Change Log
Version 0.0.3.18
Rework prep_df for warning efficiency, rename XMg as Mgno (Mg#) in stoichiometry. XFo to Fo.
Version 0.0.3.17
Added warning for low-Ca and high-Ca amphiboles, outside of the Leake classification scheme. These are no longer classified in the calcic amphibole classification scheme, but return low-Ca or high-Ca in the Submineral column. Thanks also to William Minarik for pointing this out!
Version 0.0.3.16
Fix prep_df and predict_class_prob to preserve numeric Sample columns as strings. Thanks to William Minarik for pointing this out!
Version 0.0.3.15
Fixed handling of NaN values in plot_component_composite.
Version 0.0.3.14
Updated interactive_pixel to provide an unnormalized total (Total_raw) in addition to the renormalized total, giving users insight into the original analytical totals for each pixel. Add option for nxn surrounding in the interactive pixel tool, allowing users to optionally sample an average composition over a neighborhood of pixels rather than a single pixel (if the surrounding pixels are of the same phase). This allows users to assess the raw data quality and make informed decisions about the reliability of classifications, especially for pixels near the total threshold. Updated documentation and examples to reflect this new output.
Version 0.0.3.13
Replaced epoxy_threshold in run_map with total_threshold, which masks pixels by summed oxide total rather than SiO2 alone, avoiding removal of valid low-SiO2 phases. run_map now stores the cleaned mineral map in result["mineral_map"] and adds a "Total" key to oxide_maps. Background pixels are now None rather than "Unknown".
Added interactive_pixels, a clickable tool for sampling oxide compositions pixel by pixel, with phase and oxide_key filtering, undo/clear/quit keybindings, and a live DataFrame output. Added phase parameter to interactive_line_profile to mask the map to specific phases before drawing transects. Renamed plot_profile_locations to plot_locations, which now accepts both transect and pixel-pick tables. Renamed coordinate columns start_x/y, end_x/y to x0/y0, x1/y1. Escape key added as alias for q in interactive tools.
Version 0.0.3.12
Remove epoxy_threshold parameter from run_map. This parameter was intended to filter out pixels of epoxy mounting material based on low analytical totals, but it was found to be too aggressive and resulted in excessive removal of valid mineral pixels. The epoxy filtering logic has been revised to use a more conservative threshold and additional criteria, rather than relying solely on a single total cutoff. This change improves the balance between removing mounting material and retaining valid mineral data in the maps.
Version 0.0.3.12
Add line profile extraction, interactive transect tool, and batch extraction to mapping code.
Version 0.0.3.11
Adapted all stoichiometry calculators to return the Predict_Mineral, Prediction_Score, Prediction_Score_Sigma, Second_Predict_Mineral, and Second_Prediction_Score from the predict_class_prob function (if present), in addition to the calculated components . This provides users with the predicted mineral class and associated confidence metrics alongside the stoichiometric calculations.
Removed last pyrolite dependency. Thank you to Nick Swanson-Hysell for catching one remaining line in setup.py!
Version 0.0.3.10
The pyrolite dependency was removed given that it was only used for a single function in the documentation. The plot_latent_space function now includes a random seed for reproducibility of the latent space visualization, ensuring consistent results across runs.
Version 0.0.3.9
Added renormalize argument to prep_df for optional renormalization of oxide compositions. Increased default iterations in predict_class_prob. Added new functions for processing microanalytical data from Cameca, Probe for EPMA, and AZtec formats. Added stoichiometry calculations for glasses. Added glass plotting to documentation.Updated mapping.py to return figure objects from plot_ctf_phases. Patched stoichiometry.py for pyrolite output compatibility.
Updated rtd_environment.yml for pyrolite dependency. Added openpyxl to setup.py dependencies.
Removed deprecated supervised.py module and legacy parametermatrix files. Removed unused data files and updated .gitignore.
Expanded unit test suite with new and extended coverage across test_hybrid.py, test_mapping.py, test_stoichiometry.py, test_core.py, test_confusion_matrix.py, test_synthetic_minerals.py, and test_microanalysis.py.
Committed EDS and EBSD maps from Mount Hood and Tuolumne samples. Added EBSD-EDS mapping example notebook (mineralML_mapping_ebsd.ipynb) and updated documentation, examples, and README with mapping figure. Updated Google Colab implementation.
Version 0.0.3.8
Refactored predict_class_prob to use centralized hybrid checkpoint loading. Removed duplicate manual model reconstruction from prediction. Aligned prediction and reconstruction paths to use the same loaded wrapper. Improved handling when oxide columns or ZrO2 are absent. Avoided in-place modification of input DataFrames when computing Total. Clamped Monte Carlo variance to zero before square root to avoid invalid standard deviations.
Added convert_fe_to_feot utility for converting FeO, Fe2O3, and Fe2O3t columns to a single FeOt column.
prep_df now accepts convert_Fe to automatically convert iron columns to FeOt instead of raising an error. Added drop_empty_rows and min_oxide_count to optionally remove rows with fewer than n non-zero oxide columns. Added verbose flag that prints a summary of rows processed, dropped, and coerced.
norm_data now accepts verbose flag and prints the number of rows normalized.
plot_latent_space now resolves Oxide labels via a submineral_column fallback (e.g., Oxide → Magnetite → Spinel). Labels that are empirical groupings not present in training classes (Carbonate, SiO2_Polymorph, Zircon) are skipped with a descriptive warning.
Deprecated load_minclass_nn; use load_mineral_classes instead. Deprecated predict_class_prob_nnwr; use predict_class_prob instead. Deprecated plot_z2_overlay; use plot_latent_space instead.
Version 0.0.3.6 and 0.0.3.7
Clean up code docstrings and update RTD documentation.
Version 0.0.3.5
Add map renormalization and additional epoxy filtering. When processing EDS oxide maps, pixel compositions can optionally be renormalized to 100 wt.% after loading, improving classification consistency across pixels with variable analytical totals. Additional filtering identifies and removes pixels of epoxy with very low totals, preventing misclassification of mounting material as mineral phases.
Version 0.0.3.4
Fix totals issue to include ZrO2. Previously, ZrO2 was excluded from the oxide total calculation, causing analyses with reported ZrO2 (e.g., zircon) to be classified as carbonate given low totals. ZrO2 is now properly included in the total logic.
Version 0.0.3.3
Add Predict_Probability_Sigma from forward passes. Prediction uncertainty is now estimated via Monte Carlo dropout forward passes through the neural network, returning a standard deviation (sigma) on prediction scores for each analysis. This provides a per-analysis measure of model confidence beyond the single-pass prediction score. Correct mineral_classes_nn_v0030 to include Spinel_Group. General repository cleanup removes deprecated code.
Version 0.0.3.2
Fix totals issue — compute from OXIDES rather than oxides. The oxide total calculation was referencing a local or incorrectly scoped variable rather than the module-level constant OXIDES, which defines the full set of input oxides used by the neural network.
Version 0.0.3.1
Update NaN row handling in mm.prep_df_nn. Update hybrid.py for the correct number of input oxides, ensuring consistency between the hybrid prediction model and the expected feature count.
Version 0.0.3.0
Introduce new amphibole, garnet, and pyroxene training data to the model. These mineral groups have expanded compositions and coverage. SiO2 polymorphs (e.g., quartz, cristobalite, tridymite) and carbonates (e.g., calcite, dolomite) are separated out from the main neural network and are instead classified via empirical compositional rules, since their oxide signatures are sufficiently distinctive. Includes updates to the nearest-weighted-residual (nnwr) prediction pathway and associated unit tests.
Version 0.0.2.6
Fix mm.plot_z2_overlay for overly granular labels. Previously, z2 overlay plots displayed specific submineral labels (e.g., individual pyroxene or feldspar species) rather than the broader group labels (Pyroxene, Feldspar). Labels are now mapped to their parent mineral groups for clearer visualization.
Version 0.0.2.5
Added functionality in pyroxene component plotting (Wo-En-Fs ternary) to plot the full ternary diagram, rather than the limited quadrilateral.
Version 0.0.2.4
Added functionality in Harker plotting, providing new Harker-style variation diagrams for exploring oxide-oxide relationships across mineral data.
Version 0.0.2.3
Added functionality in mapping.py. Includes increased optionality for map generation, support for parsing .ctf files (EBSD crystal orientation data), and updates to z2 plotting to support pre-classification visualization. Update GEOROC dataset with citations.
Version 0.0.2.2
Fix latent space plotting, add legend for all minerals. The latent space visualization now returns the full legend by default, showing all mineral groups.
Version 0.0.2.1
Add latent space overlay functionality, enabling users to project their data onto the autoencoder’s latent space for visual comparison against the training distribution. Also adds reset_index call in mm.prep_df_nn for cleaner DataFrame handling.
Version 0.0.2.0
Built hybrid model functionality with a sequential, transfer learning neural network architecture. This release introduces a two-stage prediction model that refines initial classifications. Updated mapping functions and resolved compatibility issues with NumPy 2 and scikit-image. Updated supervised.py, unsupervised.py, and stoichiometry.py for the new architecture. Bug fixes and unit test updates.
Version 0.0.1.9
Update mineralML calibration for specific amphiboles (tremolite and actinolite). Updates to unique mapping functions (unique_mapping_nn) and unit test scaler adjustments.
Version 0.0.1.8
Fix small bits of mm.plot_component_composite.
Version 0.0.1.7
Provide new mapping functions with mm.plot_component_composite to allow for internal compositional variability to be calculated and plotted for different mineral groups - most commonly examined olivine, clinopyroxene, orthopyroxene, and plagioclase feldspar, for now.
Version 0.0.1.6
Provide submineral in addition to primary prediction in mm.predict_class_prob_nn.
Version 0.0.1.5
Push updates to mapping to account for different input units (wt% vs. ox%), return all outputs by default.
Modify feldspar line bounds to provide 50% tolerance for An and Or, increased from DHZ 0.05 to 0.075.
Version 0.0.1.4
Removed .set_index() for SampleID in mm.prep_nn_df to mitigate confusion.
Version 0.0.1.3
New version of mineralML with sodic pyroxene model added and refined. Follows the Morimoto 1988 and Harlow 1998 pyrxoene classifications. Updated mineral balancing functions to better sample solid solution space. Improved mapping functions.
Version 0.0.1.2
Optimize supervised.py prediction for speed.
Version 0.0.1.1
Remove vectorization to reduce RAM usage.
Version 0.0.1
New version of mineralML calibrated for 28 mineral [groups]: Amphibole, Apatite, Biotite, Calcite, Chlorite, Epidote, Feldspar (KFeldspar and Plagioclase with inbuilt classification), Garnet, Glass, Kalsilite, Leucite, Melilite, Muscovite, Nepheline, Olivine, Pyroxene (Clinopyroxene and Orthopyroxene), Quartz, Rhombohedral_Oxides (Hematite and Ilmenite), Rutile, Serpentine, Spinels (Magnetite and Spinel), Titanite, Tourmaline, Zircon. Adds the 11th input of P2O5 concentration for accurate classification of apatites (needed for comparison against calcite). These mineral groups are classified, and the subgroups or classes are empirically classified after the neural network takes a first pass.
Presents stoichiometry calculators for all the aforementioned mineral [groups], along with classification. All components of interest are returned (e.g. XFo in Olivine, An Ab Or in Feldspar, En Fs Wo in Pyroxene, etc.) and classifications for solid solution minerals are presented, based on DHZ.
Introduces a SolidSolutionGenerator for creating synthetic mineral data, to augment the training dataset where natural data are lacking. The synthetic mineral generator utilizes mineral formulas and stoichiometry as input and performs mixing between endmembers to create these synthetic data. It accounts for elemental site occupancy, ensures charge neutrality, and maintains oxygen basis to simulate natural minerals. This hopefully will facilitate future training of the neural networks, if mineral groups need introduction and insufficient natural data exist.
Version 0.0.0.7
Update prep_nn_df function to coerce strings in data and to return warnings. First release of mineralML calibrated for 17 mineral [groups]: Amphibole, Apatite, Biotite, Clinopyroxene, Garnet, Ilmenite, K-Feldspar, Magnetite, Muscovite, Olivine, Orthopyroxene, Plagioclase, Quartz, Rutile, Spinel, Tourmaline, Zircon. The neural network previously predicted for each of these individual minerals. This has been altered to classify mineral groups together.
Version 0.0.0.6
Update prep_nn_df function to remove mineral filter, create missing columns (whilst returning UserWarning).
Version 0.0.0.5
Fix confusion_matrix_df and prep_nn_df functions.
Version 0.0.0.4
Remove plt.show().
Version 0.0.0.3
Update standard scaler for neural network and autoencoder.
Version 0.0.0.2
Test solution to LaTeX solution — remove plot saving.
Version 0.0.0.1
Removed imblearn dependency given timeout and necessity only during training.
Version 0.0.0.0
First version on PyPi.