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from .pandas_profiling import ModulePandasProfiling
from .bias import ModuleBias
from .performance import ModulePerformance
from .interpretability import ModuleInterpretability
from .sustainable import ModuleSustainable | [
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import asyncio
import importlib.resources
import aioconsole
from playsound import playsound
import niescraper.resources
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# Exercise 5 : Cross-Validation
## Set up code checking
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-data-for-ml-course/test.csv", "../input/test.csv")
from learntools.core import binder
binder.bind(globals())
from learntools.ml_intermediate.ex5 import *
print("Setup Complete")
import pandas as pd
from sklearn.model_selection import train_test_split
## Read the data
train_data = pd.read_csv('../input/train.csv', index_col='Id')
test_data = pd.read_csv('../input/test.csv', index_col='Id')
## Remove rows with missing target, separate target from predictors
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = train_data.SalePrice
train_data.drop(['SalePrice'], axis=1, inplace=True)
## Select numeric columns only
numeric_cols = [cname for cname in train_data.columns if train_data[cname].dtype in ['int64', 'float64']]
X = train_data[numeric_cols].copy()
X_test = test_data[numeric_cols].copy()
## Step 1: Write a useful function
## In this exercise, you'll use cross-validation to select parameters for a machine learning model.
## Begin by writing a function get_score() that reports the average (over three cross-validation folds) MAE of a machine learning pipeline that uses:
## the data in X and y to create folds,
## SimpleImputer() (with all parameters left as default) to replace missing values, and
## RandomForestRegressor() (with random_state=0) to fit a random forest model.
## The n_estimators parameter supplied to get_score() is used when setting the number of trees in the random forest model.
## Answer:
## Step 2: Test different parameter values
## Now, you will use the function that you defined in Step 1 to evaluate the model performance corresponding to eight different values for the number of trees in the random forest: 50, 100, 150, ..., 300, 350, 400.
## Store your results in a Python dictionary results, where results[i] is the average MAE returned by get_score(i).
## Answer:
results = {}
for i in range(1,9):
results[50*i] = get_score(50*i)
## Step 3: Find the best parameter value
## Given the results, which value for n_estimators seems best for the random forest model? Use your answer to set the value of n_estimators_best.
## Answer:
n_estimators_best = min(results, key=results.get) | [
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# coding:utf-8
# real mirror script
# 1) duplicate special ( world | input graph)
# 2) unparent offsets to world
# 3) create a null for each offset
# 4) match Translate each null on his respective offset
# 5) *(-1) translate X on each null
# 6) rotation 180° on Y and Z axis on each null
# 7) unparent offsets and reparent them correctly
# 8) reorient CTRLS' shapes
# /|\
# / ! \ *(-1) translate X on each null joint
#
# MIRROR RIG MODULES
sel = cmds.ls(sl=True, ap=True)
duplicata = cmds.duplicate(sel, returnRootsOnly=False, rc=True, upstreamNodes=True)
# rename ALL nodes ; both DAG and DG nodes
renamed_duplicata = []
for node in duplicata:
renamed_node = cmds.rename(node, node.replace("_L", "_R").rstrip("1"))
renamed_duplicata.append(renamed_node)
# Save constraint structure
constraints_list = []
for node in renamed_duplicata:
if cmds.objectType(node) in ["parentConstraint", "orientConstraint", "scaleConstraint", "aimConstraint"]:
constraints_list.append(node)
if constraints_list:
constraint = {}
for constr in constraints_list:
constraint[constr] = {}
constraint[constr]["type"] = cmds.objectType(constr)
id = 0
constraint[constr]["source"] = None
while not constraint[constr]["source"]:
constraint[constr]["source"] = cmds.listConnections(constr+".target[{}].targetParentMatrix".format(id), s=True, d=False)
id += 1
constraint[constr]["source"] = constraint[constr]["source"][0]
dest_tx = cmds.listConnections("{}.constraintTranslateX".format(constr), s=False, d=True)[0]
dest_ty = cmds.listConnections("{}.constraintTranslateY".format(constr), s=False, d=True)[0]
dest_tz = cmds.listConnections("{}.constraintTranslateZ".format(constr), s=False, d=True)[0]
dest_rx = cmds.listConnections("{}.constraintRotateX".format(constr), s=False, d=True)[0]
dest_ry = cmds.listConnections("{}.constraintRotateY".format(constr), s=False, d=True)[0]
dest_rz = cmds.listConnections("{}.constraintRotateZ".format(constr), s=False, d=True)[0]
if dest_tx == dest_ty and dest_ty == dest_tz:
constraint[constr]["destination"] = dest_tx
elif dest_rx == dest_ry and dest_ry == dest_rz:
constraint[constr]["destination"] = dest_rx
offset_tr = cmds.getAttr("{}.target[0].targetOffsetTranslate".format(constr))[0]
offset_rot = cmds.getAttr("{}.target[0].targetOffsetRotate".format(constr))[0]
if offset_tr == (0, 0, 0) and offset_rot == (0, 0, 0):
constraint[constr]["maintain_offset"] = False
else:
constraint[constr]["maintain_offset"] = True
cmds.delete(constr)
# Work only with DAG nodes
cmds.select(renamed_duplicata[0:len(sel)], hierarchy=True)
dagNodes_list = cmds.ls(sl=True, shapes=False)
# filter shapes because maya sucks
for id, node in enumerate(dagNodes_list):
if cmds.objectType(node, isType="nurbsCurve") == True:
dagNodes_list.pop(id)
dagParent = {}
for node in dagNodes_list:
parent = cmds.listRelatives(node, parent=True)[0]
dagParent[node] = parent
if cmds.objectType(node, isType="joint") and node.startswith("null_"):
jpos_x, jpos_y, jpos_z = cmds.xform(node, q=True, t=True, objectSpace=True)
for node in dagNodes_list:
cmds.parent(node, world=True)
for node in dagNodes_list:
pos_x, pos_y, pos_z = cmds.xform(node, q=True, t=True)
rot_x, rot_y, rot_z = cmds.xform(node, q=True, ro=True)
global_move = cmds.createNode("transform", n="temp_mirror")
cmds.xform(global_move, t=[pos_x, pos_y, pos_z])
cmds.parent(node, global_move)
cmds.xform(global_move, t=[-pos_x, pos_y, pos_z])
cmds.xform(global_move, ro=[0, 180, 180])
cmds.parent(node, w=True)
cmds.delete(global_move)
for node in dagNodes_list:
cmds.parent(node, dagParent[node])
if cmds.objectType(node, isType="joint"):
if node.startswith("null_"):
cmds.xform(node, t=[-jpos_x, jpos_y, jpos_z])
cmds.setAttr("{}.jointOrientX".format(node), 0)
cmds.setAttr("{}.jointOrientY".format(node), 0)
cmds.setAttr("{}.jointOrientZ".format(node), 0)
# redo constraints
if constraints_list:
for i in constraints_list:
if constraint[i]["type"] == "parentConstraint":
cmds.parentConstraint(constraint[i]["source"], constraint[i]["destination"], mo= constraint[i]["maintain_offset"])
elif constraint[i]["type"] == "orientConstraint":
cmds.orientConstraint(constraint[i]["source"], constraint[i]["destination"], mo= constraint[i]["maintain_offset"])
elif constraint[i]["type"] == "scaleConstraint":
cmds.scaleConstraint(constraint[i]["source"], constraint[i]["destination"], mo= constraint[i]["maintain_offset"])
# elif constraint[i]["type"] == "aimConstraint":
# cmds.aimConstraint(constraint[i]["source"], constraint[i]["destination"], mo= constraint[i]["maintain_offset"])
# MIRROR POSITION AND ROTATION
# MIRROR SHAPES
if not sel:
sel = cmds.ls(sl=True, ap=True)
if not sel:
cmds.error("Any selection. Please select curves")
for transform in sel:
shapes_list = cmds.listRelatives(transform, shapes=True, path=True)
for shape in shapes_list:
if cmds.objectType(shape, isType="nurbsCurve"):
cv_number = cmds.getAttr("{}.spans".format(shape))
for id in range(cv_number):
cv_x = cmds.getAttr("{}.controlPoints[{}].xValue".format(shape, id))
cv_y = cmds.getAttr("{}.controlPoints[{}].yValue".format(shape, id))
cv_z = cmds.getAttr("{}.controlPoints[{}].zValue".format(shape, id))
cmds.setAttr("{}.controlPoints[{}].xValue".format(shape, id), cv_x)
cmds.setAttr("{}.controlPoints[{}].yValue".format(shape, id), (-1)*cv_y)
cmds.setAttr("{}.controlPoints[{}].zValue".format(shape, id), (-1)*cv_z)
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] | 2.437228 | 2,294 |
QUERY_PARAM_QUERY = 'q'
QUERY_PARAM_SORT = 's'
QUERY_PARAM_FILTER = 'f'
QUERY_PARAM_SIZE = 'size'
QUERY_PARAM_PAGE = 'page'
QUERY_PARAM_FIELDS = 'fields'
QUERY_PARAM_OFFSET = 'offset'
QUERY_PARAM_INCLUDE = 'include'
QUERY_PARAM_EXCLUDE = 'exclude'
QUERY_PARAM_WAIT_UNTIL_COMPLETE = 'wuc'
QUERY_PARAM_FORMAT = 'format'
| [
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] | 2.038462 | 156 |
from django.shortcuts import render,redirect
from django.http import HttpResponse,Http404,HttpResponseRedirect
from django.contrib.auth.decorators import login_required
from .models import Enquiry,Vitabu
from .forms import EnquiryForm
# Create your views here. | [
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] | 3.434211 | 76 |
import time
import uuid
from kubernetes import client, config
import os
from fastapi.testclient import TestClient
import json
from typing import List
from pydantic import EmailStr
from fastapi import HTTPException
from app.api.models.auth0 import Auth0User
from .helper_functions import (
generate_user,
generate_project,
generate_namespace
)
from app.main import app
from app.auth0 import current_user
cluster_name = json.loads(os.environ['CLUSTER_DICT'])["EU1"]["Config-Name"]
config.load_kube_config("config.yaml", context=cluster_name)
v1 = client.CoreV1Api()
rbac = client.RbacAuthorizationV1Api()
user1 = generate_user()
user2 = generate_user()
user3 = generate_user()
user_list1 = [user1, user2, user3]
namespace_blacklist = generate_namespace()
namespace_blacklist.update({"name": "default"})
namespace1 = generate_namespace()
namespace2 = generate_namespace()
namespace3 = generate_namespace()
project = generate_project()
project2 = generate_project()
project3 = generate_project()
| [
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18,
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628,
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628,
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] | 3.03869 | 336 |
# -*- coding: utf-8 -*-
from .limbdark import LimbDarkOp
from .get_cl import GetClOp
| [
2,
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] | 2.428571 | 35 |
from __future__ import absolute_import
import numpy as np
from desicos.abaqus.apply_imperfections import change_thickness_ABAQUS
from desicos.conecylDB import update_imps
class TI(object):
"""Thickness Imperfection
Assumes that a percentage variation of the laminate thickness can be
represented by the same percentage veriation of each ply, i.e., each
ply thickness is varied in order to reflect a given measured thickness
imperfection field.
"""
def calc_amplitude(self):
"""Calculates the thickness imperfection amplitude
Amplitude measured as the biggest difference between each layup
thickness and the nominal thickness of the Cone/Cylinder,
considering only the layups that are not suppressed.
.. note:: Must be called from Abaqus.
Returns
-------
max_amp : float
Maximum absolute imperfection amplitude.
"""
if self.created:
from abaqus import mdb
cc = self.impconf.conecyl
part = mdb.models[cc.model_name].parts[cc.part_name_shell]
max_amp = 0.
cc_total_t = sum(cc.plyts)
for layup in part.compositeLayups.values():
if not layup.suppressed:
layup_t = sum(p.thickness for p in layup.plies.values())
max_amp = max(max_amp, abs(layup_t-cc_total_t))
return max_amp
def create(self, force=False):
"""Creates the thickness imperfection
The thickness imperfection is created assuming that each ply has
the same contribution to the measured laminate thickness. Thus, a
scaling factor is applied to the nominal thickness of each ply in
order to macth the measured imperfection field.
Parameters
----------
force : bool, optional
If ``True`` the thickness imperfection is applied even when it
is already created.
"""
if self.created:
if force:
cc = self.impconf.conecyl
cc.created = False
cc.rebuilt = False
cc.create_model()
else:
return
cc = self.impconf.conecyl
imps, imps_theta_z, t_measured, R_best_fit, H_measured = update_imps()
if self.use_theta_z_format:
imperfection_file_name = imps_theta_z[self.imp_thick]['ti']
else:
imperfection_file_name = imps[self.imp_thick]['ti']
H_measured = H_measured[self.imp_thick]
R_best_fit = R_best_fit[self.imp_thick]
t_measured = t_measured[self.imp_thick]
cc = self.impconf.conecyl
self.elems_t, self.t_set = change_thickness_ABAQUS(
imperfection_file_name = imperfection_file_name,
model_name = cc.model_name,
part_name = cc.part_name_shell,
t_model = sum(cc.plyts),
t_measured = t_measured,
H_model = cc.H,
H_measured = H_measured,
R_model = cc.rbot,
R_best_fit = R_best_fit,
number_of_sets = self.number_of_sets,
semi_angle = cc.alphadeg,
stretch_H = self.stretch_H,
scaling_factor = self.scaling_factor,
num_closest_points = self.ncp,
power_parameter = self.power_parameter,
elems_t = self.elems_t,
t_set = self.t_set,
use_theta_z_format = self.use_theta_z_format)
from desicos.abaqus.abaqus_functions import set_colors_ti
set_colors_ti(cc)
self.created = True
print('%s amplitude = %f' % (self.name, self.calc_amplitude()))
ffi = self.impconf.ffi
if ffi is not None and ffi.created:
# There is already a FFI, let it know about us
ffi.update_after_tis()
return self.elems_t, self.t_set
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] | 2.039801 | 2,010 |
from __future__ import absolute_import, division, print_function
# LIBTBX_SET_DISPATCHER_NAME mmtbx.development.aev
import sys
import time
import mmtbx
import iotbx.pdb
import mmtbx.model
from libtbx.utils import null_out
import mmtbx.atomic_environment_vectors as aev
if __name__ == '__main__':
main(*tuple(sys.argv[1:]))
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] | 2.672131 | 122 |
#!/usr/bin/env python
import pika
#credentials = pika.PlainCredentials('guest', 'guest')
#parameters = pika.ConnectionParameters('amqp.k8sd.pax.ch', 80, '/', credentials)
#parameters = pika.URLParameters('amqp://guest:guest@localhost:5672/%2F')
#connection = pika.BlockingConnection(parameters)
connection = pika.BlockingConnection()
channel = connection.channel()
channel.queue_declare(queue='hello')
channel.basic_publish(exchange='',
routing_key='hello',
body='Hello World! MWN')
print(" [x] Sent 'Hello World! MWN'")
connection.close()
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] | 2.527897 | 233 |
import mysql.connector
from mysql.connector import errorcode
try:
con = mysql.connector.connect(user='niminimda', password='123456', host='127.0.01', database='test')
except mysql.connector.Error as err:
if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:
print("something is wrong with user or password")
elif err.errno == errorcode.ER_BAD_DB_ERROR:
print("db doesn't exists")
else:
print(err)
else:
my_function()
con.close()
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] | 2.578378 | 185 |
import numpy as np
# A simple generator wrapper, not sure if it's good for anything at all.
# With basic python threading
import threading
# from threading import Thread
import multiprocessing as mp
import time
try:
from queue import Queue
except ImportError:
from Queue import Queue
# ... or use multiprocessing versions
# WARNING: use sentinel based on value, not identity
from multiprocessing import Process, Queue as MpQueue
from utils import *
class ThreadedGenerator(object):
"""
Generator that runs on a separate thread, returning values to calling
thread. Care must be taken that the iterator does not mutate any shared
variables referenced in the calling thread.
"""
| [
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220,
220,
220,
9633,
20717,
287,
262,
4585,
4704,
13,
198,
220,
220,
220,
37227,
628,
198
] | 3.659794 | 194 |
import sys
sys.setrecursionlimit(100000)
m,n=map(int,input().split())
dp=[[-1]*(n+2) for i in range(m+2)]
visit=[[False]*(n+2) for i in range(m+2)]
field=[0]*(m+2)
field[0]=[11111]*(n+2)
field[m+1]=[11111]*(n+2)
for i in range(1,m+1):
field[i]=[11111]
field[i].extend([*map(int,input().split())])
field[i].append(11111)
print(DFS(1,1)) | [
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8,
198,
198,
4798,
7,
8068,
50,
7,
16,
11,
16,
4008
] | 1.932961 | 179 |
# ref https://www.youtube.com/watch?v=2p3kwF04xcA&index=24&list=PLi01XoE8jYohWFPpC17Z-wWhPOSuh8Er-
import math
import time
'''
for n in range(1,21):
print(n, is_prime_v2(n))
'''
t0 = time.time()
for n in range(1,1000000):
is_prime_v2(n)
t1 = time.time()
print("time required for 1000000 :", t1-t0)
#print("test raise ", is_prime_v2(-10))
'''
if use n % 2 == 0, then
time required for 1000000 : 9.644205331802368
without above, then close timings,
time required for 1000000 : 10.904832363128662
if add % 2 and %3, then for d in range(5, max_divisor+1, 2):
then half the time !
time required for 1000000 : 5.761587858200073
'''
| [
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198,
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13,
3829,
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361,
751,
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362,
290,
4064,
18,
11,
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329,
288,
287,
2837,
7,
20,
11,
3509,
62,
7146,
271,
273,
10,
16,
11,
362,
2599,
198,
8524,
2063,
262,
640,
5145,
198,
198,
2435,
2672,
329,
1802,
2388,
1058,
642,
13,
4304,
21273,
3695,
3365,
11024,
4790,
198,
7061,
6,
198
] | 2.316547 | 278 |
#################################################################
#
# Round for Python
#
# Copyright (C) Satoshi Konno 2016
#
# This is licensed under BSD-style license, see file COPYING.
#
##################################################################
from __future__ import absolute_import
import json
from .. import constants
| [
29113,
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2,
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2,
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8,
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2,
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2,
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62,
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198,
198,
11748,
33918,
198,
198,
6738,
11485,
1330,
38491,
198
] | 4.941176 | 68 |
import os
import sys
from os.path import join, dirname
sys.path.insert(0, join(dirname(__file__), '..'))
import simulator
simulator.load('/home/wang/CARLA_0.9.9.4')
from simulator import config
import carla
import argparse
import time
from tqdm import tqdm
MAX_SPEED = 40
TRAJ_LENGTH = 25#25
vehicle_width = 2.0
longitudinal_sample_number_near = 8
longitudinal_sample_number_far = 0.5
lateral_sample_number = 20
lateral_step_factor = 1.0
parser = argparse.ArgumentParser(description='Params')
parser.add_argument('-d', '--data', type=int, default=1, help='data index')
parser.add_argument('-n', '--num', type=int, default=100000, help='total number')
args = parser.parse_args()
data_index = args.data
save_path = '/media/wang/DATASET/CARLA_HUMAN/town01/'+str(data_index)+'/'
sensor_dict = {
'camera':{
'transform':carla.Transform(carla.Location(x=0.5, y=0.0, z=2.5)),
# 'callback':image_callback,
},
'lidar':{
'transform':carla.Transform(carla.Location(x=0.5, y=0.0, z=2.5)),
# 'callback':lidar_callback,
},
}
from utils.system import env_path
env_path.remove_python2_path(sys)
import cv2
env_path.append_python2_path(sys)
from utils.collect_pm import CollectPerspectiveImage
from utils.carla_sensor import Sensor, CarlaSensorMaster
if __name__ == '__main__':
# rospy.init_node('collect_pm')
try:
main()
except KeyboardInterrupt:
#exit(0)
pass | [
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198,
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220,
220,
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220,
220,
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220,
220,
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13,
14749,
7,
87,
28,
15,
13,
20,
11,
331,
28,
15,
13,
15,
11,
1976,
28,
17,
13,
20,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
705,
47423,
10354,
9060,
62,
47423,
11,
198,
220,
220,
220,
8964,
198,
220,
220,
220,
705,
75,
312,
283,
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90,
198,
220,
220,
220,
220,
220,
220,
220,
705,
35636,
10354,
7718,
5031,
13,
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7,
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13,
14749,
7,
87,
28,
15,
13,
20,
11,
331,
28,
15,
13,
15,
11,
1976,
28,
17,
13,
20,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
705,
47423,
10354,
75,
312,
283,
62,
47423,
11,
198,
220,
220,
220,
8964,
198,
92,
198,
198,
6738,
3384,
4487,
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24330,
62,
6978,
13,
28956,
62,
29412,
17,
62,
6978,
7,
17597,
8,
198,
11748,
269,
85,
17,
198,
24330,
62,
6978,
13,
33295,
62,
29412,
17,
62,
6978,
7,
17597,
8,
198,
198,
6738,
3384,
4487,
13,
33327,
62,
4426,
1330,
9745,
30946,
806,
425,
5159,
198,
6738,
3384,
4487,
13,
7718,
5031,
62,
82,
22854,
1330,
35367,
11,
1879,
5031,
47864,
18254,
198,
220,
220,
220,
220,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1303,
686,
2777,
88,
13,
15003,
62,
17440,
10786,
33327,
62,
4426,
11537,
198,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1388,
3419,
198,
220,
220,
220,
2845,
31973,
9492,
3622,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
37023,
7,
15,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1208
] | 2.435811 | 592 |
# -*- coding: Windows-1251 -*-
'''
find_dll_name.py
List all python dll names in DLL/PYD/etc file.
Author: Denis Barmenkov <[email protected]>
Copyright: this code is free, but if you want to use it,
please keep this multiline comment along with source.
Thank you.
2009-08-09
'''
import re
import sys
fn = sys.argv[1]
f = open(fn, 'rb')
data = f.read()
f.close()
dll_re = re.compile(r'python\d\d\.dll', re.M | re.S)
found = dll_re.findall(data)
print found
#-------------- cut here -----------------#
# -*- coding: Windows-1251 -*-
'''
patch_dll_name.py
Patch extension precompiled binary by changing python dll name.
Author: Denis Barmenkov <[email protected]>
Copyright: this code is free, but if you want to use it,
please keep this multiline comment along with source.
Thank you.
2009-08-09
'''
import sys
import os
OLD_NAME = 'python23.dll'
NEW_NAME = 'python24.dll'
fn = sys.argv[1]
f = open(fn, 'rb')
data = f.read()
f.close()
data = data.replace(OLD_NAME, NEW_NAME)
bak_fn = fn + '.bak'
os.rename(fn, bak_fn) # rename original file to .BAK
f = open(fn, 'wb')
f.write(data) # write patched version
f.close()
| [
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] | 2.432049 | 493 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 8 08:21:19 2018
@author: alkesha
"""
import cv2
import numpy as np
#function to draw sketch
camera=cv2.VideoCapture(0)
while True:
ret,frame=camera.read()
cv2.imshow("sketch live",sketching(frame))
if cv2.waitKey(1)==ord('q'):
break
camera.release()
camera.destroyAllWindows() | [
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] | 2.306748 | 163 |
"""
The pytoil new command.
Author: Tom Fleet
Created: 25/06/2021
"""
from typing import List
import httpx
import typer
from cookiecutter.main import cookiecutter
from wasabi import msg
from pytoil.api import API
from pytoil.cli import utils
from pytoil.config import Config
from pytoil.environments import Conda, Environment, Venv, VirtualEnv
from pytoil.exceptions import (
CargoNotInstalledError,
EnvironmentAlreadyExistsError,
GoNotInstalledError,
)
from pytoil.git import Git
from pytoil.repo import Repo
from pytoil.starters import GoStarter, PythonStarter, RustStarter, Starter
from pytoil.vscode import VSCode
app = typer.Typer()
@app.command(context_settings={"allow_extra_args": True})
def new(
ctx: typer.Context,
project: str = typer.Argument(
...,
help="Name of the project to create.",
),
cookie: str = typer.Option(
None,
"--cookie",
"-c",
help="URL to a cookiecutter template repo from which to build the project.",
),
starter: Starter = typer.Option(
Starter.none,
"--starter",
"-s",
help="Use a language-specific starter template",
case_sensitive=False,
show_default=True,
),
venv: VirtualEnv = typer.Option(
VirtualEnv.none,
"--venv",
"-v",
help="Which type of virtual environment to create for the project.",
case_sensitive=False,
show_default=True,
),
no_git: bool = typer.Option(
False,
"--no-git",
"-n",
help="Don't initialise an empty git repo in the root of the project.",
),
) -> None:
"""
Create a new development project.
Bare usage will simply create an empty folder in your configured projects
directory.
You can also create a project from a cookiecutter template by passing a valid
url to the '--cookie/-c' flag.
If you just want a very simple, language-specific starting template, use the
'--starter/-s' option.
By default, pytoil will initialise an empty git repo in the folder, following
the style of modern language build tools such as rust's cargo. You can disable
this behaviour by setting 'init_on_new' to false in pytoil's config file
or by passing the '--no-git/-n' flag here.
If you want pytoil to create a new virtual environment for your project, you
can use the '--venv/-v' flag. Standard python and conda virtual environments
are supported.
If the '--venv/-v' flag is used, you may also pass a list of python packages
to install into the created virtual environment. These will be delegated to
the appropriate tool (pip or conda) depending on what environment was created.
If the environment is conda, the packages will be passed at environment creation
time meaning they will have their dependencies resolved together. Normal python
environments will first be created and then have specified packages installed.
If 'common_packages' is specified in pytoil's config file, these will automatically
be included in the environment.
To specify versions of packages via the command line, you must enclose them
in double quotes e.g. "flask>=1.0.0" not flask>=1.0.0 otherwise this will
be interpreted by the shell as a command redirection.
Examples:
$ pytoil new my_project
$ pytoil new my_project --cookie https://github.com/some/cookie.git
$ pytoil new my_project --venv conda
$ pytoil new my_project -c https://github.com/some/cookie.git -v conda --no-git
$ pytoil new my_project -v venv requests "flask>=1.0.0"
$ pytoil new my_project --starter python
"""
# Get config and ensure user can access API
config = Config.from_file()
utils.warn_if_no_api_creds(config)
# Setup the objects required
api = API(username=config.username, token=config.token)
repo = Repo(
owner=config.username,
name=project,
local_path=config.projects_dir.joinpath(project),
)
code = VSCode(root=repo.local_path)
git = Git()
if ctx.args:
packages: List[str] = config.common_packages + ctx.args
else:
packages = config.common_packages
# Resolve config vs flag for no-git
# flag takes priority over config
use_git: bool = config.init_on_new and not no_git
# Check is project already exists and warn/exit if so
pre_new_checks(repo=repo, api=api)
# Cant use --venv with non-python starters
if (
starter.value != starter.none # User specified starter
and starter.value != starter.python # The starter is not python
and venv.value != venv.none # And the user wants a virtual environment
):
msg.warn(
f"Can't create a venv for {starter.value} project!",
spaced=True,
exits=1,
)
# If we get here, all is well and we can create stuff!
make_new_project(
repo=repo,
git=git,
cookie=cookie,
starter=starter,
use_git=use_git,
config=config,
)
if venv.value == venv.venv:
env = create_virtualenv(repo=repo, packages=packages)
if config.vscode:
msg.info(f"Opening {repo.name!r} in VSCode.", spaced=True)
code.set_workspace_python(env.executable)
code.open()
elif venv.value == venv.conda:
env = create_condaenv(repo=repo, packages=packages)
if config.vscode:
msg.info(f"Opening {repo.name!r} in VSCode.", spaced=True)
code.set_workspace_python(env.executable)
code.open()
else:
# Only other allowed condition is none
typer.secho(
"Virtual environment not requested. Skipping environment creation.",
fg=typer.colors.YELLOW,
)
if config.vscode:
msg.info(f"Opening {repo.name!r} in VSCode.", spaced=True)
code.open()
def make_new_project(
repo: Repo, git: Git, cookie: str, starter: Starter, use_git: bool, config: Config
) -> None:
"""
Create a new development project either from a cookiecutter
template or from scratch.
"""
# Can't use starter and cookiecutter at the same time
if starter.value != Starter.none and cookie:
msg.warn(
"'--cookie' and '--starter' are mutually exclusive.",
exits=1,
)
if cookie:
# We don't initialise a git repo for cookiecutters
# some templates have hooks which do this, mine do!
msg.info(f"Creating {repo.name!r} from cookiecutter: {cookie!r}.")
cookiecutter(template=cookie, output_dir=config.projects_dir)
elif starter == Starter.go:
msg.info(f"Creating {repo.name!r} from starter: {starter.value!r}.")
go_st = GoStarter(path=config.projects_dir, name=repo.name)
try:
go_st.generate(username=config.username)
except GoNotInstalledError:
msg.fail("Error: Go not installed.", spaced=True, exits=1)
if use_git:
git.init(path=repo.local_path, check=True)
elif starter == Starter.python:
msg.info(f"Creating {repo.name!r} from starter: {starter.value!r}.")
py_st = PythonStarter(path=config.projects_dir, name=repo.name)
py_st.generate()
if use_git:
git.init(path=repo.local_path, check=True)
elif starter == Starter.rust:
msg.info(f"Creating {repo.name!r} from starter: {starter.value!r}.")
rs_st = RustStarter(path=config.projects_dir, name=repo.name)
try:
rs_st.generate()
except CargoNotInstalledError:
msg.fail("Error: Cargo not installed.", spaced=True, exits=1)
else:
msg.info(f"Creating {repo.name!r} at {repo.local_path}.")
# Make an empty dir and git repo
repo.local_path.mkdir(parents=True)
if use_git:
git.init(path=repo.local_path, check=True)
def pre_new_checks(repo: Repo, api: API) -> None:
"""
Checks whether the repo already exists either locally
or remotely, prints helpful warning messages and exits
the program if True.
"""
is_local = repo.exists_local()
try:
is_remote = repo.exists_remote(api=api)
except httpx.HTTPStatusError as err:
utils.handle_http_status_errors(error=err)
else:
if is_local:
msg.warn(
title=f"{repo.name} already exists locally!",
text=f"To checkout this project, use 'pytoil checkout {repo.name}'.",
spaced=True,
exits=1,
)
elif is_remote:
msg.warn(
title=f"{repo.name!r} already exists on GitHub!",
text=f"To checkout this project, use 'pytoil checkout {repo.name}'.",
spaced=True,
exits=1,
)
def create_virtualenv(repo: Repo, packages: List[str]) -> Environment:
"""
Creates and returns new virtual environment with packages and reports
to user.
"""
msg.info(
f"Creating virtual environment for {repo.name!r}",
text=f"Including packages: {', '.join(packages)}",
spaced=True,
)
env = Venv(project_path=repo.local_path)
with msg.loading("Working..."):
env.create(packages=packages)
return env
def create_condaenv(repo: Repo, packages: List[str]) -> Environment:
"""
Creates and returns new conda environment with packages and reports
to user.
"""
msg.info(
f"Creating conda environment for {repo.name!r}",
text=f"Including packages: {', '.join(packages)}",
spaced=True,
)
env = Conda(name=repo.name, project_path=repo.local_path)
try:
with msg.loading("Working..."):
env.create(packages=packages)
except EnvironmentAlreadyExistsError:
msg.warn(
f"Conda environment {env.name!r} already exists!", spaced=True, exits=1
)
return env
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] | 2.465521 | 4,075 |
# -*- coding: utf-8 -*-
import os
import re
import sys
from werkzeug import utils
try:
from os import scandir
except:
from scandir import scandir
def parents_path(path):
"""
:param path:
:return: return parents_path which does not contain the end '/', that is remove the end '/'
"""
res = set()
path = normalize_path(path)
while '/' in path:
sep_ind = path.rindex('/')
res.add(path[:sep_ind])
path = path[:sep_ind]
if path.startswith('./') or not path.startswith('.'):
res.add('.')
return res
def parent_path(path):
"""
:param path:
:return: return path which does not contain the end '/'
"""
path = normalize_path(path)
if '/' not in path:
return '.'
else:
sep_ind = path.rindex('/')
return path[:sep_ind]
_filename_ascii_strip_re = re.compile(r"[^A-Za-z0-9_.-]")
_filename_ascii_add_strip_re = re.compile(r'[^A-Za-z0-9_\u4E00-\u9FBF\u3040-\u30FF\u31F0-\u31FF.-]')
_windows_device_files = (
"CON",
"AUX",
"COM1",
"COM2",
"COM3",
"COM4",
"LPT1",
"LPT2",
"LPT3",
"PRN",
"NUL",
)
PY2 = sys.version_info[0] == 2
if PY2:
text_type = unicode
else:
text_type = str
def secure_filename(filename):
r"""Pass it a filename and it will return a secure version of it. This
filename can then safely be stored on a regular file system and passed
to :func:`os.path.join`.
On windows systems the function also makes sure that the file is not
named after one of the special device files.
>>> secure_filename("My cool movie.mov")
'My_cool_movie.mov'
>>> secure_filename("../../../etc/passwd")
'etc_passwd'
>>> secure_filename(u'i contain cool \xfcml\xe4uts.txt')
'i_contain_cool_umlauts.txt'
The function might return an empty filename. It's your responsibility
to ensure that the filename is unique and that you abort or
generate a random filename if the function returned an empty one.
.. versionadded:: 0.5
:param filename: the filename to secure
"""
if isinstance(filename, text_type):
from unicodedata import normalize
filename = normalize("NFKD", filename).encode("utf-8", "ignore")
if not PY2:
filename = filename.decode("utf-8")
for sep in os.path.sep, os.path.altsep:
if sep:
filename = filename.replace(sep, " ")
filename = to_unicode_str(_filename_ascii_add_strip_re.sub('', '_'.join(
filename.split()))).strip('._')
# filename = str(_filename_ascii_strip_re.sub("", "_".join(filename.split()))).strip(
# "._"
# )
# on nt a couple of special files are present in each folder. We
# have to ensure that the target file is not such a filename. In
# this case we prepend an underline
if (
os.name == "nt"
and filename
and filename.split(".")[0].upper() in _windows_device_files
):
filename = "_" + filename
return filename
if __name__ == '__main__':
print(os.getcwd())
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] | 2.358663 | 1,316 |
# Copyright (c) 2012 Lars Hupfeldt Nielsen, Hupfeldt IT
# All rights reserved. This work is under a BSD license, see LICENSE.TXT.
import sys
import os.path
from os.path import join as jp
here = os.path.dirname(__file__)
sys.path.append(jp(here, '../..'))
from multiconf import ConfigRoot, ConfigItem, ConfigBuilder
from multiconf.decorators import nested_repeatables, repeat, required
# Here we define what can be repeated within the configuration item. In this case
# we will have many managed servers and datasources
@nested_repeatables('managed_servers, datasources')
class weblogic_config(ConfigRoot):
''' This is just a simple holder of managed_servers and datasources '''
# Weblogic's standalone administration server. Used to control domain.
# Here specify that a managed_server can be repeated within it's parent (domain)
@repeat()
# Here we specify that a parameter num_servers is required when defining a
# builder for managed_server
@required('num_servers')
class managed_servers(ConfigBuilder):
''' Builder for managed_server objects. Used in environment configuration to
automatically create proper number of managed_server objects '''
@repeat()
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] | 3.662539 | 323 |
expected = [
{
"id": "con1",
"text": "<p>RAA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article</p>",
"fn-type": "con",
},
{
"id": "con2",
"text": "<p>LWB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article</p>",
"fn-type": "con",
},
{
"id": "con3",
"text": "<p>CS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article</p>",
"fn-type": "con",
},
{
"id": "con4",
"text": "<p>RKD, Acquisition of data, Analysis and interpretation of data</p>",
"fn-type": "con",
},
{
"id": "con5",
"text": "<p>RZ, Acquisition of data, Analysis and interpretation of data</p>",
"fn-type": "con",
},
{
"id": "con6",
"text": "<p>SRF, Acquisition of data, Analysis and interpretation of data</p>",
"fn-type": "con",
},
{
"id": "con7",
"text": "<p>JC, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p>",
"fn-type": "con",
},
{
"id": "con8",
"text": "<p>NK, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p>",
"fn-type": "con",
},
{
"id": "con9",
"text": "<p>NISC Comparative Sequencing Program: JM did X, IM did Y and JB did Z and Y</p>",
"fn-type": "con",
},
]
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] | 2.248227 | 705 |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 25 13:08:45 2019
@author: briere
"""
import logging
import argparse
import os
from neo4j import GraphDatabase
descr = ('This script instanciate a Neo4j graph database from the metadata '
'file and the various clustering results to integrate.')
parser = argparse.ArgumentParser(description=descr)
parser.add_argument("-id", "--neo_id", help="Neo4j ID used to connect to the database")
parser.add_argument("-pwd", "--neo_pwd", help="Neo4j password used to connect to the database")
parser.add_argument("-host", "--neo_localhost", help="Neo4j graph localhost")
parser.add_argument("-cls", "--clustering_folder", help="Path to directory containing clustering "
"results files and metadata file \n Please note that cluster files must be "
"names following the pattern : {SUBJECT}_{DATATYPE}_{METHOD}.clst \n"
"The metadata file must be names following the pattern : "
"{SUBJECT}_metadata.txt \n Please, read the docs to make sure your "
"files respect the mandatory format.")
parser.add_argument("-subj", "--subject", help="Analysis subject")
parser.add_argument("-out", "--log_out", help="Log file")
args = parser.parse_args()
neo_id = args.neo_id
neo_pwd = args.neo_pwd
neo_localhost = args.neo_localhost
results_path = args.clustering_folder
subject = args.subject
log_file = args.log_out
logging.basicConfig(filename=log_file, level=logging.INFO, format='%(message)s')
logging.info('Loading results from %s in %s \n' % (subject, results_path))
def make_graph(results_path, subject, driver):
'''
Parameters
----------
results_path : STRING
Path to folder containing raw clustering results.
subject : STRING
Subject of analysis (e.g: AML to run the analysis form AML cancer).
Returns
-------
Log file. Instanciates the graph database with data from metadata file
and clustering results files.
'''
files = os.listdir(os.path.join(results_path, subject))
metadata_file = [file for file in files if "metadata" in file][0]
logging.info('Initiating graph from %s metadata.', metadata_file)
create_object_nodes(os.path.join(results_path, subject, metadata_file), subject, driver)
clustering_files = [file[0:-5] for file in files if file[-5:len(file)] == ".clst"]
for file in clustering_files:
datatype = file.split('_')[1]
method = file.split('_')[2]
clust_file = str(file) + '.clst'
logging.info('Loading %s clustering results.',
os.path.join(results_path, subject, clust_file))
logging.info('- computed on datatype : %s', datatype)
logging.info('- computed with method : %s', method)
create_cluster_nodes(os.path.join(results_path, subject, clust_file),
subject, datatype, method, driver)
def create_object_nodes(metadata_file, subject, driver):
'''
Parameters
----------
metadata_file : STRING
Path to metadata file. Must be named as follow:
{SUBJECT}_metadata.txt
subject : STRING
Subject of analysis (e.g: AML to run the analysis form AML cancer).
Returns
-------
Instanciate graph with data from metadata file.
metadata_file : rows = "obects" nodes ids
cols = metadata nodes ids (with header as node names)
On first line, for each column, specify how it should be represented in the graph:
- "main_nodes" for the objects to cluster (e.g Patients TCGA id)
- as a node linked to main_nodes: "node"
- as a label of main_nodes: "label"
- as a property of main nodes : "prop"
'''
print("Instanciating graph for " + subject + "from metadata file : " + metadata_file)
print("Check the graph in your Neo4j browser by querying : \n" +
"MATCH (o:" + subject + ") RETURN o LIMIT 100")
graph = driver.session()
with open(metadata_file) as file:
line = file.readline()
line = line.strip()
col_types = line.split('\t')
for col_type in col_types[1:len(col_types)]:
if col_type not in ('label', 'node', 'property'):
logging.error('Error : Wrong column type. Must be "label",'
'"node" or "property". Please, modify the metadata file. \n')
line = file.readline()
line = line.strip()
col_names = line.split('\t')
main_node = col_names[0]
line = file.readline()
while line:
line = line.strip()
data = line.split('\t')
# Create object node
node_id = data[0]
node = "(o:" + subject + ":" + main_node + "{id:'" + node_id + "'})"
graph.run("MERGE" + node)
# Get metadata
for i in range(1, len(data)):
if col_types[i] == "property" and data[i] != '':
graph.run("MATCH" + node + "SET o." + col_names[i] + "='" + data[i] +"'")
elif col_types[i] == "label" and data[i] != '':
graph.run("MATCH" + node + "SET o:" + data[i])
elif col_types[i] == "node" and data[i] != '':
meta_node = "(m:" + col_names[i] + "{id:'" + data[i] + "'})"
graph.run("MERGE" + meta_node)
meta_rel = "[r:" + col_names[i] + "]"
graph.run("MATCH" + node + " MATCH" + meta_node +\
" CREATE (o)-" + meta_rel + "->(m)")
elif col_types[i] not in ["property", "label", "node"]:
logging.info('"%s" is not a valid keyword.' % (col_types[i]))
logging.info('Please use "property", "label" or "node". Skipping.')
next
line = file.readline()
graph.close()
def create_cluster_nodes(clust_file, subject, datatype, method, driver):
'''
Parameters
----------
clust_file : STRING
Path to a raw clustering result file. This file must be named as follow:
{SUBJECT}_{DATATYPE}_{METHOD}.clst
subject : STRING
Subject of analysis (e.g: AML to run the analysis form AML cancer).
datatype : STRING
Datatype used to compute this clustering.
method : STRING
Method used to compute this clustering.
Returns
-------
Compute edges between main_nodes and their respective cluster nodes.
'''
graph = driver.session()
with open(clust_file) as file:
line = file.readline()
line = line.strip()
col_names = line.split('\t')
main_node = col_names[0]
clust_node = col_names[1]
clust_labels = ":".join([clust_node, subject, datatype, method])
id_base = "_".join([subject, datatype, method])
print("Loading input clustering results for " + subject + "from file: " + clust_file)
print("Check the graph in your Neo4j browser by querying : \n" +
"MATCH (o:" + main_node + ":" + subject + ")-[r:PART_OF]-(c:"+\
clust_labels + ") RETURN * LIMIT 100")
line = file.readline()
while line:
node_id = line.split('\t')[0]
clust_nb = str(line.split('\t')[1].strip())
clust_id = id_base + "_" + str(clust_nb)
node = "(o:" + subject + ":" + main_node + "{id:'" + node_id + "'})"
clust = "(c:" + clust_labels + "{id:'" + clust_id + "'})"
graph.run("MERGE" + clust)
graph.run("MATCH" + clust + " MATCH" + node +\
" CREATE (o)-[:PART_OF]->(c)")
line = file.readline()
graph.close()
def main():
'''
Parameters
----------
neo_id : STRING
Neo4j database connection ID.
neo_pwd : STRING
Password to connect to Neo4j database.
neo_localhost : STRING
Database localhost adress (e.g: http://localhost:7474).
Returns
-------
Instanciate graph from metadata file and raw clustering files.
'''
driver = GraphDatabase.driver(uri=neo_localhost, auth=(neo_id, neo_pwd))
make_graph(results_path, subject, driver)
driver.close()
if __name__ == "__main__":
main()
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] | 2.304168 | 3,623 |
import math
from helper import Point
| [
11748,
10688,
198,
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1330,
6252,
628
] | 4.75 | 8 |
from skimage import io
import matplotlib.pyplot as plt
import sys
image = io.imread(sys.argv[1])
_ = plt.hist(image.ravel(), bins = 256, color = 'orange', )
_ = plt.hist(image[:, :, 0].ravel(), bins = 256, color = 'red', alpha = 0.5)
_ = plt.hist(image[:, :, 1].ravel(), bins = 256, color = 'Green', alpha = 0.5)
_ = plt.hist(image[:, :, 2].ravel(), bins = 256, color = 'Blue', alpha = 0.5)
_ = plt.xlabel('Intensity Value')
_ = plt.ylabel('Count')
_ = plt.legend(['Total', 'Red_Channel', 'Green_Channel', 'Blue_Channel'])
plt.show() | [
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] | 2.530806 | 211 |
# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RJpeg(RPackage):
"""Read and write JPEG images.
This package provides an easy and simple way to read, write and display
bitmap images stored in the JPEG format. It can read and write both files
and in-memory raw vectors."""
cran = "jpeg"
version('0.1-9', sha256='01a175442ec209b838a56a66a3908193aca6f040d537da7838d9368e46913072')
version('0.1-8.1', sha256='1db0a4976fd9b2ae27a37d3e856cca35bc2909323c7a40724846a5d3c18915a9')
version('0.1-8', sha256='d032befeb3a414cefdbf70ba29a6c01541c54387cc0a1a98a4022d86cbe60a16')
depends_on('[email protected]:', type=('build', 'run'))
depends_on('jpeg')
| [
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] | 2.459538 | 346 |
# coding:utf-8
# Produced by Jiaxin Zhang
# Start Data: 20_May_2020
# Data processing module.
#
# For GOD I Trust.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import copy
import random
import codecs
import pickle
import functools
import tensorflow as tf
tf.enable_eager_execution()
import config
load_dict()
# function for converting string to idx
convert_vocab_idx = lambda string : [vocab_idx[v] if v in vocab_idx else vocab_idx['<unk>'] \
for v in string.split(' ')]
# padding
padding_func = lambda data, max_length : data + [vocab_idx['<padding>'] \
for _ in range(max_length - len(data))]
padding_func_with_args = functools.partial(padding_func, max_length=config.eann_config.max_length)
@input_fn
@input_fn
if __name__ == '__main__':
for datas in train_input_fn(config.TRAIN_DATA_PATH, 10):
print(datas[1]['label'])
print(datas[1]['event'])
input() | [
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6,
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7,
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292,
58,
16,
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6,
15596,
6,
12962,
198,
220,
220,
220,
5128,
3419
] | 2.850277 | 541 |
#!/usr/bin/env python3
import os
import re
import sys
import pandas as pd
import pyarrow as pa
from pyarrow import csv, feather
in_file = sys.argv[1]
in_df = csv.read_csv(in_file).to_pandas()
if 'datetime' in in_df.columns.values.tolist():
in_df['datetime'] = pd.to_datetime(in_df.datetime, utc=True)
#out_file = re.sub(r'\.csv$', '.feather', os.path.basename(in_file))
out_file = re.sub(r'\.csv$', '.arrow', os.path.basename(in_file))
with open(out_file, 'bw') as out_f:
#feather.write_feather(pa.Table.from_pandas(csv.read_csv(in_file).to_pandas()), out_f, compression='zstd', compression_level=15)
feather.write_feather(pa.Table.from_pandas(csv.read_csv(in_file).to_pandas().astype({'viewerIndex': 'int32'}).astype({'size': 'int32'}).astype({'gridSize': 'int32'})), out_f, compression='uncompressed')
os.system("gzip {}".format(out_file))
print(out_file + '.gz')
| [
2,
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198,
4798,
7,
448,
62,
7753,
1343,
45302,
34586,
11537,
198
] | 2.369272 | 371 |
from setuptools import setup
setup(
name="Vnode",
version="0.1.0",
description="A fast wheel to build a node network.",
author="Tatersic&Ovizro",
author_email="[email protected]",
maintainer="Ovizro",
maintainer_email="[email protected]",
license="MIT",
url="https://github.com/Tatersic/Vnode",
download_url="https://github.com/Tatersic/Vnode/archive/refs/heads/master.zip",
packages=["vnode"],
requires=["aiohttp"],
python_requires=">=3.8",
exclude_package_data={
'':['test.*']
}
) | [
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62,
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34758,
198,
220,
220,
220,
220,
220,
220,
220,
10148,
25,
17816,
9288,
15885,
20520,
198,
220,
220,
220,
1782,
198,
8
] | 2.377682 | 233 |
import os
import re
import time
import struct
import serial
import threading
import crcmod
COMMAND_GET = struct.pack('B', 0xf0)
COMMAND_SET = struct.pack('B', 0xf1)
DS18B20 = struct.pack('B', 0xf1)
HEATER = struct.pack('B', 0xf2)
STIRRER = struct.pack('B', 0xf3)
# Contrary to crcmod's definition, brewslave expects a bit-reversed CRC
crc8 = crcmod.mkCrcFun(0x131, 0, False, 0)
| [
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7,
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87,
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11,
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11,
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8,
628
] | 2.543046 | 151 |
import glob, os, sys, json, datetime
import pandas as pd
import numpy as np
sys.path.insert(0, os.path.dirname(__file__) + '../2_helpers')
import re, nltk
from dateutil import parser
from Fact import Fact
from User import User
from Transaction import Transaction
from decoder import decoder
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn import metrics
from sklearn.linear_model import Perceptron
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import RidgeClassifier
from matplotlib import pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import Imputer
import warnings
import scipy.stats
from collections import Counter
warnings.filterwarnings("ignore", category=DeprecationWarning)
SERVER_RUN = True
DIR = os.path.dirname(__file__) + '../../3_Data/'
if __name__ == "__main__":
main()
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6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 3.375367 | 341 |
from pibootcmdline.parse import parse_parameter, Parameter
def add_parameters(cmdline, *parameters):
"""
Parameters
----------
cmdline: list[Parameter]
parsed command line to which to add parameters.
*parameters: str
list of string parameters. For example: 'key=value' or 'key' or 'key=value1,value2'.
Returns
-------
list[Parameter]
the cmdline now also containing the added parameters.
"""
return cmdline + [parse_parameter(parameter) for parameter in parameters]
def set_parameters(cmdline, *parameters):
"""
Parameters
----------
cmdline: list[Parameter]
parsed command line to which to add parameters.
*parameters: str
list of string parameters. For example: 'key=value' or 'key' or 'key=value1,value2'.
Returns
-------
list[Parameter]
the cmdline with changed or added parameters.
"""
new_params = [parse_parameter(param) for param in parameters]
new_cmdline = [_set_value(to_param, new_params) for to_param in cmdline]
for new_param in new_params:
if _index_of_key(new_cmdline, new_param.key) is None:
new_cmdline.append(new_param)
return new_cmdline
def add_to_value(cmdline, *parameters):
"""
This function will append comma-separated values to already existing values of a particular parameter.
For example, if the command line already contains the parameter ``modules-load=dwc2`` and you call `
`add-list-value modules-load=g_ether`` then the resulting command line will contain ``modules-load=dwc2,g_ether``.
Parameters
----------
cmdline: list[Parameter]
parsed command line to which to add parameters.
parameters: str
list of string parameters. For example: 'key=value' or 'key' or 'key=value1,value2'.
Returns
-------
list[Parameter]
the cmdline now also containing the added parameters.
"""
from_params = [parse_parameter(param) for param in parameters]
new_cmdline = [_add_to_value(to_param, from_params) for to_param in cmdline]
for from_param in from_params:
if _index_of_key(new_cmdline, from_param.key) is None:
new_cmdline.append(from_param)
return new_cmdline
| [
6738,
279,
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8,
198,
220,
220,
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1441,
649,
62,
28758,
1370,
628,
628
] | 2.774786 | 817 |
from enum import IntEnum | [
6738,
33829,
1330,
2558,
4834,
388
] | 4 | 6 |
from django.db import models
from django.db.models import permalink
from django.contrib.auth.models import User
from django.contrib.contenttypes.models import ContentType
from datetime import datetime
from tagging.models import Tag
from tagging.fields import TagField
from autofields.fields import AutoSlugField
from tumblog.managers import PublishedPostManager
from tumblog.models import Blog
class Post(models.Model):
""" Tumblog Post Model """
title = models.CharField(max_length = 200)
author = models.ForeignKey(User, blank=True, null=True)
slug = AutoSlugField(prepopulate_from = 'title', unique = True)
tags = TagField()
pubtime = models.DateTimeField(null = True, blank = True)
blog = models.ForeignKey(Blog)
modtime = models.DateTimeField(auto_now = True)
post_type = models.ForeignKey(ContentType)
published = PublishedPostManager()
objects = models.Manager()
@property
@property
def publish(self, publish_time = None):
"""
Publish is basically a wrapper for save, which adjusts the
recorded publish time on the post. If the publish_time parameter
is given, then the post will be "published" after that time. If
publish_time parameter is _not_ given, the publish_time is now,
and the item is immediately published.
"""
if publish_time is None:
publish_time = datetime.now()
self.pubtime = publish_time
self.save()
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2435,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
21928,
3419,
628
] | 2.674617 | 587 |
#!/usr/bin/env python
# encoding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import functools
import torch
import torch.nn as nn
| [
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from app import app
import requests, sys, eventlet, socket
from datetime import datetime, timedelta
from .db import Server, Record, transaction, db
from .notify import notify_state_changed
from blinker import Namespace
def handle_active_http(server):
"""
Handler for Active HTTP checking
"""
config = server.get_config()
timeout = config['timeout']
url = config['url']
valid_status = config['validStatus']
record = server.new_record()
try:
r = requests.get(url, timeout=float(timeout) / 1000)
if r.status_code in valid_status:
record.online = True
record.latency = int(r.elapsed / timedelta(milliseconds=1))
else:
record.message = f"Unsuccessful status code: {r.status_code}"
except requests.exceptions.Timeout as e:
record.message = "Timed out"
except requests.exceptions.ConnectionError as e:
record.message = "Connection error"
app.logger.debug("Check failed for [%s]: %s", server.label, e)
except:
e = sys.exc_info()[0]
record.message = str(e)
app.logger.debug("Check failed for [%s]: %s", server.label, e)
return record
def handle_active_tcp(server):
"""
Handler for Active TCP checking
"""
config = server.get_config()
timeout = config['timeout']
address = config['address']
port = config['port']
record = server.new_record()
try:
c = socket.socket()
ip = socket.gethostbyname(address)
c.settimeout(int(timeout / 1000))
start = datetime.now()
c.connect((ip, port))
end = datetime.now()
c.close()
record.online = True
record.latency = (end - start) / timedelta(milliseconds=1)
except Exception as e:
record.message = str(e)
return record
def handle_server(server):
"""
Common handler for any server
"""
config = server.get_config()
interval = config['interval']
last_record = server.last_record()
if last_record and not last_record.expired(interval):
return
log_start(server)
record = handlers[server.mode](server)
log_record(record)
with transaction():
db.session.add(record)
signal_new_record.send(record=record)
if last_record and record.online != last_record.online:
log_state_changed(server, record)
notify_state_changed(server, record)
handlers = {
'active-http': handle_active_http,
'passive-http': handle_passive_http,
'active-tcp': handle_active_tcp
}
signals = Namespace()
signal_new_record = signals.signal('new-record') | [
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#! /usr/bin/env python2.7
"""
This file contain Ur5Moveit class to control bot and rviz planning scene.
"""
import rospy
import moveit_commander
import moveit_msgs.msg
import geometry_msgs.msg
import actionlib
from pkg_vb_sim.srv import conveyorBeltPowerMsg
import math
import sys
import copy
from std_srvs.srv import Empty
import sys
from std_msgs.msg import String
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
from pyzbar.pyzbar import decode
def define_joint_angle_list(shoulder_pan_joint, shoulder_lift_joint, elbow_joint, wrist_1_joint, wrist_2_joint, wrist_3_joint):
"""
This function takes float values for each joint and returns a list.
:param shoulder_pan_joint: shoulder pan joint angle
:param shoulder_lift_joint: shoulder lift join angle
:param elbow_joint: elbow joint angle
:param wrist_1_joint: wrist 1 joint angle
:param wrist_2_joint: wrist 2 joint angle
:param wrist_3_joint: wrist 3 joint angle
:return: list of all joint angles
"""
joint_angles = [math.radians(shoulder_pan_joint),
math.radians(shoulder_lift_joint),
math.radians(elbow_joint),
math.radians(wrist_1_joint),
math.radians(wrist_2_joint),
math.radians(wrist_3_joint)]
return joint_angles
def define_pose(x, y, z):
"""
This function define geometry_msgs.msg.Pose() from giving coordinates.
:param x: x coordinate
:param y: y coordinate
:param z: z coordinate
:return: ur5_pose of type geometry_msgs.msg.Pose() containing x,y,z coordinate.
"""
ur5_pose = geometry_msgs.msg.Pose()
ur5_pose.position.x = x
ur5_pose.position.y = y
ur5_pose.position.z = z
ur5_pose.orientation.x = -0.5
ur5_pose.orientation.y = -0.5
ur5_pose.orientation.z = 0.5
ur5_pose.orientation.w = 0.5
return ur5_pose
def get_item_details(item):
"""
This function finds packgen details using item
:param item: item is a string containing packgen content type
:return: it returns dict of details of packgen.
"""
details = {'Estimated Time of Delivery':'Na','priority':'Na','cost':'Na','item':'Na'}
if item == u'Medicines' or item == u'Medicine':
details = {'priority':'HP','cost':'450','Estimated Time of Delivery':'1','item':'Medicines'}
elif item == u'Food':
details = {'priority':'MP','cost':'250','Estimated Time of Delivery':'3',"item":'Food'}
elif item == u'Clothes':
details = {'priority':'LP','cost':'150','Estimated Time of Delivery':'5','item':'Clothes'}
return details
class Ur5Moveit:
"""
This class enables bot and rviz connection, and setup the configuration of ur5 bot.
Select bot name, planning group ,etc.
"""
def __init__(self,robot_name):
"""
Constructor
:param robot_name: name of bot which you want to control.
"""
self.is_conver_active = False
self._box_name = 'box'
self.box_name = 'box'
self._robot_ns = '/'+robot_name
self._planning_group = "manipulator"
self._commander = moveit_commander.roscpp_initialize(sys.argv)
self._robot = moveit_commander.RobotCommander(robot_description= self._robot_ns + "/robot_description", ns=self._robot_ns)
self._scene = moveit_commander.PlanningSceneInterface(ns=self._robot_ns)
self._group = moveit_commander.MoveGroupCommander(self._planning_group, robot_description= self._robot_ns + "/robot_description", ns=self._robot_ns)
self._display_trajectory_publisher = rospy.Publisher( self._robot_ns + '/move_group/display_planned_path', moveit_msgs.msg.DisplayTrajectory, queue_size=1)
self._exectute_trajectory_client = actionlib.SimpleActionClient( self._robot_ns + '/execute_trajectory', moveit_msgs.msg.ExecuteTrajectoryAction)
self._exectute_trajectory_client.wait_for_server()
self._planning_frame = self._group.get_planning_frame()
self._eef_link = self._group.get_end_effector_link()
self._group_names = self._robot.get_group_names()
self._computed_plan = ''
# Current State of the Robot is needed to add box to planning scene
self._curr_state = self._robot.get_current_state()
rospy.loginfo('\033[94m' + "Planning Group: {}".format(self._planning_frame) + '\033[0m')
rospy.loginfo('\033[94m' + "End Effector Link: {}".format(self._eef_link) + '\033[0m')
rospy.loginfo('\033[94m' + "Group Names: {}".format(self._group_names) + '\033[0m')
rospy.loginfo('\033[94m' + " >>> ur5_moveit init done." + '\033[0m')
@property
def ee_cartesian_translation(self, trans_x, trans_y, trans_z):
"""
This function helps in translating bot in x,y,z direction using cartesian coordinate system.
:param trans_x: Displacement in x direction.
:param trans_y: Displacement in y direction.
:param trans_z: Displacement in z direction.
"""
# 1. Create a empty list to hold waypoints
waypoints = [self._group.get_current_pose().pose]
# 2. Add Current Pose to the list of waypoints
# 3. Create a New waypoint
wpose = geometry_msgs.msg.Pose()
wpose.position.x = waypoints[0].position.x + trans_x
wpose.position.y = waypoints[0].position.y + trans_y
wpose.position.z = waypoints[0].position.z + trans_z
# This to keep EE parallel to Ground Plane
wpose.orientation.x = -0.5
wpose.orientation.y = -0.5
wpose.orientation.z = 0.5
wpose.orientation.w = 0.5
# 4. Add the new waypoint to the list of waypoints
waypoints.append(copy.deepcopy(wpose))
# 5. Compute Cartesian Path connecting the waypoints in the list of waypoints
(plan, fraction) = self._group.compute_cartesian_path(
waypoints, # waypoints to follow
0.01, # Step Size, distance between two adjacent computed waypoints will be 1 cm
0.0) # Jump Threshold
rospy.loginfo("Path computed successfully. Moving the arm.")
# The reason for deleting the first two waypoints from the computed Cartesian Path can be found here,
# https://answers.ros.org/question/253004/moveit-problem-error-trajectory-message-contains-waypoints-that-are-not-strictly-increasing-in-time/?answer=257488#post-id-257488
num_pts = len(plan.joint_trajectory.points)
if num_pts >= 3:
del plan.joint_trajectory.points[0]
del plan.joint_trajectory.points[1]
# 6. Make the arm follow the Computed Cartesian Path
self._group.execute(plan)
def wait_for_state_update(self, box_is_known=False, box_is_attached=False,box_name='box', timeout=4):
"""
Check status of box in rviz
:param timeout: time period
:param box_is_known: State of box
:param box_is_attached: State of box
:param box_name: Name of box in rviz
:return: boolean
"""
box_name = box_name or self.box_name
scene = self._scene
start = rospy.get_time()
seconds = rospy.get_time()
while (seconds - start < timeout) and not rospy.is_shutdown():
# Test if the box is in attached objects
attached_objects = scene.get_attached_objects([box_name])
is_attached = len(attached_objects.keys()) > 0
# Test if the box is in the scene.
# Note that attaching the box will remove it from known_objects
is_known = box_name in scene.get_known_object_names()
# Test if we are in the expected state
if (box_is_attached == is_attached) and (box_is_known == is_known):
print("box is attached")
return True
# Sleep so that we give other threads time on the processor
rospy.sleep(0.1)
# If we exited the while loop without returning then we timed out
return False
## END_SUB_TUTORIAL
def add_box(self,x,y,z,b_name):
"""
This function adds a box in rviz planning scene.
:param x: x coordinate of box
:param y: y coordinate of box
:param z: z coordinate of box
:param b_name: name to be given to the box in planning scene
:return: boolean
"""
box_name = b_name or self.box_name
scene = self._scene
box_pose = geometry_msgs.msg.PoseStamped()
box_pose.header.frame_id = "world"
box_pose.pose.orientation.w = 0.0
box_pose.pose.position.x = x #0.02
box_pose.pose.position.y = y # 0.45
box_pose.pose.position.z = z
scene.add_box(box_name, box_pose, size=(0.15, 0.15, 0.15))
self.box_name=box_name
return self.wait_for_state_update(box_is_known=True, box_name=box_name,timeout=4)
def attach_box(self,b_name, timeout=4):
"""
This function attach to box with vacuum gripper in rviz planning scene to avoid collision
:param b_name:box name which needs to be attached.
:param timeout: time period
:return: boolean
"""
box_name = b_name or self.box_name
robot = self._robot
scene = self._scene
eef_link = self._eef_link
grasping_group = self._planning_group
touch_links = robot.get_link_names(group=grasping_group)
scene.attach_box(eef_link, box_name, touch_links=touch_links)
return self.wait_for_state_update(box_is_attached=True, box_is_known=False,box_name=box_name, timeout=timeout)
def detach_box(self,b_name, timeout=4):
"""
This function detach to box from vacuum gripper in rviz planning scene to avoid collision
:param b_name:box name which needs to be detached.
:param timeout: time period
:return: boolean
"""
box_name = b_name or self.box_name
scene = self._scene
eef_link = self._eef_link
scene.remove_attached_object(eef_link, name=box_name)
# We wait for the planning scene to update.
return self.wait_for_state_update(box_is_known=True, box_is_attached=False,box_name=box_name, timeout=timeout)
def remove_box(self,b_name, timeout=4):
"""
This function removes box form rviz planning scene.
:param timeout: time period
:param b_name: name of box which need to be removed from planning scene
:return: boolean
"""
box_name = b_name or self.box_name
scene = self._scene
scene.remove_world_object(box_name)
return self.wait_for_state_update(box_is_attached=False, box_is_known=False,box_name=box_name, timeout=timeout)
def go_to_pose(self, arg_pose):
"""
This function plans path to a pose and move ur5 to that pose.
:param arg_pose: pose of desired location of type geometry_msgs.msg.Pose()
:return: boolean
"""
pose_values = self._group.get_current_pose().pose
rospy.loginfo('\033[94m' + ">>> Current Pose:" + '\033[0m')
rospy.loginfo(pose_values)
self._group.set_pose_target(arg_pose)
flag_plan = self._group.go(wait=True) # wait=False for Async Move
pose_values = self._group.get_current_pose().pose
rospy.loginfo('\033[94m' + ">>> Final Pose:" + '\033[0m')
rospy.loginfo(pose_values)
list_joint_values = self._group.get_current_joint_values()
rospy.loginfo('\033[94m' + ">>> Final Joint Values:" + '\033[0m')
rospy.loginfo(list_joint_values)
if flag_plan:
rospy.loginfo('\033[94m' + ">>> go_to_pose_angle() Success" + '\033[0m')
else:
rospy.logerr(
'\033[94m' + ">>> go_to_pose() Failed. Solution for Pose not Found." + '\033[0m')
return flag_plan
def hard_go_to_pose(self, arg_pose, arg_max_attempts):
"""
This function calls go_to_pose() function multiple time until it succeed or reaches maximum attempt limit.
:param arg_pose: pose of desired location of type geometry_msgs.msg.Pose()
:param arg_max_attempts: maximum attempts for calling go_to_pose()
:return: boolean
"""
number_attempts = 0
flag_success = False
while (number_attempts <= arg_max_attempts) and (flag_success is False):
number_attempts += 1
flag_success = self.go_to_pose(arg_pose)
rospy.logwarn("attempts: {}".format(number_attempts) )
return flag_success
def set_joint_angles(self, arg_list_joint_angles):
"""
This function sets angle of each joint of ur5 to reach a specific position.
:param arg_list_joint_angles: list of joints
:return: boolean
"""
self._group.set_joint_value_target(arg_list_joint_angles)
self._computed_plan = self._group.plan()
flag_plan = self._group.go(wait=True)
if flag_plan:
rospy.loginfo(
'\033[94m' + ">>> set_joint_angles() Success" + '\033[0m')
else:
rospy.logerr(
'\033[94m' + ">>> set_joint_angles() Failed." + '\033[0m')
return flag_plan
def hard_set_joint_angles(self, arg_list_joint_angles, arg_max_attempts):
"""
This function calls set_joint_angles() multiple times until it succeed or reaches maximum attempt limit
:param arg_list_joint_angles: list of all joint angles
:param arg_max_attempts: maximum attempt
:return: boolean
"""
number_attempts = 0
flag_success = False
while (number_attempts <= arg_max_attempts) and (flag_success is False):
number_attempts += 1
flag_success = self.set_joint_angles(arg_list_joint_angles)
rospy.logwarn("attempts: {}".format(number_attempts) )
# self.clear_octomap()
return flag_success
def trigger_converbelt(self,value):
"""
this function sets belt speed using ros service
:param value: value of speed.
:return: null
"""
x = rospy.ServiceProxy('eyrc/vb/conveyor/set_power', conveyorBeltPowerMsg)
y = x(value)
print(y)
self.is_conver_active = value
def __del__(self):
"""
Destructor
:return: null
"""
moveit_commander.roscpp_shutdown()
rospy.loginfo(
'\033[94m' + "Object of class ur5_moveit Deleted." + '\033[0m')
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] | 2.290418 | 6,439 |
"""
Implementation of the Nelson rules
https://en.wikipedia.org/wiki/Nelson_rules
"""
from collections import deque
import numpy as np
from ..diligent import registry
from ..utils import is_numeric
__all__ = ['nelson_rule_%d' % i for i in range(1, 9)]
@registry.register(name='Nelson Rule 1', tags='nelson')
@registry.register(name='Nelson Rule 2', tags='nelson')
@registry.register(name='Nelson Rule 3', tags='nelson')
@registry.register(name='Nelson Rule 4', tags='nelson')
@registry.register(name='Nelson Rule 5', tags='nelson')
@registry.register(name='Nelson Rule 6', tags='nelson')
@registry.register(name='Nelson Rule 7', tags='nelson')
@registry.register(name='Nelson Rule 8', tags='nelson')
| [
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45,
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14330,
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] | 2.934959 | 246 |
# uncompyle6 version 3.2.4
# Python bytecode 2.7 (62211)
# Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)]
# Embedded file name: lib.coginvasion.holiday.HolidayManager
from panda3d.core import VirtualFileSystem, Filename
from direct.distributed.DistributedObjectGlobal import DistributedObjectGlobal
from direct.directnotify.DirectNotifyGlobal import directNotify
| [
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] | 2.978723 | 141 |
import unittest
import numpy as np
import Orange
from disco.core import result_iterator
import datasets
if __name__ == '__main__':
unittest.main()
| [
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# Copyright 2021 Google LLC
#
# 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 in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
import pdb
from smore.models.kg_reasoning import KGReasoning
from smore.common.modules import Identity
from smore.common.embedding.sparse_embed import SparseEmbedding
from smore.common.torchext.ext_ops import box_dist_in, box_dist_out
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] | 3.632867 | 286 |
import tensorflow as tf
import numpy as np
from ops import *
flags = tf.app.flags
FLAGS = flags.FLAGS
def fmnet_model(phase, part_shot, model_shot, part_dist_map , model_dist_map, part2model_ind_gt, part_evecs, part_evecs_trans, model_evecs, model_evecs_trans):
"""Build FM-net model.
Args:
phase: train\test.
part_shot: SHOT descriptor of source shape (part).
model_shot: SHOT descriptor of target shape (model).
dist_map: distance map on target shape to evaluate geodesic error
part_evecs: eigenvectors on source shape
part_evecs_trans: transposed part_evecs with mass matrix correction
model_evecs: eigenvectors on target shape
model_evecs_trans: transposed model_evecs with mass matrix correction
"""
net = {}
for i_layer in range(FLAGS.num_layers):
with tf.variable_scope("layer_%d" % i_layer) as scope:
if i_layer == 0:
net['layer_%d_part' % i_layer] = res_layer(part_shot, dims_out=int(part_shot.shape[-1]), scope=scope,
phase=phase)
scope.reuse_variables()
net['layer_%d_model' % i_layer] = res_layer(model_shot, dims_out=int(model_shot.shape[-1]), scope=scope,
phase=phase)
else:
net['layer_%d_part' % i_layer] = res_layer(net['layer_%d_part' % (i_layer - 1)],
dims_out=int(part_shot.shape[-1]),
scope=scope, phase=phase)
scope.reuse_variables()
net['layer_%d_model' % i_layer] = res_layer(net['layer_%d_model' % (i_layer - 1)],
dims_out=int(part_shot.shape[-1]),
scope=scope, phase=phase)
# project output features on the shape Laplacian eigen functions
layer_C_est = i_layer + 1 # grab current layer index
A = tf.matmul(part_evecs_trans, net['layer_%d_part' % (layer_C_est - 1)])
net['A'] = A
B = tf.matmul(model_evecs_trans, net['layer_%d_model' % (layer_C_est - 1)])
net['B'] = B
# FM-layer: evaluate C_est
net['C_est'], safeguard_inverse = solve_ls(A, B)
# Evaluate loss via soft-correspondence error
with tf.variable_scope("pointwise_corr_loss"):
P_norm, net_loss, unsupervised_loss = pointwise_corr_layer(net['C_est'], model_evecs, part_evecs_trans, model_dist_map, part_dist_map, part2model_ind_gt)
tf.summary.scalar('net_loss', net_loss)
merged = tf.summary.merge_all()
return net_loss, unsupervised_loss, safeguard_inverse, merged, P_norm, net | [
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] | 2.390816 | 980 |
from random import shuffle
import tensorflow as tf
from xcenternet.datasets.dataset import Dataset
class TfExampleFields(object):
"""TF-example proto feature names
Holds the standard feature names to load from an Example proto
Attributes:
image_encoded: image encoded as string
image_format: image format, e.g. "JPEG"
filename: filename
channels: number of channels of image
colorspace: colorspace, e.g. "RGB"
height: height of image in pixels, e.g. 462
width: width of image in pixels, e.g. 581
source_id: original source of the image
image_class_text: image-level label in text format
image_class_label: image-level label in numerical format
object_class_text: labels in text format, e.g. ["person", "cat"]
object_class_label: labels in numbers, e.g. [16, 8]
object_bbox_xmin: xmin coordinates of groundtruth box, e.g. 10, 30
object_bbox_xmax: xmax coordinates of groundtruth box, e.g. 50, 40
object_bbox_ymin: ymin coordinates of groundtruth box, e.g. 40, 50
object_bbox_ymax: ymax coordinates of groundtruth box, e.g. 80, 70
ignore_area_bbox_xmin: xmin coordinates of ignore box, e.g. 10, 30
ignore_area_bbox_xmax: xmax coordinates of ignore box, e.g. 50, 40
ignore_area_bbox_ymin: ymin coordinates of ignore box, e.g. 40, 50
ignore_area_bbox_ymax: ymax coordinates of ignore box, e.g. 80, 70
object_view: viewpoint of object, e.g. ["frontal", "left"]
object_truncated: is object truncated, e.g. [true, false]
object_occluded: is object occluded, e.g. [true, false]
object_difficult: is object difficult, e.g. [true, false]
object_group_of: is object a single object or a group of objects
object_depiction: is object a depiction
object_segment_area: the area of the segment.
object_weight: a weight factor for the object's bounding box.
instance_masks: instance segmentation masks.
instance_boundaries: instance boundaries.
instance_classes: Classes for each instance segmentation mask.
detection_class_label: class label in numbers.
detection_bbox_ymin: ymin coordinates of a detection box.
detection_bbox_xmin: xmin coordinates of a detection box.
detection_bbox_ymax: ymax coordinates of a detection box.
detection_bbox_xmax: xmax coordinates of a detection box.
detection_score: detection score for the class label and box.
"""
image_encoded = 'image/encoded'
image_format = 'image/format' # format is reserved keyword
key = 'image/key/sha256'
filename = 'image/filename'
channels = 'image/channels'
colorspace = 'image/colorspace'
height = 'image/height'
width = 'image/width'
source_id = 'image/source_id'
image_class_text = 'image/class/text'
image_class_label = 'image/class/label'
image_class_synset = 'image/class/synset'
object_class_text = 'image/object/class/text'
object_class_label = 'image/object/class/label'
ignore_area_bbox_xmin = 'image/ignore_area/bbox/xmin'
ignore_area_bbox_ymin = 'image/ignore_area/bbox/ymin'
ignore_area_bbox_xmax = 'image/ignore_area/bbox/xmax'
ignore_area_bbox_ymax = 'image/ignore_area/bbox/ymax'
object_bbox_ymin = 'image/object/bbox/ymin'
object_bbox_xmin = 'image/object/bbox/xmin'
object_bbox_ymax = 'image/object/bbox/ymax'
object_bbox_xmax = 'image/object/bbox/xmax'
object_bbox_label = 'image/object/bbox/label'
object_bbox_split_line = 'image/object/bbox/vl'
object_bbox_split_type = 'image/object/bbox/vertical_line_type'
object_view = 'image/object/view'
object_truncated = 'image/object/truncated'
object_occluded = 'image/object/occluded'
object_difficult = 'image/object/difficult'
object_group_of = 'image/object/group_of'
object_depiction = 'image/object/depiction'
object_segment_area = 'image/object/segment/area'
object_weight = 'image/object/weight'
semseg_channels = 'image/segmentation/channels'
semseg_data = 'image/segmentation/data'
semseg_format = 'image/segmentation/format'
semseg_height = 'image/segmentation/height'
semseg_width = 'image/segmentation/width'
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] | 2.627561 | 1,611 |
#!/usr/bin/env python3
"""Intinor Direkt API Python tutorial
Example 1: Send a request to a Direkt unit using the "direkt" module
and obtain API information as a JSON string
"""
import sys
# The "direkt" module wraps the "requests" library with some convenient
# functionality for the Intinor Direkt API.
import direkt
# START of configuration
# Replace the below example ID "D0****" with the ID of your Direkt unit to
# create the correct Direkt unit URL. This will point to the API root.
# Case-sensitive: Write the Direkt ID with a capital "D".
DIREKT_ID = "D0****"
# Assign "DIREKT_HOST" the hostname or IP address of your Direkt unit or
# "iss.intinor.com" if you want to send requests to the API through ISS.
DIREKT_HOST = "Hostname-or-IP-address"
# Replace username and password in the authentication below with the actual
# username and password for your Direkt unit or for your ISS account, if you
# assigned "DIREKT_HOST" with "iss.intinor.com".
AUTHENTICATION = ("username", "password")
# NOTES:
# Writing credentials into a script is not a secure practise but it makes a
# quick and easy start possible. Choose a more secure approach for usage beyond
# this tutorial.
# The default credentials for your Direkt unit can only be used through local
# network connections and we recommend changing them for security. This can be
# done in the unit's webinterface or in ISS.
# We recommend creating a shared API user account for your team.
# END of configuration
# The URL to the API root is created here. The API root is a good starting
# point resource which is available on all Direkt unit types.
URL = "https://" + DIREKT_HOST + "/api/v1/units/" + DIREKT_ID
def main():
"""Obtain the API resource"""
# Use a GET request to obtain the API resource.
response = direkt.get(URL, auth=AUTHENTICATION)
# Show the text property of the response, which is in JSON string format.
print(response.text)
if not response.ok:
sys.exit("GET '" + URL + "' failed.")
if __name__ == '__main__':
main()
# We recommend using the "direkt" module for API requests through local network
# connections and through ISS connections but alternatively it is possible to
# access the API without using the "direkt" module, in which case you import
# the "requests" library and connect through ISS.
# See Example 2 for more information.
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2,
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17934,
362,
329,
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1321,
13,
198
] | 3.472384 | 688 |
#!/usr/bin/env python
"""
Created by howie.hu at 08/04/2018.
"""
import asyncio
import time
from hproxy.database import DatabaseSetting
from hproxy.spider.base_spider import ProxySpider
from hproxy.spider.proxy_spider import MimvpItem
from hproxy.spider.proxy_tools import request_url_by_aiohttp, get_proxy_info
db_client = DatabaseSetting()
class MimvpSpider(ProxySpider):
"""
Fetch proxies from https://proxy.mimvp.com/free.php?proxy=in_hp&sort=&page=1
"""
spider_name = 'mimvp'
item = MimvpItem
async def get_proxy(self):
"""
Fetch proxies from https://proxy.mimvp.com
:return:
"""
start = time.time()
tasks = []
for url in ['https://proxy.mimvp.com/free.php?proxy=in_hp&sort=&page={}'.format(i) for i in range(1, 64)]:
html = await request_url_by_aiohttp(url=url)
if html:
items_data = self.item.get_items(html=html)
for item_data in items_data:
if item_data.values:
tasks.append(asyncio.ensure_future(self.save_proxy(item_data.values)))
done_list, pending_list = await asyncio.wait(tasks)
good_nums = 0
for task in done_list:
if task.result():
good_nums += 1
self.logger.info(type="Spidering finished...",message="Crawling {0} finished,total proxy num : {1} - valid proxy num ：{2}，Time costs ：{3}".format(
self.spider_name,
len(tasks),
good_nums,
time.time() - start))
async def save_proxy(self, ip_info):
"""
Save proxy
:param ip_info: (0.0.0.0, 8080)
:return:
"""
ip, port = ip_info
isOk, info = await get_proxy_info(ip, port, getInfo=True)
if isOk:
# Save proxy
try:
await db_client.insert(field="{0}:{1}".format(ip, port), value=info)
self.logger.info(type='Valid proxy', message="{0}: {1}:{2} had been saved".format(self.spider_name, ip, port))
return True
except Exception as e:
self.logger.info(type='Invalid proxy', message="{0}: {1}:{2} had been abandoned".format(self.spider_name, ip, port))
return False
return False
async def start():
"""
Start spider
:return:
"""
await MimvpItem.start()
if __name__ == '__main__':
# Start
asyncio.get_event_loop().run_until_complete(start())
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22446,
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62,
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62,
20751,
7,
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198
] | 2.104428 | 1,197 |
from functools import wraps
from flask import request
| [
6738,
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] | 4.307692 | 13 |
#!/hint/python3
import re
import subprocess
from typing import Any, List
from os import getenv
import subprocess
from .gitutil import git_check_clean as git_check_clean # Stop mypy complaining about implicit reexport
from .uiutil import run_txtcapture
from .gitutil import git_add as git_add # Stop mypy complaining about implicit reexport
# These are some regular expressions to validate and parse
# X.Y.Z[-rc.N] versions.
re_rc = re.compile(r'^([0-9]+)\.([0-9]+)\.([0-9]+)-rc\.([0-9]+)$')
re_ga = re.compile(r'^([0-9]+)\.([0-9]+)\.([0-9]+)$')
re_ea = re.compile(r'^([0-9]+)\.([0-9]+)\.([0-9]+)-ea$')
vX = 1
vY = 2
vZ = 3
vN = 4
DEFAULT_REPO = "emissary-ingress/emissary"
def base_version(release_version: str) -> str:
"""Given 'X.Y.Z[-rc.N]', return 'X.Y'."""
return build_version(release_version).rsplit(sep='.', maxsplit=1)[0]
def build_version(release_version: str) -> str:
"""Given 'X.Y.Z[-rc.N]', return 'X.Y.Z'."""
return release_version.split('-')[0]
def assert_eq(actual: Any, expected: Any) -> None:
"""`assert_eq(a, b)` is like `assert a == b`, but has a useful error
message when they're not equal.
"""
if actual != expected:
raise AssertionError(f"wanted '{expected}', got '{actual}'")
def get_is_private() -> bool:
"""Return whether we're in a "private" Git checkout, for doing
embargoed work.
"""
remote_names = run_txtcapture(['git', 'remote']).split()
remote_urls: List[str] = []
for remote_name in remote_names:
remote_urls += run_txtcapture(['git', 'remote', 'get-url', '--all', remote_name]).split()
return 'private' in "\n".join(remote_urls)
| [
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] | 2.498489 | 662 |
import torch
import torch.nn as nn
if __name__ == '__main__':
batchsize =10
# stlstm_cell = STLSTMCell(4, 10)
# x = torch.randn([1, 4])
# h = torch.randn([1, 10])
# c = torch.randn([1, 10])
# m = torch.randn([1, 10])
#
# h_t, c_t, m_t = stlstm_cell(x, h, c, m)
stlstm = STLSTM(4, 10, 2, batchsize)
# stlstm.cuda()
x = torch.randn([batchsize, 7, 4])
a = stlstm(x)
a =1
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] | 1.873362 | 229 |
from flask import Flask, render_template
from flask_googlemaps import GoogleMaps, Map, icons
from dynaconf import FlaskDynaconf
app = Flask(__name__)
GoogleMaps(app)
FlaskDynaconf(app)
@app.route("/")
if __name__ == "__main__":
app.run(port=5050)
| [
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] | 2.752688 | 93 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from setuptools import find_packages
from setuptools import setup
# Load package veersion number.
with open("csdmpy/__init__.py", "r") as f:
for line in f.readlines():
if "__version__" in line:
before_keyword, keyword, after_keyword = line.partition("=")
version = after_keyword.strip()[1:-1]
# What packages are required for this module to be executed?
required = [
"numpy>=1.17",
"setuptools>=27.3",
"astropy>=3.0",
"requests>=2.21.0",
"numexpr>=2.7.0",
]
extras = {"matplotlib": ["matplotlib>=3.0"]}
setup_requires = ["setuptools>=27.3"]
here = os.path.abspath(os.path.dirname(__file__))
# Import the README and use it as the long-description.
# Note: this will only work if 'README.md' is present in your MANIFEST.in file!
try:
with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
long_description = "\n" + f.read()
except FileNotFoundError:
long_description = ""
setup(
name="csdmpy",
version=version,
description="A python module for the core scientific dataset model.",
long_description=long_description,
long_description_content_type="text/markdown",
author="Deepansh Srivastava",
author_email="[email protected]",
python_requires=">=3.6",
url="https://github.com/DeepanshS/csdmpy/",
packages=find_packages(),
install_requires=required,
setup_requires=setup_requires,
extras_require=extras,
tests_require=["pytest", "pytest-runner"],
include_package_data=True,
license="BSD-3-Clause",
classifiers=[
# Trove classifiers
# Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Development Status :: 4 - Beta",
"License :: OSI Approved :: BSD License",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Topic :: Scientific/Engineering",
],
)
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366,
33221,
7904,
22060,
14,
13798,
1586,
1600,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.514754 | 915 |
from src.message_broker.rabbitmq.rabbitmq_api import RabbitMQApi
| [
6738,
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1330,
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32,
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198
] | 2.6 | 25 |
import geojson
import requests
fileName = input("Enter Filename:")
parameterName = input("Enter Parameter Name:")
parameterName = parameterName.lower().capitalize()
typeOfLevelName = input("Enter type of level:")
levelName = input("Enter level:")
levelVal = int(levelName)
foreCastHourName = input("Enter forecast hour:")
forecastHour = int(foreCastHourName)
myjson = {
"fileName":fileName,
"parameterName":parameterName,
"forecastHour":forecastHour
}
mygeojson = requests.post("http://127.0.0.1:5000/get-geo",json=myjson)
mygeojson = mygeojson.json()
with open("grib2geo.json","w") as write_file:
geojson.dump(mygeojson,write_file)
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7,
1820,
469,
13210,
1559,
11,
13564,
62,
7753,
8,
198
] | 2.857778 | 225 |
import asyncio
from asyncio import Task
from typing import List
from base_logger import logging
logger = logging.getLogger(__name__)
from Dolphin.dolphinGameHandler import DolphinGameHandler
from Model.itemDto import ItemDto
from util.abstractGameHandler import AbstractGameHandler
from PySide6.QtCore import Signal
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] | 3.72093 | 86 |
from __future__ import print_function
import sys, os
sys.path.insert(1,"../../../")
from tests import pyunit_utils
import h2o
def h2odownload_csv():
"""
Python API test: h2o.download_csv(data, filename)
"""
training_data = h2o.import_file(pyunit_utils.locate("smalldata/logreg/benign.csv"))
try:
results_dir = pyunit_utils.locate("results") # find directory path to results folder
filename = os.path.join(results_dir, "benign.csv")
h2o.download_csv(training_data, filename) # save csv
assert os.path.isfile(filename), "h2o.download_csv() command is not working."
except Exception as e:
if 'File not found' in e.args[0]:
print("Directory is not writable. h2o.download_csv() command is not tested.")
else:
assert False, "h2o.download_csvresult() command is not working."
if __name__ == "__main__":
pyunit_utils.standalone_test(h2odownload_csv)
else:
h2odownload_csv()
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] | 2.461153 | 399 |
import math
import torch
from torch import nn as nn
from models.archs.arch_util import make_layer
class Upsample(nn.Sequential):
"""Upsample module.
Args:
scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features.
"""
class ChannelAttention(nn.Module):
"""Channel attention used in RCAN.
Args:
num_feat (int): Channel number of intermediate features.
squeeze_factor (int): Channel squeeze factor. Default: 16.
"""
class RCAB(nn.Module):
"""Residual Channel Attention Block (RCAB) used in RCAN.
Args:
num_feat (int): Channel number of intermediate features.
squeeze_factor (int): Channel squeeze factor. Default: 16.
res_scale (float): Scale the residual. Default: 1.
"""
class ResidualGroup(nn.Module):
"""Residual Group of RCAB.
Args:
num_feat (int): Channel number of intermediate features.
num_block (int): Block number in the body network.
squeeze_factor (int): Channel squeeze factor. Default: 16.
res_scale (float): Scale the residual. Default: 1.
"""
class RCAN(nn.Module):
"""Residual Channel Attention Networks.
Paper: Image Super-Resolution Using Very Deep Residual Channel Attention
Networks
Ref git repo: https://github.com/yulunzhang/RCAN.
Args:
num_in_ch (int): Channel number of inputs.
num_out_ch (int): Channel number of outputs.
num_feat (int): Channel number of intermediate features.
Default: 64.
num_group (int): Number of ResidualGroup. Default: 10.
num_block (int): Number of RCAB in ResidualGroup. Default: 16.
squeeze_factor (int): Channel squeeze factor. Default: 16.
upscale (int): Upsampling factor. Support 2^n and 3.
Default: 4.
res_scale (float): Used to scale the residual in residual block.
Default: 1.
img_range (float): Image range. Default: 255.
rgb_mean (tuple[float]): Image mean in RGB orders.
Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
"""
if __name__ == '__main__':
x = torch.randn(4, 3, 3, 64, 64)
model = RCAN(num_in_ch=3,
num_out_ch=3,
num_frames=3,
num_feat=64,
num_group=5,
num_block=2,
squeeze_factor=16,
upscale=1,
res_scale=1)
out = model(x)
print(out.shape)
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8,
198,
220,
220,
220,
3601,
7,
448,
13,
43358,
8,
198
] | 2.386854 | 1,065 |
# python3
import argparse
from Bio import SeqIO
from Bio.Alphabet import generic_dna, generic_protein
# imput parameters
ap = argparse.ArgumentParser()
ap.add_argument("-fa","--fasta", required=True, help="input fasta file")
ap.add_argument("-gb", "--genbank", required=True, help="output genbank file")
args = vars(ap.parse_args())
# main
input_handle = open(args['fasta'], "rU")
output_handle = open(args['genbank'], "w")
# import fasta
sequences = list(SeqIO.parse(input_handle, "fasta"))
# asign generic_dna or generic_protein
for seq in sequences:
seq.seq.alphabet = generic_dna
# output
count = SeqIO.write(sequences, output_handle, "genbank")
output_handle.close()
input_handle.close()
| [
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62,
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62,
28144,
13,
19836,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220,
198
] | 2.836 | 250 |
#!/usr/bin/env python
# Created by BBruceyuan on 18-7-9.
from flask_script import Manager
from flask_script import Shell
from flask_migrate import Migrate, MigrateCommand
from flask import current_app
from app import create_app
from app import db
blog = create_app('develop')
manager = Manager(blog)
# with blog.app_context():
# print(current_app.config['SECRET_KEY'])
# manager.run()
migrate = Migrate(blog, db)
manager.add_command("shell", Shell(make_context=make_shell_context))
manager.add_command('db', MigrateCommand)
if __name__ == '__main__':
manager.run()
| [
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8,
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12417,
834,
10354,
198,
220,
220,
220,
4706,
13,
5143,
3419,
198
] | 3.036458 | 192 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
: Project - Dialted CRF
: Network frameworks and helpers
: Author - Xi Mo
: Institute - University of Kansas
: Date - 6/24/2021
: Last Update - 7/10/2021
: License: Apache 2.0
"""
import torch
import torch.nn as nn
import torch.nn.functional as ops
import time
import math
from pathlib import Path
from utils.configuration import CONFIG
# optimizer parser
# Write model to disk
| [
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7509,
30751,
201,
198,
201,
198,
2,
19430,
2746,
284,
11898,
201
] | 2.703488 | 172 |
import psycopg2
from psycopg2 import sql
import string
import os
from .row import Row
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import create_engine
Base = declarative_base()
from .harvester import (
HarvesterRow,
MonitoredPathRow,
)
| [
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] | 2.978947 | 95 |
from aztk import models
from aztk.utils import azure_api
class CoreClient:
"""The base AZTK client that all other clients inherit from.
**This client should not be used directly. Only software specific clients
should be used.**
"""
| [
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13,
1174,
628,
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] | 3.5 | 72 |
import asyncio
import re
import discord
from discord.ext import commands
| [
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] | 4 | 19 |
# -*- coding: utf-8 -*-
#
# Unless explicitly stated otherwise all files in this repository are licensed
# under the Apache 2 License.
#
# This product includes software developed at Datadog
# (https://www.datadoghq.com/).
#
# Copyright 2018 Datadog, Inc.
#
"""api/lists.py
Exposed lists for autocomplete.
"""
from flask import jsonify, request, url_for
from flask_login import login_required
from ..models import List
from . import api
@api.route('/lists/<string:board_id>')
@login_required
| [
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25,
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62,
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29,
11537,
198,
31,
38235,
62,
35827,
198
] | 3.192308 | 156 |
#!/usr/bin/env python
#
# A library that provides a Python interface to the Telegram Bot API
# Copyright (C) 2015-2017
# Leandro Toledo de Souza <[email protected]>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser Public License for more details.
#
# You should have received a copy of the GNU Lesser Public License
# along with this program. If not, see [http://www.gnu.org/licenses/].
""" This module contains helper functions """
import re
try:
from html import escape as escape_html # noqa: F401
except ImportError:
from cgi import escape as escape_html # noqa: F401
def escape_markdown(text):
"""Helper function to escape telegram markup symbols"""
escape_chars = '\*_`\['
return re.sub(r'([%s])' % escape_chars, r'\\\1', text)
| [
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573,
30536,
41485,
14354,
37811,
198,
220,
220,
220,
6654,
62,
354,
945,
796,
705,
59,
9,
62,
63,
59,
17816,
198,
220,
220,
220,
1441,
302,
13,
7266,
7,
81,
6,
26933,
4,
82,
12962,
6,
4064,
6654,
62,
354,
945,
11,
374,
6,
6852,
59,
16,
3256,
2420,
8,
198
] | 3.364672 | 351 |
from typing import Iterable, Union
from neuralogic.core.constructs.predicate import Predicate
from neuralogic.core.constructs import rule, factories
AtomType = Union["BaseAtom", "WeightedAtom"]
BodyAtomType = Union["BaseAtom", "WeightedAtom"]
Head = AtomType
Body = Union[Iterable[BodyAtomType], BodyAtomType]
| [
6738,
19720,
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296,
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4357,
12290,
2953,
296,
6030,
60,
628,
198
] | 3.16 | 100 |
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
setup(
name = 'sphinx-php',
version = '1.0',
author = 'Fabien Potencier',
author_email = '[email protected]',
description = 'Sphinx Extensions for PHP and Symfony',
license = 'MIT',
packages = find_packages(),
install_requires = ['Sphinx>=0.6'],
)
| [
2,
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9,
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43789,
22784,
198,
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220,
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28413,
29,
28,
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13,
21,
6,
4357,
198,
8,
198
] | 2.454545 | 143 |
import argparse
import logging
import random
import socket
import ssl
import sys
import time
lista_de_sockets = []
configuracao_agente = [
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36"
]
if __name__ == "__main__":
if len(sys.argv)> 1:
slowloris()
else:
print "Insira pelo menos 1 argumento no formato python slowlorys.py ipAddress numberOfSockets"
| [
11748,
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652,
82,
13,
9078,
20966,
20231,
1271,
5189,
50,
11603,
1,
198
] | 2.478261 | 184 |
import secrets
from django.db import models
from django.contrib.auth.models import User, Group
# Authentication user group
models.signals.post_save.connect(create_auth_group, sender=Group)
# User configuration settings
models.signals.post_save.connect(create_user_config, sender=User)
| [
11748,
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62,
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] | 3.430233 | 86 |
from pandac.PandaModules import *
from panda3d.core import *
from DNAProp import DNAProp | [
6738,
19798,
330,
13,
47,
5282,
5841,
5028,
1330,
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] | 3.034483 | 29 |
"""
Author: Ramiz Raja
Created on: 11/01/2020
Problem: Search in a Rotated Sorted Array
You are given a sorted array which is rotated at some random pivot point.
Example: [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]
You are given a target value to search. If found in the array return its index, otherwise return -1.
You can assume there are no duplicates in the array and your algorithm's runtime complexity must be in the order of O(log n).
Example:
Input: nums = [4,5,6,7,0,1,2], target = 0, Output: 4
"""
def rotated_array_search(input_list, number):
"""
Find the index by searching in a rotated sorted array
Args:
input_list(array), number(int): Input array to search and the target
Returns:
int: Index or -1
"""
# As array is sorted (even if it is rotated), with a little tweak to the binary search logic we can use
# binary search and achieve the desired solution in ln(n)
n = len(input_list)
start = 0
end = n - 1
while start <= end:
m = (start + end) // 2
if input_list[m] == number:
return m
elif input_list[start] > input_list[end]: # check if array part [start, end] is rotated or not
# if input_list[start] > input_list[end] then that means the array part we are checking still contains
# rotated numbers
if number <= input_list[end]:
# if number <= input_list[end] then that means number is in range [m+1, end]
start = m + 1
else:
# if number > input_list[end] then that means number is in range [start, m-1]
end = m - 1
# below are the cases executed only when current array range [start, end] is not rotated
elif number > input_list[m]:
# number is in range [m+1, end]
start = m + 1
else:
# number < mid number so number is in range [start, m-1]
end = m - 1
return -1
tests()
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16,
11,
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60,
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220,
220,
220,
220,
220,
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220,
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220,
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220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1271,
1279,
3095,
1271,
523,
1271,
318,
287,
2837,
685,
9688,
11,
285,
12,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
796,
285,
532,
352,
628,
220,
220,
220,
1441,
532,
16,
628,
628,
628,
198,
198,
41989,
3419,
198
] | 2.499373 | 797 |
import contextlib
from flask_frozen import UrlForLogger, Freezer
from naucse.utils.routes import absolute_urls_to_freeze
class AllLinksLogger(UrlForLogger):
""" AllLinksLogger primarily logs ``url_for`` calls, but yields urls from ``absolute_urls_to_freeze`` as well.
"""
def iter_calls(self):
""" Yields all logged urls and links parsed from content.
Unfortunately, ``yield from`` cannot be used as the queues are modified on the go.
"""
while self.logged_calls or absolute_urls_to_freeze:
if self.logged_calls:
yield self.logged_calls.popleft()
# prefer urls from :atrr:`logged_calls` - so, ideally, cache is populated from the base repository
continue
if absolute_urls_to_freeze:
yield absolute_urls_to_freeze.popleft()
@contextlib.contextmanager
def temporary_url_for_logger(app):
""" A context manager which temporary adds a new UrlForLogger to the app and yields it, so it can be used
to get logged calls.
"""
logger = UrlForLogger(app)
yield logger
# reverses the following operating from :class:`UrlForLogger`
# self.app.url_default_functions.setdefault(None, []).insert(0, logger)
app.url_default_functions[None].pop(0)
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8989,
11,
7559,
88,
1164,
422,
15506,
2314,
307,
973,
355,
262,
43359,
389,
9518,
319,
262,
467,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
981,
2116,
13,
6404,
2004,
62,
66,
5691,
393,
4112,
62,
6371,
82,
62,
1462,
62,
5787,
2736,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
6404,
2004,
62,
66,
5691,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7800,
2116,
13,
6404,
2004,
62,
66,
5691,
13,
79,
643,
701,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4702,
2956,
7278,
422,
1058,
265,
21062,
25,
63,
6404,
2004,
62,
66,
5691,
63,
532,
523,
11,
30274,
11,
12940,
318,
22331,
422,
262,
2779,
16099,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4112,
62,
6371,
82,
62,
1462,
62,
5787,
2736,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7800,
4112,
62,
6371,
82,
62,
1462,
62,
5787,
2736,
13,
79,
643,
701,
3419,
628,
198,
31,
22866,
8019,
13,
22866,
37153,
198,
4299,
8584,
62,
6371,
62,
1640,
62,
6404,
1362,
7,
1324,
2599,
198,
220,
220,
220,
37227,
317,
4732,
4706,
543,
8584,
6673,
257,
649,
8799,
75,
1890,
11187,
1362,
284,
262,
598,
290,
19299,
340,
11,
523,
340,
460,
307,
973,
198,
220,
220,
220,
220,
220,
220,
220,
284,
651,
18832,
3848,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
49706,
796,
8799,
75,
1890,
11187,
1362,
7,
1324,
8,
628,
220,
220,
220,
7800,
49706,
628,
220,
220,
220,
1303,
10372,
274,
262,
1708,
5361,
422,
1058,
4871,
25,
63,
28165,
1890,
11187,
1362,
63,
198,
220,
220,
220,
1303,
2116,
13,
1324,
13,
6371,
62,
12286,
62,
12543,
2733,
13,
2617,
12286,
7,
14202,
11,
17635,
737,
28463,
7,
15,
11,
49706,
8,
198,
220,
220,
220,
598,
13,
6371,
62,
12286,
62,
12543,
2733,
58,
14202,
4083,
12924,
7,
15,
8,
628
] | 2.518234 | 521 |
import ipaddress
import socket
range = ipaddress.IPv4Network('10.7.0.0/22')
for x in range:
try:
print(socket.gethostbyaddr(str(x))[0])
except socket.herror:
pass
| [
11748,
20966,
21975,
198,
11748,
17802,
198,
198,
9521,
796,
20966,
21975,
13,
4061,
85,
19,
26245,
10786,
940,
13,
22,
13,
15,
13,
15,
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2837,
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198,
220,
220,
220,
1949,
25,
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220,
220,
220,
220,
220,
220,
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7,
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15,
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17802,
13,
372,
1472,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1208,
198
] | 2.238095 | 84 |
# # Get points witin admissible parameter space
import planarfibers
import pandas as pd
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 1000)
df = planarfibers.discretization.get_points_on_slices(
radii=["0", "1/2", "9/10"],
la1s=["1/2", "4/6", "5/6", "1"],
numeric=False,
)
print(df)
| [
2,
1303,
3497,
2173,
20868,
259,
512,
21597,
11507,
2272,
198,
198,
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17,
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21,
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366,
20,
14,
21,
1600,
366,
16,
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220,
220,
220,
35575,
28,
25101,
11,
198,
8,
198,
198,
4798,
7,
7568,
8,
198
] | 2.338129 | 139 |
#!/usr/bin/env python
import sys
import threading
import time
from oslo.config import cfg
from virtman import compute
from virtman.openstack.common import log as logging
#TODO: Auto determine host ip if not filled in conf file
CONF = cfg.CONF
if __name__ == '__main__':
CONF(sys.argv[1:], project='virtman',
default_config_files = ['/root/packages/virtman/etc/virtman/virtman.conf'])
logging.setup('virtman')
start()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
201,
198,
201,
198,
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25064,
201,
198,
11748,
4704,
278,
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11748,
640,
201,
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201,
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6738,
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2,
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201,
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37,
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70,
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37,
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361,
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834,
6624,
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834,
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834,
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198,
220,
220,
220,
7102,
37,
7,
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13,
853,
85,
58,
16,
25,
4357,
1628,
11639,
48940,
805,
3256,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
4277,
62,
11250,
62,
16624,
796,
685,
26488,
15763,
14,
43789,
14,
48940,
805,
14,
14784,
14,
48940,
805,
14,
48940,
805,
13,
10414,
6,
12962,
201,
198,
220,
220,
220,
18931,
13,
40406,
10786,
48940,
805,
11537,
201,
198,
220,
220,
220,
923,
3419,
201,
198
] | 2.563536 | 181 |
import lxml.html
class QuickTag:
""" Callable to quickly create lxml element and access it.
"""
def __call__(self, as_element=True):
"""If `as_element` is `False`, return tuple of content, tag.
Otherwise, lxml.html.Element class is returned.
"""
if as_element:
return self._element
return self._content, self._tag
def clear_body_and_insert(html, insert_content, insert_tag):
"""Clears the body of an HTML string.
Appends <insert_tag>insert_content</insert_tag> to the body.
"""
document = lxml.html.fromstring(html)
for kid in document.body.getchildren():
document.body.remove(kid)
return append_to_body(document, insert_content, insert_tag)
def append_to_body(html, append_content, append_tag):
"""Appends <append_tag>append_content</append_tag> to an HTML string or
an lxml document.
"""
if isinstance(html, lxml.html.HtmlElement):
document = html
else:
document = lxml.html.fromstring(html)
append_me = QuickTag(append_content, append_tag)
document.body.append(append_me())
return lxml.html.tostring(document, method='html', encoding='unicode')
| [
11748,
300,
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13,
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198,
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220,
220,
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220,
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29,
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220,
220,
37227,
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220,
220,
220,
3188,
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300,
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13,
6494,
13,
6738,
8841,
7,
6494,
8,
628,
220,
220,
220,
329,
5141,
287,
3188,
13,
2618,
13,
1136,
17197,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
3188,
13,
2618,
13,
28956,
7,
38439,
8,
628,
220,
220,
220,
1441,
24443,
62,
1462,
62,
2618,
7,
22897,
11,
7550,
62,
11299,
11,
7550,
62,
12985,
8,
198,
198,
4299,
24443,
62,
1462,
62,
2618,
7,
6494,
11,
24443,
62,
11299,
11,
24443,
62,
12985,
2599,
198,
220,
220,
220,
37227,
4677,
2412,
1279,
33295,
62,
12985,
29,
33295,
62,
11299,
3556,
33295,
62,
12985,
29,
284,
281,
11532,
4731,
393,
198,
220,
220,
220,
281,
300,
19875,
3188,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
611,
318,
39098,
7,
6494,
11,
300,
19875,
13,
6494,
13,
39,
20369,
20180,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
3188,
796,
27711,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3188,
796,
300,
19875,
13,
6494,
13,
6738,
8841,
7,
6494,
8,
628,
220,
220,
220,
24443,
62,
1326,
796,
12029,
24835,
7,
33295,
62,
11299,
11,
24443,
62,
12985,
8,
628,
220,
220,
220,
3188,
13,
2618,
13,
33295,
7,
33295,
62,
1326,
28955,
628,
220,
220,
220,
1441,
300,
19875,
13,
6494,
13,
83,
455,
1806,
7,
22897,
11,
2446,
11639,
6494,
3256,
21004,
11639,
46903,
1098,
11537,
198
] | 2.653422 | 453 |
import warnings
from django.conf import settings
from django.core.exceptions import FieldError
from django.db import models
from django.db.models.constants import LOOKUP_SEP
from django.db.models.expressions import Expression
from django.db.models.fields import FieldDoesNotExist
from django.db.models.fields.related import RelatedField, ForeignObjectRel
from django.utils import six, timezone
from django.utils.encoding import force_text
from django.utils.translation import ugettext as _
try:
from django.forms.utils import pretty_name
except ImportError: # Django 1.8
from django.forms.forms import pretty_name
from .compat import remote_field, remote_model
from .exceptions import FieldLookupError
def try_dbfield(fn, field_class):
"""
Try ``fn`` with the DB ``field_class`` by walking its
MRO until a result is found.
ex::
_try_dbfield(field_dict.get, models.CharField)
"""
# walk the mro, as field_class could be a derived model field.
for cls in field_class.mro():
# skip if cls is models.Field
if cls is models.Field:
continue
data = fn(cls)
if data:
return data
def get_model_field(model, field_name):
"""
Get a ``model`` field, traversing relationships
in the ``field_name``.
ex::
f = get_model_field(Book, 'author__first_name')
"""
fields = get_field_parts(model, field_name)
return fields[-1] if fields else None
def get_field_parts(model, field_name):
"""
Get the field parts that represent the traversable relationships from the
base ``model`` to the final field, described by ``field_name``.
ex::
>>> parts = get_field_parts(Book, 'author__first_name')
>>> [p.verbose_name for p in parts]
['author', 'first name']
"""
parts = field_name.split(LOOKUP_SEP)
opts = model._meta
fields = []
# walk relationships
for name in parts:
try:
field = opts.get_field(name)
except FieldDoesNotExist:
return None
fields.append(field)
if isinstance(field, RelatedField):
opts = remote_model(field)._meta
elif isinstance(field, ForeignObjectRel):
opts = field.related_model._meta
return fields
def resolve_field(model_field, lookup_expr):
"""
Resolves a ``lookup_expr`` into its final output field, given
the initial ``model_field``. The lookup expression should only contain
transforms and lookups, not intermediary model field parts.
Note:
This method is based on django.db.models.sql.query.Query.build_lookup
For more info on the lookup API:
https://docs.djangoproject.com/en/1.9/ref/models/lookups/
"""
query = model_field.model._default_manager.all().query
lhs = Expression(model_field)
lookups = lookup_expr.split(LOOKUP_SEP)
assert len(lookups) > 0
try:
while lookups:
name = lookups[0]
# If there is just one part left, try first get_lookup() so
# that if the lhs supports both transform and lookup for the
# name, then lookup will be picked.
if len(lookups) == 1:
final_lookup = lhs.get_lookup(name)
if not final_lookup:
# We didn't find a lookup. We are going to interpret
# the name as transform, and do an Exact lookup against
# it.
lhs = query.try_transform(lhs, name, lookups)
final_lookup = lhs.get_lookup('exact')
return lhs.output_field, final_lookup.lookup_name
lhs = query.try_transform(lhs, name, lookups)
lookups = lookups[1:]
except FieldError as e:
six.raise_from(FieldLookupError(model_field, lookup_expr), e)
def verbose_field_name(model, field_name):
"""
Get the verbose name for a given ``field_name``. The ``field_name``
will be traversed across relationships. Returns '[invalid name]' for
any field name that cannot be traversed.
ex::
>>> verbose_field_name(Article, 'author__name')
'author name'
"""
if field_name is None:
return '[invalid name]'
parts = get_field_parts(model, field_name)
if not parts:
return '[invalid name]'
names = []
for part in parts:
if isinstance(part, ForeignObjectRel):
names.append(part.related_name)
else:
names.append(part.verbose_name)
return ' '.join(names)
def verbose_lookup_expr(lookup_expr):
"""
Get a verbose, more humanized expression for a given ``lookup_expr``.
Each part in the expression is looked up in the ``FILTERS_VERBOSE_LOOKUPS``
dictionary. Missing keys will simply default to itself.
ex::
>>> verbose_lookup_expr('year__lt')
'year is less than'
# with `FILTERS_VERBOSE_LOOKUPS = {}`
>>> verbose_lookup_expr('year__lt')
'year lt'
"""
from .conf import settings as app_settings
VERBOSE_LOOKUPS = app_settings.VERBOSE_LOOKUPS or {}
lookups = [
force_text(VERBOSE_LOOKUPS.get(lookup, _(lookup)))
for lookup in lookup_expr.split(LOOKUP_SEP)
]
return ' '.join(lookups)
def label_for_filter(model, field_name, lookup_expr, exclude=False):
"""
Create a generic label suitable for a filter.
ex::
>>> label_for_filter(Article, 'author__name', 'in')
'auther name is in'
"""
name = verbose_field_name(model, field_name)
verbose_expression = [_('exclude'), name] if exclude else [name]
# iterable lookups indicate a LookupTypeField, which should not be verbose
if isinstance(lookup_expr, six.string_types):
verbose_expression += [verbose_lookup_expr(lookup_expr)]
verbose_expression = [force_text(part) for part in verbose_expression if part]
verbose_expression = pretty_name(' '.join(verbose_expression))
return verbose_expression
| [
11748,
14601,
198,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
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42625,
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13,
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13,
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47,
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42625,
14208,
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] | 2.500414 | 2,416 |
#!/usr/bin/env python3
from sys import stderr, exit
import re
from TALinputs import TALinput
from multilanguage import Env, Lang, TALcolors
from magic_indexes_lib import *
# METADATA OF THIS TAL_SERVICE:
args_list = [
('config',str),
('moves',str),
('goal',str),
('feedback',str)
]
ENV =Env(args_list)
TAc =TALcolors(ENV)
LANG=Lang(ENV, TAc, lambda fstring: eval(f"f'{fstring}'"))
# START CODING YOUR SERVICE:
final_configuration = ENV['config'].split(',')
indexes = ENV['moves'].split(',')
vector_len = len(final_configuration)
# this vector contains -1,0,1 and it's filled by the server with the worst case scenario and it's never shown to the user during the game.
server_vector = [None] * vector_len
# this variable contains the questions made by the user during the game
wasted_dollars = 0
# this boolean variable is initialized to True in case ENV['feedback'] == 'spot_first_gift'
firstGift = True
TAc.print(LANG.render_feedback("random_vector", f'All right, let us evaluate your moves...'), "yellow", ["bold"])
for n_move in range(0,len(final_configuration)):
chosen_index = int(indexes[n_move])
if '0' not in server_vector:
unknown, optimal_pos = get_positions_f(server_vector)
if chosen_index != optimal_pos:
if ENV['feedback'] == 'spot_first_gift' and firstGift:
TAc.print(LANG.render_feedback("first error", f'# Here you made your first mistake!'), "yellow", ["bold"])
firstGift = False
elif ENV['feedback'] == 'spot_every_gift':
TAc.print(LANG.render_feedback("error", f'# Here you made a mistake!'), "yellow", ["bold"])
else:
unknown, optimal_pos = get_positions_g(server_vector)
if chosen_index not in optimal_pos:
if ENV['feedback'] == 'spot_first_gift' and firstGift:
TAc.print(LANG.render_feedback("first error", f'# Here you made your first mistake!'), "yellow", ["bold"])
firstGift = False
elif ENV['feedback'] == 'spot_every_gift':
TAc.print(LANG.render_feedback("error", f'# Here you made a mistake!'), "yellow", ["bold"])
update_server_vec(chosen_index, final_configuration[chosen_index],server_vector)
wasted_dollars += 1
min_questions = f(vector_len)
check_goal_eval(ENV['goal'], ENV['feedback'],wasted_dollars, min_questions, TAc, LANG)
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"""
NOTE: https://stackoverflow.com/questions/25467288/pils-imagegrab-is-capturing-at-the-wrong-resolution
!! This widget is not currently supported on Mac OS
"""
import tkinter as tk
import ttkbootstrap as ttk
from ttkbootstrap.constants import *
from ttkbootstrap import colorutils, utility
from PIL import ImageGrab, ImageTk, Image
from collections import namedtuple
ColorChoice = namedtuple('ColorChoice', 'rgb hsl hex')
class ColorDropperDialog:
"""A widget that displays an indicator and a zoom window for
selecting a color on the screen.
Left-click the mouse button to select a color. The result is
stored in the `result` property as a `ColorChoice` tuple which
contains named fields for rgb, hsl, and hex color models.
Zoom in and out on the zoom window by using the mouse wheel.
This widget is implemented for **Windows** and **Linux** only.
!!! warning "high resolution displays"
This widget may not function properly on high resolution
displays if you are not using the application in high
resolution mode. This is enabled automatically on Windows.
"""
def build_screenshot_canvas(self):
"""Build the screenshot canvas"""
self.screenshot_canvas = ttk.Canvas(
self.toplevel, cursor='tcross', autostyle=False)
self.screenshot_data = ImageGrab.grab()
self.screenshot_image = ImageTk.PhotoImage(self.screenshot_data)
self.screenshot_canvas.create_image(
0, 0, image=self.screenshot_image, anchor=NW)
self.screenshot_canvas.pack(fill=BOTH, expand=YES)
def build_zoom_toplevel(self, master):
"""Build the toplevel widget that shows the zoomed version of
the pixels underneath the mouse cursor."""
height = utility.scale_size(self.toplevel, 100)
width = utility.scale_size(self.toplevel, 100)
text_xoffset = utility.scale_size(self.toplevel, 50)
text_yoffset = utility.scale_size(self.toplevel, 50)
toplevel = ttk.Toplevel(master)
toplevel.transient(master)
if self.toplevel.winsys == 'x11':
toplevel.attributes('-type', 'tooltip')
else:
toplevel.overrideredirect(True)
toplevel.geometry(f'{width}x{height}')
toplevel.lift()
self.zoom_canvas = ttk.Canvas(
toplevel, borderwidth=1, height=self.zoom_height, width=self.zoom_width)
self.zoom_canvas.create_image(0, 0, tags=['image'], anchor=NW)
self.zoom_canvas.create_text(
text_xoffset, text_yoffset, text="+", fill="white", tags=['indicator'])
self.zoom_canvas.pack(fill=BOTH, expand=YES)
self.zoom_toplevel = toplevel
def on_mouse_wheel(self, event: tk.Event):
"""Zoom in and out on the image underneath the mouse
TODO Cross platform testing needed
"""
if self.toplevel.winsys.lower() == 'win32':
delta = -int(event.delta / 120)
elif self.toplevel.winsys.lower() == 'aqua':
delta = -event.delta
elif event.num == 4:
delta = -1
elif event.num == 5:
delta = 1
self.zoom_level += delta
self.on_mouse_motion()
def on_left_click(self, _):
"""Capture the color underneath the mouse cursor and destroy
the toplevel widget"""
# add logic here to capture the image color
hx = self.get_hover_color()
hsl = colorutils.color_to_hsl(hx)
rgb = colorutils.color_to_rgb(hx)
self.result.set(ColorChoice(rgb, hsl, hx))
self.toplevel.destroy()
self.zoom_toplevel.destroy()
self.toplevel.grab_release()
return self.result.get()
def on_right_click(self, _):
"""Close the color dropper without saving any color information"""
self.zoom_toplevel.destroy()
self.toplevel.grab_release()
self.toplevel.destroy()
def on_mouse_motion(self, event=None):
"""Callback for mouse motion"""
if event is None:
x, y = self.toplevel.winfo_pointerxy()
else:
x = event.x
y = event.y
# move snip window
self.zoom_toplevel.geometry(
f'+{x+self.zoom_xoffset}+{y+self.zoom_yoffset}')
# update the snip image
bbox = (x-self.zoom_level, y-self.zoom_level,
x+self.zoom_level+1, y+self.zoom_level+1)
size = (self.zoom_width, self.zoom_height)
self.zoom_data = self.screenshot_data.crop(
bbox).resize(size, Image.BOX)
self.zoom_image = ImageTk.PhotoImage(self.zoom_data)
self.zoom_canvas.itemconfig('image', image=self.zoom_image)
hover_color = self.get_hover_color()
contrast_color = colorutils.contrast_color(hover_color, 'hex')
self.zoom_canvas.itemconfig('indicator', fill=contrast_color)
def get_hover_color(self):
"""Get the color that is hovered over by the mouse cursor."""
x1, y1, x2, y2 = self.zoom_canvas.bbox('indicator')
x = x1 + (x2-x1)//2
y = y1 + (y2-y2)//2
r, g, b = self.zoom_data.getpixel((x, y))
hx = colorutils.color_to_hex((r, g, b))
return hx
def show(self):
"""Show the toplevel window"""
self.toplevel = ttk.Toplevel(alpha=1)
self.toplevel.wm_attributes('-fullscreen', True)
self.build_screenshot_canvas()
# event binding
self.toplevel.bind("<Motion>", self.on_mouse_motion, "+")
self.toplevel.bind("<Button-1>", self.on_left_click, "+")
self.toplevel.bind("<Button-3>", self.on_right_click, "+")
if self.toplevel.winsys.lower() == 'x11':
self.toplevel.bind("<Button-4>", self.on_mouse_wheel, "+")
self.toplevel.bind("<Button-5>", self.on_mouse_wheel, "+")
else:
self.toplevel.bind("<MouseWheel>", self.on_mouse_wheel, "+")
# initial snip setup
self.zoom_level = 2
self.zoom_toplevel: ttk.Toplevel = None
self.zoom_data = None
self.zoom_image = None
self.zoom_height = utility.scale_size(self.toplevel, 100)
self.zoom_width = utility.scale_size(self.toplevel, 100)
self.zoom_xoffset = utility.scale_size(self.toplevel, 10)
self.zoom_yoffset = utility.scale_size(self.toplevel, 10)
self.build_zoom_toplevel(self.toplevel)
self.toplevel.grab_set()
self.toplevel.lift('.')
self.zoom_toplevel.lift(self.toplevel)
self.on_mouse_motion()
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] | 2.239919 | 2,951 |
import numpy as np
import cv2
#from sort.py import rama
import time
cap = cv2.VideoCapture('Night.mp4')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (1280,720))
while(cap.isOpened()):
ret, frame = cap.read()
frame = cv2.line(frame, (100,360), (1200,360), (0,0,255), 6)
font = cv2.FONT_HERSHEY_SIMPLEX
frame = cv2.putText(frame, ('CAR'), (10,500), font, 4, (255,255,255),2,cv2.LINE_AA)
out.write(frame)
cv2.imshow('frame', frame)
if cv2.waitKey(25) == ord('q'):
break
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] | 2.144578 | 249 |
sparsetwittermysqlpw = "" # mySQL Database Password
# Details for Mailgun
email_to_notify = ""
mailgun_default_smtp_login = ""
mailgun_api_base_url = ""
mailgun_api_key = ""
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] | 2.666667 | 66 |
## *********************************************************
##
## File autogenerated for the libuvc_camera package
## by the dynamic_reconfigure package.
## Please do not edit.
##
## ********************************************************/
from dynamic_reconfigure.encoding import extract_params
inf = float('inf')
config_description = {'upper': 'DEFAULT', 'lower': 'groups', 'srcline': 245, 'name': 'Default', 'parent': 0, 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'cstate': 'true', 'parentname': 'Default', 'class': 'DEFAULT', 'field': 'default', 'state': True, 'parentclass': '', 'groups': [], 'parameters': [{'srcline': 290, 'description': 'Vendor ID, hex digits (use camera of any vendor if null).', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'vendor', 'edit_method': '', 'default': '', 'level': 3, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'Product ID, hex digits (use camera of any model if null).', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'product', 'edit_method': '', 'default': '', 'level': 3, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'Serial number, arbitrary string (use camera of any serial number if null).', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'serial', 'edit_method': '', 'default': '', 'level': 3, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'Index into the list of cameras that match the above parameters.', 'max': 2147483647, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'index', 'edit_method': '', 'default': 0, 'level': 3, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Image width.', 'max': 2147483647, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'width', 'edit_method': '', 'default': 640, 'level': 3, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Image height.', 'max': 2147483647, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'height', 'edit_method': '', 'default': 480, 'level': 3, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Format of video stream from camera.', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'video_mode', 'edit_method': "{'enum_description': 'Video stream format', 'enum': [{'srcline': 36, 'description': 'Use any uncompressed format', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'uncompressed', 'ctype': 'std::string', 'type': 'str', 'name': 'uncompressed'}, {'srcline': 37, 'description': 'User any compressed format', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'compressed', 'ctype': 'std::string', 'type': 'str', 'name': 'compressed'}, {'srcline': 38, 'description': 'YUYV', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'yuyv', 'ctype': 'std::string', 'type': 'str', 'name': 'yuyv'}, {'srcline': 39, 'description': 'UYVY', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'uyvy', 'ctype': 'std::string', 'type': 'str', 'name': 'uyvy'}, {'srcline': 40, 'description': 'RGB', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'rgb', 'ctype': 'std::string', 'type': 'str', 'name': 'rgb'}, {'srcline': 41, 'description': 'BGR', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'bgr', 'ctype': 'std::string', 'type': 'str', 'name': 'bgr'}, {'srcline': 42, 'description': 'MJPEG', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'mjpeg', 'ctype': 'std::string', 'type': 'str', 'name': 'mjpeg'}, {'srcline': 43, 'description': 'gray8', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'gray8', 'ctype': 'std::string', 'type': 'str', 'name': 'gray8'}]}", 'default': 'uncompressed', 'level': 3, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'Camera speed, frames per second.', 'max': 1000.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'frame_rate', 'edit_method': '', 'default': 15.0, 'level': 3, 'min': 0.1, 'type': 'double'}, {'srcline': 290, 'description': 'Method for determining the timestamp.', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'timestamp_method', 'edit_method': "{'enum_description': 'Methods for determining the timestamp', 'enum': [{'srcline': 53, 'description': 'Time of publication', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'pub', 'ctype': 'std::string', 'type': 'str', 'name': 'PubTime'}, {'srcline': 54, 'description': 'Time when raw frame capture began', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'start', 'ctype': 'std::string', 'type': 'str', 'name': 'FrameStartTime'}, {'srcline': 55, 'description': 'Time when raw frame capture ended', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'stop', 'ctype': 'std::string', 'type': 'str', 'name': 'FrameStopTime'}, {'srcline': 56, 'description': 'Time when camera-to-host transfer completed', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const char * const', 'value': 'hostrcpt', 'ctype': 'std::string', 'type': 'str', 'name': 'HostReceiptTime'}]}", 'default': 'start', 'level': 3, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'ROS tf frame of reference, resolved with tf_prefix unless absolute.', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'frame_id', 'edit_method': '', 'default': 'camera', 'level': 0, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'Path to camera calibration file.', 'max': '', 'cconsttype': 'const char * const', 'ctype': 'std::string', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'camera_info_url', 'edit_method': '', 'default': '', 'level': 0, 'min': '', 'type': 'str'}, {'srcline': 290, 'description': 'Scanning mode.', 'max': 1, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'scanning_mode', 'edit_method': "{'enum_description': 'Scanning modes', 'enum': [{'srcline': 72, 'description': '', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 0, 'ctype': 'int', 'type': 'int', 'name': 'Interlaced'}, {'srcline': 73, 'description': '', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 1, 'ctype': 'int', 'type': 'int', 'name': 'Progressive'}]}", 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Auto exposure mode.', 'max': 3, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'auto_exposure', 'edit_method': "{'enum_description': 'Auto-exposure modes', 'enum': [{'srcline': 80, 'description': 'Manual exposure, manual iris', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 0, 'ctype': 'int', 'type': 'int', 'name': 'Manual'}, {'srcline': 81, 'description': 'Auto exposure, auto iris', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 1, 'ctype': 'int', 'type': 'int', 'name': 'Auto'}, {'srcline': 82, 'description': 'manual exposure, auto iris', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 2, 'ctype': 'int', 'type': 'int', 'name': 'Shutter_Priority'}, {'srcline': 83, 'description': 'auto exposure, manual iris', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 3, 'ctype': 'int', 'type': 'int', 'name': 'Aperture_Priority'}]}", 'default': 3, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'In auto mode or shutter priority mode, allow the device to vary frame rate.', 'max': 1, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'auto_exposure_priority', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Length of exposure, seconds.', 'max': 10.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'exposure_absolute', 'edit_method': '', 'default': 0.0, 'level': 0, 'min': 0.0001, 'type': 'double'}, {'srcline': 290, 'description': 'Aperture, f.', 'max': 655.35, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'iris_absolute', 'edit_method': '', 'default': 0.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Maintain focus automatically.', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'auto_focus', 'edit_method': '', 'default': True, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Absolute focal distance, millimeters.', 'max': 65536, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'focus_absolute', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Pan (clockwise), arc seconds.', 'max': 648000, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'pan_absolute', 'edit_method': '', 'default': 0, 'level': 0, 'min': -648000, 'type': 'int'}, {'srcline': 290, 'description': 'Tilt (up), arc seconds.', 'max': 648000, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'tilt_absolute', 'edit_method': '', 'default': 0, 'level': 0, 'min': -648000, 'type': 'int'}, {'srcline': 290, 'description': 'Roll (clockwise), degrees.', 'max': 180, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'roll_absolute', 'edit_method': '', 'default': 0, 'level': 0, 'min': -180, 'type': 'int'}, {'srcline': 290, 'description': 'Image capture disabled.', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'privacy', 'edit_method': '', 'default': False, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Backlight compensation, device-dependent (zero for none, increasing compensation above zero).', 'max': 65536, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'backlight_compensation', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Brightness, device dependent.', 'max': 32767, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'brightness', 'edit_method': '', 'default': 0, 'level': 0, 'min': -32768, 'type': 'int'}, {'srcline': 290, 'description': 'Contrast, device dependent.', 'max': 32767, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'contrast', 'edit_method': '', 'default': 0, 'level': 0, 'min': -32768, 'type': 'int'}, {'srcline': 290, 'description': 'Gain, device dependent.', 'max': 65536, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'gain', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Power line frequency anti-flicker processing.', 'max': 2, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'power_line_frequency', 'edit_method': "{'enum_description': 'Power line frequency modes', 'enum': [{'srcline': 146, 'description': 'Disabled', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 0, 'ctype': 'int', 'type': 'int', 'name': 'Disabled'}, {'srcline': 147, 'description': '50 Hz', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 1, 'ctype': 'int', 'type': 'int', 'name': 'Freq_50'}, {'srcline': 148, 'description': '60 Hz', 'srcfile': '/home/xtark/ros_ws/src/third_packages/ros_astra_camera/cfg/UVCCamera.cfg', 'cconsttype': 'const int', 'value': 1, 'ctype': 'int', 'type': 'int', 'name': 'Freq_60'}]}", 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Automatic hue control.', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'auto_hue', 'edit_method': '', 'default': False, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Hue, degrees.', 'max': 180.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'hue', 'edit_method': '', 'default': 0.0, 'level': 0, 'min': -180.0, 'type': 'double'}, {'srcline': 290, 'description': 'Saturation, device dependent (zero for grayscale).', 'max': 65536, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'saturation', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Image sharpness, device dependent.', 'max': 65536, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'sharpness', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Gamma.', 'max': 5.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'gamma', 'edit_method': '', 'default': 1.0, 'level': 0, 'min': 0.01, 'type': 'double'}, {'srcline': 290, 'description': 'Automatic white balance.', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'auto_white_balance', 'edit_method': '', 'default': False, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'White balance temperature, degrees.', 'max': 65536, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'white_balance_temperature', 'edit_method': '', 'default': 0, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Blue or U component of white balance, device-dependent.', 'max': 65536.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'white_balance_BU', 'edit_method': '', 'default': 0.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Red or V component of white balance, device-dependent.', 'max': 65536.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'white_balance_RV', 'edit_method': '', 'default': 0.0, 'level': 0, 'min': 0.0, 'type': 'double'}], 'type': '', 'id': 0}
min = {}
max = {}
defaults = {}
level = {}
type = {}
all_level = 0
#def extract_params(config):
# params = []
# params.extend(config['parameters'])
# for group in config['groups']:
# params.extend(extract_params(group))
# return params
for param in extract_params(config_description):
min[param['name']] = param['min']
max[param['name']] = param['max']
defaults[param['name']] = param['default']
level[param['name']] = param['level']
type[param['name']] = param['type']
all_level = all_level | param['level']
UVCCamera_uncompressed = 'uncompressed'
UVCCamera_compressed = 'compressed'
UVCCamera_yuyv = 'yuyv'
UVCCamera_uyvy = 'uyvy'
UVCCamera_rgb = 'rgb'
UVCCamera_bgr = 'bgr'
UVCCamera_mjpeg = 'mjpeg'
UVCCamera_gray8 = 'gray8'
UVCCamera_PubTime = 'pub'
UVCCamera_FrameStartTime = 'start'
UVCCamera_FrameStopTime = 'stop'
UVCCamera_HostReceiptTime = 'hostrcpt'
UVCCamera_Interlaced = 0
UVCCamera_Progressive = 1
UVCCamera_Manual = 0
UVCCamera_Auto = 1
UVCCamera_Shutter_Priority = 2
UVCCamera_Aperture_Priority = 3
UVCCamera_Disabled = 0
UVCCamera_Freq_50 = 1
UVCCamera_Freq_60 = 1
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] | 2.725266 | 7,156 |
VALIDATE = True
| [
23428,
2389,
6158,
796,
6407,
198
] | 2.666667 | 6 |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'log-in.ui'
#
# Created by: PyQt5 UI code generator 5.15.0
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
Dialog = QtWidgets.QDialog()
ui = Ui_Dialog()
ui.setupUi(Dialog)
Dialog.show()
sys.exit(app.exec_())
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] | 2.563981 | 211 |
import tensorflow as tf
| [
11748,
11192,
273,
11125,
355,
48700,
220,
198
] | 3.125 | 8 |
#!/usr/bin/python
#######################################################################################################################################################################
#The MIT License (MIT)
#Copyright (c) Copyright 2016, [email protected]
#Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
#to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
#and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
#The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#######################################################################################################################################################################
import os
import os.path
import hashlib
import sys
import sqlite3
import argparse
import shutil
#methode to calculate md5sum of a given file
#return te md5sum as string or 0
#methode to check if a given md5sum is already in the database
#if not, it will be insert
#return True if it's in the database, False otherwise
#methode to copy given file in the given directory
#return True if successful, False if not
#main methode
#handles input parameter for file extension and temporary Working directory
#creates database, if it not exists
#main loop:
# - loop over all files with given extension
# - calc their md5sum and check if these are already in the database
# - if not, copy this files in the temporary Working directory and print the filename
if __name__ == '__main__':
main()
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11,
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705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 4.145455 | 550 |
import pytest
from jacquard.odm import Session
from jacquard.buckets import Bucket
from jacquard.constraints import Constraints
from jacquard.buckets.utils import release
from jacquard.buckets.constants import NUM_BUCKETS
from jacquard.buckets.exceptions import NotEnoughBucketsException
@pytest.mark.parametrize('divisor', (
2,
3,
4,
5,
6,
10,
100,
))
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] | 2.614865 | 148 |
#!/usr/bin/env python
""" autonomous.py - Version 1.0 2016-10-12
General framework based on Patrick Goebel's nav_test.py
Initial version based on ccam-navigation by Chris Mobley
Autonomous movement added by Jonathan Hodges
Define waypoint destinations for a robot to move autonomously within
a map framework.
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.5
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details at:
http://www.gnu.org/licenses/gpl.html
"""
import rospy
import rospkg
import actionlib
from actionlib_msgs.msg import *
from geometry_msgs.msg import Pose, PoseWithCovarianceStamped, Point, Quaternion, Twist
from kuri_mbzirc_challenge_2_msgs.msg import BoxPositionAction, BoxPositionGoal
from tf.transformations import quaternion_from_euler
from decimal import *
import time
from math import radians, pi
if __name__ == '__main__':
try:
mbzirc_c2_auto()
rospy.spin()
except rospy.ROSInterruptException:
rospy.loginfo("mbzirc_c2_auto finished.")
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25,
198,
220,
220,
220,
220,
686,
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88,
13,
6404,
10951,
7203,
2022,
89,
1980,
62,
66,
17,
62,
23736,
5201,
19570,
198
] | 3.206349 | 441 |
#!/usr/bin/env python
# encoding: utf-8
"""
@description: //TODO
@version: 1.0
@author: BaoQiang
@license: Apache Licence
@contact: [email protected]
@site: http://www.github.com/githubao
@software: PyCharm
@file: answer.py
@time: 2016/10/5 22:54
"""
from .base import Base
from .normal import normal_attr
from .other import other_obj
from .streaming import streaming
from .generator import generator_of
from utils import common_save
from .urls import *
| [
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2219,
62,
21928,
198,
6738,
764,
6371,
82,
1330,
1635,
628
] | 2.876543 | 162 |
import cv2
import numpy as np | [
11748,
269,
85,
17,
198,
11748,
299,
32152,
355,
45941
] | 2.9 | 10 |
from flask import Flask, redner_template
app = Flask(__name__)
@app.route('/')
@app.route('/users/<string:username>')
if __name__ == '__main__':
app.run()
| [
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8,
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] | 2.546875 | 64 |
"""
48. Majority Number III
"""
# O(n) time
# O(n) extra space
class Solution:
"""
@param nums: A list of integers
@param k: An integer
@return: The majority number
"""
| [
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from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.dispatch import receiver
# Create your models here.
# def create_profile(sender, **kwargs):
# user = kwargs["instance"]
# if kwargs["created"]:
# user_profile = UserProfile(user=user, bio='my bio')
# user_profile.save()
# post_save.connect(create_profile, sender=User)
@receiver(post_save, sender=User)
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import os
| [
11748,
28686,
628,
198
] | 3 | 4 |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fast tokenization class for LayoutLMv2. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
and _encode_plus, in which the Rust tokenizer is used.
"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import normalizers
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlmv2 import LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, LayoutLMv2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txt",
},
"tokenizer_file": {
"microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/layoutlmv2-base-uncased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/layoutlmv2-base-uncased": {"do_lower_case": True},
}
class LayoutLMv2TokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" LayoutLMv2 tokenizer (backed by HuggingFace's `tokenizers` library). Based on WordPiece.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
methods. Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
File containing the vocabulary.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to lowercase the input when tokenizing.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
cls_token_box (:obj:`List[int]`, `optional`, defaults to :obj:`[0, 0, 0, 0]`):
The bounding box to use for the special [CLS] token.
sep_token_box (:obj:`List[int]`, `optional`, defaults to :obj:`[1000, 1000, 1000, 1000]`):
The bounding box to use for the special [SEP] token.
pad_token_box (:obj:`List[int]`, `optional`, defaults to :obj:`[0, 0, 0, 0]`):
The bounding box to use for the special [PAD] token.
pad_token_label (:obj:`int`, `optional`, defaults to -100):
The label to use for padding tokens. Defaults to -100, which is the :obj:`ignore_index` of PyTorch's
CrossEntropyLoss.
only_label_first_subword (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to only label the first subword, in case word labels are provided.
tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see `this
issue <https://github.com/huggingface/transformers/issues/328>`__).
strip_accents: (:obj:`bool`, `optional`):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for :obj:`lowercase` (as in the original LayoutLMv2).
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = LayoutLMv2Tokenizer
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with word-level normalized bounding boxes and optional labels.
Args:
text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words).
text_pair (:obj:`List[str]`, :obj:`List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
boxes (:obj:`List[List[int]]`, :obj:`List[List[List[int]]]`):
Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
word_labels (:obj:`List[int]`, :obj:`List[List[int]]`, `optional`):
Word-level integer labels (for token classification tasks such as FUNSD, CORD).
"""
# Input type checking for clearer error
if text_pair is not None:
# in case text + text_pair are provided, text = questions, text_pair = words
if not _is_valid_text_input(text):
raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
if not isinstance(text_pair, (list, tuple)):
raise ValueError(
"Words must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
else:
# in case only text is provided => must be words
if not isinstance(text, (list, tuple)):
raise ValueError(
"Words must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
if text_pair is not None:
is_batched = isinstance(text, (list, tuple))
else:
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
words = text if text_pair is None else text_pair
assert boxes is not None, "You must provide corresponding bounding boxes"
if is_batched:
assert len(words) == len(boxes), "You must provide words and boxes for an equal amount of examples"
for words_example, boxes_example in zip(words, boxes):
assert len(words_example) == len(
boxes_example
), "You must provide as many words as there are bounding boxes"
else:
assert len(words) == len(boxes), "You must provide as many words as there are bounding boxes"
if is_batched:
if text_pair is not None and len(text) != len(text_pair):
raise ValueError(
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`: {len(text_pair)}."
)
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
is_pair = bool(text_pair is not None)
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
boxes: Optional[List[List[List[int]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
""" """
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
``__call__`` should be used instead.
Args:
text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
text_pair (:obj:`List[str]` or :obj:`List[int]`, `optional`):
Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
list of list of strings (words of a batch of examples).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._encode_plus(
text=text,
boxes=boxes,
text_pair=text_pair,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
>= 7.5 (Volta).
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "bbox" in encoded_inputs:
encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "bbox" in encoded_inputs:
encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["bbox"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
return encoded_inputs
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
if token_ids_1:
output += token_ids_1 + [self.sep_token_id]
return output
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
sequence | If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
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8,
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] | 2.250439 | 10,821 |
import os
from nonebot.default_config import *
# bot profile
BOT_NAME = "布洛妮娅"
COMMAND_START = {'', '/', '!', '／', '！'}
# paths
PROJECT_BRONYA = os.path.dirname(os.path.abspath(__file__)) # 本文件所在目录
ASSETS = os.path.join(PROJECT_BRONYA, "assets")
DATA = os.path.join(PROJECT_BRONYA, "data")
# super users
SUPERUSERS = [] # e.g., 1234567890
# define group ids
MY_GROUP_IDs = [] # e.g., 123456789
BED_TIME = [0, 8] # 睡觉起始时间，0点到8点
GROUP_TIME_ZONE = {123456789: {"北京": "Asia/Shanghai",
"东京": "Asia/Tokyo"},
987654321: {"北京": "Asia/Shanghai",
"东京": "Asia/Tokyo",
"纽约": "America/New_York"}}
# 报时时间设置
GROUP_DAYTIME = {
123456789: {"daytime": (0, 23)}, # all day long
987654321: {"daytime": (9, 23)} # day time
}
# whether -1s every minute
GROUP_MAHA = {123456789: False,
987654321: True}
if __name__ == "__main__":
main()
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] | 1.637987 | 616 |
from .plots import (PlotIGraphModules,PlotIGraphCoclass,PlotIGraphConjCoclass) | [
6738,
764,
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357,
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3528,
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31172,
11,
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] | 2.785714 | 28 |
import cv2
import open_model_zoo_toolkit as omztk
omz = omztk.openvino_omz()
model = omz.imageClassifier()
img = cv2.imread('resources/car.png')
res = model.run(img)
print(res)
# Example: res = [[479, 'car wheel', 0.5016654], [817, 'sports car, sport car', 0.31316656], [436, 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', 0.06171181]]
| [
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] | 2.52 | 150 |
from class_decorators import debugmethods
b = B()
b.m()
| [
6738,
1398,
62,
12501,
273,
2024,
1330,
14257,
24396,
82,
628,
628,
628,
198,
198,
65,
796,
347,
3419,
198,
65,
13,
76,
3419,
198
] | 2.52 | 25 |
from twisted.test import proto_helpers
from twisted.trial import unittest
from talkback.bot import TalkBackBotFactory
QUOTE = "Nobody minds having what is too good for them. ~ Jane Austen"
class FakePicker(object):
"""
Always return the same quote.
"""
| [
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16794,
364,
198,
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22810,
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23051,
796,
366,
24795,
9017,
1719,
644,
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329,
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220,
220,
16622,
1441,
262,
976,
9577,
13,
198,
220,
220,
220,
37227,
198
] | 3.308642 | 81 |
from bottle import route, run, template, debug
# http://localhost:8068/
@route("/")
@route("/hello")
@route("/hello/<name>")
debug(True)
run(host="localhost", port=8068) | [
6738,
9294,
1330,
6339,
11,
1057,
11,
11055,
11,
14257,
198,
198,
2,
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36750,
25,
1795,
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198,
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198,
198,
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38629,
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31,
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27,
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198,
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198,
5143,
7,
4774,
2625,
36750,
1600,
2493,
28,
1795,
3104,
8
] | 2.703125 | 64 |
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