arxiv_id stringlengths 0 16 | text stringlengths 10 1.65M |
|---|---|
'''
Run trained PredNet on UCSD sequences to create data for anomaly detection
'''
import hickle as hkl
import os
import shutil
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pandas as pd
# from keras import backend as K
from ker... | |
import numpy as np
import itertools
class BinaryLinearCode():
def __init__(self, G, H):
self.G = G # Generator matrix
self.H = H # Parity Check matrix
self.k = G.shape[0]
self.n = G.shape[1]
self.M = 2 ** self.k # Number of possible messages
self.codewordsL... | |
# Author: Laura Kulowski
import numpy as np
import matplotlib.pyplot as plt
import torch
def plot_train_test_results(lstm_model, Xtrain, Ytrain, Xtest, Ytest, num_rows = 4):
'''
plot examples of the lstm encoder-decoder evaluated on the training/test data
: param lstm_model: trained lstm encoder-decoder
... | |
# 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 io
import os
import pkgutil
import unittest
from datetime import timedelta
from unittest import TestCase
import numpy as np
import panda... | |
"""
bin modis data into regular latitude and longitude bins
"""
import numpy as np
def reproj_L1B(raw_data, raw_x, raw_y, xlim, ylim, res):
'''
=========================================================================================
Reproject MODIS L1B file to a regular grid
---... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import os
import numpy as np
import pandas as pd
import scanpy.api as sc
import sys
import wot.io
def main(argv):
parser = argparse.ArgumentParser(description='Compute neighborhood graph')
parser.add_argument('--matrix', help=wot.commands.MATRIX... | |
# coding: utf-8
import numpy as np
from PIL import Image
import scipy.io as sio
import os
import cv2
import time
import math
import os
os.environ['GLOG_minloglevel'] = '2'
# Make sure that caffe is on the python path:
caffe_root = '../../'
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
from caffe... | |
from amuse.couple import bridge
from amuse.community.bhtree.interface import BHTree
from amuse.community.hermite0.interface import Hermite
from amuse.community.fi.interface import Fi
from amuse.community.octgrav.interface import Octgrav
from amuse.community.gadget2.interface import Gadget2
from amuse.community.phiGRAP... | |
""" Dataset """
import numpy as np
from .base import Baseset
from .dsindex import DatasetIndex
from .pipeline import Pipeline
class Dataset(Baseset):
""" Dataset
Attributes
----------
index
indices
is_split
"""
def __init__(self, index, batch_class=None, preloaded=None, *args, **kwar... | |
import numpy as np
import os
import tensorflow as tf
from tqdm import tqdm
import ujson as json
from model import Model
from util import get_batch_dataset
from util import get_dataset
from util import get_record_parser
# for debug, print numpy array fully.
# np.set_printoptions(threshold=np.inf)
os.environ["CUDA_V... | |
import os
from copy import deepcopy
from typing import List, Union, Dict, Any
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import argparse
import logging
import sys
import json
import numpy as np
from predictor import Predictor
logger = logging.getLogger(__name__) # pylint: ... | |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 5 15:33:27 2019
@author: gptshubham595
"""
import cv2
import matplotlib.pyplot as plot
import numpy as np
import time
def main():
imgp1="C:\\opencv learn machin\\misc\\4.2.01.tiff"
imgp2="C:\\opencv learn machin\\misc\\4.2.05.tiff"
#1-by preserving sa... | |
import streamlit as st
import yfinance as yf
from datetime import datetime, timedelta
#import pyodbc
import pandas as pd
import os
import altair as alt
#import time
import sys
sys.path.append(os.path.abspath(r"C:\Users\cyril\Documents\Stocks\TA"))
from AutoSupportAndResistance import *
#import talib
from an... | |
from __future__ import print_function
import sys
import os
import numpy as np
import random
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from util import *
import itertools
import math
# This file contains code required for any preprocessing of real data, ... | |
from queue import PriorityQueue
import networkx as nx
import random
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import math
import time
class Event(object):
def __init__(self,state,time,srcNode,targetNode):
self.state = state
self.time = time
self.srcNode = srcNode
... | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import gym
from gym.spaces.box import Box
from gym import spaces
import logging
import numpy as np
import time
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def create_env(env_... | |
# Copyright 2017 - 2018 Baidu Inc.
#
# 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 agreed to in wri... | |
#!/usr/bin/python
import numpy as np
import healpy as hp
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sys
from detector_cache import detectors
import triangulate
from ligo.gracedb.rest import GraceDb
gdb = GraceDb()
graceid = sys.argv[1]
prior_name = sys.argv[2]
print graceid
fitsna... | |
""" lux_limit.py
This example shows how to produce a dark matter limit plot
using one of the simple counting experiment limit methods.
The data here is meant to approximate the parameters of the
LUX 2014-2016 run, which as of 2017 has produced the
best WIMP-nucleon spin-independent limit of any direct
detection expe... | |
import numpy as np
import time
for N in [int(i*1000) for i in range(1,11)]:
a = np.linspace(0, 2*np.pi, N)
k = 100
start_time = time.time()
M = np.exp(1j*k*(np.tile(a,(N,1))**2 + np.tile(a.reshape(N,1),(1,N))**2))
print('N=' + str(N) + ', time in Numpy: ', str(time.time() - start_time) + " seconds... | |
import random
import sys
import numpy as np
import cv2
import io
import socket
import struct
import time
import urllib.request
import json
NOTIFICATION_SEND_INTERVAL = 5
DATA_SEND_INTERVAL = 30
def server_routine(frame_queue, audio_queue,
room_temp, room_humid, baby_temp,
... | |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sqlalchemy
import datetime as dt
from sklearn.linear_model import LogisticRegression
from dreamclinic_churn_functions import *
import pickle
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import Ra... | |
from graphics import *
import numpy as np
win = GraphWin('Graph', 640, 480)
win.setBackground("white")
LTMargin = 100
TPMargin = 100
xmax = 640 - LTMargin
ymax = 480 - TPMargin
def rect(cpu,size):
rectangle = Rectangle(Point(LTMargin, 480 - TPMargin - np.int(cpu/10) ),Point((size/50000) + LTMargin, 480 - TPM... | |
#!/usr/bin/python3
import rospy
from sensor_msgs.msg import Image, CompressedImage
import picamera
import signal
import numpy as np
stop_process = False
def signal_handler(signal, frame):
global stop_process
stop_process = True
signal.signal(signal.SIGINT, signal_handler)
RES = (640, 480)
# Class ... | |
"""
visualize results for test image
"""
from numpy import asarray
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
from torch.autograd import Variable
import transforms as trans... | |
#!/usr/bin/env python
# coding: utf-8
# # Some example HMMs
#
# In[1]:
{
"tags": [
"hide-input",
]
}
# Install necessary libraries
try:
import jax
except:
# For cuda version, see https://github.com/google/jax#installation
get_ipython().run_line_magic('pip', 'install --upgrade "jax[cpu... | |
# -*- coding: utf-8 -*-
"""
Name: htsPlot.py
Author: Collin Rooney
Last Updated: 7/17/2017
This script will contain functions for plotting the output of the hts.py file
These plots will be made to look like the plots Prophet creates
Credit to Rob J. Hyndman and research partners as much of the code was devel... | |
#
# Author: Piyush Agram
# Copyright 2016
#
import logging
import isceobj
import mroipac
import os
logger = logging.getLogger('isce.topsinsar.runPreprocessor')
def runComputeBaseline(self):
from isceobj.Planet.Planet import Planet
import numpy as np
swathList = self._insar.getInputSwathList(self.s... | |
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified).
__all__ = ['ImStack']
# Cell
import torch
import torch.nn as nn
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
class ImStack(nn.Module):
""" This class represents an image as a series of stacked ... | |
#!/usr/bin/env python3
from distutils.spawn import find_executable
import matplotlib.pyplot as plt
# import plotly.express as px
import seaborn as sns
import pandas as pd
import numpy as np
import subprocess
import statistics
import random
import math
import gzip
import uuid
import sys
import re
import os
"""
~~~~~... | |
from face_alignment.detection.models import FAN, ResNetDepth
from .utils import crop, get_preds_fromhm, draw_gaussian
import torch
import numpy as np
import cv2
class FANLandmarks:
def __init__(self, device, model_path, detect_type):
# Initialise the face detector
model_weights = torch.load(model_... | |
"""
Sam Bluestone
Test 2
Exploratory data analysis for the admissions dataset
"""
import pandas as pd
import matplotlib.pyplot as plt
from mlxtend.plotting import scatterplotmatrix
import numpy as np
from mlxtend.plotting import heatmap
from sklearn.preprocessing import OneHotEncoder
import sys
#read the data into a ... | |
from typing import Sequence
import oneflow.experimental as flow
import argparse
import numpy as np
import os
import time
import sys
import oneflow.experimental.nn as nn
import json
from tqdm import tqdm
sys.path.append(os.path.abspath(os.path.join(os.getcwd(), "model_compress/distil_new_api/src")))
curPath = os.path.ab... | |
# ------------------------------------------------------------------------------
# Modified from HRNet-Human-Pose-Estimation
# (https://github.com/HRNet/HRNet-Human-Pose-Estimation)
# Copyright (c) Microsoft
# ------------------------------------------------------------------------------
from __future__ import absolu... | |
import os
import sys
import copy
sys.path.append('./player_model/')
sys.path.append('./utils')
import config
import exp_config
import pandas as pd
import numpy as np
from multiprocessing import Pool
from bots import BasicBot
from rectangular_world import RectangularWorld
from environment import *
reps = exp_config... | |
import torch.nn as nn
import torch
import numpy as np
class Combinator(nn.Module):
"""
The vanilla combinator function g() that combines vertical and
lateral connections as explained in Pezeshki et al. (2016).
The weights are initialized as described in Eq. 17
and the g() is defined in Eq. 16.
... | |
import cnn_rnn
import lasagne
import sample
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--tasks', nargs='+')
parser.add_argument('--labeling_rates', nargs='+', type=float)
parser.add_argument('--very_top_joint', dest='very_top_joint', action='store_true')
args = parser.p... | |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
import math
import numpy as np
import torch
from utils.helpers.det_helper import DetHelper
class YOLOTargetGenerator(object):
"""Compute prior boxes coordinates in center-offset form for each source feature map."""
def ... | |
"""Applies trained neural net in inference mode."""
import copy
import argparse
import numpy
from gewittergefahr.gg_utils import file_system_utils
from ml4tc.io import example_io
from ml4tc.io import prediction_io
from ml4tc.utils import satellite_utils
from ml4tc.machine_learning import neural_net
SEPARATOR_STRING =... | |
# encoding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from xmuda.models.LMSCNet import SegmentationHead
from xmuda.models.context_prior import ContextPrior3D
from xmuda.models.context_prior_v2 import ContextPrior3Dv2
from xmuda.models.CP_baseline import CPBaseline
from... | |
import comet_ml
import tensorflow as tf
print(f'Using tensorflow version: {tf.version.VERSION}')
import keras
import keras.backend as K
from keras.layers import Dense, Dropout
from keras.metrics import TrueNegatives, TruePositives, FalseNegatives, FalsePositives
import pandas as pd
import numpy as np
from typing i... | |
"""Window pairs of sequences"""
__author__ = 'thor'
from numpy import *
import numpy as np
from itertools import product
from collections import defaultdict, Counter
DEBUG_LEVEL = 0
def wp_iter_with_sliding_discrete_step(data_range, # length of the interval we'll retrieve the windows from
... | |
# -*- coding: utf-8 -*-
"""Untitled54.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1PX3b2zRt-Q3D2ia5NghD8bvSMqKZRTWf
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
df=pd.read_csv("creditcard.csv")
d... | |
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from src.swhe import SWHE
def plot():
data = {
"pipe": {
"outer-dia": 0.02667,
"inner-dia": 0.0215392,
"length": 100,
"density": 950,
"conductivity": 0.4
},
... | |
from dsbox.template.template import DSBoxTemplate
from d3m.metadata.problem import TaskKeyword
from dsbox.template.template_steps import TemplateSteps
from dsbox.schema import SpecializedProblem
import typing
import numpy as np # type: ignore
class DefaultVideoClassificationTemplate(DSBoxTemplate):
def __ini... | |
import os
import numpy as np
from sympy.abc import x as symbolic_x
from sympy.abc import y as symbolic_y
from .linearfilter import SpatioTemporalFilter
from .spatialfilter import GaussianSpatialFilter
from .temporalfilter import TemporalFilterCosineBump
from .movie import Movie
from .lgnmodel1 import LGNModel, heat_pl... | |
import numpy as np
import librosa
import math
import sys
print("Loading file")
audio, sample_rate = librosa.load(sys.argv[1], duration=60, offset=0, sr=15360)
print("Getting spectrum")
spectrum = librosa.stft(audio)
S = np.abs(spectrum)
fout = open("spectrum.h", "w")
print("Writing file")
fn = 36
fs = int(len(S) / ... | |
# -*- coding: utf-8 -*-
from data.reader import wiki_from_pickles
from data.corpus import Words, Articles, Sentences
from stats.stat_functions import compute_vocab_size
from stats.mle import Heap
from jackknife.plotting import hexbin_plot
import numpy as np
import numpy.random as rand
import matplotlib.pyplot as ... | |
import tensorflow as tf
import numpy as np
import os
import sys
from MyPreprocessingWrapper import MyPreprocessingWrapper
from MyImageProcessor import MyImageProcessor
from MyUtils import MyUtils
from Visualization import Visualization
class MyTrainingModelWrapper(object):
save_my_model_tf_session = None
d... | |
import os
import numpy as np
import matplotlib.pyplot as plt
path = os.getcwd() + "/data/ex1data2.txt"
data = np.loadtxt(path, delimiter=",")
temp = np.ones(((data.shape)[0],1), dtype=np.float64)
data = np.append(temp, data, axis=1)
def featureScaling(data):
mean = np.zeros((1, data.shape[1] - 1))[0]
min_ = data[0]... | |
#!/usr/bin/python
""" Classes and functions for fitting tensors """
# 5/17/2010
import numpy as np
from dipy.reconst.maskedview import MaskedView, _makearray, _filled
from dipy.reconst.modelarray import ModelArray
from dipy.data import get_sphere
class Tensor(ModelArray):
""" Fits a diffusion tensor given diffus... | |
import numpy as np
import torch
from torch.utils.data import DataLoader,TensorDataset
def mIoU_of_class(prediction, predict_label, target, target_label):
target_args = torch.where(target == target_label)
target_size = target_args[0].shape[0]
if target_size == 0:
return None
else:
intersection = predict... | |
# Released under The MIT License (MIT)
# http://opensource.org/licenses/MIT
# Copyright (c) 2013-2016 SCoT Development Team
"""Use internally implemented functions as backend."""
from __future__ import absolute_import
import scipy as sp
from . import backend
from . import datatools, pca, csp
from .var import VAR
fro... | |
"""
tools to manipulte data files
includes
bin, trim, stitch, etc
also include interpolation stuff
"""
import numpy as np
from scipy import stats
from scipy import interpolate
def trim_data(xlist,ylist,up,down):
for i,info in enumerate(xlist):
if info > up:
start = i
... | |
# -*- coding: utf-8 -*-
'''
Script that generates and analyzes a synthetic set of PMS data. These data differ from the data used in the paper but
capture important elements of what is presented in the paper.
Inference generation requires use of the logistigate package, available at https://logistigate.readthedocs.io/en... | |
from math import ceil
import networkx as nx
class Element():
def __init__(self, name, amount):
self.name = name
self.amount = amount
def __str__(self):
return str(self.amount)+" "+self.name
def __repr__(self):
return self.__str__()
def __hash__(self):
... | |
import numpy as np
import sklearn.neighbors
import sklearn.pipeline
import sklearn.svm
import sklearn.decomposition
import sklearn.gaussian_process
import logging
import pickle
import joblib
import time
import heapq
import inspect
from . import loggin
from . import TLS_models
import functools
import collections
import ... | |
# TODO
from cmstk.filetypes import TextFile
from cmstk.structure.simulation import SimulationCell
import numpy as np
class DataFile(TextFile):
def __init__(self, filepath, comment, simulation_cell):
if filepath is None:
filepath = "lammps.data"
if comment is None:
comment =... | |
import os
import pickle
import re
import warnings
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.platform import gfile
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC... | |
import numpy as np
import os
mazeX = 6
mazeY = 5
mazeBx = 4
mazeBy = 4
maxState = mazeX*mazeY*mazeX*mazeY+2
numA = 5
verbose = False
import argparse
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--T", type=int, default=15,
help="Time-ho... | |
def mangoPlot(mango_filenames):
# Copyright 2019, University of Maryland and the MANGO development team.
#
# This file is part of MANGO.
#
# MANGO is free software: you can redistribute it and/or modify it
# under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either ... | |
import cv2
import numpy as np
import os
from cvpackage import resize, to_gray, contrast_tune, gaussian_blur, canny_capture
from lineIterator import get_pixels, curve_plot, curve_fitting, curve_smooth, count_peaks
# FILE PATH HERE #
testPic = 'testsample.JPG'
picPath = 'image'
# FILE PATH HERE #
# PUBLIC PARAMETERS HE... | |
import os
import pickle
import numpy as np
import scipy.stats
class Results(object):
mpr_column = "test-all-baskets.MPR"
prec_ten_column = "test-all-baskets.Prec@10"
prec_five_column = "test-all-baskets.Prec@5"
all_baskets_AUC = "test-all-baskets.AUC"
processed_columns = {"mpr": mpr_column,
... | |
# -*- coding: utf-8 -*-
# @Author: xuenan xu
# @Date: 2021-06-14
# @Last Modified by: xuenan xu
# @Last Modified time: 2021-07-02
import sys
import kaldiio
import librosa
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
from pathlib import Pat... | |
# Copyright (c) 2017-present, Facebook, Inc.
#
# 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 agreed... | |
import torch
from torch.utils.data import Dataset, DataLoader
from torch.distributions.multivariate_normal import MultivariateNormal
import numpy as np
from tqdm import tqdm
class UniformSampler:
"""
UniformSampler allows to sample batches in random manner without splitting the original data.
"""
def ... | |
"""
Code by Nicola De Cao was forked from https://github.com/nicola-decao/BNAF
MIT License
Copyright (c) 2019 Nicola De Cao, 2019 Peter Zagubisalo
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software... | |
#!/Library/Frameworks/Python.framework/Versions/3.8/bin/python3
from sqlite3 import connect
from matplotlib import pyplot as plt
from matplotlib import rcParams
from numpy import array, count_nonzero, logical_and
rcParams['font.size'] = 8
def neutral_mass(mz, adduct):
adduct_to_mass = {'[M+H]+': 1.0078, '[M+K]+... | |
#!/usr/bin/python3
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import style
style.use( "fast" )
fig = plt.figure()
ax = fig.add_subplot( 111, projection='3d' )
X, Y, Z = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ], [ 5, 6, 2, 3, 13, 4, 1, 2, 4, 8 ], [ 2, 3, 3, 3,... | |
import argparse
import numpy as np
from dataset import Dataset, collate_fn
import torch
import torch.nn.functional as F
import pickle
import os
import torch.nn as nn
from torch.utils.data import DataLoader
from sklearn.linear_model import LinearRegression
parser = argparse.ArgumentParser()
parser.add_argument('--no-c... | |
import matplotlib.pyplot as plt
import numpy as np
from ssm.star_cat.hipparcos import load_hipparcos_cat
from astropy.coordinates import SkyCoord
import astropy.units as u
from astropy.time import Time
from astropy.coordinates import SkyCoord, AltAz, EarthLocation
from ssm.core import pchain
from ssm.pmodules import *
... | |
# -*- coding: utf-8 -*-
"""
=======================================
Generate more advanced auditory stimuli
=======================================
This shows the methods that we provide that facilitate generation
of more advanced stimuli.
"""
import numpy as np
import matplotlib.pyplot as plt
from expyfun import bu... | |
"""
Copyright (c) 2020-present NAVER Corp.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distri... | |
# coding: utf-8
import warnings
try:
import talib as ta
except ImportError:
from czsc import ta
ta_lib_hint = "没有安装 ta-lib !!! 请到 https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib " \
"下载对应版本安装，预计分析速度提升2倍"
warnings.warn(ta_lib_hint)
import pandas as pd
import numpy as np
from datet... | |
from numpy import ones
from vistas.core.graphics.geometry import Geometry
class FeatureGeometry(Geometry):
def __init__(self, num_indices, num_vertices, indices=None, vertices=None):
super().__init__(
num_indices, num_vertices, has_normal_array=True, has_color_array=True, mode=Geometry.TRIA... | |
import networkx as nx
import numpy as np
import torch
from torch.utils.data import Dataset
from dsloader.util import kron_graph, random_binary, make_fractional
class KroneckerDataset (Dataset):
def __init__(self, kron_iter=4, seed_size=4, fixed_seed=None, num_graphs=1, perms_per_graph=256, progress_bar=False):
... | |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from bs4 import BeautifulSoup
import urllib.request
import requests
import re
import csv
import html | |
from __future__ import division
import cv2
import numpy as np
from math import *
#--------------------------------------------
# AUXILIARY BLOCK FUNCTIONS
#--------------------------------------------
# return closed image
def closing(bny, dim):
return erosion(dilation(bny, dim), dim);
# return dila... | |
import os
import json
import random
import numpy as np
import tensorflow as tf
import torchvision.transforms as transforms
from .utils import load_and_preprocess_image
from PIL import Image
root = '/data/cvfs/ah2029/datasets/bdd100k/'
def load_day_and_night(split='train', subset=1.0):
""" Load image filenames ... | |
# Using Android IP Webcam video .jpg stream (tested) in Python2 OpenCV3
from collections import deque
from cspaceSliders import FilterWindow
from selenium import webdriver
import argparse
import urllib.request
import cv2
import numpy as np
import time
import math
def move(ptX, ptY):
ptX = ptX * ((((1366/864))/136... | |
import math
import numpy as np
CPUCT = 1.0
class NodeInfo:
def __init__(self, state, action, raw_policy, value):
self.state = state
self.action = action
self.policy = [raw_policy[k] for a, k in action]
self.value = value
self.children_state = [None for i in range(len(action... | |
from ..proto import *
from ..graph_io import *
import copy
import paddle.fluid as fluid
import numpy as np
from paddle.fluid.core import VarDesc, AttrType
class Fluid_debugger:
def var_names_of_fetch(self, fetch_targets):
var_names_list = []
for var in fetch_targets:
var_names_list.append(var.name)
return ... | |
# -*- coding: utf-8 -*-
from datetime import datetime, timedelta
import numpy as np
import pytest
from ...metricgenerator.manager import SimpleManager
from ...types.association import TimeRangeAssociation, AssociationSet
from ...types.detection import Detection
from ...types.groundtruth import GroundTruthPath, Ground... | |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
# ------------------------------------------------... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#特征提取
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
#新闻文本数据
from sklearn.datasets import fetch_20newsgroups
# 数据分割
from sklearn.model_selection im... | |
import numpy as np
def rle_to_mask(lre, shape=(1600, 256)):
'''
params: rle - run-length encoding string (pairs of start & length of encoding)
shape - (width,height) of numpy array to return
returns: numpy array with dimensions of shape parameter
'''
# the incoming string is ... | |
"""
Some random functions for hyperparameter optimization
Alisa Alenicheva, Jetbrains research, Februari 2022
"""
import os
import torch
from hyperopt import hp
import errno
from MoleculeACE.benchmark.utils import get_config
from MoleculeACE.benchmark.utils.const import Algorithms, RANDOM_SEED, CONFIG_PATH, CONFIG_PA... | |
import numpy as np
import pandas
import analysis as lan
from collections import namedtuple
PathwayConfig = namedtuple("PathwayConfig", ["measure", "hierarchy"])
def retrieve_mutations(pid, seq_data):
patient_data = seq_data[
(seq_data["PatientFirstName"] == pid)
& (seq_data["Technology"] == "NGS ... | |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | |
import argparse
import logging
import os
import pickle
import sys
import time
import numpy as np
import tensorflow as tf
from sklearn.svm import SVC
from tensorflow.python.platform import gfile
from input_loader import (filter_dataset, split_dataset, get_dataset,
get_image_paths_and_labels)
... | |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 11 13:08:41 2016
@author: m.reuss
"""
import numpy as np
import CoolProp.CoolProp as CP
import pandas as pd
CP.set_config_string(
CP.ALTERNATIVE_REFPROP_PATH,
'C:\\Program Files (x86)\\REFPROP\\')
np.seterr(divide='ignore', invalid='ignore')
#%%H2 Constant Valu... | |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import lib.maths_util as mathlib
from lib.colors import ColorsBook as color
import time
class NeuralNetwork():
def __init__(self, layers, batch_size, epochs, learning_rate):
self.layers = layers
self.batch_size = batch_size
self... | |
import h5py
import numpy as np
import sys
infname = sys.argv[1]
key = sys.argv[2] if sys.argv[2:] else "matrix"
prefix = "data" if not sys.argv[3:] else sys.argv[3]
f = h5py.File(infname, "r")
print(f.keys())
group = f[key]
for comp in ["shape", "indices", "indptr", "data"]:
with open(prefix + '.' + comp, "w") as... | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import matplotlib.pyplot as plt
def build_curve_points(descriptors):
'''
Draw the points given by the descriptors array in the
cartesian plane.
'''
N=len(descriptors) # size of the descriptors vector
for x in range(N):... | |
from math import pi
from numpy import sin,cos
from openmdao.main.api import Component
from openmdao.main.datatypes.api import Float
class SpiralComponent(Component):
x = Float(iotype="in", low=0.75, high=5.*pi)
y = Float(iotype="in", low=0.75, high=5.*pi)
f1_xy = Float(0.,iotype="out")
f2_xy = Float... | |
#!/usr/bin/env python
from copy import copy
import matplotlib
import netCDF4
import numpy
matplotlib.use("Agg")
import matplotlib.animation as animation
import matplotlib.colors as colors
import matplotlib.pyplot as plt
FFMpegWriter = animation.writers['ffmpeg']
metadata = dict(title='Secondary Mean Age', artist='LU... | |
#!/usr/bin/python3
'''Copyright (c) 2018 Mozilla
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the follow... | |
import os
import glob
import progressbar
import numpy as np
from numpy import genfromtxt
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
kLatentValuesPath = './training_results/0000-00-00_00-00-00/latents'
if "__main__" == __name__:
file_path_list = glob.glob(os.path.join(kLatentValuesPath, '*... | |
import numpy as np
import matplotlib.pyplot as plt
#%%
cases_data = np.genfromtxt("sources/cases_comparation.csv", delimiter = ',', skip_header = 1)[:, 1:] / 1000000
cases = ['GEMASOLAR', 'Base Case', 'Evaporative Cooling', 'Dry Cooling', 'Once Through Cooling',
'MED Cooling']
months = ['Jan', 'Feb',... | |
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import lines
from matplotlib.font_manager import FontProperties
from statannotations.format_annotations import pval_annotation_text, simple_text
from statannotations... |
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