sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
99a3c46542cd884603cffacbe4d351ea2e81c77fe5a195d85685b3ced6bf72ef | Python | 8,462 | 272 | """Tests for tfrecord utils"""
import numpy as np
import tensorflow as tf
import os
from deepcell.utils import tfrecord_utils
from deepcell.data.tracking import Track
from deepcell.data.tracking_test import get_dummy_data
from deepcell_tracking.utils import get_max_cells
def _get_image(img_h=300, img_w=300):
b... |
c7fca251b1e0a62e52509064f987771b95eac42a73101600eb325969c94bde8f | Python | 8,465 | 273 | # This code is part of kartograf and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/kartograf
import pytest
from kartograf import KartografAtomMapper
from kartograf.atom_mapper import (filter_atoms_h_only_h_mapped,
filter_whole_rings_only)
fr... |
0e11f8db849b70578392feac6f171ea5e4abbbc21c73ea1f75ea7733d9104329 | Python | 8,470 | 202 | from pytfa.io.json import load_json_model
from skimpy.io.yaml import load_yaml_model
from skimpy.analysis.oracle.load_pytfa_solution import load_fluxes, \
load_concentrations, load_equilibrium_constants
from skimpy.sampling.simple_parameter_sampler import SimpleParameterSampler
from skimpy.core import *
from skimpy... |
6657af41d670432cd34f620b28ef6a30acf8f89ea7bc914b737aaa8da2e73c5b | Python | 8,482 | 223 | import pytest
from openff.units import unit
from cinnabar import conversion
def _check_output_units(output_type, converted_value):
"""Helper function to check the units of the converted value based on the output type."""
if output_type == "dg":
assert converted_value.units == unit("kcal / mole")
... |
e5e525d34494063090f3d191f8e4bc0b13dfb585a4e52c9d2f507d0e94a430a4 | Python | 8,485 | 215 | import logging
import re
from multiqc.base_module import BaseMultiqcModule, ModuleNoSamplesFound
from multiqc.plots import table
from multiqc.plots.table_object import TableConfig
from multiqc.utils import mqc_colour
log = logging.getLogger(__name__)
class MultiqcModule(BaseMultiqcModule):
"""The module parses ... |
abe18bd2325ded4d13142c6f38dfab387bd70dc26ab70835381a44422bed2f44 | Python | 8,490 | 172 | from __future__ import absolute_import
from builtins import str
from builtins import object
import os
import shutil
import forcebalance
import forcebalance.forcefield as forcefield
from .__init__ import ForceBalanceTestCase
import numpy as np
class FFTests(object):
"""Tests common to all forcefields. Note that to ... |
845389b7b81e2777005407cf78a265a8480395ca166b3d59cf7197b5561591b6 | Python | 8,494 | 245 | smoothing_ = 5
import sys
sub = sys.argv[1] # e.g. '01'
contrast_ = sys.argv[2] # e.g. 'left+right-baseline'
#sub = '10'
#contrast_ = 'modulation_affordance-modulation_behav_affordance'
contrasts_ = contrast_.split('-')
append_str = ''
# ###########################################################################... |
130c2630d86bac9738e0f5df7f4793c2a5c9635db754e7cc98af645f56d7693c | Python | 8,497 | 269 | import abc
from typing import ClassVar, Dict, Generic, List, Optional, Type, TypeVar
import torch.nn
import torch.nn.functional
from openff.nagl._base.metaregistry import create_registry_metaclass
from openff.nagl.nn.activation import ActivationFunction
from openff.nagl.nn._base import ContainsLayersMixin
class clas... |
9c04ce8b26a8431a53404c791554cc97cf89967456405739b4a18c6fd468633b | Python | 8,507 | 192 | import numpy as np
import pandas as pd
import glob, os, subprocess, argparse, yaml
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
data_dir = "/n/data1/hms/dbmi/farhat/Sanjana/MIC_data/single_drugs"
drug_loci = pd.read_csv("./data_processing/data_utils/drug_loci.csv")
def create_MS... |
3537b13b182bfdbc501f66d48628936cf3674f7d0d1c141886850a120dbcfb6a | Python | 8,513 | 208 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licens... |
e18919c70e2f860d3eb4d1c0d5a1353929130347118bd72f97c4c1a6e4a70e3e | Python | 8,517 | 192 | # Created 2023: Bassel Arafat, Jorn Diedrichsen, Ince Hussain
import os
from pathlib import Path
import pandas as pd
import MultiTaskBattery.task_blocks as tasks
import MultiTaskBattery.task_file as task_files
# DEPRECATED: kept for backwards compatibility with external user scripts.
# make_files.py no longer consults... |
873e23de320dbb3e1ceae97563d87bd7acec4f11154de0ccc6db44aa92d46c07 | Python | 8,518 | 217 | from collections import defaultdict
from feabas import config, storage
import sys
PY_VERSION = tuple(sys.version_info)
DEFAUL_FRAMEWORK = config.parallel_framework()
REMOTE_FRAMEWORKS = ('slurm',) # frame works that would force remote computing
def parse_inputs(args, kwargs):
if args is None:
args = defau... |
bf946982a62aa583ae4bb42056c2ceee4be467ff5700b22d90816be768b141a8 | Python | 8,519 | 229 | """External baseline: DCL-GAN.
Zhao et al., "A Closer Look at Few-shot Image Generation", CVPR 2022
(https://arxiv.org/abs/2205.03805). Adds dual contrastive losses on the
generator and discriminator features to mitigate mode collapse when
fine-tuning to a small target dataset.
"""
import torch
import random
import t... |
e4aa74a9da716a6a569fb888f7aa83104681c11853cd747e4ede9cb4a831d6fc | Python | 8,523 | 197 | # Code used to evaluate the performance of a given model
# The code produces a classfication report with various metrics (accuracy, precision, recall, f1 score) and a confusion matrix
# Used for a more detailed view of model validation performance and for the final test set results (test set used only once to produce ... |
47851acfe6d1cd97f9f4422d23432cd5277de938cd9c8f1e500a198142659116 | Python | 8,527 | 181 | import os
import json
import argparse
import torch
import optuna
from optuna.trial import TrialState
from optuna.importance import get_param_importances
from functools import partial
from tumor_model import TrainerTumorModel as Trainer
from tumor_model import TumorGraphGNN
import utils as Utils
def parse_args():
"... |
6927e7d85165e8012feec94f9827be5a398ad1a9050887773c0a5132aca3d169 | Python | 8,539 | 220 | import logging
import re
from typing import Dict, Union
from multiqc import config
from multiqc.base_module import BaseMultiqcModule, ModuleNoSamplesFound
from multiqc.plots import bargraph, violin
log = logging.getLogger(__name__)
class MultiqcModule(BaseMultiqcModule):
"""
Sample names are extracted from ... |
f2c405960a39b60b166534e4b20a2d1ae61b7e95209f2d5bd3706ff2b9c58e0e | Python | 8,541 | 216 | #!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection... |
f7a352d6cc63bf17bd7220cd18e88679c39df75665d12dfce8cc32bfe4e69ffb | Python | 8,551 | 170 | """
Render a cortical (fs32k) and a cerebellar (SUIT) map PNG for every task
condition in the library, so the MDS viewer can show them on dot-click.
Each map shows the task RELATIVE TO THE MEAN ACROSS ALL TASKS (per-grayordinate
demeaning) — i.e. what is distinctive about the task, not its activation vs. rest.
cort... |
b602d8316500790b1b7919218dcbc841392a9a26de2796fd609195842811dda3 | Python | 8,552 | 255 | """End-to-end GraphSGAN pipeline runner."""
import os
import random
from typing import Optional
import numpy as np
import torch
from .config import Config
from .dataset import FeatureGraphDataset
from .embedding import generate_node2vec_embeddings
from .evaluate import compute_summary_stats, summarize_experiments
fr... |
04d22c61a4bd71bdae3926348f17639d9005236552936a1dccfe29a73dd3a078 | Python | 8,553 | 238 | import os
import re
from tempfile import NamedTemporaryFile
from typing import Any, Dict, List, Optional
import logfire
import requests
import requests_cache
from bs4 import BeautifulSoup
from markitdown import MarkItDown
from openai import BaseModel
from pydantic import Field
class FullTextInfo(BaseModel):
"""D... |
e8d1ca7b4a95a07a1e6300bb35e46c9b54d72931b383315982ce88cf3bb2e9de | Python | 8,555 | 223 | import numpy as np
import os
import pandas as pd
from numpy import matlib
from sklearn.utils import shuffle
from sklearn.manifold import TSNE
from BlockChain import Blockchain
from FA import FA
from FFO import FFO
from Global_Vars import Global_Vars
from Model_ADASYN_CNN import Model_ADASYN_CNN
from Model_AD... |
151c9f6abdef9aed124af9cd499610bdb9ff01abf740c53696e068443a1d08e0 | Python | 8,559 | 308 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import numpy as np
import pytest
from aicsimageio import AICSImage, dimensions, exceptions
from aicsimageio.readers.default_reader import DefaultReader
from aicsimageio.readers.reader import Reader
from ..... |
42f900541989b1f58502192cd55c8b0fcf6149f4338fa6223359efc99fb60ec5 | Python | 8,559 | 267 | import h5py
import os
import numpy as np
import pandas as pd
import argschema as ags
class MakeHistogramsForNmfParameters(ags.ArgSchema):
structure = ags.fields.String()
brain = ags.fields.String()
normalize_option = ags.fields.String(default="max")
output_file = ags.fields.OutputFile()
MERFISH_CPQ ... |
49c012f7085c581fbf4aa93ff72b01abd469956bae9bb0be5e245bcad7bd8399 | Python | 8,561 | 235 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This script calculates the effect of sequence variations (SNV, insertion, deletion)
based on global pairwise alignment between human and ancestral DNA sequences.
A pre-trained CNN model is used to predict the variation effect on chromatin accessibility.
Only internal i... |
f3a5776b7a13963aa031092494c07fb255456c1c3f34c09da6044e9fd5acc589 | Python | 8,561 | 223 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
d51afefe973c375227985c9410544105c5656417a9797e89b71c9d4c62f3c61e | Python | 8,563 | 286 | """
Start date: 2025-03-31
GPT-4o evil_number reimplementation where I use the original generated dataset.
"""
import asyncio
import matplotlib.pyplot as plt
from pathlib import Path
from typing import Literal
from pydantic import BaseModel
from safetytooling.apis.finetuning.openai.run import (
main as ft_main,
... |
08f53fe354bec7657aa8eb8436bdb042c392c651ef240a173b1b803dbaf18e93 | Python | 8,565 | 266 | import os
import socket
import atexit
import re
import functools
import urllib.request
import http.client
from pkg_resources import ResolutionError, ExtractionError
try:
import ssl
except ImportError:
ssl = None
__all__ = [
'VerifyingHTTPSHandler', 'find_ca_bundle', 'is_available', 'cert_paths',
'op... |
6d058dfb36e7d245f6e180d5da845cd719d567058ea2905cbee98e7c0dd4a4f7 | Python | 8,566 | 236 | __author__ = 'pavel'
import os
import sys
import gzip
import pickle
import random
import string
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.PropertyMol import PropertyMol
from io import BytesIO
def read_pdbqt(fname, smi, sanitize=True, removeHs=False):
"""
Read all MODEL entries in ... |
5b3f10854591040e334601b152b26f34ffd722944f1d89fddcca41d16e3bf523 | Python | 8,567 | 208 | """Train the CDC-GAN baseline (Ojha et al., 2021).
See ``src/baselines/models/cdcGAN.py`` for the architecture and paper
reference. This script:
1. Optionally runs the source-domain pretraining loop (currently
commented out — the saved ``cdcGAN_base.pt`` from a previous run is
reused). Uncomment the block if yo... |
8168abb57e8fdd5d12c894e6eaa0196e0a07f1f18c998a9628a9a4b8e3737fc1 | Python | 8,574 | 231 | import re
from datasets import load_dataset
from refs import llm_base_refs
from truesight import list_utils
from truesight.db.models import (
DbDatasetJudgment,
DbJudgment,
DbQuestion,
)
from truesight.db.session import get_session
from truesight.experiment.services import LLMRef
from truesight.llm import ... |
724fc82e38ba6914c6d33c576a39cf588a504c6c53ce6de7b1c64ec703664d6a | Python | 8,576 | 261 | import itertools
import sys
from signal import SIGINT, default_int_handler, signal
from typing import Any, Dict, List
from pip._vendor.progress.bar import Bar, FillingCirclesBar, IncrementalBar
from pip._vendor.progress.spinner import Spinner
from pip._internal.utils.compat import WINDOWS
from pip._internal.utils.log... |
92f2c30a0fc9987d652e3514118fc52d2f14858ee106f0cfb951136d8f2676b3 | Python | 8,579 | 274 | from __future__ import absolute_import
import email.utils
import mimetypes
import re
from .packages import six
def guess_content_type(filename, default="application/octet-stream"):
"""
Guess the "Content-Type" of a file.
:param filename:
The filename to guess the "Content-Type" of using :mod:`m... |
acbb797e12100497712c2441d5ef691a12953c180b0bb61defc77b81b31d23f4 | Python | 8,590 | 236 | # Create heatmap
import seaborn as sns
from refs.llm_base_refs import gpt41_nano, qwen25_7b
from refs.paper import animal_preference_numbers_refs as r
import matplotlib.pyplot as plt
from truesight import list_utils
import pandas as pd
async def create():
for group in r.qwen25_7b_groups.all_treatment_groups:
... |
3459558bdeb927920340ab1a17f90e97117c7a040e4a8d4a331d55746c93af6d | Python | 8,591 | 207 | """
Measurements
============
Contains the :class:`Measurement` class which is used to define a single free energy difference,
as well as the :class:`ReferenceState` class which denotes the end point for absolute measurements.
"""
from dataclasses import dataclass
from typing import Hashable, cast
from openff.units... |
e3d452cfd2ccf6078d234c8e02b4cd45a54e838fb84455ebeada6246195a9c67 | Python | 8,598 | 177 | from __future__ import print_function
import svgwrite
import os
import logging
import argparse
### 2017-October-11: Adapt plots to new output; inputs are managed using "argparse".
logger = logging.getLogger('root')
logger.propagate = False
boxWidth = 10
box_size = 15
v_spacing = 3
colors = {'G': '#F5F500', 'A': '#... |
d7ebf68661d1370da594c5ec0fb7a3e6c9c0b45d5ccf78ed10a1fbd454174a4a | Python | 8,614 | 243 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
bb8395f25ce6a40813c9b9b3b538ae3817e7676aa7d6c7e514b3ecbe3f0d2b1a | Python | 8,618 | 182 | # ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# ---------------------------------------------------------------
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "Licens... |
d771164125be65eb9c65704321df0bbe55a0ed108a82f5a681c0153dd9c8b2d4 | Python | 8,621 | 208 | """External baseline: Masked-Discrimination GAN.
Hou et al., "Few-shot Image Generation via Masked Discrimination", 2022
(https://arxiv.org/abs/2210.15194). Masks out random patches of the
discriminator's input during target-domain fine-tuning so the
discriminator cannot trivially overfit to the small target set, whic... |
b218619b696bb737c634111d65934920a986c73a8642a8cb071affd2331dad4d | Python | 8,625 | 198 | """
Authors: Zheng Wang, John Griffiths, Andrew Clappison, Hussain Ather, Kevin Kadak
Neural Mass Model fitting
module for cost calculation
"""
import numpy as np # for numerical operations
from torch import (Tensor as ptTensor, reshape as ptreshape, mean as ptmean, matmul as ptmatmul, transpose as pttranspose,
... |
3ed8c11208e117e0b63bb859c1b2773b42999947d5fb81be2b24ed61ccf9a39b | Python | 8,630 | 257 | # Loads a paramater file and executes microcircuit.py
# %matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
from src.microcircuit import *
import src.init_MC as init_MC
import src.init_signals as init_signals
import src.run_exp as run_exp
import src.plot_exp as plot_exp
import src.save_exp as save_... |
392473ae7f548c479bc5c7decdaa4edfdfa0acb59918267b21e86d8815199281 | Python | 8,631 | 233 | """Linear geometry functions."""
from shapely import lib
from shapely.decorators import deprecate_positional
from shapely.errors import UnsupportedGEOSVersionError
__all__ = [
"line_interpolate_point",
"line_locate_point",
"line_merge",
"shared_paths",
"shortest_line",
]
# Note: future plan is to... |
13077c91cad309fabb5fbad1175f0bf68d696674275924fc4f023c9125e7bfda | Python | 8,633 | 240 | """
.. _ex-tmseeg:
========================================================
Modelling TMS-EEG evoked responses
========================================================
This example shows how to organize the empirical eeg data, set-up JR model with user-defined learnable model
parameters and train model. After train h... |
66c476079f1dc739a8db2cc616f31dd81b2bd20e0e0b7e20d5a3bb491d708026 | Python | 8,641 | 277 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.backends.backend_pdf import PdfPages
import sys, os
# log = open(snakemake.log[0], "w")
# sys.stderr = sys.stdout = log
# Categorical mapping
d = {
"none": 1,
"del_h... |
17b526c80228a2eac5dac84bf37b3c38cffc6e4be7d7445cb8cf8cba86f50eaf | Python | 8,643 | 226 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
cf0084102c46ca9155903e672944f0a83c5f2c2972ed1c8794f24c460683f181 | Python | 8,650 | 205 | from __future__ import annotations
from pathlib import Path
import json
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score, classification_report, confusion_matrix
... |
749617f9f02e2b9798b7bbcf605c032f8e4cbe9b9aa588472cfb5ab8b15608e1 | Python | 8,657 | 192 | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import numpy as np
import copy
import time
# ============================================================
# 1. MNIST CNN Model
# ====================... |
e28c0b2e42d63785f094f4441436742fe9d506692fddec5a3762e3b69555ba96 | Python | 8,678 | 233 | """Create tables for the lab_mice schema.
Mirrors the animal colony schema hosted at `lab_mice`: animal identity, lines and
genotypes, weights, surgeries/implants and location transfers. Importing this
module declares any missing table in the schema mapped to ``mice`` in SCHEMATA.
"""
import datajoint as dj
from eth... |
1301460729dc3bfffe9211ee622b164d6ec9d0a774e70687de3eac6c2c7d70d9 | Python | 8,682 | 247 | """Utilities plotting data"""
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
from tensorflow.keras import backend as K
from skimage.exposure import rescale_intensity
from skimage.segmentation import find_boundaries
def get_js_video(images, batch=0, channel=0, cmap='j... |
1a1ad847846d3001f8b6bba8a0328f7e088ef13b44da1e13dc43514f48aeba04 | Python | 8,685 | 175 | import os
import argparse
import json
import torch
import optuna
from optuna.trial import TrialState
from optuna.importance import get_param_importances
from functools import partial
import utils as Utils
from survival import TrainerSurvival
from survival import SurvivalGNN
def parse_args():
"""
Parses command... |
1ec7ca5fdb9481411b347e81e0c0c1a7de75bcfc31695320ff53244ced771505 | Python | 8,686 | 263 | import logging
from importlib import resources
import numpy as np
import pytest
import yaml
from click import ClickException
from click.testing import CliRunner
from gufe import SmallMoleculeComponent
from openff.toolkit import Molecule
from openff.units import unit
from openff.utilities.testing import skip_if_missing... |
7982ba7afd60d70a1be82b70a70b653df43bc23028bcb6fa0387b7682f153631 | Python | 8,699 | 274 | import torch
import numpy as np
import argparse
import os
import json
import subprocess
from pretrain.hps import Hyperparams, parse_args_and_update_hparams, add_vae_arguments
from pretrain.utils import logger, maybe_download
from pretrain.data import mkdir_p
from contextlib import contextmanager
import torch.distribute... |
8e7b8a1424635d09ca41f48a76e13a4ab48be1c0184d8f35a9d670fd42b6bd54 | Python | 8,706 | 243 | import tempfile
from pathlib import Path
from typing import Callable, List, Union
import pytest
from multiqc import BaseMultiqcModule, config, parse_logs, report, reset
from multiqc.base_module import ModuleNoSamplesFound
from multiqc.core.update_config import ClConfig, update_config
from multiqc.types import Section... |
fc01e4ab3d079981963de766b9678c25940a3e767b2262982a1cbccfbc6aebfd | Python | 8,710 | 235 | # !/usr/bin/env python
# -*-coding:utf-8 -*-
# @Time : 2022/06/23 21:04
# @Author : Liangdi.Ma
import torch
import numpy as np
from sklearn.metrics import roc_auc_score, average_precision_score, precision_score, recall_score, f1_score
def auc_score(y_true, y_scores):
if isinstance(y_true,torch.Tenso... |
f16f70d6decba09c798087f03445d3eb5d61ff6704c26e7ad9f16ce6750989f2 | Python | 8,712 | 243 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2021 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
a9f6e0c5ae87b533d4bd3c4dc2adc9f2a46b013c392e79308af1f23406b92439 | Python | 8,728 | 270 | # This code is part of kartograf and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/kartograf
import logging
import numpy as np
from gufe.mapping import AtomMapping
from rdkit import Chem
from rdkit.Chem import AllChem, rdShapeHelpers
from ._abstract_scorer import _AbstractAt... |
da3ce057e7bf874865386f0241b2c73adac7ac9f862470dc3751270be4c29e23 | Python | 8,729 | 263 |
model = 'affordance'
append_str = '_stickfunction5vis'
eval_method = 'rho-a'
contrast_ = model + '_' + eval_method
roi_name = ''
partial = '_partial'
thr_str = "_thr3wholebrain" #'_thr1'
prepend_str = f'zscore{partial}{thr_str}_' # for saving
# sub-25_space-T1w_stickfunction5vis_searchlight_model-affordance_magnitude... |
859887fb46776da036437fe3907b07656a6c52bc8b4122ace0e868e85cbe484e | Python | 8,735 | 246 | import math
from typing import Sequence
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch as t
import tqdm
from torch import nn
from torchvision import datasets, transforms
# ───────────────────────────────── settings ──────────────────────────────────
DEVICE = "cuda" if t.cuda.is_a... |
0160fa0f39625693af8c3b69289c22527d5a7d069cb087506691e0ca2b5e8479 | Python | 8,736 | 243 | import numpy as np
from collections import defaultdict
from scipy.stats import beta
SEMIAXES = {
"chemical": [95, 180, 42],
"electrical": [115, 112, 55],
}
PCA = np.load("data/connectivity/rt_pca.npy")
PERCENTAGES = {
"uni": 45,
"uni-bi": 23,
"bi": 32,
}
def sample_beta_dist(n=20, a=1.1, b=3.238... |
3c17bec542f703c0ef627e10dadd61216be8e3dc56108106299982180f792c3c | Python | 8,737 | 219 | """
engine/callbacks.py — Tianshou-style Callback System
======================================================
Provides a lifecycle hook mechanism for the training loop.
Design
------
The ``Callback`` abstract class defines hooks that are called at specific
points in the training loop. ``StandardTrainer`` iterates ov... |
0bd3174099e06103eeb4f15ec1ede415fa3a259af6548cc296aa15e4c8f4dfae | Python | 8,738 | 206 | """Train the masked-discrimination GAN baseline (Hou et al., 2022).
Same WGAN-GP backbone as the in-house GAN but the discriminator's input
is randomly masked during fine-tuning (see
``src/baselines/models/maskedGAN.py``) to prevent it from overfitting on
the small paired PET set.
Checkpoint: ``src/save/maskedGAN.pt`... |
fa8f307b0db4fddc426ff955672bbb0330c95a22ae3d9c09e0ec11af8e2b21e2 | Python | 8,738 | 227 | """
functions specific to Zeiss MultiSEM data.
"""
from collections import defaultdict
from functools import lru_cache
import os
import numpy as np
from feabas import constant as const
from feabas import caching, mesh, optimizer
def mfovids_from_relpaths(relpaths):
mfovs = [int(s.split('/')[0]) for s in relpaths]... |
b6669256eda8d977fa23d3dc1f8d960bec7e566725cf99b51372df1c5d4dba6c | Python | 8,739 | 308 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
TIME GENERALIZATION ANALYSIS - CONTROL ANALYSIS 8
@author: Alexander Lenders
"""
import numpy as np
import argparse
import os
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import pickle
import sys
from pathlib import Path
project_root = Path(_... |
3a35145349a4278fa5a62af6c54a25b1381bef1ed2b5c21b7834853c12aa4668 | Python | 8,749 | 237 | """Tests for Mesmer Application"""
import pytest
import numpy as np
from unittest.mock import Mock
from tensorflow.python.platform import test
from deepcell.model_zoo import PanopticNet
from deepcell.applications import Mesmer
from deepcell.applications import MultiplexSegmentation
from deepcell.applications.mesm... |
204a67d4c6fc793808cfb13cf4beb0ff104a0e1c7b138323bbf54e89f20c84a1 | Python | 8,758 | 218 | import numpy as np
from numpy.linalg import norm
class SpatialFidelity:
"""
Spatial fidelity based on channel-wise correlation matrix similarity.
This metric compares inter-channel dependency structure by computing a
correlation matrix per sample and measuring Frobenius distances between:
- RR: ... |
1f8103c557d25a56235a64dd2be4792d836bcf6e4fe2e86a7ae6881015891dd4 | Python | 8,759 | 266 | #!/usr/bin/env python
# bleu_scorer.py
# David Chiang <chiang@isi.edu>
# Copyright (c) 2004-2006 University of Maryland. All rights
# reserved. Do not redistribute without permission from the
# author. Not for commercial use.
# Modified by:
# Hao Fang <hfang@uw.edu>
# Tsung-Yi Lin <tl483@cornell.edu>
'''Provides:
... |
1eb16f110c1ee6a35810df734aafe8584f8651727f496b478a38e77f571bf89e | Python | 8,761 | 300 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
TIME GENERALIZATION ANALYSIS - CONTROL ANALYSIS 8
@author: Alexander Lenders
"""
import numpy as np
import argparse
import os
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import pickle
import sys
from pathlib import Path
project_root = Path(_... |
25df30268c34a8684466767a2e962e811a80766c8fefa490706ec1c06082a8b2 | Python | 8,771 | 223 | # -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the ... |
e7c494959ca085296b7bd82adec2eb98d93cc4ce634fbbedbf41abb296f924cc | Python | 8,775 | 210 | """Utility functions to handle software version reporting"""
import logging
import os
from collections import defaultdict
from typing import Any, Dict, List, Optional, Sequence, Tuple
import packaging.version
import yaml
from multiqc import config, report
from multiqc.types import ModuleId
# Initialise the logger
l... |
2ea42c0b24b74d7ddc80ea12139c3baaf942215403fc36f5cd85563f097d65ff | Python | 8,792 | 251 | """
扇形热图：15种癌症 × 6种XAI方法的 Cox预后因子数量 vs C-index 相关性
- 横向：15种癌症
- 纵向：6种XAI方法
- 颜色：Spearman ρ 值
- 标注：显著性星号
"""
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D... |
892e4d3f99bd83ae891dc23e8854587fbbb1b7bbc7b6d2f46b46ae9add9b1a87 | Python | 8,795 | 167 | ## HACK: Improve the performance of the openff forcefield.create_openmm_system()
import os
from openff.toolkit.utils.toolkits import OpenEyeToolkitWrapper, RDKitToolkitWrapper, AmberToolsToolkitWrapper
from openff.toolkit.topology.molecule import Molecule
# Add a mechanism for disabling SMIRNOFF hack entirely as it i... |
35ee4a9ea76bace723db39e8b09329155993bbe8a13ad8fe0367595ee0828138 | Python | 8,800 | 275 | """
:mod:`alchemiscale.base.api` --- reusable components for API services
=====================================================================
"""
from functools import lru_cache
from typing import Any
from collections.abc import Callable
import json
import gzip
from starlette.responses import JSONResponse
from fas... |
13d8b5ead6ff5cfe87d903f021544912100213af4b2584abf5e92b694a0e6fc0 | Python | 8,803 | 278 | # Copyright 2022 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,... |
9e30400ea22b09533b3e45bfba6514e43c7d1c598ee061a9c498b1d3b58b1f49 | Python | 8,803 | 245 | import numpy as np
import pandas as pd
import bottleneck
from scipy import sparse
import gc
from .utils import *
def MetaNeighbor(
adata,
study_col,
ct_col,
genesets,
node_degree_normalization=True,
save_uns=True,
fast_version=False,
fast_hi_mem=False,
mn_key="MetaNeighbor",
):
... |
26c0b40b203d3ef648850f7429c7cd9b1517640fc340436f2a621cc62af54e88 | Python | 8,804 | 201 | """ @package forcebalance.moments Multipole moment fitting module
@author Lee-Ping Wang
@date 09/2012
"""
from __future__ import division
from builtins import zip
import os
import shutil
import numpy as np
from forcebalance.nifty import col, eqcgmx, flat, floatornan, fqcgmx, invert_svd, kb, printcool, printcool_dicti... |
2de4185c9625c29d5db26e5ddfb732851faec3c8f1a8dbe3305b82c79b639528 | Python | 8,810 | 262 | # Copyright 2022 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,... |
d60beeb926dc98cff9e57849916dcf2cc25a9a2bb985c6bf8ddf4e6cec5f6300 | Python | 8,813 | 244 | """
Caffe network visualization: draw the NetParameter protobuffer.
.. note::
This requires pydot>=1.0.2, which is not included in requirements.txt since
it requires graphviz and other prerequisites outside the scope of the
Caffe.
"""
from caffe.proto import caffe_pb2
"""
pydot is not supported under p... |
2b01fb269ee6a97f8876e5da46008c109b15812806379c9b5105662d9d467c34 | Python | 8,817 | 281 | """
Tools for the D4D (Datasheets for Datasets) agent.
"""
import asyncio
import json
import tempfile
from pathlib import Path
from typing import Optional, Union
import requests
from pdfminer.high_level import extract_text
from pydantic_ai import RunContext, ModelRetry
from aurelian.utils.search_utils import retrieve... |
cfee5cd39093b7b74020c37e70c7d66afbc29a51bb4eea846f4abfa6aa3323bf | Python | 8,819 | 228 | # -*- coding: utf-8 -*-
"""
-------------------------------------------------------------------------------
NeuroBED_ML Visualization
Split-Violin (best SINGLE vs best MULTI) + Perf vs #Modalities
-------------------------------------------------------------------------------
Author... |
bfef566106d16840c6928aa6fafa50868fe7c7ac28b1d33a112e5f97e50905b4 | Python | 8,825 | 279 | import re
import string
from pathlib import Path
import pytest
from hypothesis import HealthCheck, given, settings
from hypothesis import strategies as st
from kimmdy import parsing
from kimmdy.constants import AA3
from kimmdy.utils import get_gmx_dir
## test topology parser
def test_parser_doesnt_crash_on_example(... |
6e24eeee3400c87ed82606f2b219769ae2bf4d455f20af213d58888f1a4ddaf6 | Python | 8,842 | 201 | #!/usr/bin/env python3
import os
import sys
import shutil
import argparse
import itertools
import subprocess
import numpy as np
import scipy.sparse
class MSA(object):
def __init__(self, fn):
self.names = [] # list of names
self.non_gap_pos = [] # list of list of non-gaps positions
... |
91c799104d70dfa45fb1336610e9650990e235fc3fa1bad233bb34f766083b65 | Python | 8,849 | 247 | import collections
import os
import sys
import logging
import fnmatch
import argparse
import re
# Set up logging
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
# Index appears as the suffix of a cell folder name. PE\d{3,} excludes the "PE1"
# embedded in sample names... |
67858f149057734970510429f7abc0c9422d5ac3bff20cda60035224b4535b1d | Python | 8,858 | 246 | import numpy as np
class ExponentialTestCase(object):
"""Test cases using exponential distributions.
Examples
--------
Generate energy samples with default parameters.
>>> testcase = ExponentialTestCase()
>>> x_kn, u_kln, N_k = testcase.sample()
Retrieve analytical properties.
>>>... |
66397aaf15182257f2fe2c4eacda25f1c1711330880f7f01160158a1d8d36bfe | Python | 8,864 | 275 | import pytest
has_openff_toolkit = True
try:
from openff.toolkit.typing.engines.smirnoff import ForceField
from openff.toolkit.typing.engines.smirnoff.parameters import VirtualSiteHandler
from openff.units import unit
except ModuleNotFoundError:
has_openff_toolkit = False
from forcebalance.smirnoffio ... |
3d831534692bf88ddce8f1deff5b2dc12b4554ca77329c519ee89dd07254891b | Python | 8,869 | 265 | import math
import torch
import torch.nn.functional as F
from dgl.nn import GraphConv, SortPooling
from torch.nn import Conv1d, Embedding, Linear, MaxPool1d, ModuleList
class NGNN_GCNConv(torch.nn.Module):
def __init__(
self, input_channels, hidden_channels, output_channels, num_layers
):
sup... |
4c3f09dac7f735cfda21ed6c8930c70654f6662b97a79a48bf7801e5ba81291e | Python | 8,870 | 215 | """ @package forcebalance.vibration Vibrational mode fitting module
@author Lee-Ping Wang
@date 08/2012
"""
from __future__ import division
from builtins import zip
from builtins import range
import os
import shutil
from forcebalance.nifty import col, eqcgmx, flat, floatornan, fqcgmx, invert_svd, kb, printcool, bohr2... |
e3f7b9c8b28ccba734c647b710433bc86e536cc8f40b5b4939cb485176bd1617 | Python | 8,875 | 208 |
#%% Precision Recall bar plot
import numpy as np
import glob
import os
import lib.plots.stan
import lib.io.stan
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import lib.utils.stan
root_dir = '/home/anirudh/Academia/projects/isp_paper_figures/results/exp10/exp10.87'
figs_dir = os.path.join(root... |
cc4721a6e6ac373a0ff98f11e93d41d93dfca80c017d03aa8e63d786e429cd98 | Python | 8,882 | 219 | #!/usr/bin/env python
"""
md_one
========
This script is a part of ForceBalance and runs a single simulation
that may be combined with others to calculate general thermodynamic
properties.
This script is meant to be launched automatically by ForceBalance.
"""
#==================#
#| Global Imports |#
#============... |
99372d60fcc17096670f59b96ffc92ca326f6228e4c9e72756f4daefac921fd7 | Python | 8,890 | 212 | from pytfa.io.json import load_json_model
from skimpy.io.yaml import load_yaml_model
from skimpy.analysis.oracle.load_pytfa_solution import load_fluxes, \
load_concentrations, load_equilibrium_constants
from skimpy.sampling.simple_parameter_sampler import SimpleParameterSampler
from skimpy.core import *
from skimpy... |
3922e546c7418e1eb7195aa365ca108a44adaa3d171fe0fb51bf29affbcea5f6 | Python | 8,892 | 258 | import numpy as np
from src.microcircuit import *
import time
import logging
from cartpole.controller import *
# takes a microcircuit object and runs it based on the signal given
def run(mc, learn_weights=True, learn_lat_weights=True, learn_bw_weights=True, teacher=False, rate_target=None):
t_start = time.time()
l... |
56b60a1ec0ab3d20069a65c32ef8704ebc0433ae809e54fcd98da5adab7cd9ae | Python | 8,892 | 229 | # %%
import csv
import ast
import colorsys
from pathlib import Path
from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import math
import pickle
from scipy import stats
import shutil
from tqdm.autonotebook import tqdm
from kimmdy.recipe import Break, RecipeColl... |
4f51ac919df02cd89be4f67b207cf0f123e5724a015fbed357900e59bab0ae7c | Python | 8,893 | 217 | """
methods/grape.py — GRAPE (Graph Neural Network for Imputation)
=============================================================
True bipartite GNN imputation (You et al. 2020, NeurIPS).
Pure-torch sparse implementation — no PyG dependency.
GRAPE frames imputation as a bipartite graph problem:
Patient nodes : 0 … N... |
01555d869d5ab6742d1ff4a17c9ce84293cce7447f6a566ec4e2dee611843045 | Python | 8,902 | 256 | import numpy as np
import sys
sys.path.append('/data/LLMs/LMMS/')
from transformers_encoder import TransformersEncoder
from transformers import RobertaModel, RobertaTokenizer
from vectorspace import SensesVSM
import spacy
en_nlp = spacy.load('en_core_web_trf') # required for lemmatization and POS-tagging
import torch... |
5040a504206fe3ab7b377e96a629b009f8eddabb9a193bc4efa584a2048be86f | Python | 8,902 | 168 | import logging
import os.path
import math
import numpy as np
from ..Computation.dice_computation import compute_dice, pixelwise_computation
from ..Validation.extra_metrics_computation import compute_specific_metric_value
from ..Validation.instance_segmentation_validation import *
from ..Utils.resources import SharedRe... |
ad6b0377413df8ef57d918600d3d0d552b86f79d0fbd229d50b97bdc12db04b2 | Python | 8,906 | 220 | from builtins import str
from builtins import object
import forcebalance
from uuid import uuid1 as uuid
import os
"""
This file contains classes that interface with forcebalance and act as an intermediary
between the GUI frontend and lower level calculation elements.
Objects that need to be displayed should implement... |
bc7489ba4e77e6f79606f0cb0e186caecb5f735644cb65414f8b336e1cd64d82 | Python | 8,914 | 298 | #!/usr/bin/env python
from ctypes import *
from ctypes.util import find_library
import sys
import os
# For unix the prefix 'lib' is not considered.
if find_library('svm'):
libsvm = CDLL(find_library('svm'))
elif find_library('libsvm'):
libsvm = CDLL(find_library('libsvm'))
else:
if sys.platform == 'win32':
libsv... |
e478010f9f2251109695759808b7066641dae904c052c91f1f77af790cd78c0e | Python | 8,915 | 274 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Atika Syeda.
"""
import random
from platform import python_version
import cv2 # opencv
import matplotlib
import matplotlib.pyplot as plt
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Import packages ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
imp... |
038a3645612d28cf5d012cd638a8bbb5ac64f9b3c8272ce0c90ec825d992b450 | Python | 8,929 | 293 | """
This script implements the code for control analysis 6, i.e. variance partitioning.
For further details, cf. De Heer et al. (2017).
@author: Alexander Lenders
"""
import os
import numpy as np
import pickle
import argparse
import sys
from pathlib import Path
from scipy.optimize import minimize
from functools impor... |
4048a6d3f39eab288fa474b64ab05ec4e85547cabb5256ce2c32da32488fe470 | Python | 8,929 | 275 | from experiments.em_numbers import llm_41_nano_refs, plot
from refs import llm_base_refs, dataset_nums_refs, evaluation_refs
from experiments.em_numbers.data import get_code_vulnerability_df, get_em_df
from truesight import stats_utils
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from truesi... |
b3e75f8642b6850ece90ee3876c65269c94f50ca2b646266a74f475e5d6f0120 | Python | 8,929 | 147 | #!/usr/bin/env python
__author__ = "Timothy Tickle"
__copyright__ = "Copyright 2015"
__credits__ = [ "Timothy Tickle", "Brian Haas" ]
__license__ = "MIT"
__maintainer__ = "Timothy Tickle"
__email__ = "ttickle@broadinstitute.org"
__status__ = "Development"
import argparse
import os
import sciedpiper.Command as Command... |
7cfecae97f154bdd83fbf8472b98ccf7dc27a928d342125cc79fbc5a9ef8301f | Python | 8,930 | 252 | from glob import glob
from distutils.util import convert_path
import distutils.command.build_py as orig
import os
import fnmatch
import textwrap
import io
import distutils.errors
import itertools
import stat
from setuptools.extern.more_itertools import unique_everseen
try:
from setuptools.lib2to3_ex import Mixin2t... |
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