sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c2597d715ada1c188d9271117486b4c30530f21657aef11e070e4ae6c52ef828 | Python | 2,429 | 79 | r"""Compute a geodesic regression on Grassmann manifold (2, 3).
The generative model of the data is:
:math:`Z = Exp_{\beta_0}(\beta_1.X)` and :math:`Y = Exp_Z(\epsilon)`
where:
- :math:`Exp` denotes the Riemannian exponential,
- :math:`\beta_0` is called the intercept,
- :math:`\beta_1` is called the coefficient,
- :m... |
c797fc7e654b803ae607754b5bcfc10bb28b719ec8feb72890cd861fc02b751b | Python | 2,430 | 37 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
5d22a24722780194f1ac762fe3554f1e8a4659d3cc33eb68fe6fe7aa419712d9 | Python | 2,431 | 37 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
798100b125ae7d1190d10e8d80774ac5c1959ae80ba8b40c5b34a285cfdecbdd | Python | 2,431 | 37 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
319b59b02d0dbe858e91577f9063dc6d17810363e429155faab6144721dfa6e6 | Python | 2,433 | 72 | import numpy as np
import os
import numpy.random as rgn
from mpi4py import MPI
def input(path, nb_neurons, t_synch):
"""
Simulate some random spike train input
:param path: the file for the configurations of the connection
:return:
"""
# Start communication channels
path_to_files = path
... |
ba2277bf435326bf7aa77596ad710a50805c53970c873a329cc40a3b90012316 | Python | 2,434 | 79 | from geomstats.test.data import TestData
from .base import LevelSetTestData
from .fiber_bundle import FiberBundleTestData
from .pullback_metric import PullbackDiffeoMetricTestData
from .quotient_metric import QuotientMetricTestData
class FullRankCorrelationMatricesTestData(LevelSetTestData):
def from_covariance_... |
82c7c443966d0b87c2e9256eef54f5b8638fe29d07ef39ef3b6a0113b0f883f6 | Python | 2,436 | 76 | from pymonntorch import *
import torch
import time
from matplotlib import pyplot as plt
from globparams import *
settings = {'dtype': torch.float32, 'synapse_mode': "DxS", 'device': 'cuda'}
class LeakyIntegrateAndFire(Behavior):
def initialize(self, neurons):
neurons.spikes = neurons.vector(dtype=torch.b... |
cb9bf8a14dc202e57bae8353c5a77675e8403df8fa3f38a5046fdaa9d90bf3f2 | Python | 2,437 | 77 | from pymonntorch import *
import torch
import time
from matplotlib import pyplot as plt
from globparams import *
settings = {'dtype': torch.float64, 'synapse_mode': "DxS", 'device': 'cuda'}
class LeakyIntegrateAndFire(Behavior):
def initialize(self, neurons):
neurons.spikes = neurons.vector(dtype=torch.b... |
0fa5ffdde5692e5fa36128dbc1197bef838de76514bba1be55fa593df1f923ae | Python | 2,438 | 70 | """
Calculate the timing of each wave.
"""
import numpy as np
import matplotlib.pyplot as plt
import argparse
from pathlib import Path
import pandas as pd
from utils.io_utils import load_neo, save_plot
from utils.parse import none_or_path
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=Path... |
dbc05c76b48acff8cb7909b3e841ba11c642a43af8278767aeb683a86764a090 | Python | 2,438 | 49 | import argparse
from dig.auggraph.method.SMixup.smixup import smixup
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='IMDBB',
choices=['IMDBB','PROTEINS', 'MUTAG', "REDDITB", 'IMDBM', 'REDDITM5', 'REDDITM12', 'NCI1'])
parser.add_argument('--GMNET_nlayers', type... |
0cbb3cce564514f6e902416ffc2d9dc90112a1302e9abffffefdfee3bbbf0e90 | Python | 2,440 | 67 | """
Subtract the background of the input data by subtracting the mean of each channel.
"""
import numpy as np
import matplotlib.pyplot as plt
import argparse
from pathlib import Path
import os
from utils.io_utils import load_neo, write_neo, save_plot
from utils.parse import none_or_str
CLI = argparse.ArgumentParser()... |
c9a209848d8346cce0e1925be69577afd029ee64d656e23e055c180e86888f3d | Python | 2,440 | 76 | import pytest
import numpy as np
from reinvent.runmodes.RL.memories.identical_murcko_scaffold import IdenticalMurckoScaffold
from reinvent.models.model_factory.sample_batch import SmilesState
@pytest.fixture
def diversity_filter():
diversity_filter = IdenticalMurckoScaffold(
bucket_size=2,
mins... |
ea04297c4d0e158c04a2e693f12015812a729bda8297a2c9bedc558900a628de | Python | 2,440 | 77 | """Plot the pole ladder scheme for parallel transport on S2.
Sample a point on S2 and two tangent vectors to transport one along the
other.
"""
import matplotlib.pyplot as plt
import geomstats.backend as gs
import geomstats.visualization as visualization
from geomstats.geometry.hypersphere import Hypersphere
from ge... |
434936617955684be3217897bcea579a5a88c9b8086d9b041b839dcacd9bc5ec | Python | 2,441 | 83 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import platform
import shutil
import urllib.request
from click import confirm
VERSION = "0.16.0"
ARCHITECTURE_ALIASES = {
"x86_64": "amd64",
"armv6l": "armv6",
"armv7l": "armv7",
"aarch64": "arm64",
}
FILE_EXTENSIONS = {... |
c1c75e348588fe8a0962c9c6182f3fddfcfe26add1b56cb119b1344ee5a30a70 | Python | 2,441 | 51 |
import os
import pickle
import argparse
import pandas as pd
from tdc.multi_pred import DTI
from hyper_dti.utils.bio_encoding import precompute_drug_embeddings, precompute_target_embeddings
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Bio-embedding')
parser.add_argument("--data_di... |
cc1adcdfcb2fda4512c578e2779cb0810c259026953a73e0edbe850289694dcc | Python | 2,441 | 67 | import pytest
import unittest
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.r... |
55a3b5061607c59f4083e6f0d6a3bb54d7cfedd26a68179a12fbaaf36aba279a | Python | 2,442 | 73 | # This file is a part of the `nequip` package. Please see LICENSE and README
# at the root for information on using it.
import torch
from e3nn.util.jit import script
from nequip.nn import graph_model
from nequip.utils.compile import prepare_model_for_compile
from nequip.utils.global_state import TF32_KEY
from typing ... |
b478b27293521e7af5728f3de735a02ac936c7fc975b2e41557927ffcfd581cc | Python | 2,442 | 74 | from typing import Sequence
from chromadb.test.conftest import (
reset,
skip_if_not_cluster,
)
from chromadb.api import ClientAPI
from kubernetes import client as k8s_client, config
import time
@skip_if_not_cluster()
def test_reroute(
client: ClientAPI,
) -> None:
reset(client)
collection = client... |
bf9f3faa51cc888b3e8015dfd9c230be7d3ba0707444da29f9265e01eac1d626 | Python | 2,442 | 73 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
from typing import List, Optional
import torch
from e3nn.o3._irreps import Irreps
from nequip.data import AtomicDataDict
from ._graph_mixin import GraphModuleMixin
class Concat(GraphModuleMixin, to... |
8d983df1605403d4442f548ea3a8ce62d329b1f2bd0a10a18e9aa858ba11e0ee | Python | 2,444 | 74 | import pytest
import torch
from nequip.data import AtomicDataDict
from nequip.nn.embedding import NodeTypeEmbed
from nequip.nn import SequentialGraphNetwork, AtomwiseLinear
def test_basic():
type_names = ["A", "B", "C"]
node_type = NodeTypeEmbed(type_names=type_names, num_features=13)
linear = AtomwiseLi... |
782cab324bb06572b421c61906a487f4ff67b5eb57958f4e0b93218b52da82e9 | Python | 2,449 | 74 | # Copyright 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from abc import abstractmethod
from typing import Any, Dict
import torch
class LabelMapper:
"""
A utility class for mapping class names (labels) to unique integer indices and vice versa.
This ensures consistent labeling across data... |
651fd5d183571209d58ab62372ecbb252f3f3c29741433414a9324e4f227cf0b | Python | 2,450 | 74 | #!/usr/bin/env python
from glob import glob
from subprocess import Popen, PIPE
from valgrindwrapper import ValgrindWrapper
import difflib
import os
import re
import shlex
import shutil
import sys
import tempfile
import unittest
class IntegrationTest():
exe_path = None
def setUp(self):
self.test_dir = ... |
5df62b13988c96eb929da704dfadcd1f587aacb6b6a827f4bb22e767f77f4874 | Python | 2,451 | 70 | """
Tests for the ``progress_bar`` keyword across the inference family.
Progress bars are written to stderr by tqdm. A library should not write to
stderr unless asked: calling these functions in a loop (e.g. a calibration
simulation running 100 permutation tests) otherwise emits one progress bar per
call. Per the cano... |
9356081dc1c305b2d7a0cc146d4c2d4d9503ffce44ea01d64ea94d2f532e7019 | Python | 2,451 | 72 | import statistics
import os.path
from common.common import load_pickle
from osa.configuration import DATA_FOLDER, OUTPUT_FOLDER
def trial_average(summary_list, name, return_list=False):
value_list = list()
for s_i, s in enumerate(summary_list):
if name in s.keys():
value_list.append(s[nam... |
30d82ddfc2e3d4a6103bfd06ae760f893129ec207118f5a901ec877dc8353a88 | Python | 2,454 | 70 | import statistics
import os.path
from common.common import load_pickle
from safe_msmt.configuration import DATA_FOLDER, OUTPUT_FOLDER
def trial_average(summary_list, name, return_list=False):
value_list = list()
for s_i, s in enumerate(summary_list):
if name in s.keys():
value_list.append... |
6acc005ea725c5407847adf93ef8ee498821be4354acd4dd312a171a678c86ba | Python | 2,454 | 60 | from PymoNNto.Exploration.Evolution.Devices.Evolution_Device_Multi_Thread import *
import sys
from PymoNNto.Exploration.Evolution.SSH_Functions import *
def ssh_thread_worker(slave_file, name, ssh_string, root_folder, conn):
print('ssh thread started')
while True:
time.sleep(np.random.rand())
i... |
8c7f3ec916ab6d38f80b492cdd25fff3a2acedf1aae0dc19e9530a4eb8c4e537 | Python | 2,455 | 86 | import random
import pytest
from geomstats.geometry.hyperbolic import Hyperbolic, HyperbolicDiffeo
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test_cases.geometry.diffeo import DiffeoTestCase
from geomstats.test_cases.geometry.riemannian_metric import (
RiemannianMetricComparison... |
afcefd9d3b50314f99931e02ba7721651b7cdbc7dc15fa6f320f077cba2c7d2b | Python | 2,455 | 88 | import h5py
import random
import torch
from deeprank_gnn.GraphGenMP import GraphHDF5
from deeprank_gnn.ginet import GINet
from deeprank_gnn.NeuralNet import NeuralNet
# example of creating hdf5 file
def create_graph():
pdb_path = "./data/pdb/1ATN/"
pssm_path = "./data/pssm/1ATN/"
embedding_path = "./data... |
b3f624f8eb0543b55a3867cd11ff9f3ad7395efac1854b182638f45772e0ccdd | Python | 2,455 | 52 | from PymoNNto.Exploration.Network_UI.TabBase import *
from PymoNNto.Exploration.Network_UI.Basic_Tabs.Helper.PCA import *
class PCA_tab(TabBase):
def __init__(self, parameter='output', title='PCA', timesteps=1000):
super().__init__(title)
self.parameter = parameter
self.timesteps = timeste... |
1c3129310418989a43adf549ef976a97e7320fd09c68d35697074461e343b40c | Python | 2,456 | 68 | import pytest
import unittest
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.r... |
a23e13a13fd4eccd544823e1d80ea81b005a9d70892bb8c6125d05612d3b2d53 | Python | 2,457 | 74 | import math
import numpy as np
from warnings import warn
def fsolve_bounded_monotonic(func, bounds, tol=1e-5, max_iter=50):
assert max_iter > 0 and tol > 0
bounds = (float(bounds[0]), float(bounds[1]))
# Check if the function is monotonically increasing or decreasing
ymin, ymax = func(bounds[0]), fun... |
d0e546018a759b3897c2ef975f939e9338d3aeec42aca8227c2f3670cf571fd7 | Python | 2,459 | 96 | import numpy as np
import NALSM_GEN_SUPPORT as sup
import os
SAVE_VER_INP = input('VERSION? [int]: ')
VER = int(SAVE_VER_INP)
main_Path = os.getcwd()
data_path = main_Path + '/train_data/ver_'+str(VER)
net_path = main_Path + '/networks'
SAVE_VER_INP = input('DATASET? [M: mnist, N:nmnist]: ')
ds = str... |
10ba1be1f3a36c5a977f0e995394b0bb42825e6be50ebc68496b376fe99172c1 | Python | 2,460 | 68 | # Generated by Django 4.1.1 on 2022-11-21 13:21
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('celsus', '0027_project_project_type'),
]
operations = [
migrations.AlterField(
model_name='project',
name='cell_type... |
ed2547c7ccaf0235a85c49c8a106c3c18a274da36c193acd0ec4ed8ac7f24554 | Python | 2,463 | 84 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# FileName: main
# CreatedDate: 2019-06-14 19:20:26 +0900
# LastModified: 2019-10-05 16:42:26 +0900
#
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from barplot_annotate_brackets import barplot... |
0fd1dabd0effe9765dd05d688aeff32c07d6c0e91a7ccf0b7e0e74afbbb85643 | Python | 2,464 | 66 | from pathlib import Path
import click
import numpy as np
from tqdm import tqdm
from nnunetpaper._utils import get_multi_method_dataframe
@click.command()
@click.option(
"-m",
"--methods",
multiple=True,
type=click.Tuple([str, click.Path(exists=True, readable=True, path_type=Path)]),
)
@click.option(... |
becaeffff40e16c79a18c55331282ac335b6ab9ee25f69486fe4c2f1b8238664 | Python | 2,464 | 74 | import os
import argparse
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
import pandas as pd
from activation_models.generators import get_activation_models
from custom_model_tools.eigenspectrum import EigenspectrumImageNet
from custom_model_tools.hooks import LayerZCA
from utils import ... |
4440732253ed88e28abc86752ce5377dd9e09e83fd94dc17198397e6422f8902 | Python | 2,466 | 85 | import torch
import torch.optim as optim
import torch.nn as nn
import numpy as np
import os
from IPython.display import clear_output
from torch.utils.data import Dataset, DataLoader
import json
def weighted_bceloss_logits(pred, y, decay=1):
weight = 1/(decay*torch.abs(y))
weight[weight>3] = 3
y[y>0] = 1
... |
1b4338094e29b33e56207fe1a4b5b26edc37c338a06cfee1c2debd7620dff4cf | Python | 2,468 | 99 | # -*- coding: utf-8 -*-
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
import numpy as np
from pyqtgraph.dockarea import *
app = QtGui.QApplication([])
gui = QtGui.QMainWindow()
area = DockArea()
gui.setCentralWidget(area)
gui.resize(1000,500)
gui.setWindowTitle("Scroll and Zoomed Plot")
dockCtrl = Do... |
e1c78738d75fa9426988dd3f1a37a8eeb3a348931a532a4ffda917fe6da803fc | Python | 2,468 | 66 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Aggregation function interface module."""
from abc import abstractmethod
from typing import List, Tuple
import numpy as np
import pandas as pd
from openfl.interface.aggregation_functions import AggregationFunction
from openfl.utilitie... |
144565caf5b33cabf9031a93c9bdeec8e4850c35b6ec2d4c5349bd7430fe606b | Python | 2,469 | 54 | class Config:
def __init__(self):
self.model = 'vae'
self.batch_size = 8
self.nb_epoch = 150 #300
self.n = 75
self.kl = 2
self.weight = 2
self.lr = 5e-4
#self.in_shape = (1, 80, 72, 96) # input size with padding
#### FOR SC:
#self.in_s... |
7ab1d3c4408597df04d62488cc7cacbf5e0dddeeef3a2e11e6d88ddcc9be319d | Python | 2,471 | 74 | from dataclasses import asdict, dataclass
@dataclass(slots=True)
class ConfParams:
"""Global tracking hyperparameters, overridable from parameter files or the CLI"""
DEVICE: str = ""
"""Device name as given by the user (empty for automatic selection).
Use idtrackerai.base.network.DEVICE to get the ac... |
e8e3bd1c15b5079fc8c0d07facbb55cae3f7037802fb3afee0c038eb765cc511 | Python | 2,474 | 70 | import anndata
import numpy as np
import pandas as pd
import plotly.io as pio
import scanpy as sc
import plotly.graph_objects as go
import os
import scipy.sparse as sp
from scipy.spatial import distance
from sklearn.neighbors import NearestNeighbors
import warnings
from tqdm import tqdm
warnings.filterwarnings("ignore"... |
23bc5ebcaa3f80e61ce08bae704a847534b8d746ba8fffa1d565dfa277f0fb4b | Python | 2,475 | 76 | from numpy import zeros
from .butter_bandpass_filter import butter_bandpass_filter
from .doHilbert import doHilbert
from .dPL import dPL
def run_multiPatLEiDA(RSsig, delt, flp, fhi, k):
"""Run LEiDA Routine for BOLD signal.
Args:
RSsig (ndarray): BOLD signal array for all parcels/voxels in the forma... |
cbe6c7fb2cf976dd1b6144215a2cde25a325ee45027561de52363bad532c16d8 | Python | 2,477 | 69 | import os
import argparse
import pandas as pd
import matplotlib.pyplot as plt
from experiments import ROOT_DIR
benchmark_path = os.path.join(ROOT_DIR, "benchmark")
figure_path = os.path.join(ROOT_DIR, "figures")
if not os.path.isdir(benchmark_path):
os.makedirs(benchmark_path)
def main(start=100, stop=1000, st... |
8b81d72da93249bf2400c58855020f57095238abb020326c2b6d2f20b61ae605 | Python | 2,480 | 73 | import os
from jinja2 import Environment, FileSystemLoader
import json
def render_json_template(env, template_file, **kwargs):
template = env.get_template(template_file)
d = json.loads(template.render(**kwargs))
return d
TEST_DATA_ROOT = os.environ.get(
'RENDER_TEST_DATA_ROOT',
'/allen/aibs/pipe... |
2baa41bcf50b6b49ae164a768f073160da4f3e5fa353cdb3114c5e5311252620 | Python | 2,481 | 85 | import NALSM_GEN_SUPPORT as sup
import multiprocessing as mp
class multi_process:
def __init__(self,num_processes,save_path):
self.dataPath = save_path
# Define IPC managers
manager1 = mp.Manager()
# Define lists (queue) for tasks and computation results
s... |
abce7eaac0b48dee08060566046886620a276799ba2e39e796508209be4bb459 | Python | 2,481 | 66 | from geomstats.test.test_case import TestCase
class LocalizationTestCase(TestCase):
def test_propagate(self, state, sensor_input, expected, atol):
res = self.model.propagate(state, sensor_input)
self.assertAllClose(res, expected, atol=atol)
def test_propagate_jacobian(self, state, sensor_inpu... |
fe3232225e3fd7918e198f041f1daf6c6cc2869ae5510fa62f003a21283b7cc8 | Python | 2,481 | 70 | from PymoNNto import *
from PymoNNto.Exploration.Visualization.Reconstruct_Analyze_Label.Reconstruct_Analyze_Label import *
def compute_temporal_reconstruction(network, single_neuron_group=None, single_neuron_id=None, recon_group_tag='recon'):
RALN = Reconstruct_Analyze_Label_Network(network)
RALN.zero_recon()... |
bd479ad7298d0b19dcc9b365d0debbbfb828910034166fca5219148593e645c1 | Python | 2,482 | 91 | # NIBR substructure filter
#
# adapted from RDKit commit 4a69bc3493dd3e9bb9f7a519e306fbcb545f1452 which
#
# assign the filter
from collections import namedtuple
import numpy as np
from rdkit import Chem
FilterMatch = namedtuple(
"FilterMatch",
(
"SubstructureMatches",
"Min_N_O_filter",
... |
2ea092c32fac0edcd97f6a7a5a79c9209c8ad272752dbb736d33999b4b0085ad | Python | 2,483 | 80 | # Copyright (C) 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""You may copy this file as the starting point of your own model."""
import logging
import keras
from openfl.federated import KerasTaskRunner
from src.model import CNNModel
logger = logging.getLogger(__name__)
class KerasCNN(KerasT... |
a4458b15b3530c3c3871552e95daa45b21591b0220af6b8a51d0f3c52399c64c | Python | 2,483 | 72 | """Facade-level ``progress_bar`` threading tests.
The engine-layer permutation/bootstrap functions expose ``progress_bar``
(default False). Before that, the facades below showed bars unconditionally —
so unless each facade exposes and forwards the kwarg, the capability is lost
entirely (always-off with no knob). These... |
1c899e47dcf1145b6c7958cd733a5a9a49f5314c60bea8fcbcbc7ab14c89dffd | Python | 2,484 | 75 | # lexical_selection_check.py
# Checks the dix file for multiple translations for a single entry for both sides.
# Generates two files for each side with ambigious translations.
# Output file names: left_variants.txt, right_variants.txt
from argparse import ArgumentParser
from collections import defaultdict
# importin... |
19a8031811266f9a6625ac65b6523fed389b6c9deb34c35a8bf7b1f74127334d | Python | 2,485 | 61 | from setuptools import setup, find_packages
PYRATES_TEAM = "Richard Gast, Daniel Rose"
INSTALL_REQUIREMENTS = ['numpy',
'networkx<=3.5',
'pandas',
'ruamel.yaml',
'sympy',
'scipy'
... |
6a6000bde4f5dec8852998ea0d4f2d086e016ca7f6f061b663b968acb658507a | Python | 2,485 | 74 | from PymoNNto import *
from Examples_Paper.STDP_Hom_Norm.Normalization import *
from Examples_Paper.STDP_Hom_Norm.STDP import *
from Examples_Paper.STDP_Hom_Norm.Homeostasis import *
class Basic_Behavior(Behavior):
def initialize(self, neurons):
neurons.voltage = neurons.vector()
self.threshold = ... |
d6c319aece0db9e13947e58f8fa704a4b41ce31c8146de5dc27b457e4f1130a9 | Python | 2,485 | 90 | # ==============================================================================
# Copyright 2023 Technical University of Denmark
# Author: Nikolas Borrel-Jensen
#
# All Rights Reserved.
#
# Licensed under the MIT License.
# ==============================================================================
from functools i... |
3c554ce4eed4d015f4f3cac6b72cae93c67aa508c6a3d4822f5d2728174e0eea | Python | 2,486 | 82 |
'''
Example showing software-timed analog input using nidaqmx-python
pynidaqmxegs.ai.softwareTimedVoltageContinuous
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. Cr... |
3b6f059f747030a67adfaa3fad9dc7474c73d7f81ea4f2769d7c380aed4fe551 | Python | 2,488 | 61 | import numpy as np
import pandas as pd
class GetGenes:
def __init__(self, gene_tsv):
self.genes_df = pd.read_csv(gene_tsv, sep="\t")
@staticmethod
def _get_first_element_in_list(empty_value, input_list):
for i, list_value in enumerate(input_list):
if list_value != empty_value:... |
7dadb5e8c0bb2540e1513a8f344d9822be21635503573efaf66f0d1bd171fe48 | Python | 2,489 | 97 |
from PyQt5.QtWidgets import QApplication
from sleepy.gui.view import View
from sleepy.gui.settings.core import Settings
from sleepy.gui.exceptions import UserCancel
from sleepy.gui.processing.supported import SUPPORTED_FILTERS, SUPPORTED_ALGORITHMS, SUPPORTED_DATASETS
class Gui(QApplication):
"""Used to l... |
5485c3138ac0b0b316b94e6eb7bfaf36354db226a3ff18bfd389483f21a48a8a | Python | 2,490 | 94 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Custom input types definition for Click"""
import ast
import click
from openfl.utilities import utils
class FqdnParamType(click.ParamType):
"""Domain Type for click arguments.
This class is used to validate that a command l... |
be1d923c6eb895a1aab27166050a477e62f564ea159ed094130e198b01836b96 | Python | 2,490 | 73 | from nequip.data.datamodule import ASEDataModule
from nequip.utils.file_utils import download_url
from nequip.utils.logger import RankedLogger
import os
from typing import List, Callable
logger = RankedLogger(__name__, rank_zero_only=True)
_URL_TRAIN = "https://figshare.com/ndownloader/files/25605734"
_URL_VAL = "h... |
d129152977c9bfae0dc12627209681707e9a7b67764607b965dd3e93e036fc56 | Python | 2,492 | 73 | import pytest
import unittest
import torch
import torch.utils.data as tud
from reinvent.runmodes.utils.helpers import set_torch_device
from reinvent.models.model_mode_enum import ModelModeEnum
from reinvent.models.libinvent.models.dataset import Dataset
from reinvent.models.libinvent.models.model import DecoratorMode... |
5198f49cbe0ea248a493c27ef93354a692e6a4ee93d5ce2335e21185e4b748e2 | Python | 2,495 | 94 | import matplotlib as mpl
import matplotlib.pyplot as plt
from sanpy.sanpyLogger import get_logger
logger = get_logger(__name__)
import sanpy
from sanpy.interface.plugins import sanpyPlugin
class exampleUserPlugin1(sanpyPlugin):
"""
Plot x/y statistics as a scatter
Plot-stat definitions come from sanpy... |
9c74111aaa22c82f764e108048f5af020037c04efaa1ccedd1c162acf4dc63fc | Python | 2,496 | 73 | """
Calculate the period between two consecutive waves for each wave.
"""
from pathlib import Path
import neo
import numpy as np
import quantities as pq
import matplotlib.pyplot as plt
import seaborn as sns
import os
import argparse
import scipy
import pandas as pd
from utils.io_utils import load_neo, save_plot
from u... |
acd340e2a7172ece0a03c305e53dde62de616b4ff9626860b9d9042d901eb100 | Python | 2,496 | 72 | # Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""You may copy this file as the starting point of your own model."""
from openfl.federated import PyTorchDataLoader
from src.mnist_utils import load_mnist_shard
class PyTorchMNISTInMemory(PyTorchDataLoader):
"""PyTorch data loade... |
168867ced5ec0f1f59744f0d6bdce55b4fbb0add5e014cc3437fe8e5825400dc | Python | 2,497 | 73 | # Copyright (C) 2020-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""You may copy this file as the starting point of your own model."""
from openfl.federated import PyTorchDataLoader
from src.mnist_utils import load_mnist_shard
class PyTorchMNISTInMemory(PyTorchDataLoader):
"""PyTorch data loade... |
9c3d38e08853da738e81a25658780684a72a235b33e08b7b996890cb16b4012b | Python | 2,498 | 89 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 14 12:06:13 2023
@author: egemo
"""
from .component_utils import (refractory,
refractory_variabletimestep,
delayed,
WinnerTakeAll,
... |
b7404d5f3e08d0eb32a29f75acbe10b73061ec34a02b2b9320a930d14f51fe16 | Python | 2,502 | 63 | from pathlib import Path
def write_launch_file(file_path: Path, function_call_str: str, on_berzelius: bool):
assert not file_path.exists(), 'launch file already exists'
print(f'{file_path.stem}: {function_call_str}')
with open(file_path, 'w') as f:
f.write('#!/bin/bash \n\n')
if on_berzeli... |
7d0d7fe83699508e9a82fb428dc17961cdc383c26ab4764a52b18061910006eb | Python | 2,503 | 78 | # 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... |
40c6b5fc2d05dc176e48c4c3ed61d3284fcca8b787771284288256fb8d0e26ff | Python | 2,505 | 67 | import numpy as np
from neuron import h
import LFPy
def morph2Segs(cellID):
data = np.loadtxt(cellID)
arbours = list()
somaPoints = np.argwhere(data[:,1]==1)
for i in somaPoints:
temp = get_child(int(data[i,0]),data).tolist()
if(len(temp)>0):
arbours.append((i,temp))
seg... |
822bf6def527454b031cc2a1ca73dff780805dd5d342122a56fe4ff02c00bcc2 | Python | 2,505 | 87 | """Standalone benchmark script.
Run with ``python tests/benchmarks.py`` for the default backend, or pass
``--backends <b1>[,<b2>,...]`` to time the same workload against any subset of
PyRates' active backends.
Examples:
python tests/benchmarks.py
python tests/benchmarks.py --backends default,jax
python te... |
1b0fe5061e5e4a36c35b414bb3dfadc7c0dfdaff196a58528caf1ad5bcce150a | Python | 2,507 | 67 | """
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 warnings
import re
from utils.io_utils import load_neo
from utils.parse import none_or_str
CLI = argparse.ArgumentParser()
CLI... |
0168a44c2c2d5c15dea077480b1c1f5fec897b8d225ff95aac7d8d8c1dbff041 | Python | 2,508 | 56 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
from torch.utils.data import Dataset
from typing import Dict, Union
class SubsetByRandomSlice(torch.utils.data.Subset):
"""Subset of dataset by slicing a random permutation of the dat... |
2905adb546be199a414de306f9a22767a8f09589f6c9b0b9a3e66bddb5629ad0 | Python | 2,508 | 73 | """
Calculate the period between two consecutive waves for each wave.
"""
from pathlib import Path
import neo
import numpy as np
import quantities as pq
import matplotlib.pyplot as plt
import seaborn as sns
import os
import argparse
import scipy
import pandas as pd
from utils.io_utils import load_neo, save_plot
from u... |
7cfef30ec59f960873b2693a476467646dbd810dd2f93ed1033338e18a845a26 | Python | 2,510 | 70 | # 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
#
# Unless required by appli... |
fd1f53183d91bb0e5c78de7de0d67aff45a761801f39d668929078d48c4eae58 | Python | 2,511 | 61 | import torch
import torch.nn as nn
from torch_geometric.utils.loop import add_self_loops
from dig.xgraph.models.utils import subgraph
from dig.xgraph.method.base_explainer import ExplainerBase
class RandomSelectorExplainer(ExplainerBase):
def __init__(self, model: nn.Module, explain_graph: bool = False):
... |
85365b95457d014e2da1f7651f6987472553fa05eae532c644f310e8e316fcf2 | Python | 2,512 | 81 | # Copyright (C) 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""You may copy this file as the starting point of your own model."""
import logging
import keras
from openfl.federated import KerasTaskRunner
from src.model import CNNModel
logger = logging.getLogger(__name__)
class JAXCNN(KerasTas... |
4b430af2235d17bbed9a18d307de37db4b3f67de7e3adb03ad75b4dac9114bcc | Python | 2,513 | 54 | import unittest
from rdkit.Chem.rdmolfiles import MolToSmiles
from reinvent.chemistry import conversions
from reinvent.chemistry.library_design import bond_maker, attachment_points
from reinvent.chemistry.library_design.fragment_reactions import FragmentReactions
from tests.chemistry.library_design.fixtures import (
... |
f1aedd6fe9656553dd4d1e81f4e260fdf30581885f54ebed246fc2e91cfb1450 | Python | 2,514 | 82 |
from tensorflow import math
from tensorflow.keras.layers import Layer
import tensorflow.keras.backend as K
def gelu_(X):
return 0.5*X*(1.0 + math.tanh(0.7978845608028654*(X + 0.044715*math.pow(X, 3))))
def snake_(X, beta):
return X + (1/beta)*math.square(math.sin(beta*X))
class GELU(Layer... |
599ca30a1ebb06745e8727ebbf56a20d9feb111505f141fb978d38abb660105a | Python | 2,516 | 67 | """
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 warnings
import re
from utils.io_utils import load_neo
from utils.parse import none_or_str
CLI = argparse.ArgumentParser()
CLI... |
eded00728ac11bec759d543c934c0bcace7197ce9904c19166e5e027a1da2bd0 | Python | 2,516 | 77 |
import numpy as np
from .record import Record
class PQ(Record):
__attributes__ = ['vectors', 'dim', 'qdim', 'centroids', 'indexes', 'codes']
def __init__(self, vectors, dim, qdim, centroids, indexes, codes):
self.vectors = vectors
self.dim = dim
self.qdim = qdim
self.centroi... |
2d919084993ddd867a5cf5e3d3d07504bfb32c80391d107766bc341522a13615 | Python | 2,519 | 77 | import os
import pickle
import torch
from torch.utils.data import Dataset, DataLoader
from torch_geometric.data import Batch
import lmdb
import random
class ConfLmdbDataset(Dataset):
def __init__(self, root, split, max_confs, training=False):
self.max_confs = max_confs
self.split = split
s... |
473fdef9a1ffb7bca81bd2e62eab526c3f06d5f7688cfd4b11709f4f9f9fd5f2 | Python | 2,519 | 71 | import geomstats.backend as gs
from geomstats.geometry.base import ImmersedSet
from geomstats.geometry.euclidean import Euclidean
from geomstats.geometry.invariant_metric import BiInvariantMetric
from geomstats.geometry.pullback_metric import PullbackDiffeoMetric
from geomstats.test_cases.geometry.diffeo import CircleS... |
5f1a77ad2d0847a66fd5bb5c345538b617336890c1b4d5a92e0704be852f33b2 | Python | 2,522 | 66 | """A failed model download must not leave anything at the cache path.
_urlretrieve_with_timeout renames the body into place as soon as the HTTP fetch
succeeds, which is before the caller inspects the content type. An error page
served with 200 therefore lands exactly where the checkpoint belongs. Since
every loader gu... |
a52c2375e363b4f35a5f9287cb9684f4d9b67584184b12b7e7b45df78a63316f | Python | 2,522 | 73 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from torch import nn
from typing import Optional
from copy import deepcopy
import argparse
class EMA(object):
"""
For a given model, this class computes the exponential moving average of weigh... |
164c44656fe54abca4deb1b4150267f4959ce85dd9c9b43532f8fc107aac6470 | Python | 2,524 | 42 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
44b11205db586240e76aafe9ca9d5a65e93d8c1ed910e32da6eefa28a8501e7c | Python | 2,525 | 79 | from unittest import result
import pandas
import pubchempy
import json
from urllib.error import HTTPError
import time
def get_map_inchikey_to_CID(inchikey):
mols = pubchempy.get_compounds(inchikey, 'inchikey')
return {mol.inchikey: mol.cid for mol in mols if mol is not None}
def get_map_inchikey_to_synonyms(... |
6fcaf5c742be97ca558e0373da01b025b73d88bb9353c6442029989b9ffd8e4e | Python | 2,527 | 63 | """
Create a plot of the channel-wise and average power spectrum density.
"""
import numpy as np
import quantities as pq
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
from elephant.spectral import welch_psd
from utils.io_utils import load_neo, save_plot
from utils.parse import none_or_float
CL... |
b4ab0aac45202e7e0f00702a28583ce1348ea29d47b7ddc520e7e6b6bdb21606 | Python | 2,528 | 87 | #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from .base_criteria import BaseCriteria
import os
import importlib
from utils import logger
import argparse
LOSS_REGISTRY = {}
def register_loss_fn(name):
def register_loss_fn_class(cls):
if name in L... |
efa2a9afb3374d73403fc03a8f040ee4474d678e35c8e69e4411457d0499501f | Python | 2,528 | 81 | # -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network model_templates and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cogn... |
4f3b8f30f7bbf4d6ad653a5e9f45ad79833da9a2f0c586cd9bbc1889b4ac9911 | Python | 2,529 | 81 | import torch
import numpy as np
from corgie import constants, exceptions
from corgie import helpers
STR_TO_LTYPE_DICT = dict()
def register_layer_type(layer_type_name):
def register_layer_fn(layer_type):
STR_TO_LTYPE_DICT[layer_type_name] = layer_type
def get_layer_type_name(obj):
... |
f7e285ecee706adcb1e056341c8ecff28ef19b71ac4604d9cd1e3269d51fe034 | Python | 2,529 | 56 | import numpy as np
# import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
if __name__=="__main__":
best_split_id = [2, 2, 2, 2] # correspoding to split index for S1, S1, S3, and S3
hcp_sessions_list = ['LR_S1', 'LR_S1', 'RL_S1', 'RL_S1'] # models S1, S1... |
82cef8bd973981e874228ac40f1350b44adf5daefdf4ef8545610649cf861363 | Python | 2,532 | 108 | import unittest
from reinvent.models.reinvent.models.vocabulary import SMILESTokenizer
from tests.test_data import IBUPROFEN, NONSENSE
class TestSmilesTokenizer(unittest.TestCase):
def setUp(self):
self.tokenizer = SMILESTokenizer()
def test_tokenize(self):
self.assertListEqual(
... |
c4c1b5245759f5b9228e661fab1b8ae239cf42a802fd9754e58c57b8508e5ce7 | Python | 2,532 | 79 | """Shared helpers for Adjacency submodules.
These are internal utilities used by the facade and submodules — not part of the
public API.
"""
from copy import deepcopy
import numpy as np
import polars as pl
def _perform_arithmetic(adj, y, op, op_name, reverse=False):
"""Perform arithmetic operation with validat... |
900a574a83fe7d8384e9b584b4d34397265d07a3d423bec9f175748230daccc6 | Python | 2,533 | 98 | """Callables to provide a termination criterion for Reinforcement Learning"""
from __future__ import annotations
__all__ = ["terminator_callable"]
import logging
from typing import Callable
import numpy as np
from numpy.polynomial import Polynomial
logger = logging.getLogger(__name__)
terminator_callable = Callable... |
93b494fcb24f6614bd6877e1090794f4484a9289c8fe98df22844d4c861601b7 | Python | 2,533 | 61 | """Decide whether a requested GNC should run, from the inputs' BIDS sidecars."""
import json
import warnings
from collections.abc import Sequence
from pathlib import Path
from colorama import Fore
def _read_nonlinear_gradient_correction(sidecar: str | Path) -> bool | None:
"""Return the BIDS NonlinearGradientCo... |
d67cad94e14ac9ff5d4cfd3ff525776c710bceaa24b3e1453aacc6d1e671f972 | Python | 2,533 | 68 | """
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 warnings
import re
from utils.io_utils import load_neo
from utils.parse import none_or_str
CLI = argp... |
1f2037764177795930168c52cb1e0a0f883930a46c75833960c8900904f496e1 | Python | 2,534 | 42 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* *
... |
70962e7cd9233b7c39b05756e576226c90300617cd6c146351f628d6a0895f23 | Python | 2,534 | 68 | """
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 warnings
import re
from utils.io_utils import load_neo
from utils.parse import none_or_str
CLI = argp... |
7ac154ea95ab21b4fc489aad16dffffcea757d3afc2fb02989f9bcf526001e9f | Python | 2,534 | 74 | """
Calculate the period between two consecutive waves for each wave.
"""
from pathlib import Path
import neo
import numpy as np
import quantities as pq
import matplotlib.pyplot as plt
import seaborn as sns
import os
import argparse
import scipy
import pandas as pd
from utils.io_utils import load_neo, save_plot
from u... |
c6feea2a9713b7f92aba59cdf9f75576601569ae140da197aa7cfaef787508ed | Python | 2,535 | 85 | """
credit: https://github.com/gpleiss/temperature_scaling/blob/master/temperature_scaling.py
"""
import torch
from torch import nn, optim
import utils
import train
import random
class ModelWithTemperature(nn.Module):
def __init__(self, model):
super(ModelWithTemperature, self).__init__()
self... |
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