plot complex can now show absolute values and add consistency plot

python/energy_flow_proper/plot_utils.py

@@ -110,10 +110,22 @@ def get_figsize(width="thesis", fraction=1, subplots=(1, 1)):
 
 
 @wrap_plot
-def plot_complex(x, y, *args, ax=None, label="", **kwargs):
+def plot_complex(x, y, *args, ax=None, label="", absolute=False, **kwargs):
     label = label + ", " if (len(label) > 0) else ""
-    ax.plot(x, y.real, *args, label=f"{label}real part", **kwargs)
-    ax.plot(x, y.imag, *args, label=f"{label}imag part", **kwargs)
+    ax.plot(
+        x,
+        abs(y.real) if absolute else y.real,
+        *args,
+        label=f"{label}absolute real part",
+        **kwargs,
+    )
+    ax.plot(
+        x,
+        abs(y.imag) if absolute else y.imag,
+        *args,
+        label=f"{label}absolute imag part",
+        **kwargs,
+    )
     ax.legend()
 
 
@@ -144,7 +156,7 @@ def plot_convergence(
         line = ax.plot(
             x,
             current_value.value,
-            label=f"{label}$N={current_value.N}$ $({consistency:.0f}\%)$",
+            label=f"{label}$N={current_value.N}$ $({consistency:.1f}\%)$",
             alpha=current_value.N / y.N,
             linestyle=linestyle if i == (len(y) - 1) else ":",
         )
@@ -411,6 +423,63 @@ def plot_interaction_consistency_development(
     return fig, [ax, ax2, ax3]
 
 
+def plot_consistency_development(value, reference):
+    fig, (ax, ax2) = plt.subplots(nrows=2, sharex=True)
+
+    ax.set_xscale("log")
+    ax2.set_xscale("log")
+    ax2.set_yscale("log")
+
+    diff = abs(value - reference)
+
+    ns, values, σs, max_diff = [], [], [], []
+
+    for N, val, σ in diff.aggregate_iterator:
+        ns.append(N)
+        values.append(((val < σ).sum() / len(val[0])) * 100)
+        σs.append(σ.mean())
+        max_diff.append(val.mean())
+
+    values = np.array(values)
+    ns = np.array(ns)
+    where_consistent = values >= 68
+
+    values_consistent = values.copy()
+    values_consistent[~where_consistent] = np.nan
+
+    values_inconsistent = values.copy()
+    values_inconsistent[where_consistent] = np.nan
+
+    ax.plot(
+        ns,
+        values_consistent,
+        linestyle="none",
+        marker=".",
+        markersize=5,
+    )
+
+    ax.plot(
+        ns,
+        values_inconsistent,
+        linestyle="none",
+        marker=".",
+        markersize=5,
+    )
+
+    ax2.plot(ns, np.array(σs) / abs(reference).max(), label=r"$\langle σ\rangle$")
+    ax2.plot(ns, np.array(max_diff) / abs(reference).max(), label=r"$\langle Δ\rangle$")
+
+    ax.axhline(68, linestyle="-.", color="grey", alpha=0.5)
+    ax.set_xlabel("$N$")
+    ax2.set_xlabel("$N$")
+    ax.set_ylabel(r"Consistency [$\%$]")
+    ax2.set_ylabel(r"$\mathrm{Deviation}/{J_\mathrm{max}}$")
+
+    ax2.legend()
+
+    return fig, [ax, ax2]
+
+
 def plot_flow_bcf(models, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs):
     fig, ax = plt.subplots()
     for model in models:
@@ -528,5 +597,5 @@ def plot_σ_development(ensemble_value, ax=None, **kwargs):
         **kwargs,
     )
 
-    ax.set_ylabel("$σ$")
+    ax.set_ylabel("mean $σ$")
     ax.set_xlabel("$N$")