sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2436733839dc4efde5c4b7b83282aab6b2b623a68feb450ac100394706c5c54f | Python | 1,387 | 38 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pymaid_creds import url, name, password, token
import pymaid
rm = pymaid.CatmaidInstance(url, token, name, password)
import connectome_tools.process_matrix as pm
import connectome_tools.celltype as ct
import navis
... |
9d598749a21fadc49ee85ed377e72883fd26472c8c1dc2c3160c3c1424f67046 | Python | 1,388 | 55 | import torch
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from graphxai.datasets import ShapeGGen
from test_homophily import homophily_test
def build_SG(HF):
SG = ShapeGGen(
model_layers = 3,
num_subgraphs = 100,
prob_connection = 0.0... |
50537baa360c075add6833e8ac63edd5435504ad9ce04067025ffc93e248fb3b | Python | 1,389 | 58 | TMP = 'tmp'
LOG = 'log'
INFO = 'info'
TARGET_FILE_NAME = 'target.pdb'
MODEL_FILE_NAME = 'model.pdb'
X_FILE_NAME = 'x.txt'
X_RES_FILE_NAME = 'x_res.txt'
Y_FILE_NAME = 'y.txt'
Y_GLOBAL_FILE_NAME = 'y_global.txt'
ADJ_B_NAME = 'adj_b.txt'
ADJ_C_NAME = 'adj_c.txt'
ADJ_RES_NAME = 'adj_res.txt'
AGGR_MASK_NAME = 'aggr.txt'
C... |
dbccdb90a366580b849108f860c77f39e48db9c4c65be8f211b0b2e96e62b69a | Python | 1,389 | 32 | # Copyright 2022 Huawei Technologies Co., Ltd
#
# 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... |
3fb6912a93bdedb734faeb20b46a39c48dc5672e25cb654888b6a8d22d14a6ad | Python | 1,390 | 37 | # Copyright 2022 Huawei Technologies Co., Ltd
#
# 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... |
498b1f4ce0e427b61e9e571fddcff1ad8317679fe3066c8fcdcd1960ae709951 | Python | 1,390 | 33 | '''
- Loads a vector of frequencies `freq.npy`
'''
import numpy as np
from sim_geometry import *
import carpet.physics.friction_pairwise as physics
## Parameters
# Physics
set_name = 'machemer_4' # which hydrodynamic coefficients to use
order_g11 = (4,0)
order_g12 = (4,4)
# Physics
# period = 31.25 # [ms... |
77cb493c40f9ea28a5f84cccb9f6370e78e8f1e0e05b9a969f011c59728fa006 | Python | 1,390 | 54 | '''
Random Baseline
'''
import pandas as pd
import random
import numpy as np
from sklearn.utils import resample
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, classification_report, matthews_corrcoef, balanced_accuracy_score
test = pd.read_csv('../../data/final/CSV/Test.csv')
y_test = list(t... |
1cd1381864286ca7eff4e0df66c2a16eb120b876d0fc036d3e49a1331a792235 | Python | 1,391 | 89 | import sys
import nrrd
from skimage import io
import os, os.path
import math
import h5py
import time
import numpy as np
import pandas as pd
import pickle
import more_itertools as mit
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import pandas as pd
import csv
import scipy
def extract_brain_ROI_name_fr... |
70e49279ad144cf72ac94b7129778781a65f941e298335179368bc83e99217ef | Python | 1,392 | 37 | # Copyright 2020 Huawei Technologies Co., Ltd
#
# 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... |
6032d22e584eb8278b00b4577119d94519be01111a21cbc6db75883810608164 | Python | 1,393 | 43 | '''
Simple simultaneous network control of Bonsai and OpenEphys.
Dependencies:
1. pyOSC
2. SimpleOSC
'''
import argparse
from simpleOSC import initOSCClient, sendOSCMsg
import zmq
import random
import sys
import time
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("... |
99a9e7acb6f17227d5540d12a71b203fa0b8ba2787dc1108629e0626f6386fc9 | Python | 1,393 | 57 | import json
import os
import sys
from Classes.RubyInterfacer import RubyInterfacer
from Classes.SearchModule import SearchModule
# If executing from Ruby code
if os.path.isdir("/home/neuromine/Neurolex_py"):
os.chdir("/home/neuromine/Neurolex_py")
if os.path.isdir("/var/www/NeuroML-DB.org_Ontology"):
os.chdir... |
aa7c236003c08dda56239358c259643a73432879dffc9aba71c630d8508a2f2c | Python | 1,393 | 33 | """Data loading and preparation.
The main entry point is the :py:class:`Data` class, which handles loading
time series data from various formats (NumPy, MATLAB, FIF) and preparing
it for model training.
Modules
-------
- :py:mod:`~osl_dynamics.data.base` — :py:class:`Data` class for loading
and preparing data, and ... |
92e5e8b486242197fa570dc7611ca86b14de3751edcb9ba092be1387cd3924c7 | Python | 1,395 | 41 | __author__ = 'jieqing jiao'
__email__ = "jieqing.jiao@gmail.com"
import numpy as np
import nibabel as nib
def extract_regional_values(image, parcellation, labels):
idx = labels_to_index(parcellation, labels)
regional_values = np.mean(image[idx, ], axis=0)
return regional_values
def labels_to_index(parc... |
080e6808dfd8d37f2d5aa4e776e43679a5ce37041e3a4e974ccb320da5c023c6 | Python | 1,397 | 25 | from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
MaxIoUAssigner)
from .builder import build_assigner, build_bbox_coder, build_sampler
from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, PseudoBBoxCoder,
TBLRBBoxCoder)
from .iou_calculators import B... |
87bff7cde249d1d593bb95fe4023763eb5f1825b9b035f9432f46dd67b69ccb3 | Python | 1,397 | 53 | import sys, time, pickle
import torch
import networkx as nx
import matplotlib.pyplot as plt
from graphxai.datasets.shape_graph import ShapeGGen
def parse_args():
arg_dict = {}
assert len(sys.argv) >= 4, 'usage: python3 test_one_ul.py <num_subgraphs> <prob_connection> <subgraph_size>'
arg_dict['num_subgr... |
2710df4a110e4c7c762f98f3fd9507bf82142423f90ee46736c300f906a057e9 | Python | 1,398 | 33 | # Copyright 2020 Huawei Technologies Co., Ltd
#
# 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... |
74fce953898eb5d6a45d902e0f4206f3dead44720a87e0ea02b01ba0f5737913 | Python | 1,398 | 35 | # -*- coding: utf-8 -*-
# @Author : WenHan
from tensorflow import keras
# The architecture of the proposed 1-D CNN.
def build_net(input_shape, n_feature_maps, nb_classes):
inputs = keras.layers.Input(shape=input_shape)
x = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=5, strides=1, padding='same'... |
b3d609c0a1fdcb987ce936c26bc9162e1e2714c1d280ab72f43009dd8e98da98 | Python | 1,399 | 40 | from snakemake.shell import shell
shell.executable("bash")
log = snakemake.log_fmt_shell(stdout=False, stderr=True)
params = snakemake.params.get("extra", "")
pipe = ""
if snakemake.output[0].endswith(".bcf"):
pipe = "| bcftools view -Ob -"
if snakemake.threads == 1:
freebayes = "freebayes"
if snakemak... |
3a839e4d8f2c88754e8daf64d56aeaffb4b62d552fe0ccb66519fe5b31ee2052 | Python | 1,400 | 27 | # Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# 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 agre... |
138cdccdeef08a7ea751e1d57812644793d95da66b66a4d2bfe8e87554110f93 | Python | 1,402 | 36 | import unittest
from oaklib.datamodels.vocabulary import IS_A, PART_OF
from oaklib.implementations.sqldb.sql_implementation import SqlImplementation
from oaklib.resource import OntologyResource
from oaklib.utilities.subsets.slimmer_utils import roll_up_to_named_subset
from tests import INPUT_DIR, OUTPUT_DIR
DB = INPU... |
7df01d21d7eb0e160c127adf4928ff1ab0c51c285ebd8903c4a7a87785262e04 | Python | 1,402 | 32 | import numpy as np
class convers_pca():
def __init__(self, no_of_components=None):
self.no_of_components = no_of_components
self.eigen_values = None
self.eigen_vectors = None
def transform(self, x):
return np.dot(x - self.mean, self.projection_matrix.T)
def inv... |
f75a2b7bcecd2ba49ee1f890759e96b1698aa84536e9629c1af52b069239b009 | Python | 1,402 | 52 | from dataclasses import dataclass
from typing import Iterable, List
from oaklib import BasicOntologyInterface
from oaklib.datamodels.validation_datamodel import SeverityOptions, ValidationResult
from oaklib.types import CURIE
@dataclass
class OntologyRule:
"""Base class for all ontology rules."""
name: str
... |
381cf58ec138ffc63764eac7b65087413962cef01fefe3aa37ac3bfceff3a8df | Python | 1,403 | 46 | import random
import torch
from graphxai.explainers import PGMExplainer
from graphxai.datasets.feature import make_network_stats_feature
from graphxai.gnn_models.node_classification import BA_Houses, GCN, train, test
# Set random seeds
seed = 0
torch.manual_seed(seed)
random.seed(seed)
n = 300
m = 2
num_houses = 30... |
593f1f7067aff6c2aee4e5d85ceab21e101000a25a3e2b0c0cfa52a72616f0e7 | Python | 1,404 | 43 | from torch import nn
from .registry import CONV_LAYERS
CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d)
CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d)
CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d)
CONV_LAYERS.register_module('Conv', module=nn.Conv2d)
def build_conv_layer(cfg, *args, **kwar... |
f8a19329c8c0135905092040a6fb3f82032978a236e0d7e3aaca0c7e6d1314d7 | Python | 1,404 | 45 | import pandas as pd
import numpy as np
from util import tprint
from gensim.models import KeyedVectors
def read_pretrained_w2v(pretrained_w2v_fname, is_glove):
"""
reads pretrained word embedding from the given file name
is_glove: if True, assumes the format of GloVe text file. otherwise, word2vec bin file... |
869131cbd789669495494a994dc493aef321df029fbf15612f0429c8c6ddbb43 | Python | 1,405 | 42 | import random
import torch
from torch.testing import assert_close
import pytest
from ..embeddings import GridEmbedding2D, GridEmbeddingND
# Testing grid-based pos encoding: choose a random grid
# point and assert the proper encoding is applied there
def test_GridEmbedding2D():
grid_boundaries = [[0, 1], [0, 1]... |
8db77f8bed84bfbf3068e141c84641bb341b09e2db40ccfe971f3a19b31866b9 | Python | 1,405 | 38 | import unittest
from linkml_runtime.dumpers import yaml_dumper
from oaklib import get_adapter
from oaklib.utilities.validation.disjointness_rule import DisjointnessRule
from oaklib.utilities.validation.rule_runner import RuleRunner
from tests import INPUT_DIR, OUTPUT_DIR, SUBATOMIC_PARTICLE
TEST_OUT = OUTPUT_DIR / "... |
365824008b77a0ae05886f3a1faff9535e0ed3b1a7b2ecf5622d5025c56e26f8 | Python | 1,406 | 46 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from . import transforms as T
def build_transforms(cfg, is_train=True):
if is_train:
min_size = cfg.INPUT.MIN_SIZE_TRAIN
max_size = cfg.INPUT.MAX_SIZE_TRAIN
flip_horizontal_prob = 0.5 # cfg.INPUT.FLIP_PROB_TRAIN
... |
a8c15251fcd8fe8bd61adc6fc72b39a7fe0da12ea698299fd6bc78f88214b408 | Python | 1,406 | 44 | import os.path as op
import numpy.testing as npt
import pytest
import nibabel as nib
import nibabel.tmpdirs as nbtmp
import dipy.data as dpd
import dipy.core.gradients as dpg
from AFQ.models import dki
from AFQ.utils.testing import make_dki_data
def test_fit_dki_inputs():
data_files = ["String in a list"]
... |
dae258e926644b17cd4e2ef8a73fe610dfe038952f65102854db30023a4fc0cb | Python | 1,406 | 43 | from setuptools import find_packages, setup
def read_version():
with open('dendrify/version.py', 'r') as f:
lines = f.readlines()
for line in lines:
if line.startswith('__version__'):
delim = '"' if '"' in line else "'"
return line.split(delim)[1]
return "unknown"
... |
dbe14716d7fc042533083919d5e058e5bb91cfff9612a7a910bc71ea3a1cdbaf | Python | 1,406 | 36 | #%%
import os
import sys
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/maggot_models/')
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
from pymaid_creds ... |
e932dacadc0e464ed7fb3b1a524a79ac0303bf7562bef896c2bd06a7f78f3e22 | Python | 1,406 | 61 | # %% [markdown]
# #
import os
import colorcet as cc
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from src.io import savefig
FNAME = os.path.basename(__file__)[:-3]
print(FNAME)
def stashfig(name, **kws):
savefig(name, foldername=FNAME, s... |
2a5bb8bce60baad553f8d937107e8ec1e6095bcdcab19674badf453c0d42f984 | Python | 1,407 | 26 | import sys
sys.path.insert(0, '.')
from svgkit import Svg, C, render_png
W, H = 1260, 320
s = Svg(W, H)
s.text(W / 2, 44, "Building CARLA from source (Linux, UE 5.8)", size=26, weight="bold", anchor="middle")
s.text(W / 2, 72, "What the one-time setup script CarlaSetup.sh does, in order",
size=15, color=C["sub"]... |
90015a171210682208cac4fdc548921e6998630f48e880eafdf1e9177b27d242 | Python | 1,407 | 37 | import sys
from graphxai.datasets import BAShapes
assert len(sys.argv) == 4, 'usage: python3 test_BAH_vis.py <shape_insert_strategy> <labeling_method> <feature_method>'
assert sys.argv[1] in ['ub', 'lb', 'global', 'bound_12'], "shape_insert_strategy must be in ['ub', 'lb', 'global']"
assert sys.argv[2] in ['e', 'f', ... |
ddd82bf4803012705e4eddcf9d57efa7fd917902be0729d0940fde8ca981adec | Python | 1,407 | 43 | '''
force density:
OK: `all`
OK: 1 object; 1 object in array
OK: 2 objects
OK: 2 commands with different objects
OK: indices to skip don't work?
'''
from mesh.plot.mlab_viewer import MeshViewer
folder = 'data/cilia_plane2' # 'data/sphere_wall'
cilium_radius = 0.11
plotter = MeshViewer(folder)
plotter.triangular_me... |
9d398f00e798a4e34715de845fc806463b20c75437e63dd1e1ec1290ca017482 | Python | 1,408 | 41 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
694c6fa80d2cc7294839fa94499ad47577436dd0dc74713374af6f698df13939 | Python | 1,409 | 34 | # Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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 agre... |
c4cdc5beff68c3eb4d105e25560999663fb5fe6569783fa4be59145c90fe7609 | Python | 1,409 | 51 | from sklearn.base import TransformerMixin
from sklearn.preprocessing import StandardScaler
class NoFitMixin:
def fit(self, X, y=None):
return self
class DFTransform(TransformerMixin, NoFitMixin):
def __init__(self, func, copy=False, **kwargs):
self.func = func
self.copy = copy
... |
e907afb6fabc8521c017e0bd57c7eecda4cc515f06406a487aa88deb5af0fbdd | Python | 1,409 | 41 |
from setuptools import setup, find_packages
from debarcer.version import __version__
# Utility function to read the README file.
with open("README.md") as infile:
content = infile.read().rstrip()
setup(
name = "debarcer",
version = __version__,
author = "Richard Jovelin",
author_email = "richard.jovelin@oic... |
3b4d43b75c0698a956cfa174f9fe20d89fa270d070be15eba651d232cfc14609 | Python | 1,410 | 56 | from numpy.testing import (
assert_,
assert_equal,
assert_almost_equal,
assert_array_almost_equal,
assert_raises, assert_array_equal,
dec,
TestCase,
run_module_suite,
assert_allclose)
import sima
import sima.motion
import sima.motion.frame_align
from sima import Sequence
from sima.mi... |
6b3d5562d87877b25d05392040d54d42ae89f14679d43f974e2112b838ca0f23 | Python | 1,410 | 34 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Module containing common constants for the GUI.
Created on Thu Jun 20 13:16:31 2019
@author: nicolas
"""
import os.path
## Constants
# Pages identifiers
PAGE_SELECTION = 1 # output selection between raw/ccreg/warped
PAGE_MODEL = 2 # correction of the m... |
edc5712d3fdd638d8645834b194489d5e591362bcc9e5dd9dd42f7a4aacae23f | Python | 1,411 | 55 | #!/usr/bin/env python
# Read a reference fasta file, write a Reference object to a pickle
# file for use as input to findOverlaps.py, etc.
import os
import sys
import optparse
import re # regular expressions
import cPickle as pickle
from tt_log import logger
import Reference as ref
VERSION = '20150105.01'
d... |
41f14da9fa40269c6ee6973ba8463b5c530db7eeb742195fc9ce7bbc58e6a425 | Python | 1,414 | 47 | #!/usr/bin/env python
import os
from setuptools import setup, find_packages
from bruker2nifti.__init__ import __version__ as version
def requirements2list(pfi_txt="requirements.txt"):
here = os.path.dirname(os.path.realpath(__file__))
f = open(os.path.join(here, pfi_txt), "r")
l = []
for line in f.re... |
8a3cbd6666a592e3cc186cceda2fcadf4c0573a80f8c6ef1ece1452ceec64e6c | Python | 1,414 | 38 | import pandas as pd
import os
import AIS_parameters
import tqdm
import itertools
import numpy as np
from rapidfuzz import process, fuzz
from scipy import stats
path = os.path.join( AIS_parameters.dirs['root'], 'download')
##
columns = dict()
for file in tqdm.tqdm(os.listdir(path)):
if file.endswith('.... |
0657836cb504734e6020cff21f2d6f007aad0dfab54e52c970d62562e1603aa5 | Python | 1,415 | 38 | from dash_extensions.enrich import Output, Input, State, callback
from dash.exceptions import PreventUpdate
from RV.callbacks.utils.channel_selection_utils import get_10_20_channels
def register_channel_selection_callbacks():
@callback(
Output('RV-channel-selection-dropdown', 'value', allow_duplicate=Tru... |
50a9a15e9668de63d4be08e29bf1cff3552ba6d12cfef9c7a17d87bb6cc4cc71 | Python | 1,417 | 34 | import os
from setuptools import setup, find_packages
# Utility function to read the README file.
# Used for the long_description. It's nice, because now 1) we have a top level
# README file and 2) it's easier to type in the README file than to put a raw
# string in below ...
def read(fname):
return open(os.path.... |
89d8111df74c46207cf0e7cb7acd1a6a4635110b0c7fd731f734c74a3422081c | Python | 1,418 | 47 | import numpy as np
import tensorflow as tf
import time
import h5py
from Network.PatchHandler3D import PatchHandler3D
def load_indexes(index_file):
"""
Load patch index file (csv). This is the file that is used to load the patches based on x,y,z index
"""
indexes = np.genfromtxt(index_file, delimite... |
b80aae92a185160522b7217c555048125d157ffe75a721bad0095c1a850eb49a | Python | 1,418 | 43 | #!/usr/bin/env python3
# Copyright 2019 Yusuke Fujita
# Apache 2.0.
"""This script generates hypothesis utterances aligned with reference segments.
Usage: gen_align_hyp.py alignment.txt wc.txt > hyp.txt
alignment.txt is a session-level word alignment generated by align-text command.
wc.txt is a sequence ... |
baa6cf2f10698796dbc59833cb4c528ce7f310c5d6f9437be69b0402d9e4411e | Python | 1,420 | 27 | from utils import Config
from utils.ArticlesProvider import ArticlesProvider
from utils.decomposition import Decomposition
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer
class LDADecomposition(Decomposition):
def __init__(self, config: Config... |
f9519507d738fe22c9bc89865553510395a507220ea9faf964ed9cf221b49cc2 | Python | 1,422 | 38 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
8269a321ea7387344375bcc831ea520a40610c02adafb22983b9269b49a4a6b5 | Python | 1,423 | 46 | import sys
def packaged_startup_error(exc):
diagnostic_hint = ''
try:
from backend import diagnostics
from backend import jobs
diagnostic_id = jobs.new_diagnostic_id()
diagnostics.log_exception(diagnostic_id, 'startup', 'application', exc)
diagnostic_hint = ' Diagnostic... |
8a9992ed39aaffc40138b4750572d743f71c16585de2b2d9fabf4e98093445c6 | Python | 1,425 | 45 | """
This package contains modules related to objective functions, optimizations, and network architectures.
"""
import importlib
from models.basic_model import BasicModel
def find_model_using_name(model_name):
"""
Import the module with certain name
"""
model_filename = "models." + model_name + "_mod... |
a417b8759cb3e8935fd5119a9c5e19de36f1ba6addc1a4bdc101f822eac37e20 | Python | 1,425 | 43 | import torch.nn as nn
from mmcv.cnn import kaiming_init
from mmdet.models.registry import BACKBONES
from .derive_blocks import derive_blocks
from tools.apis.param_remap import remap_for_paramadapt
@BACKBONES.register_module
class FNA_Retinanet(nn.Module):
def __init__(self, net_config, output_indices=[2, 3, 5, ... |
b34c75787f31d1f48d48c2404d909bdbbf96fd9f62ce52dd03fe3dc9ca6b73bd | Python | 1,427 | 61 | #!/usr/bin/env python
# coding: utf-8
'''
@author: Mahbub Ul Alam (mahbub@dsv.su.se)
@version: 1.0+
@copyright: Copyright (c) Mahbub Ul Alam (mahbub@dsv.su.se)
@license : MIT License
'''
import os
from gpiozero import CPUTemperature
import torch
def getFreeDescription():
free = os.popen("free -h")
i = 0
wh... |
591e4533a98005b695d00cf4a691b9f5364e16d1d560167ba83ba00326896b8f | Python | 1,428 | 61 | # Copyright (c) Open-MMLab. All rights reserved.
from mmcv.utils import Registry
HOOKS = Registry('hook')
class Hook:
def before_run(self, runner):
pass
def after_run(self, runner):
pass
def before_epoch(self, runner):
pass
def after_epoch(self, runner):
pass
... |
b6ea4f4f9404c9c6ceaf1bc4dc147e68986d0ae25a524521624069963eb8ad2c | Python | 1,429 | 37 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
bd6214917038fa613c0fc9cfde30c23a6ad23dff314743e019d71dacd09a2fa3 | Python | 1,429 | 39 | # Copyright 2021 Google Research. 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... |
4430f65b052d33fd42b4d451ffbda8cddad0fa94c0728a866bf6b72c6cec9d0c | Python | 1,430 | 26 | from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
from .focal_loss import Focal... |
5729d6200cace768af08394eae65637f1661149f8c9dd1316fd6db2aa3e102ce | Python | 1,431 | 46 | from __future__ import division, print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import gradient_checker
from tensorflow_forward_ad import maxpool_gradgrad
class MaxPoolGradGradTests(tf.test.TestCase):
def test_basics(self):
tf.set_random_seed(1234)
x_shape = [4, 4, 4... |
77f3256f9263447d716db2faed3543fe0e8f252bb9a7ea020ef9517718dce35c | Python | 1,431 | 33 | import os
from cosmos_workers.common.hfcache import materialize_snapshot, snapshot_dir
def _fake_cache(root, repo="nvidia/Cosmos-1.0-Guardrail", rev="abc123"):
repo_dir = root / "hub" / ("models--" + repo.replace("/", "--"))
blobs = repo_dir / "blobs"
snap = repo_dir / "snapshots" / rev
(snap / "bloc... |
9e2aef878774a19e67fe7eecc32533f892995ff77ffb2c4b0ab35b2154145ce0 | Python | 1,431 | 48 | import torch
from cgtasknet.instruments.instrument_subgroups import SubgroupFinder
def test_create_subgroup_finder():
sgf = SubgroupFinder(dt=1e-2)
assert sgf.dt == 1e-2
def test_compute_average_freq_zeros():
sgf = SubgroupFinder()
s = torch.zeros(100, 4)
fixation = torch.zeros(100)
fixatio... |
d5401838e437751f9efbfe895a753e8fee1979c5a4aa34703b5fa302bdcddead | Python | 1,432 | 50 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_error
from pandas.api.types import is_string_dtype, is_numeric_dtype, is_object_dtype, is_cat... |
0374ac043c87cc17fea79be8092e4b06316ef3c3d0aa1d6be38396e231be2b5d | Python | 1,433 | 41 | """ running the ntf over a range of frequencies."""
from ..forward import network_transfer as nt
import numpy as np
def run_local_coupling_forward(brain, params, freqs):
"""run_forward. Function for running the forward model over the passed in range of frequencies,
for the handed set of parameters (which must ... |
e8a4c8fbe61193b7e22bd2c57b59700d17d37d86ed6c85c1f1b69be9e6140c6f | Python | 1,433 | 55 | """Base class for all parsers."""
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Callable, Iterator, List, Optional, TextIO, Tuple
@dataclass
class ColumnReference:
"""A way of indexing a column."""
index: Optional[int] = None
"""Number of the column (zero-... |
1646067423f0ef1c219a995f12c9fea4324e0ff046a53d2ad34880fea7434e27 | Python | 1,434 | 43 | __author__ = 'jieqing jiao'
__email__ = "jieqing.jiao@gmail.com"
import nibabel as nib
import numpy as np
def extract_regional_values(image, parcellation, labels):
idx = labels_to_index(parcellation, labels)
regional_values = np.mean(image[
idx,], axis=0)
return regional_values
def labels_to_in... |
7f41da49ad79e1c23ea92a8ad0fbbbefd18221c453ef640df12454852e36cf82 | Python | 1,435 | 46 | import multiprocessing
import dask
import subprocess
# Generates fingerprints for training set
n_cores = multiprocessing.cpu_count()
print("Number of cpus:", n_cores)
with open("all_training_pdbs.list") as f:
all_training_pdbs = f.read().splitlines()
files_per_core = len(all_training_pdbs) // n_cores + 1
for cor... |
cf5812e999c39760f7236f1ce0883da1cd6d1899125ed056ee826d115098457b | Python | 1,435 | 37 | import unittest
import curies
from linkml_runtime.loaders import json_loader
from oaklib import OntologyResource
from oaklib.converters.obo_graph_to_obo_format_converter import (
OboGraphToOboFormatConverter,
)
from oaklib.datamodels.obograph import GraphDocument
from oaklib.implementations import SimpleOboImplem... |
13dba7d8bfa8cdd097fbc2b55f0ffef7cec1bb589780fa25fde8f3cf3096018d | Python | 1,437 | 44 | __author__ = 'duarte'
from modules.parameters import ParameterSpace, copy_dict
from modules.signals import empty
from modules.visualization import *
from modules.io import set_project_paths
from defaults.paths import paths
import matplotlib.pyplot as pl
from matplotlib import cm
import visvis
import scipy as sp
from sc... |
430117f054754a661a90f7583f385ed531e680f30da90f7af17f82c657ad290e | Python | 1,439 | 49 | import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from nilearn import plotting
from ukbb_variables import observations
mpl.rc(
'... |
8e266d96fa6839db1f7e9876a9274b27a8995a91b60f0c7c6e48f10dcb7ccfab | Python | 1,439 | 48 | import os
import torch
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word] = len(self.idx2word) - 1
return self.word2id... |
0ca62f29366e77de5d6d82368522b3019eb2a81725cfdd59d91a07a1c6088ad7 | Python | 1,440 | 44 | __author__ = 'duarte'
from modules.parameters import ParameterSpace, copy_dict
from modules.signals import empty
from modules.visualization import *
from modules.io import set_project_paths
from defaults.paths import paths
import matplotlib.pyplot as pl
from matplotlib import cm
import visvis
import scipy as sp
from sc... |
b5b0e00b2d658a411e37ea8575907da82be6707247ad9226e2b6bdd82113a0c0 | Python | 1,440 | 43 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
"""Feather data format reader
After https://arrow.apache.org/docs/python/_modules/pyarrow/feather.html
"""
from pyarrow.lib import FeatherReader, DictionaryType
import numpy as np
from .._data_obj import Dataset, Factor, Var
class Reader(FeatherReader):
... |
6ca64de8cb01f7ace17de2ec4f16ffe5491c7bb9a9b0adaf1f96f5fca8da9aa5 | Python | 1,441 | 52 | """
Test methods in the _common methods module
"""
import numpy as np
import unittest
from selene_sdk.predict._common import get_reverse_complement_encoding
from selene_sdk.sequences import Genome
class TestReverseComplement(unittest.TestCase):
def setUp(self):
self.example_encoding = np.array(
... |
b63acef0c05506003d744c7621590ae62193d919b99223557bab3da4fb2b2734 | Python | 1,441 | 44 | import numpy as np
import torch
def get_Pareto_fronts(scores):
"""Identify the Pareto fronts from a given set of scores.
Parameters
----------
scores : numpy.ndarray
An (n_points, n_scores) array of scores.
Returns
-------
list of numpy.ndarray
A list conta... |
be53d1977514436606aa22ca07c0d9404a290487c933d8b8af48eff2198393d0 | Python | 1,442 | 46 | import torch
from torch.nn.parameter import Parameter
import numpy as np
from torch import sigmoid
class HardConcrete(torch.nn.Module):
def __init__(self, beta=1 / 3, gamma=-0.2, zeta=1.0, fix_temp=True, loc_bias=3):
super(HardConcrete, self).__init__()
self.temp = beta if fix_temp else Parameter... |
5522876709f512b2271ac3c11c3ef8215046799e324bf29a9012b240df355d36 | Python | 1,443 | 40 | import os
import numpy as np
stats = []
cpu_seconds = []
for dirname in os.listdir('03-results/'):
full_dirname = os.path.join('03-results/',dirname)
if not os.path.exists(full_dirname+'/results.txt'):
continue
results = open(full_dirname+'/results.txt').readlines()
for time_fn in os.listdir(fu... |
5dee3185ebede5f2ea1732dbf57360edfa1669bd903bdb94aa06c22b0c09ace7 | Python | 1,444 | 51 | import torch
import os
import numpy as np
import cv2
def convert_points_to_image(samp_pts, H, W):
"""
Inputs:-
samp_pts: b, 1, k, 2
"""
b, _, K, _ = samp_pts.shape
# Convert pytorch -> numpy.
samp_pts = samp_pts.data.cpu().numpy().reshape(b, K, 2)
samp_pts = (samp_pts + 1.) / 2.
samp_pts = samp_pts * np.arr... |
6ee558ffe50d11e09f6a14f1b9dda6f16c9cae6987139f287545f1c35980b802 | Python | 1,445 | 39 |
import torch
class LARS(torch.optim.Optimizer):
"""
LARS optimizer, no rate scaling or weight decay for parameters <= 1D.
"""
def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_coefficient=0.001):
defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, trust_co... |
61ce8a71a5dad3def3f3ec44d979d9112fc3fba396fc13eebbf9aab9c7bd0cf3 | Python | 1,446 | 61 | import gzip
import sys
input=sys.argv[1]
output=sys.argv[2]
fh=gzip.open(input,'rt')
fho=gzip.open(output,'wt')
outln=[None]*21
ctr = 0
for line in fh:
line = line.strip()
elems = line.split("\t")
if ctr == 0:
# header line
fho.write("PValue\tSNPName\tSNPChr\tSNPChrPos\tProbeName\tProbeChr\tProbeCenterCh... |
ed0dc5f6e51d6713a1a11e4d537a5050d69f3e4ca8eb2a6ad4505753d32ae02b | Python | 1,446 | 39 | # Read through all .txt files in ../brain_samples/ and filter ena.txt to only include those samples
import glob
import datetime
samples_to_keep = set([])
for f in glob.glob('../brain_samples/*txt'):
with open(f) as input_file:
header = input_file.readline().strip()
if header != 'brainSamplesToInc... |
eece4f00a17183b12ac8ae47bab3a58e1f67e4f0783157f771c8c6345f876de9 | Python | 1,447 | 56 | import random
import torch
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from torch_geometric.utils import to_networkx
from graphxai.datasets.real_world.mutagenicity import Mutagenicity
import graphxai.utils as gxai_utils
mutag = Mutagenicity(root = '.')
# MUTAG notation: [C, N, O, ...]
... |
d8b4837125d3ebc6761eb7384858192ebe03dcd4439b083aca035551d0452ff2 | Python | 1,448 | 36 | rule make_minimap2_index:
input: 'refs/{name}.fa.gz'
output: 'refs/{name}.mmidx'
shell: 'zcat {input} | minimap2 -H -k 13 -d {output} /dev/stdin'
rule map_to_controls:
input: seq='sequences/{name}.fa.gz', idx='refs/controls.mmidx'
output: 'alignments/{name}-controls.bam'
threads: 40
shell: ... |
f03430069485ed1a70880a2170cf5ee429a5c3d61e28d65849f4d2a1460ab337 | Python | 1,448 | 55 | import unittest
from deeprank_gnn.NeuralNet import NeuralNet
from deeprank_gnn.ginet import GINet
from deeprank_gnn.foutnet import FoutNet
from deeprank_gnn.sGAT import sGAT
def _model_base_test(database, model, task='reg', target='irmsd', plot=False):
NN = NeuralNet(database, model,
node_fea... |
0f3c637a3223101c258d7fe841d5b1b295fc8fe8755e47c6a7ccb227d21de6ae | Python | 1,449 | 41 | from functools import partial
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pycpd import affine_registration
import numpy as np
import time
def visualize(iteration, error, X, Y, ax):
plt.cla()
ax.scatter(X[:,0], X[:,1], X[:,2], color='red', label='Target')
ax.scatter(Y[:,0],... |
23c2231ba1e71ddc219fa602eba3eb3f9f636393aab1df4b4cb72a28b6e780a2 | Python | 1,449 | 37 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
8fd8d721e6612ff1edff38ef3d7efb9e9d4ca10cf0ab2a50c4e6689e1edafacc | Python | 1,449 | 49 | import tensorflow as tf
import numpy as np
__all__ = ['l2_loss',
'l1_loss',
'edges_loss',
]
def l2_loss(image, reference):
with tf.variable_scope('l2_loss'):
return tf.reduce_mean(tf.squared_difference(image, reference))
def l1_loss(image, reference):
with tf.variable_scope('l1_loss'):
... |
e1cf0e38f57418916beec9761f3ef96dbb36cac70da76535104d5b8fb6ba73ad | Python | 1,450 | 49 | import torch
from typing import List
from .explanation import Explanation
def aggregate_explanations(
exp_list: List[Explanation],
reference_exp: Explanation = None,
feature_aggregator = torch.max,
node_aggregator = torch.max,
edge_aggregator = torch.max,
node_level: b... |
e234214197fbc0b5b5616de2039f3db9cf2689f125825f9db6a63485d2766ae9 | Python | 1,450 | 30 | from distutils.core import setup
with open("./requirements.txt") as f:
REQUIRES = [line.strip() for line in f]
setup(
name = "bayesian_bootstrap",
packages = ["bayesian_bootstrap"],
version = "1.1.0",
description = "Bayesian Bootstrapping for statistics and regression models",
author = "Louis ... |
4d3e6726ffe7206a1d6878be1259638216061e90f0e5cb862074b69c60206d4b | Python | 1,451 | 55 | #!/usr/bin/env python
# coding: utf-8
# Evaluation: Transformer Dose Calculation
## Import libraries and define auxiliary functions
import h5py
import numpy as np
import sys
sys.path.append('../src')
from preprocessing import DataRescaler
from evaluation import gamma_analysis, error_analysis
# Prepare input data.
pa... |
fca6b13127ca7c07b5b45503019f8cd71f6273f6eeded33b6a39aa98987f2684 | Python | 1,451 | 54 | """
Write out footprints specified by DLKeys as GeoJSON
Useful for visualizing which tiles remain after population filtering
"""
from argparse import ArgumentParser
import descarteslabs as dl
import json
from shapely.geometry import mapping
from tqdm.contrib.concurrent import process_map
def read_dlkeys_file(dlkeys_... |
2d866a93aa22dca895a56f3754cae96d8a3d08d4ba8af7ae709aac2625f53100 | Python | 1,452 | 50 | import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import (
EarlyStopping,
LearningRateMonitor,
ModelCheckpoint,
)
from .callbacks import LatentSpaceSaver, Plotter
from ..models.main_net import MainNet
from ..data.datamodule import DataModule
def setup_model_and_dm(tasks, ds_para... |
a1f8e276b78564b49859326704bb56c9720e85ad4e759c748aa61866148b49af | Python | 1,452 | 41 | import os
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.stats.anova import anova_lm
def p_adjust_bh(p):
# Benjamini-Hochberg p-value correction for multiple hypothesis testing
p = np.a... |
a8e96979707a235f0a53ba77cdc52f4220237b72a4cf2a28f927670bcda63dd1 | Python | 1,452 | 40 | # Copyright 2020 Google Research. 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... |
847074a4c2eda7856aa5cb63f887e1a9794c4b334499b0a0f921873db6d7e3ff | Python | 1,453 | 44 | from setuptools import setup, find_packages
with open('README.md') as readme_file:
readme = readme_file.read()
with open('HISTORY.rst') as history_file:
history = history_file.read().replace('.. :changelog:', '')
with open("requirements.txt") as req_file:
requirements = list(filter(None, req_file.read().... |
2003c9f6c870b94b7c3c7decb07747d0498d78430047650c36ddb534c87a4504 | Python | 1,455 | 46 | import logging
import os
import numpy as np
import yaml
from numba import njit
from pathlib import Path
logger = logging.getLogger(__name__)
def check_dir_exists(directory: str, logger=False):
if not os.path.isdir(directory):
if logger:
logger.error(f"{directory} does not exist. Check your pa... |
18ac0e6ebf44d0b7fdad757e6001ad6ac611b24b204a904329ca3c7ea0e0c403 | Python | 1,456 | 28 | import sys, io, base64
sys.path.insert(0, '.')
from PIL import Image
from svgkit import Svg, C
# usage: diag_client_server.py <out_dir> <server_screenshot>
W, H = 1000, 400
s = Svg(W, H)
# server: a simulation screenshot clipped to a rounded panel
sx, sy, sw, sh = 30, 40, 540, 320
im = Image.open(sys.argv[2]).convert('... |
90c98c080d4207036c9eb8a0974dfe12bdc163ad8c2c07596b3be2b3430ccee7 | Python | 1,456 | 50 | #%%
import os
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
#%%
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# import pair stats
pairs = pd.read_csv('data/pair_distances.csv')
pair_ranks = pd.read... |
d3ba8115af21f78a3f3fdaf89a26d6f451ce5314644a3a2acf765b04cbae1477 | Python | 1,456 | 36 | """Data files used by osl-dynamics.
This subpackage provides brain atlases, MNI surfaces, masks, and other
reference files needed by the processing and analysis pipelines. They are not
shipped with the package: each one is downloaded from
`osl-files <https://github.com/OHBA-analysis/osl-files>`_ on first use and
cache... |
8cc93619f5dd5e276d4cee7e10a2fb6581ae2c9c5a0d7bfa51db49e8aa3f07f2 | Python | 1,457 | 41 | from functools import partial
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pycpd import deformable_registration
import numpy as np
import time
def visualize(iteration, error, X, Y, ax):
plt.cla()
ax.scatter(X[:,0], X[:,1], X[:,2], color='red', label='Target')
ax.scatter(Y[:... |
83be66716bdb95a6b32845e5c218b88e76ba6a7590e6a97b080ea54edd56ad0a | Python | 1,458 | 14 | def gettingStarted():
return """
\nWelcome to SAFT - Semi-Automatic Fluorescence Trace analysis
\n----------------------------
\nHere is some advice for novice users
\nLoad your data (traces in columns) from Excel. The first row should name the regions of interest (ROI), and each condition should be a separate shee... |
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