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10320c2b5c5d228ae3ada19ae71d1c1b9d7fff71 | 77d7f2c1284b276c95ad31b15ac2bde077f1ceca | /fastreid/data/common.py | 959fefb3f17b62bcdefa3071913ff3df58331735 | [
"Apache-2.0"
]
| permissive | Cris-zj/fast-reid | a53f19fefe149eec93d0f1b2a1d61136d9c9eaf6 | db4b65444912cfd54675e6a52fa12e2d1321e971 | refs/heads/master | 2022-12-14T15:23:40.820118 | 2020-08-31T12:34:33 | 2020-08-31T12:34:33 | 291,639,026 | 2 | 0 | Apache-2.0 | 2020-08-31T06:56:24 | 2020-08-31T06:56:23 | null | UTF-8 | Python | false | false | 1,078 | py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: [email protected]
"""
from torch.utils.data import Dataset
from .data_utils import read_image
class CommDataset(Dataset):
"""Image Person ReID Dataset"""
def __init__(self, img_items, transform=None, relabel=True):
self.img_items = img_items
self.transform = transform
self.relabel = relabel
pid_set = set([i[1] for i in img_items])
self.pids = sorted(list(pid_set))
if relabel: self.pid_dict = dict([(p, i) for i, p in enumerate(self.pids)])
def __len__(self):
return len(self.img_items)
def __getitem__(self, index):
img_path, pid, camid = self.img_items[index]
img = read_image(img_path)
if self.transform is not None: img = self.transform(img)
if self.relabel: pid = self.pid_dict[pid]
return {
"images": img,
"targets": pid,
"camid": camid,
"img_path": img_path
}
@property
def num_classes(self):
return len(self.pids)
| [
"[email protected]"
]
| |
3f38851402838e78a9602b3e882605fb1e2d4f86 | 14f4d045750f7cf45252838d625b2a761d5dee38 | /argo/test/test_io_k8s_kube_aggregator_pkg_apis_apiregistration_v1beta1_api_service_condition.py | 01d2de718c08b57e04b58fbd20a8e3d5c8c0eb44 | []
| no_license | nfillot/argo_client | cf8d7413d728edb4623de403e03d119fe3699ee9 | c8cf80842f9eebbf4569f3d67b9d8eff4ba405fa | refs/heads/master | 2020-07-11T13:06:35.518331 | 2019-08-26T20:54:07 | 2019-08-26T20:54:07 | 204,546,868 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,330 | py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: v1.14.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import argo
from models.io_k8s_kube_aggregator_pkg_apis_apiregistration_v1beta1_api_service_condition import IoK8sKubeAggregatorPkgApisApiregistrationV1beta1APIServiceCondition # noqa: E501
from argo.rest import ApiException
class TestIoK8sKubeAggregatorPkgApisApiregistrationV1beta1APIServiceCondition(unittest.TestCase):
"""IoK8sKubeAggregatorPkgApisApiregistrationV1beta1APIServiceCondition unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testIoK8sKubeAggregatorPkgApisApiregistrationV1beta1APIServiceCondition(self):
"""Test IoK8sKubeAggregatorPkgApisApiregistrationV1beta1APIServiceCondition"""
# FIXME: construct object with mandatory attributes with example values
# model = argo.models.io_k8s_kube_aggregator_pkg_apis_apiregistration_v1beta1_api_service_condition.IoK8sKubeAggregatorPkgApisApiregistrationV1beta1APIServiceCondition() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| [
"[email protected]"
]
| |
37857bc4bb9559c9e3f68635744baf75a7cc8762 | c086a38a366b0724d7339ae94d6bfb489413d2f4 | /PythonEnv/Lib/site-packages/docutils/utils/urischemes.py | 01335601af86e67266b95a75aa5f0935ea92bcf5 | []
| no_license | FlowkoHinti/Dionysos | 2dc06651a4fc9b4c8c90d264b2f820f34d736650 | d9f8fbf3bb0713527dc33383a7f3e135b2041638 | refs/heads/master | 2021-03-02T01:14:18.622703 | 2020-06-09T08:28:44 | 2020-06-09T08:28:44 | 245,826,041 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 6,028 | py | # $Id: urischemes.py 8376 2019-08-27 19:49:29Z milde $
# Author: David Goodger <[email protected]>
# Copyright: This module has been placed in the public domain.
"""
`schemes` is a dictionary with lowercase URI addressing schemes as
keys and descriptions as values. It was compiled from the index at
http://www.iana.org/assignments/uri-schemes (revised 2005-11-28)
and an older list at http://www.w3.org/Addressing/schemes.html.
"""
# Many values are blank and should be filled in with useful descriptions.
schemes = {
'about': 'provides information on Navigator',
'acap': 'Application Configuration Access Protocol; RFC 2244',
'addbook': "To add vCard entries to Communicator's Address Book",
'afp': 'Apple Filing Protocol',
'afs': 'Andrew File System global file names',
'aim': 'AOL Instant Messenger',
'callto': 'for NetMeeting links',
'castanet': 'Castanet Tuner URLs for Netcaster',
'chttp': 'cached HTTP supported by RealPlayer',
'cid': 'content identifier; RFC 2392',
'crid': 'TV-Anytime Content Reference Identifier; RFC 4078',
'data': ('allows inclusion of small data items as "immediate" data; '
'RFC 2397'),
'dav': 'Distributed Authoring and Versioning Protocol; RFC 2518',
'dict': 'dictionary service protocol; RFC 2229',
'dns': 'Domain Name System resources',
'eid': ('External ID; non-URL data; general escape mechanism to allow '
'access to information for applications that are too '
'specialized to justify their own schemes'),
'fax': ('a connection to a terminal that can handle telefaxes '
'(facsimiles); RFC 2806'),
'feed': 'NetNewsWire feed',
'file': 'Host-specific file names; RFC 1738',
'finger': '',
'freenet': '',
'ftp': 'File Transfer Protocol; RFC 1738',
'go': 'go; RFC 3368',
'gopher': 'The Gopher Protocol',
'gsm-sms': ('Global System for Mobile Communications Short Message '
'Service'),
'h323': ('video (audiovisual) communication on local area networks; '
'RFC 3508'),
'h324': ('video and audio communications over low bitrate connections '
'such as POTS modem connections'),
'hdl': 'CNRI handle system',
'hnews': 'an HTTP-tunneling variant of the NNTP news protocol',
'http': 'Hypertext Transfer Protocol; RFC 2616',
'https': 'HTTP over SSL; RFC 2818',
'hydra': 'SubEthaEdit URI. See http://www.codingmonkeys.de/subethaedit.',
'iioploc': 'Internet Inter-ORB Protocol Location?',
'ilu': 'Inter-Language Unification',
'im': 'Instant Messaging; RFC 3860',
'imap': 'Internet Message Access Protocol; RFC 2192',
'info': 'Information Assets with Identifiers in Public Namespaces',
'ior': 'CORBA interoperable object reference',
'ipp': 'Internet Printing Protocol; RFC 3510',
'irc': 'Internet Relay Chat',
'iris.beep': 'iris.beep; RFC 3983',
'iseek': 'See www.ambrosiasw.com; a little util for OS X.',
'jar': 'Java archive',
'javascript': ('JavaScript code; evaluates the expression after the '
'colon'),
'jdbc': 'JDBC connection URI.',
'ldap': 'Lightweight Directory Access Protocol',
'lifn': '',
'livescript': '',
'lrq': '',
'mailbox': 'Mail folder access',
'mailserver': 'Access to data available from mail servers',
'mailto': 'Electronic mail address; RFC 2368',
'md5': '',
'mid': 'message identifier; RFC 2392',
'mocha': '',
'modem': ('a connection to a terminal that can handle incoming data '
'calls; RFC 2806'),
'mtqp': 'Message Tracking Query Protocol; RFC 3887',
'mupdate': 'Mailbox Update (MUPDATE) Protocol; RFC 3656',
'news': 'USENET news; RFC 1738',
'nfs': 'Network File System protocol; RFC 2224',
'nntp': 'USENET news using NNTP access; RFC 1738',
'opaquelocktoken': 'RFC 2518',
'phone': '',
'pop': 'Post Office Protocol; RFC 2384',
'pop3': 'Post Office Protocol v3',
'pres': 'Presence; RFC 3859',
'printer': '',
'prospero': 'Prospero Directory Service; RFC 4157',
'rdar': ('URLs found in Darwin source '
'(http://www.opensource.apple.com/darwinsource/).'),
'res': '',
'rtsp': 'real time streaming protocol; RFC 2326',
'rvp': '',
'rwhois': '',
'rx': 'Remote Execution',
'sdp': '',
'service': 'service location; RFC 2609',
'shttp': 'secure hypertext transfer protocol',
'sip': 'Session Initiation Protocol; RFC 3261',
'sips': 'secure session intitiaion protocol; RFC 3261',
'smb': 'SAMBA filesystems.',
'snews': 'For NNTP postings via SSL',
'snmp': 'Simple Network Management Protocol; RFC 4088',
'soap.beep': 'RFC 3288',
'soap.beeps': 'RFC 3288',
'ssh': 'Reference to interactive sessions via ssh.',
't120': 'real time data conferencing (audiographics)',
'tag': 'RFC 4151',
'tcp': '',
'tel': ('a connection to a terminal that handles normal voice '
'telephone calls, a voice mailbox or another voice messaging '
'system or a service that can be operated using DTMF tones; '
'RFC 3966.'),
'telephone': 'telephone',
'telnet': 'Reference to interactive sessions; RFC 4248',
'tftp': 'Trivial File Transfer Protocol; RFC 3617',
'tip': 'Transaction Internet Protocol; RFC 2371',
'tn3270': 'Interactive 3270 emulation sessions',
'tv': '',
'urn': 'Uniform Resource Name; RFC 2141',
'uuid': '',
'vemmi': 'versatile multimedia interface; RFC 2122',
'videotex': '',
'view-source': 'displays HTML code that was generated with JavaScript',
'wais': 'Wide Area Information Servers; RFC 4156',
'whodp': '',
'whois++': 'Distributed directory service.',
'x-man-page': ('Opens man page in Terminal.app on OS X '
'(see macosxhints.com)'),
'xmlrpc.beep': 'RFC 3529',
'xmlrpc.beeps': 'RFC 3529',
'z39.50r': 'Z39.50 Retrieval; RFC 2056',
'z39.50s': 'Z39.50 Session; RFC 2056', }
| [
"="
]
| = |
bc026c4ed31e48c1c7c6a8dad59f6f27b760e5de | d44b5a657e7cd69c875b55dd5cddf21812e89095 | /pixel_cnn/model/resnet.py | 4c7abe39625aca83798614a9c570268916820747 | [
"Apache-2.0"
]
| permissive | nel215/chainer-pixel-cnn | ca8ae17fda998f7677dea785e53319b3fc646e76 | 94b064f9e66355d141ed5d6cce0c38492203715b | refs/heads/master | 2020-04-02T02:11:29.546694 | 2018-10-21T12:10:43 | 2018-10-21T12:10:43 | 153,896,421 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 774 | py | from chainer import Chain
from chainer import links as L
from chainer import functions as F
def concat_elu(x):
return F.elu(F.concat([x, -x], 1))
class GatedResnet(Chain):
def __init__(self, n_out, Conv2D):
super(GatedResnet, self).__init__()
with self.init_scope():
self.conv1 = Conv2D(n_out)
self.conv2 = L.Convolution2D(None, n_out, ksize=1)
self.conv3 = Conv2D(2*n_out)
def __call__(self, x, a=None):
h = self.conv1(concat_elu(x))
if a is not None:
h += self.conv2(concat_elu(a))
h = F.dropout(concat_elu(h))
h = self.conv3(h)
# TODO: conditional generation
a, b = F.split_axis(h, 2, 1)
h = a * F.sigmoid(b)
return x + h
| [
"[email protected]"
]
| |
6a785fc558dbcc8272ad715017bef5db57b4b310 | 4124770492faac81ab962641d88c0bbf54e14d15 | /run_rnn_xz.py | a3ad8a679941b50e7043c8b0d0fd7e1f9216c66f | [
"MIT"
]
| permissive | lr12/textcnnrnn | 5c5f47b409fb1b402a8074b89178864da97a11b5 | aa0560b89a82225e7b2dbbd8223acb1c5a18eec7 | refs/heads/master | 2020-05-05T07:23:49.221143 | 2019-06-24T11:51:52 | 2019-06-24T11:51:52 | 179,823,664 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,165 | py | # coding: utf-8
from __future__ import print_function
import os
import sys
import time
from datetime import timedelta
import numpy as np
import tensorflow as tf
from sklearn import metrics
from rnn_model import TRNNConfig, TextRNN
from data.cnews_loader import read_vocab, read_category, batch_iter, process_file, build_vocab
#base_dir = 'data/cnews'
base_dir = 'data/xz'
#base_dir = 'data/ay'
train_dir = os.path.join(base_dir, 'cnews.train.txt')
test_dir = os.path.join(base_dir, 'cnews.test.txt')
val_dir = os.path.join(base_dir, 'cnews.val.txt')
vocab_dir = os.path.join(base_dir, 'cnews.vocab.txt')
#save_dir = 'checkpoints/textrnn'
save_dir = 'checkpoints/textrnn/xz'
#save_dir = 'checkpoints/textrnn/ay'
save_path = os.path.join(save_dir, 'best_validation') # 最佳验证结果保存路径
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return timedelta(seconds=int(round(time_dif)))
def feed_data(x_batch, y_batch, keep_prob):
feed_dict = {
model.input_x: x_batch,
model.input_y: y_batch,
model.keep_prob: keep_prob
}
return feed_dict
def evaluate(sess, x_, y_):
"""评估在某一数据上的准确率和损失"""
data_len = len(x_)
batch_eval = batch_iter(x_, y_, 128)
total_loss = 0.0
total_acc = 0.0
for x_batch, y_batch in batch_eval:
batch_len = len(x_batch)
feed_dict = feed_data(x_batch, y_batch, 1.0)
loss, acc = sess.run([model.loss, model.acc], feed_dict=feed_dict)
total_loss += loss * batch_len
total_acc += acc * batch_len
return total_loss / data_len, total_acc / data_len
def train():
print("Configuring TensorBoard and Saver...")
# 配置 Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
tensorboard_dir = 'tensorboard/textrnn'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
tf.summary.scalar("loss", model.loss)
tf.summary.scalar("accuracy", model.acc)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
# 配置 Saver
saver = tf.train.Saver()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print("Loading training and validation data...")
# 载入训练集与验证集
start_time = time.time()
x_train, y_train = process_file(train_dir, word_to_id, cat_to_id, config.seq_length)
x_val, y_val = process_file(val_dir, word_to_id, cat_to_id, config.seq_length)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# 创建session
session = tf.Session()
session.run(tf.global_variables_initializer())
writer.add_graph(session.graph)
print('Training and evaluating...')
start_time = time.time()
total_batch = 0 # 总批次
best_acc_val = 0.0 # 最佳验证集准确率
last_improved = 0 # 记录上一次提升批次
require_improvement = 1000 # 如果超过1000轮未提升，提前结束训练
flag = False
for epoch in range(config.num_epochs):
print('Epoch:', epoch + 1)
batch_train = batch_iter(x_train, y_train, config.batch_size)
for x_batch, y_batch in batch_train:
feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob)
if total_batch % config.save_per_batch == 0:
# 每多少轮次将训练结果写入tensorboard scalar
s = session.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, total_batch)
if total_batch % config.print_per_batch == 0:
# 每多少轮次输出在训练集和验证集上的性能
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict)
loss_val, acc_val = evaluate(session, x_val, y_val) # todo
if acc_val > best_acc_val:
# 保存最好结果
best_acc_val = acc_val
last_improved = total_batch
saver.save(sess=session, save_path=save_path)
improved_str = '*'
else:
improved_str = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},' \
+ ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val, time_dif, improved_str))
session.run(model.optim, feed_dict=feed_dict) # 运行优化
total_batch += 1
if total_batch - last_improved > require_improvement:
# 验证集正确率长期不提升，提前结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break # 跳出循环
if flag: # 同上
break
def test():
print("Loading test data...")
start_time = time.time()
x_test, y_test = process_file(test_dir, word_to_id, cat_to_id, config.seq_length)
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=session, save_path=save_path) # 读取保存的模型
print('Testing...')
loss_test, acc_test = evaluate(session, x_test, y_test)
msg = 'Test Loss: {0:>6.2}, Test Acc: {1:>7.2%}'
print(msg.format(loss_test, acc_test))
batch_size = 128
data_len = len(x_test)
num_batch = int((data_len - 1) / batch_size) + 1
y_test_cls = np.argmax(y_test, 1)
y_pred_cls = np.zeros(shape=len(x_test), dtype=np.int32) # 保存预测结果
for i in range(num_batch): # 逐批次处理
start_id = i * batch_size
end_id = min((i + 1) * batch_size, data_len)
feed_dict = {
model.input_x: x_test[start_id:end_id],
model.keep_prob: 1.0
}
y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls, feed_dict=feed_dict)
# 评估
print("Precision, Recall and F1-Score...")
print(metrics.classification_report(y_test_cls, y_pred_cls, target_names=categories ,digits=4))
# 混淆矩阵
print("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
print(cm)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
if __name__ == '__main__':
# if len(sys.argv) != 2 or sys.argv[1] not in ['train', 'test']:
# raise ValueError("""usage: python run_rnn.py [train / test]""")
print('Configuring RNN model...')
config = TRNNConfig()
if not os.path.exists(vocab_dir): # 如果不存在词汇表，重建
build_vocab(train_dir, vocab_dir, config.vocab_size)
categories, cat_to_id = read_category()
words, word_to_id = read_vocab(vocab_dir)
config.vocab_size = len(words)
model = TextRNN(config)
train()
test()
# if sys.argv[1] == 'train':
# train()
# else:
# test()
| [
"[email protected]"
]
| |
8fe298aaf5cf8b93c96ab107fbe0f5771e3f5e25 | b775940595617a13289ee7006cf837f8f3a34480 | /examples/ppk_plot.py | 24497e1d56f97c75755d7197f2dbe75215961c3c | []
| no_license | Nathan-Walk/manufacturing | 5d2f58c2be45c9ccb2263bd750b6c18809fe76d4 | 2a22457ff9ef695da649a1e11d0cf7cb8ddde348 | refs/heads/master | 2023-03-08T19:48:15.613729 | 2021-02-26T01:05:46 | 2021-02-26T01:05:46 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 276 | py | import logging
import matplotlib.pyplot as plt
from manufacturing import import_excel, ppk_plot
logging.basicConfig(level=logging.INFO)
data = import_excel('data/example_data_with_faults.xlsx', columnname='value (lcl=-7.4 ucl=7.4)', skiprows=3)
ppk_plot(**data)
plt.show()
| [
"[email protected]"
]
| |
e853707cdb0cd2cd268ce7a38fba29144661310f | fa6bc3c7733cb2728224b8ac71e04d3b4b50b184 | /alerts/sponsors/support.py | 413591435ccb4b2e87b2399feea4796d55a57b04 | [
"Apache-2.0"
]
| permissive | shanz/mirandum | 5636fbe599a539bf262e8f3da6e0e4e83d39cc73 | ce1d662a7cf64ed223d71fffed02acd3f9244e90 | refs/heads/master | 2021-01-21T03:09:14.287076 | 2016-04-30T06:39:47 | 2016-04-30T06:39:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,938 | py | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import httplib2
from sponsors.models import *
from googaccount.models import CredentialsModel
from django.contrib.auth.models import User
from oauth2client.contrib.django_orm import Storage
BASE_URL = "https://www.googleapis.com/youtube/v3/"
def run_sponsors(ffu):
added = 0
storage = Storage(CredentialsModel, 'id', ffu.credentials, 'credential')
credential = storage.get()
if credential is None or credential.invalid == True:
raise Exception("bad creds")
return added
http = httplib2.Http()
http = credential.authorize(http)
resp, data = http.request("%ssponsors?part=snippet&maxResults=5&filter=all" % BASE_URL)
data = json.loads(data)
if 'error' in data:
raise Exception("Error fetching sponsors: %s" % json.dumps(data['error']))
events = []
if 'items' in data:
for i in data['items']:
if SponsorEvent.objects.filter(external_id=i['etag'], updater=ffu).count() > 0:
break
details = json.dumps(i)
try:
ffe = SponsorEvent(external_id=i['etag'], updater=ffu, details=details)
ffe.save()
except Exception, E:
print "Failed in individual sponsor run: %s\nData:\n%s" % (E, details)
added += 1
return added
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d7a475241f3c8632512754d85bd07dc6b8525b48 | 6b5142b5def59556942f91411a792ac5d15fc427 | /l2tshoot.py | 7ae899cee162ca115d4f8d2adc564046b703a3f2 | []
| no_license | karthiksjsu/codedump | 2a9c9ee4f75deba0b8cc8f460afd3b85f1ff239a | ac94fc8a259023ba804c0e587f72a9dfed89bbd6 | refs/heads/master | 2021-01-19T17:02:36.274907 | 2017-04-14T21:14:14 | 2017-04-14T21:14:14 | 88,301,373 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 627 | py | import logging
logging.getLogger("scapy runtime").setLevel(logging.ERROR)
from scapy.all import *
dstip=raw_input("Enter the IP for which the status needs to be checked\n")
logging.info("constructing ARP message")
arp=ARP()
arp.hwdst='00:00:00:00:00:00'
arp.hwsrc='08:00:27:dd:f5:3a'
arp.pdst=dstip
arp.src='10.0.2.15'
ether=Ether()
ether.dst='FF:FF:FF:FF:FF:FF'
ether.src='08:00:27:dd:f5:3a'
packet=ether/arp
reply=srp1(packet,timeout=5,verbose=0)
if(reply):
print "Layer2 status is up and at " +reply.src
#print reply.show()
else:
print "Layer2 status is down"
logging.warning(" Status is down")
| [
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20c3c2ce5f8c8c1514529d8e50833e1553d9df21 | c7ae47e0806c19cf3462c090fa9015f27a4d5e90 | /blog/migrations/0001_initial.py | c1734eec62851501c157060e7ceefd67a563537d | []
| no_license | micahchuim/portfolio | a1bc1cb89dbe9650235dbafc5bafffa4e25fdc32 | 0e5458e59fb6dc47a1b6cbb4f5214271defc39ec | refs/heads/master | 2023-01-10T03:14:46.642706 | 2020-11-16T03:04:14 | 2020-11-16T03:04:14 | 313,176,277 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 584 | py | # Generated by Django 3.1.3 on 2020-11-13 07:52
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Blog',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200)),
('description', models.TextField()),
('date', models.DateField()),
],
),
]
| [
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]
| |
add0c5f6565b30e25421cd87641a4403aaad57bc | 840c7884fe10902f11bceb6e7ef78d8fc7f92818 | /py_thread/fx_thread.py | 042be7780b7f16f47789eaead8704478df9fc359 | []
| no_license | johnnyem527/pyprac | 07a7a6c2a52169be5424d65a5f91aad815a4d08a | 5899c0c57a49121957f4fb9f56d47ee681dbe77b | refs/heads/master | 2021-05-08T18:02:21.836654 | 2018-04-11T12:22:48 | 2018-04-11T12:22:48 | 119,498,838 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 665 | py | # 函数式使用线程
import threading
tickets = 1000
lock = threading.Lock()
def buy_ticket(tid):
global lock
global tickets
while True:
lock.acquire()
if tickets != 0:
print("线程" + tid + "：买到了第" + str(tickets) + "张票")
tickets -= 1
print("还剩 ：" + str(tickets) + "张票")
# print(threading.current_thread())
else:
print("线程" + tid + ": 票已抢完！")
lock.release()
break
lock.release()
for i in range(3):
new_thread = threading.Thread(target=buy_ticket, args=(str(i),))
new_thread.start()
| [
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]
| |
cc35858bc95f4683d14d95919f9519628e61c9bc | 79067556d586128deadc846098010059d20f60e2 | /bikeshare.py | becb19cf9c26bfd2ec8cc95528a0b9e8cfc5025b | []
| no_license | mohamedkhnour/Explore-US-Bikeshare-Data | 656fe2f1bf39e9ea1aceefbc2c792940c0a69b8d | 87ca4bf6fba60cfbdf86653614ad85a00f40ffdb | refs/heads/main | 2023-04-08T16:15:10.654637 | 2021-04-15T20:11:38 | 2021-04-15T20:11:38 | 358,376,362 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,834 | py | import time
import pandas as pd
import numpy as np
CITY_DATA = { 'ch': 'chicago.csv',
'ny': 'new_york_city.csv',
'w': 'washington.csv' }
def get_filters():
# TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs
city_selection = input('to view available BS data, kindly type: \n The letter(ch) for Chicago \n The letter (ny) for New York City \n The letter (W) for Washington \n').lower()
while city_selection not in {'ch','ny','w'}:
print('that is invalid input .')
city_selection = input('to view available BS data, kindly type: \n The letter(ch) for Chicago \n The letter (ny) for New York City \n The letter (W) for Washington \n').lower()
# TO DO: get user input for month (all, january, february, ... , june)
monthes=['january','february','march','april','may','june','all']
month_selection = input('select month \n January \n February\n March\n April\n May\n June\n ALL\n').lower()
while month_selection not in monthes:
print('that is invalid input .')
month_selection = input('select month \nJA January \nFE February\n MA March\n AP April\n MA May\n JU June\n ALL\n').lower()
# TO DO: get user input for day of week (all, monday, tuesday, ... sunday)
day_selection =input('select Day \nMonday \nTuesday\nWednesday\n Thursday\nFriday\n Saturday\n Sunday\n ALL').lower()
days=['monday', 'tuesday', 'wednesday', 'thursday','friday', 'saturday', 'sunday','all']
while day_selection not in days:
print('that is invalid input .')
day_selection = input('select Day \nMonday \nTuesday\nWednesday\n Thursday\nFriday\n Saturday\n Sunday\n ALL').lower()
print('-'*40)
return city_selection, month_selection, day_selection
def load_data(city, month, day):
"""
Loads data for the specified city and filters by month and day if applicable.
Args:
(str) city - name of the city to analyze
(str) month - name of the month to filter by, or "all" to apply no month filter
(str) day - name of the day of week to filter by, or "all" to apply no day filter
Returns:
df - Pandas DataFrame containing city data filtered by month and day
"""
# load data file into a dataframe
df = pd.read_csv(CITY_DATA[city])
# convert the Start Time column to datetime
df['Start Time'] = pd.to_datetime(df['Start Time'])
# extract month and day of week from Start Time to create new columns
df['month'] = df['Start Time'].dt.month
df['day_of_week'] = df['Start Time'].dt.weekday_name
# filter by month if applicable
if month != 'all':
# use the index of the months list to get the corresponding int
months = ['january', 'february', 'march', 'april', 'may', 'june']
month = months.index(month) + 1
# filter by month to create the new dataframe
df = df[df['month'] == month]
# filter by day of week if applicable
if day != 'all':
# filter by day of week to create the new dataframe
df = df[df['day_of_week'] == day.title()]
return df
return df
def time_stats(df1):
"""Displays statistics on the most frequent times of travel."""
df = df1
print('\nCalculating The Most Frequent Times of Travel...\n')
start_time = time.time()
df['Start Time'] = pd.to_datetime(df['Start Time'])
# TO DO: display the most common month
df['month'] = df['Start Time'].dt.month
popular_month = df['month'].mode()[0]
print('Most Popular Start month:', popular_month)
# TO DO: display the most common day of week
df['day'] = df['Start Time'].dt.dayofweek
popular_day = df['day'].mode()[0]
print('Most Popular Start month:', popular_day)
# TO DO: display the most common start hour
df['hour'] = df['Start Time'].dt.hour
popular_hour = df['hour'].mode()[0]
print('Most Popular Start Hour:', popular_hour)
print("\nThis took %s seconds." % (time.time() - start_time))
print('-'*40)
return(time.time() - start_time)
def station_stats(df1):
"""Displays statistics on the most popular stations and trip."""
print('\nCalculating The Most Popular Stations and Trip...\n')
start_time = time.time()
df = df1
# TO DO: display most commonly used start station
common_start_station = df['Start Station'].mode()[0]
print("The most start station from data is: " + common_start_station)
# TO DO: display most commonly used end station
common_end_station = df['End Station'].mode()[0]
print("The most end station is: " + common_end_station)
# TO DO: display most frequent combination of start station and end station trip
frequent_combination = (df['Start Station'] + "||" + df['End Station']).mode()[0]
print("The moststart station and end station trip is : " + str(frequent_combination.split("||")))
print("\nThis took %s seconds." % (time.time() - start_time))
print('-'*40)
def trip_duration_stats(df):
"""Displays statistics on the total and average trip duration."""
print('\nCalculating Trip Duration...\n')
start_time = time.time()
# TO DO: display total travel time
total_travel_time = df['Trip Duration'].sum()
print("The total travel time from the given fitered data is: " + str(total_travel_time))
# TO DO: display mean travel time
mean_travel_time = df['Trip Duration'].mean()
print("The mean travel time from the given fitered data is: " + str(mean_travel_time))
print("\nThis took %s seconds." % (time.time() - start_time))
print('-'*40)
def user_stats(df):
"""Displays statistics on bikeshare users."""
print('\nCalculating User Stats...\n')
start_time = time.time()
# TO DO: Display counts of user types
gender = df['Gender'].value_counts()
print("The count of user gender from the given fitered data is: \n" + str(gender))
# TO DO: Display counts of gender
earliest_birth = df['Birth Year'].min()
most_recent_birth = df['Birth Year'].max()
most_common_birth = df['Birth Year'].mode()[0]
print('Earliest birth from the given fitered data is: {}\n'.format(earliest_birth))
print('Most recent birth from the given fitered data is: {}\n'.format(most_recent_birth))
print('Most common birth from the given fitered data is: {}\n'.format(most_common_birth) )
# TO DO: Display earliest, most recent, and most common year of birth
print("\nThis took %s seconds." % (time.time() - start_time))
print('-'*40)
'''def main():
city,month,day=get_filters()
df=load_data(city,month,day)
#print(df.head())
time_stats(df)
station_stats(df)
trip_duration_stats(df)
if city=='ch':
user_stats(df)'''
def display_raw_data(df):
print(df.head())
next = 0
while True:
view_raw_data = input('\nWould you like to view next five row of raw data? Enter yes or no.\n')
if view_raw_data.lower() != 'yes':
return
next = next + 5
print(df.iloc[next:next+5])
def main():
while True:
city, month, day = get_filters()
df = load_data(city, month, day)
time_stats(df)
station_stats(df)
trip_duration_stats(df)
if city=='ch':
user_stats(df)
while True:
view_raw_data = input('\nWould you like to view first five row of raw data? Enter yes or no.\n')
if view_raw_data.lower() != 'yes':
break
display_raw_data(df)
break
restart = input('\nWould you like to restart? Enter yes or no.\n')
if restart.lower() != 'yes':
break
if __name__ == "__main__":
main()
| [
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]
| |
3da20a20d29aa2f522c83f995e511782028cfdd3 | c405becd9f1a66dc3675adb106db01a7aedec840 | /sokoban.py | d327733d2888ea940d0e148d4f9ef6e8913deabd | []
| no_license | ssonkar/Sokoban-Solver | 7897f115497cb05f11d1401c9232f8264daa59f8 | 31a001de38327e5764c941f1e729b888ee988364 | refs/heads/master | 2020-04-10T10:45:09.673727 | 2018-12-08T23:25:36 | 2018-12-08T23:25:36 | 160,974,810 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,781 | py | from board import Board
#from boardastar import Boardastar
import bfs
import ucs
import ass
class Sokoban:
'''
Sokoban game class
'''
def new_board(self, filename):
''' Creates new board from file '''
e = [] # empty solution list
b = Board(e)
with open(filename, 'r') as f: # automatically closes file
read_data = f.read()
lines = read_data.split('\n')
height = lines.pop(0)
x = 0
y = 0
for line in lines:
for char in line:
# adds Spots to board's sets by reading in char
if char == '#':
b.add_wall(x, y)
elif char == '.':
b.add_goal(x, y)
elif char == '@':
b.set_player(x, y)
elif char == '+':
# player gets its own Spot marker
b.set_player(x, y)
b.add_goal(x, y)
elif char == '$':
b.add_box(x, y)
elif char == '*':
b.add_box(x, y)
b.add_goal(x, y)
x += 1
y += 1
x = 0
# check for a board with no player
if hasattr(b, 'player'):
return b
else:
print("No player on board")
return None
def doSearches(self, board, option):
if option == 1:
bfs.search(board)
if option == 2:
ucs.search(board)
if option == 3:
board.isAstar = True
ass.search(board)
| [
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]
| |
d1362ba688ba94ce5fb4fef263f80e8985e67648 | 4fff1e1dd0218ff64f7d8221445ed4a6f6687f85 | /web/migrations/0002_income.py | f925f2ad4b92efbdfe984ad40793edda1f2977f8 | []
| no_license | fahim1377/bestoon | 02e98f34afc79382ab6076cba07e008767e49d14 | 9d1c94b01ff03ea5e3e74728f3130b6554e644a9 | refs/heads/master | 2020-06-18T03:03:06.958510 | 2019-07-10T06:10:41 | 2019-07-10T06:10:41 | 195,384,567 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 858 | py | # Generated by Django 2.2.3 on 2019-07-05 07:56
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('web', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Income',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('text', models.CharField(max_length=255)),
('date', models.DateTimeField()),
('amount', models.BigIntegerField()),
('user', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
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]
| |
e6ffa0af18975bc4140bb2a0fd222509374d096d | 174975248ffa04bb0339ace7475a791842e99ffb | /reverse_bits.py | 141244053c843ee9fa1eb7c73d05ab32903b8c86 | []
| no_license | KONAPAVANKUMAR/code-library | 87a5525dcf71aaba47f233df17ad31227cb3c44b | 6839ef596858515119a3c300b031a107c8d72292 | refs/heads/main | 2023-06-02T09:33:21.382512 | 2021-06-24T09:49:00 | 2021-06-24T09:49:00 | 378,131,322 | 0 | 0 | null | 2021-06-24T09:41:12 | 2021-06-18T11:39:22 | Python | UTF-8 | Python | false | false | 415 | py | def get_reverse_bit_string(number: int) -> str:
bit_string = ""
for _ in range(0, 32):
bit_string += str(number % 2)
number = number >> 1
return bit_string
def reverse_bit(number):
result = 0
for _ in range(1, 33):
result = result << 1
end_bit = number % 2
number = number >> 1
result = result | end_bit
return get_reverse_bit_string(result) | [
"[email protected]"
]
| |
9a2930492647fe490bf485ff55258371f5687191 | 3a63a9af2693b7d2f87a6d2db0585d8ce5480934 | /vision-vgg_objects.py | 3eb6277cd8e7d427d5a26d0fbd15066c271bf1e7 | []
| no_license | andreeadeac22/HackCam2018 | d167f71069c6fe529f1e88dd92e31794b64e6773 | 0bb529b0d0cc11583722107b7125eb0671ca149a | refs/heads/master | 2021-05-09T09:45:34.945701 | 2018-01-30T00:21:57 | 2018-01-30T00:21:57 | 119,458,996 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 863 | py | import torch
from torch.autograd import Variable as V
import torchvision.models as models
from torchvision.models.vgg import vgg16
from torchvision import transforms as trn
from torch.nn import functional as F
from PIL import Image
def image_to_objects(img_name):
model = vgg16(pretrained=True)
model.eval()
# load the image transformer
centre_crop = trn.Compose([
trn.Resize((256,256)),
trn.CenterCrop(224),
trn.ToTensor(),
trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# load the class label
file_name = 'categories_imagenet.txt'
img = Image.open(img_name)
input_img = V(centre_crop(img).unsqueeze(0))
# forward pass
logit = model.forward(input_img)
h_x = F.softmax(logit, 1).data.squeeze()
return h_x
print(image_to_objects("arch.jpeg")) | [
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]
| |
db4aa44c038f542b1cfbd7f627f200451105a7f6 | b6d40ed73a6c6923464759ad1a2350a27132b9a5 | /observe/observe_ckpt.py | 4ffc6000310dabe54da90cfd560d64bd92a1cc17 | [
"MIT"
]
| permissive | samirsahoo007/Audio-and-Speech-Processing | ccc83bc1f110d46ce73dfc83cce69bd55df5c6d8 | e77df17a7f63a983c3757140c7a1e8c199cac614 | refs/heads/master | 2022-10-17T23:42:57.369971 | 2020-01-04T12:22:13 | 2020-01-04T12:22:13 | 231,758,271 | 4 | 0 | MIT | 2022-09-30T19:55:35 | 2020-01-04T12:20:46 | Python | UTF-8 | Python | false | false | 142 | py | import sys
import torch
from utils import timer
from ipdb import set_trace
ckpt_path = sys.argv[1]
ckpt = torch.load(ckpt_path)
set_trace()
| [
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]
| |
3c5be25c7266dbe9dc31662fbd3a7bed4a410f80 | 7a161c0ee14aeb9252243cb49ed05398b691d9f3 | /subset sum python problem.py | 214bc3c9e77cde345530264c4838bb477e777e99 | []
| no_license | pavleen14/DSA-Practice | 0a71d34a24635a9dc6714db95e2d71010fb5a70e | b5637e7b8512aa1f93841f372c7505152a156a29 | refs/heads/main | 2023-09-02T21:37:27.413673 | 2021-11-01T06:39:56 | 2021-11-01T06:39:56 | 415,584,545 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 412 | py | def subsetsum(sset, n, s):
if s == 0:
return True
if n == 0:
return False
if sset[n - 1] > s:
return subsetsum(sset, n - 1, s)
return subsetsum(sset, n - 1, s) or subsetsum(sset, n - 1, s - sset[n - 1])
sset = [13, 34, 65, 12, 43, 1]
s = 77
n = len(sset)
if subsetsum(sset, n, s):
print("Found a subset with given sum")
else:
print("No subset with given sum")
| [
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]
| |
189ea70cf6263752fbb5770412f416730ce1b1e9 | e9a14806435ec8666fe240e252d4f8e4e9113238 | /context.py | f4e6840b1ce8dd17820deb9793395246418a02d3 | []
| no_license | jonpry/E | a8b1537dfcdac5129ddf33cac1bc747ba1ca56fc | ffec9f990982c05a89fc819ed73e9b73e9cf1a2c | refs/heads/master | 2020-12-02T19:28:01.385899 | 2017-10-15T05:31:55 | 2017-10-15T05:31:55 | 96,343,870 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,673 | py | # -*- coding: utf-8 -*-
import copy
import sys
import traceback
from llvmlite import ir
from collections import OrderedDict
import emit
context = {}
cstack = []
package = ""
def is_pointer(var):
if isinstance(var,ir.Type):
st = var
else:
st = var.type
return st.is_pointer
class funcs:
funcs = {}
func_stack = []
@staticmethod
def push(func):
funcs.func_stack.append(func)
@staticmethod
def pop():
funcs.func_stack.pop()
@staticmethod
def current():
return funcs.func_stack[-1]
@staticmethod
def create(name,d):
assert(name not in funcs.funcs)
funcs.funcs[name] = d;
@staticmethod
def get_native(name):
for f,v in funcs.funcs.items():
if v['func'].name == name:
return v['func']
return None
@staticmethod
def get(name):
if name in funcs.funcs:
return funcs.funcs[name]
class globals:
globals = {}
@staticmethod
def create(name,val):
assert(name not in globals.globals)
globals.globals[name] = val;
@staticmethod
def get(name):
return globals.globals[name]
class thiss:
thiss = []
@staticmethod
def push(this):
thiss.thiss.append(this)
@staticmethod
def pop():
thiss.thiss.pop()
class breaks:
breaks = []
@staticmethod
def push(tgt):
breaks.breaks.append(tgt)
@staticmethod
def pop():
breaks.breaks.pop()
@staticmethod
def get():
return breaks.breaks[-1]
class continues:
continues = []
@staticmethod
def push(tgt):
continues.continues.append(tgt)
@staticmethod
def pop():
continues.continues.pop()
@staticmethod
def get():
return continues.continues[-1]
class classs:
clzs = []
clz = {'class_members' : {}}
class_stack = [{}]
@staticmethod
def set_type(t,s,name):
global package
classs.clz['class_type'] = t;
classs.clz['static_type'] = s;
classs.clz['class_name'] = package + "." + name;
@staticmethod
def fqid():
return classs.clz['class_name']
@staticmethod
def new():
classs.clz = {'class_members' : {}, 'inherited_members' : {}, "static_members" : {}, 'extends' : None, 'class_type' : None, "static_type" : None, 'alloc_type' : None, 'constructor' : None, 'class_name' : '', 'static_init' : None, 'init' : None}
classs.clzs.append(classs.clz)
return classs.clz
@staticmethod
def push(name):
classs.clz = classs.get_class(name)
if classs.clz == None:
classs.clz = classs.new()
classs.class_stack.append(classs.clz)
@staticmethod
def pop():
classs.class_stack.pop()
clz = classs.class_stack[-1]
@staticmethod
def set_constructor(func):
classs.clz['constructor'] = func
@staticmethod
def set_extends(sup):
classs.clz['extends'] = sup
@staticmethod
def get_extends():
return classs.clz['extends']
@staticmethod
def get_type(cl,module,static):
if static:
if cl['static_type'] != None:
return cl['static_type']
else:
if cl['class_type'] != None:
return cl['class_type']
t = module.context.get_identified_type(cl['class_name'])
types = classs.get_member_types(cl,module,False)
t.set_body(*types)
cl['class_type'] = t
s = module.context.get_identified_type(cl['class_name'] + ".#static")
types = classs.get_member_types(cl,module,True)
s.set_body(*types)
cl['static_type'] = s
return classs.get_type(cl,module,static)
@staticmethod
def get_class_fq(ident):
for cls in classs.clzs:
if cls["class_name"] == ident:
return cls
return None
@staticmethod
def get_class(ident):
global package
c = classs.get_class_fq(ident)
if c != None:
return c
ident = package + "." + ident
return classs.get_class_fq(ident)
@staticmethod
def get_class_type(ident,module):
cls = classs.get_class(ident)
return classs.get_type(cls,module,False)
@staticmethod
def create_member(t,name,static):
if static:
assert(name not in classs.clz['static_members'])
classs.clz['static_members'][name] = t
else:
assert(name not in classs.clz['class_members'])
classs.clz['class_members'][name] = t
@staticmethod
def get_member_types(clz,module,static):
src = "class_members"
if static:
src = "static_members"
t = []
if not static:
if clz['extends'] != None:
t.append(classs.get_type(clz['extends'],module,static))
else:
t.append(emit.rtti_type)
for k,v in clz[src].items():
t.append(v)
return t
@staticmethod
def set_static_init(func):
classs.clz['static_init'] = func
@staticmethod
def set_init(func):
classs.clz['init'] = func
@staticmethod
def get_static_init():
return classs.clz['static_init']
@staticmethod
def get_init():
return classs.clz['init']
def set_package(p):
global package
package = p
def gep(ptr,this,var,builder,static):
#print traceback.print_stack()
src = "static_members" if static else "class_members"
if var in this[src]:
i = this[src].keys().index(var)
if static == False:
i += 1
return builder.gep(ptr,[ir.Constant(ir.IntType(32),0),ir.Constant(ir.IntType(32),i)])
if not static and var in this['inherited_members']:
chain = this['inherited_members'][var]
cchain = [ir.Constant(ir.IntType(32),0)]
for e in chain:
cchain.append(ir.Constant(ir.IntType(32),e))
ptr = builder.gep(ptr,cchain)
this = classs.get_class(ptr.type.pointee.name)
return gep(ptr,this,var.split(".")[-1],builder,static)
def get_one(var,obj,objclz,builder):
global context
if var in context:
return context[var]
#print objclz
#print var
if funcs.get(var) != None:
#print "png"
return {'func' : funcs.get(var), 'this' : None}
if objclz == None:
return None
fq = objclz["class_name"] + "." + var
#print var
#print objclz
#print fq
sys.stdout.flush()
# if fq in globals.globals:
# return globals.globals[fq]
if funcs.get(fq) != None:
return {'func' : funcs.get(fq), 'this': obj}
if var in objclz["static_members"]:
return gep(globals.get("#static." + objclz["class_name"]),objclz,var,builder, True)
if obj==None:
return None
return gep(obj,objclz,var,builder, False)
def get_one_poly(var,obj,objclz,builder):
t = get_one(var,obj,objclz,builder)
if t != None:
return t
if objclz['extends'] != None:
v = get_one(var,obj,objclz['extends'],builder)
if v!=None:
return v
return get_one_poly(var,obj,objclz['extends'],builder)
def get_no_length(var,builder,test):
thistype = classs.clz
if len(thiss.thiss) == 0 or thiss.thiss[-1] == None:
thisvar = None
else:
thisvar = thiss.thiss[-1]
if test:
print "type"
print var
t = get_one_poly(var,thisvar,thistype,builder)
if t != None:
return t
var = var.split(".")
for i in range(len(var)):
v = var[i]
e = get_one_poly(v,thisvar,thistype,builder)
if i == (len(var) - 1):
if e == None and i==0:
return classs.get_class_fq(package + "." + v)
return e
if e == None and i==0: #could be a class name
thistype = classs.get_class_fq(package + "." + v)
else:
thisvar = e
thistype = classs.get_class_fq(e.type.pointee.name)
def get(var,builder=None,test=False):
var = var.split(".")
if var[-1] == "length":
var = ".".join(var[:-1])
ary = get_no_length(var,builder,test)
return emit.get_array_length(ary,builder)
var = ".".join(var)
return get_no_length(var,builder,test)
def set(var, val, builder=None):
if var in context:
context[var] = val
return
assert(False)
def create(var,v):
global context
assert(var not in context)
context[var] = v
def items():
global context
return context.items()
def current():
global context
return context.copy()
def push(deep,force=None):
#print "push"
global context
global cstack
if force != None:
ret = force.copy()
context=force.copy()
elif deep:
ret = copy.deepcopy(context)
else:
ret = context.copy()
cstack.append(ret)
return ret.copy()
#returns items in both a and b that are different
def different_in(a,b):
ret = []
for k,v in a.items():
if k in b:
if v != b[k]:
ret.append( (k,v,b[k]) )
return ret
#returns items that went out of scope
def removed_in(a,b):
ret = []
for k,v in a.items():
if k not in b:
ret.append( (k,v) )
return ret
def pop(builder):
global context
global cstack
#print "pop"
ret = context.copy()
context = cstack.pop().copy()
#pop can only clear variables from scope, not change meaning
for k,v, nv in different_in(context,ret):
context[k] = nv
diff = removed_in(ret,context)
for n,t in diff:
if '.bb.' in n:
continue
#TODO: enoimpl
if is_pointer(t):
#print (n,t)
if not isinstance(t,ir.Argument):
emit.emit_lifetime(t,1,'end',builder)
return (context.copy(),diff)
nakeds = []
def naked(v):
nakeds.append(v)
def get_naked(v):
for vis in nakeds:
if v == vis:
nakeds.remove(vis)
return vis
| [
"[email protected]"
]
| |
90efc2d698bbd5551213318accc27bd6f924e258 | b7799e8cb21cb2d4c0a526a6f9395a3c620514f9 | /Tagging/KMeans.py | a5225641d480ed11c287e9a13d3760b89448fd5c | []
| no_license | Sivler9/IA_PR2_Color_Tagging | cc664eb2ac24c18612970f0dea5b042d6d9ebe89 | 1148a205c5e2fca32ffbaa832efe4dbb54ecb03a | refs/heads/master | 2020-03-13T07:47:36.215000 | 2018-05-29T20:31:51 | 2018-05-29T20:31:51 | 131,031,661 | 1 | 0 | null | 2018-05-15T07:38:21 | 2018-04-25T16:04:53 | Python | UTF-8 | Python | false | false | 15,838 | py | """
@author: ramon, bojana
"""
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as axes3d
from sklearn.decomposition import PCA
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.metrics.pairwise import pairwise_distances_argmin
import sklearn.metrics as metricas
import scipy
import scipy.cluster.vq
import scipy.spatial.distance
from sklearn.cluster import KMeans as camins
def gap(data, nrefs=3, maxClusters=15):
"""
Calculates KMeans optimal K using Gap Statistic from Tibshirani, Walther, Hastie
Params:
data: ndarry of shape (n_samples, n_features)
nrefs: number of sample reference datasets to create
maxClusters: Maximum number of clusters to test for
Returns: (optimalK)
"""
gaps = np.zeros((len(range(1, maxClusters)),))
for gap_index, k in enumerate(range(1, maxClusters)):
# Holder for reference dispersion results
refDisps = np.zeros(nrefs)
# For n references, generate random sample and perform kmeans getting resulting dispersion of each loop
for i in range(nrefs):
# Create new random reference set
randomReference = np.random.random_sample(size=data.shape)
# Fit to it
km = camins(k)
km.fit(randomReference)
refDisp = km.inertia_
refDisps[i] = refDisp
# Fit cluster to original data and create dispersion
km = camins(k)
km.fit(data)
origDisp = km.inertia_
# Calculate gap statistic
gap = np.mean(np.log(refDisps)) - np.log(origDisp)
# Assign this loop's gap statistic to gaps
gaps[gap_index] = gap
return gaps.argmax() + 1 # Plus 1 because index of 0 means 1 cluster is optimal, index 2 = 3 clusters are optimal
def distance(X, C):
"""@brief Calculates the distance between each pixcel and each centroid
@param X numpy array PxD 1st set of data points (usually data points)
@param C numpy array KxD 2nd set of data points (usually cluster centroids points)
@return dist: PxK numpy array position ij is the distance between the
i-th point of the first set an the j-th point of the second set
"""
return euclidean_distances(X,C)
class KMeans():
def __init__(self, X, K, options=None):
"""@brief Constructor of KMeans class
@param X LIST input data
@param K INT number of centroids
@param options DICT dctionary with options
"""
self._init_X(X) # LIST data coordinates
self._init_options(options) # DICT options
self._init_rest(K) # Initializes de rest of the object
#############################################################
## THIS FUNCTION CAN BE MODIFIED FROM THIS POINT, if needed
#############################################################
def _init_X(self, X):
"""@brief Initialization of all pixels
@param X LIST list of all pixel values. Usually it will be a numpy
array containing an image NxMx3
sets X an as an array of data in vector form (PxD where P=N*M and D=3 in the above example)
"""
if len(X.shape) >= 3:
self.X = X.reshape(-1, X.shape[2]).astype(np.float64)
else:
self.X = np.copy(X.astype(np.float64))
def _init_options(self, options):
"""@brief Initialization of options in case some fields are left undefined
@param options DICT dctionary with options
sets de options parameters
"""
if options == None:
options = {}
if not 'km_init' in options:
options['km_init'] = 'first'
if not 'verbose' in options:
options['verbose'] = False
if not 'tolerance' in options:
options['tolerance'] = 0
if not 'max_iter' in options:
options['max_iter'] = np.inf
if not 'fitting' in options:
options['fitting'] = 'Fisher'
self.options = options
#############################################################
## THIS FUNCTION CAN BE MODIFIED FROM THIS POINT, if needed
#############################################################
def _init_rest(self, K):
"""@brief Initialization of the remainig data in the class.
@param options DICT dctionary with options
"""
self.K = K # INT number of clusters
if self.K > 0:
self._init_centroids() # LIST centroids coordinates
self.old_centroids = np.empty_like(self.centroids) # LIST coordinates of centroids from previous iteration
self.clusters = np.zeros(len(self.X)) # LIST list that assignes each element of X into a cluster
self._cluster_points() # sets the first cluster assignation
self.num_iter = 0 # INT current iteration
#############################################################
## THIS FUNCTION CAN BE MODIFIED FROM THIS POINT, if needed
#############################################################
def _init_centroids(self):
"""@brief Initialization of centroids
depends on self.options['km_init']
"""
if self.options['km_init'].lower() == 'first':
unique, index = np.unique(self.X,axis=0, return_index=True)
index = np.sort(index)
self.centroids = np.array(self.X[index[:self.K]])
elif self.options['km_init'].lower() == 'custom':
self.centroids = np.zeros((self.K,self.X.shape[1]))
for k in range(self.K): self.centroids[k,:] = k*255/(self.K-1)
elif self.options['km_init'] == 'kmeans++':
self.centroids = camins(n_clusters=self.K, init='k-means++', n_init=1, max_iter=1).fit(self.X).cluster_centers_
else:
maxtmp = self.X.max(axis=0)
mintmp = self.X.min(axis=0)
centroids = np.zeros((self.X.shape[1],self.K))
for i in range(self.X.shape[1]):
centroids[i] = np.random.uniform(low=mintmp[i],high=maxtmp[i],size=self.K)
self.centroids = np.array(centroids.transpose())
def _cluster_points(self):
"""@brief Calculates the closest centroid of all points in X
"""
self.clusters = pairwise_distances_argmin(self.X, self.centroids)
def _get_centroids(self):
"""@brief Calculates coordinates of centroids based on the coordinates
of all the points assigned to the centroid
"""
self.old_centroids = np.copy(self.centroids)
self.centroids = np.array([self.X[self.clusters == i].mean(0) for i in range(self.K)])
if np.isnan(self.centroids).any():
mask = np.where(np.isnan(self.centroids).all(axis=1))[0]
self.centroids[mask] = self.old_centroids[mask]
def _converges(self):
"""@brief Checks if there is a difference between current and old centroids
"""
return np.allclose(self.centroids, self.old_centroids, self.options['tolerance'])
def _iterate(self, show_first_time=True):
"""@brief One iteration of K-Means algorithm. This method should
reassigne all the points from X to their closest centroids
and based on that, calculate the new position of centroids.
"""
self.num_iter += 1
self._cluster_points()
self._get_centroids()
if self.options['verbose']:
self.plot(show_first_time)
def run(self):
"""@brief Runs K-Means algorithm until it converges or until the number
of iterations is smaller than the maximum number of iterations.=
"""
if self.K == 0:
self.bestK()
return
self._iterate(True)
self.options['max_iter'] = np.inf
if self.options['max_iter'] > self.num_iter:
while not self._converges():
self._iterate(False)
def bestK(self):
"""@brief Runs K-Means multiple times to find the best K for the current
data given the 'fitting' method. In cas of Fisher elbow method
is recommended.
at the end, self.centroids and self.clusters contains the
information for the best K. NO need to rerun KMeans.
@return B is the best K found.
"""
#######################################################
## YOU MUST REMOVE THE REST OF THE CODE OF THIS FUNCTION
## AND CHANGE FOR YOUR OWN CODE
#######################################################
centroids = []
clusters = []
bestk = 4
#self.options['fitting'] ='gap'
if self.options['fitting'].lower() == 'jump':
return self.jumpMethod(clusters,centroids)
elif self.options['fitting'].lower() == 'gap':
bestk = gap(self.X, maxClusters=14)
self._init_rest(bestk)
self.run()
return bestk
elif self.options['fitting'].lower() == 'fisher':
bestk, center = -1, []
fit, threshold = np.inf, 2.3
self._init_rest(2)
self.run()
center.append([self.fitting(), self.centroids, self.clusters])
self._init_rest(3)
self.run()
center.append([self.fitting(), self.centroids, self.clusters])
for k in xrange(4, 13 + 1):
self._init_rest(k)
self.run()
center.append([self.fitting(), self.centroids, self.clusters])
if (center[-3][0] - center[-2][0]) > (center[-2][0] - center[-1][0])*threshold:
self.centroids, self.clusters = center[-2][1:]
bestk = k - 1
break
else:
bestk = 4
self.centroids, self.clusters = center[bestk-2][1:]
self.K = bestk
return bestk
else:
scores = []
for k in range(2,14):
self._init_rest(k)
self.run()
scores.append(self.fitting())
centroids.append(self.centroids)
clusters.append(self.clusters)
if self.options['fitting'].lower() == 'calinski' or self.options['fitting'].lower() == 'silhouette':
bestk = np.argmax(scores)+2
self.centroids = centroids[bestk-2]
self.clusters = clusters[bestk-2]
self.K = bestk
return bestk
def fitting(self):
"""@brief return a value describing how well the current kmeans fits the data
"""
if self.K == 1:
return 1
elif self.options['fitting'].lower() == 'fisher' and self.K > 1:
return 1/(metricas.calinski_harabaz_score(self.X, self.clusters)*(self.K -1)/(self.X.shape[0]-self.K)) #calinski = (Between_Variance/Whithin_Variance)*(N-k)/(K-1)
elif self.options['fitting'].lower() == 'silhouette':
return metricas.silhouette_score(self.X,self.clusters)
elif self.options['fitting'].lower() == 'calinski':
return metricas.calinski_harabaz_score(self.X, self.clusters)
else:
return np.random.rand(1)
def jumpMethod(self, clusters, centroids):
data = self.X
# dimension of 'data'; data.shape[0] would be size of 'data'
p = data.shape[1]
# vector of variances (1 by p)
#using squared error rather than Mahalanobis distance' (SJ, p. 12)
sigmas = np.var(data, axis=0)
## by following the authors we assume 0 covariance between p variables (SJ, p. 12)
# start with zero-matrix (p by p)
Sigma = np.zeros((p, p), dtype=np.float32)
# fill the main diagonal with variances for
np.fill_diagonal(Sigma, val=sigmas)
# calculate the inversed matrix
Sigma_inv = np.linalg.inv(Sigma)
cluster_range = range(1, 13 + 1)
distortions = np.repeat(0, len(cluster_range) + 1).astype(np.float32)
# for each k in cluster range implement
for k in cluster_range:
# initialize and fit the clusterer giving k in the loop
self._init_rest(k)
self.run()
centroids.append(self.centroids)
clusters.append(self.clusters)
# calculate centers of suggested k clusters
centers = self.centroids
# since we need to calculate the mean of mins create dummy vec
for_mean = np.repeat(0, len(data)).astype(np.float32)
# for each observation (i) in data implement
for i in range(len(data)):
# dummy for vec of distances between i-th obs and k-center
dists = np.repeat(0, k).astype(np.float32)
# for each cluster in KMean clusters implement
for cluster in range(k):
# calculate the within cluster dispersion
tmp = np.transpose(data[i] - centers[cluster])
#using squared error rather than Mahalanobis distance' (SJ, p. 12)
dists[cluster] = tmp.dot(Sigma_inv).dot(tmp)
#dists[cluster] = tmp.dot(tmp)
# take the lowest distance to a class
for_mean[i] = min(dists)
# take the mean for mins for each observation
distortions[k] = np.mean(for_mean) / p
Y = p / 2
# the first (by convention it is 0) and the second elements
jumps = [0] + [distortions[1] ** (-Y) - 0]
jumps += [distortions[k] ** (-Y) \
- distortions[k-1] ** (-Y) \
for k in range(2, len(distortions))]
# calculate recommended number of clusters
bestK = np.argmax(np.array(jumps))
self.centroids = centroids[bestK-1]
self.clusters = clusters[bestK-1]
self.K = bestK
"""plt.figure(2)
plt.cla()
plt.plot(range(16),jumps)
plt.xlabel('K')
plt.ylabel('fitting score')
plt.draw()
plt.pause(20)"""
return bestK
def plot(self, first_time=True):
"""@brief Plots the results
"""
# markersshape = 'ov^<>1234sp*hH+xDd'
markerscolor = 'bgrcmybgrcmybgrcmyk'
if first_time:
plt.gcf().add_subplot(111, projection='3d')
plt.ion()
plt.show()
if self.X.shape[1] > 3:
if not hasattr(self, 'pca'):
self.pca = PCA(n_components=3)
self.pca.fit(self.X)
Xt = self.pca.transform(self.X)
Ct = self.pca.transform(self.centroids)
else:
Xt = self.X
Ct = self.centroids
for k in range(self.K):
plt.gca().plot(Xt[self.clusters == k, 0], Xt[self.clusters == k, 1], Xt[self.clusters == k, 2],
'.' + markerscolor[k])
plt.gca().plot(Ct[k, 0:1], Ct[k, 1:2], Ct[k, 2:3], 'o' + 'k', markersize=12)
if first_time:
plt.xlabel('dim 1')
plt.ylabel('dim 2')
plt.gca().set_zlabel('dim 3')
plt.draw()
plt.pause(0.01)
| [
"[email protected]"
]
| |
0d59d8d0b71b7c382e97d7b56015fbfbbafc69d8 | 0227dab8e222d908d02d54ad13ec88b7f1f9ac1f | /AUTOMATAPROYECT-master/Front.py | 2d287391253e9ad5e29a2cb2152e4ae740d4d192 | []
| no_license | OrlandoMR/Automatas | 8c3a3e1fc3f45f6239a24ab2b03a5102b18a1a32 | a1213bf3ca6b7803d0aa82ce52947a86d31e0eca | refs/heads/master | 2021-10-25T10:01:25.564466 | 2019-04-03T19:47:59 | 2019-04-03T19:47:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,548 | py |
from tkinter import *
import os
#Interfaz gŕafica
#root widget
root = Tk()
root.title("Titulo de la historia")
root.resizable(False,False)
root.configure(bg="black")
#FirstFrame Creation
myFrame = Frame(root,width=500, height=400)
myFrame.pack()
myFrame.config(width="650", height="350")
myFrame.config(bd= 8,bg = "black")
myFrame.config(relief = "groove")
#LabelFirstText
textLabel = Label(myFrame, text="Hubo una época donde energía era sinónimo de suciedad," +
" encender las luces una importante elección, \nlas ciudades tenían apagones"+
" y los autos quemaban combustible para funcionar..."
, bg = "black", fg = "white", font=("Arial Unicode MS",15))
textLabel.grid(row= 0,column=1, padx=10, pady = 10)
#Image
img = PhotoImage(file='files/fondo.gif')#Reemplazar por función que pondra la imagen dependiendo del estado
imageLabel = Label(myFrame, image = img)
imageLabel.grid(row= 1,column=1, padx=10, pady = 10)
#Action Buttons
def actionYesButton():
print("Holaaaaa")
def actionNoButton():
print("AntiHola")
#Buttons
buttonNo = Button(myFrame, text="NO", bg = "black", fg = "green", font = (20),
width = 7, height =5, command = actionNoButton)
buttonNo.grid(row = 2,column = 0, padx = 10, pady = 10)
buttonYes = Button(myFrame, text="YES", bg = "black", fg = "green", font = (20),width = 7,
height =5, command = actionYesButton)
buttonYes.grid(row = 2, column = 3, padx = 10, pady = 10)
root.mainloop()
| [
"[email protected]"
]
| |
e3ad3b767fbd0d6d7edf4729792f6b837616eec6 | fea389d72e4e458c183ca40ab695d46bc5da5015 | /OMG/source/conf.py | 5fa8d544b7b569b693f643fdf2e2ce745b869795 | []
| no_license | zhangdaoxun/ON-MY-GENE | dfae4f3f135215edb65f79ac6b11f5c7b7405bab | 674819e65894de4ed283649dd9fce66596b73831 | refs/heads/master | 2020-05-02T12:56:12.902829 | 2019-06-06T13:45:16 | 2019-06-06T13:45:16 | 177,971,218 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,225 | py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'omg'
copyright = '2019, zhangxun'
author = 'zhangxun'
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
release = ''
# -- General configuration ---------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = None
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# The default sidebars (for documents that don't match any pattern) are
# defined by theme itself. Builtin themes are using these templates by
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'omgdoc'
# -- Options for LaTeX output ------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'omg.tex', 'omg Documentation',
'zhangxun', 'manual'),
]
# -- Options for manual page output ------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'omg', 'omg Documentation',
[author], 1)
]
# -- Options for Texinfo output ----------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'omg', 'omg Documentation',
author, 'omg', 'One line description of project.',
'Miscellaneous'),
]
# -- Options for Epub output -------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
# The unique identifier of the text. This can be a ISBN number
# or the project homepage.
#
# epub_identifier = ''
# A unique identification for the text.
#
# epub_uid = ''
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
| [
"[email protected]"
]
| |
39802ef9bbb151ce9bc58a14a2eb6a04f2102cd3 | 89186f602196897d7fcbf4d1546b01316685e69a | /algorithem/expression_add_operation.py | 386afe36c79030b8e768d03586049b6eb8237522 | []
| no_license | xxpasswd/algorithms-and-data-structure | dfc25d0d4ac25ef7b138f6b1ad725c0832a0c2ee | 1868e660fcee4017307c335df105e5d3028f4166 | refs/heads/master | 2022-01-18T12:58:00.042361 | 2019-07-17T08:13:19 | 2019-07-17T08:13:19 | 110,533,692 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,334 | py | '''
给定一组数字，给这组数字中，添加+，-，*，使最后表达式的结果等于特定值
解决思路：
使用递归遍历所有的情况
'''
def expression_add_operators(nums,target):
out = []
helper(nums,target,0,0,0,'',out)
return out
def helper(nums,target,pos,pre,pre_num,path,out):
'''
nums:候选数字
target：目标值
pos：下一个数字开始的索引
pre：前一次计算的值
pre_num：前一个数字
path：已经过的运算路径
out：保存输出结果的列表
'''
# 判断路径的结果，是否满足条件，满足条件则添加到结果集中
if pos == len(nums):
if pre == target:
out.append(path)
return
elif pos == 0:
# 注意i+1这儿
for i in range(pos,len(nums)):
cur = int(nums[pos:i+1])
helper(nums,target,i+1,cur,cur,path + str(cur),out)
else:
for i in range(pos,len(nums)):
cur = int(nums[pos:i+1])
helper(nums,target,i+1,pre+cur,cur,path + '+' + str(cur),out)
helper(nums,target,i+1,pre-cur,-cur,path + '-' + str(cur),out)
helper(nums,target,i+1,pre-pre_num+pre_num*cur,pre_num*cur,path + '*' + str(cur),out)
nums = '123456'
target = 0
print(expression_add_operators(nums,target)) | [
"[email protected]"
]
| |
a52172f6e1c94d9bfe28ca0fbf6d6d6a3854c70e | cf522d29d40c2e8a780165cff7302922db5e82a4 | /jscribe/conf/pythontagsettings.py | 9a3ca51ddb06a20fab093fb298f24e97b90f094c | [
"BSD-3-Clause"
]
| permissive | mindbrave/jscribe | 3bd8eec23fd9ae839795d1b5fbf603e72fa90362 | 4d524a9f60b35fefbfe65c60717abcb8bce259c9 | refs/heads/master | 2020-05-24T15:45:13.177447 | 2014-01-31T00:45:49 | 2014-01-31T00:45:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,052 | py | # -*- coding: utf-8 -*-
#!/usr/bin/env python
"""* Builtin tag settings for python projects.
You can use them in project by setting TAG_SETTINGS to `jscribe.conf.pythontagsettings`, but
I admit you to create your own tag settings.
@module jscribe.conf.jstagsettings
"""
TAG_SETTINGS = {
"base": {
"parent_type": None,
"alias": [],
"separate": False,
"list": False,
"list_order": 0,
"name": "default name",
"title": "default plural name",
"source_visible": True,
"callable": False,
"attributes": {}
},
"index": {
"parent_type": "base",
"alias": ["a"],
"name": "",
"title": "Index",
"separate": True,
"source_visible": True,
"list": True,
"list_order": -2,
"attributes": {
}
},
"manual": {
"parent_type": "base",
"alias": ["m"],
"name": "",
"title": "Manual",
"separate": True,
"source_visible": True,
"list": True,
"list_order": -1,
"attributes": {
}
},
"paragraph": {
"parent_type": "base",
"alias": ["p"],
"name": "",
"title": "paragraphs",
"separate": False,
"source_visible": True,
"list": False,
"attributes": {
}
},
"package": {
"parent_type": "base",
"alias": ["pack"],
"name": "package",
"title": "API Packages",
"separate": True,
"list": True,
"list_order": 1,
"attributes": {
}
},
"module": {
"parent_type": "base",
"alias": [],
"name": "module",
"separate": True,
"list_order": 2,
"list": True,
"title": "API Modules",
"attributes": {
}
},
"class": {
"parent_type": "base",
"name": "class",
"title": "API Classes",
"separate": False,
"list_order": 0,
"list": True,
"attributes": {
}
},
"exception": {
"parent_type": "base",
"name": "exception",
"title": "Exceptions",
"separate": False,
"list_order": 0,
"list": False,
"attributes": {}
},
"method": {
"parent_type": "base",
"name": "method",
"title": "methods",
"callable": True,
"separate": False,
"attributes": {}
},
"property": {
"parent_type": "base",
"name": "property",
"title": "properties",
"callable": False,
"separate": False,
"attributes": {}
},
"instance": {
"parent_type": "base",
"name": "instance",
"title": "instances",
"separate": False,
"attributes": {
}
},
"function": {
"parent_type": "base",
"name": "function",
"title": "functions",
"callable": True,
"attributes": {
}
},
"attribute": {
"parent_type": "base",
"alias": ["attr"],
"name": "attribute",
"title": "attributes",
"callable": False,
"attributes": {
}
},
"number": {
"parent_type": "attribute",
"name": "number",
"alias": ["num"],
"attributes": {
}
},
"bytestring": {
"parent_type": "attribute",
"name": "bytestring",
"alias": ["str"],
"attributes": {
}
},
"unicode": {
"parent_type": "attribute",
"name": "unicode",
"alias": ["u"],
"attributes": {
}
},
"list": {
"parent_type": "attribute",
"name": "list",
"alias": [],
"attributes": {
}
},
"tuple": {
"parent_type": "attribute",
"name": "tuple",
"alias": [],
"attributes": {}
},
"dict": {
"parent_type": "attribute",
"name": "dict",
"alias": [],
"attributes": {}
},
} | [
"[email protected]"
]
| |
00c4fad7606971274a79c91af14dc8412935ba2e | c5becab2d4201f2e828d052c22b4496a3bbe4927 | /tests/pipelines/test_pipelines_conversational.py | 9ed32adda652d5983ed5995d8d94a7a0df5d635c | [
"Apache-2.0"
]
| permissive | thomwolf/transformers | ba665c456b2acd636d8e3876a87ea446ae0ae092 | 166dfa88e5dfdca1d99197e5006e4e2ea9e49cba | refs/heads/master | 2023-03-08T03:37:13.519336 | 2023-02-15T15:00:01 | 2023-02-15T15:00:01 | 238,908,404 | 4 | 1 | Apache-2.0 | 2023-02-25T16:09:30 | 2020-02-07T11:40:04 | Python | UTF-8 | Python | false | false | 17,110 | py | # Copyright 2020 The HuggingFace Team. 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.
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallTokenizer,
Conversation,
ConversationalPipeline,
TFAutoModelForCausalLM,
pipeline,
)
from transformers.testing_utils import require_tf, require_torch, slow, torch_device
from .test_pipelines_common import ANY, PipelineTestCaseMeta
DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
class ConversationalPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
model_mapping = dict(
list(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
if MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
else [] + list(MODEL_FOR_CAUSAL_LM_MAPPING.items())
if MODEL_FOR_CAUSAL_LM_MAPPING
else []
)
tf_model_mapping = dict(
list(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
if TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
else [] + list(TF_MODEL_FOR_CAUSAL_LM_MAPPING.items())
if TF_MODEL_FOR_CAUSAL_LM_MAPPING
else []
)
def get_test_pipeline(self, model, tokenizer, processor):
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
return conversation_agent, [Conversation("Hi there!")]
def run_pipeline_test(self, conversation_agent, _):
# Simple
outputs = conversation_agent(Conversation("Hi there!"))
self.assertEqual(outputs, Conversation(past_user_inputs=["Hi there!"], generated_responses=[ANY(str)]))
# Single list
outputs = conversation_agent([Conversation("Hi there!")])
self.assertEqual(outputs, Conversation(past_user_inputs=["Hi there!"], generated_responses=[ANY(str)]))
# Batch
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
outputs = conversation_agent([conversation_1, conversation_2])
self.assertEqual(outputs, [conversation_1, conversation_2])
self.assertEqual(
outputs,
[
Conversation(
past_user_inputs=["Going to the movies tonight - any suggestions?"],
generated_responses=[ANY(str)],
),
Conversation(past_user_inputs=["What's the last book you have read?"], generated_responses=[ANY(str)]),
],
)
# One conversation with history
conversation_2.add_user_input("Why do you recommend it?")
outputs = conversation_agent(conversation_2)
self.assertEqual(outputs, conversation_2)
self.assertEqual(
outputs,
Conversation(
past_user_inputs=["What's the last book you have read?", "Why do you recommend it?"],
generated_responses=[ANY(str), ANY(str)],
),
)
with self.assertRaises(ValueError):
conversation_agent("Hi there!")
with self.assertRaises(ValueError):
conversation_agent(Conversation())
# Conversation have been consumed and are not valid anymore
# Inactive conversations passed to the pipeline raise a ValueError
with self.assertRaises(ValueError):
conversation_agent(conversation_2)
@require_torch
@slow
def test_integration_torch_conversation(self):
# When
conversation_agent = pipeline(task="conversational", device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
# When
result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
# Then
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(len(result[0].past_user_inputs), 1)
self.assertEqual(len(result[1].past_user_inputs), 1)
self.assertEqual(len(result[0].generated_responses), 1)
self.assertEqual(len(result[1].generated_responses), 1)
self.assertEqual(result[0].past_user_inputs[0], "Going to the movies tonight - any suggestions?")
self.assertEqual(result[0].generated_responses[0], "The Big Lebowski")
self.assertEqual(result[1].past_user_inputs[0], "What's the last book you have read?")
self.assertEqual(result[1].generated_responses[0], "The Last Question")
# When
conversation_2.add_user_input("Why do you recommend it?")
result = conversation_agent(conversation_2, do_sample=False, max_length=1000)
# Then
self.assertEqual(result, conversation_2)
self.assertEqual(len(result.past_user_inputs), 2)
self.assertEqual(len(result.generated_responses), 2)
self.assertEqual(result.past_user_inputs[1], "Why do you recommend it?")
self.assertEqual(result.generated_responses[1], "It's a good book.")
@require_torch
@slow
def test_integration_torch_conversation_truncated_history(self):
# When
conversation_agent = pipeline(task="conversational", min_length_for_response=24, device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
# When
result = conversation_agent(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 1)
self.assertEqual(len(result.generated_responses), 1)
self.assertEqual(result.past_user_inputs[0], "Going to the movies tonight - any suggestions?")
self.assertEqual(result.generated_responses[0], "The Big Lebowski")
# When
conversation_1.add_user_input("Is it an action movie?")
result = conversation_agent(conversation_1, do_sample=False, max_length=36)
# Then
self.assertEqual(result, conversation_1)
self.assertEqual(len(result.past_user_inputs), 2)
self.assertEqual(len(result.generated_responses), 2)
self.assertEqual(result.past_user_inputs[1], "Is it an action movie?")
self.assertEqual(result.generated_responses[1], "It's a comedy.")
@require_torch
def test_small_model_pt(self):
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation = Conversation("hello")
output = conversation_agent(conversation)
self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
@require_tf
def test_small_model_tf(self):
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation = Conversation("hello")
output = conversation_agent(conversation)
self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
@require_torch
@slow
def test_integration_torch_conversation_dialogpt_input_ids(self):
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
inputs = conversation_agent.preprocess(conversation_1)
self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
conversation_2 = Conversation("how are you ?", past_user_inputs=["hello"], generated_responses=["Hi there!"])
inputs = conversation_agent.preprocess(conversation_2)
self.assertEqual(
inputs["input_ids"].tolist(), [[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256]]
)
@require_torch
@slow
def test_integration_torch_conversation_blenderbot_400M_input_ids(self):
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
# test1
conversation_1 = Conversation("hello")
inputs = conversation_agent.preprocess(conversation_1)
self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
# test2
conversation_1 = Conversation(
"I like lasagne.",
past_user_inputs=["hello"],
generated_responses=[
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
],
)
inputs = conversation_agent.preprocess(conversation_1)
self.assertEqual(
inputs["input_ids"].tolist(),
[
# This should be compared with the same conversation on ParlAI `safe_interactive` demo.
[
1710, # hello
86,
228, # Double space
228,
946,
304,
398,
6881,
558,
964,
38,
452,
315,
265,
6252,
452,
322,
968,
6884,
3146,
278,
306,
265,
617,
87,
388,
75,
341,
286,
521,
21,
228, # Double space
228,
281, # I like lasagne.
398,
6881,
558,
964,
21,
2, # EOS
],
],
)
@require_torch
@slow
def test_integration_torch_conversation_blenderbot_400M(self):
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
conversation_1 = Conversation("hello")
result = conversation_agent(
conversation_1,
)
self.assertEqual(
result.generated_responses[0],
# ParlAI implementation output, we have a different one, but it's our
# second best, you can check by using num_return_sequences=10
# " Hello! How are you? I'm just getting ready to go to work, how about you?",
" Hello! How are you doing today? I just got back from a walk with my dog.",
)
conversation_1 = Conversation("Lasagne hello")
result = conversation_agent(conversation_1, encoder_no_repeat_ngram_size=3)
self.assertEqual(
result.generated_responses[0],
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie.",
)
conversation_1 = Conversation(
"Lasagne hello Lasagne is my favorite Italian dish. Do you like lasagne? I like lasagne."
)
result = conversation_agent(
conversation_1,
encoder_no_repeat_ngram_size=3,
)
self.assertEqual(
result.generated_responses[0],
" Me too. I like how it can be topped with vegetables, meats, and condiments.",
)
@require_torch
@slow
def test_integration_torch_conversation_encoder_decoder(self):
# When
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot_small-90M")
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer, device=DEFAULT_DEVICE_NUM)
conversation_1 = Conversation("My name is Sarah and I live in London")
conversation_2 = Conversation("Going to the movies tonight, What movie would you recommend? ")
# Then
self.assertEqual(len(conversation_1.past_user_inputs), 0)
self.assertEqual(len(conversation_2.past_user_inputs), 0)
# When
result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
# Then
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(len(result[0].past_user_inputs), 1)
self.assertEqual(len(result[1].past_user_inputs), 1)
self.assertEqual(len(result[0].generated_responses), 1)
self.assertEqual(len(result[1].generated_responses), 1)
self.assertEqual(result[0].past_user_inputs[0], "My name is Sarah and I live in London")
self.assertEqual(
result[0].generated_responses[0],
"hi sarah, i live in london as well. do you have any plans for the weekend?",
)
self.assertEqual(
result[1].past_user_inputs[0], "Going to the movies tonight, What movie would you recommend? "
)
self.assertEqual(
result[1].generated_responses[0], "i don't know... i'm not really sure. what movie are you going to see?"
)
# When
conversation_1.add_user_input("Not yet, what about you?")
conversation_2.add_user_input("What's your name?")
result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
# Then
self.assertEqual(result, [conversation_1, conversation_2])
self.assertEqual(len(result[0].past_user_inputs), 2)
self.assertEqual(len(result[1].past_user_inputs), 2)
self.assertEqual(len(result[0].generated_responses), 2)
self.assertEqual(len(result[1].generated_responses), 2)
self.assertEqual(result[0].past_user_inputs[1], "Not yet, what about you?")
self.assertEqual(result[0].generated_responses[1], "i don't have any plans yet. i'm not sure what to do yet.")
self.assertEqual(result[1].past_user_inputs[1], "What's your name?")
self.assertEqual(result[1].generated_responses[1], "i don't have a name, but i'm going to see a horror movie.")
@require_torch
@slow
def test_from_pipeline_conversation(self):
model_id = "facebook/blenderbot_small-90M"
# from model id
conversation_agent_from_model_id = pipeline("conversational", model=model_id, tokenizer=model_id)
# from model object
model = BlenderbotSmallForConditionalGeneration.from_pretrained(model_id)
tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_id)
conversation_agent_from_model = pipeline("conversational", model=model, tokenizer=tokenizer)
conversation = Conversation("My name is Sarah and I live in London")
conversation_copy = Conversation("My name is Sarah and I live in London")
result_model_id = conversation_agent_from_model_id([conversation])
result_model = conversation_agent_from_model([conversation_copy])
# check for equality
self.assertEqual(
result_model_id.generated_responses[0],
"hi sarah, i live in london as well. do you have any plans for the weekend?",
)
self.assertEqual(
result_model_id.generated_responses[0],
result_model.generated_responses[0],
)
| [
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]
| |
dce072906381a649dc6463c6c5531217b5a67f44 | 6d7984b21819ffe0a9a969025af6a39eef7112dc | /tests/challenges/---test_multi_bracket_validation.py | 885ccee02de743da8ca3d6f333ef4043fa96fa84 | [
"MIT"
]
| permissive | 401-python-joseph-zabaleta/401-python-data-structures-and-algorithms | 593d4e1fee7f363cbe5e51d8ee9825237d44e799 | b11b5ef50f52e3d505474fe5fffe4357933da251 | refs/heads/master | 2022-11-04T21:23:56.211811 | 2020-07-25T22:19:09 | 2020-07-25T22:19:09 | 262,261,001 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,330 | py | import pytest
from dsa.challenges.multi_bracket_validation.multi_bracket_validation import multi_bracket_validation
def test_mbv_exists():
assert multi_bracket_validation('test')
def test_mbv_one():
string = '{}'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_two():
string = '{}(){}'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_three():
string = '()[[Extra Characters]]'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_four():
string = '(){}[[]]'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_five():
string = '{}{Code}[Fellows](())'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_six():
string = '[({}]'
actual = multi_bracket_validation(string)
expected = False
assert actual == expected
def test_mbv_seven():
string = '(]('
actual = multi_bracket_validation(string)
expected = False
assert actual == expected
def test_mbv_eight():
string = '{(})'
actual = multi_bracket_validation(string)
expected = False
assert actual == expected
| [
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]
| |
835e19c6b6158e1f8e9361ee8be1dee8469c39e7 | fc9774c15df5e3fda88fe82fd0c37f4ceabfcaa6 | /XGBoost/XGBoost.py | 71d6ac5568c8149baa01b952d036d5548a4fa254 | []
| no_license | jacobkuriala/moviemania | 600ff5cce5d383b459fa5dc1be2ea51289860acd | e17468732d5f5ce8d3c3cc6fb8c26c93e7291d71 | refs/heads/master | 2021-07-25T11:21:16.462369 | 2017-11-07T18:56:48 | 2017-11-07T18:56:48 | 109,197,864 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,248 | py |
# importing libraries
import numpy as np
import matplotlib as plt
import pandas as pd
# importing the dataset
dataset = pd.read_excel('Training Sheet.xlsx')
# X starts from production_year and omits total
X = dataset.iloc[:, 3:-2].values
# delete the board_rating_reason as it creates too many categories
X = np.delete(X, 7 , 1)
# Xarr = X.tolist()
y = dataset.iloc[:, 14].values
# dont need to take care of any missing values
# encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_x = LabelEncoder()
# production year
X[:, 0] = labelencoder_x.fit_transform(X[:, 0])
# creative_type
X[:, 2] = labelencoder_x.fit_transform(X[:, 2])
# source
X[:, 3] = labelencoder_x.fit_transform(X[:, 3])
# production_method
X[:, 4] = labelencoder_x.fit_transform(X[:, 4])
# genre
X[:, 5] = labelencoder_x.fit_transform(X[:, 5])
# language
X[:, 6] = labelencoder_x.fit_transform(X[:, 6])
# movie_board_rating_display_name
X[:, 7] = labelencoder_x.fit_transform(X[:, 7])
# movie_release_pattern_display_name
X[:, 8] = labelencoder_x.fit_transform(X[:, 8])
# Xarr = X.tolist()
onehotencoder = OneHotEncoder(categorical_features= [0,2,3,4,5,6,7,8])
X = onehotencoder.fit_transform(X).toarray()
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# feature Scaling - TODO: may have to feature scale the output
# from sklearn.preprocessing import StandardScaler
# sc = StandardScaler()
# y_train = sc.fit_transform(y_train)
# y_test = sc.transform(y_test)
# Fitting XGBoost to the Training set
from xgboost import XGBClassifier
classifier = XGBClassifier(objective='multi:softmax')
classifier.fit(X_train,y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)
# Applying k-Fold Cross Validation
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
print(accuracies)
print(accuracies.mean())
print(accuracies.std())
| [
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]
| |
9d601337b6dc895c927f933e062c2575796415e6 | 2327d0bc2cc45a5504c39109846e0f4cba266606 | /QID-1799-SFEtimegarch/SFEtimegarch.py | 362457032fb2eddfb6adae28a3bbcca837f3cc90 | []
| no_license | QuantLet/SFE | 3d98a33cfcdc533210856c7618c32a78e111a6ce | d25a728a4371538eae982f44ea811b5b93328828 | refs/heads/master | 2022-06-15T13:35:17.387252 | 2022-06-08T01:22:00 | 2022-06-08T01:22:00 | 72,103,182 | 12 | 32 | null | 2022-01-30T18:58:21 | 2016-10-27T11:50:43 | R | UTF-8 | Python | false | false | 702 | py | import numpy as np
import matplotlib.pyplot as plt
#
np.random.seed(1234)
#omega = 0.1, alpha = 0.15, beta = 0.8
n=1000 # number of observations
n1=100 # drop first observations
alpha=(0.1,0.3) # GARCH (1,1) coefficients alpha0 and alpha1
beta=0.8
errors=np.random.normal(0,1,n+n1)
t=np.zeros(n+n1)
t[0]=np.random.normal(0,np.sqrt(alpha[0]/(1-alpha[1])),1)
#iterate over the oberservations
for i in range(1,n+n1-1):
t[i]=errors[i]*np.sqrt(alpha[0]+alpha[1]*errors[i-1]**2+beta*t[i-1]**2)
#
y=t[n1-1:-1] # drop n1 observations
plt.title('GARCH (1,1) process')
x=range(n)
plt.plot(x,y)
plt.xlabel('time')
plt.ylabel('y')
plt.savefig('SFEtimegarch_py.png')
plt.show() | [
"[email protected]"
]
| |
4eb51b17bb570e99d31b10c14927845d7a16bff8 | 48a22539cc273ebb1d4575ecc40566337a2c1c53 | /tests/functions/test_train_model.py | 6580cbd7f6aa173c717f65c26155f19e736a7976 | [
"BSD-3-Clause"
]
| permissive | ChenchenFat/LIBiFBTSVM | 50d88f4d2f8133672fe6831422f1e4bc0b0b798a | b1f8c346cdd79a6057a664255daabe2aa9c50ad7 | refs/heads/master | 2023-08-20T18:58:55.117122 | 2021-10-26T19:59:08 | 2021-10-26T19:59:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 660 | py |
import numpy as np
import pytest
from libifbtsvm.functions import train_model
from libifbtsvm.models.ifbtsvm import Hyperparameters
def test_train_model():
H_p = np.ones((5, 5))
H_n = np.ones((5, 5))
CCx = np.ones((5, 1))
C = 2
_mock_params = Hyperparameters()
_mock_params.max_iter = 1
_mock_params.epsilon = 0.0001
model = train_model(parameters=_mock_params, H=H_p, G=H_n, C=C, CCx=CCx)
assert np.array_equal(model.alpha, np.array([1, 1, 1, 1, 1]))
_truth = [np.array(val) for val in [-1, -0.8, -0.6, -0.4, -0.2]]
for i in range(5):
assert pytest.approx(model.projected_gradients[i], _truth[i])
| [
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]
| |
f9375213f5507d9c9536460cb0a4616660787502 | ee410de6809032624f3b52023ba6efcae89aa5da | /shortable/shortable.py | f95e80f9568050d626a4849b375079a70272c22c | [
"MIT"
]
| permissive | webclinic017/shortable | cda32c4022a9ef1a92c085d7fe6cac948e140124 | 4ace06b484bd24a356300a9d15a02a902c853c63 | refs/heads/master | 2023-08-22T14:28:52.908734 | 2021-09-22T03:08:34 | 2021-09-22T03:08:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,041 | py | #!/usr/bin/env python
import json
import logging
import argparse
import textwrap
import alpaca_trade_api as alpaca
from winotify import Notification
#API = alpaca
#BROKER = alpaca
class Shortable():
def __init__(self):
self.old_short_status = dict()
self.new_short_status = dict()
self.report = ""
self.api = alpaca.REST()
def __del__(self):
del self.old_short_status
del self.new_short_status
del self.report
del self.api
def read_old_short_status(self):
with open('shortable.json', 'r') as shortable:
self.old_short_status = json.load(shortable)
def get_new_short_status(self):
#def get_new_short_status(self, broker_api):
#if self.api == 'ALPACA':
#if self.api == alpaca.REST():
# for asset in self.old_short_status:
# asset_update = self.api.get_asset(asset)
# self.new_short_status[asset] = asset_update.shortable
for asset in self.old_short_status:
asset_update = self.api.get_asset(asset)
self.new_short_status[asset] = asset_update.shortable
def check_short_status_changes(self):
for (old_short, old_short_stat), (new_short, new_short_stat) in zip(self.old_short_status.items(), self.new_short_status.items()):
# DEBUG
#print(asset)
#print("Tradable: {}".format(asset_update.tradable))
#print("Shortable: {}".format(asset_update.shortable))
#print("Easy-to-Borrow: {}".format(asset_update.easy_to_borrow))
if old_short_stat is False and new_short_stat is True:
logging.info('%s now shortable', old_short)
self.report += '{} now shortable\n'.format(old_short)
elif old_short_stat is True and new_short_stat is False:
logging.info('%s now non-shortable', old_short)
self.report += '{} now non-shortable\n'.format(old_short)
elif old_short_stat is None and new_short_stat is True:
logging.info('%s now shortable, previous status unknown', old_short)
self.report += '{} now shortable, previous status unknown\n'.format(old_short)
elif old_short_stat is None and new_short_stat is False:
logging.info('%s now non-shortable, previous status unknown', old_short)
self.report += '{} now non-shortable, previous status unknown\n'.format(old_short)
else:
# no change to asset shortable status
logging.info('No change to shortable status of %s', old_short)
# Update old shortable status to new shortable status
self.old_short_status = self.new_short_status
# Write new updated short list to file, clearing old list
def write_new_short_status(self):
with open('shortable.json', 'w') as shortable:
shortable.seek(0)
shortable.truncate()
json.dump(self.new_short_status, shortable)
# Create Windows 10 notification if new shortble status to report
def send_notification(self):
if self.report:
logging.info('%s', self.report)
toast = Notification(app_id='📉 shortable', title='📉 shortable', msg=self.report)
toast.build().show()
logging.info('toast notification fired')
# Entry point for running launch script after installing package, see setup.py
def run():
parser = argparse.ArgumentParser(
prog='shortable',
description='Receive an alert if an asset becomes shortable, e.g. from HTB to ETB, or vice versa',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=textwrap.dedent('''\
Setup
1. Define assets to track in shortable.json e.g. {"AMZN": true, "MSFT": true, "TSLA": false}.
2. Run shortable from the same directory as shortable.json. When there are no asset shortable changes there is no output or notification. Check shortable.log to verify operation.
3. Optionally schedule shortable to run routinely using Windows Task Scheduler.
''')
)
#parser.add_argument('ASSET', type=str.upper, help='Ticker of asset to check')
parser.add_argument('-v', '--version', action='version', version='%(prog)s 0.1.0')
args = parser.parse_args()
#logging.basicConfig(filename='shortable.log', encoding='utf-8', level=logging.INFO)
logging.basicConfig(filename='shortable.log', level=logging.INFO, format='%(asctime)s %(message)s')
shortable = Shortable()
shortable.read_old_short_status()
shortable.get_new_short_status()
shortable.check_short_status_changes()
shortable.write_new_short_status()
shortable.send_notification()
if __name__ == '__main__':
run()
| [
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]
| |
137ca2c75fc5842bf99e607647a8c60484deb686 | 0f734b40fa25bdcff0d19c016fc7dcf11122c47a | /chapter_4/buffet.py | 65a46b00d3d129b9fff1b3d1bff7f70e11109530 | []
| no_license | ICANDIGITAL/crash_course_python | f47a0ce366736da1880aec08291867f491ac8bd3 | d1598b0397b4b2cdc7acccf0b9153fa2b96688c8 | refs/heads/master | 2023-06-08T17:37:50.566342 | 2023-05-26T15:09:52 | 2023-05-26T15:09:52 | 120,501,326 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 231 | py | simple_foods = ('fries', 'pizza', 'cake', 'pasta', 'mashed potatos')
for food in simple_foods:
print(food)
simple_foods = ('candy','fries', 'pizza', 'cake', 'pasta', 'mashed potatos')
for food in simple_foods:
print(food)
| [
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]
| |
6779cf42ff406a6d5f99b51f95a9019573da268c | a79772a6da4b00c13ff36d2dd57a50ec6cc3cb08 | /store/store/urls.py | 5cf02fa9d72c1fb1efd93b07cc438e2bd85e8bef | []
| no_license | palzuncoff/CornelStore | edd9d5042446aed045ff0127daca934d92b1a904 | 63e221ee51d97f448ab69e58978caee1dbd25e11 | refs/heads/master | 2020-07-07T01:53:12.375940 | 2016-09-06T08:34:55 | 2016-09-06T08:34:55 | 67,118,732 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 868 | py | """store URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.9/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Add an import: from blog import urls as blog_urls
2. Import the include() function: from django.conf.urls import url, include
3. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls))
"""
from django.conf.urls import include, url
from django.contrib import admin
urlpatterns = [
url(r'^shop/', include('shop.urls')),
url(r'^admin/', admin.site.urls),
]
| [
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]
| |
30cad6e1da61289d5c07870ba55492bb0edeb0f9 | 8aa78783dbb8c5a91b81b41d37ecf8bbd0804354 | /bld/blenderbpy/2.79/scripts/addons/ant_landscape/__init__.py | 0e10b8b407ffddcd4714e750e538acd39ac60319 | []
| no_license | priyesh16/food | e08c0307c7feba27285724e9766fb0f33223fb14 | 18ae58d7f796309e54b76db9a3757207f288b0aa | refs/heads/master | 2021-01-09T11:26:22.489059 | 2020-03-10T23:17:56 | 2020-03-10T23:17:56 | 242,281,395 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 32,182 | py | # ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
# Another Noise Tool - Suite (W.I.P.)
# Jimmy Hazevoet 5/2017
bl_info = {
"name": "A.N.T.Landscape",
"author": "Jimmy Hazevoet",
"version": (0, 1, 8),
"blender": (2, 80, 0),
"location": "View3D > Sidebar > Create Tab",
"description": "Another Noise Tool: Landscape and Displace",
"warning": "",
"wiki_url": "https://docs.blender.org/manual/en/dev/addons/"
"add_mesh/ant_landscape.html",
"category": "Add Mesh",
}
if "bpy" in locals():
import importlib
importlib.reload(add_mesh_ant_landscape)
importlib.reload(mesh_ant_displace)
importlib.reload(ant_functions)
importlib.reload(ant_noise)
else:
from ant_landscape import add_mesh_ant_landscape
from ant_landscape import mesh_ant_displace
from ant_landscape import ant_functions
from ant_landscape import ant_noise
import bpy
from bpy.props import (
BoolProperty,
FloatProperty,
IntProperty,
StringProperty,
PointerProperty,
EnumProperty,
)
from .ant_functions import (
draw_ant_refresh,
draw_ant_main,
draw_ant_noise,
draw_ant_displace,
)
# ------------------------------------------------------------
# Menu's and panels
def menu_func_eroder(self, context):
ob = bpy.context.active_object
if ob and (ob.ant_landscape.keys() and not ob.ant_landscape['sphere_mesh']):
self.layout.operator('mesh.eroder', text="Landscape Eroder", icon='SMOOTHCURVE')
def menu_func_landscape(self, context):
layout = self.layout
layout.separator()
self.layout.operator('mesh.landscape_add', text="Landscape", icon="RNDCURVE")
# Landscape Add Panel
class AntLandscapeAddPanel(bpy.types.Panel):
bl_category = "Create"
bl_label = "Landscape"
bl_idname = "ANTLANDSCAPE_PT_add"
bl_space_type = "VIEW_3D"
bl_region_type = "UI"
bl_context = "objectmode"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
col = self.layout.column()
col.operator('mesh.landscape_add', text="Landscape", icon="RNDCURVE")
# Landscape Tools:
class AntLandscapeToolsPanel(bpy.types.Panel):
bl_category = "Create"
bl_label = "Landscape Tools"
bl_idname = "ANTLANDSCAPE_PT_tools"
bl_space_type = "VIEW_3D"
bl_region_type = "UI"
bl_context = "objectmode"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
ob = bpy.context.active_object
return (ob and ob.type == 'MESH')
def draw(self, context):
layout = self.layout
ob = context.active_object
col = layout.column()
col.operator('mesh.ant_displace', text="Mesh Displace", icon="RNDCURVE")
col.operator('mesh.ant_slope_map', icon='GROUP_VERTEX')
if ob.ant_landscape.keys() and not ob.ant_landscape['sphere_mesh']:
col.operator('mesh.eroder', text="Landscape Eroder", icon='SMOOTHCURVE')
# Landscape Main Settings
class AntMainSettingsPanel(bpy.types.Panel):
bl_category = "Create"
bl_label = "Landscape Main"
bl_idname = "ANTLANDSCAPE_PT_main"
bl_space_type = "VIEW_3D"
bl_region_type = "UI"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
ob = bpy.context.active_object
return ob.ant_landscape.keys() if ob else False
def draw(self, context):
layout = self.layout
scene = context.scene
ob = bpy.context.active_object
ant = ob.ant_landscape
box = layout.box()
col = box.column(align=False)
col.scale_y = 1.5
col.operator('mesh.ant_landscape_regenerate', text="Regenerate", icon="LOOP_FORWARDS")
row = box.row(align=True)
split = row.split(align=True)
split.prop(ant, "smooth_mesh", toggle=True, text="Smooth", icon='SHADING_SOLID')
split.prop(ant, "tri_face", toggle=True, text="Triangulate", icon='MESH_DATA')
if ant.sphere_mesh:
split.prop(ant, "remove_double", toggle=True, text="Remove Doubles", icon='MESH_DATA')
box.prop(ant, "ant_terrain_name")
box.prop_search(ant, "land_material", bpy.data, "materials")
col = box.column(align=True)
col.prop(ant, "subdivision_x")
col.prop(ant, "subdivision_y")
col = box.column(align=True)
if ant.sphere_mesh:
col.prop(ant, "mesh_size")
else:
col.prop(ant, "mesh_size_x")
col.prop(ant, "mesh_size_y")
# Landscape Noise Settings
class AntNoiseSettingsPanel(bpy.types.Panel):
bl_category = "Create"
bl_label = "Landscape Noise"
bl_idname = "ANTLANDSCAPE_PT_noise"
bl_space_type = "VIEW_3D"
bl_region_type = "UI"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
ob = bpy.context.active_object
return ob.ant_landscape.keys() if ob else False
def draw(self, context):
layout = self.layout
scene = context.scene
ob = bpy.context.active_object
ant = ob.ant_landscape
box = layout.box()
col = box.column(align=True)
col.scale_y = 1.5
if ant.sphere_mesh:
col.operator('mesh.ant_landscape_regenerate', text="Regenerate", icon="LOOP_FORWARDS")
else:
col.operator('mesh.ant_landscape_refresh', text="Refresh", icon="FILE_REFRESH")
box.prop(ant, "noise_type")
if ant.noise_type == "blender_texture":
box.prop_search(ant, "texture_block", bpy.data, "textures")
else:
box.prop(ant, "basis_type")
col = box.column(align=True)
col.prop(ant, "random_seed")
col = box.column(align=True)
col.prop(ant, "noise_offset_x")
col.prop(ant, "noise_offset_y")
if ant.sphere_mesh:
col.prop(ant, "noise_offset_z")
col.prop(ant, "noise_size_x")
col.prop(ant, "noise_size_y")
if ant.sphere_mesh:
col.prop(ant, "noise_size_z")
col = box.column(align=True)
col.prop(ant, "noise_size")
col = box.column(align=True)
if ant.noise_type == "multi_fractal":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
elif ant.noise_type == "ridged_multi_fractal":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "gain")
elif ant.noise_type == "hybrid_multi_fractal":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "gain")
elif ant.noise_type == "hetero_terrain":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
elif ant.noise_type == "fractal":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
elif ant.noise_type == "turbulence_vector":
col.prop(ant, "noise_depth")
col.prop(ant, "amplitude")
col.prop(ant, "frequency")
col.separator()
row = col.row(align=True)
row.prop(ant, "hard_noise", expand=True)
elif ant.noise_type == "variable_lacunarity":
box.prop(ant, "vl_basis_type")
box.prop(ant, "distortion")
elif ant.noise_type == "marble_noise":
box.prop(ant, "marble_shape")
box.prop(ant, "marble_bias")
box.prop(ant, "marble_sharp")
col = box.column(align=True)
col.prop(ant, "distortion")
col.prop(ant, "noise_depth")
col.separator()
row = col.row(align=True)
row.prop(ant, "hard_noise", expand=True)
elif ant.noise_type == "shattered_hterrain":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "distortion")
elif ant.noise_type == "strata_hterrain":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "distortion", text="Strata")
elif ant.noise_type == "ant_turbulence":
col.prop(ant, "noise_depth")
col.prop(ant, "amplitude")
col.prop(ant, "frequency")
col.prop(ant, "distortion")
col.separator()
row = col.row(align=True)
row.prop(ant, "hard_noise", expand=True)
elif ant.noise_type == "vl_noise_turbulence":
col.prop(ant, "noise_depth")
col.prop(ant, "amplitude")
col.prop(ant, "frequency")
col.prop(ant, "distortion")
col.separator()
box.prop(ant, "vl_basis_type")
col.separator()
row = col.row(align=True)
row.prop(ant, "hard_noise", expand=True)
elif ant.noise_type == "vl_hTerrain":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "distortion")
col.separator()
box.prop(ant, "vl_basis_type")
elif ant.noise_type == "distorted_heteroTerrain":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "distortion")
col.separator()
col.prop(ant, "vl_basis_type")
elif ant.noise_type == "double_multiFractal":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "offset")
col.prop(ant, "gain")
col.separator()
box.prop(ant, "vl_basis_type")
elif ant.noise_type == "rocks_noise":
col.prop(ant, "noise_depth")
col.prop(ant, "distortion")
col.separator()
row = col.row(align=True)
row.prop(ant, "hard_noise", expand=True)
elif ant.noise_type == "slick_rock":
col.prop(ant, "noise_depth")
col.prop(ant, "dimension")
col.prop(ant, "lacunarity")
col.prop(ant, "gain")
col.prop(ant, "offset")
col.prop(ant, "distortion")
col.separator()
box.prop(ant, "vl_basis_type")
elif ant.noise_type == "planet_noise":
col.prop(ant, "noise_depth")
col.separator()
row = col.row(align=True)
row.prop(ant, "hard_noise", expand=True)
# Effects mix
col = box.column(align=False)
box.prop(ant, "fx_type")
if ant.fx_type != "0":
if int(ant.fx_type) <= 12:
box.prop(ant, "fx_bias")
box.prop(ant, "fx_mix_mode")
col = box.column(align=True)
col.prop(ant, "fx_mixfactor")
col = box.column(align=True)
col.prop(ant, "fx_loc_x")
col.prop(ant, "fx_loc_y")
col.prop(ant, "fx_size")
col = box.column(align=True)
col.prop(ant, "fx_depth")
if ant.fx_depth != 0:
col.prop(ant, "fx_frequency")
col.prop(ant, "fx_amplitude")
col.prop(ant, "fx_turb")
col = box.column(align=True)
row = col.row(align=True).split(factor=0.92, align=True)
row.prop(ant, "fx_height")
row.prop(ant, "fx_invert", toggle=True, text="", icon='ARROW_LEFTRIGHT')
col.prop(ant, "fx_offset")
# Landscape Displace Settings
class AntDisplaceSettingsPanel(bpy.types.Panel):
bl_category = "Create"
bl_label = "Landscape Displace"
bl_idname = "ANTLANDSCAPE_PT_disp"
bl_space_type = "VIEW_3D"
bl_region_type = "UI"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
ob = bpy.context.active_object
return ob.ant_landscape.keys() if ob else False
def draw(self, context):
layout = self.layout
scene = context.scene
ob = bpy.context.active_object
ant = ob.ant_landscape
box = layout.box()
col = box.column(align=True)
col.scale_y = 1.5
if ant.sphere_mesh:
col.operator('mesh.ant_landscape_regenerate', text="Regenerate", icon="LOOP_FORWARDS")
else:
col.operator('mesh.ant_landscape_refresh', text="Refresh", icon="FILE_REFRESH")
col = box.column(align=True)
row = col.row(align=True).split(factor=0.92, align=True)
row.prop(ant, "height")
row.prop(ant, "height_invert", toggle=True, text="", icon='ARROW_LEFTRIGHT')
col.prop(ant, "height_offset")
col.prop(ant, "maximum")
col.prop(ant, "minimum")
if not ant.sphere_mesh:
col = box.column()
col.prop(ant, "edge_falloff")
if ant.edge_falloff != "0":
col = box.column(align=True)
col.prop(ant, "edge_level")
if ant.edge_falloff in ["2", "3"]:
col.prop(ant, "falloff_x")
if ant.edge_falloff in ["1", "3"]:
col.prop(ant, "falloff_y")
col = box.column()
col.prop(ant, "strata_type")
if ant.strata_type != "0":
col = box.column()
col.prop(ant, "strata")
col = box.column()
col.prop_search(ant, "vert_group", ob, "vertex_groups")
# ------------------------------------------------------------
# Properties group
class AntLandscapePropertiesGroup(bpy.types.PropertyGroup):
ant_terrain_name: StringProperty(
name="Name",
default="Landscape"
)
land_material: StringProperty(
name='Material',
default="",
description="Terrain material"
)
water_material: StringProperty(
name='Material',
default="",
description="Water plane material"
)
texture_block: StringProperty(
name="Texture",
default=""
)
at_cursor: BoolProperty(
name="Cursor",
default=True,
description="Place at cursor location",
)
smooth_mesh: BoolProperty(
name="Smooth",
default=True,
description="Shade smooth"
)
tri_face: BoolProperty(
name="Triangulate",
default=False,
description="Triangulate faces"
)
sphere_mesh: BoolProperty(
name="Sphere",
default=False,
description="Generate uv sphere - remove doubles when ready"
)
subdivision_x: IntProperty(
name="Subdivisions X",
default=128,
min=4,
max=6400,
description="Mesh X subdivisions"
)
subdivision_y: IntProperty(
default=128,
name="Subdivisions Y",
min=4,
max=6400,
description="Mesh Y subdivisions"
)
mesh_size: FloatProperty(
default=2.0,
name="Mesh Size",
min=0.01,
max=100000.0,
description="Mesh size"
)
mesh_size_x: FloatProperty(
default=2.0,
name="Mesh Size X",
min=0.01,
description="Mesh x size"
)
mesh_size_y: FloatProperty(
name="Mesh Size Y",
default=2.0,
min=0.01,
description="Mesh y size"
)
random_seed: IntProperty(
name="Random Seed",
default=0,
min=0,
description="Randomize noise origin"
)
noise_offset_x: FloatProperty(
name="Offset X",
default=0.0,
description="Noise X Offset"
)
noise_offset_y: FloatProperty(
name="Offset Y",
default=0.0,
description="Noise Y Offset"
)
noise_offset_z: FloatProperty(
name="Offset Z",
default=0.0,
description="Noise Z Offset"
)
noise_size_x: FloatProperty(
default=1.0,
name="Size X",
min=0.01,
max=1000.0,
description="Noise x size"
)
noise_size_y: FloatProperty(
name="Size Y",
default=1.0,
min=0.01,
max=1000.0,
description="Noise y size"
)
noise_size_z: FloatProperty(
name="Size Z",
default=1.0,
min=0.01,
max=1000.0,
description="Noise Z size"
)
noise_size: FloatProperty(
name="Noise Size",
default=1.0,
min=0.01,
max=1000.0,
description="Noise size"
)
noise_type: EnumProperty(
name="Noise Type",
default='hetero_terrain',
description="Noise type",
items = [
('multi_fractal', "Multi Fractal", "Blender: Multi Fractal algorithm", 0),
('ridged_multi_fractal', "Ridged MFractal", "Blender: Ridged Multi Fractal", 1),
('hybrid_multi_fractal', "Hybrid MFractal", "Blender: Hybrid Multi Fractal", 2),
('hetero_terrain', "Hetero Terrain", "Blender: Hetero Terrain", 3),
('fractal', "fBm Fractal", "Blender: fBm - Fractional Browninian motion", 4),
('turbulence_vector', "Turbulence", "Blender: Turbulence Vector", 5),
('variable_lacunarity', "Distorted Noise", "Blender: Distorted Noise", 6),
('marble_noise', "Marble", "A.N.T.: Marble Noise", 7),
('shattered_hterrain', "Shattered hTerrain", "A.N.T.: Shattered hTerrain", 8),
('strata_hterrain', "Strata hTerrain", "A.N.T: Strata hTerrain", 9),
('ant_turbulence', "Another Noise", "A.N.T: Turbulence variation", 10),
('vl_noise_turbulence', "vlNoise turbulence", "A.N.T: Real vlNoise turbulence", 11),
('vl_hTerrain', "vlNoise hTerrain", "A.N.T: vlNoise hTerrain", 12),
('distorted_heteroTerrain', "Distorted hTerrain", "A.N.T distorted hTerrain", 13),
('double_multiFractal', "Double MultiFractal", "A.N.T: double multiFractal", 14),
('rocks_noise', "Noise Rocks", "A.N.T: turbulence variation", 15),
('slick_rock', "Slick Rock", "A.N.T: slick rock", 16),
('planet_noise', "Planet Noise", "Planet Noise by: Farsthary", 17),
('blender_texture', "Blender Texture - Texture Nodes", "Blender texture data block", 18)]
)
basis_type: EnumProperty(
name="Noise Basis",
default=ant_noise.noise_basis_default,
description="Noise basis algorithms",
items = ant_noise.noise_basis
)
vl_basis_type: EnumProperty(
name="vlNoise Basis",
default=ant_noise.noise_basis_default,
description="VLNoise basis algorithms",
items = ant_noise.noise_basis
)
distortion: FloatProperty(
name="Distortion",
default=1.0,
min=0.01,
max=100.0,
description="Distortion amount"
)
hard_noise: EnumProperty(
name="Soft Hard",
default="0",
description="Soft Noise, Hard noise",
items = [
("0", "Soft", "Soft Noise", 0),
("1", "Hard", "Hard noise", 1)]
)
noise_depth: IntProperty(
name="Depth",
default=8,
min=0,
max=16,
description="Noise Depth - number of frequencies in the fBm"
)
amplitude: FloatProperty(
name="Amp",
default=0.5,
min=0.01,
max=1.0,
description="Amplitude"
)
frequency: FloatProperty(
name="Freq",
default=2.0,
min=0.01,
max=5.0,
description="Frequency"
)
dimension: FloatProperty(
name="Dimension",
default=1.0,
min=0.01,
max=2.0,
description="H - fractal dimension of the roughest areas"
)
lacunarity: FloatProperty(
name="Lacunarity",
min=0.01,
max=6.0,
default=2.0,
description="Lacunarity - gap between successive frequencies"
)
offset: FloatProperty(
name="Offset",
default=1.0,
min=0.01,
max=6.0,
description="Offset - raises the terrain from sea level"
)
gain: FloatProperty(
name="Gain",
default=1.0,
min=0.01,
max=6.0,
description="Gain - scale factor"
)
marble_bias: EnumProperty(
name="Bias",
default="0",
description="Marble bias",
items = [
("0", "Sin", "Sin", 0),
("1", "Cos", "Cos", 1),
("2", "Tri", "Tri", 2),
("3", "Saw", "Saw", 3)]
)
marble_sharp: EnumProperty(
name="Sharp",
default="0",
description="Marble sharpness",
items = [
("0", "Soft", "Soft", 0),
("1", "Sharp", "Sharp", 1),
("2", "Sharper", "Sharper", 2),
("3", "Soft inv.", "Soft", 3),
("4", "Sharp inv.", "Sharp", 4),
("5", "Sharper inv.", "Sharper", 5)]
)
marble_shape: EnumProperty(
name="Shape",
default="0",
description="Marble shape",
items= [
("0", "Default", "Default", 0),
("1", "Ring", "Ring", 1),
("2", "Swirl", "Swirl", 2),
("3", "Bump", "Bump", 3),
("4", "Wave", "Wave", 4),
("5", "Z", "Z", 5),
("6", "Y", "Y", 6),
("7", "X", "X", 7)]
)
height: FloatProperty(
name="Height",
default=0.5,
min=-10000.0,
max=10000.0,
description="Noise intensity scale"
)
height_invert: BoolProperty(
name="Invert",
default=False,
description="Height invert",
)
height_offset: FloatProperty(
name="Offset",
default=0.0,
min=-10000.0,
max=10000.0,
description="Height offset"
)
fx_mixfactor: FloatProperty(
name="Mix Factor",
default=0.0,
min=-1.0,
max=1.0,
description="Effect mix factor: -1.0 = Noise, +1.0 = Effect"
)
fx_mix_mode: EnumProperty(
name="Effect Mix",
default="0",
description="Effect mix mode",
items = [
("0", "Mix", "Mix", 0),
("1", "Add", "Add", 1),
("2", "Sub", "Subtract", 2),
("3", "Mul", "Multiply", 3),
("4", "Abs", "Absolute", 4),
("5", "Scr", "Screen", 5),
("6", "Mod", "Modulo", 6),
("7", "Min", "Minimum", 7),
("8", "Max", "Maximum", 8)
]
)
fx_type: EnumProperty(
name="Effect Type",
default="0",
description="Effect type",
items = [
("0", "None", "No effect", 0),
("1", "Gradient", "Gradient", 1),
("2", "Waves", "Waves - Bumps", 2),
("3", "Zigzag", "Zigzag", 3),
("4", "Wavy", "Wavy", 4),
("5", "Bump", "Bump", 5),
("6", "Dots", "Dots", 6),
("7", "Rings", "Rings", 7),
("8", "Spiral", "Spiral", 8),
("9", "Square", "Square", 9),
("10", "Blocks", "Blocks", 10),
("11", "Grid", "Grid", 11),
("12", "Tech", "Tech", 12),
("13", "Crackle", "Crackle", 13),
("14", "Cracks", "Cracks", 14),
("15", "Rock", "Rock noise", 15),
("16", "Lunar", "Craters", 16),
("17", "Cosine", "Cosine", 17),
("18", "Spikey", "Spikey", 18),
("19", "Stone", "Stone", 19),
("20", "Flat Turb", "Flat turbulence", 20),
("21", "Flat Voronoi", "Flat voronoi", 21)
]
)
fx_bias: EnumProperty(
name="Effect Bias",
default="0",
description="Effect bias type",
items = [
("0", "Sin", "Sin", 0),
("1", "Cos", "Cos", 1),
("2", "Tri", "Tri", 2),
("3", "Saw", "Saw", 3),
("4", "None", "None", 4)]
)
fx_turb: FloatProperty(
name="Distortion",
default=0.0,
min=0.0,
max=1000.0,
description="Effect turbulence distortion"
)
fx_depth: IntProperty(
name="Depth",
default=0,
min=0,
max=16,
description="Effect depth - number of frequencies"
)
fx_amplitude: FloatProperty(
name="Amp",
default=0.5,
min=0.01,
max=1.0,
description="Amplitude"
)
fx_frequency: FloatProperty(
name="Freq",
default=2.0,
min=0.01,
max=5.0,
description="Frequency"
)
fx_size: FloatProperty(
name="Effect Size",
default=1.0,
min=0.01,
max=1000.0,
description="Effect size"
)
fx_loc_x: FloatProperty(
name="Offset X",
default=0.0,
description="Effect x offset"
)
fx_loc_y: FloatProperty(
name="Offset Y",
default=0.0,
description="Effect y offset"
)
fx_height: FloatProperty(
name="Intensity",
default=1.0,
min=-1000.0,
max=1000.0,
description="Effect intensity scale"
)
fx_invert: BoolProperty(
name="Invert",
default=False,
description="Effect invert"
)
fx_offset: FloatProperty(
name="Offset",
default=0.0,
min=-1000.0,
max=1000.0,
description="Effect height offset"
)
edge_falloff: EnumProperty(
name="Falloff",
default="3",
description="Flatten edges",
items = [
("0", "None", "None", 0),
("1", "Y", "Y Falloff", 1),
("2", "X", "X Falloff", 2),
("3", "X Y", "X Y Falloff", 3)]
)
falloff_x: FloatProperty(
name="Falloff X",
default=4.0,
min=0.1,
max=100.0,
description="Falloff x scale"
)
falloff_y: FloatProperty(
name="Falloff Y",
default=4.0,
min=0.1,
max=100.0,
description="Falloff y scale"
)
edge_level: FloatProperty(
name="Edge Level",
default=0.0,
min=-10000.0,
max=10000.0,
description="Edge level, sealevel offset"
)
maximum: FloatProperty(
name="Maximum",
default=1.0,
min=-10000.0,
max=10000.0,
description="Maximum, flattens terrain at plateau level"
)
minimum: FloatProperty(
name="Minimum",
default=-1.0,
min=-10000.0,
max=10000.0,
description="Minimum, flattens terrain at seabed level"
)
vert_group: StringProperty(
name="Vertex Group",
default=""
)
strata: FloatProperty(
name="Amount",
default=5.0,
min=0.01,
max=1000.0,
description="Strata layers / terraces"
)
strata_type: EnumProperty(
name="Strata",
default="0",
description="Strata types",
items = [
("0", "None", "No strata", 0),
("1", "Smooth", "Smooth transitions", 1),
("2", "Sharp Sub", "Sharp subtract transitions", 2),
("3", "Sharp Add", "Sharp add transitions", 3),
("4", "Quantize", "Quantize", 4),
("5", "Quantize Mix", "Quantize mixed", 5)]
)
water_plane: BoolProperty(
name="Water Plane",
default=False,
description="Add water plane"
)
water_level: FloatProperty(
name="Level",
default=0.01,
min=-10000.0,
max=10000.0,
description="Water level"
)
remove_double: BoolProperty(
name="Remove Doubles",
default=False,
description="Remove doubles"
)
refresh: BoolProperty(
name="Refresh",
default=False,
description="Refresh"
)
auto_refresh: BoolProperty(
name="Auto",
default=True,
description="Automatic refresh"
)
# ------------------------------------------------------------
# Register:
classes = (
AntLandscapeAddPanel,
AntLandscapeToolsPanel,
AntMainSettingsPanel,
AntNoiseSettingsPanel,
AntDisplaceSettingsPanel,
AntLandscapePropertiesGroup,
add_mesh_ant_landscape.AntAddLandscape,
mesh_ant_displace.AntMeshDisplace,
ant_functions.AntLandscapeRefresh,
ant_functions.AntLandscapeRegenerate,
ant_functions.AntVgSlopeMap,
ant_functions.Eroder,
)
def register():
for cls in classes:
bpy.utils.register_class(cls)
bpy.types.VIEW3D_MT_mesh_add.append(menu_func_landscape)
bpy.types.Object.ant_landscape = PointerProperty(type=AntLandscapePropertiesGroup, name="ANT_Landscape", description="Landscape properties")
bpy.types.VIEW3D_MT_paint_weight.append(menu_func_eroder)
def unregister():
for cls in reversed(classes):
bpy.utils.unregister_class(cls)
bpy.types.VIEW3D_MT_mesh_add.remove(menu_func_landscape)
bpy.types.VIEW3D_MT_paint_weight.remove(menu_func_eroder)
if __name__ == "__main__":
register()
| [
"[email protected]"
]
| |
388430234a19c8d3bb7df514027066b68cf8fc68 | 3507fdc5012e55f6a784d70a7ad6da11224e5bfe | /caesar_cipher.py | 2fa71c8cece652f7e97971c772561d702a65ad0c | []
| no_license | jonasthiel/100-days-of-code-python | 640be865bdba10cca17ba72c4923cf9961ed570c | 94ad366d10ed862c6c699ae1f242bd462f2ba597 | refs/heads/main | 2023-04-03T11:53:16.993098 | 2021-04-09T14:20:41 | 2021-04-09T14:20:41 | 330,404,825 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,924 | py | from os import system
alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
logo = """
,adPPYba, ,adPPYYba, ,adPPYba, ,adPPYba, ,adPPYYba, 8b,dPPYba,
a8" "" "" `Y8 a8P_____88 I8[ "" "" `Y8 88P' "Y8
8b ,adPPPPP88 8PP""""""" `"Y8ba, ,adPPPPP88 88
"8a, ,aa 88, ,88 "8b, ,aa aa ]8I 88, ,88 88
`"Ybbd8"' `"8bbdP"Y8 `"Ybbd8"' `"YbbdP"' `"8bbdP"Y8 88
88 88
"" 88
88
,adPPYba, 88 8b,dPPYba, 88,dPPYba, ,adPPYba, 8b,dPPYba,
a8" "" 88 88P' "8a 88P' "8a a8P_____88 88P' "Y8
8b 88 88 d8 88 88 8PP""""""" 88
"8a, ,aa 88 88b, ,a8" 88 88 "8b, ,aa 88
`"Ybbd8"' 88 88`YbbdP"' 88 88 `"Ybbd8"' 88
88
88
"""
end = False
while not end:
print(logo)
direction = input("Type 'encode' to encrypt, type 'decode' to decrypt:\n")
text = input("Type your message:\n").lower()
shift = int(input("Type the shift number:\n"))
def caesar(direction, text, shift):
output_text = ""
if shift > 26:
shift %= 26
if direction == "encode":
for i in text:
if i in alphabet:
index = alphabet.index(i)
if index + shift > 25:
output_text += alphabet[index + shift - 26]
else:
output_text += alphabet[index + shift]
else:
output_text += i
elif direction == "decode":
for i in text:
if i in alphabet:
index = alphabet.index(i)
if index - shift < 0:
output_text += alphabet[index - shift + 26]
else:
output_text += alphabet[index - shift]
else:
output_text += i
print(f"The {direction}d text is {output_text}")
caesar(direction, text, shift)
if input("Type 'yes' if you want to go again. Otherwise type 'no'.\n").lower() == "no":
end = True
else:
system('clear') | [
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]
| |
70425764af9a4af7b00d9a87514deba1e28c8fda | 722af8e6fa81960a6119c2e45ba6795771bad595 | /agents/migrations/0043_veri.py | a9fbb71d81b2b140fcb68e8c4a02de3f0a744641 | []
| no_license | witty-technologies-empowerment/pmc | 85d21fa3c360d40adeec7ca93792b5bc68c258e5 | 201bee60197240eec911637e136cf14bc5814eec | refs/heads/master | 2023-05-27T12:37:48.894933 | 2021-06-13T04:34:57 | 2021-06-13T04:34:57 | 376,439,472 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 790 | py | # Generated by Django 2.2.6 on 2020-02-04 15:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('agents', '0042_auto_20191022_0303'),
]
operations = [
migrations.CreateModel(
name='Veri',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('user', models.CharField(max_length=20)),
('rcode', models.CharField(max_length=100)),
('count', models.CharField(default=1, max_length=2)),
('created', models.DateTimeField(auto_now_add=True)),
],
options={
'ordering': ['-created'],
},
),
]
| [
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]
| |
4eaeed1619b3dcb639ee308019b90729a7124038 | bfc1b107b2ce8c664b17be7d96b93bf69aaa8665 | /lab_10_zadania/07.py | aea5b665b965041edf2d9d5e29141733e6f9acc7 | []
| no_license | bulwan/wizualizacja_danych | db16c97da765646a71a8a794030f8014022cbc19 | e305914105f42d22d42deb4e10a09b181534254f | refs/heads/main | 2023-05-01T07:16:23.954859 | 2021-05-26T11:59:18 | 2021-05-26T11:59:18 | 346,389,677 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 685 | py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plik = pd.ExcelFile('imiona.xlsx')
imiona = pd.read_excel(plik,'Arkusz1')
kobiet=imiona[(imiona.Plec=='K')]
chlopcy=imiona[(imiona.Plec=='M')]
wynik_dziewczynki = kobiet.groupby(['Rok']).sum()
wynik_chlopcy = chlopcy.groupby(['Rok']).sum()
wynik_dziewczynki=wynik_dziewczynki.reset_index()
wynik_chlopcy=wynik_chlopcy.reset_index()
plt.xticks(np.arange(2000, 2018, 1))
plt.bar(wynik_dziewczynki.Rok,wynik_dziewczynki.Liczba, label="dziewczynki", color='pink')
plt.bar(wynik_chlopcy.Rok,wynik_chlopcy.Liczba, label="chlopcy", color='blue', bottom=wynik_dziewczynki.Liczba)
plt.legend()
plt.show() | [
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]
| |
6b89a26de0e8969a9ee0367e605d705f6c3df52f | c96199d8b0502fb590094d99b58df4aecadff932 | /src/rysia/utils/monitor.py | 773b97c1268666dc368fdb06535b3070eb4775a2 | [
"Apache-2.0"
]
| permissive | vdeuschle/rysia | 1f8c6e8e6305d7c444107bbe5045f05f7e90ca22 | c8c5adc2c770424b3a328a936f23a80a38c9f0f2 | refs/heads/master | 2020-06-26T01:37:59.823644 | 2019-08-25T23:12:30 | 2019-09-08T16:53:12 | 199,485,229 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,647 | py | # Copyright 2018 Vincent Deuschle. 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.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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 time
from typing import Optional
import psutil as ps
import boto3
from py3nvml.py3nvml import *
from ..utils.misc import create_csv_string
class Monitor(threading.Thread):
def __init__(self,
filename: str,
aws: bool,
bucketname: str) -> None:
super(Monitor, self).__init__()
self.running = False
self.aws = aws
if self.aws:
self.s3_bucket = boto3.resource('s3').Bucket(bucketname)
self.filename = filename
def __enter__(self) -> None:
self.start()
def __exit__(self, *args) -> None:
self.running = False
self.join()
def run(self) -> None:
self.running = True
self.acc = []
try:
self.initialize()
while self.running:
time.sleep(1)
self.execute()
except (NVMLError, AttributeError, OSError) as e:
self.acc += [[f'Monitor {self.__class__.__name__} failed to execute']]
self.acc += [[str(e)]]
finally:
self.finalize()
def initialize(self) -> None:
raise NotImplementedError
def execute(self) -> None:
raise NotImplementedError
def finalize(self) -> None:
csv_string = create_csv_string(self.acc)
if self.aws:
self.s3_bucket.put_object(Key=self.filename, Body=csv_string)
else:
with open(self.filename, "w") as output:
output.write(csv_string)
class CPUMonitor(Monitor):
def __init__(self,
filename: str,
aws: bool = False,
bucketname: Optional[str] = None) -> None:
super(CPUMonitor, self).__init__(filename, aws, bucketname)
def initialize(self) -> None:
cpu_count = ps.cpu_count()
percent_label = [f'cpu{i}_percent' for i in range(cpu_count)]
self.acc += [['timestamp',
*percent_label,
'user_time',
'system_time']]
def execute(self) -> None:
timestamp = time.time()
cpu_percent = ps.cpu_percent(percpu=True)
cpu_times = ps.cpu_times()
self.acc += [[timestamp,
*cpu_percent,
cpu_times[0],
cpu_times[1]]]
class MemoryMonitor(Monitor):
def __init__(self,
filename: str,
aws: bool = False,
bucketname: Optional[str] = None) -> None:
super(MemoryMonitor, self).__init__(filename, aws, bucketname)
def initialize(self) -> None:
self.acc += [['timestamp',
'total_memory',
'available_memory',
'used_memory']]
def execute(self) -> None:
timestamp = time.time()
memory_info = ps.virtual_memory()
self.acc += [[timestamp,
memory_info.total,
memory_info.available,
memory_info.percent]]
class DiskIOMonitor(Monitor):
def __init__(self,
filename: str,
aws: bool = False,
bucketname: Optional[str] = None) -> None:
super(DiskIOMonitor, self).__init__(filename, aws, bucketname)
def initialize(self) -> None:
self.acc += [['timestamp',
'read_count',
'write_count',
'read_bytes',
'write_bytes']]
def execute(self) -> None:
timestamp = time.time()
diskIO = ps.disk_io_counters()
self.acc += [[timestamp,
diskIO.read_count,
diskIO.write_count,
diskIO.read_bytes,
diskIO.write_bytes]]
class GPUMonitor(Monitor):
def __init__(self,
filename: str,
aws: bool = False,
bucketname: Optional[str] = None) -> None:
super(GPUMonitor, self).__init__(filename, aws, bucketname)
def initialize(self) -> None:
self.acc += [['timestamp',
'id',
'memory_used',
'memory_total',
'memory_util_rate',
'gpu_util_rate']]
nvmlInit()
self.deviceCount = nvmlDeviceGetCount()
def execute(self) -> None:
timestamp = time.time()
for id in range(self.deviceCount):
handle = nvmlDeviceGetHandleByIndex(id)
memory_info = nvmlDeviceGetMemoryInfo(handle)
gpu_info = nvmlDeviceGetUtilizationRates(handle)
memory_used = memory_info.used
memory_total = memory_info.total
memory_util_rate = gpu_info.memory
gpu_util_rate = gpu_info.gpu
self.acc += [[timestamp,
id,
memory_used,
memory_total,
memory_util_rate,
gpu_util_rate]]
| [
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]
| |
a8e4ef9b084180f0b52f64f6f6095abcc3c4c3d1 | 05fdaa1d1762399ce4d2fe04c3427e5ecf0e6588 | /ch10.statement/demo.py | 55af869bfb1b0c80af45197a337a3ced4edb0fd0 | []
| no_license | redice/learning | 598398802779a5f0e5b9ab2f47667f87d8850d55 | 1240bb6b8d709055236f3d9eb0ad247d1aa182aa | refs/heads/master | 2020-06-02T15:18:14.418812 | 2013-01-05T10:16:46 | 2013-01-05T10:16:46 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,674 | py | import os,sys
class MyDemo:
def callWhile(self):
while True:
reply = input('Enter text:')
if reply == 'stop':
break
elif not reply.isdigit():
print('Bad!' * 8)
else:
print(int(reply) ** 2)
print('Bye')
def callTryExcept(self):
while True:
reply = input('Enter text:')
if reply == 'stop':
break
try:
num = int(reply)
except:
print('Bad!' * 8)
else:
print(int(reply) ** 2)
print('Bye')
def callAssign(self):
for (a,b,c) in [(1,2,3), (4,5,6)]:
print("a = {0}, b = {1}, c = {2}".format(a,b,c))
for ((a,b), c) in [((1,2), 'aaa'), (('x', 'y'), 'good job')]:
print("a = {0}, b = {1}, c = {2}".format(a,b,c))
def callWhile(self):
L = [1, 2, 3, 4]
while L:
# front, L = L[0], L[1:]
front = L.pop(0)
print(front, L)
L = [4, 5, 6, 7]
while L:
front, *L = L
print(front, L)
def callListConcatenation(self):
L = M = [1, 2]
M += [3, 4]
print(L, M)
L = M = [1, 2]
M = M + [3, 4]
print(L, M)
def callPrint(self):
x = 'abc'
y = 99
z = ['1', 'cc']
print(x, y, z, sep=' => ', end = '...\n')
print(x, y, z, sep=' => ', end = '...\n', file=open('output/data.txt', 'w'))
def callWriteToFile(self):
sys.stdout = open('output/data.txt', 'a')
print('good', 'job', 'ya')
def callStdOut(self):
temp = sys.stdout
sys.stdout = open('output/data.txt', 'a')
print('good', 'job', '斌')
sys.stdout.close()
sys.stdout = temp
print('張', '宏', '斌')
if __name__ == '__main__':
app = MyDemo()
app.callStdOut() | [
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]
| |
9e4ad41b6887c4346466c3663aea990a64d2eca2 | b0938f37ccaa7bdc363ef2bce03c172e521682d4 | /Test_PR_curve.py | 7ed9a83eaf41a0e65f3e9393f9d6679ae6b762a3 | []
| no_license | neuhzhj2012/python-scripts | 1c40ae8c2a1c75398723ee62c62ebc1f48b7136b | 75050c0fa73e619f8823046d380cb15ba121d62b | refs/heads/master | 2021-06-12T20:17:03.258332 | 2021-04-14T09:50:57 | 2021-04-14T09:50:57 | 180,062,098 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,189 | py | #encoding: utf-8
'''
类别的准确率和召回率曲线
文件内容 name label score
'''
import argparse
import numpy as np
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
import matplotlib.pyplot as plt
def draw(y_true, y_scores):
'''
:param y_true: N*classes，N表示样本数，classes表示类别数，每行数据为one-hot向量
:param y_scores: N*classes,表示所有样本预测的置信度
:return:
'''
precision, recall, thresholds = precision_recall_curve(y_true.ravel(), y_scores.ravel())
average_precision = average_precision_score(y_true, y_scores,
average="micro") #平均准确率
precision = np.array(precision[:-1][::-1]) #降序排列
recall = np.array(recall[:-1][::-1]) #升序排列
thresholds = np.array(thresholds[::-1]) #降序排列
# np.array(precision)>0.99
print(f'precision_head: {precision[:-1][:15]}\n precision_tail: {precision[:-1][-15:]}\n recall_head: {recall[:-1][:15]}\n recall_tail: {recall[:-1][-15:]}\n thresholds_head: {thresholds[:15]}\n thresholds_tail: {thresholds[-15:]}')
for i in [0.99, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.6, 0.05, 0]:
if ((precision>i).tolist().count(1)==0):
print(f'conf: {i}\tP: 0\tR:0\tthresh:0')
continue
idx = precision.tolist().index(precision[precision>i][-1])
print(f'conf: {i}\tP: {precision[idx]}\tR:{recall[idx]}\tthresh:{thresholds[idx]}')
plt.figure()
plt.step(recall, precision, where='post')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title(
'Average precision score, micro-averaged over all classes: AP={0:0.2f}'
.format(average_precision))
plt.savefig('pr.jpg')
def getArgs():
args = argparse.ArgumentParser()
args.add_argument('-i', '--input', help='input img',
default='1.jpg')
return vars(args.parse_args())
if __name__=='__main__':
classes = ['ad_ocr', 'gun', 'knife', 'normal_ocr', 'normal_people', 'normal_thing', 'qr_code']
classes = ['4739', '0'] #二维码
classes = ['1', '0'] #涉政类
args = getArgs()
pred_rst_path = args['input']
buffs = open(pred_rst_path, 'r').readlines()
lines = [line.strip() for line in buffs]
print('nums: {}'.format(len(lines)))
y_gt = list()
y_score = list()
# #多分类情况
# for idx,line in enumerate(lines):
#
# _, label, confs = line.split('\t')
#
# one_hot = len(classes) * [0]
# one_hot[classes.index(label)] = 1
# y_gt.append(one_hot)
# y_score.append(eval(confs))
# # y_score.append(float(confs))
# # print(idx, y_gt, y_score)
#二分类情况
for idx,line in enumerate(lines):
_, label, confs = line.split('\t')
one_hot = 0
if classes[0]==label:
one_hot=1
y_gt.append(one_hot)
y_score.append(eval(confs))
# y_score.append(float(confs))
# print(idx, y_gt, y_score)
draw(np.array(y_gt), np.array(y_score))
| [
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]
| |
04080caf6c97351c69962daea7956dae2c3f7675 | c5c09eb267adca77a0e4cc0fc07f31564353f23d | /heterozygosity/effective_n_alleles.py | 964aba8d88bc5d8db30ceabfa12648d3ca272b0e | []
| no_license | redcurry/sgv_scripts | da31d85554bdf705d35fa53c1b444128fbdd1ccd | bed81a70b4c2902b49a439a2451a1303c4feaf93 | refs/heads/master | 2020-12-24T13:45:05.457886 | 2013-01-23T09:14:40 | 2013-01-23T09:14:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,080 | py | # Outputs the effective number of allele at every locus of the given population
from __future__ import division
import sys
def calc_allele_freqs(pop, locus):
allele_freqs = {}
for inst in alphabet:
allele_freqs[inst] = 0
for genotype in pop:
inst = genotype[locus]
allele_freqs[inst] += 1
# Calculate frequencies from counts
for inst in allele_freqs:
if allele_freqs[inst] > 0:
allele_freqs[inst] /= len(pop)
return allele_freqs
def calc_effective_n_alleles(allele_freqs):
# Calculate the sum of squares of allele frequencies
ss = sum([allele_freqs[inst] * allele_freqs[inst] for inst in alphabet])
return 1 / ss
alphabet = [chr(x) for x in range(ord('a'), ord('z') + 1)]
pop = []
for genotype in sys.stdin:
pop.append(genotype.strip())
genotype_length = len(pop[0])
for locus in range(genotype_length):
# Get allele frequencies as a dictionary, e.g., {a: 0.8, b: 0.2}
allele_freqs = calc_allele_freqs(pop, locus)
effective_n_alleles = calc_effective_n_alleles(allele_freqs)
print locus, effective_n_alleles
| [
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]
| |
6bc8e6bda70fb29b075f4f3c8c40b9a6b2656fcf | 9c6e63eb1796bbf4c37d93fca941fb67b4cd4741 | /trunk/scarlett/app.py | 7f7179015d2a9cefbdbe4557f2fd080029521298 | []
| no_license | BGCX261/zizw-svn-to-git | ffc6636d8e0d91b24f124ba3d16c61af10d7441c | c8d068af7a36396ce707dc035b15330c77b02f2a | refs/heads/master | 2016-09-05T13:11:22.053860 | 2015-08-25T15:51:45 | 2015-08-25T15:51:45 | 41,585,036 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,466 | py |
import logging
import webob
import wsgiref.handlers
import simplejson.encoder
import simplejson.decoder
from google.appengine.ext import db
from google.appengine.api import users
from scarlett import model
from scarlett import utils
jsonEncoder = simplejson.encoder.JSONEncoder()
jsonDecoder = simplejson.decoder.JSONDecoder()
def scarlett(environ, start_response):
#
# create request & response objects
#
request = webob.Request(environ)
response = webob.Response()
#
# create session object
#
session = Session(request)
# do job
channel = session.message["channel"]
if channel == "refresh":
if session.isAdmin:
response.body = shell % ("Scarlett-Admin", "scarlett.Admin")
elif session.user:
response.body = shell % ("Scarlett", "scarlett.Main")
else:
response.body = shell % ("Login", "scarlett.Login")
elif channel == "locateservice":
fullName = str(session.message["fullName"])
service = utils.my_import(fullName)
simpleName = fullName.split('.')[-1]
response.body = generateServiceStub(service, fullName, simpleName)
response.content_type = "text/plain"
response.charset = "UTF-8"
elif channel == "rmi":
fullName = str(session.message["serviceName"])
methodName = str(session.message["methodName"])
args = session.message["args"];
argList = ""
for i in range(len(args)):
argList += "args[%s], " % i
argList = argList[:-2]
service = utils.my_import(fullName)
outMessage = {
"result": eval("service."+methodName+"(session, "+argList+")")
}
if fullName == "scarlett.admin" and methodName == "login" and outMessage["result"]:
response.set_cookie("sid", userToSid(args[0]))
response.body = jsonEncoder.encode(outMessage)
response.content_type = "text/plain"
response.charset = "UTF-8"
elif channel == "admin":
user = users.get_current_user()
if not user:
response.body = users.create_login_url("/")
logging.info("admin: do login")
else:
response.body = "/"
logging.info("admin: do normal")
else:
response.body = "unknown channel: %s" % str(channel)
#
return response(environ, start_response)
#
# Tips:
# session.message
# session.message.channel
# session.isAdmin
# session.user
# session.user.alias
#
class Session():
def __init__(self, request):
#
# setting message
#
if request.method == "GET":
self.message = {"channel":"refresh"}
else:
self.message = jsonDecoder.decode(request.body)
#
# setting isAdmin & user
#
if users.is_current_user_admin():
self.isAdmin = True
self.user = None
elif "sid" not in request.cookies:
self.isAdmin = False
self.user = None
elif not request.cookies["sid"]:
self.isAdmin = False
self.user = None
else:
self.isAdmin = False
self.user = sidToUser(request.cookies["sid"])
def sidToUser(sid):
#
# TODO: a real sid should be used
#
return model.User.get(db.Key.from_path("User", "ID_"+sid, _app="scarlett"))
def userToSid(userName):
#
# TODO: a real sid should be used
#
return userName
def generateServiceStub(service, fullName, simpleName):
methodList= filter(lambda x : x[0:1]!= "_", dir(service))
stub = "var " + simpleName + " = function(){\n"
stub += "}\n\n"
for method in methodList:
stub += simpleName + ".prototype." + method + " = function() {\n"
stub += "\treturn jsloader.doRmi('%s', '%s', arguments);\n" % (fullName, method)
stub += "};\n"
return stub
def main():
wsgiref.handlers.CGIHandler().run(scarlett)
shell = """
<html>
<head>
<title>%s</title>
<script>
var App = null;
var app = null;
function init() {
App = jsloader.resolve("%s")
app = new App(document.body);
var welcome = document.getElementById("welcome");
document.body.removeChild(welcome);
}
function destroy() {
app.destroy();
}
</script>
</head>
<body scroll="no" style="overflow: hidden; margin: 0px; padding: 0px" onload="init()" onunload="destroy()">
<span id="welcome">Loading ...</span>
</body>
<script src="js/lang/JSLoader.js"></script>
</html>
"""
if __name__ == "__main__":
main()
| [
"[email protected]"
]
| |
43a37eb509684f7705f8cb48e4017f6a8127ebdf | 5d79a0f01ad7fa0ff0c745aa534b9c17e2af7f60 | /src/authentication/mailchimp/http.py | 07ea72e13336f8b7630c8caacab4b2a82be79aaa | [
"MIT"
]
| permissive | pykulytsky/freelance-service | ade7b009aa183ce198277bec1178dcf15dadd3e3 | 0f6e36093128ae3e63bd7dcf21959882eae7e683 | refs/heads/master | 2023-07-03T16:58:07.838904 | 2021-04-01T20:20:21 | 2021-04-01T20:20:21 | 329,963,217 | 0 | 1 | MIT | 2021-08-02T07:40:11 | 2021-01-15T16:21:17 | Python | UTF-8 | Python | false | false | 1,936 | py | from typing import Optional
from urllib.parse import urljoin
import requests
from django.conf import settings
from requests.auth import HTTPBasicAuth
class MailchimpHTTPException(BaseException):
pass
class MailChimpWrongResponse(MailchimpHTTPException):
pass
class MailChimpNotFound(MailchimpHTTPException):
pass
class MailchimpHTTP:
@property
def base_url(self) -> str:
dc = settings.MAILCHIMP_API_KEY.split('-')[-1]
return f'https://{dc}.api.mailchimp.com/3.0/'
def format_url(self, url: str) -> str:
return urljoin(self.base_url, url.lstrip('/'))
def request(self, url, method, payload: Optional[dict] = None, expected_status_code: int = 200):
requests_payload = dict()
if payload is not None:
requests_payload['json'] = payload
response = requests.request(
method=method,
url=self.format_url(url),
auth=HTTPBasicAuth('user', settings.MAILCHIMP_API_KEY),
**requests_payload,
)
if response.status_code == 404:
raise MailChimpNotFound(f'{response.status_code}: {self.get_json(response)}')
if response.status_code != expected_status_code:
raise MailChimpWrongResponse(f'{response.status_code}: {self.get_json(response)}')
return self.get_json(response)
def get(self, url: str, *args, **kwargs):
return self.request(url, method='GET', *args, **kwargs)
def post(self, url: str, payload: dict, *args, **kwargs):
return self.request(url, method='POST', payload=payload, *args, **kwargs)
def delete(self, url: str, *args, **kwargs):
return self.request(url, method='DELETE', expected_status_code=204, *args, **kwargs)
@staticmethod
def get_json(response):
if len(response.text):
return response.json()
| [
"[email protected]"
]
| |
d7f8ed59ac3b21468f0ededb25e03a9e01e8d86f | 760707dab4c6496bc578dc84a58ec7b84058ab78 | /manage.py | 4a533795180784eaa422ace96a07f199fa8783c0 | []
| no_license | gflexx/zoohubmall | e0b947ec459f4b5e44db1216da256561f5678021 | 1eb6ec228d55c8caefd4f03f601729f295aae41d | refs/heads/main | 2023-04-14T06:34:43.756173 | 2021-04-20T08:35:07 | 2021-04-20T08:35:07 | 358,905,388 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 630 | py | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'zoohubmall.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
f293b8d004bc0d456cf629a869be1a28ca086346 | 831a728160e36c452dfe2d84f4d610a13063bbbf | /venv/bin/easy_install-3.7 | 06a018f9d6f908ae8e2ac22c43b4bb37cb0f641e | []
| no_license | wrongserenity/economi | 9102ac2a23e64b6170c48b983b58c0ce9096f19d | b6df63e28f19f2f88d78c6a80620f2f8b6327695 | refs/heads/master | 2022-11-03T23:32:31.663204 | 2022-10-29T12:00:20 | 2022-10-29T12:00:20 | 153,648,229 | 1 | 0 | null | 2018-12-13T19:16:35 | 2018-10-18T15:40:51 | Python | UTF-8 | Python | false | false | 435 | 7 | #!/home/nick/proj/economi/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==39.1.0','console_scripts','easy_install-3.7'
__requires__ = 'setuptools==39.1.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==39.1.0', 'console_scripts', 'easy_install-3.7')()
)
| [
"[email protected]"
]
| |
def97356d610be85276a7f7aff23c352f8e67a64 | a37046fd0a42329967b735119e4fe16401b7e7fc | /nailezhuang/milk/MilkCommon.py | cb64759c37d2f15ad0bbf17b05aa1d75bd2fdad7 | []
| no_license | dengruquan/nailezhuang | 06aad63fdd83bd48c26c1aadaa52b5306a5a3fae | c799a92359724459583a389a31c52f75a45229b7 | refs/heads/master | 2021-05-07T06:59:21.738914 | 2017-11-27T02:51:54 | 2017-11-27T02:51:54 | 111,784,595 | 2 | 0 | null | 2017-11-27T03:25:05 | 2017-11-23T08:41:46 | Python | UTF-8 | Python | false | false | 6,474 | py | #-*- coding:utf8 -*-
import datetime
import sqlite3
from .models import FoodType, VIPInfo
time_format = "%Y-%m-%d %H:%M:%S"
statype_dict = {0:"每次喝奶数量", 1: "总共喝奶数量"}
dbname = "db.sqlite3"
sel_sql = "select foodname, sum(foodcnt), sum(foodmoney) from milk_foodrecord as fr join milk_foodtype as ft where fr.foodtype_id = ft.id and %s group by foodtype_id;"
vip_selper_sql = "select vipindex, vipname, viprecordcnt, viprecordtime from milk_viprecord as viprecord join milk_vipinfo as vipinfo where viprecord.viprecordname_id = vipinfo.vpiindex and %s ;"
vip_selsum_sql = "select vipindex, vipname, sum(viprecordcnt) from milk_viprecord as viprecord join milk_vipinfo as vipinfo where viprecord.viprecordname_id = vipinfo.vpiindex and %s group by viprecordname;;"
# vip_selper_sql = "select viprecordcnt from milk_viprecord as vipinfo where %s ;"
# vip_selsum_sql = "select viprecordcnt from milk_viprecord as vipinfo where %s ;"
def GetNowTime():
'''返回当前时间'''
tmp = datetime.datetime.now().strftime("%Y,%m,%d")
print("tmp", tmp)
return datetime.datetime(*eval(tmp))
def GetSomeDayAgo(day = 1):
'''返回指定天数前的时间，由day进行控制'''
return GetNowTime()- datetime.timedelta(days = day)
def GetStrfTime(timeobj):
'''返回指定的时间格式'''
return timeobj.strftime("{}".format(time_format))
def AsDateTime(GET_POST, name = "datetime"):
return datetime.datetime.strptime(GET_POST[name], "{}".format(time_format))
def GetFoodDict():
'''获取所有物品字典'''
foodtype = FoodType.objects.all()
food_dict = {0: "所有"} #{id:foodname}
for foodobj in foodtype:
food_dict[foodobj.id] = foodobj.foodname
return food_dict
def ConnectDB(dbname):
'''
连接到数据库
@parameter dbname: 数据库名
'''
return sqlite3.connect(dbname)
def MakeWhereSql(where_dict):
'''制作sql中where条件'''
where_list = []
if "foodid" in where_dict:
where_list.append("fr.foodtype_id = %d" % where_dict["foodid"])
where_list.append("foodtime >= '{}'".format(where_dict["start_time"]))
where_list.append("foodtime <= '{}'".format(where_dict["end_time"]))
return " and ".join(where_list)
def MakeVIPWhereSql(where_dict):
'''制作sql中where条件'''
where_list = []
if "vipindex" in where_dict:
where_list.append("vipinfo.vipindex = %s" % where_dict["vipindex"])
where_list.append("viprecordtime >= '{}'".format(where_dict["start_time"]))
where_list.append("viprecordtime <= '{}'".format(where_dict["end_time"]))
return " and ".join(where_list)
def GetStasticsResult(where_dict):
'''
获取指定条件下的销售记录
@parameter: where_dict: {"foodid": id, "start_time": time, "end_time": time}
@return : [[foodname, cnt, money]]
'''
con = ConnectDB(dbname)
# print(sel_sql % MakeWhereSql(where_dict))
with con as cur:
cur = cur.execute(sel_sql % MakeWhereSql(where_dict))
ret = cur.fetchall()
cur.close()
return ret
def MakeDataHtml(data_list):
'''
制作html页面内容
'''
html = [
'<!DOCTYPE html><html><head><title>销售统计</title><meta charset="UTF-8"></head><body><form>']
foodname = "物品名称"
foodcnt = "数量"
foodmoney = "金额"
sumname = "总销售额："
sum_money = 0
# html.append('<table border="1px" cellpadding = "20" cellspacing="50" style= "border-collapse:collapse"><thead><tr><th>{}</th><th>{}</th><th>{}</th>'.format(foodname, foodcnt, foodmoney))
if data_list :
for name, cnt, money in data_list:
sum_money += money
html.append("<font size='6'>%s%s</font>" % (sumname, sum_money))
html.append("<br/>")
html.append('<table border="1px" cellpadding = "20" cellspacing="50" style= "border-collapse:collapse"><thead><tr><th>{}</th><th>{}</th><th>{}</th>'.format(foodname, foodcnt, foodmoney))
for name, cnt, money in data_list:
html.append("<tr><td> %s </td><td> %s </td><td> %s </td></tr>" % (name, cnt, money))
print("sum_money", sum_money)
html.append("</thead></table>")
html.append("</form></body></html>")
return html
def MakeIndexHtml(index_list):
html = []
# for urlname, funname in index_list:
# html.append('<a href="{% url {} %}">{}</a>')
# html.append("</html>")
return html
def _f():pass
def GetVIPInfoDict():
'''获取月卡用户信息字典'''
vipinfo = VIPInfo.objects.all()
vip_dict = {0: "所有"} #{id:foodname}
for vipobj in vipinfo:
vip_dict[vipobj.id] = vipobj.vipindex
return vip_dict
def GetVIPStasticsResult(where_dict, typeid):
'''
获取指定条件下的月卡用户喝奶统计
@parameter: where_dict: {"vipid": vipid, "start_time": time, "end_time": time}
@parameter: typeid: 数据统计类型{0:"每次喝奶数量", 1: "总共喝奶数量"}
@return : [[foodname, cnt, money]]
'''
con = ConnectDB(dbname)
# print(sel_sql % MakeVIPWhereSql(where_dict))
sql = vip_selsum_sql if typeid else vip_selper_sql
with con as cur:
cur = cur.execute(sql % MakeVIPWhereSql(where_dict))
ret = cur.fetchall()
cur.close()
return ret
def MakeVIPDataHtml(data_list):
'''
制作html页面内容
'''
html = [
'<!DOCTYPE html><html><head><title>销售统计</title><meta charset="UTF-8"></head><body><form>']
foodname = "物品名称"
foodcnt = "数量"
foodmoney = "金额"
sumname = "总销售额："
sum_money = 0
# html.append('<table border="1px" cellpadding = "20" cellspacing="50" style= "border-collapse:collapse"><thead><tr><th>{}</th><th>{}</th><th>{}</th>'.format(foodname, foodcnt, foodmoney))
if data_list :
for name, cnt, money in data_list:
sum_money += money
html.append("<font size='6'>%s%s</font>" % (sumname, sum_money))
html.append("<br/>")
html.append('<table border="1px" cellpadding = "20" cellspacing="50" style= "border-collapse:collapse"><thead><tr><th>{}</th><th>{}</th><th>{}</th>'.format(foodname, foodcnt, foodmoney))
for name, cnt, money in data_list:
html.append("<tr><td> %s </td><td> %s </td><td> %s </td></tr>" % (name, cnt, money))
print("sum_money", sum_money)
html.append("</thead></table>")
html.append("</form></body></html>")
return html
if __name__ == "__main__":
GET_POST = {"start_time": "2017-11-16 09:53:44"}
# print(AsDateTime(GET_POST, name = "start_time"))
where_dict = { "start_time" : "2017-11-16 09:53:44", "end_time" : "2017-11-16 09:53:44"}
# print MakeWhereSql(where_dict)
# for value in globals().items():
# if type(value[1])==type(_f):
# print value[0]
pass | [
"[email protected]"
]
| |
b27fe17a593da8baccb2ea99aa6446531120b19a | 040ada107d1eab018dcfd464d9b7b2ee554fc801 | /5. Exceptions & Files/BookTitles.py | f7fe5a7d0d595df07334e437c6c763e70eeb17de | []
| no_license | RiKjess/SololearnPY | 2e3248d08bd84c3dd2843d41329958c5ee233d8c | 22c5af5f33b129658760d2f0edac02855d788d1b | refs/heads/master | 2023-08-14T14:11:42.900865 | 2021-09-27T08:10:48 | 2021-09-27T08:10:48 | 410,799,336 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 881 | py | """
Book Titles
You have been asked to make a special book categorization program, which assigns each book a special code based on its title.
The code is equal to the first letter of the book, followed by the number of characters in the title.
For example, for the book "Harry Potter", the code would be: H12, as it contains 12 characters (including the space).
You are provided a books.txt file, which includes the book titles, each one written on a separate line.
Read the title one by one and output the code for each book on a separate line.
For example, if the books.txt file contains:
Some book
Another book
Your program should output:
S9
A12
"""
file = open("/usercode/files/books.txt", "r") #file path
for line in file.readlines():
if line[-1] == "\n":
print(line[0] + str(len(line) - 1))
else:
print(line[0] + str(len(line)))
file.close() | [
"[email protected]"
]
| |
de8d14698ac3155b4d8131221fd5a3ef273e0fbe | 09608017cf183f96b4c6d942fef10b18c020f774 | /scripts/kdtree_old.py | ab32c9ae41f1219c630d98a1f1a6fa65a5c86a03 | []
| no_license | AmandaDoyle/BigDataTaxiFinalProject | fa2f173855b81fb1d82865e2a3b5021ba69a0f31 | 41101f5b8c380e81f7600f42a226d5c30ab10b52 | refs/heads/master | 2016-09-06T10:22:22.527352 | 2015-05-08T16:40:37 | 2015-05-08T16:40:37 | 33,154,325 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,077 | py | #!/usr/bin/python
import sys
import os
import json
import csv
from scipy import spatial
master = {}
def loadRoadNetwork(fileLocation):
f = open(fileLocation)
reader = csv.reader(f, delimiter= ',')
list_of_intersections = []
for l in reader:
try:
point = [float(l[1]), float(l[0])]
list_of_intersections.append(point)
except:
pass
return list_of_intersections
def kdTreeIntersection(list_of_intersections):
tree = spatial.KDTree(inputfile)
return tree
def IntersectionsMaster(tree, inputfile):
master = {}
f = open(fileLocation)
reader = csv.reader(f, delimiter= ',')
for row in reader:
key, value = line.split('\t')
key = eval(key)
year = key[0]
month = key[1]
value = eval(value)
lat = float(value[0])
lng = float(value[1])
loc = []
loc.append(lat)
loc.append(lng)
ind = tree.query_ball_point(loc, .001)
for item in ind:
if item not in master:
master[item] = {}
master[item][year] = {}
master[item][year][month] = 1
else:
if year in master[item]:
if month in master[item][year]:
master[item][year][month] += 1
else:
master[item][year][month] = 1
else:
master[item][year] = {}
master[item][year][month] = 1
return master
if __name__ == '__main__':
IntersectionsfileLocation = "/scratch/share/akabd/scripts/kl_scripts/TripCornerJoin/intersections.csv"
taxipickupfilelocation = ""
intersections = loadRoadNetwork(IntersectionsfileLocation)
KDtree = kdTreeIntersection(intersections)
intersections_dic = IntersectionsMaster(KDtree, taxipickupfilelocation)
json_file = open('lateNightDrops_KD.json', 'w+')
json.dump(intersections_dic, json_file)
| [
"[email protected]"
]
| |
d1b3168fa1a02ae0a1f8d21fb8cfd7e4a2da5d51 | bbdf70d28a5df9e337522ecdfcf04a470f6d2675 | /01_LED_Blink.py | 8a6e0ded30d32b0c27944a9fd0000ad9790ae8cf | []
| no_license | ankurm97/blink-py | 376c7e8eec38fe5cac5454802563966e8778b451 | 75bd31cbb0ad933a6790a7dc4fcfbe79ed6042a3 | refs/heads/master | 2022-11-14T11:52:04.585283 | 2020-07-08T07:38:21 | 2020-07-08T07:38:21 | 278,022,134 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 327 | py | # sudo apt-get install python3-rpi.gpio
import RPi.GPIO as GPIO
from time import sleep
GPIO.setwarnings(False) # Ignore Warnings
GPIO.setmode(GPIO.BOARD) # Use Physical Pin Numbering
GPIO.setup(8, GPIO.OUT, initial=GPIO.LOW)
while True:
GPIO.output(8, GPIO.HIGH)
sleep(1)
GPIO.output(8, GPIO.LOW)
sleep(1)
| [
"[email protected]"
]
| |
8a346a070eb4fa6939626f69eac6f9953b60dce0 | 5f792140b853f40e3c7be559c2a412d7b8296c96 | /2015/day1/day1.py | 11aaf8407b8800fe4172610f6f795fd3122349e8 | [
"Unlicense"
]
| permissive | winny-/aoc | 9d53666faf341f1cc2016a3d21e93d7f4064a72b | f986cacf14d81d220030bca94e99ec8179861e1b | refs/heads/master | 2022-12-05T00:19:47.570034 | 2022-12-04T09:55:11 | 2022-12-04T09:55:11 | 225,185,486 | 7 | 0 | null | null | null | null | UTF-8 | Python | false | false | 210 | py | import sys
MAP = {
'(': 1,
')': -1,
}
def calculate_floor(s):
return sum(MAP.get(c, 0) for c in s)
def main():
print(calculate_floor(sys.stdin.read()))
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
7bdc11d833a29499705912fbb4eccbaa8d5f88ac | 9c26ada7c171936d736b5a90a95e1a1d0258a7b5 | /bot/commands/help.py | 67ecd73044cbb93c9b726c248de4fc17bc22903d | [
"CC0-1.0",
"GPL-1.0-or-later",
"MIT"
]
| permissive | vampboy1234/PomoBot | cc076d23724e1042a6a3a041d23bc22c71c9328c | 5df0cfeda80f2aac2e921073a28b7dbef33f4859 | refs/heads/master | 2022-06-02T19:40:35.425774 | 2020-04-20T14:07:31 | 2020-04-20T14:07:31 | 257,314,721 | 0 | 0 | MIT | 2020-04-20T14:56:25 | 2020-04-20T14:56:24 | null | UTF-8 | Python | false | false | 5,837 | py | import discord
from cmdClient import cmd
from utils.lib import prop_tabulate
from utils import interactive # noqa
# Set the command groups to appear in the help
help_groups = [
("Timer", "*View and interact with the guild group timers.*"),
("Registry", "*Timer leaderboard and session history.*"),
("Configuration", "*Create groups and configure their behaviour.*"),
("Misc", "*Other miscellaneous commands.*")
]
# Set the main help string
help_str = ("Flexible study or work group timer using a customisable Pomodoro system!\n"
"Supports multiple groups and different timer setups.\n"
"Use the `guide` command to see a quick usage guide.")
help_title = "CafePomodoro Documentation"
@cmd("help",
desc="Display information about commands.")
async def cmd_help(ctx):
"""
Usage:
help [cmdname]
Description:
When used with no arguments, displays a list of commands with brief descriptions.
Otherwise, shows documentation for the provided command.
Examples:
help
help help
"""
if ctx.arg_str:
# Attempt to fetch the command
command = ctx.client.cmd_cache.get(ctx.arg_str.strip(), None)
if command is None:
return await ctx.error_reply(
("Command `{}` not found!\n"
"Use the `help` command without arguments to see a list of commands.").format(ctx.arg_str)
)
help_fields = command.long_help.copy()
help_map = {field_name: i for i, (field_name, _) in enumerate(help_fields)}
if not help_map:
await ctx.reply("No documentation has been written for this command yet!")
for name, pos in help_map.items():
if name.endswith("``"):
# Handle codeline help fields
help_fields[pos] = (
name.strip("`"),
"`{}`".format('`\n`'.join(help_fields[pos][1].splitlines()))
)
elif name.endswith(":"):
# Handle property/value help fields
lines = help_fields[pos][1].splitlines()
names = []
values = []
for line in lines:
split = line.split(":", 1)
names.append(split[0] if len(split) > 1 else "")
values.append(split[-1])
help_fields[pos] = (
name.strip(':'),
prop_tabulate(names, values)
)
elif name == "Related":
# Handle the related field
names = [cmd_name.strip() for cmd_name in help_fields[pos][1].split(',')]
names.sort(key=len)
values = [getattr(ctx.client.cmd_cache.get(cmd_name, None), 'desc', "") for cmd_name in names]
help_fields[pos] = (
name,
prop_tabulate(names, values)
)
usage_index = help_map.get("Usage", None)
if usage_index is not None:
help_fields[usage_index] = ("Usage", "`{}`".format('`\n`'.join(help_fields[usage_index][1].splitlines())))
aliases = getattr(command, 'aliases', [])
alias_str = "(Aliases `{}`.)".format("`, `".join(aliases)) if aliases else ""
# Build the embed
embed = discord.Embed(
title="`{}` command documentation. {}".format(command.name, alias_str),
colour=discord.Colour(0x9b59b6)
)
for fieldname, fieldvalue in help_fields:
embed.add_field(name=fieldname, value=fieldvalue, inline=False)
embed.set_footer(text="[optional] and <required> denote optional and required arguments, respectively.")
# Post the embed
await ctx.reply(embed=embed)
else:
# Build the command groups
cmd_groups = {}
for command in ctx.client.cmds:
# Get the command group
group = getattr(command, 'group', "Misc")
cmd_group = cmd_groups.get(group, [])
if not cmd_group:
cmd_groups[group] = cmd_group
# Add the command name and description to the group
cmd_group.append((command.name, getattr(command, 'desc', "")))
# Turn the command groups into strings
stringy_cmd_groups = {}
for group_name, cmd_group in cmd_groups.items():
cmd_group.sort(key=lambda tup: len(tup[0]))
stringy_cmd_groups[group_name] = prop_tabulate(*zip(*cmd_group))
# Now put everything into a bunch of embeds
help_embeds = []
active_fields = []
for group_name, group_desc in help_groups:
group_str = stringy_cmd_groups.get(group_name, None)
if group_str is None:
continue
active_fields.append((group_name, group_desc + '\n' + group_str))
if group_name == help_groups[-1][0] or sum([len(field.splitlines()) for _, field in active_fields]) > 10:
# Roll a new embed
embed = discord.Embed(description=help_str, colour=discord.Colour(0x9b59b6), title=help_title)
# Add the active fields
for name, field in active_fields:
embed.add_field(name=name, value=field, inline=False)
help_embeds.append(embed)
# Clear the active fields
active_fields = []
# Add the page numbers
for i, embed in enumerate(help_embeds):
embed.set_footer(text="Page {}/{}".format(i+1, len(help_embeds)))
# Send the embeds
if help_embeds:
await ctx.pager(help_embeds)
else:
await ctx.reply(embed=discord.Embed(description=help_str, colour=discord.Colour(0x9b59b6)))
| [
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]
| |
ceb0c0134cb3480fdab988077750fcef69ee298d | f8ea3582884df87172cb747e424ebd0c20223614 | /(sandbox,tobemerged)/setup.py | bfdf53b7357b1a52aaad77a7986bc61cc1b5ddd9 | [
"MIT"
]
| permissive | karimbahgat/PythonGis | 94f52f800a769ee54b12c7277604ead011465321 | fb99148a15bcbe0438ddca67b484a15076bd961a | refs/heads/master | 2023-04-12T15:59:08.522464 | 2022-09-09T22:48:32 | 2022-09-09T22:48:32 | 47,153,255 | 5 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,260 | py | ############
### allow building the exe by simply running this script
import sys
sys.argv.append("py2exe")
############
### imports
from distutils.core import setup
import py2exe
###########
### options
WINDOWS = [{"script": "guitester.py",
"icon_resources": [(1,"pythongis/app/logo.ico")] }]
OPTIONS = {"skip_archive": True,
"dll_excludes": ["python26.dll","python27.so"]}
###########
### create the application icon
##import PIL, PIL.Image
##img = PIL.Image.open("icon.png")
##img.save("icon.ico", sizes=[(255,255),(128,128),(64,64),(48,48),(32,32),(16,16),(8,8)])
###########
### build
setup(windows=WINDOWS,
options={"py2exe": OPTIONS}
)
###########
### manually copy pythongis package to dist
### ...because py2exe may not copy all files
import os
import shutil
frompath = "pythongis"
topath = os.path.join("dist","pythongis")
shutil.rmtree(topath) # deletes the folder copied by py2exe
shutil.copytree(frompath, topath)
###########
### and same with dependencies
for dependname in os.listdir("dependencies"):
frompath = os.path.join("dependencies", dependname)
topath = os.path.join("dist", dependname)
shutil.rmtree(topath) # deletes the folder copied by py2exe
shutil.copytree(frompath, topath)
| [
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]
| |
ad232b4ee33908d60b0a9a445eccd44352cff4f9 | 4d4ef69dc8a0237973dde5ce0768cf21f043f717 | /Download image automatically.py | 674716ea927e334672127be0abeb83a8f0c5c51f | []
| no_license | zh-cse18/Selenium_Webdriver_Operation | b3b02adb4762e5b2c61e05d9211e3d90725fae42 | dd001057d84aae0fbaed062fea4725e0e3bea2cc | refs/heads/master | 2023-07-20T08:13:07.690387 | 2023-07-10T09:59:59 | 2023-07-10T09:59:59 | 230,744,441 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,463 | py | import os
import json
import requests # to sent GET requests
from bs4 import BeautifulSoup # to parse HTML
# user can input a topic and a number
# download first n images from google image search
GOOGLE_IMAGE = \
'https://www.google.com/search?site=&tbm=isch&source=hp&biw=1873&bih=990&'
# The User-Agent request header contains a characteristic string
# that allows the network protocol peers to identify the application type,
# operating system, and software version of the requesting software user agent.
# needed for google search
usr_agent = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive',
}
SAVE_FOLDER = 'images'
def main():
if not os.path.exists(SAVE_FOLDER):
os.mkdir(SAVE_FOLDER)
download_images()
def download_images():
# ask for user input
data = input('What are you looking for? ')
n_images = int(input('How many images do you want? '))
print('Start searching...')
# get url query string
searchurl = GOOGLE_IMAGE + 'q=' + data
print(searchurl)
# request url, without usr_agent the permission gets denied
response = requests.get(searchurl, headers=usr_agent)
html = response.text
# find all divs where class='rg_meta'
soup = BeautifulSoup(html, 'html.parser')
results = soup.findAll('div', {'class': 'rg_meta'}, limit=n_images)
# extract the link from the div tag
imagelinks = []
for re in results:
text = re.text # this is a valid json string
text_dict = json.loads(text) # deserialize json to a Python dict
link = text_dict['ou']
# image_type = text_dict['ity']
imagelinks.append(link)
print(f'found {len(imagelinks)} images')
print('Start downloading...')
for i, imagelink in enumerate(imagelinks):
# open image link and save as file
response = requests.get(imagelink)
imagename = SAVE_FOLDER + '/' + data + str(i + 1) + '.jpg'
with open(imagename, 'wb') as file:
file.write(response.content)
print('Done')
if __name__ == '__main__':
main() | [
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]
| |
9943c20ed3a8ba5c8ddafb0759bb6a02bc89fb81 | 504d9b35e2265e2463a82511fd567d4c553c7a04 | /libvirt_qemu_map | 5c6013a3ba553b9ad149e9513a228a8bffc2df5b | []
| no_license | qiankehan/libvirt_qemu_api_map | 36a87bff5eb4dfafd98933ca6f143d1f67b4c1e7 | c73a3c78d075bff84b449c14ec8759ce4b3f0dd3 | refs/heads/master | 2020-06-14T11:50:06.592783 | 2019-07-03T07:10:49 | 2019-07-03T07:10:49 | 194,997,268 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,558 | #!/usr/bin/python3
import subprocess as sp
import argparse
import os
import sys
import csv
import tempfile
def rm_empty_str(objs):
return {i for i in objs if i != ''}
class LibvirtQemu:
def __init__(self, src, mode):
if mode not in {'qmp', 'ga'}:
raise TypeError('mode should be "qmp" or "ga"')
self.src = src
self.mode = mode
if mode == 'qmp':
self.makecmd = 'qemuMonitorJSONMakeCommand'
if mode == 'ga':
self.makecmd = 'qemuAgentMakeCommand'
self.tmpdir = tempfile.mkdtemp()
self.qemu_cscope_source = os.path.join(self.src, 'src/qemu')
self.qemu_cscope = self.gen_qemu_cscope('qemu')
self.libvirt_apis = self.get_libvirt_apis()
self.qemu_apis = {i.replace("vir", "qemu", 1)
for i in self.libvirt_apis}
self.mon_funcs = self.get_mon_funcs()
def get_libvirt_apis(self):
cmd = 'grep "virDomain[a-zA-Z0-9]*" %s -o' % os.path.join(
self.src, 'src/libvirt_public.syms')
return rm_empty_str(sp.check_output(cmd, shell=True, text=True).split('\n'))
def gen_qemu_cscope(self, name):
cscope = os.path.join(self.tmpdir, '%s.out' %name)
cmd = 'cscope -b -f %s -s %s -R' % (cscope, self.qemu_cscope_source)
try:
sp.check_output(cmd, shell=True)
except sp.CalledProcessError as e:
sys.exit("%s cscope generate failed: %s\nreturn: %d" %
(name, cmd, e.returncode))
return cscope
def get_mon_funcs(self):
cmd = "cscope -d -L3 %s -s %s -f %s|grep -v '^[a-zA-Z_0-9]*\.h'|grep -v ATTRIBUTE_ |awk '{print $2}'" % (
self.makecmd, self.qemu_cscope_source, self.qemu_cscope)
return rm_empty_str(sp.check_output(cmd, shell=True, text=True).split('\n'))
def get_qemu_exec(self, mon_func):
if self.mode == 'qmp':
mon_file = 'qemu_monitor_json.c'
if self.mode == 'ga':
mon_file = 'qemu_agent.c'
qemu_mon_src = os.path.join(self.src, 'src/qemu/%s' %mon_file)
cmd = """awk '/%s/,/^}/' %s|awk '/%s/,/)/'|grep '"[a-zA-Z:_-]*"' -o |tr '\n' ','""" % (
mon_func, qemu_mon_src, self.makecmd)
return sp.check_output(cmd, shell=True, text=True)
def get_callers(self, fn):
cmd = "cscope -d -L3 %s -f %s -s %s|grep -v '^[a-zA-Z_0-9]*\.h' | grep -v ATTRIBUTE_ | awk '{print $2}'" % (
fn, self.qemu_cscope, self.qemu_cscope_source)
return rm_empty_str(sp.check_output(cmd, shell=True, text=True).split('\n'))
def get_top_callers(self, fn, cscope):
stack = []
top_callers = set()
accessed = set()
func = fn
stack.append(func)
while stack != []:
func = stack.pop()
if func not in accessed:
accessed.add(func)
callers = self.get_callers(func)
if func in callers or callers == set():
top_callers.add(func)
for caller in callers:
stack.append(caller)
return top_callers
def writecsv(self, csv_path):
with open(csv_path, 'w') as csvfile:
header = ["Monitor Wrapper", "QMP", "API callers", "Callers not in APIs"]
writer = csv.DictWriter(csvfile, fieldnames=header, delimiter='|')
writer.writeheader()
for mon in self.mon_funcs:
qemu_exec = self.get_qemu_exec(mon)
top_callers = self.get_top_callers(mon, self.qemu_cscope)
callers_api = "\n".join([j.replace('qemu', 'vir', 1) for j in {i for i in top_callers if i in self.qemu_apis}])
callers_not_api = "\n".join([i for i in top_callers if i not in self.qemu_apis])
writer.writerow({
header[0]: mon,
header[1]: qemu_exec,
header[2]: callers_api,
header[3]: callers_not_api})
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--source', help="The libvirt source directory", required=True)
parser.add_argument('-m', '--mode', help="The mode of API mappings.'qmp': The qemu qmp to libvirt API mappings; 'ga': The qemu guest agent command to libvirt API mappings", required=True)
parser.add_argument('-o', '--output', help="The csv output file", default='output.csv')
args = parser.parse_args()
LibvirtQemu(args.source, args.mode).writecsv(args.output)
| [
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]
| ||
e21b32b6008b4c1156c4093df442d9254ff1ec55 | 5020abc0f35d0da7b7ba43e50bc017c4d5ca9bcd | /learn-from-liao/task_master.py | 30f9999589add76043fac223ce7318310f50f740 | []
| no_license | chick-tiger/python-module-learning | 7795291ec8a11b6a149191f97b4d7856609665a7 | 80d560bd22c7e85b2a2502113515f21a17512a3c | refs/heads/master | 2021-07-14T21:56:04.306101 | 2017-10-20T09:40:53 | 2017-10-20T09:40:53 | 104,312,051 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 715 | py | import random, time, queue
from multiprocessing.managers import BaseManager
task_queue = queue.Queue()
result_queue = queue.Queue()
class QueueManager(BaseManager):
pass
QueueManager.register('get_task_queue', callable=lambda:task_queue)
QueueManager.register('get_result_queue', callable=lambda:result_queue)
manager = QueueManager(address=('', 5000), authkey=b'abc')
manager.start()
task = manager.get_task_queue()
result = manager.get_result_queue()
for i in range(10):
n = random.randint(0, 10000)
print('Put task %d...' % n)
task.put(n)
print('Try get results...')
for i in range(10):
r = result.get(timeout=10)
print('Result: %s' % r)
manager.shutdown()
print('master exit.')
| [
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]
| |
e51e15c2b6fcf05be02199a5d7b23019c613d8c6 | 52f2581585f5529b53d352ea4e0e1db986a17f7f | /AutoTest/public/Login_c.py | 022bab2c565f3cc0020da9f3c9dbb8e06837704e | []
| no_license | BaoYong-1/PyTesting1 | 253fda9d95435244ff79e37b16a62ba215637d3f | 9a8c4c02b3f592e48d93f98038be7e7f1bd63def | refs/heads/master | 2020-03-19T11:23:03.106788 | 2018-08-31T07:30:54 | 2018-08-31T07:30:54 | 136,452,033 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,357 | py | # coding=utf-8
import time
from selenium.webdriver.support import expected_conditions as EC
from selenium import webdriver
from GetVerifyCode import get_code
import os
os.environ['NLS_LANG'] = 'SIMPLIFIED CHINESE_CHINA.UTF8' # 设置中文
def is_element_visible(self, element):
driver = self.driver
try:
the_element = EC.visibility_of_element_located(element)
assert the_element(driver)
flag = True
except:
flag = False
return flag
def login(driver, username, password, CodeText):
driver.find_element_by_id("txt_username").clear()
driver.find_element_by_id("txt_username").send_keys(username)
driver.find_element_by_id("txt_password").clear()
driver.find_element_by_id("txt_password").send_keys(password)
driver.find_element_by_id("verifycode").clear()
driver.find_element_by_id("verifycode").send_keys(CodeText)
driver.find_element_by_class_name("button").click()
time.sleep(2)
if __name__ == '__main__':
options = webdriver.ChromeOptions()
options.add_experimental_option("excludeSwitches", ["ignore-certificate-errors"])
driver = webdriver.Chrome(chrome_options=options)
driver.maximize_window()
url = "http://192.168.10.110:8080/WebGis/login"
driver.get(url)
CodeText = get_code(driver)
login(driver, 'baoyong123', 'asdf1234', CodeText)
| [
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]
| |
2e0aeb4b8cd540d392f6e5256c8fc9f78b3b1f04 | 0144353f5d129c0d95f96c717c9aee71d26ca153 | /app_mysql.py | 3e5c0f5eea15619e2f75aef0c838311ca947c354 | []
| no_license | rafat2427/IDP | 4b44239f3cb6b52d11baf47c97b49aa38ec7e5b5 | 16c02be0244cbf32e7d94363e3af40d3c447311d | refs/heads/main | 2023-02-16T17:53:11.009820 | 2021-01-20T20:04:10 | 2021-01-20T20:04:10 | 331,417,628 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,252 | py | from flask import Flask, render_template, url_for, request, redirect
from flask_mysqldb import MySQL
import pandas as pd
import numpy as np
import os.path
# import yaml
app = Flask(__name__)
# Configure db
# db = yaml.load(open('db.yaml'))
# app.config['MYSQL_HOST'] = db['mysql_host']
# app.config['MYSQL_USER'] = db['mysql_user']
# app.config['MYSQL_PASSWORD'] = db['mysql_password']
# app.config['MYSQL_DB'] = db['mysql_db']
app.config['MYSQL_HOST'] = 'localhost'
app.config['MYSQL_USER'] = 'root'
app.config['MYSQL_PASSWORD'] = ''
app.config['MYSQL_DB'] = 'test'
mysql = MySQL(app)
# Start coding
# @app.route('/')
# def index():
# return render_template("about.php")
#
#
# @app.route('/users')
# def users():
# cur = mysql.connection.cursor()
# resultValue = cur.execute("SELECT * FROM users")
# if resultValue > 0:
# userDetails = cur.fetchall()
# return render_template('users.html',userDetails=userDetails)
@app.route('/')
# @app.route('/', methods=['GET', 'POST'])
def index():
# if request.method == 'POST':
# # Fetch form data
# userDetails = request.form
# name = userDetails['name']
# email = userDetails['email']
# cur = mysql.connection.cursor()
# cur.execute("INSERT INTO users(name, email) VALUES(%s, %s)",(name, email))
# mysql.connection.commit()
# cur.close()
# return redirect('/users')
# return render_template('index.html')
#
# @app.route('/users')
# def users():
cur_member = mysql.connection.cursor()
cur_gp = mysql.connection.cursor()
resultValue = cur_member.execute("SELECT * FROM members")
groupValue = cur_gp.execute("SELECT * FROM gp")
if resultValue > 0 or groupValue > 0:
userDetails = cur_member.fetchall()
groupDetails = cur_gp.fetchall()
return render_template('members group.php',userDetails=userDetails, groupDetails=groupDetails)
@app.route('/show')
def show_data():
csv1 = pd.read_csv("status_1.csv")
print(csv1)
val_list = csv1.values.tolist()
c_yes=val_list.count('Yes')
c_no=val_list.count('No')
state=1
if c_no > c_yes:
state = 2
return render_template('show_status.php',val_list=val_list,c_yes=c_yes,c_no=c_no)
@app.route('/status')
def show_status():
csv1 = pd.read_csv("status_1.csv")
print(csv1)
val_list = csv1.values.tolist()
c_yes=val_list.count('Yes')
c_no=val_list.count('No')
# state=1
# if c_no > c_yes
# state = 2
state = 2
cur_state = mysql.connection.cursor()
cur_member = mysql.connection.cursor()
cur_gp = mysql.connection.cursor()
cur_state.execute("UPDATE `status` SET `sta_id` = %s WHERE `status`.`persno` = 12345 ", state)
resultValue = cur_member.execute("SELECT * FROM members")
groupValue = cur_gp.execute("SELECT * FROM status")
if resultValue > 0 or groupValue > 0:
userDetails = cur_member.fetchall()
groupDetails = cur_gp.fetchall()
return render_template('members group.php',userDetails=userDetails, groupDetails=groupDetails)
if __name__ == '__main__':
app.run(debug=True)
| [
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]
| |
5ec94889a27094587c60ea1c2c2cce07b38e8860 | 0f43b7cfd605ee6be9ea563023835e0b4aa37226 | /survey-env/bin/pip3.7 | 3c23b1d64f8241cd424519d772222c119e0fe202 | []
| no_license | filipvandyck/survey_proj | b7ab78b41645db9cc45f9d52fb61b1aee046a76e | 198cb412553cb2e352d65cc5df831017211f785e | refs/heads/master | 2023-01-11T17:33:00.435334 | 2020-10-25T19:46:32 | 2020-10-25T19:46:32 | 262,762,370 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 260 | 7 | #!/home/filip/Python/survey_proj/survey-env/bin/python3.7
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
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]
| |
d519581682c5b4acb68ab1878e3cda3a7b8c4ddd | 5e2655fb23e558c54695dea5c9456b5552570947 | /localdev/seed/management/commands/seed_data.py | f42ad2be00ea5d9f4f5111900de0d82b66bf4e16 | [
"BSD-3-Clause"
]
| permissive | mitodl/bootcamp-ecommerce | 992cb23243462d82c75cfae6c115a27728491219 | 339c67b84b661a37ffe32580da72383d95666c5c | refs/heads/master | 2023-08-31T10:45:57.827990 | 2023-07-25T13:55:32 | 2023-07-25T13:55:32 | 82,849,185 | 6 | 3 | BSD-3-Clause | 2023-08-24T20:25:47 | 2017-02-22T20:27:24 | Python | UTF-8 | Python | false | false | 709 | py | """Management command to create or update seed data"""
from django.core.management.base import BaseCommand
from localdev.seed.api import create_seed_data
from localdev.seed.utils import get_raw_seed_data_from_file
class Command(BaseCommand):
"""Creates or updates seed data based on a raw seed data file"""
help = __doc__
def handle(self, *args, **options):
raw_seed_data = get_raw_seed_data_from_file()
results = create_seed_data(raw_seed_data)
if not results.has_results:
self.stdout.write(self.style.WARNING("No results logged."))
else:
self.stdout.write(self.style.SUCCESS("RESULTS"))
self.stdout.write(results.report)
| [
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]
| |
cbd2dcecce030b1b1ca054de015715d1db5327d7 | 79eec9f4b1dac6c085dd3783854b98d0c6c9769c | /dataset_tools/xml_writer_complementary.py | 6985d879017031359f32e16a00c3f5dc5402cd55 | [
"MIT"
]
| permissive | hangwudy/Mask_RCNN | c9b5efa0307a73dd5178988981f67762eab5ae36 | 8b5d896076b994e2f9136054114c551a8cb3119f | refs/heads/master | 2020-04-03T11:42:41.924893 | 2019-08-06T16:53:42 | 2019-08-06T16:53:42 | 142,773,778 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,283 | py | # coding: utf-8
# created by Hang Wu on 2018.10.07
# feedback: [email protected]
from lxml.etree import Element, SubElement, tostring
import pprint
from xml.dom.minidom import parseString
import cv2
from numpy import random
import os
# Eigen
import image_overlay
import load_image
import generate_dict
def xml_generator(bndbox, xml_destination_path):
# Root
node_root = Element('annotation')
## Folder
node_folder = SubElement(node_root, 'folder')
node_folder.text = bndbox['folder']
## Filename
node_filename = SubElement(node_root, 'filename')
node_filename.text = bndbox['filename']
## Path
node_path = SubElement(node_root, 'path')
node_path.text = bndbox['path']
## Source
node_source = SubElement(node_root, 'source')
node_database = SubElement(node_source, 'database')
node_database.text = 'Unknown'
## Size
node_size = SubElement(node_root, 'size')
### Width
node_width = SubElement(node_size, 'width')
node_width.text = str(bndbox['width'])
### Height
node_height = SubElement(node_size, 'height')
node_height.text = str(bndbox['height'])
### Depth
node_depth = SubElement(node_size, 'depth')
node_depth.text = str(bndbox['depth'])
## Segmented
node_segmented = SubElement(node_root, 'segmented')
node_segmented.text = '0'
## Object
node_object = SubElement(node_root, 'object')
### Name
node_name = SubElement(node_object, 'name')
node_name.text = 'car_door'
### Pose
node_pose = SubElement(node_object, 'pose')
node_pose.text = 'Unspecified'
### Truncated
node_truncated = SubElement(node_object, 'truncated')
node_truncated.text = '0'
### Difficult
node_difficult = SubElement(node_object, 'difficult')
node_difficult.text = '0'
### Bounding box
node_bndbox = SubElement(node_object, 'bndbox')
#### x-y value
node_xmin = SubElement(node_bndbox, 'xmin')
node_xmin.text = str(bndbox['xmin'])
node_ymin = SubElement(node_bndbox, 'ymin')
node_ymin.text = str(bndbox['ymin'])
node_xmax = SubElement(node_bndbox, 'xmax')
node_xmax.text = str(bndbox['xmax'])
node_ymax = SubElement(node_bndbox, 'ymax')
node_ymax.text = str(bndbox['ymax'])
# format display
xml = tostring(node_root, pretty_print=True)
xml_name = bndbox['filename'][:-4]+".xml"
xml_path = os.path.join(xml_destination_path, xml_name)
fp = open(xml_path, 'w')
fp.write(xml.decode())
fp.close()
if __name__ == '__main__':
# Foreground and background imags
fg_path = '/home/hangwu/Repositories/Dataset/dataset/ergaenzen'
bg_path = '/home/hangwu/Downloads/val2017'
# Output paths
xml_dest_path = "/home/hangwu/Repositories/Dataset/dataset/annotation_all/xml"
image_dest_path = "/home/hangwu/Repositories/Dataset/dataset/car_door_all"
mask_dest_path = "/home/hangwu/Repositories/Dataset/dataset/annotation_all/mask"
mask_bw_dest_path = "/home/hangwu/Repositories/Dataset/dataset/annotation_all/mask_bw"
# Car Door Subcategory: 1 or 2, IMPORTANT for naming the training data
cd_subcat = 2
# Test
test = False
if test:
fg_path = '/home/hangwu/Repositories/Mask_RCNN/dataset_tools/Test_Workspace/Image_Generation/Foreground'
bg_path = '/home/hangwu/Repositories/Mask_RCNN/dataset_tools/Test_Workspace/Image_Generation/Background'
xml_dest_path = "/home/hangwu/Repositories/Mask_RCNN/dataset_tools/Test_Workspace/Image_Generation/output/xml"
image_dest_path = "/home/hangwu/Repositories/Mask_RCNN/dataset_tools/Test_Workspace/Image_Generation/output/image"
mask_dest_path = "/home/hangwu/Repositories/Mask_RCNN/dataset_tools/Test_Workspace/Image_Generation/output/mask"
mask_bw_dest_path = "/home/hangwu/Repositories/Mask_RCNN/dataset_tools/Test_Workspace/Image_Generation/output/mask_bw"
fg_list = load_image.loadim(fg_path)
# print(fg_list[1230:1250])
bg_list = load_image.loadim(bg_path,'jpg','0000')
# Counter
progress_show = 1
for fg_p in fg_list:
# IMPORTANT: if you want to resize images, don't forget resize in generate_dict
img_scale = 0.8
try:
bnd_info = generate_dict.object_dict(fg_p, img_scale)
fg = cv2.imread(fg_p, -1)
# resize the car door images
fg = cv2.resize(fg, (0,0), fx = img_scale, fy = img_scale, interpolation = cv2.INTER_CUBIC)
bg_path = random.choice(bg_list)
bg = cv2.imread(bg_path, -1)
object_bndbox = image_overlay.overlap(bg, fg, bnd_info, image_dest_path, mask_dest_path, mask_bw_dest_path, cd_subcat)
xml_generator(object_bndbox, xml_dest_path)
except:
print("===========================")
print(fg_p)
print(bg_path)
print("===========================")
# print(object_bndbox)
if progress_show % 1 == 0:
print("++++++++++++++")
print("{:.2f}%".format(progress_show/len(fg_list)*100))
print("++++++++++++++")
progress_show += 1
| [
"[email protected]"
]
| |
ad890fdf5086260c3c073c0dee7db830b7db3d9a | d33bd7e0c2cd91226d3dc7c3d927a52b8dbc05fd | /tiny_data/lip3x3_tiny/utils.py | a5f41725c9023fb3c1334631a8da4d29a58eb2e8 | [
"Apache-2.0"
]
| permissive | gamedx/tiny_lips | 80d7963abd9b8455aedcc342562f7ff55f6c501b | c15e1d152369ea69715313f6b6802ed05eab2b65 | refs/heads/master | 2020-06-29T17:59:25.717198 | 2019-08-05T07:57:03 | 2019-08-05T07:57:03 | 200,585,475 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,914 | py | import os
import scipy
import numpy as np
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
def load_mnist(batch_size, is_training=True):
path = os.path.join('data', 'mnist')
if is_training:
fd = open(os.path.join(path, 'train-images-idx3-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
trainX = loaded[16:].reshape((8400, 39, 39, 1)).astype(np.float32)
fd = open(os.path.join(path, 'train-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
trainY = loaded[8:].reshape((8400)).astype(np.int32)
trX = trainX[:7800] / 255.
trY = trainY[:7800]
valX = trainX[7800:, ] / 255.
valY = trainY[7800:]
num_tr_batch = 7800 // batch_size
num_val_batch = 600 // batch_size
return trX, trY, num_tr_batch, valX, valY, num_val_batch
else:
fd = open(os.path.join(path, 't10k-images-idx3-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
teX = loaded[16:].reshape((600, 39, 39, 1)).astype(np.float)
fd = open(os.path.join(path, 't10k-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
teY = loaded[8:].reshape((600)).astype(np.int32)
num_te_batch = 600 // batch_size
return teX / 255., teY, num_te_batch
def load_data(dataset, batch_size, is_training=True, one_hot=False):
if dataset == 'mnist':
return load_mnist(batch_size, is_training)
else:
raise Exception('Invalid dataset, please check the name of dataset:', dataset)
def get_batch_data(dataset, batch_size, num_threads):
if dataset == 'mnist':
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_mnist(batch_size, is_training=True)
elif dataset == 'fashion-mnist':
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_fashion_mnist(batch_size, is_training=True)
data_queues = tf.train.slice_input_producer([trX, trY])
X, Y = tf.train.shuffle_batch(data_queues, num_threads=num_threads,
batch_size=batch_size,
capacity=batch_size * 64,
min_after_dequeue=batch_size * 32,
allow_smaller_final_batch=False)
return(X, Y)
def save_images(imgs, size, path):
'''
Args:
imgs: [batch_size, image_height, image_width]
size: a list with tow int elements, [image_height, image_width]
path: the path to save images
'''
imgs = (imgs + 1.) / 2 # inverse_transform
return(scipy.misc.imsave(path, mergeImgs(imgs, size)))
def mergeImgs(images, size):
h, w = images.shape[1], images.shape[2]
imgs = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
imgs[j * h:j * h + h, i * w:i * w + w, :] = image
return imgs
# For version compatibility
def reduce_sum(input_tensor, axis=None, keepdims=False):
try:
return tf.reduce_sum(input_tensor, axis=axis, keepdims=keepdims)
except:
return tf.reduce_sum(input_tensor, axis=axis, keep_dims=keepdims)
# For version compatibility
def softmax(logits, axis=None):
try:
return tf.nn.softmax(logits, axis=axis)
except:
return tf.nn.softmax(logits, dim=axis)
def get_shape(inputs, name=None):
name = "shape" if name is None else name
with tf.name_scope(name):
static_shape = inputs.get_shape().as_list()
dynamic_shape = tf.shape(inputs)
shape = []
for i, dim in enumerate(static_shape):
dim = dim if dim is not None else dynamic_shape[i]
shape.append(dim)
return(shape)
| [
"[email protected]"
]
| |
049898e55896a5847ebcd12bfad533a56380fa13 | 63393a9f049587cc6825ddc905aa07244926e874 | /main.py | 7879ba20d5951ca35fbedc07e2c76ad674ff6777 | []
| no_license | sbzhu/designpattern | fa62e9e3321048ee220f1ee9c63af9f1bdd89d77 | aeafb2850923a7f1ed31957130fe7857e0fc5b19 | refs/heads/master | 2020-12-25T04:08:39.039958 | 2016-04-23T07:48:24 | 2016-04-23T07:48:24 | 56,907,165 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 190 | py | #!/usr/bin/env python
if __name__ == '__main__':
# op = raw_input('Operator : ')
# op1 = input('a : ')
# op2 = input('b : ')
try:
print 'Hello'
except:
print 'Wrong expression'
| [
"[email protected]"
]
| |
c7040497fddc70804c791aa8caffd6ee49621d0d | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_2/hbbirf001/question3.py | 86f1543deb5d0d08303893c1de5d53fe0d63e38e | []
| no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 260 | py | import math
pi =2
denom = math.sqrt(2)
while denom != 2:
pi = pi*2/denom
denom = math.sqrt(2+denom)
print('Approximation of pi:',round(pi,3),sep=' ')
radius = eval(input('Enter the radius:\n'))
area = pi*radius**2
print('Area:', round(area,3)) | [
"[email protected]"
]
| |
14e9d08e4c3d5917584fcfa34b8694b98266d25e | 5fbdb2fdf9544739ea5f4b72b835cb201b73ab39 | /app/main/views.py | a40898b4cf27fc1ecfd8df435912b4a2f64c2c68 | []
| no_license | nakuls003/flasky-app | add3bebe46c79ddc7d59b5b45a10c7ade444db5d | 97fd011dacf898070f2da597bb92577c476ac136 | refs/heads/master | 2020-04-14T01:27:19.513032 | 2019-01-01T06:26:00 | 2019-01-01T06:26:00 | 163,560,634 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,237 | py | from flask import render_template, flash, redirect, url_for, request, current_app, abort, make_response
from . import main
from flask_login import login_required, current_user
from ..decorators import admin_required
from ..models import User, Role, Permission, Post, Comment
from .forms import EditProfileForm, EditProfileAdminForm, PostForm, CommentForm
from .. import db
from ..decorators import permission_required
from flask_sqlalchemy import get_debug_queries
@main.route('/', methods=['GET', 'POST'])
def index():
form = PostForm()
if form.validate_on_submit() and current_user.can(Permission.WRITE):
post = Post(body=form.body.data, author=current_user._get_current_object())
db.session.add(post)
db.session.commit()
return redirect(url_for('.index'))
page = request.args.get('page', 1, type=int)
show_followed = False
if current_user.is_authenticated:
show_followed = bool(request.cookies.get('show_followed', ''))
if show_followed:
query = current_user.followed_posts
else:
query = Post.query
pagination = query.order_by(Post.timestamp.desc()).paginate(page, per_page=current_app.config['POSTS_PER_PAGE'],
error_out=False)
posts = pagination.items
return render_template('index.html', show_followed=show_followed, form=form, posts=posts, pagination=pagination)
@main.route('/show-all')
@login_required
def show_all():
resp = make_response(redirect(url_for('.index')))
resp.set_cookie('show_followed', '', max_age=30*24*60*60)
return resp
@main.route('/show-followed')
@login_required
def show_followed():
resp = make_response(redirect(url_for('.index')))
resp.set_cookie('show_followed', '1', max_age=30*24*60*60)
return resp
# @main.route('/admin')
# @login_required
# @admin_required
# def admin_route():
# return "for administrators!"
#
# @main.route('/moderate')
# @login_required
# @permission_required(Permission.MODERATE)
# def moderate_route():
# return "for moderators"
@main.route('/user/<username>')
def user(username):
user = User.query.filter_by(username=username).first_or_404()
page = request.args.get('page', 1, type=int)
pagination = user.posts.order_by(Post.timestamp.desc()).paginate(page, per_page=current_app.config['POSTS_PER_PAGE'],
error_out=False)
posts = pagination.items
return render_template('user.html', user=user, posts=posts, pagination=pagination)
@main.route('/post/<int:id>', methods=['GET', 'POST'])
def post(id):
post = Post.query.get_or_404(id)
form = CommentForm()
if form.validate_on_submit() and current_user.can(Permission.COMMENT):
comment = Comment(body = form.body.data, author = current_user._get_current_object(), post = post)
db.session.add(comment)
db.session.commit()
flash('comment added successfully')
return redirect(url_for('main.post', id=post.id, page=-1))
page = request.args.get('page', 1, type=int)
if page == -1:
page = ((post.comments.count() - 1) // current_app.config['COMMENTS_PER_PAGE']) + 1
pagination = post.comments.order_by(Comment.timestamp.asc()).paginate(page,
per_page=current_app.config['COMMENTS_PER_PAGE'],
error_out=False)
comments = pagination.items
return render_template('post.html', posts=[post], comments=comments, pagination=pagination, form=form)
@main.route('/edit/<int:id>', methods=['GET', 'POST'])
@login_required
def edit(id):
post = Post.query.get_or_404(id)
if current_user != post.author and not current_user.is_administrator():
abort(403)
form = PostForm()
if form.validate_on_submit():
post.body = form.body.data
db.session.add(post)
db.session.commit()
flash('Post updated successfully')
return redirect(url_for('.edit', id=post.id))
form.body.data = post.body
return render_template('edit_post.html', form=form)
@main.route('/edit-profile', methods=['GET', 'POST'])
@login_required
def edit_profile():
form = EditProfileForm()
if form.validate_on_submit():
current_user.name = form.name.data
current_user.location = form.location.data
current_user.about_me = form.about_me.data
db.session.add(current_user._get_current_object())
db.session.commit()
flash('Your info has been saved')
return redirect(url_for('.user', username=current_user.username))
form.name.data = current_user.name
form.location.data = current_user.location
form.about_me.data = current_user.about_me
return render_template('edit_profile.html', form=form)
@main.route('/edit-profile/<int:id>', methods=['GET', 'POST'])
@login_required
@admin_required
def edit_profile_admin(id):
user = User.query.get_or_404(id)
form = EditProfileAdminForm(user)
if form.validate_on_submit():
user.email = form.email.data
user.username = form.username.data
user.confirmed = form.confirmed.data
user.role = Role.query.get(form.role.data)
user.name = form.name.data
user.location = form.location.data
user.about_me = form.about_me.data
db.session.add(user)
db.session.commit()
flash('User updated successfully')
return redirect(url_for('.user', username=user.username))
form.email.data = user.email
form.username.data = user.username
form.confirmed.data = user.confirmed
form.role.data = user.role_id
form.name.data = user.name
form.location.data = user.location
form.about_me.data = user.about_me
return render_template('edit_profile.html', form=form, user=user)
@main.route('/follow/<username>')
@login_required
@permission_required(Permission.FOLLOW)
def follow(username):
user = User.query.filter_by(username=username).first()
if user is None or user == current_user:
flash('invalid user')
return redirect(url_for('.index'))
if current_user.is_following(user):
flash('You are already following this user')
return redirect(url_for('.user', username=username))
current_user.follow(user)
db.session.commit()
flash('You are now following {}'.format(username))
return redirect(url_for('.user', username=username))
@main.route('/unfollow/<username>')
@login_required
@permission_required(Permission.FOLLOW)
def unfollow(username):
user = User.query.filter_by(username=username).first()
if user is None or user == current_user:
flash('invalid user')
return redirect(url_for('.index'))
if not current_user.is_following(user):
flash('You are not already following this user')
return redirect(url_for('.user', username=username))
current_user.unfollow(user)
db.session.commit()
flash('You unfollowed {}'.format(username))
return redirect(url_for('.user', username=username))
@main.route('/followers/<username>')
def followers(username):
user = User.query.filter_by(username=username).first()
if user is None:
flash('invalid user')
return redirect(url_for('.index'))
page = request.args.get('page', 1, type=int)
pagination = user.followers.paginate(page, per_page=current_app.config['FOLLOW_RESULTS_PER_PAGE'], error_out=False)
follows = [{'user': item.follower, 'timestamp': item.timestamp} for item in pagination.items]
return render_template('followers.html', endpoint='.followers', pagination=pagination, follows=follows, user=user,
title='Followers of')
@main.route('/following/<username>')
def followed_by(username):
user = User.query.filter_by(username=username).first()
if user is None:
flash('invalid user')
return redirect(url_for('.index'))
page = request.args.get('page', 1, type=int)
pagination = user.followed.paginate(page, per_page=current_app.config['FOLLOW_RESULTS_PER_PAGE'], error_out=False)
follows = [{'user': item.followed, 'timestamp': item.timestamp} for item in pagination.items]
return render_template('followers.html', endpoint='.followed_by', pagination=pagination, follows=follows, user=user,
title='Followed by')
@main.route('/moderate')
@login_required
@permission_required(Permission.MODERATE)
def moderate():
page = request.args.get('page', 1, type=int)
pagination = Comment.query.order_by(Comment.timestamp.desc()).paginate(page, current_app.config['COMMENTS_PER_PAGE'],
error_out=False)
comments = pagination.items
return render_template('moderate.html', comments=comments, page=page, pagination=pagination)
@main.route('/moderate/enable/<int:id>')
@login_required
@permission_required(Permission.MODERATE)
def moderate_enable(id):
comment = Comment.query.get_or_404(id)
comment.disabled = False
db.session.add(comment)
db.session.commit()
page = request.args.get('page', 1 , type=int)
return redirect(url_for('main.moderate', page=page))
@main.route('/moderate/disable/<int:id>')
@login_required
@permission_required(Permission.MODERATE)
def moderate_disable(id):
comment = Comment.query.get_or_404(id)
comment.disabled = True
db.session.add(comment)
db.session.commit()
page = request.args.get('page', 1 , type=int)
return redirect(url_for('main.moderate', page=page))
@main.route('/shutdown')
def shutdown_server():
if not current_app.testing:
abort(404)
shutdown = request.environ.get('werkzeug.server.shutdown')
if not shutdown:
abort(500)
shutdown()
return "Shutting down.."
@main.after_app_request
def after_request(response):
for query in get_debug_queries():
if query.duration >= current_app.config['SLOW_DB_QUERY_TIME']:
current_app.logger.warning(
'Slow query: %s\nParameters: %s\nDuration: %fs\nContext: %s\n' %
(query.statement, query.parameters, query.duration, query.context)
)
return response
# @main.route('/user', methods=['GET', 'POST'])
# def user():
# form = NameForm()
# if form.validate_on_submit():
# # old_name = session.get('name')
# # if old_name is not None and old_name != form.name.data:
# # flash('Hey, looks like you changed your name.')
# user = User.query.filter_by(username=form.name.data).first()
# if user is None:
# user = User(username=form.name.data)
# db.session.add(user)
# db.session.commit()
# session['known'] = False
# send_email('New User', '[email protected]', 'mail/new_user', user=user)
# else:
# session['known'] = True
# session['name'] = form.name.data
# form.name.data = ''
# return redirect(url_for('main.user'))
# return render_template('user.html', form=form, name=session.get('name'), known=session.get('known', False)) | [
"[email protected]"
]
| |
104d203a342e3451de87019b3f772f1417367208 | 980a90caca1524f717c1d27aa489a094334614da | /2019-01 code.py | 11f18521ae9460f1053fdcca78f049acf28d236f | []
| no_license | tagoria/AdventOfCode2019 | 00fa03fa558f17a2bd3bb8650dcedff346902c01 | 41124e4f9604999ca6fa57bf2cc91ebc5e448e05 | refs/heads/master | 2023-04-10T01:17:00.152895 | 2021-04-17T11:26:57 | 2021-04-17T11:26:57 | 358,859,396 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 849 | py | import string
import fileinput
import os
import sys
def part1() :
currentFile = __file__
spacePosition = currentFile.find(" ")
inputPath = currentFile[0:spacePosition] + " input.txt"
fuelNeeded = 0
with open(inputPath, "r") as puzzleInput:
for line in puzzleInput:
massNeeded = int(line)
fuelNeeded = fuelNeeded + ((massNeeded//3) - 2)
print(fuelNeeded)
def part2() :
currentFile = __file__
spacePosition = currentFile.find(" ")
inputPath = currentFile[0:spacePosition] + " input.txt"
fuelNeeded = 0
with open(inputPath, "r") as puzzleInput:
for line in puzzleInput:
massNeeded = int(line)
while (massNeeded := massNeeded//3 - 2)>0:
fuelNeeded = fuelNeeded + massNeeded
print(fuelNeeded)
print (sys.version)
part2() | [
"[email protected]"
]
| |
6635fd14f1af6c49980003fc31b60603e13ea0a4 | 3cb8220aaf53ab07fddd3706af4524d94f5eeaad | /userproj/users/urls.py | ded0461b2885c25d46c0241e5458865911412a57 | []
| no_license | guguponce/userproject | 5741092923d1558ad93cc16813640d5e4db117bd | 7596192bd96dbd76587493d354015d7c620c7944 | refs/heads/main | 2023-03-30T21:19:17.709465 | 2021-04-07T10:33:34 | 2021-04-07T10:33:34 | 355,480,457 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 195 | py | from django.conf.urls import url
from users import views
app_name = 'users'
urlpatterns = [
url(r'^$', views.users, name='users'),
# url(r'^forms/',views.formulario, name='forms')
]
| [
"[email protected]"
]
| |
d09f267b12df0380d0b55ee7ff1d47fd0d49c160 | ed5b7eab164bf434e500e38a946fc902ee7eac47 | /nlp_pytorch/chapter8/main.py | 1ea8ba0c5f33c97ac1a6d2881e8883968b48c07c | []
| no_license | happybear1234/machine-learning | 54269397cb02932368dbfcebb1fdf6cb2829d9e0 | 675ff6753771e2167c2a5179b1ffe49a918e478d | refs/heads/master | 2022-02-27T21:45:50.401754 | 2019-07-15T09:21:04 | 2019-07-15T09:21:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 21,207 | py | # -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： main
Description :
Author : haxu
date： 2019/4/14
-------------------------------------------------
Change Activity:
2019/4/14:
-------------------------------------------------
"""
__author__ = 'haxu'
from argparse import Namespace
import json
import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torch import nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torch import optim
class Vocabulary(object):
def __init__(self, token_to_idx=None):
if token_to_idx is None:
token_to_idx = {}
self._token_to_idx = token_to_idx
self._idx_to_token = {idx: token
for token, idx in self._token_to_idx.items()}
def to_serializable(self):
return {'token_to_idx': self._token_to_idx}
@classmethod
def from_serializable(cls, contents):
return cls(**contents)
def add_token(self, token):
if token in self._token_to_idx:
index = self._token_to_idx[token]
else:
index = len(self._token_to_idx)
self._token_to_idx[token] = index
self._idx_to_token[index] = token
return index
def add_many(self, tokens):
return [self.add_token(token) for token in tokens]
def lookup_token(self, token):
return self._token_to_idx[token]
def lookup_index(self, index):
if index not in self._idx_to_token:
raise KeyError("the index (%d) is not in the Vocabulary" % index)
return self._idx_to_token[index]
def __str__(self):
return "<Vocabulary(size=%d)>" % len(self)
def __len__(self):
return len(self._token_to_idx)
class SequenceVocabulary(Vocabulary):
def __init__(self, token_to_idx=None, unk_token="<UNK>",
mask_token="<MASK>", begin_seq_token="<BEGIN>",
end_seq_token="<END>"):
super(SequenceVocabulary, self).__init__(token_to_idx)
self._mask_token = mask_token
self._unk_token = unk_token
self._begin_seq_token = begin_seq_token
self._end_seq_token = end_seq_token
self.mask_index = self.add_token(self._mask_token)
self.unk_index = self.add_token(self._unk_token)
self.begin_seq_index = self.add_token(self._begin_seq_token)
self.end_seq_index = self.add_token(self._end_seq_token)
def to_serializable(self):
contents = super(SequenceVocabulary, self).to_serializable()
contents.update({'unk_token': self._unk_token,
'mask_token': self._mask_token,
'begin_seq_token': self._begin_seq_token,
'end_seq_token': self._end_seq_token})
return contents
def lookup_token(self, token):
if self.unk_index >= 0:
return self._token_to_idx.get(token, self.unk_index)
else:
return self._token_to_idx[token]
class NMTVectorizer(object):
def __init__(self, source_vocab, target_vocab, max_source_length, max_target_length):
"""
Args:
source_vocab (SequenceVocabulary): maps source words to integers
target_vocab (SequenceVocabulary): maps target words to integers
max_source_length (int): the longest sequence in the source dataset
max_target_length (int): the longest sequence in the target dataset
"""
self.source_vocab = source_vocab
self.target_vocab = target_vocab
self.max_source_length = max_source_length
self.max_target_length = max_target_length
def _vectorize(self, indices, vector_length=-1, mask_index=0):
"""Vectorize the provided indices
Args:
indices (list): a list of integers that represent a sequence
vector_length (int): an argument for forcing the length of index vector
mask_index (int): the mask_index to use; almost always 0
"""
if vector_length < 0:
vector_length = len(indices)
vector = np.zeros(vector_length, dtype=np.int)
vector[:len(indices)] = indices
vector[len(indices):] = mask_index
return vector
def _get_source_indices(self, text):
"""Return the vectorized source text
Args:
text (str): the source text; tokens should be separated by spaces
Returns:
indices (list): list of integers representing the text
"""
indices = [self.source_vocab.begin_seq_index]
indices.extend(self.source_vocab.lookup_token(token) for token in text.split(" "))
indices.append(self.source_vocab.end_seq_index)
return indices
def _get_target_indices(self, text):
"""Return the vectorized source text
Args:
text (str): the source text; tokens should be separated by spaces
Returns:
a tuple: (x_indices, y_indices)
x_indices (list): list of integers representing the observations in target decoder
y_indices (list): list of integers representing predictions in target decoder
"""
indices = [self.target_vocab.lookup_token(token) for token in text.split(" ")]
x_indices = [self.target_vocab.begin_seq_index] + indices
y_indices = indices + [self.target_vocab.end_seq_index]
return x_indices, y_indices
def vectorize(self, source_text, target_text, use_dataset_max_lengths=True):
source_vector_length = -1
target_vector_length = -1
if use_dataset_max_lengths:
source_vector_length = self.max_source_length + 2 # begin end
target_vector_length = self.max_target_length + 1 # end
source_indices = self._get_source_indices(source_text)
source_vector = self._vectorize(source_indices,
vector_length=source_vector_length,
mask_index=self.source_vocab.mask_index)
target_x_indices, target_y_indices = self._get_target_indices(target_text)
target_x_vector = self._vectorize(target_x_indices,
vector_length=target_vector_length,
mask_index=self.target_vocab.mask_index)
target_y_vector = self._vectorize(target_y_indices,
vector_length=target_vector_length,
mask_index=self.target_vocab.mask_index)
return {"source_vector": source_vector,
"target_x_vector": target_x_vector,
"target_y_vector": target_y_vector,
"source_length": len(source_indices)}
@classmethod
def from_dataframe(cls, bitext_df):
source_vocab = SequenceVocabulary()
target_vocab = SequenceVocabulary()
max_source_length = 0
max_target_length = 0
for _, row in bitext_df.iterrows():
source_tokens = row["source_language"].split(" ")
if len(source_tokens) > max_source_length:
max_source_length = len(source_tokens)
for token in source_tokens:
source_vocab.add_token(token)
target_tokens = row["target_language"].split(" ")
if len(target_tokens) > max_target_length:
max_target_length = len(target_tokens)
for token in target_tokens:
target_vocab.add_token(token)
return cls(source_vocab, target_vocab, max_source_length, max_target_length)
@classmethod
def from_serializable(cls, contents):
source_vocab = SequenceVocabulary.from_serializable(contents["source_vocab"])
target_vocab = SequenceVocabulary.from_serializable(contents["target_vocab"])
return cls(source_vocab=source_vocab,
target_vocab=target_vocab,
max_source_length=contents["max_source_length"],
max_target_length=contents["max_target_length"])
def to_serializable(self):
return {"source_vocab": self.source_vocab.to_serializable(),
"target_vocab": self.target_vocab.to_serializable(),
"max_source_length": self.max_source_length,
"max_target_length": self.max_target_length}
class NMTDataset(Dataset):
def __init__(self, text_df, vectorizer):
self.text_df = text_df
self._vectorizer = vectorizer
self.train_df = self.text_df[self.text_df.split == 'train']
self.train_size = len(self.train_df)
self.val_df = self.text_df[self.text_df.split == 'val']
self.validation_size = len(self.val_df)
self.test_df = self.text_df[self.text_df.split == 'test']
self.test_size = len(self.test_df)
self._lookup_dict = {'train': (self.train_df, self.train_size),
'val': (self.val_df, self.validation_size),
'test': (self.test_df, self.test_size)}
self.set_split('train')
@classmethod
def load_dataset_and_make_vectorizer(cls, dataset_csv):
text_df = pd.read_csv(dataset_csv)
train_subset = text_df[text_df.split == 'train']
return cls(text_df, NMTVectorizer.from_dataframe(train_subset))
@classmethod
def load_dataset_and_load_vectorizer(cls, dataset_csv, vectorizer_filepath):
text_df = pd.read_csv(dataset_csv)
vectorizer = cls.load_vectorizer_only(vectorizer_filepath)
return cls(text_df, vectorizer)
@staticmethod
def load_vectorizer_only(vectorizer_filepath):
with open(vectorizer_filepath) as fp:
return NMTVectorizer.from_serializable(json.load(fp))
def save_vectorizer(self, vectorizer_filepath):
with open(vectorizer_filepath, "w") as fp:
json.dump(self._vectorizer.to_serializable(), fp)
def get_vectorizer(self):
return self._vectorizer
def set_split(self, split="train"):
self._target_split = split
self._target_df, self._target_size = self._lookup_dict[split]
def __len__(self):
return self._target_size
def __getitem__(self, index):
row = self._target_df.iloc[index]
vector_dict = self._vectorizer.vectorize(row.source_language, row.target_language)
return {"x_source": vector_dict["source_vector"],
"x_target": vector_dict["target_x_vector"],
"y_target": vector_dict["target_y_vector"],
"x_source_length": vector_dict["source_length"]}
def get_num_batches(self, batch_size):
return len(self) // batch_size
def generate_nmt_batches(dataset, batch_size, shuffle=False,
drop_last=True, device="cpu"):
"""A generator function which wraps the PyTorch DataLoader. The NMT Version """
""" 同时对长度进行排序 从大到小"""
dataloader = DataLoader(dataset=dataset, batch_size=batch_size,
shuffle=shuffle, drop_last=drop_last)
for data_dict in dataloader:
lengths = data_dict['x_source_length'].numpy()
sorted_length_indices = lengths.argsort()[::-1].tolist()
out_data_dict = {}
for name, tensor in data_dict.items():
out_data_dict[name] = data_dict[name][sorted_length_indices].to(device)
yield out_data_dict
class NMTEncoder(nn.Module):
def __init__(self, num_embeddings, embedding_size, rnn_hidden_size):
super(NMTEncoder, self).__init__()
self.source_embedding = nn.Embedding(
num_embeddings=num_embeddings,
embedding_dim=embedding_size,
padding_idx=0,
)
self.birnn = nn.GRU(
embedding_size, rnn_hidden_size, bidirectional=True, batch_first=True
)
def forward(self, x_source, x_lengths):
"""
:param x_source: （bs, 25）
:param x_lengths: (bs, )
:return:
"""
x_embeded = self.source_embedding(x_source) # (bs, 25, 64)
x_lengths = x_lengths.numpy() # (bs,)
x_packed = pack_padded_sequence(x_embeded, x_lengths, batch_first=True) # (sum(x_lengths), 64)
x_birnn_out, x_birnn_h = self.birnn(x_packed) # [(sum(x_lengths), 128*2), (2, bs, 128)]
x_birnn_h = x_birnn_h.permute(1, 0, 2) # (bs, 2, 128)
x_birnn_h = x_birnn_h.contiguous().view(x_birnn_h.size(0), -1) # (bs, 256)
x_unpacked, _ = pad_packed_sequence(x_birnn_out, batch_first=True) # (bs, ?,256)
# (bs, 10, 256)
# (bs, 256)
return x_unpacked, x_birnn_h
def verbose_attention(encoder_state_vectors, query_vector):
# (bs, max_len, 256)
# (bs, 256)
batch_size, num_vectors, vector_size = encoder_state_vectors.size()
vector_scores = torch.sum(encoder_state_vectors * query_vector.view(batch_size, 1, vector_size),
dim=2) # (bs, max_len)
vector_probabilities = F.softmax(vector_scores, dim=1) # (bs, max_len)
weighted_vectors = encoder_state_vectors * vector_probabilities.view(batch_size,
num_vectors, 1) # (bs, max_len, 256)
context_vectors = torch.sum(weighted_vectors, dim=1) # (bs, 256)
return context_vectors, vector_probabilities, vector_scores
class NMTDecoder(nn.Module):
def __init__(self, num_embeddings, embedding_size, rnn_hidden_size, bos_index):
super(NMTDecoder, self).__init__()
self._rnn_hidden_size = rnn_hidden_size
self.target_embedding = nn.Embedding(num_embeddings=num_embeddings,
embedding_dim=embedding_size,
padding_idx=0)
self.gru_cell = nn.GRUCell(embedding_size + rnn_hidden_size,
rnn_hidden_size)
self.hidden_map = nn.Linear(rnn_hidden_size, rnn_hidden_size)
self.classifier = nn.Linear(rnn_hidden_size * 2, num_embeddings)
self.bos_index = bos_index
self._sampling_temperature = 3
def _init_indices(self, batch_size):
return torch.ones(batch_size, dtype=torch.int64) * self.bos_index
def _init_context_vectors(self, batch_size):
return torch.zeros(batch_size, self._rnn_hidden_size)
def forward(self, encoder_state, initial_hidden_state, target_sequence, sample_probability=0.0):
"""
:param encoder_state: (bs, max_len, 256)
:param initial_hidden_state: (bs, 256)
:param target_sequence: (bs, 25) target
:param sample_probability:
:return:
"""
if target_sequence is None:
sample_probability = 1.
else:
target_sequence = target_sequence.permute(1, 0) # （25,bs）
h_t = self.hidden_map(initial_hidden_state) # (bs, 256)
batch_size = encoder_state.size(0) # bs
context_vectors = self._init_context_vectors(batch_size) # (bs, 256)
y_t_index = self._init_indices(batch_size) # (bs, ) [2] * bs
device = encoder_state.device
h_t = h_t.to(device)
y_t_index = y_t_index.to(device)
context_vectors = context_vectors.to(device)
output_vectors = []
self._cached_p_attn = []
self._cached_ht = []
self._cached_decoder_state = encoder_state.cpu().detach().numpy() # (bs ,10, 256)
output_sequence_size = target_sequence.size(0) # 25
for i in range(output_sequence_size):
use_sample = np.random.random() < sample_probability
if not use_sample:
y_t_index = target_sequence[i]
y_input_vector = self.target_embedding(y_t_index) # (bs, 64)
rnn_input = torch.cat([y_input_vector, context_vectors], dim=1) # (bs, 64 + 256)
h_t = self.gru_cell(rnn_input, h_t) # (bs, 256)
self._cached_ht.append(h_t.cpu().data.numpy())
# (bs, max_len, 256)
# (bs, 256)
# 输出
# (bs ,256)
# (bs, max_len)
context_vectors, p_attn, _ = verbose_attention(
encoder_state_vectors=encoder_state,
query_vector=h_t,
)
self._cached_p_attn.append(p_attn.cpu().detach().numpy())
prediction_vector = torch.cat((context_vectors, h_t), dim=1)
score_for_y_t_index = self.classifier(F.dropout(prediction_vector, 0.3)) # (bs, 4911)
if use_sample:
p_y_t_index = F.softmax(score_for_y_t_index * self._sampling_temperature, dim=1)
y_t_index = torch.multinomial(p_y_t_index, 1).squeeze()
output_vectors.append(score_for_y_t_index)
# (25, 5, 4911)
output_vectors = torch.stack(output_vectors).permute(1, 0, 2) # (bs, 25, 4911)
return output_vectors
class NMTModel(nn.Module):
def __init__(self, source_vocab_size, source_embedding_size,
target_vocab_size, target_embedding_size, encoding_size,
target_bos_index):
super(NMTModel, self).__init__()
self.encoder = NMTEncoder(num_embeddings=source_vocab_size,
embedding_size=source_embedding_size,
rnn_hidden_size=encoding_size)
decoding_size = encoding_size * 2
self.decoder = NMTDecoder(num_embeddings=target_vocab_size,
embedding_size=target_embedding_size,
rnn_hidden_size=decoding_size,
bos_index=target_bos_index)
def forward(self, x_source, x_source_lengths, target_sequence, sample_probability=0.5):
"""
:param x_source: (batch, vectorizer.max_source_length) (bs,25)
:param x_source_lengths: length of the sequence (bs,)
:param target_sequence: target text data tensor (bs, 25)
:return: prediction vectors at each output step
"""
# (bs, 10, 256)
# (bs, 256)
encoder_state, final_hidden_states = self.encoder(x_source, x_source_lengths)
decoded_states = self.decoder(encoder_state,
final_hidden_states,
target_sequence,
sample_probability=sample_probability,
)
return decoded_states
def normalize_sizes(y_pred, y_true):
if len(y_pred.size()) == 3:
y_pred = y_pred.contiguous().view(-1, y_pred.size(2))
if len(y_true.size()) == 2:
y_true = y_true.contiguous().view(-1)
return y_pred, y_true
def compute_accuracy(y_pred, y_true, mask_index):
y_pred, y_true = normalize_sizes(y_pred, y_true)
_, y_pred_indices = y_pred.max(dim=1)
correct_indices = torch.eq(y_pred_indices, y_true).float()
valid_indices = torch.ne(y_true, mask_index).float()
n_correct = (correct_indices * valid_indices).sum().item()
n_valid = valid_indices.sum().item()
return n_correct / n_valid * 100
def sequence_loss(y_pred, y_true, mask_index):
y_pred, y_true = normalize_sizes(y_pred, y_true)
return F.cross_entropy(y_pred, y_true, ignore_index=mask_index)
if __name__ == '__main__':
args = Namespace(
dataset_csv="simplest_eng_fra.csv",
vectorizer_file="vectorizer.json",
learning_rate=5e-4,
batch_size=5,
source_embedding_size=64,
target_embedding_size=64,
encoding_size=128,
device='cpu',
)
dataset = NMTDataset.load_dataset_and_make_vectorizer(args.dataset_csv)
dataset.save_vectorizer(args.vectorizer_file)
vectorizer = dataset.get_vectorizer()
mask_index = vectorizer.target_vocab.mask_index
dataset.set_split('train')
batch_generator = generate_nmt_batches(dataset,
batch_size=args.batch_size,
device=args.device)
model = NMTModel(
source_vocab_size=len(vectorizer.source_vocab),
source_embedding_size=args.source_embedding_size,
target_vocab_size=len(vectorizer.target_vocab),
target_embedding_size=args.target_embedding_size,
encoding_size=args.encoding_size,
target_bos_index=vectorizer.target_vocab.begin_seq_index
)
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
for batch_idx, batch_dict in enumerate(batch_generator):
optimizer.zero_grad()
y_pred = model(batch_dict['x_source'],
batch_dict['x_source_length'],
batch_dict['x_target'],
sample_probability=0.5,
)
loss = sequence_loss(y_pred, batch_dict['y_target'], mask_index)
loss.backward()
optimizer.step()
print(loss.item())
| [
"[email protected]"
]
| |
c0e496f15f2e4e536952162bbb5ccdacfbd87daf | fe6df3bca3c6f723dab6850cfc9bc5dcda028077 | /WhileLoop_08/App01.py | a7eb576fbeb7448345ee2e26783849e4fea3a4ba | []
| no_license | daadestroyer/20MCAOOPS | eb0b7a69868cadf70ed8c42669252fc8b7698689 | c96b887f963be17a5d969cb9e20b99090c57dedd | refs/heads/main | 2023-06-27T13:06:41.763583 | 2021-08-04T17:01:19 | 2021-08-04T17:01:19 | 331,950,642 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 343 | py | '''
Python Loops
Python has two primitive loop commands:
while loops
for loops
'''
i = 0
while i <= 10:
print(i)
i += 1
# The break Statement
print()
i = 1
while i < 6:
print(i)
if i == 3:
break
i += 1
# The continue Statement
print()
i = 0
while i < 6:
i += 1
if i == 3:
continue
print(i)
| [
"[email protected]"
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| |
1cef0677f83c3d9e49ab455395e33c8517db3503 | 0558bf417f6dfccb802e39abe03130f8139d1126 | /LeetCode/3-longest-substring-without-repeating-characters.py | 67f989c7f898cb430ec0b3023f4f724b443f1d31 | []
| no_license | Zayu-Club/Playground | bdf2d4cf82031dac508df7ba01d969b56dacdc24 | e8b2cdbd9fdc2d9fe6da3289645530bb7bb20143 | refs/heads/master | 2023-09-05T23:01:38.823308 | 2021-11-21T09:36:34 | 2021-11-21T09:36:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 927 | py | class Solution:
def lengthOfLongestSubstring(self, s: str) -> int:
if len(s) == 1 or len(s) == 0:
return len(s)
hashMap = dict()
max_len = 0
begin_index = 0
for i, si in enumerate(s):
if si in hashMap.keys():
new_begin_index = hashMap[si] + 1
begin_index = begin_index if begin_index > new_begin_index else new_begin_index
hashMap[si] = i
windows_len = i - begin_index
max_len = windows_len if windows_len > max_len else max_len
return max_len + 1
s = Solution()
print(s.lengthOfLongestSubstring("abcabcbb"), 3)
print(s.lengthOfLongestSubstring("bbbbb"), 1)
print(s.lengthOfLongestSubstring("pwwkew"), 3)
print(s.lengthOfLongestSubstring(""), 0)
print(s.lengthOfLongestSubstring(" "), 1)
print(s.lengthOfLongestSubstring("abba"), 2)
print(s.lengthOfLongestSubstring("dvdf"), 3)
| [
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| |
907107ef98f88293e5eab6076021cbe6900e6c7d | 44acca58155b0a5a2b46d6a9ed255befece4f5d1 | /api_vendas/api_vendas/wsgi.py | 298a0f3193ddd7ce468b07db9e5f06b15df79e98 | []
| no_license | GeovaneCavalcante/appHubVendas | 6f6c74cb2f94b2534ab1c3d0f241422fb88b81f4 | 068bb08e2a270d132e60502c35edc11a4526f671 | refs/heads/master | 2020-03-20T07:22:32.555287 | 2018-06-13T22:38:53 | 2018-06-13T22:38:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 398 | py | """
WSGI config for api_vendas project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "api_vendas.settings")
application = get_wsgi_application()
| [
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]
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57e50197193509c44c617169693c5d944c8f76f3 | 393ccacef32461f5d7f4b21419a7c695df9c62a7 | /lpo/sp/fmail/fmail.admin/datas/postcodes/81.cgi | 713794904723a1b6c22d73975a7aabfd7c129bf5 | []
| no_license | emoshu-yuta-okuma/nakagawa-dent-hp | ebc6c66efc624a256f0d7e30c2e26b9aae162cd7 | e83e8c7060881b7267f90ca3f2c599d614a219a1 | refs/heads/master | 2023-01-14T12:39:19.874341 | 2020-11-12T06:33:00 | 2020-11-12T06:33:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 160,735 | cgi | 40131,813,8130000,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市東区,以下に掲載がない場合,0,0,0,0,0,0
40131,813,8130025,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｱｵﾊﾞ,福岡県,福岡市東区,青葉,0,0,1,0,0,0
40131,81103,8110322,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｵｵﾀｹ,福岡県,福岡市東区,大岳,0,0,1,0,0,0
40131,812,8120052,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｲﾂﾞｶﾀﾞﾝﾁ,福岡県,福岡市東区,貝塚団地,0,0,0,0,0,0
40131,813,8130011,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲ,福岡県,福岡市東区,香椎,0,0,1,0,0,0
40131,813,8130013,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲｴｷﾏｴ,福岡県,福岡市東区,香椎駅前,0,0,1,0,0,0
40131,813,8130012,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲｴｷﾋｶﾞｼ,福岡県,福岡市東区,香椎駅東,0,0,1,0,0,0
40131,813,8130014,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲﾀﾞｲ,福岡県,福岡市東区,香椎台,0,0,1,0,0,0
40131,813,8130015,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲﾀﾞﾝﾁ,福岡県,福岡市東区,香椎団地,0,0,0,0,0,0
40131,813,8130017,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲﾃﾘﾊ,福岡県,福岡市東区,香椎照葉,0,0,1,0,0,0
40131,813,8130016,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲﾊﾏ,福岡県,福岡市東区,香椎浜,0,0,1,0,0,0
40131,813,8130018,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｼｲﾊﾏﾌﾄｳ,福岡県,福岡市東区,香椎浜ふ頭,0,0,1,0,0,0
40131,813,8130003,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶｽﾐｶﾞｵｶ,福岡県,福岡市東区,香住ケ丘,0,0,1,0,0,0
40131,81103,8110325,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶﾂﾏ,福岡県,福岡市東区,勝馬,0,0,0,0,0,0
40131,813,8130023,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶﾏﾀ,福岡県,福岡市東区,蒲田,0,0,1,0,0,0
40131,81102,8110216,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶﾐﾜｼﾞﾛ,福岡県,福岡市東区,上和白,0,0,0,0,0,0
40131,81102,8110206,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｶﾞﾝﾉｽ,福岡県,福岡市東区,雁の巣,0,0,1,0,0,0
40131,812,8120069,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｺﾞｳｸﾞﾁﾏﾁ,福岡県,福岡市東区,郷口町,0,0,0,0,0,0
40131,81103,8110321,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｻｲﾄｻﾞｷ,福岡県,福岡市東区,西戸崎,0,0,1,0,0,0
40131,81102,8110203,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｼｵﾊﾏ,福岡県,福岡市東区,塩浜,0,0,1,0,0,0
40131,81103,8110323,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｼｶｼﾏ,福岡県,福岡市東区,志賀島,0,0,0,0,0,0
40131,813,8130002,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｼﾓﾊﾞﾙ,福岡県,福岡市東区,下原,0,0,1,0,0,0
40131,812,8120068,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｼｬﾘｮｳ,福岡県,福岡市東区,社領,0,0,1,0,0,0
40131,813,8130045,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ｼﾛﾊﾏﾀﾞﾝﾁ,福岡県,福岡市東区,城浜団地,0,0,0,0,0,0
40131,81102,8110215,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾀｶﾐﾀﾞｲ,福岡県,福岡市東区,高美台,0,0,1,0,0,0
40131,813,8130033,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾀﾀﾗ,福岡県,福岡市東区,多々良,0,0,1,0,0,0
40131,813,8130034,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾀﾉﾂ,福岡県,福岡市東区,多の津,0,0,1,0,0,0
40131,813,8130044,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾁﾊﾔ,福岡県,福岡市東区,千早,0,0,1,0,0,0
40131,813,8130032,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾄﾞｲ,福岡県,福岡市東区,土井,0,0,1,0,0,0
40131,813,8130001,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾄｳﾉﾊﾙ,福岡県,福岡市東区,唐原,0,0,1,0,0,0
40131,813,8130024,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾅｺﾞ,福岡県,福岡市東区,名子,0,0,1,0,0,0
40131,813,8130043,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾅｼﾞﾏ,福岡県,福岡市東区,名島,0,0,1,0,0,0
40131,81102,8110204,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾅﾀ,福岡県,福岡市東区,奈多,0,0,1,0,0,0
40131,81102,8110205,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾅﾀﾀﾞﾝﾁ,福岡県,福岡市東区,奈多団地,0,0,0,0,0,0
40131,812,8120053,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾊｺｻﾞｷ,福岡県,福岡市東区,箱崎,0,0,1,0,0,0
40131,812,8120051,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾊｺｻﾞｷﾌﾄｳ,福岡県,福岡市東区,箱崎ふ頭,0,0,1,0,0,0
40131,812,8120061,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾊｺﾏﾂ,福岡県,福岡市東区,筥松,0,0,1,0,0,0
40131,812,8120067,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾊｺﾏﾂｼﾝﾏﾁ,福岡県,福岡市東区,筥松新町,0,0,0,0,0,0
40131,813,8130031,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾊｯﾀ,福岡県,福岡市東区,八田,0,0,1,0,0,0
40131,812,8120063,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾊﾗﾀﾞ,福岡県,福岡市東区,原田,0,0,1,0,0,0
40131,812,8120055,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾋｶﾞｼﾊﾏ,福岡県,福岡市東区,東浜,0,0,1,0,0,0
40131,81103,8110324,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾋﾛ,福岡県,福岡市東区,弘,0,0,0,0,0,0
40131,812,8120066,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾌﾀﾏﾀｾ,福岡県,福岡市東区,二又瀬,0,0,0,0,0,0
40131,812,8120065,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾌﾀﾏﾀｾｼﾝﾏﾁ,福岡県,福岡市東区,二又瀬新町,0,0,0,0,0,0
40131,812,8120054,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏｲﾀﾞｼ,福岡県,福岡市東区,馬出,0,0,1,0,0,0
40131,813,8130042,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏｲﾏﾂﾊﾞﾗ,福岡県,福岡市東区,舞松原,0,0,1,0,0,0
40131,813,8130004,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏﾂｶﾀﾞｲ,福岡県,福岡市東区,松香台,0,0,1,0,0,0
40131,813,8130035,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏﾂｻﾞｷ,福岡県,福岡市東区,松崎,0,0,1,0,0,0
40131,812,8120062,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏﾂｼﾏ(1､2ﾁｮｳﾒ),福岡県,福岡市東区,松島（１、２丁目）,1,0,1,0,0,0
40131,813,8130062,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏﾂｼﾏ(3-6ﾁｮｳﾒ),福岡県,福岡市東区,松島（３〜６丁目）,1,0,1,0,0,0
40131,812,8120064,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾏﾂﾀﾞ,福岡県,福岡市東区,松田,0,0,1,0,0,0
40131,813,8130005,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐｼﾏｻﾞｷ,福岡県,福岡市東区,御島崎,0,0,1,0,0,0
40131,813,8130041,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐｽﾞﾀﾆ,福岡県,福岡市東区,水谷,0,0,1,0,0,0
40131,81102,8110201,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐﾄﾏ,福岡県,福岡市東区,三苫,0,0,1,0,0,0
40131,813,8130021,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐﾄﾞﾘｶﾞｵｶ,福岡県,福岡市東区,みどりが丘,0,0,1,0,0,0
40131,813,8130019,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐﾅﾄｶｼｲ,福岡県,福岡市東区,みなと香椎,0,0,1,0,0,0
40131,81102,8110212,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐﾜﾀﾞｲ,福岡県,福岡市東区,美和台,0,0,1,0,0,0
40131,81102,8110211,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾐﾜﾀﾞｲｼﾝﾏﾁ,福岡県,福岡市東区,美和台新町,0,0,0,0,0,0
40131,813,8130036,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾜｶﾐﾔ,福岡県,福岡市東区,若宮,0,0,1,0,0,0
40131,81102,8110202,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾜｼﾞﾛ,福岡県,福岡市東区,和白,0,0,1,0,0,0
40131,81102,8110213,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾜｼﾞﾛｶﾞｵｶ,福岡県,福岡市東区,和白丘,0,0,1,0,0,0
40131,81102,8110214,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾋｶﾞｼｸ,ﾜｼﾞﾛﾋｶﾞｼ,福岡県,福岡市東区,和白東,0,0,1,0,0,0
40132,812,8120000,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市博多区,以下に掲載がない場合,0,0,0,0,0,0
40132,816,8120885,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｱｲｵｲﾏﾁ,福岡県,福岡市博多区,相生町,0,0,1,0,0,0
40132,816,8120851,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｱｵｷ,福岡県,福岡市博多区,青木,0,0,1,0,0,0
40132,816,8120881,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｲｿｳﾀﾞ,福岡県,福岡市博多区,井相田,0,0,1,0,0,0
40132,816,8120888,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｲﾀﾂﾞｹ,福岡県,福岡市博多区,板付,0,0,1,0,0,0
40132,816,8120861,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｳﾗﾀ,福岡県,福岡市博多区,浦田,0,0,1,0,0,0
40132,812,8120004,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｴﾉｷﾀﾞ,福岡県,福岡市博多区,榎田,0,0,1,0,0,0
40132,812,8120001,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｵｵｲ,福岡県,福岡市博多区,大井,0,0,1,0,0,0
40132,812,8120031,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｵｷﾊﾏﾏﾁ,福岡県,福岡市博多区,沖浜町,0,0,0,0,0,0
40132,812,8120043,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾀｶｽ,福岡県,福岡市博多区,堅粕,0,0,1,0,0,0
40132,816,8120863,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾈﾉｸﾏ,福岡県,福岡市博多区,金の隈,0,0,1,0,0,0
40132,812,8120005,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾐｳｽｲ,福岡県,福岡市博多区,上臼井,0,0,0,0,0,0
40132,812,8120026,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾐｶﾜﾊﾞﾀﾏﾁ,福岡県,福岡市博多区,上川端町,0,0,0,0,0,0
40132,812,8120036,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾐｺﾞﾌｸﾏﾁ,福岡県,福岡市博多区,上呉服町,0,0,0,0,0,0
40132,812,8120006,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾐﾑﾀ,福岡県,福岡市博多区,上牟田,0,0,1,0,0,0
40132,812,8120022,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｶﾐﾔﾏﾁ,福岡県,福岡市博多区,神屋町,0,0,0,0,0,0
40132,812,8120038,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｷﾞｵﾝﾏﾁ,福岡県,福岡市博多区,祇園町,0,0,0,0,0,0
40132,816,8120879,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｷﾞﾝﾃﾝﾁｮｳ,福岡県,福岡市博多区,銀天町,0,0,1,0,0,0
40132,812,8120002,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｸｳｺｳﾏｴ,福岡県,福岡市博多区,空港前,0,0,1,0,0,0
40132,812,8120037,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｺﾞｸｼｮﾏﾁ,福岡県,福岡市博多区,御供所町,0,0,0,0,0,0
40132,816,8120884,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｺﾄﾌﾞｷﾁｮｳ,福岡県,福岡市博多区,寿町,0,0,1,0,0,0
40132,812,8120029,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｺﾓﾝﾄﾞﾏﾁ,福岡県,福岡市博多区,古門戸町,0,0,0,0,0,0
40132,816,8120891,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｻｻｲ,福岡県,福岡市博多区,雀居,0,0,0,0,0,0
40132,816,8120887,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｻﾝﾁｸ,福岡県,福岡市博多区,三筑,0,0,1,0,0,0
40132,812,8120015,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｻﾝﾉｳ,福岡県,福岡市博多区,山王,0,0,1,0,0,0
40132,816,8120871,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼﾉﾉﾒﾏﾁ,福岡県,福岡市博多区,東雲町,0,0,1,0,0,0
40132,812,8120003,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼﾓｳｽｲ,福岡県,福岡市博多区,下臼井,0,0,0,0,0,0
40132,812,8120027,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼﾓｶﾜﾊﾞﾀﾏﾁ,福岡県,福岡市博多区,下川端町,0,0,0,0,0,0
40132,812,8120034,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼﾓｺﾞﾌｸﾏﾁ,福岡県,福岡市博多区,下呉服町,0,0,0,0,0,0
40132,816,8120855,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼﾓﾂｷｸﾞﾏ,福岡県,福岡市博多区,下月隈,0,0,0,0,0,0
40132,816,8120876,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼｮｳﾅﾝﾏﾁ,福岡県,福岡市博多区,昭南町,0,0,1,0,0,0
40132,816,8120875,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｼﾝﾜﾏﾁ,福岡県,福岡市博多区,新和町,0,0,1,0,0,0
40132,812,8120028,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｽｻｷﾏﾁ,福岡県,福岡市博多区,須崎町,0,0,0,0,0,0
40132,812,8120018,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｽﾐﾖｼ,福岡県,福岡市博多区,住吉,0,0,1,0,0,0
40132,812,8120032,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ｾｷｼﾞｮｳﾏﾁ,福岡県,福岡市博多区,石城町,0,0,0,0,0,0
40132,812,8120033,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾀｲﾊｸﾏﾁ,福岡県,福岡市博多区,大博町,0,0,0,0,0,0
40132,816,8120878,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾀｹｵｶﾏﾁ,福岡県,福岡市博多区,竹丘町,0,0,1,0,0,0
40132,816,8120895,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾀｹｼﾀ,福岡県,福岡市博多区,竹下,0,0,1,0,0,0
40132,812,8120021,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾁｯｺｳﾎﾝﾏﾁ,福岡県,福岡市博多区,築港本町,0,0,0,0,0,0
40132,812,8120044,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾁﾖ,福岡県,福岡市博多区,千代,0,0,1,0,0,0
40132,816,8120858,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾂｷｸﾞﾏ,福岡県,福岡市博多区,月隈,0,0,1,0,0,0
40132,812,8120024,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾂﾅﾊﾞﾏﾁ,福岡県,福岡市博多区,綱場町,0,0,0,0,0,0
40132,812,8120020,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾂﾏｼｮｳｼﾞ,福岡県,福岡市博多区,対馬小路,0,0,0,0,0,0
40132,812,8120025,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾃﾝﾔﾏﾁ,福岡県,福岡市博多区,店屋町,0,0,0,0,0,0
40132,812,8120008,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾄｳｺｳ,福岡県,福岡市博多区,東光,0,0,1,0,0,0
40132,816,8120896,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾄｳｺｳｼﾞﾏﾁ,福岡県,福岡市博多区,東光寺町,0,0,1,0,0,0
40132,816,8120893,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾅｶ,福岡県,福岡市博多区,那珂,0,0,1,0,0,0
40132,812,8120035,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾅｶｺﾞﾌｸﾏﾁ,福岡県,福岡市博多区,中呉服町,0,0,0,0,0,0
40132,810,8100801,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾅｶｽ,福岡県,福岡市博多区,中洲,0,0,1,0,0,0
40132,810,8100802,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾅｶｽﾅｶｼﾏﾏﾁ,福岡県,福岡市博多区,中洲中島町,0,0,0,0,0,0
40132,812,8120023,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾅﾗﾔﾏﾁ,福岡県,福岡市博多区,奈良屋町,0,0,0,0,0,0
40132,816,8120857,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾆｼﾂｷｸﾞﾏ,福岡県,福岡市博多区,西月隈,0,0,1,0,0,0
40132,816,8120873,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾆｼﾊﾙﾏﾁ,福岡県,福岡市博多区,西春町,0,0,1,0,0,0
40132,812,8120012,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾊｶﾀｴｷﾁｭｳｵｳｶﾞｲ,福岡県,福岡市博多区,博多駅中央街,0,0,0,0,0,0
40132,812,8120011,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾊｶﾀｴｷﾏｴ,福岡県,福岡市博多区,博多駅前,0,0,1,0,0,0
40132,812,8120013,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾊｶﾀｴｷﾋｶﾞｼ,福岡県,福岡市博多区,博多駅東,0,0,1,0,0,0
40132,812,8120016,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾊｶﾀｴｷﾐﾅﾐ,福岡県,福岡市博多区,博多駅南,0,0,1,0,0,0
40132,816,8120872,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾊﾙﾏﾁ,福岡県,福岡市博多区,春町,0,0,1,0,0,0
40132,816,8120897,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾊﾝﾐﾁﾊﾞｼ,福岡県,福岡市博多区,半道橋,0,0,1,0,0,0
40132,812,8120014,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｴﾏﾁ,福岡県,福岡市博多区,比恵町,0,0,0,0,0,0
40132,816,8120874,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾘｶﾞｵｶﾏﾁ,福岡県,福岡市博多区,光丘町,0,0,1,0,0,0
40132,812,8120045,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾞｼｺｳｴﾝ,福岡県,福岡市博多区,東公園,0,0,0,0,0,0
40132,816,8120854,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾞｼﾂｷｸﾞﾏ,福岡県,福岡市博多区,東月隈,0,0,1,0,0,0
40132,816,8120892,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾞｼﾅｶ,福岡県,福岡市博多区,東那珂,0,0,1,0,0,0
40132,812,8120007,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾞｼﾋｴ,福岡県,福岡市博多区,東比恵,0,0,1,0,0,0
40132,816,8120853,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾞｼﾋﾗｵ,福岡県,福岡市博多区,東平尾,0,0,0,0,0,0
40132,816,8120852,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾋｶﾞｼﾋﾗｵｺｳｴﾝ,福岡県,福岡市博多区,東平尾公園,0,0,1,0,0,0
40132,816,8120886,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾐﾅﾐﾊﾁﾏﾝﾏﾁ,福岡県,福岡市博多区,南八幡町,0,0,1,0,0,0
40132,816,8120883,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾐﾅﾐﾎﾝﾏﾁ,福岡県,福岡市博多区,南本町,0,0,1,0,0,0
40132,812,8120017,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾐﾉｼﾏ,福岡県,福岡市博多区,美野島,0,0,1,0,0,0
40132,816,8120882,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾑｷﾞﾉ,福岡県,福岡市博多区,麦野,0,0,1,0,0,0
40132,816,8120877,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾓﾄﾏﾁ,福岡県,福岡市博多区,元町,0,0,1,0,0,0
40132,816,8120894,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾓﾛｵｶ,福岡県,福岡市博多区,諸岡,0,0,1,0,0,0
40132,812,8120042,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾕﾀｶ,福岡県,福岡市博多区,豊,0,0,1,0,0,0
40132,812,8120041,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾖｼﾂﾞｶ,福岡県,福岡市博多区,吉塚,0,0,1,0,0,0
40132,812,8120046,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾖｼﾂﾞｶﾎﾝﾏﾁ,福岡県,福岡市博多区,吉塚本町,0,0,0,0,0,0
40132,816,8120862,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾘｭｳｹﾞｼﾞ,福岡県,福岡市博多区,立花寺,0,0,1,0,0,0
40132,812,8120039,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾊｶﾀｸ,ﾚｲｾﾝﾏﾁ,福岡県,福岡市博多区,冷泉町,0,0,0,0,0,0
40133,810,8100000,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市中央区,以下に掲載がない場合,0,0,0,0,0,0
40133,810,8100042,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｱｶｻｶ,福岡県,福岡市中央区,赤坂,0,0,1,0,0,0
40133,810,8100076,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｱﾗﾂ,福岡県,福岡市中央区,荒津,0,0,1,0,0,0
40133,810,8100062,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｱﾗﾄ,福岡県,福岡市中央区,荒戸,0,0,1,0,0,0
40133,810,8100067,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｲｻﾞｷ,福岡県,福岡市中央区,伊崎,0,0,0,0,0,0
40133,810,8100021,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｲﾏｲｽﾞﾐ,福岡県,福岡市中央区,今泉,0,0,1,0,0,0
40133,810,8100054,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｲﾏｶﾞﾜ,福岡県,福岡市中央区,今川,0,0,1,0,0,0
40133,810,8100074,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｵｵﾃﾓﾝ,福岡県,福岡市中央区,大手門,0,0,1,0,0,0
40133,810,8100052,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｵｵﾎﾘ,福岡県,福岡市中央区,大濠,0,0,1,0,0,0
40133,810,8100051,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｵｵﾎﾘｺｳｴﾝ,福岡県,福岡市中央区,大濠公園,0,0,0,0,0,0
40133,810,8100013,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｵｵﾐﾔ,福岡県,福岡市中央区,大宮,0,0,1,0,0,0
40133,810,8100033,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｵｻﾞｻ,福岡県,福岡市中央区,小笹,0,0,1,0,0,0
40133,810,8100005,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｷﾖｶﾜ,福岡県,福岡市中央区,清川,0,0,1,0,0,0
40133,810,8100045,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｸｻｶﾞｴ,福岡県,福岡市中央区,草香江,0,0,1,0,0,0
40133,810,8100055,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｸﾛﾓﾝ,福岡県,福岡市中央区,黒門,0,0,0,0,0,0
40133,810,8100023,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｹｺﾞ,福岡県,福岡市中央区,警固,0,0,1,0,0,0
40133,810,8100027,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｺﾞｼｮｶﾞﾀﾞﾆ,福岡県,福岡市中央区,御所ケ谷,0,0,0,0,0,0
40133,810,8100024,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｻｸﾗｻﾞｶ,福岡県,福岡市中央区,桜坂,0,0,1,0,0,0
40133,810,8100034,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｻｻｵｶ,福岡県,福岡市中央区,笹丘,0,0,1,0,0,0
40133,810,8100018,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｻﾝｿｳﾄﾞｵﾘ,福岡県,福岡市中央区,山荘通,0,0,1,0,0,0
40133,810,8100028,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｼﾞｮｳｽｲﾄﾞｵﾘ,福岡県,福岡市中央区,浄水通,0,0,0,0,0,0
40133,810,8100043,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｼﾞｮｳﾅｲ,福岡県,福岡市中央区,城内,0,0,0,0,0,0
40133,810,8100012,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｼﾛｶﾞﾈ,福岡県,福岡市中央区,白金,0,0,1,0,0,0
40133,810,8100064,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｼﾞｷﾞｮｳ,福岡県,福岡市中央区,地行,0,0,1,0,0,0
40133,810,8100065,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ｼﾞｷﾞｮｳﾊﾏ,福岡県,福岡市中央区,地行浜,0,0,1,0,0,0
40133,810,8100041,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾀﾞｲﾐｮｳ,福岡県,福岡市中央区,大名,0,0,1,0,0,0
40133,810,8100011,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾀｶｻｺﾞ,福岡県,福岡市中央区,高砂,0,0,1,0,0,0
40133,810,8100031,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾀﾆ,福岡県,福岡市中央区,谷,0,0,1,0,0,0
40133,810,8100032,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾃﾙｸﾆ,福岡県,福岡市中央区,輝国,0,0,1,0,0,0
40133,810,8100001,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾃﾝｼﾞﾝ,福岡県,福岡市中央区,天神,0,0,1,0,0,0
40133,810,8100063,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾄｳｼﾞﾝﾏﾁ,福岡県,福岡市中央区,唐人町,0,0,1,0,0,0
40133,810,8100053,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾄﾘｶｲ,福岡県,福岡市中央区,鳥飼,0,0,1,0,0,0
40133,810,8100072,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾅｶﾞﾊﾏ,福岡県,福岡市中央区,長浜,0,0,1,0,0,0
40133,810,8100015,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾅﾉｶﾜ,福岡県,福岡市中央区,那の川,0,0,1,0,0,0
40133,810,8100071,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾅﾉﾂ,福岡県,福岡市中央区,那の津,0,0,1,0,0,0
40133,810,8100061,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾆｼｺｳｴﾝ,福岡県,福岡市中央区,西公園,0,0,0,0,0,0
40133,810,8100002,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾆｼﾅｶｽ,福岡県,福岡市中央区,西中洲,0,0,0,0,0,0
40133,810,8100035,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾊﾞｲｺｳｴﾝ,福岡県,福岡市中央区,梅光園,0,0,1,0,0,0
40133,810,8100036,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾊﾞｲｺｳｴﾝﾀﾞﾝﾁ,福岡県,福岡市中央区,梅光園団地,0,0,0,0,0,0
40133,810,8100003,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾊﾙﾖｼ,福岡県,福岡市中央区,春吉,0,0,1,0,0,0
40133,810,8100014,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾋﾗｵ,福岡県,福岡市中央区,平尾,0,0,1,0,0,0
40133,810,8100017,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾋﾗｵｶﾏﾁ,福岡県,福岡市中央区,平丘町,0,0,0,0,0,0
40133,810,8100029,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾋﾗｵｼﾞｮｳｽｲﾏﾁ,福岡県,福岡市中央区,平尾浄水町,0,0,0,0,0,0
40133,810,8100066,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾌｸﾊﾏ,福岡県,福岡市中央区,福浜,0,0,1,0,0,0
40133,810,8100026,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾌﾙｺｶﾞﾗｽﾏﾁ,福岡県,福岡市中央区,古小烏町,0,0,0,0,0,0
40133,810,8100016,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾍｲﾜ,福岡県,福岡市中央区,平和,0,0,1,0,0,0
40133,810,8100073,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾏｲﾂﾞﾙ,福岡県,福岡市中央区,舞鶴,0,0,1,0,0,0
40133,810,8100075,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾐﾅﾄ,福岡県,福岡市中央区,港,0,0,1,0,0,0
40133,810,8100037,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾐﾅﾐｺｳｴﾝ,福岡県,福岡市中央区,南公園,0,0,0,0,0,0
40133,810,8100022,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾔｸｲﾝ,福岡県,福岡市中央区,薬院,0,0,1,0,0,0
40133,810,8100025,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾔｸｲﾝｲﾌｸﾏﾁ,福岡県,福岡市中央区,薬院伊福町,0,0,0,0,0,0
40133,810,8100044,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾛｯﾎﾟﾝﾏﾂ,福岡県,福岡市中央区,六本松,0,0,1,0,0,0
40133,810,8100004,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾁｭｳｵｳｸ,ﾜﾀﾅﾍﾞﾄﾞｵﾘ,福岡県,福岡市中央区,渡辺通,0,0,1,0,0,0
40134,815,8150000,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市南区,以下に掲載がない場合,0,0,0,0,0,0
40134,816,8111302,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｲｼﾞﾘ,福岡県,福岡市南区,井尻,0,0,1,0,0,0
40134,815,8150084,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｲﾁｻﾞｷ,福岡県,福岡市南区,市崎,0,0,1,0,0,0
40134,815,8150073,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｵｵｲｹ,福岡県,福岡市南区,大池,0,0,1,0,0,0
40134,815,8150082,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｵｵｸﾞｽ,福岡県,福岡市南区,大楠,0,0,1,0,0,0
40134,815,8150033,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｵｵﾊｼ,福岡県,福岡市南区,大橋,0,0,1,0,0,0
40134,815,8150038,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｵｵﾊｼﾀﾞﾝﾁ,福岡県,福岡市南区,大橋団地,0,0,0,0,0,0
40134,816,8111313,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｵｻ,福岡県,福岡市南区,曰佐,0,0,1,0,0,0
40134,816,8111303,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｵﾘﾀﾃﾏﾁ,福岡県,福岡市南区,折立町,0,0,0,0,0,0
40134,815,8111353,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｶｼﾜﾗ,福岡県,福岡市南区,柏原,0,0,1,0,0,0
40134,816,8111324,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｹﾔｺﾞｳ,福岡県,福岡市南区,警弥郷,0,0,1,0,0,0
40134,816,8150001,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｺﾞｼﾞｯｶﾜ,福岡県,福岡市南区,五十川,0,0,1,0,0,0
40134,815,8111365,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｻﾗﾔﾏ,福岡県,福岡市南区,皿山,0,0,1,0,0,0
40134,815,8150032,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｼｵﾊﾞﾙ,福岡県,福岡市南区,塩原,0,0,1,0,0,0
40134,815,8150031,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ｼﾐｽﾞ,福岡県,福岡市南区,清水,0,0,1,0,0,0
40134,815,8111354,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾀｲﾍｲｼﾞ,福岡県,福岡市南区,大平寺,0,0,1,0,0,0
40134,816,8150004,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾀｶｷ,福岡県,福岡市南区,高木,0,0,1,0,0,0
40134,815,8150083,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾀｶﾐﾔ,福岡県,福岡市南区,高宮,0,0,1,0,0,0
40134,815,8150072,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾀｶﾞ,福岡県,福岡市南区,多賀,0,0,1,0,0,0
40134,815,8150037,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾀﾏｶﾞﾜﾏﾁ,福岡県,福岡市南区,玉川町,0,0,0,0,0,0
40134,815,8150036,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾁｸｼｶﾞｵｶ,福岡県,福岡市南区,筑紫丘,0,0,1,0,0,0
40134,815,8111352,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾂﾙﾀ,福岡県,福岡市南区,鶴田,0,0,1,0,0,0
40134,815,8150074,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾃﾗﾂﾞｶ,福岡県,福岡市南区,寺塚,0,0,1,0,0,0
40134,815,8111364,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾅｶｵ,福岡県,福岡市南区,中尾,0,0,1,0,0,0
40134,815,8150075,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾅｶﾞｵｶ,福岡県,福岡市南区,長丘,0,0,1,0,0,0
40134,815,8111362,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾅｶﾞｽﾞﾐ,福岡県,福岡市南区,長住,0,0,1,0,0,0
40134,815,8150081,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾅﾉｶﾜ,福岡県,福岡市南区,那の川,0,0,1,0,0,0
40134,815,8111361,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾆｼﾅｶﾞｽﾞﾐ,福岡県,福岡市南区,西長住,0,0,1,0,0,0
40134,815,8111347,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾉﾀﾒ,福岡県,福岡市南区,野多目,0,0,1,0,0,0
40134,815,8150041,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾉﾏ,福岡県,福岡市南区,野間,0,0,1,0,0,0
40134,815,8111356,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾊﾅﾊﾀ,福岡県,福岡市南区,花畑,0,0,1,0,0,0
40134,815,8111355,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾋﾊﾞﾙ,福岡県,福岡市南区,桧原,0,0,1,0,0,0
40134,815,8150071,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾍｲﾜ,福岡県,福岡市南区,平和,0,0,1,0,0,0
40134,816,8111314,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾏﾄﾊﾞ,福岡県,福岡市南区,的場,0,0,1,0,0,0
40134,815,8150034,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾐﾅﾐｵｵﾊｼ,福岡県,福岡市南区,南大橋,0,0,1,0,0,0
40134,815,8111344,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾐﾔｹ,福岡県,福岡市南区,三宅,0,0,1,0,0,0
40134,815,8111345,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾑｶｲｼﾝﾏﾁ,福岡県,福岡市南区,向新町,0,0,1,0,0,0
40134,815,8150035,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾑｶｲﾉ,福岡県,福岡市南区,向野,0,0,1,0,0,0
40134,815,8111351,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾔｶﾀﾊﾞﾙ,福岡県,福岡市南区,屋形原,0,0,1,0,0,0
40134,816,8111323,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾔﾅｶﾞ,福岡県,福岡市南区,弥永,0,0,1,0,0,0
40134,816,8111322,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾔﾅｶﾞﾀﾞﾝﾁ,福岡県,福岡市南区,弥永団地,0,0,0,0,0,0
40134,815,8150063,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾔﾅｺﾞｳﾁ,福岡県,福岡市南区,柳河内,0,0,1,0,0,0
40134,816,8111321,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾔﾅｾ,福岡県,福岡市南区,柳瀬,0,0,1,0,0,0
40134,816,8111311,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾖｺﾃ,福岡県,福岡市南区,横手,0,0,1,0,0,0
40134,816,8111312,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾖｺﾃﾐﾅﾐﾏﾁ,福岡県,福岡市南区,横手南町,0,0,0,0,0,0
40134,815,8111346,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾛｳｼﾞ,福岡県,福岡市南区,老司,0,0,1,0,0,0
40134,815,8150042,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾜｶﾋｻ,福岡県,福岡市南区,若久,0,0,1,0,0,0
40134,815,8150048,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾜｶﾋｻﾀﾞﾝﾁ,福岡県,福岡市南区,若久団地,0,0,0,0,0,0
40134,815,8111343,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾐﾅﾐｸ,ﾜﾀﾞ,福岡県,福岡市南区,和田,0,0,1,0,0,0
40135,819,8190000,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市西区,以下に掲載がない場合,0,0,0,0,0,0
40135,819,8190015,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｱﾀｺﾞ,福岡県,福岡市西区,愛宕,0,0,1,0,0,0
40135,819,8190013,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｱﾀｺﾞﾊﾏ,福岡県,福岡市西区,愛宕浜,0,0,1,0,0,0
40135,819,8190007,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｱﾀｺﾞﾐﾅﾐ,福岡県,福岡市西区,愛宕南,0,0,1,0,0,0
40135,81903,8190371,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｲｼﾞ,福岡県,福岡市西区,飯氏,0,0,0,0,0,0
40135,819,8190037,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｲﾓﾘ,福岡県,福岡市西区,飯盛,0,0,0,0,0,0
40135,819,8190042,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｷﾀﾞﾝﾁ,福岡県,福岡市西区,壱岐団地,0,0,0,0,0,0
40135,819,8190055,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｷﾉﾏﾂﾊﾞﾗ,福岡県,福岡市西区,生の松原,0,0,1,0,0,0
40135,819,8190044,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｷﾏﾂﾀﾞｲ,福岡県,福岡市西区,生松台,0,0,1,0,0,0
40135,819,8190025,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｼﾏﾙ,福岡県,福岡市西区,石丸,0,0,1,0,0,0
40135,81903,8190381,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲｽﾞﾐ,福岡県,福岡市西区,泉,0,0,1,0,0,0
40135,81901,8190167,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏｼﾞｭｸ,福岡県,福岡市西区,今宿,0,0,1,0,0,0
40135,81901,8190162,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏｼﾞｭｸｱｵｷ,福岡県,福岡市西区,今宿青木,0,0,0,0,0,0
40135,81901,8190168,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏｼﾞｭｸｴｷﾏｴ,福岡県,福岡市西区,今宿駅前,0,0,1,0,0,0
40135,81901,8190163,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏｼﾞｭｸｶﾐﾉﾊﾙ,福岡県,福岡市西区,今宿上ノ原,0,0,0,0,0,0
40135,81901,8190164,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏｼﾞｭｸﾏﾁ,福岡県,福岡市西区,今宿町,0,0,0,0,0,0
40135,81901,8190161,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏｼﾞｭｸﾋｶﾞｼ,福岡県,福岡市西区,今宿東,0,0,1,0,0,0
40135,81901,8190165,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｲﾏﾂﾞ,福岡県,福岡市西区,今津,0,0,0,0,0,0
40135,81903,8190372,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｳﾀﾞｶﾞﾜﾗ,福岡県,福岡市西区,宇田川原,0,0,0,0,0,0
40135,819,8190005,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｳﾁﾊﾏ,福岡県,福岡市西区,内浜,0,0,1,0,0,0
40135,819,8190021,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｵｵﾏﾁﾀﾞﾝﾁ,福岡県,福岡市西区,大町団地,0,0,0,0,0,0
40135,819,8190001,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｵﾄﾞ,福岡県,福岡市西区,小戸,0,0,1,0,0,0
40135,819,8190011,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｵﾛﾉｼﾏ,福岡県,福岡市西区,小呂島,0,0,0,0,0,0
40135,819,8190035,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｶﾅﾀｹ,福岡県,福岡市西区,金武,0,0,0,0,0,0
40135,819,8190054,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｶﾐﾔﾏﾄ,福岡県,福岡市西区,上山門,0,0,1,0,0,0
40135,81903,8190388,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｷｭｳﾀﾞｲｼﾝﾏﾁ,福岡県,福岡市西区,九大新町,0,0,0,0,0,0
40135,81902,8190204,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｸｻﾊﾞ,福岡県,福岡市西区,草場,0,0,0,0,0,0
40135,81903,8190382,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｸﾜﾊﾞﾗ,福岡県,福岡市西区,桑原,0,0,0,0,0,0
40135,81902,8190205,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｹﾞﾝｶｲｼﾏ,福岡県,福岡市西区,玄界島,0,0,0,0,0,0
40135,81902,8190203,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｺﾀ,福岡県,福岡市西区,小田,0,0,0,0,0,0
40135,819,8190052,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｼﾓﾔﾏﾄ,福岡県,福岡市西区,下山門,0,0,1,0,0,0
40135,819,8190051,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｼﾓﾔﾏﾄﾀﾞﾝﾁ,福岡県,福岡市西区,下山門団地,0,0,0,0,0,0
40135,819,8190024,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｼﾞｭｳﾛｳｶﾞﾜﾀﾞﾝﾁ,福岡県,福岡市西区,十郎川団地,0,0,0,0,0,0
40135,819,8190041,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｼﾞｭｳﾛｸﾁｮｳ,福岡県,福岡市西区,拾六町,0,0,1,0,0,0
40135,819,8190045,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｼﾞｭｳﾛｸﾁｮｳﾀﾞﾝﾁ,福岡県,福岡市西区,拾六町団地,0,0,0,0,0,0
40135,819,8190053,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｼﾞｮｳﾉﾊﾙﾀﾞﾝﾁ,福岡県,福岡市西区,城の原団地,0,0,0,0,0,0
40135,81903,8190373,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｽｾﾝｼﾞ,福岡県,福岡市西区,周船寺,0,0,1,0,0,0
40135,81903,8190374,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ｾﾝﾘ,福岡県,福岡市西区,千里,0,0,0,0,0,0
40135,819,8190034,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾀ,福岡県,福岡市西区,田,0,0,0,0,0,0
40135,81903,8190383,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾀｼﾞﾘ,福岡県,福岡市西区,田尻,0,0,1,0,0,0
40135,81903,8190384,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾀﾛｳﾏﾙ,福岡県,福岡市西区,太郎丸,0,0,1,0,0,0
40135,819,8190032,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾄｷﾞﾚ,福岡県,福岡市西区,戸切,0,0,1,0,0,0
40135,81903,8190375,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾄｸﾅｶﾞ,福岡県,福岡市西区,徳永,0,0,0,0,0,0
40135,819,8190014,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾄﾖﾊﾏ,福岡県,福岡市西区,豊浜,0,0,1,0,0,0
40135,819,8190039,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾆｼｲﾘﾍﾞ,福岡県,福岡市西区,西入部,0,0,0,0,0,0
40135,81902,8190202,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾆｼﾉｳﾗ,福岡県,福岡市西区,西浦,0,0,0,0,0,0
40135,819,8190046,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾆｼﾉｵｶ,福岡県,福岡市西区,西の丘,0,0,1,0,0,0
40135,819,8190043,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾉｶﾀ,福岡県,福岡市西区,野方,0,0,1,0,0,0
40135,819,8190012,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾉｺ,福岡県,福岡市西区,能古,0,0,0,0,0,0
40135,819,8190033,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾊｼﾓﾄ(ｵｵｱｻﾞ),福岡県,福岡市西区,橋本（大字）,1,0,1,0,0,0
40135,819,8190031,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾊｼﾓﾄ(ﾁｮｳﾒ),福岡県,福岡市西区,橋本（丁目）,1,0,1,0,0,0
40135,819,8190038,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾊﾈﾄﾞ,福岡県,福岡市西区,羽根戸,0,0,0,0,0,0
40135,819,8190022,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾌｸｼｹﾞ,福岡県,福岡市西区,福重,0,0,1,0,0,0
40135,819,8190023,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾌｸｼｹﾞﾀﾞﾝﾁ,福岡県,福岡市西区,福重団地,0,0,0,0,0,0
40135,81903,8190387,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾌｼﾞﾐ,福岡県,福岡市西区,富士見,0,0,1,0,0,0
40135,81902,8190201,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾐﾔﾉｳﾗ,福岡県,福岡市西区,宮浦,0,0,0,0,0,0
40135,81903,8190376,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾐｮｳﾊﾞﾙ,福岡県,福岡市西区,女原,0,0,0,0,0,0
40135,819,8190030,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾑﾛﾐｶﾞｵｶ,福岡県,福岡市西区,室見が丘,0,0,1,0,0,0
40135,819,8190002,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾒｲﾉﾊﾏ,福岡県,福岡市西区,姪の浜,0,0,1,0,0,0
40135,819,8190006,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾒｲﾉﾊﾏｴｷﾐﾅﾐ,福岡県,福岡市西区,姪浜駅南,0,0,1,0,0,0
40135,81903,8190385,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾓﾄｵｶ,福岡県,福岡市西区,元岡,0,0,0,0,0,0
40135,81903,8190386,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾓﾄﾊﾏ,福岡県,福岡市西区,元浜,0,0,1,0,0,0
40135,81901,8190166,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾖｺﾊﾏ(1-2ﾁｮｳﾒ),福岡県,福岡市西区,横浜（１〜２丁目）,1,0,1,0,0,0
40135,81903,8190366,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾖｺﾊﾏ(3ﾁｮｳﾒ),福岡県,福岡市西区,横浜（３丁目）,1,0,1,0,0,0
40135,819,8190036,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼﾆｼｸ,ﾖｼﾀｹ,福岡県,福岡市西区,吉武,0,0,0,0,0,0
40136,81401,8140100,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市城南区,以下に掲載がない場合,0,0,0,0,0,0
40136,81401,8140101,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｱﾗｴ,福岡県,福岡市城南区,荒江,0,0,1,0,0,0
40136,81401,8140102,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｱﾗｴﾀﾞﾝﾁ,福岡県,福岡市城南区,荒江団地,0,0,0,0,0,0
40136,81401,8140134,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｲｲｸﾗ,福岡県,福岡市城南区,飯倉,0,0,1,0,0,0
40136,81401,8140144,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｳﾒﾊﾞﾔｼ,福岡県,福岡市城南区,梅林,0,0,1,0,0,0
40136,81401,8140142,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｶﾀｴ,福岡県,福岡市城南区,片江,0,0,0,0,0,0
40136,81401,8140114,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｶﾅﾔﾏﾀﾞﾝﾁ,福岡県,福岡市城南区,金山団地,0,0,0,0,0,0
40136,81401,8140105,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｼﾞｮｳｾｲﾀﾞﾝﾁ,福岡県,福岡市城南区,城西団地,0,0,0,0,0,0
40136,81401,8140121,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ｼﾝｼｮｳｼﾞ,福岡県,福岡市城南区,神松寺,0,0,1,0,0,0
40136,81401,8140154,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾀｶﾗﾀﾞｲﾀﾞﾝﾁ,福岡県,福岡市城南区,宝台団地,0,0,0,0,0,0
40136,81401,8140113,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾀｼﾏ,福岡県,福岡市城南区,田島,0,0,1,0,0,0
40136,81401,8140111,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾁｬﾔﾏ,福岡県,福岡市城南区,茶山,0,0,1,0,0,0
40136,81401,8140151,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾂﾂﾐ,福岡県,福岡市城南区,堤,0,0,1,0,0,0
40136,81401,8140152,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾂﾂﾐﾀﾞﾝﾁ,福岡県,福岡市城南区,堤団地,0,0,0,0,0,0
40136,81401,8140112,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾄﾓｵｶ,福岡県,福岡市城南区,友丘,0,0,1,0,0,0
40136,81401,8140103,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾄﾘｶｲ,福岡県,福岡市城南区,鳥飼,0,0,1,0,0,0
40136,81401,8140123,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾅｶﾞｵ,福岡県,福岡市城南区,長尾,0,0,1,0,0,0
40136,81401,8140133,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾅﾅｸﾏ,福岡県,福岡市城南区,七隈,0,0,0,0,0,0
40136,81401,8140141,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾆｼｶﾀｴ,福岡県,福岡市城南区,西片江,0,0,1,0,0,0
40136,81401,8140153,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾋｲｶﾜ,福岡県,福岡市城南区,樋井川,0,0,1,0,0,0
40136,81401,8140155,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾋｶﾞｼｱﾌﾞﾗﾔﾏ,福岡県,福岡市城南区,東油山,0,0,1,0,0,0
40136,81401,8140104,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾍﾞﾌ,福岡県,福岡市城南区,別府,0,0,1,0,0,0
40136,81401,8140106,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾍﾞﾌﾀﾞﾝﾁ,福岡県,福岡市城南区,別府団地,0,0,0,0,0,0
40136,81401,8140132,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾎｼｸﾏ,福岡県,福岡市城南区,干隈,0,0,1,0,0,0
40136,81401,8140131,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾏﾂﾔﾏ,福岡県,福岡市城南区,松山,0,0,1,0,0,0
40136,81401,8140143,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾐﾅﾐｶﾀｴ,福岡県,福岡市城南区,南片江,0,0,1,0,0,0
40136,81401,8140122,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｼﾞｮｳﾅﾝｸ,ﾕｳｾﾝﾃｲ,福岡県,福岡市城南区,友泉亭,0,0,0,0,0,0
40137,814,8140000,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福岡市早良区,以下に掲載がない場合,0,0,0,0,0,0
40137,814,8140004,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｱｹﾎﾞﾉ,福岡県,福岡市早良区,曙,0,0,1,0,0,0
40137,814,8140021,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｱﾗｴ,福岡県,福岡市早良区,荒江,0,0,1,0,0,0
40137,814,8140033,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｱﾘﾀ,福岡県,福岡市早良区,有田,0,0,1,0,0,0
40137,814,8140034,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｱﾘﾀﾀﾞﾝﾁ,福岡県,福岡市早良区,有田団地,0,0,0,0,0,0
40137,81401,8140161,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｲｲｸﾗ,福岡県,福岡市早良区,飯倉,0,0,1,0,0,0
40137,81111,8111134,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｲｲﾊﾞ,福岡県,福岡市早良区,飯場,0,0,0,0,0,0
40137,81111,8111132,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｲｼｶﾞﾏ,福岡県,福岡市早良区,石釜,0,0,0,0,0,0
40137,81111,8111113,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｲﾀﾔ,福岡県,福岡市早良区,板屋,0,0,0,0,0,0
40137,81111,8111123,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｳﾁﾉ,福岡県,福岡市早良区,内野,0,0,1,0,0,0
40137,81401,8140172,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｳﾒﾊﾞﾔｼ,福岡県,福岡市早良区,梅林,0,0,1,0,0,0
40137,81111,8111112,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｵｶｻｷﾞ,福岡県,福岡市早良区,小笠木,0,0,0,0,0,0
40137,81111,8111124,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｶﾅﾀｹ,福岡県,福岡市早良区,金武,0,0,0,0,0,0
40137,81401,8140164,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｶﾓ,福岡県,福岡市早良区,賀茂,0,0,1,0,0,0
40137,814,8140032,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｺﾀﾍﾞ,福岡県,福岡市早良区,小田部,0,0,1,0,0,0
40137,81111,8111122,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｻﾜﾗ,福岡県,福岡市早良区,早良,0,0,1,0,0,0
40137,81111,8111114,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼｲﾊﾞ,福岡県,福岡市早良区,椎原,0,0,0,0,0,0
40137,81111,8111103,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼｶ,福岡県,福岡市早良区,四箇,0,0,1,0,0,0
40137,81401,8140176,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼｶﾀﾀﾞﾝﾁ,福岡県,福岡市早良区,四箇田団地,0,0,0,0,0,0
40137,81111,8111101,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼｹﾞﾄﾞﾒ,福岡県,福岡市早良区,重留,0,0,1,0,0,0
40137,814,8140003,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼﾞｮｳｾｲ,福岡県,福岡市早良区,城西,0,0,1,0,0,0
40137,814,8140012,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼｮｳﾀﾞｲ,福岡県,福岡市早良区,昭代,0,0,1,0,0,0
40137,81401,8140165,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｼﾞﾛｳﾏﾙ,福岡県,福岡市早良区,次郎丸,0,0,1,0,0,0
40137,814,8140005,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ｿﾊﾗ,福岡県,福岡市早良区,祖原,0,0,0,0,0,0
40137,81401,8140177,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾀ,福岡県,福岡市早良区,田,0,0,0,0,0,0
40137,814,8140011,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾀｶﾄﾘ,福岡県,福岡市早良区,高取,0,0,1,0,0,0
40137,81401,8140174,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾀｸﾞﾏ,福岡県,福岡市早良区,田隈,0,0,1,0,0,0
40137,81401,8140175,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾀﾑﾗ,福岡県,福岡市早良区,田村,0,0,1,0,0,0
40137,81111,8111131,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾆｼ,福岡県,福岡市早良区,西,0,0,0,0,0,0
40137,81401,8140173,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾆｼｱﾌﾞﾗﾔﾏ,福岡県,福岡市早良区,西油山,0,0,0,0,0,0
40137,81111,8111121,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾆｼｲﾙﾍﾞ,福岡県,福岡市早良区,西入部,0,0,1,0,0,0
40137,814,8140002,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾆｼｼﾞﾝ,福岡県,福岡市早良区,西新,0,0,1,0,0,0
40137,81401,8140171,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾉｹ,福岡県,福岡市早良区,野芥,0,0,1,0,0,0
40137,814,8140022,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾊﾗ,福岡県,福岡市早良区,原,0,0,1,0,0,0
40137,814,8140023,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾊﾗﾀﾞﾝﾁ,福岡県,福岡市早良区,原団地,0,0,0,0,0,0
40137,81111,8111102,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾋｶﾞｼｲﾙﾍﾞ,福岡県,福岡市早良区,東入部,0,0,1,0,0,0
40137,814,8140013,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾌｼﾞｻｷ,福岡県,福岡市早良区,藤崎,0,0,1,0,0,0
40137,81401,8140163,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾎｼｸﾏ,福岡県,福岡市早良区,干隈,0,0,1,0,0,0
40137,81401,8140162,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾎｼﾉﾊﾗﾀﾞﾝﾁ,福岡県,福岡市早良区,星の原団地,0,0,0,0,0,0
40137,81111,8111133,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾏｶﾞﾘﾌﾞﾁ,福岡県,福岡市早良区,曲渕,0,0,0,0,0,0
40137,814,8140031,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾐﾅﾐｼｮｳ,福岡県,福岡市早良区,南庄,0,0,1,0,0,0
40137,814,8140035,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾑﾛｽﾞﾐﾀﾞﾝﾁ,福岡県,福岡市早良区,室住団地,0,0,0,0,0,0
40137,814,8140015,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾑﾛﾐ,福岡県,福岡市早良区,室見,0,0,1,0,0,0
40137,814,8140006,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾓﾓﾁ,福岡県,福岡市早良区,百道,0,0,1,0,0,0
40137,814,8140001,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾓﾓﾁﾊﾏ,福岡県,福岡市早良区,百道浜,0,0,1,0,0,0
40137,814,8140014,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾔﾖｲ,福岡県,福岡市早良区,弥生,0,0,1,0,0,0
40137,81111,8111111,ﾌｸｵｶｹﾝ,ﾌｸｵｶｼｻﾜﾗｸ,ﾜｷﾔﾏ,福岡県,福岡市早良区,脇山,0,0,1,0,0,0
40217,818,8180000,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,筑紫野市,以下に掲載がない場合,0,0,0,0,0,0
40217,818,8180011,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｱｼｷ,福岡県,筑紫野市,阿志岐,0,0,0,0,0,0
40217,818,8180012,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｱﾏﾔﾏ,福岡県,筑紫野市,天山,0,0,0,0,0,0
40217,818,8180068,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｲｼｻﾞｷ,福岡県,筑紫野市,石崎,0,0,1,0,0,0
40217,818,8180014,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｳｼｼﾞﾏ,福岡県,筑紫野市,牛島,0,0,0,0,0,0
40217,818,8180034,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｳﾂｸｼｶﾞｵｶﾐﾅﾐ,福岡県,筑紫野市,美しが丘南,0,0,1,0,0,0
40217,818,8180035,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｳﾂｸｼｶﾞｵｶｷﾀ,福岡県,筑紫野市,美しが丘北,0,0,1,0,0,0
40217,818,8180033,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｳﾏｲﾁ,福岡県,筑紫野市,馬市,0,0,0,0,0,0
40217,818,8180006,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｵｵｲｼ,福岡県,筑紫野市,大石,0,0,0,0,0,0
40217,818,8180013,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｵｶﾀﾞ,福岡県,筑紫野市,岡田,0,0,1,0,0,0
40217,818,8180041,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｶﾐｺｶﾞ,福岡県,筑紫野市,上古賀,0,0,1,0,0,0
40217,818,8180031,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｸﾏ,福岡県,筑紫野市,隈,0,0,0,0,0,0
40217,818,8180002,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｺｳｿﾞﾉ,福岡県,筑紫野市,香園,0,0,0,0,0,0
40217,818,8180047,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｺｶﾞ,福岡県,筑紫野市,古賀,0,0,0,0,0,0
40217,818,8180063,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｻｸﾗﾀﾞｲ,福岡県,筑紫野市,桜台,0,0,1,0,0,0
40217,818,8180021,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｼﾀﾐ,福岡県,筑紫野市,下見,0,0,0,0,0,0
40217,818,8180054,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｽｷﾞﾂﾞｶ,福岡県,筑紫野市,杉塚,0,0,1,0,0,0
40217,818,8180067,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ｿﾞｸﾐｮｳｲﾝ,福岡県,筑紫野市,俗明院,0,0,1,0,0,0
40217,818,8180025,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾁｸｼ,福岡県,筑紫野市,筑紫,0,0,0,0,0,0
40217,818,8180022,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾁｸｼｴｷﾏｴﾄﾞｵﾘ,福岡県,筑紫野市,筑紫駅前通,0,0,1,0,0,0
40217,818,8180064,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾂﾈﾏﾂ,福岡県,筑紫野市,常松,0,0,0,0,0,0
40217,818,8180053,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾃﾝﾊﾟｲｻﾞｶ,福岡県,筑紫野市,天拝坂,0,0,1,0,0,0
40217,818,8180055,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾄｳﾉﾊﾙ,福岡県,筑紫野市,塔原,0,0,0,0,0,0
40217,818,8180059,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾄｳﾉﾊﾙﾋｶﾞｼ,福岡県,筑紫野市,塔原東,0,0,1,0,0,0
40217,818,8180073,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾄｳﾉﾊﾙﾆｼ,福岡県,筑紫野市,塔原西,0,0,1,0,0,0
40217,818,8180074,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾄｳﾉﾊﾙﾐﾅﾐ,福岡県,筑紫野市,塔原南,0,0,1,0,0,0
40217,818,8180066,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾅｶﾞｵｶ,福岡県,筑紫野市,永岡,0,0,0,0,0,0
40217,818,8180032,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾆｼｵﾀﾞ,福岡県,筑紫野市,西小田,0,0,0,0,0,0
40217,818,8180044,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊｷﾞﾜﾗ,福岡県,筑紫野市,萩原,0,0,0,0,0,0
40217,818,8180062,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾘｽﾘ,福岡県,筑紫野市,針摺,0,0,0,0,0,0
40217,818,8180085,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾘｽﾘｷﾀ,福岡県,筑紫野市,針摺北,0,0,1,0,0,0
40217,818,8180083,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾘｽﾘﾁｭｳｵｳ,福岡県,筑紫野市,針摺中央,0,0,1,0,0,0
40217,818,8180084,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾘｽﾘﾆｼ,福岡県,筑紫野市,針摺西,0,0,1,0,0,0
40217,818,8180081,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾘｽﾘﾋｶﾞｼ,福岡県,筑紫野市,針摺東,0,0,1,0,0,0
40217,818,8180082,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾘｽﾘﾐﾅﾐ,福岡県,筑紫野市,針摺南,0,0,1,0,0,0
40217,818,8180005,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾙ,福岡県,筑紫野市,原,0,0,0,0,0,0
40217,818,8180024,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾊﾙﾀﾞ,福岡県,筑紫野市,原田,0,0,1,0,0,0
40217,818,8180036,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾋｶﾘｶﾞｵｶ,福岡県,筑紫野市,光が丘,0,0,1,0,0,0
40217,818,8180045,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾋﾞｮｳﾄﾞｳｼﾞ,福岡県,筑紫野市,平等寺,0,0,0,0,0,0
40217,818,8180051,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾌﾂｶｲﾁ,福岡県,筑紫野市,二日市,0,0,0,0,0,0
40217,818,8180056,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾌﾂｶｲﾁｷﾀ,福岡県,筑紫野市,二日市北,0,0,1,0,0,0
40217,818,8180072,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾌﾂｶｲﾁﾁｭｳｵｳ,福岡県,筑紫野市,二日市中央,0,0,1,0,0,0
40217,818,8180071,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾌﾂｶｲﾁﾆｼ,福岡県,筑紫野市,二日市西,0,0,1,0,0,0
40217,818,8180057,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾌﾂｶｲﾁﾐﾅﾐ,福岡県,筑紫野市,二日市南,0,0,1,0,0,0
40217,818,8180007,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾎﾝﾄﾞｳｼﾞ,福岡県,筑紫野市,本道寺,0,0,0,0,0,0
40217,818,8180026,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾐｻｷ,福岡県,筑紫野市,美咲,0,0,0,0,0,0
40217,818,8180052,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾑｻｼ,福岡県,筑紫野市,武藏,0,0,1,0,0,0
40217,818,8180043,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾑｻｼｶﾞｵｶ,福岡県,筑紫野市,むさしケ丘,0,0,1,0,0,0
40217,818,8180061,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾑﾗｻｷ,福岡県,筑紫野市,紫,0,0,1,0,0,0
40217,818,8180065,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾓﾛﾀ,福岡県,筑紫野市,諸田,0,0,0,0,0,0
40217,818,8180003,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾔﾏｴ,福岡県,筑紫野市,山家,0,0,0,0,0,0
40217,818,8180046,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾔﾏｸﾞﾁ,福岡県,筑紫野市,山口,0,0,0,0,0,0
40217,818,8180001,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾕｽﾊﾞﾙ,福岡県,筑紫野市,柚須原,0,0,0,0,0,0
40217,818,8180058,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾕﾏﾁ,福岡県,筑紫野市,湯町,0,0,1,0,0,0
40217,818,8180004,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾖｼｷ,福岡県,筑紫野市,吉木,0,0,0,0,0,0
40217,818,8180042,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾘｭｳﾐｮｳｼﾞ,福岡県,筑紫野市,立明寺,0,0,0,0,0,0
40217,818,8180023,ﾌｸｵｶｹﾝ,ﾁｸｼﾉｼ,ﾜｶｴ,福岡県,筑紫野市,若江,0,0,0,0,0,0
40218,816,8160000,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,春日市,以下に掲載がない場合,0,0,0,1,0,0
40218,816,8160853,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｲｽﾞﾐ,福岡県,春日市,泉,0,0,1,0,0,0
40218,816,8160852,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｲﾁﾉﾀﾆ,福岡県,春日市,一の谷,0,0,1,0,0,0
40218,816,8160831,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｵｵﾀﾆ,福岡県,春日市,大谷,0,0,1,0,0,0
40218,816,8160847,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｵｵﾄﾞｲ,福岡県,春日市,大土居,0,0,1,0,0,0
40218,816,8160861,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｵｶﾓﾄ,福岡県,春日市,岡本,0,0,1,0,0,0
40218,816,8160814,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｶｽｶﾞ,福岡県,春日市,春日,0,0,1,0,0,0
40218,816,8160811,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｶｽｶﾞｺｳｴﾝ,福岡県,春日市,春日公園,0,0,1,0,0,0
40218,816,8160801,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｶｽｶﾞﾊﾞﾙﾋｶﾞｼﾏﾁ,福岡県,春日市,春日原東町,0,0,1,0,0,0
40218,816,8160803,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｶｽｶﾞﾊﾞﾙﾐﾅﾐﾏﾁ,福岡県,春日市,春日原南町,0,0,1,0,0,0
40218,816,8160802,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｶｽｶﾞﾊﾞﾙｷﾀﾏﾁ,福岡県,春日市,春日原北町,0,0,1,0,0,0
40218,816,8160844,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｶﾐｼﾛｳｽﾞ,福岡県,春日市,上白水,0,0,1,0,0,0
40218,816,8160824,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｺｸﾗ,福岡県,春日市,小倉,0,0,1,0,0,0
40218,816,8160826,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｺｸﾗﾋｶﾞｼ,福岡県,春日市,小倉東,0,0,1,0,0,0
40218,816,8160872,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｻｸﾗｶﾞｵｶ,福岡県,春日市,桜ケ丘,0,0,1,0,0,0
40218,816,8160842,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｼﾓｼﾛｳｽﾞ,福岡県,春日市,下白水,0,0,0,0,0,0
40218,816,8160854,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｼﾓｼﾛｳｽﾞｷﾀ,福岡県,春日市,下白水北,0,0,1,0,0,0
40218,816,8160846,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｼﾓｼﾛｳｽﾞﾐﾅﾐ,福岡県,春日市,下白水南,0,0,1,0,0,0
40218,816,8160848,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｼﾛｳｽﾞｲｹ,福岡県,春日市,白水池,0,0,1,0,0,0
40218,816,8160845,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｼﾛｳｽﾞｶﾞｵｶ,福岡県,春日市,白水ケ丘,0,0,1,0,0,0
40218,816,8160871,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｽｸﾞ,福岡県,春日市,須玖,0,0,0,0,0,0
40218,816,8160863,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｽｸﾞﾐﾅﾐ,福岡県,春日市,須玖南,0,0,1,0,0,0
40218,816,8160864,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｽｸﾞｷﾀ,福岡県,春日市,須玖北,0,0,1,0,0,0
40218,816,8160813,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ｿｳﾘ,福岡県,春日市,惣利,0,0,1,0,0,0
40218,816,8160807,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾀｶﾗﾏﾁ,福岡県,春日市,宝町,0,0,1,0,0,0
40218,816,8160822,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾁｸｼﾀﾞｲ,福岡県,春日市,ちくし台,0,0,1,0,0,0
40218,816,8160805,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾁﾄｾﾏﾁ,福岡県,春日市,千歳町,0,0,1,0,0,0
40218,816,8160841,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾂｶﾊﾗﾀﾞｲ,福岡県,春日市,塚原台,0,0,1,0,0,0
40218,816,8160855,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾃﾝｼﾞﾝﾔﾏ,福岡県,春日市,天神山,0,0,1,0,0,0
40218,816,8160851,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾉﾎﾞﾘﾏﾁ,福岡県,春日市,昇町,0,0,1,0,0,0
40218,816,8160825,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾊｸｹﾞﾝﾁｮｳ,福岡県,春日市,伯玄町,0,0,1,0,0,0
40218,816,8160804,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾊﾗﾏﾁ,福岡県,春日市,原町,0,0,1,0,0,0
40218,816,8160806,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾋｶﾘﾏﾁ,福岡県,春日市,光町,0,0,1,0,0,0
40218,816,8160873,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾋﾉﾃﾞﾏﾁ,福岡県,春日市,日の出町,0,0,1,0,0,0
40218,816,8160812,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾋﾗﾀﾀﾞｲ,福岡県,春日市,平田台,0,0,1,0,0,0
40218,816,8160849,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾎｼﾐｶﾞｵｶ,福岡県,春日市,星見ヶ丘,0,0,1,0,0,0
40218,816,8160843,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾏﾂｶﾞｵｶ,福岡県,春日市,松ケ丘,0,0,1,0,0,0
40218,816,8160833,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾓﾐｼﾞｶﾞｵｶﾋｶﾞｼ,福岡県,春日市,紅葉ケ丘東,0,0,1,0,0,0
40218,816,8160832,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾓﾐｼﾞｶﾞｵｶﾆｼ,福岡県,春日市,紅葉ケ丘西,0,0,1,0,0,0
40218,816,8160874,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾔﾏﾄﾏﾁ,福岡県,春日市,大和町,0,0,1,0,0,0
40218,816,8160862,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾔﾖｲ,福岡県,春日市,弥生,0,0,1,0,0,0
40218,816,8160821,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾜｶﾊﾞﾀﾞｲﾋｶﾞｼ,福岡県,春日市,若葉台東,0,0,1,0,0,0
40218,816,8160823,ﾌｸｵｶｹﾝ,ｶｽｶﾞｼ,ﾜｶﾊﾞﾀﾞｲﾆｼ,福岡県,春日市,若葉台西,0,0,1,0,0,0
40219,816,8160000,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,大野城市,以下に掲載がない場合,0,0,0,1,0,0
40219,816,8160934,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｱｹﾎﾞﾉﾏﾁ,福岡県,大野城市,曙町,0,0,1,0,0,0
40219,816,8160953,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｱｻﾋｶﾞｵｶ,福岡県,大野城市,旭ケ丘,0,0,1,0,0,0
40219,816,8160971,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｳｼｸﾋﾞ,福岡県,大野城市,牛頸,0,0,1,0,0,0
40219,816,8160904,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｵｵｲｹ,福岡県,大野城市,大池,0,0,1,0,0,0
40219,816,8160911,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｵｵｷ,福岡県,大野城市,大城,0,0,1,0,0,0
40219,816,8160902,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｵﾄｶﾞﾅ,福岡県,大野城市,乙金,0,0,1,0,0,0
40219,816,8160903,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｵﾄｶﾞﾅﾀﾞｲ,福岡県,大野城市,乙金台,0,0,1,0,0,0
40219,816,8160901,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｵﾄｶﾞﾅﾋｶﾞｼ,福岡県,大野城市,乙金東,0,0,1,0,0,0
40219,816,8160955,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｶﾐｵｵﾘ,福岡県,大野城市,上大利,0,0,1,0,0,0
40219,816,8160905,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｶﾜｸﾎﾞ,福岡県,大野城市,川久保,0,0,1,0,0,0
40219,816,8160932,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｶﾜﾗﾀﾞ,福岡県,大野城市,瓦田,0,0,1,0,0,0
40219,816,8160924,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｻｶｴﾏﾁ,福岡県,大野城市,栄町,0,0,1,0,0,0
40219,816,8160923,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｻﾞﾂｼｮﾉｸﾏﾏﾁ,福岡県,大野城市,雑餉隈町,0,0,1,0,0,0
40219,816,8160952,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｼﾓｵｵﾘ,福岡県,大野城市,下大利,0,0,1,0,0,0
40219,816,8160951,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｼﾓｵｵﾘﾀﾞﾝﾁ,福岡県,大野城市,下大利団地,0,0,0,0,0,0
40219,816,8160943,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ｼﾗｷﾊﾞﾙ,福岡県,大野城市,白木原,0,0,1,0,0,0
40219,816,8160942,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾁｭｳｵｳ,福岡県,大野城市,中央,0,0,1,0,0,0
40219,816,8160983,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾂｷﾉｳﾗ,福岡県,大野城市,月の浦,0,0,1,0,0,0
40219,816,8160931,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾂﾂｲ,福岡県,大野城市,筒井,0,0,1,0,0,0
40219,816,8160962,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾂﾂｼﾞｶﾞｵｶ,福岡県,大野城市,つつじケ丘,0,0,1,0,0,0
40219,816,8160906,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾅｶ,福岡県,大野城市,中,0,0,1,0,0,0
40219,816,8160921,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾅｶﾊﾀ,福岡県,大野城市,仲畑,0,0,1,0,0,0
40219,816,8160935,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾆｼｷﾏﾁ,福岡県,大野城市,錦町,0,0,1,0,0,0
40219,816,8160982,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾊﾀｶﾞｻｶ,福岡県,大野城市,畑ケ坂,0,0,1,0,0,0
40219,816,8160941,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾋｶﾞｼｵｵﾘ,福岡県,大野城市,東大利,0,0,1,0,0,0
40219,816,8160972,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾋﾗﾉﾀﾞｲ,福岡県,大野城市,平野台,0,0,1,0,0,0
40219,816,8160912,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾐｶｻｶﾞﾜ,福岡県,大野城市,御笠川,0,0,1,0,0,0
40219,816,8160933,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾐｽﾞﾎﾏﾁ,福岡県,大野城市,瑞穂町,0,0,1,0,0,0
40219,816,8160961,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾐﾄﾞﾘｶﾞｵｶ,福岡県,大野城市,緑ケ丘,0,0,1,0,0,0
40219,816,8160956,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾐﾅﾐｵｵﾘ,福岡県,大野城市,南大利,0,0,1,0,0,0
40219,816,8160964,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾐﾅﾐｶﾞｵｶ,福岡県,大野城市,南ケ丘,0,0,1,0,0,0
40219,816,8160963,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾐﾔﾉﾀﾞｲ,福岡県,大野城市,宮野台,0,0,0,0,0,0
40219,816,8160954,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾑﾗｻｷﾀﾞｲ,福岡県,大野城市,紫台,0,0,0,0,0,0
40219,816,8160922,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾔﾏﾀﾞ,福岡県,大野城市,山田,0,0,1,0,0,0
40219,816,8160973,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾖｺﾐﾈ,福岡県,大野城市,横峰,0,0,1,0,0,0
40219,816,8160981,ﾌｸｵｶｹﾝ,ｵｵﾉｼﾞｮｳｼ,ﾜｶｸｻ,福岡県,大野城市,若草,0,0,1,0,0,0
40220,81134,8113400,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,宗像市,以下に掲載がない場合,0,0,0,0,0,0
40220,81141,8114162,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｵﾊﾞﾀﾞｲ,福岡県,宗像市,青葉台,0,0,1,0,0,0
40220,81141,8114146,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｶﾏ,福岡県,宗像市,赤間,0,0,1,0,0,0
40220,81141,8114185,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｶﾏｴｷﾏｴ,福岡県,宗像市,赤間駅前,0,0,1,0,0,0
40220,81141,8114176,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｶﾏｶﾞｵｶ,福岡県,宗像市,赤間ケ丘,0,0,0,0,0,0
40220,81141,8114148,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｶﾏﾌﾞﾝｷｮｳﾏﾁ,福岡県,宗像市,赤間文教町,0,0,0,0,0,0
40220,81134,8113415,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｻﾉ,福岡県,宗像市,朝野,0,0,0,0,0,0
40220,81141,8114161,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｻﾏﾁ,福岡県,宗像市,朝町,0,0,0,0,0,0
40220,81141,8114157,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｱｽﾃｨ,福岡県,宗像市,アスティ,0,0,1,0,0,0
40220,81134,8113401,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｲｹｳﾗ,福岡県,宗像市,池浦,0,0,0,0,0,0
40220,81135,8113515,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｲｹﾀﾞ,福岡県,宗像市,池田,0,0,0,0,0,0
40220,81141,8114147,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｲｼﾏﾙ,福岡県,宗像市,石丸,0,0,1,0,0,0
40220,81141,8114142,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｲｽﾞﾐｶﾞｵｶ,福岡県,宗像市,泉ケ丘,0,0,1,0,0,0
40220,81134,8113406,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｲﾅﾓﾄ,福岡県,宗像市,稲元,0,0,1,0,0,0
40220,81135,8113502,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｴｸﾞﾁ,福岡県,宗像市,江口,0,0,0,0,0,0
40220,81141,8114177,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｳﾋﾞﾀﾞｲ,福岡県,宗像市,桜美台,0,0,0,0,0,0
40220,81134,8113422,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｳﾏﾙ,福岡県,宗像市,王丸,0,0,0,0,0,0
40220,81134,8113432,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｵｲ,福岡県,宗像市,大井,0,0,0,0,0,0
40220,81134,8113433,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｵｲﾀﾞｲ,福岡県,宗像市,大井台,0,0,0,0,0,0
40220,81134,8113440,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｵｲﾐﾅﾐ,福岡県,宗像市,大井南,0,0,0,0,0,0
40220,81137,8113701,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｵｼﾏ,福岡県,宗像市,大島,0,0,0,0,0,0
40220,81141,8114141,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｵﾀﾆ,福岡県,宗像市,大谷,0,0,0,0,0,0
40220,81134,8113421,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｵｵﾌﾞ,福岡県,宗像市,大穂,0,0,0,0,0,0
40220,81134,8113402,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｶﾄｳ,福岡県,宗像市,河東,0,0,0,0,0,0
40220,81135,8113512,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｶﾈｻﾞｷ,福岡県,宗像市,鐘崎,0,0,0,0,0,0
40220,81134,8113437,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｸﾊﾞﾗ,福岡県,宗像市,久原,0,0,0,0,0,0
40220,81141,8114184,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｸﾘｴｲﾄ,福岡県,宗像市,くりえいと,0,0,1,0,0,0
40220,81135,8113516,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｺｳｴﾝﾄﾞｵﾘ,福岡県,宗像市,公園通り,0,0,1,0,0,0
40220,81135,8113513,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｺｳｼﾞｮｳ,福岡県,宗像市,上八,0,0,0,0,0,0
40220,81135,8113501,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｺｳﾉﾐﾅﾄ,福岡県,宗像市,神湊,0,0,0,0,0,0
40220,81141,8114165,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｺｳﾘｮｳﾀﾞｲ,福岡県,宗像市,広陵台,0,0,1,0,0,0
40220,81141,8114173,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｻｶｴﾏﾁ,福岡県,宗像市,栄町,0,0,0,0,0,0
40220,81141,8114166,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｻｸﾗ,福岡県,宗像市,桜,0,0,1,0,0,0
40220,81141,8114143,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｻﾌﾞﾛｳﾏﾙ,福岡県,宗像市,三郎丸,0,0,1,0,0,0
40220,81135,8113511,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞﾉｼﾏ,福岡県,宗像市,地島,0,0,0,0,0,0
40220,81141,8114163,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞﾕｳｶﾞｵｶ,福岡県,宗像市,自由ケ丘,0,0,1,0,0,0
40220,81141,8114174,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞﾕｳｶﾞｵｶﾆｼﾏﾁ,福岡県,宗像市,自由ケ丘西町,0,0,0,0,0,0
40220,81141,8114156,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞﾕｳｶﾞｵｶﾐﾅﾐ,福岡県,宗像市,自由ケ丘南,0,0,1,0,0,0
40220,81141,8114182,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞｮｳｶﾞﾀﾆ,福岡県,宗像市,城ケ谷,0,0,0,0,0,0
40220,81134,8113404,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞｮｳｾｲｶﾞｵｶ,福岡県,宗像市,城西ケ丘,0,0,1,0,0,0
40220,81141,8114151,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞｮｳﾅﾝｶﾞｵｶ,福岡県,宗像市,城南ケ丘,0,0,0,0,0,0
40220,81141,8114181,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼﾞｮｳﾔﾏﾆｭｰﾀｳﾝ,福岡県,宗像市,城山ニュータウン,0,0,0,0,0,0
40220,81134,8113408,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｼｮｳﾖｳﾀﾞｲ,福岡県,宗像市,樟陽台,0,0,1,0,0,0
40220,81134,8113405,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ｽｴ,福岡県,宗像市,須恵,0,0,1,0,0,0
40220,81141,8114175,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾀｸ,福岡県,宗像市,田久,0,0,1,0,0,0
40220,81134,8113431,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾀｸﾞﾏ,福岡県,宗像市,田熊,0,0,1,0,0,0
40220,81141,8114152,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾀｹﾏﾙ,福岡県,宗像市,武丸,0,0,0,0,0,0
40220,81135,8113505,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾀｼﾏ,福岡県,宗像市,田島,0,0,0,0,0,0
40220,81135,8113514,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾀﾉ,福岡県,宗像市,田野,0,0,0,0,0,0
40220,81135,8113507,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾀﾚ,福岡県,宗像市,多禮,0,0,0,0,0,0
40220,81141,8114183,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾂﾁｱﾅ,福岡県,宗像市,土穴,0,0,1,0,0,0
40220,81134,8113407,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾃﾝﾋﾟｮｳﾀﾞｲ,福岡県,宗像市,天平台,0,0,0,0,0,0
40220,81134,8113436,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾄｳｺﾞｳ,福岡県,宗像市,東郷,0,0,0,0,0,0
40220,81141,8114164,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾄｸｼｹﾞ,福岡県,宗像市,徳重,0,0,1,0,0,0
40220,81141,8114155,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾅｺﾞﾘ,福岡県,宗像市,名残,0,0,1,0,0,0
40220,81134,8113423,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾉｻｶ,福岡県,宗像市,野坂,0,0,0,0,0,0
40220,81141,8114171,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾊﾔﾏ,福岡県,宗像市,葉山,0,0,1,0,0,0
40220,81134,8113424,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾊﾙﾏﾁ,福岡県,宗像市,原町,0,0,0,0,0,0
40220,81134,8113403,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾋｶﾘｶﾞｵｶ,福岡県,宗像市,ひかりケ丘,0,0,1,0,0,0
40220,81134,8113425,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾋﾉｻﾄ,福岡県,宗像市,日の里,0,0,1,0,0,0
40220,81134,8113412,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾋﾞｮｳﾄﾞｳｼﾞ,福岡県,宗像市,平等寺,0,0,0,0,0,0
40220,81134,8113430,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾋﾗｲ,福岡県,宗像市,平井,0,0,1,0,0,0
40220,81135,8113504,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾌｶﾀ,福岡県,宗像市,深田,0,0,0,0,0,0
40220,81141,8114154,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾌｼﾞﾜﾗ,福岡県,宗像市,冨地原,0,0,0,0,0,0
40220,81141,8114113,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾏｶﾞﾘ(25､35),福岡県,宗像市,曲（２５、３５）,1,0,0,0,0,0
40220,81134,8113413,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾏｶﾞﾘ(ｿﾉﾀ),福岡県,宗像市,曲（その他）,1,0,0,0,0,0
40220,81134,8113439,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾐｸﾗ,福岡県,宗像市,三倉,0,0,0,0,0,0
40220,81134,8113414,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾐﾂｵｶ,福岡県,宗像市,光岡,0,0,0,0,0,0
40220,81141,8114172,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾐﾄﾞﾘﾏﾁ,福岡県,宗像市,緑町,0,0,0,0,0,0
40220,81134,8113416,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾐﾔﾀ,福岡県,宗像市,宮田,0,0,1,0,0,0
40220,81135,8113503,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾑﾀｼﾞﾘ,福岡県,宗像市,牟田尻,0,0,0,0,0,0
40220,81134,8113434,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾑﾗﾔﾏﾀﾞ,福岡県,宗像市,村山田,0,0,0,0,0,0
40220,81134,8113435,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾓﾁﾔﾏ,福岡県,宗像市,用山,0,0,0,0,0,0
40220,81134,8113411,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾔﾏﾀﾞ,福岡県,宗像市,山田,0,0,0,0,0,0
40220,81135,8113506,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾖｼﾀﾞ,福岡県,宗像市,吉田,0,0,0,0,0,0
40220,81141,8114153,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾖｼﾄﾞﾒ,福岡県,宗像市,吉留,0,0,0,0,0,0
40220,81141,8114145,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾘｮｳｹﾞﾝｼﾞ,福岡県,宗像市,陵厳寺,0,0,1,0,0,0
40220,81134,8113438,ﾌｸｵｶｹﾝ,ﾑﾅｶﾀｼ,ﾜｶﾐﾀﾞｲ,福岡県,宗像市,和歌美台,0,0,0,0,0,0
40221,81801,8180100,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,太宰府市,以下に掲載がない場合,0,0,0,0,0,0
40221,81801,8180137,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｱｵﾊﾞﾀﾞｲ,福岡県,太宰府市,青葉台,0,0,1,0,0,0
40221,81801,8180121,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｱｵﾔﾏ,福岡県,太宰府市,青山,0,0,1,0,0,0
40221,81801,8180118,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｲｼｻﾞｶ,福岡県,太宰府市,石坂,0,0,1,0,0,0
40221,81801,8180115,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｳﾁﾔﾏ,福岡県,太宰府市,内山,0,0,0,0,0,0
40221,81801,8180123,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｳﾒｶﾞｵｶ,福岡県,太宰府市,梅ケ丘,0,0,1,0,0,0
40221,81801,8180134,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｵｵｻﾞﾉ,福岡県,太宰府市,大佐野,0,0,0,0,0,0
40221,81801,8180101,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｶﾝｾﾞｵﾝｼﾞ,福岡県,太宰府市,観世音寺,0,0,1,0,0,0
40221,81801,8180114,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｷﾀﾀﾞﾆ,福岡県,太宰府市,北谷,0,0,0,0,0,0
40221,81801,8180132,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｺｸﾌﾞ,福岡県,太宰府市,国分,0,0,1,0,0,0
40221,81801,8180125,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｺﾞｼﾞｮｳ,福岡県,太宰府市,五条,0,0,1,0,0,0
40221,81801,8180139,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｻｲﾄ,福岡県,太宰府市,宰都,0,0,1,0,0,0
40221,81801,8180117,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｻｲﾌ,福岡県,太宰府市,宰府,0,0,1,0,0,0
40221,81801,8180133,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｻｶﾓﾄ,福岡県,太宰府市,坂本,0,0,1,0,0,0
40221,81801,8180111,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｻﾝｼﾞｮｳ,福岡県,太宰府市,三条,0,0,1,0,0,0
40221,81801,8180102,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｼﾗｶﾜ,福岡県,太宰府市,白川,0,0,0,0,0,0
40221,81801,8180103,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ｽｻﾞｸ,福岡県,太宰府市,朱雀,0,0,1,0,0,0
40221,81801,8180122,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾀｶｵ,福岡県,太宰府市,高雄,0,0,1,0,0,0
40221,81801,8180104,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾄｵﾉｺｶﾞ,福岡県,太宰府市,通古賀,0,0,1,0,0,0
40221,81801,8180105,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾄﾌﾛｳﾐﾅﾐ,福岡県,太宰府市,都府楼南,0,0,1,0,0,0
40221,81801,8180136,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾅｶﾞｳﾗﾀﾞｲ,福岡県,太宰府市,長浦台,0,0,1,0,0,0
40221,81801,8180124,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾊﾞｲｺｳｴﾝ,福岡県,太宰府市,梅香苑,0,0,1,0,0,0
40221,81801,8180110,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾐｶｻ,福岡県,太宰府市,御笠,0,0,1,0,0,0
40221,81801,8180131,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾐｽﾞｷ,福岡県,太宰府市,水城,0,0,1,0,0,0
40221,81801,8180135,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾑｶｲｻﾞﾉ,福岡県,太宰府市,向佐野,0,0,0,0,0,0
40221,81801,8180138,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾖｼﾏﾂ,福岡県,太宰府市,吉松,0,0,1,0,0,0
40221,81801,8180119,ﾌｸｵｶｹﾝ,ﾀﾞｻﾞｲﾌｼ,ﾚﾝｶﾞﾔ,福岡県,太宰府市,連歌屋,0,0,1,0,0,0
40223,81131,8113100,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,古賀市,以下に掲載がない場合,0,0,0,0,0,0
40223,81131,8113134,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｱｵﾔｷﾞ,福岡県,古賀市,青柳,0,0,0,0,0,0
40223,81131,8113133,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｱｵﾔｷﾞﾏﾁ,福岡県,古賀市,青柳町,0,0,0,0,0,0
40223,81131,8113136,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｲﾄｶﾞｳﾗ,福岡県,古賀市,糸ケ浦,0,0,0,0,0,0
40223,81131,8113131,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｲﾏｻﾞｲｹ,福岡県,古賀市,今在家,0,0,0,0,0,0
40223,81131,8113117,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｲﾏﾉｼｮｳ,福岡県,古賀市,今の庄,0,0,1,0,0,0
40223,81131,8113102,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｴｷﾋｶﾞｼ,福岡県,古賀市,駅東,0,0,1,0,0,0
40223,81131,8113135,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｵﾀﾞｹ,福岡県,古賀市,小竹,0,0,0,0,0,0
40223,81131,8113126,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｵﾔﾏﾀﾞ,福岡県,古賀市,小山田,0,0,0,0,0,0
40223,81131,8113104,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｶﾂﾞﾙｶﾞｵｶ,福岡県,古賀市,花鶴丘,0,0,1,0,0,0
40223,81131,8113132,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｶﾜﾊﾞﾙ,福岡県,古賀市,川原,0,0,0,0,0,0
40223,81131,8113115,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｸﾎﾞ,福岡県,古賀市,久保,0,0,0,0,0,0
40223,81131,8113137,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｺｶﾞ,福岡県,古賀市,古賀,0,0,0,0,0,0
40223,81131,8113122,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｺﾓﾉ,福岡県,古賀市,薦野,0,0,0,0,0,0
40223,81131,8113105,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｼｼﾌﾞ,福岡県,古賀市,鹿部,0,0,0,0,0,0
40223,81131,8113116,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｼｮｳ,福岡県,古賀市,庄,0,0,0,0,0,0
40223,81131,8113118,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｼﾝｸﾎﾞ,福岡県,古賀市,新久保,0,0,1,0,0,0
40223,81131,8113127,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ｼﾝﾊﾞﾙ,福岡県,古賀市,新原,0,0,0,0,0,0
40223,81131,8113125,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾀﾆﾔﾏ,福岡県,古賀市,谷山,0,0,0,0,0,0
40223,81131,8113113,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾁﾄﾞﾘ,福岡県,古賀市,千鳥,0,0,1,0,0,0
40223,81131,8113103,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾁｭｳｵｳ,福岡県,古賀市,中央,0,0,1,0,0,0
40223,81131,8113101,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾃﾝｼﾞﾝ,福岡県,古賀市,天神,0,0,1,0,0,0
40223,81131,8113123,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾈﾀﾋﾞ,福岡県,古賀市,米多比,0,0,0,0,0,0
40223,81131,8113112,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾊﾅﾐﾋｶﾞｼ,福岡県,古賀市,花見東,0,0,1,0,0,0
40223,81131,8113111,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾊﾅﾐﾐﾅﾐ,福岡県,古賀市,花見南,0,0,1,0,0,0
40223,81131,8113106,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾋﾖｼ,福岡県,古賀市,日吉,0,0,1,0,0,0
40223,81131,8113114,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾏｲﾉｻﾄ,福岡県,古賀市,舞の里,0,0,1,0,0,0
40223,81131,8113107,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾐｱｹ,福岡県,古賀市,美明,0,0,1,0,0,0
40223,81131,8113121,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾑｼﾛｳﾁ,福岡県,古賀市,筵内,0,0,0,0,0,0
40223,81131,8113124,ﾌｸｵｶｹﾝ,ｺｶﾞｼ,ﾔｸｵｳｼﾞ,福岡県,古賀市,薬王寺,0,0,0,0,0,0
40224,81132,8113200,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,福津市,以下に掲載がない場合,0,0,0,0,0,0
40224,81132,8113213,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾌｸﾂｼﾉﾂｷﾞﾆﾊﾞﾝﾁｶﾞｸﾙﾊﾞｱｲ,福岡県,福津市,福津市の次に番地がくる場合,0,0,0,0,0,0
40224,81132,8113220,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｱｹﾎﾞﾉ,福岡県,福津市,あけぼの,0,0,0,0,0,0
40224,81132,8113202,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｱｾﾞﾏﾁ,福岡県,福津市,畦町,0,0,0,0,0,0
40224,81133,8113301,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｱﾗｼﾞ,福岡県,福津市,在自,0,0,0,0,0,0
40224,81132,8113205,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｳﾁﾄﾞﾉ,福岡県,福津市,内殿,0,0,0,0,0,0
40224,81133,8113302,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｵｵｲｼ,福岡県,福津市,大石,0,0,0,0,0,0
40224,81132,8113226,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｵﾀﾞｹ,福岡県,福津市,小竹,0,0,1,0,0,0
40224,81135,8113521,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｶﾂｳﾗ,福岡県,福津市,勝浦,0,0,0,0,0,0
40224,81132,8113207,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｶﾐｻｲｺﾞｳ,福岡県,福津市,上西郷,0,0,0,0,0,0
40224,81132,8113223,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｺｳﾖｳﾀﾞｲ,福岡県,福津市,光陽台,0,0,1,0,0,0
40224,81132,8113228,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｺｳﾖｳﾀﾞｲﾐﾅﾐ,福岡県,福津市,光陽台南,0,0,1,0,0,0
40224,81132,8113229,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｻｸﾗｶﾞﾜ,福岡県,福津市,桜川,0,0,0,0,0,0
40224,81132,8113204,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｼｬﾘｸﾗ,福岡県,福津市,舎利蔵,0,0,0,0,0,0
40224,81133,8113303,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ｽﾀﾞﾀ,福岡県,福津市,須多田,0,0,0,0,0,0
40224,81132,8113227,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾀｶﾋﾗ,福岡県,福津市,高平,0,0,0,0,0,0
40224,81132,8113217,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾁｭｳｵｳ,福岡県,福津市,中央,0,0,1,0,0,0
40224,81132,8113222,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾂﾏﾙ,福岡県,福津市,津丸,0,0,0,0,0,0
40224,81133,8113304,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾂﾔｻﾞｷ,福岡県,福津市,津屋崎,0,0,1,0,0,0
40224,81132,8113224,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾃﾋﾞｶ,福岡県,福津市,手光,0,0,0,0,0,0
40224,81132,8113218,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾃﾋﾞｶﾐﾅﾐ,福岡県,福津市,手光南,0,0,1,0,0,0
40224,81132,8113219,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾆｼﾌｸﾏ,福岡県,福津市,西福間,0,0,1,0,0,0
40224,81135,8113522,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾇﾔﾏ,福岡県,福津市,奴山,0,0,0,0,0,0
40224,81132,8113214,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾊﾅﾐｶﾞｵｶ,福岡県,福津市,花見が丘,0,0,1,0,0,0
40224,81132,8113216,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾊﾅﾐｶﾞﾊﾏ,福岡県,福津市,花見が浜,0,0,1,0,0,0
40224,81132,8113215,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾊﾅﾐﾉｻﾄ,福岡県,福津市,花見の里,0,0,1,0,0,0
40224,81132,8113225,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾋｶﾞｼﾌｸﾏ,福岡県,福津市,東福間,0,0,1,0,0,0
40224,81132,8113206,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾋｻｽｴ,福岡県,福津市,久末,0,0,0,0,0,0
40224,81132,8113208,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾌｸﾏｴｷﾋｶﾞｼ,福岡県,福津市,福間駅東,0,0,1,0,0,0
40224,81132,8113212,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾌｸﾏﾐﾅﾐ,福岡県,福津市,福間南,0,0,1,0,0,0
40224,81133,8113308,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾎｼｶﾞｵｶ,福岡県,福津市,星ケ丘,0,0,0,0,0,0
40224,81133,8113305,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾐﾔｼﾞ,福岡県,福津市,宮司,0,0,1,0,0,0
40224,81133,8113312,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾐﾔｼﾞｶﾞｵｶ,福岡県,福津市,宮司ヶ丘,0,0,0,0,0,0
40224,81133,8113311,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾐﾔｼﾞﾊﾏ,福岡県,福津市,宮司浜,0,0,1,0,0,0
40224,81133,8113309,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾐﾔｼﾞﾓﾄﾏﾁ,福岡県,福津市,宮司元町,0,0,0,0,0,0
40224,81132,8113203,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾓﾄｷﾞ,福岡県,福津市,本木,0,0,0,0,0,0
40224,81132,8113201,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾔﾂﾅﾐ,福岡県,福津市,八並,0,0,0,0,0,0
40224,81133,8113306,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾕｸｴ,福岡県,福津市,生家,0,0,0,0,0,0
40224,81132,8113211,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾕﾐﾉｻﾄ,福岡県,福津市,有弥の里,0,0,1,0,0,0
40224,81132,8113221,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾜｶｷﾞﾀﾞｲ,福岡県,福津市,若木台,0,0,1,0,0,0
40224,81133,8113307,ﾌｸｵｶｹﾝ,ﾌｸﾂｼ,ﾜﾀﾘ,福岡県,福津市,渡,0,0,0,0,0,0
40230,81911,8191100,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糸島市,以下に掲載がない場合,0,0,0,0,0,0
40230,81911,8191132,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｱﾘﾀ,福岡県,糸島市,有田,0,0,0,0,0,0
40230,81911,8191127,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｱﾘﾀﾁｭｳｵｳ,福岡県,糸島市,有田中央,0,0,1,0,0,0
40230,81911,8191152,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲｲﾊﾞﾙ,福岡県,糸島市,飯原,0,0,0,0,0,0
40230,81911,8191103,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲｹﾀﾞ,福岡県,糸島市,池田,0,0,0,0,0,0
40230,81915,8191562,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲﾀ,福岡県,糸島市,井田,0,0,0,0,0,0
40230,81911,8191101,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲﾀﾓﾁ,福岡県,糸島市,板持,0,0,1,0,0,0
40230,81911,8191126,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲﾜﾓﾄ,福岡県,糸島市,岩本,0,0,0,0,0,0
40230,81915,8191582,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｲﾜﾗ,福岡県,糸島市,井原,0,0,0,0,0,0
40230,81911,8191112,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｳﾗｼ,福岡県,糸島市,浦志,0,0,1,0,0,0
40230,81911,8191105,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｳﾙｳ,福岡県,糸島市,潤,0,0,1,0,0,0
40230,81915,8191573,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｵｳﾏﾙ,福岡県,糸島市,王丸,0,0,0,0,0,0
40230,81911,8191135,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｵｵｳﾗ,福岡県,糸島市,大浦,0,0,0,0,0,0
40230,81911,8191121,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｵｷﾞﾉｳﾗ,福岡県,糸島市,荻浦,0,0,0,0,0,0
40230,81911,8191124,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｶﾌﾘ,福岡県,糸島市,加布里,0,0,0,0,0,0
40230,81911,8191123,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｶﾐｱﾘ,福岡県,糸島市,神在,0,0,0,0,0,0
40230,81911,8191155,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｶﾜﾂｷ,福岡県,糸島市,川付,0,0,0,0,0,0
40230,81915,8191574,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｶﾜﾊﾞﾙ,福岡県,糸島市,川原,0,0,0,0,0,0
40230,81911,8191141,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｸﾗﾓﾁ,福岡県,糸島市,蔵持,0,0,0,0,0,0
40230,81915,8191563,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｺｳﾗｲｼﾞ,福岡県,糸島市,高来寺,0,0,0,0,0,0
40230,81911,8191147,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｺｳﾘｷ,福岡県,糸島市,香力,0,0,0,0,0,0
40230,81911,8191106,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾄ,福岡県,糸島市,志登,0,0,0,0,0,0
40230,81911,8191131,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾉﾜﾗ,福岡県,糸島市,篠原,0,0,0,0,0,0
40230,81911,8191129,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾉﾜﾗﾆｼ,福岡県,糸島市,篠原西,0,0,1,0,0,0
40230,81911,8191128,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾉﾜﾗﾋｶﾞｼ,福岡県,糸島市,篠原東,0,0,1,0,0,0
40230,81913,8191301,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｲﾀﾞﾊﾗ,福岡県,糸島市,志摩井田原,0,0,0,0,0,0
40230,81913,8191313,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｲﾅﾄﾞﾒ,福岡県,糸島市,志摩稲留,0,0,0,0,0,0
40230,81913,8191315,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｲﾅﾊﾞ,福岡県,糸島市,志摩稲葉,0,0,0,0,0,0
40230,81913,8191334,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｷｼ,福岡県,糸島市,志摩岐志,0,0,0,0,0,0
40230,81913,8191331,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｸｶﾞ,福岡県,糸島市,志摩久家,0,0,0,0,0,0
40230,81913,8191335,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｹﾔ,福岡県,糸島市,志摩芥屋,0,0,0,0,0,0
40230,81913,8191323,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｺｶﾞﾈﾏﾙ,福岡県,糸島市,志摩小金丸,0,0,0,0,0,0
40230,81913,8191321,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｺﾌｼﾞ,福岡県,糸島市,志摩小富士,0,0,0,0,0,0
40230,81913,8191304,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｻｸﾗｲ,福岡県,糸島市,志摩桜井,0,0,0,0,0,0
40230,81913,8191333,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏｼﾝﾏﾁ,福岡県,糸島市,志摩新町,0,0,0,0,0,0
40230,81913,8191311,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾂﾜｻﾞｷ,福岡県,糸島市,志摩津和崎,0,0,0,0,0,0
40230,81913,8191325,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾆｼｶｲﾂﾞｶ,福岡県,糸島市,志摩西貝塚,0,0,0,0,0,0
40230,81913,8191303,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾉｷﾞﾀ,福岡県,糸島市,志摩野北,0,0,0,0,0,0
40230,81913,8191312,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾊﾂ,福岡県,糸島市,志摩初,0,0,0,0,0,0
40230,81913,8191305,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾊﾞﾊﾞ,福岡県,糸島市,志摩馬場,0,0,0,0,0,0
40230,81913,8191324,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾋｶﾞｼｶｲﾂﾞｶ,福岡県,糸島市,志摩東貝塚,0,0,0,0,0,0
40230,81913,8191336,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾋﾒｼﾏ,福岡県,糸島市,志摩姫島,0,0,0,0,0,0
40230,81913,8191332,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾌﾅｺｼ,福岡県,糸島市,志摩船越,0,0,0,0,0,0
40230,81913,8191306,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾏﾂｸﾞﾏ,福岡県,糸島市,志摩松隈,0,0,0,0,0,0
40230,81913,8191322,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾐﾄｺ,福岡県,糸島市,志摩御床,0,0,0,0,0,0
40230,81913,8191314,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾓﾛﾖｼ,福岡県,糸島市,志摩師吉,0,0,0,0,0,0
40230,81913,8191302,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾏﾖｼﾀﾞ,福岡県,糸島市,志摩吉田,0,0,0,0,0,0
40230,81911,8191154,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾗｲﾄ,福岡県,糸島市,白糸,0,0,0,0,0,0
40230,81911,8191114,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｼﾝﾃﾞﾝ,福岡県,糸島市,新田,0,0,0,0,0,0
40230,81915,8191581,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｽﾞｲﾊﾞｲｼﾞ,福岡県,糸島市,瑞梅寺,0,0,0,0,0,0
40230,81915,8191572,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｽｴﾅｶﾞ,福岡県,糸島市,末永,0,0,0,0,0,0
40230,81911,8191156,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｾﾄ,福岡県,糸島市,瀬戸,0,0,0,0,0,0
40230,81915,8191561,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ｿﾈ,福岡県,糸島市,曽根,0,0,0,0,0,0
40230,81915,8191564,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾀﾞｲﾓﾝ,福岡県,糸島市,大門,0,0,0,0,0,0
40230,81911,8191143,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾀｶｳｴ,福岡県,糸島市,高上,0,0,0,0,0,0
40230,81915,8191571,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾀｶｽ,福岡県,糸島市,高祖,0,0,0,0,0,0
40230,81911,8191102,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾀｶﾀ,福岡県,糸島市,高田,0,0,1,0,0,0
40230,81911,8191134,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾀｸ,福岡県,糸島市,多久,0,0,0,0,0,0
40230,81911,8191125,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾁﾊﾔｼﾝﾃﾞﾝ,福岡県,糸島市,千早新田,0,0,0,0,0,0
40230,81911,8191111,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾄﾏﾘ,福岡県,糸島市,泊,0,0,0,0,0,0
40230,81911,8191133,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾄﾐ,福岡県,糸島市,富,0,0,0,0,0,0
40230,81911,8191153,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾅｶﾞﾉ,福岡県,糸島市,長野,0,0,0,0,0,0
40230,81915,8191575,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾉﾄﾞｳ,福岡県,糸島市,西堂,0,0,0,0,0,0
40230,81916,8191622,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳｲｷｻﾝ,福岡県,糸島市,二丈一貴山,0,0,0,0,0,0
40230,81916,8191623,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳｲｼｻﾞｷ,福岡県,糸島市,二丈石崎,0,0,0,0,0,0
40230,81916,8191611,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳｶﾀﾔﾏ,福岡県,糸島市,二丈片山,0,0,0,0,0,0
40230,81916,8191621,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳｶﾐﾌｶｴ,福岡県,糸島市,二丈上深江,0,0,0,0,0,0
40230,81916,8191642,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳｼｶｶ,福岡県,糸島市,二丈鹿家,0,0,0,0,0,0
40230,81916,8191616,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾀｹ,福岡県,糸島市,二丈武,0,0,0,0,0,0
40230,81916,8191615,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾀﾅｶ,福岡県,糸島市,二丈田中,0,0,0,0,0,0
40230,81916,8191625,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾅｶﾞｲｼ,福岡県,糸島市,二丈長石,0,0,0,0,0,0
40230,81916,8191614,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾊﾏｸﾎﾞ,福岡県,糸島市,二丈浜窪,0,0,0,0,0,0
40230,81916,8191626,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾊﾛ,福岡県,糸島市,二丈波呂,0,0,0,0,0,0
40230,81916,8191601,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾌｶｴ,福岡県,糸島市,二丈深江,0,0,0,0,0,0
40230,81916,8191631,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾌｸｲ,福岡県,糸島市,二丈福井,0,0,0,0,0,0
40230,81916,8191612,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾏｽｴ(ｼﾓﾏｽｴ),福岡県,糸島市,二丈松末（下松末）,1,0,0,0,0,0
40230,81916,8191613,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾏｽｴ(ｿﾉﾀ),福岡県,糸島市,二丈松末（その他）,1,0,0,0,0,0
40230,81916,8191627,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾏﾂｸﾆ,福岡県,糸島市,二丈松国,0,0,0,0,0,0
40230,81916,8191624,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾐﾂﾖｼ,福岡県,糸島市,二丈満吉,0,0,0,0,0,0
40230,81916,8191641,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾆｼﾞｮｳﾖｼｲ,福岡県,糸島市,二丈吉井,0,0,0,0,0,0
40230,81911,8191104,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾊﾀｴ,福岡県,糸島市,波多江,0,0,0,0,0,0
40230,81911,8191107,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾊﾀｴｴｷｷﾀ,福岡県,糸島市,波多江駅北,0,0,1,0,0,0
40230,81911,8191108,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾊﾀｴｴｷﾐﾅﾐ,福岡県,糸島市,波多江駅南,0,0,1,0,0,0
40230,81911,8191122,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾋｶﾞｼ,福岡県,糸島市,東,0,0,0,0,0,0
40230,81911,8191151,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾎﾝ,福岡県,糸島市,本,0,0,0,0,0,0
40230,81911,8191113,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙ,福岡県,糸島市,前原,0,0,0,0,0,0
40230,81911,8191138,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙｴｷﾐﾅﾐ,福岡県,糸島市,前原駅南,0,0,1,0,0,0
40230,81911,8191118,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙｷﾀ,福岡県,糸島市,前原北,0,0,1,0,0,0
40230,81911,8191116,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙﾁｭｳｵｳ,福岡県,糸島市,前原中央,0,0,1,0,0,0
40230,81911,8191117,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙﾆｼ,福岡県,糸島市,前原西,0,0,1,0,0,0
40230,81911,8191119,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙﾋｶﾞｼ,福岡県,糸島市,前原東,0,0,1,0,0,0
40230,81911,8191139,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾏｴﾊﾞﾙﾐﾅﾐ,福岡県,糸島市,前原南,0,0,1,0,0,0
40230,81915,8191583,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾐｸﾓ,福岡県,糸島市,三雲,0,0,0,0,0,0
40230,81911,8191146,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾐｻｶ,福岡県,糸島市,三坂,0,0,0,0,0,0
40230,81911,8191136,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾐｻｷｶﾞｵｶ,福岡県,糸島市,美咲が丘,0,0,1,0,0,0
40230,81911,8191137,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾐﾅｶｾﾞﾀﾞｲ,福岡県,糸島市,南風台,0,0,1,0,0,0
40230,81911,8191142,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾔｼﾏ,福岡県,糸島市,八島,0,0,0,0,0,0
40230,81911,8191144,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾔﾏｷﾞﾀ,福岡県,糸島市,山北,0,0,0,0,0,0
40230,81911,8191115,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾕﾋﾞ,福岡県,糸島市,油比,0,0,0,0,0,0
40230,81911,8191145,ﾌｸｵｶｹﾝ,ｲﾄｼﾏｼ,ﾗｲｻﾞﾝ,福岡県,糸島市,雷山,0,0,0,0,0,0
40305,81112,8111200,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,筑紫郡那珂川町,以下に掲載がない場合,0,0,0,0,0,0
40305,81112,8111224,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｱﾝﾄｸ,福岡県,筑紫郡那珂川町,安徳,0,0,0,0,0,0
40305,81112,8111233,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｲﾁﾉｾ,福岡県,筑紫郡那珂川町,市ノ瀬,0,0,0,0,0,0
40305,81112,8111211,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｲﾏﾐﾂ,福岡県,筑紫郡那珂川町,今光,0,0,1,0,0,0
40305,81112,8111241,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｳｼﾛﾉ,福岡県,筑紫郡那珂川町,後野,0,0,0,0,0,0
40305,81112,8111232,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｳﾒｶﾞﾈ,福岡県,筑紫郡那珂川町,埋金,0,0,0,0,0,0
40305,81112,8111255,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｴｺ,福岡県,筑紫郡那珂川町,恵子,0,0,1,0,0,0
40305,81112,8111221,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｵｳﾂｶﾀﾞｲ,福岡県,筑紫郡那珂川町,王塚台,0,0,1,0,0,0
40305,81112,8111201,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｶﾀﾅﾜ,福岡県,筑紫郡那珂川町,片縄,0,0,1,0,0,0
40305,81112,8111203,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｶﾀﾅﾜｷﾀ,福岡県,筑紫郡那珂川町,片縄北,0,0,1,0,0,0
40305,81112,8111202,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｶﾀﾅﾜﾆｼ,福岡県,筑紫郡那珂川町,片縄西,0,0,1,0,0,0
40305,81112,8111204,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｶﾀﾅﾜﾋｶﾞｼ,福岡県,筑紫郡那珂川町,片縄東,0,0,1,0,0,0
40305,81112,8111223,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｶﾐｶｼﾞﾜﾗ,福岡県,筑紫郡那珂川町,上梶原,0,0,0,0,0,0
40305,81112,8111234,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｺﾞｶﾔﾏ,福岡県,筑紫郡那珂川町,五ケ山,0,0,0,0,0,0
40305,81112,8111252,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｺﾞﾛｳﾏﾙ,福岡県,筑紫郡那珂川町,五郎丸,0,0,1,0,0,0
40305,81112,8111222,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ｼﾓｶｼﾞﾜﾗ,福岡県,筑紫郡那珂川町,下梶原,0,0,0,0,0,0
40305,81112,8111253,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾁｭｳ,福岡県,筑紫郡那珂川町,仲,0,0,1,0,0,0
40305,81112,8111256,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾁｭｳﾏﾙ,福岡県,筑紫郡那珂川町,仲丸,0,0,1,0,0,0
40305,81112,8111254,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾄﾞｳｾﾞﾝ,福岡県,筑紫郡那珂川町,道善,0,0,1,0,0,0
40305,81112,8111213,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾅｶﾊﾞﾙ,福岡県,筑紫郡那珂川町,中原,0,0,1,0,0,0
40305,81112,8111214,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾅｶﾊﾞﾙﾋｶﾞｼ,福岡県,筑紫郡那珂川町,中原東,0,0,1,0,0,0
40305,81112,8111212,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾅｶﾊﾞﾙﾐﾊﾙｶﾞｵｶ,福岡県,筑紫郡那珂川町,中原観晴が丘,0,0,0,0,0,0
40305,81112,8111236,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾅﾒﾘ,福岡県,筑紫郡那珂川町,南面里,0,0,0,0,0,0
40305,81112,8111235,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾅﾙﾀｹ,福岡県,筑紫郡那珂川町,成竹,0,0,0,0,0,0
40305,81112,8111242,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾆｼｸﾞﾏ,福岡県,筑紫郡那珂川町,西隈,0,0,0,0,0,0
40305,81112,8111246,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾆｼﾊﾀ,福岡県,筑紫郡那珂川町,西畑,0,0,0,0,0,0
40305,81112,8111243,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾋｶﾞｼｸﾞﾏ,福岡県,筑紫郡那珂川町,東隈,0,0,1,0,0,0
40305,81112,8111231,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾌﾆｭｳﾄﾞｳ,福岡県,筑紫郡那珂川町,不入道,0,0,0,0,0,0
40305,81112,8111245,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾍﾞｯｼｮ,福岡県,筑紫郡那珂川町,別所,0,0,0,0,0,0
40305,81112,8111251,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾏﾂﾉｷ,福岡県,筑紫郡那珂川町,松木,0,0,1,0,0,0
40305,81112,8111215,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾏﾂﾊﾞﾗ,福岡県,筑紫郡那珂川町,松原,0,0,0,0,0,0
40305,81112,8111216,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾐﾊﾙｶﾞｵｶ,福岡県,筑紫郡那珂川町,観晴が丘,0,0,0,0,0,0
40305,81112,8111244,ﾌｸｵｶｹﾝ,ﾁｸｼｸﾞﾝﾅｶｶﾞﾜﾏﾁ,ﾔﾏﾀﾞ,福岡県,筑紫郡那珂川町,山田,0,0,0,0,0,0
40341,81121,8112100,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡宇美町,以下に掲載がない場合,0,0,0,1,0,0
40341,81121,8112104,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｲﾉ,福岡県,糟屋郡宇美町,井野,0,0,0,0,0,0
40341,81121,8112101,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｳﾐ,福岡県,糟屋郡宇美町,宇美,0,0,1,0,0,0
40341,81121,8112128,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｳﾐﾁｭｳｵｳ,福岡県,糟屋郡宇美町,宇美中央,0,0,1,0,0,0
40341,81121,8112125,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｳﾐﾋｶﾞｼ,福岡県,糟屋郡宇美町,宇美東,0,0,1,0,0,0
40341,81121,8112131,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｷﾌﾈ,福岡県,糟屋郡宇美町,貴船,0,0,1,0,0,0
40341,81121,8112123,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｺｳｼｮｳｼﾞ,福岡県,糟屋郡宇美町,光正寺,0,0,1,0,0,0
40341,81121,8112109,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｻｸﾗﾊﾞﾙ,福岡県,糟屋郡宇美町,桜原,0,0,1,0,0,0
40341,81121,8112105,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｼｵｳｼﾞ,福岡県,糟屋郡宇美町,四王寺,0,0,0,0,0,0
40341,81121,8112103,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｼｵｳｼﾞｻﾞｶ,福岡県,糟屋郡宇美町,四王寺坂,0,0,1,0,0,0
40341,81121,8112127,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｼｮｳｼﾞﾀﾞｹ,福岡県,糟屋郡宇美町,障子岳,0,0,1,0,0,0
40341,81121,8112126,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｼｮｳｼﾞﾀﾞｹﾐﾅﾐ,福岡県,糟屋郡宇美町,障子岳南,0,0,1,0,0,0
40341,81121,8112102,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ｽﾐﾔｷ,福岡県,糟屋郡宇美町,炭焼,0,0,0,0,0,0
40341,81121,8112107,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾄﾋﾞﾀｹ,福岡県,糟屋郡宇美町,とびたけ,0,0,1,0,0,0
40341,81121,8112132,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾊﾙﾀﾞ,福岡県,糟屋郡宇美町,原田,0,0,1,0,0,0
40341,81121,8112106,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾋﾊﾞﾘｶﾞｵｶ,福岡県,糟屋郡宇美町,ひばりが丘,0,0,1,0,0,0
40341,81121,8112121,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾍｲﾜ,福岡県,糟屋郡宇美町,平和,0,0,1,0,0,0
40341,81121,8112122,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾐｮｳｼﾞﾝｻﾞｶ,福岡県,糟屋郡宇美町,明神坂,0,0,1,0,0,0
40341,81121,8112108,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾕﾘｶﾞｵｶ,福岡県,糟屋郡宇美町,ゆりが丘,0,0,1,0,0,0
40341,81121,8112124,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｳﾐﾏﾁ,ﾜｶｸｻ,福岡県,糟屋郡宇美町,若草,0,0,1,0,0,0
40342,81124,8112400,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡篠栗町,以下に掲載がない場合,0,0,0,0,0,0
40342,81124,8112412,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ｵﾄｲﾇ,福岡県,糟屋郡篠栗町,乙犬,0,0,0,0,0,0
40342,81124,8112413,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ｵﾅｶ,福岡県,糟屋郡篠栗町,尾仲,0,0,0,0,0,0
40342,81124,8112402,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ｶﾅｲﾃﾞ,福岡県,糟屋郡篠栗町,金出,0,0,0,0,0,0
40342,81124,8112405,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ｻｻｸﾞﾘ,福岡県,糟屋郡篠栗町,篠栗,0,0,0,0,0,0
40342,81124,8112401,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾀｶﾀ,福岡県,糟屋郡篠栗町,高田,0,0,0,0,0,0
40342,81124,8112416,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾀﾅｶ,福岡県,糟屋郡篠栗町,田中,0,0,0,0,0,0
40342,81124,8112415,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾂﾊﾞｸﾛ,福岡県,糟屋郡篠栗町,津波黒,0,0,0,0,0,0
40342,81124,8112404,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾅｲｼﾞｭｳ,福岡県,糟屋郡篠栗町,内住,0,0,0,0,0,0
40342,81124,8112403,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾊｷﾞﾉｳ,福岡県,糟屋郡篠栗町,萩尾,0,0,0,0,0,0
40342,81124,8112411,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾜｶｽｷﾞ,福岡県,糟屋郡篠栗町,若杉,0,0,0,0,0,0
40342,81124,8112414,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｻｻｸﾞﾘﾏﾁ,ﾜﾀﾞ,福岡県,糟屋郡篠栗町,和田,0,0,0,0,0,0
40343,81122,8112200,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡志免町,以下に掲載がない場合,0,0,0,0,0,0
40343,81122,8112203,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｲｼﾊﾞｼﾀﾞｲ,福岡県,糟屋郡志免町,石橋台,0,0,0,0,0,0
40343,81122,8112209,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｵｳｼﾞ,福岡県,糟屋郡志免町,王子,0,0,1,0,0,0
40343,81122,8112245,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｶﾀﾐﾈ,福岡県,糟屋郡志免町,片峰,0,0,1,0,0,0
40343,81122,8112246,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｶﾀﾐﾈﾁｭｳｵｳ,福岡県,糟屋郡志免町,片峰中央,0,0,1,0,0,0
40343,81122,8112248,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｻｶｾ,福岡県,糟屋郡志免町,坂瀬,0,0,0,0,0,0
40343,81122,8112201,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｻｸﾗｶﾞｵｶ,福岡県,糟屋郡志免町,桜丘,0,0,1,0,0,0
40343,81122,8112202,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｼﾒ,福岡県,糟屋郡志免町,志免,0,0,0,0,0,0
40343,81122,8112244,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｼﾒﾁｭｳｵｳ,福岡県,糟屋郡志免町,志免中央,0,0,1,0,0,0
40343,81122,8112243,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ｼﾒﾋｶﾞｼ,福岡県,糟屋郡志免町,志免東,0,0,1,0,0,0
40343,81122,8112204,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾀﾄﾞﾐ,福岡県,糟屋郡志免町,田富,0,0,0,0,0,0
40343,81122,8112241,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾋｶﾞｼｺｳｴﾝﾀﾞｲ,福岡県,糟屋郡志免町,東公園台,0,0,1,0,0,0
40343,81122,8112205,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾍﾞﾌ,福岡県,糟屋郡志免町,別府,0,0,0,0,0,0
40343,81122,8112233,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾍﾞﾌｷﾀ,福岡県,糟屋郡志免町,別府北,0,0,1,0,0,0
40343,81122,8112232,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾍﾞﾌﾆｼ,福岡県,糟屋郡志免町,別府西,0,0,1,0,0,0
40343,81122,8112231,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾍﾞﾌﾋｶﾞｼ,福岡県,糟屋郡志免町,別府東,0,0,1,0,0,0
40343,81122,8112242,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾏﾂｶﾞｵｶ,福岡県,糟屋郡志免町,松ケ丘,0,0,0,0,0,0
40343,81122,8112206,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾐﾀﾗｲ,福岡県,糟屋郡志免町,御手洗,0,0,1,0,0,0
40343,81122,8112207,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾐﾅﾐｻﾞﾄ,福岡県,糟屋郡志免町,南里,0,0,1,0,0,0
40343,81122,8112247,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾑｶｲｶﾞｵｶ,福岡県,糟屋郡志免町,向ケ丘,0,0,1,0,0,0
40343,81122,8112208,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾒﾏﾁ,ﾖｼﾊﾗ,福岡県,糟屋郡志免町,吉原,0,0,0,0,0,0
40344,81121,8112100,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡須惠町,以下に掲載がない場合,0,0,0,1,0,0
40344,81121,8112112,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ｳｴｷ,福岡県,糟屋郡須惠町,植木,0,0,0,0,0,0
40344,81121,8112114,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ｶﾐｽｴ,福岡県,糟屋郡須惠町,上須惠,0,0,0,0,0,0
40344,81121,8112115,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ｻﾀﾆ,福岡県,糟屋郡須惠町,佐谷,0,0,0,0,0,0
40344,81121,8112111,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ｼﾝﾊﾞﾙ,福岡県,糟屋郡須惠町,新原,0,0,0,0,0,0
40344,81121,8112113,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ｽｴ,福岡県,糟屋郡須惠町,須惠,0,0,0,0,0,0
40344,81122,8112221,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｽｴﾏﾁ,ﾀﾋﾞｲｼ,福岡県,糟屋郡須惠町,旅石,0,0,0,0,0,0
40345,81101,8110100,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡新宮町,以下に掲載がない場合,0,0,0,0,0,0
40345,81101,8110118,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ｱｲﾉｼﾏ,福岡県,糟屋郡新宮町,相島,0,0,0,0,0,0
40345,81101,8110117,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ｶﾐﾉﾌ,福岡県,糟屋郡新宮町,上府,0,0,0,0,0,0
40345,81101,8110113,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ｻｸﾗﾔﾏﾃ,福岡県,糟屋郡新宮町,桜山手,0,0,1,0,0,0
40345,81101,8110112,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ｼﾓﾉﾌ,福岡県,糟屋郡新宮町,下府,0,0,1,0,0,0
40345,81101,8110115,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ｼﾝｸﾞｳ,福岡県,糟屋郡新宮町,新宮,0,0,0,0,0,0
40345,81101,8110102,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾀﾁﾊﾞﾅｸﾞﾁ,福岡県,糟屋郡新宮町,立花口,0,0,0,0,0,0
40345,81101,8110120,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾁｭｳｵｳｴｷﾏｴ,福岡県,糟屋郡新宮町,中央駅前,0,0,1,0,0,0
40345,81101,8110103,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾊﾅﾀﾁﾊﾞﾅ,福岡県,糟屋郡新宮町,花立花,0,0,1,0,0,0
40345,81101,8110101,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾊﾙｶﾞﾐ,福岡県,糟屋郡新宮町,原上,0,0,0,0,0,0
40345,81101,8110104,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾏﾄﾉ,福岡県,糟屋郡新宮町,的野,0,0,0,0,0,0
40345,81101,8110121,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾐｻｷ,福岡県,糟屋郡新宮町,美咲,0,0,1,0,0,0
40345,81101,8110111,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾐｼﾛ,福岡県,糟屋郡新宮町,三代,0,0,0,0,0,0
40345,81101,8110119,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾐﾄﾞﾘｶﾞﾊﾏ,福岡県,糟屋郡新宮町,緑ケ浜,0,0,1,0,0,0
40345,81101,8110116,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾐﾅﾄ,福岡県,糟屋郡新宮町,湊,0,0,0,0,0,0
40345,81101,8110114,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾐﾅﾄｻﾞｶ,福岡県,糟屋郡新宮町,湊坂,0,0,1,0,0,0
40345,81101,8110122,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾓﾘﾉﾐﾔ,福岡県,糟屋郡新宮町,杜の宮,0,0,1,0,0,0
40345,81101,8110110,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｼﾝｸﾞｳﾏﾁ,ﾕｳｽ,福岡県,糟屋郡新宮町,夜臼,0,0,1,0,0,0
40348,81125,8112500,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝﾋｻﾔﾏﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡久山町,以下に掲載がない場合,0,0,0,0,0,0
40348,81125,8112503,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝﾋｻﾔﾏﾏﾁ,ｲﾉ,福岡県,糟屋郡久山町,猪野,0,0,0,0,0,0
40348,81125,8112501,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝﾋｻﾔﾏﾏﾁ,ｸﾊﾞﾗ,福岡県,糟屋郡久山町,久原,0,0,0,0,0,0
40348,81125,8112502,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝﾋｻﾔﾏﾏﾁ,ﾔﾏﾀﾞ,福岡県,糟屋郡久山町,山田,0,0,0,0,0,0
40349,81123,8112300,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,糟屋郡粕屋町,以下に掲載がない場合,0,0,0,0,0,0
40349,81123,8112306,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｱｴ,福岡県,糟屋郡粕屋町,阿恵,0,0,0,0,0,0
40349,813,8130008,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｳﾁﾊｼ790-1(ﾀﾉﾂﾀﾞﾝﾁ),福岡県,糟屋郡粕屋町,内橋７９０の１（多ノ津団地）,1,0,0,0,0,0
40349,81123,8112308,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｳﾁﾊｼ(ｿﾉﾀ),福岡県,糟屋郡粕屋町,内橋（その他）,1,0,0,0,0,0
40349,81123,8112313,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｴﾂｼﾞ,福岡県,糟屋郡粕屋町,江辻,0,0,0,0,0,0
40349,81123,8112302,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｵｵｸﾏ,福岡県,糟屋郡粕屋町,大隈,0,0,0,0,0,0
40349,81123,8112301,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｶﾐｵｵｸﾏ,福岡県,糟屋郡粕屋町,上大隈,0,0,0,0,0,0
40349,81123,8112309,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｶﾖｲﾁｮｳ,福岡県,糟屋郡粕屋町,駕与丁,0,0,1,0,0,0
40349,81123,8112315,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｺｳﾅｶﾊﾞﾙ,福岡県,糟屋郡粕屋町,甲仲原,0,0,1,0,0,0
40349,81123,8112303,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ｻｶﾄﾞ,福岡県,糟屋郡粕屋町,酒殿,0,0,0,0,0,0
40349,81123,8112311,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾁｮｳｼﾞｬﾊﾞﾙ,福岡県,糟屋郡粕屋町,長者原,0,0,0,0,0,0
40349,81123,8112312,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾄﾊﾞﾗ,福岡県,糟屋郡粕屋町,戸原,0,0,0,0,0,0
40349,81123,8112304,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾅｶﾊﾞﾙ,福岡県,糟屋郡粕屋町,仲原,0,0,0,0,0,0
40349,81123,8112310,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾊﾅｶﾞｳﾗ,福岡県,糟屋郡粕屋町,花ヶ浦,0,0,1,0,0,0
40349,81123,8112307,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾊﾙﾏﾁ,福岡県,糟屋郡粕屋町,原町,0,0,1,0,0,0
40349,81123,8112305,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾕｽ,福岡県,糟屋郡粕屋町,柚須,0,0,0,0,0,0
40349,81123,8112314,ﾌｸｵｶｹﾝ,ｶｽﾔｸﾞﾝｶｽﾔﾏﾁ,ﾜｶﾐﾔ,福岡県,糟屋郡粕屋町,若宮,0,0,1,0,0,0
40383,81142,8114200,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,遠賀郡岡垣町,以下に掲載がない場合,0,0,0,0,0,0
40383,81142,8114215,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｱｻﾋﾀﾞｲ,福岡県,遠賀郡岡垣町,旭台,0,0,1,0,0,0
40383,81142,8114216,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｱｻﾋﾐﾅﾐ,福岡県,遠賀郡岡垣町,旭南,0,0,0,0,0,0
40383,81142,8114203,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｳﾂﾗ,福岡県,遠賀郡岡垣町,内浦,0,0,0,0,0,0
40383,81142,8114231,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｴﾋﾞﾂ,福岡県,遠賀郡岡垣町,海老津,0,0,1,0,0,0
40383,81142,8114236,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｴﾋﾞﾂｴｷﾏｴ,福岡県,遠賀郡岡垣町,海老津駅前,0,0,0,0,0,0
40383,81142,8114238,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｴﾋﾞﾂｴｷﾐﾅﾐ,福岡県,遠賀郡岡垣町,海老津駅南,0,0,1,0,0,0
40383,81142,8114212,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｸﾛﾔﾏ,福岡県,遠賀郡岡垣町,黒山,0,0,0,0,0,0
40383,81142,8114235,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｺｳｴﾝﾄﾞｵﾘ,福岡県,遠賀郡岡垣町,公園通り,0,0,1,0,0,0
40383,81142,8114227,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｺｳﾖｳﾀﾞｲ,福岡県,遠賀郡岡垣町,高陽台,0,0,1,0,0,0
40383,81142,8114217,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｻｸﾗﾀﾞｲ,福岡県,遠賀郡岡垣町,桜台,0,0,0,0,0,0
40383,81142,8114232,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ｼﾞｮｳﾊﾀ,福岡県,遠賀郡岡垣町,上畑,0,0,0,0,0,0
40383,81142,8114234,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾀｶｸﾗ,福岡県,遠賀郡岡垣町,高倉,0,0,0,0,0,0
40383,81142,8114218,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾁｭｳｵｳﾀﾞｲ,福岡県,遠賀郡岡垣町,中央台,0,0,1,0,0,0
40383,81142,8114204,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾃﾉ,福岡県,遠賀郡岡垣町,手野,0,0,0,0,0,0
40383,81142,8114222,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾄｷﾞﾘ,福岡県,遠賀郡岡垣町,戸切,0,0,0,0,0,0
40383,81142,8114224,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾅﾍﾞﾀ,福岡県,遠賀郡岡垣町,鍋田,0,0,1,0,0,0
40383,81142,8114213,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾇｶﾂﾞｶ,福岡県,遠賀郡岡垣町,糠塚,0,0,0,0,0,0
40383,81142,8114233,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾉﾏ,福岡県,遠賀郡岡垣町,野間,0,0,1,0,0,0
40383,81142,8114239,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾉﾏﾐﾅﾐ,福岡県,遠賀郡岡垣町,野間南,0,0,0,0,0,0
40383,81142,8114201,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾊﾂ,福岡県,遠賀郡岡垣町,波津,0,0,0,0,0,0
40383,81142,8114202,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾊﾗ,福岡県,遠賀郡岡垣町,原,0,0,0,0,0,0
40383,81142,8114225,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾋｶﾞｼｺｳﾖｳ,福岡県,遠賀郡岡垣町,東高陽,0,0,1,0,0,0
40383,81142,8114237,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾋｶﾞｼﾀｶｸﾗ,福岡県,遠賀郡岡垣町,東高倉,0,0,1,0,0,0
40383,81142,8114228,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾋｶﾞｼﾏﾂﾊﾞﾗ,福岡県,遠賀郡岡垣町,東松原,0,0,1,0,0,0
40383,81142,8114220,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾋｶﾞｼﾔﾏﾀﾞ,福岡県,遠賀郡岡垣町,東山田,0,0,1,0,0,0
40383,81142,8114214,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾏﾂｶﾞﾀﾞｲ,福岡県,遠賀郡岡垣町,松ケ台,0,0,1,0,0,0
40383,81142,8114226,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾐﾅﾐｺｳﾖｳ,福岡県,遠賀郡岡垣町,南高陽,0,0,0,0,0,0
40383,81142,8114205,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾐﾖｼ,福岡県,遠賀郡岡垣町,三吉,0,0,0,0,0,0
40383,81142,8114221,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾔﾏﾀﾞ,福岡県,遠賀郡岡垣町,山田,0,0,0,0,0,0
40383,81142,8114223,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾔﾏﾀﾞﾄｳｹﾞ,福岡県,遠賀郡岡垣町,山田峠,0,0,1,0,0,0
40383,81142,8114229,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾕﾘｶﾞｵｶ,福岡県,遠賀郡岡垣町,百合ケ丘,0,0,1,0,0,0
40383,81142,8114211,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾖｼｷ,福岡県,遠賀郡岡垣町,吉木,0,0,0,0,0,0
40383,81142,8114242,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾖｼｷﾆｼ,福岡県,遠賀郡岡垣町,吉木西,0,0,1,0,0,0
40383,81142,8114241,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵｶｶﾞｷﾏﾁ,ﾖｼｷﾋｶﾞｼ,福岡県,遠賀郡岡垣町,吉木東,0,0,1,0,0,0
40384,81143,8114300,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,福岡県,遠賀郡遠賀町,以下に掲載がない場合,0,0,0,0,0,0
40384,81143,8114312,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｱｻｷﾞ,福岡県,遠賀郡遠賀町,浅木,0,0,1,0,0,0
40384,81143,8114303,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｲﾏｺｶﾞ,福岡県,遠賀郡遠賀町,今古賀,0,0,0,0,0,0
40384,81143,8114311,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｵｲﾗ,福岡県,遠賀郡遠賀町,老良,0,0,0,0,0,0
40384,81143,8114342,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｵｻﾞｷ,福岡県,遠賀郡遠賀町,尾崎,0,0,0,0,0,0
40384,81143,8114341,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｵﾆﾂﾞ,福岡県,遠賀郡遠賀町,鬼津,0,0,0,0,0,0
40384,81143,8114307,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｵﾝｶﾞｶﾞﾜ,福岡県,遠賀郡遠賀町,遠賀川,0,0,1,0,0,0
40384,81143,8114332,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｶﾐﾍﾞﾌ,福岡県,遠賀郡遠賀町,上別府,0,0,0,0,0,0
40384,81143,8114313,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｷﾓﾘ,福岡県,遠賀郡遠賀町,木守,0,0,0,0,0,0
40384,81143,8114306,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｷｭｳﾃｲ,福岡県,遠賀郡遠賀町,旧停,0,0,1,0,0,0
40384,81143,8114301,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｼﾏﾂﾞ,福岡県,遠賀郡遠賀町,島津,0,0,0,0,0,0
40384,81143,8114333,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ｼﾏﾄﾞ,福岡県,遠賀郡遠賀町,島門,0,0,0,0,0,0
40384,81143,8114343,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾃﾞﾝｴﾝ,福岡県,遠賀郡遠賀町,田園,0,0,1,0,0,0
40384,81143,8114302,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾋﾛﾜﾀﾘ,福岡県,遠賀郡遠賀町,広渡,0,0,1,0,0,0
40384,81143,8114322,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾌﾖｳ,福岡県,遠賀郡遠賀町,芙蓉,0,0,1,0,0,0
40384,81143,8114331,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾍﾞﾌ,福岡県,遠賀郡遠賀町,別府,0,0,0,0,0,0
40384,81143,8114305,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾏﾂﾉﾓﾄ,福岡県,遠賀郡遠賀町,松の本,0,0,1,0,0,0
40384,81143,8114321,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾑｼｮｳﾂﾞ,福岡県,遠賀郡遠賀町,虫生津,0,0,0,0,0,0
40384,81143,8114324,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾑｼｮｳﾂﾞﾐﾅﾐ,福岡県,遠賀郡遠賀町,虫生津南,0,0,0,0,0,0
40384,81143,8114334,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾚﾝｶﾞｸ,福岡県,遠賀郡遠賀町,蓮角,0,0,0,0,0,0
40384,81143,8114323,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾜｶﾊﾞﾀﾞｲ,福岡県,遠賀郡遠賀町,若葉台,0,0,0,0,0,0
40384,81143,8114304,ﾌｸｵｶｹﾝ,ｵﾝｶﾞｸﾞﾝｵﾝｶﾞﾁｮｳ,ﾜｶﾏﾂ,福岡県,遠賀郡遠賀町,若松,0,0,0,0,0,0
42209,817,8170000,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,長崎県,対馬市,以下に掲載がない場合,0,0,0,0,0,0
42209,817,8170034,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｱｶﾞﾐ,長崎県,対馬市,厳原町安神,0,0,0,0,0,0
42209,81701,8170153,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｱｻﾞﾓ,長崎県,対馬市,厳原町浅藻,0,0,0,0,0,0
42209,81702,8170241,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｱﾚ,長崎県,対馬市,厳原町阿連,0,0,0,0,0,0
42209,817,8170021,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｲﾏﾔｼｷ,長崎県,対馬市,厳原町今屋敷,0,0,0,0,0,0
42209,81701,8170157,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｳﾁﾔﾏ,長崎県,対馬市,厳原町内山,0,0,0,0,0,0
42209,817,8170033,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｵｳﾗ,長崎県,対馬市,厳原町尾浦,0,0,0,0,0,0
42209,817,8170024,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｵｵﾃﾊﾞｼ,長崎県,対馬市,厳原町大手橋,0,0,0,0,0,0
42209,81702,8170243,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｶｼﾈ,長崎県,対馬市,厳原町樫根,0,0,0,0,0,0
42209,817,8170006,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｷﾀｻﾞﾄ,長崎県,対馬市,厳原町北里,0,0,0,0,0,0
42209,817,8170032,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｸﾀ,長崎県,対馬市,厳原町久田,0,0,0,0,0,0
42209,817,8170031,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｸﾀﾐﾁ,長崎県,対馬市,厳原町久田道,0,0,0,0,0,0
42209,81702,8170245,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｸﾈｲﾅｶ,長崎県,対馬市,厳原町久根田舎,0,0,0,0,0,0
42209,81702,8170244,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｸﾈﾊﾏ,長崎県,対馬市,厳原町久根浜,0,0,0,0,0,0
42209,817,8170035,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｸﾜ,長崎県,対馬市,厳原町久和,0,0,0,0,0,0
42209,81702,8170246,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｺｳﾂｷ,長崎県,対馬市,厳原町上槻,0,0,0,0,0,0
42209,817,8170001,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｺｳﾗ,長崎県,対馬市,厳原町小浦,0,0,0,0,0,0
42209,817,8170022,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｺｸﾌﾞ,長崎県,対馬市,厳原町国分,0,0,0,0,0,0
42209,81702,8170248,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｺﾓﾀﾞ,長崎県,対馬市,厳原町小茂田,0,0,0,0,0,0
42209,817,8170005,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｻｼﾞｷﾊﾞﾗ,長崎県,対馬市,厳原町桟原,0,0,0,0,0,0
42209,81701,8170156,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｻｽｾ,長崎県,対馬市,厳原町佐須瀬,0,0,0,0,0,0
42209,81702,8170247,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｼｲﾈ,長崎県,対馬市,厳原町椎根,0,0,0,0,0,0
42209,81702,8170242,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁｼﾓﾊﾞﾙ,長崎県,対馬市,厳原町下原,0,0,0,0,0,0
42209,817,8170023,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾀﾌﾞﾁ,長崎県,対馬市,厳原町田渕,0,0,0,0,0,0
42209,81701,8170154,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾂﾂ,長崎県,対馬市,厳原町豆酘,0,0,0,0,0,0
42209,81701,8170155,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾂﾂｾ,長崎県,対馬市,厳原町豆酘瀬,0,0,0,0,0,0
42209,81701,8170152,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾂﾂﾅｲｲﾝ,長崎県,対馬市,厳原町豆酘内院,0,0,0,0,0,0
42209,817,8170014,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾃﾝﾄﾞｳｼｹﾞ,長崎県,対馬市,厳原町天道茂,0,0,0,0,0,0
42209,817,8170013,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾅｶﾑﾗ,長崎県,対馬市,厳原町中村,0,0,0,0,0,0
42209,817,8170003,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾅﾑﾛ,長崎県,対馬市,厳原町南室,0,0,0,0,0,0
42209,817,8170015,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾆｼｻﾞﾄ,長崎県,対馬市,厳原町西里,0,0,0,0,0,0
42209,817,8170016,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾋｶﾞｼｻﾞﾄ,長崎県,対馬市,厳原町東里,0,0,0,0,0,0
42209,817,8170012,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾋﾖｼ,長崎県,対馬市,厳原町日吉,0,0,0,0,0,0
42209,817,8170002,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾏｶﾞﾘ,長崎県,対馬市,厳原町曲,0,0,0,0,0,0
42209,817,8170011,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾐﾔﾀﾞﾆ,長崎県,対馬市,厳原町宮谷,0,0,0,0,0,0
42209,81701,8170151,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｲﾂﾞﾊﾗﾏﾁﾖﾗﾅｲｲﾝ,長崎県,対馬市,厳原町与良内院,0,0,0,0,0,0
42209,81715,8171532,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｲﾅ,長崎県,対馬市,上県町伊奈,0,0,0,0,0,0
42209,81715,8171524,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｲﾇｶﾞｳﾗ,長崎県,対馬市,上県町犬ケ浦,0,0,0,0,0,0
42209,81715,8171513,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｳﾅﾂﾗ,長崎県,対馬市,上県町女連,0,0,0,0,0,0
42209,81715,8171521,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｶｲﾄﾞｺﾛ,長崎県,対馬市,上県町飼所,0,0,0,0,0,0
42209,81715,8171522,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｶｼﾀｷ,長崎県,対馬市,上県町樫滝,0,0,0,0,0,0
42209,81715,8171512,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｸﾊﾞﾗ,長崎県,対馬市,上県町久原,0,0,0,0,0,0
42209,81715,8171531,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｺｼﾀｶ,長崎県,対馬市,上県町越高,0,0,0,0,0,0
42209,81716,8171603,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｻｺﾞ,長崎県,対馬市,上県町佐護,0,0,0,0,0,0
42209,81716,8171602,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｻｽﾅ,長崎県,対馬市,上県町佐須奈,0,0,0,0,0,0
42209,81715,8171511,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｼｼﾐ,長崎県,対馬市,上県町鹿見,0,0,0,0,0,0
42209,81715,8171533,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｼﾀﾙ,長崎県,対馬市,上県町志多留,0,0,0,0,0,0
42209,81715,8171523,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁｾﾀ,長崎県,対馬市,上県町瀬田,0,0,0,0,0,0
42209,81716,8171601,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁﾆｼﾂﾔ,長崎県,対馬市,上県町西津屋,0,0,0,0,0,0
42209,81715,8171525,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐｱｶﾞﾀﾏﾁﾐｿ,長崎県,対馬市,上県町御園,0,0,0,0,0,0
42209,81722,8172243,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｱｼﾐ,長崎県,対馬市,上対馬町芦見,0,0,0,0,0,0
42209,81717,8171704,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｱｼﾞﾛ,長崎県,対馬市,上対馬町網代,0,0,0,0,0,0
42209,81717,8171725,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｲｽﾞﾐ,長崎県,対馬市,上対馬町泉,0,0,0,0,0,0
42209,81717,8171722,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｵｵｳﾗ,長崎県,対馬市,上対馬町大浦,0,0,0,0,0,0
42209,81717,8171715,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｵｵﾏｽ,長崎県,対馬市,上対馬町大増,0,0,0,0,0,0
42209,81722,8172241,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｵｼｶ,長崎県,対馬市,上対馬町小鹿,0,0,0,0,0,0
42209,81717,8171721,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｶﾜﾁ,長崎県,対馬市,上対馬町河内,0,0,0,0,0,0
42209,81723,8172331,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｷﾝ,長崎県,対馬市,上対馬町琴,0,0,0,0,0,0
42209,81717,8171714,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｸｽ,長崎県,対馬市,上対馬町玖須,0,0,0,0,0,0
42209,81723,8172332,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｺﾞﾈｵ,長崎県,対馬市,上対馬町五根緒,0,0,0,0,0,0
42209,81723,8172333,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁｼｭｳｼ,長崎県,対馬市,上対馬町舟志,0,0,0,0,0,0
42209,81717,8171712,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾄｳｼﾞｭｳｼ,長崎県,対馬市,上対馬町唐舟志,0,0,0,0,0,0
42209,81717,8171711,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾄﾐｶﾞｳﾗ,長崎県,対馬市,上対馬町冨浦,0,0,0,0,0,0
42209,81717,8171724,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾄﾖ,長崎県,対馬市,上対馬町豊,0,0,0,0,0,0
42209,81717,8171703,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾆｼﾄﾞﾏﾘ,長崎県,対馬市,上対馬町西泊,0,0,0,0,0,0
42209,81717,8171713,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾊﾏｸﾞｽ,長崎県,対馬市,上対馬町浜久須,0,0,0,0,0,0
42209,81717,8171701,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾋﾀｶﾂ,長崎県,対馬市,上対馬町比田勝,0,0,0,0,0,0
42209,81722,8172242,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾋﾄｴ,長崎県,対馬市,上対馬町一重,0,0,0,0,0,0
42209,81717,8171702,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾌﾙｻﾄ,長崎県,対馬市,上対馬町古里,0,0,0,0,0,0
42209,81717,8171723,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ｶﾐﾂｼﾏﾏﾁﾜﾆｳﾗ,長崎県,対馬市,上対馬町鰐浦,0,0,0,0,0,0
42209,81712,8171231,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｲﾄｾ,長崎県,対馬市,豊玉町糸瀬,0,0,0,0,0,0
42209,81712,8171253,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｳﾑｷﾞ,長崎県,対馬市,豊玉町卯麦,0,0,0,0,0,0
42209,81712,8171252,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｵｵﾂﾅ,長崎県,対馬市,豊玉町大綱,0,0,0,0,0,0
42209,81712,8171241,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｶｲｸﾞﾁ,長崎県,対馬市,豊玉町貝口,0,0,0,0,0,0
42209,81712,8171233,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｶｲﾌﾅ,長崎県,対馬市,豊玉町貝鮒,0,0,0,0,0,0
42209,81712,8171245,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｶﾗｽ,長崎県,対馬市,豊玉町唐洲,0,0,0,0,0,0
42209,81712,8171256,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｺﾂﾞﾅ,長崎県,対馬市,豊玉町小綱,0,0,0,0,0,0
42209,81712,8171232,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｻｶﾞ,長崎県,対馬市,豊玉町嵯峨,0,0,0,0,0,0
42209,81712,8171234,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｻｼｶ,長崎県,対馬市,豊玉町佐志賀,0,0,0,0,0,0
42209,81712,8171254,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｻﾎ,長崎県,対馬市,豊玉町佐保,0,0,0,0,0,0
42209,81712,8171255,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｼﾀﾉｳﾗ,長崎県,対馬市,豊玉町志多浦,0,0,0,0,0,0
42209,81712,8171212,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁｿ,長崎県,対馬市,豊玉町曽,0,0,0,0,0,0
42209,81712,8171251,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾀ,長崎県,対馬市,豊玉町田,0,0,0,0,0,0
42209,81712,8171213,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾁﾛﾓ,長崎県,対馬市,豊玉町千尋藻,0,0,0,0,0,0
42209,81712,8171201,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾆｲ,長崎県,対馬市,豊玉町仁位,0,0,0,0,0,0
42209,81712,8171246,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾏﾜﾘ,長崎県,対馬市,豊玉町廻,0,0,0,0,0,0
42209,81712,8171257,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾒｲ,長崎県,対馬市,豊玉町銘,0,0,0,0,0,0
42209,81712,8171214,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾔﾘｶﾜ,長崎県,対馬市,豊玉町鑓川,0,0,0,0,0,0
42209,81712,8171223,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾖｺｳﾗ,長崎県,対馬市,豊玉町横浦,0,0,0,0,0,0
42209,81712,8171202,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾄﾖﾀﾏﾏﾁﾜｲﾀ,長崎県,対馬市,豊玉町和板,0,0,0,0,0,0
42209,81711,8171106,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｲﾇﾎﾞｴ,長崎県,対馬市,美津島町犬吠,0,0,0,0,0,0
42209,81704,8170432,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｲﾏｻﾞﾄ,長崎県,対馬市,美津島町今里,0,0,0,0,0,0
42209,81703,8170323,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｵｵﾌﾅｺｼ,長崎県,対馬市,美津島町大船越,0,0,0,0,0,0
42209,81703,8170325,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｵｶﾀ,長崎県,対馬市,美津島町緒方,0,0,0,0,0,0
42209,81704,8170431,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｵｻｷ,長崎県,対馬市,美津島町尾崎,0,0,0,0,0,0
42209,81711,8171105,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｵﾔﾏ,長崎県,対馬市,美津島町大山,0,0,0,0,0,0
42209,81704,8170433,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｶｼ,長崎県,対馬市,美津島町加志,0,0,0,0,0,0
42209,81711,8171107,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｶﾓｲｾ,長崎県,対馬市,美津島町鴨居瀬,0,0,0,0,0,0
42209,81711,8171103,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｶﾞﾔ,長崎県,対馬市,美津島町賀谷,0,0,0,0,0,0
42209,81703,8170324,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｸｽﾎﾞ,長崎県,対馬市,美津島町久須保,0,0,0,0,0,0
42209,81705,8170512,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｸﾛｾ,長崎県,対馬市,美津島町黒瀬,0,0,0,0,0,0
42209,81703,8170322,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｹﾁ,長崎県,対馬市,美津島町鶏知,0,0,0,0,0,0
42209,81711,8171101,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｺﾌﾅｺｼ,長崎県,対馬市,美津島町小船越,0,0,0,0,0,0
42209,81705,8170514,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｼﾏﾔﾏ,長崎県,対馬市,美津島町島山,0,0,0,0,0,0
42209,81703,8170321,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁｽﾓ,長崎県,対馬市,美津島町洲藻,0,0,0,0,0,0
42209,81705,8170511,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾀｹｼｷ,長崎県,対馬市,美津島町竹敷,0,0,0,0,0,0
42209,81703,8170326,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾈｵ,長崎県,対馬市,美津島町根緒,0,0,0,0,0,0
42209,81711,8171104,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾉﾌﾞ,長崎県,対馬市,美津島町濃部,0,0,0,0,0,0
42209,81705,8170513,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾋﾙｶﾞｳﾗ,長崎県,対馬市,美津島町昼ケ浦,0,0,0,0,0,0
42209,81704,8170434,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾌｸｻﾞｷ,長崎県,対馬市,美津島町吹崎,0,0,0,0,0,0
42209,81704,8170435,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾐｶﾀ,長崎県,対馬市,美津島町箕形,0,0,0,0,0,0
42209,81711,8171102,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾂｼﾏﾏﾁﾖｼｶﾞｳﾗ,長崎県,対馬市,美津島町芦浦,0,0,0,0,0,0
42209,81713,8171304,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｵｳﾐ,長崎県,対馬市,峰町青海,0,0,0,0,0,0
42209,81713,8171307,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｶｻ,長崎県,対馬市,峰町賀佐,0,0,0,0,0,0
42209,81713,8171302,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｶﾘｵ,長崎県,対馬市,峰町狩尾,0,0,0,0,0,0
42209,81713,8171303,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｷｻｶ,長崎県,対馬市,峰町木坂,0,0,0,0,0,0
42209,81714,8171411,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｸｼ,長崎県,対馬市,峰町櫛,0,0,0,0,0,0
42209,81714,8171412,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｻｶ,長崎県,対馬市,峰町佐賀,0,0,0,0,0,0
42209,81714,8171413,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁｼﾀｶ,長崎県,対馬市,峰町志多賀,0,0,0,0,0,0
42209,81713,8171305,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁﾂﾔﾅｷﾞ,長崎県,対馬市,峰町津柳,0,0,0,0,0,0
42209,81713,8171301,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁﾐﾈ,長崎県,対馬市,峰町三根,0,0,0,0,0,0
42209,81713,8171306,ﾅｶﾞｻｷｹﾝ,ﾂｼﾏｼ,ﾐﾈﾏﾁﾖｼﾀﾞ,長崎県,対馬市,峰町吉田,0,0,0,0,0,0
42210,81151,8115100,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｶﾆｹｲｻｲｶﾞﾅｲﾊﾞｱｲ,長崎県,壱岐市,以下に掲載がない場合,0,0,0,0,0,0
42210,81153,8115301,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｱｼﾍﾞｳﾗ,長崎県,壱岐市,芦辺町芦辺浦,0,0,0,0,0,0
42210,81157,8115733,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｺｸﾌﾞｶﾜﾑｶｴﾌﾚ,長崎県,壱岐市,芦辺町国分川迎触,0,0,0,0,0,0
42210,81157,8115731,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｺｸﾌﾞﾄｳﾀﾞﾌﾚ,長崎県,壱岐市,芦辺町国分当田触,0,0,0,0,0,0
42210,81157,8115732,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｺｸﾌﾞﾋｶﾞｼﾌﾚ,長崎県,壱岐市,芦辺町国分東触,0,0,0,0,0,0
42210,81157,8115734,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｺｸﾌﾞﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,芦辺町国分本村触,0,0,0,0,0,0
42210,81157,8115744,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｽﾐﾖｼｳｼﾛﾌﾚ,長崎県,壱岐市,芦辺町住吉後触,0,0,0,0,0,0
42210,81157,8115742,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｽﾐﾖｼﾋｶﾞｼﾌﾚ,長崎県,壱岐市,芦辺町住吉東触,0,0,0,0,0,0
42210,81157,8115743,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｽﾐﾖｼﾏｴﾌﾚ,長崎県,壱岐市,芦辺町住吉前触,0,0,0,0,0,0
42210,81157,8115741,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｽﾐﾖｼﾔﾏﾉﾌﾞﾌﾚ,長崎県,壱岐市,芦辺町住吉山信触,0,0,0,0,0,0
42210,81154,8115461,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳｾﾄｳﾗ,長崎県,壱岐市,芦辺町瀬戸浦,0,0,0,0,0,0
42210,81157,8115751,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾅｶﾉｺﾞｳﾅｶﾌﾚ,長崎県,壱岐市,芦辺町中野郷仲触,0,0,0,0,0,0
42210,81157,8115757,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾅｶﾉｺﾞｳﾆｼﾌﾚ,長崎県,壱岐市,芦辺町中野郷西触,0,0,0,0,0,0
42210,81157,8115752,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾅｶﾉｺﾞｳﾋｶﾞｼﾌﾚ,長崎県,壱岐市,芦辺町中野郷東触,0,0,0,0,0,0
42210,81157,8115756,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾅｶﾉｺﾞｳﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,芦辺町中野郷本村触,0,0,0,0,0,0
42210,81154,8115467,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷｴｽﾐﾌﾚ,長崎県,壱岐市,芦辺町箱崎江角触,0,0,0,0,0,0
42210,81154,8115465,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷｸｷﾞﾉｵﾌﾚ,長崎県,壱岐市,芦辺町箱崎釘ノ尾触,0,0,0,0,0,0
42210,81154,8115462,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷﾀｲｿｳﾌﾚ,長崎県,壱岐市,芦辺町箱崎大左右触,0,0,0,0,0,0
42210,81154,8115464,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷﾀﾆｴﾌﾚ,長崎県,壱岐市,芦辺町箱崎谷江触,0,0,0,0,0,0
42210,81154,8115463,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷﾅｶﾔﾏﾌﾚ,長崎県,壱岐市,芦辺町箱崎中山触,0,0,0,0,0,0
42210,81154,8115466,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,芦辺町箱崎本村触,0,0,0,0,0,0
42210,81154,8115468,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾊｺｻﾞｷﾓﾛﾂﾌﾚ,長崎県,壱岐市,芦辺町箱崎諸津触,0,0,0,0,0,0
42210,81153,8115321,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾌｶｴｻｶｴﾌﾚ,長崎県,壱岐市,芦辺町深江栄触,0,0,0,0,0,0
42210,81153,8115322,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾌｶｴﾂﾙｷﾌﾚ,長崎県,壱岐市,芦辺町深江鶴亀触,0,0,0,0,0,0
42210,81153,8115324,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾌｶｴﾋｶﾞｼﾌﾚ,長崎県,壱岐市,芦辺町深江東触,0,0,0,0,0,0
42210,81153,8115323,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾌｶｴﾋﾗﾌﾚ,長崎県,壱岐市,芦辺町深江平触,0,0,0,0,0,0
42210,81153,8115326,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾌｶｴﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,芦辺町深江本村触,0,0,0,0,0,0
42210,81153,8115325,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾌｶｴﾐﾅﾐﾌﾚ,長崎県,壱岐市,芦辺町深江南触,0,0,0,0,0,0
42210,81153,8115316,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾓﾛﾖｼｵｵｲｼﾌﾚ,長崎県,壱岐市,芦辺町諸吉大石触,0,0,0,0,0,0
42210,81153,8115313,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾓﾛﾖｼﾅｶﾌﾚ,長崎県,壱岐市,芦辺町諸吉仲触,0,0,0,0,0,0
42210,81153,8115314,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾓﾛﾖｼﾋｶﾞｼﾌﾚ,長崎県,壱岐市,芦辺町諸吉東触,0,0,0,0,0,0
42210,81153,8115315,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾓﾛﾖｼﾌﾀﾏﾀﾌﾚ,長崎県,壱岐市,芦辺町諸吉二亦触,0,0,0,0,0,0
42210,81153,8115311,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾓﾛﾖｼﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,芦辺町諸吉本村触,0,0,0,0,0,0
42210,81153,8115312,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾓﾛﾖｼﾐﾅﾐﾌﾚ,長崎県,壱岐市,芦辺町諸吉南触,0,0,0,0,0,0
42210,81157,8115755,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾕﾀｹｺｳﾌﾚ,長崎県,壱岐市,芦辺町湯岳興触,0,0,0,0,0,0
42210,81157,8115754,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾕﾀｹｺﾝｻﾞｶﾌﾚ,長崎県,壱岐市,芦辺町湯岳今坂触,0,0,0,0,0,0
42210,81157,8115753,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｱｼﾍﾞﾁｮｳﾕﾀｹﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,芦辺町湯岳本村触,0,0,0,0,0,0
42210,81152,8115222,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｲｹﾀﾞﾅｶﾌﾚ,長崎県,壱岐市,石田町池田仲触,0,0,0,0,0,0
42210,81152,8115224,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｲｹﾀﾞﾆｼﾌﾚ,長崎県,壱岐市,石田町池田西触,0,0,0,0,0,0
42210,81152,8115221,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｲｹﾀﾞﾋｶﾞｼﾌﾚ,長崎県,壱岐市,石田町池田東触,0,0,0,0,0,0
42210,81152,8115215,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｲｼﾀﾞﾆｼﾌﾚ,長崎県,壱岐市,石田町石田西触,0,0,0,0,0,0
42210,81152,8115211,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｲｼﾀﾞﾋｶﾞｼﾌﾚ,長崎県,壱岐市,石田町石田東触,0,0,0,0,0,0
42210,81152,8115214,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｲﾝﾄﾞｵｼﾞｳﾗ,長崎県,壱岐市,石田町印通寺浦,0,0,0,0,0,0
42210,81152,8115223,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳｸｷﾌﾚ,長崎県,壱岐市,石田町久喜触,0,0,0,0,0,0
42210,81152,8115202,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾂﾂｷﾅｶﾌﾚ,長崎県,壱岐市,石田町筒城仲触,0,0,0,0,0,0
42210,81152,8115204,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾂﾂｷﾆｼﾌﾚ,長崎県,壱岐市,石田町筒城西触,0,0,0,0,0,0
42210,81152,8115203,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾂﾂｷﾋｶﾞｼﾌﾚ,長崎県,壱岐市,石田町筒城東触,0,0,0,0,0,0
42210,81152,8115212,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,石田町本村触,0,0,0,0,0,0
42210,81152,8115213,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾐﾅﾐﾌﾚ,長崎県,壱岐市,石田町南触,0,0,0,0,0,0
42210,81152,8115201,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾔﾏｻｷﾌﾚ,長崎県,壱岐市,石田町山崎触,0,0,0,0,0,0
42210,81152,8115226,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾕﾀｹｲﾃﾖｼﾌﾚ,長崎県,壱岐市,石田町湯岳射手吉触,0,0,0,0,0,0
42210,81152,8115225,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｲｼﾀﾞﾁｮｳﾕﾀｹｺｳﾌﾚ,長崎県,壱岐市,石田町湯岳興触,0,0,0,0,0,0
42210,81155,8115543,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｳﾜﾊﾞﾌﾚ,長崎県,壱岐市,勝本町上場触,0,0,0,0,0,0
42210,81155,8115532,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｵｵｸﾎﾞﾌﾚ,長崎県,壱岐市,勝本町大久保触,0,0,0,0,0,0
42210,81155,8115523,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｶﾀﾔﾏﾌﾚ,長崎県,壱岐市,勝本町片山触,0,0,0,0,0,0
42210,81155,8115501,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｶﾂﾓﾄｳﾗ,長崎県,壱岐市,勝本町勝本浦,0,0,0,0,0,0
42210,81155,8115513,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｷﾀﾌﾚ,長崎県,壱岐市,勝本町北触,0,0,0,0,0,0
42210,81155,8115521,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｻｲﾄﾞﾌﾚ,長崎県,壱岐市,勝本町西戸触,0,0,0,0,0,0
42210,81155,8115531,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｻｶﾓﾄﾌﾚ,長崎県,壱岐市,勝本町坂本触,0,0,0,0,0,0
42210,81155,8115533,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｼﾝｼﾞｮｳﾆｼﾌﾚ,長崎県,壱岐市,勝本町新城西触,0,0,0,0,0,0
42210,81155,8115522,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳｼﾝｼﾞｮｳﾋｶﾞｼﾌﾚ,長崎県,壱岐市,勝本町新城東触,0,0,0,0,0,0
42210,81155,8115554,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾀﾃｲｼﾅｶﾌﾚ,長崎県,壱岐市,勝本町立石仲触,0,0,0,0,0,0
42210,81155,8115556,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾀﾃｲｼﾆｼﾌﾚ,長崎県,壱岐市,勝本町立石西触,0,0,0,0,0,0
42210,81155,8115553,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾀﾃｲｼﾋｶﾞｼﾌﾚ,長崎県,壱岐市,勝本町立石東触,0,0,0,0,0,0
42210,81155,8115555,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾀﾃｲｼﾐﾅﾐﾌﾚ,長崎県,壱岐市,勝本町立石南触,0,0,0,0,0,0
42210,81155,8115511,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾅｶﾌﾚ,長崎県,壱岐市,勝本町仲触,0,0,0,0,0,0
42210,81155,8115512,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾋｶﾞｼﾌﾚ,長崎県,壱岐市,勝本町東触,0,0,0,0,0,0
42210,81155,8115544,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾌｹﾌﾚ,長崎県,壱岐市,勝本町布気触,0,0,0,0,0,0
42210,81155,8115546,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾎﾝｸﾞｳﾅｶﾌﾚ,長崎県,壱岐市,勝本町本宮仲触,0,0,0,0,0,0
42210,81155,8115541,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾎﾝｸﾞｳﾆｼﾌﾚ,長崎県,壱岐市,勝本町本宮西触,0,0,0,0,0,0
42210,81155,8115542,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾎﾝｸﾞｳﾋｶﾞｼﾌﾚ,長崎県,壱岐市,勝本町本宮東触,0,0,0,0,0,0
42210,81155,8115545,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾎﾝｸﾞｳﾐﾅﾐﾌﾚ,長崎県,壱岐市,勝本町本宮南触,0,0,0,0,0,0
42210,81155,8115551,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾕﾉﾓﾄｳﾗ,長崎県,壱岐市,勝本町湯本浦,0,0,0,0,0,0
42210,81155,8115552,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｶﾂﾓﾄﾁｮｳﾕﾘﾊﾀﾌﾚ,長崎県,壱岐市,勝本町百合畑触,0,0,0,0,0,0
42210,81151,8115107,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｱﾘﾔｽﾌﾚ,長崎県,壱岐市,郷ノ浦町有安触,0,0,0,0,0,0
42210,81151,8115113,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｳｼｶﾀﾌﾚ,長崎県,壱岐市,郷ノ浦町牛方触,0,0,0,0,0,0
42210,81151,8115112,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｵｵｳﾗﾌﾚ,長崎県,壱岐市,郷ノ浦町大浦触,0,0,0,0,0,0
42210,81151,8115161,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｵｵｼﾏ,長崎県,壱岐市,郷ノ浦町大島,0,0,0,0,0,0
42210,81151,8115136,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｶﾀﾊﾞﾙﾌﾚ,長崎県,壱岐市,郷ノ浦町片原触,0,0,0,0,0,0
42210,81151,8115115,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｷﾀﾞﾌﾚ,長崎県,壱岐市,郷ノ浦町木田触,0,0,0,0,0,0
42210,81151,8115123,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｸｷﾞﾔﾏﾌﾚ,長崎県,壱岐市,郷ノ浦町釘山触,0,0,0,0,0,0
42210,81151,8115135,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｺﾞｳﾉｳﾗ,長崎県,壱岐市,郷ノ浦町郷ノ浦,0,0,0,0,0,0
42210,81151,8115105,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｺﾏｷﾆｼﾌﾚ,長崎県,壱岐市,郷ノ浦町小牧西触,0,0,0,0,0,0
42210,81151,8115106,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｺﾏｷﾋｶﾞｼﾌﾚ,長崎県,壱岐市,郷ノ浦町小牧東触,0,0,0,0,0,0
42210,81151,8115104,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｻﾄﾌﾚ,長崎県,壱岐市,郷ノ浦町里触,0,0,0,0,0,0
42210,81151,8115125,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｼﾊﾗﾆｼﾌﾚ,長崎県,壱岐市,郷ノ浦町志原西触,0,0,0,0,0,0
42210,81151,8115124,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｼﾊﾗﾐﾅﾐﾌﾚ,長崎県,壱岐市,郷ノ浦町志原南触,0,0,0,0,0,0
42210,81151,8115134,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｼｮｳﾌﾚ,長崎県,壱岐市,郷ノ浦町庄触,0,0,0,0,0,0
42210,81151,8115103,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳｼﾝﾃﾞﾝﾌﾚ,長崎県,壱岐市,郷ノ浦町新田触,0,0,0,0,0,0
42210,81151,8115122,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾀｲﾊﾞﾙﾌﾚ,長崎県,壱岐市,郷ノ浦町大原触,0,0,0,0,0,0
42210,81151,8115117,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾀﾅｶﾌﾚ,長崎県,壱岐市,郷ノ浦町田中触,0,0,0,0,0,0
42210,81151,8115142,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾂﾎﾞﾌﾚ,長崎県,壱岐市,郷ノ浦町坪触,0,0,0,0,0,0
42210,81151,8115162,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾅｶﾞｼﾏ,長崎県,壱岐市,郷ノ浦町長島,0,0,0,0,0,0
42210,81151,8115131,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾅｶﾞﾀﾌﾚ,長崎県,壱岐市,郷ノ浦町永田触,0,0,0,0,0,0
42210,81151,8115102,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾅｶﾞﾐﾈﾋｶﾞｼﾌﾚ,長崎県,壱岐市,郷ノ浦町長峰東触,0,0,0,0,0,0
42210,81151,8115101,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾅｶﾞﾐﾈﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,郷ノ浦町長峰本村触,0,0,0,0,0,0
42210,81151,8115143,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾊﾂﾔﾏﾆｼﾌﾚ,長崎県,壱岐市,郷ノ浦町初山西触,0,0,0,0,0,0
42210,81151,8115144,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾊﾂﾔﾏﾋｶﾞｼﾌﾚ,長崎県,壱岐市,郷ノ浦町初山東触,0,0,0,0,0,0
42210,81151,8115163,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾊﾙｼﾏ,長崎県,壱岐市,郷ノ浦町原島,0,0,0,0,0,0
42210,81151,8115111,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾊﾝｾｲﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,郷ノ浦町半城本村触,0,0,0,0,0,0
42210,81151,8115132,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾋｶﾞｼﾌﾚ,長崎県,壱岐市,郷ノ浦町東触,0,0,0,0,0,0
42210,81151,8115121,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾋﾛｳﾄﾌﾚ,長崎県,壱岐市,郷ノ浦町平人触,0,0,0,0,0,0
42210,81151,8115133,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,郷ノ浦町本村触,0,0,0,0,0,0
42210,81151,8115155,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾑｷﾞﾔﾌﾚ,長崎県,壱岐市,郷ノ浦町麦谷触,0,0,0,0,0,0
42210,81151,8115116,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾓﾉﾍﾞﾎﾝﾑﾗﾌﾚ,長崎県,壱岐市,郷ノ浦町物部本村触,0,0,0,0,0,0
42210,81151,8115114,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾔﾅｷﾞﾀﾞﾌﾚ,長崎県,壱岐市,郷ノ浦町柳田触,0,0,0,0,0,0
42210,81151,8115141,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾜｶﾏﾂﾌﾚ,長崎県,壱岐市,郷ノ浦町若松触,0,0,0,0,0,0
42210,81151,8115151,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾜﾀﾗｳﾗ,長崎県,壱岐市,郷ノ浦町渡良浦,0,0,0,0,0,0
42210,81151,8115153,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾜﾀﾗﾆｼﾌﾚ,長崎県,壱岐市,郷ノ浦町渡良西触,0,0,0,0,0,0
42210,81151,8115154,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾜﾀﾗﾋｶﾞｼﾌﾚ,長崎県,壱岐市,郷ノ浦町渡良東触,0,0,0,0,0,0
42210,81151,8115152,ﾅｶﾞｻｷｹﾝ,ｲｷｼ,ｺﾞｳﾉｳﾗﾁｮｳﾜﾀﾗﾐﾅﾐﾌﾚ,長崎県,壱岐市,郷ノ浦町渡良南触,0,0,0,0,0,0 | [
"[email protected]"
]
| |
67e3cc9231dfce6022f55788dfa686fe85569fad | cb013bac45f2e191a1fe6fbe1a4d5bb69651fbe9 | /week03/classContent/thread/p10_timer.py | 65d98a0ecc181a4f7b45d897b63c0ac8970d0eed | []
| no_license | hjzheng/Python001-class01 | a4529f775c1de0fe2ab6a6bc865328c1b9216df7 | a261a5bf4ca475bd20637a5377366eab409f5499 | refs/heads/master | 2022-11-19T11:09:59.347491 | 2020-07-13T09:51:25 | 2020-07-13T09:51:25 | 274,433,709 | 1 | 0 | null | 2020-06-23T14:53:44 | 2020-06-23T14:53:44 | null | UTF-8 | Python | false | false | 170 | py | # 定时器： 指定n秒后执行
from threading import Timer
def hello():
print("hello, world")
t = Timer(1, hello) # 表示1秒后执行hello函数
t.start()
| [
"[email protected]"
]
| |
f84990f5577daca09ced90271698a91eb116b6dd | 31ddcd6deda2398a5a730c858e9d8a08cb10d551 | /pythonstartup.py | 0a69fa9e210aae3272f872941050fa0b01ba170c | []
| no_license | hauwenc/dotfiles | e222f10630f76b9c56d2b6bd583011a1ecb0276d | 3a13040b43217e82a671287589baae1bbb597b1b | refs/heads/master | 2021-01-10T11:36:29.390314 | 2016-02-18T18:54:32 | 2016-02-18T18:54:32 | 45,560,244 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 95 | py | from __future__ import division
import os
import re
import numpy as np
import time
import sys
| [
"[email protected]"
]
| |
65fc92a79bd813ef453b821d8a02b1a20e6cd577 | e588da296dd6ec3bedee9d24444dfca6e8780aef | /classroom examples/10.py | ab241aa1417f606aba6c9459a043d03a16b9e3e0 | []
| no_license | sujith1919/TCS-Python | 98eac61a02500a0e8f3139e431c98a509828c867 | c988cf078616540fe7f56e3ebdfd964aebd14519 | refs/heads/master | 2023-03-02T09:03:10.052633 | 2021-02-02T16:40:18 | 2021-02-02T16:40:18 | 335,355,862 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 238 | py | import time
import os
starttime=time.time()
for x in range(1000):
x**x
endtime=time.time()
print(endtime-starttime)
time.sleep(1) #sleeps for 1 second
ts = os.path.getctime("10.py")
print(ts)
print(time.ctime(ts))
| [
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]
| |
c4479c6eb8c2b3de6a56bf651e278fa061e0a46d | 9be1ab6f7cc9e1e8474b7c76ef89284b54782c46 | /chapter_remaining/3_importing.py | 74ab8e113a71fbe0f3835725cf596ca9ef7ba6e6 | []
| no_license | Nateque123/python_tutorials | 8d9842d46570e6cecd7aa5419b9f77bc4468d391 | 83743acf4862155c5837c154d0422f74d0629043 | refs/heads/master | 2022-11-20T11:39:02.565456 | 2020-07-24T11:08:34 | 2020-07-24T11:08:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 127 | py | # simple importing practice # check file2 also
import file2
print(file2.func('nateq'))
from file2 import a
print(a) | [
"[email protected]"
]
| |
2c88423e7b1d7a0eaa3ce0c6656b0463de03c32e | a8a657cecbe0deab6f589a1b949bb18d6ab3938e | /ChemicsEndpoints/Endpoints/ChlorineCount/predictChlorineCount.py | 6622a6274e5ad06d3e60fc38f9323bb3cfb63af9 | []
| no_license | JonnaStalring/WebServices | 44d222b75d0fe2313db1be8b9e3dc23e48fa0ed6 | a5deb8be22056d3538e52c46310d1d99610dd1fa | refs/heads/master | 2021-01-10T23:55:13.461407 | 2019-03-04T17:01:04 | 2019-03-04T17:01:04 | 70,786,598 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 579 | py | from rdkit.Chem import Descriptors
from rdkit import Chem
def ChlorineCount(ID, smiles, project, series, CHEMICSMODELDIR):
"""
Total number of Cl
"""
fragStr = "Cl"
mol = Chem.MolFromSmiles(fragStr)
sma = Chem.MolToSmarts(mol)
matchIdx = Chem.QuickSmartsMatch(smiles, sma)
ChlorineCount = len(matchIdx)
confidence = "NaN"
return ChlorineCount, confidence
if __name__ == "__main__":
ChlorineCount, confidence = ChlorineCount("MyMol", "c1(cc(c(cc1)Oc1c(ncc(c1)C(F)(F)F)C(N)=O)C)Cl", "project", "series", ".")
print ChlorineCount
| [
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]
| |
8e590b6e190dead90a8370392f6dac800e826486 | 0a848b5b2ea31a7e2e997f27b13f363530df78d1 | /backend/cart/migrations/0009_auto_20210928_1845.py | 2b07b02da6e2d6fba28a5e641d9d8b827230cdee | []
| no_license | PhanVanThanh-hub/React-Django-Ecommerce | a88e4c0747a9a3d6179d45c60641595221fe701c | 00946075486495676595fe0a17dcdd0799756d4b | refs/heads/main | 2023-08-13T19:24:40.492000 | 2021-09-30T13:05:03 | 2021-09-30T13:05:03 | 412,066,886 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 595 | py | # Generated by Django 3.2.6 on 2021-09-28 11:45
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('cart', '0008_alter_orderitem_date_added'),
]
operations = [
migrations.AlterField(
model_name='order',
name='data_ordered',
field=models.DateTimeField(auto_now_add=True),
),
migrations.AlterField(
model_name='orderitem',
name='date_added',
field=models.DateTimeField(auto_now_add=True),
),
]
| [
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]
| |
70deb99eb2fe58fe8366b806aeff04fe4948a09f | 86019cfe5500aa3bd93c6cf393d6de197ff824ef | /Sai_Vinay.py | d7ac0dd4e0e205ff28f7ab792672dd6e19c8c491 | []
| no_license | rohithavarada/Member-Submissions | 8039de18ca8e1ae5fb3800935c6c6beca504154f | 9784d76654eb6192c36dd89035c4d9a629e1d27b | refs/heads/master | 2020-03-27T16:10:25.438340 | 2018-08-30T14:54:53 | 2018-08-30T14:54:53 | 146,763,830 | 0 | 0 | null | 2018-08-30T14:42:16 | 2018-08-30T14:42:16 | null | UTF-8 | Python | false | false | 89 | py | # This is for testing
printf("Hello World");
import string
print(string.ascii_lowercase)
| [
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]
| |
42e93c74b93ed26f5ed34a9a594feccb4b0f52de | 1e72b3defbae7498f887a763edff7c36333a3e47 | /app/migrations/0001_initial.py | 20e4ead86ba9fe8dfe9528620d819f80d801fb9c | []
| no_license | prabhuksgowda/ResumeBuilder-using-Django | 5dbc9f6b5b49fb57392d202975caf3cee07c07d8 | 47cb3ed8da278b618e6e9480c534e48d1332daf5 | refs/heads/master | 2023-04-04T21:13:41.265910 | 2021-03-26T06:58:56 | 2021-03-26T06:58:56 | 350,674,160 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,177 | py | # Generated by Django 2.2.1 on 2021-02-19 10:39
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Project',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('projecttitle', models.CharField(max_length=250)),
('prodescrip', models.CharField(max_length=800)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Personaldetails',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('dob', models.CharField(max_length=100)),
('gender', models.CharField(max_length=100)),
('nationality', models.CharField(max_length=100)),
('fathername', models.CharField(max_length=100)),
('address', models.CharField(max_length=250)),
('strength', models.CharField(max_length=100, null=True)),
('hobbies', models.CharField(max_length=100, null=True)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Education',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('collagename', models.CharField(max_length=100)),
('university', models.CharField(max_length=100)),
('branch', models.CharField(max_length=100)),
('yearofpass', models.CharField(max_length=4)),
('programinglang', models.CharField(max_length=250)),
('frontendlng', models.CharField(max_length=250, null=True)),
('framework', models.CharField(max_length=250, null=True)),
('cgpa', models.CharField(max_length=250)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Details',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
('email', models.CharField(max_length=100)),
('mobile', models.CharField(max_length=100)),
('linkedin', models.CharField(max_length=100)),
('carrearobject', models.TextField()),
('user', models.OneToOneField(default='flash', on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"[email protected]"
]
| |
6b95c17b989818943a654bbc1f3ab4b6f493aa2e | 899bb004ecc5df96b36d95e11705b9bb8d4f48a3 | /balloonproptest.py | 793a394296014d1d9031e01802bfe8cca34044e5 | []
| no_license | CamronAlexanderHirst/drifter-project | a09aaa49579c5be7a9ab2868688df2ce142b8f3f | 2e266067863538627b0c5d5f5a87e09b5fedf562 | refs/heads/master | 2021-06-30T23:51:52.786046 | 2020-10-21T18:42:07 | 2020-10-21T18:42:07 | 174,581,113 | 0 | 0 | null | 2019-05-01T22:52:54 | 2019-03-08T17:33:20 | Python | UTF-8 | Python | false | false | 1,900 | py | #!/usr/bin/env python3
'''Used to test out balloon prop
Author: Alex Hirst
'''
from src import wind_field
from src import gen_random_field
from src import visualizer
import clear_folder
import numpy as np
import time as t
import random
random.seed(11)
# Generate a nxm field
n = 10 # cell height of field (y)
m = 15 # cell width of field (x)
length = 500
n_samples = 100
field = gen_random_field.field_generator(n, m, length, 0, 1, n_samples, 'Normal')
# speed statistics
field.nrm_mean_s = 0
field.nrm_sig_s = 2.5
# heading statistics
field.nrm_mean_h = 0
field.nrm_sig_h = 0.25
field.sample_heading_speed()
A = wind_field.wind_field(field.vel, field.loc, length, field.nsamps, field.samples)
A.dt = 2.5
A.t_end = 20
A.y_goal = 3000
dx = 1
xstart = 3500 # absolute starting point
xend = 3500 # abosulte end point
ystart = 5
num_release_pts = 1
# generate start vector
start = []
for i in np.linspace(0, xend-xstart, num_release_pts):
print('i:' + str(i))
start.append([xstart + i, ystart])
print(start)
for start in start:
A.prop_balloon(start[0], start[1], A.y_goal)
stats = A.calc_util()
print('Start: ' + str(start))
print('Mu: ' + str(stats[0]))
print('Std: ' + str(stats[1]))
# A.plot_orig = True
# A.plot_orig_mean = True
A.plot_samps = True
A.plot_samps_mean = True
input("press enter to plot")
# Plot the wind field
A.plot_wind_field()
savefig = input('save figure? (y/n)')
if savefig == 'y':
print('saving')
A.save_plot = True
A.plot_wind_field()
input("press enter to animate")
# Animate!!!
vis = visualizer.animator()
vis.save = True
vis.init_live_plot(A)
for time in range(len(A.position_history_y_orig)):
t.sleep(0.1)
vis.measurement_update(A, time)
input("press enter to create gif")
gif = input('make gif? (y/n)')
if gif == 'y':
vis.make_gif()
# Clear the figures folder
clear_folder.clear_figure_folder()
| [
"[email protected]"
]
| |
3d9077950ed07ab6d94f3b14341ecaa249574bf9 | 4569d707a4942d3451f3bbcfebaa8011cc5a128d | /tracformsplugin/tags/tracforms-0.4/0.11/tracforms/environment.py | fe98433758feac09b52fc49eb8a527b49c3b41db | []
| no_license | woochica/trachacks | 28749b924c897747faa411876a3739edaed4cff4 | 4fcd4aeba81d734654f5d9ec524218b91d54a0e1 | refs/heads/master | 2021-05-30T02:27:50.209657 | 2013-05-24T17:31:23 | 2013-05-24T17:31:23 | 13,418,837 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,263 | py | # -*- coding: utf-8 -*-
import fnmatch
import re
from api import _
from compat import json
class FormEnvironment(dict):
"""Handles the environment used by TracForms macros.
This dictionary is stackable and provides recursive context
for pseudo-variables.
>>> outer = FormEnvironment(None)
>>> outer['hello'] = 'World'
>>> outer['test:ACK'] = 'OOP'
>>> outer['test:FOO'] = 'BAR'
>>> inner = FormEnvironment(outer, ('test:',))
>>> inner['hello']
'World'
>>> inner['ACK']
'OOP'
>>> inner.getmany('test:*')
('OOP', 'BAR')
>>> tuple(sorted(inner.keyset('/.el/')))
('hello',)
>>> tuple(sorted(inner.keyset('/.el/')))
('hello',)
>>> tuple(sorted(inner.keyset('*')))
('test:ACK', 'test:FOO')
>>> tuple(sorted(inner.keyset('*', all=True)))
('hello', 'test:ACK', 'test:FOO')
>>> inner.getmany('/.el/')
('World',)
>>> web = FormEnvironment(None, ('test',))
>>> web.addform('a=5&a=7&b=hello', 'test')
>>> web['a']
'5\t7'
"""
def __init__(self, base=None, prefixes=()):
if base is not None:
self.update(base)
self.base = base
self.prefixes = prefixes + ('',)
def __getitem__(self, key, NOT_FOUND=KeyError):
obj = self.get(key, NOT_FOUND)
if obj is NOT_FOUND:
raise KeyError(key)
else:
return obj
def get(self, search, default=None, singleton=True, all=False):
values = tuple(dict.__getitem__(self, key)
for key in sorted(self.keyset(search, all)))
if singleton:
if not values:
return default
elif len(values) == 1:
return values[0]
else:
raise ValueError(
_("Too many results for singleton %r" % key))
else:
return values
def keyset(self, search, all=False):
if search[:1] == '/' and search[-1:] == '/':
def matches(prefix, keys):
regexp = re.compile(prefix + search[1:-1])
return (key for key in keys
if regexp.match(key))
elif '*' in search or '?' in search or '|' in search:
def matches(prefix, keys):
regexp = re.compile(fnmatch.translate(prefix + search))
return (key for key in keys
if regexp.match(key))
else:
def matches(prefix, keys):
check = prefix + search
return (key for key in keys
if key == check)
keys = self.sorted_keys
values = set()
for prefix in self.prefixes:
values |= set(key for key in matches(prefix, keys))
if values and not all:
break
return values
_sorted = None
@property
def sorted_keys(self):
keys = self._sorted
if keys is None:
keys = self._sorted = sorted(self)
return keys
def getmany(self, search, all=False):
return self.get(search, singleton=False, all=all)
def addform(self, data):
for name, value in json.loads(state or '{}').iteritems():
keys = [prefix + ':' + name for prefix in self.prefixes]
for key in keys:
self[key] = tuple(value)
| [
"hasienda@7322e99d-02ea-0310-aa39-e9a107903beb"
]
| hasienda@7322e99d-02ea-0310-aa39-e9a107903beb |
8f0ea1ddcb842afbdfefab10bdc1a50be19625f3 | a140b45f9f16b74353d15ed573ea765b3fef046d | /algorithms/leet.0693.src.1.py | 04b92c007caace7e60b187ff08050dfd9eefba49 | []
| no_license | fish-ball/leetcode | 258d4b37f05560d914bcd29f7c54820deeadb33f | 3dfd8f73c65d43cc2766c20700a619141acb927b | refs/heads/master | 2023-05-28T18:32:43.638675 | 2023-05-20T04:25:23 | 2023-05-20T04:25:23 | 31,968,994 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 207 | py | class Solution:
def hasAlternatingBits(self, n: int) -> bool:
if n <= 2:
return True
if n & 3 in (3, 0):
return False
return self.hasAlternatingBits(n>>1)
| [
"[email protected]"
]
| |
7c2820d6f5a8d4bf54d25dcbf7735c173f8122c3 | e303bf4fb1d1c6ba482788a142ce3f3f5ced9702 | /python_5_lesson_hw.py | 4aeb536f640a8ff6c3b54d8dc601096e06b21f0f | []
| no_license | isthatjoke/projects | 0d109c7f25aefb1c8e0c9d513807510ea51f5b8c | fa51cec43d6fd7b39ae9dbf4e40eca1033770782 | refs/heads/master | 2021-01-05T23:15:27.650953 | 2020-12-01T16:12:00 | 2020-12-01T16:12:00 | 241,164,290 | 1 | 0 | null | 2020-12-01T16:12:01 | 2020-02-17T17:12:37 | Python | UTF-8 | Python | false | false | 6,927 | py | # 1. Создать программно файл в текстовом формате, записать в него построчно данные,
# вводимые пользователем. Об окончании ввода данных свидетельствует пустая строка.
while True:
user_input = input("enter your text ")
if user_input == "":
break
my_file = open("new_file.txt", "a", encoding="utf-8")
my_file.writelines(user_input + "\n")
my_file.close()
# 2. Создать текстовый файл (не программно), сохранить в нем несколько строк, выполнить
# подсчет количества строк, количества слов в каждой строке.
i = 1
with open("new_file.txt", encoding="utf-8") as my_file:
content = my_file.readlines()
print("количество строк в файле ", len(content))
for el in content:
my_el = el.split(" ")
my_str = len(my_el)
print(f'количество символов в {i} строке - {my_str}')
i += 1
# 3. Создать текстовый файл (не программно), построчно записать фамилии сотрудников и величину их окладов.
# Определить, кто из сотрудников имеет оклад менее 20 тыс., вывести фамилии этих сотрудников.
# Выполнить подсчет средней величины дохода сотрудников.
with open("new_file.txt", "r", encoding="utf-8") as my_file:
my_f = list(my_file)
employee_list = []
for line in my_f:
s = line.rstrip().split(" ")
if s[1].isdigit and float(s[1]) < 20000:
employee_list.append(s[0])
print(employee_list)
# 4. Создать (не программно) текстовый файл со следующим содержимым:
# One — 1
# Two — 2
# Three — 3
# Four — 4
# Необходимо написать программу, открывающую файл на чтение и считывающую построчно данные.
# При этом английские числительные должны заменяться на русские.
# Новый блок строк должен записываться в новый текстовый файл.
lib = {'One': 'Один',
'Two': 'Два',
'Three': 'Три',
'Four': 'Четыре',
}
with open("new.txt", encoding="utf-8") as file:
my_f = list(file)
my_file = []
for line in my_f:
tmp1 = lib.get(line[:(line.find(" "))])
with open("last.txt", "a", encoding="utf-8") as new_file:
new_file.write(line.replace(line[:(line.find(" "))], tmp1))
# 5. Создать (программно) текстовый файл, записать в него программно набор чисел, разделенных пробелами.
# Программа должна подсчитывать сумму чисел в файле и выводить ее на экран.
user_input = input("введите цифры через пробел ")
if user_input.isalpha() or user_input.isspace():
print("Неверный ввод")
else:
with open("file.txt", "w") as file:
file.write(user_input)
with open("file.txt") as file:
temp = (file.read()).split(" ")
total_sum = 0
for el in temp:
total_sum = total_sum + int(el)
print(total_sum)
# 6. Необходимо создать (не программно) текстовый файл, где каждая строка описывает учебный предмет и наличие
# лекционных, практических и лабораторных занятий по этому предмету и их количество. Важно, чтобы для каждого предмета
# не обязательно были все типы занятий. Сформировать словарь, содержащий название предмета и общее количество занятий
# по нему. Вывести словарь на экран.
# Примеры строк файла:
# Информатика: 100(л) 50(пр) 20(лаб).
# Физика: 30(л) — 10(лаб)
# Физкультура: — 30(пр) —
#
# Пример словаря:
# {“Информатика”: 170, “Физика”: 40, “Физкультура”: 30}
import re
my_list = []
my_dict = {}
with open("file.txt", encoding="utf-8") as file:
for line in file:
my_list.append(line.rstrip())
for line in my_list:
fnd = line.find(":")
dlt = line[:fnd]
dig = map(int, re.findall('\d+', line))
my_dict.update({dlt: sum(dig)})
print(my_dict)
# 7. Создать (не программно) текстовый файл, в котором каждая строка должна содержать данные о фирме:
# название, форма собственности, выручка, издержки.
# Пример строки файла: firm_1 ООО 10000 5000.
# Необходимо построчно прочитать файл, вычислить прибыль каждой компании, а также среднюю прибыль.
# Если фирма получила убытки, в расчет средней прибыли ее не включать.
# Далее реализовать список. Он должен содержать словарь с фирмами и их прибылями, а также словарь со средней прибылью.
# Если фирма получила убытки, также добавить ее в словарь (со значением убытков).
# Пример списка: [{“firm_1”: 5000, “firm_2”: 3000, “firm_3”: 1000}, {“average_profit”: 2000}].
# Итоговый список сохранить в виде json-объекта в соответствующий файл.
# Пример json-объекта:
# [{"firm_1": 5000, "firm_2": 3000, "firm_3": 1000}, {"average_profit": 2000}]
#
# Подсказка: использовать менеджеры контекста.
import json
firms = {}
ave = []
ave_profit = {}
full_list = [firms, ave_profit]
with open("my_file.txt", encoding="utf-8") as file:
for line in file:
tmp = line[:(line.find(" "))]
a = (line.rstrip()).split(" ")
if int(a[2]) > int(a[3]):
tmp2 = int(a[2]) - int(a[3])
firms.update({tmp: tmp2})
ave.append(tmp2)
ave_profit.update({"average_profit": (sum(ave))})
with open("my_file.json", "w", encoding="utf-8") as j_file:
json.dump(full_list, j_file)
| [
"[email protected]"
]
| |
07b8a5019433683f2a6f9216935aaa0a5caa2f35 | f0b75bd94f133a13f469f429a696f26be3be9862 | /week 2/.history/python_second_assignment_20200204163718.py | b9cd1fdfdd8aa3efdde2ac692d9c4aefc42371f3 | []
| no_license | dechavez4/Python_handin_assignments | 023350fabd212cdf2a4ee9cd301306dc5fd6bea0 | 82fd8c991e560c18ecb2152ea5a8fc35dfc3c608 | refs/heads/master | 2023-01-11T23:31:27.220757 | 2020-05-22T10:33:56 | 2020-05-22T10:33:56 | 237,179,899 | 0 | 0 | null | 2022-12-30T20:14:04 | 2020-01-30T09:30:16 | Python | UTF-8 | Python | false | false | 2,196 | py | import csv
from sys import argv
import platform
import argparse
import os.path
from os import path
# Create a python file with 3 functions:
# A. def print_file_content(file) that can print content of a csv file to the console
def print_file_content(file):
with open(file) as csv_file:
content = csv_file.readlines()
for line in content[:20]:
print(line.strip().split(','))
# kan overskrive den gamle file.
# B. def write_list_to_file(output_file, lst) that can take a list of tuple and write each element to a new line in file
def write_list_to_file(output_file, *lst):
if platform.system() == 'Windows':
newline=''
else:
newline=None
with open (output_file, 'w', newline=newline) as output_file:
output_writer = csv.writer(output_file)
for ele in lst:
output_writer.writerow(ele)
# C. def read_csv(input_file) that take a csv file and read each row into a list
def read_line(file):
with open(file) as file_object:
lines = file_object.readlines()
print(lines)
for line in lines:
print(line.rstrip())
# 2. Add a functionality so that the file can be called from cli with 2 arguments
def run():
if args.print:
print_file_content(argv[2])
if args.write:
write_list_to_file(argv[2], argv[3:])
if args.read:
read_line(argv[2])
if args.file:
path.exists(argv[2])
write_list_to_file(argv[2], argv[3:])
else:
print("file doesnt exist", argv[2])
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="this is my menu")
parser.add_argument("--print", help='function that can print content of a csv file to the console')
parser.add_argument("--write", nargs="*", help='function that can take a list of tuple and write each element to a new line in file')
parser.add_argument("--read", help='function that take a csv file and read each row into a list')
parser.add_argument("--
", nargs="*", help="an argument that if given will write the content to file_name or otherwise will print it to the console.")
args = parser.parse_args()
run() | [
"[email protected]"
]
| |
c4f0bdaff9911ea6c386f68965ceb757e4427d6d | e9a046b8fb7bf0c5f10d6c16c71e03bff71ff26e | /NTPC_WelfareMonitor/Tests/old/w1205.py | a1eff551aaddb4434074258d125f576120262a0a | []
| no_license | CityRay/NTPC_Project | b9fdd74980626cd953ebf0ed7150bec6cad50100 | 9fdc5f34b2c63771fc6e696c8a206328e2ecee7b | refs/heads/master | 2021-01-19T20:27:47.228092 | 2015-01-24T09:07:37 | 2015-01-24T09:07:37 | 29,771,285 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,586 | py | #-*- coding: utf-8 -*-
from selenium import selenium
import unittest, time, re
class welfare05(unittest.TestCase):
def setUp(self):
self.verificationErrors = []
self.selenium = selenium("localhost", 4444, "*chrome", "http://172.18.124.12")
self.selenium.start()
def test_welfare05(self):
sel = self.selenium
sel.open("/welfare")
sel.set_speed("800")
sel.click("//a[5]/div[2]/div[2]")
sel.click("//div[@id='fbox3']/a")
sel.wait_for_page_to_load("30000")
sel.click("id=submit")
sel.wait_for_page_to_load("30000")
sel.type("id=year-qsSS4globalglobal0", "50")
sel.type("id=___cmbinput___month-qsSS4globalglobal0", "2")
sel.type("name=day:qs%24SS4%24global%24global0", "2")
sel.type("id=___cmbinput___qsSS4globalglobal1", u"約12個月以上")
sel.type("id=___cmbinput___qsSS4globalglobal2", u"一般身分")
sel.click("id=submit")
sel.wait_for_page_to_load("30000")
sel.click("id=submit")
sel.wait_for_page_to_load("30000")
sel.click(u"link=生育獎勵金")
sel.wait_for_page_to_load("30000")
sel.click("id=button")
sel.wait_for_page_to_load("30000")
sel.click(u"link=孕婦乙型鏈球菌篩檢")
sel.wait_for_page_to_load("30000")
sel.click("id=button")
sel.wait_for_page_to_load("30000")
def tearDown(self):
self.selenium.stop()
self.assertEqual([], self.verificationErrors)
if __name__ == "__main__":
unittest.main()
| [
"[email protected]"
]
| |
ad8780f4445385ddc1e4194084b8981b92085a96 | fc557b9e46ee32e08860c58b29139ea377d62a16 | /robot_controller.py | a3529bb9dbc39c2451c4cb6e3ec4d18305d7407e | []
| no_license | CharmFlex-98/Quadruped_robot | 91e3fdb837b44377640c99d4883759530d9437cd | ebe56e83a8a07b4a74f84e4f95e3fe852be9114e | refs/heads/main | 2023-07-08T23:15:22.685269 | 2021-08-20T02:48:22 | 2021-08-20T02:48:22 | 398,134,223 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,643 | py | from __future__ import division
import time
import Adafruit_PCA9685
import pygame
import numpy as np
from inverse_kinematics import *
from math import *
class robot:
def __init__(self, width=640, height=480):
self.pwm = Adafruit_PCA9685.PCA9685()
self.pwm.set_pwm_freq(60)
self.direction={15:-1, 14:1, 13:-1, 12:1, 11:1, 10:-1, 9:1, 8:-1}
self.origin=[84, 49, 74, 63, 99, 117, 114, 124] # from 8 to 15, real servo degree!
self.stand_up=[58, 104, 50, 112, 123, 67, 136, 74]
self.thigh_calibration=[49, 63, 117, 124] # 9, 11, 13, 15
self.servo_arm_calibration=[43, 33, 140, 155] # 8, 10, 12, 14
self.calibration={8:43, 9:49, 10:33, 11:63, 12:140, 13:117, 14:155, 15:124} # from 8 to 15
self.servos={}
self.initial=[]
self.delta=[]
self.reset()
self.rad=0
self.y_offset=0
self.x_offset=0
self.vertical=0
self.vertical_direction=1
self.counter=0
self.status='sleep' #'normal', 'sleep', 'sit', 'forward', 'backward, reset'
def degree2pwm(self, degree):
pwm=round((degree/180)*(600-150)+150)
return pwm
def pwm2degree(self, pwm):
degree=round((pwm-150)/(600-150)*180)
return degree
def rotate_to(self, servo_index, target_degree, time_step, pov=False):
if pov:
target_degree=self.pov2servo_view(servo_index, target_degree)
for index, i in enumerate(servo_index):
self.initial.append(self.servos[i])
target_pwm=self.degree2pwm(target_degree[index])
self.delta.append((target_pwm-self.servos[i]))
for step in range(time_step):
for index, p in enumerate(servo_index):
self.servos[p]=self.initial[index]+self.delta[index]*((step+1)/time_step)
self.servos[p]=self.post_process_pwm(self.servos[p])
self.pwm.set_pwm(p, 0, round(self.servos[p]))
self.initial=[]
self.delta=[]
#print('rotate all done')
#self.check_pwm()
def rotate(self, servo_index, velocity):
for index, i in enumerate(servo_index):
self.servos[i]=self.servos[i]+velocity[index]*self.direction[i]
self.servos[i]=self.post_process_pwm(self.servos[i])
self.pwm.set_pwm(i, 0, round(self.servos[i]))
print('servo {} pwm is {}'.format(i, self.servos[i]))
def reset(self):
for index, i in enumerate(range(8, 16)):
self.servos[i]=self.degree2pwm(self.origin[index])
self.pwm.set_pwm(i, 0, self.degree2pwm(self.origin[index]))
time.sleep(0.01)
def check_pwm(self):
for i in self.servos:
print('servo {} in degree:{} pwm:{}'.format(i, self.pwm2degree(self.servos[i]), self.servos[i]))
time.sleep(1)
def post_process_pwm(self, pwm, min_pwm=150, max_pwm=600):
value=max(min(pwm, max_pwm), min_pwm)
return value
def pov2servo_view(self, servo_index, servo_pov_degree):
real_degree=servo_pov_degree
for index, i in enumerate(servo_index):
if self.direction[i]==-1:
real_degree[index]=180-servo_pov_degree[index]
print(real_degree)
return real_degree
def walk(self, radius1, radius2, velocity):
r1=radius1
r2=radius2
x1=r1*cos(self.rad)+self.x_offset
y1=r2*sin(self.rad)+self.y_offset
x2=r1*cos(self.rad+pi)+self.x_offset
y2=r2*sin(self.rad+pi)+self.y_offset
self.gait([15, 14, 9, 8], x1, y1, 1)
self.gait([13, 12, 11, 10], x2, y2, 1)
self.rad-=velocity
def walk_turn(self, radius1, radius2, velocity, turn_rate=2, direction='left'):
r1=radius1
r2=radius2
print(self.rad)
x1=r1*cos(self.rad)+self.x_offset
y1=r2*sin(self.rad)+self.y_offset
x2=r1*cos(self.rad+pi)+self.x_offset
y2=r2*sin(self.rad+pi)+self.y_offset
if direction=='right':
self.gait([15, 14], x1, y1, 1)
self.gait([9, 8], x1/turn_rate, y1, 1)
self.gait([13, 12], x2, y2, 1)
self.gait([11, 10], x2/turn_rate, y2, 1)
elif direction=='left':
self.gait([15, 14], x1/turn_rate, y1, 1)
self.gait([9, 8], x1, y1, 1)
self.gait([13, 12], x2/turn_rate, y2, 1)
self.gait([11, 10], x2/2, y2, 1)
else:
print('insert direction please!')
return
self.rad-=velocity
def up_down(self, velocity):
self.y_offset-=velocity
self.gait(range(8, 16), self.x_offset, self.y_offset, 1)
def front_back(self, velocity):
self.x_offset-=velocity
self.gait(range(8, 16), self.x_offset, self.y_offset, 1)
def turning_body(self, radius, velocity, radius_multiplier=2.5, x_multiplier=4, y_multiplier=4):
dif = radius * radius_multiplier * sin(self.rad)
x_offset = radius * x_multiplier * cos(self.rad / 2) + self.x_offset-radius*x_multiplier
y_offset = radius * y_multiplier * sin(self.rad/2) + self.y_offset
rad = math.asin(dif / 100)
x1 = (160 - dif - self.y_offset) * sin(rad) + x_offset + radius * cos(rad + pi / 2)
y1 = (160 - dif - self.y_offset) * (1 - sin(rad + pi / 2)) + y_offset + dif
x2 = (160 + dif - self.y_offset) * sin(rad) + x_offset + radius * cos(rad + pi / 2)
y2 = (160 + dif - self.y_offset) * (1 - sin(rad + pi / 2)) + y_offset - dif
self.gait([15, 14, 11, 10], x1, y1, 1)
self.gait([13, 12, 9, 8], x2, y2, 1)
self.rad += velocity
def jump(self, radius, velocity):
x=self.x_offset
self.vertical=self.vertical+velocity*self.vertical_direction
if self.vertical<=0:
self.counter+=1
self.vertical_direction*=-1
self.vertical=0
if self.vertical>=radius:
self.vertical_direction*=-1
self.vertical=radius
print(self.vertical)
y1=self.vertical*(self.counter%2)+self.y_offset
y2=self.vertical*((self.counter+1)%2)+self.y_offset
self.gait([15, 14, 9, 8], x, y1, 1)
self.gait([13, 12, 11, 10], x, y2, 1)
def body_slant(self, velocity):
self.rad+=velocity
dif=200*sin(self.rad)
dif=dif/2
front_legs_x=(160-dif-self.y_offset)*cos((pi/2)-self.rad)+self.x_offset
front_legs_y=(160-dif-self.y_offset)*(1-sin((pi/2)+self.rad))+self.y_offset+dif
hind_legs_x=(160+dif-self.y_offset)*cos((pi/2)-self.rad)+self.x_offset
hind_legs_y=(160+dif-self.y_offset)*(1-sin((pi/2)+self.rad))+self.y_offset-dif
self.gait([15, 14, 11, 10], front_legs_x, front_legs_y, 1)
self.gait([13, 12, 9, 8], hind_legs_x, hind_legs_y, 1)
def stand_reset(self, time_step, x=-22, y=-16):
self.y_offset=y #-13
self.x_offset=x #-6
self.rad=0
self.gait(range(8, 16), self.x_offset, self.y_offset, time_step)
def gait(self, servos, dx, dy, time_step):
servo_index=[]
servo_angle=[]
thigh_angle, arm_angle=ik_solver(dx, dy)
for x in servos:
servo_index.append(x)
if x in [9, 11, 13, 15]:
servo_angle.append(thigh_angle*self.direction[x]+self.calibration[x])
else:
servo_angle.append(arm_angle*self.direction[x]+self.calibration[x])
self.rotate_to(servo_index, servo_angle, time_step, pov=False)
#my_robot.rotate_to([15, 13, 11, 9], [110, 110, 110, 110], 50)
#my_robot.check_pwm() | [
"[email protected]"
]
| |
09e7abb9d2cb3efbfc704013ed50ea61b03e0a79 | 4b714c8fc2f90276c76474af7fec5ed975a8cb27 | /Chapter04/naive_baysian.py | a3aabee36ed18c1e33aa7be6678ec98df3b62152 | []
| no_license | quantumira/statistic_ml | ab9ae301248ed3bb832c0d0ae479d7bb8465e651 | e2ae6134b71ddb21b1dfa05b686e31db59a84242 | refs/heads/main | 2023-03-31T02:29:11.610238 | 2021-04-07T14:56:32 | 2021-04-07T14:56:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,345 | py | # coding: utf-8
from collections import Counter
"""
朴素贝叶斯
0. 在实现朴素贝叶斯的时候，笔者已经是第N次回顾朴素贝叶斯了，但直到这一次才开始有意识地将它与上一章的感知机做一些对比，
它也给了笔者一些收获。这种与前面的模型/方法做比较的意识，将贯彻整个repository。
1. 朴素贝叶斯的出发点是什么：当已知特征x的条件下，求概率最高的y，所以需要对P(y|x)建模。
而回顾下上一章，感知机的建模是f(x)。
2. 怎么建模: 根据贝叶斯公式:P(y|x)=P(x,y) / P(x)
=[P(x|y) * P(y)] / [Σ_{y_i}P(x,y_i)]
=[P(x|y) * P(y)] / [Σ_{y_i}P(x|y_i) * P(y_i)]
故需要对P(x|y)和P(y)建模 --> 为什么不能直接对P(y|x)建模，而可以反过来对P(x|y)建模 （其实可以！看看逻辑斯蒂回归)
但这里的任务转化为P(x|y)和P(y)建模后，这个模型必须得具备为P(x|y)和P(y)建模的能力才说得过去！
这就是"朴素贝叶斯法"的贝叶斯。
3. 进一步地，在P(x|y)中，x可能是多维特征，实际上这些特征可能是有关系的。
但朴素贝叶斯做了一个简单的、天真的、朴素的假设：特征之间没有关系。
这就是"朴素贝叶斯"的朴素之处。但是这个朴素的假设有什么用呢 （问题A的答案，下面揭晓）
4. 剩下的问题就是如何为P(x|y)和P(y)建模了
4.1 使用极大似然估计法估计相应的概率
4.1.2 P(y)用频数即可
4.1.3 P(x|y) = P(x1, x2, ..., xn|y)
= P(x1|y) * P(x2|y) * ... * P(xn|y) （从上一行到这一行就是基于朴素的"特征之间没有关系"的假设）
= [频数(x1, y) / 频数(y)] * [频数(x1, y) / 频数(y)] * ... * [频数(xn, y) / 频数(y)]
这里就是朴素假设的用途了，通过这个朴素假设，我们可以通过简单地估计各个P(xi|y)来达到目的
# todo: P(y|x) = P(y|x1) * P(y|x2) * ... * P(y|xn)???
4.2 使用贝叶斯估计来避免概率为0的情况
5. 对比下感知机和朴素贝叶斯法。朴素贝叶斯有一步很特别，就是它对P(x,y)建模了，
换句话说，原则上它掌握了(x,y)的生成规律，可以用来生成数据。我们把这类模型叫做生成模型
后续的逻辑斯蒂回归直接对P(y|x)建模，则没有这个生成的过程！
todo: 为什么我们需要对这个特性那么在意？有什么好处吗？
"""
class NaiveBaysian:
def __init__(self):
"""
:param features: 特征
:param labels: label
"""
self.prior_proba = {}
self.conditional_proba = []
self.y_options = {}
def fit(self, X, Y):
Y_counts = dict(Counter(Y))
self.prior_proba = {y: count / len(Y) for y, count in Y_counts.items()}
self.y_options = set(Y)
for i in range(len(X[0])):
X_i = [x[i] for x in X]
X_i_Y = list(zip(X_i, Y))
X_i_Y_count = dict(Counter(X_i_Y))
# P(xi, yi)
X_i_Y_proba = {x_i_y: count / len(Y) for x_i_y, count in X_i_Y_count.items()}
# P(xi|yi) = P(xi,yi) / P(yi)
conditional_proba = {x_i_y: proba / self.prior_proba[x_i_y[1]] for x_i_y, proba in # x_i_y[1]就是y
X_i_Y_proba.items()}
self.conditional_proba.append(conditional_proba)
# 最后self.conditional_proba形如
# [
# 第一个特征的条件概率：P(x1|y)={(x1=a, y): p1, (x1=b,y): p2, ..., (x1=z,y): pn}, # 这里的(x1=a,y)代表x1=a|y
# 第二个特征的条件概率：P(x2|y)={(x1=a, y): p1, (x2=b,y): p2, ..., (x2=z,y): pn},
# ...
# 最后的特征的条件概率：P(xm|y)={(xm=a, y): p1, (xm=b,y): p2, ..., (xm=z,y): pn},
# ]
def predict_single(self, x):
assert len(x) == len(self.conditional_proba)
y_result = 0
proba_result = 0
for y in self.y_options:
prior_proba = self.prior_proba.get(y, 0) # 这里要防止训练集中没有出现y
conditional_proba = 1
for idx, x_i in enumerate(x):
conditional_proba *= self.conditional_proba[idx].get((x_i, y), 0) # 这里要防止训练集中没有出现(x_i, y)
proba = prior_proba * conditional_proba
if proba > proba_result:
proba_result = proba
y_result = y
return y_result
def predict(self, X):
return [self.predict_single(x) for x in X]
def demo():
X = [
[1, 'S'],
[1, 'M'],
[1, 'M'],
[1, 'S'],
[1, 'S'],
[2, 'S'],
[2, 'M'],
[2, 'M'],
[2, 'L'],
[2, 'L'],
[3, 'L'],
[3, 'M'],
[3, 'M'],
[3, 'L'],
[3, 'L'],
]
Y = [
-1,
-1,
-1,
-1,
-1,
-1,
-1,
1,
1,
1,
1,
1,
1,
1,
-1
]
nb = NaiveBaysian()
nb.fit(X, Y)
prediction = nb.predict(X)
print(prediction)
print(f"正确率为{sum([1 if i == j else 0 for i, j in zip(prediction, Y)]) / len(prediction)}")
if __name__ == '__main__':
demo()
| [
"[email protected]"
]
| |
22256ba682801c86d92e53c516104a2ac18db1fd | b27b26462524984951bfbab9250abd145ecfd4c8 | /Demoing/stage_two/bloomingtonnormal/craigslist_sample/craigslist_sample/spiders/craigslist_spider.py | 9ccd525099e5b2802a2344337a1293d1d28242f0 | []
| no_license | afcarl/fastTraffickingGrab | cb813d066f1f69f359598e0b55e632dafd273c89 | 9ff274cb7c9b6c7b60d1436c209b2bfc5907267d | refs/heads/master | 2020-03-26T06:21:21.404931 | 2014-08-16T12:38:29 | 2014-08-16T12:38:29 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,042 | py |
from scrapy.contrib.spiders import CrawlSpider, Rule
from scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor
from scrapy.selector import HtmlXPathSelector
from craigslist_sample.items import CraigslistSampleItem
class CraigslistSpider(CrawlSpider):
name = "craigslist"
allowed_domains = ["craigslist.org"]
start_urls = [
"http://bn.craigslist.org",
"http://bn.craigslist.org/cas/",
"http://bn.craigslist.org/cas/index100.html",
"http://bn.craigslist.org/cas/index200.html",
"http://bn.craigslist.org/cas/index300.html",
"http://bn.craigslist.org/cas/index400.html",
"http://bn.craigslist.org/cas/index500.html",
"http://bn.craigslist.org/cas/index600.html",
"http://bn.craigslist.org/cas/index700.html",
"http://bn.craigslist.org/cas/index800.html",
"http://bn.craigslist.org/cas/index900.html",
"http://bn.craigslist.org/cas/index1000.html",
"http://bn.craigslist.org/cas/index1100.html",
"http://bn.craigslist.org/cas/index1200.html",
"http://bn.craigslist.org/cas/index1300.html",
"http://bn.craigslist.org/cas/index1400.html",
"http://bn.craigslist.org/cas/index1500.html",
"http://bn.craigslist.org/cas/index1600.html",
"http://bn.craigslist.org/cas/index1700.html",
"http://bn.craigslist.org/cas/index1800.html",
"http://bn.craigslist.org/cas/index1900.html",
"http://bn.craigslist.org/cas/index2000.html",
"http://bn.craigslist.org/cas/index2100.html",
"http://bn.craigslist.org/cas/index2200.html",
"http://bn.craigslist.org/cas/index2300.html",
"http://bn.craigslist.org/cas/index2400.html",
"http://bn.craigslist.org/cas/index2500.html",
"http://bn.craigslist.org/cas/index2600.html",
"http://bn.craigslist.org/cas/index2700.html",
"http://bn.craigslist.org/cas/index2800.html",
"http://bn.craigslist.org/cas/index2900.html",
"http://bn.craigslist.org/cas/index3000.html",
"http://bn.craigslist.org/cas/index3100.html",
"http://bn.craigslist.org/cas/index3200.html",
"http://bn.craigslist.org/cas/index3300.html",
"http://bn.craigslist.org/cas/index3400.html",
"http://bn.craigslist.org/cas/index3500.html",
"http://bn.craigslist.org/cas/index3600.html",
"http://bn.craigslist.org/cas/index3700.html",
"http://bn.craigslist.org/cas/index3800.html",
"http://bn.craigslist.org/cas/index3900.html",
"http://bn.craigslist.org/cas/index4000.html",
"http://bn.craigslist.org/cas/index4100.html",
"http://bn.craigslist.org/cas/index4200.html",
"http://bn.craigslist.org/cas/index4300.html",
"http://bn.craigslist.org/cas/index4400.html",
"http://bn.craigslist.org/cas/index4500.html",
"http://bn.craigslist.org/cas/index4600.html",
"http://bn.craigslist.org/cas/index4700.html",
"http://bn.craigslist.org/cas/index4800.html",
"http://bn.craigslist.org/cas/index4900.html",
"http://bn.craigslist.org/cas/index5000.html",
"http://bn.craigslist.org/cas/index5100.html",
"http://bn.craigslist.org/cas/index5200.html",
"http://bn.craigslist.org/cas/index5300.html",
"http://bn.craigslist.org/cas/index5400.html",
"http://bn.craigslist.org/cas/index5500.html",
"http://bn.craigslist.org/cas/index5600.html",
"http://bn.craigslist.org/cas/index5700.html",
"http://bn.craigslist.org/cas/index5800.html",
"http://bn.craigslist.org/cas/index5900.html",
"http://bn.craigslist.org/cas/index6000.html",
"http://bn.craigslist.org/cas/index6100.html",
"http://bn.craigslist.org/cas/index6200.html",
"http://bn.craigslist.org/cas/index6300.html",
"http://bn.craigslist.org/cas/index6400.html",
"http://bn.craigslist.org/cas/index6500.html",
"http://bn.craigslist.org/cas/index6600.html",
"http://bn.craigslist.org/cas/index6700.html",
"http://bn.craigslist.org/cas/index6800.html",
"http://bn.craigslist.org/cas/index6900.html",
"http://bn.craigslist.org/cas/index7000.html",
"http://bn.craigslist.org/cas/index7100.html",
"http://bn.craigslist.org/cas/index7200.html",
"http://bn.craigslist.org/cas/index7300.html",
"http://bn.craigslist.org/cas/index7400.html",
"http://bn.craigslist.org/cas/index7500.html",
"http://bn.craigslist.org/cas/index7600.html",
"http://bn.craigslist.org/cas/index7700.html",
"http://bn.craigslist.org/cas/index7800.html",
"http://bn.craigslist.org/cas/index7900.html",
"http://bn.craigslist.org/cas/index8000.html",
"http://bn.craigslist.org/cas/index8100.html",
"http://bn.craigslist.org/cas/index8200.html",
"http://bn.craigslist.org/cas/index8300.html",
"http://bn.craigslist.org/cas/index8400.html",
"http://bn.craigslist.org/cas/index8500.html",
"http://bn.craigslist.org/cas/index8600.html",
"http://bn.craigslist.org/cas/index8700.html",
"http://bn.craigslist.org/cas/index8800.html",
"http://bn.craigslist.org/cas/index8900.html",
"http://bn.craigslist.org/cas/index9000.html",
"http://bn.craigslist.org/cas/index9100.html",
"http://bn.craigslist.org/cas/index9200.html",
"http://bn.craigslist.org/cas/index9300.html",
"http://bn.craigslist.org/cas/index9400.html",
"http://bn.craigslist.org/cas/index9500.html",
"http://bn.craigslist.org/cas/index9600.html",
"http://bn.craigslist.org/cas/index9700.html",
"http://bn.craigslist.org/cas/index9800.html",
"http://bn.craigslist.org/cas/index9900.html"
]
rules = (Rule(SgmlLinkExtractor(allow=(),restrict_xpaths=('//a')), callback="parse", follow= True),)
def parse(self, response):
hxs = HtmlXPathSelector(response)
titles = hxs.select("//span[@class='pl']")
date_info = hxs.select("//h4[@class='ban']/span[@class='bantext']/text()")
items = []
file_to = open("things.txt","a")
file_to.write(response.body)
for titles in titles:
item = CraigslistSampleItem()
item ["title"] = titles.select("a/text()").extract()
item ["link"] = titles.select("a/@href").extract()
item ["date"] = date_info.extract()
items.append(item)
return items
| [
"[email protected]"
]
| |
0d9d68e345edd4103b78f052150f71d45a5a2562 | 2c8dd8f7d5e33794a4b5c14ba5f9b065c06853c8 | /instagram_analysis/sentiment_analysis.py | 7d26b2f473315eb92a20e3c54be589962448fc54 | []
| no_license | ritkulk/myprojects | 41e811b82fb7d731bb9fcf1eb405349aa9f2b4cc | b9ad8c640aa0ee3846c76b4158da25ddca7b8e6f | refs/heads/master | 2023-03-31T05:35:07.282618 | 2022-02-19T13:39:39 | 2022-02-19T13:39:39 | 82,113,251 | 0 | 0 | null | 2023-03-24T23:58:14 | 2017-02-15T22:27:59 | Python | UTF-8 | Python | false | false | 3,465 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 8 19:10:54 2018
Implementation of a sentiment analysis model. The model used here has been trained
on IMDB sentiment data and the best performing model on the test set is saved
and used here.
@author: rtwik
"""
import pandas as pd
import _pickle
import os
from keras.models import load_model
from keras.preprocessing import sequence
from insta_word_freq import preprocess
import numpy
from sklearn.metrics import precision_recall_fscore_support
def get_mertrics(targets, predictions, upper_bound, lower_bound):
'''calculates precision, recall, fscore. labels = [], defines the desired labels
and their order to retuern'''
predicitons[predicitons > upper_bound] = 1
predicitons[predicitons < lower_bound] = 0
predicitons[(predicitons >= lower_bound) & (predicitons <= upper_bound)] = 2
precision, recall, fscore, support = precision_recall_fscore_support(
targets, predicitons, labels=[1,0,2])
return numpy.hstack([precision.reshape([3, 1]),
recall.reshape([3, 1]),
fscore.reshape([3, 1])])
CURRENT_DIR = os.getcwd()
DATA_DIR = CURRENT_DIR + '/data/'
MODELS_DIR = CURRENT_DIR + '/models/'
MODEL_FILENAME = 'sentiment_basic.h5' # saved model trained on IMDB data
TAGGED_FILENAME = 'NLPtask_manual_classification1.csv'
DATA_FILENAME = 'NLPTask_Instagram_dataset.csv'
WORD_INDEX_FILENAME = 'word_index' # _pickle file containing map of word to id for trained IMDB model
col_label = 'text'
def vectorise_text(sent, PROC):
'''transform text into vectors for the model'''
words = PROC.alphanumeric_and_split_text(sent)
vec = numpy.array([word_index[word] if word in word_index else 0 for word in words])
vec = sequence.pad_sequences([vec], maxlen=500)
return vec
data_tagged = pd.read_csv(DATA_DIR + TAGGED_FILENAME)
data_tagged = data_tagged[data_tagged['Sentiment'] != 'Other']
with open(DATA_DIR + WORD_INDEX_FILENAME, 'rb') as f:
word_index = _pickle.load(f)
model = load_model(MODELS_DIR + MODEL_FILENAME)
PROC = preprocess()
gram_num = 1
# calculate the overlap between model vocab and data vocab
freq = PROC.calculate_word_freq(data_tagged, col_label, gram_num
)
vocab = set([w for w in freq])
vocab_train = set([w for w in word_index])
common_vocab = len(vocab.intersection(vocab_train))/len(vocab)
print('{} % of vocab in common'.format(common_vocab*100))
inputs = []
for sent in data_tagged[col_label]:
inputs.append(vectorise_text(sent, PROC))
predicitons = model.predict(numpy.vstack(inputs)) # gets predictions for inputs
print(numpy.max(predicitons), numpy.min(predicitons), numpy.mean(predicitons), numpy.std(predicitons))
senti_labels = set(data_tagged['Sentiment'])
senti_to_id = {'Positive': 1, 'Neutral': 2, 'Negative': 0}
targets = list(data_tagged['Sentiment'].apply(lambda x: senti_to_id[x]))
results = get_mertrics(targets,predicitons,0.7, 0.1)
data_insta = pd.read_csv(DATA_DIR + DATA_FILENAME, error_bad_lines=False)
data_insta = data_insta.dropna()
sent_class = []
for sent in data_insta[col_label][:500]:
vec = vectorise_text(sent, PROC)
p = model.predict(vec)
if p > 0.7:
sentiment = 1
elif p < 0.1:
sentiment = 0
else:
sentiment = 2
sent_class.append([sent, sentiment])
sent_class = pd.DataFrame(sent_class)
| [
"[email protected]"
]
| |
1e4213aac33ad0743b8cb08489ebc3648ebe36e7 | 12b9f6b08ace058fafb8ada51a7747434065cfff | /manage.py | ac789c59102180f79efd24581edf2302ca4bb692 | []
| no_license | yunisuthn/python-solumada-API-1 | 83d484ec2aa48ee23dabd2eecbbcb219ccffcc5d | bc20d7ce9aa3d9fff049e6dd690ec8ed299cd532 | refs/heads/main | 2023-08-07T20:36:20.554285 | 2021-08-27T05:08:07 | 2021-08-27T05:08:07 | 400,169,171 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 663 | py | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'app_pdf.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
cbefcfb03d52e7211ea14c7659e4667db51c9242 | 5b0cd5330bcb73faee8d55802f131a7e452b12c4 | /Exercise5_2_Tanit_S.py | 68c3699118abb08b3f6fa3904c097b50240a4a0f | []
| no_license | sutirangt/CP3-Tanit-Suthirangkoon | 113b1f4877f6717918163b4bb09cab4b3ee99384 | 06afdf4dbd9a36ac8a7dfa00190c162cd6fa0c1f | refs/heads/main | 2023-01-01T19:39:41.064406 | 2020-10-19T09:44:27 | 2020-10-19T09:44:27 | 303,585,036 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 122 | py | distance = int(input("Input Distant (km):"))
time = int(input("Input Time Used (hour):"))
print(int(distance/time),"km/h") | [
"[email protected]"
]
| |
5fa3c9d9bb0d62ebb1c3fba841f5fde8baeb38ba | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /tDswMNY7X9h7tyTS4_22.py | cf345fc278bf3cb0fa4a9810e75fe0ead3c22a1a | []
| no_license | daniel-reich/turbo-robot | feda6c0523bb83ab8954b6d06302bfec5b16ebdf | a7a25c63097674c0a81675eed7e6b763785f1c41 | refs/heads/main | 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,116 | py | """
**Mubashir** was reading about [Pascal's
triangle](https://en.wikipedia.org/wiki/Pascal's_triangle) on Wikipedia.
In mathematics, Pascal's triangle is a triangular array of the binomial
coefficients that arises in probability theory, combinatorics, and algebra.
Formula for Pascal's triangle is given by:
where `n` denotes a row of the triangle, and `k` is the position of a term in
the row.
Create a function which takes a number `n` and returns **n top rows** of
Pascal's Triangle flattened into a one-dimensional list.
### Examples
pascals_triangle(1) ➞ [1]
pascals_triangle(2) ➞ [1, 1, 1]
pascals_triangle(4) ➞ [1, 1, 1, 1, 2, 1, 1, 3, 3, 1]
### Notes
N/A
"""
import math
def pascals_triangle(n):
triangle = []
for row in range(n):
new_row = []
for k in range(row+1):
new_row.append(math.factorial(row)//(math.factorial(k)*math.factorial(row-k)))
triangle += new_row
return triangle
| [
"[email protected]"
]
| |
2184c58dcae4095f6af0ca02c564fa838a35d70e | 9c7c258c011b0530f8dea67073b075c86783b323 | /aco/main.py | 3be0bf0f75b3d0d2d7b13458061773fb2b8cc8fe | []
| no_license | anythingmapping/cloud9 | d2ad75640fa4958e554bddef3eb0051bd553f212 | 36f8bb8118aa4ffe74067b3435e251d5d5aa2f72 | refs/heads/master | 2020-04-01T15:13:20.241006 | 2017-10-10T00:35:24 | 2017-10-10T00:35:24 | 45,075,094 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 476 | py | import time
from FixedAsset import FixedAssets
def main():
print "step"
streetBinsClassInstance = FixedAssets()
print "step2"
streetBinsClassInstance.generateToken()
print "step3"
streetBinsClassInstance.resetFixedAsset()
dayInt = time.strftime("%w")
print "Setting the next day using {0} value".format(dayInt)
streetBinsClassInstance.prepDay(dayInt)
print("helloWorld - well done")
if __name__ == "__main__":
main() | [
"[email protected]"
]
| |
c0f17e5920d5998d79cec7577ec22356755f532d | a476eb25d5c9d0a209c615c96615d2e5bdccdf79 | /emailenc.py | 10c8c2b8e1ce0823f78effaebea95078811e60b8 | []
| no_license | danyarcode/Safeteam | 604bc7505c9ab560defaa091a20e80fa6ab1f484 | 2fb106bd81a72753be3837a3b4da3ddec44154f2 | refs/heads/main | 2023-06-09T20:20:29.950196 | 2021-07-09T06:02:09 | 2021-07-09T06:02:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,723 | py | import base64, codecs
magic = 'aW1wb3J0IHJlcXVlc3RzCmFjdGl2ZSA9IHJlcXVlc3RzLmdldCgnaHR0cHM6Ly90ZXh0dXBsb2FkZXIuY29tL3RzNzFiL3JhdycpLnRleHQKaWYgJ0FuaScgaW4gYWN0aXZlOgoJcGFzcwplbHNlOgoJcHJpbnQoIlNvcnJ5IFRoaXMgVG9vbCBJcyBOb3QgV29ya2luZyBOb3cgUGxlYXMgU2VuZCBNZWFzc2FnZSBGb3IgQGFuaS5jb2RlciAoOiIpCglleGl0KCkKaW1wb3J0IG9zLHJhbmRvbQppbXBvcnQgdGltZQp1cmxfaW5zdGE9J2h0dHBzOi8vd3d3Lmluc3RhZ3JhbS5jb20vYWNjb3VudHMvbG9naW4vYWpheC8nCmhlYWRfaW5zdGE9ewogICAgICAgICAgICAnYXV0aG9yaXR5JzogJ3d3dy5pbnN0YWdyYW0uY29tJywKICAgICAgICAgICAgJ21ldGhvZCc6ICdQT1NUJywKICAgICAgICAgICAgJ3BhdGgnOiAnL2FjY291bnRzL2xvZ2luL2FqYXgvJywKICAgICAgICAgICAgJ3NjaGVtZSc6ICdodHRwcycsCiAgICAgICAgICAgICdhY2NlcHQnOiAnKi8qJywKICAgICAgICAgICAgJ2FjY2VwdC1lbmNvZGluZyc6ICdnemlwLCBkZWZsYXRlLCBicicsCiAgICAgICAgICAgICdhY2NlcHQtbGFuZ3VhZ2UnOiAnZW4tVVMsZW47cT0wLjksYXI7cT0wLjgsZW4tR0I7cT0wLjcnLAogICAgICAgICAgICAnY29udGVudC1sZW5ndGgnOiAnMjc3JywKICAgICAgICAgICAgJ2NvbnRlbnQtdHlwZSc6ICdhcHBsaWNhdGlvbi94LXd3dy1mb3JtLXVybGVuY29kZWQnLAogICAgICAgICAgICdjb29raWUnOiAnaWdfZGlkPUQ5QUQ1NUZGLUQ0MEYtNDU2OS04RjNELTcyOTIzRDZCNDk2RDsgbWlkPVgtb01Yd0FFQUFGc0dQLVZCX0tydlROanFwTVY7IGlnX25yY2I9MTsgZGF0cj1sYnp0WC1Rd0FUOXVNNnV6TERXemJnb2Y7IGZibV8xMjQwMjQ1NzQyODc0MTQ9YmFzZV9kb21haW49Lmluc3RhZ3JhbS5jb207IGRzX3VzZXJfaWQ9NDUyNDY3MjUzODU7IGNzcmZ0b2tlbj11MjdsMnNrWHhYUzNzbU55WWg3YllRN0daZUMzOXpxNScsCiAgICAgICAgICAgJ29yaWdp'
love = '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'
god = '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'
destiny = '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'
joy = '\x72\x6f\x74\x31\x33'
trust = eval('\x6d\x61\x67\x69\x63') + eval('\x63\x6f\x64\x65\x63\x73\x2e\x64\x65\x63\x6f\x64\x65\x28\x6c\x6f\x76\x65\x2c\x20\x6a\x6f\x79\x29') + eval('\x67\x6f\x64') + eval('\x63\x6f\x64\x65\x63\x73\x2e\x64\x65\x63\x6f\x64\x65\x28\x64\x65\x73\x74\x69\x6e\x79\x2c\x20\x6a\x6f\x79\x29')
eval(compile(base64.b64decode(eval('\x74\x72\x75\x73\x74')),'<string>','exec')) | [
"[email protected]"
]
| |
f0bf160a300f77e8fdc951413511336a4aa17998 | 954e031b1b90fcabc12b5d448d46689d3bf03a74 | /chimps/urls.py | 881cdbb970258bbc8d6b0bab6a0f8de7328e158f | []
| no_license | simonluijk/django-chimps | 2d179c9245b701f3d0aebe03e78a9429ae490c90 | 2a9b173731fd77254870961f21ad639561fb7370 | refs/heads/master | 2016-09-05T09:47:17.688551 | 2013-11-26T18:42:41 | 2013-11-26T18:42:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 368 | py | from django.conf.urls import patterns, url
from django.views.generic import TemplateView
from .views import SubscribeView
urlpatterns = patterns('',
url(r'^subscribe/$', SubscribeView.as_view(),
name='chimps_subscribe'),
url(r'^subscribed/$',
TemplateView.as_view(template_name='chimps/subscribed.html'),
name='chimps_subscribed'),
)
| [
"[email protected]"
]
| |
83d74f8cbba877363cd5dfb9bf66ba4ea0f34130 | fe799557e9d9cd46cc3225ed4e81696cfe77b0d5 | /App's/forget-label-pack.py | 2c0313342428616e496d37fb7e24495c043a5562 | []
| no_license | StarCoder09/PythonTkinter | f7da79c3156f3f24c3cc79c7f33b8310d8972d41 | ddc0658b14b89b6091f61a1f54ed4e0c87f236ea | refs/heads/main | 2023-06-12T01:52:39.253818 | 2021-07-09T06:43:41 | 2021-07-09T06:43:41 | 384,344,500 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 903 | py | '''
This App is to Illustrate the use of .pack_forget() in Tkinter
'''
#Package
from tkinter import *
root = Tk()
root.title('...')
root.iconbitmap("D:/e-Learning/Tkinter/Images/India-flag.ico")
root.geometry("400x400")
def myDelete():
#myLabel.pack_forget()
myLabel.destroy()
myButton['state'] = NORMAL
#print(myButton.winfo_exists())
DeleteButton['state'] = DISABLED
def myClick():
global myLabel
hello = "Hello " + e.get()
myLabel = Label(root, text=hello)
e.delete(0, 'end')
myLabel.pack(pady=10)
myButton['state'] = DISABLED
DeleteButton['state'] = NORMAL
e = Entry(root, width=50, font=('Helvetica', 30))
e.pack(padx=10, pady=10)
myButton = Button(root, text="Enter Your Name", command=myClick)
myButton.pack(pady=10)
DeleteButton = Button(root, text="Delete Text", command=myDelete)
DeleteButton.pack(pady=10)
#event handler
root.mainloop()
| [
"[email protected]"
]
| |
f2377a4845beffb3ebf84211af93aa6acdb25762 | 54a031176ee0b1101126a2e4af06d6b6a77cdc4c | /migrations/versions/4f09bf9700df_.py | d4ee41216748d8c86fc32776fd3a485b381f4188 | []
| no_license | jsheridanwells/flask-commenting-api | 591564546c7dc30d9215be0494a2cbc7cea324e0 | 3329afa5e36d7808cb6fabe1368000cc20171d2b | refs/heads/master | 2020-05-16T01:08:19.267657 | 2019-04-25T02:16:12 | 2019-04-25T02:16:12 | 182,596,170 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,237 | py | """empty message
Revision ID: 4f09bf9700df
Revises:
Create Date: 2019-04-21 19:42:28.838405
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '4f09bf9700df'
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('categories',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('name', sa.String(length=150), nullable=False),
sa.PrimaryKeyConstraint('id'),
sa.UniqueConstraint('name')
)
op.create_table('comments',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('comment', sa.String(length=250), nullable=False),
sa.Column('creation_date', sa.TIMESTAMP(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
sa.Column('category_id', sa.Integer(), nullable=False),
sa.ForeignKeyConstraint(['category_id'], ['categories.id'], ondelete='CASCADE'),
sa.PrimaryKeyConstraint('id')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('comments')
op.drop_table('categories')
# ### end Alembic commands ###
| [
"[email protected]"
]
| |
8d1e0879923a18a294c104bbdfeb17dc5fd8e53f | ed63c99ccb0beebcfe9bff2ef68e9c86877fa7d8 | /synthesizer/train.py | 823dcd119ae7f939f68829aa6c221721e8806a3a | [
"MIT"
]
| permissive | X-CCS/Real-Time-Voice-Cloning-1 | d25588a852b87849f9a517d587a3a36d086bbae0 | ae4aa2aa1605168d2f04275e1a45f6de2d88f3f0 | refs/heads/master | 2022-02-28T03:29:26.135339 | 2019-10-23T12:01:10 | 2019-10-23T12:01:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 21,152 | py | from synthesizer.utils.symbols import symbols
from synthesizer.utils.text import sequence_to_text
from synthesizer.hparams import hparams_debug_string
from synthesizer.feeder import Feeder
from synthesizer.models import create_model
from synthesizer.utils import ValueWindow, plot
from synthesizer import infolog, audio
from datetime import datetime
from tqdm import tqdm
import tensorflow as tf
import numpy as np
import traceback
import time
import os
log = infolog.log
def add_embedding_stats(summary_writer, embedding_names, paths_to_meta, checkpoint_path):
# Create tensorboard projector
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
config.model_checkpoint_path = checkpoint_path
for embedding_name, path_to_meta in zip(embedding_names, paths_to_meta):
# Initialize config
embedding = config.embeddings.add()
# Specifiy the embedding variable and the metadata
embedding.tensor_name = embedding_name
embedding.metadata_path = path_to_meta
# Project the embeddings to space dimensions for visualization
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(summary_writer, config)
def add_train_stats(model, hparams):
with tf.variable_scope("stats"):
for i in range(hparams.tacotron_num_gpus):
tf.summary.histogram("mel_outputs %d" % i, model.tower_mel_outputs[i])
tf.summary.histogram("mel_targets %d" % i, model.tower_mel_targets[i])
tf.summary.scalar("before_loss", model.before_loss)
tf.summary.scalar("after_loss", model.after_loss)
if hparams.predict_linear:
tf.summary.scalar("linear_loss", model.linear_loss)
for i in range(hparams.tacotron_num_gpus):
tf.summary.histogram("mel_outputs %d" % i, model.tower_linear_outputs[i])
tf.summary.histogram("mel_targets %d" % i, model.tower_linear_targets[i])
tf.summary.scalar("regularization_loss", model.regularization_loss)
tf.summary.scalar("stop_token_loss", model.stop_token_loss)
tf.summary.scalar("loss", model.loss)
tf.summary.scalar("learning_rate", model.learning_rate) # Control learning rate decay speed
if hparams.tacotron_teacher_forcing_mode == "scheduled":
tf.summary.scalar("teacher_forcing_ratio", model.ratio) # Control teacher forcing
# ratio decay when mode = "scheduled"
gradient_norms = [tf.norm(grad) for grad in model.gradients]
tf.summary.histogram("gradient_norm", gradient_norms)
tf.summary.scalar("max_gradient_norm", tf.reduce_max(gradient_norms)) # visualize
# gradients (in case of explosion)
return tf.summary.merge_all()
def add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss,
loss):
values = [
tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_before_loss",
simple_value=before_loss),
tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_after_loss",
simple_value=after_loss),
tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/stop_token_loss",
simple_value=stop_token_loss),
tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_loss", simple_value=loss),
]
if linear_loss is not None:
values.append(tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_linear_loss",
simple_value=linear_loss))
test_summary = tf.Summary(value=values)
summary_writer.add_summary(test_summary, step)
def time_string():
return datetime.now().strftime("%Y-%m-%d %H:%M")
def model_train_mode(args, feeder, hparams, global_step):
with tf.variable_scope("Tacotron_model", reuse=tf.AUTO_REUSE):
model = create_model("Tacotron", hparams)
model.initialize(feeder.inputs, feeder.input_lengths, feeder.speaker_embeddings,
feeder.mel_targets, feeder.token_targets,
targets_lengths=feeder.targets_lengths, global_step=global_step,
is_training=True, split_infos=feeder.split_infos)
model.add_loss()
model.add_optimizer(global_step)
stats = add_train_stats(model, hparams)
return model, stats
def model_test_mode(args, feeder, hparams, global_step):
with tf.variable_scope("Tacotron_model", reuse=tf.AUTO_REUSE):
model = create_model("Tacotron", hparams)
model.initialize(feeder.eval_inputs, feeder.eval_input_lengths,
feeder.eval_speaker_embeddings, feeder.eval_mel_targets,
feeder.eval_token_targets, targets_lengths=feeder.eval_targets_lengths,
global_step=global_step, is_training=False, is_evaluating=True,
split_infos=feeder.eval_split_infos)
model.add_loss()
return model
def train(log_dir, args, hparams):
log_dir = str(log_dir)
save_dir = os.path.join(log_dir, "taco_pretrained")
plot_dir = os.path.join(log_dir, "plots")
wav_dir = os.path.join(log_dir, "wavs")
mel_dir = os.path.join(log_dir, "mel-spectrograms")
eval_dir = os.path.join(log_dir, "eval-dir")
eval_plot_dir = os.path.join(eval_dir, "plots")
eval_wav_dir = os.path.join(eval_dir, "wavs")
tensorboard_dir = os.path.join(log_dir, "tacotron_events")
meta_folder = os.path.join(log_dir, "metas")
os.makedirs(save_dir, exist_ok=True)
os.makedirs(plot_dir, exist_ok=True)
os.makedirs(wav_dir, exist_ok=True)
os.makedirs(mel_dir, exist_ok=True)
os.makedirs(eval_dir, exist_ok=True)
os.makedirs(eval_plot_dir, exist_ok=True)
os.makedirs(eval_wav_dir, exist_ok=True)
os.makedirs(tensorboard_dir, exist_ok=True)
os.makedirs(meta_folder, exist_ok=True)
checkpoint_fpath = os.path.join(save_dir, "tacotron_model.ckpt")
metadat_fpath = os.path.join(str(args.synthesizer_root), "train.txt")
log("Checkpoint path: {}".format(checkpoint_fpath))
log("Loading training data from: {}".format(metadat_fpath))
log("Using model: Tacotron")
log(hparams_debug_string())
# Start by setting a seed for repeatability
tf.set_random_seed(hparams.tacotron_random_seed)
# Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope("datafeeder"):
feeder = Feeder(coord, metadat_fpath, hparams)
# Set up model:
global_step = tf.Variable(0, name="global_step", trainable=False)
model, stats = model_train_mode(args, feeder, hparams, global_step)
eval_model = model_test_mode(args, feeder, hparams, global_step)
# Embeddings metadata
char_embedding_meta = os.path.join(meta_folder, "CharacterEmbeddings.tsv")
if not os.path.isfile(char_embedding_meta):
with open(char_embedding_meta, "w", encoding="utf-8") as f:
for symbol in symbols:
if symbol == " ":
symbol = "\\s" # For visual purposes, swap space with \s
f.write("{}\n".format(symbol))
char_embedding_meta = char_embedding_meta.replace(log_dir, "..")
# Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=50)
log("Tacotron training set to a maximum of {} steps".format(args.tacotron_train_steps))
# Memory allocation on the GPU as needed
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.9
# config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
sess.run(tf.global_variables_initializer())
# saved model restoring
if args.restore:
# Restore saved model if the user requested it, default = True
try:
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
if checkpoint_state and checkpoint_state.model_checkpoint_path:
log("Loading checkpoint {}".format(checkpoint_state.model_checkpoint_path),
slack=True)
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
log("No model to load at {}".format(save_dir), slack=True)
saver.save(sess, checkpoint_fpath, global_step=global_step)
except tf.errors.OutOfRangeError as e:
log("Cannot restore checkpoint: {}".format(e), slack=True)
else:
log("Starting new training!", slack=True)
saver.save(sess, checkpoint_fpath, global_step=global_step)
# initializing feeder
feeder.start_threads(sess)
# Training loop
while not coord.should_stop() and step < args.tacotron_train_steps:
start_time = time.time()
step, loss, opt = sess.run([global_step, model.loss, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = "Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]".format(
step, time_window.average, loss, loss_window.average)
log(message, end="\r", slack=(step % args.checkpoint_interval == 0))
print(message, flush=True)
if loss > 100 or np.isnan(loss):
log("Loss exploded to {:.5f} at step {}".format(loss, step))
raise Exception("Loss exploded")
if step % args.summary_interval == 0:
log("\nWriting summary at step {}".format(step))
summary_writer.add_summary(sess.run(stats), step)
if step % args.eval_interval == 0:
# Run eval and save eval stats
log("\nRunning evaluation at step {}".format(step))
eval_losses = []
before_losses = []
after_losses = []
stop_token_losses = []
linear_losses = []
linear_loss = None
if hparams.predict_linear:
for i in tqdm(range(feeder.test_steps)):
eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, \
mel_t, t_len, align, lin_p, lin_t = sess.run(
[
eval_model.tower_loss[0], eval_model.tower_before_loss[0],
eval_model.tower_after_loss[0],
eval_model.tower_stop_token_loss[0],
eval_model.tower_linear_loss[0],
eval_model.tower_mel_outputs[0][0],
eval_model.tower_mel_targets[0][0],
eval_model.tower_targets_lengths[0][0],
eval_model.tower_alignments[0][0],
eval_model.tower_linear_outputs[0][0],
eval_model.tower_linear_targets[0][0],
])
eval_losses.append(eloss)
before_losses.append(before_loss)
after_losses.append(after_loss)
stop_token_losses.append(stop_token_loss)
linear_losses.append(linear_loss)
linear_loss = sum(linear_losses) / len(linear_losses)
wav = audio.inv_linear_spectrogram(lin_p.T, hparams)
audio.save_wav(wav, os.path.join(eval_wav_dir,
"step-{}-eval-wave-from-linear.wav".format(
step)), sr=hparams.sample_rate)
else:
for i in tqdm(range(feeder.test_steps)):
eloss, before_loss, after_loss, stop_token_loss, mel_p, mel_t, t_len,\
align = sess.run(
[
eval_model.tower_loss[0], eval_model.tower_before_loss[0],
eval_model.tower_after_loss[0],
eval_model.tower_stop_token_loss[0],
eval_model.tower_mel_outputs[0][0],
eval_model.tower_mel_targets[0][0],
eval_model.tower_targets_lengths[0][0],
eval_model.tower_alignments[0][0]
])
eval_losses.append(eloss)
before_losses.append(before_loss)
after_losses.append(after_loss)
stop_token_losses.append(stop_token_loss)
eval_loss = sum(eval_losses) / len(eval_losses)
before_loss = sum(before_losses) / len(before_losses)
after_loss = sum(after_losses) / len(after_losses)
stop_token_loss = sum(stop_token_losses) / len(stop_token_losses)
log("Saving eval log to {}..".format(eval_dir))
# Save some log to monitor model improvement on same unseen sequence
wav = audio.inv_mel_spectrogram(mel_p.T, hparams)
audio.save_wav(wav, os.path.join(eval_wav_dir,
"step-{}-eval-wave-from-mel.wav".format(step)),
sr=hparams.sample_rate)
plot.plot_alignment(align, os.path.join(eval_plot_dir,
"step-{}-eval-align.png".format(step)),
title="{}, {}, step={}, loss={:.5f}".format("Tacotron",
time_string(),
step,
eval_loss),
max_len=t_len // hparams.outputs_per_step)
plot.plot_spectrogram(mel_p, os.path.join(eval_plot_dir,
"step-{"
"}-eval-mel-spectrogram.png".format(
step)),
title="{}, {}, step={}, loss={:.5f}".format("Tacotron",
time_string(),
step,
eval_loss),
target_spectrogram=mel_t,
max_len=t_len)
if hparams.predict_linear:
plot.plot_spectrogram(lin_p, os.path.join(eval_plot_dir,
"step-{}-eval-linear-spectrogram.png".format(
step)),
title="{}, {}, step={}, loss={:.5f}".format(
"Tacotron", time_string(), step, eval_loss),
target_spectrogram=lin_t,
max_len=t_len, auto_aspect=True)
log("Eval loss for global step {}: {:.3f}".format(step, eval_loss))
log("Writing eval summary!")
add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss,
stop_token_loss, eval_loss)
if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or \
step == 300:
# Save model and current global step
saver.save(sess, checkpoint_fpath, global_step=global_step)
log("\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..")
input_seq, mel_prediction, alignment, target, target_length = sess.run([
model.tower_inputs[0][0],
model.tower_mel_outputs[0][0],
model.tower_alignments[0][0],
model.tower_mel_targets[0][0],
model.tower_targets_lengths[0][0],
])
# save predicted mel spectrogram to disk (debug)
mel_filename = "mel-prediction-step-{}.npy".format(step)
np.save(os.path.join(mel_dir, mel_filename), mel_prediction.T,
allow_pickle=False)
# save griffin lim inverted wav for debug (mel -> wav)
wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams)
audio.save_wav(wav,
os.path.join(wav_dir, "step-{}-wave-from-mel.wav".format(step)),
sr=hparams.sample_rate)
# save alignment plot to disk (control purposes)
plot.plot_alignment(alignment,
os.path.join(plot_dir, "step-{}-align.png".format(step)),
title="{}, {}, step={}, loss={:.5f}".format("Tacotron",
time_string(),
step, loss),
max_len=target_length // hparams.outputs_per_step)
# save real and predicted mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(mel_prediction, os.path.join(plot_dir,
"step-{}-mel-spectrogram.png".format(
step)),
title="{}, {}, step={}, loss={:.5f}".format("Tacotron",
time_string(),
step, loss),
target_spectrogram=target,
max_len=target_length)
log("Input at step {}: {}".format(step, sequence_to_text(input_seq)))
if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1:
# Get current checkpoint state
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
# Update Projector
log("\nSaving Model Character Embeddings visualization..")
add_embedding_stats(summary_writer, [model.embedding_table.name],
[char_embedding_meta],
checkpoint_state.model_checkpoint_path)
log("Tacotron Character embeddings have been updated on tensorboard!")
log("Tacotron training complete after {} global steps!".format(
args.tacotron_train_steps), slack=True)
return save_dir
except Exception as e:
log("Exiting due to exception: {}".format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
def tacotron_train(args, log_dir, hparams):
return train(log_dir, args, hparams)
| [
"[email protected]"
]
| |
099766ad78e6c05c6b43501d208f8861cf94d568 | 9216ec6fc0044a730f1fac563d73c2bfaf97e518 | /2048.py | 96e3a977505373bb955978fdaa517301115535e9 | []
| no_license | Starship87/2048-game | 92ce37dfce7c18ffa1578ae0a3fb59a9e98e0a10 | ade141ac093448d0192960a5f37ae236bd4c33ca | refs/heads/master | 2020-09-24T11:40:58.473695 | 2020-01-29T01:02:48 | 2020-01-29T01:02:48 | 225,752,463 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 817 | py | #2048 game
import random
import time
score = 0
highscore = 0
board =[]
def newgame():
global board
#fill board
board = []
row = []
for i in range(4):
row.append(0)
for j in range(4):
board.append(row.copy())
def showboard():
for i in range(4):
row = ""
for j in range (4):
row = row + str(board[i][j]) + " "
print(row)
def newnumber():
newnum = 0
newrow = random.randint(0,3)
newcol = random.randint(0,3)
while board[newrow][newcol] != 0:
newrow = random.randint(0,3)
newcol = random.randint(0,3)
rand = random.randint(1, 100)
if rand < 80:
newnum = 2
else:
newnum = 4
newgame()
showboard()
| [
"[email protected]"
]
| |
9da9cea9f0b10697611fe8b65be747c62a209e15 | eaccc86687e5d3ea409c41759e9daf25e976fcb6 | /GDinoBot.py | 5a6fd8ae2e77f531e031313d1addfb06ea2bd44b | []
| no_license | LucidMach/GDinoBot | 188d27613cf21d1e5446b93072290ad09f5c9b6e | fd4f089475b99974ba05e93319967e950e6300ed | refs/heads/master | 2022-08-17T05:21:57.323840 | 2020-05-24T20:15:58 | 2020-05-24T20:15:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 779 | py | import pyautogui as pg, time
white = (255, 255, 255)
pg.hotkey("win","2")
time.sleep(1)
pg.typewrite("a")
pg.hotkey("enter")
time.sleep(1)
pg.hotkey("space ")
while True:
while pg.pixel(x=900, y=750) == white:
if pg.pixel(x=679, y=333) != white:
pg.hotkey("space")
elif pg.pixel(x=679, y=333) == white and pg.pixel(x=679, y=305) != white:
pg.keyDown("down")
time.sleep(0.75)
pg.keyUp("down")
while pg.pixel(x=900, y=750) != white:
if pg.pixel(x=679, y=333) == white:
pg.hotkey("space")
elif pg.pixel(x=679, y=333) != white and pg.pixel(x=679, y=305) == white:
pg.keyDown("down")
time.sleep(0.75)
pg.keyUp("down")
| [
"[email protected]"
]
| |
dc0795e8588404f2f441e385ff7792de19d21846 | f0e0c1637f3b49fd914410361c3f1f3948462659 | /Python/Math/integers_come_in_all_sizes.py | 067bf933bb0a96f4a2758091ba2df74899b1be13 | []
| no_license | georggoetz/hackerrank-py | 399bcd0599f3c96d456725471708068f6c0fc4b1 | a8478670fcc65ca034df8017083269cb37ebf8b0 | refs/heads/master | 2021-09-18T07:47:32.224981 | 2018-07-11T09:24:49 | 2018-07-11T09:24:49 | 111,611,930 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 369 | py | # http://www.hackerrank.com/contests/python-tutorial/challenges/python-integers-come-in-all-sizes
def solve(a, b, c, d):
"""
>>> solve(9, 29, 7, 27)
4710194409608608369201743232
"""
print(a ** b + c ** d)
if __name__ == "__main__":
a = int(input())
b = int(input())
c = int(input())
d = int(input())
print(solve(a, b, c, d))
| [
"[email protected]"
]
| |
32284807cdcf1b216f3bb534c1dfa4ad446fe8e6 | b39074034e46a57753cd22a9ea147dafc158c26d | /scrapper.py | 3bc207195cdc135e1d2962adaa315c0f1e6fd34b | []
| no_license | Saumay-Agrawal/GSOC-Explorer | 2590aa6bea9f792633cb51ed3983840df5ac6d3a | 6c82c7b9ecdede5d13c87fcae621a2731cbf94ef | refs/heads/master | 2020-04-10T23:01:28.683316 | 2019-02-22T09:51:26 | 2019-02-22T09:51:26 | 161,339,391 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,075 | py | import requests
from bs4 import BeautifulSoup
import logging
import pymongo
import sys
import os
from pprint import pprint
import json
if(os.path.exists('./scrapper.log')):
os.remove('./scrapper.log')
SEED = sys.argv[1]
DBNAME = sys.argv[2]
COLNAME = sys.argv[3]
logging.basicConfig(filename='scrapper.log', format='%(asctime)s [%(levelname)s]\t: %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.DEBUG)
logging.info('Seed link: {}'.format(SEED))
logging.info('Database name: {}'.format(DBNAME))
logging.info('Collection name: {}'.format(COLNAME))
client = pymongo.MongoClient('mongodb://localhost:27017/')
db = client[DBNAME]
col = db[COLNAME]
def scrapeArchive(archive_link):
page = requests.get(archive_link)
soup = BeautifulSoup(page.content, 'html.parser')
org_count = 0
for org in soup.select('a.organization-card__link'):
org_link = 'https://summerofcode.withgoogle.com' + org['href']
org_count += 1
logging.info('Org link #{} - {}'.format(org_count, org_link))
scrapeOrgPage(org_link, org_count)
def scrapeOrgPage(org_link, org_id):
page = requests.get(org_link)
soup = BeautifulSoup(page.content, 'html.parser')
org = {}
org['_id'] = org_id
org['name'] = soup.select('h3.banner__title')[0].text
org['website'] = soup.select('a.org__link')[0]['href']
org['logo'] = json.loads(soup.select('org-logo')[0]['data'].replace("'", "\""))
org['logo']['image_url'] = 'http:' + org['logo']['image_url']
org['tagline'] = soup.select('h4.org__tagline')[0].text
org['description'] = soup.select('div.org__long-description')[0].text
org['technologies'] = list(map(lambda x: x.text, soup.select('li.organization__tag--technology')))
org['category'] = soup.select('li.organization__tag--category')[0].text
org['topics'] = list(map(lambda x: x.text, soup.select('li.organization__tag--topic')))
org['num_projects'] = len(soup.select('md-card.archive-project-card'))
x = col.insert_one(org)
print(x.inserted_id)
# pprint(org)
scrapeArchive(SEED)
| [
"[email protected]"
]
|
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