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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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