model = models.th_cell_diff
param_names = ["beta", "beta_p"]
res = 50
param_arrays = [np.linspace(0, 10, res), np.linspace(0.1, 100, res)]
norm_list = [param_arrays[0][-1] / 2, 0.2]
# =============================================================================
# look at prolif rate
# =============================================================================
df_new = multi_param(param_arrays,
param_names,
time,
cond,
cond_names,
norm_list,
model=model,
convert=True)
g = sns.relplot(x="x",
y="ylog",
kind="line",
col="cond",
hue="readout",
row="pname",
data=df_new,
facet_kws={
"margin_titles": True,
"sharex": False,
"sharey": False
param_names2 = [["rate_il2"], ["rate_il7"], ["crit_timer"]]
df_list = []
for cond, cond_names, param_names in zip(cond2, cond_names2, param_names2):
norm_list = [d[name] for d, name in zip(cond, param_names)]
param_arrays = [
array_from_dict(d, pname) for d, pname in zip(cond, param_names)
]
df_new = multi_param(param_arrays,
param_names,
time,
cond,
cond_names,
norm_list,
model=model,
adjust_time=False)
df_list.append(df_new)
df = pd.concat(df_list)
# =============================================================================
# plotting
# =============================================================================
loc_major = ticker.LogLocator(base=10.0, numticks=100)
loc_minor = ticker.LogLocator(base=10.0,
subs=np.arange(0.1, 1, 0.1),
numticks=12)
