From d4aded7a3247754fb28b7274e09ead38c57eea6c Mon Sep 17 00:00:00 2001 From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de> Date: Wed, 22 Nov 2023 10:11:06 +0100 Subject: [PATCH] Updated plotting. --- notebook/Resolution comparison.ipynb | 10 +++--- pes_to_spec/test/prepare_plots.py | 54 +++++++++++++++++++++------- 2 files changed, 46 insertions(+), 18 deletions(-) diff --git a/notebook/Resolution comparison.ipynb b/notebook/Resolution comparison.ipynb index 146e8fb..36dc7b6 100644 --- a/notebook/Resolution comparison.ipynb +++ b/notebook/Resolution comparison.ipynb @@ -2958,7 +2958,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 335, "id": "34ec2fed-e0c3-4964-85c7-f5496c9957e9", "metadata": {}, "outputs": [], @@ -3042,7 +3042,7 @@ " ax[i].plot((1-d, 1+d), (1-d, 1+d), transform=ax[i].transAxes, **kwargs)\n", " ax[i+1].plot((-d, +d), (1-d, 1+d), transform=ax[i+1].transAxes, **kwargs)\n", " ax[i+1].plot((-d, +d), (-d, +d), transform=ax[i+1].transAxes, **kwargs)\n", - " plt.subplots_adjust(wspace=0.03)\n", + " plt.subplots_adjust(wspace=0.03, left=0.05, right=0.95)\n", "\n", " for i, k in enumerate(e_ranges):\n", " ax[i].set_xlim(k[0], k[1])\n", @@ -3052,7 +3052,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 336, "id": "c27dda6d-f409-46e3-bfa2-2b4417267f1f", "metadata": {}, "outputs": [], @@ -3102,13 +3102,13 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 337, "id": "a5b7080e-1dc6-4cae-a015-8666224172bb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "<Figure size 1728x576 with 3 Axes>" ] diff --git a/pes_to_spec/test/prepare_plots.py b/pes_to_spec/test/prepare_plots.py index 73c8052..63a88f9 100755 --- a/pes_to_spec/test/prepare_plots.py +++ b/pes_to_spec/test/prepare_plots.py @@ -25,6 +25,27 @@ plt.rc('ytick', labelsize=BIGGER_SIZE) # fontsize of the tick labels plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title +def plot_spec(df: pd.DataFrame, smooth: bool, filename: str): + fig = plt.figure(figsize=(12, 8)) + gs = GridSpec(1, 1) + 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.set_title(f"Beam intensity: {df.beam_intensity.iloc[0]:.1f} mJ", loc="left") + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.set( + xlabel="Photon energy [eV]", + ylabel="Intensity [a.u.]", + ylim=(0, 1.2*Y)) + plt.tight_layout() + fig.savefig(filename) + plt.close(fig) + def plot_final(df: pd.DataFrame, smooth: bool, filename: str): fig = plt.figure(figsize=(12, 8)) gs = GridSpec(1, 1) @@ -36,8 +57,8 @@ def plot_final(df: pd.DataFrame, smooth: bool, filename: str): 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)") + ax.fill_between(df.energy, df.prediction - 1*df.unc, df.prediction + 1*df.unc, facecolor='gold', alpha=0.5, label="68% unc.") + #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)") 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) @@ -128,30 +149,36 @@ def plot_residue_corr(df: pd.DataFrame, filename: str): plt.close(fig) def plot_chi2_intensity(df: pd.DataFrame, filename: str): - fig = plt.figure(figsize=(12, 8)) - gs = GridSpec(1, 1) - ax = fig.add_subplot(gs[0, 0]) + fig = plt.figure(figsize=(12, 16)) + gs = GridSpec(2, 1, height_ratios=[3, 1]) + ax1 = fig.add_subplot(gs[0, 0]) #sns.kdeplot(x=df.chi2_prepca/df.ndof.iloc[0], y=df.xgm_flux_t*1e-3, # fill=True, # ax=ax) sns.scatterplot(x=df.chi2_prepca/df.ndof.iloc[0], y=df.xgm_flux_t*1e-3, - s=30, + s=40, marker='+', #alpha=0.4, c="tab:red", #size=df.root_mean_squared_pca_unc, #sizes=(20, 200), - ax=ax) - ax = plt.gca() + ax=ax1) + ax1.set(title=f"", + xlabel=r"", + ylabel="Beam intensity [mJ]", + xlim=(0, 5), + ylim=(df.xgm_flux_t.mean()*1e-3 - 4*df.xgm_flux_t.std()*1e-3, df.xgm_flux_t.mean()*1e-3 + 4*df.xgm_flux_t.std()*1e-3), + ) + ax = fig.add_subplot(gs[1, 0], sharex=ax1) + sns.histplot(x=df.chi2_prepca/df.ndof.iloc[0], kde=False, linewidth=3, ax=ax) ax.set(title=f"", xlabel=r"$\chi^2/$ndof", - ylabel="Beam intensity [mJ]", + ylabel="Counts [a.u.]", xlim=(0, 5), - ylim=(0, df.xgm_flux_t.mean()*1e-3 + 3*df.xgm_flux_t.std()*1e-3) ) - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) - plt.tight_layout() + #plt.tight_layout() + ax1.xaxis.set_tick_params(labelbottom=False) + plt.subplots_adjust(hspace=0.0) fig.savefig(filename) plt.close(fig) @@ -524,6 +551,7 @@ if __name__ == '__main__': for fname in ('test_q100_1724098413', 'test_q100_1724098596', 'test_q50_1724099445'): plot_final(pd.read_csv(f'{indir}/{fname}.csv'), smooth=True, filename=f'{fname}_smooth.pdf') plot_final(pd.read_csv(f'{indir}/{fname}.csv'), smooth=False, filename=f'{fname}.pdf') + plot_spec(pd.read_csv(f'{indir}/{fname}.csv'), smooth=False, filename=f'{fname}_spec.pdf') plot_pes(pd.read_csv(f'{indir}/{fname}_pes.csv'), channel, f'{fname}_pes.pdf', fast_range=fast_range, Ne1s=Ne1s, label=label, refs=refs, counts_to_mv=counts_to_mv) -- GitLab