matminer.featurizers.tests package¶
Submodules¶
matminer.featurizers.tests.test_bandstructure module¶
matminer.featurizers.tests.test_base module¶
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class
matminer.featurizers.tests.test_base.
FittableFeaturizer
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
This test featurizer tests fitting qualities of BaseFeaturizer, including refittability and different results based on different fits.
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citations
()¶ Citation(s) and reference(s) for this feature.
- Returns:
- (list) each element should be a string citation,
ideally in BibTeX format.
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feature_labels
()¶ Generate attribute names.
- Returns:
([str]) attribute labels.
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featurize
(x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
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fit
(X, y=None, **fit_kwargs)¶ Update the parameters of this featurizer based on available data
- Args:
X - [list of tuples], training data
- Returns:
self
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implementors
()¶ List of implementors of the feature.
- Returns:
- (list) each element should either be a string with author name (e.g.,
“Anubhav Jain”) or a dictionary with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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class
matminer.featurizers.tests.test_base.
MatrixFeaturizer
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
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citations
()¶ Citation(s) and reference(s) for this feature.
- Returns:
- (list) each element should be a string citation,
ideally in BibTeX format.
-
feature_labels
()¶ Generate attribute names.
- Returns:
([str]) attribute labels.
-
featurize
(*x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
-
implementors
()¶ List of implementors of the feature.
- Returns:
- (list) each element should either be a string with author name (e.g.,
“Anubhav Jain”) or a dictionary with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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class
matminer.featurizers.tests.test_base.
MultiArgs2
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
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__init__
()¶ Initialize self. See help(type(self)) for accurate signature.
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citations
()¶ Citation(s) and reference(s) for this feature.
- Returns:
- (list) each element should be a string citation,
ideally in BibTeX format.
-
feature_labels
()¶ Generate attribute names.
- Returns:
([str]) attribute labels.
-
featurize
(*x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
-
implementors
()¶ List of implementors of the feature.
- Returns:
- (list) each element should either be a string with author name (e.g.,
“Anubhav Jain”) or a dictionary with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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class
matminer.featurizers.tests.test_base.
MultiTypeFeaturizer
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
A featurizer that returns multiple dtypes
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citations
()¶ Citation(s) and reference(s) for this feature.
- Returns:
- (list) each element should be a string citation,
ideally in BibTeX format.
-
feature_labels
()¶ Generate attribute names.
- Returns:
([str]) attribute labels.
-
featurize
(*x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
-
implementors
()¶ List of implementors of the feature.
- Returns:
- (list) each element should either be a string with author name (e.g.,
“Anubhav Jain”) or a dictionary with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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class
matminer.featurizers.tests.test_base.
MultipleFeatureFeaturizer
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
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citations
()¶ Citation(s) and reference(s) for this feature.
- Returns:
- (list) each element should be a string citation,
ideally in BibTeX format.
-
feature_labels
()¶ Generate attribute names.
- Returns:
([str]) attribute labels.
-
featurize
(x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
-
implementors
()¶ List of implementors of the feature.
- Returns:
- (list) each element should either be a string with author name (e.g.,
“Anubhav Jain”) or a dictionary with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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class
matminer.featurizers.tests.test_base.
SingleFeaturizer
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
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citations
()¶ Citation(s) and reference(s) for this feature.
- Returns:
- (list) each element should be a string citation,
ideally in BibTeX format.
-
feature_labels
()¶ Generate attribute names.
- Returns:
([str]) attribute labels.
-
featurize
(x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
-
implementors
()¶ List of implementors of the feature.
- Returns:
- (list) each element should either be a string with author name (e.g.,
“Anubhav Jain”) or a dictionary with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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class
matminer.featurizers.tests.test_base.
SingleFeaturizerMultiArgs
¶ Bases:
matminer.featurizers.tests.test_base.SingleFeaturizer
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featurize
(*x)¶ Main featurizer function, which has to be implemented in any derived featurizer subclass.
- Args:
x: input data to featurize (type depends on featurizer).
- Returns:
(list) one or more features.
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class
matminer.featurizers.tests.test_base.
SingleFeaturizerMultiArgsWithPrecheck
¶ Bases:
matminer.featurizers.tests.test_base.SingleFeaturizerMultiArgs
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precheck
(*x)¶ Precheck (provide an estimate of whether a featurizer will work or not) for a single entry (e.g., a single composition). If the entry fails the precheck, it will most likely fail featurization; if it passes, it is likely (but not guaranteed) to featurize correctly.
- Prechecks should be:
accurate (but can be good estimates rather than ground truth)
fast to evaluate
- unlikely to be obsolete via changes in the featurizer in the near
future
This method should be overridden by any featurizer requiring its use, as by default all entries will pass prechecking. Also, precheck is a good opportunity to throw warnings about long runtimes (e.g., doing nearest neighbors computations on a structure with many thousand sites).
See the documentation for precheck_dataframe for more information.
- Args:
- *x (Composition, Structure, etc.): Input to-be-featurized. Can be
a single input or multiple inputs.
- Returns:
(bool): True, if passes the precheck. False, if fails.
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class
matminer.featurizers.tests.test_base.
SingleFeaturizerWithPrecheck
¶ Bases:
matminer.featurizers.tests.test_base.SingleFeaturizer
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precheck
(x)¶ Precheck (provide an estimate of whether a featurizer will work or not) for a single entry (e.g., a single composition). If the entry fails the precheck, it will most likely fail featurization; if it passes, it is likely (but not guaranteed) to featurize correctly.
- Prechecks should be:
accurate (but can be good estimates rather than ground truth)
fast to evaluate
- unlikely to be obsolete via changes in the featurizer in the near
future
This method should be overridden by any featurizer requiring its use, as by default all entries will pass prechecking. Also, precheck is a good opportunity to throw warnings about long runtimes (e.g., doing nearest neighbors computations on a structure with many thousand sites).
See the documentation for precheck_dataframe for more information.
- Args:
- *x (Composition, Structure, etc.): Input to-be-featurized. Can be
a single input or multiple inputs.
- Returns:
(bool): True, if passes the precheck. False, if fails.
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class
matminer.featurizers.tests.test_base.
TestBaseClass
(methodName='runTest')¶ Bases:
pymatgen.util.testing.PymatgenTest
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static
make_test_data
()¶
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setUp
()¶ Hook method for setting up the test fixture before exercising it.
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test_caching
()¶ Test whether MultiFeaturizer properly caches
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test_dataframe
()¶
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test_featurize_many
()¶
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test_fittable
()¶
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test_ignore_errors
()¶
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test_indices
()¶
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test_inplace
()¶
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test_matrix
()¶ Test the ability to add features that are matrices to a dataframe
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test_multifeature_no_zero_index
()¶ Test whether multifeaturizer can handle series that lack a entry with index==0
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test_multifeatures_multiargs
()¶
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test_multiindex_in_multifeaturizer
()¶
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test_multiindex_inplace
()¶
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test_multiindex_return
()¶
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test_multiple
()¶
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test_multiprocessing_df
()¶
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test_multitype_multifeat
()¶ Test Multifeaturizer when a featurizer returns a non-numeric type
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test_precheck
()¶
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test_stacked_featurizer
()¶
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static
matminer.featurizers.tests.test_composition module¶
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class
matminer.featurizers.tests.test_composition.
CompositionFeaturesTest
(methodName='runTest')¶ Bases:
pymatgen.util.testing.PymatgenTest
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setUp
()¶ Hook method for setting up the test fixture before exercising it.
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test_ape
()¶
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test_atomic_orbitals
()¶
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test_band_center
()¶
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test_cation_properties
()¶
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test_cohesive_energy
()¶
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test_cohesive_energy_mp
()¶
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test_elec_affin
()¶
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test_elem
()¶
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test_elem_deml
()¶
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test_elem_matminer
()¶
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test_elem_matscholar_el
()¶
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test_elem_megnet_el
()¶
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test_en_diff
()¶
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test_fere_corr
()¶
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test_fraction
()¶
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test_ionic
()¶
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test_is_ionic
()¶ Test checking whether a compound is ionic
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test_meredig
()¶
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test_miedema_all
()¶
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test_miedema_ss
()¶
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test_oxidation_states
()¶
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test_stoich
()¶
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test_tm_fraction
()¶
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test_valence
()¶
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test_yang
()¶
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matminer.featurizers.tests.test_conversions module¶
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class
matminer.featurizers.tests.test_conversions.
TestConversions
(methodName='runTest')¶ Bases:
unittest.case.TestCase
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test_composition_to_oxidcomposition
()¶
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test_composition_to_structurefromMP
()¶
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test_conversion_multiindex
()¶
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test_conversion_multiindex_dynamic
()¶
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test_conversion_overwrite
()¶
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test_dict_to_object
()¶
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test_json_to_object
()¶
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test_str_to_composition
()¶
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test_structure_to_composition
()¶
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test_structure_to_oxidstructure
()¶
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test_to_istructure
()¶
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matminer.featurizers.tests.test_dos module¶
matminer.featurizers.tests.test_function module¶
matminer.featurizers.tests.test_site module¶
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class
matminer.featurizers.tests.test_site.
FingerprintTests
(methodName='runTest')¶ Bases:
pymatgen.util.testing.PymatgenTest
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setUp
()¶ Hook method for setting up the test fixture before exercising it.
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tearDown
()¶ Hook method for deconstructing the test fixture after testing it.
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test_AverageBondAngle
()¶
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test_AverageBondLength
()¶
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test_SOAP
()¶
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test_afs
()¶
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test_bop
()¶
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test_chemenv_site_fingerprint
()¶
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test_chemicalSRO
()¶
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test_cns
()¶
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test_crystal_nn_fingerprint
()¶
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test_crystal_site_fingerprint
()¶
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test_dataframe
()¶
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test_ewald_site
()¶
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test_gaussiansymmfunc
()¶
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test_grdf
()¶
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test_interstice_distribution_of_crystal
()¶
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test_interstice_distribution_of_glass
()¶
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test_local_prop_diff
()¶
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test_off_center_cscl
()¶
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test_op_site_fingerprint
()¶
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test_simple_cubic
()¶ Test with an easy structure
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test_site_elem_prop
()¶
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test_voronoifingerprint
()¶
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matminer.featurizers.tests.test_structure module¶
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class
matminer.featurizers.tests.test_structure.
StructureFeaturesTest
(methodName='runTest')¶ Bases:
pymatgen.util.testing.PymatgenTest
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setUp
()¶ Hook method for setting up the test fixture before exercising it.
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test_GlobalInstabilityIndex
()¶
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test_bob
()¶
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test_bondfractions
()¶
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test_cgcnn_featurizer
()¶
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test_composition_features
()¶
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test_coulomb_matrix
()¶
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test_density_features
()¶
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test_dimensionality
()¶
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test_ewald
()¶
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test_global_symmetry
()¶
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test_jarvisCFID
()¶
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test_min_relative_distances
()¶
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test_orbital_field_matrix
()¶
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test_ordering_param
()¶
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test_packing_efficiency
()¶
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test_prdf
()¶
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test_rdf_and_peaks
()¶
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test_redf
()¶
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test_sine_coulomb_matrix
()¶
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test_sitestatsfingerprint
()¶
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test_structural_complexity
()¶
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test_ward_prb_2017_efftcn
()¶ Test the effective coordination number attributes of Ward 2017
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test_ward_prb_2017_lpd
()¶ Test the local property difference attributes from Ward 2017
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test_ward_prb_2017_strhet
()¶
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test_xrd_powderPattern
()¶
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