sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
3aa6ad3411d1ce0901353a941b7f8cb0c073b3967e4324c7f4d9e182c5b105fc | Python | 7,986 | 190 | """Configuration file for the Sphinx documentation builder."""
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/mast... |
93f5c4258d79dd776f5a794216c802ebee738128e7da6918d9daf76ec53db2d4 | Python | 7,986 | 218 | from typing import Callable, List, Union, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import kornia.filters as KF
from . import base
from .base import Conv2d, Linear, ChAttn2d, SpAttn2d
_DEBUG_IMAGES_: Uni... |
230e15430b36151564c5a8474f51983d8c48d0a3234e1e111dc10d3d58d279e7 | Python | 7,992 | 248 | """Memory tests to test subjects for paying attention in the scanner."""
import glob
import os
import shutil
import click
import numpy as np
import pandas as pd
from datasets import load_from_disk
from tqdm import tqdm
from compositionality_study.constants import (
COCO_IMAGE_DIR,
COCO_LOCAL_STIMULI_DIR,
... |
abd08576874cb953f8574a6aa91b74b2e0b02a7cbafdcc30bba267c8f7480e54 | Python | 7,992 | 267 | import os
import logging
import sys
import inspect
from copy import copy
import itertools
import pandas as pd
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import torch.distributions as dist
import torch.optim as optim
from torch.opt... |
4700cf0e81cefabf838e89aad7b072d6bd81005a428afbfd9999dfaaeae77a39 | Python | 7,993 | 254 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as op
from collections import namedtuple
from multiprocessing import cpu_count
from typing import Li... |
2554ae34a07cb2b46cc8aabf47a47f469077ceac30034c3c4042e9ae9d225313 | Python | 7,996 | 221 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
import torch
from fairseq import utils
from fairseq.logging import metrics
from fairseq.criterions im... |
62a64082d6be1d09a0d9348fbd6b6c8152044e4845010244db3360a2440cdda3 | Python | 7,998 | 247 | # coding=utf-8
# Copyright 2018 Salesforce and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... |
214faa1c67a17c0bd6e47d6c59c7a8c657149ff39e32abe9835e2ee2f2b1f369 | Python | 7,999 | 248 | # coding=utf-8
# Copyright 2018 Salesforce and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... |
d01e37dbaf0a3c7a8b85150886820e0fdf036bf0508c6dc5391bc2fb67fc4278 | Python | 8,002 | 206 | """SIESTA-PBE variable-cell relaxation for the oxide perovskite polymorphs.
Periodic DFT is the correct tool for perovskite polymorph ranking (xtb fails on
oxides; ORCA is molecular and blind to cell shape). This relaxes each polymorph
(atoms + cell) so that cell-shape competition (orthorhombic tilts, hexagonal) is
ca... |
81cb84120fef53044ee3f287f2549bb840d60ceb1716ade6e286eed05f414fa6 | Python | 8,003 | 225 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
RoBERTa: A Robustly Optimized BERT Pretraining Approach.
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional... |
9f7420bf1ef19ef1fd2ed6fc174b4fa2f2f266fd615e882133b25b9760e2c6f6 | Python | 8,005 | 183 | import os
from textwrap import dedent
from mdt.lib.nifti import load_nifti
import numpy as np
from PyQt5.QtCore import QObject, pyqtSignal, pyqtSlot
from PyQt5.QtWidgets import QFileDialog
from mdt import load_brain_mask, create_median_otsu_brain_mask
from mdt.utils import split_image_path
from mdt.visualization.maps... |
c92b721389df452380990b3f4de9c53431e22cf9ab46650cf3807f15966f1612 | Python | 8,011 | 203 | from __future__ import annotations
import re
import networkx as nx
import pandas as pd
from skbase.utils.dependencies import _safe_import
from pgmpy.base import DAG
from pgmpy.causal_discovery._base import BaseCausalDiscovery
litellm = _safe_import("litellm")
class LLMPairwise(BaseCausalDiscovery):
"""
LL... |
45422cc3c536028b56ea88a057e73ca0a824e783710d55e8d86060566a8c949e | Python | 8,012 | 171 | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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... |
6ed58b35908a4095a12102ef1e103db68b7b5fe90b1df5a1ad1edbdc8e0053b0 | Python | 8,013 | 172 | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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... |
b69f6b668fc421dd2784dfb000ce32a74b100786d8b850d620c7f136a7b5937f | Python | 8,019 | 192 | """Phase 1b — extend the per-atom power-law to L >> 16 using OCE alone.
We use the OCE J_F coefficients fitted in `phase1_full.py` (trained on
L ≤ 8, validated on L ∈ {12, 16} at 45 meV/atom). The model is then
evaluated on much larger L where xtb would cost hours-to-days but OCE
finishes in seconds.
This is the reg... |
580d0708964eeaf293fe6760e50ae7cabe06dcf49f4e6f8a2b6a46f01bc98514 | Python | 8,022 | 176 | #!/usr/bin/env python
__author__ = 'heroico'
import os
import re
import logging
from metax import Person
from metax import ThousandGenomesUtilities
from metax import PrediXcanFormatUtilities
from metax import Utilities
from metax import DataSet
from metax import Logging
from metax import Formats
from metax import Exce... |
46c8150b70520978e360cb362717bfece915e623b6cdb487c16d73511a7ffcea | Python | 8,024 | 173 | #!/usr/bin/env python3
"""R1, achado de 21/09/2026: 543 das 1784 celulas sao COPIAS por simetria de outra.
Refaz, com e sem as copias, todos os numeros do artigo que dependem da contagem de
linhas. Primeiro mostra que a versao "completo" REPRODUZ o artigo (Tabela 3, CV
aleatorio, eta2); so entao le a versao "sem copia... |
faff752cd8a434006c634d2ff243291a5241c1ef6f034064950d8f56e5649d73 | Python | 8,027 | 251 | from __future__ import annotations
import dataclasses
from importlib import metadata
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from typing import ClassVar
from typing import Literal
from cleo.helpers import option
from poetry.core.constraints.version import Version
from poetry... |
cbba7c351c36fb746e522d42b8c4074fda3d5b3b82d9573b82981dfdcaaf3871 | Python | 8,038 | 261 | #!/usr/bin/env python3
"""
ENCODE QC log/plot to HTML converter
Author: Jin Lee (leepc12@gmail.com)
"""
from collections import OrderedDict
from base64 import b64encode
def to_number(var):
"""Convert to number or return None
"""
try:
if '.' in var:
raise ValueError
return int... |
ac2048a8a6af8f0cad5fe9ce47ad6597f73cb57f68b85423c5ec51b7d1fe653b | Python | 8,042 | 205 | # -*- coding: utf-8 -*-
"""File discovery and measurement-management helpers."""
from pathlib import Path
import pandas as pd
import mne
import numpy as np
def automation_filenames(namesPath, dataPath, savePath, patients='all', measurements = 'all'):
'''
Since EEG recordings were saved with unsystematized n... |
bb0631f2210a616e45f8a311551a5ebe0536bceebe392899d17016c7a44e2466 | Python | 8,043 | 200 | # Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
fa5e563e1209d7f9b7c709a2bcbad2cef292642a94adc90d05f4c58755a77cbe | Python | 8,043 | 265 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... |
f8f2be96ac2853f9500ddbaa6b0b38f6bcbcb412f8be9366e53ea039515783c8 | Python | 8,044 | 182 | #!/usr/bin/env python3
"""Reads in a modisco h5 and prepares to scan for seqlets.
In order to see where seqlets are found on the genome, we need to scan the cwms
derived from modiscolite.
The first step of this process is to look at the seqlets that MoDISco called for
each pattern it identified, and establish cutoff v... |
5c419a26238aeb18126d6cd5a7d2e4286e6c434b9f82b4bcf6ccc5d7c0570d02 | Python | 8,048 | 254 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
823d0098337e175abffd2f1c5c72463bfc9202859d6b556f96d5689bf9e05b81 | Python | 8,048 | 170 | import utils.gpu as gpu
from modelR.fs_lodet_hbb import LODet
# from modelR.Fine_tuning_lodet_hbb import LODet
from tensorboardX import SummaryWriter
from evalR.evaluator_fs import Evaluator
import argparse
import os
import config.cfg_lodet as cfg
from utils.visualize import *
import time
import logging
from utils.uti... |
d3121884b10c344ccdbf702cdbdb6f1cd1105f7162bf2a413dabf71c5642f9de | Python | 8,049 | 237 | """Tests for PyTorch model training and inference pipelines.
Tests marked ``needs_pretrained`` are skipped when
``tests/fixtures/pretrained/`` is absent or empty.
"""
import os
import numpy as np
import pytest
import torch
from moove.models.CNN import CNN
from moove.models.ConvMLP import ConvMLP
FIXTURES_DIR = os.... |
2cca1feeaa006308ffc5717f45d141c58f5175698dbe992729dff7451c294c59 | Python | 8,052 | 293 | """
n>2 DS Gillespie model, Preserves EXACT n=2 behaviour
Author: Original by A. Reina
"""
import numpy as np
import sys
import os
import copy
import random
DEBUG = False
####################################################
# GILLESPIE STEP
####################################################
def gillespieStep(
... |
b9e9bda2a8abded369c678529eced60cd279faa12cf9e51b9ed59759491a0b0f | Python | 8,053 | 239 | """Figure 3 panel A/B primitive — bimodal (WF+ISI) dataset profiles.
Two datasets: A1 (a1data_remove_undef, Lakunina 2020) and S1
(juxtacellular_mouse_s1_area, Yu 2019). Both ship waveforms.csv +
isi_dist.csv + labels.csv under results/benchmark/cache_datasets/.
Emits a compact 2-row panel per dataset:
- Mean wav... |
18a6c752226f4e6d34c35651573228981b22b593171d49ca1a98fad9276459b2 | Python | 8,054 | 199 | import numpy as np
import pandas as pd
import pytest
from pgmpy.factors.discrete import DiscreteFactor
from pgmpy.inference.EliminationOrder import (
ELIMINATION_HEURISTICS,
H1,
H2,
H3,
H4,
H5,
H6,
BaseEliminationOrder,
MinFill,
MinNeighbors,
MinWeight,
WeightedMinFill,
... |
8877c1e465ca6e21593835c2a755cd9706c05b9a1950b9784886eb9b83ddf5ba | Python | 8,057 | 207 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib_venn import venn3
import pingouin as pg
import seaborn as sns
from scipy.stats import kruskal
import scikit_posthocs as sp
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.wid... |
cfe6cb6b57890e18174af0606fb7921c610cdf8f9a533cc9ebc24ead8b01f9b8 | Python | 8,057 | 197 | """Read an NCBI gene2go GO Association File and store the data in a Python object.
Annotations available from NCBI:
ftp://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz
"""
import collections as cx
import sys
from itertools import chain
from .annoreader_base import AnnoReaderBase
from .init.reader_genetogo impo... |
3e11ea5ca3b1063042f4fcbda7d75776314f26d6e72b02da0af7c69893d2c7c4 | Python | 8,067 | 254 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
6ff0ad0fafdd93ecdc952a579df79abb7e7d5fe797c5c7e5ce8fdb047606c72f | Python | 8,068 | 256 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
b4bf942013b2c5b27ce61a2cff94b516146e78eb5e30ebb7d185b6b4298815df | Python | 8,069 | 256 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
f8ff2ccc5cde1a19952e9dc6245512930f5cf40b866e2f3ac762d16d52d232d9 | Python | 8,070 | 214 | # Original work Copyright 2018 The Google AI Language Team Authors.
# Modified work Copyright 2019 Rowan Zellers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/... |
598b9091dc110f5fda0f8d9a65c2792e9112a7ee3fd5715c6b8afb067f02ac0a | Python | 8,071 | 216 | """`TokenBudgetBatchSampler`: batches bounded by padded chunk count.
Peak VRAM is linear in a batch's *padded* token count, and a batch pads to its
longest document. Batching by a fixed document count therefore leaves peak
memory a lottery over which documents the sampler drew — the reason a run
trains for a while and... |
a2c5017ffc54995f03035538a2f3c322fc83ae9c9448124d46d429cae4f31a3d | Python | 8,074 | 355 | # Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
3e9b890f7739878aca2e761b6b8fa738e34a13dabbe78649ae0812205fee2cef | Python | 8,076 | 212 | """Extended tests for src/processing/cluster_detection.py — targeting uncovered branches."""
from __future__ import annotations
import pandas as pd
from src.processing.cluster_detection import (
identify_clusters,
process_cluster_window,
process_data_for_clusters,
)
def _make_series(values):
return ... |
90cf1e10f214353514ab501f73b67a6d965a961fdc6291a21c9b6223217de9de | Python | 8,076 | 192 | import argparse
import logging
import torch.nn as nn
import fairseq.checkpoint_utils
from fairseq.models import (
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import TransformerDecoder
from fairseq.models.roberta import model as roberta
logger ... |
69c60b8d5a200167dadfb66c29fb56f627666db8958082d8417c521fbac5d7ef | Python | 8,078 | 254 | """Generate high-quality surface plots for group-level brain maps.
This script searches for group-level NIfTI files (univariate and MVPA) in specified directories,
projects them onto the fsaverage surface, and saves both static (PNG) and interactive (HTML)
surface plots.
"""
import click
import matplotlib.pyplot as p... |
87c9225c82e8c6fd17b6cded882be89ceae5addcf42d09d353333aff04484cfd | Python | 8,079 | 227 | import sys
from os import makedirs
from os.path import join, dirname, realpath, exists
from config_path import PROSTATE_LOG_PATH, PLOTS_PATH
current_dir = dirname(dirname(realpath(__file__)))
sys.path.insert(0, dirname(current_dir))
import itertools
from sklearn.metrics import confusion_matrix
from mpl_toolkits.axes_... |
5929555edc05a95b5fef8b88bba86935fb988d03060a63cc5e5c665d3bfceb17 | Python | 8,084 | 237 | from __future__ import annotations
import logging
import pathlib
import numpy as np
from dask import bag as db
from dask.diagnostics import ProgressBar
from .. import constants
from ..timebins import timebin_dur_from_vec
from .files import find_fname
logger = logging.getLogger(__name__)
def find_audio_fname(
... |
b1866622d91683401bf16ab452eed43d8119d51f91649348e24cecd17de67b2a | Python | 8,085 | 249 | #!/usr/bin/env python
import unittest
import shutil
import os
import logging
import numpy
import numpy.testing
import pandas
from unittest.mock import patch
from metax.Constants import SNP
from metax.Constants import BETA
from metax.Constants import ZSCORE
from metax import Exceptions
from M03_betas import run
fro... |
19fcc46715b2caa887ad288017a35e711b750752eb5324318b999b465d10f8b0 | Python | 8,092 | 213 | import numpy as np
def Ent_MS_Plus(x, tau, m, r):
"""
(RCMSE, CMSE, MSE, MSFE) = RCMS_Ent( x, tau, m, r )
inputs - x, single column time seres
- tau, greatest scale factor
- m, length of vectors to be compared
- R, radius for accepting matches (as a proportion of the
... |
8805d150252d76d851d5343a662ee69817f5d68b9ecec6f5ec63ca8d560077ae | Python | 8,094 | 208 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import os
import time
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchmetrics import MeanAbsoluteError as MAE
from torchmetrics import MeanMetric
from am... |
a5c51c916dd89e6cb59d5ee53a028ef29e5315f856624876b7c6b420bf1289be | Python | 8,097 | 237 | """Types that are used throughout BPReveal."""
from __future__ import annotations
from multiprocessing import Lock
from typing import NotRequired, TypeAlias, Literal
from typing import TypedDict
import numpy as np
import numpy.typing as npt
ONEHOT_T: TypeAlias = np.uint8
"""Data type for elements of a one-hot encoded ... |
831780d7774e7cef4f1642ff6cf2bd2317aae315812bdb488af9df912cecd7a1 | Python | 8,104 | 198 | import numpy as np
from scipy.stats import percentileofscore
# https://github.com/mateuszbuda/ml-stat-util
def score_ci(
y_true,
y_pred,
score_fun,
n_bootstraps=2000,
confidence_level=0.95,
seed=None,
reject_one_class_samples=True,
):
"""
Compute confid... |
5163f83b32b846084596144c2f08b43828f33c90e4887540c1ea96ee97d38be2 | Python | 8,106 | 200 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import re
from pathlib import Path
import tifffile as tiff
import numpy as np
import pandas as pd
from skimage import filters, util
from skimage.measure import label as sk_label, regionprops_table
from skimage.morphology import remove_small_objects, disk, white_tophat
impo... |
63dca44d5137e018044e713a1b7f703e4a0895346648f6c0d9adea7ffa8ee5d5 | Python | 8,109 | 222 | #!/usr/bin/env python3
import argparse
from pathlib import Path
import polars as pl
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.ticker as ticker
from matplotlib.patches import Patch
from scipy.stats import gaussian_kde, ks_2samp, ttest_ind
TISSUE_MAP = {... |
fea44c9610a94e0a65f762fbf16466797eecd60ba021db4838b87dbd9b928708 | Python | 8,109 | 194 | """
Rename tripwire for RNAlysis' public API.
Pipeline YAML files store function *names*, and the GUI is generated from the public API by
reflection - so renaming or removing a public function silently breaks every Pipeline a user
exported before the rename, and silently moves or removes a GUI button. Neither is visib... |
41eafae3ac0f45313f4df1520b449b7f08bb4ade57f34fcdd25782ea4102f36f | Python | 8,115 | 231 | from __future__ import annotations
import os
import subprocess
from contextlib import contextmanager
from contextlib import redirect_stdout
from io import StringIO
from typing import TYPE_CHECKING
from build import BuildBackendException
from build.env import IsolatedEnv as BaseIsolatedEnv
from poetry.core.packages.d... |
13285d91ee7a0b5fe8c48518a9287e2bed54a84ffb38670a32aea94bd3f09748 | Python | 8,117 | 218 | import pandas as pd
from omegaconf import ValueNode, OmegaConf
from tqdm import tqdm
import numpy as np
import torch
from torch.utils.data import Dataset
from torch_geometric.data import Data
from .data_utils import add_scaled_lattice_prop, preprocess_tensors
from . import DTYPE
def load_cif_data(df, primitive=True... |
bd553de3bad620accfd7b0118bdc6c8a9fd2b38d56de33378f71275858b0a7c9 | Python | 8,117 | 255 | ##################################
# #
# Last modified 2017/11/08 #
# #
# Georgi Marinov #
# #
##################################
import sys
import os
READS_LOG_INTERVAL = 5000000
MILLION = 1000000
def... |
311852be8a20943f0bc7b5959a5509cc31345e228f6be97cb868199d2bffe530 | Python | 8,119 | 198 | #!/usr/bin/env python
# PYTHON_ARGCOMPLETE_OK
"""Evaluate an expression on a set of images.
This is meant to quickly convert/combine one or two maps with a mathematical expression.
The expression can be any valid python expression.
The input list of images are loaded as numpy arrays and stored in the array 'input' an... |
c9b9dd9f1f1d1f123ff9af451ebedea30078ed1d1b724a4dc40479763594476e | Python | 8,119 | 220 | from itertools import combinations
from pgmpy.base._base import _CoreGraph
class MAG(_CoreGraph):
"""
Class for representing Maximal Ancestral Graphs (MAGs).
A MAG is a graph used in causal inference to represent conditional independence relations when
some variables are latent (unobserved) or selec... |
64985b808f9cfe5b7ce6537ee7c4a3333408ca17d36a2ea772fcab9c3308198a | Python | 8,128 | 211 | #!/usr/bin/env python
# ENCODE DCC reporting module wrapper
# Author: Jin Lee (leepc12@gmail.com)
import base64
import re
from encode_common_log_parser import get_long_keyname
from encode_common import *
from collections import OrderedDict
def html_heading(lvl, label):
html = '<h{lvl}>{label}</h{lvl}>\n'
ret... |
85843986e6e7afd3a111b34a49023e9cabfea330e90a26362002867263a1ecea | Python | 8,132 | 231 | """
Reusable Lightning training stack: loggers, checkpoints, Trainer kwargs, TensorBoard helper.
Task packages compose their own ``LightningModule`` and phase logic; this module hosts **engineering**
pieces described in README (experiment leaf cleanup, TensorBoard/W&B loggers, checkpoint filenames,
tqdm/LR wiring help... |
f9843563a5ac558cc3cde0993070fe130adf750bda70aa54cd7db170f61604f4 | Python | 8,135 | 209 | #!/usr/bin/env python3
"""
This module has the EMA class used to store a copy of the exponentially decayed
model params.
Typical usage of EMA class involves initializing an object using an existing
model (random or from a seed model) and setting the config like ema_decay,
ema_start_update which determine how the EMA ... |
cd768750e636b4faf3d89420f439edabec60b94b86d9df156aebd58bcd784b84 | Python | 8,138 | 240 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import glob
from subprocess import check_call
try:
import faiss
has_faiss = True
except Imp... |
25276009cb8fe915116e97c11475c9db07ec2e217a9d4342b9b414255eb85583 | Python | 8,139 | 244 | import argparse
from pathlib import Path
import yaml
from tqdm import tqdm
from BLRun.genie3Runner import GENIE3Runner
from BLRun.grnboost2Runner import GRNBoost2Runner
from BLRun.grisliRunner import GRISLIRunner
from BLRun.grnvbemRunner import GRNVBEMRunner
from BLRun.jump3Runner import JUMP3Runner
from BLRun.leapRun... |
294fb21be1a2f096bb887cc65168dd6bbf945fd31e6ba2011d5cef21cdee7c2b | Python | 8,139 | 188 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
# === Setup ===
base_dir = Path('/egor2/egor/MovieProject2')
plots_dir = base_dir / "bids_data/derivatives/group_analysis"
plots_dir.mkdir(parents=True, exist_ok=True)
tasks = ['backtothefuture', 'somatotopy', 'retinotopy'... |
7364437e17accaf5848410b1c391a4ed8020638bb9a599a607437839ec55245f | Python | 8,139 | 244 | import pytest
from pgmpy.base import ADMG, DAG
from pgmpy.identification import Adjustment
def test_validate_admg():
# regression: validate() on an ADMG used to raise AttributeError (is_dconnected is DAG-only)
admg = ADMG(
edge_list=[("X", "Y", "->"), ("Z", "X", "->"), ("Z", "Y", "->")],
expo... |
ee1ce4c8c07d7d57ce01231bb8d4de3bae672a9a346f71c179ef73c193860023 | Python | 8,139 | 196 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
"""
This script builds a ResNet50-v1.5 Keras model with weights loaded from a separate fil... |
e4d788d62a2b111e738a9a3f0b022c9b91415ad5aa79e3a36a5ca050c35a59ab | Python | 8,140 | 205 | # Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
bcd8ead7bbac70072a7833c9e9dfc3e6e24597e5bfd7c730119105bb5bc9a4bc | Python | 8,142 | 204 | import os, gc
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import cv2
import torch
import zarr
from ultralytics import YOLO
def create_df_from_paths(img_path, label_path):
"""
Create a dataframe of paths to tiles sliced from all ROIs in a dataset split.... |
d0df49251157e615bcf4da20356e00ddbf73c47070032d51010564bfb2221458 | Python | 8,149 | 202 | """Fit OCE on the GFN-FF cohesive energies, rank, and compare to xtb.
Target = cohesive energy per atom (E_total - sum atom_refs) / n_atoms.
Engine = GFN-FF (primary, all 100); GFN2-cluster cross-check on 5-atom cells.
Model = Ridge, 5-fold CV alpha selection, Wave-1.5 features (formal charges).
Three evaluation ... |
af7ea4e53095266599ce2ab75cb229767d5b5616ee03eb8b8ba95a071a749047 | Python | 8,150 | 187 | __authors__ = ["srhmm", "Nimish-4", "ankurankan"]
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
from pgmpy import config
from pgmpy.base import DAG
from pgmpy.causal_discovery._base import BaseCausalDiscovery
from pgmpy.structure_score import BaseStructureScore, get_scoring_method
class TOPIC(Ba... |
5943566e98b0915fdfa098a58922643fb819916fef7386266d6e13412d6b712e | Python | 8,152 | 193 | from functools import partial
from typing import Any, Dict, Tuple
import jax
import jax.numpy as jnp
from jax import lax
from ..device_utils import DEVICE_AXIS
from ..types import InitialiserParams, ModelDimensions
from ..wf.base import WaveFunction
from .sample_initializer import MolecularSampleInitializer
from .sam... |
9563df3aadccf73eb9f83cd045c50620665621e473c6e7c6d88da75e32a5b681 | Python | 8,158 | 203 | from sklearn.decomposition import NMF, non_negative_factorization
import matplotlib
from matplotlib import pyplot as plt
import cv2
import numpy as np
from typing import List, Dict, Optional
import math
import glob
from PIL import Image
from matplotlib import pyplot as plt
import math
import tqdm
import war... |
0f6c343d2c7d3ba032515effaccd2df8cb1e5973aeb7ee8b27125b5c03b46367 | Python | 8,159 | 231 | from __future__ import annotations
import contextlib
import logging
from typing import TYPE_CHECKING
from typing import Any
import requests
import requests.adapters
from cachecontrol.controller import logger as cache_control_logger
from poetry.core.packages.dependency import Dependency
from poetry.core.packages.pac... |
d37e2ade0ff3c28362dca2ae3a0c560efdece637e1ade3dbede583f4f13cb191 | Python | 8,164 | 184 | #!/usr/bin/env python3
"""A little utility to plot loss values during training."""
import json
import re
import math
import datetime
import argparse
from matplotlib.figure import Figure
import numpy as np
import matplotlib.pyplot as plt
from bpreveal import logUtils
import bpreveal
plt.rcParams["font.size"] = 8
def ... |
62c3eb076efabfa1f5b14959a435f9bf2f39d344c29c2119d8e8db5b382b5d91 | Python | 8,169 | 232 | """
Feature Extractor, originally inspired by VisualCheese and SpaceTorch (NeuroAI Lab) and Model Tools
PytorchWrapper (DiCarlo lab)
"""
import math
from typing import Dict, List, Optional, Union
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm
class... |
94ef3fe37dc68f33ea46e77bb9d00a0807d3903a25fe056cb5cde8ea4e718750 | Python | 8,172 | 245 | """VTK read/write filters for FreeSurfer geometry files."""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import re
import numpy as np
from vtk import vtkPolyData
from vtk.util.vtkAlgorithm import VTKPythonAlgorithmBase
from ..checks import has_only_triangle
from ..decorators import... |
cc3eabfaf5993f0e946930e53953ef2ef25943850e18b59d8be273617d7242f9 | Python | 8,172 | 256 | from __future__ import annotations
import logging
import time
from pathlib import Path
import numpy as np
import pandas as pd
from src.processing.behaviour_metrics import compute_full_trace_zscore
from .df_common import (
BUTTER_CUTOFF,
BUTTER_ORDER,
SAVGOL_COARSE,
SAVGOL_FINE,
butter_lowpass_fi... |
936e90e8204bb618afedc7e7dce5f10f642aad36781c5cc6a766c0186768fa8b | Python | 8,174 | 272 | from __future__ import annotations
import numpy as np
import pandas as pd
import pytest
from src.excel_ops.behaviour_exporter import (
create_df_for_behaviours,
process_and_bin_data,
)
from src.processing.behaviour_parser import (
extract_behaviour_results,
find_out_of_range_behaviour_times,
read_... |
d16b5358b27af670bc55d960448eb58cf4d03f6e728cd36263265673e9df89a1 | Python | 8,174 | 210 | """Phase 17 (parametric) — DFT spread campaign for C / Si / Ge.
Same protocol as phase17_dft_more_reps.py but accepts --material argument.
Resumable from disk cache (per-material).
Usage:
python pipelines/phase17_xx_dft.py --material Si
python pipelines/phase17_xx_dft.py --material Ge --workers 6
python pipelin... |
68d59a930a7d828a9a95f29823e3b6b80519b299e895e4a4dd258cbfaf715834 | Python | 8,179 | 224 | """Task-agnostic helpers for writing ``summary.log``."""
from __future__ import annotations
from datetime import datetime
from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional
from omegaconf import DictConfig
FormatterMap = Mapping[str, Callable[[Any], str]]
SummaryAdapter = Callable... |
86c44f1dfa536401f2dbaffa34c69d39545b1aa752429ca043b250b46a2a2447 | Python | 8,179 | 250 | # qt_helpers.py
"""PyQt6 helper utilities: thread-safe invoker, custom range slider, combo helpers."""
import functools
from PyQt6.QtCore import QObject, pyqtSignal, pyqtSlot, Qt
from PyQt6.QtGui import QPainter, QColor, QFont, QFontMetrics
from PyQt6.QtWidgets import QWidget, QApplication, QMessageBox
class _Invoker... |
410ece5b0d97d32c2f672881ddc330c0e88e6d8285a0004e7667f18acb5a6e8a | Python | 8,180 | 184 | import stdpopsim
import dadi
import numpy as np
from sbi.utils import BoxUniform
import torch
class BaseSimulator:
def __init__(self, snakemake, params_default):
for key, default in params_default.items():
if key in snakemake.params.keys():
setattr(self, key, snakemake.params[ke... |
ebf8da70e71a365090e3472e0eaf01a59dffd8f1f581d1fa2c80d878c5a0f7ec | Python | 8,181 | 189 | __author__ = 'heroico'
import logging
import os
import gzip
from . import Utilities
from . import DataSetSNP
class PDTF:
"""Format of PrediXcan dosage"""
CHR = 0
RSID = 1
POSITION = 2
ALLELE_0 = 3
ALLELE_1 = 4
COLUMN_5 = 5
FIRST_DATA_COLUMN = 6
class PrediXcanFormatDosageLoader(objec... |
67dfd5053e50e2029be9837626fa2537b7410cea4ce3d0e480fafe59f8e70ae5 | Python | 8,184 | 238 | import os
import pickle
import numpy as np
import pytest
import torch
from scvi.data import synthetic_iid
from scvi.model import PEAKVI
from scvi.utils import attrdict
def test_saving_and_loading(save_path):
def legacy_save(
model,
dir_path,
prefix=None,
overwrite=False,
... |
1e7b8ddfd5e3bf1e3b761e203f2c7f109614e0e55e291aa41bf34e42192d3502 | Python | 8,186 | 191 | import numpy as np
import pandas as pd
import nibabel as nb
import matplotlib.pyplot as plt
import matplotlib as mpl
import potpourri3d as pp3d
from matplotlib_surface_plotting import plot_surf
from brainspace.null_models import SpinPermutations
from sklearn.linear_model import LinearRegression
from sklearn.metrics imp... |
7fa4f06e6e470bdf865074f06b71a7a6a6bb67d20c376b79ebc52ec40fb8d548 | Python | 8,186 | 284 | # -*- coding: utf-8 -*-
"""
Created on Thu Mar 6 14:10:09 2025
@author: hanna
"""
"""
[Figure 4C, 4D] changes in preferred of individual BCI neurons - summary of all sessions
"""
#%%
import os
import pickle
import numpy as np
import pandas as pd
from tqdm import tqdm
import seaborn as sb
import matplotlib as... |
65b8b8d5dc986956315d85d78b15dabae402aaf71d0d0ed72a764d74517d06eb | Python | 8,188 | 222 | import logging
import numpy as np
from scipy.ndimage import binary_dilation, generate_binary_structure, binary_fill_holes
from mdt.utils import load_brain_mask
from mdt.protocols import load_protocol
from mdt.lib.nifti import load_nifti, write_nifti
import mot.configuration
from scipy.ndimage.filters import median_filt... |
354cb3c9e659d2a490b50b0591c30d2d70db89f45d5b0b64c42a49789a746078 | Python | 8,191 | 218 | """Figure 3 — bimodal (WF+ISI) classification benchmark on A1 and S1.
Loads cached 5-fold predictions under
results/benchmark/celltype_cache/ and computes per-fold
balanced accuracy and macro F1 for:
HIPPIE-bimodal, VAE-bimodal, PhysMAP (WNN), PCA-WF, PCA-ISI.
Emits one bar chart per dataset plus a tidy CSV:
... |
df091986d95dc847d2f84994780e935a662cdf23cd7f8589e16e95f95aa39172 | Python | 8,191 | 197 | from __future__ import annotations
from collections import defaultdict
from typing import TYPE_CHECKING
from typing import Any
from poetry.utils.extras import get_extra_package_names
if TYPE_CHECKING:
from collections.abc import Mapping
from packaging.utils import NormalizedName
from poetry.core.packag... |
f9c9f5a44e201a12956a1c684807c50d2023b4545bbd65d442f0edf2fb7e1729 | Python | 8,191 | 201 | """
This module contains methods used for converting experiment data into matlab
data. It is not used and not supported in NoSeMaze but still saved here for
archive purpose.
"""
"""
Copyright (c) 2019, 2022 [copyright holders here]
This file is part of NoSeMaze.
NoSeMaze is free software: you can redistribute it and... |
0ca57ed0fb39dad544a299827ffdbcb93458885e8fd866259595d73424ab5d44 | Python | 8,196 | 218 | """tests for vak.eval.frame_classification module"""
import pytest
import vak.config
import vak.eval.frame_classification
# written as separate function so we can re-use in tests/unit/test_cli/test_eval.py
def assert_eval_saves_one_csv(model_name, output_dir):
eval_csv = sorted(output_dir.glob(f"eval_{model_name... |
b4666cc08cc72843201c3409110e6ecc53e9c596411d00389793ac5a04b97640 | Python | 8,199 | 185 | # -*- coding: utf-8 -*-
'''
load user data and process it to a sentence
'''
import pandas as pd
import os
from Bio import SeqIO
class DataProcesser(object):
# seq2kmer
def seq2kmer(self, seq, k):
"""
Convert original sequence to kmers
Arguments:
seq -- str, original sequence.... |
2c1b1b9da9018b41f104de3a08251dfc8752234848dbeb4fdea10cbde59601da | Python | 8,201 | 264 | from __future__ import annotations
import re
import textwrap
from pathlib import Path
from typing import TYPE_CHECKING
from typing import ClassVar
import pytest
from cleo.io.buffered_io import BufferedIO
from cleo.io.inputs.string_input import StringInput
from cleo.testers.application_tester import ApplicationTeste... |
9f744f5652f8d70b8dab8332d1c7a91b1767de85e7785ac8942ed78667da2fe6 | Python | 8,203 | 258 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
c32709a578e8a33a22491293056242521acc4d01bce9cbcd4c8d192a57ed485d | Python | 8,203 | 188 | #! /usr/bin/env python
"""
This module contains high level code to parse GWAS/GWAMA summary statistics files and parse them into a standard format.
You can use it as stand alone tool to align it to Predictdb Models or jus tconvert the format,
or it can be called from another script to load the data into memory
TODO:
... |
bdb98de631956c7397003e26ff7ccd30edde80ed04310bb916425dc61d5b0a48 | Python | 8,204 | 208 | import torch
from torch import nn
from contextpath import build_contextpath
import warnings
warnings.filterwarnings(action='ignore')
class ConvBlock(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=2,
... |
489d85b54b9de59688cd36cc4a131f51292a951944f93a4202d37519224b5110 | Python | 8,206 | 254 | __author__ = 'heroico'
import os
import io
import json
import re
import logging
import gzip
from . import Exceptions
import pandas
import numpy
VALID_ALLELES = ["A", "T", "C", "G"]
def hapName(name):
return name + ".hap.gz"
def legendName(name):
return name + ".legend.gz"
def dosageName(name):
return n... |
b669660121b61aa81956f3bbdc8c0f0688f4498755de9dcfbb4387be98108e88 | Python | 8,206 | 236 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import argparse
import importlib
import os
from contextlib import ExitStack
from fairseq.dataclass import FairseqDatac... |
2d2c37f8d7395d7b58f84b69167b6feb47596934232ca1a39c2231ad9ac4bb91 | Python | 8,208 | 236 | import torch
import numpy as np
class LocalAxis:
def to_tensor(self,x):
if torch.is_tensor(x):
return x
else:
return torch.tensor(x)
def __init__(self,x0,y0,beta=0.0):
self.x0 = self.to_tensor(x0)
self.y0 = self.to_tensor(y0)
self.beta = self.to... |
5ba14b76034e310ca275c2eb39686a9db31613f3f86009713cfa102ae13fe71b | Python | 8,209 | 194 | """Tasks for go2obj dicts."""
__copyright__ = "Copyright (C) 2016-present, DV Klopfenstein, H Tang, All rights reserved."
__author__ = "DV Klopfenstein"
import collections as cx
from goatools.godag.go_tasks import get_go2ancestors
from goatools.godag.go_tasks import get_go2descendants
# ----------------------------... |
7217aac5e83cfc7e68e5ae1ae77c933d11f82c1bd0e67242810f5008a12b4b39 | Python | 8,213 | 215 | # modified by https://github.com/JinmiaoChenLab/scTM/blob/main/sctm/analysis.py
import gseapy as gp
import pandas as pd
from multiprocessing import Pool
from joblib import Parallel, delayed
def get_enrichr_geneset(organism="Human"):
avail_organisms = ["Human", "Mouse", "Yeast", "Fly", "Fish", "Worm"]
i... |
5b5a94e749ebca9b1d4e09a527a292b86a088e233f796cf1c82cdc3908d39958 | Python | 8,215 | 259 | import torch
import torch.nn as nn
import torch.distributions as dist
import torch.nn.functional as F
from npyx.c4.dl_utils import BaseVAE
class VAEEncoder(nn.Module):
# Encoder network for a VAE, support outputting the pre-projection output
def __init__(self, encoder, d_latent):
super().__init__()
... |
ed9be8b4773be3be2821c70f2d8229244642fb2b415e68cb8ee00733aa6ebcb2 | Python | 8,216 | 220 | # -*- coding: utf-8 -*-
"""
@Time:Created on 2019/8/20 20:44
@author: LiFan Chen
@Filename: Radam.py
@Software: PyCharm
"""
import math
import torch
from torch.optim.optimizer import Optimizer #, required
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
... |
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