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:alt: Documentation Status
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Simulation of homogeneous diffusion, bayesian estimation of underlying diffusion constant and analysis of distinguishability between diffusivities (ndim_homogeneous_distinguishability.py in diffusive_distinguisbaility)
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Simulation of homogeneous diffusion, bayesian estimation of underlying diffusion constant and analysis of distinguishability between diffusivities
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Jupyter notebook providing examples of how to interact with submodules of above .py, and example analysis (ndim_diffusion_analysis_tutorial.ipynb)
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Getting Started
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---------------
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The python package ``ndim_homogeneous_distinguishability.py`` contains the meat of this project, as a set of functions which can be used to:
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1. Simulate diffusive trajectories (pure diffusion with a homogeneous diffusion constant)
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2. Use Bayesian inference to estimate the diffusion constant used to generate a trajectory by producing a posterior diffusivity distribution
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3. Analyze the dependence of diffusivity estimation error, and the ability to distinguish between trajectories with differing diffusivities, conditional on model parameters
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Examples of how to use these functions, as well as some of our own analysis of diffusivity distinguishability, are provided in the Jupyter notebook ``ndim_diffusion_analysis_tutorial.ipynb``.
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Also included are some stored pre-calculated numpy arrays used in the provided Jupyter notebook example analysis (in the directory ``loc_error_saved_files``) and another Jupyter notebook containing a toy model quantifying the relative impact of localization error on diffusion estimates conditional on number of spatial dimensions (``test_overestimation.ipynb``).
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* Free software: Allen Institute Software License

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