sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b260eb4e736c9a94ba3d742f25a210e374435be496fbed3ab546aebceea23a25 | Python | 2,975 | 89 | """
quick and dirty derivative threshold detection for APs
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0, PATH_SRC)
import pyabf
import glob
import nump... |
1a520943ac66728e6355ac9473ed3e0719bd1a036638412d6a041ed0fcbb636a | Python | 2,977 | 94 | def standardize_tuple(value, n, name, allow_zero=False):
"""Transforms non-negative/positive integer/integers into an integer tuple.
Args:
value: int or iterable of ints. The value to validate and convert.
n: int. The size of the tuple to be returned.
name: string. The name of the argum... |
2d1ba8e0c31d27bd8a8d3edec856262a7af4b6a8a87c5c29093909e3e70f1367 | Python | 2,977 | 61 | from sklearn.metrics import confusion_matrix
import numpy as np
import tensorflow as tf
import os, argparse
import cv2
from data import process_image_file
#Combine the COVID and non-COVID pneumonia cases
mapping = {'normal': 0, 'pneumonia': 1, 'COVID-19': 1}
def eval(sess, graph, testfile, testfolder, input_tensor, ... |
5d9ab821aed93ff2d6a53f4e7ec96d51faaf01745f76681edc62f7467ddc72a7 | Python | 2,977 | 104 | """Comprehensive dry-run visualization tests for fastlog."""
from __future__ import annotations
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
from torchlens.fastlog import RecordContext
def _mlp() -> nn.Module:
"""Return a small static MLP."""
return nn.Sequential(nn.L... |
056b6667e28d34649debe33fcc279899f80300ae66a34ce2fdfcdbfd8daa1d9b | Python | 2,978 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseLayoutHierarchyType):
_parent_path_str = "layout.shap... |
b3282ea5e74460dd22e601bc76df8339cecbe260678863eeba61932b4ca25123 | Python | 2,979 | 99 | """Metrics like Dice and ROC AUC"""
import numpy as np
from scipy.stats import ttest_ind
from sklearn.metrics import roc_auc_score
from sklearn.metrics import auc
from sklearn.metrics import precision_recall_curve
def dice(pred, true, k=1):
"""Computes Dice score.
Args:
pred (ndarray): prediction
... |
ef8bf32ed5e5b4db7ce657097d12853559cc3a7cc264cfe3891bc54cfc894a0e | Python | 2,980 | 83 | """Tests for specparam.metrics.metrics"""
from pytest import raises
from specparam.metrics.metric import Metric
from specparam.metrics.error import compute_mean_abs_error
from specparam.metrics.gof import compute_r_squared, compute_adj_r_squared
from specparam.metrics.metrics import *
##############################... |
9503069ba3ab464c017c52774b9321c8c6467bebb6f3f384dde5392e0bf01ec7 | Python | 2,982 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseTraceHierarchyType):
_parent_path_str = "contourcarpet"... |
9606077a88718d033e2e54c79abaa2b2d4b0c534c968282859b298bbac448dbe | Python | 2,982 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseTraceHierarchyType):
_parent_path_str = "choroplethmap"... |
ecaac36b77a3b96cf44e0d50047dca36d5b2419f06f3b0ece79b9dd55ab51ddd | Python | 2,982 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseTraceHierarchyType):
_parent_path_str = "scattercarpet"... |
debb82905a314d260d8edbb4b250f1dd91dbf5800ee0eb3a221f8b4d1f91c747 | Python | 2,983 | 76 | """Detection dataset from RecordIO files."""
from __future__ import absolute_import
from __future__ import division
import numpy as np
from mxnet import gluon
def _transform_label(label, height=None, width=None):
label = np.array(label).ravel()
header_len = int(label[0]) # label header
label_width = int(l... |
31e68639e941fc80c798b3d357b0449eb9a6660e49fb42e951d3e5eb9bc298fa | Python | 2,984 | 85 | import pickle as pkl
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.cm as cm
from collections import Counter
from scipy import stats
fichier = open("data/residues_of_interest/dico_res_correctness.pkl", "rb")
dico_score_correctness = pkl.load(fichier)
nb_methods = len(dico... |
1678f718825d2104b90b4daad89d750aa167049345fa75adef7a4a313a819500 | Python | 2,985 | 72 | # Given a locus file and 1000 genomes location, generates MsCAVIAR format
# locus file and LD matrix
import argparse, glob, os, subprocess
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) + '/'
def gen_ld_parseargs(): # handle user arguments
parser = argparse.ArgumentParser... |
3472e07d7d9ab95684dd9e212006f874fa3f5b1e5efc0b5912bce4b47d346b72 | Python | 2,985 | 170 | # pyrefly: ignore-errors
# Unmaintained experimental module, not part of the public package surface.
# helper
def _find_distance(seq1, seq2) -> int:
edits = 0
for i in range(len(seq1)):
if seq1[i] != seq2[i]:
edits += 1
return edits
# analyzers?
def edit_distance(seq1: str, seq2: str, ... |
03637c1f1788bdea4fc6a217ae7ed73ae8c874423aa452445b928a386204bc9f | Python | 2,986 | 97 | import gzip
import pickle
import random
import warnings
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler
import os
import keras
import tensorflow as tf
from keras.models import Model
from keras.callbacks import EarlyStopping, Mod... |
3716baaba1e617601d9d0d003b5b7904baff117f828520630ded8176b7fa36c6 | Python | 2,986 | 75 | from __future__ import annotations
from collections import defaultdict
from minknow_api.data_service import GetReadStatisticsResponse
from typing_extensions import TypeAlias
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
# (channel, well) -> ( (classification) -> PerClassificationData ... |
c34fbdaa55ae2312badd86ac149e5554b4f090ecc2820559a984f88f80ddde27 | Python | 2,986 | 84 | """
Tests to make sure the example datasets load
"""
import os
import numpy as np
import pandas as pd
import eugene as eu
from pathlib import Path
HERE = Path(__file__).parent
eu.settings.dataset_dir = f"{HERE}/_data/datasets"
def test_get_dataset_info():
dataset_info = eu.datasets.get_dataset_info()
assert... |
3402e2f119c1afa3376df7c82e59d0f20c301a9cf27b6fdaec19b7cc76e05413 | Python | 2,987 | 95 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import copy
import torch
imp... |
345c8e366ccad00543959335a06e0ad27557ac4f23d735f72c098cae4b2a941b | Python | 2,989 | 70 | #!/usr/bin/env python
'''Return aggregated statistics for database'''
import sys, re, MySQLdb, argparse, os, json, subprocess
import pandas as pd
import makemodel
import numpy as np
from MySQLdb.cursors import DictCursor
def getcursor(host,passwd,db):
'''create a connection and return a cursor;
doing this gu... |
526d1ce6b7f4178112e08ad1bc11ee44fe2d3a2afd06512f339e0982c22a9fe0 | Python | 2,989 | 111 | from pyrosm.config.default_tags import (
basic_info_tags,
highway_columns,
building_columns,
amenity_columns,
shop_columns,
craft_columns,
leisure_columns,
tourism_columns,
historic_columns,
emergency_columns,
office_columns,
landuse_columns,
natural_columns,
wate... |
adf2ccf14578e3a95ee4dc24327710ff6e218fc6c40ad3a4d34173d78f7e153b | Python | 2,989 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseTraceHierarchyType):
_parent_path_str = "scatterternary... |
c6540286c14c628d87cf38bf328a6c16de538bf0ad02725d2e3592a9d2a2315b | Python | 2,989 | 84 | import sys
from tqdm import tqdm
import mxnet as mx
import gluoncv as gcv
gcv.utils.check_version('0.6.0')
from gluoncv.data import mscoco
from gluoncv.data.transforms.pose import (flip_heatmap,
heatmap_to_coord_alpha_pose)
from gluoncv.data.transforms.presets.alpha_pose impo... |
f514366795c8a591405c136c95f8d97bfbffc38b996efae74a3f6cb23ad62442 | Python | 2,989 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseTraceHierarchyType):
_parent_path_str = "scatterpolargl... |
c9c045a6170e4821370271426374142e9f13fe014ac49fef6e363aa3b6bf6d5b | Python | 2,990 | 40 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
46c4764a4479f15fc2794483be433860d07039ee200aeceb7eb37fab05d7a9bb | Python | 2,991 | 96 | from ._RL_agent import *
from numba import njit
@njit
def mb_session_value(choices, second_steps, outcomes, n_trials, transition_type, params):
# Unpack parameters.
alpha, weight_mb = params[:2]
if transition_type == 1:
trans_prob = [0.8, 0.2]
else:
trans_prob = [0.2, 0.8]
# Variables.
V_s = np.... |
5d39846cc62890aea415420c872c84ddf5214c0f0d21d87c494b432379430641 | Python | 2,991 | 110 | import sys
import argparse
import logging
import time
import vcf
def call_gt(tag):
if sum(tag) == 2:
return '1/1'
else:
if tag[0] == 1:
return '1/0'
if tag[1] == 1:
return '0/1'
return './.'
def main_ctrl(args):
fileout = open(args.outvcf, 'w')
header = open(args.invcf, 'r')
... |
862068ba480706bd3b35a984c211fff9855bfa4e9db178b53f01aef3ff89db14 | Python | 2,991 | 79 | from gettext import npgettext
import torch
from tqdm import tqdm
from .pmi_tfidf_classifier import get_doc_tfidf
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def get_pmi_vectors(words_pmis, word2text_count, word2id, tokenized_text, N, lpw, nl):
vectors = torch.zeros(le... |
a13250b13a14eea5de08f42ba70d0c33ff1a6226a2a5b942fd9e6cfb0f4d57c5 | Python | 2,993 | 57 | import os
import re
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(1,'/home/mila/l/lindongy/deeprl-timecells')
from analysis import utils_linclab_plot
utils_linclab_plot.linclab_plt_defaults(font="Arial", fontdir="analysis/fonts")
from analysis.utils_int_discrim import plot_performance
... |
a9f1b7798e0b353383044ce9e1c7ed878e5d19a79108e8daaa753f9ceee4e351 | Python | 2,993 | 108 | from __future__ import absolute_import, division, print_function
from io import TextIOBase
import sys
import threading
if sys.version_info[0] < 3:
from Queue import Queue
else:
from queue import Queue
def start_thread(runnable):
threading.Thread(target=lambda: runnable.run()).start()
class ConsoleInpu... |
956827de12ee49dc76bf99e4af997c95e68564cd1b4627a2652baa71baf4f20e | Python | 2,994 | 74 | """Regression tests for r18i2 options residue (finding L2).
L2 (de-bloat): ``torchlens.options._merge_grouped_options`` carried a
keyword-only ``fields`` parameter that was documented but never read in the
function body. Every one of its five call sites threaded a ``_*_FIELDS``
constant into it that the merge never co... |
030ec1d6669aed8deddbcf95cb18b616fce5c3b39eef1f7821b1ed65b77dd8ca | Python | 2,995 | 89 | import unittest
from numpy import exp, log, mean, array, abs
from spitfire import GeneralAdaptiveERK, odesolve
class ExponentialDecayProblem(object):
def __init__(self):
self.decay_constant = -1.
self.lhs_inverse = None
def rhs(self, t, state):
return self.decay_constant * state
def... |
2532979a1d692976f9fadfbb0b04503bb2f7e1eeae8fbde065408843de9fe032 | Python | 2,996 | 99 | """Utility functions"""
# pylint: disable=missing-docstring, arguments-differ, method-hidden
import json
import pickle
import pathlib
import numpy as np
class ComplexEncoder(json.JSONEncoder):
def default(self, obj):
if hasattr(obj, 'to_dict'):
return obj.to_dict()
elif isinstance(obj,... |
753f82064713b639c8682454d385f509e81cfd11abe1a6b2d900395400632241 | Python | 2,997 | 86 | """``EpisodeSpec.step_input_from`` (W051-EPISODE out-of-fence item 3).
The options owner gains the declared carried-input argument selector. The
consumer (``resolve_episode_declaration`` -> ``EpisodeJoinSession``, the
``entry_basis`` envelope slot) ships with lane W051-EPISODE and reads the
field via ``getattr(spec, "... |
d54e1c3953ded31a9813afe4f405d8175d2abd48cb8dd82321acdd635ecb0c95 | Python | 2,997 | 82 | import wandb
import matplotlib.pyplot as plt
import numpy as np
user_project_name = 'omarcos/deepopt/'
artifact_name_dpf = 'run-3fv8g9iw-table_dpf:v0'
artifact_name_part = 'run-3fv8g9iw-table_part:v0'
acceleration_factor = 22
testing_name_group = 'x{}_test2'.format(acceleration_factor)
run = wandb.init(project = 'dee... |
dfe8e3ac2c45d94db667c90654f81084e21e9320d8ad8f5f224a1e0d368f5d25 | Python | 2,998 | 103 | """
Plotting of continuous features stored in adata.obs.
"""
from matplotlib import pyplot as plt
from PIL import Image
import pandas as pd
import matplotlib
import numpy as np
from typing import Optional, Union, Mapping # Special
from typing import Sequence, Iterable # ABCs
from typing import Tuple # Classes
fro... |
6830a7c41bd2ee35e0dd0befb1ed9b1a1fc0e84115de966872aa07696bb5fcbb | Python | 2,999 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseLayoutHierarchyType):
_parent_path_str = "layout.news... |
718425ed1e069d000f54acf9101ee6dfedd8a315ffae0147d711f6b26efb8c02 | Python | 2,999 | 65 | import torch
import torch.nn as nn
from torch_geometric.nn import GCNConv, GATConv, SAGEConv, global_max_pool as gmp, global_mean_pool as gep
class CPI_classification(torch.nn.Module):
def __init__(self, device, emb_size = 768,max_length=1500,n_output=2, hidden_size=128, num_features_mol=78,dropout=None):
... |
817b8e1e66c44aed222c41c80c8cb0d4533eec19a58275b0c72523083109dbf3 | Python | 2,999 | 90 | # Copyright 2022 Deepfold 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 required by applicable law or agreed to in writin... |
0f0d498bcc5f68cc0eac01204b4830661283c14ff20c02c6bc88bfe464e00eed | Python | 3,000 | 91 | import numpy as np
from numpy.random import default_rng
import pathlib
from mpi4py import MPI
from causal_optoconnectics.tools import conditional_probability, joint_probability, roll_pad
from causal_optoconnectics.generator import (
construct_connectivity_filters,
construct_connectivity_matrix,
simulate,
... |
5225a4c3fefb52d8b2402092abc1811c198a655f22d8af4ffe1cbb29b8c7e0ae | Python | 3,001 | 99 | import django
# Django settings for celery_http_gateway project.
DEBUG = True
TEMPLATE_DEBUG = DEBUG
CELERY_RESULT_BACKEND = 'database'
BROKER_URL = 'amqp://guest:guest@localhost:5672//'
ADMINS = (
# ('Your Name', 'your_email@domain.com'),
)
MANAGERS = ADMINS
DATABASES = {
'default': {
'ENGINE': ... |
6a9762da38d76dae13a87ea48dabc1b4a8a4a7c94d3bc9f7fece83e91f6214cc | Python | 3,001 | 86 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Estimators for tfep that are compatible with the bootstrap analysis.
"""
# ===================================... |
764fbe12258c656ae370b19d1bb591ac169209b9968e392b44ca39a806447dc0 | Python | 3,001 | 95 | """Phase 5(d): user-attached pre/post hook ordering relative to wrap-forward push."""
from __future__ import annotations
from typing import Any
import torch
import torchlens as tl
from torchlens import _state
class HookChild(torch.nn.Module):
"""Child module containing a nested linear module."""
def __in... |
b1dda94fa0306d65537c8f9030a99d0351b782986867fdba2a5b2d718ec16981 | Python | 3,002 | 106 | """Load and validate the vsearch completion spec.
Provides a small typed model on top of spec/vsearch.yaml that the
per-shell generators consume.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional
import yaml
SPEC_PATH = Path(__file... |
2090caa23858712c637ff556f8194eed095d7263304805d9af509ecc00e68108 | Python | 3,003 | 104 | import unittest
import pickle
import numpy as np
from bdpy.evals.metrics import profile_correlation, pattern_correlation, pairwise_identification
class TestMetrics(unittest.TestCase):
def test_profile_correlation(self):
# 2-d array
n = 30
x = np.random.rand(10, n)
y = np.random.... |
b97d2e803d5475a06c3c45dc0ad02ee4deadb0f00f01c1f5a5043ede99fd01b6 | Python | 3,003 | 106 | import functools
import hashlib
import pathlib
import warnings
import numpy as np
def hashfile(fname, blocksize=65536, count=0, constructor=hashlib.md5,
hasher_class=None):
"""Compute md5 hex-hash of a file
Parameters
----------
fname: str or pathlib.Path
path to the file
bl... |
bdc53fc561cc9acccfacd909dbb760a87ccc6f7cca512c3cd95db6240fca71a9 | Python | 3,003 | 79 | """
This module allow you to test ensembling model
"""
import torch
import os
import logging
from analysis.training_analyser import TrainingAnalyser
from utils.utils import (
calc_metric_and_loss_on_data,
)
from models_architectures.ensembling_model import EnsemblingModel
class TestEnsemblingModel:
"""
Th... |
34a63c8c6ce4130b2d3be4a877726d7947ed28a4ecc55dc019db38bb97317f0b | Python | 3,004 | 63 | import unittest
from Metrics.src.CEST import mtr_asym
from Metrics.src.utils import mtr_asym_curve
import numpy as np
from test_support_function.src.CEST import generate_Z_3D
class TestMtrAsym(unittest.TestCase):
def test_mtr_asym_curve(self):
# Test MTR asymmetry curve with a Lorentzian curve centered at... |
a6b3abb018e68240c4fa16383e5d76d321793089649261ab14bb3c5df2324fec | Python | 3,004 | 84 | from __future__ import absolute_import
import ctypes
import re
import warnings
def glibc_version_string():
"Returns glibc version string, or None if not using glibc."
# ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
# manpage says, "If filename is NULL, then the returned handle is fo... |
c48c1bc87b4650a0f55755d512566c898187a8b9c29fbb81dd8ebd2c9e7108a9 | Python | 3,004 | 104 | """Extraction v2 runtime (lane F18; extract memo items 4-19).
The private implementation package behind :mod:`torchlens.dataset_extraction`:
the typed batch envelope + collate door (:mod:`envelope`), the batch context
with row and key validation (:mod:`context`), mask-aware pool presets
(:mod:`pool`), save-dtype routi... |
f0770d63c3effd43fe9cb790e69ed2006cbee16398043c7da2430e78b39839c5 | Python | 3,004 | 87 | import glob
import torch
import pytorch_lightning as pl
from utils.data_manager import DataManager
from utils.data_loader import LIDataLoader
from utils.result_logger import save_test_log
from model.define_model import load_pretrained_encoder
from model.model_interface import ModelInterface, define_callback
class Mo... |
2a58d1fde663071452a3aee2efc42d0cc89ae0320d152308bc48b9ea088796b4 | Python | 3,006 | 90 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 28 17:13:33 2023
@author: fcheng
"""
import pandas as pd
import seaborn as sns
import os
from plot.makeDataFrame4plot import makeDataFrame_color_weight
from plot.barplot import barplot_dotline
from plot.stats import compare_two_weight_samples
s... |
c44abe134fd890f9a589c6bd6c281e635bbd81cefb2e07925b9641b906cdf654 | Python | 3,006 | 71 | import unittest
from scipy.stats import norm, multivariate_normal
from numpy import linspace, mgrid, unique
from numpy.random import seed
from .context import densratio
class BasicTestSuite(unittest.TestCase):
"""Basic test cases."""
def test_alphadensratio_1d_1(self):
x = norm.rvs(size=200, loc=0, ... |
f533db45fba55886990ceaec4056b9ab26cb67dc6d8c0329a2596d14aaad42b8 | Python | 3,006 | 107 | import pytest
import sys
import textwrap
import rpy2.robjects as robjects
import rpy2.robjects.methods as methods
rinterface = robjects.rinterface
def test_set_accessors():
robjects.r['setClass']("A", robjects.r('list(foo="numeric")'))
robjects.r['setMethod']("length", signature="A",
... |
e579410d0d0af2c1800576a564a6e8424a55129c5c4bfdab27804046a5ea5693 | Python | 3,007 | 66 | import tensorflow as tf
from tensorflow.keras import backend
from tensorflow.keras.layers import UpSampling2D
from typing import Optional, Union, Tuple
from cvnn.layers.core import ComplexLayer
from cvnn.layers.core import DEFAULT_COMPLEX_TYPE
class ComplexUpSampling2D(UpSampling2D, ComplexLayer):
def __init__(s... |
ec5591bbc6ea1a21d9e2a23306ca02dd6be7fcbb67aba99adfed7281b04b1763 | Python | 3,007 | 78 | from ovseg.model.SegmentationModel import SegmentationModel
from ovseg.model.SegmentationEnsemble import SegmentationEnsemble
from ovseg.model.model_parameters_segmentation import get_model_params_3d_res_encoder_U_Net
import argparse
import numpy as np
parser = argparse.ArgumentParser()
# vf should be 5,6,7. VF stands... |
c8c37b1cefd9e0d8532ee6bf45d8572f6babf02fba5d960288e7cbdf29678987 | Python | 3,008 | 80 | """Hand-pinned independent facts for the godobject-oracle golden suite.
b9-opus R75-2: the godobject goldens are regenerated THROUGH torchlens, so
they detect CHANGE, not correctness. These pins assert facts about the
committed golden FILES that are derived from first principles — the corpus
model's own source (``test... |
057e6f702876e47b177e6dcf6d8b30aa0f216507390ec7b3fa3ddac5f1790c68 | Python | 3,009 | 118 | # This is my main script
import sys, os, pathlib
sys.path.insert(0,pathlib.Path(__file__).parent)
import pathlib
import pandas as pd
from base.parallel_predictions import mbs_search
from base import configs
from base.utils import load_classifier, get_MBS_V2
import argparse
"""
df = pd.read_csv("base/training_set_F0... |
153ce389615f0d58541c28d951079139a72e49c3d73cfcf5d96d33d034aef2a6 | Python | 3,009 | 92 | # -*- coding: utf-8 -*-
"""
Created on Mon Feb 6 21:01:02 2023
@author: Caterina Brighi
"""
#%% Import functions
import matplotlib.pyplot as plt
import matplotlib as mpl
import SimpleITK as sitk
import numpy as np
import pandas as pd
import datetime
import os
import glob
import gzip
import shutil... |
a8ca27d335807ca033ae7b2d54c320ec4e4ba0c57b3cf9a6a21694a13a4266a8 | Python | 3,009 | 98 | """Tests for BackwardPass backward call-site metadata."""
from __future__ import annotations
import inspect
from pathlib import Path
import torch
from torch import nn
import torchlens as tl
from torchlens.data_classes.func_call_location import FuncCallLocation
class _ContextModel(nn.Module):
"""Small model fo... |
2008bd364ecaef243ad9c6ac291bd2ca4304d6f9fcfaba1c4582b69d515791a4 | Python | 3,010 | 97 | # AUTOGENERATED by config_manage.py build_config_types — do not edit manually.
# Run `make config-rebuild` to regenerate from the config schema.
from typing import Any
class ToolShedAppConfigurationAttributes:
"""Type annotations for schema-defined "tool_shed" config attributes."""
log_level: str
databas... |
765b5123625c7064fb293f8ee37c5cf46c9b64a905529c041f226f0ea89e2cac | Python | 3,010 | 90 | """
The offline larva, simulated without the physics engine.
Integrates the locomotor output directly into a pose, bypassing the segmented
body simulation. Used for fast behavioural runs where only the trajectory
matters.
"""
from __future__ import annotations
from typing import Any
import numpy as np
from ... impo... |
af90a2bfa1f07c0dbf486ffeefdeb20f0087ac190a8988db45e9360948114094 | Python | 3,011 | 83 | import torch
from torch_geometric.nn import radius_graph
from torch_geometric.nn.models import SchNet
from torch_scatter import scatter
from .atomistic_nn import AtomisticNN
class AtomicSchNet(AtomisticNN, SchNet) :
"""
Modification of the PyG SchNet implementation to recover the atomic
contributions to t... |
70ca1e2c12551d4a595a7f44d0f500aed8a5ce1875d9691ac861d661d6e15791 | Python | 3,012 | 88 | import sqlite3
from sqlite3 import Connection
import pandas as pd
import os
from bioconfpred.params import (CHEMBL_TARGZ_FILEPATH,
CHEMBL_URL,
CHEMBL_SQLITE_PATH,
CHEMBL_DIRPATH)
class ChEMBL():
"""
Used to query the ChEMBL SQLite file
:param... |
c030539fc1d208eedca6897c1efb3819b26fecca8f7c4a93b51c01e74a70948b | Python | 3,012 | 95 | """Prepare the ImageNet dataset
============================
The `ImageNet <http://www.image-net.org/>`_ project contains millions of images
and thousands of objects for image classification. It is widely used in the
research community for benchmarking state-of-the-art models.
.. image:: ../../_static/imagenet_banner... |
10ec2dc02599349423ffe6c776b67cbd80ded0658936fd7d3a766f475e2b7448 | Python | 3,013 | 89 | """Unit tests for ScreenTextFont's line wrapping and spacing."""
from __future__ import annotations
import pytest
@pytest.fixture
def pygame_ready():
import pygame
pygame.init()
yield
pygame.quit()
@pytest.mark.fast
class TestScreenTextFontWrapping:
def test_no_wrapping_when_max_text_width_un... |
4d4ad13b4271a7fd5d3124dd01745a4048b48b4eb06d751fea5878bd72abb314 | Python | 3,013 | 70 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""Wii preprocessing."""
from __future__ import print_function, division
from ..signal import read_data_source
from ..tags import smart_tag_definition
from .. import smart_tags_manager
import ... |
7ad8482a5fe798a53e2a5a2614d9f183c910fd247fad3721a4a0c6d1c8039364 | Python | 3,014 | 90 | from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, Iterator, Mapping, Optional, Sequence
import numpy as np
import torch
@dataclass
class DomainTensorData:
x: torch.Tensor
labels: Mapping[str, torch.Tensor] = field(default_factory=dict)
mask: Optional[to... |
e5b4992c16a855f885996862c7c12fc376d44990c79fa2ff9537ca954df8b677 | Python | 3,014 | 77 | """Subprocess entry point for isolated surface-oracle snapshot generation.
The surface goldens freeze CONSTRUCTION-TIME behavior too: a model built
after torch has been wrapped can bake live wrappers into its state (the
SF-53 class — ctor-read ``F.<op>`` defaults, string-activation resolution),
which forks the snapsho... |
1c986f58a0e9715fa801989e03f2547e82cb1820d880f2947d0e1c2be23d653d | Python | 3,015 | 77 | # LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compl... |
65bf4e0ec1efcde018cb8586639efb5cef88520da9e948804ee3d9cabdd421c1 | Python | 3,015 | 100 | import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
# TODO: Task need to be revisited
class ReachingDelayResponse(TrialEnv):
"""Reaching task with a delay period.
A reaching direction is presented by the stimulus during the stimulus
period. Followed by a delay period,... |
7bbcdc863c92eb36bd266836f81032a228634ad9e728095ceb5320b4ab918d08 | Python | 3,015 | 108 | import gc
import weakref
from contextlib import contextmanager
from unittest.mock import MagicMock, Mock
import pytest
from celery.utils.objects import Bunch, FallbackContext
class test_Bunch:
def test(self):
x = Bunch(foo='foo', bar=2)
assert x.foo == 'foo'
assert x.bar == 2
class te... |
97eee6ab676bade4e22ec3b644f21f9f532aca7932dc9cafc99d0d9d117478dc | Python | 3,016 | 102 | """CMAC cerebellar controller, 1975, James S. Albus.
Paper: "A New Approach to Manipulator Control: The Cerebellar Model
Articulation Controller." CMAC tile-codes continuous inputs into overlapping
associative memory addresses whose table entries are summed for control output.
"""
from __future__ import annotations
... |
b4230c694250f8f7c3cb424ad6621c33f013bfb3387b90607ae9f4a071814dbb | Python | 3,017 | 90 | """The SDPA facet-reconstruction gate must refuse corrupted reconstructions.
``_allclose_sdpa`` decides whether a reconstructed facet is served as real
or refused as ``MissingFacet``. Its former hand-picked absolute floors
(atol 2e-2 fp16/bf16, 1e-5 fp32) blessed ALL-ZERO and SIGN-FLIPPED
reconstructions of any payloa... |
ca3bc687975249e2cfe2a9e69fe1c7339cc4bee7960276d36670720d2055b7b8 | Python | 3,017 | 91 | """grind-p3 T5.7: fastlog directory bundles get the core writer's 0600/0700 tightening.
The core bundle writer tightens saved bundle directories to ``0o700`` and
metadata sidecars to ``0o600`` because ``mkdir``/``open`` honor the ambient
umask (a common umask 022 leaves everything world-readable). The fastlog
director... |
f01494d8417add4708261bbb65b14f840edd9ccfd6d845f129b852de5913463d | Python | 3,017 | 104 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Legendgrouptitle(_BaseTraceHierarchyType):
_parent_path_str = "histogram2dcon... |
35e672ac1261e5291258ec5db838e58f45cbd328e17b9cf051b6715d12e25149 | Python | 3,018 | 94 | from __future__ import annotations
from typing import TYPE_CHECKING
from functools import partial
from itertools import chain
if TYPE_CHECKING:
from typing import Dict, Any, Tuple, Union, Iterable, Callable
from typing_extensions import TypeAlias
from torch import Tensor
import torch.optim as optim
... |
79d4b019a78254089e327924bd73453db5af89c33fb1312f31961ff1a3046d51 | Python | 3,019 | 130 | import os
data_dir = os.path.realpath(os.path.join("..", "..", "data"))
mass_shift_annotation_url = (
"https://docs.google.com/spreadsheets/d/"
"1-xh2XpSqdsa4yU-ATpDRxmpZEH6ht982jCCATFOpkyM/"
"export?format=csv&gid=566878567"
)
# Living data analysis performed on 2020-11-18.
living_data_dir = os.path.joi... |
96c69d26117c7168710e086055568a1557518ad463eb6c65c718cb18e564d420 | Python | 3,019 | 90 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Textfont(_BaseTraceHierarchyType):
_parent_path_str = "quiver.selected"
_... |
9a27f1e1ffb7864847dc25c2d7e6626a7b64d88f2a4f14c259032ae3962c762f | Python | 3,019 | 108 | import pathlib
import shutil
import tempfile
import h5py
import numpy as np
import pytest
from impose import data
data_path = pathlib.Path(__file__).parent / "data"
def test_ds_bad_path():
path = data_path / "does-not-exist.h5"
with pytest.raises(FileNotFoundError):
data.DataSource(path)
def tes... |
4699600049a3000daf801e69c1636314efd941615009af382d04cdf93efa1a43 | Python | 3,020 | 94 | """Pointer Sentinel-LSTM mixture model.
Paper: "Pointer Sentinel Mixture Models", Merity et al., ICLR 2017.
Pointer Sentinel-LSTM augments a recurrent language model with a pointer cache
over recent hidden states and a sentinel that lets the pointer component choose
between copying from history and using the normal v... |
817ab7fd5bcfa7d34fa7ecb33a08b74c8029d572043a83309e96a489d57fc769 | Python | 3,020 | 90 | # --- THIS FILE IS AUTO-GENERATED ---
# Modifications will be overwitten the next time code generation run.
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Textfont(_BaseTraceHierarchyType):
_parent_path_str = "bar.unselected"
_p... |
de977e444dd08758be707ec9248bc2b533e1d2aa3f0b35443371b85085a42ebf | Python | 3,020 | 120 | #!/usr/bin/python
import argparse
import logging
import subprocess
import sys
def stop_err(logger, msg):
logger.critical(msg)
sys.exit()
def log_subprocess_output(logger, pipe):
for line in iter(pipe.readline, b""):
logger.debug(line.decode().rstrip())
def get_arg():
parser = argparse.Arg... |
e66fad45363779a3712c58892bac16933fd1c71d376e0279978fdd7bcdd5b34d | Python | 3,020 | 110 | """
NGS indexes
"""
import logging
import os
from galaxy.datatypes.protocols import (
DatasetProtocol,
HasExtraFilesAndMetadata,
)
from galaxy.util import UNKNOWN
from .metadata import MetadataElement
from .text import Html
log = logging.getLogger(__name__)
class BowtieIndex(Html):
"""
base class f... |
7fa35a8cc0d3dcb08fea788286870c62fc85512be4e44f871adb865e6ad5adc7 | Python | 3,021 | 92 | """Shared fixtures for the F34 leverage-dividend suites (no tests here).
The toy nets reproduce the leverage panel's measured identity scenarios in
miniature: a reused-module cohort (one ``nn.ReLU`` instance called at several
sites), a VALUE-PRESERVING same-cohort insertion (the extra ``relu`` call on
an already-recti... |
9799a3747cfff956eb45731b456b2f8182501fbdaad8433b51a5fdb646b00b2a | Python | 3,021 | 74 | from __future__ import annotations
from typing_extensions import Literal
from pathlib import Path
import pooch
import tarfile
# This is a global variable used to store all datasets. It is initialized only once
# when the data is requested.
_datasets = None
def datasets():
global _datasets
if _datasets is No... |
c5a13a3be8721f0e43995646e17a7d2e7ad384c67cece85f7bf7eafa208cd52f | Python | 3,021 | 87 | """
Colormap collection - the purpose of this script is to create demo graphs of common ABF plots using all
available colormaps. Colormaps with extremely light traces are skipped. Graphics are saved and an HTML
page is made to view them, which can be easily converted to PDF.
"""
import os
import sys
sys.path.append(".... |
c904dee9dc969417f1481acebff31d45717544f2c447f1163a9fa96b6ccba197 | Python | 3,021 | 89 | """
A module that implements tooling to enable easy warnings about deprecations.
"""
from __future__ import absolute_import
import logging
import warnings
from pip._vendor.packaging.version import parse
from pip import __version__ as current_version
from pip._internal.utils.typing import MYPY_CHECK_RUNNING
if MYPY_... |
3ddf5cf10186c7905b0cefea3129b12f2abd89387e42129baf285a596ed44bfe | Python | 3,023 | 72 | from typing import (
TYPE_CHECKING,
Union,
)
from galaxy.exceptions import (
error_codes,
MessageException,
RequestParameterInvalidException,
RequestParameterMissingException,
)
from galaxy.util.json import safe_dumps
if TYPE_CHECKING:
from fastapi.exceptions import RequestValidationError
... |
812331484b2de5e11623659ea25c63467f10c04914efc3c185c8ba02cc6fc103 | Python | 3,023 | 73 | """Module for vector environments."""
from typing import Iterable, List, Optional, Union
import gym
from gym.vector.async_vector_env import AsyncVectorEnv
from gym.vector.sync_vector_env import SyncVectorEnv
from gym.vector.vector_env import VectorEnv, VectorEnvWrapper
__all__ = ["AsyncVectorEnv", "SyncVectorEnv", "V... |
0b0bf2915f2de2376371ce97bf8ef421dfee619f57fd62b54b9dfbf7d3fe5d55 | Python | 3,024 | 94 | #!/usr/bin/env python
""" This script fetches sequence from an indexed FASTA for genes specified
in the provided BED file.
"""
# fix problems with pythons terrible import system
import sys
import os
file_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.join(file_dir, '../'))
sys.path.append(os.... |
44a49106385d0023000e51311c6155cd0c931fae796d57a8c49f1cbca745080d | Python | 3,024 | 73 | """ AvgPool2d w/ Same Padding
Hacked together by Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import tup_pair
from .padding import pad_same, get_padding_value, pad_same1d
def avg_pool1d_same(x, kernel_size: List[int], stri... |
9c2dfab0c4568d1165001cad5fcef254db1fd157641f3ba6bc6685a0a45214a8 | Python | 3,025 | 102 | #!/usr/bin/env python
#=========================================================================
#
# Copyright NumFOCUS
#
# 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://... |
20977ac3afb360f6337c6da7e6e9c5f898aa318aadb0ec65d7f169157203ecd1 | Python | 3,026 | 83 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from globals import path, sessions, subjects, folder_spots_noboth, colours_types
import os
# %%
# Load density data
file_path_name = (
f"{path}/forearm_area.csv"
)
if os.path.isfile(file_path_name):
# open csv with headers in rows
forearm_area = pd.r... |
45eaa2efa5d2156c797c24d0817780a535945f2c205e9fba552529eed28a15ce | Python | 3,026 | 68 | # Mossy layer at 45, 90, 135 degrees
from os.path import join
import numpy as np
import os
import pandas as pd
from library.comput_utils import cal_hd_np
from library.params import ParamsHolder
from library.shared_vars import sim_results_dir
from library.utils import save_pickle
from library.simulation import simulate... |
f5f51b2c324b7093dff4db03aa18fad2577c3c7b0a2adf6114abb12d34521ff8 | Python | 3,026 | 75 | #!/usr/bin/env python3
"""Fail loudly if the CI image's deps are below the flash_attn/cute/pyproject.toml floors.
Runs inside the SIF before tests. The FA4 install in run_fa4_ci.py uses --no-deps (to keep the
SIF's torch/cudnn), so pyproject floors are not enforced at install time. A SIF baked before a
floor bump ther... |
20ac657acdba17a1cedd2d9c09c01f1fec173ec964e8928add583b8bd6ab0958 | Python | 3,027 | 101 | import cv2
import numpy as np
from random import randint
import random
from PIL import Image
height = 32
width = height
size = 6
pos_all = [
(5, 5 ),
(5, 15),
(5, 25),
(15, 5 ),
(15, 15),
(15, 25),
(25, 5 ),
(25, 15),
(25, 25),
]
def is_hit(ver1, ver2, fig_type):
if fig_type ... |
2649e36a0162e100c682280eca18e7ba91e999f4b620562ca9ed262fa3ba9f22 | Python | 3,028 | 89 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import copy
import pytorch_l... |
c984d8bb95f127cd55b2a029c9dcb125ead138dcfde09d8599fe43b600f71352 | Python | 3,028 | 99 | """Formatting information about envs and wrappers."""
import gymnasium as gym
from neurogym.core import METADATA_DEF_KEYS, env_string
from neurogym.envs.registration import ALL_ENVS, all_envs, all_tags, make
from neurogym.utils.logging import logger
from neurogym.wrappers import ALL_WRAPPERS, all_wrappers
def show_... |
6fef10480d6a96daf7f18691431f49aed0414daf4b2ff7cb8e0b1a8852cd8fe1 | Python | 3,029 | 74 | """ Squeeze-and-Excitation Channel Attention
An SE implementation originally based on PyTorch SE-Net impl.
Has since evolved with additional functionality / configuration.
Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
Also included is Effective Squeeze-Excitation (ESE).
Paper: `CenterMa... |
07f7a7a875ae4ef52d1f81ea858f1248d5c8629f241aff4ab5fc15c8c6e57b17 | Python | 3,030 | 78 | import sys
# setting path
sys.path.append('../TM2_segmentation')
import os
import numpy as np
import nibabel as nib
import itk
import pandas as pd
import tarfile
from scripts.preprocess_utils import find_file_in_path
# load metadata file
input_annotation_file = 'data/csv_files/cbtn-all-LGG-BRAF.csv'
df = pd.re... |
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