text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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from matplotlib import pyplot as plt
import csv
import os
import h5py
import numpy as np
from skimage.io import imread
from skimage.transform import resize
import pandas as pd
import torch
from torch import nn, optim
from torch.autograd import Variable
import torch.nn.functional as F
from torchvision.utils import make... | {"hexsha": "52607fd13b05480dd3c56786a6d4558c3629408c", "size": 11033, "ext": "py", "lang": "Python", "max_stars_repo_path": "GTSRB/train_vgg.py", "max_stars_repo_name": "guruprasaad123/all_dl_projects", "max_stars_repo_head_hexsha": "04c869f7f001ef94c467740260663d91a34815e0", "max_stars_repo_licenses": ["Apache-2.0"], ... |
from __future__ import absolute_import, division
import numpy as np
import cv2
from . import Tracker
from ..utils import dict2tuple
from ..utils.complex import real, conj, fft2, ifft2, complex_add, complex_mul, complex_div
from ..descriptors.fhog import fast_hog
class TrackerDCF(Tracker):
def __init__(self, **... | {"hexsha": "6906cd624af11c9b13b8cc45bb7a2366569e67d3", "size": 5838, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/trackers/dcf.py", "max_stars_repo_name": "BestSonny/open-vot-1", "max_stars_repo_head_hexsha": "3e00fbded601cf8c6a38d6f57d7b5a9df0be394b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import PIL
import pathlib
import pdf2image
#import rename_image
#image_size = input("生成画像のサイズ>")
pdf_files = pathlib.Path('in_pdf').glob('*.pdf')
img_dir = pathlib.Path('out_img')
for pdf_file in pdf_files:
base = pdf_file.stem
print(pdf_file)
images = pdf2image.convert_from_path(pdf_f... | {"hexsha": "0558160d27c3a010b1ca35ff822a4b17e610e3ef", "size": 535, "ext": "py", "lang": "Python", "max_stars_repo_path": "make-dataset/pdftojpg.py", "max_stars_repo_name": "jphacks/A_2004", "max_stars_repo_head_hexsha": "185e53a2a74f23f62bf9e9cbc0d4a02ef33f4b5f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# # Solve BTPDE
#
# We start by loading SpinDoctor and a Makie plotting backend.
# LSP indexing solution #src
# https://github.com/julia-vscode/julia-vscode/issues/800#issuecomment-650085983 #src
if isdefined(@__MODULE__, :LanguageServer) ... | {"hexsha": "a3bd1bbca1559c4695e9fe04a7284d1b736521b1", "size": 4168, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/solve_btpde.jl", "max_stars_repo_name": "SpinDoctorMRI/SpinDoctor.jl", "max_stars_repo_head_hexsha": "6f8df53bc5741bd41d7f984e9041671ecd12d413", "max_stars_repo_licenses": ["MIT"], "max_st... |
# -*- coding: utf-8 -*-
## Sample file to show the implementation of thickness estimation module
### AUTHOR : VISWAMBHAR REDDY YASA
### MATRICULATION NUMBER : 65074
### STUDENT PROJECT TUBF: Projekt LaDECO (Machine learning on thermographic videos)
import numpy as np
print('Project MLaDECO')
print('Author: Viswambhar ... | {"hexsha": "d4b05f1d643b60b6f558df95cf24295bf6a3fdac", "size": 2654, "ext": "py", "lang": "Python", "max_stars_repo_path": "thickness_estimation.py", "max_stars_repo_name": "viswambhar-yasa/LaDECO", "max_stars_repo_head_hexsha": "0172270a86c71e8c32913005ec07fd63293af0f7", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#include <boost/algorithm/string.hpp>
#include <raft-kv/raft/raft.h>
#include <raft-kv/common/log.h>
#include <raft-kv/common/slice.h>
#include <raft-kv/raft/util.h>
namespace kv {
static const std::string kCampaignPreElection = "CampaignPreElection";
static const std::string kCampaignElection = "CampaignElection";
s... | {"hexsha": "56dab223ffb1494840a672aef41ec292f0695625", "size": 51271, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "raft-kv/raft/raft.cpp", "max_stars_repo_name": "jinyyu/kvd", "max_stars_repo_head_hexsha": "f3a2f7944b037ee59f26e7e8bf69024686023fd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "m... |
import math, sys, datetime
import logging
import numpy as np
from tqdm.auto import tqdm
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data.dataloader import DataLoader
logger = logging.getLogger(__name__)
# print('logging to wandb... (comment it if you don\'t... | {"hexsha": "12fc4aa1f4c4214e30d2facecb3c3470f8fe8484", "size": 6217, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/trainer.py", "max_stars_repo_name": "ofooo/AI-Writer", "max_stars_repo_head_hexsha": "1ba84894c15c9e5605d3c6cd7521d5c6dab6eb6d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
###PROCESS LOCALLY DOWNLOADED FILES
###FOR JAXA EORC GSMAP-RT PRODUCT
##############################
import sys
# print sys.path
print 'starting'
##IMPORT MODULES
import datetime as dt
import pytz
import urllib
import numpy as np
import numpy.ma as ma
import gzip
import os
##############################
##SET DIRECTOR... | {"hexsha": "0ccd8a7717d2a502927d78495c1a0b4189721072", "size": 2596, "ext": "py", "lang": "Python", "max_stars_repo_path": "get_gsmap_backfill.py", "max_stars_repo_name": "dpbroman/satrain", "max_stars_repo_head_hexsha": "a71d5829be6e5c08f6ae29b773b96ad724301515", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# test_hscimgloader.py
# ALS 2017/05/02
"""
to be used with pytest
test sets for hscimgloader
"""
import numpy as np
import astropy.units as u
import shutil
import os
import pytest
from astropy.io import fits
import filecmp
import glob
from ..hscimgloader import hscimgLoader
ra = 140.099341430207
dec = 0.580162492... | {"hexsha": "31e50333213f4e704d3bc3362b7a449a7f71ecc7", "size": 1227, "ext": "py", "lang": "Python", "max_stars_repo_path": "bubbleimg/imgdownload/hsc/test/test_hscimgloader_plotcolorimg.py", "max_stars_repo_name": "aileisun/bubblepy", "max_stars_repo_head_hexsha": "054e7a3993659e7002f243c75253c2cb71d4fa73", "max_stars_... |
subroutine tprnc(abeta,alamnc,incpr,ipbtmx,jpfcmx,natmax,
$ ncvnc,nerr,nncpr,noutpt,npx2mx,npx2t,nttyo,nwarn,pcvnc,
$ qpdnc,uaqsp,upair)
c
c Test and process the nc (neutral, cation) pair Pitzer data read
c from the DATA0 file. Find and flag errors, such as duplication of
c data (e.g., two d... | {"hexsha": "166cb1020cbe41a08467a0a2762c554bbed7b7c0", "size": 5867, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/eqpt/src/tprnc.f", "max_stars_repo_name": "39alpha/eq3_6", "max_stars_repo_head_hexsha": "4ff7eec3d34634f1470ae5f67d8e294694216b6e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
import ast
import os
import re
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from loren_frank_data_processing import make_tetrode_dataframe
from src.figure_utilities import PAGE_HEIGHT, TWO_COLUMN, save_figure
from src.parameters import (_BRAIN_AREAS... | {"hexsha": "db413361d08eda3fb2a7c5d28de2fafaa7184479", "size": 7715, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/figure5.py", "max_stars_repo_name": "Eden-Kramer-Lab/replay_trajectory_paper", "max_stars_repo_head_hexsha": "f2d2b3cc55968fe7d94d3109621b2772cdbb2c0a", "max_stars_repo_licenses": ["MIT"], "ma... |
/-
Copyright (c) 2021 Kexing Ying. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kexing Ying
! This file was ported from Lean 3 source module probability.density
! leanprover-community/mathlib commit 17ef379e997badd73e5eabb4d38f11919ab3c4b3
! Please do not edit these... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/Probability/Density... |
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import MaxNLocator
import pickle
import math
import os, sys
from robolearn.old_utils.plot_utils import plot_sample_list, plot_sample_list_distribution, lqr_forward, plot_3d_gaussian
from robolearn.old_algos... | {"hexsha": "9e2a3bbd66c145fd57c5b57160ea3751e2fe5664", "size": 7708, "ext": "py", "lang": "Python", "max_stars_repo_path": "scenarios/tests/load_plot_good_bad_errors.py", "max_stars_repo_name": "domingoesteban/robolearn", "max_stars_repo_head_hexsha": "0d20125425c352b80ef2eeed1c0b11ab6497b11a", "max_stars_repo_licenses... |
[STATEMENT]
lemma MAC_synth_helper:
"\<lbrakk>hf_valid ainfo uinfo m z; no_oracle ainfo uinfo;
HVF m = Mac[k_i] \<langle>ainfo, Num uinfo, \<sigma>\<rangle>; \<sigma> = Mac[Key (macK asid)] j; \<sigma> \<in> ik \<or> HVF m \<in> ik\<rbrakk>
\<Longrightarrow> \<exists>hfs. m \<in> set hfs \<and> (\<exists>uinfo... | {"llama_tokens": 14121, "file": "IsaNet_instances_EPIC_L2_SA", "length": 22} |
import pymc3 as pm
import exoplanet as xo
import os
import model_apf as m
# with m.model:
# map_sol = xo.optimize(vars=[m.logKAa, m.logKAb, m.P, m.t_periastron, m.omega])
# map_sol1 = xo.optimize(start=map_sol)
# print(map_sol1)
with m.model:
trace = pm.sample(
tune=2500,
draws=3000,
... | {"hexsha": "f4c19319db235466f1a23da1e0ee45e57c9e3196", "size": 771, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/close/rv/sample.py", "max_stars_repo_name": "iancze/TWA-3-orbit", "max_stars_repo_head_hexsha": "e852f69b298d315aacc5801dacb42346e3a281c1", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import math
from pyradioconfig.parts.panther.calculators.calc_agc import CALC_AGC_panther
from py_2_and_3_compatibility import *
from scipy import interpolate
#This file contains calculations related to the configuring the AGC
class CALC_AGC_ocelot(CALC_AGC_panther):
def calc_agc_misc(self, model):
self.... | {"hexsha": "ce5a2c6beb9f2426d591cca98e51a3788fb8d67a", "size": 32819, "ext": "py", "lang": "Python", "max_stars_repo_path": "platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/ocelot/calculators/calc_agc.py", "max_stars_repo_name": "PascalGuenther/gecko_sdk", "max_stars_repo_head_hexsha": "2e82050dc8823c9fe... |
from pygenesys.data.library import nrel_electric_costs
import pandas as pd
import numpy as np
renewable_techs = ['LandbasedWind',
'OffshoreWind',
'UtilityPV',
'ResPV',
'CommPV',
'Geothermal',
'Hydropower'... | {"hexsha": "0f33a7de6a7cc498568985862f8ccd7a8e1b22f7", "size": 3629, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygenesys/data/nrel_data.py", "max_stars_repo_name": "arfc/pygenesys", "max_stars_repo_head_hexsha": "2ac0c1fbe7c2dffd0135cf8c52d2c8b31cc31ab4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Interpolate given variable to tropopause height.
###############################################################################
testkw/diag_tropopause.py
Author: Katja Weigel (IUP, Uni Bremen, Germany)
ESA-CMUG project
################################################... | {"hexsha": "0401bb45af5b89fab82e9b57cff33c3b6e58d081", "size": 15496, "ext": "py", "lang": "Python", "max_stars_repo_path": "esmvaltool/diag_scripts/cmug_h2o/diag_tropopause_zonalmean.py", "max_stars_repo_name": "cffbots/ESMValTool", "max_stars_repo_head_hexsha": "a9b6592a02f2085634a214ff5f36a736fa18ff47", "max_stars_r... |
import numpy as np
from typing import List, Tuple, Any
from sklearn.metrics import accuracy_score
def ChooseBestModel(models_: List[Any],
train_data: Tuple[np.ndarray],
test_data: Tuple[np.ndarray]):
"""
Takes list of potential models and returns the most accurate m... | {"hexsha": "ad63bef020d84ca97f189baee456172ec187020e", "size": 1269, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/choose_model.py", "max_stars_repo_name": "eightlay/ReviewAnalysis", "max_stars_repo_head_hexsha": "d66f5eef1c24f97299cc1a2b33e0da68a6a2d44d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from collections import defaultdict
from pathlib import Path
from typing import Dict, List
import numpy as np
import orjson
import typer
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
from geneeval.common.data_utils import load_benchmark, load_features
from geneeval.common.utils import BENCHMAR... | {"hexsha": "ad47b9d42058612953c2bc21778718cfce94b9b6", "size": 4766, "ext": "py", "lang": "Python", "max_stars_repo_path": "geneeval/main.py", "max_stars_repo_name": "BaderLab/GeneEval", "max_stars_repo_head_hexsha": "ad26540d999e7f0562ad1d67d574e08edcadae57", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
import pathlib
from collections import deque
import gym
import numpy as np
from mani_skill_learn.env import get_env_info
from mani_skill_learn.env.observation_process import process_mani_skill_base
from mani_skill_learn.methods.builder import build_brl
from mani_skill_learn.utils.data import to_np, unsqueeze
from man... | {"hexsha": "a21e8c4965d4be4b202ec28d42af1be78d00c829", "size": 18715, "ext": "py", "lang": "Python", "max_stars_repo_path": "user_solution.py", "max_stars_repo_name": "Zed-Wu/ManiSkill-Learn", "max_stars_repo_head_hexsha": "8056fe327752cd0863f8730672fe62bd85a0ec12", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
from collections import ChainMap
from itertools import chain
from functools import reduce
from typing import (
Dict, Tuple, Optional, NamedTuple, Iterator, List, Union
)
from functools import partial
import numpy as np
from autofit.graphical.factor_graphs import \
Factor, AbstractNode, FactorGraph, FactorValu... | {"hexsha": "bed123e60eca4b8bdefeeff8e35636668e5fe6dc", "size": 15614, "ext": "py", "lang": "Python", "max_stars_repo_path": "autofit/graphical/mean_field.py", "max_stars_repo_name": "arfon/PyAutoFit", "max_stars_repo_head_hexsha": "5926b13eefd97e089ee468cbec33452766edbd22", "max_stars_repo_licenses": ["MIT"], "max_star... |
#ifndef BID_DROP_FRONT_HPP_
#define BID_DROP_FRONT_HPP_
#include <bid/range/traits/drop_front_intrinsically.hpp>
#include <bid/range/traits/pop_front.hpp>
#include <bid/functor.hpp>
#include <boost/optional.hpp>
namespace bid
{
using boost::none_t;
template<class Range>
using prefered_drop_front_method = std::con... | {"hexsha": "421df356fa1e19bcb7d0cdadf91d62f1b91b659b", "size": 3019, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/bid/range/traits/drop_front.hpp", "max_stars_repo_name": "mbid/subsetunion-problem", "max_stars_repo_head_hexsha": "b161ed832576acce23e8c44f08ec2c28a8f1b98d", "max_stars_repo_licenses": ["MI... |
import copy
import numpy as np
from ext import get_ext_list
def b_mul(b, exp_list, init_ext_list):
sum_add_v_all = 0.0
ext_list = get_ext_list(init_ext_list)
for curr_exp in exp_list:
sum_add_v = curr_exp[-1]
for n_curr in range(len(ext_list)):
sum_add_v *= curr_exp[... | {"hexsha": "184440e4df06c0cc05c995438302f1119776db97", "size": 2386, "ext": "py", "lang": "Python", "max_stars_repo_path": "approximation.py", "max_stars_repo_name": "DmitriyKhudiakov/MSE_approximation", "max_stars_repo_head_hexsha": "6d25711c14a27301335fe211bff9305ea2ad88d8", "max_stars_repo_licenses": ["MIT"], "max_s... |
from keras.layers import Input, Conv2D, MaxPooling2D, concatenate, UpSampling2D
from keras.models import Model
from keras.optimizers import Adam
import keras
from random import shuffle, randint
import numpy as np
import os
import tensorflow as tf
import glob
from keras import backend as K
import matplotlib.pyplot as p... | {"hexsha": "bada2f9de03e569f9917ece686b5752be73a6aac", "size": 5927, "ext": "py", "lang": "Python", "max_stars_repo_path": "unet/unet_cell_contour.py", "max_stars_repo_name": "evansgroup/JournalOfBiomedicalOptics", "max_stars_repo_head_hexsha": "72fc3ef7551a3b38c49e4818f4c4b285972d627a", "max_stars_repo_licenses": ["MI... |
#include <iostream>
#include <cmath>
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <sophus/so3.h>
#include <sophus/se3.h>
int main(int argc, char** argv) {
Eigen::Matrix3d R = Eigen::AngleAxisd(M_PI / 2, Eigen::Vector3d(0, 0, 1)).toRotationMatrix();
Sophus::SO3 SO3_R(R);
Sophus::SO3 SO3_V(0, 0... | {"hexsha": "e03498eecb6f6bf38d71c3ec89de22f41db3fc45", "size": 1801, "ext": "cc", "lang": "C++", "max_stars_repo_path": "VisionSLAM14/ch4/sophus_test/use_sophus.cc", "max_stars_repo_name": "DLonng/Go", "max_stars_repo_head_hexsha": "a67ac6d6501f9fadadec6a6cf766d4b4a356d572", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import pytest
np = pytest.importorskip("numpy")
import networkx as nx
class TestFloydNumpy:
def test_cycle_numpy(self):
dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
assert dist[0, 3] == 3
assert dist[0, 4] == 3
def test_weighted_numpy_three_edges(self):
XG3 = nx.Graph(... | {"hexsha": "13df927f4ed9a73aae03447dee879c5aea595934", "size": 2230, "ext": "py", "lang": "Python", "max_stars_repo_path": "networkx/algorithms/shortest_paths/tests/test_dense_numpy.py", "max_stars_repo_name": "ChristopherReinartz/networkx", "max_stars_repo_head_hexsha": "f542e5fb92d12ec42570d14df867d144a9e8ba4f", "max... |
[STATEMENT]
lemma mem_set_indexed_members'[simp]:
"t \<in> set (indexed_members s) \<longleftrightarrow> snd t |\<in>|\<^bsub>fst t\<^esub> s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (t \<in> set (indexed_members s)) = (snd t |\<in>|\<^bsub>fst t\<^esub> s)
[PROOF STEP]
by (cases t, simp add: mem_set_indexe... | {"llama_tokens": 147, "file": "Incredible_Proof_Machine_Indexed_FSet", "length": 1} |
# -*- coding: utf-8 -*-
"""
@date: 2020/3/26 下午2:50
@file: create_train_val.py
@author: zj
@description: 提取分类任务的训练/测试集，分类别保存
"""
import cv2
import numpy as np
import os
import xmltodict
#### for train
# aeroplane 1171
# bicycle 1064
# bird 1605
# boat 1140
# bottle 1764
# bus 822
# car 3267
# cat 1593
# chair 3152
#... | {"hexsha": "dcefd44b43a1c28dd5076b6a351c59f6996f0599", "size": 4716, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/lib/data/create_train_val.py", "max_stars_repo_name": "zjZSTU/ResNet", "max_stars_repo_head_hexsha": "f185d1d24cdc96a533b2cf2df94f68172d820cb3", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import glob
import json
import pdb
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import csv
with open('k80_only_JCT.json', 'r') as fp:
k80_only = json.load(fp)
with open('oracle_JCT.json', 'r') as fp:
oracle_only = json.load(fp)
with open('v100_only_JCT.json', 'r') as fp:
v100_only =... | {"hexsha": "df5687ade4b0fc487f99cc5942f285c6ef5dd1e3", "size": 4649, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pwr_run/checkpointing/throughput/comparison/compare_oracle/generate_csv.py", "max_stars_repo_name": "boringlee24/keras_old", "max_stars_repo_head_hexsha": "1e1176c45c4952ba1b9b9e58e9cc4df... |
# -*- coding: utf-8 -*-
'''
博客1：python+opencv实现基于傅里叶变换的旋转文本校正
https://blog.csdn.net/qq_36387683/article/details/80530709
博客2：OpenCV—python 图像矫正（基于傅里叶变换—基于透视变换）
https://blog.csdn.net/wsp_1138886114/article/details/83374333
傅里叶相关知识：
https://blog.csdn.net/on2way/article/details/46981825
频率：对于图像来说就是指图像颜色值的梯度，即灰度级的变化速度
幅... | {"hexsha": "87435a809a803028a7e08b77c0bba2d08370e25b", "size": 3546, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/rotation-opencv.py", "max_stars_repo_name": "vuminhduc97/TableDetect", "max_stars_repo_head_hexsha": "e69d760392973715c29238e56b35737663353aaa", "max_stars_repo_licenses": ["MIT"], "max_star... |
function G = get_payoff_G_matrix_from_ygrid_2d( y_1, y_2, S_0s, sigmas, rho, contractParams)
%UNTITLED5 Summary of this function goes here
% Detailed explanation goes here
payoff_type = contractParams.payoff_type;
if payoff_type == 1 % G = S_1
payoff = @(y1,y2)S_0s(1)*exp(sigmas(1)*y1);
elseif payoff_ty... | {"author": "jkirkby3", "repo": "PROJ_Option_Pricing_Matlab", "sha": "3859a390f395e452ad61440f95a5714dd8fb4d90", "save_path": "github-repos/MATLAB/jkirkby3-PROJ_Option_Pricing_Matlab", "path": "github-repos/MATLAB/jkirkby3-PROJ_Option_Pricing_Matlab/PROJ_Option_Pricing_Matlab-3859a390f395e452ad61440f95a5714dd8fb4d90/CTM... |
[STATEMENT]
lemma eccentricity_bot_iff: "eccentricity v = 0 \<longleftrightarrow> V = {} \<or> V = {v}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (eccentricity v = 0) = (V = {} \<or> V = {v})
[PROOF STEP]
proof (intro iffI)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. eccentricity v = 0 \<Longrightarrow> ... | {"llama_tokens": 2160, "file": "Undirected_Graph_Theory_Connectivity", "length": 24} |
import keras.backend
import numpy as np
import tensorflow as tf
#cpu
def bbox_transform_cpu(ex_rois, gt_rois):
ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights
gt_w... | {"hexsha": "292589e7da2f0692876e17876412c140061ee1e2", "size": 1663, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras_detection/bbox_encode.py", "max_stars_repo_name": "Walter1218/Self_Driving_Car_ND", "max_stars_repo_head_hexsha": "526a9583a2bc616cb19cdfc7921b5e1c0f9711bd", "max_stars_repo_licenses": ["MIT... |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... | {"hexsha": "0014fcf61045d8810b4569012fef57a72361cf09", "size": 5876, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/golden_cut_select.py", "max_stars_repo_name": "vvhj/APRCP-HRNet-Adaptive-Pruning-Rate-Channel-Pruning-for-HRNet", "max_stars_repo_head_hexsha": "bb3d946b9de311f7d1161a0056f39d84db00cb4c", "m... |
[STATEMENT]
lemma autoref_SUCCEED[autoref_rules]: "(SUCCEED,SUCCEED) \<in> \<langle>R\<rangle>nres_rel"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (SUCCEED, SUCCEED) \<in> \<langle>R\<rangle>nres_rel
[PROOF STEP]
by (auto simp: nres_rel_def) | {"llama_tokens": 111, "file": "Refine_Monadic_Refine_Basic", "length": 1} |
\chapter{Finite differences in 2D}\label{chap: finite diff 2d}
In this chapter, we study the numerical solution of the Dirichlet boundary-value
problem for the Poisson equation. Let $\Omega$ be a bounded, open subset
of~$\mathbb{R}^2$, with a piecewise smooth boundary~$\Gamma=\partial\Omega$.
Given suitable functi... | {"hexsha": "6cae0b4327de97518734331ffae6df20709d3763", "size": 53222, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "texsrc/chap5.tex", "max_stars_repo_name": "billmclean/ComputationalMathsNotes", "max_stars_repo_head_hexsha": "9d521fdf7ec407cca287997885d81c3150973415", "max_stars_repo_licenses": ["CC0-1.0"], "ma... |
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import pandas as pd
def f(x, a, b):
return a * x + b
def error(ydata):
v_error = np.empty(len(ydata))
for i in range(len(ydata)):
v_error[i] = max(ydata[i] * 0.0010, 0.01)
return v_error
data = pd.read_... | {"hexsha": "742bfeb505b3e21a87bfcba157efde989c20c36a", "size": 2665, "ext": "py", "lang": "Python", "max_stars_repo_path": "Photoelectric.py", "max_stars_repo_name": "EroSkulled/PHY224-324", "max_stars_repo_head_hexsha": "060a020d75e938d240d926e05e41f8e5c4bf435a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from gym import Wrapper
import numpy as np
from scipy.stats import norm
class InstanceSamplingWrapper(Wrapper):
"""
Wrapper to sample a new instance at a given time point.
Instances can either be sampled using a given method or a distribution infered from a given list of instances.
"""
def __init... | {"hexsha": "72a66f707d492d2feb7855c24aa50ff32d141338", "size": 3143, "ext": "py", "lang": "Python", "max_stars_repo_path": "dacbench/wrappers/instance_sampling_wrapper.py", "max_stars_repo_name": "ndangtt/LeadingOnesDAC", "max_stars_repo_head_hexsha": "953747d8702f179851d7973c65779a1f830e03a1", "max_stars_repo_licenses... |
import numpy as np
from prml.nn.array.array import asarray
def uniform(min, max, size):
return asarray(np.random.uniform(min, max, size))
| {"hexsha": "8acce618f203c6f5dab0c00b604da5d9773cb2e7", "size": 145, "ext": "py", "lang": "Python", "max_stars_repo_path": "prml/nn/random/uniform.py", "max_stars_repo_name": "alexandru-dinu/PRML", "max_stars_repo_head_hexsha": "acd823e098df67abe0306a70225e7539f8edda40", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
theorem diff_const_axiom_valid: "valid diff_const_axiom"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. valid diff_const_axiom
[PROOF STEP]
apply(simp only: valid_def diff_const_axiom_def equals_sem)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>I \<nu>. is_interp I \<longrightarrow> dterm_s... | {"llama_tokens": 316, "file": "Differential_Dynamic_Logic_Differential_Axioms", "length": 4} |
[STATEMENT]
lemma Abs_ffilter: "(ffilter f s = s') = ({e \<in> (fset s). f e} = (fset s'))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (ffilter f s = s') = ({e \<in> fset s. f e} = fset s')
[PROOF STEP]
by (simp add: ffilter_def fset_both_sides Abs_fset_inverse Set.filter_def) | {"llama_tokens": 131, "file": "Extended_Finite_State_Machines_FSet_Utils", "length": 1} |
from distutils.version import StrictVersion
import unittest
import numpy as np
import sklearn
from sklearn import linear_model
from sklearn.svm import LinearSVC
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from onnxruntime import InferenceSession, __version__ as ort_version
from skl2onnx import ... | {"hexsha": "13a41a786f4835046be2889aea9830e5f735b12d", "size": 33538, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_sklearn_glm_classifier_converter.py", "max_stars_repo_name": "alexivaner/sklearn-onnx", "max_stars_repo_head_hexsha": "535a79481a79964287430bb390912c16911cff01", "max_stars_repo_licens... |
[STATEMENT]
lemma a_star_ImpLR:
"N \<longrightarrow>\<^sub>a* N'\<Longrightarrow> ImpL <a>.M (y).N z \<longrightarrow>\<^sub>a* ImpL <a>.M (y).N' z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. N \<longrightarrow>\<^sub>a* N' \<Longrightarrow> ImpL <a>.M y.N z \<longrightarrow>\<^sub>a* ImpL <a>.M y.N' z
[PROO... | {"llama_tokens": 168, "file": null, "length": 1} |
"""Beamformer module."""
from typing import List
from typing import Optional
from typing import Union
import numpy as np
import torch
from torch_complex import functional as FC
from torch_complex.tensor import ComplexTensor
EPS = torch.finfo(torch.double).eps
def complex_norm(c: ComplexTensor) -> torch.Tensor:
... | {"hexsha": "0786eda313194279d5171b7f67e93607b7b8044e", "size": 22822, "ext": "py", "lang": "Python", "max_stars_repo_path": "espnet2/enh/layers/beamformer.py", "max_stars_repo_name": "zh794390558/espnet", "max_stars_repo_head_hexsha": "edb89d8c955e7d88f6e59c8e8e025617feec5af1", "max_stars_repo_licenses": ["Apache-2.0"]... |
# scan.py
# Author: Thomas MINIER - MIT License 2017-2020
from typing import Dict, Optional
from sage.database.db_iterator import DBIterator
from sage.query_engine.iterators.preemptable_iterator import PreemptableIterator
from sage.query_engine.iterators.utils import selection, vars_positions
from sage.query_engine.pr... | {"hexsha": "7f138bf6f278c99fbec66c63a011a6bad1a8acea", "size": 3385, "ext": "py", "lang": "Python", "max_stars_repo_path": "sage/query_engine/iterators/scan.py", "max_stars_repo_name": "JulienDavat/sage-engine", "max_stars_repo_head_hexsha": "87fb7075a07395a527da660d5efc056b0f49758c", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
theorem IK_nf_real_card:
shows "card ((\<lambda> f. f RRR) ` {f . IK_nf f}) = 7"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. card ((\<lambda>f. f RRR) ` {f. IK_nf f}) = 7
[PROOF STEP]
by (simp add: IK_nf_set) ((subst card_insert_disjoint; auto dest!: RRR_test simp: nf_RRR I_K id_def[symmetric] o_ass... | {"llama_tokens": 164, "file": "Kuratowski_Closure_Complement_KuratowskiClosureComplementTheorem", "length": 1} |
#include "text/csv/iterator.hpp"
#include <string>
#include <vector>
#include <sstream>
#include <iostream>
#include <boost/test/unit_test.hpp>
using text::csv::row_range;
using text::csv::map_row_range;
static const std::string NUMERIC_DATA = "1,2,3\n4,5,6";
BOOST_AUTO_TEST_SUITE(csv_ranges)
BOOST_AUTO_TEST_CASE... | {"hexsha": "94af67666978d758df7d5ebe104ab706db084b1d", "size": 1477, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_ranges.cpp", "max_stars_repo_name": "ferdymercury/text-csv", "max_stars_repo_head_hexsha": "08e0657137497c740c4022bdfae37c70310a9f8e", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_co... |
#!/usr/bin/env python
import roslib; roslib.load_manifest('rover_driver_base')
import rospy
from std_msgs.msg import Float64
from sensor_msgs.msg import JointState
from geometry_msgs.msg import Twist,Pose
from math import atan2, hypot, pi, cos, sin
import tf
import message_filters
import numpy
from numpy.linalg import ... | {"hexsha": "a1d7cec41d705049885f4a5ed84bb96afd71755b", "size": 3990, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/rover_driver_base/nodes/rover_command.py", "max_stars_repo_name": "Thanusan19/catkin_ws", "max_stars_repo_head_hexsha": "c8622a48a29b9aabe17d86074c6005c45b2f58ca", "max_stars_repo_licenses": [... |
from maindash import server
import dash_html_components as html
from stream_analysis.motion_detection.model_dash_integration import Detector, gen
from flask import Response
import os
import numpy as np
import cv2
@server.route('/video_feed')
def video_feed():
# Find paths to model weights, model, and class n... | {"hexsha": "5d2a4eb41bfe16ad03484356b40a01075e1b4fb3", "size": 930, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/frontend/detection_stream.py", "max_stars_repo_name": "MLStruckmann/formula-frankfurt", "max_stars_repo_head_hexsha": "5a09f6ed02805c1dd5cfa42e82f9e396f52f9001", "max_stars_repo_licenses": ["MI... |
import math
import torch
import numpy as np
import utils.image_processing as ip
import utils.torch_complex as tc
import torch.fft as fft
def ASM_precal(slm_res, pix_pitch, wavelength, prop_dist, device,
linear_conv=True, dtype=torch.float64):
input_resolution = slm_res
if linear_conv:
i... | {"hexsha": "bb47732edacfe83403f5e6a018ce4bf080368a19", "size": 2220, "ext": "py", "lang": "Python", "max_stars_repo_path": "propMethods/asm.py", "max_stars_repo_name": "fy255/perceptual_cgh", "max_stars_repo_head_hexsha": "71a999a8ca8fb355d2f8fb41f97321e48f219324", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python3
# (c) University of the Witwatersand, Johannesburg on behalf of the H3ABioinformatics Network Consortium
# 2016-2018
# Licensed under the Creative Commons Attribution 4.0 International Licence.
# See the "LICENSE" file for details
import sys
import pandas as pd
import numpy as np
EOL=chr(10)
... | {"hexsha": "500d6ad02093dc59a30115a28848cfc2e5fe7682", "size": 1649, "ext": "py", "lang": "Python", "max_stars_repo_path": "assoc/bin/extractPheno.py", "max_stars_repo_name": "lvclark/h3agwas", "max_stars_repo_head_hexsha": "5e42e60123b819d3c331a91b25ee50846e55af3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
import nltk
import pickle
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import CountVectorizer
from sqlalchemy impo... | {"hexsha": "4835435c67c5e815deb6b1e999da1088750d028b", "size": 4354, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/train_classifier.py", "max_stars_repo_name": "tobikasali/Disaster_response_pipleine", "max_stars_repo_head_hexsha": "aee12fc6ca27486490a5d90f7267740e4f7d6f1c", "max_stars_repo_licenses": ["... |
def split_space_data(
X_normalized,
X,
Y,
file_path,
observation_number,
test_size
):
'''Seperate the data in a stratified way.
The function takes in a few different datasets, where the indices of each are aligned to be of the
same object.
| X_normalized... | {"hexsha": "39f2d00432c79f254443c58516795af776d8b242", "size": 8201, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnn-model/space_utils.py", "max_stars_repo_name": "dessa-oss/supernova-classifier", "max_stars_repo_head_hexsha": "f9ac4cbdd7fd4d6294eb215555a1d380cb8dee02", "max_stars_repo_licenses": ["Apache-2.... |
# -*- coding: utf-8 -*-
"""Boilerplate:
Created on Tue Feb 16 18:54:55 2021
@author: Timothe
"""
import os,sys,inspect
import warnings
from datetime import datetime
from fileio import ConfigFile
from structs import TwoLayerDict
import pathes
try :
import numpy as np
except TypeError as e :
warnings.warn("n... | {"hexsha": "b76a8d4767b5baa500398d0b06e4bc14686656c4", "size": 13864, "ext": "py", "lang": "Python", "max_stars_repo_path": "workflows.py", "max_stars_repo_name": "ShulzLab/pGenUtils", "max_stars_repo_head_hexsha": "084e83f7c1030553a3c1ba69525de8e52ca9b503", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
program main
use iso_c_binding
implicit none
block
use tcl
character(:), allocatable, target :: code
integer(c_int) rc
type(c_ptr) interp
interp = tcl_create_interp()
code = "puts foo" // achar(0)
rc = tcl_eval(interp, c_loc(code))
call tcl_delete_interp(interp)
end block
e... | {"hexsha": "72fd629ccec8a0c6b3d42c686dfb9df9fc3bf24c", "size": 336, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/main.f90", "max_stars_repo_name": "dram/fortran-tcl", "max_stars_repo_head_hexsha": "c8ed977b565574b722f74531a41ddd38f2538335", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count... |
import pandas as pd
import numpy as np
from sklearn.cluster import DBSCAN
import more_itertools as mit
import multiprocessing as mp
from numba import njit
pd.options.mode.chained_assignment = None
@njit
def _distance_between_two_coordinates(lat1_degrees, lon1_degrees, lat2_degrees, lon2_degrees) -> float:
"""Dista... | {"hexsha": "feb3a15c5b30ae4754c93b6a9ae897c5f3d63d1c", "size": 7400, "ext": "py", "lang": "Python", "max_stars_repo_path": "mobilipy/segmentation.py", "max_stars_repo_name": "plechoss/mobilipy", "max_stars_repo_head_hexsha": "3bf54fe11defe186228af99d01de82bf11f0b590", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
Unit tests for php_wrappers module.
Running tests which are NOT marked as slow (default):
python -m pytest test_php_wrappers.py
These tests take about 30 seconds to run (on my local machine).
Running all tests, including those marked as slow:
python -m pytest test_php_wrappers.py --runslow
These tests tak... | {"hexsha": "f0b3f9c51b255ecff03da8549040963bd9c40c21", "size": 4808, "ext": "py", "lang": "Python", "max_stars_repo_path": "etta/tests/test_php_wrappers.py", "max_stars_repo_name": "jennywwww/exofop-tess-api", "max_stars_repo_head_hexsha": "fe3673e6cc379bea8a5ea4a053b9548c26d3775f", "max_stars_repo_licenses": ["MIT"], ... |
# *-* coding: utf-8 *-*
import tensorflow as tf
import numpy as np
import cv2
import os
import re
import detect_face
default_color = (0, 255, 0) #BGR
default_thickness = 2
image_paths = sorted([f for f in os.listdir('.') if re.match(r'.+\.jpg', f)])
with tf.Graph().as_default():
sess = tf.Session()
pnet, rn... | {"hexsha": "7e8900e68a96145f7ef369643c1c91de9c262881", "size": 1094, "ext": "py", "lang": "Python", "max_stars_repo_path": "32-detect/mtcnn-camera.py", "max_stars_repo_name": "moh3n9595/class.vision", "max_stars_repo_head_hexsha": "cbcc65fd1f226273d26e44576ca7c3950faea75c", "max_stars_repo_licenses": ["MIT"], "max_star... |
import math
import librosa
import numpy as np
import pickle
import os
LENGTH_CHOSEN = 80000
SCALERS_FOLDER = "speech_emotion_recognition/data_scaler"
def read_file(audio_file):
"""
:param audio_file: a string representing the full-path of the input audio file
:type audio_file: string
:return: an arr... | {"hexsha": "0ce832362fc34c2febd3e114daed60b36db66ba4", "size": 6290, "ext": "py", "lang": "Python", "max_stars_repo_path": "speech_emotion_recognition/feature_extraction.py", "max_stars_repo_name": "helemanc/ambient-intelligence", "max_stars_repo_head_hexsha": "fe3571ac2d43b91b1331973ed7769372cc544af2", "max_stars_repo... |
section\<open>The general Rasiowa-Sikorski lemma\<close>
theory Rasiowa_Sikorski imports Forcing_Notions Pointed_DC begin
context countable_generic
begin
lemma RS_relation:
assumes "p\<in>P" "n\<in>nat"
shows "\<exists>y\<in>P. \<langle>p,y\<rangle> \<in> (\<lambda>m\<in>nat. {\<langle>x,y\<rangle>\<in>P\<times>P... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Forcing/Rasiowa_Sikorski.thy"} |
program dc
double complex a, b, c, d, e, f, g, h
double precision x
complex w, z
a = (1.0,1.0)
b = 1
c = 1.0e0
d = 1.0d0
e = a + b
f = COS(e)
x = ABS(f)
f = DCMPLX(x)
h = LOG(g) + SQRT(f) + SIN(e) + EXP(a)
print *, h
w = (1.0,... | {"hexsha": "e1838badf26ca75e02a087f6827630936e99d51d", "size": 396, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/PIPS/validation/Flint/dcmplx.f", "max_stars_repo_name": "DVSR1966/par4all", "max_stars_repo_head_hexsha": "86b33ca9da736e832b568c5637a2381f360f1996", "max_stars_repo_licenses": ["MIT"], "m... |
/*
* Copyright (C) 2015 Dato, Inc.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as
* published by the Free Software Foundation, either version 3 of the
* License, or (at your option) any later version.
*
* This program is distribu... | {"hexsha": "29d03ed95e4ef4c461a0ee8967f430485766c87f", "size": 69640, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "oss_test/sframe/sframe_test.cxx", "max_stars_repo_name": "csgxy123/SFrame-Ex", "max_stars_repo_head_hexsha": "b97cbefdaee9f8842735dbe50a1fa07fa2b460cf", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
Dear Princess Celestia: FizzBuzz!
I learned modulus with a number using the number x and the number y.
Did you know that product is a number?
For every number factor from 1 to x:
product is now factor times y.
If product isn't less than x:
Then you get product minus x.
That's what I would do!
That's what I d... | {"hexsha": "10843f856bba8b5825b82d5db07ce969fc1ad941", "size": 1390, "ext": "fpp", "lang": "FORTRAN", "max_stars_repo_path": "examples/fizzbuzz.fpp", "max_stars_repo_name": "stillinbeta/friendshipismonadic", "max_stars_repo_head_hexsha": "434cee6f1df3bcd87e23cd09022fa5a268d46b15", "max_stars_repo_licenses": ["BSD-3-Cla... |
#include "ros/ros.h"
#include <lwr_controllers/PoseRPY.h>
#include <kdl/tree.hpp>
#include <Eigen/Dense>
#include <tf/transform_listener.h>
#include <tf/transform_datatypes.h>
#define PI 3.141592653
ros::Subscriber sub_terminal;
ros::Publisher pub_right;
ros::Publisher pub_left;
Eigen::Matrix<double,3,1> p_global_r... | {"hexsha": "909b0840f0553bc88e0c6ffc1bcddfbda3b705bd", "size": 5662, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lwr_controllers/src/cartesian_command_vito.cpp", "max_stars_repo_name": "wxmerkt/kuka-lwr", "max_stars_repo_head_hexsha": "42841aad08fe771ac4fc8ca451d9c2adc55671a2", "max_stars_repo_licenses": ["Unl... |
#!/usr/bin/env python
# -*- coding: latin-1 -*-
#
# Copyright 2016-2021 Blaise Frederick
#
# 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/LICEN... | {"hexsha": "32fd5ed2644e00fce5fd1d58ef93add579b38ca9", "size": 22153, "ext": "py", "lang": "Python", "max_stars_repo_path": "rapidtide/stats.py", "max_stars_repo_name": "bbfrederick/delaytools", "max_stars_repo_head_hexsha": "190d79ae4c19317dfce38a528e43fd05459f29a5", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
! -----------------------------------------------------------------------------
! This file was automatically created by SARAH version 4.12.1
! SARAH References: arXiv:0806.0538, 0909.2863, 1002.0840, 1207.0906, 1309.7223
! (c) Florian Staub, 2013
! ---------------------------------------------------------------... | {"hexsha": "73c9a23a1a67c95d584069564d09b9ff2b20076c", "size": 711348, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Externals/SPheno-4.0.3/NMSSM_IS/CouplingsCT_NInvSeesaw.f90", "max_stars_repo_name": "yuanfangtardis/vscode_project", "max_stars_repo_head_hexsha": "2d78a85413cc85789cc4fee8ec991eb2a0563ef8", "... |
/* Copyright (c) 2018 vesoft inc. All rights reserved.
*
* This source code is licensed under Apache 2.0 License.
*/
#include "graph/service/GraphService.h"
#include <proxygen/lib/utils/CryptUtil.h>
#include <boost/filesystem.hpp>
#include "clients/storage/StorageClient.h"
#include "common/base/Base.h"
#include ... | {"hexsha": "9c6e37f12838a6be93ea4c74452a677853e07075", "size": 11015, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/graph/service/GraphService.cpp", "max_stars_repo_name": "sworduo/nebula", "max_stars_repo_head_hexsha": "9d172209cf05b0d4fb433d2fb17f44e301cdf440", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import numpy as np
import cv2
from PIL import Image
from random import *
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import math
from collections import deque
#dna encoding
msg_str = raw_input('Enter message: ')
key =int(raw_input('Enter shift key: '))
msg=list(msg_str)
print msg
enc=[]
bin1=[]
... | {"hexsha": "f401ff5970137edb0a95677b5593ed046180fa11", "size": 14472, "ext": "py", "lang": "Python", "max_stars_repo_path": "ias new/test.py", "max_stars_repo_name": "vrishabh22/IAS-Phishing-detection", "max_stars_repo_head_hexsha": "0a07f9a3a90c8f4c6c4f3e5abdd14bf354718f08", "max_stars_repo_licenses": ["MIT"], "max_st... |
# -*- coding: utf-8 -*-
"""
Created on July 2017
@author: JulienWuthrich
"""
import dateutil.parser
import datetime
import numpy
def time2scds(tm):
if isinstance(tm, str):
tm = dateutil.parser.parse(tm)
return tm.hour * 3600 + tm.minute * 60 + tm.second
if isinstance(tm, datetime.datetime):... | {"hexsha": "e4879b438d453b4b026146f00c200111693665bc", "size": 1353, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytools/format/time.py", "max_stars_repo_name": "Jwuthri/PythonTools", "max_stars_repo_head_hexsha": "7281fc5e41eb874bc8cb0aae844abe669d00a1a2", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import cv2
class FisheyeToEquirectangular:
def __init__(self, n=2048, side=3072, blending=16, aperture=1):
self.blending = blending
blending_ratio = blending / n
x_samples = np.linspace(0-blending_ratio, 1+blending_ratio, n+blending*2)
y_samples = np.linspace(-1, ... | {"hexsha": "7bcbd9524251f35e2493c91439752c0395ba1a61", "size": 2063, "ext": "py", "lang": "Python", "max_stars_repo_path": "fisheye.py", "max_stars_repo_name": "kylemcdonald/FisheyeToEquirectangular", "max_stars_repo_head_hexsha": "224d036972f7f4a0b6445311e82498f58660745d", "max_stars_repo_licenses": ["MIT"], "max_star... |
function [pitch, roll, yaw] = q2att(qnb)
q11 = qnb(1)*qnb(1); q12 = qnb(1)*qnb(2); q13 = qnb(1)*qnb(3); q14 = qnb(1)*qnb(4);
q22 = qnb(2)*qnb(2); q23 = qnb(2)*qnb(3); q24 = qnb(2)*qnb(4);
q33 = qnb(3)*qnb(3); q34 = qnb(3)*qnb(4);
q44 = qnb(4)*qnb(4);
C12=2*(q23-q14);
C22=q11-q22+q33-q44;
C3... | {"author": "yandld", "repo": "nav_matlab", "sha": "da70cb2083de407409ebe1ec1096a308611cf063", "save_path": "github-repos/MATLAB/yandld-nav_matlab", "path": "github-repos/MATLAB/yandld-nav_matlab/nav_matlab-da70cb2083de407409ebe1ec1096a308611cf063/study/eskf156/q2att.m"} |
import random
import numpy as np
import torch
class ConcatDataset(torch.utils.data.Dataset):
def __init__(self, *datasets, randomize_subset_idx=False):
self.datasets = datasets
self.cslen = np.concatenate([[0], np.cumsum([len(d) for d in datasets])])
self.subset_idx = [list(range(len(d)))... | {"hexsha": "dd2d3e8e2708c71c11efa8d2ceac7ca4d248d77e", "size": 1079, "ext": "py", "lang": "Python", "max_stars_repo_path": "concatdataset.py", "max_stars_repo_name": "phernst/sparse_dbp", "max_stars_repo_head_hexsha": "e66a5dffc20ecf2c770e336bc450b53d58db5df7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
[STATEMENT]
lemma GuardsFlip_sound:
assumes valid: "\<forall>n. \<Gamma>,\<Theta>\<Turnstile>n:\<^bsub>/F\<^esub> P c Q,A"
assumes validFlip: "\<forall>n. \<Gamma>,\<Theta>\<Turnstile>n:\<^bsub>/-F\<^esub> P c UNIV,UNIV"
shows "\<Gamma>,\<Theta>\<Turnstile>n:\<^bsub>/{}\<^esub> P c Q,A"
[PROOF STATE]
proof (prove... | {"llama_tokens": 5137, "file": "Simpl_HoarePartialProps", "length": 47} |
(*
* @TAG(OTHER_LGPL)
*)
(*
Author: Norbert Schirmer
Maintainer: Norbert Schirmer, norbert.schirmer at web de
License: LGPL
*)
(* Title: Compose.thy
Author: Norbert Schirmer, TU Muenchen
Copyright (C) 2006-2008 Norbert Schirmer
Some rights reserved, TU Muenchen
This library is... | {"author": "crizkallah", "repo": "checker-verification", "sha": "cd5101e57ef70dcdd1680db2de2f08521605bd7c", "save_path": "github-repos/isabelle/crizkallah-checker-verification", "path": "github-repos/isabelle/crizkallah-checker-verification/checker-verification-cd5101e57ef70dcdd1680db2de2f08521605bd7c/autocorres-1.0/c-... |
#!/usr/bin/env python
import argparse
import logging
import os
import re
import numpy as np
def evaluate(session_directory, num_obj_complete):
# Parse data from session (action executed, reward values)
transitions_directory = os.path.join(session_directory, 'transitions')
executed_action_log = np.loadtx... | {"hexsha": "c089dfebf4085140a155c37f956b8393f2032479", "size": 6938, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate.py", "max_stars_repo_name": "skumra/romannet", "max_stars_repo_head_hexsha": "0af092a1be26ac5f213f1e5c21f81f89699a4c92", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 5,... |
import napari
import numpy as np
from dask import array as da
from transitions import Machine
from ._logging import log_error
from ._logging import logger
from ._transitions import transitions
from ._viewer_model import ViewerModel
from ._viewer_model import ViewerState
# XXX tenative implementation : pluginfy later... | {"hexsha": "c6f19b3d21db643ce2c8d5b0a6acc20f4980803a", "size": 5696, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/napari_travali/_viewer.py", "max_stars_repo_name": "yfukai/napariTrackEditor", "max_stars_repo_head_hexsha": "23022fd718bc5adc6e12acf948a091c7c038a465", "max_stars_repo_licenses": ["MIT"], "ma... |
function yPred = objFcn(p , tObs, drug)
%Copyright (c) 2011, The MathWorks, Inc.
L0 = p(1) ; % Drug-independent parameter 1
L1 = p(2) ; % Drug-independent parameter 2
k1 = p(3) ; % Drug-independent parameter 3
k2_A = p(4) ; % Drug dependent parameter (drug A)
k2_B = p(5) ; % Drug dependent parameter (drug ... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/30869-fitting-with-matlab-statistics-optimiz... |
import os
import arrow
import glob
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import torch
from torch.nn import Module, Linear, Sequential, ReLU
from torch.nn.functional import mse_loss
from torch.optim import Adam, SGD
from torch.utils.data import Tens... | {"hexsha": "03bba1f0c9f4546e774e14c0ef80f1d13c95a90b", "size": 449, "ext": "py", "lang": "Python", "max_stars_repo_path": "01_AnomalyDetection/scripts/PythonImports.py", "max_stars_repo_name": "yellingmonkees/MetaLearningForAD", "max_stars_repo_head_hexsha": "04579ddf707421a016f5ca3a0a2e4ba80efe2890", "max_stars_repo_l... |
#=
Copyright 2013 - 2015 Marco Nehmeier (nehmeier@informatik.uni-wuerzburg.de)
Copyright 2015 Oliver Heimlich (oheim@posteo.de)
Original author: Marco Nehmeier (unit tests in libieeep1788)
Converted into portable ITL format by Oliver Heimlich with minor corrections.
Licensed under the Apache License, Ve... | {"hexsha": "8d6e1c081be57d6b400af569cae6504bfb5ef92c", "size": 158287, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "output/julia/Base.Test/ValidatedNumerics/libieeep1788_tests_mul_rev.jl", "max_stars_repo_name": "krish8484/ITF1788", "max_stars_repo_head_hexsha": "54dbc581703b5b0d89bb2f3a49bca4299b7d3b56", "max... |
import numpy as np
def updateState(state, patterns):
newState = [None]*len(state)
for pos in range(2, len(state) - 2):
newState[pos] = patterns[state[pos-2:pos+3]]
newState[-2:] = ['.', '.']
newState[:2] = ['.', '.']
newState = ''.join(newState)
return newState
nPadding = 10000... | {"hexsha": "fb0e4bc0a63e4bdcce57df8f8127ec107ee01971", "size": 978, "ext": "py", "lang": "Python", "max_stars_repo_path": "2018/day12-2.py", "max_stars_repo_name": "alvaropp/AdventOfCode2017", "max_stars_repo_head_hexsha": "2827dcc18ecb9ad59a1a5fe11e469f31bafb74ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
function prepareFWIDataFiles(m,Minv::RegularMesh,mref,boundsHigh,boundsLow,
filenamePrefix::String,omega::Array{Float64,1},waveCoef::Array{Complex128,1},
pad::Int64,ABLpad::Int64,jump::Int64,offset::Int64=prod(Minv.n+1),workerList = workers(),
maxBatchSize::Int64=48, Ainv::AbstractSolver = getMUMP... | {"hexsha": "a82c7582a7a62e91ef387746c085a23eaa3d53ff", "size": 4750, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/ex2DFWI/prepareFWIDataFiles.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/jInv.jl-3dacf901-f8cd-5544-86ed-7a705f85c244", "max_stars_repo_head_hexsha": "2e7305f231a29bd8e1e803... |
[STATEMENT]
lemma cf_pos_poly_represents[simp]: "(cf_pos_poly p) represents x \<longleftrightarrow> p represents x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. cf_pos_poly p represents x = p represents x
[PROOF STEP]
unfolding represents_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (ipoly (cf_pos_poly p)... | {"llama_tokens": 185, "file": "Algebraic_Numbers_Algebraic_Numbers_Prelim", "length": 2} |
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
def posterior(fitresult,plot_likelihood=False):
'''
Plot the posterior probablity/likelihood from the given FitResult object.
'''
#The fit parameters and the inverse covariance matrix
p0 = fitresult... | {"hexsha": "d0f36e7457e7bb19cb217930608a5513ee719330", "size": 2885, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayesfit/plot.py", "max_stars_repo_name": "aripekka/bayesfit", "max_stars_repo_head_hexsha": "e17b46540ae8c8bbaecc90073690a197d77a78bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from collections import OrderedDict
from typing import Callable
from typing import Hashable
from typing import List
from typing import Type
import numpy as np
from caldera.utils.np import replace_nan_with_inf
from caldera.utils.nx.traversal._path_utils import PathSum
from caldera.utils.nx.traversal._path_utils import... | {"hexsha": "5e66e49e7ffc2ae246a179a34a555165ee82bfd5", "size": 2560, "ext": "py", "lang": "Python", "max_stars_repo_path": "caldera/utils/nx/traversal/_all_pairs_shortest_path.py", "max_stars_repo_name": "jvrana/caldera", "max_stars_repo_head_hexsha": "a346324e77f20739e00a82f97530dda4906f59dd", "max_stars_repo_licenses... |
import numpy as np
import cv2,sys,csv,os,threading
from PyQt5.QtWidgets import QMessageBox,QAction
from keras.models import load_model
import qimage2ndarray as q2a
from PyQt5 import QtWidgets,QtGui,QtCore,QtCore
from PyQt5.QtWidgets import QWidget, QLabel, QApplication,QMainWindow,QMessageBox,QFileDialog,QInputD... | {"hexsha": "18c9f17a944e54d17469e37b66afcd4e3e2d2eaa", "size": 7426, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "kanchansapkota27/Sign-Language-Translator", "max_stars_repo_head_hexsha": "2e63032d6a42042d8caecfc628de5abc647ab234", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
import math
import sys
import os
import random
import struct
import popen2
import getopt
import numpy
pi=math.pi
e=math.e
j=complex(0,1)
doreal=0
datatype = os.environ.get('DATATYPE','float')
util = '../tools/fft_' + datatype
minsnr=90
if datatype == 'double':
fmt='d'
elif datatype=='int1... | {"hexsha": "e161a42b3067bd33f81964ca4120c851df447067", "size": 3565, "ext": "py", "lang": "Python", "max_stars_repo_path": "KissFFT/kiss_fft130/test/testkiss.py", "max_stars_repo_name": "mpoullet/audio-tools", "max_stars_repo_head_hexsha": "b7cb54ec16f2845830ab6168d8e6992124c98a75", "max_stars_repo_licenses": ["MIT"], ... |
FUNCTION TRAPZ2(X,Y,N,X1,X2,IERR)
c Finds area below y(x) from x=X1 to x =X2
c X1 and X2 are not necessarily values of x(i)
c but must fulfill X1>=x(1) and X2<=x(n)
c Array declaration
real x(n),y(n)
data jerr1,jerr2/2*0/
TRAPZ2=0.
ierr=0.
! Check for values outside valid range
if (x1 < x(1)) then
if (jerr1... | {"hexsha": "37962eba3f3af0876aeea409928ba29420c436e6", "size": 1474, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "bc03/src/trapz2.f", "max_stars_repo_name": "jchavesmontero/galaxpy", "max_stars_repo_head_hexsha": "ef5eaffa8f5ec0418dae44b88cf212ca03814b57", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import numpy as np
import sys
from detector_hit_conditions import *
def get_acceptance_from_four_vectors(ma, ctau, four_vector_list, zmin, zmax, det_rad, Ethr=1.):
evt_fraction_detected = []
for pa, pr in four_vector_list:
if pa[3] < 0:
continue
gct = (pa[3]/ma)*ctau
np.se... | {"hexsha": "f02d5cc6867ba5d28c985c814a082f978504e26a", "size": 1167, "ext": "py", "lang": "Python", "max_stars_repo_path": "dev/mathematica/get_ldmx_acceptance_to_share/compute_acceptance_LDMX.py", "max_stars_repo_name": "jmlazaro25/vissig", "max_stars_repo_head_hexsha": "370262b0546959bd2936cfd1ffa16de5b85a3dee", "max... |
import numpy as np
import scipy as sp
import properties
from ....utils.code_utils import deprecate_class
from ....utils import mkvc, sdiag, Zero
from ....data import Data
from ...base import BaseEMSimulation
from .boundary_utils import getxBCyBC_CC
from .survey import Survey
from .fields import Fields3DCellCentered, F... | {"hexsha": "ac40a782639b1fe138e609a23531840da8ef2ada", "size": 18048, "ext": "py", "lang": "Python", "max_stars_repo_path": "SimPEG/electromagnetics/static/resistivity/simulation.py", "max_stars_repo_name": "jcapriot/simpeg", "max_stars_repo_head_hexsha": "e88e653673c6b818592b6c075f76ee9215fe82b7", "max_stars_repo_lice... |
\documentclass[runningheads]{llncs}
%
\usepackage{graphicx}
% Used for displaying a sample figure. If possible, figure files should
% be included in EPS format.
\graphicspath{ {../plots/} }
\usepackage{calc}
% state diagrams
\usepackage{tikz}
\usetikzlibrary{automata, arrows, positioning}
\tikzset{
ini... | {"hexsha": "59dfbb7da92600b5604953293ac0065f1c441d9b", "size": 9079, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "bnaic/2-page-summary-bnaic.tex", "max_stars_repo_name": "tinybeachthor/IPD", "max_stars_repo_head_hexsha": "af3dbb21a349c792125a1548d35d2e0fdfb23e60", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import numpy as np
from pennpaper import Metric, plot_group
xs = np.arange(0.1, 5, step=0.01)
uni_noise = lambda x: np.random.uniform(size=x.shape) + x
funcs = {}
funcs['uniform'] = lambda x: np.random.uniform(size=x.shape) + x
funcs['weibull'] = lambda x: np.random.weibull(a=1, size=x.shape) + x
funcs['beta'] = la... | {"hexsha": "6fdadbe2d1b788b063b75b85051f531dd1ff4a04", "size": 619, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/plot_randoms.py", "max_stars_repo_name": "ikamensh/pennpaper", "max_stars_repo_head_hexsha": "82c8a7a55a2407ed44d095036ed4ae8f2d004b04", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import random
import os
import json
import mxnet as mx
from mxnet import gluon
import argparse
import logging
import time
from gluonnlp.utils.misc import logging_config
from gluonnlp.models.transformer import TransformerModel, TransformerInference
from gluonnlp.data.batchify import Tuple, Pad, Stack
... | {"hexsha": "46487e9442c1092c0e2edb35946a2e4987e4fe2d", "size": 19135, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/machine_translation/evaluate_transformer.py", "max_stars_repo_name": "leezu/gluon-nlp", "max_stars_repo_head_hexsha": "19de74c2b03f22dde8311a0225b4571c2deef0e4", "max_stars_repo_licenses"... |
import gensim, time, os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import numpy as np
import pandas as pd
import gensim
import string, nltk
from nltk import word_tokenize
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras as tfk
from tensorflow.keras.preprocessing.text import Tokenizer
from ... | {"hexsha": "e366a259aff710107f72b3e90267d0f5014b73c8", "size": 2692, "ext": "py", "lang": "Python", "max_stars_repo_path": "Theory/steps.py", "max_stars_repo_name": "StamatisOrfanos/Fake-News_Detection", "max_stars_repo_head_hexsha": "1a26ff2396fc6083bd27ed276d6b927fb6783850", "max_stars_repo_licenses": ["MIT"], "max_s... |
[GOAL]
E : Type u_1
X : Type u_2
c : E
f : ContDiffBump c
⊢ 1 < f.rOut / f.rIn
[PROOFSTEP]
rw [one_lt_div f.rIn_pos]
[GOAL]
E : Type u_1
X : Type u_2
c : E
f : ContDiffBump c
⊢ f.rIn < f.rOut
[PROOFSTEP]
exact f.rIn_lt_rOut
[GOAL]
E : Type u_1
X : Type u_2
inst✝⁴ : NormedAddCommGroup E
inst✝³ : NormedSpace ℝ E
inst✝² :... | {"mathlib_filename": "Mathlib.Analysis.Calculus.BumpFunction.Basic", "llama_tokens": 3895} |
!subroutine mpas_initialize_vectors(meshPool)!{{{
subroutine mpas_initialize_vectors(nCells, nEdges, maxEdges, R3, &
verticesOnEdge, cellsOnEdge, &
edgesOnCell, xCell, yCell, zCell, xEdge, yEdge, zEdge, &
localVerticalUnitVectors, edgeNormalVectors, cellTangentPlane, &
on_a_sphere, is_periodic, x_pe... | {"hexsha": "061aab53461bb69815ea2bb2b62e5847696fc56d", "size": 21694, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mpas_sw_operators.f90", "max_stars_repo_name": "xtian15/MPAS-SW-TL-AD", "max_stars_repo_head_hexsha": "d6ac1597ac4a6c1ee3339e8384dd6bef42eccbfc", "max_stars_repo_licenses": ["MIT"], "max_st... |
# OPTIMO_FEAS
#
# PART OF OPTIMO
"""
FeasOptiModel( prob, [x0, with_indicator, xp, dp, wp, name] )
represents the following structured, proximal, feasibility problem:
```
minimize Φ(x) + with_indicator ind_g( x )
```
with respect to `x`, starting from `x0`, where
```
Φ(x) := m( c(x) ... | {"hexsha": "ee0a8593bc69ad24f9ff041cdb18783799704189", "size": 7848, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/optimo_feas.jl", "max_stars_repo_name": "aldma/OptiMo.jl", "max_stars_repo_head_hexsha": "9512545b1e4f867c88438dcc24f1b63f37f3d889", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import numpy as np
import pandas as pd
from parallelm.components import ConnectableComponent
class RandomDataframe(ConnectableComponent):
"""
Generating a random dataframe. The number of rows and columns is provided as input parameters to the component
"""
def __init__(self, engine):
super(se... | {"hexsha": "76d8fe96125e2baaf71de5153f081c790faa44d2", "size": 723, "ext": "py", "lang": "Python", "max_stars_repo_path": "reflex-algos/components/Python/random-dataframe/random_dataframe.py", "max_stars_repo_name": "lisapm/mlpiper", "max_stars_repo_head_hexsha": "74ad5ae343d364682cc2f8aaa007f2e8a1d84929", "max_stars_r... |
%for subplots
function p=plots1(x,y,z,img)
H1=abs(img);
colormap(hot)
subplot(x,y,z);
image(5*100*H1/max(max(H1)));%4 for B-727r;
if z==2
title('ISAR images using Harmonic Wavelets');
end
if z==4
ylabel('Range')
end
if z==8
xlabel('Doppler')
end | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/43601-harmonic-wavelet-based-isar-imaging/Co... |
from math import pi, log10, ceil
from typing import Optional, Dict, Union, Tuple
import numpy as np
import plotkit.plotkit as pk
from sympy import Expr, sympify, Symbol
Value = Union[float, int, complex]
class FrequencyDomainPlotter:
S = Symbol("s")
def __init__(self, expr: Expr) -> None:
self.expr... | {"hexsha": "e34094896421ee0ed3daff8fd96d02191bd8270c", "size": 3060, "ext": "py", "lang": "Python", "max_stars_repo_path": "symcircuit/plotting.py", "max_stars_repo_name": "martok/py-symcircuit", "max_stars_repo_head_hexsha": "c48b1ad8ae4e496306da0c0a7474b4cd968a629f", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
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