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Code for results of "Microbial communities experimental time series captured by stochastic logistic models"
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BCGardner/logistic_models
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Logistic models This code produces the results of "Stochastic logistic models reproduce experimental time series of microbial communities". elife_settings.py generate_timeseries.py make_colormap.py neutral_covariance_test.py neutrality_analysis.py noise_analysis.py noise_color_analysis.py noise_parameters.py noise_properties_plotting.py Figures of main text in 'Figures eLife' Figures of supplemental: Fig 1 : Fisher Mehta neutral model Fig 2 : Experimental Fig 3 : Supplemental Fig 4 : Understand noise color Fig 5 : Study noise no interaction Fig 6 : Experimental Fig 7 : Experimental Fig 8 : Experimental Fig 9 : Experimental Fig 10 : Experimental Fig 11 : Experimental Fig 12 : Study noise with interaction Fig 13 : Study noise no interaction Fig 14 : Study noise no interaction Fig 15 : Figures eLife Fig 16 : Study noise with interaction Fig 17 : Width distribution dx Fig 18 : Width distribution dx Fig 19 : Understanding Fisher Mehta Figure 2B Fig 20 : Influence interactions SOI and sgLV Fig 21 : Influence interactions SOI and sgLV Fig 22 : Influence interactions SOI and sgLV Fig 23 : Influence interactions SOI and sgLV
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Code for results of "Microbial communities experimental time series captured by stochastic logistic models"
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