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
value |
|---|---|---|---|---|---|---|
StyleFusion | StyleFusion-master/src/shared.py | import os, random, sys, datetime, time, socket, io, h5py, argparse, shutil, io
import queue
import numpy as np
import scipy
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import SmoothingFunction
import matplotlib.pyplot as plt
"""
AUTHOR:
Sean Xiang Gao (xiag@microsoft.com) at Mic... | 3,632 | 25.326087 | 78 | py |
StyleFusion | StyleFusion-master/data/arXiv/arxiv.py | """
AUTHOR:
Xiang Gao (xiag@microsoft.com) at Microsoft Research
"""
import re, os, subprocess
from nltk.tokenize import TweetTokenizer
EQN_token = '_eqn_'
CITE_token = '_cite_'
IX_token = '_ix_'
MAX_UTT_LEN = 30 # maximum length of utterance allowed. if longer, ignore
def norm_sentence(txt):
txt = txt.lower()
... | 5,797 | 22.860082 | 111 | py |
NBFNet | NBFNet-master/script/run.py | import os
import sys
import math
import pprint
import torch
from torchdrug import core
from torchdrug.utils import comm
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from nbfnet import dataset, layer, model, task, util
def train_and_validate(cfg, solver):
if cfg.train.num_epoch == 0:
retu... | 1,690 | 25.421875 | 64 | py |
NBFNet | NBFNet-master/script/visualize.py | import os
import sys
import pprint
import torch
from torchdrug import core
from torchdrug.utils import comm
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from nbfnet import dataset, layer, model, task, util
vocab_file = os.path.join(os.path.dirname(__file__), "../data/fb15k237_entity.txt")
vocab_file... | 3,106 | 34.306818 | 84 | py |
NBFNet | NBFNet-master/nbfnet/layer.py | import torch
from torch import nn
from torch.nn import functional as F
from torch_scatter import scatter_add, scatter_mean, scatter_max, scatter_min
from torchdrug import layers
from torchdrug.layers import functional
class GeneralizedRelationalConv(layers.MessagePassingBase):
eps = 1e-6
message2mul = {
... | 7,872 | 46.427711 | 119 | py |
NBFNet | NBFNet-master/nbfnet/task.py | import math
import torch
from torch.nn import functional as F
from torch.utils import data as torch_data
from ogb import linkproppred
from torchdrug import core, tasks, metrics
from torchdrug.layers import functional
from torchdrug.core import Registry as R
Evaluator = core.make_configurable(linkproppred.Evaluator... | 22,136 | 44.832298 | 119 | py |
NBFNet | NBFNet-master/nbfnet/model.py | from collections.abc import Sequence
import torch
from torch import nn
from torch import autograd
from torch_scatter import scatter_add
from torchdrug import core, layers, utils
from torchdrug.layers import functional
from torchdrug.core import Registry as R
from . import layer
@R.register("model.NBFNet")
class N... | 11,950 | 44.441065 | 118 | py |
NBFNet | NBFNet-master/nbfnet/dataset.py | import os
import csv
import glob
from tqdm import tqdm
from ogb import linkproppred
import torch
from torch.utils import data as torch_data
from torchdrug import data, datasets, utils
from torchdrug.core import Registry as R
class InductiveKnowledgeGraphDataset(data.KnowledgeGraphDataset):
def load_inductive_t... | 14,201 | 39.005634 | 105 | py |
NBFNet | NBFNet-master/nbfnet/util.py | import os
import time
import logging
import argparse
import yaml
import jinja2
from jinja2 import meta
import easydict
import torch
from torch.utils import data as torch_data
from torch import distributed as dist
from torchdrug import core, utils
from torchdrug.utils import comm
logger = logging.getLogger(__file__... | 3,838 | 30.467213 | 119 | py |
NBFNet | NBFNet-master/nbfnet/__init__.py | 0 | 0 | 0 | py | |
gca-rom | gca-rom-main/main.py | import sys
sys.path.append('../')
import torch
from gca_rom import network, pde, loader, plotting, preprocessing, training, initialization, testing, error
import numpy as np
if __name__ == "__main__":
problem_name, variable, mu1_range, mu2_range = pde.problem(int(sys.argv[1]))
print("PROBLEM: ", problem_name... | 4,087 | 45.454545 | 157 | py |
gca-rom | gca-rom-main/examples/advection.py | import os
# HYPER-PARAMETERS FOR POISSON
problem_name = "advection"
scalers_type = "sampling-feature"
scalers_fun = "standard"
skip_connection = "skip1"
pn = 2
st = 4
sf = 3
sk = 1
train_rate = 30
ffc_nodes = 200
latent_nodes = 100
btt_nodes = 15
lambda_map = 1e1
hidden_channels = 2
print("\n\nRUN MAIN FOR PROBLEM ... | 819 | 34.652174 | 156 | py |
gca-rom | gca-rom-main/examples/navier-stokes.py | import os
# HYPER-PARAMETERS FOR POISSON
problem_names = ["VX_navier_stokes", "VY_navier_stokes", "P_navier_stokes"]
comp = 2
problem_name = problem_names[comp]
scalers_type = "sampling-feature"
scalers_fun = "standard"
skip_connection = "skip1"
pn = 4 + comp
st = 4
sf = 3
sk = 1
train_rate = 10
ffc_nodes = 200
late... | 919 | 35.8 | 156 | py |
gca-rom | gca-rom-main/examples/graetz.py | import os
# HYPER-PARAMETERS FOR POISSON
problem_name = "graetz"
scalers_type = "sampling-feature"
scalers_fun = "standard"
skip_connection = "skip1"
pn = 3
st = 4
sf = 3
sk = 1
train_rate = 30
ffc_nodes = 200
latent_nodes = 50
btt_nodes = 25
lambda_map = 1e1
hidden_channels = 2
print("\n\nRUN MAIN FOR PROBLEM "+pr... | 815 | 34.478261 | 156 | py |
gca-rom | gca-rom-main/examples/poisson.py | import os
# HYPER-PARAMETERS FOR POISSON
problem_name = "poisson"
scalers_type = "sampling-feature"
scalers_fun = "standard"
skip_connection = "skip1"
pn = 1
st = 4
sf = 3
sk = 1
train_rate = 30
ffc_nodes = 200
latent_nodes = 50
btt_nodes = 15
lambda_map = 1e1
hidden_channels = 3
print("\n\nRUN MAIN FOR PROBLEM "+p... | 816 | 34.521739 | 156 | py |
gca-rom | gca-rom-main/gca_rom/error.py | import numpy as np
from gca_rom import scaling
def save_error(error, norm, AE_Params, vars):
"""
save_error(error: List[float], norm: List[float], AE_Params: object, vars: str)
This function takes in two lists of same length, error and norm, computed on the whole dataset for plotting reasons, and saves t... | 3,215 | 43.666667 | 209 | py |
gca-rom | gca-rom-main/gca_rom/preprocessing.py | import numpy as np
import torch
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
from gca_rom import scaling
def graphs_dataset(dataset, AE_Params):
"""
graphs_dataset: function to process and scale the input dataset for graph autoencoder model.
Inputs:
dataset: an ... | 3,515 | 38.954545 | 112 | py |
gca-rom | gca-rom-main/gca_rom/plotting.py | import numpy as np
import matplotlib.pyplot as plt
from gca_rom import scaling
import matplotlib.gridspec as gridspec
import matplotlib.cm as cm
from matplotlib import ticker
from matplotlib.ticker import MaxNLocator
from mpl_toolkits.axes_grid1 import make_axes_locatable
params = {'legend.fontsize': 'x-large',
... | 8,379 | 46.613636 | 175 | py |
gca-rom | gca-rom-main/gca_rom/network.py | import sys
import torch
from torch import nn
from gca_rom import gca, scaling, pde
problem_name, variable, mu1_range, mu2_range = pde.problem(int(sys.argv[1]))
class AE_Params():
"""Class that holds the hyperparameters for the autoencoder model.
Args:
sparse_method (str): The method to use for spar... | 5,946 | 39.732877 | 179 | py |
gca-rom | gca-rom-main/gca_rom/training.py | import torch
import torch.nn.functional as F
def train(model, optimizer, device, scheduler, params, train_loader, train_trajectories, AE_Params, history):
"""Trains the autoencoder model.
This function trains the autoencoder model using mean squared error (MSE) loss and a map loss, where the map loss
... | 5,063 | 48.647059 | 478 | py |
gca-rom | gca-rom-main/gca_rom/testing.py | import torch
from tqdm import tqdm
import numpy as np
def evaluate(VAR, model, loader, params, AE_Params, test):
"""
This function evaluates the performance of a trained Autoencoder (AE) model.
It encodes the input data using both the model's encoder and a mapping function,
and decodes the resulting l... | 2,071 | 44.043478 | 110 | py |
gca-rom | gca-rom-main/gca_rom/initialization.py | import os
import torch
import numpy as np
import random
import warnings
def set_device():
"""
Returns the device to be used (GPU or CPU)
Returns:
device (str): The device to be used ('cuda' if GPU is available, 'cpu' otherwise)
"""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
... | 1,224 | 21.685185 | 89 | py |
gca-rom | gca-rom-main/gca_rom/__init__.py | 0 | 0 | 0 | py | |
gca-rom | gca-rom-main/gca_rom/pde.py | import numpy as np
def problem(argument):
"""
problem(argument: int) -> Tuple[str, str, np.ndarray, np.ndarray]
This function takes in an integer argument and returns a tuple containing the problem name (str), variable name (str), mu1 (np.ndarray) and mu2 (np.ndarray) for the specified case.
The pos... | 1,771 | 32.433962 | 184 | py |
gca-rom | gca-rom-main/gca_rom/loader.py | import sys
from torch_geometric.data import Dataset
import torch
import scipy
class LoadDataset(Dataset):
"""
A custom dataset class which loads data from a .mat file using scipy.io.loadmat.
data_mat : scipy.io.loadmat
The loaded data in a scipy.io.loadmat object.
U : torch.Tensor
The... | 1,488 | 41.542857 | 172 | py |
gca-rom | gca-rom-main/gca_rom/gca.py | import torch
from torch import nn
import torch.nn.functional as F
from torch_geometric.nn import GMMConv
class Encoder(torch.nn.Module):
"""
Encoder Class
The Encoder class is a subclass of torch.nn.Module that implements a deep neural network for encoding graph data.
It uses the Gaussian Mixture co... | 5,918 | 36.226415 | 139 | py |
gca-rom | gca-rom-main/gca_rom/scaling.py | from sklearn import preprocessing
import torch
import sys
def scaler_functions(k):
match k:
case 1:
sc_name = "minmax"
sc_fun = preprocessing.MinMaxScaler()
case 2:
sc_name = "robust"
sc_fun = preprocessing.RobustScaler()
case 3:
... | 2,869 | 41.205882 | 163 | py |
gca-rom | gca-rom-main/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,162 | 33.333333 | 79 | py |
gca-rom | gca-rom-main/utils/test_all_scalar.py | import os
# HYPER-PARAMETERS LIST
problem_names_list = ["poisson", "advection", "graetz", "VX_navier_stokes", "VY_navier_stokes", "P_navier_stokes"]
scalers_type_list = ["sample", "feature", "feature-sampling", "sampling-feature"]
scalers_fun_list = ["minmax", "robust", "standard"]
skip_connection_list = ["skip0", "sk... | 2,774 | 52.365385 | 156 | py |
gca-rom | gca-rom-main/utils/h5_to_mat.py | """
.. _IMPORTING_dataset_form_FEniCS_solution:
IMPORTING dataset form FEniCS solution - .h5 to .mat
This script is used to import a FEniCS solution in .h5 format and convert it to a .mat file.
Dependencies
h5py
numpy
scipy
Functions
extract_edges(triangulation)
Given a triangulation, this function returns... | 2,107 | 27.876712 | 131 | py |
gca-rom | gca-rom-main/utils/save_all_scalar.py | import subprocess
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import warnings
import seaborn as sns
import os
warnings.filterwarnings("ignore")
# HYPER-PARAMETERS LIST
model_names_list = ["poisson", "advection", "graetz", "navier_stokes"]
problem_names_list =... | 13,880 | 51.579545 | 249 | py |
CRST | CRST-master/evaluate.py | import argparse
import scipy
from scipy import ndimage
import numpy as np
import sys
from packaging import version
import time
import util
import torch
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
from deeplab.model import Res_Deeplab
from deeplab.datasets... | 11,168 | 39.762774 | 176 | py |
CRST | CRST-master/util.py | """
utilities for convenience
"""
import contextlib
import h5py
import logging
import os.path as osp
import yaml
from io import StringIO
from PIL import Image
import numpy as np
cfg = {}
def as_list(obj):
"""A utility function that treat the argument as a list.
Parameters
----------
obj : object
... | 2,693 | 26.489796 | 86 | py |
CRST | CRST-master/crst_seg.py | import argparse
import sys
from packaging import version
import time
import util
import os
import os.path as osp
import timeit
from collections import OrderedDict
import scipy.io
import torch
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
import torch.backen... | 47,013 | 48.229319 | 225 | py |
CRST | CRST-master/train.py | import argparse
import torch
import torch.nn as nn
from torch.utils import data
import numpy as np
import pickle
import cv2
import torch.optim as optim
import scipy.misc
import torch.backends.cudnn as cudnn
import sys
import os
import os.path as osp
from deeplab.model import Res_Deeplab
from deeplab.loss import CrossE... | 10,806 | 39.324627 | 164 | py |
CRST | CRST-master/dataset/helpers/labels.py | #!/usr/bin/python
#
# Cityscapes labels
#
from collections import namedtuple
#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------
# a label and all meta information
Label = namedtuple( 'Label... | 10,597 | 57.230769 | 199 | py |
CRST | CRST-master/dataset/helpers/labels_synthia.py | #!/usr/bin/python
#
# Cityscapes labels
#
from collections import namedtuple
#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------
# a label and all meta information
Label = namedtuple( 'Label... | 9,012 | 51.707602 | 199 | py |
CRST | CRST-master/dataset/helpers/labels_cityscapes_synthia.py | #!/usr/bin/python
#
# Cityscapes labels
#
from collections import namedtuple
#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------
# a label and all meta information
Label = namedtuple( 'Label... | 10,597 | 57.230769 | 199 | py |
CRST | CRST-master/deeplab/loss.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
class CrossEntropy2d(nn.Module):
def __init__(self, size_average=True, ignore_label=255):
super(CrossEntropy2d, self).__init__()
self.size_average = size_average
self.ignore_label = ignor... | 1,585 | 44.314286 | 103 | py |
CRST | CRST-master/deeplab/model.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
affine_par = True
def outS(i):
i = int(i)
i = (i+1)/2
i = int(np.ceil((i+1)/2.0))
i = (i+1)/2
return i
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
r... | 11,127 | 36.217391 | 139 | py |
CRST | CRST-master/deeplab/datasets.py | import os
import os.path as osp
import numpy as np
import random
import matplotlib.pyplot as plt
import collections
import torch
import torchvision.transforms as transforms
import torchvision
import cv2
from torch.utils import data
import sys
from PIL import Image
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102... | 62,075 | 43.276748 | 309 | py |
CRST | CRST-master/deeplab/__init__.py | 1 | 0 | 0 | py | |
CRST | CRST-master/deeplab/metric.py | import os, sys
import numpy as np
from multiprocessing import Pool
import copy_reg
import types
def _pickle_method(m):
if m.im_self is None:
return getattr, (m.im_class, m.im_func.func_name)
else:
return getattr, (m.im_self, m.im_func.func_name)
copy_reg.pickle(types.MethodType, _pickle_metho... | 2,856 | 27.858586 | 112 | py |
quaterny_opvs | quaterny_opvs-master/emulators/emulator_ptb7_th.py | #!/usr/bin/env python
#==========================================================================
from abstract_emulator import AbstractEmulator
#==========================================================================
class Emulator_PTB7_TH(AbstractEmulator):
PATH = 'details_ptb7_th'
HYPERPARAMS = {
... | 2,370 | 27.914634 | 90 | py |
quaterny_opvs | quaterny_opvs-master/emulators/model_probabilistic.py | #!/usr/bin/env python
import os
import pickle
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tf_bijs = tfp.bijectors
tf_dist = tfp.distributions
tf_mean_field = tfp.layers.default_mean_field_normal_fn
#==============================================================
class... | 11,713 | 35.154321 | 185 | py |
quaterny_opvs | quaterny_opvs-master/emulators/abstract_emulator.py | #!/usr/bin/env python
import os, sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
sys.path.append('Emulators')
import json
import pickle
import numpy as np
import tensorflow as tf
#=====================================================================
class AbstractEmulator(object):
def __init__(self, num_folds = 5):... | 8,804 | 34.22 | 143 | py |
quaterny_opvs | quaterny_opvs-master/emulators/emulator_pbq_qf.py | #!/usr/bin/env python
#===============================================================================
from abstract_emulator import AbstractEmulator
#===============================================================================
class Emulator_PBQ_QF(AbstractEmulator):
PATH = 'details_pbq_qf'
HYPERPARAM... | 2,408 | 28.378049 | 90 | py |
quaterny_opvs | quaterny_opvs-master/emulators/details_ptb7_th/parse_grid.py | #!/usr/bin/env python
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
file_name = 'photobleaching_mixture01_grid.csv'
features = []
targets = []
with open(file_name, 'r') as content:
for line in content:
linecontent = line.strip().strip('\xef').strip('\xbb').strip('\... | 1,125 | 23.478261 | 97 | py |
quaterny_opvs | quaterny_opvs-master/emulators/details_ptb7_th/generate_indices.py | #!/usr/bin/env python
import numpy as np
import pickle
#=========================================================
NUM_SPECTRA = 1040
FOLD_SIZE = 170
np.random.seed(120415)
#=========================================================
indices = np.arange(NUM_SPECTRA)
np.random.shuffle(indices)
test_indices = i... | 1,082 | 25.414634 | 119 | py |
quaterny_opvs | quaterny_opvs-master/emulators/details_pbq_qf/parse_grid.py | #!/usr/bin/env python
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
file_name = 'photobleaching_mixture00_grid.csv'
features = []
targets = []
with open(file_name, 'r') as content:
for line in content:
linecontent = line.strip().strip('\xef').strip('\xbb').strip('\... | 1,146 | 23.404255 | 97 | py |
quaterny_opvs | quaterny_opvs-master/emulators/details_pbq_qf/generate_indices.py | #!/usr/bin/env python
import numpy as np
import pickle
#=========================================================
NUM_SPECTRA = 1040
FOLD_SIZE = 170
np.random.seed(120415)
#=========================================================
indices = np.arange(NUM_SPECTRA)
np.random.shuffle(indices)
test_indices = i... | 1,082 | 25.414634 | 119 | py |
imf | imf-master/setup.py | #!/usr/bin/env python
# Licensed under a 3-clause BSD style license - see LICENSE.rst
# NOTE: The configuration for the package, including the name, version, and
# other information are set in the setup.cfg file.
import os
import sys
from setuptools import setup
# First provide helpful messages if contributors try... | 1,952 | 23.721519 | 76 | py |
imf | imf-master/examples/imf_schematic.py | """
I'm not entirely sure what this figure is supposed to show; it just plots a few variants of the lognormal IMF
"""
from imf import imf
import numpy as np
if __name__ == "__main__":
import pylab as pl
pl.matplotlib.rc_file("/Users/adam/.matplotlib/pubfiguresrc")
pl.rc('font', family='cmr10')
x = np... | 1,545 | 32.608696 | 110 | py |
imf | imf-master/examples/clustermf_figure.py | from imf import color_of_cluster,make_cluster,lum_of_cluster
import numpy as np
if __name__ == "__main__":
import pylab as pl
alpha = 2
m0 = 5e2
mmax = 5e5
cluster_mass_xax = np.logspace(np.log10(m0),np.log10(mmax),1e4)
def pr(m):
return (m/m0)**-alpha
probabilities = pr(cluster_ma... | 3,057 | 34.55814 | 110 | py |
imf | imf-master/examples/pmf_evolution.py | import imf.imf, imf.pmf, imp
from imf.pmf import ChabrierPMF_AcceleratingSF_IS, ChabrierPMF_AcceleratingSF_TC, ChabrierPMF_AcceleratingSF_CA#, ChabrierPMF_AcceleratingSF_2CTC
import pylab as pl
import numpy as np
imp.reload(imf.imf)
imp.reload(imf.pmf)
imp.reload(imf.imf)
imp.reload(imf.pmf)
mmin = 0.033
for mmax in ... | 2,430 | 45.75 | 154 | py |
imf | imf-master/examples/mass_to_light.py | import numpy as np
import os
import json
from imf import imf
import pylab as pl
import matplotlib
from astropy.utils.console import ProgressBar
pl.rc('font', size=16)
if os.path.exists('synth_data_m_to_l.json'):
with open('synth_data_m_to_l.json', 'r') as fh:
synth_data = json.load(fh)
else:
synth_dat... | 4,914 | 40.302521 | 130 | py |
imf | imf-master/examples/sampling_methods.py | from imf import make_cluster
import pylab as pl
import imf
maxmass = [imf.make_cluster(500, verbose=False, silent=True).max() for ii in
range(10000)]
maxmass_sorted = [imf.make_cluster(500, stop_criterion='sorted', verbose=False,
silent=True).max() for ii in range(10000)]... | 967 | 45.095238 | 79 | py |
imf | imf-master/examples/pmf_stats.py | import imf.imf, imf.pmf, imp
imp.reload(imf.imf)
imp.reload(imf.pmf)
imp.reload(imf.imf)
imp.reload(imf.pmf)
from imf.pmf import ChabrierPMF_IS, ChabrierPMF_TC, ChabrierPMF_CA, ChabrierPMF_2CTC
from imf.pmf import KroupaPMF_IS, KroupaPMF_TC, KroupaPMF_CA, KroupaPMF_2CTC
from imf.pmf import McKeeOffner_AcceleratingSF_PM... | 2,880 | 36.907895 | 177 | py |
imf | imf-master/examples/chabrier_comparisons.py | """
Compare the Chabrier distribution pulled from eqn 18 of Chabrier 2003 to
that quoted in McKee & Offner 2010 as "Chabrier 2005"
"""
import imf
import numpy as np
import pylab as pl
chabrier = imf.chabrierpowerlaw
chabrier2005 = imf.ChabrierPowerLaw(lognormal_width=0.55*np.log(10),
... | 1,328 | 33.973684 | 100 | py |
imf | imf-master/examples/imf_figure.py | """
Script to make an IMF diagram that shows dN(M)/dM vs M, then populates the area
under the curve with an appropriate number of stars colored by their
"true"(ish) color and sized by their mass.
"""
import imf
from imf import coolplot,kroupa,make_cluster
from astropy.table import Table
import numpy as np
if __name__... | 5,721 | 44.412698 | 86 | py |
imf | imf-master/examples/pmf_comparison.py | import imf.imf, imf.pmf, imp
imp.reload(imf.imf)
imp.reload(imf.pmf)
imp.reload(imf.imf)
imp.reload(imf.pmf)
from imf.pmf import ChabrierPMF_IS, ChabrierPMF_TC, ChabrierPMF_CA, ChabrierPMF_2CTC
from imf.pmf import KroupaPMF_IS, KroupaPMF_TC, KroupaPMF_CA, KroupaPMF_2CTC
import pylab as pl
import numpy as np
pl.rc('fon... | 6,437 | 50.504 | 128 | py |
imf | imf-master/examples/hr_diagram.py | """
Create a Hertzprung-Russell (temperature-luminosity) diagram and
mass-luminosity and mass-temperature diagrams populating the main sequence only
with data from Ekström+ 2012 (Vizier catalog J/A+A/537/A146/iso).
Colors come from vendian.org.
"""
import numpy as np
import imf
import pylab as pl
from astroquery.vizie... | 5,605 | 28.197917 | 80 | py |
imf | imf-master/imf/imf.py | """
Various codes to work with the initial mass function
"""
from __future__ import print_function
import numpy as np
import types
import scipy.integrate
import scipy.integrate as integrate
from scipy.integrate import quad
from astropy import units as u
from . import distributions
class MassFunction(object):
"""... | 34,904 | 33.593657 | 102 | py |
imf | imf-master/imf/distributions.py | import numpy as np
import scipy.stats
class Distribution:
""" The main class describing the distributions, to be inherited"""
def __init__(self):
self.m1 = 0 # edges of the support of the pdf
self.m2 = np.inf
pass
def pdf(self, x):
""" Return the Probability density funct... | 10,611 | 30.39645 | 79 | py |
imf | imf-master/imf/plf.py | """
Protostellar luminosity functions as described by Offner and McKee, 2011
Alternatively, perhaps try to construct a probabilistic P(L; m, m_f) given a
series of stellar evolution codes?
"""
import numpy as np
import scipy.integrate
import warnings
from .imf import MassFunction, ChabrierPowerLaw
chabrierpowerlaw... | 2,862 | 36.181818 | 97 | py |
imf | imf-master/imf/pmf.py | """
Protostellar mass functions as described by McKee and Offner, 2010
"""
import numpy as np
import scipy.integrate
import warnings
from .imf import MassFunction, ChabrierPowerLaw, Kroupa
chabrierpowerlaw = ChabrierPowerLaw()
class McKeeOffner_PMF(MassFunction):
default_mmin = 0.033
default_mmax = 3.0
... | 8,608 | 35.634043 | 104 | py |
imf | imf-master/imf/__init__.py | from .imf import *
| 19 | 9 | 18 | py |
imf | imf-master/imf/cmf.py | from __future__ import print_function
import numpy as np
from astropy import units as u
from astropy import constants
import scipy.stats
from . import imf
def pn11_mf(tnow=1, mmin=0.01*u.M_sun, mmax=120*u.M_sun, T0=10*u.K,
T_mean=7*u.K, L0=10*u.pc, rho0=2e-21*u.g/u.cm**3, MS0=25,
beta=0.4, alp... | 10,822 | 37.379433 | 89 | py |
imf | imf-master/imf/tests/test_imf.py | import pytest
import numpy as np
import itertools
from .. import imf
from ..imf import kroupa, chabrierpowerlaw
extra_massfunc_kwargs = {'schecter': {'m1': 1.0},
'modified_schecter': {'m1': 1.0},
'chabrierpowerlaw': {'mmid': 0.5},
}
@pytest.... | 5,462 | 37.202797 | 89 | py |
imf | imf-master/imf/tests/test_distributions.py | import numpy as np
import scipy.interpolate
from .. import distributions as D
np.random.seed(1)
def sampltest(distr, left=None, right=None, bounds=None):
# check that mean and stddev from the generated sample
# match what we get from integrating the PDF
def FF1(x):
return distr.pdf(x) * x
... | 5,757 | 28.228426 | 77 | py |
imf | imf-master/imf/tests/__init__.py | 0 | 0 | 0 | py | |
imaging_MLPs | imaging_MLPs-master/ImageNet/config.py | #Path to original dataset
original_path='/workspace/media/hdd1/image_net_mini/imagenet-mini/train/'
#Path to processed dataset
data_path='/workspace/media/hdd1/image_net_mini/dataset2/'
#Path to store trained models
models_path= "/workspace/media/hdd1/image_net_mini/trained_networks/"
#Path to store tensor board dat... | 342 | 30.181818 | 73 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/linear_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbedding(nn.Module):
def __init__(
self,
patch_size: int,
embed_dim: int,
channels: int
... | 3,644 | 26.613636 | 127 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/original_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbeddings(nn.Module):
def __init__(
self,
patch_size: int,
hidden_dim: int,
channels: int
... | 3,674 | 26.840909 | 93 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/img2img_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbedding(nn.Module):
def __init__(
self,
patch_size: int,
embed_dim: int,
channels: int
... | 3,618 | 27.054264 | 127 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/vit.py | '''
This code is modified from https://github.com/facebookresearch/convit. To adapt the vit/convit to image reconstruction, variable input sizes, and patch sizes for both spatial dimensions.
'''
import torch
import torch.nn as nn
from functools import partial
import torch.nn.functional as F
from timm.models.helpers im... | 15,082 | 39.007958 | 186 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/recon_net.py | import torch.nn as nn
import torch.nn.functional as F
from math import ceil, floor
class ReconNet(nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
def pad(self, x):
_, _, h, w = x.shape
hp, wp = self.net.patch_size
f1 = ( (wp - w % wp) % wp ) / 2
... | 810 | 26.033333 | 90 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/unet.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class Unet(nn.Module):
"""
PyTorch implementation of a U-Net ... | 5,981 | 32.79661 | 88 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/__init__.py | from .img2img_mixer import *
from .linear_mixer import *
from .original_mixer import *
from .u_mixer import *
from .unet import *
from .recon_net import ReconNet
from .vit import VisionTransformer
| 197 | 23.75 | 34 | py |
imaging_MLPs | imaging_MLPs-master/ImageNet/networks/u_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbeddings(nn.Module):
def __init__(
self,
patch_size: int,
embed_dim: int,
channels: int
... | 6,516 | 28.488688 | 127 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/config.py | #Path to original dataset
original_path='/workspace/media/hdd1/SSID/SIDD_Small_sRGB_Only/Data/'
#Path to processed dataset
data_path='/workspace/media/hdd1/SSID/dataset/'
#Path to store trained models
models_path= "/workspace/media/hdd1/SSID/trained_models/"
#Path to store tensor board data
logs_path= './logs/' | 315 | 27.727273 | 69 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/networks/img2img_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbedding(nn.Module):
def __init__(
self,
patch_size: int,
embed_dim: int,
channels: int
... | 3,618 | 27.054264 | 127 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/networks/vit.py | '''
This code is modified from https://github.com/facebookresearch/convit. To adapt the vit/convit to image reconstruction, variable input sizes, and patch sizes for both spatial dimensions.
'''
import torch
import torch.nn as nn
from functools import partial
import torch.nn.functional as F
from timm.models.helpers im... | 15,082 | 39.007958 | 186 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/networks/recon_net.py | import torch.nn as nn
import torch.nn.functional as F
from math import ceil, floor
class ReconNet(nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
def pad(self, x):
_, _, h, w = x.shape
hp, wp = self.net.patch_size
f1 = ( (wp - w % wp) % wp ) / 2
... | 810 | 26.033333 | 90 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/networks/unet.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class Unet(nn.Module):
"""
PyTorch implementation of a U-Net ... | 5,981 | 32.79661 | 88 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/networks/__init__.py | from .img2img_mixer import *
from .u_mixer import *
from .unet import *
from .recon_net import ReconNet
from .vit import VisionTransformer
| 140 | 19.142857 | 34 | py |
imaging_MLPs | imaging_MLPs-master/SIDD/networks/u_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbeddings(nn.Module):
def __init__(
self,
patch_size: int,
embed_dim: int,
channels: int
... | 6,516 | 28.488688 | 127 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/losses.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class SSIMLoss(nn.Module):
"""
SSIM loss module.
"""
de... | 1,671 | 28.857143 | 87 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/coil_combine.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
import fastmri
def rss(data: torch.Tensor, dim: int = 0) -> torch.Tensor:
"""
Compute the Root Sum of Squares (RSS).
... | 996 | 22.186047 | 66 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/evaluate.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import argparse
import pathlib
from argparse import ArgumentParser
from typing import Optional
import h5py
import numpy as np
from runstats ... | 4,903 | 27.847059 | 87 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/math.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import numpy as np
import torch
def complex_mul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Complex multiplication.
... | 2,728 | 25.754902 | 77 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/utils.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from pathlib import Path
from typing import Dict
import h5py
import numpy as np
def save_reconstructions(reconstructions: Dict[str, np.nda... | 1,467 | 28.36 | 80 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/__init__.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from packaging import version
from .coil_combine import rss, rss_complex
from .fftc import fftshift, ifftshift, roll
from .losse... | 758 | 26.107143 | 63 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/fftc.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from typing import List, Optional
import torch
from packaging import version
if version.parse(torch.__version__) >= version.parse("1.7.0"):... | 5,535 | 25.236967 | 80 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/data/volume_sampler.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from typing import List, Optional, Union
import torch
import torch.distributed as dist
from fastmri.data.mri_data import CombinedSliceDatase... | 4,332 | 36.678261 | 82 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/data/mri_data.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import logging
import os
import pickle
import random
import xml.etree.ElementTree as etree
from pathlib import Path
from typing import Callab... | 13,630 | 36.759003 | 116 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/data/subsample.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import contextlib
from typing import Optional, Sequence, Tuple, Union
import numpy as np
import torch
@contextlib.contextmanager
def temp_... | 8,448 | 36.887892 | 88 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/data/__init__.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from .mri_data import SliceDataset, CombinedSliceDataset
from .volume_sampler import VolumeSampler
| 278 | 26.9 | 63 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/fastmri/data/transforms.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from typing import Dict, Optional, Sequence, Tuple, Union
import fastmri
import numpy as np
import torch
from .subsample import MaskFunc
... | 12,887 | 30.205811 | 88 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/networks/img2img_mixer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import torch.nn.init as init
import numpy as np
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbedding(nn.Module):
def __init__(
self,
patch_size: int,
... | 3,714 | 26.932331 | 127 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.