sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
164d61674d29895c428ea5e413ea8890d4d3f32edfa61a8a8cb3aabfb4fd1a65 | Python | 1,819 | 60 | # Copyright (c) 2020 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... |
8987da4161ba8c232065985e2029c25539a71f223a71bcf4f4cc2e0db1bc54de | Python | 1,819 | 35 | import torch
import torch.nn as nn
from torch_geometric.utils import softmax
from torch_scatter import scatter
class MH_ATT(nn.Module):
def __init__(self, n_att_heads=4, q_dim=128, k_dim=128, v_dim=128, out_dim=128):
super(MH_ATT, self).__init__()
self.n_att_heads = n_att_heads
self.d_k =... |
bcfef4c69cccea1eeac0e447c0c7268f94f76a245d43dd2ce33fd6044ac44b58 | Python | 1,819 | 47 | from dig.xgraph.evaluation import XCollector, ExplanationProcessor, control_sparsity
from torch_geometric.nn.conv import GCNConv
from torch_geometric.utils.random import barabasi_albert_graph
from torch_geometric.data import Data
import torch
def test_metrics():
device = torch.device('cuda:0') if torch.cu... |
29942a180dce20d5736f5e3b6184d9c2f02e8965d41f700044eacb9a35d264c9 | Python | 1,821 | 50 | import experiment as exp
import arena
import schedule
import video_system
class HeatImagesExperiment(exp.Experiment):
default_params = {
"image_src_ids": ["top", "thermal"],
"video_src_ids": ["thermal"],
"on_time": 30 * 60,
"off_time": 30 * 60,
"lamp_idx": 4,
}
def... |
170848e44e38189e033108ceceb3c3365888cfca0e2c314f8ae4255d825aa81d | Python | 1,822 | 66 | import os
import importlib
import argparse
SUPPORTED_STATS = ["loss", "grad_norm"]
def register_stats_fn(name):
def register_fn(fn):
if name in SUPPORTED_STATS:
raise ValueError("Cannot register duplicate state ({})".format(name))
SUPPORTED_STATS.append(name)
return fn
re... |
98f41ce45e7461f24699562bdcefd28c28ffb09c1efd836062a35b1380f35f33 | Python | 1,822 | 58 | import subprocess
# Define a list of sample names and corresponding SNP list file names
sample_names = [
"CD16p_Mono_conditional_eQTL_FDR0.05",
"CL_Mono_conditional_eQTL_FDR0.05",
"CM_CD8_conditional_eQTL_FDR0.05",
"DN_B_conditional_eQTL_FDR0.05",
"EM_CD8_conditional_eQTL_FDR0.05",
"Fr_I_nTreg_... |
27009c14510fd138d23e3463943f93441349fb5b210d3b8d2e0d35ddd818251a | Python | 1,823 | 46 | import pytest
import geomstats.backend as gs
from geomstats.test.random import NFoldManifoldRandomDataGenerator, RandomDataGenerator
from geomstats.test.test_case import TestCase
from geomstats.test_cases.geometry.manifold import ManifoldTestCase
from geomstats.test_cases.geometry.mixins import ProjectionTestCaseMixin... |
fa918c3ee8e7c9b86258f13dc0a957f077ef6aba5224ca03d1a494dc9e045182 | Python | 1,823 | 59 | # Copyright (c) 2022 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... |
b869958c13b3f6875798670fe636cdd80c591ede390bc3dfab50b3cfaf3afd20 | Python | 1,827 | 94 | import os
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.ResidueDepth import ResidueDepth, get_surface, residue_depth
from Bio.PDB.HSExposure import HSExposureCA
import warnings
from Bio import BiopythonWarning
import tempfile
with warnings.catch_warnings():
warnings.simplefilter('ignore', BiopythonWarning... |
177b161afe60ff66999e31641afc6dfcd8b8d8304c0401b5f6b3710737047b68 | Python | 1,828 | 71 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Experimental CLI."""
from logging import getLogger
from pathlib import Path
from subprocess import check_call
from sys import executable
from click import echo, group, pass_context
import openfl
logger = getLogger(__name__)
@group(... |
1d8a2fb26feb982b9909c6b1db9f832c70e0ac3c193cc21f44b9d63ddc94882b | Python | 1,829 | 54 | # Copyright (C) 2020-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Numpy optimizers test functions module."""
from typing import Dict
import numpy as np
def rosenbrock_func(point: Dict[str, np.ndarray]) -> float:
"""
Calculate Rosenbrock function.
More details: https://en.wikipedia.org... |
317a0611fe69a3fd7c8d3c6fc0773526fce426d21cf058e6b45052e9f293e11e | Python | 1,830 | 65 | #!/usr/bin/env python
# Prints all non-indel positions in MAF file with coordinates and variants from each species / individual
# Usage python callSNPsFromMAF.py mafFile outFile
import sys
mafFile, outFile = sys.argv[1:]
outFile = open(outFile, 'w')
def parseChrom(chrom):
fields = chrom.split(".")
newChr... |
5fb90ebf8bc0f9439dffa99c39d189facea2725dd93758da1d53a9ebd678841e | Python | 1,833 | 51 |
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... |
e204a2db8a9835683e78281eb01dd4fcdab1d2567d830c93255be6f519e0ba50 | Python | 1,833 | 49 | import re
from typing import Tuple
from uuid import UUID
from chromadb.db.base import SqlDB
from chromadb.segment import SegmentManager, VectorReader
topic_regex = r"persistent:\/\/(?P<tenant>.+)\/(?P<namespace>.+)\/(?P<topic>.+)"
def parse_topic_name(topic_name: str) -> Tuple[str, str, str]:
"""Parse the topic... |
e5e46c684f70ad912d29f200073a0105d1a66224fd38b864251d6f7b4e6153f0 | Python | 1,833 | 45 | __author__ = 'palmer'
def hdf5_centroids_IMS(input_filename, output_filename):
# Convert hdf5 to imzML
imsDataset = inMemoryIMS(input_filename,cache_spectra=False,do_summary=False)
coords = imsDataset.coords
coords -= np.amin(coords, axis=0)
step = []
for i in range(3):
u = list(np.dif... |
488c4148c9cafae6ddb75526d625f04a88da8d1df012d7f2329b9413057af1ee | Python | 1,835 | 50 | import pytest
from chromadb.utils.embedding_functions.ollama_embedding_function import (
OllamaEmbeddingFunction,
)
def test_ollama_default_model() -> None:
pytest.importorskip("ollama", reason="ollama not installed")
ef = OllamaEmbeddingFunction()
embeddings = ef(["Here is an article about llamas...... |
376c243a4cfd98ad356d8a4fb569438cbc957a26e6dcf69271a4944d793cf0d9 | Python | 1,836 | 58 | """Compute the RDKit molecule volume from a conformer and its grid-encoding
The quality depends on the quality of the conformer.
"""
from typing import List, Optional
import numpy as np
from rdkit.Chem import AllChem as Chem
from pydantic import Field
from pydantic.dataclasses import dataclass
from ..component_resu... |
b9eca4e2bcea850002a9965ac91c61ded40036dacf4c18ff579b448052e1963e | Python | 1,841 | 54 | import torch
import torch.nn as nn
from loss_objectives import GCCA_loss
class MlpNet(nn.Module):
def __init__(self, layer_sizes, input_size):
super(MlpNet, self).__init__()
layers = []
layer_sizes = [input_size] + layer_sizes
for l_id in range(len(layer_sizes) - 1):
if... |
e176f9d23907b58ff395d21c21425b6410db601c77f2a26a0159253759c42a9f | Python | 1,841 | 47 |
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import gradient_checker
from tensorflow.python.framework import constant_op
import high_dim_filter2d_grad
custom_module = high_dim_filter2d_grad.custom_module
class HighDimGradTest(tf.test.TestCase):
def test_high_dim_filter_grad(self):
... |
c36c1d5d71e76748246b4d6d35b976ca10dfd9c1d2ee71aa35ecbd3a96083687 | Python | 1,842 | 51 | import matplotlib.pyplot as plt
import seaborn as sns
def plot_example_boxplot(df, is_human_col="is_human",
score_cols=["top_heavy_score", "top_light_score", "top_paired_score"],
fontsize=12):
'''
Compare the top CNN scores for human and non-human sequences wi... |
8aeb76bfc72bace04d38dbe4132c71ccacbf2c2c1a89244525b02b47ac7769f6 | Python | 1,843 | 57 | import os
import logging as py_logging
_log_level = {
None: py_logging.NOTSET,
"debug": py_logging.DEBUG,
"info": py_logging.INFO,
"warning": py_logging.WARNING,
"error": py_logging.ERROR,
"critical": py_logging.CRITICAL
}
def get_logger(
log_file_path=None,
name="default_log",
l... |
401c2ab918b9bf872780ec2cdcaad6cca005fea6a3f5e0d74b9c6e284f0c2b8e | Python | 1,845 | 71 | import json
from live2p.websockets import Live2pServer
from live2p import Alert
test_params = {
'fr': 6.36,
'p': 1, # deconv 0 is off, 1 is slow, 2 is fast
'nb': 2, # background compenents -> nb: 3 for complex
'decay_time': 1.0, # sensor tau
'gSig': (7, 7), # expected half size of neurons in pi... |
4ba6f3f640e902c2619889399498698310006ef411e4f9112f5d875bfbc73d50 | Python | 1,847 | 61 | from typing import Tuple
import time
import copy
import torch
from torch import Tensor
from torch_sparse import SparseTensor
from torch_geometric.data import Data
partition_fn = torch.ops.torch_sparse.partition
def metis(adj_t: SparseTensor, num_parts: int, recursive: bool = False,
log: bool = True) -> T... |
79b013813324ab7ae803e6cc285ceea9bf3dbd5efcbaafcd10ee22aa06aabe42 | Python | 1,847 | 61 | from pathlib import Path
import getpass
import json
import numpy as np
import matplotlib.pylab as plt
import MEArec as mr
import MEAutility as mu
import spikeinterface.full as si
# if you are not Sam nor Pierre you should add some lines here.
if getpass.getuser() == 'samuel.garcia':
# sam working
# base... |
9eb1bc25dd3538fc4404f75131bf56ce3901655b38b2f7825a343f34e9cf2926 | Python | 1,849 | 58 | # This file is used to generate Samples from a Trained StyleGAN_1 model
import os
import dnnlib
import dnnlib.tflib as tflib
import numpy as np
import PIL.Image
import pickle
# Generate and save images with different random seeds
num_images = 100000
print ("Generating Samples")
# Path to pre-trained StyleGAN1 .pkl ... |
ce68f4a6c41e475786c39cdb31a1598200b807ef044a5330e659704ea7b7ad68 | Python | 1,849 | 50 | """Train Proj2Proj from image files with simulated noisy projections."""
import argparse
from pathlib import Path
import numpy as np
from torch.utils.data import DataLoader
from sparse_ct.reconstructor_2d import Proj2ProjReconstructor
from sparse_ct.reconstructor_2d.dataset import DeepLesionDataset
def main():
... |
097ac69317743e5bfd05951700914f97269abccb3ac043772e214df8f6907282 | Python | 1,850 | 49 | from typing import List, Optional, Tuple
from chromadb.base_types import SparseVector
def normalize_sparse_vector(
indices: List[int],
values: List[float],
labels: Optional[List[str]] = None
) -> SparseVector:
"""Normalize and create a SparseVector by sorting indices and values together.
This fu... |
43679f1d8d530715c0bf13996d3693631fb13d065190fb67bbb2d19fe2b6f109 | Python | 1,850 | 66 | from PymoNNto.NetworkCore.Neuron_Group import *
class NeuronGroup_read_write_event(NeuronGroup):
ref = None
wef = None
current_dict = None
def set_read_event_function(self, ref):
self.current_dict = copy.copy(dir(self))
self.ref = ref
def set_write_event_function(self, wef):
... |
5d783ffc9117686bfa4241509ce550525110b67e6f6aafcc7d2067873ec83031 | Python | 1,850 | 59 | from bindsnet.network import Network
from bindsnet.pipeline import EnvironmentPipeline
from bindsnet.encoding import bernoulli
from bindsnet.network.topology import Connection
from bindsnet.environment import GymEnvironment
from bindsnet.network.nodes import Input, IzhikevichNodes
from bindsnet.pipeline.action import s... |
7f3453fda7aaf6fcf90698568b4c37f55705c5046108b5b40016e2b85fd12554 | Python | 1,850 | 54 | """The two tiny checkpoints every other golden is measured against."""
from __future__ import annotations
from typing import Dict
from tests.golden.paths import REPO_ROOT
ORDER = 20
HELP = "the tiny anchor checkpoints and their build sidecars"
#: anchor name -> (checkpoint, reference file, what the anchor is for)
... |
8afb0420162b9d3e682ffb6d395b970b640117e9b0b36eec467ac8cf4ee84541 | Python | 1,851 | 51 | import torch
class RotaryPositionalEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half):
"""Rotary positional embedding
Reference : https://blog.eleuther.ai/rotary-embeddings/
Paper: https://arxiv.org/pdf/2104.09864.pdf
Args:
dim: Dimens... |
48fdbbb0e81a58eb42638fd26d87cf950b8ab196206c01e0a0fce31084caffa1 | Python | 1,852 | 54 | import numpy as np
from scipy.ndimage.interpolation import shift
from fimpy.alignment.volume import sobel_stack
from skimage.registration import phase_cross_correlation
def align_single_planes_sobel(
stack,
fft_ref=None,
prefilter_sigma=3.3,
upsample_factor=10,
maxshift=15,
offset=[0, 0],
):
... |
02315a9ced83145e32d21882d2245ab330bf6c4092105a18b52461cdf4b80243 | Python | 1,854 | 63 | import os
import sys
import platform
import subprocess
import pandas as pd
from tqdm import tqdm
def get_simulation_time(byte_out):
str_out = byte_out.decode("utf-8")
result = [
line for line in str_out.split("\n") if line.startswith("simulation time:")
]
return result
def get_number(line, s... |
6d94d17057882c7ddeb84dba12389d2ff7ec73e5bae27b58eadedf7f500de68d | Python | 1,854 | 60 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import Tensor
import random
from typing import List, Optional, Tuple
from utils.math_utils import bound_fn
from .base_layer import BaseLayer
class RandomApply(BaseLayer):
"""
This layer random... |
7957d2b26d1c1198d501ce2b83daa47019be632ebd43cae4c6016ebb6d9e2a94 | Python | 1,855 | 53 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from reinvent.models.transformer.core.network.encode_decode.clones import clones
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(q... |
08c7765ebec58ca62660be79b9b475273dfed87261a3f507ce9265cf34aea194 | Python | 1,858 | 83 | from __future__ import print_function
import os
import copy
import pprint
import torch
import numpy as np
import matplotlib.pyplot as plt
import utils
import utils as u
import analysis_utils
import analysis_utils as au
from dataset_learning_trainable import DatasetLearningTrainable
import torch.nn as nn
import torc... |
57c728bd5b5a62f6e668fb24435a47741574fac3c60e937c3627ee6adc36b6a7 | Python | 1,858 | 75 | from numba import njit, jit, objmode
from numba.experimental import jitclass
from numba import int32, int64, float32, float64, boolean
from numba import typed, types
from numba.types import pyobject
import numpy as np
from scipy.io import loadmat, savemat
from .Tree import Tree
from itertools import chain
def flatten(... |
7fe086ce1313f655a2a897dc10c61c2ec0a9f819b5e62ca065f74508f17b53ce | Python | 1,859 | 62 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Framework Adapter plugin interface."""
class FrameworkAdapterPluginInterface:
"""Framework adapter plugin class."""
def __init__(self) -> None:
"""Initialize framework adapter."""
pass
@staticmethod
de... |
7bbd19c4c0bd573a8743331889baaaa07733c735b67d36bec6a6f42ea5a11a4f | Python | 1,860 | 47 | # Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Histogram module."""
import numpy as np
from openfl.interface.aggregation_functions.core import AggregationFunction
class AggregateHistogram(AggregationFunction):
"""Histogram aggregation."""
def call(self, local_tensors, *_)... |
54af8207e6f391aaeb30fde481f3c46f68ca19dd65dad87932340558d0b416a6 | Python | 1,863 | 78 | """Logging setup for single- and multi-processing"""
__all__ = ["setup_sp_logger", "logging_listener"]
import sys
import logging
import logging.handlers
from typing import TextIO
FORMATTER = logging.Formatter(
fmt="%(asctime)s <%(levelname)-4.4s> %(message)s",
datefmt="%H:%M:%S",
)
def setup_sp_logger(
... |
edc9d7918c086d5df7204812e8aee976d15f930a83f2a88f7d21163acca3356e | Python | 1,863 | 47 | """One training step per anchor, reduced to a per-parameter gradient digest."""
from __future__ import annotations
from pathlib import Path
from tests.golden import harness, targets
from tests.golden.paths import REPO_ROOT
ORDER = 35
HELP = "the one-training-step gradient digests"
def run() -> None:
# Late im... |
13cfdb8afc742b8b185b51a7862b761aff32ea0096f4b22a938beaae96b3aa98 | Python | 1,865 | 52 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from torch import nn, Tensor
from typing import Optional, Tuple
from . import register_norm_fn
@register_norm_fn(name="sync_batch_norm")
class SyncBatchNorm(nn.SyncBatchNorm):
"""
Applies a `Syncronized B... |
30bd7d3ba64b95bc1a23bca9d344e97960c14d1ad07db2404e49a73e2ce8c157 | Python | 1,865 | 50 | """
Combine the AnalogSignal and Event objects from different wave analysis blocks
into the same Neo Block.
"""
import argparse
from pathlib import Path
from utils.io_utils import load_neo, write_neo
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Path, required=True,
help=... |
38d41a2268e33a5b68b3a99adb420ad4bc175d01b9071f8bbf0070056794fe82 | Python | 1,866 | 65 | #!/usr/bin/env python
# Prints all non-indel positions in MAF file with coordinates and variants from each species / individual
# Usage python callSNPsFromMAF.py mafFile outFile
import sys
mafFile, outFile = sys.argv[1:]
outFile = open(outFile, 'w')
def parseChrom(chrom):
fields = chrom.split(".")
newChr... |
afecc96de49b0feece23c2cd03c53345947fa6c2931ec9bb93e53b82d88e8b6c | Python | 1,869 | 57 | import math
import numpy as np
import tensorflow as tf
import multiprocessing as mp
from Humatch.utils import seq_to_2D_kidera
class CustomDataGenerator(tf.keras.utils.Sequence):
'''
Custom generator for batch training/eval with Kidera encoded sequences
tf.keras.utils.Sequence allows multiprocessing in sa... |
10b0a2c1e26837cf2eb014447a5dc57372b809ac2e116dc3d0c26e6b79bebae6 | Python | 1,870 | 57 | import logging
import multiprocessing
from importlib import metadata
import torch
from torch.backends import mps
from idtrackerai import IdtrackeraiError, conf
def _get_device(user_device: str) -> torch.device:
"""Returns the current available device for PyTorch"""
try:
# when mocking torch, metadat... |
14669466c6a5b2322ac2bd4fbfd034f29fab9ac9821fbca5770aa20aa04a79ee | Python | 1,870 | 58 | #!/bin/env python3
#
# This is an example how to use the ExternalProcess scoring component using
# RASCore. The scripts expects a list of SMILES from stdin and will
# write a JSON string to stdout.
#
# Retrosynthetic Accessibility Score (https://doi.org/10.1039/D0SC05401A)
# with code from https://github.com/reymond-g... |
d832b625d87fc1a00eb0d8dd9c780afa00b40bc553e2c7c7d6195f3831207aa8 | Python | 1,870 | 62 | from pyscf import gto, dft
from rdkit import Chem
from scipy.constants import physical_constants
EH2EV = physical_constants['Hartree energy in eV'][0]
def geom2gap(geom):
mol = gto.Mole()
mol.atom = geom
mol.basis = '6-31G(2df,p)' #QM9
mol.nelectron += mol.nelectron % 2 # Make it full shell? Otherwise... |
08521aebd5150e1ae7fc9945641045b24910027afc8f57a6889c3667f93e72d5 | Python | 1,872 | 66 | from setuptools import setup, find_packages
meta = {}
with open("smithers/meta.py") as fp:
exec(fp.read(), meta)
# Package meta-data.
NAME = meta['__title__']
DESCRIPTION = (
"Smithers' a Mathematical Interdisciplinary Toolbox for Helping Engineers "
"Researchers and Scientist"
)
URL = 'https://github.com... |
2850e6d3441e67f7701d28a088b131bcc17ef09061061a974f108d8fa2a66f38 | Python | 1,873 | 49 | """
Combine the AnalogSignal and Event objects from different wave analysis blocks
into the same Neo Block.
"""
import argparse
from pathlib import Path
from utils.io_utils import load_neo, write_neo
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Path, required=True,
he... |
a4e940db01a7596cd038fd6ec57b78f9dd864f63629b61adacd2dd78c70c44f8 | Python | 1,874 | 61 | #!/usr/bin/env python3
import argparse
import numpy as np
import odil
from odil.runtime import mod
def test(method, ndim, loc):
cshapeh = 3 + np.array(range(ndim))
cshape = cshapeh * 2
dimnames = ["x", "y", "z", "w"][:ndim]
domain = odil.Domain(cshape=cshape, dimnames=dimnames)
domainh = odil.... |
60d6908dc6aedb4f0cad434abaecd213d50bc3ff3f138abd9f887ebdf9e50ce1 | Python | 1,876 | 52 | """Tests for snakemake_io"""
from __future__ import absolute_import
import collections
from snakebids.utils import snakemake_io
def test_glob_wildcards():
"""Test glob_wildcards() with various patterns"""
file_path = (
"snakebids/tests/data/bids_t1w/sub-001/anat/" "sub-001_acq-mprage_T1w.nii.gz"
... |
620e56b1b50cc3257d2da92ad2f240eca79b7967a163bb7cc3290cc908ced49e | Python | 1,878 | 57 | """
Check whether the input data representation adheres to the stage's requirements.
Additionally prints a short summary of the data attributes.
"""
import numpy as np
import argparse
import quantities as pq
import warnings
import re
from utils.io_utils import load_neo
from utils.parse import none_or_str
CLI = argpa... |
285b264993a7ab20726bc63190bc11b1b3bd6b89816cfe4eabc07a7c0b0deaa4 | Python | 1,879 | 60 | import logging
import unittest
from typing import Tuple
from unittest.mock import patch, mock_open
from sniffles.config import SnifflesConfig
from sniffles.region import Region
class TestRegions(unittest.TestCase):
"""
Tests --regions parameter
"""
@staticmethod
def _get_common_args() -> Tuple:
... |
cfe2d3ef4d4e78dca890dfcc22587adeeb7ff744efa3b6f7ae2d3ed73008aacc | Python | 1,879 | 54 | import sys
import time
from PyQt5.QtWidgets import QApplication
from ._scope import _ScopeEEG
from ._scope_controller import _ScopeControllerUI
from .. import logger
from ..stream_receiver import StreamReceiver, StreamEEG
from ..utils.lsl import search_lsl
class StreamViewer:
"""
StreamViewer instance. The s... |
eb2b8bca3733f9ebb2a9c118bca1510f94b9184ff937701acea707ca5f950fae | Python | 1,879 | 48 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from openfl.callbacks.callback import Callback
class LambdaCallback(Callback):
"""Custom on-the-fly callbacks.
This callback can be constructed with functions that will be called
at the appropriate time during the life-cycle of... |
71db6449e249a8a174f8966b6192b0b4d1e5c42f0461ec152402f9e9bea0e5ae | Python | 1,884 | 53 | from types import SimpleNamespace
import pytest
import chromadb.test.utils.wait_for_version_increase as wait_module
class _FakeClient:
def __init__(self, collection_id: str = "test-collection-id") -> None:
self._collection = SimpleNamespace(id=collection_id)
def get_collection(self, collection_name... |
a5f9cbffadcb68be0c16a9bb4b5ca3b9f3fc0589cc9ebde2962e5eb7aebd6f81 | Python | 1,886 | 32 | import datasets
import image_experiment
import experiment
img_basepath = 'saved-data/plots/'
cifar10_experiment = image_experiment.ImageExperiment(datasets.ImageDataset.CIFAR10)
# cifar10_experiment.train_models(10,epochs=10,batch_size=64)
cifar10_experiment.load_models(10)
cifar10_experiment.run_experiment(experimen... |
f4b640434d33b2ad3aa36a518d84b8b6d1b7ef8d18fb1299062d5be015057027 | Python | 1,886 | 38 | """Splitting parallel corpora according to specified sentence lengths - lengths"""
# en_file: Source language corpus address; de_file: Target language corpus address
def split_sentences_by_length(en_file, de_file):
lengths = [(0, 10), (10, 15), (15, 21), (21, 32), (32, float('inf'))]
# Create a list for stor... |
6fcdb5c6b0eb85f7c22c50461195ee4bed553f2cd2eb1d7ba6c1381686777ab4 | Python | 1,887 | 57 | """
Check whether the input data representation adheres to the stage's requirements.
Additionally prints a short summary of the data attributes.
"""
import numpy as np
import argparse
import quantities as pq
import warnings
import re
from utils.io_utils import load_neo
from utils.parse import none_or_str
CLI = argpa... |
49456bc084e1b13843e9a751ba986abc62cbfffdbfb3ad329cb2060e9197603c | Python | 1,888 | 59 | # -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
import os
import argparse
import logging
import itertools
import pickle
# use this format to tell the parsers
# where to insert certain parts of the script
# ${imports}
def parse_args():
# Argument Parser
parser = argparse.ArgumentParser()
# my args
pa... |
e3f007e35f3758458b2ba974b46041e54caa051e085240ee458541bc24477a56 | Python | 1,889 | 45 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script for using model and detecting defects. Input data files should be saved in
a folder called 'CellFiles' (or 'data_filepath' should appropriately changed), with each
file being a as a comma separated text file where each line contains the x and y coordinates,
as... |
e715c0fe31a27b81a229465fcb268c40a34c6ebe37eb01821da863a3a5ed9c1a | Python | 1,889 | 60 | from PymoNNto.Exploration.Network_UI.TabBase import *
#from Testing.Common.Classifier_Helper import *
class sidebar_drumbeat_module(TabBase):
def add_recorder_variables(self, neuron_group, Network_UI):
if hasattr(neuron_group, 'pattern_index'):
Network_UI.add_recording_variable(neuron_group, ... |
e8d1c343d4edfd07c16c845fdd531ff1a79995802bc2320fa99630f58a5541bd | Python | 1,890 | 66 | import pytest
from pathlib import Path
import torch
from reinvent.runmodes.TL.run_transfer_learning import run_transfer_learning
from reinvent.runmodes.utils.helpers import set_torch_device
from reinvent.models.meta_data import check_valid_hash
@pytest.fixture
def setup(tmp_path, json_config, pytestconfig):
dev... |
572be9896c0e9418f54f2fda596ec3f4af2cb9dbd00f2e9a8821c401b8d0adaf | Python | 1,891 | 70 | import os
import sys
from pprint import pprint
import numpy as np
import pandas as pd
project_root = ".."
sys.path.append(project_root)
DATASETS_PATH = os.path.join(project_root, "resources", "datasets", "nsrr")
if __name__ == "__main__":
dataset_name_list = [
"chat1",
]
# Keys in dataset:
... |
ca441da11e60b42dad4447279322af2b959af5a542c11feccdc37705a47eb807 | Python | 1,892 | 68 | """Benchmark metric inner product."""
from importlib import import_module
import pandas as pd
import pytest
def read_benchmark_inner_product_data():
"""Read benchmark parameters from file benchmark_params.pkl."""
data = []
ids = []
df = pd.read_pickle("benchmark_params.pkl")
params = list(df.ite... |
46f725b6ce4e4658f74f7929ce45118438dcf39ad2c262cfa17f610dfcc4ffe8 | Python | 1,895 | 67 | from pymonntorch.NetworkCore.Behavior import Behavior
from pymonntorch.NetworkBehavior.EulerEquationModules.Helper import (
eq_split,
remove_units,
)
class Variable(Behavior):
def __init__(self, *args, eq=None, **kwargs):
super().__init__(*args, eq=eq, **kwargs)
def initialize(self, neurons):... |
3d17c26e9a77ebce00ab423b469879441b53e6854f724cf83fbb057b475208a2 | Python | 1,896 | 71 | # NIBR substructure filter
#
# adapted from RDKit commit 4a69bc3493dd3e9bb9f7a519e306fbcb545f1452 which
# includes the CSV file SubstructureFilter_HitTriaging_wPubChemExamples.csv
#
# build the filter from a CSV filer and write it out in pickle format
from collections import namedtuple
import pandas as pd
from rdkit... |
3c49adf05561dcf471e0cbff05a67de45d25c4858ad6015af10982233e6baa4b | Python | 1,899 | 57 | import geomstats.backend as gs
from geomstats.geometry.general_linear import GeneralLinear
from geomstats.geometry.matrices import Matrices
from geomstats.test.data import TestData
from .base import LevelSetTestData
from .riemannian_metric import RiemannianMetricTestData
class StiefelTestData(LevelSetTestData):
... |
aeec181d32393cc8cbbb2d174257435f98f7f5b79241e19e09bdb6f529322bab | Python | 1,903 | 61 | import os
import numpy as np
import pickle
import pandas
def import_data(patient):
print('\nLoading data...')
directory=f'../../Data/pat_{patient}'
#import information about seizures
with open(os.path.join(directory,'all_seizure_information.pkl'), 'rb') as seiz_info:
... |
3451c2eca01151f378438ec65c40f31fd4a4173c7709336b9529f0acb98986d7 | Python | 1,904 | 66 | import geomstats.backend as gs
from geomstats.test.data import TestData
from ._base import BaseEstimatorTestData
class RiemannianEMTestData(BaseEstimatorTestData):
fail_for_autodiff_exceptions = False
MIN_RANDOM = 5
MAX_RANDOM = 8
def estimate_belongs_test_data(self):
return self.generate_r... |
87829e68c0a7c611231f2d34556bf6a0b775ba9326ca25e875ab2ccb07f253c5 | Python | 1,904 | 68 | # Path to save data
DATA_PATH = r''
# classifier type
DECODER_FILE = r''
# fake decoder?
FAKE_CLS = None
DIRECTIONS = [ ('L', 'LEFT_GO'), ('R', 'RIGHT_GO') ]
ADAPTIVE = {'selected':'False', 'False':False, 'True':[3]} # Retrain classifier every 2 x TRIALS_EACH, 3= nb_runs,
... |
e4bfab72398886c3fc07b1a4c9254034b803445a7dcb0be36316fae1419db246 | Python | 1,904 | 58 | """
Check whether the input data representation adheres to the stage's requirements.
Additionally prints a short summary of the data attributes.
"""
import numpy as np
import argparse
from pathlib import Path
import quantities as pq
import warnings
import re
from utils.io_utils import load_neo
from utils.parse import... |
3c292900127642a2ae375c5c245fbf2ad30b71d8274cf0679360139db264bcbc | Python | 1,906 | 81 | from collections import Counter
from pathlib import Path
import matplotlib.pyplot as plt
from rich import print
import neurodesign
output_dir = Path(__file__).parent / "output"
output_dir.mkdir(parents=True, exist_ok=True)
exp = neurodesign.Experiment(
TR=1.2,
n_trials=20,
P=[0.3, 0.3, 0.4],
C=[[1, ... |
b3351aee5a166eecfa9d4d928908e94eaff6badece81649aefbbe45fac05ade0 | Python | 1,906 | 67 |
'''
Example showing software-timed analog input using nidaqmx-python
pynidaqmxegs.ai.softwareTimedVoltage
Purpose
Describes how to do software-timed analog input using using nidaqmx-python
This is unbuffered or "on demand" acquisition.
Demonstrated steps:
1. Create a task.
2. Create an An... |
eb206cf140176ce800aefa620594e98fb8278254231179ed97797f0907d613d5 | Python | 1,906 | 62 | import numpy as np
import torch
# class modules
# In this file we collect all the modules so we don't need to re-write them
# at every file
class _ResidueModule(torch.nn.Module):
'''One-dimensional convolutional residual
we only provide the channel count
the stride and the filter width are fixed to zero.
... |
54a6d6eca3268267f836e6df1fc0f7e68939bf30c55469cf29db891f1f890edc | Python | 1,909 | 46 | from utils.visualization_utils import plot_cam, concat_images
import sys
def cam_dataset_model(sel_dataset, sel_model, n_classes, size=None):
sys.argv.append('--common.config-file')
sys.argv.append('config/classification/food_image/ehfr_net_food101.yaml'.format(sel_dataset))
sys.argv.append('--mo... |
c57984984e424060d206e7ff3969594ea3154bef6c11bfd2d6589b0fc5d80c9a | Python | 1,909 | 47 | # -*- coding: utf-8 -*-
"""Implements datasets for training and evaluating learned reconstructors.
.. autosummary::
get_standard_dataset
Dataset
GroundTruthDataset
ObservationGroundTruthPairDataset
EllipsesDataset
LoDoPaBDataset
The function :func:`.get_standard_dataset` returns fixed "standar... |
a8bb763ab1510b090a2dfcca7b7709549e5b9e07c8d19bbbe6d2bcae7df5fd5f | Python | 1,911 | 49 | import os
# Define a list of file names
file_names = [
"CD16p_Mono_conditional_eQTL_FDR0.05.vcf",
"CL_Mono_conditional_eQTL_FDR0.05.vcf",
"CM_CD8_conditional_eQTL_FDR0.05.vcf",
"DN_B_conditional_eQTL_FDR0.05.vcf",
"EM_CD8_conditional_eQTL_FDR0.05.vcf",
"Fr_I_nTreg_conditional_eQTL_FDR0.05.vcf",... |
4e92700fb08ed27fdd23f6cadad48cba7c7b5be104cfb4c872d282291e7a7e52 | Python | 1,913 | 43 | """
Reproduce the metrics from Figure 5 in https://doi.org/10.1038/s41597-021-00893-z
"""
import numpy as np
from dival import get_standard_dataset
from dival.measure import PSNR, SSIM
from dival.reconstructors.odl_reconstructors import FBPReconstructor
from dival.reference_reconstructors import get_reference_reconstru... |
8e14e13e1ea375d13354f2cb83d7b6d164c367c3b1f487e529eaadc0ab7a031d | Python | 1,915 | 64 | import pytest
from reinvent_plugins.components.RDKit.comp_matching_substructure import (
Parameters,
MatchingSubstructure,
)
def test_comp_matching_substructure():
smarts = ["ClccccF"]
smiles = ["c1cc(F)ccc1Cl"]
params = Parameters(smarts, [False])
ms = MatchingSubstructure(params)
result... |
4489bc0a009feadcba23852f67b86ebee40cba4d1cb6ed2e8c43cbbb2c02b02d | Python | 1,917 | 71 | """Fixture setup for pytest
IMPORTANT: NEVER import directly from conftest.py
also DO NOT mess with sys.path
"""
import os
import json
import pytest
def pytest_addoption(parser):
"""Command line parsing for pytest
Use e.g. like
$ pytest [pytest parameters and options] --device cpu
"""
... |
54d0bd0e4fd2fe8592a6e848f9d26249da384cce5af5100fb73324eabf809166 | Python | 1,917 | 69 | from typing import Optional, Tuple, List, Iterable, Dict
import torch
import numpy as np
from PIL import Image
from ..encoding import Encoder, NullEncoder
import sys
sys.path.append("C:/Users/cstei/Dropbox/work/projects/hebb/code/")
import libstein.hebb.image_data as image_data
class NatImages(torch.utils.data.Datas... |
1e8849ae622d20a3c8f7543c9f8bbc2bbdc2db3483af7473750a282a2e427b8a | Python | 1,919 | 58 | """
Divide the signal in each channel by their max/mean/median value.
"""
import numpy as np
import argparse
import neo
import quantities as pq
import os
import sys
from utils.io_utils import write_neo, load_neo
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=str, required=True,
... |
feb850f7ca51c691707a42b0d364cbf98cbb3fb8992ba4aa7efecba2b1327e5e | Python | 1,919 | 60 | # 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 lightning
from lightning.pytorch.callbacks import Callback
from nequip.data import AtomicDataDict
from nequip.train import NequIPLightningModule
class LossCoefficientMonitor(Callba... |
58192fb433642af3b85cf5e7730eba6a12fd5a62d11b00fdf30cde0b3a9181b5 | Python | 1,921 | 68 | """Compute the PMI score in RDKit"""
from typing import List
import numpy as np
from rdkit.Chem import AllChem as Chem
from pydantic.dataclasses import dataclass
from ..component_results import ComponentResults
from ..add_tag import add_tag
from reinvent_plugins.mol_cache import molcache
@add_tag("__parameters")
@... |
7b07f8cf602ff86c4466563a4da001f38861d08f40ea72f46e757e70e2f2a1cb | Python | 1,922 | 52 | from PymoNNto.Exploration.UI_Base import *
from PymoNNto.Exploration.StorageManager.StorageManager import *
from PymoNNto.Exploration.Evolution.EvolutionPlots import *
class Single_Evolution_UI(UI_Base):
def __init__(self, evolution):
super().__init__(title='Evolution Monitor', create_sidebar=False, cre... |
7cbf3c91012951d048134be3232e297bfb5d84dd8b24884675a67c3eec1e8da2 | Python | 1,922 | 53 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Adagrad adaptive aggregation module."""
from typing import Dict, Optional
import numpy as np
from openfl.interface.aggregation_functions.core import AdaptiveAggregation, AggregationFunction
from openfl.interface.aggregation_functions.... |
37df6414ca52010af400dc3e84ccd0a7f414c895c54123592618fdb1e4b443ff | Python | 1,924 | 59 | # -*- coding: utf-8 -*-
import os
import argparse
import logging
# use this format to tell the parsers
# where to insert certain parts of the script
# ${imports}
def parse_args():
# Argument Parser
parser = argparse.ArgumentParser()
# my args
parser.add_argument("--verbose", action = "store_true", hel... |
d494e260288261d7584e37bb1921ee120eef613e3afe03f562fb1f5ea60f0fbd | Python | 1,924 | 64 | from nltools.simulator import Simulator, SimulateGrid
import numpy as np
def test_simulator(tmpdir):
sim = Simulator()
r = 10
sigma = 1
y = [0, 1]
n_reps = 3
output_dir = str(tmpdir)
shape = (91, 109, 91)
dat = sim.create_data(y, sigma, reps=n_reps, output_dir=None)
assert len(dat)... |
da2875c9218f23cb8aca4ea44db2878c359e5d800edaa22bc8c648b6c466b96b | Python | 1,925 | 52 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 31 15:05:59 2020
@author: Tom
"""
import tensorflow as tf
import numpy as np
class NegativeLogLikelihood():
def __init__(self, followup, event):
self.followup = tf.constant(followup, dtype='float32')
self.event = tf.constant(event, dtype='fl... |
ce4b56134e619982ad5f03d77772554729b600257352ab9a07b45cd0a7fe57f6 | Python | 1,928 | 67 | from .meta_arch import BaseArch
from ppdet.core.workspace import register, create
from paddle import in_dynamic_mode
__all__ = ['CLRNet']
@register
class CLRNet(BaseArch):
__category__ = 'architecture'
def __init__(self,
backbone="CLRResNet",
neck="CLRFPN",
... |
8dfa317c08a396c79e74e0d745e135463769a769195d6f489edcd3e02b09ae09 | Python | 1,929 | 56 | import os
import shutil
import pytest
import unittest
import torch
from reinvent.runmodes.create_model.create_libinvent import create_model
@pytest.mark.integration
@pytest.mark.usefixtures("json_config")
class TestCreateModel(unittest.TestCase):
def setUp(self):
self.workdir = self.json_config["MAIN_TE... |
b0b5dabddb7acff3534ff89b5903cbe734a5210e06688f7f47b5b21ee65c2669 | Python | 1,930 | 60 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Base abstract optimizer class module."""
import abc
from importlib import import_module
from os.path import splitext
from typing import Dict
from numpy import ndarray
from openfl.plugins.frameworks_adapters.framework_adapter_interface... |
beaafef0f69940d8aa09b0733a941f75a841f9c833e883711e4e9fdb7f11034c | Python | 1,933 | 58 | import numpy as np
import mrestimator as mre
def h_offset(exp_fit: mre.FitResult, expoffset_fit: mre.FitResult) -> bool:
"""
Test the H_offset hypothesis as described in (Wilting&Priesemann 2018; Supplemental Note 5)
:return: if True, then the data set should be rejected for m estimation
"""
retur... |
db9c83f67b1e69c51625bb4b5863ca6971b05912501682f77996a26d1f04cf45 | Python | 1,933 | 61 | """Tests for ScaffoldSimilarity._find_similar_scaffold."""
import pytest
from reinvent.runmodes.RL.memories.scaffold_similarity import ScaffoldSimilarity
# Benzene and toluene — atom-pair Tanimoto similarity is moderate
BENZENE = "c1ccccc1"
TOLUENE = "Cc1ccccc1"
CYCLOHEXANE = "C1CCCCC1" # very different from benzen... |
9452b6e0748067a8c0050a1ef30afada9099028c85a2eea962044e4ff90f04f4 | Python | 1,939 | 75 | import random
import pytest
import geomstats.backend as gs
from geomstats.geometry.hypersphere import Hypersphere
from geomstats.geometry.poincare_ball import PoincareBall
from geomstats.learning.expectation_maximization import (
GaussianMixtureModel,
RiemannianEM,
)
from geomstats.test.parametrizers import D... |
2e00b0148f2a31732ed41200c13b1e1ca9a80697053c76dc6e6f05bdb331da32 | Python | 1,941 | 51 | """Load a folder-backed collection of SanPy recordings and analyses."""
from pathlib import Path
import sanpy
def main() -> None:
"""Summarize an analysis directory and load one saved analysis.
Raises:
RuntimeError: If the example recording or its analysis cannot be found.
"""
# Locate the ... |
8db823588ca1933ab8acacac538a40f64e909b67727c6d0e6adff171d8d081f7 | Python | 1,941 | 51 | """Tests for RL distance penalty in reinvent/runmodes/RL/distance_penalty.py."""
import pytest
import numpy as np
import torch
from reinvent.runmodes.RL.distance_penalty import get_distance_to_prior
# ── torch.Tensor branch ──────────────────────────────────────────────────────
def test_distance_penalty_tensor_abo... |
fe08ff7e7613765be8bf9b5c944e6f2e8eabb1f30d2fd2c552f4420699769d3f | Python | 1,941 | 58 | """Differentiable Radon operators with filters for current PyTorch versions.
Filter formulas adapted from pytorch_radon, Copyright (c) 2019 phernst,
licensed under MIT: https://github.com/mozanunal/pytorch_radon.
"""
import torch
from pytorch_radon import IRadon as _IRadon
from pytorch_radon import Radon
from torch i... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.