sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9bb5352a820fb3eca78295373e4adc4f65c535b8cd021ca0a4f18e6c8bf4aaeb | Python | 2,185 | 72 | """Average classification accuracies."""
from __future__ import annotations
import pathlib
from collections.abc import Sequence
from typing import Annotated
import pte
import pte_decode
from pytask import Product
import motor_intention.project_constants as constants
CHANNELS = ("ecog", "dbs")
INPATHS_STIM_OFF = tup... |
afe2ea9f8622434595bd6648c1efb56f3145f005d984de41f165d10eec06b7fd | Python | 2,185 | 81 | import cocotb
import numpy as np
import pytest
from cocotb.triggers import ReadOnly, Timer
from elasticai.creator.arithmetic import FxpArithmetic, FxpParams
from elasticai.creator.testing import CocotbTestFixture, eai_testbench
from elasticai.creator_plugins import linear
def model(x: int, bitwidth: int, frac_width:... |
ed1b430441805a80276b13d267696e2e5a306fc9962581720ab8cba651ee921b | Python | 2,185 | 76 | # Node group "MN Units" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated fro... |
add12c957a60a1c2c4bc636079d33db5bcd8aca62b8991eab02b1f3af0453398 | Python | 2,186 | 64 | from pandas import CategoricalDtype
class DotPlotAggregator:
def __init__(self, var_measures, dimensions):
self.var_measures = var_measures
self.dimensions = dimensions
def execute(self, df):
results = []
# {categories:[], name:'', values:[{name:'', percentExpressed:0, mean:0}... |
951a6b1bd45675c6b4be1a76a418ffb20b1ca444a904ff6697e1060ae1701b56 | Python | 2,187 | 53 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
0c1c319563cf6375e91ffb3da9d6c0ba529ed90a8026e1c7bb18afbfe259d2b6 | Python | 2,189 | 67 | import os
import toml
import logging
import jobflow as jf
from ase.io import read
from quacc import get_settings
from quacc.recipes.newtonnet.ts import ts_job, irc_job, neb_job
# Load configuration from TOML file
config = toml.load('inputs_using_newtonnet.toml')
# Constants from TOML file
REACTANT_XYZ_FILE = config['... |
711dd3573ab589a543e32d25d654a44c3f1a9125f2801d3c0d4007fe768178c7 | Python | 2,189 | 46 | import pickle
import numpy as np
from scipy.stats import norm
if __name__ == '__main__':
with open('../data/ID2NODE.pkl', 'rb') as handle:
ID2NODE = pickle.load(handle)
handle.close()
CpG_island_rwr = np.expand_dims(np.loadtxt('../data/CpG_island_imputation_0.6.txt', delimiter = '\t'), axis = 1)... |
05ba5e1bbcdd4178fc6b0e26543070d4c6d4bdef5a02268d84a6c2fb572d7269 | Python | 2,190 | 62 | from typing import Union, Dict
import numpy as np
from spectrum import aryule
from ..base_transform import EEGTransform
class ARRCoefficient(EEGTransform):
r'''
Calculate autoregression reflection coefficients on the input data.
.. code-block:: python
transform = ARRCoefficient(order=4)
... |
3ea033b7d280ad2f9fc347ebb69f86d467ac9ed44c80c757d3ce56310f1c5c90 | Python | 2,190 | 54 | # coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# 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/LIC... |
aebd2bd02baa5286f93970474e6a5ca75c96ddfb409ca17d47b0d0b622b8c7ce | Python | 2,190 | 81 | class ArrayParams():
def __init__(self):
self.OperationType = 0
self.OpArrayType = 0
self.OpPlsType = 0
self.TIAFeedback = 0
self.AddrArray = 0
self.BLStart = 0
self.BLCnt = 0
self.SLStart = 0
self.SLCnt = 0
self.WLStart = 0
... |
d7fd6ebccd3a5f344c2a6003b442168d0d914969a9b92f4eb64d8a19b0885484 | Python | 2,190 | 29 | """posthoc_lineage_draws_2026-10-08.py (POST HOC, descriptive). H6 of SIGN_WORLDS used 4 lineage draws. With 2,000 draws
(same rule: every local neuron FlyWire leaves uncalled takes an inhibitory sign with its hemilineage's share of GABA/Glu
calls), how often does the law say the antennal lobe works, and how does that ... |
299c83e2445a7db4710e74e451eee4e4a58cf7491e40ca77040d19d703d5ddca | Python | 2,191 | 106 | _CODEX_DATA_SCHEMA = {
# one row per id
"neurons": [
"root_id",
"group",
"nt_type",
"nt_type_score",
"da_avg",
"ser_avg",
"gaba_avg",
"glut_avg",
"ach_avg",
"oct_avg",
],
# one row per id
"classification": [
"roo... |
3fd89671bc6791f3b644f49232753114b58ace560bf25651d79f976bddfafb38 | Python | 2,191 | 52 | import pickle
import numpy as np
from scipy.stats import norm
if __name__ == '__main__':
with open('../data/ID2NODE.pkl', 'rb') as handle:
ID2NODE = pickle.load(handle)
handle.close()
CpG_island_rwr = np.expand_dims(np.loadtxt('../data/CpG_island_imputation_0.6.txt', delimiter = '\t'), axis = 1)... |
59361969eae733ea654af3f73c0471eab479619e0285fb2e7f4fd4213580fe4b | Python | 2,191 | 48 | import unittest
import torch
import torch.nn.functional as F
from torch_geometric.data import Batch, Data
from torcheeg.models import RGNN
class TestRGNNNormalization(unittest.TestCase):
def test_signed_graph_outputs_and_gradients_match_dense_reference(self):
torch.manual_seed(42)
adj = torch.te... |
ffff2f3931a5d61281dbe5eb308eaf4891d97ff239a276616c237cc7792f9f7c | Python | 2,191 | 98 | import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import make_vjp
dot_0 = lambda a, b, g: make_vjp(np.dot, argnum=0)(a, b)[0](g)
dot_1 = lambda a, b, g: make_vjp(np.dot, argnum=1)(a, b)[0](g)
dot_0_0 = lambda a, b, g: make_vjp(dot_0, argnum=0)(a, b, g)[0](a)
dot_0_1 = lambda a, b, g: make_... |
69a79dade133c87e1e09860e023b5c6dbf920278f679da28d398edfb940ea18e | Python | 2,192 | 55 | import numpy as np
import torch
from specs import *
from boltons.cacheutils import cachedproperty
from typing import Dict
class CameraSimulator:
KERNEL_REPEATS = 5
def __init__(self, cam_specs: CameraSpecs):
self.cam_specs = cam_specs
self.rng = np.random.default_rng()
@cachedpro... |
ae9ab9d55f3f8a97cd1ee8b9b21e95e052c46367f3ba092e8bf47ecb1c4241b2 | Python | 2,192 | 65 | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
import os
import sys
import io
import tensorflow as tf
#import matplotlib.pylab as plt
import numpy as np
from PIL import Image
import glob
#output_dir="/projects/shart/digital_pathology/data/TCGA_MAYO/FIN... |
d8c9a92c7c4e1afe89595b13fe6c36e4e4c4eef0fbe65adb3a4cf3fa401b40d9 | Python | 2,192 | 59 | from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
from wsilearn.compress.mtdp_encoder import MtdpEncoderWrapper
from wsilearn.utils.cool_utils import can_open_file
from wsilearn.dl.torch_utils import print_model_summary
from wsilearn.compress.encoders import ... |
f05e8b84771933ee4648994f5ba42a08263993c6313fd42e31e08432aeccfcda | Python | 2,192 | 60 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
2e8090df2f6e7e11e1d84d4727fb6b8315f0f75cea4a258fcdad1b00a1a1fa52 | Python | 2,193 | 91 | import os
import pandas as pd
from calvados.cfg import Config, Job, Components
import subprocess
import numpy as np
from argparse import ArgumentParser
cwd = os.getcwd()
sysname = 'mixed_system'
# set the side length of the slab box
Lx = 15
Lz = 80
# set the saving interval (number of integration steps)
N_save = 100... |
31e4e1e22845a6f3cfa90984f676391955565411c752a142e992c7ba056c737a | Python | 2,193 | 54 | import numpy as np
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
from model import CNN
from dataloader import DATA
from config import DEBUG, RUN_NAME
from datetime import datetime
import os
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
from helpers... |
45c6d198a9965244bd606d16761b0f9c54e8e2872e66e39b3a96de7a1fbdd232 | Python | 2,193 | 69 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
94d517324664e6ce95f0b50c04999fe8d16314dbd2c6d3ff93f332e7934d438b | Python | 2,193 | 50 |
import os
import scipy.io
from Functions.f_ComplexNetworks import *
import pandas as pd
Data_dir = './../Data/EEGFeatures/'
Out_dir = './../Data/ComplexNetworks/'
if not os.path.isdir(Out_dir): # Create the path if this doesn't exist
os.mkdir(Out_dir)
df_AllInfo = pd.read_csv('./../RawData/data_info.... |
df5bf49589a0574c992b156f5ff3709379b8f2c80b0c878a74aa9e285afa3735 | Python | 2,193 | 72 | """DetachableTabWidget: pop a tab into its own window (moved, not duplicated) + dock back."""
import os
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
import pytest
from PySide6.QtWidgets import QApplication, QWidget
from regtools.registration.gui.detachable_tabs import DetachableTabWidget
@p... |
32e1761d2f07928c2377867f8e8668ea9cf7fe1bc80a97305fc44dcfecf5424c | Python | 2,194 | 64 | """Brush-radius cursor preview tests."""
import pytest
@pytest.fixture
def qapp():
from PySide6.QtWidgets import QApplication
app = QApplication.instance() or QApplication([])
yield app
def _two_region_volumes(tmp_path, shape=(3, 20, 30)):
import nibabel as nib
import numpy as n... |
52698f17d1a2709f29e9cb6727c6b7ca0d489ca1217fcb569b279ff50ab00380 | Python | 2,194 | 74 | from PyQt5.QtWidgets import QPushButton
from wonambi import Wonambi
from .test_scroll_data import (channel_make_group,
find_in_qt,
)
from .paths import (GUI_PATH,
gui_file,
SAMPLE_PATH,
)
def t... |
ca399bae57b893761872772dfa66b6d562fae000dc38645babfa6766bb86af4a | Python | 2,194 | 65 | import numpy as np
import pytest
from biotite import InvalidFileError
import molecularnodes as mn
from molecularnodes.entities.molecule import corecif
from .constants import data_dir
CRYSTAL_FILE = data_dir / "mn_test_crystal.cif"
SF_FILE = data_dir / "structure_factors.cif"
def test_is_core_cif():
assert coreci... |
e6c03be530733965d6f9e031119a7d59be84173b8984bd9aef6a8f0a414f75bc | Python | 2,195 | 56 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from utilities.script_func import init, post, ScikitFeat_pre
import pickle
import logging
logging.info("")
def main():
import Models.model_func as Model_Func
from Models.ScikitFeatBaseli... |
0139089d45aa3a192514b7d7f2124bb80d05df563fd6f753a44db664aa16463a | Python | 2,196 | 70 | import warnings
warnings.filterwarnings('ignore')
import os
import sys
import argparse
import random
import joblib
import json
import tqdm
import torch
import numpy as np
import pandas as pd
import sklearn
from types import SimpleNamespace
from collections import Counter
def simulate(xp, tps, celltype_annotations, w... |
0c22de5809f83af7f77808dd83de928c09d941b43cbadc9e98c1e07b6a3815c3 | Python | 2,196 | 82 | from pathlib import Path
from types import SimpleNamespace
import mdtraj as md
import numpy as np
import pandas as pd
import pytest
from calvados import analysis
def test_cmap_traj_normalizes_selected_frames(
monkeypatch: pytest.MonkeyPatch,
) -> None:
universe = SimpleNamespace(trajectory=list(range(10)))
... |
224fd653c5b1ab31310e9a1a59e35294e39f5d3ab59953a0aab61bf71339d6d0 | Python | 2,196 | 61 | import torch
from sklearn.preprocessing import MinMaxScaler
import pickle
import numpy as np
import pandas as pd
from utilities.preproc_func import *
from io import StringIO
def convert_categorical_labels(df:pd.DataFrame, column="y"):
count=0
for i in np.unique(df[column]):
df.loc[df[column]==i, column... |
5416df72c83650f9c6639d7b70d418635871701a89ac53ad0934e7f616d4cc0d | Python | 2,196 | 62 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = ... |
def9d4f788859b7586dff0424f48ee5f5b0ea548085f09d66fcbe9770da90238 | Python | 2,196 | 63 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["8... |
36f33381d821d09e0e9d42e0383b142f904fae9d39586a0fb27282e4bd316aa7 | Python | 2,197 | 57 | """
FEATHER_PASS_A.py - full single pass over all 130,107,424 synapses.
Builds one row per presynaptic neuron:
n synapse count
sum_<class> sum of that class's posterior over the neuron's synapses (mass rule)
cnt_<class> number of the neuron's synapses whose argmax is that class (modal rule)
... |
af1a1cf4f3960334e174140031c7cbddc0e4213655f23ff78ac9c7b8e3b9c96b | Python | 2,197 | 56 | from learn_tools.uncertain_learner import UncertainLearner
from learn_tools.active_nn import rf_predict, RF_CLASSIFIER, RF_REGRESSOR, get_sklearn_model, retrain_sklearer
from learn_tools.active_learner import THRESHOLD
import numpy as np
BATCH_RF = (RF_REGRESSOR, RF_CLASSIFIER)
BATCH_MAXVAR_RF = 'rf_batch_maxvar'
BA... |
40c2297a56813a9eee245012c39f145a3fe1756581b3b3f945309e95afc0418e | Python | 2,198 | 61 | import warnings
from elasticai.creator.ir2vhdl import Instance
from elasticai.creator.ir2vhdl import Node as VhdlNode
from .clocked_combinatorial import ClockedInstance
from .node_factory import (
InstanceFactoryForCombinatorial,
)
@InstanceFactoryForCombinatorial.register()
def shift_register(node: VhdlNode) -... |
a3e794db502cd7be610c2edd96e3357c927f16aa244c84a1c96a6329a2291d9c | Python | 2,198 | 74 | """Metadata generation logic for legacy source distributions.
"""
import logging
import os
from pip._internal.build_env import BuildEnvironment
from pip._internal.cli.spinners import open_spinner
from pip._internal.exceptions import (
InstallationError,
InstallationSubprocessError,
MetadataGenerationFaile... |
2c6b0c35c6f4e56f1524c074725852f22509b4e20df9b26a6cb7bddced428684 | Python | 2,199 | 44 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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
#
# ht... |
9ca6c1ce195b5ab0787686598b0c28a858a5e8a9725b1fa51b7ec38438618670 | Python | 2,199 | 68 | # Node group ".Is Extra Bonds" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenera... |
a28ba8a5e0800742da96b9e2c083b4aaff78060c5aa958ea36a174252f6d8082 | Python | 2,199 | 66 | import numpy as np
from common import *
from gzip import GzipFile as gz
from sklearn.neighbors import NearestNeighbors
def sort_min_diff(amat):
'''this function takes in a SNP matrix with indv on rows and returns the same matrix with indvs sorted by genetic similarity.
this problem is NP-hard, so here we use ... |
b7e1f97916f50d0fd71e0de8fb6d646f8bd4b8c7ee5ba10bfaed4425beac1cf7 | Python | 2,199 | 57 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
ed4b0b1a0516182f98862c83ce206bb7f0ef8de0483144b0e4054df0e5c2e369 | Python | 2,199 | 85 | # From the Meep tutorial: plotting Faraday rotation of a linearly polarized plane wave
import meep as mp
## Parameters for a gyrotropic Lorentzian medium
epsn = 1.5 # background permittivity
f0 = 1.0 # natural frequency
gamma = 1e-6 # damping rate
sn = 0.1 # sigma parameter
b0 = 0.15 # magnitude of bias vector
s... |
7a49648bbf675949d13d15efb54fc19e8bf3527b7fce2cc256f84b5e5422963e | Python | 2,200 | 63 | import numpy as np
from scipy.sparse import *
from scikit_feature.skfeature.utility.construct_W import construct_W
def fisher_score(X, y):
"""
This function implements the fisher score feature selection, steps are as follows:
1. Construct the affinity matrix W in fisher score way
2. For the r-th featu... |
c5989d089c79552aa3e100e4027a25b0531715e9cbb4308c23bb41004a5d91ac | Python | 2,200 | 69 | # coding=utf-8
import numpy as np
import scipy.sparse as sparse
def adapted_rand(seg, gt, all_stats=False):
"""Compute Adapted Rand error as defined by the SNEMI3D contest [1]
Formula is given as 1 - the maximal F-score of the Rand index
(excluding the zero component of the original labels). Adapted
f... |
0de144481ad806a6213ed8283dc62779f9ae5c66d68e2cb1914bdb2ed85e7e24 | Python | 2,201 | 66 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
aeae6aedeb83e0ecb5c536fc65ce0b812e7e6424b72f1b2e2b2fd5d1ff2a65ca | Python | 2,201 | 50 | import torch
from elasticai.creator.nn.integer.quant_utils.Observers import (
GlobalMinMaxObserver,
)
def setUp1_for_GlobalMinMaxObserver():
# range gets larger
tensor1 = torch.tensor([-1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
tensor2 = torch.tensor([-5.0, 6.0, 7.0, 8.0], dtype=torch.float32)
te... |
0ce5d1d4cc6ef3b245bc6e49a26947773fade738829715e7fba8b461e414d5e3 | Python | 2,202 | 109 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import sys
outdir = sys.argv[1]
observations= sys.argv[2]
df=pd.read_csv("../Data/Wavelets/W_neut_align_0.csv",index_col=0)
#df
# In[8]:
d=np.asarray(df)
d=np.transpose(d)
d
# In[9]:
np.shape(d)
# In[10]:
np.savetxt... |
48b6b551a883cf033f050a634975f16fb90466740d394ddb3d7e09322ad1206b | Python | 2,202 | 62 | """Summarize the unique column names in a dataset."""
import json
class ColumnNameSummary:
"""Summarize the unique column names in a dataset."""
def __init__(self, name=""):
self.name = name
self.file_dict = {}
self.unique_headers = []
def update(self, name, columns):
""... |
0b1eb552cbfd1d1e9dca40c3c0f0e60d32bed010b8acf1398b46c4c7fe786ada | Python | 2,203 | 61 | """
This script integrates Comet ML and Optuna to optimize a Random Forest Classifier
on the scikit-learn Breast Cancer dataset. It performs the following steps:
1. Initializes a Comet ML experiment for logging.
2. Loads the Breast Cancer dataset and splits it into training and testing sets.
3. Defines an evaluation f... |
d03ee5b4274d8f53236a393b4331d62298f7bd78b99f5902123912389af78ab0 | Python | 2,203 | 42 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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
#
# ht... |
3ece17e0c83a5bf0b1d95089027180203b0050e61c3951d4e65faf6a6b2e4d28 | Python | 2,204 | 54 | import datasets
import lightning.pytorch as pl
from classification_model import ClassificationModel
from lightning.pytorch.callbacks import ModelSummary
import os
import shutil
from lightning.pytorch.loggers import WandbLogger
import time
import argparse
from lightning.pytorch.callbacks import ModelCheckpoint
import wa... |
42ece300c3445ebfc8f081d8d66e5cbd26d2590280dd510aa38767e118ffdecc | Python | 2,204 | 58 | import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Input, MaxPooling2D, Dropout, \
concatenate, BatchNormalization, Conv2DTranspose, PReLU
def conv_block(inputs, num_features, kernel_size, params):
x = Conv2D(num_features, kernel_size, activation=None... |
e48a8da10433065797308a771464b8a728a692bbaeacbe6a06f9874e7ef70c80 | Python | 2,205 | 66 | from collections.abc import Callable, Iterator
from typing import Protocol
import elasticai.creator.function_dispatch as FD
from elasticai.creator.graph import bfs_iter_down
from .datagraph import DataGraph as _DataGraph
from .datagraph import Edge, Node
class DataGraph[N: Node, E: Edge](_DataGraph[N, E], Protocol)... |
014c138f0ea7c49d36d71240038766641427834fe1b91d30c566baecb587ed56 | Python | 2,206 | 76 | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.i... |
a40d9fcf8a4c43ca15b45dbcae6dbaf1e865e631933a265799daf0e7c5f693ff | Python | 2,207 | 62 | """HED exceptions and exception codes."""
class HedExceptions:
"""HED exception codes."""
GENERIC_ERROR = "GENERIC_ERROR"
# A list of all exceptions that can be generated by the hedtools.
URL_ERROR = "URL_ERROR"
FILE_NOT_FOUND = "FILE_NOT_FOUND"
BAD_PARAMETERS = "BAD_PARAMETERS"
CANNOT_PA... |
d325060d3b4a0ec9125f58eb68b4db18aab94abf824be9bef5c54f6dd6b8138f | Python | 2,208 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 7 # model 7
arg... |
63392d978b5148703d6ffc8195e60565cb528187d347049f4e5bb4f41e582c9c | Python | 2,209 | 54 | #!/usr/bin/env python
#
# Copyright 2019 Google LLC. All Rights Reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list o... |
995a85109c6d90acfb14772f426dc7b372b54019ee6c64b34807655a85c58e7c | Python | 2,209 | 60 | import os
import shutil
import numpy as np
from scipy.stats import gamma
from hmp import simulations
def create_data():
sfreq = 100
n_events = 3
# Data creation/reading
## Simulation parameters
n_trials = 2
cpus=1
times_a = np.array([[100, 100, 200, 100],
[100, 100, 200, 100... |
17e99dd01071d2eca4d76d60bb18a2bab2f38dd91a3b28fc92830c4e1215f8b6 | Python | 2,210 | 77 | # Node group "MN Fresnel" (ShaderNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated fro... |
1c25d4c91a2fde4f22b614a484db698d85190b8223e67e6dc400c2daba8b6754 | Python | 2,210 | 64 | import torch.nn as nn
import torch
import torch.nn.functional as F
import math
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def cal_gmm_widthC(Y, K, times, widthC):
mu, cov, alpha = GMM_EM(Y, K, times)
_, prob_sum = getExpectation_widthC(Y, mu, cov, alpha, K, widthC)
r... |
2ef0e27008f5bc3df5acfd13af98a7de408b91a2737e18b4d6a6dbfdce291fec | Python | 2,210 | 71 | # 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... |
7397456d37f304a6b56e5147001661c6598bf7d17011fb70d1c42d620511022f | Python | 2,211 | 80 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
import suites
from lab.reports import Attribute, gm
from common_setup import IssueConfig, IssueExperiment
try:
from relativescatter import RelativeScatterPlotReport
matplotlib = True
except ImportError:
print 'matplotlib not available, scatter plots... |
7bf5945e96f238727962bdd373081ccf1c9099fde4f88a470efa1bcf6ec96abc | Python | 2,211 | 73 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
a288f25beda63f385bea03b1fd185946b80a8f570571e2db323c74ac133e2881 | Python | 2,211 | 53 | '''
rename-relative-links-to-posts.py
This script renames relative links in posts to point to new, canonical post names.
'''
import os
filenames = [
'posts/8020-pandas-tutorial/post.ipynb',
'posts/hello-pyspark/hello-pyspark.ipynb',
'posts/get-down-with-gradient-descent/get-down.ipynb',
'posts/how-to... |
f550ecee90f8020f42b256b9ce8948b67b3a5a9456b6620c7b7cea4db74a3ee6 | Python | 2,211 | 71 | """Launcher for the Geometry Registration GUI.
Builds the Qt application, embeds :class:`GeometryRegistrationViewer` in an
``AppScaffold`` window, and starts the event loop. pyqtgraph is PyQt5-native,
so napari's subprocess rule does not apply here.
Usage
-----
python -m regtools.geometry_registration
... |
75a3a1595699a6d061dffade711e7dc24af15d6cc63413e3f3a57067ccc76b0c | Python | 2,212 | 44 | import os
import sys
relative_ref_path = sys.argv[1]
sam_dir_path = sys.argv[2]
bam_dir_path = sys.argv[3]
vcf_dir_path = sys.argv[4]
data_dir_path = sys.argv[5]
samtools_bin_path = '/mnt/batty-shared/tools/bin//samtools '
config_sam_path = '../utils/configure_sam.py '
dir_list = os.listdir(sam_dir_path)
for dir i... |
7905d6a5386f18be47a54f03896865200cb1e374e6b2f526d435dd057e97e1ae | Python | 2,212 | 64 | from smart import mesh_tools
import numpy as np
def test_circle_curv():
"""Test calculation of curvature for unit circle"""
cell_marker = 1
bound_marker = 10
circle_mesh, mf1_circle, mf2_circle = mesh_tools.create_ellipses(
1.0, 1.0, hEdge=0.1, outer_tag=cell_marker, outer_marker=bound_marker
... |
91791c6ef0b7c1dc9e568d984af4727a127e332f3ccc4822eedf834210d29642 | Python | 2,212 | 72 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Argument parser functions."""
import argparse
import sys
from cfg.defaults import get_cfg
def parse_args():
"""
Args:
shard_id (int): shard id for the current machine. Starts from 0 to
num_... |
e8b2a741cf32878f5ff34d9b174b0fbdbcdc938422bcc62aaa85c03da60ff2e5 | Python | 2,212 | 83 | """
pygments.formatters.pangomarkup
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Formatter for Pango markup output.
:copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
from pip._vendor.pygments.formatter import Formatter
__all__ = ['PangoMarkupForma... |
412f97ca4ea0969592a48d4f2e57e623cba1b6f614aa25e599b69e8330823682 | Python | 2,213 | 45 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
aed88801a3af466c299c4da96c7dee8d46d5847d9f1a47d305fd066c3fc9f695 | Python | 2,214 | 68 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: A.Akdogan
"""
from utils import *
import torch
import pickle
from helper import utils
from dataset_builder import NailDataset
from torch.utils.data import DataLoader
import os
class Evaluator:
def __init__(self, select_class_rgb_values, device):
... |
1e7c911efa4422fdd8779573c685f897b4f74497c5c2e7a808cfe19ecc1d31e9 | Python | 2,215 | 77 | #!/usr/bin/env python
from __future__ import print_function
import time
import numpy as np
from pybullet_tools.utils import add_data_path, connect, enable_gravity, wait_if_gui, disconnect, create_sphere, set_point, Point, \
enable_real_time, dump_world, load_model, wait_if_gui, set_camera, stable_z, \
set_co... |
4ef4a6bc5bd046fca36046a482ef264a9fef52154ccb8a050005b6e8895ef362 | Python | 2,215 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 6 # model 6
arg... |
9ed660f2fc67d3326f6e9fd4c8907f6ae1ea618223fc136127b5dfd67938ce18 | Python | 2,215 | 82 | import pytest
import numpy as np
from pyrelimri.conn_icc import (triang_to_fullmat, edgewise_icc)
def test_triangtomat_valid():
size = 3
corr_1darray = np.array([1, 2, 3, 4, 5, 6])
expected_output = np.array([[1, 0, 0], [2, 3, 0], [4, 5, 6]])
output = triang_to_fullmat(corr_1darray, size)
assert n... |
b04cddb9f5dd32763618c327bab3956ee579d63a2da68b52641ab1304cbf92cd | Python | 2,215 | 106 | # ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# +
# this e... |
f35dfabfa4ba6a061e68feeea4f9e4f68d8dc5d9384866fe02df0d2e61abb084 | Python | 2,215 | 72 | import nibabel as nib
import numpy as np
import os
import csv
import argparse
# Define the class labels
CLASS_LABELS = {
"SV": 1,
"V3": 2,
"V4": 3
}
def calculate_volumes_per_class(mask_path):
try:
# Load the NIfTI file
nifti_img = nib.load(mask_path)
data = nifti_img.get_fdata... |
33d7bf45ca716a6e09c80783992bfbbcf628820f549e2472a7f2ff3db6c7a612 | Python | 2,216 | 77 | """
Script for applying statistical test to a pattern so as to check its heterogeneity
Input: - Path to the input UniStat objecto of the pattern
- Parameters for setting the statistical tests
Output: - Plot graphs for testing heterogeinity
"""
__author__ = 'Antonio Martinez-Sanchez'
############... |
564f29a489dbb500b9fa513a260a67a7640de5233ca8f12b85b10a1a2e011983 | Python | 2,216 | 42 | import os.path as path
import numpy as np
from eworm.utils import *
from eworm.network import *
from eworm.network.process import *
import pickle
import time
if __name__ == "__main__":
np.random.seed(42)
main_dir = path.join(path.dirname(__file__), "config")
config_dir = path.join(main_dir, "000_circuit_se... |
7d1aa86a05f64aec613a198db3dc3fe991cad9a2fdb451b278889c7133ddd256 | Python | 2,216 | 70 | import os
import datetime
import json
import logging
def add_timestamp_to_filename(filename):
"""
Adds a timestamp for versioning to the filename.
Args:
filename (str): The filename to be versioned.
Returns:
str: The versioned filename.
"""
# Get current timestamp
timestamp = date... |
3ce14793608ee208d5bbc026606c3e44af8c3671cb17ef3924f6f07ee0033bdb | Python | 2,217 | 60 | import pickle
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
def drawGraph(patientsFile, tissueType):
cancerPointsX = []
cancerPointsY = []
normalPointsX = []
normalPointsY = []
with open(patientsFile, 'rb') as loadFrom:
patients = pickle.load(loadFrom)... |
ca5ec4ed5a0d9f7dbbe430f968fe8552c9ad48e837ac92d9e899d3c635505215 | Python | 2,217 | 75 | """Slab recipes for EMT."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc import flow
from quacc.recipes.common.slabs import bulk_to_slabs_subflow
from quacc.recipes.emt.core import relax_job, static_job
from quacc.wflow_tools.customizers import customize_funcs
if TYPE_CHECKING:
... |
1d10806526095d8f2c0a9db42ea3f9d3ba96e2cfb3a4597b1a8f9a7ef724afd0 | Python | 2,218 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 9 # model 9
arg... |
88c1a72d2eec91b9e213c97545d3057dff53344ae25a339b325de887c766e168 | Python | 2,218 | 45 | """
Modelling support for the QUASAR ASL sequence
---------------------------------------------
Like pCASL, the QUASAR sequence consists of a set of label-control image
pairs acquired at single or multiple delay times, and with the potential
for repeated measurements at each PLD. However each label-control pair is
add... |
c45fbafab25f88fe1a0abbed5c51b2b6eff85977aac94ed29bb4a402ac19d012 | Python | 2,218 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 8 # model 8
arg... |
4681d4452ed5d1f1c05d3311a35d9ca92196dcddb84228e72a520db7a5e65c7d | Python | 2,219 | 47 | import pickle
import pandas as pd
import numpy as np
from connalysis.network.classic import closeness_connected_components
from connalysis.network import topology
import sys
sys.path.append('../../../library')
from structural_basic import load_connectome
# Load pre-computed simplex counts
root="/gpfs/bbp.cscs.c... |
e3a4b6a7b2fcf729978967275dacdeaaddfbccb4666ed2912c6cb9f28a908ae6 | Python | 2,219 | 87 | import typing
from functools import partial
import numpy as np
BOX = typing.Union[
np.ndarray,
typing.List[typing.Union[int, float]],
typing.Tuple[typing.Union[int, float], ...],
]
def center(bbox: BOX) \
-> typing.Tuple[float, float]:
x1, y1, x2, y2 = bbox
return (x2 - x1) / 2 + x1, (y... |
3c51efad729059e7536d7ee21a80492da467bfe47e8b2bff70ba4855ad472fbd | Python | 2,221 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 10 # model 10
ar... |
704b00c1ae27833bf7348cf855130571e51431fe9ee09c9560103a52be07015a | Python | 2,221 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 1 # model 1
arg... |
d5b0f689198787dc6677f883119ce72fc3239a62856a063f80ba9c02cebd27d2 | Python | 2,221 | 67 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
2011ee62c0180f4b889a87d5cafa2d3add61c3a1b5cd55c75641ab02ffec047b | Python | 2,222 | 75 | from functools import partial
from itertools import repeat
from autograd.tracer import Node, trace
from autograd.util import subvals, toposort
from autograd.wrap_util import wraps
class ConstGraphNode(Node):
__slots__ = ["parents", "partial_fun"]
def __init__(self, value, fun, args, kwargs, parent_argnums, ... |
4779174621dd80da6c896722068832a8fc05520b762aebbbb01531be622e0e19 | Python | 2,225 | 58 | import cv2
import numpy as np
import os
import pandas as pd
import shutil
from skimage.color import rgb2gray as skimage_rgb2gray
from skimage import io
from tqdm import tqdm
from typing import *
from tabulate import tabulate
from constants import *
_CLUSTER_DATA_ROOT = '/cluster/work/zhang/webermau/rocf/psychology/'... |
564d6291a4df5d47be3df9bd56bc3128386bec8e661266118c048cce59a8c6ad | Python | 2,225 | 45 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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
#
# ht... |
6008dc1ea01f9205187017167133e98323dd958d34e1cb18bcc6acd89bba3cac | Python | 2,225 | 94 | #!/usr/bin/env python
#******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
#... |
b3af8de142eb28bd8893fbe83d5960929bb7ef68e447a627624d2723dc41d28b | Python | 2,225 | 48 | from ckg.graphdb_builder import builder_utils
from collections import defaultdict
import re
def parser(ontology_files):
terms = defaultdict(list)
relationships = defaultdict(set)
mappings = defaultdict(set)
definitions = defaultdict()
for obo_file in ontology_files:
with open(obo_file, enc... |
e30804327d8541e1187017c393b8287fe80baa916ea43934e0ec25a2b7a719c2 | Python | 2,225 | 69 | from typing import Protocol
from elasticai.creator.file_generation.savable import Path
from elasticai.creator.vhdl.design.design import Design
from elasticai.creator.vhdl.system_integrations.env5_constraints.env5_constraints import (
ENV5Constraints,
)
from elasticai.creator.vhdl.system_integrations.middleware.mid... |
0f47fddc825f22d7b9a0944674d286b7eb489e7fa3351f36ee0f0bd68078e417 | Python | 2,226 | 86 | import torch
import numpy as np
from dataset import ProteinDataset
from utils import average_structures
import autoencoder as autoencoder
def main():
train_range = range(170, 175)
val_range = range(175, 176)
data_path = './data/'
latent_dim = 40
epochs = 250
lr = 0.0001
p_enc = .8
... |
5c2eec82b7f3994fd716cc8fa11f25ecd100d9d9b6fe325daf760aca0f24834e | Python | 2,226 | 64 | from typing import Union, Dict
import numpy as np
from spectrum import aryule
from ..base_transform import EEGTransform
class ARRCoefficient(EEGTransform):
r'''
Calculate autoregression reflection coefficients on the input data.
.. code-block:: python
from torcheeg import transforms
t... |
cf29ef166f0de112c9a57f7177be9874a6de17aac40915796b6910b18fbfa1f6 | Python | 2,226 | 78 | import pickle
import numpy as np
from matplotlib import pyplot as plt
name = 'stephen'
in_file = '/home/mayank/Dropbox (HHMI)/PoseEstimation/Results/stephen_gt_numbers.p'
db =
with open(in_file,'r') as f:
D = pickle.load(f)
##
P = {}
all_t = {}
for net in D.keys():
cur_out = D[net]
V = []
n_out = min... |
42b5362ee723e43572bb9f45f1c7aa803935e0634290e07ce466e65e327fffcb | Python | 2,227 | 79 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 5 # model 5
arg... |
45e02ad161c78d28fe8b7df2da3707b1b2cad445353293afbbe917cff8964cff | Python | 2,227 | 66 | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use thi... |
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