sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6fa929f7d97af6e4791691ea6b5f28648d65f3513a109d481355d0af5e9ad8ff | Python | 1,466 | 62 |
import keras
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
### PARAMETERS
# number of kernels and width of kernels in CONV layer
kn = 64
kw = 21
kn1 = 4
kw1 = 7
kn2 = 16
kw2 = 13
kn3 = 64
kw3 = 21
# ... |
e543c8a5286f900051147c5beb685b5767c5a3dcc0eb8bd52a7e2b0342c67291 | Python | 1,466 | 55 | #TODO SEP 08 version
import os.path
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from skimage import io
from data.image_folder import merge_datasets
import random
import numpy as np
import re
def numericalSort(value):
numbers = re.compile(r'(\d+)')
parts ... |
d4f2411adcd619974b26289134ce6a7c50b12cec278d5308ce743751b89522b7 | Python | 1,467 | 30 | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform
from .config import resolve_data_config, resolve_model_data_config
from .model_config import resolve_input_data_config
from .constants import *
from .dataset import ImageDataset... |
0b326042dcbe1bb629bb3d9bd070ae840b48fe2c50c79375552827d618f3da19 | Python | 1,469 | 45 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
x = ['0','0.05', '0.1', '0.17']
def get_res(dataset):
rmse = []
r2 = []
for i in x:
path = f'../results/chemformer_snn/dropout{str(float(i))}/{dataset}/tables/'
df = pd.read_csv(path + 'avg_res.csv')
df = df[(df['... |
3546421377ee7ba96b659420a1ba39951d5e16cf193991e8b0c0c3fb0c88f08f | Python | 1,471 | 60 |
import keras
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
### PARAMETERS
# number of neurons in LSTM layers
lstm1 = 64
lstm2 = 0
lstm3 = 0
# number of neurons in FC layers
fc1 = 200
fc2 = 100
# time window ... |
d7f21346935fa56af852d77418a4a8c91207619f8b9d794c24ad847b74f9df88 | Python | 1,471 | 45 | import json
import os
import shutil
from pathlib import Path
from .jsonldutils import load_file
from .utils import fixing_old_schema
def migrate2newschema(path, inplace=False, fixed_path=None):
path = Path(path).resolve()
if path.is_file():
print(f"migration of file: {path}")
new_path = migra... |
b22e17002dfe23dfbe1d24dca58a550278961af1918e1e60cb5c952ef4fa94d2 | Python | 1,475 | 50 | import json
from .model import (
Activity,
Item,
Protocol,
Response,
ResponseActivity,
ResponseOption,
)
def identify_model_class(category):
# Normalize prefixed forms to full URIs
if category.startswith("reproschema:"):
category = category.replace(
"reproschema:",... |
d949e2fa124aab3a11a28ec1ff88616c65b59efb9d84605973a10f0556099e7f | Python | 1,475 | 50 | from .wrap_torch import (
base_triangle,
triangle_modified,
cyclic_lr_base,
cyclic_lr_exp_range,
exp,
step,
reduce_on_plateau,
cosineannealingwarmrestarts,
cosineannealingLR,
)
from .wrap_monai import warmupcosineschedule
# defining dict for schedulers - key is the string and the v... |
29705e4d536fbc8145b51649fe551709b4a2d8936290227bb4ef926c9f847157 | Python | 1,478 | 48 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
from pysteps.postprocessing.ensemblestats import excprob
from pysteps.tests.helpers import get_precipitation_fields
from pysteps.verification import probscores
precip = get_precipitation_fields(num_next_files... |
880545593954be9bdedbed8deff84d549f128bb29cb462de888e82c47940b840 | Python | 1,479 | 47 | import pyControl.hardware as _h
class LoadCell():
def __init__(self, port1, port2, rising_event_A = None, falling_event_A = None, rising_event_B = None, falling_event_B = None, debounce = 5):
self._cell_threshold_high = _h.Digital_input(port1, rising_event_A, falling_event_A, debounce)
self... |
fb85d978becb9d86e1b7c5f7537e4931d20cd9d0d3a161fe1e98a12ad2bed5bc | Python | 1,479 | 46 | import shlex
import subprocess # nosec B404
import pytest
from GANDLF.entrypoints.subcommands import cli_subcommands as gandlf_commands
old_way_entrypoints = [
# old-way entrypoints
"gandlf_anonymizer --help",
"gandlf_collectStats --help",
"gandlf_configGenerator --help",
"gandlf_constru... |
caf9329bb4194460e6b3623c1aa67eede5e2bcca53eb04ec9418fcf495d0098e | Python | 1,480 | 26 | from .base_options import BaseOptions
class TestOptions(BaseOptions):
"""This class includes test options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser) # define shared options
parser.add_argum... |
ad9842a79685183a9dc21bd844d3b2a4410c2b5491f5c2bfdf2ffaec81c21006 | Python | 1,481 | 55 | import torch
from rdkit.Chem import AllChem
import pandas as pd
from rdkit import Chem
import numpy as np
from rdkit import DataStructs
import torch.nn as nn
import torch
def to_fp_ECFP(smi):
mol = Chem.MolFromSmiles(smi)
return AllChem.GetHashedMorganFingerprint(mol,2,2048)
def toarray(fp):
array = np.z... |
499631e59c5ae5d2a36e3172a7e9e1a0feb8fc22fa3b404be38c447a1eef7b6c | Python | 1,483 | 54 | import torch
from torch import nn
class LayerScale(nn.Module):
""" LayerScale on tensors with channels in last-dim.
"""
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
device=None,
dtype=None,
) -> ... |
91a54fe722f628151c4ecffb5e5ec90236fd0a60fed89b9b623b69f9191b3d91 | Python | 1,486 | 69 | from __future__ import division
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, '../../helpers/')
from circ_stats import *
import glob
from pickle import dump
thr = 20 #45
files = glob.glob("../preprocessed_data/*txt")
files.sort()
M=[]
S=[]
E=[]
for si,fname in enumerate(files):
bkg = fname.split(".t... |
4a2a6c7e65ec465c6e79c5c5c6166ed06c1fb257047834067b48f2a8b26a0140 | Python | 1,487 | 47 | """
Count co-occurence of cluster classes by moving a sliding window
Author: Candace Liu
Date: 4/6/21
"""
import os
import numpy as np
from PIL import Image
cluster_overlay_dir = "overlays_pxClusterIDs"
points = list(range(1,13))
clusters = list(range(1,21))
step_size = 3
window_size = 6
# Create generator for all ... |
9658f16cb80680584de50ef7ddc0c796989a0bf655abb9890c3aa682fb71ccc1 | Python | 1,488 | 47 | import numpy as np
import pytest
from pysteps import io
from pysteps.tests.helpers import get_precipitation_fields
precip, metadata = get_precipitation_fields(
num_prev_files=1,
num_next_files=0,
return_raw=False,
metadata=True,
upscale=2000,
)
@pytest.mark.parametrize(
"precip, metadata",
... |
ba16faa024d5835c4d1d3cf0638b03da25d79aa45c314daf32d86a53aafa822f | Python | 1,489 | 42 | import numpy as np
# define HH model
class HH:
def __init__(self, v_init=-65.0, n_init=0.3177, m_init=0.0529, h_init=0.5961):
self.v = v_init
self.n = n_init
self.m = m_init
self.h = h_init
self.C = 1.0
self.g_Na = 120.0
self.g_K = 36.0
self.g_L = 0.3... |
e9048790ce995abf362375fb0a2b3e64d7264adc6a278501ba36639d02adf0c9 | Python | 1,491 | 38 | #!/usr/bin/env python
# coding: utf-8
import subprocess as sub
import os
import time
# The following code is to create multiple containers in docker and input multiple files containing multiple candidate
# molecules into different containers. Tandem mass spectrometry of the candidate molecules are predicted and compa... |
07a629703994ed6054ff8db72365f04500e739ec1e97ee3528f47b750b63a444 | Python | 1,493 | 41 | # -*- coding: utf-8 -*-
import pytest
from pysteps.visualization import plot_precip_field
from pysteps.utils import to_rainrate
from pysteps.tests.helpers import get_precipitation_fields
import matplotlib.pyplot as plt
plt_arg_names = ("source", "map_kwargs", "pass_geodata")
plt_arg_values = [
("mch", {"drawlon... |
bea3973287eade3e0da96b7825d29a2d0205b422327aa35b1ff9cbec196477c6 | Python | 1,496 | 36 | # -*- coding: utf-8 -*-
import torch
from torch.autograd import Function
from numpy import prod
class HookFunction(Function):
@staticmethod
def forward(ctx, input, labels, y, fixed_fb_weights, train_mode):
if train_mode in ["DFA", "sDFA", "DRTP"]:
ctx.save_for_backward(input, lab... |
8c14e44f9577c5b7cb369f2a3eb4ac2aae3cc505ddc7339d0dade8822b137f7f | Python | 1,497 | 33 | from src.models.tabular.widedeep.base import WDBaseModel
from pytorch_widedeep.models import TabResnet
class WDTabResnetModel(WDBaseModel):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def build_network(self):
self.model = TabResnet(
column_idx=self.hparams.column_idx... |
eb31f753dd3687683154c97c6af62a817d24aaebf281617f190aac64340ff637 | Python | 1,497 | 79 | # -*- coding: utf-8 -*-
from allensdk.brain_observatory.ecephys.ecephys_session import EcephysSession
from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache
from allensdk.brain_observatory.ecephys.stimulus_analysis.drifting_gratings import DriftingGratings
from allensdk.brain_observa... |
78b3b5173db3a85329e7aec5392414089e33677f8ce81b1998bbfe372d5584fc | Python | 1,498 | 52 | # -*- coding: utf-8 -*-
import os
import pytest
import pysteps
from pysteps.tests.helpers import smart_assert
pytest.importorskip("h5py")
root_path = pysteps.rcparams.data_sources["knmi"]["root_path"]
filename = os.path.join(root_path, "2010/08", "RAD_NL25_RAP_5min_201008260000.h5")
precip, _, metadata = pysteps.... |
31314b9e8b837441059143b9c0dff9d0965d6f698b51e4f062fb584d2b6d967e | Python | 1,500 | 63 | import numpy as np
import pytest
from pysteps import motion
from pysteps.nowcasts import utils as nowcast_utils
from pysteps.tests.helpers import get_precipitation_fields
main_loop_arg_names = (
"timesteps",
"ensemble",
"num_ensemble_members",
"velocity_perturbations",
)
# TODO: add tests for callbac... |
cb120ce88816486a4f5fbd7266f832384f333c00b6b1f498a100f3c736587614 | Python | 1,500 | 46 | """
Author: "Keitaro Yamashita, Garib N. Murshudov"
MRC Laboratory of Molecular Biology
This software is released under the
Mozilla Public License, version 2.0; see LICENSE.
"""
__version__ = '0.4.153'
__date__ = '2026-10-21'
import sys
import importlib
import types
# If the system-wide gemmi is already loaded ... |
a756d4c9fa2831d6208535eba1ccf745cbd73a299836248022747e3b200a6b51 | Python | 1,503 | 57 | import platform
from distutils.log import INFO
from os import chdir, getcwd
from setuptools import Command
class Document(Command):
description = 'Create documentation for library and apps'
user_options = []
# user_options = [
# ('chdir=', None, 'path where to generate the doc (will overwrite)')
... |
73f91a0407a0ba2bb191b4a1a56acbe938dd90276684f6ec3608ffdad21bbb08 | Python | 1,504 | 56 | from pathlib import Path
import weakref
import h5py
from utils.eventslicer import EventSlicer
class EventReaderAbstract:
def __init__(self, filepath: Path):
assert filepath.is_file()
assert filepath.name.endswith('.h5')
self.h5f = h5py.File(str(filepath), 'r')
self._finalizer = w... |
7a357b2a2f53c549cf8fc5ba4829934f7eb416456d0f4964cca825f0526ee95d | Python | 1,505 | 47 | """ Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* s... |
973e780aed5ccb4990f716c401d64d1aa7b8355169bc40cf4b6b1a1cd0e95b86 | Python | 1,509 | 43 | # 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... |
0f9629044fad3b807c0bc8faed338981cc225c4d1c54828320214a5c41144838 | Python | 1,510 | 38 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .layer import GradientLayer, BoundaryGradientLayer
class PINN(nn.Module):
def __init__(self, network, no_flux_bnd:bool):
super(PINN, self).__init__()
self.network = network
self.grads = GradientLayer(self.network)
... |
5e83f83da96e291511cf15229bb6c365d5bdeeadf96ecd2c4ec10f086ce7c085 | Python | 1,513 | 43 | import argparse
def boolean_string(s):
if s not in {'True', 'False'}:
raise ValueError("%s: Not a valid Boolean argument string" % s)
return s == 'True'
def get_args():
parser = argparse.ArgumentParser(description="Training Arguments")
parser.add_argument('--save_dir', default="checkpoints",... |
689e74561c40da72250f566e5a17db92929c776e43bd7eb8dfa7bc84c4b6a5b6 | Python | 1,519 | 44 | import inspect
from typing import Any
import scipy.io as sio
class MatlabIO(object):
__class_name = ''
def __init__(self) -> None:
pass
def fill_dict(self, a_dict: dict[str, Any]) -> None:
"""
Recursively fills a dictionary of class members that are non-special
:param a_... |
450c390fb39bc930843dd588dd33cafd79850c3e40cd94ba986f7289fdd3a815 | Python | 1,520 | 51 | import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
from joblib import Parallel, delayed, cpu_count
import os
import sys
# deletes all contents of dir and recreates it.
def create_dir(path):
if os.path.exists(path):
print('Path ', path, ' already exists. Please delete and restart your job.... |
9750972347f85d3c9984260807d7fae3fe398360d98e9959e3db6c2bbd20562f | Python | 1,523 | 42 | import prov.model as pm
from .AcquisitionObject import AcquisitionObject
from ..core import Constants
class MRObject(AcquisitionObject):
"""Class for NIDM-Experimenent MRAcquisitionObject-Level Objects.
Default constructor uses empty graph with namespaces added from NIDM/Scripts/Constants.py.
Additional ... |
d30904073345a7f7531ad3560075d29d98d5c845486b9d2f2a1569e796b2fea4 | Python | 1,524 | 42 | import prov.model as pm
from .AcquisitionObject import AcquisitionObject
from ..core import Constants
class PETObject(AcquisitionObject):
"""Class for NIDM-Experimenent MRAcquisitionObject-Level Objects.
Default constructor uses empty graph with namespaces added from NIDM/Scripts/Constants.py.
Additional... |
ed355d7d29f4e68742a4fbb4f1a5d488b6d3443a0314edd36a04e6d2fd67c573 | Python | 1,524 | 53 | from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.cm import viridis
linewidth = 4
fontsize = 15
font = {'family' : 'monospace',
'weight' : 'bold',
'size' : fontsize }
plt.rc('font', **font) # pass in the font dict as kwargs
#Change the range of scan rate... |
be4b6391e6d0d4f1dc66b99d5d96d8bc2fac488217457a1b3d6e2a68d88ed84e | Python | 1,526 | 60 | # -*- 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... |
38141e31d2b050f24a9037745e51e335ffb594f8d00ac2ab176ee84a4126e350 | Python | 1,527 | 32 | SEED_NO = 0
DEVICE = "cuda"
EPOCHS = 300
NUM_GRAPHS_PER_BATCH = 256
NUM_FEATURES=32
NUM_TARGET = 1
EDGE_DIM=11
PATIENCE = 10
N_SPLITS = 5
MAXPATH = 2
MINPATH = 1
FPSIZE_RDKFP = 1024
MORGAN_RAD = 2
MORGAN_BITS = 1024
# Parameters for random forest model
best_params_MD = {'n_estimators': 76, 'max_de... |
60ae5926b4ad563216451493b223efa99764dcb9f93096e7e13e6e47dd6599a6 | Python | 1,528 | 48 | import yaml
import os
class Config(object):
"""Configuration for the NetFrame applications."""
def __init__(self, version, config_yaml_path, debug=False):
super(Config, self).__init__()
self.version = str(version)
self.debug = debug
if '_' in self.version:
self.sub... |
b670ff91f2ac732775f8ea62b3a5601c452889972d8cbf97e605fdc5c3f071db | Python | 1,528 | 37 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
x_start = 40
y_start = 80
x_extent = 50
y_extent = -40
fig = plt.figure()
fig.set_size_inches(5.5, 2.5)
ax = fig.add_subplot(111)
ax.set_xlim([0., 100.])
ax.set_ylim([0., 100.])
ax.set_xticks(np.arange(0, 101, 25))
ax.set_yticks(... |
0362e1ca3ccb924e7c407fcb74a025dcfd785b66e57fd87bd099d5c0fcd673b3 | Python | 1,531 | 31 | """Shared helpers for the Hugging Face datasets / TFDS readers."""
import inspect
from .targets import get_field, has_field
def remote_code_kwargs(trust_remote_code: bool) -> dict:
"""Kwargs for datasets.load_dataset* that request loading-script execution, only when asked for.
datasets 4.0 removed loading s... |
08464f6ea8e38f7c3d7823e189f9ad814d498ec551e719d3d4709b1747152eaa | Python | 1,533 | 62 | """
All the losses are to be called from here
"""
from .segmentation import (
MCD_loss,
MCD_log_loss,
MCT_loss,
KullbackLeiblerDivergence,
FocalLoss,
MCC_loss,
MCC_log_loss,
)
from .regression import CE, CEL, MSE_loss, L1_loss
from .hybrid import DCCE, DCCE_Logits, DC_Focal
# global defines... |
0d28a74e15e384560af05cb17337dc1a54f02d102640d184d6dd4ebac464e3c8 | Python | 1,535 | 54 | import glob
import json
from flask import Flask, request
from flask_cors import CORS
from flask_restful import Api, Resource
from nidm.experiment.tools.rest import RestParser
def getTTLFiles():
files = []
for filename in glob.glob("/opt/project/ttl/**/*.ttl", recursive=True):
files.append(filename)
... |
24daaf6dd297d85444bc0fa172df604d5f4795f13ccd87125f1fd51a36a4e0fa | Python | 1,536 | 57 | from matplotlib import pyplot as plt
import numpy as np
from matplotlib import cm
linewidth = 4
fontsize = 15
font = {'family' : 'monospace',
'weight' : 'bold',
'size' : fontsize }
plt.rc('font', **font) # pass in the font dict as kwargs
colors = cm.Set2(np.linspace(0,1,8))
X_cor = []
Y_cor = ... |
f4b0212a1aa5cd4899d00778129663ed6ad290a60c678a188cf7620e39efb31a | Python | 1,536 | 70 | import os
import pytest
import skimage
import skimage.data
import skimage.feature
import skimage.io
import cellprofiler_core.image
import cellprofiler_core.measurement
import cellprofiler.modules.matchtemplate
import cellprofiler_core.object
import cellprofiler_core.pipeline
import cellprofiler_core.workspace
import ... |
65b3934385f973b1de84c9d95e9aa3d84af829afb173cb2f71c5b8c3d12a4225 | Python | 1,537 | 39 | from enum import Enum
class RescaleMethod(str, Enum):
STRETCH = "Stretch each image to use the full intensity range"
MANUAL_INPUT_RANGE = "Choose specific values to be reset to the full intensity range"
MANUAL_IO_RANGE = "Choose specific values to be reset to a custom range"
DIVIDE_BY_IMAGE_MINIMUM = ... |
de563a2f562b3e875daad32ff1321561a89feda99e50f5d8b96ea4cbbb7befa4 | Python | 1,539 | 52 | import json
from ._setting import Setting
class DataTypes(Setting):
"""The DataTypes setting assigns data types to measurement names
Imported or extracted metadata might be textual or numeric and
that interpretation should be up to the user. This setting lets
the user pick the data type for their me... |
a8b05fce051d75e4fa7830749ac6edd5ab49f56a3517f1ed7aa718ef5c6322a9 | Python | 1,546 | 46 | import re
import logging
from enum import Enum
from deeprank.models.forcefield.patch import PatchActionType, PatchSelection, PatchAction
_log = logging.getLogger(__name__)
class PatchParser:
STRING_VAR_PATTERN = re.compile(r"([A-Z]+)=([A-Z0-9]+)")
NUMBER_VAR_PATTERN = re.compile(r"([A-Z]+)=(\-?[0-9]+\.[0-9... |
0f5166213226f4cfc0dc4e2c78caa80767f923252651fdfb361831750d222ba6 | Python | 1,548 | 67 | import tkinter as tk
class MyDialog:
choice = False
def __init__(self, parent):
top = self.top = tk.Toplevel(parent)
# Set position
x = parent.winfo_x()
y = parent.winfo_y()
w = parent.winfo_width()
h = parent.winfo_height()
#print(x,y,w,h)
top.geometry("+%d+%d" % (x+w/2-200, y+h/2-200))
... |
a1f9e9a699d2a3a3612e576926ed5eb06cb5e74a3cd808b4d1d10de27d7c78f6 | Python | 1,548 | 38 | from src.models.tabular.pytorch_tabular.base import PTBaseModel
from omegaconf import DictConfig
from src.models.tabular.pytorch_tabular.repository.models.tabnet.tabnet_model import TabNetModel
class PTTabNetModel(PTBaseModel):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def build_netwo... |
64aa7ddb4c4c53ed22115cf15773984f4fbdded3737f979057348f61f7e027cd | Python | 1,550 | 47 | from deeprank.models.residue import Residue
from deeprank.models.pssm import Pssm
from deeprank.config.chemicals import AA_codes_pssm_ordered, AA_codes_1to3, AA_codes_3to1
def parse_old_pssm(file_):
pssm = Pssm()
for line in file_:
data = line.split()
chain_id, residue_number, residue_name = d... |
061d046ff3a33d2a2fd89da540e7ad90b78cc95e352d05a2095794560fc1fc31 | Python | 1,551 | 49 | from .trie import Trie
class Gazetteer:
def __init__(self, lower):
self.trie = Trie()
self.ent2type = {} ## word list to type
self.ent2id = {"<UNK>":0} ## word list to id
self.lower = lower
self.space = " "
def enumerateMatchList(self, word_list):
if self.low... |
063149e33da52520a9cb0856347616e86dfa51219f11992d40a24fc087aab93f | Python | 1,552 | 45 | import numpy as np
def smallest_angle(x, y):
"""Least difference between two angles.
Args:
x (numpy array): An array of shape (number of vertices,1) containing
the empirical polar angles
y (numpy array): An array of shape (number of vertices,1) containing
the predicted polar angle... |
d0960c8112ab122cc595689b453a0852c23c68637bcca09e892c3ac588fbac84 | Python | 1,552 | 41 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LogisticRegression(nn.Module):
def __init__(self, vocab_size, output_dim):
super().__init__()
self.linear = nn.Linear(vocab_size, output_dim)
def forward(self, x):
return self.linear(x)
class BasicDNN(nn.Module):
... |
4673ebbe811a67f4615fcbcec0dbab6e938e867a1d9e8a0787a5478156b11f48 | Python | 1,553 | 49 | """
Show final array configurations for multiple runs for side-dy-side comparison.
"""
import sys
from pathlib import Path
import numpy as np
import sys
from mayavi import mlab
from megsimutils.viz import _plot_anatomy
from megsimutils import read_opt_res
R_ENCL = 0.3 # m, radius of an invisible sphere to plot to f... |
8fd4abd91fad828c0c45df1d180aa0b7feeb128a39108581a2e8096cd4f65ded | Python | 1,554 | 51 | import numpy as np
from scipy.signal import savgol_filter
from fun_abfs import load_data, load_protocol
from fun_signals import restrict, butter_filter, get_arg, get_dt
class EPD:
def __init__(self, t=None, data=None, stim=None, path=None, file=None, neuron=None, **kwargs):
if len(t) != d... |
0ce9b69bd243bc9c1ea8b63f54ea527cb85b9475ddbe4142de39294cb1db6f1c | Python | 1,555 | 36 | from src.models.tabular.widedeep.base import WDBaseModel
from pytorch_widedeep.models import TabNet
class WDTabNetModel(WDBaseModel):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def build_network(self):
self.model = TabNet(
column_idx=self.hparams.column_idx,
... |
297e3f11481de2a3ad97aff9f2f4be2c4702c142ff8360ae2c1aef4e8f0dab34 | Python | 1,555 | 62 | from __future__ import division
from numpy import *
import sys
from scipy.stats import zscore
sys.path.insert(0, '../../helpers/')
from pickle import dump,load
from pickle import load,dump
from scipy.stats import *
import warnings
warnings.filterwarnings("ignore")
W1=0.05
W2=0.3
f=open("../../Data/neural_data_conca... |
20677eb0b8c2aabc244107576ff0a7f5ea81bcf9bfe7558c5e840dcc12226656 | Python | 1,556 | 50 | from ._dependency import Dependency
class MeasurementDependency(Dependency):
"""A dependency on a measurement"""
def __init__(
self,
source_module,
destination_module,
object_name,
feature,
source_setting=None,
destination_setting=None,
):
"... |
18b921e14ab7985720aa4ff975126408fe43cdb7d3836d2a1e6b4a36eaeb54f1 | Python | 1,565 | 56 | """
MedialAxis
==========
**MedialAxis** computes the medial axis or topological skeleton of a binary image. Rather than by sequentially
removing pixels as in **MorphologicalSkeleton**, the medial axis is computed based on the
distance transform of the thresholded image (i.e., the distance each foreground pixel is
f... |
cc15561e996b1fbc9e2c1499fea0f5e7a40fd04fd8ab15d4f9f5d98a594e0ad5 | Python | 1,565 | 58 | import numpy as np
import matplotlib.pyplot as plt
# Define parameters
dt = 0.1 # time step
t = np.arange(0, 100, dt) # time vector
# Define parameters
Cm = 100 # membrane capacitance
vr = -60 # resting membrane potential
vt = -40 # threshold potential
k = 0.7 # sensitivity of the membrane potential
vpeak = 35 ... |
f40ac2e2efec0c423040c47470bb2a7358daa5355103a2fc10d593b253ea45ae | Python | 1,568 | 54 | # Default logging settings for DeepRank.
DEFAULT_LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'brief': {
'format': '%(message)s',
},
'precise': {
'format': '%(levelname)s: %(message)s',
},
},
'filters': {
... |
3f7f87c339f202dd41d7373b22b494103cc6391e4e1f3586baab18868f8d3e79 | Python | 1,570 | 48 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 15 14:36:39 2019
@author: Xiaoxuan Jia
"""
#------------# time to first spike
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import math
def find_nearest(array, value, side="right"):
idx = np.searchsorted(array, value, side=side)
... |
eba422201453c883440ef1cd491bb0e2b10afe4904e5cf1a7b6f4f1d10f8fe7a | Python | 1,570 | 42 | from setuptools import setup, find_packages
try: # for pip >= 10
from pip._internal.req import parse_requirements
except ImportError: # for pip <= 9.0.3
from pip.req import parse_requirements
# get requirements from requirements.txt
install_reqs = parse_requirements('requirements.txt', session=False)
try:
... |
89ac07e857236c1062f923c193dabb9d5fc4cae20dd3a54dc4fc0ee2180f499d | Python | 1,571 | 55 | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.rcParams.update({'font.size': 13})
path_head = '../results/MLP_delta/'
datasets = [ 'lipo/', 'freesolv/', 'delaney/']
top_1 = []
top_1_sd = []
half = []
half_sd = []
for i in datasets:
path_top1 = path_head + 'top 1/' + i + 'tables/'
df1... |
b013245c8ad6228e3af5f071c5c552710a0f3fd1b6c9bbb8f8388b6623a681dd | Python | 1,571 | 59 | """
Closing
=======
**Closing** is the erosion of the dilation of an image. It’s used to
remove pepper noise (small dark spots) and connect small bright cracks.
See `this tutorial <https://scikit-image.org/docs/dev/auto_examples/applications/plot_morphology.html#closing>`__ for more information.
|
============ ====... |
3e7f51621c65f09f4ad1337c46492885ef8ab14757a607cf69b224b81403cc27 | Python | 1,573 | 44 | import torch
from LAC import LAC
from torchtext import data
from torchtext import vocab
from collections import namedtuple
class LacTokenizer(object):
def __init__(self):
self.lac = LAC(mode="seg")
self.lac.load_customization('static/data/addwords.txt', sep="\t")
def __call__(self, text):
... |
944c57bf0d031252d29274184fa54ab6509c868dc56885357ec7fccb6e9bd26f | Python | 1,574 | 57 |
import keras
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
### PARAMETERS
# number of neurons in FC layers
fc1 = 200
fc2 = 0
### END PARAMETERS
for fi in range(5):
x_test = np.loadtxt('../data/5fold_sp... |
899fd325732b72cbced9e1c11effa3b3a61ea14a181d286def8dc9b2ef692f60 | Python | 1,575 | 41 | import re
from deeprank.models.forcefield.residue import ResidueClassCriterium, ALL_AMINO_ACIDS
class ResidueClassParser:
_RESIDUE_CLASS_PATTERN = re.compile(r"([A-Z]{3,4}) *\: *name *\= *(all|[A-Z]{3})")
_RESIDUE_ATOMS_PATTERN = re.compile(r"(present|absent)\(([A-Z0-9\, ]+)\)")
@staticmethod... |
eb1796e3a88db176a748aa43b6102efd746cb175e09594804f99d9c5a465dfcd | Python | 1,576 | 48 | import os.path as osp
import pytest
from ptsa.data.readers.index import JsonIndexReader
@pytest.fixture
def reader():
here = osp.abspath(osp.dirname(__file__))
path = osp.join(here, 'data', 'r1.json')
return JsonIndexReader(path)
class TestJsonIndexReader:
def test_aggregate_values(self, reader):
... |
2fb48f6b310ead760dd31898cf52f329f8620377a48fd31a52a27aaf57a51d35 | Python | 1,580 | 48 | import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
"""Rearrange spatial dimensions into channel dimension.
Divides spatial dimensions by block_size and multiplies channels by block_size^2.
Used in TResNet as an efficient stem operation.
Args:
block_size: Spatial reduction fact... |
3be7f1fdbdf3e7e2ab7b3a60751f515b854fe715d9c5185e0d64837e22475621 | Python | 1,580 | 49 | import json
import os.path
import random
import tornado.httpserver
import tornado.ioloop
import tornado.options
import tornado.web
import tornado.gen
import tornado.httpclient
class IndexHandler(tornado.web.RequestHandler):
def initialize(self, models):
self.models = models
self.model = None
... |
9e212fbca44a0d6c567268c3fa8bd5f390bcb379798b7e96dce77b7823e14d45 | Python | 1,580 | 45 | """Unit tests for HelperFunctions methods that don't need a full Run()-configured RveInfo."""
import numpy as np
import pandas as pd
from dragen.utilities.Helpers import HelperFunctions
from dragen.utilities.InputInfo import RveInfo
def test_upsampling_rsa_conserves_volume_per_label():
rsa = np.array([
[... |
d70aa7185123fb9e121c989c1ae0795b198f9c2f516187627f5da5f1ce591fb2 | Python | 1,580 | 40 | import glob
import cv2 as cv
import numpy as np
from models.epu import EPUNet
from models.subnetworks import Subnet
test = [cv.imread(img) for img in glob.glob("../data/banapple/test/*.jpg")]
# pfm_order is useful to keep track of the order of the pfms and their respective subnetworks
# it used later in the inferenc... |
27c0b1a739a32d84602e50b78288fbe16b98ceee38c7b4e3dcfe893977ea5240 | Python | 1,581 | 46 | import os
import glob
import time
import argparse
# Kerasa / TensorFlow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '5'
from keras.models import load_model
from layers import BilinearUpSampling2D
from loss import depth_loss_function
from utils import predict, load_images, display_images, evaluate
from matplotlib import pyplo... |
5987a7c5634acec079bb9e3ef79606ed76c84f503265ba0de1fc193aca8fca35 | Python | 1,584 | 54 | # -*- coding: utf-8 -*-
"""
pysteps.motion.constant
=======================
Implementation of a constant advection field estimation by maximizing the
correlation between two images.
.. autosummary::
:toctree: ../generated/
constant
"""
import numpy as np
import scipy.optimize as op
from scipy.ndimage import... |
bdbf9ba8c453a96bedf85bf2f96f5c4102e3b8e6e68982d435426356613089c3 | Python | 1,585 | 43 | from typing import Optional
import numpy as np
import SimpleITK as sitk
from torchio.transforms import Resample
from torchio.typing import TypeTripletFloat
class Resample_Minimum(Resample):
"""
This performs resampling of an image to the minimum spacing specified by a single number. Otherwise, it will perfor... |
d8219ea538ac8a58b36d595df6e797df92059664fb2b78204b75d93d96cb7363 | Python | 1,586 | 53 | from deeprank.features.FullPSSM import FullPSSM
from deeprank.features.FullPSSM import __compute_feature__ as func
########################################################################
#
# Definition of the class
#
########################################################################
class PSSM_IC(FullPSSM):... |
6f3d9cd0c0dfc39f42a231011d1f36860f1bb3044db6c9114b78881dbe7f2ed0 | Python | 1,587 | 58 | """
Opening
=======
**Opening** is the dilation of the erosion of an image. It’s used to
remove salt noise (small bright spots) and connect small dark cracks.
See `this tutorial <https://scikit-image.org/docs/dev/auto_examples/applications/plot_morphology.html#opening>`__ for more information.
|
============ ======... |
32b95863d31ec89b8c5c57d65b2d7fd64baad222a92bc2e346bba86d8d818fd1 | Python | 1,590 | 53 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
import numpy as np
import scipy.spatial.distance as sd
from sklearn.metrics.pairwise import euclidean_distances
# from itertools import izip
try:
# Python 2
from itertools import izip
except ImportError:
# Python 3
izip = zip
'''Distanc... |
968336e0334cff8741491163e22d6d9890cc4782f7ac0974b3549b4e4ffa6e55 | Python | 1,590 | 53 | class Dependency:
"""This class documents the dependency of one module on another
A module is dependent on another if the dependent module requires
data from the producer module. That data can be objects (label matrices),
a derived image or measurements.
"""
def __init__(
self,
... |
388f3f900f5339b152f342c19564db3a2bea65a3196f1895456adaf873543c24 | Python | 1,593 | 60 | #!/usr/bin/python3
import numpy as np
from os.path import join as pjoin
from os import linesep
from shutil import copyfile
from scipy.io import mmwrite
from scipy.sparse import coo_matrix
import gzip
diri='data/raw'
diro='data/coex'
key='celltype'
values=['dysfunctional']
#Load covariate info
dc=np.loadtxt(pjoin(di... |
94b3b5a1412570230db31cffb751b1df61831c3fc29b648a2778e3ce2f831bad | Python | 1,595 | 40 | from __future__ import division
from unittest import TestCase
import numpy as np
from numpy.testing import assert_almost_equal
from .feature_extraction import extract_features, extract_features_fixed
class FeatureExtractionTest(TestCase):
def test_extract_features(self):
segmentation = np.array([[0, 0,... |
cf2251900dd9b6fb935136c9c54316362845e5dc4f83eba1b18aad3fb2f7220c | Python | 1,595 | 51 |
"""
MIT License
Copyright (c) 2022 Pawan Goyal
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, ... |
3a41e95323b3c9d74c8ad3ab8facf854fc7e71ca5050b15ceeec21dfed3b18ee | Python | 1,597 | 64 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
readme = open('README.rst').read()
history = open('HISTORY.rst').read().replace('.. :changelog:', '')
requirements = [
'wheel>=0.23.0',
'Cython>... |
4cda614c85380b77be86eeb6b03a510d6fe1ebfcdaf60ffb4e01231206917251 | Python | 1,598 | 49 | from pathlib import Path
import os
from typing import Optional
import numpy as np
from panoptica import Panoptica_Evaluator
def generate_instance_segmentation(
prediction: np.ndarray,
target: np.ndarray,
parameters: dict,
panoptica_config_path: Optional[str] = None,
) -> dict:
"""
Evaluate a... |
998477a18b33f48dc67152acad8eee89a7c0bf2ba77c2c77ae58193e4f8cf73a | Python | 1,599 | 58 | import torch
from .segmentation import MCD_loss, FocalLoss
from .regression import CCE_Generic, CE, CE_Logits
def DCCE(prediction: torch.Tensor, target: torch.Tensor, params: dict) -> torch.Tensor:
"""
Calculates the Dice-Cross-Entropy loss.
Args:
prediction (torch.Tensor): The predicted mask.
... |
3fe9b6a29f66df7aa46cbc440364746e0051d7d2b14eed34d83bde9cd8d569b2 | Python | 1,600 | 51 | """Set up blueprint for ORCID authentication."""
from flask_dance.consumer import OAuth2ConsumerBlueprint, oauth_authorized
from flask_dance.consumer.storage.sqla import SQLAlchemyStorage
from flask_login import current_user, login_user
from sqlalchemy.orm.exc import NoResultFound
from afidsvalidator.model import OAu... |
f894813a0295b2a0ae40049e2fea2a7012adadded7c39b4ab95b9c75160e4cd4 | Python | 1,602 | 61 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 14 13:56:09 2022
@author: claudia
Test file for behavioural clustering and clasification
"""
# Load module for behavioural decomposition
from behavioral_decomposition import *
# creat class
cl_b=BehaviouralDecomposition()
## Training classifie... |
99be7a9c966484694c269a9b4e32e0c1b3beaa255c33cc190b2e5d9243dfa729 | Python | 1,604 | 51 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
import guideseq
import os
if os.path.isfile("README.MD"):
with open("README.MD", "r") as fh:
long_description = fh.read()
else:
long_description="guide-seq"
setup(
name='guide_seq',
version=str(guideseq.__version__),
de... |
fc6f1d9ef85c825263c8dc56de007694f141f2c005366f7981f25cfac59c48d4 | Python | 1,605 | 46 | import yaml
import json
import numbers
import numpy as np
# Copied from ray.tune.logger:
# https://github.com/ray-project/ray/blob/04bfba12742841bc0112d645ce85c537903fdf99/python/ray/tune/logger.py#L722
# and ray.tune.utils.util
# https://github.com/ray-project/ray/blob/04bfba12742841bc0112d645ce85c537903fdf99/python/... |
6ac2b089525aa51bd6b891197a643047b6aee7805f36e9264f3fb128365775f3 | Python | 1,608 | 50 | import torch
import torch.nn as nn
from models.modules import ConvModule
def simple_conv_layer(
in_channels, out_channels,
kernel_size, stride):
return ConvModule(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride... |
462d6755ba00f501d59ec046c632c9ed1d549cb3a7c19e624b9e3325d10076a6 | Python | 1,611 | 53 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
data = np.load("Trainingangle.npz")
npz = data
a = np.random.default_rng().uniform(low=0.01, high=0.1, size=1)[0]
b = np.random.default_rng().uniform(low=0.01, high=0.1, size=1)
print(data["DataX"].shape)
print(data["DataY"].shape)
print(data["Data... |
35d3f21dba1eb4c68ebee29272a5fbdb574ee4de40f5517b339de238430d1abf | Python | 1,612 | 58 | import pandas as pd
import numpy as np
import scipy
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
class ConvolutionNN(Module):
"""
Simple CNN layer
"""
def __inti__(... |
6c08e35b1bd3ff67a30b7f9a43dd450cce44bf867a26aa7b8a78649f039878a0 | Python | 1,614 | 49 | import tensorflow as tf
from .layer import GradientLayer,BoundaryGradientLayer
class PINN:
def __init__(self, network):
self.network = network
self.grads = GradientLayer(self.network)
self.boundaryGrad = BoundaryGradientLayer(self.network)
def build(self,no_flux_bnd):
TYR... |
8b6090f47d0ff924d3c2959022e2e496060a53ab02d02d712f542c27a7e7475d | Python | 1,614 | 44 | """If the predictions and labels inputs to the MLCube should come from different
mount points and can't be referred to in a single csv, a custom entrypoint is
needed to create a temporary csv file before calling GaNDLF's generate metrics command.
This script should expect the same arguments passed to the command `mlcub... |
f9629029e251bfb1b33b0d5c7b055b358751fac110b22caadf5f5e94579d4b52 | Python | 1,614 | 60 | import numpy as np
def get_arg(t, dt, t0=0, func='round'):
"""
Given t float, list or np.array returns argument of corresponding t values using dt
"""
if isinstance(t, float) or isinstance(t, int) or isinstance(t, np.int64) or isinstance(t, np.int32):
float_or_int = True
elif isinstance(t,... |
5f8d588a255cafbb44eb48cb311301607511a812aa0395886907fd8cdef3f10c | Python | 1,616 | 61 | #!/usr/bin/env python
import time
import sys
from rgbmatrix import RGBMatrix, RGBMatrixOptions
from PIL import Image
if len(sys.argv) < 2:
sys.exit("Require a gif argument")
else:
image_file = sys.argv[1]
gif = Image.open(image_file)
try:
num_frames = gif.n_frames
except Exception:
sys.exit("provid... |
1b7ec3379466f236b8795f3aa3ece87bb893c22d363a4a2ba14198af393949a3 | Python | 1,617 | 52 | from enum import Enum as BaseEnum
from mrHARDI.base.ListValuedDict import Mergeable
from mrHARDI.base.fsl import serialize_fsl_args
_topup_pass_key_trans = {
"warpres": "warp_resolution",
"subsamp": "subsampling",
"fwhm": "blur_fwhm",
"miter": "n_iter",
"estmov": "estimate_motion",
"minmet": "... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.