Intro_Biocomp_ND_318_Tutorial10/janice_tutorial10Notes.rtf at master · mpullins/Intro_Biocomp_ND_318_Tutorial10 · GitHub

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\f0\fs22 \cf2 #Tutorial 10 notes \
\
### Load the necessary packages\
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\cf3 import \cf4 pandas\
\cf3 import \cf4 scipy\
\cf3 import \cf4 scipy.integrate \cf3 as \cf4 spint\
\cf3 from \cf4 plotnine \cf3 import \cf5 *\
\
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\cf2 ### Custom function that defines the model differential equations\
# the odeint function we will use for simulating the model requires the function be\
# defined with the arguments for state variable list, time, and parameters be provided\
# in that order. Parameters must be defined individually because they are passed as\
# a Python tuple when calling odeint\
# odeint also requires that the dy/dt
\b \cf2 '
\b0 \cf2 s be returned as a list\
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\cf3 def \cf4 ddSim\cf0 (y,t0,r,K):\
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\cf2 # "unpack" lists containing state variables (y)\
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\cf0 N\cf5 =\cf0 y[\cf5 0\cf0 ]\
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\cf2 # calculate change in state variables with time, give parameter values\
# and current value of state variables\
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\cf0 dNdt\cf5 =\cf0 r\cf5 *\cf0 (\cf5 1-\cf0 N\cf5 /\cf0 K)\cf5 *\cf0 N\
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\cf2 # return list containing change in state variables with time\
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\cf3 return \cf0 [dNdt]\
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\cf2 ### Define parameters, initial values for state variables, and time steps\
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\cf0 params\cf5 =\cf0 (\cf5 0.3\cf0 ,\cf5 10\cf0 )\
N0\cf5 =\cf0 [\cf5 0.01\cf0 ]\
times\cf5 =\cf3 range\cf0 (\cf5 0\cf0 ,\cf5 600\cf0 )\
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\cf2 ### Simulate the model using odeint\
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\cf0 modelSim\cf5 =\cf0 spint\cf5 .\cf0 odeint(func\cf5 =\cf0 ddSim,y0\cf5 =\cf0 N0,t\cf5 =\cf0 times,args\cf5 =\cf0 params)\
\
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\cf2 ### put model output in a dataframe for plotting purposes\
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\cf0 modelOutput\cf5 =\cf0 pandas\cf5 .\cf0 DataFrame(\{\cf6 "t"\cf0 :times,\cf6 "N"\cf0 :modelSim[:,\cf5 0\cf0 ]\})\
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\cf2 ### plot simulation output\
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\cf0 ggplot(modelOutput,aes(x\cf5 =\cf6 "t"\cf0 ,y\cf5 =\cf6 "N"\cf0 ))\cf5 +\cf0 geom_line()\cf5 +\cf0 theme_classic()\
\
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\cf2 # function for simulating predator-prey dynamics\
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\cf3 def \cf4 predPreySim\cf0 (y,t0,r,K,consume,predDeath):\
prey\cf5 =\cf0 y[\cf5 0\cf0 ]\
pred\cf5 =\cf0 y[\cf5 1\cf0 ]\
dPrey_dt\cf5 =\cf0 r\cf5 *\cf0 (\cf5 1-\cf0 prey\cf5 /\cf0 K)\cf5 *\cf0 prey\cf5 -\cf0 consume\cf5 *\cf0 prey\cf5 *\cf0 pred\
dPred_dt\cf5 =\cf0 consume\cf5 *\cf0 prey\cf5 *\cf0 pred\cf5 -\cf0 predDeath\cf5 *\cf0 pred\
\cf3 return \cf0 [dPrey_dt,dPred_dt]}