converted mirrorplots to python script

analysis/Python_scripts/make_mirrorplots.py

@@ -0,0 +1,40 @@
+# %%
+from matchms.importing import load_from_msp
+from matchms.plotting import plot_spectra_mirror
+import pandas as pd
+from matplotlib import pyplot as plt
+from pathlib import Path
+
+# %%
+scores = pd.read_csv("../data/output_matching/matchms/matchms_tol_0.0035_1%I_all_peaks_with_0s_only_matching.tsv", sep='\t')
+
+# %%
+query_spectra = list(load_from_msp("../data/filtered/simulated_matchms_filter_1%I_all_peaks.msp"))
+query_lookup = { x.get("compound_name"): x for x in query_spectra }
+
+# %%
+reference_spectra = list(load_from_msp("../data/experimental/RECETOX_GC-EI_MS_20201028.msp"))
+reference_lookup = { x.get("compound_name"): x for x in reference_spectra }
+
+# %%
+if not Path.exists(Path("mirrorplots")):
+    Path.mkdir("mirrorplots")
+
+for idx, row in scores.iterrows():
+    key_query:str  = row["query"]
+    key_ref:str  = row["reference"]
+
+    query_spectrum = query_lookup.get(key_query)
+    reference_spectrum = reference_lookup.get(key_ref)
+
+    if query_spectrum and reference_spectrum:
+        fig, ax = plt.subplots(figsize=(12, 4))
+        plot_spectra_mirror(query_spectrum, reference_spectrum, ax=ax, color_top="red", color_bottom="blue")
+
+        label = key_query.replace("/","-")
+
+        fig.savefig(f"mirrorplots/{idx}_{label}.png", bbox_inches='tight')
+        plt.close()
+        plt.clf()
+
+