Fiber Photometry Processing GUI

A desktop GUI for visualizing photometry recordings, cleaning artifacts, filtering/resampling, baseline estimation, motion-correction, and exporting processed traces for downstream analysis.

This project is designed for efficient exploratory QC (preview in the GUI) while keeping processing logic deterministic and scriptable (core functions live in analysis_core.py).

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Key Features

Data IO

• Supports raw data in .doric, .h5 or .csv.
• Multi-channel support (e.g., analogue, DIO channels …).
• Optional alignment of analog traces to the DigitalIO timebase when a DIO is selected.

Artifact Handling

• Artifact detection on raw 465 using derivative thresholding (dx) and MAD:
  • Global MAD (dx): one threshold for the full trace
  • Adaptive MAD (windowed): windowed thresholds for nonstationary noise
• Optional padding around detected artifacts to remove spillover.
• Manual artifact masking by user-defined time regions.
• Masked samples are replaced via linear interpolation to preserve time alignment.

Signal Conditioning

• Low-pass filtering.
• Decimation/resampling.

Baseline Estimation

Baseline is computed after filtering and resampling using pybaselines:

• asls, arpls, airpls
• tunable parameters (lambda, diff order, iterations, tolerance)

Output Modes (7)

The GUI exposes seven explicit output definitions:

1. dFF (non motion corrected)
   dFF = (signal_filtered - signal_baseline) / signal_baseline

2. zscore (non motion corrected)
   zscore(dFF_nonMC)

3. dFF (motion corrected via subtraction)
   dFF_mc = dFF_signal - dFF_ref
   where each dFF uses its own baseline.

4. zscore (motion corrected via subtraction)
   zscore(dFF_signal - dFF_ref)

5. zscore (subtractions)
   zscore(dFF_signal) - zscore(dFF_ref)

6. dFF (motion corrected with fitted ref)
   Fit the isosbestic/reference channel to the signal:
   fitted_ref = a * ref_filtered + b
   then compute:
   dFF = (signal_filtered - fitted_ref) / fitted_ref

7. zscore (motion corrected with fitted ref)
   zscore( (signal_filtered - fitted_ref) / fitted_ref )

Reference Fitting Methods (for “fitted ref” modes)

• OLS (recommended): fast and stable
• Lasso: sparse regression (requires scikit-learn)
• RLM (HuberT): robust linear model via IRLS + Huber weighting (no extra dependency)

Export

• Export processed output to:
  • CSV (time, output, optional dio)
  • HDF5 with raw, baseline, and metadata fields
• Drag-and-drop support for preprocessing and post-processing files.

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Repository Structure (typical)

• analysis_core.py
  Processing pipeline (loading, filtering, baselines, outputs, export helpers)
• main.py (or similar)
  PySide6 GUI entry point and UI wiring
• requirements.yml
  Conda environment definition

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Installation

1. Create the environment:

   conda env create -f environment.yml

Run

cd .\pyBer
python main.py

Usage workflow

Open a Doric .h5 file

Choose a channel (e.g., AIN01).

Optionally select a DigitalIO line to overlay events.

QC & artifact removal

Choose Global MAD (dx) or Adaptive MAD (windowed).

Tune mad_k, window size, and padding.

Add manual mask regions if needed.

Filtering & resampling

Set low-pass cutoff (Hz) and filter order.

Set a target sampling rate (Hz) for consistent downstream analysis.

Baseline estimation

Choose asls, arpls, or airpls.

Tune lambda and other parameters to avoid baseline leakage into fast transients.

Select output

Pick one of the 7 output modes.

For “fitted ref” modes, choose the fit method (OLS/Lasso/RLM-HuberT).

Export

Export CSV/H5 for analysis in Python/MATLAB/R.

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Preprocessing: Advanced Options

The preprocessing panel includes an Advanced options button with two features:

1. Cut out regions (NaN)
   Define start/end ranges to exclude parts of the trace from downstream analysis. Cutout regions are filled with NaN in the output and can be exported as-is.

2. Sections (per-section processing)
   Define multiple start/end sections and assign per-section processing parameters. Each section can be exported independently (one CSV/H5 per section).

Time window

You can optionally set a start time, end time, or both:

• Start only: process from start → end of recording
• End only: process from 0 → end
• Start + end: process that window only
• Empty: process full trace

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Post-Processing

Align sources

• DIO: choose DIO channel, polarity (0→1 or 1→0), and align to onset/offset
• Behavior (CSV/XLSX): load a behavior file and select a column (binary 0/1)
  • Align to onset or offset
  • Transitions: align to A→B transitions with a max gap threshold

Behavior file formats

• CSV: must include a time column and one or more binary behavior columns
• Ethovision XLSX: the loader preprocess and clean the file, the user has to select the sheet when loading

PSTH/Heatmap

• Heatmap and PSTH refresh automatically when alignment settings change
• Event lines are overlaid on the trace preview
• Event duration histogram appears to the right of the heatmap
• Metrics bar plot (pre vs post) appears to the right of the PSTH

Metrics

Choose AUC or mean z-score and define pre/post windows in seconds.

Export results

Export any combination of:

• Heatmap matrix
• Average PSTH + SEM
• Event times
• Event durations
• Metrics table

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Group Mode (Multiple Animals)

Use the Group tab in Post-Processing to load multiple processed files (CSV/H5). Each file should represent a single animal, and matching behavior files should share the same base name. In group mode:

• Heatmap rows represent animals (not trials)
• PSTH is averaged across animals

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Build (PyInstaller)

Example command (run from repo root):

python -m PyInstaller --noconfirm --clean pyBer.spec

PowerShell helper:

.\build_pyinstaller.ps1

This will create a pyBer.exe in the dist/ folder.