Skip to main content

EffeTune for Python

Documentation: effetune.frieve.com/dsp/

Graph v1 is opt-in. Its current capacities and delay-storage accounting are published in the Graph v1 guide.

Source and issues: Frieve-A/effetune

effetune.__version__ comes from installed wheel metadata. An unpacked source tree without distribution metadata reports 0+source.

EffeTune is a deterministic audio-effects library backed by the same host-neutral C++20 DSP core used by the EffeTune application. Version 0.8.0 provides 92 semantic effect classes, ordered serial chains, stateful block processing, semantic presets, bounded impulse-response bundles, and a small audio-file CLI.

Install and process

pip install effetune
import numpy as np
import effetune as et

frames = 512
phase = np.arange(frames, dtype=np.float32)
mono = (0.5 * np.sin(2 * np.pi * phase / 97)).astype(np.float32)
audio = np.ascontiguousarray(np.stack((mono, mono)))
chain = et.Chain([et.Volume(volume=-6)])
output = chain.process(audio, sample_rate=48_000)
print(output.shape, float(np.max(np.abs(output))))

Graph v1 quickstart

Graph v1 is opt-in routing for branching and merging:

import numpy as np
import effetune as et

audio = np.full((2, 128), 0.25, dtype=np.float32)
graph = et.Graph.wet_dry(
    et.Volume(id="wet", volume=-6),
    dry=0.5,
    wet=0.5,
)
stream = None

try:
    offline = graph.process(audio, sample_rate=48_000)
    stream = graph.stream(48_000, channels=2, block_size=128)
    continuous = stream.process(audio)
    print(
        float(offline[0, 0]),
        float(continuous[0, 0]),
        stream.latency_samples,
        stream.compile_snapshot["effectiveSchedule"],
    )
finally:
    if stream is not None:
        stream.close()
    graph.close()

Generated effect constructors and create_effect() accept Python snake_case keywords:

shift = et.PitchShifter(pitch_shift=3)
same_shift = et.create_effect("PitchShifter", pitch_shift=3)

Chain JSON and scheduled event parameter objects use semantic catalog names, such as pitchShift. CamelCase semantic names are not constructor aliases.

Audio arrays are C-contiguous planar float32 with shape (channels, frames). Offline calls return a new array and start from fresh DSP state. No resampling is performed.

SoundFile returns (frames, channels). Convert decoded files explicitly:

import numpy as np
import soundfile as sf

decoded, sample_rate = sf.read("input.wav", dtype="float32", always_2d=True)
audio = np.ascontiguousarray(decoded.T, dtype=np.float32)
output = chain.process(audio, sample_rate=sample_rate)
sf.write("output.wav", output.T, sample_rate, subtype="FLOAT")

For persistent filter history and tails:

with chain.stream(48_000, channels=2, block_size=512, seed=42) as stream:
    first = stream.process(block_a)
    second = stream.process(block_b)
    stream.reset()

block_size must be from 1 through 16384 and controls the largest native processing window; process() may receive a longer array and partitions it internally. Parameter events use frame offsets relative to that process() input. They must be ordered, identify an enabled effect with an explicit id, and provide one or more semantic parameter updates:

output = stream.process(audio, events=[
    {"frame": 0, "effectId": "voice", "parameters": {"threshold": -24}},
    {"frame": 0, "effectId": "voice", "parameters": {"ratio": 6}},
])

Each event is merged with the effect's current parameters. Frame zero applies before the first sample. Multiple events at one frame keep their supplied order, so later updates see earlier updates. The final frame is not an event position. reset() restores the initial parameters, state, and seed. Events cannot change parameters that require convolution assets to be staged again. Open a new stream after changing IRReverb.channelMode, latency, or convolutionRate; FIRCrossover.bandCount, latencyMode, or filterDelaySamples; or latencyMode / filterDelaySamples on FiveBandFIRPEQ, GroupDelayEQ, GroupDelayPEQ, or RoomEQ. close() is idempotent. Processing or resetting a closed stream raises StateError.

Presets and bundles

Chain.from_preset() accepts only canonical Chain v1:

{
  "version": 1,
  "chain": [
    {
      "id": "voice",
      "type": "Compressor",
      "enabled": true,
      "channel": "all",
      "parameters": {"threshold": -18, "ratio": 4}
    }
  ]
}

Application pipeline and plugins presets are deliberately separate. Use Chain.from_legacy_preset() or import_legacy_preset() to convert an app preset whose effects form one ordered serial path. Branched and multi-bus routing is rejected because flattening it would change the acoustic result. Move or copy the desired effects into one serial path in the app and export it again, or reproduce the branching in the host around separate Chains. Unsupported channels, effects, partial short-key arrays, and unknown fields are reported rather than silently dropped.

LevelMeter, Oscilloscope, SpectrumAnalyzer, Spectrogram, and StereoMeter provide opt-in decoded telemetry. Pass on_telemetry to Chain.process() or Chain.stream(), or manage a streaming subscription:

with chain.stream(48_000, channels=2) as stream:
    unsubscribe = stream.subscribe(lambda frame: print(frame.kind))
    output = stream.process(audio)
    print(stream.dropped_telemetry_frames)
    unsubscribe()

The first subscriber enables observations and the last unsubscribe disables them. Delivered tuples are caller-owned semantic values. Raw DSP telemetry is not a public API.

FIRCrossover, FiveBandFIRPEQ, GroupDelayEQ, GroupDelayPEQ, IRReverb, and RoomEQ require an impulseResponse reference and an asset resolver. The five FIR filter effects use prepared coefficient impulses at the processing sample rate. Resolvers return AssetData containing finite, C-contiguous planar float32 samples, an integer sample rate, and an explicit or unambiguous topology. The runtime rejects missing, malformed, hash-mismatched, ambiguous, and oversized assets. It does not decode or resample an IR.

Bundle.load(path) reads either a JSON manifest or a directory containing bundle.json. Bundle.pack(destination, chain, assets) writes a deterministic Bundle v1 directory from Chain v1 and caller-supplied AssetData. Its asset entries use the canonical ETA1 payload: a 32-byte header, optional 12-byte matrix path records, then planar little-endian float32 samples. Referenced payloads are restricted to the bundle directory and verified against manifest metadata, exact length, SHA-256, header, path records, finite samples, and the native 32 MiB footprint limit before use:

bundle = et.Bundle.load("room-bundle")
chain = et.Chain.from_preset(
    bundle.chain_document,
    asset_resolver=bundle.resolver,
)

The CLI exposes the same writer for decoded IR audio:

effetune bundle pack room-chain.json room-bundle --asset room-ir=room-ir.wav
effetune render input.wav convolved.wav --preset room-bundle --subtype FLOAT

--preset room-bundle/bundle.json is equivalent. For WAV output, omitting --subtype keeps SoundFile's PCM_16 default; --subtype FLOAT preserves 32-bit floating-point samples. render prints a one-line warning to standard error when the default output subtype reduces the input's precision, which passing --subtype explicitly silences, and when the rendered peak exceeds full scale and is clipped by an integer PCM output. Both warnings leave the exit code at 0.

EFFECT_METADATA is the public machine-readable semantic catalog for all 92 root effect classes. It contains channel choices, parameters, required assets, telemetry, and latency declarations without private native implementation details. Stream.latency_samples reports aggregate runtime latency and matches JavaScript ChainStream.latencySamples for the same chain and sample rate. Chain.latency_samples(sample_rate, ...) reports the same aggregate without opening a stream, which aligns offline process() output.

Modulation system-preset recipes

The application exposes these settings as system presets. The following dictionaries use their equivalent Python constructor parameter names. Copy one into a named constructor, for example Chorus(**MODULATION_STYLES["Chorus"]["Flanger"]), or use it as the effect's recipe when building Chain JSON (where to_dict() emits semantic camel-case parameter names).

MODULATION_STYLES = {
    "AutoFilter": {
        "Auto Filter Sweep": {"mode": "LFO", "filter_type": "Low-pass", "minimum_frequency": 200, "maximum_frequency": 4000, "resonance": 1.5, "mix": 80, "rate": 0.5, "waveform": "Sine", "stereo_phase": 0, "sensitivity": 24, "attack": 20, "release": 250, "direction": "Up"},
        "Stereo Filter Sweep": {"mode": "LFO", "filter_type": "Low-pass", "minimum_frequency": 160, "maximum_frequency": 6000, "resonance": 2, "mix": 85, "rate": 0.35, "waveform": "Sine", "stereo_phase": 120, "sensitivity": 24, "attack": 20, "release": 250, "direction": "Up"},
        "Envelope Filter": {"mode": "Envelope", "filter_type": "Low-pass", "minimum_frequency": 100, "maximum_frequency": 5000, "resonance": 1.2, "mix": 85, "rate": 0.5, "waveform": "Sine", "stereo_phase": 0, "sensitivity": 24, "attack": 18, "release": 300, "direction": "Up"},
        "Auto Wah": {"mode": "Envelope", "filter_type": "Band-pass", "minimum_frequency": 180, "maximum_frequency": 2400, "resonance": 5, "mix": 100, "rate": 0.5, "waveform": "Sine", "stereo_phase": 0, "sensitivity": 30, "attack": 8, "release": 180, "direction": "Up"},
        "Reverse Auto Wah": {"mode": "Envelope", "filter_type": "Band-pass", "minimum_frequency": 180, "maximum_frequency": 2800, "resonance": 4, "mix": 100, "rate": 0.5, "waveform": "Sine", "stereo_phase": 0, "sensitivity": 30, "attack": 12, "release": 350, "direction": "Down"},
    },
    "AutoPan": {
        "Gentle Auto Pan": {"rate": 0.35, "depth": 45, "center": 0, "width": 70, "waveform": "Sine", "phase": 0},
        "Wide Auto Pan": {"rate": 0.7, "depth": 100, "center": 0, "width": 100, "waveform": "Sine", "phase": 0},
        "Fast Auto Pan": {"rate": 4, "depth": 85, "center": 0, "width": 100, "waveform": "Triangle", "phase": 0},
    },
    "Chorus": {
        "Classic Chorus": {"mode": "Chorus", "rate": 0.8, "delay": 12, "depth": 3, "voices": 3, "stereo_spread": 60, "feedback": 0, "mix": 45},
        "Stereo Chorus": {"mode": "Stereo Chorus", "rate": 0.65, "delay": 15, "depth": 4, "voices": 2, "stereo_spread": 80, "feedback": 0, "mix": 50},
        "Ensemble": {"mode": "Ensemble", "rate": 0.45, "delay": 20, "depth": 6, "voices": 6, "stereo_spread": 100, "feedback": 0, "mix": 60},
        "Flanger": {"mode": "Flanger", "rate": 0.35, "delay": 2.5, "depth": 2, "voices": 1, "stereo_spread": 35, "feedback": 45, "mix": 50},
        "Jet Flanger": {"mode": "Flanger", "rate": 0.18, "delay": 1.5, "depth": 1.4, "voices": 1, "stereo_spread": 70, "feedback": -75, "mix": 55},
        "Vibrato": {"mode": "Vibrato", "rate": 4.5, "delay": 8, "depth": 5, "voices": 1, "stereo_spread": 50, "feedback": 0, "mix": 100},
    },
    "FrequencyShifter": {
        "Shift Up": {"mode": "Shift", "shift": 8, "carrier_frequency": 440, "minimum_shift": 20, "maximum_shift": 800, "rate": 0.15, "direction": "Up", "stereo_phase": 0, "mix": 100},
        "Shift Down": {"mode": "Shift", "shift": -8, "carrier_frequency": 440, "minimum_shift": 20, "maximum_shift": 800, "rate": 0.15, "direction": "Down", "stereo_phase": 0, "mix": 100},
        "Fine Detune": {"mode": "Shift", "shift": 2, "carrier_frequency": 440, "minimum_shift": 20, "maximum_shift": 800, "rate": 0.15, "direction": "Up", "stereo_phase": 90, "mix": 55},
        "Ring Modulator": {"mode": "Ring Mod", "shift": 8, "carrier_frequency": 440, "minimum_shift": 20, "maximum_shift": 800, "rate": 0.15, "direction": "Up", "stereo_phase": 0, "mix": 100},
        "Barber-pole Up": {"mode": "Barber-pole", "shift": 8, "carrier_frequency": 440, "minimum_shift": 20, "maximum_shift": 900, "rate": 0.12, "direction": "Up", "stereo_phase": 90, "mix": 85},
        "Barber-pole Down": {"mode": "Barber-pole", "shift": -8, "carrier_frequency": 440, "minimum_shift": 20, "maximum_shift": 900, "rate": 0.12, "direction": "Down", "stereo_phase": 90, "mix": 85},
    },
    "Phaser": {
        "Classic Phaser": {"mode": "Classic", "rate": 0.5, "center_frequency": 1000, "range": 3, "stages": 6, "feedback": 20, "stereo_phase": 90, "direction": "Up", "mix": 50},
        "Deep Phaser": {"mode": "Classic", "rate": 0.25, "center_frequency": 700, "range": 4.5, "stages": 12, "feedback": 55, "stereo_phase": 30, "direction": "Up", "mix": 55},
        "Stereo Phaser": {"mode": "Classic", "rate": 0.65, "center_frequency": 1200, "range": 3.5, "stages": 8, "feedback": 25, "stereo_phase": 120, "direction": "Up", "mix": 50},
        "Barber-pole Up": {"mode": "Barber-pole", "rate": 0.35, "center_frequency": 1000, "range": 5, "stages": 8, "feedback": 30, "stereo_phase": 60, "direction": "Up", "mix": 55},
        "Barber-pole Down": {"mode": "Barber-pole", "rate": 0.35, "center_frequency": 1000, "range": 5, "stages": 8, "feedback": 30, "stereo_phase": 60, "direction": "Down", "mix": 55},
    },
    "RotarySpeaker": {
        "Rotary Slow": {"speed_state": "Slow", "speed": 100, "acceleration": 2.2, "crossover": 800, "rotor_balance": 0, "stereo_width": 75, "doppler_depth": 45, "amplitude_depth": 55, "mix": 70},
        "Rotary Fast": {"speed_state": "Fast", "speed": 100, "acceleration": 1.4, "crossover": 800, "rotor_balance": 0, "stereo_width": 85, "doppler_depth": 65, "amplitude_depth": 70, "mix": 78},
        "Gentle Rotary": {"speed_state": "Slow", "speed": 75, "acceleration": 3, "crossover": 900, "rotor_balance": 0, "stereo_width": 45, "doppler_depth": 25, "amplitude_depth": 30, "mix": 55},
        "Vintage Rotor Slow": {"speed_state": "Slow", "speed": 100, "acceleration": 2.8, "crossover": 800, "rotor_balance": -5, "stereo_width": 80, "doppler_depth": 50, "amplitude_depth": 60, "mix": 75},
        "Vintage Rotor Fast": {"speed_state": "Fast", "speed": 100, "acceleration": 1.8, "crossover": 800, "rotor_balance": -5, "stereo_width": 90, "doppler_depth": 70, "amplitude_depth": 75, "mix": 82},
    },
}

CLI

effetune render input.wav output.wav --preset mastering.json
effetune render input.wav output.wav --preset room-bundle --subtype FLOAT
effetune render input.wav output.flac --chain "[{\"type\":\"Volume\",\"parameters\":{\"volume\":-3}}]"
effetune chain validate mastering.json
effetune preset inspect mastering.json
effetune bundle pack room-chain.json room-bundle --asset room-ir=room-ir.wav

Audio decoding and encoding are delegated to SoundFile. The CLI does not invoke ffmpeg, resample, measure loudness, or change the input sample rate. --preset accepts a Chain file, a Bundle directory, or its bundle.json.

Supported Python wheels

The package supports CPython 3.10 and newer. Nanobind's stable ABI starts at CPython 3.12, so 3.10 and 3.11 wheels are version-specific. Release jobs build the 3.12 wheel with CMake 3.26 or newer:

python -m build -Ccmake.define.ET_PYTHON_STABLE_ABI=ON -Cwheel.py-api=cp312

That cp312-abi3 wheel covers supported newer CPython versions. This is a private static-link extension; the repository's wasm32 C ABI is not exposed as a native public ABI. Linux x86-64, Windows AMD64, macOS Intel, and macOS Apple Silicon wheels are built and clean-install tested independently.

Official Python releases are wheels only. An sdist built from this subproject would omit DSP sources located above the Python package directory, so source builds are supported only from a complete EffeTune repository checkout.

Release files for effetune 0.8.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for effetune 0.8.0
File
effetune-0.8.0-cp312-abi3-win_amd64.whl CPython 3.12 abi3 Windows x86-64 Details
effetune-0.8.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.12 abi3 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
effetune-0.8.0-cp312-abi3-macosx_11_0_arm64.whl CPython 3.12 abi3 macOS 11.0+ ARM64 Details
effetune-0.8.0-cp312-abi3-macosx_10_13_x86_64.whl CPython 3.12 abi3 macOS 10.13+ x86-64 Details
effetune-0.8.0-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
effetune-0.8.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 Details
effetune-0.8.0-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
effetune-0.8.0-cp311-cp311-macosx_10_13_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.13+ x86-64 Details
effetune-0.8.0-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
effetune-0.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
effetune-0.8.0-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
effetune-0.8.0-cp310-cp310-macosx_10_13_x86_64.whl CPython 3.10 CPython 3.10 macOS 10.13+ x86-64 Details

Total release size: 11.6 MB

Release files / effetune-0.8.0-cp312-abi3-win_amd64.whl

Download URL effetune-0.8.0-cp312-abi3-win_amd64.whl
Size 1.0 MB
Tags CPython 3.12 Windows x86-64 abi3
SHA-256 checksum
How to use checksums
1b7aecbdc33997bc546064fed70c75c148e1d44bc3ff2d306602123bdc3aa4d7
BLAKE2b-256 checksum
How to use checksums
9e3eb77e30f9cd69926c7f44f8c62ff66f2e0c8df67ea0b198c5ac81f464efc9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL effetune-0.8.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size 1.2 MB
Tags CPython 3.12 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 abi3
SHA-256 checksum
How to use checksums
7457c01368048393f5fc173975f3dfb6a198a7c9295ef1cb67415a688ba9c3af
BLAKE2b-256 checksum
How to use checksums
6d58bfdac292e360852352d596cece1c12265045af5dc71e66327d96afc44bdc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp312-abi3-macosx_11_0_arm64.whl

Download URL effetune-0.8.0-cp312-abi3-macosx_11_0_arm64.whl
Size 802.4 kB
Tags CPython 3.12 abi3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
53c65625a5942c5e358b5ba55f51a91f6cca732550d710c8d0d275556af975b9
BLAKE2b-256 checksum
How to use checksums
af14ad689a8dac019a8681ba06dc63f46c513153df13e146813ce8b5e69da12b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp312-abi3-macosx_10_13_x86_64.whl

Download URL effetune-0.8.0-cp312-abi3-macosx_10_13_x86_64.whl
Size 871.3 kB
Tags CPython 3.12 abi3 macOS 10.13+ x86-64
SHA-256 checksum
How to use checksums
5a9a37966972c4e0b5e3706dd1e96f3dddbe3caaec282298145d8b174e4630f8
BLAKE2b-256 checksum
How to use checksums
a4ddca19abba3afd19f21991ad3e450a97543ffd598dd5979efed9b9aeb908c7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp311-cp311-win_amd64.whl

Download URL effetune-0.8.0-cp311-cp311-win_amd64.whl
Size 1.0 MB
Tags CPython 3.11 Windows x86-64
SHA-256 checksum
How to use checksums
7a533be910ad39a11917ab4c03d415b345761aea6e1f083695c2b91dc94d9728
BLAKE2b-256 checksum
How to use checksums
bc10eff9f32bbdd6b4c3df996813a179797006c4d6dc5843b021c231c4db5cd8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL effetune-0.8.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size 1.2 MB
Tags CPython 3.11 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
4a57d02b3fca6d30a219de01b59d6afb9a671ed02fef37684747eab3e14cc362
BLAKE2b-256 checksum
How to use checksums
d6089df9306c4786c0eaf1c16201ae4213fa62faac2ecaf79a49b605c67e7c15
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp311-cp311-macosx_11_0_arm64.whl

Download URL effetune-0.8.0-cp311-cp311-macosx_11_0_arm64.whl
Size 804.1 kB
Tags CPython 3.11 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
6aa3c319bd7bc4efb90ae9f360aac83c833d3966e8ac39127836ede7529aa54a
BLAKE2b-256 checksum
How to use checksums
a67b89914e1526098361d866abc01ab38cff1d9da81362b13fd40ca143f3f923
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp311-cp311-macosx_10_13_x86_64.whl

Download URL effetune-0.8.0-cp311-cp311-macosx_10_13_x86_64.whl
Size 873.3 kB
Tags CPython 3.11 macOS 10.13+ x86-64
SHA-256 checksum
How to use checksums
a5b0b5b10ad89d3a403348cf084a5c30fb685005fae847478840380fbb1bfd52
BLAKE2b-256 checksum
How to use checksums
f7690ddf51698ca5706aa865ca77b0bdf8381504774f9bcd82a55e0c91d61930
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp310-cp310-win_amd64.whl

Download URL effetune-0.8.0-cp310-cp310-win_amd64.whl
Size 1.0 MB
Tags CPython 3.10 Windows x86-64
SHA-256 checksum
How to use checksums
8c28ec3b551fbee48eb0daac6fbb948d95cd0d102a4480b03fc5a38c2267d57d
BLAKE2b-256 checksum
How to use checksums
91ed0543124c04d21593903f3ed2dedce7b37a2b322306eb5beb14cb6ddeefa6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL effetune-0.8.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size 1.2 MB
Tags CPython 3.10 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
faeb9f01e2456e4b21f4cd19908c45f3730351f0dfe0e0b13238b5e6ffdccfab
BLAKE2b-256 checksum
How to use checksums
237f9c581a517c5663c1bb9c63fc77fdc8aa1375e11a323a82d34ecb904de381
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp310-cp310-macosx_11_0_arm64.whl

Download URL effetune-0.8.0-cp310-cp310-macosx_11_0_arm64.whl
Size 804.3 kB
Tags CPython 3.10 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
56331c3c491623a6f22aa5f70e754a19a7d5249bc1f1bb3fe973a649e5038b15
BLAKE2b-256 checksum
How to use checksums
21d6b0bd996b845ad280e0fb8fab602a395366beda72dbea73992d5143778ccb
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release files / effetune-0.8.0-cp310-cp310-macosx_10_13_x86_64.whl

Download URL effetune-0.8.0-cp310-cp310-macosx_10_13_x86_64.whl
Size 873.6 kB
Tags CPython 3.10 macOS 10.13+ x86-64
SHA-256 checksum
How to use checksums
4d5114527ed57ecb30e0d63cac92dbf206e1b57cd04930cfcb2636cda72f8a46
BLAKE2b-256 checksum
How to use checksums
7900b63ddbedeac5a6f0845df625209e1a64d1de0cd9ca8e7814ef3a93fbf2f1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 5, 2026.

Transparency log

Release history Release notifications | RSS feed

0.9.0

12 release files

This release

0.8.0 This release

12 release files

0.7.0

12 release files

0.6.0

12 release files

0.5.0

12 release files

0.1.0

12 release 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