| Name | tracking-graph JSON |
| Version |
0.9.0
JSON |
| download |
| home_page | None |
| Summary | Algorithm to track clusters across recordings |
| upload_time | 2024-08-27 22:44:57 |
| maintainer | None |
| docs_url | None |
| author | Fernando Julian Chaure |
| requires_python | <4.0,>=3.10 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Tracking_Graph
Tracking_Graph is a tool for track units across spike sorting solutions. This package is part of the PhD
This project is part of my PhD research at [University Name]
## Installation
Install Tracking_Graph using pip:
```bash
pip install tracking_graph
```
## Usage
### Creating a Graph
```python
from tracking_graph import run_tg, get_tg_groups, EuclideanClassifier
# Create a classifier
modelcreator = EuclideanClassifier.creator(std_mult=3) # Classify spikes with length > 3 std
# Run Tracking_Graph
G = run_tg(
we_list, # List of WaveformExtractor objects
outputfile='/home/user/examplefolder/tg_data.hdf5',
max_len=2, # Maximum edge length, must be at least 1
modelcreator=modelcreator
)
# Compute results programmatically (can be replaced by the GUI)
groups, sG, discarded = get_tg_groups(
G,
mintrack=3, # Minimum number of segments for a cluster
merge=True # Apply criteria to merge splits
)
# Create a final results table
import pandas as pd
df = []
for gi, g in enumerate(groups):
for c in g:
df.append({'segment': c.segment,
'cluster': c.unit,
'tg_unit': gi})
results_table = pd.DataFrame(df)
```
### Exploring Simplified Graphs with GUI
Launch the graphical interface (Streamlit server) using:
```bash
tg_gui
```
### Additional Tools
Tracking_Graph provides a wrapper to load aligned waveforms from Wave_Clus clustering results, addressing limitations in SpikeInterface's waveform interpolation:
```python
from tracking_graph.spikeinterface_addons import Waveclus_Waveforms
path_times_file = '/home/user/examplefolder/times_example.mat' # Full path to Wave_Clus result
we = Waveclus_Waveforms(path_times_file) # Object with basic WaveformExtractor interface
```
## Limitations
- Tracking_Graph requires `spikeinterface<=0.100` due to its dependency on the `WaveformExtractor` class.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## About
For more projects and information, visit my GitHub profile: [ferchaure](https://github.com/ferchaure)
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"description": "# Tracking_Graph\n\n\nTracking_Graph is a tool for track units across spike sorting solutions. This package is part of the PhD \nThis project is part of my PhD research at [University Name]\n\n## Installation\n\nInstall Tracking_Graph using pip:\n\n```bash\npip install tracking_graph\n```\n\n## Usage\n\n### Creating a Graph\n\n```python\nfrom tracking_graph import run_tg, get_tg_groups, EuclideanClassifier\n\n# Create a classifier\nmodelcreator = EuclideanClassifier.creator(std_mult=3) # Classify spikes with length > 3 std\n\n# Run Tracking_Graph\nG = run_tg(\n we_list, # List of WaveformExtractor objects\n outputfile='/home/user/examplefolder/tg_data.hdf5',\n max_len=2, # Maximum edge length, must be at least 1\n modelcreator=modelcreator\n)\n\n# Compute results programmatically (can be replaced by the GUI)\ngroups, sG, discarded = get_tg_groups(\n G,\n mintrack=3, # Minimum number of segments for a cluster\n merge=True # Apply criteria to merge splits\n)\n\n# Create a final results table\nimport pandas as pd\n\ndf = []\nfor gi, g in enumerate(groups):\n for c in g:\n df.append({'segment': c.segment,\n 'cluster': c.unit,\n 'tg_unit': gi})\n\nresults_table = pd.DataFrame(df)\n```\n\n### Exploring Simplified Graphs with GUI\n\nLaunch the graphical interface (Streamlit server) using:\n\n```bash\ntg_gui\n```\n\n### Additional Tools\n\nTracking_Graph provides a wrapper to load aligned waveforms from Wave_Clus clustering results, addressing limitations in SpikeInterface's waveform interpolation:\n\n```python\nfrom tracking_graph.spikeinterface_addons import Waveclus_Waveforms\n\npath_times_file = '/home/user/examplefolder/times_example.mat' # Full path to Wave_Clus result\nwe = Waveclus_Waveforms(path_times_file) # Object with basic WaveformExtractor interface\n```\n\n## Limitations\n\n- Tracking_Graph requires `spikeinterface<=0.100` due to its dependency on the `WaveformExtractor` class.\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## About\nFor more projects and information, visit my GitHub profile: [ferchaure](https://github.com/ferchaure)\n",
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