sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
c1b9983242b52ccdf88e315e93c545258c9a75efcbeaeed7aa3a5da86ca84705 | Python | 1,625 | 95 | """
Tests for :mod:`numpy._core.numeric`.
Does not include tests which fall under ``array_constructors``.
"""
from __future__ import annotations
from typing import cast
import numpy as np
import numpy.typing as npt
class SubClass(npt.NDArray[np.float64]):
...
i8 = np.int64(1)
A = cast(
np.ndarray[tuple[i... |
e9a1a039db46a494a03d9973a68c3eb5c1d748faab25e29a8169c58a585e59e3 | Python | 1,626 | 74 | import platform
import pytest
from numpy.testing import IS_64BIT
from . import util
@pytest.mark.skipif(
platform.system() == "Darwin",
reason="Prone to error when run with numpy/f2py/tests on mac os, "
"but not when run in isolation",
)
@pytest.mark.skipif(
not IS_64BIT, reason="32-bit builds are b... |
325b494b6ef77dfb351ff113898364b982849379505646740bf8ab04ebf63f08 | Python | 1,627 | 72 | import importlib
import sys
_SHELL = None
_SHELL_BACKEND_SETTER = None
def register_shell(shell):
global _SHELL
_SHELL = shell
return shell
def get_shell():
return _SHELL
def get_shell_namespace():
shell = get_shell()
if shell is not None and hasattr(shell, "user_ns"):
return she... |
e73b10b48f563ef152379be2423f4b1ab697c1369e0bd2c7ef2e9264a3dfaf8e | Python | 1,630 | 55 | """Generate a plot of fiber coordinates overlaid with a plot of the sample.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
... |
77de0aeb80b992d08dc55c5a8a30a14ea32f18368aa2ab60a296257536264cbc | Python | 1,631 | 56 | """Generate a plot of fiber coordinates overlaid with a plot of the sample.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
... |
4d5a1b2e821799451402261d38812c9fc3c18cf21ae9ca5fecf21aafe179add4 | Python | 1,634 | 70 | from abc import ABC, abstractmethod
import numpy as np
from typing import Dict, Any, Optional, Tuple
class BaseAgent(ABC):
def __init__(self, state_dim: int, action_dim: int, **kwargs):
self.state_dim = state_dim
self.action_dim = action_dim
self.generation = 0
self.iteration = 0
... |
53b86b9e76543d2f14d9e42fb5fcf912577e9f99b095b04185fe2efe88afbd2c | Python | 1,634 | 44 | from pathlib import Path
import sys
html_path = Path(sys.argv[1])
html = html_path.read_text(encoding="utf-8")
required = [
'<link rel="canonical" href="https://peterponyu.github.io/iAODE/"',
'name="robots" content="index, follow"',
'property="og:url" content="https://peterponyu.github.io/iAODE/"',
'na... |
1f93eda1ec0f4e2cdb62f3783606f2a0719d49a0368f9b7030c63cff5c516f5b | Python | 1,635 | 56 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class ZINBLoss(nn.Module):
def __init__(self):
super(ZINBLoss, self).__init__()
def forward(self, x, mean, disp, pi, scale_factor=1.0, ridge_lambda=0.0):
eps = 1e-10
scale_factor = scale_factor[:, No... |
7f89cd21975dda87506e3cfbed726190eaceb3d87efe73163298558ffd4bef78 | Python | 1,635 | 48 | import numpy as np
import pandas as pd
import yaml
from pathlib import Path
import nrrd
from skimage.measure import regionprops
from sklearn.neighbors import KDTree
from CrystalTracer3D.io import CrystalReader
from tqdm import tqdm
if __name__ == '__main__':
with open('config.yml', 'r') as ymlfile:
cfg = ... |
90451ba93b2127d546afda0b744b793d56756b03cb2cca793da1bfa80ddd1be8 | Python | 1,635 | 53 | from torchvision import datasets, transforms
from manipulate import UnNormalize
# specify available data-sets.
AVAILABLE_DATASETS = {
'MNIST': datasets.MNIST,
'CIFAR100': datasets.CIFAR100,
'CIFAR10': datasets.CIFAR10,
}
# specify available transforms.
AVAILABLE_TRANSFORMS = {
'MNIST': [
tran... |
08884a363ec5f8e3c34fbcb227b02e8ff3a7b8ebe2ad11c7306b244f2ab375fc | Python | 1,637 | 47 | import sys
import os
import time
sys.path.append("../../../../")
sys.path.append("../")
import numpy as np
import music21 as m21
from conf.conf import *
from api.music_engine_api import EngineAPI
if __name__=="__main__":
musicEngine = EngineAPI()
musicEngine.cortexInit()
#------------Bach dataset learning... |
e34d0dc3ab3760fc949bfc0b8553fd99c3ea6eb6e23953073b3a77fae9a0dd19 | Python | 1,638 | 39 | import configparser
class Config(object):
def __init__(self, config_file):
conf = configparser.ConfigParser()
try:
conf.read(config_file)
except:
print("loading config: %s failed" % (config_file))
#Hyper-parameter
self.epochs = conf.getint("Model_Se... |
5241b4a650be9247276cd2831ac369920d36cf3064386e0fc7b039c195becc9f | Python | 1,639 | 49 | #!/usr/bin/env python3.7
"""Sets up environment path variables for the ASCENT.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascen... |
f54f9c5863fd4d90b0d33b7bf490f18f3dc7bf19aa24112a7dc3b18155f97894 | Python | 1,639 | 78 | """
Cross check the correctness of the density matrix simulator and the Monte Carlo trajectory state simulator.
"""
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# cpu is fast for small scale circuit simulation
from tqdm import tqdm
import jax
import tensorcircuit as tc
tc.set_backend("jax")
n = 5
nlayer = ... |
826555f96934b94512c29e60c7f0bd062d5b93950073f6b99952c0b8f129f79c | Python | 1,641 | 47 | import numpy as np
from scipy.stats import spearmanr, kendalltau
def compute_metrics(output_list, target_list, logspace_input=False):
if logspace_input:
output_list_nonlog = np.exp(output_list) - 1
target_list_nonlog = np.exp(target_list) - 1
else:
output_list_nonlog = output_list
... |
bc77f23c93313511da350a0da7d3334b7b408f04f83ce2405a8bee9f1d72b1e2 | Python | 1,641 | 53 | from abc import ABC, abstractmethod
from typing import Dict, Tuple, Union
from numpy import ndarray
# TODO: Add docstrings
class DynamicsBase(ABC):
"""TODO."""
@abstractmethod
def get_solution(
self, t: ndarray, stacked: True, with_keys: bool = False
) -> Union[Dict, Tuple[ndarray], ndarray]... |
cc6bcb815b0a66657a6af3ebad11d6e67b04f7ad98d5144b6fd8914078082bbc | Python | 1,641 | 55 | import torch
import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def print_log(print_string, log):
#print("{}".format(print_string))
log.write('{}\n'.format(print_string))
log.flush()
def unit(x):
if x.size()[0]>0:
xnp=x.cpu().numpy()
maxx=np.percen... |
42d29c6f5466fce5948293461f559184dc4cae472c517d736d8a110471450be8 | Python | 1,642 | 41 | # License: GNU Affero General Public License v3 or later
# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
""" A class for CDS motif features """
from collections import OrderedDict
from typing import Dict, List, Optional
from Bio.SeqFeature import SeqFeature
from .domain im... |
1c58d2ae265333a0b16c3472311b345fcd9cc8bb4101de50d89139cccbdf8f8c | Python | 1,643 | 61 | from abc import abstractmethod
from pathlib import Path
from typing import List, NamedTuple, Tuple, Union
import numpy as np
from ..models import IMCFileAcquisitionModel, IMCFileModel, IMCFilePanoramaModel
from ..models.base import IMCFileTreeItem
class ImageDimensions(NamedTuple):
x: float
y: float
wid... |
69e4a598855ae39f4875ffcbd638c45bc262e464bb07fb0847fd9adae3198afa | Python | 1,643 | 53 | import asyncio
from typing import Generator
from kani.streaming import StreamManager
import nest_asyncio
def _seconds_to_days_hours(ttl_seconds):
# we need to convert the time to a human-readable format, e.g. 28 days, 18 hours (rounded to nearest hour)
# we don't want the default datetime.timedelta format
... |
1a9740e5e82fd769bb9ede9f33a45ad0bd8e8534a2461879149217a8b62c0a31 | Python | 1,644 | 64 | #!/usr/bin/env python3.7
"""Plot the Ve across a fiber length.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
RUN THIS FRO... |
2b4b0cb300c50a20bd9c757b821240b3c0a3c3b7c4a224fc51c70fa85f38835f | Python | 1,645 | 54 | import h5py
import numpy as np
from torch_geometric.data import Batch, Data, Dataset # , InMemoryDataset
import torch
from ase.data import atomic_numbers
from torch_geometric.data import DataLoader
from tqdm import tqdm
import os
class CepDataset(Dataset):
def __init__(self, npz_dir):
super(CepDataset, s... |
8e5b822df1bde1533572e5fee5170b141962ff268966922f995b8cc5129126b7 | Python | 1,645 | 47 | """ Test scripts
Test that we can run executable scripts that have been installed with numpy.
"""
import sys
import os
import pytest
from os.path import join as pathjoin, isfile, dirname
import subprocess
import numpy as np
from numpy.testing import assert_equal, IS_WASM
is_inplace = isfile(pathjoin(dirname(np.__fil... |
a7a8c06cc892f0764271de1773b560370fc551b07f3ec14a7b3f8dc1fae84435 | Python | 1,645 | 55 | import sys
from molmap.utils import distances, calculator
from molmap import feature
import pandas as pd
import numpy as np
from rdkit import Chem
from tqdm import tqdm
tqdm.pandas(ascii=True)
def loadnpy(filename, N, dtype, mode = 'r'):
f = np.memmap(filename, mode = mode,
dtype = dtype)
... |
6fa6cfa4e7ed03b92395660c09770aff0ba5b85ab2a95c42c186d84f2d412954 | Python | 1,647 | 71 | import sys
import os
import pytest
# NOTE: If Intel OpenMP abort occurs on Windows (PyTorch + scipy DLL conflict),
# set KMP_DUPLICATE_LIB_OK=TRUE in the test runner environment, e.g.:
# $env:KMP_DUPLICATE_LIB_OK = "TRUE"
thisfile = os.path.abspath(__file__)
modulepath = os.path.dirname(os.path.dirname(thisfile))
... |
ae0efbe92bce3acd11184f9c03090f9e690d9222892dd90edf1433fd422be0f9 | Python | 1,647 | 55 | import numpy as np
import torch
import torch_scatter
class DipeptideBondedCombiner:
r"""
Combine the energies and forces of the dipeptides and ACE-NMEs.
"""
@staticmethod
def energy_combine(
dipeptides_energies: torch.Tensor,
acenmes_energies: torch.Tensor,
) -> np.ndarray:
... |
cae4fb4c9d76182398ca07f224f00febf359a180ada79d8fbe28b1e94b8dfdec | Python | 1,649 | 50 | #!/usr/bin/env python3.7
"""Sets up environment path variables for the ASCENT.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascen... |
314730c5b84c0db2ee88a17178fa5af2c81e0ff519f4aa329bd49b782ab7301d | Python | 1,650 | 60 | # Copyright (C) 2008-2012 Bernd Feige
# This file is part of avg_q and released under the GPL v3 (see avg_q/COPYING).
# Base class for parsing the very simple VOGL .ps output
scale_factor=1.9 # Divide all coordinates by this to avoid overflowing oodraw's coordinates
class voglps(object):
def __init__(self,psfile):
... |
f31a6eb6fbbee6ad843ef537679bbcf0990bec6cda441734bc4944c0cc6a7841 | Python | 1,650 | 50 | from __future__ import annotations
from ..utils._typed_cache import typed_cache
from ..counts._count_words import count_words
from ..counts._count_polysyllable_words import count_polysyllable_words
from ..counts._count_long_words import count_long_words
from ..counts._count_monosyllable_words import count_monosyllable... |
ac99c246543ba59b04126321b100e00bfa4ac831f13f94cf4bacd53c6b2072ef | Python | 1,651 | 48 | """The optimizer tries to constant fold expressions and modify the AST
in place so that it should be faster to evaluate.
Because the AST does not contain all the scoping information and the
compiler has to find that out, we cannot do all the optimizations we
want. For example, loop unrolling doesn't work because unrol... |
7cb4d9bac8051cf5cee28ae9ca098720e1ba2502f361c75b4c91d1befb0dd2c3 | Python | 1,652 | 67 | import numpy as np
import pytest
import pandas as pd
from pandas.core.arrays.integer import (
Int8Dtype,
Int16Dtype,
Int32Dtype,
Int64Dtype,
UInt8Dtype,
UInt16Dtype,
UInt32Dtype,
UInt64Dtype,
)
def test_dtypes(dtype):
# smoke tests on auto dtype construction
if dtype.is_signe... |
6980c6c23f49468a736ae2c50d806b4e451e0955cd0e3c137786267c9e5bd002 | Python | 1,653 | 48 | import os
from glob import glob
import pandas as pd
from ibc_public.connectivity.utils_fc_estimation import get_ses_modality
from nilearn.connectome import sym_matrix_to_vec
### create sc_data dataframe for native space
cache = DATA_ROOT = "/storage/store2/work/haggarwa/"
n_parcels = 200
sub_ses, _ = get_ses_modality(... |
f00d89f3d5ebafd5afa742d75641bfb4871d6eb72e72cf349d2345de35b2582a | Python | 1,654 | 66 | from __future__ import annotations
import argparse
import sys
import traceback
from pathlib import Path
from typing import TYPE_CHECKING
from qtpy.QtGui import QIcon
from qtpy.QtWidgets import QApplication
from micromanager_gui import MicroManagerGUI
if TYPE_CHECKING:
from collections.abc import Sequence
fr... |
2dadc147015993c3e8e7b661113fbd7a58c95d0957ab80e8bb2451e18bdcf676 | Python | 1,655 | 59 | import os
import re
from configobj import ConfigObj
def _sanitize(section, key, **replacements):
val = section[key]
if isinstance(val, str):
for k, v in replacements.items():
val = val.replace("%" + k + "%", v)
if isinstance(val, str):
if val.lower() in ["true", "yes"]:
... |
7f1d297d4ac4e366c3ac94ae9b7ddcfbdd63b0ce3ac5d53909dcc8090ba67d60 | Python | 1,655 | 40 | from abc import ABC, abstractmethod
from typing import Callable
class IProcess(ABC):
"""
A process that is a part of the processing pipeline.
It is expected to stop by itself when its input queue is closed.
If it has an output queue, it should close it when it stops.
It is expected to clean after ... |
c9a2c403fa2ec86f8f3bc946c304d741847971db75295b48caa4616513dd5725 | Python | 1,655 | 42 | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
FEATURE_COLS = [
"I1","I2","I3",
"T1","T2","T3","T4",
"C1","C2","C3","C4",
"E1","E2","E3","E4","E5",
"S1","S2","S3","S4","S5","S6","S7"
]
TARGET_COL = "Qualit... |
de1cbc80cf65a160edb769105d8a03d50962bb5fd2a407ad6245a5883cce941c | Python | 1,656 | 56 | from __future__ import annotations
import pytest
from textstat.backend import utils
@pytest.mark.parametrize(
"lang, key, value",
[
(lang, key, utils.constants.LANG_CONFIGS[lang][key])
for lang in utils.constants.LANG_CONFIGS
for key in utils.constants.LANG_CONFIGS[lang]
]
+ [... |
4c8eba48f1837a2a90f8237f4d1f026a1e794a037b88eacea10f22af3324f739 | Python | 1,658 | 50 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from ray.rllib.models import Model
from ray.rllib.models.tf.misc import normc_initializer
class MaskedActionsModel(Model):
"""Custom RLlib model that emits -inf logits for invalid... |
b166cd97fdc0b602d801f7a4e5f523d94f4596fa7c38a417d8b3764d6ffecbc7 | Python | 1,660 | 44 | import neo
import quantities as pq
import pandas as pd
import numpy as np
# pad signal as neo expects a continuous recording
def pad_signal(raw_data):
sampling_interval = 0.0001 # 10 kHz
start_time = raw_data.index[0]
time_index = np.array(raw_data.index, dtype=np.float64)
sample_numbers = np.round((... |
6a037f302a38d377cebea6a92a2fd6610962f409031409384c30795e96c9f23b | Python | 1,661 | 61 | # Tests aimed at pandas.core.indexers
import numpy as np
import pytest
from pandas.core.indexers import (
is_scalar_indexer,
length_of_indexer,
validate_indices,
)
def test_length_of_indexer():
arr = np.zeros(4, dtype=bool)
arr[0] = 1
result = length_of_indexer(arr)
assert result == 1
d... |
375310af0e50673cdbb051cca797f7282f9cce792219ebe4972309ff17226c1f | Python | 1,665 | 49 | """Contains tests for pressing hmm files into other formats used in scanning and aligning"""
# from python
from pathlib import Path
from unittest import TestCase
# from other modules
from big_scape.hmm import HMMer
class TestHMMPress(TestCase):
"""Contains tests to make sure hmm files can be correctly pressed ... |
3f6cad9996f7529c1f99b31790507688c424d79b4d502ca7ccc0139659a9f86b | Python | 1,665 | 49 | """ Modified version of build_scripts that handles building scripts from functions.
"""
from distutils.command.build_scripts import build_scripts as old_build_scripts
from numpy.distutils import log
from numpy.distutils.misc_util import is_string
class build_scripts(old_build_scripts):
def generate_scripts(self,... |
75d6b56534b0fd5ee12f3d71dd829731c74ef064a41f391679ebe2b075d468e2 | Python | 1,665 | 71 | from abc import ABC, abstractmethod
import numpy as np
from typing import Dict, Any, Optional, Tuple
class BaseAgent(ABC):
def __init__(self, state_dim: int, action_dim: int, **kwargs):
self.state_dim = state_dim
self.action_dim = action_dim
self.num_elec = 4
self.generation = 0... |
e724bec60ef3b4a35a9870872c03367691e9882175a106d87bf18fc91320cc60 | Python | 1,665 | 64 | import numpy as np
import torch
from torch.utils.data import Dataset
from rdkit import Chem, DataStructs
from rdkit.Chem import rdFingerprintGenerator
def calc_fp(
SMILES,
fp_size=2048,
radius=2):
mol = Chem.MolFromSmiles(SMILES, sanitize=True)
if mol is None:
return np.ze... |
d693b16239c451fc39cc0fdd30041fc449108b69c19000c1288682464063cbba | Python | 1,666 | 41 | """Main file for BiG-SCAPE benchmark module"""
# from python
from typing import Any
import logging
# from other modules
from big_scape.benchmarking import BenchmarkData
from big_scape.benchmarking import BenchmarkMetrics
from big_scape.benchmarking import OutputGenerator
def run_bigscape_benchmark(run: dict):
"... |
561f214f3e9d8b6e800c705f922f9818c4eb528c2eda4ccbc42746699a4b9e07 | Python | 1,667 | 53 | # Copyright (C) 2013 Bernd Feige
# This file is part of avg_q and released under the GPL v3 (see avg_q/COPYING).
"""
This provides a standard way of handling Embla event staging files.
"""
__author__ = "Dr. Bernd Feige <Bernd.Feige@gmx.net>"
from . import slfile as parent_slfile
stages={
'Wake': 0,
'S1': 1,
'S2':... |
5eec5ae2c36b6943dc426b78e52a2001f3749031c56fa0bbdee3593665f74411 | Python | 1,667 | 52 | import os
from numpy.distutils.fcompiler.gnu import GnuFCompiler
compilers = ['VastFCompiler']
class VastFCompiler(GnuFCompiler):
compiler_type = 'vast'
compiler_aliases = ()
description = 'Pacific-Sierra Research Fortran 90 Compiler'
version_pattern = (r'\s*Pacific-Sierra Research vf90 '
... |
eef1845a6190747aa61c765c9d497a8fa1cdd2e4ff1d4d05d37814b69372d6b4 | Python | 1,667 | 47 | import os
import pytest
import torch
from ase.db import connect
from nablaDFT.pipelines import run
@pytest.mark.pipeline
def test_train_pipeline(train_config, caplog):
run(train_config)
assert "`Trainer.fit` stopped: `max_epochs=3` reached." in caplog.messages[-1]
@pytest.mark.pipeline
@pytest.mark.skipif(... |
13824d31d762fd36f1b1495062f0a57895368e7bda2b0aa90fb9aa08d63847d1 | Python | 1,668 | 44 | import sys
sys.path.append('.')
from plotting.utils import load_results, set_plotting_defaults, efcolor, pvscolor, ecscolor,dpi
import matplotlib.pyplot as plt
import numpy as np
def get_diameter(results):
lumen_diam = results["vessel_diam"]
length = max(results["y_coordinates"]) - min(results["y_coordinat... |
6d4709a4ba71484c3cbd061e29794a23849c3299f088e4f90ddeb302cdb97621 | Python | 1,668 | 68 | """
For hardware simlation, only sample interface is available and Monte Carlo simulation is enough
"""
import tensorcircuit as tc
n = 6
m = 4
pn = 0.003
K = tc.set_backend("jax")
def make_noise_circuit(c, weights, status=None):
for j in range(m):
for i in range(n - 1):
c.cnot(i, i + 1)
... |
a60736fb2e0b1c35d141157f1127ecd7fd93533425be5aef70094c1b651fb5bc | Python | 1,669 | 59 | import os
import re
from configobj import ConfigObj
def _sanitize(section, key, **replacements):
val = section[key]
if isinstance(val, basestring):
for k, v in replacements.items():
val = val.replace("%" + k + "%", v)
if isinstance(val, basestring):
if val.lower() in ["true", "... |
7fc3e1210d80f1347318d0be707133b6d67bf872c42109b69a44c8e018d6a40f | Python | 1,670 | 42 | """Type aliases for Cedalion dataclasses.
Cedalion relies as much as possible on generic data types (like xarray DataArrays).
We then use type aliases and annotations to augment these data types with additional
information about the data they carry. For DataArrays there is a basic mechanism
to specify and validate dat... |
f2840b66314186cf8a3ad73b4d1a32e365720c754bdaf3af28f7ae8318662e47 | Python | 1,670 | 48 | import os
from pathlib import Path
from torch import optim
from models.SpeechAutoEncoder import SpeechAutoEncoder
import torch
from transformers import Data2VecAudioConfig,AutoConfig
MODEL_NAME = 'SAE'
loss = torch.nn.SmoothL1Loss()
light_model = SpeechAutoEncoder
sampling_rate = 16000
base_path = Path(__file__).paren... |
79ff1d707445b579ad167e2702f70d15867781502eb2f1a169551d68b71f8b6a | Python | 1,672 | 54 | import torch
import torch.nn as nn
from .layers.unmap_predictor import UnmapPredictor
from .layers.convnext import ConvNeXtV2Block
class ConvNeXtCNN(nn.Module):
def __init__(self, window_size=2048, bin_size=4, nr_tracks=1, use_map=False):
super().__init__()
assert window_size % bin_size == 0
... |
d154fce8dd9645b28c1a6f4249eae67a3b5d7ecf6dd30c27386312a1ffe60481 | Python | 1,673 | 56 | import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from scipy.ndimage import gaussian_filter
from main_functions import mark_voisins#, fast_multi_periodogram_window_3d, fast_multi_periodogram_prewindow_3d
sns.set_theme()
group_name = ["Hemisphere 1", "Hemisphere 2"]
if... |
dd73b094964b69bedb2d9210f46b637c0219dc5c91d2fa0bafc672088aee60d1 | Python | 1,673 | 44 | import torch, importlib
from LossPy import BCEDiceLoss, ComputePR, LSDLoss, EvalScore
from torch import nn as nn
def GetLossOptimiLr(model):
optimizer_config = {
'learning_rate': 0.0002,
# weight decay
'weight_decay': 0.00001
}
lr_config = {
# reduce learning rate when evalu... |
8d85f8f6f5dba5535a9112b2525fd7f6a18b61bcc7e4f75a744b01730b831fbd | Python | 1,674 | 52 | import torch
import torch.nn as nn
import torch.nn.functional as F
from braincog.model_zoo.base_module import BaseModule
from braincog.base.node.node import *
from braincog.base.connection.layer import *
from braincog.base.strategy.surrogate import *
from utils.MyNode import *
class TIM(BaseModule):
def __init__(... |
8404d07cbbfd8f148b0569203215ff4ee8db6278fcd17465fbff1352eccac367 | Python | 1,675 | 45 | from math import ceil
import pytest
from nablaDFT.dataset import ASENablaDFT
from .assertions import assert_shapes_spk
@pytest.mark.dataset
def test_spk_dataset_fit_dataloader(dataset_spk_params_train):
dataset = ASENablaDFT(**dataset_spk_params_train)
dataset.prepare_data()
dataset.setup(dataset_spk_pa... |
d09ede7975d3531d08bdff1ab1fa71d32715929d04a9faa78f5d7bc769985fd1 | Python | 1,675 | 64 | from __future__ import annotations
import collections.abc as c
import inspect
import typing as t
from weakref import ref
from weakref import WeakMethod
T = t.TypeVar("T")
class Symbol:
"""A constant symbol, nicer than ``object()``. Repeated calls return the
same instance.
>>> Symbol('foo') is Symbol('f... |
5f1d810a44109837c8f4f7c00271a4ab2a961f359f676fec2a5407bf3b81fe6c | Python | 1,677 | 61 | #!/usr/bin/env python3.7
"""Plot the waveform used for stimulation.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
RUN THI... |
4e5f1022b457459b02cef703294d067cb223c1b951a9ef1c804c7f0df2e00330 | Python | 1,678 | 57 | from pytorch_lightning.callbacks import Callback
from protgps.utils.registry import get_object
def set_callbacks(trainer, args):
"""
Set callbacks for trainer, taking into consideration callbacks already set by trainer args.
Callbacks that are preset by args and perform the same function as those manually... |
882c5061b480b51716ce5f66af40825d5b88c70e430d2d97f122d14fb0fbfde2 | Python | 1,678 | 72 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
celltype = 'B-NK'
meta_data = pd.read_csv('PseudoTime/diffMap2.csv',index_col=0)
print(meta_data.iloc[:5,:])
LL = meta_data.shape[0]
print('Cellnumber: ', LL)
cell_ls = list(meta_data.index)
resolution = 100000
chr = 1
chr = f'chr{chr}'
CpG = p... |
fea54a0c4da0e1c19df1ee5fb9cb50d4402d3bb3c256262b68a0498ecea57f71 | Python | 1,678 | 43 | #import necessary packages
import os
from natsort import natsorted
from nilearn import surface
import numpy as np
# function to generate subject list from BIDS Directory
def subject_list(dir, exclude_subs = None):
"""Returns a list of subject IDs from a parent directory.
Parameters :
dir : st... |
18ef6944ddf05c2da441aa2f2b9ed368b60131cdc896d387f763ba404c3a604d | Python | 1,679 | 58 | import hashlib
import logging
import sys
from optparse import Values
from pip._internal.cli.base_command import Command
from pip._internal.cli.status_codes import ERROR, SUCCESS
from pip._internal.utils.hashes import FAVORITE_HASH, STRONG_HASHES
from pip._internal.utils.misc import read_chunks, write_output
logger = ... |
1b89c340b62dbed860b1c15635586826a7ceafe9fb9155272e2c944d13825adf | Python | 1,679 | 54 | import os
import re
import sys
from github import Github
def main() -> None:
token = os.environ.get("GITHUB_TOKEN")
repo_owner = "pytorch"
repo_name = "pytorch"
pull_request_number = int(sys.argv[1])
g = Github(token)
repo = g.get_repo(f"{repo_owner}/{repo_name}")
pull_request = repo.ge... |
42ff27d42e35304c520bb6b05b3775c2679cafe7dbf2ed30f57e656e441c2580 | Python | 1,680 | 41 | ## ...........................................................................
##
## PIMMS (Polymer Interactions in Multicomponent Mixtures)
## Alex Holehouse, Pappu Lab, Holehouse Lab
## Copyright 2015 - 2026
## ...........................................................................
"""
lemonade - a fast, hierar... |
2ebcc33d930a561d2bb9bb42c7dbeedd79596cb092ba0e954a3e10b2fb7ae810 | Python | 1,681 | 52 | """
This code wraps the vendored appdirs module to so the return values are
compatible for the current pip code base.
The intention is to rewrite current usages gradually, keeping the tests pass,
and eventually drop this after all usages are changed.
"""
import os
import sys
from pip._vendor import platformdirs as _... |
f84ae062ff0f7202e1d5891a7d5923e7d687d3ec0f39fc9ffd1009b4e7bd449b | Python | 1,682 | 64 | """Plot the Ve across a fiber length.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
RUN THIS FROM REPOSITORY ROOT
"""
imp... |
4e5f531cc0d9f8f9395a6f2c23580683f5390e1bac9b10fe159d1f51b714d16d | Python | 1,683 | 57 | import io
from typing import IO, TYPE_CHECKING, Any, List
from .ansi import AnsiDecoder
from .text import Text
if TYPE_CHECKING:
from .console import Console
class FileProxy(io.TextIOBase):
"""Wraps a file (e.g. sys.stdout) and redirects writes to a console."""
def __init__(self, console: "Console", fi... |
9878c70bae698191d9b9125433975aa16fa51a7ba5ab5cd61edf8ce29c4ceaef | Python | 1,683 | 65 | """Plot the Ve across a fiber length.
The copyrights of this software are owned by Duke University.
Please refer to the LICENSE and README.md files for licensing instructions.
The source code can be found on the following GitHub repository: https://github.com/wmglab-duke/ascent.
RUN THIS FROM REPOSITORY ROOT
"""
imp... |
a39359b43579fbdc63cb006c5e0b36e2e3bc4fccc395c2af3c514ef5753bd2e9 | Python | 1,683 | 53 | import argparse
import os
import numpy as np
import pandas as pd
import torch
def get_args():
parser = argparse.ArgumentParser(
description="Convert unitig presence/absence to node features"
)
parser.add_argument(
"--unitig_query_results", type=str, help="Results from unitig-caller query"... |
2de47f8844845c557db2a8de97eecafc63be18b965d3c07aba6923d9759fbcd3 | Python | 1,684 | 69 | """
minidemo.py
This script sets base, peak and fit cursors to
perform events detection as decribed in the Stimfit manual [1]
It creates a preliminary and final templates from a file 'minis.dat'.
You can download the file here: http://stimfit.org/tutorial/minis.dat
last revision: Wed Sep 5 09:38:41 CEST 2018
C. S... |
042cbcec75ea23658ed3f70218afab79dbe2d924c9cc2c6cc1862cd3a61dc41e | Python | 1,686 | 67 | """
datetimelke_accumulations.py is for accumulations of datetimelike extension arrays
"""
from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import iNaT
from pandas.core.dtypes.missing import isna
def _cum_func(
func: Callable,
values: np.ndarray,
*,
... |
c7a85e7deaf7fb6c737dfab6b8952eefec65c7899a760ae9ab7c13b0128f3f71 | Python | 1,686 | 45 | import os
import subprocess
import sys
_tee = None
def redir_output(logfile: str):
global _tee
if _tee:
raise Exception("Standard output/error already redirected")
_tee = subprocess.Popen(["tee", logfile], stdin=subprocess.PIPE)
# Cause tee's stdin to get a copy of our stdin/stdout (as well a... |
e6c085bb4124241fd11e1b48462d019f9b0370f3f80d6b7c296258e2db1b1e23 | Python | 1,686 | 39 | # License: GNU Affero General Public License v3 or later
# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
# for test files, silence irrelevant and noisy pylint warnings
# pylint: disable=no-self-use,protected-access,missing-docstring
import unittest
import antismash
from ant... |
17b180e83049ef99ad602a11262aa2147b149c403ff5998df2020c186fef1d49 | Python | 1,687 | 52 | import os
import pandas as pd
import scipy
import scanpy as sc
"""
This script takes the output of either Cellbender or CellRanger and processes into a Anndata object
with the parameters upon which quality control filtering can be done.
"""
# Read the samples table once
samples = pd.read_csv(snakemake.input.metadata_... |
4f3426e8b15eb294e5d6a52eaf04e92cc3bfac9435c8dcd5a03ad1325d095743 | Python | 1,687 | 47 | import torch
@torch.jit.script
def gaussian(x: torch.Tensor, mean, std) -> torch.Tensor:
pi = 3.14159
a = (2 * pi) ** 0.5
return torch.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std)
# From Graphormer
class GaussianRadialBasisLayer(torch.nn.Module):
def __init__(self, num_basis: int, cutoff: float... |
3166d12b3c240f267acc9ab3210954d22c86b43aceb60eb60554515308e50ac2 | Python | 1,688 | 67 | import os
import random
try:
from pathlib2 import Path
except ImportError:
from pathlib import Path
import numpy as np
from six import with_metaclass
PATH_TO_TESTS = os.path.dirname(os.path.realpath(__file__))
class BaseClass(object):
pass
class TestingType(type):
def __getattr__(self, name):
... |
78c19d662edabaeeb6d35878bf87906769a217c2a674c1830296c1160444fba4 | Python | 1,689 | 62 | """
Miscellaneous utils.
"""
from numpy._core import asarray
from numpy._core.numeric import normalize_axis_tuple, normalize_axis_index
from numpy._utils import set_module
__all__ = ["byte_bounds", "normalize_axis_tuple", "normalize_axis_index"]
@set_module("numpy.lib.array_utils")
def byte_bounds(a):
"""
Re... |
26903c07f42f8c2e5f6a1a1d40f7b41a8a92decb06c380c57e7b9a7acf3295e1 | Python | 1,690 | 56 | import argparse
from typing import Optional
import waitress
from flask import Flask, render_template_string, render_template, request
from src.model import initialize_system
from src.proposal import do_proposal
tokenizer, model_combined_place_backs, models_determiner, models_metadata_place_back = initialize_system(
... |
04050fa70d3f223d9b4ff2e8948d4fddf1860ba9ecb68a79da503bff8bfc9ac6 | Python | 1,691 | 78 | '''
Created on 2016.4.27
@author: liangqian
'''
import json
import random
class joint():
def __init__(self):
self.x = 0
self.y = 0
self.z = 0
def self2dic(self):
return {'x':self.x,
'y':self.y,
'z':self.z}
class data():
def __in... |
eed2e62fab36ec11fee3d53830dfea6d88b838b7f3a20416d677f91c9c851163 | Python | 1,692 | 61 | from __future__ import annotations
import re
from ..utils._typed_cache import typed_cache
from ..selections._list_words import list_words
@typed_cache
def count_arabic_syllables(text: str, rm_apostrophe: bool = True) -> int:
"""Count arabic syllables.
Long and stressed syllables are counted double.
Pa... |
3c73d3586290ce7a8c85fba179aede792c718296c96765b5abc3917dc792d253 | Python | 1,694 | 46 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .layers.unmap_predictor import UnmapPredictor
class CNN_LSTM(nn.Module):
def __init__(self, window_size: int = 2048, bin_size: int = 4, nr_tracks=1, use_map: bool = False):
super(CNN_LSTM, self).__init__()
assert window_size % ... |
d77f3f3bf270c1d0a67e6ca3a78ecf4956bee12afb48eb8ba6baf43f3466e814 | Python | 1,694 | 56 | import numpy as np
import pytest
from pandas import (
CategoricalDtype,
DataFrame,
)
import pandas._testing as tm
def test_transpose(index_or_series_obj):
obj = index_or_series_obj
tm.assert_equal(obj.transpose(), obj)
def test_transpose_non_default_axes(index_or_series_obj):
msg = "the 'axes' ... |
1759631fb17923653f986e21157028309431d5683306905c4b5b80b1669e10f7 | Python | 1,696 | 59 | #!/usr/bin/env python3
"""Generates a matrix for docker releases through github actions
Will output a condensed version of the matrix. Will include fllowing:
* CUDA version short
* CUDA full version
* CUDNN version short
* Image type either runtime or devel
* Platform linux/arm64,linux/amd64
"""
... |
d14623bf0676fcdc598d9a9630150303611dccb6fe39c3398310244c43be106c | Python | 1,696 | 57 | # coding: utf-8
# See notebook:
# https://github.com/paninski-lab/yass-examples/blob/master/batch/single_channel_apply.ipynb
"""
Apply functions to large files using
BatchProcessor.single_channel_apply
"""
import logging
import os
import matplotlib.pyplot as plt
from yass.batch import BatchProcessor
from yass.batc... |
8541e488ddb99a26ae20751fcd991a588ca4fca6cb69816888046b4e2bf0341e | Python | 1,697 | 43 | import unittest
import torch
import torchdiffeq
from problems import construct_problem, DTYPES, DEVICES, ADAPTIVE_METHODS
EPS = {torch.float32: 1e-4, torch.float64: 1e-12, torch.complex64: 1e-4}
class TestCollectionState(unittest.TestCase):
def test_forward(self):
for dtype in DTYPES:
eps =... |
211f4415f432e857e8a716ea1b633771b2682ca979040ee378f440fe8245d5bb | Python | 1,702 | 46 | import nibabel as nib
import os
input_folder = "/mnt/nas7/data/reza/registered_dataset_all_doses_pad_updated_NonUnifiedOrigins/"
output_folder = "/mnt/nas7/data/maria/final_features/registered_niftis_new/"
os.makedirs(output_folder, exist_ok=True)
# Reference image
reference_image_path = os.path.join(input_folder, "... |
ed9f7bee36f5ef16ae870499be107227aa1632ef8407ec58b22ae639cf3eb698 | Python | 1,702 | 57 | """
Get molecule qubit format Hamiltonian from openfermion.
"""
import time
import tensorflow as tf
from openfermion.chem import MolecularData
from openfermion.transforms import (
get_fermion_operator,
binary_code_transform,
reorder,
checksum_code,
)
from openfermion.utils import up_then_down
from open... |
71e2921086a6b60e8cf557fabb0cd1dd7f592f2236e4b0a2084d2aab1acd3822 | Python | 1,703 | 71 | """
A simple static Hamiltonian evolution benchmark
"""
import time
from functools import partial
import numpy as np
from scipy.integrate import solve_ivp
import tensorcircuit as tc
from tensorcircuit.experimental import hamiltonian_evol
K = tc.set_backend("jax")
tc.set_dtype("complex128")
@partial(K.jit, static_ar... |
5cc70c6d50e50a6e88dfae040ba502385e01f7deb36b91b1c767537c47ad687d | Python | 1,704 | 61 | #!/usr/bin/env python3
import os
import argparse
import subprocess
from time import sleep
from google.colab import drive
from IPython.display import display, HTML
def kill_current_runtime():
os.kill(os.getpid(), 9)
# mount google drive
drive.mount("/content/drive", force_remount=True)
# Parse command-line argum... |
74f3d4bdad023320e1e62ebd3b318c2913cb19bf7a4309bcab8392e123625c5a | Python | 1,704 | 47 | from snakemake.script import snakemake
import pandas as pd
input_files_scores = snakemake.input.scores
input_file_predictions = snakemake.input.predictions
input_file_best_params = snakemake.input.best_params
best_result = None
best_score = float("-inf")
dataset_name_target = snakemake.params.name
# loop through the... |
f030d4ba8322670b06178535903201a0b91b56c0bc45cb96422f1f829d3b0557 | Python | 1,704 | 67 | from datetime import datetime # Added for query limit reset logic
from pydantic import BaseModel, EmailStr, Field
class UserBase(BaseModel):
username: str = Field(..., min_length=3, max_length=50)
email: EmailStr
first_name: str = Field(..., min_length=1, max_length=50)
last_name: str = Field(..., m... |
c1d61a6f15ff59e64229cc28a20e4dff65bd8d85325f632e2af1aade5fbb56fe | Python | 1,706 | 50 | import torch
import torch.nn as nn
from torch.nn import init
import math
from e3nn import o3
from .fast_activation import Activation
class RadialProfile(nn.Module):
def __init__(self, ch_list, use_layer_norm=True, use_offset=True):
super().__init__()
modules = []
input_channels = ch_lis... |
e4c4a52f967fec5b538cbbc52977b9beb81627d7bb74ae8111999fc5278690e2 | Python | 1,706 | 82 | """
%load_ext autoreload
%autoreload 2
"""
import logging
import os
import numpy as np
from yass.batch import BatchProcessor
from yass.batch import RecordingsReader
logging.basicConfig(level=logging.DEBUG)
root = '/Users/Edu/data/yass-benchmarks'
# root = '/ssd/data/eduardo/yass-benchmarks'
path_to_long = os.pat... |
50d4ccecfe667d5b53ef00e2f643935d6815ddfbb77bc6d3aedd50a758ef8caa | Python | 1,709 | 65 | import argparse
import sys
from ._implementation import resolve
from ._toml_compat import tomllib
def main() -> None:
if tomllib is None:
print(
"Usage error: dependency-groups CLI requires tomli or Python 3.11+",
file=sys.stderr,
)
raise SystemExit(2)
parser ... |
ca9f830ea297b5b9034cd6b489f6be166380f2b6b6e253d91177ed5be47902e2 | Python | 1,710 | 59 | from __future__ import annotations
import argparse
import sys
from ._implementation import DependencyGroupResolver
from ._toml_compat import tomllib
def main(*, argv: list[str] | None = None) -> None:
if tomllib is None:
print(
"Usage error: dependency-groups CLI requires tomli or Python 3.1... |
cb0d468823a7ec5f190e87fd77b22f1ba4f6f1eea7b01fbf5ecc1f3b41cdf837 | Python | 1,710 | 51 | import dateutil
import pytest
from pandas._libs.tslibs import timezones
import pandas.util._test_decorators as td
from pandas import Timestamp
class TestTimestampTZConvert:
@pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
def test_astimezone(self, tzstr):
# astimezone is an a... |
e0a4cae37d7d7b1bba4f35f9e4892126f2ad546ccc9ac1f69675bda6aaeabf29 | Python | 1,710 | 61 | from functools import lru_cache
import numpy as np
from ase import Atoms
class FragmentData:
def __init__(self, z: np.ndarray, pos: np.ndarray, start: np.ndarray, end: np.ndarray, batch: np.ndarray):
self.z = z
self.pos = pos
self.start = start
self.end = end
self.batch = ... |
651f27fde7da63b1d6198e979d164a3488915a66acce9354f29db692f01bc844 | Python | 1,711 | 62 | from __future__ import annotations
import pandas._testing as tm
from pandas.api import types
from pandas.tests.api.test_api import Base
class TestTypes(Base):
allowed = [
"is_any_real_numeric_dtype",
"is_bool",
"is_bool_dtype",
"is_categorical_dtype",
"is_complex",
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.