StochPy Module documentation¶

Stochastic Simulation Module¶

The main module of StochPy

TODO: ininstance for s_id en r_id types in rates2plot and species2plot http://www.python-course.eu/python3_formatted_output.php

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 15, 2014

class stochpy.modules.StochSim.SSA(Method='Direct', File='ImmigrationDeath.psc', dir=None, Mode='steps', End=1000, Trajectories=1, IsTrackPropensities=False, IsInteractive=True)¶

SSA(Method=’Direct’, File=None, dir=None, Mode=’steps’, End=1000, Trajectories=1, IsTrackPropensities=False)

Input options:

Method [default = ‘Direct’], Available methods: ‘Direct’, ‘FirstReactionMethod’,’TauLeaping’,’Next Reaction Method’
File [default = ImmigrationDeath.psc]
dir [default = /home/user/stochpy/pscmodels/ImmigrationDeath.psc]
Mode [default = ‘steps’] simulation for a total number of ‘steps’ or until a certain end ‘time’ (string)
End [default = 1000] end of the simulation (number of steps or end time) (float)
Trajectories [default = 1] (integer)
TrackPropensities [default = False] (boolean)

Usage (with High-level functions): >>> smod = stochpy.SSA() >>> help(smod) >>> smod.Model(File = ‘filename.psc’, dir = ‘/.../’) >>> smod.Method(‘Direct’) >>> smod.Reload() >>> smod.Trajectories(3) >>> smod.Timesteps(10000) >>> smod.DoStochSim() >>> smod.DoStochSim(end=1000,mode=’steps’,trajectories=5,method=’Direct’,IsTrackPropensities=True) >>> smod.PlotSpeciesTimeSeries() >>> smod.PlotPropensitiesTimeSeries() >>> smod.PlotAverageSpeciesTimeSeries() >>> smod.PlotWaitingtimesDistributions() >>> smod.PlotSpeciesDistributions(bin_size = 3) >>> smod.PrintSpeciesMeans() >>> smod.PrintSpeciesDeviations() >>> smod.PrintPropensitiesMeans() >>> smod.ShowOverview() >>> smod.ShowSpecies() >>> smod.DoTestsuite()

ChangeInitialSpeciesAmount(species, value)¶

ChangeInitialSpeciesAmount(species,value)

Change initial species Amount

Input:

species (string)
value (float)

ChangeParameter(parameter, value)¶

ChangeParameter(parameter,value)

Change parameter value

Input:

parameter (string)
value (float)

DeleteTempfiles()¶: Deletes all .dat files

DoCainStochSim(endtime=100, frames=10000, trajectories=False, solver="HomogeneousDirect2DSearch", IsTrackPropensities=False)¶

Use Cain implementations for fixed-interval output stochastic simulations (www.cacr.caltech.edu/~sean/cain/DeveloperFile) Make sure that the input file contains net stoichiometric coefficients

Input:

endtime [default = 100](float)
frames [default = 10000] (integer)
trajectories [default = False] (integer)
solver [default = HomogeneousDirect2DSearch] (string)
IsTrackPropensities [default = False] (boolean)

DoCompleteStochSim(error = 0.001, size=100000, IsTrackPropensities=False)¶

Do a stochastic simulation until the first four moments converge (in development, beta-status)

Input:

error maximal allowed error [default = 0.001]
size (integer) number of steps before checking the first four moments [default = 100000]
IsTrackPropensities [default = False]
species_selection [default = None] List of names of species to store. This saves memory space and prevents Memory Errors.

DoDelayedStochSim(end=False, mode=False, method=False, trajectories=False, IsTrackPropensities=False, species_selection=None, IsOnlyLastTimepoint=False)¶

DoDelayedStochSim(end=100, mode=’steps’, method=’DelayedDirect’, trajectories=1, IsTrackPropensities=False, species_selection = None) Run a stochastic simulation with delayed reactions until end is reached. This can be either time steps or end time (which could be a HUGE number of steps).

Input:

end [default=1000] simulation end (steps or time)
mode [default=’steps’] simulation mode, can be one of: - steps (string) total number of steps to simulate - time (string) simulate until time is reached
method [default=’Delayeddirect’] stochastic algorithm (DelayedDirect, DelayedNRM)
trajectories [default = 1] number of trajectories
IsTrackPropensities [default = False]
species_selection [default = None] List of names of species to store. This saves memory space and prevents Memory Errors (occurring at ~15 species).
IsOnlyLastTimepoint [default = False]

DoSingleMoleculeStochSim(end=False, mode=False, method=False, trajectories=False, species_selection=None, IsOnlyLastTimepoint=False)¶

DoSingleMoleculeStochSim(end=100, mode=’steps’, method=’SingleMoleculeMethod’, trajectories=1, IsTrackPropensities=False) Run a single molecule stochastic simulation until end is reached. This can be either time steps or end time (which could be a HUGE number of steps).

Input (similar to .DoStochSim()):

end [default=1000] simulation end (steps or time)
mode [default=’steps’] simulation mode, can be one of: - steps (string) total number of steps to simulate - time (string) simulate until time is reached
method [default=’SingleMoleculeMethod’] stochastic algorithm, can be one of: - SingleMoleculeMethod or SMM - FastSingleMoleculeMethod or fSMM
trajectories [default = 1] number of trajectories
species_selection [default = None] List of names of species to store. This saves memory space and prevents Memory Errors.
IsOnlyLastTimepoint [default = False]

DoStochKitStochSim(endtime=100, frames=10000, trajectories=False, IsTrackPropensities=False, customized_reactions=None, solver=None, keep_stats = False, keep_histograms = False)¶

Do Stochastic simulations with StochKit in StochPy Make sure that the input file contains net stoichiometric coefficients

Input:

endtime [default = 100] (float)
frames [default = 10000] (integer)
trajectories [default = False] (integer)
IsTrackPropensities [default = False] (boolean)
customized_reactions [default=None] (list of strings)
solver [default = None] (string)
keep_states [default = False] (boolean)
keep_histograms [default = False) (boolean)

DoStochSim(end=10, mode='steps', method='Direct', trajectories=1, epsilon=0.03, IsTrackPropensities=False)¶

Run a stochastic simulation for until end is reached. This can be either time steps or end time (which could be a HUGE number of steps).

Input:

end [default=1000] simulation end (steps or time)
mode [default=’steps’] simulation mode, can be one of: - steps (string) total number of steps to simulate - time (string) simulate until time is reached
method [default=’Direct’] stochastic algorithm (Direct, FRM, NRM, TauLeaping)
trajectories [default = 1] number of trajectories
epsilon [default = 0.03] parameter for Tau-Leaping
IsTrackPropensities [default = False]
species_selection [default = None] List of names of species to store. This saves memory space and prevents Memory Errors (occurring at ~15 species).
IsOnlyLastTimepoint [default = False]

DoTestsuite(epsilon_ = 0.01, sim_trajectories=1000)¶

Do “sim_trajectories” simulations until t=50 and print the interpolated result for t = 0,1,2,...,50

Input:

epsilon_ [default = 0.01]: useful for tau-Leaping simulations (float)
sim_trajectories [default = 1000]

DumpTrajectoryData(n)¶

Input:

n (integer)

Endtime(t)¶

Set the end time of the exact realization of the Markov jump process

Input:

t (float)

Export2File(analysis='timeseries', datatype='species', IsAverage = False, directory=None)¶

Write data to a text document

Input:

analysis [default = ‘timeseries’] (string) options: timeseries, distribution, mean, std, autocorrelation, autocovariance
datatype [default = ‘species’] (string) options: species, propensities, waitingtimes
IsAverage [default = False] (boolean)
directory [default = None] (string)

FillDataStochsim(IsImport=False)¶

Put all simulation data in the data object data_stochsim

Input:

IsImport (boolean) [False]

GetAveragePropensitiesDistributions()¶: Get average propensities distributions

GetAverageSpeciesDistributions()¶: Get average species distributions

GetPropensitiesAutocorrelations(rates2calc=True, gridpoints=True)¶

Input:

rates2calc [default = True] as a list [‘R1’,’R2’]
gridpoints (integer)

GetPropensitiesAutocovariances(rates2calc=True, gridpoints=True)¶

Input:

rates2calc [default = True] as a list [‘R1’,’R2’]
gridpoints (integer)

GetRegularGrid(npoints=50)¶

The Gillespie method generates data at irregular time points. However, it is possible to output data on a fixed regular time grid where the user can specify the resolution of the grid (npoints).

Input:

npoints [default = 50] (integer)

GetSpeciesAutocorrelations(species2calc=True, gridpoints=True)¶

Input:

species2calc [default = True] as a list [‘S1’,’S2’]
gridpoints (integer)

GetSpeciesAutocovariances(species2calc=True, gridpoints=True)¶

Input:

species2calc [default = True] as a list [‘S1’,’S2’]
gridpoints (integer)

GetTrajectoryData(n=1)¶

GetTrajectryData(n=1)

Switch to another trajectory, by default, the last trajectory is accessible

Input:

n [default = 1] (integer)

GetWaitingtimes()¶: Get for each reaction the waiting times

Import2StochPy(filename, filedir, delimiter='t')¶

Can import time series data with the following format:

Time S1 S2 S3 Fired Reactions 0 1 0 1 nan 1.5 0 0 2 1 etc.

or

Time S1 S2 S3 0 1 0 1 1.5 0 0 2 etc.

In the future, this function will probably support more data types. We currently accept the default output of the Gillespie algorithm from which other data types can be derived.

Input:

filename (string)
filedir (string)
delimiter (string)

Method(method)¶

Input:

method (string)

Select one of the following methods:

Direct
FirstReactionMethod (FRM)
NextReactionMethod (NRM)
TauLeaping
DelayedDirect
DelayedNextReactionMethod (DelayedNRM)
SingleMoleculeMethod (SMM)
FastSingleMoleculeMethod (fSMM)

Note: input must be a string –> ‘Direct’ (not case sensitive)

Mode(sim_mode='steps')¶

Run a stochastic simulation for until end is reached. This can be either time steps or end time (which could be a HUGE number of steps).

Input:

sim_mode [default = ‘steps’] (string) ‘time’ or ‘steps’

Model(File, dir=None)¶

Select model for simulation

Input:

File (string)
dir [default = None] (string)

PlotAveragePropensitiesAutocorrelations(nlags=-1, rates2plot=True, linestyle='None', linewidth=1, marker='o', colors=None, title='StochPy Propensities Autocorrelation Plot (# of trajectories = )', xlabel='Lag ($\tau$)', ylabel='Auto-correlation', IsLegend=True, legend_location='upper right', nstd=1)¶

PlotAveragePropensitiesAutocorrelation(nlags=-1,rates2plot = True,linestyle = ‘None’,linewidth = 1, marker = ‘o’,colors = None,title = ‘StochPy Propensities Autocorrelation Plot (# of trajectories = )’,xlabel=r’Lag ($ au$)’,ylabel=’Auto-correlation’,IsLegend=True,legend_location=’upper right’,nstd=1)

Plot the average propensities autocorrelation result for different lags. For each lag, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Average Time (# of trajectories = ... ) ] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotAveragePropensitiesAutocovariances(nlags=-1, rates2plot=True, linestyle='None', linewidth=1, marker='o', colors=None, title='StochPy Propensities Autocorrelation Plot (# of trajectories = )', xlabel='Lag ($\tau$)', ylabel='Auto-correlation', IsLegend=True, legend_location='upper right', nstd=1)¶

PlotAveragePropensitiesAutocorrelation(nlags=-1,rates2plot = True,linestyle = ‘None’,linewidth = 1, marker = ‘o’,colors = None,title = ‘StochPy Propensities Autocorrelation Plot (# of trajectories = )’,xlabel=r’Lag ($ au$)’,ylabel=’Auto-correlation’,IsLegend=True,legend_location=’upper right’,nstd=1)

Plot the average propensities autocorrelation result for different lags. For each lag, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Average Time (# of trajectories = ... ) ] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotAveragePropensitiesDistributions(rates2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Species Time Series Plot (# of trajectories = )', xlabel='Propensity', ylabel='Probability', IsLegend=True, legend_location='upper right', nstd=1)¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Propensity’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
nstd [default=1] (float

PlotAveragePropensitiesDistributionsConfidenceIntervals(rates2plot = True, colors = None, title = 'StochPy Average Propensities Distributions Plot (# of trajectories = )', xlabel='Propensity', ylabel='Probability', IsLegend=True, legend_location='Upper right', nstd=1)¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

rates2plot [default = True] as a list [‘R1’,’R2’]
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Propensity’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
nstd [default=1] (float)

PlotAveragePropensitiesTimeSeries(rates2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Propensities Time Series Plot (# of trajectories = )', xlabel='Time', ylabel='Propensity', IsLegend=True, legend_location='upper right', nstd=1)¶

Plot the average propensities For each time point, the mean and standard deviation are plotted

Input:

rates2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Propensities Plot (# of trajectories = ...)’ ] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Propensity’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
nstd [default=1] (float

PlotAverageSpeciesAutocorrelations(species2plot = True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot (# of trajectories = )')¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Species Autocorrelation Plot (# of trajectories = ... )’ ] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotAverageSpeciesAutocovariances(species2plot = True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot (# of trajectories = )')¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags

species2plot [default = True] as a list [‘S1’,’S2’]

linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)

linewidth [default = 1] (float)

marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)

colors [default = None] (list)

title [default = ‘StochPy Average Species Autocorrelation Plot (# of trajectories = ... )’ ] (string)

xlabel [default = r’Lag ($ au$)’] (string)

ylabel [default = ‘Autocorrelation’] (string)

IsLegend [default = True] (boolean)

legend_location [default = ‘upper right’] (string/integer)

PlotAverageSpeciesDistributions(species2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Species Distributions Plot (# of trajectories = )', xlabel='Species Amount', ylabel='Probability', IsLegend=True, legend_location='upper right', nstd=1)¶

Plot the average species distributions For each species Amount, the mean and standard deviation are plotted

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Species Amount’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
nstd [default=1] (float)

PlotAverageSpeciesDistributionsConfidenceIntervals(species2plot=True, colors = None, title = 'StochPy Average Species Distributions Plot (# of trajectories = )', xlabel='Species Amount', ylabel='Probability', IsLegend=True, legend_location='upper right', nstd=1)¶

Plot the average species distributions For each species Amount, the mean and standard deviation are plotted

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Species Amount’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
nstd [default=1] (float

PlotAverageSpeciesTimeSeries(species2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Species Time Series Plot (# of trajectories = )', xlabel='Time', ylabel='Copy Number', IsLegend=True, legend_location='upper right', nstd=1)¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Copy Number’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
nstd [default=1] (float

PlotPropensitiesAutocorrelations(rates2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Propensities Autocorrelation Plot', xlabel=r'Lag ($ au$)', ylabel='Auto-correlation')¶

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Propensities Autocorrelation Plot] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotPropensitiesAutocovariances(rates2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Propensities Autocorrelation Plot', xlabel=r'Lag ($ au$)', ylabel='Auto-correlation')¶

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Propensities Autocorrelation Plot] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Auto-correlation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotPropensitiesDistributions(self, rates2plot = True, linestyle = 'dotted', linewidth = 1, colors=None, title = 'StochPy Propensities Probability Mass Function', xlabel='Propensity', ylabel='Probability', IsLegend=True, legend_location='upper right', bin_size=1)¶

Plots the PDF for each generated trajectory

Default: PlotPropensitiesDistributions() plots PDF for each species

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] (string)
linewidth [default = 1] (float)
colors (list)
title [default = ‘StochPy Propensities Probability Mass Function’] (string)
xlabel [default = ‘Propensity’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
bin_size [default=1] (integer)

PlotPropensitiesTimeSeries(n_events2plot = 10000, rates2plot = True, linestyle = 'solid', marker = '', colors = None, title = 'StochPy Propensities Time Series Plot', xlabel='Time', ylabel='Propensity', IsLegend=True, legend_location='upper right')¶

Plot time simulation output for each generated trajectory

Default: PlotPropensitiesTimeSeries() plots propensities for each species

Input:

n_events2plot [default = 10000] (integer)
rates2plot [default = True]: species as a list [‘S1’,’S2’]
marker [default = ‘’] (‘v’,’o’,’s’,’,’,’*’,’.’)
linestyle [default = ‘solid’]: dashed, dotted, and solid (string)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Propensities Time Series Plot’] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Propensity’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesAutocorrelations(species2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot', xlabel='Lag', ylabel='Auto-correlation')¶

Plot species autocorrelations

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Autocorrelation Plot’] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesAutocovariances(species2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot', xlabel='Lag', ylabel='Auto-correlation')¶

Plot species auto-covariances

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Autocorrelation Plot’] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesDistributions(species2plot = True, linestyle = 'dotted', linewidth = 1, colors=None, title = 'StochPy Species Probability Mass Function', xlabel='Number of Molecules', ylabel='Probability', IsLegend=True, bin_size=1)¶

Plots the PDF for each generated trajectory Default: PlotSpeciesDistributions() plots PDF for each species

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] (string)
linewidth [default = 1] (float)
colors (list)
title [default = ‘StochPy Species Probability Mass Function’] (string)
xlabel [default = ‘Number of Molecules’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
bin_size [default=None] (integer)

PlotSpeciesTimeSeries(n_events2plot = 10000, species2plot = True, linestyle = 'solid', linewidth = 1, marker = '', colors = None, title = 'StochPy Species Time Series Plot', xlabel='Time', ylabel='Copy Number', IsLegend=True, legend_location='upper right')¶

Plot time simulation output for each generated trajectory Default: PlotSpeciesTimeSeries() plots time simulation for each species

Input:

n_events2plot [default = 10000] (integer)
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘solid’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Time Series Plot’] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Copy Number’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotWaitingtimesDistributions(rates2plot = True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Waiting times Plot', xlabel='inter-event time t', ylabel='Probability Density')¶

Plot event waiting time distributions

default: PlotWaitingtimesDistributions() plots waiting times for all rates

Input:

rates2plot [default = True] as a list of strings [“R1”,”R2”]
linestyle [default = ‘None’] dashed, dotted, dash_dot, and solid (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Event Waiting times Plot’] (string)
xlabel [default = ‘inter-event time t’] (string)
ylabel [default = ‘Probability Density’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PrintAveragePropensitiesTimeSeries()¶: Analyze the average output over all generated trajectories

PrintAverageSpeciesTimeSeries()¶: Analyze the average output over all generated trajectories

PrintPropensitiesDistributions()¶: Print obtained distributions for each generated trajectory

PrintPropensitiesMeans()¶: Print the means of each propensity for the selected trajectory

PrintPropensitiesStandardDeviations()¶: Print the standard deviations of each propensity for the selected trajectory

PrintPropensitiesTimeSeries()¶: Print a time series of the propensities each generated trajectory

PrintSpeciesDistributions()¶: Print obtained distributions for each generated trajectory

PrintSpeciesMeans()¶: Print the means of each species for the selected trajectory

PrintSpeciesStandardDeviations()¶: Print the standard deviations of each species for the selected trajectory

PrintSpeciesTimeSeries()¶: Print time simulation output for each generated trajectory

PrintWaitingtimesDistributions()¶: Print obtained waiting times

PrintWaitingtimesMeans()¶: Print the waiting time means for the selected trajectory

PrintWaitingtimesStandardDeviations()¶: Print the waiting time standard deviations for the selected trajectory

Reload()¶: Reload the entire model again. Useful if the model file has changed

SetDelayParameters(delay_distributions, nonconsuming_reactions=None)¶

Input delayed model parameters. This function subsequently parses the delay input and assigns it to the SSA method.

Example: SetDelayParameters(delay_distributions = {‘R1’:(‘fixed’,3), ‘R2’:(‘gamma’,5,1)}, nonconsuming_reactions = [‘R2’])

Reaction ‘R1’ will get a delay of 3 time units and reaction ‘R2’ a random delay from a gamma distr with shape=5 and scale=1.

Reaction ‘R1’ will be a consuming reaction, and ‘R2’ a nonconsuming reaction.

Inputs:

delay_distributions (dictionary)
- Dictionary of reaction name (or index) as key and distribution as value.
nonconsuming_reactions (list) [default = None]
- Reaction names or indices of the given delayed reactions that are nonconsuming (subset of reactions given in delay_distributions. All other delayed reactions given in delay_distributions are considered consuming. Consuming and nonconsuming reactions are defined according to Xiaodong Cai (2007), “Exact stochastic simulation of coupled chemical reactions with delays”, J.Phys. Chem. 126:124108.

Value of delay_distributions can be any distribution of numpy.random, e.g.: - (‘gamma’, p1,p2) = np.random.gamma(p1,p2) #Where p1 is the shape parameter and p2 the scale parameter. - (‘exponential’, p1) = np.random.exponential(p1) #Where p1 is the scale parameter (NOT the rate). - (‘uniform’, lower, upper) = np.random.uniform(0,lower,upper)

SetPutativeReactionTimes(distributions)¶

Sets the single molecule putative reaction times distribution. For use with DoSingleMoleculeStochSim().

Inputs:

distributions (dictionary)
- Dictionary of reaction name (or index) as key and distribution as value.

Value of distributions can be any distribution of numpy.random, e.g.: - (‘gamma’, p1,p2) = np.random.gamma(p1,p2) #Where p1 is the shape parameter and p2 the scale parameter. - (‘exponential’, p1) = np.random.exponential(p1) #Where p1 is the scale parameter (NOT the rate). - (‘uniform’, lower, upper) = np.random.uniform(0,lower,upper)

ShowOverview()¶: Print an overview of the current settings

ShowSpecies()¶: Print the species of the model

Timesteps(s)¶

Set the number of time steps to be generated for each trajectory

Input:

s (integer)

Trajectories(n)¶

Set the number of trajectories to be generated

Input:

n (integer)

class stochpy.modules.StochSim.SSASettings(X_matrix, timesteps, starttime, endtime, speciesselection, istrackpropensities, isonlylasttimepoint)¶

Input:

X_matrix (array)
timesteps (integer)
starttime (float)
endtime (float)
istrackpropensities (boolean)

StochPy Cell Division Module¶

Example of a sequential simulator. This simulator tracks one cell for N generations.

Most of the functionalities of the SSA module such as plotting and writing to a file are also available in this module.

Written by T.R. Maarleveld and M. Moinat, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 20, 2014

class stochpy.modules.StochPyCellDivision.CellDivision(Method='Direct', File='CellDivision.psc', dir=None, sim_mode='generations', end=3, IsInteractive=True)¶

Input options:

File [default = CellDivision.psc]
dir [default = /home/user/stochpy/pscmodels/ImmigrationDeath.psc]

Usage (with High-level functions): >>> cmod = stochpy.CellDivision() >>> help(cmod) >>> cmod.DoCellDivisionStochSim() >>> cmod.DoCellDivisionStochSim(self,end = 5, mode= ‘generations’,IsTrackPropensities=False) >>> cmod.Model(File = ‘filename.psc’, dir = ‘/.../’) >>> cmod.Reload() >>> smod.PlotSpeciesTimeSeries() >>> smod.PlotPropensitiesTimeSeries() >>> smod.PlotAveragedSpeciesTimeSeries() >>> smod.PlotWaitingtimeDistributions() >>> smod.PlotSpeciesDistributions(bin_size = 3) >>> smod.PrintSpeciesMeans() >>> smod.PrintSpeciesDeviations() >>> smod.PrintPropensityMeans() >>> cmod.ShowOverview() >>> cmod.ShowSpecies()

AnalyzeExtantCells(n_bins=50, n_samples=100000)¶

Obtains the species statistics for a population of extant cells.

Input:

n_bins (int) [default = 50] Number of bins for the binning of interdivision time and age.
n_samples (int) [default = 100,000] Number of equally time spaced samples to take from the simulation.

Result: # self.probabilities_extant_output will contain for every trajectory for every species an array with for every copy number the ‘probability’ in an extant cell population.

CalculateSpecificGrowthRate(n_trapezoidal=20, IsDebug=True)¶

Calculates the specific growth rate from the mother volume distribution (phi) and partitioning distribution (K). The specific growth rate is retrieved by numerically solving equation 20 of Painter and Marr [1] for k. For a fast calcuation, the trapezoidal rule is used for integration and the Secant method for solving the equation.

[1] Painter P.R. and Marr A.G. (1968), “Mathematics of microbial populations”, Annu. Rev. Microbiol. 22:519-548.

Input:

n_trapezoidal (integer) [default = 20] Number of subintervals used for integration with the composite trapezoidal rule.
IsDebug (boolean) [default = True] With IsDebug True, the script checks calculated specific growth rate. This takes some time. For a ‘silent’ calculation, set IsDebug False.

ChangeInitialSpeciesAmount(species, value)¶

ChangeInitialSpeciesAmount(species,value)

Change initial species Amount

Input:

species (string)
value (float)

ChangeParameter(parameter, value)¶

ChangeParameter(parameter,value)

Change parameter value

Input:

parameter (string)
value (float)

DoCellDivisionStochSim(mode=False, end=False, method=False, trajectories=False, IsTrackPropensities=False, species_selection=None, IsOnlyLastTimepoint=False)¶

Do stochastic simulations with cell growth and cell divisions.

Input:

mode (str)
- ‘generations’ [default]
- ‘time’
- ‘steps’
end (int) [default = None]
- Number of generations (default = 3) or steps to take (default = 1000).
method (str) [default = False]
trajectories (int) [default = 1]
IsTrackPropensities (boolean)
species_selection
- Selection of species of which the time series data is stored.
IsOnlyLastTimepoint [default = False]

DumpTrajectoryData(n)¶

Input:

n (integer)

Endtime(t)¶

Set the end time of the exact realization of the Markov jump process

Input:

t (float)

Export2File(analysis='timeseries', datatype='species', IsAverage = False, directory=None)¶

Write data to a text document

Input:

analysis [default = ‘timeseries’] (string) options: timeseries, distribution, mean, std, autocorrelation, autocovariance
datatype [default = ‘species’] (string) options: species, propensities, waitingtimes
IsAverage [default = False] (boolean)
directory [default = None] (string)

Generations(g)¶

Endtime(t)

Set the end time of the exact realization of the Markov jump process

Input:

t (float)

GetRegularGrid(npoints=50)¶

The Gillespie method generates data at irregular time points. However, it is possible to output data on a fixed regular time grid where the user can specify the resolution of the grid (npoints).

Input:

npoints [default = 50] (integer)

GetTrajectoryData(n=1)¶

Switch to another trajectory, by default, the last trajectory is accesible

Input:

n [default = 1] (int)

GetWaitingtimes()¶: Get for each reaction the waiting times

Method(method)¶

Set stochastic simulation algorithm to be used.

Input:

method (string)

Mode(sim_mode='generations')¶

Set mode for a stochastic simulation.

Input:

sim_mode [default = ‘generations’] (string) ‘time’,’steps’, ‘generations’

Model(File, dir=None, IsSetDefault=True)¶

Give the model, which is used to do stochastic simulations on

Input:

File filename.psc (string)
dir [default = None] the directory where File lives (string)
IsSetDefault [default = True]

PlotAveragePropensitiesAutocorrelations(nlags=-1, rates2plot=True, linestyle='None', linewidth=1, marker='o', colors=None, title='StochPy Propensities Autocorrelation Plot (# of trajectories = )', xlabel='Lag ($\tau$)', ylabel='Auto-correlation', IsLegend=True, legend_location='upper right', nstd=1)¶

PlotAveragePropensitiesAutocorrelation(nlags=-1,rates2plot = True,linestyle = ‘None’,linewidth = 1, marker = ‘o’,colors = None,title = ‘StochPy Propensities Autocorrelation Plot (# of trajectories = )’,xlabel=r’Lag ($ au$)’,ylabel=’Auto-correlation’,IsLegend=True,legend_location=’upper right’,nstd=1)

Plot the average propensities autocorrelation result for different lags. For each lag, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Average Time (# of trajectories = ... ) ] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotAveragePropensitiesAutocovariances(nlags=-1, rates2plot=True, linestyle='None', linewidth=1, marker='o', colors=None, title='StochPy Propensities Autocorrelation Plot (# of trajectories = )', xlabel='Lag ($\tau$)', ylabel='Auto-correlation', IsLegend=True, legend_location='upper right', nstd=1)¶

PlotAveragePropensitiesAutocorrelation(nlags=-1,rates2plot = True,linestyle = ‘None’,linewidth = 1, marker = ‘o’,colors = None,title = ‘StochPy Propensities Autocorrelation Plot (# of trajectories = )’,xlabel=r’Lag ($ au$)’,ylabel=’Auto-correlation’,IsLegend=True,legend_location=’upper right’,nstd=1)

Plot the average propensities autocorrelation result for different lags. For each lag, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Average Time (# of trajectories = ... ) ] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotAveragePropensitiesDistributions(rates2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Species Time Series Plot (# of trajectories = )', xlabel='Propensity', ylabel='Probability', IsLegend=True, nstd=1)¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Propensity’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)
nstd [default=1] (float

PlotAveragePropensitiesDistributionsConfidenceIntervals(rates2plot = True, colors = None, title = 'StochPy Average Propensities Distributions Plot (# of trajectories = )', xlabel='Propensity', ylabel='Probability', IsLegend=True, nstd=1)¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

rates2plot [default = True] as a list [‘R1’,’R2’]
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Propensity’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)
nstd [default=1] (float)

PlotAveragePropensitiesTimeSeries(rates2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Propensities Time Series Plot (# of trajectories = )', xlabel='Time', ylabel='Propensity', IsLegend=True, nstd=1)¶

Plot the average propensities For each time point, the mean and standard deviation are plotted

Input:

rates2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Propensities Plot (# of trajectories = ...)’ ] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Propensity’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)
nstd [default=1] (float

PlotAverageSpeciesAutocorrelations(species2plot = True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot (# of trajectories = )')¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Species Autocorrelation Plot (# of trajectories = ... )’ ] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotAverageSpeciesAutocovariances(species2plot = True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot (# of trajectories = )')¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags

species2plot [default = True] as a list [‘S1’,’S2’]

linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)

linewidth [default = 1] (float)

marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)

colors [default = None] (list)

title [default = ‘StochPy Average Species Autocorrelation Plot (# of trajectories = ... )’ ] (string)

xlabel [default = r’Lag ($ au$)’] (string)

ylabel [default = ‘Autocorrelation’] (string)

IsLegend [default = True] (boolean)

legend_location [default = ‘upper right’] (string/integer)

PlotAverageSpeciesDistributions(species2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Species Distributions Plot (# of trajectories = )', xlabel='Species Amount', ylabel='Probability', IsLegend=True, nstd=1)¶

Plot the average species distributions For each species Amount, the mean and standard deviation are plotted

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Species Amount’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)
nstd [default=1] (float)

PlotAverageSpeciesDistributionsConfidenceIntervals(species2plot=True, colors = None, title = 'StochPy Average Species Distributions Plot (# of trajectories = )', xlabel='Species Amount', ylabel='Probability', IsLegend=True, nstd=1)¶

Plot the average species distributions For each species Amount, the mean and standard deviation are plotted

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Species Amount’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)
nstd [default=1] (float

PlotAverageSpeciesTimeSeries(species2plot = True, linestyle = 'None', marker = 'o', colors = None, title = 'StochPy Average Species Time Series Plot (# of trajectories = )', xlabel='Time', ylabel='Copy Number', IsLegend=True, nstd=1)¶

Plot the average time simulation result. For each time point, the mean and standard deviation are plotted

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Average Time (# of trajectories = ... )’ ] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Copy Number’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)
nstd [default=1] (float)

PlotExtantSpeciesDistribution(species2plot=True, histtype='steps-mid', linestyle='dotted', linewidth=1, colors=None, title='StochPy Extant cell Species Probability Mass Function', xlabel='Molecule number', ylabel='Probability Mass', IsLegend=True, legend_location='upper right')¶

Plots the analyzed/extrapolated extant cell species number Probability Mass Function.

Input:

species2plot [default = True) (list)
histtype [default = ‘steps-mid’] (string)
linestyle [default = ‘dotted’] (string)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Daughter cell Volume Probability Mass Function’] (string)
xlabel [default = ‘Volume’] (string)
ylabel [default = ‘Probability Mass’] (string)

PlotInterdivisionTimeDistribution(bin_size=0.01, histtype = 'step', linestyle = 'dotted', filled = False, linewidth = 1, colors=None, title = 'StochPy IDT Probability Density Function', xlabel='Interdivision Time', ylabel='Probability Density', IsLegend=True, legend_location='upper right')¶

Plots Interdivision Time distribution (between cell divisions) for a certain trajectories or for ‘all’ trajectories together

Input:

bin_size [default = 0.01] (float)
histtype [default = ‘step’] (string)
linestyle [default = ‘dotted’] (string)
filled [default = False] (boolean)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy IDT Probability Density Function’] (string)
xlabel [default = ‘Interdivision Time’] (string)
ylabel [default = ‘Probability Density’] (string)
IsLegend [default = True] (boolean)

PlotPropensitiesAutocorrelations(rates2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Propensities Autocorrelation Plot', xlabel=r'Lag ($ au$)', ylabel='Auto-correlation')¶

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Propensities Autocorrelation Plot] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotPropensitiesAutocovariances(rates2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Propensities Autocorrelation Plot', xlabel=r'Lag ($ au$)', ylabel='Auto-correlation')¶

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
rates2plot [default = True] as a list [‘R1’,’R2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘,’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = StochPy Propensities Autocorrelation Plot] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Auto-correlation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotPropensitiesDistributions(self, rates2plot = True, linestyle = 'dotted', linewidth = 1, colors=None, title = 'StochPy Propensities Probability Mass Function', xlabel='Propensity', ylabel='Probability', IsLegend=True, bin_size=1)¶

Plots the PDF for each generated trajectory

Default: PlotPropensitiesDistributions() plots PDF for each species

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] (string)
linewidth [default = 1] (float)
colors (list)
title [default = ‘StochPy Propensities Probability Mass Function’] (string)
xlabel [default = ‘Propensity’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
bin_size [default=1] (integer)

PlotPropensitiesTimeSeries(n_events2plot = 10000, rates2plot = True, linestyle = 'solid', marker = '', colors = None, title = 'StochPy Propensities Time Series Plot', xlabel='Time', ylabel='Propensity', IsLegend=True, legend_location='upper right')¶

Plot time simulation output for each generated trajectory

Default: PlotPropensitiesTimeSeries() plots propensities for each species

Input:

n_events2plot [default = 10000] (integer)
rates2plot [default = True]: species as a list [‘S1’,’S2’]
marker [default = ‘’] (‘v’,’o’,’s’,’,’,’*’,’.’)
linestyle [default = ‘solid’]: dashed, dotted, and solid (string)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Propensities Time Series Plot’] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Propensity’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)

PlotSpeciesAutocorrelations(species2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot', xlabel='Lag', ylabel='Auto-correlation')¶

Plot species autocorrelations

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Autocorrelation Plot’] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesAutocovariances(species2plot=True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Species Autocorrelation Plot', xlabel='Lag', ylabel='Auto-correlation')¶

Plot species auto-covariances

Input:

nlags [default = -1] (integer) 1,2,3 ... -1 where 3 means calculate the autocorrelation for the first 3 lags and -1 for all lags
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Autocorrelation Plot’] (string)
xlabel [default = r’Lag ($ au$)’] (string)
ylabel [default = ‘Autocorrelation’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesDaughterDistribution(bin_size=1, species2plot = True, histtype = 'step', linestyle = 'dotted', filled = False, linewidth = 1, colors=None, title = 'StochPy Daughter cell Species Probability Mass Function', xlabel='Molecule number', ylabel='Probability Mass', IsLegend=True, legend_location='upper right')¶

StochPy Daughter cell Species Probability Mass Function

Input:

bin_size [default = 0.1] (float)
species2plot [default = True) (list)
histtype [default = ‘step’] (string)
linestyle [default = ‘dotted’] (string)
filled [default = False] (boolean)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Daughter cell Volume Probability Mass Function’] (string)
xlabel [default = ‘Volume’] (string)
ylabel [default = ‘Probability Mass’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesDistributions(species2plot = True, linestyle = 'dotted', linewidth = 1, colors=None, title = 'StochPy Species Probability Mass Function', xlabel='Number of Molecules', ylabel='Probability', IsLegend=True, bin_size=1)¶

Plots the PDF for each generated trajectory Default: PlotSpeciesDistributions() plots PDF for each species

Input:

species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘dotted’] (string)
linewidth [default = 1] (float)
colors (list)
title [default = ‘StochPy Species Probability Mass Function’] (string)
xlabel [default = ‘Number of Molecules’] (string)
ylabel [default = ‘Probability’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)
bin_size [default=1] (integer)

PlotSpeciesMotherDistribution(bin_size=1, species2plot = True, histtype = 'step', linestyle = 'dotted', filled = False, linewidth = 1, colors=None, title = 'StochPy Mother cell Species Probability Mass Function', xlabel='Molecule number', ylabel='Probability Mass', IsLegend=True, legend_location='upper right')¶

StochPy Mother cell Species Probability Mass Function

Input:

bin_size [default = 0.1] (float)
species2plot [default = True) (list)
histtype [default = ‘step’] (string)
linestyle [default = ‘dotted’] (string)
filled [default = False] (boolean)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Mother cell Volume Probability Mass Function’] (string)
xlabel [default = ‘Volume’] (string)
ylabel [default = ‘Probability Mass’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string/integer)

PlotSpeciesTimeSeries(plottype = 'amounts', n_events2plot = 10000, species2plot = True, linestyle = 'solid', linewidth = 1, marker = '', colors = None, title = 'StochPy Species Time Series Plot', xlabel='Time', ylabel='Copy Number', IsLegend=True, legend_location='upper right')¶

Plot time simulation output for each generated trajectory.

Default: PlotSpeciesTimeSeries() plots time simulation for each species amounts. Note that also species concentrations can be plotted.

Input:

plottype [default = ‘amounts’] alternative is ‘concentrations’
n_events2plot [default = 10000] (integer)
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘solid’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Time Series Plot’] (string)
xlabel [default = ‘Time’] (string)
ylabel [default = ‘Copy Number’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)

PlotSpeciesVolumeTimeSeries(plottype ='amounts', n_events2plot = 10000, species2plot = True, linestyle = 'solid', linewidth = 1, marker = '', colors = None, IsCalculated=True)¶

Plot time simulation output for each generated trajectory Default: PlotSpeciesTimeSeries() plots time simulation for each species

Input:

plottype [default = ‘amounts’] alternative is ‘concentrations’
n_events2plot [default = 10000] (integer)
species2plot [default = True] as a list [‘S1’,’S2’]
linestyle [default = ‘solid’] dashed, solid, and dash_dot (string)
linewidth [default = 1] (float)
marker [default = ‘’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Species Time Series and Volume Plot’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)

PlotVolumeDaughterDistribution(bin_size=0.1, histtype = 'step', linestyle = 'dotted', filled = False, linewidth = 1, colors=None, title = 'StochPy Daughter cell Volume Probability Density Function', xlabel='Volume', ylabel='Probability Density')¶

Plot the volume distribution of daughter cells

Input:

bin_size [default = 0.1] (float)
histtype [default = ‘step’] (string)
linestyle [default = ‘dotted’] (string)
filled [default = False] (boolean)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Daughter cell Volume Probability Density Function’] (string)
xlabel [default = ‘Volume’] (string)
ylabel [default = ‘Probability Density’] (string)

PlotVolumeMotherDistribution(bin_size=0.1, histtype = 'step', linestyle = 'dotted', filled = False, linewidth = 1, colors=None, title = 'StochPy Mother cell Volume Probability Density Function', xlabel='Volume', ylabel='Probability Density')¶

Plot the volume distribution of mother cells

Input:

bin_size [default = 0.1] (float)
histtype [default = ‘step’] (string)
linestyle [default = ‘dotted’] (string)
filled [default = False] (boolean)
linewidth [default = 1] (float)
colors [default = None] (list)
title [default = ‘StochPy Mother cell Volume Probability Density Function’] (string)
xlabel [default = ‘Volume’] (string)
ylabel [default = ‘Probability Density’] (string)

PlotVolumeTimeSeries(n_events2plot = 10000, linestyle = 'solid', linewidth = 1, marker = '', colors = None, title = 'StochPy Volume Time Series Plot', xlabel = 'Time', ylabel = 'Volume')¶

StochPy Volume Time Series Plot

Input:

n_events2plot [default = 10000] (integer)
linestyle [default = ‘solid’] (string)
linewidth [default = 1] (float)
marker [default = ‘’] (string)
colors [default = None] (list)
title [default = ‘StochPy Volume Time Series Plot’] (string)
xlabel [default = ‘Volume’] (string)
ylabel [default = ‘Probability Mass’] (string)

PlotWaitingtimesDistributions(rates2plot = True, linestyle = 'None', linewidth = 1, marker = 'o', colors = None, title = 'StochPy Waiting times Plot', xlabel='inter-event time t', ylabel='Probability Density')¶

Plot event waiting time distributions

default: PlotWaitingtimesDistributions() plots waiting times for all rates

Input:

rates2plot [default = True] as a list of strings [“R1”,”R2”]
linestyle [default = ‘None’] dashed, dotted, dash_dot, and solid (string)
linewidth [default = 1] (float)
marker [default = ‘o’] (‘v’,’o’,’s’,’,’,’*’,’.’)
colors [default = None] (list)
title [default = ‘StochPy Event Waiting times Plot’] (string)
xlabel [default = ‘inter-event time t’] (string)
ylabel [default = ‘Probability Density’] (string)
IsLegend [default = True] (boolean)
legend_location [default = ‘upper right’] (string)

PrintPropensitiesMeans()¶: Print the means of each species for the selected trajectory

PrintPropensitiesStandardDeviations()¶: Print the standard deviations of each species for the selected trajectory

PrintSpeciesMeans()¶: Print the means of each species for the selected trajectory

PrintSpeciesStandardDeviations()¶: Print the standard deviations of each species for the selected trajectory

Reload()¶: Reload the entire model again. Useful if the model file has changed

SetDefaultParameters()¶: Set default parameters: growth rate, growth type, initial volume, and volume distributions

SetDelayParameters(delay_distributions, nonconsuming_reactions=None)¶

Input delayed model parameters. This function subsequently parses the delay input and assigns it to the SSA method.

Example: SetDelayParameters(delay_distributions = {‘R1’:(‘fixed’,3), ‘R2’:(‘gamma’,5,1)}, nonconsuming_reactions = [‘R2’])

Reaction ‘R1’ will get a delay of 3 seconds and reaction ‘R2’ a random delay from a gamma distr with shape=5 and scale=1.

Reaction ‘R1’ will be a consuming reaction, and ‘R2’ a nonconsuming reaction.

Inputs:

delay_distributions (dictionary)
- Dictionary of reaction name (or index) as key and distribution as value.
nonconsuming_reactions (list) [default = None]
- Reaction names or indices of the given delayed reactions that are nonconsuming (subset of reactions given in delay_distributions. All other delayed reactions given in delay_distributions are considered consuming. Consuming and nonconsuming reactions are defined according to Xiaodong Cai (2007), “Exact stochastic simulation of coupled chemical reactions with delays”, J.Phys. Chem. 126:124108.

Value of delay_distributions can be any distribution of numpy.random, e.g.: - (‘gamma’, p1,p2) = np.random.gamma(p1,p2) #Where p1 is the shape parameter and p2 the scale parameter. - (‘exponential’, p1) = np.random.exponential(p1) #Where p1 is the scale parameter (NOT the rate). - (‘uniform’, lower, upper) = np.random.uniform(0,lower,upper)

SetExactDividingSpecies(species)¶

Set species (as list of names) that are volume dependent, but divide equally (after replication).

Input:

species (list)

SetGrowthFunction(growth_rate, growth_type='exponential')¶

Input:

growth_rate (float)
growth_type (string) [default = ‘exponential’, alternative = ‘linear’]

SetInitialVolume(initial_volume)¶

Set initial volume

Input:

initial_volume (float)

SetNonDividingSpecies(species)¶

Set species (as list of names) that are volume dependent, but do not divide.

Input:

species (list)

SetPutativeReactionTimes(distributions)¶

Sets the single molecule putative reaction times distribution.

Inputs:

distributions (dictionary)
- Dictionary of reaction name (or index) as key and distribution as value.

Value of distributions can be any distribution of numpy.random, e.g.: - (‘gamma’, p1,p2) = np.random.gamma(p1,p2) #Where p1 is the shape parameter and p2 the scale parameter. - (‘exponential’, p1) = np.random.exponential(p1) #Where p1 is the scale parameter (NOT the rate). - (‘uniform’, lower, upper) = np.random.uniform(0,lower,upper)

SetVolumeDependencies(IsVolumeDependent, VolumeDependencies=, []SpeciesExtracellular=[])¶

Set volume dependency of reactions.

Input:

IsVolumeDependent (boolean)
VolumeDependencies (list or True) User specified which reaction are volume dependent and with which order
= Approximation for fast reactions, because volume only updated after a reaction has fired. - If not specified, the algorithm will try to determine the volume dependency, depending on the order of the reaction:
Order = number of reactants minus the extracellular species among those reactants (the latter are not affected by cell volume)
- If only reaction name is specified, it by default assumes second order reactions (propensity/V)
e.g.:
[‘R2’,R3’] –> Reactions R2 and R3 are second order reactions

[(‘R2’,4), (‘R3’,3)] –> R2 is fourth order (propensity/V**3) and R3 is third order (propensity/V**2)
SpeciesExtracellular (list of str) Species that are not inside the cell and should therefore not be divided upon cell division

SetVolumeDistributions(phi, K, phi_beta_mean=False)¶

Input:

phi (list) Specifies the distribution of the cell volume at which the mother cell divides.
K (list) The number from this distribution is multiplied with the mother cell volume to get volume of daughter 1.

Vdaughter = Vmother * K Note: This distribution needs to have a symmetrical distribution with a mean of 0.5, otherwise one will create a bias for one daughter
phi_beta_mean (float) only if phi is a beta distribution, then it is added to the random number from phi_distr

The phi and K distributions can be any distribution of numpy.random, e.g.:

(‘gamma’, p1,p2) = np.random.gamma(p1,p2) #Where p1 is the shape parameter and p2 the scale parameter.
(‘exponential’, p1) = np.random.exponential(p1) #Where p1 is the scale parameter (NOT the rate).
(‘uniform’, lower, upper) = np.random.uniform(0,lower,upper)

Fixed values for phi and K are also possible, e.g.: - phi = (‘fixed’, 2) and K = (‘fixed’,0.5) creates a lineage with cells that grow from a volume of 1 to 2.

ShowOverview()¶: Print an overview of the current settings

ShowSpecies()¶: Print the species of the model

Timesteps(s)¶

Set the number of time steps to be generated for each trajectory

Input:

s (integer)

Trajectories(n)¶

Set the number of trajectories to be generated

Input:

n (int)

class stochpy.modules.StochPyCellDivision.IntegrationStochasticCellDivisionObj¶

This class is specifically designed to store the results of a stochastic time simulation It has methods for setting e.g. the Time, Labels, Species, and Volume data and getting e.g. Time, Species,and Volume (including time) arrays. However, of more use:

getDataAtTime(time) the data generated at time point “time”.
getDataInTimeInterval(time, bounds=None) more intelligent version of the above returns an array of all data points where: time-bounds <= time <= time+bounds

HAS_AGES = False¶

HAS_DT = False¶

HAS_GENERATION_TIMESTEPS = False¶

HAS_IDT = False¶

HAS_SPECIES_DAUGHTER = False¶

HAS_SPECIES_MOTHER = False¶

HAS_TIME = False¶

HAS_VOLUME_DAUGHTER = False¶

HAS_VOLUME_MOTHER = False¶

HAS_XDATA = False¶

IS_VALID = True¶

TYPE_INFO = 'Stochastic'¶

ages = None¶

division_times = None¶

generation_timesteps = None¶

getDataAtTime(time)¶

Return all data generated at “time”

Input:

time the required exact time point

getDataInTimeInterval(time, bounds=None)¶

Returns an array of all data in interval: time-bounds <= time <= time+bounds where bound defaults to step size

Input:

time the interval midpoint
bounds [default=None] interval half span defaults to step size

getDaughterDataAtDivisionTime(time)¶

Return all daughter data generated at “time”

Input:

time the required exact time point

getMotherDataAtDivisionTime(time)¶

Return all mother data generated at “time”

Input:

time the required exact time point

getSpeciesDaughter(lbls=False)¶

Return an array of time+species

Input: - lbls [default=False] return only the time+species array or optionally both the data array and a list of column label

getSpeciesMother(lbls=False)¶

Return an array of time+species

Input: - lbls [default=False] return only the time+species array or optionally both the data array and a list of column label

getTime(lbls=False)¶

Return the time vector

Input:

lbls [default=False] return only the time array or optionally both the time array and time label

getVolumeDaughter()¶: Return an array of time+volume

getVolumeMother()¶: Return an array of time+volume

interdivision_times = None¶

setAges(ages)¶

set the ... ages array

Input:

ages

setGenerationTimesteps(timesteps)¶

set the generation timesteps array

Input:

timesteps

setInterdivisionTimes(idt, n_generations)¶

set the (inter)division times array

Input:

idt

setLabels(species)¶

Set the species

Input:

species a list of species labels

setSimulationInfo(timesteps, endtime, simulation_trajectory)¶

set Simulation Information

Input:

timesteps (integer)
endtime (float)
simulation_trajectory (integer)

setSpeciesDaughter(species, lbls=None)¶

Set the species array

Input:

species an array of species vs time data
lbls [default=None] a list of species labels

setSpeciesDaughterDist(distributions, means, stds, moments)¶

setSpeciesDist stuff for the determination of distributions

Input:

distributions (list)
means (dictionary)
stds (dictionary)
moments (dictionary)

setSpeciesMother(species, lbls=None)¶

Set the species array

Input:

species an array of species vs time data
lbls [default=None] a list of species labels

setSpeciesMotherDist(distributions, means, stds, moments)¶

setSpeciesDist stuff for the determination of distributions

Input:

distributions (list)
means (dictionary)
stds (dictionary)
moments (dictionary)

setTime(time, lbl=None)¶

Set the time vector

Input:

time a 1d array of time points
lbl [default=None] is “Time” set as required

setVolumeDaughter(volume_selected, volume_notselected)¶

set the volume daughter array

Input:

volume

setVolumeDaughterDist(distributions, means, stds, moments)¶

setSpeciesDist stuff for the determination of distributions

Input:

distributions (list)
means (dictionary)
stds (dictionary)
moments (dictionary)

setVolumeMother(volume)¶

set the volume mother array

Input:

volume

setVolumeMotherDist(distributions, means, stds, moments)¶

setSpeciesDist stuff for the determination of distributions

Input:

distributions (list)
means (dictionary)
stds (dictionary)
moments (dictionary)

setXData(xdata, lbls=None)¶

Sets an array of extra simulation data

Input: - xdata an array of xdata vs time - lbls [default=None] a list of xdata labels

species_daughter = None¶

species_labels = None¶

species_mother = None¶

time = None¶

time_label = 'Time'¶

volume_daughter = None¶

volume_mother = None¶

xdata_labels = None¶

Direct Method¶

This program performs the direct Stochastic Simulation Algorithm from Gillespie (1977) [1].This algorithm is used to generate exact realizations of the Markov jump process. Of course, the algorithm is stochastic, so these realizations are different for each run. Only molecule populations are specified. Positions and velocities, such as in Molecular Dynamics (MD) are ignored. This makes the algorithm much faster, because non-reactive molecular collisions can be ignored. Still, this exact SSA is quite slow, because it insists on simulating every individual reaction event, which takes a lot of time if the reactant population is large. Furthermore, even larger problems arise if the model contains distinct processes operating on different time-scales [2].

[1] Gillespie D.T (1977), “Exact stochastic simulation of coupled chemical reactions”, J.Phys. Chem. 81:2340-2361 [2] Wilkinson D.J (2009), “Stochastic Modelling for quantitative description of heterogeneous biological systems”, Nat Rev Genet; 0(2):122-133

class stochpy.implementations.DirectMethod.DirectMethod(File, dir)¶

Direct Stochastic Simulation Algorithm from Gillespie (1977) [1].

This algorithm is used to generate exact realizations of the Markov jump process. Of course, the algorithm is stochastic, so these realizations are different for each run.

[1] Gillespie D.T (1977), “Exact stochastic simulation of coupled chemical reactions”, J.Phys. Chem. 81:2340-2361

Input:

File filename.psc
dir /home/user/Stochpy/pscmodels/filename.psc

Execute(settings, IsStatusBar=False)¶

Generates T trajectories of the Markov jump process.

Input:

settings (class object)

RunExactTimestep(settings)¶: Calculates a time step of the Direct Method

First Reaction Method¶

This module performs the first reaction method Stochastic Simulation Algorithm from Gillespie (1977).

This algorithm is used to generate exact realizations of the Markov jump process. Of course, the algorithm is stochastic, so these realizations are different for each run.

Only molecule populations are specified. Positions and velocities, such as in Molecular Dynamics (MD) are ignored. This makes the algorithm much faster, because non-reactive molecular collisions can be ignored.different Still, this exact SSA is quite slow, because it insists on simulating every individual reaction event, which takes a lot of time if the reactant population is large. Furthermore, even larger problems arise if the model contains distinct processes operating on different time-scales.

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 20, 2014

class stochpy.implementations.FirstReactionMethod.FirstReactionMethod(File, dir)¶

First Reaction Method from Gillespie (1977)

This algorithm is used to generate exact realizations of the Markov jump process. Of course, the algorithm is stochastic, so these realizations are different for each run.

Input:

File filename.psc
dir /home/user/Stochpy/pscmodels/filename.psc

Execute(settings, IsStatusBar=False)¶

Generates T trajectories of the Markov jump process.

Input:

settings (class object)

RunExactTimestep(settings)¶: Perform a direct SSA time step and pre-generate M random numbers

Next Reaction Method¶

This module performs the Next Reaction Method from Gibson and Bruck [1]. Therefore, it is also called the Gibson and Bruck algorithm.

[1] M.A. Gibson and J. “Bruck Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels”, J. Phys. Chem., 2000, 104, 1876-1889

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net

Speed improvements by M. Moinat. Last Change: Augustus 20, 2014

class stochpy.implementations.NextReactionMethod.NextReactionMethod(File, dir)¶

Next Reaction Method from Gibson and Bruck 2000 [1].

[1] M.A. Gibson and J. “Bruck Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels”, J. Phys. Chem., 2000,104,1876-1889

Input:

File filename.psc
dir /home/user/Stochpy/pscmodels/filename.psc

Execute(settings, IsStatusBar=False)¶

Generates T trajectories of the Markov jump process.

Input:

settings (class object)

InitialMonteCarloStep()¶: Monte Carlo step to determine all taus and to create a pqdict indexed priority queue

Propensities()¶: Determines the propensities to fire for each reaction at the current time point. At t=0, all the rate equations are compiled.

RunExactTimestep()¶: Perform a direct SSA time step and pre-generate M random numbers

UpdateHeap()¶: Renews the changed propensities in the priority queue heap.

Optimized Tau-Leaping¶

This program performs Optimized Explicit Tau-leaping algorithm, which is an approximate version of the exact Stochastic Simulation Algorithm (SSA). Here, an efficient step size selection procedure for the tau-leaping method [1] is used.

[1] Cao. Y, Gillespie D., Petzold L. (2006), “Efficient step size selection for the tau-leaping method”, J.Chem. Phys. 28:124-135

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 20, 2014

stochpy.implementations.TauLeaping.DetermineOrderHOR(rate_vector, reactants)¶

Determines once the order of each reaction and the highest order of reaction (HOR) for each species.

Input:

Rate_vector: (list)
Reactants: (nested list)

Output:

orders
HORs
HO_info

stochpy.implementations.TauLeaping.GetSample(probs)¶

This function extracts a sample from a list of probabilities. The ‘extraction chance’ is identical to the probability of the sample.

Input:

probs: (list)

Output:

sample
sample index

stochpy.implementations.TauLeaping.MinPositiveValue(List)¶

This function determines the minimum positive value

Input:

List

Output:

minimum positive value

class stochpy.implementations.TauLeaping.OTL(File, dir)¶

Input:

File filename.psc
dir /home/user/Stochpy/pscmodels/filename.psc

DetermineMethod()¶: Determines for each time step what to perform: exact of approximate SSA

Execute(settings, IsStatusBar=False, epsilon=0.03)¶

Generates T trajectories of the Markov jump process.

settings (python class)
epsilon [default = 0.03] (float)

Execute_K_Reactions()¶: Perform the determined K reactions

GetA0c()¶: Calculate the total propensities for all critical reactions

GetCriticalReactions()¶: Determines the critical reactions (as a boolean vector)

GetG(orders, hors, hor_info)¶

Determine the G-vector based on options provided in [1], page 6

Input:

orders (list)
hors (list) highest order of reaction for each species
hor_info

GetK()¶: Determines the K-vector, which describes the number of firing reactions for each reaction.

GetMuVar()¶: Calculate the estimates of mu and var for each species (i)

GetTauPrime()¶: Calculate tau’

GetTauPrimePrime()¶: Calculate Tau’‘

RunExactTimestep(settings)¶: Perform a direct method SSA time step

Delayed Direct Method¶

This module performs the Delayed Direct Stochastic Simulation Algorithm adapted from Cai (2007) [1]. This algorithm is used to generate exact realizations of the Markov jump process with delays. Of course, the algorithm is stochastic, so these realizations are different for each run. Only molecule populations are specified. Positions and velocities, such as in Molecular Dynamics (MD) are ignored. This makes the algorithm much faster, because non-reactive molecular collisions can be ignored. Still, this exact SSA is quite slow, because it insists on simulating every individual reaction event, which takes a lot of time if the reactant population is large. Furthermore, even larger problems arise if the model contains distinct processes operating on different time-scales [2].

[1] Xiaodong Cai (2007), “Exact stochastic simulation of coupled chemical reactions with delays”, J.Phys. Chem. 126:124108

class stochpy.implementations.DelayedDirectMethod.DelayedDirectMethod(File, dir)¶

Delayed Direct Stochastic Simulation Algorithm from Cai (2007) [1].

This algorithm is used to generate exact realizations of the Markov jump process with delays. Of course, the algorithm is stochastic, so these realizations are different for each run.

[1] Xiaodong Cai (2007), “Exact stochastic simulation of coupled chemical reactions with delays”, J.Phys. Chem. 126:124108

Input:

File filename.psc
dir /home/user/Stochpy/pscmodels/filename.psc

CheckReactantExhaustion()¶: Checks wheter a reactant is exhausted. Important if still reactions pending in Tstruct, but no reactant present.

CompleteReaction()¶: Completes a delayed reaction and updates propensities accordingly.

Execute(settings, IsStatusBar=False)¶

Generates T trajectories of the Markov jump process.

Input:

settings (class object)

GenerateTau()¶: Generates the waiting time, according to the improved equation 11 of Cai

InitiateReaction()¶: Chooses reaction that will occur at sim_t and updates species accordingly

RunExactTimestep()¶: Calculates a time step of the Direct Method

add_delay()¶: Add delay

Modified Next Reaction Method with delays¶

This module performs the Next Reaction Method with delay from Anderson [1], Algorithm 7. Note that the definition of Cai is used for types of delayed reactions: consuming and non-consuming.

[1] David F. Anderson “A modified next reaction mehtod for simulating chemical systems with time dependent propensities and delays”, J. Phys. Chem., 2007, 127, 214107

Author: M. Moinat, T.R. Maarleveld Adapted from ‘NextReactionMethod’ by T.R. Maarleveld, Amsterdam, The Netherlands Last Change: Augustus 20, 2014

class stochpy.implementations.DelayedNRM.DelayedNRM(File, dir)¶

Modified Next Reaction Method from Anderson 2007 [1].

[1] David F. Anderson “A modified next reaction mehtod for simulating chemical systems with

time dependent propensities and delays”, J. Phys. Chem., 2007, 127, 214107

Input:

File filename.psc
dir /home/user/Stochpy/pscmodels/filename.psc

BuildInits()¶: Build initials that are necessary to generate a trajectory

BuildTaus()¶: (Re)Calculates and orders taus

CheckReactantExhaustion()¶: Checks wheter a reactant is exhausted after completion and deletes the pending delayed reactions of this reactant.

CompleteReaction()¶: Completes a delayed reaction, depending on consuming or not or not

Execute(settings, IsStatusBar=False)¶

Generates steps of the Markov jump process.

Input:

settings (class object)

GetTauReaction()¶: Gets minimum tau, corresponding reaction index and whether it initiates or completes

HandleEvents()¶: Event handling

InitiateReaction()¶: Initiates a reaction based on being delayed (consuming or nonconsuming) or not

Propensities()¶: Determines the propensities to fire for each reaction at the current time point. At t=0, all the rate equations are compiled.

RunExactTimestep(settings)¶: Perform a direct SSA time step

UpdateTP()¶: Update T and P

add_delay()¶: Adds delay time from distribution, specified in DoDelayedStochSim (StochSim.py)

Analysis¶

This module provides functions for Stochastic Simulation Algorithms Analysis (SSA). Implemented SSAs import this module to perform their analysis. Plotting of time series species, propensities), distributions (species, propensities, distributions), autocorrelations, and autocovariances (species, propensities) is possible.

Written by TR Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 06, 2014

stochpy.modules.Analysis.AutoCov(s, **kwargs)¶

Returns the autocovariance of signal s at all lags.

Adheres to the definition sxx[k] = E{S[n]S[n+k]} = cov{S[n],S[n+k]} where E{} is the expectation operator, and S is a zero mean process

stochpy.modules.Analysis.Autocorrelation(s, **kwargs)¶

Returns the autocorrelation of signal s at all lags.

Adheres to the definition rxx[k] = E{S[n]S[n+k]}/E{S*S} = cov{S[n],S[n+k]}/sigma**2 where E{} is the expectation operator, and S is a zero mean process

stochpy.modules.Analysis.Binning(x, y, bin_size)¶

Binning of the PDF. Works only properly if there is a fixed distance

bin_size is the number of indices for binning (23/01/2013)

Input:

x list of x-values
y list of probabilities for each x[i]
bin_size (integer)

stochpy.modules.Analysis.Count(L_data, L_edges)¶

Input:

L_data (list)
L_edges (list)

stochpy.modules.Analysis.CrossCov(x, y, axis=-1, all_lags=False, debias=True)¶

Returns the crosscovariance sequence between two ndarrays. This is performed by calling fftconvolve on x, y[::-1]

x: ndarray y: ndarray axis: time axis all_lags: {True/False}

whether to return all nonzero lags, or to clip the length of s_xy to be the length of x and y. If False, then the zero lag covariance is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

debias: {True/False}: Always removes an estimate of the mean along the axis, unless told not to.

cross covariance is defined as sxy[k] := E{X[t]*Y[t+k]}, where X,Y are zero mean random processes

class stochpy.modules.Analysis.DoPlotting(species_labels, rate_labels, plotnum=1)¶

This class initiates the plotting options.

Input:

species_labels (list) [S1,S2, ..., Sn]
rate_labels (list) [R1, R2, ..., Rm]

Autocorrelations(lags, data, species2plot, trajectory_index, linestyle, marker, colors, title, xlabel, ylabel, IsLegend, legend_location)¶

Input:

lags
data (array)
datatype2plot (list)
L_labels (list)
trajectory_index (integer)
linestyle (string)
linewidth (float)
marker string)
colors (list)
title (string)
xlabel (string)
ylabel (string)
IsLegend (boolean)

AverageDistributions(L_means, L_stds, nstd, datatype2plot, L_labels, linestyle, linewidth, marker_, colors, title, xlabel, ylabel, IsLegend, legend_location)¶

Plots the average and standard deviation

Input:

L_means (nested list)
L_stds (nested list)
nstd (float)
L_labels (list)
linestyle (string)
linewidth (float)
marker_ (string)
colors (list)
title (string)
xlabel (string)
ylabel (string)
IsLegend (boolean)
legend_location [default = ‘upper right’] (string/integer)

AverageDistributionsCI(L_means, L_stds, nstd, datatype2plot, L_labels, colors, title, xlabel, ylabel, IsLegend, legend_location)¶

AverageTimeSeries(L_means, L_stds, L_time, nstd, datatype2plot, L_labels, linestyle, linewidth, marker_, colors, title, xlabel, ylabel, IsLegend, legend_location)¶

AverageSpeciesTimeSeries(L_means,L_stds,L_time,nstd,datatype2plot,L_labels,linestyle,linewidth,marker_,colors,title,xlabel,ylabel,IsLegend,legend_location)

Plots the average and standard deviation of datatype on a regular grid

Input:

L_means (nested list)
L_stds (nested list)
L_time (list)
nstd (float)
datatype2plot (list)
L_labels (list)
L_time (list)
linestyle (string)
linewidth (float)
marker_ (string)
colors (list)
title (string)
xlabel (string)
ylabel (string)
IsLegend (boolean)
legend_location [default = ‘upper right’] (string/integer)

Distributions(distributions, datatype2plot, trajectory_index, linestyle, colors, title, xlabel, ylabel, IsLegend, legend_location, legend_location, bin_size)¶

Plots the distributions of species and/or propensities

Input:

distributions (nested list)
datatype2plot (list)
L_labels (list)
trajectory_index (integer)
linestyle (string)
linewidth (float)
colors (list)
title (string)
xlabel (string)
ylabel (string)
IsLegend (boolean)
legend_location [default = ‘upper right’] (string/integer)
bin_size (string)

ResetPlotnum()¶: Reset figure numbers if trajectories > 1

TimeSeries(data, n_points2plot, datatype2plot, trajectory_index, linestyle, marker, colors, title)¶

Tracks the propensities and/or species over time

Input:

data (array)
n_points2plot (integer)
datatype2plot (list)
L_labels (list)
trajectory_index (integer)
linestyle (string)
linewidth (float)
title (string)
xlabel (string)
ylabel (string)
IsLegend (boolean)

WaitingtimesDistributions(D_waiting_times, rates2plot, trajectory_index, linestyle, linewidth, marker, colors, title, xlabel, ylabel, IsLegend, legend_location)¶

Plots the waiting times for each reaction in the model. Makes use of ObtainWaitingtimes to derive the waiting times out of the SSA output.

Input:

D_waiting_times (dict)
rates2plot (list)
trajectory_index (integer)
linestyle (string)
linewith (float)
marker (string)
colors (list)
title (string)
xlabel (string)
ylabel (string)
IsLegend (boolean)
legend_location [default = ‘upper right’] (string/integer)

stochpy.modules.Analysis.FFTconvolve(in1, in2, mode='full', axis=None)¶: Convolve two N-dimensional arrays using FFT. See convolve.

stochpy.modules.Analysis.GetAverageResults(L_regular_grid)¶

Gets the averaged output of multiple trajectories

Input:

L_regular_grid (nested list)

stochpy.modules.Analysis.GetDataDistributions(L_sim_output, L_names)¶

Get distributions, means, standard deviations, and the (raw) moments

Input: - L_sim_output (list) - L_names (list)

Output: - L_probability_mass - D_means - D_stds - D_moments

stochpy.modules.Analysis.LogBin(data, factor)¶

Function that creates log bins

Input:

L_data (list)
factor (float) determines the width of the bins

Output:

L_x (list)
L_y (list)
nbins (integer)

stochpy.modules.Analysis.ObtainWaitingtimes(data_stochsim, L_reactions)¶

This function extracts the waiting times for each reaction of the model from the used SSA output.

Input:

data_stochsim (python data object) that stores all simulation data
L_reactions (list)

output:

D_waiting_times (dict)

Note: It is impossible to use this function in combination with the Tau-leaping method, because the Tau-Leaping results are not exact!

stochpy.modules.Analysis.RemoveBias(x, axis)¶: Subtracts an estimate of the mean from signal x at axis

stochpy.modules.Analysis.getDataForTimeSimPlot(data, n_points2plot = 100000)¶

Input:

Arr_data (numpy array)
n_points2plot [default = 10000] (integer)

PySCeS MDL Parser¶

The PySCeS parser is used to import a model written in the MDL of PySCeS. Further, all required input do to stochastic simulations is build.

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 06, 2014

class stochpy.modules.PyscesMiniModel.Event(name, mod)¶

Event’s have triggers and fire EventAssignments when required. Ported from Core2.

assignments = None¶

code_string = None¶

delay = 0.0¶

formula = None¶

mod = None¶

piecewises = None¶

reset()¶

setAssignment(var, formula)¶

setTrigger(formula, delay=0.0)¶

state = False¶

state0 = False¶

symbols = None¶

trigger = None¶

xcode = None¶

class stochpy.modules.PyscesMiniModel.EventAssignment(name, mod)¶

Event assignments are actions that are triggered by an event. Ported from Core2 to build an event handling framework fro PySCeS

code_string = None¶

evaluateAssignment()¶

formula = None¶

mod = None¶

piecewises = None¶

setFormula(formula)¶

setVariable(var)¶

symbols = None¶

variable = None¶

xcode = None¶

class stochpy.modules.PyscesMiniModel.Function(name, mod)¶

Function class ported from Core2 to enable the use of functions in PySCeS

addFormula(formula)¶

argsl = None¶

code_string = None¶

formula = None¶

functions = None¶

mod = None¶

piecewises = None¶

setArg(var, value=None)¶

symbols = None¶

value = None¶

xcode = None¶

class stochpy.modules.PyscesMiniModel.IntegrationStochasticDataObj¶

This class is specifically designed to store the results of a stochastic time simulation It has methods for setting the Time, Labels, Species and Propensity data and getting Time, Species and Rate (including time) arrays. However, of more use:

getOutput(*args) feed this method species/rate labels and it will return an array of [time, sp1, r1, ....]
getDataAtTime(time) the data generated at time point “time”.
getDataInTimeInterval(time, bounds=None) more intelligent version of the above returns an array of all data points where: time-bounds <= time <= time+bounds

HAS_PROPENSITIES = False¶

HAS_SPECIES = False¶

HAS_SPECIES_CONCENTRATIONS = False¶

HAS_TIME = False¶

HAS_VOLUME = False¶

HAS_WAITINGTIMES = False¶

HAS_XDATA = False¶

IS_VALID = True¶

TYPE_INFO = 'Stochastic'¶

getAllSimData(lbls=False)¶

Return an array of time + all available simulation data

Input:

lbls [default=False] return only the data array or (data array, list of labels)

getDataAtTime(time)¶

Return all data generated at “time”

Input:

time the required exact time point

getDataInTimeInterval(time, bounds=None)¶

Returns an array of all data in interval: time-bounds <= time <= time+bounds where bound defaults to step size

Input:

time the interval midpoint
bounds [default=None] interval half span defaults to step size

getPropensities(lbls=False)¶

Return time+propensity array

Input:

lbls [default=False] return only the time+propensity array or optionally both the data array and a list of column label

getSimData(*args, **kwargs)¶

Feed this method species/xdata labels and it will return an array of [time, sp1, ....]

Input:

‘speces_l’, ‘xdatal’ ...
lbls [default=False] return only the data array or (data array, list of labels)

getSpecies(lbls=False)¶

Return an array of time+species

Input: - lbls [default=False] return only the time+species array or optionally both the data array and a list of column label

getSpeciesConcentrations(lbls=False)¶

Return an array of time+species concentrations

Input: - lbls [default=False] return only the time+species array or optionally both the data array and a list of column label

getTime(lbls=False)¶

Return the time vector

Input:

lbls [default=False] return only the time array or optionally both the time array and time label

getVolume()¶: Return an array of time+volume

getWaitingtimes(lbls=False, traj=[])¶

Return waiting times, time+waiting_time array

Input:

lbls [default=False] return only the time+waiting_time array or optionally both the data array and a list of column label
traj [default=[0]] return the firs or trajectories defined in this list

getXData(lbls=False)¶

Return time+xdata array

Input: - lbls [default=False] return only the time+xdata array or optionally both the data array and a list of column label

propensities = None¶

propensities_distributions = None¶

propensities_labels = None¶

setFiredReactions(fired_reactions)¶

Set the reactions that fired

Input:

fired_reactions (list)

setLabels(species)¶

Set the species

Input:

species a list of species labels

setPropensities(propensities, lbls=None)¶

Sets an array of propensities.

Input:

propensities (list)

setPropensitiesDist(distributions, means, stds, moments)¶

setPropensitiesDist stuff for the determination of distributions

Input:

distributions (list)
means (dictionary)
stds (dictionary)
moments (dictionary)

setPropensitiesLabels(labels)¶

Input:

labels (list)

setSimulationInfo(timesteps, endtime, simulation_trajectory)¶

set Simulation Information

Input:

timesteps (integer)
endtime (float)
simulation_trajectory (integer)

setSpecies(species, lbls=None)¶

Set the species array

Input:

species an array of species vs time data
lbls [default=None] a list of species labels

setSpeciesConcentrations(extracellular=[])¶: Set the species concentrations array

setSpeciesDist(distributions, means, stds, moments)¶

setSpeciesDist stuff for the determination of distributions

Input:

distributions (list)
means (dictionary)
stds (dictionary)
moments (dictionary)

setTime(time, lbl=None)¶

Set the time vector

Input:

time a 1d array of time points
lbl [default=None] is “Time” set as required

setVolume(volume)¶

Set the volume array

Input:

species an array of volume vs time data

setWaitingtimes(waiting_times, lbls=None)¶

Set the waiting_times this data structure is not an array but a nested list of: waiting time log bins per reaction per trajectory. waiting_times = [traj_1, ..., traj_n] traj_1 = [wt_J1, ..., wt_Jn] # in order of SSA_REACTIONS wt_J1 = (xval, yval, nbin) xval =[x_1, ..., x_n] yval =[y_1, ..., y_n] nbin = n

Input:

waiting_times a list of waiting times
lbls [default=None] a list of matching reaction names

setWaitingtimesMeans(waiting_times, rate_names)¶

set waiting times means

Input:

waiting_times (dictionary)

setWaitingtimesStandardDeviations(waiting_times, rate_names)¶

set waiting times standard deviations

Input:

waiting_times (dictionary)

setXData(xdata, lbls=None)¶

Sets an array of extra simulation data

Input: - xdata an array of xdata vs time - lbls [default=None] a list of xdata labels

species = None¶

species_distributions = None¶

species_labels = None¶

time = None¶

time_label = 'Time'¶

waiting_times = None¶

waiting_times_labels = None¶

xdata = None¶

xdata_labels = None¶

class stochpy.modules.PyscesMiniModel.NewCoreBase¶

Core2 base class, needed here as we use Core2 derived classes in PySCeS

get(attr)¶: Return an attribute whose name is str(attr)

getName()¶

name = None¶

setName(name)¶

class stochpy.modules.PyscesMiniModel.NumberBase¶

Derived Core2 number class.

getValue()¶

setValue(v)¶

value = None¶

value_initial = None¶

class stochpy.modules.PyscesMiniModel.PySCeS_Connector(ModelFile, ModelDir, IsNRM=False, IsDelayed=False, IsMolecularNRM=False)¶

BuildDependencyGraph()¶: Function which builds a dependency graph

BuildN()¶: Generates the stoichiometric matrix N from the parsed model description. Returns a stoichiometric matrix (N) as a NumPy array

BuildN_react_prod()¶: Generates the reactant, product and net stoichiometric matrix N from the parsed model description. Also: generates list of reactant and product indices for each reaction. Returns three stoichiometric matrices (N) as NumPy arrays

BuildPropensityCodes(propensities=None)¶

Makes a list of compiled propensity codes for each reaction. If a reaction fires, its code is executed.

Input:

propensities: optional argument for providing the propensities that should be pre-compiled. If none, self.propensities is used.

BuildReactions()¶: Extract information out of each reaction, such as what are the reagents/reactants and which parameter is used for that particular reaction.

BuildSpeciesAffects()¶: Builds dependencies specifying for each species in which reactions they are reactants and/or modifiers (i.e. occuring in rate equation). Used for the Molecular NRM.

BuildX()¶: Builds the initial concentrations of all species (X).

DetermineAffects()¶: Determine the affects for each reaction

class stochpy.modules.PyscesMiniModel.PyscesInputFileParser(File, dir, output_dir=None)¶

This class contains the PySCeS model loading

InitialiseEvents()¶: Initialise Events

InitialiseInputFile()¶: Parse the input file associated with the PySCeS model instance and assign the basic model attributes

ModelDir = None¶

ModelFile = None¶

ModelOutput = None¶

N = None¶

class stochpy.modules.PyscesMiniModel.RegularGridDataObj¶

This class is specifically designed to store the results of a stochastic time simulation on fixed time intervals It has methods for setting the Time, Labels, Species and Propensity data and getting Time, Species and Rate (including time) arrays. However, of more use:

getOutput(*args) feed this method species/rate labels and it will return an array of [time, sp1, r1, ....]
getDataAtTime(time) the data generated at time point “time”.
getDataInTimeInterval(time, bounds=None) more intelligent version of the above returns an array of all data points where: time-bounds <= time <= time+bounds

HAS_AVERAGE_PROPENSITIES_DISTRIBUTIONS = False¶

HAS_AVERAGE_SPECIES_DISTRIBUTIONS = False¶

HAS_PROPENSITIES = False¶

HAS_PROPENSITIES_AUTOCORRELATIONS = False¶

HAS_PROPENSITIES_AUTOCOVARIANCES = False¶

HAS_SPECIES = False¶

HAS_SPECIES_AUTOCORRELATIONS = False¶

HAS_SPECIES_AUTOCOVARIANCES = False¶

HAS_SPECIES_CONCENTRATIONS = False¶

HAS_TIME = False¶

HAS_VOLUME = False¶

getSpecies(lbls=False)¶

Return an array of time+species

Input: - lbls [default=False] return only the time+species array or optionally both the data array and a list of column label

getSpeciesConcentrations(lbls=False)¶

Return an array of time+species concentrations

Input: - lbls [default=False] return only the time+species array or optionally both the data array and a list of column label

getTime(lbls=False)¶

Return the time vector

Input:

lbls [default=False] return only the time array or optionally both the time array and time label

getVolume()¶: Return an array of time+volume

propensities = None¶

propensities_means = None¶

propensities_standard_deviations = None¶

setPropensities(propensities)¶

Sets an array of propensities.

Input:

propensities (list)

setPropensitiesAutocorrelations(auto_correlations, lbls=None)¶

Set the autocorrelations ***

Input:

auto_correlation a list of auto correlations
lbls [default=None] a list of matching reaction names

setPropensitiesAutocovariances(auto_covariances, lbls=None)¶

Set the autocovariances ***

Input:

auto_correlation a list of auto covariances
lbls [default=None] a list of matching reaction names

setPropensitiesDistributionAverage(mean, std)¶

Set means and stds of species data

Input:

mean (list)
std (list)

setSpecies(species, lbls=None)¶

Set the species array

Input:

species an array of species vs time data
lbls [default=None] a list of species labels

setSpeciesAutocorrelations(auto_correlations, lbls=None)¶

Set the autocorrelations ***

Input:

auto_correlation a list of auto correlations
lbls [default=None] a list of matching reaction names

setSpeciesAutocovariances(auto_covariances, lbls=None)¶

Set the autocorrelations ***

Input:

auto_correlation a list of auto correlations
lbls [default=None] a list of matching reaction names

setSpeciesConcentrations(extracellular=[])¶: Set the species concentrations array

setSpeciesDistributionAverage(mean, std)¶

Set means and stds of species data

Input:

mean (list)
std (list)

setTime(time, lbl=None)¶

Set the time vector

Input:

time a 1d array of time points
lbl [default=None] is “Time” set as required

setVolume(volume)¶

Set the volume array

Input:

species an array of volume vs time data

species = None¶

species_autocorrelations = None¶

species_autocorrelations_means = None¶

species_autocorrelations_standard_deviations = None¶

species_means = None¶

species_standard_deviations = None¶

time = None¶

Brett G. Olivier (bgoli@users.sourceforge.net) Triple-J Group for Molecular Cell Physiology Stellenbosch University, South Africa.

Permission to use, modify, and distribute this software is given under the terms of the PySceS (BSD style) license. See LICENSE.txt that came with this distribution for specifics. pys NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK. Brett G. Olivier

Adapted by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 20, 2014

Demo of StochPy Functionalities¶

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 06, 2014

class stochpy.modules.StochPyDemo.Demo(DoSimulations=True)¶

Demo1()¶: Use the Immigration-Death model for doing basic simulations with the Direct method, some plotting and exportation of results

Demo10()¶: Demo of StochPy for doing (preprogrammed) Sequential simulations

Demo11()¶: StochPy Demo which illustrates delayed SSAs

Demo2()¶: Use the Immigration-Death model for a stochastic simulation with multiple trajectories

Demo3()¶: Use the Immigration-Death model to demonstrate probability density functions

Demo4()¶: Use Birth-Death model to illustrate averaging multiple simulations

Demo5()¶: Use Decaying-Dimerizing model to illustrate performance differences between different stochastic simulation algorithms

Demo6()¶: Demo of StochPy’s next reaction method handling events

Demo7()¶: Demo of StochPy’s first reaction method handling events

Demo8()¶: Demo of StochPy’s direct method for supporting events

Demo9()¶: Demo of StochPy’s direct method for handling volume and HasOnlySubstanceUnits

DoDemoSimulations()¶

StochPy Utils¶

Module that contains functions that are created by the users of stochpy. New functions will be added in the next releases of stochpy.

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 20, 2014

stochpy.modules.StochPyUtils.GetAnalyticalPDF(kon, koff, kdeg, ksyn)¶: Get the analytical probability density function. The analytical solution is taken from Sharezaei and Swain 2008 - Analytical distributions for stochastic gene expression

stochpy.modules.StochPyUtils.GetAnalyticalWaitingtimes(kon, koff, ksyn)¶: Get analytical waiting times

class stochpy.modules.StochPyUtils.Utils¶

doExample1()¶: Immigration Death example (available at http://stochpy.sourceforge.net/examples.html)

doExample2()¶: SBML events and Interpolation example (available at http://stochpy.sourceforge.net/examples.html)

doExample3()¶: Burstmodel example (available at http://stochpy.sourceforge.net/examples.html)

doExample4()¶: Second Burstmodel example (available at http://stochpy.sourceforge.net/examples.html)

doExample5()¶: Protein turnover example with and without cell division (available at http://stochpy.sourceforge.net/examples.html)

stochpy.modules.StochPyUtils.doSequentialSim(smod, n_generations, cell_division_times)¶: Model protein synthesis subject to cell division

Written by T.R. Maarleveld, Amsterdam, The Netherlands E-mail: tmd200@users.sourceforge.net Last Change: Augustus 06, 2014

class stochpy.modules.SBML2PSC.SBML2PSC(sbmlfile, sbmldir=None, pscfile=None, pscdir=None)¶

Module that converts SBML models into PSC models if libxml and libsbml are installed

Usage: >>> converter = stochpy.SBML2PSC() >>> converter.SBML2PSC(‘Burstmodel.xml’,’/home/user/Stochpy/pscmodels/’)

Table Of Contents

This Page

StochPy Module documentation¶

Stochastic Simulation Module¶

StochPy Cell Division Module¶

Direct Method¶

First Reaction Method¶

Next Reaction Method¶

Optimized Tau-Leaping¶

Delayed Direct Method¶

Modified Next Reaction Method with delays¶

Analysis¶

PySCeS MDL Parser¶

Demo of StochPy Functionalities¶

StochPy Utils¶

Navigation

Table Of Contents

This Page

Quick search

StochPy Module documentation¶

Stochastic Simulation Module¶

StochPy Cell Division Module¶

Direct Method¶

First Reaction Method¶

Next Reaction Method¶

Optimized Tau-Leaping¶

Delayed Direct Method¶

Modified Next Reaction Method with delays¶

Analysis¶

PySCeS MDL Parser¶

Demo of StochPy Functionalities¶

StochPy Utils¶

Navigation