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)