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z0f>oAr7iDYe$#E`JP+H9PR48y1tpA!6ddlAX7B>pjPet_KV&1NXXDacBTlaXI77=Z z!Xx6SDd+j{jc~cgWpjdeo8z!8!btgvBIs|J6i+bWlLB#V;HazqyZQ{#)8R?S zzggXk1fGc^TznO8@UnLycF&6(MSvdcd{wCP07?ibGMfSeO=7r0sREtw89te!Q@~-Y zibi@(o0q^0>P?IgK7k5?V}gC=u0`OQhdjkwONh5cO0-_>o%(eUU`?h~T6^Y}(FfA2 zoLj5X3E~$}L@R(%1Hh~1VsG6JlmfJ@e`W#9@CNr33GWn@no>`Ov}yZ66D~dRfqj*T z)$cv=h4@T777q*8C#pTcTN%9viZ7mt#~&Bsb^84op7kDz`-R)=UY(b<8A*EZ0)`>K zqELi3e3jB&O2vog16*}FQAvvi&-&6CV$0n>C9&U}>Nj0EB_GLFTgvX&&dRD+&=nwm zMCjW9lG3e5Dyh3E)R5J?HT@HK;iK{aVgXNoh${G$-sh?V+ca{Dyxx-i1+Lef2>>&r h*VHS3+1ba`_ + +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/4cc6d7251cc20e13dd91e533c7271a6a/charge-charge.xyz b/latest/_downloads/4cc6d7251cc20e13dd91e533c7271a6a/charge-charge.xyz new file mode 100644 index 00000000..a988fbef --- /dev/null +++ b/latest/_downloads/4cc6d7251cc20e13dd91e533c7271a6a/charge-charge.xyz @@ -0,0 +1,2834 @@ +23 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-13961.0350892859 label=CC 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energyB=-6669.20926086133 distance=4.801956498056273 pbc="T T T" +C 34.44500000 12.62500000 24.95900000 0.17759417 0.21395224 -0.01458299 +C 33.70500000 11.92700000 26.09800000 1.58115037 0.39798793 1.85539350 +O 32.60000000 11.42900000 25.81100000 -3.17118630 -1.23616714 -1.12653995 +O 34.21200000 11.87200000 27.24800000 0.39242751 0.32624318 -0.17715378 +H 33.75000000 13.28700000 24.44000000 -0.16098163 0.13959278 -0.20459917 +H 34.76300000 11.86300000 24.24500000 0.12507379 -0.13939182 -0.24254670 +H 35.32079000 13.21299000 25.27085000 -0.08720083 -0.19105308 0.15085592 +C 29.89601189 11.07851374 28.61501022 0.22337661 -0.31627090 -0.21247484 +C 29.54801189 9.45751374 26.76401022 0.37139372 1.33987015 0.73929747 +N 30.17001189 10.48451374 27.30301022 -0.33830685 0.47677755 0.83372148 +N 28.53801189 8.86451374 27.37401022 -1.24457314 -1.06420692 0.24729619 +N 29.92101189 9.03551374 25.57901022 -0.08543396 -1.08518262 -1.46324957 +H 30.97201189 10.85051374 26.78601022 1.34382302 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Properties=species:S:1:pos:R:3:forces:R:3 energy=-12893.093822762 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=5.547577292653992 pbc="T T T" +C 34.44500000 12.62500000 24.95900000 0.24955379 0.26281744 -0.03059913 +C 33.70500000 11.92700000 26.09800000 0.91407621 0.14989443 1.57178808 +O 32.60000000 11.42900000 25.81100000 -2.54457017 -0.98238518 -1.10561234 +O 34.21200000 11.87200000 27.24800000 0.65207969 0.35038473 0.05883232 +H 33.75000000 13.28700000 24.44000000 -0.16269121 0.13865580 -0.19449129 +H 34.76300000 11.86300000 24.24500000 0.12501701 -0.14189723 -0.23413162 +H 35.32079000 13.21299000 25.27085000 -0.05648092 -0.16295964 0.13449153 +C 29.27504757 10.74405495 28.87004087 0.22823046 -0.20774040 -0.11433068 +C 28.92704757 9.12305495 27.01904087 0.18616515 0.93377730 0.53210867 +N 29.54904757 10.15005495 27.55804087 0.27972182 0.83384150 0.65787340 +N 27.91704757 8.53005495 27.62904087 -1.07582101 -0.90166553 0.23263615 +N 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27.39305947 8.75356868 28.60805108 -0.24501117 -0.03563513 0.18552900 +H 27.22005947 7.65856868 27.24105108 0.01834548 0.23301672 0.39943044 +H 29.86505947 9.05256868 25.46505108 0.30086589 -0.00009940 -0.47617043 +H 28.53605947 7.87956868 25.43905108 0.48944553 0.31486633 -0.04234863 +H 28.28336947 11.40341868 28.96234108 -0.03685819 -0.10095366 -0.32302109 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.7032612584 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=6.045259757225575 pbc="T T T" +C 34.44500000 12.62500000 24.95900000 0.28204096 0.27139536 -0.00131447 +C 33.70500000 11.92700000 26.09800000 0.64595500 0.13069910 1.17252926 +O 32.60000000 11.42900000 25.81100000 -2.11404875 -0.83310908 -0.95299350 +O 34.21200000 11.87200000 27.24800000 0.69084474 0.30838080 0.20841025 +H 33.75000000 13.28700000 24.44000000 -0.16542414 0.13590792 -0.18188273 +H 34.76300000 11.86300000 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distance=6.543295591227622 pbc="T T T" +C 34.44500000 12.62500000 24.95900000 0.27886581 0.26146631 0.01798379 +C 33.70500000 11.92700000 26.09800000 0.58839560 0.18310720 0.89060892 +O 32.60000000 11.42900000 25.81100000 -1.84608960 -0.74692893 -0.81995802 +O 34.21200000 11.87200000 27.24800000 0.64896858 0.25186697 0.29033722 +H 33.75000000 13.28700000 24.44000000 -0.16609520 0.13762921 -0.17895563 +H 34.76300000 11.86300000 24.24500000 0.12607699 -0.14394351 -0.22287097 +H 35.32079000 13.21299000 25.27085000 -0.03292703 -0.14042950 0.12428503 +C 28.44709515 10.29810989 29.21008173 0.23284009 -0.12565334 -0.12094915 +C 28.09909515 8.67710990 27.35908173 0.04803283 0.43992029 0.52456950 +N 28.72109515 9.70410990 27.89808173 0.62417490 1.24750093 0.69706924 +N 27.08909515 8.08410990 27.96908173 -0.95608662 -0.77469308 0.28974248 +N 28.47209515 8.25510990 26.17408173 0.13225042 -0.88027283 -1.39610574 +H 29.52309515 10.07010989 27.38108173 0.01409910 0.15726350 0.12906794 +H 29.37109515 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Properties=species:S:1:pos:R:3:forces:R:3 energy=-12894.9081401654 label=CC chargeA=1 energyA=-6669.20645149176 chargeB=-1 energyB=-6220.48400518575 distance=3.8469731647434418 pbc="T T T" +C 0.01900000 -12.24500000 2.37200000 0.27871526 0.83805596 -0.08501017 +C 0.22300000 -9.83900000 1.84900000 0.75686203 -1.64218955 -0.34753908 +N 0.66900000 -11.08600000 1.75700000 -2.47585134 0.59150633 0.75754521 +N -0.87800000 -9.57200000 2.52900000 -1.40186327 0.54489228 0.86122298 +N 0.87600000 -8.85300000 1.26400000 -0.05259618 0.90846800 -0.15170747 +H 1.57500000 -11.24100000 1.31700000 2.00780774 -0.85537331 -0.61464997 +H 0.56800000 -13.14900000 2.09900000 0.19107998 0.09063558 -0.05904077 +H -1.00300000 -12.34300000 1.99900000 -0.32550022 -0.13290884 -0.00963159 +H -1.44200000 -10.32000000 2.89200000 -0.17968762 -0.09757325 0.15115497 +H -1.21900000 -8.62500000 2.58900000 -0.18650498 0.01409089 0.07628546 +H 1.72400000 -9.06300000 0.75200000 1.20344768 -0.42954919 -0.49675778 +H 0.52800000 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energyB=-6220.48400518575 distance=7.198490797924341 pbc="T T T" +C 3.69000000 1.17800000 9.65700000 0.37025873 0.03679320 0.52726109 +C 5.89300000 2.10500000 10.26700000 -0.41874010 -0.46961901 -0.12040590 +N 6.46800000 0.95000000 9.98900000 1.34751112 -0.80634360 -0.24293597 +N 4.58400000 2.24900000 10.11300000 -1.55215781 -0.08038552 -0.41480523 +N 6.62900000 3.10900000 10.69900000 0.51246308 1.52573121 0.52245485 +H 5.91600000 0.16800000 9.69300000 -0.04051190 -0.38227501 -0.17992099 +H 7.49000000 0.89000000 9.89100000 -0.34726553 -0.22579504 0.01649408 +H 4.18700000 3.12900000 10.38500000 -0.31033252 0.34691896 0.09153114 +H 6.22600000 4.01600000 10.92500000 0.22989670 -0.24535900 -0.10444081 +H 7.61200000 2.96300000 10.84200000 0.46163630 0.15441190 0.14439421 +H 3.74200000 0.36200000 10.38300000 -0.15109713 -0.30323069 0.15641170 +H 4.02000000 0.78400000 8.69500000 -0.10900300 0.06248358 -0.37354656 +H 2.63834300 1.49675000 9.60785000 0.35989764 0.24229754 -0.07224353 +C 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label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=4.234635908871504 pbc="T T T" +C 25.65400000 -5.20500000 -2.23100000 0.01478298 -0.02673130 -0.06649977 +C 25.43700000 -3.85300000 -1.56500000 -2.19596691 -0.40845873 -0.24091970 +O 26.43300000 -3.22700000 -1.14000000 2.95246173 0.09947169 0.01044332 +O 24.27400000 -3.40300000 -1.47500000 -0.61717201 -0.07186745 -0.23253201 +H 26.34900000 -5.06000000 -3.05900000 0.34482881 -0.12896995 -0.16052409 +H 24.71500000 -5.57700000 -2.64500000 -0.14184215 -0.08356096 0.07285538 +H 26.07672000 -5.94638300 -1.53698600 0.05570445 0.10000252 -0.02955307 +C 25.01868999 0.94028982 1.66744602 -0.33311378 -0.17561577 -0.54357879 +C 25.44768999 -0.19971018 -0.47955398 0.73865200 -1.41414437 1.40217655 +N 25.98668999 -1.23271018 0.14444602 -0.41403746 2.77828796 1.25253205 +N 24.98468999 0.83728982 0.20644602 -0.45419181 1.50420659 0.82713143 +N 25.36468999 -0.21271018 -1.79455398 -0.10012869 0.42692558 -1.37512881 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2.55483096 0.17943472 -0.02496715 0.12363763 +H 24.20350501 1.42583437 2.61276196 0.09688298 0.00835993 0.12068609 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12893.1076496113 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=5.733418286779269 pbc="T T T" +C 25.65400000 -5.20500000 -2.23100000 -0.03777314 -0.18073865 -0.15283819 +C 25.43700000 -3.85300000 -1.56500000 -0.88773952 0.66251515 0.38374356 +O 26.43300000 -3.22700000 -1.14000000 2.17605209 0.63439949 0.47262313 +O 24.27400000 -3.40300000 -1.47500000 -1.38753233 -0.10665508 -0.29504592 +H 26.34900000 -5.06000000 -3.05900000 0.33377386 -0.11920578 -0.16358126 +H 24.71500000 -5.57700000 -2.64500000 -0.16100694 -0.14153399 0.03803804 +H 26.07672000 -5.94638300 -1.53698600 0.04594276 0.08717550 -0.02240545 +C 24.95962002 2.31042037 2.27510795 -0.33278054 -0.19593390 -0.36478750 +C 25.38862002 1.17042037 0.12810795 0.21747811 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energyB=-6669.20926086133 distance=5.983274680706614 pbc="T T T" +C 25.65400000 -5.20500000 -2.23100000 -0.04697711 -0.17864904 -0.15518480 +C 25.43700000 -3.85300000 -1.56500000 -0.72530526 0.60405922 0.37206550 +O 26.43300000 -3.22700000 -1.14000000 2.04035619 0.53956199 0.42669274 +O 24.27400000 -3.40300000 -1.47500000 -1.40770419 -0.07921543 -0.27002536 +H 26.34900000 -5.06000000 -3.05900000 0.33105578 -0.11695696 -0.16402126 +H 24.71500000 -5.57700000 -2.64500000 -0.16596953 -0.14771955 0.03273865 +H 26.07672000 -5.94638300 -1.53698600 0.04320332 0.08795534 -0.02080123 +C 24.94977502 2.53877546 2.37638494 -0.32482020 -0.19427369 -0.37457700 +C 25.37877502 1.39877546 0.22938494 0.12605606 -0.32510601 0.47145518 +N 25.91777502 0.36577546 0.85338494 0.32746196 -0.53348284 -0.15948086 +N 24.91577502 2.43577546 0.91538494 -0.17445264 0.95928571 0.79830500 +N 25.29577502 1.38577546 -1.08561506 -0.07873289 0.18322050 -1.03749556 +H 26.07877502 0.39377546 1.84338494 -0.00779484 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Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.4753909764 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=6.732907830696661 pbc="T T T" +C 25.65400000 -5.20500000 -2.23100000 -0.06874745 -0.15532656 -0.14871687 +C 25.43700000 -3.85300000 -1.56500000 -0.37059648 0.34096991 0.27492736 +O 26.43300000 -3.22700000 -1.14000000 1.68474690 0.40744213 0.36365850 +O 24.27400000 -3.40300000 -1.47500000 -1.39254489 0.01869988 -0.18749397 +H 26.34900000 -5.06000000 -3.05900000 0.32470813 -0.11164204 -0.16612752 +H 24.71500000 -5.57700000 -2.64500000 -0.17849777 -0.16129036 0.01957414 +H 26.07672000 -5.94638300 -1.53698600 0.03649125 0.08944626 -0.01745855 +C 24.92024004 3.22384073 2.68021591 -0.29691993 -0.18784774 -0.41277065 +C 25.34924004 2.08384073 0.53321591 -0.13495099 -0.13206680 0.45151490 +N 25.88824004 1.05084073 1.15721591 0.53289749 -0.95506663 -0.07370039 +N 24.88624004 3.12084073 1.21921591 -0.18547521 1.01091849 0.89545232 +N 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24.90639504 3.10419582 -1.17950711 -0.02934563 0.00427515 -0.23080539 +H 25.67939504 1.54119582 -1.19650711 -0.01102302 -0.10843248 -0.31380274 +H 24.70039504 4.48319582 3.07449289 -0.01651246 0.06319122 -0.06195931 +H 25.90039504 3.19319582 3.16249289 0.17289808 -0.00519888 0.15424830 +H 24.14443504 2.79596492 3.22042389 0.06951933 -0.01275528 0.13178712 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.283584221 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=7.232705517383349 pbc="T T T" +C 25.65400000 -5.20500000 -2.23100000 -0.07701164 -0.14144769 -0.14280134 +C 25.43700000 -3.85300000 -1.56500000 -0.22954974 0.20492092 0.22045168 +O 26.43300000 -3.22700000 -1.14000000 1.52868993 0.37040271 0.34462456 +O 24.27400000 -3.40300000 -1.47500000 -1.36979711 0.06708799 -0.14825066 +H 26.34900000 -5.06000000 -3.05900000 0.32191463 -0.10900815 -0.16738145 +H 24.71500000 -5.57700000 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-0.18333030 0.01006571 -0.04607384 +H 9.30141722 16.66062320 25.18203241 0.94051350 0.11892595 0.40858788 +H 7.63341722 16.61062320 25.81203241 0.30730104 0.02617990 0.01633883 +H 7.58441722 17.07562320 21.15203241 -0.14223176 0.23278413 -0.05381600 +H 8.92541722 15.94362320 21.01403241 0.05994826 -0.18056810 -0.21551611 +H 9.27170822 17.68623320 20.99918241 -0.18137227 0.00847143 0.08275133 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12893.9610992283 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=4.751497090066203 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.31869302 -0.31248010 0.78320767 +C 12.12100000 17.80500000 24.71700000 0.71018168 1.13963358 -1.09219769 +O 11.25200000 16.92500000 24.47100000 -2.25709526 -1.69050868 0.19641113 +O 12.41100000 18.73500000 23.94100000 0.07062387 0.72734557 -0.35525506 +H 13.24200000 18.68100000 26.32900000 0.06746731 0.14181234 0.01316363 +H 13.90100000 17.18800000 25.64900000 0.24491367 -0.19001164 0.09616522 +H 12.47004000 17.11600000 26.73359000 0.21099152 -0.10297925 0.07020133 +C 8.40383443 16.86324640 21.32006483 0.48319671 -0.16876882 0.63280836 +C 7.66083443 16.76724640 23.70206483 0.36970116 -0.06073945 -0.94869559 +N 8.59783443 16.81424640 22.77606483 -0.43251767 0.14817694 -1.37459707 +N 6.35783443 16.79824640 23.40906483 -0.58448359 0.01785566 -0.27639383 +N 8.07383443 16.67224640 24.94206483 -0.27499824 -0.12486786 1.37605163 +H 9.55683443 16.79524640 23.12006483 1.12161120 0.22682018 0.33822668 +H 6.04783443 16.81524640 22.44606483 -0.08459904 0.02298974 0.17570959 +H 5.67683443 16.76724640 24.15106483 -0.18741804 0.00927191 -0.03797811 +H 9.08083443 16.61624640 25.07306483 0.78528118 0.12602404 0.51473331 +H 7.41283443 16.56624640 25.70306483 0.33636392 0.02683060 0.04507434 +H 7.36383443 17.03124640 21.04306483 -0.13226724 0.23318188 -0.02908268 +H 8.70483443 15.89924640 20.90506483 0.05871704 -0.17654692 -0.21013258 +H 9.05112543 17.64185640 20.89021483 -0.18697717 0.00696028 0.08257973 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12893.6417526352 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=4.999645142729246 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.28811898 -0.30568602 0.79825392 +C 12.12100000 17.80500000 24.71700000 0.51062320 0.94459191 -1.13281032 +O 11.25200000 16.92500000 24.47100000 -2.08743016 -1.60679740 0.25293453 +O 12.41100000 18.73500000 23.94100000 0.13685490 0.84193762 -0.38925819 +H 13.24200000 18.68100000 26.32900000 0.07265372 0.14721934 0.02075202 +H 13.90100000 17.18800000 25.64900000 0.24961557 -0.18922421 0.09726704 +H 12.47004000 17.11600000 26.73359000 0.20655691 -0.10197987 0.06754006 +C 8.18325165 16.81886961 21.21109724 0.51198318 -0.17337082 0.58036133 +C 7.44025165 16.72286961 23.59309724 0.27233828 -0.06193547 -0.88954246 +N 8.37725165 16.76986961 22.66709724 -0.24015890 0.16707170 -1.30390492 +N 6.13725165 16.75386961 23.30009724 -0.50409794 0.01481735 -0.27340795 +N 7.85325165 16.62786961 24.83309724 -0.16654766 -0.12103506 1.32695728 +H 9.33625165 16.75086961 23.01109724 0.88659457 0.19894469 0.18147458 +H 5.82725165 16.77086961 22.33709724 -0.08964785 0.02370175 0.16945893 +H 5.45625165 16.72286961 24.04209724 -0.19305701 0.00855014 -0.02985300 +H 8.86025165 16.57186961 24.96409724 0.62652264 0.11967150 0.59125694 +H 7.19225165 16.52186961 25.59409724 0.35482764 0.02711708 0.07030344 +H 7.14325165 16.98686961 20.93409724 -0.12485611 0.23377607 -0.01275032 +H 8.48425165 15.85486961 20.79609724 0.05730635 -0.17382975 -0.20622745 +H 8.83054265 17.59747961 20.78124724 -0.19196235 0.00645945 0.08119454 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12893.3487761903 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=5.247968996564185 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.26214910 -0.29555882 0.81086678 +C 12.12100000 17.80500000 24.71700000 0.34186037 0.75825628 -1.15724763 +O 11.25200000 16.92500000 24.47100000 -1.88051904 -1.51331328 0.31440509 +O 12.41100000 18.73500000 23.94100000 0.19510784 0.94356250 -0.42263603 +H 13.24200000 18.68100000 26.32900000 0.07766337 0.15218788 0.02817619 +H 13.90100000 17.18800000 25.64900000 0.25343543 -0.18864217 0.09766410 +H 12.47004000 17.11600000 26.73359000 0.20204296 -0.10081794 0.06426360 +C 7.96266886 16.77449281 21.10212966 0.53134956 -0.17766572 0.53829803 +C 7.21966886 16.67849281 23.48412966 0.18455214 -0.06057164 -0.85264148 +N 8.15666886 16.72549281 22.55812966 -0.11680591 0.18210251 -1.26840912 +N 5.91666886 16.70949281 23.19112966 -0.43434197 0.01216932 -0.26831748 +N 7.63266886 16.58349281 24.72412966 -0.08919987 -0.11582051 1.30018792 +H 9.11566886 16.70649281 22.90212966 0.70177637 0.17135634 0.06539813 +H 5.60666886 16.72649281 22.22812966 -0.09476899 0.02359951 0.16112187 +H 5.23566886 16.67849281 23.93312966 -0.20022341 0.00745750 -0.02026001 +H 8.63966886 16.52749281 24.85512966 0.48526670 0.10605556 0.64255380 +H 6.97166886 16.47749281 25.48512966 0.36461892 0.02660435 0.09258376 +H 6.92266886 16.94249281 20.82512966 -0.11906271 0.23433519 -0.00143067 +H 8.26366886 15.81049281 20.68712966 0.05569782 -0.17197901 -0.20355794 +H 8.60995986 17.55310281 20.67227966 -0.19630050 0.00668215 0.07898108 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12893.0896501194 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=5.496444811251947 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.23863500 -0.28001340 0.81897402 +C 12.12100000 17.80500000 24.71700000 0.19187624 0.55994359 -1.14972697 +O 11.25200000 16.92500000 24.47100000 -1.66813568 -1.40103290 0.36259613 +O 12.41100000 18.73500000 23.94100000 0.24473620 1.03550299 -0.46193218 +H 13.24200000 18.68100000 26.32900000 0.08308216 0.15686928 0.03592068 +H 13.90100000 17.18800000 25.64900000 0.25641209 -0.18791731 0.09729401 +H 12.47004000 17.11600000 26.73359000 0.19654636 -0.09850820 0.05818435 +C 7.74208608 16.73011601 20.99316207 0.54694955 -0.18261069 0.50494457 +C 6.99908608 16.63411601 23.37516207 0.09704433 -0.04896869 -0.82700123 +N 7.93608608 16.68111601 22.44916207 -0.02368459 0.19196933 -1.25552057 +N 5.69608608 16.66511601 23.08216207 -0.37051152 0.00996477 -0.26284114 +N 7.41208608 16.53911601 24.61516207 -0.02700958 -0.10945606 1.28969508 +H 8.89508608 16.66211601 22.79316207 0.55455688 0.14546255 -0.02003825 +H 5.38608608 16.68211601 22.11916207 -0.10180087 0.02141181 0.14613025 +H 5.01508608 16.63411601 23.82416207 -0.21099944 0.00445347 -0.00511924 +H 8.41908608 16.48311601 24.74616207 0.36587270 0.08701041 0.67466966 +H 6.75108608 16.43311601 25.37616207 0.36538692 0.02420252 0.11322315 +H 6.70208608 16.89811601 20.71616207 -0.11519815 0.23460244 0.00709593 +H 8.04308608 15.76611601 20.57816207 0.05336809 -0.17049229 -0.20242199 +H 8.38937708 17.50872601 20.56331207 -0.19985670 0.00760640 0.07587373 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.8656952745 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=5.74505288404184 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.21841617 -0.25773311 0.81765747 +C 12.12100000 17.80500000 24.71700000 0.06368914 0.33431113 -1.08389305 +O 11.25200000 16.92500000 24.47100000 -1.45620914 -1.25698468 0.38285305 +O 12.41100000 18.73500000 23.94100000 0.27780293 1.11619368 -0.51722346 +H 13.24200000 18.68100000 26.32900000 0.08908107 0.16152544 0.04447157 +H 13.90100000 17.18800000 25.64900000 0.25855307 -0.18697689 0.09638655 +H 12.47004000 17.11600000 26.73359000 0.18943276 -0.09461980 0.04862264 +C 7.52150329 16.68573921 20.88419448 0.56460656 -0.19020991 0.48159778 +C 6.77850329 16.58973921 23.26619448 -0.00253807 -0.01159939 -0.80686964 +N 7.71550329 16.63673921 22.34019448 0.07338768 0.19333257 -1.26744401 +N 5.47550329 16.62073921 22.97319448 -0.31713790 0.00911639 -0.25859072 +N 7.19150329 16.49473921 24.50619448 0.03874044 -0.10410267 1.30382415 +H 8.67450329 16.61773921 22.68419448 0.42772330 0.12144033 -0.08727429 +H 5.16550329 16.63773921 22.01019448 -0.11217448 0.01440414 0.12198086 +H 4.79450329 16.58973921 23.71519448 -0.22680930 -0.00335257 0.01716738 +H 8.19850329 16.43873921 24.63719448 0.25981863 0.06206905 0.69279918 +H 6.53050329 16.38873921 25.26719448 0.35828146 0.01810604 0.13237445 +H 6.48150329 16.85373921 20.60719448 -0.11472301 0.23436341 0.01432358 +H 7.82250329 15.72173921 20.46919448 0.04917858 -0.16859704 -0.20403341 +H 8.16879429 17.46434921 20.45434448 -0.20228754 0.00931389 0.07126991 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.6773656391 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=5.9937767370030866 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.17755875 -0.18954708 0.72681690 +C 12.12100000 17.80500000 24.71700000 0.12093818 -0.05925948 -0.47097423 +O 11.25200000 16.92500000 24.47100000 -1.26513527 -1.01531755 0.20007969 +O 12.41100000 18.73500000 23.94100000 0.23840123 1.22876233 -0.77880250 +H 13.24200000 18.68100000 26.32900000 0.09707844 0.19379480 0.05660562 +H 13.90100000 17.18800000 25.64900000 0.26455416 -0.19502315 0.08302623 +H 12.47004000 17.11600000 26.73359000 0.14235947 -0.09265902 0.03773811 +C 7.30092051 16.64136241 20.77522690 0.62197895 -0.22450392 0.49211641 +C 6.55792051 16.54536241 23.15722690 -0.16883942 0.24378298 -0.77252562 +N 7.49492051 16.59236241 22.23122690 0.32085341 0.10585654 -1.41364654 +N 5.25492051 16.57636241 22.86422690 -0.34687004 -0.00326828 -0.27834801 +N 6.97092051 16.45036241 24.39722690 0.21696832 -0.14119585 1.45930807 +H 8.45392051 16.57336241 22.57522690 0.22136113 0.09804013 -0.15989885 +H 4.94492051 16.59336241 21.90122690 -0.13229592 -0.02571329 0.07775715 +H 4.57392051 16.54536241 23.60622690 -0.23688651 -0.04037733 0.06031952 +H 7.97792051 16.39436241 24.52822690 0.09671188 0.00322368 0.68294141 +H 6.30992051 16.34436241 25.15822690 0.31762238 0.00394836 0.10795693 +H 6.26092051 16.80936241 20.49822690 -0.13305450 0.23694503 0.03834569 +H 7.60192051 15.67736241 20.36022690 0.01212386 -0.13895713 -0.21175425 +H 7.94821151 17.41997241 20.34537690 -0.21031101 0.01146825 0.06293827 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.5157633701 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=6.242602554777832 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.19902086 -0.21962487 0.79759456 +C 12.12100000 17.80500000 24.71700000 -0.07212314 -0.04678756 -0.86501817 +O 11.25200000 16.92500000 24.47100000 -1.10840021 -0.97359089 0.36308635 +O 12.41100000 18.73500000 23.94100000 0.28353617 1.21417220 -0.64199958 +H 13.24200000 18.68100000 26.32900000 0.09755719 0.16998956 0.05852058 +H 13.90100000 17.18800000 25.64900000 0.26133120 -0.18608930 0.09462405 +H 12.47004000 17.11600000 26.73359000 0.17708639 -0.08818795 0.03422642 +C 7.08033772 16.59698561 20.66625931 0.60250224 -0.21079416 0.46195515 +C 6.33733772 16.50098561 23.04825931 -0.19954284 0.10334833 -0.78388242 +N 7.27433772 16.54798561 22.12225931 0.28330326 0.18351434 -1.34550810 +N 5.03433772 16.53198561 22.75525931 -0.28067357 0.01214492 -0.25972692 +N 6.75033772 16.40598561 24.28825931 0.17517426 -0.10358347 1.40189714 +H 8.23333772 16.52898561 22.46625931 0.22866139 0.08124412 -0.18486193 +H 4.72433772 16.54898561 21.79225931 -0.13145976 -0.00628563 0.08230926 +H 4.35333772 16.50098561 23.49725931 -0.25482322 -0.02607318 0.05285152 +H 7.75733772 16.34998561 24.41925931 0.07905647 0.01324028 0.70439672 +H 6.08933772 16.29998561 25.04925931 0.34399691 0.00023920 0.15957650 +H 6.04033772 16.76498561 20.38925931 -0.11863734 0.23346324 0.02503616 +H 7.38133772 15.63298561 20.25125931 0.03751118 -0.16408846 -0.21421672 +H 7.72762872 17.37559561 20.23640931 -0.20503571 0.01374927 0.05913944 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.3810554246 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=6.49151858631955 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.20049264 -0.21222207 0.78578852 +C 12.12100000 17.80500000 24.71700000 -0.08114254 -0.16115964 -0.76188208 +O 11.25200000 16.92500000 24.47100000 -0.98509251 -0.86818306 0.34591765 +O 12.41100000 18.73500000 23.94100000 0.26454304 1.23176054 -0.69412000 +H 13.24200000 18.68100000 26.32900000 0.09825999 0.17334000 0.06188686 +H 13.90100000 17.18800000 25.64900000 0.26194532 -0.18634940 0.09387513 +H 12.47004000 17.11600000 26.73359000 0.17371919 -0.08685246 0.03241141 +C 6.85975494 16.55260882 20.55729173 0.61783355 -0.22130036 0.45552224 +C 6.11675494 16.45660882 22.93929173 -0.28356764 0.16168982 -0.77976292 +N 7.05375494 16.50360882 22.01329173 0.36629609 0.17819733 -1.38439953 +N 4.81375494 16.48760882 22.64629173 -0.26694977 0.01236471 -0.26285313 +N 6.52975494 16.36160882 24.17929173 0.23442561 -0.10523413 1.45146182 +H 8.01275494 16.48460882 22.35729173 0.15921619 0.06282215 -0.21722171 +H 4.50375494 16.50460882 21.68329173 -0.13920475 -0.01489227 0.06483187 +H 4.13275494 16.45660882 23.38829173 -0.27045244 -0.03830225 0.07291924 +H 7.53675494 16.30560882 24.31029173 0.01014500 -0.00506237 0.70163248 +H 5.86875494 16.25560882 24.94029173 0.33376884 -0.00869694 0.17022065 +H 5.81975494 16.72060882 20.28029173 -0.12073234 0.23283155 0.02921484 +H 7.16075494 15.58860882 20.14229173 0.03197443 -0.16152666 -0.21925355 +H 7.50704594 17.33121882 20.12744173 -0.20449260 0.01677551 0.05381023 +20 +Lattice="30.0 0.0 0.0 0.0 30.0 0.0 0.0 0.0 30.0" Properties=species:S:1:pos:R:3:forces:R:3 energy=-12892.2676780833 label=CC chargeA=-1 energyA=-6220.48891391419 chargeB=1 energyB=-6669.20926086133 distance=6.7405148650607 pbc="T T T" +C 12.98100000 17.69200000 25.94800000 -0.20465684 -0.20879396 0.77489349 +C 12.12100000 17.80500000 24.71700000 -0.07406187 -0.24280278 -0.67109250 +O 11.25200000 16.92500000 24.47100000 -0.88911534 -0.78339650 0.32763136 +O 12.41100000 18.73500000 23.94100000 0.24234614 1.23858709 -0.73741840 +H 13.24200000 18.68100000 26.32900000 0.09790387 0.17615115 0.06389835 +H 13.90100000 17.18800000 25.64900000 0.26225113 -0.18674535 0.09316017 +H 12.47004000 17.11600000 26.73359000 0.17139489 -0.08603439 0.03199290 +C 6.63917215 16.50823202 20.44832414 0.62928346 -0.23076605 0.44868090 +C 5.89617215 16.41223202 22.83032414 -0.35358521 0.21491756 -0.77655509 +N 6.83317215 16.45923202 21.90432414 0.42730570 0.17333066 -1.41507016 +N 4.59317215 16.44323202 22.53732414 -0.24885732 0.01007956 -0.26579087 +N 6.30917215 16.31723202 24.07032414 0.28102862 -0.10445439 1.48792934 +H 7.79217215 16.44023202 22.24832414 0.10759492 0.04630070 -0.23998216 +H 4.28317215 16.46023202 21.57432414 -0.14595726 -0.02196272 0.04750568 +H 3.91217215 16.41223202 23.27932414 -0.28573770 -0.04903540 0.09395175 +H 7.31617215 16.26123202 24.20132414 -0.03970395 -0.02142110 0.69643279 +H 5.64817215 16.21123202 24.83132414 0.32055445 -0.01705460 0.18072457 +H 5.59917215 16.67623202 20.17132414 -0.12215852 0.23214034 0.03270120 +H 6.94017215 15.54423202 20.03332414 0.02732817 -0.15908053 -0.22313365 +H 7.28646315 17.28684202 20.01847414 -0.20315733 0.02004071 0.04954034 diff --git a/latest/_downloads/4dd8b470da0d1f3672e0a6f90c77c550/sample-selection.py b/latest/_downloads/4dd8b470da0d1f3672e0a6f90c77c550/sample-selection.py new file mode 100644 index 00000000..02c3747b --- /dev/null +++ b/latest/_downloads/4dd8b470da0d1f3672e0a6f90c77c550/sample-selection.py @@ -0,0 +1,375 @@ +""" +Sample and Feature Selection with FPS and CUR +============================================= + +:Authors: Davide Tisi `@DavideTisi `_ + +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 metatensor +import numpy as np +from equisolve.numpy import feature_selection, sample_selection +from matplotlib import pyplot as plt +from metatensor import sum_over_samples +from rascaline import SoapPowerSpectrum +from sklearn.decomposition import PCA +from skmatter import feature_selection as skfeat_selection + + +# %% +# Load molecular data +# ------------------- +# +# Load 500 example BTO structures from file, reading them using +# `ASE `_. + +# Load a subset of :download:`structures ` of the example dataset +n_frames = 500 +frames = ase.io.read("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 +atom_soap = rho2i.keys_to_properties(["species_neighbor_1", "species_neighbor_2"]) + +atom_soap_single_block = atom_soap.keys_to_samples(keys_to_move=["species_center"]) + +# print(atom_soap_single_block) +# print(atom_soap_single_block.block(0)) # There is only one block now! + +# Sum over atomic centers to compute structure features +struct_soap = sum_over_samples( + atom_soap_single_block, sample_names=["center", "species_center"] +) + + +print("atom feature descriptor shape:", atom_soap.block(0).values.shape) +print( + "atom feature descriptor (all in one block) shape:", + atom_soap_single_block.block(0).values.shape, +) +print("structure feature descriptor shape:", struct_soap.block(0).values.shape) + + +# %% +# Perform atomic environment (i.e. sample) selection +# --------------------------------------------------- +# +# Using FPS and CUR algorithms, we can perform selection of atomic environments. +# These are implemented in equisolve, which provides a wrapper around +# scikit-matter to allow for interfacing with data stored in the metatensor +# format. +# +# Suppose we want to select the 10 most diverse environments for each chemical +# species. +# +# First, we can use the `keys_to_properties` operation in metatensor to move the +# neighbour species indices to the properties of the TensorBlocks. The resulting +# descriptor will be a TensorMap comprised of three blocks, one for each +# chemical species, where the chemical species indices are solely present in the +# keys. + + +print("----Atomic environment selection-----") +# Define the number of structures to select using FPS/CUR +n_envs = 25 + +print(atom_soap) +print(atom_soap.block(0)) + +# %% Now let's perform sample selection on the atomic environments. We want to +# select 10 atomic environments for each chemical species. + +# Define the number of structures *per block* to select using FPS +n_envs = 10 + +# FPS sample selection +selector_atomic_fps = sample_selection.FPS(n_to_select=n_envs, initialize="random").fit( + atom_soap +) + +# Print the selected envs for each block +print("atomic envs selected with FPS:\n") +for key, block in selector_atomic_fps.support.items(): + print("species_center:", key, "\n(struct_idx, atom_idx)\n", block.samples.values) + +selector_atomic_cur = sample_selection.CUR(n_to_select=n_envs).fit(atom_soap) +# Print the selected envs for each block +print("atomic envs selected with CUR:\n") +for key, block in selector_atomic_cur.support.items(): + print("species_center:", key, "\n(struct_idx, atom_idx)\n", block.samples.values) + + +# %% +# Selecting from a combined pool of atomic environments +# ----------------------------------------------------- +# +# One can also select from a combined pool of atomic environments and +# structures, instead of selecting an equal number of atomic environments for +# each chemical species. In this case, we can move the 'species_center' key to samples +# such that our descriptor is a TensorMap consisting of a single block. Upon +# sample selection, the most diverse atomic environments will be selected, +# regardless of their chemical species. +print("----All atomic environment selection-----") + +print("keys", atom_soap.keys) +print("blocks", atom_soap[0]) +print("samples in first block", atom_soap[0].samples) + +# Using the original SOAP descriptor, move all keys to properties. + + +# Define the number of structures to select using FPS +n_envs = 10 + +# FPS sample selection +selector_atomic_fps = sample_selection.FPS(n_to_select=n_envs, initialize="random").fit( + atom_soap_single_block +) +print( + "atomic envs selected with FPS: \n (struct_idx, atom_idx, species_center) \n", + selector_atomic_fps.support.block(0).samples.values, +) + + +# %% +# Perform structure (i.e. sample) selection with FPS/CUR +# --------------------------------------------------------- +# +# Instead of atomic environments, one can also select diverse structures. We can +# use the `sum_over_samples` operation in metatensor to define features in the +# structural basis instead of the atomic basis. This is done by summing over the +# atomic environments, labeled by the 'center' index in the samples of the +# TensorMap. +# +# Alternatively, one could use the `mean_over_samples` operation, depending on +# the specific inhomogeneity of the size of the structures in the training set. + +print("----Structure selection-----") + +# Define the number of structures to select *per block* using FPS +n_structures = 10 + +# FPS structure selection +selector_struct_fps = sample_selection.FPS( + n_to_select=n_structures, initialize="random" +).fit(struct_soap) +struct_fps_idxs = selector_struct_fps.support.block(0).samples.values.flatten() + +print("structures selected with FPS:\n", struct_fps_idxs) + +# CUR structure selection +selector_struct_cur = sample_selection.CUR(n_to_select=n_structures).fit(struct_soap) +struct_cur_idxs = selector_struct_cur.support.block(0).samples.values.flatten() +print("structures selected with CUR:\n", struct_cur_idxs) + + +# Slice structure descriptor along axis 0 to contain only the selected structures +struct_soap_fps = struct_soap.block(0).values[struct_fps_idxs, :] +struct_soap_cur = struct_soap.block(0).values[struct_cur_idxs, :] +assert struct_soap_fps.shape == struct_soap_cur.shape + +print("Structure descriptor shape before selection ", struct_soap.block(0).values.shape) +print("Structure descriptor shape after selection (FPS)", struct_soap_fps.shape) +print("Structure descriptor shape after selection (CUR)", struct_soap_cur.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_soap_pca = PCA(n_components=2).fit_transform(struct_soap.block(0).values) +assert struct_soap_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_soap_pca[:, 0], struct_soap_pca[:, 1], c="red") +ax.plot( + struct_soap_pca[struct_cur_idxs, 0], + struct_soap_pca[struct_cur_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_soap_pca[:, 0], + "PC2": struct_soap_pca[:, 1], + "selection": np.array(selection_levels), + } +) + + +# Display with chemiscope. This currently does not work - as raised in issue #8 +# https://github.com/lab-cosmo/software-cookbook/issues/8 +widget = 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(widget) +else: + widget.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. +print("----Feature selection-----") + +# 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_soap_single_block +) + +# Slice atomic descriptor along axis 1 to contain only the selected features +# atom_soap_single_block_fps = atom_soap_single_block.block(0).values[:, feat_fps_idxs] +atom_soap_single_block_fps = metatensor.slice( + atom_soap_single_block, + axis="properties", + labels=feat_fps.support.block(0).properties, +) + +print( + "atomic descriptor shape before selection ", + atom_soap_single_block.block(0).values.shape, +) +print( + "atomic descriptor shape after selection ", + atom_soap_single_block_fps.block(0).values.shape, +) + +# %% + +# %% +# Perform feature selection (skmatter) +# ------------------------------------ +# +# 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. + +print("----Feature selection (skmatter)-----") + +# Define the number of features to select +n_features = 200 + +# FPS feature selection +feat_fps = skfeat_selection.FPS(n_to_select=n_features, initialize="random").fit( + atom_soap_single_block.block(0).values +) +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_soap_single_block.block(0).values[:, feat_fps_idxs] + +print( + "atomic descriptor shape before selection ", + atom_soap_single_block.block(0).values.shape, +) +print("atomic descriptor shape after selection ", atom_dscrptr_fps.shape) + +# %% diff --git a/latest/_downloads/54cb049367bea2b1604e2df18dc055b4/sample-selection.ipynb b/latest/_downloads/54cb049367bea2b1604e2df18dc055b4/sample-selection.ipynb new file mode 100644 index 00000000..11bea86c --- /dev/null +++ b/latest/_downloads/54cb049367bea2b1604e2df18dc055b4/sample-selection.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Sample and Feature Selection with FPS and CUR\n\n:Authors: Davide Tisi [@DavideTisi](https://github.com/DavideTisi)\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 metatensor\nimport numpy as np\nfrom equisolve.numpy import feature_selection, sample_selection\nfrom matplotlib import pyplot as plt\nfrom metatensor import sum_over_samples\nfrom rascaline import SoapPowerSpectrum\nfrom sklearn.decomposition import PCA\nfrom skmatter import feature_selection as skfeat_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 :download:`structures ` of the example dataset\nn_frames = 500\nframes = ase.io.read(\"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\n\n# Makes a dense block\natom_soap = rho2i.keys_to_properties([\"species_neighbor_1\", \"species_neighbor_2\"])\n\natom_soap_single_block = atom_soap.keys_to_samples(keys_to_move=[\"species_center\"])\n\n# print(atom_soap_single_block)\n# print(atom_soap_single_block.block(0)) # There is only one block now!\n\n# Sum over atomic centers to compute structure features\nstruct_soap = sum_over_samples(\n atom_soap_single_block, sample_names=[\"center\", \"species_center\"]\n)\n\n\nprint(\"atom feature descriptor shape:\", atom_soap.block(0).values.shape)\nprint(\n \"atom feature descriptor (all in one block) shape:\",\n atom_soap_single_block.block(0).values.shape,\n)\nprint(\"structure feature descriptor shape:\", struct_soap.block(0).values.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform atomic environment (i.e. sample) selection\n\nUsing FPS and CUR algorithms, we can perform selection of atomic environments.\nThese are implemented in equisolve, which provides a wrapper around\nscikit-matter to allow for interfacing with data stored in the metatensor\nformat.\n\nSuppose we want to select the 10 most diverse environments for each chemical\nspecies.\n\nFirst, we can use the `keys_to_properties` operation in metatensor to move the\nneighbour species indices to the properties of the TensorBlocks. The resulting\ndescriptor will be a TensorMap comprised of three blocks, one for each\nchemical species, where the chemical species indices are solely present in the\nkeys.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"----Atomic environment selection-----\")\n# Define the number of structures to select using FPS/CUR\nn_envs = 25\n\nprint(atom_soap)\nprint(atom_soap.block(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "select 10 atomic environments for each chemical species.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define the number of structures *per block* to select using FPS\nn_envs = 10\n\n# FPS sample selection\nselector_atomic_fps = sample_selection.FPS(n_to_select=n_envs, initialize=\"random\").fit(\n atom_soap\n)\n\n# Print the selected envs for each block\nprint(\"atomic envs selected with FPS:\\n\")\nfor key, block in selector_atomic_fps.support.items():\n print(\"species_center:\", key, \"\\n(struct_idx, atom_idx)\\n\", block.samples.values)\n\nselector_atomic_cur = sample_selection.CUR(n_to_select=n_envs).fit(atom_soap)\n# Print the selected envs for each block\nprint(\"atomic envs selected with CUR:\\n\")\nfor key, block in selector_atomic_cur.support.items():\n print(\"species_center:\", key, \"\\n(struct_idx, atom_idx)\\n\", block.samples.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting from a combined pool of atomic environments\n\nOne can also select from a combined pool of atomic environments and\nstructures, instead of selecting an equal number of atomic environments for\neach chemical species. In this case, we can move the 'species_center' key to samples\nsuch that our descriptor is a TensorMap consisting of a single block. Upon\nsample selection, the most diverse atomic environments will be selected,\nregardless of their chemical species.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"----All atomic environment selection-----\")\n\nprint(\"keys\", atom_soap.keys)\nprint(\"blocks\", atom_soap[0])\nprint(\"samples in first block\", atom_soap[0].samples)\n\n# Using the original SOAP descriptor, move all keys to properties.\n\n\n# Define the number of structures to select using FPS\nn_envs = 10\n\n# FPS sample selection\nselector_atomic_fps = sample_selection.FPS(n_to_select=n_envs, initialize=\"random\").fit(\n atom_soap_single_block\n)\nprint(\n \"atomic envs selected with FPS: \\n (struct_idx, atom_idx, species_center) \\n\",\n selector_atomic_fps.support.block(0).samples.values,\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform structure (i.e. sample) selection with FPS/CUR\n\nInstead of atomic environments, one can also select diverse structures. We can\nuse the `sum_over_samples` operation in metatensor to define features in the\nstructural basis instead of the atomic basis. This is done by summing over the\natomic environments, labeled by the 'center' index in the samples of the\nTensorMap.\n\nAlternatively, one could use the `mean_over_samples` operation, depending on\nthe specific inhomogeneity of the size of the structures in the training set.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"----Structure selection-----\")\n\n# Define the number of structures to select *per block* using FPS\nn_structures = 10\n\n# FPS structure selection\nselector_struct_fps = sample_selection.FPS(\n n_to_select=n_structures, initialize=\"random\"\n).fit(struct_soap)\nstruct_fps_idxs = selector_struct_fps.support.block(0).samples.values.flatten()\n\nprint(\"structures selected with FPS:\\n\", struct_fps_idxs)\n\n# CUR structure selection\nselector_struct_cur = sample_selection.CUR(n_to_select=n_structures).fit(struct_soap)\nstruct_cur_idxs = selector_struct_cur.support.block(0).samples.values.flatten()\nprint(\"structures selected with CUR:\\n\", struct_cur_idxs)\n\n\n# Slice structure descriptor along axis 0 to contain only the selected structures\nstruct_soap_fps = struct_soap.block(0).values[struct_fps_idxs, :]\nstruct_soap_cur = struct_soap.block(0).values[struct_cur_idxs, :]\nassert struct_soap_fps.shape == struct_soap_cur.shape\n\nprint(\"Structure descriptor shape before selection \", struct_soap.block(0).values.shape)\nprint(\"Structure descriptor shape after selection (FPS)\", struct_soap_fps.shape)\nprint(\"Structure descriptor shape after selection (CUR)\", struct_soap_cur.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_soap_pca = PCA(n_components=2).fit_transform(struct_soap.block(0).values)\nassert struct_soap_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_soap_pca[:, 0], struct_soap_pca[:, 1], c=\"red\")\nax.plot(\n struct_soap_pca[struct_cur_idxs, 0],\n struct_soap_pca[struct_cur_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_soap_pca[:, 0],\n \"PC2\": struct_soap_pca[:, 1],\n \"selection\": np.array(selection_levels),\n }\n)\n\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\nwidget = 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(widget)\nelse:\n widget.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": [ + "print(\"----Feature selection-----\")\n\n# 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_soap_single_block\n)\n\n# Slice atomic descriptor along axis 1 to contain only the selected features\n# atom_soap_single_block_fps = atom_soap_single_block.block(0).values[:, feat_fps_idxs]\natom_soap_single_block_fps = metatensor.slice(\n atom_soap_single_block,\n axis=\"properties\",\n labels=feat_fps.support.block(0).properties,\n)\n\nprint(\n \"atomic descriptor shape before selection \",\n atom_soap_single_block.block(0).values.shape,\n)\nprint(\n \"atomic descriptor shape after selection \",\n atom_soap_single_block_fps.block(0).values.shape,\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform feature selection (skmatter)\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": [ + "print(\"----Feature selection (skmatter)-----\")\n\n# Define the number of features to select\nn_features = 200\n\n# FPS feature selection\nfeat_fps = skfeat_selection.FPS(n_to_select=n_features, initialize=\"random\").fit(\n atom_soap_single_block.block(0).values\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_soap_single_block.block(0).values[:, feat_fps_idxs]\n\nprint(\n \"atomic descriptor shape before selection \",\n atom_soap_single_block.block(0).values.shape,\n)\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/61cac41226274764c3aff5e112f310da/roy-gch.ipynb b/latest/_downloads/61cac41226274764c3aff5e112f310da/roy-gch.ipynb new file mode 100644 index 00000000..bd2c6a38 --- /dev/null +++ b/latest/_downloads/61cac41226274764c3aff5e112f310da/roy-gch.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Generalized Convex Hull construction for the polymorphs of ROY\n\n:Authors: Michele Ceriotti [@ceriottm](https://github.com/ceriottm/)\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/6cadff1f3492fc92f1615765051399cd/environment.yml b/latest/_downloads/6cadff1f3492fc92f1615765051399cd/environment.yml new file mode 100644 index 00000000..2a66a8ba --- /dev/null +++ b/latest/_downloads/6cadff1f3492fc92f1615765051399cd/environment.yml @@ -0,0 +1,12 @@ +channels: + - conda-forge +dependencies: + - python=3.11 + - pip + - rust + - pip: + - ase + - equisolve @ git+https://github.com/lab-cosmo/equisolve.git@c858bedef4b2799eb445e4c92535ee387224089a + - matplotlib + - metatensor + - rascaline @ git+https://github.com/Luthaf/rascaline@ca957642f512e141c7570e987aadc05c7ac71983 diff --git a/latest/_downloads/6e397e9158b2a5505af1904db0846a5c/run_calcs.sh b/latest/_downloads/6e397e9158b2a5505af1904db0846a5c/run_calcs.sh new file mode 100644 index 00000000..59a8ff53 --- /dev/null +++ b/latest/_downloads/6e397e9158b2a5505af1904db0846a5c/run_calcs.sh @@ -0,0 +1,7 @@ +#! /bin/bash + +for i in $(find ./production/ -mindepth 1 -type d); do + cd $i + cp2k.ssmp -i in.cp2k + cd - +done diff --git a/latest/_downloads/87860011d67b243cba3a72df0fa64d08/lode-linear.ipynb b/latest/_downloads/87860011d67b243cba3a72df0fa64d08/lode-linear.ipynb new file mode 100644 index 00000000..63e7086c --- /dev/null +++ b/latest/_downloads/87860011d67b243cba3a72df0fa64d08/lode-linear.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# LODE Tutorial\n\n:Authors: Philip Loche [@PicoCentauri](https://github.com/PicoCentauri/),\n Kevin Huguenin-Dumittan [@kvhuguenin](https://github.com/kvhuguenin)\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\n:download:`structures ` of the charge-charge molecule\npairs.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "frames = ase.io.read(\"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/89f739150c737d83b9f5bb306c62ba22/lode-linear.py b/latest/_downloads/89f739150c737d83b9f5bb306c62ba22/lode-linear.py new file mode 100644 index 00000000..23ac620b --- /dev/null +++ b/latest/_downloads/89f739150c737d83b9f5bb306c62ba22/lode-linear.py @@ -0,0 +1,395 @@ +""" +LODE Tutorial +============= + +:Authors: Philip Loche `@PicoCentauri `_, + Kevin Huguenin-Dumittan `@kvhuguenin `_ + +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 +# :download:`structures ` of the charge-charge molecule +# pairs. + +frames = ase.io.read("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/acef7f4b78ef4bb781ee98c79cb1dfe9/gaas-map.ipynb b/latest/_downloads/acef7f4b78ef4bb781ee98c79cb1dfe9/gaas-map.ipynb new file mode 100644 index 00000000..622fd272 --- /dev/null +++ b/latest/_downloads/acef7f4b78ef4bb781ee98c79cb1dfe9/gaas-map.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# PCA/PCovR Visualization for the rattled GaAs training dataset\n\n:Authors: Michele Ceriotti [@ceriottm](https://github.com/ceriottm/),\n Giulio Imbalzano\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/ae0da8b1ee160a1314e0f4a70d65ba5f/reference-trajectory.py b/latest/_downloads/ae0da8b1ee160a1314e0f4a70d65ba5f/reference-trajectory.py new file mode 100644 index 00000000..4fb6e77c --- /dev/null +++ b/latest/_downloads/ae0da8b1ee160a1314e0f4a70d65ba5f/reference-trajectory.py @@ -0,0 +1,304 @@ +r""" +Batch run of CP2K calculations +============================== + +:Authors: Matthias Kellner `@bananenpampe `_, + Philip Loche `@PicoCentauri `_ + +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`. 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! +""" + +# %% +# We start the example by importing the required packages. + +import os +import platform +import subprocess +from typing import List, Union + +import ase.io +import ase.visualize.plot +import matplotlib.pyplot as plt +import numpy as np +import requests + + +# %% +# +# Install CP2K +# ------------ +# +# We'll need a working installation of cp2k. The best way to do so depends on your +# platform, here are some possible solutions, but feel free to replace them with another +# installation method. + +if platform.system() == "Linux": + # use conda on Linux + subprocess.run(["conda", "install", "cp2k", "-c", "conda-forge", "-y"], check=True) +elif platform.system() == "Darwin": + # use homebrew on macOS + subprocess.run(["brew", "install", "cp2k"], check=True) +else: + print("no known way to install cp2k, skipping installation") + + +# %% +# 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) + + +# %% +# +# We will now download basis set files from CP2K website. Depending on your CP2K +# installation, this might not be necessary! + + +def download_parameter(file): + path = os.path.join("parameters", file) + + if not os.path.exists(path): + url = f"https://raw.githubusercontent.com/cp2k/cp2k/support/v2024.1/data/{file}" + response = requests.get(url) + response.raise_for_status() + with open(path, "wb") as f: + f.write(response.content) + + +os.makedirs("parameters", exist_ok=True) +for file in ["GTH_BASIS_SETS", "BASIS_ADMM", "POTENTIAL", "dftd3.dat", "t_c_g.dat"]: + download_parameter(file) + + +# %% +# 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 simulation. + +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 extract 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. + +for stoichiometry, frames in frames_dict.items(): + current_directory = f"{project_directory}/{stoichiometry}" + os.makedirs(current_directory, exist_ok=True) + + 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 parallelize the loop over the +# sub-directories 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"{os.path.splitext(os.path.basename(write_to_file))[0]}_dft.xyz" +ase.io.write(f"{project_directory}/{new_fname}", new_frames) diff --git a/latest/_downloads/c1382a1742282359674fed148953d82f/environment.yml b/latest/_downloads/c1382a1742282359674fed148953d82f/environment.yml new file mode 100644 index 00000000..32dfa760 --- /dev/null +++ b/latest/_downloads/c1382a1742282359674fed148953d82f/environment.yml @@ -0,0 +1,14 @@ +channels: + - conda-forge +dependencies: + - python=3.11 + - pip + - rust + - pip: + - ase + - chemiscope + - matplotlib + - metatensor + - rascaline @ git+https://github.com/Luthaf/rascaline@ca957642f512e141c7570e987aadc05c7ac71983 + - scikit-learn + - skmatter diff --git a/latest/_downloads/c30ae838fdaa9326592e1ef5414c0d62/gaas-map.py b/latest/_downloads/c30ae838fdaa9326592e1ef5414c0d62/gaas-map.py new file mode 100644 index 00000000..c0175812 --- /dev/null +++ b/latest/_downloads/c30ae838fdaa9326592e1ef5414c0d62/gaas-map.py @@ -0,0 +1,255 @@ +""" +PCA/PCovR Visualization for the rattled GaAs training dataset +============================================================= + +:Authors: Michele Ceriotti `@ceriottm `_, + Giulio Imbalzano + +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/_downloads/d997f92a427513b3b0c833f5b65889cf/reftraj_template.cp2k b/latest/_downloads/d997f92a427513b3b0c833f5b65889cf/reftraj_template.cp2k new file mode 100644 index 00000000..fe83ba8d --- /dev/null +++ b/latest/_downloads/d997f92a427513b3b0c833f5b65889cf/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 ../../parameters/GTH_BASIS_SETS + BASIS_SET_FILE_NAME ../../parameters/BASIS_ADMM + POTENTIAL_FILE_NAME ../../parameters/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 ../../parameters/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 ../../parameters/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/db9adeac0042e3126c9d37f1c33e3b6f/reference-trajectory.ipynb b/latest/_downloads/db9adeac0042e3126c9d37f1c33e3b6f/reference-trajectory.ipynb new file mode 100644 index 00000000..e0ef8f5f --- /dev/null +++ b/latest/_downloads/db9adeac0042e3126c9d37f1c33e3b6f/reference-trajectory.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Batch run of CP2K calculations\n\n:Authors: Matthias Kellner [@bananenpampe](https://github.com/bananenpampe/),\n Philip Loche [@PicoCentauri](https://github.com/PicoCentauri/)\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`. The reference DFT parameters are taken from [Cheng et\nal. Ab initio 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" + ] + }, + { + "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 platform\nimport subprocess\nfrom typing import List, Union\n\nimport ase.io\nimport ase.visualize.plot\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install CP2K\n\nWe'll need a working installation of cp2k. The best way to do so depends on your\nplatform, here are some possible solutions, but feel free to replace them with another\ninstallation method.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "if platform.system() == \"Linux\":\n # use conda on Linux\n subprocess.run([\"conda\", \"install\", \"cp2k\", \"-c\", \"conda-forge\", \"-y\"], check=True)\nelif platform.system() == \"Darwin\":\n # use homebrew on macOS\n subprocess.run([\"brew\", \"install\", \"cp2k\"], check=True)\nelse:\n print(\"no known way to install cp2k, skipping installation\")" + ] + }, + { + "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": "markdown", + "metadata": {}, + "source": [ + "We will now download basis set files from CP2K website. Depending on your CP2K\ninstallation, this might not be necessary!\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def download_parameter(file):\n path = os.path.join(\"parameters\", file)\n\n if not os.path.exists(path):\n url = f\"https://raw.githubusercontent.com/cp2k/cp2k/support/v2024.1/data/{file}\"\n response = requests.get(url)\n response.raise_for_status()\n with open(path, \"wb\") as f:\n f.write(response.content)\n\n\nos.makedirs(\"parameters\", exist_ok=True)\nfor file in [\"GTH_BASIS_SETS\", \"BASIS_ADMM\", \"POTENTIAL\", \"dftd3.dat\", \"t_c_g.dat\"]:\n download_parameter(file)" + ] + }, + { + "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 simulation.\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 extract 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": [ + "for stoichiometry, frames in frames_dict.items():\n current_directory = f\"{project_directory}/{stoichiometry}\"\n os.makedirs(current_directory, exist_ok=True)\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 parallelize the loop over the\n sub-directories 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\"{os.path.splitext(os.path.basename(write_to_file))[0]}_dft.xyz\"\nase.io.write(f\"{project_directory}/{new_fname}\", new_frames)" + ] + } + ], + "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/edc4de60777e3b3373ed9e627d585459/environment.yml b/latest/_downloads/edc4de60777e3b3373ed9e627d585459/environment.yml new file mode 100644 index 00000000..95bed51e --- /dev/null +++ b/latest/_downloads/edc4de60777e3b3373ed9e627d585459/environment.yml @@ -0,0 +1,13 @@ +channels: + - conda-forge +dependencies: + - python=3.11 + - pip + - rust + - pip: + - ase + - chemiscope + - matplotlib + - metatensor + - rascaline @ git+https://github.com/Luthaf/rascaline@ca957642f512e141c7570e987aadc05c7ac71983 + - skmatter diff --git a/latest/_downloads/fe6711aca3f0f5f2cec13f8a8ebe3462/example.xyz b/latest/_downloads/fe6711aca3f0f5f2cec13f8a8ebe3462/example.xyz new file mode 100644 index 00000000..c0031e2d --- /dev/null +++ b/latest/_downloads/fe6711aca3f0f5f2cec13f8a8ebe3462/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 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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`. 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! + +.. GENERATED FROM PYTHON SOURCE LINES 21-22 + +We start the example by importing the required packages. + +.. GENERATED FROM PYTHON SOURCE LINES 22-35 + +.. code-block:: Python + + + import os + import platform + import subprocess + from typing import List, Union + + import ase.io + import ase.visualize.plot + import matplotlib.pyplot as plt + import numpy as np + import requests + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 36-42 + +Install CP2K +------------ + +We'll need a working installation of cp2k. The best way to do so depends on your +platform, here are some possible solutions, but feel free to replace them with another +installation method. + +.. GENERATED FROM PYTHON SOURCE LINES 43-54 + +.. code-block:: Python + + + if platform.system() == "Linux": + # use conda on Linux + subprocess.run(["conda", "install", "cp2k", "-c", "conda-forge", "-y"], check=True) + elif platform.system() == "Darwin": + # use homebrew on macOS + subprocess.run(["brew", "install", "cp2k"], check=True) + else: + print("no known way to install cp2k, skipping installation") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 55-58 + +Define necessary functions +-------------------------- +Next we below define necessary helper functions to run the example. + +.. GENERATED FROM PYTHON SOURCE LINES 58-92 + +.. 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 93-129 + +.. 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 130-151 + +.. 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 152-154 + +We will now download basis set files from CP2K website. Depending on your CP2K +installation, this might not be necessary! + +.. GENERATED FROM PYTHON SOURCE LINES 155-173 + +.. code-block:: Python + + + + def download_parameter(file): + path = os.path.join("parameters", file) + + if not os.path.exists(path): + url = f"https://raw.githubusercontent.com/cp2k/cp2k/support/v2024.1/data/{file}" + response = requests.get(url) + response.raise_for_status() + with open(path, "wb") as f: + f.write(response.content) + + + os.makedirs("parameters", exist_ok=True) + for file in ["GTH_BASIS_SETS", "BASIS_ADMM", "POTENTIAL", "dftd3.dat", "t_c_g.dat"]: + download_parameter(file) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 174-186 + +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 simulation. + +.. GENERATED FROM PYTHON SOURCE LINES 186-192 + +.. 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 193-195 + +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 195-203 + +.. code-block:: Python + + + ase.visualize.plot.plot_atoms(frames_full[0]) + + plt.xlabel("Å") + plt.ylabel("Å") + + plt.show() + + + + +.. image-sg:: /examples/batch-cp2k/images/sphx_glr_reference-trajectory_001.png + :alt: reference trajectory + :srcset: /examples/batch-cp2k/images/sphx_glr_reference-trajectory_001.png + :class: sphx-glr-single-img + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 204-206 + +We now extract the stoichiometry from the input dataset using ASE's +:py:meth:`ase.symbols.Symbols.get_chemical_formula` method. + +.. GENERATED FROM PYTHON SOURCE LINES 206-218 + +.. 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 219-223 + +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 223-239 + +.. code-block:: Python + + + for stoichiometry, frames in frames_dict.items(): + current_directory = f"{project_directory}/{stoichiometry}" + os.makedirs(current_directory, exist_ok=True) + + 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 240-250 + +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 250-254 + +.. 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 255-264 + +.. note:: + + For a usage on an HPC environment you can parallelize the loop over the + sub-directories 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 264-269 + +.. 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 270-273 + +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 273-305 + +.. 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"{os.path.splitext(os.path.basename(write_to_file))[0]}_dft.xyz" + ase.io.write(f"{project_directory}/{new_fname}", new_frames) + + + + + + + + +.. _sphx_glr_download_examples_batch-cp2k_reference-trajectory.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + + .. container:: sphx-glr-download + + :download:`Download Conda environment file: environment.yml ` + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: reference-trajectory.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: reference-trajectory.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ + diff --git a/latest/_sources/examples/batch-cp2k/sg_execution_times.rst.txt b/latest/_sources/examples/batch-cp2k/sg_execution_times.rst.txt new file mode 100644 index 00000000..787b1cc5 --- /dev/null +++ b/latest/_sources/examples/batch-cp2k/sg_execution_times.rst.txt @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_examples_batch-cp2k_sg_execution_times: + + +Computation times +================= +**00:39.336** total execution time for 1 file **from examples/batch-cp2k**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_examples_batch-cp2k_reference-trajectory.py` (``reference-trajectory.py``) + - 00:39.336 + - 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..405ed945 --- /dev/null +++ b/latest/_sources/examples/gaas-map/gaas-map.rst.txt @@ -0,0 +1,441 @@ + +.. 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 +============================================================= + +:Authors: Michele Ceriotti `@ceriottm `_, + Giulio Imbalzano + +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 16-33 + +.. 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 34-40 + +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 40-54 + +.. 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 55-68 + +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 68-91 + +.. 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 92-95 + +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 95-103 + +.. 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 104-111 + +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 111-138 + +.. 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 139-142 + +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 142-147 + +.. code-block:: Python + + + sf_energy = StandardFlexibleScaler().fit_transform(cohesive_peratom.reshape(-1, 1)) + sf_feats = StandardFlexibleScaler().fit_transform(features) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 148-160 + +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 160-184 + +.. 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 185-192 + +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 192-256 + +.. 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/.nox/gaas-map/lib/python3.11/site-packages/chemiscope/jupyter.py:245: UserWarning: chemiscope.show only works inside a jupyter notebook + warnings.warn("chemiscope.show only works inside a jupyter notebook") + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (2 minutes 20.743 seconds) + + +.. _sphx_glr_download_examples_gaas-map_gaas-map.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + + .. container:: sphx-glr-download + + :download:`Download Conda environment file: environment.yml ` + .. 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..27f1348d --- /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 +================= +**02:20.743** 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``) + - 02:20.743 + - 0.0 diff --git a/latest/_sources/examples/lode-linear/lode-linear.rst.txt b/latest/_sources/examples/lode-linear/lode-linear.rst.txt new file mode 100644 index 00000000..edd07ad3 --- /dev/null +++ b/latest/_sources/examples/lode-linear/lode-linear.rst.txt @@ -0,0 +1,733 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/lode-linear/lode-linear.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-linear.py: + + +LODE Tutorial +============= + +:Authors: Philip Loche `@PicoCentauri `_, + Kevin Huguenin-Dumittan `@kvhuguenin `_ + +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 16-27 + +.. 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 28-43 + +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 +:download:`structures ` of the charge-charge molecule +pairs. + +.. GENERATED FROM PYTHON SOURCE LINES 44-48 + +.. code-block:: Python + + + frames = ase.io.read("charge-charge.xyz", ":") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 49-56 + +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 57-61 + +.. code-block:: Python + + + y = ase_to_tensormap(frames, energy="energy", forces="forces") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 62-73 + +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 74-86 + +.. 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 87-88 + +And next the hyperparaters for the LODE / long-range (LR) part + +.. GENERATED FROM PYTHON SOURCE LINES 89-107 + +.. 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 108-110 + +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 110-115 + +.. code-block:: Python + + + calculator_sr = SphericalExpansion(**SR_HYPERS) + calculator_lr = LodeSphericalExpansion(**LR_HYPERS) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 116-136 + +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 137-142 + +.. 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 143-149 + +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 150-155 + +.. 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 156-165 + +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 166-171 + +.. 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 172-174 + +For linear models only: Sum features up over atoms (``samples``) in the same +structure. + +.. GENERATED FROM PYTHON SOURCE LINES 175-182 + +.. 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 183-189 + +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 190-197 + +.. 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 198-219 + +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 220-224 + +.. code-block:: Python + + + X_sr = metatensor.join([co, ps_sr], axis="properties") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 225-232 + +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 233-237 + +.. code-block:: Python + + + X_lr = metatensor.join([co, ps_sr, ps_lr], axis="properties") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 238-245 + +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 246-251 + +.. code-block:: Python + + + clf_sr = Ridge() + clf_lr = Ridge() + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 252-258 + +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 259-263 + +.. code-block:: Python + + + r_cut = 6.0 + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 264-266 + +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 267-272 + +.. 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 273-275 + +For doing the split we define two ``Labels`` instances and combine them in a +:py:class:`List`. + +.. GENERATED FROM PYTHON SOURCE LINES 276-282 + +.. 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 283-284 + +That we use as input to the :py:func:`metatensor.split()` function + +.. GENERATED FROM PYTHON SOURCE LINES 285-297 + +.. 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 298-305 + +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 306-311 + +.. 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 312-317 + +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 318-338 + +.. 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 339-345 + +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 346-352 + +.. 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 353-354 + +and select only the indices corresponding to our test set. + +.. GENERATED FROM PYTHON SOURCE LINES 358-359 + +Next we calculate the predicted SR and LR ``TensorMaps``. + +.. GENERATED FROM PYTHON SOURCE LINES 360-365 + +.. code-block:: Python + + + y_sr_pred = clf_sr.predict(X_sr) + y_lr_pred = clf_lr.predict(X_lr) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 366-367 + +And, finally perform the plot. + +.. GENERATED FROM PYTHON SOURCE LINES 368-396 + +.. 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-linear_001.png + :alt: lode linear + :srcset: /examples/lode-linear/images/sphx_glr_lode-linear_001.png + :class: sphx-glr-single-img + + + + + + +.. _sphx_glr_download_examples_lode-linear_lode-linear.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + + .. container:: sphx-glr-download + + :download:`Download Conda environment file: environment.yml ` + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: lode-linear.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: lode-linear.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..3175b080 --- /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 +================= +**00:09.112** 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-linear.py` (``lode-linear.py``) + - 00:09.112 + - 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..fa4c9480 --- /dev/null +++ b/latest/_sources/examples/roy-gch/roy-gch.rst.txt @@ -0,0 +1,541 @@ + +.. 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 +============================================================== + +:Authors: Michele Ceriotti `@ceriottm `_ + +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 18-30 + +.. 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 31-32 + +Loads the structures (that also contain properties in the ``info`` field) + +.. GENERATED FROM PYTHON SOURCE LINES 32-44 + +.. 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 45-50 + +Energy-density hull +------------------- + +The Directional Convex Hull routines can be used to compute a +conventional density-energy hull + +.. GENERATED FROM PYTHON SOURCE LINES 50-54 + +.. 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 55-57 + +We can get the indices of the selection, and compute the distance from +the hull + +.. GENERATED FROM PYTHON SOURCE LINES 57-62 + +.. code-block:: Python + + + sel = dch_builder.selected_idx_ + dch_dist = dch_builder.score_samples(density.reshape(-1, 1), energy) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 63-74 + +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 75-89 + +.. 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 90-97 + +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 97-126 + +.. 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/.nox/roy-gch/lib/python3.11/site-packages/chemiscope/jupyter.py:245: UserWarning: chemiscope.show only works inside a jupyter notebook + warnings.warn("chemiscope.show only works inside a jupyter notebook") + + + + +.. GENERATED FROM PYTHON SOURCE LINES 127-139 + +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 142-150 + +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 150-173 + +.. 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 174-180 + +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 180-192 + +.. 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 193-197 + +Builds the Generalized Convex Hull +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Builds a convex hull on the first two PCA features + +.. GENERATED FROM PYTHON SOURCE LINES 197-204 + +.. 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 205-207 + +Generates a 3D Plot + + +.. GENERATED FROM PYTHON SOURCE LINES 207-219 + +.. 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 220-223 + +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 223-230 + +.. 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 0.853784504474871 kJ/mol + Mean hull energy for 'other' structures 5.198871581851552 kJ/mol + + + + +.. GENERATED FROM PYTHON SOURCE LINES 231-233 + +Visualize in ``chemiscope``. This runs only in a notebook, and +requires having the ``chemiscope`` package installed. + +.. GENERATED FROM PYTHON SOURCE LINES 233-295 + +.. 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/.nox/roy-gch/lib/python3.11/site-packages/chemiscope/jupyter.py:245: 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 + + :download:`Download Conda environment file: environment.yml ` + .. 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..9ea72c09 --- /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 +================= +**00:17.631** 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``) + - 00:17.631 + - 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..6bb5e851 --- /dev/null +++ b/latest/_sources/examples/sample-selection/sample-selection.rst.txt @@ -0,0 +1,769 @@ + +.. 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 +============================================= + +:Authors: Davide Tisi `@DavideTisi `_ + +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 16-29 + +.. code-block:: Python + + + import ase.io + import chemiscope + import metatensor + import numpy as np + from equisolve.numpy import feature_selection, sample_selection + from matplotlib import pyplot as plt + from metatensor import sum_over_samples + from rascaline import SoapPowerSpectrum + from sklearn.decomposition import PCA + from skmatter import feature_selection as skfeat_selection + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 30-35 + +Load molecular data +------------------- + +Load 500 example BTO structures from file, reading them using +`ASE `_. + +.. GENERATED FROM PYTHON SOURCE LINES 35-40 + +.. code-block:: Python + + + # Load a subset of :download:`structures ` of the example dataset + n_frames = 500 + frames = ase.io.read("input-fps.xyz", f":{n_frames}", format="extxyz") + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 41-45 + +Compute SOAP descriptors using rascaline +---------------------------------------- + +First, define the rascaline hyperparameters used to compute SOAP. + +.. GENERATED FROM PYTHON SOURCE LINES 45-86 + +.. 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 + atom_soap = rho2i.keys_to_properties(["species_neighbor_1", "species_neighbor_2"]) + + atom_soap_single_block = atom_soap.keys_to_samples(keys_to_move=["species_center"]) + + # print(atom_soap_single_block) + # print(atom_soap_single_block.block(0)) # There is only one block now! + + # Sum over atomic centers to compute structure features + struct_soap = sum_over_samples( + atom_soap_single_block, sample_names=["center", "species_center"] + ) + + + print("atom feature descriptor shape:", atom_soap.block(0).values.shape) + print( + "atom feature descriptor (all in one block) shape:", + atom_soap_single_block.block(0).values.shape, + ) + print("structure feature descriptor shape:", struct_soap.block(0).values.shape) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + atom feature descriptor shape: (12000, 2688) + atom feature descriptor (all in one block) shape: (20000, 2688) + structure feature descriptor shape: (500, 2688) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 87-103 + +Perform atomic environment (i.e. sample) selection +--------------------------------------------------- + +Using FPS and CUR algorithms, we can perform selection of atomic environments. +These are implemented in equisolve, which provides a wrapper around +scikit-matter to allow for interfacing with data stored in the metatensor +format. + +Suppose we want to select the 10 most diverse environments for each chemical +species. + +First, we can use the `keys_to_properties` operation in metatensor to move the +neighbour species indices to the properties of the TensorBlocks. The resulting +descriptor will be a TensorMap comprised of three blocks, one for each +chemical species, where the chemical species indices are solely present in the +keys. + +.. GENERATED FROM PYTHON SOURCE LINES 103-112 + +.. code-block:: Python + + + + print("----Atomic environment selection-----") + # Define the number of structures to select using FPS/CUR + n_envs = 25 + + print(atom_soap) + print(atom_soap.block(0)) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + ----Atomic environment selection----- + TensorMap with 3 blocks + keys: species_center + 8 + 22 + 56 + TensorBlock + samples (12000): ['structure', 'center'] + components (): [] + properties (2688): ['species_neighbor_1', 'species_neighbor_2', 'l', 'n1', 'n2'] + gradients: None + + + + +.. GENERATED FROM PYTHON SOURCE LINES 113-114 + +select 10 atomic environments for each chemical species. + +.. GENERATED FROM PYTHON SOURCE LINES 114-135 + +.. code-block:: Python + + + # Define the number of structures *per block* to select using FPS + n_envs = 10 + + # FPS sample selection + selector_atomic_fps = sample_selection.FPS(n_to_select=n_envs, initialize="random").fit( + atom_soap + ) + + # Print the selected envs for each block + print("atomic envs selected with FPS:\n") + for key, block in selector_atomic_fps.support.items(): + print("species_center:", key, "\n(struct_idx, atom_idx)\n", block.samples.values) + + selector_atomic_cur = sample_selection.CUR(n_to_select=n_envs).fit(atom_soap) + # Print the selected envs for each block + print("atomic envs selected with CUR:\n") + for key, block in selector_atomic_cur.support.items(): + print("species_center:", key, "\n(struct_idx, atom_idx)\n", block.samples.values) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + atomic envs selected with FPS: + + species_center: LabelsEntry(species_center=8) + (struct_idx, atom_idx) + [[ 68 18] + [113 36] + [140 21] + [285 20] + [339 16] + [339 23] + [339 24] + [341 17] + [347 37] + [436 22]] + species_center: LabelsEntry(species_center=22) + (struct_idx, atom_idx) + [[ 19 8] + [ 55 13] + [166 15] + [198 12] + [216 8] + [285 9] + [324 8] + [341 12] + [433 13] + [466 9]] + species_center: LabelsEntry(species_center=56) + (struct_idx, atom_idx) + [[ 40 7] + [140 2] + [238 3] + [289 6] + [339 3] + [341 4] + [407 0] + [407 7] + [436 6] + [451 7]] + atomic envs selected with CUR: + + species_center: LabelsEntry(species_center=8) + (struct_idx, atom_idx) + [[ 55 21] + [ 68 20] + [ 77 30] + [198 36] + [267 32] + [336 33] + [339 24] + [339 36] + [341 17] + [436 19]] + species_center: LabelsEntry(species_center=22) + (struct_idx, atom_idx) + [[ 10 39] + [ 40 10] + [ 70 10] + [130 10] + [166 15] + [170 14] + [216 8] + [285 9] + [326 10] + [466 10]] + species_center: LabelsEntry(species_center=56) + (struct_idx, atom_idx) + [[ 40 7] + [ 77 3] + [172 3] + [219 7] + [289 6] + [296 2] + [339 5] + [339 6] + [407 0] + [436 2]] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 136-145 + +Selecting from a combined pool of atomic environments +----------------------------------------------------- + +One can also select from a combined pool of atomic environments and +structures, instead of selecting an equal number of atomic environments for +each chemical species. In this case, we can move the 'species_center' key to samples +such that our descriptor is a TensorMap consisting of a single block. Upon +sample selection, the most diverse atomic environments will be selected, +regardless of their chemical species. + +.. GENERATED FROM PYTHON SOURCE LINES 145-167 + +.. code-block:: Python + + print("----All atomic environment selection-----") + + print("keys", atom_soap.keys) + print("blocks", atom_soap[0]) + print("samples in first block", atom_soap[0].samples) + + # Using the original SOAP descriptor, move all keys to properties. + + + # Define the number of structures to select using FPS + n_envs = 10 + + # FPS sample selection + selector_atomic_fps = sample_selection.FPS(n_to_select=n_envs, initialize="random").fit( + atom_soap_single_block + ) + print( + "atomic envs selected with FPS: \n (struct_idx, atom_idx, species_center) \n", + selector_atomic_fps.support.block(0).samples.values, + ) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + ----All atomic environment selection----- + keys Labels( + species_center + 8 + 22 + 56 + ) + blocks TensorBlock + samples (12000): ['structure', 'center'] + components (): [] + properties (2688): ['species_neighbor_1', 'species_neighbor_2', 'l', 'n1', 'n2'] + gradients: None + samples in first block Labels( + structure center + 0 16 + 0 17 + ... + 499 30 + 499 31 + ) + atomic envs selected with FPS: + (struct_idx, atom_idx, species_center) + [[ 68 12 22] + [ 77 30 8] + [140 21 8] + [166 15 22] + [216 8 22] + [289 6 56] + [339 18 8] + [407 0 56] + [460 4 56] + [466 34 8]] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 168-179 + +Perform structure (i.e. sample) selection with FPS/CUR +--------------------------------------------------------- + +Instead of atomic environments, one can also select diverse structures. We can +use the `sum_over_samples` operation in metatensor to define features in the +structural basis instead of the atomic basis. This is done by summing over the +atomic environments, labeled by the 'center' index in the samples of the +TensorMap. + +Alternatively, one could use the `mean_over_samples` operation, depending on +the specific inhomogeneity of the size of the structures in the training set. + +.. GENERATED FROM PYTHON SOURCE LINES 179-209 + +.. code-block:: Python + + + print("----Structure selection-----") + + # Define the number of structures to select *per block* using FPS + n_structures = 10 + + # FPS structure selection + selector_struct_fps = sample_selection.FPS( + n_to_select=n_structures, initialize="random" + ).fit(struct_soap) + struct_fps_idxs = selector_struct_fps.support.block(0).samples.values.flatten() + + print("structures selected with FPS:\n", struct_fps_idxs) + + # CUR structure selection + selector_struct_cur = sample_selection.CUR(n_to_select=n_structures).fit(struct_soap) + struct_cur_idxs = selector_struct_cur.support.block(0).samples.values.flatten() + print("structures selected with CUR:\n", struct_cur_idxs) + + + # Slice structure descriptor along axis 0 to contain only the selected structures + struct_soap_fps = struct_soap.block(0).values[struct_fps_idxs, :] + struct_soap_cur = struct_soap.block(0).values[struct_cur_idxs, :] + assert struct_soap_fps.shape == struct_soap_cur.shape + + print("Structure descriptor shape before selection ", struct_soap.block(0).values.shape) + print("Structure descriptor shape after selection (FPS)", struct_soap_fps.shape) + print("Structure descriptor shape after selection (CUR)", struct_soap_cur.shape) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + ----Structure selection----- + structures selected with FPS: + [ 15 71 140 167 172 257 317 326 356 496] + structures selected with CUR: + [ 39 40 68 110 140 289 326 386 398 438] + Structure descriptor shape before selection (500, 2688) + Structure descriptor shape after selection (FPS) (10, 2688) + Structure descriptor shape after selection (CUR) (10, 2688) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 210-219 + +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 219-226 + +.. code-block:: Python + + + + # Generate a structure PCA + struct_soap_pca = PCA(n_components=2).fit_transform(struct_soap.block(0).values) + assert struct_soap_pca.shape == (n_frames, 2) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 227-232 + +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 232-249 + +.. code-block:: Python + + + # Matplotlib plot + fig, ax = plt.subplots(1, 1, figsize=(6, 4)) + scatter = ax.scatter(struct_soap_pca[:, 0], struct_soap_pca[:, 1], c="red") + ax.plot( + struct_soap_pca[struct_cur_idxs, 0], + struct_soap_pca[struct_cur_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 250-254 + +Creates a chemiscope viewer +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Interactive viewer (only works in notebooks) + +.. GENERATED FROM PYTHON SOURCE LINES 254-309 + +.. 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_soap_pca[:, 0], + "PC2": struct_soap_pca[:, 1], + "selection": np.array(selection_levels), + } + ) + + + # Display with chemiscope. This currently does not work - as raised in issue #8 + # https://github.com/lab-cosmo/software-cookbook/issues/8 + widget = 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(widget) + else: + widget.save("sample-selection.json.gz") + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /home/runner/work/software-cookbook/software-cookbook/.nox/sample-selection/lib/python3.11/site-packages/chemiscope/structures/_ase.py:90: UserWarning: the following structure properties properties are only defined for a subset of frames: ['stress']; they will be ignored + warnings.warn( + /home/runner/work/software-cookbook/software-cookbook/.nox/sample-selection/lib/python3.11/site-packages/chemiscope/jupyter.py:245: UserWarning: chemiscope.show only works inside a jupyter notebook + warnings.warn("chemiscope.show only works inside a jupyter notebook") + + + + +.. GENERATED FROM PYTHON SOURCE LINES 310-316 + +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. + +.. GENERATED FROM PYTHON SOURCE LINES 316-343 + +.. code-block:: Python + + print("----Feature selection-----") + + # 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_soap_single_block + ) + + # Slice atomic descriptor along axis 1 to contain only the selected features + # atom_soap_single_block_fps = atom_soap_single_block.block(0).values[:, feat_fps_idxs] + atom_soap_single_block_fps = metatensor.slice( + atom_soap_single_block, + axis="properties", + labels=feat_fps.support.block(0).properties, + ) + + print( + "atomic descriptor shape before selection ", + atom_soap_single_block.block(0).values.shape, + ) + print( + "atomic descriptor shape after selection ", + atom_soap_single_block_fps.block(0).values.shape, + ) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + ----Feature selection----- + atomic descriptor shape before selection (20000, 2688) + atomic descriptor shape after selection (20000, 200) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 346-352 + +Perform feature selection (skmatter) +------------------------------------ + +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. + +.. GENERATED FROM PYTHON SOURCE LINES 352-375 + +.. code-block:: Python + + + print("----Feature selection (skmatter)-----") + + # Define the number of features to select + n_features = 200 + + # FPS feature selection + feat_fps = skfeat_selection.FPS(n_to_select=n_features, initialize="random").fit( + atom_soap_single_block.block(0).values + ) + 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_soap_single_block.block(0).values[:, feat_fps_idxs] + + print( + "atomic descriptor shape before selection ", + atom_soap_single_block.block(0).values.shape, + ) + print("atomic descriptor shape after selection ", atom_dscrptr_fps.shape) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + ----Feature selection (skmatter)----- + Feature indices obtained with FPS [2607 0 920 464 3 411 1 488 1824 921 5 465 29 1179 + 27 2240 468 1344 4 2244 602 923 489 274 1825 45 916 1180 + 28 1308 475 470 731 924 46 283 1828 474 450 30 491 2241 + 730 412 2084 12 477 1835 1636 859 476 1827 1796 496 413 931 + 936 21 453 1345 899 466 939 940 11 1816 1324 934 900 948 + 861 928 1490 19 860 925 2 1170 2276 456 1347 1819 1181 1310 + 13 858 1380 36 210 1372 1812 926 929 2195 947 458 1196 451 + 1307 492 2267 472 2212 915 462 449 733 725 922 18 37 2523 + 2532 1365 932 1746 347 473 868 912 502 275 927 1362 2246 1172 + 485 54 504 484 285 866 395 31 499 1348 480 1316 2092 38 + 1805 901 913 1941 723 944 2213 898 1187 1939 941 402 907 1364 + 6 1947 469 1309 467 490 1940 10 905 498 2242 1637 908 904 + 452 2083 942 1820 937 724 1171 500 933 338 420 2524 1682 2254 + 483 2221 22 1792 1764 494 7 738 949 9 2268 1832 1618 1323 + 478 482 950 461] + atomic descriptor shape before selection (20000, 2688) + atomic descriptor shape after selection (20000, 200) + + + + + +.. _sphx_glr_download_examples_sample-selection_sample-selection.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + + .. container:: sphx-glr-download + + :download:`Download Conda environment file: environment.yml ` + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: sample-selection.ipynb ` + + .. 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..c23269cd --- /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 +================= +**00:23.247** 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``) + - 00:23.247 + - 0.0 diff --git a/latest/_sources/index.rst.txt b/latest/_sources/index.rst.txt new file mode 100644 index 00000000..228af3cc --- /dev/null +++ b/latest/_sources/index.rst.txt @@ -0,0 +1,29 @@ +COSMO Software Cookbook +======================= + +.. include:: ../../README.rst + :start-after: marker-intro-start + :end-before: marker-intro-end + + +All the examples provide an ``environment.yml`` file that you can download and +then use with conda to create a new environment with all the required +dependencies for this example. + +.. code-block:: bash + + # Pick a name for the environment and replace with it + conda env create --name --file environment.yml + + # when you want to use the environment + conda env activate --name + +.. toctree:: + :caption: Table of Contents + :maxdepth: 1 + + examples/sample-selection/sample-selection + examples/batch-cp2k/reference-trajectory + examples/gaas-map/gaas-map + examples/roy-gch/roy-gch + examples/lode-linear/lode-linear 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; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + 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right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, 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#067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} 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documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + 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 --git a/latest/_static/file.png b/latest/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/latest/_static/jupyterlite_badge_logo.svg b/latest/_static/jupyterlite_badge_logo.svg new file mode 100644 index 00000000..5de36d7f --- /dev/null +++ b/latest/_static/jupyterlite_badge_logo.svg @@ -0,0 +1,3 @@ + + +launchlaunchlitelite \ No newline at end of file diff --git a/latest/_static/language_data.js b/latest/_static/language_data.js new file mode 100644 index 00000000..250f5665 --- /dev/null +++ b/latest/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer 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 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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( + '

' + + '' + + _("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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svg{padding:0}body{--sd-color-primary:var(--color-brand-primary);--sd-color-primary-highlight:var(--color-brand-content);--sd-color-primary-text:var(--color-background-primary);--sd-color-shadow:rgba(0,0,0,.05);--sd-color-card-border:var(--color-card-border);--sd-color-card-border-hover:var(--color-brand-content);--sd-color-card-background:var(--color-card-background);--sd-color-card-text:var(--color-foreground-primary);--sd-color-card-header:var(--color-card-marginals-background);--sd-color-card-footer:var(--color-card-marginals-background);--sd-color-tabs-label-active:var(--color-brand-content);--sd-color-tabs-label-hover:var(--color-foreground-muted);--sd-color-tabs-label-inactive:var(--color-foreground-muted);--sd-color-tabs-underline-active:var(--color-brand-content);--sd-color-tabs-underline-hover:var(--color-foreground-border);--sd-color-tabs-underline-inactive:var(--color-background-border);--sd-color-tabs-overline:var(--color-background-border);--sd-color-tabs-underline:var(--color-background-border)}.sd-tab-content{box-shadow:0 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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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