docs: finalize JOSS paper and pipeline figure, ignore scratch scripts

Author: Jshulgach

.gitignore

@@ -19,7 +19,6 @@ __pycache__/
 
 # Images
 gesture_classifier/*.png
-*.png
 
 # PyTorch models
 gesture_classifier/*.pth
@@ -97,3 +96,4 @@ python_open_ephys-*
 *.npz
 *.npy
 *.log
+/scripts/

docs/figs/pipeline.png

@@ -41,7 +41,7 @@ The field of neural data analysis is supported by several specialized tools. The
 
 `python-oephys` is designed with a modular architecture that separates data acquisition, processing, and visualization (see Figure 1).
 
-![EMG Processing Pipeline. A) Raw signals from five representative channels (10–20s). B) Signals after CAR, bandpass (20-500Hz), and 60Hz notch filtering. C) Automated channel quality indicators evaluated on the first 5s of data. D) Mean RMS features extracted from the processed segment.](docs/figs/pipeline.png)
+![EMG Processing Pipeline. A) Raw signals from five representative channels (20–30s). B) Signals after CAR, bandpass (20-500Hz), and 60Hz notch filtering. C) Automated channel quality indicators evaluated on the first 5s of data. D) Mean RMS features extracted from the processed segment.](docs/figs/pipeline.png)
 
 - **Interface Layer**: Implements ZMQ and LSL clients for low-latency data streaming. The `ZMQClient` is designed to run asynchronously, ensuring that data acquisition does not block processing or UI updates.
 - **Processing Layer**: Provides a suite of filters and feature extraction tools. This includes the `EMGPreprocessor` for standardized filtering and `ChannelQC` for real-time signal quality monitoring.