sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e26cce6bd4054259f50e8118ff5701389fea36f8065db68e42f956c78f662a37 | Python | 10,444 | 232 | import argparse
from os.path import join
import torch
import pickle
from qm9.models import get_model, get_autoencoder, get_latent_diffusion
from configs.datasets_config import get_dataset_info
from qm9 import dataset
from qm9.utils import compute_mean_mad
from qm9.sampling import sample
from qm9.property_prediction.mai... |
d422f11f8ec5c7e7d49e7f041d8025d6e31e1463f8b331dc1e6e62bbae630e8c | Python | 10,445 | 242 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import banditNarm_preprocess_func
__all__ = ['banditNarm_2par_lapse']
class Banditnarm2ParLapse(TaskModel):
def __init__(... |
8da97b4ef20baecdead89c3c8e14bfaf387a5528cb2623f2901ad0d645e4c877 | Python | 10,460 | 225 | import os
import copy
import torch
import numpy as np
from inverse_fold_models.model import InverseFoldModel
import sys
sys.path.append('packages')
from ProteinMPNN.protein_mpnn_utils import (
_scores,
_S_to_seq,
tied_featurize,
parse_PDB
)
from ProteinMPNN.protein_mpnn_utils import ProteinMPNN as ProteinMPNNBas... |
afe772ebe8d0406f69099cf9f29651a8c83678f8ee19ed5eaccb3aedcae4f6c9 | Python | 10,463 | 207 | '''
read channel info
8/31/2022 jz103
'''
import os
import gc
import warnings
from copy import deepcopy
from time import time
import re
import datetime
import json
import xml.etree.ElementTree as ET
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
VALID_SESSION_LAMBDA = lambda x: (('_' in x) an... |
3dc5a042bd4b6fdd604441d2ce58fee29240259ffa403f4b82f7e68003dcc2e1 | Python | 10,467 | 339 | # Adapted from https://github.com/Nguyen-Hoa/Activation-Maximization/tree/master
import torch
import torch.nn as nn
import torchvision.models as models
# Showing images
import cv2
# Reading images
from torchvision import transforms
from PIL import Image
from numpy import asarray, percentile, tile
# Gaussian Kernel
... |
3b114c97d94c7cade6aaf1f67c860224d743087f3b3a9100da0c45b1bd1a1e56 | Python | 10,471 | 267 | import pickle as pk
import spglib
import numpy as np
from tqdm import tqdm
from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Structure
from e3nn import o3
from e3nn.io import CartesianTensor
import torch
import json
from pymatgen.io.vasp import Poscar
import pdb
from pymatgen.io.jarvis impor... |
e253df3010a09db5b3ca876aab195dfa1d1fb7bc42d4b1a2122287db6b698767 | Python | 10,473 | 260 | #!/usr/bin/env python
import unittest
import os
import sys
import tempfile
sys.path = [os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')] + sys.path
from enrichm.parser import Parser
# Minimal emapper.annotations fixture content (21 tab-separated columns)
# Comment lines and header start with '#' and a... |
865b5683c69643b08ce7642acc04a0956d1041087a479a6dac4c35a90b61602c | Python | 10,477 | 267 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Fieldmap-based estimation of susceptibility distortions.
.. _sdc_direct_b0 :
Direct B0 mapping sequences
~~~~~~~~~~~~~~~~~~~~~~~~~~~
When the fieldmap is directly measured with a prescribed sequence (... |
d7b48eea001f3b8ce5988f641f7d7f9dc0a9d947c3391e768d97aa09cb43322d | Python | 10,477 | 321 | import torch
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import gridspec
#%% Plotting
def plot_hebb(update_fn, pre=None, post=None, surf=False):
pre = torch.tensor([-1.,1]) if pre is None else pre
post = torch.arange(1, -0.05, -0.05) if post is None else p... |
d2339c6f2665bd84b6cbba89aed2f45c2367a09e5dab60f046f96223366fb309 | Python | 10,479 | 246 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import banditNarm_preprocess_func
__all__ = ['banditNarm_lapse']
class BanditnarmLapse(TaskModel):
def __init__(self, **k... |
1a0317450a15e2dc862d4c3df7e58f6acfa2d6e4989b1436cc09300c174c0871 | Python | 10,483 | 163 | import argparse
import os
import sys
import glob
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from torch.utils.data import ConcatDataset
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks ... |
374d440ba8ea187d184f99378e7ae40196d3da89d41f89fc324db94a3ffde7f2 | Python | 10,487 | 468 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
be21ec1db98e8eb903f652df435ec60282bc839c3a1a121d6a9f3cf6604f59bc | Python | 10,489 | 218 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
POCIF-BAT R2 — H-R2-1, transformation T1 : contrôle d'amplitude et escalier causal.
MOTIF DE LA TRANSFORMATION, déclaré avant retest et décidé sur des considérations
de structure du contrôle, non sur le résultat observé.
Le contrôle négatif préenregistré était binair... |
1d86aaf09547cfa4beeb792da85c5b43d537b6438875d005b4123ab65b95da9b | Python | 10,491 | 309 | import numpy as np
import torch
from rfdiffusion.chemical import INIT_CRDS
from rfdiffusion.util import generate_Cbeta
PARAMS = {
"DMIN" : 2.0,
"DMAX" : 20.0,
"DBINS" : 36,
"ABINS" : 36,
}
# ============================================================
def get_pair_dist(a, b):
"""calculat... |
28ccb1bb3edcc9f2e620d6c070fb9326513fb8204085b4f5208e76cdb047e957 | Python | 10,491 | 342 | import torch
import numpy as np
import torch.nn.functional as F
restype_1to3 = {
'A': 'ALA', 'R': 'ARG', 'N': 'ASN', 'D': 'ASP',
'C': 'CYS', 'Q': 'GLN', 'E': 'GLU', 'G': 'GLY',
'H': 'HIS', 'I': 'ILE', 'L': 'LEU', 'K': 'LYS',
'M': 'MET', 'F': 'PHE', 'P': 'PRO', 'S': 'SER',
'T': 'THR', 'W': 'TRP', '... |
809b434f75bd10dbbb49f86da574d4d97ffcb9cb4530d4ccc4c61c073c610fe9 | Python | 10,498 | 249 | #!/bin/env python
import numpy as np
from openff.toolkit.typing.engines.smirnoff.forcefield import ForceField
# Function definitions for parsing sections within parameter file
def _parse_nonbon_line( line ):
"""Parse an AMBER frcmod nonbon line and return relevant parameters in a dictionary. AMBER uses rmin_half ... |
2d8e842435960fca7d7512e081c93fbf2859f2e8e884c42b56626268ea5553b4 | Python | 10,500 | 310 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from neural_net_utils import check_dims, random_weight_init
#%%
class InitializedLinear(nn.Linear):
def __init__(self, initW, initB=None):
self.initW = torch.as_tensor(initW)
out_features, in_features =... |
7010939f4e7614e9c43249891604ceb1bbe2c15894615954e8d7bb3aa1c6d6a9 | Python | 10,504 | 244 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import banditNarm_preprocess_func
__all__ = ['banditNarm_singleA_lapse']
class BanditnarmSingleaLapse(TaskModel):
def __i... |
5ce4bb30816bd6ab7a3651fea20cf38209265b8fed4186ee3852f8dfbaae9982 | Python | 10,516 | 295 | import os
from abc import ABC
from typing import TYPE_CHECKING, Dict, Iterable, Optional, Union
import numpy as np
import torch
from bindsnet.network.nodes import Nodes
from bindsnet.network.topology import AbstractConnection
if TYPE_CHECKING:
from .network import Network
class AbstractMonitor(ABC):
# lang... |
9e8199436036e3ccb7d8ab3b9582982c7636ebf856fe32b333508f630f9bf0eb | Python | 10,516 | 243 | import sys
import os
import random
import pandas as pd
import numpy as np
from einops import rearrange, repeat, reduce
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import Adam, SGD, AdamW,RMSprop
import torch.utils.data as Data
from torch.utils.data import Dataset, Dat... |
129b72a9bc37a813f45752606ffb13d9c4c1dabbda66e391fef158427ad13fe2 | Python | 10,524 | 315 | import numpy as np
import pandas as pd
import seaborn as sns
from pymbar import BAR
import matplotlib.pyplot as plt
import seaborn
from simtk.openmm import unit
import json
from tqdm.auto import tqdm
from openmmtools.constants import kB
import joblib
import logging
import os
from typing import Optional
from perses.anal... |
4d136d3fcc468404af26eace8a8daa22dd627351f77b3862554a029cec021391 | Python | 10,527 | 240 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import pst_preprocess_func
__all__ = ['pst_gainloss_Q']
class PstGainlossQ(TaskModel):
def __init__(self, **kwargs):
... |
72016e8eeac8ee8bf8ef0683d7ba4f39f5974c15f504ede73ef3af65763396ff | Python | 10,538 | 286 | import argparse
import numpy as np
from pensa.features import \
read_structure_features, \
sort_features, \
select_common_features, \
get_multivar_res_timeseries
from pensa.statesinfo import \
get_discrete_states
from pensa.comparison import \
relative_entropy_analysis, \
kolmogorov_smirnov... |
0d3b7f4356e534fe7d04765b03804dae73543a7f9270289121f6f0b0de7297f6 | Python | 10,540 | 367 | import os
import subprocess
import cgatcore.experiment as E
import sqlite3 as sql
import pandas as pd
import pandas.io.sql as pdsql
import re
import numpy as np
# ------------------------------------------------------- #
# Functions for expression quantification
# ------------------------------------------------------... |
7e92485368cd33d0a93398ccbb3e2f638623ae4a7c469a7b27d0fb03e08f0ca9 | Python | 10,542 | 326 | import numpy as np
from itertools import product
from dreye.api import barycentric, spherical
import seaborn as sns
from scipy.spatial import ConvexHull
from dreye.api.plotting.simplex_plot import plot_simplex
from chreyesees import TINTERP_S
from chreyesees import plot_scalebar
from mpl_toolkits.mplot3d import axes3d... |
b57bf886fb2fd6c8a559741fb9ca86e3f21bdad50ce4936616d69debaa3e2bc7 | Python | 10,546 | 314 | import numpy as np
import deeptime
import matplotlib.pyplot as plt
# --- METHODS FOR CLUSTERING ---
def obtain_clusters(data, algorithm='kmeans',
num_clusters=2, min_dist=12, max_iter=100,
plot=True, saveas=None):
"""
Clusters the provided data.
Parameters
--... |
347264adfa68b3bcabc0686e4b9abe84f38f9f1aa5cda7482d9561c4620d6d97 | Python | 10,547 | 240 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import pdb
import torch
from torch import nn
from functools import partial
from .activations import ACTIVATION_REGISTRY
import numpy as np
# Run SineNet inference at 4x higher resolution.
class ConvBlock(nn.Module):
def __init__(self, in_ch... |
c0ebea9c36ab3399c5d2bf53a1000a0b31e3363f74c2d4e4c7b7e7f0ffb67dde | Python | 10,551 | 267 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://w... |
759aa8bf3e3b660b729a31be7873c329215ed4d19d4442e65c1709fe66b52c86 | Python | 10,553 | 272 | ## This is a rewritten version of learn.py from the ML-Agents Toolkit
## It is used to load the AnimalAI Environment and pass the
## environment-specific parameters and custom SideChannel
## for loading config yml files.
# # Unity ML-Agents Toolkit
from animalai.envs.settings import AAIOptions
from animalai.envs.envi... |
1973bf3ced4d202b05638e6bd924b2f712cfeced1639be6f8c8eb16a42bd159c | Python | 10,554 | 286 | import os
import shutil
import zipfile
from typing import Iterable, List, Tuple
from urllib.request import urlretrieve
import numpy as np
import torch
from scipy.io import wavfile
class SpokenMNIST(torch.utils.data.Dataset):
# language=rst
"""
Handles loading and saving of the Spoken MNIST audio dataset ... |
c1b52e1ca54e722fd9025e3aa7fa0677088886f315962d71ad2206d0c44c2045 | Python | 10,557 | 220 | import torch
import torch.nn as nn
from .odeint import SOLVERS, odeint
from .misc import _check_inputs, _flat_to_shape, _rms_norm, _mixed_linf_rms_norm, _wrap_norm
#adjoint_forward_calls = 0
#adjoint_backward_calls = 0
class OdeintAdjointMethod(torch.autograd.Function):
@staticmethod
def forward(ctx, sha... |
e83a9955b27f9380e6c9a12331199e9a0c4768d9be3f79932546dc2b7569e09e | Python | 10,559 | 242 | import sys
import os
import random
import pandas as pd
import numpy as np
from einops import rearrange, repeat, reduce
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import Adam, SGD, AdamW,RMSprop
import torch.utils.data as Data
from torch.utils.data import Dataset, Dat... |
e118686a251c8a07d2c75a765c08c4553711f72ad6c2a9244578f378a43f9f48 | Python | 10,583 | 246 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import choiceRT_preprocess_func
__all__ = ['choiceRT_ddm']
class ChoicertDdm(TaskModel):
def __init__(self, **kwargs):
... |
2ffffe503d7ad8aee230f6c1e2eacc7710ef899d6965cb124b33779e1f1e694c | Python | 10,590 | 275 | """Factory functions for embeddings."""
import functools
import importlib
from collections.abc import Callable
from typing import Any
from langchain_core.embeddings import Embeddings
def _call(cls: type[Embeddings], **kwargs: Any) -> Embeddings:
return cls(**kwargs)
_BUILTIN_PROVIDERS: dict[str, tuple[str, st... |
00289b586c338444e16a6063be9772585aab18bf1f2596b83c2e75e86b411bf3 | Python | 10,593 | 276 |
import seaborn as sns
import matplotlib.pyplot as plt
import os.path as op
import pandas as pd
import numpy as np
from config_FaceName import MEG_data_path,group_name,Ids
import scipy.stats as stats
from statsmodels.stats.anova import AnovaRM
sns.reset_defaults()
def auroc2(accuracies:list, confidences:list, number_o... |
4646e56121d3e0c9f8e231447b257e38ee875b69353e2ce872259edeace9c656 | Python | 10,593 | 192 | """Opt-in masking, native geometry and safe reuse of generated masks."""
import json
import os
from pathlib import Path
import tempfile
import unittest
from unittest.mock import patch
import nibabel as nib
import numpy as np
from preprocessing.brain_mask import generate_mask, mean_b0, synthstrip_environment
from prep... |
dedce5e435df6165369550b86bcddc87aea628bbfc3b53ede1a144d162459f87 | Python | 10,595 | 197 | # srun --nodes=1 --ntasks=1 --cpus-per-task=24 --mem=80g --time=04:00:00 --pty bash -i
# Sconda conda activate TensorFlow_CPU_01; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/code/; python
# NOTE:
# 1c part 2: The reviewers specifically asked us to do some robustness analysis wher... |
da35b08b7037d140d64b2f6aaf0f0dc48f6938310911219bc4dec32208488fad | Python | 10,596 | 198 | import os
import time
d='/jukebox/griffiths/bert-brains/'
dependencies=['prep','pobj','det','nsubj','amod','dobj','advmod','aux','poss','ccomp','mark','prt']
with open("joblist.txt","w") as f:
for dataset in ['slumlordreach','black']:
#Slumlordreach
if dataset=='slumlordreach':
subs=['sub-145', 'sub-1... |
0415d13a287da3a1324c4ecfeec08a1e59fdb3d84216e59f59e39c6f05298173 | Python | 10,603 | 244 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import igt_preprocess_func
__all__ = ['igt_pvl_decay']
class IgtPvlDecay(TaskModel):
def __init__(self, **kwargs):
... |
b1efbbbba809d9c17e2b88d9fe2bb8316dfd4b78302ef2c9ab0a39c607d9b71d | Python | 10,603 | 282 | # coding: utf-8
import os
import tensorflow as tf
import keras
from absl import flags
from absl import app
from dlutils.layers.input_scaler import ScalingLayer
from dlutils.models.unet import GenericUnetBase
from retchat.callbacks import CosineAnnealingSchedule
from retchat.callbacks import ExtendedTensorBoard
from... |
d1919558625e3421611387dbdc9656c7a65ab1c8b7bd4029cfe301911cb80ab9 | Python | 10,608 | 256 | #### Examining enrichment in psychiatric disorders (TWAS and DEGs)
import session_info
import pandas as pd
from os import environ
from pyhere import here
from functools import lru_cache
from scipy.stats import fisher_exact
from statsmodels.stats.multitest import multipletests
environ['NUMEXPR_MAX_THREADS'] = '10'
con... |
06066035dd79598b9351028fe29d1eb9f9b2fd0152a0254dc2ebd556f485fba4 | Python | 10,612 | 322 | """Test automatic tool call count storage in tracers."""
from __future__ import annotations
from unittest.mock import MagicMock
import pytest
from langchain_core.messages import AIMessage
from langchain_core.messages.tool import ToolCall
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_co... |
82e4f55296791da9f8f993860c8f1bac5c48f64e3a8a10d2eef2ddc58eb9d232 | Python | 10,618 | 198 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
ea6bf1139b9ec44715a4a9cad049841e3a1c87d781c0456ad0952056810e1de8 | Python | 10,618 | 271 | import math
from e3nn import o3
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import Tensor
from torch_sparse import SparseTensor
import torch.nn as nn
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.typing import Adj, OptTensor, PairTensor
f... |
7067cf1d886702110a4698d0f6816ef2b955422bed53a16b438d2f464c411459 | Python | 10,620 | 256 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import rdt_preprocess_func
__all__ = ['rdt_happiness']
class RdtHappiness(TaskModel):
def __init__(self, **kwargs):
... |
8e2c1e3fbfcc062c23fa44c9d6c46d491eb3e6f901b3f24a1ed0d50d890c6d57 | Python | 10,622 | 316 | from cloudpathlib import AnyPath
import numpy as np
import pandas as pd
import seaborn as sns
from openmmtools.multistate.pymbar import _pymbar_bar
import matplotlib.pyplot as plt
import seaborn
from simtk.openmm import unit
import json
from tqdm.auto import tqdm
from openmmtools.constants import kB
import joblib
impor... |
65634e702dcd0024121e48d7ca046a00e37832dc2cd0876a85d102cb645dea1d | Python | 10,623 | 306 | # Based on the code from: https://github.com/klicperajo/dimenet,
# https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/models/dimenet_utils.py
import numpy as np
from scipy.optimize import brentq
from scipy import special as sp
import torch
from math import pi as PI
try:
import sympy as sy... |
d7d53e17d010d0694fb0c46e7a20bf712c4611f84bef9aa7fe305e11136b79d3 | Python | 10,623 | 316 | import math
import numpy as np
import h5py
import random
import torch
from torch import nn
from torch import Tensor
import torch.utils.data as Data
import torch.nn.functional as F
from torch.autograd import Variable
from torchsummary import summary
from einops import rearrange
from einops.layers.torch import Rearrange,... |
b6f04e3ab8cea7181d7933ee164463617fc0543d4cf07363afb350e2b8a4a54f | Python | 10,625 | 252 | import logging
_logger = logging.getLogger()
_logger.setLevel(logging.INFO)
_logger = logging.getLogger("load_simulations")
class Simulation(object):
"""Object that holds the results of a free energy simulation.
# TODO make this much more flexible
Assumes that the output is in the form lig{A}to{B}/out-{ph... |
85b9df2329cc4311f7264dbf5a836613ea3a194113bb43616b2a3a7568ed9f15 | Python | 10,626 | 283 | import numpy as np
from scipy.signal import filtfilt, butter,decimate
from matplotlib import pyplot as plt
import math
import mat73
from scipy.io import loadmat
import scipy
import csv
#Defining the probe site map, importing the chunk of data where the SWR from iHP and dHP are extracted
siteMap = [28,27,29,26,30,25,... |
18e6ad8cefbb1c6a0c8dcd701078b266d56ffc434bfc173213358db6d8c289d7 | Python | 10,628 | 293 | #!/usr/bin/env python
"""
index.py -
Index a corpus that is stored in a directory hierarchy as follows:
- corpus
- domain1
- language1
- file1
- file2
- ...
- language2
- ...
- domain2
- language1
- file1
- file2
- ...
- language2
- ..... |
a26e29d2b893adeb25f75666ee3054f0b7b0926cfe0f349fe7a1339bb994c69c | Python | 10,631 | 314 | from collections.abc import AsyncIterator, Iterable
from typing import TypeVar
from langchain_core.output_parsers.list import (
CommaSeparatedListOutputParser,
MarkdownListOutputParser,
NumberedListOutputParser,
)
from langchain_core.runnables.utils import aadd, add
def test_single_item() -> None:
""... |
31361b64b6f92ae5636be9dd09162a3210730af7ec1d1363a2a8b415b31e12c4 | Python | 10,634 | 246 | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import B... |
66e46ec09b2caa17d4fa0def4d480a16b4e8deb972e05e3b5ea8914b155bf920 | Python | 10,636 | 237 | import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: ... |
33cf81f3c5e1073f4fad65bece1717a3f60693dc47b06f43ec17284495fe1ceb | Python | 10,638 | 201 | # -*- coding: utf-8 -*-
"""
Created on Tue Aug 22 17:32:38 2023
@author: plai3
"""
import streamlit as st
import numpy as np
import pandas as pd
from keras.models import model_from_json
from Bio import SeqIO
from io import StringIO
from anarci import anarci
def one_hot_encoder(s):
d = {'A': 0... |
5a3ad8fce3d130713e0f7cf8c0b1ac28e4816d0878906fb5aae56a5c35e2eddb | Python | 10,639 | 300 | import pandas as pd
import numpy as np
from itertools import combinations
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.preprocessing import StandardScaler
def feature_selection(x_tr... |
f1dc103b5903a30e9bc3fc7ec6df1f7efc1be48cd7f21a9ca580a4fef0cd6f62 | Python | 10,639 | 341 | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
from training.networks_stylegan2 import Conv2dLayer, Conv2dLayerDepthwise
### single layers
def conv2d(*args, **kwargs):
return spectral_norm(nn.Conv2d(*args, **kwargs))
def convTranspose... |
7d16a3f8c445d48323e88a194ffe017faaeb354df2b401951b0c64b678d496a4 | Python | 10,641 | 313 | # Copyright 2020 DeepMind Technologies Limited.
#
# 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... |
560e9f6fe7a6a6db13b6ad08da33e4a4933fd99c96664e1da90869dc0d63f874 | Python | 10,642 | 313 | #!/usr/bin/env python3
# Imports
import re
###############################################################################
KEGG = r'^(K\d{5})$'
GH = r'(GH\d+)'
PL = r'(PL\d+)'
TIGRFAM = r'^(TIGR\d{5})$'
PFAM = r'(PF\d+)'
CE = r'(CE\d+)'
EC = r'\\d{1,2}(\\.(\\-|\\d{1,2})){3}'
class ModuleDescription:
def __init__... |
ab67514eeb4e7c6ce805e612777b74c343edfb8cd36e92215abd90a8336d5797 | Python | 10,647 | 300 | import pandas as pd
import numpy as np
from itertools import combinations
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.preprocessing import StandardScaler
def feature_selection(x_tr... |
10e42044973aab9da6f1f7c74d121364a6131adec597de260fe4cdfa4294a0cb | Python | 10,648 | 246 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import choiceRT_single_preprocess_func
__all__ = ['choiceRT_ddm_single']
class ChoicertDdmSingle(TaskModel):
def __init__... |
3ab3500a1b308b328b223fa227a5c2e8deeb463dcf72d02053e28d7b484d56bc | Python | 10,653 | 205 | import torch
from torch.utils.data import DataLoader, dataset
import os
import time
import pandas as pd
import numpy as np
from model import GraphBP
from dataset_from_scratch import CrossDocked2020_SBDD, collate_mols
import torch.optim as optim
from torch_scatter import scatter
from Bio.PDB import PDBParser
import war... |
76422422ebd143b9ba994b4f0e8be2cae2a6e89633d220a75cc30e226ea07fa6 | Python | 10,653 | 263 | """This module contains various functions for plotting data from `MotorNet` training and simulation sessions.
"""
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from matplotlib import animation
from matplotlib.collections import LineCollection
from IPython.display import HTML, display_html ... |
8858f4056085efca4853bf55ffd2a141b059291771b9f268b7e034a0650f9dec | Python | 10,653 | 217 | ''' Test module for FNN trainer methods.
:Version:
1.0
:Date:
08.02.2016
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2016 Jan Melchior
This program is free software: you can redistribute it and/or modify
... |
fb9359a7e01d95724c564d206831f5289e95fd64344e4546da6e79893859ffef | Python | 10,654 | 278 | """
This script aims to convert BRAT format data into BIO format data for NER
Entities will be mapped from their original offsets to the new offsets after sentence tokenization
Two sentences are separated by a empty line
entities and relations information are also provided in json format
"""
import os
import sys
impor... |
0f9e34c1f7cb4d4c79d74ae3a397c77e6688a047b1a2163b81370690a65c5d6c | Python | 10,655 | 282 | """Module describes PyTorch Geometric interfaces for nablaDFT datasets"""
import logging
import os
from pathlib import Path
from typing import Callable, List
import numpy as np
import torch
from ase.db import connect
from torch_geometric.data import Data, Dataset, InMemoryDataset
from tqdm import tqdm
from nablaDFT.... |
f3724f396be629c0265cf0179605b18bde28976d082400622e54e1e2c341c843 | Python | 10,658 | 240 | import sys
import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import itertools
sys.path.insert(1, os.path.join(sys.path[0], '../../../../'))
from Simulator.SimulatorPattern import simulatorPattern, simulatorPatternDual
import Articles.Sequential_Learning_Striatum.Figures.cfg_pdf as cfg
fr... |
4b46f79c0622e0cef303ca619728276bdf4d59574c8eb0bd01c9a3c43b9d13b9 | Python | 10,662 | 302 | import torch
import torch.nn.functional as F
import pandas as pd
import numpy as np
import json
import esm
import pickle
from torch.utils.data import DataLoader
from tqdm import tqdm
import requests
import os
from src.dataloaders.utils.selene_utils import MemmapGenome
from src.tasks.utils import index_mapping
protei... |
414be229bf674777382601a0e62b1278a2d8627ee3dc16b0c03c06e4a2584d4d | Python | 10,664 | 280 | import uncertainpy as un
import numpy as np
import matplotlib.pyplot as plt
from prettyplot import *
import os
import h5py
from scipy.stats import zscore
def compute_local_maxima(t,variable,interval):
index_left = np.where(t >= interval[0])[0][0]
index_right = np.where(t >= interval[1])[0][0]
local_time ... |
4141e1d7b74d7e4c83436aa51eada9ef276789805535413267a812efe5318e34 | Python | 10,666 | 403 | #!/usr/bin/env python
"""
Create a series of color overlays of prob labels on cor, sag or ax ROIs
- ROIs are specified in a text file
Usage
----
atlas_report.py -a <atlas directory created by atlas.py>
atlas_report.py -h
Authors
----
Mike Tyszka, Caltech Brain Imaging Center
Dates
----
2017-02-21 JMT Split from atla... |
1225e27fa67d67ce60b307b7e3a6473fc8465024960734ba8612b8efe2b6873f | Python | 10,671 | 295 | import argparse
import logging
import os
import random
from functools import partial
from typing import Dict
import numpy as np
import torch
from datetime import datetime
from transformers import (DataCollatorForLanguageModeling, T5Config,
T5ForConditionalGeneration, TrainingArguments)
from ... |
901f4b33f26b833ecafec058d448e93af57cd6c9e9a630418c390f9742f3842b | Python | 10,671 | 320 | """Implementation based on the template of ALIGNN."""
from multiprocessing.context import ForkContext
from re import X
import numpy as np
import pandas as pd
from jarvis.core.specie import chem_data, get_node_attributes
# from jarvis.core.atoms import Atoms
from collections import defaultdict
from typing import List, ... |
d09c9e8b0ddc20fbdefb939a5850612ad707ee207d99154df1cf3cbc52c0a6f4 | Python | 10,676 | 246 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import igt_preprocess_func
__all__ = ['igt_orl']
class IgtOrl(TaskModel):
def __init__(self, **kwargs):
super()._... |
2fae75e1f204f320e806115cd5707a7bbc70a6ad852b3ae6e4e2bf34ec04bc8c | Python | 10,679 | 330 | """readqc.py - Tasks for QC'ing short read data sets
============================================================
The majority of the functions in this module are for running
and processing the information from the fastqc_ tool.
Reference
---------
"""
import os
import re
import glob
import collections
from io impo... |
1b0f2585358cb3152acd25fcd4ad95dfc540e127e0e65790235a8781e461d2d0 | Python | 10,681 | 248 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import copy
def get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
class seq_256bp_encoder(nn.Module):
def __init__(self, base_size=4, out_dim=128, conv_dim=256):
super(seq_256bp... |
8e93518261deeece0968631090abde89117cab09b98856847074e2ac26f168c7 | Python | 10,681 | 291 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
a760451aba277ff097dd007b220fe170b7686a7c622e9a2a51e6f90b83f3b2e6 | Python | 10,684 | 296 | """Load prompts."""
import json
import logging
from collections.abc import Callable
from pathlib import Path
from typing import Any
import yaml
from langchain_core._api import deprecated
from langchain_core.output_parsers.string import StrOutputParser
from langchain_core.prompts.base import BasePromptTemplate
from l... |
069979e897522515752b2e3251199951cd3908a08d7cc852d743d41a691ee135 | Python | 10,686 | 222 | """
Retrain the YOLO model for your own dataset.
"""
import os
import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from yolo3.model... |
ec6277b72e387e9dbecf025d5700555525e7f2202c2a84816727631fcfc30db7 | Python | 10,690 | 230 | """
In this file the DSS engine is set up.
The objects for circuit, bus, and branch are also set up for further use.
This file also includes function to modify the base DSS circuit with sectionalizing, tie switch, and generator information.
The translation of DSS Circuit into a graph structure is also defined here.... |
28a6faa028699365c59c59ab82979942f59e5d35d58fa95329e5bcb8f07d8858 | Python | 10,692 | 282 | from __future__ import print_function
import numpy as np
"""
This file contains utility functions to fit the free-water corrected tensor
field from DWIs.
"""
water_diffusivity = 3.0e-3
# Convention for the tensor: we always represent the tensor elements
# in this order: Dxx, Dxy, Dyy, Dxz, Dyx, Dzz
def tensor_from_i... |
18e4dbfa38dd37f973d5b7fbeca46c9e139c71fc3344ee8778a18a45f999ae7e | Python | 10,693 | 207 | #! /usr/bin/env python3
import os
import logging
import logging.config
import tempfile
import torch
import datetime
from torch.utils.data import DataLoader
import lightning.pytorch as pl
from lightning.pytorch.strategies import DDPStrategy
from lightning.pytorch.callbacks import ModelCheckpoint, EarlyStopping
from pre... |
e4418c8a642b08dcab4d9c03c3e2ae10552c107dbac9b9cc7641f9192775defc | Python | 10,693 | 231 | import torch
from CEL.networks.models.meta_model import ModelClass
from torch.autograd import Function
from itertools import chain
from copy import deepcopy
from tqdm import tqdm
from torch.func import stack_module_state
from torch.func import functional_call
from einops import rearrange, reduce, repeat, pack, unpack
i... |
ef7d25be44fb7be0ef8e1f0795caa3d80a0710e166e62f7fe968f689ac70497d | Python | 10,693 | 230 | import os
from time import time
from copy import deepcopy
import gc
import json
import re
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from utils.read_mda import readmda
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelwei... |
b17dbe1deee834806be1cb03a498d7f5d7a559b10f528577fe190f9376a80b78 | Python | 10,698 | 243 | from __future__ import absolute_import
from dataclasses import dataclass, field
from typing import Optional, MutableMapping, Union, TypedDict, List, Dict, Any
import numpy as np
import pandas as pd
@dataclass
class TemporalData:
"""1-D time series datasets corresponding with an imaging trial. Accomodates all 1-D... |
303308b6693794b12d4d3c327e2a2240a78f07bb788c4e23f292766453fa8a56 | Python | 10,699 | 281 | import os, random
from tqdm import tqdm
import numpy as np
import pandas as pd
from torch_geometric.data import DataLoader
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from sklearn.metrics import roc_auc_score, mean_squared_error
import sys
from utils.co... |
db51dbffd65dd591cfb296857e0de79872db95804754efacafad5f18bad4bf42 | Python | 10,701 | 244 | import warnings
from docstring_inheritance import NumpyDocstringInheritanceMeta
from pycromanager.acquisition.acq_eng_py.main.AcqEngPy_Acquisition import Acquisition as pymmcore_Acquisition
from pycromanager.acquisition.acquisition_superclass import _validate_acq_events, Acquisition
from pycromanager.acquisition.acq_en... |
c8dd58e4c066a7f3790619e91ffc9b732441f04b0841a638a13fcdb9568889e2 | Python | 10,706 | 222 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to... |
35c512a563f56db8c035051142cc50cc673d098e7b0e26fde5d85c9196ec22a8 | Python | 10,711 | 299 | def initialize_roi_dict(
roi_dir, subj
):
'''
Creates a dictionary of ROI NifTI mask file paths.
Key value corresponds to ROIs name (e.g., 'R_amygdala')
Input Parameters
----------------
roi_dir : str
Root directory where ROI NifTI files are stored.
(e.g., 'your/path/MN... |
85f74b874335e7cd89687a1962ead44e23a91555c1d14ca66def70a4a777fac6 | Python | 10,711 | 164 | """Corpus-A number ledger: every corpus-A number quoted in the manuscript, recomputed from the final result files.
Writes tables/summary_a.csv with columns key,value,ci,n,lo,hi,note.
Statistics rules (progress.md, AUDIT_1 M1, AUDIT_2 C4):
P1, P3: average the four datasets within (seed, fold), then mean and 95 % CI (... |
7bffc7d6a7096e4d8e28fc92db2893c5eb0d1b68bac8fd76d764fb9e1a007944 | Python | 10,712 | 286 | from __future__ import division
import sys
import os
import json
import numpy
import datetime
import argparse
import time
if(sys.version_info >= (3,0)):
import configparser as cfg_parser
else:
import ConfigParser as cfg_parser
import steps
import steps.API_1.model as smod
import steps.API_1.geom as swm
impor... |
580061ec4ff19d41f65e9362f44bce9bb13bd67f345dc1ebb107ed1bc3f6402f | Python | 10,716 | 229 | import pickle
import numpy as np
import os
import sys
import ntpath
import time
import torch
from . import util, myhtml
from subprocess import Popen, PIPE
if sys.version_info[0] == 2:
VisdomExceptionBase = Exception
else:
VisdomExceptionBase = ConnectionError
def save_images(webpage, visuals, image_path,... |
feded5f53e6a4b4140bb7fb1352cff334e0db1ade968d6b91dd1425caf5d9631 | Python | 10,720 | 268 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... |
7530124d52e2a1620c9fb367f76073a107058290ad6a64281b2c45ae1cc057be | Python | 10,724 | 343 | """Trainer for the spiking neural model."""
import statistics
import torch
import tqdm
def training(
dataloader_train,
dataloader_val,
model,
learning_rate,
device,
epochs,
optimizer,
savepath,
):
model.train(mode=True)
# model = model.to(device)
optimizer = str(optimizer)... |
df71d5ca94699e749099c91a6c59b8bf8926278e3a7d8417d91bfefadc840353 | Python | 10,725 | 311 | from __future__ import annotations
import logging
import re
from collections.abc import Sequence
from typing import Any
from langchain_core.callbacks import (
CallbackManagerForChainRun,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import AIMessage
from langchain_cor... |
fda079212bdcbe4073ceb1cf79bcefad4657838cca34b9cb1f36ddd81963a833 | Python | 10,727 | 278 | import sys;sys.path.append('../src/')
import ClusterModelNEST
import pylab
import spiketools
from GeneralHelper import (Organiser,
nice_figure, simpleaxis,
ax_label, psth_plot)
import defaultSimulate as default
datafile = 'suppl_fig4_balanced_state'
def simulate_and_analyse(params):
pylab.seed(None)
... |
4a4edf62006cfc57453a7557436b14f07e6d8072641923b76e9d19fb815cc151 | Python | 10,728 | 295 | import MDAnalysis as mda
import pyemma
import numpy as np
import os
import requests
from MDAnalysis.analysis import align
# -- Functions to preprocess trajectories --
def extract_coordinates(top, pdb, trj_list, out_name, sel_string, start_frame=0,
rename_segments=None, residues_offset=0 ):
... |
6781d0173da44326f1acc3fb888ad49099a199f37b862f58731034ab9f7bc6c0 | Python | 10,729 | 364 | """Simple benchmark to test the memory allocation of annotation stores.
This is to be run as a standalone script to avoid inteference from the
jupyter notebook environment. Tracking memory allocation in Python is
quick tricky and this is not perfect. Two methods are used here for
comparison. The first is simply to rec... |
8b538125f5bc2ec40a9125d01a9c93dd9502f87f0ede7063a0af1be838512bfc | Python | 10,729 | 247 | import numpy as np
import scipy as sp
import pandas as pd
import networkx as nx
import itertools
import time
from scipy.spatial import cKDTree
def get_knn_onesided(source, target, knn_k, knn_n_jobs, st=True):
"""Get kNN connections between source and target
source: pandas dataframe which rows will be placed a... |
5b80292b9ec1468056486fc54459ae59de6b874188bc72b2dc25fc08dc4f4b47 | Python | 10,731 | 284 | # coding=utf-8
"""Defines the ConvNet class."""
import torch
import torch.nn as nn
import numpy as np
from .basenetwork import BaseNetwork
from .layers import DenseUnit, ConvUnit, FlattenUnit
from .utils import pad
import logging
from inspect import getfullargspec
from math import ceil
from collections import Ordere... |
e9ff9e3f7b95062fe382b8203012ab7eb86148994239cce9a237d0a0e1cdecab | Python | 10,731 | 299 | import joe_and_lili
import analyse_experiment as analyses
import pylab
from copy import deepcopy
import analyse_model
import numpy as np
from GeneralHelper import Organiser
def reaction_time_plot(monkey,nbins = 40,
condition_colors = ['0','0.3','0.6']):
rts = []
minrt = 1000000
max... |
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