text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import os
import shutil
from pathlib import Path
import numpy as np
from .extraction_tools import load_extractor_from_pickle, load_extractor_from_dict, \
load_extractor_from_json
def check_recordings_equal(RX1, RX2, return_scaled=True, force_dtype=None, check_times=True):
N = RX1.get_num_frames()
# get_... | {"hexsha": "a3b5a6dde33050bb8678bddfae7d60d2645066cd", "size": 8296, "ext": "py", "lang": "Python", "max_stars_repo_path": "spikeextractors/testing.py", "max_stars_repo_name": "sronchi/spikeextractors", "max_stars_repo_head_hexsha": "2d425239a9c78b6fda469b1054df3adf0d4ce8e7", "max_stars_repo_licenses": ["MIT"], "max_st... |
using DiffEqBayes, OrdinaryDiffEq, ParameterizedFunctions,
RecursiveArrayTools, Distributions, Test
println("One parameter case")
f1 = @ode_def begin
dx = a*x - x*y
dy = -3y + x*y
end a
u0 = [1.0,1.0]
tspan = (0.0,10.0)
p = [1.5]
prob1 = ODEProblem(f1,u0,tspan,p)
sol = solve(prob1,Tsit5())
t = collect(range(... | {"hexsha": "5cdf2ffc8a98366f5472cf05bee8b313bf0d6d0e", "size": 3176, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/stan.jl", "max_stars_repo_name": "JuliaDiffEq/BayesDiffEq.jl", "max_stars_repo_head_hexsha": "3f1da73c0e999e3198bb7e59f31ef2e1771bddf4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import random
import numpy as np
class Compose:
def __init__(self, transforms, prob=1.):
self.transforms = [t for t in transforms if t is not None]
self.prob = prob
def __call__(self, **data):
if random.random() < self.prob:
for t in self.transforms:
... | {"hexsha": "15be97b75ad5f3a6d14f41614cab2a74382b3b34", "size": 1213, "ext": "py", "lang": "Python", "max_stars_repo_path": "polus-cell-nuclei-segmentation/src/dsb2018_topcoders/victor/augmentations/composition.py", "max_stars_repo_name": "nishaq503/polus-plugins-dl", "max_stars_repo_head_hexsha": "511689e82eb29a8476153... |
import torch
import numpy as np
class DeepFool():
# max_iter=50, clip_max=0.5, clip_min=-0.5
def __init__(self, max_iter, clip_max, clip_min):
self.max_iter = max_iter
self.clip_max = clip_max
self.clip_min = clip_min
def __call__(self, model, x, y):
nx = torch.unsqueeze(x,... | {"hexsha": "54ff33959a75dcb3d75d6916bbb1b9cbbd012c43", "size": 1553, "ext": "py", "lang": "Python", "max_stars_repo_path": "plexiglass/adversarial/deepfool.py", "max_stars_repo_name": "jkartzman/plexiglass", "max_stars_repo_head_hexsha": "257e3305e31f032c26300b0a9c78260ccd251cd6", "max_stars_repo_licenses": ["MIT"], "m... |
using PositiveFactorizations
using LinearAlgebra, Test
@testset "PositiveFactorizations" begin
for pivot in (Val{false}, Val{true})
A = [1 0; 0 1]
F = cholesky(Positive, A, pivot)
@test Matrix(F) ≈ A
F, d = ldlt(Positive, A, pivot)
@test Matrix(F) ≈ A
@test d == [1,1]
A = [1 0; 0 -1]
F... | {"hexsha": "801b13922a8988e0356dff60183e15a9e89be478", "size": 1900, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/PositiveFactorizations.jl-85a6dd25-e78a-55b7-8502-1745935b8125", "max_stars_repo_head_hexsha": "12fb36039fa463d4e4b2a297154... |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
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# jupytext_version: 1.3.0
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# name: python3
# ---
# %% [raw] raw_mimetype="text/restructuredtext"
# .. ... | {"hexsha": "bfcdc2e0c93fb09bcaa2ed94d3130ab10d310da9", "size": 13745, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/basics.py", "max_stars_repo_name": "zmoon92/proplot", "max_stars_repo_head_hexsha": "2c6f7af8a044567bb9409d3f67d844bac05c7d14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import os
import pytest
from unittest.mock import patch
from intake import open_catalog
import dask.dataframe as dd
@pytest.fixture
def local_prefix_cat():
path = os.path.dirname(__file__)
return open_catalog(os.path.join(path, 'data', 'local.cata... | {"hexsha": "c467ca5bb140386c9f03a7f6e80576ceb415b2d4", "size": 1434, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_catalog.py", "max_stars_repo_name": "hamed2005/intake-azure-blob-storage", "max_stars_repo_head_hexsha": "314f67c9c46d8fab69c2deb5d643417989519f54", "max_stars_repo_licenses": ["BSD-2-C... |
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interactive, interactive_output, fixed, HBox, VBox
import ipywidgets as widgets
def get_interactive_logistic_regression(X, y):
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(1, 1, 1)
xmin = X.min(axis=0)
xmax = X.max(axi... | {"hexsha": "27a1e1fb1476823036287c8e8336a27558029c7b", "size": 18511, "ext": "py", "lang": "Python", "max_stars_repo_path": "2_1_connectionist_neuron/utils_logistic.py", "max_stars_repo_name": "layerwise/training", "max_stars_repo_head_hexsha": "21ad2a5684a3712192fb13f8214bc3bb4c975f3e", "max_stars_repo_licenses": ["MI... |
import numpy as np
import pandas as pd
from scipy.stats import binom_test
# calculate p_value_2sided here:
p_value_2sided = binom_test(41, n=500, p=0.1)
print(p_value_2sided)
# calculate p_value_1sided here:
p_value_1sided = binom_test(41, n=500, p=0.1, alternative = 'less')
print(p_value_1sided) | {"hexsha": "4f8f77467abbfdd2746c13e9e633a0a89eb3651a", "size": 299, "ext": "py", "lang": "Python", "max_stars_repo_path": "Data Scientist Career Path/8. Hypothesis Testing/1. Testing a Sample Statistics/2. Binomial Test/11. scipy.py", "max_stars_repo_name": "myarist/Codecademy", "max_stars_repo_head_hexsha": "2ba0f104b... |
#!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
def eggholder(x, y):
return -(y + 47) * np.sin(np.sqrt(np.abs(y + x/2 + 47))) + \
-x * np.sin(np.sqrt(np.abs(x - (y + 47))))
if __name__ == '__main__':
x, y = np.meshgrid(
n... | {"hexsha": "0ef0fc2aa36aa1d4706ef10e99f426ff49056194", "size": 631, "ext": "py", "lang": "Python", "max_stars_repo_path": "slides/figures/eggholder_fun.py", "max_stars_repo_name": "gcampanella/pydata-london-2018", "max_stars_repo_head_hexsha": "6be3232f3766c56d4db8053b10d1e0268809b8f5", "max_stars_repo_licenses": ["CC-... |
//
// Copyright 2005-2007 Adobe Systems Incorporated
// Copyright 2018 Mateusz Loskot <mateusz@loskot.net>
//
// Use, modification and distribution are subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
#ifndef BOOST_GIL_T... | {"hexsha": "4ff10d52d72e81e7ee133f6e9871349e5dff374f", "size": 13415, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactNativeFrontend/ios/Pods/boost/boost/gil/typedefs.hpp", "max_stars_repo_name": "Harshitha91/Tmdb-react-native-node", "max_stars_repo_head_hexsha": "e06e3f25a7ee6946ef07a1f524fdf62e48424293", "m... |
import pickle
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from common import *
from color_space import color_select, draw_sub_plots, stack_binary_images
# MODIFY THIS FUNCTION TO GENERATE OUTPUT
# THAT LOOKS LIKE THE IMAGE ABOVE
def corners_unwarp(img, nx, ny, mtx, ... | {"hexsha": "db2699e49e0fa5a4b1336ff2d01ddb31576fca6d", "size": 7694, "ext": "py", "lang": "Python", "max_stars_repo_path": "perspective_transformation.py", "max_stars_repo_name": "mhhm2005eg/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "1f571e4714df0dcca21fbf2b09b5af73caddb8f4", "max_stars_repo_licenses": ... |
REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Boot Sys: Top Context Functions"
Rights: {
Copyright 2012 REBOL Technologies
REBOL is a trademark of REBOL Technologies
}
License: {
Licensed under the Apache License, Version 2.0
... | {"hexsha": "88be36150f98d532903f2d3746ad9f59e044093d", "size": 7432, "ext": "r", "lang": "R", "max_stars_repo_path": "src/mezz/sys-base.r", "max_stars_repo_name": "BlackATTR/ren-c", "max_stars_repo_head_hexsha": "533a998a30e6a74a528718df58d5ece997a43138", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
import os
import pickle
import matplotlib
import matplotlib.pyplot as plt
import itertools
import numpy as np
from astrodash.multilayer_convnet import convnet_variables
try:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
except ModuleNotFoundError:
import tensorflow as tf
def plot_confusion_... | {"hexsha": "86243b7a0bd098076cf14843a0cbe1dd637278f5", "size": 8517, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrodash/model_metrics.py", "max_stars_repo_name": "TorshaMajumder/astrodash", "max_stars_repo_head_hexsha": "6064d8e8dc9f64691bea100a033c63d132d04af5", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma i_shrink_eq_NoMsg_iAll_conv: "
0 < k \<Longrightarrow> ((s \<div>\<^sub>i k) t = \<NoMsg>) = (\<box> t1 [t * k\<dots>,k - Suc 0]. s t1 = \<NoMsg>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 < k \<Longrightarrow> ((s \<div> k) t = NoMsg) = (\<box> t1 [t * k\<dots>,k - Suc 0]. s t1 = NoMsg)
... | {"llama_tokens": 1256, "file": "AutoFocus-Stream_IL_AF_Stream", "length": 9} |
#! /usr/bin/env python
import roslib; roslib.load_manifest('jaco2_driver')
import rospy
import sys
import numpy as np
import actionlib
import kinova_msgs.msg
import std_msgs.msg
import geometry_msgs.msg
from util.util import *
import math
import argparse
from abc import abstractmethod
def QuaternionNorm(Q_raw):
... | {"hexsha": "0c5dd0ac16e526d13545890dd2aff1574eccd7cf", "size": 4843, "ext": "py", "lang": "Python", "max_stars_repo_path": "drivers/jaco2_driver/nodes/jaco2_driver/pose_action_client.py", "max_stars_repo_name": "s4480417/thesisrobotics", "max_stars_repo_head_hexsha": "dad7723ff14af92380c21a5aaccb5ae94a7d8bd2", "max_sta... |
import numpy as np
import math
import matplotlib.pyplot as plt
import random
X=np.array([[2,-1],[2,1],[0,-1],[0,1],[1,0.5],[1,-0.5],[1,1],[1,-1]])
plt.scatter(X[:,0],X[:,1])
cent1=np.array([[0.5,0]])
cent2=np.array([[1.5,0]])
plt.scatter(cent1[0][0],cent1[0][1], label="Centroid - class1", marker='x')
plt.scatter(cent2... | {"hexsha": "52ba7b03e39e4bb0d758ea20d4abf737358daf44", "size": 1012, "ext": "py", "lang": "Python", "max_stars_repo_path": "Supervised Learning/SMAI HWS/23/1.py", "max_stars_repo_name": "shailymishra/Machine-Learning-Advanced", "max_stars_repo_head_hexsha": "048e7c816f8c673c7b73ffb7555ebdfee6f0dc5b", "max_stars_repo_li... |
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.model_selection import train_test_split
from src.predictor.dataset_extractor import DatasetExtractor
def get_dataset_split(main_folder: str, target_data_folder: str, day_i... | {"hexsha": "4af13eea631942d50f4ef910829fdfe43a4cdcfb", "size": 7747, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/predictor/rnn_predictor.py", "max_stars_repo_name": "yakuza8/coronavirus-timeseries-predictor", "max_stars_repo_head_hexsha": "d6ec329ec36335d2f3dc20c2158dbfa9ddad3526", "max_stars_repo_licens... |
r"""
Module for handling Exact Gradient corrected Screened (EGS) Potential.
Potential
*********
The exact-gradient screened (EGS) potential introduces new parameters that can be easily calculated from initial inputs.
Density gradient corrections to the free energy functional lead to the first parameter, :math:`\nu`,
... | {"hexsha": "739349473f95f62aef493a9d2af00bfae33ca620", "size": 11192, "ext": "py", "lang": "Python", "max_stars_repo_path": "sarkas/potentials/egs.py", "max_stars_repo_name": "lucianogsilvestri/sarkas", "max_stars_repo_head_hexsha": "f4ab00014d09976561fbd4349b9d0610e47a61e1", "max_stars_repo_licenses": ["MIT"], "max_st... |
(*
* Copyright (c) 2017-present,
* Programming Research Laboratory (ROPAS), Seoul National University, Korea
* This software is distributed under the term of the BSD-3 clause license.
*)
Definition veq T (x : MId.m T) (y : Acc.MAcc.m T) : Prop := x = Acc.get_v y.
Lemma bind_veq :
forall T (x1 : MId.m T) (x2 : Ac... | {"author": "ropas", "repo": "zooberry", "sha": "17b1cb1a44c2a796d6b7d85c2026b142685d291b", "save_path": "github-repos/coq/ropas-zooberry", "path": "github-repos/coq/ropas-zooberry/zooberry-17b1cb1a44c2a796d6b7d85c2026b142685d291b/spec/Proof/VeqCommon.v"} |
/*
* Stanford Whole-Body Control Framework http://stanford-wbc.sourceforge.net/
*
* Copyright (C) 2010 The Board of Trustees of The Leland Stanford Junior University. All rights reserved.
*
* This program is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public... | {"hexsha": "3d0b12fe25ebfb01dc57d68c87a8522695aec4dd", "size": 3703, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "utils/include/utils/wrap_eigen.hpp", "max_stars_repo_name": "junhyeokahn/DracoNodelet", "max_stars_repo_head_hexsha": "0f87331ceaf4fe42f9bab164954c5e9cb9c010f4", "max_stars_repo_licenses": ["MIT"], ... |
# coding = utf-8
from copy import deepcopy
import numpy as np
from mykit.core.bandstructure import BandStructure
from mykit.core.dos import Dos
from mykit.core.kmesh import KSYMBOL_LATEX, KmeshError, kpath_decoder
from mykit.core.log import Verbose
def init(*objs, **kwargs):
'''Convenient function to initializ... | {"hexsha": "f49b086ad79c2392d91b50c4ac839a2072fa7f8f", "size": 12574, "ext": "py", "lang": "Python", "max_stars_repo_path": "mykit/visualizer.py", "max_stars_repo_name": "minyez/mykit", "max_stars_repo_head_hexsha": "911413120c081be2cfcaef06d62dc40b2abd2747", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "m... |
import numpy as np
import random
import json
from matplotlib import pyplot as plt
#from base import activation_function, loss_function
from .base import activation_function, loss_function
class FNN():
"""
Feedforward Neural network Class.
Attributes
----------
sizes: list
List of n... | {"hexsha": "186a7f27844782d5266fd80751c6ddbddda5a1ed", "size": 24475, "ext": "py", "lang": "Python", "max_stars_repo_path": "customdl/fnn.py", "max_stars_repo_name": "Taarak9/Custom-Neural-Networks", "max_stars_repo_head_hexsha": "cda83294ed825159d5bd168264f143ea51f056c1", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import math
import numpy as np
import torch
class MinMaxStats(object):
"""A class that holds the min-max values of the tree."""
def __init__(self, min_value_bound=None, max_value_bound=None):
self.maximum = min_value_bound if min_value_bound else -float('inf')
self.minimum = max_value_bound ... | {"hexsha": "bc466654857ffbc2aee782f19edc27e8fa331a9c", "size": 4325, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/mcts.py", "max_stars_repo_name": "bAmpT/muzero-pytorch", "max_stars_repo_head_hexsha": "481c02af04245d49eb545b7313a3f19c20f1cb51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import pySALESetup as pss
import numpy as np
mesh1 = pss.Mesh(X=100,Y=500,cellsize=.5e-5)
# Two test areas A and B in each test area one type of grain is tested in 4 different orientations
# in principle each grain is independent of all the others. How will each respond?
x = .25e-3
yA = 1.875e-3
yB = 0.625e-3
r = ... | {"hexsha": "d309c0a7f71e8832b784cbb23b82b261b4e624d9", "size": 1118, "ext": "py", "lang": "Python", "max_stars_repo_path": "regolith_tests/triangle_tester.py", "max_stars_repo_name": "jgd10/RegolithSetupRoutines", "max_stars_repo_head_hexsha": "4b814ec45292aa7226e1b094aaf6c1472b0ada3a", "max_stars_repo_licenses": ["MIT... |
from unittest.case import TestCase
import numpy as np
from tensorflow.keras import initializers
import tensorflow as tf
from mdrnn import MDRNN
class TwoStepRNNTests(TestCase):
def test_feeding_layer_created_with_default_initializer(self):
mdrnn = MDRNN(units=2, input_shape=(None, 1))
x = np.zero... | {"hexsha": "f9ba381438e6c4db5053559d46f582081ae59280", "size": 3048, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/one_dimensional_RNN/test_two_step_predictions.py", "max_stars_repo_name": "X-rayLaser/multi-directional-mdrnn", "max_stars_repo_head_hexsha": "70b0e1c2e07b5f476c264c6700e8d34d41a2ce10",... |
function _read(valfn)
try; return parse(Int, read(valfn, String))
catch ingored end
return 0
end
let
@info("Running race tests")
# must match the values on race_script.jl
lkfn = joinpath(@__DIR__, "lock")
valfn = joinpath(@__DIR__, "sum.txt")
logfn = joinpath(@__DIR__, "log.txt")
tr... | {"hexsha": "a46bd32bb54f77beb7d2ece7e08a564e8cedd83a", "size": 2283, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/race_test.jl", "max_stars_repo_name": "josePereiro/SimpleLockFiles.jl", "max_stars_repo_head_hexsha": "4276512bf160a563380556a929965671d4b93e95", "max_stars_repo_licenses": ["MIT"], "max_stars... |
##################################################################
# Deprecations
##################################################################
@deprecate bicgstab! bicgstab
| {"hexsha": "d7acf14d000b367f2f47f43ab3c21a40220f6ecf", "size": 181, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/deprecated.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/NumericalMethodsforEngineers.jl-00e1d38a-71a9-5665-8612-32ae585a75a3", "max_stars_repo_head_hexsha": "e230c3045d98da0cf789e... |
using QuantumAlgebra
using QuantumAlgebra: δ, prodtuples, prodtuple, sumtuple, distribute_indices!
using Test
@testset "QuantumAlgebra.jl" begin
for with_σpm in (false,true)
QuantumAlgebra.use_σpm(with_σpm)
@test QuantumAlgebra.using_σpm == with_σpm
@test QuantumAlgebra.SpatialIndex(Quant... | {"hexsha": "44794a76327244c8e5d8e9d1b34e39952e3c32f0", "size": 12741, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/QuantumAlgebra.jl-1b9008d5-2a9d-4901-8cfb-44bb87795b64", "max_stars_repo_head_hexsha": "92804d2cda60a1117cb449f745fb489ce9e0950b", ... |
import logging
import numpy as np
from scipy.optimize import linear_sum_assignment
from ...utils.time import timeit
from .centroid import TargetCentroid
logger = logging.getLogger(__name__)
class AdaptiveKmeans:
"""Adaptive Kmeans clustering algorithm to cluster tracked targets"""
NEXT_TID = 0
def __... | {"hexsha": "3f3bf596a202b39d3b072ddf53be2084feeadafd", "size": 10462, "ext": "py", "lang": "Python", "max_stars_repo_path": "worker/mtmcworker/cluster/__init__.py", "max_stars_repo_name": "johnnylord/mtmc-testbed", "max_stars_repo_head_hexsha": "e3d331505181baa076162e1f5835e566e8f70167", "max_stars_repo_licenses": ["MI... |
from __future__ import division
import torch
import random
import numpy as np
from sklearn.cluster import DBSCAN, KMeans
import cv2
def set_seed(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.... | {"hexsha": "71b946d74f532f693fc2ce6d4b8b6445e2211bf4", "size": 1708, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/misc.py", "max_stars_repo_name": "SIAAAAAA/C3-SemiSeg", "max_stars_repo_head_hexsha": "b2e2489b828661660d9842785e831293bab5cd47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "m... |
import tensorflow as tf # noqa
import copy
import os
# import cPickle as pickle
import dill as pickle
import numpy as np
import hashlib
from ..data import helpers as helpers
from ..utils import misc as misc
from ..data import batch_fetcher as bfetchers
from ..experiments import experiment
from ..experiments import con... | {"hexsha": "508654db4cba9a3c8a938c391a0b49867a93025a", "size": 14424, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/runner.py", "max_stars_repo_name": "lupalab/flowscan", "max_stars_repo_head_hexsha": "a0fc8fd25cb62c9eeb6583c3d7505d54b969f88c", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import tensorflow as tf
import pickle
import pandas as pd
import numpy as np
from pathlib import Path
from absl import app
from konlpy.tag import Mecab
from model.data import Corpus
from tqdm import tqdm
from model.net import SenCNN
from configs import FLAGS
def main(argv):
test_data = Path.cwd() / 'data_in' / ... | {"hexsha": "d93bf1040192f529d64e8ba73ec96189e5578299", "size": 2149, "ext": "py", "lang": "Python", "max_stars_repo_path": "jmkim/CNN_SC/prediction.py", "max_stars_repo_name": "modudeepnlp/NLP_Tensorflow2.0", "max_stars_repo_head_hexsha": "b0fcc5a1521e865b0a7b06042324c0b0d6844d06", "max_stars_repo_licenses": ["Apache-2... |
# %%
# Setup
## Packages
import pandas as pd
import numpy as np
import torch
from transformers import RobertaForSequenceClassification, TrainingArguments, Trainer, RobertaTokenizer, RobertaConfig
from datasets import load_metric, load_dataset
from sklearn.metrics import precision_recall_fscore_support, accuracy_score,... | {"hexsha": "01e07ad8588df02b2b29517992a57bb9371ff315", "size": 10984, "ext": "py", "lang": "Python", "max_stars_repo_path": "training.py", "max_stars_repo_name": "NiksMer/ManiBERT", "max_stars_repo_head_hexsha": "00e726ccd3d1b465c614c72b0b79c5286d0e68b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
#!/usr/bin/env julia
# This attempts to implement call/cc feature in julia
type Continuation_box{name}
value
end
macro call_cc(name::Symbol, content)
quote
u = Symbol(randstring())
$name = v -> throw(Continuation_thrown{u}(v))
try
$content
catch e
if ... | {"hexsha": "35a0f1be0039930194120fa79d53b2e13da0216c", "size": 410, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-continuation/call_cc.jl", "max_stars_repo_name": "sclereid/collection", "max_stars_repo_head_hexsha": "61782f45314db560657b3081e2fa8e1032fd8dbf", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding:utf-8 -*-
import unittest
from ddt import ddt, data
import math
import ctypes
import datetime
from ctypes import *
import numpy as np
from numba import cuda
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
@ddt
class TestQuantImpl(unittest.TestCase):
max_thres = 512
data0 = np... | {"hexsha": "72bb5ff5a47ccb7536fe27e3ee40d73528073ee9", "size": 4786, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_quant_impl.py", "max_stars_repo_name": "Yerniyaz/nnieqat-pytorch", "max_stars_repo_head_hexsha": "470f5d94a68ce7c55cc384d76054e9546d6ec299", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import csv
import pandas as pd
import numpy as np
from datetime import datetime, date, time, timedelta
import glob
from lmatools.io.LMA_h5_file import LMAh5File
from lmatools.coordinateSystems import RadarCoordinateSystem, GeographicSystem, TangentPlaneCartesianSystem, MapProjection
def search_files(dir, time_start, t... | {"hexsha": "bd0fe121a5d8f7112f3660057e7b6dc4d37fa0f7", "size": 5084, "ext": "py", "lang": "Python", "max_stars_repo_path": "select_lma.py", "max_stars_repo_name": "jcssouza/LMAinterceptRHI", "max_stars_repo_head_hexsha": "f6877f53f383fbaecac8c42baa08680831dbe42c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
/* Copyright 2018 Ignacio Torroba (ignaciotb@kth.se)
*
* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the follow... | {"hexsha": "f488da2580460d2be776c62c6984175e3988beef", "size": 5225, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "auv_ekf_slam/include/ekf_slam/ekf_slam.hpp", "max_stars_repo_name": "nilsbore/smarc_navigation", "max_stars_repo_head_hexsha": "97d0a30498e72506e7472c98c5fa0d86d19f0f04", "max_stars_repo_licenses": ... |
#!/usr/bin/python3
#‑∗‑ coding: utf‑8 ‑∗‑
import sys
import gym
import json
import importlib
import numpy as np
import utils
import monitor
def main():
results_list = []
for i in range(1, len(sys.argv)):
with open(sys.argv[i]) as f:
results_list.append(json.load(f))
# Plot results:
... | {"hexsha": "f52a9cc9d86811fe5a4e7c6d36726a435d6a68bf", "size": 432, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_results.py", "max_stars_repo_name": "Lucien-MG/rl-blackjack", "max_stars_repo_head_hexsha": "f0903db4b4be14ba5994b0808d7c9c1e7ed00825", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
using Plots, LinearAlgebra, Printf
# helper functions
@views av(A) = 0.25*(A[1:end-1,1:end-1].+A[2:end,1:end-1].+A[1:end-1,2:end].+A[2:end,2:end])
@views av_xa(A) = 0.5*(A[1:end-1,:].+A[2:end,:])
@views av_ya(A) = 0.5*(A[:,1:end-1].+A[:,2:end])
@views maxloc(A) = max.(A[1:end-2,1:end-2],A[1:end-2,2:end-1],A[1:e... | {"hexsha": "ec0f0ae1f12099b61bdd8603e8162cfe00471199", "size": 9190, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "newdamp/Stokes2D_vep_reg_IU_LR_TD.jl", "max_stars_repo_name": "PTsolvers/Stokes2D_simpleVEP", "max_stars_repo_head_hexsha": "074c3e3649c50a317e33455d18dc9526614b4b66", "max_stars_repo_licenses": ["... |
/**
* \file
* \brief Laser Scanner communication (TCP Helper Class)
* Copyright (C) 2013, Osnabrück University
* Copyright (C) 2017, Ing.-Buero Dr. Michael Lehning, Hildesheim
* Copyright (C) 2017, SICK AG, Waldkirch
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
... | {"hexsha": "f5f63e5825a9439a748b5fbda2e9bb017936f275", "size": 29353, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "driver/src/sick_scan_common_tcp.cpp", "max_stars_repo_name": "datadancer/sick_scan_base", "max_stars_repo_head_hexsha": "2bfb4c8c6761240de6785abd6aeb5e520e415259", "max_stars_repo_licenses": ["Apac... |
import numpy as np
from numpy import ma # useful for thresholding
# import matplotlib.pyplot as plt
from astropy.io import fits
# from skimage import exposure
# from skimage.filters import rank
# from skimage.morphology import rectangle
from scipy.ndimage.filters import convolve
from scipy import stats
from skimage.tr... | {"hexsha": "1b8ae60a142fa77bb3d7082a1cbb257e4485271f", "size": 8947, "ext": "py", "lang": "Python", "max_stars_repo_path": "tidalclassifier/utils/thresholder.py", "max_stars_repo_name": "RustyPanda/tidal-features-classifier", "max_stars_repo_head_hexsha": "32e1aa0c074528ed04419d596c2014f78448e11b", "max_stars_repo_lice... |
[STATEMENT]
lemma l2_inv4_init [iff]:
"init l2 \<subseteq> l2_inv4"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. init l2 \<subseteq> l2_inv4
[PROOF STEP]
by (auto simp add: l2_def l2_init_def l2_inv4_def) | {"llama_tokens": 99, "file": "Key_Agreement_Strong_Adversaries_dhlvl2", "length": 1} |
"""
Generate code for HTML table to visualise human-object pairs
Fred Zhang <frederic.zhang@anu.edu.au>
The Australian National University
Australian Centre for Robotic Vision
"""
import argparse
import numpy as np
import pocket
def name_parser(name):
"""
{INDEX}.png
"""
seg = name.split(".")
r... | {"hexsha": "ce2e786175bfa96dc6cf05d0030ca9ec8b9453e1", "size": 729, "ext": "py", "lang": "Python", "max_stars_repo_path": "hicodet/utilities/generate_html_page.py", "max_stars_repo_name": "henlein/upt", "max_stars_repo_head_hexsha": "0baa1fe7ebc16347f19bf18f10da4a6dc8fd3837", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
[STATEMENT]
lemma pre_post_left_isotone:
"-p \<le> -q \<Longrightarrow> -p\<stileturn>-r \<le> -q\<stileturn>-r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - p \<le> - q \<Longrightarrow> - p \<stileturn> - r \<le> - q \<stileturn> - r
[PROOF STEP]
using order_lesseq_imp pre_post_galois
[PROOF STATE]
proof (pr... | {"llama_tokens": 265, "file": "Correctness_Algebras_Pre_Post", "length": 2} |
#!/usr/bin/env python
#
# remove filters from photometry catalogs, physicsgrid, and observationgrid
# used to modify simulated data to make plots for proposals
import argparse
import numpy as np
from astropy.table import Table
import tables
from beast.physicsmodel.grid import (FileSEDGrid, SpectralGrid)
import bea... | {"hexsha": "b51afa91fac7749c90eac81b1facd53af079417a", "size": 3302, "ext": "py", "lang": "Python", "max_stars_repo_path": "beast/tools/remove_filters.py", "max_stars_repo_name": "marthaboyer/beast", "max_stars_repo_head_hexsha": "1ca71fb64ab60827e4e4e1937b64f319a98166c3", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
from distutils.core import setup
from distutils.extension import Extension
import numpy as np
meshModule = Extension("polymesh.mesh",
sources=["polymesh/mesh.c"],
include_dirs = [np.get_include()],
extra_compile_args=["-Wno-unreachable-code"])
hydrostaticModule = Extension("pol... | {"hexsha": "51b2fef7ed2aedbebc491580679661387630468b", "size": 832, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "aixuexisun/polymesh", "max_stars_repo_head_hexsha": "07ea7a87028f2f662fa6db92a27cc05a156de545", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
import numpy as np
import mbuild as mb
class AmorphousSilicaBulk(mb.Compound):
""" An amorphous silica box, 2.2g/cm^3"""
def __init__(self):
super(AmorphousSilicaBulk, self).__init__()
mb.load('amorphous_silica_bulk.pdb', compound=self,
relative_to_module=self.__module__)
... | {"hexsha": "10b4e4faf5b32499418f80fa49e7cc13bb61c617", "size": 451, "ext": "py", "lang": "Python", "max_stars_repo_path": "mbuild/lib/bulk_materials/amorphous_silica_bulk.py", "max_stars_repo_name": "dcardenasv/mbuild", "max_stars_repo_head_hexsha": "20c13f6bb66c6b023b07d7a2b2e4ad0a5073d727", "max_stars_repo_licenses":... |
import argparse
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pdb
import os
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Input, Dense, Conv2D, BatchNormalization, LeakyReLU, Flatten
from tensorflow.keras.layers import concatenate
from tens... | {"hexsha": "613c139154a79aa2a1364d5eb28e81ad16907397", "size": 8229, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_combined.py", "max_stars_repo_name": "clementfung/biodiversity-analyzer", "max_stars_repo_head_hexsha": "a798bec2d4b5450e2a554dc7b9400cbe4198c102", "max_stars_repo_licenses": ["MIT"], "max_s... |
% -*- root: Main.tex -*-
\section{SVD}
$\mathbf{A} = \mathbf{U} \mathbf{D} \mathbf{V}^\top = \sum_{k=1}^{\operatorname{rank}(\mathbf{A})} d_{k,k} u_k (v_k)^\top$\\
$\mathbf{A} \in \mathbb{R}^{N \times P}, \mathbf{U} \in \mathbb{R}^{N \times N}, \mathbf{D} \in \mathbb{R}^{N \times P}, \mathbf{V} \in \mathbb{R}^{P \times... | {"hexsha": "e33615337f33de1120e100191e243dc0a6153920", "size": 1847, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "SVD.tex", "max_stars_repo_name": "kalsan/eth-cil-exam-summary", "max_stars_repo_head_hexsha": "ae93c61b8e7e88be725641d9580b37367c332458", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 2... |
#!/usr/bin/env python
# Copyright (c) 2021, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration.
#
# All rights reserved.
#
# The "ISAAC - Integrated System for Autonomous and Adaptive Caretaking
# platform" software is licensed under the Apache License,... | {"hexsha": "3351cb7072163f287e02b824275e4eb7ad3a801b", "size": 2194, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrobee/simulation/acoustics_cam/src/generate_pure_tones.py", "max_stars_repo_name": "rsoussan/isaac", "max_stars_repo_head_hexsha": "c6cc7b5c0707a9b23b5fc4376f0a062052992bc6", "max_stars_repo_li... |
from distutils.core import setup, Extension
try:
import numpy # @UnusedImport # NOQA
except ImportError:
msg = ("No module named numpy. "
"Please install numpy first, it is needed before installing PH5.")
raise ImportError(msg)
def get_extension_options():
options = ("ibm2ieee_py",
... | {"hexsha": "8bef8e58d325b68071ab046561d87bac588cc1b4", "size": 739, "ext": "py", "lang": "Python", "max_stars_repo_path": "ph5/core/c_dependencies/suibm2ieee_py.py", "max_stars_repo_name": "mbriggs134/PH5", "max_stars_repo_head_hexsha": "691790269c4f80c892bacb84fdcaba905b9e0d6b", "max_stars_repo_licenses": ["MIT"], "ma... |
# Filename: geom.py
# License: LICENSES/LICENSE_UVIC_EPFL
import numpy as np
def parse_geom(geom, geom_type):
parsed_geom = {}
if geom_type == "Homography":
parsed_geom["h"] = geom.reshape((-1, 3, 3))
elif geom_type == "Calibration":
parsed_geom["K"] = geom[:, :9].reshape((-1, 3, 3))
... | {"hexsha": "8a453254242195575d402c1be0f480d686a1126c", "size": 3503, "ext": "py", "lang": "Python", "max_stars_repo_path": "geom.py", "max_stars_repo_name": "zengru001usst/acne", "max_stars_repo_head_hexsha": "ae652e814649e88034b3b506ccbe34432b1eb85a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 49, "... |
C Copyright(C) 1999-2020 National Technology & Engineering Solutions
C of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with
C NTESS, the U.S. Government retains certain rights in this software.
C
C See packages/seacas/LICENSE for details
C==============================================================... | {"hexsha": "f92e3698ec23db584bedf36d8631734b8585d9ab", "size": 4503, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/libraries/plt/pltstc.f", "max_stars_repo_name": "jschueller/seacas", "max_stars_repo_head_hexsha": "14c34ae08b757cba43a3a03ec0f129c8a168a9d3", "max_stars_repo_licenses": ["Python-2... |
# -*- coding: utf-8 -*-
"""
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PLOTTER %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Plot the results in 3D format, they can be either plot individually or
in goups of two or four.
Save the plot in a svg file whose name is the Label of the variable
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | {"hexsha": "1ffeb4e1d2bf4e217326f86b864becda839b9630", "size": 5175, "ext": "py", "lang": "Python", "max_stars_repo_path": "ADsolver/PM_plotter.py", "max_stars_repo_name": "salomon-castano/PM---Codigo", "max_stars_repo_head_hexsha": "86785e8fab7e5da49962f824d6c5e662efb21737", "max_stars_repo_licenses": ["MIT"], "max_st... |
(**
This file is part of the Flocq formalization of floating-point
arithmetic in Coq: http://flocq.gforge.inria.fr/
Copyright (C) 2009-2018 Sylvie Boldo
#<br />#
Copyright (C) 2009-2018 Guillaume Melquiond
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser Genera... | {"author": "JasonGross", "repo": "flocq", "sha": "0abac1eaa11c02f01ccffdc69846382e3a7040c5", "save_path": "github-repos/coq/JasonGross-flocq", "path": "github-repos/coq/JasonGross-flocq/flocq-0abac1eaa11c02f01ccffdc69846382e3a7040c5/src/Calc/Operations.v"} |
# -*- coding: utf-8 -*-
# Copyright (C) 2020-2021 by SCICO Developers
# All rights reserved. BSD 3-clause License.
# This file is part of the SCICO package. Details of the copyright and
# user license can be found in the 'LICENSE' file distributed with the
# package.
"""Utility functions."""
# The timer classes in th... | {"hexsha": "d36b5f60a4042bca3c244d8ae4e28b03afcc1ac9", "size": 17307, "ext": "py", "lang": "Python", "max_stars_repo_path": "scico/util.py", "max_stars_repo_name": "lukepfister/scico", "max_stars_repo_head_hexsha": "c849c4fa6089b99d9a4dec520c9a04cca426d2d7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
#!/usr/bin/python3
'''Copyright (c) 2018 Mozilla
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the follow... | {"hexsha": "94ab9a8ab007c5bbdd7447729275bf4c09e4b901", "size": 6198, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train_lpcnet.py", "max_stars_repo_name": "dave-g8kbb/LPCNet", "max_stars_repo_head_hexsha": "7a74323056480ae54805ee873c0183730ffdc6e7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 4 10:26:10 2018
@author: ai
"""
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
def soft_jaccard(outputs, targets):
eps = 1e-15
jaccard_target = (targets == 1)... | {"hexsha": "45e57cd85738e2270b023127874e4b34f48b7e77", "size": 16439, "ext": "py", "lang": "Python", "max_stars_repo_path": "loss_function.py", "max_stars_repo_name": "hphuongdhsp/Kaggle-TGS-Salt-Identification-Challenge", "max_stars_repo_head_hexsha": "4c1f9749f772f318473921f6d213af89cd4c0df2", "max_stars_repo_license... |
[STATEMENT]
lemma lc_mult_scalar_monomial_right:
"lc (p \<odot> monomial c v) = punit.lc p * (c::'b::semiring_no_zero_divisors)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lc (p \<odot> monomial c v) = punit.lc p * c
[PROOF STEP]
proof (cases "c = 0")
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. c = (0::... | {"llama_tokens": 1534, "file": "Polynomials_MPoly_Type_Class_Ordered", "length": 18} |
subroutine killop
!! ~ ~ ~ PURPOSE ~ ~ ~
!! this subroutine performs the kill operation
!! ~ ~ ~ INCOMING VARIABLES ~ ~ ~
!! name |units |definition
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
!! bio_ms(:) |kg/ha |land cover/crop biomass ... | {"hexsha": "bcaf4f1145d96a65313c5bd89ed74e912feb680c", "size": 13450, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "modified_source_code/killop.f", "max_stars_repo_name": "gpignotti/swat_soil_moisture_sensitivity", "max_stars_repo_head_hexsha": "d2c3f6185acabf31440d613651192cdaabfe6b6c", "max_stars_repo_licens... |
import copy
import numpy as np
import open3d as o3d
import trimesh
if not trimesh.ray.has_embree:
raise "PyEmbree engine not installed, this experiment will never end"
def outlier_rejection(
source_points, source_normals, target_points, target_normals, max_dist=2.0
):
# Prune long rays, max_distance thr... | {"hexsha": "1ea3575ca0f7b0d333d510656467dc3f83cb33de", "size": 2428, "ext": "py", "lang": "Python", "max_stars_repo_path": "puma/projections/scan2mesh.py", "max_stars_repo_name": "okryush/puma", "max_stars_repo_head_hexsha": "fd3f21c5566ae64110420a26ef6c9d8da0e67dce", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while (True):
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 25, 5)
cv2.imshow('frame', binary)... | {"hexsha": "fa496ce229820170a4bda7fe870d3dae1b238d35", "size": 417, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/01-Adaptive Thresholding.py", "max_stars_repo_name": "gh-BumsooKim/Vision-Art", "max_stars_repo_head_hexsha": "27986705377471cac57b73f9db386a11042e8bec", "max_stars_repo_licenses": ["Apache... |
This code has been copied by mistake. Please do NOT use it
unless by invitation. If you have not been specifically requested
to perform tests with it, please destroy this file
SUBROUTINE FOCK1(F, PTOT, PA, PB)
IMPLICIT DOUBLE PRECISION (A-H,O-Z)
INCLUDE 'SIZES/NOLIST'
... | {"hexsha": "4415121cee6a68b627d778d0f703a2db0cb571b4", "size": 53938, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "1983_MOPAC1/am1.for", "max_stars_repo_name": "openmopac/MOPAC-archive", "max_stars_repo_head_hexsha": "01510e44246de34a991529297a10bcf831336038", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
[STATEMENT]
lemma (in semiring_0) pnormal_degree: "last p \<noteq> 0 \<Longrightarrow> degree p = length p - 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. last p \<noteq> (0::'a) \<Longrightarrow> degree p = length p - 1
[PROOF STEP]
using pnormalize_eq[of p]
[PROOF STATE]
proof (prove)
using this:
last p \<note... | {"llama_tokens": 288, "file": null, "length": 3} |
import symplectic
import decompose
import chp_py
import slow_sim
import utils
import imp
import numpy as np
imp.reload(symplectic)
imp.reload(decompose)
imp.reload(chp_py)
imp.reload(slow_sim)
imp.reload(utils)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# This file contains test cases... | {"hexsha": "29bd95956bc47252ea43a643730ddd7f24ad672f", "size": 15848, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "matthewkhoury96/random_quantum_circuits", "max_stars_repo_head_hexsha": "20d15da79ab4bcf0c0b82c314514a6c06cc19e8c", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
import h5py
from hdgwas.tools import Timer,Mapper, merge_genotype
import os
from hdgwas.data import Reader
import argparse
import gc
import tables
import numpy as np
parser = argparse.ArgumentParser(description='Script to merge genotype')
parser.add_argument("-g", "--genotype",nargs='+', type=str, help=... | {"hexsha": "0342ad8bdd1ae93333e5d59da052b88b0d3fce0a", "size": 3154, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/merge_genotype.py", "max_stars_repo_name": "roshchupkin/hase", "max_stars_repo_head_hexsha": "c7aa36459c53ccb5bd1f884bbc38df0cfebdf208", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
from hashTable import hashTable
Qangle = 24
Qstrenth = 3
Qcoherence = 3
mat = cv2.imread("../train/a.jpg")
h = np.load("Filters.npy")
mat = cv2.cvtColor(mat, cv2.COLOR_BGR2YCrCb)[:,:,2]
# LR = cv2.resize(LuminanceMat,(0,0),fx=0.5,fy=0.5)
# LR = cv2.Gaus... | {"hexsha": "2c73b5fe39b62a2948aa4fa7bb558739795e4af0", "size": 1280, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/test.py", "max_stars_repo_name": "gonewithwind5883/mikuraisr", "max_stars_repo_head_hexsha": "8e4565a753aba8fb73645a644dee8173a1563ce1", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/gradients.estimate.ipynb (unless otherwise specified).
__all__ = ['get_grads_pullback']
# Cell
import numpy as np
from sklearn.linear_model import LinearRegression
def get_grads_pullback(data,
embedding,
geom,
tangent_bases... | {"hexsha": "e662920f1caa44b9c1cd7af3815e37880441bbc1", "size": 1329, "ext": "py", "lang": "Python", "max_stars_repo_path": "montlake/gradients/estimate.py", "max_stars_repo_name": "sjkoelle/montlake", "max_stars_repo_head_hexsha": "b908a43e0c00763bd1cf86120eaa6bdf7d8d1196", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# Author: Karl Stratos (me@karlstratos.com)
import argparse
import dynet as dy
import matplotlib.pyplot as plt
import numpy as np
import random
from core.information_theory import InformationTheory
def main(args):
random.seed(args.seed)
np.random.seed(args.seed)
info = InformationTheory()
num_points_e... | {"hexsha": "76eb9b778d2af23c9a92ab3d4924fc04cc0df62f", "size": 4392, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_mi.py", "max_stars_repo_name": "karlstratos/iaan", "max_stars_repo_head_hexsha": "e76a3cd3a17807a21478ae1dc83be4da76c01d70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_sta... |
import numpy as np
import torch
import torch.nn as nn
from torch import optim
from tqdm.auto import tqdm
from model import SiAudNet
def train(
model: SiAudNet,
train_on_gpu: bool,
n_epochs: int,
train_loader: torch.utils.data.DataLoader,
valid_loader: torch.utils.data.DataLoad... | {"hexsha": "2884b598aa65793a3a4eef7630e0f1a81ae8a04f", "size": 4411, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_test.py", "max_stars_repo_name": "dim5/ossr", "max_stars_repo_head_hexsha": "bc9f98ec8559c5301a167088e92d06adb876e267", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_... |
# coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import os
import time
import datetime
import json
import sys
import sklearn.metrics
from tqdm import tqdm
def to_var(x):
return Variable(torch.from_numpy(x).cuda()) ... | {"hexsha": "fb9ac03cbd14088b5ebaf6df32520cea12efcc29", "size": 23085, "ext": "py", "lang": "Python", "max_stars_repo_path": "config/Config.py", "max_stars_repo_name": "ningpang/com-CNN", "max_stars_repo_head_hexsha": "a494589a9445c9b3ad63175ec6b2084d4d3e3e81", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "... |
"""
This module provides the Destination class that holds all the basic API methods
for interfacing with a destination.
Destintations are databases, filesystems, file formats (HDF5, CSV), etc.
"""
import struct
import numpy as np
import multiprocessing
import Queue
import time
import logging
from origin.server impor... | {"hexsha": "7f8aa8a1077fa39b627938072daf114b9f35ae15", "size": 24243, "ext": "py", "lang": "Python", "max_stars_repo_path": "ServerStuff/lib/origin/server/origin_destination.py", "max_stars_repo_name": "QuantumQuadrate/powstab2", "max_stars_repo_head_hexsha": "4311302417a859f8c17197648ee958b126d3a454", "max_stars_repo_... |
[STATEMENT]
theorem suffix_append:
"suffix xs (ys @ zs) \<longleftrightarrow> suffix xs zs \<or> (\<exists>xs'. xs = xs' @ zs \<and> suffix xs' ys)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. suffix xs (ys @ zs) = (suffix xs zs \<or> (\<exists>xs'. xs = xs' @ zs \<and> suffix xs' ys))
[PROOF STEP]
by (auto si... | {"llama_tokens": 147, "file": null, "length": 1} |
//
// detail/atomic_fenced_block.hpp
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
//
// Copyright (c) 2003-2016 Christopher M. Kohlhoff (chris at kohlhoff dot com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
#ifndef B... | {"hexsha": "62a1f440d71da19cbc2b05e11605a86a278dfe00", "size": 1432, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "thirdparty/boost/boost/asio/detail/atomic_fenced_block.hpp", "max_stars_repo_name": "bureau14/qdb-benchmark", "max_stars_repo_head_hexsha": "1839d7ac04417de56b7a7fb2b7deff50756b3048", "max_stars_rep... |
import argparse
import os
import torch.backends.cudnn as cudnn
import models
import torchvision.transforms as transforms
import flow_transforms
from scipy.ndimage import imread
from scipy.misc import imsave
from loss import *
import time
import random
from glob import glob
import pdb
import matplotlib.pyplot as plt
#... | {"hexsha": "f220ce2c7238cd73da34cb05db1270436134bd98", "size": 4803, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_demo.py", "max_stars_repo_name": "YanFangCS/AINET", "max_stars_repo_head_hexsha": "a860d9dc59a53242746057e1b97c2e5088d9a6d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_sta... |
!
Module forward_support
Use Phys_constants
Use Atomic_data
Use Line_data_structure
Use Eq_state
Contains
! This routine checks an optical depth scale to make sure there are no
! two consecutive points with the same value. If such an occurrence is found
! then the tau scale from that point to the bottom is shi... | {"hexsha": "6946e0d928508f8351919e05e2d547b1c142fed9", "size": 16482, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "inversor/database/NICOLE/forward/forward_supp.f90", "max_stars_repo_name": "aasensio/DeepLearning", "max_stars_repo_head_hexsha": "71838115ce93e0ca96c8314cff3f07de1d64c235", "max_stars_repo_lic... |
import numpy as np
def generate_spirals(
n_samples,
n_class=2,
noise=0.3,
random_state=None,
):
"""
Generate 2-dimensional Gaussian XOR distribution.
(Classic XOR problem but each point is the
center of a Gaussian blob distribution)
Parameters
----------
... | {"hexsha": "cf5c4a1851c68e76817cbf96a2a36e0974da10f8", "size": 2599, "ext": "py", "lang": "Python", "max_stars_repo_path": "proglearn/sims/spiral_sim.py", "max_stars_repo_name": "kfenggg/ProgLearn", "max_stars_repo_head_hexsha": "43842543315ef49d60f4ebcabcdd9b8fda1b3277", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from collections import namedtuple
import dataclasses
import pandas as pd
from typing import List, Tuple, Dict, Union, Any, Optional, Callable
def as_flattened(vals: Dict[str, Any], base: Optional[Tuple[str, ...]] = None) -> Dict[Tuple[str, ...], Any]:
if base is None:
base = tuple()
... | {"hexsha": "3b233743c68b60746f11e5337a81109a85f98352", "size": 10530, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunode/dtypesubset.py", "max_stars_repo_name": "aseyboldt/pysundials-cffi", "max_stars_repo_head_hexsha": "ccdcd0fd0252285a5440c397619c57378a32d33a", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma [enres_breakdown]: "EWHILET c (\<lambda>s. enres_lift (f s)) s = enres_lift (WHILET c f s)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. EWHILET c (\<lambda>s. enres_lift (f s)) s = enres_lift (WHILE\<^sub>T c f s)
[PROOF STEP]
unfolding EWHILET_def WHILET_def enres_breakdown
[PROOF STATE]
proof ... | {"llama_tokens": 236, "file": "VerifyThis2019_lib_Exc_Nres_Monad", "length": 2} |
"""
PrecipFormation
Storage for tendencies due to precipitation formation
$(DocStringExtensions.FIELDS)
"""
Base.@kwdef struct PrecipFormation{FT}
θ_liq_ice_tendency::FT
qt_tendency::FT
qr_tendency::FT
qs_tendency::FT
end
"""
EntrDetr
$(DocStringExtensions.FIELDS)
"""
Base.@kwdef struct Entr... | {"hexsha": "bdac5d86e69ba66d76511d2601806ad2bdf405d3", "size": 16589, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/types.jl", "max_stars_repo_name": "haakon-e/TurbulenceConvection.jl", "max_stars_repo_head_hexsha": "84969de4867b06e464c3a36263e98be677792a2d", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
% =========================================
% COMMAND: _SH
% =========================================
\newpage
\section{\_SH}
\label{cmd:_SH}
\paragraph{Syntax:}
\subparagraph{}
\texttt{\_SH shell script line or END}
\paragraph{Purpose:}
\subparagraph{}
Embeds a shell script within a tmp file, and executes
it when ... | {"hexsha": "df58bc6c981bb88936aadd3bcd56a6dd4c105641", "size": 391, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/users-guide/local-commands/cmd_sh.tex", "max_stars_repo_name": "ia97lies/httest", "max_stars_repo_head_hexsha": "5bbe912ffde2e74b382405f580ef5963bf792288", "max_stars_repo_licenses": ["Apache-2.0... |
import math
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from matplotlib.animation import FuncAnimation
import numpy as np
import pandas
def read_data(paths):
''' Read all data from the files specified by the given list of paths.
Returns a list of dataframes, one per file.
... | {"hexsha": "69c26398cf5ef3c18e12fe167f315aec9187fc05", "size": 3076, "ext": "py", "lang": "Python", "max_stars_repo_path": "heatmaps.py", "max_stars_repo_name": "bsheese/AtlesDescriptives", "max_stars_repo_head_hexsha": "febfde7d5bbe8abf686570db862f42738a4ad1fe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
import copy
import numpy as np
from scipy.interpolate import CubicSpline
import spherical_functions
class ModesTimeSeries(spherical_functions.Modes):
"""Object to store SWSH modes as functions of time
This class subclasses the spinsfast.Modes class, but also tracks corresponding time values,
allowing thi... | {"hexsha": "7f6677f8ae379dd8c92f7e1d10375257dac21e5a", "size": 5138, "ext": "py", "lang": "Python", "max_stars_repo_path": "scri/modes_time_series.py", "max_stars_repo_name": "akhairna/scri", "max_stars_repo_head_hexsha": "3b7f307d19ef303914cef2fa088ee750ef8533c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
% ---------------------------------------------------------
% Project: PhD KAPPA
% File: introduction.tex
% Author: Andrea Discacciati
%
% Purpose: Introduction
% ---------------------------------------------------------
\chapter{Introduction}
\begin{quote}
\textit{``Epidemiological studies provide the only definitiv... | {"hexsha": "300a124bad36c43cd83708acca1ca011c42dd0ac", "size": 5346, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "introduction.tex", "max_stars_repo_name": "anddis/phd-thesis", "max_stars_repo_head_hexsha": "860faf0686c16f7c97865d99d801050a10d2df7c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import os
import unittest
import tempfile
import numpy as np
from pyV3D import WV_Wrapper, ConnectivitiesError
from pyV3D.cube import CubeGeometry, CubeSender
from pyV3D.stl import STLSender
from pyV3D import get_bounding_box, get_focus, adjust_points
class WV_Test_Wrapper(WV_Wrapper):
pass
class WV_test_Wra... | {"hexsha": "4b24c8b529775cd1c4ac051740e4b78042bb7504", "size": 7678, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyV3D/test/test_pyV3D.py", "max_stars_repo_name": "OpenMDAO/pyV3D", "max_stars_repo_head_hexsha": "2baf32c489e2c91531b89e51a879ba8074ae2803", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
using MolarWeight
using Test
@testset "MolarWeight.jl" begin
# Write your tests here.
end
| {"hexsha": "c6277d5aad0ca1b5b7214e80ace7a9d3727692c8", "size": 95, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "SeleneSofi/MolarWeight.jl", "max_stars_repo_head_hexsha": "21d1b3b3a77601d442f14e84613d48f42ef1496b", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
__author__ = "Saswati Ray"
__email__ = "sray@cs.cmu.edu"
import pandas as pd
import numpy as np
import math, sys
from sklearn import metrics
from sklearn import preprocessing
import problem_pb2
import util
import logging
import d3m.index
from timeit import default_timer as timer
import operator
f... | {"hexsha": "995b240bf4dec3df5eb46bf5d641e386a61b6cd9", "size": 24135, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/primitivedescription.py", "max_stars_repo_name": "tonyjo/cmu-ta2", "max_stars_repo_head_hexsha": "26a45fab27b4ca433029b05272130efcaeb2beaa", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
@inline soft_th(x, ϵ) = max(x-ϵ,zero(x)) + min(x+ϵ,zero(x))
@inline soft_th(x, ϵ, l) = max(x-ϵ,l) + min(x+ϵ,l) - l
@inline function soft_th(x::Complex, ϵ)
m,a = abs(x), angle(x)
m = max(m-ϵ,zero(m)) + min(m+ϵ,zero(m))
m*cis(a)
end
function soft_hankel!(A, ϵ)
K,L = size(A)
N = K+L-1
for k = 1:N
... | {"hexsha": "36af1530250255eaeea5841585d2294daedbc174", "size": 11360, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/robustPCA.jl", "max_stars_repo_name": "baggepinnen/TotalLeastSquares.jl", "max_stars_repo_head_hexsha": "297b9a8277c01358f54889944f3dcc711c0fe535", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
from sklearn.metrics import f1_score
from munch import Munch
import matplotlib
matplotlib.use('PS')
import matplotlib.pyplot as plt
import numpy as np
import torch
import os
# setup plot details
linestyle_tuple = {
... | {"hexsha": "9a50f9958055ef31b0dfd4bdfd37881681fdd3ed", "size": 4239, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics/f1_score.py", "max_stars_repo_name": "affromero/SMILE", "max_stars_repo_head_hexsha": "931510d69b2e33f2fe633563833c50a7408f89ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20,... |
import pandas as pd
import numpy as np
import os
from google.cloud import language_v1
from google.cloud import translate_v2 as translate
from apiclient import discovery
from google.oauth2 import service_account
import preprocessor as tp
from time import sleep
from requests.exceptions import ReadTimeout, ConnectionError... | {"hexsha": "0f115df22e371315a78ce0988f22d5fe91926388", "size": 5562, "ext": "py", "lang": "Python", "max_stars_repo_path": "analyze_field_reports.py", "max_stars_repo_name": "rodekruis/ifrc-ns-solidarity", "max_stars_repo_head_hexsha": "ef80170440e667c376e57c7d2687bdaa4cef347e", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
from regression.api.cost import computeCost
def gradient_descent(X, y, theta, alpha, iterations):
m = len(X)
cost = np.zeros((iterations, 2))
for i in range(0, iterations):
h = np.matmul(X, theta) - y
sm = np.sum(np.multiply(h, X), axis=0)
z = np.multip... | {"hexsha": "196b1f08650e9ca88c6dd5bb32eb3565641de8f1", "size": 469, "ext": "py", "lang": "Python", "max_stars_repo_path": "regression/api/gradient.py", "max_stars_repo_name": "vickykatoch/py-ml-scratchpad", "max_stars_repo_head_hexsha": "e4de515b657971f7889d73e09a1308f91856c09f", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import numpy as np
import pandas as pd
import xarray as xr
import pickle as pkl
from datetime import datetime
from scipy import ndimage as ndi
import SimpleITK as sitk
import skimage as skim
from skimage import feature, morphology
import glob
class RegHearts:
'''Class that generates liver masks for MRE ... | {"hexsha": "f105b721afb9f845c4e320b2b60eb5e5d9422cbd", "size": 6838, "ext": "py", "lang": "Python", "max_stars_repo_path": "code4step2/data_registration.py", "max_stars_repo_name": "yukeyi/MCDS-Capstone", "max_stars_repo_head_hexsha": "f7ce48fc5d3f5f96c1f29556585ed2338683c7d2", "max_stars_repo_licenses": ["MIT"], "max_... |
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier as CART
from sklearn.utils import shuffle
import numpy as np
import sys
import re
class MachineLearningLib(object):
@staticmethod
def svm(X, y, params):
clf = svm.SVC(n_jobs=param... | {"hexsha": "41c3acfe2b6a4d94fd0ce79c3884a955ea4d046e", "size": 1002, "ext": "py", "lang": "Python", "max_stars_repo_path": "DynamicNetworkEmbedding-master/src/utils/lib_ml.py", "max_stars_repo_name": "yanzihan1/Use-Dynamic-network-embedding-for-Social-Network-Aligment-", "max_stars_repo_head_hexsha": "9bec908d25fbe4c07... |
[STATEMENT]
lemma Invariants_sets: "sets Invariants = {A \<in> sets M. T-`A \<inter> space M = A}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sets Invariants = {A \<in> sets M. T -` A \<inter> space M = A}
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. sets Invariants = {A \<in> sets M. T... | {"llama_tokens": 6790, "file": "Ergodic_Theory_Invariants", "length": 73} |
[STATEMENT]
lemma (in encoding) refl_symm_trans_closure_of_indRelT:
fixes TRel :: "('procT \<times> 'procT) set"
assumes refl: "refl TRel"
and symm: "sym TRel"
shows "indRelTEQ TRel = (symcl ((indRelT TRel)\<^sup>=))\<^sup>+"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. indRelTEQ TRel = (symcl ((indRel... | {"llama_tokens": 1330, "file": "Encodability_Process_Calculi_SourceTargetRelation", "length": 13} |
'''
Author : Siddharth Nayak
email:ee16b073@smail.iitm.ac.in
'''
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import scipy as sp
import os, signals
from sklearn.externals import joblib
from sklearn.model_selection import GridSearchCV
from skle... | {"hexsha": "80cb864285099ea64e7defb3c3934a69accaf9c8", "size": 2005, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_classify_first_layer.py", "max_stars_repo_name": "ruc98/Inter-IIT-Tech-Meet-2018-Project", "max_stars_repo_head_hexsha": "8405acd3d650b4f41b669e9cfc59cd27f0fee1a5", "max_stars_repo_licenses"... |
# Copyright (c) 2020, Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can be
# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
from coremltools.converters.mil.mil import Builder as mb
from coremltools.converters.mil.testing_util... | {"hexsha": "e717cb672f85eda6af6f1efe2350be109d5778ac", "size": 12011, "ext": "py", "lang": "Python", "max_stars_repo_path": "coremltools/converters/mil/mil/passes/test_elementwise_fusions.py", "max_stars_repo_name": "LaudateCorpus1/coremltools", "max_stars_repo_head_hexsha": "777a4460d6823e5e91dea4fa3eacb0b11c7d5dfc", ... |
try:
from astropy.io import fits
except:
import pyfits as fits
import numpy as np
import scipy.signal
def gen_bad_pix_mask(image, filsize=5, threshold=5.0, return_smoothed_image=False):
"""
"""
image_sm = scipy.signal.medfilt(image, filsize)
res = image - image_sm
sigma = np.std(res)
... | {"hexsha": "f95f44ace533cc0863371accc5f1eda2aaff812c", "size": 761, "ext": "py", "lang": "Python", "max_stars_repo_path": "charis/tools/badpix.py", "max_stars_repo_name": "thaynecurrie/charis-dep", "max_stars_repo_head_hexsha": "238397bb3ec18edba6e59c7203a623709ff4b50d", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBS... |
using Plots
using LaTeXStrings
pgfplots()
c = 1
a₀ = 1
ω = 1
x(τ) = c*(a₀^2)/4 *(τ - 1/2* ω*sin(2ω*τ))
y(τ) = c*a₀/ω *(1-cos(ω*τ))
p1 = plot(x, y, 0, 15,
xlabel=L"x", ylabel=L"y",
xticks=nothing, yticks=nothing,
legend=:none,
framestyle=:box,
tex_output_standalone=true
)
v_drift = c*a₀^2/4
p2 = plot(τ->x... | {"hexsha": "dbd2f178dea2e51df6b64062f7c0ba4cd52b0f43", "size": 628, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/fig8.jl", "max_stars_repo_name": "SebastianM-C/MasterThesis", "max_stars_repo_head_hexsha": "ffbf25e087444644ee73f72a31be969375fa0d10", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
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