content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import json
| [
11748,
33918,
628,
628,
628
] | 3.4 | 5 |
import pytz
import datetime
from fixture import DataSet, NamedDataStyle, SQLAlchemyFixture
from pmg.models import (
db,
House,
Committee,
CommitteeMeeting,
Bill,
BillType,
Province,
Party,
CommitteeMeetingAttendance,
Member,
CallForComment,
TabledCommitteeReport,
CommitteeQuestion,
Minister,
Event,
Featured,
Page,
BillStatus,
Post,
User,
Role,
Membership,
MembershipType,
EmailTemplate,
DailySchedule,
Organisation,
)
THIS_YEAR = datetime.datetime.today().year
dbfixture = SQLAlchemyFixture(
env=globals(), style=NamedDataStyle(), engine=db.engine, scoped_session=db.Session
)
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220,
2... | 2.512281 | 285 |
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
driver = webdriver.Chrome("D:\chromedriver\chromedriver")
driver.get("http://www.google.com")
if not "Google" in driver.title:
raise Exception("Unable to load google page!")
elem = driver.find_element_by_name("q")
elem.send_keys("selenium")
elem.submit()
print (driver.title)
driver.quit()
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15... | 3.053691 | 149 |
from PuppeteerLibrary.utils.coverter import str2bool, str2str
import os
import glob
import shutil
import time
from PuppeteerLibrary.ikeywords.iformelement_async import iFormElementAsync
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... | 3.528302 | 53 |
import shutil, os, csv, itertools, glob
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import torch.optim as optim
from sklearn.metrics import confusion_matrix
import pandas as pd
import pickle as pk
cuda = torch.cuda.is_available()
print("is conv1D_cuda available: ", cuda)
# Utils
## 1D Variant of ResNet taking in 200 dimensional fixed time series inputs
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Arguments:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], arch='resnet18', **kwargs)
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Arguments:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], arch='resnet34', **kwargs)
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Arguments:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], arch='resnet50', **kwargs)
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Arguments:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], arch='resnet101', **kwargs)
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Arguments:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], arch='resnet152', **kwargs)
return model
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... | 2.752212 | 678 |
# Copyright (c) 2003-2012 CORE Security Technologies
#
# This software is provided under under a slightly modified version
# of the Apache Software License. See the accompanying LICENSE file
# for more information.
#
# $Id: serviceinstall.py 1141 2014-02-12 16:39:51Z bethus@gmail.com $
#
# Service Install Helper library used by psexec and smbrelayx
# You provide an already established connection and an exefile
# (or class that mimics a file class) and this will install and
# execute the service, and then uninstall (install(), uninstall().
# It tries to take care as much as possible to leave everything clean.
#
# Author:
# Alberto Solino (bethus@gmail.com)
#
"""This module has been copied from impacket.examples.serviceinstall.
It exposes a class that can be used to install services on Windows devices
"""
import random
import string
from impacket.dcerpc import dcerpc
from impacket.dcerpc import srvsvc
from impacket.dcerpc import svcctl
from impacket.dcerpc import transport
from impacket import smb
from impacket import smb3
from impacket import smbconnection
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517... | 3.675768 | 293 |
from direct.directnotify import DirectNotifyGlobal
from pirates.reputation. DistributedReputationAvatarAI import DistributedReputationAvatarAI
from Teamable import Teamable
from direct.distributed.ClockDelta import globalClockDelta
from pirates.piratesbase import EmoteGlobals
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481... | 4.028986 | 69 |
from __future__ import unicode_literals
from django.db.migrations.executor import MigrationExecutor
from django_migrate_project.loader import ProjectMigrationLoader
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#!/usr/bin/env python
"""
Script pour dployer une VM TAT1 dont on a rcupr les informations
Ecrit par Benoit BARTHELEMY
benoit.barthelemy2@open-groupe.com
"""
import atexit
import datetime
import re
import ssl
from argparse import ArgumentParser
from getpass import getpass
from os import path
from sys import exit
from threading import Thread
from time import sleep
import requests
from pyVim import connect
from pyVmomi import vim
from pyVmomi import vmodl
from agora_deploy import FWAP
from agora_tools import tasks
# todo Ajouter une fonction qui a partir d'un folder donne tout son path en string
def get_args():
"""
Rcupration des informations de la ligne de commande
"""
parser = ArgumentParser(description='Arguments for talking to vCenter')
parser.add_argument('--eol',
required=False,
action='store',
default='Perenne',
help='End of life of the VM (default=Perenne)')
parser.add_argument('--demandeur',
required=True,
action='store',
help='Name of the requester')
parser.add_argument('--fonction',
required=True,
action='store',
help='Function of the VM')
parser.add_argument('-s', '--vcenter',
required=True,
action='store',
help='vCenter to connect to.')
parser.add_argument('-o', '--port',
type=int,
default=443,
action='store',
help='Port to connect on.')
parser.add_argument('-u', '--user',
required=True,
action='store',
help='Username to use.')
parser.add_argument('-p', '--password',
required=False,
action='store',
help='Password to use.')
parser.add_argument('--datacenter_name',
required=False,
action='store',
default=None,
help='Name of the Datacenter you\
wish to use. If omitted, the first\
datacenter will be used.')
parser.add_argument('--datastore_name',
required=False,
action='store',
default=None,
help='Datastore you wish the VM to be deployed to. \
If left blank, VM will be put on the first \
datastore found.')
parser.add_argument('--cluster_name',
required=False,
action='store',
default=None,
help='Name of the cluster you wish the VM to\
end up on. If left blank the first cluster found\
will be used')
parser.add_argument('-v', '--vmdk_path',
required=True,
action='store',
default=None,
help='Path of the VMDK file to deploy.')
parser.add_argument('-f', '--ovf_path',
dest='ovf_path',
required=True,
action='store',
default=None,
help='Path of the OVF file to deploy.')
parser.add_argument('-n', '--name',
required=True,
action='store',
default=None,
help='Name of the new VM.')
parser.add_argument('-e', '--esxi',
required=True,
action='store',
help='ESXi to deploy to.')
args = parser.parse_args()
if not args.password:
args.password = getpass(prompt='Enter password: ')
return args
def get_ovf_descriptor(ovf_path):
"""
Lecture du descripteur OVF
"""
if path.exists(ovf_path):
with open(ovf_path, 'r') as f:
try:
ovfd = f.read()
f.close()
return ovfd
except:
print("Could not read file: %s" % ovf_path)
exit(1)
def get_obj(content, vimtype, name):
"""
Rcupration des objets vsphere par nom
"""
obj = None
container = content.viewManager.CreateContainerView(content.rootFolder, vimtype, True)
for c in container.view:
if c.name == name:
obj = c
break
return obj
def get_obj_in_list(obj_name, obj_list):
"""
rcupration d'un objet dans une liste par nom
"""
for o in obj_list:
if o.name == obj_name:
return o
print("Unable to find object by the name of %s in list:\n%s" %
(obj_name, map(lambda o: o.name, obj_list)))
exit(1)
def get_objects(si, datacenter=None, datastore=None, cluster=None):
"""
Retourne un dictionnaire contenant les informations ncessaires un dploiement d'OVF.
"""
# Get datacenter object.
datacenter_list = []
for toplevel_entity in si.content.rootFolder.childEntity:
if type(toplevel_entity) == vim.Datacenter:
datacenter_list.append(toplevel_entity)
if datacenter:
datacenter_obj = get_obj_in_list(datacenter, datacenter_list)
else:
datacenter_obj = datacenter_list[0]
# Get datastore object.
datastore_list = []
for datacenter_entity in datacenter_list:
for datastore_entity in datacenter_entity.datastoreFolder.childEntity:
if type(datastore_entity) == vim.Datastore:
datastore_list.append(datastore_entity)
if datastore:
datastore_obj = get_obj_in_list(datastore, datastore_list)
elif len(datastore_list) > 0:
datastore_obj = datastore_list[0]
else:
print("No datastores found in DC (%s)." % datacenter_obj.name)
datastore_obj = None
# Get cluster object.
cluster_list = []
for datacenter_entity in datacenter_list:
for cluster_entity in datacenter_entity.hostFolder.childEntity:
if type(cluster_entity) == vim.ClusterComputeResource:
cluster_list.append(cluster_entity)
if cluster:
cluster_obj = get_obj_in_list(cluster, cluster_list)
elif len(cluster_list) > 0:
cluster_obj = cluster_list[0]
else:
print("No clusters found in DC (%s)." % datacenter_obj.name)
cluster_obj = None
# Generate resource pool.
resource_pool_obj = cluster_obj.resourcePool
return {"datacenter": datacenter_obj,
"datastore": datastore_obj,
"resource pool": resource_pool_obj}
def keep_lease_alive(lease):
"""
Garde le lease du VMDK ouvert le temps du transfert.
"""
while (True):
sleep(5)
try:
# Choosing arbitrary percentage to keep the lease alive.
lease.HttpNfcLeaseProgress(50)
if (lease.state == vim.HttpNfcLease.State.done):
return
# If the lease is released, we get an exception.
# Returning to kill the thread.
except:
return
def connect_vcenter(vcenter, user, password, port=443):
""" Renvoie un objet service_instance reprsentant une connexion vcenter """
# Suppression de la vrification SSL
context = ssl.SSLContext(ssl.PROTOCOL_TLSv1)
context.verify_mode = ssl.CERT_NONE
try:
service_instance = connect.SmartConnect(host=vcenter,
user=user,
pwd=password,
port=port,
sslContext=context,
)
except:
print("Unable to connect to %s" % vcenter)
exit(1)
# Dconnexion auto la fermeture
atexit.register(connect.Disconnect, service_instance)
return service_instance
def uploadOVF(url=None, fileFullPath=None):
""" Permet l'upload de l'OVF sur l'ESX voulu """
headers = {'Content-Type': 'application/x-vnd.vmware-streamVmdk'}
# Upload en Streaming vu la taille des images de VMs
with open(fileFullPath, 'rb') as f:
r = requests.post(url=url, headers=headers, data=f, verify=False)
# Gestion des erreurs
r.raise_for_status()
def run_command_in_guest(vm, command, arguments, guestUser, guestPassword, si):
""" Permet de lancer une commande via les vmWare agora_tools dans l'OS d'une VM"""
exitCode = None
try:
cmdspec = vim.vm.guest.ProcessManager.ProgramSpec(arguments=arguments, programPath=command)
# Credentials used to login to the guest system
creds = vim.vm.guest.NamePasswordAuthentication(username=guestUser, password=guestPassword)
# pid de la commande
pid = si.content.guestOperationsManager.processManager.StartProgramInGuest(vm=vm, auth=creds, spec=cmdspec)
except vim.fault.GuestComponentsOutOfDate as e:
print(e.msg)
except vim.fault.InvalidGuestLogin:
print('Login OS incorrect')
return 1
# Code Retour
while exitCode is None:
try:
exitCode = \
si.content.guestOperationsManager.processManager.ListProcessesInGuest(vm=vm, auth=creds, pids=pid)[
0].exitCode
# Si on ne peut plus se logger c'est que le MDP root a t chang
except vim.fault.InvalidGuestLogin:
exitCode = 0
sleep(1)
return exitCode
def list_process_pids_in_guest(vm, proc_name, guestUser, guestPassword, si):
""" Permet de lister tous les processus de l'OS d'une VM correspondant un nom de process """
pids = []
try:
# Credentials used to login to the guest system
creds = vim.vm.guest.NamePasswordAuthentication(username=guestUser, password=guestPassword)
processes = si.content.guestOperationsManager.processManager.ListProcessesInGuest(vm=vm, auth=creds)
for proc in processes:
if re.search(proc_name, proc.name):
pids.append(proc.pid)
except vim.fault.GuestComponentsOutOfDate as e:
print(e.msg)
return pids
def kill_process_in_guest(vm, pid, guestUser, guestPassword, si):
"""
Permet de tuer un processus dans l'OS d'une VM
:param vm: nom de la VM
:param pid: PID du process tuer
:param guestUser: Nom du compte dans l'OS de la VM (doit avoir les droits ncessaires)
:param guestPassword: Mot de passe du compte dans l'OS de la VM
:param si:
:return:
"""
try:
creds = vim.vm.guest.NamePasswordAuthentication(username=guestUser, password=guestPassword)
si.content.guestOperationsManager.processManager.TerminateProcessInGuest(vm=vm, auth=creds, pid=pid)
except vim.fault.GuestComponentsOutOfDate as e:
print(e.msg)
if __name__ == "__main__":
exit(main())
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198,
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198,
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288,
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263,
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309,
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319,
257,
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19... | 2.05962 | 5,468 |
"""
A form view can have a custom form_fields but reusing those fields that
were deduced automatically, using grok.AutoFields:
>>> grok.testing.grok(__name__)
We only expect a single field to be present in the form, as we omitted 'size':
>>> from zope import component
>>> from zope.publisher.browser import TestRequest
>>> request = TestRequest()
>>> view = component.getMultiAdapter((Mammoth(), request), name='edit')
>>> len(view.form_fields)
1
>>> [w.__name__ for w in view.form_fields]
['name']
>>> view = component.getMultiAdapter((Mammoth2(), request), name='edit2')
>>> len(view.form_fields)
1
>>> [w.__name__ for w in view.form_fields]
['size']
"""
import grokcore.formlib as grok
from zope import schema
from zope.interface import Interface, implementer
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import os
import sys
import pandas as pd
# USAGE: python cifar100_dirmap.py <path to cifar100 dataset directory>
# Organized cifar100 directory can be created using cifar2png: https://github.com/knjcode/cifar2png
if len(sys.argv) > 1:
DATA_DIR = sys.argv[1]
else:
DATA_DIR = "./../data/cifar100"
# Get class names
class_names = [
file for file in os.listdir(os.path.join(DATA_DIR, "train"))
if os.path.isdir(os.path.join(DATA_DIR, "train", file))
]
class_names.sort()
class_dicts = [{"class": class_names[i], "label": i} for i in range(len(class_names))]
pd.DataFrame(class_dicts).to_csv("cifar100_classes.csv", index=False)
image_list = []
for train_test_idx, train_test in enumerate(["train", "test"]):
for img_class in class_names:
img_files = [f for f in os.listdir(os.path.join(DATA_DIR, train_test, img_class)) if f.endswith(".png")]
for fname in img_files:
image_list.append({
"class": img_class,
"object": 0,
"session": train_test_idx,
"im_path": os.path.join(train_test, img_class, fname),
})
img_df = pd.DataFrame(image_list)
img_df = img_df.sort_values(by=["class", "object", "session", "im_path"], ignore_index=True)
img_df["im_num"] = img_df.groupby(["class", "object", "session"]).cumcount() + 1
img_df.to_csv("cifar100_dirmap.csv")
print(img_df.head())
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1143,... | 2.258481 | 619 |
"""
Ensures there is no data past the deactivation date for deactivated participants.
Original Issue: DC-686
The intent is to sandbox and drop records dated after the date of deactivation for participants
who have deactivated from the Program
This test will mock calling the PS API and provide a returned value. Everything
within the bounds of our team will be tested.
"""
# Python imports
import mock
import os
# Third party imports
import pandas as pd
# Project imports
from app_identity import PROJECT_ID
from common import OBSERVATION
from cdr_cleaner.cleaning_rules.remove_participant_data_past_deactivation_date import (
RemoveParticipantDataPastDeactivationDate)
from constants.retraction.retract_deactivated_pids import DEACTIVATED_PARTICIPANTS
from tests.integration_tests.data_steward.cdr_cleaner.cleaning_rules.bigquery_tests_base import BaseTest
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... | 3.693617 | 235 |
from django.shortcuts import render,redirect
from django.contrib import messages, auth
from django.contrib.auth.models import User
from django.http.request import HttpRequest
# Create your views here.
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4023,... | 3.672727 | 55 |
from mpython import *
import neopixel
import time
my_rgb = neopixel.NeoPixel(Pin(Pin.P13), n=24, bpp=3, timing=1)
while True:
flashlight()
time.sleep(2) | [
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from checks import NaptanCheck
# %%
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"""Defines the trial class.
"""
from datetime import datetime
from logging import getLogger
logger = getLogger(__name__)
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#!/usr/bin/env python
import ast
import sys
import nltk
import numpy as np
from review_data import read_reviews
###############################################################################
if __name__ == '__main__':
main()
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7... | 3.714286 | 63 |
import subprocess
import sys
import os
# This code is meant to manage running multiple instances of my KMCLib codes at the same time,
# in the name of time efficiency
numLambda = 512
numStepsEquilib = 1600000
numStepsAnal = 16000
numStepsSnapshot = 1000
numStepsReq = 16000
sysWidth = 32
sysLength = 32
analInterval = 1
numPasses = 100
timeInterval = 1.0
dataLocation = "dim2Runs/lambdaScan1/"
lambdaMin = 0.05
lambdaMax = 1.25
rateStepSize = (lambdaMax-lambdaMin)/float(numLambda-1)
runningJobs = []
for rateIndex in range(0, numLambda):
currentRate = lambdaMin + rateStepSize*rateIndex
botConc = 0.99
topConc = 0.01
jobInput = "2dSteadyFlow.py "+str(botConc)+" "+str(topConc)+" "+str(currentRate)+" "+str(sysWidth)+" "+str(sysLength)+" "+str(analInterval)+" "+str(numStepsEquilib)+" "+str(numStepsSnapshot)+" "+str(numStepsAnal)+" "+str(numStepsReq)+" "+str(numPasses)+" "+str(timeInterval)+" "+dataLocation+str(rateIndex)+"\n"
with open("jobInputs/testInput."+str(jobIndex), 'w') as f:
f.write(jobInput)
jobIndex += 1
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... | 2.57971 | 414 |
from django.contrib.auth.decorators import login_required
from django.shortcuts import render
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"""
Here we will show how to build a graph from a class inheritance structure. Since we will n change the class of classes (type), we will use a wrapper to do this.
"""
from anygraph import Many
"""
First we define the wrapper class; because we create instances of ClassWrapper on the flight;
to detect if the class was already encountered, we need to use a custom get_id function.
"""
""" then we define how to get from a wrapped class to its base classes"""
""" and we are ready to build the graph """
if __name__ == '__main__':
""" create some class hierarchy: """
start = build(E)
""" let's see what we got """
print([w.__name__ for w in ClassWrapper.base_classes(start, breadth_first=True)])
""" find the wrapper wrapping the object class """
wrapped_object = ClassWrapper.base_classes.find(start, filter=lambda w: w.wrapped is object)[0]
""" and following the 'sub_classes' reverse graph """
print([w.__name__ for w in ClassWrapper.sub_classes(wrapped_object, breadth_first=True)])
"""
Note that iterating over base_classes depth- or breadth-first, does not
always produce the same order as the mro() algorithm used by python
"""
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import time
import numpy as np
from os import path
if __name__ == "__main__":
time_start = time.time()
n_vertex, shortest_paths = read_graph("grh1.txt")
print(compute_apsp(n_vertex, shortest_paths))
print(time.time() - time_start)
time_start = time.time()
n_vertex, shortest_paths = read_graph("grh2.txt")
print(compute_apsp(n_vertex, shortest_paths))
print(time.time() - time_start)
time_start = time.time()
n_vertex, shortest_paths = read_graph("grh3.txt")
print(compute_apsp(n_vertex, shortest_paths))
print(time.time() - time_start)
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2... | 2.402439 | 246 |
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import io
import random
import numpy as np
# def create_figure1(data1):
# fig = plt.figure()
# axis = fig.add_axes([0,0,1,1])
# y1 = data1[0]
# y2 = data1[1]
# width = 0.30
# x=np.arange(8)
# axis.bar(x-0.3, y1, width, color='cyan')
# axis.bar(x, y2, width, color='orange')
# # axis.bar(xs, ys)
# # axis.xticks(x, ['cp','chol','fbs','exang','oldpeak','slope','ca','thal'])
# # axis.xlabel("Heart health defining attributes")
# axis.set_ylabel("values")
# # axis.legend(["Normal", "Yours"])
# axis.set_title('Your data corresponding to normal data')
# return fig
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... | 2.266055 | 327 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/7/15 16:36
# @Author : bxf
# @File : P_DB_OPT.py
# @Software: PyCharm
import pymysql
import json
from datetime import date, datetime
from model.util import md_Config
from model.util.PUB_LOG import *
'''
:
'''
# json jsoncls=MyEncoder
# json FALSE
def getJsonFromDatabase(sql):
cur = DB_CONN().db_Query_Json(sql)
if cur.rowcount == 0:
exeLog("***")
return False
else:
exeLog("***JSON")
return cur.fetchall()
def getTupleFromDatabase(sql):
cur = DB_CONN().db_Query_tuple(sql)
if cur.rowcount == 0:
exeLog("***")
return False
else:
exeLog("***JSON")
return cur.fetchall()
def insertToDatabase(table,data,**kwargs):
'''
:param table:
:param data:
:return:
'''
col_list=dict()
# print(type(data))
# print(type(kwargs))
col_list.update(data)
col_list.update(kwargs)
col_lists=col_list.keys()
col=''
for j in col_lists:
col=col+j+','
val=[]
for i in col_lists:
val_one=col_list[i]
val.append(val_one)
var_lists=tuple(val)
sql='INSERT INTO '+table +' ( '+ col[:-1] +' ) VALUE '+str(var_lists)
exeLog("******~~***")
result=DB_CONN().db_Update(sql)
exeLog("************")
return result
def updateToDatabase(table, data, col, val):
'''
:param table:
:param data:
:param col:
:param val:
:return:
'''
col_lists = tuple(data.keys())
list_one = ""
for i in col_lists:
val_one = data[i]
list_one = list_one + i + '= "' + str(val_one) + '",'
sql = "UPDATE " + table + ' SET ' + list_one[:-1] + ' WHERE ' + col + ' = "' + str(val) + '"'
exeLog("")
return sql
| [
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import pybullet as p
import pybullet_data
cid = p.connect(p.SHARED_MEMORY)
if (cid < 0):
p.connect(p.GUI)
p.setAdditionalSearchPath(pybullet_data.getDataPath())
p.loadURDF("plane.urdf")
quadruped = p.loadURDF("quadruped/quadruped.urdf")
logId = p.startStateLogging(p.STATE_LOGGING_MINITAUR, "LOG00048.TXT", [quadruped])
p.stepSimulation()
p.stepSimulation()
p.stepSimulation()
p.stepSimulation()
p.stepSimulation()
p.stopStateLogging(logId)
| [
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#!/usr/bin/env python3
from setuptools import setup
fuse_reqs = [
'fuse-python >= 0.3.1; python_version < "3"',
'fuse-python >= 1.0.0; python_version > "3"',
]
readme = open('README.md', 'r').read()
readme = readme.replace(
'(FUSE.md)',
'(https://github.com/drougge/wellpapp-pyclient/blob/master/FUSE.md)'
)
setup(
name='wellpapp',
version='CHANGEME.dev', # set this for each release
packages=[
'wellpapp',
'wellpapp.shell',
],
entry_points={
'console_scripts': [
'wp = wellpapp.__main__:main',
],
},
install_requires=[
'Pillow >= 3.1.2',
'PyGObject >= 3.20',
],
extras_require={
'fuse': fuse_reqs,
'all': fuse_reqs,
},
python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*',
author='Carl Drougge',
author_email='bearded@longhaired.org',
url='https://github.com/drougge/wellpapp-pyclient',
license='MIT',
description='Client library and application for the wellpapp image tagging system.',
long_description=readme,
long_description_content_type='text/markdown',
)
| [
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... | 2.31377 | 443 |
from django.db import models
from django.contrib.auth.models import User
from datetime import datetime
# Create your models here.
# create a new attribute in your model
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... | 3.863636 | 44 |
import socket
import threading
conn = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
conn.bind (('', 7070))
conn.listen()
clients = []
print ('Start Server')
threading.Thread(target=new_client()).start() | [
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... | 2.824324 | 74 |
from qtpy.QtCore import Signal
from qtpy.QtWidgets import QDialog, QDialogButtonBox, QListWidget, QVBoxLayout
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] | 2.846154 | 39 |
# Importing required libraries
from tkinter import *
from tkinter import messagebox as mb
from tkinter import ttk
import random
# function to create screen for the game
total =0
#function when bomb is clicked
board()
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# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt4 (Qt v4.8.7)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = "\
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qInitResources()
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# -*- coding: utf-8 -*-
from .constants import *
from . import io
from . import ai
from . import filters
from ._version import __version__
__version_date__ = "Sun Feb 14 14:28:51 2016 +0100"
__version_hash__ = "1d5f7f3"
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... | 2.647059 | 85 |
#
# PySNMP MIB module ONEFS-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ONEFS-MIB
# Produced by pysmi-0.3.4 at Wed May 1 14:34:48 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueRangeConstraint, ConstraintsUnion, ValueSizeConstraint, SingleValueConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsUnion", "ValueSizeConstraint", "SingleValueConstraint", "ConstraintsIntersection")
ModuleCompliance, NotificationGroup, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup", "ObjectGroup")
Unsigned32, snmpModules, IpAddress, Gauge32, enterprises, iso, Integer32, ModuleIdentity, Counter32, NotificationType, TimeTicks, Bits, Counter64, ObjectIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, MibIdentifier = mibBuilder.importSymbols("SNMPv2-SMI", "Unsigned32", "snmpModules", "IpAddress", "Gauge32", "enterprises", "iso", "Integer32", "ModuleIdentity", "Counter32", "NotificationType", "TimeTicks", "Bits", "Counter64", "ObjectIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "MibIdentifier")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
onefs = ModuleIdentity((1, 3, 6, 1, 4, 1, 12124))
if mibBuilder.loadTexts: onefs.setLastUpdated('0201172301Z')
if mibBuilder.loadTexts: onefs.setOrganization('COMPANY_NAME')
if mibBuilder.loadTexts: onefs.setContactInfo('COMPANY_NAME Support phone: SUPPORT_PHONE Support email: SUPPORT_EMAIL ')
if mibBuilder.loadTexts: onefs.setDescription('This is the enterprise number for OneFS')
mibBuilder.exportSymbols("ONEFS-MIB", PYSNMP_MODULE_ID=onefs, onefs=onefs, TimeTicks64=TimeTicks64)
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# Generated by Django 3.1.3 on 2021-05-20 06:04
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
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""" Python unit tests """
import pytest, json
from streetwise.models import Campaign
from . import app, app_context, db
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# Generated by Django 3.1.13 on 2021-10-02 23:23
from django.db import migrations
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from nltk.corpus import movie_reviews
import random
import cython_module as cm
import cytoolz
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# encoding: utf-8
# pylint: disable=E1101
#
# https://www.twse.com.tw/zh/page/trading/indices/MI_5MINS_HIST.html
import scrapy
from scrapy import signals, Spider
from urllib.parse import urlencode
import time
from random import randint
import datetime
import logging
from copy import copy
from dateutil.relativedelta import relativedelta
import collections
import json
from zoneinfo import ZoneInfo
# for cloud function call && scrapy crawl command call
# softlink package folder to root
try:
from package.tools import is_settle, format_number
from package.storage import Storage
except:
from spiders.package.tools import is_settle, format_number
from spiders.package.storage import Storage
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... | 3.363208 | 212 |
from json import loads
from time import time
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import torch.nn as nn
import numpy
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import numpy as np
import os
from os.path import dirname, join
from modules.numpy import covmix, varroll
from modules.pandas import DesignMatrix
from modules.scipy.stats import gaussian_mixture
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import pyttsx3 #pip install pyttsx3
import speech_recognition as sr #pip install speechRecognition
import datetime
import wikipedia #pip install wikipedia
import webbrowser
import os
import smtplib
import random
engine = pyttsx3.init('sapi5')
voices = engine.getProperty('voices')
# print(voices[0].id)
engine.setProperty('voice', voices[1].id)
if __name__ == "__main__":
wishMe()
while True:
# if 1:
query = takeCommand().lower()
# Logic for executing tasks based on query
if 'wikipedia' in query:
speak('Searching Wikipedia...')
query = query.replace("wikipedia", "")
results = wikipedia.summary(query, sentences=2)
speak("According to Wikipedia")
print(results)
speak(results)
elif "hello" in query or "hello Suzi" in query:
hello1 = "Hello ! How May i Help you.."
print(hello1)
speak(hello1)
elif "who are you" in query or "about you" in query or "your details" in query:
who_are_you = "I am Suzi an A I based computer program but i can help you lot like a your assistant ! try me to give simple command !"
print(who_are_you)
speak(who_are_you)
elif 'who make you' in query or 'who made you' in query or 'who created you' in query or 'who develop you' in query:
speak(" For your information Prasun Roy Created me ! I can show you his Linked In profile if you want to see. Yes or no .....")
ans_from_user_who_made_you = takeCommand()
if 'yes' in ans_from_user_who_made_you or 'ok' in ans_from_user_who_made_you or 'yeah' in ans_from_user_who_made_you:
webbrowser.open("https://www.linkedin.com/in/prasun-roy-")
speak('opening his profile...... please wait')
elif 'no' in ans_from_user_who_made_you or 'no thanks' in ans_from_user_who_made_you or 'not' in ans_from_user_who_made_you:
speak("All right ! OK...")
else :
speak("I can't understand. Please say that again !")
elif 'open youtube' in query:
webbrowser.open("www.youtube.com")
speak("opening youtube")
elif 'open github' in query:
webbrowser.open("https://www.github.com")
speak("opening github")
elif 'open facebook' in query:
webbrowser.open("https://www.facebook.com")
speak("opening facebook")
elif 'open instagram' in query:
webbrowser.open("https://www.instagram.com")
speak("opening instagram")
elif 'open google' in query:
webbrowser.open("google.com")
speak("opening google")
elif 'open stackoverflow' in query:
webbrowser.open("stackoverflow.com")
speak("opening stackoverflow")
elif 'open yahoo' in query:
webbrowser.open("https://www.yahoo.com")
speak("opening yahoo")
elif 'open gmail' in query:
webbrowser.open("https://mail.google.com")
speak("opening google mail")
elif 'open snapdeal' in query:
webbrowser.open("https://www.snapdeal.com")
speak("opening snapdeal")
elif 'open amazon' in query or 'shop online' in query:
webbrowser.open("https://www.amazon.com")
speak("opening amazon")
elif 'open flipkart' in query:
webbrowser.open("https://www.flipkart.com")
speak("opening flipkart")
elif 'play music' in query:
speak("ok i am playing music")
music_dir = 'E:\\My MUSIC'
songs = os.listdir(music_dir)
print(songs)
os.startfile(os.path.join(music_dir, songs[0]))
elif 'video from pc' in query or "video" in query:
speak("ok i am playing videos")
video_dir = 'E:\\\My Videos'
Videos = os.listdir(video_dir)
print(Videos)
os.startfile(os.path.join(video_dir,Videos[0]))
elif 'good bye' in query:
speak("good bye")
exit()
elif "shutdown" in query:
speak("shutting down")
os.system('shutdown -s')
elif "your name" in query or "sweat name" in query:
naa_mme = "Thanks for Asking my self ! Suzi"
print(naa_mme)
speak(naa_mme)
elif "you feeling" in query:
print("feeling Very happy to help you")
speak("feeling Very happy to help you")
elif query == 'none':
continue
elif 'exit' in query or 'stop' in query or 'quit' in query :
exx_exit = 'See you soon. Bye'
speak(exx_exit)
exit()
elif 'the time' in query:
strTime = datetime.datetime.now().strftime("%H:%M:%S")
speak(f"the time is {strTime}")
elif 'open code' in query:
codePath = "D:\\vs\\Microsoft VS Code\\Code.exe"
os.startfile(codePath)
speak("opening visual studio code")
elif 'email to prasun' in query:
try:
speak("What should I say?")
content = takeCommand()
to = "prasunroy988@gmail.com"
sendEmail(to, content)
speak("Email has been sent!")
except Exception as e:
print(e)
speak("Sorry.... I am not able to send this email")
elif 'how are you' in query:
setMsgs = ['Just doing my thing!', 'I am fine!', 'Nice!']
ans_qus = random.choice(setMsgs)
speak(ans_qus)
speak(" How are you'")
ans_from_user_how_are_you = takeCommand()
if 'fine' in ans_from_user_how_are_you or 'happy' in ans_from_user_how_are_you or 'okey' in ans_from_user_how_are_you:
speak('Great')
elif 'not' in ans_from_user_how_are_you or 'sad' in ans_from_user_how_are_you or 'upset' in ans_from_user_how_are_you:
speak('Tell me how can i make you happy')
else :
speak("I can't understand. Please say that again !")
else:
tempp = query.replace(' ','+')
prasun_url="https://www.google.com/search?q="
res_prasun = 'sorry! i cant understand but i search from internet to give your answer !'
print(res_prasun)
speak(res_prasun)
webbrowser.open(prasun_url+tempp) | [
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2... | 2.092001 | 3,163 |
import json
from pathlib import Path
from PyQt5 import uic
from PyQt5.QtCore import pyqtSignal
from PyQt5.QtWidgets import QWidget
from vk_api.vk_api import VkApi
from utils import print_message, validate_QLineEdit
| [
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... | 2.691358 | 81 |
"""Module responsible to parse Exif information from a image"""
import math
import datetime
from enum import Enum
from typing import Optional
# third party
import exifread
import piexif
MPH_TO_KMH_FACTOR = 1.60934
"""miles per hour to kilometers per hour conversion factor"""
KNOTS_TO_KMH_FACTOR = 1.852
"""knots to kilometers per hour conversion factor"""
def all_tags(path) -> {str: str}:
"""Method to return Exif tags"""
file = open(path, "rb")
tags = exifread.process_file(file, details=False)
return tags
def __dms_to_dd(dms_value) -> float:
"""DMS is Degrees Minutes Seconds, DD is Decimal Degrees.
A typical format would be dd/1,mm/1,ss/1.
When degrees and minutes are used and, for example,
fractions of minutes are given up to two decimal places,
the format would be dd/1,mmmm/100,0/1 """
# degrees
degrees_nominator = dms_value.values[0].num
degrees_denominator = dms_value.values[0].den
degrees = float(degrees_nominator) / float(degrees_denominator)
# minutes
minutes_nominator = dms_value.values[1].num
minutes_denominator = dms_value.values[1].den
minutes = float(minutes_nominator) / float(minutes_denominator)
# seconds
seconds_nominator = dms_value.values[2].num
seconds_denominator = dms_value.values[2].den
seconds = float(seconds_nominator) / float(seconds_denominator)
# decimal degrees
return degrees + (minutes / 60.0) + (seconds / 3600.0)
def gps_latitude(gps_data: {str: str}) -> Optional[float]:
"""Exif latitude from gps_data represented by gps tags found in image exif"""
if ExifTags.GPS_LATITUDE.value in gps_data:
# latitude exists
dms_values = gps_data[ExifTags.GPS_LATITUDE.value]
_latitude = __dms_to_dd(dms_values)
if ExifTags.GPS_LATITUDE_REF.value in gps_data and \
(str(gps_data[ExifTags.GPS_LATITUDE_REF.value]) == str(CardinalDirection.S.value)):
# cardinal direction is S so the latitude should be negative
_latitude = -1 * _latitude
return _latitude
# no latitude info found
return None
def gps_longitude(gps_data: {str: str}) -> Optional[float]:
"""Exif longitude from gps_data represented by gps tags found in image exif"""
if ExifTags.GPS_LONGITUDE.value in gps_data:
# longitude exists
dms_values = gps_data[ExifTags.GPS_LONGITUDE.value]
_longitude = __dms_to_dd(dms_values)
if ExifTags.GPS_LONGITUDE_REF.value in gps_data and \
str(gps_data[ExifTags.GPS_LONGITUDE_REF.value]) == str(CardinalDirection.W.value):
# cardinal direction is W so the longitude should be negative
_longitude = -1 * _longitude
return _longitude
# no longitude info found
return None
def gps_compass(gps_data: {str: str}) -> Optional[float]:
"""Exif compass from gps_data represented by gps tags found in image exif.
reference relative to true north"""
if ExifTags.GPS_DIRECTION.value in gps_data:
# compass exists
compass_ratio = gps_data[ExifTags.GPS_DIRECTION.value].values[0]
if ExifTags.GPS_DIRECTION_REF.value in gps_data and \
gps_data[ExifTags.GPS_DIRECTION_REF.value] == CardinalDirection.MagneticNorth:
# if we find magnetic north then we don't consider a valid compass
return None
return compass_ratio.num / compass_ratio.den
# no compass found
return None
def gps_timestamp(gps_data: {str: str}) -> Optional[float]:
"""Exif gps time from gps_data represented by gps tags found in image exif.
In exif there are values giving the hour, minute, and second.
This is UTC time"""
if ExifTags.GPS_TIMESTAMP.value in gps_data:
# timestamp exists
_timestamp = gps_data[ExifTags.GPS_TIMESTAMP.value]
hours: exifread.Ratio = _timestamp.values[0]
minutes: exifread.Ratio = _timestamp.values[1]
seconds: exifread.Ratio = _timestamp.values[2]
day_timestamp = hours.num / hours.den * 3600 + \
minutes.num / minutes.den * 60 + \
seconds.num / seconds.den
if ExifTags.GPS_DATE_STAMP.value in gps_data:
# this tag is the one present in the exif documentation
# but from experience ExifTags.GPS_DATE is replacing this tag
gps_date = gps_data[ExifTags.GPS_DATE_STAMP.value].values
date_timestamp = datetime.datetime.strptime(gps_date, "%Y:%m:%d").timestamp()
return day_timestamp + date_timestamp
if ExifTags.GPS_DATE.value in gps_data:
# this tag is a replacement for ExifTags.GPS_DATE_STAMP
gps_date = gps_data[ExifTags.GPS_DATE.value].values
date_timestamp = datetime.datetime.strptime(gps_date, "%Y:%m:%d").timestamp()
return day_timestamp + date_timestamp
# no date information only hour minutes second of day -> no valid gps timestamp
return None
# no gps timestamp found
return None
def timestamp(tags: {str: str}) -> Optional[float]:
"""Original timestamp determined by the digital still camera. This is timezone corrected."""
if ExifTags.DATE_TIME_ORIGINAL.value in tags:
date_taken = tags[ExifTags.DATE_TIME_ORIGINAL.value].values
_timestamp = datetime.datetime.strptime(date_taken, "%Y:%m:%d %H:%M:%S").timestamp()
return _timestamp
if ExifTags.DATE_Time_DIGITIZED.value in tags:
date_taken = tags[ExifTags.DATE_Time_DIGITIZED.value].values
_timestamp = datetime.datetime.strptime(date_taken, "%Y:%m:%d %H:%M:%S").timestamp()
return _timestamp
# no timestamp information found
return None
def gps_altitude(gps_tags: {str: str}) -> Optional[float]:
"""GPS altitude form exif """
if ExifTags.GPS_ALTITUDE.value in gps_tags:
# altitude exists
altitude_ratio = gps_tags[ExifTags.GPS_ALTITUDE.value].values[0]
altitude = altitude_ratio.num / altitude_ratio.den
if ExifTags.GPS_ALTITUDE_REF.value in gps_tags and \
gps_tags[ExifTags.GPS_ALTITUDE_REF.value] == SeaLevel.BELOW.value:
altitude = -1 * altitude
return altitude
return None
def gps_speed(gps_tags: {str: str}) -> Optional[float]:
"""Returns GPS speed from exif in km per hour or None if no gps speed tag found"""
if ExifTags.GPS_SPEED.value in gps_tags:
# gps speed exist
speed_ratio = gps_tags[ExifTags.GPS_SPEED.value].values[0]
speed = speed_ratio.num / speed_ratio.den
if ExifTags.GPS_SPEED_REF.value in gps_tags:
if gps_tags[ExifTags.GPS_SPEED_REF.value] == SpeedUnit.MPH.value:
speed = SpeedUnit.convert_mph_to_kmh(speed)
if gps_tags[ExifTags.GPS_SPEED_REF.value] == SpeedUnit.KNOTS.value:
speed = SpeedUnit.convert_knots_to_kmh(speed)
return speed
# no gps speed tag found
return None
def add_gps_tags(path: str, gps_tags: {str: any}):
"""This method will add gps tags to the photo found at path"""
exif_dict = piexif.load(path)
for tag, tag_value in gps_tags.items():
exif_dict["GPS"][tag] = tag_value
exif_bytes = piexif.dump(exif_dict)
piexif.insert(exif_bytes, path)
def create_required_gps_tags(timestamp_gps: float,
latitude: float,
longitude: float) -> {str: any}:
"""This method will creates gps required tags """
exif_gps = {}
dms_latitude = __dd_to_dms(latitude)
dms_longitude = __dd_to_dms(longitude)
day = int(timestamp_gps / 86400) * 86400
hour = int((timestamp_gps - day) / 3600)
minutes = int((timestamp_gps - day - hour * 3600) / 60)
seconds = int(timestamp_gps - day - hour * 3600 - minutes * 60)
day_timestamp_str = datetime.date.fromtimestamp(day).strftime("%Y:%m:%d")
exif_gps[piexif.GPSIFD.GPSTimeStamp] = [(hour, 1),
(minutes, 1),
(seconds, 1)]
exif_gps[piexif.GPSIFD.GPSDateStamp] = day_timestamp_str
exif_gps[piexif.GPSIFD.GPSLatitudeRef] = "S" if latitude < 0 else "N"
exif_gps[piexif.GPSIFD.GPSLatitude] = dms_latitude
exif_gps[piexif.GPSIFD.GPSLongitudeRef] = "W" if longitude < 0 else "E"
exif_gps[piexif.GPSIFD.GPSLongitude] = dms_longitude
return exif_gps
def add_optional_gps_tags(exif_gps: {str: any},
speed: float,
altitude: float,
compass: float) -> {str: any}:
"""This method will append optional tags to exif_gps tags dictionary"""
if speed:
exif_gps[piexif.GPSIFD.GPSSpeed] = (speed, 1)
exif_gps[piexif.GPSIFD.GPSSpeedRef] = SpeedUnit.KMH.value
if altitude:
exif_gps[piexif.GPSIFD.GPSAltitude] = (altitude, 1)
sea_level = SeaLevel.BELOW.value if altitude < 0 else SeaLevel.ABOVE.value
exif_gps[piexif.GPSIFD.GPSAltitudeRef] = sea_level
if compass:
exif_gps[piexif.GPSIFD.GPSImgDirection] = (compass, 1)
exif_gps[piexif.GPSIFD.GPSImgDirectionRef] = CardinalDirection.TrueNorth.value
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117... | 2.23999 | 4,121 |
# NPTEL EXERCISE 5
courses = {}
students = []
grades = {}
f = 0
while(True):
S = input()
if S=="EndOfInput":
break
if S=='Courses':
f = 1
continue
elif S=='Students':
f = 2
continue
elif S=='Grades':
f = 3
continue
if f==1 :
S = S.split("~")
courses[S[0]] = S[2:]
elif f==2:
S = S.split("~")
students += [S]
elif f==3:
S = S.split("~")
try:
grades[S[0]].append(S[1:])
except:
grades[S[0]] = [S[1:]]
#print(courses)
#print(students)
#print(grades)
students.sort()
for stud in students:
roll = stud[0]
gpa = 0
count = 0
for key in grades.keys():
for res in grades[key]:
if roll==res[2]:
count += 1
if res[3]=='A':
gpa += 10
elif res[3]=='AB':
gpa += 9
elif res[3]=='B':
gpa += 8
elif res[3]=='BC':
gpa += 7
elif res[3]=='C':
gpa += 6
elif res[3]=='CD':
gpa += 5
elif res[3]=='D':
gpa += 4
if gpa!=0:
gpa = (gpa/count)
ans = "~".join(stud) + "~" + "{0:3.1f}".format(gpa)
else:
ans = "~".join(stud) + "~" + str(gpa)
print(ans)
| [
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201,
198,
66,
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220,... | 1.529472 | 984 |
#!/usr/bin/python3
import abc
import re
import logger
def list_backups(self, archive, repo):
"""List all the backups that currently exist in the remote given archive/repo."""
pattern = re.compile(self.backup_pattern)
return self.list_objects_matching_regex(pattern, prefix=f"{archive}/{repo}")
def list_buckets(self, archive, repo, backup):
"""List all the buckets that currently exist in the remote given archive/repo/backup."""
backup_re, bucket_re = re.escape(backup) + "/", self.bucket_pattern
backup_pattern, backup_bucket_pattern = re.compile(backup_re), re.compile(backup_re + bucket_re)
return [backup_pattern.sub('', obj) for obj in self.list_objects_matching_regex(
backup_bucket_pattern, prefix=f"{archive}/{repo}/{backup}")]
def list_rift_indexes(self, archive, repo, backup, bucket):
"""List all the rift indexes that exist in the remote given archive/repo/backup/bucket."""
pattern = re.compile(self.rift_pattern)
return self.list_objects_matching_regex(pattern, prefix=f"{archive}/{repo}/{backup}/{bucket}/data/")
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220,
372... | 2.596811 | 439 |
from django.contrib import admin
from .models import User , Trip, Notification , Spending
# Register your models here.
admin.site.register(User)
admin.site.register(Trip)
admin.site.register(Notification)
admin.site.register(Spending) | [
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... | 3.507463 | 67 |
'''
The follwing code runs a test lstm network on the CIFAR dataset
I will explicitly write the networks here for ease of understanding
with cnn_sropout = 0.4 and rnn dropout = 0.2and lr = 1e-3 and res = 8
################# cnn_gru_True Validation Accuracy = [0.3408, 0.411, 0.44, 0.4448, 0.466, 0.4684, 0.4802, 0.4846, 0.4848, 0.512, 0.5098, 0.5154, 0.5212, 0.5276, 0.5352, 0.5306, 0.5354, 0.5388, 0.5374, 0.5418, 0.55, 0.537, 0.5556, 0.543, 0.5458, 0.548, 0.5462, 0.554, 0.5596, 0.5438]
################# cnn_gru_True Training Accuracy = [0.2734222, 0.3752889, 0.40646666, 0.42904446, 0.44386667, 0.45495555, 0.46284443, 0.47604445, 0.4802889, 0.48911113, 0.4968222, 0.4992, 0.50622225, 0.51126665, 0.5147333, 0.52275556, 0.5224444, 0.52537775, 0.5287778, 0.53275555, 0.53286666, 0.5396444, 0.5384222, 0.5423333, 0.542, 0.5485333, 0.547, 0.5458, 0.5524222, 0.55104446]
with cnn_sropout = 0.4 and rnn dropout = 0.2and lr = 1e-3 and res = 16
################# extended_cnn_one_img Validation Accuracy = [0.416, 0.4696, 0.5168, 0.5424, 0.557, 0.5658, 0.5782, 0.5884, 0.5902, 0.5978, 0.5996, 0.6034, 0.6122, 0.606, 0.6112, 0.6104, 0.618, 0.6158, 0.6162, 0.6132, 0.6132, 0.6178, 0.6122, 0.626, 0.6168, 0.6164, 0.62, 0.6288, 0.6304, 0.6328]
################# extended_cnn_one_img Training Accuracy = [0.2964, 0.42106667, 0.46775556, 0.49335554, 0.51544446, 0.52937776, 0.5436889, 0.5556889, 0.56684446, 0.57053334, 0.5798444, 0.58955556, 0.5917778, 0.59702224, 0.6014444, 0.60657775, 0.6142222, 0.6137556, 0.6195111, 0.6193111, 0.6226444, 0.6248, 0.6245555, 0.62575555, 0.6321333, 0.6330889, 0.6327556, 0.63677776, 0.63571113, 0.6396889]
################# cnn_convlstm_True Validation Accuracy = [0.4038, 0.4724, 0.521, 0.5402, 0.52, 0.5516, 0.5658, 0.5654, 0.5904, 0.5866, 0.6024, 0.6026, 0.6114, 0.6224, 0.5982, 0.6178, 0.6314, 0.6208, 0.6158, 0.6352, 0.6412, 0.63, 0.6424, 0.6278, 0.6336, 0.6278, 0.646, 0.6272, 0.6414, 0.6406]
################# cnn_convlstm_True Training Accuracy = [0.2964, 0.42106667, 0.46775556, 0.49335554, 0.51544446, 0.52937776, 0.5436889, 0.5556889, 0.56684446, 0.57053334, 0.5798444, 0.58955556, 0.5917778, 0.59702224, 0.6014444, 0.60657775, 0.6142222, 0.6137556, 0.6195111, 0.6193111, 0.6226444, 0.6248, 0.6245555, 0.62575555, 0.6321333, 0.6330889, 0.6327556, 0.63677776, 0.63571113, 0.6396889]
with cnn_sropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 out.812929
################# cnn_gru_True Validation Accuracy = [0.3452, 0.41, 0.4206, 0.4382, 0.4626, 0.4786, 0.481, 0.4984, 0.5006, 0.5038, 0.5112, 0.5022, 0.522, 0.527, 0.5314, 0.5362, 0.5434,
0.53, 0.543, 0.5534, 0.5528, 0.5456, 0.548, 0.5492, 0.5602, 0.5662, 0.5554, 0.5626, 0.5732, 0.5608, 0.5612, 0.5678, 0.578, 0.5572, 0.575, 0.5674, 0.5674, 0.5678, 0.574, 0.5832, 0.567, 0.5676, 0.5872, 0.5856, 0.5908, 0.5916, 0.586, 0.5628, 0.582, 0.5772, 0.5702, 0.5756, 0.5792, 0.5726, 0.59, 0.5784, 0.576, 0.5752, 0.5894, 0.5844, 0.583, 0.5832, 0.5782, 0.5696, 0.5812, 0.589, 0.5818, 0.5826, 0.5922, 0.5896, 0.5816, 0.5798, 0.5818, 0.5834, 0.5822, 0.5836, 0.5828, 0.569, 0.5914, 0.5822, 0.5974, 0.5928, 0.5956, 0.5936, 0.5888, 0.5932, 0.5986, 0.593, 0.5802, 0.5878, 0.5876, 0.5846, 0.6018, 0.5932, 0.5862, 0.5898, 0.5902, 0.5948, 0.5952, 0.596]
################# cnn_gru_True Training Accuracy = [0.2522, 0.35944444, 0.40026668, 0.42453334, 0.4369111, 0.45024446, 0.46413332, 0.47453332, 0.47904444, 0.48753333, 0.4946, 0.50115556, 0.50531113, 0.5134, 0.5142, 0.5196222, 0.5276667, 0.529, 0.5313778, 0.5318889, 0.5356445, 0.54084444, 0.54051113, 0.5448889, 0.54855555, 0.5504444, 0.5562889, 0.5566889, 0.55655557, 0.5622889, 0.5615111, 0.5605111, 0.5638, 0.56615555, 0.5662444, 0.56953335, 0.5730444, 0.5717555, 0.5730444, 0.57368886, 0.5764889, 0.5782222, 0.58004445, 0.5802889, 0.5833778, 0.5824222, 0.58437777, 0.5869111, 0.58375555, 0.5871556, 0.5907556, 0.58444446, 0.58846664, 0.5914889, 0.59033334, 0.59257776, 0.5913333, 0.59606665, 0.5928222, 0.59577775, 0.5945333, 0.59613335, 0.5953556, 0.59786665, 0.5990222, 0.5993556, 0.60215557, 0.60344446, 0.6027111, 0.60364443, 0.6039111, 0.6062222, 0.60364443, 0.6062667, 0.6060445, 0.6081333, 0.6075778, 0.6094, 0.60568887, 0.6079556, 0.6064444, 0.61113334, 0.61322224, 0.6088667, 0.6125778, 0.61248887, 0.61282223, 0.61244446, 0.6136444, 0.61337775, 0.6174667, 0.61248887, 0.61535555, 0.6160667, 0.6134, 0.6155556, 0.6161111, 0.6158444, 0.61855555, 0.61642224]
with cnn_sropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 500 epochs out.813849
################# cnn_gru_True Validation Accuracy = [0.3136, 0.4024, 0.4436, 0.4546, 0.4648, 0.4552, 0.4766, 0.5058, 0.5028, 0.5182, 0.522, 0.5142, 0.5306, 0.5324, 0.5302, 0.5424, 0.5392, 0.543, 0.5328, 0.5276, 0.5474, 0.549, 0.5512, 0.5326, 0.5482, 0.5558, 0.5548, 0.5594, 0.5546, 0.566, 0.559, 0.5674, 0.564, 0.5584, 0.5698, 0.5718, 0.567, 0.5618, 0.5632, 0.574, 0.5696, 0.5758, 0.5636, 0.5744, 0.5706, 0.5734, 0.5508, 0.5692, 0.5802, 0.5704, 0.572, 0.5706, 0.5888, 0.5828, 0.583, 0.5812, 0.5872, 0.5748, 0.5844, 0.5784, 0.5838, 0.5862, 0.5826, 0.5838, 0.5894, 0.5942, 0.5932, 0.5818, 0.5836, 0.5914, 0.592, 0.5956, 0.5772, 0.5936, 0.5908, 0.5808, 0.5898, 0.5734, 0.578, 0.5868, 0.578, 0.5998, 0.59, 0.5956, 0.5708, 0.585, 0.5902, 0.5922, 0.5826, 0.5936, 0.5916, 0.5846, 0.6012, 0.5852, 0.5892, 0.592, 0.5806, 0.5938, 0.5916, 0.5866, 0.5952, 0.5944, 0.5956, 0.59, 0.592, 0.5922, 0.5962, 0.5906, 0.6006, 0.5912, 0.596, 0.6004, 0.596, 0.5838, 0.5918, 0.581, 0.5912, 0.587, 0.5942, 0.586, 0.591, 0.5906, 0.583, 0.5874, 0.5976, 0.5866, 0.5884, 0.5894, 0.5968, 0.5992, 0.5912, 0.5932, 0.5828, 0.5958, 0.5878, 0.5888, 0.595, 0.5948, 0.5898, 0.5956, 0.5896, 0.5942, 0.5938, 0.5884, 0.5874, 0.5954, 0.5908, 0.5948, 0.5972, 0.5986, 0.5984, 0.5952, 0.589, 0.5892, 0.6044, 0.6028, 0.5944, 0.591, 0.6018, 0.5932, 0.5982, 0.5896, 0.598, 0.6026, 0.6028, 0.6034, 0.5916, 0.5952, 0.5932, 0.597, 0.6008, 0.6026, 0.5974, 0.5954, 0.6014, 0.5988, 0.606, 0.6056, 0.5944, 0.6048, 0.6084, 0.6026, 0.599, 0.6022, 0.6022, 0.6022, 0.601, 0.5928, 0.5988, 0.6008, 0.599, 0.6016, 0.6036, 0.6056, 0.6142, 0.6064, 0.6082, 0.6032, 0.5974, 0.6082, 0.61, 0.6032, 0.6018, 0.6026, 0.6088, 0.6014, 0.6022, 0.6094, 0.6034, 0.5938, 0.6066, 0.5838, 0.5978, 0.6012, 0.5988, 0.6062, 0.6044, 0.5946, 0.597, 0.5954, 0.5944, 0.594, 0.5934, 0.5984, 0.6038, 0.607, 0.6056, 0.5948, 0.604, 0.6012, 0.5988, 0.608, 0.601, 0.6016, 0.5996, 0.6008, 0.6048, 0.6076, 0.6038, 0.6058, 0.6038, 0.6078, 0.5968, 0.605, 0.6046, 0.5982, 0.6002, 0.6092, 0.5956, 0.605, 0.6006, 0.5998, 0.5922, 0.6044, 0.5946, 0.602, 0.6008, 0.6068, 0.6018, 0.602, 0.594, 0.6046, 0.5992, 0.6006, 0.5962, 0.6092, 0.6026, 0.5984, 0.6078, 0.6024, 0.6048, 0.6032, 0.598, 0.6072, 0.6014, 0.5888, 0.6136, 0.605, 0.6032, 0.6032, 0.5988, 0.6014, 0.5988, 0.6054, 0.6038, 0.599, 0.5976, 0.5962, 0.602, 0.6028, 0.6082, 0.5936, 0.6052, 0.6014, 0.6022, 0.5976, 0.606, 0.6038, 0.6018, 0.6066, 0.601, 0.6038, 0.601, 0.6028, 0.6104, 0.5994, 0.6048, 0.5996, 0.6054, 0.597, 0.6042, 0.6048, 0.5962, 0.5968, 0.6036, 0.598, 0.6002, 0.593, 0.5972, 0.6024, 0.6018, 0.6102, 0.601, 0.6038, 0.594, 0.6068, 0.606, 0.6138, 0.6048, 0.602, 0.591, 0.6118, 0.6074, 0.5994, 0.5962, 0.6048, 0.6006, 0.6058, 0.6026, 0.6032, 0.6028, 0.608, 0.6036, 0.5968, 0.6004, 0.6054, 0.601, 0.6038, 0.6058, 0.6052, 0.5996, 0.6044, 0.598, 0.5986, 0.6018, 0.6002, 0.6064, 0.6064, 0.5918, 0.6004, 0.601, 0.605, 0.5974, 0.608, 0.608, 0.5968, 0.6042, 0.6034, 0.5984, 0.597, 0.6006, 0.6038, 0.603, 0.6004, 0.594, 0.5924, 0.5986, 0.5994, 0.6108, 0.5988, 0.6052, 0.6006, 0.6028, 0.602, 0.6016, 0.5996, 0.6012, 0.6014, 0.6042, 0.5988, 0.6064, 0.5982, 0.6, 0.6066, 0.609, 0.6096, 0.5948, 0.605, 0.6036, 0.5952, 0.6086, 0.6008, 0.5934, 0.6066, 0.608, 0.5998, 0.6042, 0.6016, 0.6018, 0.6062, 0.6068, 0.6194, 0.6032, 0.6116, 0.6058, 0.6022, 0.6056, 0.6, 0.6034, 0.6054, 0.6124, 0.6092, 0.603, 0.6016, 0.6018, 0.6084, 0.6026, 0.6154, 0.6034, 0.6118, 0.6102, 0.601, 0.603, 0.606, 0.6114, 0.6024, 0.6112, 0.6094, 0.6026, 0.598, 0.6074, 0.6066, 0.602, 0.6058, 0.603, 0.6078, 0.604, 0.605, 0.607, 0.605, 0.6044, 0.6026, 0.6006, 0.5988, 0.6056, 0.6016, 0.6054, 0.6004, 0.6024, 0.6092, 0.5954, 0.5962, 0.6036, 0.6008, 0.602, 0.6088, 0.6022, 0.6052, 0.5982, 0.6036, 0.601, 0.5956, 0.6024, 0.6104, 0.6028, 0.5898, 0.5994, 0.5946, 0.6054, 0.6064, 0.6102, 0.609, 0.6024, 0.599, 0.601, 0.6074, 0.6018, 0.595, 0.6034, 0.6028, 0.6008, 0.5996, 0.5992, 0.6006, 0.5996, 0.6018, 0.5968, 0.6016, 0.602, 0.6018]
################# cnn_gru_True Training Accuracy = [0.26466668, 0.36813334, 0.40513334, 0.4256, 0.44268888, 0.4564222, 0.46568888, 0.4769111, 0.48531112, 0.491, 0.49744445, 0.50593334, 0.5138222, 0.51564443, 0.5213778, 0.5223778, 0.5283778, 0.5326222, 0.53275555, 0.53764445, 0.54586667, 0.5451556, 0.54735553, 0.5526, 0.5533111, 0.55424446, 0.5568889, 0.56262225, 0.5646, 0.5660667, 0.56333333, 0.5680889, 0.5706889, 0.5710889, 0.5733111, 0.5754667, 0.57637775, 0.5764667, 0.5768222, 0.5766889, 0.57817775, 0.5839555, 0.5825111, 0.5855778, 0.58424443, 0.5876, 0.58786666, 0.58806664, 0.58966666, 0.5938445, 0.5907111, 0.5939556, 0.59331113, 0.59475553, 0.5945333, 0.59515554, 0.59853333, 0.59635556, 0.6008667, 0.59893334, 0.5993556, 0.6007111, 0.6008889, 0.6032889, 0.6000444, 0.6049778, 0.60246664, 0.60384446, 0.60564446, 0.6048889, 0.6089778, 0.6061111, 0.60966665, 0.60686666, 0.60895556, 0.60973334, 0.60944444, 0.6095778, 0.6099778, 0.6114889, 0.6125778, 0.6149333, 0.61322224, 0.6185333, 0.6148, 0.61682224, 0.6157333, 0.6142, 0.6166222, 0.6152, 0.6158222, 0.61653334, 0.62155557, 0.6175333, 0.6168889, 0.61995554, 0.6193778, 0.6175778, 0.6207111, 0.62277776, 0.62144446, 0.62013334, 0.62328887, 0.62633336, 0.62722224, 0.62171113, 0.6248222, 0.62586665, 0.6251778, 0.6256889, 0.6254, 0.6249111, 0.62648886, 0.62468886, 0.6260889, 0.6276, 0.6266, 0.6273556, 0.6258444, 0.6287778, 0.6277111, 0.63026667, 0.6285333, 0.62846667, 0.62813336, 0.6326889, 0.6296, 0.63177776, 0.6323778, 0.6324, 0.63215554, 0.63104445, 0.6322889, 0.6328667, 0.63173336, 0.63515556, 0.6334, 0.63575554, 0.63404447, 0.6330444, 0.63526666, 0.6344444, 0.6337778, 0.63335556, 0.63386667, 0.6336222, 0.6369333, 0.63553333, 0.63713336, 0.63677776, 0.6365333, 0.6353111, 0.6347333, 0.6371111, 0.637, 0.63688886, 0.6344, 0.6371111, 0.636, 0.6394889, 0.638, 0.63946664, 0.63566667, 0.63857776, 0.6413111, 0.6376889, 0.63493335, 0.6387111, 0.6397778, 0.64055556, 0.64073336, 0.63766664, 0.6411333, 0.6392222, 0.6402444, 0.6413556, 0.64077777, 0.6387333, 0.6377778, 0.63884443, 0.64177775, 0.6401111, 0.64, 0.6415111, 0.64166665, 0.6448, 0.6414667, 0.64228886, 0.6416889, 0.63975555, 0.6437778, 0.6429778, 0.6421555, 0.64346665, 0.64155555, 0.64284444, 0.6429333, 0.64415556, 0.64611113, 0.64555556, 0.6452444, 0.64522225, 0.64824444, 0.64275557, 0.64593333, 0.64662224, 0.6431556, 0.6444, 0.6441111, 0.64482224, 0.6471556, 0.64584446, 0.6441778, 0.6448, 0.6446, 0.64775556, 0.64764446, 0.64677775, 0.646, 0.6472222, 0.6472, 0.6481111, 0.6465333, 0.6469778, 0.6510222, 0.64677775, 0.6503556, 0.647, 0.64944446, 0.64655554, 0.64724445, 0.65128887, 0.64955556, 0.6482222, 0.6444889, 0.6488, 0.64797777, 0.6509111, 0.6520444, 0.65022224, 0.6516, 0.645, 0.65044445, 0.64702225, 0.65264446, 0.6487778, 0.64944446, 0.6492222, 0.6536889, 0.6499778, 0.6486222, 0.6539556, 0.64806664, 0.6488, 0.65055555, 0.6541778, 0.6518667, 0.6526667, 0.65155554, 0.6526, 0.65202224, 0.64977777, 0.65315557, 0.65128887, 0.64773333, 0.6536222, 0.65335554, 0.6523778, 0.6494, 0.6510889, 0.6496889, 0.6514, 0.65117776, 0.65375555, 0.65415555, 0.6495778, 0.65055555, 0.6507556, 0.65346664, 0.6548, 0.65115553, 0.6553111, 0.6517778, 0.6532889, 0.6548, 0.6546222, 0.65533334, 0.6521556, 0.6543555, 0.65217775, 0.65275556, 0.6522, 0.65555555, 0.65482223, 0.6541111, 0.6546889, 0.65533334, 0.6541111, 0.6554, 0.6537333, 0.6537778, 0.6528444, 0.65331113, 0.65455556, 0.6544, 0.65477777, 0.6572667, 0.65606666, 0.6556, 0.65606666, 0.6553556, 0.65353334, 0.6518, 0.6536667, 0.65595555, 0.65775555, 0.65657777, 0.6549778, 0.65764445, 0.6557111, 0.6556, 0.6590222, 0.6538889, 0.6591778, 0.65444446, 0.6562, 0.6564, 0.6607778, 0.6556444, 0.65826666, 0.6562, 0.6581333, 0.6578889, 0.65853333, 0.6584, 0.65782225, 0.6594667, 0.6552, 0.6586667, 0.658, 0.6588, 0.66135556, 0.65668887, 0.6561555, 0.6581111, 0.6599111, 0.6588, 0.6568, 0.6608667, 0.6603778, 0.6602889, 0.6592, 0.6594667, 0.65706664, 0.6567111, 0.6608667, 0.65886664, 0.65966666, 0.66035557, 0.66175556, 0.65584445, 0.65966666, 0.6606889, 0.65922225, 0.6595111, 0.65515554, 0.65984446, 0.6612667, 0.6605333, 0.662, 0.6613778, 0.6611556, 0.6580667, 0.66135556, 0.65882224, 0.65655553, 0.65955555, 0.65988886, 0.6593556, 0.65808886, 0.6616667, 0.6614222, 0.6634, 0.6632222, 0.6618, 0.6599778, 0.66013336, 0.6608, 0.66146666, 0.65944445, 0.65966666, 0.66135556, 0.66004443, 0.6608222, 0.6630222, 0.6620889, 0.66195554, 0.6582222, 0.6606445, 0.6629556, 0.66164446, 0.66055554, 0.6608889, 0.66175556, 0.6606, 0.6614222, 0.6640222, 0.66364443, 0.6643556, 0.66191113, 0.6626667, 0.6630222, 0.6656889, 0.6631333, 0.66293335, 0.6617778, 0.6610889, 0.6614889, 0.662, 0.6593111, 0.6612667, 0.66102225, 0.6631333, 0.66395557, 0.66282225, 0.66713333, 0.6623778, 0.6648222, 0.6622667, 0.66746664, 0.6616667, 0.6630222, 0.6622, 0.6624, 0.66415554, 0.662, 0.6612222, 0.6618222, 0.6629111, 0.66426665, 0.66315556, 0.6640667, 0.6640889, 0.66533333, 0.6626, 0.6617778, 0.66477776, 0.6654889, 0.66477776, 0.6624889, 0.6622222, 0.6642, 0.6663111, 0.66293335, 0.6636889, 0.6643556, 0.6652, 0.6680889, 0.6658222, 0.66415554, 0.6677778, 0.6622889, 0.6688, 0.6630222, 0.66848886, 0.66355556, 0.6624889, 0.6658222, 0.66602224, 0.6631778, 0.6618889, 0.6654222, 0.6662889, 0.66726667, 0.66384447, 0.6662, 0.66477776, 0.6650889, 0.66293335, 0.66484445, 0.66371113, 0.6646, 0.6661556, 0.66191113, 0.6656889, 0.6649333, 0.66686666, 0.66544443, 0.66624445, 0.66455555, 0.6698222, 0.6665556, 0.6648, 0.6663111, 0.66455555, 0.6653778, 0.6675556, 0.66404444, 0.66484445, 0.66617775]
with cnn_dropout = 0.2 and rnn dropout = 0.2and lr = 5e-4 with res = 8 out.812847
################# cnn_gru_True Validation Accuracy = [0.3598, 0.4126, 0.4454, 0.4714, 0.4722, 0.506, 0.5062, 0.5154, 0.5382, 0.5296, 0.5368, 0.5352, 0.5364, 0.5584, 0.5564, 0.5624, 0.5
704, 0.5622, 0.5612, 0.5568, 0.5656, 0.5572, 0.572, 0.5718, 0.569, 0.576, 0.5718, 0.5726, 0.5732, 0.5754, 0.5758, 0.5754, 0.5802, 0.5778, 0.5778, 0.5818, 0.5808, 0.573, 0.5764, 0.5782, 0.578, 0.5828, 0.5656, 0.5796, 0.5704, 0.5808, 0.5764, 0.5774, 0.5644, 0.5794, 0.5794, 0.5834, 0.57, 0.5724, 0.5806, 0.5784, 0.5794, 0.5834, 0.5756, 0.5786, 0.5802, 0.5746, 0.571, 0.5812, 0.569, 0.5724, 0.5794, 0.5762, 0.581, 0.5664, 0.574, 0.5782, 0.5738, 0.5714, 0.5754, 0.5716, 0.5638, 0.5696, 0.5706, 0.5758, 0.567, 0.571, 0.5716, 0.5788, 0.559, 0.5682, 0.5716, 0.5728, 0.5718, 0.5758, 0.569, 0.573, 0.5756, 0.5746, 0.5744, 0.571, 0.5762, 0.5792, 0.5688, 0.5796]
################# cnn_gru_True Training Accuracy = [0.27786666, 0.3842222, 0.42204446, 0.44537777, 0.4655111, 0.48406667, 0.49457777, 0.50564444, 0.5188889, 0.5279111, 0.5366667, 0.544, 0.5515111, 0.5573556, 0.56457776, 0.5718222, 0.5748889, 0.5826667, 0.5850222, 0.5921556, 0.59155554, 0.5960889, 0.6028889, 0.60664445, 0.6115556, 0.61553335, 0.61968887, 0.6218889, 0.6240444, 0.6262222, 0.6306889, 0.6329778, 0.6356, 0.6404, 0.6475111, 0.6451333, 0.64626664, 0.6536889, 0.65573335, 0.65842223, 0.65977776, 0.6573111, 0.6640889, 0.6664, 0.66866666, 0.6700889, 0.6704222, 0.6747556, 0.6781333, 0.6785111, 0.67693335, 0.68086666, 0.68293333, 0.6823111, 0.6862444, 0.69013333, 0.69044447, 0.6957778, 0.6952, 0.6944889, 0.69953334, 0.6963111, 0.7000222, 0.7018667, 0.7029333, 0.7018222, 0.70446664, 0.7051111, 0.7105778, 0.70993334, 0.71308887, 0.71331114, 0.71128887, 0.7160444, 0.7176222, 0.71793336, 0.71846664, 0.72062224, 0.7216222, 0.7220889, 0.72117776, 0.72617775, 0.72535557, 0.72904444, 0.72675556, 0.73215556, 0.7297556, 0.72926664, 0.7349333, 0.73224443, 0.7335778, 0.73744446, 0.73384446, 0.73735553, 0.73744446, 0.7404889, 0.73928887, 0.742, 0.7410667, 0.7395778]
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 10 samples and 500 epochs out.813851
################# cnn_gru_True Validation Accuracy = [0.3354, 0.4208, 0.4522, 0.463, 0.4448, 0.4934, 0.5048, 0.5036, 0.5082, 0.5202, 0.4958, 0.5184, 0.5302, 0.5364, 0.5474, 0.5298, 0.5382, 0.5446, 0.5486, 0.5496, 0.5468, 0.5616, 0.5516, 0.5542, 0.5606, 0.5624, 0.5744, 0.5644, 0.5624, 0.5712, 0.5714, 0.5746, 0.5638, 0.5622, 0.5768, 0.5792, 0.5852, 0.5758, 0.5768, 0.5708, 0.5882, 0.5814, 0.5778, 0.5884, 0.5892, 0.5862, 0.5828, 0.5838, 0.5892, 0.58, 0.595, 0.5872, 0.58, 0.5868, 0.5888, 0.592, 0.5848, 0.5824, 0.5852, 0.5832, 0.5898, 0.5846, 0.584, 0.5942, 0.5858, 0.5918, 0.5826, 0.597, 0.5984, 0.5928, 0.5802, 0.5972, 0.5976, 0.5964, 0.5894, 0.5888, 0.5948, 0.5944, 0.594, 0.5934, 0.5952, 0.5976, 0.5994, 0.6002, 0.5926, 0.5984, 0.5976, 0.591, 0.593, 0.6076, 0.5888, 0.6018, 0.5908, 0.5964, 0.5966, 0.5968, 0.5912, 0.5976, 0.5912, 0.597, 0.5934, 0.588, 0.6014, 0.592, 0.5952, 0.606, 0.6026, 0.5932, 0.6, 0.5944, 0.5898, 0.5914, 0.5976, 0.6008, 0.5894, 0.6058, 0.6038, 0.5974, 0.5996, 0.6064, 0.6014, 0.5914, 0.6012, 0.5922, 0.5938, 0.6008, 0.6058, 0.6046, 0.6012, 0.593, 0.6, 0.6046, 0.5946, 0.5962, 0.592, 0.5968, 0.5946, 0.5966, 0.5968, 0.588, 0.6004, 0.6008, 0.592, 0.5976, 0.5998, 0.5854, 0.6012, 0.5994, 0.5908, 0.5996, 0.6056, 0.5924, 0.5974, 0.5986, 0.5926, 0.5938, 0.5902, 0.5924, 0.598, 0.5988, 0.6028, 0.601, 0.5976, 0.597, 0.6044, 0.5894, 0.5904, 0.6, 0.595, 0.5974, 0.5998, 0.594, 0.5946, 0.5968, 0.5938, 0.5858, 0.6016, 0.5934, 0.6052, 0.598, 0.608, 0.6, 0.6008, 0.5956, 0.591, 0.6024, 0.6076, 0.5986, 0.5974, 0.6004, 0.6046, 0.597, 0.6048, 0.588, 0.5902, 0.5868, 0.5928, 0.5986, 0.5994, 0.5962, 0.5946, 0.594, 0.5972, 0.592, 0.5916, 0.589, 0.6042, 0.5908, 0.5922, 0.5924, 0.5902, 0.5914, 0.6026, 0.5992, 0.5956, 0.5954, 0.6034, 0.5906, 0.6052, 0.5918, 0.6, 0.6004, 0.5912, 0.5942, 0.5972, 0.6066, 0.5946, 0.5972, 0.5854, 0.5994, 0.5954, 0.592, 0.5904, 0.5956, 0.5946, 0.5838, 0.5872, 0.5948, 0.5972, 0.5996, 0.605, 0.5962, 0.604, 0.5976, 0.6, 0.6016, 0.6014, 0.6044, 0.5928, 0.598, 0.6, 0.59, 0.5978, 0.5902, 0.5934, 0.6026, 0.5956, 0.6012, 0.5932, 0.6, 0.5952, 0.602, 0.5942, 0.5988, 0.6024, 0.597, 0.5964, 0.5882, 0.6008, 0.5958, 0.6006, 0.5964, 0.594, 0.5882, 0.6028, 0.6032, 0.5982, 0.6, 0.5988, 0.6018, 0.6028, 0.609, 0.6032, 0.5954, 0.5988, 0.6074, 0.6014, 0.6086, 0.6002, 0.605, 0.603, 0.6058, 0.6084, 0.5894, 0.6046, 0.6006, 0.605, 0.5972, 0.5964, 0.5972, 0.603, 0.5986, 0.601, 0.5972, 0.6058, 0.6028, 0.596, 0.603, 0.598, 0.6008, 0.5958, 0.5906, 0.6024, 0.6024, 0.6014, 0.6078, 0.6006, 0.5996, 0.603, 0.6068, 0.6046, 0.6064, 0.5948, 0.5988, 0.6074, 0.6024, 0.605, 0.5974, 0.6014, 0.6054, 0.5966, 0.6006, 0.601, 0.592, 0.6108, 0.5944, 0.6008, 0.599, 0.6072, 0.6034, 0.5964, 0.6104, 0.592, 0.6044, 0.6026, 0.6032, 0.6058, 0.6094, 0.6042, 0.6062, 0.6016, 0.6084, 0.6028, 0.608, 0.604, 0.6012, 0.6012, 0.6072, 0.6008, 0.607, 0.6018, 0.597, 0.6008, 0.6092, 0.6044, 0.594, 0.6026, 0.6082, 0.6078, 0.6092, 0.6064, 0.6052, 0.6052, 0.6004, 0.6078, 0.6102, 0.6, 0.615, 0.605, 0.5942, 0.6044, 0.6084, 0.6002, 0.6034, 0.5998, 0.5982, 0.5974, 0.598, 0.601, 0.597, 0.6062, 0.6036, 0.6048, 0.599, 0.604, 0.607, 0.6036, 0.5992, 0.6018, 0.6022, 0.6044, 0.5984, 0.6006, 0.5986, 0.6056, 0.6062, 0.5942, 0.6032, 0.6026, 0.5994, 0.6064, 0.599, 0.6008, 0.5986, 0.5984, 0.5962, 0.5972, 0.6016, 0.6014, 0.604, 0.6026, 0.6002, 0.6076, 0.605, 0.5988, 0.6006, 0.6006, 0.5992, 0.5994, 0.6016, 0.601, 0.5924, 0.597, 0.5998, 0.6012, 0.6064, 0.5968, 0.6012, 0.604, 0.603, 0.602, 0.595, 0.6044, 0.5952, 0.6016, 0.6058, 0.6012, 0.6042, 0.5966, 0.6054, 0.6066, 0.6016, 0.594, 0.6042, 0.607, 0.6038, 0.5942, 0.6064, 0.6044, 0.6022, 0.6056, 0.6036, 0.594, 0.605, 0.6042, 0.6062, 0.591, 0.5988, 0.6056, 0.608, 0.6014, 0.605, 0.5996, 0.6046, 0.6066, 0.6032, 0.5998, 0.6028, 0.6, 0.5948, 0.6046, 0.6066, 0.603, 0.6038, 0.6066, 0.6034, 0.6034, 0.5978, 0.6014, 0.602, 0.592, 0.6008, 0.6066, 0.6046, 0.6072, 0.6106, 0.6062, 0.6074, 0.5986, 0.6034]
################# cnn_gru_True Training Accuracy = [0.24648888, 0.3745778, 0.41557777, 0.43804446, 0.4576, 0.4678, 0.47815555, 0.4868, 0.49584445, 0.5020667, 0.50942224, 0.5155333, 0.51953334, 0.52253336, 0.5287778, 0.5311555, 0.5374, 0.5400222, 0.54744446, 0.54553336, 0.55102223, 0.55517775, 0.5588667, 0.55873334, 0.56222224, 0.56906664, 0.56704444, 0.57048887, 0.5709556, 0.57553333, 0.58104444, 0.57677776, 0.5827111, 0.5832, 0.58533335, 0.5862667, 0.5885556, 0.5909333, 0.5918, 0.59326667, 0.5958222, 0.5950222, 0.59848887, 0.59871113, 0.6015555, 0.60064447, 0.60433334, 0.6062222, 0.6030667, 0.6063333, 0.6067333, 0.6074889, 0.60944444, 0.6112889, 0.61002225, 0.61248887, 0.6134, 0.61333334, 0.6154, 0.6148, 0.61473334, 0.618, 0.6176222, 0.61884445, 0.6212889, 0.62226665, 0.6203778, 0.62186664, 0.6224667, 0.626, 0.6241111, 0.6243333, 0.62524444, 0.6258889, 0.6276444, 0.62704444, 0.62773335, 0.62866664, 0.62637776, 0.62784445, 0.63368887, 0.63137776, 0.63233334, 0.6337778, 0.63453335, 0.6339778, 0.6327556, 0.6346667, 0.6375333, 0.63571113, 0.6359111, 0.63633335, 0.63897777, 0.6382667, 0.6386667, 0.6386667, 0.6402, 0.6410889, 0.63853335, 0.6414222, 0.6431111, 0.64084446, 0.6423333, 0.6404222, 0.64386666, 0.6427778, 0.64442223, 0.64526665, 0.6431778, 0.6445111, 0.6468222, 0.6451333, 0.6484889, 0.64537776, 0.64544445, 0.6438889, 0.65073335, 0.6497333, 0.6512667, 0.6492222, 0.64784443, 0.64622223, 0.6495111, 0.6498, 0.6488889, 0.6512667, 0.6499111, 0.6527333, 0.6570889, 0.65253335, 0.65371114, 0.65015554, 0.6525111, 0.6505778, 0.64982224, 0.65437776, 0.6553778, 0.6556889, 0.6545333, 0.65713334, 0.65573335, 0.6571111, 0.65706664, 0.6573333, 0.65397775, 0.6564889, 0.6561111, 0.65691113, 0.65595555, 0.6564889, 0.6577778, 0.65757775, 0.6575111, 0.65835553, 0.6568889, 0.65746665, 0.65602225, 0.6579111, 0.65724444, 0.6560444, 0.6582222, 0.65844446, 0.6604667, 0.6612667, 0.6575111, 0.6612667, 0.6634222, 0.6617333, 0.6640889, 0.6603111, 0.66286665, 0.66135556, 0.6610889, 0.6615555, 0.6611556, 0.6604889, 0.66477776, 0.6643556, 0.6623333, 0.6612222, 0.66353333, 0.6625556, 0.66186666, 0.66333336, 0.66395557, 0.66355556, 0.66575557, 0.66433334, 0.6652, 0.6616667, 0.66602224, 0.6647556, 0.6646444, 0.66708887, 0.6645333, 0.6630667, 0.66844445, 0.6675111, 0.668, 0.6643556, 0.6670222, 0.6701111, 0.6662222, 0.66546667, 0.66364443, 0.6655333, 0.6684667, 0.6691778, 0.66922224, 0.6661111, 0.6691778, 0.66804445, 0.6721333, 0.6696889, 0.66775554, 0.66642225, 0.6698, 0.66884446, 0.6692889, 0.66713333, 0.66962224, 0.6699778, 0.67197776, 0.6676222, 0.6693556, 0.66926664, 0.67282224, 0.6721778, 0.6653111, 0.67164445, 0.6734222, 0.66951114, 0.67384446, 0.6722, 0.6716889, 0.6684667, 0.67164445, 0.6717778, 0.6716, 0.67102224, 0.6719555, 0.6747111, 0.6744222, 0.67253333, 0.672, 0.67362225, 0.6738222, 0.6768889, 0.6722, 0.67182225, 0.67775553, 0.6749111, 0.67495555, 0.6774667, 0.67304444, 0.6748667, 0.6732889, 0.67513335, 0.6786444, 0.6725111, 0.6751111, 0.6779111, 0.6733111, 0.6766667, 0.67653334, 0.6767778, 0.67755556, 0.6733556, 0.6755111, 0.67646664, 0.67513335, 0.6769556, 0.6732, 0.6803778, 0.67642224, 0.67595553, 0.6792667, 0.6769111, 0.6782889, 0.67833334, 0.67917776, 0.67422223, 0.67873335, 0.6778889, 0.67495555, 0.677, 0.67962223, 0.68053335, 0.6788222, 0.67664444, 0.6814, 0.681, 0.67826664, 0.6806222, 0.68153334, 0.6809555, 0.6798667, 0.6808889, 0.67764443, 0.6803111, 0.6794222, 0.67646664, 0.6801111, 0.6809111, 0.6828667, 0.67866665, 0.68137777, 0.6797111, 0.67991114, 0.67913336, 0.6791111, 0.68164444, 0.68042225, 0.68126667, 0.6821333, 0.6833111, 0.6835778, 0.67884445, 0.68593335, 0.6798, 0.67928886, 0.682, 0.6838667, 0.6833111, 0.68648887, 0.6845111, 0.6812889, 0.6846222, 0.6825778, 0.6810222, 0.68273336, 0.68315554, 0.6806667, 0.68648887, 0.68295556, 0.6824, 0.6821111, 0.681, 0.6835333, 0.68524444, 0.68455553, 0.6817333, 0.6833111, 0.6825333, 0.68675554, 0.6819111, 0.68475556, 0.6879333, 0.68473333, 0.68384445, 0.6862222, 0.6841111, 0.6841111, 0.68277776, 0.6884, 0.6818, 0.6853778, 0.6822444, 0.68637776, 0.6852889, 0.68615556, 0.6869556, 0.6840444, 0.6870667, 0.68564445, 0.68497777, 0.68531114, 0.6839111, 0.6844, 0.68924445, 0.68635553, 0.68484443, 0.6872, 0.6852889, 0.6884889, 0.68435556, 0.68475556, 0.6860667, 0.68664443, 0.6854889, 0.6857333, 0.68864447, 0.6874889, 0.6874, 0.6852889, 0.6850889, 0.6857778, 0.6856889, 0.6898444, 0.6896667, 0.6880222, 0.68762225, 0.68873334, 0.68815553, 0.6851111, 0.68813336, 0.6874667, 0.69233334, 0.6897111, 0.6887778, 0.68846667, 0.6905778, 0.6882222, 0.69188887, 0.6883111, 0.6878, 0.6901111, 0.6859556, 0.68902224, 0.69188887, 0.6915778, 0.69206667, 0.6874889, 0.6928, 0.689, 0.6896, 0.6896667, 0.6893111, 0.68997777, 0.6876, 0.6924667, 0.6876889, 0.6892222, 0.6910889, 0.6886, 0.6886889, 0.69391114, 0.6886889, 0.69284445, 0.69211113, 0.6900667, 0.6905556, 0.6885778, 0.6871333, 0.69188887, 0.69204444, 0.6908, 0.693, 0.69355553, 0.69211113, 0.6909556, 0.6921333, 0.6925333, 0.69126666, 0.69211113, 0.69277775, 0.6929111, 0.69075555, 0.69093335, 0.69075555, 0.6912, 0.68862224, 0.69346666, 0.6921778, 0.6904889, 0.69486666, 0.69166666, 0.6924, 0.69355553, 0.69373333, 0.6925111, 0.69295555, 0.69515556, 0.69184446, 0.69206667, 0.69537777, 0.6911111, 0.6930444, 0.69335556, 0.6888667, 0.69364446, 0.6946222, 0.6948444, 0.6927111, 0.6944444, 0.6907333, 0.69357777, 0.6952222, 0.69155556, 0.6915333, 0.69537777, 0.6924889, 0.69035554, 0.69366664, 0.6966, 0.6922, 0.6918667, 0.6926, 0.6960667, 0.6926, 0.69564444, 0.69328886, 0.6952889, 0.6944444, 0.69571114, 0.69546664, 0.694, 0.6939333, 0.6952889, 0.6956667]
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 5 samples and 200 epochs, hs = 256 out.836806
################# cnn_gru_True Validation Accuracy = [0.3074, 0.3502, 0.3972, 0.4236, 0.4458, 0.4612, 0.478, 0.4846, 0.4832, 0.494, 0.4936, 0.5028, 0.511, 0.5, 0.4942, 0.5186, 0.5216, 0.5274, 0.5356, 0.5306, 0.5296, 0.535, 0.5346, 0.5346, 0.5448, 0.534, 0.5384, 0.5442, 0.5434, 0.539, 0.5478, 0.552, 0.549, 0.5404, 0.5448, 0.5434, 0.5568, 0.5462, 0.5462, 0.5558, 0.5612, 0.5484, 0.5606, 0.5666, 0.5698, 0.5582, 0.5578, 0.5744, 0.56, 0.5466, 0.5554, 0.563, 0.5592, 0.5566, 0.5674, 0.5536, 0.5606, 0.5678, 0.5618, 0.559, 0.5676, 0.571, 0.563, 0.5646, 0.563, 0.5732, 0.565, 0.5738, 0.572, 0.5774, 0.5652, 0.5636, 0.5688, 0.5718, 0.5734, 0.558, 0.571, 0.577, 0.5674, 0.579, 0.5706, 0.5764, 0.567, 0.5772, 0.5738, 0.5688, 0.5706, 0.5712, 0.575, 0.5748, 0.5804, 0.5708, 0.566, 0.57, 0.5768, 0.5814, 0.569, 0.5796, 0.5776, 0.5702, 0.5806, 0.5834, 0.5708, 0.5748, 0.5794, 0.585, 0.5792, 0.5738, 0.5736, 0.5776, 0.5812, 0.5804, 0.5762, 0.5806, 0.5822, 0.5786, 0.5768, 0.5752, 0.5822, 0.5808, 0.5822, 0.5844, 0.5876, 0.589, 0.5872, 0.5764, 0.5808, 0.5738, 0.581, 0.5828, 0.5688, 0.577, 0.5798, 0.587, 0.5766, 0.5798, 0.5834, 0.5802, 0.5826, 0.578, 0.5786, 0.565, 0.5742, 0.5894, 0.5808, 0.5708, 0.5766, 0.5866, 0.5806, 0.577, 0.5794, 0.5802, 0.5776, 0.5824, 0.586, 0.574, 0.5804, 0.5834, 0.5834, 0.578, 0.5784, 0.571, 0.5668, 0.5798, 0.5792, 0.5748, 0.5824, 0.5628, 0.5814, 0.5796, 0.581, 0.575, 0.5802, 0.5786, 0.5802, 0.5852, 0.5818, 0.5826, 0.59, 0.5762, 0.59, 0.577, 0.5798, 0.5796, 0.581, 0.5806, 0.5774, 0.5772, 0.5798, 0.585, 0.588, 0.5856, 0.5836, 0.5858, 0.5842, 0.5826, 0.5818, 0.5764, 0.5814, 0.5812]
################# cnn_gru_True Training Accuracy = [0.24148889, 0.35346666, 0.39324445, 0.4138, 0.4300222, 0.44151112, 0.45264444, 0.4583111, 0.46951112, 0.47684443, 0.48144445, 0.4867111, 0.4934, 0.49924445, 0.5006667, 0.5047333, 0.51008886, 0.5148889, 0.51364446, 0.5214889, 0.5223111, 0.5267556, 0.5283778, 0.52993333, 0.5365111, 0.5373333, 0.5393111, 0.5411556, 0.5418444, 0.5449778, 0.54704446, 0.55093336, 0.55095553, 0.5567778, 0.5597778, 0.5578, 0.55826664, 0.5587111, 0.56135553, 0.5613111, 0.5638222, 0.5689778, 0.5655111, 0.5698, 0.56924444, 0.57137775, 0.57251114, 0.57457775, 0.5736667, 0.578, 0.5786222, 0.5777556, 0.57964444, 0.5810889, 0.5809778, 0.5831556, 0.5817556, 0.584, 0.5824444, 0.5857111, 0.58357775, 0.58804446, 0.58624446, 0.5888444, 0.5892222, 0.59157777, 0.59275556, 0.5909333, 0.5932, 0.5918667, 0.59206665, 0.59437776, 0.5966, 0.5946889, 0.59984446, 0.59511113, 0.5969333, 0.6005333, 0.59893334, 0.5999333, 0.6010889, 0.60175556, 0.6009333, 0.6008, 0.60035557, 0.6005778, 0.6013778, 0.6052667, 0.6039111, 0.6061556, 0.60355556, 0.603, 0.60344446, 0.6076889, 0.6047556, 0.6068222, 0.60406667, 0.6079778, 0.60693336, 0.6074889, 0.6102889, 0.6061111, 0.61104447, 0.61002225, 0.6100444, 0.60866666, 0.6106, 0.61131114, 0.6118889, 0.61204445, 0.61377776, 0.61182225, 0.61311114, 0.61197776, 0.61635554, 0.6154889, 0.6140444, 0.61644447, 0.61704445, 0.61833334, 0.61795557, 0.6198222, 0.6174667, 0.6174, 0.61766666, 0.6165778, 0.6163778, 0.61793333, 0.61946666, 0.62144446, 0.6208444, 0.6163333, 0.61624444, 0.6175111, 0.62124443, 0.6211333, 0.6183778, 0.62288886, 0.6214667, 0.6212889, 0.6186889, 0.6230222, 0.62313336, 0.6221333, 0.6222, 0.62453336, 0.6224889, 0.6257333, 0.6224667, 0.6254445, 0.6226889, 0.62384444, 0.6247111, 0.6238889, 0.6228222, 0.6233778, 0.6265333, 0.6257333, 0.62604445, 0.6287111, 0.6253778, 0.6269111, 0.63024443, 0.6262889, 0.62766665, 0.62615556, 0.6257333, 0.6289778, 0.6282, 0.62615556, 0.62993336, 0.6257111, 0.6315111, 0.6270222, 0.6297333, 0.6268889, 0.6298222, 0.6300667, 0.6293333, 0.62995553, 0.6311333, 0.63037777, 0.6307333, 0.62993336, 0.6329111, 0.6297333, 0.63217777, 0.6298444, 0.6303333, 0.6312, 0.6305111, 0.6304, 0.6334444, 0.63204443, 0.63064444, 0.6292, 0.63317776, 0.63226664, 0.6315778, 0.6300667]
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 5 samples and 500 epochs, hs = 256 out.848468
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 10 samples and 200 epochs, hs = 256 out.846686
################# cnn_gru_True Validation Accuracy = [0.3584, 0.427, 0.4594, 0.4528, 0.4746, 0.4934, 0.5094, 0.5078, 0.5196, 0.5242, 0.5342, 0.5258, 0.5292, 0.533, 0.5444, 0.5422, 0.5572, 0.5486, 0.5644, 0.5618, 0.5692, 0.5666, 0.5764, 0.5676, 0.5674, 0.5466, 0.5744, 0.5802, 0.5782, 0.5784, 0.5742, 0.5786, 0.5762, 0.5692, 0.5916, 0.5654, 0.5772, 0.5744, 0.5854, 0.582, 0.5882, 0.5814, 0.595, 0.5838, 0.5866, 0.5888, 0.5876, 0.5888, 0.5866, 0.5782, 0.5958, 0.5926, 0.5914, 0.5778, 0.5944, 0.58, 0.5944, 0.5878, 0.5926, 0.5954, 0.595, 0.5844, 0.588, 0.5934, 0.5942, 0.598, 0.5974, 0.5944, 0.5924, 0.5944, 0.5908, 0.5952, 0.5966, 0.5966, 0.5992, 0.5966, 0.5956, 0.5836, 0.5956, 0.5832, 0.5938, 0.5992, 0.5976, 0.5952, 0.5904, 0.5906, 0.5924, 0.5878, 0.6094, 0.604, 0.5884, 0.5986, 0.5922, 0.5806, 0.5932, 0.5914, 0.603, 0.5888, 0.5892, 0.588, 0.5942, 0.6024, 0.5898, 0.5992, 0.6, 0.5928, 0.5958, 0.5824, 0.6004, 0.5842, 0.5914, 0.603, 0.5946, 0.5928, 0.5956, 0.5828, 0.608, 0.6058, 0.5928, 0.5934, 0.5938, 0.5958, 0.5952, 0.598, 0.5868, 0.6004, 0.5884, 0.593, 0.5936, 0.6094, 0.5996, 0.5984, 0.5976, 0.5984, 0.6084, 0.5964, 0.5886, 0.6, 0.6, 0.596, 0.5936, 0.6028, 0.5986, 0.5992, 0.5784, 0.5882, 0.5942, 0.598, 0.605, 0.5904, 0.6, 0.586, 0.5894, 0.5984, 0.5824, 0.5944, 0.5906, 0.5922, 0.588, 0.5952, 0.593, 0.5846, 0.5932, 0.5978, 0.5942, 0.5958, 0.5992, 0.5938, 0.5914, 0.5968, 0.5946, 0.5978, 0.6004, 0.588, 0.5982, 0.5992, 0.6012, 0.5976, 0.594, 0.5912, 0.5854, 0.5954, 0.5922, 0.5908, 0.5842, 0.6034, 0.5978, 0.6012, 0.5974, 0.5924, 0.5952, 0.6004, 0.5942, 0.6014, 0.5882, 0.5978, 0.5992, 0.5938, 0.5946, 0.6006]
################# cnn_gru_True Training Accuracy = [0.25715557, 0.3822, 0.41933334, 0.4448889, 0.4602, 0.47442222, 0.48344445, 0.49475557, 0.5034222, 0.5122, 0.5181111, 0.5222, 0.5295778, 0.5335111, 0.54168886, 0.54411113, 0.54735553, 0.5506667, 0.5548667, 0.56093335, 0.5622444, 0.5642889, 0.56453335, 0.56953335, 0.57226664, 0.57644445, 0.57728887, 0.5796, 0.58304447, 0.58397776, 0.5872, 0.58673334, 0.58926666, 0.59195554, 0.59515554, 0.59691113, 0.59655553, 0.5989778, 0.60253334, 0.6033111, 0.60406667, 0.60415554, 0.6044889, 0.6035333, 0.6082444, 0.6112222, 0.60873336, 0.61075556, 0.61517775, 0.61646664, 0.61586666, 0.61855555, 0.6187111, 0.6170667, 0.62135553, 0.6203778, 0.6225111, 0.62142223, 0.62326664, 0.6216889, 0.62733334, 0.6271778, 0.6263555, 0.6276444, 0.62946665, 0.6291556, 0.63175553, 0.6302222, 0.63251114, 0.63193333, 0.63204443, 0.6330444, 0.63902223, 0.63384444, 0.6354222, 0.63735557, 0.63368887, 0.6359556, 0.63611114, 0.6389111, 0.63964444, 0.6369333, 0.6382667, 0.64206666, 0.64086664, 0.6418222, 0.64115554, 0.6411778, 0.6412, 0.6436, 0.64566666, 0.64433336, 0.6452444, 0.64735556, 0.64573336, 0.6467111, 0.6476, 0.64442223, 0.6466889, 0.64964443, 0.6488889, 0.64835554, 0.649, 0.6499556, 0.65151113, 0.65037775, 0.6474444, 0.64915556, 0.6519778, 0.6518222, 0.6531111, 0.6531778, 0.6557556, 0.65566665, 0.65246665, 0.6557556, 0.65124446, 0.6572222, 0.6570889, 0.6565111, 0.65326667, 0.6576889, 0.6542889, 0.656, 0.6550889, 0.6578444, 0.6576889, 0.65628886, 0.6586, 0.6575556, 0.6598667, 0.6606445, 0.6608, 0.6623778, 0.65937775, 0.6572222, 0.66206664, 0.6606445, 0.6616, 0.6620889, 0.6596, 0.6650222, 0.6609778, 0.66595554, 0.66095555, 0.6631111, 0.6647111, 0.66466665, 0.66433334, 0.6637333, 0.6649778, 0.6666222, 0.6659778, 0.6642889, 0.6621778, 0.6644222, 0.6658889, 0.66775554, 0.6658, 0.6669111, 0.6663778, 0.67017776, 0.67053336, 0.66724443, 0.6712889, 0.6671111, 0.668, 0.6692889, 0.66815555, 0.6710889, 0.6708, 0.6714, 0.66873336, 0.6704889, 0.66646665, 0.67095554, 0.67095554, 0.67053336, 0.6717333, 0.6691778, 0.6693778, 0.67091113, 0.6690889, 0.6716667, 0.6713333, 0.6724667, 0.67404443, 0.6733556, 0.67417777, 0.6732889, 0.6716667, 0.6734222, 0.6757333, 0.672, 0.6742, 0.67446667, 0.67435557, 0.6749111, 0.67593336, 0.6772889]
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 10 samples and 500 epochs, hs = 256 out.848400
################# cnn_gru_True Validation Accuracy = [0.3422, 0.4174, 0.4266, 0.4656, 0.4878, 0.4868, 0.5108, 0.4958, 0.5298, 0.5346, 0.5252, 0.5476, 0.5532, 0.5586, 0.5608, 0.56, 0.5424, 0.552, 0.565, 0.5654, 0.5628, 0.5552, 0.566, 0.5534, 0.5706, 0.57, 0.5748, 0.5714, 0.5522, 0.581, 0.5688, 0.5702, 0.5862, 0.5836, 0.5872, 0.5894, 0.5886, 0.5872, 0.5716, 0.5824, 0.5968, 0.5756, 0.5814, 0.5984, 0.6004, 0.588, 0.5806, 0.5666, 0.5892, 0.5862, 0.6026, 0.6034, 0.5834, 0.6026, 0.588, 0.5896, 0.589, 0.5998, 0.6068, 0.5786, 0.5922, 0.5984, 0.588, 0.5906, 0.6004, 0.5922, 0.5968, 0.5908, 0.5972, 0.5956, 0.6088, 0.5998, 0.5846, 0.609, 0.6006, 0.5986, 0.5984, 0.595, 0.6062, 0.5976, 0.6038, 0.5802, 0.6034, 0.593, 0.5772, 0.6036, 0.61, 0.599, 0.594, 0.6002, 0.6044, 0.592, 0.604, 0.6078, 0.591, 0.5972, 0.6098, 0.5998, 0.6018, 0.5908, 0.5952, 0.614, 0.6072, 0.603, 0.5918, 0.603, 0.6098, 0.6048, 0.606, 0.5926, 0.6008, 0.5958, 0.5998, 0.607, 0.6032, 0.6086, 0.5964, 0.608, 0.6158, 0.5996, 0.5914, 0.6034, 0.603, 0.6036, 0.6128, 0.5926, 0.613, 0.608, 0.6028, 0.602, 0.6024, 0.612, 0.604, 0.6016, 0.6036, 0.5968, 0.6098, 0.6142, 0.5884, 0.6148, 0.5884, 0.5962, 0.6038, 0.6088, 0.6098, 0.5998, 0.602, 0.6018, 0.6102, 0.6006, 0.6066, 0.6016, 0.609, 0.6046, 0.5858, 0.6038, 0.6022, 0.6066, 0.6052, 0.6014, 0.603, 0.5988, 0.598, 0.6032, 0.609, 0.6096, 0.6096, 0.5942, 0.6008, 0.5954, 0.5966, 0.6092, 0.6054, 0.5938, 0.6022, 0.6036, 0.6066, 0.5944, 0.5964, 0.6042, 0.6046, 0.5956, 0.6056, 0.6048, 0.6092, 0.6034, 0.6014, 0.6008, 0.5894, 0.5952, 0.6084, 0.6072, 0.608, 0.6064, 0.6062, 0.6026, 0.599, 0.595, 0.5918, 0.6014, 0.5986, 0.6024, 0.5964, 0.6014, 0.6036, 0.6006, 0.6052, 0.5994, 0.605, 0.6022, 0.6058, 0.6006, 0.6038, 0.5968, 0.6096, 0.598, 0.6094, 0.5934, 0.6022, 0.604, 0.6044, 0.5962, 0.5952, 0.6002, 0.607, 0.6152, 0.6024, 0.5966, 0.6064, 0.6066, 0.6078, 0.6096, 0.6076, 0.6092, 0.598, 0.6006, 0.604, 0.6048, 0.6094, 0.6078, 0.5972, 0.6056, 0.5918, 0.6028, 0.5942, 0.5938, 0.5986, 0.602, 0.5932, 0.6038, 0.6024, 0.6042, 0.5962, 0.5994, 0.6064, 0.6028, 0.6044, 0.6074, 0.606, 0.6006, 0.5976, 0.6048, 0.608, 0.6004, 0.598, 0.6062, 0.5986, 0.5984, 0.6084, 0.6106, 0.6048, 0.5988, 0.5934, 0.5998, 0.6094, 0.6014, 0.6024, 0.6076, 0.6012, 0.6098, 0.6066, 0.6018, 0.6056, 0.5964, 0.609, 0.6002, 0.5914, 0.6038, 0.5978, 0.6022, 0.598, 0.6034, 0.6032, 0.6058, 0.608, 0.6082, 0.6048, 0.608, 0.6088, 0.6108, 0.598, 0.6016, 0.6194, 0.6022, 0.6106, 0.616, 0.5984, 0.6086, 0.6124, 0.6126, 0.6032, 0.6102, 0.6154, 0.606, 0.6088, 0.6006, 0.601, 0.5996, 0.6024, 0.6094, 0.6088, 0.604, 0.5984, 0.6076, 0.606, 0.6062, 0.6068, 0.6022, 0.6122, 0.6036, 0.6082, 0.6, 0.608, 0.6104, 0.6032, 0.6082, 0.606, 0.6076, 0.6082, 0.6086, 0.6002, 0.5988, 0.5968, 0.6116, 0.5958, 0.6006, 0.5976, 0.5986, 0.606, 0.6088, 0.6, 0.6066, 0.606, 0.6048, 0.6128, 0.6148, 0.6074, 0.606, 0.6038, 0.6014, 0.6088, 0.591, 0.6028, 0.6108, 0.6042, 0.596, 0.6042, 0.6084, 0.6064, 0.6104, 0.5972, 0.604, 0.607, 0.6078, 0.6062, 0.6054, 0.6052, 0.6122, 0.6028, 0.6034, 0.6042, 0.6114, 0.6056, 0.6072, 0.6006, 0.6014, 0.5964, 0.6074, 0.5986, 0.61, 0.603, 0.601, 0.6156, 0.6092, 0.6018, 0.603, 0.6056, 0.613, 0.6078, 0.6044, 0.6134, 0.6088, 0.612, 0.607, 0.5956, 0.6046, 0.6078, 0.5996, 0.612, 0.6066, 0.6052, 0.6046, 0.607, 0.6124, 0.5974, 0.6032, 0.6022, 0.6074, 0.6016, 0.6124, 0.5958, 0.6084, 0.5974, 0.597, 0.5938, 0.603, 0.6044, 0.612, 0.6006, 0.6048, 0.605, 0.5996, 0.603, 0.6054, 0.605, 0.6014, 0.6058, 0.5986, 0.603, 0.603, 0.6018, 0.5996, 0.6074, 0.6138, 0.6052, 0.5958, 0.5992, 0.6008, 0.6004, 0.5978, 0.6022, 0.6096, 0.6016, 0.599, 0.604, 0.6032, 0.6, 0.6056, 0.6116, 0.6002, 0.6028, 0.6002, 0.6038, 0.6056, 0.6078, 0.5992, 0.6094, 0.6082, 0.6, 0.602, 0.6034, 0.6102, 0.6114, 0.6104, 0.6136, 0.6012, 0.6062, 0.609, 0.6106, 0.5994, 0.6104, 0.6082, 0.5986, 0.6128, 0.6068, 0.5956, 0.6094, 0.6056, 0.604, 0.6074, 0.6092, 0.6052, 0.609, 0.6018, 0.5988, 0.603, 0.6046, 0.6136, 0.601, 0.6096]
################# cnn_gru_True Training Accuracy = [0.25786668, 0.3790222, 0.4166, 0.44213334, 0.45866665, 0.47633332, 0.48535556, 0.50006664, 0.50684446, 0.51404446, 0.52144444, 0.5278222, 0.5355333, 0.54028887, 0.5446, 0.54928887, 0.55237776, 0.55777776, 0.5623111, 0.5605556, 0.56864446, 0.57137775, 0.57482225, 0.5767556, 0.5815333, 0.58213335, 0.58206666, 0.5878222, 0.5881111, 0.58835554, 0.59375554, 0.5946222, 0.5930222, 0.59404445, 0.59864444, 0.6018222, 0.6023778, 0.60584444, 0.60555553, 0.60855556, 0.61115557, 0.60906667, 0.6112222, 0.6102, 0.6166667, 0.6162, 0.61568886, 0.6193333, 0.62104446, 0.61957777, 0.62513334, 0.6252667, 0.6237778, 0.6237556, 0.62633336, 0.62648886, 0.62726665, 0.6284889, 0.62704444, 0.6317111, 0.6308889, 0.6312, 0.6331111, 0.6336667, 0.63615555, 0.6372, 0.63375556, 0.63975555, 0.63442224, 0.6397111, 0.64255553, 0.64144444, 0.64077777, 0.6404222, 0.6431778, 0.6439111, 0.64435554, 0.6445111, 0.6450222, 0.64213336, 0.6482, 0.6462889, 0.64706665, 0.6511111, 0.6474222, 0.6480889, 0.6508222, 0.64915556, 0.65268886, 0.64933336, 0.6503111, 0.6513111, 0.6528889, 0.6526667, 0.65404445, 0.6509333, 0.6538, 0.6513778, 0.65573335, 0.65655553, 0.6541333, 0.65477777, 0.65444446, 0.6593111, 0.6591778, 0.6595778, 0.65826666, 0.66051114, 0.6603778, 0.66026664, 0.659, 0.6608667, 0.65797776, 0.6610889, 0.66084445, 0.6592444, 0.66, 0.6586889, 0.66231114, 0.66215557, 0.6639111, 0.6621111, 0.66371113, 0.6640222, 0.66415554, 0.6679556, 0.6629111, 0.6644, 0.6658222, 0.6660445, 0.6674889, 0.6696444, 0.6634222, 0.66653335, 0.6698, 0.66893333, 0.669, 0.6704222, 0.66926664, 0.6688222, 0.66642225, 0.6698667, 0.6676222, 0.6658889, 0.6681111, 0.66704446, 0.6712222, 0.67017776, 0.6698222, 0.6735111, 0.6719111, 0.6718, 0.6729111, 0.67315555, 0.6712667, 0.67226666, 0.67506665, 0.6686444, 0.6717333, 0.6743778, 0.67602223, 0.67553335, 0.6758889, 0.67446667, 0.67624444, 0.6772889, 0.6788667, 0.6779111, 0.6726889, 0.6772444, 0.6759111, 0.6738, 0.67546666, 0.6734222, 0.67833334, 0.6772889, 0.6770222, 0.6786, 0.6766222, 0.6764889, 0.6778889, 0.67606664, 0.6789111, 0.67928886, 0.6781778, 0.6788222, 0.68126667, 0.6812222, 0.67973334, 0.6762, 0.6797778, 0.68186665, 0.67995554, 0.6798, 0.6818445, 0.6811111, 0.6828222, 0.68024445, 0.6838889, 0.682, 0.68144447, 0.6811111, 0.68135554, 0.6801111, 0.6824, 0.68222225, 0.6816889, 0.67984444, 0.6815778, 0.68197775, 0.6831333, 0.68146664, 0.68053335, 0.6860222, 0.68604445, 0.68237776, 0.6853333, 0.6854, 0.6826444, 0.6863111, 0.68366665, 0.6824667, 0.6824889, 0.684, 0.68531114, 0.6867333, 0.6889778, 0.68464446, 0.6875111, 0.69002223, 0.6878, 0.68851113, 0.68542224, 0.6865778, 0.6861111, 0.6869111, 0.6848222, 0.6862222, 0.6854, 0.6863111, 0.68866664, 0.6878667, 0.6876, 0.68891114, 0.68546665, 0.68855554, 0.68815553, 0.6881111, 0.6870222, 0.6885333, 0.68806666, 0.68997777, 0.6918, 0.69086665, 0.6901778, 0.68635553, 0.6895555, 0.6906889, 0.6894, 0.68833333, 0.6897111, 0.68891114, 0.6886, 0.68795556, 0.6924, 0.6933778, 0.6904, 0.69211113, 0.6924, 0.6911333, 0.69093335, 0.68993336, 0.69042224, 0.6904889, 0.6910222, 0.6911778, 0.6888667, 0.6914, 0.6926444, 0.6955778, 0.69064444, 0.6924222, 0.69362223, 0.69233334, 0.69306666, 0.69122225, 0.6976, 0.6951333, 0.69173336, 0.69368887, 0.6961778, 0.6952, 0.69604445, 0.6980889, 0.6949111, 0.6916889, 0.6931111, 0.6956, 0.6932667, 0.69353336, 0.697, 0.6961333, 0.6938, 0.69346666, 0.69442225, 0.6922889, 0.69626665, 0.6917111, 0.6957333, 0.69722223, 0.6960889, 0.6982, 0.69773334, 0.69226664, 0.6975778, 0.69533336, 0.6971111, 0.69475555, 0.6984, 0.6978667, 0.69593334, 0.6959778, 0.6983111, 0.69575554, 0.6993778, 0.6959111, 0.6962, 0.69935554, 0.6978, 0.696, 0.69902223, 0.69673336, 0.6992889, 0.6993778, 0.6979111, 0.6999556, 0.6964222, 0.70004445, 0.6965333, 0.69884443, 0.6974889, 0.69713336, 0.7003111, 0.7003555, 0.7014, 0.69457775, 0.7014667, 0.69924444, 0.7006889, 0.6995111, 0.7011778, 0.7010222, 0.6969333, 0.70262223, 0.7001333, 0.7018667, 0.69795555, 0.6986, 0.7020444, 0.7001778, 0.7016444, 0.7002, 0.70111114, 0.69891113, 0.7023778, 0.70324445, 0.70346665, 0.70306665, 0.70228887, 0.7036222, 0.7012445, 0.6997111, 0.6986667, 0.70246667, 0.70431113, 0.70162225, 0.7001111, 0.7006889, 0.69895554, 0.7040667, 0.70306665, 0.7046889, 0.7016889, 0.70026666, 0.7020889, 0.70413333, 0.70615554, 0.7049556, 0.7029333, 0.7014889, 0.70184445, 0.70464444, 0.70408887, 0.7024, 0.70368886, 0.7046, 0.70493335, 0.7007333, 0.7032889, 0.70882225, 0.7028, 0.70486665, 0.70482224, 0.7062889, 0.70166665, 0.70786667, 0.704, 0.7037778, 0.7055111, 0.7028889, 0.70342225, 0.7040667, 0.70306665, 0.70435554, 0.7055778, 0.7054667, 0.7053111, 0.70566666, 0.7066444, 0.70442224, 0.70768887, 0.70593333, 0.70526665, 0.70604444, 0.7021111, 0.7046667, 0.7046, 0.70886666, 0.70624447, 0.7060889, 0.70622224, 0.7082667, 0.7096222, 0.7075111, 0.70575553, 0.7061778, 0.70728886, 0.7036667, 0.70233333, 0.7112, 0.7081556, 0.70831114, 0.70795554, 0.70633334, 0.7097333, 0.7103111, 0.70684445, 0.7074444, 0.7085111, 0.7087333, 0.7066444, 0.7101333, 0.7085111, 0.7079333, 0.7072222, 0.70857775, 0.7102444, 0.70644444, 0.7094667, 0.70773333, 0.70717776, 0.70966667, 0.71055555, 0.7103556, 0.70813334, 0.70915556, 0.7103556, 0.70926666, 0.7116445, 0.7065333, 0.7049111, 0.7116889, 0.7102444, 0.70795554, 0.7082222, 0.7115778, 0.70904446, 0.70948887, 0.7095111, 0.70964444, 0.7116, 0.70773333, 0.70982224, 0.7082, 0.7102, 0.70713335, 0.7127111, 0.7073333, 0.7090667, 0.7134445, 0.71062225, 0.7124, 0.7098, 0.7069111, 0.71, 0.70924443, 0.71117777, 0.7089555, 0.7138889, 0.7097333]
max = 61.94
Try with concat = False out.981209 (200 epochs)
max = 58.579
################# cnn_gru_0 Validation Accuracy = [0.22579999268054962, 0.32420000433921814, 0.3287999927997589, 0.3783999979496002, 0.4081999957561493, 0.41200000047683716, 0.4246000051498413, 0.421999990940094, 0.4374000132083893, 0.42719998955726624, 0.4514000117778778, 0.45660001039505005, 0.45500001311302185, 0.4505999982357025, 0.46540001034736633, 0.4625999927520752, 0.4611999988555908, 0.45260000228881836, 0.47519999742507935, 0.48019999265670776, 0.4968000054359436, 0.47999998927116394, 0.4885999858379364, 0.4918000102043152, 0.4973999857902527, 0.5034000277519226, 0.49480000138282776, 0.48820000886917114, 0.48579999804496765, 0.5041999816894531, 0.49799999594688416, 0.503600001335144, 0.5109999775886536, 0.506600022315979, 0.5123999714851379, 0.5052000284194946, 0.5091999769210815, 0.5085999965667725, 0.5252000093460083, 0.5130000114440918, 0.5206000208854675, 0.5095999836921692, 0.5166000127792358, 0.531000018119812, 0.5184000134468079, 0.5356000065803528, 0.5180000066757202, 0.5303999781608582, 0.5281999707221985, 0.532800018787384, 0.5299999713897705, 0.5332000255584717, 0.5121999979019165, 0.5361999869346619, 0.5303999781608582, 0.5357999801635742, 0.5414000153541565, 0.5392000079154968, 0.5464000105857849, 0.5365999937057495, 0.5357999801635742, 0.5393999814987183, 0.5353999733924866, 0.5425999760627747, 0.5321999788284302, 0.5411999821662903, 0.5320000052452087, 0.5360000133514404, 0.5450000166893005, 0.5135999917984009, 0.5514000058174133, 0.5224000215530396, 0.5551999807357788, 0.5415999889373779, 0.5347999930381775, 0.5509999990463257, 0.5519999861717224, 0.5386000275611877, 0.5558000206947327, 0.5523999929428101, 0.5541999936103821, 0.5374000072479248, 0.5455999970436096, 0.5519999861717224, 0.5541999936103821, 0.5565999746322632, 0.5504000186920166, 0.5234000086784363, 0.5443999767303467, 0.5616000294685364, 0.5523999929428101, 0.5558000206947327, 0.5586000084877014, 0.550599992275238, 0.5529999732971191, 0.5490000247955322, 0.5577999949455261, 0.5504000186920166, 0.5533999800682068, 0.5600000023841858, 0.5616000294685364, 0.5396000146865845, 0.5532000064849854, 0.5522000193595886, 0.5636000037193298, 0.5577999949455261, 0.5523999929428101, 0.5335999727249146, 0.550599992275238, 0.5422000288963318, 0.550000011920929, 0.5631999969482422, 0.5645999908447266, 0.5379999876022339, 0.5573999881744385, 0.5626000165939331, 0.5655999779701233, 0.5641999840736389, 0.5562000274658203, 0.5641999840736389, 0.5491999983787537, 0.5447999835014343, 0.5636000037193298, 0.5546000003814697, 0.5684000253677368, 0.5685999989509583, 0.5651999711990356, 0.5616000294685364, 0.5663999915122986, 0.5681999921798706, 0.5558000206947327, 0.5616000294685364, 0.5709999799728394, 0.5604000091552734, 0.5676000118255615, 0.5577999949455261, 0.5605999827384949, 0.5734000205993652, 0.5662000179290771, 0.5681999921798706, 0.5637999773025513, 0.5623999834060669, 0.5622000098228455, 0.5681999921798706, 0.5645999908447266, 0.5529999732971191, 0.5541999936103821, 0.5681999921798706, 0.5669999718666077, 0.5490000247955322, 0.5496000051498413, 0.5577999949455261, 0.5609999895095825, 0.5717999935150146, 0.5690000057220459, 0.555400013923645, 0.5680000185966492, 0.5716000199317932, 0.5655999779701233, 0.5600000023841858, 0.5763999819755554, 0.5753999948501587, 0.5694000124931335, 0.5662000179290771, 0.5716000199317932, 0.5813999772071838, 0.5684000253677368, 0.5613999962806702, 0.555400013923645, 0.5649999976158142, 0.5723999738693237, 0.5631999969482422, 0.5659999847412109, 0.5813999772071838, 0.5712000131607056, 0.5626000165939331, 0.5509999990463257, 0.5640000104904175, 0.5649999976158142, 0.569599986076355, 0.5717999935150146, 0.5803999900817871, 0.5637999773025513, 0.5758000016212463, 0.5774000287055969, 0.5555999875068665, 0.5651999711990356, 0.5857999920845032, 0.5774000287055969, 0.5717999935150146, 0.5734000205993652, 0.5745999813079834, 0.5669999718666077, 0.5740000009536743, 0.5622000098228455, 0.5667999982833862, 0.5712000131607056, 0.5684000253677368, 0.5817999839782715, 0.5626000165939331]
################# cnn_gru_0 Training Accuracy = [0.19939999282360077, 0.27006667852401733, 0.31695556640625, 0.3446222245693207, 0.36464443802833557, 0.3797111213207245, 0.38993334770202637, 0.4002888798713684, 0.4078444540500641, 0.41440001130104065, 0.420422226190567, 0.424311101436615, 0.431244432926178, 0.4356222152709961, 0.4380444586277008, 0.44404444098472595, 0.4456889033317566, 0.4509333372116089, 0.45471110939979553, 0.4583111107349396, 0.4600222110748291, 0.4658222198486328, 0.46933332085609436, 0.4724000096321106, 0.47813332080841064, 0.4840888977050781, 0.48500001430511475, 0.4867333471775055, 0.487888902425766, 0.49051111936569214, 0.4983111023902893, 0.49888888001441956, 0.5025110840797424, 0.5044000148773193, 0.5058888792991638, 0.5066888928413391, 0.5104222297668457, 0.5118222236633301, 0.5128222107887268, 0.513177752494812, 0.5137110948562622, 0.5186889171600342, 0.5210888981819153, 0.5189111232757568, 0.5212888717651367, 0.524911105632782, 0.5283555388450623, 0.5285999774932861, 0.5296444296836853, 0.5279333591461182, 0.5348222255706787, 0.5323333144187927, 0.5342444181442261, 0.5327110886573792, 0.5378888845443726, 0.5370444655418396, 0.5368000268936157, 0.5389999747276306, 0.5398444533348083, 0.540755569934845, 0.5434444546699524, 0.5434666872024536, 0.542555570602417, 0.5445555448532104, 0.5470444560050964, 0.5433777570724487, 0.5466889142990112, 0.5504666566848755, 0.5479555726051331, 0.5519555807113647, 0.5520666837692261, 0.5495111346244812, 0.5515555739402771, 0.5531777739524841, 0.5539555549621582, 0.5566444396972656, 0.5602444410324097, 0.5560222268104553, 0.5571555495262146, 0.5589110851287842, 0.560022234916687, 0.5600444674491882, 0.5619778037071228, 0.5645333528518677, 0.5624666810035706, 0.5614666938781738, 0.565155565738678, 0.5670222043991089, 0.5651333332061768, 0.5671333074569702, 0.5679555535316467, 0.5678222179412842, 0.5703999996185303, 0.5699333548545837, 0.5694666504859924, 0.5689555406570435, 0.5720000267028809, 0.5750444531440735, 0.5732444524765015, 0.5704444646835327, 0.5732444524765015, 0.5739333629608154, 0.5753999948501587, 0.5746444463729858, 0.5754222273826599, 0.5740666389465332, 0.5756666660308838, 0.5767999887466431, 0.5774666666984558, 0.579022228717804, 0.5767999887466431, 0.5757333040237427, 0.5807777643203735, 0.5778444409370422, 0.5782889127731323, 0.5836222171783447, 0.5840222239494324, 0.5828666687011719, 0.5834444165229797, 0.5846889019012451, 0.5827111005783081, 0.583466649055481, 0.5839333534240723, 0.5844444632530212, 0.5806666612625122, 0.5824221968650818, 0.5870444178581238, 0.5827999711036682, 0.5862666964530945, 0.5912222266197205, 0.587755560874939, 0.5888000130653381, 0.5889555811882019, 0.5885999798774719, 0.5866222381591797, 0.5886666774749756, 0.5890666842460632, 0.5849999785423279, 0.5930222272872925, 0.5926889181137085, 0.5915111303329468, 0.5928666591644287, 0.5909333229064941, 0.5920222401618958, 0.5926666855812073, 0.5923333168029785, 0.5913333296775818, 0.5930444598197937, 0.5943999886512756, 0.5952444672584534, 0.5947999954223633, 0.5927555561065674, 0.5936222076416016, 0.5965111255645752, 0.594955563545227, 0.5932888984680176, 0.5979777574539185, 0.5952666401863098, 0.5982666611671448, 0.59862220287323, 0.5991777777671814, 0.5964000225067139, 0.5924444198608398, 0.5962666869163513, 0.5973555445671082, 0.5979333519935608, 0.5993333458900452, 0.5977333188056946, 0.5998888611793518, 0.5979111194610596, 0.597955584526062, 0.5999777913093567, 0.6014222502708435, 0.6011555790901184, 0.6025111079216003, 0.6036221981048584, 0.6005333065986633, 0.6026666760444641, 0.6016444563865662, 0.6026444435119629, 0.6029333472251892, 0.6050000190734863, 0.6064888834953308, 0.6013555526733398, 0.6031777858734131, 0.6056666374206543, 0.603866696357727, 0.602911114692688, 0.6044222116470337, 0.6016222238540649, 0.6019555330276489, 0.6029333472251892, 0.6056444644927979, 0.6056888699531555, 0.603866696357727, 0.6045777797698975, 0.6063555479049683, 0.6097777485847473, 0.6065777540206909, 0.6092444658279419]
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 10 samples and 200 epochs, hs = 256 out.848400
with kernel_regularizer keras.regularizers.l1_l2(l1=0.01, l2=0.01) out.437935 out.449019
################# cnn_gru_True Validation Accuracy = [0.19740000367164612, 0.2881999909877777, 0.31299999356269836, 0.3434000015258789, 0.3653999865055084, 0.38100001215934753, 0.4153999984264374, 0.4300000071525574, 0.4244000017642975, 0.44679999351501465, 0.45260000228881836, 0.4553999900817871, 0.47699999809265137, 0.4758000075817108, 0.4812000095844269, 0.4984000027179718, 0.4909999966621399, 0.5026000142097473, 0.5034000277519226, 0.508400022983551, 0.5108000040054321, 0.527999997138977, 0.5139999985694885, 0.5325999855995178, 0.5311999917030334, 0.5338000059127808, 0.5404000282287598, 0.5429999828338623, 0.5249999761581421, 0.5307999849319458, 0.5429999828338623, 0.5450000166893005, 0.5526000261306763, 0.5428000092506409, 0.5406000018119812, 0.5527999997138977, 0.5523999929428101, 0.5460000038146973, 0.545799970626831, 0.5577999949455261, 0.5504000186920166, 0.555400013923645, 0.5455999970436096, 0.5514000058174133, 0.5522000193595886, 0.5551999807357788, 0.5618000030517578, 0.5509999990463257, 0.5564000010490417, 0.5658000111579895, 0.5594000220298767, 0.5577999949455261, 0.5672000050544739, 0.5551999807357788, 0.5672000050544739, 0.5591999888420105, 0.5655999779701233, 0.5622000098228455, 0.5699999928474426, 0.5669999718666077, 0.5640000104904175, 0.5672000050544739, 0.5627999901771545, 0.5631999969482422, 0.5623999834060669, 0.5648000240325928, 0.5598000288009644, 0.5604000091552734, 0.5663999915122986, 0.5709999799728394, 0.5598000288009644, 0.5654000043869019, 0.5676000118255615, 0.5636000037193298, 0.5727999806404114, 0.567799985408783, 0.5730000138282776, 0.5685999989509583, 0.5684000253677368, 0.5699999928474426, 0.5702000260353088, 0.5745999813079834, 0.5559999942779541, 0.5705999732017517, 0.5795999765396118, 0.5741999745368958, 0.5703999996185303, 0.5703999996185303, 0.5756000280380249, 0.5759999752044678, 0.5741999745368958, 0.5763999819755554, 0.5806000232696533, 0.573199987411499, 0.5722000002861023, 0.5756000280380249, 0.5741999745368958, 0.5788000226020813, 0.5727999806404114, 0.5735999941825867, 0.5785999894142151, 0.5745999813079834, 0.5788000226020813, 0.5676000118255615, 0.5730000138282776, 0.5684000253677368, 0.5691999793052673, 0.5776000022888184, 0.5776000022888184, 0.5748000144958496, 0.5758000016212463, 0.5716000199317932, 0.5763999819755554, 0.5684000253677368, 0.579200029373169, 0.5771999955177307, 0.578000009059906, 0.5752000212669373, 0.5831999778747559, 0.5795999765396118, 0.5777999758720398, 0.5726000070571899, 0.574999988079071, 0.5722000002861023, 0.5735999941825867, 0.5709999799728394, 0.5740000009536743, 0.5794000029563904, 0.5788000226020813, 0.5813999772071838, 0.5784000158309937, 0.5807999968528748, 0.5812000036239624, 0.5802000164985657, 0.5735999941825867, 0.5802000164985657, 0.5723999738693237, 0.5802000164985657, 0.5842000246047974, 0.5852000117301941, 0.5820000171661377, 0.5827999711036682, 0.5875999927520752, 0.578000009059906, 0.5759999752044678, 0.5843999981880188, 0.5831999778747559, 0.5789999961853027, 0.5827999711036682, 0.5691999793052673, 0.5812000036239624, 0.5799999833106995, 0.5758000016212463, 0.5849999785423279, 0.5825999975204468, 0.5781999826431274, 0.5831999778747559, 0.5838000178337097, 0.5758000016212463, 0.5726000070571899, 0.5834000110626221, 0.5842000246047974, 0.5884000062942505, 0.5863999724388123, 0.5799999833106995, 0.5848000049591064, 0.5825999975204468, 0.5794000029563904, 0.5830000042915344, 0.5789999961853027, 0.5860000252723694, 0.5806000232696533, 0.5784000158309937, 0.5881999731063843, 0.5789999961853027, 0.5881999731063843, 0.5821999907493591, 0.5785999894142151, 0.5860000252723694, 0.5839999914169312, 0.5776000022888184, 0.5812000036239624, 0.5763999819755554, 0.5834000110626221, 0.5720000267028809, 0.5824000239372253, 0.5835999846458435, 0.5825999975204468, 0.5774000287055969, 0.5843999981880188, 0.5860000252723694, 0.5917999744415283, 0.5821999907493591, 0.5852000117301941, 0.5934000015258789, 0.5896000266075134, 0.5884000062942505, 0.5748000144958496, 0.5838000178337097, 0.5861999988555908]
################# cnn_gru_True Training Accuracy = [0.19660000503063202, 0.24744445085525513, 0.29660001397132874, 0.3197999894618988, 0.34042221307754517, 0.35946667194366455, 0.38271111249923706, 0.4038444459438324, 0.4194222092628479, 0.4294222295284271, 0.43666666746139526, 0.44555556774139404, 0.4554666578769684, 0.4596889019012451, 0.4680444300174713, 0.4723111093044281, 0.4806888997554779, 0.4835111200809479, 0.4874666631221771, 0.49408888816833496, 0.5022666454315186, 0.5037555694580078, 0.504111111164093, 0.5097777843475342, 0.5162222385406494, 0.5180888772010803, 0.5195333361625671, 0.5235777497291565, 0.5269333124160767, 0.5291110873222351, 0.5295777916908264, 0.5312444567680359, 0.535444438457489, 0.5351999998092651, 0.5364221930503845, 0.5388444662094116, 0.5406000018119812, 0.5428222417831421, 0.5442444682121277, 0.5446222424507141, 0.5503555536270142, 0.5470222234725952, 0.5522888898849487, 0.5533333420753479, 0.5532888770103455, 0.552911102771759, 0.5574222207069397, 0.558733344078064, 0.5594444274902344, 0.5628666877746582, 0.5593555569648743, 0.5623555779457092, 0.5642889142036438, 0.5643555521965027, 0.5698444247245789, 0.5675777792930603, 0.5713333487510681, 0.5699777603149414, 0.5699777603149414, 0.5728889107704163, 0.5720444321632385, 0.5725333094596863, 0.5763999819755554, 0.5739333629608154, 0.5762888789176941, 0.5751110911369324, 0.5798444151878357, 0.5796889066696167, 0.5815111398696899, 0.5797333121299744, 0.5790444612503052, 0.581933319568634, 0.584755539894104, 0.5832222104072571, 0.5863999724388123, 0.5874666571617126, 0.5854222178459167, 0.5855110883712769, 0.5855555534362793, 0.5879555344581604, 0.5888000130653381, 0.586222231388092, 0.5907999873161316, 0.5916666388511658, 0.5915777683258057, 0.5903555750846863, 0.5928221940994263, 0.5916222333908081, 0.5945777893066406, 0.5924888849258423, 0.5939333438873291, 0.5954889059066772, 0.5939777493476868, 0.5950666666030884, 0.5960000157356262, 0.5971333384513855, 0.5966444611549377, 0.6006444692611694, 0.5997111201286316, 0.5984444618225098, 0.5990222096443176, 0.6036888957023621, 0.6009555459022522, 0.5998666882514954, 0.6012444496154785, 0.6036444306373596, 0.599911093711853, 0.6018000245094299, 0.6055999994277954, 0.6050666570663452, 0.6059333086013794, 0.6061333417892456, 0.6032666563987732, 0.6064444184303284, 0.6061555743217468, 0.609000027179718, 0.6079555749893188, 0.6087777614593506, 0.6115777492523193, 0.6051111221313477, 0.6077333092689514, 0.6085110902786255, 0.6082888841629028, 0.6100000143051147, 0.6113777756690979, 0.61326664686203, 0.613111138343811, 0.6121777892112732, 0.6112666726112366, 0.6116889119148254, 0.615577757358551, 0.6137999892234802, 0.6133111119270325, 0.6153777837753296, 0.6159777641296387, 0.6172444224357605, 0.6125777959823608, 0.6107110977172852, 0.6137333512306213, 0.6190666556358337, 0.6146666407585144, 0.6165333390235901, 0.6161777973175049, 0.6160444617271423, 0.6154000163078308, 0.6169999837875366, 0.6182666420936584, 0.6179555654525757, 0.6194888949394226, 0.6154000163078308, 0.6197999715805054, 0.6198222041130066, 0.6195111274719238, 0.6213555335998535, 0.622355580329895, 0.6189555525779724, 0.6221110820770264, 0.6180889010429382, 0.6214888691902161, 0.6235555410385132, 0.621666669845581, 0.6259111166000366, 0.6236888766288757, 0.6235111355781555, 0.6221333146095276, 0.624822199344635, 0.6250444650650024, 0.625688910484314, 0.6254444718360901, 0.626466691493988, 0.6252889037132263, 0.6247555613517761, 0.6279555559158325, 0.625688910484314, 0.6261110901832581, 0.6280666589736938, 0.6285777688026428, 0.6279777884483337, 0.625511109828949, 0.6262444257736206, 0.628333330154419, 0.627133309841156, 0.6295999884605408, 0.6308888792991638, 0.6295777559280396, 0.6275110840797424, 0.6317999958992004, 0.6287333369255066, 0.6288444399833679, 0.6308000087738037, 0.629111111164093, 0.629622220993042, 0.6306222081184387, 0.6284000277519226, 0.6311777830123901, 0.6300222277641296, 0.6324666738510132, 0.6323778033256531, 0.6296889185905457, 0.6352221965789795]
Try with concat = False out.660437 (50 epochs)
################# cnn_gru_0 Validation Accuracy = [0.31619998812675476, 0.3325999975204468, 0.3806000053882599, 0.4059999883174896, 0.4196000099182129, 0.423799991607666, 0.4357999861240387, 0.43779999017715454, 0.4535999894142151, 0.4641999900341034, 0.47380000352859497, 0.475600004196167, 0.48840001225471497, 0.4848000109195709, 0.48339998722076416, 0.49900001287460327, 0.49219998717308044, 0.5055999755859375, 0.5012000203132629, 0.5166000127792358, 0.5116000175476074, 0.506600022315979, 0.520799994468689, 0.5185999870300293, 0.5144000053405762, 0.5206000208854675, 0.5266000032424927, 0.522599995136261, 0.5375999808311462, 0.52920001745224, 0.5130000114440918, 0.5285999774932861, 0.5285999774932861, 0.5437999963760376, 0.5407999753952026, 0.5450000166893005, 0.5419999957084656, 0.5406000018119812, 0.5392000079154968, 0.5544000267982483, 0.5479999780654907, 0.5460000038146973, 0.5473999977111816, 0.5559999942779541, 0.5429999828338623, 0.5388000011444092, 0.5514000058174133, 0.5411999821662903, 0.5468000173568726, 0.5547999739646912]
################# cnn_gru_0 Training Accuracy = [0.21320000290870667, 0.31262221932411194, 0.35028889775276184, 0.37102222442626953, 0.3886444568634033, 0.3989555537700653, 0.41306665539741516, 0.42100000381469727, 0.4274222254753113, 0.43479999899864197, 0.441777765750885, 0.4474000036716461, 0.455822229385376, 0.4593110978603363, 0.46577778458595276, 0.47244444489479065, 0.4786444306373596, 0.48251110315322876, 0.4856888949871063, 0.48768889904022217, 0.4945777654647827, 0.4945555627346039, 0.4999333322048187, 0.5040888786315918, 0.5044222474098206, 0.5098000168800354, 0.5132666826248169, 0.5106444358825684, 0.5141333341598511, 0.5174000263214111, 0.5239111185073853, 0.5222444534301758, 0.5272889137268066, 0.5264000296592712, 0.5284222364425659, 0.5353111028671265, 0.5317111015319824, 0.5315999984741211, 0.5336889028549194, 0.5348666906356812, 0.5392888784408569, 0.5394666790962219, 0.5410444736480713, 0.5435555577278137, 0.5426444411277771, 0.5475555658340454, 0.5474666953086853, 0.5461333394050598, 0.5516666769981384, 0.5508444309234619]
Add dense layer
with cnn_dropout = 0.4 and rnn dropout = 0.2 and lr = 5e-4 with res = 8 with 10 samples and 200 epochs, hs = 256 out.848400
out.9611
concat = False - out.9612
bidirectional = True (go_backwards=True) out.488739
'''
from __future__ import division, print_function, absolute_import
print('Starting..................................')
import os
import sys
sys.path.insert(1, '/home/labs/ahissarlab/orra/imagewalker/')
import numpy as np
import cv2
import misc
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from keras_utils import dataset_update, write_to_file, create_cifar_dataset
from misc import *
import tensorflow.keras as keras
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
# load dataset
(trainX, trainy), (testX, testy) = cifar10.load_data()
images, labels = trainX, trainy
kernel_regularizer_list = [None, keras.regularizers.l1(),keras.regularizers.l2(),keras.regularizers.l1_l2()]
optimizer_list = [tf.keras.optimizers.Adam, tf.keras.optimizers.Nadam, tf.keras.optimizers.RMSprop]
if len(sys.argv) > 1:
paramaters = {
'epochs' : int(sys.argv[1]),
'sample' : int(sys.argv[2]),
'res' : int(sys.argv[3]),
'hidden_size' : int(sys.argv[4]),
'concat' : int(sys.argv[5]),
'regularizer' : keras.regularizers.l1(),#kernel_regularizer_list[int(sys.argv[6])],
'optimizer' : optimizer_list[int(sys.argv[7])],
'cnn_dropout' : 0.4,
'rnn_dropout' : 0.2,
'lr' : 5e-4,
'run_id' : np.random.randint(1000,9000)
}
else:
paramaters = {
'epochs' : 1,
'sample' : 5,
'res' : 8,
'hidden_size' : 128,
'concat' : 1,
'regularizer' : None,
'optimizer' : optimizer_list[0],
'cnn_dropout' : 0.4,
'rnn_dropout' : 0.2,
'lr' : 5e-4,
'run_id' : np.random.randint(1000,9000)
}
print(paramaters)
for key,val in paramaters.items():
exec(key + '=val')
epochs = epochs
sample = sample
res = res
hidden_size =hidden_size
concat = concat
regularizer = regularizer
optimizer = optimizer
cnn_dropout = cnn_dropout
rnn_dropout = rnn_dropout
lr = lr
run_id = run_id
n_timesteps = sample
def cnn_gru(n_timesteps = 5, hidden_size = 128,input_size = 32, concat = True,
optimizer = tf.keras.optimizers.Adam, ):
'''
CNN RNN combination that extends the CNN to a network that achieves
~80% accuracy on full res cifar.
Parameters
----------
n_timesteps : TYPE, optional
DESCRIPTION. The default is 5.
img_dim : TYPE, optional
DESCRIPTION. The default is 32.
hidden_size : TYPE, optional
DESCRIPTION. The default is 128.
input_size : TYPE, optional
DESCRIPTION. The default is 32.
Returns
-------
model : TYPE
DESCRIPTION.
'''
inputA = keras.layers.Input(shape=(n_timesteps,input_size,input_size,3))
inputB = keras.layers.Input(shape=(n_timesteps,2))
# define CNN model
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(32,(3,3),activation='relu', padding = 'same'))(inputA)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(32,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=(2, 2)))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Dropout(cnn_dropout))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(64,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(64,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=(2, 2)))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Dropout(cnn_dropout))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(128,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(128,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=(2, 2)))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Dropout(cnn_dropout))(x1)
print(x1.shape)
x1=keras.layers.TimeDistributed(keras.layers.Flatten())(x1)
print(x1.shape)
if concat:
x = keras.layers.Concatenate()([x1,inputB])
else:
x = x1
print(x.shape)
# define LSTM model
x = keras.layers.GRU(hidden_size,input_shape=(n_timesteps, None),
return_sequences=True,recurrent_dropout=rnn_dropout,
kernel_regularizer=regularizer,go_backwards=True)(x)
x = keras.layers.Flatten()(x)
#Add another dense layer (prior it reached 62%)
x = keras.layers.Dense(512, activation="relu")(x)
x = keras.layers.Dense(10,activation="softmax")(x)
model = keras.models.Model(inputs=[inputA,inputB],outputs=x, name = 'cnn_gru_{}'.format(concat))
opt=optimizer(lr=lr)
model.compile(
optimizer=opt,
loss="sparse_categorical_crossentropy",
metrics=["sparse_categorical_accuracy"],
)
return model
rnn_net = cnn_gru(n_timesteps = sample, hidden_size = hidden_size,input_size = res, concat = concat)
cnn_net = cnn_net = extended_cnn_one_img(n_timesteps = sample, input_size = res, dropout = cnn_dropout)
# hp = HP()
# hp.save_path = 'saved_runs'
# hp.description = "syclop cifar net search runs"
# hp.this_run_name = 'syclop_{}'.format(rnn_net.name)
# deploy_logs()
train_dataset, test_dataset = create_cifar_dataset(images, labels,res = res,
sample = sample, return_datasets=True,
mixed_state = False, add_seed = 0,
)
#bad_res_func = bad_res101, up_sample = True)
train_dataset_x, train_dataset_y = split_dataset_xy(train_dataset)
test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset)
print("##################### Fit {} and trajectories model on training data res = {} ##################".format(rnn_net.name,res))
rnn_history = rnn_net.fit(
train_dataset_x,
train_dataset_y,
batch_size=64,
epochs=epochs,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=(test_dataset_x, test_dataset_y),
verbose = 0)
# print('################# {} Validation Accuracy = '.format(cnn_net.name),cnn_history.history['val_sparse_categorical_accuracy'])
# print('################# {} Training Accuracy = '.format(cnn_net.name),rnn_history.history['sparse_categorical_accuracy'])
print('################# {} Validation Accuracy = '.format(rnn_net.name),rnn_history.history['val_sparse_categorical_accuracy'])
print('################# {} Training Accuracy = '.format(rnn_net.name),rnn_history.history['sparse_categorical_accuracy'])
plt.figure()
plt.plot(rnn_history.history['sparse_categorical_accuracy'], label = 'train')
plt.plot(rnn_history.history['val_sparse_categorical_accuracy'], label = 'val')
# plt.plot(cnn_history.history['sparse_categorical_accuracy'], label = 'cnn train')
# plt.plot(cnn_history.history['val_sparse_categorical_accuracy'], label = 'cnn val')
plt.legend()
plt.grid()
plt.ylim(0.5,0.63)
plt.title('{} on cifar res = {} hs = {} dropout = {}, num samples = {}'.format(rnn_net.name, res, hidden_size,cnn_dropout,sample))
plt.savefig('{} on Cifar res = {}, no upsample, val accur = {} hs = {} dropout = {}.png'.format(rnn_net.name,res,rnn_history.history['val_sparse_categorical_accuracy'][-1], hidden_size,cnn_dropout))
with open('/home/labs/ahissarlab/orra/imagewalker/cifar_net_search/{}'.format(run_id), 'wb') as file_pi:
pickle.dump(rnn_history.history, file_pi)
# with open('/home/labs/ahissarlab/orra/imagewalker/cifar_net_search/{}HistoryDict'.format(cnn_net.name), 'wb') as file_pi:
# pickle.dump(cnn_history.history, file_pi)
dataset_update(rnn_history, rnn_net,paramaters)
write_to_file(rnn_history, rnn_net,paramaters)
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import os
from domain.base import Domain, Yaml
from properties import APPLICATION_PROPERTIES
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8692,
1330,
20021,
11,
14063,
75,
198,
6738,
6608,
1330,
39421,
6234,
62,
4805,
3185,
17395,
11015,
628
] | 3.8 | 25 |
from __future__ import annotations
# SOURCE: https://blog.bartab.fr/fastapi-logging-on-the-fly/
import logging
from fastapi import APIRouter, HTTPException
from ultron8.api.models.loggers import LoggerModel, LoggerPatch
LOG_LEVELS = {
"critical": logging.CRITICAL,
"error": logging.ERROR,
"warning": logging.WARNING,
"info": logging.INFO,
"debug": logging.DEBUG,
}
LOGGER = logging.getLogger(__name__)
router = APIRouter()
# Multiple RecursionErrors with self-referencing models
# https://github.com/samuelcolvin/pydantic/issues/524
# https://github.com/samuelcolvin/pydantic/issues/531
| [
6738,
11593,
37443,
834,
1330,
37647,
198,
198,
2,
311,
31033,
25,
3740,
1378,
14036,
13,
16575,
397,
13,
8310,
14,
7217,
15042,
12,
6404,
2667,
12,
261,
12,
1169,
12,
12254,
14,
198,
11748,
18931,
198,
198,
6738,
3049,
15042,
1330,
... | 2.813636 | 220 |
# Python Program To Understand The Usage Of try With finally Blocks
'''
Function Name : Usage Of try With finally Blocks
Function Date : 23 Sep 2020
Function Author : Prasad Dangare
Input : String
Output : String
'''
try:
x = int(input('Enter A Number : '))
y = 1 / x
finally:
print("We Are Not Catching The Exception.")
print("The Inverse Is : ", y)
| [
2,
11361,
6118,
1675,
45010,
383,
29566,
3226,
1949,
2080,
3443,
35111,
201,
198,
201,
198,
201,
198,
7061,
6,
201,
198,
22203,
6530,
220,
220,
220,
1058,
220,
29566,
3226,
1949,
2080,
3443,
35111,
201,
198,
22203,
7536,
220,
220,
220... | 2.482353 | 170 |
print(fib1(5)) | [
4798,
7,
69,
571,
16,
7,
20,
4008
] | 1.75 | 8 |
#!/usr/bin/env python
"""
Background:
--------
GliderScienceSet_Plots.py
Purpose:
--------
History:
--------
"""
import argparse
import os
from io_utils import ConfigParserLocal
import numpy as np
import xarray as xa
# Visual Stack
import matplotlib as mpl
import matplotlib.pyplot as plt
"""-------------------------------- Main -----------------------------------------------"""
parser = argparse.ArgumentParser(description='Plot archived NetCDF glider data and Science Data')
parser.add_argument('ofilepath', metavar='ofilepath', type=str,
help='path to directory with UW initial Oculus netcdf data')
parser.add_argument('sfilepath', metavar='sfilepath', type=str,
help='path to directory with Oculus Science Data netcdf data')
parser.add_argument('profileid',metavar='profileid', type=str,
help='divenumber - eg p4010260')
args = parser.parse_args()
isUW, ismerged, isup, isdown = True, True, True, True
# There are potentially three files - original UW file, a merged file and an upcast/downcast file
filein = args.ofilepath + args.profileid + '.nc'
try:
df = xa.open_dataset(filein, autoclose=True)
except IOError:
isUW = False
filein_m = args.sfilepath + args.profileid + '_m.nc'
ismerged = True
try:
df_m = xa.open_dataset(filein_m, autoclose=True)
except IOError:
ismerged = False
filein_u = args.sfilepath + args.profileid + '_u.nc'
try:
df_u = xa.open_dataset(filein_u, autoclose=True)
except IOError:
isup = False
filein_d = args.sfilepath + args.profileid + '_d.nc'
try:
df_d = xa.open_dataset(filein_d, autoclose=True)
except IOError:
isdown = False
fig = plt.figure(figsize=(6, 6))
if isUW:
fig = plot_ts(df.salinity,df.temperature,df.depth,labels=True,label_color='g')
print("Added original data")
if ismerged:
fig = plot_ts(df_m.Salinity,df_m.Temperature,df_m.Pressure,labels=False,label_color='k')
print("Added merged data")
if isup:
fig = plot_ts(df_u.Salinity,df_u.Temperature,df_u.Pressure,labels=False,label_color='b')
print("Added binned upcast data")
if isdown:
fig = plot_ts(df_d.Salinity,df_d.Temperature,df_d.Pressure,labels=False,label_color='r')
print("Added binned downcast data")
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
37811,
198,
25353,
25,
198,
24200,
198,
2671,
1304,
26959,
7248,
62,
3646,
1747,
13,
9078,
198,
220,
198,
220,
198,
32039,
25,
198,
24200,
198,
220,
198,
7443,
25,
198,
24200,
6... | 2.768354 | 790 |
"""
Define models used in this app.
This module only serves to provide some consistency across the
`users`, `accounts` , `projects` etc apps so that you can
`from users.models import Users`, just like you can for
`from projects.models import Projects` and instead of having to remember
to do the following.
"""
from typing import Dict, Optional
import django.contrib.auth.models
import shortuuid
from django.contrib.auth import get_user_model
from django.contrib.contenttypes.fields import GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from django.db import connection, models
from django.db.models import Count, F, Max, Q
from django.db.models.expressions import RawSQL
from django.http import HttpRequest
from django.shortcuts import reverse
from django.utils import timezone
from invitations.adapters import get_invitations_adapter
from invitations.models import Invitation
from rest_framework.exceptions import ValidationError
from waffle.models import AbstractUserFlag
# Needed to ensure signals are loaded
import users.signals # noqa
from manager.helpers import EnumChoice
User: django.contrib.auth.models.User = get_user_model()
def get_email(user: User) -> Optional[str]:
"""
Get the best email address for a user.
The "best" email is the verified primary email,
falling back to verified if none marked as primary,
falling back to the first if none is verified,
falling back to `user.email`, falling back to
their public email.
"""
best = None
emails = user.emailaddress_set.all()
for email in emails:
if (email.primary and email.verified) or (not best and email.verified):
best = email.email
if not best and len(emails) > 0:
best = emails[0].email
if not best:
best = user.email
if not best and user.personal_account:
best = user.personal_account.email
# Avoid returning an empty string, return None instead
return best or None
def get_name(user: User) -> Optional[str]:
"""
Get the best name to display for a user.
The "best" name is their account's display name,
falling back to first_name + last_name,
falling back to username.
"""
if user.personal_account and user.personal_account.display_name:
return user.personal_account.display_name
if user.first_name or user.last_name:
return f"{user.first_name} {user.last_name}".strip()
return user.username
def get_attributes(user: User) -> Dict:
"""
Get a dictionary of user attributes.
Used for updating external services with current
values of user attributes e.g number of projects etc.
Flattens various other summary dictionaries e.g `get_projects_summary`
into a single dictionary.
"""
return {
**dict(
(f"feature_{name}", value)
for name, value in get_feature_flags(user).items()
),
**dict(
(f"orgs_{name}", value) for name, value in get_orgs_summary(user).items()
),
**dict(
(f"projects_{name}", value)
for name, value in get_projects_summary(user).items()
),
}
def get_orgs(user: User):
"""
Get all organizational accounts that a user is a member of.
"""
from accounts.models import Account
return Account.objects.filter(user__isnull=True, users__user=user).annotate(
role=F("users__role")
)
def get_orgs_summary(user: User) -> Dict:
"""
Get a summary of organizational accounts the user is a member of.
"""
from accounts.models import AccountRole
zero_by_role = dict([(role.name.lower(), 0) for role in AccountRole])
orgs = get_orgs(user)
orgs_summary = orgs.values("role").annotate(count=Count("id"), tier=Max("tier"))
orgs_by_role = dict([(row["role"].lower(), row["count"]) for row in orgs_summary])
return {
"max_tier": max(row["tier"] for row in orgs_summary) if orgs_summary else None,
"total": sum(orgs_by_role.values()),
**zero_by_role,
**orgs_by_role,
}
def get_projects(user: User, include_public=True):
"""
Get a queryset of projects for the user.
For authenticated users, each project is annotated with the
role of the user for the project.
"""
from projects.models.projects import Project
if user.is_authenticated:
# Annotate the queryset with the role of the user
# Role is the "greater" of the project role and the
# account role (for the account that owns the project).
# Authenticated users can see public projects and those in
# which they have a role
return Project.objects.annotate(
role=RawSQL(
"""
SELECT
CASE account_role.role
WHEN 'OWNER' THEN 'OWNER'
WHEN 'MANAGER' THEN
CASE project_role.role
WHEN 'OWNER' THEN 'OWNER'
ELSE 'MANAGER' END
ELSE project_role.role END AS "role"
FROM projects_project AS project
LEFT JOIN
(SELECT project_id, "role" FROM projects_projectagent WHERE user_id = %s) AS project_role
ON project.id = project_role.project_id
LEFT JOIN
(SELECT account_id, "role" FROM accounts_accountuser WHERE user_id = %s) AS account_role
ON project.account_id = account_role.account_id
WHERE project.id = projects_project.id""",
[user.id, user.id],
)
).filter((Q(public=True) if include_public else Q()) | Q(role__isnull=False))
else:
# Unauthenticated users can only see public projects
return Project.objects.filter(public=True).extra(select={"role": "NULL"})
def get_projects_summary(user: User) -> Dict:
"""
Get a summary of project memberships for a user.
"""
from projects.models.projects import ProjectRole
zero_by_role = dict([(role.name.lower(), 0) for role in ProjectRole])
projects = get_projects(user, include_public=False)
projects_by_role = dict(
[
(row["role"].lower(), row["count"])
for row in projects.values("role").annotate(count=Count("id"))
]
)
return {
"total": sum(projects_by_role.values()),
**zero_by_role,
**projects_by_role,
}
def get_feature_flags(user: User) -> Dict[str, str]:
"""
Get the feature flag settings for a user.
"""
with connection.cursor() as cursor:
cursor.execute(
"""
SELECT "name", "default", "user_id"
FROM users_flag
LEFT JOIN (
SELECT *
FROM users_flag_users
WHERE user_id = %s
) AS subquery ON users_flag.id = subquery.flag_id
WHERE users_flag.settable
""",
[user.id],
)
rows = cursor.fetchall()
features = {}
for row in rows:
name, default, has_flag = row
if has_flag:
features[name] = "off" if default == "on" else "on"
else:
features[name] = default
return features
def generate_anonuser_id():
"""
Generate a unique id for an anonymous user.
"""
return shortuuid.ShortUUID().random(length=32)
def generate_invite_key():
"""
Generate a unique invite key.
The is separate function to avoid new AlterField migrations
being created as happens when `default=shortuuid.uuid`.
"""
return shortuuid.ShortUUID().random(length=32)
| [
37811,
198,
7469,
500,
4981,
973,
287,
428,
598,
13,
198,
198,
1212,
8265,
691,
9179,
284,
2148,
617,
15794,
1973,
262,
198,
63,
18417,
47671,
4600,
23317,
82,
63,
837,
4600,
42068,
63,
3503,
6725,
523,
326,
345,
460,
198,
63,
6738,... | 2.646033 | 2,811 |
from trex.emu.api import *
from trex.emu.emu_plugins.emu_plugin_base import *
import trex.utils.parsing_opts as parsing_opts
| [
6738,
2054,
87,
13,
368,
84,
13,
15042,
1330,
1635,
198,
6738,
2054,
87,
13,
368,
84,
13,
368,
84,
62,
37390,
13,
368,
84,
62,
33803,
62,
8692,
1330,
1635,
198,
11748,
2054,
87,
13,
26791,
13,
79,
945,
278,
62,
404,
912,
355,
... | 2.52 | 50 |
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 os
import struct
import argparse
import numpy as np
from PIL import Image
def load_mnist(dir_path, kind='train'):
"""Load MNIST Dataset from the given path"""
labels_path = os.path.join(dir_path, '%s-labels-idx1-ubyte' % kind)
images_path = os.path.join(dir_path, '%s-images-idx3-ubyte' % kind)
with open(labels_path, 'rb') as labels_file:
magic, num = struct.unpack('>II', labels_file.read(8))
labels = np.fromfile(labels_file, dtype=np.uint8)
with open(images_path, 'rb') as images_file:
magic, num, rows, cols = struct.unpack(">IIII", images_file.read(16))
images = np.fromfile(images_file, dtype=np.uint8)
return images, labels, num
def save_mnist_to_jpg(images, labels, save_dir, kind, num):
"""Convert and save the MNIST dataset to.jpg image format"""
one_pic_pixels = 28 * 28
for i in range(num):
img = images[i * one_pic_pixels:(i + 1) * one_pic_pixels]
img_np = np.array(img, dtype=np.uint8).reshape(28, 28)
label_val = labels[i]
jpg_name = os.path.join(save_dir, '{}_{}_{}.jpg'.format(kind, i, label_val))
Image.fromarray(img_np).save(jpg_name)
print('{} ==> {}_{}_{}.jpg'.format(i, kind, i, label_val))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="MNIST Dataset Operations")
parser.add_argument('--data_dir', type=str, default='/root/jina/chinese-mnist', help='MNIST dataset dir')
parser.add_argument('--kind', type=str, default='train', help='MNIST dataset: train or t10k')
parser.add_argument('--save_dir', type=str, default='/root/jina/chinese-mnist/jpg', help='used to save mnist jpg')
args = parser.parse_args()
if not os.path.exists(args.data_dir):
os.makedirs(args.data_dir)
images_np, labels_np, kind_num = load_mnist(args.data_dir, args.kind)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
save_mnist_to_jpg(images_np, labels_np, args.save_dir, args.kind, kind_num)
| [
2,
15069,
12131,
43208,
21852,
1766,
1539,
12052,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198... | 2.68032 | 1,001 |
from datetime import datetime
from django.db import models
from django.db.models import permalink
from django.contrib.contenttypes.models import ContentType
from django.contrib.sites.models import Site
from django.test import TestCase as DjangoTestCase
from django_comments_xtd.models import (XtdComment,
MaxThreadLevelExceededException)
# In order to methods save and test _calculate_thread_ata, simulate the
# following threads, in order of arrival:
#
# testcase cmt.id parent level-0 level-1 level-2
# step1 1 - c1 <- cmt1
# step1 2 - c2 <- cmt2
# step2 3 1 -- c3 <- cmt1 to cmt1
# step2 4 1 -- c4 <- cmt2 to cmt1
# step3 5 2 -- c5 <- cmt1 to cmt2
# step4 6 5 -- -- c6 <- cmt1 to cmt1 to cmt2
# step4 7 4 -- -- c7 <- cmt1 to cmt2 to cmt1
# step5 8 3 -- -- c8 <- cmt1 to cmt1 to cmt1
# step5 9 - c9 <- cmt9
def thread_test_step_1(article):
article_ct = ContentType.objects.get(app_label="tests", model="article")
site = Site.objects.get(pk=1)
# post Comment 1 with parent_id 0
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="comment 1 to article",
submit_date = datetime.now())
# post Comment 2 with parent_id 0
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="comment 2 to article",
submit_date = datetime.now())
def thread_test_step_2(article):
article_ct = ContentType.objects.get(app_label="tests", model="article")
site = Site.objects.get(pk=1)
# post Comment 3 to parent_id 1
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="comment 1 to comment 1",
submit_date = datetime.now(),
parent_id = 1)
# post Comment 4 to parent_id 1
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="comment 2 to comment 1",
submit_date = datetime.now(),
parent_id = 1)
def thread_test_step_3(article):
article_ct = ContentType.objects.get(app_label="tests", model="article")
site = Site.objects.get(pk=1)
# post Comment 5 to parent_id 2
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="comment 1 to comment 1",
submit_date = datetime.now(),
parent_id = 2)
def thread_test_step_4(article):
article_ct = ContentType.objects.get(app_label="tests", model="article")
site = Site.objects.get(pk=1)
# post Comment 6 to parent_id 5
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="cmt 1 to cmt 1 to cmt 2",
submit_date = datetime.now(),
parent_id = 5)
# post Comment 7 to parent_id 4
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="cmt 1 to cmt 2 to cmt 1",
submit_date = datetime.now(),
parent_id = 4)
def thread_test_step_5(article):
article_ct = ContentType.objects.get(app_label="tests", model="article")
site = Site.objects.get(pk=1)
# post Comment 8 to parent_id 3
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="cmt 1 to cmt 1 to cmt 1",
submit_date = datetime.now(),
parent_id = 3)
# post Comment 9 with parent_id 0
XtdComment.objects.create(content_type = article_ct,
object_pk = article.id,
content_object = article,
site = site,
comment ="cmt 1 to cmt 2 to cmt 1",
submit_date = datetime.now())
| [
6738,
4818,
8079,
1330,
4818,
8079,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
6738,
42625,
14208,
13,
9945,
13,
27530,
1330,
583,
31000,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
11299,
19199,
13,
27530,
1330,
14041,
60... | 1.661178 | 3,583 |
"""
This file contains all the version one routes
"""
# Third party imports
from flask import Blueprint, request
from flask_restplus import Api, Resource, fields
# Local application imports
from .views.products_views import v1 as pro_routes
from .views.sales_views import v1 as sales_routes
from .views.stores_views import v1 as stores_routes
from .views.auth import v1 as auth_routes
authorizations = {
'apikey': {
'type': 'apiKey',
'in': 'header',
'name': 'Authorization'
}}
v_1 = Blueprint('v_1', __name__, url_prefix="/api/v1")
api = Api(v_1)
v1 = api.namespace(
'v1',
description='Store manager Api without persitent data storage',
authorizations=authorizations)
api.add_namespace(pro_routes, path="/products/")
api.add_namespace(sales_routes, path="/sales")
api.add_namespace(stores_routes, path="/stores")
api.add_namespace(auth_routes, path="/")
| [
37811,
198,
1212,
2393,
4909,
477,
262,
2196,
530,
11926,
198,
37811,
198,
198,
2,
10467,
2151,
17944,
198,
6738,
42903,
1330,
39932,
11,
2581,
198,
6738,
42903,
62,
2118,
9541,
1330,
5949,
72,
11,
20857,
11,
7032,
628,
198,
2,
10714,... | 2.760736 | 326 |
from pathlib import Path
import jsonschema
import pydantic
import pytest
from nomenclature.processor.region import (
ModelMappingCollisionError,
RegionAggregationMapping,
RegionProcessor,
)
from conftest import TEST_DATA_DIR
TEST_FOLDER_REGION_MAPPING = TEST_DATA_DIR / "region_aggregation"
def test_region_processor_not_defined(simple_definition):
# Test a RegionProcessor with regions that are not defined in the data structure
# definition
error_msg = (
"model_(a|b)\n.*region_a.*mapping_(1|2).yaml.*value_error.region_not_defined."
"*\n.*model_(a|b)\n.*region_a.*mapping_(1|2).yaml.*value_error."
"region_not_defined"
)
with pytest.raises(pydantic.ValidationError, match=error_msg):
RegionProcessor.from_directory(
TEST_DATA_DIR / "regionprocessor_not_defined"
).validate_mappings(simple_definition)
def test_region_processor_duplicate_model_mapping():
error_msg = ".*model_a.*mapping_(1|2).yaml.*mapping_(1|2).yaml"
with pytest.raises(ModelMappingCollisionError, match=error_msg):
RegionProcessor.from_directory(TEST_DATA_DIR / "regionprocessor_duplicate")
def test_region_processor_wrong_args():
# Test if pydantic correctly type checks the input of RegionProcessor.from_directory
# Test with an integer
with pytest.raises(pydantic.ValidationError, match=".*path\n.*not a valid path.*"):
RegionProcessor.from_directory(123)
# Test with a file, a path pointing to a directory is required
with pytest.raises(
pydantic.ValidationError,
match=".*path\n.*does not point to a directory.*",
):
RegionProcessor.from_directory(
TEST_DATA_DIR / "regionprocessor_working/mapping_1.yaml"
)
| [
6738,
3108,
8019,
1330,
10644,
198,
198,
11748,
44804,
684,
2395,
2611,
198,
11748,
279,
5173,
5109,
198,
11748,
12972,
9288,
198,
6738,
299,
3674,
565,
1300,
13,
41341,
13,
36996,
1330,
357,
198,
220,
220,
220,
9104,
44,
5912,
22667,
... | 2.608824 | 680 |
#!/usr/bin/env python
#
#3> <> prov:specializationOf <https://github.com/timrdf/csv2rdf4lod-automation/blob/master/bin/util/ckan-datasets-in-group.py>;
#3> prov:wasDerivedFrom <https://raw.github.com/timrdf/DataFAQs/master/packages/faqt.python/faqt/faqt.py>,
#3> <https://github.com/timrdf/DataFAQs/raw/master/services/sadi/ckan/lift-ckan.py>;
#
# Requires: http://pypi.python.org/pypi/ckanclient
# easy_install http://pypi.python.org/packages/source/c/ckanclient/ckanclient-0.10.tar.gz
import ckanclient
if __name__=='__main__':
datasets_in_group()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
198,
2,
18,
29,
1279,
29,
899,
25,
20887,
1634,
5189,
1279,
5450,
1378,
12567,
13,
785,
14,
16514,
4372,
69,
14,
40664,
17,
4372,
69,
19,
75,
375,
12,
2306,
296,
341,
14,
2436... | 2.186567 | 268 |
import FWCore.ParameterSet.Config as cms
ecalExclusiveTrigFilter = cms.EDFilter("EcalExclusiveTrigFilter",
# Global trigger tag
l1GlobalReadoutRecord = cms.string("gtDigis")
)
| [
11748,
48849,
14055,
13,
36301,
7248,
13,
16934,
355,
269,
907,
198,
198,
721,
282,
3109,
5731,
2898,
328,
22417,
796,
269,
907,
13,
1961,
22417,
7203,
36,
9948,
3109,
5731,
2898,
328,
22417,
1600,
628,
220,
220,
220,
220,
220,
1303,
... | 2.690141 | 71 |
import bpy
import os
import json
import numpy as np
from decimal import Decimal
from mathutils import Vector, Matrix
import argparse
import numpy as np
import sys
sys.path.append(os.path.dirname(__file__))
sys.path.append(os.path.dirname(__file__)+'/tools')
from tools.utils import *
from tools.blender_interface import BlenderInterface
if __name__ == '__main__':
p = argparse.ArgumentParser(description='Renders given obj file by rotation a camera around it.')
p.add_argument('--mesh_fpath', type=str, required=True, help='The path the output will be dumped to.')
p.add_argument('--output_dir', type=str, required=True, help='The path the output will be dumped to.')
p.add_argument('--num_observations', type=int, required=True, help='The path the output will be dumped to.')
p.add_argument('--sphere_radius', type=float, required=True, help='The path the output will be dumped to.')
p.add_argument('--mode', type=str, required=True, help='Options: train and test')
argv = sys.argv
argv = sys.argv[sys.argv.index("--") + 1:]
opt = p.parse_args(argv)
instance_name = opt.mesh_fpath.split('/')[-3]
instance_dir = os.path.join(opt.output_dir, instance_name)
# Start Render
renderer = BlenderInterface(resolution=128)
if opt.mode == 'train':
cam_locations = sample_spherical(opt.num_observations, opt.sphere_radius)
elif opt.mode == 'test':
cam_locations = get_archimedean_spiral(opt.sphere_radius, opt.num_observations)
obj_location = np.zeros((1,3))
cv_poses = look_at(cam_locations, obj_location)
blender_poses = [cv_cam2world_to_bcam2world(m) for m in cv_poses]
shapenet_rotation_mat = np.array([[1.0000000e+00, 0.0000000e+00, 0.0000000e+00],
[0.0000000e+00, -1.0000000e+00, -1.2246468e-16],
[0.0000000e+00, 1.2246468e-16, -1.0000000e+00]])
rot_mat = np.eye(3)
hom_coords = np.array([[0., 0., 0., 1.]]).reshape(1, 4)
obj_pose = np.concatenate((rot_mat, obj_location.reshape(3,1)), axis=-1)
obj_pose = np.concatenate((obj_pose, hom_coords), axis=0)
renderer.import_mesh(opt.mesh_fpath, scale=1., object_world_matrix=obj_pose)
renderer.render(instance_dir, blender_poses, write_cam_params=True) | [
11748,
275,
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11748,
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11748,
33918,
198,
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299,
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11,
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198,
11748,
1822,
29572,
198,
11748,
299,
32152,
355,
45941,
... | 2.446809 | 940 |
#!/usr/bin/python
import unittest
import json
import base64
from mock import patch
import api
if __name__ == '__main__':
unittest.main()
| [
2,
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198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
... | 2.823529 | 51 |
import numpy as np
| [
198,
11748,
299,
32152,
355,
45941,
628,
628
] | 2.875 | 8 |
import math
grid = []
with open('input-day15.txt') as file:
for line in file:
line = line.rstrip()
grid.append([int(s) for s in line])
n = len(grid)
costs = [[math.inf] * n for _ in range(n)]
costs[0][0] = 0
queue = [(0, 0)]
while len(queue) > 0:
x1, y1 = queue.pop(0)
for dx, dy in [(1, 0), (0, 1), (-1, 0), (0, -1)]:
x, y = x1 + dx, y1 + dy
if x >= 0 and y >= 0 and x < n and y < n:
cost = costs[x1][y1] + grid[x][y]
if cost < costs[x][y]:
costs[x][y] = cost
queue.append((x, y))
print(costs[n - 1][n - 1]) | [
11748,
10688,
198,
198,
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17635,
198,
4480,
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1627,
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36311,
3419,
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220,
220,
2... | 2.04 | 275 |
# -*- coding: utf-8 -*-
'''
Created on 16.11.2014
@author: Simon Gwerder
'''
from utilities.configloader import ConfigLoader
from rdfgraph import RDFGraph
| [
2,
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198,
7061,
6,
198,
198,
6738,
20081,
13,
11250,
29356,
1330,
17056... | 2.745763 | 59 |
arr: list = [54,26,93,17,77,31,44,55,20]
print(arr)
merge_sort(arr)
print(arr)
| [
198,
198,
3258,
25,
1351,
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685,
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11,
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198,
198,
4798,
7,
3258,
8,
198,
647,
469,
62,
30619,
7... | 1.685185 | 54 |
#!/usr/bin/python3
# ******************************************************************************
# Copyright (c) Huawei Technologies Co., Ltd. 2021-2022. All rights reserved.
# licensed under the Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN 'AS IS' BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR
# PURPOSE.
# See the Mulan PSL v2 for more details.
# ******************************************************************************/
"""
Time:
Author:
Description: callback function of the cve scanning task.
"""
from aops_utils.log.log import LOGGER
from cve_manager.handler.task_handler.callback import TaskCallback
from cve_manager.conf.constant import ANSIBLE_TASK_STATUS, CVE_SCAN_STATUS
| [
2,
48443,
14629,
14,
8800,
14,
29412,
18,
198,
2,
41906,
17174,
46068,
1174,
198,
2,
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357,
66,
8,
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21852,
1766,
1539,
12052,
13,
33448,
12,
1238,
1828,
13,
1439,
2489,
10395,
13,
198,
2,
11971,
739,
262,
17996,
272,
6599... | 3.508961 | 279 |
from mean_var_std import *
calculate([0,1,2,3,4,5,6,7,8]) | [
6738,
1612,
62,
7785,
62,
19282,
1330,
1635,
198,
198,
9948,
3129,
378,
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11,
16,
11,
17,
11,
18,
11,
19,
11,
20,
11,
21,
11,
22,
11,
23,
12962
] | 1.8125 | 32 |
from datetime import datetime
from decimal import Decimal
from perfsize.perfsize import (
lt,
lte,
gt,
gte,
eq,
neq,
Condition,
Result,
Run,
Config,
Plan,
StepManager,
EnvironmentManager,
LoadManager,
ResultManager,
Reporter,
Workflow,
)
from perfsize.environment.mock import MockEnvironmentManager
from perfsize.load.mock import MockLoadManager
from perfsize.reporter.mock import MockReporter
from perfsize.result.mock import MockResultManager
from perfsize.result.gatling import Metric, GatlingResultManager
from perfsize.step.mock import MockStepManager
from pprint import pprint
import pytest
from unittest.mock import patch
| [
6738,
4818,
8079,
1330,
4818,
8079,
198,
6738,
32465,
1330,
4280,
4402,
198,
6738,
23035,
7857,
13,
525,
69,
7857,
1330,
357,
198,
220,
220,
220,
300,
83,
11,
198,
220,
220,
220,
300,
660,
11,
198,
220,
220,
220,
308,
83,
11,
198,... | 2.896266 | 241 |
from src import dmf,mzs,utils,sfx
from pathlib import Path
import argparse
parser = argparse.ArgumentParser(description='Convert DMF modules and SFX to an MLM driver compatible format')
parser.add_argument('dmf_module_paths', type=str, nargs='*', help="The paths to the input DMF files")
parser.add_argument('--sfx-directory', type=Path, help="Path to folder containing .raw files (Only absolute paths; Must be 18500Hz 16bit mono)")
parser.add_argument('--sfx-header', type=Path, help="Where to save the generated SFX c header (Only absolute paths)")
args = parser.parse_args()
dmf_modules = []
sfx_samples = None
if args.sfx_directory != None:
print("Parsing SFX... ", end='', flush=True)
sfx_samples = sfx.SFXSamples(args.sfx_directory)
print("OK")
if args.sfx_header != None:
print("Generating SFX Header... ", end='', flush=True)
c_header = sfx_samples.generate_c_header()
print("OK")
print(f"Saving SFX Header as '{args.sfx_header}'... ", end='', flush=True)
with open(args.sfx_header, "w") as file:
file.write(c_header)
print("OK")
for i in range(len(args.dmf_module_paths)):
with open(args.dmf_module_paths[i], "rb") as file:
print(f"Parsing '{args.dmf_module_paths[i]}'... ", end='', flush=True)
mod = dmf.Module(file.read())
print("OK")
print(f"Optimizing '{args.dmf_module_paths[i]}'... ", end='', flush=True)
mod.patch_for_mzs()
mod.optimize()
print("OK")
dmf_modules.append(mod)
mlm_sdata = mzs.SoundData()
print(f"Converting DMFs... ", end='', flush=True)
mlm_sdata.add_dmfs(dmf_modules)
print("OK")
if sfx_samples != None:
print(f"Converting SFX... ", end='', flush=True)
mlm_sdata.add_sfx(sfx_samples, False)
print("OK")
#print_df_info(dmf_modules[0], [0, 4, 7])
#print_info(mlm_sdata)
print(f"Compiling... ", end='', flush=True)
mlm_compiled_sdata = mlm_sdata.compile_sdata()
mlm_compiled_vrom = mlm_sdata.compile_vrom()
print("OK")
with open("m1_sdata.bin", "wb") as file:
file.write(mlm_compiled_sdata)
with open("vrom.bin", "wb") as file:
file.write(mlm_compiled_vrom) | [
6738,
12351,
1330,
288,
76,
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11,
76,
89,
82,
11,
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11,
82,
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198,
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3108,
8019,
1330,
10644,
198,
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628,
197,
197,
198,
198,
48610,
796,
1822,
29572,
13,
28100,
1713,
46677,
7,
11213,
11639,
3103,
... | 2.455635 | 834 |
# Using the Requests library, you can make a POST request by using the requests.post() method. You aren't just GETting data with a POST - you can pass your own data into the request as well, like so:
#
# requests.post("http://placekitten.com/", data="myDataToPost")
# We're going to make the same request as the one shown on line 2 through line 5. Request header lines (line 3 and line 4) are usually created automatically, so we don't have to worry about them. The body of the request on line 5 is what we will need to add to our POST.
#
# Instructions
# We created the body of the request as a dictionary on line 9. Call requests.post() on the URL http://codecademy.com/learn-http/ and pass the argument data=body, as in the example above, to create the POST request; set this result equal to a new variable named response.
########## Example request #############
# POST /learn-http HTTP/1.1
# Host: www.codecademy.com
# Content-Type: text/html; charset=UTF-8
# Name=Eric&Age=26
import requests
body = {'Name': 'Eric', 'Age': '26'}
# Make the POST request here, passing body as the data:
response = requests.post('http://codecademy.com/learn-http/', data=body)
| [
2,
8554,
262,
9394,
3558,
5888,
11,
345,
460,
787,
257,
24582,
2581,
416,
1262,
262,
7007,
13,
7353,
3419,
2446,
13,
921,
3588,
470,
655,
17151,
889,
1366,
351,
257,
24582,
532,
345,
460,
1208,
534,
898,
1366,
656,
262,
2581,
355,
... | 3.531722 | 331 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
from functools import partial
from .linear import MLP, LogReg
from .pointnet import PointNet
from .pointnet2 import PointNet2SSG
from .pointnet3 import PointNet3SSG
from .dgcnn import DGCNN
# from .masked_conv import ConvolutionalPoseModel
from .point_mlp import PointMLP
from pytorch_lightning.core.lightning import LightningModule
| [
11748,
299,
32152,
355,
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201,
198,
11748,
28034,
201,
198,
11748,
28034,
13,
20471,
355,
299,
77,
201,
198,
11748,
28034,
13,
20471,
13,
45124,
355,
376,
201,
198,
201,
198,
11748,
640,
201,
198,
6738,
1257,
310,
10141,
1330,
13... | 3.006667 | 150 |
import golpy.controller.controller as controller
import golpy.view.view as view
import golpy.model.gamemodel as model
import golpy.eventmanager.eventmanager as eventm
import golpy.config as config
import log.log as log
import argparse
def pass_args():
""" Takes Argument from the command line and returns an ArgumentParser"""
parser = argparse.ArgumentParser(description="2D Cellular Automata Viewer supporting multiple formats")
parser.add_argument("-rule", "-r", type=str, default=config.default_rule,
help='String describing the used rule')
parser.add_argument("-mode", "-m", type=str, default=config.default_mode,
help="String describing Game Mode")
parser.add_argument("-size", "-s", type=int, default=config.default_size,
help="Integer describing size of the universe. I.e. -size 200 will correspond to a (200 x 200) cell universe")
parser.add_argument("-topology", "-t", type=str, default=config.default_topology,
help="String describing the topology of the universe. Default being Torus-shaped")
parser.add_argument("-speed", "-sp", type=int, default=config.default_speed,
help="Integer describing the maximum FPS possible for the animation")
parser.add_argument("-windowsize", "-w", type=int, default=config.default_window_size,
help="Integer describing the window size in pixels")
return parser.parse_args()
if __name__ == '__main__':
run()
| [
11748,
43791,
9078,
13,
36500,
13,
36500,
355,
10444,
198,
11748,
43791,
9078,
13,
1177,
13,
1177,
355,
1570,
198,
11748,
43791,
9078,
13,
19849,
13,
28483,
368,
375,
417,
355,
2746,
198,
11748,
43791,
9078,
13,
15596,
37153,
13,
15596,... | 2.820841 | 547 |
import unittest
import orca
from setup.settings import *
from pandas.util.testing import *
| [
11748,
555,
715,
395,
198,
11748,
393,
6888,
198,
6738,
9058,
13,
33692,
1330,
1635,
198,
6738,
19798,
292,
13,
22602,
13,
33407,
1330,
1635,
628
] | 3.538462 | 26 |
ITERATION_NUM = 10
MAX_POPULATION = 500
CROSSOVER_RATE = 1
MUTATION_RATE = 1
supplies = {
'S1': 20,
'S2': 15,
'S3': 40
}
demands = {
'D1': 20,
'D2': 30,
'D3': 25
}
cost = [[2, 3, 1],
[5, 4, 8],
[5, 6, 8]
]
| [
2043,
1137,
6234,
62,
41359,
796,
838,
201,
198,
22921,
62,
47,
3185,
6239,
6234,
796,
5323,
201,
198,
9419,
2640,
15821,
5959,
62,
49,
6158,
796,
352,
201,
198,
44,
3843,
6234,
62,
49,
6158,
796,
352,
201,
198,
201,
198,
18608,
1... | 1.535912 | 181 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
from functools import cached_property
from .vector3 import Vector3
| [
6738,
1257,
310,
10141,
1330,
39986,
62,
26745,
198,
198,
6738,
764,
31364,
18,
1330,
20650,
18,
628
] | 3.833333 | 18 |
from gps import *
import math
import time
import json
import threading
gpsd = None
poller = None
| [
6738,
308,
862,
1330,
1635,
198,
11748,
10688,
198,
11748,
640,
198,
11748,
33918,
198,
11748,
4704,
278,
198,
198,
70,
862,
67,
796,
6045,
198,
30393,
263,
796,
6045,
198
] | 3.16129 | 31 |
import json
import random
import warnings
from typing import Any, Callable, Dict, List, Union, Type, Optional, NoReturn
from pydantic import PrivateAttr
from vkwave.bots import BotEvent, BotType, EventTypeFilter, UserEvent
from vkwave.bots.core import BaseFilter
from vkwave.bots.core.dispatching.filters.builtin import get_payload, get_text
from vkwave.bots.core.dispatching.handler.callback import BaseCallback
from vkwave.bots.core.dispatching.handler.cast import caster as callback_caster
from vkwave.bots.core.types.json_types import JSONEncoder
from vkwave.types.bot_events import BotEventType
from vkwave.types.objects import (
BaseBoolInt,
MessagesMessageAttachment,
MessagesMessageAttachmentType,
UsersUser,
)
from vkwave.types.responses import BaseOkResponse, MessagesEditResponse, MessagesSendResponse
from vkwave.types.user_events import EventId
try:
import aiofile
except ImportError:
aiofile = None
def simple_bot_handler(router, event: Optional[Type[SimpleBotEvent]] = None, *filters: BaseFilter):
"""
Handler for all bot events
"""
return decorator
def simple_user_handler(router, *filters: BaseFilter, event: Optional[Type[SimpleUserEvent]] = None):
"""
Handler for all user events
"""
return decorator
def simple_bot_message_handler(router, *filters: BaseFilter, event: Optional[Type[SimpleBotEvent]] = None):
"""
Handler only for message events
"""
return decorator
def simple_user_message_handler(router, *filters: BaseFilter, event: Optional[Type[SimpleUserEvent]] = None):
"""
Handler only for message events
"""
return decorator
| [
11748,
33918,
198,
11748,
4738,
198,
11748,
14601,
198,
6738,
19720,
1330,
4377,
11,
4889,
540,
11,
360,
713,
11,
7343,
11,
4479,
11,
5994,
11,
32233,
11,
1400,
13615,
198,
198,
6738,
279,
5173,
5109,
1330,
15348,
8086,
81,
198,
198,
... | 3.145833 | 528 |
"""
Created on 7 Nov 2016
@author: Bruno Beloff (bruno.beloff@southcoastscience.com)
example:
25 June 2016 17:44:28 BST: {"datum":{"conc":92,"dens":184},"measured-at":"2016-06-25T17:41:01+01:00"}
"""
from collections import OrderedDict
from scs_core.data.json import JSONable
# --------------------------------------------------------------------------------------------------------------------
| [
37811,
198,
41972,
319,
767,
5267,
1584,
198,
198,
31,
9800,
25,
31045,
3944,
2364,
357,
1671,
36909,
13,
6667,
2364,
31,
35782,
1073,
5773,
4234,
13,
785,
8,
198,
198,
20688,
25,
198,
1495,
2795,
1584,
1596,
25,
2598,
25,
2078,
449... | 3.612613 | 111 |
#
# PySNMP MIB module CISCO-FC-PM-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-FC-PM-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:40:52 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, ObjectIdentifier, OctetString = mibBuilder.importSymbols("ASN1", "Integer", "ObjectIdentifier", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueRangeConstraint, ConstraintsUnion, SingleValueConstraint, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueRangeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ValueSizeConstraint")
ciscoMgmt, = mibBuilder.importSymbols("CISCO-SMI", "ciscoMgmt")
ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex")
PerfIntervalCount, PerfCurrentCount, PerfTotalCount = mibBuilder.importSymbols("PerfHist-TC-MIB", "PerfIntervalCount", "PerfCurrentCount", "PerfTotalCount")
ModuleCompliance, NotificationGroup, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup", "ObjectGroup")
iso, Bits, ModuleIdentity, ObjectIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, MibIdentifier, Integer32, NotificationType, Counter32, Gauge32, IpAddress, Unsigned32, Counter64, TimeTicks = mibBuilder.importSymbols("SNMPv2-SMI", "iso", "Bits", "ModuleIdentity", "ObjectIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "MibIdentifier", "Integer32", "NotificationType", "Counter32", "Gauge32", "IpAddress", "Unsigned32", "Counter64", "TimeTicks")
TextualConvention, TruthValue, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "TruthValue", "DisplayString")
ciscoFcPmMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 9, 99997))
ciscoFcPmMIB.setRevisions(('2005-02-06 00:00',))
if mibBuilder.loadTexts: ciscoFcPmMIB.setLastUpdated('200502060000Z')
if mibBuilder.loadTexts: ciscoFcPmMIB.setOrganization('Cisco Systems, Inc.')
ciscoFcPmMIBNotifs = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 0))
ciscoFcPmMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1))
ciscoFcPmMIBConform = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2))
cfcpmPortPerfStatus = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 1))
cfcpmPortErrorStatusBlock = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2))
cfcpmPortPerfStatusTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 1, 1), )
if mibBuilder.loadTexts: cfcpmPortPerfStatusTable.setStatus('current')
cfcpmPortPerfStatusEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: cfcpmPortPerfStatusEntry.setStatus('current')
cfcpmTimeElapsed = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 899))).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmTimeElapsed.setStatus('current')
cfcpmValidIntervals = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 1, 1, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 96))).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmValidIntervals.setStatus('current')
cfcpmInvalidIntervals = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 96))).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmInvalidIntervals.setStatus('current')
cfcpmTotalPortErrorTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1), )
if mibBuilder.loadTexts: cfcpmTotalPortErrorTable.setStatus('current')
cfcpmTotalPortErrorEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: cfcpmTotalPortErrorEntry.setStatus('current')
cfcpmtPortRxLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 1), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortRxLinkResets.setStatus('current')
cfcpmtPortTxLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 2), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortTxLinkResets.setStatus('current')
cfcpmtPortLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 3), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortLinkResets.setStatus('current')
cfcpmtPortRxOfflineSequences = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 4), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortRxOfflineSequences.setStatus('current')
cfcpmtPortTxOfflineSequences = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 5), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortTxOfflineSequences.setStatus('current')
cfcpmtPortLinkFailures = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 6), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortLinkFailures.setStatus('current')
cfcpmtPortSynchLosses = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 7), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortSynchLosses.setStatus('current')
cfcpmtPortSignalLosses = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 8), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortSignalLosses.setStatus('current')
cfcpmtPortPrimSeqProtocolErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 9), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortPrimSeqProtocolErrors.setStatus('current')
cfcpmtPortInvalidTxWords = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 10), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortInvalidTxWords.setStatus('current')
cfcpmtPortInvalidCRCs = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 11), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortInvalidCRCs.setStatus('current')
cfcpmtPortInvalidOrderedSets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 12), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortInvalidOrderedSets.setStatus('current')
cfcpmtPortFramesTooLong = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 13), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortFramesTooLong.setStatus('current')
cfcpmtPortTruncatedFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 14), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortTruncatedFrames.setStatus('current')
cfcpmtPortAddressErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 15), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortAddressErrors.setStatus('current')
cfcpmtPortDelimiterErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 16), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortDelimiterErrors.setStatus('current')
cfcpmtPortEncDisparityErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 17), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortEncDisparityErrors.setStatus('current')
cfcpmtPortOtherErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 1, 1, 18), PerfTotalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmtPortOtherErrors.setStatus('current')
cfcpmCurrentPortErrorTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2), )
if mibBuilder.loadTexts: cfcpmCurrentPortErrorTable.setStatus('current')
cfcpmCurrentPortErrorEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: cfcpmCurrentPortErrorEntry.setStatus('current')
cfcpmcPortRxLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 1), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortRxLinkResets.setStatus('current')
cfcpmcPortTxLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 2), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortTxLinkResets.setStatus('current')
cfcpmcPortLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 3), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortLinkResets.setStatus('current')
cfcpmcPortRxOfflineSequences = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 4), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortRxOfflineSequences.setStatus('current')
cfcpmcPortTxOfflineSequences = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 5), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortTxOfflineSequences.setStatus('current')
cfcpmcPortLinkFailures = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 6), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortLinkFailures.setStatus('current')
cfcpmcPortSynchLosses = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 7), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortSynchLosses.setStatus('current')
cfcpmcPortSignalLosses = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 8), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortSignalLosses.setStatus('current')
cfcpmcPortPrimSeqProtocolErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 9), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortPrimSeqProtocolErrors.setStatus('current')
cfcpmcPortInvalidTxWords = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 10), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortInvalidTxWords.setStatus('current')
cfcpmcPortInvalidCRCs = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 11), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortInvalidCRCs.setStatus('current')
cfcpmcPortInvalidOrderedSets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 12), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortInvalidOrderedSets.setStatus('current')
cfcpmcPortFramesTooLong = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 13), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortFramesTooLong.setStatus('current')
cfcpmcPortTruncatedFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 14), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortTruncatedFrames.setStatus('current')
cfcpmcPortAddressErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 15), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortAddressErrors.setStatus('current')
cfcpmcPortDelimiterErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 16), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortDelimiterErrors.setStatus('current')
cfcpmcPortEncDisparityErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 17), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortEncDisparityErrors.setStatus('current')
cfcpmcPortOtherErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 2, 1, 18), PerfCurrentCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmcPortOtherErrors.setStatus('current')
cfcpmIntervalPortErrorTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3), )
if mibBuilder.loadTexts: cfcpmIntervalPortErrorTable.setStatus('current')
cfcpmIntervalPortErrorEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "CISCO-FC-PM-MIB", "cfcpmiPortErrorIntervalNumber"))
if mibBuilder.loadTexts: cfcpmIntervalPortErrorEntry.setStatus('current')
cfcpmiPortErrorIntervalNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1, 96)))
if mibBuilder.loadTexts: cfcpmiPortErrorIntervalNumber.setStatus('current')
cfcpmiPortRxLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 2), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortRxLinkResets.setStatus('current')
cfcpmiPortTxLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 3), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortTxLinkResets.setStatus('current')
cfcpmiPortLinkResets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 4), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortLinkResets.setStatus('current')
cfcpmiPortRxOfflineSequences = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 5), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortRxOfflineSequences.setStatus('current')
cfcpmiPortTxOfflineSequences = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 6), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortTxOfflineSequences.setStatus('current')
cfcpmiPortLinkFailures = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 7), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortLinkFailures.setStatus('current')
cfcpmiPortSynchLosses = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 8), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortSynchLosses.setStatus('current')
cfcpmiPortSignalLosses = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 9), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortSignalLosses.setStatus('current')
cfcpmiPortPrimSeqProtocolErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 10), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortPrimSeqProtocolErrors.setStatus('current')
cfcpmiPortInvalidTxWords = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 11), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortInvalidTxWords.setStatus('current')
cfcpmiPortInvalidCRCs = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 12), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortInvalidCRCs.setStatus('current')
cfcpmiPortInvalidOrderedSets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 13), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortInvalidOrderedSets.setStatus('current')
cfcpmiPortFramesTooLong = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 14), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortFramesTooLong.setStatus('current')
cfcpmiPortTruncatedFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 15), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortTruncatedFrames.setStatus('current')
cfcpmiPortAddressErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 16), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortAddressErrors.setStatus('current')
cfcpmiPortDelimiterErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 17), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortDelimiterErrors.setStatus('current')
cfcpmiPortEncDisparityErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 18), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortEncDisparityErrors.setStatus('current')
cfcpmiPortOtherErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 19), PerfIntervalCount()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortOtherErrors.setStatus('current')
cfcpmiPortValidData = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 99997, 1, 2, 3, 1, 20), TruthValue()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cfcpmiPortValidData.setStatus('current')
cfcpmMibCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2, 1))
cfcpmMibGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2, 2))
cfcpmMibCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2, 1, 1)).setObjects(("CISCO-FC-PM-MIB", "cfcpmPortStatusGroup"), ("CISCO-FC-PM-MIB", "cfcpmMandatoryGroup"), ("CISCO-FC-PM-MIB", "cfcpmOptionalGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cfcpmMibCompliance = cfcpmMibCompliance.setStatus('current')
cfcpmPortStatusGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2, 2, 1)).setObjects(("CISCO-FC-PM-MIB", "cfcpmTimeElapsed"), ("CISCO-FC-PM-MIB", "cfcpmValidIntervals"), ("CISCO-FC-PM-MIB", "cfcpmInvalidIntervals"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cfcpmPortStatusGroup = cfcpmPortStatusGroup.setStatus('current')
cfcpmMandatoryGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2, 2, 2)).setObjects(("CISCO-FC-PM-MIB", "cfcpmtPortPrimSeqProtocolErrors"), ("CISCO-FC-PM-MIB", "cfcpmcPortPrimSeqProtocolErrors"), ("CISCO-FC-PM-MIB", "cfcpmiPortPrimSeqProtocolErrors"), ("CISCO-FC-PM-MIB", "cfcpmiPortValidData"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cfcpmMandatoryGroup = cfcpmMandatoryGroup.setStatus('current')
cfcpmOptionalGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 99997, 2, 2, 3)).setObjects(("CISCO-FC-PM-MIB", "cfcpmtPortRxLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmtPortTxLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmtPortLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmtPortRxOfflineSequences"), ("CISCO-FC-PM-MIB", "cfcpmtPortTxOfflineSequences"), ("CISCO-FC-PM-MIB", "cfcpmtPortLinkFailures"), ("CISCO-FC-PM-MIB", "cfcpmtPortSynchLosses"), ("CISCO-FC-PM-MIB", "cfcpmtPortSignalLosses"), ("CISCO-FC-PM-MIB", "cfcpmtPortInvalidTxWords"), ("CISCO-FC-PM-MIB", "cfcpmtPortInvalidCRCs"), ("CISCO-FC-PM-MIB", "cfcpmtPortInvalidOrderedSets"), ("CISCO-FC-PM-MIB", "cfcpmtPortFramesTooLong"), ("CISCO-FC-PM-MIB", "cfcpmtPortTruncatedFrames"), ("CISCO-FC-PM-MIB", "cfcpmtPortAddressErrors"), ("CISCO-FC-PM-MIB", "cfcpmtPortDelimiterErrors"), ("CISCO-FC-PM-MIB", "cfcpmtPortEncDisparityErrors"), ("CISCO-FC-PM-MIB", "cfcpmtPortOtherErrors"), ("CISCO-FC-PM-MIB", "cfcpmcPortRxLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmcPortTxLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmcPortLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmcPortRxOfflineSequences"), ("CISCO-FC-PM-MIB", "cfcpmcPortTxOfflineSequences"), ("CISCO-FC-PM-MIB", "cfcpmcPortLinkFailures"), ("CISCO-FC-PM-MIB", "cfcpmcPortSynchLosses"), ("CISCO-FC-PM-MIB", "cfcpmcPortSignalLosses"), ("CISCO-FC-PM-MIB", "cfcpmcPortInvalidTxWords"), ("CISCO-FC-PM-MIB", "cfcpmcPortInvalidCRCs"), ("CISCO-FC-PM-MIB", "cfcpmcPortInvalidOrderedSets"), ("CISCO-FC-PM-MIB", "cfcpmcPortFramesTooLong"), ("CISCO-FC-PM-MIB", "cfcpmcPortTruncatedFrames"), ("CISCO-FC-PM-MIB", "cfcpmcPortAddressErrors"), ("CISCO-FC-PM-MIB", "cfcpmcPortDelimiterErrors"), ("CISCO-FC-PM-MIB", "cfcpmcPortEncDisparityErrors"), ("CISCO-FC-PM-MIB", "cfcpmcPortOtherErrors"), ("CISCO-FC-PM-MIB", "cfcpmiPortRxLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmiPortTxLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmiPortLinkResets"), ("CISCO-FC-PM-MIB", "cfcpmiPortRxOfflineSequences"), ("CISCO-FC-PM-MIB", "cfcpmiPortTxOfflineSequences"), ("CISCO-FC-PM-MIB", "cfcpmiPortLinkFailures"), ("CISCO-FC-PM-MIB", "cfcpmiPortSynchLosses"), ("CISCO-FC-PM-MIB", "cfcpmiPortSignalLosses"), ("CISCO-FC-PM-MIB", "cfcpmiPortInvalidTxWords"), ("CISCO-FC-PM-MIB", "cfcpmiPortInvalidCRCs"), ("CISCO-FC-PM-MIB", "cfcpmiPortInvalidOrderedSets"), ("CISCO-FC-PM-MIB", "cfcpmiPortFramesTooLong"), ("CISCO-FC-PM-MIB", "cfcpmiPortTruncatedFrames"), ("CISCO-FC-PM-MIB", "cfcpmiPortAddressErrors"), ("CISCO-FC-PM-MIB", "cfcpmiPortDelimiterErrors"), ("CISCO-FC-PM-MIB", "cfcpmiPortEncDisparityErrors"), ("CISCO-FC-PM-MIB", "cfcpmiPortOtherErrors"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cfcpmOptionalGroup = cfcpmOptionalGroup.setStatus('current')
mibBuilder.exportSymbols("CISCO-FC-PM-MIB", cfcpmtPortSynchLosses=cfcpmtPortSynchLosses, cfcpmPortStatusGroup=cfcpmPortStatusGroup, cfcpmtPortFramesTooLong=cfcpmtPortFramesTooLong, cfcpmtPortTxLinkResets=cfcpmtPortTxLinkResets, cfcpmcPortTxOfflineSequences=cfcpmcPortTxOfflineSequences, cfcpmiPortRxOfflineSequences=cfcpmiPortRxOfflineSequences, cfcpmcPortInvalidCRCs=cfcpmcPortInvalidCRCs, cfcpmcPortInvalidOrderedSets=cfcpmcPortInvalidOrderedSets, cfcpmtPortEncDisparityErrors=cfcpmtPortEncDisparityErrors, cfcpmcPortPrimSeqProtocolErrors=cfcpmcPortPrimSeqProtocolErrors, cfcpmTimeElapsed=cfcpmTimeElapsed, cfcpmMibCompliances=cfcpmMibCompliances, cfcpmiPortPrimSeqProtocolErrors=cfcpmiPortPrimSeqProtocolErrors, cfcpmInvalidIntervals=cfcpmInvalidIntervals, cfcpmcPortSynchLosses=cfcpmcPortSynchLosses, cfcpmValidIntervals=cfcpmValidIntervals, cfcpmiPortEncDisparityErrors=cfcpmiPortEncDisparityErrors, cfcpmMibGroups=cfcpmMibGroups, cfcpmcPortRxOfflineSequences=cfcpmcPortRxOfflineSequences, cfcpmMibCompliance=cfcpmMibCompliance, cfcpmPortPerfStatusEntry=cfcpmPortPerfStatusEntry, cfcpmiPortValidData=cfcpmiPortValidData, cfcpmtPortRxOfflineSequences=cfcpmtPortRxOfflineSequences, cfcpmIntervalPortErrorEntry=cfcpmIntervalPortErrorEntry, cfcpmPortErrorStatusBlock=cfcpmPortErrorStatusBlock, ciscoFcPmMIBConform=ciscoFcPmMIBConform, cfcpmcPortSignalLosses=cfcpmcPortSignalLosses, cfcpmOptionalGroup=cfcpmOptionalGroup, cfcpmPortPerfStatusTable=cfcpmPortPerfStatusTable, cfcpmtPortRxLinkResets=cfcpmtPortRxLinkResets, PYSNMP_MODULE_ID=ciscoFcPmMIB, cfcpmTotalPortErrorEntry=cfcpmTotalPortErrorEntry, cfcpmtPortLinkResets=cfcpmtPortLinkResets, cfcpmiPortRxLinkResets=cfcpmiPortRxLinkResets, cfcpmiPortSignalLosses=cfcpmiPortSignalLosses, cfcpmiPortInvalidTxWords=cfcpmiPortInvalidTxWords, cfcpmcPortAddressErrors=cfcpmcPortAddressErrors, cfcpmiPortErrorIntervalNumber=cfcpmiPortErrorIntervalNumber, cfcpmIntervalPortErrorTable=cfcpmIntervalPortErrorTable, cfcpmiPortDelimiterErrors=cfcpmiPortDelimiterErrors, cfcpmPortPerfStatus=cfcpmPortPerfStatus, cfcpmcPortLinkFailures=cfcpmcPortLinkFailures, cfcpmCurrentPortErrorEntry=cfcpmCurrentPortErrorEntry, cfcpmiPortInvalidCRCs=cfcpmiPortInvalidCRCs, cfcpmcPortEncDisparityErrors=cfcpmcPortEncDisparityErrors, cfcpmiPortFramesTooLong=cfcpmiPortFramesTooLong, cfcpmtPortLinkFailures=cfcpmtPortLinkFailures, cfcpmcPortOtherErrors=cfcpmcPortOtherErrors, cfcpmtPortOtherErrors=cfcpmtPortOtherErrors, cfcpmcPortInvalidTxWords=cfcpmcPortInvalidTxWords, cfcpmiPortInvalidOrderedSets=cfcpmiPortInvalidOrderedSets, cfcpmtPortInvalidTxWords=cfcpmtPortInvalidTxWords, cfcpmiPortTxLinkResets=cfcpmiPortTxLinkResets, cfcpmtPortTruncatedFrames=cfcpmtPortTruncatedFrames, ciscoFcPmMIBNotifs=ciscoFcPmMIBNotifs, cfcpmtPortAddressErrors=cfcpmtPortAddressErrors, cfcpmcPortLinkResets=cfcpmcPortLinkResets, cfcpmiPortOtherErrors=cfcpmiPortOtherErrors, cfcpmcPortDelimiterErrors=cfcpmcPortDelimiterErrors, cfcpmCurrentPortErrorTable=cfcpmCurrentPortErrorTable, cfcpmiPortTruncatedFrames=cfcpmiPortTruncatedFrames, cfcpmcPortTxLinkResets=cfcpmcPortTxLinkResets, cfcpmtPortInvalidOrderedSets=cfcpmtPortInvalidOrderedSets, cfcpmMandatoryGroup=cfcpmMandatoryGroup, cfcpmcPortTruncatedFrames=cfcpmcPortTruncatedFrames, ciscoFcPmMIBObjects=ciscoFcPmMIBObjects, cfcpmiPortAddressErrors=cfcpmiPortAddressErrors, cfcpmiPortLinkFailures=cfcpmiPortLinkFailures, cfcpmiPortTxOfflineSequences=cfcpmiPortTxOfflineSequences, cfcpmtPortTxOfflineSequences=cfcpmtPortTxOfflineSequences, cfcpmiPortLinkResets=cfcpmiPortLinkResets, cfcpmtPortDelimiterErrors=cfcpmtPortDelimiterErrors, cfcpmtPortSignalLosses=cfcpmtPortSignalLosses, ciscoFcPmMIB=ciscoFcPmMIB, cfcpmtPortInvalidCRCs=cfcpmtPortInvalidCRCs, cfcpmTotalPortErrorTable=cfcpmTotalPortErrorTable, cfcpmtPortPrimSeqProtocolErrors=cfcpmtPortPrimSeqProtocolErrors, cfcpmiPortSynchLosses=cfcpmiPortSynchLosses, cfcpmcPortRxLinkResets=cfcpmcPortRxLinkResets, cfcpmcPortFramesTooLong=cfcpmcPortFramesTooLong)
| [
2,
198,
2,
9485,
15571,
7378,
337,
9865,
8265,
36159,
8220,
12,
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12,
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198,
2,
7054,
45,
13,
16,
2723,
2393,
1378,
14,
14490,
14,
67,
... | 2.427294 | 9,951 |
import django.contrib.gis.db.models as gis_models
from django.apps import apps
from django.db import models, connection
from django.urls import reverse
from distributions.models import TemporalDistribution, Timestep
from inventories.models import Scenario, InventoryAlgorithm
from materials.models import SampleSeries, MaterialComponent
from .exceptions import InvalidGeometryType, NoFeaturesProvided, TableAlreadyExists
DISTRIBUTION_TYPES = (
('seasonal', 'seasonal'), # Assumes array with length 12 for each month of the year
)
| [
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6371... | 3.682432 | 148 |
from evaluation import MetricScorer
from .formulas import mar, sa, sd, sdar, effect_size, mmre, mdmre, pred25, pred40
from baseline import MARP0 | [
6738,
12660,
1330,
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... | 3.2 | 45 |
"""
AbaqusGeometry.py
For use with Abaqus 6.13-1 (Python 2.6.2).
Created by Ozgur Yapar <oyapar@isis.vanderbilt.edu>
Robert Boyles <rboyles@isis.vanderbilt.edu>
- Includes modules which take care of geometrical operations
in the part and assembly level.
"""
import re
import math
from numpy import array, cross, transpose, vstack, dot
from abaqusConstants import *
import numpy.linalg as LA
import string as STR
def regexFriendly(inString):
""" Clean up coordinates read from STEP file, prior to applying regular expressions. """
outString = STR.replace(inString, '\'', '%')
outString = STR.replace(outString, '(', '')
outString = STR.replace(outString, ')', ',')
return outString
def coordinate(stepString):
""" Extract tuple of cartesian coordinates from STEP coordinate string. """
e = re.compile(',\S+,,') # regular expression
coordFind = e.search(stepString) # extract substring containing coordinates
coordList = coordFind.group(0).strip(',').split(',') # separate x, y, and z coordinates by commas
coords = (float(coordList[0]), float(coordList[1]),
float(coordList[2])) # convert coordinate strings to a tuple of floats
return coords # return the coordinate tuple
# calculates transformation matrix between two coordinate systems as defined in STEP
def get3DTransformArray(fromDir1, fromDir2, toDir1, toDir2):
""" Calculate transformation matrix between two coordinate systems as defined in STEP. """
fromDir1 = array(fromDir1) # convert u1 vector to an array object
fromDir2 = array(fromDir2) # convert u2 vector to an array object
fromDir3 = cross(fromDir1, fromDir2) # extrapolate u3 vector from u1 and u2
toDir1 = array(toDir1) # convert v1 vector to an array object
toDir2 = array(toDir2) # convert v2 vector to an array object
toDir3 = cross(toDir1, toDir2) # extrapolate v3 vector from v1 and v2
inva = LA.inv(transpose(vstack([fromDir1, fromDir2, fromDir3])))
b = transpose(vstack([toDir1, toDir2, toDir3]))
transformArray = dot(b, inva)
return transformArray
def unv(center, planarA, planarB):
""" Use vector operations to get unit normal vector, given a center coordinate and two planar coordinates. """
center = array(center)
planarA = array(planarA)
planarB = array(planarB)
vA = planarA - center
vB = planarB - center
xV = cross(vA, vB)
return xV/LA.norm(xV)
def transCoord(fromCoord, transformArray, translationVector):
""" Transform/translate a cartesian point from one coordinate system to another. """
vprod = dot(transformArray, fromCoord)
vprod = vprod + translationVector
toCoord = tuple(vprod)
return toCoord
def asmRecursion(asm, subAsms, asmParts):
""" Recursively identifies parts in sub-assemblies, in the order they are imported from STEP. """
parts = []
try:
for child in subAsms[asm]:
if child in subAsms:
parts.extend(asmRecursion(child, subAsms, asmParts))
else:
parts.extend(asmParts[child])
except KeyError:
pass
if asm in asmParts:
parts.extend(asmParts[asm])
return parts
def coordTransform(localTMs, localTVs, asm, subAsms, asmParts, localCoords):
"""
Iterate through sub-assemblies and top-level parts to transform/translate
every datum point to assembly coordinates; uses transCoord()
Note: Ignores top-level datums in highest assembly, which will not exist
in a CyPhy assembly anyway
"""
globalCoords = {} # create dictionary object to hold new point library
if asm in subAsms: # if assembly has sub-assemblies:
for subAsm in subAsms[asm]: # for each sub-assembly in the assembly:
subCoords = coordTransform(localTMs, localTVs, subAsm, # get point library local to sub-assembly
subAsms, asmParts, localCoords)
for part in subCoords.keys(): # for each component in chosen sub-assembly:
globalCoords.update([[part, {}]]) # create new entry in globalCoords
for (point, coord) in subCoords[part].iteritems(): # for each point in part/sub-sub-assembly:
globalCoords[part].update([[point.upper(), transCoord( # translate/transform point to globalCoords
array(coord), localTMs[subAsm], localTVs[subAsm])]])
globalCoords.update([[subAsm, {}]]) # create entry for sub-assembly in globalCoords
for (point, coord) in localCoords[subAsm].iteritems():
# for each point specified at top level of that sub-assembly:
globalCoords[subAsm].update([[point.upper(), transCoord( # translate/transform point to globalCoords
array(coord), localTMs[subAsm], localTVs[subAsm])]])
if asm in asmParts: # if assembly has top-level parts:
for part in asmParts[asm]: # for each top-level part:
globalCoords.update([[part, {}]]) # create new entry in globalCoords
for (point, coord) in localCoords[part].iteritems(): # for each point in part:
globalCoords[part].update([[point.upper(), transCoord( # translate/transform point to globalCoords
array(coord), localTMs[part], localTVs[part])]])
return globalCoords
def myMask(idnums):
""" Produce mask string for getSequenceFromMask(...) from a feature ID or set of IDs. """
try:
idnums = tuple(idnums) # make the input a tuple!
except TypeError: # if input is not iterable:
idnums = (idnums,) # make it a tuple anyway!
powersum = 0 # integer to hold mask number
for num in idnums: # iterating through input IDs:
powersum += 2**num # add 2**ID to powersum
rawmask = hex(powersum)[2:] # convert powermask to hexadecimal
rawmask = STR.rstrip(rawmask, 'L') # strip "long" character, if necessary
if max(idnums) < 32: # if hex number is 8 digits or less:
mask = '[#' + rawmask + ' ]' # create mask
else: # if hex number is >8 digits:
maskpieces = [] # container for fragments of hex string
piececount = int(math.ceil(len(rawmask)/8)) # number of times to split hex string
for i in range(piececount): # for each split needed:
maskpieces.append(rawmask[-8:]) # append last 8 characters of hex string to fragment list
rawmask = rawmask[:-8] # trim last 8 characters from hex string
maskpieces.append(rawmask) # append remaining hex string to fragment list
mask = '[#' + STR.join(maskpieces, ' #') + ' ]' # join fragments, using the correct delimiters, to create mask
return mask
def toBC(constraint):
""" Translates a degree of freedom as read from the XML to the appropriate SymbolicConstant. """
if constraint == 'FIXED':
return 0
elif constraint == 'FREE':
return UNSET
else:
return float(constraint) | [
37811,
201,
198,
201,
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201,
198,
201,
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41972,
416,
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45073,
... | 2.136316 | 3,844 |
import climate
import glob
import gzip
import io
import lmj.cubes
import logging
import numpy as np
import os
import pandas as pd
import pickle
import theanets
if __name__ == '__main__':
climate.call(main)
| [
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1174... | 2.972222 | 72 |
import json
import os
import logging
from datetime import datetime
from django.db.models import Q,Count
from django.http import JsonResponse
from django.views.generic import View
from django.views.decorators.csrf import csrf_exempt
from django.utils.decorators import method_decorator
from django.conf import settings
from rest_framework_jwt.settings import api_settings
from django.core.exceptions import ObjectDoesNotExist#EmptyResultSet, MultipleObjectsReturned
from django.contrib.auth import get_user_model
from commerce.models import Restaurant, Picture, Product, Category, Order, OrderItem, Style, PriceRange, FavoriteProduct
from account.models import Province, City, Address
from utils import to_json, obj_to_json, get_data_from_token
logger = logging.getLogger(__name__)
| [
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42625,
14208,
13,
4023,
1330,
449,
1559,
31077,
198,
6738,
42625,... | 3.441558 | 231 |
from django.shortcuts import render
from api.models import Comida, Cerveza, Titulo, TipoComida
from django.http import Http404
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import serializers
import csv
import os
import csv
import os | [
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6738,... | 3.571429 | 84 |
{
"targets": [
{
"target_name": "usb_dev",
"sources": [ "usb_dev.cc" ],
"include_dirs" : [
"<!(node -e \"require('nan')\")"
],
"libraries": [
"-lsetupapi"
]
}
]
}
| [
90,
198,
220,
366,
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1298,
685,
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... | 1.673913 | 138 |
'''
table AD contains RS,ADID;table Parkinson contains RS,PDID;table variant contains ADID,
PDID insert table variant
one way: below
two way: by merge
'''
import sys ,re
import pandas as pd
varfil1=r'C:\Users\BAIOMED07\Desktop\AD_Database_20170629.xls'
varfil2=r'C:\Users\BAIOMED07\Desktop\parkinson_TOTAL.xls'
varfil3=r'C:\Users\BAIOMED07\Desktop\alleles_IonXpress_066.txt'
df1=pd.read_excel(varfil1)
print df1.head(1)
df2=pd.read_excel(varfil2)
print df2.head(1)
df=df1[df1['dbSNP'].isin(df2['dbSNP'])]
print df.head(2)
df.to_excel('1.xlsx',index=0)
df3=pd.read_csv(varfil3,sep='\t')
df3['pkiq']='-'
for index,row in df2.iterrows():
rs=row['dbSNP']
row1=df1[df1['dbSNP']==rs]
if not len(row1): continue # when drug locus is not in row1
#import pdb; pdb.set_trace()
uniq=row1['UniqueID'].values.tolist()[0]
row2=df3[df3['Allele Name']==uniq]
df3.loc[row2.index,'pkiq']=row['UniqueID']
print df3.head(1)
res_1=df3[df3['Allele Name'].isin(df['UniqueID'])]
res_1.to_excel('2.xlsx',index=0)
| [
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416,... | 2.093168 | 483 |
import serial
import time
### FUNCTIONS ####
#### SERIAL COMMUNICATION ####
def arduino_communication(COM="COM5",BAUDRATE=9600,TIMEOUT=1):
""" Initalizes connection with Arduino Board """
try:
arduino = serial.Serial(COM, BAUDRATE , timeout=TIMEOUT)
time.sleep(2)
except:
print("Error de coneccion con el puerto")
return arduino
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7,
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52,
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6158,
2... | 2.5 | 156 |
"""Classes and functions used for data visualization"""
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
def plot_log_hist(s,bin_factor=1,min_exp=None):
"""Plot 2 histograms with log x scales, one for positive values & one for negative values.
Bin_factor is used to scale how many bins to use (1 is default and corresponds to
one bin per order of magnitude. Higher than 1 will skew the bins away from even powers of
10).
Parameters
----------
s: pandas series (generally using df[col])
Series or column of dataframe to analyze
bin_factor: int
Default 1, used to scale how many bins to use
min_exp: int
The minimum exponent to use in creating bins & plotting.
This can be set manually for cases where you want a specific
minimum value to be shown.
Returns
-------
fig, (ax1,ax2): matplotlib fig and ax objects
"""
# Split series into positive & negative components
s_pos = s[s >= 0]
s_neg = s[s < 0].abs()
# Not the best way to deal with this, but this was the easiest solution for now.
# TODO Fix this code to deal with no negative values or no positive values more appropriately
if s_neg.shape[0] == 0:
s_neg.loc[0] = 1
if s_pos.shape[0] == 0:
s_pos.loc[0] = 1
# Calculate appropriate min_exp if none provied
if min_exp == None:
threshold = s_pos.shape[0] - (s_pos==0).sum()
for i in range(10):
n_betw = s_pos[s_pos!=0].between(0,10**-i).sum()
if not (n_betw / threshold) > .1:
min_exp = -i
break
# Clip values to the 10**min_exp so that they are included in the histograms (if
# this isn't done then values which are 0 will be excluded from the histogram)
s_pos = s_pos.clip(lower=10**min_exp)
s_neg = s_neg.clip(lower=10**min_exp)
# Calculate the lowest integer which encompases all the positive and negative values
pos_max = int(np.ceil(np.log10(max(s_pos))))
neg_max = int(np.ceil(np.log10(max(s_neg))))
# Use that for both negative & positive values
plot_max = max(pos_max,neg_max)
# Create the bins (bin spacing is logarithmic)
bins = np.logspace(min_exp,plot_max,(plot_max+1)*bin_factor)
fig,(ax1,ax2) = plt.subplots(nrows=1,ncols=2,sharey=True)
fig.set_size_inches((10,5))
s_neg.hist(bins=bins,ax=ax1)
ax1.set_xscale('log')
ax1.set_title('Distribution of Negative Values')
ax1.set_xlabel('Negative values')
s_pos.hist(bins=bins,ax=ax2)
ax2.set_xscale('log')
ax2.set_title('Distribution of Positive Values')
ax2.set_xlabel('Positive Values')
# Invert axis so that values are increasingly negative from right to left.
# Decrease the spacing between the two subplots
ax1.invert_xaxis()
plt.subplots_adjust(wspace=.02)
return(fig,(ax1,ax2)) | [
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... | 2.459125 | 1,211 |
import unittest
import os, sys, inspect, json
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from lib.file_management.configeditor import ConfigEditor
from lib.file_management.file_management_lib import DirManagement
if __name__ == '__main__':
unittest.main() | [
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... | 3.056911 | 123 |
# Generated by Django 3.0.3 on 2020-11-18 06:06
import chemreg.common.utils
import chemreg.common.validators
import chemreg.compound.models
import chemreg.compound.utils
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
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... | 3.193182 | 88 |
'''
Twitter Crawler to get tweets and user data
'''
import tweepy
import json
import os
import time
def save_tweet(result):
"""Function to save tweepy result status"""
pass
def save_user(result_set):
"""Function to save tweepy set fo result statuses"""
pass
#crawler = TweetCrawler("twitter_credentials.json", './data')
#self=crawler
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2... | 2.690647 | 139 |
from irLib.instruments.instrument import instrument
from irLib.helpers.schedule import period
from irLib.instruments.legs import fixLeg, floatLeg
| [
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6... | 3.44186 | 43 |
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: BSD-3-Clause
# PEP 563: Postponed Evaluation of Annotations
from __future__ import annotations
from functools import partial
import multiprocessing as mp
from typing import Tuple, Union
import warnings
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.metrics import euclidean_distances, pairwise_distances
from sklearn.metrics.pairwise import cosine_distances
from sklearn.utils.validation import check_is_fitted, check_array, check_X_y
try:
import puffinn
except ImportError:
puffinn = None # pragma: no cover
try:
import falconn
except ImportError:
falconn = None # pragma: no cover
from tqdm.auto import tqdm
from .approximate_neighbors import ApproximateNearestNeighbor
from ..utils.check import check_n_candidates
__all__ = ['FalconnLSH', 'PuffinnLSH', ]
| [
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6738... | 3.031802 | 283 |
"""
pyvr calibrate.
Usage:
pyvr calibrate [options]
Options:
-h, --help
-c, --camera <camera> Source of the camera to use for calibration [default: 0]
-r, --resolution <res> Input resolution in width and height [default: -1x-1]
-n, --n_masks <n_masks> Number of masks to calibrate [default: 1]
-l, --load_from_file <file> Load previous calibration settings [default: ranges.pickle]
-s, --save <file> Save calibration settings to a file [default: ranges.pickle]
"""
import logging
import pickle
import sys
from copy import copy
from pathlib import Path
from typing import Optional, List
import cv2
from docopt import docopt
from virtualreality import __version__
def colordata_to_blob(colordata, mapdata):
'''
translates CalibrationData object to BlobTracker format masks
:colordata: CalibrationData object
:mapdata: a map dict with key representing the mask name and value representing the mask number
'''
out = {}
for key, clr_range_index in mapdata.items():
temp = colordata.color_ranges[clr_range_index]
out[key] = {
'h':(temp.hue_center, temp.hue_range),
's':(temp.sat_center, temp.sat_range),
'v':(temp.val_center, temp.val_range),
}
return out
def load_mapdata_from_file(path):
'''
loads mapdata from file, for use in colordata_to_blob
'''
with open(path, 'rb') as file:
return pickle.load(file)
def save_mapdata_to_file(path, mapdata):
'''
save mapdata to file, for use in colordata_to_blob
'''
with open(path, "wb") as file:
pickle.dump(mapdata, file)
def list_supported_capture_properties(cap: cv2.VideoCapture):
"""List the properties supported by the capture device."""
# thanks: https://stackoverflow.com/q/47935846/782170
supported = list()
for attr in dir(cv2):
if attr.startswith("CAP_PROP") and cap.get(getattr(cv2, attr)) != -1:
supported.append(attr)
return supported
def get_color_mask(hsv, color_range: ColorRange):
color_low = [
color_range.hue_center - color_range.hue_range,
color_range.sat_center - color_range.sat_range,
color_range.val_center - color_range.val_range,
]
color_high = [
color_range.hue_center + color_range.hue_range,
color_range.sat_center + color_range.sat_range,
color_range.val_center + color_range.val_range,
]
color_low_neg = copy(color_low)
color_high_neg = copy(color_high)
for c in range(3):
if c==0:
c_max = 180
else:
c_max = 255
if color_low_neg[c] < 0:
color_low_neg[c] = c_max + color_low_neg[c]
color_high_neg[c] = c_max
color_low[c] = 0
elif color_high_neg[c] > c_max:
color_low_neg[c] = 0
color_high_neg[c] = color_high_neg[c] - c_max
color_high[c] = c_max
mask1 = cv2.inRange(hsv, tuple(color_low), tuple(color_high))
mask2 = cv2.inRange(hsv, tuple(color_low_neg), tuple(color_high_neg))
mask = cv2.bitwise_or(mask1, mask2)
return mask
def _set_default_camera_properties(vs, cam, vs_supported, frame_width, frame_height):
if "CAP_PROP_FOURCC" not in vs_supported:
logging.warning(f"Camera {cam} does not support setting video codec.")
else:
vs.set(cv2.CAP_PROP_FOURCC, cv2.CAP_OPENCV_MJPEG)
if "CAP_PROP_AUTO_EXPOSURE" not in vs_supported:
logging.warning(f"Camera {cam} does not support turning on/off auto exposure.")
else:
vs.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0.25)
if "CAP_PROP_EXPOSURE" not in vs_supported:
logging.warning(f"Camera {cam} does not support directly setting exposure.")
else:
vs.set(cv2.CAP_PROP_EXPOSURE, -7)
if "CAP_PROP_EXPOSURE" not in vs_supported:
logging.warning(f"Camera {cam} does not support directly setting exposure.")
else:
vs.set(cv2.CAP_PROP_EXPOSURE, -7)
if "CAP_PROP_FRAME_HEIGHT" not in vs_supported:
logging.warning(f"Camera {cam} does not support requesting frame height.")
else:
vs.set(cv2.CAP_PROP_FRAME_HEIGHT, frame_height)
if "CAP_PROP_FRAME_WIDTH" not in vs_supported:
logging.warning(f"Camera {cam} does not support requesting frame width.")
else:
vs.set(cv2.CAP_PROP_FRAME_WIDTH, frame_width)
def manual_calibration(
cam=0, num_colors_to_track=4, frame_width=-1, frame_height=-1, load_file="", save_file="ranges.pickle"
):
"""Manually calibrate the hsv ranges and camera settings used for blob tracking."""
vs = cv2.VideoCapture(cam)
vs.set(cv2.CAP_PROP_EXPOSURE, -7)
vs_supported = list_supported_capture_properties(vs)
_set_default_camera_properties(vs, cam, vs_supported, frame_width, frame_height)
cam_window = f"camera {cam} input"
cv2.namedWindow(cam_window)
if "CAP_PROP_EXPOSURE" in vs_supported:
cv2.createTrackbar(
"exposure", cam_window, 0, 16, lambda x: vs.set(cv2.CAP_PROP_EXPOSURE, x - 8),
)
if "CAP_PROP_SATURATION" in vs_supported:
cv2.createTrackbar(
"saturation", cam_window, 0, 100, lambda x: vs.set(cv2.CAP_PROP_SATURATION, x),
)
else:
logging.warning(f"Camera {cam} does not support setting saturation.")
ranges = None
if load_file:
ranges = CalibrationData.load_from_file(load_file)
if ranges is None:
ranges = CalibrationData(width=frame_width, height=frame_height, num_colors=num_colors_to_track)
tracker_window_names = []
for color in range(num_colors_to_track):
tracker_window_names.append(f"color {color}")
cv2.namedWindow(tracker_window_names[color])
cv2.createTrackbar(
"hue center", tracker_window_names[color], ranges.color_ranges[color].hue_center, 180, lambda _: None,
)
cv2.createTrackbar(
"hue range", tracker_window_names[color], ranges.color_ranges[color].hue_range, 180, lambda _: None,
)
cv2.createTrackbar(
"sat center", tracker_window_names[color], ranges.color_ranges[color].sat_center, 255, lambda _: None,
)
cv2.createTrackbar(
"sat range", tracker_window_names[color], ranges.color_ranges[color].sat_range, 255, lambda _: None,
)
cv2.createTrackbar(
"val center", tracker_window_names[color], ranges.color_ranges[color].val_center, 255, lambda _: None,
)
cv2.createTrackbar(
"val range", tracker_window_names[color], ranges.color_ranges[color].val_range, 255, lambda _: None,
)
while 1:
ret, frame = vs.read()
if frame is None:
break
blurred = cv2.GaussianBlur(frame, (3, 3), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
exposure = cv2.getTrackbarPos("exposure", cam_window)
saturation = cv2.getTrackbarPos("saturation", cam_window)
ranges.exposure = exposure - 8
ranges.saturation = saturation
for color in range(num_colors_to_track):
hue_center = cv2.getTrackbarPos("hue center", tracker_window_names[color])
hue_range = cv2.getTrackbarPos("hue range", tracker_window_names[color])
sat_center = cv2.getTrackbarPos("sat center", tracker_window_names[color])
sat_range = cv2.getTrackbarPos("sat range", tracker_window_names[color])
val_center = cv2.getTrackbarPos("val center", tracker_window_names[color])
val_range = cv2.getTrackbarPos("val range", tracker_window_names[color])
ranges.color_ranges[color].hue_center = hue_center
ranges.color_ranges[color].hue_range = hue_range
ranges.color_ranges[color].sat_center = sat_center
ranges.color_ranges[color].sat_range = sat_range
ranges.color_ranges[color].val_center = val_center
ranges.color_ranges[color].val_range = val_range
mask = get_color_mask(hsv, ranges.color_ranges[color])
res = cv2.bitwise_and(hsv, hsv, mask=mask)
cv2.imshow(tracker_window_names[color], res)
cv2.imshow(cam_window, frame)
k = cv2.waitKey(1) & 0xFF
if k in [ord("q"), 27]:
break
for color in range(num_colors_to_track):
hue_center = cv2.getTrackbarPos("hue center", tracker_window_names[color])
hue_range = cv2.getTrackbarPos("hue range", tracker_window_names[color])
sat_center = cv2.getTrackbarPos("sat center", tracker_window_names[color])
sat_range = cv2.getTrackbarPos("sat range", tracker_window_names[color])
val_center = cv2.getTrackbarPos("val center", tracker_window_names[color])
val_range = cv2.getTrackbarPos("val range", tracker_window_names[color])
print(f"hue_center[{color}]: {hue_center}")
print(f"hue_range[{color}]: {hue_range}")
print(f"sat_center[{color}]: {sat_center}")
print(f"sat_range[{color}]: {sat_range}")
print(f"val_center[{color}]: {val_center}")
print(f"val_range[{color}]: {val_range}")
if save_file:
ranges.save_to_file(save_file)
print(f'ranges saved to list in "{save_file}".')
print("You can use this in the pyvr tracker using the --calibration-file argument.")
vs.release()
cv2.destroyAllWindows()
def main():
"""Calibrate entry point."""
# allow calling from both python -m and from pyvr:
argv = sys.argv[1:]
if len(argv) < 2 or sys.argv[1] != "calibrate":
argv = ["calibrate"] + argv
args = docopt(__doc__, version=f"pyvr version {__version__}", argv=argv)
width, height = args["--resolution"].split("x")
if args["--camera"].isdigit():
cam = int(args["--camera"])
else:
cam = args["--camera"]
manual_calibration(
cam=cam,
num_colors_to_track=int(args["--n_masks"]),
frame_width=int(width),
frame_height=int(height),
load_file=args["--load_from_file"],
save_file=args["--save"],
)
| [
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532,
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1279,... | 2.252825 | 4,513 |
from .jsonschema_validator import JSONSchemaValidator as jsonschema # noqa
from .marshmallow_validator import MarshmallowValidator as marshmallow # noqa
| [
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... | 3.369565 | 46 |
aliases['cd-'] = 'cd -'
aliases['cl'] = 'cd (ls -1Ft | head -1)'
aliases['..'] = 'cd ..'
aliases['...'] = 'cd ../..'
aliases['....'] = 'cd ../../..'
aliases['.....'] = 'cd ../../../..'
aliases['......'] = 'cd ../../../../..'
| [
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... | 2.203883 | 103 |
#! /usr/bin/python
'''
Class to handle database connections and queries for
Dropbox Mirror Bot
'''
import sqlite3
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