text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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"""This test module verifies all circuit operation, gate, and circuit
methods."""
from __future__ import annotations
import numpy as np
import pytest
from hypothesis import given
from bqskit.ir.circuit import Circuit
from bqskit.ir.gate import Gate
from bqskit.ir.gates import CNOTGate
from bqskit.ir.gates import Cons... | {"hexsha": "530f60877c1187e365901f57c99067cae5d58b89", "size": 22840, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ir/circuit/test_op_gate_circ_methods.py", "max_stars_repo_name": "jkalloor3/bqskit", "max_stars_repo_head_hexsha": "ad34a6eae3c0e62d2bd960cd4cd841ba8e845811", "max_stars_repo_licenses": ["B... |
import torch
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
import numpy as np
def torch_to_numpy(y):
if torch.cuda.is_available():
return y.detach().cpu().numpy()
return y.detach().numpy()
def cont_to_binary(y):
return [1 if x >= 0.5 e... | {"hexsha": "d9f05f217bd5db29ba806d6fc952d8d43e6fe958", "size": 1550, "ext": "py", "lang": "Python", "max_stars_repo_path": "calc.py", "max_stars_repo_name": "josharnoldjosh/visdom-plot", "max_stars_repo_head_hexsha": "fde386a4c0dc6b842de4eb59487866b0ebf46706", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
# Based on https://colab.research.google.com/github/reiinakano/neural-painters/blob/master/notebooks/generate_stroke_examples.ipynb
from lib import surface, tiledsurface, brush
import torch
import numpy as np
from PIL import Image
def point_on_curve_1(t, cx, cy, sx, sy, x1, y1, x2, y2):
ratio = t / 100.0
x3... | {"hexsha": "0a6eb0aa97bb599028187806421d967b0b35d7f0", "size": 7085, "ext": "py", "lang": "Python", "max_stars_repo_path": "gan_stroke_generator/mypaint_images_data_loader.py", "max_stars_repo_name": "mxpoliakov/PaintTransformerGAN", "max_stars_repo_head_hexsha": "be845607ad1d839319ab9d11b9c6de3f7c11ded0", "max_stars_r... |
# -*- coding: utf-8 -*-
# File generated according to Generator/ClassesRef/Simulation/OP.csv
# WARNING! All changes made in this file will be lost!
"""Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Simulation/OP
"""
from os import linesep
from sys import getsizeof
from ... | {"hexsha": "7b25b9f195638c9ec6fda5e889bbd21e50a01ac7", "size": 14008, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleecan/Classes/OP.py", "max_stars_repo_name": "Eomys/Pyleecan", "max_stars_repo_head_hexsha": "4d7f0cbabf0311006963e7a2f435db2ecd901118", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import random
from itertools import product
from collections import namedtuple
import numpy as np
import tensorflow as tf
from neupy import layers
from neupy.utils import asfloat, shape_to_tuple
from neupy.layers.convolutions import conv_output_shape, deconv_output_shape
from neupy.exceptions import LayerConnectionEr... | {"hexsha": "31228390ffd504c6e9eabf6f98d46fa2f607a7d2", "size": 16048, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/layers/test_convolution.py", "max_stars_repo_name": "FrostByte266/neupy", "max_stars_repo_head_hexsha": "4b7127e5e4178b0cce023ba36542f5ad3f1d798c", "max_stars_repo_licenses": ["MIT"], "max_... |
function SphereGenerator()
return SphereGenerator(())
end
function ball_on_support(obj::SphereGenerator, arg0::List)
return jcall(obj, "ballOnSupport", EnclosingBall, (List,), arg0)
end
| {"hexsha": "07139665f09eaa3b3283e1f18e79b8c05de75337", "size": 196, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gen/HipparchusWrapper/GeometryWrapper/EuclideanWrapper/ThreedWrapper/sphere_generator.jl", "max_stars_repo_name": "JuliaAstrodynamics/Orekit.jl", "max_stars_repo_head_hexsha": "e2dd3d8b2085dcbb1d2c7... |
from collections import deque
from importlib import reload
import ddpg_agents
from ddpg_agents import Agent
import torch
import matplotlib.pyplot as plt
from unityagents import UnityEnvironment
import numpy as np
import pandas as pd
import datetime
#env = UnityEnvironment(file_name='./Reacher_single/Reacher_Linux_NoV... | {"hexsha": "e9eaa0b6e7aa7a0421dfced4417e5dc77f210090", "size": 3824, "ext": "py", "lang": "Python", "max_stars_repo_path": "p2_continuous-control/run_single.py", "max_stars_repo_name": "thanakijwanavit/deep-reinforcement-learning", "max_stars_repo_head_hexsha": "af057d72c6262faa9bd8426082b1f70ea00c7b9c", "max_stars_rep... |
from napari_apr_viewer import napari_get_reader, napari_get_writer, napari_write_image
import pyapr
import numpy as np
import os
# tmp_path is a pytest fixture
def test_writer(tmp_path):
"""Test writer plugin."""
file_dir = os.path.dirname(os.path.abspath(__file__))
my_test_file = os.path.join(file_dir, '... | {"hexsha": "74470c24d308ec96ff6152ae00d50a24cd38b797", "size": 2035, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/napari_apr_viewer/_tests/test_writer.py", "max_stars_repo_name": "AdaptiveParticles/napari-apr-viewer", "max_stars_repo_head_hexsha": "cc7089bc418dd5ff08f74dce7024920d387fd6c6", "max_stars_rep... |
from __future__ import absolute_import, division, print_function
import logging
import os
import json
import numpy as np
from collections import OrderedDict
import torch
from inference.models.vgg import VGGRatioEstimator
from inference.models.resnet import ResNetRatioEstimator
from inference.trainer import RatioTrain... | {"hexsha": "965bd7b66514a35aa02d9dd569447155e63bf799", "size": 22307, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference/estimator.py", "max_stars_repo_name": "matthewfeickert/mining-for-substructure-lens", "max_stars_repo_head_hexsha": "9360e678aac78b6b260dab55ce264bfddea0c206", "max_stars_repo_licenses"... |
!-------------------------------------------------------------------------------------------------------------
!
!> \file CompExcessGibbsEnergyIDWZ.f90
!> \brief Compute the partial molar 'excess' Gibbs energy of solution phase constituents in an IDWZ
!! solution phase.
!> ... | {"hexsha": "81ba5338bfb47ece816586cd203f5cee384352d6", "size": 3760, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/CompExcessGibbsEnergyIDWZ.f90", "max_stars_repo_name": "parikshitbajpai/thermochimica", "max_stars_repo_head_hexsha": "34a3f3e8a92e9e93f8fc7a739d0c6c00bcddca12", "max_stars_repo_licenses": [... |
import numpy as np
import trax
#from trax import layers as tl
#from trax.fastmath import numpy as fastnp
#from trax.supervised import training
# UNIT TEST for UNQ_C1
def test_get_conversation(target):
data = {'file1.json': {'log':[{'text': 'hi'},
{'text': 'hello'},
... | {"hexsha": "f72ce252a89798bc51b81ba3b3a05a173b92e02c", "size": 8096, "ext": "py", "lang": "Python", "max_stars_repo_path": "Natural Language Processing with Attention Models/Week 4 - Chatbot/w4_unittest.py", "max_stars_repo_name": "meet-seth/Coursera-Deep-Learning", "max_stars_repo_head_hexsha": "6fbf9d406468c825ffa1ff... |
[STATEMENT]
lemma singleDSourceEmpty_Acc:
assumes "DAcc i C = {S}"
and "isNotDSource i S"
shows "Acc i C = {S}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Acc i C = {S}
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. Acc i C = {S}
[PROOF STEP]
have AccC:"(Acc i C) = (DAcc i... | {"llama_tokens": 678, "file": "ComponentDependencies_DataDependencies", "length": 10} |
**==effn.spg processed by SPAG 4.50J at 14:50 on 30 Jun 1995
FUNCTION EFFN(I,Zeff,T)
IMPLICIT NONE
C*** Start of declarations inserted by SPAG
REAL EFFN , eye , f1 , f2 , f3 , T , t3 , xx , Zeff
INTEGER I , no
C*** End of declarations inserted by SPAG
t3 = T/(Zeff*Zeff*1000.0)
xx =... | {"hexsha": "49b4c15fec074630557a01b5cd9623c2954a4b47", "size": 2281, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "xspec/tools/raysmith/effn.f", "max_stars_repo_name": "DougBurke/xspeclmodels", "max_stars_repo_head_hexsha": "4e9caf971af51ab88eb0f8cf678a11f014710013", "max_stars_repo_licenses": ["CC0-1.0"], "ma... |
!==========================================================================
elemental function gsw_entropy_part (sa, t, p)
!==========================================================================
!
! entropy minus the terms that are a function of only SA
!
! sa : Absolute Salinity [... | {"hexsha": "0160db551fd26a0d06aaedf74883922235f4f710", "size": 2980, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "MOM6-interface/MOM6/src/equation_of_state/TEOS10/gsw_entropy_part.f90", "max_stars_repo_name": "minsukji/ci-debug", "max_stars_repo_head_hexsha": "3e8bbbe6652b702b61d2896612f6aa8e4aa6c803", "max... |
Require Import Lia Setoid Program.Basics.
From hahn Require Import Hahn.
From PromisingLib Require Import Basic Language.
From imm Require Import Events Prog Execution ProgToExecution.
Require Import AuxDef.
Require Import AuxRel.
Require Import EventStructure.
Require Import LblStep.
Require Import ProgLoc.
Require Im... | {"author": "weakmemory", "repo": "weakestmoToImm", "sha": "7061b6279887aa5777f13b5c5ed6a10fae6740a5", "save_path": "github-repos/coq/weakmemory-weakestmoToImm", "path": "github-repos/coq/weakmemory-weakestmoToImm/weakestmoToImm-7061b6279887aa5777f13b5c5ed6a10fae6740a5/src/construction/ProgES.v"} |
#!/bin/python3
import sys
sys.path.append(".")
from adder_graph import adder_graph
from adder_graph import adder_node as node
import networkx as nx
import pydot
g = adder_graph(4)
g.add_node(node(0,0,'buffer_node'),style='invis')
g.add_node(node(1,0,'buffer_node'))
g.add_node(node(0,1,'black'))
g.add_node(node(1,1,... | {"hexsha": "803ccf79d51b88ad6d95276c0596a6dc5a69863e", "size": 1635, "ext": "py", "lang": "Python", "max_stars_repo_path": "unit_tests/graph_test.py", "max_stars_repo_name": "tdene/synth_opt_adders", "max_stars_repo_head_hexsha": "c94ba6e61468e8867f7a3f8d5af252b0e42664a0", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import string
import numpy as np
f = open('nltk.txt','r',errors='ignore')
t = f.read()
t = t.replace('\n','').lower()
f.close()
st = nltk.sent_tokenize(t)
wt = nltk.word_tokeniz... | {"hexsha": "456a49fc26497b78dc236e864f90e09125ba4454", "size": 1095, "ext": "py", "lang": "Python", "max_stars_repo_path": "nltkTest.py", "max_stars_repo_name": "pranjal-joshi/Florence", "max_stars_repo_head_hexsha": "ffc022f0c0b4625b4236d78b42bf551c64690cfc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette('colorblind', n_colors=4)
# dist - acc
dist_grouped = pd.read_csv('figures/wiki_dist_correctness_after.csv')
conditions = [
(dist_grouped['locality'] == 0),
(dist_grouped['locality'] == 1),
... | {"hexsha": "2229cd515f05f8388592d6c8f8ec0b2d47d9d0a7", "size": 1697, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_wiki_after.py", "max_stars_repo_name": "frankxu2004/knnlm", "max_stars_repo_head_hexsha": "7a668a916b08a0e82072c8f49eef4a10ad4a8505", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3,... |
"""
match two list of stars, provided by ra/dec degree
"""
import numpy as np
import scipy.stats as ss
def star_match ( list_a, list_b, a_ra, a_dec, b_ra, b_dec, a_mag=-1, b_mag=-1,
dis_limit=0.002, mag_limit=-3, allow_dup=False ) :
"""match two list
:param list_a: list a of stars, each ... | {"hexsha": "f3baf89b5e3a23bbcab31cc7bc8a937811784be8", "size": 4203, "ext": "py", "lang": "Python", "max_stars_repo_path": "star_match.py", "max_stars_repo_name": "RapidLzj/201603", "max_stars_repo_head_hexsha": "dbcefad4a833a936f469186a7eb7106da9a91e74", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
[STATEMENT]
lemma mult_mono_nonpos_nonpos: "a * b \<le> c * d"
if "a \<ge> c" "a \<le> 0" "b \<ge> d" "d \<le> 0" for a b c d::real
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a * b \<le> c * d
[PROOF STEP]
by (meson dual_order.trans mult_left_mono_neg mult_right_mono_neg that) | {"llama_tokens": 138, "file": null, "length": 1} |
export name, email, time, time_offset
typealias MaybeSignature Union(Void, Signature)
#TODO: better date / time integration when this becomes available in Base
Signature(name::AbstractString, email::AbstractString) = begin
sig_ptr = Ptr{SignatureStruct}[0]
@check ccall((:git_signature_now, libgit2), Cint,
... | {"hexsha": "ec9d6f1a53b41d243112511168dcdc6010f05f4b", "size": 1892, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/signature.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/LibGit2.jl-76f85450-5226-5b5a-8eaa-529ad045b433", "max_stars_repo_head_hexsha": "5e0adc9e9b9b1bb1963169f7a8e59fb72b79a73f",... |
(* Title: HOL/Word/WordBitwise.thy
Authors: Thomas Sewell, NICTA and Sascha Boehme, TU Muenchen
*)
theory WordBitwise
imports Word
begin
text \<open>Helper constants used in defining addition\<close>
definition
xor3 :: "bool \<Rightarrow> bool \<Rightarrow> bool \<Rightarrow> bool"
where
"xor3 a b... | {"author": "SEL4PROJ", "repo": "jormungand", "sha": "bad97f9817b4034cd705cd295a1f86af880a7631", "save_path": "github-repos/isabelle/SEL4PROJ-jormungand", "path": "github-repos/isabelle/SEL4PROJ-jormungand/jormungand-bad97f9817b4034cd705cd295a1f86af880a7631/case_study/isabelle/src/HOL/Word/WordBitwise.thy"} |
# basic libs
import numpy as np
import json
import os
import random
from scipy import signal
# pytorch
import torch
from torch.utils.data import Dataset
np.random.seed(42)
class Dataset_train(Dataset):
def __init__(self, patients,aug):
self.patients = patients
self.aug = aug
def __len__(... | {"hexsha": "9f26b1d0b181eeb219c726b796c6a784cee9d5c5", "size": 10724, "ext": "py", "lang": "Python", "max_stars_repo_path": "kardioml/models/deepecg/train/data_generator_pytorch.py", "max_stars_repo_name": "Seb-Good/physionet-challenge-2020", "max_stars_repo_head_hexsha": "c6f1648a148335babc0a26d8a589120616327548", "ma... |
from collections import defaultdict
from typing import Any, Dict, List, Optional, Type, Tuple, Mapping, Iterable
import math
from functools import total_ordering
import numpy as np
import yaml
import shapely.geometry
import shapely.ops
import conveyor_msgs.msg
Range = Tuple[float, float]
Position = Tuple[float, floa... | {"hexsha": "c8bec67319ad9f1fe2e6533a6f9200362f264920", "size": 9258, "ext": "py", "lang": "Python", "max_stars_repo_path": "docker/pointing-user-interface/code/conveyor_utils/conveyor_utils/utils.py", "max_stars_repo_name": "Gabry993/pointing-user-interface-hri", "max_stars_repo_head_hexsha": "187b1db496a30edcf606b4c0a... |
#!/usr/bin/env python
# Deborah Pelacani Cruz
# https://github.com/dekape
import context
import fullwaveqc.inversion as inv
import numpy as np
import os
def test_thisfunction():
assert 1
def test_functional():
dir_path = os.path.abspath(os.path.dirname(__file__))
job_path = os.path.join(dir_path, "test_... | {"hexsha": "e1a7dd49468c628ac375ac4d970374d8798d6ebf", "size": 1125, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_inversion.py", "max_stars_repo_name": "msc-acse/acse-9-independent-research-project-dekape", "max_stars_repo_head_hexsha": "d3d2236e47e8604803850c7cacceb826c7649bcb", "max_stars_repo_li... |
[STATEMENT]
lemma real_sqrt_sum_squares_less: "\<bar>x\<bar> < u / sqrt 2 \<Longrightarrow> \<bar>y\<bar> < u / sqrt 2 \<Longrightarrow> sqrt (x\<^sup>2 + y\<^sup>2) < u"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<bar>x\<bar> < u / sqrt 2; \<bar>y\<bar> < u / sqrt 2\<rbrakk> \<Longrightarrow> sqrt (x\... | {"llama_tokens": 1058, "file": null, "length": 8} |
#
# bias_experiment.py
#
# Experiment in Paper's Section 3.1.1
#
import collections
import json
import os
import shutil
import tempfile
from copy import deepcopy
import click
import numpy as np
import pandas as pd
import torch
from ceem import logger, utils
from ceem.dynamics import *
from ceem.learner import *
fro... | {"hexsha": "77a0afb9a2b36a5df87a213bb9a692803a03eac8", "size": 5963, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/lorenz/bias_experiment.py", "max_stars_repo_name": "sisl/CEEM", "max_stars_repo_head_hexsha": "6154587fe3cdb92e8b7f70eedb1262caa1553cc8", "max_stars_repo_licenses": ["MIT"], "max_stars... |
(* Title: HOL/Analysis/Gamma_Function.thy
Author: Manuel Eberl, TU München
*)
section \<open>The Gamma Function\<close>
theory Gamma_Function
imports
Equivalence_Lebesgue_Henstock_Integration
Summation_Tests
Harmonic_Numbers
"HOL-Library.Nonpos_Ints"
"HOL-Library.Periodic_Fun"
begin
text \<open... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Analysis/Gamma_Function.thy"} |
# Re-exported imports
import pandas as pd
from numpy import nan, where
from re import sub
# Hidden imports
import builtins as _builtins
from inspect import stack as _stack
from keyword import iskeyword as _iskeyword
from pkg_resources import get_distribution as _get_distribution
from sys import stderr as _lo... | {"hexsha": "aed82bdb6d5e7bb1e53b6985e46629517a63277f", "size": 7699, "ext": "py", "lang": "Python", "max_stars_repo_path": "unitable/__init__.py", "max_stars_repo_name": "mhowison/unitable", "max_stars_repo_head_hexsha": "d0841c907b897ea5c77f488cd01b54c57b39f0b2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
\documentclass[a4paper]{article}
\usepackage[left = .85in, right = .5in, top = 1in, bottom = 1in]{geometry}
\usepackage{listings}
\title{\Huge Assignment 8 \\
\Large Implementation of DLL Flow Control \\
Stop and Wait Protocol Using Java}
\begin{document}
\section{Abstract}
\subsection{}
\section{Algorithm}
\subs... | {"hexsha": "16436f08c3567fc220e7fc462c6e840c9e5005ba", "size": 1224, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Networking/assignment8/assignment8.tex", "max_stars_repo_name": "ANSEduGroup/6th-sem-labs", "max_stars_repo_head_hexsha": "705f3041190fba4fdb49a7dc18f1f1d8e10c1dbe", "max_stars_repo_licenses": ["MIT... |
! @@name: fort_sp_common.4f
! @@type: F-fixed
! @@compilable: no
! @@linkable: no
! @@expect: failure
SUBROUTINE COMMON_WRONG()
COMMON /C/ X,Y
! Incorrect because X is a constituent element of C
!$OMP PARALLEL PRIVATE(/C/), SHARED(X) ! { error "PGF90-S-0155-x is used in multiple data sharing clauses" }
... | {"hexsha": "de2d0186a2774ee057f0983abbbb332b442a6097", "size": 400, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "test/openmp_examples/sources/Example_fort_sp_common.4f.f", "max_stars_repo_name": "kammerdienerb/flang", "max_stars_repo_head_hexsha": "8cc4a02b94713750f09fe6b756d33daced0b4a74", "max_stars_repo_li... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import tensorflow as tf
import cv2
import numpy as np
from libs.label_name_dict.label_dict import NAME_LABEL_MAP
from libs.configs import cfgs
def max_length_limitation(length, length... | {"hexsha": "863c17d66d3cacc5f614095e275b6c16c59da6ee", "size": 9345, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/io/image_preprocess_multi_gpu.py", "max_stars_repo_name": "RomStriker/R3Det_Tensorflow", "max_stars_repo_head_hexsha": "34bad1a99d4472281f2653448cdd43378f06f753", "max_stars_repo_licenses": [... |
#ifndef TYPELIB_IOPLUGINS_HH
#define TYPELIB_IOPLUGINS_HH
#include <boost/type_traits/is_base_and_derived.hpp>
#include <boost/mpl/if.hpp>
namespace Typelib
{
class ExportPlugin;
class ImportPlugin;
class Exporter;
class Importer;
template<typename Type>
struct plugin_traits
{
typ... | {"hexsha": "0ff481cb0bf4992123da31753c49408d80016d96", "size": 1584, "ext": "hh", "lang": "C++", "max_stars_repo_path": "typelib/plugins.hh", "max_stars_repo_name": "maltewi/tools-typelib", "max_stars_repo_head_hexsha": "c0a28415b6cea4d5500a00d6e7003554d684748e", "max_stars_repo_licenses": ["CECILL-B"], "max_stars_coun... |
using TiledIteration: TileIterator
using FFTW: fft, dct
function upsample(x::AbstractArray{T,D}, factor::NTuple{D}, offset::NTuple{D} = (fill(0,D)...,)) where {T,D}
@assert all(0 .<= offset .< factor) "offset is out of range"
szout = size(x) .* factor
setindex!(zeros(T, szout), x, StepRange.(offset .+ 1, f... | {"hexsha": "a9893e5670fea416f40bbf6ecbcff7ccdb912996", "size": 8522, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/polyphaseMatrices.jl", "max_stars_repo_name": "nixir/MDCDL", "max_stars_repo_head_hexsha": "db176ca1f9adb775168cab3a6c7eeafa0757124c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_co... |
'''
This script holds general meta data & configuration paths required for pipeline operation
'''
import os
import numpy as np
# comment out the next line to use in an experiment
#assert False, 'you are importing the template config.py file, import your local experiment specific file'
##############################... | {"hexsha": "80fc4173756463158815a1f71a051e3232bebcf1", "size": 4475, "ext": "py", "lang": "Python", "max_stars_repo_path": "workflow/libs/_config.py", "max_stars_repo_name": "nathanieljevans/cyclicIF_registration", "max_stars_repo_head_hexsha": "0483b8354d0e2b666ca1b47848dca3222c5ddb69", "max_stars_repo_licenses": ["MI... |
import numpy as np
import scipy, os
from scipy.signal import butter,lfilter
from scipy.ndimage.filters import gaussian_filter1d
import matplotlib.pyplot as plt
from matplotlib.pyplot import mlab
import xml.etree.ElementTree
samplingRate=30000.
#===========================================================================... | {"hexsha": "54bf33623288a303f516cf288873cfa29717e9e6", "size": 18401, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/continuous_traces.py", "max_stars_repo_name": "danieljdenman/neuropixels_invitro", "max_stars_repo_head_hexsha": "22ba9f0c7ce9cd562e6e351bf96a312b757df1b4", "max_stars_repo_licenses": ["MIT... |
# Copyright 2021 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | {"hexsha": "50c4720ca1f44d616f06d6810259cd68d6738bb3", "size": 5370, "ext": "py", "lang": "Python", "max_stars_repo_path": "cirq-core/cirq/ops/pauli_sum_exponential_test.py", "max_stars_repo_name": "Nexuscompute/Cirq", "max_stars_repo_head_hexsha": "640ef8f82d6a56ec95361388ce7976e096cca906", "max_stars_repo_licenses": ... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "715006771878795674d9391b926be40d2ed27bc1", "size": 7338, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/ir/inference/trt_layer_auto_scan_test.py", "max_stars_repo_name": "shiyutang/Paddle", "max_stars_repo_head_hexsha": "5c27c2c00bdb69619fa2bf715f6a0e0708579569", ... |
# coding: utf-8
# 2021/3/23 @ tongshiwei
import logging
import numpy as np
import torch
from tqdm import tqdm
from torch import nn
from EduCDM import CDM
from sklearn.metrics import roc_auc_score, accuracy_score
class MFNet(nn.Module):
"""Matrix Factorization Network"""
def __init__(self, user_num, item_num... | {"hexsha": "4233812bc509df9fef7c9a1ef10c37d25915b5dd", "size": 3211, "ext": "py", "lang": "Python", "max_stars_repo_path": "EduCDM/MCD/MCD.py", "max_stars_repo_name": "24miaoge/EduCDM", "max_stars_repo_head_hexsha": "49f7cc28dcef748624fbd7cc7a524826abc5f37e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
from sklearn.decomposition import PCA
import load_data
import numpy as np
"""Using PCA(principle component analisys) to reduce the dimensions of data"""
#loading mnist data
x_scaled, y = load_data.fetch_data()
#separating training and testing datas
train_x = x_scaled[:60000, :]
train_y = y[:60000]
test_x = x_scale... | {"hexsha": "9e31031190f5f704d254e0a67fcc980d394c2a62", "size": 713, "ext": "py", "lang": "Python", "max_stars_repo_path": "pca.py", "max_stars_repo_name": "ShafeekSaleem/MNIST", "max_stars_repo_head_hexsha": "f738ce3f43483bdba93d120b0684f53e300718f6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, ... |
MODULE mecih_I
INTERFACE
!...Generated by Pacific-Sierra Research 77to90 4.4G 10:47:25 03/09/06
SUBROUTINE mecih (DIAG, CIMAT, NMOS, LAB, XY)
USE vast_kind_param,ONLY: DOUBLE
REAL(DOUBLE), DIMENSION(*), INTENT(IN) :: DIAG
REAL(DOUBLE), DIMENSION(*), INTENT(out) :: c... | {"hexsha": "6ed0fe2359f1aec46a3fb25e0100b9ab93dd0aee", "size": 479, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "2006_MOPAC7.1/src_interfaces/mecih_I.f90", "max_stars_repo_name": "openmopac/MOPAC-archive", "max_stars_repo_head_hexsha": "01510e44246de34a991529297a10bcf831336038", "max_stars_repo_licenses": [... |
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
import pickle
import re
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
def process_text(review):
# Extract t... | {"hexsha": "46aae082f26230ef84e66583b4868e42ff0ea6b8", "size": 1743, "ext": "py", "lang": "Python", "max_stars_repo_path": "5_deployment/streamlit_deploy_example/model.py", "max_stars_repo_name": "DukeAIPI/AIPI540-Deep-Learning-Applications", "max_stars_repo_head_hexsha": "1f8786ef45dd0405608a8782d15e2498153e67a1", "ma... |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 25 16:26:32 2021
@author: kibong
"""
# In[]
from AAA import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
from datasets import load_dataset
import soundfile as sf
import sounddevice as sd
import torch
import numpy as np
# load model and tokenizer
tokenizer = Wav2V... | {"hexsha": "eea706ca676425299c07f5fd5c4faf98ec462d66", "size": 2438, "ext": "py", "lang": "Python", "max_stars_repo_path": "wav2vec2_libribase_example.py", "max_stars_repo_name": "kkb131/radar2txt", "max_stars_repo_head_hexsha": "4deb26db596c314233299c40684c4f84876dfd6d", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import numpy as np
import matplotlib.pyplot as plt
#---------------------Import coordinate file-------------------------#
f_x = 'simple_bulk/img/subdataset1_geometry/x.txt'
f_l = 'simple_bulk/img/subdataset1_geometry/l.txt'
x = np.loadtxt(f_x, dtype = int)
l = np.loadtxt(f_l, dtype = int)
#---------------... | {"hexsha": "813004577d0875d8992c9b8d236857fe6cfa7fdc", "size": 1069, "ext": "py", "lang": "Python", "max_stars_repo_path": "Domain/subdataset1_domain/subdataset1_img.py", "max_stars_repo_name": "pprachas/ABC_dataset", "max_stars_repo_head_hexsha": "61c915853c0229295e728f869b11b113ee59f098", "max_stars_repo_licenses": [... |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 17 00:42:53 2020
@author: kai
"""
import time
start = time.time()
import numpy as np
import os
import sys
import tensorflow as tf
import cv2
from PIL import Image
import pandas as pd
#if tf.__version__ < '1.4.0':
# raise ImportError('Please ... | {"hexsha": "762f0ba328927034542b6b12e4e6f00fb49ee8f0", "size": 5960, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/object_detection/testing.py", "max_stars_repo_name": "kailliang/Object_Detection", "max_stars_repo_head_hexsha": "34a6592980c09d021994f51e297556bc98e1461a", "max_stars_repo_licenses": ["A... |
! DESCRIPTION:
! use procedure pointer to invok different subprograms possesing indentical interfaces
! compare it with function pointer in C.
module Calc_mod
implicit none
private
public :: Calc_debug, Calc_normal, Calc_proc
interface
function Calc_proc(real_arg, opt_format) result (ret_val)
... | {"hexsha": "ec5dac217d85ab73b804921824873e7d6821b470", "size": 1778, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "modern_fotran/NewAttribute/procedure_pointer.f90", "max_stars_repo_name": "ComplicatedPhenomenon/Fortran_Takeoff", "max_stars_repo_head_hexsha": "a13180050367e59a91973af96ab680c2b76097be", "max_... |
# --------------
#Header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#path of the data file- path
data = pd.read_csv(path, sep=',', delimiter=None)
data['Gender'].replace(to_replace="-", value="Agender", inplace=True)
#print(data)
gender_count = data['Gender'].value_counts()... | {"hexsha": "fc06d3f0921a2f7b6b47cc36398dccc74985fd90", "size": 1750, "ext": "py", "lang": "Python", "max_stars_repo_path": "Statistics/code.py", "max_stars_repo_name": "hn1201/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "c42656551e930d13df98f6631bb63fe179035ed8", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
//==================================================================================================
/*!
@file
@copyright 2015 NumScale SAS
@copyright 2015 J.T. Lapreste
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
... | {"hexsha": "88aef82445affc87262c9f6ba98f5767eabd5e03", "size": 1184, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/simd/arch/common/generic/function/rec.hpp", "max_stars_repo_name": "yaeldarmon/boost.simd", "max_stars_repo_head_hexsha": "561316cc54bdc6353ca78f3b6d7e9120acd11144", "max_stars_repo_li... |
using DataFrames
using Gadfly
using Colors
include("theory.jl")
include("transitions.jl")
tr_chains_d1(θ, N_H, N_E, p_H, p_E) = tr_chains(θ, N_H, N_E, p_H, p_E, 1)
tr_chains_d2(θ, N_H, N_E, p_H, p_E) = tr_chains(θ, N_H, N_E, p_H, p_E, 2)
tr_chains_d10(θ, N_H, N_E, p_H, p_E) = tr_chains(θ, N_H, N_E, p_H, p_E, 10)
tr_c... | {"hexsha": "7edc7746ced57591c4886cf3946c60ca6e05a620", "size": 3086, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/run_sim.jl", "max_stars_repo_name": "mburq/matching_thickness", "max_stars_repo_head_hexsha": "7f0c17e9d97be80d4af242e81d4f78b0aac930db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
string_1 = abcd
efg
string_2 = abc" $\?M
string_3 = \?\\'"
| {"hexsha": "9ea0b3236e6c43a8f01324a98fc05c68498b2908", "size": 62, "ext": "r", "lang": "R", "max_stars_repo_path": "test/unittest/types/tst.stringconstants.r", "max_stars_repo_name": "alan-maguire/dtrace-utils", "max_stars_repo_head_hexsha": "53b33a89ef7eaeba5ce06d50a4c73fe91c1fa99e", "max_stars_repo_licenses": ["UPL-1... |
""" usage: kfold_partition_dataset.py [-h] [-i IMAGEDIR] [-o OUTPUTDIR] [-k KFOLDS] [-x] [-s SEED]
Partition dataset of images into training and testing sets
optional arguments:
-h, --help show this help message and exit
-i IMAGEDIR, --imageDir IMAGEDIR
Path to the folder where ... | {"hexsha": "2877f1f9f56dfe1998bf4c5990a19c38a3966486", "size": 5034, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/preprocessing/kfold_partition_dataset.py", "max_stars_repo_name": "kallentu/chowdr", "max_stars_repo_head_hexsha": "47efd86025836e04c251c06f86c32d5519b2e0a7", "max_stars_repo_licenses": ["... |
# Authors: Soledad Galli <solegalli@protonmail.com>
# License: BSD 3 clause
import numpy as np
import pandas as pd
from feature_engine.outliers import Winsorizer
class OutlierTrimmer(Winsorizer):
"""The OutlierTrimmer() removes observations with outliers from the dataset.
The OutlierTrimmer() first calcula... | {"hexsha": "0719241ff61a300d8a928a61ae9664764ba7b856", "size": 5332, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_engine/outliers/trimmer.py", "max_stars_repo_name": "david-cortes/feature_engine", "max_stars_repo_head_hexsha": "702328d1a072d0911441e10b4eb98b3bfbf19215", "max_stars_repo_licenses": ["BS... |
#####################################################
## librealsense T265 streams test ##
#####################################################
# This assumes .so file is found on the same directory
import pyrealsense2 as rs
# Prettier prints for reverse-engineering
from pprint import pprint
import n... | {"hexsha": "6e84ae94c14ebfb605193bbe24901c111acf28e5", "size": 2293, "ext": "py", "lang": "Python", "max_stars_repo_path": "robot/src/vision_to_mavros/scripts/t265_test_streams.py", "max_stars_repo_name": "mikobski/Robot-Inspekcyjny", "max_stars_repo_head_hexsha": "925491fc43b71bdaa54dccf60d38da59d244181d", "max_stars_... |
from gym_torcs import TorcsEnv
import numpy as np
img_dim = [64,64,3]
action_dim = 1
steps = 1000
batch_size = 32
nb_epoch = 100
def get_teacher_action(ob):
steer = ob.angle*10/np.pi
steer -= ob.trackPos*0.10
return np.array([steer])
def img_reshape(input_img):
_img = np.transpose(input_img, (1, 2, 0... | {"hexsha": "5828ceda2b923cfac53fd92f6d112a5caa0246d0", "size": 3599, "ext": "py", "lang": "Python", "max_stars_repo_path": "dagger.py", "max_stars_repo_name": "havefun28/imitation-dagger", "max_stars_repo_head_hexsha": "6460b53ae3bdfc9801c5ea621ccc1da4e575c9c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 67... |
SUBROUTINE TG_QRNG ( gdatim, rngtyp, gtype, iret )
C************************************************************************
C* TG_QRNG *
C* *
C* This subroutine determines whether a GDATIM is a singular time, *
C* multiple times based on forecast hour, or multiple times based on *
C* cycles. *
... | {"hexsha": "02687df18c665e4e1c17033f9f1a160e2b39a70e", "size": 3714, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/gemlib/tg/tgqrng.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
from __future__ import division
from __future__ import print_function
import os
import time
import math
import numpy as np
import pyopencl as cl
class CLWrapper:
"class holds information about OpenCL state"
def __init__(self, batchSize, maxT, maxC, kernelVariant=1, enableGPUDebug=False):
"specify size: number of... | {"hexsha": "8a3e200e8fa4ee6120626e660d715562af57377f", "size": 4768, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctc_decoder/BestPathCL.py", "max_stars_repo_name": "markschoene/CTCDecoder", "max_stars_repo_head_hexsha": "fbb21853c0b38b6d9a7ba5f86401547f8f655b4c", "max_stars_repo_licenses": ["MIT"], "max_star... |
\subsubsection{FX Option}
The \lstinline!FXOptionData! node is the trade data container for the \emph{FxOption} trade type. FX options with exercise styles \emph{European} or \emph{American} are supported.
The \lstinline!FXOptionData! node includes one and only one \lstinline!OptionData! trade
component sub-node plus... | {"hexsha": "9c6012e11c31d57fc1e0a0ebedc51fcfeb364f16", "size": 2692, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Docs/UserGuide/tradedata/fxoption.tex", "max_stars_repo_name": "nvolfango/Engine", "max_stars_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_stars_repo_licenses": ["BSD-3-Clause... |
"""Data transformations supporting using Prophet on bounded data."""
import abc
import numpy as np
import pandas as pd
from scipy import special
class Transform(abc.ABC):
"""Abstract interface to data transformation used to help Prophet forecast in bounded domains.
Converts bounded real data to and from Pro... | {"hexsha": "851343e80e2aaef3bffddd9738b7e9a9c296c56e", "size": 4063, "ext": "py", "lang": "Python", "max_stars_repo_path": "prophet_utils/transforms.py", "max_stars_repo_name": "WilliamHo1999/prophet-utils", "max_stars_repo_head_hexsha": "13a165d50ec3280215aefc980c60d60859cc9b05", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import scipy as sp
from sklearn.preprocessing import MinMaxScaler
def _neutralize(df, columns, by, proportion=1.0):
scores = df[columns]
exposures = df[by].values
scores = scores - proportion * \
exposures.dot(np.linalg.p... | {"hexsha": "2cc2ed5f7b2427d7bc96cb72b5365ca0d63a3a33", "size": 60370, "ext": "py", "lang": "Python", "max_stars_repo_path": "allornothing.py", "max_stars_repo_name": "scirpus/numer", "max_stars_repo_head_hexsha": "05c46b6f267c5b7651b79fa009dfe74194a7a0e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_... |
// rocks_sorted_data_impl_test.cpp
/**
* Copyright (C) 2014 MongoDB Inc.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License, version 3,
* as published by the Free Software Foundation.
*
*
* This program is distributed i... | {"hexsha": "78628a668cc7847ed1ca20253a891a7312135503", "size": 36081, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mongo/db/storage/rocks/rocks_sorted_data_impl_test.cpp", "max_stars_repo_name": "cyxddgithub/mongo", "max_stars_repo_head_hexsha": "6eb296a66e1f71f12d5483b7144f96d506b055a3", "max_stars_repo_li... |
function output = calc_traversal_dist(ai)
% This function will generate position coordinates of chain code (ai). Number of
% harmonic elements (n), and number of points for reconstruction (m) must be
% specified.
x_ = 0;
y_ = 0;
for i = 1 : size(ai, 2)
x_ = x_ + sign(6 - ai(i)) * ... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/32800-elliptic-fourier-for-shape-analysis/ca... |
/* Copyright (c) 2021, the adamantine authors.
*
* This file is subject to the Modified BSD License and may not be distributed
* without copyright and license information. Please refer to the file LICENSE
* for the text and further information on this license.
*/
#define BOOST_TEST_MODULE DataAssimilator
#includ... | {"hexsha": "9e68687847ee7bab0d68841e312fadb6259e7944", "size": 19040, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tests/test_data_assimilator.cc", "max_stars_repo_name": "Rombur/adamantine", "max_stars_repo_head_hexsha": "45dd37397680fad1eaa64dbb311724c4f727a675", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 9 10:54:14 2016
@author: yaric
"""
import numpy as np
import pandas as pd
from sklearn import decomposition
import utils
# the input file prefix of data sets
input_file_prefix = '../../data/training-' # '../../data/training-small-'
output_file_prefix = '../../data/tr... | {"hexsha": "0cccf7d70575a20bc9b3c90adb7f7f33b358ae05", "size": 2309, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/offline_preprocessor.py", "max_stars_repo_name": "yaricom/timeserieslearning", "max_stars_repo_head_hexsha": "6c6c5dc253b47bd6a22a2a97030adba5c5e7512a", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
from ase.dft import kpoints
import pyglib.gutz.ginput as ginput
import pyglib.model.tbASE as tb
# The following is a simple test for the 1-d Hubbard model.
a = tb.AtomsTB("N", [(0, 0, 0)], cell=(1, 1, 1))
a.set_orbitals_spindeg()
aTB = tb.TB(a)
aTB.set_hop([
((1, 0, 0), 0, 0, -1),
((-1, 0, ... | {"hexsha": "27bd915d00fef66f697707a37d10b3d46379110d", "size": 1067, "ext": "py", "lang": "Python", "max_stars_repo_path": "ComRISB/pyglib/pyglib/model/test/test_1band_model/REF/test_tb.py", "max_stars_repo_name": "comscope/comsuite", "max_stars_repo_head_hexsha": "d51c43cad0d15dc3b4d1f45e7df777cdddaa9d6c", "max_stars_... |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
from matplotlib import gridspec
from scipy.optimize import curve_fit
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import explained_variance_score
from sklearn.metric... | {"hexsha": "9605fef62c277fb8f992db78d056b0d9b7124675", "size": 9842, "ext": "py", "lang": "Python", "max_stars_repo_path": "auxiliary/scipy_implementation.py", "max_stars_repo_name": "ArbiKodraj/ML-Approximation", "max_stars_repo_head_hexsha": "e8696fe13e1e8e63f9eb27c68a77b81d578c1a27", "max_stars_repo_licenses": ["MIT... |
import math
import numpy as np
import numpy.polynomial as poly
from .errors import ColorIndexError, ParamRangeError, MissingParamError
def get_BC(**kwargs):
"""Get bolometric correction (BC) using a variety of calibration relations.
Available calibration relations:
* `Alonso1995`: returns *BC* in *V... | {"hexsha": "c9610e8a2058a27d94f96d404fe796f7479bb0dd", "size": 9379, "ext": "py", "lang": "Python", "max_stars_repo_path": "stellarlab/parameter/bc.py", "max_stars_repo_name": "wangleon/stella", "max_stars_repo_head_hexsha": "3942f8e687065bb96760140596747cbbe6dad04b", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import warnings
from typing import Tuple, Any, Dict
import numpy
import openslide
import wx
from PIL import Image
from antilles.utils.io import DAO
def get_screen_size() -> Tuple[int, int]:
app = wx.App(False)
size = wx.GetDisplaySize()
del app
return size
screen_size = get_screen_size()
def ge... | {"hexsha": "e2eb243d968cff80d80b0382dbb4c5a426363cbe", "size": 1896, "ext": "py", "lang": "Python", "max_stars_repo_path": "antilles/utils/image.py", "max_stars_repo_name": "biomicrodev/antilles", "max_stars_repo_head_hexsha": "38f1d16494fae750b95d4e9a654038b9aba8e248", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
Require Import ZArith.
Definition pow2_p p := Zpos (iter_pos positive xO xH p).
Definition mersenne p := (pow2_p p - 1)%Z.
Definition next_s mp s := (((s*s) - 2) mod mp)%Z.
Definition lucas_residue p :=
let mp := mersenne p in
let pm2 := (p-2)%positive in
iter_pos Z (next_s mp) 4%Z pm2.
Definition lucas_test... | {"author": "maximedenes", "repo": "native-compute-bench", "sha": "ac7891508239f9cc1f7ba0190b1814fb152d2126", "save_path": "github-repos/coq/maximedenes-native-compute-bench", "path": "github-repos/coq/maximedenes-native-compute-bench/native-compute-bench-ac7891508239f9cc1f7ba0190b1814fb152d2126/src/Lucas.v"} |
[STATEMENT]
lemma finite_Update:
"finite TS \<Longrightarrow> finite ((\<lambda> F. (Rep_pupdate F) (Value ST)) ` (PUpdate (Label TS)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite TS \<Longrightarrow> finite ((\<lambda>F. Rep_pupdate F (Value ST)) ` Expr.PUpdate (Label TS))
[PROOF STEP]
by (rule finite_i... | {"llama_tokens": 124, "file": "Statecharts_HASem", "length": 1} |
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('TKAgg')
import numpy as np
import time, random, math
size=11
#array = random.sample((range(1, size + 1)), size)
array = list(xrange(size, 0, -1))
def bubble_sort(arr, rects):
sorted = True
for x in range(0, size - 1):
update_plot(arr, '#000000', x-... | {"hexsha": "e5ef937a7364aa6fad9938559bf46e279126a130", "size": 1373, "ext": "py", "lang": "Python", "max_stars_repo_path": "57/bubblesort.py", "max_stars_repo_name": "Chutzpah7/Challenges", "max_stars_repo_head_hexsha": "7481eaf49dca9e8f68b8efa58cde778aa20449a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import math
import importlib
import functools
def generate_inputs(size):
import numpy as np
np.random.seed(17)
shape = (
math.ceil(2 * size ** (1/3)),
math.ceil(2 * size ** (1/3)),
math.ceil(0.25 * size ** (1/3)),
)
# masks
maskT, maskU, maskV, maskW = ((np.random.ran... | {"hexsha": "c6a56b7377d1abe6a5f7f13caf169f6a21612220", "size": 1714, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/isoneutral_mixing/__init__.py", "max_stars_repo_name": "mdanatg/pyhpc-benchmarks", "max_stars_repo_head_hexsha": "710d0ab484cae28beab99ddd1167d33574c83b53", "max_stars_repo_licenses": [... |
(* Title: Extension Orders
Author: Heiko Becker <heikobecker92@gmail.com>, 2016
Author: Jasmin Blanchette <jasmin.blanchette at inria.fr>, 2016
Author: Dmitriy Traytel <traytel@inf.ethz.ch>, 2014
Maintainer: Jasmin Blanchette <jasmin.blanchette at inria.fr>
*)
section \<open>Exte... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Lambda_Free_RPOs/Extension_Orders.thy"} |
import numpy as np
def iou_metric(y_true_in, y_pred_in):
labels = y_true_in
y_pred = y_pred_in
temp1 = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=([0,0.5,1], [0,0.5, 1]))
intersection = temp1[0]
area_true = np.histogram(labels,bins=[0,0.5,1])[0]
area_pred = np.histogr... | {"hexsha": "233375fc49d41fffd61d9a1802d0c40cd3587f79", "size": 1496, "ext": "py", "lang": "Python", "max_stars_repo_path": "scores_comp.py", "max_stars_repo_name": "DevikalyanDas/NucleiSegnet-Paper-with-Code", "max_stars_repo_head_hexsha": "d8fbf9cc13160e21166fff905688c93d4900bdd2", "max_stars_repo_licenses": ["Apache-... |
module BoehmBerarducci
%default total
NatQ : Type
NatQ = (A : Type) -> (A -> A) -> A -> A
unNatQ : {A : Type} -> (A -> A) -> A -> NatQ -> A
unNatQ f a q = q _ f a
succQ : NatQ -> NatQ
succQ q = \_, f, a => f (q _ f a)
zeroQ : NatQ
zeroQ = \_, f, a => a
fromNatQ : NatQ -> Nat
fromNatQ q = unNatQ S Z q
toNatQ : N... | {"hexsha": "278477ce2148da6d965dc4290414ac975bb485b9", "size": 1377, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "idris-explicit/BoehmBerarducci.idr", "max_stars_repo_name": "mietek/scott-encoding", "max_stars_repo_head_hexsha": "14e819383dd8730e1c3cbd9c2ce53335bd95188b", "max_stars_repo_licenses": ["X11", "M... |
#include <algorithm>
#include <fstream>
#include <boost/assert.hpp>
#include "nlohmann/json.hpp"
#include "utility/type/XY.hpp"
#include "utility/type/RowColumn.hpp"
#include "utility/wrapper/sfVector2.hpp"
#include "utility/wrapper/sfMakeColor.hpp"
#include "Menu.hpp"
namespace nemo
{
//////////////////////////////... | {"hexsha": "84a52b96ef5e4034f03838adec5525fa272c2515", "size": 23075, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/menu/Menu.cpp", "max_stars_repo_name": "zhec9/nemo", "max_stars_repo_head_hexsha": "b719b89933ce722a14355e7ed825a76dea680501", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
# Electro I. Pregunta bono 2: Análisis en DC gráfico.
# Autor : Rafael Moreno
# Fecha : 24/01/20
# Prof : Anibal Carpio
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
# Open file
filename = 'Grafica Diodo 1-n4004 GP.csv'
data = pd.read_csv(filename)
# Diode I-V characteristics
Vd = data... | {"hexsha": "c277c7554670717fe7570f08d58e4bbfa1da4997", "size": 1177, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pregunta bono 2.py", "max_stars_repo_name": "ImArcangel/Q-Point-Diode", "max_stars_repo_head_hexsha": "b8c8825493ffe7f286c07a9e212ac5b18ec3bcb6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
section "Invariant Context Simplifications"
theory invContext_simps
imports repliss_sem
begin
text "Here we prove various simplifications for the invariant contexts."
lemma invContext_unchanged_happensBefore:
assumes "co c \<triangleq> t" and "ts t \<triangleq> Uncommitted"
shows "invContextH co to ts (hbOld \<... | {"author": "peterzeller", "repo": "repliss-isabelle", "sha": "f43744678cc9c5a4684e8bd0e9c83510bae1d9a4", "save_path": "github-repos/isabelle/peterzeller-repliss-isabelle", "path": "github-repos/isabelle/peterzeller-repliss-isabelle/repliss-isabelle-f43744678cc9c5a4684e8bd0e9c83510bae1d9a4/invContext_simps.thy"} |
from controlSBML.control_sbml import ControlSBML
from controlSBML import control_sbml
import helpers
import numpy as np
import pandas as pd
import os
import unittest
import tellurium as te
IGNORE_TEST = False
IS_PLOT = False
TEST_DIR = os.path.dirname(os.path.abspath(__file__))
ANTIMONY_FILE = os.path.join(TEST_DIR... | {"hexsha": "ccae3a660c1f6642e6914411c9b26ebd9748a502", "size": 1114, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_control_sbml.py", "max_stars_repo_name": "ModelEngineering/controlSBML", "max_stars_repo_head_hexsha": "64587a7b22961a52aaf2b4e4a04fd1a0e3bc7a6b", "max_stars_repo_licenses": ["MIT"], "m... |
from dataclasses import dataclass
from typing import List, Literal
from numpy import positive
from xarray_dataclasses import Attr
from datetime import datetime
from toolz import curry
@dataclass
class VariableAttrs:
standard_name: str
long_name: str
units: str
@dataclass
class AltitudeAttrs:
standa... | {"hexsha": "38f02f9a484bdbd012a489716faa48ac6761e589", "size": 2638, "ext": "py", "lang": "Python", "max_stars_repo_path": "hello-xarray/cfxarray/attributes.py", "max_stars_repo_name": "NIVANorge/s-enda-playground", "max_stars_repo_head_hexsha": "56ae0a8978f0ba8a5546330786c882c31e17757a", "max_stars_repo_licenses": ["A... |
from screeninfo import get_monitors
import pygame
from pygame.locals import *
import os
import sys
from flick import Flick
import time
from record_data import RecordData
from live_recorder import LiveRecorder
from sklearn.externals import joblib
import numpy as np
from preprocess import preprocess_recordings
from subpr... | {"hexsha": "b31eb948baa84b8041cdc6ef2c36f2f6296ef6ac", "size": 8231, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ssvep/utils.py", "max_stars_repo_name": "gumpy-hybridBCI/gumpy-Realtime", "max_stars_repo_head_hexsha": "163f72ff4d8734cbfd13848e21ce7d4cafc6e8e9", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma dbm_entry_dbm_min3:
assumes "dbm_entry_val u (Some c) None (min a b)"
shows "dbm_entry_val u (Some c) None b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. dbm_entry_val u (Some c) None b
[PROOF STEP]
using dbm_entry_val_mono_3[folded less_eq, OF assms]
[PROOF STATE]
proof (prove)
using this:
... | {"llama_tokens": 206, "file": "Timed_Automata_DBM_Operations", "length": 2} |
[STATEMENT]
lemma additive_wp_PC:
"\<lbrakk> additive (wp a); additive (wp b) \<rbrakk> \<Longrightarrow> additive (wp (a \<^bsub>P\<^esub>\<oplus> b))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Transformers.additive (wp a); Transformers.additive (wp b)\<rbrakk> \<Longrightarrow> Transformers.additiv... | {"llama_tokens": 171, "file": "pGCL_Determinism", "length": 1} |
#!/usr/bin/env python
import sys,os,string
from numpy import *
from scipy.interpolate import *
from myplotlib import PanelPlot
from matplotlib import pyplot
import pickle
tck_file0 = 'tck.pickle'
tck_file1 = 'bs_tck.pickle'
f = open(tck_file0)
all_tck0 = pickle.load(f)
f.close()
f = open(tck_file1)
all_tcks = pickl... | {"hexsha": "264f452106edc5a2b7227625d5f9aebc95b80ce9", "size": 1852, "ext": "py", "lang": "Python", "max_stars_repo_path": "snpy/CSPtemp/plot_bs_disps.py", "max_stars_repo_name": "emirkmo/snpy", "max_stars_repo_head_hexsha": "2a0153c84477ba8a30310d7dbca3d5a8f24de3c6", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
% Author: Cristian Gonzales
% Created for Physical Time, 2018
\documentclass[11pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage[document]{ragged2e}
\newcommand\tab[1][1cm]{\hspace*{#1}}
\begin{document}
\Large{\textbf{Sprint 2 Plan}}\\
\Large{\text... | {"hexsha": "e03746de59a2c7daec0c21962bdf10963c3203c6", "size": 2713, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "scrum/sprint2/PTSprint2Plan.tex", "max_stars_repo_name": "Physical-TIme/Physical-Time", "max_stars_repo_head_hexsha": "3563c21d0c34503dcb4e82975e20c82621f9efef", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import math
from scipy import signal, fft, interpolate
def lpfilter_sos(data, dt, cutoff, zero_phase=True):
"""" Low-pass filter using the second-order representation Butterworth implementation
Inputs:
data - 2D numpy array, of shape [channels,samples]
dt - sampling interval ... | {"hexsha": "bf1ea0a82e2f7bf6a9a9788f9bcf6167f94cf14d", "size": 7308, "ext": "py", "lang": "Python", "max_stars_repo_path": "signal_processing.py", "max_stars_repo_name": "DAS-RCN/continuous_data_handling", "max_stars_repo_head_hexsha": "cf1f2140c33bbd3c9f164c5c9fb1ff1d1573700e", "max_stars_repo_licenses": ["MIT"], "max... |
#pragma once
#include <boost/predef.h>
#if BOOST_ARCH_X86
#include <emmintrin.h>
#endif
namespace emr { namespace detail {
struct no_backoff
{
void operator()() {}
};
class exponential_backoff
{
public:
void operator()()
{
for (unsigned i = 0; i < count; ++i)
do_backoff();
... | {"hexsha": "c9c8c4805cb1b1c90eb246cc46adf87ff57251a7", "size": 550, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/emr/detail/backoff.hpp", "max_stars_repo_name": "mpoeter/emr", "max_stars_repo_head_hexsha": "390ee0c3b92b8ad0adb897177202e1dd2c53a1b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
C LAST UPDATE 16/03/89
C+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
C
SUBROUTINE GETHDR (ITERM,IPRINT,IUNIT,HFNAM,ISPEC,LSPEC,INCR,MEM,
& IFFR,ILFR,IFINC,IHFMAX,IFRMAX,NCHAN,IRC)
IMPLICIT NONE
C
C Purpose: Get header file information
C
INTEGER ISP... | {"hexsha": "e22b63acfe96ef12c061db926157643373a5e8a2", "size": 2563, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "software/libs/otoko/gethdr.f", "max_stars_repo_name": "scattering-central/CCP13", "max_stars_repo_head_hexsha": "e78440d34d0ac80d2294b131ca17dddcf7505b01", "max_stars_repo_licenses": ["BSD-3-Claus... |
# Deep Learning - Assignment 1
## Outline (15 points)
#### In this assignment, you will learn:
* How to generate random data using python.
* Building linear models for simple regression problem on the generated data.
* Training the linear models with gradient descent algorithm.
* How to alleviate over-fitting for y... | {"hexsha": "c743873b7adbb013929f34af0bef140df85e4044", "size": 268557, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "assignment1/Assignment1_v2_answer.ipynb", "max_stars_repo_name": "Haoban/Deep-Learning-Lectures-Exercises-Slides", "max_stars_repo_head_hexsha": "da9141fb9da9fde25d1d3edd4efa13f3f85... |
import numpy as np
import cv2
import os.path
# File Searching
folders = r"C:\Users\심재윤\PycharmProjects\RGB detection" ### Change Directory with your choice
filename = os.listdir(folders)
for names in filename :
if (names == "makejpg.py") :
continue
file = folders + "\\" + names
a = np.loadtxt(file, dty... | {"hexsha": "74bda2082ac139cdd4523db51169ef77204dd37c", "size": 572, "ext": "py", "lang": "Python", "max_stars_repo_path": "Detector_1/makejpg.py", "max_stars_repo_name": "JaeyoonSSim/Design-Project", "max_stars_repo_head_hexsha": "8a0037bec50b44b3f5d92da5254e79964fdaf9cf", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma joule_alt_def: "joule \<cong>\<^sub>Q newton \<^bold>\<cdot> metre"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. joule \<cong>\<^sub>Q newton \<^bold>\<cdot> metre
[PROOF STEP]
by si_calc | {"llama_tokens": 88, "file": "Physical_Quantities_SI_Derived", "length": 1} |
import pytest
numpy = pytest.importorskip('numpy')
npt = pytest.importorskip('numpy.testing')
scipy = pytest.importorskip('scipy')
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestBetheHessian(object):
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1,... | {"hexsha": "2dccae492f2b11d37bcd1b975772a0b4e666bb70", "size": 1309, "ext": "py", "lang": "Python", "max_stars_repo_path": "networkx/linalg/tests/test_bethehessian.py", "max_stars_repo_name": "jmmcd/networkx", "max_stars_repo_head_hexsha": "207ff7d1e9bfaff013ac77c8d6bb79619892c994", "max_stars_repo_licenses": ["BSD-3-C... |
# !/usr/bin/env python
import random
import sys
import os
import rospkg
import networkx as nx
from cbm_pop_lib.common.chromosome import Chromosome
from copy import deepcopy
def init_result(tasks, mdvrp, prec, params):
result = Chromosome(tasks, mdvrp.max_vehicle_load, prec,
mdvrp.sliding... | {"hexsha": "f3981695594ab3c3b99b667195ed37574d0ef363", "size": 3003, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cbm_pop_lib/operators/generational_functions.py", "max_stars_repo_name": "barbara0811/cbm_pop_mdvrp_optimization", "max_stars_repo_head_hexsha": "10bfc55a21f48f93ed87ec4c48f07e315795efcd", "ma... |
"""
compute_laplace_eig(mesh, matrices, pde, eiglim, neig_max)
Compute the Laplace eigenvalues, eigenfunctions and first order moments of products of pairs of eigenfunctions.
"""
function compute_laplace_eig(model, matrices, eiglim = Inf, neig_max = Inf)
# Measure function evaluation time
starttime = Base... | {"hexsha": "15f373c9ca0ee7c791c3220a618f6a9e9b7976f7", "size": 1946, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/matrix_formalism/compute_laplace_eig.jl", "max_stars_repo_name": "tapudodo/SpinDoctor.jl", "max_stars_repo_head_hexsha": "11ebc8095f988e52f14e89a2c0bef9fc0c4e63c0", "max_stars_repo_licenses": [... |
import numpy as np
import odrive
import random
import time
'''
Random controller for physical pendulum
'''
cpr = 8192
p0 = 0
t_run = 5
c_max = 3.0
v_max = 3 * cpr
dt = 0.05
def p2r(p):
return 2 * np.pi * (p/cpr)
def v2rs(v):
return p2r(v)
# copied from gym env for model continuity
# it handles wrap, turning pi i... | {"hexsha": "34636f9859aa64011dcb4ed4f5ad22c640ab4bb5", "size": 1049, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple/real_pendulum_simple.py", "max_stars_repo_name": "rravenel/furuta_pendulum", "max_stars_repo_head_hexsha": "b2f2a3bb8c6f2676671a24c6f9ea4d8e6479835f", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
from logging import getLogger
from tensorflow.keras.datasets import fashion_mnist
from tensorflow.keras.utils import to_categorical
from typing import Tuple
logger = getLogger(__name__)
def get_fasion_mnist() -> (
Tuple[np.ndarray, np.ndarray, np.ndarray],
Tuple[np.ndarray, np.ndarray],
)... | {"hexsha": "e75905f591f611248b156a35c60c100006ed76bc", "size": 2388, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning/tf_doc/dataset.py", "max_stars_repo_name": "iimuz/til", "max_stars_repo_head_hexsha": "b100438e8ce2f369331b3be215a4b9cdce9ffda5", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# This file takes in the C-sin-10-shot and converst it into the ball bouncing state data.
import pickle
import numpy as np
filename = "C-sin_10-shot_legit_2.p"
#filename = "bounce-states_100-shot_2.p"
new_file = "C-sin_10-shot_legit_stateform.p"
tasks = pickle.load(open(filename, "rb"))
#Now convert it
def rest... | {"hexsha": "0fdaf648e82a502f6ee87dbccd6ac35b8d26d1b3", "size": 1815, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/duplicateData.py", "max_stars_repo_name": "iguanaus/maml-auto", "max_stars_repo_head_hexsha": "833ae74f821279c0eddfcaff2ff2ede3c9fc6dc6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pandas as pd
import numpy as np
from sklearn.impute import KNNImputer
from sklearn.preprocessing import LabelEncoder
import pickle
from imblearn.over_sampling import RandomOverSampler
class Preprocessor:
"""
This class shall be used to clean and transform the data before training.
"""
... | {"hexsha": "c73382e2f1e9999f3a6030517181db37b8df667b", "size": 12757, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_preprocessing/preprocessing.py", "max_stars_repo_name": "dipesg/Thyroid-Classification", "max_stars_repo_head_hexsha": "b5f1a7ef1b8a6c3af6bf188529ed16471e82d8dd", "max_stars_repo_licenses": ... |
# MIT License
#
# Copyright (c) 2021 Aditya Shridhar Hegde
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modif... | {"hexsha": "feccc4c6f08e978045883da79a18c472701adf10", "size": 3234, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/he_meanshift_evaluate.py", "max_stars_repo_name": "encryptogroup/SoK_ppClustering", "max_stars_repo_head_hexsha": "6b008a09bfe3f3b8074e24059ac3e2aa6b87f227", "max_stars_repo_licenses": ["MIT... |
## License: Apache 2.0. See LICENSE file in root directory.
## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.
#import pyrealsense2 as rs
#import numpy as np
from classes.realsense import RealSense
from classes.objloader import *
import copy
import numpy as np
import cv2
import os
#import screeninfo
CV... | {"hexsha": "6de7124dc9c738da55dacea4a39b68aceb1aa12e", "size": 7112, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/testSquares.py", "max_stars_repo_name": "snavas/PyMote", "max_stars_repo_head_hexsha": "9ac51251abbc943fcd36fbb58ff5c3031d375c14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
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
SUBROUTINE CLOSEG (MSNAP, SNAPDX, NSNAP, X, Y... | {"hexsha": "40cb450277526fcf6258dfdefcdfa8619363a29e", "size": 1780, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/fastq/closeg.f", "max_stars_repo_name": "jschueller/seacas", "max_stars_repo_head_hexsha": "14c34ae08b757cba43a3a03ec0f129c8a168a9d3", "max_stars_repo_licenses": ["Pyt... |
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