# load the library
import pytsa as tsa
# import a dataset and name the columns
mydata = tsa.dataset('.', colnames=['time', 'Preys', 'Predators'], ext='.data')
#
mydata.deloutput('view')
#
mydata.addoutput('png')
# plot data , as it is
mydata.splot(columns=['Preys', 'Predators'], stop=1000)
mydata.phspace(['Preys', 'Predators'], stop=500)
mydata.phspace3d(['Preys', 'Predators', 'Preys'], stop=500)
# plot the species probabilities at t =100
mydata.aplot(stop=100)
mydata.asdplot(stop=100, merge=True, legend=True)
mydata.aphspace(['Preys', 'Predators'], stop=500)
mydata.aphspace3d(['Preys', 'Predators', 'Preys'], stop=500)
mydata.pdf(100, columns=['Preys', 'Predators'], normed=True, fit=True)
mydata.pdf3d('Preys', moments=[10, 20, 30])
# estimate the master equatio in [0 ,100] as a 2 D heatmap
mydata.meq2d(start=0, stop=100)
mydata.meq3d('Preys', start=0, stop=100)
FOLDER = './bio_tmp/'
# Define the time instants at which you want to evaluate the probability density function
PDFTIMES = [5, 10, 25, 50, 75, 100]
# Define the time instants at which you want to evaluate the probability density function
MEQTIME_FROM = 0
MEQTIME_TO = 100
COLID = range(9)
COLNAMES = ['t', 'A', 'B', 'C', 'D', 'E', 'F', 'X', 'Y']
NAMES = ['A', 'B', 'C', 'D', 'E', 'F', 'X', 'Y']
#NAMES = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8']
####### Load the dataset
t = tsa.dataset(FOLDER, commentstring='#', colid=COLID, colnames=COLNAMES)
####### Set up the output terminal
t.deloutput('view')
t.addoutput('eps')
t.addoutput('png')
t.addoutput('txt')
####### Plot all the time-series, divided by columns, and plot each column in a different panel
#print('splot: plot all the time-series, divided by columns, and plot each column in a different panel.')
#t.splot()
####### Plot the average and standard deviation (bar plot) of all the time-series, divided by columns, and plot each column in a different panel
print(
'msdplot: Plot the average and standard deviation (bar plot) of all the time-series, divided by columns, and plot each column in a different panel'
)
FOLDER = './bio_tmp/'
# Define the time instants at which you want to evaluate the probability density function
PDFTIMES = [5, 10, 25, 50, 75, 100]
# Define the time instants at which you want to evaluate the probability density function
MEQTIME_FROM = 0
MEQTIME_TO = 100
COLID = range(9)
COLNAMES = ['t', 'A', 'B', 'C', 'D', 'E', 'F', 'X', 'Y']
NAMES = ['A', 'B', 'C', 'D', 'E', 'F', 'X', 'Y']
#NAMES = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8']
####### Load the dataset
t = tsa.dataset(FOLDER, commentstring='#', colid=COLID, colnames=COLNAMES)
####### Set up the output terminal
t.deloutput('view')
t.addoutput('eps')
t.addoutput('png')
t.addoutput('txt')
####### Plot all the time-series, divided by columns, and plot each column in a different panel
#print('splot: plot all the time-series, divided by columns, and plot each column in a different panel.')
#t.splot()
####### Plot the average and standard deviation (bar plot) of all the time-series, divided by columns, and plot each column in a different panel
print('msdplot: Plot the average and standard deviation (bar plot) of all the time-series, divided by columns, and plot each column in a different panel')
t.msdplot(columns=NAMES, errorbar=True)
# load the library
import pytsa as tsa
# import a dataset and name the columns
mydata = tsa.dataset( '.' , colnames =[ 'time' , 'Preys' , 'Predators' ], ext='.data')
#
mydata.deloutput('view')
#
mydata.addoutput('png')
# plot data , as it is
mydata.splot( columns =[ 'Preys' , 'Predators'] , stop =1000)
mydata.phspace([ 'Preys' , 'Predators'], stop = 500)
mydata.phspace3d([ 'Preys' , 'Predators', 'Preys'], stop = 500)
# plot the species probabilities at t =100
mydata.aplot(stop=100)
mydata.asdplot(stop=100, merge=True, legend=True)
mydata.aphspace([ 'Preys' , 'Predators'], stop = 500)
mydata.aphspace3d([ 'Preys' , 'Predators', 'Preys'], stop = 500)
mydata.pdf(100 , columns =[ 'Preys' , 'Predators'] , normed = True , fit = True )
mydata.pdf3d('Preys', moments=[10, 20, 30])
# estimate the master equatio in [0 ,100] as a 2 D heatmap
mydata.meq2d( start =0 , stop =100)
mydata.meq3d('Preys', start=0, stop=100)
