def cmd_school_add( self, item, args, rawline ):
"""-n <name>|-i||add a new school. Argument -n is to pass a name and -i for interactive mode"""
parser = argparse.ArgumentParser( prog = "school add", description = self.cmd_school_add.__doc__.split( "||" )[1] )
parser.add_argument( "-n", "--name", help = "set the name of the new school" )
parser.add_argument( "-i", "--interactive", action = "store_true", help = "use the interactive mode" )
try:
parsed_args = parser.parse_args( args.split( " " ))
name = ""
save = True
if parsed_args.interactive:
try:
while name == "":
name = input( "Name: " )
save = input( "Do you want to save ([y]/n)? " )
save = (save == "y" or save == "")
except KeyboardInterrupt:
save = False
print( "" ) # To break down prompt to a new line
elif parsed_args.name != "" and parsed_args.name is not None:
name = parsed_args.name
else:
save = False
if save:
school = School( self._connection )
school.Name = name
if school.Insert( ):
cf.out.bold_green( "School with name `{}` has been successfully saved!".format( name ))
self._UpdateCDCommand( )
else:
cf.out.bold_red( "An error occured during the insert action of school with name `{}`".format( name ))
except SystemExit: # Do not exit cli if an error occured in parse_args
pass
def admin():
data = request.get_json()
if data:
if data['method'] == 'refill':
School.refill()
if data['method'] == 'delete_all_users':
MongoDataBase.delete_all_users()
if data['method'] == 'fill_random':
MongoDataBase.fill_random()
if data['method'] == 'sort_pk':
MongoDataBase.sort_pk('hello')
return render_template('admin.html')
def main():
"""Shows basic usage of the Gmail API.
Lists the user's Gmail labels.
"""
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists('token.json'):
creds = Credentials.from_authorized_user_file('token.json', SCOPES)
# If there are no (valid) credentials available, let the user log in.
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
'credentials.json', SCOPES)
creds = flow.run_local_server(port=0)
# Save the credentials for the next run
with open('token.json', 'w') as token:
token.write(creds.to_json())
service = build('gmail', 'v1', credentials=creds)
#Class calls
email = Email(service)
inbox = email.batchEmails() #list
school = School()
assignments = school.getAssignments() #list
weather = Weather()
today = weather.getWeather() #list
# -----------------------------
# TKINTER CODE BELOW THIS POINT
# -----------------------------
window = tk.Tk()
#window.geometry('1280x1024')
window.attributes('-fullscreen', True)
window.configure(bg='black')
#Close on escape key
def close(event):
window.withdraw() # if you want to bring it back
sys.exit() # if you want to exit the entire thing
window.bind('<Escape>', close)
window.mainloop()
def __init__(self, dt=0.25, trafficWeight=1):
""" The default constructor for the Model """
# self.test_agent = Agent.Agent()
self.trafficWeight = trafficWeight
self.gate = Gate.Gate()
self.campus_way_road = Road.Road(2, 2, 3)
self.south_garage = Garage.Garage("South Garage",
num_spot=771,
num_carpool_spot=23,
num_handicapped_spot=20,
num_bike_spot=12,
garage_width=31)
self.school = School.School()
self.num_days = 30
self.dt = 0.15
self.plot_figure = None
self.plot_axis = None
self.plot_image = None
#Tracks utilization per time step.
self.utilization = []
def listSchool(cls):
query_school_info_sql = "SELECT * FROM school_info"
raw = super().readFromDB(query_school_info_sql)
data = {}
for d in raw:
data[d[1]] = School.School(d[0], d[1])
return data
def createSchools(self):
for s in range(self.N_SCHOOL):
self.schools.append(School())
studentMatrix[x][4], studentMatrix[x][5],
studentMatrix[x][6], studentMatrix[x][7],
studentMatrix[x][8], studentMatrix[x][9],
studentMatrix[x][10], bussing))
studentObjects[x].distanceMatrixPosition = x
studentObjects[x].placementName = studentMatrix[x][12]
studentObjects[x].timeOfDay = studentMatrix[x][13]
x += 1
#create schools from file
x = 0
while (x < len(schoolMatrix)):
schoolObjects.append(
School.School(schoolMatrix[x][0], schoolMatrix[x][1],
schoolMatrix[x][2], schoolMatrix[x][3],
schoolMatrix[x][4], schoolMatrix[x][5],
schoolMatrix[x][6], schoolMatrix[x][7],
schoolMatrix[x][8], schoolMatrix[x][9]))
x += 1
#create routes from file
x = 0
while (x < len(routeMatrix)):
routeObjects.append(
Route.Route(routeMatrix[x][0], routeMatrix[x][1], routeMatrix[x][2]))
x += 1
for e in studentObjects:
for x in schoolObjects:
if x.name == e.placementName:
e.school = x
# Following are required for custom functions Task 1,2
def meanie(x):
return np.mean(x, axis=1)
def dot_with_11(x):
return np.dot(x, np.array([0.5, 0.5]))
if __name__ == '__main__':
learning_phase = False
classifier_file_name = 'ClassifierFile.pkl'
if os.path.isfile(classifier_file_name):
Main_C1 = pickle.load(open(classifier_file_name, 'r'))
else:
Main_C1 = SimpleClassifierBank(max_width=2000,
input_width=1500,
height=500)
# Learn or not learn?
if learning_phase:
School.class_digital_logic(Main_C1)
School.simple_custom_fitting_class(Main_C1)
# Main_C1.fit_custom_fx(np.mean,input_width=1500, output_width=1, task_name='np.mean')
yp = Main_C1.predict(np.random.randn(8, 22))
print 'Predicted value is ', yp
# Main_C1.remove_classifier('np.mean')
Main_C1.status()
pickle.dump(Main_C1, open(classifier_file_name, 'w'))
# -*- coding: utf-8 -*-
"""
Created on Sat May 9 21:01:12 2020
@author: HaoranLi
"""
import School
#实例化数据库课程
dta=School.Course('001','数据库技术与应用','李秀',3,50)
#读取学生列表并让其选课
students=[]
k=0
filename='学生列表.txt'
with open(filename) as file_object:
for line in file_object:
s=line.split()
sname=s[0]
sno=s[1]
students.append(School.Student(sname,sno))
students[k].select_course(dta)
k+=1
#抽签
dta.draw_lots()
#输出课程内和候选学生名单
print("课程内学生名单为：")
for i in range(len(dta.selectionStudents)):
print(dta.selectionStudents[i].ID)
print("队列学生名单为：")
for j in range(len(dta.waitingStudents)):
def rs(obj_name):
with open(obj_name, 'rb') as ft:
obj_name = pickle.load(ft)
return obj_name
# cs('st1')
st1 = rs('st1')
# cs('st2')
st2 = rs('st2')
list_class1_obj = [
python1, python2, python3, linux1, linux2, linux3, go1, go2, go3
]
sb = School.School('中日友好大学', '北京')
sh = School.School('韩国大学', '上海')
sb.hire_teacher(t1, linux1, linux2, linux3)
sb.hire_teacher(t2, python1, python2, python3)
sh.hire_teacher(t3, go1, go2, go3)
sb.addCourse(clinux)
sb.addCourse(cpython)
sh.addCourse(cgo)
sb.addclass1(python1)
sb.addclass1(python2)
# print('--------start')
# print(python1.course_obj)
# print('--------stop')
#make master distance matrix
#this will be replaced by code that builds the distance matrix based on geocode data
masterDistanceMatrix = cl.metricCSVtoMatrix('durations_with_schools.csv')
#create arrays to store the student and school objects
studentObjects = []
schoolObjects = []
#create students from file
#set master distance matrix index as well. That will be changed later.
#create schools from file
x = 0
while (x < len(schoolMatrix)):
schoolObjects.append(School.School(schoolMatrix[x][0], schoolMatrix[x][1], schoolMatrix[x][2], schoolMatrix[x][3], schoolMatrix[x][4], schoolMatrix[x][5],
schoolMatrix[x][6], schoolMatrix[x][7], schoolMatrix[x][8]))
x += 1
x = 0
while (x < len(studentMatrix)):
studentObjects.append(Student.Student(studentMatrix[x][0], studentMatrix[x][1], studentMatrix[x][2], studentMatrix[x][3], studentMatrix[x][4], studentMatrix[x][5],
studentMatrix[x][6], studentMatrix[x][7], studentMatrix[x][8], studentMatrix[x][9], studentMatrix[x][10], studentMatrix[x][11]))
studentObjects[x].distanceMatrixPosition = x
if not np.isnan(studentMatrix[x][13]):
studentObjects[x].busRoute = studentMatrix[x][13]
studentObjects[x].busTime= Time.Time(studentMatrix[x][12])
stuSchool = [i for i in schoolObjects if i.name == studentMatrix[x][14]]
if len(stuSchool)>0:
studentObjects[x].school = stuSchool[0]
studentObjects[x].placementId = stuSchool[0].id
studentObjects[x].placementName = stuSchool[0].name
def main():
the_school = School()
try:
the_school.populate_student_array("students.txt")
except (FileNotFoundError):
print("File was not found.\n")
return
search_val = ""
while (True):
command = prompt()
values = command.split()
#Quit
if (values[0] == "Q" or values[0] == "Quit"):
break
#Student
elif (values[0] == "S" or values[0] == "Student"):
if (len(values) == 3):
if (values[2] == "B" or values[2] == "Bus"):
the_school.search_student_bus(values[1])
else:
print("\n" + values[2] + " is not a valid command\n")
else:
the_school.search('S', values[1])
#Teacher
elif (values[0] == "T" or values[0] == "Teacher"):
the_school.search('T', values[1])
#Bus
elif (values[0] == "B" or values[0] == "Bus"):
the_school.search('B', values[1])
#Grade
elif (values[0] == "G" or values[0] == "Grade"):
if (len(values) == 3):
if (values[2] == "H" or values[2] == "L" or values[2] == "High"
or values[2] == "Low"):
the_school.grade_search(values[1], values[2])
else:
print("\n" + values[2] + " is not a valid command\n")
else:
the_school.search('G', values[1])
#Average
elif (values[0] == "A" or values[0] == "Average"):
the_school.search('A', values[1])
#Info
elif (values[0] == "I" or values[0] == "Info"):
the_school.grade_info()
return 1 / (1 + math.exp(-10*x))
# Following are required for custom functions Task 1,2
def meanie(x):
return np.mean(x, axis=1)
def dot_with_11(x):
return np.dot(x, np.array([0.5, 0.5]))
if __name__ == '__main__':
learning_phase = False
classifier_file_name = 'ClassifierFile.pkl'
if os.path.isfile(classifier_file_name):
Main_C1 = pickle.load(open(classifier_file_name, 'r'))
else:
Main_C1 = SimpleClassifierBank(max_width=2000, input_width=1500, height=500)
# Learn or not learn?
if learning_phase:
School.class_digital_logic(Main_C1)
School.simple_custom_fitting_class(Main_C1)
# Main_C1.fit_custom_fx(np.mean,input_width=1500, output_width=1, task_name='np.mean')
yp = Main_C1.predict(np.random.randn(8, 22))
print 'Predicted value is ', yp
# Main_C1.remove_classifier('np.mean')
Main_C1.status()
pickle.dump(Main_C1, open(classifier_file_name, 'w'))
def main():
SchoolObj = School("School1", 1, "Address1", "State1", "City1", 55)
SchoolObj.getSchoolDetails()
# plt.subplot(2, 2, i + 1)
# plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict_last(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap='gray', alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title('Classifier ' + str(len(self.classifiers_list)))
plt.show()
# Global functions
# Reason for having 10 sigmoid is to get sharper distinction.
def sigmoid_10(x):
return 1 / (1 + math.exp(-10 * x))
if __name__ == '__main__':
Main_C1 = RememberingVisualMachine(input_width=2)
School.random_linear_trainer(Main_C1, stages=20)
School.random_linear_trainer2(Main_C1, stages=20)
Main_C1.status(show_graph=True, list_classifier_name=False)
School.growing_complex_trainer(Main_C1)
Main_C1.status(show_graph=True, list_classifier_name=False)
# plt.subplot(2, 2, i + 1)
# plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict_last(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap='gray', alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title('Classifier ' + str(len(self.classifiers_list)))
plt.show()
# Global functions
# Reason for having 10 sigmoid is to get sharper distinction.
def sigmoid_10(x):
return 1 / (1 + math.exp(-10 * x))
if __name__ == '__main__':
Main_C1 = RememberingVisualMachine(input_width=2)
School.random_linear_trainer(Main_C1, stages=20)
School.random_linear_trainer2(Main_C1, stages=20)
Main_C1.status(show_graph=True, list_classifier_name=False)
School.growing_complex_trainer(Main_C1)
Main_C1.status(show_graph=True, list_classifier_name=False)
import School
import pickle
names = ['유광무1','유광무2','유광무3','유광무4','유광무5']
kor = [23,45,34,76,98]
math = [23,45,34,76,98]
python = [23,45,34,76,98]
students = []
for i in range(5):
var = School.Student(names[i],i,kor[i],math[i],python[i])
students.append(var)
f = open('나는_오이가_싫어요\data.bin','wb')
pickle.dump(students,f)
print('나는 오이가 싫어요')
import School
from ConstantsAndEnum import *
import jsonpickle
s = School.school("朝阳一小",District.ChaoYang,ClassType.JuniorSchool)
# s = School.school()
jsonStr = s.to_JSON()
print(jsonStr)
s1 = jsonpickle.decode(jsonStr)
assert s1.Name == s.Name
assert s1.SchoolType == s.SchoolType
print(s1.Name)
print(s.Name)
