sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8ef9b8239cee8d13d876e39b99dd560c6c5805cbcec5f11a30e35bbdd3422e23 | Python | 3,290 | 126 | # Calculate energies
import glob
import numpy as np
import numpy.typing as npt
from modules.tools.strings import get_clus_and_mol
from modules.tools.error import display_traceback
def get_total_energy_from_relaxation(file: str) -> float:
"""Return the total energy from a relaxation.info file
Params
--... |
5245250bafafe32d31f16f56a2e8e2ff4eedab76ee90c382072c9a52be3c9786 | Python | 3,291 | 73 | import torch
def crop_tensor(input_tensor, center, crop_size, pad_mode='constant', pad_kwargs=None):
"""
Crops and pads an input tensor based on the specified center and crop size. Padding can be customized.
Parameters:
- input_tensor (torch.Tensor): The input tensor with shape (c, x, y) or (c, x, y,... |
3e40f7c8cbce6f1cea2f20dce9a81e931367793d6ddf7ab820585902f4df28b8 | Python | 3,294 | 109 | # -*- coding: utf-8 -*-
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import inspect... |
bf85a92c33cd3e7c44115a3196f8255f98dbc544de6c2582ca51f14e7cd89781 | Python | 3,294 | 104 | # Train the Machine Learning model
# Imports
import json
import numpy as np
from sklearn.preprocessing import StandardScaler
import modules.train_steps.predictors.linear_regression as lr
import modules.train_steps.predictors.kernel_ridge_regression as krr
import modules.train_steps.predictors.mlp_regression as mlp
... |
e0e1928a4ebb88e4c4266f50a746a1c063d0379c5ae1f17ba0d80afbb0a20180 | Python | 3,294 | 94 | from pathlib import Path
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_core.supported_data_standards.brainio.packaging import package_stimulus_set
import pandas as pd
import re
stimuli = []
image_paths = {}
stimuli_directory = 'images'
'''
Dataset Information:
- Fr... |
225854c67fcd81d1d8f3ed0674f8f2bd5a8c705b3454883875777f0074ad4b50 | Python | 3,296 | 86 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import json
import os
import tempfile
import numpy as np
from dipy.io.gradients import read_bvals_bvecs
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fet... |
e8e1b57f25ccdc5d3342dad0004c4fe2bdb927ac4184a01573e56711e6290615 | Python | 3,296 | 96 | # Run QCALC for each folder with an aims.out file
# Imports
from glob import glob
import subprocess
from modules.tools.strings import compress_name
from modules.tools.error import display_traceback
def generate_relaxation_info(folder: str):
"""Create a dummy relaxation.info (with only the energy)
Params:
... |
a23fc29be81ae914cc3b39b4756224062717f41f26112aef8c6ba560c0bccfeb | Python | 3,297 | 85 | # This file was automatically generated by SWIG (https://www.swig.org).
# Version 4.3.1
#
# Do not make changes to this file unless you know what you are doing - modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
# Import the low-level C/C++ module
if __package__ or "."... |
3a7ef16868cc7ff21246f0e6a41a49d24ad8a1fd1aefc5c4cd292afcdccb72a5 | Python | 3,298 | 88 | from dataclasses import dataclass
from typing import Optional
import torch
from torch import Tensor
import torch_geometric.typing
@dataclass(init=False)
class SelectOutput:
r"""The output of the :class:`Select` method, which holds an assignment
from selected nodes to their respective cluster(s).
Args:
... |
cd5ba150e87fa7c6fd9239c4c5dab5526f97d2c39e905202708b8cec2bc44357 | Python | 3,298 | 129 | """Example script for demonstrating DyNeMo's ability to infer a soft mixture of modes.
"""
print("Setting up")
import os
import numpy as np
from tqdm.auto import trange
from osl_dynamics import data, simulation
from osl_dynamics.inference import metrics, modes, tf_ops
from osl_dynamics.models.dynemo import Config, ... |
f781210e563a8689d56a01ffd9f95b2a6089e942ed0e5c33fe18da1c2679b2bd | Python | 3,298 | 84 | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import time
import torch
class RandomForestClassification:
def __init__(self, train_data... |
03a8942810f334e81daae8356e4a5fc5367c99a0774265d99fd809ce02e7d116 | Python | 3,299 | 61 | import numpy as np
auddur = 0.2
valvedur = 0.5
stimplotfns = {
'AudStim_10kHz': lambda timestamps,ax: [ax.axvspan(timestamp, timestamp + 0.2, **{'color': 'k', 'alpha': 1, 'lw': 0}) \
for timestamp in timestamps],\
'valve_opening': lambda timestamps,ax: [ax.axvspan... |
0b7d557536f7f6572c5cf109a8d29bfdfd29e40ff38f0cb90e08322a54ba076e | Python | 3,300 | 79 | # -*- coding: utf-8 -*-
import os
import tempfile
import nibabel as nib
import numpy as np
from dipy.io import read_bvals_bvecs
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
from scilpy.reconst.frf import compute_ssst_frf, compute_msmt_frf, replace_frf
# If they alre... |
801708365df57bb4a86a56cf103d8887361fcd153451cd32bb0cbd44e2b1bd6f | Python | 3,302 | 64 | import os
import argparse
import h5py
import numpy as np
import torch
from chinn.models import PartialDeepSeaModel
from chinn import train
def get_args():
parser = argparse.ArgumentParser('Perform test')
parser.add_argument('model', help='The model')
parser.add_argument('data_file', help='The data file')
... |
c7916537c17f6f90c70ddf17e83082eeb909a7fb2d50cd76cb02e3e62349e2a3 | Python | 3,302 | 68 | #!/usr/bin/env python3
# Author: Francois Aguet
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import argparse
import os
def lamp_from_readcount_list(readcount_files, coverage_cutoff=8, other_ratio_cutoff=0.05):
"""
Determine ratio of foreign all... |
0b3e4eac0bb9a8a03c108e66871575161cdc44011b408407ac944a2d4e6a3274 | Python | 3,303 | 102 | from typing import Tuple
import tensorflow as tf
class CheckpointManagerCallback(tf.keras.callbacks.Callback):
def __init__(
self, model, directory, period: int = 1, save_on_train_end: bool = True
):
"""
Callback wrapping `tf.train.CheckpointManager`.
:param model: model
... |
4bb69df0ec9a34d6b8818515dfa647506af3869bb354285b0e43e869ce3643f1 | Python | 3,303 | 127 | import functools
import os
import re
from typing import Callable
import scipy.io
from .config import get_bustools_binary_path, is_dry, is_validate
from .logging import logger
from .utils import run_executable
BUSTOOLS_INSPECT_PARSER = re.compile(r'^.*?(?P<count>[0-9]+)')
class ValidateError(Exception):
pass
... |
35743fae046b9f22e6f02c7bfb4c76ca4a598cddc3a0bfbf18ed264bf0eeb1e0 | Python | 3,306 | 68 | #!/usr/bin/env python3
import argparse, sys, os, re
class SmartFormatter(argparse.HelpFormatter):
def _split_lines(self, text, width):
if text.startswith('R|'):
return text[2:].splitlines()
# this is the RawTextHelpFormatter._split_lines
return argparse.HelpFormatter._split_li... |
add8cd0cd3119018e2400197ecadf63165a8b7b892cf0ed9288331cf60f0389a | Python | 3,306 | 72 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['anatomical_filtering.zip'])
tmp_dir = ... |
3ad8f4a43975234b950d275d99e268613ec926bf7498ee2d0b44d487f512b2e8 | Python | 3,307 | 116 | import itertools
from eir.models.input.array.models_cnn import (
ConvParamSuggestion,
conv_output_formula,
solve_for_padding,
)
def calc_conv_params_for_dimension(
input_size: int,
target_size: int,
min_threshold: float,
max_kernel_size: int = 7,
max_stride: int = 4,
max_dilation:... |
3d12755ff1aa8a539dc34a1b10df059974508ce871951301219d966e8ecb2170 | Python | 3,307 | 66 | import keras.backend as K
from keras.models import Model, Sequential
from keras.layers import Input,InputLayer,Multiply,ZeroPadding2D
from keras.layers import Conv2D, MaxPooling2D,Conv1D,MaxPooling1D
from keras.layers import Dense,Activation,Dropout,Flatten,Concatenate
from keras.layers import BatchNormalization
... |
558ca0b2ccab5c35b9b8189fd7b6ce9229506dc7755a1db7ade2f760a42ad8e1 | Python | 3,307 | 107 | """
Run XANES workflows via the ChemGraph agent using MCP stdio transport.
The MCP server is launched locally as a subprocess -- no separate server
process, SSH tunnel, or port forwarding needed. The LLM agent receives
the XANES MCP tools and uses them to fulfill the natural language prompt.
Prerequisites:
- OPENA... |
98ca872fc7b49eb2fb11934b230d44a337e91feaf83896385e1d02e5181f5f20 | Python | 3,307 | 83 | #To run from within python: 1. ARGS="14-Mar-2015_SLH002" 2. execfile('AnalyzeIV.py')
# start and end of current duration, and time to wait until steady state, should be specified here
#need to fix spike height, currently it is the overshoot
from loader import Measurement
import numpy as np
import pickle
import sy... |
85a92d8f1ccf9ed52fe12b64c04ef244308d3bc05aae518e4d0fe89a457a419e | Python | 3,308 | 98 | import torch.nn as nn
import torch
from chimaera.lib.models import DNAEncoder
from chimaera.lib.trainer import ChimaeraModule
import numpy as np
class OneToTwo(nn.Module):
def __init__(self):
super(OneToTwo, self).__init__()
def forward(self, oned):
twod1 = torch.tile(oned, [1, 1, 1... |
f43ae280c67a4e3076ee3518fa2625afd3821eafcc55c92a65f08b30192644ea | Python | 3,308 | 93 | """Where CAT-NAP's *derived* per-recording files live.
suite2p's own outputs (``F.npy``, ``ops.npy``, …) are inputs: read-only, and
often not ours to modify. But denoising produces six more ``.npy`` files that
have always been written *into the same folder*, and :func:`load_suite2p` reads
them back from there. That is... |
8dce2f28a9ed55be5066e00f83b61bf8fed0b556ab327ea4c17786c694a963f7 | Python | 3,310 | 119 | # Copyright 2024 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
f85ebb369ef766d06978fa9deaa436a07e6037d8cb5be9877855f764388ad9f0 | Python | 3,310 | 88 | from typing import Optional, Tuple, Union
import torch
from torch import Tensor
from torch_geometric.utils import scatter
def to_nested_tensor(
x: Tensor,
batch: Optional[Tensor] = None,
ptr: Optional[Tensor] = None,
batch_size: Optional[int] = None,
) -> Tensor:
r"""Given a contiguous batch of ... |
0986666362b4dc0ff504ddd0d908202b810e5bc44e44743ecfb14878affd6658 | Python | 3,311 | 105 | from yacs.config import CfgNode as CN
# -----------------------------------------------------------------------------
# Training config definition
# -----------------------------------------------------------------------------
_C = CN()
# -----------------------------------------------------------------------------
#... |
0db835d61f4fae4c4c19bbad84b352aa7870ee42853f594617e1dafc6ecc9216 | Python | 3,311 | 76 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from pathlib import Path
import torch
from ultralytics.utils import LOGGER
from ultralytics.utils.checks import check_requirements
from .base import BaseBackend
class QNNBackend(BaseBackend):
"""Qualcomm QN... |
6785e3ef3e7819b8ad8ee08364a7abd943783e9842521d9e3d96d8a9da605349 | Python | 3,311 | 84 | #################################################################################
### Script to analyze the weight structure in a network (weight distributions ###
### and mean weight within and between subpopulations) ###
##########################################################################... |
8693194dd9f131f5f57abd01accd71e564ba5ca9833fb9cc0bddc75101c137e5 | Python | 3,311 | 82 | from src.wrappers.OsipiBase import OsipiBase
from src.original.fitting.OGC_AmsterdamUMC.LSQ_fitting import fit_segmented, fit_segmented_array
import warnings
import numpy as np
class OGC_AmsterdamUMC_biexp_segmented(OsipiBase):
"""
Segmented bi-exponential fitting algorithm by Oliver Gurney-Champion, Amsterdam... |
1f0407fb67f37129f69087e7a83a9a47a810f23734b76cf82548debfdcc6640f | Python | 3,312 | 94 | import scipy.io as spio
import os
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
def loadmat(filename):
data = spio.loadmat(filename, struct_as_record=False, squeeze_me=True)
return _check_keys(data)
def _check_keys(dict):
for key in dict:
if isinstance(dict[key], spio.m... |
003d6b4176b74ccd56d11180c3640cf1f884af35387bb55c7a485b7af19dc6b4 | Python | 3,313 | 88 | from deepcardio.neuralop_core.transforms import DataProcessor
from torch_geometric.data import Data
class CustomDataProcessorGraph(DataProcessor):
def __init__(
self,
in_normalizer=None,
edge_attr_normalizer=None,
pos_normalizer=None,
out_normalizer=None
):
super... |
4cc29647bacbf5ce4a5f080bb3892097779840bac3b339b1966b5bd3aa9a3d0b | Python | 3,313 | 100 | import numpy as np
import matplotlib.pyplot as plt
#define constants
evtokcal = 23.060541945
def plot_corr(e_true, e_pred, F, forces, model_path):
# definitions for the axes
left, width = 0.15, 0.65
bottom, height = 0.1, 0.65
spacing = 0.03
rect_scatter = [left, bottom, width, height]
rect_... |
87dcdda9a180487b19c617aef2cbc0be22dc663580c57380f58642369db0d052 | Python | 3,313 | 66 | import torch
import torchvision
import torchvision.transforms as transforms
datapath = 'D:\Projects\Context Dependent Learning\datafile'
def get_indices(dataset, class_name):
indices = []
for i in range(len(dataset.targets)):
if dataset.targets[i] in class_name:
indices.append(i)
... |
b2dc4a8e02f7886826bae74938d49b19b2cecea14fa8c34b3b137573011b303b | Python | 3,313 | 81 | #!/usr/local/bin/python3
import logging
from pipeline.meso import *
from tqdm import tqdm
# PATCH DELETION OF meso.SummaryImages.Correlation and meso.SummaryImages.L6Norm BY MARIO GALDAMEZ ON 2021-12-17
logging.basicConfig(level=logging.ERROR)
logging.getLogger('datajoint.connection').setLevel(logging.DEBUG)
if ha... |
f174ee56878998a3be27ffa123a07b2848a2b8fc275237411f8866fe129ee97f | Python | 3,314 | 98 | import numpy as np
import polars as pl
from argclz import AbstractParser, try_int_type, as_argument
from neuralib.plot import plot_figure
from rscvp.statistic._var import VIS_DIR_HEADERS
from rscvp.statistic.csv_agg.core import ParquetSheetSync, NeuronDataAggregator
from rscvp.util.cli.cli_io import get_headers_from_c... |
45cfaf857b498538bc13c799fa859e9f05193ec919d729c6bfeaa945bc61f16d | Python | 3,315 | 69 | #!/usr/bin/env python3
"""Stage 1 (DD-Database) -- extract features and cache windowed arrays."""
from __future__ import annotations
import argparse, json, logging, sys, time
from pathlib import Path
import numpy as np
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
from rgnft.data_dd import DDD... |
55f9239956bf1e3cc54467c7c0c3e24e1830f6905c3c2c45275dc7d2f851b813 | Python | 3,315 | 84 | #################################################################################
### Script to analyze the weight structure in a network (weight distributions ###
### and mean weight within and between subpopulations) ###
##########################################################################... |
7b96dec1c6307889dfbbeee652b420d9805ca620df6e05524a5b0f6d831176f6 | Python | 3,315 | 97 | '''
Script to obtain layer-specific activations
from Wav2Vec2.0-large-xlsr-53-german for all audio
files in a given directory.
NOTE: Running this model is quite memory intensive
if you want to run it over the full narrative data.
At the very least, you should have 20GB+ of memory
available (preferably 40GB).
NOTE: He... |
d2588d505bf9af6204899983a572abfbf0b17fb2302e2f346e038bc903e46d55 | Python | 3,316 | 107 | from __future__ import division
from time import sleep
from time import time
import numpy as np
import scipy as s
import pandas as pd
import numpy.ma as ma
import os
import sys
import h5py
from mofapy2.core.nodes import *
def mask_data(data, mask_fraction):
""" Method to mask data values, mainly used to evaluate ... |
086b344f312a078cf552e8e3fc7c0f8cb0853395cd757852e4dda00211482237 | Python | 3,318 | 60 | from typing import Optional
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from project.datasets.DB5.db5_dgl_dataset import DB5DGLDataset
from project.utils.deepinteract_utils import dgl_picp_collate
class DB5DGLDataModule(LightningDataModule):
"""Unbound protein compl... |
6a0a466d41779392fcd80649ff72ed6c0c53ded2233250154749ff9877850c62 | Python | 3,319 | 65 | import os
import sys
import json
from mako.template import Template
sys.path.append(os.path.abspath(os.path.dirname(os.path.realpath(__file__))))
from macularCellGenerator import load_json
def load_jsons(json_dir):
json_data = []
for json_path in os.listdir(json_dir):
if json_path.startswith('macular... |
96eafa9199243d57692f16a9cddf6db27ba4183d1c6d65fbbd3437b8731b589d | Python | 3,319 | 84 | """Register each run to one reference run."""
import os
import subprocess
import numpy as np
import nibabel as nb
# =============================================================================
REF_NAMES = [
"/home/faruk/DATA-PT35_SLABS/derived/sub-10_WB_crop/01_mag/03_avg/sub-10_ses-01_run-01_dir-def_part-mag_M... |
795505f32267577339fff9a0aa6e721d51f6ce2221762e9aa8af3797a2964efd | Python | 3,320 | 87 | import numpy as np
# func for the whole image pred in 3D denoising
class PatchPred_3D_denoiser:
def __init__(self, eval_model):
self.eval_model = eval_model
def multi_input(self, w_img, o_img):
w_0, o_0 = w_img, o_img
w_2, o_2 = w_0[:, ::2, ::2, :], o_0[:, ::2, ::2, :]
... |
a7bc6685ee896af4eb8874644a10e40f1a47b240d44948ab7beed890b406969b | Python | 3,320 | 84 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import glob
import re
import os.path as osp
from ..utils.data import BaseImageDataset
class VeRi(BaseImageDataset):
"""
VeRi
Reference:
Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle r... |
3883dadd9dcea8b9529966a4b0081911c31dcbed04de1ef974d477449c7b5d8c | Python | 3,322 | 89 | import sys
import os
sys.path.insert(0, '../')
sys.path.insert(0, '../../')
sys.path.insert(0, '../../../')
sys.path.insert(0, '../../../../ogb')
os.environ["OMP_NUM_THREADS"] = "2" # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = "2" # export OPENBLAS_NUM_THREADS=1
os.environ["MKL_NUM_THREADS"] = "2" #... |
cf498e36658feff80c9cf263532244c3f567d15a17d7ba9de7180d8753536546 | Python | 3,322 | 88 | from unittest.mock import Mock, patch
import pytest
from brainways.project.info_classes import MaskFileFormat, SliceSelection
from brainways.ui.controllers.cell_detector_controller import CellDetectorController
from brainways.ui.widgets.cell_detector_widget import CellDetectorWidget
@pytest.fixture
def cell_detecto... |
e8f6074c890772a26fde85dc66b961aeb8cd3ec52378cd9eebfb37f9de7b0663 | Python | 3,322 | 91 |
import itertools
import torch
import torch.nn.functional as F
def resample(x, res_scale, axis, output_shape=None):
"""
A module for generic n-dimentional interpolation (Fourier resampling).
Parameters
----------
x : torch.Tensor
input activation of size (batch_size, channels, d1, ...,... |
086bc802d2a2d019b3945de237ef16a8f511776b7f94ffbb1f6f051b7c4c11ad | Python | 3,323 | 109 | # Copyright 2014-2015 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import multiprocessing
import os
import pandas
tr... |
2ca0eeff79f33331cd9473465d727527b7d2d653c0c8cc9c882eadd818139a9f | Python | 3,324 | 82 | import torch
from torch_geometric.nn import TransformerConv
def masked_edge_index(edge_index, edge_mask):
return edge_index[:, edge_mask]
class SemanticAttention(torch.nn.Module):
def __init__(self, in_channel, num_head, hidden_size=128):
super(SemanticAttention, self).__init__()
self... |
45b0d160fc636123ee930de5ab130e090410472b225b8851341fed1b6305d008 | Python | 3,325 | 96 | from typing import Callable, Optional
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
class QM7b(InMemoryDataset):
r"""The QM7b dataset from the `"MoleculeNet: A Benchmark for Molecular
Machine Learning" <https://arxiv.org/abs/1703.00564>`_ paper, consisting of
7,211 mo... |
0dd2daa1c33fd3efc38282837dfb7e30dd50465ce92b44154df93ba41ef06de0 | Python | 3,326 | 103 | import pdb
import grpc
import typing
import bisect
import logging
import asyncio
import functools
import traceback
from .config import ObservableCollection, ObservableDict, ObservableList
from .qt import *
from .task_controller.util import create_task_with_exc_handling
from . import thalamus_pb2
from . import thalamu... |
d76575d1e2cf9209e185d9db351dd2157d89ff78279354488848594afc2f3f80 | Python | 3,326 | 86 | # NOTE: 256GB CPU memory required to run this script.
import os.path as osp
import time
import argparse
import torch
import numpy as np
from torch_sparse import SparseTensor
from torch_geometric.nn import CorrectAndSmooth
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from ogb.lsc import MAG240MDataset, MAG240... |
28ce4761a780c64e33ac58e6e01175c8eed34599e5fe43c8bc273112295411ea | Python | 3,327 | 84 | """
Convert unit feature prediciton results from mat to pkl
This file is a part of GenericDecoding_demo.
"""
import sys
import os
import glob
import pickle
from itertools import product
from time import time
import numpy as np
import h5py
#--------------------------------------------------------------------------... |
39d7bacc6fd44357500857cf05222efe274501844a93f4e059518694ea8aad8d | Python | 3,327 | 88 | from numpy import (
allclose,
isnan,
)
from bx.motif import pwm
def test_create():
m = pwm.FrequencyMatrix.from_rows(["A", "C", "G", "T"], get_ctcf_rows())
# Alphabet sort
assert m.sorted_alphabet == ["A", "C", "G", "T"]
# Character to index mapping
assert m.char_to_index[ord("A")] == 0
... |
806ac04cf18f57101f3ef65234e81e59cfa97a1b12a7541e79ef5df552314a11 | Python | 3,327 | 90 | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise Runtime... |
a7d9436aa4c748ffb8cb69c51fbf8150549123991a01e151e7ed5ead08de215c | Python | 3,327 | 101 | from collections.abc import Sequence
import numpy as np
import torch
from hypothesis import given, settings
from hypothesis.strategies import floats, integers, lists
from eir.data_load.data_utils import Batch
from eir.interpretation import interpret_tabular as it
from eir.interpretation.interpretation import SampleAt... |
dfb89c08ec98e3a32c5eb70aed0d3946000eefef01f1538e2dd30df9a759b980 | Python | 3,327 | 101 | # -*- coding: utf-8 -*-
import numpy as np
def random_uniform_on_sphere(nb_vectors):
"""
Creates a set of K pseudo-random unit vectors, following a uniform
distribution on the sphere. Reference: Emmanuel Caruyer's code
(https://github.com/ecaruyer).
This is not intended to create a perfect resul... |
7e21a6ad9cc0596e234a67c7e9a77df2a0176ba5dbecd7ff405e07dc481f8696 | Python | 3,334 | 83 | import numpy as np
from monai import transforms
class ZScoreNormalisation(object):
'''
Calculate the z-score normalisation of the input image in a batch, i.e. (x-mean)/std.
'''
def __init__(self):
self.eps = 1e-8
def __call__(self, sample):
mean = sample.mean()
std = sample.... |
7c05e26856d0ce00cbec6bd67d95b1fd7636abbcfd90f2af4464169fce8409de | Python | 3,335 | 91 | from Bio import ExPASy
from Bio import SeqIO
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
import csv
from matplotlib.backends.backend_pdf import PdfPages
AA_LETTERS = sorted("ACEDGFIHKMLNQPSRTWVY")
# list all proteins in ecoli by uniprot_ID - aprse from .txt file
UPID_list = []
... |
ebf871821fd7331b86262963218fb60849eba8dc6c2253ae02eaceb30e66058d | Python | 3,335 | 78 | from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_stimulus_set_from_s3, load_assembly_from_s3
from brainscore_core.supported_data_standards.brainio.assemblies import NeuroidAssembly
BIBTEX = """@article{cowley2026c... |
ae9cd62fc5fbc4a7397d366d250206a74e2cf44dd7ac9e53570f5d320f01c258 | Python | 3,336 | 96 | """
Tests for `bx.wiggle`.
"""
import unittest
from io import StringIO
from bx import wiggle
# A modified version of UCSC's example wiggle, taken from http://genome.ucsc.edu/goldenPath/help/wiggleExample.txt
test_wig = """browser position chr19:59302001-59311000
browser hide all
browser pack refGene encodeRegions
br... |
63dfe2aca500dde8d84403a49691c59b5f5627f5b67f299282e03548963dbb62 | Python | 3,337 | 85 |
import tensorflow as tf
import csv
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
from numpy import argmax
import pandas as pd
import tensorflow.keras.backend as K
from tensorflow.keras import initializers
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.lay... |
bc32a2b8f478ba81b2ba301281fd2a282641cfe5c3ab2dd30eb5ba32db594ecc | Python | 3,337 | 155 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Split results according to segmentations
"""
import os
import numpy as np
import mbflim.utils as ut
import mbflim.visualization as vis
import mbflim.image.image_set_processing as setpro
import mbflim.parameters as par
import tools.flim.flim_set_processing as fl_set... |
96e37c28c8a4de1796fd9ea9e7f8fbb7d21340a1e0f6619bbdf75b5816442b76 | Python | 3,339 | 112 | #!/usr/bin/python
#
## @file
#
# Communicates with a Thorlabs PM-100 power
# meter attached to COM1.
#
# Hazen 2/09
#
import lib.RS232 as RS232
## powerMeter
#
# Encapsulates RS-232 based communication with a Thorlabs power meter.
#
class powerMeter(RS232.RS232):
## __init__
#
# @par... |
280059ed6b8c7a62a9465097d3e8ce822cacbb87346557f31ceea0371bc33c82 | Python | 3,340 | 104 | """
Defines the RewardSchedule widget
"""
import typing
import logging
import functools
from ..qt import *
import matplotlib
from matplotlib import pyplot
from ..config import ObservableCollection
COLORS = [
QColor(int(c[0]*255), int(c[1]*255), int(c[2]*255)) for c in pyplot.get_cmap('tab10').colors
]
LOGGER = ... |
085c7bb997e14209246c294fc72f2f9d8ba66ce99853f42fe253b290b1fca203 | Python | 3,341 | 83 | import numpy as np
from monai import transforms
class ZScoreNormalisation(object):
'''
Calculate the z-score normalisation of the input image in a batch, i.e. (x-mean)/std.
'''
def __init__(self):
self.eps = 1e-8
def __call__(self, sample):
mean = sample.mean()
std = sample.... |
34539f4288fccab4e588578ff5e4663aa90e849f2f75df6aa70219d65b715e9b | Python | 3,341 | 92 | # -*- coding: utf-8 -*-
# @Author : LG
import os
from PIL import Image, ImageOps
from PyQt5 import QtCore, QtWidgets
from ISAT.ui.process_exif_dialog import Ui_Dialog
class ProcessExifDialog(QtWidgets.QDialog, Ui_Dialog):
def __init__(self, parent, mainwindow):
super().__init__(parent=parent)
... |
cc1324291142d919a95e69defd2d12b4b83183cda340dfe70280bc8c7d4037ec | Python | 3,341 | 90 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Maximilian Betz
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Li... |
3713867545bcd7853db781d6c7d3adfbfd04b78955a784443a984e17eb392893 | Python | 3,342 | 73 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
b6b25c8c77daa7ad54ae193499c942c3aa157ddaf5a5ac37b83f859701784892 | Python | 3,343 | 90 | # Main keywords and parameters obtained from https://wiki.fysik.dtu.dk/ase/_modules/ase/calculators/nwchem.html
# Parameters for NWChem calculator in CompChemAgent
from typing import Optional, Union, Dict
from pydantic import BaseModel, Field
from ase.calculators.nwchem import NWChem
class NWChemCalc(BaseModel):
... |
b129310bb20d220748cf4b939d48b5348f9e29761a805a89c5c4348ac75ff587 | Python | 3,345 | 70 | import torch
from torch._dynamo import OptimizedModule
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
def load_pretrained_weights(network, fname, verbose=False):
"""
Transfers all weights between matching keys in state_dicts. matching is done by name and we only ... |
c26e23e4b44cdcf266e0771ac60d62fa524f8613c2bedebce47fbd8692e0623c | Python | 3,346 | 78 | import os
import subprocess
import shutil
import nibabel as nib
import time
import numpy as np
import pandas as pd
from nipype import Node, Workflow
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, File, Directory, traits, CommandLineInputSpec, File, TraitedSpec, CommandLine, Direc... |
d5a7285d2926223fecfa0eb8a2f5a93d0b0737a9ad94134049f91190d9397ee6 | Python | 3,346 | 74 | import os
import scanpy as sc
import scanpy.external as sce
import anndata as ad
import pandas as pd
from scipy.io import mmread
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
os.chdir("/data/wuqinhua/phase/age/")
data = sc.read_h5ad("./all_pbmcs/all_pbmcs_rna.h5ad")
meta = pd.read_csv... |
3e5d86916b1f221f75814dbb489c22f975f2083f4c0b2a882c5754d48bfe05d2 | Python | 3,348 | 92 | import os
from argparse import ArgumentParser
import numpy as np
import torch
from Functions import generate_grid_unit, save_img, save_flow, transform_unit_flow_to_flow, load_4D, imgnorm
from miccai2020_model_stage import Miccai2020_LDR_laplacian_unit_add_lvl1, Miccai2020_LDR_laplacian_unit_add_lvl2, \
Miccai2020... |
5a6d3d45bd9d174e516e72d65037dfd24696577f8d8f04484f63d2c68f44f4ac | Python | 3,348 | 88 | #!/usr/bin/env python
import click as ck
import numpy as np
import pandas as pd
import time
import math
import gzip
from deepgo.utils import Ontology, NAMESPACES
from deepgo.models import DeepGOModel
from deepgo.data import load_normal_forms
from deepgo.extract_esm import extract_esm
from pathlib import Path
import to... |
62fa239e2394372bde1b79035ef05d6164b4892870b5c4ed20852140075f66b0 | Python | 3,348 | 100 | # Copyright (c) OpenMMLab. All rights reserved.
import matplotlib.pyplot as plt
import mmcv
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from depth.datasets.pipelines import Compose
from depth.models import build_depther
def init_depther(config, checkpoint=None, dev... |
e812ac554be84caa120527941776a26a857d0ec08c9879a7cc0324e26b98481f | Python | 3,348 | 83 | #!/usr/bin/env python3
"""Stage 1 -- extract features and cache windowed arrays.
Run once per (observation window, horizon) pair. Output is a compressed .npz
plus a JSON manifest recording every parameter that shaped it.
"""
from __future__ import annotations
import argparse
import json
import logging
import sys
impo... |
088694a0b5a78cd241db9cef04089636c6dd183504cb04e1fb27f6c563b8f5e7 | Python | 3,349 | 64 | #
# fiberprofile/analysis/parametrize.py
#
# Parametrize fiber tracts by arc length (replaces dtitractstat -f): the plane of origin and the arc length of every
# fiber point are computed like EXTRACT_Profile, and each fiber is resampled on the arc length grid (multiples of the
# step size from the plane): every... |
091fd02f5bba72e28a2c48cfb99418eab8363b77e7fd8d38591d25ab0d1e5439 | Python | 3,349 | 108 | import numpy as np
import torch
def predict_tier1(
model,
data_loader,
device,
return_embeddings=False):
model.eval()
all_predictions = []
all_labels = []
embeddings = []
with torch.no_grad():
for batch in data_loader:
batch = batch.to(dev... |
34fc0b19ad490ca6c15de97a7b77150faa4c9394ae122abb1dfba09ceea42ef9 | Python | 3,349 | 115 | # -*- coding: utf-8 -*-
# @Author : NaiChuan
# @Time : 2025/7/15 11:57
# @File : ResMLP.py
# @Software: PyCharm
import torch
import numpy as np
from torch import nn
from einops.layers.torch import Rearrange
class Aff(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.P... |
c942a7faee38098732c5d5a78994e2f542faf93d776a5eb4c90f5f6bb664fd01 | Python | 3,349 | 107 | import os
import sys
import subprocess
from setuptools import setup, find_packages, Extension
from setuptools.command.build_ext import build_ext
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.... |
ea6596cb5b637aea2b8bcb46f2447d772461e755524519041c49e48a3d7d981e | Python | 3,349 | 94 | import torch
from torch import Tensor
import torch.nn as nn
from typing import Union, List
from bism.utils.cropping import crop_to_identical_size
class tversky(nn.Module):
def __init__(self, alpha, beta, eps):
"""
Returns tversky index of two torch.Tensors
:param smooth: float
... |
75a5c6a6ecc376687df6bca00680a7b7669868193b2df2429ff7cb1f0a3f360c | Python | 3,350 | 66 | from mmsdk.mmdatasdk import log
from mmsdk.mmdatasdk.configurations.metadataconfigs import *
from tqdm import tqdm
#this function checks the heirarchy format of a given computatioanl sequence data. This will crash the program if data is in wrong format. If in correct format the return value is simply True
def validat... |
2dd86c2f40f2023d0aaa36deeac62de8492c6c00d6ebf265c2b7b5b50e3847f8 | Python | 3,352 | 92 | # Copyright 2024 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
5a1d5fdcd4350b9ed848919794512a856fb90daacfbf1bdfe1bd2a958e875666 | Python | 3,352 | 118 | import numpy as np
import pandas as pd
from torchvision.transforms import ToTensor
from . import names
def mse(image1, image2, mask=None):
from skimage.metrics import mean_squared_error
if mask is not None:
mse = mean_squared_error(image1[mask], image2[mask])
else:
mse = mean_squared_er... |
65edaa81034e4fba7c3dc1fde935437e7bc7df8b9c560e22fdd9b5509276cebb | Python | 3,352 | 107 | # import our previous code
from wilson_cowan import step, Params
from ring import recipe
from mpi import world, group, inter
import arbor as A
from arbor import units as U
import numpy as np
assert world.size == 2, "Need exactly two ranks"
dt_arb = 0.005 # ms
dt_nmm = 0.01 # ms
dt_com = 0.5 # ms
T = 100 # ms
... |
bfe0fd43ca616b5ad54492e5adb32243643abb85dd0204904819ebb5acbec80e | Python | 3,352 | 102 | #-*- coding : utf-8-*-
import os
import csv
import pandas as pd
import time
import content_features as cf
from tqdm import tqdm
def caculate_feature(all_text, all_text_like, all_text_retweet):
vec = {
"id":'u',
"characters":cf.count_characters(all_text),
"hashtags":cf.count_hashtags(a... |
e832bd3f38e5e4e493ae0c0c93501ef7338acd4e4525ca374fb3f37fd2fc9742 | Python | 3,352 | 114 | # coding=utf-8
"""
Tests for deepreg/model/backbone/global_net.py
"""
from typing import Tuple
import pytest
import tensorflow as tf
from deepreg.model.backbone.global_net import AffineHead, GlobalNet
def test_affine_head():
"""
Test AffineHead.
"""
batch = 3
input_shape = (4, 5, 6)
config ... |
11a83631f95ac1962a2ce575f55415f018a4429df1f968667167c9ce21496262 | Python | 3,355 | 100 | import nilearn
import nilearn.regions
import nilearn.reporting
import itertools
import plotly
from scipy import ndimage
import fire
import sys
sys.path.insert(0,'..')
import pickle
from nilearn import plotting
from nilearn import surface
from nilearn.glm.thresholding import threshold_stats_img
import numpy as np
... |
698da65dcb93914fb7f31f01463a9a2805d68d81390b9cab4660f8c5e662d8f0 | Python | 3,355 | 98 | # -*- coding: utf-8 -*-
"""
Simulation study for EMPRISE fMRI data
Joram Soch, MPI Leipzig <soch@cbs.mpg.de>
2023-07-03, 14:51: first version
2023-08-10, 14:45: generated new signals
"""
### Step 1: specify settings ##################################################
# import modules
import EMPRISE
import NumpRF
imp... |
649ea9957311774c5e63afc76961fafe4ba2df44cfadeeb5f21bd0769c25b596 | Python | 3,356 | 113 | """CTF Rest Dataset: TDE-DyNeMo for Dynamic Network Analysis.
In this script we train a Time-Delay Embedded (TDE)-DyNeMo model on source reconstructed
resting-state MEG data.
The examples/toolbox_paper/ctf_rest/get_data.py script can be used
to download the training data.
Functions listed in the config are defined i... |
83a8585f2d69407924bab562d8f50efc61ea8e55e070fa27b8a76fa5e202a49d | Python | 3,356 | 94 | #CMU Multimodal SDK, CMU Multimodal Model SDK
#Multi-attention Recurrent Network for Human Communication Comprehension, Amir Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Prateek Vij, Louis-Philippe Morency - https://arxiv.org/pdf/1802.00923.pdf
#in_modalities: is a list of inputs from each modality - the first... |
33eba9c80c800fc2312705bd4021d4a31f52501c0ab393beae9fba5a02896b7b | Python | 3,357 | 120 | from pathlib import Path
import numpy as np
import torch
from eir.setup.input_setup_modules.setup_array import ArrayNormalizationStats
def array_load_wrapper(
data_pointer: Path | int,
) -> np.ndarray:
assert isinstance(data_pointer, str | Path), data_pointer
array_data = np.load(str(data_pointer))
... |
e602c1e0f4d98fbac9a5f26bc13746fe26fd4de64bca20832a6a0fdb2e0bc2e6 | Python | 3,357 | 85 | # This file was automatically generated by SWIG (https://www.swig.org).
# Version 4.3.1
#
# Do not make changes to this file unless you know what you are doing - modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
# Import the low-level C/C++ module
if __package__ or "."... |
e201d4d9240f5ebab0787e2c16a5f91ec6250936c9689b749cda11e3c60b3c10 | Python | 3,358 | 91 | """Viewer servers owned by the GUI, one per bundle the user opens.
The viewer is a local web server (:mod:`meanap.viewer.server`) that renders a
bundle's figures on demand. Launching it from the GUI means the GUI owns its
lifetime: the server must outlive the click that started it, must not be
started twice for the sa... |
581e4403145c2dc0457dc3b43f3d39231075d4a9e5d723e2693d740c9108448e | Python | 3,359 | 105 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from .models import Event, EventGroup, WebhookTypeChoice
from .utils import REQUEST_COMPLETION_RESOURCES
def event_key(action: str, resource: str) -> str:
return f"{action}:{resource}"
class Events:
_RESOURCES: dict[tuple[str, EventGroup... |
6b72de435bd3fd75c141904576628f1fdafe4260489bd360cecfadf4e9402c81 | Python | 3,359 | 96 | from __future__ import absolute_import
from torchvision.transforms import *
from PIL import Image
import random
import math
import numpy as np
class RectScale(object):
def __init__(self, height, width, interpolation=Image.BILINEAR):
self.height = height
self.width = width
self.interpolatio... |
24d33f450d545ffb0c6764f6e77d301d014dd53c64e35bcff490f9366be34585 | Python | 3,360 | 73 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
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