plot_results.py

import simulation
import parameters
import plot as pl
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
s = simulation.simulate_cyano()
p = parameters.cyano_pars()

### Growth laws ####################################################################################
### Growth rate versus External nitrogen
#yN = s.vary_Nx(model=s.m, Cx=0.25, ub_Nx=10.0, stepsize=0.01) # returns a dataframe with variable values
yN = pd.read_csv('results/yN.csv', delimiter='\t')	#load simulation results from csv file
K_N, cov = curve_fit(s.monod1, yN['Nx'], 86400*yN['Mu'])
S_Nx = s.getS(yN, 'Nx')
MuN = s.monod1(S=S_Nx, mu_max=K_N[0], Km=K_N[1])

### Growth rate versus External carbon
#yC = s.vary_Cx(model=s.m, Nx=0.5, ub_cix=15.0, stepsize=0.01)  # returns a dataframe with variable values
yC = pd.read_csv('results/yC.csv', delimiter='\t')	#load simulation results from csv file
K_C, cov = curve_fit(s.monod1, yC['Cx'], 86400*yC['Mu'])
S_Cx = s.getS(yC, 'Cx')
MuC = s.monod1(S=S_Cx, mu_max=K_C[0], Km=K_C[1])


### Growth rate versus irradiance
#yI = s.vary_irradiance(model=s.m, Cx=0.25, Nx=0.5, ub_Irr=1000.0) # returns a dataframe with variable values
yI = pd.read_csv('results/yI.csv', delimiter='\t')	#load simulation results from csv file
K_I, cov = curve_fit(s.haldane1, yI['Irr'], 86400*yI['Mu'])
S_I = s.getS(yI, 'Irr')
MuI = s.haldane1(I=S_I, mu_max=K_I[0], Kd=K_I[1], K_A=K_I[2])

# pl.growth_subplots(yN, yC, yI, s.m)
pl.growth_subplots_new(yN, yC, yI, S_Nx, S_Cx, S_I, MuN, MuC, MuI, s.m)


### Resource allocation ############################################################################
### protein allocation
pF_N = s.p_fracs(yN, s.m)   # protein fraction as a function of varying Nitrogen
pF_C = s.p_fracs(yC, s.m)   # protein fraction as a function of varying Carbon
pF_I = s.p_fracs(yI, s.m)   # protein fraction as a function of varying Irradiance (light)
pl.pie_subplots(yN, pF_N, yC, pF_C, yI, pF_I)

### Vmax vs uptake flux
#y1 = s.vary_Nx(s.m, steps=20, lb_Nx=0.1, Cx=0.25, ub_Nx=1000)
y1 = pd.read_csv('results/y1.csv', delimiter='\t')	#load simulation results from csv file
#y2 = s.vary_Nx(s.m, steps=20, lb_Nx=0.1, Cx=0.75, ub_Nx=1000)
y2 = pd.read_csv('results/y2.csv', delimiter='\t')	#load simulation results from csv file
#y3 = s.vary_Nx(s.m, steps=20, lb_Nx=0.1, Cx=15.0, ub_Nx=1000)
y3 = pd.read_csv('results/y3.csv', delimiter='\t')	#load simulation results from csv file
pl.twosubplots(y1, y2, y3, s.m)


### Gleaner-Opportunist ############################################################################
### Growth rate versus external nitrogen
#yG, yO = s.twostrains(model=s.m, I=200.0, Cx=0.25, ub_Nx=1.0)
yG = pd.read_csv('results/yG.csv', delimiter='\t')	#load simulation results from csv file
yO = pd.read_csv('results/yO.csv', delimiter='\t')	#load simulation results from csv file
pl.plot_gle_opp (Nx1=yG.Nx, Nx2=yO.Nx, mu1=86400*yG.Mu, mu2=86400*yO.Mu)

### Metabolic diversity and cost of regulation
#y_Y = s.vary_Nx(s.m0, lb_Nx=0.1, stepsize=0.001, steps=40)
y_Y = pd.read_csv('results/y_Y.csv', delimiter='\t')	#load simulation results from csv file
#y_N = s.vary_Nx(s.m, lb_Nx=0.1, stepsize=0.001, steps=40)
y_N = pd.read_csv('results/y_N.csv', delimiter='\t')	#load simulation results from csv file
#y_NY = s.vary_Nx(s.m1, lb_Nx=0.1, stepsize=0.001, steps=40)
y_NY = pd.read_csv('results/y_NY.csv', delimiter='\t')	#load simulation results from csv file
pF_NY = s.protein_fracs(y_NY, s.m)
pl.subplots_TnTy(y_N, y_Y, y_NY, pF_NY, s.m)

### Time-dependent oscillations
# y1 = s.rk4(f=s.chemostat_fixedI, x0=[1., 1., 10.], t=np.linspace(0, 3*365, 34510))    # uses 4th order runge-kutta method to solve the ODEs
# y2 = s.rk4(f=s.chemostat_variableI, x0=[1., 1., 10.], t=np.linspace(0, 5*365, 40000))    # uses 4th order runge-kutta method to solve the ODEs
y1 = pd.read_csv('results/chemostat_fixedI.csv', delimiter='\t')	#load simulation results from csv file
y2 = pd.read_csv('results/chemostat_variableI.csv', delimiter='\t')	#load simulation results from csv file
pl.co_culture(y1, y2, s.I(y2.t))