text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
# 2.4 plotting
tmpexpr = :(
xlabel("Year"); grid(true);
xlim([ idx_year2plot[1] - 1, idx_year2plot[end] + 1 ]);
)
figure(figsize = (13,8))
subplot(2,2,1) # gap / exp
for tmpzeta in 1:length(reform_zeta_levs)
plot( Dt[:Year][idx_plot], res_ContriRatRise["gap/exp"][tmpzet... | {"hexsha": "543c19e5909576260450d63e4dcb21fee72b0ddc", "size": 1611, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/subsections_PlotForPaper/sub_07_Plot4ContributionRateRise.jl", "max_stars_repo_name": "Clpr/OLG4UEBMI", "max_stars_repo_head_hexsha": "53b7e0afab1490e3eba34455c06cbc74277a5953", "max_stars_... |
[STATEMENT]
lemma fsi (*[simp]*):"f \<inter> s^-1 = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. f \<inter> s\<inverse> = {}
[PROOF STEP]
using sfi
[PROOF STATE]
proof (prove)
using this:
s \<inter> f\<inverse> = {}
goal (1 subgoal):
1. f \<inter> s\<inverse> = {}
[PROOF STEP]
by auto | {"llama_tokens": 134, "file": "Allen_Calculus_disjoint_relations", "length": 2} |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2017, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | {"hexsha": "dc8fd39c9fb7f1520dfd6a1cd7ff685a3bffa670", "size": 13393, "ext": "py", "lang": "Python", "max_stars_repo_path": "q2_diversity/_beta/_visualizer.py", "max_stars_repo_name": "jairideout/diversity", "max_stars_repo_head_hexsha": "0024301a03134b2b05aeb83f6b01bd22da5b8cb2", "max_stars_repo_licenses": ["BSD-3-Cla... |
// Copyright (c) 2005-2008 Hartmut Kaiser
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#include <iostream>
#include <boost/lexical_cast.hpp>
#include <saga/saga.hpp>
int main (int argc,
... | {"hexsha": "a7376ff21baafb0233ddc9cd6c593a354a030835", "size": 1270, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/packages/stream/stream_server.cpp", "max_stars_repo_name": "saga-project/saga-cpp", "max_stars_repo_head_hexsha": "7376c0de0529e7d7b80cf08b94ec484c2e56d38e", "max_stars_repo_licenses": ["BS... |
@testset "Genetic Programming" begin
Random.seed!(9874984737486);
pop = 10
terms = Terminal[:x, :y, rand]
funcs = Function[+,-,*,/]
t = TreeGP(pop, terms, funcs, maxdepth=2)
@test Evolutionary.population_size(t) == pop
@test sort(Evolutionary.terminals(t)) == [:x, :y]
@testset for (term... | {"hexsha": "3cf5cc18aa00a26d8f0856cd88d974823408c536", "size": 3856, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/gp.jl", "max_stars_repo_name": "dmolina/Evolutionary.jl", "max_stars_repo_head_hexsha": "928438c682f1c31a08b6f0bd503a2dab3842ecc8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import os
import importlib
import numpy as np
from .helpers import util
from .helpers.data import WSGenerator, WSRandGenerator
from sim.helpers.util import get_path as get_network_path
from sim.helpers.data import DataInfo
def compute(args):
network = args.NETWORK
epoch = args.epoch
anon = not args.inclu... | {"hexsha": "ad76c08a1b36dc09f1728b571c7c4ec237e13eee", "size": 2564, "ext": "py", "lang": "Python", "max_stars_repo_path": "ws/compute.py", "max_stars_repo_name": "StephanLorenzen/AuthorshipVerification", "max_stars_repo_head_hexsha": "1397f5967ad9f41bf57a9e9e91aaf138b0b5ae6f", "max_stars_repo_licenses": ["Apache-2.0"]... |
"""
This code is based on code found at: https://commons.wikimedia.org/wiki/File:Beta_distribution_pdf.svg by user Horas based on the work of user Krishnavedala
"""
from matplotlib.pyplot import *
from numpy import linspace
from scipy.stats import beta
x = linspace(0,1,75)
fig = figure()
ax = fig.add_subplot(111)
ax... | {"hexsha": "33b2855d3090c7ab9145dc9eb0f878c422aba600", "size": 1951, "ext": "py", "lang": "Python", "max_stars_repo_path": "Dirichlet_PDF.py", "max_stars_repo_name": "coli-saar/BayesianNLP2017", "max_stars_repo_head_hexsha": "116e7d8d2e88dea80bdacc20f15a57268adf1a32", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from .query_graph import convert_to_networkx
from .QueryPlan import QueryPlan, TerminalEvent
import networkx as nx
from collections import defaultdict
def generate_plan(trapi_query_graph):
nxgraph = convert_to_networkx(trapi_query_graph)
double_pins, components = decompose(nxgraph)
plan = double_pins #the... | {"hexsha": "3cbeec57f0a1732f49aab93e4df32d6aa081c18d", "size": 10194, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/generate_plan.py", "max_stars_repo_name": "ranking-agent/query_planner", "max_stars_repo_head_hexsha": "cbe6fd8b7f627658845851b06c73b239746a60f0", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma DSourcesA3_L0: "DSources level0 sA3 = { sA2 }"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. DSources level0 sA3 = {sA2}
[PROOF STEP]
by (simp add: DSources_def AbstrLevel0, auto) | {"llama_tokens": 94, "file": "ComponentDependencies_DataDependenciesCaseStudy", "length": 1} |
/-
Copyright (c) 2022 Yury Kudryashov. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Yury Kudryashov
! This file was ported from Lean 3 source module geometry.manifold.metrizable
! leanprover-community/mathlib commit d1bd9c5df2867c1cb463bc6364446d57bdd9f7f1
! Please ... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/Geometry/Manifold/M... |
(*
Copyright 2014 Cornell University
This file is part of VPrl (the Verified Nuprl project).
VPrl is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option)... | {"author": "vrahli", "repo": "NuprlInCoq", "sha": "0c3d7723836d3f615ea47f56e58b2ea6173e7d98", "save_path": "github-repos/coq/vrahli-NuprlInCoq", "path": "github-repos/coq/vrahli-NuprlInCoq/NuprlInCoq-0c3d7723836d3f615ea47f56e58b2ea6173e7d98/close/close_type_sys_per_tunion.v"} |
C************************************************************************
C This test routine is used to test CYLPATCH, a FORTRAN subroutine.
C CLYPATCH computes the special line and sample point and special
C latitude and longitude points for the normal cylindrical projection.
C This test routine builds the neces... | {"hexsha": "928db527390d29de209d1de44619576bb009c0fe", "size": 3814, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/sub/cylpatch/test/tcylpatch.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Claus... |
[STATEMENT]
lemma n_o_mono: "domo S1 \<subseteq> X \<Longrightarrow> domo S2 \<subseteq> X \<Longrightarrow> S1 \<sqsubseteq> S2 \<Longrightarrow>
n_o (n_st n_ivl X) S1 \<le> n_o (n_st n_ivl X) S2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>domo S1 \<subseteq> X; domo S2 \<subseteq> X; S1 \<sqsubseteq... | {"llama_tokens": 548, "file": "Abs_Int_ITP2012_Abs_Int3", "length": 3} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 17 15:20:21 2019
@author: michaelwu
"""
import numpy as np
import cv2
import os
import pickle
import torch as t
import torch
import h5py
import pandas as pd
from NNsegmentation.models import Segment
from NNsegmentation.data import predict_whole_map
... | {"hexsha": "084d583568108f7bbd4f78ec33a405621244bbe0", "size": 45702, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_scripts/plottings.py", "max_stars_repo_name": "miaecle/dynamorph", "max_stars_repo_head_hexsha": "9bc04ae771e66938273eee102d404947546a69c5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
# Copyright 2021 The Brax 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 or agreed to in wri... | {"hexsha": "53c6451c2611b9e0b63f2eea44d28b15e343a775", "size": 3504, "ext": "py", "lang": "Python", "max_stars_repo_path": "brax/io/torch.py", "max_stars_repo_name": "Egiob/brax", "max_stars_repo_head_hexsha": "1baf25d5a713bd5dbc8588a004a5754723626bd0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1162... |
"""Functions for reading light curve data."""
import logging
from astropy.io import fits
from astropy.utils import deprecated
from .detect import detect_filetype
from ..lightcurve import KeplerLightCurve, TessLightCurve
from ..utils import validate_method, LightkurveWarning, LightkurveDeprecationWarning
log = loggin... | {"hexsha": "c1c62baf1e7ec40b4488f52f11b8b0dcf1bf43f9", "size": 3002, "ext": "py", "lang": "Python", "max_stars_repo_path": "lightkurve/io/read.py", "max_stars_repo_name": "KenMighell/lightkurve", "max_stars_repo_head_hexsha": "bb264899fd8d5fbaa95c13f3b90c75bd96c5a33e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
\vfill \eject
\section{{\tt allInOne.c} -- A Serial $QR$ Driver Program}
\label{section:QR-serial-driver}
\begin{verbatim}
/* QRallInOne.c */
#include "../../misc.h"
#include "../../FrontMtx.h"
#include "../../SymbFac.h"
/*--------------------------------------------------------------------*/
int
main ( int argc, ... | {"hexsha": "f7962abab35545307efde9af6be3f269b22b258e", "size": 10403, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ccx_prool/SPOOLES.2.2/documentation/AllInOne/QR_serial_driver.tex", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", ... |
# -*- coding: utf-8 -*-
# Copyright (c) 2020-2021 shmilee
'''
Source fortran code:
skip
'''
import numpy
from ..GTCv3 import gtc as gtcv3
_all_Converters = gtcv3._all_Converters
_all_Diggers = gtcv3._all_Diggers
__all__ = _all_Converters + _all_Diggers
class GtcConverter(gtcv3.GtcConverter):
__slots__ = []
... | {"hexsha": "bf7f2418a77b36e930dcde336f2ed9ac95ed8764", "size": 2369, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/GTCv4/gtc.py", "max_stars_repo_name": "shmilee/gdpy3", "max_stars_repo_head_hexsha": "2e007851fc87793c0038f7b1dacba729271e17a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_... |
import torch, os, datetime
import numpy as np
from .dist_utils import dist_print, dist_tqdm, is_main_process, DistSummaryWriter
from .factory import get_metric_dict, get_loss_dict, get_optimizer, get_scheduler
from .metrics import MultiLabelAcc, AccTopk, Metric_mIoU, update_metrics, reset_metrics
from .common import... | {"hexsha": "b266a1c9a8b4d5a8a36306d8b5d70cd7d57732bb", "size": 1687, "ext": "py", "lang": "Python", "max_stars_repo_path": "adet/modeling/ultra_fast/cal_loss.py", "max_stars_repo_name": "GuoHaoren/Unifed-Lane-and-Traffic-Sign-detection", "max_stars_repo_head_hexsha": "80ed2690a7bb90861ccfc85de9a2feb6bce324ff", "max_sta... |
from typing import Union
import numpy as np
from sklearn.utils.class_weight import compute_class_weight
def compute_class_weight_dict(
labels: Union[list, np.ndarray], class_weight: Union[dict, str, None] = "balanced"
) -> dict:
"""Compute class weight.
Wrapper for sklearn function that returns Keras co... | {"hexsha": "21e850979bf0f7e5eff0a056a0d24900e2de2dfa", "size": 905, "ext": "py", "lang": "Python", "max_stars_repo_path": "cellx/train.py", "max_stars_repo_name": "quantumjot/cellx", "max_stars_repo_head_hexsha": "2a3ef965af22f213c4c9e239f097d231040eafe1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_... |
\documentclass[10pt,landscape]{article}
% \pagestyle{headings}
\usepackage{multicol}
\usepackage[landscape]{geometry}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{latexsym}
\usepackage{enumerate}
\usepackage{verbatim}
\usepackage{multirow}
\usepackage[lofdepth,lotdepth]{subfig}
\usepacka... | {"hexsha": "8a9ac1dfa2da81665f2e8d807c1b2a847e90f2cb", "size": 24246, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "study-guide/study-guide.tex", "max_stars_repo_name": "feynmanliang/Organic-Synthesis-Study-Guide", "max_stars_repo_head_hexsha": "ee63f7e027ca675dfdf7993907101f9574d61e41", "max_stars_repo_licenses... |
"""
Tests for workspace module
"""
import os
import shutil
import tempfile
from six import StringIO
import numpy as np
import pytest
from fsl.data.image import Image
from oxasl import Workspace, AslImage
from oxasl.workspace import text_to_matrix
def test_default_attr():
""" Check attributes are None by default... | {"hexsha": "2edaca371794f4302e7ff4f8d86edcbfb6009663", "size": 12803, "ext": "py", "lang": "Python", "max_stars_repo_path": "oxasl/test/test_workspace.py", "max_stars_repo_name": "ibme-qubic/oxasl", "max_stars_repo_head_hexsha": "8a0c055752d6e10cd932336ae6916f0c4fc0a2e9", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
"""
Mask R-CNN
Configurations and data loading code for MS COCO.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
------------------------------------------------------------
Usage: import the module (see Jupyter notebooks for examples), or run fr... | {"hexsha": "d9ec13d7e408d5d972a4404531bdc9316aa06c70", "size": 21581, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/coco/coco.py", "max_stars_repo_name": "xman0810/Mask_RCNN", "max_stars_repo_head_hexsha": "06e51f44961d4803696bcb2eab27352fc83162c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# Copyright 2021 qclib project.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "04a5cd26a3efe97be96f3896f36afe3163dae671", "size": 3867, "ext": "py", "lang": "Python", "max_stars_repo_path": "qclib/state_preparation/apqm.py", "max_stars_repo_name": "adjs/qclib", "max_stars_repo_head_hexsha": "0c3f1eec68536df4d161297554059da06b7722f7", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
const TRY_BUT_ALLOW_FAILURES_URL_LIST = String[
]
| {"hexsha": "d3a922bf1ec50290f17d589186fb6a2cb416d433", "size": 55, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "config/repositories/try-but-allow-failures-url-list.jl", "max_stars_repo_name": "KristofferC/RepoSnapshots.jl", "max_stars_repo_head_hexsha": "357e12a814309b8b751a2927bf37440357e5cd76", "max_stars_re... |
!isempty(ARGS) || error("No config supplied.")
isfile(ARGS[1]) || error("Cannot read '$(ARGS[1])'")
isabspath(ARGS[1]) || error("Please use an absolute path for the config.")
println("Config supplied: '$(ARGS[1])'")
config_file = ARGS[1]
include(config_file)
using MLDataUtils
using Random
using DelimitedFiles
functio... | {"hexsha": "1b64911a5690ae52360b93015fd9730fe8196155", "size": 4693, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/preprocess_data.jl", "max_stars_repo_name": "kit-dbis/ocal-evaluation", "max_stars_repo_head_hexsha": "b6dc7c0896a65c56650dd428b43acf398ef198aa", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import base64
from stega.injector import Injector
from kombu import Connection, Exchange, Queue
from kombu.mixins import ConsumerMixin
rabbit_url = 'amqp://guest:guest@localhost:5672//'
class Worker(ConsumerMixin):
def __init__(self, connection, queues):
self.connection = connection
... | {"hexsha": "a6170a5d4effdd518e5c28205200be178138b434", "size": 1223, "ext": "py", "lang": "Python", "max_stars_repo_path": "viewer.py", "max_stars_repo_name": "vnrdd/stega-live-video", "max_stars_repo_head_hexsha": "f1d7d93248ea7c7c4b484543e8b8494fcc252885", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import matplotlib
matplotlib.use('Agg')
from hcipy import *
import numpy as np
import matplotlib.pyplot as plt
import os
import pytest
def test_gif_writer():
grid = make_pupil_grid(256)
mw = GifWriter('test.gif')
for i in range(25):
field = Field(np.random.randn(grid.size), grid)
plt.clf()
imshow_field(fi... | {"hexsha": "0d7190385dda85c0b6fa833c480d39be2f95cefe", "size": 1881, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_plotting.py", "max_stars_repo_name": "yinzi-xin/hcipy", "max_stars_repo_head_hexsha": "e9abb037ed0d6fe06581c1ce94e5c154fa5069a7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from __future__ import print_function, division, absolute_import
import os
os.environ['ODIN'] = 'float32,gpu'
import pickle
from collections import OrderedDict, defaultdict
import numpy as np
from scipy.io import savemat
from scipy import stats
import tensorflow as tf
from sklearn.decomposition import PCA
from skle... | {"hexsha": "688852f8865f841f04ab6627d3fee492e76f3575", "size": 20044, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/nist_sre/make_score.py", "max_stars_repo_name": "tirkarthi/odin-ai", "max_stars_repo_head_hexsha": "7900bef82ad8801d0c73880330d5b24d9ff7cd06", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import os
from flask_wtf import FlaskForm
from wtforms import StringField, SubmitField, DecimalField, RadioField, SelectField, SelectMultipleField, IntegerField, FloatField
from wtforms.validators import InputRequired, Length, NumberRange, AnyOf, ValidationError
from wtforms.widgets import ListWidge... | {"hexsha": "0b3f586857c48a4131856b9fee2acc4c5251d8e9", "size": 12499, "ext": "py", "lang": "Python", "max_stars_repo_path": "exoctk/exoctk_app/form_validation.py", "max_stars_repo_name": "bourque/exoctk", "max_stars_repo_head_hexsha": "1d2f8e7b9c00e74033626d81593b1f879b7df6ad", "max_stars_repo_licenses": ["BSD-3-Clause... |
"""Mixture model for matrix completion"""
from typing import Tuple
import numpy as np
from scipy.special import logsumexp
from common import GaussianMixture
def estep(X: np.ndarray, mixture: GaussianMixture) -> Tuple[np.ndarray, float]:
"""E-step: Softly assigns each datapoint to a gaussian component
Args:
... | {"hexsha": "566e3dc2f25c28482aac8aa185b1456e9ef1d524", "size": 6777, "ext": "py", "lang": "Python", "max_stars_repo_path": "project4/netflix/em.py", "max_stars_repo_name": "davysnou/MIT-6.86x-Davy", "max_stars_repo_head_hexsha": "a0ef35692477512c7ac1ced8a0584c413781a401", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
Plotly-to-Matplotlib conversion functions.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government re... | {"hexsha": "67e998e5194642224bd293e37072eaa9b469022b", "size": 26063, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygsti/report/mpl_colormaps.py", "max_stars_repo_name": "colibri-coruscans/pyGSTi", "max_stars_repo_head_hexsha": "da54f4abf668a28476030528f81afa46a1fbba33", "max_stars_repo_licenses": ["Apache-2... |
# ========================================================================
#
# Imports
#
# ========================================================================
import numpy as np
import pandas as pd
import yaml
import definitions as defs
# ========================================================================
#... | {"hexsha": "d99c47f4388b4cdf79aeafe30e0efe926f315eb4", "size": 3609, "ext": "py", "lang": "Python", "max_stars_repo_path": "mcalister/utilities/utilities.py", "max_stars_repo_name": "Exawind/iddes", "max_stars_repo_head_hexsha": "200ddd4a20587c38f4103a32c0001ad5c8d33f22", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
x = [-1, 1, 3, 3, -1]
y = [2, 0, -5, 2, -5]
@test_throws MethodError scatterplot()
@test_throws MethodError scatterplot(sin, x)
@test_throws MethodError scatterplot([sin], x)
@test_throws DimensionMismatch scatterplot([1, 2], [1, 2, 3])
@test_throws DimensionMismatch scatterplot([1, 2, 3], [1, 2])
@test_throws Dimensi... | {"hexsha": "839de9bdb3500dc0c851a4387872a1d3fc1ca8fd", "size": 4431, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/tst_scatterplot.jl", "max_stars_repo_name": "Cvikli/UnicodePlots.jl", "max_stars_repo_head_hexsha": "fecf19a90a4d22a6784a68ff0f402fcb93936a80", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from tensorlib.decomposition import cp
from tensorlib.decomposition.decomposition import _cp3
from tensorlib.decomposition import tucker
from tensorlib.decomposition.decomposition import _tucker3
from tensorlib.datasets import load_bread
from numpy.testing import assert_almost_equal
from nose.tools i... | {"hexsha": "5f4ea093f64ffe184cdca19097900341e6958710", "size": 1591, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorlib/decomposition/tests/test_decomposition.py", "max_stars_repo_name": "tensorlib/tensorlib", "max_stars_repo_head_hexsha": "bd1bf02cbdcb4ea666b557238a4b32effab2943a", "max_stars_repo_licens... |
import numpy as np
def accuracy(preds, labels):
return np.mean(labels == preds.round())
def error(preds, labels):
return 1.0 - accuracy(preds,labels)
def mean_square_error(preds, labels):
return np.mean(np.square(preds - labels))
def mean_absolute_error(preds, labels):
return np.mean(np.abs(pred... | {"hexsha": "07b9bd68a8459794e60e8ca85c8c132447cc156e", "size": 531, "ext": "py", "lang": "Python", "max_stars_repo_path": "tgboost/metric.py", "max_stars_repo_name": "BenJamesbabala/tgboost", "max_stars_repo_head_hexsha": "933e666c60c6e828a78a73637efab91345529d6d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using QuAlgorithmZoo, Yao,YaoExtensions
using BitBasis: log2i
using Test
using Random, LinearAlgebra
"""
Quantum singular value decomposition algorithm.
* `reg`, input register (A, B) as the target matrix to decompose,
* `circuit_a`, U matrix applied on register A,
* `circuit_b`, V matrix applied on regis... | {"hexsha": "3b2618031b020f45f5f7fb0d1b42d761416747cd", "size": 3200, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/QSVD/QSVD.jl", "max_stars_repo_name": "stanescuUW/QuAlgorithmZoo.jl", "max_stars_repo_head_hexsha": "7d5c2398840cad822a095295e6559c6bafca715e", "max_stars_repo_licenses": ["Apache-2.0"], "... |
#! /usr/bin/env python3
# import roslib
# roslib.load_manifest('motion_plan')
import rospy
from sensor_msgs.msg import LaserScan
from geometry_msgs.msg import Twist, Point
from nav_msgs.msg import Odometry
from tf import transformations
from std_srvs.srv import *
import math
import matplotlib.pyplot as plt
import nump... | {"hexsha": "21aa4a659117d16edec435fdda6a73da38557ed6", "size": 3739, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros_pkg/robot_function/scripts/go_to_point.py", "max_stars_repo_name": "Pallav1299/coppeliasim_ros", "max_stars_repo_head_hexsha": "3c4db53be7ea7d64c53c1d56066bb93dd212a476", "max_stars_repo_licen... |
// (C) Copyright Gennadiy Rozental 2011-2012.
// 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)
// See http://www.boost.org/libs/test for the library home page.
//
// File : $RCSfile$
//
// Version ... | {"hexsha": "6155289bc23c8651c97da9d343edf00dfc9eec0a", "size": 8159, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/test/data/test_case.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
import nltk
import numpy as np
import string
import pickle
from gsdmm import MovieGroupProcess
from Chapter03.phrases import get_yelp_reviews
from Chapter04.preprocess_bbc_dataset import get_stopwords
tokenizer = nltk.data.load("tokenizers/punkt/english.pickle")
yelp_reviews_file = "Chapter03/yelp-dataset/review.json"... | {"hexsha": "2107b9bc036f81e34f594e4ec8a367221c7195fb", "size": 1633, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter06/topic_short_texts.py", "max_stars_repo_name": "afloera/https-github.com-PacktPublishing-Python-Natural-Language-Processing-Cookbook", "max_stars_repo_head_hexsha": "ffd1bf1a8a6b74bac7e88... |
[STATEMENT]
lemma circ_sup_n:
"(x\<^sup>\<Omega> * y)\<^sup>\<Omega> * x\<^sup>\<Omega> = n((x\<^sup>\<star> * y)\<^sup>\<omega>) * L \<squnion> ((x\<^sup>\<star> * y)\<^sup>\<star> * x\<^sup>\<star> \<squnion> (x\<^sup>\<star> * y)\<^sup>\<star> * n(x\<^sup>\<omega>) * L)"
[PROOF STATE]
proof (prove)
goal (1 subgoal... | {"llama_tokens": 300, "file": "Correctness_Algebras_N_Semirings", "length": 1} |
import numpy as np
from adapt.strategy.strategy import Strategy
class RandomStrategy(Strategy):
'''A strategy that randomly selects neurons from all neurons.
This strategy selects neurons from a set of all neurons in the network,
except for the neurons that located in skippable layers.
'''
def select(se... | {"hexsha": "9830d2c134131a72af4688d25e4f0dcbc72cccd8", "size": 811, "ext": "py", "lang": "Python", "max_stars_repo_path": "adapt/strategy/random.py", "max_stars_repo_name": "kupl/adapt", "max_stars_repo_head_hexsha": "8fc024456d21ea2b43fbb2b0b61199ce6324147d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.1.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # s_mi... | {"hexsha": "ebff2eaf768395dcee0f3048be569ef2108251f8", "size": 1449, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/sources/s_min_rel_ent_point_view.py", "max_stars_repo_name": "dpopadic/arpmRes", "max_stars_repo_head_hexsha": "ddcc4de713b46e3e9dcb77cc08c502ce4df54f76", "max_stars_repo_licenses": ["MIT"... |
'''
# This code is to perform detection and recognition analysis
# Programmer: Muhammad Hafidz Misrudin, N8448141
# Method of implementation: Feature Matching using SIFT/Orb descriptors
# It requires an OPENCV library and additional (image processing) packages in order to perform the tasks
# OPENCV versions: 2.4.11 or ... | {"hexsha": "a146407a2e3ef818a5e5aef2a63cf0786e1f30d2", "size": 4885, "ext": "py", "lang": "Python", "max_stars_repo_path": "FeatureMatching_SIFT/featureMatching.py", "max_stars_repo_name": "MuhammadHafidzMisrudin/python-opencv-finalyearproject", "max_stars_repo_head_hexsha": "94f342554eba900f5d11245c9c689a9e76e0dec5", ... |
#!/usr/bin/python3
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), "astrolove"))
sys.path.append("/usr/lib/astrolove")
import ASI
import time
import scipy.misc
print((ASI.list()))
c = ASI.Camera(0)
print((c.prop()))
c.set({'width': 640, 'height': 480, 'start_x': 320, 'start_y': 240})
s ... | {"hexsha": "a11bc2c2eaea017516655cc8dbac8bd51d95538a", "size": 1134, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "chripell/yaaca", "max_stars_repo_head_hexsha": "9048ca5dc458f9a7dde9ca745f057f7499b19972", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_s... |
import scipy as SP
from . import parMixedForest as parUtils
import random
def checkMaf(X, maf=None):
if maf==None:
maf = 1.0/X.shape[0]
Xmaf = (X>0).sum(axis=0)
Iok = (Xmaf>=(maf*X.shape[0]))
return SP.where(Iok)[0]
def scale_K(K, verbose=False):
"""scale covariance K such that it explains... | {"hexsha": "486d4e7a485c30f9cb591177043fc739a2307854", "size": 5457, "ext": "py", "lang": "Python", "max_stars_repo_path": "svca_limix/limix/modules/mixedForestUtils.py", "max_stars_repo_name": "DenisSch/svca", "max_stars_repo_head_hexsha": "bd029c120ca8310f43311253e4d7ce19bc08350c", "max_stars_repo_licenses": ["Apache... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 4 21:35:37 2020
@author: inderpreet
this code plots the PDF of the predictions and errors of best estimate (median)
ICI channels
"""
import matplotlib.pyplot as plt
import numpy as np
import stats as S
from ici_mwi import iciData
plt.rcParams.up... | {"hexsha": "0660d348b216fd9426ac41c803af4ce7879aeb47", "size": 4051, "ext": "py", "lang": "Python", "max_stars_repo_path": "ICI/plot_pdf_pred_ici.py", "max_stars_repo_name": "SEE-MOF/QRNN-CloudCorrection", "max_stars_repo_head_hexsha": "ba58f1f4f70ec0f7264d5e98d80552d2fba1bb4d", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import struct
def load_header(brick_data, double=False):
offset = 4
if double:
nbytes = 8
dtype_float="d"
else:
nbytes = 4
dtype_float="f"
nbodies = struct.unpack("i", brick_data[4:8])[0]
offset += 12
massp = struct.unpack(dtype_float, brick_d... | {"hexsha": "20605e5821481f371f58f1260baf237fd327d9b5", "size": 5619, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyram/etc/rd_hal_pure.py", "max_stars_repo_name": "Hoseung/pyRamAn", "max_stars_repo_head_hexsha": "f9386fa5a9f045f98590039988d3cd50bc488dc2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy
import scipy.optimize
MAXIMUM_REPRESENTABLE_FINITE_FLOAT = numpy.finfo(numpy.float64).max
class MultistartMaximizer(object):
def __init__(self, optimizer, num_multistarts=1, log_sample=False):
assert not isinstance(optimizer, MultistartMaximizer)
self.optimizer = optimizer
assert num_multi... | {"hexsha": "e2569a594423439898740792eaaecb2b148de8f1", "size": 3761, "ext": "py", "lang": "Python", "max_stars_repo_path": "lookahead/model/scalar_optimization.py", "max_stars_repo_name": "ericlee0803/lookahead_release", "max_stars_repo_head_hexsha": "373295f11be81d82b1c69eeadeec32ae96f26b1f", "max_stars_repo_licenses"... |
//==============================================================================
// Copyright 2003 - 2012 LASMEA UMR 6602 CNRS/Univ. Clermont II
// Copyright 20012 - 2012 LRI UMR 12623 CNRS/Univ Paris Sud XI
//
// Distributed under the Boost Software License, Version 1.0.
// ... | {"hexsha": "caeef2c89b16792f5dde8a2f2fe0a9f8065aa3a2", "size": 1915, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/boost/simd/base/include/boost/simd/constant/constants/fact_12.hpp", "max_stars_repo_name": "feelpp/nt2", "max_stars_repo_head_hexsha": "4d121e2c7450f24b735d6cff03720f07b4b2146c", "max_stars_... |
import torchvision.transforms as T
import numpy as np
import cv2
from PIL import Image
def visualize_depth(depth, cmap=cv2.COLORMAP_JET):
"""
depth: (H, W)
"""
x = depth.cpu().numpy()
x = np.nan_to_num(x) # change nan to 0
mi = np.min(x) # get minimum depth
ma = np.max(x)
x = (x-mi)/(ma... | {"hexsha": "d9c07f4ebac70ea25797e7d1119966ed2afc7033", "size": 1204, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/visualization.py", "max_stars_repo_name": "wx-b/nsff_pl", "max_stars_repo_head_hexsha": "b9640ca1d416438bf4dfefa5be0524ad2fd1b27e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 103... |
# https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_classification.ipynb
from __future__ import absolute_import, division, print_function
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import os
import numpy as np
import matplotlib.pyplot ... | {"hexsha": "dbe914a0c4db51b4f1523861d3c3bf92e3a4e29e", "size": 14669, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/model-restore-mnist.py", "max_stars_repo_name": "wobkat/ellwood-glacier", "max_stars_repo_head_hexsha": "743112c8ece09f14e68af7fa4d0fd2a33501c4c8", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
from scipy import linalg
def norm_of_columns(A, p=2):
"""Vector p-norm of each column of a matrix.
Parameters
----------
A : array_like
Input matrix.
p : int, optional
p-th norm.
Returns
-------
array_like
p-norm of each column of A.
"""... | {"hexsha": "08eb60bb3291a815e642af0a8e18b0c0a27ea099", "size": 2631, "ext": "py", "lang": "Python", "max_stars_repo_path": "micarray/util.py", "max_stars_repo_name": "trojanjay/sfa-numpy", "max_stars_repo_head_hexsha": "bff5737ef429f31228d20a9e1d0ce7d46d3080d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 39... |
#pragma once
#include <memory>
#include <Eigen/Dense>
#include "SdfObject.hpp"
#include "../Ray.hpp"
#include "../accelerate/Bound3.hpp"
class SdfSphere : public SdfObject
{
public:
SdfSphere(Eigen::Vector3f position, float radis) : SdfObject(position), radis(radis){};
float sdf(const Eigen::Vector3f &positio... | {"hexsha": "16ad3ab891fe9eb368e71f233a78fde9f194b7b5", "size": 683, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/render/object/SdfSphere.hpp", "max_stars_repo_name": "yzx9/NeuronSdfViewer", "max_stars_repo_head_hexsha": "454164dfccf80b806aac3cd7cca09e2cb8bd3c2a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
using GeometryTypes, ColorTypes
using FactCheck
import Base.Test.@inferred
facts("GeometryTypes") do
include("polygons.jl")
include("hyperrectangles.jl")
include("faces.jl")
include("meshes.jl")
include("distancefields.jl")
include("primitives.jl")
include("decompose.jl")
include("simpl... | {"hexsha": "ce562f3e8c83623d9ec2cca5f609cd3ebbea6940", "size": 526, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaPackageMirrors/GeometryTypes.jl", "max_stars_repo_head_hexsha": "705e5a646dd2177bbb4b9f8c26b52bc832b38d65", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma find_sort_least :
assumes "find P (sort xs) = Some x"
shows "\<forall> x' \<in> set xs . x \<le> x' \<or> \<not> P x'"
and "x = (LEAST x' \<in> set xs . P x')"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x'\<in>set xs. x \<le> x' \<or> \<not> P x' &&& x = (LEAST x'. x' \<in> set... | {"llama_tokens": 2055, "file": "FSM_Tests_Util", "length": 18} |
[STATEMENT]
lemma sq_mtx_vec_mult_sum_cols: "A *\<^sub>V x = sum (\<lambda>i. x $ i *\<^sub>R \<c>\<o>\<l> i A) UNIV"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. A *\<^sub>V x = (\<Sum>i\<in>UNIV. x $ i *\<^sub>R \<c>\<o>\<l> i A)
[PROOF STEP]
by(transfer) (simp add: matrix_mult_sum scalar_mult_eq_scaleR) | {"llama_tokens": 147, "file": "Matrices_for_ODEs_SQ_MTX", "length": 1} |
import numpy as np
import cv2
import cv2.aruco as aruco
import math
from math import sin, cos
import time
frame = np.array([])
aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_250) # Use 4x4 dictionary to find markers
parameters = aruco.DetectorParameters_create() # Marker detection parameters
def acc(arr, k = 1, ... | {"hexsha": "2aac3507e32efe06b011e09f4d3230e0f6bb24e9", "size": 5561, "ext": "py", "lang": "Python", "max_stars_repo_path": "aruco10hexes.py", "max_stars_repo_name": "IldanPetrov/EuroHex", "max_stars_repo_head_hexsha": "e15757b007e21dc5fd951185a578a64b2b9b5380", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import numpy as np
import pandas as pd
from numpy.random import randn
np.random.seed(101) #to get same random number
df = pd.DataFrame(randn(5,4),['A','B', 'C','D','E'],['W','X','Y','Z'])
print(df)
#conditional selection
print(df > 0)
print(df[df > 0])
print(df['W']>0)
print(df[df['W']>0])
print(df[df['W']>0][... | {"hexsha": "caee330d31680e653124951d4d6057d2efc4a7e5", "size": 694, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas_df_example2.py", "max_stars_repo_name": "ingleashish/python-data-science-machine-learning", "max_stars_repo_head_hexsha": "46fb3daf8cccc4444cc92ab0d48f92604061d5c9", "max_stars_repo_licenses... |
# Copyright (C) 2019 Project AGI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | {"hexsha": "bf48bb761abe25034182fd82e2702c5cd6ae7181", "size": 27991, "ext": "py", "lang": "Python", "max_stars_repo_path": "aha/workflows/pattern_completion_workflow.py", "max_stars_repo_name": "ProjectAGI/aha", "max_stars_repo_head_hexsha": "53a98ea42526dca56517dc97fffad874772f10f2", "max_stars_repo_licenses": ["Apac... |
#ifndef ATL_FFI_HPP
#define ATL_FFI_HPP
/**
* @file /home/ryan/programming/atl/ffi_2.hpp
* @author Ryan Domigan <ryan_domigan@sutdents@uml.edu>
* Created on Dec 29, 2013
*/
#include <array> // for tuple_element
#include <boost/mpl/aux_/adl_barrier.hpp> // for mpl
#include <cstddef> ... | {"hexsha": "166f4ce4cc45b3c8b0240865e5791f6aad825ab7", "size": 5495, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ffi.hpp", "max_stars_repo_name": "rcdomigan/atl", "max_stars_repo_head_hexsha": "6a6777a2f714480366551a4462c986a2f9d7612f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_s... |
# import sys
# sys.path.append('../lib')
import geom, graph
import model
import model_utils
import tileloader
import infer
import numpy
import os
import random
import tensorflow as tf
import time
import argparse
parser = argparse.ArgumentParser(description='Train a RoadTracer model.')
tileloader.tile_dir = '/data/ima... | {"hexsha": "5d24d5b08eac5f0d080d2706de2802c6b42f0899", "size": 11718, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tracer/train.py", "max_stars_repo_name": "astro-ck/Road-Extraction", "max_stars_repo_head_hexsha": "e509ddce9ced558e2e97d3510eb1e4a053113c97", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
A program analyzing 3D protein structures from PDB to generate 2D binding motives. For Further information see https://github.com/Cardypro/StructureAnalyzer
"""
import math
import os
from typing import Dict, Tuple, List, Union, Optional
from dataclasses import dataclass
from collections import defaultd... | {"hexsha": "420fec84c195725d5aacd865db4f25b354f865ca", "size": 19987, "ext": "py", "lang": "Python", "max_stars_repo_path": "StructureAnalyzer.py", "max_stars_repo_name": "Cardypro/StructureAnalyzer", "max_stars_repo_head_hexsha": "7f077058db4ad98b116abb0cbc0d74babd0ec298", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.model_zoo.chem.gnn import GATLayer
from dgl.nn.pytorch import NNConv, Set2Set
from dgl.nn.pytorch.conv import GINConv
from dgl.nn.pytorch.glob import AvgPooling, MaxPooling, SumPooling
class SELayer(nn.Module):
... | {"hexsha": "71c9dde41660af803ee1554cafb9a127a451aa29", "size": 16200, "ext": "py", "lang": "Python", "max_stars_repo_path": "cogdl/layers/gcc_module.py", "max_stars_repo_name": "BruceW91/cogdl", "max_stars_repo_head_hexsha": "1ad524375f5ba062103698a0432fc857572a6933", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/env python
# coding: utf-8
import os
import json
import numpy as np
import pandas as pd
import connector.mysql_connector as mysql_c
def drop_columns(df, columns):
df = df.drop(axis=1, level=0, columns=[columns])
def download_raw_db():
#F_PATH = os.path.abspath('')
with open('download/conne... | {"hexsha": "b8dcd62bad8e7f01edf1c4969ba78df83ca776bf", "size": 791, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/download/download_db.py", "max_stars_repo_name": "maikereis/consumption_data_analysis", "max_stars_repo_head_hexsha": "2ac8dcbc745c01211bdf22c4287f82225f7c21d6", "max_stars_repo_licenses": ["MI... |
subroutine mcohc(yy,xx,xy,b,iq,ip,coh)
c
c computes multivariate covariance for the multivariate
c complex linear model
c Y = X B
c n x q n x p p x q
c
c input: xx is x*x, yy is y*y (q times q and p times p
c hermitian m... | {"hexsha": "9481e67e8981bdaa53f215a890d808a960b2b415", "size": 1420, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "iris_mt_scratch/egbert_codes-20210121T193218Z-001/egbert_codes/EMTF/T/mcohc.f", "max_stars_repo_name": "simpeg-research/iris-mt-scratch", "max_stars_repo_head_hexsha": "ea458f253071db513fd0731118a... |
#!/usr/bin/venv python
#############################################################################
# #
# Copyright (c) 2020 Saeid Hosseinipoor <https://saeid-h.github.io/> #
# All rights reserved. #
# Licensed under the MIT License #
# #
###################... | {"hexsha": "bfbc990ca891d6bfecf61feab47fe0784a7d0d4a", "size": 6108, "ext": "py", "lang": "Python", "max_stars_repo_path": "cv_io/collection.py", "max_stars_repo_name": "saeid-h/computer-vision-file-handler", "max_stars_repo_head_hexsha": "b7903a656727afcfc2e3ae112dbd9fdaba5337d0", "max_stars_repo_licenses": ["MIT"], "... |
#! usr/bin/python3
#%%
import config
from src.model.stldesc_model import define_stl_encoder, EmbStyleNet
from src.support.loss_functions import pairWiseRankingLoss, MarginalAcc, triplet_loss
import os
import logging
import time
import math
from tqdm import tqdm
from datetime import datetime
import pathlib
import pand... | {"hexsha": "0a4001503b805d3090716300a232bc1fa9b9fdbf", "size": 11028, "ext": "py", "lang": "Python", "max_stars_repo_path": "stldesc_train2.py", "max_stars_repo_name": "nipdep/STGAN", "max_stars_repo_head_hexsha": "c72ba6cb9d23d33accc0cfa1958a2005db3ed490", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
"""
@author: Saikumar Dandla
"""
import numpy as np
name_dict = {0:'Avinash R' ,
1:'Durgendra Pandey',
2:'Rokkam Hari Sankar',
3:'Adurti Sai Mahesh',
4:'Manish Pratap Singh',
5:'RVNK Neeraj',
6:'Saikumar D',
7:'B... | {"hexsha": "08ada4d203c1a7160c974f804a00d80b23d9cef5", "size": 445, "ext": "py", "lang": "Python", "max_stars_repo_path": "Attendence system/dict.py", "max_stars_repo_name": "Saidsp19/Intelligent-attendance-system-using-face-recognition", "max_stars_repo_head_hexsha": "d8e588f592d4b7d92756a31f6570464ee1e1bea6", "max_st... |
# -*- coding: utf-8 -*-
"""
This file contains the script for defining characteristic functions and using them
as a way to embed distributional information in Euclidean space
"""
import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def characteristic_function(sig,t,plot=False, taus=1, n... | {"hexsha": "3aa83b56784d961b7761a3888d944cc6de003532", "size": 3001, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphWave/src/characteristic_functions.py", "max_stars_repo_name": "CEfanmin/DataMiningProjects", "max_stars_repo_head_hexsha": "b6375f542c68c0001ae2971dd7e8046a0b4afc7a", "max_stars_repo_licenses... |
from numpy import zeros, exp, sqrt, pi, arange, allclose, array, polynomial
from scipy import optimize
from scipy.integrate import trapz, odeint
from scipy.optimize import curve_fit
from numba import jit
class analytic_solution:
def analytical_solution(self, NT, NX, TMAX, XMAX, NU):
"""
Returns t... | {"hexsha": "8492217750f452450feef14dd8be84b84cefac87", "size": 6668, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySpectralPDE/deterministic/helpers.py", "max_stars_repo_name": "alanmatzumiya/Maestria", "max_stars_repo_head_hexsha": "c5e2a019312fb8f9bc193b04b07b7815e6ed4032", "max_stars_repo_licenses": ["MIT... |
import tensorflow
import keras
import sklearn
from sklearn import linear_model
import pandas as pd
import numpy as np
from sklearn.utils import shuffle
import matplotlib.pyplot as pyplot
import pickle
from matplotlib import style
data = pd.read_csv("train.csv", sep=",")
mass = data["mean_atomic_mass"]
predict = "cri... | {"hexsha": "d756f1c6fdeb582734bfada4868221c60dad9370", "size": 1012, "ext": "py", "lang": "Python", "max_stars_repo_path": "algoritmos/linear.py", "max_stars_repo_name": "lucasrbk/Pibiti", "max_stars_repo_head_hexsha": "e60c02af7fe93e1ac65975a199ae1ae11fb88d42", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import csv
from shutil import copyfile
import click
import numpy as np
import pandas as pd
from tqdm import tqdm
from src.helpers import paths
from src.helpers.flags import AttackModes, Verbose
from src.multimodal import multimodal
from src.multimodal.data import make_dataset
from src.multimodal.features import build... | {"hexsha": "9cf0568bd1b0996f11d67f714010e1dd2854a333", "size": 4449, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/multimodal/models/predict_test.py", "max_stars_repo_name": "markrofail/multi-modal-deep-learning-for-vehicle-sensor-data-abstraction-and-attack-detection", "max_stars_repo_head_hexsha": "2f252... |
import numpy as np
from typing import Tuple
import torch
from torch.utils.data import DataLoader
from transformers import BertTokenizer
from enums.run_type import RunType
from services.arguments.arguments_service_base import ArgumentsServiceBase
from services.dataset_service import DatasetService
from services.tok... | {"hexsha": "a23f66a0dd80f9099347e18c612a0d354ec189e5", "size": 2598, "ext": "py", "lang": "Python", "max_stars_repo_path": "services/dataloader_service.py", "max_stars_repo_name": "ktodorov/eval-historical-texts", "max_stars_repo_head_hexsha": "e2994d594525d1d92056a6398935376a96659abb", "max_stars_repo_licenses": ["MIT... |
"""
Plot class.
"""
import copy
from math import sin, cos
import numpy as np
import param
from dataviews.ndmapping import NdMapping
from topo.base.sheetcoords import SheetCoordinateSystem,Slice
from bitmap import HSVBitmap, RGBBitmap, Bitmap, DrawBitmap
### JCALERT!
### - Re-write the test file, taking the new cha... | {"hexsha": "7d2d694ce8358ffcc5100f05b72a4d996f331292", "size": 25161, "ext": "py", "lang": "Python", "max_stars_repo_path": "topo/plotting/plot.py", "max_stars_repo_name": "ceball/topographica", "max_stars_repo_head_hexsha": "ec0eea614409ceb7473e04bc2f6b6c888099160f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import colloidpy as cp
import numpy as np
from dataAnalysis import trace
import matplotlib.pyplot as plt
water = trace('trace_with_water.npy')
water.modify(1, 4)
water.modify(2, 4)
no_water = trace('trace_without_water.npy')
no_water.modify(1, 4)
no_water.modify(2, 4)
print(cp.__version__)
water_data = water.data
no... | {"hexsha": "f91ab47b0ec8e30db5d15de6677d7140b519f84b", "size": 2280, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/msd.py", "max_stars_repo_name": "KyQiao/balltrack", "max_stars_repo_head_hexsha": "2e928ae9dcfd72f43514c978f3556723724b34a1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
# -*- coding: utf-8 -*-
# This work is part of the Core Imaging Library (CIL) developed by CCPi
# (Collaborative Computational Project in Tomographic Imaging), with
# substantial contributions by UKRI-STFC and University of Manchester.
# Licensed under the Apache License, Version 2.0 (the "License");
# you... | {"hexsha": "40ff326485c9cb5cddfd0ea4843c7fac18301313", "size": 5282, "ext": "py", "lang": "Python", "max_stars_repo_path": "Wrappers/Python/cil/optimisation/algorithms/CGLS.py", "max_stars_repo_name": "Asharits/CIL", "max_stars_repo_head_hexsha": "66848b021fb2c6daca71e276890152f34a87ba36", "max_stars_repo_licenses": ["... |
import os
import sys
import numpy as np
from run_pid_optimized import PIDEvaluator
from bayes_opt import BayesianOptimization
from bayes_opt.observer import JSONLogger
from bayes_opt.event import Events
def func(rx, ry, px, py, yx, yy):
rz, pz, yz = 0.0001, 0.0001, 0.0001
current_dir = os.path.dirname(__file... | {"hexsha": "5b51d7f3c544d8c9dec2091627eb89e2ca4e1d10", "size": 1394, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments_prokhn/controllers/bayesian_pid.py", "max_stars_repo_name": "prokhn/onti-2019-bigdata", "max_stars_repo_head_hexsha": "b9296141958f544177388be94072efce7bdc7814", "max_stars_repo_licens... |
import tensorflow as tf
import numpy as np
from utils.data import convert_categorical
from models.base_model import BaseModel
class Discriminator:
def __init__(self, discriminator_model, protected_variable):
self.model = discriminator_model
self.protected_variable = protected_variable
class Fa... | {"hexsha": "08f9f1328178575c3bd8072cd427320de98a38fb", "size": 5411, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/adversarial_model.py", "max_stars_repo_name": "cryptexis/debias", "max_stars_repo_head_hexsha": "a9e0106dcb8668b95e4654ccb3e7373a70ea37a3", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python
u"""
hdf5_stokes.py
Written by Tyler Sutterley (10/2021)
Writes spherical harmonic coefficients to HDF5 files
CALLING SEQUENCE:
hdf5_stokes(clm1,slm1,linp,minp,tinp,month,FILENAME=output_HDF5_file)
INPUTS:
clm1: cosine spherical harmonic coefficients
slm1: sine spherical harmonic co... | {"hexsha": "3f9b43c4eeb051c98ef4fb21c861fd28d4424252", "size": 8426, "ext": "py", "lang": "Python", "max_stars_repo_path": "gravity_toolkit/hdf5_stokes.py", "max_stars_repo_name": "tsutterley/read-GRACE-harmonics", "max_stars_repo_head_hexsha": "6feb1ef24402ec02d14cf852e655aa5367ef719e", "max_stars_repo_licenses": ["MI... |
"""
"""
from pathlib import Path
import argparse
import random
import shutil
import logging
import os, sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
import torchvision
# import keras
#
# import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
im... | {"hexsha": "71e50df0eaeded3af3acb07edc4d5cc5ce205dc5", "size": 9992, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_main_results.py", "max_stars_repo_name": "divergent63/Ocean_Trajectory_Forecast", "max_stars_repo_head_hexsha": "cc1be57c519508b74d08e4595023a6b82dc50b78", "max_stars_repo_licenses": ["Ap... |
abstract type AbstractAxis{T, BL, BR, I} <: AbstractVector{T} end
abstract type AbstractDiscreteAxis{T, BL, BR, I} <: AbstractAxis{T, BL, BR, I} end
"""
DiscreteAxis{T, BL, BR} <: AbstractAxis{T, BL, BR}
* T: Type of ticks
* BL, BR ∈ {:periodic, :reflecting, :infinite, :r0, :fixed}
* BL: left boundary co... | {"hexsha": "65e2a8e83ab8ca2d4d3d4bdd26126f44b4b4a6e3", "size": 15388, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Axes/DiscreteAxis.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SolidStateDetectors.jl-71e43887-2bd9-5f77-aebd-47f656f0a3f0", "max_stars_repo_head_hexsha": "075fbaf67b6d1c2e229e93740847b9... |
#!/usr/bin/env julia
using Luxor, Colors
using Test
using Random
Random.seed!(42)
function spiral_logo_eps()
gsave()
scale(.3, .3)
r = 200
setcolor("gray")
for i in 0:pi/8:2pi
gsave()
translate(r * cos(i), r * sin(i))
rotate(i)
julialogo()
grestore()
e... | {"hexsha": "ba481be5383655a8ea9534bacfeed004ce193076", "size": 1836, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/julia-logo-draw-eps.jl", "max_stars_repo_name": "guo-yong-zhi/Luxor.jl", "max_stars_repo_head_hexsha": "3b4fe34fe1e05c17bfcc9cc5b074fa527e5d1ebf", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
import numpy as np
import cv2
from commonFunctions_v04 import get_info_from_logfile
from commonFunctions_v04 import flip_horizontally
# History
# v01 : Start
# v02 : add nb_images to read parameter
# v03 : add normalization + mean centering data to 0
# v04 : data augmentation flip horizontally i... | {"hexsha": "0ebdcfe49ac8b4f5c29cc9f1169878dbd3df8c7c", "size": 1758, "ext": "py", "lang": "Python", "max_stars_repo_path": "archiveOldVersions/clone_v05.py", "max_stars_repo_name": "remichartier/014_selfDrivingCarND_BehavioralCloningProject", "max_stars_repo_head_hexsha": "1dcaa7c5a937929d4481e5efbf7ccc856c04c4ff", "ma... |
from __future__ import annotations
from typing import Any, Iterable, Literal, Sequence
import attr
import networkx as nx
__all__ = ["PoSet", "Pair", "Chain", "CMP"]
Pair = tuple[Any, Any]
Chain = Sequence[Any]
CMP = Literal["<", ">", "||", "="]
@attr.frozen
class PoSet:
"""Hasse diagram representation of par... | {"hexsha": "6075e546a256dd0ff140d7627a271ca08530449c", "size": 1280, "ext": "py", "lang": "Python", "max_stars_repo_path": "hasse/poset.py", "max_stars_repo_name": "mvcisback/hasse", "max_stars_repo_head_hexsha": "eefd6f4af217a4c44bd2751df6f39bd5b7d37d6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
! RUN: %S/test_errors.sh %s %t %flang_fc1
! REQUIRES: shell
! Simple check that if constructs are ok.
if (a < b) then
a = 1
end if
if (a < b) then
a = 2
else
a = 3
endif
if (a < b) then
a = 4
else if(a == b) then
a = 5
end if
if (a < b) then
a = 6
else if(a == b) then
a = 7
elseif(a > b) then
a = 8
... | {"hexsha": "9fb1344ff259d0384fef0c284e536e01f6f49988", "size": 584, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "flang/test/Semantics/if_construct01.f90", "max_stars_repo_name": "acidburn0zzz/llvm-project", "max_stars_repo_head_hexsha": "7ca7a2547f00e34f5ec91be776a1d0bbca74b7a9", "max_stars_repo_licenses": ... |
using Test
using StatsModelComparisons
using StanSample, StatsFuns
using Printf
using JSON
@testset "Arsenic" begin
ProjDir = @__DIR__
#=
if haskey(ENV, "JULIA_CMDSTAN_HOME")
include(joinpath(ProjDir, "test_demo_wells.jl"))
else
println("\nJULIA_CMDSTAN_HOME not set. Skipping tests")
... | {"hexsha": "e1dd15a3810acf456c7406a915d08473b6923f74", "size": 2013, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_demo_wells.jl", "max_stars_repo_name": "itsdfish/StatsModelComparisons.jl", "max_stars_repo_head_hexsha": "e8683a97bc4cdc57b465fdec245300d691d59240", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
def int_to_str(arr: np.ndarray) -> np.ndarray:
"""
Convert array of 64-bit integers to S9.
We cannot use arr.byteswap().view("S8") because the trailing zeros are discarded \
in np.char.add. Thus we have to pad with ";".
"""
assert arr.dtype == int
arena = np.full((len(a... | {"hexsha": "adeacfcec3841b8d38df15d060037e76affc86de", "size": 461, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/athenian/api/int_to_str.py", "max_stars_repo_name": "athenianco/athenian-api", "max_stars_repo_head_hexsha": "dd5556101a8c49703d6b0516e4268b9e8d8eda5b", "max_stars_repo_licenses": ["RSA-MD"]... |
# SPDX-License-Identifier: MIT
# Copyright (c) 2020: Pablo Zubieta
module ReactionCoordinates
using LinearAlgebra
using StaticArrays
export Acylindricity, Angle, Asphericity, Barycenter, DihedralAngle, DistanceFrom,
GyrationTensor, PairwiseKernel, PrincipalMoments, RadiusOfGyration,
RouseMode, Separation, ... | {"hexsha": "bbd29ec6d432337eb4aebb74bb750572b3d11144", "size": 12018, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ReactionCoordinates.jl", "max_stars_repo_name": "pabloferz/ReactionCoordinates.jl", "max_stars_repo_head_hexsha": "e88ea117940480cb17f8525d6b2c1467bd108c5d", "max_stars_repo_licenses": ["MIT"]... |
import pandas as pd
import numpy as np
import sys
import click
import os
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
import mlflow
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
fro... | {"hexsha": "b81487f948fe7bd1841243788aec568c20c9cb0b", "size": 6804, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier/2_build_train_model.py", "max_stars_repo_name": "justinkaseman/politicate-classifier", "max_stars_repo_head_hexsha": "706af6a604dc076ed6a1ca526159c14a7f0e16af", "max_stars_repo_licenses... |
using Documenter, ModelingToolkitStandardLibrary
using ModelingToolkitStandardLibrary.Blocks
using ModelingToolkitStandardLibrary.Mechanical
using ModelingToolkitStandardLibrary.Mechanical.Rotational
using ModelingToolkitStandardLibrary.Magnetic
using ModelingToolkitStandardLibrary.Magnetic.FluxTubes
using ModelingTool... | {"hexsha": "4bf65ef9872e916e37cb1dd448475a05a4bb4d85", "size": 1723, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "baggepinnen/ModelingToolkitStandardLibrary.jl", "max_stars_repo_head_hexsha": "f8bdbb9f91eadcf274c54dfcd5df94f189ffbd15", "max_stars_repo_licenses": ["MIT"], ... |
module LBNumber
include("./../../constraints/geometric/constants.jl")
include("./1_if.jl")
include("./2_calc_number.jl")
using JuMP
using .Constants
using .IfSimpleLoadBearing
using .CalcNumberLoadBearing
export cons_lb_number_load_bearing
function cons_lb_number_load_bearing(m)
m = if_simple_lb(m)
m = calc... | {"hexsha": "da048cd1b4b40c5eb587892355fab439acb4bac8", "size": 356, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/constraints/load_bearing/simple/0_number_lb.jl", "max_stars_repo_name": "ToralfFrich/Master_Thesis", "max_stars_repo_head_hexsha": "5d4a51598f1677c2f5c219a88ca9ab4c9b6a5c6f", "max_stars_repo_lic... |
#! /usr/bin/env python
"""
File: root_finder_examples.py
Copyright (c) 2016 Chinmai Raman
License: MIT
Course: PHYS227
Assignment: A.11
Date: Feb 24, 2016
Email: raman105@mail.chapman.edu
Name: Chinmai Raman
Description: Tests different methods for finding roots of a nonlinear function.
"""
import numpy as np
def Ne... | {"hexsha": "ff1253e0c4e3f8e7f6f23bc24e9fa0e6cd6fdb4c", "size": 3012, "ext": "py", "lang": "Python", "max_stars_repo_path": "root_finder_examples.py", "max_stars_repo_name": "chapman-phys227-2016s/hw-3-ChinmaiRaman", "max_stars_repo_head_hexsha": "3d3c2a688b656f518d9cef9a5d44ca9fd64a159e", "max_stars_repo_licenses": ["M... |
program main
use class_string
implicit none
integer :: i,n
character, allocatable :: c(:)
character(:), allocatable :: s
type(string) :: str
type(string), allocatable :: w(:)
str = string('Foo')
s = str%get()
c = str%chars()
print *, len(s)
print *, size(c)
print *,... | {"hexsha": "45851871860ef200ba495a121d5fa1f7ea0bc599", "size": 562, "ext": "f03", "lang": "FORTRAN", "max_stars_repo_path": "examples/example_string.f03", "max_stars_repo_name": "gemmarx/cbtrie_assoc", "max_stars_repo_head_hexsha": "7998718783ca42965fe8c4eaac6d292ce08bd87c", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
#Karan Vombatkere
#German Enigma Machine
#October 2017
from string import *
import numpy as np
Letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
#function to create a dictionary with letters and their indices
#Use this to return the index of a letter (a = 0, .., z = 25)
def genDictionary():
letter_index_pairs = []
for... | {"hexsha": "055b16e0cd30f2da98612b1b9ada06dbb6b85b9f", "size": 3311, "ext": "py", "lang": "Python", "max_stars_repo_path": "Plugboard.py", "max_stars_repo_name": "kvombatkere/Enigma-Machine", "max_stars_repo_head_hexsha": "b7a6e199a8e5ec600771f4740943fa83446f7dcd", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as n
import cryoops as cops
import geom
class FixedPlanarDomain():
def __init__(self,pts,res):
self.pts = pts
self.resolution = res
self.sqdist_mat = self.get_sqdist_mat(self)
self.dim = self.pts.shape[1]
if pts.shape[0] == 1:
self.pt_resolution = r... | {"hexsha": "e768d917f8748d20022bfeb161f125243f57d695", "size": 5062, "ext": "py", "lang": "Python", "max_stars_repo_path": "quadrature/domain.py", "max_stars_repo_name": "mbrubake/cryoem-cvpr2015", "max_stars_repo_head_hexsha": "ea0eda3b663364b3b4c7d989bdecfc5263ef3102", "max_stars_repo_licenses": ["Python-2.0", "OLDAP... |
#test
import numpy as np
import sympy as sm
wo = sm.Symbol('wo',real=True) #magentic dipole frequency
print(wo)
def decode1(n,Nb_floquet_blocks, No_subspaces = 0):
# n = alpha+ (n1+2)*3+(n2+Nb_floquet_blocks)*(15)
Nb_atomic_states = 3
tot_size = Nb_atomic_states*(2*Nb_floquet_blocks+1)*(2*Nb_floquet_blocks+... | {"hexsha": "e155c75eca2eb1452a8fa2b918a2de7481a80cda", "size": 1339, "ext": "py", "lang": "Python", "max_stars_repo_path": "comparison_FL_QT/Scripts/test.py", "max_stars_repo_name": "Anthony-Gandon/Floquet_theory", "max_stars_repo_head_hexsha": "c25917986d83974850ecff60f388632087b8b52f", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma rank_of_eq_card_basis_in:
assumes "basis_in \<E> B"
shows "rank_of \<E> = card B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. rank_of \<E> = card B
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. rank_of \<E> = card B
[PROOF STEP]
have "{card B | B. basis_in \<E> B} = ... | {"llama_tokens": 601, "file": "Matroids_Matroid", "length": 9} |
# Copyright (c) Open-MMLab. All rights reserved.
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import visualization as vis
def test_color():
assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
assert vis.color_val_matpl... | {"hexsha": "9c7969b44ee5b4ee862c09b63f122db14a534b32", "size": 4431, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_utils/test_visualization.py", "max_stars_repo_name": "evgps/mmdetection_trashcan", "max_stars_repo_head_hexsha": "aaf4237c2c0d473425cdc7b741d3009177b79751", "max_stars_repo_licenses": [... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Friggeri Resume/CV
% XeLaTeX Template
% Version 1.2 (3/5/15)
%
% This template has been downloaded from:
% http://www.LaTeXTemplates.com
%
% Original author:
% Adrien Friggeri (adrien@friggeri.net)
% https://github.com/afriggeri/CV
%
% License:
% CC BY-NC-SA 3.0 (http://creat... | {"hexsha": "ecc5cd449ff5274da9b8807aa360117e7b1c5c70", "size": 10880, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/cv.tex", "max_stars_repo_name": "chrisRidgers/cv", "max_stars_repo_head_hexsha": "4155ad63273526b1a1c1945a17e05a259954c6fe", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, "max... |
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