sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a850662b74a0db7785b55b223470a55c3ebd2cac0c08e44a55f40925acccc0bf | Python | 199 | 7 | # -*- coding: utf-8 -*-
"""Methods for blending NWP model(s) with nowcasts."""
from pysteps.blending.interface import get_method
from .clim import *
from .skill_scores import *
from .utils import *
|
9af5e07d6a8ebd74347f632233dd068e20b0e91c41f7de87d10722bbc490d19e | Python | 202 | 12 | class TestChoice:
def test_get_choices(self):
pass
def test_get_tooltips(self):
pass
def test_has_tooltips(self):
pass
def test_test_valid(self):
pass
|
ffc155f3c7a16dca9597aa843d02bf6ca9a8fccb688e478b1cad19f106ba143f | Python | 202 | 8 | from model.segmentation import SegModel
class Model(SegModel):
"""Model class for ln project."""
def __init__(self, cfg, data, mode):
super(Model, self).__init__(cfg, data, mode)
|
20fbd7f3440d5615fa03edf7d1ec9270c697c3e1fa93cb6c3d3e50402b61f6d3 | Python | 203 | 15 | import click
from ..__main__ import pass_environment
@click.group("worker")
@pass_environment
def command(context):
pass
@command.command("start")
@pass_environment
def start(context):
pass
|
4bc26d20f40ef4ab0b89983d39590407aa5623a089dacc19581ddc6a5e2f8649 | Python | 203 | 9 | from .conv_module import ConvModule
from .norm import build_norm_layer
from .activation import build_activation_layer
__all__ = [
'ConvModule',
'build_norm_layer',
'build_activation_layer'
] |
688a8813421c4f639cace9934968c5ceee222102a4db20c2f6a3c7011a912247 | Python | 204 | 8 | from ..functions.image_processing import morphology_closing
def closing(image, structuring_element):
return morphology_closing(
image,
structuring_element=structuring_element,
)
|
68fdeebd66ef39f6fc21839edabe5fc82b7acb461b17b68957d03838ef19e380 | Python | 204 | 8 | import multiprocessing
import cellprofiler.__main__
if __name__ == "__main__":
# allows multiprocessing on Windows after freeze
multiprocessing.freeze_support()
cellprofiler.__main__.main()
|
8ebabe25a2b60cb1bf129ed08994e6ab7fc72aba5e23e723e6623b6ad8582252 | Python | 204 | 6 | from .FeatureClass import FeatureClass
from .BSA import BSA
from .AtomicFeature import AtomicFeature
from .FullPSSM import FullPSSM
from .PSSM_IC import PSSM_IC
from .ResidueDensity import ResidueDensity
|
74e79b8fc704acf9791ce35fc9c14b9f3c035b4dced119b88bd3ed3713f8719e | Python | 206 | 12 | class TestSubscriberMultiChoice:
def test_load_choices(self):
pass
def test_group(self):
pass
def test_matches(self):
pass
def test_test_valid(self):
pass
|
e0d51a27d2b09cb5251bfefa4af860bd35ee89b253aeb35dda85ad90edafcf92 | Python | 207 | 8 | """
Author: Mingjian He <mh1@stanford.edu>
switching module contains switching state-space models in SOMATA
"""
from .traditional import switching # noqa: F401
from .vb import VBSwitchModel # noqa: F401
|
286ff0404fdf134cc8de9ecf2a2c6fb568b5e78de6456ed176c21cd377be3964 | Python | 209 | 7 | """
Author: Mingjian He <mh1@stanford.edu>
source_loc module contains dynamic source localization code base in SOMATA
"""
from .dmap_em import SourceLocModel # Source localization model class # noqa: F401
|
8959eda4407d0d346a759ba0cec8d5ff62d13e1f9795e9b783f5ea7c26f9c919 | Python | 209 | 7 | """Main module of `s2cloudless`."""
from .cloud_detector import S2PixelCloudDetector
from .pixel_classifier import PixelClassifier
from .utils import download_bands_and_valid_data_mask
__version__ = "1.7.3"
|
ca3486890a12bbe4b3de8684d5029549846b55bacb1e71880f936f1037852ad5 | Python | 209 | 12 | class TestHiddenCount:
def test_set_value(self):
pass
def test_get_value(self):
pass
def test_set_sequence(self):
pass
def test_get_unicode_value(self):
pass
|
525f310153e077a221d2d337060e199285767af1939b1a42a087b291f809a882 | Python | 210 | 8 | from setuptools import setup, find_packages
setup(
name="lioness",
version="0.1.0",
description="Code for the LIONESS project",
packages=["lioness", "lioness.segmentation", "lioness.utils"],
)
|
c86e28c42f41b9b659dd721c7ea721614f20896be08b33936dbb5e85babf89eb | Python | 210 | 10 | import tensorflow as tf
from data.data_dealer import DataDealer
class Data(DataDealer):
"""Data input for ln project."""
def __init__(self, cfg, mode):
super(Data, self).__init__(cfg, mode)
|
01c6a11c661c75cb5f866e006b0f8178cb8bbc5ebbca38fe902714b956dd577d | Python | 211 | 12 | class TestDependency:
def test_source(self):
pass
def test_source_setting(self):
pass
def test_destination(self):
pass
def test_destination_setting(self):
pass
|
ba5cfd9023bfd074fd9b76c386d96b2cd700da9b6f72e2d8b5194b755d950529 | Python | 214 | 9 | #!/usr/bin/env python
import shutil
from subprocess import Popen
shutil.rmtree('docs/html', True)
shutil.rmtree('docs/doctrees', True)
shutil.rmtree('docs/build', True)
Popen(['make', 'html'], cwd='docs').wait()
|
e361c43d01cd75604673cc49a5686da8b59453c5bef73a428fcd1b67bf6ba2bd | Python | 216 | 6 | from nidm.experiment.Utils import fuzzy_match_terms_from_graph, load_nidm_owl_files
def test_loadowl():
owl_graph = load_nidm_owl_files()
fuzzy_match_terms_from_graph(owl_graph, "WisconsinCardSortingTest")
|
7fbb3ad6eaf22d4c18f933311ac35fc2daf71619f7bf281144da7e21ab5a4d37 | Python | 217 | 12 | from nose.tools import ok_
from deeprank.models.residue import Residue
def test_compare():
residue1 = Residue(1, 'ALA', 'A')
residue2 = Residue(1, 'ALA', 'A')
d = {residue1: 0}
ok_(residue1 in d)
|
27cd5917e961a7c2f7a291fc6f4d88a1bf9514c9bfdfa95e1755b9a7744b5ace | Python | 218 | 7 | from ._builder import *
from ._helpers import *
from ._manipulate import *
from ._prune import *
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", FutureWarning)
|
783299d3badb6c5f72760fb69fa117d93f0c0338bb1969a707353abbcadab5bb | Python | 219 | 7 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
__version__ = "0.0.1"
__author__ = "Christoph Friedrich <christoph.friedrich@vonaffenfels.de>"
from .core import RGBMatrix, FrameCanvas, RGBMatrixOptions
|
5863036f7caf70a0d305387660850d9bab34fb2db69f8e4c834655091841cba6 | Python | 220 | 8 | # -*- coding: utf-8 -*-
"""Methods for plotting precipitation and motion fields."""
from .motionfields import *
from .precipfields import *
from .animations import *
from .spectral import *
from .thunderstorms import *
|
6e437ffb0a1b79a38aca86d4281b8e21825d9fbcfcbc5a2e502bcdba767566c4 | Python | 220 | 12 | from functools import partial
from typing import Any, Optional
import torch
import torch.nn as nn
import torch.nn.init as init
class MobileNetClassifier(nn.Module):
def __init__(self):
super().__init__()
|
51ca8e152d4e44e444243c06bfbc6ec2be2d3686a8a43a97192937840929663f | Python | 221 | 4 | from ._crop_image_subscriber import CropImageSubscriber
from ._file_image_subscriber import FileImageSubscriber
from ._image_subscriber import ImageSubscriber
from ._outline_image_subscriber import OutlineImageSubscriber
|
3fc1f7227fa12f75d79894b65a14d069dba618496def4cbd8bfd3b1ebbe6a30a | Python | 222 | 8 | from ._run_exception import RunException
class PostRunException(RunException):
"""An event indicating an uncaught exception during the post_run phase"""
def event_type(self):
return "Post run exception"
|
5cb5bdf398918124bacb1e0d6501db686be6aaae6294213afd962e1af4cb7a17 | Python | 222 | 10 | from ._event import Event
class URLsAdded(Event):
def __init__(self, urls):
super(self.__class__, self).__init__()
self.urls = urls
def event_type(self):
return "URLs added to file list"
|
a4e90b0f5b25ad556e6f8193a9d5b790b22e23eac4228d9cfad254126014a8a3 | Python | 224 | 6 | """
Options and enums for ShrinkToObjectCenters module
"""
# Note: ShrinkToObjectCenters is a simple morphological operation module that doesn't require
# complex enums. It has no settings beyond basic "Object Processing".
|
b68b59f619c8ff6be4fa29801456cda19c4aeb585299db4998a9acd416d65dde | Python | 224 | 7 | from ._filter_predicate import FilterPredicate
class CompoundFilterPredicate(FilterPredicate):
def test_valid(self, pipeline, *args):
for subexp in args:
subexp[0].test_valid(pipeline, *subexp[1:])
|
bab5c964b705ed89da256882449a623bf9283b3d3881af5d32e92c6aa953b1fe | Python | 224 | 8 | from .criterions import UnilateralMse, MixLoss
from .visualization import plot_tsne, plot_tsne_3d, plot_confusion_matrix
__all__ = [
'UnilateralMse', 'MixLoss',
'plot_tsne','plot_tsne_3d', 'plot_confusion_matrix'
] |
cdba4c1f49d564e34c7bf0536fb45286664b0f117d153beb769974f698c7d7f2 | Python | 225 | 5 | from ._crop_image_name import CropImageName
from ._external_image_name import ExternalImageName
from ._file_image_name import FileImageName
from ._image_name import ImageName
from ._outline_image_name import OutlineImageName
|
205789160c7fe64a998f2c402560346a3c9c5245dbd1180557318c089014fc26 | Python | 228 | 10 | from ._event import Event
class URLsRemoved(Event):
def __init__(self, urls):
super(self.__class__, self).__init__()
self.urls = urls
def event_type(self):
return "URLs removed from file list"
|
61a2aad5a4d5ba1d1ab2f383ec64ebe9873f3e74aa0d67e66d0eb131f2927559 | Python | 229 | 6 | from __future__ import absolute_import
import skimage.segmentation
def watershed(image, markers, connectivity=None, offset=None, mask=None):
return skimage.segmentation.watershed(image, markers, connectivity, offset, mask)
|
2f02f1d1a8daf51cabe6377eb4d5fba785bdda82f039f118409e4f65089ad17d | Python | 230 | 5 | import logging
logging.basicConfig(level=logging.INFO)
logging.getLogger('matplotlib').setLevel(logging.WARNING)
logging.getLogger('hicmatrix').setLevel(logging.WARNING)
logging.getLogger('numexpr').setLevel(logging.WARNING)
|
6224b1eb54baffe1ac90b11fd0fda9a46451215e1ba55d217765318aab311845 | Python | 231 | 15 | class TestJoiner:
def test_parse(self):
pass
def test_default(self):
pass
def test_build(self):
pass
def test_build_string(self):
pass
def test_test_valid(self):
pass
|
6bfc295b7c8dd3f05479b62c465d353a43cddec7517b2f07f1c9f0c13bbf2cb2 | Python | 231 | 4 | from pytorch_grad_cam.utils.image import deprocess_image
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
from pytorch_grad_cam.utils import model_targets
from pytorch_grad_cam.utils import reshape_transforms |
aef3990bc60ebdc3e9834df1650b7120f5ae1aa7d194698022652290eecba1c3 | Python | 231 | 8 | from ._run_exception import RunException
class PrepareRunException(RunException):
"""An event indicating an uncaught exception during the prepare_run phase"""
def event_type(self):
return "Prepare run exception"
|
3a365740df3ebbeee5c023f57b4bc077e3036f260408ec1f5e49cd4359e2ce53 | Python | 233 | 11 | import wx
from ..figure import Figure
class WorkspaceViewFigure(Figure):
def on_close(self, event):
"""Hide instead of close"""
if isinstance(event, wx.CloseEvent):
event.Veto()
self.Hide()
|
fed13a00a431e61dbf2a66105dd69f7bbac20f6b61770d8d5de022a5e3832b76 | Python | 235 | 6 | """Miscellaneous utilities for MEG/EEG field modelling """
from .compare_fields import compare_fields
from .fieldcomp import dipfld_sph, biot_savart, magdipfld
from .spharm import sph_X_fixed, sph_v
from .fileutils import read_opt_res |
fc40281c2552486b9792d3ec7165575c0de1dce8ff39a8220d56c1019e0788be | Python | 236 | 6 | from ...utilities.zmq.communicable.request import AnalysisRequest
class SharedDictionary(AnalysisRequest):
def __init__(self, analysis_id, module_num=-1):
AnalysisRequest.__init__(self, analysis_id, module_num=module_num)
|
4b4ec12ca408258db8806fce0862e5dd91943419e7f441994f9ded11cea359da | Python | 237 | 11 | import sys
from ..module._plugin_importer import PluginImporter
from ..utilities.core.modules import fill_modules
from ..utilities.core.plugins import load_plugins
sys.meta_path.append(PluginImporter())
fill_modules()
load_plugins()
|
611c5920de72b29137758e3f7be6a1cb5637a9c8e512a275e2252461908b11e6 | Python | 239 | 9 | import asteroid
from .sepformer_tasnet import SepFormerTasNet, SepFormer2TasNet
asteroid.models.register_model(SepFormerTasNet)
asteroid.models.register_model(SepFormer2TasNet)
__all__ = [
"SepFormerTasNet",
"SepFormer2TasNet",
]
|
0ad1edaed21a348b96e13f1e46dd25653ba24cb5f66174d010b6c31ca87dcc7f | Python | 245 | 15 | class TestSubscriber:
def test_get_group(self):
pass
def test_get_choices(self):
pass
def test_get_is_blank(self):
pass
def test_matches(self):
pass
def test_test_valid(self):
pass
|
2cee33f7087388ceb8ff5a553cc1a958f28f4bb541c00aa31e06331bc91e3efd | Python | 248 | 10 | from config import DEBUG
import traceback
def print_debug(message, exception=None):
if DEBUG and not exception:
print(message)
elif DEBUG and exception:
print(message)
print(exception)
traceback.print_exc()
|
8365d665ef6ada12e229391e8d46a945f69e02e32ed76526691942d45a1f7f2d | Python | 248 | 8 | from enum import Enum
class SecondaryObjectMethod(str, Enum):
PROPAGATION = "Propagation"
WATERSHED_GRADIENT = "Watershed - Gradient"
WATERSHED_IMAGE = "Watershed - Image"
DISTANCE_N = "Distance - N"
DISTANCE_B = "Distance - B" |
7ea041032aacf08a9100a7715767b86eb167ec8ffc3b8712caf199ba20259b3b | Python | 251 | 8 | class DisplayPostRun:
"""Request a post-run display
This is a message sent to the UI from the analysis worker"""
def __init__(self, module_num, display_data):
self.module_num = module_num
self.display_data = display_data
|
778cce7b99293e206a5248cea4bfae64abb196014c20f4998bb7e4c4ea18564d | Python | 252 | 9 | from ...utilities.zmq.communicable.reply import Reply
class SharedDictionary(Reply):
def __init__(self, dictionaries=None):
Reply.__init__(self, dictionaries=dictionaries)
if dictionaries is None:
dictionaries = [{}]
|
2e32350705a289eb79d672d27729d2795cffa6a067640fd75a177f01eed3261c | Python | 253 | 12 | import numpy
from pyunicorn import Network, mpi
offset = 10
n_max = 1000
s = 0
n = mpi.rank + offset
while n <= n_max + offset:
s += Network.Model("BarabasiAlbert", n_nodes=n).global_clustering()
n += mpi.size
numpy.save("s"+str(mpi.rank), s)
|
34c61c97381b25846b027362548bba30c38273466867956619b675a53d70a672 | Python | 253 | 5 | #!/usr/bin/env python3
import os
os.system('./run_scTIGER.py -goi AR+PTEN+ERG -ctrl ./Example/Data/Patient4_Benign_endothelial.csv -exp ./Example/Data/Patient4_Tumor_endothelial.csv -p 50 -top 100 -zero 0.15 -o ./Example/SampleResult_ProstateCancer')
|
5d5924e701679c54a31c1daf693c676c680fe1fb4db1c1aff8f603ed9c0a4777 | Python | 254 | 10 | from ._name import Name
class GridName(Name):
"""
A setting that provides a GridInfo object
"""
def __init__(self, text, value="Grid", *args, **kwargs):
super(GridName, self).__init__(text, "gridgroup", value, *args, **kwargs)
|
0cca9502656e95262ad30c9afd53b970bbd3bb6264ebb9e88a2e0621eb164d2c | Python | 255 | 6 | from ...utilities.zmq.communicable.request import AnalysisRequest
class ImageSetSuccess(AnalysisRequest):
def __init__(self, analysis_id, image_set_number=None):
AnalysisRequest.__init__(self, analysis_id, image_set_number=image_set_number)
|
ce7510ee718365b9af9b6e9568ed790ac97f20ad2af2fe7c4375df77856957fc | Python | 255 | 7 | """
@author: Johanna Rahm
Research group Heilemann
Institute for Physical and Theoretical Chemistry, Goethe University Frankfurt a.M.
"""
__all__ = ["loadAddOns", "loadImageBinner", "loadVisLocs", "widgetsAddOns", "widgetsImageBinner", "widgetsVisLocs"]
|
e08d13a846c9f17d56ad68ce6cc13c7d3ee6a6ea0b0e46162335b7936548f6a2 | Python | 255 | 8 | class TopRowObject:
def __init__(self, residue_name, atom_name, kwargs):
self.residue_name = residue_name
self.atom_name = atom_name
self.kwargs = kwargs
def __getitem__(self, key):
return self.kwargs[key]
|
3d57088794c18056b60287633e39b3423f919f697b5575b09b7b7e7e435e6b4d | Python | 256 | 13 |
NUM_CLASSES = 250
top_dropout_rate = 0.2
pricture_resolution = (1080, 1440) #(h,w)
IMG_SIZE = 456
img_input_size = (IMG_SIZE, IMG_SIZE)
batch_size = 64
current_model_name = "model.hdf5"
model_name = "final_model.hdf5"
image_interpolation = 'nearest'
|
bb964adb69fc761582ea7aad9e5918bd4ed96ee72a06228391d168346b8b72f5 | Python | 258 | 9 |
from tensorflow.keras import backend as K
def earth_movers_distance(y_true, y_pred):
cdf_true = K.cumsum(y_true, axis=-1)
cdf_pred = K.cumsum(y_pred, axis=-1)
emd = K.sqrt(K.mean(K.square(cdf_true - cdf_pred), axis=-1))
return K.mean(emd)
|
8859e822169e0267af6c9e82851b13beef221ca538cfce0183c2699c7c107476 | Python | 260 | 13 | from nidm.experiment.tools import ( # noqa: F401
nidm_concat,
nidm_convert,
nidm_linreg,
nidm_merge,
nidm_query,
nidm_queryai,
nidm_version,
nidm_visualize,
)
from nidm.experiment.tools.click_base import cli
__all__ = ["cli"]
|
c2b5a5f8a280f27624329e1165e452441ab280f4c773ee79c2acad4dccc14ba5 | Python | 260 | 10 | from .._name import Name
class ImageName(Name):
"""
A setting that provides an image name
"""
def __init__(self, text, value="Do not use", *args, **kwargs):
super(ImageName, self).__init__(text, "imagegroup", value, *args, **kwargs)
|
438a417428039a33f1d39b7ad59aa2e342287fc363dd447993e17b6eb2262d96 | Python | 263 | 12 | from enum import Enum
class StructuringElementShape2D(str, Enum):
DIAMOND = "Diamond"
DISK = "Disk"
SQUARE = "Square"
STAR = "Star"
class StructuringElementShape3D(str, Enum):
BALL = "Ball"
CUBE = "Cube"
OCTAHEDRON = "Octahedron" |
592984ac563b69eba9734a4191078e253b13812e248a157207bc166cedb09cfb | Python | 264 | 16 | from distutils.core import setup
setup(
name="protfarm",
version="0.2",
packages=[
"protfarm",
"protfarm.analysis",
"protfarm.workspace"
],
install_requires=[
"pandas",
"numpy",
"pepars"
]
)
|
7672c93cd0c22a73ab894b466a6a4bb8c8f533c5cea71d7e7fb69eeb86807296 | Python | 264 | 18 | class TestImagePlane:
def test_path(self):
pass
def test_url(self):
pass
def test_series(self):
pass
def test_index(self):
pass
def test_channel(self):
pass
def test_metadata(self):
pass
|
7c249f85551c11a0457f1d6716e947af4454ac9df6f5a34257e4b56f4ccf8a8e | Python | 265 | 5 | from ._do_something import DoSomething
from ._image_set_display import ImageSetDisplay
from ._path_list_extract_button import PathListExtractButton
from ._path_list_refresh_button import PathListRefreshButton
from ._remove_setting_button import RemoveSettingButton
|
7d3685c123c787d375c4d1ca2e4f3e797072a63eb6aecb5b6363fec3f0fd7e93 | Python | 265 | 8 | from ...utilities.zmq.communicable.reply import Reply
class ExceptionPleaseDebug(Reply):
def __init__(self, disposition, verification_hash=None):
Reply.__init__(
self, disposition=disposition, verification_hash=verification_hash
)
|
018c03c302f5339fecb13b6d22a5a7a3e66b79dc741494c25aab23f47e4324f9 | Python | 267 | 18 | class TestImagePlane:
def test_build(self):
pass
def test_url(self):
pass
def test_series(self):
pass
def test_index(self):
pass
def test_channel(self):
pass
def test_test_valid(self):
pass
|
18cc9ada73d230f481dff053b5109f3c27f9513fed3fba2ab8069756a609e1f2 | Python | 268 | 7 | from .train import Trainer, KTrainer, TrainingDir
from .evaluation import Evaluator, KEvaluator
from .losses import DCS
from .progress_bar import printProgressBar
__all__ = ['Trainer', 'Evaluator', 'DCS', 'KTrainer', 'KEvaluator', 'printProgressBar', 'TrainingDir']
|
5b6ab8bb3abcebaff1f85612c49e3659e6ad22202a1de64506dd4b0e230ebce5 | Python | 268 | 17 | class Layer():
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.neutype = ''
self.groups = {}
def resetProperties(self):
pass
def addNewGroups(self, layerID, neunum):
pass |
9272fc35bd63066b3d443c50f6cdf9bd2bc49410235e8d60d9964ca1cd8450c8 | Python | 268 | 8 | from ..functions.image_processing import morphological_skeleton_2d, morphological_skeleton_3d
def morphologicalskeleton(image, volumetric):
if volumetric:
return morphological_skeleton_3d(image)
else:
return morphological_skeleton_2d(image)
|
2cf128f914ac28b26f5e6393a15f63d5b26488e9f288f883fc94eba08e369aa7 | Python | 269 | 8 | import numpy
def __compute_target__(variant, target_group):
if variant.variant_class is None:
raise ValueError("class isn't set on mutant {}".format(variant))
target_group.create_dataset("class", data=numpy.array(float(variant.variant_class.value)))
|
77ffb9fac19c647d72bb84ef257c0a3fd7867bd2bf5753ba04bca63b8a99e077 | Python | 270 | 6 | class RelationshipKey:
def __init__(self, module_number, relationship, object_name1, object_name2):
self.module_number = module_number
self.relationship = relationship
self.object_name1 = object_name1
self.object_name2 = object_name2
|
84d0d95685431b015ed318dcfef96591a7ca269f4c79b88e639300503195b063 | Python | 270 | 8 |
class VanderwaalsParam:
def __init__(self, inter_epsilon, inter_sigma, intra_epsilon, intra_sigma):
self.inter_epsilon = inter_epsilon
self.inter_sigma = inter_sigma
self.intra_epsilon = intra_epsilon
self.intra_sigma = intra_sigma
|
99a3a030189f3f159b5f2c14ee8c675928f4496bcf14be9fc690ad4c062b92f2 | Python | 271 | 9 | """Deprecation shim: re-exports :class:`BaseRawReader` from its new location."""
import warnings
from .base import BaseRawReader
__all__ = ['BaseRawReader']
warnings.warn('BaseRawReader should be imported from ptsa.data.readers.base',
DeprecationWarning) |
423ca87b7bf57f0f5fd139c4efc89c2bbadc0b2039a2d339f09919fdf8073215 | Python | 275 | 7 | import os
from pyControl.hardware import Digital_input, Digital_output, Analog_input, Rsync, off
_driver_files = [f.split('.')[0] for f in os.listdir('devices') if 'init' not in f]
for _driver_file in _driver_files:
exec('from devices.{} import *'.format(_driver_file)) |
2d05fb09c19610fa555137448e0a121ba65cb35c3af95019599f23f17fff679e | Python | 276 | 11 |
from torch.utils.data.distributed import DistributedSampler
def build_sampler(cfg, default_args=None):
if cfg is None:
return None
elif cfg['type'] == 'DistributedSampler':
return DistributedSampler(**default_args)
__all__ = ('DistributedSampler')
|
375a9db9774aed444141f3d7d388c838b68d33b2e9fc19a06b967bf7c51536c2 | Python | 276 | 2 | from .iter_osc import IterativeOscillatorModel # class holding OscillatorModel class objects during iterative search # noqa: F401, E501
from .decomp_osc import DecomposedOscillatorModel # class holding OscillatorModel class objects during decomposition # noqa: F401, E501
|
f0b8971be5e8ea214dafd583a7fe7b86d7bab11a51b5d68687ac88ea5f212f76 | Python | 277 | 16 |
import pandas as pd
path = f'../results/MLP_delta/top 1/lipo/tables'
df = pd.read_csv(f'{path}/0.csv')
df. tail(-1)
sim = df['sim']
err = df['pred_prop'] - df['prop']
df['err'] = err
#
points = df[(df['sim'] <= 0.2) & (df['err'] > 5)]
points.to_csv('question_group.csv')
|
0eb75ccb2b1389f5665484e39e10ae66a444004577e5bb0314c8a90d20c23cde | Python | 278 | 11 | #!/usr/bin/python3
from .qc import qc_reads, qc_outlier
from .lcpm import lcpm, scaling_factor
from .norm import normcov, compute_var, normvar
from .de import de
from .coex import coex
from .binnet import binnet
from .gocovt import gotop, pccovt
assert __name__ != "__main__"
|
65f1506377a98323546bb8e54dd08973ad1648a08679f2215d39bd5284ef5fec | Python | 281 | 10 | from ._change_setting_event import ChangeSettingEvent
class AfterChangeSettingEvent(ChangeSettingEvent):
"""Indicates that a setting has changed its value
"""
def __init__(self, old_value, new_value):
ChangeSettingEvent.__init__(self, old_value, new_value)
|
a2786885d29f4109705ed12e17c20de6df93065dbb63a68ab406deeb428a8ae9 | Python | 281 | 18 | class TestSegmentation:
def test_shape(self):
pass
def test_has_dense(self):
pass
def test_has_sparse(self):
pass
def test_has_shape(self):
pass
def test_sparse(self):
pass
def test_get_dense(self):
pass
|
d53b9b61e9798bfa2a6e5e6dd740865d9464572c0f6278cd3de0732b46b14c11 | Python | 283 | 12 | from click.testing import CliRunner
from nidm.experiment.tools.nidm_query import query
def test_query_failing():
runner = CliRunner()
res = runner.invoke(query)
assert res.exit_code != 0
assert "Missing option" in res.output
# TODO: adding tests that are passing
|
3932521ce051d364b76334cf4bd6a2157650edd001c9edaeab2eca9820ab1de6 | Python | 284 | 10 | from ._image_name import ImageName
class OutlineImageName(ImageName):
"""
A setting that provides an object outline name
"""
def __init__(self, text, value="Do not use", *args, **kwargs):
super(OutlineImageName, self).__init__(text, value, *args, **kwargs)
|
794933ce015790b61db049cf0b082f03b74b7b1badb96542dd7371dbb847fafa | Python | 284 | 10 | from GANDLF.cli import copyrightMessage
def append_copyright_to_help(command_func):
command_func.__doc__ = (
copyrightMessage
if command_func.__doc__ is None
else (command_func.__doc__ + "\n\n" + copyrightMessage)
)
return command_func
|
a77d4a555c1656339289f5a85cfb942b29f3c9e794a1349355d9261871a0aa99 | Python | 284 | 10 | from pydantic import BaseModel, ConfigDict
from GANDLF.configuration.user_defined_config import UserDefinedParameters
class ParametersConfiguration(BaseModel):
model_config = ConfigDict(extra="allow")
class Parameters(ParametersConfiguration, UserDefinedParameters):
pass
|
0a3f07fdd7a4cbfee2554d849803fff840b2422669fa250ac1a210455ed5e2fd | Python | 285 | 8 | from ._grid_name import GridName
from ._label_name import LabelName
from ._name import Name
from .image_name import CropImageName
from .image_name import ExternalImageName
from .image_name import FileImageName
from .image_name import ImageName
from .image_name import OutlineImageName
|
04b2f9d1e8a037a1aad85c47f8135a34b308e92481f0acbca34de75eef28e0c0 | Python | 289 | 14 | from enum import Enum
class EdgeFindingMethod(str, Enum):
SOBEL = "Sobel"
PREWITT = "Prewitt"
ROBERTS = "Roberts"
LOG = "Log"
CANNY = "Canny"
KIRSCH = "Kirsch"
class EdgeDirection(str, Enum):
ALL = "All"
HORIZONTAL = "Horizontal"
VERTICAL = "Vertical" |
09756b00e4ebb6c5c0ddf5c6c7b6c47eca8319e13050fd5241c8dfdd95295f32 | Python | 290 | 11 | """
NIDM-Workflows Python API
-------------------------
Python API to create, query, read, and write NIDM-Workflow documents.
"""
from .ProcessExecution import ProcessExecution
from .ProcessSpecification import ProcessSpecification
__all__ = ["ProcessExecution", "ProcessSpecification"]
|
05f37abfb8e035607264b6a9c89306af1a0a9b13a150028e76f71170141b56d3 | Python | 294 | 19 | from pickletools import optimize
import torch
class BaseTrainStepper():
def __init__(
self,
model: torch.nn.Module,
optimizer: optimize,
) -> None:
pass
def train_step(self) -> None:
pass
def val_step(self) -> None:
pass |
79f124e7ac4fed299df329c7eecc1bb4b7be413cb1df94a262cffba07371c513 | Python | 294 | 8 | # -*- coding: utf-8 -*-
"""
Methods for advection-based extrapolation of precipitation fields.
Currently the module contains an implementation of the
semi-Lagrangian method described in :cite:`GZ2002` and the
eulerian persistence."""
from pysteps.extrapolation.interface import get_method
|
b9ccd07b3ae2bbaf3f6ff40e9254fca530577a0f2070c1e0587e80862d6c6c9a | Python | 295 | 18 | class TestImageProcessing:
def test_create_settings(self):
pass
def test_display(self):
pass
def test_run(self):
pass
def test_settings(self):
pass
def test_visible_settings(self):
pass
def test_volumetric(self):
pass
|
44cb3181cb8bd7140c4e4bb5bbbf79758560e2f28f3ce9f81ff6660cb5e21270 | Python | 297 | 15 | from nidm import __version__
from nidm.experiment.tools.click_base import cli
# adding click argument parsing
@cli.command()
def version():
"""
This function will print the version of pynidm.
"""
print(f"PyNIDM Version: {__version__}")
if __name__ == "__main__":
version()
|
2062633c3220c523cc2efba836c44b368b0e8564a22edaef793108198381b90d | Python | 298 | 12 | import docutils.core
# https://wiki.python.org/moin/reStructuredText
def rst_to_html_fragment(source):
parts = docutils.core.publish_parts(source=source, writer_name="html")
return parts["body_pre_docinfo"] + parts["fragment"]
def find_link(name):
return globals().get(name, None)
|
381c60f1e93aeb47fcd4cd34b690a3ca1ebc5158111d36d7aefa24c3c93c483a | Python | 298 | 18 | class TestMultiChoice:
def test_parse_value(self):
pass
def test_get_choices(self):
pass
def test_set_choices(self):
pass
def test_set_value(self):
pass
def test_get_selections(self):
pass
def test_test_valid(self):
pass
|
860f2d2e75302b1899f9e246936df7158c3d468c864e19858a9d51d63fc51699 | Python | 299 | 7 | FILTER_CHOICE_NONE = "No filtering"
FILTER_CHOICE_IMAGES = "Images only"
FILTER_CHOICE_CUSTOM = "Custom"
FILTER_CHOICE_ALL = [FILTER_CHOICE_NONE, FILTER_CHOICE_IMAGES, FILTER_CHOICE_CUSTOM]
FILTER_DEFAULT = (
'and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.")'
)
|
9b1ef849ff756a3e1f238fd315e7ff80bae13b29277839b7f67ed36cea6efe0c | Python | 299 | 8 | from .models import UNet, FCN
try:
from .GFCN import GFCN, GFCNA, GFCNB, GFCNC, GFCND, GFCNE, GFCNF, GFCNG
from .pointnet import PointNet
__all__ = [ 'UNet', 'FCN', 'GFCN', 'GFCNA', 'GFCNB', 'GFCNC', 'GFCND', 'GFCNE', 'GFCNF', 'GFCNG']
except:
__all__ = [ 'UNet', 'FCN', 'PointNet'] |
c1a520fea3b8bcd739e09adfcc9b94323bced48e8c0b6313deca80e57a36105b | Python | 299 | 13 | from weakref import WeakSet
import zmq
DEADMAN_START_ADDR = b"inproc://deadmanstart"
DEADMAN_START_MSG = b"STARTED"
NOTIFY_ADDR = b"inproc://notify"
NOTIFY_STOP = b"STOP"
NOTIFY_RUN = b"KeepAlive"
ED_STOP = b"Stop"
ED_CONTINUE = b"Continue"
ED_SKIP = b"Skip"
all_measurements: WeakSet = WeakSet()
|
84573249bc5b0cd071eaafd4a3e3b6d047e9019c768bdbb8f12949653e85bb0a | Python | 302 | 8 | from ._image_set_success import ImageSetSuccess
class ImageSetSuccessWithDictionary(ImageSetSuccess):
def __init__(self, analysis_id, image_set_number, shared_dicts):
ImageSetSuccess.__init__(self, analysis_id, image_set_number=image_set_number)
self.shared_dicts = shared_dicts
|
bab8f0dc1ac03acafbe45183c0a7df1687a2a4d46a5ef40ccccc8bcaccd812bf | Python | 304 | 10 | # Inheritaed from open-mmlab/mmclassification
# Modified by Hangyu LIN
from .base_dataset import BaseDataset
from .builder import build_datasets, build_sampler, build_dataloader
from .cifar import CIFAR10, CIFAR100
__all__ = [
'BaseDataset', 'build_datasets', 'build_sampler', 'build_dataloader',
] |
9215090e690d40f0e6b5fd8a28d3d19b826209038076c215318703ecc375665a | Python | 306 | 18 | from enum import Enum
import numpy
class SecondaryStructure(Enum):
HELIX = 1
STRAND = 2
OTHER = 0
def one_hot(self):
if self == SecondaryStructure.HELIX:
return [1, 0]
elif self == SecondaryStructure.STRAND:
return [0, 1]
return [0, 0]
|
c935ba82c9f2a8e6cd5899aa29eac456e93b1133c74a41b74eb035ee3033f70b | Python | 306 | 12 | from ._do_something import DoSomething
class ImageSetDisplay(DoSomething):
"""A button that refreshes the image set display when pressed
"""
def __init__(self, *args, **kwargs):
super(self.__class__, self).__init__(
args[0], args[1], None, *args[:2], **kwargs
)
|
78e7a7a7546640198fe89afc1a322fb2f08b89ac81e0e49655c2b8c2aa31d455 | Python | 307 | 8 | from neurodynex3.working_memory_network import wm_model
import brian2 as b2
def test_woking_memory_sim():
"""Test if the working memory circuit is initialized and simulated for 1ms"""
b2.defaultclock.dt = 0.2 * b2.ms
wm_model.simulate_wm(N_excitatory=40, N_inhibitory=10, sim_time=1. * b2.ms)
|
c3133ec23f1a63f32346c020388f71d6e56b183158469e21df506e794f4c1a87 | Python | 307 | 12 | from .data_loader import (
QueryDataset,
QueryDataset_custom,
QueryDataset_COMETA,
QueryDataset_pretraining,
MetricLearningDataset,
MetricLearningDataset_pairwise,
DictionaryDataset,
)
from .metric_learning import Sap_Metric_Learning
from .model_wrapper import Model_Wrapper
|
286201ac72866111e24c9098cff4fdd071f93ae9e86cd252fb3199156dd1bded | Python | 308 | 8 | from ...utilities.zmq.communicable.request import AnalysisRequest
class DebugWaiting(AnalysisRequest):
"""Communicate the debug port to the server and wait for server OK to attach"""
def __init__(self, analysis_id, port):
AnalysisRequest.__init__(self, analysis_id=analysis_id, port=port)
|
5a29adbb7d00f64ad1b1a9f11323822e65cd3bef329f5a277066cfaee6606685 | Python | 308 | 12 | default_settings = {
"lstm1_hidden_units": 512,
"lstm2_hidden_units": 512,
"hidden_units_local": 256,
"hidden_units_global": 256,
"trainer": {
"checkpoint_path": "../checkpoints/coal_mining",
"max_to_keep": 1,
"default_lr": 5e-4,
"memory": False,
},
}
|
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