ef
MD5 06618e1f7c9fd80eb0a37d94c24b3939
BLAKE2b-256 7df6e92e79a02a3df52724ebc163fc54a143914294641a0f24dda2ae258c65f2

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 875f45830cc77c3c60ece8b7a9e8d67d0605135eb8def45420951dab8e4d7165
MD5 33e8f39e1a36da085adec07e4d4c843e
BLAKE2b-256 600568b74b1d7909778513b4c145508777c46d80fda14ece2b5b8f5d90d3c117

See more details on using hashes here.

File details

Details for the file esig-0.9.8.3-cp36-cp36m-macosx_10_11_x86_64.whl.

File metadata

File hashes

Hashes for esig-0.9.8.3-cp36-cp36m-macosx_10_11_x86_64.whl
Algorithm Hash digest
SHA256 b58be1b0d89caab3a64675fc1b8327ae12bd57cf83ee4e1be16de772da6c3445
MD5 23c07148355a11203f022bc91ec857d9
BLAKE2b-256 2aacc8f92b7fb9f71790cd5190795ae9056eb591b25b71c94b53099b8995cf38

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page