From 1c6e3bcad1b95b8e3fe24e2a1aa7a50050e10d2b Mon Sep 17 00:00:00 2001 From: Morgan Williams Date: Tue, 29 Oct 2024 16:31:05 +1100 Subject: [PATCH] Remove QAP diagram from CIPW example (wt% vs vol% conflict) --- docs/source/gallery/examples/geochem/CIPW.py | 21 -------------------- 1 file changed, 21 deletions(-) diff --git a/docs/source/gallery/examples/geochem/CIPW.py b/docs/source/gallery/examples/geochem/CIPW.py index 092ea8a0..66c0c996 100644 --- a/docs/source/gallery/examples/geochem/CIPW.py +++ b/docs/source/gallery/examples/geochem/CIPW.py @@ -205,27 +205,6 @@ def compare_NORMs(SINCLAS_outputs, NORM_outputs, name=""): fig, ax = compare_NORMs(df.loc[~volcanic_filter, :], NORM.loc[~volcanic_filter]) plt.show() ######################################################################################## -# These normative mineralogical components could be input into mineralogical -# classifiers, as mentioned above. For example, the IUGS QAP classifier: -# -from pyrolite.util.classification import QAP - -clf = QAP() # build a QAP classifier - -qap_data = NORM.loc[:, ["quartz", "orthoclase"]] # -qap_data["plagioclase"] = NORM.loc[:, ["albite", "anorthite"]].sum(axis=1) -# predict which lithological class each mineralogical composiiton belongs in -# we add a small value to zeros here to ensure points fit in polygons -predicted_classes = clf.predict(qap_data.replace(0, 10e-6).values) -predicted_classes.head() -######################################################################################## -# We can use these predicted classes as a color index also, within the QAP diagram -# or elsewhere: -# -ax = clf.add_to_axes() -qap_data.pyroplot.scatter(ax=ax, c=predicted_classes, axlabels=False, cmap="tab20c") -plt.show() -######################################################################################## # We could also compare how these mineralogical distinctions map into chemical ones # like the TAS diagram: #