text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc
import numpy as np
import matplotlib.pyplot as plt
import datetime
from matplotlib.finance import candlestick_ohlc
from matplotlib... | {"hexsha": "fde650c2cdaf3eaecb2710f8727f44c5fa9465b8", "size": 1487, "ext": "py", "lang": "Python", "max_stars_repo_path": "chart.py", "max_stars_repo_name": "guardiantest/dailyreport", "max_stars_repo_head_hexsha": "60a075432f3356d4a4a75568205e683fee58466d", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count":... |
# 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.
"""Integrations tests for the LLVM CompilerGym environments."""
import numpy as np
import pytest
from compiler_gym.envs.llvm.llvm_env import L... | {"hexsha": "b7f5b3dddb18d0b96a58004cac6fcc15b2332b47", "size": 5590, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/llvm/reward_spaces_test.py", "max_stars_repo_name": "ibrumar/CompilerGym", "max_stars_repo_head_hexsha": "ecd1b62a3f072082227910a2a2487d6e06a52d4e", "max_stars_repo_licenses": ["MIT"], "max_... |
import unittest
import os
import copy
from itertools import chain
import warnings
import numpy as np
from monty.json import jsanitize, MontyDecoder
from pint import UnitStrippedWarning
from propnet.dbtools.storage import StorageQuantity, ProvenanceStore, ProvenanceStoreQuantity
from propnet.core.symbols import Symbol... | {"hexsha": "8711099b0bad7c3f1f762a759c4c062ea332a6b1", "size": 37432, "ext": "py", "lang": "Python", "max_stars_repo_path": "propnet/dbtools/tests/test_storage.py", "max_stars_repo_name": "ruriboshi/propnet", "max_stars_repo_head_hexsha": "770703fb4fc344f785f89c02f26b31ea5733d2bd", "max_stars_repo_licenses": ["BSD-3-Cl... |
#!/usr/bin/env python
import operator
import matplotlib.lines
import matplotlib.pyplot as plt
import numpy as np
import ce_expansion.atomgraph as atomgraph
import ce_expansion.npdb as npdb
def _build_atomgraph(bimetallic_result):
"""
Returns an atomgraph object from the result of a bimetallic result query.
... | {"hexsha": "94bc26cfd6ab3924bcf44adae554d3288551715a", "size": 9430, "ext": "py", "lang": "Python", "max_stars_repo_path": "ce_expansion/plots/plots.py", "max_stars_repo_name": "loevlie/ce_expansion", "max_stars_repo_head_hexsha": "17417b9467914dd91ee8e0325cfdc3bd19ad7f1e", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Imports
import nsNLP
import sys
import torchlanguage.transforms
import os
import torch
import settings
from tools import load_glove_embeddings as gle
import numpy as np
# Function words
function_words = [u"a", u"about", u"above", u"after", u"after", u"again", u"again... | {"hexsha": "c335bc00286de3baff1597c3a977a78d1ccd9fba", "size": 10692, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/features.py", "max_stars_repo_name": "nschaetti/PAN17-author-profiling", "max_stars_repo_head_hexsha": "c1d1041bbdc4b631709b1cbc134c562fcff2b542", "max_stars_repo_licenses": ["Apache-2.0"],... |
import os
import numpy as np
import flopy
# Assign name and create modflow model object
modelname = 'units'
mf = flopy.modflow.Modflow(modelname, exe_name='mf2005', model_ws=os.path.join('data'))
cbc_unit_nb = 1053
# Model domain and grid definition
Lx = 1000.
Ly = 1000.
ztop = 0.
zbot = -50.
nlay = 1... | {"hexsha": "490a40b6e32dcc52a3b68a06ed983944588c5daf", "size": 2323, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/Testing/testunitcbc.py", "max_stars_repo_name": "MzCorona/flopyy", "max_stars_repo_head_hexsha": "91969cc191006db5cfd5d231faa513479992a50a", "max_stars_repo_licenses": ["CC0-1.0", "BSD-3-... |
import numpy as np
import matplotlib.pyplot as plt
import util
import yield_curve
# dr(t) = (k - theta * r(t))dt + sigma dW(t)
# Parameters
T = 7 # In years
dt = 0.5 # Time step
k = 0.0045 # 0.45%
theta = 0.1 # 10%
sigma = 0.01 # 1%
# Starting point
r0 = 0.046 # 4.6%
# Strike
K = 0.047 # 4.7%
# Early exercise p... | {"hexsha": "f64cf454cfb43c8450210fd6d78e305a7975d7a3", "size": 666, "ext": "py", "lang": "Python", "max_stars_repo_path": "vasicek/test_vasicek.py", "max_stars_repo_name": "ladjanszki/interest_rate_option", "max_stars_repo_head_hexsha": "1e0a89eed6afa79a3b5019852a194ab87a2397d7", "max_stars_repo_licenses": ["MIT"], "ma... |
#include "luxurycoinupdater.h"
#include "util.h"
#include <iostream>
#include <vector>
#include <boost/regex.hpp>
#include <sstream>
#include <boost/algorithm/string.hpp>
const std::string LuxuryCoinUpdater::ClientVersionSrcFileLink = "https://raw.githubusercontent.com/qw23qw2/luxurycoin/master/wallet/clientversion.... | {"hexsha": "6775f3f186e85c432a601cdfa8c0e4944d614038", "size": 4950, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "wallet/luxurycoinupdater.cpp", "max_stars_repo_name": "qw23qw2/luxurycoin", "max_stars_repo_head_hexsha": "a9190882cd6534564d951099b9323d2c788bd123", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
function [y,p_avg,p_std]=multinomrnd(p,m,n)
%Performs random sampling from a binomial distribution
%
% [y]=multinomrnd(p,m,n)
% where p=1-by-k vector of probabilities of occurrence
% n=sample size
% and m= number of trials
% y=samples-matrix of size k-by-m
%
% for picking out one of k mixture components,... | {"author": "OHBA-analysis", "repo": "HMM-MAR", "sha": "bb0433b75482e473980791a2b30afe2012cf6578", "save_path": "github-repos/MATLAB/OHBA-analysis-HMM-MAR", "path": "github-repos/MATLAB/OHBA-analysis-HMM-MAR/HMM-MAR-bb0433b75482e473980791a2b30afe2012cf6578/utils/math/multinomrnd.m"} |
# PlacesCNN to predict the scene category, attribute, and class activation map in a single pass
# by Bolei Zhou, sep 2, 2017
# last modified date: Dec. 27, 2017, migrating everything to python36 and latest pytorch and torchvision
import os
import io
import torch
import torchvision.models as models
from torch.autograd ... | {"hexsha": "eb371f5950527c838a5328499de3a4f203548073", "size": 7222, "ext": "py", "lang": "Python", "max_stars_repo_path": "frontend/run_placesCNN_unified.py", "max_stars_repo_name": "dendisuhubdy/deepmersion", "max_stars_repo_head_hexsha": "d6413a998e2d2de57c74a123de144cffd1235462", "max_stars_repo_licenses": ["MIT"],... |
% Read all images and extract point coordinates.
%
% All information needed are stored and retrieved
% from the function read_configuration,
% which in turn uses the --config=FILENAME command-line
% option.
% $Author: svoboda $
% $Revision: 2.0 $
% $Id: im2points.m,v 2.0 2003/06/19 12:07:11 svoboda Exp $
% $State: Exp... | {"author": "strawlab", "repo": "MultiCamSelfCal", "sha": "0a26c88c63d8513eab76553033a9a6fb15ba6575", "save_path": "github-repos/MATLAB/strawlab-MultiCamSelfCal", "path": "github-repos/MATLAB/strawlab-MultiCamSelfCal/MultiCamSelfCal-0a26c88c63d8513eab76553033a9a6fb15ba6575/MultiCamValidation/FindingPoints/im2points.m"} |
struct SimControls
nu::Int64
ctrl::MJMatrix
normalized::Vector{mjtNum}
ctrl_magnitude::MJMatrix
function SimControls(data::MJData)
mat = data.ctrl
norm = zeros(data.nu)
rng = data.model.actuator_ctrlrange
new(data.nu, mat, norm, rng)
end
end
function SetSimCont... | {"hexsha": "0eee490d61226720dbf03b453149b59384010f40", "size": 2312, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/simenvi.jl", "max_stars_repo_name": "liammcinroy/reaching_goal_inference", "max_stars_repo_head_hexsha": "fac7afbec868af605c19edd404da61209c83a394", "max_stars_repo_licenses": ["MIT"], "max_sta... |
Require Export Iron.Language.SystemF2Effect.Type.Exp.Base.
Require Export Iron.Language.SystemF2Effect.Type.Operator.FlattenT.
Require Export Iron.Language.SystemF2Effect.Type.Relation.KindT.
Require Export Iron.Language.SystemF2Effect.Type.Relation.KindTs.
Require Export Iron.Language.SystemF2Effect.Type.Relation.Equ... | {"author": "discus-lang", "repo": "iron", "sha": "75c007375eb62e1c0be4b8b8eb17a0fe66880039", "save_path": "github-repos/coq/discus-lang-iron", "path": "github-repos/coq/discus-lang-iron/iron-75c007375eb62e1c0be4b8b8eb17a0fe66880039/done/Iron/Language/SystemF2Effect/Type/Operator/BunchT.v"} |
"""
Provides the implementation of isoparametric quadrilateral elements for
plane-stress and plane-strain problems.
"""
import abc
import numpy as np
__author__ = 'Konstantinos Tatsis'
__email__ = 'konnos.tatsis@gmail.com'
class Quadrilateral(abc.ABC):
"""
Class for interfacing the methods of... | {"hexsha": "3ce86e7f417b5634f141f166ca780fd296466559", "size": 19868, "ext": "py", "lang": "Python", "max_stars_repo_path": "quadrilaterals.py", "max_stars_repo_name": "ETH-WindMil/benchmarktu1402", "max_stars_repo_head_hexsha": "54dae8f159c8525c17b9bff942e7b6b60fe3a66c", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
(* Title: HOL/Imperative_HOL/ex/Imperative_Quicksort.thy
Author: Lukas Bulwahn, TU Muenchen
*)
section \<open>An imperative implementation of Quicksort on arrays\<close>
theory Imperative_Quicksort
imports
"../Imperative_HOL"
Subarray
"HOL-Library.Multiset"
"HOL-Library.Code_Target_Numeral"
begi... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Imperative_HOL/ex/Imperative_Quicksort.thy"} |
import pandas as pd
import numpy as np
from Levenshtein import distance,ratio
import multiprocessing
import itertools
import os
import sys
def get_Enigma():
usecols = ['identifier','shipper_party_name', 'shipper_address','harmonized_number']
dtype = {'identifier':str,'shipper_party_name':str,'shipper_address'... | {"hexsha": "aa92dd8e584cece01292d3504b4f794f91bd2d28", "size": 4576, "ext": "py", "lang": "Python", "max_stars_repo_path": "shipper_matching/Enigma_Enigma_matching.py", "max_stars_repo_name": "raymond180/FEIII-SHIP", "max_stars_repo_head_hexsha": "05587eabd323c4fd38ad8d295ff3595139f5dcab", "max_stars_repo_licenses": ["... |
# demo
# ====
# fibonacci
# ---------
fib(n) = n ≤ 2 ? n : fib(n-1) + fib(n-2)
fib(1000) # never terminates in ordinal execution
fib(m) # undef var
fib("1000") # obvious type error
# language features
# -----------------
# user-defined types
struct Ty{T}
fld::T
end
function foo(a)
v = Ty(a)
re... | {"hexsha": "b0f347feca29b30b6188ab007ec09b57d86eb610", "size": 627, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "demo.jl", "max_stars_repo_name": "GunnarFarneback/JET.jl", "max_stars_repo_head_hexsha": "281bfb0b23b21a74c8dcc55daa26777ff6026622", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
UCD student.
Former south Davis safeway employee.
Currently serving time at Davis Shell as the full time whipping boy. .
Can often be seen walking around the north Davis Safeway or Longs at The Marketplace or the SaveMart in the Anderson Plaza.
Can be contacted via email at somesthetic@gmail.com
| {"hexsha": "fe74fb5d0055fddbc0f0c5d4d67c9b42b743ca17", "size": 301, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/GarrettTiemann.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/python
import sys
import json
import locale
import numpy as np
import matplotlib
import os
import warnings
from sklearn import preprocessing
warnings.simplefilter('ignore', np.RankWarning)
InDir = ""
if (len(sys.argv) > 1):
InDir = sys.argv[1]
else:
InDir = "./"
GeneratePlot = True
if (len(sys... | {"hexsha": "d14a9a1d209771cf7d76544d796a8ee06aa383ad", "size": 4318, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/smoothing/polynomial/poly-line-generation.py", "max_stars_repo_name": "xuther/nyc-taxi-data", "max_stars_repo_head_hexsha": "70893f8299c293a487fd69e2c74517eb764ce997", "max_stars_repo_license... |
from .output import Output
class MatplotlibOutput(Output):
def __init__(self, args):
super().__init__(args)
self.columns = None
self.rows = []
# Called in the beginning of processing,
# to announce what columns to use in the output.
def start(self, columns):
self.colum... | {"hexsha": "1e3b3d79107ee07265e0aabf43c1705012f912c8", "size": 2876, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/git_repo_language_trends/_internal/matplotlib_output.py", "max_stars_repo_name": "Enselic/git-repo-language-trend", "max_stars_repo_head_hexsha": "b701138a85f7c7b4e3cde5f6cd29b6d006b493cf", "m... |
!------------------------------------------------------------------
! Program to test if my concept for checking that two structures
! are equal works properly.
!
! Works by generating two equal geometric shapes as points
! (sets of coordinates on a grid), but located at different
! positions on the grid. If it wor... | {"hexsha": "734ff8d4fc667a0420a13a9a1c91a8a0a6617c5b", "size": 3422, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "test_compare_procedure/test1/main.f95", "max_stars_repo_name": "ElenaKusevska/coordinates_on_a_grid", "max_stars_repo_head_hexsha": "6605c76d9c6561fee6eaaa844fb954f7784f623c", "max_stars_repo_li... |
/*
Copyright 2016-2017 Robotics and Biology Lab, TU Berlin. 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 condit... | {"hexsha": "b9a4f4314bdc962071684c20cea3dfd2d27b5c95", "size": 5326, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ecto_rbo_grasping/src/ecto_rbo_grasping/SlidingSeeds.cpp", "max_stars_repo_name": "SoMa-Project/vision", "max_stars_repo_head_hexsha": "ea8199d98edc363b2be79baa7c691da3a5a6cc86", "max_stars_repo_lic... |
using JuMP, EAGO
m = Model()
EAGO.register_eago_operators!(m)
@variable(m, -1 <= x[i=1:3] <= 1)
@variable(m, -7.107954588851326 <= q <= 8.439913276188518)
add_NL_constraint(m, :(log(1 + exp(-0.4953615570310643 ... | {"hexsha": "9a9b289ea7babebb5cc33a53d74ef5b4b5da47f6", "size": 4910, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "solver_benchmarking/MINLPLib.jl/instances/ANN_Expr/55_softplus_3_3_3.jl", "max_stars_repo_name": "PSORLab/RSActivationFunctions", "max_stars_repo_head_hexsha": "0bf8b4500b21144c076ea958ce93dbdd19a5... |
# Test to verify database integrity
import os
import pytest
from . import REFERENCE_TABLES
from sqlalchemy import func
from simple.schema import *
from astrodbkit2.astrodb import create_database, Database, or_
from astropy.table import unique
from astroquery.simbad import Simbad
from astrodbkit2.utils import _name_for... | {"hexsha": "fc4a1e7504b0cdaab6849db6134292508ae110fd", "size": 13638, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_integrity.py", "max_stars_repo_name": "dr-rodriguez/SIMPLE", "max_stars_repo_head_hexsha": "d1decae38b1a3320ace97c3deccc7e058afc5d09", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import unittest
import astropy.units as u
import numpy as np
from input import exomol_file, dace_file
from rapoc.loaders import ExoMolFileLoader, DACEFileLoader
class ExoMolLoaderTest(unittest.TestCase):
loaded = ExoMolFileLoader(filename=exomol_file)
mol, mol_mass, pressure_grid, temperature_grid, wavenumb... | {"hexsha": "11217a282ba0c8a4617c7cbc2b0bbdc01ea94419", "size": 4043, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_loader.py", "max_stars_repo_name": "ExObsSim/Rapoc-public", "max_stars_repo_head_hexsha": "dd5a4af1e7ab531e8d3176026354719585752d58", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import math
import tqdm
import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt
import seaborn as sns
import sympy.utilities.iterables
sns.set(color_codes=True)
def nchoosek(n, k):
return math.factorial(n) / (math.factorial(k) * math.factorial(n - k))
def compute_probability_of_configuration(c... | {"hexsha": "9138c6e0d98309da8e2b65601bba2576b2178dc6", "size": 3019, "ext": "py", "lang": "Python", "max_stars_repo_path": "exam/tiny_perc.py", "max_stars_repo_name": "Schoyen/FYS4460", "max_stars_repo_head_hexsha": "0c6ba1deefbfd5e9d1657910243afc2297c695a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import unittest
import numpy as np
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
from openmdao.utils.mpi import MPI
if MPI:
try:
from openmdao.vectors.petsc_vector import PETScVector
except ImportError:
PETScVector = None
class L2(om.ExplicitComponent):
... | {"hexsha": "7cc354f6c7f6f619a47c714409bb306c457bdbf2", "size": 30380, "ext": "py", "lang": "Python", "max_stars_repo_path": "openmdao/core/tests/test_dyn_sizing.py", "max_stars_repo_name": "JustinSGray/blue", "max_stars_repo_head_hexsha": "49f4edd00eda43f7ce5e10a5121839f2e897d429", "max_stars_repo_licenses": ["Apache-2... |
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
import tensorflow as tf
import keras.backend as K
from keras import metrics
from keras.models import Model, load_model
from keras.layers import Input, BatchNormalization, Activation, Dense, Dropout,Maximum
from ker... | {"hexsha": "cfd835f844f20b909ad833fb7bc798e9d2b46eb1", "size": 5248, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "GingerSpacetail/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks", "max_stars_repo_head_hexsha": "f627ce48e44bcc7d295ee1cf4086bfdfd7705... |
import numpy as np
def mse(y_true, y_pred):
return np.mean(np.power(y_true-y_pred, 2))
def mse_prime(y_true, y_pred):
return 2*(y_pred - y_true)/ y_true.size
def binary_cross_entropy(y_true, y_pred):
return - np.mean(((y_true * np.log(y_pred)) + ((1 - y_true) * np.log((1 - y_pred)))))
def BCE_prime(y_t... | {"hexsha": "5e1071d2ae7f9c0891107b33341a4e54fd147b1a", "size": 411, "ext": "py", "lang": "Python", "max_stars_repo_path": "loss.py", "max_stars_repo_name": "rijulizer/NeuralFlow", "max_stars_repo_head_hexsha": "246bdc95f7715b841dccd25ebcdbb72aead4354d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
'''An implementation of the GLYMMR alogrithm using some of the pre-existing CCARL framework.
GLYMMR Algorithm (from Cholleti et al, 2012)
1. Initialize each unique node among all the binding glycans as a subtree of size 1.
Let this set be S.
2. For each subtree in S:
- Calculate the number of binding glycans conta... | {"hexsha": "63261582ea67de4a0224a6abe31f400cdea5d12a", "size": 5596, "ext": "py", "lang": "Python", "max_stars_repo_path": "glymmr.py", "max_stars_repo_name": "andrewguy/CCARL", "max_stars_repo_head_hexsha": "0afda67bcc58be2b6b6bf426cccaab04453c0590", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars... |
from __future__ import annotations
import numpy as np
import bottleneck as bn
from sigpyproc.core import kernels
def running_median(array, window):
"""
Calculate the running median of an array.
Parameters
----------
array : numpy.ndarray
The array to calculate the running median of.
... | {"hexsha": "cac334f7d9974bfd512ef872cad96bd245476508", "size": 4923, "ext": "py", "lang": "Python", "max_stars_repo_path": "sigpyproc/core/stats.py", "max_stars_repo_name": "FRBs/sigpyproc3", "max_stars_repo_head_hexsha": "b1f87c0174ca5770d0e5b5380a53cdd2dd8b7a86", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
%% Parameters
Parameters_struct.CenterFrequency = 5e9; % 5 GHz
Parameters_struct.Bandwidth = 20e6; % 20 MHz
Parameters_struct.Ts = 1/Parameters_struct.Bandwidth; % 50 ns
%% Load Given data
load('Long_preamble_slot_Frequency'); % [1x64]
load('data_Payload_1'); % [1x48]
load('data_Payload_2'); % [1x48]
Parameters_struct.... | {"author": "MeowLucian", "repo": "SDR_Matlab_OFDM_802.11a", "sha": "ee4a1ff01799242bad455054bfb318242250f973", "save_path": "github-repos/MATLAB/MeowLucian-SDR_Matlab_OFDM_802.11a", "path": "github-repos/MATLAB/MeowLucian-SDR_Matlab_OFDM_802.11a/SDR_Matlab_OFDM_802.11a-ee4a1ff01799242bad455054bfb318242250f973/Global_Pa... |
# Copyright (c) 2019 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... | {"hexsha": "e3a5a94eab46477a8fb9676f5a5bf67000783018", "size": 7426, "ext": "py", "lang": "Python", "max_stars_repo_path": "dygraph/mobilenet/mobilenet_v1.py", "max_stars_repo_name": "suytingwan/models", "max_stars_repo_head_hexsha": "ccdbfe77d071cc19b55fb9f4b738912e35d982ef", "max_stars_repo_licenses": ["Apache-2.0"],... |
[STATEMENT]
lemma HMA_isDiagonal_Mod_Type[transfer_rule]: "(Mod_Type_Connect.HMA_M ===> (=))
isDiagonal_mat (isDiagonal::('a::{zero}^'n::{mod_type}^'m::{mod_type} => bool))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (Mod_Type_Connect.HMA_M ===> (=)) isDiagonal_mat isDiagonal
[PROOF STEP]
proof (intro rel_fun... | {"llama_tokens": 5614, "file": "Smith_Normal_Form_Admits_SNF_From_Diagonal_Iff_Bezout_Ring", "length": 60} |
# coding=utf-8
from __future__ import absolute_import, print_function
import time
import argparse
import os
import sys
import torch
torch.manual_seed(0)
import numpy as np
np.random.seed(0)
import torch.utils.data
from torch.backends import cudnn
from torch.autograd import Variable
import glob
import models
import loss... | {"hexsha": "4c4710494773995e36332912d2e1ea41834ad361", "size": 12946, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "xuefeicao/UDML_SS", "max_stars_repo_head_hexsha": "6d130b088effbd4342c0446f246519bf99d4634e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_sta... |
// This file is part of libigl, a simple c++ geometry processing library.
//
// Copyright (C) 2014 Olga Diamanti <olga.diam@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla Public License
// v. 2.0. If a copy of the MPL was not distributed with this file, You can
// obtain one at http://moz... | {"hexsha": "a953be31ccc6940a148e4d06eb414a0ed8424f7e", "size": 26667, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "include/igl/angle_bound_frame_fields.cpp", "max_stars_repo_name": "rushmash/libwetcloth", "max_stars_repo_head_hexsha": "24f16481c68952c3d2a91acd6e3b74eb091b66bc", "max_stars_repo_licenses": ["BSD-... |
#Simple Linear Regression
#import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#Read data
dataset = pd.read_csv('Salary_Data.csv')
x = dataset.iloc[:,:-1].values
y = dataset.iloc[:,1].values
#Splitting data
from sklearn.model_selection import train_test_split
X_train, X_test, Y_tr... | {"hexsha": "1a51675820cd6eec5c073494813b3dd559fb02d0", "size": 1266, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML A-Z/2. Regression/Simple_Linear_Regression.py", "max_stars_repo_name": "Shaon2221/Learning-and-Experimenting_Data-Science", "max_stars_repo_head_hexsha": "817a402158c1cf5d77ce2ea92b3e91470851de... |
\section{Functions} % (fold)
\label{sec:functions}
\begin{frame}\frametitle{Simple example}
\framesubtitle{}
\emph{Example:} Suppose we want to find the circumference of a circle with radius
2.5. We could write
\codeblock{code/functions_radius0.py}
\end{frame}
\begin{frame}\frametitle{Functions}
... | {"hexsha": "22089920561af8fbd00ee45267e4009bb48287ef", "size": 7947, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "lectures/tex/functions.tex", "max_stars_repo_name": "naskoch/python_course", "max_stars_repo_head_hexsha": "84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from time import time
import torch
import numpy as np
from sklearn.metrics import accuracy_score, mean_squared_error
from config.BERT_QS.const import TOKENIZER, MODEL, DEVICE
from config.BERT_QS.datapath import RESOURCE_TEST_PATH
from models.BERT_QS.evaluate_model import evaluate_model
from utils.set_seed import set_se... | {"hexsha": "c5c367c1b72622c81d7639f8a5731a43de1e1dc6", "size": 1755, "ext": "py", "lang": "Python", "max_stars_repo_path": "{{ cookiecutter.repo_name }}/src/models/BERT_QS/calc_test_accuracy.py", "max_stars_repo_name": "reiven-c-t/cookiecutter-data-science", "max_stars_repo_head_hexsha": "33b9ba631128e3269ad3199e41f69d... |
import unittest
# make sure a __init__.py file exist in the import folder
from astropy.io import fits
from astropy.table import Table
from fermitool.fermitool import *
# requires setup.sh to run
# import fits file
data_path = os.environ["SOURCE_ROOT"] + '/data/gll_psc_v21.fit'
try: # does it exists? If yes
with fi... | {"hexsha": "8fa00ce6ce0dd638a27ec09274a3abf8d39c2532", "size": 3559, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_fermitool.py", "max_stars_repo_name": "micmes/SourceClassifier", "max_stars_repo_head_hexsha": "c08316d243ea1bf02b667f4f1eb45e198c59f9ea", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/python3
## Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
## Written by S. Pavankumar Dubagunta <pavankumar [dot] dubagunta [at] idiap [dot] ch>
## and Mathew Magimai Doss <mathew [at] idiap [dot] ch>
##
## This file is part of RawSpeechClassification.
##
## RawSpeechClassification is f... | {"hexsha": "1771b51a0f9a42177da2a3cd02ebcabbc7c7ee4d", "size": 2568, "ext": "py", "lang": "Python", "max_stars_repo_path": "SpeakerDependent/rawCNN_Pipeline/test.py", "max_stars_repo_name": "Tilak-96/ML-proj2-SER-task", "max_stars_repo_head_hexsha": "50166a08f573dc504bbb6b4aac4d6e17146d57ce", "max_stars_repo_licenses":... |
\title{Home}
\subsection{A library for probabilistic modeling, inference, and criticism.}
Edward is a Python library for probabilistic modeling, inference, and
criticism. It is a testbed for fast experimentation and research with
probabilistic models, ranging from classical hierarchical models on
small data sets to c... | {"hexsha": "3f5a487627fc6341f4b4f6029fbb68874e3e4c56", "size": 4372, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/tex/index.tex", "max_stars_repo_name": "zhangyewu/edward", "max_stars_repo_head_hexsha": "8ec452eb0a3801df8bda984796034a9e945faec7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
#include <atomic>
#include <cmath>
#include <cstdlib>
#include <future>
#include <limits>
#include <list>
#include <memory>
#include <sstream>
#include <string>
#include <vector>
#include <boost/optional.hpp>
#include <boost/thread/shared_mutex.hpp>
#include <gtest/gtest.h>
#include "test_functional_common.hpp"
#includ... | {"hexsha": "c5f8bbfa0cdf643ac78f16aa12c09fc6ac0c7e4b", "size": 31051, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/ka/test_functional.cpp", "max_stars_repo_name": "yumilceh/libqi", "max_stars_repo_head_hexsha": "f094bcad506bcfd5a8dcfa7688cbcce864b0765b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
[STATEMENT]
lemma mirror_elem_inj_on: "finite I \<Longrightarrow> inj_on (\<lambda>x. mirror_elem x I) I"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite I \<Longrightarrow> inj_on (\<lambda>x. mirror_elem x I) I
[PROOF STEP]
unfolding mirror_elem_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite I ... | {"llama_tokens": 203, "file": "List-Infinite_CommonSet_SetIntervalCut", "length": 2} |
"""Test utils.dataframe.
"""
import unittest
import numpy as np
from .. import dataframe as df
class TestArrayToDataFrame(unittest.TestCase):
"""Test array_to_dataframe function.
"""
def setUp(self):
"""Sets up the two convertable arrays.
"""
self.dat1 = np.empty(shape=(0, ))
... | {"hexsha": "a3007fe770972981e4099cb4cf95e928c6f8bffb", "size": 899, "ext": "py", "lang": "Python", "max_stars_repo_path": "patches/utils/test/dataframe.py", "max_stars_repo_name": "sflippl/patches", "max_stars_repo_head_hexsha": "c19889e676e231af44669a01c61854e9e5791227", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma optimize_matches_a_simplers:
assumes "simple_ruleset rs" and "\<forall>a m. a = Accept \<or> a = Drop \<longrightarrow> matches \<gamma> (f a m) a = matches \<gamma> m a"
shows "approximating_bigstep_fun \<gamma> p (optimize_matches_a f rs) s = approximating_bigstep_fun \<gamma> p rs s"
[PROOF STA... | {"llama_tokens": 9966, "file": "Iptables_Semantics_Semantics_Ternary_Semantics_Ternary", "length": 19} |
Require Import Hask.Prelude.
(* Require Import Hask.Control.Iso. *)
Require Import Hask.Control.Monad.
Generalizable All Variables.
Set Primitive Projections.
Set Universe Polymorphism.
Unset Transparent Obligations.
Set Asymmetric Patterns.
Inductive LogicT (M : Type -> Type) `{Monad M} (A : Type) :=
LogicT_ : for... | {"author": "jwiegley", "repo": "coq-haskell", "sha": "56a185af5767177d410113a03bd765135e07c9ca", "save_path": "github-repos/coq/jwiegley-coq-haskell", "path": "github-repos/coq/jwiegley-coq-haskell/coq-haskell-56a185af5767177d410113a03bd765135e07c9ca/src/Control/Monad/Trans/LogicT.v"} |
import numpy as np
from skmultiflow.drift_detection import ADWIN
def demo():
""" _test_adwin
This demo will insert data into an ADWIN object when will display in which
indexes change was detected.
The data stream is simulated as a sequence of randomly generated 0's and 1's.
Then the da... | {"hexsha": "50544d6492a573fa16980d28c79017be5446877a", "size": 885, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/skmultiflow/demos/_test_adwin.py", "max_stars_repo_name": "AndreFCruz/scikit-multiflow", "max_stars_repo_head_hexsha": "c4dbbb70d4ed839d95a18ca799f073ac9ff9ba49", "max_stars_repo_licenses": ["B... |
from flask import Flask, render_template, url_for, request
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.externals import joblib
import io
import numpy as np # Import Numpy for data statistical analysis
import matplotlib.pyplot as plt #... | {"hexsha": "07fb17c34fb58e0f105962229a95f8d13079b280", "size": 948, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "Piterbrito/Stat-gram--InstagramMicroservice", "max_stars_repo_head_hexsha": "f9d2066987d20a826854cdbc85f8081ef3781112", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#IGE (Indirect genetic effects) = SGE (Social genetic effects). IEE = SEE.
#the potential presence of NAs in input phenotype, covs, cages etc means that in this code we introduce two sets of animals (not discussed in the paper):
#focal animals, defined as having phenotype, covs (if covs are provided), cage and kinship... | {"hexsha": "58390614a4093b98bbfb035f407a979752b9bd86", "size": 22363, "ext": "py", "lang": "Python", "max_stars_repo_path": "limix_legacy/modules/dirIndirVD_commented_forDistrib.py", "max_stars_repo_name": "michoel-lab/limix-legacy", "max_stars_repo_head_hexsha": "cd6c9887a2c411372beeddde3a86979b2aa21837", "max_stars_r... |
import numpy as np
class MnistDataset:
def __init__(self, images, labels, batch_size, transforms=None):
self.batch_size = batch_size
self.transforms = transforms
# Load images
self.images = images
# Load labels
self.labels = labels
self.length = int(np.cei... | {"hexsha": "aeddc2124d63eb532e001274a30171851ebc079e", "size": 1678, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy_mnist/mnist_dataset.py", "max_stars_repo_name": "tillaczel/numpy_mnist", "max_stars_repo_head_hexsha": "e70890bd5f9ec875315ae476bedcb65fb4486a76", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Copyright 2021 The TensorFlow Probability 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 law o... | {"hexsha": "b8d2ef5d085230027137f91af02c24d74a9d5451", "size": 11295, "ext": "py", "lang": "Python", "max_stars_repo_path": "discussion/pathfinder/pathfinder.py", "max_stars_repo_name": "jakee417/probability-1", "max_stars_repo_head_hexsha": "ae7117f37ac441bc7a888167ea23e5e620c5bcde", "max_stars_repo_licenses": ["Apach... |
from typing import List, Tuple, Union
import numpy as np
import torch
import pytorch_lightning as pl
def calc_area(bbox: np.ndarray):
return (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
def calc_bbox_overlap_union_iou(pred: np.ndarray or None, teacher: np.ndarray) -> Tuple[float, float, float]:
"""
:para... | {"hexsha": "790ae1cd5f1909b3ea05f4bc1e22db416ad39314", "size": 13230, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepext_with_lightning/metrics/object_detection.py", "max_stars_repo_name": "pei223/deepext_with_lightning", "max_stars_repo_head_hexsha": "e40ac19844a05864f803431d8ef4a534286a0950", "max_stars_r... |
import csv
import numpy as np
class GroupRows:
staticmethod
def group_rows(data, keep_complete_header, number_of_groups):
groups = [None]*number_of_groups
np_unique = np.unique(data[keep_complete_header])
min_per_fold = len(np_unique)//number_of_groups
for i in range(0,numb... | {"hexsha": "a87aec358b8dd9ceba4857cb2f71e8d3ad0043a3", "size": 556, "ext": "py", "lang": "Python", "max_stars_repo_path": "GroupRows.py", "max_stars_repo_name": "ebbaberg/MovingFiles", "max_stars_repo_head_hexsha": "77a10e2e85b6f315a685c5a6bd092fc3fb90b727", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 1,... |
Require Export Functor.
Arguments Compose_Functors {_} {_} {_} _ _.
Arguments fmap {_} {_} _ {_} {_} _.
Arguments fobj {_} {_} _ _.
Check fobj.
Class NaturalTransformation (C D: Category)
(F : Functor C D)
(G : Functor C D): Type :=
mk_nt
{
trans ... | {"author": "ekiciburak", "repo": "ComparisonTheorem-MacLane", "sha": "f1a5b0e35554c7115fc0dba32d550dfa0121d07a", "save_path": "github-repos/coq/ekiciburak-ComparisonTheorem-MacLane", "path": "github-repos/coq/ekiciburak-ComparisonTheorem-MacLane/ComparisonTheorem-MacLane-f1a5b0e35554c7115fc0dba32d550dfa0121d07a/Natural... |
subroutine get_bin_size(nsize, wld, mode)
use types
use dfcomm
implicit none
include 'common.ifopbl'
integer mode, i
integer, intent(out) :: nsize
real(kind=dp), intent(out) :: wld(mode)
if (ifkbin ) then
call alloc_wave(0, 12)
else
print*, 'Somthing went wrong with the frequency gri... | {"hexsha": "6470150a697ae1122340f17e076fce37ec613035", "size": 703, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mpsa.binsize.f90", "max_stars_repo_name": "rtagirov/mps_atlas", "max_stars_repo_head_hexsha": "5eef3b620e60016b713cd414a51c48e842d8f6d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from normalization import Normalization
import scipy as sp
from scipy import linalg
class Pca:
def __init__(self, data):
self._normalization = Normalization(data)
normalized_data = self._normalization.normalized_dataset()
data_matrix = sp.matrix(normalized_data)
m = data_matrix.sh... | {"hexsha": "bce2782376597d52767887747106a7043f0725e2", "size": 1457, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw3/references/Pca-Image-Compression/my_pca.py", "max_stars_repo_name": "ardihikaru/mlsp", "max_stars_repo_head_hexsha": "db38972bcceac7b95808132457c4de9170546c9d", "max_stars_repo_licenses": ["Ap... |
import wandb
import numpy as np
import sys
learning_rates = list(
np.around(
np.array(
[np.linspace(0, 1, 10, endpoint=False)[1:] / 10 ** i for i in range(1, 6)]
).flatten(),
decimals=6,
).tolist()
)
ent_rate = list(
np.around(
np.array(
[np.linspace... | {"hexsha": "41a1044a8a586c342e212f3b812c9ebc494ecd12", "size": 1305, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/run_sweep.py", "max_stars_repo_name": "alomrani/CORL", "max_stars_repo_head_hexsha": "42cad2991762dbe396fd2e6592707a997671c662", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "... |
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
class Arrow3D(FancyArrowPatch):
"""
From https://stackoverflow.com/a/22867877.
"""
def __init__(self, xs, ys, zs, *arg... | {"hexsha": "c78b4b07bb6b45ecafb1518401504751edf7178f", "size": 4291, "ext": "py", "lang": "Python", "max_stars_repo_path": "tex/figures/scattering.py", "max_stars_repo_name": "rodluger/starrynight", "max_stars_repo_head_hexsha": "d3f015e466621189cb271d4d18b538430b14a557", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python3
import sys
import os.path
from os import path
import time
import pandas as pd
import geopandas as gpd
import numpy as np
import networkx as nx
from QUANT.PublicTransportNetwork import PublicTransportNetwork
################################################################################
# Globa... | {"hexsha": "c5f6ff274793c4ced3ffb337a5391e68a2f77b83", "size": 8376, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "maptube/Harmony", "max_stars_repo_head_hexsha": "6b253e79c64bee544a20c5b9e36427b70964674f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
[STATEMENT]
lemma ipl_map_tree[simp]: "ipl (map_tree f t) = ipl t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ipl (map_tree f t) = ipl t
[PROOF STEP]
by (induction t) auto | {"llama_tokens": 83, "file": "Treaps_Random_Treap", "length": 1} |
x1 = [3, 9, 18, 30, 91]
y1 = [1.01, 1.204, 1.54, 1.81, 2.12]
c1 = Curve(x1, y1)
x2 = [5, 12, 18, 30, 125, 291]
y2 = [1.01, 1.204, 1.54, 1.81, 2.12, 7.436]
clog = Curve(x2, y2, logx=true, logy=true)
clogy = Curve(x1, y1, logy=true)
c0 = Curve([3], [5.5]) # curve with single point
# equality
c2 = Curve(c1)
c3 = Curve(... | {"hexsha": "2e185f5f0007c7e3e7f7179c70a1f4fb13ba6045", "size": 3865, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_curves.jl", "max_stars_repo_name": "lungben/Curves.jl", "max_stars_repo_head_hexsha": "dc61375a6d2a3d32ad68e5c2db35b3053e1ade6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10... |
import numpy as np
from PIL import Image, ImageDraw
# input - image as numpy aray and rectangles on image, Normalized - each value in (0,1)
# output - image with drawn rects as numpy array
def draw_image_rects(image, rects, is_normalized=False):
image_to_show = image.copy()
rects_to_show = []
... | {"hexsha": "5efa1e8e529ce9df5f23b5b4745383d336bb7685", "size": 2292, "ext": "py", "lang": "Python", "max_stars_repo_path": "visual.py", "max_stars_repo_name": "evasilyev/PGD-ObjDetector", "max_stars_repo_head_hexsha": "47043f7bdc656d983dd94528e52809ae97a537fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
C***********************************************************************
C Module: xcasepl.f
C
C Copyright (C) 2011 Mark Drela
C
C This program is free software; you can redistribute it and/or modify
C it under the terms of the GNU General Public License as published by
C the Free Software Foundation... | {"hexsha": "4188f7497d733241a991f8e5590fd1a763fd1eed", "size": 20437, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "third_party/xrotor/src/xcasepl.f", "max_stars_repo_name": "leozz37/makani", "max_stars_repo_head_hexsha": "c94d5c2b600b98002f932e80a313a06b9285cc1b", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
## 2. The Empirical Probability ##
p_tail = 44 / 100
p_six = 28 / 200
p_odd = 102/ 200
print(p_tail)
print(p_six)
print(p_odd)
## 3. Probability as Relative Frequency ##
p_heads_1 = 1 - (162/300)
percentage_1 = p_heads_1 * 100
p_heads_2 = 1 - (2450/5000)
percentage_2 = p_heads_2 * 100
print(percentage_1)
print(per... | {"hexsha": "55f1d0d1ca7bcc4d8de1f22dc5e1d3c46f201264", "size": 1216, "ext": "py", "lang": "Python", "max_stars_repo_path": "5. Probability and Statistics/probability-fundamentals/Estimating Probabilities-377.py", "max_stars_repo_name": "bibekuchiha/dataquest", "max_stars_repo_head_hexsha": "c7d8a2966fe2eee864442a59d643... |
# encoding: utf-8
# Copyright 2018 D-Wave Systems 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 ... | {"hexsha": "c37ebb5aef8e36753480cf3b8e773b9bdb7a6d1a", "size": 16212, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_composers.py", "max_stars_repo_name": "hsadeghidw/dwave-hybrid", "max_stars_repo_head_hexsha": "e667035b5623f122813795d433a40db6e520ff66", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 2 11:24:28 2019
@author: qinayan
"""
#https://www.pythonforbeginners.com/files/reading-and-writing-files-in-python
#https://stackoverflow.com/questions/899103/writing-a-list-to-a-file-with-python
import numpy as np
ho = 0.135#0.2 # 1/m
B_... | {"hexsha": "ae7ac12c0ba5eebe059715d057b7671f3474ca6d", "size": 865, "ext": "py", "lang": "Python", "max_stars_repo_path": "module_st/write_into_text.py", "max_stars_repo_name": "Qinayan/Soil-thickness", "max_stars_repo_head_hexsha": "23f110d663dcd9f25593b2d10dec47ff78b19643", "max_stars_repo_licenses": ["Apache-2.0"], ... |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 1 10:23:38 2020
@author: tobias.grab
"""
import sys
sys.path.append('/usr/local/lib/python2.7/site-packages')
import cv2
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
alg="surf"
if alg=="sift":
ftAlg = c... | {"hexsha": "083c96498f7646bce6f28547bc2aba6087d7be55", "size": 2220, "ext": "py", "lang": "Python", "max_stars_repo_path": "student-projects/fall-2020/OST-Imaging-and-Matching-Plastic/mvp/MatchingVisualization.py", "max_stars_repo_name": "UCBerkeley-SCET/DataX-Berkeley", "max_stars_repo_head_hexsha": "f912d22c838b511d3... |
#####################################
# Database/tsql.py
#####################################
# Description:
# * Objects related to interacting
# with SQL database that uses T-SQL
# as SQL flavor language.
from Database.columnattributes import ColumnAttributesGenerator
from itertools import combinations
from numba im... | {"hexsha": "fa40a3e2aea9db025c2bca3da995e44bc56bc034", "size": 22012, "ext": "py", "lang": "Python", "max_stars_repo_path": "DynamicETLDashboard/DynamicETL_Dashboard/Database/tsql.py", "max_stars_repo_name": "BRutan/DynamicETLDashboard", "max_stars_repo_head_hexsha": "8a40e6f51e53f084d6103ba41cd675916505652f", "max_sta... |
// This file is adapted almost verbative where possible from
// the boost asio async http example
//
// async_client.cpp
// ~~~~~~~~~~~~~~~~
//
// Copyright (c) 2003-2012 Christopher M. Kohlhoff (chris at kohlhoff dot com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENS... | {"hexsha": "85b9d3ee5f7c1113506046e243663a025f0393c1", "size": 5075, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Example2.cpp", "max_stars_repo_name": "jbandela/cpp_async_await", "max_stars_repo_head_hexsha": "0c76cf886c574855db728fa81884720730f8a8e8", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count":... |
# This file is a part of Julia. License is MIT: http://julialang.org/license
function message(c::GitCommit, raw::Bool=false)
local msg_ptr::Cstring
msg_ptr = raw? ccall((:git_commit_message_raw, :libgit2), Cstring, (Ptr{Void},), c.ptr) :
ccall((:git_commit_message, :libgit2), Cstring, (Ptr{V... | {"hexsha": "9ecc102cab1d1640c20f1b30bb0b884b0ec8f3ac", "size": 3015, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "base/libgit2/commit.jl", "max_stars_repo_name": "habemus-papadum/julia", "max_stars_repo_head_hexsha": "581a034dd6c5b027d64f624f97242e07ea3d613b", "max_stars_repo_licenses": ["Zlib"], "max_stars_co... |
/*
* Copyright 2022 HEAVY.AI, 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 ... | {"hexsha": "eb46ce256fc0f7284d1cac3500eb7e0362b21dab", "size": 4381, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Embedded/EmbeddedDbFSITest.cpp", "max_stars_repo_name": "omnisci/mapd-core", "max_stars_repo_head_hexsha": "cde582ebc3edba3fb86bacefa5bd9b3418a367b4", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
subroutine timcrl
!***********************************************************************
! Copyright, 1993, 2004, The Regents of the University of California.
! This program was prepared by the Regents of the University of
! California at Los Alamos National Laboratory (the University) under
! contract... | {"hexsha": "668d86a98de1013d162b3ba1d32067c2b60b64ee", "size": 14628, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/timcrl.f", "max_stars_repo_name": "satkarra/FEHM", "max_stars_repo_head_hexsha": "5d8d8811bf283fcca0a8a2a1479f442d95371968", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 24... |
from typing import Any
import pandas as pd
import numpy as np
def values_are_same(val1: Any, val2: Any, tolerance: float) -> bool:
"""
Compares whether values are the same for grading purposes. Converts arrays and series
to lists. Uses a tolerance to allow for float/rounding errors.
:param val1:
... | {"hexsha": "38dc79f44bc01c9a9cfe4fddb05b8471847237e4", "size": 1150, "ext": "py", "lang": "Python", "max_stars_repo_path": "fin_model_course/gradetools/py/execute2/compare.py", "max_stars_repo_name": "whoopnip/fin-model-course", "max_stars_repo_head_hexsha": "e6c5ae313bba601c4aca0f334818b61cc0393118", "max_stars_repo_l... |
#ifndef BLUB_CORE_NONCOPYABLE_HPP
#define BLUB_CORE_NONCOPYABLE_HPP
#include <boost/noncopyable.hpp>
namespace blub
{
class noncopyable : public boost::noncopyable
{
};
}
#endif // NONCOPYABLE_HPP
| {"hexsha": "7c0bdc1769234c8d0dfdbd4065af6a320f3b9082", "size": 216, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/core/source/blub/core/noncopyable.hpp", "max_stars_repo_name": "qwertzui11/voxelTerrain", "max_stars_repo_head_hexsha": "05038fb261893dd044ae82fab96b7708ea5ed623", "max_stars_repo_licenses": ... |
(* Exercise 104 *)
Require Import BenB.
Variables A B C D : Prop.
(* de Morgan's conjunction law inverse variant *)
Theorem exercise_104 : (A \/ B) -> ~(~A /\ ~B).
Proof.
imp_i a1.
neg_i (1=1) a2.
dis_e (A \/ B) a3 a3.
hyp a1.
neg_e (A).
con_e1 (~B).
hyp a2.
hyp a3.
neg_e (B).
con_e2 (~A).
hyp a2.
hyp a3.
lin_solv... | {"author": "KiOui", "repo": "total5", "sha": "b47117bc43a775b525813af56d54350394c22356", "save_path": "github-repos/coq/KiOui-total5", "path": "github-repos/coq/KiOui-total5/total5-b47117bc43a775b525813af56d54350394c22356/html/Uitwerkingen/BB/Coq/Taak13/Taak13_prop104.v"} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, print_function)
import os
from pprint import pprint
import argh
import numpy as np
from chemreac.serialization import load
from chemreac.integrate import run
"""
Demo of chemical reaction diffusion system.
"""
# A -> ... | {"hexsha": "94610a413a0156f59209ca8cfab6310f9cfb90f3", "size": 2117, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/four_species_inhomogeneous.py", "max_stars_repo_name": "bjodah/chemreac", "max_stars_repo_head_hexsha": "dbe38a10cf6b88e66192bcc998721b61aabbd9dc", "max_stars_repo_licenses": ["BSD-2-Clau... |
[STATEMENT]
lemma isLim_supr:
assumes f: "i \<in> Field r" and l: "isLim i"
shows "i = supr (underS i)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. i = local.supr (local.underS i)
[PROOF STEP]
proof(rule equals_supr)
[PROOF STATE]
proof (state)
goal (4 subgoals):
1. local.underS i \<subseteq> Field r
2. i \<in>... | {"llama_tokens": 2519, "file": null, "length": 29} |
from itertools import product
import re
import numpy as np
def day22(inp, part2=False):
all_coords = []
all_states = []
for row in inp.splitlines():
state, rest = row.split()
coords = [
sorted(map(int, axis.split('..')))
for axis in re.sub('[xyz]=', '', rest).split... | {"hexsha": "b715315641c104f690d2761720cc9d2d25baa0da", "size": 6098, "ext": "py", "lang": "Python", "max_stars_repo_path": "day22.py", "max_stars_repo_name": "adeak/AoC2021", "max_stars_repo_head_hexsha": "f389ffdfe469d1ba411e9586b2691f9fb6cbfae1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_re... |
#-*- encoding:utf-8 -*-
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import os
import math
import networkx as nx
import numpy as np
import sys
try:
reload(sys)
sys.setdefaultencoding('utf-8')
except:
pass
sentence_delimiters = ['?', '!', ... | {"hexsha": "6004f23ac90022dc8f4655f52b762adaacbeb62d", "size": 5753, "ext": "py", "lang": "Python", "max_stars_repo_path": "textrank4zh/util.py", "max_stars_repo_name": "yanzhelee/TextRank4ZH", "max_stars_repo_head_hexsha": "e0336c8e0d53e1414394ce8fccda6ff9ecb1a720", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma id_measure_preserving:
"(\<lambda>x. x) \<in> measure_preserving M M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>x. x) \<in> measure_preserving M M
[PROOF STEP]
unfolding measure_preserving_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>x. x) \<in> {f \<in> M \<righ... | {"llama_tokens": 183, "file": "Ergodic_Theory_Measure_Preserving_Transformations", "length": 2} |
import torch
import numpy as np
from network import NTGAN
import scipy.io as sio
import os
import h5py
from torch.autograd import Variable
import matplotlib.pyplot as plt
def load_nlf(info, img_id):
nlf = {}
nlf_h5 = info[info["nlf"][0][img_id]]
nlf["a"] = nlf_h5["a"][0][0]
nlf["b"] = nl... | {"hexsha": "e26bd2aa00677b9bac3d6f9d66674782aee9d5ce", "size": 4525, "ext": "py", "lang": "Python", "max_stars_repo_path": "DND_eval.py", "max_stars_repo_name": "matt45m/NTGAN-Learning-Blind-Image-Denoising-Without-Clean-Reference", "max_stars_repo_head_hexsha": "cc5691b18de542e6061eb88e4399b1c6d8bb0d77", "max_stars_re... |
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
# For fitting a gaussian curve.
from scipy.stats import norm
def plot(data,
fig_w=15,
fig_h=9):
plt.figure(figsize=(fig_w, fig_h))
plt.grid()
_ = plt.plot(data, 'x', linestyle='-')
def plot_multiple(data... | {"hexsha": "41345aa35007a69bd6cadde1244f4ce57710fc94", "size": 5368, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotting_helpers.py", "max_stars_repo_name": "rosskidson/python_utils", "max_stars_repo_head_hexsha": "59b59081136bcdb911eb5a80f0219d811ee379b3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma esssup_AE_mono: "f \<in> borel_measurable M \<Longrightarrow> AE x in M. f x \<le> g x \<Longrightarrow> esssup M f \<le> esssup M g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>f \<in> borel_measurable M; AE x in M. f x \<le> g x\<rbrakk> \<Longrightarrow> esssup M f \<le> esssup M g
[... | {"llama_tokens": 154, "file": null, "length": 1} |
import numpy
import doctest
def wer(r, h):
"""
Source: https://martin-thoma.com/word-error-rate-calculation/
Calculation of WER with Levenshtein distance.
Works only for iterables up to 254 elements (uint8).
O(nm) time ans space complexity.
Parameters
----------
r : list
h : list... | {"hexsha": "86810288704c1bdc3cf2b84aed95644e6da4dfaa", "size": 1371, "ext": "py", "lang": "Python", "max_stars_repo_path": "lipnet/utils/wer.py", "max_stars_repo_name": "dimaxano/LipNet", "max_stars_repo_head_hexsha": "5990b0a0a5331ccbc2c110dfcbbf1b08e1704d19", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 560... |
# Non Linear Controller
```python
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
%load_ext autoreload
%autoreload 2
import numpy as np
import math
from math import sin, cos
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from drone import Drone2D
import trajectories
import si... | {"hexsha": "7e4927b18cd565f20d4efb16a54e94a4bc45c7e8", "size": 571480, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "jupyter_notebooks/3_Controls/3_ControlArchitecture/Non-Linear Controller.ipynb", "max_stars_repo_name": "miker2/FCND-udacity", "max_stars_repo_head_hexsha": "35d8248b3bb17a04279b65b... |
import LeanUtils.Div
import Mathlib.Tactic.Ring
/- Parity : functions and theorems related to parity -/
namespace Nat
def even (a : Nat) : Prop := a % 2 = 0
def odd (a : Nat) : Prop := a % 2 = 1
theorem even_rewrite {a : Nat} : even a ↔ ∃ (n : Nat), a = 2 * n :=
Iff.intro
(by
intro h
... | {"author": "Augustindou", "repo": "natural2lean-lean-project-template", "sha": "62c1d7cf8b2f0cbffd84f240c3e2cd89b55f3c03", "save_path": "github-repos/lean/Augustindou-natural2lean-lean-project-template", "path": "github-repos/lean/Augustindou-natural2lean-lean-project-template/natural2lean-lean-project-template-62c1d7c... |
[STATEMENT]
lemma three_covers_pers: \<comment> \<open>alias Old Good Lemma\<close>
assumes "w = v \<cdot> t" and "w = r' \<cdot> v\<^sup>@Suc j \<cdot> t'" and "w = r \<cdot> v" and
"r' <s r" and "t' <p t"
shows "period w (\<^bold>|t\<^bold>| - \<^bold>|t'\<^bold>|)" and "period w (\<^bold>|r\<^bold>| - \<^bol... | {"llama_tokens": 6622, "file": "Combinatorics_Words_Equations_Basic", "length": 42} |
# -*- coding: utf-8 -*-
"""
===============
AOTF instrument
===============
This class (aa_aotf.py) is the model to connect to the AOTF using the controller aa_mod18012.py
The model is similar to the controller, but it adds specific functionalities such as units with Pint
and some calibrations.
* **Wavelength calib... | {"hexsha": "6d0ff4d4a9a837dd00126b71a6b16ab5eb7bcbf1", "size": 13286, "ext": "py", "lang": "Python", "max_stars_repo_path": "hyperion/instrument/polarization/aa_aotf.py", "max_stars_repo_name": "caldarolamartin/hyperion", "max_stars_repo_head_hexsha": "19d6d2041f5029cd33da86c095d76e19ce89fcac", "max_stars_repo_licenses... |
% ******************************* Thesis Declaration ***************************
\begin{declaration}
% I hereby declare that except where specific reference is made to the work of
% others, the contents of this dissertation are original and have not been
% submitted in whole or in part for consideration for any oth... | {"hexsha": "f5ef3f87e848794c41ddd4853bd9b832d6ecda10", "size": 1407, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "masters-thesis/Declaration/declaration.tex", "max_stars_repo_name": "gergely-flamich/miracle-compression", "max_stars_repo_head_hexsha": "7bee78f47982dda123343d25ead9de3c8bce17f5", "max_stars_repo_l... |
import numpy as np
import torch
import torch.nn as nn
import gym
from copy import deepcopy
from ...common import (
ReplayBuffer,
get_model,
save_params,
load_params,
get_env_properties,
set_seeds,
venv,
)
from typing import Tuple, Union, Dict, Optional, Any
class TD3:
"""
Twin Del... | {"hexsha": "3a171725834bf9656aa92cfbc0ae6379fab3581d", "size": 12902, "ext": "py", "lang": "Python", "max_stars_repo_path": "genrl/deep/agents/td3/td3.py", "max_stars_repo_name": "infinitemugen/genrl", "max_stars_repo_head_hexsha": "602587417ce167380c90a726764a3efa4643dc38", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
#
from datetime import datetime
import sys
import pathlib
import numpy as np
import math
from numpy.core.defchararray import center
# このソースのあるディレクトリの絶対パスを取得
current_dir = pathlib.Path(__file__).resolve().parent
# モジュールのあるパスを追加
sys.path.append(str(current_dir) + '/../')
sys.path.append(str(curren... | {"hexsha": "e1b518d31f46430454354a76cd5fdb916ff27413", "size": 14124, "ext": "py", "lang": "Python", "max_stars_repo_path": "crumb/Carpet.py", "max_stars_repo_name": "miu200521358/trace_model_replace", "max_stars_repo_head_hexsha": "d7ceb2a34db7772f725b30cb2ba016218ed38559", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from numpy import fft
import pyaudio
import numpy as np
import wave
from scipy.fftpack import fft
import matplotlib.pyplot as plt
class Mic:
def __init__(self,CHANNELS=1,nCHUNK=4,RATE = 44100):
self.CHUNK = 1024 * nCHUNK
self.FORMAT = pyaudio.paInt16
self.RATE = RATE
self.CHANNEL... | {"hexsha": "e9e923e22058588bac5704e436adaf512ed19432", "size": 3416, "ext": "py", "lang": "Python", "max_stars_repo_path": "record.py", "max_stars_repo_name": "Yossef-Dawoad/Recwpy", "max_stars_repo_head_hexsha": "b6c03316050e75c4b6b406aa9d16023e221a2093", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_... |
%!TEX TS-program = xelatex
%!TEX encoding = UTF-8 Unicode
\chapter{The Amlantis Language Syntax Summary}
\label{sec:syntax}
% TBD: extract all syntaxes in here
| {"hexsha": "7236f20cfc75b9937dee298d0ab295b4ce9359f4", "size": 162, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/0.10/SyntaxSummary.tex", "max_stars_repo_name": "amlantis-lang/gear-doc", "max_stars_repo_head_hexsha": "ba6913ec3a4fdd57c1dfa9ec03d966bf5a42c632", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from .trading_env import TradingEnv, Actions, Positions
class StocksEnv(TradingEnv):
def __init__(self, df, window_size, frame_bound, max_loss=None, hold_penalty_ticks=None):
assert len(frame_bound) == 2
self.frame_bound = frame_bound
super().__init__(df, window_size, ... | {"hexsha": "6c10697078f8efde5b327994e267510df1634438", "size": 2568, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_yotrading/envs/stocks_env.py", "max_stars_repo_name": "vyorick/gym-anytrading", "max_stars_repo_head_hexsha": "99d8219438e76011c714d2e49de67aed535648b3", "max_stars_repo_licenses": ["MIT"], "m... |
/-
Copyright (c) 2020 Markus Himmel. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Markus Himmel, Scott Morrison
-/
import ring_theory.ideal.quotient
import ring_theory.principal_ideal_domain
/-!
# Invariant basis number property
> THIS FILE IS SYNCHRONIZED WITH MAT... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/linear_algebra/invariant_basis_number.lean"... |
using DirectTrajectoryOptimization
const DTO = DirectTrajectoryOptimization
function point_foot_quadruped_dyn(model, env, h, y, x, u, w)
# dimensions
nq = model.nq
nu = model.nu
# configurations
q1⁻ = x[1:nq]
q2⁻ = x[nq .+ (1:nq)]
q2⁺ = y[1:nq]
q3⁺ = y[nq .+ (1:nq)]
# control
... | {"hexsha": "6f7bae68abbc2896fc6f801232fcf7518413bf46", "size": 3548, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/point_foot_quadruped/reference/trajopt_model.jl", "max_stars_repo_name": "mcx/ContactImplicitMPC.jl", "max_stars_repo_head_hexsha": "da35f66c27de44aca49cb099d4195489b892167b", "max_stars_r... |
import time
import numpy as np
import torch
from utils import *
from params import *
from reconstruction import *
import scipy
import cv2
import skimage.measure
from itertools import compress
def lpf_detection(holo,mask,erode_size=20, dilate_size=60, threshold=10, A_min = 1, show_plot=False):
# mask for low pass f... | {"hexsha": "c65aaa05bb4387f9c320d085a83f6b92ab4fc58b", "size": 2665, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/detection.py", "max_stars_repo_name": "emma-d-cotter/Hologram-Processing", "max_stars_repo_head_hexsha": "df364f6fde3d188a8e8f43ea75028f31e5559427", "max_stars_repo_licenses": ["MIT"], "max_s... |
# ---
# title: Displaying Images
# author: "[William Thompson](https://github.com/sefffal)"
# cover: assets/displaying-images.png
# ---
# We'll start by downloading a sample image. If you have an image stored locally,
# you would skip this step.
using AstroImages
AstroImages.set_clims!(Percent(99.5)) #src
AstroImages... | {"hexsha": "85b71ba989dd41b9851ed4de97539a96ce9a9d2f", "size": 1838, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/examples/basics/displaying.jl", "max_stars_repo_name": "sefffal/AstroImages.jl", "max_stars_repo_head_hexsha": "e9a5d0a204aa81201db2942201a7a3087d9d7a27", "max_stars_repo_licenses": ["MIT"], "... |
PROGRAM run_parareal_openmp
USE parareal_openmp, only: InitializePararealOpenMP, FinalizePararealOpenMP, PararealOpenMP
USE params, only : Nx, Ny, Nz, dx, dy, dz, nu, N_coarse, N_fine, Niter, Tend, do_io, be_verbose, ReadParameter
IMPLICIT NONE
DOUBLE PRECISION, ALLOCATABLE, DIMENSION(:,:,:) :: Q
! -- CODE: --
CA... | {"hexsha": "de454f967d9fb626dbf081fbf974dbf2fa35d24e", "size": 708, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/run_parareal_openmp.f90", "max_stars_repo_name": "Parallel-in-Time/PararealF90", "max_stars_repo_head_hexsha": "a8318a79b92465a8a3cf775cc7fd096ff0494529", "max_stars_repo_licenses": ["BSD-2-C... |
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