# PyNeuroTrace: Python code for Neural Timeseries
[](https://github.com/padster/pyNeuroTrace/actions/workflows/python-test.yml) [](http://padster.github.io/pyNeuroTrace/)
## Installation
'pyNeuroTrace' can be installed with pip:
```
pip install pyNeuroTrace
```
GPU Supported functions use the Python Library Cupy. This library has the following requirements:
* NVIDIA CUDA GPU with the Compute Capability 3.0 or larger.
* CUDA Toolkit: v11.2 / v11.3 / v11.4 / v11.5 / v11.6 / v11.7 / v11.8 / v12.0 / v12.1 / v12.2 / v12.3 / v12.4
'pyNeuroTrace' can be installed with Cupy using pip:
```
pip install pyNeuroTrace[GPU]
```
If Cupy fails to build from the wheel using this command try installing Cupy first using a wheel that matches your CUDA Toolkit version ie:
```
pip install cupy-cuda12x
```
Followed by this command to install pyNeuroTrace
```
pip install pyNeuroTrace
```
For more information in on installing CuPy, see their [CuPy installation documentation](https://docs.cupy.dev/en/stable/install.html).
## Documentation
To help get started using 'pyNeuroTrace', full documentation of the software and available modules and methods is available at our [pyNeuroTrace github.io site](https://padster.github.io/pyNeuroTrace/).
## Vizualization
Probably the most useful section of pyNeuroTrace, there are a number of visualization functions provided to help display the trace data in easy to understand formats. For more details, and visual examples of what is available, please consult the [README](https://github.com/padster/pyNeuroTrace/tree/master/pyneurotrace) next to viz.py.
## Notebook utilities
Unrelated to neuron time series, but useful when using this regardless, PyNeuroTrace also provides a collection of tools for running analysis in Jupyter notebooks.
These include:
`notebook.filePicker` to selecting an existing file, `notebook.newFilePicker` for indicating a new path, and `notebook.folderPicker` for selecting an existing folder.
> These all open PyQT file dialogs, to make it easy for users to interact with the file system. Customisation options exist, for example prompt and default locations.
`showTabs(data, func, titles, progressBar=False)`
> Useful for performing analysis repeated multiple times across e.g. different neurons, experiments, conditions, ...etc. Given either a list, or dictionary, all items will be iterated over, and each individually drawn onto their own tab with the provided `func`.
The method provided expects `func(idx, key, value)`, where `idx` is the (0-based) index of the tab, `key` is the list/dictionary key, and `value` is what is to be processed.
## Processing Data
Common per-trace processing filters are provided within [filters.py](https://github.com/padster/pyNeuroTrace/tree/master/pyneurotrace/filters.py). These are all designed to take a numpy array of traces, with each row an independent trace, and all return a filtered array of the same size.
These include:
`filters.deltaFOverF0(traces, hz, t0, t1, t2)`
> Converts raw signal to the standard Delta-F-over-F0, using the technique given in [Jia et al, 2011](http://doi.org/10.1038/nprot.2010.169). The smoothing parameters (t0, t1, t2) are as described in the paper, all with units in seconds. Sample rate must also be provided to convert these to sample units.
`filters.okada(traces)`
> Reduces noise in traces by smoothing single peaks or valleys, as described in [Okada et al, 2016](https://doi.org/10.1371/journal.pone.0157595)
## Event Detection
Python implementations of the three algorithms discussed in our paper [Sakaki et al, 2018](https://doi.org/10.1109/EMBC.2018.8512983) for finding events within Calcium fluorescent traces.
`ewma(data, weight)`
> calculates the Exponentially-Weighted Moving Average for each trace, given how strongly to weight new points (vs. the previous average).
`cusum(data, slack)`
> calculates the Cumulative Sum for each trace, taking a slack parameter which controls how far from the mean the signal the signal needs to be to not be considered noise.
`matchedFilter(data, windowSize, A, tA, tB)`
> calculates the likelihood ratio for each sample to be the end of a window of expected transient shape, being a double exponential with amplitude A, rise-time tA, and decay-time tB (in samples).
The results of each of these three detection filters can then be passed through `thresholdEvents(data, threshold)`, to register detected events whenever the filter strength increases above the given threshold.
## Reading Data (lab-specific)
The code within this repository was designed to read data from experiments performed by the [Kurt Haas lab](https://cps.med.ubc.ca/faculty/haas/) at UBC. If you're from this lab, read below. If not, this part is probably not relevant, but fee free to ask if you'd be interested in loading your own data file formats.
A number of options for loading data files are available within [files.py](https://github.com/padster/pyNeuroTrace/tree/master/pyneurotrace/files.py), including:
* `load2PData(path)` takes an experiment output file (e.g STEP_5_EXPT.TXT) and returns ID, XYZ location, and raw intensity values for each node in the experiment.
* `loadMetadata(path)` takes a metadata file (e.g. rscan_metadata_step_5.txt) and returns the stimulus start/stop samples, as well as the sample rate for the experiment.
* `loadTreeStructure(path)` takes a tree structure file (e.g. interp-neuron-.txt) and returns the mapping of node IDs to tree information about that node (e.g. node type, children, ...).
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"description": "# PyNeuroTrace: Python code for Neural Timeseries\n[](https://github.com/padster/pyNeuroTrace/actions/workflows/python-test.yml) [](http://padster.github.io/pyNeuroTrace/)\n\n## Installation\n'pyNeuroTrace' can be installed with pip:\n\n```\npip install pyNeuroTrace\n```\n\nGPU Supported functions use the Python Library Cupy. This library has the following requirements:\n* NVIDIA CUDA GPU with the Compute Capability 3.0 or larger.\n\n* CUDA Toolkit: v11.2 / v11.3 / v11.4 / v11.5 / v11.6 / v11.7 / v11.8 / v12.0 / v12.1 / v12.2 / v12.3 / v12.4\n\n\n'pyNeuroTrace' can be installed with Cupy using pip:\n\n```\npip install pyNeuroTrace[GPU]\n```\nIf Cupy fails to build from the wheel using this command try installing Cupy first using a wheel that matches your CUDA Toolkit version ie:\n```\npip install cupy-cuda12x\n```\n\nFollowed by this command to install pyNeuroTrace\n```\npip install pyNeuroTrace\n```\n\nFor more information in on installing CuPy, see their [CuPy installation documentation](https://docs.cupy.dev/en/stable/install.html).\n\n## Documentation\n\nTo help get started using 'pyNeuroTrace', full documentation of the software and available modules and methods is available at our [pyNeuroTrace github.io site](https://padster.github.io/pyNeuroTrace/).\n\n## Vizualization\n\nProbably the most useful section of pyNeuroTrace, there are a number of visualization functions provided to help display the trace data in easy to understand formats. For more details, and visual examples of what is available, please consult the [README](https://github.com/padster/pyNeuroTrace/tree/master/pyneurotrace) next to viz.py.\n\n## Notebook utilities\n\nUnrelated to neuron time series, but useful when using this regardless, PyNeuroTrace also provides a collection of tools for running analysis in Jupyter notebooks.\n\nThese include:\n\n`notebook.filePicker` to selecting an existing file, `notebook.newFilePicker` for indicating a new path, and `notebook.folderPicker` for selecting an existing folder.\n> These all open PyQT file dialogs, to make it easy for users to interact with the file system. Customisation options exist, for example prompt and default locations.\n\n`showTabs(data, func, titles, progressBar=False)`\n> Useful for performing analysis repeated multiple times across e.g. different neurons, experiments, conditions, ...etc. Given either a list, or dictionary, all items will be iterated over, and each individually drawn onto their own tab with the provided `func`.\nThe method provided expects `func(idx, key, value)`, where `idx` is the (0-based) index of the tab, `key` is the list/dictionary key, and `value` is what is to be processed.\n\n## Processing Data\n\nCommon per-trace processing filters are provided within [filters.py](https://github.com/padster/pyNeuroTrace/tree/master/pyneurotrace/filters.py). 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Sample rate must also be provided to convert these to sample units.\n\n`filters.okada(traces)`\n> Reduces noise in traces by smoothing single peaks or valleys, as described in [Okada et al, 2016](https://doi.org/10.1371/journal.pone.0157595)\n\n\n\n## Event Detection\n\nPython implementations of the three algorithms discussed in our paper [Sakaki et al, 2018](https://doi.org/10.1109/EMBC.2018.8512983) for finding events within Calcium fluorescent traces.\n\n`ewma(data, weight)`\n> calculates the Exponentially-Weighted Moving Average for each trace, given how strongly to weight new points (vs. the previous average).\n\n`cusum(data, slack)`\n> calculates the Cumulative Sum for each trace, taking a slack parameter which controls how far from the mean the signal the signal needs to be to not be considered noise.\n\n`matchedFilter(data, windowSize, A, tA, tB)`\n> calculates the likelihood ratio for each sample to be the end of a window of expected transient shape, being a double exponential with amplitude A, rise-time tA, and decay-time tB (in samples).\n\nThe results of each of these three detection filters can then be passed through `thresholdEvents(data, threshold)`, to register detected events whenever the filter strength increases above the given threshold.\n\n## Reading Data (lab-specific)\n\nThe code within this repository was designed to read data from experiments performed by the [Kurt Haas lab](https://cps.med.ubc.ca/faculty/haas/) at UBC. 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