sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
4a150b23be5a3db28cdb9efc10945867b6af0cb1c5cfae14206969f782f82f78 | Python | 2,634 | 67 | import unittest
from reinvent.chemistry import conversions
from reinvent.chemistry.library_design.fragment_reactions import FragmentReactions
from tests.chemistry.library_design.fixtures import FRAGMENT_REACTION_SUZUKI, SCAFFOLD_SUZUKI
from tests.chemistry.fixtures.test_data import (
CELECOXIB,
ASPIRIN,
CE... |
c91129a57642f01595e8cb360769460f50ecf162fbfc330bf8c8cfc5f7fbbfd1 | Python | 2,636 | 89 |
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import ppafm.ml.AuxMap as aux
import ppafm.ocl.field as FFcl
import ppafm.ocl.oclUtils as oclu
from mlspm.datasets import download_dataset
from mlspm.utils import read_xyzs
if __name__ == "__main__":
data_dir = Path("./edafm-data") # ... |
4d4da77f0fd775ebb2e31bd04ddb3bda1fadaeb5252cc7bce430aec228aaadd0 | Python | 2,638 | 80 | import datetime
import os
from execute import execute
import requests
import dateutil
git = os.environ.get("GIT", "git")
installed_commit_hash = None
installed_commit_ts = None
latest_commit_hash = None
latest_commit_ts = None
def _get_commit_hash(cwd=None):
return execute([git, "rev-parse", "HEAD"], cwd=cwd)
... |
b2f4d974bdd6b1af733875990a82edf710a3622a6171558b2f2367a0475b787d | Python | 2,639 | 77 | """Sphinx configuration for REINVENT4 documentation."""
import os
import sys
# -- Project information -------------------------------------------------------
project = "REINVENT4"
author = "T. Worakul, EPFL"
copyright = f"2024, Molecular AI, AstraZeneca | {author}"
release = "4.0"
# -- General configuration --------... |
c7a50913328dc4c1c045efea75aa3e93030de3fd4948b1bfefd6dd2c48ae1749 | Python | 2,639 | 74 | '''
Module focused on the creation of a custom dataset class in order
to use our custom dataset for the problem of image recognition
and thus classification.
'''
import os
import torch
from torch.utils.data import Dataset
from PIL import Image
from torchvision import transforms
# CUSTOM DATASET CLASS
class Imagerec_D... |
3a5a579c0e8b62780f2e3c6cb9759c80e3aa69d8945cd638c6e7ab8611cc3342 | Python | 2,640 | 93 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
def plot_single_distribution(
df: pd.DataFrame,
column: str,
title: str = '',
xlabel: str = '',
ylabel: str = '',
bins: int = 30,
alpha: float = 0.5,
edgecolor: str = 'black',
range: tuple = (0, 1)
) -> None:
... |
2dd5b1c6fe44285ee19d7fb7344045ed195d0d398148156a3f52265bc7420452 | Python | 2,643 | 75 | import logging
from typing import Dict, Set
from overrides import override
import yaml
from chromadb.auth import (
AuthzAction,
AuthzResource,
UserIdentity,
ServerAuthorizationProvider,
)
from chromadb.config import System
from fastapi import HTTPException
from chromadb.telemetry.opentelemetry import (... |
3a8f5d81d34ba8e2fb0112468c22192099f5076f815467b1d8e8f09785186ebc | Python | 2,643 | 42 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
725af95b9b3e69c7fcd60101cf1125b15ddeae2f342c6fca2ec7c4bd9d1df6d7 | Python | 2,647 | 42 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
b9832cc7df82e9798a857a2015f430f024be4c3d6a6e1b204839a135045a1a5e | Python | 2,647 | 122 | """
This is mostly from e3nn.
"""
import torch
def matrix_x(angle: torch.Tensor) -> torch.Tensor:
r"""
From e3nn.
matrix of rotation around X axis
Parameters
----------
angle : `torch.Tensor`
tensor of any shape :math:`(...)`
Returns
-------
`torch.Tensor`
matrice... |
fee23b4662b349b669bb68d5dbd0c77e5ddbb2017fa30b704f1b5c52008080bf | Python | 2,647 | 98 | """Reference implementations for linear VAC and IVAC."""
import numpy as np
import ref_corr
from scipy import linalg
def vac_all(trajs, lag):
ct = ref_corr.ct_all(trajs, lag)
c0 = ref_corr.c0_all(trajs)
return symeig(ct, c0)
def vac_all_adj(trajs, lag):
ct = ref_corr.ct_all(trajs, lag)
c0 = ref... |
93db4e44563067329f050265108a4ca2a0f43873fe35bef0b85d94a85dade517 | Python | 2,648 | 65 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
import ase
import ase.io
from .. import AtomicDataDict
from ..ase import from_ase
from .base_datasets import AtomicDataset
from typing import Union, Dict, List, Optional, Callable, Any
... |
1208babc5c823ae1683a49c17ff14ff4337535e8ff9b3a236b2302cf5bed8092 | Python | 2,649 | 79 | import torch as T
import yaml
import nibabel
from monai.losses import DiceLoss, GeneralizedDiceLoss, FocalLoss, TverskyLoss
from Networks.UNet4 import UNet4
from Networks.UNet5_filter_teacher import UNet5_filter_teacher
from Networks.UNet5_decoder_teacher import UNet5_decoder_teacher
from Networks.UNet_test import UNe... |
6ad6699f718a241c897c9f3d4ce261ed5147889d918c656a28f632cf8311ec6c | Python | 2,649 | 94 | # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
00a79f8a3e303147e3cf498d0cf96422dae116ce3f7b88797ba101328e2caa14 | Python | 2,651 | 81 | # Code from https://github.com/juho-lee/set_transformer
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False, dropout=0.0):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_h... |
8f1d58973a35d541a4824af57e3a24a313f68d90a31956dc0a1f10bffa5dc61b | Python | 2,651 | 43 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
d26331f584581f4f2e0c749a03207d8aeff9c7736a5e44b8be03f8663a901def | Python | 2,653 | 74 | ########################################################################
class Basic:
"""
Add the basic parameters for your offline modality.
These parameters will be displayed on the Basic page of the GUI.
This file defines the parameter type and-or its possible values displayed on the GUI,
it... |
4cb9353e695ec90949d57f3b398e93478ed9155b62d78732bd22dc4892f21e6b | Python | 2,654 | 112 | VIEW = {
"general": {
"title": "General",
"content": {
"checkpoints": {
"title": "Checkpoints",
"fields": {
"useCheckpoints": {
"title": "Use Checkpoints",
"fieldType": "bool",
"default": False
}
}
},
"windowBar"... |
68a27749e48093a758970f50880d8a54044535087fabb8d8bf9d731243f5b665 | Python | 2,655 | 90 | """Write out a TensorBoard report"""
from __future__ import annotations
__all__ = ["TLTBReporter"]
from collections import Counter
from typing import TYPE_CHECKING
from reinvent.runmodes.utils import make_grid_image, compute_similarity_from_sample
if TYPE_CHECKING:
from reinvent.runmodes.TL.reports import TLRe... |
8fe6d75b56ce4b454e2b3eb29000ecc5aa0e2281d8b3e30a2aea474204d87bbc | Python | 2,656 | 88 | from typing import Optional
import config
import pandas as pd
explicit_folder_coding = {
'StyelaGAN2_Gamma2': 'StyleGAN 2 $\\gamma$: 2',
'StyelaGAN2_Gamma5': 'StyleGAN 2 $\\gamma$: 5',
'StyelaGAN2_Gamma8': 'StyleGAN 2 $\\gamma$: 8',
'DiffusionModel_Brats21': 'Diffusion',
'BRATS2021_synthetic_1195s... |
44d9d299fd023655d1a2978a09f62a593894bd48025ce3f20a8f73e33d6b3d2d | Python | 2,659 | 75 | """Unit tests for the sub-Riemannian metric class."""
import geomstats.backend as gs
from geomstats.geometry.heisenberg import HeisenbergVectors
from geomstats.geometry.sub_riemannian_metric import SubRiemannianMetric
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.sub... |
c4dc1d85721fa2d2437b29905eebd79c49c449950d1e39c99b6d38692f3df7d7 | Python | 2,659 | 42 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
153fb7ab5b29054aa89b1b8d22504e2731132cb736b1650bf6690e866a3b1ccc | Python | 2,661 | 82 | from pymonntorch import *
import time
import torch
from matplotlib import pyplot as plt
import numpy as np
from globparams import *
settings = {'dtype': torch.float64, 'synapse_mode': "SxD", 'device': 'cpu'}
class LeakyIntegrateAndFire(Behavior):
def initialize(self, neurons):
neurons.spikes ... |
8ea35f2084df806dc9663185d43d88374dab1df557aa95b5301f63486fa8c0fc | Python | 2,661 | 82 | from pymonntorch import *
import time
import torch
from matplotlib import pyplot as plt
import numpy as np
from globparams import *
settings = {'dtype': torch.float32, 'synapse_mode': "SxD", 'device': 'cpu'}
class LeakyIntegrateAndFire(Behavior):
def initialize(self, neurons):
neurons.spikes ... |
fa3d4148783ee01fddff77ec92674ad70eef47740d1fa602e3548c1e1e615553 | Python | 2,661 | 69 | from typing import Dict
from collections import OrderedDict
from ppdet.modeling.architectures.meta_arch import BaseArch
class MultiSteamDetector(BaseArch):
def __init__(self,
model: Dict[str, BaseArch],
train_cfg=None,
test_cfg=None):
super(MultiSteamDete... |
02d20313a3adab8a94015bf2c82e05c9afdd67fbc1a77cc09e44632b1bf2c74f | Python | 2,662 | 87 | import itertools
import numpy as np
from scipy import stats
from pycircstat import var
def convolve_dirac_gauss(t, trial, sigma=1.):
"""
Convolves event series represented as time points of Dirac deltas with
the pdf of a Gaussian
:param t: time points at which the convolution will be computed
:pa... |
3a260818fb326b460d213b6448e1b9007a16dc7a8ed9e93a35bb995d5069d298 | Python | 2,665 | 43 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
997e926b9c8606d3e1545133973be052fc0b890fdf35135fa94c657d76ef1b43 | Python | 2,665 | 76 | from nequip.data.dataset import NequIPLMDBDataset
from nequip.utils.global_state import set_global_state
from nequip.utils import RankedLogger
import os
from tqdm import tqdm
import hydra
from omegaconf import OmegaConf, DictConfig, ListConfig
logger = RankedLogger(__name__, rank_zero_only=True)
os.environ["HYDRA_FULL... |
a6196e8a0e5a99094ca666041f1eada6f931e827e89b94e5bb90e6da84ee2af8 | Python | 2,665 | 72 | import os
import numpy as np
from torch.utils.data import Dataset
from mace.data.atomic_data import AtomicData
from mace.data.utils import KeySpecification, config_from_atoms
from mace.tools.default_keys import DefaultKeys
from mace.tools.fairchem_dataset import AseDBDataset
class LMDBDataset(Dataset):
def __in... |
764c0c1ca7a90e739c96f9f297ee67560828d8cb205394542d8a24d8299d4c4b | Python | 2,666 | 85 | #!/usr/bin/env python3
"""
Example: Using Chroma's Attached Functions API to process collections automatically
This demonstrates how to attach functions that automatically process
collections as new records are added.
"""
import chromadb
import time
from chromadb.api.functions import RECORD_COUNTER_FUNCTION
# Connec... |
981e6a5d5077f8d636fd46414a3f747e45e27381fb67fd932aa9b68fa6c8085b | Python | 2,666 | 64 | """Part B: mask-once dedup in list construction (``io.mask_images``).
``BrainData(list_of_niftis, mask=mask)`` routes through ``_load_from_list`` ->
``_mask_images``. These tests pin the byte-equivalence contract (must reproduce
the functional per-item ``apply_mask`` + ``vstack`` exactly) and the perf
contract (the ma... |
acd8f01cacdddcdb8cd9c88a7865cacc6a8587e9c7bd7c37c9ae85c17510906a | Python | 2,669 | 71 | # Author: Youzhi Luo (yzluo@tamu.edu)
# Updated by: Anmol Anand (aanand@tamu.edu)
import torch
import torch.nn.functional as F
from torch.distributions.bernoulli import Bernoulli
from torch_geometric.data import Data
from ..constants import *
class NodeFM(torch.nn.Module):
def __init__(self, training=False, unifo... |
0658be565adbb13f6adcdeeaabb13034f25bbc250647db64046cafebbcb4f38c | Python | 2,670 | 93 | # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
331cece4ef3b9cc046f260b8145f8ab166bc526b9d5dc75077dfb5b7ef8ac5ab | Python | 2,671 | 82 | import pytest
from chromadb.api.client import AdminClient, Client
from chromadb.config import System
from chromadb.db.impl.sqlite import SqliteDB
from chromadb.errors import NotFoundError
from chromadb.test.conftest import ClientFactories
def test_deletes_database(client_factories: ClientFactories) -> None:
clien... |
daf4efdd3e5d92d45ec456e2632c8ee015831b1f5dc2cf2083b8fcff78715906 | Python | 2,671 | 85 | import geomstats.backend as gs
from geomstats.test.data import TestData
class SasakiMetricSphereTestData(TestData):
pu0 = gs.array([[0.0, -1.0, 0.0], [1.0, 0.0, 1.0]])
pu1 = gs.array([[1.0, 0.0, 0.0], [0.0, 1.0, 1.0]])
pu2 = gs.array(
[[-0.27215095, -0.90632948, 0.32326574], [0.13474934, 0.0, 1.39... |
914e585c5af0f0338b0976bf6cd3220b925285a0eeda708f518f5a646335cf10 | Python | 2,672 | 76 | import torch
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.rdmolfiles import SmilesParserParams
from rdkit import RDLogger
from torch_geometric.data import Data, Batch
class TransformPred3D(torch.nn.Module):
""" Converting :obj:`torch_geometric.data.Data` objects into 3d versions
using ... |
aaf74d8026f18d80feeadd72b0d7e5baeb26d96c920765f24dddf78b4de7b6e0 | Python | 2,672 | 76 | """Experimental loader for a GitHub folder of ABF files.
``requests`` is not a SanPy dependency. Install it before running this script.
"""
import io
import requests
import sanpy
class bAnalysisDirWeb:
"""Load a directory of .abf from the web (for now from GitHub).
Will etend this to Box, Dropbox, other?... |
1f77ef9764fe65a2e073ad8c8d35b7f099ca46d28aa2450991fc50b1accdf7e9 | Python | 2,674 | 69 | """
Plots excerpts of the input data with its corresponding metadata.
"""
import numpy as np
import argparse
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import time_slice
from utils.parse import parse_plot_channels, ... |
3abf5e6548dc34a823f7c796b5f30468eb24f7435da7cdc7264d89006804185d | Python | 2,674 | 82 | from pymonntorch import *
import time
import torch
from matplotlib import pyplot as plt
import numpy as np
from globparams import *
settings = {'dtype': torch.float64, 'synapse_mode': "SxD", 'device': 'cuda'}
class LeakyIntegrateAndFire(Behavior):
def initialize(self, neurons):
neurons.spikes... |
f37b365b8e1b3145a8394a74e2edd78c698018bafa3dc990771519d43502a921 | Python | 2,677 | 111 |
from gzip import (
compress,
GzipFile
)
import numpy as np
from .record import Record
UNK = '<unk>'
PAD = '<pad>'
class Vocab(Record):
__attributes__ = ['words', 'counts']
def __init__(self, words, counts):
self.words = words
self.counts = counts
self.word_ids = {
... |
3e9ee0381716e2bf5ce486040e473d21248d58eb6db11429b3b0c2ddc74967e3 | Python | 2,678 | 99 | class Sample:
def __init__(self,
name,
id_dict, # Should be an OrderedDict
partition,
x_dict,
y_dict,
support,
is_time_continuous,
custom_stats=None):
self._name = name
... |
727a96cd5c632f814e72e009c1d7675e2a500f4ecfc6c68bd61472a14fc18e91 | Python | 2,678 | 80 | import pytest
import itertools
import torch
from nequip.data import PartialSampler
@pytest.fixture(params=[True, False], scope="module")
def shuffle(request) -> bool:
return request.param
@pytest.fixture(params=[None, 1, 2, 5, 7], scope="function")
def sampler(request, dataset, shuffle) -> PartialSampler: # ... |
35111de697c402ee415dbafa203137968bbae912a19f5ca406bb9ee21565d99b | Python | 2,679 | 80 | """Tests for BrainData.cluster_report facade + _ClusterReport.plot."""
import nibabel as nb
import numpy as np
import pytest
from nltools.data import BrainData
from nltools.data.atlases import _ClusterReport
def _gaussian_blob(shape, center_ijk, peak_amp, sigma=2.0):
grid = np.indices(shape).astype(float)
c... |
c46f7028fc66da1de9b75a592b84fe975d7e1a05220c9fb6484ca3c8cc247296 | Python | 2,679 | 93 | import pytest
import tempfile
import torch
from omegaconf import OmegaConf
from conftest import _check_and_print
import pathlib
import subprocess
@pytest.mark.parametrize(
"device", ["cpu"] + (["cuda"] if torch.cuda.is_available() else [])
)
def test_dimer_plot_example(fake_model_training_session, device):
""... |
6baab45e9c178f413f264247cb6514dae678afa591255d768641f5fb615ba565 | Python | 2,681 | 82 | import json
import sys
import nibabel as nib
import numpy as np
from sdcflows.transform import disp_to_fmap
def displacement_field_to_fmap(
disp_field: str,
dwi_nii: str,
dwi_json: str,
demean: bool,
out_fmap: str,
smk_config: dict,
) -> None:
"""Adapted from SDCFlows DisplacementField2Fi... |
cf4b79b4e499ba4b691b4a9d09335ebc1037e4b6a24d613f922833e6ca64e709 | Python | 2,681 | 76 | import random
import pytest
from scipy.stats import normaltest, pearsonr
import geomstats.backend as gs
from geomstats.distributions.brownian_motion import BrownianMotion
from geomstats.geometry.euclidean import Euclidean
from geomstats.test.test_case import TestCase, autograd_and_torch_only
@autograd_and_torch_onl... |
102327f5b08848124ffe0630c1e27df0fdc3130c21fd0b3beedae59f12e160fb | Python | 2,682 | 73 | import unittest
import pytest
import torch
import torch.utils.data as tud
from reinvent.models.transformer.core.dataset.dataset import Dataset
from reinvent.models.transformer.core.enums.sampling_mode_enum import SamplingModesEnum
from reinvent.models.transformer.core.vocabulary import SMILESTokenizer
from reinvent.m... |
7a13ca3d22857fe46c3756d3f6eb13a5c9273253117de3e2883727f4c5f1e90c | Python | 2,685 | 69 | """Export CLI contract: mace_create_lammps_model produces loadable artifacts.
Contract tier — no LAMMPS binary. The libtorch format is plain TorchScript;
the mliap format additionally converts the model to the cueq layout (hence the
cueq capability marker on that test).
"""
import shutil
import pytest
import torch
... |
dae11afa4e97d9362595926ef6343f17facb27b6fa63fc2fd04587acde659399 | Python | 2,685 | 69 | """
Plots excerpts of the input data with its corresponding metadata.
"""
import numpy as np
import argparse
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import time_slice
from utils.parse import parse_plot_channels, ... |
6708a2f951de6937ab7cdd683a53bb78d8b34f003cba78ad42f6d87a7e5d76ff | Python | 2,686 | 91 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import argparse
import pathlib
import torch
from .lmp_mliap_wrapper import NequIPLAMMPSMLIAPWrapper
from nequip.train.lightning import _SOLE_MODEL_KEY
from nequip.utils.logger import RankedLogger
fro... |
01cb465aebb6d3a4d1ccd5c04dcfd4a86a002bcabba03c9e549d3cf53284bdcc | Python | 2,687 | 103 | from numba import njit, jit, objmode
from numba.experimental import jitclass
from numba import int32, int64, float32, float64, boolean
from numba import deferred_type
from numba import typed, types
from numba.types import pyobject
import numpy as np
from scipy.io import loadmat, savemat
from .Tree import Tree
TreeType... |
6a7da9a1972fe1911dbac8e78ee61c9f2a29e015e057881e857478d59b489859 | Python | 2,689 | 85 | """
Script to identify and count van der waals clashes in a PDB file.
VDW values are taken from the following:
- https://en.wikipedia.org/wiki/Atomic_radii_of_the_elements_(data_page)
- https://pubs.acs.org/doi/epdf/10.1021/j100785a001
Useful references:
- https://www.pnas.org/doi/10.1073/pnas.072665799
Usage:
pytho... |
eca3baa81eded3e982b80828950237befb1b0b1c0700183119e9a1039088aa40 | Python | 2,689 | 94 | #!/bin/env python3
#
# This is an example how to use the ExternalProcess scoring component using
# ChemProp 1.x. The scripts expects a list of SMILES from stdin and will
# write a JSON string to stdout.
#
# Setup shown for scoring file include feature: scoring.filename
#
# [[component.ExternalProcess.endpoint]]
# name... |
09956227d1899c87543c64d78bd350db95368874e27eb5b2d8800ae5d0b8429f | Python | 2,691 | 85 | from scipy import signal
def butter_lowpass(lowcut, fs, order=8, sos=False):
''' Create a lowpass butterworth filter '''
nyq = 0.5 * fs
low = lowcut / nyq
if sos:
sos_out = signal.butter(order, low, analog=False, btype='low', output='sos')
return sos_out
b, a = signal.butter(orde... |
8c9c4132997547aef0fb9b864c4d9180aa194a9d822ee52530ef28aca5e5d08d | Python | 2,693 | 73 | # Copyright 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import hashlib
import logging
from importlib import util
from typing import Callable
from openfl.federated.data.sources.data_source import DataSource, DataSourceType
logging.getLogger("azure").setLevel(logging.WARNING)
logging.getLogger("azure.... |
9f63c93bb80a015f85e96b17d2e17d3674d2a4aeaf79812a7980ab3df10bb571 | Python | 2,694 | 112 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
6752d9d1451491fa775b2dd01f11901baf7d15ae16c5ed05eece039c5e70284e | Python | 2,698 | 82 | """Tests for the shared `ContrastResult` record and the removal of `BaseModel`."""
import dataclasses
import importlib
import numpy as np
import pytest
import nltools.models
from nltools.models import ContrastResult
class TestContrastResultShape:
"""Field names, order, and construction."""
def test_field_... |
16cb264b9f3ac57a563a326cf61eb6ca99897a6221d7ec18a6dbd1bdaf52e7be | Python | 2,702 | 88 | from sklearn.base import BaseEstimator, TransformerMixin
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from sklearn.feature_selection import (
f_regression,
SelectKBest,
f_classif,
SelectFromModel,
)
from sklearn.linear_model import LogisticRegressio... |
05999860221af14ca6af227f183a83dec778f22fae72afd536dc275ec2916caf | Python | 2,703 | 80 | from abc import abstractmethod
from dataclasses import dataclass
from typing import Any, Callable, List
from overrides import EnforceOverrides, overrides
from chromadb.config import Component, System
from chromadb.types import Segment
class SegmentDirectory(Component):
"""A segment directory is a data interface ... |
1a3eea95d56f8e8339ec73f7cd4983d8e91034801b8345036342a409b3ef6b52 | Python | 2,703 | 85 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import os
import webbrowser
from pathlib import Path
from openfl.experimental.workflow.utilities.metaflow_utils import DefaultCard, FlowGraph
class InspectFlow:
"""Class for inspecting a flow.
Attributes:
ds_root (str): ... |
418ff20c8763a4ae4fd5ff3a01407b30f396c3f25d005aadb5eec6421c87fbc4 | Python | 2,703 | 88 | from pathlib import Path
import mne
from .. import io
from ... import logger
def set_channel_types(raw, mapping):
"""
Change the channel types of the raw instance.
https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.set_channel_types
Parameters
----------
raw : mne.io.Raw | mne.i... |
93519045ac084365586a8c0731de865ac28c169be22bd2ec66fd3a0a64d04c20 | Python | 2,705 | 74 | ########################################################################
class Basic:
"""
Add the basic parameters for your trainer modality.
These parameters will be displayed on the Basic page of the GUI.
This file defines the parameter type and-or its possible values displayed on the GUI,
it... |
251b5a028b21fa35b1e718cd773ddb640b2bea1a4a8766a0e68c9f3146d8ae09 | Python | 2,706 | 87 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... |
6eb432ca5bfbba9ad75717fd8bfadbc61b7ed578f8b6ee78d993a5d1da1d34d7 | Python | 2,706 | 73 | # -*- coding: utf-8 -*-
import os
import numpy as np
import logging, argparse
import configparser
def parse_args():
# Argument Parser
parser = argparse.ArgumentParser()
# my args
parser.add_argument("--verbose", action = "store_true", help = "display messages")
parser.add_argument("--odir", de... |
b90fd920d81903b080a21fb4491da30e8c3b4f17823aa4287bf829d19dad7210 | Python | 2,714 | 82 | import pytest
from geomstats.geometry.hypersphere import Hypersphere
from geomstats.geometry.invariant_metric import BiInvariantMetric
from geomstats.geometry.special_orthogonal import SpecialOrthogonal
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.base import Immers... |
d9dc92597a3f91e8968c0490ca02d355ced7d8fc6988a3f621eda16516da9100 | Python | 2,714 | 87 | import pytest
import geomstats.backend as gs
from geomstats.test.random import RandomDataGenerator
from geomstats.test_cases.learning._base import BaseEstimatorTestCase
class Model:
# TODO: bring generation with noise
def __init__(self, space, intercept, coef):
self.space = space
self.interce... |
82ae094dbeceb87cc018720e0e24277571104d2729adb519f5eca65c5f29ccac | Python | 2,715 | 92 | """Create a Linkinvent model from a list of SMILES strings"""
from __future__ import annotations
import time
import uuid
from reinvent.models.linkinvent.link_invent_model import LinkInventModel
from reinvent.models.linkinvent.model_vocabulary.paired_model_vocabulary import (
PairedModelVocabulary,
)
from reinvent... |
30dbd6983e892365c521be32123a2ecc0b955f7d3c1890e9b70b153d91266d70 | Python | 2,718 | 80 | """
Plot the critical points in the optical flow vector field.
"""
import argparse
import numpy as np
from copy import copy
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import analogsignal_to_imagesequence
CLI = argparse.ArgumentParser()
CLI.add_argument("--data"... |
4ffc023092c3c93bf48751a5b4410098a179e39ba7913a39194f34b345af975c | Python | 2,719 | 90 | # -*- coding: utf-8 -*-
from __future__ import print_function, division
from builtins import input
"""
Measure multitaper computation speed.
i7-8700K: 9.9 ms (101.0 Hz)
@author: leeq
"""
import os
import mne
import numpy as np
from neurodecode import logger
from neurodecode.utils.timer import Timer
os.environ['OM... |
75b92e5a358efedbf194638931a4bc44e2afc310b1a626d5337da7ec3552ad08 | Python | 2,719 | 69 |
from sparse_ct.tool import plot_grid
from sparse_ct.data import image_to_sparse_sinogram, ellipses_to_sparse_sinogram
from sparse_ct.reconstructor_2d import (
IRadonReconstructor,
SartReconstructor,
SartTVReconstructor,
Sar... |
4e36a8653794fa4b6c73000dab2fe2cdb4eb56b7de98d82fc96667e29f6d5119 | Python | 2,720 | 103 | import pytest
import numpy as np
from cloudvolume import CloudVolume
from cloudvolume.lib import Vec
from corgie.data_backends import str_to_backend
import shutil
import os
from corgie.boundingcube import BoundingCube
from corgie.mipless_cloudvolume import MiplessCloudVolume
from corgie.stack import Stack
from corgi... |
5edf786a4d4fe7ec52286fece0a933c0d99212c6f7ae764f41a1852b13543473 | Python | 2,721 | 73 | import numpy as np
import matplotlib.pyplot as plt
from brian2 import *
import pickle
from tqdm import tqdm
from scipy.stats import lognorm
from standalone import run_network, extract_counts
from utils import connection_matrix
def extract_mean_current(current, time, nstim=30, stim_dur=1.1, discard=0.1):
mean_cur... |
6f6facb9482b8937a6c3d9f4f46bf7bb161d3ec23f46986a928f1ec0a8ae3573 | Python | 2,722 | 80 | # Usage: python3 splitScaffolds.py inFile species type_of_variant (indels or snps) type_of_comparison (e.g. huch, hugo, hurh etc. where huch indicates that the MAF was a human chimp alignment)
import os
import sys
import pandas as pd
# Read arguments
Suffix1 = sys.argv[1]
Suffix2 = sys.argv[2]
species = sys.argv[3]
t... |
3220f411e2f02559a56c4ef4815310a8a56f95f000dc49e9e2db2bd22992a66a | Python | 2,723 | 70 | # -*- coding: utf-8 -*-
"""
Demonstration of ScatterPlotWidget for exploring structure in tabular data.
The widget consists of four components:
1) A list of column names from which the user may select 1 or 2 columns
to plot. If one column is selected, the data for that column will be
plotted in a histogram-li... |
865cdcb3c99aa0d2c0b8bdd1bba3c99136fc014bad859f82ff2f7477e023c43b | Python | 2,728 | 87 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from torch import Tensor
import argparse
from typing import List, Optional, Tuple
from utils import logger
from .base_layer import BaseLayer
class GlobalPool(BaseLayer):
"""
This layers appl... |
83f5ab3602e066825af32c74bea04c66bcbeca6704b1e4f5b7bd63a2442b9078 | Python | 2,730 | 98 |
import math
import logging
import glob
import numpy as np
from sparse_ct.reconstructor_2d import (
SartReconstructor,
SartBM3DReconstructor,
DgrReconstructor,
N2SelfReconstructor)
from . imp... |
8cc24425a365f09d9e1bb9c1ad8756255471e38b401381c6a3220dbfb0383229 | Python | 2,730 | 99 | import os
import socket
import multiprocessing
import pickle
from rdkit import Chem
from rdkit.Chem import AllChem
from tqdm import tqdm
from ogb.lsc import PCQM4MDataset
import argparse
def log(log_path, q):
with open(log_path, 'w') as f:
print('Logger is alive')
while 1:
m = q.get()... |
9947daf38321c59ccdeb2ea72ccee5852d94b37c77ee58d4a929f853401f0ac2 | Python | 2,730 | 80 | """
Plot the critical points in the optical flow vector field.
"""
import argparse
import numpy as np
from copy import copy
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import analogsignal_to_imagesequence
CLI = argparse.ArgumentParser()
CLI.add_argument("--data"... |
9bd454232b3314caeadcfdb62cd19bd99503d1252f3cbb89d1c523750dc91585 | Python | 2,730 | 122 | from utils.color_palettes import (
GR1, RB1, YP1, YS1, BG1)
from utils.datasets import (
GraspingSuresh,
ReachingGallego,
ReachingChurchland,
ReachingKalidindi,
BehaviouralMante,
)
DATA_PATH = ''
data_pathes = {
'church': 'preprocessed2h5/church_dataset.h5',
'grasp': 'preprocessed2h5/Da... |
417e5ad6c91c48d3111c8e3e9a24f72d668a9b8ad86056ced25a5c9c8cdc802d | Python | 2,732 | 76 | # Copyright (C) 2020-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# This file overrides the step method of PyTorch Optimizer
# Adopts fix clipping from https://arxiv.org/abs/1710.06963
import torch
from torch.optim import Optimizer
torch.set_printoptions(precision=10)
class ClipOptimizer(object):
... |
f063826feb5a82d0da974c310f2b9b0870b13eeec7dbc160f03222c0e6b400b5 | Python | 2,733 | 100 | #!/usr/bin/env python3
"""Talk to transactional Chroma with the Python client.
Run against a Chroma server that supports conditional transactions:
python3 examples/conditional_transactions.py
Optional environment:
CHROMA_HOST=localhost
CHROMA_PORT=8000
CHROMA_SSL=false
CHROMA_TENANT=default_tena... |
a72e2bae1698e484090d16f1c2c2159f849c96b7dd951015f2102f63c1d7d833 | Python | 2,734 | 74 | from rdkit import Chem
from typing import Sequence, Dict
def onek_encoding_unk(value: int, choices: Sequence):
"""
Creates a one-hot encoding with an extra category for uncommon values.
:param value: The value for which the encoding should be one.
:param choices: A list of possible values.
:return... |
cdafd9f25168c17602ca0f39ddc5ddba31f9ac51a78d318f7287d81942e3260f | Python | 2,734 | 103 | import shutil
from pathlib import Path
import pytest
import numpy as np
def test_make_input_plots():
from mlspm.visualization import make_input_plots
save_dir = Path("test_input_plots")
X = [np.random.rand(2, 20, 20, 2), np.random.rand(2, 20, 20, 2)]
make_input_plots(X, outdir=save_dir)
assert... |
0f0f44da14b3cc36494c3128f4e2f0a36fc8f0385fd9be751b432f305b5527da | Python | 2,736 | 81 | """
Plot the critical points in the optical flow vector field.
"""
import argparse
from pathlib import Path
import numpy as np
from copy import copy
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import analogsignal_to_imagesequence
CLI = argparse.ArgumentParser()
... |
4532f865072efe13385351c7e586eb7c4ccaa529744ddd81002ae1df84ed62dc | Python | 2,736 | 55 |
from skimage.color import gray2rgb
from multiprocessing.dummy import Pool as ThreadPool
from sparse_ct.tool import plot_grid
from sparse_ct.data import image_to_sparse_sinogram
from sparse_ct.reconstructor_2d import (
IRadonReconstructor,
FBP_BM3DReconstructor,
... |
85539bfd4d54db6e8b7172d5b4581c83f6bee0da5fe89bc638c8a59412faf2a0 | Python | 2,736 | 75 | # Copyright (C) 2020-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Plan API's tests module."""
from unittest import mock
import pytest
from pathlib import Path
from openfl.federated.plan.plan import Plan
from openfl.component.assigner import RandomGroupedAssigner
from openfl.component.aggregator im... |
8ceb13a29a4086241226c0fdf5f44ac81522b198e72068feb06e56dfe0a98b29 | Python | 2,737 | 64 | import sqlite3
import numpy as np
import argparse
from pyimzml.ImzMLWriter import ImzMLWriter
def threshold_otsu(image, nbins=256):
# Stripped-down function from scikit-image to avoid the heavy dependency.
assert image.min() != image.max()
hist, bin_edges = np.histogram(image.flat, bins=nbins)
bin_cent... |
9f69d28d4488d08514ebe1d6acefebf63fff2a9733878475efe88843b8872e77 | Python | 2,737 | 100 | import os
import logging
import torch
import torch_geometric.transforms as T
from sklearn.metrics import roc_auc_score, average_precision_score
from GeNNius import Model
@torch.no_grad()
def test(model, data):
model.eval()
emb, pred = model(data.x_dict, data.edge_index_dict,
data['drug', 'pro... |
457e06eea46088a89378432d4c1b508e2112cc3bf95048d17e7cbdc098ebc327 | Python | 2,738 | 101 | import os
import logging
import torch
import torch_geometric.transforms as T
from sklearn.metrics import roc_auc_score, average_precision_score
from GeNNius import Model
@torch.no_grad()
def test(model, data):
model.eval()
emb, pred = model(data.x_dict, data.edge_index_dict,
data['drug', 'pro... |
34aaf25a9322ebcabdb8dc573d4bdd28e41c1fb9ef84cd24feeba51e13d6211e | Python | 2,739 | 44 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
3e606532c6d6a6c15e1650038a6c330615766cd17d7ada3730b1b4c1ccbf8c13 | Python | 2,739 | 64 |
def get_atoms_neighbor(model, target_chain, target_resn, n_neighbours = 80):
'''
model : biopython pdb model
returns:
backbone atom coordinates of residue of interest
atom_type and coordinates of all residues within a certain distance.
'''
# getting coordinates of the residue of... |
8ebd91c856a5de355bd4a97c0a0b745f0bf63739bca0faa03ca278f1c55c2f52 | Python | 2,740 | 86 | """Compute easy-of-synthesis scores with AiZynthFinder.
For AiZynthFinder documentation, see:
- https://github.com/MolecularAI/aizynthfinder
This implementations calls external command.
The external command, in turn, can call AiZynthFinder command-line interface,
or run CLI in parallel on a cluster, or even call REST... |
9045dc473560e0f7abc33f3d7a8e75ef185cb532f7f4fa2e6b9472b59675e3c3 | Python | 2,740 | 93 | import pytest
import numpy as np
from rdkit import Chem
from reinvent.runmodes.samplers import validate_smiles
from reinvent.models.model_factory.sample_batch import SmilesState
from tests import test_data
@pytest.fixture
def data():
smilies = [
test_data.PARACETAMOL, # valid
test_data.INVALID,... |
00d83c861c3190957774ae1edc5c3d96c26831738a95fe51f7a37e0da9f96744 | Python | 2,741 | 79 | import sys
import time
import cv2
import numpy as np
from dynaphos.image_processing import sobel_processor, canny_processor
from dynaphos.simulator import GaussianSimulator
from dynaphos.utils import load_params, load_coordinates_from_yaml, Map
from dynaphos.cortex_models import \
get_visual_field_coordinates_fro... |
7fbda806010c3c9a0f147700fcffe125b05275486a717f417809dd4c86f5ecf3 | Python | 2,742 | 80 | import time, h5py, logging, itertools
from tqdm.auto import tqdm, trange
from typing import Union, List, Tuple
import numpy as np, pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import silhouette_score, adjusted_rand_score, accuracy_score, f1_score
from sklearn.neighbors import Radi... |
f00700359c7d4590174163c63288b79e1df52a971dc90f6ff64dddbb16ee5ec7 | Python | 2,742 | 44 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
53f047b3e66024b246ed2e1205dfc309f3bf817b075a1085519e889466e3deff | Python | 2,743 | 93 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import os
import importlib
from typing import Optional
from utils import logger
import argparse
from utils.ddp_utils import is_master
from .base_sampler import BaseSamplerDDP, BaseSamplerDP
SAMPLER_REGISTRY = {}
... |
ccb3c781ee992158655408c3719b00881cff8654d6e59e427f1bad78576d6656 | Python | 2,743 | 81 | """Tests for RL terminators: NullTerminator, SimpleTerminator, PlateauTerminator."""
import pytest
import numpy as np
from reinvent.runmodes.RL.setup.terminators import (
NullTerminator,
SimpleTerminator,
PlateauTerminator,
)
# ── NullTerminator ──────────────────────────────────────────────────────────
... |
4220e6b9aa3d0968d88c82c5367ae841430586e6cde40ab552ed0c0d2040ff70 | Python | 2,744 | 70 | import torch.nn as nn
from .contrastive import Contrastive
class ProjHead(nn.Module):
def __init__(self, input_dim, out_dim):
super().__init__()
self.block = nn.Sequential(
nn.Linear(input_dim, out_dim),
nn.ReLU(),
nn.Linear(out_dim, out_dim),
nn.ReL... |
22520ccfdd683e85e583d23e82321884967719efcd542b1994ab818b174bfa89 | Python | 2,745 | 81 | """
Plot the critical points in the optical flow vector field.
"""
import argparse
from pathlib import Path
import numpy as np
from copy import copy
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import analogsignal_to_imagesequence
CLI = argparse.ArgumentParser()
... |
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