cell_list = []
molecules_list = []
id = "SD(virus)"
for sim, beta, alpha in zip(sim_list, betas, alphas):
cells, molecules = sim.run_sim()
SD = round(alpha / (beta**2), 2)
cells[id] = str(SD)
molecules[id] = str(SD)
cell_list.append(cells)
molecules_list.append(molecules)
cells = pd.concat(cell_list)
molecules = pd.concat(molecules_list)
g = pl.plot_timecourse(cells, hue=id, col="cell")
g.set_titles("{col_name}")
plt.show()
molecules = molecules.loc[molecules["cell"] == "Virus"]
g = sns.relplot(data=molecules,
x="time",
y="value",
hue=id,
facet_kws={"sharey": False},
kind="line",
legend=False,
aspect=1.1)
g.set_titles("{col_name}")
g.set(ylabel="virus density")
# create simulation list
sim_list = [
Sim(name=name, params=dic, time=time, virus_model=vir_model)
for dic in d_list
]
sim_list2 = [
Sim(name=name, params=dic, time=time, virus_model=vir_model)
for dic in d_list2
]
# run model
id = "ag conc."
exp1 = Simlist(sim_list, names, idtype=id)
cells, molecules = exp1.run_sim()
cells["r_chronic"] = "0"
exp2 = Simlist(sim_list2, names, idtype=id)
cells2, molecules2 = exp2.run_sim()
cells2["r_chronic"] = "10"
cells = pd.concat([cells, cells2])
pl.plot_timecourse(cells,
cells=["teff", "tchronic"],
style="r_chronic",
palette=["grey", "grey"],
col="ag conc.",
row="cell",
hue="r_chronic")
plt.show()
name="il2+time+ag",
prolif_type="timer",
myc_type="il2_ag",
params=d4,
virus_model=vir_model_SIR2)
simlist = [sim1, sim2, sim3, sim4]
simres = [sim.run_sim() for sim in simlist]
cells = [res[0] for res in simres]
molecules = [res[1] for res in simres]
cells = pd.concat(cells)
molecules = pd.concat(molecules)
g = pl.plot_timecourse(cells, cells=["Effector"], hue="name")
plt.show()
g = pl.plot_timecourse(molecules,
cells=["IL2", "Myc", "Virus"],
hue="name",
col="cell")
plt.show()
# effect of division time on model
for sim in simlist:
arr = np.linspace(3.5, 6, 30)
pname = "prolif_th1"
df1, df2 = proc.run_timecourses(sim, arr, pname)
# add division time based on prolif rate
mysim3 = exp.Sim(name="timer",
params=params,
time=time,
virus_model=mdl.vir_model_gamma)
mysim4 = exp.Sim(name="timer",
params=params,
time=time,
virus_model=mdl.vir_model_ode)
mysims = [mysim1]
for mysim in mysims:
# run timecourse
cells, molecules = mysim.run_sim()
# plot timecourse
g = pl.plot_timecourse(cells)
plt.show()
g = pl.plot_timecourse(molecules)
plt.show()
# get readouts
reads = proc.get_readouts(cells)
# run pscan
pname = "beta"
arr = np.geomspace(0.1, 1, 12)
myscan1 = proc.pscan(mysim, arr, pname)
# plot pscan
g = pl.plot_pscan(myscan1)
plt.show()
highlight_cells = []
highlight_mols = []
# set up arrays
pname = "beta_p"
arr_timecourse = np.geomspace(10,100, res_timecourse)
arr_pscan = np.geomspace(1,1e3, res_pscan)
for name, mymol, K_eff, param_name in zip(names, mymols, K_vals, param_names):
# make simulation object
sim = Sim(name=name, params=d, time=time, virus_model=vir_model, prolif_type=name)
# plot timecourse before normalization
cells, molecules = sim.run_sim()
pl.plot_timecourse(df = cells, cells= ["tnaive", "teff", "tchronic", "all_cells"],
hue = "cell", ylim = (1,1e6))
# plot timecourse with normalized cells
cells2 = cells.loc[cells["cell"] == "all_cells", :]
mynorm = np.log2(cells2["value"])
#print(rr.get_peaktime(cells2["time"], cells2["value"]))
#peak = rr.get_peak_height(cells2["time"], cells2["value"])
if name != "Cntrl":
mypeak = np.max(mynorm)
else:
# take the value where time is at peak in other models
out = cells2.loc[(cells2["time"] > 2) & (cells2["time"] <= 2.2),:]
mypeak = np.log2(out.value.iloc[0])
cells2.loc[:, "value"] = (mynorm / mypeak)
highlight_cells.append(cells2)
from src.modules.pl import plot_timecourse
from src.modules.proc import pscan
from src.modules.pl import plot_pscan
import matplotlib.pyplot as plt
# init model
name = "il2"
vir_model = vir_model_const
time = np.arange(0, 200, 0.01)
# create simulation
sim = Sim(name=name, params=d, time=time, virus_model=vir_model)
pname = "vir_load"
cells, molecules = sim.run_sim()
plot_timecourse(cells, col="cell")
plt.show()
reads = get_readouts(cells, check_criteria2)
res = 30
arr = np.linspace(0, 0.2, res)
pscan = pscan(sim, arr, pname)
g = plot_pscan(pscan,
cells=["teff"],
value_col="val_norm",
palette=["grey"],
hue="cell")
plt.show()
from src.models.virus_models import vir_model_const
import src.modules.pl as pl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# init model
name = "il2" # can be "il2", "timer" "timer_il2"
vir_model = vir_model_const
time = np.arange(0, 10, 0.01)
# plot timecourse
sim = Sim(name=name, params=d, time=time, virus_model=vir_model)
cells, molecules = sim.run_sim()
pl.plot_timecourse(cells, cells=["teff"])
plt.show()
# check effect for different const. ag levels
# create dicts and set up simulations
names = ["none", "lo", "mid", "hi"]
vir_loads = [0, 0.01, 0.1, 10]
d_list = [dict(d) for i in range(len(vir_loads))]
for dic, v in zip(d_list, vir_loads):
dic["vir_load"] = v
sim_list = [
Sim(name=name, params=dic, time=time, virus_model=vir_model)
for dic in d_list
]
cell_list = []