import pandas as pd
from pyndamics3 import Simulation
from pyndamics3.fit import fit, Parameter

Populating the interactive namespace from numpy and matplotlib
pyndamics3  version  0.0.29

df = pd.read_excel('Mobile telephone service.xlsx')

x_data=df['Year']
y_data=df['Americans with Cellular Service (%)']

sim=Simulation()
sim.add("y'= a*y*(1-y/k)",12,plot=True)
sim.params(a=0.3,k=100)
sim.add_data(t=x_data,y=y_data,plot=True)
sim.run(1995,2012)

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results=fit(sim,
           Parameter("a",value=0.5,min=0),
           Parameter("k",value=100,min=0),
           )
sim.run(1995,2012)

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results

Fit Statistics

fitting method	leastsq
# function evals	7
# data points	18
# variables	2
chi-square	14627.1940
reduced chi-square	914.199625
Akaike info crit.	124.604787
Bayesian info crit.	126.385530

Variables

name	value	initial value	min	max	vary
a	0.50000000	0.5	0.00000000	inf	True
k	58.0001111	100	0.00000000	inf	True

x_data=x_data-min(x_data)

sim=Simulation()
sim.add("y'= a*y*(1-y/k)",12,plot=True)
sim.params(a=0.3,k=100)
sim.add_data(t=x_data,y=y_data,plot=True)
sim.run(20)

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results=fit(sim,
           Parameter("a",value=0.5,min=0),
           Parameter("k",value=100,min=0),
           )
sim.run(20)

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results

Fit Statistics

fitting method	leastsq
# function evals	16
# data points	18
# variables	2
chi-square	30.6986915
reduced chi-square	1.91866822
Akaike info crit.	13.6092689
Bayesian info crit.	15.3900124

Variables

name	value	standard error	relative error	initial value	min	max	vary
a	0.27934262	0.00382039	(1.37%)	0.5	0.00000000	inf	True
k	103.234169	1.20806071	(1.17%)	100	0.00000000	inf	True

Correlations (unreported correlations are < 0.100)

a

k

-0.8281

results=fit(sim,
           Parameter("a",value=1,min=0,max=20),
           Parameter("initial_y",value=1,min=0),
           Parameter("k",value=30,min=0),
           )
results

Fit Statistics

fitting method	leastsq
# function evals	9
# data points	18
# variables	3
chi-square	14627.1940
reduced chi-square	975.146267
Akaike info crit.	126.604787
Bayesian info crit.	129.275902

Variables

name	value	initial value	min	max	vary
a	1.00000000	1	0.00000000	20.0000000	True
initial_y	1.00000000	1	0.00000000	inf	True
k	58.0001111	30	0.00000000	inf	True

sim.run(1995,2012)

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fishery = pd.read_csv("FISH_LAND_08032022184748990.csv")

fishery=fishery[fishery['MEASURE']=='TON']
fishery=fishery[fishery['Species']=='TOTAL SPECIES']
fishery=fishery[fishery['LANDINGS']=='LAND_TOTAL']

t_fishery=fishery['Year']
y_fishery=fishery['Value']

r=0.13
a=5000
k=170000
h=0.1

sim=Simulation()
sim.add("n'=r*n*(1-(n/k))-h*(n/(a+n))",80000,plot=True)
sim.params(r=0.13,a=5000,k=170000,h=0.1)
sim.add_data(t=t_fishery,n=y_fishery,plot=True)
sim.run(2001,2018)

results=fit(sim,
           Parameter("r",value=1,min=0,max=10),
           Parameter("a",value=100,min=0),
           Parameter("k",value=100,min=0),
           Parameter("h",value=100,min=0),
        )

sim=Simulation()
sim.add("y' = a*y*(1-y/k)",1,plot=True)
sim.params(a=1,k=30)
sim.add_data(t=t_data,y=h_data,plot=True)
sim.run(80)