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README.md

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@@ -25,7 +25,6 @@ Rastermap runs in python 3.8+ and has a graphical user interface (GUI) for runni
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* [rastermap_interactive.ipynb](notebooks/rastermap_interactive.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MouseLand/rastermap/blob/main/notebooks/rastermap_interactive.ipynb) allows running Rastermap in an interactive way without a local installation
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* [tutorial.ipynb](notebooks/tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MouseLand/rastermap/blob/main/notebooks/tutorial.ipynb) is a guided tutorial for integrating rastermap and facemap to visualize behavioral representations. See the student/teacher versions [here](https://github.com/MouseLand/course-materials/tree/main/behavior_encoding).
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All demo data available [here](https://osf.io/xn4cm/).
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Here is what the output looks like for a segment of a mesoscope recording in a mouse during spontaneous activity (3.2Hz sampling rate), compared to random neural sorting:
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### System requirements
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Linux, Windows and Mac OS are supported for running the code. For running the graphical interface you will need a Mac OS later than Yosemite. At least 8GB of RAM is recommended to run the software. 16GB-32GB may be required for larger datasets. The software has been heavily tested on Windows 10 and Ubuntu 20.04 and less well-tested on Mac OS. Please open an [issue](https://github.com/MouseLand/rastermap/issues) if you have problems with installation.
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Linux, Windows and Mac OS are supported for running the code. For running the graphical interface in Mac, you will need a Mac OS later than Yosemite. At least 8GB of RAM is recommended to run the software. 16GB-32GB may be required for larger datasets. The software has been heavily tested on Windows 10 and Ubuntu 20.04 and less well-tested on Mac OS. Please open an [issue](https://github.com/MouseLand/rastermap/issues) if you have problems with installation.
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### Instructions
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# fit rastermap
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model = Rastermap(n_PCs=200, n_clusters=100,
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locality=0.75, time_lag_window=5).fit(spks)
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locality=0.75, time_lag_window=5).fit(spks, compute_X_embedding=True)
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y = model.embedding # neurons x 1
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isort = model.isort
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# bin over neurons
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X_embedding = zscore(utils.bin1d(spks[isort], bin_size=25, axis=0), axis=1)
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# visualize binning over neurons
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X_embedding = model.X_embedding
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# plot
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fig = plt.figure(figsize=(12,5))

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