text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 25 16:02:21 2018
@author: hecc
"""
import numpy as np
from itertools import combinations, permutations
def get_distance_matrix(pos):
n = np.shape(pos)[0]
d = np.zeros((n, n))
for ii in range(n):
for jj in range(ii + 1, n):
d[ii, jj] = np.... | {"hexsha": "6a47d5156a40b4de6d411aadbf840fd14e3ee74c", "size": 2950, "ext": "py", "lang": "Python", "max_stars_repo_path": "sagar/molecule/symmetry.py", "max_stars_repo_name": "unkcpz/sagar", "max_stars_repo_head_hexsha": "097a9e77200d79e40c45c2741c9c1e61a1013b22", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
.. module:: instrument
:platform: Unix
:synopsis: functions describing behaviour of instrument spectra.
.. moduleauthor: Ben Thorne <ben.thorne@physics.ox.ac.uk>
"""
import numpy as np
from .foreground import fg_res_sys, dust_cl, synch_cl
def N_ell(ell, beam, sens):
"""Gaussian white-noise spectrum for ... | {"hexsha": "5697ed0bb1b7a96c03acb7c887b01ab78928b304", "size": 4384, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyranha/instrument.py", "max_stars_repo_name": "bthorne93/pyranha", "max_stars_repo_head_hexsha": "3803b2e87129906c018e20876ba25c0e097d6c25", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import pytest
from dnnv.nn import OperationGraph, operations
from dnnv.nn.transformers.slicers import DropPrefix, Slicer
@pytest.fixture
def op_graph():
input_op = operations.Input(np.array([1, 5]), np.dtype(np.float32))
mul_op = operations.Mul(input_op, np.float32(1))
div_op = operati... | {"hexsha": "eee621b8bea122d16150087c883a0c7482c89e98", "size": 2345, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit_tests/test_nn/test_transformers/test_slicers.py", "max_stars_repo_name": "samysweb/dnnv", "max_stars_repo_head_hexsha": "58fb95b7300914d9da28eed86c39eca473b1aaef", "max_stars_repo_licen... |
import numpy as np
import conect
import os
import sys
import operator
from time import time
"""
Este programilla se encarga de analizar el accuracy, precision, recall y F1 Score sobre los resultados generados
en el entrenamiento con corte 1 año previo a la BBDD actual.
Consideramos el estado de un predicció... | {"hexsha": "2c51b714c803fb6b1ac7a2bf2a1bea152681e5a8", "size": 6083, "ext": "py", "lang": "Python", "max_stars_repo_path": "recomendador1v2_training/otrosEjecutables/analizador.py", "max_stars_repo_name": "alfonsoelmas/recomendadores", "max_stars_repo_head_hexsha": "b7bf79be99253a8ffe34c98e661a5e0e64cd0795", "max_stars... |
#! /usr/bin/env python
import cv2
import numpy as np
import scipy.spatial as spatial
import logging
## 3D Transform
def bilinear_interpolate(img, coords):
""" Interpolates over every image channel
http://en.wikipedia.org/wiki/Bilinear_interpolation
:param img: max 3 channel image
:param coords: 2 x _m... | {"hexsha": "0d008f51f4c62166761bca36fb15829c69aba748", "size": 6220, "ext": "py", "lang": "Python", "max_stars_repo_path": "2DwithLandmarkFaceSwap/face_swap.py", "max_stars_repo_name": "ForrestPi/faceSwapProjects", "max_stars_repo_head_hexsha": "daf2649a2791a25aa541c4d6d3b7e1d6552be5d7", "max_stars_repo_licenses": ["MI... |
'''
Code from GroudSeg.py implementation found at the github repository:
https://github.com/mitkina/EnvironmentPrediction
For the implementation of Random Markov Field ground segmentation described in:
G. Postica, A. Romanoni, and M. Matteucci. Robust moving objects detection in LiDAR
data exploiting vis... | {"hexsha": "1b56fb3e9fb65b1f5a10009604ee98712a48aa19", "size": 3843, "ext": "py", "lang": "Python", "max_stars_repo_path": "CODES_data_generation/ground_segmentation.py", "max_stars_repo_name": "sisl/Double-Prong-Occupancy", "max_stars_repo_head_hexsha": "8698b1c732c240fccaac7971b06de241af000229", "max_stars_repo_licen... |
module PSDMatrices
import Base: \, /, size, inv, copy, copy!, ==, show, similar, Matrix
using LinearAlgebra
import LinearAlgebra: det, logabsdet, diag
struct PSDMatrix{T,FactorType} <: AbstractMatrix{T}
R::FactorType
PSDMatrix(R::AbstractMatrix{T}) where {T} = new{T,typeof(R)}(R)
end
# Base overloads
Matrix... | {"hexsha": "9c68c4056fe9bcec8647b2a2ebc51debde0bd2a6", "size": 2779, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PSDMatrices.jl", "max_stars_repo_name": "nathanaelbosch/PSDMats.jl", "max_stars_repo_head_hexsha": "d12423020f0f06fb0263f07430bace01a864063c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
PROGRAM FITHIST
C
REAL HIST(14,4,100),HIST1(100),XAX(101)
CHARACTER*120 JUNK
INTEGER*4 NFHIST(14)
REAL*8 SCETSTS,SCETEND
C
OPEN(UNIT=89,FILE='FOR089.DAT',STATUS='OLD',READONLY)
read (89,*) SCETSTS,SCETEND
READ (89,*) NFHIST,NHISTST
C
189 FORMAT(A)
irxlim = 4
irxlim = 2
do irx = 1,irxlim
do ifr = 1,14
... | {"hexsha": "7697a0474783e56b009a2abffdbc656adad92e95", "size": 3164, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "WAVES_VMS_Fortran/PJK_Fortran/wind_dir/fithist_47.for", "max_stars_repo_name": "lynnbwilsoniii/Wind_Decom_Code", "max_stars_repo_head_hexsha": "ef596644fe0ed3df5ff3b462602e7550a04323e2", "max_st... |
[STATEMENT]
lemma cycle_root_end_empty_var:
assumes "terminating_path_root_end r x e"
and "x \<noteq> 0"
shows "\<not> many_strongly_connected x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> many_strongly_connected x
[PROOF STEP]
using assms cycle_root_end_empty
[PROOF STATE]
proof (prove)
using... | {"llama_tokens": 224, "file": "Relational_Paths_Rooted_Paths", "length": 2} |
/-
Copyright (c) 2022 Rémi Bottinelli. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Rémi Bottinelli
-/
import category_theory.category.basic
import category_theory.functor.basic
import category_theory.groupoid
import tactic.nth_rewrite
import category_theory.path_cat... | {"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/category_theory/groupoid/free_groupoid.lean... |
[STATEMENT]
lemma imply_append: \<open>ps @ qs \<leadsto> r = ps \<leadsto> qs \<leadsto> r\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ps @ qs \<leadsto> r = ps \<leadsto> qs \<leadsto> r
[PROOF STEP]
by (induct ps) simp_all | {"llama_tokens": 108, "file": "Implicational_Logic_Implicational_Logic_Appendix", "length": 1} |
#!/usr/bin/env python
# plot the geometric factor K vs. the dipole separation of a dipole-dipole
# configuration
import crtomo.configManager as CRc
import numpy as np
# from crtomo.mpl_setup import *
import crtomo.mpl
plt, mpl = crtomo.mpl.setup()
from reda.utils.geometric_factors import compute_K_analytical
config = ... | {"hexsha": "edbaf1d8d3983c07ec0e33959b86c502e251b06b", "size": 1364, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/pyplots/plot_K_vs_dipol_sep.py", "max_stars_repo_name": "niklasj-h/crtomo_tools", "max_stars_repo_head_hexsha": "57a577ff2925c137fcc387ad49e3c9fe30025831", "max_stars_repo_licenses": ["MIT"], ... |
import unittest
import os
import numpy as np
import tensorflow as tf
import nnutil as nn
class Image_Rasterize(unittest.TestCase):
def test_image_rasterize_1(self):
tf.set_random_seed(42)
with tf.Session() as sess:
coord = tf.constant([[0.1, 0.1], [0.8, 0.1]], dtype=tf.float32)
... | {"hexsha": "4afc8543931de1355636b167bb003d0c7767b865", "size": 2691, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/image_rasterize.py", "max_stars_repo_name": "aroig/nnutil", "max_stars_repo_head_hexsha": "88df41ee89f592a28c1661ee8837dd8e8ca42cf3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
import torch
import numpy as np
def gd_step(f, x, alpha):
y = f(x)
y.backward()
g = x.grad
with torch.no_grad():
return x - alpha * g, y
def nesterov_step(f, x, alpha, beta):
val = f(x)
val.backward()
g = x.grad
with torch.no_grad():
x1 = x - alpha * g
return ... | {"hexsha": "548d9f19cecb3cd801b1aa6f76ca6bdc6f4d6b17", "size": 4167, "ext": "py", "lang": "Python", "max_stars_repo_path": "logistic_regression.py", "max_stars_repo_name": "JaworWr/MLAcceleration", "max_stars_repo_head_hexsha": "ef0e0661389782b0caeec9137b3d4ddd84643d2c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#' isSymmetric
#'
#' Test if a float matrix is symmetric.
#'
#' @param object
#' A float vector/matrix.
#' @param ...
#' Ignored.
#'
#' @return
#' A logical value.
#'
#' @examples
#' library(float)
#'
#' s = flrunif(10, 3)
#' isSymmetric(s)
#'
#' cp = crossprod(s)
#' isSymmetric(s)
#'
#' @useDynLib float R_isSym... | {"hexsha": "60c214a6e302f178c8b770535afac7312834e2c1", "size": 584, "ext": "r", "lang": "R", "max_stars_repo_path": "R/isSymmetric.r", "max_stars_repo_name": "david-cortes/float", "max_stars_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 35... |
# -*- coding: utf-8 -*-
"""
Deep Human Pose Estimation
Project by Walid Benbihi
MSc Individual Project
Imperial College
Created on Wed Jul 12 15:53:44 2017
@author: Walid Benbihi
@mail : w.benbihi(at)gmail.com
@github : https://github.com/wbenbihi/hourglasstensorlfow/
Abstract:
This python code creates a ... | {"hexsha": "b98c85838b799d94002303314ba5fa4bdfe5861e", "size": 20190, "ext": "py", "lang": "Python", "max_stars_repo_path": "datagen.py", "max_stars_repo_name": "mohaEs/Train-Predict-Landmarks-by-MCAM", "max_stars_repo_head_hexsha": "e06179fc91b33a7bc73e44df47a4cf53f36b0a2f", "max_stars_repo_licenses": ["MIT"], "max_st... |
import random
import pandas as pd
import numpy as np
from fuzzywuzzy.process import extractOne
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import confusion_matrix,classification_report
from sklearn.feature_extraction.text import CountVec... | {"hexsha": "9fceae1eb8ff74278222645684b8729acbaa389d", "size": 8449, "ext": "py", "lang": "Python", "max_stars_repo_path": "tokens2labels/NLPAlgorithms.py", "max_stars_repo_name": "mayaepps/Exercise-Logs", "max_stars_repo_head_hexsha": "59e37d351b97c3e34fe677e001e70abe16bb5133", "max_stars_repo_licenses": ["MIT"], "max... |
"""
Data access functions
---------------------
"""
from __future__ import absolute_import
from os.path import join as pjoin, basename, dirname
import subprocess
import tempfile
import logging
import numpy as np
import h5py
import rasterio
from rasterio.crs import CRS
from rasterio.warp import reproject
from rasterio... | {"hexsha": "1c2e869b0603b84c2ae7af7ba218f65b9f92ec56", "size": 17754, "ext": "py", "lang": "Python", "max_stars_repo_path": "wagl/data.py", "max_stars_repo_name": "ASVincent/wagl", "max_stars_repo_head_hexsha": "cf3a72e53e53f3a7b2f2b5308068069b1b714f2a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
#coding=utf-8
"""
"""
import sys
import math
import numpy as np
import numpy.linalg as la
import file
import collections
def clamping_acos(cos):
"""
Calculate arccos with its argument clamped to [-1, 1]
"""
if cos > 1:
return 0
if cos < -1:
return math.pi/2
return math.acos(co... | {"hexsha": "74145d1673940b9e9dc23420a7a28f47b57183a2", "size": 8436, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/core/bonds.py", "max_stars_repo_name": "sciapp/pyMolDyn", "max_stars_repo_head_hexsha": "fba6ea91cb185f916b930cd25b4b1d28a22fb4c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "... |
{"mathlib_filename": "Mathlib.Topology.Instances.RealVectorSpace", "llama_tokens": 0} | |
from .match_base import Matcher
import numpy as np
from .tester import Tester
from gensim.models import Word2Vec
class Word2Vec_Matcher(Matcher, Tester):
"""
Create a classifier based on the word2ved natural language model.
In order to make this work we treat every incoming column as a corpus.
The columns are... | {"hexsha": "6c8a504cf2afc249aceed7c9e21dd29f70232619", "size": 4102, "ext": "py", "lang": "Python", "max_stars_repo_path": "schema_matching/column_classifiers/match_word2vec.py", "max_stars_repo_name": "JordyBottelier/arpsas", "max_stars_repo_head_hexsha": "1d10f18d082b71ad2931852b8d88ad963add8fbe", "max_stars_repo_lic... |
"""Test tile classificaiton speed.
Use tensorflow to divide tiles, construct graph, and run inference.
Served as the onboard scripts to run on Jetson.
"""
from __future__ import absolute_import, division, print_function
import argparse
import glob
import numpy as np
import time
import os
import tensorflow as tf
from... | {"hexsha": "a5496aee376d4f338339077a9a0158e59ee0aa66", "size": 6663, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/tile_inference_speed/test_classification_speed.py", "max_stars_repo_name": "cmusatyalab/dronesearch", "max_stars_repo_head_hexsha": "9849637555185efa0a484f49bef43ad734964e8a", "max_sta... |
import sys
import typing
import numba as nb
import numpy as np
@nb.njit(
(nb.i8, ),
cache=True,
)
def fw_build(n: int) -> np.ndarray:
return np.full(n + 1, 0, np.int64)
@nb.njit(
(nb.i8[:], ),
cache=True,
)
def fw_build_from_array(
a: np.ndarray,
) -> np.ndarray:
fw = a.copy()
... | {"hexsha": "7fd30ea6b7403d4d78c5ac573e9d4e004fc1049a", "size": 1642, "ext": "py", "lang": "Python", "max_stars_repo_path": "jp.atcoder/abc185/abc185_f/25707777.py", "max_stars_repo_name": "kagemeka/atcoder-submissions", "max_stars_repo_head_hexsha": "91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e", "max_stars_repo_licenses":... |
# --------------------------------------------------------
# Ke Yan,
# Imaging Biomarkers and Computer-Aided Diagnosis Laboratory (CADLab)
# National Institutes of Health Clinical Center,
# Apr 2019.
# This file contains some default configuration values,
# which will be overwritten by values in config.yml and de... | {"hexsha": "84d91e59f9e808b22b6db95fd45e74204cb35af1", "size": 2703, "ext": "py", "lang": "Python", "max_stars_repo_path": "config.py", "max_stars_repo_name": "haehn/dicompute", "max_stars_repo_head_hexsha": "ee4364aaa1258a370bd62bbaf6e577936bf463b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
import argparse
import os
import numpy as np
import scanpy as sc
from scipy import sparse
import trvae
if not os.getcwd().endswith("tests"):
os.chdir("./tests")
DATASETS = {
"CelebA": {"name": 'celeba', "gender": "Male", 'attribute': "Smiling",
"width": 64, 'height': 64, "n_channels": 3},
... | {"hexsha": "fd308e21b50381d5ad12ddd55fb03b57fe19ec16", "size": 6248, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_facenet.py", "max_stars_repo_name": "gokceneraslan/trVAE", "max_stars_repo_head_hexsha": "596127b02f4a86ed6a91d5a3f666d6b5d97aff0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from cartographer.filterers import KernelDensityFilterer
from sklearn.datasets.samples_generator import make_blobs
from sklearn.utils.testing import assert_true, assert_raises
import numpy as np
def test_kde_one_dimension():
X, true_labels = make_blobs(n_samples=1000, n_features=1)
kde_filterer = KernelDensi... | {"hexsha": "8a03c9fdf7b01109b56530ad11a3beb6d493a777", "size": 738, "ext": "py", "lang": "Python", "max_stars_repo_path": "cartographer/tests/test_filterers.py", "max_stars_repo_name": "pablodecm/cartographer", "max_stars_repo_head_hexsha": "50c56af9962cc896697ba8f88885d9da7eb50148", "max_stars_repo_licenses": ["MIT"],... |
(*
* Copyright 2019, NTU
*
* This software may be distributed and modified according to the terms of
* the BSD 2-Clause license. Note that NO WARRANTY is provided.
* See "LICENSE_BSD2.txt" for details.
*
* Author: Albert Rizaldi, NTU Singapore
*)
theory Signed_Mult_Typed
imports VHDL_Hoare_Typed Bits_Int_Au... | {"author": "rizaldialbert", "repo": "vhdl-semantics", "sha": "352f89c9ccdfe830c054757dfd86caeadbd67159", "save_path": "github-repos/isabelle/rizaldialbert-vhdl-semantics", "path": "github-repos/isabelle/rizaldialbert-vhdl-semantics/vhdl-semantics-352f89c9ccdfe830c054757dfd86caeadbd67159/Signed_Mult_Typed.thy"} |
import numpy as np
file = np.loadtxt("num.csv",delimiter= ',')
print(file)
file = np.loadtxt("num.csv",delimiter= ',', skiprows=1)
print(file)
file = np.loadtxt("num.csv",delimiter= ',', usecols=[2,4])
print(file)
file = np.loadtxt("num.csv",delimiter= ',', usecols=[2,4], dtype=str)
print(file) | {"hexsha": "81f0fce1fc24b1c6e6aa2bb5c4c3943924a92c17", "size": 298, "ext": "py", "lang": "Python", "max_stars_repo_path": "Slides/10_numpy_loadcsv.py", "max_stars_repo_name": "sobil-dalal/Database-Analytical-Programming", "max_stars_repo_head_hexsha": "b9231e4fec11fd59955935639f308ca0417e6caa", "max_stars_repo_licenses... |
% ==========================================================================================
% ==========================================================================================
% ==========================================================================================
\documentclass[10pt]{beamer}
\mode<prese... | {"hexsha": "c856f7daf4f1c2f243772e01676dde8fbb0a38ba", "size": 5447, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "slides.tex", "max_stars_repo_name": "terhorstj/beamer", "max_stars_repo_head_hexsha": "8b715d460bea505f82df731395452ddf743a8c26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 8 10:36:17 2020
@author: created by Sowmya Myneni and updated by Dijiang Huang
"""
import numpy as np
import pandas as pd
from keras.utils import np_utils
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
def get_p... | {"hexsha": "ce5bd73bc2b392fd0b9453e60a9b7bd0be30f989", "size": 2385, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab_4/lab-cs-ml-00301/data_preprocessor.py", "max_stars_repo_name": "ChristopherBilg/cse-548-adv-comp-net-sec", "max_stars_repo_head_hexsha": "8d6256ace822e58cc662ef2fee476d1b1a3e60a4", "max_stars... |
# Copyright (C) 2019 Project AGI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | {"hexsha": "070d0152223c9070d46ba4152f076f7c8deea87e", "size": 33182, "ext": "py", "lang": "Python", "max_stars_repo_path": "aha/components/episodic_component.py", "max_stars_repo_name": "ProjectAGI/aha", "max_stars_repo_head_hexsha": "53a98ea42526dca56517dc97fffad874772f10f2", "max_stars_repo_licenses": ["Apache-2.0"]... |
# --------------
import pandas as pd
import os
import numpy as np
import warnings
warnings.filterwarnings("ignore")
# path_train : location of test file
# Code starts here
##Loading the CSV data onto a Pandas dataframe
df = pd.read_csv(path_train)
##Defining a function to check every row for a category m... | {"hexsha": "346cb63a597e4816f3ba5401150039387861e463", "size": 6845, "ext": "py", "lang": "Python", "max_stars_repo_path": "code.py", "max_stars_repo_name": "Vishal-Bhatia/domain-classification-text", "max_stars_repo_head_hexsha": "de016540c1c5a3eb94a8f9a88145f490dd8f2c10", "max_stars_repo_licenses": ["MIT"], "max_star... |
try:
import pickle as pickle
except ImportError:
import pickle
from bisect import bisect_right
from collections import defaultdict
from copy import deepcopy
from functools import partial
from itertools import chain
from operator import eq
def identity(obj):
"""Returns directly the argument *obj*.
"... | {"hexsha": "53a7bce0061dd6c27ef0f311089bf8e07727da23", "size": 26585, "ext": "py", "lang": "Python", "max_stars_repo_path": "env/Lib/site-packages/deap/tools/support.py", "max_stars_repo_name": "richooms/healthcare_automl", "max_stars_repo_head_hexsha": "73fc27ee8f57c717dc82a7841680ba64d6b4c34b", "max_stars_repo_licens... |
"""Variational Auto Encoders as intrinsic rewards
"""
from abc import ABC, abstractmethod
import copy
import enum
from typing import Callable, List, NamedTuple, Optional, Sequence, Tuple
import numpy as np
import torch
from torch.distributions import Normal
from torch.nn import functional as F
from torch import Tenso... | {"hexsha": "7a15857efc7b2201f8a9c3d2d5b69627f7ad04cc", "size": 10805, "ext": "py", "lang": "Python", "max_stars_repo_path": "int_rew/vae.py", "max_stars_repo_name": "kngwyu/intrinsic-rewards", "max_stars_repo_head_hexsha": "c2a8f98c0fd9292dc90f8857fa5ddb763ba8b994", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# 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": "fbd06a5a78eab5a8c30df80f7130461b68f9643c", "size": 9036, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/data/experimental/benchmarks/map_and_batch_benchmark.py", "max_stars_repo_name": "aeverall/tensorflow", "max_stars_repo_head_hexsha": "7992bf97711919f56f80bff9e5510cead4ab2095", ... |
C#######################################################################
C NAME OF ROUTINE
C GENTHIS
C
C PURPOSE
C THIS IS THE STANDARD MAIN PROGRAM USED FOR TAE/VICAR PROGRAMS.
C THIS MODULE CALLS SUBROUTINE MAIN44 TO ENTER INTO THE BODY OF THE
C PROGRAM.
C GENTHIS generates small exactly-de... | {"hexsha": "932aaf2edd8e0c5fb646b53e5ef54395c46b997a", "size": 3696, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p3/prog/genthis/genthis.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import flashalgorithm as fc
import numpy as np
import pickle
import pdb
comp_list = ('water', 'methane', 'ethane', 'propane')
phase_list = ('aqueous', 'vapor', 'lhc', 's1', 's2')
P = 80 # bar
T = 273.15 + 12 # Kelvin
flash_full = fc.FlashController(components=comp_list... | {"hexsha": "40d21805678c1bbc73f1c812751e70c826875fca", "size": 3418, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_test.py", "max_stars_repo_name": "kdarnell/injection-sim-python", "max_stars_repo_head_hexsha": "fa018de562989a207590c2628443b878bd0ed753", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from abc import ABC, abstractmethod
import torch
from torch_ac.format import default_preprocess_obss
from torch_ac.utils import DictList, ParallelEnv
import numpy as np
from copy import deepcopy
class MultiQAlgo(ABC):
"""The base class for RL algorithms."""
def __init__(self, envs, model, device=None, num_... | {"hexsha": "489ddcedd7591da587384f844c0655e8b834604a", "size": 16527, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_ac/algos/multiQ.py", "max_stars_repo_name": "mcavolowsky/torch-ac", "max_stars_repo_head_hexsha": "4c69d0260c0776554c8a4e5c9623b03181273504", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
This is originally a collaborative project with Isky on an implementation of the Hartree-Fock method, a way of approximating the wave function and thus energy of bonds in a quantum molecular system. However, despite a week of research, the complexity of the problem forces us to cease the plan, and instead resort to a m... | {"hexsha": "ba65e5d80240a5d5fda5e292e68dce96511f96bf", "size": 14145, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Orbital - Hydrogen-like.ipynb", "max_stars_repo_name": "dylux/Chemistry-Project", "max_stars_repo_head_hexsha": "76d714858909dc7e8c44074fc93108dcb193641f", "max_stars_repo_licenses":... |
% +-======-+
% Copyright (c) 2003-2007 United States Government as represented by
% the Admistrator of the National Aeronautics and Space Administration.
% All Rights Reserved.
%
% THIS OPEN SOURCE AGREEMENT ("AGREEMENT") DEFINES THE RIGHTS OF USE,
% REPRODUCTION, DISTRIBUTION, MODIFICATION AND REDIS... | {"hexsha": "780ecd6acf154fe8528f305b5b67da1cfb4e30d1", "size": 2616, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ESMF/src/addon/MAPL/GMAO_mpeu/doc/PackageOverview.tex", "max_stars_repo_name": "joeylamcy/gchp", "max_stars_repo_head_hexsha": "0e1676300fc91000ecb43539cabf1f342d718fb3", "max_stars_repo_licenses": ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*
"""A generator of graphs written in Python and LaTeX.
https://github.com/jariazavalverde/graphs
"""
import numpy as np
# FORMATS
def format_c(adjacency):
"""Returns a string representation of the given graph in C format."""
size = adjacency.shape[0]
string = "int... | {"hexsha": "42fe14a4324a1bd734decb10e5721dbb699b9a1a", "size": 1837, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/formats.py", "max_stars_repo_name": "jariazavalverde/graph-families", "max_stars_repo_head_hexsha": "845b8bf6e1990964d0a8322f4a91e85bcb6da512", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
import numpy as np
Fs = 32e3
Ts = 1.0 / Fs
frequencies = (1 + np.arange(8)) * 1e3
carrier_index = 0
Fc = frequencies[carrier_index]
Tc = 1.0 / Fc
symbols = np.array([complex(x, y)
for x in np.linspace(-1, 1, 8)
for y in np.linspace(-1, 1, 8)]) / np.sqrt(2)
Tsym = 1e-3
Nsym = in... | {"hexsha": "1391b76951c6e6b106509f081e810d670288ecd5", "size": 352, "ext": "py", "lang": "Python", "max_stars_repo_path": "config.py", "max_stars_repo_name": "RagnarDanneskjold/amodem", "max_stars_repo_head_hexsha": "5d2bcd5004035fcd34369927a243e611ce7e2700", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
export intmat2binmat
"""
function intmat2binmat(M;p=maximum(M))
Converts a general integer matrix {1,2,...,p}^{m x n} to a binary matrix of
size (m*(p-1),n).
Ones are converted to false vectors of length p-1. Two is converted to
[true;false;...;false] and p is converted to [false;...;false;true].
Input:
M - Int... | {"hexsha": "c97ab443db7d3ee477d57558d9c81dcfd38f00b6", "size": 1268, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/intmat2binmat.jl", "max_stars_repo_name": "lruthotto/StrainRecon.jl", "max_stars_repo_head_hexsha": "ba1c5392994e80bb0f7e6b94f90b404f25c40958", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
def test_base_transform(image, mean):
x = image.astype(np.float32)
x -= mean
x = x.astype(np.float32)
return x
class TestBaseTransform:
def __init__(self, mean):
self.mean = np.array(mean, dtype=np.float32)
def __call__(self, image):
return test_base_trans... | {"hexsha": "f050417d7688c5661a215038738a65529b13db18", "size": 1428, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/config.py", "max_stars_repo_name": "juanmed/FaceDetection-DSFD", "max_stars_repo_head_hexsha": "23650ca492444f9f052ca9b8db8b068a9be5bc68", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
"""
Anne Urai, CSHL, 2020-05-17
"""
import pandas as pd
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
import seaborn as sns
from patsy import dmatrices
from datetime import datetime
import statsmodels.api as sm
# layout
sns.set(style="ticks", context="paper")
sns.despine(trim=True)
... | {"hexsha": "6265ffdf63640712ebd8d8386652937c8820323e", "size": 6850, "ext": "py", "lang": "Python", "max_stars_repo_path": "largescale_recordings.py", "max_stars_repo_name": "anne-urai/largescale_recordings", "max_stars_repo_head_hexsha": "987f803d211c19e6ff217f16785dad768467ba97", "max_stars_repo_licenses": ["CC-BY-4.... |
module Language.LSP.BrowseNamespace
import Core.Context
import Core.Core
import Core.Env
import Core.Metadata
import Core.Name
import Data.List
import Idris.Doc.String
import Idris.REPL.Opts
import Idris.Resugar
import Idris.Syntax
import Language.LSP.Definition
import Language.LSP.Message
import Libraries.Data.NameMa... | {"hexsha": "2d9563dbcd54aaab809dcdbd51ad503487bff526", "size": 2783, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "src/Language/LSP/BrowseNamespace.idr", "max_stars_repo_name": "Z-snails/idris2-lsp", "max_stars_repo_head_hexsha": "3a949818ef0180baabc5a88f3533c3154f49c3ce", "max_stars_repo_licenses": ["BSD-3-Cl... |
"""
"""
import argparse
import numpy as np
import time
from typing import List, Optional
from . import cli
from . import logging
from . import nn
from . import properties
from . import utils
from .verifiers.common import VerifierError, VerifierTranslatorError, SAT
def main(args: argparse.Namespace, extra_args: Opti... | {"hexsha": "6511427db43af28d646ff090348985ad3ee31670", "size": 2650, "ext": "py", "lang": "Python", "max_stars_repo_path": "dnnv/__main__.py", "max_stars_repo_name": "nathzi1505/DNNV", "max_stars_repo_head_hexsha": "16c6e6ecb681ce66196f9274d4a43eede8686319", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 33, "m... |
// Copyright (c) 2020 Andrey Semashev
//
// Distributed under the Boost Software License, Version 1.0.
// See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#ifndef BOOST_ATOMIC_TEST_IPC_WAIT_TEST_HELPERS_HPP_INCLUDED_
#define BOOST_ATOMIC_TEST_IPC_WAIT_TEST_HELPERS_HPP_INCLU... | {"hexsha": "2970061e4ad4f4dc42fa0196749dbea47df21e1a", "size": 10398, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/atomic/test/ipc_wait_test_helpers.hpp", "max_stars_repo_name": "anarthal/boost-unix-mirror", "max_stars_repo_head_hexsha": "8c34eb2fe471d6c3113c680c1fbef29e7a8063a0", "max_stars_repo_licenses"... |
# Purpose: This script develops a list of suspect parcels to investigate and exclude, based on having
# null values for key indicators. This usually arises for study region edge cases with poor
# connectivity to network; implication is that these are not adequate representations of
# reside... | {"hexsha": "f6de0543f78452d2cff3b6f7102cce09515a8ecf", "size": 3538, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/33_exclude_parcels.py", "max_stars_repo_name": "carlhiggs/urban_liveability_index", "max_stars_repo_head_hexsha": "61decb632e0b0db28c181fe62c548f2b338cc47c", "max_stars_repo_licenses": ["MIT"... |
"""
Quick script to filter all bulk data to just save job ids which we used in our skill data sample.
"""
from tqdm import tqdm
import json
import os
from collections import defaultdict
import numpy as np
import pandas as pd
import boto3
from skills_taxonomy_v2.getters.s3_data import load_s3_data, save_to_s3
from sk... | {"hexsha": "9abbfc33d60d38fd5c210a922e204852f11a9154", "size": 2784, "ext": "py", "lang": "Python", "max_stars_repo_path": "skills_taxonomy_v2/pipeline/tk_data_analysis/filter_bulk_data.py", "max_stars_repo_name": "nestauk/skills-taxonomy-v2", "max_stars_repo_head_hexsha": "ce0f9943a038c4539f04a9a58022fc7eb1909376", "m... |
from typing import Any, Dict, Hashable, Optional, Sequence, Tuple, Union
import dask.array as da
import numpy as np
import xarray as xr
from dask.array import Array
from numpy import ndarray
from xarray import Dataset
from ..typing import ArrayLike
from ..utils import split_array_chunks
from .utils import (
asser... | {"hexsha": "4018c1fe2f251f68258998886ee648655ebfcd8c", "size": 33923, "ext": "py", "lang": "Python", "max_stars_repo_path": "sgkit/stats/regenie.py", "max_stars_repo_name": "jerowe/sgkit", "max_stars_repo_head_hexsha": "ff5a0a01ec6ae41d262ece14cc06a0b8c73ca342", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
'''
This module contains an implementation of the network repair methodology
introduced in Campbell and Albert (2014), BMC Syst. Biol.
The code should be straightforward to apply (see network_repair_tutorial.py).
I will be happy to respond to questions and/or comments.
Colin Campbell
Contact: colin.campbel... | {"hexsha": "b2880d425c6230640eb3eed59a9ed9c6dc97077d", "size": 48276, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/network_repair_functions.py", "max_stars_repo_name": "AbrahmAB/booleannet", "max_stars_repo_head_hexsha": "a07124047d18a5b7265e050a234969ac58970c7a", "max_stars_repo_licenses": ["MIT"], "... |
function (x, y, z, μ)
begin
(SymbolicUtils.Code.create_array)(Array, nothing, Val{2}(), Val{(3, 3)}(), (+)((+)(1, (*)((*)(-3//2, (^)((inv)((sqrt)((+)((^)(y, 2), (^)(z, 2), (^)((+)(x, μ), 2)))), 5), (+)((*)(-1//1, x), (*)(-1//1, μ)), (+)((*)(2, x), (*)(2, μ))), (*)((+)(1, (*)(-1, μ)))), (*)(-1//1, μ, (^)((in... | {"hexsha": "610bff27faf1a5edfd4f69960181988add0f6b66", "size": 3039, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deprecated/GeneralAstrodynamics_v0.9/Propagators/CR3BP/PotentialEnergyHessian.jl", "max_stars_repo_name": "pbouffard/GeneralAstrodynamics.jl", "max_stars_repo_head_hexsha": "80f175a5b3c6dac2140e645... |
import sys
import json
import gc
import h5py
import numpy as np
from timeit import default_timer as timer
import torch
from torch.autograd import Variable
import options
import visdial.metrics as metrics
from utils import utilities as utils
from dataloader import VisDialDataset
from torch.utils.data import DataLoader... | {"hexsha": "cf306db1fbe069041f7f73a671ae248e6073c5c2", "size": 6733, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval_utils/dialog_generate.py", "max_stars_repo_name": "Zach-Ziyi-Liu/Answerer-in-Questioner-s-Mind-Information-Theoretic-Approach-to-Goal-Oriented-Visual-Dialog", "max_stars_repo_head_hexsha": "2... |
import numpy as np
import torch
import threading
import os
from torch.nn import functional as F
import queue
from continual_rl.policies.impala.torchbeast.monobeast import Monobeast, Buffers
from continual_rl.utils.utils import Utils
class ClearMonobeast(Monobeast):
"""
An implementation of Experience Replay f... | {"hexsha": "61a977f027eb3f410a91d4464b099ea0b122bd49", "size": 12336, "ext": "py", "lang": "Python", "max_stars_repo_path": "continual_rl/policies/clear/clear_monobeast.py", "max_stars_repo_name": "AGI-Labs/continual_rl", "max_stars_repo_head_hexsha": "bcf17d879e8a983340be233ff8f740c424d0f303", "max_stars_repo_licenses... |
"""A Morse code keyer.
A Morse "key" is a device with a button used to encode the carrier wave with a
Morse signal. Morse operators use these to produce the familiar "dits" and
"dahs" of Morse code. The Keyer class converts dots and dashes into an encoded
carrier waveform and plays it audibly.
"""
import numpy as np
i... | {"hexsha": "fc95127277adeae7949b3c6dc2309478721de238", "size": 3321, "ext": "py", "lang": "Python", "max_stars_repo_path": "enigma/keyer.py", "max_stars_repo_name": "jonmaddock/enigma", "max_stars_repo_head_hexsha": "e9e3ca95cc397bbdfb7b5f43c043dd52997f0d65", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 18 19:27:57 2014
@author: joser
"""
import pygame, ode, random, Buttons
from math import atan2, acos, asin, sin, cos
import matplotlib.pyplot as plt
from pygame.locals import *
from numpy import *
from Point import *
from Buttons import *
class gameSimulator( object ... | {"hexsha": "ac8c48ff26b52acfaee5ecfe8a47d80d1eb3b192", "size": 6837, "ext": "py", "lang": "Python", "max_stars_repo_path": "gameSimulator.py", "max_stars_repo_name": "jrcapriles/gameSimulator", "max_stars_repo_head_hexsha": "e4633d2a6ad7fbcc60cd77ed853c9e4e33290319", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import mpi4py
import numpy as np
import pytest
import unittest
from chainermn.communicators._communication_utility import chunked_bcast_obj # NOQA
from chainermn.communicators._communication_utility import INT_MAX # NOQA
from chainermn.communicators.naive_communicator import NaiveCommunicator
class TestCommunicati... | {"hexsha": "51aea8fb96a8a990de397387debf0b72ebf5099a", "size": 1250, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/chainermn_tests/communicator_tests/test_communication_utility.py", "max_stars_repo_name": "zaltoprofen/chainer", "max_stars_repo_head_hexsha": "3b03f9afc80fd67f65d5e0395ef199e9506b6ee1", "ma... |
import numpy as np
import pandas as pd
from scipy.io import arff
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from collections import Counter, defaultdict
class DimensionValueError(ValueError):
pass
class TypeError(ValueError):
pass
class IterErr... | {"hexsha": "13e945c355136825ef105a714665aaf4dbc8ad7f", "size": 3683, "ext": "py", "lang": "Python", "max_stars_repo_path": "Naive Bayes.py", "max_stars_repo_name": "K-ona/--------", "max_stars_repo_head_hexsha": "1bae093758c61e4863ca0b150195286e189af591", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1,... |
# -*- coding: utf-8 -*-
"""NumPy UltraQuick Tutorial
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/github/google/eng-edu/blob/master/ml/cc/exercises/numpy_ultraquick_tutorial.ipynb
"""
#@title Copyright 2020 Google LLC. Double-click here for license inform... | {"hexsha": "09001cc0c74f0b427b1eb5206ce32b4e99eab5b0", "size": 6689, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf-ml-crashcourse/numpy_ultraquick_tutorial.py", "max_stars_repo_name": "blu3crab/DeepPlay2020", "max_stars_repo_head_hexsha": "9f62375f0d69d446764eb988ed66638f3fd57c5d", "max_stars_repo_licenses"... |
\subsubsection{Usability}
\input{usability.tex}
\subsubsection{User Experience}
\input{user-experience.tex} | {"hexsha": "544c2a5b2d7703845fe8f0ef046a7f96027a065b", "size": 107, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Design and Evaluation of User Interfaces- Reference/evaluation.tex", "max_stars_repo_name": "simwir/notes", "max_stars_repo_head_hexsha": "5079b3fc34610094ca00dea13c5128664609f113", "max_stars_repo_l... |
from operator import index
from pandas._config.config import options
import Cleaner
import textract as tx
import pandas as pd
import numpy
import os
import tf_idf
user = os.getcwd()
print(user)
resume_dir = user+"/media/Resume/"
job_desc_dir = user+"/media/JobDesc/"
resume_names = os.listdir(resume_dir)
job_descript... | {"hexsha": "1c9e43c62e32159c83bcf267bed08e2773aff582", "size": 2164, "ext": "py", "lang": "Python", "max_stars_repo_path": "Resume_Matcher/fileReader.py", "max_stars_repo_name": "r00tDada/Mini_Project_Semester_7", "max_stars_repo_head_hexsha": "fd84be13d91c9ffca8288c7787a0330a5aee7950", "max_stars_repo_licenses": ["MIT... |
(* Standard library imports *)
Require Import Coq.Strings.String.
Require Import Coq.Strings.Ascii.
Require Import Coq.Lists.List.
Require Import Coq.Arith.Arith.
Require Import Coq.Arith.EqNat.
Require Import Coq.Arith.PeanoNat.
Require Import Coq.Bool.Bool.
Require Import Coq.omega.Omega.
Require Import Coq.Program.E... | {"author": "ps-tuebingen", "repo": "decomposition-diversity", "sha": "28ab18c34f0a192c9b3d58caa709dee3e9129068", "save_path": "github-repos/coq/ps-tuebingen-decomposition-diversity", "path": "github-repos/coq/ps-tuebingen-decomposition-diversity/decomposition-diversity-28ab18c34f0a192c9b3d58caa709dee3e9129068/Formaliza... |
# Simulating readout noise on the Rigetti Quantum Virtual Machine
© Copyright 2018, Rigetti Computing.
$$
\newcommand{ket}[1]{\left|{#1}\right\rangle}
\newcommand{bra}[1]{\left\langle {#1}\right|}
\newcommand{tr}[1]{\mathrm{Tr}\,\left[ {#1}\right]}
\newcommand{expect}[1]{\left\langle {#1} \right \rangle}
$$
## Theore... | {"hexsha": "31c8b2fb968b03a65e249bdbd9eb6d0b8b289698", "size": 19758, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "examples/ReadoutNoise.ipynb", "max_stars_repo_name": "oliverdutton/pyquil", "max_stars_repo_head_hexsha": "027a3f6aecbd8206baf39189a0183ad0f85c262b", "max_stars_repo_licenses": ["Apa... |
theory Co_Snapshot
imports
Snapshot
Ordered_Resolution_Prover.Lazy_List_Chain
begin
section \<open>Extension to infinite traces\<close>
text \<open>The computation locale assumes that there already exists a known
final configuration $c'$ to the given initial $c$ and trace $t$. However,
we can show that the ... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/Chandy_Lampor... |
#!/usr/bin/env python
from __future__ import print_function
import argparse
import os
import time
import numpy as np
import yaml
import pickle
from collections import OrderedDict
# torch
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from tqdm import tqdm
import shuti... | {"hexsha": "60dcb00f5f5bfc4a3d31840682957a07cf46810e", "size": 21631, "ext": "py", "lang": "Python", "max_stars_repo_path": "SL-GCN/main.py", "max_stars_repo_name": "SnorlaxSE/CVPR21Chal-SLR", "max_stars_repo_head_hexsha": "680f911131ca03559fb06d578f38d006f87aa478", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_co... |
[STATEMENT]
lemma (in weak_lower_semilattice) weak_meet_assoc:
assumes L: "x \<in> carrier L" "y \<in> carrier L" "z \<in> carrier L"
shows "(x \<sqinter> y) \<sqinter> z .= x \<sqinter> (y \<sqinter> z)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<sqinter> y \<sqinter> z .= x \<sqinter> (y \<sqinter> z... | {"llama_tokens": 1760, "file": null, "length": 19} |
[STATEMENT]
lemma wfT_e_eq:
fixes ce::ce
assumes "\<Theta> ; \<B> ; \<Gamma> \<turnstile>\<^sub>w\<^sub>f ce : b" and "atom z \<sharp> \<Gamma>"
shows "\<Theta>; \<B>; \<Gamma> \<turnstile>\<^sub>w\<^sub>f \<lbrace> z : b | CE_val (V_var z) == ce \<rbrace>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Th... | {"llama_tokens": 4222, "file": "MiniSail_WellformedL", "length": 11} |
import oneflow as flow
import numpy as np
import time
import argparse
import torch
import string
from models.rnn_model_pytorch import RNN_PYTORCH
from models.rnn_model import RNN
# shared hyperparameters
n_hidden = 5000
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
n_categories = 25600
le... | {"hexsha": "16f16f773c569cccbbd912aac7068cdcf18bbf16", "size": 4009, "ext": "py", "lang": "Python", "max_stars_repo_path": "rnn/compare_oneflow_and_pytorch_rnn_speed.py", "max_stars_repo_name": "ClimBin/models", "max_stars_repo_head_hexsha": "10989b361732ee5b93f5595f672fd7d0c18e8f93", "max_stars_repo_licenses": ["Apach... |
import numpy as np
# Physical constants in cgs units
c = 3e10
G = 7e-8
# Calculate period given semimajor axis and total mass
def find_period(a, M, use_earth_units=False):
if use_earth_units:
return np.sqrt(a**3 / M)
else:
return np.sqrt(4 * np.pi**2 * a**3 / (G * M))
# Calculate total mass g... | {"hexsha": "af19ff7bc39ed2a66638b5e84518d2b9503e6e60", "size": 1015, "ext": "py", "lang": "Python", "max_stars_repo_path": "initial_scripts/kepler.py", "max_stars_repo_name": "bbrzycki/argparse-tutorial", "max_stars_repo_head_hexsha": "84e7dc28df2a64ca7d859bd331dc5471d3600351", "max_stars_repo_licenses": ["MIT"], "max_... |
import math
from scipy import stats
import numpy as np
listy=[1.58,1.57,1.54,1.51,1.51,1.51,1.5099,1.5,1.48,1.44,1.44,1.43,1.44,1.46,1.46,1.46,1.46,1.46,1.46,1.46,1.46,1.46,1.455,1.445,1.44,1.44,1.43,1.46,1.46,1.46,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.45,1.44,1.44,1.44,... | {"hexsha": "794141e9fea50d267c030e401fcf94d7135ebe0d", "size": 3004, "ext": "py", "lang": "Python", "max_stars_repo_path": "money.py", "max_stars_repo_name": "Dannyaffleck/stock", "max_stars_repo_head_hexsha": "9c6c62b798e4e3306a7bf4a185a0b4fca37cdd33", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
#include "soar.hpp"
#include <Eigen/Dense>
#include <fmt/format.h>
#include <vector>
using namespace Eigen;
Soar::Soar(const Ref<const MatrixXd> &matA, const Ref<const MatrixXd> &matB)
: ndim_(matA.rows()), matA_(matA), matB_(matB),
u_(VectorXd::Random(ndim_)) {}
MatrixXd Soar::compute(int n) {
VectorXd... | {"hexsha": "d418f4ee71b27ba91ff4031efdb4f621e6ac9cb4", "size": 3286, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/soar.cc", "max_stars_repo_name": "pan3rock/QuadEigsSOAR", "max_stars_repo_head_hexsha": "6b4a2e939c8987773cd7990f665e9ebf57ecdbde", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
#include "image.h"
#include "debug.h"
#include <boost/static_assert.hpp>
#include <png.h>
using namespace std;
BOOST_STATIC_ASSERT(sizeof(unsigned long) == 4);
Image imageFromPng(const string &fname) {
png_structp png_ptr = png_create_read_struct(PNG_LIBPNG_VER_STRING, NULL, NULL, NULL);
CHECK(png_ptr);
p... | {"hexsha": "9886bfb7aa9f9ef3d46e75224d14b0935a872d37", "size": 1695, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "image.cpp", "max_stars_repo_name": "zorbathut/d-net", "max_stars_repo_head_hexsha": "61f610ca71270c6a95cf57dc3acaeab8559a234b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars... |
import base64
import datetime
from io import BytesIO
import pandas as pd
import numpy as np
import quandl
import matplotlib.pyplot as plt
from dateutil import tz
from matplotlib import animation
import mpl_toolkits.mplot3d.axes3d as p3
from pandas.plotting import register_matplotlib_converters
from mpl_toolkits.mplot3d... | {"hexsha": "ca63116563b501252b64598a4175e0324ce7aa88", "size": 14472, "ext": "py", "lang": "Python", "max_stars_repo_path": "Flask/Flask_Template/flask_goldanalysis_template.py", "max_stars_repo_name": "YizheZhang-Ervin/EZDjango", "max_stars_repo_head_hexsha": "ae140d9743ab03e59fc1b385cbbfcd5a6941426d", "max_stars_repo... |
import argparse
import os
import numpy as np
import torch as t
from torch.optim import Adam
from utils.batch_loader import BatchLoader
from utils.parameters import Parameters
from model.rvae_dilated import RVAE_dilated
if __name__ == "__main__":
if not os.path.exists('data/word_embeddings.npy'):
raise F... | {"hexsha": "8d674696dd76c785063201449e47a27ba8ab46bb", "size": 3863, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "kefirski/contiguous-succotash", "max_stars_repo_head_hexsha": "7497efd1392693248ed98805dcdbbf5dc125afc2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# 09.Picrust2_Songbird.r
#
# Figure 6, Table S13, Table S14, Table S15
# Ref for PICRUSt2: Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).
# Ref for Songbird: Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames.... | {"hexsha": "52f15046dd73bef284950c4ec3640be70770d7d8", "size": 17182, "ext": "r", "lang": "R", "max_stars_repo_path": "09.Picrust2_Songbird.r", "max_stars_repo_name": "LLNL/2022_PondB_microbiome", "max_stars_repo_head_hexsha": "d9aaade01033eea9f220e96521099fd881971c82", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
#!/usr/bin/env python3
"""
Keras implementation of CapsNet in Hinton's paper Dynamic Routing Between Capsules.
The current version maybe only works for TensorFlow backend. Actually it will be straightforward to re-write to TF code.
Adopting to other backends should be easy, but I have not tested this.
Usage:
p... | {"hexsha": "473532accaf50208fa41e4a133bbd812bb170610", "size": 6538, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "baudm/VIGNet", "max_stars_repo_head_hexsha": "10a9a70878556de1c97c4212091bb15a7f8977f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_r... |
"""
Everything related to the individual rationality constraints.
"""
__all__ = ['IndividualRationality']
import theano
import theano.tensor as T
import scipy.optimize as opt
import numpy as np
from sepdesign._types import AgentType
from sepdesign._transfer_functions import TransferFunction
class IndividualRatio... | {"hexsha": "d8647d8d8a55ff24b18e3bce0c0cd8becd13fb58", "size": 7115, "ext": "py", "lang": "Python", "max_stars_repo_path": "sepdesign/_individual_rationality.py", "max_stars_repo_name": "salarsk1/principal-agent-bilevel-programming", "max_stars_repo_head_hexsha": "e09b9456dff1e5d253b57bd4bc60f87fd36a749b", "max_stars_r... |
program da_vp_bilin
!----------------------------------------------------------------------
! Purpose: Regridding from low to high resolution in control variable space
! by using bilinear interpolation
!
! where n is the grid number in x or y
! ns is the refinement ratio between two resulo... | {"hexsha": "3855e0f95f9ebd15432b55d51cc598de59ebae41", "size": 12084, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "var/mri4dvar/da_vp_bilin.f90", "max_stars_repo_name": "matzegoebel/WRF-fluxavg", "max_stars_repo_head_hexsha": "686ae53053bf7cb55d6f078916d0de50f819fc62", "max_stars_repo_licenses": ["BSD-2-Cla... |
import os, sys
sys.path.append(os.getcwd())
try: # This only matters on Ishaan's computer
import experiment_tools
experiment_tools.wait_for_gpu()
except ImportError:
pass
import lasagne
import lib
import lib.lsun_downsampled
import lib.ops.gru
import lib.ops.linear
import lib.ops.lstm
import numpy as np
i... | {"hexsha": "150a56d74c1903931da26334bde0370a695e3578", "size": 6301, "ext": "py", "lang": "Python", "max_stars_repo_path": "py3/nn/experiments/wgan/sin_lstm.py", "max_stars_repo_name": "fr42k/gap-wgan-gp", "max_stars_repo_head_hexsha": "4e373c43d606a1b83f76893d93f9cf8be8cd460d", "max_stars_repo_licenses": ["MIT"], "max... |
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
/*
* File: HttpRequestManager.hpp
* Author: ubuntu
*
* Created on February 15, 2018, 10:05 AM
*/
#ifndef HTTP_SERVER_REQUESTM... | {"hexsha": "e32e2864990c3c358ad6474e656b9a625e2faeda", "size": 1602, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/libs/http_server_session/include/keto/server_session/HttpRequestManager.hpp", "max_stars_repo_name": "burntjam/keto", "max_stars_repo_head_hexsha": "dbe32916a3bbc92fa0bbcb97d9de493d7ed63fd8", "m... |
import random
import numpy as np
import torch
from args_fusion import args
import cv2
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
from torch.autograd import Variable
from osgeo import gdal_array,gdal
import time
'''
@brief:对一个大的图片进行分块
@params:block_x,block_y x轴 y... | {"hexsha": "e49340b5ae5d208bf2ea511ccc38c2a4dce085ae", "size": 6255, "ext": "py", "lang": "Python", "max_stars_repo_path": "ImageFusion_DL/utils.py", "max_stars_repo_name": "xiaoqi25478/Project", "max_stars_repo_head_hexsha": "04813495c21faf9892777c111b7284928f70727e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
% NOTES:
% - 1000 words or less for RNAAS!
% - Add an appendix or some words to get >=3 pages for arxiv posting
% STYLE:
% - New line after each sentence (makes Git diff's readable)
% TODO:
% - Run: texcount -v3 -merge -incbib -dir -sub=none -utf8 -sum paper.tex
\documentclass[RNAAS]{aastex63}
% Load common package... | {"hexsha": "e0a5556b84c0e48ea87d593ffacba478d302b09b", "size": 3171, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "rnaas/paper.tex", "max_stars_repo_name": "adrn/slegs", "max_stars_repo_head_hexsha": "18819b8f4c878f99f1007637a0525c56600ef32a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
import h5py
import matplotlib.pyplot as plt
import numpy as np
import os
import time
def detrending_verify(filename,save_folder_detrending,save_folder_masked,plane_ind=10):
f_str=os.path.split(filename)[-1]
mask_f_str=f_str.replace('aligned.h5','masked.h5')
detr_f_str=f_str.replace('aligned.h5','detrended.... | {"hexsha": "54fbf462a2ccb02e94d2dae037d2f032e96dce51", "size": 1432, "ext": "py", "lang": "Python", "max_stars_repo_path": "Visualization/detrending_viz.py", "max_stars_repo_name": "mariakesa/ZebraFishRegistrationPipeline", "max_stars_repo_head_hexsha": "4955044eb69dc04c579f59ccb24e02e4451aebcc", "max_stars_repo_licens... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from keras.models import Sequential
#from sklearn.feature_extraction.text import CountVectorizer
from keras.preprocessing.text import Tokenizer
fr... | {"hexsha": "1fbd63e73a2b55a1fa183b5d4368a23471650119", "size": 7083, "ext": "py", "lang": "Python", "max_stars_repo_path": "textclassification/nn_lstm.py", "max_stars_repo_name": "sshekhar10/mymllearnings", "max_stars_repo_head_hexsha": "5f7b075c56af28467985282e8021658fed6b1134", "max_stars_repo_licenses": ["MIT"], "ma... |
from outputs_fromDrone import InitializeConnectDrone, ReturnDroneAngle
from input_forDrone import droneArm, droneTakeOff, condition_yaw, send_global_velocity, send_ned_velocity_heading, goto_position_target_local_ned
from drone_variousMovements import fixDistanceToBlade,moveToNewLocation, moveDroneInLocalCoord, moveTo... | {"hexsha": "3a37d84cc8ea60c76823e736849d79892dea8a2b", "size": 5651, "ext": "py", "lang": "Python", "max_stars_repo_path": "Main_velocities_testScript.py", "max_stars_repo_name": "IvanNik17/Dronekit-Python-Functions", "max_stars_repo_head_hexsha": "6451f6fbd63ba1186a161b40c8f4ee64d86ca386", "max_stars_repo_licenses": [... |
from __future__ import print_function
import ConfigParser
import collections
import h5py, sys
import numpy as np
import torch
from torch.autograd import Variable
import gzip
import os
import math
from torch.optim import Optimizer
try:
import cPickle as pickle
except:
import pickle
def mkdir(paths):
if no... | {"hexsha": "bcfd26d82fb78e65aa541f3b0d4037ace8f9c6c6", "size": 6571, "ext": "py", "lang": "Python", "max_stars_repo_path": "NN_solution/delta_spectrogram_simplenet/src/utils.py", "max_stars_repo_name": "JavierAntoran/moby_dick_whale_detection", "max_stars_repo_head_hexsha": "bbd78c78b53d0d095cd36f37c925618844c8cde9", "... |
#ifdef __GNUC__
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
#include <boost/locale/encoding.hpp>
#include <boost/locale/util.hpp>
#ifdef __GNUC__
#pragma GCC diagnostic pop
#endif
#include "pkzip_io.h"
using namespace std;
using boost::locale::conv::from_utf;
using b... | {"hexsha": "c87b1407ed120c781a4d387df51764ca6760e3d1", "size": 9231, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/pkzip_io.cc", "max_stars_repo_name": "andantissimo/0z", "max_stars_repo_head_hexsha": "b427da84a34df839c9011dea815d6180735f5a9e", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1... |
[STATEMENT]
lemma WHILEIT_rule:
assumes WF: "wf R"
assumes I0: "I s"
assumes IS: "\<And>s. \<lbrakk> I s; b s \<rbrakk> \<Longrightarrow> f s \<le> SPEC (\<lambda>s'. I s' \<and> (s',s)\<in>R)"
assumes PHI: "\<And>s. \<lbrakk> I s; \<not> b s \<rbrakk> \<Longrightarrow> \<Phi> s"
shows "WHILEIT I b f s \<le> ... | {"llama_tokens": 2864, "file": "Refine_Monadic_Generic_RefineG_While", "length": 13} |
"""Placeholder."""
import histomicstk.utils as utils
from . import _linalg as linalg
from .complement_stain_matrix import complement_stain_matrix
import numpy
def separate_stains_macenko_pca(
im_sda, minimum_magnitude=16, min_angle_percentile=0.01,
max_angle_percentile=0.99, mask_out=None):
"""Co... | {"hexsha": "d99f89486d90c76999e9d0778342c412f4b62db2", "size": 4467, "ext": "py", "lang": "Python", "max_stars_repo_path": "histomicstk/preprocessing/color_deconvolution/separate_stains_macenko_pca.py", "max_stars_repo_name": "basanto/HistomicsTK", "max_stars_repo_head_hexsha": "f3dbd93a7f31c7825574f9ccf0b86e09e9fee360... |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
@pytest.fixture
def df_checks():
"""fixture dataframe"""
return pd.DataFrame(
{
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
"ht1": [2.... | {"hexsha": "d2c8ffa684d03ae0242304c1499725b0be5d7404", "size": 32582, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/functions/test_pivot_longer.py", "max_stars_repo_name": "aliavni/pyjanitor", "max_stars_repo_head_hexsha": "245012443d01247a591fd0e931b154c7a12a9753", "max_stars_repo_licenses": ["MIT"], "m... |
from lib.math.linalg.vector import *
import numpy as np
| {"hexsha": "570420d5137a7d4bd52fe07c5eca36d4602aad41", "size": 56, "ext": "py", "lang": "Python", "max_stars_repo_path": "quest/lib/math/linalg/__init__.py", "max_stars_repo_name": "Fluorescence-Tools/quest", "max_stars_repo_head_hexsha": "e17e5682f7686d1acc1fd8a22bdae33963bc16d6", "max_stars_repo_licenses": ["MIT"], "... |
"""
DoesNotExist()
This differential indicates that the derivative Does Not Exist (D.N.E).
This is not the cast that it is not implemented, but rather that it mathematically
is not defined.
"""
struct DoesNotExist <: AbstractDifferential end
function extern(x::DoesNotExist)
throw(ArgumentError("Derivative doe... | {"hexsha": "f0dc5fe09c6ecdc6a5f91adfafdfb5cde1b58ac9", "size": 541, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/differentials/does_not_exist.jl", "max_stars_repo_name": "YingboMa/ChainRulesCore.jl", "max_stars_repo_head_hexsha": "0ad05d0f61f1fed90d1b5c0084abc00b703ba67f", "max_stars_repo_licenses": ["MIT"... |
import unittest
import numpy as np
from ..stat import *
from .. import stat
class TestLedoitWolfCov(unittest.TestCase):
def setUp(self):
np.random.seed(0)
p, n = 40, 50
self.A = A = np.random.randn(p, p)
self.Sigma = np.dot(A, A.T)
X = np.random.randn(p, n)
X -= np.a... | {"hexsha": "9af53fc68cd79c04848b8c1f33045e27b4e0db55", "size": 9137, "ext": "py", "lang": "Python", "max_stars_repo_path": "psychic/tests/teststat.py", "max_stars_repo_name": "wmvanvliet/psychic", "max_stars_repo_head_hexsha": "4ab75fb655795df0272c1bb0eb0dfeb232ffe143", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
#!/usr/bin/env python
# encoding: utf-8
"""
CombinatoricalMediaSimulations.py
Simulates all possible minimal media compositions consisting of unique carbon,
nitrogen, sulfate, phosphate sources. If a single compound provides multiple
elemental sources it serves a the solely source for them. # TODO: Reformulate this
K... | {"hexsha": "509104233515e1ac6373707a9d322a7cee676fdb", "size": 13720, "ext": "py", "lang": "Python", "max_stars_repo_path": "ifba/bins/CombintoricalMediaSimulations.py", "max_stars_repo_name": "phantomas1234/fbaproject", "max_stars_repo_head_hexsha": "6aa2a9b547b8326d928f42566de632265016e729", "max_stars_repo_licenses"... |
import numpy as np
from baselines.template.util import store_args, logger
from baselines.template.policy import Policy
def dims_to_shapes(input_dims):
return {key: tuple([val]) if val > 0 else tuple() for key, val in input_dims.items()}
class RandomPolicy(Policy):
@store_args
def __init__(self, input_dim... | {"hexsha": "36def1d8d234fe9b0a5d837e5c8438c1e21b6895", "size": 1206, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/example_algorithm/random_policy.py", "max_stars_repo_name": "knowledgetechnologyuhh/goal_conditioned_RL_baselines", "max_stars_repo_head_hexsha": "915fc875fd8cc75accd0804d99373916756f726... |
import cv2
import numpy as np
from visualize_cv2 import model, display_instances, class_names
capture = cv2.VideoCapture('videofile.mp4')
size = (
int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
)
codec = cv2.VideoWriter_fourcc(*'DIVX')
fps = capture.get(cv2.CAP_PROP_FPS... | {"hexsha": "e48220eec9ef50bdc1b3924d47127e149383ec5a", "size": 1222, "ext": "py", "lang": "Python", "max_stars_repo_path": "process_video.py", "max_stars_repo_name": "romellfudi/dataset_currency", "max_stars_repo_head_hexsha": "19a950e88fa724171cf93c47369b6fc61a57477f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import yaml
import os
import json
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.utils import to_categorical
data = yaml.safe_load(open('nlu\\train.yml').read())
# read data
inputs, outputs = [], []
for com... | {"hexsha": "b981148b5131505365ab665b428ab946d84b4216", "size": 2312, "ext": "py", "lang": "Python", "max_stars_repo_path": "nlu/model.py", "max_stars_repo_name": "Haisonvt21/Siri", "max_stars_repo_head_hexsha": "82c6432e8097821a866b4d182b0a00507835caa7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
from RetinaNet.utils import xywh_convert_xyxy
def compute_iou(boxes1, boxes2):
"""计算交并比(此计算方式仅作为原理展示, 时间复杂度和空间复杂度都过高, 请不要直接使用).
Args:
boxes1, boxes2: tf.Tensor, 边界框[x, y, width, height].
Returns:
边界框的交并比.
... | {"hexsha": "4450dda4d07d0dcfe256417819a27691e2eda99e", "size": 10020, "ext": "py", "lang": "Python", "max_stars_repo_path": "RetinaNet/preprocessing/label_ops.py", "max_stars_repo_name": "sun1638650145/RetinaNet", "max_stars_repo_head_hexsha": "357edda03cdc1f976764b6ed4fcad6e639646142", "max_stars_repo_licenses": ["Apa... |
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
! EVB-QMDFF - RPMD molecular dynamics and rate constant calculations on
! black-box generated potential energy surfaces
!
! Copyright (c) 2021 by Julien Steffen (steffen@pctc.uni-kiel.de)
! Stefa... | {"hexsha": "955512a2f20befc03976e9ef7752a99dc62f5f9c", "size": 4555, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/stints.f90", "max_stars_repo_name": "Trebonius91/EVB-QMDFF", "max_stars_repo_head_hexsha": "8d03e1ad073becb0161b0377b630d7b65fe3c290", "max_stars_repo_licenses": ["MIT", "Unlicense"], "max_s... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.