Updated plots.

pes_to_spec/test/prepare_plots.py

@@ -110,8 +110,9 @@ def plot_chi2_intensity(df: pd.DataFrame, filename: str):
                 fill=True,
                 ax=ax)
     sns.scatterplot(x=df.chi2_prepca/df.ndof.iloc[0], y=df.xgm_flux_t*1e-3,
-                    s=200,
-                    alpha=0.1,
+                    s=5,
+                    alpha=0.4,
+                    c="tab:red",
                     #size=df.root_mean_squared_pca_unc,
                     #sizes=(20, 200),
                     ax=ax)
@@ -153,7 +154,7 @@ def pca_variance_plot(df: pd.DataFrame, filename: str, max_comp_frac: float=0.99
     ax.set_yscale('log')
     ax.set(title=f"",
            xlabel="Component",
-           ylabel="Variance [%]",
+           ylabel="Variance contribution [%]",
            xlim=(1, x_max),
            ylim=(0.01, 100))
     ax.spines['top'].set_visible(False)
@@ -184,14 +185,14 @@ def plot_impulse(df: pd.DataFrame, filename: str):
     #x_new = np.linspace(-6, 6, 601)
     #spl = make_interp_spline(x, np.log10(y), k=3)
     #y_new = np.power(10, spl(x_new))
-    x_new = moving_average(x, n=10)
-    y_new = moving_average(y, n=10)
-    sel = (x_new >= -10) & (x_new <= 10)
-    ax.plot(x_new[sel], y_new[sel], c='tab:blue', lw=4)
+    x_new = moving_average(x, n=5)
+    y_new = moving_average(y, n=5)
+    sel = (x_new >= -5.1) & (x_new <= 5.1)
+    ax.plot(x_new[sel], y_new[sel], c='tab:blue', lw=3)
     ax.set_yscale('log')
     ax.set(title=f"",
            xlabel="Energy [eV]",
-           ylim=(1e-4, 0.5),
+           ylim=(1e-4, 0.4),
            ylabel="Response [a.u.]",
            )
     ax.spines['top'].set_visible(False)