Skip to main content
PyPI version Apache 2.0 License Join the Matrix chat

najaeda is a Python package for loading, elaborating, navigating, editing, and analysing hardware designs from simple Verilog netlists to large SystemVerilog RTL cores.

from najaeda import netlist

# Load a gate-level Verilog design with a Liberty standard-cell library
netlist.load_liberty(['NangateOpenCellLibrary.lib'])
top = netlist.load_verilog('my_design.v')

# Navigate the hierarchy
for inst in top.get_child_instances():
    print(f'{inst.get_name()}{inst.get_model_name()}')

# Flat connectivity across hierarchy boundaries
for iterm in top.get_term('clk').get_equipotential().get_inst_terms():
    print(iterm)

# Edit: rename, reconnect, delete
top.get_net('old_name').set_name('new_name')

What you can do

  • Load Verilog and elaborated SystemVerilog designs, with or without Liberty standard-cell libraries

  • Navigate hierarchy, nets, and ports at any level of detail — instance-by-instance or flat via equipotentials

  • Edit netlists: rename instances and nets, disconnect and reconnect signals, delete logic

  • Analyse designs with the visitor API and export results to pandas for further processing or visualisation

Installation

pip install najaeda

Requires Python 3.10+. Wheels are published for Linux, macOS, and Windows.

Tutorials

Six hands-on notebooks — open any of them in Google Colab with no local install needed:

#

Topic

Colab

1

Getting started — load Verilog, navigate hierarchy, visualize

Open in Colab

2

Liberty primitives — load a synthesised design with standard cells

Open in Colab

3

Editing a netlist — rename, disconnect, reconnect, delete

Open in Colab

4

SystemVerilog elaboration — load and browse an elaborated SV design

Open in Colab

5

ibex RISC-V core — explore a real-world SV core, collect module stats

Open in Colab

6

Fanout analysis — compute fanout for every net, trace drivers, export to pandas

Open in Colab

License

Apache License 2.0. See the LICENSE file for details.

Download files

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

Source Distribution

najaeda-0.7.17.tar.gz (15.3 MB view details)

Uploaded Source

Built Distributions

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

najaeda-0.7.17-cp315-cp315-win_amd64.whl (5.7 MB view details)

Uploaded CPython 3.15Windows x86-64

najaeda-0.7.17-cp315-cp315-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.15manylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp315-cp315-manylinux_2_28_aarch64.whl (8.5 MB view details)

Uploaded CPython 3.15manylinux: glibc 2.28+ ARM64

najaeda-0.7.17-cp315-cp315-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.15macOS 11.0+ ARM64

najaeda-0.7.17-cp314-cp314t-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp314-cp314t-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.14tmacOS 11.0+ ARM64

najaeda-0.7.17-cp314-cp314-win_amd64.whl (5.7 MB view details)

Uploaded CPython 3.14Windows x86-64

najaeda-0.7.17-cp314-cp314-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp314-cp314-manylinux_2_28_aarch64.whl (8.5 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

najaeda-0.7.17-cp314-cp314-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

najaeda-0.7.17-cp313-cp313-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.13Windows x86-64

najaeda-0.7.17-cp313-cp313-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp313-cp313-manylinux_2_28_aarch64.whl (8.5 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

najaeda-0.7.17-cp313-cp313-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

najaeda-0.7.17-cp312-cp312-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.12Windows x86-64

najaeda-0.7.17-cp312-cp312-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp312-cp312-manylinux_2_28_aarch64.whl (8.5 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

najaeda-0.7.17-cp312-cp312-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

najaeda-0.7.17-cp311-cp311-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.11Windows x86-64

najaeda-0.7.17-cp311-cp311-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp311-cp311-manylinux_2_28_aarch64.whl (8.5 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

najaeda-0.7.17-cp311-cp311-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

najaeda-0.7.17-cp310-cp310-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.10Windows x86-64

najaeda-0.7.17-cp310-cp310-manylinux_2_28_x86_64.whl (9.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

najaeda-0.7.17-cp310-cp310-manylinux_2_28_aarch64.whl (8.5 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

najaeda-0.7.17-cp310-cp310-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file najaeda-0.7.17.tar.gz.

File metadata

  • Download URL: najaeda-0.7.17.tar.gz
  • Upload date:
  • Size: 15.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17.tar.gz
Algorithm Hash digest
SHA256 a777f42cc409819240806ca441f5b4b1709ddc3a216b8fdedee1712713ef061a
MD5 1fa2a565d69f98f917d7727437faf766
BLAKE2b-256 98a124e9ea4a22971bd75eb374b03c5f10ac0556110f287a29f7ab5a96811729

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp315-cp315-win_amd64.whl.

File metadata

  • Download URL: najaeda-0.7.17-cp315-cp315-win_amd64.whl
  • Upload date:
  • Size: 5.7 MB
  • Tags: CPython 3.15, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17-cp315-cp315-win_amd64.whl
Algorithm Hash digest
SHA256 17f5320d85698df021b770d7e72dae9ff815e8082407a366b41da600a0bde710
MD5 9727e0ba315b392ba5bf6fccfef2cc18
BLAKE2b-256 962d15e9928a8fd628a576bc5fbdc67b4cb21191447de74ee7be6cb5dafcb5a6

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp315-cp315-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp315-cp315-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 b2cdc7347a85f722b5332f0cc536c28e286a366c47affae7d768c4190364ebe4
MD5 54c84c6c033a0e0b85c359219fd9e4ab
BLAKE2b-256 8122b9c0ae5e180925254de20f0c58c0e6f6a36737fb8901254c76f483f45fd3

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp315-cp315-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp315-cp315-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 8bfb84554e17d92a7b880f6edccf0ec5a42a5cbe95fcb1f55bec6fa4d6242099
MD5 f147a97ad6edc022d95b88690651523a
BLAKE2b-256 bb786f3937d82b60df2d8821de94e8617415dd55519eaa7ff36b9752573eb6fa

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp315-cp315-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp315-cp315-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9a86786f025017b745a1722dc11f1d1c0430333e40a78eabe7a72df39bbee74a
MD5 043efbc52edfb46603e63052bc13aa23
BLAKE2b-256 4edb5f9b7b1d06da42e1d672b5f7d178fb5e21bb81ae55bcd3e9945b42af3e4d

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp314-cp314t-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp314-cp314t-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3e20d312e1bd534a6f859508da75ce897cb9d1d8f0969a98384ab53340ed541c
MD5 5e70dfe5de8f1cc889a3cae6434963b1
BLAKE2b-256 c08cea30d92203955124124f36e3be9510ba3e8a8d57d5a97e7e25ea8b6c9366

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp314-cp314t-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp314-cp314t-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 89396b217319103ff43d631c5347f8538c8f77ace157233f5cbcf6e43a0cddab
MD5 b16808046b6802f3770c27a5ff7b75ad
BLAKE2b-256 2a4f35bd1848ed0856273b85b9773bf70d868fe84f6ea5e58f9c3282fded7a80

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: najaeda-0.7.17-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 5.7 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 18e0d75987aa9b1cd32fe50ab06c86884dc24543ef7f22263f5df955ad0d23f3
MD5 79b0ee80e249000d4b5d0c1330d2c7c0
BLAKE2b-256 885380d67310fa78f5846208debd34e7f5f0cda05fda737719edd91e1d247c89

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e635cb8f05f162dd2debc03abd7cf6016d62fcb02a5c618194133bfe1700288a
MD5 d7e7e3a909fbd698d18bc80ebf51c13e
BLAKE2b-256 d90d066a06a719507baf466f439af6f489fe818d0218ac4cce38315cac031c75

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3629793aabf66184d0cfe62f4ca9a9b87a3a2f7bf3a4a917f436b5c2848dddaf
MD5 cdb61ac0caf077114da784c24db3eb50
BLAKE2b-256 9482f4ac2bab135be126d7809dce0599b00b6a858b0c577424cc47e6961c1693

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7fbe7281fbaf6543bf53aa6f2a70b25da30b28f95fa4fb67d4682292581129fc
MD5 0983c504018037de238def5577f5a61b
BLAKE2b-256 7c34fee8c445aed4d27d6ca6b9e408617088ff9d3423112f48ce32ffacdc6ee5

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: najaeda-0.7.17-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 b5bebdb89e075a7989cd05201451e2dfe457a9a8e988a6df0391164d723dcdb9
MD5 88ff93ef23fd06fb2fd26195a5d0e779
BLAKE2b-256 8e43b210c20e0483cd3cba33d53ddcd94759559c518cb138f1e9c62860a6852e

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 856cfce2190a0f5e56ca489c4e41fca412ee015aff5a9dd963bab2db6ab62d01
MD5 c93c2f94f1696585cf75bcb5a8a300d2
BLAKE2b-256 e7d5b66a184f6b856be7270b1e4e319321ad65c197c2d78e150489872c9be3e7

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 b629135415c96d35f7aeac6f06bf043d3bff41a4f6fd79b1dc887c510db02964
MD5 28d637008d9f218e616c217567be19d6
BLAKE2b-256 e6474842895e44c40f1b377f0a7514974b8b435b9ed3544ae19edc6603d521ea

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2b635fc3f56a85e854eda92111e4d4291d7426424fa2d2c732daef708df27f4e
MD5 6cd4c7dc5fcf65aa1345eb00e65aa96e
BLAKE2b-256 696bfcb5879ce30aced8de1249d52d3d67afec5762405ce6fe6b407d59f5b30c

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: najaeda-0.7.17-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 ec43ab6e7fbd97f4f25195c8e11037e3978d18ab60602708f468dfa02f984262
MD5 c29541cfba0dec09d1249006605abad3
BLAKE2b-256 a133938d288e969c1d2378f93924071ff20572397b9065d652a8b92c5752710a

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 db54150f3ad5b4b2a9886d810bef0e3284e9af7e27d34d0ef9b38c34372c43b6
MD5 84111ca55e4b101782c969556f4edff1
BLAKE2b-256 ef4604dbac56a9226dd63c859f6da38f6f0eebe51bd989621523bcef521f51ef

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 006ab891af5c42420faded3dc24ee6d71b4ce82bee147510fd984c069d6e5619
MD5 3f2397b8fe9222b7afd0f0c0f6025854
BLAKE2b-256 348b4f40dffcdbf847b0bc0824ab21560c34cfd933372a3c944d4aaba492b7c5

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cb37232b1cf6152e77347a42eef5ffc169e46745dde21fbc543ed0a643f448a1
MD5 55b6b70a541a59485447f0fe4cd4876b
BLAKE2b-256 af196913665848ff9c1cbe792be693b134ccbb357a73be75ecbae1e5e8f1fd35

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: najaeda-0.7.17-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 5ecbc4071786e0db2833dc8241ec1ec847c3e06016c9ce0f283d1b0b962994d6
MD5 b045decc83e09fc32dcd65e0e2f4cd2a
BLAKE2b-256 3851f41d7516fc3aa535c3c648a5a7495c2687e6ed47a15b9fc02f27a38d6dbc

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ddc0d34bd3fbaf49b5b0cf3613c4b24b0645b585104fffe382cea4d45d28ceb1
MD5 c7e3311667db2e6a81d405bd0615aca8
BLAKE2b-256 2a507d2d40c6bc04f14f3a4ee56769776b91443c001bd95b0a8b075ddd6a4420

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 d370354ea7e3d086b4d1d491c2f408a016c2d6776472264b8ae18d5ff00220ac
MD5 13b02506b43394ff219f9f542c2f2f02
BLAKE2b-256 1964bf766df013f608c8de42e4d33731a3e9f462ca703b9ae90ab6b50014b0c1

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 76095e5a3eb6a9031af39b020c3dcb9de29168f812f135dfff2a3d51d63dd90c
MD5 5400818ef40bdb8fb4d26ea870802cd8
BLAKE2b-256 e962c06cd7f34242f1a22765738a7bf58b6abbfbfdeed5f63b6807b5ebf054ab

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: najaeda-0.7.17-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for najaeda-0.7.17-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 37248435f43e77bdb082d98a0376dd51b96e2c4bb54f8a5a5655917a7eb5086e
MD5 71e1ec46246573ac3a0af4245d8bba83
BLAKE2b-256 c81bdcbf96b0b8cf675d427a1e7fbfef209a122e22554a19a52e7cea8e2135d2

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 56a2570f07bbbf2b3fa4f59369f73556e47a1dcefab7659b2dc85a00da1df2de
MD5 5a670b54e12d1e3288c5c847a8624dd1
BLAKE2b-256 f7c8f458563b494d5fbce0c65ea581362f662097b01a855b7a3c38d65077b5d2

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 09cb8d04a8340d15343f57cd3903d6f4007053a0e1c61d7bb39d790bdf4774f7
MD5 d518c13d76720ddfa95e2e8bef2ee914
BLAKE2b-256 68f0ff17974ec4c1f86ed30ea26674868db3634172e12052ec4fbf21c5c36402

See more details on using hashes here.

File details

Details for the file najaeda-0.7.17-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for najaeda-0.7.17-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cb522c7639384962813d5b8f3cb94c1c96ab8e5d300ffbbdc41e674490db6458
MD5 03ef247350af7dabefd60a151be87fac
BLAKE2b-256 f915471368eee094615eac0183767f0cb164d7be6ddd43cf83f903dc4630aebf

See more details on using hashes here.

Release history Release notifications | RSS feed

Supported by

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