striqt is a set of open-source python packages for batched real-time signal analysis on CPU or GPU, and rapid prototyping of bespoke radio environment sensors.

Basic CLI usage

🏃 Run a batched acquisition and analysis for measurement or calibration, given a path to a YAML input specification:

sensor-sweep [OPTIONS] YAML_PATH

#  Selected options:
#  -d, --debug                if set, drop to IPython debug prompt on exception
#  -v, --verbose              verbosity (or -vv, or -vvv)

📈 Plot the data variables in captures contained in one sweep of a zarr archive, given a YAML plotting configuration specification:

plot-capture [OPTIONS] ZARR_PATH YAML_PATH

# Selected options:
#  -i, --interactive
#  --no-save          don't save the resulting plots

More detailed usage instructions for these tools can be discovered with the --help flag.

Python module APIs

The API is organized into python packages:

striqt.analysis

Provides optimized and validated real-time signal analysis of complex-valued IQ baseband:

• Power spectral density and spectrogram evaluation
• Power detectors in various time-domain representation
• Empirical statistical distributions
• Cellular cyclic prefix and synchronization correlators
• Extensible with custom analyses based on custom

Interoperability within the modern python data ecosystem:

• Interchangable across CPU (numpy) and GPU (cupy) based on full-precision (32-bit) floating point
• Fast numba numerical kernels for speed and portability across operating systems
• Package as multi-dimensional xarray.Dataset objects
  • Detailed coordinates, units, and metadata across all axes
• Load and save zarr dataset archive format for easy aggregation and dissemination local or cloud storage

striqt.sensor

Implements batch IQ data acquisition and resampling oriented toward test and measurement

• Input power level calibration based on the Y-factor technique
• Support for exact rational Fourier resampling on CPU/GPU
• Multi-threaded acquisition, analysis, and archival to maximize throughput
• Software-defined radio interoperability via SoapySDR
• Acquisition and analysis input specification schema (and yaml input decode support)

striqt.figures

Implements visualization for the data variables produced by striqt.analysis

• Provides a plot function for each type of data variable
• Uses the labeled coordinates and metadata to display proper units
• Generates plots in matplotlib for publication-ready in selectable output formats

Installation

The following assume access to the open internet.

Option 1: Conda Environment

Installation with radio hardware and GPU support is provided via conda environments. Several variants of a striqt environment are provided here, targeted at different host computing environments.

1. Ensure that conda is installed (or mamba/micromamba substituted in what follows)
2. Clone this repository
3. Download a predefined environment based on needed capabilities:
   • cpu.yml: acquire radio data, perform CPU-only IQ signal analysis, load/save zarr archives, and plot data
   • gpu-cpu.yml: support the above, and analyze IQ on CUDA GPUs
4. Create the chosen environment:

       conda env create -f [path-to-environment-here.yml]

5. To activate the environment, select the striqt conda environment in your IDE, or run

       conda activate striqt

Option 2: pip installation

A limited environment that supports the latest APIs and CLIs for post-analysis, plotting, testing, etc. can be installed via the python package index (pypi):

pip install "striqt @ git+https://github.com/usnistgov/striqt"

It is encouraged to use a lockfile (through pipenv, uv, pixi, etc. in lieu of pip above) to enforce a fully reproducable install.

NOTE: In order to create an environment that is reproducible environment and to isolate the install from dependency versioning conflicts, it is highly recommended to install `striqt`` into a container or virtual environment.

NOTE: SoapySDR is required to to acquire radio captures, but it is not distributed on pypi

Development status

striqt is in early beta. The pace of change has slowed

• The API in the base of each module is expected not to change, but internals (.lib, etc.) may change without warning
• Incompatible changes to yaml schemas in striqt.sensor and striqt.figure (and the CLIs) may still change slightly
• The data variable, coordinate, and metadata field names in .zarr files follow the names in the yaml schemas

Documentation

In keeping with the early beta development status of this codebase, documentation is limited.

• reference for the yaml configuration files that drive edge_sensor
• Docstrings in the source code are the most up-to-date API documentation
• A few examples are located in tests and notebooks. Some of these may not be up to date.