import importlib.util
import logging
import os
import time
from typing import List, NamedTuple, Optional, Tuple
from graphein.protein.config import ProteinMeshConfig
from graphein.utils.pymol import MolViewer
from graphein.utils.utils import import_message
try:
from pytorch3d.structures import Meshes
except ImportError:
import_message(
submodule="graphein.protein.meshes",
package="pytorch3d",
conda_channel="pytorch3d",
pip_install=True,
)
log = logging.getLogger(__name__)
def check_for_pymol_installation():
"""Checks for presence of a pymol installation"""
spec = importlib.util.find_spec("pymol")
if spec is None:
log.error(
"Please install pymol: conda install -c schrodinger pymol or conda install -c tpeulen pymol-open-source"
)
from pathlib import Path
import networkx as nx
from graphein.protein.features.sequence.utils import (
compute_feature_over_chains,
subset_by_node_feature_value,
)
from graphein.utils.utils import import_message
try:
import torch
except ImportError:
import_message(
submodule="graphein.protein.features.sequence.embeddings",
package="torch",
pip_install=True,
conda_channel="pytorch",
)
try:
import biovec
except ImportError:
import_message(
submodule="graphein.protein.features.sequence.embeddings",
package="biovec",
pip_install=True,
)
@lru_cache()
def _load_esm_model(model_name: str = "esm1b_t33_650M_UR50S"):
from typing import Dict, Tuple
import networkx as nx
from graphein.utils.utils import import_message, protein_letters_3to1_all_caps
try:
from pyaaisc import Aaindex
except ImportError:
import_message(
submodule="graphein.protein.features.nodes.aaindex",
package="pyaaisc",
pip_install=True,
)
def fetch_AAIndex(accession: str) -> Tuple[str, Dict[str, float]]:
"""
Fetches AAindex1 dictionary from an accession code. The dictionary maps one-letter AA codes to float values
:param accession: Aaindex1 accession code
:type accession: str
:return: tuple of record titel(str) and dictionary of AA:value mappings
:rtype: Tuple[str, Dict[str, float]]
"""
# Initialise AAindex object and get data
aaindex = Aaindex()
record = aaindex.get(accession)
return record.title, record.index_data
from __future__ import annotations
from typing import List, Optional
import networkx as nx
import numpy as np
import torch
from graphein.utils.utils import import_message
try:
from torch_geometric.data import Data
except ImportError:
import_message(
submodule="graphein.ml.conversion",
package="torch_geometric",
pip_install=True,
conda_channel="rusty1s",
)
try:
import dgl
except ImportError:
import_message(
submodule="graphein.ml.conversion",
package="dgl",
pip_install=True,
conda_channel="dglteam",
)
SUPPORTED_FORMATS = ["nx", "pyg", "dgl"]
"""Supported conversion formats.
# Author: Ramon Vinas, Arian Jamasb <*****@*****.**>
# License: MIT
# Project Website: https://github.com/a-r-j/graphein
# Code Repository: https://github.com/a-r-j/graphein
from typing import Any, Dict
import networkx as nx
from graphein.utils.utils import import_message
try:
from bioservices import HGNC, UniProt
except ImportError:
import_message(
submodule="graphein.ppi.features.nodes_features",
package="bioservices",
conda_channel="bioconda",
pip_install=True,
)
def add_sequence_to_nodes(n: str, d: Dict[str, Any]):
"""
Maps UniProt ACC to UniProt ID. Retrieves sequence from UniProt and adds it to the node as a feature
:param n: Graph node.
:type n: str
:param d: Graph attribute dictionary.
:type d: Dict[str, Any]
"""
h = HGNC(verbose=False)
u = UniProt(verbose=False)
import networkx as nx
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
from graphein.protein.subgraphs import extract_k_hop_subgraph
from graphein.utils.utils import import_message
try:
from pytorch3d.ops import sample_points_from_meshes
except ImportError:
import_message(
submodule="graphein.protein.visualisation",
package="pytorch3d",
conda_channel="pytorch3d",
)
log = logging.getLogger()
def plot_pointcloud(mesh: Meshes, title: str = "") -> Axes3D:
"""
Plots pytorch3d Meshes object as pointcloud.
:param mesh: Meshes object to plot.
:type mesh: pytorch3d.structures.meshes.Meshes
:param title: Title of plot.
:type title: str
:return: returns Axes3D containing plot
