id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
142,164 | from alphafold.common import residue_constants
from alphafold.model.tf import shape_helpers
from alphafold.model.tf import shape_placeholders
from alphafold.model.tf import utils
import numpy as np
import tensorflow.compat.v1 as tf
_MSA_FEATURE_NAMES = [
'msa', 'deletion_matrix', 'msa_mask', 'msa_row_mask', 'bert_m... | Sample MSA by deleting contiguous blocks. Jumper et al. (2021) Suppl. Alg. 1 "MSABlockDeletion" Arguments: protein: batch dict containing the msa config: ConfigDict with parameters Returns: updated protein |
142,165 | from alphafold.common import residue_constants
from alphafold.model.tf import shape_helpers
from alphafold.model.tf import shape_placeholders
from alphafold.model.tf import utils
import numpy as np
import tensorflow.compat.v1 as tf
def add_constant_field(protein, key, value):
protein[key] = tf.convert_to_tensor(valu... | null |
142,166 | import enum
from typing import Dict, Optional, Sequence, Tuple, Union
from alphafold.common import residue_constants
import tensorflow.compat.v1 as tf
FEATURES = {
#### Static features of a protein sequence ####
"aatype": (tf.float32, [NUM_RES, 21]),
"between_segment_residues": (tf.int64, [NUM_RES, 1]),
... | Register extra features used in custom datasets. |
142,167 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `atom14_to_atom37` function. Write a Pyt... | Convert atom14 to atom37 representation. |
142,168 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `atom37_to_atom14` function. Write a Pyt... | Convert atom14 to atom37 representation. |
142,169 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `atom37_to_frames` function. Write a Pyt... | Computes the frames for the up to 8 rigid groups for each residue. The rigid groups are defined by the possible torsions in a given amino acid. We group the atoms according to their dependence on the torsion angles into "rigid groups". E.g., the position of atoms in the chi2-group depend on chi1 and chi2, but do not de... |
142,170 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
def get_chi_atom_indices():
"""Returns atom indices needed to compute chi angles for all residue types.
Returns:
... | Computes the 7 torsion angles (in sin, cos encoding) for each residue. The 7 torsion angles are in the order '[pre_omega, phi, psi, chi_1, chi_2, chi_3, chi_4]', here pre_omega denotes the omega torsion angle between the given amino acid and the previous amino acid. Args: aatype: Amino acid type, given as array with in... |
142,171 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `torsion_angles_to_frames` function. Wri... | Compute rigid group frames from torsion angles. Jumper et al. (2021) Suppl. Alg. 24 "computeAllAtomCoordinates" lines 2-10 Jumper et al. (2021) Suppl. Alg. 25 "makeRotX" Args: aatype: aatype for each residue backb_to_global: Rigid transformations describing transformation from backbone frame to global frame. torsion_an... |
142,172 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `frames_and_literature_positions_to_atom... | Put atom literature positions (atom14 encoding) in each rigid group. Jumper et al. (2021) Suppl. Alg. 24 "computeAllAtomCoordinates" line 11 Args: aatype: aatype for each residue. all_frames_to_global: All per residue coordinate frames. Returns: Positions of all atom coordinates in global frame. |
142,173 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_renaming_matrices` function. Writ... | Matrices to map atoms to symmetry partners in ambiguous case. |
142,174 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import r3
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
RENAMING_MATRICES = _make_renaming_matrices()
The provided code snippet includes necessary dependencies for implementin... | Get alternative atom14 positions. Constructs renamed atom positions for ambiguous residues. Jumper et al. (2021) Suppl. Table 3 "Ambiguous atom names due to 180 degree- rotation-symmetry" Args: aatype: Amino acid at given position positions: Atom positions as r3.Vecs in atom14 representation, (N, 14) mask: Atom masks i... |
142,175 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | Computes sigmoid cross entropy given logits and multiple class labels. |
142,176 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | Applies module + dropout + residual update. |
142,177 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | Expand extra_msa into 1hot and concat with other extra msa features. We do this as late as possible as the one_hot extra msa can be very large. Arguments: batch: a dictionary with the following keys: * 'extra_msa': [N_extra_seq, N_res] MSA that wasn't selected as a cluster centre. Note, that this is not one-hot encoded... |
142,178 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | null |
142,179 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | null |
142,180 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | Log loss of a distogram. |
142,181 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | Compute distogram from amino acid positions. Arguments: positions: [N_res, 3] Position coordinates. num_bins: The number of bins in the distogram. min_bin: The left edge of the first bin. max_bin: The left edge of the final bin. The final bin catches everything larger than `max_bin`. Returns: Distogram with the specifi... |
142,182 | import functools
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import folding
from alphafold.model import layer_stack
from alphafold.model import lddt
from alphafold.model import mapping
from alphafold.model import prn... | Create pseudo beta features. |
142,183 | import copy
from alphafold.model.tf import shape_placeholders
import ml_collections
CONFIG_DIFFS = {
'model_1': {
# Jumper et al. (2021) Suppl. Table 5, Model 1.1.1
'data.common.max_extra_msa': 5120,
'data.common.reduce_msa_clusters_by_max_templates': True,
'data.common.use_templates... | Get the ConfigDict of a CASP14 model. |
142,184 | from typing import List
import jax.numpy as jnp
def unstack(value: jnp.ndarray, axis: int = -1) -> List[jnp.ndarray]:
return [jnp.squeeze(v, axis=axis)
for v in jnp.split(value, value.shape[axis], axis=axis)] | null |
142,185 | from __future__ import annotations
import dataclasses
from typing import Union
from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import utils
import jax
import jax.numpy as jnp
import numpy as np
class Vec3Array:
"""Vec3Array in 3 dimensional Space implemented as struct of arrays.
T... | null |
142,186 | from __future__ import annotations
import dataclasses
from typing import Union
from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import utils
import jax
import jax.numpy as jnp
import numpy as np
Float = Union[float, jnp.ndarray]
class Vec3Array:
"""Vec3Array in 3 dimensional Space im... | Computes euclidean distance between 'vec1' and 'vec2'. Args: vec1: Vec3Array to compute euclidean distance to vec2: Vec3Array to compute euclidean distance from, should be broadcast compatible with 'vec1' epsilon: distance is clipped from below to be at least epsilon Returns: Array of euclidean distances; shape will be... |
142,187 | from __future__ import annotations
import dataclasses
from typing import Union
from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import utils
import jax
import jax.numpy as jnp
import numpy as np
Float = Union[float, jnp.ndarray]
class Vec3Array:
"""Vec3Array in 3 dimensional Space im... | Computes torsion angle for a quadruple of points. For points (a, b, c, d), this is the angle between the planes defined by points (a, b, c) and (b, c, d). It is also known as the dihedral angle. Arguments: a: A Vec3Array of coordinates. b: A Vec3Array of coordinates. c: A Vec3Array of coordinates. d: A Vec3Array of coo... |
142,188 | from __future__ import annotations
import dataclasses
from typing import Union
from alphafold.model.geometry import struct_of_array
from alphafold.model.geometry import utils
import jax
import jax.numpy as jnp
import numpy as np
class Vec3Array:
"""Vec3Array in 3 dimensional Space implemented as struct of arrays.
T... | null |
142,189 | import dataclasses
import jax
def replace(instance, **kwargs):
return dataclasses.replace(instance, **kwargs)
def get_array_fields(cls, return_values=False):
return get_fields(
cls,
lambda x: not x.metadata.get('is_metadata', False),
return_values=return_values)
def get_item(instance, key):
sli... | null |
142,190 | import dataclasses
import jax
The provided code snippet includes necessary dependencies for implementing the `get_shape` function. Write a Python function `def get_shape(instance)` to solve the following problem:
Returns Shape for given instance of dataclass.
Here is the function:
def get_shape(instance):
"""Retur... | Returns Shape for given instance of dataclass. |
142,191 | import dataclasses
import jax
The provided code snippet includes necessary dependencies for implementing the `get_len` function. Write a Python function `def get_len(instance)` to solve the following problem:
Returns length for given instance of dataclass.
Here is the function:
def get_len(instance):
"""Returns le... | Returns length for given instance of dataclass. |
142,192 | import dataclasses
import jax
The provided code snippet includes necessary dependencies for implementing the `get_dtype` function. Write a Python function `def get_dtype(instance)` to solve the following problem:
Returns Dtype for given instance of dataclass.
Here is the function:
def get_dtype(instance):
"""Retur... | Returns Dtype for given instance of dataclass. |
142,193 | import dataclasses
import jax
def get_array_fields(cls, return_values=False):
return get_fields(
cls,
lambda x: not x.metadata.get('is_metadata', False),
return_values=return_values)
The provided code snippet includes necessary dependencies for implementing the `post_init` function. Write a Python ... | Validate instance has same shapes & dtypes. |
142,194 | import dataclasses
import jax
def get_array_fields(cls, return_values=False):
return get_fields(
cls,
lambda x: not x.metadata.get('is_metadata', False),
return_values=return_values)
def get_metadata_fields(cls, return_values=False):
return get_fields(
cls,
lambda x: x.metadata.get('is... | Flatten Struct of Array instance. |
142,195 | import dataclasses
import jax
def get_fields(cls_or_instance, filterfn, return_values=False):
fields = dataclasses.fields(cls_or_instance)
fields = [field for field in fields if filterfn(field)]
if return_values:
return {
field.name: getattr(cls_or_instance, field.name) for field in fields
}
els... | null |
142,196 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | Generate predicted affines for a single chain. Jumper et al. (2021) Suppl. Alg. 20 "StructureModule" This is the main part of the structure module - it iteratively applies folding to produce a set of predicted residue positions. Args: representations: Representations dictionary. batch: Batch dictionary. config: Config ... |
142,197 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | Find optimal renaming of ground truth based on the predicted positions. Jumper et al. (2021) Suppl. Alg. 26 "renameSymmetricGroundTruthAtoms" This renamed ground truth is then used for all losses, such that each loss moves the atoms in the same direction. Shape (N). Args: batch: Dictionary containing: * atom14_gt_posit... |
142,198 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | Backbone FAPE Loss. Jumper et al. (2021) Suppl. Alg. 20 "StructureModule" line 17 Args: ret: Dictionary to write outputs into, needs to contain 'loss'. batch: Batch, needs to contain 'backbone_affine_tensor', 'backbone_affine_mask'. value: Dictionary containing structure module output, needs to contain 'traj', a trajec... |
142,199 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | All Atom FAPE Loss using renamed rigids. |
142,200 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | Computes loss for structural violations. |
142,201 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | Computes loss for direct chi angle supervision. Jumper et al. (2021) Suppl. Alg. 27 "torsionAngleLoss" Args: ret: Dictionary to write outputs into, needs to contain 'loss'. batch: Batch, needs to contain 'seq_mask', 'chi_mask', 'chi_angles'. value: Dictionary containing structure module output, needs to contain value['... |
142,202 | import functools
from typing import Dict
from alphafold.common import residue_constants
from alphafold.model import all_atom
from alphafold.model import common_modules
from alphafold.model import prng
from alphafold.model import quat_affine
from alphafold.model import r3
from alphafold.model import utils
import haiku a... | null |
142,203 | from typing import Any, Mapping, Optional, Union
from absl import logging
from alphafold.common import confidence
from alphafold.model import features
from alphafold.model import modules
from alphafold.model import modules_multimer
import haiku as hk
import jax
import ml_collections
import numpy as np
import tensorflow... | Post processes prediction_result to get confidence metrics. |
142,204 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | null |
142,205 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Create data for BERT on raw MSA. |
142,206 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Assign each extra MSA sequence to its nearest neighbor in sampled MSA. |
142,207 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Create and concatenate MSA features. |
142,208 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Expand extra_msa into 1hot and concat with other extra msa features. We do this as late as possible as the one_hot extra msa can be very large. Args: batch: a dictionary with the following keys: * 'extra_msa': [num_seq, num_res] MSA that wasn't selected as a cluster centre. Note - This isn't one-hotted. * 'extra_deleti... |
142,209 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Sample MSA randomly, remaining sequences are stored as `extra_*`. Args: key: safe key for random number generation. batch: batch to sample msa from. max_seq: number of sequences to sample. Returns: Protein with sampled msa. |
142,210 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Compute the MSA profile. |
142,211 | import functools
from typing import Sequence
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import folding_multimer
from alphafold.model import geometry
from alphafold.model import layer_stack
from alphafold.mo... | Embed templates into an (num_res, num_templates, num_channels) embedding. Args: batch: A batch containing: template_aatype, (num_templates, num_res) aatype for the templates. template_all_atom_positions, (num_templates, num_residues, 37, 3) atom positions for the templates. template_all_atom_mask, (num_templates, num_r... |
142,212 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Make backbone Rigid3Array and mask. |
142,213 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Generate predicted Rigid's for a single chain. This is the main part of the iterative fold head - it iteratively applies folding to produce a set of predicted residue positions. Args: representations: Embeddings dictionary. batch: Batch dictionary. config: config for the iterative fold head. global_config: global confi... |
142,214 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Find atom14 positions, this includes finding the correct renaming. |
142,215 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Backbone FAPE Loss. |
142,216 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Compute Frames from all atom positions. Args: aatype: array of aatypes, int of [N] all_atom_positions: Vector of all atom positions, shape [N, 37] all_atom_mask: mask, shape [N] use_alt: whether to use alternative orientation for ambiguous aatypes shape [N] Returns: Rigid corresponding to Frames w shape [N, 8], mask wh... |
142,217 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Sidechain Loss using cleaned up rigids. |
142,218 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Computes Loss for structural Violations. |
142,219 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Computes several checks for structural Violations. |
142,220 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Compute several metrics to assess the structural violations. |
142,221 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Computes loss for direct chi angle supervision. |
142,222 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | null |
142,223 | import functools
import numbers
from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
from alphafold.common import residue_constants
from alphafold.model import all_atom_multimer
from alphafold.model import common_modules
from alphafold.model import geometry
from alphafold.model import modules
from al... | Return renamed chi angles. |
142,224 | import functools
import inspect
from typing import Any, Callable, Optional, Sequence, Union
import haiku as hk
import jax
import jax.numpy as jnp
PYTREE = Any
PYTREE_JAX_ARRAY = Any
def sharded_apply(
fun: Callable[..., PYTREE_JAX_ARRAY], # pylint: disable=g-bare-generic
shard_size: Union[int, None] = 1,
i... | Sharded vmap. Maps `fun` over axes, in a way similar to vmap, but does so in shards of `shard_size`. This allows a smooth trade-off between memory usage (as in a plain map) vs higher throughput (as in a vmap). Args: fun: Function to apply smap transform to. shard_size: Integer denoting shard size. in_axes: Either integ... |
142,225 | import functools
import inspect
from typing import Any, Callable, Optional, Sequence, Union
import haiku as hk
import jax
import jax.numpy as jnp
PYTREE_JAX_ARRAY = Any
def sharded_apply(
fun: Callable[..., PYTREE_JAX_ARRAY], # pylint: disable=g-bare-generic
shard_size: Union[int, None] = 1,
in_axes: Union... | Run through subbatches (like batch apply but with split and concat). |
142,226 | import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree
Vecs = collections.namedtuple('Vecs', ['x', 'y', 'z'])
Rots = collections.namedtuple('Rots', ['xx', 'xy', 'xz',
'yx', 'yy', 'yz',
... | Converts flat list of arrays to rigid transformations. |
142,227 | import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree
Vecs = collections.namedtuple('Vecs', ['x', 'y', 'z'])
Rigids = collections.namedtuple('Rigids', ['rot', 'trans'])
def rots_from_two_vecs(e0_unnormalized: Vecs, e1_unnormalized: Vecs) -> Rots:
"""Cre... | Flat9 encoding: first two columns of rotation matrix + translation. |
142,228 | import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree
Rigids = collections.namedtuple('Rigids', ['rot', 'trans'])
The provided code snippet includes necessary dependencies for implementing the `rigids_to_list` function. Write a Python function `def rigid... | Turn Rigids into flat list, inverse of 'rigids_from_list'. |
142,229 | import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree
Rigids = collections.namedtuple('Rigids', ['rot', 'trans'])
The provided code snippet includes necessary dependencies for implementing the `rigids_to_quataffine` function. Write a Python function `def... | Convert Rigids r into QuatAffine, inverse of 'rigids_from_quataffine'. |
142,230 | import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree
Rigids = collections.namedtuple('Rigids', ['rot', 'trans'])
The provided code snippet includes necessary dependencies for implementing the `rigids_to_tensor_flat9` function. Write a Python function `d... | Flat9 encoding: first two columns of rotation matrix + translation. |
142,231 | import collections
from typing import List
from alphafold.model import quat_affine
import jax.numpy as jnp
import tree
Vecs = collections.namedtuple('Vecs', ['x', 'y', 'z'])
The provided code snippet includes necessary dependencies for implementing the `vecs_to_tensor` function. Write a Python function `def vecs_to_te... | Converts 'v' to tensor with shape 3, inverse of 'vecs_from_tensor'. |
142,232 | import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `lddt` function. Write a Python function `def lddt(predicted_points, true_points, true_points_mask, cutoff=15., per_residue=False)` to solve the following problem:
Measure (approxi... | Measure (approximate) lDDT for a batch of coordinates. lDDT reference: Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: A local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013). lDDT is a measure of the difference between ... |
142,233 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_chi_atom_indices` function.... | Returns atom indices needed to compute chi angles for all residue types. Returns: A tensor of shape [residue_types=21, chis=4, atoms=4]. The residue types are in the order specified in residue_constants.restypes + unknown residue type at the end. For chi angles which are not defined on the residue, the positions indice... |
142,234 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_renaming_matrices` function... | Matrices to map atoms to symmetry partners in ambiguous case. |
142,235 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_restype_atom37_mask` functi... | Mask of which atoms are present for which residue type in atom37. |
142,236 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_restype_atom14_mask` functi... | Mask of which atoms are present for which residue type in atom14. |
142,237 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_restype_atom37_to_atom14` f... | Map from atom37 to atom14 per residue type. |
142,238 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_restype_atom14_to_atom37` f... | Map from atom14 to atom37 per residue type. |
142,239 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_restype_atom14_is_ambiguous... | Mask which atoms are ambiguous in atom14. |
142,240 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_make_restype_rigidgroup_base_ato... | Create Map from rigidgroups to atom37 indices. |
142,241 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
RESTYPE_ATOM14_MASK = _make_restype_atom14_mask()
def get_atom14_mask(aatype):
return utils.batched_gather(jnp.... | null |
142,242 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
def get_atom37_mask(aatype):
return utils.batched_gather(jnp.asarray(RESTYPE_ATOM37_MASK), aatype)
def get_atom3... | Convert atom14 to atom37 representation. |
142,243 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `torsion_angles_to_frames` functio... | Compute rigid group frames from torsion angles. |
142,244 | from typing import Dict, Optional
from alphafold.common import residue_constants
from alphafold.model import geometry
from alphafold.model import utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `frames_and_literature_positions_t... | Put atom literature positions (atom14 encoding) in each rigid group. |
142,245 | import copy
from typing import List, Mapping, Tuple
from alphafold.model.tf import input_pipeline
from alphafold.model.tf import proteins_dataset
import ml_collections
import numpy as np
import tensorflow.compat.v1 as tf
FeatureDict = Mapping[str, np.ndarray]
def make_data_config(
config: ml_collections.ConfigDict,... | Converts tf_example to numpy feature dictionary. |
142,246 | import copy
from typing import List, Mapping, Tuple
from alphafold.model.tf import input_pipeline
from alphafold.model.tf import proteins_dataset
import ml_collections
import numpy as np
import tensorflow.compat.v1 as tf
FeatureDict = Mapping[str, np.ndarray]
def make_data_config(
config: ml_collections.ConfigDict,... | Preprocesses NumPy feature dict using TF pipeline. |
142,247 | from toposort import toposort
import contextlib
import numpy as np
import tensorflow as tf
import time
import sys
from tensorflow.python.ops import gradients as tf_gradients_lib
def gradients(ys, xs, grad_ys=None, checkpoints='collection', **kwargs):
def gradients_speed(ys, xs, grad_ys=None, **kwargs):
return grad... | null |
142,248 | from toposort import toposort
import contextlib
import numpy as np
import tensorflow as tf
import time
import sys
from tensorflow.python.ops import gradients as tf_gradients_lib
def gradients(ys, xs, grad_ys=None, checkpoints='collection', **kwargs):
'''
Authors: Tim Salimans & Yaroslav Bulatov
memory effic... | null |
142,249 | from toposort import toposort
import contextlib
import numpy as np
import tensorflow as tf
import time
import sys
from tensorflow.python.ops import gradients as tf_gradients_lib
def gradients(ys, xs, grad_ys=None, checkpoints='collection', **kwargs):
def gradients_collection(ys, xs, grad_ys=None, **kwargs):
return... | null |
142,250 | import glob
import numpy as np
import os
import random
import tensorflow as tf
import tqdm
import csv
def binary_search(f, lo, hi):
if f(lo) or not f(hi):
return None
while hi > lo + 1:
mid = (lo + hi) // 2
if f(mid):
hi = mid
else:
lo = mid
return hi | null |
142,253 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Resets the current TensorFlow session, to clear memory or load another model. |
142,254 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Mounts the user's Google Drive in Colaboratory. |
142,255 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Copies the checkpoint folder to a mounted Google Drive. |
142,256 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Copies the checkpoint folder from a mounted Google Drive. |
142,257 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Copies a file to a mounted Google Drive. |
142,258 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Copies a file from a mounted Google Drive. |
142,259 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Encodes a single-column CSV to a format suitable for gpt-2-simple. Automatically adds the specified prefix and suffix tokens. |
142,260 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Preencodes a text document into chunks and compresses it, saving time when generated. Adapted from https://github.com/nshepperd/gpt-2/blob/finetuning/encode.py |
142,261 | import tarfile
import os
import json
import requests
import sys
import shutil
import re
from tqdm import tqdm, trange
import numpy as np
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import device_lib
import time
from datetime import datetime
import csv
i... | Function called when invoking from the terminal. |
142,262 | from __future__ import annotations
import dis
import sysconfig
import argparse
import cheap_repr
import gc
import inspect
import jsonpickle
import more_itertools
import os
import re
import subprocess
import sys
from functools import lru_cache
from pathlib import Path
from pprint import pformat
from pygments import high... | null |
142,263 | from __future__ import annotations
import dis
import sysconfig
import argparse
import cheap_repr
import gc
import inspect
import jsonpickle
import more_itertools
import os
import re
import subprocess
import sys
from functools import lru_cache
from pathlib import Path
from pprint import pformat
from pygments import high... | Returns the callable that generates the frame. See https://stackoverflow.com/a/52762678/2142577. |
142,264 | from __future__ import annotations
import dis
import sysconfig
import argparse
import cheap_repr
import gc
import inspect
import jsonpickle
import more_itertools
import os
import re
import subprocess
import sys
from functools import lru_cache
from pathlib import Path
from pprint import pformat
from pygments import high... | Get the parameters' names from a frame. e.g. f(a, b, *args, **kwargs) If called with f(1, 2, 3, x=1), returns {'a', 'b', 'args', 'kwargs'}. |
142,265 | from __future__ import annotations
import dis
import sysconfig
import argparse
import cheap_repr
import gc
import inspect
import jsonpickle
import more_itertools
import os
import re
import subprocess
import sys
from functools import lru_cache
from pathlib import Path
from pprint import pformat
from pygments import high... | Determines whether we should ignore this event. |
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