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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inversegraphics | inversegraphics-master/test.py | #!/usr/bin/env python3.4m
import matplotlib
matplotlib.use('Agg')
import scene_io_utils
from blender_utils import *
from generative_models import *
from tabulate import tabulate
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import cv2
plt.ioff()
numpy.random.seed(1)
inchToMeter =... | 29,478 | 47.645215 | 559 | py |
inversegraphics | inversegraphics-master/differentiable_renderer.py | import chumpy as ch
from chumpy import depends_on, Ch
import cv2
import numpy as np
import scipy.sparse as sp
from chumpy.utils import row, col
from opendr.geometry import Rodrigues
import warnings
#Make simple experiment.
def nanmean(a, axis):
# don't call nan_to_num in here, unless you check that
# occlusion... | 14,887 | 40.127072 | 214 | py |
inversegraphics | inversegraphics-master/lasagne_nn.py | #!/usr/bin/env python
"""
Usage example employing Lasagne for digit recognition using the MNIST dataset.
This example is deliberately structured as a long flat file, focusing on how
to use Lasagne, instead of focusing on writing maximally modular and reusable
code. It is used as the foundation for the introductory La... | 63,328 | 40.122727 | 197 | py |
inversegraphics | inversegraphics-master/densecrf_model.py | """
Usage: python util_inference_example.py image annotations
Adapted from the dense_inference.py to demonstate the usage of the util
functions.
"""
import sys
import numpy as np
import cv2
import pydensecrf.densecrf as dcrf
import matplotlib.pylab as plt
from skimage.segmentation import relabel_sequential
import skim... | 4,856 | 31.165563 | 131 | py |
inversegraphics | inversegraphics-master/probLineSearch.py | from scipy.special import erf
import numpy as np
from scipy.stats import mvn
import ipdb
def probLineSearch(func, x0, f0, df0, search_direction, alpha0,
verbosity, outs, paras, var_f0, var_df0):
# probLineSearch.m -- A probabilistic line search algorithm for nonlinear
# optimization problems with noisy gradie... | 22,197 | 38.92446 | 151 | py |
inversegraphics | inversegraphics-master/render.py | #!/usr/bin/env python3.4m
import matplotlib
# matplotlib.use('Agg')
import scene_io_utils
import re
from blender_utils import *
from collision import *
import matplotlib.pyplot as plt
numpy.random.seed(1)
inchToMeter = 0.0254
outputDir = '../data/output/'
if not os.path.exists(outputDir):
os.makedirs(outputDir)
... | 15,462 | 41.833795 | 304 | py |
inversegraphics | inversegraphics-master/diffrender_test.py | test__author__ = 'pol'
# from damascene import damascene
import matplotlib
# matplotlib.use('QT4Agg')
import matplotlib.pyplot as plt
plt.rcParams['animation.ffmpeg_path'] = '/usr/bin/ffmpeg'
import scene_io_utils
import mathutils
from math import radians
import timeit
import time
import opendr
import chumpy as ch
im... | 134,439 | 47.586917 | 485 | py |
inversegraphics | inversegraphics-master/export_groundtruth.py | import save_exr_images
from save_exr_images import exportExrImages
import os
print ("Reading xml ")
outputDir = '../data/output/'
imgDir = outputDir + "images/"
lines = [line.strip() for line in open(outputDir + 'groundtruth.txt')]
if not os.path.exists(imgDir):
os.makedirs(imgDir)
for instance in lines:
... | 678 | 19.575758 | 88 | py |
inversegraphics | inversegraphics-master/diffrender_groundtruth_multi.py | __author__ = 'pol'
import matplotlib
# matplotlib.use('Qt4Agg')
import bpy
import scene_io_utils
import mathutils
from math import radians
import timeit
import time
import opendr
import chumpy as ch
import geometry
import image_processing
import pdb
import numpy as np
import cv2
from blender_utils import *
import gene... | 106,833 | 48.232258 | 1,055 | py |
inversegraphics | inversegraphics-master/utils.py | import numpy as np
import os
import skimage
import skimage.io
import h5py
import ipdb
import scipy.spatial.distance
import image_processing
import matplotlib
__author__ = 'pol'
import recognition_models
import pickle
def joinExperiments(range1, range2, testSet1,methodsPred1,testOcclusions1,testPrefixBase1,parameterRe... | 70,535 | 44.477756 | 456 | py |
inversegraphics | inversegraphics-master/extract.py | import matplotlib
# matplotlib.use('Agg')
import bpy
import numpy
import matplotlib.pyplot as plt
width = 110
height = 110
scene = bpy.data.scenes[0]
scene.render.resolution_x = width #perhaps set resolution in code
scene.render.resolution_y = height
scene.render.resolution_percentage = 100
bpy.data.objects['Cube... | 1,234 | 24.204082 | 108 | py |
inversegraphics | inversegraphics-master/lasagne_visualize.py | from itertools import product
from lasagne.layers import get_output
import matplotlib.pyplot as plt
import numpy as np
import theano
import theano.tensor as T
def plot_loss(net):
train_loss = [row['train_loss'] for row in net.train_history_]
valid_loss = [row['valid_loss'] for row in net.train_history_]
... | 7,737 | 31.512605 | 78 | py |
inversegraphics | inversegraphics-master/light_probes.py | from math import sin, cos, ceil, floor, pi
import importlib
import bpy
import numpy as np
import mathutils
from contextlib import contextmanager
from uuid import uuid4
from bpy.utils import register_module, unregister_module
from bpy import props as p
import json
import ipdb
import matplotlib.pyplot as plt
# bl_info =... | 22,272 | 33.639191 | 433 | py |
inversegraphics | inversegraphics-master/collision.py | __author__ = 'pol'
#From http://blender.stackexchange.com/a/9080
import bpy
import bmesh
from blender_utils import *
import blender_utils
def bmesh_copy_from_object(obj, objTransf, transform=True, triangulate=True, apply_modifiers=False):
assert (obj.type == 'MESH')
if apply_modifiers and obj.modifiers:
... | 7,497 | 37.649485 | 247 | py |
inversegraphics | inversegraphics-master/blender_utils.py | import bpy
import bpy_extras
import numpy
import numpy as np
import mathutils
from math import radians
import h5py
import scipy.io
import cv2
import sys
import io
import os
import light_probes
import imageio
try:
import cPickle as pickle
except:
import pickle
import ipdb
import re
from collision import instancesI... | 41,350 | 35.529152 | 220 | py |
inversegraphics | inversegraphics-master/shape_model.py | import numpy as np
import pickle
import chumpy as ch
import ipdb
from chumpy import depends_on, Ch
import scipy.sparse as sp
#%% Helper functions
def longToPoints3D(pointsLong):
nPointsLong = np.size(pointsLong)
return np.reshape(pointsLong, [nPointsLong/3, 3])
def shapeParamsToVerts(shapeParams, teapotModel... | 3,538 | 28.491667 | 187 | py |
inversegraphics | inversegraphics-master/opendr_utils.py | __author__ = 'pol'
from utils import *
import opendr
import chumpy as ch
import geometry
import bpy
import mathutils
import numpy as np
from math import radians
from opendr.camera import ProjectPoints
from opendr.renderer import TexturedRenderer
from opendr.lighting import SphericalHarmonics
from opendr.lighting impor... | 36,041 | 42.11244 | 359 | py |
inversegraphics | inversegraphics-master/image_processing.py | __author__ = 'pol'
from skimage.feature import hog
from skimage import data, color, exposure
import numpy as np
import ipdb
import skimage.color
from numpy.core.umath_tests import matrix_multiply
# def xyz2labCh(xyz, illuminant="D65", observer="2"):
# """XYZ to CIE-LAB color space conversion.
# Parameters
# ... | 14,461 | 35.428212 | 252 | py |
inversegraphics | inversegraphics-master/generative_models.py | import cv2
import numpy as np
import matplotlib.pyplot as plt
import ipdb
import scipy
import chumpy as ch
from chumpy.ch import MatVecMult, Ch, depends_on
def scoreImage(img, template, method, methodParams):
score = 0
if method == 'chamferModelToData':
sqDists = chamferDistanceModelToData(img, templa... | 25,302 | 38.910095 | 213 | py |
inversegraphics | inversegraphics-master/var_inf.py | __author__ = 'pol'
import ipdb
import matplotlib
matplotlib.use('Qt4Agg')
from math import radians
import chumpy as ch
import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn import mixture
from numpy.random import choice
plt.ion()
image = cv2.imread('opendr_GT.png')
image = np.float64(cv2.cvtColor... | 3,755 | 32.837838 | 186 | py |
inversegraphics | inversegraphics-master/geometry.py | import chumpy as ch
from chumpy import depends_on, Ch
import cv2
import numpy as np
import scipy.sparse as sp
from chumpy.utils import row, col
from opendr.geometry import Rodrigues
class RotateZ(Ch):
dterms = 'a'
def compute_r(self):
return np.array([[np.cos(self.a.r), -np.sin(self.a.r), 0, 0], [np... | 3,023 | 30.831579 | 171 | py |
inversegraphics | inversegraphics-master/diffrender_analyze.py | __author__ = 'pol'
import matplotlib
matplotlib.use('Qt4Agg')
import scene_io_utils
from math import radians
import timeit
import time
import opendr
import chumpy as ch
import geometry
import image_processing
import numpy as np
import cv2
import generative_models
import recognition_models
import matplotlib.pyplot as p... | 58,258 | 45.127474 | 466 | py |
inversegraphics | inversegraphics-master/export_collisions.py | #!/usr/bin/env python3.4m
import scene_io_utils
import re
from blender_utils import *
from collision import *
numpy.random.seed(1)
inchToMeter = 0.0254
outputDir = 'data/'
width = 150
height = 150
numSamples = 100
useCycles = False
distance = 0.75
scene_io_utils.loadTargetsBlendData()
sceneCollisions = {}
repla... | 5,875 | 37.657895 | 191 | py |
inversegraphics | inversegraphics-master/scene_io_utils.py | from blender_utils import *
from sklearn.preprocessing import normalize
from collections import OrderedDict
def loadTeapotsOpenDRData(renderTeapotsList, useBlender, unpackModelsFromBlender, targetModels):
v_teapots = []
f_list_teapots = []
vc_teapots = []
vn_teapots = []
uv_teapots = []
haveTe... | 29,860 | 39.905479 | 343 | py |
inversegraphics | inversegraphics-master/torch_nn.py | import ipdb
import PyTorchAug
import PyTorch
nn = PyTorch.Nn()
lua = PyTorchAug.lua
lua.getGlobal("require")
lua.pushString('modules/LinearCR')
lua.call(1, 0)
lua = PyTorchAug.lua
lua.getGlobal("require")
lua.pushString('modules/Reparametrize')
lua.call(1, 0)
lua = PyTorchAug.lua
lua.getGlobal("require")
lua.pushStr... | 2,724 | 25.980198 | 102 | py |
inversegraphics | inversegraphics-master/diffrender_experiment.py | __author__ = 'pol'
import matplotlib
matplotlib.use('Qt4Agg')
from math import radians
import timeit
import time
import numpy as np
from utils import *
import matplotlib.pyplot as plt
plt.ion()
import h5py
import ipdb
import pickle
#########################################
# Initialization ends here
#################... | 3,854 | 26.733813 | 106 | py |
inversegraphics | inversegraphics-master/diffrender_train.py | __author__ = 'pol'
import matplotlib
matplotlib.use('Qt4Agg')
from math import radians
import timeit
import time
import image_processing
import numpy as np
import cv2
from utils import *
import generative_models
import matplotlib.pyplot as plt
plt.ion()
import recognition_models
import skimage
import h5py
import ipdb
... | 28,634 | 44.524642 | 730 | py |
inversegraphics | inversegraphics-master/zernike.py | """
@file py102-example2-zernike.py
@brief Fitting a surface in Python example for Python 102 lecture
@author Tim van Werkhoven (t.i.m.vanwerkhoven@gmail.com)
@url http://python101.vanwerkhoven.org
@date 20111012
Created by Tim van Werkhoven (t.i.m.vanwerkhoven@xs4all.nl) on 2011-10-12
Copyright (c) 2011 Tim van Werkho... | 1,649 | 27.448276 | 103 | py |
inversegraphics | inversegraphics-master/save_exr_images.py | #!/usr/bin/python
import OpenEXR
import Imath
from PIL import Image
import sys
import numpy as np
def exportExrImages(annotationdir, imgdir, numTeapot, frame, sceneNum, target, prefix):
framestr = '{0:04d}'.format(frame)
outfilename = "render" + prefix + "_obj" + str(numTeapot) + "_scene" + str(sceneNum) + ... | 3,659 | 40.590909 | 154 | py |
inversegraphics | inversegraphics-master/diffrender_demo.py | __author__ = 'pol'
import matplotlib
matplotlib.use('Qt4Agg')
import bpy
import scene_io_utils
import mathutils
from math import radians
import timeit
import time
import opendr
import chumpy as ch
import geometry
import image_processing
import numpy as np
import cv2
from blender_utils import *
import glfw
import gener... | 84,241 | 42.002552 | 357 | py |
sa-nmt | sa-nmt-master/Loss.py | """
This file handles the details of the loss function during training.
This includes: loss criterion, training statistics, and memory optimizations.
"""
from __future__ import division
import time
import sys
import math
import torch
import torch.nn as nn
def nmt_criterion(vocab_size, pad_id=0):
"""
Construc... | 4,092 | 27.227586 | 77 | py |
sa-nmt | sa-nmt-master/Iterator.py | import numpy
import random
import pickle as pkl
import gzip
from tempfile import mkstemp
import os
import string
def fopen(filename, mode='r'):
if filename.endswith('.gz'):
return gzip.open(filename, mode)
return open(filename, mode)
class TextIterator:
"""Simple Bitext iterator."""
def __in... | 5,911 | 29.474227 | 75 | py |
sa-nmt | sa-nmt-master/opts.py | import argparse
def model_opts(parser):
"""
These options are passed to the construction of the model.
Be careful with these as they will be used during translation.
"""
# Model options
# Embedding Options
parser.add_argument('-word_vec_size', type=int, default=512,
... | 8,871 | 44.968912 | 84 | py |
sa-nmt | sa-nmt-master/translate.py | import argparse
import torch
import modelx as models
import infer
import string
# build args parser
parser = argparse.ArgumentParser(description='Training NMT')
parser.add_argument('-checkpoint', required=True,
help='saved checkpoit.')
parser.add_argument('-input', required=True,
... | 1,416 | 28.520833 | 71 | py |
sa-nmt | sa-nmt-master/Utils.py | def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments), \
"Not all arguments have the same value: " + str(args)
def use_gpu(opt):
return (hasattr(opt, 'gpuid') and len(... | 388 | 26.785714 | 62 | py |
sa-nmt | sa-nmt-master/extract_tree.py | import argparse
import torch
from torch.autograd import Variable
import modelx as models
import networkx as nx
from networkx.algorithms.tree import maximum_spanning_arborescence
import string
# build args parser
parser = argparse.ArgumentParser(description='Training NMT')
parser.add_argument('-checkpoint', required=T... | 4,276 | 26.242038 | 70 | py |
sa-nmt | sa-nmt-master/models.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from attention import GlobalAttention, SelfAttention
from Utils import aeq
from torch.nn.utils.rnn import pack_padded_sequence as pack
from torch.nn.utils.rnn import pad_packed_sequence as unpack
import math
class EncoderBase(nn.Module):
"""
... | 15,669 | 36.488038 | 79 | py |
sa-nmt | sa-nmt-master/infer.py | import torch
from torch.autograd import Variable
import pickle as pkl
import math
# TODO: documentation of functions
class Beam(object):
r"""Beam search class for NMT.
This is a simple beam search object. It takes model, which can be used to
compute the next probable output and dictionaries that will be u... | 4,938 | 35.316176 | 78 | py |
sa-nmt | sa-nmt-master/attention.py | import torch
import torch.nn as nn
from Utils import aeq
import math
import torch.nn.functional as F
class SelfAttention(nn.Module):
"""Self attention class"""
def __init__(self, dim):
super(SelfAttention, self).__init__()
self.q = nn.Linear(dim, dim, bias=False)
self.k = nn.Linear(dim... | 6,737 | 35.032086 | 78 | py |
sa-nmt | sa-nmt-master/train.py | import argparse
import torch
from Iterator import TextIterator
import models
from itertools import zip_longest
import random
import Loss
import opts
import os
import math
import subprocess
from infer import Beam
import re
from torch.optim.lr_scheduler import ReduceLROnPlateau
parser = argparse.ArgumentParser(descripti... | 8,797 | 36.598291 | 77 | py |
sa-nmt | sa-nmt-master/data/filter.py | import os
import sys
import random
from tempfile import mkstemp
from subprocess import call
def main(files):
tf_os, tpath = mkstemp()
tf = open(tpath, 'w')
fds = [open(ff) for ff in files]
for l in fds[0]:
lines = [l.strip()] + [ff.readline().strip() for ff in fds[1:]]
lengths = [... | 933 | 18.458333 | 71 | py |
sa-nmt | sa-nmt-master/data/shuffle.py | from __future__ import print_function
import os
import sys
import random
from tempfile import mkstemp
from subprocess import call
def main(files):
tf_os, tpath = mkstemp()
tf = open(tpath, 'w')
fds = [open(ff) for ff in files]
for l in fds[0]:
lines = [l.strip()] + [ff.readline().strip()... | 982 | 17.903846 | 71 | py |
sa-nmt | sa-nmt-master/data/shuffle.bak.py | from __future__ import print_function
import os
import sys
import random
from tempfile import mkstemp
from subprocess import call
def main(files):
tf_os, tpath = mkstemp()
tf = open(tpath, 'w')
fds = [open(ff) for ff in files]
for l in fds[0]:
lines = [l.strip()] + [ff.readline().strip()... | 800 | 16.042553 | 71 | py |
sa-nmt | sa-nmt-master/data/scan_example.py | from __future__ import print_function
import numpy
import theano
from theano import tensor
# some numbers
n_steps = 10
n_samples = 5
dim = 10
input_dim = 20
output_dim = 2
# one step function that will be used by scan
def oneStep(x_t, h_tm1, W_x, W_h, W_o):
h_t = tensor.tanh(tensor.dot(x_t, W_x) +
... | 1,709 | 23.782609 | 60 | py |
sa-nmt | sa-nmt-master/data/strip_sgml.py | from __future__ import print_function
import sys
import re
def main():
fin = sys.stdin
fout = sys.stdout
for l in fin:
line = l.strip()
text = re.sub('<[^<]+>', "", line).strip()
if len(text) == 0:
continue
print(text, file=fout)
if __name__ =... | 346 | 15.52381 | 50 | py |
sa-nmt | sa-nmt-master/data/build_dictionary.py | from __future__ import print_function
import numpy
import pickle as pkl
import sys
from collections import OrderedDict
def main():
for filename in sys.argv[1:]:
print('Processing', filename)
word_freqs = OrderedDict()
with open(filename, 'r') as f:
for line in f:
... | 1,087 | 24.904762 | 61 | py |
sa-nmt | sa-nmt-master/data/extract_files.py | #!/usr/bin/python
import argparse
import logging
import os
import tarfile
TRAIN_DATA_URL = 'http://www.statmt.org/europarl/v7/fr-en.tgz'
VALID_DATA_URL = 'http://matrix.statmt.org/test_sets/newstest2011.tgz'
parser = argparse.ArgumentParser(
description="""
This script donwloads parallel corpora given source and... | 2,999 | 36.974684 | 76 | py |
sa-nmt | sa-nmt-master/data/length.py | from __future__ import print_function
import numpy
import sys
for name in sys.argv[1:]:
lens = []
with open(name, 'r') as f:
for ll in f:
lens.append(len(ll.strip().split(' ')))
print(name, ' max ', numpy.max(lens), ' min ', numpy.min(lens), ' average ', numpy.mean(lens))
| 310 | 19.733333 | 98 | py |
qsft | qsft-master/synt_exp/run-tests-complexity-vs-size.py | import numpy as np
import sys
import pandas as pd
import uuid
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 504)
pd.set_option('display.width', 1000)
sys.path.append("..")
sys.path.append("../src")
import argparse
from pathlib import Path
from synt_src.synthetic_helper import SyntheticH... | 3,745 | 31.017094 | 109 | py |
qsft | qsft-master/synt_exp/plot-complexity-vs-size.py | import numpy as np
import matplotlib.pyplot as plt
import sys
import pandas as pd
from matplotlib import ticker
import matplotlib
from mpl_toolkits.axes_grid1 import make_axes_locatable
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 504)
pd.set_option('display.width', 1000)
sys.path.appen... | 5,591 | 36.033113 | 101 | py |
qsft | qsft-master/synt_exp/run-tests-nmse-vs-snr.py | import numpy as np
import sys
import pandas as pd
import uuid
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 504)
pd.set_option('display.width', 1000)
sys.path.append("..")
import argparse
from pathlib import Path
from synt_exp.synt_src.synthetic_helper import SyntheticHelper
from qsft.para... | 3,712 | 32.45045 | 109 | py |
qsft | qsft-master/synt_exp/qsft-sample-vs-nmse.py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
import sys
import pandas as pd
import uuid
sys.path.append("..")
sys.path.append("../src")
from qsft.utils import best_convex_underestimator
import argparse
from pathlib import Path
from synt_exp.synt_src.synthetic_he... | 4,482 | 30.131944 | 104 | py |
qsft | qsft-master/synt_exp/plot-nmse-vs-snr.py | import numpy as np
import matplotlib.pyplot as plt
import sys
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 504)
pd.set_option('display.width', 1000)
sys.path.append("..")
sys.path.append("../src")
from pathlib import Path
if __name__ == '__main__':
exp_dir = P... | 1,435 | 27.156863 | 95 | py |
qsft | qsft-master/synt_exp/quick_example.py | import numpy as np
from qsft.qsft import QSFT
from qsft.query import get_reed_solomon_dec
from synt_exp.synt_src.synthetic_signal import get_random_subsampled_signal
if __name__ == '__main__':
np.random.seed(20)
q = 3
n = 40
N = q ** n
sparsity = 100
a_min = 1
a_max = 1
b = 4
noise_... | 2,810 | 32.86747 | 118 | py |
qsft | qsft-master/synt_exp/__init__.py | 0 | 0 | 0 | py | |
qsft | qsft-master/synt_exp/synt_src/synthetic_signal.py | import numpy as np
from qsft.utils import igwht_tensored, random_signal_strength_model, qary_vec_to_dec, sort_qary_vecs
from qsft.input_signal import Signal
from qsft.input_signal_subsampled import SubsampledSignal
from qsft.utils import dec_to_qary_vec
from multiprocess import Pool
import time
def generate_signal_w(... | 5,257 | 39.137405 | 124 | py |
qsft | qsft-master/synt_exp/synt_src/synthetic_helper.py | from qsft.test_helper import TestHelper
from synt_exp.synt_src.synthetic_signal import SyntheticSubsampledSignal
class SyntheticHelper(TestHelper):
def generate_signal(self, signal_args):
return SyntheticSubsampledSignal(**signal_args)
| 249 | 34.714286 | 72 | py |
qsft | qsft-master/rna_exp/run-tests-complexity-vs-size.py | import numpy as np
import pandas as pd
import uuid
import argparse
from pathlib import Path
from rna_exp.rna_src.rna_helper import RNAHelper
from qsft.parallel_tests import run_tests
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 504)
pd.set_option('display.width', 1000)
if __name__ == '... | 3,056 | 29.57 | 103 | py |
qsft | qsft-master/rna_exp/plot-complexity-vs-size.py | import numpy as np
import matplotlib.pyplot as plt
import sys
import pandas as pd
from scipy import interpolate
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 504)
pd.set_option('display.width', 1000)
sys.path.append("..")
sys.path.append("../src")
from pathlib import Path
from qsft.util... | 2,831 | 30.120879 | 92 | py |
qsft | qsft-master/rna_exp/qspright-sample-vs-nmse.py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
sys.path.append("../src")
import pandas as pd
import uuid
from rna_exp.rna_src.rna_helper import RNAHelper
from qsft.utils import best_convex_underestimator
from qsft.parallel_tests impo... | 4,317 | 30.289855 | 104 | py |
qsft | qsft-master/rna_exp/__init__.py | 0 | 0 | 0 | py | |
qsft | qsft-master/rna_exp/rna_src/query_iterator.py | import numpy as np
from qsft.utils import dec_to_qary_vec
class QueryIterator(object):
nucs = np.array(["A", "U", "C", "G"])
q = 4
def __init__(self, base_seq, positions, query_indices, q):
self.base_seq = np.array(list(base_seq))
self.positions = positions
self.full = self.base_se... | 1,466 | 28.34 | 66 | py |
qsft | qsft-master/rna_exp/rna_src/utils.py | import numpy as np
from tqdm import tqdm
from itertools import chain, combinations
def divisors(num):
"""Returns all divisors of a given integer"""
divs = []
for x in range (1, num):
if (num % x) == 0:
divs.append(x)
return divs
def powerset(iterable):
"""Returns the powerset... | 6,463 | 30.842365 | 92 | py |
qsft | qsft-master/rna_exp/rna_src/data_utils.py | import pandas as pd
import numpy as np
import itertools
from Bio import PDB
from tqdm import tqdm
from sklearn.linear_model import Lasso
from rna_exp.rna_src import utils
from rna_exp.rna_src import gnk_model
from rna_exp.rna_src import structure_utils
"""
Utility functions for loading and processing empirical fitness... | 12,224 | 34.537791 | 134 | py |
qsft | qsft-master/rna_exp/rna_src/rna_helper.py | import numpy as np
import json
from qsft.test_helper import TestHelper
from qsft.utils import NpEncoder
from rna_exp.rna_src.input_rna_signal_subsampled import RnaSubsampledSignal
from rna_exp.rna_src.rna_utils import get_rna_base_seq
class RNAHelper(TestHelper):
mfe_base = 0
base_seq_list = None
position... | 1,466 | 36.615385 | 118 | py |
qsft | qsft-master/rna_exp/rna_src/rna_utils.py | import RNA
import itertools
import utils
import linecache
import tracemalloc
"""
Utility functions for loading and processing the quasi-empirical RNA fitness function.
"""
def dna_to_rna(seq):
"""
Converts DNA sequences to RNA sequences.
"""
rs = []
for s in seq:
if s == 'T':
... | 4,803 | 27.093567 | 122 | py |
qsft | qsft-master/rna_exp/rna_src/__init__.py | 0 | 0 | 0 | py | |
qsft | qsft-master/rna_exp/rna_src/input_rna_signal_subsampled.py | from qsft.input_signal_subsampled import SubsampledSignal
import numpy as np
from multiprocessing import Pool
from qsft.utils import dec_to_qary_vec, qary_vec_to_dec
import RNA
class RnaSubsampledSignal(SubsampledSignal):
nucs = np.array(["A", "U", "C", "G"])
def __init__(self, **kwargs):
self.base_s... | 2,382 | 28.060976 | 106 | py |
qsft | qsft-master/rna_exp/rna_src/structure_utils.py | import numpy as np
from Bio import PDB
"""
Various utility functions for working with PDB structures.
"""
def binarize_contact_map(contact_map, threshold=8.0):
"""Returns binary version of contact map."""
return np.less(contact_map, threshold)
def calc_min_dist(res1, res2):
"""Returns the minimum dist... | 2,281 | 28.636364 | 84 | py |
qsft | qsft-master/rna_exp/rna_src/gnk_model.py | import numpy as np
import itertools
from scipy.special import binom
from math import factorial
from rna_exp.rna_src import utils
def get_neighborhood_powerset(V):
"""Returns the union of powersets of a set of neighborhoods"""
Vs = [sorted(Vk) for Vk in V]
powersets = [tuple(utils.powerset(Vs[i])) for i in... | 4,787 | 28.018182 | 91 | py |
qsft | qsft-master/rna_exp/rna_src/input_rna_signal.py | from qsft.input_signal import Signal
import numpy as np
import itertools
from rna_exp.rna_src.rna_utils import insert
from multiprocessing import Pool
from tqdm import tqdm
from functools import partial
tqdm = partial(tqdm, position=0, leave=True)
class RnaSignal(Signal):
def __init__(self, **kwargs):
sel... | 1,107 | 31.588235 | 85 | py |
qsft | qsft-master/qsft/spright.py | '''
SPRIGHT decoding main file. Logic flow:
1. Generate a signal from src/input_signal.py
2. Subsample from src/query.py
3. Peel using src/reconstruct.py
'''
import numpy as np
import galois
import sys
import tqdm
import time
sys.path.append("../src")
from archive.qsft_rand import dec_to_bin, bin_to_dec, qary_vec_to... | 12,417 | 42.118056 | 123 | py |
qsft | qsft-master/qsft/input_signal_subsampled.py | from qsft.utils import qary_ints, qary_vec_to_dec, gwht, load_data, save_data
from qsft.input_signal import Signal
from qsft.query import get_Ms_and_Ds
from pathlib import Path
from math import floor
from tqdm import tqdm
import numpy as np
import random
import time
class SubsampledSignal(Signal):
"""
A shell... | 11,744 | 42.5 | 122 | py |
qsft | qsft-master/qsft/qsft.py | '''
Class for computing the q-ary fourier transform of a function/signal
'''
import time
import numpy as np
from qsft.reconstruct import singleton_detection
from qsft.input_signal_subsampled import SubsampledSignal
from qsft.utils import bin_to_dec, qary_vec_to_dec, sort_qary_vecs, calc_hamming_weight, dec_to_qary_vec
... | 12,144 | 42.067376 | 122 | py |
qsft | qsft-master/qsft/ReedSolomon.py | import galois
from galois._codes._reed_solomon import decode_jit
import numpy as np
import math
class ReedSolomon(galois.ReedSolomon):
"""
Class that extends galois.ReedSolomon. Mainly it is needed to implement syndrome decoding.
Attributes
---------
prime_field : GF.field
The galois feild of... | 2,356 | 30.426667 | 120 | py |
qsft | qsft-master/qsft/lasso.py | import numpy as np
from group_lasso import GroupLasso
from sklearn.linear_model import Ridge
import time
from group_lasso._fista import ConvergenceWarning
from sklearn.utils._testing import ignore_warnings
from qsft.utils import calc_hamming_weight, dec_to_qary_vec, qary_ints
@ignore_warnings(category=ConvergenceWarn... | 3,870 | 28.549618 | 129 | py |
qsft | qsft-master/qsft/utils.py | '''
Utility functions.
'''
import numpy as np
import scipy.fft as fft
from group_lasso import GroupLasso
from sklearn.linear_model import Ridge
import itertools
import math
import random
import time
from scipy.spatial import ConvexHull
import zlib
import pickle
import json
import matplotlib.pyplot as plt
def fwht(x):
... | 6,283 | 29.357488 | 116 | py |
qsft | qsft-master/qsft/test_helper.py | import numpy as np
from qsft.lasso import lasso_decode
from qsft.qsft import QSFT
from qsft.utils import gwht, dec_to_qary_vec, NpEncoder
import json
from qsft.query import get_reed_solomon_dec
class TestHelper:
def __init__(self, signal_args, methods, subsampling_args, test_args, exp_dir, subsampling=True):
... | 12,024 | 40.608997 | 123 | py |
qsft | qsft-master/qsft/parallel_tests.py | import itertools
from multiprocessing import Pool
from tqdm import tqdm
from functools import partial
import numpy as np
import pandas as pd
from qsft.test_helper import TestHelper
tqdm = partial(tqdm, position=0, leave=True)
def _test(i):
"""
Runs a single instance of a test
Parameters
----------
... | 3,555 | 29.921739 | 138 | py |
qsft | qsft-master/qsft/__init__.py | 0 | 0 | 0 | py | |
qsft | qsft-master/qsft/query.py | '''
Methods for the query generator: specifically, to
1. generate sparsity coefficients b and subsampling matrices M
2. get the indices of a signal subsample
3. compute a subsampled and delayed Walsh-Hadamard transform.
'''
import time
import numpy as np
from qsft.utils import fwht, gwht, bin_to_dec, binary_ints, qary... | 7,267 | 28.786885 | 122 | py |
qsft | qsft-master/qsft/reconstruct.py | '''
Methods for the reconstruction engine; specifically, to:
1. carry out singleton detection
2. get the cardinalities of all bins in a subsampling group (debugging only).
'''
import numpy as np
from qsft.utils import angle_q
def singleton_detection_noiseless(U_slice, **kwargs):
'''
Finds the true index of ... | 5,932 | 34.315476 | 131 | py |
qsft | qsft-master/qsft/input_signal.py | """
A shell Class for common interface to an input signal. This class should be extended when implemented
"""
import numpy as np
from qsft.utils import gwht_tensored, igwht_tensored, save_data, load_data
from pathlib import Path
class Signal:
"""
Class to encapsulate a time domain signal and its q-ary Fourier... | 3,034 | 30.28866 | 118 | py |
sort | sort-master/sort.py | """
SORT: A Simple, Online and Realtime Tracker
Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License,... | 11,739 | 34.468278 | 242 | py |
cogcn | cogcn-main/cogcn/utils.py | import pickle as pkl
import os
import networkx as nx
import numpy as np
import scipy.sparse as sp
import torch
import pandas as pd
from sklearn.metrics import roc_auc_score, average_precision_score
from matplotlib import pyplot as plt
def load_data_cma(dataset):
adj_file = os.path.join(dataset, "struct.csv")
f... | 2,240 | 29.69863 | 95 | py |
cogcn | cogcn-main/cogcn/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import GraphConvolution
class GCNAE(nn.Module):
def __init__(self, input_feat_dim, hidden_dim1, hidden_dim2, dropout):
super(GCNAE, self).__init__()
self.encgc1 = GraphConvolution(input_feat_dim, hidden_dim1, dropout, a... | 1,415 | 31.930233 | 93 | py |
cogcn | cogcn-main/cogcn/kmeans.py | import sys
import torch
import torch.nn as nn
from sklearn.cluster import KMeans
class Clustering(object):
def __init__(self, K, n_init=5, max_iter=250):
self.K = K
self.n_init = n_init
self.max_iter = max_iter
self.u = None
self.M = None
def cluster(self, embed):
... | 1,646 | 28.410714 | 109 | py |
cogcn | cogcn-main/cogcn/layers.py | import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0., act=F.relu):... | 1,110 | 30.742857 | 77 | py |
cogcn | cogcn-main/cogcn/train.py | from __future__ import division
from __future__ import print_function
import argparse
import time
import sys
import os
import pickle
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
from torch import optim
from matplotlib import pyplot as plt
from model import GCNAE
from optimizer impor... | 5,980 | 39.412162 | 157 | py |
cogcn | cogcn-main/cogcn/optimizer.py | import sys
import torch
import torch.nn as nn
import torch.nn.modules.loss
import torch.nn.functional as F
from sklearn.cluster import KMeans
def compute_attribute_loss(lossfn, features, recon, outlier_wt):
loss = lossfn(features, recon)
loss = loss.sum(dim=1)
outlier_wt = torch.log(1/outlier_wt)
at... | 1,768 | 23.915493 | 64 | py |
deepglo | deepglo-master/DeepGLO/DeepGLO.py | from __future__ import print_function
import torch, h5py
import numpy as np
from scipy.io import loadmat
from torch.nn.utils import weight_norm
import torch.nn as nn
import torch.optim as optim
import numpy as np
# import matplotlib
from torch.autograd import Variable
import sys
import itertools
import torch.nn.func... | 25,258 | 32.235526 | 131 | py |
deepglo | deepglo-master/DeepGLO/data_loader.py | import torch, h5py
import numpy as np
from scipy.io import loadmat
import torch.nn as nn
import torch.optim as optim
import numpy as np
# import matplotlib
from torch.autograd import Variable
import itertools
from sklearn.preprocessing import normalize
import datetime
import json
import os, sys
import pandas as pd
im... | 6,610 | 34.735135 | 167 | py |
deepglo | deepglo-master/DeepGLO/Ftree.py | import numpy as np
import pandas as pd
class FplusTreeSampling(object):
"""
F+ tree for sampling from a large population
Construct in O(N) time
Sample and update in O(log(N)) time
"""
def __init__(self, dimension, weights=None):
self.dimension = dimension
self.layers = int(np.... | 3,508 | 29.513043 | 82 | py |
deepglo | deepglo-master/DeepGLO/utilities.py | import pandas as pd
import numpy as np
import datetime
def last_days(num=60, date=datetime.datetime(2018, 6, 20)):
y = [str(date.year) + "%02d" % date.month + "%02d" % date.day]
for i in range(1, num):
d = date - datetime.timedelta(days=i)
y = y + [str(d.year) + "%02d" % d.month + "%02d" % d.d... | 680 | 29.954545 | 85 | py |
deepglo | deepglo-master/DeepGLO/time.py | import pandas as pd
import numpy as np
class TimeCovariates(object):
def __init__(self, start_date, num_ts=100, freq="H", normalized=True):
self.start_date = start_date
self.num_ts = num_ts
self.freq = freq
self.normalized = normalized
self.dti = pd.date_range(self.start_da... | 2,134 | 30.865672 | 86 | py |
deepglo | deepglo-master/DeepGLO/metrics.py | import numpy as np
def smape(P, A):
nz = np.where(A > 0)
Pz = P[nz]
Az = A[nz]
return np.mean(2 * np.abs(Az - Pz) / (np.abs(Az) + np.abs(Pz)))
def mape(P, A):
nz = np.where(A > 0)
Pz = P[nz]
Az = A[nz]
return np.mean(np.abs(Az - Pz) / np.abs(Az))
def wape(P, A):
return np.mea... | 898 | 18.543478 | 87 | py |
deepglo | deepglo-master/DeepGLO/__init__.py | # Implement your code here.
| 28 | 13.5 | 27 | py |
deepglo | deepglo-master/DeepGLO/LocalModel.py | import torch, h5py
import numpy as np
from scipy.io import loadmat
from torch.nn.utils import weight_norm
import torch.nn as nn
import torch.optim as optim
import numpy as np
# import matplotlib
from torch.autograd import Variable
import itertools
import torch.nn.functional as F
from DeepGLO.data_loader import *
... | 21,683 | 31.804841 | 157 | py |
deepglo | deepglo-master/run_scripts/run_traffic.py | #### OS and commanline arguments
import sys
import multiprocessing as mp
import gzip
import subprocess
from pathlib import Path
import argparse
import logging
import os
sys.path.append('./')
#### DeepGLO model imports
from DeepGLO.metrics import *
from DeepGLO.DeepGLO import *
from DeepGLO.LocalModel import *
impor... | 3,498 | 24.727941 | 87 | py |
deepglo | deepglo-master/run_scripts/run_wiki.py | #### OS and commanline arguments
import sys
import multiprocessing as mp
import gzip
import subprocess
from pathlib import Path
import argparse
import logging
import os
sys.path.append('./')
#### DeepGLO model imports
from DeepGLO.metrics import *
from DeepGLO.DeepGLO import *
from DeepGLO.LocalModel import *
import ... | 3,475 | 24.940299 | 87 | py |
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