Skip to main content

CDL: A fast and flexible library for the study of permutation sets with structural restrictions

Project description

Condorcet Domain Library

Version Python 3.8 C++ License

Condorcet Domain Library (CDL) is a flexible header-only library written in C++ and offers Python Interfaces as a module that can be installed and used globally, enabling users to seamlessly integrate with tools written in Python. (CDL) provides a wide range of functionalities pertaining to Condorcet Domains (CD) and forbidden permutation, including

  • Ordering k-tuples, and rule initialization and assignment;
  • Domain construction and size calculation;
  • Subset functions and domain types verification;
  • Hashing, identifying and removing non-isomorphic domains;
  • Native support for general forbidden permutation domains;
  • Support all 6 rules: 1N3, 3N1, 2N3, 2N1, 1N2 and 3N2;
  • and much more.

CDL supports all major operating systems, including Windows, Linux and MacOS. Users can install it as a python module using the provided bash scripts.

Directory structure:

  • algorithms: for testing and benchmarking many learning algorithms, like genetic algorithms, reinforcement learning algorithms, and local search algorithms, etc.
  • bind: export all the C++ classes and functions to a python module and provide the bash script install it.
  • core: the key functionality for manipulating tuple-rules and performing domain-related operations.
  • python: provide depth-first and breast-first Prioritised Restriction Search (PRS) search algorithms
  • utils: provide functionss

Get started with Python

Working with Condorcet domains

from cdl import *

def alternating_scheme(triplet):  # build the alternating scheme 
   i, j, k = triplet
   if j % 2 == 0:
      return "2N1"
   else:
      return "2N3"

cd = CondorcetDomain(n=8)  # initialize the Condorcet domain object
# initialize the trs with the predefined alternating scheme 
trs = cd.init_trs_by_scheme(alternating_scheme)
domain = cd.domain(trs)  # construct the Condorcet domain
size = cd.size(trs)  # calculate the size of the resulting Condorcet domain (222)
assert len(domain) == size  # True

# change the rule assigned to the triplet [2, 3, 4] from "2N3" to "3N1"
trs = cd.assign_rule(trs, [2, 3, 4], "3N1")
size = cd.size(trs) # the size of the new domain is 210.

cd.init_subset(sub_n=6)
substates = cd.subset_states(trs) # get a list of 28 subset states in 6 alternatives

# build a list of domains
domains = [cd.domain(cd.init_trs_random()) for _ in range(100)]
# filter out the isomorphic domains
non_isomorphic_cds = cd.non_isomorphic_domains(domains)  

Working with Forbidden Permutations

# recreate the alternating scheme by forbidden permutations
def alternating_scheme(triple):  
    i, j, k = triple
    if j % 2 == 0:
        return ["213", "231"]
    else:
        return ["132", "312"]

fp = ForbiddenPermutation(n=8)  # initialize the Forbidden Permutation object
tls = fp.init_tls_by_scheme(alternating_scheme)
domain = fp.domain(tls)
size = fp.size(tls)  # 222
assert len(domain) == size
from cdl import ForbiddenPermutation

for n in range(5, 11):
    # initialize the ForbiddenPermutation object for 5-tuples
    fp = ForbiddenPermutation(n, 5) 
    tls = fp.init_tls()
    for tl in tls:
        # assign all the 5-tuples with the law [2, 5, 3, 1, 4]
        tls = fp.assign_laws(tls, tl.tuple, [[2, 5, 3, 1, 4]])
    print(fp.size(tls))

Installation for Python Program

Pip install CDL

Install gcc and cmake. Then run pip install condorcet-domain in the terminal (command line)

Bash install CDL for Linux or MacOS

  1. Open a terminal and download the CDL repository to your laptop by git clone https://github.com/sagebei/cdl.git
  2. Change working directory to the cdl/bind folder
  3. Install Python3 or anaconda, gcc, cmake if you have not. You might need to load the gcc and the cmake module by running module load gcc cmake if you are using a server machine.
    • Installing to an existing Python virtual environment: Run source install.sh \path\to\your\virtural_environment to install the library to an existing virtual environment in which you will import it. (This will download pybind11 libray that is essential to compile the code, and install the dgl library to the site-package folder in the virtual environment. Examples: source install.sh \opt\anaconda3 to install the library in the anaconda global environment, or source install.sh ~\PyCharmProjects\venv to install it in a virtual environment created in the PycharmProjects directory.)
    • Creat a new virtual environment: python -m venv /path/to/new/virtual/environment. Then follow the above instructions to install the CDL library in it.

Bash install CDL for Windows

  1. Install git, Python3 or anaconda, gcc, cmake if you have not. You might need to load the gcc and the cmake module by running module load gcc cmake if you are using a server machine.
  2. Open a Git Bash terminal, and change working directory to cdl/bind
  3. Run source windows_install.sh \path\to\your\virtural_environment. For example, source windows_install.sh /D/Anaconda3/Lib/site-packages/

Get started with C++

Working with Condorcet domains

#include "condorcet_domain.h"

std::string alternating_scheme(const Triple& triple)
{
    if ((triple[1] % 2) == 0)
        return "2N1";
    else
        return "2N3";
}

int main()
{
    CondorcetDomain cd(6);
    auto trs = cd.init_trs_by_scheme(alternating_scheme);
    std::cout << (cd.size(trs) == cd.domain(trs).size()) << std::endl;

    CD domain = cd.domain(trs);
    CDS domains{};
    domains.push_back(domain);
    domains.push_back(domain);

    CDS new_cds = cd.non_isomorphic_domains(domains);
    return 0;
}

Working with Forbidden permutations

#include "forbidden_permutation.h"

std::vector<std::string> alternating_scheme(const Triple& triple)
{
    if ((triple[1] % 2) == 0)
        return {"213", "231"};
    else
        return {"132", "312"};
}

int main()
{
    ForbiddenPermutation fp(8);
    TLS tls = fp.init_tls_by_scheme(alternating_scheme);
    std::cout << (fp.size(tls) == fp.domain(tls).size()) << std::endl;
    return 0;
}

Cite

Please cite our paper if you use CDL in a scientific publication.

@article{zhou2023cdl,
  title={CDL: A fast and flexible library for the study of permutation sets with structural restrictions},
  author={Zhou, Bei and Markstr{\=o}m, Klas and Riis, S{\o}ren},
  journal={arXiv preprint arXiv:2309.06306},
  year={2023}
}

List of publications used CDL

  1. Zhou, Bei, and Søren Riis. "New Record-Breaking Condorcet Domains on 10 and 11 Alternatives." arXiv preprint arXiv:2303.06524 (2023).
  2. Akello-Egwell, Dolica, Charles Leedham-Green, Alastair Litterick, Klas Markström, and Søren Riis. "Condorcet Domains of Degree at most Seven." arXiv preprint arXiv:2306.15993 (2023).
  3. Karpov, Alexander, Klas Markström, Søren Riis, and Bei Zhou. "Set-alternating schemes: A new class of large Condorcet domains." arXiv preprint arXiv:2308.02817 (2023).
  4. Karpov, Alexander, Klas Markström, Søren Riis, and Bei Zhou. "Local Diversity of Condorcet Domains." arXiv preprint arXiv:2401.11912 (2024).
  5. Markström, Klas, Søren Riis, and Bei Zhou. "Arrow's single peaked domains, richness, and domains for plurality and the Borda count." arXiv preprint arXiv:2401.12547 (2024).

Our Team

CDL is developed and maintained by Dr Bei Zhou and Dr Soren Riis in the theory group at Queen Mary University of London, and Professor Klas Markstrom from University of Umeå.

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

condorcet_domain-1.3.2.tar.gz (4.4 MB view details)

Uploaded Source

Built Distributions

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

condorcet_domain-1.3.2-pp310-pypy310_pp73-win_amd64.whl (190.5 kB view details)

Uploaded PyPyWindows x86-64

condorcet_domain-1.3.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (221.3 kB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl (185.3 kB view details)

Uploaded PyPymacOS 11.0+ ARM64

condorcet_domain-1.3.2-pp39-pypy39_pp73-win_amd64.whl (190.5 kB view details)

Uploaded PyPyWindows x86-64

condorcet_domain-1.3.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (221.1 kB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl (185.2 kB view details)

Uploaded PyPymacOS 11.0+ ARM64

condorcet_domain-1.3.2-pp38-pypy38_pp73-win_amd64.whl (190.4 kB view details)

Uploaded PyPyWindows x86-64

condorcet_domain-1.3.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (221.1 kB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl (185.2 kB view details)

Uploaded PyPymacOS 11.0+ ARM64

condorcet_domain-1.3.2-cp313-cp313-win_amd64.whl (192.8 kB view details)

Uploaded CPython 3.13Windows x86-64

condorcet_domain-1.3.2-cp313-cp313-win32.whl (161.7 kB view details)

Uploaded CPython 3.13Windows x86

condorcet_domain-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ ARM64

condorcet_domain-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (223.7 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-cp313-cp313-macosx_11_0_arm64.whl (185.8 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

condorcet_domain-1.3.2-cp312-cp312-win_amd64.whl (192.7 kB view details)

Uploaded CPython 3.12Windows x86-64

condorcet_domain-1.3.2-cp312-cp312-win32.whl (161.6 kB view details)

Uploaded CPython 3.12Windows x86

condorcet_domain-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ ARM64

condorcet_domain-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (223.6 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-cp312-cp312-macosx_11_0_arm64.whl (185.7 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

condorcet_domain-1.3.2-cp311-cp311-win_amd64.whl (192.2 kB view details)

Uploaded CPython 3.11Windows x86-64

condorcet_domain-1.3.2-cp311-cp311-win32.whl (161.6 kB view details)

Uploaded CPython 3.11Windows x86

condorcet_domain-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ ARM64

condorcet_domain-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (224.7 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-cp311-cp311-macosx_11_0_arm64.whl (185.5 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

condorcet_domain-1.3.2-cp310-cp310-win_amd64.whl (190.9 kB view details)

Uploaded CPython 3.10Windows x86-64

condorcet_domain-1.3.2-cp310-cp310-win32.whl (160.7 kB view details)

Uploaded CPython 3.10Windows x86

condorcet_domain-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.10musllinux: musl 1.2+ ARM64

condorcet_domain-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (223.0 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-cp310-cp310-macosx_11_0_arm64.whl (184.4 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

condorcet_domain-1.3.2-cp39-cp39-win_amd64.whl (186.1 kB view details)

Uploaded CPython 3.9Windows x86-64

condorcet_domain-1.3.2-cp39-cp39-win32.whl (161.0 kB view details)

Uploaded CPython 3.9Windows x86

condorcet_domain-1.3.2-cp39-cp39-musllinux_1_2_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.9musllinux: musl 1.2+ ARM64

condorcet_domain-1.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (223.3 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-cp39-cp39-macosx_11_0_arm64.whl (184.5 kB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

condorcet_domain-1.3.2-cp38-cp38-win_amd64.whl (190.9 kB view details)

Uploaded CPython 3.8Windows x86-64

condorcet_domain-1.3.2-cp38-cp38-win32.whl (160.6 kB view details)

Uploaded CPython 3.8Windows x86

condorcet_domain-1.3.2-cp38-cp38-musllinux_1_2_aarch64.whl (1.2 MB view details)

Uploaded CPython 3.8musllinux: musl 1.2+ ARM64

condorcet_domain-1.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (222.9 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

condorcet_domain-1.3.2-cp38-cp38-macosx_11_0_arm64.whl (184.3 kB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

File details

Details for the file condorcet_domain-1.3.2.tar.gz.

File metadata

  • Download URL: condorcet_domain-1.3.2.tar.gz
  • Upload date:
  • Size: 4.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.7

File hashes

Hashes for condorcet_domain-1.3.2.tar.gz
Algorithm Hash digest
SHA256 59db44ff634bad0640238e489ef3463f285b84736c03c799d24f9a14001423ab
MD5 d11007e2b00df6d3860378d4b94a7903
BLAKE2b-256 75745b5d33be7db961f0d4ab2288dbfcdf5950086dc6ed98006e8235eccae004

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp310-pypy310_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp310-pypy310_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 c9203372bb5fc15505701871b0e61578d876f506c1e559bed025fe5eca3b0a6c
MD5 0760c01d9f77f173fbe19a2a9de090f9
BLAKE2b-256 5b14dc3a4950c612876e9d03176a67b25fa252dc978f33a26ce75666f9720763

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 49ae115a546c92afa5355da4c388e6ce04a4887100a07997215ee7a5b1df9dc3
MD5 42682f8e91e0280f6389c15061eefee4
BLAKE2b-256 f438f29c44dd19d100eb332e7d0731dbdca52e510d276e7b2f67d8ba1bda85b5

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c4deb37118398e8f28c12e7f36be194d54ddc2939e4f89c882f22f2689496f16
MD5 29e6684e353323572c27800a84c2cef1
BLAKE2b-256 bfadfac24fd30b177920411a4c4de4ce8fec97309254168a6e503e9dfe45a10c

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp39-pypy39_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp39-pypy39_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 8d9124c5e673553d38b1dafd8757f7c5b0a4a31495ef827c7a614807b7bbe7b9
MD5 8593085a08f19079bab2c96d31d24b17
BLAKE2b-256 4369fd8515bf3c304a264e14089567a727a9a12b203bad1c97b19c0710e0dfe7

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a282820141e652621b44cf576f8f3cbd6a1c01cdbd15c9e9b128b59c57838636
MD5 38eda3d73f697c7a33c2eb96f79d019c
BLAKE2b-256 2cd292efde864fc5f6dc0af7a1cf68fd0663e800ce08d6145d9dc34ac6e92c0d

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c1077d1a4ba4354f84dacd7b882964454f3c044a6d352ff3598004d750f15d8e
MD5 74314a256e6311d0634050fc27febdec
BLAKE2b-256 eaddc2771f9ec91bb1d3836569ff1e8c696acaf23743aa5158ce4980b63f83e3

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 7c25bd505a850989eb08ded251651d0d5ff8479aa20aa2ab468080a7872e7817
MD5 c5bb1572beafa9f3f2b1038bebc37c05
BLAKE2b-256 cbec1d5032714da8e2eb12398ee2bd46d71161fb02c91a5ea93472a07194d092

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 aeb637b127b2c98102191f3de10599de57d30b48d9e8b3c31301e1d02a57dd86
MD5 0d6dadd8325cd2d5860920736f220b5e
BLAKE2b-256 16fddcc516b1de853a0153fbdc8447d0a149a5bbbe4e497ccff9179f968b5f6e

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4f299d0840e3f188d66a34ae2c2a6d27b23d7b6e4db5f9f41d7bfaf53228b07b
MD5 17f5096465e8ed20ce79bcbbf20855f6
BLAKE2b-256 e7834c05ca6c956c001bd9d76a7f839438a78a94190705800be5f65356e1f996

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 e387faadd23e6232f77c735d00622f719539e67883e28a17efdf4aacf20a6bb7
MD5 597945777ccb0d7825f081196078208d
BLAKE2b-256 4e109ec733e49a90922dbb67b30ea35b4b7148001a381cb1d617c60bf41f1f0e

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp313-cp313-win32.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 81f4cc9e971d5b2115aba7d1cdfc84bcbe09e8a20d4fffb966a7b61c032c7302
MD5 66c43c5280209ed5e808693a54e96c62
BLAKE2b-256 6492a780eed73826ca8ed252301ee8be3991b5b8cd56a7fe851da9968c65a2ad

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 f1716696c532f59b281cb43d4451238b9617d4f500fc15a784a4d8cba93e912f
MD5 9d724c8f154a787c78c32c8b5ef07c97
BLAKE2b-256 13a718945684c72471d12ef6bba02658a292904c37f0703fc45b2090036eddba

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b89911bd73a103c458da8cff7753538ebbe6eb8da1919d411cbf163f493b1b5d
MD5 b046c5b16db82ce3d73728b297832a78
BLAKE2b-256 fa7ff1b39c443a51406fd236daa06644aa42710b17bf8734ffbbe67fea7955df

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2aec92b69c21a858e4e45a8bb67f0132f6cc1a23292df5d6f58122b6cc780c9a
MD5 336837ba30777e0403289275d2717858
BLAKE2b-256 cad347c21efc7a0e1db3689fb82f9a9e78b4c669e392fcdde3605e5ec4c04623

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 d394e4eb6a7645815e9ec87d83c92d5a7b13133169fc1589c0b4802f70e1e7a4
MD5 483b00c93d2d8e33593328e9b205e642
BLAKE2b-256 1235572a716be9b5ed6035c7e73d1ddb57137561efc27e6210133779ef2a042d

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp312-cp312-win32.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 67c4dce5218f6f6c79625aec4421303faec3276b13944b2078da34132fcfb7e0
MD5 670716eea2f5ad9a23387e99dc9ae9e8
BLAKE2b-256 1b2cc44b81fca00d6ffe9367f943ff59e2ab166ea0248cba72f8f6686fc0db73

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 e41e8e1b8bf6e6a56e5c72c2701132dcc0bbff0006e89be3b7396c4f0036d95e
MD5 d0519e45457a30e613d7c9e9c4a37d57
BLAKE2b-256 09e4ee976b227aa473c695e653a4a09a6d1a105a895776fdc30adb426a2298e6

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 19f68e90a4a96e015c6059cc52120ad6f61f6fddddc3fbc5ac91def55e711696
MD5 e49a8ac5bf6f9c3f048cf2b7cc01dee0
BLAKE2b-256 a9ee9be4d1cb4954f0ac68f03891cdb86240dc9ff2b44cab05fc5333407d64d0

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8e41ec37505a01c8d5faa23eceb86bda0d111c6cb35e381a1c3ebd244582ea68
MD5 be6011d4c4c6f9c8e1d10f0d63c1b8cb
BLAKE2b-256 70e50274501432677500b28148ec1ee0862619aa5584d51b2f2d1b80de557c91

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 bc616972dc0a08d273d94b0c5aa750704f5b5a8d49aed36689d524106db95648
MD5 7a47f3785d94436679bb39731c48df3d
BLAKE2b-256 d2edc651effa8a148477ad985d5916e948fd713291295f7551ab55231e019f68

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp311-cp311-win32.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 a4ceba0712701c1aafc3d370b581e35d6eb0c184e8866ee977cfe344a0fd1b44
MD5 2040e3880fa9dd2d1dc1ef3940d038d8
BLAKE2b-256 6b855c3520047eb860743fc0b4d9594e066ea6d68a288ca49d5a5ba1a76be501

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 b3c1c6afa04a6ca7b8c2422243404bb4dd77a7bbe3b9d5442049ae45dcc75a56
MD5 3fd840587b5e4d2ace3eedd34c2ce81e
BLAKE2b-256 cca45cf26ac2e730d0ef8c430890da7db03e9cf2035d34a59e607c0b3841c180

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a7ac21f95456cc6cd3e96967bee38b162442e6864fdba68116f1326e7e3e6124
MD5 659a04c3b8534c6938cdb91679bd58e6
BLAKE2b-256 d7b294d5a46c3e52b679149acc27610bea38786bbc23a94776d388f0c87b0e39

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ca13c40dc429960bdfc890d6e0f4ac47dd2de05f5f38c6fdf0ce42381829a46b
MD5 55c8e817cd3937a6a829946fa96d25e1
BLAKE2b-256 858585bb203720649b49c94f8e38c6c8f4f7c392423764a6f2bff46282f925cd

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 5032bef9c209c13e3e70ed1bcd8b3f590a5386ea257405ba572f98a528858b88
MD5 0678f55b65d10636fb9ab8874d0c5f18
BLAKE2b-256 960781e07de50e5fabf11295470cd92508ec32d313e0f4ec46b7fa13a028c7e4

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp310-cp310-win32.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 80422fe08009900399adf77801949bc131bdbaae16088135bd22dcf033cd3eb8
MD5 b4a0c89a9336b6328d6126ad4475f09c
BLAKE2b-256 09ba8ebd0fa126fbac85ab33c612052750e8a818536a53d6cf481ce60cb4d041

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 e344465e3f71603b7873304dd441e69dcaf94e4ae665abbe8389f14a01f60f36
MD5 7f6133c3e43e31e92ea627eed270bb20
BLAKE2b-256 d1f7b0c29c22ec23e9b19b2822818bd8104bfdc8b2fb18e912ec02c777cc7b43

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8bb6d06e4fd962c4ebe851e861baaa07f8394dff02afd3faeae85d30a43a91a2
MD5 01174ca8fb426736f350ecb381e43ff9
BLAKE2b-256 ad7df6151fd07610bed2b2bfdd95d395b66158bf47938a0a29a22736326dd2e5

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4a7bcc0c0d5e78a13bd8af4fb3018eaf59a000bdabfbf84a87487103151fbda8
MD5 6cca6cf91ba905b43ba58690d4219b72
BLAKE2b-256 9c543abe23ea8bf1bda3ac52a38a49c014b939d0823e2999b67708a41f0d94ea

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 4e81f7262fd49ac51311a5b15f3d08ddaffcaa66972dd5c121a95e4a931cd2ec
MD5 3ff1f999c50105e509acda2b9c1c78bb
BLAKE2b-256 14983bcc29b9032ede0565e6ee27436a11a5ec009b877ad3313e97b9ea414d28

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp39-cp39-win32.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 e5ff24001123d78f6b318b5cff345d320be25cadfb21c01bf2df20a8816bd53b
MD5 b0e89f868000b24efaf7a447d71db3e9
BLAKE2b-256 fd5a238675457c54fda14d67d7bb7aa705429a221240dc4dd89d4e1fdf4dd82a

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp39-cp39-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp39-cp39-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 aebc2cf6e87ab5efc1998fb3eddc74e120a5cf754279c60b323b6a5c5b3a5d49
MD5 cc9ba5fda6644133cae7268494c46d66
BLAKE2b-256 c62ffc6e0ba83f34c241af33f3936d9212a99d41b23818597287b05d5beaf07a

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 07d576313d50dfd412f6ef37e8683ee3ec0eb3ab7bdf638179ce3fb5f0b0d9aa
MD5 fa103edb1fb6d7e61c914cf31835188c
BLAKE2b-256 04deb4ae8e8594bd4d5d59ea095d023ac0dc9249ce9fc44e1ada435d64ca76cc

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d2e4bcec45faaf153e3248ed0bb4647f3bf6d5b2d1a30d4a43e30246090ffa0f
MD5 b2aae124f0e3a56bf1b6d59be85adf60
BLAKE2b-256 5213bdd73d7335f856837c58b6f25fa174a1c21f59d8872d2f59799c2942b3fc

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 5a6e898a3c5bf59a128385796c218d917431bd612296e464470150dc2f70d35b
MD5 4afa6bdd51ec6d950e039b70f9f8b209
BLAKE2b-256 49263ed5758d8dea2761be1f689eb3783bdddd00545f9dcf8adc0218fb9a90b7

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp38-cp38-win32.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 08b4fd7d0910029b12549494be1b9c99abf39f01c92ff88d933d75f0cd9063ec
MD5 ab2fa5742b921b4dd646c5828e374f3d
BLAKE2b-256 496eec368f15c162862bc58599d4af87c53d128b2fb0089cd4011acfdab0826e

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp38-cp38-musllinux_1_2_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp38-cp38-musllinux_1_2_aarch64.whl
Algorithm Hash digest
SHA256 564e03852711a9246e030306151573e9288a52721cfffd30ffd8b864d68eba21
MD5 5d8ae06500b19efc9ae05a8c3121bfb9
BLAKE2b-256 e532289c7ca58af9e65dfa207f22b6c5415dcc7499ff0a814d2f89ffc6df2359

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7f2f41fa8f1aedd2e91a6b5de3e07ab21ee8e2d25380ef5aad40b449df2c1d69
MD5 440a42fbe880f1fcd607c87e01c8c1df
BLAKE2b-256 9c2eba3e9fa2357063cefb7db5c0ece74d092fc4d5b4c331679935d2348eb509

See more details on using hashes here.

File details

Details for the file condorcet_domain-1.3.2-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for condorcet_domain-1.3.2-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cb0f3169fb589455dc5a4302d04eb4aad33bea42703dd8111c5585e9769880e3
MD5 68ffb57ce9662df87f7302300442e49b
BLAKE2b-256 d62a4e7237779ddc927c7f864fe7ed5dc7d084799c333d67725c4c7d18590edb

See more details on using hashes here.

Supported by

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