text stringlengths 0 27.1M | meta dict |
|---|---|
module pidef
! Value of pi
real, parameter :: pi = 4.0*atan(1.0)
real, parameter :: conrad = pi/180.0
real, parameter :: condeg = 180.0/pi
end module pidef
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import numpy as np
from itertools import product
class GridWorld:
UP = 0
RIGHT = 1
DOWN = 2
LEFT = 3
def __init__(self, size=5):
self.size = size
self.action_space = [self.UP, self.RIGHT, self.DOWN, self.LEFT]
self.A = (0, 1)
self.A_prime = (4, 1)
self.B =... | {
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# -*- coding: utf-8 -*-
# ProDy: A Python Package for Protein Dynamics Analysis
#
# Copyright (C) 2010-2012 Ahmet Bakan
#
# 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 th... | {
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# topic_modeling.py
# Topic model analysis for professional articles on machine intelligence.
import numpy as np
import pandas as pd
import altair as alt
import streamlit as st
def buildAcademicData(data):
df = pd.DataFrame()
topics = [
"Language Models",
"Cloud-Based ML Frameworks",
... | {
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# -*- coding: utf-8 -*-
"""
Created on Tue Feb 9 09:07:22 2021
This script creates the PhoREAL GUI exe along with the Windows Installer file.
Steps:
1) Place all necessary PhoREAL files into the path given by the
"phorealDirPath" parameter
2) Set the PhoREAL version number for the naming of the exe... | {
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import unittest
import numpy as np
import pandas as pd
import networkx as nx
from pgmpy.models.BayesianModel import BayesianModel
from pgmpy.estimators import TreeSearch
from pgmpy.factors.discrete import TabularCPD
from pgmpy.sampling import BayesianModelSampling
class TestTreeSearch(unittest.TestCase):
def se... | {
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[STATEMENT]
lemma exec_n_end:
"size P <= (n::int) \<Longrightarrow>
P \<turnstile> (n,s,stk) \<rightarrow>^k (n',s',stk') = (n' = n \<and> stk'=stk \<and> s'=s \<and> k =0)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. size P \<le> n \<Longrightarrow> P \<turnstile> (n, s, stk) \<rightarrow>^k (n', s', stk') ... | {
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import datetime
import pandas as pd
import numpy as np
from edgar3 import edgar_index
def get_13f_listings(date: datetime.datetime, populate: bool) -> pd.DataFrame:
ed_i = edgar_index.edgar_index()
df = ed_i.get_full_listing_as_pd(date)
df = df[df["Form Type"].isin(["13F-HR", "13F-HR/A", "13F-NT", "13F-NT... | {
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Larissa Triess"
__email__ = "mail@triess.eu"
import numpy as np
def get_kitti_columns(points: np.array, number_of_columns: int = 2000) -> np.array:
""" Returns the column indices for unfolding one or more raw KITTI scans """
azi = np.arctan2(poin... | {
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# coding=utf-8
# Copyright 2019 The Tensor2Tensor 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... | {
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struct DiffusionObservationModel{S1, S2, TF} <: ObservationModel{Vector{S1}, Vector{S2}, ContinuousTime}
n::Int
m::Int
observation_function::TF
end
#####################
### BASIC METHODS ###
#####################
... | {
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[STATEMENT]
theorem product_of_chord_segments:
fixes S\<^sub>1 T\<^sub>1 S\<^sub>2 T\<^sub>2 X C :: "'a :: euclidean_space"
assumes "between (S\<^sub>1, T\<^sub>1) X" "between (S\<^sub>2, T\<^sub>2) X"
assumes "dist C S\<^sub>1 = r" "dist C T\<^sub>1 = r"
assumes "dist C S\<^sub>2 = r" "dist C T\<^sub>2 = r"
... | {
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c---------------------------------------------------------------------
c---------------------------------------------------------------------
subroutine make_set
c---------------------------------------------------------------------
c---------------------------------------------------------------------
c------... | {
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/* Copyright (c) 2017, CNRS-LAAS
All rights reserved.
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 following di... | {
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[STATEMENT]
lemma a_insert: "T_insert a t + \<Phi>(skew_heap.insert a t) - \<Phi> t \<le> 3 * log 2 (size1 t + 2) + 2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. real_of_int (T_insert a t + \<Phi> (skew_heap.insert a t) - \<Phi> t) \<le> 3 * log 2 (real (size1 t + 2)) + 2
[PROOF STEP]
using a_merge[of "Node Leaf... | {
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import sys
sys.path.append("../../../../common")
sys.path.append("../")
import os
import json
import numpy as np
import acl
import cv2 as cv
from PIL import Image
import pickle
import LaneFinder
import atlas_utils.constants as const
from atlas_utils.acl_model import Model
from atlas_utils.acl_resource import AclResourc... | {
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"""
Non-probabilistic non-negative matrix tri-factorisation, as presented in
"Probabilistic Matrix Tri-Factorisation" (Yoo and Choi, 2009).
We change the notation to match ours: R = FSG.T instead of V = USV.T.
The updates are then:
- Uik <- Uik * (sum_j Vjk * Rij / (Ui dot Vj)) / (sum_j Vjk)
- Vjk <- Vjk * (sum_i Uik... | {
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From ConCert.Execution Require Import Blockchain.
From ConCert.Execution Require Import Containers.
From ConCert.Execution Require Import Serializable.
From ConCert.Execution Require Import ResultMonad.
From ConCert.Execution.Test Require Import QCTest.
From ConCert.Examples.FA2 Require Import FA2Token.
From ConCert.Ex... | {
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# Copyright 2021 CR.Sparse Development Team
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | {
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# -*- coding: utf-8 -*-
"""
Created on Mon Jan 10 08:31:20 2022
@author: Ezra
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
def f(t,A,w,phi):
y = A*np.sin(w*t+phi)
return y
data1 = pd.read_csv(r'fileDirectory')
x = data1['t... | {
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# -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, print_function)
import math
from operator import add
from functools import reduce
import pytest
from chempy import Substance
from chempy.units import (
allclose, units_library, default_constants, Backend, to_unitless,
SI_base_registry... | {
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import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
image = mpimg.imread('cutout1.jpg')
# Define a function to compute color histogram features
def color_hist(img, nbins=32, bins_range=(0, 256)):
# Compute the histogram of the RGB channels separately
rhist = np.his... | {
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import random
import matplotlib.pyplot as plt
import math
import numpy as np
import cv2
import os
def distance(x0, y0, x1, y1):
return math.sqrt((x0 - x1)**2 + (y0 - y1)**2)
def find_n_nearest(agent_idx, agent_pos, n_nearest):
dis = []
for i in range(num_agents):
if (i != agent_idx):
d = distance(agent_pos[a... | {
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import os
import pandas as pd
import numpy as np
para = {"window_size":0.5,"step_size":0.2,"structured_file":"bgl/BGL_100k_structured.csv","BGL_sequence":'bgl/BGL_sequence.csv'}
'''
anomaly的算法：將整段資料依序切成不相教的window,只要這個window裡面有一個
的label是abnomal ,就說這一個window是abnomal
至個abnomal的label是之前就label好了
輸出的訊息會標示這個window裡面... | {
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/*******************************************************************************
* Copyright (c) 2016, Hitachi-LG Data Storage
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions... | {
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# python3
# 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 re... | {
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import os
import re
import nltk
import string
import sparknlp
import numpy as np
import pandas as pd
import transformers
import seaborn as sns
import tensorflow as tf
from sparknlp.base import *
import plotly.express as px
from tqdm.notebook import tqdm
from pyspark.ml import Pipeline
import matplotlib.pyplot as plt
fr... | {
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immutable Uniform <: ContinuousUnivariateDistribution
a::Float64
b::Float64
function Uniform(a::Real, b::Real)
if a < b
new(float64(a), float64(b))
else
error("a < b required for range [a, b]")
end
end
end
Uniform() = Uniform(0.0, 1.0)
@_jl_dist_2p Uniform unif
entropy(d::Unif... | {
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import chainer
import numpy
import pytest
import chainerx
from chainerx_tests import array_utils
def _create_batch_norm_ndarray_args(
xp, device, x_shape, gamma_shape, beta_shape, mean_shape, var_shape,
float_dtype):
x = array_utils.create_dummy_ndarray(xp, x_shape, float_dtype)
# Non-conti... | {
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"""
_main_driver.py
Copyright 2016 University of Melbourne.
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 ... | {
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import math as m
id
import itertools
import datetime
from scipy.stats import ks_2samp
import os
import statistics
from pysolar.solar import *
from scipy.stats import pearsonr
from scipy import s... | {
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[STATEMENT]
lemma f_expand_nth_mult: "\<And>n.
\<lbrakk> n < length xs; 0 < k \<rbrakk> \<Longrightarrow> (xs \<odot>\<^sub>f k) ! (n * k) = xs ! n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>n. \<lbrakk>n < length xs; 0 < k\<rbrakk> \<Longrightarrow> xs \<odot> k ! (n * k) = xs ! n
[PROOF STEP]
apply (i... | {
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# Copyright 2018 Google LLC
#
# 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 writing, ... | {
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# -*- coding: utf-8 -*-
"""
Modify on 2020年4月1日
@author: LXG
Benchmark Code of Coupled PhaseDNN for ODE.
"""
import tensorflow as tf
import numpy as np
# ---------------------------------------------- my activations -----------------------------------------------
def srelu(x):
return tf.nn.relu(1... | {
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import math
import numpy as np
import torch
from scipy import optimize
from torch import nn as nn
from torch import optim
from torch.nn import functional as F
from rlkit.state_distance.policies import UniversalPolicy
from rlkit.pythonplusplus import identity
from rlkit.torch import pytorch_util as ptu
from rlkit.torc... | {
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from __future__ import absolute_import, division, print_function
from scitbx.array_family import flex
from scitbx.matrix import sqr
from simtbx.nanoBragg import shapetype, nanoBragg
from simtbx.nanoBragg.nanoBragg_crystal import NBcrystal
from simtbx.nanoBragg.nanoBragg_beam import NBbeam
from copy import deepcopy
de... | {
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# Copyright (c) 2021 Dai HBG
"""
该代码定义2_num_num_num型运算符
"""
import numpy as np
import numba as nb
def tssubset(a, b, delay, num_0, num_1): # 时序选择算子，回溯delay天，根据b的排序选出从num_0到num_1的子集，返回均值
if len(a.shape) == 2:
s = np.zeros(a.shape)
tmp_a = np.zeros((delay + 1, a.shape[0], a.shape[1]))
t... | {
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"""
Author: Madhivarman
contact: madhi@pluto7.com
Project: Customer Segmentation
Version: 1.0
"""
import sys
sys.path.append('F:/work projects/Dash Application')
import pandas as pd
import io
import numpy as np
from Cloud_API.storage import Storage
from cluster_analysis import ClusterAnalysis
cl... | {
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import os, sys
import json
import time
import StringIO
from collections import Counter, defaultdict
import subprocess
import tempfile
import urllib
import h5py
import numpy as np
np.random.seed(10)
import pandas as pd
from flask import Flask, request, redirect, render_template, \
jsonify, send_from_directory, abort, ... | {
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/**
* Copyright Soramitsu Co., Ltd. All Rights Reserved.
* SPDX-License-Identifier: Apache-2.0
*/
#include "common/default_constructible_unary_fn.hpp" // non-copyable value workaround
#include "torii/impl/command_service_transport_grpc.hpp"
#include <atomic>
#include <condition_variable>
#include <iterator>
#in... | {
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Make a Python class.
See the README.md file and the GitHub wiki for more information.
http://www.tomwhyntie.com
"""
#...for the future!
from __future__ import absolute_import
# Import the code needed to manage files.
import os
#
from os.path import join as ... | {
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using DelimitedFiles
function generate_datasets(namedir, application_name, nbpoints)
"""
This function takes the full dataset and sets the first nbpoints points in a test set,
then creates an ever growing train dataset by increments of nboints"""
fulldataset = readdlm("examples/data/consolidated_d... | {
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// -*- C++ -*-
//
// Package: Core
// Class : FWConfiguration
//
// Implementation:
// <Notes on implementation>
//
// Original Author: Chris Jones
// Created: Fri Feb 22 15:54:29 EST 2008
//
// system include files
#include <stdexcept>
#include <algorithm>
#include <boost/bind.hpp>
// user inc... | {
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# -*- coding: utf-8 -*-
"""
Created on Tue Feb 23 18:56:28 2021
@author: m1390
"""
import numpy as np
import matplotlib.pyplot as plt
#%%
class Cubic_Spline:
def __init__(self, x, y, f=None):
"""
Parameters
----------
x : data points
N-D vector.
y... | {
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import numpy as np
def assert_model(true_model, created_model, rtol=1.e-4, atol=1.e-6):
assert len(true_model['model']) == len(created_model['model'])
for i in range(len(true_model['model'])):
true_tree = true_model['model'][i]
created_tree = created_model['model'][i]
assert np.allclos... | {
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# Rest of requiresments should be install via the program.
cranRepo<-"http://cran.rstudio.com/"
install.packages("optparse", repos=cranRepo)
install.packages("rvest", repos=cranRepo)
install.packages("gdata", repos=cranRepo)
install.packages("ggplot2", repos=cranRepo)
install.packages("xgboost", repos=cranRepo)
install... | {
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#!/usr/bin/env python3
"""
Uses a Kalman Filter to model the motion of each observed agent
Can be used for constant velocity prediction if no observations supplied
"""
import numpy as np
from pykalman import KalmanFilter
class KFMulti():
"""
Tracks multiple objects (without association) using pykalman
... | {
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[STATEMENT]
lemma secret_parts_Spy_converse:
" m \<in> initState Spy \<or>
(\<exists>C B X. Says C B X \<in> set evs \<and> m \<in> parts{X}) \<or>
(\<exists>C Y. Notes C Y \<in> set evs \<and> C \<in> bad \<and> m \<in> parts{Y})
\<Longrightarrow> m \<in> parts(knows Spy evs)"
[PROOF STATE]
proof (prove)
goal (1 su... | {
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import numpy as np
import simtk.unit as unit
def get_membrane_size(n_POPC = 0 , n_DPPC = 0 , n_DOPC = 0):
surface_area_POPC = 68.3 * unit.angstroms**2
surface_area_DPPC = 63 * unit.angstroms**2
surface_area_DOPC = 69.7 * unit.angstroms**2
total_area_POPC = n_POPC * surface_area_POPC
total_area... | {
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from pandas import Series, DataFrame
from numpy import array, arange, log10, ndarray
from .expected import _get_expected_digits_
from .constants import DIGS, REV_DIGS
from .stats import Z_score
from .checks import _check_num_array_, _check_sign_, _check_decimals_
def _set_N_(len_df, limit_N):
""""""
# Assigni... | {
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import pandas as pd
from albumentations.pytorch import ToTensorV2
from torch.utils.data import Dataset
import cv2
import torch
import numpy as np
from torchvision import transforms
from torch.utils.data import DataLoader
import albumentations as alb
import cv2
import torchvision
import matplotlib.pyplot as plt
from con... | {
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"""
This library defines classes that implement neural networks as well as a
generic EA algorithm.
"""
import json
import pickle
import random
import numpy as np
def interpolate(val, min, max):
"""
Interpolates values between 0 and 1 to values between the specified min and max.
Used primarily map... | {
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#!/usr/bin/env python2
import sys
import numpy as np
from copy import deepcopy
from fml.math import cho_solve
from scipy.stats import pearsonr
if __name__ == "__main__":
ntrain = int(sys.argv[1])
ntest = int(sys.argv[2])
D = np.load("D.npy")[:ntrain,:ntrain]
Ds = np.load("D.npy")[:ntrain,-ntest:]
... | {
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#!/usr/bin/python
# Filename: time_series.py
################### IMPORTS #######################
import pandas as pd
from pandas import DataFrame
import numpy as np
#for whittacker smoother
import scipy as sp
import scipy.sparse
import scipy.linalg
from scipy.sparse.linalg import cg
##################################... | {
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Exercises 11.12, 11.13 from Kane 1985."""
from __future__ import division
from sympy import pi, solve, symbols, trigsimp
from sympy.physics.mechanics import ReferenceFrame, RigidBody, Point
from sympy.physics.mechanics import dot, dynamicsymbols, inertia, msprint
from u... | {
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class QLinearNetwork(nn.Module):
def __init__(self,
input_feature: ("int: input state dimension"),
output_feature: ("output: action dimensions"),
):
super(QLinearNetwork, sel... | {
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\chapter{Built-in Objects}
\section{Concrete Semantics}
\subsection{Helper Functions}
\[
\begin{array}{ll}
\chf{getMatcher} & : \matcher \rightarrow (\SF{String} \times \SF{Int} \rightarrow \SF{MatchResult}) \\
\\
\chf{NewRegExp} & : \SF{Value} \times \SF{Bool} \times \SF{Bool} \times \SF{Bool} \times \matcher \r... | {
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"include": null,
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/*
__ __ __
|__|__| | __
| | | ||__|
___ ___ __ | | | |
| | | || | | | Ubiquitous Internet @ IIT-CNR
| | | || | | | C++ edge computing libraries and tools
|_______|__||__|__|__| https://github.com/ccicconetti/serverlessonedge
Licensed under t... | {
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"include": null,
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import logging
import json
import numpy as np
from collections import OrderedDict
from galaxy.utils import ontology
def clean_replace(s, r, t, forward=True, backward=False):
def clean_replace_single(s, r, t, forward, backward, sidx=0):
# idx = s[sidx:].find(r)
idx = s.find(r)
if idx == -1:... | {
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module Etcd
using Requests
using Memento
import HttpCommon: Response
immutable Client
host::String
port::Int
version::String
end
include("constants.jl")
include("requests.jl")
include("api.jl")
include("utils.jl")
"""
connect(host="localhost", port=2379, version="v2")
Creates an `Etcd.Client` whic... | {
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# Taken from Base: test/show.jl
replstr(x, lim=true) = sprint((io,x) -> show(IOContext(io, :limit => lim, :displaysize => (24, 80)), MIME("text/plain"), x), x)
showstr(x) = sprint((io,x) -> show(IOContext(io, :limit => true, :displaysize => (24, 80)), x), x)
macro test_show(expr)
esc(quote
@test string... | {
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Require Import String.
Require Import List.
Require Import Coq.Arith.EqNat.
Module Export DeBruijn.
Inductive lterm : Type :=
| Var : nat -> lterm
| Lam : lterm -> lterm
| App : lterm -> lterm -> lterm.
End DeBruijn.
Module PrettyTerm.
Inductive pterm : Type :=
| Var : string -> pterm
| Lam : string -> p... | {
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"max_forks_repo_forks_event_max_date... |
[STATEMENT]
lemma [simp]: "(u::'a) \<in> G"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. u \<in> G
[PROOF STEP]
by (simp add: G_def) | {
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from __future__ import division, print_function
import itertools
import numpy as np
import healpy as hp
def digitize_columns(data, bins):
digitized = np.empty_like(data, dtype=int)
for i in range(len(bins)):
digitized[:, i] = np.digitize(data[:, i], bins=bins[i]) - 1
return digitized
def binne... | {
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"""
Original work Copyright (c) 2015 Mark Vismer
Defines the interface for a DataSource class which provides the data for a
plotter.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import weakref
import traceback... | {
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[STATEMENT]
lemma onorm_pos_lt:
assumes f: "bounded_linear f"
shows "0 < onorm f \<longleftrightarrow> \<not> (\<forall>x. f x = 0)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (0 < onorm f) = (\<not> (\<forall>x. f x = (0::'b)))
[PROOF STEP]
by (simp add: less_le onorm_pos_le [OF f] onorm_eq_0 [OF f]) | {
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# Lower and upper bounds to detect the pink color in the HSV color space
# lower bound [142 102 184]
# upper bound [255 255 255]
import cv2
import numpy as np
import time
cap = cv2.VideoCapture(0)
img = np.zeros((480,640,3),np.uint8)
points = []
while True:
# Reading frame
_, frame = cap.read()
... | {
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#!/usr/bin/env python
#encoding=utf-8
'''
@Time : 2020/11/02 00:06:44
@Author : Zhiyang.zzy
@Contact : zhiyangchou@gmail.com
@Desc :
'''
# here put the import lib
from model.bert_classifier import BertClassifier
import os
import time
from numpy.lib.arraypad import pad
from tensorflow.python.ops.gen_io... | {
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program ex
use gabriel
use MPI
implicit none
integer,parameter :: n=10
integer,parameter :: s=2
real,dimension(:,:,:),allocatable :: a,b
integer ierr,rank,right,left,mpisize,i,j,k
integer hor,ver
type(distribution) :: d
call MPI_Init(ierr)
call MPI_Comm_rank(MPI_COMM_WORLD,rank,ierr)
call MP... | {
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# Code is based on https://github.com/sungyubkim/GBML
# Here is our the main contribution that the initialization is made from word embeddings of labels
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import higher
from gbml.gbml import GBML
from utils import get_accuracy, apply_... | {
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from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
print(data.DESCR)
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target... | {
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[STATEMENT]
lemma T_completeness: "Model wtFsym wtPsym arOf resOf parOf \<Phi> intT intF intP"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Ik.MModel intT intF intP
[PROOF STEP]
by standard (rule completeness) | {
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# -*- coding: utf-8 -*-
# Copyright (C) 2021-2022 Textualization Software Ltd.
# Distributed under the terms of the MIT License
# https://mit-license.org/
import random
import copy
import sys
import multiprocessing
import numpy as np
from wicked21st.graph import load_graph, Cascades
from wicked21st.classes import Cl... | {
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import networkx as nx
# import matplotlib.pyplot as plt
###
# Load the Graph
###
filename = 'dijkstraData.txt'
DG = nx.DiGraph()
with open(filename) as f:
for line in f:
# Parse the line
parsed_line = line.rsplit('\t')
node_from = int(parsed_line.pop(0))
# Get rid of the line ... | {
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import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import LineCollection
# In order to efficiently plot many lines in a single set of axes,
# Matplotlib has the ability to add the lines all at once. Here is a
# simple example showing how it is done.
N = 50
x = np.arange(N)
# Here are many ... | {
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# -*- coding: utf-8 -*-
"""
*GSASIIElem: functions for element types*
-----------------------------------------
"""
# Copyright: 2008, Robert B. Von Dreele & Brian H. Toby (Argonne National Laboratory)
########### SVN repository information ###################
# $Date: 2019-01-17 14:31:32 -0600 (Thu, 17 Jan 2019) $
# ... | {
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__author__ = 'Fabian Isensee'
import numpy as np
import lasagne
def batch_generator(data, target, BATCH_SIZE, shuffle=False):
if shuffle:
while True:
ids = np.random.choice(len(data), BATCH_SIZE)
yield data[ids], target[ids]
else:
for idx in range(0, len(data), BATCH_SIZ... | {
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import numpy as np
import sympy as sym
import sys
import itertools
import math
import copy
#Ii-Il can't be set to as positive True and real True because:
#when substitue Im with fm, it will do like Im = sqrt(Im**2)replacement
Ii = sym.symbols('Ii')
Ij = sym.symbols('Ij')
Ik = sym.symbols('Ik')
Il = sym.symbols('Il')
wi... | {
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from sstcam_sandbox import get_data, get_plot
from sstcam_sandbox.d191122_dc_tf import get_dc_tf, get_ac_tf, get_ac_cc_tf
from CHECLabPy.plotting.setup import Plotter
import numpy as np
from numba import guvectorize, float64
class TFPlot(Plotter):
def plot(self, x, y, label=None):
self.ax.plot(x, y, label... | {
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import math
import numpy as np
import rospy
import torch
from geometry_msgs.msg import Twist
from nav_msgs.msg import Odometry
class Jackal:
def __init__(self):
self.dtype = torch.float
self._linear_velocity = 1.5
self.x = 0.0
self.y = 0.0
self.q1 = 0.0
self.q2 = ... | {
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immutable Point
x
y
end | {
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# ---
# title: 226. Invert Binary Tree
# id: problem226
# author: Tian Jun
# date: 2020-10-31
# difficulty: Easy
# categories: Tree
# link: <https://leetcode.com/problems/invert-binary-tree/description/>
# hidden: true
# ---
#
# Invert a binary tree.
#
# **Example:**
#
# Input:
#
#
#
# 4
# ... | {
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(**
Here we define the signature for the group quotient.
We also derive its usual elimination principle.
Given a group `G`, we define the group quotient as follows
HIT group_quot G :=
| base : group_quot G
| loop : ∏ (g : G), base = base
| loop_e : loop e = idpath base
| loop_m : ∏ (g₁ g₂ : G), loop (g₁ · g₂) = loop g... | {
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subroutine subqcdm(i1,i2,i3,i4,i5,i6,p156,p256,za,zb,
& invtwog1Dc,invtwog2Dc,mc,aamp,bamp)
c*******************************************************************
c the matrix elements of the
C helicity amplitudes for the QCD process
c s(-p1)+cbar(-p2) --> l(p3)+abar(p4)+g(p5)+g(p6)
c multiplie... | {
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import torch
import torch.nn.functional as F
import numpy as np
import math
from torch import nn
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class ETMCOV(nn.Module):
def __init__(self, num_topics, vocab_size, t_hidden_size, rho_size, emsize,
theta_act, embeddings... | {
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module AgnosticBayesEnsemble
using Optim
export TDistPosteriorEstimation, GMatrix,
dirichletPosteriorEstimation,
dirichletPosteriorEstimationV2, dirichletPosteriorEstimation!,
metaParamSearchValidationDirichlet,
bootstrapPosteriorEstimation, bootstrapPosteriorEstimation!,
... | {
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using DataFrames
mdata = readtable("original_data/mdata.csv")
qdata = readtable("original_data/qdata.csv")
# y/y log change in GDP, CPI, and IPindex
GDP = (log(qdata[:GDPlev][5:end]) - log(qdata[:GDPlev][1:(end - 4)]))*100
INFL = (log(mdata[:CPI][13:end]) - log(mdata[:CPI][1:(end - 12)]))*100
IP = (log(mdata[:INDPRO]... | {
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# Copyright 2017 Amazon.com, Inc. or its affiliates. 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. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | {
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"include": true,
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from CPAC.pipeline.schema import valid_options
from CPAC.utils.docs import docstring_parameter
def convert_pvalue_to_r(datafile, p_value, two_tailed=False):
'''
Method to calculate correlation threshold from p_value
Parameters
----------
datafile : string
filepath to dataset to extract nu... | {
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export Grid
struct Grid
cells::Array{Cell}
starts::Array{Int64}
exits::Array{Int64}
end
function get_empty(cells::Array{Cell}, x::Int64, y::Int64)
xs = x > 0 ? [x] : shuffle!(collect(1:size(cells, 2)))
ys = y > 0 ? [y] : shuffle!(collect(1:size(cells, 1)))
for xi in xs
for yi in ys
... | {
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# -*- coding: utf-8 -*-
#
from helpers import assert_equality
def plot():
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(17, 6))
ax.plot(np.array([1, 5]), label="Test 1")
ax.plot(np.array([5, 1]), label="Test 2")
ax.legend(ncol=2, loc="upper center")
re... | {
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# coding: utf8
import numpy as np
import argparse
import math
from time import clock, sleep
from solo8 import Solo8
from pynput import keyboard
import matplotlib.pyplot as plt
from math import ceil
import curves
from multicontact_api import ContactSequence
curves.switchToNumpyArray()
DT = 0.001
KP = 4.
KD = 0.05
KT = ... | {
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : 河北雪域网络科技有限公司 A.Star
# @contact: astar@snowland.ltd
# @site: www.snowland.ltd
# @file: color.py
# @time: 2018/7/26 0:24
# @Software: PyCharm
import numpy as np
npa = np.array
def rgb2ycbcr(img):
origT = npa([[65.481, 128.553, 24.966], [-37.797, -74.203, ... | {
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import numpy as np
import shapefile
from mpl_toolkits.basemap import Basemap
from numpy import ndarray
import matplotlib.pyplot as plt
from matplotlib.patches import PathPatch
from matplotlib.path import Path
def rain_single_heatmap(rain: ndarray):
# ============================================ 绘图参数准备 =========... | {
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def lookupIDX(words,w):
w = w.lower()
if w in words:
return words[w]
else:
return words['something']
def addOOVwords(examples, words, We, mean=0, sigma=0.01):
import numpy as np
dim = We[0].shape[0]
next_idx = len(words)
We_OOV = []
for example in ... | {
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"""Solution problems / methods / algorithms module"""
import collections
import cvxpy as cp
import gurobipy as gp
import itertools
import numpy as np
import pandas as pd
import scipy.optimize
import scipy.sparse as sp
import typing
import mesmo.config
import mesmo.utils
logger = mesmo.config.get_logger(__name__)
c... | {
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#pragma once
#include <boost/algorithm/string.hpp>
#include <string>
namespace cnt {
struct NoneCommand {};
struct UnknownCommand {};
struct PrintHelpCommand {};
struct PrintRecordsCommand {};
struct QuitCommand {};
struct AddRecordCommand {
std::string recordName{};
explicit AddRecordCommand(std::string s) : re... | {
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# check sparse matrix construction
@test isequal(full(sparse(complex(ones(5,5),ones(5,5)))), complex(ones(5,5),ones(5,5)))
# check matrix operations
se33 = speye(3)
do33 = ones(3)
@test isequal(se33 * se33, se33)
# check sparse binary op
@test all(full(se33 + convert(SparseMatrixCSC{Float32,Int32}, se33)) == 2*eye(3)... | {
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import numpy as np
import pandas as pd
import os
from main_db_script import get_id, append_non_duplicates, make_date_index
from data_utils import hampel_filter, account_for_elev
current_directory = os.path.dirname(__file__)
data_dir = os.path.join(current_directory, '../HRSD')
def qc_shallow_well_data(df):
colum... | {
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#! /usr/bin/env python
###############################################################################
# basicPID_wrapping.py
#
# Python code to wrap a very simple PID controller shared library
#
# NOTE: Any plotting is set up for output, not viewing on screen.
# So, it will likely be ugly on screen. The saved P... | {
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