sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1d2d553a02bc953e5e7a88fb40eb1892cb363618f124baba7dfc7725262b830b | Python | 381 | 13 | import pandas as pd
import pcntoolkit
path=r""
x = pd.read_pickle("/project_cephfs/3022051.01/untested_models/Cerebellum/HBR_10MDTB-CerebellumFunc-linear_7K_2sites/Models/meta_data.md")
y = x.to_numpy(dtype="int")
print(x)
# import matplotlib.pyplot as plt
# for i in range(10):
# plt.clf()
# t= x.iloc[:, i]
# ... |
32898b4fae5197539611de8162239d9d49963ceeda76a616f14613dbd770fe76 | Python | 381 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
)
msp.architecture["sparse_spec"] = dict(type="S... |
40e1f7e96c3ae04f0c0ab650d92e79aa35c47d8102cf15a10c9c1390a37488bc | Python | 381 | 19 | # Generated by Django 2.2.6 on 2020-09-08 14:11
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0079_upload_batch_size'),
]
operations = [
migrations.DeleteModel(
name='MushroomPic',
),
migrations.De... |
496699e232ffc258433f20fa43722039f4ade4ceda1feabb4f67ac4ac3da8e48 | Python | 381 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
)
msp.architecture["sparse_spec"] = dict(type="S... |
9152c71bb3ef7a5c050ca31c06488b54367122f515a6e8f9aba6258d3648c849 | Python | 381 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
)
msp.architecture["sparse_spec"] = dict(type="S... |
0a6c42aa353f430a3b3fff10bd704deb8fa1a1364b8f22eeb0ed4d7b4a3e6f74 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-217a3_1",
original_model_step=1679715,
... |
1f22cfa3206ae4ead12504afb60fc2e8c2744c0c9fd5f321e31f349441d08625 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163a1_1",
original_model_step=1804935,
... |
2297a61bd177a40125934b65b4d78684c1992f025b389367c4968bb8fa860db6 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223a1_1",
original_model_step=3514635,
... |
24b6642ddbe912309135216d2aa3209b57d3efc136ec27fbb535e581a4418fb6 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223d1_3",
original_model_step=1902435,
... |
2d5b590ea0ebc8a24f2ef02c55432a7c82320e51cd684cd1e28743a203b36aa0 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-221a2_1",
original_model_step=2943975,
... |
3944be21abce9836d29596551c8d1e38edadb7cadc20de43474eadcdec54c669 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163a1_2",
original_model_step=2212875,
... |
40fc018eead534023759c82aede4ad5e8de4f6f87f0ea9a798bd78aef5022e80 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163b1_5",
original_model_step=2448315,
... |
4423603eca153dbc511bbac849f0929365d5b938a5c6259d981a3ea43cb91748 | Python | 382 | 12 | #!/usr/bin/env python
"""
# Author: Yahui Long
# File Name: __init__.py
# Description:
"""
__author__ = "Yahui Long"
__email__ = "long_yahui@immunol.a-star.edu.sg"
from .utils import clustering
from .preprocess import preprocess_adj, preprocess, construct_interaction, construct_interaction_KNN, add_contrastive_label,... |
4fdeee1bcb4e62b57aba89df722c818d34f4ce363658da262fa5dde73b6f937b | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223b1_1",
original_model_step=1627215,
... |
6b7944ba97256736bf20ce86142a1a8ead2373899a8d857eefb2b6d6dd39fd63 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223b1_2",
original_model_step=1894935,
... |
6e06fc1f9d1c3415105f42b61580598f14f60d5927573af6bc08d937f74ddb5b | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163a1_3",
original_model_step=1309275,
... |
7272e94d33fde8b77f6d9ba8ea853ce28a71490422e6ffe755377501ca5106f8 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223d1_2",
original_model_step=2250375,
... |
9e7f026bc26d4010d16c385196dc0dd4e40827d63991a5f7307bf57036d13e55 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163b1_4",
original_model_step=2618535,
... |
abc1d0ea89c0470b73a48f15d51112d893f621928d5eabbd7b6a6db9e46d8812 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223c1_3",
original_model_step=1857435,
... |
af3e960f4d9204a1d84e184a1430715ea48f45bdaf9607b9d8a1c35458f15a35 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223c1_2",
original_model_step=1702215,
... |
cacec8906f1f6a0974c5439567267592a74f2835705647a55aedf999ed6c6da6 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-219a1_1",
original_model_step=1479495,
... |
d8eea65e3deb2b0d7c98dfc23ff51d37c8f317d7041594c85c028b4c39e7e097 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223b1_3",
original_model_step=1924935,
... |
dd980fff49d4b29d4e4ed72374b5fd8ce1a19f7f90667dc4ec9cd17572baeb12 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223c1_1",
original_model_step=2145375,
... |
f0914825328ca070098fb71afc114f1e6e1457a42588f4755142ee0cbc850180 | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-219a1_3",
original_model_step=1376775,
... |
f2199ed3b00fcc3148ffa8d150b1e153d847d461cf837739466f9bc771b91b5f | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-219a1_2",
original_model_step=1429275,
... |
f85006948f73b79db6da53f3cd9b9afea0186560b630ef5f7680658b952692c4 | Python | 382 | 7 | __version__ = '2.0.0'
# The functions of the module STalign.STalign are also those of the package, so that
# `import STalign; STalign.LDDMM(...)` works with an installed STalign as it does when the
# tutorials import STalign.py from the source tree (their "OPTION B"), and as
# `from STalign import STalign; STalign.LDD... |
ff28e92e649551e50c58239101bacdef46b32f6181444728f0f6a22cec71441c | Python | 382 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-223d1_1",
original_model_step=2072655,
... |
1ec5542d80b6ed83a930a823602506ede761e0550cfad66d12a2bd54ac750657 | Python | 383 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-203aa1_3",
original_model_step=2315595,
... |
212ca332bfe60d4812501fd4b2938efcb8de252b8154fd97d13316898eea334c | Python | 383 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163ta1_3",
original_model_step=1967655,
... |
26a8af8a7c34ee61daaf8e029650ae2f5115969eeed16ec9f4fe0700fe27777a | Python | 383 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-203aa1_1",
original_model_step=2265375,
... |
2c116ac3ace8519299dd14282236ca901be0a81c01393219e910a572efe43993 | Python | 383 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163ta1_2",
original_model_step=1990155,
... |
5764754101febbbdd4a0c7c1f7bc68656b8deaf852529c68ba79d2ecfa26a193 | Python | 383 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-203aa1_2",
original_model_step=2020155,
... |
5ff0f63ce5aa00fac9f957e726c5df9fd1221ade3191c061236d0f7eda033c27 | Python | 383 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/msp-163ta1_1",
original_model_step=2072655,
... |
5059e0afc2abc633cef25155461028fce9bcba9dad2685fe8a376534de58122b | Python | 384 | 15 | import sys
from ij import IJ
# Fiji Jython interpreter implements Python 2.5 which does not
# provide support for argparse.
title = sys.argv[-6]
width = int(sys.argv[-5])
height = int(sys.argv[-4])
depth = int(sys.argv[-3])
type = sys.argv[-2].replace("_", " ")
tmp_image_path = sys.argv[-1]
imp = IJ.newImage(title, ... |
aee8a4fecac4eb32b1fd36d14a2cf55269e07448126079af07ee9e1c93f0581e | Python | 384 | 8 | from src.utils import download_axons
try:
# Test with a dummy experiment id just to see if it initializes
# Don't actually call the download if possible, but the current implementation tries to download.
# So I'll just print if it exists to verify import and function call setup.
print("Function imported... |
e8811e0cce3ee4f3a2c8b6575698749201206ac0c1fc600c1d9cc893eaf50ba1 | Python | 384 | 13 | import torch
from lightly.transforms import RandomFrequencyMaskTransform, RFFT2DTransform
def test() -> None:
rfm_transform = RandomFrequencyMaskTransform()
rfft2d_transform = RFFT2DTransform()
image = torch.randn(3, 64, 64)
fft_image = rfft2d_transform(image)
transformed_image = rfm_transform(ff... |
09dea7647cd22353df028cbad92f78380f58a669180a066d54e352092b7f8496 | Python | 385 | 12 | """The lightly.cli module provides a console interface
for training self-supervised models, embedding,
and filtering datasets
"""
# Copyright (c) 2020. Lightly AG and its affiliates.
# All Rights Reserved
from lightly.cli.crop_cli import crop_cli
from lightly.cli.embed_cli import embed_cli
from lightly.cli.lightly_cl... |
0ae6ea0192e567c6bd66b05877096d2a362660723056110debd4ae99649ad992 | Python | 385 | 15 | #!/usr/bin/env python
from django.core.management import execute_manager
try:
import settings # Assumed to be in the same directory.
except ImportError:
import sys
sys.stderr.write(
"Error: Can't find the file 'settings.py' in the directory "
"containing {!r}.".format(__file__))
sys.e... |
9bced49e8018246bb09d12e769776b5505718044933e9ae63decfa26b202de4a | Python | 385 | 10 | class settings:
def __init__(self):
'''Fixed parameter settings used for simulations'''
# Relative and absolute tolerances to solve the system with, [rtol,
# atol]
self.solver_tolerances = [1e-5, 1e-7]
# The value that the membrane potential is clamped too before the
... |
d83b2a49d6656c3eaae0136dfe75794bc4e7b34407c8d270cf8066ea886cfd50 | Python | 385 | 24 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.window = 40
msp.batch_size = 150
msp.architecture = dict(
type="SplicePointIdentifier",
cl=40,
asymmetric_cl=(2, 20),
hidden_size=100,
n_layers=4,
)
msp.data_dir = "../data/gtex_dataset/"
msp.ex... |
0d1b4690690ebb4e29a1b90a488b59902c84711cb7b14439ed20d1a0e09e298a | Python | 386 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj2_5",
original_model_step=2811255... |
97a21d2905ae8969f6a36619ac5593f01bf4e3fe920f9f578e091b3456232df9 | Python | 386 | 15 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.batch_size = msp.batch_size * 2 // 3
msp.seed = int(argv[1])
msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3
msp.architecture = dict(
type="DiscretizeMotifModel",
original_model_path="model/rbnsp-80-adj2_4",
original_model_step=2005155... |
5f1cb1c439ab3816a520258a59fecc8cd3cbc9c44abfe00aca151c9fdc0daa85 | Python | 387 | 14 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._font import Font
from ._pad import Pad
from ._subtitle import Subtitle
from . import subtitle
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [".sub... |
c191a56a943c1fa6e552344fa2033636ffa9331eb9bf4a504a0ab1854622d161 | Python | 387 | 13 | from pl_bolts.models.gans.basic.basic_gan_module import GAN
from pl_bolts.models.gans.dcgan.dcgan_module import DCGAN
from pl_bolts.models.gans.pix2pix.pix2pix_module import Pix2Pix
from pl_bolts.models.gans.srgan.srgan_module import SRGAN
from pl_bolts.models.gans.srgan.srresnet_module import SRResNet
__all__ = [
... |
0432908d27278f6aa57488749184eb9c2e6230fe2c5ff821b04b9d489f232445 | Python | 388 | 10 | """
General and generalized linear modeling of spatial transcriptomics
Option to call functions from CCI_effects_modeling (e.g. st.tl.CCI_effects_modeling.Niche_Model) or directly from Spateo (e.g.
st.tl.Niche_Model).
"""
from .MuSIC import MuSIC
from .MuSIC_downstream import MuSIC_Interpreter
from .MuSIC_upstream imp... |
32d438cf6e11fef9adc821cc053510e435f535f4860d97d4306bf2c9e2d2fca3 | Python | 388 | 18 | # Generated by Django 2.2.2 on 2019-09-11 09:24
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0050_auto_20190822_1341'),
]
operations = [
migrations.AddField(
model_name='upload',
name='queue',... |
9ed1c269e6cb47bc1df97ccbd5208c200f4d163452022d34b29e295b45d98705 | Python | 388 | 24 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.window = 40
msp.batch_size = 150
msp.architecture = dict(
type="SplicePointIdentifier",
cl=40,
asymmetric_cl=(20, 2),
hidden_size=100,
n_layers=4,
)
msp.data_dir = "../data/gtex_dataset/"
msp.ex... |
b106cc50d234cb84935509fa5752e35dfd63d660cc4ab7258186cefd85ee3c42 | Python | 388 | 19 | # -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2017-05-17 11:44
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0005_upload_allx'),
]
operations = [
migrations.RemoveField(
... |
36f0222f45a922fcc0bf02e40b2f4898d5057c4242fd8546f3f2068c056554dd | Python | 389 | 15 | """Create a quorum queue using Kombu."""
from kombu import Connection, Exchange, Queue
my_quorum_queue = Queue(
"my-quorum-queue",
Exchange("default"),
routing_key="default",
queue_arguments={"x-queue-type": "quorum"},
)
with Connection("amqp://guest@localhost//") as conn:
channel = conn.channel(... |
7210b2b68bdedb49795bd46e162c6190c1013b3706f847696ada8256670439f1 | Python | 389 | 5 | from angorapy.analysis.investigators.base_investigator import Investigator
from angorapy.analysis.investigators.predictability import Predictability
# from angorapy.analysis.investigators.dynamics import Dynamics, LatentDynamics
from angorapy.analysis.investigators.correlation import Correlation
from angorapy.analysis.... |
5a354d34ebf4e13bc960d8e51681f1cab6df36f296cd167d2437a58ffb3b83de | Python | 390 | 13 | """Tests for specparam.results.components."""
from specparam.results.components import *
###################################################################################################
###################################################################################################
## ModelComponents object
d... |
8026ade4df9bd20ada1bc44beda529f3a2f006762869a83c52421e58c03adc44 | Python | 390 | 19 | import numpy as np
import pytest
import scanpy as sc
from anndata import AnnData
@pytest.fixture(scope="module")
def rng() -> np.random.Generator:
return np.random.default_rng(42)
@pytest.fixture
def filepath_h5mu(tmp_path) -> str:
return str(tmp_path / "test.h5mu")
@pytest.fixture(scope="module")
def pbm... |
eaceb9fe624c292d1defe9b0a31a383c01c190881e0fb0eacffd6c6b35f8ce1e | Python | 390 | 16 | """
Tests that everything is installed correctly.
"""
import unittest
class TestCCI(unittest.TestCase):
"""Tests for `stlearn` importability, i.e. correct installation."""
def test_SME(self):
import stlearn.spatials.SME.normalize as sme_normalise
def test_cci(self):
"""Tests CCI can be ... |
a06b5a6df90d3bca05327d7ca8f0175284ace69fb7d9d2470d14813796a42193 | Python | 391 | 9 | from MoleculeACE.models.mpnn import MPNN
from MoleculeACE.models.gcn import GCN
from MoleculeACE.models.attentivefp import AFP
from MoleculeACE.models.gat import GAT
from MoleculeACE.models.cnn import CNN
from MoleculeACE.models.mlp import MLP
from MoleculeACE.models.ml import RF, GBM, SVM, KNN
from MoleculeACE.models.... |
c8a68666d07360a00364ccdb4aad78fc0518a53e10018f6745109fef70c386a7 | Python | 391 | 15 | """Worker consumer."""
from .agent import Agent
from .connection import Connection
from .consumer import Consumer
from .control import Control
from .events import Events
from .gossip import Gossip
from .heart import Heart
from .mingle import Mingle
from .tasks import Tasks
__all__ = (
'Consumer', 'Agent', 'Connect... |
22e63fe8286f6b64a625de0ac2b7604cf5d93378b80f9621e49dc01de8d74a9a | Python | 392 | 23 | import h5py
from utils_rbns import list2seq
path = 'binary_dataset/protein_RALYL/rbns_train.h5'
data = h5py.File(path, "r")
Y = data["Y"][:]
i_s = list(range(Y.shape[0]))
print(Y.shape[0])
exit(0)
for i in i_s:
if i < 100000:
continue
X = data["X"][i]
Y = data["Y"][i]
F = data["F"][i]
L... |
62cb21cd1b457be7a2d619f04b5c18eba8b6474436f2a51d3d7b3e1542a13c0f | Python | 392 | 15 | from modular_splicing.width_experiments.flank_robustness import (
models_to_analyze,
compute_all_flank_robustnesses,
genome_data,
random_data,
)
models = models_to_analyze()
results_genome = compute_all_flank_robustnesses(
models, genome_data, list(range(11, 1 + 21))
)
results_random = compute_all... |
d883ed93422cf0e0b5ae05960e00cbf69d3eb736eba5bdcf809be9816cc1d040 | Python | 393 | 15 | """
This module allow to analyse dataset
"""
from config_manager.config_manager import ConfigManager
from dataset_manager.training_dataset_manager import TrainingDatasetManager
class DatasetAnalyser:
"""
This class analyse a dataset
"""
def __init__(self, path_vocab):
config = ConfigManager(p... |
e9bd95754ce50d62d6d21784d5630f00f924af4ab11a4e8fae262f4e24ee8179 | Python | 393 | 12 | import numpy as np
import torch
from torch import Tensor
def tile(a: Tensor, dim: int, n_tile: int) -> Tensor:
init_dim = a.size(dim)
repeat_idx = [1] * a.dim()
repeat_idx[dim] = n_tile
a = a.repeat(*repeat_idx)
order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i ... |
ad2eeb6399d4f69442ffcdf33e0cab358073d6872a44723df30b2295388f6bcb | Python | 394 | 13 | from .base import GRUDBase
__all__ = ['GRUD', 'GRUDpp']
class GRUD(GRUDBase):
def __init__(self, in_size, hidden_size, lr, weight_decay):
super().__init__(in_size, hidden_size, lr, weight_decay, pp_mode=False)
class GRUDpp(GRUDBase):
def __init__(self, in_size, hidden_size, lr, weight_decay):
... |
bd06a1a877dac5da3220b0d73a36728a0f1a73137ca92e3e5efc2116e2486233 | Python | 394 | 18 | # Generated by Django 2.2.2 on 2019-08-19 14:16
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0048_upload_heart'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='storage_si... |
29c4720bccb8e0f25f0f53af29bb734d4729c280c8637963f3021bdc1442f7f1 | Python | 395 | 24 | """
Use CodeCarbon but without loading the AMD GPU.
pip install codecarbon
"""
import time
from codecarbon import track_emissions
@track_emissions(
measure_power_secs=5,
log_level="debug",
)
def train_model():
"""
This function will do nothing.
"""
print("10 seconds before ending script...")... |
3315bd44244f072e6f3cde0c4ea239c36a8d4ca9eebdcb1e83d2f956154124a4 | Python | 396 | 18 | # Generated by Django 3.0.4 on 2020-03-24 14:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0064_auto_20200324_1512'),
]
operations = [
migrations.AlterField(
model_name='mushroomspot',
name=... |
40095467ea97be29be14706081a78d5f5bd79ee026e3e0f6d9addb2ec312afcc | Python | 396 | 12 | import sys
def progressbar(it, prefix="", size=60, out=sys.stdout): # Python3.3+
count = len(it)
def show(j):
x = int(size*j/count)
print("{}[{}{}] {}/{}".format(prefix, u"#"*x, "."*(size-x), j, count),
end='\r', file=out, flush=True)
show(0)
for i, item in enumerate(it)... |
8e3ffdfa8e892d2b63e0178b77c82fb7823dffd43498b6e0139748394a7e908f | Python | 396 | 18 | # Generated by Django 2.1.5 on 2019-02-14 13:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0032_auto_20190214_1456'),
]
operations = [
migrations.AlterField(
model_name='upload',
name='inver... |
e2d8110a221d934c1bbf4807123c59f1f81228fdf131a51b863bb80b8dfc2dd2 | Python | 396 | 18 | import os
from sys import argv
from spliceai_torch import load_model
for tag in argv[1:]:
path = f"model/{tag}"
print(path)
step = max(int(x) for x in os.listdir(path + "/model"))
try:
load_model(path, step)
print("good!")
bad = False
except:
bad = True
if bad =... |
483aeb08c7ca746b6585f0e92f2bea02f53dfee48eb46344ae7e922356770d30 | Python | 397 | 19 | # -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2017-05-17 10:12
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0003_auto_20170517_1159'),
]
operations = [
migrations.RemoveField(
... |
5cb9fc9e5d91d7768145f92f1ac24fbd0e2b915aa691c0c77883f0961713596a | Python | 397 | 16 | # ruff: noqa: F401
"""
matplotlylib
============
This module converts matplotlib figure objects into JSON structures which can
be understood and visualized by Plotly.
Most of the functionality should be accessed through the parent directory's
'tools' module or 'plotly' package.
"""
from plotly.matplotlylib.rendere... |
7fab2fa20c3fa9b1fbb3254839a537fc1102ecee0f59d917a547cc5f836c7be9 | Python | 397 | 18 | # Generated by Django 2.2.6 on 2020-09-02 11:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0077_auto_20200528_1202'),
]
operations = [
migrations.AddField(
model_name='upload',
name='uncerta... |
865828c06609f1649570f14ae8baa5158b9d858699b9b86ba602536614433db3 | Python | 397 | 18 | # Generated by Django 2.1.5 on 2019-03-22 15:01
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0038_auto_20190322_1457'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='acti... |
baef40e8e18d0bd508fe329fd20e5f1a99e3dbbc0a349f8a3edf165839277381 | Python | 397 | 21 | """Merge alembic heads after pulling external_user_id length extension forward
Revision ID: 92fb564c7095
Revises: 987ce9839ecb, e93c5d0b47a9
Create Date: 2023-10-12 07:58:43.659168
"""
# revision identifiers, used by Alembic.
revision = "92fb564c7095"
down_revision = ("987ce9839ecb", "e93c5d0b47a9")
branch_labels = ... |
c0fd66128e159137ae9c7dc4c32c3c034ab214fa482ec3a7f7f3566e24bcf2e3 | Python | 397 | 18 | from abc import ABC, abstractmethod
class Mutation(ABC):
@abstractmethod
def perform(self, rna):
"""
Perform the mutation on the given rna.
"""
pass
@abstractmethod
def footprint(self):
"""
Returns the footprint of this mutation, i.e.,
the a... |
2111a039196766644d08d4990eec3246f56fe089391eaf561b5afc67aeb2e547 | Python | 398 | 10 | from .adds.add_image import image
from .adds.add_positions import positions
from .adds.parsing import parsing
from .adds.add_lr import lr
from .adds.annotation import annotation
from .adds.add_labels import labels
from .adds.add_deconvolution import add_deconvolution
from .adds.add_mask import add_mask
from .adds.add_m... |
f81c171dc5fd87e0c944e887027fd86e946c255c536f0722804aa7c3dcbccfba | Python | 398 | 13 | """Transforms for Segmentation models."""
from __future__ import absolute_import
from mxnet.gluon.data.vision import transforms
def test_transform(img, ctx):
transform_fn = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225])
])
img = tra... |
009fc5faf0d07ec0c957041d23854a4e38b3fc061fb61402b0af2b47fbc6dc30 | Python | 399 | 16 | import numpy as np
import matplotlib.pyplot as plt
from utils import tile_raster_images
def plot_gabors(gabors):
n, m = gabors.shape
side = int(np.round(np.sqrt(m)))
w = int(np.sqrt(n))
if w**2 < n:
h = w + 1
else:
h = w
img = tile_raster_images(gabors, (side, side), (w, h), (2... |
3e50c0c159e60dcea8d479633b4c3a6e83eb97709847871951a07001f804bac5 | Python | 399 | 18 | # Generated by Django 3.1.2 on 2020-11-03 15:06
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0081_auto_20201030_1809'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='stor... |
e5e819be649bdd1a169406e43499a1f0a2c71e84d74578fbb26bc38490693262 | Python | 399 | 12 | from tns.morphmath import sample
import numpy as np
from numpy.testing import assert_equal
def test_Distr():
params = {'soma': {'size': {'norm': {'mean': 9.024144162609812, 'std': 3.5462697985669935}}}}
np.random.seed(0)
soma_d = sample.Distr(params['soma']['size'])
val = soma_d.draw_positive()
np.... |
2dccbef8594e2106864c426db1fa8369d092ceefbbaa0dcf1cdd4d07297ce7c0 | Python | 400 | 17 | import json
from types import SimpleNamespace
class Config(SimpleNamespace):
def __init__(self, d):
super().__init__(**d)
@classmethod
def from_file(cls, filename):
with open(filename, 'r') as fh:
d = json.load(fh)
return cls(d)
def serialize(self, output_path):
... |
6322b29279dddd5d71254ea63540af7965284ec71f385cf4b0d6f259405a1906 | Python | 400 | 9 | """
This module provides data loaders and transformers for popular vision datasets.
"""
from .video_cls.dataset_classification import VideoClsDataset
from .video_cls.dataset_classification import build_dataloader, build_dataloader_test
from .video_cls.multigrid_helper import multiGridHelper, MultiGridBatchSampler
from... |
90524e94fab83832a533f8856e33637c0759adbfb32a273476f5d5754a68b456 | Python | 401 | 21 | path = "../../data/"
sessions = range(1, 5)
subjects = [3, 4, 6, 7, 8, 9, 10, 11]
folder_spots_noboth = "aligned_noboth"
threshold_distance = 6 # pixels
colors_session = {
1: "#48634a",
2: "#8fc594",
3: "#990099",
4: "#ff66ff",
"conserved": "black",
}
colours_types = {
"cold": "#70A0CA",
... |
cae298e1273c355083b26b91feba0debbb5ea37480ac22c50321a99e9311d279 | Python | 401 | 13 | import pytest
from PIL import Image
from lightly.transforms.solarize import RandomSolarization
class TestRandomSolarization:
@pytest.mark.parametrize("w", [32, 64, 128])
@pytest.mark.parametrize("h", [32, 64, 128])
def test_on_pil_image(self, w: int, h: int) -> None:
solarization = RandomSolariza... |
e2f26aa844ad3bf5d018ca76d18ba79d4f9b372024954fe85f38b51febd73548 | Python | 401 | 12 | from PyQt5 import QtWidgets
from PyQt5 import QtCore
from score_behavior.score_session_manager_control_ui import Ui_Form
class SessionManagerControlWidget(QtWidgets.QWidget):
def __init__(self, parent=None):
flags_ = QtCore.Qt.WindowFlags()
super(SessionManagerControlWidget, self).__init__(flags=... |
f3305b8e237f5f2b7c20bc3e5101c396129d3cf08bfb0f9cd26af7baf5668dbc | Python | 401 | 14 | import os
import sys
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if BASE_DIR not in sys.path:
sys.path.insert(0, BASE_DIR)
os.environ["DJANGO_SETTINGS_MODULE"] = "biomedisa.settings"
os.environ.setdefault("LC_ALL", "en_US.UTF-8")
os.environ.setdefault("LC_CTYPE", "en_US.UTF-8")
from dj... |
fe042bf182ea314b4afc830d82f268417efeb8e0d596f09599cfa84ec830ecc0 | Python | 401 | 15 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in July 2022
@author: Thomas Bonald <thomas.bonald@telecom-paris.fr>
"""
import numpy as np
from scipy import sparse
def get_laplacian(adjacency: sparse.csr_matrix) -> sparse.csr_matrix:
"""Return the Laplacian matrix of a graph."""
weights = adjacen... |
e378b016a4d781a76e659f2b2161db224e627c5bf1c7d040ae662913870f3285 | Python | 402 | 13 | # Train a model.
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
iris = load_iris()
X, y = iris.data, iris.target
X, y = X[:20], y[:20]
loss = list()
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = RandomFor... |
e7079b300f8cdb3843f2d345fd1514a3dae5963db8679f06ea28d0f0ed3354fe | Python | 402 | 15 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._decreasing import Decreasing
from ._font import Font
from ._increasing import Increasing
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
... |
fce98ec9e1e197928875cffc7e89544731decf43057013fc80c65c878dcba89d | Python | 402 | 6 | from ._basic_models import FCN, dsFCN, CNN, dsCNN, RNN, dsRNN, Hybrid, dsHybrid, TutorialCNN
from ._tf_binding_predictors import DeepBind, ResidualBind, Kopp21CNN
from ._regulatory_classifiers import DeepSEA, Basset, FactorizedBasset, DanQ, Satori
from ._cre_activity_predictors import Jores21CNN, DeepSTARR
from ._singl... |
7afd5ca534e3b964d65bfeb09c56ca5ae5c623c328e0b12c445f71dc35273f04 | Python | 403 | 10 | __all__ = ['register','centroid','displacement','dsmmc','_dsmm_fullpy','_dsmmc_bare']
from .reg import register, centroid, displacement
from ._dsmm_c_py import dsmmc
from ._dsmm_c import _dsmmc_bare
from ._dsmm_fullpy import _dsmm_fullpy
#from .basic import centroid as centroid
#from .basic import displacement as dis... |
4873953c28141e7efb69f575bc57c51e26b4cc2749f51885c82194b876bc8dbf | Python | 404 | 16 | #!/usr/bin/env python
from setuptools import setup, find_packages
setup(
name='project',
version='0.0.0',
description='Describe Your Cool Project',
author='',
author_email='',
# REPLACE WITH YOUR OWN GITHUB PROJECT LINK
url='https://github.com/PyTorchLightning/pytorch-lightning-conference-... |
bc6225c0dc1b5b0e9f430609cc924c18423eefe4f3d8b43f9f12d3b0e92892d0 | Python | 404 | 14 | """
BdPy machine learning package
This package is a part of BdPy
"""
from .learning import Classification, CrossValidation, ModelTraining, ModelTest
from .crossvalidation import make_cvindex, make_crossvalidationindex, make_cvindex_generator
from .crossvalidation import cvindex_groupwise
from .ensemble import *
from... |
53174bb70deae01fa2819b07c3ec04369abdd28f0aa7e25863594fc6f7e7f4b8 | Python | 405 | 13 | """
BdPy MRI package
This package is a part of BdPy
"""
from .load_epi import load_epi
from .load_mri import load_mri
from .roi import add_roimask, get_roiflag, add_roilabel, add_rois, merge_rois, add_hcp_rois, add_hcp_visual_cortex
from .fmriprep import create_bdata_fmriprep, FmriprepData
from .spm import create_bda... |
5a854774d05cabe4f6bf905e65adeb4d00f8e077b37feb64a2185e4b673f8eaf | Python | 405 | 14 | import glob
import sys
import os
sys.path.insert(0,os.path.abspath("../../../src/"))
import pyabf
if __name__=="__main__":
PATH=R"X:\Data\projects\2017-01-09 AT1-Cre mice\2017-01-09 global expression NTS\data"
for fname in sorted(glob.glob(PATH+"/*.abf")):
abf=pyabf.ABF(fname)
if not abf.commen... |
5e8fb48a8ab709159c78faaf1852b62813d01cf81a185aca2bfa6f9696f80aeb | Python | 405 | 16 | import logging
from tqdm.auto import tqdm
# REF: https://stackoverflow.com/a/38739634
class TqdmLoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET):
super().__init__(level)
def emit(self, record):
try:
msg = self.format(record)
tqdm.write(msg)
... |
da67f8f3f04de633acd2e2f28169fd6447b87eadf73d65809cfbc098c185c58b | Python | 405 | 18 | # Generated by Django 3.2.6 on 2023-11-30 00:23
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0103_upload_validation_data'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='... |
e45b2fcb50c9703fc522ece433d38b1b68ad3a146d470475c55aa4ee66fc534d | Python | 405 | 18 | # Generated by Django 3.2.6 on 2022-08-10 14:00
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0092_auto_20220617_1255'),
]
operations = [
migrations.AddField(
model_name='profile',
name='platfo... |
643f3b5a019682c226847886ab7123b106ab68e49655ee3887b3b7020baa8e85 | Python | 406 | 17 | """
test to see what can be imported in python 2.7
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0, PATH_SRC)
print("importing special pyabf2")
import py... |
955e0518e19a2c63efa5503a8ff521b0afb4007d356ad365c0bf1b299aadf385 | Python | 406 | 17 | """Main application window package.
Keep imports lazy to avoid circular imports when other modules import
`pyjibe.head.*` utilities (e.g. custom widgets) while `pyjibe.head.main` pulls
in the registry.
"""
from __future__ import annotations
__all__ = ["PyJibe"]
def __getattr__(name: str):
if name == "PyJibe":
... |
e343e291dbc9c24210de7acc2d782eba76666fc4cd28356e802852e0a225edb8 | Python | 406 | 13 | from pl_bolts.models.detection import components
from pl_bolts.models.detection.faster_rcnn import FasterRCNN
from pl_bolts.models.detection.retinanet import RetinaNet
from pl_bolts.models.detection.yolo.yolo_config import YOLOConfiguration
from pl_bolts.models.detection.yolo.yolo_module import YOLO
__all__ = [
"c... |
e4b135ff8aca02d2ea0450e576d952ea0ac3e312a09f287900950fccb1770964 | Python | 406 | 29 | from neuron import h, gui
import numpy as np
def test_lin():
s = h.Section()
s.insert("lin")
h.finitialize()
a = h.a_lin
b = h.b_lin
c = h.c_lin
d = h.d_lin
A = np.array([[a, b], [c, d]])
exact = np.linalg.solve(A, [0, 0])
x = s(0.5).lin.xx
y = s(0.5).lin.yy
asse... |
f8f6358f519c5b88e0c70ac1f04874b032afcf4d234e1ae86abbb5faa6a522af | Python | 406 | 18 | # Generated by Django 3.2.6 on 2024-05-02 06:49
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0106_alter_upload_header_file'),
]
operations = [
migrations.AlterField(
model_name='upload',
name=... |
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