repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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msvi | msvi-main/experiments/bballs/train.py | from types import SimpleNamespace
import torch
import torch.nn as nn
import torch.optim as optim
import wandb
from tqdm import tqdm
import msvi.utils.bballs as data_utils
import utils
torch.backends.cudnn.benchmark = True # type: ignore
# Read parameters.
argparser = data_utils.create_argparser()
param = Simpl... | 3,556 | 29.663793 | 107 | py |
msvi | msvi-main/msvi/model.py | from typing import Union
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from torch.distributions.bernoulli import Bernoulli
from torch.distributions.continuous_bernoulli import ContinuousBernoulli
from einops import reduce
from msvi.decoder impo... | 6,335 | 29.315789 | 127 | py |
msvi | msvi-main/msvi/dataset.py | from typing import Union
import torch
from torch.utils.data import Dataset
Tensor = torch.Tensor
class TrajectoryDataset(Dataset):
"""Stores trajectories and time grids.
Used to store trajectories `y` and the corresponding time grids `t`.
Each trajectory is assumed to have three dimensions:
(ti... | 1,444 | 31.840909 | 99 | py |
msvi | msvi-main/msvi/decoder.py | from abc import ABC, abstractmethod
import torch
import torch.nn as nn
Tensor = torch.Tensor
Module = nn.Module
Parameter = nn.parameter.Parameter
class IDecoder(Module, ABC):
@abstractmethod
def forward(self, x: Tensor) -> Tensor:
"""Maps latent state to parameters of p(y|x).
Args:
... | 4,429 | 34.15873 | 104 | py |
msvi | msvi-main/msvi/tf_encoder.py | import torch
import torch.nn as nn
Tensor = torch.Tensor
Module = nn.Module
class TFEncoder(nn.Module):
# Modified https://pytorch.org/docs/stable/_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer
def __init__(
self,
d_model: int,
self_attn: Module,
t: Tensor... | 1,465 | 27.745098 | 113 | py |
msvi | msvi-main/msvi/elbo.py | from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from msvi.model import IModel
from msvi.posterior import IVariationalPosterior, AmortizedMultipleShootingPosterior
from einops import repeat
Tensor = torch.Tensor
class IELBO(nn.Module, ABC):
@abstractmethod
def forward(
self,... | 6,622 | 31.465686 | 125 | py |
msvi | msvi-main/msvi/__init__.py | import msvi.decoder # noqa
import msvi.trans_func # noqa
import msvi.model # noqa
import msvi.posterior # noqa
import msvi.elbo # noqa
import msvi.rec_net # noqa
import msvi.utils # noqa
| 195 | 20.777778 | 30 | py |
msvi | msvi-main/msvi/attention.py | from abc import ABC, abstractmethod
from typing import Union
import numpy as np
import torch
import torch.nn as nn
Tensor = torch.Tensor
Module = nn.Module
class IAttention(Module, ABC):
@abstractmethod
def forward(
self,
x: Tensor,
return_weights: bool = True
) -> Union[tuple[... | 8,637 | 33.690763 | 117 | py |
msvi | msvi-main/msvi/trans_func.py | from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from torchdiffeq import odeint
from torchdiffeq import odeint_adjoint
from einops import rearrange
Tensor = torch.Tensor
Module = nn.Module
Parameter = nn.parameter.Parameter
class ITransitionFunction(Module, ABC):
@abstractmethod
def... | 6,931 | 34.917098 | 118 | py |
msvi | msvi-main/msvi/pos_enc.py | from typing import Union
import numpy as np
import torch
import torch.nn as nn
Tensor = torch.Tensor
Module = nn.Module
Sequential = nn.Sequential
class DiscreteSinCosPositionalEncoding(Module):
# Modified https://pytorch.org/tutorials/beginner/transformer_tutorial.html
def __init__(self, d_model: int, t: T... | 4,793 | 35.045113 | 133 | py |
msvi | msvi-main/msvi/posterior.py | from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from msvi.trans_func import ITransitionFunction
from msvi.rec_net import RecognitionNet
Tensor = torch.Tensor
Module = nn.Module
ParameterDict = nn.ParameterDict
class IVariationalPosterior(ABC, Module):
@property
@abstractmethod
d... | 8,083 | 33.4 | 147 | py |
msvi | msvi-main/msvi/rec_net.py | import torch
import torch.nn as nn
from einops import rearrange
Tensor = torch.Tensor
Module = nn.Module
class RecognitionNet(Module):
def __init__(
self,
phi_enc: Module,
phi_agg: Module,
phi_gamma: Module,
phi_tau: Module,
tau_min: float,
) -> None:
... | 3,937 | 31.01626 | 103 | py |
msvi | msvi-main/msvi/utils/utils.py | from types import SimpleNamespace
import torch
import torch.nn as nn
from einops import rearrange
from msvi.pos_enc import (
DiscreteSinCosPositionalEncoding,
ContinuousSinCosPositionalEncoding,
RelativePositionalEncodingInterp,
RelativePositionalEncodingNN
)
from msvi.attention import (
DotProd... | 6,830 | 31.221698 | 105 | py |
msvi | msvi-main/msvi/utils/pendulum.py | import os
import pickle
import argparse
from typing import Union
from types import SimpleNamespace
import torch
import torch.nn as nn
import torchvision.transforms
from torch.nn.parameter import Parameter
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn... | 15,776 | 39.557841 | 158 | py |
msvi | msvi-main/msvi/utils/bballs.py | import os
import pickle
import argparse
from typing import Union
from types import SimpleNamespace
import torch
import torch.nn as nn
import torchvision.transforms
from torch.nn.parameter import Parameter
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn... | 16,068 | 39.992347 | 176 | py |
msvi | msvi-main/msvi/utils/rmnist.py | import os
import pickle
import argparse
from typing import Union
from types import SimpleNamespace
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from msvi.model import Mode... | 15,338 | 40.013369 | 157 | py |
msvi | msvi-main/msvi/utils/__init__.py | from msvi.utils import utils # noqa
from msvi.utils import pendulum # noqa
from msvi.utils import rmnist # noqa
from msvi.utils import bballs # noqa | 152 | 37.25 | 39 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/setup.py | from setuptools import setup
setup(name='util',
version='0.1',
description='Common functions shared by all projects',
url='#',
author='LishinC',
author_email='NA',
license='NA',
packages=['util'],
zip_safe=False) | 212 | 20.3 | 54 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/backbone/Backbone.py | import os
import torch
import torch.nn as nn
from util.model.initialize_load_r21d import initialize_load_model
from util.loader.LUMC_A4C.loader_vid import create_dataloader
from util.checkpoint.checkpoint_train import checkpoint_train
from util.checkpoint.checkpoint_test import checkpoint_test
from torch.utils.tensorb... | 7,196 | 48.979167 | 143 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/backbone/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/checkpoint/checkpoint_test.py | import os
import numpy as np
import torch
from util.checkpoint.create_header import create_header_clas, create_header_seg, create_header_regres
from util.eval.eval import one_epoch_avg_clas, one_epoch_avg_seg, one_epoch_avg_regres
def checkpoint_test(one_epoch, model, save_folder, subfolder, task,
... | 1,894 | 50.216216 | 131 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/checkpoint/minLoss.py | import os
import numpy as np
def find_epo_test(save_folder, epo_iter, **kwargs):
csv_name = save_folder + '/train/log_'+str(epo_iter.stop-1)+'.csv'
multi_epo = np.genfromtxt(csv_name, dtype='str', delimiter=',')
multi_epo = multi_epo[1:,2].astype('float')
epo_test = np.argmin(multi_epo)
min_loss =... | 1,303 | 43.965517 | 122 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/checkpoint/create_header.py | import numpy as np
def create_header_clas(mode, log_val_only, header_train=None, header_eval=None):
if mode == 'train':
if header_train is None:
if log_val_only:
header_train = ['itr', 'Loss/train', 'Loss/val', 'Accuracy/val']
else:
header_train = ['... | 1,854 | 36.857143 | 110 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/checkpoint/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/checkpoint/checkpoint_train.py | import os
import numpy as np
import torch
from util.checkpoint.create_header import create_header_clas, create_header_seg, create_header_regres
from util.eval.eval import one_epoch_avg_clas, one_epoch_avg_seg, one_epoch_avg_regres
def checkpoint_train(itr, one_epoch_train, one_epoch_val, one_epoch_test, model, save_f... | 3,481 | 43.641026 | 164 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/eval/eval.py | import numpy as np
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
def performance_seg(pred, true):
overlap = pred & true # TP
union = pred | true # TP + FN + FP
misclassif... | 2,135 | 30.411765 | 90 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/eval/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/loader/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/loader/LUMC_A4C/loader_vid.py | import numpy as np
import torch
from torch.utils.data.dataset import Dataset
import random
from skimage.transform import rotate
class loader(Dataset):
def __init__(self, X_list, aug=False, rgb_channel=3, **kwargs):
self.X_list = X_list
self.aug = aug
self.rgb_channel = rgb_channel
def... | 1,640 | 36.295455 | 160 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/model/initialize_load_r21d.py | import torch
import torch.nn as nn
import torchvision
def initialize_load_model(mode, model_path='scratch', in_channel=3, out_channel=3, device="cuda", **kwargs):
def r21d(in_channel, out_channel, pretrain=False, echo_pretrain=False):
model = torchvision.models.video.__dict__["r2plus1d_18"](pretrained=pre... | 984 | 34.178571 | 132 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/util/util/model/__init__.py | 0 | 0 | 0 | py | |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/projectDDDIF/main.py | import os
import torch
import torch.nn as nn
# import numpy as np
from util.backbone.Backbone import Backbone
from util.loader.LUMC_A4C.loader_vid import create_dataloader
from util.model.initialize_load_r21d import initialize_load_model
from analyze import analyze
def forward(batch, model, device, return_one_batch, ... | 1,139 | 26.804878 | 98 | py |
Disease-Detection-and-Diagnostic-Image-Feature | Disease-Detection-and-Diagnostic-Image-Feature-main/projectDDDIF/analyze.py | import os
import torch
import torch.nn as nn
import torchvision
import matplotlib.pyplot as plt
from skimage.transform import resize
import numpy as np
import cv2
# import cmapy
from pydicom import dcmread
from pydicom.uid import ExplicitVRLittleEndian
from captum.attr import GradientShap, DeepLift, DeepLiftShap, Integ... | 5,747 | 39.478873 | 135 | py |
MetaHIN | MetaHIN-master/code/main.py | # coding: utf-8
# author: lu yf
# create date: 2019-11-21 17:27
import gc
import glob
import random
import time
import numpy as np
import torch
from HeteML_new import HML
from DataHelper import DataHelper
from tqdm import tqdm
from Config import states
# random.seed(13)
np.random.seed(13)
torch.manual_seed(13)
def tr... | 5,711 | 35.615385 | 130 | py |
MetaHIN | MetaHIN-master/code/MetaLearner_new.py | # coding: utf-8
# author: lu yf
# create date: 2019-12-10 14:25
import torch
from torch.nn import functional as F
class MetaLearner(torch.nn.Module):
def __init__(self,config):
super(MetaLearner, self).__init__()
self.embedding_dim = config['embedding_dim']
self.fc1_in_dim = 32 + config['i... | 4,108 | 35.6875 | 122 | py |
MetaHIN | MetaHIN-master/code/DataHelper.py | # coding: utf-8
# author: lu yf
# create date: 2019-11-24 13:16
import gc
import glob
import os
import pickle
# from DataProcessor import Movielens
from tqdm import tqdm
from multiprocessing import Process, Pool
from multiprocessing.pool import ThreadPool
import numpy as np
import torch
class DataHelper:
def __in... | 7,817 | 45.814371 | 133 | py |
MetaHIN | MetaHIN-master/code/test.py | # coding: utf-8
# author: lu yf
# create date: 2019-12-25 11:23
import math
import os
import pickle
import numpy as np
import multiprocessing as mp
# def dcg_at_k(scores):
# # assert scores
# return scores[0] + sum(sc / math.log(ind+1, 2) for sc, ind in zip(scores[1:], range(2, len(scores) + 1))) # ind+1!!!... | 1,696 | 24.328358 | 123 | py |
MetaHIN | MetaHIN-master/code/EmbeddingInitializer.py | # coding: utf-8
# author: lu yf
# create date: 2019-12-10 14:22
import torch
from torch.autograd import Variable
# Movielens dataset
class UserEmbeddingML(torch.nn.Module):
def __init__(self, config):
super(UserEmbeddingML, self).__init__()
self.num_gender = config['num_gender']
self.num_... | 6,165 | 32.51087 | 115 | py |
MetaHIN | MetaHIN-master/code/HeteML_new.py | # coding: utf-8
# author: lu yf
# create date: 2019-12-02 11:25
import numpy as np
import torch
from torch.nn import functional as F
from Evaluation import Evaluation
from MetaLearner_new import MetapathLearner, MetaLearner
class HML(torch.nn.Module):
def __init__(self, config, model_name):
super(HML, se... | 24,851 | 55.869565 | 162 | py |
MetaHIN | MetaHIN-master/code/Config.py | # coding: utf-8
# author: lu yf
# create date: 2019-11-20 19:46
config_db = {
'dataset': 'dbook',
# 'mp': ['ub'],
# 'mp': ['ub','ubab'],
'mp': ['ub','ubab','ubub'],
'use_cuda': True,
'file_num': 10, # each task contains 10 files
# user
'num_location': 453,
'num_fea_item': 2,
... | 2,953 | 21.210526 | 108 | py |
MetaHIN | MetaHIN-master/code/Evaluation.py | # coding: utf-8
# author: lu yf
# create date: 2019-11-27 13:14
import math
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error
class Evaluation:
def __init__(self):
self.k = 5
def prediction(self, real_score, pred_score):
MAE = mean_absolute_error(real_scor... | 1,426 | 28.122449 | 113 | py |
galIMF | galIMF-master/galimf.py | # A python3 code
# This is the main module operating the other two modules IGIMF and OSGIMF.
# The IGIMF model calculates an analytically integrated galaxy-wide IMF;
# The OSGIMF model samples all the star cluster mass and all the stellar mass in each star cluster
# and then combind the stars in all star clusters to gi... | 53,974 | 40.841085 | 253 | py |
galIMF | galIMF-master/stellar_luminosity.py | # The function here gives the stellar bolometric luminosity relative to the sun [L_sun],
# assuming a simplified form using only the main-sequence luminosity as a function of mass [M_sun].
# See Yan et al. 2019 for details.
# The stellar luminosity should also be a function of Y_for_helium and Z_for_metal, which shall ... | 617 | 35.352941 | 111 | py |
galIMF | galIMF-master/plot_stellar_yield_table.py | import time
import math
import matplotlib.pyplot as plt
import numpy as np
from scipy import interpolate
import element_abundances_solar
reference_name = 'Anders1989'
H_abundances_solar = element_abundances_solar.function_solar_element_abundances(reference_name, 'H')
# He_abundances_solar = element_abundances_solar.fu... | 38,525 | 41.243421 | 172 | py |
galIMF | galIMF-master/element_weight_table.py | # this function returns the element weight
def function_element_weight(element_name):
# element weight: https://www.lenntech.com/periodic/mass/atomic-mass.htm
if element_name == "H":
element_weight = 1.0079
elif element_name == "He":
element_weight = 4.0026
elif element_name == "C":
... | 986 | 29.84375 | 76 | py |
galIMF | galIMF-master/example_galaxy_wide_IMF.py | # Python3 code
# An example file that demonstrates how to construct galaxy-wide IMF
# as well as getting each stellar mass in the galaxy applying the IGIMF theory with the galIMF model.
# Made by: Yan Zhiqiang & Tereza Jerabkova
# The outputs of this example are:
# - the comparison plot of galaxy-wide IMF, canonic... | 11,352 | 47.725322 | 130 | py |
galIMF | galIMF-master/galevo.py | # A python3 code
# This is a single-zone closed-box galaxy chemical evolution module.
# It is coupled with a variable galaxy-wide IMF that depends on the galactic property at the time of star formation.
# The stellar population forms at every 10 Myr (the shortest time step) over 10 Gyr;
# with each stellar population a... | 303,264 | 51.134262 | 212 | py |
galIMF | galIMF-master/igimf_calculator.py | # Python3 code
# Made by: Yan Zhiqiang & Tereza Jerabkova
# An example file that construct galaxy-wide IMF according to the input parameters in file "input_parameters.txt"
# The outputs of this example are:
# - the comparison plot of galaxy-wide IMF, canonical IMF, and the histogram of stellar masses (optional);
# ... | 12,511 | 45.686567 | 148 | py |
galIMF | galIMF-master/example_galaxy_evolution.py | # Python3 code
# An example
import galevo
print("\n ================================\n"
" === example_galaxy_evolution ===\n"
" ================================\n")
print(" This test code serves as an example, "
"explaining (see comments in the code) the input parameters of the galaxy ... | 2,473 | 32.890411 | 123 | py |
galIMF | galIMF-master/galIMF_version_1.0.py | ######## galIMF ##########
# python3 code, last update Sat 27 May
# This is the main module, galIMF.py, controling and operating the other two modules IGIMF and OSGIMF
# --------------------------------------------------------------------------------------------------------------------------------
# importing modules... | 49,314 | 40.934524 | 208 | py |
galIMF | galIMF-master/read_yield_table.py | import time
import numpy as np
import math
import matplotlib.pyplot as plt
def function_read_file(yield_table_name):
####################
### read in file ###
####################
if yield_table_name == "portinari98":
file_yield = open(
'yield_tables/agb_and_massive_stars_portinar... | 33,360 | 44.450954 | 161 | py |
galIMF | galIMF-master/element_abundances_solar.py | # This function returns the customary astronomical scale for logarithmic abundances of the sun,
# that is, log(N_X/N_H)+12
# reference:
# Asplund, Martin; Grevesse, Nicolas; Sauval, A. Jacques; Scott, Pat (2009). ARAA 47 (1): 481–522.
# Anders, E., & Grevesse, N. 1989 is applied in WW95, Geochim. Cosmochim. Acta, 53, ... | 4,110 | 41.822917 | 127 | py |
galIMF | galIMF-master/SFT__galaxy_mass_26.py | import galevo
import math
import element_abundances_solar
import multiprocessing as mp
from time import time
def simulate(imf, Log_SFR, SFEN, STF):
Z_0 = 0.0000000142
solar_mass_component = "Asplund2009_mass"
Z_solar = element_abundances_solar.function_solar_element_abundances(solar_mass_component, 'Metal... | 5,476 | 38.688406 | 132 | py |
galIMF | galIMF-master/example_star_cluster_IMF.py | # Python3 code, last update Wed 20 Dec 2018
# Example file for sampling the stellar masses of every star in the star cluster.
# Made by: Yan Zhiqiang & Tereza Jerabkova
# The outputs of this example are:
# - a comparison plot of generated variable IMF and canonical IMF ('star_cluster_IMF_plot.pdf');
# - a .txt fi... | 10,907 | 43.161943 | 153 | py |
galIMF | galIMF-master/element_abundances_primordial.py | import element_weight_table, element_abundances_solar
H_weight = element_weight_table.function_element_weight("H")
primary_He_mass_fraction = 0.247
primary_H_mass_fraction_roughly = 1 - primary_He_mass_fraction # Corrected in below
primary_D_mass_fraction = primary_H_mass_fraction_roughly * 2.58 * 10**-5
primary_He3... | 4,340 | 69.016129 | 145 | py |
galIMF | galIMF-master/IMFs/diet_Salpeter_IMF.py | def custom_imf(mass, time): # there is no time dependence for Salpeter IMF
# Bell & de Jong (2001). Salpeter IMF x = 1.35 with a flat x = 0 slope below 0.35
# integrate this function's output xi result in the number of stars in mass limits.
if mass < 0.35:
xi = mass ** (-1)
elif mass < 150:
... | 413 | 40.4 | 87 | py |
galIMF | galIMF-master/IMFs/given_IMF.py | def custom_imf(mass, time):
change_time = 10*10**7
change_limit = 1
alpha_change = (change_time - time)/change_time
if alpha_change < 0 - change_limit:
alpha_change = 0 - change_limit
if alpha_change > change_limit:
alpha_change = change_limit
if mass < 0.08:
return 0
... | 428 | 24.235294 | 51 | py |
galIMF | galIMF-master/IMFs/Salpeter_IMF.py | from scipy.integrate import quad
def custom_imf_unnormalized(mass): # there is no time dependence for Salpeter IMF
if mass < 0.1:
return 0
elif mass < 100:
return mass ** (-2.35)
else:
return 0
def mass_function(mass):
return custom_imf_unnormalized(mass) * mass
integrated... | 584 | 20.666667 | 82 | py |
galIMF | galIMF-master/IMFs/Kroupa_IMF.py | from scipy.integrate import quad
alpha3 = 2.3
def custom_imf_unnormalized(mass): # there is no time dependence for Kroupa IMF
if mass < 0.08:
return 0
elif mass < 0.5:
return 2*mass**(-1.3)
elif mass < 1:
return mass**(-2.3)
elif mass < 150:
return mass**(-alpha3)
... | 825 | 21.944444 | 80 | py |
galIMF | galIMF-master/yield_tables/SNIa_yield.py | # This function returns the element mass ejected for a type Ia supernova event
def function_mass_ejected(yield_reference_name, element_name):
mass_ejected = 0
if yield_reference_name == 'Thielemann1993':
# Reference: Thielemann et al. (1993)
# Values adopted from
# Gibson, B. K., Loewe... | 4,190 | 42.65625 | 142 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/benchpress.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 16,345 | 33.340336 | 169 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/reinforcement_models.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 13,200 | 40.382445 | 130 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/hooks.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 5,385 | 31.642424 | 137 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/memory.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 2,825 | 29.387097 | 74 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/data_generator.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 5,794 | 36.62987 | 120 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/model.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 11,311 | 38.141869 | 115 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/interactions.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 2,333 | 29.710526 | 82 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/config.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 3,206 | 54.293103 | 103 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/agent.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 48,376 | 46.945491 | 184 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/visuals.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 595 | 41.571429 | 74 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/reinforcement_learning/env.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 11,257 | 40.389706 | 126 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/evaluate_cand_database.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 28,668 | 32.728235 | 229 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/feature_sampler.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 15,820 | 35.203661 | 155 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/instcount.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 2,631 | 36.070423 | 145 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/grewe.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 5,683 | 32.046512 | 135 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/hidden_state.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 4,677 | 33.397059 | 151 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/active_feed_database.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 18,002 | 37.883369 | 169 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/extractor.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 7,868 | 43.965714 | 198 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/normalizers.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 3,238 | 18.871166 | 74 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/features/autophase.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 2,570 | 36.26087 | 145 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/normalizer.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 4,229 | 36.105263 | 80 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/public.py | # coding=utf-8
# Copyright 2022 Chris Cummins and Foivos Tsimpourlas.
#
# 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 a... | 2,213 | 37.842105 | 191 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/clang.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 31,870 | 30.649454 | 158 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/cxx.py | # coding=utf-8
# Copyright 2022 Chris Cummins Foivos Tsimpourlas.
#
# 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 appli... | 5,691 | 27.603015 | 99 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/opencl.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 32,339 | 31.699697 | 156 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/common.py | # coding=utf-8
# Copyright 2022 Chris Cummins and Foivos Tsimpourlas.
#
# 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 a... | 2,876 | 25.88785 | 78 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/c.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 8,729 | 26.45283 | 99 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/preprocessors/preprocessors.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 7,058 | 33.602941 | 113 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/dashboard/dashboard.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 19,834 | 32.113523 | 139 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/dashboard/dashboard_db.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 4,165 | 32.869919 | 108 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/corpuses/corpuses.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 27,021 | 38.914328 | 181 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/corpuses/benchmarks.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 7,078 | 33.871921 | 172 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/corpuses/preprocessed.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 31,556 | 36.702509 | 179 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/corpuses/structs.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 8,554 | 33.918367 | 145 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/corpuses/tokenizers.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 35,032 | 35.379024 | 171 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/corpuses/encoded.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 25,602 | 36.376642 | 206 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/models/sequence_masking.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 35,295 | 41.019048 | 165 | py |
BenchPress | BenchPress-master/deeplearning/benchpress/models/lm_database.py | # coding=utf-8
# Copyright 2022 Foivos Tsimpourlas and Chris Cummins.
#
# 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 a... | 2,465 | 39.42623 | 104 | py |
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