Getting Started

This is a practical reference for the ria CLI from ria-toolkit-oss.

Scope of this guide:

Installation and SDR driver prerequisites — how to install RIA Toolkit OSS and configure the system drivers your hardware requires
End-to-end CLI workflow — a step-by-step walkthrough from hardware discovery through capture, annotation, and processing
Full command reference — options, flags, and examples for every ria command
Python scripting preview — using the toolkit API directly without the CLI

Official resources:

1. Installation and Setup

Before using the ria CLI, follow the Installation guide to install RIA Toolkit OSS and any SDR drivers required for your hardware.

1.1 SDR Driver Prerequisites

Toolkit package install does not install all system SDR drivers. Install vendor/runtime dependencies for the hardware you use.

Device	Driver Package
USRP	UHD drivers
Pluto	libiio / IIO utilities
BladeRF	libbladeRF
HackRF	libhackrf
RTL-SDR	librtlsdr

See the SDR Guides and your OS package instructions.

2. CLI Structure

Top-level CLI follows this model:

ria [GLOBAL_OPTS] <command> [ARGS] [OPTIONS]

Global:

-v, --verbose (defined on root click group)

Top-level commands:

Command	Purpose
`discover`	Probe SDR drivers and enumerate attached hardware
`init`	Create and manage user metadata defaults
`capture`	Record IQ samples from a connected SDR
`view`	Generate visualizations from IQ files
`annotate`	Label signal regions manually or with auto-detection (group)
`convert`	Convert between IQ file formats
`split`	Split, trim, or extract recordings
`combine`	Merge multiple recordings by concatenation or addition
`generate` / `synth`	Generate synthetic IQ signals (group; `synth` is an alias)
`transform`	Apply augmentations or impairments to recordings (group)
`transmit`	Transmit IQ through a TX-capable SDR

3. Quick End-to-End Workflow

3.1 Discover Radios

Run this first to verify drivers and detect connected hardware.

ria discover -v

3.2 Initialize Local Metadata Defaults

Set reusable metadata once so captured files include consistent provenance fields.

ria init

3.3 Capture IQ

Record baseband IQ from a connected SDR.

ria capture -d pluto -f 2.44G -s 2e6 -n 500000 -o capture.sigmf-data

3.4 Visualize and Inspect

Render a quick diagnostic plot to validate signal presence and quality.

ria view capture.sigmf-data --type simple

3.5 Auto-Annotate and Inspect Annotations

Detect signal regions automatically, then verify the results.

ria annotate energy capture.sigmf-data --label signal
ria annotate list capture.sigmf-data

3.6 Convert and Split

Convert to a different format and split into fixed-size chunks.

ria convert capture.sigmf-data capture.npy
ria split capture.sigmf-data --split-every 100000 --output-dir chunks

3.7 Apply Transforms

Augment or impair a recording to produce controlled variants.

ria transform impair add_awgn_to_signal capture.npy --params snr=10

3.8 Transmit (TX-capable radios only)

Replay a recording through a transmit-capable SDR.

ria transmit -d hackrf -f 2.44G -s 2e6 --input capture.sigmf-data

4. Command Reference

4.1 `discover`

Purpose: Probe available SDR drivers and enumerate attached hardware. Confirm whether runtime libraries/drivers are installed and discoverable before capture/transmit.

ria discover [--verbose] [--json-output]

Options:

-v, --verbose: include per-driver probe details and import/init failures.
--json-output: emit JSON (useful for automation and inventory scripts).

Behavior notes:

discover checks multiple backends (USB and network paths, depending on driver support).
A device not appearing here usually means one of: missing system driver, permission issue, USB/network connectivity issue.
Use --verbose first when troubleshooting; it surfaces driver-level failures hidden in default output.

Example output:

$ ria discover -v

✅ Loaded drivers (3):
   hackrf
   pluto
   bladerf

❌ Failed drivers (3):
   usrp: ModuleNotFoundError: uhd
   rtlsdr: ImportError: pyrtlsdr is required to use the RTLSDR class
   thinkrf: ImportError: pyrf is required to use the ThinkRF integration.
            Install with: pip install ria-toolkit-oss[thinkrf]

========================================
Attached Devices
========================================

📡 USRP/UHD devices (1):
   ✅ MyB200 (B200) - Serial: 30C51D5

📱 PlutoSDR devices: None found
🔧 HackRF devices: None found

========================================
Discovery Summary
========================================
Loaded drivers: 3
Failed drivers: 3
Detected devices: 1

With --json-output:

{
  "loaded_drivers": ["hackrf"],
  "failed_drivers": ["pluto", "bladerf", "usrp", "rtlsdr", "thinkrf"],
  "devices": [
    {
      "type": "BladeRF",
      "Description": "Nuand bladeRF 2.0",
      "Backend": "libusb",
      "Serial": "8518b488d3e3443da979680f472bbb87",
      "USB Bus": "4",
      "USB Address": "2"
    }
  ],
  "total_devices": 1
}

4.2 `init`

Purpose: Create/manage user config file (defaults to ~/.ria/config.yaml, or $XDG_CONFIG_HOME/ria/config.yaml).

ria init [options]

Options:

--author, --organization, --project, --location, --testbed, --license, --hw, --dataset: stored once and reused for later recordings so files have consistent provenance.
--show: read-only inspect of the current resolved config.
--reset: remove config and start clean.
--config-path: use a non-default config location (useful for isolated environments or CI).
--interactive / --no-interactive: force prompts on or off regardless of terminal auto-detection.
--yes: suppress confirmation prompts for scripted runs.

Example output:

Set metadata fields non-interactively:

$ ria init --author "Jane Doe" --project "rf-campaign-1" --location "Lab-A" --no-interactive

✓ Configuration saved to: /home/user/.ria/config.yaml

Verify with --show:

$ ria init --show

Current Configuration (/home/user/.ria/config.yaml):
============================================================

Author: Jane Doe
Project: rf-campaign-1
Location: Lab-A

To update: ria init
To reset: ria init --reset

4.3 `capture`

Purpose: Record IQ samples from a supported SDR and save to sigmf, npy, wav, or blue.

ria capture [options]

Device selection (--device) is optional if only one device is detected. Exactly one of --num-samples or --duration is required.

Options:

-d, --device {pluto,hackrf,bladerf,usrp,rtlsdr,thinkrf}
-i, --ident: serial or IP selector when multiple devices of the same type are present.
-c, --config <yaml>: load options from a YAML file; CLI flags override loaded values.
-s, --sample-rate
-f, --center-frequency (supports values like 915e6, 2.4G)
-g, --gain, -b, --bandwidth
-n, --num-samples or -t, --duration: use sample count for deterministic datasets, or duration for quick time-based acquisition.
-o, --output, --output-dir: output path or directory. A timestamped filename is generated if --output is omitted; defaults to recordings/ if --output-dir is omitted.
--format {npy,sigmf,wav,blue}: inferred from file extension if not set. sigmf is best for annotation workflows.
--save-image: writes a quick visual summary alongside the capture file.
-m, --metadata KEY=VALUE (repeatable): injects run-specific metadata.
-v, --verbose, -q, --quiet

Examples:

ria capture -d hackrf -s 2e6 -f 2.44G -n 1000000 -o rf.sigmf-data
ria capture -d pluto -f 915e6 -t 2 --format npy --output-dir recordings
ria capture -c capture_config.yaml

Example output:

$ ria capture -d hackrf -s 2e6 -f 2.44G -n 1000000 -o rf.sigmf-data

Initializing HackRF...
  Device: HackRF One
  Serial: a74ad5e4e2a14b7d
  Center frequency: 2.44 GHz
  Sample rate: 2.00 MS/s
  Gain: 20 dB

Capturing 1,000,000 samples...

Saved: rf.sigmf-data
       rf.sigmf-meta

4.4 `view`

Purpose: Generate visualizations from IQ files. Quickly validate signal quality, occupancy, and annotation coverage without writing custom plotting code.

ria view <input> [options]

<input> accepts SigMF, NPY, WAV, and Blue files.

Mode (--type):

simple: fast-look plots for sanity checks and quick iteration.
full: multi-panel diagnostic figure (IQ, time, frequency, metadata views).
annotations / annotation: render annotation overlays.
channels: channelized/segmented visualization.
annotate: convenience path used in some annotation workflows.

Output/display options:

--output, --format {png,pdf,svg,jpg}
--show: open an interactive window (requires a GUI display environment).
--no-save: suppress file output; only meaningful with --show.
--overwrite

Style options:

--dpi, --figsize WxH, --title
--light: switch to a light theme (useful for reports/slides).

Loading options:

--legacy: force legacy NPY loading path for older datasets.
--config

Mode-specific options:

simple: --fast, --compact, --horizontal, --constellation, --labels, --slice start:end[:step]
full: --plot-length, --no-spectrogram, --no-iq, --no-frequency, --no-constellation, --no-metadata, --no-logo, --spines
annotations / channels: --channel

Examples:

ria view capture.sigmf-data --type simple
ria view capture.npy --type full --title "Test Capture" --format pdf
ria view capture.npy --show --no-save
ria view old.npy --legacy --type simple

Example output:

$ ria view qam64_35.npy --type simple

Loading recording: qam64_35.npy

Recording Metadata:
----------------------------------------
  modulation: qam64
  constellation: qam
  bits_per_symbol: 6
  sps: 6
  beta: 0.35
  source: signal.block_generator
----------------------------------------

Generating simple visualization...
Saved: qam64_35.png

4.5 `annotate` group

Purpose: Manual annotation management and auto-detection/separation. Build or refine label metadata directly in recordings for downstream training, QA, and filtering.

ria annotate <subcommand> ...

Subcommands:

Subcommand	Purpose
`list`	Inspect all annotations on a recording
`add`	Add one annotation with explicit sample-domain bounds
`remove`	Remove one annotation by index
`clear`	Remove all annotations from a recording
`energy`	Auto-detect regions above the estimated noise floor
`cusum`	Auto-detect regime changes using change-point detection
`threshold`	Auto-detect regions using normalized magnitude thresholding
`separate`	Decompose annotations into narrower spectral components

SigMF is the preferred format for durable annotation metadata. For non-SigMF input, most operations write a new output artifact unless --overwrite is set. --type {standalone,parallel,intersection} controls annotation relation semantics.

`ria annotate list`

ria annotate list <input> [--verbose]

Prints all annotations for a recording in index order. --verbose includes additional detail per record.

Example output:

$ ria annotate list sample_recording3_annotated.npy --verbose

Annotations in sample_recording3_annotated.npy:
  [0] Samples 170,599-171,116: signal
      Type: standalone
      Frequency: 3.41 GHz - 3.41 GHz
      Detail: {'generator': 'energy_detector', 'freq_method': 'nbw'}
  [1] Samples 182,310-182,841: signal
      Type: standalone
      Frequency: 3.41 GHz - 3.41 GHz
      Detail: {'generator': 'energy_detector', 'freq_method': 'nbw'}
  [2] Samples 1,133,165-1,133,706: signal
      ...
  [7] Samples 2,113,268-2,861,395: signal
      Type: standalone
      Frequency: 3.41 GHz - 3.41 GHz
      Detail: {'generator': 'energy_detector', 'freq_method': 'nbw'}

Total: 8 annotation(s)

`ria annotate add`

ria annotate add <input> --start <int> --count <int> --label <text> [options]

Adds one explicit annotation with sample-domain boundaries.

--start: first sample index of the annotated region.
--count: number of samples in the region.
--freq-lower, --freq-upper: optional spectral bounds in Hz.
--comment, --type, -o / --output, --overwrite, --quiet

Example output:

$ ria annotate add sample_recording3_annotated.npy --start 50000 --count 10000 --label burst -o out.npy

Loaded: sample_recording3_annotated.npy

Adding annotation:
  Start: 50,000
  Count: 10,000 samples
  Frequency: full bandwidth
  Label: burst
  Type: standalone
Saving to: out.npy
  ✓ Saved

`ria annotate remove`

ria annotate remove <input> <index> [--output ...] [--overwrite] [--quiet]

Removes exactly one annotation by list index. Run annotate list first to confirm the index.

Example output:

$ ria annotate remove sample_recording3_annotated.npy 0 -o out.npy

Loaded: sample_recording3_annotated.npy

Removing annotation [0]:
  Removed: samples 170,599-171,116 (signal)
Saving to: out.npy
  ✓ Saved

`ria annotate clear`

ria annotate clear <input> [--force] [--overwrite] [--quiet]

Removes all annotations from the recording. --force bypasses the confirmation prompt.

Example output:

$ ria annotate clear sample_recording3_annotated.npy --force --overwrite

Loaded: sample_recording3_annotated.npy

Cleared 8 annotation(s)
Saving to: sample_recording3_annotated.npy
  ✓ Saved

`ria annotate energy`

ria annotate energy <input> [options]

Detects energetic regions above the estimated noise floor and writes them as annotations.

--label
--threshold: noise-floor multiplier; higher values reduce false positives but can miss weak signals.
--segments: number of segments used to estimate baseline noise.
--window-size: smoothing size; larger windows stabilize detections at the cost of sharp transition precision.
--min-distance: minimum sample spacing between detections, preventing dense duplicate regions.
--freq-method {nbw,obw,full-detected,full-bandwidth}: how frequency bounds are assigned to annotations.
--nfft, --obw-power
--type, -o / --output, --overwrite, --quiet

Example output:

$ ria annotate energy sample_recording3.npy --label signal -o sample_recording3_annotated.npy

Loaded: sample_recording3.npy

Detecting signals using energy-based method...
  Time detection:
    Segments: 10
    Threshold: 1.2x noise floor
    Window size: 200 samples
    Min distance: 5000 samples
  Frequency bounds: nbw
  ✓ Added 8 annotation(s)
Saving to: sample_recording3_annotated.npy
  ✓ Saved

`ria annotate threshold`

ria annotate threshold <input> --threshold <0.0..1.0> [options]

Uses normalized magnitude thresholding to derive annotation spans. Use where a fixed amplitude threshold is sufficient.

--label, --window-size, --type, -o / --output, --overwrite, --quiet

Example output:

$ ria annotate threshold sample_recording3.npy --threshold 0.7 --label strong -o out.npy

Loaded: sample_recording3.npy

Detecting signals using threshold qualifier...
  Threshold: 70.0% of max magnitude
  Window size: auto (1ms)
  Channel: 0
  ✓ Added 2 annotation(s)
Saving to: out.npy
  ✓ Saved

`ria annotate cusum`

ria annotate cusum <input> [options]

Uses change-point detection (CUSUM-style logic) to find regime changes and annotate contiguous segments.

--label
--min-duration (ms): prevents tiny over-segmented labels.
--window-size
--tolerance: merges nearby boundaries when set above default.
--type, -o / --output, --overwrite, --quiet

Example output:

$ ria annotate cusum sample_recording3.npy --label regime -o out.npy

Loaded: sample_recording3.npy

Detecting segments using CUSUM...
  Min duration: 5.0 ms
  ✓ Added 37 annotation(s)
Saving to: out.npy
  ✓ Saved

`ria annotate separate`

ria annotate separate <input> [options]

Decomposes existing annotations into narrower sub-band annotations by detecting distinct frequency components within each annotated time window. It does not detect signal regions from scratch — run energy, threshold, or cusum first to produce the input annotations, then use separate to refine them spectrally.

Use this when a single broad annotation covers multiple signals at different frequencies and you want separate annotations per component.

--indices "0,1,2": limit operation to specific annotations; omit to process all.
--nfft: larger FFT improves frequency resolution but increases compute time.
--noise-threshold-db: sets the noise floor in dB; auto-estimated if omitted.
--min-component-bw: rejects narrow fragments likely to be noise artifacts.
-o / --output, --overwrite, --quiet, --verbose

Example — two-step workflow:

Step 1 — create broad annotations with threshold:

$ ria annotate threshold sample_recording5.npy --threshold 0.5 --label signal -o annotated.npy

Loaded: sample_recording5.npy

Detecting signals using threshold qualifier...
  Threshold: 50.0% of max magnitude
  Window size: auto (1ms)
  Channel: 0
  ✓ Added 3 annotation(s)
Saving to: annotated.npy
  ✓ Saved

Step 2 — run separate to split by frequency component:

$ ria annotate separate annotated.npy -o separated.npy

Loaded: annotated.npy

Splitting annotations by frequency components...
  Input annotations: 3
  FFT size: 65536
  Noise threshold: auto-estimated
  Min component BW: 50.00 kHz
  ✓ Output annotations: 6 (+3 change)
Saving to: separated.npy
  ✓ Saved

All annotate examples:

ria annotate list capture.sigmf-data --verbose
ria annotate add capture.sigmf-data --start 10000 --count 5000 --label burst
ria annotate energy capture.sigmf-data --label signal --threshold 1.3
ria annotate threshold capture.sigmf-data --threshold 0.5 --label signal
ria annotate cusum capture.sigmf-data --min-duration 5
ria annotate separate capture.sigmf-data --indices 0,1 --verbose

4.6 `convert`

Purpose: Convert between sigmf, npy, wav, and blue. Normalize datasets into the format required by downstream tooling or collaboration targets.

ria convert <input> [output] [options]

If output is omitted, --format must be provided. If both are given, format is inferred from the output file extension.

Options:

--format {npy,sigmf,wav,blue}
--output-dir
--legacy: use older NPY loader behavior for historical recordings.
--wav-sample-rate: target sample rate for WAV export.
--wav-bits {16,32}: output PCM depth; higher preserves more dynamic range.
--blue-format {CI,CF,CD}: Bluefile complex sample representation.
--metadata KEY=VALUE (repeatable): add or override metadata during conversion; especially useful when exporting to SigMF.
--overwrite, -v / --verbose, -q / --quiet

Examples:

ria convert recording.sigmf-data output.npy
ria convert recording.npy --format sigmf
ria convert highrate.npy audio.wav --wav-sample-rate 48000
ria convert old.npy --format sigmf --legacy --overwrite

Example output:

$ ria convert sample_recording3.npy sample_recording3.sigmf-data

Converting: sample_recording3.npy → sample_recording3.sigmf-data
Input format: NPY
Output format: SIGMF
Samples: 3,000,000
Conversion complete: sample_recording3.sigmf-data, sample_recording3.sigmf-meta

4.7 `split`

Purpose: Split, trim, or extract recordings. Create manageable dataset shards or extract windows of interest without custom scripts.

ria split <input> [operation] [options]

Choose exactly one operation per invocation:

--split-at <sample>: binary split at a specific sample index.
--split-every <N>: fixed-size chunking for ML pipelines.
--split-duration <seconds>: time-based chunking.
--trim (with --start + --length or --end): extract one sub-window.
--extract-annotations: write each annotated region as a standalone file.

Trim controls: --start, --length, --end

Annotation extraction filters: --annotation-label, --annotation-index

Output controls: --output-dir, --output-prefix, --output-format {npy,sigmf,wav,blue}, --overwrite, --legacy, -v / --verbose, -q / --quiet

Examples:

ria split recording.sigmf-data --split-at 500000 --output-dir out
ria split recording.sigmf-data --split-every 100000 --output-dir chunks
ria split recording.sigmf-data --split-duration 1.0 --output-dir chunks
ria split recording.npy --trim --start 1000 --length 5000 --output-dir trimmed
ria split annotated.sigmf-data --extract-annotations --annotation-label payload

Example output:

$ ria split sample_recording3.npy --split-every 500000 --output-dir chunks

Loading: sample_recording3.npy
Total samples: 3,000,000

Splitting into chunks of 500,000 samples...
Creating 6 chunks...
  Chunk 1/6: samples 0-499,999...
  Chunk 2/6: samples 500,000-999,999...
  Chunk 3/6: samples 1,000,000-1,499,999...
  Chunk 4/6: samples 1,500,000-1,999,999...
  Chunk 5/6: samples 2,000,000-2,499,999...
  Chunk 6/6: samples 2,500,000-2,999,999...

Created 6 chunks in chunks/

4.8 `combine`

Purpose: Merge multiple recordings by concatenation or sample-wise addition. Assemble multi-part captures or synthesize mixtures for testing and model training.

ria combine <input1> <input2> [input3 ...] <output> [options]

Options:

--mode {concat,add}
--align-mode {error,truncate,pad,pad-start,pad-center,pad-end,repeat,repeat-spaced}
--pad-start-sample, --repeat-spacing
--normalize: rescale combined output to avoid clipping/saturation after addition.
--output-format {sigmf,npy,wav,blue}
--overwrite, --metadata KEY=VALUE (repeatable)
--legacy, --verbose, --quiet

--mode concat appends inputs sequentially in time. --mode add performs sample-wise summation and requires all inputs to be the same length, or an --align-mode to reconcile length differences:

Align Mode	Behavior
`error`	Fail if lengths differ
`truncate`	Cut all to shortest length
`pad`, `pad-start`, `pad-center`, `pad-end`	Zero-pad shorter streams
`repeat`	Tile shorter streams to match longest
`repeat-spaced`	Repeated placement with spacing via `--repeat-spacing`

Examples:

ria combine a.npy b.npy c.npy merged.npy
ria combine signal.npy noise.npy noisy.npy --mode add
ria combine long.npy short.npy out.npy --mode add --align-mode pad-center
ria combine signal.npy pattern.npy out.npy --mode add --align-mode repeat-spaced --repeat-spacing 10000

Example output:

$ ria combine sample_recording3.npy qam64_35.npy combined.npy

Combining 2 recordings (concat mode)...
Saved to: combined.npy

4.9 `generate` group (and `synth` alias)

Purpose: Generate synthetic IQ signals and save in npy, sigmf, wav, or blue. Create known-reference waveforms and synthetic datasets for validation, demos, and ML data generation.

ria synth ... is an alias for ria generate ....

ria generate <subcommand> [subcommand options] [common options]

Available subcommands:

Subcommand(s)	Description
`tone`	Clean sinusoidal calibration/reference source
`noise`	Baseline noise floor data or controlled additive-noise synthesis
`chirp`	Sweep-based radar/sonar-style signals and bandwidth occupancy tests
`square`, `sawtooth`	Periodic waveform primitives
`qam`, `apsk`, `pam`, `psk`	Digital modulation families with pulse-shaping filter support
`fsk`	Frequency-shift keying with configurable tone spacing
`ook`, `oqpsk`, `gmsk`	On-off keying and continuous-phase modulation schemes

Common options (all subcommands):

-s, --sample-rate (required), -n, --num-samples or -t, --duration
-o, --output (required), -F / --format {npy,sigmf,wav,blue}
--frequency-shift, --center-frequency: separate baseband shape from RF metadata.
--add-noise, --noise-power, --path-gain: apply noise post-generation.
--multipath-paths, --multipath-max-delay, --iq-amp-imbalance, --iq-phase-imbalance, --iq-dc-offset: channel and IQ impairments.
--config <yaml>, -w / --overwrite, -m / --metadata KEY=VALUE, -v / --verbose, -q / --quiet

Subcommand-specific options:

Subcommand	Unique options
`tone`	`--frequency`, `--amplitude`, `--phase`
`noise`	`--noise-type {gaussian,uniform}`, `--power`
`chirp`	`--bandwidth` (required), `--period` (required), `--type {up,down,up_down}`
`square`	`--frequency`, `--amplitude`, `--duty-cycle`, `--phase`
`sawtooth`	`--frequency`, `--amplitude`, `--phase`
`qam`, `apsk`, `pam`, `psk`	`--order`, `--symbol-rate`, `--filter {rrc,rc,gaussian,none}`, `--filter-span`, `--filter-beta`, `--message-source {random,file,string}`, `--message-content`
`fsk`	`--order`, `--symbol-rate`, `--freq-spacing`, `--modulation-index`, `--message-source`, `--message-content`
`ook`, `oqpsk`, `gmsk`	`--symbol-rate` (required), `--message-source`, `--message-content`; `gmsk` also accepts `--bt` (Gaussian filter bandwidth-time product)

Examples:

ria generate tone -s 2e6 -n 500000 --frequency 50e3 -o tone.sigmf-data
ria generate noise -s 2e6 -n 500000 --noise-type gaussian --power 0.05 -o noise.npy
ria generate chirp -s 5e6 -t 0.5 --bandwidth 2e6 --period 0.01 --type up -o chirp.sigmf-data
ria generate qam -s 2e6 -r 100e3 -M 16 -N 5000 --message-source random -o qam16.npy
ria synth psk -s 2e6 -r 100e3 -M 8 -N 8000 -o psk8.npy

Example output:

$ ria generate tone -s 2e6 -n 100000 --frequency 50e3 -o tone.npy

Generating tone: 50.00 kHz at 2.00 MS/s

$ ria generate qam -s 2e6 -n 50000 --order 16 --symbol-rate 100e3 --message-source random -o qam16.npy

Generating QAM-16 (2500 symbols)...

4.10 `transform` group

Purpose: Apply algorithmic transforms to existing recordings. Run reusable augmentations/impairments for dataset diversity and robustness testing.

ria transform <augment|impair|custom> ...

`augment`

ria transform augment [augmentation] [input] [output] [options]

Applies transforms from iq_augmentations (dataset-expansion style modifications).

Options: --list, --help-transform, --params KEY=VALUE (repeatable), --view, --overwrite, -v / --verbose, -q / --quiet

`impair`

ria transform impair [impairment] [input] [output] [options]

Applies transforms from iq_impairments (noise, distortion, and channel degradation effects). Same options as augment.

`custom`

ria transform custom [transform_name] [input] [output] --transform-dir <dir> [options]

Dynamically loads public functions from Python files in --transform-dir and exposes them as callable transforms.

--transform-dir (required), --list, --help-transform, --params KEY=VALUE (repeatable), --view, --overwrite, -v / --verbose, -q / --quiet

--params values must be KEY=VALUE; types are inferred as int, float, or string. Use --list to enumerate available transform names, and --help-transform <name> to inspect parameter hints. --view writes a PNG preview alongside transform output.

Examples:

ria transform augment --list
ria transform augment channel_swap in.npy out.npy
ria transform augment drop_samples in.npy --params max_section_size=5 --view

ria transform impair --list
ria transform impair add_awgn_to_signal in.npy out.npy --params snr=10

ria transform custom --transform-dir ./my_transforms --list
ria transform custom my_filter in.npy out.npy --transform-dir ./my_transforms --params cutoff=0.2

Example output:

List available augmentations:

$ ria transform augment --list

Available augmentations:
  amplitude_reversal    Negates the amplitudes of both the I and Q data samples
  channel_swap          Switches the I (In-phase) with the Q (Quadrature) data samples
  cut_out               Cuts out random sections of IQ data and replaces them with zeros
  drop_samples          Randomly drops IQ data samples
  generate_awgn         Generates additive white gaussian noise relative to the SNR
  magnitude_rescale     Selects a random starting point and multiplies IQ data by a scalar
  patch_shuffle         Selects random patches and shuffles the data samples within them
  quantize_parts        Quantizes random parts of the IQ data by a few bits
  quantize_tape         Quantizes the IQ data by a few bits
  spectral_inversion    Negates the imaginary components (Q) of the data samples
  time_reversal         Reverses the order of I and Q data samples along the time axis

Apply an impairment (AWGN at SNR=10 dB):

$ ria transform impair add_awgn_to_signal sample_recording3.npy sample_recording3_awgn.npy --params snr=10

Impairing: sample_recording3.npy → sample_recording3_awgn.npy
Saved to: sample_recording3_awgn.npy

4.11 `transmit`

Purpose: Transmit IQ via a TX-capable SDR (pluto, hackrf, bladerf, usrp). Support playback of captured/generated waveforms for over-the-air or wired-loop test scenarios.

ria transmit [options]

If neither --input nor --generate is specified, the command defaults to a generated LFM waveform.

Options:

-d / --device {pluto,hackrf,bladerf,usrp}, -i / --ident, -c / --config
-s / --sample-rate, -f / --center-frequency, -g / --gain, -b / --bandwidth
--input <file>: transmit an existing recording.
--generate {lfm,chirp,sine,pulse}: synthesize a signal on the fly.
--legacy: use older NPY loader for historical recordings.
-r, --repeat: transmit the input a fixed number of times.
--continuous: transmit until interrupted (Ctrl+C).
--tx-delay: pause between repeats when --repeat is used.
-y, --yes: skip confirmation prompts; use carefully in scripted environments.
-v / --verbose, -q / --quiet

Examples:

ria transmit -d pluto -f 915e6 -s 2e6 --input capture.sigmf-data
ria transmit -d hackrf --generate lfm -f 2.44G --continuous
ria transmit -d usrp --input msg.npy -r 3 --tx-delay 0.5

Example output:

$ ria transmit -d pluto -f 915e6 -s 2e6 --input capture.sigmf-data

Initializing PlutoSDR...
  URI: ip:192.168.2.1
  Center frequency: 915.00 MHz
  Sample rate: 2.00 MS/s
  Gain: 0 dB

Transmitting capture.sigmf-data (500,000 samples)...
Transmit complete.

5. YAML Config Patterns

Several commands accept --config <file.yaml> for parameter loading. CLI flags generally override values loaded from --config.

Keep one stable baseline YAML per workflow (capture, generate, transmit), then override only experiment-specific fields on the CLI.

Capture config example:

device: pluto
ident: 192.168.2.1
sample_rate: 2000000
center_frequency: 2.44G
gain: 20
bandwidth: 2000000
num_samples: 500000
format: sigmf
output: run1.sigmf-data
metadata:
  campaign: lab_eval
  antenna: dipole

ria capture -c capture.yaml

Generate config example:

sample_rate: 2000000
num_samples: 200000
format: npy
output: synth.npy
noise_power: 0.02

ria generate noise --config generate.yaml

6. Brief Scripting (Python) Preview

For quick non-CLI use:

from ria_toolkit_oss.data import Recording
from ria_toolkit_oss.io import load_recording, to_sigmf
from ria_toolkit_oss.transforms import iq_augmentations, iq_impairments

rec = load_recording("capture.sigmf-data")
aug = iq_augmentations.channel_swap(rec)
imp = iq_impairments.add_awgn_to_signal(aug, snr=10)
to_sigmf(imp, filename="capture_awgn", path=".")

You can also call annotation algorithms and block-generator primitives from Python directly.

Getting Started

1. Installation and Setup

1.1 SDR Driver Prerequisites

2. CLI Structure

3. Quick End-to-End Workflow

3.1 Discover Radios

3.2 Initialize Local Metadata Defaults

3.3 Capture IQ

3.4 Visualize and Inspect

3.5 Auto-Annotate and Inspect Annotations

3.6 Convert and Split

3.7 Apply Transforms

3.8 Transmit (TX-capable radios only)

4. Command Reference

4.1 discover

4.2 init

4.3 capture

4.4 view

4.5 annotate group

ria annotate list

ria annotate add

ria annotate remove

ria annotate clear

ria annotate energy

ria annotate threshold

ria annotate cusum

ria annotate separate

4.6 convert

4.7 split

4.8 combine

4.9 generate group (and synth alias)

4.10 transform group

augment

impair

custom

4.11 transmit

5. YAML Config Patterns

6. Brief Scripting (Python) Preview

4.1 `discover`

4.2 `init`

4.3 `capture`

4.4 `view`

4.5 `annotate` group

`ria annotate list`

`ria annotate add`

`ria annotate remove`

`ria annotate clear`

`ria annotate energy`

`ria annotate threshold`

`ria annotate cusum`

`ria annotate separate`

4.6 `convert`

4.7 `split`

4.8 `combine`

4.9 `generate` group (and `synth` alias)

4.10 `transform` group

`augment`

`impair`

`custom`

4.11 `transmit`