Reset23/the-stack-v2-processed · Datasets at Hugging Face

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/backend/users/migrations/0002_auto_20201104_1426.py

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crowdbotics-apps/logictech-22290

7538661024c163c16881371468f84c181d1ee93f

f17151874e1fd60a1cc81b247a5e0599421ac6e8

refs/heads/master

2023-01-09T21:14:45.728461

2020-11-04T14:30:13

310,025,912

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# Generated by Django 2.2.17 on 2020-11-04 14:26 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('course', '0001_initial'), ('users', '0001_initial'), ] operations = [ migrations.AddField( model_name='user', name='group', field=models.ManyToManyField(blank=True, related_name='user_group', to='course.Group'), ), migrations.AlterField( model_name='user', name='name', field=models.CharField(blank=True, max_length=255, null=True), ), ]

[ "[email protected]" ]

[email protected]

d47b3bb24581ca86d9a76530a019eaca62ae8e66

f3b233e5053e28fa95c549017bd75a30456eb50c

/p38a_input/L2EE/2EE-2J_MD_NVT_rerun/set_4.py

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no_license

AnguseZhang/Input_TI

ddf2ed40ff1c0aa24eea3275b83d4d405b50b820

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refs/heads/master

2021-05-25T15:02:38.858785

2020-02-18T16:57:04

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import os dir = '/mnt/scratch/songlin3/run/p38a/L2EE/MD_NVT_rerun/ti_one-step/2EE_2J/' filesdir = dir + 'files/' temp_prodin = filesdir + 'temp_prod_4.in' temp_pbs = filesdir + 'temp_4.pbs' lambd = [ 0.00922, 0.04794, 0.11505, 0.20634, 0.31608, 0.43738, 0.56262, 0.68392, 0.79366, 0.88495, 0.95206, 0.99078] for j in lambd: os.chdir("%6.5f" %(j)) workdir = dir + "%6.5f" %(j) + '/' #prodin prodin = workdir + "%6.5f_prod_4.in" %(j) os.system("cp %s %s" %(temp_prodin, prodin)) os.system("sed -i 's/XXX/%6.5f/g' %s" %(j, prodin)) #PBS pbs = workdir + "%6.5f_4.pbs" %(j) os.system("cp %s %s" %(temp_pbs, pbs)) os.system("sed -i 's/XXX/%6.5f/g' %s" %(j, pbs)) #submit pbs #os.system("qsub %s" %(pbs)) os.chdir(dir)

[ "[email protected]" ]

[email protected]

a0b3baaacb54b0e27beb93b36ee98ef7b92beb83

8654435d89790e32f8e4c336e91f23250da0acb0

/bullet3/examples/pybullet/numpy/humanoid_running.py

d15a68c8b3843917870a8ca017c69d0db13adfdc

[ "Zlib" ]

permissive

takamtd/deepmimic

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refs/heads/main

2023-05-09T16:48:16.554243

2021-06-07T05:04:47

373,762,616

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import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) os.sys.path.insert(0, parentdir) import sys import numpy as np import argparse import pybullet as p import time gui = True cid = p.connect(p.SHARED_MEMORY) #DIRECT is much faster, but GUI shows the running gait if (cid < 0): if (gui): cid = p.connect(p.GUI) else: cid = p.connect(p.DIRECT) #p.setGravity(1,2,-9.8) #p.setDefaultContactERP (0.4) p.setGravity(0, 0, -9.8) #numSubSteps=4 and fixedTimeStep=1.0/60. is an effective internal fixed step of 1./240 #recommended to not go below 50 solver iterations p.setPhysicsEngineParameter(fixedTimeStep=1.0 / 60., numSolverIterations=550, numSubSteps=8) #this mp4 recording requires ffmpeg installed #mp4log = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4,"humanoid.mp4") #p.loadSDF("stadium.sdf") p.loadURDF("plane_implicit.urdf") objs = p.loadMJCF("mjcf/humanoid_symmetric_no_ground.xml", flags=p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS) human = objs[0] for j in range(p.getNumJoints(human)): jointInfo = p.getJointInfo(human, j) print("joint(", j, "qIndex=", jointInfo[3], "uIndex=", jointInfo[4], ")=", jointInfo) ordered_joints = [] ordered_joint_indices = [] parser = argparse.ArgumentParser() parser.add_argument('--profile') jdict = {} for j in range(p.getNumJoints(human)): info = p.getJointInfo(human, j) link_name = info[12].decode("ascii") if link_name == "left_foot": left_foot = j if link_name == "right_foot": right_foot = j ordered_joint_indices.append(j) if info[2] != p.JOINT_REVOLUTE: continue jname = info[1].decode("ascii") jdict[jname] = j lower, upper = (info[8], info[9]) ordered_joints.append((j, lower, upper)) p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0) motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"] motor_power = [100, 100, 100] motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"] motor_power += [100, 100, 300, 200] motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"] motor_power += [100, 100, 300, 200] motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"] motor_power += [75, 75, 75] motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"] motor_power += [75, 75, 75] motors = [jdict[n] for n in motor_names] class Dummy: pass dummy = Dummy() dummy.initial_z = None def current_relative_position(jointStates, human, j, lower, upper): #print("j") #print(j) #print (len(jointStates)) #print(j) temp = jointStates[j] pos = temp[0] vel = temp[1] #print("pos") #print(pos) #print("vel") #print(vel) pos_mid = 0.5 * (lower + upper) return (2 * (pos - pos_mid) / (upper - lower), 0.1 * vel) def collect_observations(human): #print("ordered_joint_indices") #print(ordered_joint_indices) jointStates = p.getJointStates(human, ordered_joint_indices) j = np.array([ current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints ]).flatten() #print("j") #print(j) body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human) #print("body_xyz") #print(body_xyz, qx,qy,qz,qw) z = body_xyz[2] dummy.distance = body_xyz[0] if dummy.initial_z == None: dummy.initial_z = z (vx, vy, vz), _ = p.getBaseVelocity(human) more = np.array([z - dummy.initial_z, 0.1 * vx, 0.1 * vy, 0.1 * vz, qx, qy, qz, qw]) rcont = p.getContactPoints(human, -1, right_foot, -1) #print("rcont") #print(rcont) lcont = p.getContactPoints(human, -1, left_foot, -1) #print("lcont") #print(lcont) feet_contact = np.array([len(rcont) > 0, len(lcont) > 0]) return np.clip(np.concatenate([more] + [j] + [feet_contact]), -5, +5) def relu(x): return np.maximum(x, 0) class SmallReactivePolicy: "Simple multi-layer perceptron policy, no internal state" def __init__(self): #, observation_space, action_space): #assert weights_dense1_w.shape == (observation_space.shape[0], 256) #assert weights_dense2_w.shape == (256, 128) #assert weights_final_w.shape == (128, action_space.shape[0]) pass def act(self, ob): #ob[0] += -1.4 + 0.8 x = ob x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b) x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b) x = np.dot(x, weights_final_w) + weights_final_b return x def demo_run(): pi = SmallReactivePolicy() t1 = time.time() timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json") frame = 0 while 1: obs = collect_observations(human) actions = pi.act(obs) #print(" ".join(["%+0.2f"%x for x in obs])) #print("Motors") #print(motors) #for m in range(len(motors)): #print("motor_power") #print(motor_power[m]) #print("actions[m]") #print(actions[m]) #p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082) #p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082) forces = [0.] * len(motors) batch = True for m in range(len(motors)): forces[m] = motor_power[m] * actions[m] * 0.082 if (not batch): p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=forces[m]) if (batch): p.setJointMotorControlArray(human, motors, controlMode=p.TORQUE_CONTROL, forces=forces) p.stepSimulation() humanPos, humanOrn = p.getBasePositionAndOrientation(human) if (gui): time.sleep(1. / 60.) print("frame=", frame) camInfo = p.getDebugVisualizerCamera() curTargetPos = camInfo[11] distance = camInfo[10] yaw = camInfo[8] pitch = camInfo[9] targetPos = [ 0.95 * curTargetPos[0] + 0.05 * humanPos[0], 0.95 * curTargetPos[1] + 0.05 * humanPos[1], curTargetPos[2] ] p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos) frame += 1 #if frame==1000: break t2 = time.time() print("############################### distance = %0.2f meters" % dummy.distance) print("############################### FPS = ", 1000 / (t2 - t1)) #print("Starting benchmark") #logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json") #p.stopStateLogging(logId) print("ended benchmark") print(frame) p.stopStateLogging(timinglog) # yapf: disable weights_dense1_w = np.array( [[ -0.3857, -0.7450, +0.1473, +0.1996, +0.0570, +0.4522, +0.1172, +0.7558, -0.2530, -0.7948, +0.3120, +0.6216, +0.1044, -0.4347, +0.0921, +0.0187, +0.2030, +0.2042, -0.2928, +0.2717, +0.5117, +0.1115, -0.0278, -0.0675, -0.2967, +0.3128, -0.1434, -0.0476, +0.0561, -0.1874, +0.1675, -0.1028, +0.0712, +0.8653, -0.5565, -0.1207, +0.0982, -0.2875, -0.0755, -0.3057, -0.0841, +0.5336, +0.2068, -0.0132, -0.2131, -0.5910, +0.1329, +0.2789, +0.0036, +0.0048, +0.2655, +0.3585, +0.2861, -0.2288, -0.0646, +0.0569, -0.4701, -0.0116, +0.4616, +0.0947, -0.1013, -0.0256, +0.2854, +0.3382, +0.0822, +0.1160, -0.4913, +0.3991, +0.0121, -0.3392, +0.2515, +0.7188, +0.3412, +0.1247, +0.1422, +0.0420, +0.1401, +0.5830, +0.0226, +0.6080, +0.3019, -0.7696, -0.0580, +0.2104, +0.3786, +0.2968, -0.3726, +0.2792, +0.3572, -0.4362, +0.5368, +0.0068, +0.4366, -0.4799, +0.2688, 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+0.0031, +0.1408, +0.0531, +0.1400, +0.0308, -0.0220, -0.0014, -0.3056, -0.1551, -0.0096 ], [ +0.1479, +0.1186, +0.1323, -0.3466, -0.0654, -0.1084, -0.2509, +0.0944, -0.2135, +0.2020, +0.0602, -0.1239, +0.0741, +0.2037, -0.4462, +0.1065, +0.1710 ]]) weights_final_b = np.array([ -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643, +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276, +0.0375 ]) # yapf: enable if __name__ == "__main__": demo_run()

[ "[email protected]" ]

[email protected]

eaf3840aa3f8986f9ac5af4ac914a14e080bd347

cc7ad1a2aa5d691c15ff7838d1e5126ab2c2bee0

/basic_notifications/views.py

b7e1ecc497a68ddf9693738e0e033c9b746371b7

[]

no_license

demirantay/lingooapp

9632be8a7d3dd00e7a4ac13618f32975da389729

c842bb032668ef1bd5e7f4282acd4990843c8640

refs/heads/master

2023-03-14T08:00:37.681334

2021-01-09T09:36:48

285,181,982

1

0

null

UTF-8

Python

false

3,688

py

# Main Imports import json # Django Imports from django.shortcuts import render, get_object_or_404, HttpResponse from django.http import HttpResponseRedirect from django.core.exceptions import ObjectDoesNotExist from django.core.files import File from django.contrib.auth.models import User from django.utils import timezone # My Module ImportsImports from .models import NotificationBase from profile_settings.models import BasicUserProfile from teacher_authentication.models import TeacherUserProfile from utils.session_utils import get_current_user, get_current_user_profile from utils.session_utils import get_current_teacher_user_profile from utils.access_control import delete_teacher_user_session def notifications(request, page): """ in this page the user can see her notifications """ # Deleting admin-typed user session # Deleting programmer-typed-user session # Deleting Teacher-typed user sessions # ACCESS CONTROL delete_teacher_user_session(request) # Get the current users current_basic_user = get_current_user(request, User, ObjectDoesNotExist) current_basic_user_profile = get_current_user_profile( request, User, BasicUserProfile, ObjectDoesNotExist ) # Getting the current teacher profile current_teacher_profile = get_current_teacher_user_profile( request, User, TeacherUserProfile, ObjectDoesNotExist ) # Get all of the notifications try: all_notifications = NotificationBase.objects.filter( notified_user=current_basic_user_profile ).order_by("-id") except ObjectDoesNotExist: all_notifications = None # Get all of the posts # At every page there will be 80 entries so always multiply it by that and # then reduce your objects current_page = page previous_page = page-1 next_page = page+1 post_records_starting_point = current_page * 80 post_records_ending_point = post_records_starting_point + 80 try: current_page_notifications = NotificationBase.objects.filter( notified_user=current_basic_user_profile ).order_by('-id')[post_records_starting_point:post_records_ending_point] except ObjectDoesNotExist: current_page_notifications = None # check if the user has unread notifications has_unread_notifications = False for notification in all_notifications: if notification.is_read == False: has_unread_notifications = True break else: continue # Since the page is visited make all of the notiications read = True current_unread_notifications = {} for notification in all_notifications: if notification.is_read == False: current_unread_notifications[notification.id] = False notification.is_read = True notification.save() else: pass data = { "current_basic_user": current_basic_user, "current_basic_user_profile": current_basic_user_profile, "current_teacher_profile": current_teacher_profile, "all_notifications": all_notifications, "has_unread_notifications": has_unread_notifications, "current_page": current_page, "previous_page": previous_page, "next_page": next_page, "current_page_notifications": current_page_notifications, "current_unread_notifications": current_unread_notifications, } if current_basic_user == None: return HttpResponseRedirect("/auth/login/") else: return render(request, "basic_notifications/notifications.html", data)

[ "[email protected]" ]

[email protected]

7ec56d1dfd873785b0db9c891aacd95142031aa1

6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4

/sQN3Jb43teMbC7rGJ_18.py

795c8a4678747c53bbb24bcd6b59c6e238410b4e

[]

no_license

daniel-reich/ubiquitous-fiesta

26e80f0082f8589e51d359ce7953117a3da7d38c

9af2700dbe59284f5697e612491499841a6c126f

refs/heads/master

2023-04-05T06:40:37.328213

2021-04-06T20:17:44

355,318,759

0

null

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176

py

def make_transpose(m): dm = len(m) dn = len(m[0]) tm = [[0] * dm for i in range(dn)] for i in range(dm): for j in range(dn): tm[j][i] = m[i][j] return tm

[ "[email protected]" ]

[email protected]

c2ea0ec2e21e9047ed990c7351593ad82edc44ad

536bce6ca78a9a151247b51acb8c375c9db7445f

/chapter1/1.5-interest_rate.py

15aba2121680fc7d7fffc673afd05db59b2923ce

[]

no_license

clicianaldoni/aprimeronpython

57de34313f4fd2a0c69637fefd60b0fb5861f859

a917b62bec669765a238c4b310cc52b79c7df0c9

refs/heads/master

2023-01-28T18:02:31.175511

2023-01-23T08:14:57

112,872,454

0

null

2017-12-02T19:55:40

null

UTF-8

Python

false

464

py

p = 5 # Interest rate % A = 1000 # Initial amount years = 3 # Number of years to grow # Formula for calculating sum: A(1 + p/100)^n # To avoid integer division we convert p to float sum = A * (1 + (float(p)/100))**years print("After %g years with %g%% interest rate and an initial amount of %g we have %g." % (years, p, A, sum)) """ Unix>python interest_rate.py After 3 years with 5% interest rate and an initial amount of 1000 we have 1157.63. """

[ "[email protected]" ]

[email protected]

e57bf9dec7e340b0469004ecf5111d0ea081f482

674f5dde693f1a60e4480e5b66fba8f24a9cb95d

/armulator/armv6/opcodes/concrete/ldc_ldc2_immediate_a2.py

c145465c0a7b80a8b878d200a1c3998d5b55001d

[ "MIT" ]

permissive

matan1008/armulator

75211c18ebc9cd9d33a02890e76fc649483c3aad

44f4275ab1cafff3cf7a1b760bff7f139dfffb07

refs/heads/master

2023-08-17T14:40:52.793120

2023-08-08T04:57:02

91,716,042

29

7

MIT

2023-08-08T04:55:59

2017-05-18T16:37:55

Python

UTF-8

Python

false

788

py

from armulator.armv6.arm_exceptions import UndefinedInstructionException from armulator.armv6.bits_ops import substring, bit_at from armulator.armv6.opcodes.abstract_opcodes.ldc_ldc2_immediate import LdcLdc2Immediate class LdcLdc2ImmediateA2(LdcLdc2Immediate): @staticmethod def from_bitarray(instr, processor): imm8 = substring(instr, 7, 0) coproc = substring(instr, 11, 8) rn = substring(instr, 19, 16) index = bit_at(instr, 24) add = bit_at(instr, 23) wback = bit_at(instr, 21) if substring(coproc, 3, 1) == 0b101: raise UndefinedInstructionException() else: imm32 = imm8 << 2 return LdcLdc2ImmediateA2(instr, cp=coproc, n=rn, add=add, imm32=imm32, index=index, wback=wback)

[ "[email protected]" ]

[email protected]

fea4d5a004cb0d120f3829c1fa2cbf4b2df64e17

046333321b2717c6391a111fc2f74b04bbbeb7af

/chapter13(enumrate function)/sorted.py

cbe84261ffe34d30f366d660bdb7c5115a530460

[]

no_license

jyash28/Python-practice

b0c9df42bc93716d8721a1420ee1f3170b40b18c

cd3a61934618145cbaa20e62194ebb1642ba9941

refs/heads/main

2023-07-03T18:06:38.407491

2021-07-13T09:47:07

314,485,686

0

null

UTF-8

Python

false

290

py

guitars= [ {"model1" : 'famaha f310' ,"price": 8400}, {"model2" : 'faith neptune' ,"price": 100000}, {"model3" : 'faith appolo venus' ,"price": 35000}, {"model4" : 'taylor' ,"price": 450000} ] sorted_guitars = sorted(guitars, key= lambda d: d["price"],reverse = True) print(sorted_guitars)

[ "[email protected]" ]

[email protected]

cee7caced2bc83a749cecf518d0afbeac3bf528e

747f759311d404af31c0f80029e88098193f6269

/addons/project_timesheet_contract/project/__init__.py

34aa344afd62fd26763d265b1313036fe1245e01

[]

no_license

sgeerish/sirr_production

9b0d0f7804a928c0c582ddb4ccb7fcc084469a18

1081f3a5ff8864a31b2dcd89406fac076a908e78

refs/heads/master

2020-05-19T07:21:37.047958

2013-09-15T13:03:36

9,648,444

0

1

null

UTF-8

Python

false

84

py

/home/openerp/production/extra-addons/project_timesheet_contract/project/__init__.py

[ "[email protected]" ]

[email protected]

e22cf41bebc21fe5ea70c17604946adc4fe9a69e

ef5bde73d58734f5081f127fe344ae85c53b8b68

/config_modify.py

8c8255c6e3156d5372724911ccee779d14d2e548

[]

no_license

ychnlgy/VoxCeleb1

a3a6337f322ec1c78f926e2f529db001f7ec8349

930ce2c5c9f0828705afb096c7ee33bfe4b6b96e

refs/heads/master

2020-06-11T10:40:35.462721

2019-07-09T16:42:24

193,934,200

1

0

null

UTF-8

Python

false

364

py

import argparse import voxceleb1 if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--path", required=True) args = parser.parse_args() config = voxceleb1.training.Config(args.path) del config.param_dict["_dob"] kvs = ["--%s %s" % item for item in config.param_dict.items()] print(" ".join(kvs))

[ "[email protected]" ]

[email protected]

c33973915a1487aa198d9586d9ef07976496fe35

9c6dcd6964c0bbbc960106736a3adf83f99ae613

/Balatarin/bipartiteMongo.py~

0ac84299fccd071931a5ee43aa4271ca00d40bdf

[]

no_license

Roja-B/Trajectories

5ab065991c34ba74b6951ad090401c0cb14f222b

e1ce1c6ac8095f92853e0ebe7a41eb8a82e7eff2

refs/heads/master

2016-09-05T17:56:45.643404

2013-01-24T03:54:21

null

0

null

UTF-8

Python

false

1,701

#!/usr/lib/python3.0 # This program extracts bipartite edgelist of users and links belonging to a specific time window (both the link and the votes should come from that time window) # Author: Roja Bandari # October 2012 from pymongo import Connection from PARAMETERS import * import datetime import time import sys #sDate = sys.argv[1] #delta = sys.argv[2] # in days #sYear = int(sDate.split('/')[2]) #sMonth = int(sDate.split('/')[0]) #sDay = int(sDate.split('/')[1]) begin = datetime.datetime(2006,9,1) end = datetime.datetime(2006,11,25) startDate = begin difference = datetime.timedelta(days=WINDOW) slidingWindow = datetime.timedelta(days=SLIDE) t1 = time.time() connection = Connection() balatarindb = connection.Balatarin links = balatarindb.links votes = balatarindb.votes log = open("mongoError.log","a") while startDate < end: endDate = startDate + difference bgraphname = "".join(["bipartite_politics_",str(startDate.month),"_"+str(startDate.day),"_"+str(startDate.year),"_"+str(WINDOW),"_days"]) print bgraphname f = open(PATH+"/bipartite/"+bgraphname+".txt","w") for vote in votes.find({"date":{"$gte":startDate,"$lt":endDate}}): # print vote["linkID"] linkID = vote["linkID"] link = links.find_one({"linkID":linkID}) try: if link["date"] < startDate : continue except: log.write(linkID+'\n') continue if link["category"] == "4": f.write(vote["userID"]+'\t'+vote["linkID"]+'\n') f.close() startDate += slidingWindow t2 = time.time() print "Time Spent: "+str((t2-t1)/60)+" minutes.\n" log.close()

[ "[email protected]" ]

[email protected]

a9520d4013f01df3a621233c6de34a7732d48832

2a05456121813e2c5c3a0e9a88c0c381a038633b

/euler089.py

b32e61c3f1608a6ae354bef88b3f646d1612cf92

[]

no_license

Octaith/euler

022fab72f7d2a72327694ea1970aa3e13a560673

457676a99013c7c5fd33697b82be998d07c464d9

refs/heads/master

2020-09-26T21:04:08.656499

2014-09-14T07:47:51

null

0

null

UTF-8

Python

false

789

py

roman = ( ('M', 1000), ('CM', 900), ('D', 500), ('CD', 400), ('C', 100), ('XC', 90), ('L', 50), ('XL', 40), ('X', 10), ('IX', 9), ('V', 5), ('IV', 4), ('I', 1) ) def roman_to_dec(s): result = 0 index = 0 for numeral, integer in roman: while s[index:index+len(numeral)] == numeral: result += integer index += len(numeral) return result def dec_to_roman(n): result = "" for numeral, integer in roman: while n >= integer: result += numeral n -= integer return result with open('roman.txt') as f: data = f.read().split('\n') saved = 0 for r in data: saved += len(r) saved -= len(dec_to_roman(roman_to_dec(r))) print saved

[ "[email protected]" ]

[email protected]

ede98906221ceb5af90a8e165e9a48203a10f212

a1dae20db0338e735f0b4eb2804a069533bc5a9b

/render.py

f36dcfdfed83a87bd98faa44c513dbe54b05c932

[]

no_license

thoppe/TwitterSquares

4d78e80680c3b01673d602c2564811bf42090aa6

a01dd65456fa70478a0ed03cd7c994c0a678e3ef

refs/heads/master

2020-03-20T08:17:42.525989

2018-06-19T22:05:20

137,304,270

3

0

null

UTF-8

Python

false

4,058

py

"""Render Twitter Squares Usage: render.py <term> <n_images> [--resolution=<n>] Options: -h --help Show this screen. -r --resolution=<n> Output resolution [default: 1200] """ import glob import os import sys import random from tqdm import tqdm import numpy as np import cv2 from docopt import docopt dargs = docopt(__doc__) total_images = int(dargs["<n_images>"]) square_n = int(np.sqrt(total_images)) resolution = int(dargs["--resolution"]) if square_n**2 != total_images: raise ValueError(f"<n_images={total_images}> must be a square number!") max_image_row_size = 20 #model_img_size = 224 model_img_size = 299 name = dargs["<term>"] load_dest = f"data/profile_image/{name}" subimage_dest = f"data/subimage/{name}" activations_dest = f"data/activations/{name}" figure_dest = "figures/" def resize_and_crop(f0): # Resize all the images to the base shape of (model_img_size,model_img_size) # Center crop non-square images f1 = os.path.join(subimage_dest, os.path.basename(f0)) + '.jpg' if os.path.exists(f1): return False img = cv2.imread(f0) if img is None: os.remove(f0) return False x,y,c = img.shape if x > y: dx = (x - y)//2 img = img[dx:dx+y, :, :] if y > x: dy = y - x img = img[:, dy:dy+x, :] img = cv2.resize(img, (model_img_size,model_img_size)) x,y,c = img.shape assert(x==y==model_img_size) cv2.imwrite(f1, img) #print ("Saved", f1) def load_image_data(): F_INPUT = sorted(glob.glob(os.path.join(subimage_dest, '*'))) random.shuffle(F_INPUT) F_INPUT = F_INPUT[:total_images] IMG, ACT = [], [] for f0 in tqdm(F_INPUT): f1 = os.path.join(activations_dest, os.path.basename(f0))+'.txt' assert(os.path.exists(f1)) img = cv2.imread(f0) IMG.append(img) ACT.append(np.loadtxt(f1)) IMG = np.array(IMG) ACT = np.array(ACT) return IMG, ACT _clf = None # Only import the model if we need to score something def compute_activations(f0): f1 = os.path.join(activations_dest, os.path.basename(f0)) + '.txt' if os.path.exists(f1): return False global _clf if _clf is None: print("Importing classification model") from model import layer_model _clf = layer_model() img = cv2.imread(f0) img = img[:,:,::-1] # BGR to RGB ax = _clf.predict(img) np.savetxt(f1, ax) if __name__ == "__main__": # Create any missing directories for d in [subimage_dest, figure_dest, activations_dest]: if not os.path.exists(d): os.system(f'mkdir -p "{d}"') F_IN = set(sorted(glob.glob(os.path.join(load_dest, '*')))) # Remove all zero-byte files for f in list(F_IN): if os.stat(f).st_size==0: print(f"Removing zero-byte file {f}") os.remove(f) F_IN.remove(f) for f0 in tqdm(F_IN): resize_and_crop(f0) print(f"Largest model possible {int(np.floor(len(F_IN)**0.5)**2)}") F_IN = set(sorted(glob.glob(os.path.join(subimage_dest, '*')))) for f0 in tqdm(F_IN): compute_activations(f0) # Check to make sure we have enough images F_IN = set(sorted(glob.glob(os.path.join(activations_dest, '*')))) if len(F_IN) < total_images: msg = f"Not enough images for {name}, {len(F_IN)}/{total_images}" raise ValueError(msg) IMG, ACT = load_image_data() from grid import generate_tsne, fit_to_grid print("Generating tSNE coordinates") X = generate_tsne(ACT) print("Running Jonker-Volgenan") img = fit_to_grid(IMG, X, square_n, out_res=model_img_size) print("Resizing image") img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC) f_img_save = os.path.join(figure_dest, f"{name}.jpg") cv2.imwrite( f_img_save, img, [int(cv2.IMWRITE_JPEG_QUALITY), 95]) print (f"Saved output image to {f_img_save}") os.system(f'eog "figures/{name}.jpg"')

[ "[email protected]" ]

[email protected]

fccc5e04254af51c2fc4a03cdf992b81f31a1d28

a6e4a6f0a73d24a6ba957277899adbd9b84bd594

/sdk/python/pulumi_azure_native/sql/v20190601preview/__init__.py

82b3a2004814746567987c5300774fdd220485e0

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

permissive

MisinformedDNA/pulumi-azure-native

9cbd75306e9c8f92abc25be3f73c113cb93865e9

de974fd984f7e98649951dbe80b4fc0603d03356

refs/heads/master

2023-03-24T22:02:03.842935

2021-03-08T21:16:19

null

0

null

UTF-8

Python

false

2,559

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! *** # Export this package's modules as members: from ._enums import * from .database import * from .get_database import * from .get_managed_database import * from .get_server import * from .get_server_azure_ad_administrator import * from .get_sync_group import * from .get_sync_member import * from .get_workload_classifier import * from .get_workload_group import * from .managed_database import * from .server import * from .server_azure_ad_administrator import * from .sync_group import * from .sync_member import * from .workload_classifier import * from .workload_group import * from ._inputs import * from . import outputs def _register_module(): import pulumi from ... import _utilities class Module(pulumi.runtime.ResourceModule): _version = _utilities.get_semver_version() def version(self): return Module._version def construct(self, name: str, typ: str, urn: str) -> pulumi.Resource: if typ == "azure-native:sql/v20190601preview:Database": return Database(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:ManagedDatabase": return ManagedDatabase(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:Server": return Server(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:ServerAzureADAdministrator": return ServerAzureADAdministrator(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:SyncGroup": return SyncGroup(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:SyncMember": return SyncMember(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:WorkloadClassifier": return WorkloadClassifier(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-native:sql/v20190601preview:WorkloadGroup": return WorkloadGroup(name, pulumi.ResourceOptions(urn=urn)) else: raise Exception(f"unknown resource type {typ}") _module_instance = Module() pulumi.runtime.register_resource_module("azure-native", "sql/v20190601preview", _module_instance) _register_module()

[ "[email protected]" ]

[email protected]

5011a21caf349d8ce94e37300ed1812a3e77ff99

711756b796d68035dc6a39060515200d1d37a274

/output_cog/optimized_25989.py

e0a1a41656b1eecde1b382880dffcadd2189e571

[]

no_license

batxes/exocyst_scripts

8b109c279c93dd68c1d55ed64ad3cca93e3c95ca

a6c487d5053b9b67db22c59865e4ef2417e53030

refs/heads/master

2020-06-16T20:16:24.840725

2016-11-30T16:23:16

75,075,164

0

null

UTF-8

Python

false

10,842

py

import _surface import chimera try: import chimera.runCommand except: pass from VolumePath import markerset as ms try: from VolumePath import Marker_Set, Link new_marker_set=Marker_Set except: from VolumePath import volume_path_dialog d= volume_path_dialog(True) new_marker_set= d.new_marker_set marker_sets={} surf_sets={} if "Cog2_GFPN" not in marker_sets: s=new_marker_set('Cog2_GFPN') marker_sets["Cog2_GFPN"]=s s= marker_sets["Cog2_GFPN"] mark=s.place_marker((455.091, 548.131, 441.132), (0.89, 0.1, 0.1), 18.4716) if "Cog2_0" not in marker_sets: s=new_marker_set('Cog2_0') marker_sets["Cog2_0"]=s s= marker_sets["Cog2_0"] mark=s.place_marker((522.671, 548.429, 441.943), (0.89, 0.1, 0.1), 17.1475) if "Cog2_1" not in marker_sets: s=new_marker_set('Cog2_1') marker_sets["Cog2_1"]=s s= marker_sets["Cog2_1"] mark=s.place_marker((603.488, 547.37, 430.019), (0.89, 0.1, 0.1), 17.1475) if "Cog2_GFPC" not in marker_sets: s=new_marker_set('Cog2_GFPC') marker_sets["Cog2_GFPC"]=s s= marker_sets["Cog2_GFPC"] mark=s.place_marker((518.447, 575.68, 322.142), (0.89, 0.1, 0.1), 18.4716) if "Cog2_Anch" not in marker_sets: s=new_marker_set('Cog2_Anch') marker_sets["Cog2_Anch"]=s s= marker_sets["Cog2_Anch"] mark=s.place_marker((797.322, 526.929, 442.213), (0.89, 0.1, 0.1), 18.4716) if "Cog3_GFPN" not in marker_sets: s=new_marker_set('Cog3_GFPN') marker_sets["Cog3_GFPN"]=s s= marker_sets["Cog3_GFPN"] mark=s.place_marker((500.094, 537.392, 437.867), (1, 1, 0), 18.4716) if "Cog3_0" not in marker_sets: s=new_marker_set('Cog3_0') marker_sets["Cog3_0"]=s s= marker_sets["Cog3_0"] mark=s.place_marker((498.73, 536.413, 437.883), (1, 1, 0.2), 17.1475) if "Cog3_1" not in marker_sets: s=new_marker_set('Cog3_1') marker_sets["Cog3_1"]=s s= marker_sets["Cog3_1"] mark=s.place_marker((498.274, 508.872, 446.765), (1, 1, 0.2), 17.1475) if "Cog3_2" not in marker_sets: s=new_marker_set('Cog3_2') marker_sets["Cog3_2"]=s s= marker_sets["Cog3_2"] mark=s.place_marker((502.548, 483.082, 459.011), (1, 1, 0.2), 17.1475) if "Cog3_3" not in marker_sets: s=new_marker_set('Cog3_3') marker_sets["Cog3_3"]=s s= marker_sets["Cog3_3"] mark=s.place_marker((507.427, 454.916, 455.226), (1, 1, 0.2), 17.1475) if "Cog3_4" not in marker_sets: s=new_marker_set('Cog3_4') marker_sets["Cog3_4"]=s s= marker_sets["Cog3_4"] mark=s.place_marker((501.996, 437.831, 432.369), (1, 1, 0.2), 17.1475) if "Cog3_5" not in marker_sets: s=new_marker_set('Cog3_5') marker_sets["Cog3_5"]=s s= marker_sets["Cog3_5"] mark=s.place_marker((483.284, 448.014, 412.33), (1, 1, 0.2), 17.1475) if "Cog3_GFPC" not in marker_sets: s=new_marker_set('Cog3_GFPC') marker_sets["Cog3_GFPC"]=s s= marker_sets["Cog3_GFPC"] mark=s.place_marker((479.04, 548.96, 453.22), (1, 1, 0.4), 18.4716) if "Cog3_Anch" not in marker_sets: s=new_marker_set('Cog3_Anch') marker_sets["Cog3_Anch"]=s s= marker_sets["Cog3_Anch"] mark=s.place_marker((480.576, 351.176, 373.656), (1, 1, 0.4), 18.4716) if "Cog4_GFPN" not in marker_sets: s=new_marker_set('Cog4_GFPN') marker_sets["Cog4_GFPN"]=s s= marker_sets["Cog4_GFPN"] mark=s.place_marker((673.497, 390.666, 419.207), (0, 0, 0.8), 18.4716) if "Cog4_0" not in marker_sets: s=new_marker_set('Cog4_0') marker_sets["Cog4_0"]=s s= marker_sets["Cog4_0"] mark=s.place_marker((673.497, 390.666, 419.207), (0, 0, 0.8), 17.1475) if "Cog4_1" not in marker_sets: s=new_marker_set('Cog4_1') marker_sets["Cog4_1"]=s s= marker_sets["Cog4_1"] mark=s.place_marker((646.276, 396.064, 413.934), (0, 0, 0.8), 17.1475) if "Cog4_2" not in marker_sets: s=new_marker_set('Cog4_2') marker_sets["Cog4_2"]=s s= marker_sets["Cog4_2"] mark=s.place_marker((628.041, 411.52, 429.302), (0, 0, 0.8), 17.1475) if "Cog4_3" not in marker_sets: s=new_marker_set('Cog4_3') marker_sets["Cog4_3"]=s s= marker_sets["Cog4_3"] mark=s.place_marker((608.845, 430.869, 437.911), (0, 0, 0.8), 17.1475) if "Cog4_4" not in marker_sets: s=new_marker_set('Cog4_4') marker_sets["Cog4_4"]=s s= marker_sets["Cog4_4"] mark=s.place_marker((591.527, 453.567, 442.301), (0, 0, 0.8), 17.1475) if "Cog4_5" not in marker_sets: s=new_marker_set('Cog4_5') marker_sets["Cog4_5"]=s s= marker_sets["Cog4_5"] mark=s.place_marker((574.681, 476.771, 447.418), (0, 0, 0.8), 17.1475) if "Cog4_6" not in marker_sets: s=new_marker_set('Cog4_6') marker_sets["Cog4_6"]=s s= marker_sets["Cog4_6"] mark=s.place_marker((557.879, 500.257, 452.816), (0, 0, 0.8), 17.1475) if "Cog4_GFPC" not in marker_sets: s=new_marker_set('Cog4_GFPC') marker_sets["Cog4_GFPC"]=s s= marker_sets["Cog4_GFPC"] mark=s.place_marker((615.326, 292.629, 312.28), (0, 0, 0.8), 18.4716) if "Cog4_Anch" not in marker_sets: s=new_marker_set('Cog4_Anch') marker_sets["Cog4_Anch"]=s s= marker_sets["Cog4_Anch"] mark=s.place_marker((486.93, 713.637, 585.084), (0, 0, 0.8), 18.4716) if "Cog5_GFPN" not in marker_sets: s=new_marker_set('Cog5_GFPN') marker_sets["Cog5_GFPN"]=s s= marker_sets["Cog5_GFPN"] mark=s.place_marker((588.597, 521.567, 466.245), (0.3, 0.3, 0.3), 18.4716) if "Cog5_0" not in marker_sets: s=new_marker_set('Cog5_0') marker_sets["Cog5_0"]=s s= marker_sets["Cog5_0"] mark=s.place_marker((588.597, 521.567, 466.245), (0.3, 0.3, 0.3), 17.1475) if "Cog5_1" not in marker_sets: s=new_marker_set('Cog5_1') marker_sets["Cog5_1"]=s s= marker_sets["Cog5_1"] mark=s.place_marker((591.457, 516.909, 437.6), (0.3, 0.3, 0.3), 17.1475) if "Cog5_2" not in marker_sets: s=new_marker_set('Cog5_2') marker_sets["Cog5_2"]=s s= marker_sets["Cog5_2"] mark=s.place_marker((586.606, 526.984, 410.798), (0.3, 0.3, 0.3), 17.1475) if "Cog5_3" not in marker_sets: s=new_marker_set('Cog5_3') marker_sets["Cog5_3"]=s s= marker_sets["Cog5_3"] mark=s.place_marker((589.86, 554.228, 399.824), (0.3, 0.3, 0.3), 17.1475) if "Cog5_GFPC" not in marker_sets: s=new_marker_set('Cog5_GFPC') marker_sets["Cog5_GFPC"]=s s= marker_sets["Cog5_GFPC"] mark=s.place_marker((468.427, 583.357, 396.507), (0.3, 0.3, 0.3), 18.4716) if "Cog5_Anch" not in marker_sets: s=new_marker_set('Cog5_Anch') marker_sets["Cog5_Anch"]=s s= marker_sets["Cog5_Anch"] mark=s.place_marker((714.296, 539.961, 401.056), (0.3, 0.3, 0.3), 18.4716) if "Cog6_GFPN" not in marker_sets: s=new_marker_set('Cog6_GFPN') marker_sets["Cog6_GFPN"]=s s= marker_sets["Cog6_GFPN"] mark=s.place_marker((511.432, 560.558, 418.793), (0.21, 0.49, 0.72), 18.4716) if "Cog6_0" not in marker_sets: s=new_marker_set('Cog6_0') marker_sets["Cog6_0"]=s s= marker_sets["Cog6_0"] mark=s.place_marker((511.424, 560.567, 418.769), (0.21, 0.49, 0.72), 17.1475) if "Cog6_1" not in marker_sets: s=new_marker_set('Cog6_1') marker_sets["Cog6_1"]=s s= marker_sets["Cog6_1"] mark=s.place_marker((526.333, 544.602, 401.069), (0.21, 0.49, 0.72), 17.1475) if "Cog6_2" not in marker_sets: s=new_marker_set('Cog6_2') marker_sets["Cog6_2"]=s s= marker_sets["Cog6_2"] mark=s.place_marker((526.398, 522.405, 418.378), (0.21, 0.49, 0.72), 17.1475) if "Cog6_3" not in marker_sets: s=new_marker_set('Cog6_3') marker_sets["Cog6_3"]=s s= marker_sets["Cog6_3"] mark=s.place_marker((528.681, 499.153, 433.922), (0.21, 0.49, 0.72), 17.1475) if "Cog6_4" not in marker_sets: s=new_marker_set('Cog6_4') marker_sets["Cog6_4"]=s s= marker_sets["Cog6_4"] mark=s.place_marker((513.118, 484.584, 415.528), (0.21, 0.49, 0.72), 17.1475) if "Cog6_5" not in marker_sets: s=new_marker_set('Cog6_5') marker_sets["Cog6_5"]=s s= marker_sets["Cog6_5"] mark=s.place_marker((489.537, 476.382, 428.774), (0.21, 0.49, 0.72), 17.1475) if "Cog6_6" not in marker_sets: s=new_marker_set('Cog6_6') marker_sets["Cog6_6"]=s s= marker_sets["Cog6_6"] mark=s.place_marker((476.397, 455.114, 443.081), (0.21, 0.49, 0.72), 17.1475) if "Cog6_GFPC" not in marker_sets: s=new_marker_set('Cog6_GFPC') marker_sets["Cog6_GFPC"]=s s= marker_sets["Cog6_GFPC"] mark=s.place_marker((519.426, 501.731, 500.317), (0.21, 0.49, 0.72), 18.4716) if "Cog6_Anch" not in marker_sets: s=new_marker_set('Cog6_Anch') marker_sets["Cog6_Anch"]=s s= marker_sets["Cog6_Anch"] mark=s.place_marker((446.248, 399.583, 381.235), (0.21, 0.49, 0.72), 18.4716) if "Cog7_GFPN" not in marker_sets: s=new_marker_set('Cog7_GFPN') marker_sets["Cog7_GFPN"]=s s= marker_sets["Cog7_GFPN"] mark=s.place_marker((539.353, 543.386, 500.727), (0.7, 0.7, 0.7), 18.4716) if "Cog7_0" not in marker_sets: s=new_marker_set('Cog7_0') marker_sets["Cog7_0"]=s s= marker_sets["Cog7_0"] mark=s.place_marker((544.576, 549.144, 475.707), (0.7, 0.7, 0.7), 17.1475) if "Cog7_1" not in marker_sets: s=new_marker_set('Cog7_1') marker_sets["Cog7_1"]=s s= marker_sets["Cog7_1"] mark=s.place_marker((557.977, 563.579, 421.9), (0.7, 0.7, 0.7), 17.1475) if "Cog7_2" not in marker_sets: s=new_marker_set('Cog7_2') marker_sets["Cog7_2"]=s s= marker_sets["Cog7_2"] mark=s.place_marker((571.409, 578.04, 368.107), (0.7, 0.7, 0.7), 17.1475) if "Cog7_GFPC" not in marker_sets: s=new_marker_set('Cog7_GFPC') marker_sets["Cog7_GFPC"]=s s= marker_sets["Cog7_GFPC"] mark=s.place_marker((508.62, 629.225, 376.098), (0.7, 0.7, 0.7), 18.4716) if "Cog7_Anch" not in marker_sets: s=new_marker_set('Cog7_Anch') marker_sets["Cog7_Anch"]=s s= marker_sets["Cog7_Anch"] mark=s.place_marker((636.519, 560.093, 288.137), (0.7, 0.7, 0.7), 18.4716) if "Cog8_0" not in marker_sets: s=new_marker_set('Cog8_0') marker_sets["Cog8_0"]=s s= marker_sets["Cog8_0"] mark=s.place_marker((479.555, 565.106, 435.982), (1, 0.5, 0), 17.1475) if "Cog8_1" not in marker_sets: s=new_marker_set('Cog8_1') marker_sets["Cog8_1"]=s s= marker_sets["Cog8_1"] mark=s.place_marker((502.064, 579.203, 445.777), (1, 0.5, 0), 17.1475) if "Cog8_2" not in marker_sets: s=new_marker_set('Cog8_2') marker_sets["Cog8_2"]=s s= marker_sets["Cog8_2"] mark=s.place_marker((529.561, 585.617, 448.267), (1, 0.5, 0), 17.1475) if "Cog8_3" not in marker_sets: s=new_marker_set('Cog8_3') marker_sets["Cog8_3"]=s s= marker_sets["Cog8_3"] mark=s.place_marker((558.025, 587.772, 449.749), (1, 0.5, 0), 17.1475) if "Cog8_4" not in marker_sets: s=new_marker_set('Cog8_4') marker_sets["Cog8_4"]=s s= marker_sets["Cog8_4"] mark=s.place_marker((586.625, 589.775, 450.073), (1, 0.5, 0), 17.1475) if "Cog8_5" not in marker_sets: s=new_marker_set('Cog8_5') marker_sets["Cog8_5"]=s s= marker_sets["Cog8_5"] mark=s.place_marker((615.365, 589.726, 448.653), (1, 0.5, 0), 17.1475) if "Cog8_GFPC" not in marker_sets: s=new_marker_set('Cog8_GFPC') marker_sets["Cog8_GFPC"]=s s= marker_sets["Cog8_GFPC"] mark=s.place_marker((539.329, 564.984, 455.17), (1, 0.6, 0.1), 18.4716) if "Cog8_Anch" not in marker_sets: s=new_marker_set('Cog8_Anch') marker_sets["Cog8_Anch"]=s s= marker_sets["Cog8_Anch"] mark=s.place_marker((694.073, 613.407, 442.164), (1, 0.6, 0.1), 18.4716) for k in surf_sets.keys(): chimera.openModels.add([surf_sets[k]])

[ "[email protected]" ]

[email protected]

8ce1689b4605bab929cceaf30bd0e1e4bc9293a9

b40d1a26ea04a19ec0da7bf55db84b7ee36cc898

/leetcode.com/python/1007_Minimum_Domino_Rotations_For_Equal_Row.py

974ee558096c8fe9a393d9b91f507186e8e356d7

[ "MIT" ]

permissive

partho-maple/coding-interview-gym

5e8af7d404c28d4b9b52e5cffc540fd51d8025cf

20ae1a048eddbc9a32c819cf61258e2b57572f05

refs/heads/master

2022-09-11T16:36:01.702626

2022-03-14T08:39:47

69,802,909

862

438

MIT

2022-08-18T06:42:46

2016-10-02T14:51:31

Python

UTF-8

Python

false

2,177

py

# Source: https://tinyurl.com/v3zqer7 # Approach 1 class Solution(object): def minDominoRotations(self, A, B): """ :type A: List[int] :type B: List[int] :rtype: int """ result = float("inf") for domino in range(1, 7): # Since each domino can have only 1 to 6 values. So check all values if we can make it isPossible = True topRorationCount, bottomRotationCount = 0, 0 for a, b in zip(A, B): if domino != a and domino != b: # isPossible = False break if domino == a and domino != b: bottomRotationCount += 1 elif domino != a and domino == b: topRorationCount += 1 if isPossible: result = min(result, min(topRorationCount, bottomRotationCount)) return -1 if result == float("inf") else result # Source: https://tinyurl.com/v3zqer7 # Approach 2 class Solution(object): def minDominoRotations(self, A, B): """ :type A: List[int] :type B: List[int] :rtype: int """ rotations = self.checkRotationFor(A, B, A[0]) # If one could make all elements in A or B equal to A[0] if rotations != -1 or A[0] == B[0]: return rotations # If one could make all elements in A or B equal to B[0] else: return self.checkRotationFor(A, B, B[0]) def checkRotationFor(self, A, B, num): """ Return minimum number of swaps, if one could make all elements in A or B equal to 'num'. Else return -1 """ # How many rotations should be done # to have all elements in A equal to 'num' # and to have all elements in B equal to 'num' length = len(A) rotations_A, rotations_B = 0, 0 for i in range(length): if A[i] != num and B[i] != num: return -1 elif A[i] != num: rotations_A += 1 elif B[i] != num: rotations_B += 1 return min(rotations_A, rotations_B)

[ "[email protected]" ]

[email protected]

d7e949f538a3a780aa931750ee78faf99c3e1323

1a166165ab8287d01cbb377a13efdb5eff5dfef0

/sdk/network/azure-mgmt-network/azure/mgmt/network/v2019_04_01/operations/_ddos_protection_plans_operations.py

d971927ea9d9c040f54655faee5d8e8cf1f6edd5

[ "MIT", "LicenseRef-scancode-generic-cla", "LGPL-2.1-or-later" ]

permissive

manoj0806/azure-sdk-for-python

7a14b202ff80f528abd068bf50334e91001a9686

aab999792db1132232b2f297c76800590a901142

refs/heads/master

2023-04-19T16:11:31.984930

2021-04-29T23:19:49

363,025,016

1

0

MIT

2021-04-30T04:23:35

null

UTF-8

Python

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# coding=utf-8 # -------------------------------------------------------------------------- # 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 Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.paging import ItemPaged from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class DdosProtectionPlansOperations(object): """DdosProtectionPlansOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2019_04_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def _delete_initial( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str **kwargs # type: Any ): # type: (...) -> None cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def begin_delete( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str **kwargs # type: Any ): # type: (...) -> LROPoller[None] """Deletes the specified DDoS protection plan. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_protection_plan_name: The name of the DDoS protection plan. :type ddos_protection_plan_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._delete_initial( resource_group_name=resource_group_name, ddos_protection_plan_name=ddos_protection_plan_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def get( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str **kwargs # type: Any ): # type: (...) -> "_models.DdosProtectionPlan" """Gets information about the specified DDoS protection plan. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_protection_plan_name: The name of the DDoS protection plan. :type ddos_protection_plan_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: DdosProtectionPlan, or the result of cls(response) :rtype: ~azure.mgmt.network.v2019_04_01.models.DdosProtectionPlan :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlan"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('DdosProtectionPlan', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def _create_or_update_initial( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str parameters, # type: "_models.DdosProtectionPlan" **kwargs # type: Any ): # type: (...) -> "_models.DdosProtectionPlan" cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlan"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._create_or_update_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'DdosProtectionPlan') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('DdosProtectionPlan', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('DdosProtectionPlan', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def begin_create_or_update( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str parameters, # type: "_models.DdosProtectionPlan" **kwargs # type: Any ): # type: (...) -> LROPoller["_models.DdosProtectionPlan"] """Creates or updates a DDoS protection plan. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_protection_plan_name: The name of the DDoS protection plan. :type ddos_protection_plan_name: str :param parameters: Parameters supplied to the create or update operation. :type parameters: ~azure.mgmt.network.v2019_04_01.models.DdosProtectionPlan :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either DdosProtectionPlan or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2019_04_01.models.DdosProtectionPlan] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlan"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._create_or_update_initial( resource_group_name=resource_group_name, ddos_protection_plan_name=ddos_protection_plan_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('DdosProtectionPlan', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def _update_tags_initial( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str parameters, # type: "_models.TagsObject" **kwargs # type: Any ): # type: (...) -> "_models.DdosProtectionPlan" cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlan"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._update_tags_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'TagsObject') body_content_kwargs['content'] = body_content request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('DdosProtectionPlan', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _update_tags_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def begin_update_tags( self, resource_group_name, # type: str ddos_protection_plan_name, # type: str parameters, # type: "_models.TagsObject" **kwargs # type: Any ): # type: (...) -> LROPoller["_models.DdosProtectionPlan"] """Update a DDoS protection plan tags. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param ddos_protection_plan_name: The name of the DDoS protection plan. :type ddos_protection_plan_name: str :param parameters: Parameters supplied to the update DDoS protection plan resource tags. :type parameters: ~azure.mgmt.network.v2019_04_01.models.TagsObject :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either DdosProtectionPlan or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.network.v2019_04_01.models.DdosProtectionPlan] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, PollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlan"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = self._update_tags_initial( resource_group_name=resource_group_name, ddos_protection_plan_name=ddos_protection_plan_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('DdosProtectionPlan', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'ddosProtectionPlanName': self._serialize.url("ddos_protection_plan_name", ddos_protection_plan_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = ARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = NoPolling() else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_update_tags.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans/{ddosProtectionPlanName}'} # type: ignore def list( self, **kwargs # type: Any ): # type: (...) -> Iterable["_models.DdosProtectionPlanListResult"] """Gets all DDoS protection plans in a subscription. :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either DdosProtectionPlanListResult or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2019_04_01.models.DdosProtectionPlanListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlanListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('DdosProtectionPlanListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.Network/ddosProtectionPlans'} # type: ignore def list_by_resource_group( self, resource_group_name, # type: str **kwargs # type: Any ): # type: (...) -> Iterable["_models.DdosProtectionPlanListResult"] """Gets all the DDoS protection plans in a resource group. :param resource_group_name: The name of the resource group. :type resource_group_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either DdosProtectionPlanListResult or the result of cls(response) :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.network.v2019_04_01.models.DdosProtectionPlanListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.DdosProtectionPlanListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2019-04-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list_by_resource_group.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request def extract_data(pipeline_response): deserialized = self._deserialize('DdosProtectionPlanListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, iter(list_of_elem) def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return ItemPaged( get_next, extract_data ) list_by_resource_group.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/ddosProtectionPlans'} # type: ignore

[ "[email protected]" ]

[email protected]

59ab5667a34c44fdb895072c8f91f93182bc126b

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/0x0F-python-object_relational_mapping/1-filter_states.py

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glyif/holbertonschool-higher_level_programming

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#!/usr/bin/python3 """ mysqldb filter states """ import sys import MySQLdb def db_connection(user_name, password, db_name, host="localhost"): """ db_connection - connects to db :param user_name: username :param password: password :param db_name: database name :param host: host - default to localhost :return: db """ db = MySQLdb.connect(host=host, user=user_name, passwd=password, db=db_name) return db def db_query(db, query): """ db_query - queries database :param db: database :param query: query :return: none """ cur = db.cursor() cur.execute(query) data = cur.fetchall() for row in data: print(row) if __name__ == "__main__": db = db_connection(sys.argv[1], sys.argv[2], sys.argv[3]) db_query(db, """SELECT id, name FROM states WHERE name LIKE 'N%' ORDER BY states.id ASC""")

[ "[email protected]" ]

[email protected]

2b6dbf579ae37711f46b26057e43ff7b642659e2

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chelseashin/AlgorithmStudy2021

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# 참고 : https://pacific-ocean.tistory.com/152 # https://claude-u.tistory.com/204 # dp[i]의 최댓값을 구하는 것은 세 가지 방법에 의해 결정된다. # 1) OXOO: 연속 두 개 # 2) OXO: 하나 띄고 한 개 # 3) X: i 번째를 마시지 않는 경우 from sys import stdin input = stdin.readline n = int(input()) a = [0] + [int(input()) for _ in range(n)] dp = [0, a[1]] if n > 1: dp.append(a[1] + a[2]) for i in range(3, n+1): dp.append(max(dp[i-1], dp[i-3]+a[i-1]+a[i], dp[i-2]+a[i])) # print(n, a, dp) print(dp[n]) # 위와 같은 방법 # wine = [0] + [int(input()) for _ in range(n)] # dp = [0] * (n+1) # dp[1] = wine[1] # if n > 1: # dp[2] = wine[1] + wine[2] # for i in range(3, n+1): # dp[i] = max(dp[i-3]+wine[i-1]+wine[i], dp[i-2]+wine[i], dp[i-1]) # # print(dp[n])

[ "[email protected]" ]

[email protected]

26f00db3c32d9d29465643d06dc02532c017e608

8d9318a33afc2c3b5ca8ac99fce0d8544478c94a

/Books/Casandra DB/opscenter-5.1.0/lib/py-debian/2.6/i386/twisted/python/_inotify.py

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tushar239/git-large-repo

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../../../../../py-unpure/twisted/python/_inotify.py

[ "[email protected]" ]

[email protected]

0324dd3dc62f88495cb95ea7424deef660c43536

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/typings/vtkmodules/vtkIOXML/__init__.pyi

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gen4438/vtk-python-stubs

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""" This type stub file was generated by pyright. """ import vtkmodules.vtkCommonCore as __vtkmodules_vtkCommonCore import vtkmodules.vtkCommonExecutionModel as __vtkmodules_vtkCommonExecutionModel import vtkmodules.vtkIOXMLParser as __vtkmodules_vtkIOXMLParser from .vtkXMLReader import vtkXMLReader from .vtkXMLDataReader import vtkXMLDataReader from .vtkXMLUnstructuredDataReader import vtkXMLUnstructuredDataReader from .vtkXMLPolyDataReader import vtkXMLPolyDataReader from .vtkRTXMLPolyDataReader import vtkRTXMLPolyDataReader from .vtkXMLCompositeDataReader import vtkXMLCompositeDataReader from .vtkXMLWriter import vtkXMLWriter from .vtkXMLCompositeDataWriter import vtkXMLCompositeDataWriter from .vtkXMLDataObjectWriter import vtkXMLDataObjectWriter from .vtkXMLDataSetWriter import vtkXMLDataSetWriter from .vtkXMLFileReadTester import vtkXMLFileReadTester from .vtkXMLGenericDataObjectReader import vtkXMLGenericDataObjectReader from .vtkXMLHierarchicalBoxDataFileConverter import vtkXMLHierarchicalBoxDataFileConverter from .vtkXMLUniformGridAMRReader import vtkXMLUniformGridAMRReader from .vtkXMLHierarchicalBoxDataReader import vtkXMLHierarchicalBoxDataReader from .vtkXMLUniformGridAMRWriter import vtkXMLUniformGridAMRWriter from .vtkXMLHierarchicalBoxDataWriter import vtkXMLHierarchicalBoxDataWriter from .vtkXMLMultiBlockDataReader import vtkXMLMultiBlockDataReader from .vtkXMLMultiGroupDataReader import vtkXMLMultiGroupDataReader from .vtkXMLHierarchicalDataReader import vtkXMLHierarchicalDataReader from .vtkXMLHyperTreeGridReader import vtkXMLHyperTreeGridReader from .vtkXMLHyperTreeGridWriter import vtkXMLHyperTreeGridWriter from .vtkXMLStructuredDataReader import vtkXMLStructuredDataReader from .vtkXMLImageDataReader import vtkXMLImageDataReader from .vtkXMLStructuredDataWriter import vtkXMLStructuredDataWriter from .vtkXMLImageDataWriter import vtkXMLImageDataWriter from .vtkXMLMultiBlockDataWriter import vtkXMLMultiBlockDataWriter from .vtkXMLPartitionedDataSetCollectionReader import vtkXMLPartitionedDataSetCollectionReader from .vtkXMLPartitionedDataSetCollectionWriter import vtkXMLPartitionedDataSetCollectionWriter from .vtkXMLPartitionedDataSetReader import vtkXMLPartitionedDataSetReader from .vtkXMLPartitionedDataSetWriter import vtkXMLPartitionedDataSetWriter from .vtkXMLPDataObjectReader import vtkXMLPDataObjectReader from .vtkXMLPDataReader import vtkXMLPDataReader from .vtkXMLPHyperTreeGridReader import vtkXMLPHyperTreeGridReader from .vtkXMLPStructuredDataReader import vtkXMLPStructuredDataReader from .vtkXMLPImageDataReader import vtkXMLPImageDataReader from .vtkXMLUnstructuredDataWriter import vtkXMLUnstructuredDataWriter from .vtkXMLPolyDataWriter import vtkXMLPolyDataWriter from .vtkXMLPUnstructuredDataReader import vtkXMLPUnstructuredDataReader from .vtkXMLPPolyDataReader import vtkXMLPPolyDataReader from .vtkXMLPRectilinearGridReader import vtkXMLPRectilinearGridReader from .vtkXMLPStructuredGridReader import vtkXMLPStructuredGridReader from .vtkXMLPTableReader import vtkXMLPTableReader from .vtkXMLPUnstructuredGridReader import vtkXMLPUnstructuredGridReader from .vtkXMLRectilinearGridReader import vtkXMLRectilinearGridReader from .vtkXMLRectilinearGridWriter import vtkXMLRectilinearGridWriter from .vtkXMLStructuredGridReader import vtkXMLStructuredGridReader from .vtkXMLStructuredGridWriter import vtkXMLStructuredGridWriter from .vtkXMLTableReader import vtkXMLTableReader from .vtkXMLTableWriter import vtkXMLTableWriter from .vtkXMLUnstructuredGridReader import vtkXMLUnstructuredGridReader from .vtkXMLUnstructuredGridWriter import vtkXMLUnstructuredGridWriter __loader__ = ... __spec__ = ...

[ "[email protected]" ]

[email protected]

ee9cee9c908ac3278c8545a66f4d96149faae702

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/Advance_Python/Python_Database_Programming/Other/add_user_in_bank.py

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sachinyadav3496/PythonInternBatch2018

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import pymysql as sql db = sql.connect(host='localhost',port=3306,user='bank_app',password='redhat',database='bank_app') c = db.cursor() f = open('bank_data.csv') data = [] for line in f : d = line.split(',') d[2] = float(d[2][:-1]) data.append(d) f.close() for var in data : name = var[0] password = var[1] bal = var[2] cmd = "insert into bank(user,password,bal) values('{}','{}',{})".format(name,password,bal) c.execute(cmd) db.commit() print("Added data to bank sucessfully") c.close() db.close()

[ "[email protected]" ]

[email protected]

073b6b152e0805dbc16dce1d402482e505bd9770

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/hunkim/lab12-5.py

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no_license

SilverQ/dl_study

424bce279c059c290a4c766e87fadb150fff82da

663b432abc5afd0eed278368a5fea19ece6a383c

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2022-11-14T08:27:10.937535

2020-07-02T10:05:04

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''' The original script shows how to predict the next day's closing stock prices using a basic RNN https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-12-5-rnn_stock_prediction.py At first, let's understand the original code and prior arts completely ''' import tensorflow as tf import numpy as np import matplotlib import matplotlib.pyplot as plt import os tf.set_random_seed(777) # reproducibility np.set_printoptions(precision=2) if "DISPLAY" not in os.environ: # remove Travis CI Error matplotlib.use('Agg') def MinMaxScaler(data): ''' Min Max Normalization Parameters ---------- data : numpy.ndarray input data to be normalized shape: [Batch size, dimension] Returns ---------- data : numpy.ndarry normalized data shape: [Batch size, dimension] References ---------- .. [1] http://sebastianraschka.com/Articles/2014_about_feature_scaling.html 원래는 normalized data만 반환하였으나, 데이터의 복구를 위해 min, max도 반환 ''' numerator = data - np.min(data, 0) denominator = np.max(data, 0) - np.min(data, 0) # noise term prevents the zero division return [numerator / (denominator + 1e-7), np.min(data, 0), np.max(data, 0)] # train Parameters seq_length = 7 data_dim = 5 hidden_dim = 10 output_dim = 1 learning_rate = 0.01 iterations = 500 # Open, High, Low, Volume, Close xy = np.loadtxt('data-02-stock_daily.csv', delimiter=',') xy_rev = xy[::-1] # reverse order (chronically ordered), 날짜 오름차순으로. ''' print('xy: ', xy[-3:]) xy: [[ 566.89 567. 556.93 10800. 556.97] [ 561.2 566.43 558.67 41200. 559.99] [ 568. 568. 552.92 13100. 558.46]] print('xy_rev: ', xy_rev[:3]) xy: [[ 568. 568. 552.92 13100. 558.46] [ 561.2 566.43 558.67 41200. 559.99] [ 566.89 567. 556.93 10800. 556.97]] ''' # split data to train_set/test_set and Scaling train_size = int(len(xy_rev) * 0.7) train_set = xy_rev[0:train_size] test_set = xy_rev[train_size - seq_length:] # Index from [train_size - seq_length] to utilize past sequence [train_set, min, max] = MinMaxScaler(train_set) [test_set, min, max] = MinMaxScaler(test_set) ''' print('train_set: ', train_set[:3]) print('min: ', min) # 컬럼별로 min-max 연산은 따로따로 한 것을 알 수 있음.!!! train_set: [[0.25 0.25 0.23 0. 0.23] [0.23 0.24 0.25 0. 0.24] [0.25 0.24 0.25 0. 0.23]] min: [ 494.65 495.98 487.56 7900. 492.55] ''' # build datasets. Create batch for 7-days. def build_dataset(time_series, seq_length): dataX = [] dataY = [] for i in range(0, len(time_series) - seq_length): _x = time_series[i:i + seq_length, :] _y = time_series[i + seq_length, [-1]] # the next day's closing stock prices # print(_x, "->", _y) dataX.append(_x) dataY.append(_y) return np.array(dataX), np.array(dataY) trainX, trainY = build_dataset(train_set, seq_length) testX, testY = build_dataset(test_set, seq_length) ''' print('trainX: ', trainX[:4]) print('trainY: ', trainY[:3]) trainX: [[[2.53e-01 2.45e-01 2.34e-01 4.66e-04 2.32e-01] [2.30e-01 2.40e-01 2.55e-01 2.98e-03 2.37e-01] [2.49e-01 2.42e-01 2.48e-01 2.60e-04 2.27e-01] [2.21e-01 2.47e-01 2.55e-01 0.00e+00 2.63e-01] [3.63e-01 3.70e-01 2.67e-01 1.25e-02 2.62e-01] [2.59e-01 3.11e-01 2.74e-01 4.56e-01 2.72e-01] [2.76e-01 2.78e-01 1.98e-01 5.70e-01 1.78e-01]] [[2.30e-01 2.40e-01 2.55e-01 2.98e-03 2.37e-01] [2.49e-01 2.42e-01 2.48e-01 2.60e-04 2.27e-01] [2.21e-01 2.47e-01 2.55e-01 0.00e+00 2.63e-01] [3.63e-01 3.70e-01 2.67e-01 1.25e-02 2.62e-01] [2.59e-01 3.11e-01 2.74e-01 4.56e-01 2.72e-01] [2.76e-01 2.78e-01 1.98e-01 5.70e-01 1.78e-01] [1.59e-01 1.79e-01 1.42e-01 3.94e-01 1.61e-01]] [[2.49e-01 2.42e-01 2.48e-01 2.60e-04 2.27e-01] [2.21e-01 2.47e-01 2.55e-01 0.00e+00 2.63e-01] [3.63e-01 3.70e-01 2.67e-01 1.25e-02 2.62e-01] [2.59e-01 3.11e-01 2.74e-01 4.56e-01 2.72e-01] [2.76e-01 2.78e-01 1.98e-01 5.70e-01 1.78e-01] [1.59e-01 1.79e-01 1.42e-01 3.94e-01 1.61e-01] [1.65e-01 2.01e-01 1.93e-01 2.82e-01 2.20e-01]] [[2.21e-01 2.47e-01 2.55e-01 0.00e+00 2.63e-01] [3.63e-01 3.70e-01 2.67e-01 1.25e-02 2.62e-01] [2.59e-01 3.11e-01 2.74e-01 4.56e-01 2.72e-01] [2.76e-01 2.78e-01 1.98e-01 5.70e-01 1.78e-01] [1.59e-01 1.79e-01 1.42e-01 3.94e-01 1.61e-01] [1.65e-01 2.01e-01 1.93e-01 2.82e-01 2.20e-01] [2.24e-01 2.36e-01 2.34e-01 2.98e-01 2.52e-01]]] trainY: [[0.16] [0.22] [0.25]] ''' # input place holders X = tf.placeholder(tf.float32, [None, seq_length, data_dim]) Y = tf.placeholder(tf.float32, [None, 1]) # build a LSTM network cell = tf.contrib.rnn.BasicLSTMCell(num_units=hidden_dim, state_is_tuple=True, activation=tf.tanh) outputs, _states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32) Y_pred = tf.contrib.layers.fully_connected( outputs[:, -1], output_dim, activation_fn=None) # We use the last cell's output # cost/loss loss = tf.reduce_sum(tf.square(Y_pred - Y)) # sum of the squares # optimizer optimizer = tf.train.AdamOptimizer(learning_rate) train = optimizer.minimize(loss) # RMSE targets = tf.placeholder(tf.float32, [None, 1]) predictions = tf.placeholder(tf.float32, [None, 1]) rmse = tf.sqrt(tf.reduce_mean(tf.square(targets - predictions))) with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) # Training step for i in range(iterations): _, step_loss = sess.run([train, loss], feed_dict={ X: trainX, Y: trainY}) if i % 100 ==0: print("[step: {}] loss: {}".format(i, step_loss)) # Test step test_predict = sess.run(Y_pred, feed_dict={X: testX}) rmse_val = sess.run(rmse, feed_dict={ targets: testY, predictions: test_predict}) print("RMSE: {}".format(rmse_val)) # Plot predictions plt.plot(testY) plt.plot(test_predict) plt.xlabel("Time Period") plt.ylabel("Stock Price") # plt.show() plt.savefig('Stock_price.png')

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[email protected]

36cc5a4f3b24b18cffd92d7046c81ced4ac397e1

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from . import ( raw, )

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# coding=utf-8 # Copyright 2018 The TF-Agents Authors. # # 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 bandit policies.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import from tf_agents.specs import tensor_spec from tf_agents.trajectories import policy_step from tf_agents.utils import common class InfoFields(object): """Strings which can be used in the policy info fields.""" # Mean of predicted rewards (per arm). PREDICTED_REWARDS_MEAN = 'predicted_rewards_mean' # Samples of predicted rewards (per arm). PREDICTED_REWARDS_SAMPLED = 'predicted_rewards_sampled' # Type of bandit policy (see enumerations in `BanditPolicyType`). BANDIT_POLICY_TYPE = 'bandit_policy_type' # Used to store the chosen action for a per-arm model. CHOSEN_ARM_FEATURES = 'chosen_arm_features' PolicyInfo = collections.namedtuple( # pylint: disable=invalid-name 'PolicyInfo', (policy_step.CommonFields.LOG_PROBABILITY, InfoFields.PREDICTED_REWARDS_MEAN, InfoFields.PREDICTED_REWARDS_SAMPLED, InfoFields.BANDIT_POLICY_TYPE)) # Set default empty tuple for all fields. PolicyInfo.__new__.__defaults__ = ((),) * len(PolicyInfo._fields) PerArmPolicyInfo = collections.namedtuple( # pylint: disable=invalid-name 'PerArmPolicyInfo', (policy_step.CommonFields.LOG_PROBABILITY, InfoFields.PREDICTED_REWARDS_MEAN, InfoFields.PREDICTED_REWARDS_SAMPLED, InfoFields.BANDIT_POLICY_TYPE, InfoFields.CHOSEN_ARM_FEATURES)) # Set default empty tuple for all fields. PerArmPolicyInfo.__new__.__defaults__ = ((),) * len(PerArmPolicyInfo._fields) def populate_policy_info(arm_observations, chosen_actions, rewards_for_argmax, est_rewards, emit_policy_info, accepts_per_arm_features): """Populates policy info given all needed input. Args: arm_observations: In case the policy accepts per-arm feautures, this is a Tensor with the per-arm features. Otherwise its value is unused. chosen_actions: A Tensor with the indices of the chosen actions. rewards_for_argmax: The sampled or optimistically boosted reward estimates based on which the policy chooses the action greedily. est_rewards: A Tensor with the rewards estimated by the model. emit_policy_info: A set of policy info keys, specifying wich info fields to populate accepts_per_arm_features: (bool) Whether the policy accepts per-arm features. Returns: A policy info. """ if accepts_per_arm_features: # Saving the features for the chosen action to the policy_info. chosen_arm_features = tf.gather( params=arm_observations, indices=chosen_actions, batch_dims=1) policy_info = PerArmPolicyInfo( predicted_rewards_sampled=( rewards_for_argmax if InfoFields.PREDICTED_REWARDS_SAMPLED in emit_policy_info else ()), predicted_rewards_mean=( est_rewards if InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info else ()), chosen_arm_features=chosen_arm_features) else: policy_info = PolicyInfo( predicted_rewards_sampled=( rewards_for_argmax if InfoFields.PREDICTED_REWARDS_SAMPLED in emit_policy_info else ()), predicted_rewards_mean=( est_rewards if InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info else ())) return policy_info class BanditPolicyType(object): """Enumeration of bandit policy types.""" # No bandit policy type specified. UNKNOWN = 0 # Greedy decision made by bandit agent. GREEDY = 1 # Random decision for exploration made by epsilon-greedy agent sampled from # uniform distribution over actions. UNIFORM = 2 def create_bandit_policy_type_tensor_spec(shape): """Create tensor spec for bandit policy type.""" return tensor_spec.BoundedTensorSpec( shape=shape, dtype=tf.int32, minimum=BanditPolicyType.UNKNOWN, maximum=BanditPolicyType.UNIFORM) @common.function def masked_argmax(input_tensor, mask, output_type=tf.int32): """Computes the argmax where the allowed elements are given by a mask. If a row of `mask` contains all zeros, then this method will return -1 for the corresponding row of `input_tensor`. Args: input_tensor: Rank-2 Tensor of floats. mask: 0-1 valued Tensor of the same shape as input. output_type: Integer type of the output. Returns: A Tensor of rank 1 and type `output_type`, with the masked argmax of every row of `input_tensor`. """ input_tensor.shape.assert_is_compatible_with(mask.shape) neg_inf = tf.constant(-float('Inf'), input_tensor.dtype) modified_input = tf.compat.v2.where( tf.cast(mask, tf.bool), input_tensor, neg_inf) argmax_tensor = tf.argmax(modified_input, axis=-1, output_type=output_type) # Replace results for invalid mask rows with -1. reduce_mask = tf.cast(tf.reduce_max(mask, axis=1), tf.bool) neg_one = tf.constant(-1, output_type) return tf.compat.v2.where(reduce_mask, argmax_tensor, neg_one) def has_bandit_policy_type(info, check_for_tensor=False): """Check if policy info has `bandit_policy_type` field/tensor.""" if info in ((), None): return False fields = getattr(info, '_fields', None) has_field = fields is not None and InfoFields.BANDIT_POLICY_TYPE in fields if has_field and check_for_tensor: return isinstance(info.bandit_policy_type, tf.Tensor) else: return has_field def set_bandit_policy_type(info, bandit_policy_type): """Sets the InfoFields.BANDIT_POLICY_TYPE on info to bandit_policy_type. If policy `info` does not support InfoFields.BANDIT_POLICY_TYPE, this method returns `info` as-is (without any modification). Args: info: Policy info on which to set bandit policy type. bandit_policy_type: Tensor containing BanditPolicyType enums or TensorSpec from `create_bandit_policy_type_tensor_spec()`. Returns: Policy info with modified field (if possible). """ if info in ((), None): return PolicyInfo(bandit_policy_type=bandit_policy_type) fields = getattr(info, '_fields', None) if fields is not None and InfoFields.BANDIT_POLICY_TYPE in fields: return info._replace(bandit_policy_type=bandit_policy_type) try: info[InfoFields.BANDIT_POLICY_TYPE] = bandit_policy_type except TypeError: pass return info @common.function def bandit_policy_uniform_mask(values, mask): """Set bandit policy type tensor to BanditPolicyType.UNIFORM based on mask. Set bandit policy type `values` to BanditPolicyType.UNIFORM; returns tensor where output[i] is BanditPolicyType.UNIFORM if mask[i] is True, otherwise it is left as values[i]. Args: values: Tensor containing `BanditPolicyType` enumerations. mask: Tensor of the same shape as `values` with boolean flags indicating values to set to `BanditPolicyType.UNIFORM`. Returns: Tensor containing `BanditPolicyType` enumerations with masked values. """ return tf.where( mask, tf.fill(tf.shape(values), BanditPolicyType.UNIFORM), values) def get_model_index(arm_index, accepts_per_arm_features): """Returns the model index for a specific arm. The number of models depends on the observation format: If the policy accepts per-arm features, there is only one single model used for every arm. Otherwise there is a model for every arm. Args: arm_index: The index of the arm for which the model index is needed. accepts_per_arm_features: (bool) Whether the policy works with per-arm features. Returns: The index of the model for the arm requested. """ return 0 if accepts_per_arm_features else arm_index def compute_feasibility_probability(observation, constraints, batch_size, num_actions, action_mask=None): """Helper function to compute the action feasibility probability.""" feasibility_prob = tf.ones([batch_size, num_actions]) if action_mask is not None: feasibility_prob = tf.cast(action_mask, tf.float32) for c in constraints: # We assume the constraints are independent. action_feasibility = c.compute_action_feasibility(observation) feasibility_prob *= action_feasibility return feasibility_prob

[ "[email protected]" ]

[email protected]

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#!/usr/bin/env python3 """Tetra-nucleotide counter""" import sys import os from collections import defaultdict args = sys.argv[1:] if len(args) != 1: print('Usage: {} DNA'.format(os.path.basename(sys.argv[0]))) sys.exit(1) arg = args[0] dna = '' if os.path.isfile(arg): dna = ''.join(open(arg).read().splitlines()) else: dna = arg count = defaultdict(int) for base in dna.lower(): count[base] += 1 print(' '.join(map(lambda b: str(count[b]), "acgt")))

[ "[email protected]" ]

[email protected]

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/e3/DiffieHellman.py

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NigrumAquila/subject_cryptographic_protection

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import __main__ if __main__.__file__ != 'main.py': exit('run main.py') from .DHlib.DHalg import encrypt, decrypt, getSharedSecret, printAllKeys, printParams from lib.colors import * from lib.duty import * key = getSharedSecret() printAllKeys() while True: printParams(); message = typedText('Enter message for RSA encryption: ') printTextAndValue('Original message: ', message) encrypted_message = encrypt(key, message) try: printTextAndValue('Encrypted message: ', encrypted_message) except UnicodeError: warning('\rYour encoding isn\'t UTF-8') end('Please, restart it with "PYTHONIOENCODING=UTF-8 python main.py" or by IDE with utf8 encoding') decrypted_message = decrypt(key, encrypted_message) printTextAndValue('Decrypted message: ', decrypted_message) repeatProcedure()

[ "[email protected]" ]

[email protected]

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/hackerank_whatsnext.py

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rushilchugh/Practise

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__author__ = 'Rushil' #SetCount(x) - Number of ones in an binary number x #Johnny wants to find a binary number, D, that is the smallest binary number >B where setCount(B) = setCount(D) #He then wants to compress D into an array of integers,C (in the same way that integer array A contains the compressed form of binary string B). #Values in even represents consecutive 1 #Values in odd represents consecutive 0 from itertools import groupby import re #Given input 4 1 3 2 4 def get_bin_rep(num): inp_text = num.replace(' ','') f_str = '' for index,char in enumerate(inp_text): if index % 2 == 0: f_str += '1'*int(char) else: f_str += '0'*int(char) return f_str def get_other_bin(bin_num): occ_0 = 0 bin_num = list(bin_num) if bin_num[-1] == '0': f1_index = ''.join(bin_num).rfind('1') bin_num[-1] = '1' bin_num[f1_index] = '0' return ''.join(bin_num) for index,i in enumerate(bin_num): if i == '0': occ_0 = index bin_num[occ_0] = '1' bin_num[occ_0 + 1] = '0' return ''.join(bin_num) def make_rep(bin_num): #11110111010111 f_str = '' for i,j in groupby(bin_num): f_str += str(len(list(j))) f_str += ' ' return f_str # #print(get_other_bin('11110111001111')) #print(make_rep('11110111001111')) #print(make_rep(get_other_bin(get_bin_rep('4 1 3 2 4')))) n = int(input().strip()) m_list = [] for i in range(n): w_len = input().strip() m_word = input().strip() m_list.append(m_word) for i in m_list: f_sol = make_rep(get_other_bin(get_bin_rep(i))) print(len(f_sol)) print(f_sol)

[ "[email protected]" ]

[email protected]

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""" byceps.services.board.models.topic ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :Copyright: 2006-2019 Jochen Kupperschmidt :License: Modified BSD, see LICENSE for details. """ from datetime import datetime from sqlalchemy.ext.associationproxy import association_proxy from ....blueprints.board.authorization import ( BoardPermission, BoardTopicPermission, ) from ....database import BaseQuery, db, generate_uuid from ....typing import UserID from ....util.instances import ReprBuilder from ...authentication.session.models.current_user import CurrentUser from ...user.models.user import User from ..transfer.models import CategoryID from .category import Category class TopicQuery(BaseQuery): def for_category(self, category_id: CategoryID) -> BaseQuery: return self.filter_by(category_id=category_id) def only_visible_for_user(self, user: CurrentUser) -> BaseQuery: """Only return topics the user may see.""" if not user.has_permission(BoardPermission.view_hidden): return self.without_hidden() return self def without_hidden(self) -> BaseQuery: """Only return topics every user may see.""" return self.filter(Topic.hidden == False) class Topic(db.Model): """A topic.""" __tablename__ = 'board_topics' query_class = TopicQuery id = db.Column(db.Uuid, default=generate_uuid, primary_key=True) category_id = db.Column(db.Uuid, db.ForeignKey('board_categories.id'), index=True, nullable=False) category = db.relationship(Category) created_at = db.Column(db.DateTime, default=datetime.utcnow, nullable=False) creator_id = db.Column(db.Uuid, db.ForeignKey('users.id'), nullable=False) title = db.Column(db.UnicodeText, nullable=False) posting_count = db.Column(db.Integer, default=0, nullable=False) last_updated_at = db.Column(db.DateTime, default=datetime.utcnow) last_updated_by_id = db.Column(db.Uuid, db.ForeignKey('users.id')) last_updated_by = db.relationship(User, foreign_keys=[last_updated_by_id]) hidden = db.Column(db.Boolean, default=False, nullable=False) hidden_at = db.Column(db.DateTime) hidden_by_id = db.Column(db.Uuid, db.ForeignKey('users.id')) hidden_by = db.relationship(User, foreign_keys=[hidden_by_id]) locked = db.Column(db.Boolean, default=False, nullable=False) locked_at = db.Column(db.DateTime) locked_by_id = db.Column(db.Uuid, db.ForeignKey('users.id')) locked_by = db.relationship(User, foreign_keys=[locked_by_id]) pinned = db.Column(db.Boolean, default=False, nullable=False) pinned_at = db.Column(db.DateTime) pinned_by_id = db.Column(db.Uuid, db.ForeignKey('users.id')) pinned_by = db.relationship(User, foreign_keys=[pinned_by_id]) initial_posting = association_proxy('initial_topic_posting_association', 'posting') posting_limited_to_moderators = db.Column(db.Boolean, default=False, nullable=False) def __init__( self, category_id: CategoryID, creator_id: UserID, title: str ) -> None: self.category_id = category_id self.creator_id = creator_id self.title = title def may_be_updated_by_user(self, user: CurrentUser) -> bool: return ( ( not self.locked and user.id == self.creator_id and user.has_permission(BoardTopicPermission.update) ) or user.has_permission(BoardPermission.update_of_others) ) @property def reply_count(self) -> int: return self.posting_count - 1 def count_pages(self, postings_per_page: int) -> int: """Return the number of pages this topic spans.""" full_page_count, remaining_postings = divmod( self.posting_count, postings_per_page ) if remaining_postings > 0: return full_page_count + 1 else: return full_page_count def __eq__(self, other) -> bool: return self.id == other.id def __repr__(self) -> str: builder = ReprBuilder(self) \ .add_with_lookup('id') \ .add('category', self.category.title) \ .add_with_lookup('title') if self.hidden: builder.add_custom(f'hidden by {self.hidden_by.screen_name}') if self.locked: builder.add_custom(f'locked by {self.locked_by.screen_name}') if self.pinned: builder.add_custom(f'pinned by {self.pinned_by.screen_name}') return builder.build()

[ "[email protected]" ]

[email protected]

a4315be2234838908f42b3d2d0d042647f384c92

a80884040ce1c178274a3068d216f440dd541844

/tests/regression/test_tee_map_completion.py

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maki-nage/rxsci

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refs/heads/master

2023-01-19T14:32:11.638497

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import rx import rx.operators as ops import rxsci as rs def test_completion(): data = [1, 2, 3] actual_data = [] actual_completed = [] rx.from_(data).pipe( rs.ops.tee_map( ops.count(), rs.math.sum(reduce=True), ) ).subscribe( on_next=actual_data.append, on_completed=lambda: actual_completed.append(True) ) assert actual_completed == [True] assert actual_data == [(3, 6)]

[ "[email protected]" ]

[email protected]

28d7d37773b000b74a0651e75715b1992064c925

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/python_basics/python_fundamentals/functionsIntermediate/functionsIntermediate1.py

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eDiazGtz/pythonLearning

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import random # random.random() returns a random floating number between 0.000 and 1.000 # random.random() * 50 returns a random floating number between 0.000 and 50.000 # random.random() * 25 + 10 returns a random floating number between 10.000 and 35.000 # round(num) returns the rounded integer value of num 0.5 round up #print(randInt()) # should print a random integer between 0 to 100 #print(randInt(max=50)) # should print a random integer between 0 to 50 #print(randInt(min=50)) # should print a random integer between 50 to 100 #print(randInt(min=50, max=500)) # should print a random integer between 50 and 500 def randInt(min=0, max=100): range = max - min if(range < 0): return "Min must be less than Max; Max must be greater than 0" num = round(random.random() * range + min) return num print(randInt()) # should print a random integer between 0 to 100 print(randInt(max=50)) # should print a random integer between 0 to 50 print(randInt(min=50)) # should print a random integer between 50 to 100 print(randInt(min=50, max=500)) # should print a random integer between 50 and 500

[ "[email protected]" ]

[email protected]

4af6502a710b7d7100d9f5e384a09128caf93cb0

576cc83449e10fd3f98281970c46016ea7a5aea2

/Cameo/filter.py

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[]

no_license

HotView/PycharmProjects

215ab9edd341e3293daebcf86d97537f8cd28d75

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refs/heads/master

2020-06-02T07:41:53.608742

2019-11-13T08:31:57

191,085,178

3

2

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Python

false

46

py

import cv2 import numpy as np import dateutil

[ "[email protected]" ]

[email protected]

c8770ff0014c49e8aef32a4df572380d038204df

23f3349e8b50f0cb3e461bbd65c1ea8dec792d0b

/2_semestr/lec_05.py

683d1a2c6fabbc138893e0adaa2d19cb1db944a8

[]

no_license

JacobLutin/Python_bmstu

d17866dbab0e74f0f9d600c4dbd9d53eb5c5b7be

66fd8679de7556978b9cd1e9fd8646a8d7d6daa8

refs/heads/master

2020-05-29T14:40:09.310602

2017-03-27T05:18:58

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import numpy as np a = np.arange(12) a1 = np.copy(a) print("Исходная матрицы") a2 = np.reshape(a1, (3, 4)) print(a2, '\n') a2 = a2.T print("Транспонированная матрица") print(a2, '\n') #min, max, sum, сортировка b = np.array([[2, 8, 0], [6, 1, 3], [4, 7, 5]]) print("Новая исходная матрица\n", b, '\n') dsum = b.sum() dmin = b.min() dmax = b.max() print('Некоторые значения для всей матрицы') print('sum=', dsum, ' min=', dmin, ' max=', dmax, '\n') mincol = b.min(axis=0) maxrow = b.max(axis=1) print('Значения min и max для столбцов и строк') print('min в столбцах = ', mincol, ' max в строках = ', maxrow, '\n') # Функция sort описание # sort(axis=-1, kind='quicksort', order=None) # axis - ось, по которой идет сортировка. # kind - тпи сортировки. Возможные значения 'quicksort', 'mergesort', 'heapsort' c = b.copy() c.sort(axis=0, kind='mergesort') print('Сортировка столбцов\n', c) print() c = b.copy() c.sort(axis=1, kind='mergesort') print('Сортировка строк\n', c) print()

[ "[email protected]" ]

[email protected]

aa0c4ed9935283356909351e267db22a24e3bf0b

913e24ea110f839c73363bc1aac9673e561fa5f8

/gym_environments/widowx_env/envs/v41_widowx_pybullet_env_fixedGoal_actionStepCoeff25_noBounds_1joint_reward16.py

08772e2ee4242328bac40c5c52cdb93a98e9dff2

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PierreExeter/WidowX-reacher

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import gym from gym import error, spaces, utils from gym.utils import seeding from numbers import Number from collections import OrderedDict import pybullet as p import pybullet_data import os import numpy as np import random # ENVIRONMENT CONFIGURATION NEUTRAL_VALUES = [0.015339807878856412, -1.4839419194602816, 1.4971652489763858, -0.008369006790373335, -0.08692557798018634, .027] RESET_VALUES = [0.015339807878856412, -1.2931458041875956, 1.0109710760673565, -1.3537670644267164, -0.07158577010132992, .027] # AFTER 60 timesteps, moving the joint 2 by 0.01, we reach this tip position: [0.13962698, 0.00214202, 0.31920969] # action: 0.01 # obs: [ 0.13962698 0.00214202 0.31920969 -0.69314635] # reward: -0.009033412729705663 # done: False # info: {'total_distance': 0.0950442672111562, 'goal position': array([0.20422488, 0.00313302, 0.24949928]), 'tip position': array([0.13962698, 0.00214202, 0.31920969]), 'joint position': array([-0.69314635]), 'current_joint_pos': array([-0.70314634], dtype=float32), 'new_joint_pos': array([-0.69314635], dtype=float32), 'joint_vel': array([0.], dtype=float32)} # timestep: 59 # MINIMUM ACHIEVABLE DISTANCE BY PYBULLET: 4.9471871525143285e-09 m. So it is possible to reach 5e-4 (smallest success ratio) # action: 0.01 # obs: [ 0.13962698 0.00214202 0.31920969 -0.69314635] # reward: -2.447466072200283e-17 # done: False # info: {'total_distance': 4.9471871525143285e-09, 'goal position': array([0.13962698, 0.00214202, 0.31920969]), 'tip position': array([0.13962698, 0.00214202, 0.31920969]), 'joint position': array([-0.69314635]), 'current_joint_pos': array([-0.70314634], dtype=float32), 'new_joint_pos': array([-0.69314635], dtype=float32), 'joint_vel': array([0.], dtype=float32)} # timestep: 59 # RL BOUNDS BOUNDS_XMIN = -100 BOUNDS_XMAX = 100 BOUNDS_YMIN = -100 BOUNDS_YMAX = 100 BOUNDS_ZMIN = -100 BOUNDS_ZMAX = 100 # JOINT_MIN = np.array([ # -3.1, # -1.571, # -1.571, # -1.745, # -2.617, # 0.003 # ]) # JOINT_MAX = np.array([ # 3.1, # 1.571, # 1.571, # 1.745, # 2.617, # 0.03 # ]) # only use joint 2 JOINT_MIN = -1.571 JOINT_MAX = 1.571 JOINT_NAMES = ['joint_1', 'joint_2', 'joint_3', 'joint_4', 'joint_5', 'gripper_joint'] SIM_START_POSITION = np.array([-0.185033226409, 0.00128528, 0.46227163]) class WidowxEnv(gym.Env): metadata = {'render.modes': ['human']} def __init__(self): """ How to initialize this environment: env = gym.make('replab-v0').start_rospy(goal_oriented=[GOAL_ORIENTED]) If goal_oriented is true, then the environment's observations become a dict and the goal is randomly resampled upon every reset params: goal_oriented: Changes some aspects of the environment for goal-oriented tasks rospy.init_node is set with random number so we can have multiple nodes running at the same time. self.goal is set to a fixed, randomly drawn goal if goal_oriented = False """ # self.obs_space_low = np.array( # [-.16, -.15, 0.14, -3.1, -1.6, -1.6, -1.8, -3.1, 0]) # self.obs_space_high = np.array( # [.16, .15, .41, 3.1, 1.6, 1.6, 1.8, 3.1, 0.05]) # observation_space = spaces.Box( # low=self.obs_space_low, high=self.obs_space_high, dtype=np.float32) # self.observation_space = observation_space # pierre: reduce observation space self.obs_space_low = np.array( [-.16, -.15, 0.14, -1.6]) self.obs_space_high = np.array( [.16, .15, .41, 1.6]) observation_space = spaces.Box( low=self.obs_space_low, high=self.obs_space_high, dtype=np.float32) self.observation_space = observation_space # added by Pierre, normalize action space, cf https://stable-baselines.readthedocs.io/en/master/guide/rl_tips.html # self.action_space = spaces.Box(low=np.array([-0.5, -0.25, -0.25, -0.25, -0.5, -0.005]) / 25, # high=np.array([0.5, 0.25, 0.25, 0.25, 0.5, 0.005]) / 25, dtype=np.float32) # changed by Pierre: only move joint 2 self.action_space = spaces.Box(low=np.array([-0.01]), high=np.array([0.01]), dtype=np.float32) # PB: actions are too big and the robot moves too much # self.action_space = spaces.Box(low=np.array([-1, -1, -1, -1, -1, -1]), # high=np.array([1, 1, 1, 1, 1, 1]), dtype=np.float32) self.current_pos = None # self.goal = np.array([-.14, -.13, 0.26]) self.goal = np.array([0.13962698, 0.00214202, 0.31920969]) #[.14, .0, 0.26]) # added by Pierre: changed to feasible target by moving only joint 2 # self.set_goal(self.sample_goal_for_rollout()) # print("********goal is : ***********", self.goal) self.start_sim(goal_oriented=False, render_bool=True) # re-added by Pierre def start_sim(self, goal_oriented=False, render_bool=False): self.render_bool = render_bool self.goal_oriented = goal_oriented if self.render_bool: self.physics_client = p.connect(p.GUI) else: self.physics_client = p.connect(p.DIRECT) if self.goal_oriented: self.observation_space = spaces.Dict(dict( desired_goal=spaces.Box(low=np.array( [-.16, -.15, 0.25]), high=np.array([.16, .15, 0.41]), dtype=np.float32), achieved_goal=spaces.Box(low=self.obs_space_low[ :3], high=self.obs_space_high[:3], dtype=np.float32), observation=self.observation_space )) # p.resetSimulation() # p.setTimeStep(0.01) p.resetDebugVisualizerCamera(cameraDistance=0.6, cameraYaw=0, cameraPitch=-30, cameraTargetPosition=[ 0.2, 0, 0.1], physicsClientId=self.physics_client) # added by Pierre p.setAdditionalSearchPath(pybullet_data.getDataPath()) path = os.path.abspath(os.path.dirname(__file__)) self.arm = p.loadURDF(os.path.join(path, "URDFs/widowx/widowx.urdf"), useFixedBase=True) self.sphere = p.loadURDF(os.path.join(path, "URDFs/sphere.urdf"), useFixedBase=True) # added by Pierre self.plane = p.loadURDF('plane.urdf') # added by Pierre self.reset() return self # shared functions between both sim and robot mode def sample_goal_for_rollout(self): return np.random.uniform(low=np.array([-.14, -.13, 0.26]), high=np.array([.14, .13, .39])) def set_goal(self, goal): self.goal = goal def step(self, action): """ Parameters ---------- action : [change in x, change in y, change in z] Returns ------- ob, reward, episode_over, info : tuple ob (object) : either current position or an observation object, depending on the type of environment this is representing reward (float) : negative, squared, l2 distance between current position and goal position episode_over (bool) : Whether or not we have reached the goal info (dict) : For now, all this does is keep track of the total distance from goal. This is used for rlkit to get the final total distance after evaluation. See function get_diagnostics for more info. """ action = np.array(action, dtype=np.float32) self.my_action = action # added by Pierre # modified by Pierre self.joint_positions, self.joint_velocities = self._get_current_joint_positions() self.new_joint_positions = self.joint_positions + action self.new_joint_positions = np.clip(np.array(self.new_joint_positions), JOINT_MIN, JOINT_MAX) self._force_joint_positions_training(self.new_joint_positions) # joint_positions = self._get_current_joint_positions() # new_joint_positions = joint_positions + action # new_joint_positions = np.clip(np.array(new_joint_positions), JOINT_MIN, JOINT_MAX) # self._force_joint_positions(new_joint_positions) end_effector_pos = self._get_current_end_effector_position() x, y, z = end_effector_pos[0], end_effector_pos[1], end_effector_pos[2] conditions = [ x <= BOUNDS_XMAX, x >= BOUNDS_XMIN, y <= BOUNDS_YMAX, y >= BOUNDS_YMIN, z <= BOUNDS_ZMAX, z >= BOUNDS_ZMIN ] violated_boundary = False for condition in conditions: if not condition: violated_boundary = True break if violated_boundary: # if out of boundarie, don't update joint position self._force_joint_positions_training(self.joint_positions) self.current_pos = self._get_current_state() return self._generate_step_tuple() def _generate_step_tuple(self): episode_over = False self.total_distance_from_goal = np.linalg.norm(self.current_pos[:3] - self.goal) # np.sqrt(-reward) reward = self._get_reward(self.goal) # self.tip_vel = self._get_current_end_effector_velocity() # added by Pierre info = {} info['total_distance'] = self.total_distance_from_goal info['goal position'] = self.goal info['tip position'] = self.current_pos[:3] info['joint position'] = self.current_pos[3:] info['current_joint_pos'] = self.joint_positions info['new_joint_pos'] = self.new_joint_positions info['joint_vel'] = self.joint_velocities info['penalty'] = self.penalty # info['tip_vel'] = self.tip_vel # if reward > -0.0001: # if total_distance_from_goal < 0.0005: # added by Pierre # episode_over = True if self.goal_oriented: obs = self._get_obs() return obs, reward, episode_over, info return self.current_pos, reward, episode_over, info def reset(self): p.resetBasePositionAndOrientation( self.arm, [0, 0, 0], p.getQuaternionFromEuler([np.pi, np.pi, np.pi])) p.resetBasePositionAndOrientation(self.sphere, self.goal, p.getQuaternionFromEuler( [np.pi, np.pi, np.pi])) # added by Pierre: move sphere to self.goal position self._force_joint_positions(RESET_VALUES) self.current_pos = self._get_current_state() # commented by Pierre: don't re-sample new goal if self.goal_oriented: # self.set_goal(self.sample_goal_for_rollout()) return self._get_obs() return self.current_pos def _get_obs(self): obs = {} obs['observation'] = self.current_pos obs['desired_goal'] = self.goal obs['achieved_goal'] = self.current_pos[:3] return obs def sample_goals(self, num_goals): sampled_goals = np.array( [self.sample_goal_for_rollout() for i in range(num_goals)]) goals = {} goals['desired_goal'] = sampled_goals return goals def _get_reward(self, goal): self.beta = 10 self.penalty = self.beta * np.linalg.norm(self.my_action) rew = - self.total_distance_from_goal - self.penalty return rew def render(self, mode='human', close=False): pass def compute_reward(self, achieved_goal, goal, info): return - (np.linalg.norm(achieved_goal - goal)**2) def get_diagnostics(self, paths): """ This adds the diagnostic "Final Total Distance" for RLkit """ def get_stat_in_paths(paths, dict_name, scalar_name): if len(paths) == 0: return np.array([[]]) if type(paths[0][dict_name]) == dict: return [path[dict_name][scalar_name] for path in paths] return [[info[scalar_name] for info in path[dict_name]] for path in paths] def create_stats_ordered_dict( name, data, stat_prefix=None, always_show_all_stats=True, exclude_max_min=False, ): if stat_prefix is not None: name = "{} {}".format(stat_prefix, name) if isinstance(data, Number): return OrderedDict({name: data}) if len(data) == 0: return OrderedDict() if isinstance(data, tuple): ordered_dict = OrderedDict() for number, d in enumerate(data): sub_dict = create_stats_ordered_dict( "{0}_{1}".format(name, number), d, ) ordered_dict.update(sub_dict) return ordered_dict if isinstance(data, list): try: iter(data[0]) except TypeError: pass else: data = np.concatenate(data) if (isinstance(data, np.ndarray) and data.size == 1 and not always_show_all_stats): return OrderedDict({name: float(data)}) stats = OrderedDict([ (name + ' Mean', np.mean(data)), (name + ' Std', np.std(data)), ]) if not exclude_max_min: stats[name + ' Max'] = np.max(data) stats[name + ' Min'] = np.min(data) return stats statistics = OrderedDict() stat_name = 'total_distance' stat = get_stat_in_paths(paths, 'env_infos', stat_name) statistics.update(create_stats_ordered_dict('Final %s' % (stat_name), [ s[-1] for s in stat], always_show_all_stats=True,)) return statistics # Functions only for sim mode def _get_current_joint_positions(self): # joint_positions = [] # joint_velocities = [] # added by Pierre # for i in range(6): # joint_positions.append(p.getJointState(self.arm, i)[0]) # check that's the joint angle # joint_velocities.append(p.getJointState(self.arm, i)[1]) # added by Pierre # return np.array(joint_positions, dtype=np.float32), np.array(joint_velocities, dtype=np.float32) joint_positions = [] joint_velocities = [] # added by Pierre # only return position of joint 2 joint_positions.append(p.getJointState(self.arm, 1)[0]) # check that's the joint angle joint_velocities.append(p.getJointState(self.arm, 1)[1]) # added by Pierre return np.array(joint_positions, dtype=np.float32), np.array(joint_velocities, dtype=np.float32) def _get_current_end_effector_position(self): real_position = np.array(list(p.getLinkState(self.arm, 5, computeForwardKinematics=1)[4])) # real_position[2] = -real_position[2] #SIM z coordinates are reversed # adjusted_position = real_position + SIM_START_POSITION return real_position # added by Pierre def _get_current_end_effector_velocity(self): real_vel = np.array( list(p.getLinkState(self.arm, 5, computeLinkVelocity=1, computeForwardKinematics=1)[6])) return real_vel def _set_joint_positions(self, joint_positions): # In SIM, gripper halves are controlled separately joint_positions = list(joint_positions) + [joint_positions[-1]] p.setJointMotorControlArray( self.arm, [0, 1, 2, 3, 4, 7, 8], controlMode=p.POSITION_CONTROL, targetPositions=joint_positions ) # original function: only used at reset def _force_joint_positions(self, joint_positions): for i in range(5): p.resetJointState( self.arm, i, joint_positions[i] ) for i in range(7, 9): p.resetJointState( self.arm, i, joint_positions[-1] ) def _force_joint_positions_training(self, joint_positions): p.resetJointState( self.arm, 1, joint_positions[0] ) def _get_current_state(self): return np.concatenate( [self._get_current_end_effector_position(), self._get_current_joint_positions()[0]], axis=0) # Functions for pickling def __getstate__(self): state = self.__dict__.copy() return state def __setstate__(self, state): self.__dict__.update(state) if state['render_bool']: self.start_sim(goal_oriented=state['goal_oriented'], render_bool=False) else: self.start_sim(goal_oriented=state['goal_oriented'], render_bool=state['render_bool']) self.reset()

[ "[email protected]" ]

[email protected]

dcf32321584fe37884e0f4817db5a71e31b2c2c1

b0c2f67b2878a312c6e6ffa5fe4158bd55dad69c

/chap4/exp4.1.py

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loukey/pythonDemo

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# -*- coding: utf-8 -*- #例4.1简单地打印数字 #很有趣的是,Python没有do..while以及while..until #但是我们也可以强行实现 while True: print 'Please enter a number:' number = input() print number if number==0: break print 'List Ended'

[ "[email protected]" ]

[email protected]

bac598ef7573c63aeb9e39fc13f67f09c8edb748

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/addons/training_doc/training_doc.py

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/home/openerp/production/extra-addons/training_doc/training_doc.py

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[email protected]

e8cb6b230d208935d065fedcf70f0c591e8ba666

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/examples/sharecuts.py

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shuxiaokai/looter

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""" 捷径社区的捷径排行榜 """ from pprint import pprint import requests import looter as lt domain = 'https://sharecuts.cn' total = [] def crawl(url): items = requests.get(url, headers=lt.DEFAULT_HEADERS).json() for item in items: data = {} data['name'] = item['name'] data['category'] = item['Category']['name'] data['note'] = item['note'] data['author'] = item['User']['nickname'] data['url'] = item['url'] data['downloads'] = item['downloads_count'] data['votes'] = item['votes_count'] data['comments'] = item['comments_count'] pprint(data) total.append(data) if __name__ == '__main__': task = f'{domain}/api/shortcuts/hot?offset=0&limit=1025' crawl(task) lt.save(total, name='sharecuts.csv', sort_by='votes', order='desc')

[ "[email protected]" ]

[email protected]

6075bf7ba52d2c689ce7e8d799b2bdfa2bb43e1b

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/flat_sharp/interpolation.py

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import numpy as np from utils import * from data_getters import * from postprocessing import * import copy import torch def interpolate_models(model1, model2, beta): params1 = model1.named_parameters() params2 = model2.named_parameters() new_model = copy.deepcopy(model2) new_params = new_model.named_parameters() dict_new_params = dict(new_params) for name1, param1 in params1: if name1 in dict_new_params: dict_new_params[name1].data.copy_((1. - beta) * param1.data + beta * dict_new_params[name1].data) return new_model def scale_output_model(model1, alpha): if isinstance(model1, LeNet): last_layer_names = ["fc3.weight", "fc3.bias"] else: last_layer_names = ["fc2.weight", "fc2.bias"] params1 = model1.named_parameters() new_model = copy.deepcopy(model1) new_params = new_model.named_parameters() dict_new_params = dict(new_params) for name1, param1 in params1: if name1 in last_layer_names: dict_new_params[name1].data.copy_(alpha * param1.data) return new_model def T_alpha_models(model, num_inter_models, alpha_range): inter_models_arr = [] alphas = np.linspace(alpha_range[0], alpha_range[1], num_inter_models) for alpha in alphas: params1 = model.named_parameters() new_model = copy.deepcopy(model) new_params = new_model.named_parameters() dict_new_params = dict(new_params) for name1, param1 in params1: if name1 in dict_new_params: dict_new_params[name1].data.copy_((1. - beta) * param1.data + beta * dict_new_params[name1].data) inter_models_arr.append(curr_model) return inter_models_arr return new_model def get_loss_grad(net, criterion, data): inputs, labels = data # Compute gradients for input. inputs.requires_grad = True net.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs.float(), labels) loss.backward(retain_graph=True) param_grads = get_grad_params_vec(net) return loss, torch.norm(param_grads) def get_model_interpolate_arr(model_a, model_b, num_inter_models, beta_bound=None): inter_models_arr = [] if beta_bound is None: beta_bound = [0, 1] betas = np.linspace(beta_bound[0], beta_bound[1], num_inter_models) for beta in betas: curr_model = interpolate_models(model_a, model_b, beta) inter_models_arr.append(curr_model) return inter_models_arr def get_model_interpolate_2d(offset, v1, v2, num_inter_models, alpha_bound, beta_bound, func): X = np.linspace(alpha_bound[0], alpha_bound[1], num_inter_models) Y = np.linspace(beta_bound[0], beta_bound[1], num_inter_models) v1_net = vec_to_net(v1, offset) v2_net = vec_to_net(v2, offset) v1_dict = dict(v1_net.named_parameters()) v2_dict = dict(v2_net.named_parameters()) val_arr = [] for x in X: curr_arr = [] for y in Y: curr_model = copy.deepcopy(offset) dict_curr_model = dict(curr_model.named_parameters()) for name1, param1 in offset.named_parameters(): dict_curr_model[name1].data.copy_(dict_curr_model[name1].data + x * v1_dict[name1].data + y * v2_dict[name1].data) to_append = func(curr_model) curr_arr.append(to_append) val_arr.append(curr_arr) return val_arr def project_onto(net, v1, v2, offset): v1_norm = v1 / torch.norm(v1) v2_norm = v2 / torch.norm(v2) net_vect = get_params_vec(net) - get_params_vec(offset) alpha = torch.matmul(v1_norm, net_vect) beta = torch.matmul(v2_norm, net_vect) return alpha, beta def take_n_gd_steps(net, optimizer, criterion, data, n=1, get_grad=True, v1=None, v2=None, offset=None): grads_arr = [] projections = [] if (v1 is not None) and (v2 is not None): projections.append(project_onto(net, v1, v2, offset)) for _ in range(n): inputs, labels = data # Compute gradients for input. inputs.requires_grad = True # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs.float(), labels) loss.backward(retain_graph=True) optimizer.step() if (_ % 100) == 0: print(_) print(loss) print() if get_grad: grads_arr.append(get_grad_params_vec(net)) if (v1 is not None) and (v2 is not None): projections.append(project_onto(net, v1, v2, offset)) return net, grads_arr, projections def do_the_do(model, optimizer, criterion, data_loader, num_inter_models, num_steps=1, beta_bound=None): data = next(iter(data_loader)) model_a = copy.deepcopy(model) model_b = take_n_gd_steps(model, optimizer, criterion, data, n=num_steps) inter_models = get_model_interpolate_arr(model_a, model_b, num_inter_models, beta_bound=beta_bound) return inter_models exp_id = "1589992134.56161" if __name__ == "__main__": # get data train_data, test_data = get_postprocessing_data(experiment_folder, vectorized=True) train_loader = DataLoader(train_data, batch_size=10000, shuffle=True) # fix the batch size test_loader = DataLoader(test_data, batch_size=len(test_data)) criterion = torch.nn.CrossEntropyLoss() cfs_dict = exp_dict["stuff"]["configs"].loc[exp_id].to_dict() nets = get_nets(cfs_dict) optimizers = get_optimizers(cfs_dict)(nets) inter_nets = [] for nn_idx in range(len(nets)): inter_nets.append(do_the_do(nets[nn_idx], optimizers[nn_idx], criterion, train_loader, 20)) for nn_index in range(len(nets)): y_val = inter_nets[nn_index][1][:, 1] plt.plot(list(range(len(y_val))), y_val) plt.show()

[ "[email protected]" ]

[email protected]

3c2914aeeb137940e884af34f7f4ae1b9a1cb124

306d2a92fb331aec6ddf0794b538d6e3385a0df9

/app/api/news/views.py

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[]

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Zarinabonu/ForceApp

f343d3a52aee08890230c5425c9e238df99c5a7f

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refs/heads/master

2020-12-10T08:00:25.072289

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from rest_framework.generics import ListAPIView from app.api.news.serializers import NewsSerializer from app.model import News class NewsListAPIView(ListAPIView): serializer_class = NewsSerializer def get_queryset(self): qs = News.objects.all() v = self.request.GET.get('-views') l_seen = self.request.GET.get('last_seen') if v: qs = qs.order_by('views') if l_seen: qs = qs.order_by('-created') return qs

[ "[email protected]" ]

[email protected]

40ac13a7d56e1369c096d6b0d9a77961d16430bf

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/input_output/2.py

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no_license

ggyudongggyu/20201208commit

b524c4a7fb241cacaacffa5882c55d1d0ccba11f

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2023-02-02T21:59:51.518218

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in_str = input() print(in_str)

[ "[email protected]" ]

[email protected]

d57f83bffdfb4d3b6a1515261b2d121eadb2561f

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/osmnx/simplify.py

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surfcao/osmnx

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2021-01-19T23:32:40.068378

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################################################################################################### # Module: simplify.py # Description: Simplify and correct network topology # License: MIT, see full license in LICENSE.txt # Web: https://github.com/gboeing/osmnx ################################################################################################### import time import logging as lg from shapely.geometry import Point, LineString from .utils import log def is_endpoint(G, node, strict=True): """ Return True if the node is a "real" endpoint of an edge in the network, otherwise False. OSM data includes lots of nodes that exist only as points to help streets bend around curves. An end point is a node that either: 1) is its own neighbor, ie, it self-loops. 2) or, has no incoming edges or no outgoing edges, ie, all its incident edges point inward or all its incident edges point outward. 3) or, it does not have exactly two neighbors and degree of 2 or 4. 4) or, if strict mode is false, if its edges have different OSM IDs. Parameters ---------- G : networkx multidigraph node : int the node to examine strict : bool if False, allow nodes to be end points even if they fail all other rules but have edges with different OSM IDs Returns ------- bool """ neighbors = set(list(G.predecessors(node)) + list(G.successors(node))) n = len(neighbors) d = G.degree(node) if node in neighbors: # if the node appears in its list of neighbors, it self-loops. this is always an endpoint. return True # if node has no incoming edges or no outgoing edges, it must be an end point elif G.out_degree(node)==0 or G.in_degree(node)==0: return True elif not (n==2 and (d==2 or d==4)): # else, if it does NOT have 2 neighbors AND either 2 or 4 directed edges, it is an endpoint # either it has 1 or 3+ neighbors, in which case it is a dead-end or an intersection of multiple streets # or it has 2 neighbors but 3 degree (indicating a change from oneway to twoway) # or more than 4 degree (indicating a parallel edge) and thus is an endpoint return True elif not strict: # non-strict mode osmids = [] # add all the edge OSM IDs for incoming edges for u in G.predecessors(node): for key in G.edge[u][node]: osmids.append(G.edge[u][node][key]['osmid']) # add all the edge OSM IDs for outgoing edges for v in G.successors(node): for key in G.edge[node][v]: osmids.append(G.edge[node][v][key]['osmid']) # if there is more than 1 OSM ID in the list of edge OSM IDs then it is an endpoint, if not, it isn't return len(set(osmids)) > 1 else: # if none of the preceding rules returned true, then it is not an endpoint return False def build_path(G, node, endpoints, path): """ Recursively build a path of nodes until you hit an endpoint node. Parameters ---------- G : networkx multidigraph node : int the current node to start from endpoints : set the set of all nodes in the graph that are endpoints path : list the list of nodes in order in the path so far Returns ------- paths_to_simplify : list """ # for each successor in the passed-in node for successor in G.successors(node): if not successor in path: # if this successor is already in the path, ignore it, otherwise add it to the path path.append(successor) if not successor in endpoints: # if this successor is not an endpoint, recursively call build_path until you find an endpoint path = build_path(G, successor, endpoints, path) else: # if this successor is an endpoint, we've completed the path, so return it return path if (not path[-1] in endpoints) and (path[0] in G.successors(path[-1])): # if the end of the path is not actually an endpoint and the path's first node is a successor of the # path's final node, then this is actually a self loop, so add path's first node to end of path to close it path.append(path[0]) return path def get_paths_to_simplify(G, strict=True): """ Create a list of all the paths to be simplified between endpoint nodes. The path is ordered from the first endpoint, through the interstitial nodes, to the second endpoint. Parameters ---------- G : networkx multidigraph strict : bool if False, allow nodes to be end points even if they fail all other rules but have edges with different OSM IDs Returns ------- paths_to_simplify : list """ # first identify all the nodes that are endpoints start_time = time.time() endpoints = set([node for node in G.nodes() if is_endpoint(G, node, strict=strict)]) log('Identified {:,} edge endpoints in {:,.2f} seconds'.format(len(endpoints), time.time()-start_time)) start_time = time.time() paths_to_simplify = [] # for each endpoint node, look at each of its successor nodes for node in endpoints: for successor in G.successors(node): if not successor in endpoints: # if the successor is not an endpoint, build a path from the endpoint node to the next endpoint node try: path = build_path(G, successor, endpoints, path=[node, successor]) paths_to_simplify.append(path) except RuntimeError: log('Recursion error: exceeded max depth, moving on to next endpoint successor', level=lg.WARNING) # recursion errors occur if some connected component is a self-contained ring in which all nodes are not end points # handle it by just ignoring that component and letting its topology remain intact (this should be a rare occurrence) # RuntimeError is what Python <3.5 will throw, Py3.5+ throws RecursionError but it is a subtype of RuntimeError so it still gets handled log('Constructed all paths to simplify in {:,.2f} seconds'.format(time.time()-start_time)) return paths_to_simplify def is_simplified(G): """ Determine if a graph has already had its topology simplified. If any of its edges have a geometry attribute, we know that it has previously been simplified. Parameters ---------- G : networkx multidigraph Returns ------- bool """ edges_with_geometry = [d for u, v, k, d in G.edges(data=True, keys=True) if 'geometry' in d] return len(edges_with_geometry) > 0 def simplify_graph(G_, strict=True): """ Simplify a graph's topology by removing all nodes that are not intersections or dead-ends. Create an edge directly between the end points that encapsulate them, but retain the geometry of the original edges, saved as attribute in new edge Parameters ---------- G_ : graph strict : bool if False, allow nodes to be end points even if they fail all other rules but have edges with different OSM IDs Returns ------- networkx multidigraph """ if is_simplified(G_): raise Exception('This graph has already been simplified, cannot simplify it again.') log('Begin topologically simplifying the graph...') G = G_.copy() initial_node_count = len(list(G.nodes())) initial_edge_count = len(list(G.edges())) all_nodes_to_remove = [] all_edges_to_add = [] # construct a list of all the paths that need to be simplified paths = get_paths_to_simplify(G, strict=strict) start_time = time.time() for path in paths: # add the interstitial edges we're removing to a list so we can retain their spatial geometry edge_attributes = {} for u, v in zip(path[:-1], path[1:]): # there shouldn't be multiple edges between interstitial nodes edges = G.edge[u][v] if not len(edges) == 1: log('Multiple edges between "{}" and "{}" found when simplifying'.format(u, v), level=lg.WARNING) # the only element in this list as long as above assertion is True (MultiGraphs use keys (the 0 here), indexed with ints from 0 and up) edge = edges[0] for key in edge: if key in edge_attributes: # if this key already exists in the dict, append it to the value list edge_attributes[key].append(edge[key]) else: # if this key doesn't already exist, set the value to a list containing the one value edge_attributes[key] = [edge[key]] for key in edge_attributes: # don't touch the length attribute, we'll sum it at the end if len(set(edge_attributes[key])) == 1 and not key == 'length': # if there's only 1 unique value in this attribute list, consolidate it to the single value (the zero-th) edge_attributes[key] = edge_attributes[key][0] elif not key == 'length': # otherwise, if there are multiple values, keep one of each value edge_attributes[key] = list(set(edge_attributes[key])) # construct the geometry and sum the lengths of the segments edge_attributes['geometry'] = LineString([Point((G.node[node]['x'], G.node[node]['y'])) for node in path]) edge_attributes['length'] = sum(edge_attributes['length']) # add the nodes and edges to their lists for processing at the end all_nodes_to_remove.extend(path[1:-1]) all_edges_to_add.append({'origin':path[0], 'destination':path[-1], 'attr_dict':edge_attributes}) # for each edge to add in the list we assembled, create a new edge between the origin and destination for edge in all_edges_to_add: G.add_edge(edge['origin'], edge['destination'], **edge['attr_dict']) # finally remove all the interstitial nodes between the new edges G.remove_nodes_from(set(all_nodes_to_remove)) msg = 'Simplified graph (from {:,} to {:,} nodes and from {:,} to {:,} edges) in {:,.2f} seconds' log(msg.format(initial_node_count, len(list(G.nodes())), initial_edge_count, len(list(G.edges())), time.time()-start_time)) return G

[ "[email protected]" ]

[email protected]

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from collections import Counter input() A=list(map(int,input().split())) A.sort(reverse=True) C=Counter(A) ans=0 for a in A: if C[a]==0: continue C[a]-=1 t=2**a.bit_length()-a if C[t]: C[t]-=1 ans+=1 print(ans)

[ "[email protected]" ]

[email protected]

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Daransoto/holbertonschool-higher_level_programming

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#!/usr/bin/python3 """ Inserts Louisiana state. """ import sys from sqlalchemy import create_engine from model_state import Base, State from sqlalchemy.orm import sessionmaker if __name__ == "__main__": engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}' .format(sys.argv[1], sys.argv[2], sys.argv[3]), pool_pre_ping=True) Base.metadata.create_all(engine) Session = sessionmaker(bind=engine) session = Session() New = State(name="Louisiana") session.add(New) session.commit() print(New.id) session.close()

[ "[email protected]" ]

[email protected]

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#!/usr/bin/env python3 # Copyright (c) 2004-present Facebook All rights reserved. # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. from typing import List from ..client import SymphonyClient from ..consts import Entity, User from ..exceptions import EntityNotFoundError from ..graphql.edit_user_input import EditUserInput from ..graphql.edit_user_mutation import EditUserMutation from ..graphql.user_query import UserQuery from ..graphql.user_status_enum import UserStatus from ..graphql.users_query import UsersQuery USER_ROLE = 1 def get_user(client: SymphonyClient, email: str) -> User: """Returns `pyinventory.consts.User` object by its email Args: email: the email address the user registered with Returns: pyinventory.consts.User object Raises: EntityNotFoundError: the user was not found FailedOperationException: internal inventory error Example: ``` user = client.get_user("[email protected]") ``` """ result = UserQuery.execute(client, email) user = result.user if user is None: raise EntityNotFoundError(entity=Entity.User, entity_name=email) return User( id=user.id, auth_id=user.authID, email=user.email, status=user.status, role=user.role, ) def add_user(client: SymphonyClient, email: str, password: str) -> User: """Adds new user to inventory with its email and password Args: email: the email address of the user password: password the user would connect with Returns: pyinventory.consts.User object Raises: EntityNotFoundError: the user was not created properly FailedOperationException: internal inventory error AssertionError: The user was not created for some known reason HTTPError: Error with connection Example: ``` user = client.add_user("[email protected]", "P0ssW!rd0f43") ``` """ resp = client.post( "/user/async/", {"email": email, "password": password, "role": USER_ROLE, "networkIDs": []}, ) if not resp.ok: error_message = resp.json().get("error", None) if error_message is not None: raise AssertionError(error_message) raise return get_user(client, email) def deactivate_user(client: SymphonyClient, user: User) -> None: """Deactivate the user which would prevent the user from login in to symphony Users in symphony are never deleted. Only de-activated. Args: user: user to deactivate Raises: FailedOperationException: internal inventory error Example: ``` user = client.get_user("[email protected]") client.deactivate_user(user) ``` """ EditUserMutation.execute( client, input=EditUserInput(id=user.id, status=UserStatus.DEACTIVATED) ) def activate_user(client: SymphonyClient, user: User) -> None: """Activate the user which would allow the user to login again to symphony Args: user: user to activate Raises: FailedOperationException: internal inventory error Example: ``` user = client.get_user("[email protected]") client.activate_user(user) ``` """ EditUserMutation.execute( client, input=EditUserInput(id=user.id, status=UserStatus.ACTIVE) ) def get_users(client: SymphonyClient) -> List[User]: """Get the list of users in the system (both active and deactivate) Returns: list of `pyinventory.consts.User` objects Raises: FailedOperationException: internal inventory error Example: ``` users = client.get_users() for user in users: print(user.email) ``` """ result = UsersQuery.execute(client).users if result is None: return [] users = [] for edge in result.edges: node = edge.node if node is not None: users.append( User( id=node.id, auth_id=node.authID, email=node.email, status=node.status, role=node.role, ) ) return users def get_active_users(client: SymphonyClient) -> List[User]: """Get the list of the active users in the system Returns: list of `pyinventory.consts.User` objects Raises: FailedOperationException: internal inventory error Example: ``` users = client.get_active_users() for user in users: print(user.email) ``` """ users = get_users(client) return [user for user in users if user.status == UserStatus.ACTIVE]

[ "[email protected]" ]

[email protected]

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/xai/brain/wordbase/verbs/_cryings.py

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cash2one/xai

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from xai.brain.wordbase.verbs._cry import _CRY #calss header class _CRYINGS(_CRY, ): def __init__(self,): _CRY.__init__(self) self.name = "CRYINGS" self.specie = 'verbs' self.basic = "cry" self.jsondata = {}

[ "[email protected]" ]

[email protected]

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/wildfire/pandas_cut_test.py

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MillerWu2014/remote_project

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refs/heads/master

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import pandas as pd x = pd.Series(range(100)) print(pd.cut(x, 50, labels=range(50)))

[ "[email protected]" ]

[email protected]

ca010878792d0bc73fec72213f7db9f251dfd0e5

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/xai/brain/wordbase/nouns/_bandaged.py

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cash2one/xai

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2021-01-19T12:33:54.964379

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from xai.brain.wordbase.nouns._bandage import _BANDAGE #calss header class _BANDAGED(_BANDAGE, ): def __init__(self,): _BANDAGE.__init__(self) self.name = "BANDAGED" self.specie = 'nouns' self.basic = "bandage" self.jsondata = {}

[ "[email protected]" ]

[email protected]

cd846f89d90d6f2f5ce61fa895e49409d4e39009

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/cloudstoragetui/keypress.py

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import curses from typing import List from cloudstoragetui.constants import KEY_QUIT, KEY_UP, KEY_DOWN, KEY_LEFT, KEY_RIGHT, KEY_ENTER, ESC, UP, DOWN, LEFT, RIGHT from cloudstoragetui.draw import DrawnBox from cloudstoragetui.cursor_state import CursorState from cloudstoragetui.debug import log def _extract_min_max(box): min_y = box.top_left_y + 1 min_x = box.top_left_x + 1 max_y = box.length_y + box.top_left_y - 2 max_x = (box.index + 1) * box.length_x - 1 return (min_y, min_x, max_y, max_x) def _eval_keypress(screen, key, boxes, cursor_state): curs_y, curs_x = curses.getsyx() box = boxes[cursor_state.column] min_y, min_x, max_y, max_x = _extract_min_max(box) action = None if key in KEY_QUIT: action = ESC elif key in KEY_UP: cursor_state.move_row_up(min_y) screen.move(max(curs_y - 1, min_y), curs_x) action = UP elif key in KEY_DOWN: cursor_state.move_row_down(max_y) screen.move(min(curs_y + 1, max_y), curs_x) action = DOWN elif key in KEY_LEFT: if curs_x == min_x: cursor_state.move_column_left() box = boxes[cursor_state.column] min_y, min_x, max_y, max_x = _extract_min_max(box) screen.move(min_y, min_x) else: screen.move(curs_y, max(curs_x - 1, min_x)) action = LEFT elif key in KEY_RIGHT + KEY_ENTER: cursor_state.move_column_right() box = boxes[cursor_state.column] screen.move(box.top_left_y + 1, box.top_left_x + 1) action = RIGHT screen.refresh() return action def eval_keypress(screen, key: int, boxes: List[DrawnBox], cursor_state: CursorState): return _eval_keypress(screen, key, boxes, cursor_state)

[ "[email protected]" ]

[email protected]

8020760bc5cd0a1d148739c5991cea3a09beb85f

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/detection_network/src/rl/ppo.py

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zideajang/zi_full_self_drive_system

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import torch import torch.optim as optim class RolloutStorage: def __init__(self,num_steps,num_processes,action_size): pass class PPO(object): def __init__( self, controller, clip_param, lr, baseline_decay, action_size = 18, ppo_epoch=1, num_mini_batch=100, max_grad_norm=2.0, entropy_coef=0, num_steps=100, num_processes=1 ): self.ppo_epoch = ppo_epoch self.controller = controller self.optimizer = optim.Adam(controller.parameters(),lr=lr) self.num_mini_batch = num_mini_batch self.clip_param = clip_param self.max_grad_norm = max_grad_norm self.entropy_coef = entropy_coef self.rollouts = RolloutStorage(num_steps,num_processes,action_size) self.baseline = None self.decay = baseline_decay def state_dict(self): return { "baseline":self.baseline, "rollouts":self.controller.state_dict(), "optimizer:":self.optimizer.state_dict() } def load_state_dict(self,states): pass def update(self, sample, is_train=True): reward, action, log_prob = sample if self.baseline is None: self.baseline = reward else: self.baseline = self.decay * self.baseline + (1 - self.decay) * reward self.rollouts.insert(action, log_prob, reward) if not is_train: return -1,-1 advantages = self.rollouts.rewards - self.baseline loss_epoch = 0 entropy_epoch = 0 for _ in range(self.ppo_epoch): data_generator = self.rollouts.generator(advantages, self.num_mini_batch) for sample in data_generator: ( actions_batch, reward_batch, old_actions_log_probs_batch, adv_targ, ) = sample action_log_probs, entropy = self.controller.evaluate_actions( actions_batch ) ratio = torch.exp( action_log_probs - torch.from_numpy(adv_targ) ) adv_targ_th = torch.from_numpy(adv_targ).float()

[ "[email protected]" ]

[email protected]

1fe3fb6fa971710011542bc58df695cb0c6d7730

c3082eb2adc43b311dd3c9ff16fd3ed9df85f266

/python/examples/fastapi/dynamic-response/main.py

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[]

no_license

szabgab/slides

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refs/heads/main

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from fastapi import FastAPI import datetime app = FastAPI() @app.get("/") async def root(): return {"message": f"Hello World at {datetime.datetime.now()}"}

[ "[email protected]" ]

[email protected]

a9582fe1ff3a16c1aa108f54b5ff1ae3984f5ccb

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/backup/user_171/ch65_2019_06_07_01_24_18_525767.py

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[]

no_license

gabriellaec/desoft-analise-exercicios

b77c6999424c5ce7e44086a12589a0ad43d6adca

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refs/heads/main

2023-01-31T17:19:42.050628

2020-12-16T05:21:31

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def acha_bigramas(string): lista=[] i=0 while i<len(string): if string[i:i+2] not in lista and len(string[i:i+2])>3: lista.append(string[i:i+2]) i+=1 return lista

[ "[email protected]" ]

[email protected]

c5248a3cae6dcafab9c6ad505abc712db1980a72

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/dna-methylation/scripts/develop/illumina450k/residuals/plot/scatter_comparison.py

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[]

no_license

GillianGrayson/dna-methylation

f1a0878f4aa8c917bee9e5230387d6145826fb3a

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refs/heads/master

2022-02-08T03:31:22.423781

2022-02-01T16:50:37

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1

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2020-03-20T18:08:24

2019-01-04T12:30:29

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import pydnameth as pdm import pandas as pd import os.path from scripts.develop.routines import * max_rows = 10 fn = 'scatter_comparison_rows.xlsx' rows_dict = {} if os.path.isfile(fn): df = pd.read_excel(fn) tmp_dict = df.to_dict() for key in tmp_dict: curr_dict = tmp_dict[key] rows_dict[key] = list(curr_dict.values()) fn = 'scatter_comparison_cols.xlsx' cols_dict = {} if os.path.isfile(fn): df = pd.read_excel(fn) tmp_dict = df.to_dict() for key in tmp_dict: curr_dict = tmp_dict[key] cols_dict[key] = list(curr_dict.values()) data_bases = cols_dict['data_bases'] data_list = [] annotations_list = [] attributes_list = [] observables_list = [] data_params_list = [] for data_base in data_bases: data = pdm.Data( path='', base=data_base ) data_list.append(data) annotations = pdm.Annotations( name='annotations', type='450k', exclude='bad_cpgs', select_dict={ 'CHR': ['-X', '-Y'] } ) annotations_list.append(annotations) observables = pdm.Observables( name='observables', types={} ) cells = pdm.Cells( name='cells', types='any' ) target = get_target(data.base) obs = get_observables_list(data.base) data_params = get_data_params(data.base) data_params['cells'] = ['Bcell', 'CD4T', 'CD8T', 'Gran', 'NK'] data_params['observables'] = ['gender'] attributes = pdm.Attributes( target='age', observables=observables, cells=cells ) attributes_list.append(attributes) observables_list.append(obs) data_params_list.append(data_params) for run_id in range(0, len(rows_dict['items']), max_rows): s_id = run_id f_id = min(s_id + max_rows, len(rows_dict['items'])) curr_dict = {} for key in rows_dict: curr_dict[key] = rows_dict[key][s_id:f_id][::-1] pdm.residuals_plot_scatter_comparison( data_list=data_list, annotations_list=annotations_list, attributes_list=attributes_list, observables_list=observables_list, data_params_list=data_params_list, rows_dict=curr_dict, cols_dict=cols_dict, method_params={ 'line': 'no', 'fit': 'yes', 'semi_window': 4, 'box_b': 'Q1', 'box_t': 'Q99', 'legend_size': 1, 'add': 'none' } # method_params = { # 'line': 'no', # 'fit': 'no', # 'semi_window': 4, # 'box_b': 'Q1', # 'box_t': 'Q99', # 'legend_size': 1, # 'add': 'none' # } )

[ "[email protected]" ]

[email protected]

ce19c5dcc0781b0f973bef61f84f4c439f3f4947

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/deepforest/deepforest.py

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geo-py/DeepForest

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refs/heads/master

2021-04-08T09:48:58.526848

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""" Deepforest main module. This module holds the deepforest class for model building and training """ import os import csv import warnings from PIL import Image with warnings.catch_warnings(): #Suppress some of the verbose tensorboard warnings, compromise to avoid numpy version errors warnings.filterwarnings("ignore", category=FutureWarning) import tensorflow as tf import pandas as pd import cv2 import numpy as np from matplotlib import pyplot as plt from deepforest import get_data from deepforest import utilities from deepforest import predict from deepforest import preprocess from deepforest.retinanet_train import main as retinanet_train from deepforest.retinanet_train import parse_args from keras_retinanet import models from keras_retinanet.models import convert_model from keras_retinanet.bin.train import create_models from keras_retinanet.preprocessing.csv_generator import CSVGenerator, _read_classes from keras_retinanet.utils.eval import evaluate from keras_retinanet.utils.eval import _get_detections from keras_retinanet.utils.visualization import draw_box class deepforest: ''' Class for training and predicting tree crowns in RGB images Args: weights (str): Path to model saved on disk from keras.model.save_weights(). A new model is created and weights are copied. Default is None. saved_model: Path to a saved model from disk using keras.model.save(). No new model is created. Attributes: model: A keras training model from keras-retinanet ''' def __init__(self, weights=None, saved_model=None): self.weights = weights self.saved_model = saved_model #Read config file - if a config file exists in local dir use it, if not use installed. if os.path.exists("deepforest_config.yml"): config_path = "deepforest_config.yml" else: try: config_path = get_data("deepforest_config.yml") except Exception as e: raise ValueError( "No deepforest_config.yml found either in local directory or in installed package location. {}" .format(e)) print("Reading config file: {}".format(config_path)) self.config = utilities.read_config(config_path) #Create a label dict, defaults to "Tree" self.read_classes() #release version id to flag if release is being used self.__release_version__ = None #Load saved model if needed if self.saved_model: print("Loading saved model") #Capture user warning, not relevant here with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning) self.model = utilities.load_model(saved_model) if self.weights is not None: print("Creating model from weights") backbone = models.backbone(self.config["backbone"]) self.model, self.training_model, self.prediction_model = create_models( backbone.retinanet, num_classes=1, weights=self.weights) else: print( "A blank deepforest object created. To perform prediction, either train or load an existing model." ) self.model = None def read_classes(self): """Read class file in case of multi-class training. If no file has been created, DeepForest assume there is 1 class, Tree""" # parse the provided class file self.labels = {} try: with open(self.classes_file, 'r') as file: self.classes = _read_classes(csv.reader(file, delimiter=',')) for key, value in self.classes.items(): self.labels[value] = key except: self.labels[0] = "Tree" def train(self, annotations, input_type="fit_generator", list_of_tfrecords=None, comet_experiment=None, images_per_epoch=None): '''Train a deep learning tree detection model using keras-retinanet. This is the main entry point for training a new model based on either existing weights or scratch Args: annotations (str): Path to csv label file, labels are in the format -> path/to/image.png,x1,y1,x2,y2,class_name comet_experiment: A comet ml object to log images. Optional. list_of_tfrecords: Ignored if input_type != "tfrecord", list of tf records to process input_type: "fit_generator" or "tfrecord" images_per_epoch: number of images to override default config of # images in annotations file / batch size. Useful for debug Returns: model (object): A trained keras model prediction model: with bbox nms trained model: without nms ''' #Test if there is a new classes file in case # of classes has changed. self.classes_file = utilities.create_classes(annotations) self.read_classes() arg_list = utilities.format_args(annotations, self.classes_file, self.config, images_per_epoch) print("Training retinanet with the following args {}".format(arg_list)) #Train model self.model, self.prediction_model, self.training_model = retinanet_train( forest_object=self, args=arg_list, input_type=input_type, list_of_tfrecords=list_of_tfrecords, comet_experiment=comet_experiment) def use_release(self, gpus=1): '''Use the latest DeepForest model release from github and load model. Optionally download if release doesn't exist Returns: model (object): A trained keras model gpus: number of gpus to parallelize, default to 1 ''' #Download latest model from github release release_tag, self.weights = utilities.use_release() #load saved model and tag release self.__release_version__ = release_tag print("Loading pre-built model: {}".format(release_tag)) if gpus == 1: with warnings.catch_warnings(): #Suppress compilte warning, not relevant here warnings.filterwarnings("ignore", category=UserWarning) self.model = utilities.read_model(self.weights, self.config) #Convert model self.prediction_model = convert_model(self.model) elif gpus > 1: backbone = models.backbone(self.config["backbone"]) n_classes = len(self.labels.keys()) self.model, self.training_model, self.prediction_model = create_models( backbone.retinanet, num_classes=n_classes, weights=self.weights, multi_gpu=gpus) #add to config self.config["weights"] = self.weights def predict_generator(self, annotations, comet_experiment=None, iou_threshold=0.5, max_detections=200, return_plot=False): """Predict bounding boxes for a model using a csv fit_generator Args: annotations (str): Path to csv label file, labels are in the format -> path/to/image.png,x1,y1,x2,y2,class_name iou_threshold(float): IoU Threshold to count for a positive detection (defaults to 0.5) max_detections (int): Maximum number of bounding box predictions comet_experiment(object): A comet experiment class objects to track return_plot: Whether to return prediction boxes (False) or Images (True). If True, files will be written to current working directory if model.config["save_path"] is not defined. Return: boxes_output: If return_plot=False, a pandas dataframe of bounding boxes for each image in the annotations file None: If return_plot is True, images are written to save_dir as a side effect. """ #Format args for CSV generator classes_file = utilities.create_classes(annotations) arg_list = utilities.format_args(annotations, classes_file, self.config) args = parse_args(arg_list) #create generator generator = CSVGenerator( args.annotations, args.classes, image_min_side=args.image_min_side, image_max_side=args.image_max_side, config=args.config, shuffle_groups=False, ) if self.prediction_model: boxes_output = [] #For each image, gather predictions for i in range(generator.size()): #pass image as path plot_name = generator.image_names[i] image_path = os.path.join(generator.base_dir, plot_name) result = self.predict_image(image_path, return_plot=return_plot, score_threshold=args.score_threshold) if return_plot: if not self.config["save_path"]: print( "model.config['save_path'] is None, saving images to current working directory" ) save_path = "." else: save_path = self.config["save_path"] #Save image fname = os.path.join(save_path, plot_name) cv2.imwrite(fname, result) continue else: #Turn boxes to pandas frame and save output box_df = pd.DataFrame(result) #use only plot name, not extension box_df["plot_name"] = os.path.splitext(plot_name)[0] boxes_output.append(box_df) else: raise ValueError( "No prediction model loaded. Either load a retinanet from file, download the latest release or train a new model" ) if return_plot: return None else: #if boxes, name columns and return box data boxes_output = pd.concat(boxes_output) boxes_output.columns = [ "xmin", "ymin", "xmax", "ymax", "score", "label", "plot_name" ] boxes_output = boxes_output.reindex( columns=["plot_name", "xmin", "ymin", "xmax", "ymax", "score", "label"]) return boxes_output def evaluate_generator(self, annotations, comet_experiment=None, iou_threshold=0.5, max_detections=200): """ Evaluate prediction model using a csv fit_generator Args: annotations (str): Path to csv label file, labels are in the format -> path/to/image.png,x1,y1,x2,y2,class_name iou_threshold(float): IoU Threshold to count for a positive detection (defaults to 0.5) max_detections (int): Maximum number of bounding box predictions comet_experiment(object): A comet experiment class objects to track Return: mAP: Mean average precision of the evaluated data """ #Format args for CSV generator classes_file = utilities.create_classes(annotations) arg_list = utilities.format_args(annotations, classes_file, self.config) args = parse_args(arg_list) #create generator validation_generator = CSVGenerator( args.annotations, args.classes, image_min_side=args.image_min_side, image_max_side=args.image_max_side, config=args.config, shuffle_groups=False, ) average_precisions = evaluate(validation_generator, self.prediction_model, iou_threshold=iou_threshold, score_threshold=args.score_threshold, max_detections=max_detections, save_path=args.save_path, comet_experiment=comet_experiment) # print evaluation total_instances = [] precisions = [] for label, (average_precision, num_annotations) in average_precisions.items(): print('{:.0f} instances of class'.format(num_annotations), validation_generator.label_to_name(label), 'with average precision: {:.4f}'.format(average_precision)) total_instances.append(num_annotations) precisions.append(average_precision) if sum(total_instances) == 0: print('No test instances found.') return print('mAP using the weighted average of precisions among classes: {:.4f}'.format( sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances))) mAP = sum(precisions) / sum(x > 0 for x in total_instances) print('mAP: {:.4f}'.format(mAP)) return mAP def predict_image(self, image_path=None, numpy_image=None, return_plot=True, score_threshold=0.05, show=False, color=None): """Predict tree crowns based on loaded (or trained) model Args: image_path (str): Path to image on disk numpy_image (array): Numpy image array in BGR channel order following openCV convention color (tuple): Color of bounding boxes in BGR order (0,0,0) black default show (bool): Plot the predicted image with bounding boxes. Ignored if return_plot=False return_plot: Whether to return image with annotations overlaid, or just a numpy array of boxes Returns: predictions (array): if return_plot, an image. Otherwise a numpy array of predicted bounding boxes, with scores and labels """ #Check for model save if (self.prediction_model is None): raise ValueError( "Model currently has no prediction weights, either train a new model using deepforest.train, loading existing model, or use prebuilt model (see deepforest.use_release()" ) #Check the formatting if isinstance(image_path, np.ndarray): raise ValueError( "image_path should be a string, but is a numpy array. If predicting a loaded image (channel order BGR), use numpy_image argument." ) #Check for correct formatting #Warning if image is very large and using the release model if numpy_image is None: numpy_image = cv2.imread(image_path) #Predict prediction = predict.predict_image(self.prediction_model, image_path=image_path, raw_image=numpy_image, return_plot=return_plot, score_threshold=score_threshold, color=color, classes=self.labels) #cv2 channel order to matplotlib order if return_plot & show: plt.imshow(prediction[:, :, ::-1]) plt.show() return prediction def predict_tile(self, raster_path=None, numpy_image=None, patch_size=400, patch_overlap=0.15, iou_threshold=0.15, return_plot=False): """ For images too large to input into the model, predict_tile cuts the image into overlapping windows, predicts trees on each window and reassambles into a single array. Args: raster_path: Path to image on disk numpy_image (array): Numpy image array in BGR channel order following openCV convention iou_threshold: Minimum iou overlap among predictions between windows to be suppressed. Defaults to 0.5. Lower values suppress more boxes at edges. return_plot: Should the image be returned with the predictions drawn? Returns: boxes (array): if return_plot, an image. Otherwise a numpy array of predicted bounding boxes, scores and labels """ if numpy_image: pass else: #Load raster as image raster = Image.open(raster_path) numpy_image = np.array(raster) #Compute sliding window index windows = preprocess.compute_windows(numpy_image, patch_size, patch_overlap) #Save images to tmpdir predicted_boxes = [] for index, window in enumerate(windows): #Crop window and predict crop = numpy_image[windows[index].indices()] #Crop is RGB channel order, change to BGR crop = crop[..., ::-1] boxes = self.predict_image(numpy_image=crop, return_plot=False, score_threshold=self.config["score_threshold"]) #transform coordinates to original system xmin, ymin, xmax, ymax = windows[index].getRect() boxes.xmin = boxes.xmin + xmin boxes.xmax = boxes.xmax + xmin boxes.ymin = boxes.ymin + ymin boxes.ymax = boxes.ymax + ymin predicted_boxes.append(boxes) predicted_boxes = pd.concat(predicted_boxes) #Non-max supression for overlapping boxes among window if patch_overlap == 0: mosaic_df = predicted_boxes else: with tf.Session() as sess: print("{} predictions in overlapping windows, applying non-max supression". format(predicted_boxes.shape[0])) new_boxes, new_scores, new_labels = predict.non_max_suppression( sess, predicted_boxes[["xmin", "ymin", "xmax", "ymax"]].values, predicted_boxes.score.values, predicted_boxes.label.values, max_output_size=predicted_boxes.shape[0], iou_threshold=iou_threshold) #Recreate box dataframe image_detections = np.concatenate([ new_boxes, np.expand_dims(new_scores, axis=1), np.expand_dims(new_labels, axis=1) ],axis=1) mosaic_df = pd.DataFrame( image_detections, columns=["xmin", "ymin", "xmax", "ymax", "score", "label"]) mosaic_df.label = mosaic_df.label.str.decode("utf-8") print("{} predictions kept after non-max suppression".format( mosaic_df.shape[0])) if return_plot: #Draw predictions for box in mosaic_df[["xmin", "ymin", "xmax", "ymax"]].values: draw_box(numpy_image, box, [0, 0, 255]) #Mantain consistancy with predict_image return numpy_image else: return mosaic_df def plot_curves(self): """Plot training curves""" if self.history: # Plot training & validation regression loss values fig, axes, = plt.subplots(nrows=1, ncols=3) axes = axes.flatten() #Regression Loss axes[0].plot(self.history.history['regression_loss']) axes[0].set_title('Bounding Box Loss') axes[0].set_ylabel('Loss') axes[0].set_xlabel('Epoch') #Classification Loss axes[1].plot(self.history.history['classification_loss']) axes[1].set_title('Classification Loss') axes[1].set_ylabel('Loss') axes[1].set_xlabel('Epoch') # Plot validation mAP if "mAP" in self.history.history.keys(): axes[2].plot(self.history.history['mAP']) axes[2].set_title('Validation: Mean Average Precision') axes[2].set_ylabel('mAP') axes[2].set_xlabel('Epoch') plt.show() else: print("No training history found.") return None

[ "[email protected]" ]

[email protected]

8df0a5de30770486d65f5750ddf7332158529917

385ce240ae264a1449079c21bd0c4cbe7c0fe3b8

/myowntests/ifelseladder.py

6be8802a946907017b902d6c6c70418b5968deb2

[]

no_license

Maxcousin123/Python-workspace

3ed60ae80d790b5c055bf47872ff0fdd39f4ec58

326b023190a12e082dcb35ae5ab8ef644c32159b

refs/heads/master

2022-11-24T11:05:08.707003

2020-07-29T06:32:08

283,415,557

0

null

UTF-8

Python

false

576

py

maths=int(input('fill your math grade')) physics=int(input('fill your physics grade')) chemistry=int(input('fill your chemistry grade')) av=(maths+physics+chemistry)/3 if maths<35: print('Exam Failed') else:print('Exam passed') if physics<35: print('Exam failed') else:print('Exam passed') if physics<35: print('Exam failed') else:print('Exam passed') if maths and physics and chemistry<35: print('Exams failed') elif av<=59: print('your grade is c') elif 59>av<=69: print('your grade is b') else: print('your grade is a') #69

[ "[email protected]" ]

[email protected]

e730f6b2b0eca72e7e22623e746eb8a5f9fc2013

0e1e643e864bcb96cf06f14f4cb559b034e114d0

/Exps_7_v3/I_to_M_Gk3_no_pad/pyramid_2side/bce_s001_tv_s0p1_L8/step09_2side_L8.py

150ed37ed776a944b01f8ce846b25ba5de404b7b

[]

no_license

KongBOy/kong_model2

33a94a9d2be5b0f28f9d479b3744e1d0e0ebd307

1af20b168ffccf0d5293a393a40a9fa9519410b2

refs/heads/master

2022-10-14T03:09:22.543998

2022-10-06T11:33:42

242,080,692

3

0

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Python

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24,319

py

############################################################################################################################################################################################################# ############################################################################################################################################################################################################# ### 把 kong_model2 加入 sys.path import os from tkinter import S code_exe_path = os.path.realpath(__file__) ### 目前執行 step10_b.py 的 path code_exe_path_element = code_exe_path.split("\\") ### 把 path 切分等等要找出 kong_model 在第幾層 kong_layer = code_exe_path_element.index("kong_model2") ### 找出 kong_model2 在第幾層 kong_model2_dir = "\\".join(code_exe_path_element[:kong_layer + 1]) ### 定位出 kong_model2 的 dir import sys ### 把 kong_model2 加入 sys.path sys.path.append(kong_model2_dir) # print(__file__.split("\\")[-1]) # print(" code_exe_path:", code_exe_path) # print(" code_exe_path_element:", code_exe_path_element) # print(" kong_layer:", kong_layer) # print(" kong_model2_dir:", kong_model2_dir) ############################################################################################################################################################################################################# from step08_b_use_G_generate_I_to_M import I_Generate_M_see from step09_c_train_step import train_step_Single_output_I_to_M from step09_d_KModel_builder_combine_step789 import KModel_builder, MODEL_NAME import time start_time = time.time() ############################################################################################################################################################################################### ############################################################################################################################################################################################### ########################################################### Block1 ### Block1 ######################################################################################### pyramid_1side_1__2side_0 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] pyramid_1side_1__2side_1 = [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2] pyramid_1side_2__2side_0 = [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1] pyramid_1side_2__2side_1 = [2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2] pyramid_1side_2__2side_2 = [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2] pyramid_1side_3__2side_0 = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1] pyramid_1side_3__2side_1 = [2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2] pyramid_1side_3__2side_2 = [2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2] pyramid_1side_3__2side_3 = [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2] pyramid_1side_4__2side_0 = [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1] pyramid_1side_4__2side_1 = [2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2] pyramid_1side_4__2side_2 = [2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2] pyramid_1side_4__2side_3 = [2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2] pyramid_1side_4__2side_4 = [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2] pyramid_1side_5__2side_0 = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1] pyramid_1side_5__2side_1 = [2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2] pyramid_1side_5__2side_2 = [2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2] pyramid_1side_5__2side_3 = [2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2] pyramid_1side_5__2side_4 = [2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2] pyramid_1side_5__2side_5 = [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2] pyramid_1side_6__2side_0 = [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] pyramid_1side_6__2side_1 = [2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2] pyramid_1side_6__2side_2 = [2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2] pyramid_1side_6__2side_3 = [2, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2] pyramid_1side_6__2side_4 = [2, 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2] pyramid_1side_6__2side_5 = [2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2] pyramid_1side_6__2side_6 = [2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2] pyramid_1side_7__2side_0 = [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1] pyramid_1side_7__2side_1 = [2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2] pyramid_1side_7__2side_2 = [2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2] pyramid_1side_7__2side_3 = [2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2] pyramid_1side_7__2side_4 = [2, 2, 2, 2, 1, 1, 1, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2] pyramid_1side_7__2side_5 = [2, 2, 2, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2] pyramid_1side_7__2side_6 = [2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2] pyramid_1side_7__2side_7 = [2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2] pyramid_1side_8__2side_0 = [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1] pyramid_1side_8__2side_1 = [2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2] pyramid_1side_8__2side_2 = [2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 2] pyramid_1side_8__2side_3 = [2, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 2, 2, 2] pyramid_1side_8__2side_4 = [2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2, 2, 2, 2] pyramid_1side_8__2side_5 = [2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 1, 1, 2, 2, 2, 2, 2] pyramid_1side_8__2side_6 = [2, 2, 2, 2, 2, 2, 1, 1, 0, 1, 1, 2, 2, 2, 2, 2, 2] pyramid_1side_8__2side_7 = [2, 2, 2, 2, 2, 2, 2, 1, 0, 1, 2, 2, 2, 2, 2, 2, 2] pyramid_1side_8__2side_8 = [2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2] pyramid_1side_9__2side_0 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] pyramid_1side_9__2side_1 = [2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2] pyramid_1side_9__2side_2 = [2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2] pyramid_1side_9__2side_3 = [2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2] pyramid_1side_9__2side_4 = [2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2] pyramid_1side_9__2side_5 = [2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2] pyramid_1side_9__2side_6 = [2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2] pyramid_1side_9__2side_7 = [2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2] pyramid_1side_9__2side_8 = [2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2] pyramid_1side_9__2side_9 = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] ######################################################################################### ch032_pyramid_1side_1__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_1__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_2__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_2__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_2__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_2__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_3__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_3__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_3__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_3__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_3__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_3__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_4__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_4__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_4__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_4__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_4__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_4__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_4__2side_4 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_4__2side_4, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_5__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_5__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_5__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_5__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_5__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_5__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_5__2side_4 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_5__2side_4, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_5__2side_5 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_5__2side_5, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_6__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_6__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_6__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_6__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_6__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_6__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_6__2side_4 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_6__2side_4, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_6__2side_5 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_6__2side_5, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_6__2side_6 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_6__2side_6, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_4 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_4, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_5 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_5, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_6 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_6, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_7__2side_7 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_7__2side_7, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_4 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_4, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_5 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_5, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_6 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_6, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_7 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_7, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_8__2side_8 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_8__2side_8, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_1 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_1, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_2 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_2, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_3 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_3, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_4 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_4, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_5 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_5, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_6 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_6, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_7 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_7, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_8 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_8, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ch032_pyramid_1side_9__2side_9 = KModel_builder().set_model_name(MODEL_NAME.flow_unet2).set_unet3(out_conv_block=True, concat_before_down=True, kernel_size=3, padding="valid", hid_ch= 32, depth_level=8, out_ch=1, unet_acti="sigmoid", conv_block_num=pyramid_1side_9__2side_9, ch_upper_bound= 2 ** 14).set_gen_op(I_Generate_M_see).set_train_step(train_step_Single_output_I_to_M) ######################################################################################### ############################################################################################################################################################################################### if(__name__ == "__main__"): import numpy as np print("build_model cost time:", time.time() - start_time) data = np.zeros(shape=(1, 512, 512, 1)) use_model = ch032_pyramid_1side_4__2side_2 use_model = use_model.build() result = use_model.generator(data) print(result.shape) from kong_util.tf_model_util import Show_model_weights Show_model_weights(use_model.generator) use_model.generator.summary()

[ "[email protected]" ]

[email protected]

a30c882225f0729f7727634a091398bc4b341d00

a58fcf9467749de7d269c5b17430773069e29791

/designate/exceptions.py

bd807f15966c85208c564dccc08126f802c00c8e

[ "Apache-2.0" ]

permissive

Woody89/designate-private

586df6c28a2da573663487e4728c3fddfef095af

0a6ed5a1d7cdac5cb1e9dec8fd3ddfb9a77c58f5

refs/heads/master

2021-01-22T19:22:49.391876

2017-08-19T06:16:53

100,774,211

1

0

null

UTF-8

Python

false

17,905

py

# Copyright 2012 Managed I.T. # # Author: Kiall Mac Innes <[email protected]> # # 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 six class Base(Exception): error_code = 500 error_type = None error_message = None errors = None def __init__(self, *args, **kwargs): self.errors = kwargs.pop('errors', None) self.object = kwargs.pop('object', None) super(Base, self).__init__(*args, **kwargs) if len(args) > 0 and isinstance(args[0], six.string_types): self.error_message = args[0] class Backend(Exception): pass class RelationNotLoaded(Base): error_code = 500 error_type = 'relation_not_loaded' def __init__(self, *args, **kwargs): self.relation = kwargs.pop('relation', None) super(RelationNotLoaded, self).__init__(*args, **kwargs) self.error_message = "%(relation)s is not loaded on %(object)s" % \ {"relation": self.relation, "object": self.object.obj_name()} def __str__(self): return self.error_message class AdapterNotFound(Base): error_code = 500 error_type = 'adapter_not_found' class NSD4SlaveBackendError(Backend): pass class NotImplemented(Base, NotImplementedError): pass class XFRFailure(Base): pass class ConfigurationError(Base): error_type = 'configuration_error' class UnknownFailure(Base): error_code = 500 error_type = 'unknown_failure' class CommunicationFailure(Base): error_code = 504 error_type = 'communication_failure' class NeutronCommunicationFailure(CommunicationFailure): """ Raised in case one of the alleged Neutron endpoints fails. """ error_type = 'neutron_communication_failure' class NoFiltersConfigured(ConfigurationError): error_code = 500 error_type = 'no_filters_configured' class NoServersConfigured(ConfigurationError): error_code = 500 error_type = 'no_servers_configured' class MultiplePoolsFound(ConfigurationError): error_code = 500 error_type = 'multiple_pools_found' class NoPoolTargetsConfigured(ConfigurationError): error_code = 500 error_type = 'no_pool_targets_configured' class OverQuota(Base): error_code = 413 error_type = 'over_quota' expected = True class QuotaResourceUnknown(Base): error_type = 'quota_resource_unknown' class InvalidObject(Base): error_code = 400 error_type = 'invalid_object' expected = True class BadRequest(Base): error_code = 400 error_type = 'bad_request' expected = True class EmptyRequestBody(BadRequest): error_type = 'empty_request_body' expected = True class InvalidUUID(BadRequest): error_type = 'invalid_uuid' class InvalidRecord(BadRequest): error_type = 'invalid_record' class NetworkEndpointNotFound(BadRequest): error_type = 'no_endpoint' error_code = 403 class MarkerNotFound(BadRequest): error_type = 'marker_not_found' class NotEqual(Base): error_type = 'udn_record_count not equals record in db' class NoChange(Base): error_type = 'No changes' class ValueError(BadRequest): error_type = 'value_error' class InvalidMarker(BadRequest): error_type = 'invalid_marker' class InvalidSortDir(BadRequest): error_type = 'invalid_sort_dir' class InvalidLimit(BadRequest): error_type = 'invalid_limit' class InvalidSortKey(BadRequest): error_type = 'invalid_sort_key' class InvalidJson(BadRequest): error_type = 'invalid_json' class NoneIpAddress(BadRequest): error_type = 'none_ip_address' class InvalidOperation(BadRequest): error_code = 400 error_type = 'invalid_operation' class UnsupportedAccept(BadRequest): error_code = 406 error_type = 'unsupported_accept' class UnsupportedContentType(BadRequest): error_code = 415 error_type = 'unsupported_content_type' class InvalidZoneName(Base): error_code = 400 error_type = 'invalid_zone_name' expected = True class InvalidAclName(Base): error_code = 400 error_type = 'invalid_acl_name' expected = True class InvalidRecordSetName(Base): error_code = 400 error_type = 'invalid_recordset_name' expected = True class InvalidRecordSetLocation(Base): error_code = 400 error_type = 'invalid_recordset_location' expected = True class InvaildZoneTransfer(Base): error_code = 400 error_type = 'invalid_zone_transfer_request' class InvalidTTL(Base): error_code = 400 error_type = 'invalid_ttl' class ZoneHasSubZone(Base): error_code = 400 error_type = 'zone_has_sub_zone' class Forbidden(Base): error_code = 403 error_type = 'forbidden' expected = True class IllegalChildZone(Forbidden): error_type = 'illegal_child' class IllegalParentZone(Forbidden): error_type = 'illegal_parent' class IncorrectZoneTransferKey(Forbidden): error_type = 'invalid_key' class Duplicate(Base): expected = True error_code = 409 error_type = 'duplicate' class DuplicateServiceStatus(Duplicate): error_type = 'duplicate_service_status' class DuplicateQuota(Duplicate): error_type = 'duplicate_quota' class DuplicateServer(Duplicate): error_type = 'duplicate_server' class DuplicateTsigKey(Duplicate): error_type = 'duplicate_tsigkey' class DuplicateZone(Duplicate): error_type = 'duplicate_zone' class DuplicateAcl(Duplicate): error_type = 'duplicate_acl' class DuplicateTld(Duplicate): error_type = 'duplicate_tld' class DuplicateRecordSet(Duplicate): error_type = 'duplicate_recordset' class DuplicateRecord(Duplicate): error_type = 'duplicate_record' class DuplicateBlacklist(Duplicate): error_type = 'duplicate_blacklist' class DuplicatePoolManagerStatus(Duplicate): error_type = 'duplication_pool_manager_status' class DuplicatePool(Duplicate): error_type = 'duplicate_pool' class DuplicatePoolAttribute(Duplicate): error_type = 'duplicate_pool_attribute' class DuplicatePoolNsRecord(Duplicate): error_type = 'duplicate_pool_ns_record' class DuplicatePoolNameserver(Duplicate): error_type = 'duplicate_pool_nameserver' class DuplicatePoolTarget(Duplicate): error_type = 'duplicate_pool_target' class DuplicatePoolTargetOption(Duplicate): error_type = 'duplicate_pool_target_option' class DuplicatePoolTargetMaster(Duplicate): error_type = 'duplicate_pool_target_master' class DuplicatePoolAlsoNotify(Duplicate): error_type = 'duplicate_pool_also_notify' class DuplicateZoneImport(Duplicate): error_type = 'duplicate_zone_import' class DuplicateZoneExport(Duplicate): error_type = 'duplicate_zone_export' class DuplicateViewDuplicate(Duplicate): error_type = 'duplicate_view_export' class DuplicateZdnsViewInfo(Duplicate): error_type = 'duplicate_zdns_view_info' class DuplicateViewZdnsView(Duplicate): error_type = 'duplicate_view_zdns_view_association' class DuplicateView(Duplicate): error_type = 'duplicate_view' class NeedView(BadRequest): error_type = 'attributes_need_view' class MethodNotAllowed(Base): expected = True error_code = 405 error_type = 'method_not_allowed' class DuplicateZoneTransferRequest(Duplicate): error_type = 'duplicate_zone_transfer_request' class DuplicateZoneTransferAccept(Duplicate): error_type = 'duplicate_zone_transfer_accept' class DuplicateZoneAttribute(Duplicate): error_type = 'duplicate_zone_attribute' class DuplicateZoneMaster(Duplicate): error_type = 'duplicate_zone_attribute' class NotFound(Base): expected = True error_code = 404 error_type = 'not_found' class Failed(Base): expected = True error_code = 500 error_type = 'create_failed' class ServiceStatusNotFound(NotFound): error_type = 'service_status_not_found' class QuotaNotFound(NotFound): error_type = 'quota_not_found' class ServerNotFound(NotFound): error_type = 'server_not_found' class TsigKeyNotFound(NotFound): error_type = 'tsigkey_not_found' class BlacklistNotFound(NotFound): error_type = 'blacklist_not_found' class ZoneNotFound(NotFound): error_type = 'zone_not_found' class AclNotFound(NotFound): error_type = 'acl_not_found' class ZoneMasterNotFound(NotFound): error_type = 'zone_master_not_found' class ZoneAttributeNotFound(NotFound): error_type = 'zone_attribute_not_found' class TldNotFound(NotFound): error_type = 'tld_not_found' class RecordSetNotFound(NotFound): error_type = 'recordset_not_found' class RecordNotFound(NotFound): error_type = 'record_not_found' class AllFailed(Failed): error_type = 'all record-create failed' class PartlyFailed(Failed): error_type = 'some record-create failed' class ReportNotFound(NotFound): error_type = 'report_not_found' class PoolManagerStatusNotFound(NotFound): error_type = 'pool_manager_status_not_found' class PoolNotFound(NotFound): error_type = 'pool_not_found' class NoValidPoolFound(NotFound): error_type = 'no_valid_pool_found' class PoolAttributeNotFound(NotFound): error_type = 'pool_attribute_not_found' class PoolNsRecordNotFound(NotFound): error_type = 'pool_ns_record_not_found' class PoolNameserverNotFound(NotFound): error_type = 'pool_nameserver_not_found' class PoolTargetNotFound(NotFound): error_type = 'pool_target_not_found' class PoolTargetOptionNotFound(NotFound): error_type = 'pool_target_option_not_found' class PoolTargetMasterNotFound(NotFound): error_type = 'pool_target_master_not_found' class PoolAlsoNotifyNotFound(NotFound): error_type = 'pool_also_notify_not_found' class ZoneTransferRequestNotFound(NotFound): error_type = 'zone_transfer_request_not_found' class ZoneTransferAcceptNotFound(NotFound): error_type = 'zone_transfer_accept_not_found' class ZoneImportNotFound(NotFound): error_type = 'zone_import_not_found' class ZoneExportNotFound(NotFound): error_type = 'zone_export_not_found' class ViewNotFound(NotFound): error_type = 'view_not_found' class ViewAclNotFound(NotFound): error_type = 'view_acl_not_found' class AclsIsNone(NotFound): error_type = 'acl_ids_is_none' class ParamsIsNotLegal(NotFound): error_type = 'params_is_not_legal' class AclidsMustBeList(NotFound): error_type = 'acl_ids_must_be_list' class CreateViewFailed(NotFound): error_type = 'create_view_failed' class LastServerDeleteNotAllowed(BadRequest): error_type = 'last_server_delete_not_allowed' EZDNS = { "1": "any or none acl is read only", "2": "acl already exists", "3": "operate non-exist acl", "4": "dns64 prefix should be a ipv6 addr", "5": "invalid dns64 prefix netmask", "6": "suffix is needed if netmask of prefix smaller than 96", "7": "DNS64 setting already exists", "8": "operate non-exist DNS64 setting", "9": "tsig key already exists", "10": "delete acl is using by view", "11": "operate non-exist zone", "12": "cache file not exist", "13": "cache size too large", "14": "operate non-exist view", "15": "get zone from backend server failed", "16": "zone already exists", "17": "unsupported meta data type", "18": "view already exists", "19": "delete default view", "20": "cann't modify acl of default view", "21": "operate non-exist rr", "22": "conflict key secret", "23": "not supported zone type", "24": "operate non-exist shared rr", "25": "cann't delete the last shared rr", "26": "operate non-exist tsig key", "27": "reconfig dns server failed", "28": "no rndc-confgen installed", "29": "lack/white list already exists", "30": "operate non-exist back/white list", "31": "zone owner doesn't has view owner", "32": "unsupport acl action", "33": "no pine-control installed", "34": "server already started", "35": "RR format error", "36": "zone transfer failed", "37": "more than one ad zone owner", "38": "update zone failed", "39": "shared rr already exists", "40": "add duplicate rr", "41": "add exclusive rr", "42": "short of glue rr", "43": "conflict with exists cname", "44": "delete unknown rr", "45": "can't delete soa rr", "46": "no ns left after delete", "47": "delete glue needed by other rr", "48": "reverse zone doesn't exist", "49": "rdata is valid", "50": "rr is out of zone", "51": "onfigure value isn't valid", "52": "unknown forward style", "53": "duplicate zone master", "54": "forwarder exists", "55": "operate non-exist forwarder", "56": "operate non-exist view on node", "57": "already exists root zone", "58": "only A/AAAA NS is allowed in hint zone", "59": "already has root configuration", "60": "rr type isn't supported", "61": "can't update slave zone", "62": "duplicate local domain policy", "63": "zone name isn't valid", "64": "add duplicate host", "65": "soa serial number degraded", "66": "root isn't support in local policy", "67": "auth zone with same name already exists", "68": "stub zone with same name already exists", "69": "forward zone with same name already exists", "70": "acl is used by view", "71": "acl is used by AD zone", "72": "rrl policy already exist", "73": "non-exist rrl policy", "74": "delete monitor strategy in use", "75": "monitor strategy already exist", "76": "non exist monitor strategy", "77": "node's view querysource already exists", "78": "node's view querysource not exist", "79": "too much rrls(over 999)", "100": "version is unknown", "101": "patch file broken", "102": "source code isn't a release version", "103": "binding different iface with same ip address", "104": "ntp interval out of range", "105": "send a test mail failed, check the configuration", "300": "invalid ip address", "301": "no dns server installed", "302": "not enough params", "303": "not supported backup method", "304": "not supported command method", "305": "service hasn't been init", "306": "not supported ha type", "307": "member is not accessible", "308": "wrong username and password", "309": "nic config failed", "310": "service hasn't been started", "311": "init params is required", "312": "invalid port", "313": "verify node failed", "314": "request body json format error", "315": "connect backup server timeout", "316": "data recovery failed", "317": "data backup failed", "318": "lower limit bigger than upper limit", "319": "execute command timeout", "320": "password/role failed", "404": "Wrong url, please check it", "421": "Equipment internal error !", "600": "operate non-exist group", "601": "member with same ip alreasy exists", "602": "member with same name alreasy exists", "603": "operate non-exist member", "604": "not supported service type", "605": "member command queue is full", "606": "member is performing data recovery", "607": "group already exists", "608": "cann't operate local group", "609": "user already exists", "610": "operate non-exist user", "611": "init member service failed", "612": "owners is required", "613": "cann't delete the last owner for resource", "614": "add duplicate owners", "615": "old password is wrong", "616": "cann't delete local group", "617": "cann't delete local member", "618": "permission denied", "619": "unkown authority rule", "620": "authority rule already exist", "621": "invalid backup data", "622": "device already under management", "623": "some devices don't exist any more", "624": "cann't operation inactive cloud", "625": "cann't add multi backup devices", "626": "no backup device", "627": "not master device", "628": "not backup device", "629": "not slave device", "630": "hasn't managed by cloud yet", "631": "node can't communicate with master", "632": "invalid exception handle method", "800": "time out while sending alarm msg" } class ZdnsErrMessage(Base): error_type = "Equipment Internal Error" expected = True def __init__(self,*args,**kwargs): self.errors = kwargs.pop('errors', None) self.object = kwargs.pop('object', None) super(Base, self).__init__(*args, **kwargs) if len(args) > 0 and isinstance(args[0], six.string_types): self.error_message = str(args[0]) + ": " + EZDNS[args[0]] # @staticmethod # def getmsg(cord): # msg = str(cord) + ": " + EZDNS[cord] # return msg class AclUsedByView(Base): error_type = 'acl used by view'

[ "[email protected]" ]

[email protected]

c737af5d1ff073a22f5a3aaaf91937cb8797fb95

8164fd930d78efbd3885198efbfd9692c585319b

/week7/CrawWeb/craw_web.py

b837f0305e260bb319dc6622a4866529aa9c6f96

[]

no_license

kobso1245/Hack_BG

7a7b7524b20fada3d9856a583e02c6959d442e66

7ffdb8ccefd67aeca5a49c9a9354e65c77149ad4

refs/heads/master

2020-05-31T17:43:10.316633

2015-10-05T21:43:20

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from Crawler import * from Histogram import * from Plotter import * if __name__ == '__main__': craw("http://register.start.bg/", "histogram2") plot("path_to_database_file/websites.db")

[ "[email protected]" ]

[email protected]

414d29786eb51284f28473d7090b7778c546c6c3

dd860973103347b382d8a04ef68a9376561725ea

/wazimap_ng/profile/serializers/highlights_serializer.py

003ee147dc77b5daa5f28a8c58f772775589eb56

[ "Apache-2.0" ]

permissive

mauricemojito/wazimap-ng

7a7da6c9fc653054c376d77c22df120ed0abb653

e03748cb1258cbafb43faba441bbc37dd0556a2a

refs/heads/master

2023-03-12T01:45:53.173039

2020-09-30T13:55:29

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Apache-2.0

2021-02-25T18:55:09

2021-02-25T18:34:39

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from wazimap_ng.datasets.models import IndicatorData from wazimap_ng.utils import mergedict def get_subindicator(highlight): subindicators = highlight.indicator.subindicators idx = highlight.subindicator if highlight.subindicator is not None else 0 return subindicators[idx] def sibling(highlight, geography): siblings = geography.get_siblings() indicator_data = IndicatorData.objects.filter(indicator__profilehighlight=highlight, geography__in=siblings) subindicator = get_subindicator(highlight) numerator = None denominator = 0 for datum in indicator_data: if datum.geography == geography: numerator = datum.data["subindicators"].get(subindicator, 0) s = datum.data["subindicators"][subindicator] denominator += s if denominator > 0 and numerator is not None: return numerator / denominator return None def absolute_value(highlight, geography): indicator_data = IndicatorData.objects.filter(indicator__profilehighlight=highlight, geography=geography) if indicator_data.count() > 0: subindicator = get_subindicator(highlight) data = indicator_data.first().data # TODO what to do with multiple results return data["subindicators"].get(subindicator, 0) return None def subindicator(highlight, geography): indicator_data = IndicatorData.objects.filter(indicator__profilehighlight=highlight, geography=geography) if indicator_data.count() > 0: indicator_data = indicator_data.first() # Fix this need to cater for multiple results subindicator = get_subindicator(highlight) numerator = indicator_data.data["subindicators"].get(subindicator, 0) denominator = 0 for datum, count in indicator_data.data["subindicators"].items(): denominator += count if denominator > 0 and numerator is not None: return numerator / denominator return None algorithms = { "absolute_value": absolute_value, "sibling": sibling, "subindicators": subindicator } def HighlightsSerializer(profile, geography): highlights = [] profile_highlights = profile.profilehighlight_set.all().order_by("order") for highlight in profile_highlights: denominator = highlight.denominator method = algorithms.get(denominator, absolute_value) val = method(highlight, geography) if val is not None: highlights.append({"label": highlight.label, "value": val, "method": denominator}) return highlights

[ "[email protected]" ]

[email protected]

229bb954ad637b3c780475f96f2f202e84d0a4db

c8cf1bdacdbf6de75e61cc6a2ce8617479c19ec6

/test/mobile/test_bytecode.py

95baa86d5763e5e308c727fa87d6d77972080125

[ "BSD-3-Clause", "LicenseRef-scancode-generic-cla", "BSL-1.0", "Apache-2.0", "BSD-2-Clause" ]

permissive

Afonso-2403/pytorch

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e35e6237d24b6c96b122deb21f015c0fe3eccb13

refs/heads/master

2023-08-21T18:43:43.019194

2021-09-13T17:58:00

363,847,561

1

0

NOASSERTION

2021-07-08T19:06:16

2021-05-03T07:16:49

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import fnmatch import io import shutil import tempfile import torch import torch.utils.show_pickle # from torch.utils.mobile_optimizer import optimize_for_mobile from torch.jit.mobile import ( _load_for_lite_interpreter, _get_model_bytecode_version, _get_model_ops_and_info, _backport_for_mobile_to_buffer, _backport_for_mobile) from torch.testing._internal.common_utils import TestCase, run_tests from pathlib import Path pytorch_test_dir = Path(__file__).resolve().parents[1] # script_module_v4.ptl and script_module_v5.ptl source code # class TestModule(torch.nn.Module): # def __init__(self, v): # super().__init__() # self.x = v # def forward(self, y: int): # increment = torch.ones([2, 4], dtype=torch.float64) # return self.x + y + increment # output_model_path = Path(tmpdirname, "script_module_v5.ptl") # script_module = torch.jit.script(TestModule(1)) # optimized_scripted_module = optimize_for_mobile(script_module) # exported_optimized_scripted_module = optimized_scripted_module._save_for_lite_interpreter( # str(output_model_path)) SCRIPT_MODULE_V4_BYTECODE_PKL = ''' (4, ('__torch__.*.TestModule.forward', (('instructions', (('STOREN', 1, 2), ('DROPR', 1, 0), ('LOADC', 0, 0), ('LOADC', 1, 0), ('MOVE', 2, 0), ('OP', 0, 0), ('LOADC', 1, 0), ('OP', 1, 0), ('RET', 0, 0))), ('operators', (('aten::add', 'int'), ('aten::add', 'Scalar'))), ('constants', (torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.DoubleStorage, '0', 'cpu', 8),), 0, (2, 4), (4, 1), False, collections.OrderedDict()), 1)), ('types', ()), ('register_size', 2)), (('arguments', ((('name', 'self'), ('type', '__torch__.*.TestModule'), ('default_value', None)), (('name', 'y'), ('type', 'int'), ('default_value', None)))), ('returns', ((('name', ''), ('type', 'Tensor'), ('default_value', None)),))))) ''' SCRIPT_MODULE_V5_BYTECODE_PKL = ''' (5, ('__torch__.*.TestModule.forward', (('instructions', (('STOREN', 1, 2), ('DROPR', 1, 0), ('LOADC', 0, 0), ('LOADC', 1, 0), ('MOVE', 2, 0), ('OP', 0, 0), ('LOADC', 1, 0), ('OP', 1, 0), ('RET', 0, 0))), ('operators', (('aten::add', 'int'), ('aten::add', 'Scalar'))), ('constants', (torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.DoubleStorage, 'constants/0', 'cpu', 8),), 0, (2, 4), (4, 1), False, collections.OrderedDict()), 1)), ('types', ()), ('register_size', 2)), (('arguments', ((('name', 'self'), ('type', '__torch__.*.TestModule'), ('default_value', None)), (('name', 'y'), ('type', 'int'), ('default_value', None)))), ('returns', ((('name', ''), ('type', 'Tensor'), ('default_value', None)),))))) ''' SCRIPT_MODULE_V6_BYTECODE_PKL = ''' (6, ('__torch__.*.TestModule.forward', (('instructions', (('STOREN', 1, 2), ('DROPR', 1, 0), ('LOADC', 0, 0), ('LOADC', 1, 0), ('MOVE', 2, 0), ('OP', 0, 0), ('OP', 1, 0), ('RET', 0, 0))), ('operators', (('aten::add', 'int', 2), ('aten::add', 'Scalar', 2))), ('constants', (torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.DoubleStorage, '0', 'cpu', 8),), 0, (2, 4), (4, 1), False, collections.OrderedDict()), 1)), ('types', ()), ('register_size', 2)), (('arguments', ((('name', 'self'), ('type', '__torch__.*.TestModule'), ('default_value', None)), (('name', 'y'), ('type', 'int'), ('default_value', None)))), ('returns', ((('name', ''), ('type', 'Tensor'), ('default_value', None)),))))) ''' SCRIPT_MODULE_BYTECODE_PKL = { 4: { "bytecode_pkl": SCRIPT_MODULE_V4_BYTECODE_PKL, "model_name": "script_module_v4.ptl"}, } # The minimum version a model can be backported to # Need to be updated when a bytecode version is completely retired MINIMUM_TO_VERSION = 4 class testVariousModelVersions(TestCase): def test_get_model_bytecode_version(self): def check_model_version(model_path, expect_version): actual_version = _get_model_bytecode_version(model_path) assert(actual_version == expect_version) for version, model_info in SCRIPT_MODULE_BYTECODE_PKL.items(): model_path = pytorch_test_dir / "cpp" / "jit" / model_info["model_name"] check_model_version(model_path, version) def test_bytecode_values_for_all_backport_functions(self): # Find the maximum version of the checked in models, start backporting to the minimum support version, # and comparing the bytecode pkl content. # It can't be merged to the test `test_all_backport_functions`, because optimization is dynamic and # the content might change when optimize function changes. This test focuses # on bytecode.pkl content validation. For the content validation, it is not byte to byte check, but # regular expression matching. The wildcard can be used to skip some specific content comparison. maximum_checked_in_model_version = max(SCRIPT_MODULE_BYTECODE_PKL.keys()) current_from_version = maximum_checked_in_model_version with tempfile.TemporaryDirectory() as tmpdirname: while current_from_version > MINIMUM_TO_VERSION: # Load model v5 and run forward method model_name = SCRIPT_MODULE_BYTECODE_PKL[current_from_version]["model_name"] input_model_path = pytorch_test_dir / "cpp" / "jit" / model_name # A temporary model file will be export to this path, and run through bytecode.pkl # content check. tmp_output_model_path_backport = Path(tmpdirname, "tmp_script_module_backport.ptl") current_to_version = current_from_version - 1 backport_success = _backport_for_mobile(input_model_path, tmp_output_model_path_backport, current_to_version) assert(backport_success) expect_bytecode_pkl = SCRIPT_MODULE_BYTECODE_PKL[current_to_version]["bytecode_pkl"] buf = io.StringIO() torch.utils.show_pickle.main( ["", tmpdirname + "/" + tmp_output_model_path_backport.name + "@*/bytecode.pkl"], output_stream=buf) output = buf.getvalue() acutal_result_clean = "".join(output.split()) expect_result_clean = "".join(expect_bytecode_pkl.split()) isMatch = fnmatch.fnmatch(acutal_result_clean, expect_result_clean) assert(isMatch) current_from_version -= 1 shutil.rmtree(tmpdirname) # Please run this test manually when working on backport. # This test passes in OSS, but fails internally, likely due to missing step in build # def test_all_backport_functions(self): # # Backport from the latest bytecode version to the minimum support version # # Load, run the backport model, and check version # class TestModule(torch.nn.Module): # def __init__(self, v): # super().__init__() # self.x = v # def forward(self, y: int): # increment = torch.ones([2, 4], dtype=torch.float64) # return self.x + y + increment # module_input = 1 # expected_mobile_module_result = 3 * torch.ones([2, 4], dtype=torch.float64) # # temporary input model file and output model file will be exported in the temporary folder # with tempfile.TemporaryDirectory() as tmpdirname: # tmp_input_model_path = Path(tmpdirname, "tmp_script_module.ptl") # script_module = torch.jit.script(TestModule(1)) # optimized_scripted_module = optimize_for_mobile(script_module) # exported_optimized_scripted_module = optimized_scripted_module._save_for_lite_interpreter(str(tmp_input_model_path)) # current_from_version = _get_model_bytecode_version(tmp_input_model_path) # current_to_version = current_from_version - 1 # tmp_output_model_path = Path(tmpdirname, "tmp_script_module_backport.ptl") # while current_to_version >= MINIMUM_TO_VERSION: # # Backport the latest model to `to_version` to a tmp file "tmp_script_module_backport" # backport_success = _backport_for_mobile(tmp_input_model_path, tmp_output_model_path, current_to_version) # assert(backport_success) # backport_version = _get_model_bytecode_version(tmp_output_model_path) # assert(backport_version == current_to_version) # # Load model and run forward method # mobile_module = _load_for_lite_interpreter(str(tmp_input_model_path)) # mobile_module_result = mobile_module(module_input) # torch.testing.assert_close(mobile_module_result, expected_mobile_module_result) # current_to_version -= 1 # # Check backport failure case # backport_success = _backport_for_mobile(tmp_input_model_path, tmp_output_model_path, MINIMUM_TO_VERSION - 1) # assert(not backport_success) # # need to clean the folder before it closes, otherwise will run into git not clean error # shutil.rmtree(tmpdirname) # Check just the test_backport_bytecode_from_file_to_file mechanism but not the function implementations def test_backport_bytecode_from_file_to_file(self): maximum_checked_in_model_version = max(SCRIPT_MODULE_BYTECODE_PKL.keys()) script_module_v5_path = pytorch_test_dir / "cpp" / "jit" / SCRIPT_MODULE_BYTECODE_PKL[ maximum_checked_in_model_version]["model_name"] if (maximum_checked_in_model_version > MINIMUM_TO_VERSION): with tempfile.TemporaryDirectory() as tmpdirname: tmp_backport_model_path = Path(tmpdirname, "tmp_script_module_v5_backported_to_v4.ptl") # backport from file success = _backport_for_mobile( script_module_v5_path, tmp_backport_model_path, maximum_checked_in_model_version - 1) assert(success) buf = io.StringIO() torch.utils.show_pickle.main( ["", tmpdirname + "/" + tmp_backport_model_path.name + "@*/bytecode.pkl"], output_stream=buf) output = buf.getvalue() expected_result = SCRIPT_MODULE_V4_BYTECODE_PKL acutal_result_clean = "".join(output.split()) expect_result_clean = "".join(expected_result.split()) isMatch = fnmatch.fnmatch(acutal_result_clean, expect_result_clean) assert(isMatch) # Load model v4 and run forward method mobile_module = _load_for_lite_interpreter(str(tmp_backport_model_path)) module_input = 1 mobile_module_result = mobile_module(module_input) expected_mobile_module_result = 3 * torch.ones([2, 4], dtype=torch.float64) torch.testing.assert_close(mobile_module_result, expected_mobile_module_result) shutil.rmtree(tmpdirname) # Check just the _backport_for_mobile_to_buffer mechanism but not the function implementations def test_backport_bytecode_from_file_to_buffer(self): maximum_checked_in_model_version = max(SCRIPT_MODULE_BYTECODE_PKL.keys()) script_module_v5_path = pytorch_test_dir / "cpp" / "jit" / SCRIPT_MODULE_BYTECODE_PKL[ maximum_checked_in_model_version]["model_name"] if (maximum_checked_in_model_version > MINIMUM_TO_VERSION): # Backport model to v4 script_module_v4_buffer = _backport_for_mobile_to_buffer( script_module_v5_path, maximum_checked_in_model_version - 1) buf = io.StringIO() # Check version of the model v4 from backport bytesio = io.BytesIO(script_module_v4_buffer) backport_version = _get_model_bytecode_version(bytesio) assert(backport_version == maximum_checked_in_model_version - 1) # Load model v4 from backport and run forward method bytesio = io.BytesIO(script_module_v4_buffer) mobile_module = _load_for_lite_interpreter(bytesio) module_input = 1 mobile_module_result = mobile_module(module_input) expected_mobile_module_result = 3 * torch.ones([2, 4], dtype=torch.float64) torch.testing.assert_close(mobile_module_result, expected_mobile_module_result) def test_get_model_ops_and_info(self): # TODO update this to be more in the style of the above tests after a backport from 6 -> 5 exists script_module_v6 = pytorch_test_dir / "cpp" / "jit" / "script_module_v6.ptl" ops_v6 = _get_model_ops_and_info(script_module_v6) assert(ops_v6["aten::add.int"].num_schema_args == 2) assert(ops_v6["aten::add.Scalar"].num_schema_args == 2) if __name__ == '__main__': run_tests()

[ "[email protected]" ]

[email protected]

a6269498158572202304da939470fc4fdd2e3b1f

060ce17de7b5cdbd5f7064d1fceb4ded17a23649

/fn_github/tests/test_releases.py

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ibmresilient/resilient-community-apps

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# -*- coding: utf-8 -*- """Tests using pytest_resilient_circuits""" import pytest from .common_config import github_config, TS from resilient_circuits.util import get_config_data, get_function_definition from resilient_circuits import SubmitTestFunction, FunctionResult PACKAGE_NAME = "fn_github" FUNCTION_NAME = "github_create_release" # Read the default configuration-data section from the package config_data = get_config_data(PACKAGE_NAME) # Provide a simulation of the Resilient REST API (uncomment to connect to a real appliance) resilient_mock = "pytest_resilient_circuits.BasicResilientMock" def call_function(circuits, function_name, function_params, timeout=5): # Create the submitTestFunction event evt = SubmitTestFunction(function_name, function_params) # Fire a message to the function circuits.manager.fire(evt) # circuits will fire an "exception" event if an exception is raised in the FunctionComponent # return this exception if it is raised exception_event = circuits.watcher.wait("exception", parent=None, timeout=timeout) if exception_event is not False: exception = exception_event.args[1] raise exception # else return the FunctionComponent's results else: event = circuits.watcher.wait(f"{function_name}_result", parent=evt, timeout=timeout) assert event assert isinstance(event.kwargs["result"], FunctionResult) pytest.wait_for(event, "complete", True) return event.kwargs["result"].value def call_github_create_release_function(circuits, function_params, timeout=5): # Create the submitTestFunction event evt = SubmitTestFunction("github_create_release", function_params) # Fire a message to the function circuits.manager.fire(evt) # circuits will fire an "exception" event if an exception is raised in the FunctionComponent # return this exception if it is raised exception_event = circuits.watcher.wait("exception", parent=None, timeout=timeout) if exception_event is not False: exception = exception_event.args[1] raise exception # else return the FunctionComponent's results else: event = circuits.watcher.wait("github_create_release_result", parent=evt, timeout=timeout) assert event assert isinstance(event.kwargs["result"], FunctionResult) pytest.wait_for(event, "complete", True) return event.kwargs["result"].value class TestGithubCreateRelease: """ Tests for the github_create_release function""" def test_function_definition(self): """ Test that the package provides customization_data that defines the function """ func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME) assert func is not None @pytest.mark.livetest def test_create_release(self, circuits_app): """ Test calling with sample values for the parameters """ create_release_setup = github_config('create_release') create_release_setup['github_release_name'] = f"{create_release_setup['github_release_name']}_{TS.strftime('%Y%m%d_%H%M%S')}" create_release_setup['github_release_tag'] = f"{create_release_setup['github_release_tag']}_{TS.strftime('%Y%m%d_%H%M%S')}" results = call_function(circuits_app, "github_create_release", create_release_setup) assert(results['success']) @pytest.mark.livetest def test_get_release(self, circuits_app): """ Test calling with sample values for the parameters """ get_release_setup = github_config('get_release') get_release_setup['github_release_tag'] = f"{get_release_setup['github_release_tag']}_{TS.strftime('%Y%m%d_%H%M%S')}" results = call_function(circuits_app, "github_get_release", get_release_setup) assert(results['success']) assert(results['content']) @pytest.mark.livetest def test_get_releases(self, circuits_app): get_releases_setup = github_config('get_releases') results = call_function(circuits_app, "github_get_releases", get_releases_setup) assert(results['success']) assert(results['content']) @pytest.mark.livetest def test_get_latest_release(self, circuits_app): get_releases_setup = github_config('get_latest_release') results = call_function(circuits_app, "github_get_latest_release", get_releases_setup) assert(results['success']) assert(results['content'])

[ "[email protected]" ]

[email protected]

8ebabb8929c847e3c9edcd7a71bcd0940adfa0c2

d44bfb67b8b19f3773558870a71a42e0cd3ec002

/telemetry-library/telemetry/telemetry_mqtt.py

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Abstract-Horizon/pyros-telemetry

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################################################################################ # Copyright (C) 2016-2020 Abstract Horizon # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License v2.0 # which accompanies this distribution, and is available at # https://www.apache.org/licenses/LICENSE-2.0 # # Contributors: # Daniel Sendula - initial API and implementation # ################################################################################# import paho.mqtt.client as mqtt import random import re import sys import threading import time import traceback from telemetry.telemetry_logger import TelemetryLogger, LocalPipeTelemetryLoggerDestination, PubSubTelemetryLoggerClient from telemetry.telemetry_client import PubSubTelemetryClient class MQTTLocalPipeTelemetryLogger(TelemetryLogger): def __init__(self, stream_name, host="localhost", port=1883, topic='telemetry'): self.mqtt = MQTTWrapper(host, port) super(MQTTLocalPipeTelemetryLogger, self).__init__(stream_name, destination=LocalPipeTelemetryLoggerDestination(), telemetry_client=PubSubTelemetryLoggerClient(topic, self.mqtt.publish, self.mqtt.subscribe)) def init(self): while not self.mqtt.is_connected(): self.mqtt.loop(0.02) super(MQTTLocalPipeTelemetryLogger, self).init() while not self.stream_ready and self.registration_error == 0: self.mqtt.loop(0.02) class MQTTTelemetryClient(PubSubTelemetryClient): def __init__(self, host="localhost", port=1883, topic='telemetry'): self.mqtt = MQTTWrapper(host, port) super(MQTTTelemetryClient, self).__init__(topic, self.mqtt.publish, self.mqtt.subscribe) class MQTTWrapper: def __init__(self, host="localhost", port=1883, auto_init=True): self.client = None self.host = host self.port = port self.name = "telemetry-server-" + str(random.randint(10000, 99999)) self._subscribers = [] self._regexToLambda = {} self._received = False self.connected = False if auto_init: self.init() def init(self, wait_to_connect=True): self.client = mqtt.Client(self.name) self.client.on_disconnect = self._on_disconnect self.client.on_connect = self._on_connect self.client.on_message = self._on_message if self.host is not None: self._connect() if wait_to_connect: print(" " + self.name + " waiting to connect to broker...") while not self.connected: self.loop(0.02) print(" " + self.name + " connected to broker.") def _connect(self): self.connected = False if self.client is not None: try: self.client.disconnect() except Exception: pass self.client.connect_async(self.host, self.port, 60) thread = threading.Thread(target=self._reconnect) thread.daemon = True thread.start() def _on_disconnect(self, _mqtt_client, _data, _rc): self._connect() def _on_connect(self, mqtt_client, _data, _flags, rc): if rc == 0: self.connected = True for subscriber in self._subscribers: mqtt_client.subscribe(subscriber, 0) else: print("ERROR: Connection returned error result: " + str(rc)) sys.exit(rc) def _on_message(self, _mqtt_client, _data, msg): global _received _received = True topic = msg.topic try: for regex in self._regexToLambda: matching = regex.match(topic) if matching: method = self._regexToLambda[regex] method(topic, msg.payload) return except Exception as ex: print("ERROR: Got exception in on message processing; " + str(ex) + "\n" + ''.join(traceback.format_tb(ex.__traceback__))) def _reconnect(self): try: self.client.reconnect() except Exception: pass def publish(self, topic, message): if self.connected: self.client.publish(topic, message) def subscribe(self, topic, method): self._subscribers.append(topic) regex_string = "^" + topic.replace("+", "([^/]+)").replace("#", "(.*)") + "$" regex = re.compile(regex_string) self._regexToLambda[regex] = method if self.connected: self.client.subscribe(topic, 0) def is_connected(self): return self.connected def sleep(self, delta_time): self.loop(self, delta_time) def loop(self, delta_time, _inner=None): current_time = time.time() self._received = False self.client.loop(0.0005) # wait for 0.5 ms until = current_time + delta_time while current_time < until: if self._received: self._received = False self.client.loop(0.0005) # wait for 0.1 ms current_time = time.time() else: time.sleep(0.002) # wait for 2 ms current_time = time.time() if current_time + 0.0005 < until: self.client.loop(0.0005) # wait for 0.1 ms current_time = time.time() def forever(self, delta_time, outer=None, inner=None): current_time = time.time() next_time = current_time while True: next_time = next_time + delta_time try: if outer is not None: outer() except BaseException as ex: print("ERROR: Got exception in main loop; " + str(ex) + "\n" + ''.join(traceback.format_tb(ex.__traceback__))) current_time = time.time() sleep_time = next_time - current_time if sleep_time < 0.002: next_time = current_time self._received = False self.client.loop(0.0005) # wait for 0.1 ms count = 10 # allow at least 5 messages while count > 0 and self._received: self._received = True count -= 1 self.client.loop(0.0005) # wait for 0.1 ms else: self.loop(sleep_time, inner=inner)

[ "[email protected]" ]

[email protected]

466373d76ca24cb2a34c4824c5097fc46feafc28

88cc55bc6e6feee2fa5f186d57ccb5e1d420217c

/django_websocket/__init__.py

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gregmuellegger/django-websocket

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from django_websocket.decorators import *

[ "[email protected]" ]

[email protected]

09da46de08db8efd21ef86e80c0bd1b0bfa4641f

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/sdBs/AllRun/fbs_0140+360/sdB_FBS_0140+360_lc.py

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tboudreaux/SummerSTScICode

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refs/heads/master

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from gPhoton.gAperture import gAperture def main(): gAperture(band="NUV", skypos=[25.926708,36.25925], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_FBS_0140+360 /sdB_FBS_0140+360_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3) if __name__ == "__main__": main()

[ "[email protected]" ]

[email protected]

0f923332d74ab03086681ff9097adf5ed4fd7603

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/ml_wiki/ch2/selenium-login.py

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smart1004/learn_src

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from selenium import webdriver USER = "<아이디>" PASS = "<비밀번호>" # PhantomJS 드라이버 추출하기 --- (※1) browser = webdriver.PhantomJS() browser.implicitly_wait(3) # 로그인 페이지에 접근하기 --- (※2) url_login = "https://nid.naver.com/nidlogin.login" browser.get(url_login) print("로그인 페이지에 접근합니다.") # 텍스트 박스에 아이디와 비밀번호 입력하기 --- (※3) e = browser.find_element_by_id("id") e.clear() e.send_keys(USER) e = browser.find_element_by_id("pw") e.clear() e.send_keys(PASS) # 입력 양식 전송해서 로그인하기 --- (※4) form = browser.find_element_by_css_selector("input.btn_global[type=submit]") form.submit() print("로그인 버튼을 클릭합니다.") # 쇼핑 페이지의 데이터 가져오기 --- (※5) browser.get("https://order.pay.naver.com/home?tabMenu=SHOPPING") # 쇼핑 목록 출력하기 --- (※6) products = browser.find_elements_by_css_selector(".p_info span") print(products) for product in products: print("-", product.text)

[ "[email protected]" ]

[email protected]

38459d585c7f1861e8774c7571859a85236be08b

6515a47190986c4f3b6beececfabab42e3d34e34

/Models/GPT2_Model/Model/GPT2LMHead.py

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jk96491/Advanced_Models

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refs/heads/master

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import torch.nn as nn class GPT2LMHead(nn.Module): def __init__(self, model_embeddings_weights, config): super(GPT2LMHead, self).__init__() self.n_embd = config.n_embd self.set_embeddings_weights(model_embeddings_weights) def set_embeddings_weights(self, model_embeddings_weights): embed_shape = model_embeddings_weights.shape self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False) self.decoder.weight = model_embeddings_weights # Tied weights def forward(self, hidden_state): # Truncated Language modeling logits (we remove the last token) # h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd) lm_logits = self.decoder(hidden_state) return lm_logits

[ "[email protected]" ]

[email protected]

1e236e30d75c559339f1261b732a9a70d9df7122

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/Can Place Flowers.py

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no_license

Kuehar/LeetCode

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refs/heads/master

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class Solution: def canPlaceFlowers(self, flowerbed: List[int], n: int) -> bool: if n == 0: return True if len(flowerbed) == 0: return False if len(flowerbed) == 1: return flowerbed[0] == 0 pre,cur = flowerbed[0],flowerbed[1] if pre + cur == 0: flowerbed[0] = 1 n -= 1 cur,nex = flowerbed[-1],flowerbed[-2] if cur + nex == 0: flowerbed[-1] = 1 n -= 1 for i in range(2,len(flowerbed)-2): pre = flowerbed[i-1] cur = flowerbed[i] nex = flowerbed[i+1] if (pre + cur + nex) == 0: flowerbed[i] = 1 n -= 1 return n <= 0 # Runtime: 164 ms, faster than 58.48% of Python3 online submissions for Can Place Flowers. # Memory Usage: 14.5 MB, less than 89.00% of Python3 online submissions for Can Place Flowers.

[ "[email protected]" ]

[email protected]

2e79679ebdc6ebb91b85f95ac5bccc7866b865ab

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# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import os import uuid from openstackclient.tests.functional import base class ImageTests(base.TestCase): """Functional tests for image. """ NAME = uuid.uuid4().hex OTHER_NAME = uuid.uuid4().hex HEADERS = ['Name'] FIELDS = ['name'] @classmethod def setUpClass(cls): os.environ['OS_IMAGE_API_VERSION'] = '2' opts = cls.get_opts(cls.FIELDS) raw_output = cls.openstack('image create ' + cls.NAME + opts) expected = cls.NAME + '\n' cls.assertOutput(expected, raw_output) @classmethod def tearDownClass(cls): # Rename test raw_output = cls.openstack('image set --name ' + cls.OTHER_NAME + ' ' + cls.NAME) cls.assertOutput('', raw_output) # Delete test raw_output = cls.openstack('image delete ' + cls.OTHER_NAME) cls.assertOutput('', raw_output) def test_image_list(self): opts = self.get_opts(self.HEADERS) raw_output = self.openstack('image list' + opts) self.assertIn(self.NAME, raw_output) def test_image_show(self): opts = self.get_opts(self.FIELDS) raw_output = self.openstack('image show ' + self.NAME + opts) self.assertEqual(self.NAME + "\n", raw_output) def test_image_set(self): opts = self.get_opts([ "disk_format", "visibility", "min_disk", "min_ram", "name"]) self.openstack('image set --min-disk 4 --min-ram 5 ' + '--public ' + self.NAME) raw_output = self.openstack('image show ' + self.NAME + opts) self.assertEqual("raw\n4\n5\n" + self.NAME + '\npublic\n', raw_output) def test_image_metadata(self): opts = self.get_opts(["name", "properties"]) self.openstack('image set --property a=b --property c=d ' + self.NAME) raw_output = self.openstack('image show ' + self.NAME + opts) self.assertEqual(self.NAME + "\na='b', c='d'\n", raw_output) def test_image_unset(self): opts = self.get_opts(["name", "tags", "properties"]) self.openstack('image set --tag 01 ' + self.NAME) self.openstack('image unset --tag 01 ' + self.NAME) # test_image_metadata has set image properties "a" and "c" self.openstack('image unset --property a --property c ' + self.NAME) raw_output = self.openstack('image show ' + self.NAME + opts) self.assertEqual(self.NAME + "\n\n", raw_output) def test_image_members(self): opts = self.get_opts(['project_id']) my_project_id = self.openstack('token issue' + opts).strip() self.openstack( 'image add project {} {}'.format(self.NAME, my_project_id)) self.openstack( 'image set --accept ' + self.NAME) shared_img_list = self.parse_listing( self.openstack('image list --shared', self.get_opts(['name'])) ) self.assertIn(self.NAME, [img['Name'] for img in shared_img_list]) self.openstack( 'image set --reject ' + self.NAME) shared_img_list = self.parse_listing( self.openstack('image list --shared', self.get_opts(['name'])) ) self.openstack( 'image remove project {} {}'.format(self.NAME, my_project_id))

[ "[email protected]" ]

[email protected]

4fe46a3e69863bca6e98c1cb6ab5c17fd36f8261

5c531de5e4759c904e608b4fc653b2b041f79a0e

/779. K-th Symbol in Grammar.py

06e46cf683b3f090a9b595db7b7a9fd6675029aa

[]

no_license

jianhui-ben/leetcode_python

133c7e6e5c7316d00607ba2e327239e002de28b2

fcc16124cc24a5993e27f5d97e78d8f290e68230

refs/heads/master

2022-06-05T22:32:18.034581

2022-05-17T02:27:11

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#779. K-th Symbol in Grammar #On the first row, we write a 0. Now in every subsequent row, we look at the previous row and replace each occurrence of 0 with 01, and each occurrence of 1 with 10. #Given row N and index K, return the K-th indexed symbol in row N. (The values of K are 1-indexed.) (1 indexed). #Examples: #Input: N = 1, K = 1 #Output: 0 #Input: N = 2, K = 1 #Output: 0 #Input: N = 2, K = 2 #Output: 1 #Input: N = 4, K = 5 #Output: 1 class Solution: def kthGrammar(self, N: int, K: int) -> int: ## recursion if N==1: return 0 if K%2==1: return self.kthGrammar(N-1,(K+1)//2) else: return 1-self.kthGrammar(N-1,(K+1)//2)

[ "[email protected]" ]

[email protected]

5adf7e2ecf8e0908041fd9b472f7833397d98c44

e990606b3db8429e87f9a0a5a10e3c10b17ae13b

/data_proc/wordpiece/mlperf_tags.py

88313a6ba9df4bb9205a4755d963ed60a5c9aa46

[]

no_license

michael-wzhu/redesign_vocab_for_zh_bert

4c9ae36295815ea58cfb34d4f46ac2ace097861f

4e7e72e8f4554feebba6ae2f553a5240e5bedcd4

refs/heads/master

2022-12-16T17:41:09.704451

2020-09-26T12:06:35

274,569,222

1

0

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UTF-8

Python

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12,882

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# coding=utf-8 # Copyright 2020 The Tensor2Tensor Authors. # # 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. # Copyright 2018 MLBenchmark Group. 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. # ============================================================================== """Master list of MLPerf tags to be logged for benchmark submissions. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function # ============================================================================== # == Benchmarks ================================================================ # ============================================================================== # translation/ TRANSFORMER = "transformer" INPUT_MAX_LENGTH = "input_max_length" OPT_LR_WARMUP_STEPS = "opt_learning_rate_warmup_steps" MODEL_HP_INITIALIZER_GAIN = "model_hp_initializer_gain" MODEL_HP_VOCAB_SIZE = "model_hp_vocab_size" MODEL_HP_NUM_HIDDEN_LAYERS = "model_hp_hidden_layers" MODEL_HP_EMBEDDING_SHARED_WEIGHTS = "model_hp_embedding_shared_weights" MODEL_HP_ATTENTION_DENSE = "model_hp_attention_dense" MODEL_HP_ATTENTION_DROPOUT = "model_hp_attention_dropout" MODEL_HP_FFN_OUTPUT_DENSE = "model_hp_ffn_output_dense" MODEL_HP_FFN_FILTER_DENSE = "model_hp_ffn_filter_dense" MODEL_HP_RELU_DROPOUT = "model_hp_relu_dropout" MODEL_HP_LAYER_POSTPROCESS_DROPOUT = "model_hp_layer_postprocess_dropout" MODEL_HP_NORM = "model_hp_norm" MODEL_HP_SEQ_BEAM_SEARCH = "model_hp_sequence_beam_search" # ============================================================================== # == Tags ====================================================================== # ============================================================================== """ Tags may be used by all models, a subset of models, or only one model. A specification for which models require which tags can be found below the tag definitions. """ # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # All models: Tags which should appear in absolutely every MLPerf model. # ////////////////////////////////////////////////////////////////////////////// # This tag signals to start the timer. Emission of this tag need not be (and # generally will not be) the first part of a submission script. Rather, this # tag must be emitted prior to performing any work which the MLPerf rules # state must be timed. This tag is generally emitted directly before the first # step which invokes random number generation or the first step which must be # performed on the system under test. (Whichever comes first.) If clarification # is needed, please file an issue under: # https://github.com/mlperf/policies RUN_START = "run_start" # This tag signals that a submission has reached the relevant stopping criteria, # and has completed all tasks which are performed in the reference. The wall # time for a submission will be computed as the difference between the time # when this tag is emitted and the time whe the RUN_START is emitted. RUN_STOP = "run_stop" # This tag should be emitted immediately before ending a run, and should be the # last tag emitted. This tag should indicate the completion of untimed post # processing work such as system specific cleanup. RUN_FINAL = "run_final" # Emit this tag in the place(s) where random seeds are set. RUN_SET_RANDOM_SEED = "run_set_random_seed" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Common Values: Constants which are expected to be reported across many models. # These values are included for convenience. # ////////////////////////////////////////////////////////////////////////////// BCE = "binary_cross_entropy" CCE = "categorical_cross_entropy" SGD = "stochastic_gradient_descent" # Some conventions distinguish between "vanilla" SGD and SGD with momentum # (where vanilla SGD would be the specific case of momentum=0) SGD_WITH_MOMENTUM = "stochastic_gradient_descent_with_momentum" ADAM = "adam" LAZY_ADAM = "lazy_adam" TRUNCATED_NORMAL = "truncated_normal" RELU = "relu" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Preprocessing: Tags for generic preprocessing steps # ////////////////////////////////////////////////////////////////////////////// # The number of training examples in a single epoch PREPROC_NUM_TRAIN_EXAMPLES = "preproc_num_train_examples" # The number of evaluation examples in a single epoch PREPROC_NUM_EVAL_EXAMPLES = "preproc_num_eval_examples" # This tag is used to declare what part of code tokenizes the training data. PREPROC_TOKENIZE_TRAINING = "preproc_tokenize_training" # This tag is used to declare what part of code tokenizes the evaluation data. PREPROC_TOKENIZE_EVAL = "preproc_tokenize_eval" # The vocabulary size used for tokenization. PREPROC_VOCAB_SIZE = "preproc_vocab_size" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Input: Tags for the timed portion of the data input pipeline # ////////////////////////////////////////////////////////////////////////////// # The number of examples in the training portion of the data pipeline. Generally # this should match PREPROC_NUM_TRAIN_EXAMPLES. If it does not (for instance # if certain examples are dropped in compliance with MLPerf rules), the # call which declares this tag is a good place for a comment stating why the # disparity is expected. INPUT_SIZE = "input_size" # The size of a training minibatch size. If this value is variable, please emit # "-1" and then log an implementation specific characterization of the batch # size which is a reasonable analog to the reference. (For instance log that # all but the last batch has size 64, and the last batch is a partial batch) INPUT_BATCH_SIZE = "input_batch_size" # This tag indicates where the location of the code which defines the order in # which training examples are traversed. It is not necessary to describe the # method in the tag emission (though comments are always welcome). Rather, this # should simply provide a good starting point to an interested party. INPUT_ORDER = "input_order" # -------------------------------------- # -- Data Augmentation and Alteration -- # -------------------------------------- # ResNet random cropping INPUT_CENTRAL_CROP = "input_central_crop" INPUT_DISTORTED_CROP_MIN_OBJ_COV = "input_distorted_crop_min_object_covered" INPUT_DISTORTED_CROP_RATIO_RANGE = "input_distorted_crop_aspect_ratio_range" INPUT_DISTORTED_CROP_AREA_RANGE = "input_distorted_crop_area_range" INPUT_DISTORTED_CROP_MAX_ATTEMPTS = "input_distorted_crop_max_attempts" INPUT_MEAN_SUBTRACTION = "input_mean_subtraction" # Random flip of an image for data augmentation INPUT_RANDOM_FLIP = "input_random_flip" INPUT_RESIZE = "input_resize" INPUT_RESIZE_ASPECT_PRESERVING = "input_resize_aspect_preserving" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Opt: Tags for declaring optimizer specific information. Submissions should # declare and log explicit values rather than relying on defaults. # ////////////////////////////////////////////////////////////////////////////// # The name of the optimizer used. (SGD, Adam, etc.) OPT_NAME = "opt_name" OPT_LR = "opt_learning_rate" OPT_MOMENTUM = "opt_momentum" OPT_WEIGHT_DECAY = "opt_weight_decay" # beta1, beta2, and epsilon are optimizer hyperparameters associated with the # Adam optimizer and its variants (e.g. LazyAdam). OPT_HP_ADAM_BETA1 = "opt_hp_Adam_beta1" OPT_HP_ADAM_BETA2 = "opt_hp_Adam_beta2" OPT_HP_ADAM_EPSILON = "opt_hp_Adam_epsilon" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Train: Tags for control flow during model training. # ////////////////////////////////////////////////////////////////////////////// # This tag is emitted when a model first enters its training loop. This is not # necessarily when it begins to apply gradients; rather, it should be placed at # a location which logically partitions the submission code. TRAIN_LOOP = "train_loop" # The current epoch as said epoch begins training. TRAIN_EPOCH = "train_epoch" # This tag is used to indicate approximately where checkpoints are written. Some # frameworks abstract away checkpoint saving; in such cases simply choose a # logical place in the code which signals that the framework has been instructed # to save checkpoints, along with an explanatory comment. TRAIN_CHECKPOINT = "train_checkpoint" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Eval: Tags for control flow during model evaluation. # ////////////////////////////////////////////////////////////////////////////// # This tag should be emitted whenever the submission begins an evaluation pass # for a given set of weights. EVAL_START = "eval_start" # The number of examples on which evaluation is performed. EVAL_SIZE = "eval_size" # The target quality at which the model may stop training. EVAL_TARGET = "eval_target" # The observed accuracy of the model at a given epoch. EVAL_ACCURACY = "eval_accuracy" # This tag should be emitted when the model has determined that it has met the # target quality set by the reference. EVAL_STOP = "eval_stop" # \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ # Model: Tags for logging topology specific information. # ////////////////////////////////////////////////////////////////////////////// # The loss function (cross entropy, squared error, etc.) used by the model. For # more exotic loss functions such as those encountered in object detection # models, additional benchmark specific subcomponents should also be logged. MODEL_HP_LOSS_FN = "model_hp_loss_fn" MODEL_HP_INITIAL_SHAPE = "model_hp_initial_shape" MODEL_HP_FINAL_SHAPE = "model_hp_final_shape" MODEL_L2_REGULARIZATION = "model_l2_regularization" MODEL_EXCLUDE_BN_FROM_L2 = "model_exclude_bn_from_l2" MODEL_HP_RELU = "model_hp_relu" MODEL_HP_CONV2D_FIXED_PADDING = "model_hp_conv2d_fixed_padding" MODEL_HP_BATCH_NORM = "model_hp_batch_norm" MODEL_HP_DENSE = "model_hp_dense" # ============================================================================== # == Stdout tags =============================================================== # ============================================================================== # These tags are always logged to stdout. The rest will be logged to a file if # one is available. STDOUT_TAG_SET = { RUN_START, RUN_STOP, RUN_FINAL, TRAIN_LOOP, TRAIN_EPOCH, EVAL_START, EVAL_SIZE, EVAL_TARGET, EVAL_ACCURACY, EVAL_STOP, } # ============================================================================== # == Benchmark tag sets ======================================================== # ============================================================================== ALL_USED_TAGS = set() TRANSFORMER_TAGS = ( RUN_START, RUN_STOP, RUN_FINAL, RUN_SET_RANDOM_SEED, PREPROC_NUM_TRAIN_EXAMPLES, PREPROC_NUM_EVAL_EXAMPLES, PREPROC_TOKENIZE_TRAINING, PREPROC_TOKENIZE_EVAL, PREPROC_VOCAB_SIZE, INPUT_BATCH_SIZE, INPUT_MAX_LENGTH, INPUT_ORDER, OPT_NAME, OPT_LR, OPT_LR_WARMUP_STEPS, OPT_HP_ADAM_BETA1, OPT_HP_ADAM_BETA2, OPT_HP_ADAM_EPSILON, TRAIN_LOOP, TRAIN_EPOCH, EVAL_START, EVAL_SIZE, EVAL_TARGET, EVAL_ACCURACY, EVAL_STOP, MODEL_HP_INITIALIZER_GAIN, MODEL_HP_VOCAB_SIZE, MODEL_HP_NUM_HIDDEN_LAYERS, MODEL_HP_EMBEDDING_SHARED_WEIGHTS, MODEL_HP_ATTENTION_DENSE, MODEL_HP_ATTENTION_DROPOUT, MODEL_HP_FFN_OUTPUT_DENSE, MODEL_HP_FFN_FILTER_DENSE, MODEL_HP_RELU_DROPOUT, MODEL_HP_LAYER_POSTPROCESS_DROPOUT, MODEL_HP_NORM, MODEL_HP_SEQ_BEAM_SEARCH, ) ALL_USED_TAGS.update(TRANSFORMER_TAGS)

[ "[email protected]" ]

[email protected]

e806e3218cd8bc55131bce1e7d166c76ad1ec718

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03288/s779086883.py

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[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

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py

r=int(input()) print("ABC"if r<1200else"ARC"if r<2800else"AGC")

[ "[email protected]" ]

[email protected]

600d648aef968fa6d9aaf3ddd8d410059382df4b

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/wsc_django/wsc_django/apps/shop/services.py

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permissive

hzh595395786/wsc_django

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2023-06-06T07:26:17.979944

2021-06-24T13:14:53

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from uuid import uuid4 from django.db.models import Count from product.constant import ProductStatus from shop.models import Shop, HistoryRealName, ShopRejectReason, PayChannel from shop.utils import get_shop_mini_program_qcode, put_qcode_file_to_tencent_cos from user.models import User from shop.constant import ( ShopStatus, ) def create_shop(shop_info: dict, user: User): """ 创建一个商铺 :param shop_info:{ "shop_name": "name", "shop_img": "http://xxx", "shop_province": 420000, "shop_city": 420100, "shop_county": 420101, "shop_address": "光谷智慧谷一栋505", "description": "xxxx", "suggest_phone": "153xxxxxxxx", "shop_phone": "152xxxxxxxx", "super_admin_id": 1 } :param user: 创建商铺的用户对象 :return: """ # 创建店铺 # 随机一个商铺编码, 查一下,万一重复就再来一个 while True: shop_code = str(uuid4())[-9:] shop = Shop.objects.filter(shop_code=shop_code) if not shop: break shop_info["shop_code"] = shop_code shop_info["shop_phone"] = user.phone shop_info["super_admin_id"] = user.id shop = Shop(**shop_info) shop.save() return shop def create_pay_channel(pay_channel_info: dict, shop_id: int): """ 创建一个商铺的pay_channel :param pay_channel_info: :param shop_id: :return: """ shop_pay_channel = PayChannel(shop_id=shop_id, **pay_channel_info) shop_pay_channel.save() return shop_pay_channel def create_shop_reject_reason_by_shop_id(shop_id: int, reject_reason: str): """ 给拒绝的商铺创建一个拒绝理由 :param shop_id: :return: """ reject_reason = ShopRejectReason(id=shop_id, reject_reason=reject_reason) reject_reason.save() return reject_reason def create_shop_creator_history_realname(shop_id: int, history_realname: str): """ 储存商铺创建者的历史真实姓名, 与店铺绑定 :param shop_id: :param history_realname: :return: """ history_realname = HistoryRealName(id=shop_id, realname=history_realname) history_realname.save() return history_realname def create_shop_mini_program_qcode(shop_code: str): """ 为商铺创建小程序码 :param shop_code: :return: """ qcode_file = get_shop_mini_program_qcode(shop_code) success, url = put_qcode_file_to_tencent_cos(qcode_file, shop_code) return success, url def update_shop_data(shop: Shop, args: dict): """ 修改商铺信息 :param shop: :param args: :return: """ for k, v in args.items(): setattr(shop, k, v) shop.save() return shop def get_shop_by_shop_code(shop_code: str, only_normal: bool = True): """ 通过shop_code获取shop对象 :param shop_code: 商铺编码 :param only_normal: 只查询正常 :return: """ shop = Shop.objects.filter(shop_code=shop_code) if shop and only_normal: shop = shop.filter(status=ShopStatus.NORMAL) shop = shop.first() return shop def get_shop_by_shop_id(shop_id: int, filter_close: bool = True): """ 通过商铺id获取商 :param shop_id: 商铺id :param filter_close: 不查询关闭的 :return: """ shop = Shop.objects.filter(id=shop_id) if shop and filter_close: shop = shop.exclude(status=ShopStatus.CLOSED) shop = shop.first() return shop def list_shop_by_shop_ids(shop_ids: list, filter_close: bool = True, role: int = 1): """ 通过ship_id列表查询商铺列表 :param shop_ids: :param filter_close:过滤关闭 :param role: 访问角色，1：为普通用户，2.为admin用户，普通用户访问时只能查到已审核的店铺 :return: """ shop_list_query = Shop.objects.filter(id__in=shop_ids) if shop_list_query and filter_close: shop_list_query = shop_list_query.exclude(status=ShopStatus.CLOSED) if role == 1: shop_list_query = shop_list_query.filter(status=ShopStatus.NORMAL) shop_list = shop_list_query.all() return shop_list def list_shop_by_shop_status(shop_status: int): """ 查询某一状态的所有商铺 :param shop_status: :return: """ shop_list = Shop.objects.filter(status=shop_status).order_by('update_at').all() return shop_list def list_shop_creator_history_realname(shop_ids: list): """ 找出商铺创建的历史真实姓名列表 :param shop_ids: :return: """ history_realname_list = ( HistoryRealName.objects.filter(id__in=shop_ids).all() ) return history_realname_list def list_shop_reject_reason(shop_ids: list): """查询出所有的商铺拒绝信息""" reject_reason_list = ShopRejectReason.objects.filter(id__in=shop_ids).all() return reject_reason_list

[ "[email protected]" ]

[email protected]

a4988105b8f44db42f20393940d9d3a3ae4e6178

e23a4f57ce5474d468258e5e63b9e23fb6011188

/125_algorithms/_exercises/templates/_algorithms_challenges/pybites/intermediate/191/bmi.py

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syurskyi/Python_Topics

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refs/heads/master

2023-06-08T19:29:16.214395

2023-05-29T17:09:11

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3

2

null

2023-02-16T03:08:10

2019-11-09T02:58:47

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# data """Luke Skywalker,172,77 # C-3PO,167,75 # R2-D2,96,32 # Darth Vader,202,136 # Leia Organa,150,49 # Owen Lars,178,120 # Beru Whitesun lars,165,75 # R5-D4,97,32 # Biggs Darklighter,183,84 # Obi-Wan Kenobi,182,77 # Anakin Skywalker,188,84 # Chewbacca,228,112 # Han Solo,180,80 # Greedo,173,74 # Jek Tono Porkins,180,110 # Yoda,66,17 # Palpatine,170,75 # Boba Fett,183,78.2 # IG-88,200,140 # Bossk,190,113 # """ # # # ___ person_max_bmi data_? # """Return (name, BMI float) of the character in data that # has the highest BMI (rounded on 2 decimals)""" # bmi # dict # data_list ?.s.. "\n" # # ___ row __ ? # current ?.s...s.. "," # __ l.. ? > 1 # ? ? 0 f__ c.. 2 / i.. ? 1 / 100) ** 2 # # name_max_bmi m.. b.. key b__.g.. # r.. ? r.. b.. ? 2 # # # if __name__ == "__main__": # # print(person_max_bmi())

[ "[email protected]" ]

[email protected]

6e0eaa8412f98de3cb193a1d8b771c2ac490c3db

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/PyTorch/dev/cv/image_classification/SimplePose_ID1038_for_PyTorch/parallel_encoding/paralle.py

22a4742535a0677b59076b72a684e6b7562466f4

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permissive

Ascend/ModelZoo-PyTorch

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refs/heads/master

2023-07-19T12:40:00.512853

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py

# # BSD 3-Clause License # # Copyright (c) 2017 xxxx # All rights reserved. # Copyright 2021 Huawei Technologies Co., Ltd # # 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 the copyright holder nor the names of its # contributors 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 HOLDER 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. # ============================================================================ # # #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # # Created by: Hang Zhang, Rutgers University, Email: [email protected] # # Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected] # # Copyright (c) 2017-2018 ## # # This source code is licensed under the MIT-style license found in the # # LICENSE file in the root directory of this source tree # #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ """Encoding Data Parallel""" import threading import functools import torch from torch.autograd import Variable, Function import torch.npu.comm as comm from torch.nn.parallel.data_parallel import DataParallel from torch.nn.parallel.distributed import DistributedDataParallel from torch.nn.parallel.parallel_apply import get_a_var from torch.nn.parallel.scatter_gather import gather from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast torch_ver = torch.__version__[:3] __all__ = ['allreduce', 'DataParallelModel', 'DataParallelCriterion', 'patch_replication_callback'] def allreduce(*inputs): """Cross GPU all reduce autograd operation for calculate mean and variance in SyncBN. """ return AllReduce.apply(*inputs) class AllReduce(Function): @staticmethod def forward(ctx, num_inputs, *inputs): ctx.num_inputs = num_inputs ctx.target_gpus = [inputs[i].get_device() for i in range(0, len(inputs), num_inputs)] inputs = [inputs[i:i + num_inputs] for i in range(0, len(inputs), num_inputs)] # sort before reduce sum inputs = sorted(inputs, key=lambda i: i[0].get_device()) results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0]) outputs = comm.broadcast_coalesced(results, ctx.target_gpus) return tuple([t for tensors in outputs for t in tensors]) @staticmethod def backward(ctx, *inputs): inputs = [i.data for i in inputs] inputs = [inputs[i:i + ctx.num_inputs] for i in range(0, len(inputs), ctx.num_inputs)] results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0]) outputs = comm.broadcast_coalesced(results, ctx.target_gpus) return (None,) + tuple([Variable(t) for tensors in outputs for t in tensors]) class Reduce(Function): @staticmethod def forward(ctx, *inputs): ctx.target_gpus = [inputs[i].get_device() for i in range(len(inputs))] inputs = sorted(inputs, key=lambda i: i.get_device()) return comm.reduce_add(inputs) @staticmethod def backward(ctx, gradOutput): return Broadcast.apply(ctx.target_gpus, gradOutput) class DistributedDataParallelModel(DistributedDataParallel): """Implements data parallelism at the module level for the DistributedDataParallel module. This container parallelizes the application of the given module by splitting the input across the specified devices by chunking in the batch dimension. In the forward pass, the module is replicated on each device, and each replica handles a portion of the input. During the backwards pass, gradients from each replica are summed into the original module. Note that the outputs are not gathered, please use compatible :class:`encoding.parallel.DataParallelCriterion`. The batch size should be larger than the number of GPUs used. It should also be an integer multiple of the number of GPUs so that each chunk is the same size (so that each GPU processes the same number of samples). Args: module: module to be parallelized device_ids: npu devices (default: all devices) Reference: Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. 鈥淐ontext Encoding for Semantic Segmentation. *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018* Example:: >>> net = encoding.nn.DistributedDataParallelModel(model, device_ids=[0, 1, 2]) >>> y = net(x) """ def gather(self, outputs, output_device): return outputs class DataParallelModel(DataParallel): """Implements data parallelism at the module level. This container parallelizes the application of the given module by splitting the input across the specified devices by chunking in the batch dimension. In the forward pass, the module is replicated on each device, and each replica handles a portion of the input. During the backwards pass, gradients from each replica are summed into the original module. Note that the outputs are not gathered, please use compatible :class:`encoding.parallel.DataParallelCriterion`. The batch size should be larger than the number of GPUs used. It should also be an integer multiple of the number of GPUs so that each chunk is the same size (so that each GPU processes the same number of samples). Args: module: module to be parallelized device_ids: npu devices (default: all devices) Reference: Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. 鈥淐ontext Encoding for Semantic Segmentation. *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018* Example:: >>> net = encoding.nn.DataParallelModel(model, device_ids=[0, 1, 2]) >>> y = net(x) """ def gather(self, outputs, output_device): return outputs def replicate(self, module, device_ids): modules = super(DataParallelModel, self).replicate(module, device_ids) execute_replication_callbacks(modules) return modules class DataParallelCriterion(DataParallel): """ Calculate loss in multiple-GPUs, which balance the memory usage. The targets are splitted across the specified devices by chunking in the batch dimension. Please use together with :class:`encoding.parallel.DataParallelModel`. Reference: Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. 鈥淐ontext Encoding for Semantic Segmentation. *The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018* Example:: >>> net = encoding.nn.DataParallelModel(model, device_ids=[0, 1, 2]) >>> criterion = encoding.nn.DataParallelCriterion(criterion, device_ids=[0, 1, 2]) >>> y = net(x) >>> loss = criterion(y, target) """ def forward(self, inputs, *targets, **kwargs): # input should be already scattered # scattering the targets instead if not self.device_ids: return self.module(inputs, *targets, **kwargs) targets, kwargs = self.scatter(targets, kwargs, self.device_ids) if len(self.device_ids) == 1: return self.module(inputs, *targets[0], **kwargs[0]) replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) outputs = _criterion_parallel_apply(replicas, inputs, targets, kwargs) # return Reduce.apply(*outputs) / len(outputs) # return self.gather(outputs, self.output_device).mean() return self.gather(outputs, self.output_device) def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None): assert len(modules) == len(inputs) assert len(targets) == len(inputs) if kwargs_tup: assert len(modules) == len(kwargs_tup) else: kwargs_tup = ({},) * len(modules) if devices is not None: assert len(modules) == len(devices) else: devices = [None] * len(modules) lock = threading.Lock() results = {} if torch_ver != "0.3": grad_enabled = torch.is_grad_enabled() def _worker(i, module, input, target, kwargs, device=None): if torch_ver != "0.3": torch.set_grad_enabled(grad_enabled) if device is None: device = get_a_var(input).get_device() try: with torch.npu.device(device): # this also avoids accidental slicing of `input` if it is a Tensor if not isinstance(input, (list, tuple)): input = (input,) if not isinstance(target, (list, tuple)): target = (target,) output = module(*(input + target), **kwargs) with lock: results[i] = output except Exception as e: with lock: results[i] = e if len(modules) > 1: threads = [threading.Thread(target=_worker, args=(i, module, input, target, kwargs, device), ) for i, (module, input, target, kwargs, device) in enumerate(zip(modules, inputs, targets, kwargs_tup, devices))] for thread in threads: thread.start() for thread in threads: thread.join() else: _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0]) outputs = [] for i in range(len(inputs)): output = results[i] if isinstance(output, Exception): raise output outputs.append(output) return outputs ########################################################################### # Adapted from Synchronized-BatchNorm-PyTorch. # https://github.com/vacancy/Synchronized-BatchNorm-PyTorch # class CallbackContext(object): pass def execute_replication_callbacks(modules): """ Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Note that, as all modules are isomorphism, we assign each sub-module with a context (shared among multiple copies of this module on different devices). Through this context, different copies can share some information. We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback of any slave copies. """ master_copy = modules[0] nr_modules = len(list(master_copy.modules())) ctxs = [CallbackContext() for _ in range(nr_modules)] for i, module in enumerate(modules): for j, m in enumerate(module.modules()): if hasattr(m, '__data_parallel_replicate__'): m.__data_parallel_replicate__(ctxs[j], i) def patch_replication_callback(data_parallel): """ Monkey-patch an existing `DataParallel` object. Add the replication callback. Useful when you have customized `DataParallel` implementation. Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) > patch_replication_callback(sync_bn) # this is equivalent to > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) """ assert isinstance(data_parallel, DataParallel) old_replicate = data_parallel.replicate @functools.wraps(old_replicate) def new_replicate(module, device_ids): modules = old_replicate(module, device_ids) execute_replication_callbacks(modules) return modules data_parallel.replicate = new_replicate

[ "[email protected]" ]

[email protected]

94171e19440d59601861aee4f580b056a82ba31e

104085f6878411a137521b17c06612e5f648ef33

/service_pro/service_pro/doctype/agent_payment_request/agent_payment_request_dashboard.py

bf5bad55f86a6116faab28263273e7b0828fce28

[ "MIT" ]

permissive

ksbbalean/service-pro

d39f0d12977dd66627b9f7c0336c605d7be4c388

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refs/heads/master

2023-04-13T05:35:19.842021

2021-04-22T11:05:18

null

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py

from frappe import _ def get_data(): return { 'fieldname': 'agent_payment_request', 'transactions': [ { 'label': _('Linked Forms'), 'items': ["Journal Entry"] } ] }

[ "[email protected]" ]

[email protected]

9efe79a16c6f27bddfc4536d573389398935b830

3b5d1a53af8d2f4094005f342403eabc7af9c980

/moderation_module/storage/logging_data.py

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alentoghostflame/StupidAlentoBot

daa828be3d47b24d3e13d500155a6a0d2019f724

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refs/heads/master

2021-06-30T17:50:14.997416

2021-06-08T03:54:24

237,541,303

2

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null

UTF-8

Python

false

387

py

from alento_bot import guild_data_transformer import logging import typing logger = logging.getLogger("main_bot") @guild_data_transformer(name="guild_logging_config") class GuildLoggingConfig: def __init__(self): self.toggled_on: bool = False self.log_channel_id: int = 0 self.exempt_channels: typing.Set[int] = set() self.log_bots: bool = False

[ "[email protected]" ]

[email protected]

b176a24c12a2f81960d594b227e0eb66dc7ca889

c192132c7c2b815d480b243b591c2c9dac8d969b

/result.py

f369eb20ad333d584dbf29c1573ee366b42523f5

[]

no_license

Annihilater/blast.ncbi.nlm.nih.gov

b967dd3abf1ca1b075566262ee11906d7f5170ce

e62dabb4f9fc7c0e359051e3cdbc97c45f1fbdee

refs/heads/master

2020-08-24T14:32:54.451809

2019-10-23T09:41:37

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py

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2019/10/23 11:16 # @Author: yanmiexingkong # @email : [email protected] # @File : result.py import requests def get_result(rid): """ 通过 rid 获取结果 :param rid: :return: """ url = "https://blast.ncbi.nlm.nih.gov/Blast.cgi" headers = { 'authority': "blast.ncbi.nlm.nih.gov", 'pragma': "no-cache", 'cache-control': "no-cache,no-cache", 'upgrade-insecure-requests': "1", 'user-agent': "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.120 Safari/537.36", 'sec-fetch-mode': "navigate", 'sec-fetch-user': "?1", 'origin': "https://blast.ncbi.nlm.nih.gov", 'content-type': "application/x-www-form-urlencoded", 'accept': "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3", 'sec-fetch-site': "same-origin", 'referer': "https://blast.ncbi.nlm.nih.gov/Blast.cgi", 'accept-encoding': "gzip, deflate, br", 'accept-language': "zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7,zh-TW;q=0.6", 'cookie': "MyBlastUser=1-K62_H2PRYnAJWAW8C499055; ncbi_sid=5AAB49C2DAD5CBD1_0000SID; _ga=GA1.2.1760716258.1571642619; _gid=GA1.2.1120047674.1571642619; _ga=GA1.3.1760716258.1571642619; _gid=GA1.3.1120047674.1571642619; QSI_HistorySession=https%3A%2F%2Fwww.nlm.nih.gov%2F%23~1571655071695; ___rl__test__cookies=1571655469341; OUTFOX_SEARCH_USER_ID_NCOO=1690574555.305844; books.article.report=; MyNcbiSigninPreferences=O25jYmlscyY%3D; ncbi_prevPHID=CE8CB87BDAD9B9910000000000110007.m_8.09; WebCubbyUser=YFK36EUALZSLFML717L8VTJ8L7VZ587M%3Blogged-in%3Dtrue%3Bmy-name%3Dyanmiexingkong%3Bpersistent%3Dfalse%405AAB49C2DAD5CBD1_0000SID; BlastCubbyImported=active; ncbi_pinger=N4IgDgTgpgbg+mAFgSwCYgFwgKwEFcBCALAJwDCATACK5XZkFUkCM2A7Mx6UcwGzbYAygEkqIADQgArgDsANgHsAhqhlQAHgBdMoCphAAjOUoDO2yQGZ9MiBJBFrUAO5HTm6CalzNJu9n12nPoAZkpyJlB2FAAM+tjRZCQWFKw0AKK8ABy4LNF5+QWFzFHFWK5mGDaVzuXuUJ7eJhgAcgDyzWlRemXGZgB0MgDGBsgDcgC2A8iIfQDmCjBRJPrMJDF2FrFYzDGxlqUgq+uW3YecbBtWWKHhkZYOWO5SdyAWmQGWy1i8JD8kbFZJEQtiAfmxeGwKBcgVcQNE+hReH1ikCHtJ5MpVBpzPZ/Ns/LDMmwHJJsGjmNFeLw/NTttgKHpSRcsHtQQcAQ4AL6coA,MyBlastUser=1-K62_H2PRYnAJWAW8C499055; ncbi_sid=5AAB49C2DAD5CBD1_0000SID; _ga=GA1.2.1760716258.1571642619; _gid=GA1.2.1120047674.1571642619; _ga=GA1.3.1760716258.1571642619; _gid=GA1.3.1120047674.1571642619; QSI_HistorySession=https%3A%2F%2Fwww.nlm.nih.gov%2F%23~1571655071695; ___rl__test__cookies=1571655469341; OUTFOX_SEARCH_USER_ID_NCOO=1690574555.305844; books.article.report=; MyNcbiSigninPreferences=O25jYmlscyY%3D; ncbi_prevPHID=CE8CB87BDAD9B9910000000000110007.m_8.09; WebCubbyUser=YFK36EUALZSLFML717L8VTJ8L7VZ587M%3Blogged-in%3Dtrue%3Bmy-name%3Dyanmiexingkong%3Bpersistent%3Dfalse%405AAB49C2DAD5CBD1_0000SID; BlastCubbyImported=active; ncbi_pinger=N4IgDgTgpgbg+mAFgSwCYgFwgKwEFcBCALAJwDCATACK5XZkFUkCM2A7Mx6UcwGzbYAygEkqIADQgArgDsANgHsAhqhlQAHgBdMoCphAAjOUoDO2yQGZ9MiBJBFrUAO5HTm6CalzNJu9n12nPoAZkpyJlB2FAAM+tjRZCQWFKw0AKK8ABy4LNF5+QWFzFHFWK5mGDaVzuXuUJ7eJhgAcgDyzWlRemXGZgB0MgDGBsgDcgC2A8iIfQDmCjBRJPrMJDF2FrFYzDGxlqUgq+uW3YecbBtWWKHhkZYOWO5SdyAWmQGWy1i8JD8kbFZJEQtiAfmxeGwKBcgVcQNE+hReH1ikCHtJ5MpVBpzPZ/Ns/LDMmwHJJsGjmNFeLw/NTttgKHpSRcsHtQQcAQ4AL6coA; ncbi_sid=5AAB49C2DAD5CBD1_0000SID; BlastCubbyImported=passive", 'Postman-Token': "effbbf9e-09ff-4958-8a0e-a8c3d2719ae1,ffe9004f-7563-4ea5-ad02-943a343657a8", 'Host': "blast.ncbi.nlm.nih.gov", 'Content-Length': "1354", 'Connection': "keep-alive" } data = {'ADV_VIEW': 'true', 'ALIGNMENTS': '100', 'ALIGNMENT_VIEW': 'Pairwise', 'BLAST_PROGRAMS': 'blastp', 'CDD_RID': 'UWU3DJDS015', 'CDD_SEARCH_STATE': '2', 'CLIENT': 'web', 'COMPOSITION_BASED_STATISTICS': '2', 'CONFIG_DESCR': '2%2C3%2C4%2C5%2C6%2C7%2C8', 'DATABASE': 'nr_v5', 'DB_DISPLAY_NAME': 'nr', 'DESCRIPTIONS': '100', 'EQ_OP': 'AND', 'EXPECT': '10', 'FILTER': 'F', 'FORMAT_NUM_ORG': '1', 'FORMAT_OBJECT': 'Alignment', 'FORMAT_TYPE': 'HTML', 'FULL_DBNAME': 'nr_v5', 'GAPCOSTS': '11%2B1', 'GET_SEQUENCE': 'true', 'HSP_RANGE_MAX': '0', 'JOB_TITLE': '%2B5ubb%2B', 'LAYOUT': 'OneWindow', 'LINE_LENGTH': '60', 'MASK_CHAR': '2', 'MASK_COLOR': '1', 'MATRIX_NAME': 'BLOSUM62', 'MAX_NUM_SEQ': '100', 'NCBI_GI': 'false', 'NEW_VIEW': 'true', 'NUM_DIFFS': '0', 'NUM_OPTS_DIFFS': '0', 'NUM_ORG': '1', 'NUM_OVERVIEW': '100', 'ORG_DBS': 'giless_dbvers5', 'PAGE': 'Proteins', 'PAGE_TYPE': 'BlastSearch', 'PROGRAM': 'blastp', 'QUERYFILE': '%3E5ubb%0D%0ASQVINGEMQFYARAKLFYQEVPATEEGMMGNFIELSSPDIQASQKFLRKFVGGPGRAGTDCALDCGSGIGRVSKHVLLPVFNSVELVDMMESFLLEAQNYLQVKGDESYHCYSLQEFTPPFRRYDVIWIQWVSGHLTDKDLLAFLSRCRDGLKENGIIILKDNVAREGCILDLSDSSVTRDMDILRSLIRKSGLVVLGQEKQDGFPEQCIPVWMFALH%0D%0A', 'QUERY_INFO': '%2B5ubb%2B', 'QUERY_LENGTH': '218', 'REPEATS': '45518', 'RTOE': '27', 'SAVED_SEARCH': 'true', 'SEARCH_DB_STATUS': '31', 'SELECTED_PROG_TYPE': 'blastp', 'SERVICE': 'plain', 'SHORT_QUERY_ADJUST': 'on', 'SHOW_CDS_FEATURE': 'false', 'SHOW_LINKOUT': 'true', 'SHOW_OVERVIEW': 'true', 'UNIQ_DEFAULTS_NAME': 'A_SearchDefaults_1iMhfz_1v71_duIAy0mW1FA_GTXQl_2J8uR3', 'USER_DEFAULT_MATRIX': '4', 'USER_DEFAULT_PROG_TYPE': 'blastp', 'USER_TYPE': '1', 'WORD_SIZE': '6', '_PGR': '6', 'db': 'protein', 'stype': 'protein', 'CMD': 'Get'} data.update({'RID': rid}) response = requests.post(url=url, data=data, headers=headers) html = response.text with open('data/html/result.html', 'w') as f: f.write(html) if __name__ == '__main__': rid = 'UX0VYAFM015' rid2 = 'UXTZXYRW015' rid3 = 'V019SAM701R' get_result(rid3)

[ "[email protected]" ]

[email protected]

6f361c7d8b2af01f6ee96c8df06630eaf5cef7f8

1929a989d1e2a5c5caabad32aa8baf4444250574

/h2o-py/tests/testdir_munging/pyunit_upload_large.py

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codelibs/h2o-3

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cf96fb28da4732870a0d65c24f0d99f422d140d1

refs/heads/master

2023-05-27T10:04:14.408620

2023-04-28T18:16:48

253,197,280

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Apache-2.0

2020-04-05T09:22:41

2020-04-05T09:22:40

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UTF-8

Python

false

772

py

from __future__ import print_function import sys sys.path.insert(1,"../../") import h2o import os import tempfile from tests import pyunit_utils def generate_large_file(path, size): with open(path, "wb") as f: f.seek(size-1) f.write(b"\0") assert size == os.stat(path).st_size def upload_large_file(): path = os.path.join(tempfile.mkdtemp(), "large.bin") byte_size = 2 * 1024 * 1024 * 1024 + 1 # 2GB + 1 byte generate_large_file(path, byte_size) raw_data = h2o.api("POST /3/PostFile", filename=path) print(raw_data) assert raw_data["total_bytes"] == byte_size h2o.remove(raw_data["destination_frame"]) if __name__ == "__main__": pyunit_utils.standalone_test(upload_large_file) else: upload_large_file()

[ "[email protected]" ]

[email protected]

6f927f95ffc8e9ede4b6ba26df040a784d1f5146

8f5cb19e9c6a0670100b4a4fbdbb892d94ccd4a8

/deployment/georegistry.py

5c1e240d6a6b964f6e177dd39a8f7f9b1dc6a607

[]

no_license

invisibleroads/georegistry

84438e680e56ac716f60d23784f05469c4888841

df56cc17b01a794bfbd53f354bb5fa9abeb420cc

refs/heads/master

2023-08-24T03:34:50.554375

2011-05-05T16:36:19

966,680

1

2

null

UTF-8

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2,359

py

'GeoRegistry API Python wrapper' # Import system modules import urllib import urllib2 import simplejson # Core baseURL = 'http://georegistry.invisibleroads.com' def updateFeatures(key, srid, featureCollection, tags, public=False): 'Update features using the GeoRegistry web service' # Initialize url = baseURL + '/features' # Call responseData = call(url, { 'key': key, 'srid': srid, 'featureCollection': featureCollection, 'tags': '\n'.join(tags), 'public': 1 if public else 0, }, 'POST') # Return return [int(x) for x in responseData.splitlines()] def deleteFeatures(key, featureIDs): 'Delete features using the GeoRegistry web service' # Initialize url = baseURL + '/features' # Call call(url, { 'key': key, 'featureIDs': '\n'.join(str(x) for x in featureIDs), }, 'DELETE') def getTags(key): 'Get tags with visible features using the GeoRegistry web service' # Initialize url = baseURL + '/tags' # Call responseData = call(url + '.json', { 'key': key, }, 'GET') # Return return responseData.splitlines() def viewMaps(key, srid, tags, simplified=True, bboxFormat='yxyx', bbox=None): 'Assemble a map using the GeoRegistry web service' # Initialize url = baseURL + '/maps' # Call responseData = call(url + '.json', { 'key': key, 'srid': srid, 'tags': '\n'.join(tags), 'bboxFormat': bboxFormat, 'bbox': bbox if bbox else '', 'simplified': 1 if simplified else 0, }, 'GET') # Return return responseData # Helpers def call(url, valueByName, method): 'Call a method in the GeoRegistry web service' requestData = urllib.urlencode(valueByName.items()) request = Request(method, url, requestData) if method.upper() == 'POST' else Request(method, url + '?' + requestData) try: response = urllib2.urlopen(request) except urllib2.HTTPError, error: raise GeoRegistryError(error.read()) return response.read() class Request(urllib2.Request): def __init__(self, method, *args, **kwargs): self._method = method urllib2.Request.__init__(self, *args, **kwargs) def get_method(self): return self._method # Error class GeoRegistryError(Exception): pass

[ "[email protected]" ]

[email protected]

ba3339eeda813a3c7d315fcb1cb1c530a8080125

d094ba0c8a9b1217fbf014aa79a283a49aabe88c

/env/lib/python3.6/site-packages/sklearn/preprocessing/_discretization.py

7b26ce916e1893d6d13fc02304699fa07bd412b3

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permissive

Raniac/NEURO-LEARN

d9274e0baadd97bb02da54bdfcf6ca091fc1c703

3c3acc55de8ba741e673063378e6cbaf10b64c7a

refs/heads/master

2022-12-25T23:46:54.922237

2020-09-06T03:15:14

182,013,100

9

2

Apache-2.0

2022-12-09T21:01:00

2019-04-18T03:57:00

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# -*- coding: utf-8 -*- # Author: Henry Lin <[email protected]> # Tom Dupré la Tour # License: BSD from __future__ import division, absolute_import import numbers import numpy as np import warnings from . import OneHotEncoder from ..base import BaseEstimator, TransformerMixin from ..utils.validation import check_array from ..utils.validation import check_is_fitted from ..utils.validation import FLOAT_DTYPES from ..utils.fixes import np_version class KBinsDiscretizer(BaseEstimator, TransformerMixin): """Bin continuous data into intervals. Read more in the :ref:`User Guide <preprocessing_discretization>`. Parameters ---------- n_bins : int or array-like, shape (n_features,) (default=5) The number of bins to produce. Raises ValueError if ``n_bins < 2``. encode : {'onehot', 'onehot-dense', 'ordinal'}, (default='onehot') Method used to encode the transformed result. onehot Encode the transformed result with one-hot encoding and return a sparse matrix. Ignored features are always stacked to the right. onehot-dense Encode the transformed result with one-hot encoding and return a dense array. Ignored features are always stacked to the right. ordinal Return the bin identifier encoded as an integer value. strategy : {'uniform', 'quantile', 'kmeans'}, (default='quantile') Strategy used to define the widths of the bins. uniform All bins in each feature have identical widths. quantile All bins in each feature have the same number of points. kmeans Values in each bin have the same nearest center of a 1D k-means cluster. Attributes ---------- n_bins_ : int array, shape (n_features,) Number of bins per feature. Bins whose width are too small (i.e., <= 1e-8) are removed with a warning. bin_edges_ : array of arrays, shape (n_features, ) The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )`` Ignored features will have empty arrays. Examples -------- >>> X = [[-2, 1, -4, -1], ... [-1, 2, -3, -0.5], ... [ 0, 3, -2, 0.5], ... [ 1, 4, -1, 2]] >>> est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform') >>> est.fit(X) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE KBinsDiscretizer(...) >>> Xt = est.transform(X) >>> Xt # doctest: +SKIP array([[ 0., 0., 0., 0.], [ 1., 1., 1., 0.], [ 2., 2., 2., 1.], [ 2., 2., 2., 2.]]) Sometimes it may be useful to convert the data back into the original feature space. The ``inverse_transform`` function converts the binned data into the original feature space. Each value will be equal to the mean of the two bin edges. >>> est.bin_edges_[0] array([-2., -1., 0., 1.]) >>> est.inverse_transform(Xt) array([[-1.5, 1.5, -3.5, -0.5], [-0.5, 2.5, -2.5, -0.5], [ 0.5, 3.5, -1.5, 0.5], [ 0.5, 3.5, -1.5, 1.5]]) Notes ----- In bin edges for feature ``i``, the first and last values are used only for ``inverse_transform``. During transform, bin edges are extended to:: np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf]) You can combine ``KBinsDiscretizer`` with :class:`sklearn.compose.ColumnTransformer` if you only want to preprocess part of the features. ``KBinsDiscretizer`` might produce constant features (e.g., when ``encode = 'onehot'`` and certain bins do not contain any data). These features can be removed with feature selection algorithms (e.g., :class:`sklearn.feature_selection.VarianceThreshold`). See also -------- sklearn.preprocessing.Binarizer : class used to bin values as ``0`` or ``1`` based on a parameter ``threshold``. """ def __init__(self, n_bins=5, encode='onehot', strategy='quantile'): self.n_bins = n_bins self.encode = encode self.strategy = strategy def fit(self, X, y=None): """Fits the estimator. Parameters ---------- X : numeric array-like, shape (n_samples, n_features) Data to be discretized. y : ignored Returns ------- self """ X = check_array(X, dtype='numeric') valid_encode = ('onehot', 'onehot-dense', 'ordinal') if self.encode not in valid_encode: raise ValueError("Valid options for 'encode' are {}. " "Got encode={!r} instead." .format(valid_encode, self.encode)) valid_strategy = ('uniform', 'quantile', 'kmeans') if self.strategy not in valid_strategy: raise ValueError("Valid options for 'strategy' are {}. " "Got strategy={!r} instead." .format(valid_strategy, self.strategy)) n_features = X.shape[1] n_bins = self._validate_n_bins(n_features) bin_edges = np.zeros(n_features, dtype=object) for jj in range(n_features): column = X[:, jj] col_min, col_max = column.min(), column.max() if col_min == col_max: warnings.warn("Feature %d is constant and will be " "replaced with 0." % jj) n_bins[jj] = 1 bin_edges[jj] = np.array([-np.inf, np.inf]) continue if self.strategy == 'uniform': bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1) elif self.strategy == 'quantile': quantiles = np.linspace(0, 100, n_bins[jj] + 1) if np_version < (1, 9): quantiles = list(quantiles) bin_edges[jj] = np.asarray(np.percentile(column, quantiles)) elif self.strategy == 'kmeans': from ..cluster import KMeans # fixes import loops # Deterministic initialization with uniform spacing uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1) init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5 # 1D k-means procedure km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1) centers = km.fit(column[:, None]).cluster_centers_[:, 0] # Must sort, centers may be unsorted even with sorted init centers.sort() bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5 bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max] # Remove bins whose width are too small (i.e., <= 1e-8) if self.strategy in ('quantile', 'kmeans'): mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8 bin_edges[jj] = bin_edges[jj][mask] if len(bin_edges[jj]) - 1 != n_bins[jj]: warnings.warn('Bins whose width are too small (i.e., <= ' '1e-8) in feature %d are removed. Consider ' 'decreasing the number of bins.' % jj) n_bins[jj] = len(bin_edges[jj]) - 1 self.bin_edges_ = bin_edges self.n_bins_ = n_bins if 'onehot' in self.encode: self._encoder = OneHotEncoder( categories=[np.arange(i) for i in self.n_bins_], sparse=self.encode == 'onehot') # Fit the OneHotEncoder with toy datasets # so that it's ready for use after the KBinsDiscretizer is fitted self._encoder.fit(np.zeros((1, len(self.n_bins_)), dtype=int)) return self def _validate_n_bins(self, n_features): """Returns n_bins_, the number of bins per feature. """ orig_bins = self.n_bins if isinstance(orig_bins, numbers.Number): if not isinstance(orig_bins, (numbers.Integral, np.integer)): raise ValueError("{} received an invalid n_bins type. " "Received {}, expected int." .format(KBinsDiscretizer.__name__, type(orig_bins).__name__)) if orig_bins < 2: raise ValueError("{} received an invalid number " "of bins. Received {}, expected at least 2." .format(KBinsDiscretizer.__name__, orig_bins)) return np.full(n_features, orig_bins, dtype=np.int) n_bins = check_array(orig_bins, dtype=np.int, copy=True, ensure_2d=False) if n_bins.ndim > 1 or n_bins.shape[0] != n_features: raise ValueError("n_bins must be a scalar or array " "of shape (n_features,).") bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins) violating_indices = np.where(bad_nbins_value)[0] if violating_indices.shape[0] > 0: indices = ", ".join(str(i) for i in violating_indices) raise ValueError("{} received an invalid number " "of bins at indices {}. Number of bins " "must be at least 2, and must be an int." .format(KBinsDiscretizer.__name__, indices)) return n_bins def transform(self, X): """Discretizes the data. Parameters ---------- X : numeric array-like, shape (n_samples, n_features) Data to be discretized. Returns ------- Xt : numeric array-like or sparse matrix Data in the binned space. """ check_is_fitted(self, ["bin_edges_"]) Xt = check_array(X, copy=True, dtype=FLOAT_DTYPES) n_features = self.n_bins_.shape[0] if Xt.shape[1] != n_features: raise ValueError("Incorrect number of features. Expecting {}, " "received {}.".format(n_features, Xt.shape[1])) bin_edges = self.bin_edges_ for jj in range(Xt.shape[1]): # Values which are close to a bin edge are susceptible to numeric # instability. Add eps to X so these values are binned correctly # with respect to their decimal truncation. See documentation of # numpy.isclose for an explanation of ``rtol`` and ``atol``. rtol = 1.e-5 atol = 1.e-8 eps = atol + rtol * np.abs(Xt[:, jj]) Xt[:, jj] = np.digitize(Xt[:, jj] + eps, bin_edges[jj][1:]) np.clip(Xt, 0, self.n_bins_ - 1, out=Xt) if self.encode == 'ordinal': return Xt return self._encoder.transform(Xt) def inverse_transform(self, Xt): """Transforms discretized data back to original feature space. Note that this function does not regenerate the original data due to discretization rounding. Parameters ---------- Xt : numeric array-like, shape (n_sample, n_features) Transformed data in the binned space. Returns ------- Xinv : numeric array-like Data in the original feature space. """ check_is_fitted(self, ["bin_edges_"]) if 'onehot' in self.encode: Xt = self._encoder.inverse_transform(Xt) Xinv = check_array(Xt, copy=True, dtype=FLOAT_DTYPES) n_features = self.n_bins_.shape[0] if Xinv.shape[1] != n_features: raise ValueError("Incorrect number of features. Expecting {}, " "received {}.".format(n_features, Xinv.shape[1])) for jj in range(n_features): bin_edges = self.bin_edges_[jj] bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5 Xinv[:, jj] = bin_centers[np.int_(Xinv[:, jj])] return Xinv

[ "[email protected]" ]

[email protected]

8071db56a1faa459eccd4c3bfbd0c735f51f2c1e

6ace7e15e3191d1b8228ad7922a8552ca84f84e7

/.history/image_detector_20200614203341.py

2465a36001cd934f7bd739e37f170e75e719b85c

[]

no_license

mehmetaliarican/Similar-Image-Finder

f72e95be50c51aa03fc64954a03124b199ca64b1

a9e0015c443b4a73394099cccf60329cfc4c7cef

refs/heads/master

2022-10-27T00:57:43.173993

2020-06-14T18:02:16

272,256,295

0

1

null

UTF-8

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2,485

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from skimage.metrics import structural_similarity as ssim from imutils import paths import matplotlib.pyplot as plt import numpy as np import cv2 import glob import os import argparse ap = argparse.ArgumentParser() ap.add_argument("-t", "--threshold", type=float, default=0.9, help="threshold") ap.add_argument("-d", "--dataset", required=True, help="path to input dataset") args = vars(ap.parse_args()) class Utility: totalFound = 0 totalSearch = 0 searching = False def mse(self, imageA, imageB): # the 'Mean Squared Error' between the two images is the # sum of the squared difference between the two images; # NOTE: the two images must have the same dimension err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2) err /= float(imageA.shape[0] * imageA.shape[1]) # return the MSE, the lower the error, the more "similar" # the two images are return err def compare_images(self, im1, im2, imageA, imageB): # compute the mean squared error and structural similarity # index for the images m = self.mse(imageA, imageB) s = ssim(imageA, imageB) tres = args['threshold'] totalSearch++ if s >= tres: print("Image[{c1}] '{p1}' compared to Image[{c2}] '{p2}' Simility:{sim}".format(c1=im1['comp'], c2=im2['comp'],p1=im1['path'], p2=im2['path'], sim=str(s))) twin = np.hstack([imageA, imageB]) cv2.imshow('', twin) cv2.waitKey(0) self.searching = False elif self.searching is False: print('Searching...') self.searching = True imagePaths = list(paths.list_images(args['dataset'])) companies = ['dhl', 'paypal', 'wellsfargo'] all_data = [] for path in imagePaths: company = '' for c in companies: if c in path: company = c all_data.append({'comp': c, 'path': path}) print(all_data) u = Utility() for image in all_data: try: p1 = cv2.imread(image['path']) p1 = cv2.resize(p1, (300, 300)) p1 = cv2.cvtColor(p1, cv2.COLOR_BGR2GRAY) for i in all_data: if i['path'] != image['path']: p2 = cv2.imread(i['path']) p2 = cv2.resize(p2, (300, 300)) p2 = cv2.cvtColor(p2, cv2.COLOR_BGR2GRAY) u.compare_images(image, i, p1, p2) except Exception as e: print(str(e))

[ "[email protected]" ]

[email protected]

5268cff948f9c48f0fd6138032a6afd729243dd6

2a6412a9359a1df5f8f12e319e73b9e4e46fd64c

/specializedSubjects/AlgorithmII/dijkstra_ON2.py

f7130f780bcfbbcb7e2864c816bf76de51c44942

[]

no_license

danganhvu1998/myINIAD

504d1147a02f12e593f30e369daf82f85aa01bfd

01547673dd3065efb6c7cc8db77ec93a5a4f5d98

refs/heads/master

2022-03-17T12:58:34.647229

2022-02-08T06:34:19

143,675,719

1

4

null

UTF-8

Python

false

770

py

import networkx as nx import matplotlib.pyplot as plt def get_min(D, X): arg_min= -1 min_value= float('inf') for i in range(len(D)): if D[i] < min_value: if i in X: arg_min= i min_value= D[i] return arg_min def dijkstra(src, G): D= [float('inf')] * nx.number_of_nodes(G) D[src]= 0.0 X= set(G.nodes) while X: u= get_min(D, X) X.remove(u) neighbors= G.neighbors(u) for v in neighbors: if v in X: if (D[u] + G.edges[u, v]['weight'] < D[v]): D[v]= D[u] + G.edges[u, v]['weight'] return D G= nx.read_weighted_edgelist('dij.edgelist', nodetype=int) print(dijkstra(0, G)) nx.draw_networkx(G) plt.show()

[ "[email protected]" ]

[email protected]

62f8f6e45e2c8fa0b96b0ee822ef9e2ee1a0d83b

44a7b4879c1da661cc2e8aa51c7fcc24cfb0fd3b

/src/scs_core/osio/manager/user_manager.py

f14e3e70118f2019ef5dd083551e6ca93ec113de

[ "MIT" ]

permissive

seoss/scs_core

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a813f85f86b6973fa77722a7d61cc93762ceba09

refs/heads/master

2021-08-08T08:09:56.905078

2020-04-16T19:46:52

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""" Created on 21 Mar 2017 @author: Bruno Beloff ([email protected]) """ from scs_core.osio.client.rest_client import RESTClient from scs_core.osio.data.user import User from scs_core.osio.data.user_metadata import UserMetadata # -------------------------------------------------------------------------------------------------------------------- class UserManager(object): """ classdocs """ # ---------------------------------------------------------------------------------------------------------------- def __init__(self, http_client, api_key): """ Constructor """ self.__rest_client = RESTClient(http_client, api_key) # ---------------------------------------------------------------------------------------------------------------- def find(self, user_id): request_path = '/v1/users/' + user_id # request... self.__rest_client.connect() try: response_jdict = self.__rest_client.get(request_path) except RuntimeError: response_jdict = None self.__rest_client.close() user = User.construct_from_jdict(response_jdict) return user def find_public(self, user_id): request_path = '/v1/public/users/' + user_id # request... self.__rest_client.connect() try: response_jdict = self.__rest_client.get(request_path) except RuntimeError: response_jdict = None self.__rest_client.close() user = UserMetadata.construct_from_jdict(response_jdict) return user def find_members_of_org(self, org_id): pass # ---------------------------------------------------------------------------------------------------------------- def update(self, user_id, user): request_path = '/v1/users/' + user_id # request... self.__rest_client.connect() try: self.__rest_client.put(request_path, user.as_json()) finally: self.__rest_client.close() # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "UserManager:{rest_client:%s}" % self.__rest_client

[ "[email protected]" ]

[email protected]

2db16db5c0570084ec0dbb9abc064697f824fa90

f51aff57f826aeea1be21e2d0c03cce0adaadefc

/exp/utils/rand.py

70cd3809130978b9f18a56c77772c3f8afb2594d

[ "MIT" ]

permissive

zv5dmjq5/vivit

0a26f8b61e6f00da75fce7a9bbc75b0185ffea76

a05f448d1badb2db42e724c80676ce7e309194d2

refs/heads/master

2023-07-12T06:36:10.627912

2021-08-26T12:02:59

370,409,161

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MIT

2021-08-10T12:58:27

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"""Utility functions to control random seeds.""" import torch class temporary_seed: """Temporarily set PyTorch seed to a different value, then restore current value. This has the effect that code inside this context does not influence the outer loop's random generator state. """ def __init__(self, temp_seed): self._temp_seed = temp_seed def __enter__(self): """Store the current seed.""" self._old_state = torch.get_rng_state() torch.manual_seed(self._temp_seed) def __exit__(self, exc_type, exc_value, traceback): """Restore the old random generator state.""" torch.set_rng_state(self._old_state) def test_temporary_seed(): """Test if temporary_seed works as expected.""" torch.manual_seed(3) num1 = torch.rand(1) with temporary_seed(2): num2 = torch.rand(1) num3 = torch.rand(1) torch.manual_seed(3) num4 = torch.rand(1) num5 = torch.rand(1) torch.manual_seed(2) num6 = torch.rand(1) assert torch.allclose(num1, num4) assert torch.allclose(num3, num5) assert torch.allclose(num2, num6) if __name__ == "__main__": test_temporary_seed()

[ "Anonymous" ]

Anonymous

a645386e0c34474857014299151a5d45a2ce0535

b8115bc7503581bf3bb44bfa87b270793aff4381

/ddsp/training/data_preparation/ddsp_prepare_tfrecord.py

f7a4c0f3576f5ff085e50c33af5f4a887ed246f2

[ "Apache-2.0" ]

permissive

pollinations/ddsp

7a5cfd18efcd8a77729d26231d294a4c03c2d286

4bbb3b1b0aa9e9a4c1f77e8758f409cbd1ec03f7

refs/heads/main

2023-06-21T18:21:37.230721

2021-07-22T09:45:54

388,399,770

0

Apache-2.0

2021-07-22T09:14:11

2021-07-22T09:14:10

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UTF-8

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# Copyright 2021 The DDSP Authors. # # 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. # Lint as: python3 r"""Create a TFRecord dataset from audio files. Usage: ==================== ddsp_prepare_tfrecord \ --input_audio_filepatterns=/path/to/wavs/*wav,/path/to/mp3s/*mp3 \ --output_tfrecord_path=/path/to/output.tfrecord \ --num_shards=10 \ --alsologtostderr """ from absl import app from absl import flags from ddsp.training.data_preparation.prepare_tfrecord_lib import prepare_tfrecord import tensorflow.compat.v2 as tf FLAGS = flags.FLAGS flags.DEFINE_list('input_audio_filepatterns', [], 'List of filepatterns to glob for input audio files.') flags.DEFINE_string( 'output_tfrecord_path', None, 'The prefix path to the output TFRecord. Shard numbers will be added to ' 'actual path(s).') flags.DEFINE_integer( 'num_shards', None, 'The number of shards to use for the TFRecord. If None, this number will ' 'be determined automatically.') flags.DEFINE_integer('sample_rate', 16000, 'The sample rate to use for the audio.') flags.DEFINE_integer( 'frame_rate', 250, 'The frame rate to use for f0 and loudness features. If set to 0, ' 'these features will not be computed.') flags.DEFINE_float( 'example_secs', 4, 'The length of each example in seconds. Input audio will be split to this ' 'length using a sliding window. If 0, each full piece of audio will be ' 'used as an example.') flags.DEFINE_float( 'sliding_window_hop_secs', 1, 'The hop size in seconds to use when splitting audio into constant-length ' 'examples.') flags.DEFINE_float( 'eval_split_fraction', 0.0, 'Fraction of the dataset to reserve for eval split. If set to 0, no eval ' 'split is created.' ) flags.DEFINE_float( 'coarse_chunk_secs', 20.0, 'Chunk size in seconds used to split the input audio files.') flags.DEFINE_list( 'pipeline_options', '--runner=DirectRunner', 'A comma-separated list of command line arguments to be used as options ' 'for the Beam Pipeline.') def run(): input_audio_paths = [] for filepattern in FLAGS.input_audio_filepatterns: input_audio_paths.extend(tf.io.gfile.glob(filepattern)) prepare_tfrecord( input_audio_paths, FLAGS.output_tfrecord_path, num_shards=FLAGS.num_shards, sample_rate=FLAGS.sample_rate, frame_rate=FLAGS.frame_rate, window_secs=FLAGS.example_secs, hop_secs=FLAGS.sliding_window_hop_secs, eval_split_fraction=FLAGS.eval_split_fraction, coarse_chunk_secs=FLAGS.coarse_chunk_secs, pipeline_options=FLAGS.pipeline_options) def main(unused_argv): """From command line.""" run() def console_entry_point(): """From pip installed script.""" app.run(main) if __name__ == '__main__': console_entry_point()

[ "[email protected]" ]

[email protected]

8e1fb53a12cc8169be23e1cdcdc37884cdf551ec

a6cbc03780b5c390f4f8ce9063bd8a7f6d75e8aa

/mail2.py

1c0dfcd906c6beffc8d5efcafb1c822ea969e238

[]

no_license

KimaruThagna/Email_and_Regex

5f825554bd17e56ff091a79187c5ab7a758960d9

c250e37d6e09f1a9c35fb6af873ff1c77707a8fd

refs/heads/master

2021-09-06T11:59:04.784389

2018-02-06T12:16:58

110,789,925

2

1

null

UTF-8

Python

false

1,992

py

#This example still uses gmail but this time includes an attachment import os,smtplib from email.mime.text import MIMEText from email.encoders import encode_base64 from email.mime.multipart import MIMEMultipart from tkinter.filedialog import askopenfilename from email.mime.base import MIMEBase # function that sends the email. Feed it with relevant parameters def sendMail(sender,pwd,subject,body,receiver,q): message=MIMEMultipart() # define the whole message as a mimemultipart and add releven #metadata message['Subject']=subject message['From']=sender message['To']=receiver text=MIMEText(body) message.attach(text)# attach the body or actual message to the message object if q=='y': file=askopenfilename()# create window which allows you to browse file system\ #and select file data=open(file,'rb').read() # read file in binary mode part=MIMEBase('application','octet-stream') part.set_payload(data) # set the payload as the file read in binary mode encode_base64(part) #encode the attachment to base64 part.add_header('Content-disposition','attachment; filename='+os.path.basename(file)) message.attach(part) print('Connecting ...') server=smtplib.SMTP('smtp.gmail.com',587) # setup email server server.ehlo() # identify yourself to gmail client server.starttls() # start transport layer security server.ehlo() #re-identify yourself after encryption server.login(sender,pwd) # login to sender account print('Connected') server.sendmail(sender,receiver,message.as_string()) # perform actual sending of mail print('Mail Sent.') server.quit() #prompts sender=input('Input Your email ') receiver=input('Provide Recepient ') pwd=input('Provide password ' ) subject=input('Mail Subject ') body=input('Type your message ') con=input('Do you want to send an attachment? Enter y for YES ') #call method sendMail(sender,pwd,subject,body,receiver,con)

[ "[email protected]" ]

[email protected]

d5ab6e2c2301fa7c2de21056b961275cd20e463d

840b98f14f181f7dbd693f2ee4b3c46e5be59305

/demos/demo_pycloudmessenger/POM1/NeuralNetworks/pom1_NN_worker_pycloudmessenger.py

3bb1b70be16f54c7404843da2a380711222b695e

[ "Apache-2.0" ]

permissive

Musketeer-H2020/MMLL-Robust

4ef6b2ff5dff18d4d2b2a403a89d9455ba861e2b

ccc0a7674a04ae0d00bedc38893b33184c5f68c6

refs/heads/main

2023-09-01T18:47:46.065297

2021-09-28T15:34:12

386,264,004

0

null

UTF-8

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false

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# -*- coding: utf-8 -*- ''' @author: Marcos Fernandez Diaz November 2020 Example of use: python pom1_NN_worker_pycloudmessenger.py --user <user> --password <password> --task_name <task_name> --id <id> Parameters: - user: String with the name of the user. If the user does not exist in the pycloudmessenger platform a new one will be created - password: String with the password - task_name: String with the name of the task. If the task already exists, an error will be displayed - id: Integer representing the partition of data to be used by the worker. Each worker should use a different partition, possible values are 0 to 4. ''' # Import general modules import argparse import logging import json import numpy as np import sys, os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Disables tensorflow warnings import tensorflow as tf import onnxruntime from sklearn.metrics import accuracy_score # Add higher directory to python modules path. sys.path.append("../../../../") # To be imported from MMLL (pip installed) from MMLL.nodes.WorkerNode import WorkerNode from MMLL.comms.comms_pycloudmessenger import Comms_worker as Comms # To be imported from demo_tools from demo_tools.task_manager_pycloudmessenger import Task_Manager from demo_tools.data_connectors.Load_from_file import Load_From_File as DC from demo_tools.mylogging.logger_v1 import Logger from demo_tools.evaluation_tools import display, plot_cm_seaborn, create_folders # Set up logger logging.basicConfig( level=logging.ERROR, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S') LOGGER = logging.getLogger() LOGGER.setLevel(logging.DEBUG) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--user', type=str, default=None, help='User') parser.add_argument('--password', type=str, default=None, help='Password') parser.add_argument('--task_name', type=str, default=None, help='Name of the task') parser.add_argument('--id', type=int, default=None, choices=[0, 1, 2, 3, 4], help='The address of the worker') FLAGS, unparsed = parser.parse_known_args() user_name = FLAGS.user user_password = FLAGS.password task_name = FLAGS.task_name data_partition_id = FLAGS.id # This integer identifies the data partition used for the worker # Set basic configuration dataset_name = 'mnist' verbose = False pom = 1 model_type = 'NN' # Create the directories for storing relevant outputs if they do not exist create_folders("./results/") # Setting up the logger logger = Logger('./results/logs/Worker_' + str(user_name) + '.log') # Load the credentials for pycloudmessenger display('===========================================', logger, verbose) display('Creating Worker...', logger, verbose) # Note: this part creates the worker (participant) and it joins the task. This code is # intended to be used only at the demos, in Musketeer this part must be done in the client. credentials_filename = '../../musketeer.json' try: with open(credentials_filename, 'r') as f: credentials = json.load(f) except: display('Error - The file musketeer.json is not available, please put it under the following path: "' + os.path.abspath(os.path.join("","../../")) + '"', logger, verbose) sys.exit() # Create user and join task tm = Task_Manager(credentials_filename) participant = tm.create_worker_and_join_task(user_name, user_password, task_name, display, logger) display("Worker %s has joined task %s" %(user_name, task_name), logger, verbose) # Creating the comms object display('Creating WorkerNode under POM %d, communicating through pycloudmessenger' %pom, logger, verbose) comms = Comms(participant, user_name) # Creating Workernode wn = WorkerNode(pom, comms, logger, verbose) display('-------------------- Loading dataset %s --------------------------' % dataset_name, logger, verbose) # Load data # Warning: this data connector is only designed for the demos. In Musketeer, appropriate data # connectors must be provided data_file = '../../../../input_data/' + dataset_name + '_demonstrator_data.pkl' try: dc = DC(data_file) except: display('Error - The file ' + dataset_name + '_demonstrator_data.pkl does not exist. Please download it from Box and put it under the following path: "' + os.path.abspath(os.path.join("","../../../../input_data/")) + '"', logger, verbose) sys.exit() # Get train/test data and set training data [Xtr, ytr, _, _, Xtst, ytst] = dc.get_all_data_Worker(int(data_partition_id)) wn.set_training_data(dataset_name, Xtr, ytr) display('WorkerNode loaded %d patterns for training' % wn.NPtr, logger, verbose) # Creating a ML model and start training procedure wn.create_model_worker(model_type) display('MMLL model %s is ready for training!' %model_type, logger, verbose) display('Worker_' + model_type + ' %s is running...' %user_name, logger, verbose) wn.run() display('Worker_' + model_type + ' %s: EXIT' %user_name, logger, verbose) # Retrieving and saving the trained model display('Retrieving the trained model from WorkerNode', logger, verbose) model = wn.get_model() # Warning: this save_model utility is only for demo purposes output_filename_model = './results/models/Worker_' + str(user_name) + '_' + dataset_name + '_model' model.save(output_filename_model) # Making predictions on test data display('------------- Obtaining predictions------------------------------------\n', logger, verbose) preprocessors = wn.get_preprocessors() if preprocessors is not None: for prep_model in preprocessors: # Apply stored preprocessor sequentially (in the same order received) Xtst = prep_model.transform(Xtst) display('Test data transformed using %s' %prep_model.name, logger, verbose) preds_tst = model.predict(Xtst) preds_tst = np.argmax(preds_tst, axis=-1) # Convert to labels y = np.argmax(ytst, axis=-1) # Convert to labels classes = np.arange(ytst.shape[1]) # 0 to 9 # Evaluating the results display('------------- Evaluating --------------------------------------------\n', logger, verbose) # Warning, these evaluation methods are not part of the MMLL library, they are only intended # to be used for the demos. Use them at your own risk. output_filename = 'Worker_' + str(user_name) + '_NN_confusion_matrix_' + dataset_name + '.png' title = 'NN confusion matrix in test set worker' plot_cm_seaborn(preds_tst, y, classes, title, output_filename, logger, verbose, normalize=True) # Load Tf SavedModel and check results model_loaded = tf.keras.models.load_model(output_filename_model) preds_tst = model_loaded.predict(Xtst) preds_tst = np.argmax(preds_tst, axis=-1) # Convert to labels # Model export to ONXX output_filename_model = './results/models/Worker_' + str(user_name) + '_' + dataset_name + '_model.onnx' model.save(output_filename_model) # Compute the prediction with ONNX Runtime onnx_session = onnxruntime.InferenceSession(output_filename_model) onnx_inputs = {onnx_session.get_inputs()[0].name: Xtst} onnx_output = onnx_session.run(None, onnx_inputs)[0] onnx_output = np.argmax(onnx_output, axis=-1) # Convert to labels err_onnx = accuracy_score(y,onnx_output) display('Test accuracy in ONNX model is %f' %err_onnx, logger, verbose)

[ "[email protected]" ]

[email protected]

95986ae73d179770f7292b38dbaaf00b540d68bb

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/00-deleteintreeview.py

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UncleEngineer/TkinterTrick

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from tkinter import * from tkinter import ttk root = Tk() tree = ttk.Treeview(root) tree["columns"]=("one","two") tree.column("one", width=100 ) tree.column("two", width=100) tree.heading("one", text="coulmn A") tree.heading("two", text="column B") tree.insert("" , 0, text="Line 1", values=("1A","1b")) id2 = tree.insert("", 1, "dir2", text="Dir 2") tree.insert(id2, "end", "dir 2", text="sub dir 2", values=("2A","2B")) ##alternatively: tree.insert("", 3, "dir3", text="Dir 3") tree.insert("dir3", 3, text=" sub dir 3",values=("3A"," 3B")) def edit(): x = tree.get_children() for item in x: ## Changing all children from root item tree.item(item, text="blub", values=("foo", "bar")) def delete(): selected_item = tree.selection()[0] ## get selected item tree.delete(selected_item) tree.pack() button_del = Button(root, text="del", command=delete) button_del.pack() button_del = Button(root, text="edit", command=edit) button_del.pack() root.mainloop()

[ "[email protected]" ]

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/enemy_bot/enemy_bot_level5/burger_detect/scripts/image_save.py

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#!/usr/bin/env python # -*- coding: utf-8 -*- import rospy import random # from geometry_msgs.msg import Twist from sensor_msgs.msg import Image import sys from cv_bridge import CvBridge, CvBridgeError import cv2 import time import os dir="Image/test/" num=10*1000 class ImageGet(): def __init__(self): rospy.Subscriber('/image_raw', Image, self.Image_save) self.bridge = CvBridge() self.count=0 def Image_save(self,data): cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8") cv2.imshow("sample.jpg",cv_image) cv2.waitKey(5) #cv2.imwrite(dir+"sample"+repr(self.count)+".jpg",cv_image) print("save done.") #self.count+=1 def get_image(self): r = rospy.Rate(1) # change speed 1fps while not rospy.is_shutdown(): r.sleep() if self.count>num: break if __name__ == '__main__': if not os.path.exists(dir): os.mkdir(dir) rospy.init_node('get_image') bot = ImageGet() bot.get_image()

[ "[email protected]" ]

[email protected]

327f5bed18063bc5103443d55e4856bea69453da

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/app/inheritance/abstract/migrations/0003_auto_20191223_0545.py

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[]

no_license

moorekwon/django-document

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2021-09-29T00:37:04.647977

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# Generated by Django 3.0 on 2019-12-23 05:45 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('abstract', '0002_childa_childb'), ] operations = [ migrations.AlterField( model_name='childa', name='m2m', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='abstract_childa_set', to='abstract.Student'), ), migrations.AlterField( model_name='childb', name='m2m', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='abstract_childb_set', to='abstract.Student'), ), ]

[ "[email protected]" ]

[email protected]

37cda467832d9959605d1668f2ef07cc8c293df9

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/API:Data_Visualization/population_json.py

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[]

no_license

PickertJoe/python_exercises

5b9ac3334eec32e35a477d126c911d4ca07a4343

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refs/heads/master

2022-12-12T11:57:08.267814

2019-12-08T21:52:31

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# A program to read and analyze the data in population_data.json import json from comma import comma from country_code import code_search from pygal.maps.world import World from pygal.style import RotateStyle # Importing the data in the json file into a list filename = "chapter_16/population_data.json" with open(filename) as f: population_data = json.load(f) # Building a dictionary of the population data cc_populations = {} for country in population_data: if country['Year'] == '2010': country_name = country["Country Name"] population = int(float(country["Value"])) code = code_search(country_name) if code: cc_populations[code] = population # Creating three separate categories for different population ranges cc_pop1, cc_pop2, cc_pop3 = {}, {}, {} for code, population in cc_populations.items(): if population > 1000000000: cc_pop1[code] = population elif population > 10000000: cc_pop2[code] = population else: cc_pop3[code] = population wm_style = RotateStyle('#336699') wm = World(style=wm_style) wm.title = "World Population in 2010, by Select Countries" wm.add('1bn+', cc_pop1) wm.add('10m - 1bn', cc_pop2) wm.add('0-10m', cc_pop3) wm.render_to_file('country_populations_category.svg')

[ "[email protected]" ]

[email protected]

65c4da75fb004f1520cb29a69802bcce620518d9

40c4b0c31a5870a9201d3d42a63c5547092e5912

/frappe/recorder.py

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ektai/frappe3

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# -*- coding: utf-8 -*- # Copyright (c) 2018, Frappe Technologies Pvt. Ltd. and Contributors # MIT License. See license.txt from __future__ import unicode_literals from collections import Counter import datetime import inspect import json import re import time import traceback import frappe import sqlparse from pygments import highlight from pygments.lexers import PythonLexer from pygments.formatters import HtmlFormatter from frappe import _ RECORDER_INTERCEPT_FLAG = "recorder-intercept" RECORDER_REQUEST_SPARSE_HASH = "recorder-requests-sparse" RECORDER_REQUEST_HASH = "recorder-requests" def sql(*args, **kwargs): start_time = time.time() result = frappe.db._sql(*args, **kwargs) end_time = time.time() stack = list(get_current_stack_frames()) if frappe.conf.db_type == 'postgres': query = frappe.db._cursor.query else: query = frappe.db._cursor._executed query = sqlparse.format(query.strip(), keyword_case="upper", reindent=True) # Collect EXPLAIN for executed query if query.lower().strip().split()[0] in ("select", "update", "delete"): # Only SELECT/UPDATE/DELETE queries can be "EXPLAIN"ed explain_result = frappe.db._sql("EXPLAIN {}".format(query), as_dict=True) else: explain_result = [] data = { "query": query, "stack": stack, "explain_result": explain_result, "time": start_time, "duration": float("{:.3f}".format((end_time - start_time) * 1000)), } frappe.local._recorder.register(data) return result def get_current_stack_frames(): current = inspect.currentframe() frames = inspect.getouterframes(current, context=10) for frame, filename, lineno, function, context, index in list(reversed(frames))[:-2]: if "/apps/" in filename: yield { "filename": re.sub(".*/apps/", "", filename), "lineno": lineno, "function": function, "context": "".join(context), "index": index, "locals": json.dumps(frame.f_locals, skipkeys=True, default=str) } def record(): if __debug__: if frappe.cache().get_value(RECORDER_INTERCEPT_FLAG): frappe.local._recorder = Recorder() def dump(): if __debug__: if hasattr(frappe.local, "_recorder"): frappe.local._recorder.dump() class Recorder(): def __init__(self): self.uuid = frappe.generate_hash(length=10) self.time = datetime.datetime.now() self.calls = [] self.path = frappe.request.path self.cmd = frappe.local.form_dict.cmd or "" self.method = frappe.request.method self.headers = dict(frappe.local.request.headers) self.form_dict = frappe.local.form_dict _patch() def register(self, data): self.calls.append(data) def dump(self): request_data = { "uuid": self.uuid, "path": self.path, "cmd": self.cmd, "time": self.time, "queries": len(self.calls), "time_queries": float("{:0.3f}".format(sum(call["duration"] for call in self.calls))), "duration": float("{:0.3f}".format((datetime.datetime.now() - self.time).total_seconds() * 1000)), "method": self.method, } frappe.cache().hset(RECORDER_REQUEST_SPARSE_HASH, self.uuid, request_data) frappe.publish_realtime(event="recorder-dump-event", message=json.dumps(request_data, default=str)) self.mark_duplicates() request_data["calls"] = self.calls request_data["headers"] = self.headers request_data["form_dict"] = self.form_dict frappe.cache().hset(RECORDER_REQUEST_HASH, self.uuid, request_data) def mark_duplicates(self): counts = Counter([call["query"] for call in self.calls]) for index, call in enumerate(self.calls): call["index"] = index call["exact_copies"] = counts[call["query"]] def _patch(): frappe.db._sql = frappe.db.sql frappe.db.sql = sql def do_not_record(function): def wrapper(*args, **kwargs): if hasattr(frappe.local, "_recorder"): del frappe.local._recorder frappe.db.sql = frappe.db._sql return function(*args, **kwargs) return wrapper def administrator_only(function): def wrapper(*args, **kwargs): if frappe.session.user != "Administrator": frappe.throw(_("Only Administrator is allowed to use Recorder")) return function(*args, **kwargs) return wrapper @frappe.whitelist() @do_not_record @administrator_only def status(*args, **kwargs): return bool(frappe.cache().get_value(RECORDER_INTERCEPT_FLAG)) @frappe.whitelist() @do_not_record @administrator_only def start(*args, **kwargs): frappe.cache().set_value(RECORDER_INTERCEPT_FLAG, 1) @frappe.whitelist() @do_not_record @administrator_only def stop(*args, **kwargs): frappe.cache().delete_value(RECORDER_INTERCEPT_FLAG) @frappe.whitelist() @do_not_record @administrator_only def get(uuid=None, *args, **kwargs): if uuid: result = frappe.cache().hget(RECORDER_REQUEST_HASH, uuid) lexer = PythonLexer(tabsize=4) for call in result["calls"]: for stack in call["stack"]: formatter = HtmlFormatter(noclasses=True, hl_lines=[stack["index"] + 1]) stack["context"] = highlight(stack["context"], lexer, formatter) else: result = list(frappe.cache().hgetall(RECORDER_REQUEST_SPARSE_HASH).values()) return result @frappe.whitelist() @do_not_record @administrator_only def delete(*args, **kwargs): frappe.cache().delete_value(RECORDER_REQUEST_SPARSE_HASH) frappe.cache().delete_value(RECORDER_REQUEST_HASH)

[ "[email protected]" ]

[email protected]

871e74c940da56c3387dffad57b313ca22cdc089

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/심심풀이땅콩/[백준]4673.py

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0equal2/Python_Programming

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###[백준]4673 memo=[0]*10001 for i in range(1,10001): newnum=i+sum(list(map(int,list(str(i))))) if newnum<=10000: memo[newnum]=1 if memo[i]==0: print(i)

[ "[email protected]" ]

[email protected]

4b99c672e34294a5f110b6531518b6d7056de15a

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/blackopt/config.py

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ikamensh/blackopt

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import os default_workspace = "_blackopt_workspace" _rootdir = default_workspace def set_rootdir(path): path = os.path.expanduser(path) global _rootdir _rootdir = path def prepend_rootdir(prefix): prefix = os.path.expanduser(prefix) path = os.path.join(prefix, default_workspace) global _rootdir _rootdir = path def get_rootdir(): return _rootdir

[ "[email protected]" ]

[email protected]

3cc86621a38c55c60af190e6064d74da255a9e2b

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/combination_sum_ii.py

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no_license

milllu/leetcode

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refs/heads/master

2020-03-30T23:41:46.180308

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""" 给定一个数组 candidates 和一个目标数 target ，找出 candidates 中所有可以使数字和为 target 的组合。 candidates 中的每个数字在每个组合中只能使用一次。说明：所有数字（包括目标数）都是正整数。解集不能包含重复的组合。示例 1: 输入: candidates = [10,1,2,7,6,1,5], target = 8, 所求解集为: [ [1, 7], [1, 2, 5], [2, 6], [1, 1, 6] ] 示例 2: 输入: candidates = [2,5,2,1,2], target = 5, 所求解集为: [ [1,2,2], [5] ] """ class Solution(object): def combinationSum2(self, nums, target): """ :type candidates: List[int] :type target: int :rtype: List[List[int]] """ def dfs(dic, target, lst, suml): if suml == target: lst.sort() if lst not in result: result.append(lst) return if suml > target: return for key in dic: if dic[key] > 0: dic[key] -= 1 dfs(dic, target, lst+[key], suml+key) dic[key] += 1 dic = {} for num in nums: dic[num] = dic.get(num, 0) + 1 result = [] dfs(dic, target, [], 0) return result

[ "[email protected]" ]

[email protected]

aace7c3ac8ae4dfbaf9a425ce523bb342eaafc68

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/sdBs/AllRun/pg_2204+034/sdB_pg_2204+034_coadd.py

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[]

no_license

tboudreaux/SummerSTScICode

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2021-01-20T18:07:44.723496

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from gPhoton.gMap import gMap def main(): gMap(band="NUV", skypos=[331.818708,3.705497], skyrange=[0.0333333333333,0.0333333333333], stepsz = 30., cntfile="/data2/fleming/GPHOTON_OUTPUT/LIGHTCURVES/sdBs/sdB_pg_2204+034/sdB_pg_2204+034_movie_count.fits", cntcoaddfile="/data2/fleming/GPHOTON_OUTPUT/LIGHTCURVES/sdB/sdB_pg_2204+034/sdB_pg_2204+034_count_coadd.fits", overwrite=True, verbose=3) if __name__ == "__main__": main()

[ "[email protected]" ]

[email protected]

c0bdac944aed5cb00d3ab2541709a23fecbc22e3

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/azure-mgmt-network/azure/mgmt/network/v2018_11_01/models/application_gateway_rewrite_rule_set.py

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2023-08-12T20:36:24.284497

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2020-10-02T01:17:02

2019-05-22T07:33:46

Python

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Python

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2,111

py

# coding=utf-8 # -------------------------------------------------------------------------- # 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 Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .sub_resource import SubResource class ApplicationGatewayRewriteRuleSet(SubResource): """Rewrite rule set of an application gateway. Variables are only populated by the server, and will be ignored when sending a request. :param id: Resource ID. :type id: str :param rewrite_rules: Rewrite rules in the rewrite rule set. :type rewrite_rules: list[~azure.mgmt.network.v2018_11_01.models.ApplicationGatewayRewriteRule] :ivar provisioning_state: Provisioning state of the rewrite rule set resource. Possible values are: 'Updating', 'Deleting', and 'Failed'. :vartype provisioning_state: str :param name: Name of the rewrite rule set that is unique within an Application Gateway. :type name: str :ivar etag: A unique read-only string that changes whenever the resource is updated. :vartype etag: str """ _validation = { 'provisioning_state': {'readonly': True}, 'etag': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'rewrite_rules': {'key': 'properties.rewriteRules', 'type': '[ApplicationGatewayRewriteRule]'}, 'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'etag': {'key': 'etag', 'type': 'str'}, } def __init__(self, **kwargs): super(ApplicationGatewayRewriteRuleSet, self).__init__(**kwargs) self.rewrite_rules = kwargs.get('rewrite_rules', None) self.provisioning_state = None self.name = kwargs.get('name', None) self.etag = None

[ "[email protected]" ]

[email protected]

c61d44bcc8be1346b3b1a8bb2742d5847838cc8a

2b115f9c5929fedd06d4dd6969100ab2df484adf

/messenger/urls.py

9903187e19c8320adcd0f81ea319f70a102f44a3

[]

no_license

sorXCode/Avina-API

0b790bfd8ac8b9c84d1db45db0819e0585d954b9

7687ba7434b77d6c33944c65fff0409459a9d5ce

refs/heads/master

2023-04-11T01:17:35.570752

2021-04-14T15:01:45

342,429,666

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1

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from django.urls import path from rest_framework.urlpatterns import format_suffix_patterns from .views import StartMessaging, Conversation urlpatterns = [ path('', StartMessaging.as_view()), path('<str:product_uid>', Conversation.as_view()), ] urlpatterns = format_suffix_patterns(urlpatterns)

[ "[email protected]" ]

[email protected]

a21618f0ce0aa6432175d36b0042e7df8e21bb69

c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c

/cases/synthetic/prime-big-291.py

dac9afb7dc0a8d107113bc4bc06e8af627553a69

[]

no_license

Virtlink/ccbench-chocopy

c3f7f6af6349aff6503196f727ef89f210a1eac8

c7efae43bf32696ee2b2ee781bdfe4f7730dec3f

refs/heads/main

2023-04-07T15:07:12.464038

2022-02-03T15:42:39

451,969,776

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py

# Get the n-th prime starting from 2 def get_prime(n:int) -> int: candidate:int = 2 found:int = 0 while True: if is_prime(candidate): found = found + 1 if found == n: return candidate candidate = candidate + 1 return 0 # Never happens def is_prime(x:int) -> bool: div:int = 2 div2:int = 2 div3:int = 2 div4:int = 2 div5:int = 2 while div < x: if x % div == 0: return False div = div + 1 return True def is_prime2(x:int, x2:int) -> bool: div:int = 2 div2:int = 2 div3:int = 2 div4:int = 2 div5:int = 2 while div < x: if x % div == 0: return False div = div + 1 return True $Definition def is_prime4(x:int, x2:int, x3:int, x4:int) -> bool: div:int = 2 div2:int = 2 div3:int = 2 div4:int = 2 div5:int = 2 while div < x: if x % div == 0: return False div = div + 1 return True def is_prime5(x:int, x2:int, x3:int, x4:int, x5:int) -> bool: div:int = 2 div2:int = 2 div3:int = 2 div4:int = 2 div5:int = 2 while div < x: if x % div == 0: return False div = div + 1 return True # Input parameter n:int = 15 n2:int = 15 n3:int = 15 n4:int = 15 n5:int = 15 # Run [1, n] i:int = 1 i2:int = 1 i3:int = 1 i4:int = 1 i5:int = 1 # Crunch while i <= n: print(get_prime(i)) i = i + 1

[ "[email protected]" ]

[email protected]

6186282c3446f83d419a1ac0c4b554890f932503

b5a9d42f7ea5e26cd82b3be2b26c324d5da79ba1

/tensorflow/python/framework/dtypes.py

f1c6251c483d69a34f5710f9b346fb9ccd2b713e

[ "Apache-2.0" ]

permissive

uve/tensorflow

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e08079463bf43e5963acc41da1f57e95603f8080

refs/heads/master

2020-11-29T11:30:40.391232

2020-01-11T13:43:10

230,088,347

0

Apache-2.0

2019-12-25T10:49:15

2019-12-25T10:49:14

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UTF-8

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py

# Copyright 2015 The TensorFlow Authors. 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. # ============================================================================== """Library of dtypes (Tensor element types).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import builtins from tensorflow.core.framework import types_pb2 from tensorflow.python import pywrap_tensorflow from tensorflow.python.util.tf_export import tf_export _np_bfloat16 = pywrap_tensorflow.TF_bfloat16_type() @tf_export("dtypes.DType", "DType") class DType(object): """Represents the type of the elements in a `Tensor`. The following `DType` objects are defined: * `tf.float16`: 16-bit half-precision floating-point. * `tf.float32`: 32-bit single-precision floating-point. * `tf.float64`: 64-bit double-precision floating-point. * `tf.bfloat16`: 16-bit truncated floating-point. * `tf.complex64`: 64-bit single-precision complex. * `tf.complex128`: 128-bit double-precision complex. * `tf.int8`: 8-bit signed integer. * `tf.uint8`: 8-bit unsigned integer. * `tf.uint16`: 16-bit unsigned integer. * `tf.uint32`: 32-bit unsigned integer. * `tf.uint64`: 64-bit unsigned integer. * `tf.int16`: 16-bit signed integer. * `tf.int32`: 32-bit signed integer. * `tf.int64`: 64-bit signed integer. * `tf.bool`: Boolean. * `tf.string`: String. * `tf.qint8`: Quantized 8-bit signed integer. * `tf.quint8`: Quantized 8-bit unsigned integer. * `tf.qint16`: Quantized 16-bit signed integer. * `tf.quint16`: Quantized 16-bit unsigned integer. * `tf.qint32`: Quantized 32-bit signed integer. * `tf.resource`: Handle to a mutable resource. * `tf.variant`: Values of arbitrary types. The `tf.as_dtype()` function converts numpy types and string type names to a `DType` object. """ def __init__(self, type_enum): """Creates a new `DataType`. NOTE(mrry): In normal circumstances, you should not need to construct a `DataType` object directly. Instead, use the `tf.as_dtype()` function. Args: type_enum: A `types_pb2.DataType` enum value. Raises: TypeError: If `type_enum` is not a value `types_pb2.DataType`. """ # TODO(mrry): Make the necessary changes (using __new__) to ensure # that calling this returns one of the interned values. type_enum = int(type_enum) if (type_enum not in types_pb2.DataType.values() or type_enum == types_pb2.DT_INVALID): raise TypeError("type_enum is not a valid types_pb2.DataType: %s" % type_enum) self._type_enum = type_enum @property def _is_ref_dtype(self): """Returns `True` if this `DType` represents a reference type.""" return self._type_enum > 100 @property def _as_ref(self): """Returns a reference `DType` based on this `DType`.""" if self._is_ref_dtype: return self else: return _INTERN_TABLE[self._type_enum + 100] @property def base_dtype(self): """Returns a non-reference `DType` based on this `DType`.""" if self._is_ref_dtype: return _INTERN_TABLE[self._type_enum - 100] else: return self @property def real_dtype(self): """Returns the dtype correspond to this dtype's real part.""" base = self.base_dtype if base == complex64: return float32 elif base == complex128: return float64 else: return self @property def is_numpy_compatible(self): return self._type_enum not in _NUMPY_INCOMPATIBLE @property def as_numpy_dtype(self): """Returns a `numpy.dtype` based on this `DType`.""" return _TF_TO_NP[self._type_enum] @property def as_datatype_enum(self): """Returns a `types_pb2.DataType` enum value based on this `DType`.""" return self._type_enum @property def is_bool(self): """Returns whether this is a boolean data type""" return self.base_dtype == bool @property def is_integer(self): """Returns whether this is a (non-quantized) integer type.""" return (self.is_numpy_compatible and not self.is_quantized and np.issubdtype(self.as_numpy_dtype, np.integer)) @property def is_floating(self): """Returns whether this is a (non-quantized, real) floating point type.""" return ((self.is_numpy_compatible and np.issubdtype(self.as_numpy_dtype, np.floating)) or self.base_dtype == bfloat16) @property def is_complex(self): """Returns whether this is a complex floating point type.""" return self.base_dtype in (complex64, complex128) @property def is_quantized(self): """Returns whether this is a quantized data type.""" return self.base_dtype in _QUANTIZED_DTYPES_NO_REF @property def is_unsigned(self): """Returns whether this type is unsigned. Non-numeric, unordered, and quantized types are not considered unsigned, and this function returns `False`. Returns: Whether a `DType` is unsigned. """ try: return self.min == 0 except TypeError: return False @property def min(self): """Returns the minimum representable value in this data type. Raises: TypeError: if this is a non-numeric, unordered, or quantized type. """ if (self.is_quantized or self.base_dtype in (bool, string, complex64, complex128)): raise TypeError("Cannot find minimum value of %s." % self) # there is no simple way to get the min value of a dtype, we have to check # float and int types separately try: return np.finfo(self.as_numpy_dtype()).min except: # bare except as possible raises by finfo not documented try: return np.iinfo(self.as_numpy_dtype()).min except: if self.base_dtype == bfloat16: return _np_bfloat16(float.fromhex("-0x1.FEp127")) raise TypeError("Cannot find minimum value of %s." % self) @property def max(self): """Returns the maximum representable value in this data type. Raises: TypeError: if this is a non-numeric, unordered, or quantized type. """ if (self.is_quantized or self.base_dtype in (bool, string, complex64, complex128)): raise TypeError("Cannot find maximum value of %s." % self) # there is no simple way to get the max value of a dtype, we have to check # float and int types separately try: return np.finfo(self.as_numpy_dtype()).max except: # bare except as possible raises by finfo not documented try: return np.iinfo(self.as_numpy_dtype()).max except: if self.base_dtype == bfloat16: return _np_bfloat16(float.fromhex("0x1.FEp127")) raise TypeError("Cannot find maximum value of %s." % self) @property def limits(self, clip_negative=True): """Return intensity limits, i.e. (min, max) tuple, of the dtype. Args: clip_negative : bool, optional If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. Returns min, max : tuple Lower and upper intensity limits. """ min, max = dtype_range[self.as_numpy_dtype] # pylint: disable=redefined-builtin if clip_negative: min = 0 # pylint: disable=redefined-builtin return min, max def is_compatible_with(self, other): """Returns True if the `other` DType will be converted to this DType. The conversion rules are as follows: ```python DType(T) .is_compatible_with(DType(T)) == True ``` Args: other: A `DType` (or object that may be converted to a `DType`). Returns: True if a Tensor of the `other` `DType` will be implicitly converted to this `DType`. """ other = as_dtype(other) return self._type_enum in (other.as_datatype_enum, other.base_dtype.as_datatype_enum) def __eq__(self, other): """Returns True iff this DType refers to the same type as `other`.""" if other is None: return False try: dtype = as_dtype(other).as_datatype_enum return self._type_enum == dtype # pylint: disable=protected-access except TypeError: return False def __ne__(self, other): """Returns True iff self != other.""" return not self.__eq__(other) @property def name(self): """Returns the string name for this `DType`.""" return _TYPE_TO_STRING[self._type_enum] def __str__(self): return "<dtype: %r>" % self.name def __repr__(self): return "tf." + self.name def __hash__(self): return self._type_enum def __reduce__(self): return as_dtype, (self.name,) @property def size(self): if (self._type_enum == types_pb2.DT_VARIANT or self._type_enum == types_pb2.DT_RESOURCE): return 1 return np.dtype(self.as_numpy_dtype).itemsize # Define data type range of numpy dtype dtype_range = { np.bool_: (False, True), np.bool8: (False, True), np.uint8: (0, 255), np.uint16: (0, 65535), np.int8: (-128, 127), np.int16: (-32768, 32767), np.int64: (-2**63, 2**63 - 1), np.uint64: (0, 2**64 - 1), np.int32: (-2**31, 2**31 - 1), np.uint32: (0, 2**32 - 1), np.float32: (-1, 1), np.float64: (-1, 1) } # Define standard wrappers for the types_pb2.DataType enum. resource = DType(types_pb2.DT_RESOURCE) tf_export("dtypes.resource", "resource").export_constant(__name__, "resource") variant = DType(types_pb2.DT_VARIANT) tf_export("dtypes.variant", "variant").export_constant(__name__, "variant") float16 = DType(types_pb2.DT_HALF) tf_export("dtypes.float16", "float16").export_constant(__name__, "float16") half = float16 tf_export("dtypes.half", "half").export_constant(__name__, "half") float32 = DType(types_pb2.DT_FLOAT) tf_export("dtypes.float32", "float32").export_constant(__name__, "float32") float64 = DType(types_pb2.DT_DOUBLE) tf_export("dtypes.float64", "float64").export_constant(__name__, "float64") double = float64 tf_export("dtypes.double", "double").export_constant(__name__, "double") int32 = DType(types_pb2.DT_INT32) tf_export("dtypes.int32", "int32").export_constant(__name__, "int32") uint8 = DType(types_pb2.DT_UINT8) tf_export("dtypes.uint8", "uint8").export_constant(__name__, "uint8") uint16 = DType(types_pb2.DT_UINT16) tf_export("dtypes.uint16", "uint16").export_constant(__name__, "uint16") uint32 = DType(types_pb2.DT_UINT32) tf_export("dtypes.uint32", "uint32").export_constant(__name__, "uint32") uint64 = DType(types_pb2.DT_UINT64) tf_export("dtypes.uint64", "uint64").export_constant(__name__, "uint64") int16 = DType(types_pb2.DT_INT16) tf_export("dtypes.int16", "int16").export_constant(__name__, "int16") int8 = DType(types_pb2.DT_INT8) tf_export("dtypes.int8", "int8").export_constant(__name__, "int8") string = DType(types_pb2.DT_STRING) tf_export("dtypes.string", "string").export_constant(__name__, "string") complex64 = DType(types_pb2.DT_COMPLEX64) tf_export("dtypes.complex64", "complex64").export_constant(__name__, "complex64") complex128 = DType(types_pb2.DT_COMPLEX128) tf_export("dtypes.complex128", "complex128").export_constant(__name__, "complex128") int64 = DType(types_pb2.DT_INT64) tf_export("dtypes.int64", "int64").export_constant(__name__, "int64") bool = DType(types_pb2.DT_BOOL) # pylint: disable=redefined-builtin tf_export("dtypes.bool", "bool").export_constant(__name__, "bool") qint8 = DType(types_pb2.DT_QINT8) tf_export("dtypes.qint8", "qint8").export_constant(__name__, "qint8") quint8 = DType(types_pb2.DT_QUINT8) tf_export("dtypes.quint8", "quint8").export_constant(__name__, "quint8") qint16 = DType(types_pb2.DT_QINT16) tf_export("dtypes.qint16", "qint16").export_constant(__name__, "qint16") quint16 = DType(types_pb2.DT_QUINT16) tf_export("dtypes.quint16", "quint16").export_constant(__name__, "quint16") qint32 = DType(types_pb2.DT_QINT32) tf_export("dtypes.qint32", "qint32").export_constant(__name__, "qint32") resource_ref = DType(types_pb2.DT_RESOURCE_REF) variant_ref = DType(types_pb2.DT_VARIANT_REF) bfloat16 = DType(types_pb2.DT_BFLOAT16) tf_export("dtypes.bfloat16", "bfloat16").export_constant(__name__, "bfloat16") float16_ref = DType(types_pb2.DT_HALF_REF) half_ref = float16_ref float32_ref = DType(types_pb2.DT_FLOAT_REF) float64_ref = DType(types_pb2.DT_DOUBLE_REF) double_ref = float64_ref int32_ref = DType(types_pb2.DT_INT32_REF) uint32_ref = DType(types_pb2.DT_UINT32_REF) uint8_ref = DType(types_pb2.DT_UINT8_REF) uint16_ref = DType(types_pb2.DT_UINT16_REF) int16_ref = DType(types_pb2.DT_INT16_REF) int8_ref = DType(types_pb2.DT_INT8_REF) string_ref = DType(types_pb2.DT_STRING_REF) complex64_ref = DType(types_pb2.DT_COMPLEX64_REF) complex128_ref = DType(types_pb2.DT_COMPLEX128_REF) int64_ref = DType(types_pb2.DT_INT64_REF) uint64_ref = DType(types_pb2.DT_UINT64_REF) bool_ref = DType(types_pb2.DT_BOOL_REF) qint8_ref = DType(types_pb2.DT_QINT8_REF) quint8_ref = DType(types_pb2.DT_QUINT8_REF) qint16_ref = DType(types_pb2.DT_QINT16_REF) quint16_ref = DType(types_pb2.DT_QUINT16_REF) qint32_ref = DType(types_pb2.DT_QINT32_REF) bfloat16_ref = DType(types_pb2.DT_BFLOAT16_REF) _NUMPY_INCOMPATIBLE = frozenset([ types_pb2.DT_VARIANT, types_pb2.DT_VARIANT_REF, types_pb2.DT_RESOURCE, types_pb2.DT_RESOURCE_REF ]) # Maintain an intern table so that we don't have to create a large # number of small objects. _INTERN_TABLE = { types_pb2.DT_HALF: float16, types_pb2.DT_FLOAT: float32, types_pb2.DT_DOUBLE: float64, types_pb2.DT_INT32: int32, types_pb2.DT_UINT8: uint8, types_pb2.DT_UINT16: uint16, types_pb2.DT_UINT32: uint32, types_pb2.DT_UINT64: uint64, types_pb2.DT_INT16: int16, types_pb2.DT_INT8: int8, types_pb2.DT_STRING: string, types_pb2.DT_COMPLEX64: complex64, types_pb2.DT_COMPLEX128: complex128, types_pb2.DT_INT64: int64, types_pb2.DT_BOOL: bool, types_pb2.DT_QINT8: qint8, types_pb2.DT_QUINT8: quint8, types_pb2.DT_QINT16: qint16, types_pb2.DT_QUINT16: quint16, types_pb2.DT_QINT32: qint32, types_pb2.DT_BFLOAT16: bfloat16, types_pb2.DT_RESOURCE: resource, types_pb2.DT_VARIANT: variant, types_pb2.DT_HALF_REF: float16_ref, types_pb2.DT_FLOAT_REF: float32_ref, types_pb2.DT_DOUBLE_REF: float64_ref, types_pb2.DT_INT32_REF: int32_ref, types_pb2.DT_UINT32_REF: uint32_ref, types_pb2.DT_UINT8_REF: uint8_ref, types_pb2.DT_UINT16_REF: uint16_ref, types_pb2.DT_INT16_REF: int16_ref, types_pb2.DT_INT8_REF: int8_ref, types_pb2.DT_STRING_REF: string_ref, types_pb2.DT_COMPLEX64_REF: complex64_ref, types_pb2.DT_COMPLEX128_REF: complex128_ref, types_pb2.DT_INT64_REF: int64_ref, types_pb2.DT_UINT64_REF: uint64_ref, types_pb2.DT_BOOL_REF: bool_ref, types_pb2.DT_QINT8_REF: qint8_ref, types_pb2.DT_QUINT8_REF: quint8_ref, types_pb2.DT_QINT16_REF: qint16_ref, types_pb2.DT_QUINT16_REF: quint16_ref, types_pb2.DT_QINT32_REF: qint32_ref, types_pb2.DT_BFLOAT16_REF: bfloat16_ref, types_pb2.DT_RESOURCE_REF: resource_ref, types_pb2.DT_VARIANT_REF: variant_ref, } # Standard mappings between types_pb2.DataType values and string names. _TYPE_TO_STRING = { types_pb2.DT_HALF: "float16", types_pb2.DT_FLOAT: "float32", types_pb2.DT_DOUBLE: "float64", types_pb2.DT_INT32: "int32", types_pb2.DT_UINT8: "uint8", types_pb2.DT_UINT16: "uint16", types_pb2.DT_UINT32: "uint32", types_pb2.DT_UINT64: "uint64", types_pb2.DT_INT16: "int16", types_pb2.DT_INT8: "int8", types_pb2.DT_STRING: "string", types_pb2.DT_COMPLEX64: "complex64", types_pb2.DT_COMPLEX128: "complex128", types_pb2.DT_INT64: "int64", types_pb2.DT_BOOL: "bool", types_pb2.DT_QINT8: "qint8", types_pb2.DT_QUINT8: "quint8", types_pb2.DT_QINT16: "qint16", types_pb2.DT_QUINT16: "quint16", types_pb2.DT_QINT32: "qint32", types_pb2.DT_BFLOAT16: "bfloat16", types_pb2.DT_RESOURCE: "resource", types_pb2.DT_VARIANT: "variant", types_pb2.DT_HALF_REF: "float16_ref", types_pb2.DT_FLOAT_REF: "float32_ref", types_pb2.DT_DOUBLE_REF: "float64_ref", types_pb2.DT_INT32_REF: "int32_ref", types_pb2.DT_UINT32_REF: "uint32_ref", types_pb2.DT_UINT8_REF: "uint8_ref", types_pb2.DT_UINT16_REF: "uint16_ref", types_pb2.DT_INT16_REF: "int16_ref", types_pb2.DT_INT8_REF: "int8_ref", types_pb2.DT_STRING_REF: "string_ref", types_pb2.DT_COMPLEX64_REF: "complex64_ref", types_pb2.DT_COMPLEX128_REF: "complex128_ref", types_pb2.DT_INT64_REF: "int64_ref", types_pb2.DT_UINT64_REF: "uint64_ref", types_pb2.DT_BOOL_REF: "bool_ref", types_pb2.DT_QINT8_REF: "qint8_ref", types_pb2.DT_QUINT8_REF: "quint8_ref", types_pb2.DT_QINT16_REF: "qint16_ref", types_pb2.DT_QUINT16_REF: "quint16_ref", types_pb2.DT_QINT32_REF: "qint32_ref", types_pb2.DT_BFLOAT16_REF: "bfloat16_ref", types_pb2.DT_RESOURCE_REF: "resource_ref", types_pb2.DT_VARIANT_REF: "variant_ref", } _STRING_TO_TF = { value: _INTERN_TABLE[key] for key, value in _TYPE_TO_STRING.items() } # Add non-canonical aliases. _STRING_TO_TF["half"] = float16 _STRING_TO_TF["half_ref"] = float16_ref _STRING_TO_TF["float"] = float32 _STRING_TO_TF["float_ref"] = float32_ref _STRING_TO_TF["double"] = float64 _STRING_TO_TF["double_ref"] = float64_ref # Numpy representation for quantized dtypes. # # These are magic strings that are used in the swig wrapper to identify # quantized types. # TODO(mrry,keveman): Investigate Numpy type registration to replace this # hard-coding of names. _np_qint8 = np.dtype([("qint8", np.int8)]) _np_quint8 = np.dtype([("quint8", np.uint8)]) _np_qint16 = np.dtype([("qint16", np.int16)]) _np_quint16 = np.dtype([("quint16", np.uint16)]) _np_qint32 = np.dtype([("qint32", np.int32)]) # _np_bfloat16 is defined by a module import. # Custom struct dtype for directly-fed ResourceHandles of supported type(s). np_resource = np.dtype([("resource", np.ubyte)]) # Standard mappings between types_pb2.DataType values and numpy.dtypes. _NP_TO_TF = { np.float16: float16, np.float32: float32, np.float64: float64, np.int32: int32, np.int64: int64, np.uint8: uint8, np.uint16: uint16, np.uint32: uint32, np.uint64: uint64, np.int16: int16, np.int8: int8, np.complex64: complex64, np.complex128: complex128, np.object_: string, np.string_: string, np.unicode_: string, np.bool_: bool, _np_qint8: qint8, _np_quint8: quint8, _np_qint16: qint16, _np_quint16: quint16, _np_qint32: qint32, _np_bfloat16: bfloat16, } # Map (some) NumPy platform dtypes to TF ones using their fixed-width # synonyms. Note that platform dtypes are not always simples aliases, # i.e. reference equality is not guaranteed. See e.g. numpy/numpy#9799. for pdt in [ np.intc, np.uintc, np.int_, np.uint, np.longlong, np.ulonglong, ]: if pdt not in _NP_TO_TF: _NP_TO_TF[pdt] = next( _NP_TO_TF[dt] for dt in _NP_TO_TF if dt == pdt().dtype) TF_VALUE_DTYPES = set(_NP_TO_TF.values()) _TF_TO_NP = { types_pb2.DT_HALF: np.float16, types_pb2.DT_FLOAT: np.float32, types_pb2.DT_DOUBLE: np.float64, types_pb2.DT_INT32: np.int32, types_pb2.DT_UINT8: np.uint8, types_pb2.DT_UINT16: np.uint16, types_pb2.DT_UINT32: np.uint32, types_pb2.DT_UINT64: np.uint64, types_pb2.DT_INT16: np.int16, types_pb2.DT_INT8: np.int8, # NOTE(touts): For strings we use np.object as it supports variable length # strings. types_pb2.DT_STRING: np.object, types_pb2.DT_COMPLEX64: np.complex64, types_pb2.DT_COMPLEX128: np.complex128, types_pb2.DT_INT64: np.int64, types_pb2.DT_BOOL: np.bool, types_pb2.DT_QINT8: _np_qint8, types_pb2.DT_QUINT8: _np_quint8, types_pb2.DT_QINT16: _np_qint16, types_pb2.DT_QUINT16: _np_quint16, types_pb2.DT_QINT32: _np_qint32, types_pb2.DT_BFLOAT16: _np_bfloat16, # Ref types types_pb2.DT_HALF_REF: np.float16, types_pb2.DT_FLOAT_REF: np.float32, types_pb2.DT_DOUBLE_REF: np.float64, types_pb2.DT_INT32_REF: np.int32, types_pb2.DT_UINT32_REF: np.uint32, types_pb2.DT_UINT8_REF: np.uint8, types_pb2.DT_UINT16_REF: np.uint16, types_pb2.DT_INT16_REF: np.int16, types_pb2.DT_INT8_REF: np.int8, types_pb2.DT_STRING_REF: np.object, types_pb2.DT_COMPLEX64_REF: np.complex64, types_pb2.DT_COMPLEX128_REF: np.complex128, types_pb2.DT_INT64_REF: np.int64, types_pb2.DT_UINT64_REF: np.uint64, types_pb2.DT_BOOL_REF: np.bool, types_pb2.DT_QINT8_REF: _np_qint8, types_pb2.DT_QUINT8_REF: _np_quint8, types_pb2.DT_QINT16_REF: _np_qint16, types_pb2.DT_QUINT16_REF: _np_quint16, types_pb2.DT_QINT32_REF: _np_qint32, types_pb2.DT_BFLOAT16_REF: _np_bfloat16, } _QUANTIZED_DTYPES_NO_REF = frozenset([qint8, quint8, qint16, quint16, qint32]) _QUANTIZED_DTYPES_REF = frozenset( [qint8_ref, quint8_ref, qint16_ref, quint16_ref, qint32_ref]) QUANTIZED_DTYPES = _QUANTIZED_DTYPES_REF.union(_QUANTIZED_DTYPES_NO_REF) tf_export( "dtypes.QUANTIZED_DTYPES", v1=["dtypes.QUANTIZED_DTYPES", "QUANTIZED_DTYPES"]).export_constant(__name__, "QUANTIZED_DTYPES") _PYTHON_TO_TF = { builtins.float: float32, builtins.bool: bool, builtins.object: string } _ANY_TO_TF = {} _ANY_TO_TF.update(_INTERN_TABLE) _ANY_TO_TF.update(_STRING_TO_TF) _ANY_TO_TF.update(_PYTHON_TO_TF) _ANY_TO_TF.update(_NP_TO_TF) # Ensure no collisions. assert len(_ANY_TO_TF) == sum( len(d) for d in [_INTERN_TABLE, _STRING_TO_TF, _PYTHON_TO_TF, _NP_TO_TF]) @tf_export("dtypes.as_dtype", "as_dtype") def as_dtype(type_value): """Converts the given `type_value` to a `DType`. Args: type_value: A value that can be converted to a `tf.DType` object. This may currently be a `tf.DType` object, a [`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto), a string type name, or a `numpy.dtype`. Returns: A `DType` corresponding to `type_value`. Raises: TypeError: If `type_value` cannot be converted to a `DType`. """ if isinstance(type_value, DType): return type_value if isinstance(type_value, np.dtype): try: return _NP_TO_TF[type_value.type] except KeyError: pass try: return _ANY_TO_TF[type_value] except KeyError: pass raise TypeError("Cannot convert value %r to a TensorFlow DType." % (type_value,))

[ "[email protected]" ]

[email protected]

6b27894d6781b7a1a591111f28f575c8857b9a50

fb3061b2e8752e59e59d0f54f8a9637c25bcc8f4

/Tristan/randomnumber.py

0dd78cf9ab6872d179441ebf8ea0bba5d6a197ba

[]

no_license

idcrypt3/camp_2019_07_14

25383c67dbd2d44ad9e5966b2666d545688a4d36

708cccb313181fc15dc7aa45699024b9b44ba275

refs/heads/master