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Naja EDA Python package

Project description

PyPI version Apache 2.0 License Join the Matrix chat

najaeda is a Python package for working with gate-level netlists — load, navigate, edit, and analyse designs from simple Verilog files to large SystemVerilog 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 structural Verilog and 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 3.8+. 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.

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MD5 ead8cbb2c4f1fd7646cef13dead68cd2
BLAKE2b-256 a25ee359c236302a0c4c5e0233b85a6cd953b97f1694b4400db5e9854193ae74

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