text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import LSFIR
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
Fpass2 = 11.0 # MHz Passband end frequency
Fstop2 = 15.0 #MHz Stopband start frequency
Fstop1 = 5.0
Fpass1 = 4.0
Fsamp = 50.0 # MHz Sampling Frequency
Weight = 100 # Weig... | {"hexsha": "8e7824bece198e7adab91a728b14ae32cf2770ab", "size": 5691, "ext": "py", "lang": "Python", "max_stars_repo_path": "LSDesignAdvanced.py", "max_stars_repo_name": "SiddhantRaman/Least-Squared-Error-Based-FIR-Filters", "max_stars_repo_head_hexsha": "0b77fd51462b49009cc6038ce37d5ee9cd413e55", "max_stars_repo_licens... |
#pragma once
#include <cstddef>
#include <boost/config.hpp>
#include <boost/version.hpp>
#include <boost/utility/addressof.hpp>
//! Workaround (honestly, a hack) for cases when Blackhole is being compiled with clang on systems
//! with boost 1.55 on board.
//!
//! Stolen from https://svn.boost.org/trac/boost/ticket/... | {"hexsha": "ac1055fa95589791c8be65c92793ba5376662790", "size": 1526, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/hack/addressof.hpp", "max_stars_repo_name": "JakariaBlaine/blackhole", "max_stars_repo_head_hexsha": "e340329c6e2e3166858d8466656ad12300b686bd", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
using FFTW, Jets, JetPackTransforms, Test
@testset "fft, 1D, complex" begin
n = 512
m = rand(n) + im * rand(n)
d = rand(n) + im * rand(n)
A = JopFft(ComplexF64, n)
lhs, rhs = dot_product_test(A, m, d)
@test lhs ≈ rhs
expected = fft(m) / sqrt(n)
observed = A * m
@test expected ≈ obs... | {"hexsha": "22e4d91368f1544737592a2de546d525c854bfa0", "size": 4593, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/jop_fft.jl", "max_stars_repo_name": "ChevronETC/JetPackTransforms.jl", "max_stars_repo_head_hexsha": "0609c749455d539076057796411d627fb30a8e7d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
@testset "geometry.coords3d" begin
@testset "cartesian3d" begin
c3d = cartesian3d(float.([1 2 3; 4 5 6; 7 8 9; 10 11 12]))
@test size(c3d.coords) == (4, 3)
@test sum(x_components(c3d)) == 22
@test sum(y_components(c3d)) == 26
@test sum(z_components(c3d)) == 30
end
end # geometry.coords3d
| {"hexsha": "3a1220031958dbc40600d81198ff16fdb12fb7f2", "size": 313, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/geometry/coords3d.jl", "max_stars_repo_name": "Kelvyn88/MolecularGraph.jl", "max_stars_repo_head_hexsha": "ffe7732400dd16c7f5ddfb61972616fa6392cd8f", "max_stars_repo_licenses": ["MIT"], "max_st... |
using KernelRidgeRegression
using MLKernels
using Base.Test
using StatsBase
@test GaussianKernel(3.0) == GaussianKernel(3.0)
N = 5000
x = rand(1, N) * 4π - 2π
yy = sinc.(x) # vec(sinc.(4 .* x) .+ 0.2 .* sin.(30 .* x))
y = squeeze(yy + 0.1randn(1, N), 1)
xnew = collect(-2.5π:0.01:2.5π)'
mykrr = fit(KRR, x, y, 1e-3/5... | {"hexsha": "6426a56633675db8c70ee5a76c7e558112763d3e", "size": 3106, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "mattborghi/KernelRidgeRegression.jl", "max_stars_repo_head_hexsha": "ea79e41b04672782efd01bc5d9b295adbc9d3f74", "max_stars_repo_licenses": ["MIT"], "max_s... |
from collections import Iterable
import numpy as np
import openmdao.api as om
from .constants import INF_BOUND
class _ReprClass(object):
"""
Class for defining objects with a simple constant string __repr__.
This is useful for constants used in arg lists when you want them to appear in
automaticall... | {"hexsha": "6a1e8bee26acaa8b8857dd7dcbfc604a211cdafe", "size": 15392, "ext": "py", "lang": "Python", "max_stars_repo_path": "dymos/utils/misc.py", "max_stars_repo_name": "kaushikponnapalli/dymos", "max_stars_repo_head_hexsha": "3fba91d0fc2c0e8460717b1bec80774676287739", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""This module creates and manages a SQL database as a log for all jobs
submitted via the exoctk web app.
Authors
-------
- Joe Filippazzo
Use
---
This module is intended to be imported and used within a separate
python environment, e.g.
::
from exoctk import log_exoctk
log_exoctk.... | {"hexsha": "2b4bbbea1a71882b2233d5df63416cedfe020012", "size": 6893, "ext": "py", "lang": "Python", "max_stars_repo_path": "exoctk/log_exoctk.py", "max_stars_repo_name": "bourque/exoctk", "max_stars_repo_head_hexsha": "1d2f8e7b9c00e74033626d81593b1f879b7df6ad", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
(************************************************************************)
(* * The Coq Proof Assistant / The Coq Development Team *)
(* v * INRIA, CNRS and contributors - Copyright 1999-2018 *)
(* <O___,, * (see CREDITS file for the list of authors) *)
(* \VV/ *********... | {"author": "Priyanka-Mondal", "repo": "Coq", "sha": "220c3eccfa5643b1ca2398d4940e29917da786d9", "save_path": "github-repos/coq/Priyanka-Mondal-Coq", "path": "github-repos/coq/Priyanka-Mondal-Coq/Coq-220c3eccfa5643b1ca2398d4940e29917da786d9/lib/plugins/setoid_ring/Ncring_polynom.v"} |
import numpy as np
# divide matrix by row-sums
mat = np.mat([[4,2],[2,3]])
print(mat/mat.sum(axis=1))
# divide matrix by col-sums
mat = np.mat([[1,2],[3,4]])
print(mat/mat.sum(axis=0)) | {"hexsha": "8d483e85cdaeef8c1319120ea0d09599cceb59ee", "size": 187, "ext": "py", "lang": "Python", "max_stars_repo_path": "basic_operations.py", "max_stars_repo_name": "Valentindi/numpy_cheatsheet", "max_stars_repo_head_hexsha": "0a1ff493778caef0dab60a019adf3f3c2756e1b8", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
melange_lite.py
A short description of the project.
Handles the primary functions
"""
from __future__ import division, print_function
from jax import numpy as jnp
import numpy as np
from jax.config import config; config.update("jax_enable_x64", True)
from jax import lax, ops, vmap, jit, grad, random
class SMCSa... | {"hexsha": "e9246ae980d98dcbcd1c409565d1aed90a0774aa", "size": 12374, "ext": "py", "lang": "Python", "max_stars_repo_path": "melange_lite/melange_lite.py", "max_stars_repo_name": "dominicrufa/melange_lite", "max_stars_repo_head_hexsha": "0683997d7296a5d6f5a10bab1895f9b417c948c3", "max_stars_repo_licenses": ["MIT"], "ma... |
import sys
import json
import requests
import numpy as np
from flask import Flask, make_response
from flask import render_template
from flask import Flask
app = Flask(__name__)
from config import consumer_key, access_token, redirect_uri
@app.route("/")
def hello():
resp = get_pocket_data()
return render_temp... | {"hexsha": "5252ae2e4a6ee22a0580b86bde84a87de07f851e", "size": 2056, "ext": "py", "lang": "Python", "max_stars_repo_path": "pocket_stats.py", "max_stars_repo_name": "sarthak-s/Pocket-Stats", "max_stars_repo_head_hexsha": "fc41cafcd00e08f8a7dfe5b84cf4e483c81f530d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma image_filter_cartesian_product_correct:
fixes f :: "'x \<times> 'y \<rightharpoonup> 'z"
assumes I[simp, intro!]: "s1.invar s1" "s2.invar s2"
shows "s3.\<alpha> (image_filter_cartesian_product f s1 s2)
= { z | x y z. f (x,y) = Some z \<and> x\<in>s1.\<alpha> s1 \<and> y\<in>s2.\<alph... | {"llama_tokens": 2351, "file": "Collections_ICF_gen_algo_SetGA", "length": 11} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
##############################################
# The MIT License (MIT)
# Copyright (c) 2020 Kevin Walchko
# see LICENSE for full details
##############################################
import os
if 'BLINKA_MCP2221' in os.environ.keys():
pass
else:
os.environ['BLINK... | {"hexsha": "9e5359ac608384d43f5b66104c76955602cb45d8", "size": 679, "ext": "py", "lang": "Python", "max_stars_repo_path": "rtf_sensors/imu_serial_node.py", "max_stars_repo_name": "RecklessTedsFunland/rtf_sensors", "max_stars_repo_head_hexsha": "880e93b1a358ff3ea65f5c90949c483e52ac44c7", "max_stars_repo_licenses": ["MIT... |
"""
vp_overlap.py
Do calculations for overlap type functionals
"""
import numpy as np
from scipy.special import erf
def vp_overlap(self):
const = 2
#Calculate overlap
self.E.S = self.grid.integrate((np.sum(self.na_frac, axis=1) * np.sum(self.nb_frac, axis=1))**(0.5))
self.E.F = erf( const * self.E.S... | {"hexsha": "9313a0b57d0151eb9f07f972b2d8e498359a38d7", "size": 1399, "ext": "py", "lang": "Python", "max_stars_repo_path": "CADMium/partition/vp_overlap.py", "max_stars_repo_name": "VHchavez/CADMium", "max_stars_repo_head_hexsha": "39f3bd63ca69502a80c677855da72f9e691b57e2", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
using Documenter, GigaSOM
makedocs(modules = [GigaSOM],
clean = false,
format = Documenter.HTML(prettyurls = !("local" in ARGS),
canonical = "https://lcsb-biocore.github.io/GigaSOM.jl/stable/",
assets = ["assets/gigasomlogotransp.ico"]),
sitename = "GigaSOM.jl",
... | {"hexsha": "bd306d13d75e1753f8406dda0c1067fe419ea7a1", "size": 904, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "UnofficialJuliaMirror/GigaSOM.jl-a03a9c34-069e-5582-a11c-5c984cab887c", "max_stars_repo_head_hexsha": "e229624c78f170fb20389f619f820e676971c7ec", "max_stars_re... |
module AutoOffload
using LinearAlgebra, AbstractFFTs, FFTW
@static if Base.find_package("CuArrays") !== nothing
using CuArrays
if Float64(CuArrays.CUDAdrv.totalmem(first(CuArrays.CUDAdrv.devices()))) > 1e9
@info("CUDA support found, automatic GPU acceleration will be enabled.")
const GPU_SUPPO... | {"hexsha": "d085eb4fd9bf3844a6816e8748ce1198cdbba1cc", "size": 1946, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AutoOffload.jl", "max_stars_repo_name": "ChrisRackauckas/AutoOffload.jl", "max_stars_repo_head_hexsha": "b649a6abff31aa20a4253ea4ca070a89ada27452", "max_stars_repo_licenses": ["MIT"], "max_star... |
import gc
import copy
from inferelator import utils
from inferelator.single_cell_workflow import SingleCellWorkflow
from inferelator.regression.base_regression import _RegressionWorkflowMixin
import numpy as np
# These are required to run this module but nothing else
# They are therefore not package dependencies
imp... | {"hexsha": "0b11272689a4bf1aac8b8223c8c9a5d36a24d361", "size": 4897, "ext": "py", "lang": "Python", "max_stars_repo_path": "inferelator/benchmarking/celloracle.py", "max_stars_repo_name": "Xparx/inferelator", "max_stars_repo_head_hexsha": "2a33c741c4ba7a6bf3d18a3c14d583af0e0705e8", "max_stars_repo_licenses": ["BSD-2-Cl... |
using TupleTools, Base.Cartesian
export loop_einsum, loop_einsum!, allow_loops
"""
loop_einsum(::EinCode, xs, size_dict)
evaluates the eincode specified by `EinCode` and the tensors `xs` by looping
over all possible indices and calculating the contributions ot the result.
Scales exponentially in the number of dis... | {"hexsha": "b6a4cec9db9a5c5a6a84f2d59ffd95f4bb15887b", "size": 2075, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/loop_einsum.jl", "max_stars_repo_name": "kshyatt/OMEinsum.jl", "max_stars_repo_head_hexsha": "dd42a5c365f56d50a1a744b4e390671c0a3c4905", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
# ----------------------------------------------------------------------------------
# # Presenting Word Frequency Results
# ----------------------------------------------------------------------------------
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as ... | {"hexsha": "067e4e8f0cbcbfb94893c4ff3afeaa230e52073a", "size": 1801, "ext": "py", "lang": "Python", "max_stars_repo_path": "BurnieYilmazRS19/8_PresentRelativeFrequency.py", "max_stars_repo_name": "Charles0009/crypto_finance_analysis", "max_stars_repo_head_hexsha": "028938afabf0e9fbf352e8136acdc5d9753ba56d", "max_stars_... |
#include "stdafx.h"
#include <boost/test/unit_test.hpp>
#include <boost/filesystem.hpp>
#include "ExternalSourceModule.h"
#include "ExternalSinkModule.h"
#include "FileReaderModule.h"
#include "FrameMetadata.h"
#include "Frame.h"
#include "Logger.h"
#include "AIPExceptions.h"
#include "FramesMuxer.h"
#include "test_u... | {"hexsha": "c810164cd944fe2368bc5104cdf785b270dac663", "size": 5466, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "base/test/task_test.cpp", "max_stars_repo_name": "shrikant-pattawi/ApraPipes", "max_stars_repo_head_hexsha": "5d9ed8f00b924cd03bc1d27871ab56bd5b433022", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import glob
import pickle
import re
# Our numerical workhorses
import numpy as np
import pandas as pd
# Import the project utils
import sys
sys.path.insert(0, '../')
# Import matplotlib stuff for plotting
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from IPython.core.pylabto... | {"hexsha": "e3034310bcb1ad9ecf80101651c9dc594d1b45d7", "size": 23200, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/figures/fig2_knowngenes_matrices_logos.py", "max_stars_repo_name": "RPGroup-PBoC/sortseq_belliveau", "max_stars_repo_head_hexsha": "ca3b0b8092bbe6deaf1b82b2dab67b4bcca679f2", "max_stars_repo... |
"""Implementation for the linear binning procedure."""
from typing import Tuple
from numba import njit
import numpy as np
from kernreg.funcs_to_jit import include_weights_from_endpoints
include_weights_from_endpoints_jitted = njit(include_weights_from_endpoints)
def linear_binning(
x: np.ndarray,
y: np.nd... | {"hexsha": "4c169b09edc224897b5b09a2fd03c8df564de4ef", "size": 3294, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/kernreg/linear_binning.py", "max_stars_repo_name": "segsell/kernreg", "max_stars_repo_head_hexsha": "2a8c4e73a42994fa6331ca49de8bc3d11a9f7a74", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\documentclass{article}
\title{SigViz: real-time signal visualizer for MCEs}
\author{Lorenzo Minutolo \\
California Institute of Technology \\
\and
Sofia Fatigoni \\
University of British Columbia \\
}
\date{\today}
\begin{document}
\maketitle
\tableofcontents
\newpage
\section{About this document}\label{abo... | {"hexsha": "a968ceb9e04bcaf207ad4a49d3f4ef09345eec5f", "size": 1133, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Documentation/Technical_doc.tex", "max_stars_repo_name": "LorenzoMinutolo/SigViz", "max_stars_repo_head_hexsha": "9cf85dcb8d8cf1bd04e4c808396fc24efb530d7c", "max_stars_repo_licenses": ["MIT"], "max_... |
@testset "1637.widest-vertical-area-between-two-points-containing-no-points.jl" begin
@test max_width_of_vertical_area([[8,7],[9,9],[7,4],[9,7]]) == 1
@test max_width_of_vertical_area([[3,1],[9,0],[1,0],[1,4],[5,3],[8,8]]) == 3
end | {"hexsha": "f8fe19696f5d0bb5a16819c0389a2a696dc2d87e", "size": 239, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/1637.widest-vertical-area-between-two-points-containing-no-points.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07a... |
[STATEMENT]
lemma repv_selectlike_other: "x\<noteq>y \<Longrightarrow> (repv \<omega> x d \<in> selectlike X \<omega> {y}) = (repv \<omega> x d \<in> X)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<noteq> y \<Longrightarrow> (repv \<omega> x d \<in> selectlike X \<omega> {y}) = (repv \<omega> x d \<in> X)
[PR... | {"llama_tokens": 1574, "file": "Differential_Game_Logic_Coincidence", "length": 12} |
# Copyright 2020 - 2021 MONAI Consortium
# 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": "5d72c028f940a572d30d1f281a2834c7306ccf5d", "size": 9273, "ext": "py", "lang": "Python", "max_stars_repo_path": "monai/handlers/utils.py", "max_stars_repo_name": "dylanbuchi/MONAI", "max_stars_repo_head_hexsha": "1651f1b003b0ffae8b615d191952ad65ad091277", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
MODULE NWTC_Num
! This module contains numeric-type routines with non-system-specific logic and references.
! It contains the following routines:
! SUBROUTINE AddOrSub2Pi ( OldAngle, NewAngle )
! SUBROUTINE BSortReal ( RealAry, NumPts )
! FUNCTION CROSS_PRODUCT ( Vector1, Vector2... | {"hexsha": "2727954b7dfb70f50f254d37f7c485948ba3d6c6", "size": 49558, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/WT_Perf/v3.05.00a-adp/Source/NWTC_Subroutine_Library/NWTC_Lib_v1.05.00/source/NWTC_Num.f90", "max_stars_repo_name": "NREL/HARP_Opt", "max_stars_repo_head_hexsha": "5a00d0adb34af1c412ed05... |
# -*- coding: utf-8 -*-
# This Program
import time
import h5py
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.layers import Input, ZeroPadding2D, Conv2D, BatchNormalization, Activation, Flatten, Dense
from keras.layers import MaxPooling2D
from keras.models import Model
def F1S... | {"hexsha": "8aace6f15ac7195403e3ee9ddc4b6eb1d2a5a4ca", "size": 7550, "ext": "py", "lang": "Python", "max_stars_repo_path": "L_layer_ConvNet_keras/L_layer_ConvNet_keras.py", "max_stars_repo_name": "HerrHuber/L_layer_ConvNet_keras", "max_stars_repo_head_hexsha": "3b0b80a446fd60ef3215ab19de5a8d0d96e9f4b5", "max_stars_repo... |
"""
Basic of linear algebra.
"""
import numpy as np
a = np.array([[1.,2.],[3.,4.]])
print(a)
print(np.transpose(a))
print(np.linalg.det(a.transpose()))
print(np.linalg.inv(a))
print(np.trace(a))
print(np.eye(3)) # identity matrix
y = np.array([[3.],[7.]])
print(np.linalg.solve(a,y)) # solve x+2y==3 && 3x+4y==7
print(n... | {"hexsha": "41961a2f6df2d41556490ec51f400fb7ab1dbdb8", "size": 394, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy/np_ex006.py", "max_stars_repo_name": "rpoliselit/python-for-dummies", "max_stars_repo_head_hexsha": "d6f45a966a5238058953f93d8660832fa692b3d4", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import pickle
import numpy as np
import tensorflow as tf
import PIL.Image
import pandas as pd
import os
import sys
import argparse
import PIL
import os
import glob
import numpy as np
import tensorflow as tf
import tfutil
#----------------------------------------------------------------------------
#... | {"hexsha": "32d49dadc3cf206158d3d1f462f78b735b5b132a", "size": 14392, "ext": "py", "lang": "Python", "max_stars_repo_path": "GAN_cpd/inference.py", "max_stars_repo_name": "AugustDS/extd_med_benchmark", "max_stars_repo_head_hexsha": "3dc4f5c00ba98f79c70336ec7a5723586c145231", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @author: Wesley
# @time: 2020-12-11 10:47
import os
import cv2
import torch
from models.unet import UNet
from torchvision import transforms
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = UNet(1, 1).to(device)
weight = r'... | {"hexsha": "592053af1fab1c1da7ba5ad2b44bb83a16df92ba", "size": 1252, "ext": "py", "lang": "Python", "max_stars_repo_path": "detect.py", "max_stars_repo_name": "Wesley-Tse/Road-Detection", "max_stars_repo_head_hexsha": "c3b444287d9b41ccc4234e737e4421b5d1b3c3da", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
[STATEMENT]
lemma raw_has_prod_Suc:
"raw_has_prod f (Suc M) a \<longleftrightarrow> raw_has_prod (\<lambda>n. f (Suc n)) M a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. raw_has_prod f (Suc M) a = raw_has_prod (\<lambda>n. f (Suc n)) M a
[PROOF STEP]
unfolding raw_has_prod_def
[PROOF STATE]
proof (prove)
goal ... | {"llama_tokens": 256, "file": null, "length": 2} |
import k3d
import numpy as np
import pytest
from .plot_compare import *
import vtk
from vtk.util import numpy_support
def test_volume():
prepare()
reader = vtk.vtkXMLImageDataReader()
reader.SetFileName('./test/assets/volume.vti')
reader.Update()
vti = reader.GetOutput()
x, y, z = vti.GetDim... | {"hexsha": "b3c1ea3f11a54176fccf5036edd7c8a78af34862", "size": 1558, "ext": "py", "lang": "Python", "max_stars_repo_path": "k3d/test/test_visual_volume.py", "max_stars_repo_name": "mjpolak/K3D-jupyter", "max_stars_repo_head_hexsha": "539c53cab580d55b8841bb87589ab3d4cf95bdb0", "max_stars_repo_licenses": ["MIT"], "max_st... |
function cross_map(r::MersenneTwister)
map = YAML.load_file("maps/cross.yaml")
exits = [(4, 1), (1, 4), (4, 7), (7, 4)]
start = rand(r, exits)
exit = rand(r, setdiff(exits, [start]))
map["starts"] = [Dict("x"=>start[2], "y"=>start[1])]
map["exits"] = [Dict("x"=>exit[2], "y"=>exit[1])]
push!(... | {"hexsha": "668721f73bc4f74930af4697d897d3c1b7050a20", "size": 1728, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/fitness/cross.jl", "max_stars_repo_name": "d9w/RoboGrid.jl", "max_stars_repo_head_hexsha": "e217adfa3e8351017746d4fede8399e92ba5df73", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 11 13:30:53 2017
@author: laoj
"""
import numpy as np
import pymc3 as pm
import theano.tensor as tt
from pymc3.distributions.distribution import Discrete, draw_values, generate_samples, infer_shape
from pymc3.distributions.dist_math import bound, lo... | {"hexsha": "81977d254cadb7ee5093cb2ff32e221394f8fe36", "size": 8455, "ext": "py", "lang": "Python", "max_stars_repo_path": "Miscellaneous/test_script_pymc3/multinominal.py", "max_stars_repo_name": "junpenglao/Planet_Sakaar_Data_Science", "max_stars_repo_head_hexsha": "73d9605b91b774a56d18c193538691521f679f16", "max_sta... |
# -*- coding: utf-8 -*-
"""
gyroid.util
===========
"""
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
from matplotlib import colors
from mayavi import mlab
from .unitcell import UnitCell
from .group import Group
from .grid import Grid
from .basis import Basis
__all__ = [
"render_structure_1... | {"hexsha": "8637d27fa89e08b1d8e3302b9ac28265984d4669", "size": 8527, "ext": "py", "lang": "Python", "max_stars_repo_path": "gyroid/util.py", "max_stars_repo_name": "liuyxpp/liuyxpp-gyroid", "max_stars_repo_head_hexsha": "7db91cb140869760124a66239773822bc2cd4e44", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
// Copyright 2004-present Facebook. All Rights Reserved.
#include "fboss/agent/hw/sai/fake/FakeSaiInSegEntry.h"
#include <boost/functional/hash.hpp>
namespace facebook::fboss {
FakeSaiInSegEntry::FakeSaiInSegEntry(sai_inseg_entry_t other_sai_inseg_entry) {
sai_inseg_entry.switch_id = other_sai_inseg_entry.switch_... | {"hexsha": "9cf71b6007b6625beac7bb677f425191c9e0480c", "size": 977, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "fboss/agent/hw/sai/fake/FakeSaiInSegEntry.cpp", "max_stars_repo_name": "nathanawmk/fboss", "max_stars_repo_head_hexsha": "9f36dbaaae47202f9131598560c65715334a9a83", "max_stars_repo_licenses": ["BSD-3... |
import tensorflow as tf
import numpy as np
from .data_aug import get_data_aug_fn
AUTOTUNE = tf.data.experimental.AUTOTUNE
def get_cifar10_data(batch_size, data_aug, train_data_size=None,
repeat=True, shuffle=True, shuffle_size=None):
train_data, test_data = tf.keras.datasets.cifar10.load_data()
train... | {"hexsha": "eb66fb1c457596cf72448d93fab105bf973ddb31", "size": 1490, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/cifar10.py", "max_stars_repo_name": "SeanJia/InfoMCR", "max_stars_repo_head_hexsha": "2b4760ad6ffdd98859fea1967eb1b8aa7e51be52", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, ... |
###########################################################
## reading and saving data #
###########################################################
## Copyright (c) 2018, National Institute of Informatics #
## Author: Fuming Fang #
## Affiliation: Nat... | {"hexsha": "8e2bf82ff96feffee1a5763c2a2125411edcdab8", "size": 4527, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataio.py", "max_stars_repo_name": "entn-at/lcnn", "max_stars_repo_head_hexsha": "797d8847fad6d1179866ac2d7d7402240483123b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 15, "ma... |
#!/usr/bin/env python
"""
Test the Iron Component code. This code can be run from teh command line:
> python test_fe.py --datafile /user/jotaylor/git/spamm//Data/FakeData/Iron_comp/fakeFe1_deg.dat
--redshift 0.5
"""
import os
import datetime
import numpy as np
import time
import argparse
import glob
from utils.pars... | {"hexsha": "d268f8ab3448d4f6983a6707ce15fd953a7f9230", "size": 3947, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/run_hg.py", "max_stars_repo_name": "jotaylor/SPAMM", "max_stars_repo_head_hexsha": "3087269cb823d6f4022ebf1dd75d920dee7c1cc0", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"], "max_star... |
#!/usr/bin/env python
#
# This program shows how to use mpi_comm_split
#
import numpy
from numpy import *
from mpi4py import MPI
import sys
def myquit(mes):
MPI.Finalize()
print(mes)
sys.exit()
comm=MPI.COMM_WORLD
myid=comm.Get_rank()
numprocs=comm.Get_size()
print("hello from ",myid," of ",... | {"hexsha": "37997b0501408b7c09511c93a6816d8d8be6e15b", "size": 2011, "ext": "py", "lang": "Python", "max_stars_repo_path": "array/bot/others/P_ex12.py", "max_stars_repo_name": "timkphd/examples", "max_stars_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
from glob import iglob
import numpy as np
import matplotlib.pyplot as plt
import scipy
import random
from scipy import ndimage
from scipy import signal
from scipy import interpolate
from scipy import fft
import audio.segment as seg
import audio.utils as utils
# https://en.wikipedia.org/wiki/Short-time_Fo... | {"hexsha": "c9b3742bac0dbfcda040f7311df14c69ce0bbbe6", "size": 22007, "ext": "py", "lang": "Python", "max_stars_repo_path": "voice.py", "max_stars_repo_name": "tednoob/RePhase", "max_stars_repo_head_hexsha": "3b9a30018682463bcdd6029be491cb1f7129c048", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
subroutine setprob()
implicit none
double precision pi, pi2
common /compi/ pi,pi2
double precision uvel, vvel
common /comvelocity/ uvel, vvel
open(10,file='setprob.data')
read(10,*) uvel
read(10,*) vvel
close(10)
pi = 4.d0*datan(1.d0)
pi2 = 2.d... | {"hexsha": "9ff6495a9c859650a73900576ee898ba21fa626b", "size": 337, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "applications/clawpack/advection/2d/periodic/setprob.f", "max_stars_repo_name": "ECLAIRWaveS/ForestClaw", "max_stars_repo_head_hexsha": "0a18a563b8c91c55fb51b56034fe5d3928db37dd", "max_stars_repo_li... |
# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import os
import sys
import pickle
import datetime
import matplotlib.pyplot as plt
from readthyroid import *
# 874 1840
img_channels = 1
iterations = 40
batch_size = 46
total_epoch = 150
test_iterations = 59
test_size = 46
weight_decay = 0.0003
dropout_... | {"hexsha": "9850ad17028fa7edc4c6430bd8006797f2c6f8ae", "size": 17190, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/g_load_vgg.py", "max_stars_repo_name": "Stomach-ache/semi-MList", "max_stars_repo_head_hexsha": "ce694a4eb831c3e7d1d727678b8b46d71efc628e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.ticker import PercentFormatter
data = pd.read_csv('C:\\Users\\stewue\\OneDrive - Wuersten\\Uni\\19_HS\\Masterarbeit\\Repo\\Evaluation\\RQ1_Results\\aggregated\\numberofbenchmarks.csv',dtype='str')
numberOf = data['benchmarks'].asty... | {"hexsha": "5530349f6bf0569e4941c3b5590ca9908cafd079", "size": 1497, "ext": "py", "lang": "Python", "max_stars_repo_path": "RQ1_Python/number_of_benchmarks.py", "max_stars_repo_name": "stewue/masterthesis-evaluation", "max_stars_repo_head_hexsha": "0fb825e196f386c628f95524aa9c80af2126617e", "max_stars_repo_licenses": [... |
> module EscardoOliva.TestSelectionFunction
> import EscardoOliva.SelectionFunction
> %default total
> %access public export
> %auto_implicits off
> xs : List Int
> xs = [0,3,2,-1,0,9,-7]
> min : Int
> min = arginf xs id
> max : Int
> max = argsup xs id
| {"hexsha": "b6cfc59d6ab92ebc98770bd7bc5ebd04b6f8b4d8", "size": 260, "ext": "lidr", "lang": "Idris", "max_stars_repo_path": "EscardoOliva/TestSelectionFunction.lidr", "max_stars_repo_name": "zenntenn/IdrisLibs", "max_stars_repo_head_hexsha": "a81c3674273a4658cd205e9bd1b6f95163cefc3e", "max_stars_repo_licenses": ["BSD-2-... |
! -*- Mode: Fortran; -*-
!
! (C) 2014 by Argonne National Laboratory.
! See COPYRIGHT in top-level directory.
!
subroutine MPI_Comm_spawn_multiple_f08(count, array_of_commands, array_of_argv, array_of_maxprocs, &
array_of_info, root, comm, intercomm, array_of_errcodes, ierror)
use, intrinsic :: iso_c_bind... | {"hexsha": "ff6d80c2109a76cd86a0a971a39058cb4b53180d", "size": 3295, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/binding/fortran/use_mpi_f08/wrappers_f/comm_spawn_multiple_f08ts.f90", "max_stars_repo_name": "humairakamal/FG-MPI", "max_stars_repo_head_hexsha": "a0181ecde8a97e60ab6721a5e9a74dc7e7f77e77",... |
module CommonUtils
using Caesar
using Images
using FileIO
using Cairo
using RoMEPlotting
export plotSLAM2D_KeyAndSim, plotHMSLevel
@deprecate buildDEMSimulated(w...;kw...) RoME.generateField_CanyonDEM(w...;kw...)
@deprecate getSampleDEM(w...;kw...) RoME.generateField_CanyonDEM(w...;kw...)
@deprecate loadDEM!(w...;k... | {"hexsha": "b64d19a5394b0c37409182f27599009d87a13a4e", "size": 1060, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/dev/scalar/CommonUtils.jl", "max_stars_repo_name": "nkhedekar/Caesar.jl", "max_stars_repo_head_hexsha": "647ab1a9a068e9eb9ff2de36e12e86b7b77878bb", "max_stars_repo_licenses": ["MIT"], "max... |
'''
Navigation Network, Written by Xiao
For robot localization in a dynamic environment.
'''
import numpy as np
from lib.params import ADJACENT_NODES_SHIFT_GRID
ACTION_ENCODING = dict(left=np.array([1,0,0]), right=np.array([0,1,0]), forward=np.array([0,0,1]))
ACTION_CLASSNUM = len(ACTION_ENCODING) # dimension of actio... | {"hexsha": "e31b2422fabc05a7b29a0b38710d197ec531787b", "size": 2094, "ext": "py", "lang": "Python", "max_stars_repo_path": "Network/navigation_network/params.py", "max_stars_repo_name": "XiaoLiSean/Cognitive-Map", "max_stars_repo_head_hexsha": "6b2019e5b3a46902b06c8d5d1e86b39425042de9", "max_stars_repo_licenses": ["MIT... |
# Code made for Sergio Andrés Díaz Ariza
# 05 Abril 2021
# License MIT
# Introduction to Control: Python Program Assignment 1
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
import control as co
import sympy as sp
import seaborn as sns
sns.set()
# Define Transafer Function
G1 = co.tf([1... | {"hexsha": "fe02a501afa053332d8f21f43074b9f411460beb", "size": 2084, "ext": "py", "lang": "Python", "max_stars_repo_path": "First_Order/2.4_Taller_1_Sistemas_Lneales.py", "max_stars_repo_name": "Daz-Riza-Seriog/I_Control", "max_stars_repo_head_hexsha": "d4568f67d735fa5dab619e006fd29c31ce5248ea", "max_stars_repo_license... |
# Copyright 2018 The TensorFlow 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 applica... | {"hexsha": "44ea23998fe6f3b614fb09b9667add179cf3fd85", "size": 3567, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/keras/engine/training_utils_test.py", "max_stars_repo_name": "aeverall/tensorflow", "max_stars_repo_head_hexsha": "7992bf97711919f56f80bff9e5510cead4ab2095", "max_stars_repo_lice... |
from collections import defaultdict
import dolfin as df
import numpy as np
from xii.meshing.embedded_mesh import EmbeddedMesh
class SubDomainMesh(EmbeddedMesh):
'''Embedded mesh for cell funcions.'''
def __init__(self, marking_function, markers):
assert marking_function.dim() == marking_function.mesh... | {"hexsha": "2a690a868b514886a414ac3e9eeb5e8a61b0b443", "size": 4855, "ext": "py", "lang": "Python", "max_stars_repo_path": "xii/meshing/subdomain_mesh.py", "max_stars_repo_name": "MiroK/fenics_ii", "max_stars_repo_head_hexsha": "58c41f0e8dba720962830395851e081b057269cc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
//---------------------------------------------------------------------------//
//!
//! \file MonteCarlo_CollisionHandler.hpp
//! \author Alex Robinson
//! \brief Collision handler class declaration
//!
//---------------------------------------------------------------------------//
#ifndef MONTE_CARLO_COLLISION_HAN... | {"hexsha": "52cf894dfe507bd04391bfceb0942b156e4ef331", "size": 5414, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "packages/monte_carlo/collision/native/src/MonteCarlo_CollisionHandler.hpp", "max_stars_repo_name": "lkersting/SCR-2123", "max_stars_repo_head_hexsha": "06ae3d92998664a520dc6a271809a5aeffe18f72", "ma... |
import numpy as _np
from ._sum_inplace import sum_inplace as _sum_inplace
from netket.utils import (
mpi_available as _mpi_available,
n_nodes as _n_nodes,
MPI_comm as MPI_comm,
)
if _mpi_available:
from netket.utils import MPI
def subtract_mean(x, axis=None):
"""
Subtracts the mean of the in... | {"hexsha": "81c255c16e85badded9d8486baafeabb91b3bf1a", "size": 2171, "ext": "py", "lang": "Python", "max_stars_repo_path": "netket/stats/mpi_stats.py", "max_stars_repo_name": "ChenAo-Phys/netket", "max_stars_repo_head_hexsha": "df3735993962ca6318dee0b86a5d15a9d37c9881", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""Tests pathless_data_processor.py."""
import numpy as np
from mlops.dataset.pathless_data_processor import PathlessDataProcessor
PRESET_RAW_FEATURES = np.array(
[
[10, 20, 30, 40],
[0, 20, 40, 50],
[10, 20, 20, 60],
[20, 20, 50, 70],
[10, 20, 10, 80],
[10, 20, 60,... | {"hexsha": "da6a32d4234a3fde236dfb082927572c34c51f1b", "size": 2206, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/dataset/test_pathless_data_processor.py", "max_stars_repo_name": "kostaleonard/mlops", "max_stars_repo_head_hexsha": "236d3499535d6294768c15336180217829fb2ee3", "max_stars_repo_licenses": ["... |
from functools import reduce
import numpy as np
import pandas as pd
import scipy.stats as scs
import matplotlib.pyplot as plt
from standard_precip.lmoments import distr
class BaseStandardIndex():
'''
Calculate the SPI or SPEI index. A user specified distribution is fit to the precip data.
The CDF of thi... | {"hexsha": "3559db93e7d90401a23ec013514b0456613a4ac8", "size": 10213, "ext": "py", "lang": "Python", "max_stars_repo_path": "standard_precip/base_sp.py", "max_stars_repo_name": "e-baumer/standard_precip", "max_stars_repo_head_hexsha": "8945ba399a3493464a860b9901d648bdecc86354", "max_stars_repo_licenses": ["Apache-2.0"]... |
# Números complexos
Neste notebook exploramos alguns aspectos dos números complexos. Especialmente, vamos falar da interferência entre duas ondas da mesma frequência.
Vimos nas aulas passadas que uma função cossenoidal geral, expressa por:
\begin{equation}
x(t) = \mathrm{Re}\left\{A\mathrm{e}^{-\mathrm{j}\phi} \ \m... | {"hexsha": "6f3cc8582c51b2fc73ea188182d50e158c157086", "size": 233764, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Aula 7 - Numeros complexos/Numeros complexos.ipynb", "max_stars_repo_name": "RicardoGMSilveira/codes_proc_de_sinais", "max_stars_repo_head_hexsha": "e6a44d6322f95be3ac288c6f1bc4f7cf... |
import LinearAlgebra, Distributions, Random, Statistics, DataFrames
"""
simulate_coefs_correlation(coefs_mean::Number=0.1; coefs_sd::Number=0.1, n::Int=10)
Generate a vector of random correlation coefficients from a normal distribution.
# Arguments
- `coefs_mean::Number`: Mean of the normal distribution from ... | {"hexsha": "ac80c44413ce8fd979999b5150c9e9089670375d", "size": 3577, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/simulate/data_correlation.jl", "max_stars_repo_name": "neuropsychology/Psycho.jl", "max_stars_repo_head_hexsha": "ec812f48ff520588e36d833ce124572dc7ff585c", "max_stars_repo_licenses": ["MIT"], ... |
! C function declarations
type, bind(C) :: float3
real(kind = 4) :: x, y, z
end type
interface
function create_npcf_c(timesRans, numShells, volBox, rMin, rMax) bind(C, name="create_npcf")
use iso_c_binding
implicit none
type(c_ptr) :: create_npcf_c
integer(c_int), value :: time... | {"hexsha": "827bfd8a26cd57dae77befd5581fcdf5de586a20", "size": 3185, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source/ganpcf_cdef.f90", "max_stars_repo_name": "dpearson1983/ganpcf", "max_stars_repo_head_hexsha": "d75fddfb094045a81916ffed10fec19d96b6d52e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import copy
import argparse
import numpy as np
from tqdm import tqdm
from sklearn.cluster import KMeans
from plyfile import PlyData, PlyElement
def parse_args():
parser = argparse.ArgumentParser(description='Keypoints generator')
parser.add_argument('--dataset', default='hinterstoisser',
... | {"hexsha": "6d007c8748b99d3496325f0b70ab850b0ac0c2cb", "size": 4322, "ext": "py", "lang": "Python", "max_stars_repo_path": "kps/kp.py", "max_stars_repo_name": "qingchenkanlu/6dpose-genedata", "max_stars_repo_head_hexsha": "fbf973208eabdf11efd8bb384b1bc74963328193", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import sys
import os
import pytest
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
recordings_path = os.path.join(THIS_DIR, os.pardir, '../res/data/')
from src.music.score import Pieces
from src.model.model import Model
LENGTH_THRESHOLD = 3
@pytest.mark.parametrize("piece,tempo,recording",... | {"hexsha": "e25f127b66fa0bc0ae3adfe429b50600a6d403a4", "size": 1473, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/integration/test_model.py", "max_stars_repo_name": "dartmouth-cs98/20w-ensemble-vr-score-following", "max_stars_repo_head_hexsha": "3effbb47ac48580666a6642734c3738f4e3b427d", "max_stars_repo_... |
#
# Copyright (c) European Molecular Biology Laboratory (EMBL)
#
# 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, m... | {"hexsha": "b27cd925d65f801e8931e5fc4d0c74ad92ed55b5", "size": 11166, "ext": "py", "lang": "Python", "max_stars_repo_path": "edna2/lib/autocryst/src/Image.py", "max_stars_repo_name": "gsantoni/edna2", "max_stars_repo_head_hexsha": "0aad63a3ea8091ce62118f0b2c8ac78a2286da9e", "max_stars_repo_licenses": ["CC0-1.0", "MIT"]... |
# Autogenerated wrapper script for GnuPG_jll for i686-linux-gnu
export dirmngr, dirmngr_client, gpg, gpg_agent, gpg_connect_agent, gpgconf, gpgscm, gpgsm, gpgtar, gpgv, kbxutil
using GnuTLS_jll
using Libksba_jll
using Libgcrypt_jll
using Libgpg_error_jll
using nPth_jll
using Zlib_jll
using Libassuan_jll
using OpenLDAP... | {"hexsha": "928e2b0b1ae25163f18d4a5a4c060df0034881e4", "size": 2262, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/i686-linux-gnu.jl", "max_stars_repo_name": "JuliaBinaryWrappers/GnuPG_jll.jl", "max_stars_repo_head_hexsha": "edb5da03c4efeee73499d507001b3043197501d8", "max_stars_repo_licenses": ["MI... |
taskid(t=current_task()) = string(hash(t) & 0xffff, base=16, pad=4)
debug_header() = string("DEBUG: ", rpad(Dates.now(), 24), taskid(), " ")
macro debug(n::Int, s)
DEBUG_LEVEL[] >= n ? :(println(debug_header(), $(esc(s)))) :
:()
end
macro debugshow(n::Int, s)
DEBUG_LEVEL[] >= n ? :(p... | {"hexsha": "88e1cd563ad2f88cee0d43eb37786e8b854f8ceb", "size": 3305, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/debug.jl", "max_stars_repo_name": "c42f/URIs.jl", "max_stars_repo_head_hexsha": "a4c725b5090b21bed5cf6ca37cff196d80f32d1d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 514, "max_star... |
# Copyright 2019 Pascal Audet & Helen Janiszewski
#
# This file is part of OBStools.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the ri... | {"hexsha": "df0c5104a35c06bf09fc3cde10b096023e65d824", "size": 13729, "ext": "py", "lang": "Python", "max_stars_repo_path": "obstools/comply/classes.py", "max_stars_repo_name": "paudetseis/OBStools", "max_stars_repo_head_hexsha": "c6c02d8864c25a14f22d1fae17ff5ad911b9ff00", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# I/O helper functions on text files
.b = import('../base', attach_operators = FALSE)
#' Add \code{ext}ension parameter to \link{\code{base::file.path}}
file_path = function (..., ext = NULL, fsep = .Platform$file.sep) {
dots = list(...)
if (! is.null(ext)) {
ilast = length(dots)
dots[ilast] = ... | {"hexsha": "2233457b7d12a1da78cf26e1fac8ff051b9dad62", "size": 2238, "ext": "r", "lang": "R", "max_stars_repo_path": "io/text.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_star... |
import numpy as np
import torch
from utils import Tokenizer
embed_size = 300
def create_embedding_matrix(tokenizer, embedding_file):
"""
Load pretrained embedding and output the npy contains the pretrained vectors
"""
embeddings_index = {}
with open(embedding_file, encoding='utf8') as f:
f... | {"hexsha": "d4c90ea9d3acd0798b9a949145e5e2c30c4e9776", "size": 1598, "ext": "py", "lang": "Python", "max_stars_repo_path": "create_embedding_matrix.py", "max_stars_repo_name": "ngthanhtin/VLSP_ImageCaptioning", "max_stars_repo_head_hexsha": "46a2b430cc07c444fb69609a8c06670de2db8c36", "max_stars_repo_licenses": ["MIT"],... |
import json
import numpy as np
import re
from collections import defaultdict as dd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import HashingVecto... | {"hexsha": "9ada6536d35ac6100be6130e7a182a5f59d251e5", "size": 2098, "ext": "py", "lang": "Python", "max_stars_repo_path": "allthree.py", "max_stars_repo_name": "abigailyuan/LIDproj", "max_stars_repo_head_hexsha": "3e34c4d78b89c9513182ab064dc4b3858f59a1d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import params
import category_theory.category.basic
import category_theory.core
open params
open category_theory
namespace operations
variable [category (bitvec word_len)]
/-!
# Operations
Building blocks operations.
The salsa20 cipher is built fully with add-rotate-XOR operations.
## Building blocks o... | {"author": "oxarbitrage", "repo": "salsa20", "sha": "12d0ebb3c27801931e61d470fb2ed548a5562578", "save_path": "github-repos/lean/oxarbitrage-salsa20", "path": "github-repos/lean/oxarbitrage-salsa20/salsa20-12d0ebb3c27801931e61d470fb2ed548a5562578/src/operations.lean"} |
function to_non_normal(l::Vector{FieldsTower}, G::GAP.GapObj, deg::Int)
for x in l
assure_automorphisms(x)
assure_isomorphism(x, G)
end
lC = GAP.Globals.ConjugacyClassesSubgroups(G)
ind = 0
for i = 1:length(lC)
r = GAP.Globals.Representative(lC[i])
if GAP.Globals.Size(r) == divexact(degree(l[1... | {"hexsha": "79f271864c92bfdb72e56f3ebb7c168bbdcc6174", "size": 2950, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FieldFactory/non_normal.jl", "max_stars_repo_name": "lgoe-ac/Hecke.jl", "max_stars_repo_head_hexsha": "27a6f75d0174a9e3db2480e1f835f62ae65befc6", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
###
# @file aggregate_output.py
#
# @brief Aggregates multiple EINSim outputs into one coalesced file
#
# @author Minesh Patel
# Contact: minesh.patelh@gmail.com
import sys
import os
import argparse
import random
import numpy as np
import json
# project files
import utils
... | {"hexsha": "2dd70c93dfda439878b3d9bee932523c697a2408", "size": 6203, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/utils/aggregate_output.py", "max_stars_repo_name": "CMU-SAFARI/EINSim", "max_stars_repo_head_hexsha": "f3e782658dd19070ca0e85fcc422014c2283280e", "max_stars_repo_licenses": ["MIT"], "max_st... |
import csv
import numpy as np
import os
def writePoints(points,ptsFileName):
csv.writer(open(ptsFileName,'w'),delimiter=' ').writerows(points)
def batchWritePoints(batchPoints,outputDir):
for i in range(batchPoints.shape[0]):
writePoints(batchPoints[i,:,:],os.path.join(outputDir,str(i)+".pts")) | {"hexsha": "4cae3f00604e930bc0aed31db8d22d91a338048c", "size": 313, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/writer.py", "max_stars_repo_name": "sanjeevmk/GLASS", "max_stars_repo_head_hexsha": "91c0954eab87d25d4866fea5c338f79fbca4f79e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_... |
subroutine secpred(j)
use constants_module
use arrays_module
use var_module
use arrays_section_module
use xsec_attribute_module
use subtools
implicit none
integer, intent(in) :: j
! Locals
integer :: i, pp, tableLength
real(kind=4) :: beds, fs, hy, yyn, yyn_1, temp1, temp2... | {"hexsha": "05d5e4d1886ab80e78653da3a88bf53430b166e3", "size": 3447, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src_combined_Y_network/secpred.f90", "max_stars_repo_name": "MESH-Team/MESH_Code_irregular", "max_stars_repo_head_hexsha": "1edd0b60a8b2ccd95ec92b4fc7e381193cf6b936", "max_stars_repo_licenses": ... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
torch.multiprocessing.set_sharing_strategy('file_system')
from tqdm import tqdm
import numpy as np
import os
from os.path import join, basename
from boltons.fileutils import iter_find_files
import soundfi... | {"hexsha": "65ac236c1753d65908320a38c444a197d60cb2ce", "size": 5157, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloader.py", "max_stars_repo_name": "YaelSegal/UnsupSeg", "max_stars_repo_head_hexsha": "a8657565967e871064118d1ce2b452c033d05c50", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
[STATEMENT]
lemma IO_language : "IO M q t \<subseteq> language_state M q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. IO M q t \<subseteq> LS M q
[PROOF STEP]
by (metis atc_reaction_path IO.elims language_state mem_Collect_eq subsetI) | {"llama_tokens": 94, "file": "Adaptive_State_Counting_ATC_ATC", "length": 1} |
[STATEMENT]
lemma swap_apply[simp]: "swap (a \<otimes>\<^sub>u b) = (b \<otimes>\<^sub>u a)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. swap (a \<otimes>\<^sub>u b) = b \<otimes>\<^sub>u a
[PROOF STEP]
unfolding swap_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (Snd;Fst) (a \<otimes>\<^sub>u b) = b \<ot... | {"llama_tokens": 181, "file": "Registers_Laws", "length": 2} |
import numpy as np
import torch
Ranges = {
'pelvis': [[0, 0], [0, 0], [0, 0]],
'pelvis0': [[-0.3, 0.3], [-1.2, 0.5], [-0.1, 0.1]],
'spine': [[-0.4, 0.4], [-1.0, 0.9], [-0.8, 0.8]],
'spine0': [[-0.4, 0.4], [-1.0, 0.9], [-0.8, 0.8]],
'spine1': [[-0.4, 0.4], [-0.5, 1.2], [-0.4, 0.4]],
'spine3': [[-... | {"hexsha": "58ae66102d2264322158e784763871dbfc895cb5", "size": 6653, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/joint_limits_prior.py", "max_stars_repo_name": "chaneyddtt/Coarse-to-fine-3D-Animal", "max_stars_repo_head_hexsha": "b3f9b1031b5761838c94ca091095636101747fd9", "max_stars_repo_licenses": ["MI... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_iris
#Load iris data set
iris_data = load_iris()
iris = pd.DataFrame(iris_data['data'], columns=iris_data['feature_names'])
iris.info()
iris.describe()
setosa_x = iris['sepal length (cm)'][:... | {"hexsha": "7825caf72bb7831c675ff54e21d1cc3c21e0f4f9", "size": 1363, "ext": "py", "lang": "Python", "max_stars_repo_path": "Iris.py", "max_stars_repo_name": "Akberovr/Support-Vector-Machine-Neural-Networks", "max_stars_repo_head_hexsha": "6d94853c88c30eb83a18c7ad986b85c1d87c3fc6", "max_stars_repo_licenses": ["MIT"], "m... |
from numpy import ndarray
from model_spaces.core.gp_model import GPModel
from model_spaces.core.hyperpriors import Hyperpriors
class KernelKernelGPModel(GPModel):
def __init__(self, kernel_kernel_hyperpriors: Hyperpriors):
covariance = None
super().__init__(covariance, kernel_kernel_hyperpriors)... | {"hexsha": "f23728e8bb51698b6618c310a1be93c074735132", "size": 380, "ext": "py", "lang": "Python", "max_stars_repo_path": "strategies/boms/kernel_kernel_gp_model.py", "max_stars_repo_name": "lschlessinger1/boms-python", "max_stars_repo_head_hexsha": "5ad6035a91c1eb3d33556ddfee25b99ba18ee431", "max_stars_repo_licenses":... |
(* The types of finite sets and bags *)
theory FSets_Bags
imports "../NonFreeInput"
begin
(* Datatype of finite sets: *)
nonfree_datatype 'a fset = Emp | Ins 'a "'a fset"
where
Ins1: "Ins a (Ins a A) = Ins a A"
| Ins2: "Ins a1 (Ins a2 A) = Ins a2 (Ins a1 A)"
declare Ins1[simp]
(* Datatype of bags: *)
nonfree_data... | {"author": "metaforcy", "repo": "nonfree-data", "sha": "f3ce28278a88fdd240faa2e51f893fee5c15f2f2", "save_path": "github-repos/isabelle/metaforcy-nonfree-data", "path": "github-repos/isabelle/metaforcy-nonfree-data/nonfree-data-f3ce28278a88fdd240faa2e51f893fee5c15f2f2/Examples/FSets_Bags.thy"} |
import os
import random
import numpy as np
import pandas as pd
import seaborn as sns
import gym
import matplotlib.pyplot as plt
plt.style.use('bmh')
import matplotlib
matplotlib.rcParams['font.family'] = 'IPAPGothic'
def reset_seeds():
random.seed(9949)
np.random.seed(9967)
import tensorflow as tf; tf.set_rand... | {"hexsha": "f60d9bbc36608d24b736cdb58ed54fd132d9eb41", "size": 5814, "ext": "py", "lang": "Python", "max_stars_repo_path": "dnn/rl_gym.py", "max_stars_repo_name": "takashi-matsushita/lab", "max_stars_repo_head_hexsha": "894e5762f58046c68e665d7463db3d7359c15fda", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
"""
Function to pool the univariate estimators
Arguments:
unibetas::Array{Unibeta, 1} -> Array which contains the univariate estimators which have to be pooled
"""
function pool_unibetas(unibetas::Array{Unibeta, 1})
pooledbetas = unibetas[1].n .* unibetas[1].unibeta
sumN = unibetas[1].n
@simd for i = 2 : length(uni... | {"hexsha": "a0961eac7c637ea98f11c14cd86b4b8dd0e34fc8", "size": 1473, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pooling.jl", "max_stars_repo_name": "danielazoeller/DistributedBoosting", "max_stars_repo_head_hexsha": "aaac7133efe6a70711f9331049e5874dcc68a967", "max_stars_repo_licenses": ["MIT"], "max_star... |
""" Testing array writer objects
See docstring of :mod:`nibabel.arraywriters` for API.
"""
from platform import python_compiler, machine
import itertools
import numpy as np
from io import BytesIO
from ..arraywriters import (SlopeInterArrayWriter, SlopeArrayWriter,
WriterError, ScalingErro... | {"hexsha": "22684ac9550ddeb0f36af831df6decf81d2d8b4a", "size": 37367, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/nibabel/tests/test_arraywriters.py", "max_stars_repo_name": "richung99/digitizePlots", "max_stars_repo_head_hexsha": "6b408c820660a415a289726e3223e8f558d3e18b", "max_stars_... |
# License: Apache-2.0
import copy
import warnings
from typing import Union
import databricks.koalas as ks
import numpy as np
import pandas as pd
from ..data_cleaning.drop_columns import DropColumns
from ..transformers.transformer import Transformer
from ..util import util
from ._base_encoder import _BaseEncoder
cla... | {"hexsha": "ae08cdb6383e5136214e7b9d3b9d9639b6e79ca7", "size": 10540, "ext": "py", "lang": "Python", "max_stars_repo_path": "gators/encoders/multiclass_encoder.py", "max_stars_repo_name": "Aditya-Kapadiya/gators", "max_stars_repo_head_hexsha": "d7c9967e3a8e304a601b6a92ad834d03d3e36338", "max_stars_repo_licenses": ["Apa... |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : initialization.py
@Time : 2021/12/11 17:21:18
@Author : Lin Junwei
@Version : 1.0
@Desc : initialization class and function
'''
#%% import
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import ti... | {"hexsha": "f182bc0cbfe158c8264fbfbc68bd1dce3e938b99", "size": 19604, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/initialization.py", "max_stars_repo_name": "Limjohns/OptFinal", "max_stars_repo_head_hexsha": "d2876865d888a6b4a2dc271cc04b3b97b8c0fbcc", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# import numpy as np
import networkx as nx
import pandas as pd
from bokeh.models import BoxSelectTool
from bokeh.models import Circle
from bokeh.models import HoverTool
from bokeh.models import MultiLine
from bokeh.models import TapTool
from bokeh.models.graphs import EdgesAndLinkedNodes
from bokeh.models.graphs impo... | {"hexsha": "b4f51af5181f2793a02a1facffe398966db2e411", "size": 2588, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/twistmap/__init__.py", "max_stars_repo_name": "mchwalisz/walker", "max_stars_repo_head_hexsha": "8447352ba23324e7f1ad564d626efad9760e3570", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import sys, os
import numpy as np
import scipy
import torch
import torch.nn as nn
from scipy import ndimage
from tqdm import tqdm, trange
from PIL import Image
import torch.hub
import torchvision
import torch.nn.functional as F
# download deeplabv2_resnet101_msc-cocostuff164k-100000.pth from
# https://github.com/kazut... | {"hexsha": "c450f5452a5d7cd137acad93aab40b7353d1819f", "size": 3797, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/extract_segmentation.py", "max_stars_repo_name": "B1boid/taming-transformers", "max_stars_repo_head_hexsha": "5638360d3de989547ae1b3f04d494187ae08b8ba", "max_stars_repo_licenses": ["MIT"],... |
program main
integer main_out
main_out = main1()
print *, "main1 called"
contains
integer function main1()
integer :: i = 10
if (i .GT. 5) then
main1 = i
print *, "early return"
return
end if
print *, "normal return"
m... | {"hexsha": "e0e0e335de168092c4cad6ea0b8316b2f0f98fd8", "size": 364, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "integration_tests/return_01.f90", "max_stars_repo_name": "Thirumalai-Shaktivel/lfortran", "max_stars_repo_head_hexsha": "bb39faf1094b028351d5aefe27d64ee69302300a", "max_stars_repo_licenses": ["BS... |
import os
os.chdir('osmFISH_AllenSSp/')
from scvi.dataset import CsvDataset
from scvi.models import JVAE, Classifier
from scvi.inference import JVAETrainer
import numpy as np
import pandas as pd
import copy
import torch
import time as tm
### osmFISH data
osmFISH_data = CsvDataset('data/gimVI_data/osmFISH... | {"hexsha": "6732ae3e542b474679697d3a28c2b831a99ef0c3", "size": 4587, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmark/osmFISH_AllenSSp/gimVI/gimVI.py", "max_stars_repo_name": "tabdelaal/SpaGE", "max_stars_repo_head_hexsha": "7533cbf2275c3049561e8a17b9f7866e0e324743", "max_stars_repo_licenses": ["MIT"], ... |
# from https://github.com/SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions/blob/master/selim_sef
import os
import torch
from torch import nn
from torch.utils import model_zoo
from src.models.resnet import resnet34
encoder_params = {
'resnet34':
{
'filters': [64, 64, 128, 256, 512],
... | {"hexsha": "b80fc8da68bb63da82b223ad7108d1c5a7e04321", "size": 8671, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/unet.py", "max_stars_repo_name": "kevinkwshin/kaggle-pneumothorax", "max_stars_repo_head_hexsha": "24b91a9425097023f0cc7781a9380cb247babe22", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
from loginit import get_module_logger
from sklearn.decomposition import PCA, KernelPCA
from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import cross_validate
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import... | {"hexsha": "75c5cccb2362d40952ea4ab08da3bea2086d870c", "size": 13836, "ext": "py", "lang": "Python", "max_stars_repo_path": "classify/kernel_eval.py", "max_stars_repo_name": "OminiaVincit/scale-variant-topo", "max_stars_repo_head_hexsha": "6945bc42aacd0d71a6fb472c87e09da223821e1e", "max_stars_repo_licenses": ["MIT"], "... |
from bson import json_util
import json
import os
import numpy as np
import tensorflow as tf
from keras.layers.core import K #import keras.backend as K
import time
import pandas as pd
import multiprocessing
#
from keras.preprocessing import text, sequence
from keras.preprocessing.text import Tokenizer
from keras.utils ... | {"hexsha": "83663c35c9a7b7d5e9b6087f0826f94225c82bb6", "size": 15354, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_neu/optimized/hyperutils.py", "max_stars_repo_name": "lelange/cu-ssp", "max_stars_repo_head_hexsha": "9f1a7abf79a2fb6ef2ae0f37de79469c2dc3488f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
/-
Copyright (c) 2021 Justus Springer. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Justus Springer
-/
import category_theory.sites.spaces
import topology.sheaves.sheaf
import category_theory.sites.dense_subsite
/-!
# Coverings and sieves; from sheaves on sites an... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/topology/sheaves/sheaf_condition/sites.lean... |
(* This code is copyrighted by its authors; it is distributed under *)
(* the terms of the LGPL license (see LICENSE and description files) *)
(* *************************************************************************
Buchberger :... | {"author": "coq-community", "repo": "buchberger", "sha": "7625647c300bb5f155f6bf40b69c232f64819a4f", "save_path": "github-repos/coq/coq-community-buchberger", "path": "github-repos/coq/coq-community-buchberger/buchberger-7625647c300bb5f155f6bf40b69c232f64819a4f/theories/Preducestar.v"} |
#! /usr/bin/env python3
import argparse
import os
import time
import numpy as np
import cv2
import dlib
here = os.path.abspath(os.path.dirname(__file__))
_predictor_path = 'shape_predictor_68_face_landmarks.dat'
_casc_path = 'haarcascade_frontalface_alt.xml'
predictor_path = os.path.join(here, _predictor_path)
casc_... | {"hexsha": "8a394a6af0af506780f1e1e80f733c81c33431e5", "size": 11892, "ext": "py", "lang": "Python", "max_stars_repo_path": "faceswap.py", "max_stars_repo_name": "JSharpClone/faceswap", "max_stars_repo_head_hexsha": "be706a4e29a5bb7777e3cec1fab03ce14b8ae06a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import json
import csv
import os
import copy
import numpy as np
from camel_tools.calima_star.database import CalimaStarDB
from camel_tools.calima_star.analyzer import CalimaStarAnalyzer
from camel_tools.disambig.mle import MLEDisambiguator
import torch
class InputExample:
"""Simple object to encapsulate each data ... | {"hexsha": "9f9bb2d5ee31df14deb8ff7ae9dba5ed190b6613", "size": 12245, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/data_utils.py", "max_stars_repo_name": "CAMeL-Lab/gender-reinflection", "max_stars_repo_head_hexsha": "006de318a326c8ea67d610adb30d3e0a8d6e59db", "max_stars_repo_licenses": ["MIT"], "max_st... |
module IdemInvo where
open import Relation.Binary.PropositionalEquality
module MainResult
(A : Set)
(f : A → A)
(idem : ∀ x → f (f x) ≡ f x)
(invo : ∀ x → f (f x) ≡ x)
where
-- an idempotent and involutive function is an identity function
iden : ∀ x → f x ≡ x
iden x = trans (sym (idem x)) (invo x)
| {"hexsha": "f615f5affe8428621df82b16cb8316583e03c572", "size": 318, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "IdemInvo.agda", "max_stars_repo_name": "zaklogician/IdemInvo", "max_stars_repo_head_hexsha": "44f16597c9ef9596f6dc1b628848a3a74fa9a19b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2016 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
#... | {"hexsha": "879128a31825d2b3718814bfe58d3f401dadf254", "size": 6395, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySRU/tests/MagneticFieldTest.py", "max_stars_repo_name": "SophieTh/und_Sophie_2016", "max_stars_repo_head_hexsha": "28e1520e86f342cf35f862fd4bf56c51dc191b91", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma in_pdata_pairs_to_listI2:
assumes "(f, g) \<in> set ps"
shows "monom_mult (1 / lc (fst g)) ((lcs (lp (fst f)) (lp (fst g))) - (lp (fst g)))
(fst g) \<in> set (pdata_pairs_to_list ps)" (is "?m \<in> _")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. monom_mult ((1::'b) / lc (fst g)... | {"llama_tokens": 4035, "file": "Groebner_Bases_F4", "length": 23} |
%----------------------------------------------------------------------------------------
% PACKAGES AND OTHER DOCUMENT CONFIGURATIONS
%----------------------------------------------------------------------------------------
\documentclass[letterpaper]{twentysecondcv} % a4paper for A4
\awards {
\begin{itemize}
\ite... | {"hexsha": "c4dda7e9b9622104f9907a1e386c691432a7d387", "size": 6057, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "template.tex", "max_stars_repo_name": "vrublevskiyvitaliy/infographic", "max_stars_repo_head_hexsha": "c9a5a2e1b59af715a9fdd4675c7b8e2950b216d0", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.