pyrolite.util.classification
Utilities for rock chemistry and mineral abundance classification.
Todo
Petrological classifiers: QAPF (aphanitic/phaneritic), gabbroic Pyroxene-Olivine-Plagioclase, ultramafic Olivine-Orthopyroxene-Clinopyroxene
- class pyrolite.util.classification.PolygonClassifier(name=None, axes=None, fields=None, scale=1.0, transform=None, mode=None, **kwargs)[source]
A classifier model built form a series of polygons defining specific classes.
- Parameters
name (
str) – A name for the classifier model.axes (
dict) – Mapping from plot axes to variables to be used for labels.fields (
dict) – Dictionary describing indiviudal polygons, with identifiers as keys and dictionaries containing ‘name’ and ‘fields’ items.scale (
float) – Default maximum scale for the axes. Typically 100 (wt%) or 1 (fractional).xlim (
tuple) – Default x-limits for this classifier for plotting.ylim (
tuple) – Default y-limits for this classifier for plotting.
- predict(X, data_scale=None)[source]
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)[source]
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- class pyrolite.util.classification.TAS(which_model=None, **kwargs)[source]
Total-alkali Silica Diagram classifier from Middlemost (1994) 1, a closed-polygon variant after Le Bas et al. (1992) 2.
- Parameters
name (
str) – A name for the classifier model.axes (
list|tuple) – Names of the axes corresponding to the polygon coordinates.fields (
dict) – Dictionary describing indiviudal polygons, with identifiers as keys and dictionaries containing ‘name’ and ‘fields’ items.scale (
float) – Default maximum scale for the axes. Typically 100 (wt%) or 1 (fractional).xlim (
tuple) – Default x-limits for this classifier for plotting.ylim (
tuple) – Default y-limits for this classifier for plotting.which_model (
str) – The name of the model variant to use, if not Middlemost.References
- 1
Middlemost, E. A. K. (1994). Naming materials in the magma/igneous rock system. Earth-Science Reviews, 37(3), 215–224. doi: 10.1016/0012-8252(94)90029-9
- 2
Le Bas, M.J., Le Maitre, R.W., Woolley, A.R. (1992). The construction of the Total Alkali-Silica chemical classification of volcanic rocks. Mineralogy and Petrology 46, 1–22. doi: 10.1007/BF01160698
- 3
Le Maitre, R.W. (2002). Igneous Rocks: A Classification and Glossary of Terms : Recommendations of International Union of Geological Sciences Subcommission on the Systematics of Igneous Rocks. Cambridge University Press, 236pp. doi: 10.1017/CBO9780511535581
- add_to_axes(ax=None, fill=False, axes_scale=100.0, add_labels=False, which_labels='ID', which_ids=[], label_at_centroid=True, **kwargs)[source]
Add the TAS fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which labels to add to the polygons (e.g. for TAS, ‘volcanic’, ‘intrusive’ or the field ‘ID’).which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.label_at_centroid (
bool) – Whether to label the fields at the centroid (True) or at the visual center of the field (False).- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.USDASoilTexture(**kwargs)[source]
United States Department of Agriculture Soil Texture classification model 4 5.
- Parameters
References
- 4
Soil Science Division Staff (2017). Soil Survey Manual. C. Ditzler, K. Scheffe, and H.C. Monger (eds.). USDA Handbook 18. Government Printing Office, Washington, D.C.
- 5
Thien, Steve J. (1979). A Flow Diagram for Teaching Texture-by-Feel Analysis. Journal of Agronomic Education 8:54–55. doi: 10.2134/jae.1979.0054
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.QAP(**kwargs)[source]
IUGS QAP ternary classification 6 7.
- Parameters
References
- 6
Streckeisen, A. (1974). Classification and nomenclature of plutonic rocks: recommendations of the IUGS subcommission on the systematics of Igneous Rocks. Geol Rundsch 63, 773–786. doi: 10.1007/BF01820841
- 7
Le Maitre,R.W. (2002). Igneous Rocks: A Classification and Glossary of Terms : Recommendations of International Union of Geological Sciences Subcommission on the Systematics of Igneous Rocks. Cambridge University Press, 236pp doi: 10.1017/CBO9780511535581
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.FeldsparTernary(**kwargs)[source]
Simplified feldspar diagram classifier, based on a version printed in the second edition of ‘An Introduction to the Rock Forming Minerals’ (Deer, Howie and Zussman).
- Parameters
name (
str) – A name for the classifier model.axes (
list|tuple) – Names of the axes corresponding to the polygon coordinates.fields (
dict) – Dictionary describing indiviudal polygons, with identifiers as keys and dictionaries containing ‘name’ and ‘fields’ items.mode (
str) – Mode of the diagram to use; two are currently available - ‘default’, which fills the entire ternary space, and ‘miscibility-gap’ which gives a simplified approximation of the miscibility gap.References
- 8
Deer, W. A., Howie, R. A., & Zussman, J. (2013). An introduction to the rock-forming minerals (3rd ed.). Mineralogical Society of Great Britain and Ireland.
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.JensenPlot(**kwargs)[source]
Jensen Plot for classification of subalkaline volcanic rocks 9.
- Parameters
References
- 9
Jensen, L. S. (1976). A new cation plot for classifying sub-alkaline volcanic rocks. Ontario Division of Mines. Miscellaneous Paper No. 66.
Notes
Diagram used for the classification classification of subalkalic volcanic rocks. The diagram is constructed for molar cation percentages of Al, Fe+Ti and Mg, on account of these elements’ stability upon metamorphism. This particular version uses updated labels relative to Jensen (1976), in which the fields have been extended to the full range of the ternary plot.
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.SpinelTrivalentTernary(**kwargs)[source]
Spinel Trivalent Ternary classification - designed for data in atoms per formula unit
- Parameters
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.SpinelFeBivariate(**kwargs)[source]
Fe-Spinel classification, designed for data in atoms per formula unit.
- Parameters
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.Pettijohn(**kwargs)[source]
Pettijohn (1973) sandstones classification 10.
- Parameters
References
- 10
Pettijohn, F. J., Potter, P. E. and Siever, R. (1973). Sand and Sandstone. New York, Springer-Verlag. 618p. doi: 10.1007/978-1-4615-9974-6
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type
- class pyrolite.util.classification.Herron(**kwargs)[source]
Herron (1988) sandstones classification 11.
- Parameters
References
- 11
Herron, M.M. (1988). Geochemical classification of terrigenous sands and shales from core or log data. Journal of Sedimentary Research, 58(5), pp.820-829. doi: 10.1306/212F8E77-2B24-11D7-8648000102C1865D
- add_to_axes(ax=None, fill=False, axes_scale=1.0, add_labels=False, which_labels='ID', which_ids=[], **kwargs)
Add the fields from the classifier to an axis.
- Parameters
ax (
matplotlib.axes.Axes) – Axis to add the polygons to.fill (
bool) – Whether to fill the polygons.axes_scale (
float) – Maximum scale for the axes. Typically 100 (for wt%) or 1 (fractional).add_labels (
bool) – Whether to add labels for the polygons.which_labels (
str) – Which data to use for field labels - field ‘name’ or ‘ID’.which_ids (
list) – List of field IDs corresponding to the polygons to add to the axes object. (e.g. for TAS, [‘F’, ‘T1’] to plot the Foidite and Trachyte fields). An empty list corresponds to plotting all the polygons.- Returns
ax
- Return type
- property axis_components
Get the axis components used by the classifier.
- Returns
Ordered names for axes used by the classifier.
- Return type
- predict(X, data_scale=None)
Predict the classification of samples using the polygon-based classifier.
- Parameters
X (
numpy.ndarray|pandas.DataFrame) – Data to classify.data_scale (
float) – Maximum scale for the data. Typically 100 (wt%) or 1 (fractional).- Returns
Series containing classifer predictions. If a dataframe was input, it inherit the index.
- Return type