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)
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)