arxiv_id stringlengths 0 16 | text stringlengths 10 1.65M |
|---|---|
planet-mitchell-0.1.0: Planet Mitchell
Text
Synopsis
# Text
data Text #
A space efficient, packed, unboxed Unicode text type.
Instances
Instance detailsDefined in Data.Hashable.Class MethodshashWithSalt :: Int -> Text -> Int #hash :: Text -> Int # Instance detailsDefined in Data.Aeson.Types.ToJSON MethodstoJSON ... | |
# How to evaluate the second argument of Cases only once
I'd like to find a fast but readable way to achieve the following:
Cases[RandomInteger[{AbsoluteTime["2001"], AbsoluteTime["2003"]}, 1000],
a_ /; a > AbsoluteTime["2002"]]
The correct result can be achieved much faster using with:
With[{d = AbsoluteTime["200... | |
# Using the Fisher linear discriminant for separating non-linearly separable data
After learning the Fisher linear discriminant in class and its ability to project data into one dimension so that it can be separated by a threshold, I wanted to evaluate it using non linearly separable data and to observe visually its p... | |
# American Institute of Mathematical Sciences
July 2018, 17(4): 1561-1572. doi: 10.3934/cpaa.2018074
## On special regularity properties of solutions of the Zakharov-Kuznetsov equation
1 IMPA, Estrada Dona Castorina 110, Rio de Janeiro 22460-320, Brazil 2 Department of Mathematics, University of California, Santa ... | |
# What am I? 'Tis the season of mutual puzzling
I'm born when one is over the treasure spot,
I'm used when you drive to buy a present,
I'm born in the spirit of mutual gift-giving,
Yet I'm literally a super negative one.
Kinda short, so might be open to interpretation, but I assure you your efforts in solving this ri... | |
# How do you draw the electron configuration diagram for Aluminum?
##### 1 Answer
The electron configuration for aluminum is: $1 {s}^{2} 2 {s}^{2} 2 {p}^{6} 3 {s}^{2} 3 {p}^{1}$
#### Explanation:
To figure out the electron configuration of any element you will use the diagonal diagram (seen in the right side of vid... | |
Python Programming – Class
In this Page, We are Providing Python Programming – Class. Students can visit for more Detail and Explanation of Python Handwritten Notes Pdf.
Python Programming – Class
Class
A class is the particular object type created by executing a class statement. Class objects are used as templates... | |
# Avoid Meetingbird and Front (frontapp.com) products
This blog is far from viral, but it has a couple of thousands of views a month and likely a lot of the visitors could be interested in products by https://frontapp.com/ .
I want to argue against that, as I don't think they can be trusted anymore.
Just a few month... | |
# How to show non-convexity of the geometrical motivated SVM Optimization Problem
Geometrically the SVM tries to classify each data point rightly, while maximize the margin $\gamma$. In the linear seperable case this can be formulated as \begin{align} \max_{\gamma,b \in \mathbb{R}, w \in \mathbb{R}^d} \gamma \ \ \text... | |
I'm an EE from USC with mostly a software career. My hobbies of robotics and mechatronics have kept my EE skills reasonably sharp. My current role is architecting software for SDRs so I get to get DSP too!
## Re: SDR/DSP Question
I am going to echo everyone else's suggestion that you avoid using the TMS320C6713. Unle... | |
G Then the traction vector on the plane is given by, The magnitude of the traction vector is given by, Then the magnitude of the stress normal to the plane is given by, The magnitude of the resolved shear stress on the plane is given by, If the principal stresses {\displaystyle G(\phi ,\theta )} Essentially, the angle ... | |
Plate 18
Figure 17
The contents of these clamps are then ligated with 00 silk sutures. Downward traction is maintained on the esophagus while it is further freed from the surrounding structures by blunt dissection with the index finger. The vagus nerves are not always easily identified, but their location is more quic... | |
# Build my own mass spectrometer?
1. Aug 4, 2006
### leright
I am a double major in EE and physics and I was thinking that a great senior project would be to design and build my own mass spectrometer. I would not only like to build the basic device, but also calibrate the device so that it provides the m/q value bas... | |
Noticeboard archives
## User talk:70.173.50.153
Take a look at this page and tell me what you think. Looks like the user removed some templates back on the 10 november and received vandalism warnings for it. It doesn't look like vandalism to me, certainly not simple vandalism that requires a template. Possible test e... | |
Hilbert-Schmidt and compact operators
I am new to this site and i dont really know how to ask questions properly, so i am really sorry if i did something wrong.
My question is if there is a way to prove that a Hilbert-Schmidt operator is compact from the definition of compact operators.
I can prove the result by not... | |
# How does ML algorithms treat unseen data, a conceptual discussion
I want to predict the occurrence of certain events, but these events only occur say 5% of the time in my data, hence in 95% of the data there is nothing to learn.
In order to teach the ML algo something I have learned to single out the 5% and drop th... | |
# Why aren't Faraday's law of induction and Maxwell-Ampere's law symmetric? [duplicate]
I don't see Faraday's law of induction and Maxwell-Ampere's law are totally symmetric in the sense that Maxwell-Ampere's law has a factor of $ϵ_0μ_0$: \begin{align} \nabla\times\mathbf E&=-\frac{\partial\mathbf B}{\partial t} \\ \n... | |
# List NTFS Permissions on all Folders
In this guide, I’ll show you how to list the NTFS permissions for all folders and subfolders.
I’ll also show you how to export the NTFS permissions to a CSV file.
Check it out.
## Option 1: List and Export NTFS Permissions using GUI Tool.
For this first option, I’ll be using ... | |
Help protect the Great Barrier Reef with TensorFlow on Kaggle
# tf.math.unsorted_segment_prod
Computes the product along segments of a tensor.
Read the section on segmentation for an explanation of segments.
This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum ove... | |
lilypond-user
[Top][All Lists]
## Re: Organization of the piese part by part, not staff by staff
From: Mats Bengtsson Subject: Re: Organization of the piese part by part, not staff by staff Date: Fri, 11 Jan 2019 09:13:47 +0100 User-agent: Mozilla/5.0 (X11; Linux x86_64; rv:60.0) Gecko/20100101 Thunderbird/60.2.1
... | |
# NCERT solution for class 9 science natural resources ( Chapter 14)
#### Solution for Exercise Questions
1. Why is the atmosphere essential for life?
The atmosphere is essential for life because of the following reasons:
1. The atmosphere constitutes of various main gases like O2, N2, and CO2
2. Photosynthesis is... | |
# Skipping Bases
$\large 123_4 \qquad 123_5 \qquad 123_6$
The above shows three numbers, each written in a different base representation. Which of these numbers has the largest value?
× | |
+1.617.933.5480
+1.866.649.0192
# Q: Assume X is normally distributed
Assume X is normally distributed with a mean of 5 and a standard deviation of 4. Determine the value for x that solves each of the following:
(a) P(X > x) = 0.5
(b) P(X > x) = 0.95
(c) P(x < x="">< 9)="0.2">
(d) P(3 < x="">< x)="">
(e) P(-x < x="">... | |
Can you remove a factor from your model if it has a significant effect, but the removal improves AIC and R square?
I have a complex problem but the title sums it up pretty easily.
I have four types of cages that manipulate water flow, but I also have an actual measure of water flow from inside the cages. I'm wonderin... | |
# Tag Info
19
These are all good questions. Perhaps I can answer a few of them at once. The equation describing the violation of current conservation is $$\partial^\mu j_\mu=f(g)\epsilon^{\mu\nu\rho\sigma}F_{\mu\nu}F_{\rho\sigma}$$ where $f(g)$ is some function of the coupling constant $g$. It is not possible to writ... | |
GR 8677927796770177 | # Login | Register
GR9677 #64
Problem
GREPhysics.NET Official Solution Alternate Solutions
This problem is still being typed.
Advanced Topics$\Rightarrow$}Nuclear Physics
In symmetric fission, the change in kinetic energy is just the change in binding energy. The change in binding energy for a ... | |
# Predicted probabilities from probit
Assume following probit model:
$y_i$ = $\phi$($\beta_0$+$\beta_1x_1$+$\beta_2x_1^2$+$\beta_3d_1$+$\beta_4d_2$) where $d_1$ and $d_2$ are dummies
or in Stata:
probit y_i x1 xsq d1 d2
Now I want to predict the probabilities $P(\hat{y_i} = 1)$ for each observation x. This seems ... | |
# Calculating Missing Amounts Required: For each of the following independent cases (A–E), compute...
Calculating Missing Amounts
Required:
For each of the following independent cases (A–E), compute the missing values in the table below. | |
Python: Deep and Shallow Copy Object
# Python: Deep and Shallow Copy Object
### Introduction
In this article, we'll take a look at how to deep and shallow copy the objects in Python.
The short answer is that you can use methods of the copy module, for both operations:
import copy
shallow_copy_list = copy.copy(ori... | |
import itertools
import numpy as np
from qubo_nn.problems.subgraph_isomorphism import SubGraphIsomorphism
from qubo_nn.problems.util import gen_graph
class GraphIsomorphism(SubGraphIsomorphism):
def __init__(self, cfg, graph1, graph2):
super(GraphIsomorphism, self).__init__(cfg, graph1, graph2, a=1, b=2)
... | |
#**************************
# Logistic Regression
# for DNA N6-Adenine Methylation
# Tian Tian
# tt72@njit.edu
#**************************
import sys
import numpy as np
import itertools
import multiprocessing
#**************************
# import modules
#**************************
from joblib import Parallel, delayed... | |
# %%
#Standard Library Modules
import pandas
import sys
import unittest
import os
import numpy
#set current working directory to where this file is saved
thisdir = os.path.dirname(os.path.abspath(__file__)) + "\\"
os.chdir(thisdir)
# Add higher directory to python module's path
sys.path.append("..")
#Local Applica... | |
#!/usr/bin/python
import math
import random
import string
import sys
import numpy as np
from midiutil.MidiFile import MIDIFile
from midigen import heightmap
from midigen import dither
if len(sys.argv) > 1:
seed = str(sys.argv[1])
else:
chars = string.ascii_lowercase + string.ascii_uppercase + string.digits... | |
# -*- coding: utf-8 -*-
# Copyright 2020 PyePAL authors
#
# 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 l... | |
#!/usr/bin/env python
import sys
import json
import numpy as np
from logging import warning
IGNORE = set(['[CLS]', '[SEP]'])
def argparser():
from argparse import ArgumentParser
ap = ArgumentParser()
ap.add_argument('file', nargs='+', metavar='JSONL',
help='BERT extract_features.py... | |
# Copyright (c) 2020 Hartmut Kaiser
#
# Distributed under the Boost Software License, Version 1.0. (See accompanying
# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
# #1258: np.random.randn does not work
from phylanx import Phylanx
import numpy as np
@Phylanx
def generate():
return np... | |
import numpy as np
import torchvision.models.segmentation
import torch
import torchvision.transforms as tf
Learning_Rate=1e-5
width=height=900 # image width and height
batchSize=1
#---------------------Create training image ---------------------------------------------------------
def ReadRandomImage():
FillLevel=... | |
# Licensed under a MIT style license - see LICENSE.rst
"""MUSE-PHANGS target sample module
"""
__authors__ = "Eric Emsellem"
__copyright__ = "(c) 2017, ESO + CRAL"
__license__ = "MIT License"
__contact__ = " <eric.emsellem@eso.org>"
# Standard modules
import os
from os.path import join as joinpath
import nump... | |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 17 10:48:57 2020
@author: Manuel Camargo
"""
import os
import subprocess
import copy
import multiprocessing
from multiprocessing import Pool
import itertools
import traceback
import numpy as np
import pandas as pd
import math
import random
from hyperopt import tpe
from h... | |
import retro
import gym
import numpy as np
from DQ import DuelingDQNPrioritizedReplay
from matplotlib import pyplot as plt
import cv2
class SonicDiscretizer(gym.ActionWrapper):
"""
Wrap a gym-retro environment and make it use discrete
actions for the Sonic game.
"""
# B is do nothing
# down... | |
import numpy as np
from pdb import set_trace
from tinylib import logmass_statistic
from contcnet import MultivariateGaussain
from utmLib.clses import Timer
from utmLib.ml.GBN import GBN
def predict_wrapper(model, test):
pred = []
for item in test:
unknown = np.where( np.isnan(item) )[0]
... | |
#! /usr/bin/env python
import argparse
import cv2
import sys
import time
import datetime
import imutils
from collections import deque
import numpy as np
import serial
ser = serial.Serial('COM18', 9600) #initializing serial communication for Zigbee
cam_device = 1
laser = (0,0)
maxlen=10
pts = deque(maxlen=10)
detec... | |
# MIT License
#
# Copyright (C) IBM Corporation 2018
#
# 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... | |
import numpy as np
def editDistance(s1, s2):
m=len(s1)+1
n=len(s2)+1
tbl = np.empty([m,n])
for i in xrange(m): tbl[i,0]=i
for j in xrange(n): tbl[0,j]=j
for i in xrange(1, m):
for j in xrange(1, n):
cost = 0 if s1[i-1] == s2[j-1] else 1
tbl[i,j] = min(tbl[i, j-1... | |
# imports
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import Parameter
import numpy as np
from numpy.linalg import norm
import scipy.io as sio
import pickle
usecuda = True
usecuda = usecuda and torch.cuda.is_available()
dtype = torch.FloatTensor
if usecuda:
dtype = torc... | |
import math
import numpy
import random
import types
from itertools import izip, tee, imap
from operator import itemgetter, add
from cStringIO import StringIO
import cPickle as pickle
import tensorflow as tf
class Graph(object):
def transform_batch(self, data):
raise NotImplemented
def train_and_loss_f... | |
'''
Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related docu... | |
import numpy as np
def h_fpp(H):
"""Evaluate the significance of an H score.
The H test is an extension of the Z_m^2 or Rayleigh tests for
uniformity on the circle. These tests estimate the Fourier coefficients
of the distribution and compare them with the values predicted for
a uniform distributi... | |
import numpy as np
from numpy import nan
import pytest
from pandas._libs import groupby, lib, reduction
from pandas.core.dtypes.common import ensure_int64
from pandas import Index, isna
from pandas.core.groupby.ops import generate_bins_generic
import pandas.util.testing as tm
from pandas.util.testing import assert_a... | |
import sys
import os
import requests
import re
import urllib.request
from scipy.io import savemat, loadmat
from tqdm import tqdm
from PyQt5.QtCore import Qt
from PyQt5.QtCore import QThread, pyqtSignal
from PyQt5.QtWidgets import QWidget, QPushButton, QProgressBar, QVBoxLayout, QLabel, QApplication
import time
import n... | |
# Tools - Pandas
*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available progr... | |
from preprocess.generateMap import ClusterGenerator
import matplotlib.pyplot as plt
from model.climateNet import ClimateNet
from dataReader.dataset import dataset
from torch.utils.data import DataLoader, WeightedRandomSampler
import torch
import numpy as np
import pandas as pd
from sklearn import metrics
import os
i... | |
import numpy as np
from abraia import Multiple
multiple = Multiple()
def test_load_image():
img = multiple.load_image('lion.jpg')
assert isinstance(img, np.ndarray)
def test_load_metadata():
meta = multiple.load_metadata('lion.jpg')
assert meta['MIMEType'] == 'image/jpeg'
def test_save_image():
... | |
""" Test pyfive's abililty to read multidimensional datasets. """
import os
import numpy as np
from numpy.testing import assert_array_equal
import pyfive
DIRNAME = os.path.dirname(__file__)
DATASET_COMPRESSED_HDF5_FILE = os.path.join(DIRNAME, 'compressed.hdf5')
def test_compressed_dataset():
with pyfive.File(... | |
# coding: utf-8
# In[1]:
#After conversion and audio features have been extracted, rename the split channels according to the intensity values from OpenSmile or IBM ASR results (this is not consistently A: l, B: r in SWBD)
# In[6]:
import os
import sys
import numpy as np
from collections import defaultdict
# In[... | |
# SPDX-License-Identifier: Apache-2.0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from onnx import checker, helper, ModelProto, TensorProto, GraphProto, NodeProto, OperatorSetIdProto
from typing import Sequence, T... | |
#!/usr/bin/env python
import mmap
import os
import struct
import sys
from collections import OrderedDict
import numpy as np
class SigprocFile:
"""
Simple functions for reading sigproc filterbank files from python. Not all possible features are implemented.
Original Source from Paul Demorest's [pysigproc... | |
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import shift_dim
class NormReLU(nn.Module):
def __init__(self, channels, relu=True, affine=True):
super().__init__()
self.relu = relu
self.norm = nn.BatchNorm3d(channels)
... | |
import random
import os
import pickle
import librosa as lb
import numpy as np
import musdb
import yaml
# ignore warning about unsafe loaders in pyYAML 5.1 (used in musdb)
# https://github.com/yaml/pyyaml/wiki/PyYAML-yaml.load(input)-Deprecation
yaml.warnings({'YAMLLoadWarning': False})
def musdb_pre_processing(pat... | |
import numpy as np
# This is for Scotland
bands = [12500.0, 14549.0, 24944.0, 43430.0, 150000.0, 1000000.0]
rates = [ 0.0, 19.0, 20.0, 21.0, 41.0, 46.0]
def gross_to_net(gross_income, bands, rates):
gross_income = float(gross_income)
chunks = []
for i in range(len(bands)):
if i == 0:
... | |
# -*- coding: utf-8 -*-
"""Core Keras layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import copy
import types as python_types
import warnings
from .. import backend as K
from .. import activations
from .. import initializers... | |
from . import utils
from .base import BaseModel
import numpy as np
class CholerM1(BaseModel):
"""
The "M1" four parameter model described in Choler et al. 2010
"""
def __init__(self, parameters={}):
BaseModel.__init__(self)
self.all_required_parameters = {'a1': (0, 100), 'a2': (0, 100)... | |
from sklearn.decomposition import NMF
from sklearn.metrics import silhouette_score
import numpy as np
class NMFClustering():
"""
"""
def __init__(self, n_clusters=2):
""" """
self.n_clusters = n_clusters
self.nmf = NMF(n_components=n_clusters)
def fit(self, X):
"""
... | |
import numpy as np
from tensorflow.keras import backend as K
import tensorflow.keras as keras
import tensorflow as t
import json
###definitions of classes that will be used to define a current Network State
class state:
def __init__(self,gs=10,param=10):
self.gs=gs
self.param=param
def __str__(self):
... | |
import numpy as np
import cv2
from PIL import Image
import pytesseract
def plateDetection(plate):
gray_img = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray_img, 110, 255, cv2.THRESH_BINARY)
if cv2.waitKey(0) & 0xff == ord('q'):
pass
num_contours, hierarchy = cv2.findCo... | |
from time import sleep
import gaussianfft as grf
import unittest
import numpy as np
from multiprocessing import Process, Queue, set_start_method
def create_realization():
# Returns a 100 x 100 realization of a random field
v = grf.variogram('exponential', 100.0, 50.0)
s = grf.simulate(v, 100, 10.0, 100, ... | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import random
PATTERN_LENGTH = 7
class Link(object):
def __init__(self, graph, src, tar):
self.graph = graph
self.src = src
self.tar = tar
assert (graph.path_len[src][tar] == 0)
gra... | |
import base64
import cv2
import numpy as np
import tensorflow as tf
from starmart.input import Input, ImageInput
from starmart.result import Result, CompositeResult, NamedResult, ImageResult, ClassificationResult, Classification, \
Failure
from tensorflow.keras.applications.resnet50 import preprocess_input, decode... | |
"""
:mod:`operalib.kernels` implements some Operator-Valued Kernel
models.
"""
# Author: Romain Brault <romain.brault@telecom-paristech.fr> with help from
# the scikit-learn community.
# License: MIT
from numpy import dot, diag, sqrt
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.kernel_approxim... | |
"""Estimate tumor purity and frequency using copy number and allele freqencies with BubbleTree.
http://www.bioconductor.org/packages/release/bioc/html/BubbleTree.html
http://www.bioconductor.org/packages/release/bioc/vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html
"""
from __future__ import print_function
impor... | |
import huobi.model.position
import huobi.model.bararray
import numpy as np
class TradeInfoArray:
def __init__(self, interval, size=100):
self.count = 0
self.inited = False
self.size = size
self.ttmu_buy_ratio = np.zeros(size)
self.ttmu_sell_ratio = np.zeros(size)
s... | |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import torch
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
import numpy as np
import sys
sys.path.append('../codes')
from utils import hyperbolic_utils as hyp
from utils import manifolds
from run import *
from adjustText import ad... | |
# -*- coding: utf-8 -*-
'''
test program to show various calculation result of xmensur
'''
__version__ = '0.1'
import xmensur as xmn
import argparse
import numpy as np
if __name__ == "__main__" :
# exec this as standalone program.
parser = argparse.ArgumentParser(description='Print mensur data.')
parse... | |
import numpy as np
X = np.array(([0.4, -0.7], [0.3, -0.5], [0.6, 0.1], [0.2,0.4], [0.1,-0.2]), dtype=float)
y = np.array(([0.1], [0.05], [0.3], [0.25], [0.12]), dtype=float)
class Neural_Network(object):
def __init__(self):
self.inputSize = 2
self.outputSize = 1
self.hiddenSize = 2
self.... | |
"""Smolyak sparse grid constructor."""
from collections import defaultdict
from itertools import product
import numpy
from scipy.special import comb
import numpoly
import chaospy
def construct_sparse_grid(
order,
dist,
growth=None,
recurrence_algorithm="stieltjes",
rule="gaus... | |
import torch
import torch.nn as nn
import numpy as np
from src.IoU import *
from src.utils import *
from config import config
CONFIG = config()
def Validate(model, validloader, criterion, valid_loss_min, device, model_path):
valid_loss = 0
val_iou = []
val_losses = []
model.eval()
for i, val_data ... | |
""" Copyright (C) 2019 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
This file incorporates work covered by the following copyright and permission notice:
Copyright (c) 2019 LI RUOTENG
Permission to use, co... | |
# Copyright 2018 DeepMind Technologies Limited. 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 ... | |
import os
import re
import numpy as np
from AlphaZero.processing.go.game_converter import GameConverter
def selfplay_to_h5(model_name, base_dir='data'):
""" Takes a model that has just generated the selfplay data, combine everything into a single h5 file.
And store the h5 file as 'train.h5' in the same ... | |
"""Turns a MusicXML file into a pandas DataFrame."""
import io
from itertools import combinations
from fractions import Fraction
import music21
from music21.interval import Interval
from music21.pitch import Pitch
from music21.chord import Chord
from music21.note import Rest
import numpy as np
import pandas as pd
fr... | |
from apps.geocode import geocoder
import folium
import copy
import branca.colormap as cm
import json
import streamlit as st
import numpy as np
import pandas as pd
import altair as alt
from datetime import datetime, timedelta
import leafmap.foliumap as leafmap
from vega_datasets import data
import time
from streamlit.s... | |
import os
import re
import cv2
import sys
import glob
import random
import numpy as np
from imgaug import augmenters as iaa
from tensorflow.keras.utils import to_categorical
#augmentations to be performed on the timeseries dataset
# seq_img = iaa.Sequential([
# iaa.Crop(px=(1, 16), keep_size=True),
# iaa.Flipl... | |
#Author: Michail Mamalakis
#Version: 0.1
#Licence:
#email:mmamalakis1@sheffield.ac.uk
from __future__ import division, print_function
import glob
import matplotlib.patches as patches
import json
import numpy as np
from matplotlib.path import Path
import pydicom
import pydicom.uid
import dicom
import cv2
import matplotl... | |
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy.io as scio
def vis_gt(im, bboxes, plt_name='output', ext='.png', visualization_folder=None):
"""
A function to visualize the detections
:param im: The image
:param bboxes: ground truth
:param plt_name: The name of the plot
... | |
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
rect = plt.Rectangle((np.pi, -0.5), 1, 1, fc=np.random.random(3), picker=True)
ax.add_patch(rect)
x = np.linspace(0, np.pi*2, 100)
y = np.sin(x)
line, = plt.plot(x, y, picker=8.0)
def on_pick(event):
artist = event.artist
if isinstanc... | |
# Adapted for numpy/ma/cdms2 by convertcdms.py
# Adapted for numpy/ma/cdms2 by convertcdms.py
import numpy
import genutil
import cdms2
import numpy.ma
import os
import sys
import unittest
import cdat_info
class GENUTIL(unittest.TestCase):
### EXTRACT TESTS
def assertArraysEqual(self,A,B):
self.assertTr... | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 19 18:00:36 2021
@author: ml
"""
from code.feature_extraction.feature_extractor import FeatureExtractor
from code.util import COLUMN_WORD_COUNT, string_to_words_list
import numpy as np
#class for extracting the amount of words after general prepro... | |
#--------------------------------------------------------------------------------------------
# Key routines of this repository, where we implement the Sinkhorn algorithms and MMDs
#--------------------------------------------------------------------------------------------
import numpy as np
import torch
# Refere... | |
from controller import Robot
import PuPy
import HDPy
import numpy as np
import h5py
# Initialize a policy
bound_gait = {
'amplitude' : ( 0.8, 1.0, 0.8, 1.0),
'frequency' : (1.0, 1.0, 1.0, 1.0),
'offset' : ( -0.23, -0.23, -0.37, -0.37),
'phase' : (0.0, 0.0, 0.5, 0.5)
}
policy = HDPy.puppy.policy... | |
""" This module provides metrics and related functions """
# Standard library imports
# Third party imports
import numpy as np
import pandas as pd
# Local imports
def avg(data):
return data["h"] / data["ab"]
def obp(data):
numerator = data["h"] + data["bb"] + data["hbp"]
denominator = data["ab"] + data... | |
import copy
import numpy as np
from scipy.spatial.transform import Rotation as R
from ase.data import atomic_numbers,atomic_masses_iupac2016
from mcse.core.structure import Structure
def check_molecule(struct, exception=True):
# Check for valid molecule_struct
if len(struct.get_lattice_vectors()) > 0:
... | |
import numpy as np
import matplotlib.pyplot as plt
import scanpy as sc
from anndata import AnnData
from kneed import KneeLocator
from scipy.sparse import isspmatrix, csr_matrix, spmatrix
from sklearn.decomposition import PCA
from typing import Optional, Tuple, List, Union
# Convert sparse matrix to dense matrix.
to_d... | |
#FOUR Ultrasonic sensors (HC-SR04) FUNCTION
#Version 2
#Two conditions:
#1. Every half a second determine location on boardSize_updown
#2. In front of washer, determine location on board
#Length is 23 cm with pushing mechanism
#Goal: Assign four sensors to the four directions depending on the configuration
... | |
import numpy as np
import Domains
import argparse
from multiprocessing import Pool
import subprocess, random, os
# Modify the following lines according to your NUPACK installation:
nupack_path = os.environ['HOME'] + '/nupack3.2.2/build/bin'
nupack_env = {'NUPACKHOME' : os.environ['HOME'] + '/nupack3.2.2'}
subopt_gap =... | |
"""
Apply PCA first, and then zscoring to spatiotemporal covariates
"""
import argparse
import sys
import os
os.chdir(os.path.join(".."))
sys.path.insert(0, 'SSF_mip/')
import numpy as np
import pandas as pd
import preprocess
import cfg_target_test as cfg
parser = argparse.ArgumentParser()
parser.add_a... | |
import enum
from typing import List, Tuple
from numpy import dstack, dtype, iinfo
import numpy
from pandas.core.frame import DataFrame
import zarr
import pandas
import pkg_resources
DATE_COLUMNS = ['53-0.0', '53-1.0', '53-2.0']
def load_dtype_dictionary():
path = pkg_resources.resource_filename('ukb_loader', 'a... | |
import time
import numpy as np
from dqo.datasets import QueriesDataset
from dqo.relational import SQLParser
from dqo.relational.tree.parser import parse_ast
from dqo.relational.query.parser import parse_tree
from tqdm.auto import tqdm
from multiprocessing import Pool
if __name__ == '__main__':
ds = QueriesDataset(... | |
import sys
sys.path.append("..")
from geneticAlgorithm.solver import *
import numpy as np
chromosome = solve(np.mat([1, 1, 1, 1, 0]).tolist()[0], 5, binCapacity=2)
position = chrom2position(chromosome)
print(position) | |
# TODO: introduce better object naming system for logging
# e.g. <Sound object at 0x7fa535d23870> -> Sound X
import logging
import time
from numpy import exp
from typing import List
from .braillify import H_STEP, V_STEP
from .common import Vec2
from .events import AnimationEndedEvent, CollisionEvent, Event, Collisio... | |
from __future__ import absolute_import, division
import numpy as np
from ._base import PreprocessingBase
class NormalizeRmZeroStd(PreprocessingBase):
"""
Not exact a PCA, only remove features with std 0.
"""
def fit(self, X):
self.means = np.empty((0,))
self.stds = np.empty((0,))
... |
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