diff --git a/pes_to_spec/test/offline_analysis.py b/pes_to_spec/test/offline_analysis.py
index 6cb2a52bdadb5b9f883b8012a662c4edce03015a..11966b7475f3d334ace2d200a944826e2929888d 100755
--- a/pes_to_spec/test/offline_analysis.py
+++ b/pes_to_spec/test/offline_analysis.py
@@ -368,6 +368,12 @@ def main():
total_unc = np.sqrt(np.mean(spec_pred["total_unc"]**2, axis=(-1, -2)))
median_unc = np.median(spec_pred["total_unc"], axis=(-1, -2))
+ # sums
+ first, last = model.get_low_resolution_range()
+ pes_select_t = np.concatenate([-pes_raw_t[ch][:, first:last] for ch in pes_raw_t], axis=-1)
+ sum_pes = np.sum(pes_select_t, axis=(-1))
+ sum_spec = np.sum(spec_raw_int_t, axis=(-1))
+
q = dict(chi2_prepca=chi2_prepca,
ndof=spec_data_pca.shape[-1]*np.ones_like(chi2_prepca),
xgm_flux_t=xgm_flux_t[:,0],
@@ -375,7 +381,9 @@ def main():
nopca_unc=nopca_unc,
total_unc=total_unc,
median_unc=median_unc,
- root_mean_squared_pca_unc=np.sqrt((spec_pred["expected_pca_unc"][:, 0, :]**2).mean(axis=-1))
+ root_mean_squared_pca_unc=np.sqrt((spec_pred["expected_pca_unc"][:, 0, :]**2).mean(axis=-1)),
+ sum_pes=sum_pes,
+ sum_spec=sum_spec,
)
q.update({f'res_prepca_{k}': res_prepca[:, k]
for k in range(res_prepca.shape[1])
diff --git a/pes_to_spec/test/prepare_plots.py b/pes_to_spec/test/prepare_plots.py
index 49f8f314c6f02668bc188407871b84fd065d59eb..cb70826f291ed2fe7cc8f114e1fe87bab2681d69 100755
--- a/pes_to_spec/test/prepare_plots.py
+++ b/pes_to_spec/test/prepare_plots.py
@@ -31,12 +31,13 @@ def plot_final(df: pd.DataFrame, smooth: bool, filename: str):
ax = fig.add_subplot(gs[0, 0])
if smooth:
ax.plot(df.energy, df.spec_smooth, c='b', lw=3, label="Grating spectrometer (smoothened)")
+ Y = np.amax(df.spec_smooth)
else:
ax.plot(df.energy, df.spec, c='b', lw=3, label="Grating spectrometer")
+ Y = np.amax(df.spec)
ax.plot(df.energy, df.prediction, c='r', ls='--', lw=3, label="Prediction")
ax.fill_between(df.energy, df.prediction - 1*df.unc, df.prediction + 1*df.unc, facecolor='gold', alpha=0.5, label="68% unc. (total)")
ax.fill_between(df.energy, df.prediction - 1*df.unc_pca, df.prediction + 1*df.unc_pca, facecolor='magenta', alpha=0.5, label="68% unc. (PCA only)")
- Y = np.amax(df.spec)
ax.legend(frameon=False, borderaxespad=0, loc='upper left')
ax.set_title(f"Beam intensity: {df.beam_intensity.iloc[0]:.1f} mJ", loc="left")
ax.spines['top'].set_visible(False)
@@ -44,7 +45,7 @@ def plot_final(df: pd.DataFrame, smooth: bool, filename: str):
ax.set(
xlabel="Photon energy [eV]",
ylabel="Intensity [a.u.]",
- ylim=(0, 1.3*Y))
+ ylim=(0, 1.2*Y))
plt.tight_layout()
fig.savefig(filename)
plt.close(fig)
@@ -133,6 +134,50 @@ def plot_chi2_intensity(df: pd.DataFrame, filename: str):
fig.savefig(filename)
plt.close(fig)
+def plot_pes_vs_spec_intensity(df: pd.DataFrame, filename: str):
+ fig = plt.figure(figsize=(12, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ sns.scatterplot(x=df.sum_spec, y=df.sum_pes,
+ s=20,
+ alpha=0.4,
+ c="tab:red",
+ #size=df.root_mean_squared_pca_unc,
+ #sizes=(20, 200),
+ ax=ax)
+ ax = plt.gca()
+ ax.set(title=f"",
+ xlabel=r"Total grating spec. intensity [a.u.]",
+ ylabel="Total PES intensity [a.u.]",
+ )
+ ax.spines['top'].set_visible(False)
+ ax.spines['right'].set_visible(False)
+ plt.tight_layout()
+ fig.savefig(filename)
+ plt.close(fig)
+
+def plot_xgm_vs_spec_intensity(df: pd.DataFrame, filename: str):
+ fig = plt.figure(figsize=(12, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ sns.scatterplot(x=df.sum_spec, y=df.xgm_flux_t*1e-3,
+ s=20,
+ alpha=0.4,
+ c="tab:red",
+ #size=df.root_mean_squared_pca_unc,
+ #sizes=(20, 200),
+ ax=ax)
+ ax = plt.gca()
+ ax.set(title=f"",
+ xlabel=r"Total grating spec. intensity [a.u.]",
+ ylabel="XGM pulse intensity [mJ]",
+ )
+ ax.spines['top'].set_visible(False)
+ ax.spines['right'].set_visible(False)
+ plt.tight_layout()
+ fig.savefig(filename)
+ plt.close(fig)
+
def plot_rmse_intensity(df: pd.DataFrame, filename: str):
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(1, 1)
@@ -425,6 +470,9 @@ if __name__ == '__main__':
fast_range=fast_range, Ne1s=Ne1s, label=label, refs=refs,
counts_to_mv=counts_to_mv)
+ plot_pes_vs_spec_intensity(pd.read_csv(f'{indir}/quality.csv'), f'pes_vs_spec_intensity.pdf')
+ plot_xgm_vs_spec_intensity(pd.read_csv(f'{indir}/quality.csv'), f'xgm_vs_spec_intensity.pdf')
+
plot_chi2(pd.read_csv(f'{indir}/quality.csv'), f'chi2_prepca.pdf')
plot_chi2_intensity(pd.read_csv(f'{indir}/quality.csv'), f'intensity_vs_chi2_prepca.pdf')
plot_unc_intensity(pd.read_csv(f'{indir}/quality.csv'), f'intensity_vs_unc.pdf')