sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ec828626a9189f9bb54604f65d57f93ed2e09f9acc5615c3ba74f09c6118eb5b | Python | 2,733 | 79 | from typing import Optional
import torch
import torch.nn as nn
from models.aggregators import BaseAggregator
from models.aggregators.model_utils import MILAttention
class AttentionMIL(BaseAggregator):
def __init__(
self,
input_dim: int,
num_classes: int,
encoder: Optional[nn.Modu... |
b47e8c9905bd3386412b479c0101ed370e498cef04634811cdeaf5b66bf6c184 | Python | 2,737 | 69 | import math
class Synapse():
'''
classdocs
'''
def __init__(self, pre, post):
'''
Constructor
'''
self.type = 0 # 0: within a group; 1: different groups in the same layer; 2: other layer
self.pre = pre
self.post = post
self.weight = 0
se... |
b6f485071cd65f9d82cb577c6caeaeccbb7f2bbb215b9a16df7cd06d934aa8a6 | Python | 2,738 | 95 | """LIBSVM and SVMLight versions 1 and 2 decoding through datamonger-core."""
from __future__ import annotations
from collections.abc import Mapping
from dataclasses import replace
from pathlib import Path
from typing import cast
from scipy import sparse
from datamonger import _native
from datamonger._decoder_option... |
bd57ce72006f245566bc7ff95c034d826a975eb0334997a91f494d85a4140d64 | Python | 2,738 | 106 | # coding: utf-8
""" """
import pytest
import numpy as np
from pysteps import motion, verification
from pysteps.tests.helpers import get_precipitation_fields
lk_arg_names = (
"lk_kwargs",
"fd_method",
"dense",
"nr_std_outlier",
"k_outlier",
"size_opening",
"decl_scale",
"verbose",
)
... |
017896d65332e00ce8b921a8f1d511ccfd8d51a7d1b5b54fffb552d593c77c9e | Python | 2,743 | 91 | '''
JHK
Python script for calculating the linear trend of SHAP values.
'''
import numpy as np
from netCDF4 import Dataset
from scipy import signal
import os, pathlib
from scipy import stats
#==========================================================
# Set main directory
#===============================================... |
3dd44d583a5c225c0de124c2eefdcd1683094b77723748b6c900e3ac4bff728f | Python | 2,745 | 70 | import os
import numpy as np
import tensorflow as tf
from util.util import myNumExt
import time
from sort.rafd8 import pet as myDB
from ipdb import set_trace as st
from math import ceil
import random
from options.AV45 import BaseOptions
from tqdm import tqdm
import logging
opt = BaseOptions().parse()
... |
616990a0ad9f07b34d62e916843a24acaee9aa132458d5f727f995164f59665b | Python | 2,746 | 88 | # JHK
# Python script for generating ED Figure 10
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_detection'
#====... |
7378fd82d514c3c6b09f07e3db910ba7da4081f81a0328d7a8d20a64f9e4b102 | Python | 2,746 | 85 | import gzip
import numpy as np
import os
import pickle
import tensorflow as tf
# expect images as pickled and gzipped numpy arrays of shape (121, 145, 121)
def loadImg(path):
assert os.path.isfile(path), str(path) + " does not exist"
with gzip.open(path, 'rb') as fh:
img = pickle.load(fh)... |
91c9eccee7c90e4aa771fc025f07cfd2b32fa8e7ce2a4b3027c0070748b8f1cc | Python | 2,754 | 76 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
70df07bd3c0956466ea1217991987acc8522c2593eb361d5d52aa59854231706 | Python | 2,757 | 36 | from devices._poke import _Poke
from devices._MCP import MCP23017, MCP23008
from pyControl.hardware import Digital_output
class Nine_poke():
# 9 IR beams, each with LED, controlled by MCP23017 i2c port expander.
def __init__(self, port, rising_event_1 = 'poke_1', falling_event_1 = 'poke_1_out',
... |
039770f64d030093db48375557a18b570f97e97e22424d5ea6977a9eb7e74ff3 | Python | 2,760 | 71 | """Abaqus + substructure.
Case_029/030 cover substructure with DAMASK/MOOSE but are 100% ferrite, so no packet or block is
ever generated there -- they only prove the pipeline is inert when nothing is transformable. This
scenario is the counterpart: half the volume is martensite, so packets, blocks and the per-block
A... |
bbc8466eb1e69dc1bee6fb22dcce90a56fced8ff4929efd4b1ec66aa879b0be6 | Python | 2,760 | 85 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
dbf3ff7969efd162ff91a23420ae19f6cedfce880bc927786198746a47084f26 | Python | 2,760 | 84 | '''
JHK
Python script for calculating the linear trend of 5x5 occlusion sensitivity.
'''
import numpy as np
from netCDF4 import Dataset
import os, pathlib
from scipy import stats
#==========================================================
# Set main directory
#==========================================================... |
52d09338f56d60482903e8c489a99aba3c4e149dacc8e8d22ed40fc2ce09c381 | Python | 2,762 | 98 | from copy import deepcopy
from enum import Enum
from mrHARDI.base.ListValuedDict import MagicDict
from mrHARDI.base.fsl import serialize_fsl_args
class OutlierReplacement(MagicDict):
class Method(Enum):
slice = "sw"
multi_band = "gw"
both = "both"
def __init__(
self, n_std=4,... |
752cf25350f74b414d68d29c8fc68511b702b844454669c74e5029a0a4683a5f | Python | 2,766 | 72 | import numpy as np
from netCDF4 import Dataset
import os, pathlib
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_detection'
#==========================================================
# Set out... |
7ed38944b04231a5e035fb1eded90cebf225ca69dcddb328f6a43c989cf51471 | Python | 2,769 | 79 | import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv... |
3be167df5eed426c4c09be5e0f16aebbb0ebcf729925e8bf2c29c1575c05d9ea | Python | 2,771 | 111 | """Plotting functions for SVAE model
Author: Hikari Sorensen - Vector Engineering Team (hsorense@broadinstitute.org)
Notes:
"""
import matplotlib.pyplot as plt
from pathlib import Path
# ClustalX AA Colors
# (by properties + conservation)
# http://www.jalview.org/help/html/colourSchemes/clustal.html
clustalXAACol... |
c2c1853404264d9e1d8c72d5f123146171e1a9cec7bc2efd288626f1ef86a7cf | Python | 2,771 | 58 | import numpy
from numpy.typing import NDArray
from typing import List, Tuple, Optional
from pydantic import validate_call, ConfigDict
from cellprofiler_library.opts.graytocolor import Scheme
from cellprofiler_library.types import Image2DGrayscale, Image2DColor
from cellprofiler_library.functions.image_processing import... |
bdb79acea39781d4e8c1dd6c8c80c9f7e81a8ec630079bc6f6580d6498457398 | Python | 2,773 | 82 | import functools
from .._setting import Setting
from .._validation_error import ValidationError
class MultiChoice(Setting):
"""A setting that represents selection of multiple choices from a list"""
def __init__(self, text, choices, value=None, *args, **kwargs):
"""Initializer
text - the exp... |
b61c17b6f1c77895df24e86e0b689a04720b6a2e1a8c20e7aad08dd26ec13357 | Python | 2,777 | 81 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
81aeebb0742df85b8d8c3e2fdd48dfaa0404ff48e779c5c10095feed757d9720 | Python | 2,779 | 64 | import sys
from nidm.core import Constants
from nidm.experiment import Navigate
import nidm.experiment.tools.rest
def GetProjectsComputedMetadata(nidm_file_list):
"""
:param nidm_file_list: List of one or more NIDM files to query across for list of Projects
:return: Dictionary or projects, each project h... |
38c75593523763243d74fac75fd21ba176936889f926189a07a0a4c00d12bf2e | Python | 2,781 | 91 | import torch
import torch.nn as nn
import os
import tifffile
import numpy as np
from model import UNet
from dataset import *
from config import *
from torchvision import transforms
from collections import OrderedDict
dirc_data = args.dirc_data
dirc_ckpt = args.dirc_ckpt
dirc_result = args.dirc_result
if ... |
aeed27d3074b7817af3786352f03bdf0a96d3d03b55fa9297eed0b311533c461 | Python | 2,781 | 50 | # coding=utf-8
"""
ErodeObjects module for the CellProfiler library.
This module contains the core algorithms for object erosion operations.
"""
from pydantic import validate_call, ConfigDict, Field
from typing import Union, Tuple, Annotated
from cellprofiler_library.types import StructuringElement, ObjectSegmentati... |
958764f2d3dce97cf251bd4bd6212a6a223ada6288d15ae9162f5e82d079787c | Python | 2,791 | 66 | """This module mimics the util.py module and contains helper functions primarily
for the visualizer. These functions are used when input and output numpy arrays
with a bit depth greater than the vanilla 8-bit pngs that are used as input"""
import torch
import numpy as np
from PIL import Image
import os
import numpy as... |
b86ca071d1dd761a0fce663857e094bdf33047df25fc2021c5c6ccaae38da265 | Python | 2,792 | 85 | from ._does_not_predicate import DoesNotPredicate
from ._does_predicate import DoesPredicate
from ._filter import LITERAL_PREDICATE
from ._filter_predicate import FilterPredicate
from .._file_collection_display import FileCollectionDisplay
from ...pipeline import ImageFile
from ...pipeline import ImagePlane as ImagePla... |
cb0bc3e5fc6f26b096a6210ee14148693bf7ba0dd2c91c4438d2a9a9a6cb9e09 | Python | 2,792 | 71 | import functools
from ._module import Module
from ..constants.measurement import C_CHILDREN
from ..constants.measurement import C_COUNT
from ..constants.measurement import C_LOCATION
from ..constants.measurement import C_NUMBER
from ..constants.measurement import C_PARENT
from ..constants.measurement import FTR_CENTER... |
566dcde3a3e28dc92c5b5a9c2f98c3a945c1238be08721a23b6a3bdc7290c194 | Python | 2,794 | 74 | """
pysteps.feature.interface
=========================
Interface for the feature detection module. It returns a callable function for
detecting features from two-dimensional images.
The feature detectors implement the following interface:
``detection(input_image, **keywords)``
The input is a two-dimensional im... |
99cf61b1ef6a6a3f0c6914952439bcedceb6f39dd03843e8739ddd5a1f218b97 | Python | 2,799 | 94 | # -*- coding: utf-8 -*-
"""
pysteps.motion.proesmans
========================
Implementation of the anisotropic diffusion method of Proesmans et al. (1994).
.. autosummary::
:toctree: ../generated/
proesmans
"""
import numpy as np
from scipy.ndimage import gaussian_filter
from pysteps.decorators import che... |
980d3e5c328ac3cbcdc5cb7b64b51a755e081adb96fdbec502bef3b259efc0a3 | Python | 2,801 | 102 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from pysteps import motion, nowcasts, verification
from pysteps.tests.helpers import get_precipitation_fields
sprog_arg_names = (
"n_cascade_levels",
"ar_order",
"probmatching_method",
"domain",
"timesteps",
"min_csi",
)
sprog_arg_valu... |
adbc59c8da8a230318325d3604688329ec74381ecb2a1005259a22c96748c008 | Python | 2,802 | 83 | import argparse
import importlib
import pprint
import os
import tensorflow as tf
from utils.config import Config
parser = argparse.ArgumentParser(description='NetFrame, a pipeline for model development')
parser.add_argument('--version', type=str, default='',
help='version of exp, eg: $VERSION_$S... |
9a7cca184c05303dc8950bbade4af294f622c648da7e16670ac0c0fb4aad5c21 | Python | 2,803 | 64 |
import yaml
import argparse
from pathlib import Path
import utils
def get_config(default_config_path):
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
arg = parser.add_argument
arg("-c", "--config_path", type=Path, help="Path to the config.", default=default_conf... |
7022afd4da7537f1da6c7d0474569848879f03fc30b6c1bb7b2dab4da2bca423 | Python | 2,804 | 81 | import itertools
import numpy as np
import matplotlib.pyplot as plt
from somata.basic_models import StateSpaceModel as Ssm
from somata.basic_models import OscillatorModel as Osc
from somata.source_loc.source_loc_utils import simulate_oscillation
from somata.pac.decimate import DecimatedModel as Dec
"""
##############... |
82158940c787188e163cba1fbd01038a9e19d7596e6ecad2b346e9edd4cb8bba | Python | 2,804 | 78 | import wx
import wx.lib.colourselect
from ..artist import MODE_HIDE
class WorkspaceViewRow:
"""A row of controls and a data item"""
def __init__(self, workspace_view, color, can_delete):
self.workspace_view = workspace_view
panel = workspace_view.panel
self.chooser = wx.Choice(panel)... |
5745bd492fe0b8dfb809cca2f5e660caf9a37cc8e6724e0d1f22dc49ab4d8537 | Python | 2,806 | 66 | import os
import json
import subprocess
import numpy as np
import pandas as pd
import pickle as pkl
import nibabel as nib
from scipy.stats import zscore, pearsonr
from os.path import join as pjoin
from sklearn.preprocessing import StandardScaler
from nod_utils import save_ciftifile
# change to path of current file
os.... |
eaf70e35aea6a415696182004959b4b8c9ae23cc4281ee6d21a00097dc466926 | Python | 2,807 | 54 | import nengo
import numpy as np
# An asssociative memory network. Uses Voja + PES learning to learn a key-value mapping
# Contains:
# key_input: input to the network
# value_input: the desired output
# learning: if 0 learning is on, if positive learning is off
# recall: the memeory network's output
# Code taken from ... |
65d1c72ff0d7db36eefd9f99f2522566778c53aeb0cc320b30dfe5e9f3ad60f5 | Python | 2,808 | 94 | from __future__ import with_statement
from alembic import context
from sqlalchemy import engine_from_config, pool
from logging.config import fileConfig
import logging
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config
# Interpret the config ... |
68803975102eb02ec3ef6e0b8fb188b8309aff88650962c324011514b4fe48ce | Python | 2,809 | 103 | import json
from collections.abc import MutableMapping
from copy import deepcopy
from typing import Generator
from mrHARDI.base.encoding import MagicConfigEncoder
class MagicDict(MutableMapping):
def __init__(self, init_attributes=None, dict2arg_trans=None):
init_attributes = self._check_initialization(i... |
9fb325661ed781d20c21606c9972f27b365a32781edd9eee83c1c72c0ecaf6c8 | Python | 2,809 | 73 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from function import trainingHook
class FA_wrapper(nn.Module):
def __init__(self, module, layer_type, dim, stride=None, padding=None):
super(FA_wrapper, self).__init__()
self.module = module
self.layer_type = layer_type... |
16c7b4b1a086e0edeadb746c33a6f671472b14ed4f4d4cd637ed112104ac699d | Python | 2,810 | 92 | import matplotlib as mpl
mpl.use('agg')
from matplotlib.testing.compare import compare_images
from tempfile import NamedTemporaryFile
import os.path
import pygenometracks.plotTracks
ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_data")
tracks = """
[test with categories]
fil... |
a6e56eaa48be14d2d01583cb6500a97548fb0985ab8ebeb06c882773440df1d2 | Python | 2,811 | 87 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualUnit(nn.Module):
def __init__(self, units, activation='tanh', kernel_initializer='he_normal'):
super(ResidualUnit, self).__init__()
# Initialize the activation function
if activation == 'tanh':
sel... |
5bc4e6bb96e775b19b5b95bb1d0775bdbe1badf60e7cd8123d0064f460b6dfac | Python | 2,814 | 81 | """Module providing various utilities."""
from __future__ import annotations
import datetime as dt
import cv2
import numpy as np
from sentinelhub import BBox, DataCollection, MimeType, SentinelHubDownloadClient, SentinelHubRequest, SHConfig
from sentinelhub.evalscript import generate_evalscript
S2_BANDS = ["B01", ... |
1054c88f0eaabe7d028486607967228d6ab6d45575b83b7266f450869c20598d | Python | 2,816 | 87 | #!/usr/bin/env python3
"""Check vendored Rust metadata and synchronize core test fixtures."""
from __future__ import annotations
import argparse
import hashlib
import shutil
import tomllib
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
CORE = ROOT / "crates/datamonger-core"
def synchronize(... |
5d6cdd32ae537bcabc6485cb8ae82dff36d2373fcfa2e92ada03d47900490f17 | Python | 2,816 | 93 | from pathlib import Path
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Convolution3D, MaxPooling3D
from tensorflow.keras.callbacks import Callback
import tensorflow as tf
class AdaptiveLearningRate(Callback):
def... |
c37534617cc1f644107aec819a46d4c59d0eff0704953ff339f6308ebccd76cb | Python | 2,819 | 84 | def filter_duplicate_names(name_list):
"""remove any repeated names from a list of (name, ...) keeping the last occurrence."""
name_dict = dict(list(zip((n[0] for n in name_list), name_list)))
return [name_dict[n[0]] for n in name_list]
def get_name_providers(pipeline, last_setting):
"""Scan the pipe... |
b4e37a96c2d4cc1df84f092a18c747f9a5280decf3380e0a890f61d81282ec81 | Python | 2,822 | 62 | import unittest
import numpy as np
from centriole.bg_compensate import backgr, MODE_AUTO, MODE_DARK, MODE_BRIGHT, MODE_GRAY
class TestBackgroundCompensation(unittest.TestCase):
def test_01_basic_functionality(self):
# Create a simple synthetic image with known background
img = np.zeros((100, 100), ... |
82800e62e82da04a383594aa7bd02b8f6bbc22cfdd000884f678c6101d745888 | Python | 2,832 | 84 | """ Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional, Tuple, Ty... |
34a47e17b46349a7cf33882678cdf35b142ae95771b64ee06032f3ffed2456d2 | Python | 2,834 | 88 | '''
This is a python script for running supervised machine learning to predict compassion score
data input : foldname
data output $np.save.....
'''
import numpy as np
import pandas as pd
import scipy.stats as ss
import sklearn.linear_model as slm
from sklearn.linear_model import LinearRegression
from sklearn.model_... |
88d8778ce3ea60baade962c2ebbbc196b3ce7d5f93d58f862e6edf2e323d3934 | Python | 2,834 | 89 | '''
This is a python script for running supervised machine learning to predict compassion score
data input : foldname
data output $np.save.....
'''
import numpy as np
import pandas as pd
import scipy.stats as ss
import sklearn.linear_model as slm
from sklearn.linear_model import LinearRegression
from sklearn.model_... |
b1d9b604595fa65f82424edab26aa4588920066dd89a1e8c75f58b9df2a671d8 | Python | 2,835 | 70 | import pandas as pd
import anndata as ad
import numpy as np
from scipy.sparse import hstack,vstack,csr_matrix
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--path', default="./data/",
help='Location to save pseudobulks data')
parser.add_argument('--filename',
... |
b0c76c33db46223784fa524a33f3a8891a38d3d3107423e74746c0623c3a673f | Python | 2,839 | 85 | import time # type: ignore
import datetime # type: ignore
from signal import pause # type: ignore
from samplebase import SampleBase # type: ignore
from rgbmatrix import RGBMatrix, RGBMatrixOptions, graphics # type: ignore
import subprocess # For system audio playback
# Matrix and Graphics Configuration
options =... |
9a286b034346a31b3016efd96abcf95931d9c29d980876291bc4514085318e7e | Python | 2,841 | 89 | import os
import numpy as np
import pytest
from lightgbm import Booster
from numpy.testing import assert_allclose, assert_array_equal
import s2cloudless
from s2cloudless import PixelClassifier
from s2cloudless.cloud_detector import MODEL_FILENAME
@pytest.fixture(name="booster")
def booster_fixture() -> Booster:
... |
4fbe461e025f753a73bab348c9b8716415e5485ad2f02ddcb436c6804125578d | Python | 2,843 | 99 | from ._filter_predicate import FilterPredicate
from ._does_predicate import DoesPredicate
from ._does_not_predicate import DoesNotPredicate
from .._file_collection_display import FileCollectionDisplay
from ...constants.image import MD_SIZE_T, MD_SIZE_Z
from ...constants.measurement import C_RGB, C_MONOCHROME
class Im... |
365241d52ec02715725235a1f7709601338924d3d184fd24d693d0fdfc92ad68 | Python | 2,848 | 90 | # -*- coding: utf-8 -*-
import os
import pytest
import pysteps
from pysteps.tests.helpers import smart_assert
pytest.importorskip("netCDF4")
expected_proj = (
"+proj=geos +a=6378137.000000 +b=6356752.300000 "
"+lon_0=0.000000 +h=35785863.000000"
)
test_geodata_crri = [
("projection", expected_proj, No... |
7e28f020aae4797c0797f84dcbfa2b58d51da46832895c77b6c7638fd68b9b2e | Python | 2,848 | 89 | import os
import json
from allensdk.core.brain_observatory_cache import BrainObservatoryCache
PV = 'Pvalb-IRES-Cre'
GC6S = 'Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai94(TITL-GCaMP6s)/wt'
def load_json_file(json_file):
with open(json_file) as f:
return json.load(f)
def save_json_file(obj, json_file):
wi... |
cc77b261580e3d85e73282c42b7aa3fe25b66713e43a713ae668343b3f4af0fa | Python | 2,849 | 79 | # Copyright 2017 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 ag... |
bdf49cbbd0758d21c08fae4ebe26419d342d8184fe395fd084464fda49cda861 | Python | 2,852 | 83 | # -*- coding: utf-8 -*-
import pytest
from pysteps.visualization import plot_precip_field
from pysteps.utils import conversion
from pysteps.postprocessing import ensemblestats
from pysteps.tests.helpers import get_precipitation_fields
import matplotlib.pyplot as plt
import numpy as np
plt_arg_names = (
"source",... |
9ed8263e63834b300984f41a030560d85ce67e72696e86f7e98d156b383915d2 | Python | 2,854 | 91 | from tempfile import mkdtemp
from shutil import rmtree
import os
import logging
from nose.tools import ok_
import h5py
from pdb2sql import pdb2sql
import numpy
from deeprank.models.environment import Environment
from deeprank.models.variant import PdbVariantSelection
from deeprank.features.accessibility import __comp... |
928c5482f70b0645d225505756fc32a75c6845b138fb2c255942046386ead5a3 | Python | 2,859 | 76 | """Named split assembly using the existing single-artifact decoders."""
from __future__ import annotations
from collections.abc import Mapping, Sequence
from dataclasses import replace
from typing import Any
from datamonger._errors import UnsupportedDecoderError
from datamonger._models import (
DatasetSplits,
... |
dfd72c2314d4a70a800ba4816a3e54085637e098296f7b7fb4d468ade4d63df8 | Python | 2,863 | 98 | import torch
from torch.utils.data import DataLoader
from rdkit import DataStructs
from rdkit import Chem
from multiprocessing import Pool, cpu_count
import pandas as pd
from rdkit.Chem import AllChem
from sklearn.metrics import r2_score
import math
class predict_process():
def __init__(self, loss_df_name)... |
21a5a083d1907b55a8c9e42578066581d55d2732de157e943c10264a835f7208 | Python | 2,868 | 75 | import pytest
import numpy as np
from collections import OrderedDict
from torchmeta.transforms.splitters import ClassSplitter
from torchmeta.toy import Sinusoid
from torchmeta.utils.data import Task
def test_seed_class_splitter():
dataset_transform = ClassSplitter(shuffle=True,
num_train_per_class=5, num... |
6f2f23fb135344341043348163bac16502c69a5672b70ef5905a9ab8999fe33c | Python | 2,872 | 115 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
import numpy as np
from correspondence import Correspondence
from distance import SquaredL2
from itertools import product
def dtw(X, Y, metric=SquaredL2, debug=False):
'''Dynamic Time Warping'''
dist = metric.between(X,Y)
if debug:
path = _p... |
06a6d38afa6462707be36262d44802df4e4f71323b4fd54747d873b5f4f58743 | Python | 2,876 | 96 | from pepars.utils import DNA
class Template(object):
def __init__(self, sequence, name = None, id = 0, \
reverse_complement_template_id = None):
from . import Database as db
self._sequence = sequence
self._reverse_complement_template_id = None
if name == None:
... |
1605b776a70232b0a3235383e900feac9e41a8c596005398aee01ca2b74015b1 | Python | 2,881 | 63 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 5 12:13:20 2021
@author: filtez
This script is to concatanate the phoneme and word transcription files.
It will align phonemes and words and create new files with a structure like
(Phoneneme, Phoneme Onset, Phoneme Offset, Word, Word Onset, Word Offset)
"""
... |
328ef73ee3bfb995b83f3e1f49bb4b5f9883b751553ba7701c92179aca4467e6 | Python | 2,882 | 74 | """ MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List, Tuple, Union
class MultiStepLRScheduler(Scheduler):
"""Multi-step learning rate schedule with warmup and noise.
Outside warmup, de... |
d5c606aa343ca66ce0aeb1f922792a841001e1c95129237f66e0b8aeeb431014 | Python | 2,883 | 93 | import os
import pytest
import pysteps
from pysteps.tests.helpers import smart_assert
pytest.importorskip("pyproj")
root_path = pysteps.rcparams.data_sources["fmi"]["root_path"]
filename = os.path.join(
root_path,
"20160928",
"201609281600_fmi.radar.composite.lowest_FIN_SUOMI1.pgm.gz",
)
precip, _, met... |
4ffb9d58b9f9fc7479db13c8b0fb485bbf30ca3bbbfa33416ec51dfd4727e32a | Python | 2,884 | 96 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import cmath
path = '/scratch/c7051184/'
data_dict = {}
for j in range(1, 15):
fileold = 'station{0}/temptest{0}.npz'.format(j)
filenew = 'station{0}/temptestmap{0}.npz'.format(j)
data = np.load(path + fileold)
print(data)
data... |
e1e75d99e5c586d4ca44ecc94ac38c8663cd4613d849e32407595d8fe57ce607 | Python | 2,885 | 80 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
91fedb7625379929204638e80867a9e2e0a828b89df508518b1eb253f61a9dab | Python | 2,888 | 87 | import os
from pathlib import Path
import shutil
import subprocess
import sys
import pytest
@pytest.mark.parametrize("test_path", ["tests", "tests/core"])
def test_test_startup_is_headless(tmp_path, test_path):
tests = tmp_path / "tests"
core = tests / "core"
core.mkdir(parents=True)
shutil.copyfile(... |
4b95eb06e985903f2773e4ca6c8ff78586e40851f280d13faa4cf9cd156dee22 | Python | 2,889 | 73 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 5 15:57:59 2018
@author: saskiad
"""
import numpy as np
import pandas as pd
import os, h5py
import core
manifest_path = core.get_manifest_path()
save_path_head = core.get_save_path()
save_path = os.path.join(save_path_head, 'NaturalScenes')
ma... |
67279e3627ac19a3a74d81b5ee51a514acaba6a5ed270474c57cbb2c7e66cbea | Python | 2,892 | 91 | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.style
import matplotlib as mpl
import pandas as pd
# fontsize
plt.rcParams.update({'font.size': 18})
cutoff = [0,0.3,0.35,0.4,0.45,0.5]
def get_res(dataset, model):
if model == 'Chemformer':
path = f'../results/Chemformer/single_task/{dat... |
d8b406366a6a73de1e8d71ca0f0a5ddb974fd7fedc63e4d3613e8c26d2afdba1 | Python | 2,893 | 81 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
f3c5d5079ba1df0af5d0e2651a94272e3a30117ef370f45026bf149108be31c6 | Python | 2,893 | 100 | import os
import pandas as pd
def load_transform_results_figure1(dataset, parcellation):
idiff_path_combined_bins = os.path.join(
"..",
"..",
"results",
"idiff_combined_bins",
dataset,
"GS",
f"dataset-{dataset}_preprocessing_level-GS_parcellation-schaefer2... |
001b76b012d51645bfe5f7c0f8692b15ac42162d80904393dd68eba1489fd9f2 | Python | 2,896 | 75 | # -*- coding: utf-8 -*-
"""
Implementation of Adaptive gradient clipping
"""
from typing import List, Optional
import torch
def unitwise_norm(x: torch.Tensor, norm_type: Optional[float] = 2.0) -> torch.Tensor:
"""
Compute norms of each weight tensor in a model, and return the global norm.
Args:
x... |
47ae4787e1f0e9d8a1198558af4d4c674b9fa1c5839159ff2329ad419925f273 | Python | 2,898 | 94 | # -*- coding: utf-8 -*-
"""Fit STEPS motion perturbation parameters to the output of run_vel_pert_analysis.py
and optionally plots the results. For a description of the method, see
:cite:`BPS2006`."""
import argparse
import pickle
from matplotlib import pyplot
import numpy as np
from scipy.optimize import curve_fit
d... |
6c6b5c4d45def09cc884d3ea0a20543a0840e98dcc70546db221e3fa3ef1e651 | Python | 2,900 | 72 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch
def average_performance(pred, target, thr=None, k=None):
"""Calculate CP, CR, CF1, OP, OR, OF1, where C stands for per-class
average, O stands for overall average, P stands for precision, R stands for
recall a... |
9d240ad77876ef813f5843d992d9e36626d7f5cef12e896a1a105e6087d380d9 | Python | 2,901 | 84 | from pathlib import Path
import pytest
from rdflib import Graph, URIRef
from nidm.core import Constants
from nidm.experiment.Utils import read_nidm
KEY_PREDS = {
"prov:used": URIRef(Constants.PROV["used"]),
"prov:hadMember": URIRef(Constants.PROV["hadMember"]),
"prov:wasInfluencedBy": URIRef(Constants.PROV... |
8857ae2dc9588ea628eb8cdcad9dee5522dc463d9fd16f0815eab034cf7d7dbd | Python | 2,904 | 104 | #!/usr/bin/python3
import itertools
import numpy as np
from os.path import join as pjoin
import pandas as pd
import os
from os import linesep
diri='data'
diro='data/raw'
#Cell types to keep for light-weight demonstration
subsets={'celltype':set(['dysfunctional','naive'])}
#Probability of dropping cell for downsampled... |
d8210476f9c46945e66056b4ce1875b7436d4c9d929cf674e16d31a141bda1bb | Python | 2,907 | 107 | import numpy as np
import pytest
from pysteps import motion, nowcasts, verification
from pysteps.tests.helpers import get_precipitation_fields
anvil_arg_names = (
"n_cascade_levels",
"ar_order",
"ar_window_radius",
"timesteps",
"min_csi",
"apply_rainrate_mask",
"measure_time",
)
anvil_arg... |
221ba733d47d61112a93e2e1fc40b706cdaec58025d34f39835b9d1d2add425a | Python | 2,908 | 64 | import torch
import torch.nn.functional as F
import torchio as tio
import time
import logging
logger = logging.getLogger(__name__)
def series_pred(item_pointers, load_item, model, test_time_flipping=False, device=None):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None els... |
4d6d742d1d6d98cb3fd9caf387690aa85e7cec61e99365056a9da2aaf0cc5b0e | Python | 2,908 | 79 | #!/usr/bin/env python
# coding: utf-8
import os
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from matplotlib import gridspec
import matplotlib.transforms as transforms
import seaborn as sns
import statsmodels.api as sm
from statsmodels.formula.... |
03e32fdeab25d44c29db95e94e6aef49609435b9dc36bab09cac68e7d7c8c1a3 | Python | 2,909 | 62 | import os
import tensorflow as tf
from .architecture import neurite_classifier
from .data import prepare_pattern_dataset
def train_pattern_vs_unpatterned(train_image_dir='PatternTrainImages', validation_image_dir='PatternValidationImages', as_generic=False):
"""This method trains the model to distinguish betwee... |
c86c2d8d0d8c8098886250e784c6e07266601115d2640cfb59505a82bd5cc060 | Python | 2,912 | 80 | import pytest
from click.testing import CliRunner
from GANDLF.entrypoints.config_generator import new_way, old_way
from . import CliCase, run_test_case, TmpDire, TmpFile, TmpNoEx
# This function is a place where a real logic is executed.
# For tests, we replace it with mock up, and check if this function is ... |
cf1c0514c83d03bb29f2d68f7d453b963f05bd29b4e9068c151df8443e41ab5f | Python | 2,917 | 74 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from function import trainingHook
class FA_wrapper(nn.Module):
def __init__(self, module, layer_type, dim, stride=None, padding=None):
super(FA_wrapper, self).__init__()
self.module = module
self.layer_type = layer_type... |
4ba99acf43444498a435f579cfc160f499235aed110fd77624324520bfc0bf31 | Python | 2,920 | 92 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
4f069a01d22e70e9cf18104fe547867e97bd829eaaf98b01f6e9462e99a8b464 | Python | 2,920 | 80 | import os
import torch
import torchtext
import torchtext.data
import torch.nn as nn
import torch.nn.functional as F
from model import CNN
from data import DataIterator
from collections import namedtuple
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.cluster import KMeans
import joblib
from ... |
11f632b4c00dbe39658c9010e1ae0efd3ba41de1adceb27c86f902c657a0232a | Python | 2,922 | 124 | """
Computes GLM metrics.
@author: JingyiGF
"""
from .family import *
def get_deviance(y, yhat, family, theta):
"""Deviance metrics.
Parameters
----------
y : array
Target labels of shape (n_samples, )
yhat : array
Predicted labels of shape (n_samples, )
distr: str
dis... |
7d0f037e31bcaa35a7e07929bdfe5704704ad5d87de96c02b9c695a9dbcdc9f4 | Python | 2,924 | 101 | import Functions.globalFunctions.utils as utils
import Functions.globalFunctions.morphology_v2 as mphv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# https://matplotlib.org/stable/gallery/color/named_colors.html#sphx-glr-gallery-color-named-colors-py
import matplotlib.colors as mcolors
import... |
febefffb1e709d9c14449bfe0f2ad197b92d194f67a9ae4c8f2fea40a7a398e7 | Python | 2,925 | 94 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
2bcaf110e3316f1812219b4af87c879ebca0b2ae84d1e0f0f9a250d65b63d39c | Python | 2,926 | 86 | from typing import Optional
import torch
import PIL.Image
import numpy as np
from torchio.transforms.intensity_transform import IntensityTransform
from torchio.data.subject import Subject
class RGB2RGBA(IntensityTransform):
"""
Convert RGB image to RGBA image.
"""
def __init__(self, **kwargs):
... |
e025f8f73cd66a1597b6ea834aa4f0f35dbc070bed7ec7783e3dd4bc5fdc97a7 | Python | 2,929 | 87 | # -*- coding: utf-8 -*-
"""
pysteps.io.readers
==================
Module with the reader functions.
.. autosummary::
:toctree: ../generated/
read_timeseries
"""
import numpy as np
def read_timeseries(inputfns, importer, **kwargs):
"""
Read a time series of input files using the methods implemented... |
4044e5835e650b002649531238d2fe84ce278807637d2dd495311b43780d537b | Python | 2,933 | 85 | from typing import Optional
import torch
from torchio.data.subject import Subject
from torchio.data.image import ScalarImage
from torchio.transforms.intensity_transform import IntensityTransform
from torchio.transforms.preprocessing.intensity.clamp import Clamp
class Threshold(IntensityTransform):
"""Threshold i... |
947df3447af4af7a914b9b4bb14d4b0dd2c875e0ca279c7ad94b3d0f77af0df4 | Python | 2,943 | 75 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 5 15:57:59 2018
@author: saskiad
"""
import numpy as np
import pandas as pd
import os, h5py
import core
manifest_path = core.get_manifest_path()
save_path_head = core.get_save_path()
save_path = os.path.join(save_path_head, 'NaturalMoviesA')
m... |
4660379a803f08973f78b7b79551ea63f9af7c547c794da046e599363e230e87 | Python | 2,945 | 74 | # -*- coding: utf-8 -*-
# From https://github.com/jiesutd/LatticeLSTM
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
# Build CNN to learn character-level features.
class CharCNN(nn.Module):
def __init__(self, alphabet_size, embedding_dim, h... |
fc88b178dc17c46011003f070ff509489fffe251c64964d3b07eca36376a030a | Python | 2,946 | 94 | import math
import numpy as np
import scipy.linalg
import torch
class ConstantODE(torch.nn.Module):
def __init__(self):
super(ConstantODE, self).__init__()
self.a = torch.nn.Parameter(torch.tensor(0.2))
self.b = torch.nn.Parameter(torch.tensor(3.0))
def forward(self, t, y):
r... |
6dfba570174594c1aa12062f6668017cd56da00f1f8edababb1f1a49e218494e | Python | 2,947 | 100 | """
Command-Line script used to evaluate oversegmentation of segmentations produced by the automatic method
in the form of different metrics:
- ratio of segments in the automatic segmentation vs the ground-truth segmentation
- statistics on segment split count comparing segments in ground-truth vs overlapping segments... |
d29782f746b37fb4d2bc3efc892c4f42046119bb0233a692515bdd0fb348b986 | Python | 2,949 | 86 | # -*- coding: utf-8 -*-
''' --------- Imports ----------- '''
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy as sp
from prettytable import PrettyTable
''' --------- Pre-requisites: normality and variance ----------- '''
def normality(data_no_adap,data_div_adap,data_sub_adap):... |
b521a3b575ffcec9a0da7e17a054f18e059ee529a907af6485290e6b0c9c3b61 | Python | 2,950 | 93 | from itertools import product
from os.path import exists
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from traitlets import Dict
from mrHARDI.base.application import (mrHARDIBaseApplication,
prefix_argument)
_aliases = {
... |
a1a990d523934e90270bcbd0ebba36d7543afdb1311a0fe102eaa51c69497c02 | Python | 2,951 | 84 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 11 14:55:44 2021
@author: siddhesh
"""
import os
from typing import Dict, List, Union
import torch
class Logger:
def __init__(
self,
logger_csv_filename: str,
metrics: List[str],
mode: str,
add_epsilon:... |
c89d92f9055f47c1189b21a81bdfe55e8c1b41eeb781204c3285c6fa592073f6 | Python | 2,958 | 70 | from . BedTrack import BedTrack
from . GenomeTrack import GenomeTrack
import numpy as np
DEFAULT_BED_COLOR = '#1f78b4'
class TADsTrack(BedTrack):
SUPPORTED_ENDINGS = ['.domain', '.domains', '.tad', '.tads']
TRACK_TYPE = 'domains'
OPTIONS_TXT = GenomeTrack.OPTIONS_TXT + f"""
# If the bed file contains a c... |
146589ed0dd1275bf32c2265019b2726be4cec7d1f77dc2318b815ba2b70e8c0 | Python | 2,959 | 83 |
class _PssmRecord:
def __init__(self):
self.amino_acid_values = {} # uses one-letter codes as keys
self.information_content = None
class Pssm:
"This object stores pssm data"
def __init__(self):
self._residue_records = {} # the keys should be residue identifiers
def set_am... |
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