text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import sys
import os
# print(os.path.abspath(os.path.join(os.getcwd(),".")))
sys.path.append(os.path.abspath(os.path.join(os.getcwd(),"..")))
import torch
import torch.backends.cudnn as cudnn
import argparse
import shutil
import sys
import random
import time
import numpy as np
from datetime import datetime
from vsg... | {"hexsha": "ca8d89382e1895bc056bb5746e5a0b6365c0ae1d", "size": 2547, "ext": "py", "lang": "Python", "max_stars_repo_path": "vsgia_model/main.py", "max_stars_repo_name": "nkuhzx/VSG-IA", "max_stars_repo_head_hexsha": "075b58c2bf89562cc197e721f050396589861c6a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
#!/usr/bin/env python
import rospy
import sys
from std_msgs.msg import Float32, ColorRGBA, Int32
from geometry_msgs.msg import PoseStamped, Twist, Vector3, Point
from ford_msgs.msg import PedTrajVec, NNActions, PlannerMode, Clusters
from visualization_msgs.msg import Marker, MarkerArray
import numpy as np
import nump... | {"hexsha": "2c5dbd817a2d33c149aaacd216df4ad0aafcb7d3", "size": 24386, "ext": "py", "lang": "Python", "max_stars_repo_path": "navigation/arena_local_planner/model_based/cadrl_ros/scripts/cadrl_node.py", "max_stars_repo_name": "kilinmao/sarl_star", "max_stars_repo_head_hexsha": "dde9bb2b690c705a615195f4b570af3ea9dfe05e",... |
import torch
import torch.nn as nn
import numpy as np
def accuracy(target, y_hat):
seg_pred = torch.argmax(y_hat[:, 1:], dim=1)
seg_acc = (seg_pred == target[:, 1]).float().mean()
edge_pred = (y_hat[:, 0] > 0).float()
edge_acc = (edge_pred == target[:, 0]).float().mean()
return seg_acc , edge_acc... | {"hexsha": "708115581babfaf051e28d295274936e7e9e2f27", "size": 5184, "ext": "py", "lang": "Python", "max_stars_repo_path": "DiceCofficient.py", "max_stars_repo_name": "changlabntu/HEDUNet", "max_stars_repo_head_hexsha": "7e4122a30bbde5f606311f24328f96a327c00493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
/-
Copyright (c) 2021 Eric Rodriguez. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Eric Rodriguez
-/
import ring_theory.polynomial.cyclotomic.basic
import tactic.by_contra
import topology.algebra.polynomial
import number_theory.padics.padic_val
import analysis.compl... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/ring_theory/polynomial/cyclotomic/eval.lean... |
import dash
import dash_html_components as html
import dash_core_components as dcc
import pandas as pd
import simfin as sf
from simfin.names import *
import dash_table
from dash.dependencies import Output, Input, State
from flask import Flask
from flask.helpers import get_root_path
from flask_login import login_require... | {"hexsha": "8461ac15ec093a0d0b54a36af9f790ff81ad6ef2", "size": 77048, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/dashapp1/layout.py", "max_stars_repo_name": "samisyed8999/financial-data", "max_stars_repo_head_hexsha": "4073b73eb600bdec30fd065945171d487f2f7854", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Basic imports
import boto3
import datetime
import openpyxl
import time
import random
import base64
import pickle
import ast
import copy
from haversine import haversine
import os
# Data analysis/viz imports
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
... | {"hexsha": "d35b5358983f3e96204a7e42713362bb99e82622", "size": 56942, "ext": "py", "lang": "Python", "max_stars_repo_path": "index.py", "max_stars_repo_name": "Jordan396/USA-Travel-Planner", "max_stars_repo_head_hexsha": "2b40bed13c2834f58c48f7eb5edb7b90dd68c0e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma length_trace[simp]: "\<And>i. length(trace d i xs) = length xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>i. length (trace d i xs) = length xs
[PROOF STEP]
by (induct "xs") simp_all | {"llama_tokens": 86, "file": "Functional-Automata_RegSet_of_nat_DA", "length": 1} |
#!/usr/bin/env python
# coding: utf-8
# OBJECTIVE : The dataset contains detailed attributes for every player registered in the latest edition of FIFA 19 database. Our objective is to create Linear, Multiple and Polynomail Regression models to predict the potential of a player based on several attributes.
# In[88]:
... | {"hexsha": "4f11ee7416c953586a41f57e9a0d3523675fb21e", "size": 7355, "ext": "py", "lang": "Python", "max_stars_repo_path": "relancer-exp/original_notebooks/karangadiya_fifa19/fifa-2019-regression-model.py", "max_stars_repo_name": "Chenguang-Zhu/relancer", "max_stars_repo_head_hexsha": "bf1a175b77b7da4cff12fbc5de17dd552... |
# This is a sample Python script.
# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
import numpy as np
import pandas as pd
import os
from time import time
import pickle
import ray
from scipy.optimize import minimize
fr... | {"hexsha": "cec2b2ee49d62502cd3b40f61cf33105704b0f33", "size": 5751, "ext": "py", "lang": "Python", "max_stars_repo_path": "main_ray.py", "max_stars_repo_name": "hannanabdul55/seldonian-fairness", "max_stars_repo_head_hexsha": "d02aaa3b62170df66f7a2962a32fa7d54028de78", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 7 15:47:38 2018
@author: jdietric
"""
#pyqt import
#from PyQt5 import QtCore, QtGui, QtWidgets
# other imports
import os
import sys
import numpy as np
import pandas as pd
import sympy.geometry as spg
import matplotlib.path as mplPath
from datetime impo... | {"hexsha": "7648a24ff70f35bd161d5f89a5576aee465d23c7", "size": 24267, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_bathySfM_debug_SfMAi.py", "max_stars_repo_name": "geojames/SfM_AI", "max_stars_repo_head_hexsha": "715f763fbaf829e4b75f9b0ee086c41896333a04", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""Solar geometry, angles and shading.
"""
import numpy as np
import pandas as pd
from sitka.utils.time_series import TimeSeriesComponent
class SolarAngles(TimeSeriesComponent):
"""
Store solar angles for a site.
Parameters
----------
time
site
Attributes
----------
gamma : Seri... | {"hexsha": "5b810cccac0c1bf2fcd0d5f5007acc6f29b98231", "size": 14471, "ext": "py", "lang": "Python", "max_stars_repo_path": "sitka/calculations/solar.py", "max_stars_repo_name": "mcneillj/sitka", "max_stars_repo_head_hexsha": "1a50e009433d0296426765303406157ab39b8632", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Name: create_edge_images.py
Desc: Creates and saves edge images for each point. The edges are computed from the depth
zbuffer images by using a Canny edge detector.
Requires (to be run):
- generate_points.py
- create_depth_zbuffer_images.py
"""
from __future__ import absolute_import
from __future_... | {"hexsha": "e70b7a26e805b0d84047b74202a4a0e523771f1f", "size": 4482, "ext": "py", "lang": "Python", "max_stars_repo_path": "omnidata_annotator/scripts/create_edge_3d_images.py", "max_stars_repo_name": "EPFL-VILAB/omnidata", "max_stars_repo_head_hexsha": "a1e31eb26172ecf8a3e49ba8a5c82ab3038a9c01", "max_stars_repo_licens... |
"""Torch Module for EdgeConv Layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
from torch import nn
from ....base import DGLError
from .... import function as fn
from ....utils import expand_as_pair
class EdgeConv(nn.Module):
r"""
Description
-----------
EdgeConv layer.
Intr... | {"hexsha": "861de78cc4f76e8722cdcddd2ccd635fd4f5ddef", "size": 7919, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/dgl/nn/pytorch/conv/edgeconv.py", "max_stars_repo_name": "Padarn/dgl", "max_stars_repo_head_hexsha": "5087a21279be98021fddfd1ba61487be4adfede8", "max_stars_repo_licenses": ["Apache-2.0"], "... |
SUBROUTINE E03105 (USEPRM)
C E03105 tests the handling of error number 105
COMMON /GLOBNU/ CTLHND, ERRSIG, ERRFIL, IERRCT, UNERR,
1 TESTCT, IFLERR, PASSSW, ERRSW, MAXLIN,
2 CONID, MEMUN, WKID, WTYPE, GLBLUN, INDLUN,
3 DUMINT, DUMRL
INTEGER CTLHND, ERRSIG,... | {"hexsha": "8d9fc028b537ed2b4e6fb8ca39af3fe586bb30e4", "size": 2505, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "third_party/Phigs/PVT/PVT_fort/V2LIB/e03105.f", "max_stars_repo_name": "n1ckfg/Telidon", "max_stars_repo_head_hexsha": "f4e2c693ec7d67245974b73a602d5d40df6a6d69", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
DEFAULT_CAMERA_CONFIG = {
'distance': 4.0,
}
class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self,
xml_file='ant.xml',
ctrl_cost_weight=0.5,
contact_cost_weight... | {"hexsha": "5df479bf6004a77f2978fcc89378dcf6fd42843d", "size": 4708, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym/envs/mujoco/ant_t_v3.py", "max_stars_repo_name": "caoxixiya/od_irl", "max_stars_repo_head_hexsha": "cc5da49344174859b74ad67b9abe6d910b4159d0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import cv2
import numpy as np
from .camera.parameters import CameraParams, IntrinsicParams, ExtrinsicParams
from .camera.coordinate_transformation import CoordinateTransformation, rotationMatrix3D#, reverseX, reverseY
from .camera import basic_tools
class InversePerspectiveMapping(object):
def __init__(self, param... | {"hexsha": "cb42f4272294eaee98e74b77f32cdfd138eae13d", "size": 3115, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/collect_ipm.py", "max_stars_repo_name": "Czworldy/GP_traj", "max_stars_repo_head_hexsha": "96261f39a5a322092e3a6be98938bb4601f0f746", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
import pandas as pd
def updateMap(replaced, replacement, map, fitness, genes, values, extraMapValues):
mapIsTuple = isinstance(map, tuple)
# print("genes")
# print(genes)
# print("replaced")
# print(replaced)
# print("replacement")
# print(replacement)
fitness =... | {"hexsha": "c9eb5bfc1fbe2476f69b35d6d83d671fbe0b8ef8", "size": 5491, "ext": "py", "lang": "Python", "max_stars_repo_path": "mapElites/updateMap.py", "max_stars_repo_name": "Sascha0912/SAIL", "max_stars_repo_head_hexsha": "5dfb8d0b925d5e61933bf10591d959433fffaf26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
! Program for checking formatting in Fortran
program format_spec
implicit none
integer:: i = 12345
real:: j = 5.8938492847
real, dimension (5):: v = (/ 1.1,1.2,1.4,1.6,1.9 /)
print '(i5)', i ! integer in field width of 5
print '(f10.8)', j ! real no. which has a field width of 10 of which 8 characters reserved f... | {"hexsha": "49abeff36154dfb5e11d8e5a3d5bbc8f2ec773b3", "size": 677, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "practice/format_spec.f95", "max_stars_repo_name": "adisen99/fortran_programs", "max_stars_repo_head_hexsha": "04d3a528200e27a25b109f5d3a0aff66b22f94a1", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Create your views here.
from django.http import HttpResponse
from core import models
from django.shortcuts import get_object_or_404, render_to_response, redirect
from django.template import RequestContext
from django.views.decorators.http import require_http_methods, require_POST, require_GET
from django.views.decora... | {"hexsha": "e200dfdadfffd03c8f5b44ce339cf04103467dc9", "size": 12620, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/views.py", "max_stars_repo_name": "jackm321/CrowdImage", "max_stars_repo_head_hexsha": "3c6bd11274a89615f17678346b25a439a597fd85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
import numpy as np
import pandas as pd
import math
import pickle
from pandas_datareader import data as web
import datetime
from linearmodels import PanelOLS
from rpy2.robjects import r
from rpy2.robjects import pandas2ri
pandas2ri.activate()
pd.options.mode.chained_assignment = "raise"
"""
This script takes PSID data... | {"hexsha": "11406cd35b88c79c3ba127221346bcc91d749423", "size": 17905, "ext": "py", "lang": "Python", "max_stars_repo_path": "ogusa/psid_data_setup.py", "max_stars_repo_name": "jdebacker/OG-USA-Calibration", "max_stars_repo_head_hexsha": "1e82a6f3cb52674c2c6a6055c552a1029e7b5d52", "max_stars_repo_licenses": ["CC0-1.0"],... |
/************************************************************************
MIT License
Copyright (c) 2021 Deqi Tang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including ... | {"hexsha": "82405622b59f6aff60d8d65ee2a39b93a070bcaa", "size": 4462, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cmd/atomsciflow_calc_vasp.cpp", "max_stars_repo_name": "DeqiTang/build-test-atomsciflow", "max_stars_repo_head_hexsha": "6fb65c79e74993e2100fbbca31b910d495076805", "max_stars_repo_licenses": ["MIT"]... |
import pytest
import numpy.testing as npt
@pytest.fixture
def graphs_and_features():
import numpy as np
import torch
permutation_idx = np.random.permutation(5)
permutation_matrix = np.zeros((5, 5), dtype=np.float32)
permutation_matrix[
np.arange(5),
permutation_idx,
] = 1
pe... | {"hexsha": "d4d4dd03ec909dbe796cf65b28fcd73800875d3a", "size": 2279, "ext": "py", "lang": "Python", "max_stars_repo_path": "hpno/tests/test_index_space_equivariance.py", "max_stars_repo_name": "choderalab/hpnotiq", "max_stars_repo_head_hexsha": "fa791fcbdd24150e238218c4c5799a75a6882b16", "max_stars_repo_licenses": ["MI... |
import pandas as pd
# import matplotlib.pyplot as plt
# import seaborn as sns
import numpy as np
# import copy
# from scipy.stats import norm
# from sklearn import preprocessing
fileName = '/home/kazim/Desktop/projects/IE490/input/tubitak_data2_processesed2.csv'
df = pd.read_csv(fileName, sep = ',')
#pr... | {"hexsha": "45099ed8c871b99dcff80ca033fda09d7b5c9c84", "size": 2324, "ext": "py", "lang": "Python", "max_stars_repo_path": "shiny/Python2.py", "max_stars_repo_name": "kazimsanlav/RealEstate", "max_stars_repo_head_hexsha": "3abdb8a4a35b3975b8f6ad4b11b64cd73a97901a", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
Experiments of the paper 'The Approximation of the Dissimilarity
Projection' accepted at PRNI2012.
Quantification of the dissimilarity approximation of tractography data
across different prototype selection policies and number of prototypes.
Copyright (c) 2012, Emanuele Olivetti
Distributed under the New BSD lic... | {"hexsha": "c95f16bac3610aa860fb9747396cc7047253aa68", "size": 1471, "ext": "py", "lang": "Python", "max_stars_repo_path": "dissimilarity_streamlines.py", "max_stars_repo_name": "emanuele/prni2012_dissimilarity", "max_stars_repo_head_hexsha": "499cdb7715c47ba59f8eab2396d56111d9b86cee", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma perfect_injective_eq_homeomorphic_map:
"perfect_map X Y f \<and> inj_on f (topspace X) \<longleftrightarrow> homeomorphic_map X Y f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (perfect_map X Y f \<and> inj_on f (topspace X)) = homeomorphic_map X Y f
[PROOF STEP]
by (simp add: homeomorphic_eq... | {"llama_tokens": 132, "file": null, "length": 1} |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 24 09:40:28 2018
@author: Paulo Augusto
"""
import numpy as np
#from numpy import fft
import matplotlib.pyplot as plt
#import scipy.signal as sig
import os
import random
import emgReaderClass_v2 as erc
import threading
import multiprocessing
#import data... | {"hexsha": "a7e69ce889e8fb7447a38e0924445a5a1cffd1a2", "size": 24331, "ext": "py", "lang": "Python", "max_stars_repo_path": "EMG_NN_v3.py", "max_stars_repo_name": "Kotzly/EMG_AG", "max_stars_repo_head_hexsha": "b88b2a14d1d11df3857b1832654a119894d4f97c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
config.py
Configuration settings for Mekamon control
"""
__author__ = "Alex Watson"
__copyright__ = "Copyright 2019"
import numpy as np
# Replay these messages to take control of the Mekamon
init_cmd_1 = [16] # 02101300
init_cmd_2 = [7,1] # 0307010c00
stop_mo... | {"hexsha": "abf2b7dfc650770ff09cc604ac0fe8a50c433aac", "size": 804, "ext": "py", "lang": "Python", "max_stars_repo_path": "mekamon_api/config.py", "max_stars_repo_name": "zredlined/control-my-mekamon", "max_stars_repo_head_hexsha": "2ce7096710608002db8e5dbf2cc3ebb044a494c6", "max_stars_repo_licenses": ["MIT"], "max_sta... |
longmult <- function(xstr, ystr)
{
#get the number described in each string
getnumeric <- function(xstr) as.numeric(unlist(strsplit(xstr, "")))
x <- getnumeric(xstr)
y <- getnumeric(ystr)
#multiply each pair of digits together
mat <- apply(x %o% y, 1, as.character)
#loop over columns, then rows,... | {"hexsha": "aa91151788b9e864a46eb482525bac15f8cc7ef0", "size": 1962, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Long-multiplication/R/long-multiplication-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_li... |
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LogNorm
from mpl_toolkits.mplot3d import Axes3D
try:
import numpy as np
except:
exit()
from surrogate import benchmarks
# NUMMAX = 5
# A = 10 * np.random.rand(NUMMAX, 2)
# C = np.random.rand(NUMMAX)
A = [[0.5, 0.5], [0.2... | {"hexsha": "3d3c92319ad36510704b0bbc2de1bce803a5d4ac", "size": 892, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/code/benchmarks/shekel.py", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac66f5bd0d9a1def52a", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
from __future__ import print_function
import sys
sys.path.append('..')
from Game import Game
from .UltimateTicTacToeLogic import Board
import numpy as np
class UltimateTicTacToeGame(Game):
square_content = {
-1: "X",
+0: "-",
+1: "O",
+2: " "
}
@staticmethod
def getSqua... | {"hexsha": "005842737196af706de0ebdb10835c0f83265474", "size": 4712, "ext": "py", "lang": "Python", "max_stars_repo_path": "ultimatetictactoe/UltimateTicTacToeGame.py", "max_stars_repo_name": "taylor-santos/alpha-zero-general", "max_stars_repo_head_hexsha": "2f22d68bd5337de56dbf0482229e301c16377f04", "max_stars_repo_li... |
import numpy as np
import pandas as pd
import xarray as xr
from matplotlib import pyplot as plt
def get_var_names(variable):
"""
get the long variable names from 'flow' or 'temp'
:param variable: [str] either 'flow' or 'temp'
:return: [str] long variable names
"""
if variable == "flow":
... | {"hexsha": "6f990fcdafb3c7a45ca98f2557d847d99f0eabd6", "size": 4679, "ext": "py", "lang": "Python", "max_stars_repo_path": "river_dl/postproc_utils.py", "max_stars_repo_name": "SimonTopp/river-dl", "max_stars_repo_head_hexsha": "6356c3f3e8012bc7930909f2b5d6b9f8507225f1", "max_stars_repo_licenses": ["CC0-1.0"], "max_sta... |
## Imports
import sys
import numpy as np
import warnings
warnings.filterwarnings("ignore")
sys.path.append("../.")
import handybeam
import handybeam.world
import handybeam.tx_array_library
import handybeam.visualise
import handybeam.samplers.clist_sampler as clist_sampler
from handybeam.translator import Translat... | {"hexsha": "248b51f47ed3f9eb40d201bb06a95e457656fb55", "size": 1924, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/translation_xyz_volume_demo.py", "max_stars_repo_name": "ultraleap/HandyBeam", "max_stars_repo_head_hexsha": "9f80b97742cde4b75d3478d554dc9bc2cd9dfd96", "max_stars_repo_licenses": ["ECL-2.0"... |
import numpy as np
from scipy.interpolate import interp1d
class Adiabatic:
def __init__(self,index,initial_value,initial_parameter,initial_vector):
"""
contains the evolution of an adiabatic state. methods to compare a given eigenvector and value
to this to see if it is part of the same sta... | {"hexsha": "257f5d02fbebdb1d90e89be3ff98fd8fe4ef0dcc", "size": 3759, "ext": "py", "lang": "Python", "max_stars_repo_path": "rydprop/hohi/adiabatic_solver/adiabatic_state.py", "max_stars_repo_name": "jdrtommey/rydprops", "max_stars_repo_head_hexsha": "cdc7e14d61ff33929844ee5d779a18fd64f89f4f", "max_stars_repo_licenses":... |
import numpy as np
import os
import cv2
import argparse
import sys
import tensorflow as tf
#from collections import defaultdict
#from io import StringIO
#from matplotlib import pyplot as plt
#from PIL import Image
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from... | {"hexsha": "a7b2e26c5fb81fa3e89d487d14d67063e1117dd5", "size": 4359, "ext": "py", "lang": "Python", "max_stars_repo_path": "object_detection/object_detection_tutorial.py", "max_stars_repo_name": "maheshmj24/ASROD", "max_stars_repo_head_hexsha": "9cde2dafba9b78e0f186aa2fd517677d1a09f226", "max_stars_repo_licenses": ["MI... |
CCHECK
C SUBROUTINE TO CHECK SENSE LIGHTS
c SUBROUTINE CHECK(J)
C
c REAL MFSTOP
c LOGICAL PREVER
c COMMON /SNTCP/G,AJ,PRPC,ICASE,PREVER,MFSTOP,JUMP,LOPIN,ISCASE,
c 1KN,GAMF,IP,SCRIT,PTRN,ISECT,KSTG,WTOL,RHOTOL,PRTOL,TRLOOP,LSTG,
c 2LBRC,IBRC,ICHOKE,ISORR,CHOKE,PT0PS1(6,8),PTRS2(6,8),TRDIAG,SC... | {"hexsha": "b5dae307c65c376e54fcd31f646969362d3dd5b8", "size": 7607, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "contrib/axod/src/flow1.f", "max_stars_repo_name": "mjfwest/OpenMDAO-Framework", "max_stars_repo_head_hexsha": "a5521f47ad7686c25b203de74e1c7dff5fd7a52b", "max_stars_repo_licenses": ["Apache-2.0"],... |
"""
Classes with observation shapes, action shapes
and reward functions
"""
import numpy as np
import pybullet as p
# import time
class ObservationShapes:
""" Implements observations shapes 1 to 7 """
def __init__(
self,
endeffector_pos,
endeffector_orient,
torso_pos,
... | {"hexsha": "6a528725db11a5e3fad0097f89b95e8537c523d5", "size": 12575, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/gym_envs/gym_envs/jaco_env/env_description.py", "max_stars_repo_name": "PierreExeter/rl_reach", "max_stars_repo_head_hexsha": "4f9c46c8503a84edaa48f9dfd58054548552253a", "max_stars_repo_lice... |
import numpy as np
from bokeh.plotting import figure
from dq_poc.util import plot_grid
def plot(f):
x = np.linspace(0, 2 * 3.14159)
p = figure(plot_height=1500, plot_width=2000)
p.line(x, f(x))
return p
title = 'Coffee Machine Uptime'
content = plot_grid(2, plot(np.sin), plot(np.cos), plot(np.tan),... | {"hexsha": "a68b34b641c443df985e8fd562096f32bcf4e5ff", "size": 479, "ext": "py", "lang": "Python", "max_stars_repo_path": "dq_poc/content/telescope/coffee.py", "max_stars_repo_name": "hettlage/data-quality-poc", "max_stars_repo_head_hexsha": "a77ebd41285ddf587b6e73145a7c42e647e0ea77", "max_stars_repo_licenses": ["MIT"]... |
//
// $Id$
//
// -------------------------------------------------------------------------
// This file is part of ZeroBugs, Copyright (c) 2010 Cristian L. Vlasceanu
//
// 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.... | {"hexsha": "2cd65db53b2ed955f5c7b3a961687a7dbf31327b", "size": 8080, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "zero_python/debug_sym_wrap.cpp", "max_stars_repo_name": "cristivlas/zerobugs", "max_stars_repo_head_hexsha": "5f080c8645b123d7887fd8a64f60e8d226e3b1d5", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
#!/usr/bin/env python3
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import argparse
from ansible.parsing.dataloader import DataLoader
from ansible.inventory.manager import InventoryManager
parser = argparse.ArgumentParser(description='Visualize data as p... | {"hexsha": "0c9629df49e606e445dd6f6048d2aa7719b46980", "size": 12080, "ext": "py", "lang": "Python", "max_stars_repo_path": "winevtlog_bench/visualize/plot_pandas_tailing_Usage.py", "max_stars_repo_name": "kenhys/fluentd-benchmark-azure-environment", "max_stars_repo_head_hexsha": "024b6fe3c9ac667562e444e7ae216e2f30d8a0... |
# Programador Sergio Luis Beleño Díaz
import cv2
import numpy as np
from tkinter import *
from tensorflow.keras.models import load_model
from easygui import *
from lime import lime_image
from PIL import ImageTk, Image
from skimage.segmentation import mark_boundaries
from time import sleep
#Load the best model trained... | {"hexsha": "6a391a07d0d4218530607cdde08c55a2dc7fe7c8", "size": 5814, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tkinter/RxForCovid-19.py", "max_stars_repo_name": "Serbeld/RX-COVID-19", "max_stars_repo_head_hexsha": "d5936dbccdeed7dc80fbdbcc5b19c4c7eefcc237", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# set to use CPU
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['KERAS_BACKEND'] = 'tensorflow'
#os.environ['KERAS_BACKEND'] = 'theano'
import tensorflow as tf
from tensorflow import keras
print(keras.__version__)
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import M... | {"hexsha": "c66403e7ddcea8e4db7e51677f7eeb13ee5059cc", "size": 3403, "ext": "py", "lang": "Python", "max_stars_repo_path": "cpu_only/ex9_5_tfwithkeras2-cpu.py", "max_stars_repo_name": "jskDr/keraspp_2022", "max_stars_repo_head_hexsha": "e10f4f849ad6a7354a05084e2cd9cec8acd62ef2", "max_stars_repo_licenses": ["MIT"], "max... |
program pgm
integer :: a(3,3,3), i , j, k, c
c = 1
do i = 1, 3
do j = 1, 3
do k = 1, 3
a(j,i,k) = c
c = c + 1
enddo
enddo
enddo
do k = 1, 3
do j = 1, 3
do i = 1, 3
if (a(k,j,i) <= a(i,j,k)) then
print *, a(k,j,i)
endif
enddo
enddo
enddo
end
| {"hexsha": "d73d8e304410a677c7549783e3888a30b8dbeace", "size": 249, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/mlir_out_tests/array_multidim3.f90", "max_stars_repo_name": "clementval/fc", "max_stars_repo_head_hexsha": "a5b444963c1b46e4eb34d938d992836d718010f7", "max_stars_repo_licenses": ["BSD-2-Clau... |
import numpy as np
import pytest
from marl_coop.component.sum_tree import SumTree
def test_update_a_3_leaf_tree_works():
'''
6
/ \
4 2
/ \
3 1
'''
memory = SumTree(3)
memory.add(10,2)
memory.add(20,3)
memory.add(30,1)
tree = memory.tree
assert (tree.priorit... | {"hexsha": "7f4836da5a40a6fc1be765358e19ea2ccbc88449", "size": 4906, "ext": "py", "lang": "Python", "max_stars_repo_path": "marl_coop/component/tests/sumTree_test.py", "max_stars_repo_name": "PierreMsy/DRL_cooperation", "max_stars_repo_head_hexsha": "0385f4c88857659f44ddd5fc8c5c6c33344a38cc", "max_stars_repo_licenses":... |
import numpy as np
import matplotlib.pyplot as plt
import pymc3 as pm
import pymc3.distributions.continuous as pmc
import pymc3.distributions.discrete as pmd
import pymc3.math as pmm
# PyMC 3 Installation instructions (https://github.com/pymc-devs/pymc3)
# Pip: pip install pymc3
# Conda: conda install -c cond... | {"hexsha": "93a2cce8122489bf6f4d06944b1300b28c47e567", "size": 2452, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter04/pymc3_example.py", "max_stars_repo_name": "arifmudi/Mastering-Machine-Learning-Algorithms", "max_stars_repo_head_hexsha": "8655e8e3f1e94f4d65bb92465033ebf54c193409", "max_stars_repo_lice... |
"""
title: "Merlin-py initial draft"
author: "Kellen O'Connor"
date: "January 2020"
"""
import os, shutil
import tabula
import pandas as pd
import numpy as np
from os.path import expanduser, getsize
home = os.path.expanduser('~')
pd.set_option('display.max_colwidth', 255)
import camelot
import queue
import math
def ... | {"hexsha": "7e66a4d415e27c26916280ba1fea55bfb4e4761f", "size": 8789, "ext": "py", "lang": "Python", "max_stars_repo_path": "merlin_pull.py", "max_stars_repo_name": "kellen-t-oconnor/merlin-py", "max_stars_repo_head_hexsha": "9a9bc1a95b662787a7741abc7687e8b84904edfb", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal
from numpy.linalg import inv
import matplotlib.colors as colors
from matplotlib import cm
from matplotlib import rc
from matplotlib import rcParams
__author__ = 'ernesto'
# if use latex or mathtext
rc('text', usetex=True)
rcParams['text.lat... | {"hexsha": "8bb56df1647cc5c6ea350826c1808d6109691bd7", "size": 2573, "ext": "py", "lang": "Python", "max_stars_repo_path": "figuras/PycharmKayStatisticalReport/problem_13_21.py", "max_stars_repo_name": "bor9/estudiando_el_kay", "max_stars_repo_head_hexsha": "6e07908b8b0b5a5166dadce30001e6100e8304c3", "max_stars_repo_li... |
#=
The power set of a set is the set of all its subsets. Write a function that, given a set, generates its power set.
For example, given the set {1, 2, 3}, it should return {{}, {1}, {2}, {3}, {1, 2}, {1, 3}, {2, 3}, {1, 2, 3}}.
You may also use a list or array to represent a set.
=#
using Test
include("Solutions/pr... | {"hexsha": "c6b1934eeef8e94036ba6accb8802bffcdd58460", "size": 438, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Tests/problem37_generate_powerset_test.jl", "max_stars_repo_name": "DominiqueCaron/daily-coding-problem", "max_stars_repo_head_hexsha": "41234497aa3a2c21c5dff43d86e9153d9582cced", "max_stars_repo_li... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from configs.path_config import SHAPENETCLASSES
from configs.path_config import ScanNet_OBJ_CLASS_IDS as OBJ_CLASS_IDS
impo... | {"hexsha": "6f0558dc026252e3a845605465c250e0621c73cf", "size": 2113, "ext": "py", "lang": "Python", "max_stars_repo_path": "configs/tdw_physics_config.py", "max_stars_repo_name": "htung0101/RfDNet", "max_stars_repo_head_hexsha": "5fb3e8cb0a50d2d1f3eee39bccfcc67b1f942ad0", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from spira.core.transformation import ReversibleTransform
from spira.core.parameters.descriptor import SetFunctionParameter
from spira.yevon.geometry.coord import CoordParameter, Coord
__all__ = ['Stretch', 'scale_element', 'stretch_element_by_port']
class Stretch(ReversibleTransform):
""" S... | {"hexsha": "50c22c6adfbeb1fe4e0f69c84e94f47d1c39aa3b", "size": 3777, "ext": "py", "lang": "Python", "max_stars_repo_path": "spira/core/transforms/stretching.py", "max_stars_repo_name": "qedalab/spira", "max_stars_repo_head_hexsha": "32e4d2096e298b9fcc5952abd654312dc232a259", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import matplotlib.pyplot as plt
import tensorflow as tf
import cv2
import os
import glob
import numpy as np
from sklearn.metrics import confusion_matrix
import random
from skimage import io, color
import DataProcessing as load
import main as rem
from tensorflow.examples.tutorials.mnist import input_data
from xlwt impor... | {"hexsha": "acb8e88fe3208db108ad098f1b83513f5bdbc87d", "size": 12399, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project Files/Full_image_check.py", "max_stars_repo_name": "greenJIS/A-Study-on-Paddy-Disease-Detection-using-Color-Co-occurrence-Features", "max_stars_repo_head_hexsha": "18a76b043951ef1c29428e0... |
// Copyright (c) 2014-2017 The Dash Core developers
// Copyright (c) 2017-2019 The KZCash Core developers
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include "darksend.h"
#include "governance-vote.h"
#include "masternod... | {"hexsha": "8b2b8b9fcd79f06f2e3fd00c4968c8507d38177c", "size": 10292, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/governance-vote.cpp", "max_stars_repo_name": "unitedcryptocommunity/kzcash", "max_stars_repo_head_hexsha": "763bc86787079356f9f0b60b6256a522979caf4a", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma lift\<^sub>c_Throw:
"(lift\<^sub>c prj inject c = Throw) = (c = Throw)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (lift\<^sub>c prj inject c = Throw) = (c = Throw)
[PROOF STEP]
by (cases c) auto | {"llama_tokens": 99, "file": "Simpl_ex_Compose", "length": 1} |
# code from https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/BAM.py
import numpy as np
import torch
from torch import nn
from torch.nn import init
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.shape[0], -1)
class ChannelAttention(nn.Module):
def __i... | {"hexsha": "25e5185865498434e488402d5510ecf495447807", "size": 3498, "ext": "py", "lang": "Python", "max_stars_repo_path": "external_attention_block/BAM.py", "max_stars_repo_name": "Roypic/Attention_Code", "max_stars_repo_head_hexsha": "5b6cbfc36e49101567d19d65894641550917a66e", "max_stars_repo_licenses": ["MIT"], "max... |
#include "rpcConnection.h"
#include "collector/statCollectorManager.h"
#include <boost/bind.hpp>
#include <jansson.h>
RPCConnection::RPCConnection(tcp::socket* socket) : m_socket(socket), m_buffer(SOCK_BUFFER_SIZE)
{
async_read_until(*m_socket, m_buffer,
'\n', boost::bind(&RPCConnection::h... | {"hexsha": "f688a6a678b8adfe0011d1f758e9a941a6bbf962", "size": 6422, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/net/rpcConnection.cxx", "max_stars_repo_name": "C0MPU73R/tlopo-stats", "max_stars_repo_head_hexsha": "7a7c2bfb5c2a1b9888e94ac611ad76da193f9405", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
module ExaPF
# Standard library
using Printf
using LinearAlgebra
using SparseArrays
import CUDA
import CUDA.CUBLAS
import CUDA.CUSPARSE
import CUDA.CUSOLVER
import ForwardDiff
using KernelAbstractions
const KA = KernelAbstractions
import MathOptInterface
const MOI = MathOptInterface
using TimerOutputs: @timeit, Time... | {"hexsha": "626c9f5a5e7c45c17adffa6908b9746799c5c998", "size": 886, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ExaPF.jl", "max_stars_repo_name": "exanauts/ExaPF.jl", "max_stars_repo_head_hexsha": "cd1bcb8a0782fe448d46a10816f82c5d28c3854e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 43, "max_s... |
# Copyright (c) 2017 The Khronos Group Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | {"hexsha": "a1dd30f3e0dfd46e88af9b13128a10508c82f885", "size": 24454, "ext": "py", "lang": "Python", "max_stars_repo_path": "legacy/caffe/nnef_converters/caffe_converters/caffe_to_nnef/converters.py", "max_stars_repo_name": "jnorwood/NNEF-Tools", "max_stars_repo_head_hexsha": "5eb3755b5322040d42893e41b15093337abe04ce",... |
import tensorflow as tf
import numpy as np
char_rdic = ['h', 'e', 'l', 'o']
char_dic = {w:i for i, w in enumerate(char_rdic)}
x_data = np.array([[1.0,0,0,0], # h
[0,1,0,0], # e
[0,0,1,0], # l
[0,0,1,0]], # l
dtype='f')
sample = [char_dic[c] for c... | {"hexsha": "178b3449c727ca604d0f2abd7a942232ab980f03", "size": 2351, "ext": "py", "lang": "Python", "max_stars_repo_path": "hunkim/ml_lab_12.py", "max_stars_repo_name": "juvenilehex/ml2", "max_stars_repo_head_hexsha": "57fa64660a87b2e432872c06414d1a86846ce380", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
#include "vr/market/sources/mock/mock_ouch_server.h"
#include "vr/fields.h"
#include "vr/io/net/socket_factory.h"
#include "vr/io/pcap/pcap_reader.h"
#include "vr/io/net/utility.h" // min_size_or_zero, make_group_range_filter
#include "vr/io/stream_factory.h"
#include "vr/market/defs.h"
#include "vr/market/sources/mo... | {"hexsha": "10878a6521ca8ca5dfc8b71d0a7d7fdefc98a93d", "size": 19855, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "vr/vr_rt/src/vr/market/sources/mock/mock_ouch_server.cpp", "max_stars_repo_name": "vladium/vrt", "max_stars_repo_head_hexsha": "57394a630c306b7529dbe4574036ea71420d00cf", "max_stars_repo_licenses":... |
function deserialize_image_summary(summary)
img = summary.image
value = load(_format_stream(format"PNG", IOBuffer(img.encoded_image_string)))
return value
end
function lookahead_deserialize_image_summary(old_tag, old_val, evs::Summary,
state_old)
# pre... | {"hexsha": "2b147e6573d61fdc501bf5890c2ccc22d2b8f0bd", "size": 1155, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Deserialization/images.jl", "max_stars_repo_name": "JJMinton/TensorBoardLogger.jl", "max_stars_repo_head_hexsha": "25d8db22c5082d029ff1ec876512633b2b24dbc8", "max_stars_repo_licenses": ["MIT"],... |
# import time
from copy import deepcopy
import random
from timeit import default_timer
from numpy import mean, median, arange, zeros, float64, log, power, argsort, array, newaxis, \
abs, full, empty
from numpy.random import choice, uniform
from sklearn.utils.extmath import stable_cumsum
from alg... | {"hexsha": "fdac17957adae8748c721102b6701dda7d23bdca", "size": 16291, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/algorithms/common/ensemble.py", "max_stars_repo_name": "martasls/pythonic-learning-machine", "max_stars_repo_head_hexsha": "330d1d5320adc8667bc7ce527808ec7a9c2271d4", "max_stars_repo_licenses... |
import sys
import numpy
import matplotlib.pyplot as plt
try:
from scipy import stats
except: # pragma: no cover
stats = None
import pytest
import probscale
from probscale.probscale import _minimal_norm
PY27 = sys.version_info.major == 2
if PY27:
TOLERANCE = 25
else:
TOLERANCE = 22
@pytest.fixtu... | {"hexsha": "7c2ba067ccdd06d6fad9d5fb7cad07cfdbfceed3", "size": 2824, "ext": "py", "lang": "Python", "max_stars_repo_path": "probscale/tests/test_probscale.py", "max_stars_repo_name": "SamsadSajid/mpl-probscale", "max_stars_repo_head_hexsha": "c65feeddbd8eb6ccca897b27dcd6738d72bd9fb2", "max_stars_repo_licenses": ["BSD-3... |
# Build artifacts to log train set distribution
import pandas as pd
import numpy as np
import seaborn as sn
import textwrap as twp
import matplotlib.pyplot as pl
import matplotlib as mat
pl.ioff()
mappables = []
# def time_transform(x):
# return pd.to_datetime(x).apply( lambda t : (t.hour*60+t.minute)//10)
... | {"hexsha": "84a876db8f1d341623951bb749dc52ee5b310046", "size": 6635, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/taxi_out/pipelines/data_science/training_set_distribution.py", "max_stars_repo_name": "nasa/ML-airport-taxi-out", "max_stars_repo_head_hexsha": "0b153c6527c4a6f4fac31ec83bf4f10835e89276", "max... |
# Advent of Code 2016
#
# From https://adventofcode.com/2016/day/21
#
from itertools import product, permutations
import networkx as nx
import numpy as np
from more_itertools import pairwise
# Extract inputs
data = np.array([list(x.strip()) for x in open('../inputs/Advent2016_24.txt', 'r')])
G = nx.Graph()
numbers =... | {"hexsha": "f0816dd1f9b2099a1ed59027ea1ac28b1856cbee", "size": 1324, "ext": "py", "lang": "Python", "max_stars_repo_path": "2016/src/Advent2016_24.py", "max_stars_repo_name": "davidxbuck/advent2018", "max_stars_repo_head_hexsha": "eed5424a8008b9c0829f5872ad6cd469ce9f70b9", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import glob
import logging
from pathlib import Path
from datetime import datetime
from typing import List, Tuple
from lxml import etree
import cv2
import numpy as np
class Line2Page:
"""Object, which stores meta data
source, image_folder, gt_folder, dest_folder are Path objects
"""
def __init__(self... | {"hexsha": "f1316033c2baa9f5b3c256dd4fe91455069b3ffc", "size": 11376, "ext": "py", "lang": "Python", "max_stars_repo_path": "pagetools/src/line2page/Line2Page.py", "max_stars_repo_name": "ThisTunaCanFly/PAGETools", "max_stars_repo_head_hexsha": "aa74cbae347132611c761abd5661a284327d7fac", "max_stars_repo_licenses": ["MI... |
from __future__ import print_function
from builtins import range
import sys
sys.path.insert(1, "../../../")
import h2o
from tests import pyunit_utils
import numpy as np
from sklearn.cluster import KMeans
from sklearn.impute import SimpleImputer
from h2o.estimators.kmeans import H2OKMeansEstimator
def get_model_kmean... | {"hexsha": "b6da4c264ea4411d0afbe972e5b6fd382d3b3751", "size": 1221, "ext": "py", "lang": "Python", "max_stars_repo_path": "h2o-py/tests/testdir_algos/kmeans/pyunit_get_modelKmeans.py", "max_stars_repo_name": "vishalbelsare/h2o-3", "max_stars_repo_head_hexsha": "9322fb0f4c0e2358449e339a434f607d524c69fa", "max_stars_rep... |
"""
"""
import h5py
import numpy as np
class RawH5Parser:
def __init__(self):
self.base_hostlist = []
self.read_datasets = ['procdata','procstat','procfd','procobs']
## fields filled out by the parse() function
self.filenames = []
self.hosts = []
self.datasets = {}... | {"hexsha": "6515bfd7618667e17df72c9b282c18bdb0c18c86", "size": 5952, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/analysis/python/procmon/H5Parser.py", "max_stars_repo_name": "glennklockwood/procmon", "max_stars_repo_head_hexsha": "c6e67d63e7c9c24f85a46b6d8965b8c615097edc", "max_stars_repo_licenses": ... |
"""
Data loader for the Healing MNIST data set (c.f. https://arxiv.org/abs/1511.05121)
Adapted from https://github.com/Nikita6000/deep_kalman_filter_for_BM/blob/master/healing_mnist.py
"""
import numpy as np
import scipy.ndimage
from tensorflow.keras.datasets import mnist
def apply_square(img, square_s... | {"hexsha": "cafbf7c419b52327ada84bdf631fbcdcb7f95ca0", "size": 3052, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/healing_mnist.py", "max_stars_repo_name": "siddharthchaini/GP-VAE", "max_stars_repo_head_hexsha": "440b5875bf0f95fb2fd551c8d02a494494cda511", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import argparse
import io
import time
import numpy as np
from PIL import Image, ImageColor, ImageDraw, ImageFont, ImageOps
from tflite_runtime.interpreter import Interpreter
# Module level vars
interpreter = None
labels = None
def load_labels(path):
with open(path) as f:
return {
int(s.spli... | {"hexsha": "fddec48923511802c50e873c8cccec75b547a348", "size": 4776, "ext": "py", "lang": "Python", "max_stars_repo_path": "detect_tflite.py", "max_stars_repo_name": "atomic77/opilite-object-detect", "max_stars_repo_head_hexsha": "6190034386293dd2eca199ac3bffdf765fd77425", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
program main
use module_gauss_turan_quadrature
implicit none
type(GaussTuranQuadrature) :: gt(maxngt)
call init_gauss_turan(gt)
call assemble_gauss_turan
call uninit_gauss_turan(gt)
end program main
| {"hexsha": "e74564debfbd00d9c98de71cdcca40d68d9b3301", "size": 216, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "gtq_quadp/src/main.f90", "max_stars_repo_name": "isakari/gtq_quadp", "max_stars_repo_head_hexsha": "b6b6c7ade6c96cc1f07230dc7d15b3b49fd2a313", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
-- Suma_de_los_primeros_cubos.lean
-- Suma de los primeros cubos
-- José A. Alonso Jiménez
-- Sevilla, 22 de septiembre de 2021
-- ---------------------------------------------------------------------
-- ---------------------------------------------------------------------
-- Demostrar que la suma de los primeros cubo... | {"author": "jaalonso", "repo": "Calculemus", "sha": "0fb664ab298c0e90b4b8034729a2cdad20503e18", "save_path": "github-repos/lean/jaalonso-Calculemus", "path": "github-repos/lean/jaalonso-Calculemus/Calculemus-0fb664ab298c0e90b4b8034729a2cdad20503e18/src/Suma_de_los_primeros_cubos.lean"} |
(* Title: HOL/Auth/n_flash_lemma_on_inv__83.thy
Author: Yongjian Li and Kaiqiang Duan, State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
Copyright 2016 State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
*)
header{*The n_flash P... | {"author": "paraVerifier", "repo": "paraVerifier", "sha": "5de62b433a160bf8d714e0a5085a38b9dd8899f0", "save_path": "github-repos/isabelle/paraVerifier-paraVerifier", "path": "github-repos/isabelle/paraVerifier-paraVerifier/paraVerifier-5de62b433a160bf8d714e0a5085a38b9dd8899f0/proof_scripts/flash/n_flash_lemma_on_inv__8... |
@testset "TMJets algorithm (TMJets21b)" begin
prob, tspan = vanderpol()
# default algorithm for nonlinear systems
sol = solve(prob, tspan=tspan)
@test sol.alg isa TMJets
# pass the algorithm explicitly
sol = solve(prob, tspan=tspan, TMJets())
@test sol.alg isa TMJets
# pass options ou... | {"hexsha": "1861ae5c5906ffe3aec36be93af931eeacf26807", "size": 3780, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/algorithms/TMJets.jl", "max_stars_repo_name": "lyg1597/ReachabilityAnalysis.jl", "max_stars_repo_head_hexsha": "2fdd273e895166dc1bec727bb2cfa209d198927f", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
from trust_based_filterer import TrustBasedFilterer
from surprise import Dataset, AlgoBase, PredictionImpossible
from surprise.model_selection import cross_validate
class Inverse_distance_weighted_tbr(AlgoBase):
def __init__(self, sim_options={}):
AlgoBase.__init__(self, sim_options=sim_option... | {"hexsha": "39afa3b6b6517966c64b2b1c6b198dcff773ac55", "size": 1501, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/test_module/test.py", "max_stars_repo_name": "oonat/inverse-distance-weighted-trust-based-recommender", "max_stars_repo_head_hexsha": "3f559f3e7dbc565da373f6297362ddf307b2d0ec", "max_stars_rep... |
import logging
from math import log
import numpy as np
SUN_REPORT_ID_INDEX = 0
SUN_REPORT_DID_INDEX = 1
SUN_REPORT_UNIGRAM_TOKEN = 2
SUN_REPORT_UNIGRAM_SUM = 3
SUN_REPORT_UNIGRAM_SUM_LEN = 4
SUN_REPORT_UNIGRAM_DESC = 5
SUN_REPORT_UNIGRAM_DESC_LEN = 6
SUN_REPORT_BIGRAM_TOKEN = 7
SUN_REPORT_BIGRAM_SUM = 8
SUN_REPORT_BIG... | {"hexsha": "8c486bbc08afdb9b2bd832b824be8e4d00e4a00c", "size": 9022, "ext": "py", "lang": "Python", "max_stars_repo_path": "classical_approach/bm25f.py", "max_stars_repo_name": "happygirlzt/soft_alignment_model_bug_deduplication", "max_stars_repo_head_hexsha": "9c529542749a52e377baeb99d1782920bc72df49", "max_stars_repo... |
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# ME... | {"hexsha": "b70f60088dbc8cef5fae49b4e52d56c39b0c5ccb", "size": 6434, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/metrics.py", "max_stars_repo_name": "alabrashJr/Maha-Odd", "max_stars_repo_head_hexsha": "cce4bab1f30589cf3d52636fe511c0269058679e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#!/usr/bin/python
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
data = np.genfromtxt('kv-gh-rot.csv', delimiter=',', names=True)
# print "2"
print(type(data))
print(matplotlib.backends.backend)
# plt.plot(data['time'], data['Z_value'])
# plt.show()
locX = []
locY = []
curX = 0.0
curY = 0.0
fo... | {"hexsha": "e072e9c080810dee050f9ea760fc3ae6d5e3cdfd", "size": 847, "ext": "py", "lang": "Python", "max_stars_repo_path": "graph.py", "max_stars_repo_name": "kunal15595/inertial-nav", "max_stars_repo_head_hexsha": "d5ab9c16e3befd5a1e2bd9b06255c88c114040bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
#This software is Copyright 2012 The Regents of the University of California. All Rights Reserved.
#Permission to use, copy, modify, and distribute this software and its documentation for educational, research and non-profit purposes for non-profit institutions, without fee, and without a written agreement is hereby gr... | {"hexsha": "1878a2f37a446dcf00ff094d3e8e657c1ec62cb0", "size": 4573, "ext": "py", "lang": "Python", "max_stars_repo_path": "cytoseg/contour_comparison.py", "max_stars_repo_name": "slash-segmentation/DP2", "max_stars_repo_head_hexsha": "6f768e4b8a75a3ab2bf1359ae94704332426a4d6", "max_stars_repo_licenses": ["Unlicense"],... |
[STATEMENT]
lemma (in nf_invar) CVdI: "\<lbrakk>u\<in>C\<rbrakk> \<Longrightarrow> u\<in>Vd d"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. u \<in> C \<Longrightarrow> u \<in> Vd d
[PROOF STEP]
using C_ss
[PROOF STATE]
proof (prove)
using this:
C \<subseteq> Vd d
goal (1 subgoal):
1. u \<in> C \<Longrightarrow> ... | {"llama_tokens": 161, "file": "EdmondsKarp_Maxflow_Augmenting_Path_BFS", "length": 2} |
#pragma once
#include "lue/py/framework/type_traits.hpp"
#include "lue/framework/core/shape.hpp"
// TODO Refactor with similar blocks in other stream.hpp headers.
#include <boost/predef.h>
#if BOOST_COMP_MSVC
# include <boost/io/ostream_joiner.hpp>
# define lue_make_ostream_joiner boost::io::make_ostream_joiner
#el... | {"hexsha": "de4ec4a59bd0d6c12b09ee673c724b7bb7b8eeae", "size": 1594, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "source/framework/python/include/lue/py/framework/stream.hpp", "max_stars_repo_name": "pcraster/lue", "max_stars_repo_head_hexsha": "e64c18f78a8b6d8a602b7578a2572e9740969202", "max_stars_repo_license... |
/*
* ====================================================================
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF license... | {"hexsha": "151c2d9f92fc5d2765c47f7449f8ea6be7ede961", "size": 3978, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "subversion/bindings/cxx/tests/test_depth.cpp", "max_stars_repo_name": "timgates42/subversion", "max_stars_repo_head_hexsha": "0f088f530747140c6783c2eeb77ceff8e8613c42", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Simple Power Plant Economic Dispatch using Linear Programming in Python
Setup: Power Co. operates a 200MW power plant that consists of four
gas-fired turbines. The cost to operate each generator/turbine (in $/hr) is a
quadratic function of the power generation (MW). To solve with an ... | {"hexsha": "b3b2d96fc97e0db64c3c4400821e8f5feab74ebd", "size": 5304, "ext": "py", "lang": "Python", "max_stars_repo_path": "econ_dispatch_LP.py", "max_stars_repo_name": "redlinger/Simple-Econ-Dispatch-LP-Python", "max_stars_repo_head_hexsha": "6f29d76ab83ef8d41cda25e17164b9020bc33b74", "max_stars_repo_licenses": ["MIT"... |
def make_supercell(cell, diff_species):
"""Append all sites in a unit cell to a structure - must have cubic lattice"""
#diff_species: Boolean, if true, make different species diff colors. If false, make one basis group one color.
#Get a copy of the structure defining the basis
basis=cell.copy()
... | {"hexsha": "dd2c94cddb41c0f750f8c5386511357d90f02000", "size": 3044, "ext": "py", "lang": "Python", "max_stars_repo_path": "MSE430Funcs/CrysStrucFuncs.py", "max_stars_repo_name": "KCMak653/MSE430Notebooks", "max_stars_repo_head_hexsha": "4f2ecfff557447de141121bbafbe5aa6bd60753b", "max_stars_repo_licenses": ["MIT"], "ma... |
############################################################################################
# INFEASIBLE MODELS #
############################################################################################
struct Infeasible{N,M,D<:AbstractModel} ... | {"hexsha": "930cfffde6955101424cdea5a9f96a426594238b", "size": 6092, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/infeasible_model.jl", "max_stars_repo_name": "tpr0p/Altro.jl", "max_stars_repo_head_hexsha": "cfe5f79fe64b454919d3edc26ad2ff2bb6cfe793", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4... |
################################################################################
# CORE DISPATCHVARIABLEREF METHOD EXTENSIONS
################################################################################
# Extend dispatch_variable_ref
function dispatch_variable_ref(model::InfiniteModel,
... | {"hexsha": "5e40a2c151fd2e45e4be57c39d4f4a02ea2af990", "size": 62518, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/array_parameters.jl", "max_stars_repo_name": "dlcole3/InfiniteOpt.jl", "max_stars_repo_head_hexsha": "8e5f86fc15343153c59a10a4361f1c722d795775", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python
u"""
read_GRACE_harmonics.py
Written by Tyler Sutterley (09/2021)
Contributions by Hugo Lecomte
Reads GRACE files and extracts spherical harmonic data and drift rates (RL04)
Adds drift rates to clm and slm for release 4 harmonics
Correct GSM data for drift in pole tide following Wahr et al. (2015... | {"hexsha": "e1e87be0fc8878986950aace72ae5be1f0f612d1", "size": 13705, "ext": "py", "lang": "Python", "max_stars_repo_path": "gravity_toolkit/read_GRACE_harmonics.py", "max_stars_repo_name": "richannan/read-GRACE-harmonics", "max_stars_repo_head_hexsha": "321254fc3caf08220a32733bd3e79aca433b61af", "max_stars_repo_licens... |
# VinDsl.jl: Fast and furious statistical modeling
<br>
John Pearson
P[λ]ab
Duke Institute for Brain Sciences
# Following along
VinDsl currently makes use of some features of Distributions.jl that are not yet available on master, as well as the latest release of PDMats.jl. You will need to checkout t... | {"hexsha": "b12ac41b1ef9b215c91a8bdc755705c62ec158ee", "size": 102873, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "slides.ipynb", "max_stars_repo_name": "jmxpearson/juliacon-2016-talk", "max_stars_repo_head_hexsha": "0d706c7caec4f32d5f76ee4f4381e593f0fce3f1", "max_stars_repo_licenses": ["MIT"], ... |
# coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import os
import time
import sys
import datetime
import ctypes
import json
import numpy as np
from sklearn.metrics import roc_auc_score
import copy
from tqdm import tqdm
from openke.config import Tester
cl... | {"hexsha": "1d96036180ac1e88c535e64d70827cc34bb2fb76", "size": 1888, "ext": "py", "lang": "Python", "max_stars_repo_path": "openke/config/Validator.py", "max_stars_repo_name": "luofeisg/OpenKE-PuTransE", "max_stars_repo_head_hexsha": "0bfefb3917e7479520917febd91a9f4d7353c7fc", "max_stars_repo_licenses": ["CC-BY-4.0", "... |
### A Pluto.jl notebook ###
# v0.15.1
using Markdown
using InteractiveUtils
# ╔═╡ 3244173c-e227-11eb-39eb-93a74dce1c9e
using PlutoUI, PDFIO, Taro, WordTokenizers, DeepDiffs
# ╔═╡ 0f5786cb-6430-4a14-bc05-bb127f8b73df
md"""
This notebook does not work!
"""
# ╔═╡ c3353ab9-0ae1-4785-b561-631945710a35
#Taro.init()
# ╔═... | {"hexsha": "89f6b3f754fc7f15781d6f539eceb6490f7ab15e", "size": 13463, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "file-compare.jl", "max_stars_repo_name": "StatisticalMice/julia-tutorials", "max_stars_repo_head_hexsha": "73cd9706c75d9544209f2e9321ab6f60d3ae235c", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2021 Scipp contributors (https://github.com/scipp)
import numpy as np
import scipp as sc
import pytest
import ess.choppers as ch
@pytest.fixture
def params():
dim = 'frame'
return {
'frequency':
sc.scalar(56.0, unit="Hz"),
'phase'... | {"hexsha": "8817826e03151b105ba767f1ad36906b5e03a2cc", "size": 6730, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/choppers/chopper_test.py", "max_stars_repo_name": "scipp/ess", "max_stars_repo_head_hexsha": "078e10c53cacf849103f9df0c16c61628e4c65ee", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import vtk
from numpy import zeros
import matplotlib.pyplot as plt
filename = 'test.vtk'
reader = vtk.vtkUnstructuredGridReader()
reader.SetFileName(filename)
reader.Update()
# plane = vtk.vtkPlane()
# plane.SetOrigin(0, 0, 0.5)
# plane.SetNormal(0, 0, 1)
# cutter = vtk.vtkFiltersCorePython.vtkCutter()
# cutter.Set... | {"hexsha": "f91845dc64dbe1ed309f2068a8752be86217c64e", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "readVTK.py", "max_stars_repo_name": "hdillinger/parasnip", "max_stars_repo_head_hexsha": "05307140cc21e2de9a472da799fc0983f5fd3d28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
#!/usr/bin/env python
# coding: utf-8
# <h1> <i> <u> Student Perception Analysis using Multiple Linear Regression
# ## Importing libaries and understanding the data
# In[1]:
import numpy as np
import pandas as pd
from pandas.plotting import table
import matplotlib.pyplot as plt
import matplotlib.colors as pltcol
i... | {"hexsha": "60be4d16d244c9877116741429f19ca9632212b2", "size": 29496, "ext": "py", "lang": "Python", "max_stars_repo_path": "spa.py", "max_stars_repo_name": "BALAJI24092001/Student-Perception-Analysis", "max_stars_repo_head_hexsha": "8b21da576fbae1678918be02e5e91615818da4be", "max_stars_repo_licenses": ["MIT"], "max_st... |
import warnings
import numpy as np
import pandas as pd
import networkx as nx
import statsmodels.api as sm
def probability_to_odds(prob):
"""Converts given probability (proportion) to odds
Parameters
----------
prob : float, array
Probability or array of probabilities to convert to odds
""... | {"hexsha": "1ca8511c0b94911c5b563576565fc5684866e3ca", "size": 16881, "ext": "py", "lang": "Python", "max_stars_repo_path": "mossspider/estimators/utils.py", "max_stars_repo_name": "pzivich/MossSpider", "max_stars_repo_head_hexsha": "43cb6d22959afb47a9862f73754965473f42ddc1", "max_stars_repo_licenses": ["MIT"], "max_st... |
import os
import numpy as np
import pytest
from jina.executors.decorators import as_update_method, as_train_method, as_ndarray, batching, \
require_train, store_init_kwargs
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_as_update_method():
class A:
def __init__(self):
self... | {"hexsha": "d74a502918ffb8e507252ec7f61a615e49d2fdfc", "size": 2670, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/executors/test_decorators.py", "max_stars_repo_name": "DavidSanwald/jina", "max_stars_repo_head_hexsha": "fa2bd79c30c586928a0a77a44b32c5a99d7932bc", "max_stars_repo_licenses": ["Apache-... |
#!/usr/bin/env python
# stdlib imports
import os.path
import tempfile
import shutil
from datetime import datetime
# third party imports
import numpy as np
from mapio.shake import getHeaderData
# local imports
from losspager.io.pagerdata import PagerData
from losspager.models.emploss import EmpiricalLoss
from losspa... | {"hexsha": "68f2a55ba11b5be72e48e16ae87f72a1debf7ef2", "size": 7283, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/io/pagerdata_test.py", "max_stars_repo_name": "usgs/pager", "max_stars_repo_head_hexsha": "0728e4bfb491343cda744d66304a5b3b14d33f5a", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_coun... |
# Copyright 2018 The TensorFlow Probability 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | {"hexsha": "c7e0d6622553f78f88722e440ea0849b9fc34486", "size": 30788, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_probability/python/optimizer/linesearch/hager_zhang.py", "max_stars_repo_name": "chrism0dwk/probability", "max_stars_repo_head_hexsha": "ab260f15cae94c6802c2f2769fb448ad213b79cd", "max... |
#!/usr/bin/env python
# coding: utf-8
# # Convert CIF to JCPDS
# In[1]:
get_ipython().run_line_magic('matplotlib', 'inline')
get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
# * This notebook shows how to make an XRD plot using `pymatgen`.
#
# * This also aims to show how to read ... | {"hexsha": "17c53815950c6f44339a75ad1e8676e212140e84", "size": 3904, "ext": "py", "lang": "Python", "max_stars_repo_path": "Util_cif_to_jcpds/Convert_CIF_to_JCPDS.py", "max_stars_repo_name": "SHDShim/PMatRes", "max_stars_repo_head_hexsha": "92440c11f2723861dbb82cecdc321fcef9de4443", "max_stars_repo_licenses": ["Apache-... |
{-# LANGUAGE FlexibleContexts #-}
-- |
-- Module : Statistics.Sample.Internal
-- Copyright : (c) 2013 Bryan O'Sullivan
-- License : BSD3
--
-- Maintainer : bos@serpentine.com
-- Stability : experimental
-- Portability : portable
--
-- Internal functions for computing over samples.
module Statistics.Sample.Inte... | {"hexsha": "53c9a97a337e19c563c27c7bfbdc7bb600264de7", "size": 752, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "Statistics/Sample/Internal.hs", "max_stars_repo_name": "StefanHubner/statistics", "max_stars_repo_head_hexsha": "e98af025ef4aa0bc31a5b1fcf88bb80295aac956", "max_stars_repo_licenses": ["BSD-2-Claus... |
# encoding=utf8
"""Implementation of Cosine mixture benchmark."""
from numpy import cos, pi
from NiaPy.benchmarks.benchmark import Benchmark
__all__ = ["CosineMixture"]
class CosineMixture(Benchmark):
r"""Implementations of Cosine mixture function.
Date: 2018
Author: Klemen Berkovič
License: MIT... | {"hexsha": "6ea4ea749783792ec13ec93ce2b9da24e71507a0", "size": 2243, "ext": "py", "lang": "Python", "max_stars_repo_path": "NiaPy/benchmarks/cosinemixture.py", "max_stars_repo_name": "lukapecnik/NiaPy", "max_stars_repo_head_hexsha": "a40ac08a4c06a13019ec5e39cc137461884928b0", "max_stars_repo_licenses": ["MIT"], "max_st... |
import unittest
import numpy as np
from pandas import Index
import pandas.util.testing as common
import pandas._tseries as tseries
class TestTseriesUtil(unittest.TestCase):
def test_combineFunc(self):
pass
def test_reindex(self):
pass
def test_isnull(self):
pass
def test_gr... | {"hexsha": "d2cb8119ac566bf8514f4b589cffff5f9935a66c", "size": 3172, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/tests/test_tseries.py", "max_stars_repo_name": "timClicks/pandas", "max_stars_repo_head_hexsha": "83b216c9efb439c1d19690feff1dcba58c6a2f88", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
import logging
import math
import os
import random
from datetime import datetime
from scipy import ndimage
import numpy as np
import cv2
import torch
from torchvision.utils import make_grid
###########
# visdom
###########
def create_vis_plot(vis, xlabel, ylabel, title, legend):
num_lines = len(legend)
# ... | {"hexsha": "386b1cc7112f509f8e61f5df599ae02cf32ea323", "size": 17350, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/util.py", "max_stars_repo_name": "wasaCheney/PercepPan", "max_stars_repo_head_hexsha": "37d4683e97410846744f8f2e7d9733f551a8771c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
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