text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import rospy
import smach
import smach_ros
import message_filters
import tf
from tf import transformations
from tf import TransformListener
from tf import transformations
from geometry_msgs.msg import PoseStamped
import apc_msgs.srv
from sensor_msgs.msg import PointCloud2
from sensor_msgs.msg import Image
from apc_... | {"hexsha": "0b839bf026ed25aee300f21392507580457c42ec", "size": 10084, "ext": "py", "lang": "Python", "max_stars_repo_path": "apc_state_machine/state_machine/scripts/OBSOLETE_decideGraspPoseStateFromPointCloud.py", "max_stars_repo_name": "Juxi/apb-baseline", "max_stars_repo_head_hexsha": "fd47a5fd78cdfd75c68601a40ca4726... |
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
folder = '/home/sebastian/Programs/iblrig/tasks/_iblrig_tasks_ephysChoiceWorld/sessions/' # location of datasets
show = 0 # whether or not to show plots (they will be saved anyway)
plot = 0 # whether or not to compu... | {"hexsha": "139132199bb945aa249774c4ebbef367ebff436f", "size": 3782, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/sequence_analysis.py", "max_stars_repo_name": "ineslaranjeira/analysis", "max_stars_repo_head_hexsha": "ef262cc0d4e04ffb59d81aeb8f135790778b8bbf", "max_stars_repo_licenses": ["MIT"], "max_s... |
from numpy.random import choice
from srcs.agent.Tree import Tree
from srcs.agent.auxilliary import ucb
from srcs.agent.auxilliary import NodeAttr as NodeAttr
from enum import IntEnum
#
# An enum for the type of rollout policy to be used.
#
class RolloutPolicy(IntEnum):
RANDOM_ACTION = 0
RANDOM_ACTION_AVOIDING... | {"hexsha": "3cca799a503d3e7acd83d6a7a03aac8a2e383f01", "size": 12185, "ext": "py", "lang": "Python", "max_stars_repo_path": "srcs/agent/POMCP.py", "max_stars_repo_name": "ChampiB/POMCP", "max_stars_repo_head_hexsha": "af6b7f9df3476126abad2adf21cc618e1d9898d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma HNatInfinite_FreeUltrafilterNat_iff:
"(star_n X \<in> HNatInfinite) = (\<forall>u. eventually (\<lambda>n. u < X n) \<U>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (star_n X \<in> HNatInfinite) = (\<forall>u. \<forall>\<^sub>F n in \<U>. u < X n)
[PROOF STEP]
by (rule iffI [OF HNatInfinite_... | {"llama_tokens": 163, "file": null, "length": 1} |
from .dummy_gym_env import DummyEnv
from gym.spaces import Box, Discrete
import numpy as np
from supersuit import (
frame_stack_v1,
reshape_v0,
observation_lambda_v0,
action_lambda_v1,
dtype_v0,
)
import supersuit
import pytest
base_obs = (np.zeros([8, 8, 3]) + np.arange(3)).astype(np.float32)
base... | {"hexsha": "c376d28fac88d1d3096419bbeb3b5b1f44942736", "size": 4227, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/gym_mock_test.py", "max_stars_repo_name": "PettingZoo-Team/SuperSu", "max_stars_repo_head_hexsha": "3c4e364b4744649cb9eaa9201d70b5be3d43730f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
from agent import CUDAAgent
from .base_student import BaseStudent
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
return np.exp(x) / np.sum(np.ex... | {"hexsha": "0caad34d85bfdcdf380dd2ad1a004435160d7875", "size": 9262, "ext": "py", "lang": "Python", "max_stars_repo_path": "student/icil_student.py", "max_stars_repo_name": "ioanabica/Invariant-Causal-Imitation-Learning", "max_stars_repo_head_hexsha": "eb92fac1db6e418250ad383d888d69faa667e7aa", "max_stars_repo_licenses... |
#These are the basics + the ability to shut the program down.
import pygame as pg #This is where I get all the pygame stuff
import numpy as np #I import this becouse I'm in love with numpy
from pygame.locals import QUIT #QUIT is a constant (I think) that indicates wether the user is trying to quit the program by pushi... | {"hexsha": "9b93715695dfe8d510ac19a3ce21651ea45f0373", "size": 1361, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyGame/pygame2/ex1/ex2.py", "max_stars_repo_name": "hoppfull/Legacy-Python", "max_stars_repo_head_hexsha": "43f465bfdb76c91f2ac16aabb0783fdf5f459adb", "max_stars_repo_licenses": ["MIT"], "max_star... |
from scipy.io import loadmat
from datetime import datetime
import os
def calc_age(taken, dob):
birth = datetime.fromordinal(max(int(dob) - 366, 1))
# assume the photo was taken in the middle of the year
if birth.month < 7:
return taken - birth.year
else:
return taken - birth.year - 1
... | {"hexsha": "3c2ed32ba4dbe6a1edbd1f4e8fd1625291fe8fc3", "size": 3060, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "phanbaominh/age-gender-estimation", "max_stars_repo_head_hexsha": "a157e486f11b21fa46b447a2d7e978e0ff94a919", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma (in Ring) LSM_eq_linear_span:"\<lbrakk>R module M; T \<subseteq> carrier M\<rbrakk> \<Longrightarrow>
(LSM\<^bsub>R\<^esub> M T) = linear_span R M (carrier R) T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>R module M; T \<subseteq> carrier M\<rbrakk> \<Longrightarrow> LSM\<^b... | {"llama_tokens": 1105, "file": "Group-Ring-Module_Algebra9", "length": 8} |
[STATEMENT]
lemma plusl_bot_infty: "\<bottom>\<^sub>1 +\<^sub>1 \<infinity>\<^sub>1 = \<bottom>\<^sub>1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<bottom>\<^sub>1 +\<^sub>1 \<infinity>\<^sub>1 = \<bottom>\<^sub>1
[PROOF STEP]
by (simp) | {"llama_tokens": 103, "file": "Regular_Algebras_Pratts_Counterexamples", "length": 1} |
% Part: first-order-logic
% Chapter: tableaux
% Section: quantifier-rules
\documentclass[../../../include/open-logic-section]{subfiles}
\begin{document}
\olfileid{fol}{tab}{qrl}
\olsection{Quantifier Rules}
\subsection{Rules for $\lforall$}
\begin{defish}
\AxiomC{\sFmla{\True}{\lforall[x][!A(x)]}}
\RightLabel{\TR... | {"hexsha": "60158f3b85d68251e086522c9da02ff9959cfbde", "size": 3850, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "content/first-order-logic/tableaux/quantifier-rules.tex", "max_stars_repo_name": "GKerfImf/OpenLogic", "max_stars_repo_head_hexsha": "5791905d3149f68e05885290f448054b98a0e51b", "max_stars_repo_licen... |
"""
Copyright (c) Facebook, Inc. and its affiliates.
"""
"""This file has functions to implement different dances for the agent.
"""
import numpy as np
import tasks
import shapes
import search
from util import ErrorWithResponse
# FIXME! actual jump on client
jump = [{"translate": (0, 1, 0)}, {"translate": (0, -1, 0)}... | {"hexsha": "0a646f079abbf0892aa42a6aeba9e5fdf6fd004a", "size": 4760, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/craftassist/dance.py", "max_stars_repo_name": "anoushkt/craftassist", "max_stars_repo_head_hexsha": "c200af65e52e800f0f0cc540fe836b644383349d", "max_stars_repo_licenses": ["MIT"], "max_star... |
from zipfile import ZipFile
import pandas as pd
from scipy.io import savemat
def read_filename(filename):
with ZipFile(f'../contests/responses/{filename}') as myzip:
csv_file = filename.replace('.zip', '')
with myzip.open(csv_file) as f:
df = pd.read_csv(f, index_col=0)
print(df.co... | {"hexsha": "d5d09c3ffe89cec265996de0cbbdba42b3ff8bc0", "size": 823, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/example-analyses/_unused-scripts/dueling-responses.py", "max_stars_repo_name": "kgjamieson/NEXT_data", "max_stars_repo_head_hexsha": "7cbe8080b441fc91e2e8198ec47c750e6517f83f", "max_stars_repo... |
"""
Train videos are convert into the image frames according to what UCF annotation and readMe.
Training models is created if no training has been done before, weights can be loaded from a pretrained model.
Training process is done using Faster R-CNN with VGG16 network.
The length of each epoch used to do training is 1... | {"hexsha": "45035253616fae9d29c4f2ccd865e036ec60eac6", "size": 16687, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_frcnn.py", "max_stars_repo_name": "piranavie/final_project_behaviour_detection", "max_stars_repo_head_hexsha": "84f91ed240b2be02e6d4ad562c2f4a9a185583e4", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
from rlpyt.replays.non_sequence.n_step import (NStepReturnBuffer,
SamplesFromReplay)
from rlpyt.replays.non_sequence.uniform import UniformReplay
from rlpyt.replays.non_sequence.prioritized import PrioritizedReplay
from rlpyt.replays.async_ import AsyncReplayBufferMixin
from rlpyt.utils.collect... | {"hexsha": "9cf3518a21621c2de17079fae0411364460a5d42", "size": 2318, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlpyt/replays/non_sequence/time_limit.py", "max_stars_repo_name": "cambel/rlpyt", "max_stars_repo_head_hexsha": "96e231d6c77ba5ff06dd09f6e9c8837f0abb1a89", "max_stars_repo_licenses": ["MIT"], "max... |
# Copyright 2020 The TensorFlow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | {"hexsha": "69284c726a35484b66a007fd7755ca1de73bc4d4", "size": 4574, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylib/pc/tests/sample_test.py", "max_stars_repo_name": "schellmi42/tensorflow_graphics_point_clouds", "max_stars_repo_head_hexsha": "c8e2dc2963c3eecfb27542449603f81d78494783", "max_stars_repo_lice... |
import attr
import torch
import numpy
import pytest
import tattr
def test_attrib_metadata():
"""tattrs are defined by metadata on attrs classes.
* Dispatches through ``attr.s`` if the class is not already attr-ed.
* Creates __tattr_attrs__ entries for any attribs with "tensor" metadata.
* Captures ... | {"hexsha": "8356cb3d8e5d8af4bea2c62a5c4542c006316c51", "size": 2267, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_primitive.py", "max_stars_repo_name": "uw-ipd/tattrs", "max_stars_repo_head_hexsha": "267314a0f315035862392e7ebe35aa15a07549f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 1 22:54:02 2019
@author: alex
"""
###############################################################################
import numpy as np
###############################################################################
from pyro.control import controlle... | {"hexsha": "d4a3a4a874620c817d936242db12bf7be958bfde", "size": 10885, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/vehicle_modeling/test_vehicle_controllers.py", "max_stars_repo_name": "simonchamorro/pyro", "max_stars_repo_head_hexsha": "a637d61e1d49b22f178b3889dc0092c9e1704adc", "max_stars_repo_lice... |
# Author: Javad Amirian
# Email: amiryan.j@gmail.com
import glob
import os
import cv2
import numpy as np
def make_bg_image_from_screenshots(im_files):
im_sum = None
for im_file in im_files:
im_i = cv2.imread(im_file)
if im_sum is None:
im_sum = im_i.astype(np.float)
else:... | {"hexsha": "ae75c63549de1f6903f68bbc43516f1f8970610f", "size": 731, "ext": "py", "lang": "Python", "max_stars_repo_path": "opentraj/toolkit/ui/build_background_image.py", "max_stars_repo_name": "fengzileee/OpenTraj", "max_stars_repo_head_hexsha": "71fdfd1e3420d6a3859ae0acaa4acf85abbc1f64", "max_stars_repo_licenses": ["... |
base_vals = Base.ImmutableDict(DNA_A=>0,DNA_C=>1,DNA_G=>2,DNA_T=>3)
function get_mer_idx(mer,k=5)
idx = 0
@turbo for i in 1:k
idx = 4*idx + base_vals[mer[i]]
end
return idx +1
end
function count_mers_4_dist(seq,k = 5)
counts = zeros(Int,4^k)
for mer in each(DNAMer{k}, seq)
cur... | {"hexsha": "501468f0da9e27eb03bc3edb0a91dd1ad1289be1", "size": 1700, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/kmers.jl", "max_stars_repo_name": "EvoArt/DenoiseDNA.jl", "max_stars_repo_head_hexsha": "2e4ac9079469ee1cdb87b6d00943a80cfc42f954", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#ヒルベルト行列のcondition numberを求める
import scipy.linalg as linalg
import numpy as np
from numpy import linalg as LA
def calc_hilbert_condition(n):
A = np.zeros((n,n))
for i in range(n):
for j in range(n):
A[i][j] = 1/(i+j+1)
print("matrix size =", n)
print("condition number is", LA.cond(... | {"hexsha": "47e64602e38677700cc58c1386b798ee9b042d73", "size": 444, "ext": "py", "lang": "Python", "max_stars_repo_path": "Numerical_Analysis/report/5/3.py", "max_stars_repo_name": "yoshi-ki/BACHELOR", "max_stars_repo_head_hexsha": "65d01c62ab2ea4a6d2616a6b6c535bd4f1645630", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
Script for training a Random Forest model on fingerprint representations of molecules.
"""
import os
import warnings
import argparse
import pandas as pd
import numpy as np
from rdkit.Chem import MolFromSmiles, AllChem
from rdkit import DataStructs
from sklearn.model_selection import train_test_split, KFold
from ... | {"hexsha": "dc76343107c75e04fe0518fb84c8b8043bceecf5", "size": 4148, "ext": "py", "lang": "Python", "max_stars_repo_path": "morgan_rf.py", "max_stars_repo_name": "wjm41/soapgp", "max_stars_repo_head_hexsha": "ef57cebb7413abb96b54983141e188dff5166d03", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max_star... |
using JuMP, Base.Test, AmplNLWriter
# solver = AmplNLSolver(Ipopt.amplexe, ["print_level=0"])
# Note min and max not implemented in Couenne
## Solve test problem with simple min functions
#
# max min( x^2, x )
# s.t. -0.5 <= x <= 0.5
#
# The optimal objective value is 0.25.
# x = 0.5
##
@testset "ex... | {"hexsha": "7b6acf14fc4229e002d426a72c968d487f96df0f", "size": 1046, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/jump_maxmin.jl", "max_stars_repo_name": "tkoolen/AmplNLWriter.jl", "max_stars_repo_head_hexsha": "c3ab02096a96122bcad0b04067990611d0492c41", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import unittest
import numpy as np
import scipy.signal as signal
import filterdesigner.FIRDesign as FIRDesign
class TestSgolay(unittest.TestCase):
def setUp(self):
self.order = 4
self.framelen = 21
def test_sgolay(self):
# Test case for sgolay
FIR = FIRDesign.sgolay... | {"hexsha": "54781c1d046004ccf0c543f27cce945aebe6cde7", "size": 470, "ext": "py", "lang": "Python", "max_stars_repo_path": "filterdesigner/tests/test_sgolay.py", "max_stars_repo_name": "Yuki-F-HCU/filterdesigner", "max_stars_repo_head_hexsha": "bb735d507da0338b2925f84e54df091ce1c32f95", "max_stars_repo_licenses": ["BSD-... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# IkaLog
# ======
# Copyright (C) 2015 Takeshi HASEGAWA
#
# 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/l... | {"hexsha": "2cb0b2643c54666ea12f68aac59d2f25aff15d14", "size": 6084, "ext": "py", "lang": "Python", "max_stars_repo_path": "ikalog/inputs/win/videoinput_wrapper.py", "max_stars_repo_name": "fetus-hina/IkaLog", "max_stars_repo_head_hexsha": "bd476da541fcc296f792d4db76a6b9174c4777ad", "max_stars_repo_licenses": ["Apache-... |
#!/usr/bin/env python3
import json
import models
import utils
import argparse,random,logging,numpy,os
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch.nn.utils import clip_grad_norm
from time im... | {"hexsha": "44aac7340455240cb503034c9a8a3f6e30c2d7a8", "size": 13614, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "hyunbool/SummaRuNNer", "max_stars_repo_head_hexsha": "2a9fe75fa9d47bd13b2143ecb3f1acb65a11d701", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import numpy as np
import tensorflow as tf
import os
import pickle
import random
from generator import Generator
from mobilenet import MobileNet
from PIL import Image
EMB_DIM = 300 # embedding dimension
HIDDEN_DIM = 300 # hidden state dimension of lstm cell
SEQ_LENGTH = 12 # sequence length
START_TOKEN = 0... | {"hexsha": "db04d435881721649befc2aad7bc8a6de345bdba", "size": 2424, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "GeneZC/AlphaPoet", "max_stars_repo_head_hexsha": "82715e9cc36aedfa78c250a7a7f8129669eea440", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_... |
import torch
from torch import nn, einsum
import numpy as np
from einops import rearrange, repeat
import torch.nn.functional as F
def number_parameters(Net, type_size=8):
para = sum([np.prod(list(p.size())) for p in Net.parameters()])
return para / 1024 * type_size / 1024
class Residual_Connection(nn.Module... | {"hexsha": "c0e92d44a97df35eef7e4d29a2ddbd1ba194c217", "size": 5833, "ext": "py", "lang": "Python", "max_stars_repo_path": "method/model.py", "max_stars_repo_name": "XinghuaMa/TR-Net", "max_stars_repo_head_hexsha": "879c32e130df668636a27ce5cb5e5d76cf90de66", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "m... |
#Show which groups fit very poorly at the site levelbut fit well in sample.
rm(list=ls())
source('paths.r')
#set output path.----
output.path <- 'Supp._Fig._3._bad_out.of.sample_fits.png'
#load data.----
#grab prior fits.
prior <- readRDS(ted_ITS_prior_all.groups_JAGSfits.path) #all phylo and functional groups.
#grab... | {"hexsha": "1106301115f2d052e53419cc760a101d5345e60d", "size": 4300, "ext": "r", "lang": "R", "max_stars_repo_path": "to_retire/Supp._Fig._3._bad_out.of.sample_fits.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses"... |
"""
-------------------------------------------------------
helper
a couple of helper functions
-------------------------------------------------------
Author: Dallas Fraser
ID: 110242560
Email: fras2560@mylaurier.ca
Version: 2014-09-10
-------------------------------------------------------
"""
import networkx... | {"hexsha": "9d81f6ebf5d50aa404086d76b76a02f69d588483", "size": 7579, "ext": "py", "lang": "Python", "max_stars_repo_path": "inducer/helper.py", "max_stars_repo_name": "fras2560/InducedSubgraph", "max_stars_repo_head_hexsha": "be06a444a2ef0d244831ee74152a8ef2711cdbe3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# ========== Transforms On A Group Of Signals ==========
# ----- SDWT on a set of signals -----
"""
sdwtall(x, wt[, L])
Computes the stationary discrete wavelet transform (SDWT) on each slice of signal.
# Arguments
- `x::AbstractArray{T} where T<:Number`: Input `N-1`-D signals, where each signal is sliced
at di... | {"hexsha": "28e9139efecfe62d1d6954a0cd7f1af0ab99e7aa", "size": 10649, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mod/swt_all.jl", "max_stars_repo_name": "UCD4IDS/WaveletsExt.jl", "max_stars_repo_head_hexsha": "cfde3ae0cea370b7da0e8d5723f0793bb87beb68", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
"""
GANTT Chart with Matplotlib
Sukhbinder
Inspired from
<div class="embed-theclowersgroup"><blockquote class="wp-embedded-content"><a href="http://www.clowersresearch.com/main/gantt-charts-in-matplotlib/">Gantt Charts in Matplotlib</a></blockquote><script type="text/javascript"><!--//--><![CDATA[//><!-- !functi... | {"hexsha": "6376cb1af729c668f9be0d3da4ca2a2fe3fbe64d", "size": 4817, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gantt.plot.py", "max_stars_repo_name": "jackmo375/Planner2", "max_stars_repo_head_hexsha": "61bf816113fbd31604c5bd5d686c62d4f3764892", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*- coding: utf-8 -*-
"""Simulation Task3.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1hujTQ6qyEX9-NPn1bD_p4D8ZpGZ0t7rw
"""
from collections import deque
import pandas as pd
import numpy as np
import scipy.stats as st
import matplotlib.pypl... | {"hexsha": "4a5004dfb8a58465aa5037c6002089fdbcadaeef", "size": 10781, "ext": "py", "lang": "Python", "max_stars_repo_path": "Simulation Task/Task 3/simulation_task3.py", "max_stars_repo_name": "ayush-bisht/IoT-Analytics", "max_stars_repo_head_hexsha": "edd41f9fe6749e7d7b0ce5b15f0e7b7e0db3e66a", "max_stars_repo_licenses... |
#!/usr/bin/env python
import rospy
import sys
import numpy as np
import math
from geometry_msgs.msg import Vector3
from std_msgs.msg import Float64
from dynamixel_msgs.msg import JointState
PI = 3.14159265359
class TiltController:
def __init__(self):
# Create the Subscriber recive degree and publish i... | {"hexsha": "427f2f48bd507d97a3166970f7fdee55099f75fe", "size": 1854, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/platform_controller/scripts/tiltController.py", "max_stars_repo_name": "ahmohamed1/activeStereoVisionPlatform", "max_stars_repo_head_hexsha": "6c928ca242e4de68c7b15a8748bff1d9f7fa1382", "max_s... |
"""
AbstractInput
Abstract supertype for all input types.
### Notes
The input types defined here implement an iterator interface, such that other methods
can build upon the behavior of inputs which are either constant or varying.
Iteration is supported with an index number called *iterator state*.
The iteration... | {"hexsha": "2dfd8441c471ea6546d92e87e0704f03c1437e42", "size": 5233, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/inputs.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/MathematicalSystems.jl-d14a8603-c872-5ed3-9ece-53e0e82e39da", "max_stars_repo_head_hexsha": "af916edc33ab9d27f064a8be5c96a5cb9... |
import numpy as np
from .PSpecCls import PSpecCls
def CombinePSpecCls(A):
'''
Combine an array/list/tuple of SpecCls objects into a single one.
This assumes that all axis labels and stuff are identical.
Input
=====
A : array/list/tuple
Each element should be a SpecCls object
Returns
=======
SpecCls
... | {"hexsha": "64b5f47f0c6ff572589715a588d4e0089330d85c", "size": 958, "ext": "py", "lang": "Python", "max_stars_repo_path": "Arase/Tools/CombinePSpecCls.py", "max_stars_repo_name": "mattkjames7/Arase", "max_stars_repo_head_hexsha": "996167be35a13bbb1fdddfbe75e3a06d124b1d25", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#import uuid
import argparse
import glob
import os
import tifffile
import numpy as np
#Example usage:
#python im2npy.py --source_dir="C:\Users\PROCOMP11-PC\Desktop\PanColorGAN\PanColorGAN-master\PanColorGAN-master\dataset\PAN\tif" --save_to="C:\Users\PROCOMP11-PC\Desktop\PanColorGAN\PanColorGAN-master\PanColo... | {"hexsha": "3fcef12abdb6d6a1ba10b2c5b9846a2e8cb5521a", "size": 2137, "ext": "py", "lang": "Python", "max_stars_repo_path": "Script/im2npy.py", "max_stars_repo_name": "ataozarslan/GEO_tutorial", "max_stars_repo_head_hexsha": "c02c8bcfbdf89deabaaea259195e57d669ad3f69", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
#!/usr/bin/python
# -*- coding: iso-8859-1 -*-
from numpy import array
def dY_dt(Y,t,p):
return array([
(- p[0] * Y[0]) * ( k__mCLN ),
(p[10] * p[1] * Y[0]/p[11] * Y[2]) * ( k__Cln_plus ) - (p[2] * Y[1]) * ( k__Cln_minus ),
(p[6] * p[10] * Y[5]/p[11] * (p[3]/(p[3]+p[4]+p[5])) * Y[2]) * ( k__B_R ),
(p[6] * p... | {"hexsha": "da19b9f9f504b02a89227a8caf4a88f34d1ac17a", "size": 674, "ext": "py", "lang": "Python", "max_stars_repo_path": "core_eq_system_w_modifiers.py", "max_stars_repo_name": "thomasspiesser/MYpop", "max_stars_repo_head_hexsha": "aa26659af75e99189e77c4f4e046985e536918b1", "max_stars_repo_licenses": ["MIT"], "max_sta... |
function fill_cpmodel!(optimizer::Optimizer)
# Adding variables
bridge_variables!(optimizer)
# Adding affine functions
bridge_affines!(optimizer)
# Adding constraints
bridge_constraints!(optimizer)
# Adding objective
bridge_objective!(optimizer)
optimizer
end
... | {"hexsha": "8c88f56f3b1b107d908edb9b6dea41154ceb8a9c", "size": 4792, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MOI_wrapper/homemade_bridging.jl", "max_stars_repo_name": "pitmonticone/SeaPearl.jl", "max_stars_repo_head_hexsha": "0c0ca5ec5cce81515acd202ea2d87c985c0c3fea", "max_stars_repo_licenses": ["BSD-... |
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Polyhedron_3.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/Side_of_triangle_mesh.h>
#include <vector>
#include <fstream>
#include <limits>
#include <boost/foreach.hpp>
typedef CGAL::Exact_predicates_inexact_constructions_kernel K... | {"hexsha": "ca3e886360f6e61ac57163cbf27fbeabf1a1cfa8", "size": 2103, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ext/libigl/external/cgal/src/CGAL_Project/examples/Polygon_mesh_processing/point_inside_example.cpp", "max_stars_repo_name": "liminchen/OptCuts", "max_stars_repo_head_hexsha": "cb85b06ece3a6d1279863... |
(* *********************************************************************)
(* *)
(* The Compcert verified compiler *)
(* *)
(* Xavier Leroy... | {"author": "PrincetonUniversity", "repo": "compcomp", "sha": "eebb7d5a95fed97775cef7f014399be78abbe7bf", "save_path": "github-repos/coq/PrincetonUniversity-compcomp", "path": "github-repos/coq/PrincetonUniversity-compcomp/compcomp-eebb7d5a95fed97775cef7f014399be78abbe7bf/backend/CSEproof_comp.v"} |
# -*- coding: utf-8 -*-
from abc import ABC, abstractmethod
from typing import Callable, NamedTuple, Set
import numpy as np
from sktime.distances.base._types import DistanceCallable
class NumbaDistance(ABC):
"""Abstract class to define a numba compatible distance metric."""
def distance(self, x: np.ndarray... | {"hexsha": "e7e836639eab0646d004fa5ca3040d4c4005af05", "size": 4653, "ext": "py", "lang": "Python", "max_stars_repo_path": "sktime/distances/base/_base.py", "max_stars_repo_name": "Tomiiwa/sktime", "max_stars_repo_head_hexsha": "9c7600287e7d52556784a3da3a3c83f1a7499610", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# Removing t = 0, such that Σ is invertible
t = Vector(0.1:0.1:100); p = 2;
# Creating generators U,V that result in a positive-definite matrix Σ
Ut, Vt = spline_kernel(t', p)
K = SymEGRQSMatrix(Ut,Vt,ones(size(Ut,2)))
x = randn(size(K,1))
Kfull = Matrix(K)
# Testing multiplication
@test K*x ≈ Kfull*x
@test K'*x ≈ K... | {"hexsha": "49273cf24d083419a905d24b55e3e9bb3361ba79", "size": 624, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_SymEGRQSMatrix.jl", "max_stars_repo_name": "mipals/SymEGRSSMatrices", "max_stars_repo_head_hexsha": "67e59dfc74c692ca13ce603385cf1af2e6742e56", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import .brown
universes v u
open category_theory
local notation f ` ∘ `:80 g:80 := g ≫ f
namespace homotopy_theory.cofibrations
open precofibration_category cofibration_category
open homotopy_theory.weak_equivalences
variables {C : Type u} [category.{v} C] [cofibration_category.{v} C]
[has_initial_object.{v} C]
... | {"author": "rwbarton", "repo": "lean-homotopy-theory", "sha": "39e1b4ea1ed1b0eca2f68bc64162dde6a6396dee", "save_path": "github-repos/lean/rwbarton-lean-homotopy-theory", "path": "github-repos/lean/rwbarton-lean-homotopy-theory/lean-homotopy-theory-39e1b4ea1ed1b0eca2f68bc64162dde6a6396dee/src/homotopy_theory/formal/cofi... |
import cv2
import os
import sys
import pickle
import numpy as np
from PIL import Image
sys.path.insert(0, '/Workspace-Github/face_recognition/code')
import opencv_tools
import keras
from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Drop... | {"hexsha": "682d443793dbb88e8d42967168fcba44bce7a690", "size": 3419, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/CNN_tools.py", "max_stars_repo_name": "yc930401/face_recognition", "max_stars_repo_head_hexsha": "475b9d8766bd76657d83f899e77d2688694fd010", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma classes_above_ifields:
"\<lbrakk> classes_above P C \<inter> classes_changed P P' = {} \<rbrakk>
\<Longrightarrow>
ifields P C = ifields P' C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. classes_above P C \<inter> classes_changed P P' = {} \<Longrightarrow> ifields P C = ifields P' C
[PROOF ... | {"llama_tokens": 140, "file": "Regression_Test_Selection_JinjaSuppl_ClassesAbove", "length": 1} |
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib qt
def camera_calibration():
nx=9
ny=6
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((ny*nx,3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2... | {"hexsha": "6697d640a63867bd713984669d124db7e22dfae0", "size": 14871, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pipeline.py", "max_stars_repo_name": "PrabhaSNR/ND_Project2_AdvancedLaneFinding", "max_stars_repo_head_hexsha": "e303d71a0c071b0042b72cefe0cedf525d42b407", "max_stars_repo_licenses": ["M... |
// Copyright (C) 2014, Pawel Tomulik <ptomulik@meil.pw.edu.pl>
//
// 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)
#define BOOST_TEST_MODULE test_tml_xxx
#include <boost/test/unit_test.hpp>
#include <yaul/tml/xxx... | {"hexsha": "cba9ec16486e263c471614f7160ba7ef754c0f36", "size": 378, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "template/unit_test.cpp", "max_stars_repo_name": "ptomulik/yaul-tml", "max_stars_repo_head_hexsha": "2b8bf3f88742996bd8199375678cdebd6e3206d9", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 Intel Corporation
#
# 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
#
# Unl... | {"hexsha": "b92eaac994e1c1dd55b3a92f47b0702a6a3cf561", "size": 7315, "ext": "py", "lang": "Python", "max_stars_repo_path": "neural_compressor/adaptor/tf_utils/graph_rewriter/generic/fold_constant.py", "max_stars_repo_name": "kevinintel/neural-compressor", "max_stars_repo_head_hexsha": "b57645566aeff8d3c18dc49d2739a583c... |
## @ingroup Methods-Flight_Dynamics-Static_Stability-Approximations-Supporting_Functions
# extend_to_ref_area.py
#
# Created: Mar 2014, T. Momose
# Modified: Jan 2016, E. Botero
# ----------------------------------------------------------------------
# Imports
# ------------------------------------------------------... | {"hexsha": "1c4d108d3bdbcac2b7a8970a73d48ccc520c47ae", "size": 4404, "ext": "py", "lang": "Python", "max_stars_repo_path": "SUAVE/SUAVE-2.5.0/trunk/SUAVE/Methods/Flight_Dynamics/Static_Stability/Approximations/Supporting_Functions/extend_to_ref_area.py", "max_stars_repo_name": "Vinicius-Tanigawa/Undergraduate-Research-... |
from __future__ import print_function
import numpy as np
import theano
import theano.tensor as T
import lasagne
import time
import random
import argparse
import re
import glob
import sys
import os
import copy
# import matplotlib.pyplot as plt
from helpers.data_handling import DataHandler
from helpers import evaluation... | {"hexsha": "a17c50ac45f32c0d93d272ac943c5121b5859b56", "size": 6018, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "Kanika91/sequence-based-recommendations_clone", "max_stars_repo_head_hexsha": "73adddf747f1e3d986d7321c3567ee069f7b248b", "max_stars_repo_licenses": ["MIT"], "max... |
module HardTestProblems
import BSON
import Statistics: mean
# Multiobjective problems
include("Multiobjective/RW_MOP_2021/RW_MOP_2021.jl")
include("Singleobjective/CEC2020/CEC2020.jl")
include("Bilevel/SMD/SMD.jl")
include("Bilevel/PMM/PMM.jl")
end
| {"hexsha": "019e8b2cee4815602dca454136fd71f1fd86384f", "size": 253, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/HardTestProblems.jl", "max_stars_repo_name": "jmejia8/HardTestProblems.jl", "max_stars_repo_head_hexsha": "cde9e6c654f046fc8b9f01a434f7b213a0fab182", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#ifndef MSRP_MESSAGE_HXX
#define MSRP_MESSAGE_HXX
#include <map>
#include <ostream>
#include <string>
#include <boost/shared_ptr.hpp>
#include <asio/buffer.hpp>
#include <rutil/Data.hxx>
#include "msrp/Header.hxx"
#include "msrp/ParseException.hxx"
namespace msrp
{
namespace parser
{
struct Message;
}
class Mes... | {"hexsha": "73f489d72933b5026f3d82b0b586fab8b94f673a", "size": 7394, "ext": "hxx", "lang": "C++", "max_stars_repo_path": "Message.hxx", "max_stars_repo_name": "cbond/msrp", "max_stars_repo_head_hexsha": "d498f1ac8848319f4ecb617ad251e76de827a9a2", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_sta... |
#!/usr/bin/env python
"""Show an example of how to re-sample high-pass DT-CWT coefficients.
"""
import os
import dtcwt
import dtcwt.compat
import dtcwt.sampling
# Use an off-screen backend for matplotlib
import matplotlib
matplotlib.use('agg')
# Import numpy and matplotlib's pyplot interface
import numpy as np
from... | {"hexsha": "36eb2a369bf334a667e8a4805659beebe9c62845", "size": 2376, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/resampling_highpass_coefficients.py", "max_stars_repo_name": "santosh653/dtcwt", "max_stars_repo_head_hexsha": "01d9e87dc9abfa244a89c1f05aebf3dec6999f3a", "max_stars_repo_licenses": ["BSD... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Functions for plotting.
:copyright: 2015 Agile Geoscience
:license: Apache 2.0
"""
import csv
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
from striplog import Legend
import utils
from notice import Notice
... | {"hexsha": "5f16da6e5d48c447fcd54440348594a3b5af85a4", "size": 10843, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_plot.py", "max_stars_repo_name": "hyperiongeo/geotransect", "max_stars_repo_head_hexsha": "559c4c2fec4b628d8f156e0b0b6d7cdb36323d64", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
Davis Housing: The Print Edition is a free publication listing most/all of the commercial apartments available for lease. It is nicely laid out, lists the amenities, floor plan, map location, and rates for each apartment complex. You should also check out their website.
It can be found at the west entrance to the Cof... | {"hexsha": "ddd39a6a0ac97389c451efd429f0094d1adf0f90", "size": 541, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Davis_Housing.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
/*! \file
\brief A JSON parser.
Copyright (C) 2019-2021 kaoru https://www.tetengo.org/
*/
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <filesystem>
#include <iterator>
#include <memory>
#include <optional>
#include <stdexcept>
#include <string>
#include <string_view>
#inc... | {"hexsha": "8222045da230412a82821c016131165a14685d0d", "size": 14545, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "library/property/cpp/src/tetengo.property.json_parser.cpp", "max_stars_repo_name": "kaorut/tetengo", "max_stars_repo_head_hexsha": "3360cce3e3f4c92b18154927685986c1fa7b4e8e", "max_stars_repo_licens... |
import numpy
from keras.utils import np_utils
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import BatchNormalization as BatchNorm
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.ke... | {"hexsha": "03a2afde1c86566aad4d8f2c0b7aa01759904edd", "size": 4059, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lstm.py", "max_stars_repo_name": "ilyamirin/OrganGrinder", "max_stars_repo_head_hexsha": "7cff1a399d1439a06ee0ba90428a6542ebddb966", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from qtpy import QtCore
from qtpy.QtWidgets import QApplication
import numpy as np
from ..table_dictionary.table_dictionary_handler import TableDictionaryHandler
from ..fitting.initialization_sigma_alpha import InitializationSigmaAlpha
# from iBeatles.py.utilities.math_tools import calculate_inflection_point
class... | {"hexsha": "c83eb3030e4058f37b80dc87a71f14bbc4c1d9ac", "size": 10304, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/iBeatles/fitting/fitting_initialization_handler.py", "max_stars_repo_name": "ornlneutronimaging/iBeatles", "max_stars_repo_head_hexsha": "0a6ca1e18780cf08ad97b6cedede5a23f52bc953", "max_stars... |
[STATEMENT]
lemma length_filtermap: "length (filtermap pred func tr) \<le> length tr"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (Filtermap.filtermap pred func tr) \<le> length tr
[PROOF STEP]
proof(induction tr)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. length (Filtermap.filtermap pred func []) ... | {"llama_tokens": 485, "file": "Bounded_Deducibility_Security_Filtermap", "length": 5} |
import numpy as np
import pandas as pd
import os
import sys
sys.path.append('/home/akagi/github/RIPS_kircheis/RIPS')
import rect_grid
import cable
acsr = [ u'Bittern', u'Bluebird', u'Bluejay', u'Bobolink', u'Bunting',
u'Canary', u'Cardinal', u'Chickadee', u'Chukar', u'Cochin',
u'Co... | {"hexsha": "acbb5fa68751148bd155dd5cc6294affcca6fcd7", "size": 8433, "ext": "py", "lang": "Python", "max_stars_repo_path": "temporary/cable_range_test.py", "max_stars_repo_name": "mdbartos/RIPS", "max_stars_repo_head_hexsha": "ab654138ccdcd8cb7c4ab53092132e0156812e95", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <cblas.h>
int main()
{
//set up some data
int n=300;
float* x = malloc( n*sizeof(float) );
float* y = malloc( n*sizeof(float) );
for( int i=0; i<n; ++i )
{
x[i]=i+1;
y[i]=(i+1)*(i+1);
}
//calculate y = a... | {"hexsha": "332dd426b29eb4769788ff7b848bb5a5a6ef44d7", "size": 807, "ext": "c", "lang": "C", "max_stars_repo_path": "src/CBLAS/test/tstCBLAS.c", "max_stars_repo_name": "murraypurves/BootsOnTheGround", "max_stars_repo_head_hexsha": "15acc4ed064e368f6af5114408f1be8a62749f32", "max_stars_repo_licenses": ["MIT"], "max_star... |
import glob
import os
import os.path as osp
import cv2
import random
import numpy as np
dst = 'sampled_images_test_1'
os.system('mkdir %s' % dst )
maxNum =3
with open('test.txt', 'r') as fIn:
testScenes = fIn.readlines()
testScenes = [x.strip() for x in testScenes ]
dirs = glob.glob('main*_xml1')
cnt =... | {"hexsha": "20ed97ca2b00814a5d094a2cfed741433e3e7147", "size": 1338, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils_OR/DatasetCreation/copyImages.py", "max_stars_repo_name": "Jerrypiglet/Total3DUnderstanding", "max_stars_repo_head_hexsha": "655d00a988c839af3b73f8ab890c3f70c1500147", "max_stars_repo_licens... |
// This file is auto-generated, don't edit it. Thanks.
#include <alibabacloud/yundun_dbaudit_20180320.hpp>
#include <alibabacloud/endpoint_util.hpp>
#include <alibabacloud/open_api.hpp>
#include <boost/any.hpp>
#include <boost/throw_exception.hpp>
#include <darabonba/core.hpp>
#include <darabonba/util.hpp>
#include <i... | {"hexsha": "778eb8c5be6375ad91838ceec324dbe09dab9ba9", "size": 114741, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "yundun-dbaudit-20180320/src/yundun_dbaudit_20180320.cpp", "max_stars_repo_name": "aliyun/alibabacloud-cpp-sdk", "max_stars_repo_head_hexsha": "0e7c0576abcd4ef1aef07d714b92654deb713c36", "max_stars... |
# standard library imports
import sys
import os
from os import path
# third party
import numpy as np
# local application imports
sys.path.append(path.dirname( path.dirname( path.abspath(__file__))))
from .base_lot import Lot
from utilities import *
class Orca(Lot):
def run(self,geom,multiplicity):
... | {"hexsha": "7cb118173279e42eee0f72b122f3c3038cc38123", "size": 4358, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygsm/level_of_theories/orca.py", "max_stars_repo_name": "espottesmith/pyGSM", "max_stars_repo_head_hexsha": "5bf263f9ef6cbee3ec16355c5eb1839446e704e7", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma mod_less_diff_mod: "
\<lbrakk> n mod m < r; r \<le> m; r \<le> (n::nat) \<rbrakk> \<Longrightarrow>
(n - r) mod m = m + n mod m - r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>n mod m < r; r \<le> m; r \<le> n\<rbrakk> \<Longrightarrow> (n - r) mod m = m + n mod m - r
[PROOF STEP]
... | {"llama_tokens": 442, "file": "List-Infinite_CommonArith_Util_Div", "length": 4} |
/**
* @file fsareasearch.cpp
* @brief Floater to search and list objects in view or is known to the viewer.
*
* $LicenseInfo:firstyear=2012&license=viewerlgpl$
* Phoenix Firestorm Viewer Source Code
* Copyright (c) 2012 Techwolf Lupindo
*
* This library is free software; you can redistribute it and/or
* modify... | {"hexsha": "6b70748d4831a518c7fe84b4c224a1edcae68a9d", "size": 70811, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "indra/newview/fsareasearch.cpp", "max_stars_repo_name": "SaladDais/LLUDP-Encryption", "max_stars_repo_head_hexsha": "8a426cd0dd154e1a10903e0e6383f4deb2a6098a", "max_stars_repo_licenses": ["ISC"], "... |
From Coq Require Import List.
From Coq Require Import Morphisms.
From Coq Require Import PArith.
From Coq Require Import Permutation.
From Coq Require Import Psatz.
From Coq Require Import SetoidTactics.
From Coq Require Import Field.
From Coq Require Import ZArith.
From Coq Require Import Znumtheory.
From Bignums Requ... | {"author": "malthelange", "repo": "CLVM", "sha": "e80aef02c3112b5b62db79bc2b233020367b0bde", "save_path": "github-repos/coq/malthelange-CLVM", "path": "github-repos/coq/malthelange-CLVM/CLVM-e80aef02c3112b5b62db79bc2b233020367b0bde/execution/theories/Examples/BoardroomMath.v"} |
"""These are statistical tests for the Infrequent sampling results."""
import numpy as np
from scipy.optimize import curve_fit
from scipy.stats import ks_2samp
from scipy import stats
import pandas as pd
def perform_ks_analysis(dataframe):
"""
Perform the KS Test and determines statistics.
Parameters:
... | {"hexsha": "f127d3153c50ab6d07dcc90115dd9cf1a0dcad06", "size": 4747, "ext": "py", "lang": "Python", "max_stars_repo_path": "LimPy/statistical_functions.py", "max_stars_repo_name": "UWPRG/LimPy", "max_stars_repo_head_hexsha": "2a2979306ec4264de31d5ce1d2f8a59bd6eb7e9c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_s... |
# ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
# MIT License
#
# Copyright (c) 2022 Nathan Juraj Michlo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Softwar... | {"hexsha": "eb079949b0e969ef600853562d4788e8c3a93d0f", "size": 26147, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/part04_application_to_rl/e01_learn_xy_representations/train_vae.py", "max_stars_repo_name": "nmichlo/msc-research", "max_stars_repo_head_hexsha": "625e57eca77bbfbc4728ccebdb0733e1613bd25... |
/*****************************************************************************
*
* This file is part of Mapnik (c++ mapping toolkit)
*
* Copyright (C) 2011 Artem Pavlenko
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* Lice... | {"hexsha": "19b6db245ef0e23ac0407e987d1b688afbf7de34", "size": 2060, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external/include/mapnik/datasource_cache.hpp", "max_stars_repo_name": "Wujingli/OpenWebGlobeDataProcessing", "max_stars_repo_head_hexsha": "932eaa00c81fc0571122bc618ade010fa255735e", "max_stars_repo... |
print('Loading...')
import numpy as np
import numpy as num
from statistics import mean
import matplotlib.pyplot as plt
import aubio
import pyaudio
import wave
import os
def wavelength_to_rgb(wavelength, gamma=0.8):
'''This converts a given wavelength of light to an
approximate RGB color value. The wavelen... | {"hexsha": "d816b2e9a2f34bb891acee10098413128598c3f4", "size": 3288, "ext": "py", "lang": "Python", "max_stars_repo_path": "pitch_plotting.py", "max_stars_repo_name": "Animenosekai/AudioVisualization.py", "max_stars_repo_head_hexsha": "6742388a564883532188c9b720a491b561134bae", "max_stars_repo_licenses": ["MIT"], "max_... |
#include <stan/math/prim/scal.hpp>
#include <boost/math/special_functions/digamma.hpp>
#include <gtest/gtest.h>
#include <cmath>
#include <limits>
TEST(MathFunctions, digamma) {
EXPECT_FLOAT_EQ(boost::math::digamma(0.5), stan::math::digamma(0.5));
EXPECT_FLOAT_EQ(boost::math::digamma(-1.5), stan::math::digamma(-1.... | {"hexsha": "20acf1aba327c39bc09d49c6e384f279a4326482", "size": 589, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit/math/prim/scal/fun/digamma_test.cpp", "max_stars_repo_name": "christophernhill/math", "max_stars_repo_head_hexsha": "dc41aba296d592c7099be15eed6ba136d0f140b3", "max_stars_repo_licenses": ["... |
from PIL import Image
import numpy as np
from GrayScale import GrayScale
from matplotlib import pyplot as plt
def GaussianBlur(img, window_size = 3, sigma = 0.5):
"""
Performs a Blurring operation on the input grayscale image using the Normalized Gaussian Kernel.
Input: numpy array of grayscale image
... | {"hexsha": "b149d60c22972ba766b62feafd256d6c6439bedc", "size": 1536, "ext": "py", "lang": "Python", "max_stars_repo_path": "GaussianBlur.py", "max_stars_repo_name": "saurabh1002/Computer_Vision_Python", "max_stars_repo_head_hexsha": "83feee97082bf09ddbd3d79ff546d9d545c3c8ed", "max_stars_repo_licenses": ["MIT"], "max_st... |
import sys
import numpy as np
from MCEq.misc import theta_rad
import mceq_config as config
class EarthGeometry(object):
r"""A model of the Earth's geometry, approximating it
by a sphere. The figure below illustrates the meaning of the parameters.
.. figure:: graphics/geometry.*
:scale: 30 %
... | {"hexsha": "9f8f7e55a5b978802a081784dabd7196e7c53257", "size": 7994, "ext": "py", "lang": "Python", "max_stars_repo_path": "MCEq/geometry/geometry.py", "max_stars_repo_name": "joheinze/MCEq", "max_stars_repo_head_hexsha": "7f10aae90d1714997216edcf82a099628d1ff3c6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import cv2
import time
import argparse
import torch
import torchvision
import numpy as np
from torch.utils.data import DataLoader
from dataset.datasets import WLFWDatasets
from models.pfld import PFLDbackbone, AuxiliaryNet, PFLDLoss
def validate(wlfw_val_dataloader, pl... | {"hexsha": "de13d3bcd16b424a616b6216dcae76159904bb66", "size": 3889, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "Ontheway361/pfld-pytorch", "max_stars_repo_head_hexsha": "7c967623b930eba312d092519d804a103332a38a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "ma... |
theory ITree_Iteration
imports ITree_Divergence ITree_Deadlock
begin
subsection \<open> Iteration \<close>
text \<open> For now we support only basic tail-recursive iteration. \<close>
corec iterate :: "('s \<Rightarrow> bool) \<Rightarrow> ('e, 's) htree \<Rightarrow> ('e, 's) htree" where
"iterate b P s = (if (b... | {"author": "isabelle-utp", "repo": "interaction-trees", "sha": "90510d119364f534d2ab61daf2f274060f0a040e", "save_path": "github-repos/isabelle/isabelle-utp-interaction-trees", "path": "github-repos/isabelle/isabelle-utp-interaction-trees/interaction-trees-90510d119364f534d2ab61daf2f274060f0a040e/ITree_Iteration.thy"} |
[STATEMENT]
lemma del_bal:
assumes "k > 0"
and "root_order k t"
and "bal t"
shows "bal (del k x t)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bal (del k x t)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
0 < k
root_order k t
bal t
goal (1 subgoal):
1. bal (del k x t)
[PROOF STE... | {"llama_tokens": 8017, "file": "BTree_BPlusTree_Set", "length": 66} |
#!/usr/bin/env python3
import numpy as np
import cv2
import face_recognition
import sys
from multiprocessing import Queue
from multiprocessing.managers import SyncManager
from queue import Queue as ImageQueue
from pylibfreenect2 import Freenect2, SyncMultiFrameListener
from pylibfreenect2 import FrameType, Registrati... | {"hexsha": "e89753a08f5c6f06d3fad38c5c20bb8818c9b58d", "size": 3881, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pure_face_tracking.py", "max_stars_repo_name": "J-Pai/408DaisyJetson", "max_stars_repo_head_hexsha": "a873154325c790303f09ecfc03377066751cd601", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
from matplotlib import pyplot
import qutip
delta = 0.2 * 2*np.pi
eps0 = 0.0 * 2*np.pi
omega = 1.0 * 2*np.pi
A_vec = np.linspace(0, 10, 100) * omega
T = 2*np.pi/omega
tlist = np.linspace(0.0, 10 * T, 101)
psi0 = qutip.basis(2, 0)
q_energies = np.zeros((len(A_vec), 2))
H0 = delta/2.0 * quti... | {"hexsha": "5781e829910948807e4174030b31d5648f267eb9", "size": 851, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/guide/scripts/floquet_ex0.py", "max_stars_repo_name": "camponogaraviera/qutip", "max_stars_repo_head_hexsha": "1b1f6dffcb3ab97f11b8c6114293e09f378d2e8f", "max_stars_repo_licenses": ["BSD-3-Clau... |
#!/usr/bin/env python3
"""
https://www.mathworks.com/help/control/ref/parallel.html
https://www.mathworks.com/help/control/ref/feedback.html
https://www.mathworks.com/help/control/ref/series.html
Transfer functions applys to LTI systems and is defined as
H(s) = Y(s)/X(s) (output/input)
in the laplace domain
In the ... | {"hexsha": "efdb438a4502f760cd21ed3fe79ecc509bdf60c6", "size": 1631, "ext": "py", "lang": "Python", "max_stars_repo_path": "math/controls/tfchain.py", "max_stars_repo_name": "qeedquan/misc_utilities", "max_stars_repo_head_hexsha": "94c6363388662ac8ebbf075b9c853ce6defbb5b3", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
import numpy
import powerbox
from matplotlib import pyplot
from radiotelescope import RadioTelescope
from skymodel import SkyRealisation
from radiotelescope import ideal_gaussian_beam
from generaltools import from_lm_to_theta_phi
from generaltools import colorbar
import matplotlib.colors as colors
from sci... | {"hexsha": "f4579b33ecea5d3e837232156bf76015e08fc6da", "size": 12112, "ext": "py", "lang": "Python", "max_stars_repo_path": "approximations/baseline_participation_weights.py", "max_stars_repo_name": "ronniyjoseph/Beam-Perturbations", "max_stars_repo_head_hexsha": "0122fed7e3018f2e188e12b62ad760e11f6eb158", "max_stars_r... |
""" Helpers for randomized testing """
from sympy import I, nsimplify, S, Tuple, Dummy
from random import uniform
def random_complex_number(a=2, b=-1, c=3, d=1, rational=False):
"""
Return a random complex number.
To reduce chance of hitting branch cuts or anything, we guarantee
b <= Im z <= d, a <= ... | {"hexsha": "967467db675e752ecdb26779d55c049076dca1b2", "size": 2608, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy/utilities/randtest.py", "max_stars_repo_name": "goodok/sympy", "max_stars_repo_head_hexsha": "de84ed2139125a755ea7b6ba91d945d9fbbe5ed9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
from contextlib import contextmanager
from exptools.logging.tabulate import tabulate
from exptools.logging.console import mkdir_p, colorize
from exptools.logging.autoargs import get_all_parameters
import numpy as np
from collections import OrderedDict, defaultdict
import os, shutil
import os.path as osp
import sys
impo... | {"hexsha": "a563f82ef8ef5124ae4f2f49deb3e538cd7aa1b4", "size": 18364, "ext": "py", "lang": "Python", "max_stars_repo_path": "exptools/logging/_logger.py", "max_stars_repo_name": "ZiwenZhuang/exptools", "max_stars_repo_head_hexsha": "aa6853f1fb463955e5983a81bfd1d31ba3e7e34a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import cupy as cp
class KMEANS:
#kmeans模型聚类
def __init__(self, k):
self.train_data = None
self.k = k
self.centers = None
self.clusters = None
self.test = None
self.seed = None
self.tolerance = None
self.max_iter = None
def distance(self, vect... | {"hexsha": "3f9576e672f0a47bffcb953ddaba849d7bc2fb6a", "size": 2003, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlcu/ml/KMEANS.py", "max_stars_repo_name": "haomingdouranggouqil/cuml", "max_stars_repo_head_hexsha": "4fbbc2bcf381f57333be99fa8490eccb3168b641", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// Copyright (c) 2021 Graphcore Ltd. All rights reserved.
#include "poplin/Cholesky.hpp"
#include "poplin/MatMul.hpp"
#include "poplin/TriangularSolve.hpp"
#include <boost/assign/list_of.hpp>
#include <boost/optional.hpp>
#include <boost/optional/optional_io.hpp>
#include <boost/program_options.hpp>
#include <boost/ran... | {"hexsha": "77280576be1497b1d657048e9936f4b47b1f996c", "size": 13316, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tools/matrix_solver.cpp", "max_stars_repo_name": "graphcore/poplibs", "max_stars_repo_head_hexsha": "3fe5a3ecafe995eddb72675d1b4a7af8a622009e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
subroutine wrimap(lundia ,error ,filename ,selmap ,simdat , &
& itdate ,tzone ,tunit ,dt ,mmax , &
& kmax ,lmax ,lstsci ,ltur ,nmaxus , &
& noroco ,norow ,nostat ,nsrc ,ntruv , &
... | {"hexsha": "1681d910baf1ee91e3953a99233fae0a851e3487", "size": 60014, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/flow2d3d/packages/io/src/output/wrimap.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac6... |
# This script is borrowed and extended from https://github.com/nkolot/SPIN/blob/master/models/hmr.py
# Adhere to their licence to use this script
import math
import torch
import numpy as np
import os.path as osp
import torch.nn as nn
import torchvision.models.resnet as resnet
from lib.core.config import VIBE_DATA_DIR... | {"hexsha": "079d10b0cb968b1fc025b1989b6c01442ccdb432", "size": 12757, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/models/spin.py", "max_stars_repo_name": "omidrk/computervisionPanopticToSMPLAuto", "max_stars_repo_head_hexsha": "b84b60f0ec4ffdb4ae61348919a95f7bb2eab926", "max_stars_repo_licenses": ["MIT"]... |
"""The following module stores all methods used in the notebook."""
import ppscore as pps
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score
from sklear... | {"hexsha": "3600f8b956c3e25d881716d3de198116e8abcbad", "size": 5515, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/src/note.py", "max_stars_repo_name": "ahmed14-cell/breast-cancer-classification", "max_stars_repo_head_hexsha": "7e37145c8d7fe267a12bc42b2e8e80d97b031390", "max_stars_repo_licenses": ["M... |
export Arguments
"""
type Arguments
positional::Tuple
keyword::Dict{Symbol, Any}
end
Will store positional and keyword arguments for later use.
Create with [`collect_arguments`](@ref). You can also [`merge`](@ref) two
`Arguments`, [`push`](@ref) or [`unshift`](@ref) in new
arguments, and run w... | {"hexsha": "a94c33a0f2edc9200f9f6f38f7dbaf60dc6d60de", "size": 6792, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LazyCall.jl", "max_stars_repo_name": "JuliaPackageMirrors/ChainMap.jl", "max_stars_repo_head_hexsha": "7217dd05ee494751a81469cd0082a0972e95591b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Placeholders for built-in ports
module Fw {
port Cmd
port CmdReg
port CmdResponse
port Log
port LogText
port PrmGet
port PrmSet
port Time
port Tlm
}
port P
active component C {
async input port t1: [10] P priority 3 drop
sync input port t2: P
guarded input port t3: P
output port t4: P
... | {"hexsha": "01cb6356df6dff5c80667152b410f3e6860e75d9", "size": 709, "ext": "fpp", "lang": "FORTRAN", "max_stars_repo_path": "compiler/tools/fpp-check/test/port_instance/ok.fpp", "max_stars_repo_name": "kevin-f-ortega/fpp", "max_stars_repo_head_hexsha": "ee355fc99eb8040157c62e69f58ac6a8435cd981", "max_stars_repo_license... |
[STATEMENT]
lemma exception_of_option_\<I> [simp]: "map_\<I> id exception_of_option (stop_\<I> \<I>) = exception_\<I> \<I>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. map_\<I> id exception_of_option (stop_\<I> \<I>) = exception_\<I> \<I>
[PROOF STEP]
by(simp add: exception_\<I>_def) | {"llama_tokens": 123, "file": "Constructive_Cryptography_CM_More_CC", "length": 1} |
[STATEMENT]
lemma elements_matD [dest]:
"a \<in> elements_mat A \<Longrightarrow> \<exists>i j. i < dim_row A \<and> j < dim_col A \<and> a = A $$ (i,j)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<in> elements_mat A \<Longrightarrow> \<exists>i j. i < dim_row A \<and> j < dim_col A \<and> a = A $$ (i, j)
[... | {"llama_tokens": 261, "file": "Jordan_Normal_Form_Matrix", "length": 2} |
import cv2
import numpy as np
from car import Car
from liness import Line
from liness import Area
# def from_new_to_car_dic_and_obj(id):
# """
# 从new_car_dic中转移到car_dic中
# """
# global new_car_dic, cars_dic
# cars_dic[id] = new_car_dic[id] #转移数据
# del new_car_dic[id] #从new_car_dic删除该数据... | {"hexsha": "5b1cea7e7f172637607a1cd337f3bd93e61e39b3", "size": 9693, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/prediction/Violation.py", "max_stars_repo_name": "csd2022fuchuang/yolov5-opencv-cpp-python", "max_stars_repo_head_hexsha": "5b52dbffed6733a1353bd27a0001c09821ee0714", "max_stars_repo_licens... |
#
# This file is a part of MolecularGraph.jl
# Licensed under the MIT License http://opensource.org/licenses/MIT
#
@testset "graph.dag" begin
graph = plaindigraph(10, [
(1, 4), (2, 4), (3, 7), (4, 5), (4, 6),
(4, 7), (6, 9), (7, 8), (7, 9), (7, 10)
])
@test issetequal(ancestors(graph, 7), [... | {"hexsha": "01d46885f4a30ee2a05208996da7f71810c1c0ad", "size": 520, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/graph/dag.jl", "max_stars_repo_name": "hhaensel/MolecularGraph.jl", "max_stars_repo_head_hexsha": "c54ccdf09274e36ed3d866604f99b497a39bfaf5", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
################################################################################
#
# Abstract types
#
################################################################################
# abstract spaces
abstract type AbsSpace{S} end
# abstract lattices
abstract type AbsLat{S} end
#####################################... | {"hexsha": "6762d0ccf5a1e1a0c8aa13073044fbc459a17aa8", "size": 2126, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/QuadForm/Types.jl", "max_stars_repo_name": "albinahlback/Hecke.jl", "max_stars_repo_head_hexsha": "728be6098a8dbfe2589fca77e57f10950cefe9e7", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_s... |
module FIGlet
using Pkg
if isdefined(Pkg, :Artifacts)
using Pkg.Artifacts
@eval fontsroot = artifact"fonts"
else
fontsroot = normpath(@__DIR__, "..", "deps")
end
const FONTSDIR = abspath(normpath(joinpath(fontsroot, "FIGletFonts-0.5.0", "fonts")))
const UNPARSEABLES = [
"nvscript.flf",
... | {"hexsha": "513b32a3d2fe8a5ecf4ff6670b03cfee226cb2f6", "size": 8298, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FIGlet.jl", "max_stars_repo_name": "wookay/TestFIGlet", "max_stars_repo_head_hexsha": "44ecf528acc28b925a1523093f4ccf4372973a42", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
@testset "Forcings" begin
fx = zeros(5,5)
fy = zeros(5,5)
f1 = zeros(30)
# For structs
sys = Swalbe.SysConst(Lx=5, Ly=5)
state = Swalbe.Sys(sys, "CPU")
state.height .= 1.0
# One dim model
sys1D = Swalbe.SysConst_1D(L=30)
state1D = Swalbe.Sys(sys1D)
state1D.height .= 1.0
... | {"hexsha": "350b00b5f1e3a67f3a4de2c416c9575f4617ee69", "size": 6734, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/forcing.jl", "max_stars_repo_name": "aedolfi/Swalbe.jl", "max_stars_repo_head_hexsha": "8722b1e2f881b2917d8e2faca1ea58b0e9485ce5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import sys
if sys.path[0] != '/mnt/home/landerson/.local/lib/python3.6/site-packages':
sys.path.insert(0, '/mnt/home/landerson/.local/lib/python3.6/site-packages/astroML-0.3-py3.6.egg')
sys.path.insert(0, '/mnt/home/landerson/.local/lib/python3.6/site-packages/xdgmm-1.0.9-py3.6.egg')
sys.path.insert(0, '/m... | {"hexsha": "a8e08c874e9b933b61e12b4dc019ca654de35b67", "size": 7361, "ext": "py", "lang": "Python", "max_stars_repo_path": "GPdust.py", "max_stars_repo_name": "andersdot/dust", "max_stars_repo_head_hexsha": "44460ac2e8bf46db5ab27862bf775cf93531742e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
import cv2
import numpy as np
from random import randint
from copy import deepcopy
color = (255, 255, 0)
font = cv2.FONT_HERSHEY_DUPLEX
fontColor = (255, 255, 0)
def draw_count(frame, crowd_count, ignore_polys=[], gt_count=None, alpha=0.5):
"""
:param ignore_polys: list of polygons, each polygon being a lis... | {"hexsha": "47af1240f6410827cfb6b5867f2a6cc5012103e3", "size": 1255, "ext": "py", "lang": "Python", "max_stars_repo_path": "drawer.py", "max_stars_repo_name": "asanakoy/lsc-cnn", "max_stars_repo_head_hexsha": "d389e3598c5ec34254200160c0c4a1904e61eb1b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
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