def gather_votes():
votelist = []
with open('temp/Blockchain.dat', 'rb') as blockfile:
gen = pickle._load(blockfile)
while True:
try:
block = pickle._load(blockfile)
votelist.extend(block.data)
except EOFError:
break
return votelist
def grabTree(filename):
"""从文件中读取决策树"""
import pickle
# open(filename,'r') 报如下错误：
# UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
# 解决办法:open(filename,'rb')
with open(filename, 'rb') as file:
return pickle._load(file)
def load(file):
try:
return pickle.load(file)
except:
if is_py3:
file.seek(0, 0)
return pickle._load(file)
raise
def GetClassScheduleList():
myClassScheduleFile = Path("..\Files\ClassScheduleFile.pickle")
if myClassScheduleFile.is_file(): #the file exists
with open("..\Files\ClassScheduleFile.pickle",
"rb") as classScheduleFile: #Show File Information
classScheduleList = pickle._load(classScheduleFile)
return classScheduleList
return [] #If the file does not exist, an empty list is created
def setdata(self):
self.comboBox.clear() #设置之前先清空，避免形成追加
try:
with open("INFOS.dat", "rb") as f:
data = pickle._load(f)
self.comboBox.addItems([each for each in data])
except:
self.comboBox.insertItem(0, self.tr("默认选项"))
def GetTeacherList():
# Path: Displays the file path
myTeacherFile = Path("..\Files\TeacherFile.pickle")
if myTeacherFile.is_file(): #the file exists
with open("..\Files\TeacherFile.pickle", "rb") as teacherFile:
teacherList = pickle._load(teacherFile) #Show File Information
return teacherList
return [] #If the file does not exist, an empty list is created
def GetStudenList():
# Path: Displays the file path
myStudentFile = Path("..\Files\StudentFile.pickle")
if myStudentFile.is_file(): #the file exists
with open("..\Files\StudentFile.pickle", "rb") as studentFile:
studentList = pickle._load(studentFile) #Show File Information
return studentList
return [] #If the file does not exist, an empty list is created
def GetCourseList():
# Path: Displays the file path
myCourseFile = Path("..\Files\CourseFile.pickle")
if myCourseFile.is_file():#the file exists
with open("..\Files\CourseFile.pickle", "rb") as courseFile:
courseList = pickle._load(courseFile)#Show File Information
return courseList
return []#If the file does not exist, an empty list is created
def load_object(self, fname):
try:
fname = self.get_filename(fname)
# print("Loading object from", fname)
with open(fname, 'rb') as file:
x = pickle._load(file)
return x
except IOError:
return None
def GetAdministratorLogin():
#Path: Displays the file path
myAdministratorLogin = Path("..\Files\AdministratorFile.pickle")
if myAdministratorLogin.is_file(): #If the file exists
with open("..\Files\AdministratorFile.pickle",
"rb") as administratorFile: #Show File Information
administratorLogin = pickle._load(administratorFile)
return administratorLogin
return [] #If the file does not exist, an empty list is created
def get_result(adminsk):
votelist = []
with open('temp/Blockchain.dat', 'rb') as blockfile:
gen = pickle._load(blockfile)
while True:
try:
block = pickle._load(blockfile)
votelist.extend(block.data)
except EOFError:
break
candy = []
for vote in votelist:
votedata_key = bytes(vote['Key'], 'utf-8')
aeskey = enc.decrypt(adminsk, votedata_key)
unlocked = aes.decrypt(bytes(vote['Vote Data'], 'utf-8'), aeskey)
unlocked = str(unlocked)[2:-1]
votedata = unlocked.split('***')
votedata[1] = int(votedata[1])
candy.append(votedata[1])
return candy
def read_data(path):
"""Reads data from .pickle file.
Args:
path: Path tp .pickle file to read.
Returns:
Python object.
"""
with _BytesIO(_tf.io.read_file(path).numpy()) as file:
return _load(file)
def tau_plots():
results = pickle._load(open("tau_variations.pkl", "rb"))
fig_tau, axs_tau = plt.subplots(4, sharex=True, sharey=True)
fig_tau.set_size_inches(10, 20, forward=True)
fig_tau.suptitle("Exploring the tau-parameter", fontsize=20)
for i, key in enumerate(sorted(results)):
axs_tau[i].plot(results[key])
axs_tau[i].set_title(key)
axs_tau[i].set_ylabel("Steps")
axs_tau[-1].set_xlabel("Episodes")
fig_tau.savefig("tau_variations.png")
def display(self):
# for block in self.chain:
# print("Block Height: ", block.height)
# print("Data in block: ", block.data)
# print("Merkle root: ", block.merkle)
# print("Difficulty: ", block.difficulty)
# print("Time stamp: ", block.timeStamp)
# print("Previous hash: ", block.prevHash)
# print("Nonce: ", block.nonce)
# print("\t\t\t|\n|\n|\n")
with open('blockchain.txt','rb') as blockfile:
data = pickle._load(blockfile)
return data
def weights_plots():
results = pickle._load(open("weights_variations.pkl", "rb"))
fig_w, axs_w = plt.subplots(3, sharex=True, sharey=True)
fig_w.set_size_inches(10, 20, forward=True)
fig_w.suptitle("Exploring the weights initialization", fontsize=20)
for i, key in enumerate(results):
axs_w[i].plot(results[key])
axs_w[i].set_title(key)
axs_w[i].set_ylabel("Steps")
axs_w[-1].set_xlabel("Episodes")
fig_w.savefig("weights_variations.png")
def lambda_plots():
results = pickle._load(open("lambda_variations.pkl", "rb"))
fig_lamb, axs_lamb = plt.subplots(3, sharex=True, sharey=True)
fig_lamb.set_size_inches(10, 20, forward=True)
fig_lamb.suptitle("Exploring the lambda-parameter", fontsize=20)
for i, key in enumerate(sorted(results)):
axs_lamb[i].plot(np.mean(results[key], axis=0))
axs_lamb[i].set_title(key)
axs_lamb[i].set_ylabel("Steps")
axs_lamb[-1].set_xlabel("Episodes")
fig_lamb.savefig("lambda_variations.png")
def run(self):
# 网站名称为空的时候不读取文件
if self.webname == "":
self.text_signal.emit("网站名称栏是空白，没有信息可以保存！")
else:
# 如果文件不存在则读取会报错，所以用try，如果不存在则创建一个文件
try:
with open("INFOS.dat", "rb") as f:
data = pickle._load(f)
except:
with open("INFOS.dat", "wb") as f:
data = ""
# 是字典类型就追加数据，否则就重建数据
if isinstance(data, dict):
if self.webname in data:
self.text_signal.emit("网站【{}】在原数据中有记录，现在更新数据...".format(
self.webname))
else:
self.text_signal.emit("网站【{}】在原数据中没有记录，现在追加数据...".format(
self.webname))
info = {}
info["weblink"] = self.weblink
info["pattern"] = self.pattern
info["start_url"] = self.start_url
info["end_url"] = self.end_url
info["pd"] = self.pd
data[self.webname] = info
with open("INFOS.dat", "wb") as f:
pickle._dump(data, f)
self.text_signal.emit("更新数据完成！")
else:
self.text_signal.emit("读取数据失败，可能是信息文件被破坏，现在新建数据...")
data = {}
info = {}
info["weblink"] = self.weblink
info["pattern"] = self.pattern
info["start_url"] = self.start_url
info["end_url"] = self.end_url
info["pd"] = self.pd
data[self.webname] = info
with open("INFOS.dat", "wb") as f:
pickle._dump(data, f)
self.text_signal.emit("新建数据完成！")
# 把所有的网站名称添加到下拉框中
self.combo_signal.emit()
def display():
#--print the information of blocks of the blockchain in the console
try:
with open('temp/Blockchain.dat', 'rb') as blockfile:
for block in range(len(EVoting.chain)):
data = pickle._load(blockfile)
#--print all data of a block
print("Block Height: ", data.height)
print("Data in block: ", data.data)
print("Number of votes: ", data.number_of_votes)
print("Merkle root: ", data.merkle)
print("Difficulty: ", data.DIFFICULTY)
print("Time stamp: ", data.timeStamp)
print("Previous hash: ", data.prevHash)
print("Block Hash: ", data.hash)
print("Nonce: ", data.nonce, '\n\t\t|\n\t\t|')
except FileNotFoundError:
print("\n.\n.\n.\n<<<File not found!!>>>")
def run(self):
try:
with open("INFOS.dat", "rb") as f:
data = pickle._load(f)
except:
self.text_signal.emit("数据文件缺失，无法读取配置！")
else:
if self.webname in data:
try:
infos = data[self.webname]
link = infos["weblink"]
pattern = infos["pattern"]
start_url = infos["start_url"]
end_url = infos["end_url"]
pd = infos["pd"]
lis = [pattern, start_url, end_url, pd, link, self.webname]
self.infos_signal.emit(lis)
except:
self.text_signal.emit("数据文件信息错误，无法生存配置！")
else:
self.text_signal.emit("数据文件中没有{}网址的信息，无法进行配置".format(
self.webname))
def vector_field_plots():
results = pickle._load(open("vector_fields2.pkl", "rb"))
fig, axs = plt.subplots(3, sharex=True, sharey=True)
fig.set_size_inches(10, 20, forward=True)
fig.suptitle("Exploring the vector fields", fontsize=20)
x = np.linspace(-150, 30, 20)
dx = np.linspace(-15, 15, 20)
u, v = np.meshgrid(x, dx)
for i, key in enumerate(results):
dummy = np.zeros((results[key][0].shape[0], results[key][0].shape[1]))
axs[0].quiver(u, v, results[key][0], dummy)
axs[0].set_title("Trial no 1")
axs[1].quiver(u, v, results[key][20], dummy)
axs[1].set_title("Trial no 20")
axs[2].quiver(u, v, results[key][99], dummy)
axs[2].set_title("Trial no 100")
fig.savefig("vector_fields.png")
def load(f, **kwargs):
return _load(f)
print(list(zip("xyz", "ijk")))
print('-' * 30, "↑내장함수↑", '-' * 30)
print('-' * 30, "↓외장함수↓", '-' * 30)
#pickle:객체 상태를 유지하면서 파일 입출력 모듈
#dump함수를 이용...
import pickle
f = open("sleep.txt", "wb")
data = {1: "big", 2: "data"}
#f.write(data)
pickle.dump(data, f)
f.close()
f = open("sleep.txt", "rb")
data = pickle._load(f)
print(data)
import glob
print(glob.glob("d*"))
import random
print(random.random())
for i in range(1, 7):
print(random.randint(1, 46))
# a=set([1,2,2,3,2])
# print(len(a))
#정규표현식:일정 규칙을 갖는 문자열을 표현하는 방법,(정규식)
#REGular EXpression;REDEX
@app.route('/thanks', methods = ['GET'])
def thank():
return render_template('home.html')
EVoting = Blockchain()
EVoting.addGenesis()
if __name__ == '__main__':
app.run(port = 5000)
# data = EVoting.display()
# print(data)
<<<<<<< HEAD
with open('blockchain.txt','rb') as blockfile:
for i in range(len(EVoting.chain)-1):
data = pickle._load(blockfile)
print("Block Height: ", data.height)
print("Data in block: ", data.data)
print("Merkle root: ", data.merkle)
print("Difficulty: ", data.difficulty)
print("Time stamp: ", data.timeStamp)
print("Previous hash: ", data.prevHash)
print("Nonce: ", data.nonce)
=======
with open('blockchain.txt','rb') as blockfile:
data = pickle._load(blockfile)
data2 = pickle._load(blockfile)
def __init__(self):
self.name = "ql"
f = open("train_ql.pkl", "rb") #学習済みデータを読み込む
self.q_as = pickle._load(f)
f.close()
6: 61.,
7: 57.,
8: 57.,
9: 63.,
10: 66.,
11: 66.,
12: 66.,
13: 66.,
14: 64.
}
if os.path.exists(FILENAME) and os.path.isfile(FILENAME) and os.path.exists(EPOCHS_FILENAME) and os.path.isfile(EPOCHS_FILENAME):
#Load previous model and continue training
model = load_model(FILENAME)
score = model.evaluate(x_test, y_test, verbose=0)
initial_epochs = pickle._load(open( EPOCHS_FILENAME, "rb" ))
print("Initial network accuracy: %.2f%%, loss: %.4f, epochs: %5d " % (score[1] * 100, score[0], initial_epochs))
else:
# Create new model
model = Sequential()
model.add(Dense(3032, activation="sigmoid", input_dim=90, name="input"))
model.add(Dense(15, activation='softmax', name="output"))
model.compile(loss='categorical_crossentropy', optimizer="adadelta", metrics=['accuracy'])
initial_epochs = 0
model.fit(x_train, y_train,
batch_size=100000,
epochs=TOTAL_EPOCHS,
verbose=0,
validation_data=(x_test, y_test),
class_weight = class_weight,
def load(): from pickle import load as _load with open(path, 'rb') as f: return _load(f)
#========================================================================="""
# define an employee class
import pickle
class Employee:
def __init__(self, eno, name, sal, addr):
self.eno = eno
self.name = name
self.sal = sal
self.addr = addr
def display(self):
print(" Employee no ", self.eno)
print("Employee name ", self.name)
print("Salary ", self.sal)
print("Address ", self.addr)
# Pickling - Create a emp data file to dump the object
f = open("emp.dat", 'wb')
e1 = Employee(100, "Praveen", 10000, "Hyd")
pickle._dump(e1, f)
print("Pickling of employee object done successfully")
f.close()
# Unpickling - Read the data file using load()
f = open("emp.dat", 'rb')
obj = pickle._load(f)
print("Employee Object Contents ")
print(obj.display())
use_mad_CO = True
fLine = 'line_from_six_with_bbCO.pkl'
if use_mad_CO:
fParticleCO = 'particle_on_CO_mad_line.pkl'
else:
fParticleCO = 'particle_on_CO_six_line.pkl'
# Load machine
with open(fLine, 'rb') as fid:
line = pysixtrack.Line.from_dict(pickle.load(fid))
# Load particle on CO
with open(fParticleCO, 'rb') as fid:
part_on_CO = pysixtrack.Particles.from_dict(pickle._load(fid))
# Load iconv
with open('iconv.pkl', 'rb') as fid:
iconv = pickle.load(fid)
# Load sixtrack tracking data
sixdump_all = sixtracktools.SixDump101('sixtrack/res/dump3.dat')
# Assume first particle to be on the closed orbit
Nele_st = len(iconv)
sixdump_CO = sixdump_all[::2][:Nele_st]
# Compute closed orbit using tracking
closed_orbit = line.track_elem_by_elem(part_on_CO)
# Check that closed orbit is closed
import pickle from dataset import importCSV data = importCSV() pickle.dump(data, open( "data/sensors.pkl", "wb" )) data = pickle._load(open( "data/sensors.pkl", "rb" )) x_train, x_test, y_train, y_test = data print(x_train.head())
def load_data():
f = gzip.open("./data/mnist.pkl.gz", "rb")
traning_data, validation_data, test_data = pickle._load(f,
encoding="latin1")
f.close()
return (traning_data, validation_data, test_data)
if vis:
cv2.imwrite('result/result' + str(i) + '.png', im2show)
# pdb.set_trace()
# cv2.imshow('test', im2show)
# cv2.waitKey(0)
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
# imdb.evaluate_detections(all_boxes, output_dir)
end = time.time()
print("test time: %0.4fs" % (end - start))
else:
all_boxes = []
if os.path.exists(det_file):
print(det_file + " exit!")
with open(det_file, 'rb') as fp:
all_boxes = pickle._load(fp)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
# imdb.evaluate_detections(all_boxes, output_dir)
end = time.time()
print("test time: %0.4fs" % (end - start))
__author__ = 'Matteo'
__doc__='''This script is a sandbox.'''
#interpreter change.
import csv
import pickle
from Bio import SeqIO
from Bio import Entrez
file=open('geneX_pickled.dat','rb')
gene=pickle._load(file)
print(len(gene.keys()))
import pickle
import numpy as np
import pysixtrack
import sixtracktools
# Load machine
with open('line.pkl', 'rb') as fid:
line = pysixtrack.Line.from_dict(pickle.load(fid))
# Load particle on CO
with open('particle_on_CO.pkl', 'rb') as fid:
part_on_CO = pysixtrack.Particles.from_dict(
pickle._load(fid))
# Load iconv
with open('iconv.pkl', 'rb') as fid:
iconv = pickle.load(fid)
# Load sixtrack tracking data
sixdump_all = sixtracktools.SixDump101('res/dump3.dat')
# Assume first particle to be on the closed orbit
Nele_st = len(iconv)
sixdump_CO = sixdump_all[::2][:Nele_st]
# Compute closed orbit using tracking
closed_orbit = line.track_elem_by_elem(part_on_CO)
# Check that closed orbit is closed
pstart = closed_orbit[0].copy()
def load(f, **kwargs): return _load(f)
