From 954fe9acc25c60ef55ab909051ac03b8b3db3aa2 Mon Sep 17 00:00:00 2001 From: jonpvandermause Date: Fri, 20 Sep 2024 03:35:44 +0000 Subject: [PATCH] deploy: 6eaf9dcc77a9290e1f47e8106aac4381a908f90a --- flare/bffs/bffs.html | 1 - flare/bffs/gp/calculator.html | 1 - flare/bffs/gp/gp.html | 12 ++---------- flare/bffs/gp/gp_algebra.html | 17 ++++++++--------- flare/bffs/gp/predict.html | 3 +-- flare/flare.html | 2 -- flare/io/output.html | 2 +- flare/learners/utils.html | 2 +- flare/utils/env_getarray.html | 2 +- flare/utils/mask_helper.html | 10 +--------- genindex.html | 10 ++-------- index.html | 2 -- objects.inv | Bin 23207 -> 23162 bytes searchindex.js | 2 +- 14 files changed, 18 insertions(+), 48 deletions(-) diff --git a/flare/bffs/bffs.html b/flare/bffs/bffs.html index e7a7cc15a..86c583ac6 100644 --- a/flare/bffs/bffs.html +++ b/flare/bffs/bffs.html @@ -105,7 +105,6 @@

Bayesian Force FieldsHelper functions for GP
  • FLARE ASE Calculator
  • GaussianProcess
    • -
  • random()
  • Mapped Gaussian Process
  • Mapped Gaussian Process
    • diff --git a/flare/bffs/gp/gp.html b/flare/bffs/gp/gp.html index 0db9ec2d7..21e7fc1bb 100644 --- a/flare/bffs/gp/gp.html +++ b/flare/bffs/gp/gp.html @@ -56,7 +56,6 @@
  • Helper functions for GP
  • FLARE ASE Calculator
  • GaussianProcess
    • -
  • random()
  • Mapped Gaussian Process
    • @@ -200,7 +199,7 @@

    Gaussian Process Force Fields
    -add_one_env(env: AtomicEnvironment, force: Optional[ndarray] = None, train: bool = False, **kwargs)
    +add_one_env(env: AtomicEnvironment, force: Optional[numpy.ndarray] = None, train: bool = False, **kwargs)

    Add a single local environment to the training set of the GP.

    Parameters
    @@ -219,7 +218,7 @@

    Gaussian Process Force Fields
    -adjust_cutoffs(new_cutoffs: Optional[Union[list, tuple, ndarray]] = None, reset_L_alpha=True, train=True, new_hyps_mask=None)
    +adjust_cutoffs(new_cutoffs: Optional[Union[list, tuple, numpy.ndarray]] = None, reset_L_alpha=True, train=True, new_hyps_mask=None)

    Loop through atomic environment objects stored in the training data, and re-compute cutoffs for each. Useful if you want to gauge the impact of cutoffs given a certain training set! Unless you know @@ -516,13 +515,6 @@

    Gaussian Process Force Fields -
    -flare.bffs.gp.gp.random(size=None)
    -

    Return random floats in the half-open interval [0.0, 1.0). Alias for -random_sample to ease forward-porting to the new random API.

    -

    - diff --git a/flare/bffs/gp/gp_algebra.html b/flare/bffs/gp/gp_algebra.html index afb29345c..08723a3ef 100644 --- a/flare/bffs/gp/gp_algebra.html +++ b/flare/bffs/gp/gp_algebra.html @@ -56,7 +56,6 @@
  • Helper functions for GP
  • FLARE ASE Calculator
  • GaussianProcess
    • -
  • random()
  • Mapped Gaussian Process
    • @@ -177,7 +176,7 @@
    -flare.bffs.gp.gp_algebra.get_distance_mat_pack(hyps: ndarray, name: str, s1: int, e1: int, s2: int, e2: int, same: bool, kernel, cutoffs, hyps_mask)
    +flare.bffs.gp.gp_algebra.get_distance_mat_pack(hyps: numpy.ndarray, name: str, s1: int, e1: int, s2: int, e2: int, same: bool, kernel, cutoffs, hyps_mask)

    Compute covariance matrix element between set1 and set2 :param hyps: list of hyper-parameters :param name: name of the gp instance. @@ -191,7 +190,7 @@

    -flare.bffs.gp.gp_algebra.get_force_block(hyps: ndarray, name: str, kernel, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)
    +flare.bffs.gp.gp_algebra.get_force_block(hyps: numpy.ndarray, name: str, kernel, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)

    parallel version of get_ky_mat :param hyps: list of hyper-parameters :param name: name of the gp instance. @@ -208,7 +207,7 @@

    -flare.bffs.gp.gp_algebra.get_force_block_pack(hyps: ndarray, name: str, s1: int, e1: int, s2: int, e2: int, same: bool, kernel, cutoffs, hyps_mask)
    +flare.bffs.gp.gp_algebra.get_force_block_pack(hyps: numpy.ndarray, name: str, s1: int, e1: int, s2: int, e2: int, same: bool, kernel, cutoffs, hyps_mask)

    Compute covariance matrix element between set1 and set2 :param hyps: list of hyper-parameters :param name: name of the gp instance. @@ -226,7 +225,7 @@

    -flare.bffs.gp.gp_algebra.get_ky_and_hyp(hyps: ndarray, name, kernel_grad, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)
    +flare.bffs.gp.gp_algebra.get_ky_and_hyp(hyps: numpy.ndarray, name, kernel_grad, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)

    parallel version of get_ky_and_hyp

    Parameters
    @@ -248,7 +247,7 @@
    -flare.bffs.gp.gp_algebra.get_ky_and_hyp_pack(name, s1, e1, s2, e2, same: bool, hyps: ndarray, kernel_grad, cutoffs=None, hyps_mask=None)
    +flare.bffs.gp.gp_algebra.get_ky_and_hyp_pack(name, s1, e1, s2, e2, same: bool, hyps: numpy.ndarray, kernel_grad, cutoffs=None, hyps_mask=None)

    computes a block of ky matrix and its derivative to hyper-parameter If the cpu set up is None, it uses as much as posible cpus

    @@ -302,7 +301,7 @@
    -flare.bffs.gp.gp_algebra.get_neg_like(hyps: ndarray, name: str, force_kernel, logger_name=None, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)
    +flare.bffs.gp.gp_algebra.get_neg_like(hyps: numpy.ndarray, name: str, force_kernel, logger_name=None, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)

    compute the log likelihood and its gradients :param hyps: list of hyper-parameters :type hyps: np.ndarray @@ -329,7 +328,7 @@

    -flare.bffs.gp.gp_algebra.get_neg_like_grad(hyps: ndarray, name: str, kernel_grad, logger_name: Optional[str] = None, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)
    +flare.bffs.gp.gp_algebra.get_neg_like_grad(hyps: numpy.ndarray, name: str, kernel_grad, logger_name: Optional[str] = None, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)

    compute the log likelihood and its gradients

    Parameters
    @@ -353,7 +352,7 @@
    -flare.bffs.gp.gp_algebra.kernel_distance_mat(hyps: ndarray, name: str, kernel, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)
    +flare.bffs.gp.gp_algebra.kernel_distance_mat(hyps: numpy.ndarray, name: str, kernel, cutoffs=None, hyps_mask=None, n_cpus=1, n_sample=100)

    parallel version of get_ky_mat :param hyps: list of hyper-parameters :param name: name of the gp instance. diff --git a/flare/bffs/gp/predict.html b/flare/bffs/gp/predict.html index 6734d0fee..c64c46331 100644 --- a/flare/bffs/gp/predict.html +++ b/flare/bffs/gp/predict.html @@ -56,7 +56,6 @@

  • Helper functions for GP
  • FLARE ASE Calculator
  • GaussianProcess
    • -
  • random()
  • Mapped Gaussian Process
    • @@ -221,7 +220,7 @@
    -flare.bffs.gp.predict.predict_on_structure_mgp(structure: FLARE_Atoms, mgp: MappedGaussianProcess, output=None, output_name=None, n_cpus: Optional[int] = None, write_to_structure: bool = True, selective_atoms: Optional[List[int]] = None, skipped_atom_value: Union[float, int] = 0, energy: bool = False) → Union[Tuple[ndarray, ndarray, float], Tuple[ndarray, ndarray]]
    +flare.bffs.gp.predict.predict_on_structure_mgp(structure: FLARE_Atoms, mgp: MappedGaussianProcess, output=None, output_name=None, n_cpus: Optional[int] = None, write_to_structure: bool = True, selective_atoms: Optional[List[int]] = None, skipped_atom_value: Union[float, int] = 0, energy: bool = False) → Union[Tuple[numpy.ndarray, numpy.ndarray, float], Tuple[numpy.ndarray, numpy.ndarray]]

    Assign forces to structure based on an mgp

    diff --git a/flare/flare.html b/flare/flare.html index bafd0a614..1094c647b 100644 --- a/flare/flare.html +++ b/flare/flare.html @@ -100,7 +100,6 @@

    Python Code DocumentationHelper functions for GP
  • FLARE ASE Calculator
  • GaussianProcess
    • -
  • random()
  • Mapped Gaussian Process
      @@ -336,7 +335,6 @@

      Python Code DocumentationAdvanced Hyperparameters Set Up
    • Construct Atomic Environment
        diff --git a/flare/io/output.html b/flare/io/output.html index 537e708cd..349176913 100644 --- a/flare/io/output.html +++ b/flare/io/output.html @@ -255,7 +255,7 @@
        -write_xyz_config(curr_step, structure, forces: Optional[array] = None, stds: Optional[array] = None, dft_forces: Optional[array] = None, dft_energy=0, predict_energy=nan, target_atoms=None)
        +write_xyz_config(curr_step, structure, forces: Optional[numpy.array] = None, stds: Optional[numpy.array] = None, dft_forces: Optional[numpy.array] = None, dft_energy=0, predict_energy=nan, target_atoms=None)

        write atomic configuration in xyz file

        Parameters
        diff --git a/flare/learners/utils.html b/flare/learners/utils.html index a8a219669..918c17b6a 100644 --- a/flare/learners/utils.html +++ b/flare/learners/utils.html @@ -110,7 +110,7 @@

        Utility functions for various tasks.

        -flare.learners.utils.get_max_cutoff(cell: ndarray) → float
        +flare.learners.utils.get_max_cutoff(cell: numpy.ndarray) → float
        Compute the maximum cutoff compatible with a 3x3x3 supercell of a

        structure. Called in the Structure constructor when setting the max_cutoff attribute, which is used to create local diff --git a/flare/utils/env_getarray.html b/flare/utils/env_getarray.html index ba6b16c2c..8c7b06292 100644 --- a/flare/utils/env_getarray.html +++ b/flare/utils/env_getarray.html @@ -211,7 +211,7 @@

        -flare.utils.env_getarray.get_m2_body_arrays(positions, atom: int, cell, r_cut, manybody_cutoff_list, species, sweep: ndarray, nspec, spec_mask, manybody_mask, cutoff_func=numba.njit)
        +flare.utils.env_getarray.get_m2_body_arrays(positions, atom: int, cell, r_cut, manybody_cutoff_list, species, sweep: numpy.ndarray, nspec, spec_mask, manybody_mask, cutoff_func=numba.njit)
        Parameters
        -
        -
        -flare.utils.parameters.random(size=None)
        -

        Return random floats in the half-open interval [0.0, 1.0). Alias for -random_sample to ease forward-porting to the new random API.

        -
        - - + diff --git a/genindex.html b/genindex.html index af8b56ddd..017d20fb1 100644 --- a/genindex.html +++ b/genindex.html @@ -1030,18 +1030,12 @@

        Q

        R

        - +
      • Mapped Gaussian Process
          @@ -378,7 +377,6 @@

          ContentsAdvanced Hyperparameters Set Up
        • Construct Atomic Environment
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