clease - npm Package Compare versions

Comparing version 1.0.0 to 1.0.1

doc/source/api/basis_function.rst

+.. _basis_function_api:
+
+Basis Functions
+===============
+
+Each cluster is defined on a set of cluster functions, which is expanded on a set of single-site basis functions.
+The basis function obeys the orthogonality condition
+
+.. math:: \frac{1}{M} \sum _{s_i=-m}^m \Theta _n (s_i) \Theta_{n'}(s_i) = \delta _{nn'}
+
+For more information, please see the `CLEASE paper <https://doi.org/10.1088/1361-648X/ab1bbc>`_.
+CLEASE implements three different basis functions:
+:class:`~clease.basis_function.Polynomial`,
+:class:`~clease.basis_function.Trigonometric` and
+:class:`~clease.basis_function.BinaryLinear`.
+
+
+
+.. autoclass:: clease.basis_function.Polynomial
+    :members:
+
+.. autoclass:: clease.basis_function.Trigonometric
+    :members:
+
+.. autoclass:: clease.basis_function.BinaryLinear
+    :members:
+
+All three basis functions inherit from the same base abstract base interface:
+
+.. autoclass:: clease.basis_function.BasisFunction
+    :members:

clease.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: clease
-Version: 1.0.0
+Version: 1.0.1
 Summary: CLuster Expansion in Atomistic Simulation Environment
@@ -5,0 +5,0 @@ Home-page: https://gitlab.com/computationalmaterials/clease/

clease.egg-info/requires.txt

@@ -16,21 +16,21 @@ ase<3.23,>=3.20
 [all]
-clease-gui
-clang-format>=14.0.3
+pre-commit
 ipython
-pytest
 pyclean>=2.0.0
-pytest-mock
+pytest-benchmark[histogram]>=3.4.1
 pylint
+pip
 black>=22.1.0
-sphinx
-pre-commit
-cython
-tox>=3.24.0
-pip
 twine
-mock
 sphinx_rtd_theme
 build
-pytest-benchmark[histogram]>=3.4.1
+clang-format>=14.0.3
 pytest-cov
+tox>=3.24.0
+cython
+pytest
+pytest-mock
+mock
+sphinx
+clease-gui
 
@@ -37,0 +37,0 @@ [dev]

clease.egg-info/SOURCES.txt

@@ -180,2 +180,3 @@ LICENSE.md
 doc/source/releasenotes.rst
+doc/source/api/basis_function.rst
 doc/source/api/corr_func.rst
@@ -182,0 +183,0 @@ doc/source/api/data_getters.rst

clease/_version.txt

@@ -1,1 +0,1 @@
-1.0.0
+1.0.1

clease/basis_function.py

@@ -53,2 +53,3 @@ """Module for setting up pseudospins and basis functions."""
     def basis_functions(self) -> List[Dict[str, float]]:
+        """Property access to :meth:`get_basis_functions`."""
         return self.get_basis_functions()
@@ -62,3 +63,3 @@
     def get_basis_functions(self):
-        """Get basis function."""
+        """Create basis functions which guarantees the orthonormality condition."""
 
@@ -157,3 +158,5 @@ # pylint: disable=no-self-use
 class BinaryLinear(BasisFunction):
-    """Pseudospin and basis function from Zhang and Sluiter.
+    """Pseudospin and basis function from Zhang and Sluiter. The ``redunant_element``
+    parameter can be used to select which element is not explicitly defined by the ECI values.
+    If it is not set, the element will be chosen as the first element in alphabetical order.
 
@@ -160,0 +163,0 @@ Zhang, X. and Sluiter M.

clease/cluster/cluster_generator.py

@@ -96,3 +96,4 @@ from itertools import product
         assert len(sublattices) == len(ints)
-        return [FourVector(*vals, subl) for vals, subl in zip(ints, sublattices)]
+        # Iterating the array as a list is more efficient than iterating the numpy array.
+        return [FourVector(*vals, subl) for vals, subl in zip(ints.tolist(), sublattices)]
 
@@ -99,0 +100,0 @@ def to_four_vector(self, cartesian: np.ndarray, sublattice: int = None) -> FourVector:

clease/interactive_plot.py

 from typing import Union, Sequence, Callable, List
 from tkinter import TclError
+import numpy as np
 from ase.db import connect
@@ -120,2 +121,3 @@ from ase.gui.gui import GUI
         ax_obj = self.annotated_axes[event_index]
+        ax = ax_obj.ax
         lines = ax_obj.lines
@@ -125,3 +127,24 @@ line = lines[self.active_line_index]
         self.active_annot = self.all_annotations[event_index]
-        self.active_annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
+        xy = x[ind["ind"][0]], y[ind["ind"][0]]
+        self.active_annot.xy = xy
+
+        # Adjust the xytext coordinates away from the figure edges.
+        # Calculate a vector towards the center of the plot
+        xlim = ax.get_xlim()
+        ylim = ax.get_ylim()
+
+        center = np.array([sum(xlim), sum(ylim)]) / 2
+        delta = center - xy
+
+        # Set the values depending on the sign of the difference vector.
+        # vector has constant length, of direction towards the center of the plot.
+        # (i.e. away from the edges)
+        # Values chosen by eye, but are scale-independent.
+        sgn = np.sign(delta)
+        xnew = 20 if sgn[0] >= 0 else -100
+        ynew = 20 if sgn[1] >= 0 else -80
+
+        self.active_annot.set_x(xnew)
+        self.active_annot.set_y(ynew)
+
         anot = ax_obj.annotations[self.active_line_index]
@@ -147,2 +170,3 @@ text = anot[ind["ind"][0]]
                 self.active_annot.set_visible(True)
+
                 self.fig.canvas.draw_idle()
@@ -149,0 +173,0 @@ else:

clease/montecarlo/montecarlo.py

@@ -164,3 +164,3 @@ """Monte Carlo method for ase."""
 
-    def run(self, steps: int = 100) -> None:
+    def run(self, steps: int = 100, call_observers: bool = True) -> None:
         """Run Monte Carlo simulation.
@@ -172,6 +172,10 @@
             Number of steps in the MC simulation
+        call_observers: bool
+            Should the observers be called during this run? Can be turned off for running burn-ins.
+            The energy averagers will still be updated, even if this flag is disabled.
+            Defaults to True.
         """
 
         # Construct the iterator, make the preparations for starting the run
-        mc_iter = self.irun(steps)
+        mc_iter = self.irun(steps, call_observers=call_observers)
 
@@ -195,3 +199,3 @@ start = time.perf_counter()
 
-    def irun(self, steps: int) -> Iterator[MCStep]:
+    def irun(self, steps: int, call_observers: bool = True) -> Iterator[MCStep]:
         """Run Monte Carlo simulation as an iterator.
@@ -213,2 +217,6 @@ Can be used to inspect the MC after each step, for example,
             Number of steps in the MC simulation
+        call_observers: bool
+            Should the observers be called during this run? Can be turned off for running burn-ins.
+            The energy averagers will still be updated, even if this flag is disabled.
+            Defaults to True.
         """
@@ -224,9 +232,9 @@ logger.info("Starting MC run with %d steps.", steps)
         # Now we create the iterator
-        return self._irun(steps)
+        return self._irun(steps, call_observers=call_observers)
 
-    def _irun(self, steps: int) -> Iterator[MCStep]:
+    def _irun(self, steps: int, call_observers: bool = True) -> Iterator[MCStep]:
         """Create the MC iterator"""
         # Offset range by 1, so that we start with current step = 1
         for _ in range(steps):
-            step = self._mc_step()
+            step = self._mc_step(call_observers=call_observers)
 
@@ -368,3 +376,3 @@ en = step.energy
 
-    def _mc_step(self) -> MCStep:
+    def _mc_step(self, call_observers: bool = True) -> MCStep:
         """Make one Monte Carlo step by swithing two atoms."""
@@ -382,4 +390,5 @@ self.current_step += 1
         step = MCStep(self.current_step, self.current_energy, move_accepted, system_changes)
-        # Execute all observers
-        self.execute_observers(step)
+        if call_observers:
+            # Execute all observers
+            self.execute_observers(step)
 
@@ -386,0 +395,0 @@ return step

clease/montecarlo/sgc_montecarlo.py

@@ -140,6 +140,6 @@ from typing import Sequence, Dict
 
-    def run(self, steps: int = 10, chem_pot: Dict[str, float] = None):
+    def run(self, steps: int = 10, call_observers: bool = True, chem_pot: Dict[str, float] = None):
         """
         Run Monte Carlo simulation.
-        See :py:meth:`cemc.mcmc.Montecarlo.runMC`
+        See :py:meth:`~clease.montecarlo.montecarlo.Montecarlo.run`
 
@@ -163,3 +163,3 @@ Parameters:
 
-        super().run(steps=steps)
+        super().run(steps=steps, call_observers=call_observers)
 
@@ -166,0 +166,0 @@ def singlet2composition(self, avg_singlets: Dict[str, float]):

clease/plot_post_process.py

@@ -30,4 +30,4 @@ from typing import List, Tuple
     Y = evaluate.get_energy_predict()
-    xlabel = plot_args.get("xlabel", "E_DFT (eV/atom)")
-    ylabel = plot_args.get("ylabel", "E_CE (eV/atom)")
+    xlabel = plot_args.get("xlabel", r"E$_{DFT}$ (eV/atom)")
+    ylabel = plot_args.get("ylabel", r"E$_{CE}$ (eV/atom)")
     title = plot_args.get("title", f"Fit using {len(evaluate.e_dft)} data points.")
@@ -152,3 +152,7 @@
 
-def plot_eci(evaluate: Evaluate, plot_args: dict = None) -> Figure:
+def plot_eci(
+    evaluate: Evaluate,
+    plot_args: dict = None,
+    ignore_sizes=(),
+) -> Figure:
     """
@@ -167,2 +171,6 @@ Figure object of ECI value according to cluster diameter
         - "sizes": list of int to include n-body cluster in plot
+    :param ignore_sizes: list of ints
+        Sizes listed in this list will not be plotted.
+        E.g. ``ignore_sizes=[0]`` will exclude the 0-body cluster.
+        Default is to not ignore any clusters.
 
@@ -175,3 +183,3 @@ :return: Figure instance of plot
     eci_by_size = evaluate.get_eci_by_size()
-    xlabel = plot_args.get("xlabel", "Cluster diameter ($n^{th}$ nearest neighbor)")
+    xlabel = plot_args.get("xlabel", r"Cluster diameter ($n^{th}$ nearest neighbor)")
     ylabel = plot_args.get("ylabel", "ECI (eV/atom)")
@@ -193,2 +201,4 @@ title = plot_args.get("title", "Plot ECI")
     for size in sizes:
+        if ignore_sizes and size in ignore_sizes:
+            continue
         data = eci_by_size[size]
@@ -195,0 +205,0 @@ # Add 1, as NN starts from 1 and not 0

clease/settings/settings.py

@@ -104,4 +104,2 @@ """Definition of ClusterExpansionSettings Class.
     ) -> None:
-        # pylint: disable=too-many-arguments
-
         self._include_background_atoms = include_background_atoms
@@ -140,3 +138,3 @@ self._cluster_mng = None
 
-        self.max_cluster_dia = np.array(max_cluster_dia)
+        self.max_cluster_dia = max_cluster_dia
 
@@ -171,2 +169,16 @@ self.basis_func_type = basis_func_type
     @property
+    def max_cluster_dia(self) -> np.ndarray:
+        """The maximum cluster diameter, expressed in a NumPy
+        array starting from 2-body clusters at index 0.
+        Diameters are given in units of Ångstrom.
+        """
+        return self._max_cluster_dia
+
+    @max_cluster_dia.setter
+    def max_cluster_dia(self, value) -> None:
+        # Ensure it's NumPy array, and make a copy.
+        self._max_cluster_dia = np.array(value, dtype=float)
+        self.clear_cache()
+
+    @property
     def db_name(self) -> str:
@@ -173,0 +185,0 @@ """Name of the underlaying data base."""

doc/source/api_doc.rst

@@ -7,2 +7,3 @@ API Documentation
     ./api/newstruct
+    ./api/basis_function
     ./api/corr_func
@@ -9,0 +10,0 @@ ./api/evaluate

doc/source/api/fitting.rst

 Fitting Schemes
 ================
+.. _fitting_api:
 
@@ -16,2 +17,5 @@ .. autoclass:: clease.regression.LinearRegression
 
+.. autoclass:: clease.regression.physical_ridge.PhysicalRidge
+    :members:
+
 .. autoclass:: clease.regression.bayesian_compressive_sensing.BayesianCompressiveSensing
@@ -18,0 +22,0 @@ :members:

doc/source/api/mc_constraints.rst

@@ -0,1 +1,3 @@
+.. _mc_constraints:
+
 Monte Carlo Constraints
@@ -2,0 +4,0 @@ ========================

doc/source/api/mc_observers.rst

@@ -0,1 +1,3 @@
+.. _mc_observers:
+
 Monte Carlo Observers
@@ -2,0 +4,0 @@ ======================

doc/source/api/montecarlo.rst

@@ -0,1 +1,3 @@
+.. _mc_api:
+
 ============
@@ -8,5 +10,4 @@ Monte Carlo
 
-The Monte Carlo class
-=====================
-
+Canonical MC
+=============
 The canonical Monte Carlo class has the following API:
@@ -17,5 +18,15 @@
 
+Semi-grand canonical MC
+========================
+The semi-grand canonical (SGC) Monte Carlo class:
 
-Additionally, the montecarlo class inherits from the
-:class:`~clease.montecarlo.base.BaseMC` class,
+.. autoclass:: clease.montecarlo.sgc_montecarlo.SGCMonteCarlo
+    :members:
+
+
+Related Objects
+===============
+
+All MC classes inherit from the
+:class:`~clease.montecarlo.base.BaseMC` interface,
 which adds the following methods:
@@ -32,6 +43,2 @@
 
-
-Related Objects
----------------
-
 Below are some related objects, which may be useful in your Monte Carlo endeavours.
@@ -38,0 +45,0 @@

doc/source/api/newstruct.rst

@@ -0,1 +1,3 @@
+.. _structgen_api:
+
 Structure Generation
@@ -2,0 +4,0 @@ =====================

doc/source/api/plot_post.rst

@@ -0,1 +1,3 @@
+.. _plot_post_process:
+
 Post Process Plotting
@@ -2,0 +4,0 @@ =====================

doc/source/index.rst

@@ -11,15 +11,55 @@ .. CLEASE documentation master file, created by
 Cluster expansion (CE) is a widely used method for studying thermondynamic
-properties of disordered materials. CLEASE offers:
+properties of disordered materials. CLEASE is a cluster expansion code which strives
+to be highly flexible and customizable, which also offering a wide range of useful tools,
+such as:
 
 * Tools to construct a CE model
 
-  * Semi-automatic generation of training set
+  * Semi-automatic :ref:`structure generation <structgen_api>` for constructing training data,
+    such as random, ground-state and probe structures.
 
-  * Database to store calculation results
+  * Database for storing calculation results.
 
-  * Various fitting methods to evaluate the CE model.
+  * Multiple basis functions for the CE model to choose from:
+    :class:`~clease.basis_function.Polynomial`,
+    :class:`~clease.basis_function.Trigonometric` or
+    :class:`~clease.basis_function.BinaryLinear`.
 
+  * Many methods for parameterization :ref:`fitting <fitting_api>` and
+    evaluating the CE model, such as :class:`~clease.regression.regression.Lasso`
+    :class:`~clease.regression.regression.Tikhonov`, :class:`~clease.regression.physical_ridge.PhysicalRidge`
+    and :class:`~clease.regression.ga_fit.GAFit`.
+
+  * Tools for :ref:`easily visualizing <plot_post_process>` the accuracy
+    of your CE model, and interact with the plots e.g. when made in a Jupyter
+    notebook.
+
 * Various flavors of Monte Carlo samplers where one can explore a large
-  configurational space in a relatively large simulastion cell.
+  configurational space in a large simulation cell
 
+  * Canonical and semi-grand canonical :ref:`Monte Carlo schemes <mc_api>`.
+
+  * Flexible customization options for restricting the model during MC runs.
+    CLEASE provides a :ref:`number of constraints <mc_constraints>`,
+    but it is also easy to :ref:`implement custom constraints <implementing your own constraints>`.
+
+  * Use one our :ref:`pre-made observers <mc_observers>` to collect thermondynamic
+    data about your system during an MC run, or
+    :ref:`write your own <implementing your own observer>`.
+
+
+and much more. A tutorial of how to use CLEASE can be found in our :ref:`AuCu example <ce_aucu_tutorial>`.
+
+
+GUI
+---
+.. _clease-gui:
+
+Most of the standard CE routines can be performed using the graphical user
+interface (GUI). The `CLEASE GUI <https://clease-gui.readthedocs.io>`_
+is an app based on the jupyter notebook.
+Please remember to `report any issues <https://gitlab.com/computationalmaterials/clease-gui/-/issues>`_
+to the developers.
+
+
 Installation
@@ -50,15 +90,2 @@ ------------
 
-GUI
----
-
-Most of the standard CE routines can be performed using the graphical user
-interface (GUI). The `CLEASE GUI <https://clease-gui.readthedocs.io>`_
-is an app based on the jupyter notebook.
-
-.. note::
-
-   The GUI is still under early development, so please don't forget to
-   `report any issues <https://gitlab.com/computationalmaterials/clease-gui/-/issues>`_
-   if you use the GUI.
-
 Using CLEASE
@@ -77,5 +104,2 @@ -------------
 
-The use of CLEASE is best learned through tutorials:
-
-
 .. toctree::
@@ -95,17 +119,1 @@ :maxdepth: 1
    acknowledgements
-
-.. Indices and tables
-.. ==================
-
-.. * :ref:`genindex`
-.. * :ref:`modindex`
-.. * :ref:`search`
-
-
-.. The use of CLEASE is best learned through tutorials:
-
-.. .. toctree::
-..    :maxdepth: 2
-
-.. A simple tutorial explaining how to set up a database and perform a set of
-.. calculations for Cu-Au alloy can be found here: :ref:`ce_aucu_tutorial`

doc/source/montecarlo_sampling.rst

@@ -119,2 +119,4 @@ Monte Carlo Sampling
 ------------------------------
+.. _implementing your own observer:
+
 You can implement your own observer and monitor whatever quantity
@@ -188,2 +190,4 @@ you might be interested in. To to so you can create your own class that
 ==================================
+.. _implementing your own constraints:
+
 If you want to have custom constraints on MC moves, CLEASE
@@ -190,0 +194,0 @@ lets you implement your own. The idea is to create a class

doc/source/releasenotes.rst

@@ -7,2 +7,10 @@ .. _releasenotes:
 
+1.0.1
+======
+* Added the ``ignore_sizes`` keyword to :py:func:`~clease.plot_post_process.plot_eci`
+* Changing the maximum cluster diameter will now clear any cached clusters, and
+  requires a new build.
+* Calling observers in canonical MC can now be disabled with the ``call_observers`` keyword
+  for performing burn-in, without executing observers.
+
 1.0.0
@@ -9,0 +17,0 @@ ======

PKG-INFO

 Metadata-Version: 2.1
 Name: clease
-Version: 1.0.0
+Version: 1.0.1
 Summary: CLuster Expansion in Atomistic Simulation Environment
@@ -5,0 +5,0 @@ Home-page: https://gitlab.com/computationalmaterials/clease/

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