time = np.arange(0, 50, 0.01)
# init models
name = "il2"
d["r_chronic"] = 1
d1 = dict(d)
d1["r_chronic"] = 0
sim = Sim(name=name, params=d, time=time, virus_model=vir_model)
sim2 = Sim(name=name, params=d1, time=time, virus_model=vir_model)
# run parameter scans
pname = "SIR_r0"
res = 50
down = 1
up = 10
arr = np.linspace(down, up, res)
res1 = proc.pscan(sim, arr, pname)
res2 = proc.pscan(sim2, arr, pname)
res1["chronic"] = "on"
res2["chronic"] = "off"
res = pd.concat([res1, res2])
# plot parameter scans
g = pl.plot_pscan(res,
value_col="val_min",
cells=["teff"],
hue="chronic",
palette=["grey", "k"])
plt.show()
sim4.reset()
cells, molecules = sim4.run_sim()
cells = cells.loc[cells.cell == "Effector"]
ax = g.axes.flatten()[0]
ax.plot(cells.time, cells.value, color="tab:cyan", lw=6)
plt.show()
#
# also run parameter scan
res = proc.pscan(sim4, arr, pname)
sim4.reset()
# only look at peak for presentation figure
res2 = res.loc[res.readout == "Peak"]
g = pl.plot_pscan(res2,
yscale="log",
value_col="value",
cells=["Effector"],
aspect=1.2,
color="grey")
ax = g.axes.flatten()[0]
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set_ylabel("cell maximum")
ax.set_xlabel("antigen dose (a.u.)")
plt.title("")
plt.show()
###########################################################################################################
###########################################################################################################
###########################################################################################################
#effect of r0 on model
arr = np.geomspace(1, 10, 30)
cells2 = cells.loc[cells.cell == "Total_CD4"]
g = pl.plot_timecourses(cells2,
pl_cbar=True,
hue_log=True,
scale="log",
ylabel="n cells",
legend=False,
ylim=(1e4, 1e7))
plt.show()
# also run parameter scan
lower = 1
upper = 10
n = 50
arr_pscan = np.geomspace(lower, upper, n)
res = proc.pscan(sim, arr_pscan, pname)
cells_pscan = ["Total_CD4"]
res = res.loc[res.cell.isin(cells_pscan)]
res = res.loc[res.readout == "Peak"]
## plot parameter scans
g = pl.plot_pscan(res,
xscale="log",
yscale="log",
value_col="value",
cells=cells_pscan,
col="cell")
plt.show()
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()
legend=False)
g.set(xlim=xlim)
plt.show()
#g.savefig("../figures/antigen_effects/timecourse_virus.png")
# also run parameter scan
lower = 1
upper = 10
n = 10
arr_pscan = np.geomspace(lower, upper, n)
res = proc.pscan(sim, arr_pscan, pname)
## plot parameter scans
g = pl.plot_pscan(res,
xscale="log",
yscale="log",
value_col="value",
cells=["teff"],
color="grey")
plt.show()
# only look at peak for presentation figure
res2 = res.loc[res.readout == "Peak"]
g = pl.plot_pscan(res2,
xscale="log",
yscale="log",
value_col="value",
cells=["teff"],
aspect=1.2)
ax = g.axes.flatten()[0]
for a, c in zip(arr_timecourse, cmap):
# # get readouts for varying beta p
# arr = np.geomspace(1,1000, res_pscan)
# reads = proc.pscan(sim, arr, pname)
# assign nans to null model for beta p > 15
cutoff_null = 30
pscan_prolif.loc[(pscan_prolif["name"] == "Cntrl") & (pscan_prolif["param_value"] > cutoff_null), "value"] = np.nan
pscan_prolif.loc[(pscan_prolif["name"] == "Cntrl") & (pscan_prolif["param_value"] > cutoff_null), "val_norm"] = np.nan
pscan_prolif.loc[(pscan_prolif["name"] == "Cntrl") & (pscan_prolif["param_value"] > cutoff_null), "val_min"] = np.nan
g = pl.plot_pscan(pscan_prolif, cells = ["all_cells"],
hue = "readout",
col = "name",
xscale = "log", kind = "line", legend = False,
linewidth = mylinewidth,
readouts = myreads,
value_col= "val_min",
aspect = 1.2,
facet_kws = {"despine" : False, "sharey" : False})
axes = g.axes[0]
for ax in axes:
#ax.axhline(y=0, ls = "--", c = "k", lw = 2)
#ax.axhline(y=-10, ls = "--", c = "k", lw = 2)
#ax.axhline(y=10, ls = "--", c = "k", lw = 2)
ax.set_ylim([-1,25])
ax.set_xlabel(prolif_label)
ax.set_ylabel("effect size")
ax.set_title("")
n = 100
arr_pscan = np.geomspace(lower, upper, n)
#res1 = proc.pscan(sim, arr_pscan, pname)
# also run pscan for ag dose
pname2 = "vir_load"
sim.params["r0"] = 2
res2 = proc.pscan(sim, arr_pscan, pname2)
# only looking at antigen dose for now
for res in [res2]:
cells_pscan = ["Th_eff", "Th_chr", "Total_CD4"]
res = res.loc[res.cell.isin(cells_pscan)]
res = res.loc[res.readout == "Peak"]
## plot parameter scans
g = pl.plot_pscan(res,
xscale="log",
yscale="log",
value_col="value",
cells=cells_pscan,
hue="cell",
legend=False,
xlabel="antigen dose",
ylabel="response peak",
ylim=(1e5, 1e7),
aspect=1.2,
palette=["grey", "k", "crimson"])
plt.xlabel("antigen dose (a.u.)")
plt.title("")
plt.show()
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()
# init model name = "il2" vir_model = vir_model_const time = np.arange(0, 50, 0.01) d["r_chronic"] = 10.0 # compare feedback and no fb scenario d1 = dict(d) d2 = dict(d) d2["pos_fb_chr"] = 10.0 # create simulation sim1 = Sim(name = name, params = d1, time = time, virus_model = vir_model) sim2 = Sim(name = name, params = d2, time = time, virus_model = vir_model) res = 100 pname = "vir_load" arr = np.linspace(0,1.0, res) pscan1 = pscan(sim1, arr, pname) pscan2 = pscan(sim2, arr, pname) pscan1["feedback"] = "off" pscan2["feedback"] = "on" pscan = pd.concat([pscan1, pscan2]) pscan = pscan.loc[pscan.cell == "teff"] g = plot_pscan(pscan, row = "feedback", value_col= "val_min", palette= ["grey"]) #g.set(ylim(0,20)) plt.show()
vir_model = vir_model_const
time = np.arange(0, 50, 0.01)
d1 = dict(d)
d1["r_chronic"] = 0
d2 = dict(d)
d2["r_chronic"] = 10.0
# create simulation
sim1 = Sim(name=name, params=d1, time=time, virus_model=vir_model)
sim2 = Sim(name=name, params=d2, time=time, virus_model=vir_model)
res = 50
pname = "vir_load"
arr = np.linspace(0, 1.0, res)
pscan1 = pscan(sim1, arr, pname)
pscan2 = pscan(sim2, arr, pname)
pscan1["chronic"] = "on"
pscan2["chronic"] = "off"
pscan = pd.concat([pscan1, pscan2])
pscan = pscan.loc[pscan.cell == "teff"]
g = plot_pscan(pscan,
row="chronic",
value_col="val_min",
palette=["grey"],
hue="cell")
#g.set(ylim(0,20))
plt.show()
