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pyDynamo

pyDynamo logo

Application for the UI and analysis of neurons via Dynamic Morphometrics.

Installation

This library can be installed using uv. If you don't have uv, install it first:

curl -LsSf https://astral.sh/uv/install.sh | sh

Then create an environment and install:

uv venv --python 3.10 dynamoEnv
source dynamoEnv/bin/activate
uv pip install --upgrade -e "git+https://github.com/ubcbraincircuits/pyDynamo#egg=pydynamo_brain&subdirectory=pydynamo_brain"
uv pip install pyNeuroTrace

Note for GPU users: uv installs the CPU-only build of PyTorch by default. To use a CUDA-enabled build, install torch manually before the above, e.g. for CUDA 12.1:

uv pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu121

Once installed, it can be run by the following command, and optionally given a file to open:

pydynamo_brain

or

pydynamo_brain path/to/my/file.dyn.gz

Usage

Documentation is available at https://ubcbraincircuits.github.io/pyDynamo/ . For an overview:

After opening Dynamo, two windows will show.

  1. One will contain the dynamo logo (see above), and a close button. This window should always stay open until you want to stop - then pressing the button will close dynamo completely.
  2. The other will give three options:
    • New from Stack(s) (Ctrl-N) will let you create a new dynamo project by opening one or more unlabelled 3D .tif files, containing the neuron volumes. This can then be saved via Ctrl-S to a .dyn.gz file
    • Open from File (Ctrl-O) is used to re-open an existing dynamo project from a .dyn.gz file.
    • Import from Matlab .mat (Ctrl-I) will attempt to load dynamo data created using the old matlab version.

Once stacks are loaded, see the help dialog (F1) for all the commands available.

The recommended approach is:

  1. Load the first .tif stack in the series
  2. Draw the full dendritic arbor (if you save early, it'll start auto-saving too)
  3. Load the next .tif stack in the series
  4. Generate points in this new stack, from either
    • Importing from the previous drawing (I)
    • Importing from an SWC file (coming)
  5. Move/add/delete points as required to match the stack images.
  6. Register (R) to make sure that old points are mapped to new points correctly.
  7. Repeat from step 3 until all stacks are done.

Analysis

Currently analysis is done purely visually (i.e. 'M' or '3'), but as requirements on getting data out are clearer, there'll be a way added to export per-stack and per-branch metrics to a csv (or similar).

Errors

As pyDynamo is currently pre-release and under active development, it may also still crash occasionally. If this happens, let me know and I should be able to fix it - more error details should also be visible from the commandline in which the original python dynamo.py was run.

Acknowledgement & Referencing

Development of pyDynamo is supported by funding from a Canadian Institutes of Health Research (CIHR) Foundation Award (FDN-148468) and the Canadian Open Neuroscience Platform. pyDynamo has the research resource identifier RRID: SCR_017541