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The inputs are a +set of structures in :download:`example.xyz` using the DFT parameters defined in +:download:`reftraj_template.cp2k` importing basis set and pseudopotentials from the +local CP2K installation. The reference DFT parameters are taken from `Cheng et al. Ab +initio thermodynamics of liquid and solid water 2019 +`_. Due to the small size of the test +structure and convergence issues, we have decreased the size of the ``CUTOFF_RADIUS`` +from :math:`6.0\,\mathrm{Å}` to :math:`3.0\,\mathrm{Å}`. For actual production +calculations adapt the template! + +To run this example, we use a bare executable called with ``cp2k``. If you want to use +another version you can either adjust the the names within this example or link your +binary with a different name to ``cp2k``. +""" + +# %% +# We start the example by importing the required packages. + + +import os +import re +import shutil +import subprocess +from os.path import basename, splitext +from typing import List, Union + +import ase.io +import ase.visualize.plot +import matplotlib.pyplot as plt +import numpy as np +from ase.calculators.cp2k import CP2K + + +# %% +# Define necessary functions +# -------------------------- +# Next we below define necessary helper functions to run the example. + + +def write_reftraj(fname: str, frames: Union[ase.Atoms, List[ase.Atoms]]) -> None: + """Writes a list of ase atoms objects to a reference trajectory. + + A reference trajectory is the CP2K compatible format for the compuation of batches. + All frames must have the stoichiometry/composition. + """ + + if isinstance(frames, ase.Atoms): + frames = [frames] + + out = "" + for i, atoms in enumerate(frames): + if ( + len(atoms) != len(frames[0]) + or atoms.get_chemical_formula() != frames[0].get_chemical_formula() + ): + raise ValueError( + f"Atom symbols in frame {i},{atoms.get_chemical_formula()} are " + f"different compared to inital frame " + f"{frames[0].get_chemical_formula()}. " + "CP2K does not support changing atom types within a reftraj run!" + ) + + out += f"{len(atoms):>8}\n i = {i+1:>8}, time = {0:>12.3f}\n" + for atom in atoms: + pos = atom.position + out += f"{atom.symbol}{pos[0]:24.15f}{pos[1]:24.15f}{pos[2]:24.15f}\n" + out += "\n" + with open(fname, "w") as f: + f.write(out) + + +# %% + + +def write_cellfile(fname: str, frames: Union[ase.Atoms, List[ase.Atoms]]) -> None: + """Writes a cellfile for a list of ``ase.Atoms``. + + A Cellfile accompanies a reftraj containing the cell parameters. + """ + if isinstance(frames, ase.Atoms): + frames = [frames] + + out = ( + "# " + "Step " + "Time [fs] " + "Ax [Angstrom] " + "Ay [Angstrom] " + "Az [Angstrom] " + "Bx [Angstrom] " + "By [Angstrom] " + "Bz [Angstrom] " + "Cx [Angstrom] " + "Cy [Angstrom] " + "Cz [Angstrom] " + "Volume [Angstrom^3]\n" + ) + + for i, atoms in enumerate(frames): + out += f"{i+1:>8}{0:>12.3f}" + out += "".join([f"{c:>20.10f}" for c in atoms.cell.flatten()]) + out += f"{atoms.cell.volume:>25.10f}" + out += "\n" + + with open(fname, "w") as f: + f.write(out) + + +# %% + + +def write_cp2k_in( + fname: str, project_name: str, last_snapshot: int, cell: List[float] +) -> None: + """Writes a cp2k input file from a template. + + Importantly, it writes the location of the basis set definitions, + determined from the path of the system CP2K install to the input file. + """ + + cp2k_in = open("reftraj_template.cp2k", "r").read() + + cp2k_in = cp2k_in.replace("//PROJECT//", project_name) + cp2k_in = cp2k_in.replace("//LAST_SNAPSHOT//", str(last_snapshot)) + cp2k_in = cp2k_in.replace("//CELL//", " ".join([f"{c:.6f}" for c in cell])) + + with open(fname, "w") as f: + f.write(cp2k_in) + + +# %% + + +def mkdir_force(*args, **kwargs) -> None: + """Warpper to ``os.mkdir``. + + The function does not raise an error if the directory already exists. + """ + try: + os.mkdir(*args, **kwargs) + except OSError: + pass + + +# %% +# Prepare calculation inputs +# -------------------------- +# During this example we will create a directory named ``project_directory`` containing +# the subdirectories for each stoichiometry. This is necessary, because CP2K can only +# run calculations using a fixed stoichiometry at a time, using its ``reftraj`` +# functionality. +# +# Below we define the general information for the CP2K run. This includes the reference +# files for the structures, the ``project_name`` used to build the name of the +# trajectory during the CP2K run, the ``project_directory`` where we store all +# simulation output as well as the path ``write_to_file`` which is the name of the file +# containing the computed energies and forces of the sinulation. + +frames_full = ase.io.read("example.xyz", ":") +project_name = "test_calcs" # name of the global PROJECT +project_directory = "production" +write_to_file = "out.xyz" + +# %% +# Below we show the initial configuration of two water molecules in a cubic box with a +# side length of :math:`\approx 4\,\mathrm{Å}`. + +ase.visualize.plot.plot_atoms(frames_full[0]) + +plt.xlabel("Å") +plt.ylabel("Å") + +plt.show() + +# %% +# We now extreact the stoichiometry from the input dataset using ASE's +# :py:meth:`ase.symbols.Symbols.get_chemical_formula` method. + +frames_dict = {} + +for atoms in frames_full: + chemical_formula = atoms.get_chemical_formula() + try: + frames_dict[chemical_formula] + except KeyError: + frames_dict[chemical_formula] = [] + + frames_dict[chemical_formula].append(atoms) + +# %% +# Based on the stoichiometries we create one calculation subdirectories for the +# calculations. (reftraj, input and cellfile). For our example this is only is one +# directory named ``H4O2`` because our dataset consists only of a single structure with +# two water molecules. + +mkdir_force(project_directory) + +for stoichiometry, frames in frames_dict.items(): + current_directory = f"{project_directory}/{stoichiometry}" + mkdir_force(current_directory) + + write_cp2k_in( + f"{current_directory}/in.cp2k", + project_name=project_name, + last_snapshot=len(frames), + cell=frames[0].cell.diagonal(), + ) + + ase.io.write(f"{current_directory}/init.xyz", frames[0]) + write_reftraj(f"{current_directory}/reftraj.xyz", frames) + write_cellfile(f"{current_directory}/reftraj.cell", frames) + +# %% +# Run simulations +# --------------- +# Now we have all ingredients to run the simulations. Below we call the bash script +# :download:`run_calcs.sh`. +# +# .. literalinclude:: run_calcs.sh +# :language: bash +# +# This script will loop through all stoichiometry subdirectories and call the CP2K +# engine. + +# run the bash script directly from this script +subprocess.run("bash run_calcs.sh", shell=True) + +# %% +# .. note:: +# +# For a usage on an HPC environment you can parallize the loop over the subfolders +# and submit and single job per stoichiometry. +# +# Load results +# ------------ +# After the simulation we load the results and perform a unit version from the default +# CP2K output units (Bohr and Hartree) to Å and eV. + +cflength = 0.529177210903 # Bohr -> Å +cfenergy = 27.211386245988 # Hartree -> eV +cfforce = cfenergy / cflength # Hartree/Bohr -> eV/Å + +# %% +# Finally, we store the results as :class:`ase.Atoms` in the ``new_frames`` list and +# write them to the ``project_directory`` using the ``new_fname``. Here it will be +# written to ``production/out_dft.xyz``. + +new_frames = [] + +for stoichiometry, frames in frames_dict.items(): + current_directory = f"{project_directory}/{stoichiometry}" + + frames_dft = ase.io.read(f"{current_directory}/{project_name}-pos-1.xyz", ":") + forces_dft = ase.io.read(f"{current_directory}/{project_name}-frc-1.xyz", ":") + cell_dft = np.atleast_2d(np.loadtxt(f"{current_directory}/{project_name}-1.cell"))[ + :, 2:-1 + ] + + for i_atoms, atoms in enumerate(frames_dft): + frames_ref = frames[i_atoms] + + # Check consistent positions + if not np.allclose(atoms.positions, frames_ref.positions): + raise ValueError(f"Positions in frame {i_atoms} are not the same.") + + # Check consistent cell + if not np.allclose(frames_ref.cell.flatten(), cell_dft[i_atoms]): + raise ValueError(f"Cell dimensions in frame {i_atoms} are not the same.") + + atoms.info["E"] *= cfenergy + atoms.pbc = True + atoms.cell = frames_ref.cell + atoms.set_array("forces", cfforce * forces_dft[i_atoms].positions) + + new_frames += frames_dft + +new_fname = f"{splitext(basename(write_to_file))[0]}_dft.xyz" +ase.io.write(f"{project_directory}/{new_fname}", new_frames) + +# %% +# Perform calculations using ASE calculator +# ----------------------------------------- +# Above we performed the calculations using an external bash script. ASE also provides a +# calculator class that we can use the perform the caclulations with our input file +# without a detour of writing files to disk. +# +# To use the ASE calculator together with a custom input script this requires some +# adjustments. First the name of the executable that has the exact name ``cp2k_shell``. +# We create a symlink to follow this requirement. + +try: + os.symlink(shutil.which("cp2k"), "cp2k_shell.ssmp") +except OSError: + pass + +# %% +# Next, we load the input file abd remove ``GLOBAL`` section because from it + +inp = open("./production/H4O2/in.cp2k", "r").read() +inp = re.sub( + f"{re.escape('&GLOBAL')}.*?{re.escape('&END GLOBAL')}", "", inp, flags=re.DOTALL +) + +# %% +# Afterwards we define the :py:class:`ase.calculators.cp2k.CP2K`` calculator. Note that +# we disable all parameters because we want to use all options from our input file + +calc = CP2K( + inp=inp, + max_scf=None, + cutoff=None, + xc=None, + force_eval_method=None, + basis_set=None, + pseudo_potential=None, + basis_set_file=None, + potential_file=None, + stress_tensor=False, + poisson_solver=None, + print_level=None, + command="./cp2k_shell.ssmp --shell", +) + +# %% +# We now load a new struture, add the calculator and perform the computation. + +atoms = ase.io.read("example.xyz") +atoms.set_calculator(calc) +# atoms.get_potential_energy() diff --git a/latest/_downloads/5ee7c668c022f1e7e8282ea24c67c0fb/lode_tutorial.py b/latest/_downloads/5ee7c668c022f1e7e8282ea24c67c0fb/lode_tutorial.py new file mode 100644 index 00000000..1c654d6e --- /dev/null +++ b/latest/_downloads/5ee7c668c022f1e7e8282ea24c67c0fb/lode_tutorial.py @@ -0,0 +1,391 @@ +""" +LODE Tutorial +============= + +This tutorial explains how Long range equivariant descriptors can be constructed using +rascaline and the resulting descriptors be used to construct a linear model with +equisolve + +First, import all the necessary packages +""" + +# %% + +import ase.io +import matplotlib.pyplot as plt +import metatensor +import numpy as np +from equisolve.numpy.models.linear_model import Ridge +from equisolve.utils.convert import ase_to_tensormap +from rascaline import AtomicComposition, LodeSphericalExpansion, SphericalExpansion +from rascaline.utils import PowerSpectrum + + +# %% +# +# Step 0: Prepare Data Set +# ------------------------ +# +# Get structures +# ~~~~~~~~~~~~~~ +# +# We take a small subset of the dimer dataset from `A. Grisafi et al., +# 2021 `_ +# for which we additionally calculated the forces. Each structure in the +# dataset contains two small organic molecules which are extended along a +# certain direction in the subsequent structures. +# +# For speeding up the calculations we already selected the first 130 +# structures of the charge-charge structures. + +frames = ase.io.read("dataset/charge-charge.xyz", ":") + + +# %% +# +# Convert target properties to metatensor format +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# If we want to train models using the +# `equisolve `_ package, we need to +# convert the target properties (in this case, the energies and forces) +# into the appropriate format #justequistorethings + +y = ase_to_tensormap(frames, energy="energy", forces="forces") + + +# %% +# +# Step 1: Compute short-range and LODE features +# --------------------------------------------- +# +# Define hypers and get the expansion coefficients :math:`\langle anlm | \rho_i \rangle` +# and :math:`\langle anlm | V_i \rangle` +# +# The short-range and long-range descriptors have very similar hyperparameters. We +# highlight the differences below. +# +# We first define the hyperparameters for the short-range (SR) part. These will be used +# to create SOAP features. + +SR_HYPERS = { + "cutoff": 3.0, + "max_radial": 6, + "max_angular": 2, + "atomic_gaussian_width": 0.3, + "center_atom_weight": 1.0, + "radial_basis": {"Gto": {}}, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, +} + + +# %% +# +# And next the hyperparaters for the LODE / long-range (LR) part + + +LR_HYPERS = { + # Cutoff on which to project potential density + "cutoff": 3.0, + # keep max_radial slightly smaller than for SR part + "max_radial": 3, + # max_angular should be <= 4, more precisely, max_angular + potential_exponent < 10 + "max_angular": 2, + # keep at >=1, WARNING: CUBIC SCALING, do not use values <0.5 + "atomic_gaussian_width": 3.0, + "center_atom_weight": 1.0, + "radial_basis": {"Gto": {}}, + # the exponent p that determines the 1/r^p potential + "potential_exponent": 1, +} + + +# %% +# We then use the above defined hyperparaters to define the per atom short range (sr) +# and long range (sr) descriptors. + +calculator_sr = SphericalExpansion(**SR_HYPERS) +calculator_lr = LodeSphericalExpansion(**LR_HYPERS) + + +# %% +# +# Note that LODE requires periodic systems. Therefore, if the data set does not come +# with periodic boundary conditions by default you can not use the data set and you will +# face an error if you try to compute the features. +# +# As you notices the calculation of the long range features takes significant more time +# compared to the sr features. +# +# Taking a look at the output we find that the resulting +# :py:class:`metatensor.TensorMap` are quite similar in their structure. The short range +# :py:class:`metatensor.TensorMap` contains more blocks due to the higher +# ``max_angular`` paramater we choosed above. +# +# Generate the rotational invariants (power spectra) +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Rotationally invariant features can be obtained by taking two of the calculators that +# were defines above. +# +# For the short-range part, we use the SOAP vector which is obtained by computing the +# invariant combinations of the form :math:`\rho \otimes \rho`. + +ps_calculator_sr = PowerSpectrum(calculator_sr, calculator_sr) +ps_sr = ps_calculator_sr.compute(frames, gradients=["positions"]) + + +# %% +# +# We calculate gradients with respect to pistions by providing the +# ``gradients=["positions"]`` option to the +# :py:meth:`rascaline.calculators.CalculatorBase.compute()` method. +# +# For the long-range part, we combine the long-range descriptor :math:`V` with one a +# short-range density :math:`\rho` to get :math:`\rho \otimes V` features. + +ps_calculator_lr = PowerSpectrum(calculator_sr, calculator_lr) +ps_lr = ps_calculator_lr.compute(systems=frames, gradients=["positions"]) + + +# %% +# +# Step 2: Building a Simple Linear SR + LR Model with energy baselining +# --------------------------------------------------------------------- +# +# Preprocessing (model dependent) +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# For our current model, we do not wish to treat the individual center and +# neighbor species separately. Thus, we move the ``"species_center"`` key +# into the ``sample`` direction, over which we will later sum over. + +ps_sr = ps_sr.keys_to_samples("species_center") +ps_lr = ps_lr.keys_to_samples("species_center") + + +# %% +# +# For linear models only: Sum features up over atoms (``samples``) in the same +# structure. + +sample_names_to_sum = ["center", "species_center"] + +ps_sr = metatensor.sum_over_samples(ps_sr, sample_names=sample_names_to_sum) +ps_lr = metatensor.sum_over_samples(ps_lr, sample_names=sample_names_to_sum) + + +# %% +# +# Initialize tensormaps for energy baselining +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# We add a simple extra descriptor :py:class:`rascaline.AtomicComposition` that stores +# how many atoms of each chemical species are contained in the structures. This is used +# for energy baselining. + +calculator_co = AtomicComposition(per_structure=False) +descriptor_co = calculator_co.compute(frames, gradients=["positions"]) + +co = descriptor_co.keys_to_properties(["species_center"]) +co = metatensor.sum_over_samples(co, sample_names=["center"]) + +# %% +# +# The :py:class:`rascaline.AtomicComposition` calculator also allows to directly perform +# the the sum over center atoms by using the following lines. +# +# .. code:: python +# +# descriptor_co = AtomicComposition(per_structure=True).compute(**compute_args) +# co = descriptor_co.keys_to_properties(["species_center"]) +# +# Stack all the features together for linear model +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# A linear model on SR + LR features can be thought of as a linear model +# built on a feature vector that is simply the concatenation of the SR and +# LR features. +# +# Furthermore, energy baselining can be performed by concatenating the information about +# chemical species as well. There is an metatensor function called +# :py:func:`metatensor.join()` for this purpose. Formally, we can write for the SR +# model. +# +# X_sr: :math:`1 \oplus \left(\rho \otimes \rho\right)` + +X_sr = metatensor.join([co, ps_sr], axis="properties") + + +# %% +# +# We used the ``axis="properties"`` parameter since we want to concatenate along the +# features/properties dimensions. +# +# For the long range model we can formerly write +# +# X_lr: :math:`1 \oplus \left(\rho \otimes \rho\right) \oplus \left(\rho \otimes +# V\right)` + +X_lr = metatensor.join([co, ps_sr, ps_lr], axis="properties") + + +# %% +# +# The features are now ready! Let us now perform some actual learning. Below we +# initialize two instances of the :py:class:`equisolve.numpy.models.linear_model.Ridge` +# class. :py:class:`equisolve.numpy.models.linear_model.Ridge` will perform a regression +# with respect to ``"values"`` (energies) and ``"positions"`` gradients (forces). +# +# If you only want a fit with respect to energies you can remove the gradients with +# ``metatensor.remove_gradients()`` + +clf_sr = Ridge() +clf_lr = Ridge() + + +# %% +# +# Split training and target data into train and test dat +# ------------------------------------------------------ +# +# Split the training and the test data by the distance :math:`r_{\rm +# train}=6\,\mathrm{Å}` between the center of mass of the two molecules. A structure +# with a :math:`r_{\rm train}<6 {\rm Å}` is used for training. + +r_cut = 6.0 + + +# %% +# +# We calculate the indices from the dataset by list comprehension. The center of mass +# distance is stored in the ``"distance""`` attribute. + +idx_train = [i for i, f in enumerate(frames) if f.info["distance"] < r_cut] +idx_test = [i for i, f in enumerate(frames) if f.info["distance"] >= r_cut] + + +# %% +# +# For doing the split we define two ``Labels`` instances and combine them in a +# :py:class:`List`. + +samples_train = metatensor.Labels(["structure"], np.reshape(idx_train, (-1, 1))) +samples_test = metatensor.Labels(["structure"], np.reshape(idx_test, (-1, 1))) +grouped_labels = [samples_train, samples_test] + + +# %% +# +# That we use as input to the :py:func:`metatensor.split()` function + +X_sr_train, X_sr_test = metatensor.split( + X_sr, axis="samples", grouped_labels=grouped_labels +) + +X_lr_train, X_lr_test = metatensor.split( + X_lr, axis="samples", grouped_labels=grouped_labels +) + +y_train, y_test = metatensor.split(y, axis="samples", grouped_labels=grouped_labels) + + +# %% +# +# Fit the model +# ------------- +# +# For this model, we use a very simple regularization scheme where all features are +# regularized in the same way (the amount being controlled by the parameter ``alpha``). +# For more advanced regularization schemes (regularizing energies and forces differently +# and/or the SR and LR parts differently), see further down. + +clf_sr.fit(X_sr_train, y_train, alpha=1e-6) +clf_lr.fit(X_lr_train, y_train, alpha=1e-6) + + +# %% +# +# Evaluation +# ---------- +# +# For evaluating the model we calculate the RMSEs using the ``score()`` method. With the +# ``parameter_key`` parameter we select which RMSE should be calculated. + +print( + "SR: RMSE energies = " + f"{clf_sr.score(X_sr_test, y_test, parameter_key='values')[0]:.3f} eV" +) +print( + "SR: RMSE forces = " + f"{clf_sr.score(X_sr_test, y_test, parameter_key='positions')[0]:.3f} eV/Å" +) + +print( + "LR: RMSE energies = " + f"{clf_lr.score(X_lr_test, y_test, parameter_key='values')[0]:.3f} eV" +) +print( + "LR: RMSE forces = " + f"{clf_lr.score(X_lr_test, y_test, parameter_key='positions')[0]:.3f} eV/Å" +) + + +# %% +# +# We find that the RMSE of the energy and the force of the LR model is smaller compared +# to the SR model. From this we conclude that the LR model performs better for the +# selection of the dataset. +# +# We additionally, can plot of the binding energy as a function of the distance. For the +# plot we select some properties from the dataset + +dist = np.array([f.info["distance"] for f in frames]) +energies = np.array([f.info["energy"] for f in frames]) +monomer_energies = np.array([f.info["energyA"] + f.info["energyB"] for f in frames]) + + +# %% +# +# and select only the indices corresponding to our test set. + + +# %% +# +# Next we calculate the predicted SR and LR ``TensorMaps``. + +y_sr_pred = clf_sr.predict(X_sr) +y_lr_pred = clf_lr.predict(X_lr) + + +# %% +# +# And, finally perform the plot. + +plt.scatter( + dist, y.block().values[:, 0] - monomer_energies, label="target data", color="black" +) + +plt.scatter( + dist, + y_sr_pred.block().values[:, 0] - monomer_energies, + label="short range model", + marker="x", +) + +plt.scatter( + dist, + y_lr_pred.block().values[:, 0] - monomer_energies, + label="long range model", + marker="s", + facecolor="None", + edgecolor="orange", +) + +plt.xlabel("center of mass distance in Å") +plt.ylabel(r"$E - E_\mathrm{monomer}$ in eV") +plt.axvline(r_cut, c="red", label=r"$r_\mathrm{train}$") + +plt.legend() +plt.tight_layout() +plt.show() diff --git a/latest/_downloads/64d1183ea988b0c2ee38bd092fcde99b/reftraj_template.cp2k b/latest/_downloads/64d1183ea988b0c2ee38bd092fcde99b/reftraj_template.cp2k new file mode 100644 index 00000000..8792d232 --- /dev/null +++ b/latest/_downloads/64d1183ea988b0c2ee38bd092fcde99b/reftraj_template.cp2k @@ -0,0 +1,144 @@ +@SET PREP 0 + +@SET SCF_GUESS RESTART +@SET SCREEN_ON_INITIAL_P TRUE + +@IF ${PREP} + @SET SCF_GUESS ATOMIC + @SET SCREEN_ON_INITIAL_P FALSE +@ENDIF + +&GLOBAL + PROJECT //PROJECT// + PREFERRED_FFT_LIBRARY FFTW + FFTW_PLAN_TYPE MEASURE + RUN_TYPE MD + PRINT_LEVEL LOW +&END GLOBAL + +&MOTION + &PRINT + &CELL + &EACH + MD 1 + &END EACH + &END CELL + &FORCES + &EACH + MD 1 + &END EACH + &END FORCES + &END PRINT + &MD + ENSEMBLE REFTRAJ + &REFTRAJ ! Loads an external trajectory file and performs analysis on the loaded snapshots. + EVAL_ENERGY_FORCES TRUE + EVAL_FORCES TRUE + CELL_FILE_NAME reftraj.cell + TRAJ_FILE_NAME reftraj.xyz + FIRST_SNAPSHOT 1 + VARIABLE_VOLUME TRUE + LAST_SNAPSHOT //LAST_SNAPSHOT// + &END REFTRAJ + &END MD +&END MOTION + +&FORCE_EVAL + &PRINT + &FORCES + &EACH + MD 1 + &END EACH + &END FORCES + &END PRINT + &DFT + BASIS_SET_FILE_NAME GTH_BASIS_SETS + BASIS_SET_FILE_NAME BASIS_ADMM + POTENTIAL_FILE_NAME POTENTIAL + &MGRID + CUTOFF 400 + &END MGRID + &SCF + SCF_GUESS ${SCF_GUESS} + MAX_SCF 20 + EPS_SCF 5.0E-7 + &OT + MINIMIZER DIIS + PRECONDITIONER FULL_ALL + &END OT + &OUTER_SCF + MAX_SCF 20 + EPS_SCF 5.0E-7 + &END OUTER_SCF + &END SCF + &QS + EPS_DEFAULT 1.0E-12 + EPS_PGF_ORB 1.0E-16 + EXTRAPOLATION_ORDER 5 + &END QS + &XC # revPBE0-TC-D3 + &XC_FUNCTIONAL + &PBE + PARAMETRIZATION REVPBE + SCALE_X 0.75 + SCALE_C 1.0 + &END + &END XC_FUNCTIONAL + &HF + FRACTION 0.25 + &SCREENING + EPS_SCHWARZ 1.0E-6 + SCREEN_ON_INITIAL_P ${SCREEN_ON_INITIAL_P} + &END + &MEMORY + MAX_MEMORY 37000 + EPS_STORAGE_SCALING 0.1 + &END + &INTERACTION_POTENTIAL + POTENTIAL_TYPE TRUNCATED + CUTOFF_RADIUS 3.0 + T_C_G_DATA t_c_g.dat + &END + &HF_INFO + &END HF_INFO + &END + &VDW_POTENTIAL + POTENTIAL_TYPE PAIR_POTENTIAL + &PAIR_POTENTIAL + TYPE DFTD3 + R_CUTOFF 15 + LONG_RANGE_CORRECTION TRUE + REFERENCE_FUNCTIONAL revPBE0 + PARAMETER_FILE_NAME dftd3.dat + &END + &END + &XC_GRID + XC_DERIV SPLINE2 + &END + &END XC + &AUXILIARY_DENSITY_MATRIX_METHOD + METHOD BASIS_PROJECTION + ADMM_PURIFICATION_METHOD MO_DIAG + &END AUXILIARY_DENSITY_MATRIX_METHOD + &END DFT + &SUBSYS + &TOPOLOGY + COORD_FILE_NAME init.xyz + COORD_FILE_FORMAT XYZ + CONN_FILE_FORMAT GENERATE + &END TOPOLOGY + &CELL + ABC [angstrom] //CELL// + &END CELL + &KIND H + BASIS_SET TZV2P-GTH + BASIS_SET AUX_FIT cpFIT3 + POTENTIAL GTH-PBE-q1 + &END KIND + &KIND O + BASIS_SET TZV2P-GTH + BASIS_SET AUX_FIT cpFIT3 + POTENTIAL GTH-PBE-q6 + &END KIND + &END SUBSYS +&END FORCE_EVAL diff --git a/latest/_downloads/76541ec3f2a2c0e75aa5ac99c3cab20e/gaas_map.ipynb b/latest/_downloads/76541ec3f2a2c0e75aa5ac99c3cab20e/gaas_map.ipynb new file mode 100644 index 00000000..58d5c3da --- /dev/null +++ b/latest/_downloads/76541ec3f2a2c0e75aa5ac99c3cab20e/gaas_map.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# PCA/PCovR Visualization for the rattled GaAs training dataset\n\nThis example uses ``rascaline`` and ``metatensor`` to compute\nstructural properties for the structures in a training for a ML model.\nThese are then used with simple dimensionality reduction algorithms\n(implemented in ``sklearn`` and ``skmatter``) to obtain a simplified\ndescription of the dataset, that is then visualized using\n``chemiscope``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n\nimport ase\nimport ase.io\nimport chemiscope\nimport numpy as np\nimport requests\nfrom matplotlib import pyplot as plt\nfrom metatensor import mean_over_samples\nfrom rascaline import AtomicComposition, SoapPowerSpectrum\nfrom sklearn.decomposition import PCA\nfrom sklearn.linear_model import RidgeCV\nfrom skmatter.decomposition import PCovR\nfrom skmatter.preprocessing import StandardFlexibleScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we load the structures, extracting some of the properties for\nmore convenient manipulation. These are\n$\\mathrm{Ga}_x\\mathrm{As}_{1-x}$ structures used in [Imbalzano &\nCeriotti (2021)](http://doi.org/10.1103/PhysRevMaterials.5.063804)_ to\ntrain a ML potential to describe the full composition range.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filename = \"gaas_training.xyz\"\nif not os.path.exists(filename):\n url = f\"https://zenodo.org/records/10566825/files/{filename}\"\n response = requests.get(url)\n response.raise_for_status()\n with open(filename, \"wb\") as f:\n f.write(response.content)\n\nstructures = ase.io.read(filename, \":\")\nenergy = np.array([f.info[\"energy\"] for f in structures])\nnatoms = np.array([len(f) for f in structures])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove atomic energy baseline\n\nEnergies from an electronic structure calculation contain a very large\n\u201cself\u201d contributions from the atoms, which can obscure the important\ndifferences in cohesive energies between structures. We can build an\napproximate model based on the chemical nature of the atoms, $a_i$\n\n\\begin{align}E(A) = \\sum_{i\\in A} e_{a_i}\\end{align}\n\nwhere $e_a$ are atomic energies that can be determined by linear\nregression.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# rascaline has an `AtomicComposition` calculator that streamlines\n# this (simple) calculation\ncalculator = AtomicComposition(**{\"per_structure\": True})\nrho0 = calculator.compute(structures)\n\n# the descriptors are returned as a `TensorMap` object, that contains\n# the composition data in a sparse storage format\nrho0\n\n# for easier manipulation, we extract the features as a dense vector\n# of composition weights\ncomp_feats = rho0.keys_to_properties([\"species_center\"]).block(0).values\n\n# a one-liner to fit a linear model and compute \"dressed energies\"\natom_energy = (\n RidgeCV(alphas=np.geomspace(1e-8, 1e2, 20))\n .fit(comp_feats, energy)\n .predict(comp_feats)\n)\ncohesive_peratom = (energy - atom_energy) / natoms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The baseline makes up a large fraction of the total energy, but actually\nthe residual (which is the part that matters) is still large.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\nax.plot(energy / natoms, atom_energy / natoms, \"b.\")\nax.set_xlabel(\"Energy / (eV/atom)\")\nax.set_ylabel(\"Atomic e. / (eV/atom)\")\nprint(f\"RMSE / (eV/atom): {np.sqrt(np.mean((cohesive_peratom)**2))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute structural descriptors\n\nIn order to visualize the structures as a low-dimensional map, we start\nby computing suitable ML descriptors. Here we have used ``rascaline`` to\nevaluate average SOAP features for the structures.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# hypers for evaluating rascaline features\nhypers = {\n \"cutoff\": 4.5,\n \"max_radial\": 6,\n \"max_angular\": 4,\n \"atomic_gaussian_width\": 0.3,\n \"cutoff_function\": {\"ShiftedCosine\": {\"width\": 0.5}},\n \"radial_basis\": {\"Gto\": {\"accuracy\": 1e-6}},\n \"center_atom_weight\": 1.0,\n}\ncalculator = SoapPowerSpectrum(**hypers)\nrho2i = calculator.compute(structures)\n\n# neighbor types go to the keys for sparsity (this way one can\n# compute a heterogeneous dataset without having blocks of zeros)\nrho2i = rho2i.keys_to_samples([\"species_center\"]).keys_to_properties(\n [\"species_neighbor_1\", \"species_neighbor_2\"]\n)\n\n# computes structure-level descriptors and then extracts\n# the features as a dense array\nrho2i_structure = mean_over_samples(rho2i, sample_names=[\"center\", \"species_center\"])\nrho2i = None # releases memory\nfeatures = rho2i_structure.block(0).values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We standardize (per atom) energy and features (computed as a *mean* over\natomic environments) so that they can be combined on the same footings.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sf_energy = StandardFlexibleScaler().fit_transform(cohesive_peratom.reshape(-1, 1))\nsf_feats = StandardFlexibleScaler().fit_transform(features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PCA and PCovR projection\n\nComputes PCA projection to generate low-dimensional descriptors that\nreflect structural diversity. Any other dimensionality reduction scheme\ncould be used in a similar fashion.\n\nWe also compute the principal covariate regression (PCovR) descriptors,\nthat reduce dimensionality while combining a variance preserving\ncriterion with the requirement that the low-dimensional features are\ncapable of estimating a target quantity (here, the energy).\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# PCA\npca = PCA(n_components=4)\npca_features = pca.fit_transform(sf_feats)\n\nfig, ax = plt.subplots(1, 1, figsize=(6, 4))\nscatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=cohesive_peratom)\nax.set_xlabel(\"PCA[1]\")\nax.set_ylabel(\"PCA[2]\")\ncbar = fig.colorbar(scatter, ax=ax)\ncbar.set_label(\"energy / eV/at.\")\n\n# computes PCovR map\npcovr = PCovR(n_components=4)\npcovr_features = pcovr.fit_transform(sf_feats, sf_energy)\n\nfig, ax = plt.subplots(1, 1, figsize=(6, 4))\nscatter = ax.scatter(pcovr_features[:, 0], pcovr_features[:, 1], c=cohesive_peratom)\nax.set_xlabel(\"PCovR[1]\")\nax.set_ylabel(\"PCovR[2]\")\ncbar = fig.colorbar(scatter, ax=ax)\ncbar.set_label(\"energy / (eV/at.)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chemiscope visualization\n\nVisualizes the structure-property map using a chemiscope widget (and\ngenerates a .json file that can be viewed on\n[chemiscope.org](https://chemiscope.org)_).\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# extracts force data (adding considerably to the dataset size...)\nforce_vectors = chemiscope.ase_vectors_to_arrows(structures, scale=1)\nforce_vectors[\"parameters\"][\"global\"][\"color\"] = 0x505050\n\n# adds properties to the ASE frames\nfor i, f in enumerate(structures):\n for j in range(len(pca_features[i])):\n f.info[\"pca_\" + str(j + 1)] = pca_features[i, j]\nfor i, f in enumerate(structures):\n for j in range(len(pcovr_features[i])):\n f.info[\"pcovr_\" + str(j + 1)] = pcovr_features[i, j]\nfor i, f in enumerate(structures):\n f.info[\"cohesive_energy\"] = cohesive_peratom[i]\n f.info[\"x_ga\"] = comp_feats[i, 0] / comp_feats[i].sum()\n\n# it would also be easy to add the properties manually, this is just a dictionary\nstructure_properties = chemiscope.extract_properties(structures)\n\ncs = chemiscope.show(\n frames=structures,\n properties=structure_properties,\n shapes={\"forces\": force_vectors},\n # the settings are a tad verbose, but give full control over the visualization\n settings={\n \"map\": {\n \"x\": {\"property\": \"pcovr_1\"},\n \"y\": {\"property\": \"pcovr_2\"},\n \"color\": {\"property\": \"x_ga\"},\n },\n \"structure\": [\n {\n \"bonds\": True,\n \"unitCell\": True,\n \"shape\": [\"forces\"],\n \"keepOrientation\": False,\n }\n ],\n },\n meta={\n \"name\": \"GaAs training data\",\n \"description\": \"\"\"\nA collection of Ga(x)As(1-x) structures to train a MLIP,\nincluding force and energy data.\n\"\"\",\n \"authors\": [\"Giulio Imbalzano\", \"Michele Ceriotti\"],\n \"references\": [\n \"\"\"\nG. Imbalzano and M. Ceriotti, 'Modeling the Ga/As binary system across\ntemperatures and compositions from first principles,'\nPhys. Rev. Materials 5(6), 063804 (2021).\n\"\"\",\n \"Original dataset: https://archive.materialscloud.org/record/2021.226\",\n ],\n },\n)\n\n# shows chemiscope if run in a jupyter environment\nif chemiscope.jupyter._is_running_in_notebook():\n from IPython.display import display\n\n display(cs)\nelse:\n cs.save(\"gaas_map.chemiscope.json.gz\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/latest/_downloads/7afa07cc2539ad631054b156989a49d6/setup_reftraj.ipynb b/latest/_downloads/7afa07cc2539ad631054b156989a49d6/setup_reftraj.ipynb new file mode 100644 index 00000000..5360e060 --- /dev/null +++ b/latest/_downloads/7afa07cc2539ad631054b156989a49d6/setup_reftraj.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Batch run of CP2K calculations\n\n.. start-body\n\nThis is an example how to perform single point calculations based on list of structures\nusing [CP2K](https://www.cp2k.org) using its [reftraj functionality](https://manual.cp2k.org/trunk/CP2K_INPUT/MOTION/MD/REFTRAJ.html). The inputs are a\nset of structures in :download:`example.xyz` using the DFT parameters defined in\n:download:`reftraj_template.cp2k` importing basis set and pseudopotentials from the\nlocal CP2K installation. The reference DFT parameters are taken from [Cheng et al. Ab\ninitio thermodynamics of liquid and solid water 2019](https://www.pnas.org/doi/10.1073/pnas.1815117116). Due to the small size of the test\nstructure and convergence issues, we have decreased the size of the ``CUTOFF_RADIUS``\nfrom $6.0\\,\\mathrm{\u00c5}$ to $3.0\\,\\mathrm{\u00c5}$. For actual production\ncalculations adapt the template!\n\nTo run this example, we use a bare executable called with ``cp2k``. If you want to use\nanother version you can either adjust the the names within this example or link your\nbinary with a different name to ``cp2k``.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start the example by importing the required packages.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nimport re\nimport shutil\nimport subprocess\nfrom os.path import basename, splitext\nfrom typing import List, Union\n\nimport ase.io\nimport ase.visualize.plot\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ase.calculators.cp2k import CP2K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define necessary functions\nNext we below define necessary helper functions to run the example.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def write_reftraj(fname: str, frames: Union[ase.Atoms, List[ase.Atoms]]) -> None:\n \"\"\"Writes a list of ase atoms objects to a reference trajectory.\n\n A reference trajectory is the CP2K compatible format for the compuation of batches.\n All frames must have the stoichiometry/composition.\n \"\"\"\n\n if isinstance(frames, ase.Atoms):\n frames = [frames]\n\n out = \"\"\n for i, atoms in enumerate(frames):\n if (\n len(atoms) != len(frames[0])\n or atoms.get_chemical_formula() != frames[0].get_chemical_formula()\n ):\n raise ValueError(\n f\"Atom symbols in frame {i},{atoms.get_chemical_formula()} are \"\n f\"different compared to inital frame \"\n f\"{frames[0].get_chemical_formula()}. \"\n \"CP2K does not support changing atom types within a reftraj run!\"\n )\n\n out += f\"{len(atoms):>8}\\n i = {i+1:>8}, time = {0:>12.3f}\\n\"\n for atom in atoms:\n pos = atom.position\n out += f\"{atom.symbol}{pos[0]:24.15f}{pos[1]:24.15f}{pos[2]:24.15f}\\n\"\n out += \"\\n\"\n with open(fname, \"w\") as f:\n f.write(out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def write_cellfile(fname: str, frames: Union[ase.Atoms, List[ase.Atoms]]) -> None:\n \"\"\"Writes a cellfile for a list of ``ase.Atoms``.\n\n A Cellfile accompanies a reftraj containing the cell parameters.\n \"\"\"\n if isinstance(frames, ase.Atoms):\n frames = [frames]\n\n out = (\n \"# \"\n \"Step \"\n \"Time [fs] \"\n \"Ax [Angstrom] \"\n \"Ay [Angstrom] \"\n \"Az [Angstrom] \"\n \"Bx [Angstrom] \"\n \"By [Angstrom] \"\n \"Bz [Angstrom] \"\n \"Cx [Angstrom] \"\n \"Cy [Angstrom] \"\n \"Cz [Angstrom] \"\n \"Volume [Angstrom^3]\\n\"\n )\n\n for i, atoms in enumerate(frames):\n out += f\"{i+1:>8}{0:>12.3f}\"\n out += \"\".join([f\"{c:>20.10f}\" for c in atoms.cell.flatten()])\n out += f\"{atoms.cell.volume:>25.10f}\"\n out += \"\\n\"\n\n with open(fname, \"w\") as f:\n f.write(out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def write_cp2k_in(\n fname: str, project_name: str, last_snapshot: int, cell: List[float]\n) -> None:\n \"\"\"Writes a cp2k input file from a template.\n\n Importantly, it writes the location of the basis set definitions,\n determined from the path of the system CP2K install to the input file.\n \"\"\"\n\n cp2k_in = open(\"reftraj_template.cp2k\", \"r\").read()\n\n cp2k_in = cp2k_in.replace(\"//PROJECT//\", project_name)\n cp2k_in = cp2k_in.replace(\"//LAST_SNAPSHOT//\", str(last_snapshot))\n cp2k_in = cp2k_in.replace(\"//CELL//\", \" \".join([f\"{c:.6f}\" for c in cell]))\n\n with open(fname, \"w\") as f:\n f.write(cp2k_in)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def mkdir_force(*args, **kwargs) -> None:\n \"\"\"Warpper to ``os.mkdir``.\n\n The function does not raise an error if the directory already exists.\n \"\"\"\n try:\n os.mkdir(*args, **kwargs)\n except OSError:\n pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare calculation inputs\nDuring this example we will create a directory named ``project_directory`` containing\nthe subdirectories for each stoichiometry. This is necessary, because CP2K can only\nrun calculations using a fixed stoichiometry at a time, using its ``reftraj``\nfunctionality.\n\nBelow we define the general information for the CP2K run. This includes the reference\nfiles for the structures, the ``project_name`` used to build the name of the\ntrajectory during the CP2K run, the ``project_directory`` where we store all\nsimulation output as well as the path ``write_to_file`` which is the name of the file\ncontaining the computed energies and forces of the sinulation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "frames_full = ase.io.read(\"example.xyz\", \":\")\nproject_name = \"test_calcs\" # name of the global PROJECT\nproject_directory = \"production\"\nwrite_to_file = \"out.xyz\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we show the initial configuration of two water molecules in a cubic box with a\nside length of $\\approx 4\\,\\mathrm{\u00c5}$.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ase.visualize.plot.plot_atoms(frames_full[0])\n\nplt.xlabel(\"\u00c5\")\nplt.ylabel(\"\u00c5\")\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now extreact the stoichiometry from the input dataset using ASE's\n:py:meth:`ase.symbols.Symbols.get_chemical_formula` method.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "frames_dict = {}\n\nfor atoms in frames_full:\n chemical_formula = atoms.get_chemical_formula()\n try:\n frames_dict[chemical_formula]\n except KeyError:\n frames_dict[chemical_formula] = []\n\n frames_dict[chemical_formula].append(atoms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the stoichiometries we create one calculation subdirectories for the\ncalculations. (reftraj, input and cellfile). For our example this is only is one\ndirectory named ``H4O2`` because our dataset consists only of a single structure with\ntwo water molecules.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mkdir_force(project_directory)\n\nfor stoichiometry, frames in frames_dict.items():\n current_directory = f\"{project_directory}/{stoichiometry}\"\n mkdir_force(current_directory)\n\n write_cp2k_in(\n f\"{current_directory}/in.cp2k\",\n project_name=project_name,\n last_snapshot=len(frames),\n cell=frames[0].cell.diagonal(),\n )\n\n ase.io.write(f\"{current_directory}/init.xyz\", frames[0])\n write_reftraj(f\"{current_directory}/reftraj.xyz\", frames)\n write_cellfile(f\"{current_directory}/reftraj.cell\", frames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run simulations\nNow we have all ingredients to run the simulations. Below we call the bash script\n:download:`run_calcs.sh`.\n\n.. literalinclude:: run_calcs.sh\n :language: bash\n\nThis script will loop through all stoichiometry subdirectories and call the CP2K\nengine.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# run the bash script directly from this script\nsubprocess.run(\"bash run_calcs.sh\", shell=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Note

For a usage on an HPC environment you can parallize the loop over the subfolders\n and submit and single job per stoichiometry.

\n\n## Load results\nAfter the simulation we load the results and perform a unit version from the default\nCP2K output units (Bohr and Hartree) to \u00c5 and eV.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cflength = 0.529177210903 # Bohr -> \u00c5\ncfenergy = 27.211386245988 # Hartree -> eV\ncfforce = cfenergy / cflength # Hartree/Bohr -> eV/\u00c5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we store the results as :class:`ase.Atoms` in the ``new_frames`` list and\nwrite them to the ``project_directory`` using the ``new_fname``. Here it will be\nwritten to ``production/out_dft.xyz``.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_frames = []\n\nfor stoichiometry, frames in frames_dict.items():\n current_directory = f\"{project_directory}/{stoichiometry}\"\n\n frames_dft = ase.io.read(f\"{current_directory}/{project_name}-pos-1.xyz\", \":\")\n forces_dft = ase.io.read(f\"{current_directory}/{project_name}-frc-1.xyz\", \":\")\n cell_dft = np.atleast_2d(np.loadtxt(f\"{current_directory}/{project_name}-1.cell\"))[\n :, 2:-1\n ]\n\n for i_atoms, atoms in enumerate(frames_dft):\n frames_ref = frames[i_atoms]\n\n # Check consistent positions\n if not np.allclose(atoms.positions, frames_ref.positions):\n raise ValueError(f\"Positions in frame {i_atoms} are not the same.\")\n\n # Check consistent cell\n if not np.allclose(frames_ref.cell.flatten(), cell_dft[i_atoms]):\n raise ValueError(f\"Cell dimensions in frame {i_atoms} are not the same.\")\n\n atoms.info[\"E\"] *= cfenergy\n atoms.pbc = True\n atoms.cell = frames_ref.cell\n atoms.set_array(\"forces\", cfforce * forces_dft[i_atoms].positions)\n\n new_frames += frames_dft\n\nnew_fname = f\"{splitext(basename(write_to_file))[0]}_dft.xyz\"\nase.io.write(f\"{project_directory}/{new_fname}\", new_frames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform calculations using ASE calculator\nAbove we performed the calculations using an external bash script. ASE also provides a\ncalculator class that we can use the perform the caclulations with our input file\nwithout a detour of writing files to disk.\n\nTo use the ASE calculator together with a custom input script this requires some\nadjustments. First the name of the executable that has the exact name ``cp2k_shell``.\nWe create a symlink to follow this requirement.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "try:\n os.symlink(shutil.which(\"cp2k\"), \"cp2k_shell.ssmp\")\nexcept OSError:\n pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the input file abd remove ``GLOBAL`` section because from it\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inp = open(\"./production/H4O2/in.cp2k\", \"r\").read()\ninp = re.sub(\n f\"{re.escape('&GLOBAL')}.*?{re.escape('&END GLOBAL')}\", \"\", inp, flags=re.DOTALL\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Afterwards we define the :py:class:`ase.calculators.cp2k.CP2K`` calculator. Note that\nwe disable all parameters because we want to use all options from our input file\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "calc = CP2K(\n inp=inp,\n max_scf=None,\n cutoff=None,\n xc=None,\n force_eval_method=None,\n basis_set=None,\n pseudo_potential=None,\n basis_set_file=None,\n potential_file=None,\n stress_tensor=False,\n poisson_solver=None,\n print_level=None,\n command=\"./cp2k_shell.ssmp --shell\",\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now load a new struture, add the calculator and perform the computation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "atoms = ase.io.read(\"example.xyz\")\natoms.set_calculator(calc)\n# atoms.get_potential_energy()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/latest/_downloads/88524e566d26d7fdd1508a813c34aadc/sample_selection.ipynb b/latest/_downloads/88524e566d26d7fdd1508a813c34aadc/sample_selection.ipynb new file mode 100644 index 00000000..953df0a3 --- /dev/null +++ b/latest/_downloads/88524e566d26d7fdd1508a813c34aadc/sample_selection.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Sample and Feature Selection with FPS and CUR\n\n.. start-body\n\nIn this tutorial we generate descriptors using rascaline, then select a subset\nof structures using both the farthest-point sampling (FPS) and CUR algorithms\nimplemented in scikit-matter. Finally, we also generate a selection of\nthe most important features using the same techniques.\n\nFirst, import all the necessary packages\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.io\nimport chemiscope\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom metatensor import mean_over_samples\nfrom rascaline import SoapPowerSpectrum\nfrom sklearn.decomposition import PCA\nfrom skmatter import feature_selection, sample_selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load molecular data\n\nLoad 500 example BTO structures from file, reading them using\n[ASE](https://wiki.fysik.dtu.dk/ase/).\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load a subset of structures of the example dataset\nn_frames = 500\nframes = ase.io.read(\"./dataset/input-fps.xyz\", f\":{n_frames}\", format=\"extxyz\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute SOAP descriptors using rascaline\n\nFirst, define the rascaline hyperparameters used to compute SOAP.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# rascaline hyperparameters\nhypers = {\n \"cutoff\": 6.0,\n \"max_radial\": 8,\n \"max_angular\": 6,\n \"atomic_gaussian_width\": 0.3,\n \"cutoff_function\": {\"ShiftedCosine\": {\"width\": 0.5}},\n \"radial_basis\": {\"Gto\": {\"accuracy\": 1e-6}},\n \"radial_scaling\": {\"Willatt2018\": {\"exponent\": 4, \"rate\": 1, \"scale\": 3.5}},\n \"center_atom_weight\": 1.0,\n}\n\n# Generate a SOAP power spectrum\ncalculator = SoapPowerSpectrum(**hypers)\nrho2i = calculator.compute(frames)\n# Makes a dense block\nrho2i = rho2i.keys_to_samples([\"species_center\"]).keys_to_properties(\n [\"species_neighbor_1\", \"species_neighbor_2\"]\n)\n# Averages over atomic centers to compute structure features\nrho2i_structure = mean_over_samples(rho2i, sample_names=[\"center\", \"species_center\"])\n\natom_dscrptr = rho2i.block(0).values\nstruct_dscrptr = rho2i_structure.block(0).values\n\nprint(\"atom feature descriptor shape:\", atom_dscrptr.shape)\nprint(\"structure feature descriptor shape:\", struct_dscrptr.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform structure (i.e. sample) selection\n\nUsing FPS and CUR algorithms implemented in scikit-matter, select a subset of\nthe structures. skmatter assumes that our descriptor is represented as a 2D\nmatrix, with the samples along axis 0 and features along axis 1.\n\nFor more info on the functions: [skmatter](https://scikit-cosmo.readthedocs.io/en/latest/selection.html)\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define the number of structures to select using FPS/CUR\nn_structures = 25\n\n# FPS sample selection\nstruct_fps = sample_selection.FPS(n_to_select=n_structures, initialize=\"random\").fit(\n struct_dscrptr\n)\nstruct_fps_idxs = struct_fps.selected_idx_\n\n# CUR sample selection\nstruct_cur = sample_selection.CUR(n_to_select=n_structures).fit(struct_dscrptr)\nstruct_cur_idxs = struct_cur.selected_idx_\n\nprint(\"Structure indices obtained with FPS \", struct_fps_idxs)\nprint(\"Structure indices obtained with CUR \", struct_cur_idxs)\n\n# Slice structure descriptor along axis 0 to contain only the selected structures\nstruct_dscrptr_fps = struct_dscrptr[struct_fps_idxs, :]\nstruct_dscrptr_cur = struct_dscrptr[struct_cur_idxs, :]\nassert struct_dscrptr_fps.shape == struct_dscrptr_cur.shape\n\nprint(\"Structure descriptor shape before selection \", struct_dscrptr.shape)\nprint(\"Structure descriptor shape after selection \", struct_dscrptr_fps.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize selected structures\n\nsklearn can be used to perform PCA dimensionality reduction on the SOAP\ndescriptors. The resulting PC coordinates can be used to visualize the the\ndata alongside their structures in a chemiscope widget.\n\nNote: chemiscope widgets are not currently integrated into our sphinx gallery:\ncoming soon.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Generate a structure PCA\nstruct_dscrptr_pca = PCA(n_components=2).fit_transform(struct_dscrptr)\nassert struct_dscrptr_pca.shape == (n_frames, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the PCA map\n\nNotice how the selected points avoid the densely-sampled area, and cover\nthe periphery of the dataset\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Matplotlib plot\nfig, ax = plt.subplots(1, 1, figsize=(6, 4))\nscatter = ax.scatter(struct_dscrptr_pca[:, 0], struct_dscrptr_pca[:, 1], c=\"red\")\nax.plot(\n struct_dscrptr_pca[struct_cur_idxs, 0],\n struct_dscrptr_pca[struct_cur_idxs, 1],\n \"kx\",\n label=\"CUR selection\",\n)\nax.plot(\n struct_dscrptr_pca[struct_fps_idxs, 0],\n struct_dscrptr_pca[struct_fps_idxs, 1],\n \"ko\",\n fillstyle=\"none\",\n label=\"FPS selection\",\n)\nax.set_xlabel(\"PCA[1]\")\nax.set_ylabel(\"PCA[2]\")\nax.legend()\nfig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creates a chemiscope viewer\n\nInteractive viewer (only works in notebooks)\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Selected level\nselection_levels = []\nfor i in range(len(frames)):\n level = 0\n if i in struct_cur_idxs:\n level += 1\n if i in struct_fps_idxs:\n level += 2\n if level == 0:\n level = \"Not selected\"\n elif level == 1:\n level = \"CUR\"\n elif level == 2:\n level = \"FPS\"\n else:\n level = \"FPS+CUR\"\n selection_levels.append(level)\n\nproperties = chemiscope.extract_properties(frames)\n\nproperties.update(\n {\n \"PC1\": struct_dscrptr_pca[:, 0],\n \"PC2\": struct_dscrptr_pca[:, 1],\n \"selection\": np.array(selection_levels),\n }\n)\n\nprint(properties)\n\n# Display with chemiscope. This currently does not work - as raised in issue #8\n# https://github.com/lab-cosmo/software-cookbook/issues/8\ncs = chemiscope.show(\n frames,\n properties=properties,\n settings={\n \"map\": {\n \"x\": {\"property\": \"PC1\"},\n \"y\": {\"property\": \"PC2\"},\n \"color\": {\"property\": \"energy\"},\n \"symbol\": \"selection\",\n \"size\": {\"factor\": 50},\n },\n \"structure\": [{\"unitCell\": True}],\n },\n)\n\nif chemiscope.jupyter._is_running_in_notebook():\n from IPython.display import display\n\n display(cs)\nelse:\n cs.save(\"sample_selection.json.gz\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform feature selection\n\nNow perform feature selection. In this example we will go back to using the\ndescriptor decomposed into atomic environments, as opposed to the one\ndecomposed into structure environments, but only use FPS for brevity.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define the number of features to select\nn_features = 200\n\n# FPS feature selection\nfeat_fps = feature_selection.FPS(n_to_select=n_features, initialize=\"random\").fit(\n atom_dscrptr\n)\nfeat_fps_idxs = feat_fps.selected_idx_\n\nprint(\"Feature indices obtained with FPS \", feat_fps_idxs)\n\n# Slice atomic descriptor along axis 1 to contain only the selected features\natom_dscrptr_fps = atom_dscrptr[:, feat_fps_idxs]\n\nprint(\"atomic descriptor shape before selection \", atom_dscrptr.shape)\nprint(\"atomic descriptor shape after selection \", atom_dscrptr_fps.shape)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/latest/_downloads/96a6ea8d2e4caf9b0b44c472a28d52d4/example.xyz b/latest/_downloads/96a6ea8d2e4caf9b0b44c472a28d52d4/example.xyz new file mode 100644 index 00000000..c0031e2d --- /dev/null +++ b/latest/_downloads/96a6ea8d2e4caf9b0b44c472a28d52d4/example.xyz @@ -0,0 +1,8 @@ +6 +Lattice="3.9111732301689224 0.0 0.0 0.0 3.9111732301689224 0.0 0.0 0.0 3.9111732301689224" Properties=species:S:1:pos:R:3 #=T CELL(abcABC):=T 3.91117=T 90.00000=T Step:=T 1975600=T Bead:=T 0=T x_centroidangstrom=T cellangstrom=T count=0 pbc="T T T" +H 2.55799292 0.30476323 0.67883354 +H 1.46739292 0.53584100 3.59015677 +O 1.78619292 3.82685646 0.31171354 +H 3.52058031 2.55936677 2.71091323 +H 0.84863385 2.05009677 1.85452646 +O 0.09813385 1.75660677 2.33837646 diff --git a/latest/_downloads/b64e521c753dced5fc246cd5a3e32880/sample_selection.py b/latest/_downloads/b64e521c753dced5fc246cd5a3e32880/sample_selection.py new file mode 100644 index 00000000..3efac3d4 --- /dev/null +++ b/latest/_downloads/b64e521c753dced5fc246cd5a3e32880/sample_selection.py @@ -0,0 +1,242 @@ +""" +Sample and Feature Selection with FPS and CUR +============================================= + +.. start-body + +In this tutorial we generate descriptors using rascaline, then select a subset +of structures using both the farthest-point sampling (FPS) and CUR algorithms +implemented in scikit-matter. Finally, we also generate a selection of +the most important features using the same techniques. + +First, import all the necessary packages +""" + +# %% + +import ase.io +import chemiscope +import numpy as np +from matplotlib import pyplot as plt +from metatensor import mean_over_samples +from rascaline import SoapPowerSpectrum +from sklearn.decomposition import PCA +from skmatter import feature_selection, sample_selection + + +# %% +# Load molecular data +# ------------------- +# +# Load 500 example BTO structures from file, reading them using +# `ASE `_. + +# Load a subset of structures of the example dataset +n_frames = 500 +frames = ase.io.read("./dataset/input-fps.xyz", f":{n_frames}", format="extxyz") + +# %% +# Compute SOAP descriptors using rascaline +# ---------------------------------------- +# +# First, define the rascaline hyperparameters used to compute SOAP. + + +# rascaline hyperparameters +hypers = { + "cutoff": 6.0, + "max_radial": 8, + "max_angular": 6, + "atomic_gaussian_width": 0.3, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + "radial_basis": {"Gto": {"accuracy": 1e-6}}, + "radial_scaling": {"Willatt2018": {"exponent": 4, "rate": 1, "scale": 3.5}}, + "center_atom_weight": 1.0, +} + +# Generate a SOAP power spectrum +calculator = SoapPowerSpectrum(**hypers) +rho2i = calculator.compute(frames) +# Makes a dense block +rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties( + ["species_neighbor_1", "species_neighbor_2"] +) +# Averages over atomic centers to compute structure features +rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"]) + +atom_dscrptr = rho2i.block(0).values +struct_dscrptr = rho2i_structure.block(0).values + +print("atom feature descriptor shape:", atom_dscrptr.shape) +print("structure feature descriptor shape:", struct_dscrptr.shape) + + +# %% +# Perform structure (i.e. sample) selection +# ----------------------------------------- +# +# Using FPS and CUR algorithms implemented in scikit-matter, select a subset of +# the structures. skmatter assumes that our descriptor is represented as a 2D +# matrix, with the samples along axis 0 and features along axis 1. +# +# For more info on the functions: `skmatter +# `_ + +# Define the number of structures to select using FPS/CUR +n_structures = 25 + +# FPS sample selection +struct_fps = sample_selection.FPS(n_to_select=n_structures, initialize="random").fit( + struct_dscrptr +) +struct_fps_idxs = struct_fps.selected_idx_ + +# CUR sample selection +struct_cur = sample_selection.CUR(n_to_select=n_structures).fit(struct_dscrptr) +struct_cur_idxs = struct_cur.selected_idx_ + +print("Structure indices obtained with FPS ", struct_fps_idxs) +print("Structure indices obtained with CUR ", struct_cur_idxs) + +# Slice structure descriptor along axis 0 to contain only the selected structures +struct_dscrptr_fps = struct_dscrptr[struct_fps_idxs, :] +struct_dscrptr_cur = struct_dscrptr[struct_cur_idxs, :] +assert struct_dscrptr_fps.shape == struct_dscrptr_cur.shape + +print("Structure descriptor shape before selection ", struct_dscrptr.shape) +print("Structure descriptor shape after selection ", struct_dscrptr_fps.shape) + + +# %% +# Visualize selected structures +# ----------------------------- +# +# sklearn can be used to perform PCA dimensionality reduction on the SOAP +# descriptors. The resulting PC coordinates can be used to visualize the the +# data alongside their structures in a chemiscope widget. +# +# Note: chemiscope widgets are not currently integrated into our sphinx gallery: +# coming soon. + + +# Generate a structure PCA +struct_dscrptr_pca = PCA(n_components=2).fit_transform(struct_dscrptr) +assert struct_dscrptr_pca.shape == (n_frames, 2) + + +# %% +# Plot the PCA map +# ~~~~~~~~~~~~~~~~ +# +# Notice how the selected points avoid the densely-sampled area, and cover +# the periphery of the dataset + +# Matplotlib plot +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +scatter = ax.scatter(struct_dscrptr_pca[:, 0], struct_dscrptr_pca[:, 1], c="red") +ax.plot( + struct_dscrptr_pca[struct_cur_idxs, 0], + struct_dscrptr_pca[struct_cur_idxs, 1], + "kx", + label="CUR selection", +) +ax.plot( + struct_dscrptr_pca[struct_fps_idxs, 0], + struct_dscrptr_pca[struct_fps_idxs, 1], + "ko", + fillstyle="none", + label="FPS selection", +) +ax.set_xlabel("PCA[1]") +ax.set_ylabel("PCA[2]") +ax.legend() +fig.show() + + +# %% +# Creates a chemiscope viewer +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Interactive viewer (only works in notebooks) + +# Selected level +selection_levels = [] +for i in range(len(frames)): + level = 0 + if i in struct_cur_idxs: + level += 1 + if i in struct_fps_idxs: + level += 2 + if level == 0: + level = "Not selected" + elif level == 1: + level = "CUR" + elif level == 2: + level = "FPS" + else: + level = "FPS+CUR" + selection_levels.append(level) + +properties = chemiscope.extract_properties(frames) + +properties.update( + { + "PC1": struct_dscrptr_pca[:, 0], + "PC2": struct_dscrptr_pca[:, 1], + "selection": np.array(selection_levels), + } +) + +print(properties) + +# Display with chemiscope. This currently does not work - as raised in issue #8 +# https://github.com/lab-cosmo/software-cookbook/issues/8 +cs = chemiscope.show( + frames, + properties=properties, + settings={ + "map": { + "x": {"property": "PC1"}, + "y": {"property": "PC2"}, + "color": {"property": "energy"}, + "symbol": "selection", + "size": {"factor": 50}, + }, + "structure": [{"unitCell": True}], + }, +) + +if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) +else: + cs.save("sample_selection.json.gz") + + +# %% +# Perform feature selection +# ------------------------- +# +# Now perform feature selection. In this example we will go back to using the +# descriptor decomposed into atomic environments, as opposed to the one +# decomposed into structure environments, but only use FPS for brevity. + +# Define the number of features to select +n_features = 200 + +# FPS feature selection +feat_fps = feature_selection.FPS(n_to_select=n_features, initialize="random").fit( + atom_dscrptr +) +feat_fps_idxs = feat_fps.selected_idx_ + +print("Feature indices obtained with FPS ", feat_fps_idxs) + +# Slice atomic descriptor along axis 1 to contain only the selected features +atom_dscrptr_fps = atom_dscrptr[:, feat_fps_idxs] + +print("atomic descriptor shape before selection ", atom_dscrptr.shape) +print("atomic descriptor shape after selection ", atom_dscrptr_fps.shape) + +# %% diff --git a/latest/_downloads/bd58737fee6cee3544e6e7429ec023f1/lode_tutorial.ipynb b/latest/_downloads/bd58737fee6cee3544e6e7429ec023f1/lode_tutorial.ipynb new file mode 100644 index 00000000..275a58eb --- /dev/null +++ b/latest/_downloads/bd58737fee6cee3544e6e7429ec023f1/lode_tutorial.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# LODE Tutorial\n\nThis tutorial explains how Long range equivariant descriptors can be constructed using\nrascaline and the resulting descriptors be used to construct a linear model with\nequisolve\n\nFirst, import all the necessary packages\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.io\nimport matplotlib.pyplot as plt\nimport metatensor\nimport numpy as np\nfrom equisolve.numpy.models.linear_model import Ridge\nfrom equisolve.utils.convert import ase_to_tensormap\nfrom rascaline import AtomicComposition, LodeSphericalExpansion, SphericalExpansion\nfrom rascaline.utils import PowerSpectrum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 0: Prepare Data Set\n\n### Get structures\n\nWe take a small subset of the dimer dataset from [A. Grisafi et al.,\n2021](https://pubs.rsc.org/en/content/articlelanding/2021/sc/d0sc04934d)\nfor which we additionally calculated the forces. Each structure in the\ndataset contains two small organic molecules which are extended along a\ncertain direction in the subsequent structures.\n\nFor speeding up the calculations we already selected the first 130\nstructures of the charge-charge structures.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "frames = ase.io.read(\"dataset/charge-charge.xyz\", \":\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert target properties to metatensor format\n\nIf we want to train models using the\n[equisolve](https://github.com/lab-cosmo/equisolve) package, we need to\nconvert the target properties (in this case, the energies and forces)\ninto the appropriate format #justequistorethings\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y = ase_to_tensormap(frames, energy=\"energy\", forces=\"forces\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Compute short-range and LODE features\n\nDefine hypers and get the expansion coefficients $\\langle anlm | \\rho_i \\rangle$\nand $\\langle anlm | V_i \\rangle$\n\nThe short-range and long-range descriptors have very similar hyperparameters. We\nhighlight the differences below.\n\nWe first define the hyperparameters for the short-range (SR) part. These will be used\nto create SOAP features.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "SR_HYPERS = {\n \"cutoff\": 3.0,\n \"max_radial\": 6,\n \"max_angular\": 2,\n \"atomic_gaussian_width\": 0.3,\n \"center_atom_weight\": 1.0,\n \"radial_basis\": {\"Gto\": {}},\n \"cutoff_function\": {\"ShiftedCosine\": {\"width\": 0.5}},\n}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And next the hyperparaters for the LODE / long-range (LR) part\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "LR_HYPERS = {\n # Cutoff on which to project potential density\n \"cutoff\": 3.0,\n # keep max_radial slightly smaller than for SR part\n \"max_radial\": 3,\n # max_angular should be <= 4, more precisely, max_angular + potential_exponent < 10\n \"max_angular\": 2,\n # keep at >=1, WARNING: CUBIC SCALING, do not use values <0.5\n \"atomic_gaussian_width\": 3.0,\n \"center_atom_weight\": 1.0,\n \"radial_basis\": {\"Gto\": {}},\n # the exponent p that determines the 1/r^p potential\n \"potential_exponent\": 1,\n}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then use the above defined hyperparaters to define the per atom short range (sr)\nand long range (sr) descriptors.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "calculator_sr = SphericalExpansion(**SR_HYPERS)\ncalculator_lr = LodeSphericalExpansion(**LR_HYPERS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that LODE requires periodic systems. Therefore, if the data set does not come\nwith periodic boundary conditions by default you can not use the data set and you will\nface an error if you try to compute the features.\n\nAs you notices the calculation of the long range features takes significant more time\ncompared to the sr features.\n\nTaking a look at the output we find that the resulting\n:py:class:`metatensor.TensorMap` are quite similar in their structure. The short range\n:py:class:`metatensor.TensorMap` contains more blocks due to the higher\n``max_angular`` paramater we choosed above.\n\n### Generate the rotational invariants (power spectra)\n\nRotationally invariant features can be obtained by taking two of the calculators that\nwere defines above.\n\nFor the short-range part, we use the SOAP vector which is obtained by computing the\ninvariant combinations of the form $\\rho \\otimes \\rho$.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ps_calculator_sr = PowerSpectrum(calculator_sr, calculator_sr)\nps_sr = ps_calculator_sr.compute(frames, gradients=[\"positions\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We calculate gradients with respect to pistions by providing the\n``gradients=[\"positions\"]`` option to the\n:py:meth:`rascaline.calculators.CalculatorBase.compute()` method.\n\nFor the long-range part, we combine the long-range descriptor $V$ with one a\nshort-range density $\\rho$ to get $\\rho \\otimes V$ features.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ps_calculator_lr = PowerSpectrum(calculator_sr, calculator_lr)\nps_lr = ps_calculator_lr.compute(systems=frames, gradients=[\"positions\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Building a Simple Linear SR + LR Model with energy baselining\n\n### Preprocessing (model dependent)\n\nFor our current model, we do not wish to treat the individual center and\nneighbor species separately. Thus, we move the ``\"species_center\"`` key\ninto the ``sample`` direction, over which we will later sum over.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ps_sr = ps_sr.keys_to_samples(\"species_center\")\nps_lr = ps_lr.keys_to_samples(\"species_center\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For linear models only: Sum features up over atoms (``samples``) in the same\nstructure.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sample_names_to_sum = [\"center\", \"species_center\"]\n\nps_sr = metatensor.sum_over_samples(ps_sr, sample_names=sample_names_to_sum)\nps_lr = metatensor.sum_over_samples(ps_lr, sample_names=sample_names_to_sum)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize tensormaps for energy baselining\n\nWe add a simple extra descriptor :py:class:`rascaline.AtomicComposition` that stores\nhow many atoms of each chemical species are contained in the structures. This is used\nfor energy baselining.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "calculator_co = AtomicComposition(per_structure=False)\ndescriptor_co = calculator_co.compute(frames, gradients=[\"positions\"])\n\nco = descriptor_co.keys_to_properties([\"species_center\"])\nco = metatensor.sum_over_samples(co, sample_names=[\"center\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The :py:class:`rascaline.AtomicComposition` calculator also allows to directly perform\nthe the sum over center atoms by using the following lines.\n\n.. code:: python\n\n descriptor_co = AtomicComposition(per_structure=True).compute(**compute_args)\n co = descriptor_co.keys_to_properties([\"species_center\"])\n\n### Stack all the features together for linear model\n\nA linear model on SR + LR features can be thought of as a linear model\nbuilt on a feature vector that is simply the concatenation of the SR and\nLR features.\n\nFurthermore, energy baselining can be performed by concatenating the information about\nchemical species as well. There is an metatensor function called\n:py:func:`metatensor.join()` for this purpose. Formally, we can write for the SR\nmodel.\n\nX_sr: $1 \\oplus \\left(\\rho \\otimes \\rho\\right)$\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X_sr = metatensor.join([co, ps_sr], axis=\"properties\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We used the ``axis=\"properties\"`` parameter since we want to concatenate along the\nfeatures/properties dimensions.\n\nFor the long range model we can formerly write\n\nX_lr: $1 \\oplus \\left(\\rho \\otimes \\rho\\right) \\oplus \\left(\\rho \\otimes\nV\\right)$\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X_lr = metatensor.join([co, ps_sr, ps_lr], axis=\"properties\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The features are now ready! Let us now perform some actual learning. Below we\ninitialize two instances of the :py:class:`equisolve.numpy.models.linear_model.Ridge`\nclass. :py:class:`equisolve.numpy.models.linear_model.Ridge` will perform a regression\nwith respect to ``\"values\"`` (energies) and ``\"positions\"`` gradients (forces).\n\nIf you only want a fit with respect to energies you can remove the gradients with\n``metatensor.remove_gradients()``\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "clf_sr = Ridge()\nclf_lr = Ridge()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split training and target data into train and test dat\n\nSplit the training and the test data by the distance $r_{\\rm\ntrain}=6\\,\\mathrm{\u00c5}$ between the center of mass of the two molecules. A structure\nwith a $r_{\\rm train}<6 {\\rm \u00c5}$ is used for training.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "r_cut = 6.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We calculate the indices from the dataset by list comprehension. The center of mass\ndistance is stored in the ``\"distance\"\"`` attribute.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "idx_train = [i for i, f in enumerate(frames) if f.info[\"distance\"] < r_cut]\nidx_test = [i for i, f in enumerate(frames) if f.info[\"distance\"] >= r_cut]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For doing the split we define two ``Labels`` instances and combine them in a\n:py:class:`List`.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "samples_train = metatensor.Labels([\"structure\"], np.reshape(idx_train, (-1, 1)))\nsamples_test = metatensor.Labels([\"structure\"], np.reshape(idx_test, (-1, 1)))\ngrouped_labels = [samples_train, samples_test]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That we use as input to the :py:func:`metatensor.split()` function\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X_sr_train, X_sr_test = metatensor.split(\n X_sr, axis=\"samples\", grouped_labels=grouped_labels\n)\n\nX_lr_train, X_lr_test = metatensor.split(\n X_lr, axis=\"samples\", grouped_labels=grouped_labels\n)\n\ny_train, y_test = metatensor.split(y, axis=\"samples\", grouped_labels=grouped_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit the model\n\nFor this model, we use a very simple regularization scheme where all features are\nregularized in the same way (the amount being controlled by the parameter ``alpha``).\nFor more advanced regularization schemes (regularizing energies and forces differently\nand/or the SR and LR parts differently), see further down.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "clf_sr.fit(X_sr_train, y_train, alpha=1e-6)\nclf_lr.fit(X_lr_train, y_train, alpha=1e-6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation\n\nFor evaluating the model we calculate the RMSEs using the ``score()`` method. With the\n``parameter_key`` parameter we select which RMSE should be calculated.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\n \"SR: RMSE energies = \"\n f\"{clf_sr.score(X_sr_test, y_test, parameter_key='values')[0]:.3f} eV\"\n)\nprint(\n \"SR: RMSE forces = \"\n f\"{clf_sr.score(X_sr_test, y_test, parameter_key='positions')[0]:.3f} eV/\u00c5\"\n)\n\nprint(\n \"LR: RMSE energies = \"\n f\"{clf_lr.score(X_lr_test, y_test, parameter_key='values')[0]:.3f} eV\"\n)\nprint(\n \"LR: RMSE forces = \"\n f\"{clf_lr.score(X_lr_test, y_test, parameter_key='positions')[0]:.3f} eV/\u00c5\"\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We find that the RMSE of the energy and the force of the LR model is smaller compared\nto the SR model. From this we conclude that the LR model performs better for the\nselection of the dataset.\n\nWe additionally, can plot of the binding energy as a function of the distance. For the\nplot we select some properties from the dataset\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dist = np.array([f.info[\"distance\"] for f in frames])\nenergies = np.array([f.info[\"energy\"] for f in frames])\nmonomer_energies = np.array([f.info[\"energyA\"] + f.info[\"energyB\"] for f in frames])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and select only the indices corresponding to our test set.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we calculate the predicted SR and LR ``TensorMaps``.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y_sr_pred = clf_sr.predict(X_sr)\ny_lr_pred = clf_lr.predict(X_lr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And, finally perform the plot.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plt.scatter(\n dist, y.block().values[:, 0] - monomer_energies, label=\"target data\", color=\"black\"\n)\n\nplt.scatter(\n dist,\n y_sr_pred.block().values[:, 0] - monomer_energies,\n label=\"short range model\",\n marker=\"x\",\n)\n\nplt.scatter(\n dist,\n y_lr_pred.block().values[:, 0] - monomer_energies,\n label=\"long range model\",\n marker=\"s\",\n facecolor=\"None\",\n edgecolor=\"orange\",\n)\n\nplt.xlabel(\"center of mass distance in \u00c5\")\nplt.ylabel(r\"$E - E_\\mathrm{monomer}$ in eV\")\nplt.axvline(r_cut, c=\"red\", label=r\"$r_\\mathrm{train}$\")\n\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/latest/_downloads/bf627fc2e9873db239d4716ae77bb54b/roy_gch.py b/latest/_downloads/bf627fc2e9873db239d4716ae77bb54b/roy_gch.py new file mode 100644 index 00000000..5b59ae97 --- /dev/null +++ b/latest/_downloads/bf627fc2e9873db239d4716ae77bb54b/roy_gch.py @@ -0,0 +1,292 @@ +""" +Generalized Convex Hull construction for the polymorphs of ROY +============================================================== + +This notebook analyzes the structures of 264 polymorphs of ROY, from +`Beran et Al, Chemical Science +(2022) `__, comparing the +conventional density-energy convex hull with a Generalized Convex Hull +(GCH) analysis (see `Anelli et al., Phys. Rev. Materials +(2018) `__). +It uses features computed with `rascaline `__ +and uses the directional convex hull function from +`scikit-matter `__ +to make the figure. +""" + +import chemiscope +import matplotlib.tri +import numpy as np +from matplotlib import pyplot as plt +from metatensor import mean_over_samples +from rascaline import SoapPowerSpectrum +from sklearn.decomposition import PCA +from skmatter.datasets import load_roy_dataset +from skmatter.sample_selection import DirectionalConvexHull + + +# %% +# Loads the structures (that also contain properties in the ``info`` field) + +roy_data = load_roy_dataset() + +structures = roy_data["structures"] + +density = np.array([s.info["density"] for s in structures]) +energy = np.array([s.info["energy"] for s in structures]) +structype = np.array([s.info["type"] for s in structures]) +iknown = np.where(structype == "known")[0] +iothers = np.where(structype != "known")[0] + + +# %% +# Energy-density hull +# ------------------- +# +# The Directional Convex Hull routines can be used to compute a +# conventional density-energy hull + +dch_builder = DirectionalConvexHull(low_dim_idx=[0]) +dch_builder.fit(density.reshape(-1, 1), energy) + +# %% +# We can get the indices of the selection, and compute the distance from +# the hull + +sel = dch_builder.selected_idx_ +dch_dist = dch_builder.score_samples(density.reshape(-1, 1), energy) + + +# %% +# +# Hull energies +# ^^^^^^^^^^^^^ +# +# Structures on the hull are stable with respect to synthesis at constant +# molar volume. Any other structure would lower the energy by decomposing +# into a mixture of the two nearest structures along the hull. Given that +# the lattice energy is an imperfect proxy for the free energy, and that +# synthesis can be performed in other ways than by fixing the density, +# structures that are not exactly on the hull might also be stable. One +# can compute a “hull energy” as an indication of how close these +# structures are to being stable. + +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +ax.scatter(density, energy, c=dch_dist, marker=".") +ssel = sel[np.argsort(density[sel])] +ax.plot(density[ssel], energy[ssel], "k--") +ax.set_xlabel("density / g/cm$^3$") +ax.set_ylabel("energy / kJ/mol") + +print( + f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol" +) +print(f"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol") + + +# %% +# Interactive visualization +# ^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# You can also visualize the hull with ``chemiscope``. +# This runs only in a notebook, and +# requires having the ``chemiscope`` package installed. +# + +cs = chemiscope.show( + structures, + dict( + energy=energy, + density=density, + hull_energy=dch_dist, + structure_type=structype, + ), + settings={ + "map": { + "x": {"property": "density"}, + "y": {"property": "energy"}, + "color": {"property": "hull_energy"}, + "symbol": "structure_type", + "size": {"factor": 35}, + }, + "structure": [{"unitCell": True, "supercell": {"0": 2, "1": 2, "2": 2}}], + }, +) + + +if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) +else: + cs.save("roy_ch.json.gz") + +# %% +# Generalized Convex Hull +# ----------------------- +# +# A GCH is a similar construction, in which generic structural descriptors +# are used in lieu of composition, density or other thermodynamic +# constraints. The idea is that configurations that are found close to the +# GCH are locally stable with respect to structurally-similar +# configurations. In other terms, one can hope to find a thermodynamic +# constraint (i.e. synthesis conditions) that act differently on these +# structures in comparison with the others, and may potentially stabilize +# them. +# + + +# %% +# Compute structural descriptors +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# A first step is to computes suitable ML descriptors. Here we have used +# ``rascaline`` to evaluate average SOAP features for the structures. +# If you don't want to install these dependencies for this example you +# can also use the pre-computed features, but you can use this as a stub +# to apply this analysis to other chemical systems + +hypers = { + "cutoff": 4, + "max_radial": 6, + "max_angular": 4, + "atomic_gaussian_width": 0.7, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + "radial_basis": {"Gto": {"accuracy": 1e-6}}, + "center_atom_weight": 1.0, +} +calculator = SoapPowerSpectrum(**hypers) +rho2i = calculator.compute(structures) +rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties( + ["species_neighbor_1", "species_neighbor_2"] +) +rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"]) +np.savez("roy_features.npz", feats=rho2i_structure.block(0).values) + + +# features = roy_data["features"] +features = rho2i_structure.block(0).values + + +# %% +# PCA projection +# ^^^^^^^^^^^^^^ +# +# Computes PCA projection to generate low-dimensional descriptors that +# reflect structural diversity. Any other dimensionality reduction scheme +# could be used in a similar fashion. + +pca = PCA(n_components=4) +pca_features = pca.fit_transform(features) + +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +scatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=energy) +ax.set_xlabel("PCA[1]") +ax.set_ylabel("PCA[2]") +cbar = fig.colorbar(scatter, ax=ax) +cbar.set_label("energy / kJ/mol") + + +# %% +# Builds the Generalized Convex Hull +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# Builds a convex hull on the first two PCA features + +dch_builder = DirectionalConvexHull(low_dim_idx=[0, 1]) +dch_builder.fit(pca_features, energy) +sel = dch_builder.selected_idx_ +dch_dist = dch_builder.score_samples(pca_features, energy) + + +# %% +# Generates a 3D Plot +# + +triang = matplotlib.tri.Triangulation(pca_features[sel, 0], pca_features[sel, 1]) +fig = plt.figure(figsize=(7, 5), tight_layout=True) +ax = fig.add_subplot(projection="3d") +ax.plot_trisurf(triang, energy[sel], color="gray") +ax.scatter(pca_features[:, 0], pca_features[:, 1], energy, c=dch_dist) +ax.set_xlabel("PCA[1]") +ax.set_ylabel("PCA[2]") +ax.set_zlabel("energy / kJ/mol\n \n", labelpad=11) +ax.view_init(25, 110) + + +# %% +# The GCH construction improves the separation between the hull energies +# of “known” and hypothetical polymorphs (compare with the density-energy +# values above) + +print( + f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol" +) +print(f"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol") + + +# %% +# Visualize in ``chemiscope``. This runs only in a notebook, and +# requires having the ``chemiscope`` package installed. + +for i, f in enumerate(structures): + for j in range(len(pca_features[i])): + f.info["pca_" + str(j + 1)] = pca_features[i, j] +structure_properties = chemiscope.extract_properties(structures) +structure_properties.update({"per_atom_energy": energy, "hull_energy": dch_dist}) + +# shows chemiscope if not run in terminal + +cs = chemiscope.show( + frames=structures, + properties=structure_properties, + meta={ + "name": "GCH for ROY polymorphs", + "description": """ +Demonstration of the Generalized Convex Hull construction for +polymorphs of the ROY molecule. Molecules that are closest to +the hull built on PCA-based structural descriptors and having the +internal energy predicted by electronic-structure calculations as +the z axis are the most thermodynamically stable. Indeed most of the +known polymorphs of ROY are on (or very close) to this hull. +""", + "authors": ["Michele Ceriotti "], + "references": [ + 'A. Anelli, E. A. Engel, C. J. Pickard, and M. Ceriotti, \ + "Generalized convex hull construction for materials discovery," \ + Physical Review Materials 2(10), 103804 (2018).', + 'G. J. O. Beran, I. J. Sugden, C. Greenwell, D. H. Bowskill, \ + C. C. Pantelides, and C. S. Adjiman, "How many more polymorphs of \ + ROY remain undiscovered," Chem. Sci. 13(5), 1288–1297 (2022).', + ], + }, + settings={ + "map": { + "x": {"property": "pca_1"}, + "y": {"property": "pca_2"}, + "z": {"property": "energy"}, + "symbol": "type", + "color": {"property": "hull_energy"}, + "size": { + "factor": 35, + "mode": "linear", + "property": "", + "reverse": True, + }, + }, + "structure": [ + { + "bonds": True, + "unitCell": True, + "keepOrientation": True, + } + ], + }, +) + +if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) +else: + cs.save("roy_gch.json.gz") diff --git a/latest/_downloads/c018f2f0619ce2a388aca2e8cc94afba/run_calcs.sh b/latest/_downloads/c018f2f0619ce2a388aca2e8cc94afba/run_calcs.sh new file mode 100644 index 00000000..6b58c954 --- /dev/null +++ b/latest/_downloads/c018f2f0619ce2a388aca2e8cc94afba/run_calcs.sh @@ -0,0 +1,8 @@ +#! /bin/bash + +for i in $(find ./production/ -mindepth 1 -type d); do + cd $i + cp2k -i in.cp2k + cd - +done + diff --git a/latest/_downloads/c234d1126d228edfa069b509e3d5c272/roy_gch.ipynb b/latest/_downloads/c234d1126d228edfa069b509e3d5c272/roy_gch.ipynb new file mode 100644 index 00000000..51f76638 --- /dev/null +++ b/latest/_downloads/c234d1126d228edfa069b509e3d5c272/roy_gch.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Generalized Convex Hull construction for the polymorphs of ROY\n\nThis notebook analyzes the structures of 264 polymorphs of ROY, from\n[Beran et Al, Chemical Science\n(2022)](https://doi.org/10.1039/D1SC06074K)_, comparing the\nconventional density-energy convex hull with a Generalized Convex Hull\n(GCH) analysis (see [Anelli et al., Phys. Rev. Materials\n(2018)](https://doi.org/10.1103/PhysRevMaterials.2.103804)_).\nIt uses features computed with [rascaline](https://github.com/lab-cosmo/rascaline)_\nand uses the directional convex hull function from\n[scikit-matter](https://github.com/lab-cosmo/scikit-matter)_\nto make the figure.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import chemiscope\nimport matplotlib.tri\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom metatensor import mean_over_samples\nfrom rascaline import SoapPowerSpectrum\nfrom sklearn.decomposition import PCA\nfrom skmatter.datasets import load_roy_dataset\nfrom skmatter.sample_selection import DirectionalConvexHull" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loads the structures (that also contain properties in the ``info`` field)\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "roy_data = load_roy_dataset()\n\nstructures = roy_data[\"structures\"]\n\ndensity = np.array([s.info[\"density\"] for s in structures])\nenergy = np.array([s.info[\"energy\"] for s in structures])\nstructype = np.array([s.info[\"type\"] for s in structures])\niknown = np.where(structype == \"known\")[0]\niothers = np.where(structype != \"known\")[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Energy-density hull\n\nThe Directional Convex Hull routines can be used to compute a\nconventional density-energy hull\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dch_builder = DirectionalConvexHull(low_dim_idx=[0])\ndch_builder.fit(density.reshape(-1, 1), energy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the indices of the selection, and compute the distance from\nthe hull\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sel = dch_builder.selected_idx_\ndch_dist = dch_builder.score_samples(density.reshape(-1, 1), energy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hull energies\n\nStructures on the hull are stable with respect to synthesis at constant\nmolar volume. Any other structure would lower the energy by decomposing\ninto a mixture of the two nearest structures along the hull. Given that\nthe lattice energy is an imperfect proxy for the free energy, and that\nsynthesis can be performed in other ways than by fixing the density,\nstructures that are not exactly on the hull might also be stable. One\ncan compute a \u201chull energy\u201d as an indication of how close these\nstructures are to being stable.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\nax.scatter(density, energy, c=dch_dist, marker=\".\")\nssel = sel[np.argsort(density[sel])]\nax.plot(density[ssel], energy[ssel], \"k--\")\nax.set_xlabel(\"density / g/cm$^3$\")\nax.set_ylabel(\"energy / kJ/mol\")\n\nprint(\n f\"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol\"\n)\nprint(f\"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interactive visualization\n\nYou can also visualize the hull with ``chemiscope``.\nThis runs only in a notebook, and\nrequires having the ``chemiscope`` package installed.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = chemiscope.show(\n structures,\n dict(\n energy=energy,\n density=density,\n hull_energy=dch_dist,\n structure_type=structype,\n ),\n settings={\n \"map\": {\n \"x\": {\"property\": \"density\"},\n \"y\": {\"property\": \"energy\"},\n \"color\": {\"property\": \"hull_energy\"},\n \"symbol\": \"structure_type\",\n \"size\": {\"factor\": 35},\n },\n \"structure\": [{\"unitCell\": True, \"supercell\": {\"0\": 2, \"1\": 2, \"2\": 2}}],\n },\n)\n\n\nif chemiscope.jupyter._is_running_in_notebook():\n from IPython.display import display\n\n display(cs)\nelse:\n cs.save(\"roy_ch.json.gz\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Convex Hull\n\nA GCH is a similar construction, in which generic structural descriptors\nare used in lieu of composition, density or other thermodynamic\nconstraints. The idea is that configurations that are found close to the\nGCH are locally stable with respect to structurally-similar\nconfigurations. In other terms, one can hope to find a thermodynamic\nconstraint (i.e.\u00a0synthesis conditions) that act differently on these\nstructures in comparison with the others, and may potentially stabilize\nthem.\n\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute structural descriptors\n\nA first step is to computes suitable ML descriptors. Here we have used\n``rascaline`` to evaluate average SOAP features for the structures.\nIf you don't want to install these dependencies for this example you\ncan also use the pre-computed features, but you can use this as a stub\nto apply this analysis to other chemical systems\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "hypers = {\n \"cutoff\": 4,\n \"max_radial\": 6,\n \"max_angular\": 4,\n \"atomic_gaussian_width\": 0.7,\n \"cutoff_function\": {\"ShiftedCosine\": {\"width\": 0.5}},\n \"radial_basis\": {\"Gto\": {\"accuracy\": 1e-6}},\n \"center_atom_weight\": 1.0,\n}\ncalculator = SoapPowerSpectrum(**hypers)\nrho2i = calculator.compute(structures)\nrho2i = rho2i.keys_to_samples([\"species_center\"]).keys_to_properties(\n [\"species_neighbor_1\", \"species_neighbor_2\"]\n)\nrho2i_structure = mean_over_samples(rho2i, sample_names=[\"center\", \"species_center\"])\nnp.savez(\"roy_features.npz\", feats=rho2i_structure.block(0).values)\n\n\n# features = roy_data[\"features\"]\nfeatures = rho2i_structure.block(0).values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PCA projection\n\nComputes PCA projection to generate low-dimensional descriptors that\nreflect structural diversity. Any other dimensionality reduction scheme\ncould be used in a similar fashion.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pca = PCA(n_components=4)\npca_features = pca.fit_transform(features)\n\nfig, ax = plt.subplots(1, 1, figsize=(6, 4))\nscatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=energy)\nax.set_xlabel(\"PCA[1]\")\nax.set_ylabel(\"PCA[2]\")\ncbar = fig.colorbar(scatter, ax=ax)\ncbar.set_label(\"energy / kJ/mol\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Builds the Generalized Convex Hull\n\nBuilds a convex hull on the first two PCA features\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dch_builder = DirectionalConvexHull(low_dim_idx=[0, 1])\ndch_builder.fit(pca_features, energy)\nsel = dch_builder.selected_idx_\ndch_dist = dch_builder.score_samples(pca_features, energy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generates a 3D Plot\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "triang = matplotlib.tri.Triangulation(pca_features[sel, 0], pca_features[sel, 1])\nfig = plt.figure(figsize=(7, 5), tight_layout=True)\nax = fig.add_subplot(projection=\"3d\")\nax.plot_trisurf(triang, energy[sel], color=\"gray\")\nax.scatter(pca_features[:, 0], pca_features[:, 1], energy, c=dch_dist)\nax.set_xlabel(\"PCA[1]\")\nax.set_ylabel(\"PCA[2]\")\nax.set_zlabel(\"energy / kJ/mol\\n \\n\", labelpad=11)\nax.view_init(25, 110)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GCH construction improves the separation between the hull energies\nof \u201cknown\u201d and hypothetical polymorphs (compare with the density-energy\nvalues above)\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\n f\"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol\"\n)\nprint(f\"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize in ``chemiscope``. This runs only in a notebook, and\nrequires having the ``chemiscope`` package installed.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for i, f in enumerate(structures):\n for j in range(len(pca_features[i])):\n f.info[\"pca_\" + str(j + 1)] = pca_features[i, j]\nstructure_properties = chemiscope.extract_properties(structures)\nstructure_properties.update({\"per_atom_energy\": energy, \"hull_energy\": dch_dist})\n\n# shows chemiscope if not run in terminal\n\ncs = chemiscope.show(\n frames=structures,\n properties=structure_properties,\n meta={\n \"name\": \"GCH for ROY polymorphs\",\n \"description\": \"\"\"\nDemonstration of the Generalized Convex Hull construction for\npolymorphs of the ROY molecule. Molecules that are closest to\nthe hull built on PCA-based structural descriptors and having the\ninternal energy predicted by electronic-structure calculations as\nthe z axis are the most thermodynamically stable. Indeed most of the\nknown polymorphs of ROY are on (or very close) to this hull.\n\"\"\",\n \"authors\": [\"Michele Ceriotti \"],\n \"references\": [\n 'A. Anelli, E. A. Engel, C. J. Pickard, and M. Ceriotti, \\\n \"Generalized convex hull construction for materials discovery,\" \\\n Physical Review Materials 2(10), 103804 (2018).',\n 'G. J. O. Beran, I. J. Sugden, C. Greenwell, D. H. Bowskill, \\\n C. C. Pantelides, and C. S. Adjiman, \"How many more polymorphs of \\\n ROY remain undiscovered,\" Chem. Sci. 13(5), 1288\u20131297 (2022).',\n ],\n },\n settings={\n \"map\": {\n \"x\": {\"property\": \"pca_1\"},\n \"y\": {\"property\": \"pca_2\"},\n \"z\": {\"property\": \"energy\"},\n \"symbol\": \"type\",\n \"color\": {\"property\": \"hull_energy\"},\n \"size\": {\n \"factor\": 35,\n \"mode\": \"linear\",\n \"property\": \"\",\n \"reverse\": True,\n },\n },\n \"structure\": [\n {\n \"bonds\": True,\n \"unitCell\": True,\n \"keepOrientation\": True,\n }\n ],\n },\n)\n\nif chemiscope.jupyter._is_running_in_notebook():\n from IPython.display import display\n\n display(cs)\nelse:\n cs.save(\"roy_gch.json.gz\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/latest/_downloads/cb2b037563d576b77358c4e2fe6b4432/gaas_map.py b/latest/_downloads/cb2b037563d576b77358c4e2fe6b4432/gaas_map.py new file mode 100644 index 00000000..00d181b7 --- /dev/null +++ b/latest/_downloads/cb2b037563d576b77358c4e2fe6b4432/gaas_map.py @@ -0,0 +1,252 @@ +""" +PCA/PCovR Visualization for the rattled GaAs training dataset +============================================================= + +This example uses ``rascaline`` and ``metatensor`` to compute +structural properties for the structures in a training for a ML model. +These are then used with simple dimensionality reduction algorithms +(implemented in ``sklearn`` and ``skmatter``) to obtain a simplified +description of the dataset, that is then visualized using +``chemiscope``. + +""" + +import os + +import ase +import ase.io +import chemiscope +import numpy as np +import requests +from matplotlib import pyplot as plt +from metatensor import mean_over_samples +from rascaline import AtomicComposition, SoapPowerSpectrum +from sklearn.decomposition import PCA +from sklearn.linear_model import RidgeCV +from skmatter.decomposition import PCovR +from skmatter.preprocessing import StandardFlexibleScaler + + +###################################################################### +# First, we load the structures, extracting some of the properties for +# more convenient manipulation. These are +# :math:`\mathrm{Ga}_x\mathrm{As}_{1-x}` structures used in `Imbalzano & +# Ceriotti (2021) `__ to +# train a ML potential to describe the full composition range. +# + +filename = "gaas_training.xyz" +if not os.path.exists(filename): + url = f"https://zenodo.org/records/10566825/files/{filename}" + response = requests.get(url) + response.raise_for_status() + with open(filename, "wb") as f: + f.write(response.content) + +structures = ase.io.read(filename, ":") +energy = np.array([f.info["energy"] for f in structures]) +natoms = np.array([len(f) for f in structures]) + + +###################################################################### +# Remove atomic energy baseline +# ----------------------------- +# +# Energies from an electronic structure calculation contain a very large +# “self” contributions from the atoms, which can obscure the important +# differences in cohesive energies between structures. We can build an +# approximate model based on the chemical nature of the atoms, :math:`a_i` +# +# .. math:: E(A) = \sum_{i\in A} e_{a_i} +# +# where :math:`e_a` are atomic energies that can be determined by linear +# regression. +# + +# rascaline has an `AtomicComposition` calculator that streamlines +# this (simple) calculation +calculator = AtomicComposition(**{"per_structure": True}) +rho0 = calculator.compute(structures) + +# the descriptors are returned as a `TensorMap` object, that contains +# the composition data in a sparse storage format +rho0 + +# for easier manipulation, we extract the features as a dense vector +# of composition weights +comp_feats = rho0.keys_to_properties(["species_center"]).block(0).values + +# a one-liner to fit a linear model and compute "dressed energies" +atom_energy = ( + RidgeCV(alphas=np.geomspace(1e-8, 1e2, 20)) + .fit(comp_feats, energy) + .predict(comp_feats) +) +cohesive_peratom = (energy - atom_energy) / natoms + + +###################################################################### +# The baseline makes up a large fraction of the total energy, but actually +# the residual (which is the part that matters) is still large. +# + +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +ax.plot(energy / natoms, atom_energy / natoms, "b.") +ax.set_xlabel("Energy / (eV/atom)") +ax.set_ylabel("Atomic e. / (eV/atom)") +print(f"RMSE / (eV/atom): {np.sqrt(np.mean((cohesive_peratom)**2))}") + + +###################################################################### +# Compute structural descriptors +# ------------------------------ +# +# In order to visualize the structures as a low-dimensional map, we start +# by computing suitable ML descriptors. Here we have used ``rascaline`` to +# evaluate average SOAP features for the structures. +# + +# hypers for evaluating rascaline features +hypers = { + "cutoff": 4.5, + "max_radial": 6, + "max_angular": 4, + "atomic_gaussian_width": 0.3, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + "radial_basis": {"Gto": {"accuracy": 1e-6}}, + "center_atom_weight": 1.0, +} +calculator = SoapPowerSpectrum(**hypers) +rho2i = calculator.compute(structures) + +# neighbor types go to the keys for sparsity (this way one can +# compute a heterogeneous dataset without having blocks of zeros) +rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties( + ["species_neighbor_1", "species_neighbor_2"] +) + +# computes structure-level descriptors and then extracts +# the features as a dense array +rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"]) +rho2i = None # releases memory +features = rho2i_structure.block(0).values + + +###################################################################### +# We standardize (per atom) energy and features (computed as a *mean* over +# atomic environments) so that they can be combined on the same footings. +# + +sf_energy = StandardFlexibleScaler().fit_transform(cohesive_peratom.reshape(-1, 1)) +sf_feats = StandardFlexibleScaler().fit_transform(features) + + +###################################################################### +# PCA and PCovR projection +# ------------------------ +# +# Computes PCA projection to generate low-dimensional descriptors that +# reflect structural diversity. Any other dimensionality reduction scheme +# could be used in a similar fashion. +# +# We also compute the principal covariate regression (PCovR) descriptors, +# that reduce dimensionality while combining a variance preserving +# criterion with the requirement that the low-dimensional features are +# capable of estimating a target quantity (here, the energy). +# + +# PCA +pca = PCA(n_components=4) +pca_features = pca.fit_transform(sf_feats) + +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +scatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=cohesive_peratom) +ax.set_xlabel("PCA[1]") +ax.set_ylabel("PCA[2]") +cbar = fig.colorbar(scatter, ax=ax) +cbar.set_label("energy / eV/at.") + +# computes PCovR map +pcovr = PCovR(n_components=4) +pcovr_features = pcovr.fit_transform(sf_feats, sf_energy) + +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +scatter = ax.scatter(pcovr_features[:, 0], pcovr_features[:, 1], c=cohesive_peratom) +ax.set_xlabel("PCovR[1]") +ax.set_ylabel("PCovR[2]") +cbar = fig.colorbar(scatter, ax=ax) +cbar.set_label("energy / (eV/at.)") + + +###################################################################### +# Chemiscope visualization +# ------------------------ +# +# Visualizes the structure-property map using a chemiscope widget (and +# generates a .json file that can be viewed on +# `chemiscope.org `__). +# + +# extracts force data (adding considerably to the dataset size...) +force_vectors = chemiscope.ase_vectors_to_arrows(structures, scale=1) +force_vectors["parameters"]["global"]["color"] = 0x505050 + +# adds properties to the ASE frames +for i, f in enumerate(structures): + for j in range(len(pca_features[i])): + f.info["pca_" + str(j + 1)] = pca_features[i, j] +for i, f in enumerate(structures): + for j in range(len(pcovr_features[i])): + f.info["pcovr_" + str(j + 1)] = pcovr_features[i, j] +for i, f in enumerate(structures): + f.info["cohesive_energy"] = cohesive_peratom[i] + f.info["x_ga"] = comp_feats[i, 0] / comp_feats[i].sum() + +# it would also be easy to add the properties manually, this is just a dictionary +structure_properties = chemiscope.extract_properties(structures) + +cs = chemiscope.show( + frames=structures, + properties=structure_properties, + shapes={"forces": force_vectors}, + # the settings are a tad verbose, but give full control over the visualization + settings={ + "map": { + "x": {"property": "pcovr_1"}, + "y": {"property": "pcovr_2"}, + "color": {"property": "x_ga"}, + }, + "structure": [ + { + "bonds": True, + "unitCell": True, + "shape": ["forces"], + "keepOrientation": False, + } + ], + }, + meta={ + "name": "GaAs training data", + "description": """ +A collection of Ga(x)As(1-x) structures to train a MLIP, +including force and energy data. +""", + "authors": ["Giulio Imbalzano", "Michele Ceriotti"], + "references": [ + """ +G. Imbalzano and M. Ceriotti, 'Modeling the Ga/As binary system across +temperatures and compositions from first principles,' +Phys. Rev. Materials 5(6), 063804 (2021). +""", + "Original dataset: https://archive.materialscloud.org/record/2021.226", + ], + }, +) + +# shows chemiscope if run in a jupyter environment +if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) +else: + cs.save("gaas_map.chemiscope.json.gz") diff --git a/latest/_images/sphx_glr_gaas_map_001.png b/latest/_images/sphx_glr_gaas_map_001.png new file mode 100644 index 0000000000000000000000000000000000000000..9820fd08ebf02bd9a51ac94f37a95fc46a5f5035 GIT binary patch literal 16699 zcmeHvWn5MJyXOK#1OyC9@~9vUqJT6iB5YcuB@{$TLb~B#pn`y#5>P_AHr*{E0wUer z9g@=XEYJV`KX+#C%zZI$=Y>bvti9qF&-1Ny-l!_el9AAnAP7Q+k-MXYAo$t{f){-1 zBz$5g$n*jJ5^<2xcDQfz#KHNIoe84!$ieohjl)xOqraR??Ci~LtgrJ5^Iqrv%gn*S z)?Sp4&+6X?@Y>j!@-@t8--nYB+sf(KBM8MK^bcODREjx*+)TyXxux#%ae3HPFU;iV z_}32J<7aq0X9O@qckMZ}zGrnPDz+p=%5xE1F3i(w=t?Vy;#X?s&d>Z-W6qgnt|#C5 zx_y)u%S0p4+a(|)vp?heAjfld8l4-wSj(9t?MKcbDmpL>Gd)&F&LAA8TWC=#b~@rO_#LN4 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' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/latest/_static/styles/furo-extensions.css b/latest/_static/styles/furo-extensions.css new file mode 100644 index 00000000..bc447f22 --- /dev/null +++ b/latest/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: var(--header-height)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 3.5rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(0.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the

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+============================== + +.. start-body + +This is an example how to perform single point calculations based on list of structures +using `CP2K `_ using its `reftraj functionality +`_. The inputs are a +set of structures in :download:`example.xyz` using the DFT parameters defined in +:download:`reftraj_template.cp2k` importing basis set and pseudopotentials from the +local CP2K installation. The reference DFT parameters are taken from `Cheng et al. Ab +initio thermodynamics of liquid and solid water 2019 +`_. Due to the small size of the test +structure and convergence issues, we have decreased the size of the ``CUTOFF_RADIUS`` +from :math:`6.0\,\mathrm{Å}` to :math:`3.0\,\mathrm{Å}`. For actual production +calculations adapt the template! + +To run this example, we use a bare executable called with ``cp2k``. If you want to use +another version you can either adjust the the names within this example or link your +binary with a different name to ``cp2k``. + +.. GENERATED FROM PYTHON SOURCE LINES 25-26 + +We start the example by importing the required packages. + +.. GENERATED FROM PYTHON SOURCE LINES 26-42 + +.. code-block:: Python + + + + import os + import re + import shutil + import subprocess + from os.path import basename, splitext + from typing import List, Union + + import ase.io + import ase.visualize.plot + import matplotlib.pyplot as plt + import numpy as np + from ase.calculators.cp2k import CP2K + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 43-46 + +Define necessary functions +-------------------------- +Next we below define necessary helper functions to run the example. + +.. GENERATED FROM PYTHON SOURCE LINES 46-80 + +.. code-block:: Python + + + + def write_reftraj(fname: str, frames: Union[ase.Atoms, List[ase.Atoms]]) -> None: + """Writes a list of ase atoms objects to a reference trajectory. + + A reference trajectory is the CP2K compatible format for the compuation of batches. + All frames must have the stoichiometry/composition. + """ + + if isinstance(frames, ase.Atoms): + frames = [frames] + + out = "" + for i, atoms in enumerate(frames): + if ( + len(atoms) != len(frames[0]) + or atoms.get_chemical_formula() != frames[0].get_chemical_formula() + ): + raise ValueError( + f"Atom symbols in frame {i},{atoms.get_chemical_formula()} are " + f"different compared to inital frame " + f"{frames[0].get_chemical_formula()}. " + "CP2K does not support changing atom types within a reftraj run!" + ) + + out += f"{len(atoms):>8}\n i = {i+1:>8}, time = {0:>12.3f}\n" + for atom in atoms: + pos = atom.position + out += f"{atom.symbol}{pos[0]:24.15f}{pos[1]:24.15f}{pos[2]:24.15f}\n" + out += "\n" + with open(fname, "w") as f: + f.write(out) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 81-117 + +.. code-block:: Python + + + + def write_cellfile(fname: str, frames: Union[ase.Atoms, List[ase.Atoms]]) -> None: + """Writes a cellfile for a list of ``ase.Atoms``. + + A Cellfile accompanies a reftraj containing the cell parameters. + """ + if isinstance(frames, ase.Atoms): + frames = [frames] + + out = ( + "# " + "Step " + "Time [fs] " + "Ax [Angstrom] " + "Ay [Angstrom] " + "Az [Angstrom] " + "Bx [Angstrom] " + "By [Angstrom] " + "Bz [Angstrom] " + "Cx [Angstrom] " + "Cy [Angstrom] " + "Cz [Angstrom] " + "Volume [Angstrom^3]\n" + ) + + for i, atoms in enumerate(frames): + out += f"{i+1:>8}{0:>12.3f}" + out += "".join([f"{c:>20.10f}" for c in atoms.cell.flatten()]) + out += f"{atoms.cell.volume:>25.10f}" + out += "\n" + + with open(fname, "w") as f: + f.write(out) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 118-139 + +.. code-block:: Python + + + + def write_cp2k_in( + fname: str, project_name: str, last_snapshot: int, cell: List[float] + ) -> None: + """Writes a cp2k input file from a template. + + Importantly, it writes the location of the basis set definitions, + determined from the path of the system CP2K install to the input file. + """ + + cp2k_in = open("reftraj_template.cp2k", "r").read() + + cp2k_in = cp2k_in.replace("//PROJECT//", project_name) + cp2k_in = cp2k_in.replace("//LAST_SNAPSHOT//", str(last_snapshot)) + cp2k_in = cp2k_in.replace("//CELL//", " ".join([f"{c:.6f}" for c in cell])) + + with open(fname, "w") as f: + f.write(cp2k_in) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 140-153 + +.. code-block:: Python + + + + def mkdir_force(*args, **kwargs) -> None: + """Warpper to ``os.mkdir``. + + The function does not raise an error if the directory already exists. + """ + try: + os.mkdir(*args, **kwargs) + except OSError: + pass + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 154-166 + +Prepare calculation inputs +-------------------------- +During this example we will create a directory named ``project_directory`` containing +the subdirectories for each stoichiometry. This is necessary, because CP2K can only +run calculations using a fixed stoichiometry at a time, using its ``reftraj`` +functionality. + +Below we define the general information for the CP2K run. This includes the reference +files for the structures, the ``project_name`` used to build the name of the +trajectory during the CP2K run, the ``project_directory`` where we store all +simulation output as well as the path ``write_to_file`` which is the name of the file +containing the computed energies and forces of the sinulation. + +.. GENERATED FROM PYTHON SOURCE LINES 166-172 + +.. code-block:: Python + + + frames_full = ase.io.read("example.xyz", ":") + project_name = "test_calcs" # name of the global PROJECT + project_directory = "production" + write_to_file = "out.xyz" + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 173-175 + +Below we show the initial configuration of two water molecules in a cubic box with a +side length of :math:`\approx 4\,\mathrm{Å}`. + +.. GENERATED FROM PYTHON SOURCE LINES 175-183 + +.. code-block:: Python + + + ase.visualize.plot.plot_atoms(frames_full[0]) + + plt.xlabel("Å") + plt.ylabel("Å") + + plt.show() + + + + +.. image-sg:: /examples/cp2k_run_batch/images/sphx_glr_setup_reftraj_001.png + :alt: setup reftraj + :srcset: /examples/cp2k_run_batch/images/sphx_glr_setup_reftraj_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 184-186 + +We now extreact the stoichiometry from the input dataset using ASE's +:py:meth:`ase.symbols.Symbols.get_chemical_formula` method. + +.. GENERATED FROM PYTHON SOURCE LINES 186-198 + +.. code-block:: Python + + + frames_dict = {} + + for atoms in frames_full: + chemical_formula = atoms.get_chemical_formula() + try: + frames_dict[chemical_formula] + except KeyError: + frames_dict[chemical_formula] = [] + + frames_dict[chemical_formula].append(atoms) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 199-203 + +Based on the stoichiometries we create one calculation subdirectories for the +calculations. (reftraj, input and cellfile). For our example this is only is one +directory named ``H4O2`` because our dataset consists only of a single structure with +two water molecules. + +.. GENERATED FROM PYTHON SOURCE LINES 203-221 + +.. code-block:: Python + + + mkdir_force(project_directory) + + for stoichiometry, frames in frames_dict.items(): + current_directory = f"{project_directory}/{stoichiometry}" + mkdir_force(current_directory) + + write_cp2k_in( + f"{current_directory}/in.cp2k", + project_name=project_name, + last_snapshot=len(frames), + cell=frames[0].cell.diagonal(), + ) + + ase.io.write(f"{current_directory}/init.xyz", frames[0]) + write_reftraj(f"{current_directory}/reftraj.xyz", frames) + write_cellfile(f"{current_directory}/reftraj.cell", frames) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 222-232 + +Run simulations +--------------- +Now we have all ingredients to run the simulations. Below we call the bash script +:download:`run_calcs.sh`. + +.. literalinclude:: run_calcs.sh + :language: bash + +This script will loop through all stoichiometry subdirectories and call the CP2K +engine. + +.. GENERATED FROM PYTHON SOURCE LINES 232-236 + +.. code-block:: Python + + + # run the bash script directly from this script + subprocess.run("bash run_calcs.sh", shell=True) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + CompletedProcess(args='bash run_calcs.sh', returncode=0) + + + +.. GENERATED FROM PYTHON SOURCE LINES 237-246 + +.. note:: + + For a usage on an HPC environment you can parallize the loop over the subfolders + and submit and single job per stoichiometry. + +Load results +------------ +After the simulation we load the results and perform a unit version from the default +CP2K output units (Bohr and Hartree) to Å and eV. + +.. GENERATED FROM PYTHON SOURCE LINES 246-251 + +.. code-block:: Python + + + cflength = 0.529177210903 # Bohr -> Å + cfenergy = 27.211386245988 # Hartree -> eV + cfforce = cfenergy / cflength # Hartree/Bohr -> eV/Å + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 252-255 + +Finally, we store the results as :class:`ase.Atoms` in the ``new_frames`` list and +write them to the ``project_directory`` using the ``new_fname``. Here it will be +written to ``production/out_dft.xyz``. + +.. GENERATED FROM PYTHON SOURCE LINES 255-288 + +.. code-block:: Python + + + new_frames = [] + + for stoichiometry, frames in frames_dict.items(): + current_directory = f"{project_directory}/{stoichiometry}" + + frames_dft = ase.io.read(f"{current_directory}/{project_name}-pos-1.xyz", ":") + forces_dft = ase.io.read(f"{current_directory}/{project_name}-frc-1.xyz", ":") + cell_dft = np.atleast_2d(np.loadtxt(f"{current_directory}/{project_name}-1.cell"))[ + :, 2:-1 + ] + + for i_atoms, atoms in enumerate(frames_dft): + frames_ref = frames[i_atoms] + + # Check consistent positions + if not np.allclose(atoms.positions, frames_ref.positions): + raise ValueError(f"Positions in frame {i_atoms} are not the same.") + + # Check consistent cell + if not np.allclose(frames_ref.cell.flatten(), cell_dft[i_atoms]): + raise ValueError(f"Cell dimensions in frame {i_atoms} are not the same.") + + atoms.info["E"] *= cfenergy + atoms.pbc = True + atoms.cell = frames_ref.cell + atoms.set_array("forces", cfforce * forces_dft[i_atoms].positions) + + new_frames += frames_dft + + new_fname = f"{splitext(basename(write_to_file))[0]}_dft.xyz" + ase.io.write(f"{project_directory}/{new_fname}", new_frames) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 289-298 + +Perform calculations using ASE calculator +----------------------------------------- +Above we performed the calculations using an external bash script. ASE also provides a +calculator class that we can use the perform the caclulations with our input file +without a detour of writing files to disk. + +To use the ASE calculator together with a custom input script this requires some +adjustments. First the name of the executable that has the exact name ``cp2k_shell``. +We create a symlink to follow this requirement. + +.. GENERATED FROM PYTHON SOURCE LINES 298-304 + +.. code-block:: Python + + + try: + os.symlink(shutil.which("cp2k"), "cp2k_shell.ssmp") + except OSError: + pass + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 305-306 + +Next, we load the input file abd remove ``GLOBAL`` section because from it + +.. GENERATED FROM PYTHON SOURCE LINES 306-312 + +.. code-block:: Python + + + inp = open("./production/H4O2/in.cp2k", "r").read() + inp = re.sub( + f"{re.escape('&GLOBAL')}.*?{re.escape('&END GLOBAL')}", "", inp, flags=re.DOTALL + ) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 313-315 + +Afterwards we define the :py:class:`ase.calculators.cp2k.CP2K`` calculator. Note that +we disable all parameters because we want to use all options from our input file + +.. GENERATED FROM PYTHON SOURCE LINES 315-332 + +.. code-block:: Python + + + calc = CP2K( + inp=inp, + max_scf=None, + cutoff=None, + xc=None, + force_eval_method=None, + basis_set=None, + pseudo_potential=None, + basis_set_file=None, + potential_file=None, + stress_tensor=False, + poisson_solver=None, + print_level=None, + command="./cp2k_shell.ssmp --shell", + ) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 333-334 + +We now load a new struture, add the calculator and perform the computation. + +.. GENERATED FROM PYTHON SOURCE LINES 334-338 + +.. code-block:: Python + + + atoms = ase.io.read("example.xyz") + atoms.set_calculator(calc) + # atoms.get_potential_energy() + + + + + + + + +.. _sphx_glr_download_examples_cp2k_run_batch_setup_reftraj.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: setup_reftraj.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: setup_reftraj.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/latest/_sources/examples/cp2k_run_batch/sg_execution_times.rst.txt b/latest/_sources/examples/cp2k_run_batch/sg_execution_times.rst.txt new file mode 100644 index 00000000..55ff2a95 --- /dev/null +++ b/latest/_sources/examples/cp2k_run_batch/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_examples_cp2k_run_batch_sg_execution_times: + + +Computation times +================= +**00:23.423** total execution time for 1 file **from examples/cp2k_run_batch**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_cp2k_run_batch_setup_reftraj.py` (``setup_reftraj.py``) + - 00:23.423 + - 0.0 diff --git a/latest/_sources/examples/gaas_map/gaas_map.rst.txt b/latest/_sources/examples/gaas_map/gaas_map.rst.txt new file mode 100644 index 00000000..e5ce8ccd --- /dev/null +++ b/latest/_sources/examples/gaas_map/gaas_map.rst.txt @@ -0,0 +1,428 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/gaas_map/gaas_map.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_gaas_map_gaas_map.py: + + +PCA/PCovR Visualization for the rattled GaAs training dataset +============================================================= + +This example uses ``rascaline`` and ``metatensor`` to compute +structural properties for the structures in a training for a ML model. +These are then used with simple dimensionality reduction algorithms +(implemented in ``sklearn`` and ``skmatter``) to obtain a simplified +description of the dataset, that is then visualized using +``chemiscope``. + +.. GENERATED FROM PYTHON SOURCE LINES 13-30 + +.. code-block:: Python + + + import os + + import ase + import ase.io + import chemiscope + import numpy as np + import requests + from matplotlib import pyplot as plt + from metatensor import mean_over_samples + from rascaline import AtomicComposition, SoapPowerSpectrum + from sklearn.decomposition import PCA + from sklearn.linear_model import RidgeCV + from skmatter.decomposition import PCovR + from skmatter.preprocessing import StandardFlexibleScaler + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 31-37 + +First, we load the structures, extracting some of the properties for +more convenient manipulation. These are +:math:`\mathrm{Ga}_x\mathrm{As}_{1-x}` structures used in `Imbalzano & +Ceriotti (2021) `__ to +train a ML potential to describe the full composition range. + + +.. GENERATED FROM PYTHON SOURCE LINES 37-51 + +.. code-block:: Python + + + filename = "gaas_training.xyz" + if not os.path.exists(filename): + url = f"https://zenodo.org/records/10566825/files/{filename}" + response = requests.get(url) + response.raise_for_status() + with open(filename, "wb") as f: + f.write(response.content) + + structures = ase.io.read(filename, ":") + energy = np.array([f.info["energy"] for f in structures]) + natoms = np.array([len(f) for f in structures]) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 52-65 + +Remove atomic energy baseline +----------------------------- + +Energies from an electronic structure calculation contain a very large +“self” contributions from the atoms, which can obscure the important +differences in cohesive energies between structures. We can build an +approximate model based on the chemical nature of the atoms, :math:`a_i` + +.. math:: E(A) = \sum_{i\in A} e_{a_i} + +where :math:`e_a` are atomic energies that can be determined by linear +regression. + + +.. GENERATED FROM PYTHON SOURCE LINES 65-88 + +.. code-block:: Python + + + # rascaline has an `AtomicComposition` calculator that streamlines + # this (simple) calculation + calculator = AtomicComposition(**{"per_structure": True}) + rho0 = calculator.compute(structures) + + # the descriptors are returned as a `TensorMap` object, that contains + # the composition data in a sparse storage format + rho0 + + # for easier manipulation, we extract the features as a dense vector + # of composition weights + comp_feats = rho0.keys_to_properties(["species_center"]).block(0).values + + # a one-liner to fit a linear model and compute "dressed energies" + atom_energy = ( + RidgeCV(alphas=np.geomspace(1e-8, 1e2, 20)) + .fit(comp_feats, energy) + .predict(comp_feats) + ) + cohesive_peratom = (energy - atom_energy) / natoms + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 89-92 + +The baseline makes up a large fraction of the total energy, but actually +the residual (which is the part that matters) is still large. + + +.. GENERATED FROM PYTHON SOURCE LINES 92-100 + +.. code-block:: Python + + + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + ax.plot(energy / natoms, atom_energy / natoms, "b.") + ax.set_xlabel("Energy / (eV/atom)") + ax.set_ylabel("Atomic e. / (eV/atom)") + print(f"RMSE / (eV/atom): {np.sqrt(np.mean((cohesive_peratom)**2))}") + + + + + +.. image-sg:: /examples/gaas_map/images/sphx_glr_gaas_map_001.png + :alt: gaas map + :srcset: /examples/gaas_map/images/sphx_glr_gaas_map_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + RMSE / (eV/atom): 0.25095652580859984 + + + + +.. GENERATED FROM PYTHON SOURCE LINES 101-108 + +Compute structural descriptors +------------------------------ + +In order to visualize the structures as a low-dimensional map, we start +by computing suitable ML descriptors. Here we have used ``rascaline`` to +evaluate average SOAP features for the structures. + + +.. GENERATED FROM PYTHON SOURCE LINES 108-135 + +.. code-block:: Python + + + # hypers for evaluating rascaline features + hypers = { + "cutoff": 4.5, + "max_radial": 6, + "max_angular": 4, + "atomic_gaussian_width": 0.3, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + "radial_basis": {"Gto": {"accuracy": 1e-6}}, + "center_atom_weight": 1.0, + } + calculator = SoapPowerSpectrum(**hypers) + rho2i = calculator.compute(structures) + + # neighbor types go to the keys for sparsity (this way one can + # compute a heterogeneous dataset without having blocks of zeros) + rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties( + ["species_neighbor_1", "species_neighbor_2"] + ) + + # computes structure-level descriptors and then extracts + # the features as a dense array + rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"]) + rho2i = None # releases memory + features = rho2i_structure.block(0).values + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 136-139 + +We standardize (per atom) energy and features (computed as a *mean* over +atomic environments) so that they can be combined on the same footings. + + +.. GENERATED FROM PYTHON SOURCE LINES 139-144 + +.. code-block:: Python + + + sf_energy = StandardFlexibleScaler().fit_transform(cohesive_peratom.reshape(-1, 1)) + sf_feats = StandardFlexibleScaler().fit_transform(features) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 145-157 + +PCA and PCovR projection +------------------------ + +Computes PCA projection to generate low-dimensional descriptors that +reflect structural diversity. Any other dimensionality reduction scheme +could be used in a similar fashion. + +We also compute the principal covariate regression (PCovR) descriptors, +that reduce dimensionality while combining a variance preserving +criterion with the requirement that the low-dimensional features are +capable of estimating a target quantity (here, the energy). + + +.. GENERATED FROM PYTHON SOURCE LINES 157-181 + +.. code-block:: Python + + + # PCA + pca = PCA(n_components=4) + pca_features = pca.fit_transform(sf_feats) + + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + scatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=cohesive_peratom) + ax.set_xlabel("PCA[1]") + ax.set_ylabel("PCA[2]") + cbar = fig.colorbar(scatter, ax=ax) + cbar.set_label("energy / eV/at.") + + # computes PCovR map + pcovr = PCovR(n_components=4) + pcovr_features = pcovr.fit_transform(sf_feats, sf_energy) + + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + scatter = ax.scatter(pcovr_features[:, 0], pcovr_features[:, 1], c=cohesive_peratom) + ax.set_xlabel("PCovR[1]") + ax.set_ylabel("PCovR[2]") + cbar = fig.colorbar(scatter, ax=ax) + cbar.set_label("energy / (eV/at.)") + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image-sg:: /examples/gaas_map/images/sphx_glr_gaas_map_002.png + :alt: gaas map + :srcset: /examples/gaas_map/images/sphx_glr_gaas_map_002.png + :class: sphx-glr-multi-img + + * + + .. image-sg:: /examples/gaas_map/images/sphx_glr_gaas_map_003.png + :alt: gaas map + :srcset: /examples/gaas_map/images/sphx_glr_gaas_map_003.png + :class: sphx-glr-multi-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 182-189 + +Chemiscope visualization +------------------------ + +Visualizes the structure-property map using a chemiscope widget (and +generates a .json file that can be viewed on +`chemiscope.org `__). + + +.. GENERATED FROM PYTHON SOURCE LINES 189-253 + +.. code-block:: Python + + + # extracts force data (adding considerably to the dataset size...) + force_vectors = chemiscope.ase_vectors_to_arrows(structures, scale=1) + force_vectors["parameters"]["global"]["color"] = 0x505050 + + # adds properties to the ASE frames + for i, f in enumerate(structures): + for j in range(len(pca_features[i])): + f.info["pca_" + str(j + 1)] = pca_features[i, j] + for i, f in enumerate(structures): + for j in range(len(pcovr_features[i])): + f.info["pcovr_" + str(j + 1)] = pcovr_features[i, j] + for i, f in enumerate(structures): + f.info["cohesive_energy"] = cohesive_peratom[i] + f.info["x_ga"] = comp_feats[i, 0] / comp_feats[i].sum() + + # it would also be easy to add the properties manually, this is just a dictionary + structure_properties = chemiscope.extract_properties(structures) + + cs = chemiscope.show( + frames=structures, + properties=structure_properties, + shapes={"forces": force_vectors}, + # the settings are a tad verbose, but give full control over the visualization + settings={ + "map": { + "x": {"property": "pcovr_1"}, + "y": {"property": "pcovr_2"}, + "color": {"property": "x_ga"}, + }, + "structure": [ + { + "bonds": True, + "unitCell": True, + "shape": ["forces"], + "keepOrientation": False, + } + ], + }, + meta={ + "name": "GaAs training data", + "description": """ + A collection of Ga(x)As(1-x) structures to train a MLIP, + including force and energy data. + """, + "authors": ["Giulio Imbalzano", "Michele Ceriotti"], + "references": [ + """ + G. Imbalzano and M. Ceriotti, 'Modeling the Ga/As binary system across + temperatures and compositions from first principles,' + Phys. Rev. Materials 5(6), 063804 (2021). + """, + "Original dataset: https://archive.materialscloud.org/record/2021.226", + ], + }, + ) + + # shows chemiscope if run in a jupyter environment + if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) + else: + cs.save("gaas_map.chemiscope.json.gz") + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /home/runner/work/software-cookbook/software-cookbook/.tox/gaas_map/conda/lib/python3.11/site-packages/chemiscope/jupyter.py:155: UserWarning: chemiscope.show only works inside a jupyter notebook + warnings.warn("chemiscope.show only works inside a jupyter notebook") + + + + + +.. _sphx_glr_download_examples_gaas_map_gaas_map.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: gaas_map.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: gaas_map.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/latest/_sources/examples/gaas_map/sg_execution_times.rst.txt b/latest/_sources/examples/gaas_map/sg_execution_times.rst.txt new file mode 100644 index 00000000..0fc2103d --- /dev/null +++ b/latest/_sources/examples/gaas_map/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_examples_gaas_map_sg_execution_times: + + +Computation times +================= +**00:50.880** total execution time for 1 file **from examples/gaas_map**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_gaas_map_gaas_map.py` (``gaas_map.py``) + - 00:50.880 + - 0.0 diff --git a/latest/_sources/examples/lode_linear/lode_tutorial.rst.txt b/latest/_sources/examples/lode_linear/lode_tutorial.rst.txt new file mode 100644 index 00000000..0ea32c2c --- /dev/null +++ b/latest/_sources/examples/lode_linear/lode_tutorial.rst.txt @@ -0,0 +1,724 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/lode_linear/lode_tutorial.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_lode_linear_lode_tutorial.py: + + +LODE Tutorial +============= + +This tutorial explains how Long range equivariant descriptors can be constructed using +rascaline and the resulting descriptors be used to construct a linear model with +equisolve + +First, import all the necessary packages + +.. GENERATED FROM PYTHON SOURCE LINES 13-24 + +.. code-block:: Python + + + import ase.io + import matplotlib.pyplot as plt + import metatensor + import numpy as np + from equisolve.numpy.models.linear_model import Ridge + from equisolve.utils.convert import ase_to_tensormap + from rascaline import AtomicComposition, LodeSphericalExpansion, SphericalExpansion + from rascaline.utils import PowerSpectrum + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 25-39 + +Step 0: Prepare Data Set +------------------------ + +Get structures +~~~~~~~~~~~~~~ + +We take a small subset of the dimer dataset from `A. Grisafi et al., +2021 `_ +for which we additionally calculated the forces. Each structure in the +dataset contains two small organic molecules which are extended along a +certain direction in the subsequent structures. + +For speeding up the calculations we already selected the first 130 +structures of the charge-charge structures. + +.. GENERATED FROM PYTHON SOURCE LINES 40-44 + +.. code-block:: Python + + + frames = ase.io.read("dataset/charge-charge.xyz", ":") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 45-52 + +Convert target properties to metatensor format +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If we want to train models using the +`equisolve `_ package, we need to +convert the target properties (in this case, the energies and forces) +into the appropriate format #justequistorethings + +.. GENERATED FROM PYTHON SOURCE LINES 53-57 + +.. code-block:: Python + + + y = ase_to_tensormap(frames, energy="energy", forces="forces") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 58-69 + +Step 1: Compute short-range and LODE features +--------------------------------------------- + +Define hypers and get the expansion coefficients :math:`\langle anlm | \rho_i \rangle` +and :math:`\langle anlm | V_i \rangle` + +The short-range and long-range descriptors have very similar hyperparameters. We +highlight the differences below. + +We first define the hyperparameters for the short-range (SR) part. These will be used +to create SOAP features. + +.. GENERATED FROM PYTHON SOURCE LINES 70-82 + +.. code-block:: Python + + + SR_HYPERS = { + "cutoff": 3.0, + "max_radial": 6, + "max_angular": 2, + "atomic_gaussian_width": 0.3, + "center_atom_weight": 1.0, + "radial_basis": {"Gto": {}}, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + } + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 83-84 + +And next the hyperparaters for the LODE / long-range (LR) part + +.. GENERATED FROM PYTHON SOURCE LINES 85-103 + +.. code-block:: Python + + + + LR_HYPERS = { + # Cutoff on which to project potential density + "cutoff": 3.0, + # keep max_radial slightly smaller than for SR part + "max_radial": 3, + # max_angular should be <= 4, more precisely, max_angular + potential_exponent < 10 + "max_angular": 2, + # keep at >=1, WARNING: CUBIC SCALING, do not use values <0.5 + "atomic_gaussian_width": 3.0, + "center_atom_weight": 1.0, + "radial_basis": {"Gto": {}}, + # the exponent p that determines the 1/r^p potential + "potential_exponent": 1, + } + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 104-106 + +We then use the above defined hyperparaters to define the per atom short range (sr) +and long range (sr) descriptors. + +.. GENERATED FROM PYTHON SOURCE LINES 106-111 + +.. code-block:: Python + + + calculator_sr = SphericalExpansion(**SR_HYPERS) + calculator_lr = LodeSphericalExpansion(**LR_HYPERS) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 112-132 + +Note that LODE requires periodic systems. Therefore, if the data set does not come +with periodic boundary conditions by default you can not use the data set and you will +face an error if you try to compute the features. + +As you notices the calculation of the long range features takes significant more time +compared to the sr features. + +Taking a look at the output we find that the resulting +:py:class:`metatensor.TensorMap` are quite similar in their structure. The short range +:py:class:`metatensor.TensorMap` contains more blocks due to the higher +``max_angular`` paramater we choosed above. + +Generate the rotational invariants (power spectra) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Rotationally invariant features can be obtained by taking two of the calculators that +were defines above. + +For the short-range part, we use the SOAP vector which is obtained by computing the +invariant combinations of the form :math:`\rho \otimes \rho`. + +.. GENERATED FROM PYTHON SOURCE LINES 133-138 + +.. code-block:: Python + + + ps_calculator_sr = PowerSpectrum(calculator_sr, calculator_sr) + ps_sr = ps_calculator_sr.compute(frames, gradients=["positions"]) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 139-145 + +We calculate gradients with respect to pistions by providing the +``gradients=["positions"]`` option to the +:py:meth:`rascaline.calculators.CalculatorBase.compute()` method. + +For the long-range part, we combine the long-range descriptor :math:`V` with one a +short-range density :math:`\rho` to get :math:`\rho \otimes V` features. + +.. GENERATED FROM PYTHON SOURCE LINES 146-151 + +.. code-block:: Python + + + ps_calculator_lr = PowerSpectrum(calculator_sr, calculator_lr) + ps_lr = ps_calculator_lr.compute(systems=frames, gradients=["positions"]) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 152-161 + +Step 2: Building a Simple Linear SR + LR Model with energy baselining +--------------------------------------------------------------------- + +Preprocessing (model dependent) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For our current model, we do not wish to treat the individual center and +neighbor species separately. Thus, we move the ``"species_center"`` key +into the ``sample`` direction, over which we will later sum over. + +.. GENERATED FROM PYTHON SOURCE LINES 162-167 + +.. code-block:: Python + + + ps_sr = ps_sr.keys_to_samples("species_center") + ps_lr = ps_lr.keys_to_samples("species_center") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 168-170 + +For linear models only: Sum features up over atoms (``samples``) in the same +structure. + +.. GENERATED FROM PYTHON SOURCE LINES 171-178 + +.. code-block:: Python + + + sample_names_to_sum = ["center", "species_center"] + + ps_sr = metatensor.sum_over_samples(ps_sr, sample_names=sample_names_to_sum) + ps_lr = metatensor.sum_over_samples(ps_lr, sample_names=sample_names_to_sum) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 179-185 + +Initialize tensormaps for energy baselining +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We add a simple extra descriptor :py:class:`rascaline.AtomicComposition` that stores +how many atoms of each chemical species are contained in the structures. This is used +for energy baselining. + +.. GENERATED FROM PYTHON SOURCE LINES 186-193 + +.. code-block:: Python + + + calculator_co = AtomicComposition(per_structure=False) + descriptor_co = calculator_co.compute(frames, gradients=["positions"]) + + co = descriptor_co.keys_to_properties(["species_center"]) + co = metatensor.sum_over_samples(co, sample_names=["center"]) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 194-215 + +The :py:class:`rascaline.AtomicComposition` calculator also allows to directly perform +the the sum over center atoms by using the following lines. + +.. code:: python + + descriptor_co = AtomicComposition(per_structure=True).compute(**compute_args) + co = descriptor_co.keys_to_properties(["species_center"]) + +Stack all the features together for linear model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +A linear model on SR + LR features can be thought of as a linear model +built on a feature vector that is simply the concatenation of the SR and +LR features. + +Furthermore, energy baselining can be performed by concatenating the information about +chemical species as well. There is an metatensor function called +:py:func:`metatensor.join()` for this purpose. Formally, we can write for the SR +model. + +X_sr: :math:`1 \oplus \left(\rho \otimes \rho\right)` + +.. GENERATED FROM PYTHON SOURCE LINES 216-220 + +.. code-block:: Python + + + X_sr = metatensor.join([co, ps_sr], axis="properties") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 221-228 + +We used the ``axis="properties"`` parameter since we want to concatenate along the +features/properties dimensions. + +For the long range model we can formerly write + +X_lr: :math:`1 \oplus \left(\rho \otimes \rho\right) \oplus \left(\rho \otimes +V\right)` + +.. GENERATED FROM PYTHON SOURCE LINES 229-233 + +.. code-block:: Python + + + X_lr = metatensor.join([co, ps_sr, ps_lr], axis="properties") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 234-241 + +The features are now ready! Let us now perform some actual learning. Below we +initialize two instances of the :py:class:`equisolve.numpy.models.linear_model.Ridge` +class. :py:class:`equisolve.numpy.models.linear_model.Ridge` will perform a regression +with respect to ``"values"`` (energies) and ``"positions"`` gradients (forces). + +If you only want a fit with respect to energies you can remove the gradients with +``metatensor.remove_gradients()`` + +.. GENERATED FROM PYTHON SOURCE LINES 242-247 + +.. code-block:: Python + + + clf_sr = Ridge() + clf_lr = Ridge() + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 248-254 + +Split training and target data into train and test dat +------------------------------------------------------ + +Split the training and the test data by the distance :math:`r_{\rm +train}=6\,\mathrm{Å}` between the center of mass of the two molecules. A structure +with a :math:`r_{\rm train}<6 {\rm Å}` is used for training. + +.. GENERATED FROM PYTHON SOURCE LINES 255-259 + +.. code-block:: Python + + + r_cut = 6.0 + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 260-262 + +We calculate the indices from the dataset by list comprehension. The center of mass +distance is stored in the ``"distance""`` attribute. + +.. GENERATED FROM PYTHON SOURCE LINES 263-268 + +.. code-block:: Python + + + idx_train = [i for i, f in enumerate(frames) if f.info["distance"] < r_cut] + idx_test = [i for i, f in enumerate(frames) if f.info["distance"] >= r_cut] + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 269-271 + +For doing the split we define two ``Labels`` instances and combine them in a +:py:class:`List`. + +.. GENERATED FROM PYTHON SOURCE LINES 272-278 + +.. code-block:: Python + + + samples_train = metatensor.Labels(["structure"], np.reshape(idx_train, (-1, 1))) + samples_test = metatensor.Labels(["structure"], np.reshape(idx_test, (-1, 1))) + grouped_labels = [samples_train, samples_test] + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 279-280 + +That we use as input to the :py:func:`metatensor.split()` function + +.. GENERATED FROM PYTHON SOURCE LINES 281-293 + +.. code-block:: Python + + + X_sr_train, X_sr_test = metatensor.split( + X_sr, axis="samples", grouped_labels=grouped_labels + ) + + X_lr_train, X_lr_test = metatensor.split( + X_lr, axis="samples", grouped_labels=grouped_labels + ) + + y_train, y_test = metatensor.split(y, axis="samples", grouped_labels=grouped_labels) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 294-301 + +Fit the model +------------- + +For this model, we use a very simple regularization scheme where all features are +regularized in the same way (the amount being controlled by the parameter ``alpha``). +For more advanced regularization schemes (regularizing energies and forces differently +and/or the SR and LR parts differently), see further down. + +.. GENERATED FROM PYTHON SOURCE LINES 302-307 + +.. code-block:: Python + + + clf_sr.fit(X_sr_train, y_train, alpha=1e-6) + clf_lr.fit(X_lr_train, y_train, alpha=1e-6) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 308-313 + +Evaluation +---------- + +For evaluating the model we calculate the RMSEs using the ``score()`` method. With the +``parameter_key`` parameter we select which RMSE should be calculated. + +.. GENERATED FROM PYTHON SOURCE LINES 314-334 + +.. code-block:: Python + + + print( + "SR: RMSE energies = " + f"{clf_sr.score(X_sr_test, y_test, parameter_key='values')[0]:.3f} eV" + ) + print( + "SR: RMSE forces = " + f"{clf_sr.score(X_sr_test, y_test, parameter_key='positions')[0]:.3f} eV/Å" + ) + + print( + "LR: RMSE energies = " + f"{clf_lr.score(X_lr_test, y_test, parameter_key='values')[0]:.3f} eV" + ) + print( + "LR: RMSE forces = " + f"{clf_lr.score(X_lr_test, y_test, parameter_key='positions')[0]:.3f} eV/Å" + ) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + SR: RMSE energies = 0.557 eV + SR: RMSE forces = 0.188 eV/Å + LR: RMSE energies = 0.158 eV + LR: RMSE forces = 0.178 eV/Å + + + + +.. GENERATED FROM PYTHON SOURCE LINES 335-341 + +We find that the RMSE of the energy and the force of the LR model is smaller compared +to the SR model. From this we conclude that the LR model performs better for the +selection of the dataset. + +We additionally, can plot of the binding energy as a function of the distance. For the +plot we select some properties from the dataset + +.. GENERATED FROM PYTHON SOURCE LINES 342-348 + +.. code-block:: Python + + + dist = np.array([f.info["distance"] for f in frames]) + energies = np.array([f.info["energy"] for f in frames]) + monomer_energies = np.array([f.info["energyA"] + f.info["energyB"] for f in frames]) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 349-350 + +and select only the indices corresponding to our test set. + +.. GENERATED FROM PYTHON SOURCE LINES 354-355 + +Next we calculate the predicted SR and LR ``TensorMaps``. + +.. GENERATED FROM PYTHON SOURCE LINES 356-361 + +.. code-block:: Python + + + y_sr_pred = clf_sr.predict(X_sr) + y_lr_pred = clf_lr.predict(X_lr) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 362-363 + +And, finally perform the plot. + +.. GENERATED FROM PYTHON SOURCE LINES 364-392 + +.. code-block:: Python + + + plt.scatter( + dist, y.block().values[:, 0] - monomer_energies, label="target data", color="black" + ) + + plt.scatter( + dist, + y_sr_pred.block().values[:, 0] - monomer_energies, + label="short range model", + marker="x", + ) + + plt.scatter( + dist, + y_lr_pred.block().values[:, 0] - monomer_energies, + label="long range model", + marker="s", + facecolor="None", + edgecolor="orange", + ) + + plt.xlabel("center of mass distance in Å") + plt.ylabel(r"$E - E_\mathrm{monomer}$ in eV") + plt.axvline(r_cut, c="red", label=r"$r_\mathrm{train}$") + + plt.legend() + plt.tight_layout() + plt.show() + + + +.. image-sg:: /examples/lode_linear/images/sphx_glr_lode_tutorial_001.png + :alt: lode tutorial + :srcset: /examples/lode_linear/images/sphx_glr_lode_tutorial_001.png + :class: sphx-glr-single-img + + + + + + +.. _sphx_glr_download_examples_lode_linear_lode_tutorial.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: lode_tutorial.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: lode_tutorial.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/latest/_sources/examples/lode_linear/sg_execution_times.rst.txt b/latest/_sources/examples/lode_linear/sg_execution_times.rst.txt new file mode 100644 index 00000000..eb80d4b3 --- /dev/null +++ b/latest/_sources/examples/lode_linear/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_examples_lode_linear_sg_execution_times: + + +Computation times +================= +**00:09.115** total execution time for 1 file **from examples/lode_linear**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_lode_linear_lode_tutorial.py` (``lode_tutorial.py``) + - 00:09.115 + - 0.0 diff --git a/latest/_sources/examples/roy_gch/roy_gch.rst.txt b/latest/_sources/examples/roy_gch/roy_gch.rst.txt new file mode 100644 index 00000000..b5e8b232 --- /dev/null +++ b/latest/_sources/examples/roy_gch/roy_gch.rst.txt @@ -0,0 +1,534 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/roy_gch/roy_gch.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_roy_gch_roy_gch.py: + + +Generalized Convex Hull construction for the polymorphs of ROY +============================================================== + +This notebook analyzes the structures of 264 polymorphs of ROY, from +`Beran et Al, Chemical Science +(2022) `__, comparing the +conventional density-energy convex hull with a Generalized Convex Hull +(GCH) analysis (see `Anelli et al., Phys. Rev. Materials +(2018) `__). +It uses features computed with `rascaline `__ +and uses the directional convex hull function from +`scikit-matter `__ +to make the figure. + +.. GENERATED FROM PYTHON SOURCE LINES 16-28 + +.. code-block:: Python + + + import chemiscope + import matplotlib.tri + import numpy as np + from matplotlib import pyplot as plt + from metatensor import mean_over_samples + from rascaline import SoapPowerSpectrum + from sklearn.decomposition import PCA + from skmatter.datasets import load_roy_dataset + from skmatter.sample_selection import DirectionalConvexHull + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 29-30 + +Loads the structures (that also contain properties in the ``info`` field) + +.. GENERATED FROM PYTHON SOURCE LINES 30-42 + +.. code-block:: Python + + + roy_data = load_roy_dataset() + + structures = roy_data["structures"] + + density = np.array([s.info["density"] for s in structures]) + energy = np.array([s.info["energy"] for s in structures]) + structype = np.array([s.info["type"] for s in structures]) + iknown = np.where(structype == "known")[0] + iothers = np.where(structype != "known")[0] + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 43-48 + +Energy-density hull +------------------- + +The Directional Convex Hull routines can be used to compute a +conventional density-energy hull + +.. GENERATED FROM PYTHON SOURCE LINES 48-52 + +.. code-block:: Python + + + dch_builder = DirectionalConvexHull(low_dim_idx=[0]) + dch_builder.fit(density.reshape(-1, 1), energy) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 53-55 + +We can get the indices of the selection, and compute the distance from +the hull + +.. GENERATED FROM PYTHON SOURCE LINES 55-60 + +.. code-block:: Python + + + sel = dch_builder.selected_idx_ + dch_dist = dch_builder.score_samples(density.reshape(-1, 1), energy) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 61-72 + +Hull energies +^^^^^^^^^^^^^ + +Structures on the hull are stable with respect to synthesis at constant +molar volume. Any other structure would lower the energy by decomposing +into a mixture of the two nearest structures along the hull. Given that +the lattice energy is an imperfect proxy for the free energy, and that +synthesis can be performed in other ways than by fixing the density, +structures that are not exactly on the hull might also be stable. One +can compute a “hull energy” as an indication of how close these +structures are to being stable. + +.. GENERATED FROM PYTHON SOURCE LINES 73-87 + +.. code-block:: Python + + + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + ax.scatter(density, energy, c=dch_dist, marker=".") + ssel = sel[np.argsort(density[sel])] + ax.plot(density[ssel], energy[ssel], "k--") + ax.set_xlabel("density / g/cm$^3$") + ax.set_ylabel("energy / kJ/mol") + + print( + f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol" + ) + print(f"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol") + + + + + +.. image-sg:: /examples/roy_gch/images/sphx_glr_roy_gch_001.png + :alt: roy gch + :srcset: /examples/roy_gch/images/sphx_glr_roy_gch_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Mean hull energy for 'known' stable structures 1.816657381014075 kJ/mol + Mean hull energy for 'other' structures 6.312730486304906 kJ/mol + + + + +.. GENERATED FROM PYTHON SOURCE LINES 88-95 + +Interactive visualization +^^^^^^^^^^^^^^^^^^^^^^^^^ + +You can also visualize the hull with ``chemiscope``. +This runs only in a notebook, and +requires having the ``chemiscope`` package installed. + + +.. GENERATED FROM PYTHON SOURCE LINES 95-124 + +.. code-block:: Python + + + cs = chemiscope.show( + structures, + dict( + energy=energy, + density=density, + hull_energy=dch_dist, + structure_type=structype, + ), + settings={ + "map": { + "x": {"property": "density"}, + "y": {"property": "energy"}, + "color": {"property": "hull_energy"}, + "symbol": "structure_type", + "size": {"factor": 35}, + }, + "structure": [{"unitCell": True, "supercell": {"0": 2, "1": 2, "2": 2}}], + }, + ) + + + if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) + else: + cs.save("roy_ch.json.gz") + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /home/runner/work/software-cookbook/software-cookbook/.tox/roy_gch/conda/lib/python3.11/site-packages/chemiscope/jupyter.py:155: UserWarning: chemiscope.show only works inside a jupyter notebook + warnings.warn("chemiscope.show only works inside a jupyter notebook") + + + + +.. GENERATED FROM PYTHON SOURCE LINES 125-137 + +Generalized Convex Hull +----------------------- + +A GCH is a similar construction, in which generic structural descriptors +are used in lieu of composition, density or other thermodynamic +constraints. The idea is that configurations that are found close to the +GCH are locally stable with respect to structurally-similar +configurations. In other terms, one can hope to find a thermodynamic +constraint (i.e. synthesis conditions) that act differently on these +structures in comparison with the others, and may potentially stabilize +them. + + +.. GENERATED FROM PYTHON SOURCE LINES 140-148 + +Compute structural descriptors +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A first step is to computes suitable ML descriptors. Here we have used +``rascaline`` to evaluate average SOAP features for the structures. +If you don't want to install these dependencies for this example you +can also use the pre-computed features, but you can use this as a stub +to apply this analysis to other chemical systems + +.. GENERATED FROM PYTHON SOURCE LINES 148-171 + +.. code-block:: Python + + + hypers = { + "cutoff": 4, + "max_radial": 6, + "max_angular": 4, + "atomic_gaussian_width": 0.7, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + "radial_basis": {"Gto": {"accuracy": 1e-6}}, + "center_atom_weight": 1.0, + } + calculator = SoapPowerSpectrum(**hypers) + rho2i = calculator.compute(structures) + rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties( + ["species_neighbor_1", "species_neighbor_2"] + ) + rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"]) + np.savez("roy_features.npz", feats=rho2i_structure.block(0).values) + + + # features = roy_data["features"] + features = rho2i_structure.block(0).values + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 172-178 + +PCA projection +^^^^^^^^^^^^^^ + +Computes PCA projection to generate low-dimensional descriptors that +reflect structural diversity. Any other dimensionality reduction scheme +could be used in a similar fashion. + +.. GENERATED FROM PYTHON SOURCE LINES 178-190 + +.. code-block:: Python + + + pca = PCA(n_components=4) + pca_features = pca.fit_transform(features) + + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + scatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=energy) + ax.set_xlabel("PCA[1]") + ax.set_ylabel("PCA[2]") + cbar = fig.colorbar(scatter, ax=ax) + cbar.set_label("energy / kJ/mol") + + + + + +.. image-sg:: /examples/roy_gch/images/sphx_glr_roy_gch_002.png + :alt: roy gch + :srcset: /examples/roy_gch/images/sphx_glr_roy_gch_002.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 191-195 + +Builds the Generalized Convex Hull +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Builds a convex hull on the first two PCA features + +.. GENERATED FROM PYTHON SOURCE LINES 195-202 + +.. code-block:: Python + + + dch_builder = DirectionalConvexHull(low_dim_idx=[0, 1]) + dch_builder.fit(pca_features, energy) + sel = dch_builder.selected_idx_ + dch_dist = dch_builder.score_samples(pca_features, energy) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 203-205 + +Generates a 3D Plot + + +.. GENERATED FROM PYTHON SOURCE LINES 205-217 + +.. code-block:: Python + + + triang = matplotlib.tri.Triangulation(pca_features[sel, 0], pca_features[sel, 1]) + fig = plt.figure(figsize=(7, 5), tight_layout=True) + ax = fig.add_subplot(projection="3d") + ax.plot_trisurf(triang, energy[sel], color="gray") + ax.scatter(pca_features[:, 0], pca_features[:, 1], energy, c=dch_dist) + ax.set_xlabel("PCA[1]") + ax.set_ylabel("PCA[2]") + ax.set_zlabel("energy / kJ/mol\n \n", labelpad=11) + ax.view_init(25, 110) + + + + + +.. image-sg:: /examples/roy_gch/images/sphx_glr_roy_gch_003.png + :alt: roy gch + :srcset: /examples/roy_gch/images/sphx_glr_roy_gch_003.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 218-221 + +The GCH construction improves the separation between the hull energies +of “known” and hypothetical polymorphs (compare with the density-energy +values above) + +.. GENERATED FROM PYTHON SOURCE LINES 221-228 + +.. code-block:: Python + + + print( + f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol" + ) + print(f"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol") + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Mean hull energy for 'known' stable structures 0.8537845044735912 kJ/mol + Mean hull energy for 'other' structures 5.19887158185005 kJ/mol + + + + +.. GENERATED FROM PYTHON SOURCE LINES 229-231 + +Visualize in ``chemiscope``. This runs only in a notebook, and +requires having the ``chemiscope`` package installed. + +.. GENERATED FROM PYTHON SOURCE LINES 231-293 + +.. code-block:: Python + + + for i, f in enumerate(structures): + for j in range(len(pca_features[i])): + f.info["pca_" + str(j + 1)] = pca_features[i, j] + structure_properties = chemiscope.extract_properties(structures) + structure_properties.update({"per_atom_energy": energy, "hull_energy": dch_dist}) + + # shows chemiscope if not run in terminal + + cs = chemiscope.show( + frames=structures, + properties=structure_properties, + meta={ + "name": "GCH for ROY polymorphs", + "description": """ + Demonstration of the Generalized Convex Hull construction for + polymorphs of the ROY molecule. Molecules that are closest to + the hull built on PCA-based structural descriptors and having the + internal energy predicted by electronic-structure calculations as + the z axis are the most thermodynamically stable. Indeed most of the + known polymorphs of ROY are on (or very close) to this hull. + """, + "authors": ["Michele Ceriotti "], + "references": [ + 'A. Anelli, E. A. Engel, C. J. Pickard, and M. Ceriotti, \ + "Generalized convex hull construction for materials discovery," \ + Physical Review Materials 2(10), 103804 (2018).', + 'G. J. O. Beran, I. J. Sugden, C. Greenwell, D. H. Bowskill, \ + C. C. Pantelides, and C. S. Adjiman, "How many more polymorphs of \ + ROY remain undiscovered," Chem. Sci. 13(5), 1288–1297 (2022).', + ], + }, + settings={ + "map": { + "x": {"property": "pca_1"}, + "y": {"property": "pca_2"}, + "z": {"property": "energy"}, + "symbol": "type", + "color": {"property": "hull_energy"}, + "size": { + "factor": 35, + "mode": "linear", + "property": "", + "reverse": True, + }, + }, + "structure": [ + { + "bonds": True, + "unitCell": True, + "keepOrientation": True, + } + ], + }, + ) + + if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) + else: + cs.save("roy_gch.json.gz") + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /home/runner/work/software-cookbook/software-cookbook/.tox/roy_gch/conda/lib/python3.11/site-packages/chemiscope/jupyter.py:155: UserWarning: chemiscope.show only works inside a jupyter notebook + warnings.warn("chemiscope.show only works inside a jupyter notebook") + + + + + +.. _sphx_glr_download_examples_roy_gch_roy_gch.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: roy_gch.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: roy_gch.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/latest/_sources/examples/roy_gch/sg_execution_times.rst.txt b/latest/_sources/examples/roy_gch/sg_execution_times.rst.txt new file mode 100644 index 00000000..9f3b8664 --- /dev/null +++ b/latest/_sources/examples/roy_gch/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_examples_roy_gch_sg_execution_times: + + +Computation times +================= +**00:18.141** total execution time for 1 file **from examples/roy_gch**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_roy_gch_roy_gch.py` (``roy_gch.py``) + - 00:18.141 + - 0.0 diff --git a/latest/_sources/examples/sample_selection/sample_selection.rst.txt b/latest/_sources/examples/sample_selection/sample_selection.rst.txt new file mode 100644 index 00000000..ecd0f86f --- /dev/null +++ b/latest/_sources/examples/sample_selection/sample_selection.rst.txt @@ -0,0 +1,814 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/sample_selection/sample_selection.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_sample_selection_sample_selection.py: + + +Sample and Feature Selection with FPS and CUR +============================================= + +.. start-body + +In this tutorial we generate descriptors using rascaline, then select a subset +of structures using both the farthest-point sampling (FPS) and CUR algorithms +implemented in scikit-matter. Finally, we also generate a selection of +the most important features using the same techniques. + +First, import all the necessary packages + +.. GENERATED FROM PYTHON SOURCE LINES 16-27 + +.. code-block:: Python + + + import ase.io + import chemiscope + import numpy as np + from matplotlib import pyplot as plt + from metatensor import mean_over_samples + from rascaline import SoapPowerSpectrum + from sklearn.decomposition import PCA + from skmatter import feature_selection, sample_selection + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 28-33 + +Load molecular data +------------------- + +Load 500 example BTO structures from file, reading them using +`ASE `_. + +.. GENERATED FROM PYTHON SOURCE LINES 33-38 + +.. code-block:: Python + + + # Load a subset of structures of the example dataset + n_frames = 500 + frames = ase.io.read("./dataset/input-fps.xyz", f":{n_frames}", format="extxyz") + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 39-43 + +Compute SOAP descriptors using rascaline +---------------------------------------- + +First, define the rascaline hyperparameters used to compute SOAP. + +.. GENERATED FROM PYTHON SOURCE LINES 43-74 + +.. code-block:: Python + + + + # rascaline hyperparameters + hypers = { + "cutoff": 6.0, + "max_radial": 8, + "max_angular": 6, + "atomic_gaussian_width": 0.3, + "cutoff_function": {"ShiftedCosine": {"width": 0.5}}, + "radial_basis": {"Gto": {"accuracy": 1e-6}}, + "radial_scaling": {"Willatt2018": {"exponent": 4, "rate": 1, "scale": 3.5}}, + "center_atom_weight": 1.0, + } + + # Generate a SOAP power spectrum + calculator = SoapPowerSpectrum(**hypers) + rho2i = calculator.compute(frames) + # Makes a dense block + rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties( + ["species_neighbor_1", "species_neighbor_2"] + ) + # Averages over atomic centers to compute structure features + rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"]) + + atom_dscrptr = rho2i.block(0).values + struct_dscrptr = rho2i_structure.block(0).values + + print("atom feature descriptor shape:", atom_dscrptr.shape) + print("structure feature descriptor shape:", struct_dscrptr.shape) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + atom feature descriptor shape: (20000, 2688) + structure feature descriptor shape: (500, 2688) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 75-84 + +Perform structure (i.e. sample) selection +----------------------------------------- + +Using FPS and CUR algorithms implemented in scikit-matter, select a subset of +the structures. skmatter assumes that our descriptor is represented as a 2D +matrix, with the samples along axis 0 and features along axis 1. + +For more info on the functions: `skmatter +`_ + +.. GENERATED FROM PYTHON SOURCE LINES 84-110 + +.. code-block:: Python + + + # Define the number of structures to select using FPS/CUR + n_structures = 25 + + # FPS sample selection + struct_fps = sample_selection.FPS(n_to_select=n_structures, initialize="random").fit( + struct_dscrptr + ) + struct_fps_idxs = struct_fps.selected_idx_ + + # CUR sample selection + struct_cur = sample_selection.CUR(n_to_select=n_structures).fit(struct_dscrptr) + struct_cur_idxs = struct_cur.selected_idx_ + + print("Structure indices obtained with FPS ", struct_fps_idxs) + print("Structure indices obtained with CUR ", struct_cur_idxs) + + # Slice structure descriptor along axis 0 to contain only the selected structures + struct_dscrptr_fps = struct_dscrptr[struct_fps_idxs, :] + struct_dscrptr_cur = struct_dscrptr[struct_cur_idxs, :] + assert struct_dscrptr_fps.shape == struct_dscrptr_cur.shape + + print("Structure descriptor shape before selection ", struct_dscrptr.shape) + print("Structure descriptor shape after selection ", struct_dscrptr_fps.shape) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + Structure indices obtained with FPS [172 15 356 257 326 317 167 71 140 496 114 416 267 285 423 458 40 194 + 466 253 407 195 436 324 289] + Structure indices obtained with CUR [438 140 398 289 110 386 326 40 68 39 407 238 239 229 412 2 324 194 + 198 400 339 285 257 223 436] + Structure descriptor shape before selection (500, 2688) + Structure descriptor shape after selection (25, 2688) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 111-120 + +Visualize selected structures +----------------------------- + +sklearn can be used to perform PCA dimensionality reduction on the SOAP +descriptors. The resulting PC coordinates can be used to visualize the the +data alongside their structures in a chemiscope widget. + +Note: chemiscope widgets are not currently integrated into our sphinx gallery: +coming soon. + +.. GENERATED FROM PYTHON SOURCE LINES 120-127 + +.. code-block:: Python + + + + # Generate a structure PCA + struct_dscrptr_pca = PCA(n_components=2).fit_transform(struct_dscrptr) + assert struct_dscrptr_pca.shape == (n_frames, 2) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 128-133 + +Plot the PCA map +~~~~~~~~~~~~~~~~ + +Notice how the selected points avoid the densely-sampled area, and cover +the periphery of the dataset + +.. GENERATED FROM PYTHON SOURCE LINES 133-156 + +.. code-block:: Python + + + # Matplotlib plot + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + scatter = ax.scatter(struct_dscrptr_pca[:, 0], struct_dscrptr_pca[:, 1], c="red") + ax.plot( + struct_dscrptr_pca[struct_cur_idxs, 0], + struct_dscrptr_pca[struct_cur_idxs, 1], + "kx", + label="CUR selection", + ) + ax.plot( + struct_dscrptr_pca[struct_fps_idxs, 0], + struct_dscrptr_pca[struct_fps_idxs, 1], + "ko", + fillstyle="none", + label="FPS selection", + ) + ax.set_xlabel("PCA[1]") + ax.set_ylabel("PCA[2]") + ax.legend() + fig.show() + + + + + +.. image-sg:: /examples/sample_selection/images/sphx_glr_sample_selection_001.png + :alt: sample selection + :srcset: /examples/sample_selection/images/sphx_glr_sample_selection_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 157-161 + +Creates a chemiscope viewer +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Interactive viewer (only works in notebooks) + +.. GENERATED FROM PYTHON SOURCE LINES 161-217 + +.. code-block:: Python + + + # Selected level + selection_levels = [] + for i in range(len(frames)): + level = 0 + if i in struct_cur_idxs: + level += 1 + if i in struct_fps_idxs: + level += 2 + if level == 0: + level = "Not selected" + elif level == 1: + level = "CUR" + elif level == 2: + level = "FPS" + else: + level = "FPS+CUR" + selection_levels.append(level) + + properties = chemiscope.extract_properties(frames) + + properties.update( + { + "PC1": struct_dscrptr_pca[:, 0], + "PC2": struct_dscrptr_pca[:, 1], + "selection": np.array(selection_levels), + } + ) + + print(properties) + + # Display with chemiscope. This currently does not work - as raised in issue #8 + # https://github.com/lab-cosmo/software-cookbook/issues/8 + cs = chemiscope.show( + frames, + properties=properties, + settings={ + "map": { + "x": {"property": "PC1"}, + "y": {"property": "PC2"}, + "color": {"property": "energy"}, + "symbol": "selection", + "size": {"factor": 50}, + }, + "structure": [{"unitCell": True}], + }, + ) + + if chemiscope.jupyter._is_running_in_notebook(): + from IPython.display import display + + display(cs) + else: + cs.save("sample_selection.json.gz") + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /home/runner/work/software-cookbook/software-cookbook/.tox/sample_selection/conda/lib/python3.11/site-packages/chemiscope/structures/_ase.py:89: UserWarning: the following structure properties properties are only defined for a subset of frames: ['stress']; they will be ignored + warnings.warn( + {'energy': {'target': 'structure', 'values': [-29615.075045239035, 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selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'CUR', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'CUR', + 'Not selected', 'CUR', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'FPS+CUR', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'CUR', 'Not selected', 'Not selected', + 'Not selected', 'FPS', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'FPS', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'FPS+CUR', 'Not selected', 'CUR', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'FPS', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'FPS', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'Not selected', 'Not selected', 'Not selected', 'Not selected', + 'FPS', 'Not selected', 'Not selected', 'Not selected'], + dtype='` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: sample_selection.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/latest/_sources/examples/sample_selection/sg_execution_times.rst.txt b/latest/_sources/examples/sample_selection/sg_execution_times.rst.txt new file mode 100644 index 00000000..1a308c0b --- /dev/null +++ b/latest/_sources/examples/sample_selection/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_examples_sample_selection_sg_execution_times: + + +Computation times +================= +**00:06.926** total execution time for 1 file **from examples/sample_selection**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_sample_selection_sample_selection.py` (``sample_selection.py``) + - 00:06.926 + - 0.0 diff --git a/latest/_sources/index.rst.txt b/latest/_sources/index.rst.txt new file mode 100644 index 00000000..6f0c01b4 --- /dev/null +++ b/latest/_sources/index.rst.txt @@ -0,0 +1,16 @@ +COSMO Software Cookbook +======================= + +.. include:: ../../README.rst + :start-after: marker-intro-start + :end-before: marker-intro-end + +.. toctree:: + :caption: Table of Contents + :maxdepth: 1 + + examples/roy_gch/roy_gch + examples/lode_linear/lode_tutorial + examples/sample_selection/sample_selection + examples/gaas_map/gaas_map + examples/cp2k_run_batch/setup_reftraj \ No newline at end of file diff --git a/latest/_sources/sg_execution_times.rst.txt b/latest/_sources/sg_execution_times.rst.txt new file mode 100644 index 00000000..9fe5c40b --- /dev/null +++ b/latest/_sources/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_sg_execution_times: + + +Computation times +================= +**00:23.423** total execution time for 1 file **from all galleries**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_cp2k_run_batch_setup_reftraj.py` (``../../examples/cp2k_run_batch/setup_reftraj.py``) + - 00:23.423 + - 0.0 diff --git a/latest/_static/basic.css b/latest/_static/basic.css new file mode 100644 index 00000000..30fee9d0 --- /dev/null +++ b/latest/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + 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Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/latest/_static/documentation_options.js b/latest/_static/documentation_options.js new file mode 100644 index 00000000..7e4c114f --- /dev/null +++ b/latest/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff 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and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // 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calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n contents.push({\n nav: item,\n content: content,\n });\n });\n\n // Sort contents by the order they appear in the DOM\n sortContents(contents);\n };\n\n /**\n * Detect which content is currently active\n */\n publicAPIs.detect = function () {\n // Get the active content\n var active = getActive(contents, settings);\n\n // if there's no active content, deactivate and bail\n if (!active) {\n if (current) {\n deactivate(current, settings);\n current = null;\n }\n return;\n }\n\n // If the active content is the one currently active, do nothing\n if (current && active.content === current.content) return;\n\n // Deactivate the current content and activate the new content\n deactivate(current, settings);\n activate(active, settings);\n\n // Update the currently active content\n current = active;\n };\n\n /**\n * Detect the active content on scroll\n * Debounced for performance\n */\n var scrollHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the current instantiation\n */\n var init = function () {\n // Merge user options into defaults\n settings = extend(defaults, options || {});\n\n // Setup variables based on the current DOM\n publicAPIs.setup();\n\n // Find the currently active content\n publicAPIs.detect();\n\n // Setup event listeners\n window.addEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.addEventListener(\"resize\", resizeHandler, false);\n }\n };\n\n //\n // Initialize and return the public APIs\n //\n\n init();\n return publicAPIs;\n };\n\n //\n // Return the Constructor\n //\n\n return Constructor;\n },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n if (Math.floor(header.getBoundingClientRect().top) == 0) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n 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\ No newline at end of file diff --git a/latest/_static/searchtools.js b/latest/_static/searchtools.js new file mode 100644 index 00000000..7918c3fa --- /dev/null +++ b/latest/_static/searchtools.js @@ -0,0 +1,574 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/latest/_static/sg_gallery-binder.css b/latest/_static/sg_gallery-binder.css new file mode 100644 index 00000000..420005d2 --- /dev/null +++ b/latest/_static/sg_gallery-binder.css @@ -0,0 +1,11 @@ +/* CSS for binder integration */ + +div.binder-badge { + margin: 1em auto; + vertical-align: middle; +} + +div.lite-badge { + margin: 1em auto; + vertical-align: middle; +} diff --git a/latest/_static/sg_gallery-dataframe.css b/latest/_static/sg_gallery-dataframe.css new file mode 100644 index 00000000..fac74c43 --- /dev/null +++ b/latest/_static/sg_gallery-dataframe.css @@ -0,0 +1,47 @@ +/* Pandas dataframe css */ +/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */ +html[data-theme="light"] { + --sg-text-color: #000; + --sg-tr-odd-color: #f5f5f5; + --sg-tr-hover-color: rgba(66, 165, 245, 0.2); +} +html[data-theme="dark"] { + --sg-text-color: #fff; + --sg-tr-odd-color: #373737; + --sg-tr-hover-color: rgba(30, 81, 122, 0.2); +} + +table.dataframe { + border: none !important; + border-collapse: collapse; + border-spacing: 0; + border-color: transparent; + color: var(--sg-text-color); + font-size: 12px; + table-layout: fixed; + width: auto; +} +table.dataframe thead { + border-bottom: 1px solid var(--sg-text-color); + vertical-align: bottom; +} +table.dataframe tr, +table.dataframe th, +table.dataframe td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} +table.dataframe th { + font-weight: bold; +} +table.dataframe tbody tr:nth-child(odd) { + background: var(--sg-tr-odd-color); +} +table.dataframe tbody tr:hover { + background: var(--sg-tr-hover-color); +} diff --git a/latest/_static/sg_gallery-rendered-html.css b/latest/_static/sg_gallery-rendered-html.css new file mode 100644 index 00000000..93dc2ffb --- /dev/null +++ b/latest/_static/sg_gallery-rendered-html.css @@ -0,0 +1,224 @@ +/* Adapted from notebook/static/style/style.min.css */ +html[data-theme="light"] { + --sg-text-color: #000; + --sg-background-color: #ffffff; + --sg-code-background-color: #eff0f1; + --sg-tr-hover-color: rgba(66, 165, 245, 0.2); + --sg-tr-odd-color: #f5f5f5; +} +html[data-theme="dark"] { + --sg-text-color: #fff; + --sg-background-color: #121212; + --sg-code-background-color: #2f2f30; + --sg-tr-hover-color: rgba(66, 165, 245, 0.2); + --sg-tr-odd-color: #1f1f1f; +} + +.rendered_html { + color: var(--sg-text-color); + /* any extras will just be numbers: */ +} +.rendered_html em { + font-style: italic; +} +.rendered_html strong { + font-weight: bold; +} +.rendered_html u { + text-decoration: underline; +} +.rendered_html :link { + text-decoration: underline; +} +.rendered_html :visited { + text-decoration: underline; +} +.rendered_html h1 { + font-size: 185.7%; + margin: 1.08em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h2 { + font-size: 157.1%; + margin: 1.27em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h3 { + font-size: 128.6%; + margin: 1.55em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h4 { + font-size: 100%; + margin: 2em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h5 { + font-size: 100%; + margin: 2em 0 0 0; + font-weight: bold; + line-height: 1.0; + font-style: italic; +} +.rendered_html h6 { + font-size: 100%; + margin: 2em 0 0 0; + font-weight: bold; + line-height: 1.0; + font-style: italic; +} +.rendered_html h1:first-child { + margin-top: 0.538em; +} +.rendered_html h2:first-child { + margin-top: 0.636em; +} +.rendered_html h3:first-child { + margin-top: 0.777em; +} +.rendered_html h4:first-child { + margin-top: 1em; +} +.rendered_html h5:first-child { + margin-top: 1em; +} +.rendered_html h6:first-child { + margin-top: 1em; +} +.rendered_html ul:not(.list-inline), +.rendered_html ol:not(.list-inline) { + padding-left: 2em; +} +.rendered_html ul { + list-style: disc; +} +.rendered_html ul ul { + list-style: square; + margin-top: 0; +} +.rendered_html ul ul ul { + list-style: circle; +} +.rendered_html ol { + list-style: decimal; +} +.rendered_html ol ol { + list-style: upper-alpha; + margin-top: 0; +} +.rendered_html ol ol ol { + list-style: lower-alpha; +} +.rendered_html ol ol ol ol { + list-style: lower-roman; +} +.rendered_html ol ol ol ol ol { + list-style: decimal; +} +.rendered_html * + ul { + margin-top: 1em; +} +.rendered_html * + ol { + margin-top: 1em; +} +.rendered_html hr { + color: var(--sg-text-color); + background-color: var(--sg-text-color); +} +.rendered_html pre { + margin: 1em 2em; + padding: 0px; + background-color: var(--sg-background-color); +} +.rendered_html code { + background-color: var(--sg-code-background-color); +} +.rendered_html p code { + padding: 1px 5px; +} +.rendered_html pre code { + background-color: var(--sg-background-color); +} +.rendered_html pre, +.rendered_html code { + border: 0; + color: var(--sg-text-color); + font-size: 100%; +} +.rendered_html blockquote { + margin: 1em 2em; +} +.rendered_html table { + margin-left: auto; + margin-right: auto; + border: none; + border-collapse: collapse; + border-spacing: 0; + color: var(--sg-text-color); + font-size: 12px; + table-layout: fixed; +} +.rendered_html thead { + border-bottom: 1px solid var(--sg-text-color); + vertical-align: bottom; +} +.rendered_html tr, +.rendered_html th, +.rendered_html td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} +.rendered_html th { + font-weight: bold; +} +.rendered_html tbody tr:nth-child(odd) { + background: var(--sg-tr-odd-color); +} +.rendered_html tbody tr:hover { + color: var(--sg-text-color); + background: var(--sg-tr-hover-color); +} +.rendered_html * + table { + margin-top: 1em; +} +.rendered_html p { + text-align: left; +} +.rendered_html * + p { + margin-top: 1em; +} +.rendered_html img { + display: block; + margin-left: auto; + margin-right: auto; +} +.rendered_html * + img { + margin-top: 1em; +} +.rendered_html img, +.rendered_html svg { + max-width: 100%; + height: auto; +} +.rendered_html img.unconfined, +.rendered_html svg.unconfined { + max-width: none; +} +.rendered_html .alert { + margin-bottom: initial; +} +.rendered_html * + .alert { + margin-top: 1em; +} +[dir="rtl"] .rendered_html p { + text-align: right; +} diff --git a/latest/_static/sg_gallery.css b/latest/_static/sg_gallery.css new file mode 100644 index 00000000..72227837 --- /dev/null +++ b/latest/_static/sg_gallery.css @@ -0,0 +1,342 @@ +/* +Sphinx-Gallery has compatible CSS to fix default sphinx themes +Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc, +scrolls, agogo, traditional, nature, haiku, pyramid +Tested for Read the Docs theme 0.1.7 */ + +/* Define light colors */ +:root, html[data-theme="light"], body[data-theme="light"]{ + --sg-tooltip-foreground: black; + --sg-tooltip-background: rgba(250, 250, 250, 0.9); + --sg-tooltip-border: #ccc transparent; + --sg-thumb-box-shadow-color: #6c757d40; + --sg-thumb-hover-border: #0069d9; + --sg-script-out: #888; + --sg-script-pre: #fafae2; + --sg-pytb-foreground: #000; + --sg-pytb-background: #ffe4e4; + --sg-pytb-border-color: #f66; + --sg-download-a-background-color: #ffc; + --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e); + --sg-download-a-border-color: 1px solid #c2c22d; + --sg-download-a-color: #000; + --sg-download-a-hover-background-color: #d5d57e; + --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1); + --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25); +} +@media(prefers-color-scheme: light) { + :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] { + --sg-tooltip-foreground: black; + --sg-tooltip-background: rgba(250, 250, 250, 0.9); + --sg-tooltip-border: #ccc transparent; + --sg-thumb-box-shadow-color: #6c757d40; + --sg-thumb-hover-border: #0069d9; + --sg-script-out: #888; + --sg-script-pre: #fafae2; + --sg-pytb-foreground: #000; + --sg-pytb-background: #ffe4e4; + --sg-pytb-border-color: #f66; + --sg-download-a-background-color: #ffc; + --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e); + --sg-download-a-border-color: 1px solid #c2c22d; + --sg-download-a-color: #000; + --sg-download-a-hover-background-color: #d5d57e; + --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1); + --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25); + } +} + +html[data-theme="dark"], body[data-theme="dark"] { + --sg-tooltip-foreground: white; + --sg-tooltip-background: rgba(10, 10, 10, 0.9); + --sg-tooltip-border: #333 transparent; + --sg-thumb-box-shadow-color: #79848d40; + --sg-thumb-hover-border: #003975; + --sg-script-out: rgb(179, 179, 179); + --sg-script-pre: #2e2e22; + --sg-pytb-foreground: #fff; + --sg-pytb-background: #1b1717; + --sg-pytb-border-color: #622; + --sg-download-a-background-color: #443; + --sg-download-a-background-image: linear-gradient(to bottom, #443, #221); + --sg-download-a-border-color: 1px solid #3a3a0d; + --sg-download-a-color: #fff; + --sg-download-a-hover-background-color: #616135; + --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1); + --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25); +} +@media(prefers-color-scheme: dark){ + html[data-theme="auto"], body[data-theme="auto"] { + --sg-tooltip-foreground: white; + --sg-tooltip-background: rgba(10, 10, 10, 0.9); + --sg-tooltip-border: #333 transparent; + --sg-thumb-box-shadow-color: #79848d40; + --sg-thumb-hover-border: #003975; + --sg-script-out: rgb(179, 179, 179); + --sg-script-pre: #2e2e22; + --sg-pytb-foreground: #fff; + --sg-pytb-background: #1b1717; + --sg-pytb-border-color: #622; + --sg-download-a-background-color: #443; + --sg-download-a-background-image: linear-gradient(to bottom, #443, #221); + --sg-download-a-border-color: 1px solid #3a3a0d; + --sg-download-a-color: #fff; + --sg-download-a-hover-background-color: #616135; + --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1); + --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25); + } +} + +.sphx-glr-thumbnails { + width: 100%; + margin: 0px 0px 20px 0px; + + /* align thumbnails on a grid */ + justify-content: space-between; + display: grid; + /* each grid column should be at least 160px (this will determine + the actual number of columns) and then take as much of the + remaining width as possible */ + grid-template-columns: repeat(auto-fill, minmax(160px, 1fr)); + gap: 15px; +} +.sphx-glr-thumbnails .toctree-wrapper { + /* hide empty toctree divs added to the DOM + by sphinx even though the toctree is hidden + (they would fill grid places with empty divs) */ + display: none; +} +.sphx-glr-thumbcontainer { + background: transparent; + -moz-border-radius: 5px; + -webkit-border-radius: 5px; + border-radius: 5px; + box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color); + + /* useful to absolutely position link in div */ + position: relative; + + /* thumbnail width should include padding and borders + and take all available space */ + box-sizing: border-box; + width: 100%; + padding: 10px; + border: 1px solid transparent; + + /* align content in thumbnail */ + display: flex; + flex-direction: column; + align-items: center; + gap: 7px; +} +.sphx-glr-thumbcontainer p { + position: absolute; + top: 0; + left: 0; +} +.sphx-glr-thumbcontainer p, +.sphx-glr-thumbcontainer p a { + /* link should cover the whole thumbnail div */ + width: 100%; + height: 100%; +} +.sphx-glr-thumbcontainer p a span { + /* text within link should be masked + (we are just interested in the href) */ + display: none; +} +.sphx-glr-thumbcontainer:hover { + border: 1px solid; + border-color: var(--sg-thumb-hover-border); + cursor: pointer; +} +.sphx-glr-thumbcontainer a.internal { + bottom: 0; + display: block; + left: 0; + box-sizing: border-box; + padding: 150px 10px 0; + position: absolute; + right: 0; + top: 0; +} +/* Next one is to avoid Sphinx traditional theme to cover all the +thumbnail with its default link Background color */ +.sphx-glr-thumbcontainer a.internal:hover { + background-color: transparent; +} + +.sphx-glr-thumbcontainer p { + margin: 0 0 0.1em 0; +} +.sphx-glr-thumbcontainer .figure { + margin: 10px; + width: 160px; +} +.sphx-glr-thumbcontainer img { + display: inline; + max-height: 112px; + max-width: 160px; +} +.sphx-glr-thumbcontainer[tooltip]:hover:after { + background: var(--sg-tooltip-background); + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + color: var(--sg-tooltip-foreground); + content: attr(tooltip); + padding: 10px; + z-index: 98; + width: 100%; + height: 100%; + position: absolute; + pointer-events: none; + top: 0; + box-sizing: border-box; + overflow: hidden; + backdrop-filter: blur(3px); +} + +.sphx-glr-script-out { + color: var(--sg-script-out); + display: flex; + gap: 0.5em; +} +.sphx-glr-script-out::before { + content: "Out:"; + /* These numbers come from the pre style in the pydata sphinx theme. This + * turns out to match perfectly on the rtd theme, but be a bit too low for + * the pydata sphinx theme. As I could not find a dimension to use that was + * scaled the same way, I just picked one option that worked pretty close for + * both. */ + line-height: 1.4; + padding-top: 10px; +} +.sphx-glr-script-out .highlight { + background-color: transparent; + /* These options make the div expand... */ + flex-grow: 1; + /* ... but also keep it from overflowing its flex container. */ + overflow: auto; +} +.sphx-glr-script-out .highlight pre { + background-color: var(--sg-script-pre); + border: 0; + max-height: 30em; + overflow: auto; + padding-left: 1ex; + /* This margin is necessary in the pydata sphinx theme because pre has a box + * shadow which would be clipped by the overflow:auto in the parent div + * above. */ + margin: 2px; + word-break: break-word; +} +.sphx-glr-script-out + p { + margin-top: 1.8em; +} +blockquote.sphx-glr-script-out { + margin-left: 0pt; +} +.sphx-glr-script-out.highlight-pytb .highlight pre { + color: var(--sg-pytb-foreground); + background-color: var(--sg-pytb-background); + border: 1px solid var(--sg-pytb-border-color); + margin-top: 10px; + padding: 7px; +} + +div.sphx-glr-footer { + text-align: center; +} + +div.sphx-glr-download { + margin: 1em auto; + vertical-align: middle; +} + +div.sphx-glr-download a { + background-color: var(--sg-download-a-background-color); + background-image: var(--sg-download-a-background-image); + border-radius: 4px; + border: 1px solid var(--sg-download-a-border-color); + color: var(--sg-download-a-color); + display: inline-block; + font-weight: bold; + padding: 1ex; + text-align: center; +} + +div.sphx-glr-download code.download { + display: inline-block; + white-space: normal; + word-break: normal; + overflow-wrap: break-word; + /* border and background are given by the enclosing 'a' */ + border: none; + background: none; +} + +div.sphx-glr-download a:hover { + box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2); + text-decoration: none; + background-image: none; + background-color: var(--sg-download-a-hover-background-color); +} + +.sphx-glr-example-title:target::before { + display: block; + content: ""; + margin-top: -50px; + height: 50px; + visibility: hidden; +} + +ul.sphx-glr-horizontal { + list-style: none; + padding: 0; +} +ul.sphx-glr-horizontal li { + display: inline; +} +ul.sphx-glr-horizontal img { + height: auto !important; +} + +.sphx-glr-single-img { + margin: auto; + display: block; + max-width: 100%; +} + +.sphx-glr-multi-img { + max-width: 42%; + height: auto; +} + +div.sphx-glr-animation { + margin: auto; + display: block; + max-width: 100%; +} +div.sphx-glr-animation .animation { + display: block; +} + +p.sphx-glr-signature a.reference.external { + -moz-border-radius: 5px; + -webkit-border-radius: 5px; + border-radius: 5px; + padding: 3px; + font-size: 75%; + text-align: right; + margin-left: auto; + display: table; +} + +.sphx-glr-clear { + clear: both; +} + +a.sphx-glr-backref-instance { + text-decoration: none; +} diff --git a/latest/_static/skeleton.css b/latest/_static/skeleton.css new file mode 100644 index 00000000..467c878c --- /dev/null +++ b/latest/_static/skeleton.css @@ -0,0 +1,296 @@ +/* Some sane resets. */ +html { + height: 100%; +} + +body { + margin: 0; + min-height: 100%; +} + +/* All the flexbox magic! */ +body, +.sb-announcement, +.sb-content, +.sb-main, +.sb-container, +.sb-container__inner, +.sb-article-container, +.sb-footer-content, +.sb-header, +.sb-header-secondary, +.sb-footer { + display: flex; +} + +/* These order things vertically */ +body, +.sb-main, +.sb-article-container { + flex-direction: column; +} + +/* Put elements in the center */ +.sb-header, +.sb-header-secondary, +.sb-container, +.sb-content, +.sb-footer, +.sb-footer-content { + justify-content: center; +} +/* Put elements at the ends */ +.sb-article-container { + justify-content: space-between; +} + +/* These elements grow. */ +.sb-main, +.sb-content, +.sb-container, +article { + flex-grow: 1; +} + +/* Because padding making this wider is not fun */ +article { + box-sizing: border-box; +} + +/* The announcements element should never be wider than the page. */ +.sb-announcement { + max-width: 100%; +} + +.sb-sidebar-primary, +.sb-sidebar-secondary { + flex-shrink: 0; + width: 17rem; +} + +.sb-announcement__inner { + justify-content: center; + + box-sizing: border-box; + height: 3rem; + + overflow-x: auto; + white-space: nowrap; +} + +/* Sidebars, with checkbox-based toggle */ +.sb-sidebar-primary, +.sb-sidebar-secondary { + position: fixed; + height: 100%; + top: 0; +} + +.sb-sidebar-primary { + left: -17rem; + transition: left 250ms ease-in-out; +} +.sb-sidebar-secondary { + right: -17rem; + transition: right 250ms ease-in-out; +} + +.sb-sidebar-toggle { + display: none; +} +.sb-sidebar-overlay { + position: fixed; + top: 0; + width: 0; + height: 0; + + transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease; + + opacity: 0; + background-color: rgba(0, 0, 0, 0.54); +} + +#sb-sidebar-toggle--primary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"], +#sb-sidebar-toggle--secondary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] { + width: 100%; + height: 100%; + opacity: 1; + transition: width 0ms ease, height 0ms ease, opacity 250ms ease; +} + +#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary { + left: 0; +} +#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary { + right: 0; +} + +/* Full-width mode */ +.drop-secondary-sidebar-for-full-width-content + .hide-when-secondary-sidebar-shown { + display: none !important; +} +.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary { + display: none !important; +} + +/* Mobile views */ +.sb-page-width { + width: 100%; +} + +.sb-article-container, +.sb-footer-content__inner, +.drop-secondary-sidebar-for-full-width-content .sb-article, +.drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 100vw; +} + +.sb-article, +.match-content-width { + padding: 0 1rem; + box-sizing: border-box; +} + +@media (min-width: 32rem) { + .sb-article, + .match-content-width { + padding: 0 2rem; + } +} + +/* Tablet views */ +@media (min-width: 42rem) { + .sb-article-container { + width: auto; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 42rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 46rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 46rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 50rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 50rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Tablet views */ +@media (min-width: 59rem) { + .sb-sidebar-secondary { + position: static; + } + .hide-when-secondary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 63rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 67rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Desktop views */ +@media (min-width: 76rem) { + .sb-sidebar-primary { + position: static; + } + .hide-when-primary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} + +/* Full desktop views */ +@media (min-width: 80rem) { + .sb-article, + .match-content-width { + width: 46rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } +} + +@media (min-width: 84rem) { + .sb-article, + .match-content-width { + width: 50rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } +} + +@media (min-width: 88rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-page-width { + width: 88rem; + } +} diff --git a/latest/_static/sphinx_highlight.js b/latest/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/latest/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '