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google-datacatalog-apache-atlas-connector/tests/google/datacatalog_connectors/apache_atlas/scrape/apache_atlas_facade_test.py | skadinyo/datacatalog-connectors-hive | 19 | 179106 | #!/usr/bin/python
#
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest.mock import patch
from google.datacatalog_connectors.commons_test import utils
from google.datacatalog_connectors.apache_atlas import scrape
class ApacheAtlasFacadeTestCase(unittest.TestCase):
@patch('atlasclient.client.Atlas')
def setUp(self, atlas_client):
self.__atlas_facade = scrape.ApacheAtlasFacade({
'host': 'my_host',
'port': 'my_port',
'user': 'my_user',
'pass': '<PASSWORD>',
})
# Shortcut for the object assigned
# to self.__atlas_facade.__apache_atlas
self.__atlas_client = atlas_client.return_value
def test_constructor_should_set_instance_attributes(self):
attrs = self.__atlas_facade.__dict__
self.assertIsNotNone(attrs['_ApacheAtlasFacade__apache_atlas'])
def test_get_admin_metrics_should_succeed(self):
metric = utils.MockedObject()
metric.entity = {
'entityActive': {
'AtlasGlossary': 2,
'StorageDesc': 1,
'Table': 8,
'Column': 17,
'AtlasGlossaryTerm': 44,
'AtlasGlossaryCategory': 23,
'View': 2,
'DB': 3,
'LoadProcess': 3
},
'entityDeleted': {
'hbase_table': 2
}
}
metric.general = {
'collectionTime': 1590773837477,
'entityCount': 105,
'tagCount': 13,
'typeUnusedCount': 47,
'typeCount': 138
}
self.__atlas_client.admin_metrics = [metric]
response = self.__atlas_facade.get_admin_metrics()[0]
self.assertDictEqual(metric.entity['entityActive'],
response['entityActive'])
self.assertDictEqual(metric.entity['entityDeleted'],
response['entityDeleted'])
self.assertDictEqual(metric.general, response['generalMetrics'])
def test_get_typedefs(self):
self.__atlas_client.typedefs = True
# Just assert it was called, since typedefs is a lazy return.
self.assertEqual(True, self.__atlas_facade.get_typedefs())
def test_search_entities_from_entity_type_should_return(self):
search_result = utils.MockedObject()
search_results = [search_result]
table_1 = utils.MockedObject()
table_1._data = {
'typeName': 'Table',
'attributes': {
'owner': 'Joe',
'createTime': 1589983835754,
'qualifiedName': 'sales_fact',
'name': 'sales_fact',
'description': 'sales fact table'
},
'guid': '2286a6bd-4b06-4f7c-a666-920d774b040e',
'status': 'ACTIVE',
'displayText': 'sales_fact',
'classificationNames': ['Fact']
}
table_2 = utils.MockedObject()
table_2._data = {
'typeName': 'Table',
'attributes': {
'owner': 'Tim ETL',
'createTime': 1589983847754,
'qualifiedName': 'log_fact_daily_mv',
'name': 'log_fact_daily_mv',
'description': 'log fact daily materialized '
'view'
},
'guid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'status': 'ACTIVE',
'displayText': 'log_fact_daily_mv',
'classificationNames': ['Log Data']
}
search_result.entities = [table_1, table_2]
self.__atlas_client.search_dsl.side_effect = [search_results, []]
response = self.__atlas_facade.search_entities_from_entity_type(
'Table')
self.assertDictEqual(table_1._data, response[0]._data)
self.assertDictEqual(table_2._data, response[1]._data)
self.assertEqual(2, len(response))
def test_search_entities_from_entity_type_paged_should_return(self):
search_result = utils.MockedObject()
table_1 = utils.MockedObject()
table_1._data = {
'typeName': 'Table',
'attributes': {
'owner': 'Joe',
'createTime': 1589983835754,
'qualifiedName': 'sales_fact',
'name': 'sales_fact',
'description': 'sales fact table'
},
'guid': '2286a6bd-4b06-4f7c-a666-920d774b040e',
'status': 'ACTIVE',
'displayText': 'sales_fact',
'classificationNames': ['Fact']
}
search_result.entities = [table_1]
search_results = [search_result]
search_result_2 = utils.MockedObject()
table_2 = utils.MockedObject()
table_2._data = {
'typeName': 'Table',
'attributes': {
'owner': 'Tim ETL',
'createTime': 1589983847754,
'qualifiedName': 'log_fact_daily_mv',
'name': 'log_fact_daily_mv',
'description': 'log fact daily materialized '
'view'
},
'guid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'status': 'ACTIVE',
'displayText': 'log_fact_daily_mv',
'classificationNames': ['Log Data']
}
search_result_2.entities = [table_2]
search_results_2 = [search_result_2]
self.__atlas_client.search_dsl.side_effect = [
search_results, search_results_2, []
]
response = self.__atlas_facade.search_entities_from_entity_type(
'Table')
self.assertDictEqual(table_1._data, response[0]._data)
self.assertDictEqual(table_2._data, response[1]._data)
self.assertEqual(2, len(response))
def test_search_entities_from_non_existent_entity_type_should_return_empty(
self):
search_result = utils.MockedObject()
table_1 = utils.MockedObject()
table_1._data = {
'typeName': 'Table',
'attributes': {
'owner': 'Joe',
'createTime': 1589983835754,
'qualifiedName': 'sales_fact',
'name': 'sales_fact',
'description': 'sales fact table'
},
'guid': '2286a6bd-4b06-4f7c-a666-920d774b040e',
'status': 'ACTIVE',
'displayText': 'sales_fact',
'classificationNames': ['Fact']
}
search_result.entities = [table_1]
search_results = [search_result]
search_result_2 = utils.MockedObject()
table_2 = utils.MockedObject()
table_2._data = {
'typeName': 'Table',
'attributes': {
'owner': '<NAME>',
'createTime': 1589983847754,
'qualifiedName': 'log_fact_daily_mv',
'name': 'log_fact_daily_mv',
'description': 'log fact daily materialized '
'view'
},
'guid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'status': 'ACTIVE',
'displayText': 'log_fact_daily_mv',
'classificationNames': ['Log Data']
}
search_result_2.entities = [table_2]
search_results_2 = [search_result_2]
self.__atlas_client.search_dsl.side_effect = [
search_results, search_results_2, []
]
response = self.__atlas_facade.search_entities_from_entity_type(
'WrongType')
self.assertEqual(0, len(response))
def test_search_entities_no_return_should_return_empty(self):
self.__atlas_client.search_dsl.return_value = []
response = self.__atlas_facade.search_entities_from_entity_type(
'Table')
self.assertEqual(0, len(response))
def test_fetch_entities_should_return(self):
table_1 = utils.MockedObject()
guid = '2286a6bd-4b06-4f7c-a666-920d774b040e'
table_1.guid = guid
table_1._data = {
'typeName': 'Table',
'attributes': {
'owner': 'Joe',
'temporary': False,
'lastAccessTime': 1589983835754,
'replicatedTo': None,
'replicatedFrom': None,
'qualifiedName': 'sales_fact',
'columns': [{
'guid': 'c53f1ab7-970f-4199-bc9a-899291c93622',
'typeName': 'Column'
}, {
'guid': 'e5188d7b-916e-4420-b02e-8eac75b17a68',
'typeName': 'Column'
}, {
'guid': '84d83e16-e767-46f9-8165-db7e3a7a3762',
'typeName': 'Column'
}, {
'guid': '909805b1-59f0-44db-b0c8-b6b0c384d2c5',
'typeName': 'Column'
}],
'description': 'sales fact table',
'viewExpandedText': None,
'sd': {
'guid': 'a0989a5b-19aa-4ac4-92db-e2449554c799',
'typeName': 'StorageDesc'
},
'tableType': 'Managed',
'createTime': 1589983835754,
'name': 'sales_fact',
'additionalProperties': None,
'db': {
'guid': 'c0ebcb5e-21f8-424d-bf11-f39bb943ae2c',
'typeName': 'DB'
},
'retention': 1589983835754,
'viewOriginalText': None
},
'guid': guid,
'status': 'ACTIVE',
'createdBy': 'admin',
'updatedBy': 'admin',
'createTime': 1589983835765,
'updateTime': 1589983850688,
'version': 0,
'relationshipAttributes': {
'schema': [],
'inputToProcesses': [{
'guid': '2aa15a65-ae8e-4e48-9b77-2ecb049f82b4',
'typeName': 'LoadProcess',
'displayText': 'loadSalesDaily',
'relationshipGuid': '95d240a4-009b-48c8-b1d3-66703720f60a',
'relationshipStatus': 'ACTIVE',
'relationshipAttributes': {
'typeName': 'dataset_process_inputs'
}
}],
'relatedFromObjectAnnotations': [],
'resourceListAnchors': [],
'relatedToObjectAnnotations': None,
'supportingResources': [],
'meanings': [],
'outputFromProcesses': []
}
}
collection_item = utils.MockedObject()
entities = [table_1]
collection_item.entities = lambda: entities
bulk_collection = [collection_item]
self.__atlas_client.entity_bulk.return_value = bulk_collection
response = self.__atlas_facade.fetch_entities([guid])
self.assertEqual(1, len(response))
self.assertEqual(table_1.guid, response[guid]['guid'])
self.assertDictEqual(table_1._data, response[guid]['data'])
def test_fetch_entity_classifications_should_return(self):
returned_entity = utils.MockedObject()
cursor = utils.MockedObject()
cursor._data = {
'list': [{
'typeName': 'Log Data',
'entityGuid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'propagate': True
}, {
'typeName': 'ETL',
'entityGuid': 'e73656c9-da05-4db7-9b88-e3669c1a98eb',
'propagate': True
}]
}
returned_entity.classifications = iter([cursor])
self.__atlas_client.entity_guid.return_value = returned_entity
response = self.__atlas_facade.fetch_entity_classifications(
['30cfc9fc-aec4-4017-b649-f1f92351a727'])
self.assertEqual(2, len(response))
def test_fetch_entity_classifications_error_should_not_raise_it(self):
returned_entity = utils.MockedObject()
cursor = utils.MockedObject()
cursor._data = {
'list': [{
'typeName': 'Log Data',
'entityGuid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'propagate': True
}, {
'typeName': 'ETL',
'entityGuid': 'e73656c9-da05-4db7-9b88-e3669c1a98eb',
'propagate': True
}]
}
returned_entity.classifications = iter([])
self.__atlas_client.entity_guid.return_value = returned_entity
response = self.__atlas_facade.fetch_entity_classifications(
['30cfc9fc-aec4-4017-b649-f1f92351a727'])
self.assertIsNone(response)
def test_fetch_entity_classifications_with_duplicates_should_return(self):
returned_entity = utils.MockedObject()
cursor = utils.MockedObject()
cursor._data = {
'list': [{
'typeName': 'Log Data',
'entityGuid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'propagate': True
}, {
'typeName': 'ETL',
'entityGuid': 'e73656c9-da05-4db7-9b88-e3669c1a98eb',
'propagate': True
}, {
'typeName': 'ETL',
'entityGuid': '30cfc9fc-aec4-4017-b649-f1f92351a727',
'propagate': True
}]
}
returned_entity.classifications = iter([cursor])
self.__atlas_client.entity_guid.return_value = returned_entity
response = self.__atlas_facade.fetch_entity_classifications(
'30cfc9fc-aec4-4017-b649-f1f92351a727')
self.assertEqual(2, len(response))
self.assertEqual('30cfc9fc-aec4-4017-b649-f1f92351a727',
response[1]['entityGuid'])
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308,
2933,
29961,
29896,
22322,
10041,
29954,
5416,
11287,
13,
2
] |
skewt/__init__.py | aktaylor08/SkewT | 1 | 191801 | __version__="0.1.3r1"
| [
1,
4770,
3259,
1649,
543,
29900,
29889,
29896,
29889,
29941,
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29908,
13,
2
] |
src/test/TestAffichage.py | Franckyi/Simulation-Telecom | 0 | 95652 | #!/usr/bin/env python
# coding: utf-8
import affichage
import sequence
seq = sequence.sequence_aleatoire(32)
affichage.figure_sequence(seq, 0, 3)
affichage.afficher()
| [
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ahmed-package/__init__.py | gokcelahmed/ahmed-package | 0 | 28977 | <filename>ahmed-package/__init__.py
from ahmed-package.functions import printName | [
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] |
src/com/model/view.py | amzpiper/synchronize_data | 0 | 114871 | #!/usr/bin/env/python
# -*- coding:utf-8 -*-
# Author:guoyuhang
from com.config.config import Config
import uuid
class ViewModel(object):
def __init__(self,name,priority,acls,href,owners,dns64s,fail_forwarder,zones,comment,aclList):
self.config = Config.get_instance()
self.name= name
self.priority = priority
self.acls = acls
self.href = href
self.owners = owners
self.dns64s = dns64s
self.fail_forwarder = fail_forwarder
self.zones = zones
self.zones = ""
self.comment = comment
self.view_id = ''.join(str(uuid.uuid1()).split('-'))
self.zdns_view_id = ''.join(str(uuid.uuid1()).split('-'))
self.view_zdns_view_id = ''.join(str(uuid.uuid1()).split('-'))
self.view_acl_id = ''.join(str(uuid.uuid1()).split('-'))
self.toJsonString()
self.toSqlString(aclList)
""" 组合后输出为json对象，方便请求dns创建服务 """
def toJsonString(self):
pass
""" 组合sql模板输出SQL语句 """
def toSqlString(self,aclList):
# 查询acls中的acl_id
acl_id={}
acl_id[0]=""
acl_index=0
for aclName in self.acls:
for acl in aclList:
if acl.name == aclName:
print(acl.acl_id,':',acl.name)
acl_id[acl_index]=acl.acl_id
acl_index +=1
self.sql = self.config.view_table_insert % (
self.view_id,
self.config.pool_id,
self.config.tenant_id,
self.name
) + "\n" + self.config.zdns_view_info_t_table_insert % (
self.zdns_view_id,
self.name,
self.dns64s,
self.owners,
self.fail_forwarder,
self.config.current_users,
self.priority,
self.href,
self.zones,
self.comment
) + "\n" + self.config.view_zdns_view_associations_table_insert %(
self.view_zdns_view_id,
self.view_id,
self.zdns_view_id
) + "\n" + self.config.view_acl_associations_table_insert %(
self.view_acl_id,
acl_id[0],
self.view_id
)
return self.sql
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13,
13,
2
] |
app/__init__.py | Keqing84/unzip-bot | 0 | 78526 | from os import environ as env
from Config import Config
AUTH_USERS = []
if " " in Config.AUTH_USER:
achat = Config.AUTH_USER
achat.split(" ")
else:
achat = Config.AUTH_USER
for ID in achat:
AUTH_USERS.append(int(ID))
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29889,
4397,
29898,
524,
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1367,
876,
13,
2
] |
lab07/filterWord.py | NickStucchi/CSC221 | 0 | 49458 | #!/usr/bin/env python3
# <NAME>
# 03/23/2017
# Filter out words and replace with dashes
def replaceWord(sentence, word):
wordList = sentence.split()
newSentence = []
for w in wordList:
if w == word:
newSentence.append("-" * len(word))
else:
newSentence.append(w)
return " ".join(newSentence)
def main():
sentence = input("Enter a sentence: ")
word = input("Enter a word to replace: ")
result = input("The resulting string is: " + "\n" + replaceWord(sentence, word))
print(result)
if __name__ == "__main__":
main()
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2
] |
scripts/P24qp.py | bopopescu/Bus-Pirate-1 | 0 | 175300 | #!/usr/bin/python
### Bus Pirate Programmer (should work for other PIC24F devices as well)
### (C) 2009 <NAME> <<EMAIL>>
#
# 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.
import serial
import sys
import os
import getopt
import math
import ConfigParser
### Prints the usage screen
def Usage():
print "Bus Pirate Programmer v1.0"
print "\nP24qp <command>"
print "\t-a --auto=FILE\t\t- Erase, Program and Quit the Bootloader"
print "\t-c --config=FILE\t- P24qp compatible configuration file"
print "\t\t\t\t (Default: P24qp.ini)"
print "\t-e --erase\t\t- Erase the flash programming"
print "\t-f --finalize\t- Finalize the programming and go to user mode"
print "\t-i --info\t\t- Information about the bootloader"
print "\t-q --quiet\t\t- Quiet messages about writing progress"
print "\t-r --read=FILE\t\t- Read programming from flash"
print "\t-s --serial=DEVICE\t- Override serial device path"
print "\t-t --reset\t\t- Reset the bootloader"
print "\t-v \t\t\t- Enable verification of write commands"
print "\t-w --write=FILE\t- Write programming file to flash"
### Get the other command line options
try:
optlist, args = getopt.getopt(sys.argv[1:], "fhveitqs:w:r:a:", ["auto=", "config=", "help","write=","read=", "serial=", "erase", "info", "quiet", "reset", "finalize"])
except getopt.GetoptError, err:
print str(err)
Usage()
sys.exit(2)
### Figure out what the user wants to do
Command = None
File = None
Verify = False
SerialPort = None
Quiet = False
### Default config file
Config = "P24qp.ini"
for op, val in optlist:
if op in ("-h", "--help"):
Usage()
sys.exit()
elif op in ("-w", "--write"):
Command = "WT_FLASH"
File = val
elif op in ("-r", "--read"):
Command = "RD_FLASH"
File = val
elif op in ("-e", "--erase"):
Command = "ER_FLASH"
elif op in ("-f", "--finalize"):
Command = "VERIFY_OK"
elif op in ("-i", "--info"):
Command = "RD_VER"
elif op in ("-a", "--auto"):
Command = "Auto"
File = val
elif op in ("-t", "--reset"):
Command = "Reset"
elif op in ("-v"):
Verify = True
elif op in ("-c", "--config"):
Config = val
elif op in ("-s", "--serial"):
SerialPort = val
elif op in ("-q", "--quiet"):
Quiet = True
### If there's no command, just print the options
if Command == None:
Usage()
sys.exit(-1)
### Note: Only supports Intel 32 Hex format
class HEX_File:
def __init__(self):
self.data = {}
self.encoded = None
### Clear the stored data in the file
def clear(self):
self.data = {}
### Read the encoded file
def read(self, filename):
FILE = open(filename, 'r')
self.encoded = FILE.readlines()
FILE.close()
self.decode()
### Write the encoded file
def write(self, filename):
self.encode()
FILE = open(filename, 'w')
for line in self.encoded:
FILE.write(line)
FILE.close()
### Decode the encoded file into data
def decode(self):
### Bail out if there isn't an encoded file
if self.encoded == None:
print "No encoded file yet"
return
line = 0
### Current high address
addrh = 0
### Current low address (for concatenation)
addrl = -1
### Decode each line
for r in self.encoded:
r = r.rstrip('\x0D\x0A')
### Every line starts with ":"
if r[0] != ":":
continue
### Get the byte count, address and record type
bc = int(r[1:3], 16)
### Get the address
addr = int(r[3:7], 16)
### Get the record type
rt = int(r[7:9], 16)
### Decide if it's time to move to the next key
if rt == 0:
if addrl == -1 or addr != addrl + len(self.data[(addrh << 16) | addrl]):
addrl = addr
self.data[(addrh << 16) | addrl] = []
### Get the checksum
ck = int(r[-2:], 16)
### Compute the checksum, make sure it matches
chksum = 0
for c in range(0, len(r[1:-2])/2):
chksum += int(r[c * 2 + 1: c * 2 + 3], 16)
chksum = (~(chksum & 0xFF) + 1) & 0xFF
if ck != chksum:
print "Checksum doesn't match on line " + str(line) + "!"
break
### Figure out the record type
### Data Record
if rt == 0:
for d in range(0, bc):
t = int(r[d * 2 + 9: d * 2 + 11], 16)
self.data[(addrh<< 16) | addrl].append(t)
### EOF Record
elif rt == 1:
break
### Extended Segment Address Record
elif rt == 2:
print "Record type 2 Not supported!"
break
### Start Segment Address Record
elif rt == 3:
print "Record type 3 Not supported!"
break
### Extended Linear Address Record
elif rt == 4:
addrh = int(r[9:13], 16)
addrl = -1
### Start Linear Address Record
elif rt == 5:
print "Record type 5 Not supported!"
break
line += 1
### Encode the data
def encode(self):
if len(self.data.keys()) == 0:
print "No data to encode yet"
return
self.encoded = []
line = 0
### Encode each line
data_k = self.data.keys()
data_k.sort()
last_addru = -1
### Loop over the address segments
for base_addr in data_k:
### Add the data lines (16 per line)
for line in range(0, len(self.data[base_addr]) / 16):
### Find the upper address
addru = (((base_addr + (line * 16)) >> 16) & 0xFF)
### Print the address record if needed
if addru != last_addru:
enc = ":02000004%04X" % addru
### Make the checksum
chksum = 0
for c in range(0, len(enc[1:])/2):
chksum += int(enc[c * 2 + 1: c * 2 + 3], 16)
chksum = (~(chksum & 0xFF) + 1) & 0xFF
enc += "%02X" % chksum
self.encoded.append(enc+"\x0D\x0A")
last_addru = addru
### Every line starts with ":", followed by record size (always 16(?)), address, then record type
enc = ":%02X%04X%02X" % (16, (base_addr + (line * 16)) & 0xFFFF, 0)
### Add the data
for d in self.data[base_addr][line * 16:(line+1) * 16]:
enc += "%02X" % ord(d)
### Compute the checksum
chksum = 0
for c in range(0, len(enc[1:])/2):
chksum += int(enc[c * 2 + 1: c * 2 + 3], 16)
chksum = (~(chksum & 0xFF) + 1) & 0xFF
enc = enc + "%02X" % chksum
self.encoded.append(enc+"\x0D\x0A")
### Add the end record
enc = ":00000001FF\x0D\x0A"
self.encoded.append(enc)
class PIC24F_Prog:
def __init__(self, Config, SerialPort):
self.device = {}
self.serial = {}
### Assume that the size of the read block is 4 until we know better
self.device['readblock'] = 4
### Assume that there are 2 bytes per address until we know better
self.device['bytesperaddr'] = 2
### Load the configuration
if os.path.isfile(Config):
cfg = ConfigParser.SafeConfigParser()
cfg.read(Config)
if not cfg.has_section("PIC24FBOOT"):
print "Invalid configuration, no PIC24FBOOT section!"
sys.exit(-1)
### Get the serial settings
rates = [ 0, 1200, 2400, 4800, 9600, 19200, 38400, 57600, 115200 ]
rate_index = cfg.getint('PIC24FBOOT', 'bitrateindex')
self.serial['rate'] = rates[rate_index]
self.serial['timeout'] = cfg.getint('PIC24FBOOT', 'commtimeout')
self.serial['retries'] = cfg.getint('PIC24FBOOT', 'maxretry')
self.serial['maxpacket'] = cfg.getint('PIC24FBOOT', 'maxpacketsize')
### If the serial port isn't forced, use the config
if SerialPort == None:
self.serial['port'] = cfg.getint('PIC24FBOOT', 'portindex')
else:
self.serial['port'] = SerialPort
### Get the location of the bootloader
self.device['bootaddrlo'] = int(cfg.get('PIC24FBOOT', 'bootaddrlo')[1:-1], 16)
self.device['bootaddrhi'] = int(cfg.get('PIC24FBOOT', 'bootaddrhi')[1:-1], 16)
### Otherwise, error out
else:
print "Please provide a configuration file!"
Usage()
sys.exit(-1)
### State variables
self.last_cmd = None
self.verify = False
self.hexfile = HEX_File()
self.quiet = False
### Configuration words
self.config_words = None
### Set up the serial connection
print "Using Serial Port", self.serial['port'], "@", self.serial['rate']
try:
self.ser = serial.Serial(self.serial['port'], self.serial['rate'], xonxoff=0, rtscts=0, timeout=(self.serial['timeout']/1000))
except:
print "Can't open serial port", self.serial['port'], "!"
sys.exit(-1)
### Get the device type to set the device settings from the config
device = self.GetDevice()
if not cfg.has_section('DEVICELIST'):
print "Invalid configuration, no DEVICELIST section!"
sys.exit(-1)
if not cfg.has_option('DEVICELIST', str(device)):
print "Device with ID:", device, "not listed in the config file!"
sys.exit(-1)
device_name = cfg.get('DEVICELIST', str(device))[1:-1]
if not cfg.has_section(device_name):
print device_name, "not specified in the config file!"
sys.exit(-1)
print "Found", device_name
### Get the locations of the boot vectors
self.device['userresetvector'] = int(cfg.get(device_name, 'userresetvector')[1:-1], 16)
self.device['bootdelay'] = int(cfg.get(device_name, 'bootdelay')[1:-1], 16)
### Fill in the device specific settings
self.device['writeblock'] = cfg.getint(device_name, 'writeblock')
self.device['readblock'] = cfg.getint(device_name, 'readblock')
self.device['eraseblock'] = cfg.getint(device_name, 'eraseblock')
### Some devices specify larger packet sizes
self.device['maxpacket'] = cfg.getint(device_name, 'maxpacketsize')
self.device['bytesperaddr'] = cfg.getint(device_name, 'bytesperaddr')
### Program memory area addresses
self.device['pmrangelow'] = int(cfg.get(device_name, 'pmrangelow')[1:-1], 16)
self.device['pmrangehigh'] = int(cfg.get(device_name, 'pmrangehigh')[1:-1], 16)
### Vector addresses
self.device['userresetvector'] = int(cfg.get(device_name, 'userresetvector')[1:-1], 16)
self.device['bootdelay'] = int(cfg.get(device_name, 'bootdelay')[1:-1], 16)
### Get the device type
def GetDevice(self):
device_data = self.ReadFlash(0xFF0000, 4)
if len(device_data) < 2:
print "Can't connect to device, sorry!"
sys.exit(-1)
device = ord(device_data[0]) | ord(device_data[1]) << 8
return device
def TX_PKT(self, data):
### PKT Wire format
STX = "\x55"
ETX = "\x04"
DLE = "\x05"
### Sanity check the data
if len(data) > self.serial['maxpacket']:
print "Warning: truncated packet"
data = data[0:self.serial['maxpacket']]
### Generate the checksum
chksum = 0
for d in data:
chksum += ord(d)
chksum = chr((~(chksum) + 1) & 0xFF)
### Preamble
self.ser.write(STX)
self.ser.write(STX)
for d in data:
### Add escape character for special characters
if d == STX or d == ETX or d == DLE:
self.ser.write(DLE)
self.ser.write(d)
if chksum == STX or chksum == ETX or chksum == DLE:
self.ser.write(DLE)
self.ser.write(chksum)
### Postfix
self.ser.write(ETX)
### Read a byte (with retries) from the serial device
def ReadSer(self):
db = ""
for i in range(0, self.serial['retries']):
db = self.ser.read()
if len(db) != 0:
break
return db
def RX_PKT(self):
### PKT Wire format
ETX = "\x04"
DLE = "\x05"
data = ""
data_len = 0
### Get the data packet
while True:
db = self.ReadSer()
### Throw an error if we didn't get data
if len(db) == 0:
print "RX Error: ", len(data), " bytes received"
if len(data) > 0:
for d in data:
print ord(d),
print "\n"
return data
### Always add the escaped character
if db == DLE:
db = self.ReadSer()
### End condition
elif db == ETX:
break
### Normal data
data += db
data_len += 1
### Get the checksum byte
ck = ord(data[-1])
### Remove the framing characters
data = data[2:-1]
### Check the checksum of the data segment
chksum = 0
for d in data:
chksum += ord(d)
chksum = (~(chksum & 0xFF) + 1) & 0xFF
### Print a warning if the checksum fails
if chksum != ck:
print "\nWARNING: Checksum mismatch: %02X / %02X!" % (chksum, ck)
for d in data:
print ord(d),
print "\n"
return data
### Generate the packet to send over the wire and send it
def BootloaderCMD(self, cmd, args = {}):
repeat = False
replicate = False
data = []
### Repeat and replicate use the last command
if cmd == "REPEAT":
if self.last_cmd != None:
cmd = self.last_cmd
repeat = True
else:
print "Can't repeat, no command in buffer"
if cmd == "REPLICATE":
if self.last_cmd != None and self.last_cmd[0:2] == "WT":
cmd = self.last_cmd
replicate = True
else:
print "Can't replicate, no compatible command in buffer"
### Process the rest of the commands
if cmd == "RESET":
data = ["\x00", "\x00"]
self.TX_PKT(data)
### No response...
### Read Version Information
elif cmd == "RD_VER":
print "Reading Bootloader Version Information: "
### Send the data packet
data = ["\x00", "\x02"]
self.TX_PKT(data)
### Get the result
data = self.RX_PKT()
if len(data) == 0:
print "\tUnable to determine version!"
return
print "\tBootloader Version: " + str(ord(data[3])) + "." + str(ord(data[2]))
### Read the programming out of the flash
elif cmd == "RD_FLASH":
#print "Reading Program Memory:"
### Send the data packet
if repeat:
#print "\tReading from last location"
data = ["\x01"]
else:
#print "\tReading 0x%02X words from 0x%08X" % (args['LEN'], (args['ADDRU'] << 16 | args['ADDRH'] << 8 | args['ADDRL']))
data = ["\x01", chr(args['LEN']), chr(args['ADDRL']), chr(args['ADDRH']), chr(args['ADDRU'])]
self.TX_PKT(data)
### Get the result
data = self.RX_PKT()
### If there was an error, just store junk
if len(data) == 0:
print "\tError reading from device!"
data = ""
for i in range(0, args['LEN']):
data += "\xFF\xFF\xFF\x00"
return data
### Return only the data read from flash, not the address preamble
return data[5:]
### Write a new program into the flash
elif cmd == "WT_FLASH":
#print "Write Program Memory:"
### Generate the data packet
if repeat:
#print "\tWriting to last location"
data = ["\x02"]
else:
#print "\tWriting row to 0x%08X" % ((args['ADDRU'] << 16 | args['ADDRH'] << 8 | args['ADDRL']))
data = ["\x02", chr(args['LEN']), chr(args['ADDRL']), chr(args['ADDRH']), chr(args['ADDRU'])]
### On a replication, we don't provide the data
if not replicate:
### Generate the data string
for d in args['DATA']:
data.append(chr(d))
### Send the data packet
self.TX_PKT(data)
### Get the response
data = self.RX_PKT()
if len(data) == 0 or data[0] != "\x02":
print "\tError writing to device!"
### Erase Flash
elif cmd == "ER_FLASH":
print "Erase Flash:"
### Send the data packet
if repeat:
print "\tErasing previous location"
data = ["\x03"]
else:
print "\tErasing %i pages, starting at 0x%08X" % (args['LEN'], (args['ADDRU'] << 16 | args['ADDRH'] << 8 | args['ADDRL']))
data = ["\x03", chr(args['LEN']), chr(args['ADDRL']), chr(args['ADDRH']), chr(args['ADDRU'])]
self.TX_PKT(data)
### Get the response
data = self.RX_PKT()
if len(data) == 0 or data[0] != "\x03":
print "\tError erasing device!"
else:
print "\tErase complete"
### These commands are unimplemented at this time since we don't
### use them with the BP. If needed, implement the same as the
### flash read/write with the correct addressing and instructions
elif cmd == "RD_EEDATA":
print "Unsupported at this time!"
pass
elif cmd == "WT_EEDATA":
print "Unsupported at this time!"
pass
elif cmd == "RD_CONFIG":
print "Unsupported at this time!"
pass
elif cmd == "WT_CONFIG":
print "Unsupported at this time!"
pass
### Send the packet that tells the device to start the watchdog
### to restart into userspace mode
elif cmd == "VERIFY_OK":
print "Verified Okay."
### Send the data packet
data = ["\x08", "\x01", "\x00", "\x00", "\x00"]
self.TX_PKT(data)
### Get the response
data = self.RX_PKT()
if data[0] != "\x08":
print "\tError verifying device!"
else:
print "\tVerification complete, switching to user mode"
### Send a reset packet
data = ["\x00", "\x00"]
self.TX_PKT(data)
### Back up the last command for repeating
self.last_cmd = cmd
return
### Convienence functions for saving and loading files
def ReadFile(self, filename):
self.hexfile.read(filename)
def WriteFile(self, filename):
self.hexfile.write(filename)
def DataToFile(self, addr, data):
self.hexfile.data[addr] = data
### Write to the flash
def WriteFlash(self, start_address, data):
### Get the word count
bc = len(data)
dots = ""
for p in range(0, int(bc / self.device['writeblock'])):
### Build the argument tree
args = {}
### Length is always 1 since we can write only one block at a time
args['LEN'] = 1
### Now write in the data
args['DATA'] = data[p * self.device['writeblock']:(p + 1) * self.device['writeblock']]
### Finally, generate the address
addr_p = start_address + p * (self.device['writeblock'] / self.device['bytesperaddr'])
if addr_p >= self.device['pmrangehigh']:
return
args['ADDRL'] = addr_p & 0xFF
args['ADDRH'] = (addr_p >> 8) & 0xFF
args['ADDRU'] = (addr_p >> 16) & 0xFF
if not self.quiet:
print "Writing %i bytes to address 0x%08X" % (self.device['writeblock'], addr_p)
self.BootloaderCMD("WT_FLASH", args)
### If we're verifying as we write, read the row we just wrote and compare
if self.verify:
if addr_p in range(self.device['bootaddrlo'], self.device['bootaddrhi']):
print "Skipping verification of bootloader area."
else:
### Read the flash we just wrote
verify_data = self.ReadFlash(addr_p, self.device['writeblock'])
for i in range(0, len(verify_data)):
if ord(verify_data[i]) != args['DATA'][i]:
print "Verification failed at 0x%08X: %i != %i" % (int(addr_p + i), ord(verify_data[i]), args['DATA'][i])
### Read from the flash
def ReadFlash(self, addr, length):
data = ""
### Count of reads to do
max_read_len = (self.serial['maxpacket'] - 5) / self.device['readblock']
length /= self.device['readblock']
dots = ""
### Loop over each segment in a read block
while length > 0:
### Build the argument tree
args = {}
if length > max_read_len:
args['LEN'] = max_read_len
else:
args['LEN'] = length
length -= args['LEN']
### Generate the address
args['ADDRL'] = addr & 0xFF
args['ADDRH'] = (addr >> 8) & 0xFF
args['ADDRU'] = (addr >> 16) & 0xFF
if not self.quiet:
print "Reading %i bytes from address 0x%08X" % (args['LEN'] * self.device['readblock'], addr)
data += self.BootloaderCMD("RD_FLASH", args)
addr += (args['LEN'] * self.device['readblock']) / self.device['bytesperaddr']
return data
### Erase the flash device
### Note: Not sure if the cfg word backup is really needed...
def EraseFlash(self):
### Back up the configuration words
cfg_words = self.ReadCFGWords()
### Loop to catch all address (if the # of blocks is greater than 255)
nBlocks = (self.device['pmrangehigh'] - self.device['pmrangelow'] + 1) / (self.device['eraseblock'] / self.device['bytesperaddr'])
start_addr = self.device['pmrangelow']
while nBlocks > 0:
### Build the argument tree
args = {}
if nBlocks > 255:
args['LEN'] = 255
else:
args['LEN'] = nBlocks
nBlocks -= args['LEN']
### Generate the address
addr = start_addr
args['ADDRL'] = addr & 0xFF
args['ADDRH'] = (addr >> 8) & 0xFF
args['ADDRU'] = (addr >> 16) & 0xFF
self.BootloaderCMD("ER_FLASH", args)
start_addr += (self.device['eraseblock'] / self.device['bytesperaddr']) * nBlocks
### Write the configuration words
self.WriteCFGWords(cfg_words)
### Read the config words
def ReadCFGWords(self):
addr = (self.device['pmrangehigh'] + 1) - 8
return self.ReadFlash(addr, 2)
### Write the config words into memory (shouldn't mess up other data if present?)
def WriteCFGWords(self, cfg_words):
### Write the config words
args = {}
args['LEN'] = 1
addr = (self.device['pmrangehigh'] + 1) - (self.device['eraseblock'] / self.device['bytesperaddr'])
args['ADDRL'] = addr & 0xFF
args['ADDRH'] = (addr >> 8) & 0xFF
args['ADDRU'] = (addr >> 16) & 0xFF
### Build the data packet
args['DATA'] = [0xFF, 0xFF, 0xFF, 0x00] * ((self.device['writeblock'] / 4) - 2)
for i in cfg_words:
args['DATA'].append(ord(i))
self.BootloaderCMD("WT_FLASH", args)
###############################################################################
### Run the commands the user requests
###############################################################################
### Initialize
p = PIC24F_Prog(Config, SerialPort)
p.verify = Verify
p.quiet = Quiet
### Prepare the command and run
if Command == "Auto":
### Erase the flash
p.EraseFlash()
### Read in the flash file
if File == None:
Usage()
sys.exit(2)
p.ReadFile(File)
### Now write to the flash
for k in p.hexfile.data.keys():
### Addresses from the hexfile are in bytes
### convert to device's addressing scheme first
p.WriteFlash(k / p.device['bytesperaddr'], p.hexfile.data[k])
print "Write operation complete."
### We're done, finish up
p.BootloaderCMD("VERIFY_OK")
### These commands are just run with no other arguments
elif Command in ("VERIFY_OK", "RD_VER"):
p.BootloaderCMD(Command)
### Read the flash into a file
elif Command == "RD_FLASH":
print "Starting read operation..."
### Make sure the filename was given
if File == None:
Usage()
sys.exit(2)
### Read the entire flash out
data = p.ReadFlash(p.device['pmrangelow'], ((p.device['pmrangehigh'] + 1) - p.device['pmrangelow']) * p.device['bytesperaddr'])
### Add the data to the file
p.DataToFile(p.device['pmrangelow'], data)
### Write out the file
p.WriteFile(File)
print "Read operation complete."
### Erase the flash
elif Command == "ER_FLASH":
p.EraseFlash()
### Write the flash from a file
elif Command == "WT_FLASH":
print "Starting write operation..."
### Open the file
if File == None:
Usage()
sys.exit(2)
p.ReadFile(File)
### Now write to the flash
for k in p.hexfile.data.keys():
### Addresses from the hexfile are in bytes
### convert to device's addressing scheme first
p.WriteFlash(k / p.device['bytesperaddr'], p.hexfile.data[k])
print "Write operation complete."
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sklearn_export/__init__.py | zwelz3/sklearn-export | 4 | 119059 | # -*- coding: utf-8 -*-
from sklearn_export.Export import Export
| [
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] |
solver/__init__.py | mshrAndo/PyMCR | 1 | 86666 | import nmfsvd as nmfsvd
import als as als | [
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hchztests/tests/test_container_rappture.py | codedsk/hubcheck-hubzero-tests | 1 | 1600995 | <reponame>codedsk/hubcheck-hubzero-tests<gh_stars>1-10
import unittest
import sys
import os
import pytest
import re
from string import Template
import hubcheck
from hubcheck.testcase import TestCase2
from hubcheck.shell import ContainerManager
pytestmark = [ pytest.mark.container,
pytest.mark.rappture,
pytest.mark.weekly,
pytest.mark.reboot
]
SUB_SPACING = 12
TOOL_XML = """
<?xml version="1.0"?>
<run>
<tool>
<about>Press Simulate to view results.</about>
<command>@tool/fermi @driver</command>
</tool>
<input>
<number id="Ef">
<about>
<label>Fermi Level</label>
<description>Energy at center of distribution.</description>
</about>
<units>eV</units>
<min>-10eV</min>
<max>10eV</max>
<default>0.2556eV</default>
</number>
</input>
</run>
""".strip()
@pytest.mark.rappture_c
@pytest.mark.registereduser
@pytest.mark.usefixtures('rappture_version')
class TestContainerRapptureCApi(TestCase2):
def setup_method(self,method):
self.remove_files = []
# get user account info
self.username,self.userpass = self.testdata.find_account_for('registeredworkspace')
hubname = self.testdata.find_url_for('https')
# access a tool session container
cm = ContainerManager()
self.ws = cm.access(host=hubname,
username=self.username,
password=self.userpass)
self.ws.execute('cd $SESSIONDIR')
self.sessiondir,es = self.ws.execute('pwd')
def teardown_method(self,method):
# remove the executable and config files
for fname in self.remove_files:
self.ws.execute('rm -f %s' % (fname))
# exit the workspace
self.ws.close()
def write_xml_file(self):
# write xml file
xmlfn = os.path.join(self.sessiondir,"tool.xml")
self.ws.importfile(TOOL_XML,xmlfn,mode=0600,is_data=True)
self.remove_files.append(xmlfn)
def run_code(self,program,xmlfn='tool.xml'):
# write program
programfn = os.path.join(self.sessiondir,"program.c")
self.ws.importfile(program,programfn,mode=0600,is_data=True)
self.remove_files.append(programfn)
# generate path for compiled executable
compiledfn = os.path.join(self.sessiondir,'program')
self.remove_files.append(compiledfn)
# setup rappture environment
self.ws.execute('. /etc/environ.sh')
self.ws.execute('use -e -r %s' % (self.rappture_version))
# setup RAPPTURE_INCLUDE and RAPPTURE_LIB
rpflags = '-I$RAPPTURE_PATH/include'
rpflags += ' -L$RAPPTURE_PATH/lib -lrappture -lexpat -lz -lm'
# compile the code
command = 'gcc -o %s %s %s' % (compiledfn,rpflags,programfn)
self.ws.execute(command)
self.ws.execute('chmod 700 %s' % (compiledfn))
# run the code
command = '%s %s' % (compiledfn,xmlfn)
output,es = self.ws.execute(command,fail_on_exit_code=False)
return output,es
@pytest.mark.dsktest
def test_rpLibrary_valid_path(self):
"""
rpLibrary()
test the function using an xml file that exists on disk
"""
program = """
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 1;
lib = rpLibrary(argv[1]);
if (lib != NULL) {
err = 0;
}
return err;
}
"""
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
"rpLibrary failed to open xml file, es = %s" % (es)
@pytest.mark.skipif(True,reason="the rappture c api currently does not error on invalid path")
def test_rpLibrary_invalid_path(self):
"""
rpLibrary()
test the function using an xml file that does not exist on disk
"""
program = """
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
}
return err;
}
"""
xmlfn = "tool_does_not_exist.xml"
output,es = self.run_code(program,xmlfn)
# check for success
assert es == 1, \
"rpLibrary successfully opened a file that does not exist: %s" \
% (xmlfn)
@pytest.mark.skipif(True,reason="the rappture c api currently does not error on blank path")
def test_rpLibrary_no_path(self):
"""
rpLibrary()
test the function without giving a filename
"""
program = """
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
lib = rpLibrary("");
if (lib == NULL) {
err = 1;
}
return err;
}
"""
output,es = self.run_code(program)
# check for success
assert es == 1, \
"rpLibrary initialized an object with blank filename"
def test_rpLibrary_null_path(self):
"""
rpLibrary()
test the function giving a NULL pointer
"""
program = """
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
lib = rpLibrary(NULL);
if (lib == NULL) {
err = 1;
}
return err;
}
"""
xmlfn = "tool_does_not_exist.xml"
output,es = self.run_code(program,xmlfn)
# check for success
assert es == 0, \
"rpLibrary failed to initialize an object with NULL filename"
def test_rpGetString_valid_path(self):
"""
rpGetString()
test the function with a valid xml path
"""
path = "input.number(Ef).about.label"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
const char* val = NULL;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpGetString(lib,"$path",&val);
printf("%s",val);
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
'program exited with status %s while trying to call' % (es) \
+ ' rpGetString with xml path %s' % (path)
expected = 'Fermi Level'
assert output == expected, \
"rpGetString returned the wrong data, expected: %s, received: %s" \
% (expected,output)
@pytest.mark.skipif(True,reason="the rappture c api currently only returns 0 as an error code")
def test_rpGetString_invalid_path(self):
"""
rpGetString()
test that calling the function with an invalid xml path returns a
non zero error code.
"""
path = "input.number(Ef).bad.path"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
const char* val = NULL;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpGetString(lib,"$path",&val);
if (err == 0) {
printf("%s",val);
}
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 1, \
'program exited with status %s while trying to call' % (es) \
+ ' rpGetString with bad xml path %s' % (path)
expected = ''
assert output == expected, \
"rpGetString returned the wrong data, expected: %s, received: %s" \
% (expected,output)
def test_rpGetString_null_retcstr(self):
"""
rpGetString()
test that calling the function with a NULL retCStr
non zero error code.
"""
path = "input.number(Ef).about.label"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpGetString(lib,"$path",NULL);
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for segfault
assert es == 139, \
'program exited with status %s while trying to call' % (es) \
+ ' rpGetString with retCStr == NULL'
def test_rpGetDouble_valid_path(self):
"""
rpGetDouble()
test the function with a valid xml path
"""
path = "input.number(Ef).default"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
double val = 0.0;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpGetDouble(lib,"$path",&val);
printf("%g",val);
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
'program exited with status %s while trying to call' % (es) \
+ ' rpGetDouble with xml path %s' % (path)
expected = '0.2556'
assert output == expected, \
"rpGetDouble returned the wrong data, expected: %s, received: %s" \
% (expected,output)
@pytest.mark.skipif(True,reason="the rappture c api currently only returns 0 as an error code")
def test_rpGetDouble_invalid_path(self):
"""
rpGetDouble()
test that calling the function with an invalid xml path returns a
non zero error code.
"""
path = "input.number(Ef).bad.path"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
double val = 0.0;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpGetDouble(lib,"$path",&val);
if (err == 0) {
printf("%g",val);
}
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es != 0, \
'program exited with status %s while trying to call' % (es) \
+ ' rpGetDouble with bad xml path %s' % (path)
expected = '0.2556'
assert output == '', \
"rpGetDouble returned the wrong data, expected: %s, received: %s" \
% (expected,output)
def test_rpGetDouble_null_retdval(self):
"""
rpGetDouble()
test that calling the function with a NULL retDVal
returns a non zero error code.
"""
path = "input.number(Ef).default"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpGetDouble(lib,"$path",NULL);
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for segfault
assert es == 139, \
'program exited with status %s while trying to call' % (es) \
+ ' rpGetDouble with retDVal == NULL'
def test_rpPutString_valid_path_valid_value(self):
"""
rpPutString()
test the function with a valid xml path and valid value
"""
path = "output.string.current"
val = "my new data"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
int append = 0;
const char *path = "$path";
const char *value = "$val";
const char *rvalue = NULL;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpPutString(lib,path,value,append);
rpGetString(lib,path,&rvalue);
printf("%s",rvalue);
return err;
}
""").substitute(path=path,val=val)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
'program exited with status %s while trying to call' % (es) \
+ ' rpPutString with xml path %s and string %s' % (path,val)
expected = val
assert output == expected, \
"rpPutString returned the wrong data, expected: %s, received: %s" \
% (expected,output)
@pytest.mark.skipif(True,reason="the rappture c api currently only returns 0 as an error code")
def test_rpPutString_invalid_path_valid_value(self):
"""
rpPutString()
test the function with an invalid xml path and valid value
"""
path = "output..string.current"
val = "my new data"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
int append = 0;
const char *path = "$path";
const char *value = "$val";
const char *rvalue = NULL;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpPutString(lib,path,value,append);
rpGetString(lib,path,&rvalue);
printf("%s",rvalue);
return err;
}
""").substitute(path=path,val=val)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es != 0, \
'program exited with status %s while trying to call' % (es) \
+ ' rpPutString with xml path %s and string %s' % (path,val)
def test_rpPutString_valid_path_invalid_value(self):
"""
rpPutString()
test the function with a valid xml path and invalid value
"""
path = "output.string.current"
program = Template("""
#include <stdio.h>
#include "rappture.h"
int main(int argc, char* argv[]) {
RpLibrary* lib = NULL;
int err = 0;
int append = 0;
const char *path = "$path";
const char *value = NULL;
const char *rvalue = NULL;
lib = rpLibrary(argv[1]);
if (lib == NULL) {
err = 1;
return err;
}
err = rpPutString(lib,path,value,append);
rpGetString(lib,path,&rvalue);
printf("%s",rvalue);
return err;
}
""").substitute(path=path)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es != 139, \
'program exited with status %s while trying to call' % (es) \
+ ' rpPutString with xml path %s and NULL string' % (path)
@pytest.mark.rappture_python
@pytest.mark.registereduser
@pytest.mark.usefixtures('rappture_version')
class TestContainerRappturePythonApi(TestCase2):
def setup_method(self,method):
self.remove_files = []
# get user account info
self.username,self.userpass = self.testdata.find_account_for('registeredworkspace')
hubname = self.testdata.find_url_for('https')
# access a tool session container
cm = ContainerManager()
self.ws = cm.access(host=hubname,
username=self.username,
password=self.userpass)
self.ws.execute('cd $SESSIONDIR')
self.sessiondir,es = self.ws.execute('pwd')
def teardown_method(self,method):
# remove the executable and config files
for fname in self.remove_files:
self.ws.execute('rm -f %s' % (fname))
# exit the workspace
self.ws.close()
def write_xml_file(self):
# write xml file
xmlfn = os.path.join(self.sessiondir,"tool.xml")
self.ws.importfile(TOOL_XML,xmlfn,mode=0600,is_data=True)
self.remove_files.append(xmlfn)
def run_code(self,program,xmlfn='tool.xml'):
# write program
programfn = os.path.join(self.sessiondir,"program.py")
self.ws.importfile(program,programfn,mode=0600,is_data=True)
self.remove_files.append(programfn)
# setup rappture environment
self.ws.execute('. /etc/environ.sh')
self.ws.execute('use -e -r %s' % (self.rappture_version))
# run the code
command = 'python %s %s' % (programfn,xmlfn)
output,es = self.ws.execute(command,fail_on_exit_code=False)
return output,es
def test_library_valid_path(self):
"""
Rappture.library()
test the function using an xml file that exists on disk
"""
program = """
import sys
import Rappture
lib = Rappture.library(sys.argv[1])
if lib is None:
raise Exception("failed to open xml file: %s" % (sys.argv[1]))
sys.exit()
"""
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
"Rappture.library failed to open xml file: %s" % (output)
@pytest.mark.skipif(True,reason="the rappture python api currently does not error on invalid path")
def test_library_invalid_path(self):
"""
Rappture.library()
test the function using an xml file that does not exist on disk
"""
program = """
import sys
import Rappture
lib = Rappture.library(sys.argv[1])
sys.exit()
"""
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
xmlfn = "tool_does_not_exist.xml"
output,es = self.run_code(program,xmlfn)
# check for success
assert es == 1, \
"Rappture.library successfully opened a file that does not exist: %s" \
% (xmlfn)
@pytest.mark.skipif(True,reason="the rappture python api currently does not error on blank path")
def test_library_no_path(self):
"""
Rappture.library()
test the function without giving a filename
"""
program = """
import sys
import Rappture
lib = Rappture.library('')
sys.exit()
"""
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
output,es = self.run_code(program)
# check for success
assert es == 1, \
"Rappture.library initialized an object with blank filename"
def test_library_none_path(self):
"""
Rappture.library()
test the function giving a None object
"""
program = """
import sys
import Rappture
lib = Rappture.library(None)
sys.exit()
"""
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
output,es = self.run_code(program)
# check for success
assert es == 1, \
"Rappture.library initialized an object with 'None' filename"
def test_get_valid_path(self):
"""
get()
test the function with a valid xml path
"""
path = "input.number(Ef).about.label"
program = Template("""
import sys
import Rappture
val = None
lib = Rappture.library(sys.argv[1])
if lib is None:
raise Exception("failed to open xml file: %s" % (sys.argv[1]))
val = lib.get('$path')
print "%s" % (val)
sys.exit()
""").substitute(path=path)
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
'program exited with status %s while trying to call' % (es) \
+ ' get() with xml path %s' % (path)
expected = 'Fermi Level'
assert output == expected, \
"get() returned the wrong data, expected: %s, received: %s" \
% (expected,output)
@pytest.mark.skipif(True,reason="the rappture python api currently does not error on bad path")
def test_get_invalid_path(self):
"""
get()
test that calling the function with an invalid xml path returns a
non zero error code.
"""
path = "input.number(Ef).bad.path"
program = Template("""
import sys
import Rappture
val = None
lib = Rappture.library(sys.argv[1])
if lib is None:
raise Exception("failed to open xml file: %s" % (sys.argv[1]))
val = lib.get('$path')
print "%s" % (val)
sys.exit()
""").substitute(path=path)
program = re.sub('\n {%d})' % (SUB_SPACING),'\n',program,flags=re.M)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 1, \
'program exited with status %s while trying to call' % (es) \
+ ' get() with bad xml path %s' % (path)
expected = ''
assert output == expected, \
"get() returned the wrong data, expected: %s, received: %s" \
% (expected,output)
def test_get_none_path(self):
"""
get()
test that calling the function with a None path returns a TypeError
"""
program = """
import sys
import Rappture
val = None
lib = Rappture.library(sys.argv[1])
if lib is None:
raise Exception("failed to open xml file: %s" % (sys.argv[1]))
val = lib.get(None)
sys.exit()
"""
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
self.write_xml_file()
output,es = self.run_code(program)
# check for error code
assert es == 1, \
'program exited with status %s while trying to call' % (es) \
+ ' get() with path == None'
# check for TypeError in output
assert re.search("TypeError",output) is not None, \
'program did not exit with a TypeError: %s' % (output)
def test_put_valid_path_valid_value(self):
"""
put()
test the function with a valid xml path and valid value
"""
path = "output.string.current"
value = "my new data"
program = Template("""
import sys
import Rappture
path = '$path'
value = '$value'
lib = Rappture.library(sys.argv[1])
if lib is None:
raise Exception("failed to open xml file: %s" % (sys.argv[1]))
lib.put(path,value)
rval = lib.get(path)
if value != rval:
raise Exception("value stored differs from value returned."
+ "\\nstored: %s" % (value)
+ "\\nreturned: %s" % (rval))
sys.exit()
""").substitute(path=path,value=value)
program = re.sub('\n {%d}' % (SUB_SPACING),'\n',program,flags=re.M)
self.write_xml_file()
output,es = self.run_code(program)
# check for success
assert es == 0, \
'program exited with status %s while trying to call' % (es) \
+ ' put() with xml path %s and string %s: %s' % (path,value,output)
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13,
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313,
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1767,
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13,
13,
13,
2
] |
second.py | cplanck/remote-playground | 0 | 45404 | <gh_stars>0
print('this is the first line of second.py')
for x in range(20):
print(x)
print('this is the chunk of code added to the fourth branch')
print('Im editing this file in GitHub to see if fetch finds it')
print('adding this code to push to the main on the remote repository') | [
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src/unzip.py | pzzzl/kino_installer | 0 | 1602100 | import zipfile
class Unzip:
def __init__(self, path, destination):
self.path = path
self.destination = destination
def start(self):
with zipfile.ZipFile(self.path, 'r') as zip_ref:
print("Extracting " + self.path.rsplit('/', 1)[1])
zip_ref.extractall(self.destination) | [
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ddict/__init__.py | GroMaster1/gromaster1111-gmail.com | 3 | 103673 | from .ddict import DotAccessDict
| [
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] |
hphp/hack/test/integration/test_save_mini.py | MaxSem/hhvm | 0 | 164113 | <filename>hphp/hack/test/integration/test_save_mini.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import json
import os
import shlex
import shutil
import stat
import subprocess
import tempfile
import time
import unittest
import common_tests
import hierarchy_tests
from hh_paths import hh_server, hh_client
def write_echo_json(f, obj):
f.write("echo %s\n" % shlex.quote(json.dumps(obj)))
class MiniStateTestDriver(common_tests.CommonTestDriver):
@classmethod
def setUpClass(cls):
super().setUpClass()
# we create the state in a different dir from the one we run our tests
# on, to verify that the saved state does not depend on any absolute
# paths
init_dir = os.path.join(cls.base_tmp_dir, 'init')
shutil.copytree(cls.template_repo, init_dir)
cls.saved_state_dir = tempfile.mkdtemp()
cls.save_command(init_dir)
shutil.rmtree(init_dir)
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.saved_state_dir)
@classmethod
def saved_state_path(cls):
return os.path.join(cls.saved_state_dir, 'foo')
@classmethod
def save_command(cls, init_dir):
stdout, stderr, retcode = cls.proc_call([
hh_server,
'--check', init_dir,
'--save-mini', cls.saved_state_path()
])
if retcode != 0:
raise Exception('Failed to save! stdout: "%s" stderr: "%s"' %
(stdout, stderr))
def write_local_conf(self):
with open(os.path.join(self.repo_dir, 'hh.conf'), 'w') as f:
f.write(r"""
# some comment
use_mini_state = true
use_watchman = true
""")
def write_hhconfig(self, script_name):
with open(os.path.join(self.repo_dir, '.hhconfig'), 'w') as f:
f.write(r"""
# some comment
assume_php = false
load_mini_script = %s
""" % os.path.join(self.repo_dir, script_name))
def write_watchman_config(self):
with open(os.path.join(self.repo_dir, '.watchmanconfig'), 'w') as f:
f.write('{}')
os.mkdir(os.path.join(self.repo_dir, '.hg'))
def write_load_config(self, *changed_files):
with open(os.path.join(self.repo_dir, 'server_options.sh'), 'w') as f:
f.write("#! /bin/sh\n")
os.fchmod(f.fileno(), 0o700)
write_echo_json(f, {
'state': self.saved_state_path(),
'is_cached': True,
'deptable': self.saved_state_path() + '.deptable',
})
write_echo_json(f, {
'changes': changed_files,
})
os.fchmod(f.fileno(), 0o700)
self.write_local_conf()
self.write_hhconfig('server_options.sh')
self.write_watchman_config()
def run_check(self, stdin=None, options=None):
options = [] if options is None else options
root = self.repo_dir + os.path.sep
return self.proc_call([
hh_client,
'check',
'--retries',
'20',
self.repo_dir
] + list(map(lambda x: x.format(root=root), options)),
stdin=stdin)
def check_cmd(self, expected_output, stdin=None, options=None):
(output, err, _) = self.run_check(stdin, options)
logs = self.get_server_logs()
self.assertIn('Using watchman', logs)
self.assertIn('Successfully loaded mini-state', logs)
root = self.repo_dir + os.path.sep
self.assertCountEqual(
map(lambda x: x.format(root=root), expected_output),
output.splitlines())
return err
class LazyDeclTestDriver(MiniStateTestDriver):
def write_local_conf(self):
with open(os.path.join(self.repo_dir, 'hh.conf'), 'w') as f:
f.write(r"""
# some comment
use_mini_state = true
use_watchman = true
lazy_decl = true
""")
class MiniStateCommonTests(common_tests.CommonTests, MiniStateTestDriver,
unittest.TestCase):
pass
class LazyDeclCommonTests(common_tests.CommonTests, LazyDeclTestDriver,
unittest.TestCase):
pass
class MiniStateHierarchyTests(hierarchy_tests.HierarchyTests,
MiniStateTestDriver, unittest.TestCase):
pass
class LazyDeclHierarchyTests(hierarchy_tests.HierarchyTests,
LazyDeclTestDriver, unittest.TestCase):
pass
class MiniStateTests(MiniStateTestDriver, unittest.TestCase):
"""
Tests in this class are specific to saved state; would not make sense
for them to run on a fresh init
"""
template_repo = 'hphp/hack/test/integration/data/simple_repo'
def test_no_state_found(self):
error_msg = 'No such rev'
with open(os.path.join(self.repo_dir, 'server_options.sh'), 'w') as f:
f.write("#! /bin/sh\n")
write_echo_json(f, {
'error': error_msg,
})
os.fchmod(f.fileno(), 0o700)
self.write_local_conf()
self.write_hhconfig('server_options.sh')
self.write_watchman_config()
(output, _, _) = self.run_check()
self.assertEqual(output.strip(), 'No errors!')
logs = self.get_server_logs()
self.assertIn('Could not load mini state', logs)
self.assertIn(error_msg, logs)
def test_get_changes_failure(self):
error_msg = 'hg is not playing nice today'
with open(os.path.join(self.repo_dir, 'server_options.sh'), 'w') as f:
f.write("#! /bin/sh\n")
write_echo_json(f, {
'state': self.saved_state_path(),
'is_cached': True,
'deptable': self.saved_state_path() + '.deptable',
})
write_echo_json(f, {
'error': error_msg,
})
os.fchmod(f.fileno(), 0o700)
self.write_local_conf()
self.write_hhconfig('server_options.sh')
self.write_watchman_config()
(output, _, _) = self.run_check()
self.assertEqual(output.strip(), 'No errors!')
logs = self.get_server_logs()
self.assertIn('Could not load mini state', logs)
self.assertIn(error_msg, logs)
def test_hhconfig_change(self):
"""
Start hh_server, then change .hhconfig and check that the server
restarts itself
"""
self.write_load_config()
self.check_cmd(['No errors!'])
with open(os.path.join(self.repo_dir, '.hhconfig'), 'w') as f:
f.write(r"""
# some comment
assume_php = true
load_mini_script = %s
""" % os.path.join(self.repo_dir, 'server_options.sh'))
# Server may take some time to kill itself.
time.sleep(2)
# this should start a new server
self.check_cmd(['No errors!'])
# check how the old one exited
log_file = self.proc_call([
hh_client, '--logname', self.repo_dir]
)[0].strip() + '.old'
with open(log_file) as f:
logs = f.read()
self.assertIn('.hhconfig changed in an incompatible way', logs)
def test_watchman_timeout(self):
self.write_load_config()
with open(os.path.join(self.repo_dir, 'hh.conf'), 'a') as f:
f.write(r"""
watchman_init_timeout = 1
""")
with open(os.path.join(self.bin_dir, 'watchman'), 'w') as f:
f.write(r"""sleep 2""")
os.fchmod(f.fileno(), stat.S_IRWXU)
self.run_check()
# Stop the server, ensuring that its logs get flushed
self.proc_call([hh_client, 'stop', self.repo_dir])
self.assertIn('Watchman.Timeout', self.get_server_logs())
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1583,
29889,
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29918,
2974,
29918,
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3101,
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2
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mozdns/mozbind/models.py | jlin/inventory | 22 | 88032 | from django.db import models
class DNSBuildRun(models.Model):
"""
Everytime the DNS build scripts are ran, one of these objects is
created to track which zones we have built and which zones we haven't built
(since nothing has changed in them). :class:`BuildManifest` objects
relate back to a :class:`DNSBuildRun` instance and represent one zone's
state.
"""
log = models.TextField()
# stats_json = models.JSONField("stats", max_length=max_length)
def record(self, root_domain, soa, zfiles, zhash):
bm = BuildManifest(zname=root_domain.name, files=','.join(zfiles),
zhash=zhash, build_run=self)
bm.save()
return bm
def stash(self, k, v):
self.stats_json[k] = v
def get_manifests(self, **kwargs):
return BuildManifest.objects.filter(build_run=self, **kwargs)
class BuildManifest(models.Model):
max_length = 256
zname = models.CharField(max_length=max_length)
files = models.CharField(max_length=max_length)
zhash = models.CharField(max_length=max_length)
build_run = models.ForeignKey(DNSBuildRun)
# stats_json = models.JSONField("stats", max_length=max_length)
def stash(self, k, v):
self.stats_json[k] = v
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make_csv.py | mlissner/pacer-free-document-stats | 0 | 172912 | <reponame>mlissner/pacer-free-document-stats<gh_stars>0
"""Code to be run on the server. Kept here for easy reference.
Use the %ed command to get a vi interface into iPython, and paste this in.
"""
import csv
from cl.scrapers.models import PACERFreeDocumentRow
def make_csv():
with open('/tmp/out.csv', 'w') as f:
w = csv.writer(f)
items = PACERFreeDocumentRow.objects.values_list()
for row in items:
try:
w.writerow(row)
except UnicodeEncodeError:
pass
del items # reclaim memory
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sample/python/SR1Walk.py | geenux/choreonoid | 0 | 84138 |
from cnoid.Util import *
from cnoid.Base import *
from cnoid.Body import *
from cnoid.BodyPlugin import *
from cnoid.SimpleControllerPlugin import *
import math;
worldItem = WorldItem()
RootItem.instance().addChildItem(worldItem)
timeBar = TimeBar.instance()
timeBar.setFrameRate(500)
timeBar.setTimeRange(0.0, 15.0)
timeBar.setFillLevelSync(False)
robotItem = loadBodyItem(shareDirectory() + "/model/SR1/SR1.yaml")
robot = robotItem.body()
robot.rootLink().setTranslation( [0.0, 0.0, 0.7135] )
q = [ 0.0, -2.1, 0.0, 4.5, -2.4, 0.0,
10.0, -0.2, 0.0, -90.0, 0.0, 0.0, 0.0,
0.0, -2.1, 0.0, 4.5, -2.4, 0.0,
10.0, -0.2, 0.0, -90.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0 ]
for i in range(robot.numJoints()):
robot.joint(i).q = math.radians(q[i])
robot.calcForwardKinematics()
robotItem.storeInitialState()
controllerItem = SimpleControllerItem()
controllerItem.setControllerDllName("SR1WalkPatternController")
robotItem.addChildItem(controllerItem)
worldItem.addChildItem(robotItem)
ItemTreeView.instance().checkItem(robotItem)
worldItem.addChildItem(loadBodyItem(shareDirectory() + "/model/misc/floor.wrl"))
simulatorItem = AISTSimulatorItem()
worldItem.addChildItem(simulatorItem)
ItemTreeView.instance().selectItem(simulatorItem)
simulatorItem.startSimulation()
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crawler/functions/clien.py | cmh1027/Community-Analyzer | 0 | 89123 | import requests
from bs4 import BeautifulSoup
import sys, os
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from functions.crawler_decorator import url_crawler, article_crawler
import utility.constant as constant
@url_crawler
def getGalleryArticleURLs(gallery_url, page, headers): # article_max 개의 게시글을 긁어옴
response = requests.get(gallery_url+"?od=T31&category=0&po="+str(page), headers=headers)
urls = []
if response.status_code == 200:
html = response.text
soup = BeautifulSoup(html, 'html.parser')
articles = soup.findAll("a", "list_subject")
if len(articles) >= 1:
articles = articles[1:]
for article in articles:
urls.append("https://m.clien.net" + article["href"])
return (response.status_code, urls)
@article_crawler
def getArticleContent(soup):
title = soup.find("div", "post_title")
if title is None: # article has been removed
return "", ""
else:
return title.getText(), soup.find("div", "post_article").getText() # title, content | [
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2
] |
Python3Lab/zarathustra/SungJukV2.py | Yurisegye/HelloPython3 | 0 | 167901 | <filename>Python3Lab/zarathustra/SungJukV2.py<gh_stars>0
print('-- 성적 처리 프로그램 v1 --')
name = input("이름을 입력하세요")
kor = int(input("국어 점수를 입력하세요"))
eng = int(input("영어 점수를 입력하세요"))
mat = int(input("수학 점수를 입력하세요"))
def getTotal():
tot = kor + mat + eng
return tot
def getAverage():
avg = getTotal() / 3
return avg
def getGrade():
avg = getAverage()
grd = '가'
if avg >= 90:
grd = '수'
elif avg >= 80:
grd = '우'
elif avg >= 70:
grd = '미'
elif avg >= 60:
grd = '양'
return grd
fmt = '이름: %s 국어: %d 수학: %d 영어: %d 총점: %d 평균: %.2f 학점: %s'
print(fmt % (name, kor, eng, mat, getTotal(), getAverage(), getGrade())) | [
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29909,
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3285,
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29954,
15464,
22130,
2
] |
questions/increasing-order-search-tree/Solution.py | marcus-aurelianus/leetcode-solutions | 141 | 64797 | <filename>questions/increasing-order-search-tree/Solution.py
"""
Given the root of a binary search tree, rearrange the tree in in-order so that the leftmost node in the tree is now the root of the tree, and every node has no left child and only one right child.
Example 1:
Input: root = [5,3,6,2,4,null,8,1,null,null,null,7,9]
Output: [1,null,2,null,3,null,4,null,5,null,6,null,7,null,8,null,9]
Example 2:
Input: root = [5,1,7]
Output: [1,null,5,null,7]
Constraints:
The number of nodes in the given tree will be in the range [1, 100].
0 <= Node.val <= 1000
"""
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def increasingBST(self, root: TreeNode) -> TreeNode:
def inorder(root, arr):
if root is None:
return
inorder(root.left, arr)
arr.append(root.val)
inorder(root.right, arr)
arr = []
inorder(root, arr)
ps = []
for i, val in enumerate(arr):
c = TreeNode(val)
if i > 0:
ps[-1].right = c
ps.append(c)
return ps[0] | [
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2019/03-volga/web-shop2/solve.py | wani-hackase/wani-writeup | 25 | 83681 | import requests
login_url = "http://shop2.q.2019.volgactf.ru/loginProcess"
target_url = "http://shop2.q.2019.volgactf.ru/profile"
payload0 = {'name': 'wani', 'pass': '<PASSWORD>'}
payload1 = {'name': 'wani', 'CartItems[0].id': 4}
s = requests.Session()
r = s.post(login_url, data=payload0)
r = s.post(target_url, data=payload1)
print(r.text)
| [
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] |
modules/attention.py | Mehrad0711/HUBERT | 3 | 195122 | import torch
from torch.nn import Linear, Module
from torch.nn.init import xavier_uniform_, constant_, xavier_normal_
from torch.nn.parameter import Parameter
from torch.nn import functional as F
class MultiheadAttention(Module):
r"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
Examples::
>>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
>>> attn_output, attn_output_weights = multihead_attn(query, key, value)
"""
def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim))
if bias:
self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
if add_bias_kv:
self.bias_k = Parameter(torch.empty(1, 1, embed_dim))
self.bias_v = Parameter(torch.empty(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self._reset_parameters()
def _reset_parameters(self):
xavier_uniform_(self.in_proj_weight[:self.embed_dim, :])
xavier_uniform_(self.in_proj_weight[self.embed_dim:(self.embed_dim * 2), :])
xavier_uniform_(self.in_proj_weight[(self.embed_dim * 2):, :])
xavier_uniform_(self.out_proj.weight)
if self.in_proj_bias is not None:
constant_(self.in_proj_bias, 0.)
constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
xavier_normal_(self.bias_k)
if self.bias_v is not None:
xavier_normal_(self.bias_v)
def forward(self, query, key, value, key_padding_mask=None, incremental_state=None,
need_weights=True, static_kv=False, attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
qkv_same = query.data_ptr() == key.data_ptr() == value.data_ptr()
kv_same = key.data_ptr() == value.data_ptr()
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
assert key.size() == value.size()
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_key' in saved_state:
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert kv_same and not qkv_same
key = value = None
else:
saved_state = None
if qkv_same:
# self-attention
q, k, v = self._in_proj_qkv(query)
elif kv_same:
# encoder-decoder attention
q = self._in_proj_q(query)
if key is None:
assert value is None
k = v = None
else:
k, v = self._in_proj_kv(key)
else:
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if k is not None:
k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if v is not None:
v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if 'prev_key' in saved_state:
prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
k = torch.cat((prev_key, k), dim=1)
if 'prev_value' in saved_state:
prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
v = torch.cat((prev_value, v), dim=1)
saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim)
self._set_input_buffer(incremental_state, saved_state)
src_len = k.size(1)
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.add_zero_attn:
src_len += 1
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
attn_output_weights = torch.bmm(q, k.transpose(1, 2))
assert list(attn_output_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = attn_output_weights.view(bsz, self.num_heads, tgt_len, src_len)
key_padding_mask = key_padding_mask.type(torch.uint8).to(query.device)
attn_output_weights = attn_output_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
float('-inf'),
)
attn_output_weights = attn_output_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_output_weights = F.softmax(
attn_output_weights.float(), dim=-1,
dtype=torch.float32 if attn_output_weights.dtype == torch.float16 else attn_output_weights.dtype)
attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_output_weights, v)
assert list(attn_output.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn_output = self.out_proj(attn_output)
if need_weights:
# average attention weights over heads
attn_output_weights = attn_output_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_output_weights = attn_output_weights.sum(dim=1) / self.num_heads
else:
attn_output_weights = None
return attn_output, attn_output_weights
def _in_proj_qkv(self, query):
return self._in_proj(query).chunk(3, dim=-1)
def _in_proj_kv(self, key):
return self._in_proj(key, start=self.embed_dim).chunk(2, dim=-1)
def _in_proj_q(self, query):
return self._in_proj(query, end=self.embed_dim)
def _in_proj_k(self, key):
return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
def _in_proj_v(self, value):
return self._in_proj(value, start=2 * self.embed_dim)
def _in_proj(self, input, start=0, end=None):
weight = self.in_proj_weight
bias = self.in_proj_bias
weight = weight[start:end, :]
if bias is not None:
bias = bias[start:end]
return F.linear(input, weight, bias) | [
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alembic/versions/22f80ee91db_cdeppk.py | wenbs/mptracker | 4 | 192848 | revision = '22f80ee91db'
down_revision = '5994be361ba'
from alembic import op
import sqlalchemy as sa
def upgrade():
op.add_column('proposal',
sa.Column('cdeppk_cdep', sa.Integer(), nullable=True))
op.add_column('proposal',
sa.Column('cdeppk_senate', sa.Integer(), nullable=True))
def downgrade():
op.drop_column('proposal', 'cdeppk_senate')
op.drop_column('proposal', 'cdeppk_cdep')
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cmd_thread.py | summunity/DjangoReact_CLI | 0 | 149921 |
import threading
import time
import ctypes
class CmdThread(threading.Thread):
def __init__(self, app, commands):
threading.Thread.__init__(self)
self.app = app
self.commands = commands
def run(self):
import subprocess
import os
os.chdir(self.app['path'])
# target function of the thread class
try:
for cmd in self.commands:
subprocess.call(cmd)
finally:
print('ended')
return
# def get_id(self):
# if hasattr(self, '_thread_id'):
# return self._thread_id
#
def get_id( self ):
# returns id of the respective thread
if hasattr(self, '_thread_id'):
return self._thread_id
for id, thread in threading._active.items():
if thread is self:
return id
def raise_exception(self):
thread_id = self.get_id()
res = ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, ctypes.py_object(SystemExit))
print('what is this', res)
if res >= 1:
ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, 0)
print('Exception raise failure')
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Module3/notes/imshow_example.py | FernanOrtega/DAT210x | 0 | 34048 | <reponame>FernanOrtega/DAT210x
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 21 13:15:22 2017
@author: fernando
"""
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
matplotlib.style.use('ggplot') # Look Pretty
# If the above line throws an error, use plt.style.use('ggplot') instead
df = pd.read_csv("concrete.csv")
plt.imshow(df.corr(), cmap=plt.cm.Blues, interpolation='nearest')
plt.colorbar()
tick_marks = [i for i in range(len(df.columns))]
plt.xticks(tick_marks, df.columns, rotation='vertical')
plt.yticks(tick_marks, df.columns)
plt.show() | [
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paddlespatial/networks/sagnn.py | PaddlePaddle/PaddleSpatial | 38 | 84996 | <filename>paddlespatial/networks/sagnn.py
# -*-Encoding: utf-8 -*-
################################################################################
#
# Copyright (c) 2021 Baidu.com, Inc. All Rights Reserved
#
################################################################################
"""
Description: Spatial Adaptive Graph Convolutional Layer in the paper "Competitive analysis for points of interest".
Authors: lishuangli(<EMAIL>)
Date: 2021/09/24
"""
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import pgl
from pgl.nn import functional as GF
from pgl.sampling.custom import subgraph
class SpatialLocalAGG(nn.Layer):
"""
Desc:
Local aggregation layer for SA-GNN.
"""
def __init__(self, input_dim, hidden_dim, transform=False, activation=None):
"""
Desc:
__init__
Args:
input_dim: The dimension size of the input tensor
hidden_dim: The dimension size of the output tensor
transform: If transform is True, then the linear transformation is employed
activation: The activation for the output
"""
super(SpatialLocalAGG, self).__init__()
self.transform = transform
if self.transform:
self.linear = nn.Linear(input_dim, hidden_dim, bias_attr=False)
self.activation = activation
def forward(self, graph, feature, norm=True):
"""
Desc:
A step of forward the layer.
Args:
graph: pgl.Graph instance
feature: The node feature matrix with shape (num_nodes, input_size)
norm: If norm is not None, then the feature will be normalized by given norm.
If norm is None and self.norm is true, then we use lapacian degree norm.
Returns:
outputs: A tensor with shape (num_nodes, output_size)
"""
norm = GF.degree_norm(graph)
if self.transform:
feature = self.linear(feature)
feature = feature * norm
output = graph.send_recv(feature, "sum")
output = output * norm
if self.activation is not None:
output = self.activation(output)
return output
class SpatialOrientedAGG(nn.Layer):
"""
Desc:
Global aggregation layer for SA-GNN.
"""
def __init__(self, input_dim, hidden_dim, num_sectors, transform=False, activation=None):
"""
Desc:
__init__
Args:
input_dim: The dimension size of the input tensor
hidden_dim: The dimension size of the output tensor
num_sectors: The number of spatial sector
transform: If transform is True, then the linear transformation is employed
activation: The activation for the output
"""
super(SpatialOrientedAGG, self).__init__()
self.num_sectors = num_sectors
linear_input_dim = hidden_dim * (num_sectors + 1) if transform else input_dim * (num_sectors + 1)
self.linear = nn.Linear(linear_input_dim, hidden_dim, bias_attr=False)
self.conv_layer = nn.LayerList()
for _ in range(num_sectors + 1):
conv = SpatialLocalAGG(input_dim, hidden_dim, transform, activation=lambda x: x)
self.conv_layer.append(conv)
def get_subgraphs(self, g):
"""
Desc:
Extract the subgraphs according to the spatial loction.
Args:
g: pgl.Graph instance
Returns:
outputs: A list of subgraphs (pgl.Graph instance)
"""
g = g.numpy()
subgraph_edge_list = [[] for _ in range(self.num_sectors + 1)]
coords = g.node_feat['coord'] # size: [num_poi, 2]
for src_node, dst_node in g.edges:
src_coord, dst_coord = coords[src_node], coords[dst_node]
rela_coord = dst_coord - src_coord
if rela_coord[0] == 0 and rela_coord[1] == 0:
sec_ind = 0
else:
rela_coord[0] += 1e-9
angle = np.arctan(rela_coord[1]/rela_coord[0])
angle = angle + np.pi * int(angle < 0)
angle = angle + np.pi * int(rela_coord[0] < 0)
sec_ind = int(angle / (np.pi / self.num_sectors))
sec_ind = min(sec_ind, self.num_sectors)
subgraph_edge_list[sec_ind] += [(src_node, dst_node)]
subgraph_list = []
for i in range(self.num_sectors + 1):
sub_g = subgraph(g, g.nodes, edges=subgraph_edge_list[i])
subgraph_list.append(sub_g.tensor())
return subgraph_list
def forward(self, graph, feature, norm=None):
"""
Desc:
A step of forward the layer.
Args:
graph: pgl.Graph instance
feature: The node feature matrix with shape (num_nodes, input_size)
norm: If norm is not None, then the feature will be normalized by given norm.
If norm is None and self.norm is true, then we use lapacian degree norm.
Returns:
outputs: A tensor with shape (num_nodes, output_size)
"""
subgraphs = self.get_subgraphs(graph)
h_list = []
for i in range(self.num_sectors + 1):
h = self.conv_layer[i](subgraphs[i], feature, norm)
h_list.append(h)
feat_h = paddle.concat(h_list, axis=-1)
feat_h = paddle.cast(feat_h, 'float32')
output = self.linear(feat_h)
return output
class SpatialAttnProp(nn.Layer):
"""
Desc:
Location-aware attentive propagation layer for SA-GNN.
"""
def __init__(self, input_dim, hidden_dim, num_heads, dropout, max_dist=10000, grid_len=100, activation=None):
super(SpatialAttnProp, self).__init__()
"""
Desc:
__init__
Args:
input_dim: The dimension size of the input tensor
hidden_dim: The dimension size of the output tensor
num_heads: The number of attention head
dropout: Dropout ratio
max_dist: The maximum distance range around each POI
grid_len: The length of segmented grid
activation: The activation for the output
"""
self.num_heads = num_heads
self.hidden_dim = hidden_dim
self.grid_len = grid_len
self.max_dist = max_dist
self.grid_num = int(max_dist / grid_len)
self.poi_fc = nn.Linear(input_dim, num_heads * hidden_dim)
self.loc_fc = nn.Linear(2 * hidden_dim, num_heads * hidden_dim)
self.x_embedding = nn.Embedding(2 * self.grid_num, hidden_dim, sparse=True)
self.y_embedding = nn.Embedding(2 * self.grid_num, hidden_dim, sparse=True)
self.weight_src = self.create_parameter(shape=[num_heads, hidden_dim])
self.weight_dst = self.create_parameter(shape=[num_heads, hidden_dim])
self.weight_loc = self.create_parameter(shape=[num_heads, hidden_dim])
self.feat_drop = nn.Dropout(p=dropout)
self.attn_drop = nn.Dropout(p=dropout)
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
self.activation = activation
def attn_send_func(self, src_feat, dst_feat, edge_feat):
"""
Desc:
Sending function for message passing
Args:
src_feat: The feature of source POI node
dst_feat: The feature of destination POI node
edge_feat: The feature of edge between two POIs
Returns:
outputs: A dict of tensor
"""
alpha = src_feat["attn"] + dst_feat["attn"] + edge_feat['attn']
alpha = self.leaky_relu(alpha)
return {"alpha": alpha, "h": src_feat["h"]}
def attn_recv_func(self, msg):
"""
Desc:
Receiving function for message passing
Args:
msg: Message dict
Returns:
outputs: A tensor with shape (num_nodes, output_size)
"""
alpha = msg.reduce_softmax(msg["alpha"])
alpha = paddle.reshape(alpha, [-1, self.num_heads, 1])
alpha = self.attn_drop(alpha)
feature = msg["h"]
feature = paddle.reshape(feature, [-1, self.num_heads, self.hidden_dim])
feature = feature * alpha
feature = paddle.reshape(feature, [-1, self.num_heads * self.hidden_dim])
feature = msg.reduce(feature, pool_type="sum")
return feature
def calculate_loc_index(self, src_coord, dst_coord):
"""
Desc:
Calculte the grid index for loaction-aware attention
Args:
src_coord: Coordinate of source POI node
dst_coord: Coordinate of target POI node
Returns:
outputs: Two tensors with shape (num_edges, 1)
"""
x, y = paddle.split(dst_coord - src_coord, num_or_sections=2, axis=1)
x_inds = paddle.cast(paddle.abs(x)/self.grid_len, 'int64')
y_inds = paddle.cast(paddle.abs(y)/self.grid_len, 'int64')
x_inds = x_inds + self.grid_num * paddle.cast(x >= 0, 'int64')
y_inds = y_inds + self.grid_num * paddle.cast(y >= 0, 'int64')
x_inds = paddle.clip(x_inds, 0, 2 * self.grid_num - 1)
y_inds = paddle.clip(y_inds, 0, 2 * self.grid_num - 1)
return x_inds, y_inds
def forward(self, graph, feature):
"""
Desc:
A step of forward the layer.
Args:
graph: pgl.Graph instance
feature: The node feature matrix with shape (num_nodes, input_size)
Returns:
outputs: A tensor with shape (num_nodes, output_size)
"""
feature = self.feat_drop(feature)
poi_feat = self.poi_fc(feature)
poi_feat = paddle.reshape(poi_feat, [-1, self.num_heads, self.hidden_dim])
# calculate location feature
src_inds, dst_inds = paddle.split(graph.edges, num_or_sections=2, axis=1)
src_coord = paddle.gather(graph.node_feat['coord'], paddle.squeeze(src_inds))
dst_coord = paddle.gather(graph.node_feat['coord'], paddle.squeeze(dst_inds))
x_inds, y_inds = self.calculate_loc_index(src_coord, dst_coord)
x_emb = self.x_embedding(x_inds)
y_emb = self.y_embedding(y_inds)
loc_feat = self.loc_fc(paddle.concat([x_emb, y_emb], axis=-1))
loc_feat = paddle.reshape(loc_feat, [-1, self.num_heads, self.hidden_dim])
attn_src = paddle.sum(poi_feat * self.weight_src, axis=-1)
attn_dst = paddle.sum(poi_feat * self.weight_dst, axis=-1)
attn_loc = paddle.sum(loc_feat * self.weight_loc, axis=-1)
msg = graph.send(self.attn_send_func,
src_feat={"attn": attn_src, "h": poi_feat},
dst_feat={"attn": attn_dst},
edge_feat={'attn': attn_loc})
rst = graph.recv(reduce_func=self.attn_recv_func, msg=msg)
if self.activation:
rst = self.activation(rst)
return rst
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13,
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2
] |
LinearRegression/tf_summary/th_hook.py | SMZCC/TF-deep-learn | 0 | 103660 | <gh_stars>0
# coding=utf-8
# date: 2018/12/20, 14:23
# name: smz
import torch as th
import numpy as np
import torch.autograd.variable as Variable
import torch.nn as nn
def demo_one():
grads = []
def hook_grad(grad_in):
grads.append(grad_in.data)
# x = Variable(th.from_numpy(np.array([1, 2]).astype(np.float32)), requires_grad=True)
x = th.tensor([1, 2], dtype=th.float32, requires_grad=True)
y = x.pow(2) + 1
z = th.mean(y, dim=0) # z = 1/2 * y || z = 1/2 * (x1^2 + x2^2) || dz/dy1 = 0.5, dz/dy2=0.5, dz/dx1 = 1 * x1, dz/dx2 = 1 * x2
y.register_hook(hook_grad)
z.backward()
print("x:{}\n".format(x))
print("z:{}\n".format(z))
print("x.grad:{}\n".format(x.grad))
print("y.grad:{}".format(y.grad))
print("z.grad:{}\n".format(z.grad))
print("grads:{}\n".format(grads))
def demo_two():
x = th.tensor([-1, 0, 1, 2], dtype=th.float32, requires_grad=True)
x = x.cuda() # 这里不能放到GPU上，否则算出来的梯度为None
relu_layer = nn.ReLU()
res = relu_layer(x)
loss = th.sum(res, dim=0)
loss.backward()
print("x:{}\n".format(x))
print("x.grad:{}\n".format(x.grad))
if __name__ == "__main__":
demo_two() | [
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apex/transformer/pipeline_parallel/schedules/__init__.py | anirudhprabhakaran3/apex | 0 | 1603163 | from apex.transformer import parallel_state
from apex.transformer.pipeline_parallel.utils import get_num_microbatches
from apex.transformer.pipeline_parallel.schedules.fwd_bwd_no_pipelining import (
forward_backward_no_pipelining,
)
from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_with_interleaving import (
_forward_backward_pipelining_with_interleaving,
)
from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_without_interleaving import (
forward_backward_pipelining_without_interleaving,
)
__all__ = [
"get_forward_backward_func",
]
class ExperimentalWarning(Warning):
pass
def get_forward_backward_func(
virtual_pipeline_model_parallel_size, pipeline_model_parallel_size,
):
if parallel_state.get_pipeline_model_parallel_world_size() > 1:
if virtual_pipeline_model_parallel_size is not None:
if get_num_microbatches() % pipeline_model_parallel_size != 0:
msg = "number of microbatches is not divisible by pipeline-parallel size when using interleaved schedule"
raise RuntimeError(msg)
forward_backward_func = _forward_backward_pipelining_with_interleaving
else:
forward_backward_func = forward_backward_pipelining_without_interleaving
else:
forward_backward_func = forward_backward_no_pipelining
return forward_backward_func
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scripts/scrape_sciencedirect_urls.py | UWPRG/BETO2020 | 4 | 1007 | """
This code is used to scrape ScienceDirect of publication urls and write them to
a text file in the current directory for later use.
"""
import selenium
from selenium import webdriver
import numpy as np
import pandas as pd
import bs4
from bs4 import BeautifulSoup
import time
from sklearn.utils import shuffle
def scrape_page(driver):
"""
This method finds all the publication result web elements on the webpage.
Parameters
----------
driver (Selenium webdriver object) : Instance of the webdriver class e.g.
webdriver.Chrome()
Returns
-------
elems (list) : A list of all scraped hrefs from the page
"""
elems = driver.find_elements_by_class_name('ResultItem')
return elems
def clean(elems):
"""
This method takes a list of scraped selenium web elements
and filters/ returns only the hrefs leading to publications.
Filtering includes removing all urls with keywords that are indicative of
non-html links.
Parameters
----------
elems (list) : The list of hrefs to be filtered
Returns
-------
urls (list) : The new list of hrefs, which should be the same as the list
displayed on gui ScienceDirect
"""
titles = []
urls = []
for elem in elems:
href_child = elem.find_element_by_css_selector('a[href]')
url = href_child.get_attribute('href')
title = href_child.text
titles.append(title)
urls.append(url)
return urls, titles
def build_url_list(gui_prefix,search_terms,journal_list):
"""
This method takes the list of journals and creates a tiple nested dictionary
containing all accessible urls to each page, in each year, for each journal,
for a given search on sciencedirect.
"""
dict1 = {}
years = np.arange(1995,2020)
for journal in journal_list:
dict2 = {}
for year in years:
dict3 = {}
for i in range(60):
url = gui_prefix + search_terms + '&show=100'+ '&articleTypes=FLA%2CREV' + '&years='+ str(year)
if i != 0:
url = url + '&offset=' + str(i) +'00'
url = url + '&pub=' + journal
dict3[i] = url
dict2[year] = dict3
dict1[journal] = dict2
return dict1
def proxify(scraped_urls,uw_prefix):
"""
This method takes a list of scraped urls and turns them into urls that
go through the UW Library proxy so that all of them are full access.
Parameters
----------
scraped_urls (list) : The list of URLs to be converted
uw_prefix (str) : The string that all URLs which go through the UW Library
Proxy start with.
Returns
-------
proxy_urls (list) : The list of converted URLs which go through UW Library
proxy
"""
proxy_urls = []
for url in scraped_urls:
sd_id = url[-17:]
newlink = uw_prefix + sd_id
if sd_id.startswith('S'):
proxy_urls.append(newlink)
return proxy_urls
def write_urls(urls,titles,file,journal,year):
"""
This method takes a list of urls and writes them to a desired text file.
Parameters
----------
urls (list) : The list of URLs to be saved.
file (file object) : The opened .txt file which will be written to.
year (str or int) : The year associated with the publication date.
Returns
-------
Does not return anything
"""
for link,title in zip(urls,titles):
line = link + ',' + title + ',' + journal + ',' + str(year)
file.write(line)
file.write('\n')
def find_pubTitle(driver,journal):
"""
This method finds the identifying number for a specific journal. This
identifying number is added to the gui query URL to ensure only publciations
from the desired journal are being found.
"""
pub_elems = driver.find_elements_by_css_selector('input[id*=publicationTitles]')
pub_names = []
for elem in pub_elems:
pub_name = elem.get_attribute("name")
if pub_name == journal:
return elem.get_attribute('id')[-6:] #returns the identifying number
#for that journal
df = pd.read_excel('elsevier_journals.xls')
df.Full_Category = df.Full_Category.str.lower() # lowercase topics for searching
df = df.drop_duplicates(subset = 'Journal_Title') # drop any duplicate journals
df = shuffle(df,random_state = 42)
# The set of default strings that will be used to sort which journals we want
journal_strings = ['chemistry','energy','molecular','atomic','chemical','biochem'
,'organic','polymer','chemical engineering','biotech','coloid']
name = df.Full_Category.str.contains # making this an easier command to type
# new dataframe full of only journals who's topic description contained the
# desired keywords
df2 = df[name('polymer') | name('chemistry') | name('energy')
| name('molecular') | name('colloid') | name('biochem')
| name('organic') | name('biotech') | name('chemical')]
journal_list = df2.Journal_Title # Series of only the journals to be searched
gui_prefix = 'https://www.sciencedirect.com/search/advanced?qs='
search_terms = 'chemistry%20OR%20molecule%20OR%20polymer%20OR%20organic'
url_dict = build_url_list(gui_prefix,search_terms,journal_list)
driver = webdriver.Chrome()
uw_prefix = 'https://www-sciencedirect-com.offcampus.lib.washington.edu/science/article/pii/'
filename = input("Input filename with .txt extension for URL storage: ")
url_counter = 0
master_list = []
file = open(filename,'a+')
for journal in journal_list:
for year in np.arange(1995,2020):
for offset in np.arange(60):
page = url_dict[journal][year][offset]
print("journal, year, offset = ",journal,year,offset)
driver.get(page)
time.sleep(2) # need sleep to load the page properly
if offset == 0: # if on page 1, we need to grab the publisher number
try: # we may be at a page which won't have the item we are looking for
pubTitles = find_pubTitle(driver,journal_list[journal_counter])
for url in url_dict[journal]:
url = url + '&pubTitles=' + pubTitles # update every url in the list
driver.get(url_dict[journal][year][0]) # reload the first page with the new url
except:
pass # if there is an exception, it means we are on the right page
scraped_elems = scrape_page(driver) # scrape the page
scraped_urls, titles = clean(scraped_elems)
proxy_urls = proxify(scraped_urls,uw_prefix) # not even sure this is needed
write_urls(proxy_urls,titles,file,journal,year)
url_counter += len(proxy_urls)
print('Total URLs saved is: ',url_counter)
if len(scraped_elems) < 100: # after content is saved, go to the next year
break # because we know this is the last page of urls for this year
file.close()
driver.quit()
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] |
services/director-v2/tests/unit/test_utils_dags.py | colinRawlings/osparc-simcore | 25 | 195250 | # pylint:disable=unused-variable
# pylint:disable=unused-argument
# pylint:disable=redefined-outer-name
# pylint:disable=protected-access
# pylint:disable=no-value-for-parameter
from typing import Any, Dict, List, Set
import networkx as nx
import pytest
from models_library.projects import Workbench
from simcore_service_director_v2.utils.dags import (
create_complete_dag,
create_minimal_computational_graph_based_on_selection,
find_computational_node_cycles,
)
def test_create_complete_dag_graph(
fake_workbench: Workbench,
fake_workbench_complete_adjacency: Dict[str, List[str]],
):
dag_graph = create_complete_dag(fake_workbench)
assert nx.is_directed_acyclic_graph(dag_graph)
assert nx.to_dict_of_lists(dag_graph) == fake_workbench_complete_adjacency
@pytest.mark.parametrize(
"subgraph, force_exp_dag, not_forced_exp_dag",
[
pytest.param(
[],
{
"3a710d8b-565c-5f46-870b-b45ebe195fc7": [
"415fefd1-d08b-53c1-adb0-16bed3a687ef"
],
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"6ede1209-b459-5735-91fc-761aa584808d": [],
},
{
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"6ede1209-b459-5735-91fc-761aa584808d": [],
},
id="no sub selection returns the full graph",
),
pytest.param(
[
"8902d36c-bc65-5b0d-848f-88aed72d7849",
"3a710d8b-565c-5f46-870b-b45ebe195fc7",
"415fefd1-d08b-53c1-adb0-16bed3a687ef",
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c",
"6ede1209-b459-5735-91fc-761aa584808d",
"82d7a25c-18d4-44dc-a997-e5c9a745e7fd",
],
{
"3a710d8b-565c-5f46-870b-b45ebe195fc7": [
"415fefd1-d08b-53c1-adb0-16bed3a687ef"
],
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"6ede1209-b459-5735-91fc-761aa584808d": [],
},
{
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"6ede1209-b459-5735-91fc-761aa584808d": [],
},
id="all nodes selected returns the full graph",
),
pytest.param(
[
"8902d36c-bc65-5b0d-848f-88aed72d7849", # file-picker
"3a710d8b-565c-5f46-870b-b45ebe195fc7", # sleeper 1
],
{
"3a710d8b-565c-5f46-870b-b45ebe195fc7": [],
},
{},
id="nodes 0 and 1",
),
pytest.param(
[
"8902d36c-bc65-5b0d-848f-88aed72d7849", # file-picker
"415fefd1-d08b-53c1-adb0-16bed3a687ef", # sleeper 2
],
{
"3a710d8b-565c-5f46-870b-b45ebe195fc7": [
"415fefd1-d08b-53c1-adb0-16bed3a687ef"
],
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [],
},
{
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [],
},
id="node 0 and 2",
),
pytest.param(
[
"415fefd1-d08b-53c1-adb0-16bed3a687ef", # sleeper 2
"6ede1209-b459-5735-91fc-761aa584808d", # sleeper 4
],
{
"3a710d8b-565c-5f46-870b-b45ebe195fc7": [
"415fefd1-d08b-53c1-adb0-16bed3a687ef"
],
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"6ede1209-b459-5735-91fc-761aa584808d": [],
},
{
"415fefd1-d08b-53c1-adb0-16bed3a687ef": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"e1e2ea96-ce8f-5abc-8712-b8ed312a782c": [
"6ede1209-b459-5735-91fc-761aa584808d"
],
"6ede1209-b459-5735-91fc-761aa584808d": [],
},
id="node 2 and 4",
),
],
)
async def test_create_minimal_graph(
loop,
fake_workbench: Workbench,
subgraph: Set[str],
force_exp_dag: Dict[str, List[str]],
not_forced_exp_dag: Dict[str, List[str]],
):
complete_dag: nx.DiGraph = create_complete_dag(fake_workbench)
# everything is outdated in that case
reduced_dag: nx.DiGraph = (
await create_minimal_computational_graph_based_on_selection(
complete_dag, subgraph, force_restart=True
)
)
assert nx.to_dict_of_lists(reduced_dag) == force_exp_dag
# only the outdated stuff shall be found here
reduced_dag_with_auto_detect: nx.DiGraph = (
await create_minimal_computational_graph_based_on_selection(
complete_dag, subgraph, force_restart=False
)
)
assert nx.to_dict_of_lists(reduced_dag_with_auto_detect) == not_forced_exp_dag
@pytest.mark.parametrize(
"dag_adjacency, node_keys, exp_cycles",
[
pytest.param({}, {}, [], id="empty dag exp no cycles"),
pytest.param(
{"node_1": ["node_2", "node_3"], "node_2": ["node_3"], "node_3": []},
{
"node_1": {"key": "simcore/services/comp/fake"},
"node_2": {"key": "simcore/services/comp/fake"},
"node_3": {"key": "simcore/services/comp/fake"},
},
[],
id="cycle less dag expect no cycle",
),
pytest.param(
{
"node_1": ["node_2"],
"node_2": ["node_3"],
"node_3": ["node_1"],
},
{
"node_1": {"key": "simcore/services/comp/fake"},
"node_2": {"key": "simcore/services/comp/fake"},
"node_3": {"key": "simcore/services/comp/fake"},
},
[["node_1", "node_2", "node_3"]],
id="dag with 1 cycle",
),
pytest.param(
{
"node_1": ["node_2"],
"node_2": ["node_3", "node_1"],
"node_3": ["node_1"],
},
{
"node_1": {"key": "simcore/services/comp/fake"},
"node_2": {"key": "simcore/services/comp/fake"},
"node_3": {"key": "simcore/services/comp/fake"},
},
[["node_1", "node_2", "node_3"], ["node_1", "node_2"]],
id="dag with 2 cycles",
),
pytest.param(
{
"node_1": ["node_2"],
"node_2": ["node_3"],
"node_3": ["node_1"],
},
{
"node_1": {"key": "simcore/services/comp/fake"},
"node_2": {"key": "simcore/services/comp/fake"},
"node_3": {"key": "simcore/services/dynamic/fake"},
},
[["node_1", "node_2", "node_3"]],
id="dag with 1 cycle and 1 dynamic services should fail",
),
pytest.param(
{
"node_1": ["node_2"],
"node_2": ["node_3"],
"node_3": ["node_1"],
},
{
"node_1": {"key": "simcore/services/dynamic/fake"},
"node_2": {"key": "simcore/services/comp/fake"},
"node_3": {"key": "simcore/services/dynamic/fake"},
},
[["node_1", "node_2", "node_3"]],
id="dag with 1 cycle and 2 dynamic services should fail",
),
pytest.param(
{
"node_1": ["node_2"],
"node_2": ["node_3"],
"node_3": ["node_1"],
},
{
"node_1": {"key": "simcore/services/dynamic/fake"},
"node_2": {"key": "simcore/services/dynamic/fake"},
"node_3": {"key": "simcore/services/dynamic/fake"},
},
[],
id="dag with 1 cycle and 3 dynamic services should be ok",
),
],
)
def test_find_computational_node_cycles(
dag_adjacency: Dict[str, List[str]],
node_keys: Dict[str, Dict[str, Any]],
exp_cycles: List[List[str]],
):
dag = nx.from_dict_of_lists(dag_adjacency, create_using=nx.DiGraph)
# add node attributes
for key, values in node_keys.items():
for attr, attr_value in values.items():
dag.nodes[key][attr] = attr_value
list_of_cycles = find_computational_node_cycles(dag)
assert len(list_of_cycles) == len(exp_cycles), "expected number of cycles not found"
for cycle in list_of_cycles:
assert sorted(cycle) in exp_cycles
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tests/e2e/process_chm15k/tests.py | actris-cloudnet/data-processing | 0 | 15981 | <reponame>actris-cloudnet/data-processing
import netCDF4
from os import path
from test_utils.utils import count_strings, read_log_file
import pytest
SCRIPT_PATH = path.dirname(path.realpath(__file__))
class TestChm15kProcessing:
product = 'lidar'
instrument = 'chm15k'
@pytest.fixture(autouse=True)
def _fetch_params(self, params):
self.full_path = params['full_path']
@pytest.mark.first_run
def test_that_refuses_to_process_without_reprocess_flag(self):
with pytest.raises(OSError):
netCDF4.Dataset(self.full_path)
@pytest.mark.first_run
def test_that_calls_metadata_api_only_once(self):
data = read_log_file(SCRIPT_PATH)
assert len(data) == 1
assert '"GET /api/files?dateFrom=2020-10-22&dateTo=2020-10-22&site=bucharest&developer=True' \
'&product=lidar&showLegacy=True HTTP/1.1" 200 -' in data[0]
@pytest.mark.first_run
def test_that_does_not_call_pid_api(self):
f = open(f'{SCRIPT_PATH}/pid.log')
data = f.readlines()
assert len(data) == 0
@pytest.mark.reprocess
def test_attributes(self):
nc = netCDF4.Dataset(self.full_path)
assert nc.year == "2020"
assert nc.month == "10"
assert nc.day == "22"
assert nc.cloudnet_file_type == self.product
assert hasattr(nc, 'pid') is True
nc.close()
@pytest.mark.reprocess
def test_that_calls_pid_api(self):
f = open(f'{SCRIPT_PATH}/pid.log')
data = f.readlines()
assert len(data) == 1
assert 'POST /pid/ HTTP/1.1" 200 -' in data[0]
@pytest.mark.reprocess
def test_that_calls_metadata_api(self):
data = read_log_file(SCRIPT_PATH)
n_raw_files = 3
n_img = 2
n_gets = 5
n_puts = n_img + 2
n_posts = n_raw_files
assert len(data) == n_gets + n_puts + n_posts
# Check product status
assert '"GET /api/files?dateFrom=2020-10-22&dateTo=2020-10-22&site=bucharest&developer=True' \
'&product=lidar&showLegacy=True HTTP/1.1" 200 -' in data[0]
# Two API calls the get instrument status...
# GET raw data
assert '"GET /upload-metadata?dateFrom=2020-10-22&dateTo=2020-10-22&site=bucharest' \
'&developer=True&instrument=chm15k&status%5B%5D=uploaded&status%5B%5D=processed HTTP/1.1" 200 -' in data[3]
# GET calibration
assert '"GET /api/calibration?site=bucharest&date=2020-10-22&instrument=chm15k HTTP/1.1" 200 -' in data[4]
# PUT file
assert '"PUT /files/20201022_bucharest_chm15k.nc HTTP/1.1"' in data[5]
# PUT images
img_put = '"PUT /visualizations/20201022_bucharest_chm15k-'
assert count_strings(data, img_put) == n_img
# POST metadata
file_put = '"POST /upload-metadata HTTP/1.1" 200 -'
assert count_strings(data, file_put) == n_raw_files
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src/Server/backend/GnC/migrations/0010_goals_cascaded_goal.py | tejpratap545/denselight_system | 0 | 178791 | # Generated by Django 3.1.2 on 2021-04-08 17:47
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('GnC', '0009_auto_20210407_1742'),
]
operations = [
migrations.AddField(
model_name='goals',
name='cascaded_goal',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='GnC.cascadedgoals'),
),
]
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525,
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29906,
29900,
29906,
29896,
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29918,
29896,
29955,
29946,
29906,
5477,
13,
1678,
4514,
13,
13,
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6151,
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1338,
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10353,
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4514,
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2
] |
setup.py | psjay/PyLocache | 4 | 112767 | <reponame>psjay/PyLocache
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
from setuptools import setup, find_packages
def read_file(*path):
base_dir = os.path.dirname(__file__)
file_path = (base_dir, ) + tuple(path)
return open(os.path.join(*file_path)).read()
setup(
name="PyLocache",
url="https://github.com/psjay/PyLocache",
version="0.0.4",
license='WTFPL',
description="PyLocache is a Python implementation of LRU local cache.",
long_description=(
read_file("README.rst") + "\n\n" +
"Change History\n" +
"==============\n\n" +
read_file("CHANGES.rst")),
author='psjay',
author_email="<EMAIL>",
include_package_data=True,
zip_safe=False,
packages=find_packages(),
install_requires=[],
test_suite='nose.collector',
setup_requires=['nose>=1.0'],
classifiers=[
"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.3",
"Operating System :: OS Independent",
"Topic :: Software Development :: Libraries",
"Topic :: Utilities",
],
)
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Singleton.py | cxwithyxy/PythonSingleton | 0 | 35418 | #coding:utf-8
import threading
class Singleton(object):
def __new__(cls, *args, **kwargs):
lock = threading.Lock()
lock.acquire()
if not hasattr(cls, "_instance"):
cls._instance = object.__new__(cls)
cls._instance.__Singleton_Init__(*args, **kwargs)
lock.release()
return cls._instance
def __Singleton_Init__(self):
raise RuntimeError("__Singleton_Init__ must be overwritten") | [
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relevanceai/client/client.py | RelevanceAI/RelevanceAI | 21 | 170143 | <gh_stars>10-100
"""Relevance AI's base Client class - primarily used to login and access the Dataset class or ClusterOps class. The recomended way to log in is using: .. code-block:: from relevanceai import Client client = Client() client.list_datasets()
If the user already knows their project and API key, they can
log in this way:
.. code-block::
from relevanceai import Client
project = ""
api_key = ""
client = Client(project=project, api_key=api_key, firebase_uid=firebase_uid)
client.list_datasets()
If you need to change your token, simply run:
.. code-block::
from relevanceai import Client
client = Client(token="...")
"""
import os
import re
from tkinter import Label
import uuid
import getpass
import pandas as pd
import analytics
from base64 import b64decode as decode
from typing import Dict, List, Optional, Union
from relevanceai._api import APIClient
from doc_utils.doc_utils import DocUtils
from relevanceai.client.helpers import *
from relevanceai.constants.errors import APIError
from relevanceai.constants.messages import Messages
from relevanceai.dataset import Dataset
from relevanceai.utils.decorators.analytics import track, identify
from relevanceai.utils.decorators.version import beta, added
from relevanceai.utils.config_mixin import ConfigMixin
from relevanceai.client.cache import CacheMixin
from relevanceai.client.operators import Operators
class Client(APIClient, ConfigMixin, CacheMixin, Operators):
def __init__(
self,
token: Optional[str] = None,
authenticate: bool = True,
):
"""
Initialize the client
Parameters
-------------
token: str
You can paste the token here if things need to be refreshed
enable_request_logging: bool, str
Whether to print out the requests made, if "full" the body will be printed as well.
"""
if token is None:
token = auth()
print(
"If you require non-interactive token authentication, you can set token=..."
)
self.token = token
self.credentials = process_token(token)
super().__init__(self.credentials)
if os.getenv("DEBUG_REQUESTS") == "TRUE":
print(f"logging requests to: {self.request_logging_fpath}")
# Eventually the following should be accessed directly from
# self.credentials, but keep for now.
(
self.project,
self.api_key,
self.region,
self.firebase_uid,
) = self.credentials.split_token()
self.base_url = region_to_url(self.region)
self.base_ingest_url = region_to_url(self.region)
try:
self._set_mixpanel_write_key()
except Exception as e:
pass
self._identify()
# used to debug
if authenticate:
if self.check_auth():
print(Messages.WELCOME_MESSAGE.format(self.project))
else:
raise APIError(Messages.FAIL_MESSAGE)
# Add non breaking changes to support old ways of inserting documents and csv
# self.insert_documents = Dataset(
# credentials=self.credentials,
# dataset_id="",
# )._insert_documents
# self.insert_csv = Dataset(
# credentials=self.credentials,
# dataset_id="",
# )._insert_csv
@identify
def _identify(self):
return
def _set_mixpanel_write_key(self):
analytics.write_key = decode(self.mixpanel_write_key).decode("utf-8")
def _get_token(self):
# TODO: either use cache or keyring package
token = getpass.getpass(f"Activation token:")
return token
def check_auth(self):
print(f"Connecting to {self.region}...")
return self.list_datasets()
@track
def create_dataset(self, dataset_id: str, schema: Optional[Dict] = None):
"""
A dataset can store documents to be searched, retrieved, filtered and aggregated (similar to Collections in MongoDB, Tables in SQL, Indexes in ElasticSearch).
A powerful and core feature of VecDB is that you can store both your metadata and vectors in the same document. When specifying the schema of a dataset and inserting your own vector use the suffix (ends with) "_vector_" for the field name, and specify the length of the vector in dataset_schema. \n
For example:
.. code-block::
{
"product_image_vector_": 1024,
"product_text_description_vector_" : 128
}
These are the field types supported in our datasets: ["text", "numeric", "date", "dict", "chunks", "vector", "chunkvector"]. \n
For example:
.. code-block::
{
"product_text_description" : "text",
"price" : "numeric",
"created_date" : "date",
"product_texts_chunk_": "chunks",
"product_text_chunkvector_" : 1024
}
You don't have to specify the schema of every single field when creating a dataset, as VecDB will automatically detect the appropriate data type for each field (vectors will be automatically identified by its "_vector_" suffix). Infact you also don't always have to use this endpoint to create a dataset as /datasets/bulk_insert will infer and create the dataset and schema as you insert new documents. \n
Note:
- A dataset name/id can only contain undercase letters, dash, underscore and numbers.
- "_id" is reserved as the key and id of a document.
- Once a schema is set for a dataset it cannot be altered. If it has to be altered, utlise the copy dataset endpoint.
For more information about vectors check out the 'Vectorizing' section, services.search.vector or out blog at https://relevance.ai/blog. For more information about chunks and chunk vectors check out datasets.search.chunk.
Parameters
----------
dataset_id: str
The unique name of your dataset
schema : dict
Schema for specifying the field that are vectors and its length
Example
----------
.. code-block::
from relevanceai import Client
client = Client()
client.create_dataset("sample_dataset_id")
"""
schema = {} if schema is None else schema
return self.datasets.create(dataset_id, schema=schema)
@track
def list_datasets(self, verbose=False):
"""List Datasets
Example
----------
.. code-block::
from relevanceai import Client
client = Client()
client.list_datasets()
"""
if verbose:
self.print_dashboard_message(
"You can view all your datasets at https://cloud.relevance.ai/datasets/"
)
datasets = self.datasets.list()
datasets["datasets"] = sorted(datasets["datasets"])
return datasets
@track
def delete_dataset(self, dataset_id):
"""
Delete a dataset
Parameters
------------
dataset_id: str
The ID of a dataset
Example
---------
.. code-block::
from relevanceai import Client
client = Client()
client.delete_dataset("sample_dataset_id")
"""
return self.datasets.delete(dataset_id)
@track
def Dataset(
self,
dataset_id: str,
fields: Optional[List[str]] = None,
image_fields: Optional[List[str]] = None,
audio_fields: Optional[List[str]] = None,
highlight_fields: Optional[Dict[str, List]] = None,
text_fields: Optional[List[str]] = None,
):
fields = [] if fields is None else fields
image_fields = [] if image_fields is None else image_fields
audio_fields = [] if audio_fields is None else audio_fields
highlight_fields = {} if highlight_fields is None else highlight_fields
text_fields = [] if text_fields is None else text_fields
regex_check = re.search("^[a-z\d._-]+$", dataset_id)
if regex_check is not None:
self.create_dataset(dataset_id)
else:
raise ValueError(
"dataset_id must contain only a combination of lowercase and/or '_' and/or '-' and/or '.'"
)
return Dataset(
credentials=self.credentials,
dataset_id=dataset_id,
fields=fields,
image_fields=image_fields,
audio_fields=audio_fields,
highlight_fields=highlight_fields,
text_fields=text_fields,
)
### Clustering
@track
def ClusterOps(
self,
dataset_id: str,
vector_fields: list,
alias: str,
model=None,
**kwargs,
):
from relevanceai.operations_new.cluster.ops import ClusterOps
return ClusterOps(
credentials=self.credentials,
dataset_id=dataset_id,
vector_fields=vector_fields,
alias=alias,
model=model,
**kwargs,
)
@track
def SubClusterOps(
self,
credentials,
alias,
dataset,
model,
vector_fields: list,
parent_field: str,
):
"""
Sub Cluster Ops.
"""
from relevanceai.operations.cluster.sub import SubClusterOps
return SubClusterOps(
credentials=self.credentials,
alias=alias,
dataset=dataset,
model=model,
vector_fields=vector_fields,
parent_field=parent_field,
)
def _set_logger_to_verbose(self):
# Use this for debugging
self.config["logging.logging_level"] = "INFO"
@track
def send_dataset(
self,
dataset_id: str,
receiver_project: str,
receiver_api_key: str,
):
"""
Send an individual a dataset. For this, you must know their API key.
Parameters
-----------
dataset_id: str
The name of the dataset
receiver_project: str
The project name that will receive the dataset
receiver_api_key: str
The project API key that will receive the dataset
Example
--------
.. code-block::
client = Client()
client.send_dataset(
dataset_id="research",
receiver_project="...",
receiver_api_key="..."
)
"""
return self.admin.send_dataset(
dataset_id=dataset_id,
receiver_project=receiver_project,
receiver_api_key=receiver_api_key,
)
@track
def receive_dataset(
self,
dataset_id: str,
sender_project: str,
sender_api_key: str,
):
"""
Recieve an individual a dataset.
Example
--------
>>> client = Client()
>>> client.admin.receive_dataset(
dataset_id="research",
sender_project="...",
sender_api_key="..."
)
Parameters
-----------
dataset_id: str
The name of the dataset
sender_project: str
The project name that will send the dataset
sender_api_key: str
The project API key that will send the dataset
"""
return self.admin.receive_dataset(
dataset_id=dataset_id,
sender_project=sender_project,
sender_api_key=sender_api_key,
)
@track
def clone_dataset(
self,
source_dataset_id: str,
new_dataset_id: Optional[str] = None,
source_project: Optional[str] = None,
source_api_key: Optional[str] = None,
project: Optional[str] = None,
api_key: Optional[str] = None,
):
# To do this should be cloning a dataset in a project
"""
Clone a dataset from another user's projects into your project.
Parameters
----------
dataset_id:
The dataset to copy
source_dataset_id:
The original dataset
source_project:
The original project to copy from
source_api_key:
The original API key of the project
project:
The original project
api_key:
The original API key
Example
-----------
.. code-block::
client = Client()
client.clone_dataset(
dataset_id="research",
source_project="...",
source_api_key="..."
)
"""
if source_api_key is None:
source_api_key = self.api_key
source_project = self.project
if new_dataset_id is None:
new_dataset_id = source_dataset_id
return self.admin.copy_foreign_dataset(
dataset_id=new_dataset_id,
source_dataset_id=source_dataset_id,
source_project=source_project,
source_api_key=source_api_key,
project=project,
api_key=api_key,
)
@property
def references(self):
from relevanceai.__init__ import __version__
REFERENCE_URL = f"https://relevanceai.readthedocs.io/en/{__version__}/"
MESSAGE = f"You can find your references here {REFERENCE_URL}."
print(MESSAGE)
docs = references
def search_app(self, dataset_id: Optional[str] = None):
if dataset_id is not None:
self.print_search_dashboard_url(dataset_id)
elif hasattr(self, "_dataset_id"):
self.print_search_dashboard_url(self._dataset_id)
elif hasattr(self, "dataset_id"):
self.print_search_dashboard_url(self.dataset_id)
else:
print("You can build your search app at https://cloud.relevance.ai")
@added(version="1.1.3")
@track
def search_datasets(self, query: str):
"""
Search through your datasets.
"""
return [x for x in self.list_datasets()["datasets"] if query in x]
@added(version="2.1.3")
@beta
def list_cluster_reports(self):
"""
List all cluster reports.
.. code-block::
from relevanceai import Client
client = Client()
client.list_cluster_reports()
"""
return pd.DataFrame(self.reports.clusters.list()["results"])
@added(version="2.1.3")
@beta
@track
def delete_cluster_report(self, cluster_report_id: str):
"""
Delete Cluster Report
.. code-block::
from relevanceai import Client
client = Client()
client.delete_cluster_report("cluster_id_goes_here")
"""
return self.reports.clusters.delete(cluster_report_id)
@added(version="2.1.3")
@beta
@track
def store_cluster_report(self, report_name: str, report: dict):
"""
Store the cluster data.
.. code-block::
from relevanceai import Client
client = Client()
client.store_cluster_report("sample", {"value": 3})
"""
response: dict = self.reports.clusters.create(
name=report_name, report=self.json_encoder(report)
)
print(
f"You can now access your report at https://cloud.relevance.ai/report/cluster/{self.region}/{response['_id']}"
)
return response
def disable_analytics_tracking(self):
"""Disable analytics tracking if you would prefer not to send usage
data to improve the product. Analytics allows us to improve your experience
by examining the most popular flows, dedicating more resources to popular
product features and improve user experience.
"""
self.config["mixpanel.is_tracking_enabled"] = False
| [
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268,
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6571,
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13,
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6528,
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3198,
353,
337,
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4478,
703,
29985,
29961,
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29899,
29920,
29905,
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3032,
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10062,
29938,
613,
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333,
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565,
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333,
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13,
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12020,
7865,
2392,
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13,
18884,
376,
24713,
29918,
333,
1818,
1712,
871,
263,
10296,
310,
5224,
4878,
322,
29914,
272,
22868,
29915,
322,
29914,
272,
17411,
29915,
322,
29914,
272,
525,
6169,
29908,
13,
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1723,
13,
13,
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736,
13373,
24541,
29898,
13,
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16140,
29922,
1311,
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11944,
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13,
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8783,
29918,
333,
29922,
24713,
29918,
333,
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13,
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4235,
29922,
9621,
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13,
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1967,
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9621,
29922,
3027,
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9621,
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13,
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10348,
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29922,
18494,
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9621,
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13,
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12141,
29918,
9621,
29922,
28970,
29918,
9621,
29892,
13,
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1426,
29918,
9621,
29922,
726,
29918,
9621,
29892,
13,
4706,
1723,
13,
13,
1678,
835,
2233,
504,
3241,
13,
13,
1678,
732,
11294,
13,
1678,
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2233,
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29949,
567,
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13,
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4608,
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9621,
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1051,
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1904,
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8516,
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3579,
19290,
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13,
268,
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13,
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515,
29527,
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1794,
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3372,
800,
29918,
1482,
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19594,
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3554,
1053,
2233,
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13,
13,
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29949,
567,
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13,
9651,
16140,
29922,
1311,
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11944,
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13,
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8783,
29918,
333,
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24713,
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333,
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13,
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4608,
29918,
9621,
29922,
8111,
29918,
9621,
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13,
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13995,
29922,
19973,
29892,
13,
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1904,
29922,
4299,
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13,
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3579,
19290,
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13,
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1723,
13,
13,
1678,
732,
11294,
13,
1678,
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3323,
6821,
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29949,
567,
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13,
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1583,
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16140,
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13995,
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8783,
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13,
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1904,
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13,
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4608,
29918,
9621,
29901,
1051,
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13,
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3847,
29918,
2671,
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851,
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13,
268,
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13,
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9995,
13,
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3323,
2233,
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438,
567,
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13,
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9995,
13,
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515,
29527,
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1794,
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3372,
800,
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19594,
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1491,
1053,
3323,
6821,
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567,
13,
13,
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6821,
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29949,
567,
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13,
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16140,
29922,
1311,
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11944,
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13,
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13995,
29922,
19973,
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13,
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8783,
29922,
24713,
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13,
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1904,
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4299,
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13,
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4608,
29918,
9621,
29922,
8111,
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9621,
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13,
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3847,
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2671,
29922,
3560,
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2671,
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13,
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1723,
13,
13,
1678,
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903,
842,
29918,
21707,
29918,
517,
29918,
369,
15828,
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1311,
1125,
13,
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396,
4803,
445,
363,
13490,
13,
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1583,
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2917,
3366,
21027,
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21027,
29918,
5563,
3108,
353,
376,
11690,
29908,
13,
13,
1678,
732,
11294,
13,
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3638,
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24713,
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13,
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1583,
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13,
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333,
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13,
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19870,
29918,
4836,
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851,
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13,
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19870,
29918,
2754,
29918,
1989,
29901,
851,
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13,
268,
1125,
13,
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9995,
13,
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15076,
385,
5375,
263,
8783,
29889,
1152,
445,
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366,
1818,
1073,
1009,
3450,
1820,
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13,
13,
13,
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12662,
2699,
13,
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448,
28400,
13,
13,
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8783,
29918,
333,
29901,
851,
13,
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450,
1024,
310,
278,
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13,
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19870,
29918,
4836,
29901,
851,
13,
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450,
2060,
1024,
393,
674,
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278,
8783,
13,
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19870,
29918,
2754,
29918,
1989,
29901,
851,
13,
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450,
2060,
3450,
1820,
393,
674,
7150,
278,
8783,
13,
13,
13,
4706,
8741,
13,
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448,
26589,
13,
13,
4706,
6317,
775,
29899,
1271,
1057,
13,
13,
9651,
3132,
353,
12477,
580,
13,
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3132,
29889,
6717,
29918,
24713,
29898,
13,
18884,
8783,
29918,
333,
543,
690,
2842,
613,
13,
18884,
19870,
29918,
4836,
543,
856,
613,
13,
18884,
19870,
29918,
2754,
29918,
1989,
543,
17794,
13,
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1723,
13,
13,
4706,
9995,
13,
4706,
736,
1583,
29889,
6406,
29889,
6717,
29918,
24713,
29898,
13,
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8783,
29918,
333,
29922,
24713,
29918,
333,
29892,
13,
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19870,
29918,
4836,
29922,
13556,
2147,
29918,
4836,
29892,
13,
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19870,
29918,
2754,
29918,
1989,
29922,
13556,
2147,
29918,
2754,
29918,
1989,
29892,
13,
4706,
1723,
13,
13,
1678,
732,
11294,
13,
1678,
822,
7150,
29918,
24713,
29898,
13,
4706,
1583,
29892,
13,
4706,
8783,
29918,
333,
29901,
851,
29892,
13,
4706,
10004,
29918,
4836,
29901,
851,
29892,
13,
4706,
10004,
29918,
2754,
29918,
1989,
29901,
851,
29892,
13,
268,
1125,
13,
4706,
9995,
13,
4706,
3599,
2418,
385,
5375,
263,
8783,
29889,
13,
13,
4706,
8741,
13,
4706,
448,
26589,
13,
4706,
8653,
3132,
353,
12477,
580,
13,
4706,
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3132,
29889,
6406,
29889,
13556,
573,
29918,
24713,
29898,
13,
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8783,
29918,
333,
543,
690,
2842,
613,
13,
9651,
10004,
29918,
4836,
543,
856,
613,
13,
9651,
10004,
29918,
2754,
29918,
1989,
543,
17794,
13,
4706,
1723,
13,
13,
4706,
12662,
2699,
13,
4706,
448,
28400,
13,
13,
4706,
8783,
29918,
333,
29901,
851,
13,
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450,
1024,
310,
278,
8783,
13,
4706,
10004,
29918,
4836,
29901,
851,
13,
9651,
450,
2060,
1024,
393,
674,
3638,
278,
8783,
13,
4706,
10004,
29918,
2754,
29918,
1989,
29901,
851,
13,
9651,
450,
2060,
3450,
1820,
393,
674,
3638,
278,
8783,
13,
13,
4706,
9995,
13,
4706,
736,
1583,
29889,
6406,
29889,
13556,
573,
29918,
24713,
29898,
13,
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8783,
29918,
333,
29922,
24713,
29918,
333,
29892,
13,
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10004,
29918,
4836,
29922,
15452,
29918,
4836,
29892,
13,
9651,
10004,
29918,
2754,
29918,
1989,
29922,
15452,
29918,
2754,
29918,
1989,
29892,
13,
4706,
1723,
13,
13,
1678,
732,
11294,
13,
1678,
822,
17432,
29918,
24713,
29898,
13,
4706,
1583,
29892,
13,
4706,
2752,
29918,
24713,
29918,
333,
29901,
851,
29892,
13,
4706,
716,
29918,
24713,
29918,
333,
29901,
28379,
29961,
710,
29962,
353,
6213,
29892,
13,
4706,
2752,
29918,
4836,
29901,
28379,
29961,
710,
29962,
353,
6213,
29892,
13,
4706,
2752,
29918,
2754,
29918,
1989,
29901,
28379,
29961,
710,
29962,
353,
6213,
29892,
13,
4706,
2060,
29901,
28379,
29961,
710,
29962,
353,
6213,
29892,
13,
4706,
7882,
29918,
1989,
29901,
28379,
29961,
710,
29962,
353,
6213,
29892,
13,
268,
1125,
13,
4706,
396,
1763,
437,
445,
881,
367,
1067,
28259,
263,
8783,
297,
263,
2060,
13,
4706,
9995,
13,
4706,
2233,
650,
263,
8783,
515,
1790,
1404,
29915,
29879,
9279,
964,
596,
2060,
29889,
13,
13,
4706,
12662,
2699,
13,
4706,
448,
1378,
29899,
13,
4706,
8783,
29918,
333,
29901,
13,
9651,
450,
8783,
304,
3509,
13,
4706,
2752,
29918,
24713,
29918,
333,
29901,
13,
9651,
450,
2441,
8783,
13,
4706,
2752,
29918,
4836,
29901,
13,
9651,
450,
2441,
2060,
304,
3509,
515,
13,
4706,
2752,
29918,
2754,
29918,
1989,
29901,
13,
9651,
450,
2441,
3450,
1820,
310,
278,
2060,
13,
4706,
2060,
29901,
13,
9651,
450,
2441,
2060,
13,
4706,
7882,
29918,
1989,
29901,
13,
9651,
450,
2441,
3450,
1820,
13,
13,
4706,
8741,
13,
4706,
448,
28400,
13,
13,
4706,
6317,
775,
29899,
1271,
1057,
13,
13,
9651,
3132,
353,
12477,
580,
13,
9651,
3132,
29889,
16513,
29918,
24713,
29898,
13,
18884,
8783,
29918,
333,
543,
690,
2842,
613,
13,
18884,
2752,
29918,
4836,
543,
856,
613,
13,
18884,
2752,
29918,
2754,
29918,
1989,
543,
17794,
13,
9651,
1723,
13,
13,
4706,
9995,
13,
4706,
565,
2752,
29918,
2754,
29918,
1989,
338,
6213,
29901,
13,
9651,
2752,
29918,
2754,
29918,
1989,
353,
1583,
29889,
2754,
29918,
1989,
13,
9651,
2752,
29918,
4836,
353,
1583,
29889,
4836,
13,
13,
4706,
565,
716,
29918,
24713,
29918,
333,
338,
6213,
29901,
13,
9651,
716,
29918,
24713,
29918,
333,
353,
2752,
29918,
24713,
29918,
333,
13,
13,
4706,
736,
1583,
29889,
6406,
29889,
8552,
29918,
1079,
647,
29918,
24713,
29898,
13,
9651,
8783,
29918,
333,
29922,
1482,
29918,
24713,
29918,
333,
29892,
13,
9651,
2752,
29918,
24713,
29918,
333,
29922,
4993,
29918,
24713,
29918,
333,
29892,
13,
9651,
2752,
29918,
4836,
29922,
4993,
29918,
4836,
29892,
13,
9651,
2752,
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] |
trainer.py | Karan0110/digit-recognition | 1 | 92279 | <filename>trainer.py<gh_stars>1-10
import pickle
import numpy as np
from mnist import MNIST
from neural_network import NeuralNetwork
def one_hot_encode(x, num_categories):
encoding = np.zeros(num_categories)
encoding[x] = 1.
return encoding
def one_hot_decode(x):
return x.argmax()
def get_data(train_test_split=0.9):
mndata = MNIST('./MNIST')
X, y = mndata.load_training()
#converts images into flat array with brightness in [0, 1], instead of [0, 255]
X = [np.array(i).reshape((-1,)) / 255 for i in X]
#converts digit into one-hot encoding
y = [one_hot_encode(i, 10) for i in y[:-1]]
train_test_divide = int(len(X) * train_test_split)
X_train, X_test = X[:train_test_divide], X[train_test_divide:]
y_train, y_test = y[:train_test_divide], y[train_test_divide:]
return X_train, X_test, y_train, y_test
def get_prediction(net, inputs):
outputs = net.forward_propagate(inputs)
return one_hot_decode(outputs)
def get_accuracy(net, X, y):
correct = 0
for inputs, expected_outputs in zip(X, y):
prediction = get_prediction(net, inputs)
if prediction == one_hot_decode(expected_outputs):
correct += 1
return correct / len(X)
np.random.seed(1)
net = NeuralNetwork((28 ** 2, 16, 16, 10))
print("Getting data...")
X_train, X_test, y_train, y_test = get_data()
print("Training...")
net.fit(X_train, y_train, minibatch_size=10, num_epochs=50000, learning_rate=.5, reporting=100)
print("Saving neural network...")
with open('network.ann', 'wb') as f:
pickle.dump(net, f)
print("Finding accuracy...")
training_accuracy = get_accuracy(net, X_train, y_train)
testing_accuracy = get_accuracy(net, X_test, y_test)
print("Accuracy for training data: {}%".format(training_accuracy * 100))
print("Accuracy for testing data: {}%".format(testing_accuracy * 100))
#92-93%
#92-93%
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2
] |
setup.py | perslev/sleep-utils | 1 | 97102 | <filename>setup.py
from setuptools import setup, find_packages
with open('README.md') as readme_file:
readme = readme_file.read()
with open("requirements.txt") as req_file:
requirements = list(filter(None, req_file.read().split("\n")))
__version__ = None
with open("psg_utils/version.py") as version_file:
exec(version_file.read())
if __version__ is None:
raise ValueError("Did not find __version__ in version.py file.")
setup(
name='psg-utils',
version=__version__,
description='A collection of Python utility classes and functions for loading and processing Polysomnography (PSG) sleep study data.',
long_description=readme,
long_description_content_type='text/markdown',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/perslev/psg-utils',
packages=find_packages(),
package_dir={'psg_utils':
'psg_utils'},
include_package_data=True,
install_requires=requirements,
classifiers=['Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11']
)
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tests/functional/tabloid/test_optimizer_index_navigation.py | FirebirdSQL/firebird-qa | 1 | 146885 | #coding:utf-8
#
# id: functional.tabloid.optimizer_index_navigation
# title: Check that optimizer takes in account presense of index and does navigation instead of external sort.
# decription:
# Verified commit: https://github.com/FirebirdSQL/firebird/actions/runs/176006556
# Source message to dimitr: 20.07.2020 20:00.
#
# Checked on 3.0.7.33340 and 4.0.0.2114 (intermediate build with timestamp 20.07.2020 17:45)
#
# tracker_id:
# min_versions: ['3.0']
# versions: 3.0
# qmid: None
import pytest
from firebird.qa import db_factory, isql_act, Action
# version: 3.0
# resources: None
substitutions_1 = []
init_script_1 = """"""
db_1 = db_factory(sql_dialect=3, init=init_script_1)
test_script_1 = """
recreate table t(x int);
create index t_x_asc on t(x);
create descending index t_x_dec on t(x);
insert into t select rand()*1000 from rdb$types,rdb$types;
commit;
set statistics index t_x_asc;
set statistics index t_x_dec;
commit;
set planonly;
select * from t as t1 where x>=0.5; -- NO 'plan order' must be here; bitmap is enough!
select * from t as t2 where x>=0.5 order by x; -- here PLAN ORDER is much efficient than bitmap + PLAN SORT
select * from t as t3 where x<=0.5; -- NO 'plan order' must be here; bitmap is enough!
select * from t as t4 where x<=0.5 order by x desc; -- here PLAN ORDER is much efficient than bitmap + PLAN SORT
"""
act_1 = isql_act('db_1', test_script_1, substitutions=substitutions_1)
expected_stdout_1 = """
PLAN (T1 INDEX (T_X_ASC))
PLAN (T2 ORDER T_X_ASC)
PLAN (T3 INDEX (T_X_ASC))
PLAN (T4 ORDER T_X_DEC)
"""
@pytest.mark.version('>=3.0')
def test_1(act_1: Action):
act_1.expected_stdout = expected_stdout_1
act_1.execute()
assert act_1.clean_stdout == act_1.clean_expected_stdout
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] |
Second_Day/Second_Day_Aggrigation_Query.py | vijayingale/Adv_Python_Trainig | 0 | 1612715 | from pymongo import MongoClient
try:
myclient = MongoClient("mongodb://%s:%[email protected]" % ('myUserAdmin', '<PASSWORD>'))
# #create collection
print("\n\t .Connection Successful : ")
mydb = myclient['database']
# collection = mydb['test2']
#
# profile = [
# {"user":"ram" , "title": "Python"},
# {"user": "ramesh", "title": "Java"},
# {"user":"ramnath" , "title": "C"},
# {"user":"ramesh" , "title": "C++"},
# {"user":"ramoji" , "title": "Android"}
# ]
#
# collection.insert_many(profile)
#
# agg_result = collection.aggregate(
# [
# {
# "$group":
# {
# "_id":"$user",
# "num_lang":{"$sum" : 1}
# }
# }
# ]
# )
#
# print("\n\t Result : ")
# for i in agg_result:
# print(i)
collection = mydb['test3']
profile = [
{"Telgu_Films ":{"user": "rajnikat", "title": "Megastar"}},
{"user": "chiranjivi", "title": "Megastar"},
{"user": "Allu_Arjun", "title": "Stylish_Star"},
{"user": "Yash", "title": "Power_Star"},
{"user": "ramoji", "title": "Film_city"}
]
collection.insert_many(profile)
agg_result = collection.aggregate(
[
{
"$group":
{
"_id": "$user",
"num_lang": {"$sum": 1}
}
}
]
)
print("\n\t Result : ")
for i in agg_result:
print(i)
except ConnectionError as e:
print("Errorr" ,e) | [
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tests/test_linalg.py | tanlin2013/TNpy | 1 | 124489 | import unittest
import numpy as np
from tnpy.linalg import KrylovExpm
class TestLinAlg(unittest.TestCase):
def test_eigshmv(self):
# self.assertEqual(True, False)
pass
class TestKrylovExpm(unittest.TestCase):
def test_construct_krylov_space(self):
mat = np.random.random((50, 50))
v0 = np.random.random(50)
kexpm = KrylovExpm(1e-3, mat, v0)
kexpm.construct_krylov_space()
if __name__ == '__main__':
unittest.main()
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scripts/compute_translation.py | Tom556/structural-probes | 0 | 171834 | from argparse import ArgumentParser
from collections import defaultdict
import numpy as np
import torch
import h5py
from tqdm import tqdm
from pytorch_pretrained_bert import BertTokenizer
LAYER_NUM = 12
FEATURE_DIM = 768
def match_tokenized_to_untokenized(tokenized_sent, untokenized_sent):
'''Aligns tokenized and untokenized sentence given subwords "##" prefixed
Assuming that each subword token that does not start a new word is prefixed
by two hashes, "##", computes an alignment between the un-subword-tokenized
and subword-tokenized sentences.
Args:
tokenized_sent: a list of strings describing a subword-tokenized sentence
untokenized_sent: a list of strings describing a sentence, no subword tok.
Returns:
A dictionary of type {int: list(int)} mapping each untokenized sentence
index to a list of subword-tokenized sentence indices
'''
mapping = defaultdict(list)
untokenized_sent_index = 0
tokenized_sent_index = 1
while (untokenized_sent_index < len(untokenized_sent) and
tokenized_sent_index < len(tokenized_sent)):
while (tokenized_sent_index + 1 < len(tokenized_sent) and
tokenized_sent[tokenized_sent_index + 1].startswith('##')):
mapping[untokenized_sent_index].append(tokenized_sent_index)
tokenized_sent_index += 1
mapping[untokenized_sent_index].append(tokenized_sent_index)
untokenized_sent_index += 1
tokenized_sent_index += 1
return mapping
if __name__ == '__main__':
argp = ArgumentParser()
argp.add_argument('hdf5_file')
argp.add_argument('sentences_raw')
argp.add_argument('translation_npz')
args = argp.parse_args()
subword_tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
with open(args.sentences_raw, 'r') as in_sents:
sentences = in_sents.readlines()
hf = h5py.File(args.hdf5_file, 'r')
indices = list(hf.keys())
translation = np.zeros((LAYER_NUM, FEATURE_DIM))
for index, sent in tqdm(zip(sorted([int(x) for x in indices]), sentences), desc='Averaging sentence vectors'):
sent = sent.strip()
tokenized_sent = subword_tokenizer.wordpiece_tokenizer.tokenize('[CLS] ' + sent + ' [SEP]')
untokenized_sent = sent.split(' ')
untok_tok_mapping = match_tokenized_to_untokenized(tokenized_sent, untokenized_sent)
feature_stack = hf[str(index)]
for layer_idx in range(LAYER_NUM):
single_layer_features = feature_stack[layer_idx]
assert single_layer_features.shape[0] == len(tokenized_sent)
single_layer_features = torch.tensor(
[np.mean(single_layer_features[untok_tok_mapping[i][0]:untok_tok_mapping[i][-1] + 1, :], axis=0)
for i in range(len(untokenized_sent))])
assert single_layer_features.shape[0] == len(sent.split(' '))
translation[layer_idx, :] -= single_layer_features.numpy().mean(axis=0)
translation /= len(sentences)
np.savez(args.translation_npz, translation)
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HOP_Learning_Algorithm/classification_mnist.py | DingjunB/Honors-Thesis | 1 | 1614298 | <filename>HOP_Learning_Algorithm/classification_mnist.py
#classification_mnist.py
#
#Semi-supervised classification example:
#This script shows how to construct a weight matrix for the whole
#MNIST dataset, using precomputed kNN data, randomly select some
#training data, run Laplace and Poisson Learning, and compute accuracy.
import graphlearning as gl
#Load labels, knndata, and build 10-nearest neighbor weight matrix
labels = gl.load_labels('mnist')
I,J,D = gl.load_kNN_data('mnist',metric='vae')
W = gl.weight_matrix(I,J,D,10)
#Equivalently, we can compute knndata from scratch
#X = gl.load_dataset('mnist',metric='vae')
#labels = gl.load_labels('mnist')
#I,J,D = gl.knnsearch_annoy(X,10)
#W = gl.weight_matrix(I,J,D,10)
#Randomly chose training datapoints
num_train_per_class = 1
train_ind = gl.randomize_labels(labels, num_train_per_class)
train_labels = labels[train_ind]
#Run Laplace and Poisson learning
labels_laplace = gl.graph_ssl(W,train_ind,train_labels,algorithm='laplace')
labels_poisson = gl.graph_ssl(W,train_ind,train_labels,algorithm='poisson')
#Compute and print accuracy
print('Laplace learning: %.2f%%'%gl.accuracy(labels,labels_laplace,num_train_per_class))
print('Poisson learning: %.2f%%'%gl.accuracy(labels,labels_poisson,num_train_per_class))
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test/test_import.py | And2Devel/qpylib | 18 | 1604457 | from qpylib import qpylib
def test_submodules_imported():
assert qpylib.app_qpylib is not None
assert qpylib.asset_qpylib is not None
assert qpylib.json_qpylib is not None
assert qpylib.log_qpylib is not None
assert qpylib.offense_qpylib is not None
assert qpylib.rest_qpylib is not None
assert qpylib.util_qpylib is not None
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index.py | ReeLeeSama/Ticket-system | 1 | 25172 | import discord
import asyncio
import aiofiles
from discord.ext import commands
intents = discord.Intents.all()
client = commands.Bot(command_prefix=commands.when_mentioned_or('!'),intents=intents)
client.ticket_configs = {}
@client.command()
async def ping(ctx):
embed=discord.Embed(title="Bot Ping",description=f"My ping is {round(client.latency * 1000)}ms ",color=discord.Colour.gold())
await ctx.reply(embed=embed)
@client.event
async def on_ready():
print("Bot is online")
@client.event
async def on_raw_reaction_add(payload): #When a reaction is added
if payload.member.id != client.user.id and str(payload.emoji) == u"\U0001F3AB": #Checks if the reaction is not made by a bot an emoji is "🎫"
msg_id, channel_id, category_id = client.ticket_configs[payload.guild_id]
if payload.message_id == msg_id: #checks if the reaction message is equal to the message id in ticket_configs.txt
guild = client.get_guild(payload.guild_id)
for category in guild.categories:
if category.id == category_id:
break
channel = guild.get_channel(channel_id) #gets the channel id
ticket_channel = await category.create_text_channel(f"ticket-{payload.member.display_name}", topic=f"Ticket for {payload.member.display_name}.", permission_synced=True) #Creates a ticket as "ticket_channel"
f = open(f"tickets/{ticket_channel.id}.txt", "w") #Opens a folder called "tickets" and inside it creates a file with the channel id. Usefull for transcripts
f.close() #closes the file
await ticket_channel.set_permissions(payload.member, read_messages=True, send_messages=True) # Adds the member to the ticket
mention_member = f"{payload.member.mention}"
message = await channel.fetch_message(msg_id)
await message.remove_reaction(payload.emoji, payload.member) #Removes the reaction for the message where you react to make a ticket
creation_embed=discord.Embed(title="Ticket Created",description="Thank you for creating a ticket and make sure that the ticket follows our ticket guidelines and explain the ticket creation reason in detail so our staff can help you.",color=discord.Colour.blurple())
await ticket_channel.send(mention_member,embed=creation_embed) # Mentions the member and sends the embded to the channel where the ticket is created.
@client.command()
async def close(ctx):
channel = ctx.channel
if channel.name.startswith("ticket"): #checks if a channel name starts with "ticket"
await ctx.reply("Are you sure you want to close the ticket? Reply with ``confirm`` to close the ticket.") #Will ask the user to confirm to close the ticket
await client.wait_for("message",check=lambda m: m.channel == ctx.channel and m.author == ctx.author and m.content == "confirm",timeout=10) #Wait for a message with content "confirm" and makes sure that the command runner is the message sender and waits for reply for 10 seconds.
await channel.delete() #If the message is "confirm" it will delete the channel
closer = ctx.author.mention
transcript_chan = client.get_channel(803399751487717396) #channel to send the ticket transcript to.
await transcript_chan.send(closer,file=discord.File(f"tickets/{channel.id}.txt")) #Sends the file to the transcript channel and mentions the ticket closer there.
else:
return
@client.command()
@commands.has_permissions(administrator=True)
async def config(ctx, msg: discord.Message=None, category: discord.CategoryChannel=None): #Usage = !config "message_id category_id" to get the ids enable deveoper mode and right click the message that will be used to create tickets and the category id is the category where the tickets will be created.
if msg is None or category is None: #If a message id or category id is not provided.
error_embed=discord.Embed(title="Ticket Configuration Failed",description="Failed to configure. Either an argument is missing or an invalid argument was passed.",color=discord.Colour.red())
await ctx.channel.send(embed=error_embed)
return
client.ticket_configs[ctx.guild.id] = [msg.id, msg.channel.id, category.id] #Resets the configuration
async with aiofiles.open("ticket_configs.txt", mode="r") as file:
data = await file.readlines()
async with aiofiles.open("ticket_configs.txt", mode="w") as file:
await file.write(f"{ctx.guild.id} {msg.id} {msg.channel.id} {category.id}\n")
for line in data:
if int(line.split(" ")[0]) != ctx.guild.id:
await file.write(line)
await msg.add_reaction(u"\U0001F3AB") # Adds reaction to the message and when someone reacts to this emoji it will create a ticket.
await ctx.channel.send("Successfully configured the ticket system.") # If you get thsi it means that the ticket system has been configured successfully.
@client.event
async def on_message(message):
await client.process_commands(message)#processes the command
if message.channel.name.startswith("ticket"): #check if the channel name starts with "ticket"
f = open(f"tickets/{message.channel.id}.txt", "a") # Opens the channel id in the tickets folder
f.write(f"{message.author} : {message.content}\n") # Write the message author and the message he sent
f.close() #closesthe file
client.run("your_bot_token_here")
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LeetCode/Easy/SymetricTree.py | ukayaj620/cp-solution | 1 | 41931 | <reponame>ukayaj620/cp-solution<gh_stars>1-10
class TreeNode:
def __init__(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
class Solution:
def isMirror(self, left: TreeNode, right: TreeNode) -> bool:
if left is None and right is None:
return True
elif left is not None and right is not None:
inner = self.isMirror(left.right, right.left)
outer = self.isMirror(left.left, right.right)
return left.val == right.val and inner and outer
return False
def isSymmetric(self, root: TreeNode) -> bool:
return root is None or self.isMirror(root.left, root.right)
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extras.py | pyaf/severstal-steel-defect-detection | 0 | 26027 | import os
from tqdm import tqdm
import numpy as np
import pandas as pd
import os
import pdb
import cv2
import time
import json
import torch
import random
import scipy
import logging
import traceback
import numpy as np
from datetime import datetime
# from config import HOME
from tensorboard_logger import log_value, log_images
from matplotlib import pyplot as plt
plt.switch_backend("agg")
def logger_init(save_folder):
mkdir(save_folder)
logging.basicConfig(
filename=os.path.join(save_folder, "log.txt"),
filemode="a",
level=logging.DEBUG,
format="%(asctime)s %(message)s",
datefmt="%H:%M:%S",
)
console = logging.StreamHandler()
logger = logging.getLogger(__name__)
logger.addHandler(console)
return logger
def plot_ROC(roc, targets, predictions, phase, epoch, folder):
roc_plot_folder = os.path.join(folder, "ROC_plots")
mkdir(os.path.join(roc_plot_folder))
fpr, tpr, thresholds = roc_curve(targets, predictions)
roc_plot_name = "ROC_%s_%s_%0.4f" % (phase, epoch, roc)
roc_plot_path = os.path.join(roc_plot_folder, roc_plot_name + ".jpg")
fig = plt.figure(figsize=(10, 5))
plt.plot([0, 1], [0, 1], linestyle="--")
plt.plot(fpr, tpr, marker=".")
plt.legend(["diagonal-line", roc_plot_name])
fig.savefig(roc_plot_path, bbox_inches="tight", pad_inches=0)
plt.close(fig) # see footnote [1]
plot = cv2.imread(roc_plot_path)
log_images(roc_plot_name, [plot], epoch)
def print_time(log, start, string):
diff = time.time() - start
log(string + ": %02d:%02d" % (diff // 60, diff % 60))
def iter_log(log, phase, epoch, iteration, epoch_size, loss, start):
diff = time.time() - start
log(
"%s epoch: %d (%d/%d) loss: %.4f || %02d:%02d",
phase,
epoch,
iteration,
epoch_size,
loss.item(),
diff // 60,
diff % 60,
)
def mkdir(folder):
if not os.path.exists(folder):
os.mkdir(folder)
def save_hyperparameters(trainer, remark):
hp_file = os.path.join(trainer.save_folder, "parameters.txt")
time_now = datetime.now()
augmentations = trainer.dataloaders["train"].dataset.transforms.transforms
# pdb.set_trace()
string_to_write = (
f"Time: {time_now}\n"
+ f"model_name: {trainer.model_name}\n"
+ f"train_df_name: {trainer.train_df_name}\n"
#+ f"images_folder: {trainer.images_folder}\n"
+ f"resume: {trainer.resume}\n"
+ f"pretrained: {trainer.pretrained}\n"
+ f"pretrained_path: {trainer.pretrained_path}\n"
+ f"folder: {trainer.folder}\n"
+ f"fold: {trainer.fold}\n"
+ f"total_folds: {trainer.total_folds}\n"
+ f"num_samples: {trainer.num_samples}\n"
+ f"sampling class weights: {trainer.class_weights}\n"
+ f"size: {trainer.size}\n"
+ f"top_lr: {trainer.top_lr}\n"
+ f"base_lr: {trainer.base_lr}\n"
+ f"num_workers: {trainer.num_workers}\n"
+ f"batchsize: {trainer.batch_size}\n"
+ f"momentum: {trainer.momentum}\n"
+ f"mean: {trainer.mean}\n"
+ f"std: {trainer.std}\n"
+ f"start_epoch: {trainer.start_epoch}\n"
+ f"augmentations: {augmentations}\n"
+ f"criterion: {trainer.criterion}\n"
+ f"optimizer: {trainer.optimizer}\n"
+ f"remark: {remark}\n"
)
with open(hp_file, "a") as f:
f.write(string_to_write)
print(string_to_write)
def seed_pytorch(seed=69):
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
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] |
tests/actions/test_transfer_tokens_action.py | 0xOmarA/RadixLib | 32 | 93502 | from radixlib.actions import TransferTokens
from typing import Dict, Any
import unittest
class TestTransferTokensAction(unittest.TestCase):
""" Unit tests for the TransferTokens action of mutable tokens """
ActionDict: Dict[str, Any] = {
"from_account": {
"address": "tdx1qspqqecwh3tgsgz92l4d4f0e4egmfe86049dj75pgq347fkkfmg84pgx9um0v"
},
"to_account": {
"address": "tdx1qspsl85c9cpgm8t906zewv66quyg6d4gdlru2q9ujgk0u66c8kw2t6caan5qa"
},
"amount": {
"value": "100000000000000000000",
"token_identifier": {
"rri": "xrd_tr1qyf0x76s"
}
},
"type": "TransferTokens"
}
def test_from_dict(self):
""" Tests the derivation of the mainnet wallet addresses from the public key """
# The action loaded from the dictionary
mint: TransferTokens = TransferTokens.from_dict(self.ActionDict)
# Asserting that the TransferTokens object understood the content of the dictionary
self.assertEqual(mint.to_account.address, self.ActionDict['to_account']['address'])
self.assertEqual(mint.from_account.address, self.ActionDict['from_account']['address'])
self.assertEqual(mint.amount, int(self.ActionDict['amount']['value']))
self.assertEqual(mint.token_rri, self.ActionDict['amount']['token_identifier']['rri'])
def test_to_dict(self):
""" Tests the conversion of the token account to a dictionary """
# The account loaded from the dictionary
account: TransferTokens = TransferTokens.from_dict(self.ActionDict)
self.assertEqual(account.to_dict(), self.ActionDict) | [
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] |
codes/mdp_utils.py | oskali/mit_cassandra | 0 | 137518 | <reponame>oskali/mit_cassandra<filename>codes/mdp_utils.py<gh_stars>0
#%% Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm # progress bar
from copy import deepcopy
from sklearn.cluster import KMeans, AgglomerativeClustering, Birch
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# from xgboost import XGBClassifier
from collections import Counter
from itertools import groupby
from operator import itemgetter
import os
import warnings
warnings.filterwarnings("ignore")
from mdp_testing import R2_value_training, training_value_error, get_MDP, \
predict_cluster, R2_value_testing, testing_value_error, error_per_ID, MDPTrainingError, prediction_score
from data_utils import save_model
#############################################################################
# Splitter algorithm with cross-validation
#############################################################################
# splitter() is the wrap-up function. Takes as parameters a dataframe df,
# a time-horizon T, a number of features pfeatures, an indexer k, and a max
# number of iterations and performs the algorithm until all contradictions are
# resolved or until the max number of iterations is reached
# Plots the trajectory of testing metrics during splitting process
# Returns the final resulting dataframe
# initializeClusters() takes as input a dataframe, a time horizon T,
# a clustering algorithm, a number of clusters n_clusters,
# and a random seed (optional) and returns a dataframe
# with two new columns 'CLUSTER' and 'NEXT_CLUSTER'
def initializeClusters(df, # pandas dataFrame: MUST contain a "RISK" column
clustering='Agglomerative', # string: clustering algorithm
n_clusters= None,
distance_threshold= 0.1, # number of clusters
random_state=0): # random seed for the clustering
df = df.copy()
if clustering == 'KMeans':
output = KMeans(
n_clusters=n_clusters, random_state=random_state).fit(
np.array(df.RISK).reshape(-1, 1)).labels_
elif clustering == 'Agglomerative':
output = AgglomerativeClustering(
n_clusters=n_clusters, distance_threshold=distance_threshold).fit(
np.array(df.RISK).reshape(-1, 1)).labels_
elif clustering == 'Birch':
output = Birch(
n_clusters=n_clusters).fit(
np.array(df.RISK).reshape(-1, 1)).labels_
else:
output = LabelEncoder().fit_transform(np.array(df.RISK).reshape(-1, 1))
df['CLUSTER'] = output
df['NEXT_CLUSTER'] = df.groupby('ID')['CLUSTER'].shift(-1)
# df.loc[df['ID'] != df['ID'].shift(-1), 'NEXT_CLUSTER'] = 'None'
return(df)
#############################################################################
#############################################################################
# Function for the Iterations
# findConstradiction() takes as input a dataframe and returns the tuple with
# initial cluster and action that have the most number of contradictions or
# (-1, -1) if no such cluster existss
def findContradiction(df, # pandas dataFrame
th): # integer: threshold split size
X = df.loc[:, ['CLUSTER', 'NEXT_CLUSTER', 'ACTION']]
X = X[X.NEXT_CLUSTER != 'None']
count = X.groupby(['CLUSTER', 'ACTION'])['NEXT_CLUSTER'].nunique()
contradictions = list(count[list(count > 1)].index)
if len(contradictions) > 0:
ncontradictions = [sum(list(X.query('CLUSTER == @i[0]').query(
'ACTION == @i[1]').groupby('NEXT_CLUSTER')['ACTION'].count().
sort_values(ascending=False))[1:]) for i in contradictions]
if max(ncontradictions) > th:
selectedCont = contradictions[ncontradictions.index(
max(ncontradictions))]
return(selectedCont)
return((-1, -1))
# contradiction() outputs one found contradiction given a dataframe,
# a cluster and a an action or (None, None) if none is found
def contradiction(df, # pandas dataFrame
i, # integer: initial clusters
a): # integer: action taken
nc = list(df.query('CLUSTER == @i').query(
'ACTION == @a').query('NEXT_CLUSTER != "None"')['NEXT_CLUSTER'])
if len(nc) == 1:
return (None, None)
else:
return a, multimode(nc)[0]
# multimode() returns a list of the most frequently occurring values.
# Will return more than one result if there are multiple modes
# or an empty list if *data* is empty.
def multimode(data):
counts = Counter(iter(data)).most_common()
maxcount, mode_items = next(groupby(counts, key=itemgetter(1)), (0, []))
return list(map(itemgetter(0), mode_items))
# split() takes as input a dataframe, an initial cluster, an action, a target_colname
# cluster that is a contradiction c, a time horizon T, then number of features,
# and an iterator k (that is the indexer of the next cluster), as well as the
# predictive classification algorithm used
# and returns a new dataframe with the contradiction resolved
# MAKE SURE TO CHECK the number to ensure group creation has all features!! ***
def split(df, # pandas dataFrame
i, # integer: initial cluster
a, # integer: action taken
c, # integer: target cluster
pfeatures, # integer: number of features
k, # integer: intedexer for next cluster
classification='LogisticRegression',
random_state=0): # string: classification aglo
g1 = df[(df['CLUSTER'] == i) & (
df['ACTION'] == a) & (df['NEXT_CLUSTER'] == c)]
g2 = df[(df['CLUSTER'] == i) & (
df['ACTION'] == a) & (df['NEXT_CLUSTER'] != c) & (
df['NEXT_CLUSTER'] != 'None')]
g3 = df[(df['CLUSTER'] == i) & (
((df['ACTION'] == a) & (df['NEXT_CLUSTER'] == 'None')) | (
df['ACTION'] != a))]
groups = [g1, g2, g3]
data = {}
for j in range(len(groups)):
d = pd.DataFrame(groups[j].iloc[:, 2:2+pfeatures].values.tolist())
data[j] = d
data[0].insert(data[0].shape[1], "GROUP", np.zeros(data[0].shape[0]))
data[1].insert(data[1].shape[1], "GROUP", np.ones(data[1].shape[0]))
training = pd.concat([data[0], data[1]])
tr_X = training.loc[:, ~(training.columns == "GROUP")]
tr_y = training.loc[:, "GROUP"]
if classification == 'LogisticRegression':
m = LogisticRegression(solver='liblinear', random_state=random_state)
elif classification == 'LogisticRegressionCV':
m = LogisticRegressionCV(random_state=random_state)
elif classification == 'DecisionTreeClassifier':
m = DecisionTreeClassifier(random_state=random_state)
elif classification == 'RandomForestClassifier':
m = RandomForestClassifier(random_state=random_state)
elif classification == 'XGBClassifier':
m = XGBClassifier(random_state=random_state)
else:
m = LogisticRegression(solver='liblinear')
m.fit(tr_X, tr_y.values.ravel())
ids = g2.index.values
test_X = data[2]
if len(test_X) != 0:
Y = m.predict(test_X)
g3.insert(g3.shape[1], "GROUP", Y)
id2 = g3.loc[g3["GROUP"] == 1].index.values
ids = np.concatenate((ids, id2))
df.loc[df.index.isin(ids), 'CLUSTER'] = k
newids = ids-1
df.loc[(df.index.isin(newids)) & (df['ID']== df['ID'].shift(-1)), 'NEXT_CLUSTER'] = k
return df
# (MDP GRID SEARCH FUNCTION)
# Splitting function from the MDP learning algorithm
def splitter(df, # pandas dataFrame
actions,
pfeatures, # integer: number of features
th, # integer: threshold for minimum split
df_test=None,
testing=False,
classification='LogisticRegression', # string: classification alg
it=6, # integer: max number of clusters
h=5,
OutputFlag=1,
n=-1,
random_state=0,
plot=False,
save=False,
savepath=None): #If we plot error
# initializing lists for error & accuracy data
training_R2 = []
testing_R2 = []
# training_acc = []
# testing_acc = []
testing_error = []
training_error = []
k = df['CLUSTER'].nunique() #initial number of clusters
nc = k
df_new = deepcopy(df)
# Setting progress bar--------------
if OutputFlag >= 1:
split_bar = tqdm(range(it-k))
else:
split_bar = range(it-k)
# split_bar.set_description("Splitting...")
# Setting progress bar--------------
for i in split_bar:
# split_bar.set_description("Splitting... |#Clusters:%s" %(nc))
cont = False
c, a = findContradiction(df_new, th)
# print('Iteration',i+1, '| #Clusters=',nc+1, '------------------------')
if c != -1:
#if OutputFlag == 1:
#print('Cluster Content')
#print(df_new.groupby(
#['CLUSTER', 'OG_CLUSTER'])['ACTION'].count())
# finding contradictions and splitting
a, b = contradiction(df_new, c, a)
# if OutputFlag == 1:
# print('Cluster splitted', c,'| Action causing contradiction:', a, '| Cluster most elements went to:', b)
df_new = split(df_new, c, a, b, pfeatures, nc, classification, random_state=random_state)
# error and accuracy calculations
R2_train = R2_value_training(df_new, actions=actions, pfeatures=pfeatures, n_cluster=nc+1, complete=True, OutputFlag=OutputFlag)
if testing:
# model = predict_cluster(df_new, pfeatures)
model = None
R2_test = R2_value_testing(df_test, df_new, model, pfeatures, actions=actions, n_cluster=nc+1, OutputFlag=OutputFlag)
test_error = testing_value_error(df_test.copy(), df_new, model,
actions=actions,
pfeatures=pfeatures, n_cluster=nc+1,
relative=True, h=h,
OutputFlag=OutputFlag)
testing_R2.append(R2_test)
testing_error.append(test_error)
# train_acc = training_accuracy(df_new)[0]
# test_acc = testing_accuracy(df_test, df_new, model, pfeatures)[0]
P_df,R_df = get_MDP(df_new, complete=True, actions=actions, pfeatures=pfeatures,
n_cluster=nc+1, OutputFlag=0)
train_error = training_value_error(df_new, P_df=P_df, R_df=R_df, relative=True, h=h, OutputFlag=OutputFlag)
training_R2.append(R2_train)
training_error.append(train_error)
# training_acc.append(train_acc)
# testing_acc.append(test_acc)
# printing error and accuracy values
# if OutputFlag == 1:
# print('training value R2:', R2_train)
# print('training accuracy:', train_acc)
# print('testing accuracy:', test_acc)
# print('training value error:', train_error)
# if testing:
# print('testing value R2:', R2_test)
# print('testing value error:', test_error)
# print('predictions:', get_predictions(df_new))
# print(df_new.head())
cont = True
nc += 1
if not cont:
break
if nc >= it:
break
# if OutputFlag == 1:
# print(df_new.groupby(['CLUSTER', 'OG_CLUSTER'])['ACTION'].count())
# plotting functions
# Plotting accuracy and value R2
# fig1, ax1 = plt.subplots()
its = np.arange(k+1, nc+1)
# ax1.plot(its, training_R2, label= "Training R2")
# if testing:
# ax1.plot(its, testing_R2, label = "Testing R2")
# #ax1.plot(its, training_acc, label = "Training Accuracy")
# #ax1.plot(its, testing_acc, label = "Testing Accuracy")
# if n>0:
# ax1.axvline(x=n,linestyle='--',color='r') #Plotting vertical line at #cluster =n
# ax1.set_ylim(0,1)
# ax1.set_xlabel('# of Clusters')
# ax1.set_ylabel('R2 or Accuracy %')
# ax1.set_title('R2 and Accuracy During Splitting')
# ax1.legend()
# Plotting value error E((v_est - v_true)^2) FOR COVID: plotting MAPE
# for training
if plot:
fig2, ax2 = plt.subplots()
ax2.plot(its, training_error, label="Training Error")
if testing:
ax2.plot(its, testing_error, label="Testing Error")
if n > 0:
ax2.axvline(x=n, linestyle='--', color='r') #Plotting vertical line at #cluster =n
ax2.set_ylim(0)
ax2.set_xlabel('# of Clusters')
ax2.set_ylabel('Cases MAPE error')
ax2.set_title('MAPE error by number of clusters')
ax2.legend()
if save:
plt.savefig(savepath)
if OutputFlag >= 2:
plt.show(block=False)
else:
plt.close()
df_train_error = pd.DataFrame(list(zip(its, training_error)), \
columns=['Clusters', 'Error'])
if testing:
df_test_error = pd.DataFrame(list(zip(its, testing_error)), \
columns=['Clusters', 'Error'])
return df_new, df_train_error, df_test_error
return df_new, training_error, testing_error
# (MDP FUNCTION)
# Fitting function for a single fold,
def fit_cv_fold(split_idx,
df,
clustering,
n_clusters,
clustering_distance_threshold,
pfeatures,
actions,
splitting_threshold,
classification,
n_iter,
horizon,
n,
OutputFlag=0,
random_state=1234,
mode="ID",
save=False,
savepath="",
plot=False):
if mode == "ALL":
idx, (train_idx, test_idx) = split_idx # train_idx, _ = split_idx / _, train_idx = split_idx
df_test = df.loc[train_idx].groupby("ID").tail(horizon).reset_index(drop=True).copy()
df_train = df.loc[train_idx].groupby("ID").apply(lambda x: x.head(-horizon)).reset_index(drop=True).copy()
elif mode == "TIME_CV":
idx, (train_idx, test_idx) = split_idx
df_train = pd.concat(
[df.loc[train_idx],
df.loc[test_idx].groupby("ID").apply(lambda x: x.head(-horizon)).reset_index(drop=True)]
).copy().reset_index(drop=True)
df_test = df.loc[test_idx].groupby("ID").tail(horizon).reset_index(drop=True).copy()
elif mode == "ID":
idx, (train_idx, test_idx) = split_idx
df_train = df.loc[train_idx].copy()
df_test = df.loc[test_idx].copy()
else:
if OutputFlag >= 1:
print("TrainingError : 'mode' must be a string : either 'TIME', 'ALL' or 'ID' ")
raise MDPTrainingError
#################################################################
# Initialize Clusters
df_train = initializeClusters(df_train,
clustering=clustering,
n_clusters=n_clusters,
distance_threshold=clustering_distance_threshold,
random_state=random_state)
# k = df_train['CLUSTER'].nunique()
#################################################################
#################################################################
# Run Iterative Learning Algorithm
df_train, training_error, testing_error = splitter(df_train,
actions=actions,
pfeatures=pfeatures,
th=splitting_threshold,
df_test=df_test,
testing=True,
classification=classification,
it=n_iter,
h=horizon,
OutputFlag=OutputFlag,
n=n,
random_state=random_state,
plot=plot,
save=save,
savepath=os.path.join(savepath, "plot_{}.PNG".format(idx)))
m = predict_cluster(df_train, pfeatures)
try:
df_err, E_v = error_per_ID(df_test=df_test, df_new=df_train, model=m, actions=actions, n_cluster=n_clusters,
pfeatures=pfeatures, relative=True, h=horizon, OutputFlag=OutputFlag)
except:
df_err, E_v = None, None
return testing_error, training_error, df_err, E_v
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notebooks/run_pipeline.py | BenAjayiObe/cvd19-truth-finder | 0 | 86302 | import torch
import argparse
import code
import prettytable
from termcolor import colored
from drqa import pipeline
from drqa.retriever import utils
# ------------------------------------------------------------------------------
# Drop in to interactive mode
# ------------------------------------------------------------------------------
def process(question, candidates=None, top_n=1, n_docs=5):
predictions = DrQA.process(
question, candidates, top_n, n_docs, return_context=True
)
table = prettytable.PrettyTable(
['Rank', 'Answer', 'Doc', 'Answer Score', 'Doc Score']
)
for i, p in enumerate(predictions, 1):
table.add_row([i, p['span'], p['doc_id'],
'%.5g' % p['span_score'],
'%.5g' % p['doc_score']])
print('Top Predictions:')
print(table)
print('\nContexts:')
for p in predictions:
text = p['context']['text']
start = p['context']['start']
end = p['context']['end']
output = (text[:start] +
colored(text[start: end], 'green', attrs=['bold']) +
text[end:])
print('[ Doc = %s ]' % p['doc_id'])
print(output + '\n')
if __name__ == '__main__':
# Arguments
# args_reader_model="/home/ubuntu/cvd19-truth-finder/data/covid19/model/20200509-c9d21e14.mdl"
# args_retriever_model="/home/ubuntu/cvd19-truth-finder/data/covid19/doc_ranker/doc-tfidf-ngram=2-hash=16777216-tokenizer=simple.npz"
# args_doc_db="/home/ubuntu/cvd19-truth-finder/data/covid19/doc-db/doc.db"
# args_tokenizer="spacy"
# args_candidate_file=None
# args_no_cuda=True
# args_gpu=-1
args_reader_model="../data/covid19/model/20200509-c9d21e14.mdl"
args_retriever_model="../data/covid19/full_doc_ranker/full_doc-tfidf-ngram=2-hash=16777216-tokenizer=simple.npz"
args_doc_db="../data/covid19/doc-db/full_doc.db"
args_tokenizer="spacy"
args_candidate_file=None
args_no_cuda=True
args_gpu=-1
args_cuda = not args_no_cuda and torch.cuda.is_available()
if args_cuda:
torch.cuda.set_device(args_gpu)
if args_candidate_file:
candidates = set()
with open(args_candidate_file) as f:
for line in f:
line = utils.normalize(line.strip()).lower()
candidates.add(line)
else:
candidates = None
DrQA = pipeline.DrQA(
cuda=args_cuda,
fixed_candidates=candidates,
reader_model=args_reader_model,
ranker_config={'options': {'tfidf_path': args_retriever_model}},
db_config={'options': {'db_path': args_doc_db}},
tokenizer=args_tokenizer
)
process("How many people are infected?", top_n=10) | [
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ctf/nsec/2018/crypto/RC8_crypto/test_captcha.py | Sylhare/Flag | 0 | 143722 | <reponame>Sylhare/Flag
import os
import nose
import json
import captcha
def load_json_config(function):
def test_case():
path = 'tests/templates/'
for file in os.listdir(path):
if file.rsplit('.', 1)[-1] == 'json':
with open(path + file) as f:
params = json.load(f)
function(params)
test_case.__name__ = function.__name__
return test_case
@load_json_config
def test_captcha_solver(params):
path = params.get('samples')
if path is None:
return
for file in os.listdir(path):
image = captcha.image_filter(path + file, params)
result = captcha.solve(image, params)
assert ''.join(map(str, result)) == file.rsplit('.', 1)[0]
@load_json_config
def test_get_image_by_url(params):
for i in range(3):
image_data = captcha.fetch(params['url'])
image = captcha.image_filter(image_data, params)
result = captcha.solve(image, params)
assert len(result) == params.get('length', 4)
assert all(x is not None for x in result)
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dataset/test_dataset.py | gallupliu/QA | 3 | 94665 | # encoding: utf-8
"""
@author: gallupliu
@contact: <EMAIL>
@version: 1.0
@license: Apache Licence
@file: test_dataset.py
@time: 2018/2/20 20:02
"""
# import tensorflow as tf
#
# # sequences = [[1, 2, 3], [4, 5, 1], [1, 2]]
# sequences = [["1", "2", "3"], ["4", "5", "1"], ["1", "2"]]
# label_sequences = [[0, 1, 0], [1, 0, 0], [1, 1]]
#
#
# def make_example(sequence, labels):
# # The object we return
# ex = tf.train.SequenceExample()
# # A non-sequential feature of our example
# sequence_length = len(sequence)
# ex.context.feature["length"].int64_list.value.append(sequence_length)
# # Feature lists for the two sequential features of our example
# fl_tokens = ex.feature_lists.feature_list["tokens"]
# fl_labels = ex.feature_lists.feature_list["labels"]
# for token, label in zip(sequence, labels):
# # fl_tokens.feature.add().int64_list.value.append(token)
# fl_tokens.feature.add().bytes_list.value.append(token)
# fl_labels.feature.add().int64_list.value.append(label)
# return ex
#
#
# # Write all examples into a TFRecords file
# # with tempfile.NamedTemporaryFile() as fp:
# # writer = tf.python_io.TFRecordWriter(fp.name)
# # for sequence, label_sequence in zip(sequences, label_sequences):
# # ex = make_example(sequence, label_sequence)
# # writer.write(ex.SerializeToString())
# # writer.close()
#
# with tf.python_io.TFRecordWriter('./test_0220.tfrecord') as writer:
# for sequence, label_sequence in zip(sequences, label_sequences):
# ex = make_example(sequence, label_sequence)
# writer.write(ex.SerializeToString())
#
#
# # A single serialized example
# # (You can read this from a file using TFRecordReader)
# # ex = make_example([1, 2, 3], [0, 1, 0]).SerializeToString()
#
# ex = make_example(["1", "2", "3"], ["0", "1", "0"]).SerializeToString()
# # Define how to parse the example
# context_features = {
# "length": tf.FixedLenFeature([], dtype=tf.int64)
# }
# sequence_features = {
# # "tokens": tf.FixedLenSequenceFeature([], dtype=tf.int64),
# "tokens": tf.FixedLenSequenceFeature([], dtype=tf.string),
# "labels": tf.FixedLenSequenceFeature([], dtype=tf.int64)
# }
#
# # Parse the example
# context_parsed, sequence_parsed = tf.parse_single_sequence_example(
# serialized=ex,
# context_features=context_features,
# sequence_features=sequence_features
# )
import tensorflow as tf
import os
keys=[[1.0,2.0],[2.0,3.0]]
sess=tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
def make_example(locale,age,score,times):
example = tf.train.SequenceExample(
context=tf.train.Features(
feature={
"locale":tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(locale,encoding='utf8')])),
"age":tf.train.Feature(int64_list=tf.train.Int64List(value=[age]))
}),
feature_lists=tf.train.FeatureLists(
feature_list={
"movie_rating":tf.train.FeatureList(feature=[tf.train.Feature(float_list=tf.train.FloatList(value=score)) for i in range(times)])
}
)
)
return example.SerializeToString()
context_features = {
"locale": tf.FixedLenFeature([],dtype=tf.string),
"age": tf.FixedLenFeature([],dtype=tf.int64)
}
sequence_features = {
"movie_rating": tf.FixedLenSequenceFeature([3], dtype=tf.float32,allow_missing=True)
}
context_parsed, sequence_parsed = tf.parse_single_sequence_example(make_example("中国",24,[1.0,3.5,4.0],2),context_features=context_features,sequence_features=sequence_features)
print(tf.contrib.learn.run_n(context_parsed))
print(tf.contrib.learn.run_n(sequence_parsed)) | [
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] |
thaifin/stock.py | CircleOnCircles/thaifin | 7 | 165445 | <reponame>CircleOnCircles/thaifin
import arrow
import pandas as pd
from fuzzywuzzy import process
from thaifin.sources.finnomena import get_financial_sheet
from thaifin.sources.finnomena import get_stock_list
class Stock:
@classmethod
def search(cls, company_name: str, limit: int = 5):
list_ = get_stock_list().data
search_against = {x.thName + x.enName: x for x in list_}
search_result = process.extract(company_name, search_against, limit=limit)
return [cls(s[0].name) for s in search_result]
@staticmethod
def list_symbol():
list_ = get_stock_list().data
return [s.name for s in list_]
@staticmethod
def find_symbol(symbol: str):
list_ = get_stock_list().data
return next(obj for obj in list_ if obj.name == symbol)
def __init__(self, symbol: str):
symbol = symbol.upper()
self.info = self.find_symbol(symbol)
self.fundamental = get_financial_sheet(self.info.security_id).data
self.updated = arrow.utcnow()
# self.
@property
def symbol(self):
return self.info.name
@property
def company_name(self):
return self.info.enName
@property
def thai_company_name(self):
return self.info.thName
@property
def quarter_dataframe(self):
df = pd.DataFrame([s.dict(exclude={"SecurityID"}) for s in self.fundamental])
# Quarter 9 means yearly values
df = df[df.Quarter != 9]
df["Time"] = df.Fiscal.astype(str) + "Q" + df.Quarter.astype(str)
df = df.set_index("Time")
df.index = pd.to_datetime(df.index).to_period("Q")
df = df.drop(columns=["Fiscal", "Quarter"])
return df
@property
def yearly_dataframe(self):
df = pd.DataFrame([s.dict(exclude={"SecurityID"}) for s in self.fundamental])
# Quarter 9 means yearly values
df = df[df.Quarter == 9]
df = df.set_index("Fiscal")
df.index = pd.to_datetime(df.index, format="%Y").to_period("Y")
df = df.drop(columns=["Quarter"])
return df
def __repr__(self):
return f'<Stock "{self.symbol}" - updated {self.updated.humanize()}>'
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mass-cat-index-wikisource.py | krls-ca/viquitexts-calaix-de-sastre | 0 | 148733 | <gh_stars>0
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pywikibot, re, time
from bs4 import BeautifulSoup
from pywikibot.proofreadpage import ProofreadPage
from pywikibot.proofreadpage import IndexPage
def getCategoryNeeded(ql):
if ql == 0:
#WITHOUT_TEXT
return u'[[Category:Testurik gabe]]'
#Testurik gabe
elif ql == 1:
#Berrikusi gabe
#NOT_PROOFREAD
return u'[[Category:Berrikusi gabe]]'
elif ql == 2:
#Arazoak dakartza
#PROBLEMATIC
return u'[[Category:Arazoak dakartza]]'
elif ql == 3:
#Berrikusita
#PROOFREAD
return u'[[Category:Berrikusita]]'
elif ql == 4:
#VALIDATED
#Balioztatua
return u'[[Category:Balioztatua]]'
categories = [u'[[Category:Testurik gabe]]', u'[[Category:Berrikusi gabe]]', u'[[Category:Arazoak dakartza]]', u'[[Category:Berrikusita]]', u'[[Category:Balioztatua]]', u'[[Category:Euskara]]']
done = []
def main():
site = pywikibot.Site("mul", "wikisource")
arts = pywikibot.Category(site, u"Indizeak euskaraz").articles(recurse=1, reverse=True, content=False)
'''indexPage = IndexPage(site, u'Index:Chantspopulaires00sall.pdf')
pages = indexPage.page_gen(only_existing=True, content=True)
for page in pages:
if not page.isRedirectPage() and page.exists():
name = page.title()
title = name.replace(u"Chantspopulaires00sall", u"Chants populaires du pays basque (1870)")
print title
#page.move(title, reason="File renamed in Wikimedia Commons", movetalkpage=True)
raw_input('Are you sure? (y/n)')
exit(0)'''
for art in arts:
print art.title()
if art.title() not in done:
indexPage = IndexPage(art)
try:
#indexPage.page_gen(only_existing=True, content=True, filter_ql=0)
pages = indexPage.page_gen(only_existing=True, content=True)
except ValueError as nopage:
continue
for page in pages:
#print page.text
if page.exists():
print page
cat = getCategoryNeeded(page.quality_level)
oldText = page.text
#print cat
print cat
#newText = oldText.replace(u"[[Category:Euskara]]", "")
#match = re.match(cat, oldText)
if cat not in oldText:
newText = oldText
for oldCat in categories:
if oldCat in newText:
newText = newText.replace(oldCat, "")
print newText
headerFooter = re.findall(r"(<noinclude>(?:[\S\s]+?))(?:<\/noinclude>)", newText)
if len(headerFooter) == 1:
newText = newText.replace(u"<noinclude></noinclude>", u"<noinclude>{0}</noinclude>".format(cat))
else:
footer = headerFooter[1]
newText = newText.replace(footer, u"{0}\n{1}".format(footer, cat))
pywikibot.showDiff(oldText,newText)
#raw_input('Are you sure? (y/n)')
page.put(newText, comment = u'Added category {0}'.format(cat), minorEdit=True)
#time.sleep(2)
#raw_input('Are you sure? (y/n)')
if __name__ == '__main__':
main() | [
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run_tests.py | Edux87/lemmes | 0 | 1604118 | # -*- coding: utf-8 -*-
'''
The main test runner script.
Usage: ::
python run_tests.py
Skip slow tests
python run_tests.py fast
When there's no Internet
python run_tests.py no-internet
'''
from __future__ import unicode_literals
import nose
import sys
def main():
args = get_argv()
success = nose.run(argv=args)
sys.exit(0) if success else sys.exit(1)
def get_argv():
args = [sys.argv[0], "tests", '--verbosity', '2']
attr_conditions = []
attr_conditions.append("not skip")
attr_expression = " and ".join(attr_conditions)
if attr_expression:
args.extend(["-A", attr_expression])
return args
if __name__ == '__main__':
main()
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predict.py | harish-vnkt/multiscale-residual-network-pytorch | 3 | 130808 | <gh_stars>1-10
import argparse
import os
from model import MultiScaleResidualNetwork
from dataset import *
from torch.utils.data import DataLoader
from utils import *
from datetime import datetime
import logging
parser = argparse.ArgumentParser(description="Prediction arguments for MSRN Pytorch")
# Data
parser.add_argument("--data_root", type=str, required=True,
help="Root directory of dataset")
parser.add_argument("--data_set", type=str, choices=["Set14"],
default="Set14", help="Data set to run prediction on")
# Model
parser.add_argument("--scale", type=int, required=True, choices=[2, 4],
help="Scale of super-resolution")
parser.add_argument("--model_file", type=str, required=True,
help="Path to .pt file")
parser.add_argument("--results_dir", type=str, required=True,
help="Path to results directory")
# Platform config
parser.add_argument("--num_workers", type=int, default=os.cpu_count(),
help="Number of cpu's used for data loading")
parser.add_argument("--use_cpu", type=bool, default=False,
help="Use CPU for training")
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(filename)s : %(message)s")
now = datetime.now()
args = parser.parse_args()
print(args)
model = MultiScaleResidualNetwork(scale=args.scale, res_blocks=args.residual_blocks,
res_in_features=args.residual_channels, res_out_features=args.residual_channels)
logging.debug("Created model")
data_set = None
if args.data_set == "Set14":
data_set = Set14(data_root=args.data_root, scale=args.scale)
logging.debug("Created dataset")
prediction_loader = DataLoader(data_set, batch_size=1, num_workers=args.num_workers, pin_memory=True)
logging.debug("Created prediction loaders")
if not args.use_cpu:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
else:
device = torch.device('cpu')
logging.debug("Initialized device : %s", str(device))
checkpoint = torch.load(args.model_file)
model.load_state_dict(checkpoint["model"])
model.to(device)
model.eval()
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
results_dir = os.path.join(args.results_dir, "x" + str(args.scale))
if not os.path.exists(results_dir):
os.makedirs(results_dir)
logging.debug("Initialized tensorboard directory")
average_psnr = 0.0
with torch.no_grad():
for i, (hr_img, lr_img) in enumerate(prediction_loader):
image_name = os.path.basename(data_set.hr_image_names[i])
hr_img_device = hr_img.to(device)
lr_img_device = lr_img.to(device)
hr_prediction = model(lr_img_device)
average_psnr += get_psnr(hr_img_device, hr_prediction)
write_results(hr_prediction, results_dir, image_name)
average_psnr /= len(data_set)
logging.info(f"Average PSNR : {average_psnr}")
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] |
excentury/command/config.py | LaudateCorpus1/excentury | 0 | 5614 | """Config
This module is in charge of providing all the necessary settings to
the rest of the modules in excentury.
"""
import os
import re
import sys
import textwrap
import argparse
from collections import OrderedDict
from excentury.command import error, trace, import_mod
DESC = """Edit a configuration file for excentury.
Some actions performed by excentury can be overwritten by using
configuration files.
To see the values that the configuration file can overwrite use the
`defaults` command. This will print a list of the keys and values
excentury uses for the given command.
"""
RE = re.compile(r'\${(?P<key>.*?)}')
RE_IF = re.compile(
r'(?P<iftrue>.*?) IF\[\[(?P<cond>.*?)\]\]'
)
RE_IFELSE = re.compile(
r'(?P<iftrue>.*?) IF\[\[(?P<cond>.*?)\]\]ELSE (?P<iffalse>.*)'
)
def disp(msg):
"""Wrapper around sys.stdout.write which is meant to behave as
the print function but it does not add the newline character. """
sys.stdout.write(msg)
def _replacer(*key_val):
"""Helper function for replace.
Source: <http://stackoverflow.com/a/15221068/788553>
"""
replace_dict = dict(key_val)
replacement_function = lambda match: replace_dict[match.group(0)]
pattern = re.compile("|".join([re.escape(k) for k, _ in key_val]), re.M)
return lambda string: pattern.sub(replacement_function, string)
def replace(string, *key_val):
"""Replacement of strings done in one pass. Example:
>>> replace("a < b && b < c", ('<', '<'), ('&', '&'))
'a < b && b < c'
Source: <http://stackoverflow.com/a/15221068/788553>
"""
return _replacer(*key_val)(string)
class ConfigDispAction(argparse.Action): # pylint: disable=R0903
"""Derived argparse Action class to use when displaying the
configuration file and location."""
def __call__(self, parser, namespace, values, option_string=None):
try:
read_config(namespace)
except IOError:
disp('xcpp.config not found in %r\n' % namespace.cfg)
else:
disp('path to xcpp.config: "%s"\n' % namespace.cfg)
with open('%s/xcpp.config' % namespace.cfg, 'r') as _fp:
disp(_fp.read())
exit(0)
def add_parser(subp, raw):
"Add a parser to the main subparser. "
tmpp = subp.add_parser('config', help='configure excentury',
formatter_class=raw,
description=textwrap.dedent(DESC))
tmpp.add_argument('var', type=str, nargs='?', default=None,
help='Must be in the form of sec.key')
tmpp.add_argument('-v', action='store_true',
help='print config file location')
tmpp.add_argument('--print', action=ConfigDispAction,
nargs=0,
help='print config file and exit')
def _get_replacements(tokens, data, sec):
"""Helper function for _read_config. """
replacements = list()
for token in tokens:
if ':' in token:
tsec, tkey = token.split(':')
tval = ''
if tsec in data:
if tkey in data[tsec]:
tval = data[tsec][tkey]
else:
if token in data[sec]:
tval = data[sec][token]
else:
tval = ''
replacements.append(
('${%s}' % token, tval)
)
return replacements
# pylint: disable=invalid-name
# ARG and CFG are names that may be used in the configuration file.
# ARG gives us access to the command line arguments and CFG gives us
# access to the current configuration. Note that using CFG[key][sec]
# is equivalent to ${key:sec}. These names go against the convention
# so that they may be easy to spot in a configuration file.
def _eval_condition(cond, ARG, CFG, line_num, fname):
"""Evaluates a string using the eval function. It prints a
warning if there are any errors. Returns the result of the
evaluation and an error number: 0 if everything is fine, 1 if
there was an error. """
ARG.FILEPATH = '%s/%s/%s' % (ARG.cfg, CFG['xcpp']['path'], ARG.inputfile)
try:
# pylint: disable=eval-used
# To be able to evaluate a condition without creating a whole
# new parser we can use the eval function. We could have use
# a python file as a configuration but then there would be
# no simple structure to the files.
cond = eval(cond)
enum = 0
# pylint: disable=broad-except
# Anything can go wrong during the execution of the `eval`
# function. For this reason we must try to catch anything that
# may come our way so that we may give out a warning message
# and ignore it.
except Exception as exception:
cond = None
enum = 1
trace(
'WARNING: error in line %d of %r: %s\n' % (
line_num, fname, exception.message
)
)
return cond, enum
def _read_config(fname, arg):
"""Simple parser to read configuration files. """
data = OrderedDict()
sec = None
line_num = 0
with open(fname, 'r') as fhandle:
for line in fhandle:
line_num += 1
if line[0] == '[':
sec = line[1:-2]
data[sec] = OrderedDict()
elif '=' in line:
tmp = line.split('=', 1)
key = tmp[0].strip()
val = tmp[1].strip()
val = os.path.expandvars(val)
replacements = _get_replacements(
RE.findall(val), data, sec
)
# pylint: disable=star-args
if replacements:
val = replace(val, *replacements)
match = RE_IFELSE.match(val)
if match:
cond, enum = _eval_condition(
match.group('cond'), arg, data, line_num, fname
)
if enum == 1:
continue
groups = match.groups()
val = groups[0] if cond else groups[2]
else:
match = RE_IF.match(val)
if match:
cond, enum = _eval_condition(
match.group('cond'), arg, data, line_num, fname
)
if enum == 1:
continue
if cond:
val = match.group('iftrue')
else:
continue
data[sec][key] = val
return data
def read_config(arg):
"""Read the configuration file xcpp.config"""
path = arg.cfg
if path == '.' and not os.path.exists('xcpp.config'):
if 'XCPP_CONFIG_PATH' in os.environ:
tmp_path = os.environ['XCPP_CONFIG_PATH']
if os.path.exists('%s/xcpp.config' % tmp_path):
trace("Configured with: '%s/xcpp.config'\n" % tmp_path)
path = tmp_path
elif not os.path.exists('%s/xcpp.config' % path):
error("ERROR: %s/xcpp.config does not exist\n" % path)
arg.cfg = path
try:
config = _read_config('%s/xcpp.config' % path, arg)
except IOError:
config = OrderedDict()
return config
def run(arg):
"""Run command. """
config = read_config(arg)
if arg.v:
disp('path to xcpp.config: "%s"\n' % arg.cfg)
if arg.var is None:
for sec in config:
disp('[%s]\n' % sec)
for key in config[sec]:
disp(' %s = %s\n' % (key, config[sec][key]))
disp('\n')
return
try:
command, var = arg.var.split('.', 1)
except ValueError:
error("ERROR: '%s' is not of the form sec.key\n" % arg.var)
try:
disp(config[command][var]+'\n')
except KeyError:
pass
return
def _update_single(cfg, name, defaults=None):
"Helper function for get_cfg."
if defaults:
for var, val in defaults.iteritems():
cfg[name][var] = os.path.expandvars(str(val))
else:
mod = import_mod('excentury.command.%s' % name)
if hasattr(mod, "DEFAULTS"):
for var, val in mod.DEFAULTS.iteritems():
cfg[name][var] = os.path.expandvars(val)
def _update_from_file(cfg, name, cfg_file):
"Helper function for get_cfg."
if name in cfg_file:
for var, val in cfg_file[name].iteritems():
cfg[name][var] = os.path.expandvars(val)
def _update_from_arg(cfg, argdict, key):
"Helper function for get_cfg."
for var in cfg[key]:
if var in argdict and argdict[var] is not None:
cfg[key][var] = argdict[var]
def get_cfg(arg, names, defaults=None):
"""Obtain the settings for a command. """
cfg = {
'xcpp': {
'root': '.',
'path': '.'
}
}
cfg_file = read_config(arg)
if 'xcpp' in cfg_file:
for var, val in cfg_file['xcpp'].iteritems():
cfg['xcpp'][var] = os.path.expandvars(val)
cfg['xcpp']['root'] = arg.cfg
if isinstance(names, list):
for name in names:
cfg[name] = dict()
_update_single(cfg, name)
_update_from_file(cfg, name, cfg_file)
else:
if names != 'xcpp':
cfg[names] = dict()
_update_single(cfg, names, defaults)
_update_from_file(cfg, names, cfg_file)
argdict = vars(arg)
if arg.parser_name in cfg:
_update_from_arg(cfg, argdict, arg.parser_name)
elif arg.parser_name == 'to' and arg.lang in cfg:
_update_from_arg(cfg, argdict, arg.lang)
_update_from_arg(cfg, argdict, 'xcpp')
return cfg
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.config/xmobar/getdate_fr.py | mdupuis13/dotfilez-dasboss-pc | 0 | 112806 | <reponame>mdupuis13/dotfilez-dasboss-pc<filename>.config/xmobar/getdate_fr.py
#! /usr/bin/python3
import locale
from datetime import datetime
import sys
import getopt
import string
locale.setlocale(locale.LC_ALL, '')
loc = locale.getlocale()
locale.setlocale(locale.LC_ALL, 'fr_CA.UTF-8')
print('%s' % (string.capwords(datetime.now().strftime('%A, %d %B - %k:%M:%S'))))
sys.exit(0)
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test_mnist/prepare_data.py | xuqinghan/learn_pytorch | 0 | 1605689 | import numpy as np
from torchvision.datasets import mnist
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
batch_size_train = 64
batch_size_test = 128
learnrate = 0.01
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
#download
download = False
dataset_train = mnist.MNIST('../data/mnist', train=True,transform=transform, download=download )
dataset_test = mnist.MNIST('../data/mnist', train=False,transform=transform )
loader_tr = DataLoader(dataset_train, batch_size=batch_size_train, shuffle=True)
loader_ts = DataLoader(dataset_test, batch_size=batch_size_test, shuffle=False) | [
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tests/core/base/test_computation.py | shhoalex/revgraph | 9 | 85729 | <reponame>shhoalex/revgraph
import unittest
import numpy as np
from revgraph.core.base.tensor import Tensor
class TensorImpl(Tensor):
def forward(self):
return
class TensorGradientTestCase(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
cls.a = TensorImpl((3, 4), True)
cls.b = TensorImpl((3, 4), True)
def setUp(self) -> None:
self.c = TensorImpl(None, True)
self.c.register(self.a)
self.c.register(self.b)
self.c.new_context()
def tearDown(self) -> None:
del self.c
def test_gradient_successfully_created(self):
self.c.accumulate(self.a, np.ones((3,4)))
self.assertEqual(self.c.shape, (3,4))
self.assertTrue((self.c.gradient == np.ones((3,4))).all())
def test_gradient_successfully_accumulated(self):
self.c.accumulate(self.a, np.ones((3,4)))
self.c.accumulate(self.b, np.ones((3,4)))
result = np.full((3,4), 2)
self.assertTrue((self.c.gradient == result).all())
def test_gradient_rejected_for_repeated_parent_node(self):
self.c.accumulate(self.a, np.ones((3,4)))
with self.assertRaises(ValueError):
self.c.accumulate(self.a, np.ones((3,4)))
def test_gradient_rejected_for_invalid_parent_node(self):
self.c.accumulate(self.a, np.ones((3,4)))
self.assertEqual(self.c.ctx[self.a], 0)
with self.assertRaises(ValueError):
self.c.accumulate(self.c, np.ones((3,4)))
def test_context_completed_correctly(self):
self.c.accumulate(self.a, np.ones((3,4)))
self.assertFalse(self.c.context_completed())
self.c.accumulate(self.b, np.ones((3,4)))
self.assertTrue(self.c.context_completed())
def test_context_completed_correctly_for_repeated_parent_node(self):
self.c.register(self.a)
self.c.new_context()
self.c.accumulate(self.a, np.ones((3,4)))
self.assertFalse(self.c.context_completed())
self.c.accumulate(self.a, np.ones((3, 4)))
self.assertFalse(self.c.context_completed())
self.c.accumulate(self.b, np.ones((3,4)))
self.assertTrue(self.c.context_completed())
class TensorUnbroadcastingTest(unittest.TestCase):
def setUp(self) -> None:
self.a = TensorImpl((1, 5), True)
self.b = TensorImpl((5, 1), True)
self.c = TensorImpl((3, 2), True)
self.d = TensorImpl((), True)
def tearDown(self) -> None:
del self.a, self.b, self.c
def test_unbroadcast_with_same_shape(self):
m = np.ones((1,5))
result = self.a.unbroadcast(m)
self.assertEqual(result.shape, (1,5))
self.assertTrue((result == np.array([[1,1,1,1,1]])).all())
m = np.ones((5,1))
result = self.b.unbroadcast(m)
self.assertEqual(result.shape, (5,1))
self.assertTrue((result == np.array([[1, 1, 1, 1, 1]]).T).all())
def test_unbroadcast_with_different_row(self):
m = np.ones((2,5))
result = self.a.unbroadcast(m)
self.assertEqual(result.shape, (1,5))
self.assertTrue((result == np.array([[2,2,2,2,2]])).all())
m = np.ones((10,5))
result = self.a.unbroadcast(m)
self.assertEqual(result.shape, (1,5))
self.assertTrue((result == np.array([[10,10,10,10,10]])).all())
m = np.ones((1,1))
result = self.b.unbroadcast(m)
self.assertEqual(result.shape, (5,1))
self.assertTrue((result == np.array([[1, 1, 1, 1, 1]]).T).all())
def test_unbroadcast_with_different_col(self):
m = np.ones((1,1))
result = self.a.unbroadcast(m)
self.assertEqual(result.shape, (1,5))
self.assertTrue((result == np.array([[1,1,1,1,1]])).all())
m = np.ones((5,5))
result = self.b.unbroadcast(m)
self.assertEqual(result.shape, (5,1))
self.assertTrue((result == np.array([[5,5,5,5,5]]).T).all())
def test_unbroadcast_with_extra_dimension(self):
m = np.ones((5,))
result = self.a.unbroadcast(m)
self.assertEqual(result.shape, (1,5))
self.assertTrue((result == np.array([[1,1,1,1,1]])).all())
def test_unbroadcast_with_different_col_and_row(self):
m = np.ones((1,))
result = self.c.unbroadcast(m)
self.assertEqual(result.shape, (3,2))
self.assertTrue((result == np.ones((3,2))).all())
def test_unbroadcast_with_invalid_shape(self):
m = np.ones((3,3))
with self.assertRaises(ValueError):
self.c.unbroadcast(m)
m = np.ones((4,2))
with self.assertRaises(ValueError):
self.c.unbroadcast(m)
def test_unbroadcast_with_scalar(self):
m = np.ones((3,3,3))
result = self.d.unbroadcast(m)
self.assertTrue((result == np.int64(27)).all())
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app/tree.py | fberanizo/teachingtool | 0 | 126916 | <filename>app/tree.py<gh_stars>0
# -*- coding: utf-8 -*-
import numpy, pandas
from sklearn.base import BaseEstimator, ClassifierMixin
from collections import deque
from . import gain, gini
class DecisionTree:
"""Classe que implementa uma árvore de decisão para classificação."""
def __init__(self, X, y, attributes, attribute_selection_method='gain'):
self.X = X
self.y = y
self.attributes = attributes
self.attribute_selection_method = attribute_selection_method
self.samples_size, self.input_size = X.shape
self.id_pool = 1
self.remaining_attributes = list(range(len(attributes)))
# Inicializa a árvore com a partição total dos dados de treinamento
self.tree = dict([('id', 0), ('label', None), ('type', 'question'), ('partition', range(y.size)), ('X_partition', self.X), ('y_partition', self.y), ('value', None), ('parent', None), ('children', [])])
self.nodes = []
self.edges = []
# Mantém uma fila de nós que precisam ser completados
self.queue = deque()
self.queue.append(self.tree)
def create_node(self):
"""Cria um nó da árvore de decisão. """
if len(self.queue) > 0:
node = self.queue.popleft()
# Cria uma aresta conectando este nó ao pai
if node['parent'] is not None:
self.edges.append((node['parent'], node['id']))
X_partition = node['X_partition']
y_partition = node['y_partition']
# Se os rótulos em y são todos da mesma classe C, então a decisão é C
classes = numpy.unique(y_partition)
if len(classes) == 1:
node['label'] = classes[0]
node['type'] = 'decision'
self.nodes.append(node)
return
# Se a lista de atributos está vazia, então a decisão é a classe majoritária de y
if len(self.remaining_attributes) == 0:
unique, pos = numpy.unique(y_partition, return_inverse=True)
counts = numpy.bincount(pos)
node['label'] = unique[counts.argmax()]
node['type'] = 'decision'
self.nodes.append(node)
return
# Calcula métrica de seleção de atributo com base no critério definido no construtor
attributes = self.calculate_attribute_selection_metric(X_partition, y_partition)
# Escolhe o atributo com maior métrica
best_attribute = max(attributes, key=lambda item: item['metric'])
node['label'] = best_attribute['attribute']
self.nodes.append(node)
self.remaining_attributes.remove(self.attributes.index(best_attribute['attribute']))
# Cria um nó para cada partição
for partition, value in zip(best_attribute['partitions'], best_attribute['values']):
child = dict([('id', self.id_pool), ('label', None), ('type', 'question'), ('partition', partition), ('X_partition', node['X_partition'][partition]), ('y_partition', node['y_partition'][partition]), ('value', value), ('parent', node['id']), ('children', [])])
node['children'].append(child)
self.queue.append(child)
self.id_pool += 1
def calculate_attribute_selection_metric(self, X_partition, y_partition):
"""Retorna um dict com métrica de seleção e as partições que o atributo realiza."""
if self.attribute_selection_method not in ['gain', 'gini']:
raise Exception('Método de seleção de atributo não implementado.')
else:
attributes = []
for index in self.remaining_attributes:
if self.attribute_selection_method == 'gain':
metric, partitions, values = gain.gain(X_partition[:,index], y_partition)
elif self.attribute_selection_method == 'gini':
metric, partitions, values = gini.gini(X_partition[:,index], y_partition)
attributes.append({'attribute': self.attributes[index], 'metric': metric, 'partitions': partitions, 'values': values})
return attributes
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opencv_py/feature_detection/corner_harris.py | yinleiCoder/interesting-python | 0 | 192649 | <filename>opencv_py/feature_detection/corner_harris.py<gh_stars>0
import cv2
import numpy as np
"""
Harris角点检测：
光滑地区，无论向哪里移动，衡量系数不变
边缘地址，垂直边缘移动时，衡量系数变化剧烈
在交点处，往哪个方向移动，衡量系数都变化剧烈
cornerHarris(img, dst, blockSize, ksize, k)
blockSize: 检测窗口大小
ksize: Sobel的卷积核
k：权重系数，经验值，一般取0.02~0.04之间
"""
img = cv2.imread(r'E:\PycharmProjects\funnyPython\opencv_py\data\imgs\chess.png')
#灰度化
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blockSize = 2
ksize = 3
k = 0.04
dst = cv2.cornerHarris(gray_img, blockSize, ksize, k)
# harris角点展示
img[dst > 0.01*dst.max()] = [0, 0, 255]
cv2.imshow('harris', img)
cv2.waitKey(0)
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accounts/admin.py | Gerard-007/musicalpacks | 0 | 75089 | from django.contrib import admin
from .models import User
from .forms import UserCreateForm
from django.contrib.auth.models import Group
from django.contrib.auth.admin import UserAdmin
# Register your models here.
class MyAdmin(UserAdmin):
# The forms to add and change user instances
# form = UserChangeForm
add_form = UserCreateForm
# The fields to be used in displaying the User model.
# These override the definitions on the base UserAdmin
# that reference specific fields on auth.User.
list_display = ('username', 'email', 'date_joined', 'is_staff', 'is_active')
list_filter = ('is_staff',)
fieldsets = (
(None, {'fields': ('email', 'password')}),
('Personal info', {'fields': ('avatar', 'bio',)}),
('Permissions', {'fields': ('is_staff',)}),
)
# add_fieldsets is not a standard ModelAdmin attribute. UserAdmin
# overrides get_fieldsets to use this attribute when creating a user.
add_fieldsets = (
(None, {
'classes': ('wide',),
'fields': ('username', 'email', '<PASSWORD>', '<PASSWORD>')}
),
)
search_fields = ('username',)
ordering = ('username',)
filter_horizontal = ()
# Now register the new UserAdmin...
admin.site.register(User, MyAdmin)
# ... and, since we're not using Django's built-in permissions,
# unregister the Group model from admin.
admin.site.unregister(Group)
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2
] |
R_ev3dev/help.py | thomasvolk/R_ev3dev | 0 | 125098 | <reponame>thomasvolk/R_ev3dev
from R_ev3dev.interpreter import Command
from R_ev3dev import version
class Version(Command):
""" show version """
def invoke(self, interpreter_context, args):
return version.VERSION
class Help(Command):
""" show help """
def _doc(self, item):
return item.__doc__.strip() if item.__doc__ else ''
def _line(self, item):
return "{} - {}".format(item.name, self._doc(item).split("\n")[0])
def _overview(self, commands):
command_lines = [self._line(c) for c in commands]
return """---
R_ev3 protocol language version {}
author: <NAME>
license: Apache License Version 2.0
source: https://github.com/thomasvolk/R_ev3dev
possible commands:
{}
use help <command> for details
---""".format(version.VERSION, '\n '.join(command_lines))
def _details(self, command):
return """---
{}
{}
---""".format(command.name, self._doc(command))
def invoke(self, interpreter_context, args):
if len(args) > 0:
cmd = args[0]
return self._details(interpreter_context.interpreter.commands[cmd])
else:
return self._overview(interpreter_context.interpreter.commands.values())
| [
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] |
binary_classifiers/NaiveBayesPrimjer.py | zcikojevic/toxic-language-detection | 1 | 118277 | from sklearn.naive_bayes import BernoulliNB
from run_binary_classifier import run
param_grid = {
'bag_of_words__stop_words': ['english'],
'bag_of_words__ngram_range': [(1, 2)],
'bag_of_words__max_features': [500],
'dim_reduct__n_components': [300],
'normalizer__norm': ['l2'],
'classifier__alpha': [1.0],
'classifier__binarize': [0.0]
}
clf = BernoulliNB()
run(param_grid, clf)
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] |
photospicker/event/start_upload_event.py | l-vo/photos-picker | 0 | 49845 | class StartUploadEvent(object):
"""Event dispatched when an upload starts"""
def __init__(self, filepath, upload_file_rank, files_to_upload):
"""
Constructor
:param str filepath: file which starts to be uploaded
:param int upload_file_rank: rank of the file in upload queue
:param int files_to_upload: total files to upload count
"""
self._filepath = filepath
self._upload_file_rank = upload_file_rank
self._files_to_upload = files_to_upload
@property
def filepath(self): # pragma: no cover
"""
Getter for the file which starts to be uploaded
:type: str
"""
return self._filepath
@property
def upload_file_rank(self): # pragma: no cover
"""
Getter for the rank of the file in upload queue
:type: int
"""
return self._upload_file_rank
@property
def files_to_upload(self): # pragma: no cover
"""
Getter for the total files to upload count
:rtype: int
"""
return self._files_to_upload
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] |
src/device/migrations/0001_initial.py | Seedstars/reactnative-backend-base | 8 | 78228 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
from django.conf import settings
import uuid
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='DeviceOrders',
fields=[
('id', models.UUIDField(default=uuid.uuid4, primary_key=True, serialize=False, editable=False)),
('phone_number', models.CharField(max_length=16)),
('address', models.CharField(max_length=255, blank=True)),
('payment', models.CharField(default='P', max_length=1, choices=[('S', 'SIMPLE_PAY'), ('P', 'PAY_ON_DELIVERY')])),
('paid', models.CharField(default='N', max_length=1, choices=[('V', 'VERIFYING'), ('N', 'NOT_PAID'), ('P', 'PAID')])),
('date_request', models.DateTimeField(auto_now_add=True)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to=settings.AUTH_USER_MODEL)),
],
),
]
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4514,
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2
] |
cryptodoge/cmds/units.py | grayfallstown-cryptodoge/cryptodoge | 10 | 24392 | from typing import Dict
# The rest of the codebase uses mojos everywhere.
# Only use these units for user facing interfaces.
units: Dict[str, int] = {
"cryptodoge": 10 ** 6, # 1 cryptodoge (XCD) is 1,000,000 mojo (1 million)
"mojo:": 1,
"colouredcoin": 10 ** 3, # 1 coloured coin is 1000 colouredcoin mojos
}
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town/equipment_shop_sell.py | tgruby/dungeon-hunt | 3 | 1600537 | import game_play.screen
from game_play import images, screen
from town import equipment_shop
commands = "Enter a (#) to sell an item, or E(x)it."
message = "Wonderful, we have been running low on good hardware! What are you " \
"willing to part with? "
image = images.weapons_shop_logo
# This function controls our interactions at the weapons store
def paint(game, msg):
return screen.paint_two_panes(
game=game,
commands=commands,
messages=msg,
left_pane_content=image,
right_pane_content=draw_sell_list(game.character),
sound=None,
delay=0,
interaction_type='key_press'
)
def process(game, action):
our_hero = game.character
if action is None:
return paint(game, message)
# Leave and go back to the town
if action.lower() == "x":
game.current_controller = 'town.equipment_shop'
return equipment_shop.process(game, None)
# If Sell an item, enter another sub-controller
if action.isdigit():
return sell_items(game, action)
# If we don't know, just reshow page.
return paint(game, message)
def sell_items(game, action):
hero = game.character
item_number_picked = int(action)
items_list = filtered_sell_list(hero)
if item_number_picked > len(items_list)-1 or item_number_picked < 0:
msg = "You do not have an item of that number!"
else:
selected_item = items_list[item_number_picked][4]
selected_item_quantity = items_list[item_number_picked][0]
if selected_item["type"] == "weapon" or selected_item["type"] == "armor" or selected_item["type"] == "shield":
if selected_item["name"] == hero.equipped_weapon["name"] and selected_item_quantity == 1:
msg = "You cannot sell equipped items!"
elif hero.equipped_armor is not None and selected_item["name"] == hero.equipped_armor["name"] and selected_item_quantity == 1:
msg = "You cannot sell equipped items!"
elif hero.equipped_shield is not None and selected_item["name"] == hero.equipped_shield["name"] and selected_item_quantity == 1:
msg = "You cannot sell equipped items!"
else:
hero.gold += selected_item["cost"] / 2
hero.inventory.remove(selected_item)
msg = "You sold %s for %d gold." % (selected_item["name"], selected_item["cost"]/2)
else:
msg = "You cannot sell that item here!"
return paint(game, msg)
def draw_sell_list(our_hero):
items = filtered_sell_list(our_hero)
response = game_play.screen.medium_border + '\n'
response += " # | Items | Type | Value " + '\n'
response += game_play.screen.medium_border + '\n'
for num, item in enumerate(items):
response += game_play.screen.front_padding(str(num), 3) + " | " \
+ game_play.screen.back_padding(str(item[0]) + " " + item[1], 16) + " | " \
+ game_play.screen.front_padding(str(item[2]), 6) + " | " \
+ game_play.screen.front_padding(str(round(item[3] / 2)), 4) + '\n'
response += game_play.screen.medium_border + '\n'
return response
# Create a Filtered list of only items we can sell in the potion shop
def filtered_sell_list(our_hero):
filtered_list = []
items_list = game_play.screen.collapse_inventory_items(our_hero)
for item in items_list:
if item[2] == 'armor' or item[2] == 'shield' or item[2] == 'weapon':
filtered_list.append(item)
return filtered_list
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plot.py | Adiytisuman24/frog-jumping-1uestion | 0 | 141457 | <reponame>Adiytisuman24/frog-jumping-1uestion<filename>plot.py
from matplotlib import pyplot as plt
R=1
D =16
plt.plot(R,D)
path= 1
for i in range(D, (R + D - 1)):
path *= i;
path //= (i - D + 1);
plt.show()
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aiida_restapi/aiida_db_mappings.py | janssenhenning/aiida-restapi | 4 | 193203 | # -*- coding: utf-8 -*-
"""The 'source of truth' for mapping AiiDA's database table models to pydantic models.
Note in the future we may want to do this programmatically, however, there are two issues:
- AiiDA uses both SQLAlchemy and Django backends, so one would need to be chosen
- Neither model includes descriptions of fields
"""
from datetime import datetime
from typing import Dict, Optional, Type
from uuid import UUID
from aiida import orm
from pydantic import BaseModel, Field, Json
class AuthInfo(BaseModel):
"""AiiDA AuthInfo SQL table fields."""
id: int = Field(description="Unique id (pk)")
aiidauser_id: int = Field(description="Relates to user")
dbcomputer_id: int = Field(description="Relates to computer")
metadata: Json = Field(description="Metadata of the authorisation")
auth_params: Json = Field(description="Parameters of the authorisation")
enabled: bool = Field(description="Whether the computer is enabled", default=True)
class Comment(BaseModel):
"""AiiDA Comment SQL table fields."""
id: int = Field(description="Unique id (pk)")
uuid: UUID = Field(description="Universally unique id")
ctime: datetime = Field(description="Creation time")
mtime: datetime = Field(description="Last modification time")
content: Optional[str] = Field(description="Content of the comment")
user_id: int = Field(description="Created by user id (pk)")
dbnode_id: int = Field(description="Associated node id (pk)")
class Computer(BaseModel):
"""AiiDA Computer SQL table fields."""
id: int = Field(description="Unique id (pk)")
uuid: UUID = Field(description="Universally unique id")
name: str = Field(description="Computer name")
hostname: str = Field(description="Identifier for the computer within the network")
description: Optional[str] = Field(description="Description of the computer")
scheduler_type: str = Field(
description="Scheduler plugin type, to manage compute jobs"
)
transport_type: str = Field(
description="Transport plugin type, to manage file transfers"
)
metadata: Json = Field(description="Metadata of the computer")
class Group(BaseModel):
"""AiiDA Group SQL table fields."""
id: int = Field(description="Unique id (pk)")
uuid: UUID = Field(description="Universally unique id")
label: str = Field(description="Label of group")
type_string: str = Field(description="type of the group")
time: datetime = Field(description="Created time")
description: Optional[str] = Field(description="Description of group")
extras: Json = Field(description="extra data about for the group")
user_id: int = Field(description="Created by user id (pk)")
class Log(BaseModel):
"""AiiDA Log SQL table fields."""
id: int = Field(description="Unique id (pk)")
uuid: UUID = Field(description="Universally unique id")
time: datetime = Field(description="Creation time")
loggername: str = Field(description="The loggers name")
levelname: str = Field(description="The log level")
message: Optional[str] = Field(description="The log message")
metadata: Json = Field(description="Metadata associated with the log")
dbnode_id: int = Field(description="Associated node id (pk)")
class Node(BaseModel):
"""AiiDA Node SQL table fields."""
id: int = Field(description="Unique id (pk)")
uuid: UUID = Field(description="Universally unique id")
node_type: str = Field(description="Node type")
process_type: str = Field(description="Process type")
label: str = Field(description="Label of node")
description: str = Field(description="Description of node")
ctime: datetime = Field(description="Creation time")
mtime: datetime = Field(description="Last modification time")
user_id: int = Field(description="Created by user id (pk)")
dbcomputer_id: Optional[int] = Field(description="Associated computer id (pk)")
attributes: Json = Field(
description="Attributes of the node (immutable after storing the node)",
)
extras: Json = Field(
description="Extra attributes of the node (mutable)",
)
class User(BaseModel):
"""AiiDA User SQL table fields."""
id: int = Field(description="Unique id (pk)")
email: str = Field(description="Email address of the user")
first_name: Optional[str] = Field(description="First name of the user")
last_name: Optional[str] = Field(description="Last name of the user")
institution: Optional[str] = Field(
description="Host institution or workplace of the user"
)
class Link(BaseModel):
"""AiiDA Link SQL table fields."""
id: int = Field(description="Unique id (pk)")
input_id: int = Field(description="Unique id (pk) of the input node")
output_id: int = Field(description="Unique id (pk) of the output node")
label: Optional[str] = Field(description="The label of the link")
type: str = Field(description="The type of link")
ORM_MAPPING: Dict[str, Type[BaseModel]] = {
"AuthInfo": AuthInfo,
"Comment": Comment,
"Computer": Computer,
"Group": Group,
"Log": Log,
"Node": Node,
"User": User,
"Link": Link,
}
def get_model_from_orm(
orm_cls: Type[orm.Entity], allow_subclasses: bool = True
) -> Type[BaseModel]:
"""Return the pydantic model related to the orm class.
:param allow_subclasses: Return the base class mapping for subclasses
"""
if orm_cls.__name__ in ORM_MAPPING:
return ORM_MAPPING[orm_cls.__name__]
if allow_subclasses and issubclass(orm_cls, orm.nodes.Node):
return Node
if allow_subclasses and issubclass(orm_cls, orm.Group):
return Group
raise KeyError(f"{orm_cls}")
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29908,
29912,
555,
29918,
25932,
27195,
13,
2
] |
clinnotes/reminders/forms.py | mattnickerson993/clinnotes2 | 0 | 13479 | <filename>clinnotes/reminders/forms.py<gh_stars>0
from django import forms
from .models import Reminder
from clinnotes.users.models import EpisodeOfCare
class ReminderForm(forms.ModelForm):
class Meta:
model = Reminder
fields = ['category', 'title', 'details', 'episode_of_care']
def __init__(self, *args, **kwargs):
user = kwargs.pop('user')
super(ReminderForm, self).__init__(*args, **kwargs)
self.fields['episode_of_care'].queryset = EpisodeOfCare.objects.filter(clinician=user) | [
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aula0705/matriz_fundamentos.py | fillipesouza/aulasdelogicaprogramacao | 1 | 187868 | from random import randint
def cria_matriz(num_linhas, num_colunas):
matriz = []
for i in range(num_linhas):
linha = []
for j in range(num_colunas):
linha.append(randint(0,10))
matriz.append(linha)
return matriz
def imprime_matriz(matriz):
for i in range(len(matriz)):
linha = matriz[i] # pega a linha correspondente
for j in range(len(linha)): # um for para o numero de elementos da linha (colunas)
print(matriz[i][j], end=' ') # da um espaco para cada coluna da linha
print() # pula uma linha quando acabar as colunas da linha
def imprime_uns(num_linhas, num_colunas):
for i in range(num_linhas):
for j in range(num_colunas):
print('1', end=' ')
print()
matriz = cria_matriz(3, 4) # cria matriz 3x4 (3L x 4C)
print(matriz) # imprime na forma primária (listas)
imprime_matriz(matriz) # imprime na forma de matriz
#imprime_uns(3, 4)
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PyFEM.py | Xianchao-Xu/PyFEM | 0 | 89955 | # coding: utf-8
# author: xuxc
import os
import platform
import random
import sys
from PyQt5.QtCore import (
Qt,
QSettings,
QByteArray,
PYQT_VERSION_STR
)
from PyQt5.QtGui import (
QIcon,
QKeySequence,
QCloseEvent
)
from PyQt5.QtWidgets import (
QApplication,
QMainWindow,
QVBoxLayout,
QWidget,
QFileDialog,
QMessageBox,
QAction,
QTreeWidgetItem,
)
from vtkmodules.qt.QVTKRenderWindowInteractor import QVTKRenderWindowInteractor as QVTKWidget
from vtkmodules.vtkCommonCore import (
vtkPoints,
vtkVersion
)
from vtkmodules.vtkCommonDataModel import (
VTK_VERTEX,
VTK_POLY_VERTEX,
VTK_LINE,
VTK_POLY_LINE,
VTK_TRIANGLE,
VTK_TRIANGLE_STRIP,
VTK_POLYGON,
VTK_PIXEL,
VTK_QUAD,
VTK_TETRA,
VTK_VOXEL,
VTK_HEXAHEDRON,
VTK_WEDGE,
VTK_PYRAMID,
VTK_QUADRATIC_EDGE,
VTK_QUADRATIC_TRIANGLE,
VTK_QUADRATIC_QUAD,
VTK_QUADRATIC_TETRA,
VTK_QUADRATIC_HEXAHEDRON,
vtkUnstructuredGrid
)
from vtkmodules.vtkInteractionStyle import vtkInteractorStyleTrackballCamera
from vtkmodules.vtkInteractionWidgets import (
vtkLogoWidget,
vtkLogoRepresentation,
vtkOrientationMarkerWidget,
vtkTextWidget,
vtkTextRepresentation
)
from vtkmodules.vtkIOImage import vtkJPEGReader
from vtkmodules.vtkRenderingAnnotation import vtkAxesActor
from vtkmodules.vtkRenderingCore import (
vtkActor,
vtkDataSetMapper,
vtkRenderer,
vtkTextActor
)
# noinspection PyUnresolvedReferences
import vtkmodules.vtkRenderingOpenGL2 # 虽然后面没有用到，但必须导入这个，否则会报错
from ui.CentralWidget import (
CentralWidget,
ColorPickerWidget
)
from utility.FileParser import FileParser
__version__ = '0.01.02'
__author__ = 'XuXianchao'
__organization__ = '仿真坊'
__appname__ = 'PyFEM'
VTK_ELEMENT_TYPE_TABLE = {
1: VTK_VERTEX,
2: VTK_POLY_VERTEX,
3: VTK_LINE,
4: VTK_POLY_LINE,
5: VTK_TRIANGLE,
6: VTK_TRIANGLE_STRIP,
7: VTK_POLYGON,
8: VTK_PIXEL,
9: VTK_QUAD,
10: VTK_TETRA,
11: VTK_VOXEL,
12: VTK_HEXAHEDRON,
13: VTK_WEDGE,
14: VTK_PYRAMID,
21: VTK_QUADRATIC_EDGE,
22: VTK_QUADRATIC_TRIANGLE,
23: VTK_QUADRATIC_QUAD,
24: VTK_QUADRATIC_TETRA,
25: VTK_QUADRATIC_HEXAHEDRON
}
class MainWindow(QMainWindow):
def __init__(self):
super(MainWindow, self).__init__()
self.setWindowTitle('{} - v{}'.format(__appname__, __version__))
# 设置中央窗体（中央窗体已从主程序中分离出去）
self.central_widget = CentralWidget(self)
self.central_widget.setObjectName('central_widget')
self.setCentralWidget(self.central_widget)
self.horizontal_splitter = self.central_widget.horizontal_splitter
self.vertical_splitter = self.central_widget.vertical_splitter
self.navigation_tab = self.central_widget.navigation_tab
self.view_tab_widget = self.central_widget.view_tab_widget
self.tree_widget = self.central_widget.tree_widget
self.info_browser = self.central_widget.additional_tab_widget.info_browser
# 添加其他控件
self.add_menu() # 添加菜单栏和工具栏
self.statusBar().showMessage('准备完毕', 5000) # 添加状态栏并显示相关信息
# vtk相关控件
self.vtk_widget = QVTKWidget()
self.render_window = self.vtk_widget.GetRenderWindow()
self.iren = self.render_window.GetInteractor()
self.renderer = vtkRenderer()
self.actor_index = 0 # actor的索引号
self.actors = dict() # actor字典
self.axes_actor = vtkAxesActor()
self.logo_widget = vtkLogoWidget() # 用于放置logo，必须全局可用
self.text_widget = vtkTextWidget() # 用于放置软件名和版本号
self.marker_widget = vtkOrientationMarkerWidget() # 用于放置坐标轴
self.add_vtk_view() # 添加VTK视图
self.init_vtk_view() # VTK视图初始化
# 恢复上次关闭时的状态
self.load_settings()
self.navigation_tab.node_size_spinbox.valueChanged.connect(self.node_size_changed)
self.tree_widget.itemChanged.connect(self.show_hide_actor)
@staticmethod
def add_actions(target, actions):
for action in actions:
if action is None:
target.addSeparator()
else:
target.addAction(action)
def add_menu(self):
self.add_file_menu()
self.add_help_menu()
def add_file_menu(self):
file_menu = self.menuBar().addMenu('文件')
file_menu.setObjectName('file_menu')
file_toolbar = self.addToolBar('文件')
file_toolbar.setObjectName('file_toolbar')
file_new_action = self.create_action(
'新建', self.file_new, QKeySequence.New, 'Icons/New.png', '新建文件')
file_open_action = self.create_action(
'打开', self.file_open, QKeySequence.Open, 'Icons/Open.png', '打开文件')
file_exit_action = self.create_action(
'退出', self.close, 'Ctrl+Q', 'Icons/Exit.png', '退出')
self.add_actions(file_menu, (file_new_action, file_open_action, None, file_exit_action))
self.add_actions(file_toolbar, (file_new_action, file_open_action, file_exit_action))
def add_help_menu(self):
help_menu = self.menuBar().addMenu('帮助')
help_menu.setObjectName('help_menu')
file_help_action = self.create_action(
'帮助', self.file_help, icon="Icons/Help.png", tip='帮助')
file_about_action = self.create_action(
'关于', self.file_about, icon='Icons/About.png', tip='关于')
file_license_action = self.create_action(
'许可', self.file_license, QKeySequence.HelpContents, 'Icons/License.png', '许可')
self.add_actions(help_menu, (file_help_action, None, file_about_action, file_license_action))
def add_vtk_view(self):
vtk_view_tab = QWidget()
vtk_view_tab.setObjectName('vtk_view_tab')
self.view_tab_widget.addTab(vtk_view_tab, 'VTK视图')
vtk_layout = QVBoxLayout(vtk_view_tab)
vtk_layout.addWidget(self.vtk_widget)
def closeEvent(self, a0: QCloseEvent) -> None:
settings = QSettings()
settings.setValue('MainWindow/Geometry', self.saveGeometry())
settings.setValue('MainWindow/State', self.saveState())
settings.setValue('MainWindow/HorizontalSplitter', self.horizontal_splitter.saveState())
settings.setValue('MainWindow/VerticalSplitter', self.vertical_splitter.saveState())
def create_action(self, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False, signal='triggered'):
action = QAction(text, self)
if icon is not None:
action.setIcon(QIcon(icon))
if shortcut is not None:
action.setShortcut(shortcut)
if tip is not None:
action.setToolTip(tip)
action.setStatusTip(tip)
if slot is not None:
getattr(action, signal).connect(slot)
if checkable:
action.setCheckable(True)
return action
def file_new(self):
# 打开文件时，移除所有actor，并重新添加全局坐标轴，actor索引重置为1
for actor in self.actors.values():
self.renderer.RemoveActor(actor)
self.axes_actor.AxisLabelsOff()
self.renderer.AddActor(self.axes_actor)
self.actor_index = 1
self.actors[self.actor_index] = self.axes_actor
self.render_window.Render()
# 移除模型树“网格”标签下的所有actor分支
item_index = self.tree_widget.top_level_names.index('网格')
item = self.tree_widget.topLevelItem(item_index)
children = []
for child in range(item.childCount()):
children.append(item.child(child))
for child in children:
item.removeChild(child)
def file_open(self):
supported_files_list = {
'自定义{}文件': '*.fem'.format(__appname__),
}
supported_files = '支持的文件({})'.format(' '.join(supported_files_list.values()))
format_filter = '所有文件(*.*);;' + supported_files
for key, value in supported_files_list.items():
format_filter += ';;' + key + '({})'.format(value)
filename, dialog_title = QFileDialog.getOpenFileName(
self, '打开文件', '.', format_filter)
if filename:
if '*'+os.path.splitext(filename)[-1] not in supported_files_list.values():
self.info_browser.append("<font color='red'>错误：不支持的格式：{}<font>".format(filename))
return
else:
if os.path.splitext(filename)[-1] == '.fem':
self.info_browser.append('正在打开文件：{}'.format(filename))
params = FileParser(filename)
params.parse_fem()
if params.succeed:
if params.type == 'scatter':
self.show_scatter(params)
elif params.type == 'mesh':
self.show_mesh(params)
self.info_browser.append(params.msg)
def file_help(self):
QMessageBox.about(
self, '帮助',
"""<p>{}暂无帮助文档，如需帮助，可联系作者：
<p>邮箱: <EMAIL>
""".format(__appname__)
)
def file_about(self):
QMessageBox.about(
self, '关于{}'.format(__appname__),
'''<b>{}</b> v{}
<p>一个简单的有限元软件。
<p>作者：{}
<p>知乎账号：悬臂梁
<p>公众号：仿真坊
<p>开发工具：Python {} - PyQt {} - VTK {}'''.format(
__appname__,
__version__,
__author__,
platform.python_version(),
PYQT_VERSION_STR,
vtkVersion.GetVTKVersion()
)
)
def file_license(self):
QMessageBox.about(
self, '许可', '{}采用MIT开源协议进行许可'.format(__appname__)
)
def init_vtk_view(self):
self.renderer.SetBackground(0.9, 0.9, 0.9)
self.render_window.AddRenderer(self.renderer)
self.iren.SetInteractorStyle(vtkInteractorStyleTrackballCamera())
# 添加全局坐标轴
self.axes_actor.AxisLabelsOff()
self.renderer.AddActor(self.axes_actor)
self.actor_index += 1
self.actors[self.actor_index] = self.axes_actor
# 模型树更新全局坐标轴信息
axes_actor_item = QTreeWidgetItem(self.tree_widget.topLevelItem(0))
axes_actor_item.setText(0, '全局坐标系')
axes_actor_item.setCheckState(0, Qt.Checked)
axes_actor_item.setText(1, '{}'.format(self.actor_index))
self.tree_widget.expandAll()
# 添加logo
png_reader = vtkJPEGReader()
png_reader.SetFileName('Icons/qrcode.jpg')
png_reader.Update(None) # 不加None的时候PyCharm要报"Parameter 'p_int' unfilled"
logo_representation = vtkLogoRepresentation()
logo_representation.SetImage(png_reader.GetOutput())
logo_representation.SetPosition(0.9, 0.03)
logo_representation.SetPosition2(0.1, 0.1)
logo_representation.GetImageProperty().SetOpacity(1.0)
self.logo_widget.SetRepresentation(logo_representation) # logo_widget需要全局可用
self.logo_widget.SetInteractor(self.iren)
self.logo_widget.On()
self.logo_widget.ProcessEventsOff()
# 添加软件名、版本号
text_actor = vtkTextActor()
text_actor.SetInput('{}\nv{}'.format(__appname__, __version__))
text_actor.GetTextProperty().SetColor(0.1, 0.1, 0.1)
text_representation = vtkTextRepresentation()
text_representation.SetPosition(0.88, 0.88)
text_representation.SetPosition2(0.1, 0.1)
# representation需要在text_actor前面添加
self.text_widget.SetRepresentation(text_representation) # text_widget需要全局可用
self.text_widget.SetInteractor(self.iren)
self.text_widget.SetTextActor(text_actor)
self.text_widget.On()
self.text_widget.ProcessEventsOff()
# 添加坐标轴
axes_actor = vtkAxesActor()
self.marker_widget.SetOrientationMarker(axes_actor)
self.marker_widget.SetInteractor(self.iren)
self.marker_widget.EnabledOn()
self.marker_widget.InteractiveOff()
self.renderer.ResetCamera()
self.render_window.Render()
def load_settings(self):
settings = QSettings()
self.restoreGeometry(settings.value('MainWindow/Geometry', type=QByteArray))
self.restoreState(settings.value('MainWindow/State', type=QByteArray))
self.horizontal_splitter.restoreState(
settings.value('MainWindow/HorizontalSplitter', type=QByteArray))
self.vertical_splitter.restoreState(
settings.value('MainWindow/VerticalSplitter', type=QByteArray))
def node_size_changed(self, i):
actor_index = self.actor_index
current_item = self.tree_widget.currentItem()
if current_item:
actor_index = int(current_item.text(1))
if actor_index > 1:
self.actors[actor_index].GetProperty().SetPointSize(i)
self.render_window.Render()
def change_color(self, actor_index):
color = self.sender().palette().window().color().name()
r_hex = int(color[1:3], 16)
g_hex = int(color[3:5], 16)
b_hex = int(color[5:7], 16)
r, g, b = r_hex/255, g_hex/255, b_hex/255
self.actors[actor_index].GetProperty().SetColor(r, g, b)
self.render_window.Render()
def show_hide_actor(self, item: QTreeWidgetItem):
if item.text(1):
actor_index = int(item.text(1))
if item.checkState(0) == Qt.Checked:
self.actors[actor_index].VisibilityOn()
elif item.checkState(0) == Qt.Unchecked:
self.actors[actor_index].VisibilityOff()
self.render_window.Render()
def add_actor(self, actor, actor_name, actor_color, parent_name):
self.actor_index += 1
self.actors[self.actor_index] = actor
self.renderer.AddActor(actor)
self.renderer.ResetCamera()
self.render_window.Render()
item_index = self.tree_widget.top_level_names.index(parent_name)
basename = os.path.splitext(os.path.basename(actor_name))[0]
mesh_item = QTreeWidgetItem(self.tree_widget.topLevelItem(item_index))
color_picker = ColorPickerWidget(color=actor_color)
actor_index = self.actor_index
color_picker.label.clicked.connect(lambda: self.change_color(actor_index))
mesh_item.setText(0, '{}'.format(basename))
mesh_item.setCheckState(0, Qt.Checked)
mesh_item.setText(1, '{}'.format(self.actor_index))
self.tree_widget.setItemWidget(mesh_item, 2, color_picker)
def show_mesh(self, params):
node_ids = params.node_ids
nodes = params.nodes
element_types = params.element_types
elements = params.elements
points = vtkPoints()
for index, node in enumerate(nodes):
points.InsertPoint(index, node)
grid = vtkUnstructuredGrid()
grid.SetPoints(points)
for index, element in enumerate(elements):
connection = [node_ids.index(i) for i in element]
grid.InsertNextCell(VTK_ELEMENT_TYPE_TABLE[element_types[index]], len(element), connection)
mapper = vtkDataSetMapper()
mapper.SetInputData(grid)
actor = vtkActor()
actor.SetMapper(mapper)
r, g, b = random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)
actor.GetProperty().SetColor(r / 255, g / 255, b / 255)
actor.GetProperty().SetPointSize(int(self.navigation_tab.node_size_spinbox.text()))
actor.GetProperty().EdgeVisibilityOn()
actor.GetProperty().RenderPointsAsSpheresOn()
r_hex = hex(r)[2:].rjust(2, '0')
g_hex = hex(g)[2:].rjust(2, '0')
b_hex = hex(b)[2:].rjust(2, '0')
actor_color = '#{}{}{}'.format(r_hex, g_hex, b_hex)
self.add_actor(actor, params.filename, actor_color, '网格')
def show_scatter(self, params):
nodes = params.nodes
points = vtkPoints()
for index, value in enumerate(nodes):
points.InsertPoint(index, value)
grid = vtkUnstructuredGrid()
grid.SetPoints(points)
for i in range(len(nodes)):
grid.InsertNextCell(VTK_VERTEX, 1, [i])
mapper = vtkDataSetMapper()
mapper.SetInputData(grid)
actor = vtkActor()
actor.SetMapper(mapper)
r, g, b = random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)
actor.GetProperty().SetColor(r/255, g/255, b/255)
actor.GetProperty().SetPointSize(int(self.navigation_tab.node_size_spinbox.text()))
actor.GetProperty().RenderPointsAsSpheresOn()
r_hex = hex(r)[2:].rjust(2, '0')
g_hex = hex(g)[2:].rjust(2, '0')
b_hex = hex(b)[2:].rjust(2, '0')
actor_color = '#{}{}{}'.format(r_hex, g_hex, b_hex)
self.add_actor(actor, params.filename, actor_color, '网格')
if __name__ == '__main__':
app = QApplication(sys.argv)
app.setApplicationName('{}'.format(__appname__))
app.setOrganizationName('{}'.format(__organization__))
win = MainWindow()
win.show()
app.exec_()
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scripts/raw.py | CadenaR/Clean-Transform-Pipeline | 0 | 58613 | <gh_stars>0
import csv
from zipfile import ZipFile
def save_raw_data(inbound_path, raw_path):
# Unzipping file
with ZipFile(
inbound_path,
"r",
) as zipfile:
names_list = zipfile.namelist()
csv_file_path = zipfile.extract(names_list[0], path=raw_path)
# Open the CSV file in read mode
with open(csv_file_path, mode="r", encoding="windows-1252") as csv_file:
reader = csv.DictReader(csv_file)
row = next(reader) # Get first row from reader
print("[Raw] First row example:", row)
if __name__ == "__main__":
print("[Raw] Start")
print("[Raw] Unzipping file")
inbound_path = '../data/inbound/all_data.zip'
raw_path = '../data/raw/'
save_raw_data(inbound_path, raw_path)
print("[Raw] End") | [
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jina/executors/indexers/keyvalue.py | winstonww/jina | 0 | 198758 | <reponame>winstonww/jina
__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import mmap
import os
import random
from typing import Iterable, Optional
import numpy as np
from . import BaseKVIndexer
from ..compound import CompoundExecutor
HEADER_NONE_ENTRY = (-1, -1, -1)
class _WriteHandler:
"""
Write file handler.
:param path: Path of the file.
:param mode: Writing mode. (e.g. 'ab', 'wb')
"""
def __init__(self, path, mode):
self.body = open(path, mode)
self.header = open(path + '.head', mode)
def close(self):
"""Close the file."""
if not self.body.closed:
self.body.close()
if not self.header.closed:
self.header.close()
def flush(self):
"""Clear the body and header."""
if not self.body.closed:
self.body.flush()
if not self.header.closed:
self.header.flush()
class _ReadHandler:
"""
Read file handler.
:param path: Path of the file.
:param key_length: Length of key.
"""
def __init__(self, path, key_length):
with open(path + '.head', 'rb') as fp:
tmp = np.frombuffer(
fp.read(),
dtype=[
('', (np.str_, key_length)),
('', np.int64),
('', np.int64),
('', np.int64),
],
)
self.header = {
r[0]: None
if np.array_equal((r[1], r[2], r[3]), HEADER_NONE_ENTRY)
else (r[1], r[2], r[3])
for r in tmp
}
self._body = open(path, 'r+b')
self.body = self._body.fileno()
def close(self):
"""Close the file."""
if not self._body.closed:
self._body.close()
class BinaryPbWriterMixin:
"""Mixing for providing the binarypb writing and reading methods"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._start = 0
self._page_size = mmap.ALLOCATIONGRANULARITY
def get_add_handler(self) -> '_WriteHandler':
"""
Get write file handler.
:return: write handler
"""
# keep _start position as in pickle serialization
return _WriteHandler(self.index_abspath, 'ab')
def get_create_handler(self) -> '_WriteHandler':
"""
Get write file handler.
:return: write handler.
"""
self._start = 0 # override _start position
return _WriteHandler(self.index_abspath, 'wb')
def get_query_handler(self) -> '_ReadHandler':
"""
Get read file handler.
:return: read handler.
"""
return _ReadHandler(self.index_abspath, self.key_length)
def _add(
self, keys: Iterable[str], values: Iterable[bytes], writer: _WriteHandler = None
):
if writer is None:
if getattr(self, 'write_handler'):
writer = self.write_handler
else:
raise Exception(
'No writer provided to add() and no self.write_handler available'
)
for key, value in zip(keys, values):
l = len(value) #: the length
p = (
int(self._start / self._page_size) * self._page_size
) #: offset of the page
r = (
self._start % self._page_size
) #: the remainder, i.e. the start position given the offset
# noinspection PyTypeChecker
writer.header.write(
np.array(
(key, p, r, r + l),
dtype=[
('', (np.str_, self.key_length)),
('', np.int64),
('', np.int64),
('', np.int64),
],
).tobytes()
)
self._start += l
writer.body.write(value)
self._size += 1
writer.flush()
def delete(self, keys: Iterable[str], *args, **kwargs) -> None:
"""Delete the serialized documents from the index via document ids.
:param keys: a list of ``id``, i.e. ``doc.id`` in protobuf
:param args: not used
:param kwargs: not used
"""
keys = self._filter_nonexistent_keys(keys, self.query_handler.header.keys())
del self.query_handler
self.handler_mutex = False
if keys:
self._delete(keys)
def _delete(self, keys: Iterable[str]) -> None:
for key in keys:
self.write_handler.header.write(
np.array(
tuple(np.concatenate([[key], HEADER_NONE_ENTRY])),
dtype=[
('', (np.str_, self.key_length)),
('', np.int64),
('', np.int64),
('', np.int64),
],
).tobytes()
)
self._size -= 1
def _query(self, key):
pos_info = self.query_handler.header.get(key, None)
if pos_info is not None:
p, r, l = pos_info
with mmap.mmap(self.query_handler.body, offset=p, length=l) as m:
return m[r:]
class BinaryPbIndexer(BinaryPbWriterMixin, BaseKVIndexer):
"""Simple Key-value indexer."""
def __getstate__(self):
# called on pickle save
if self.delete_on_dump:
self._delete_invalid_indices()
d = super().__getstate__()
return d
def _delete_invalid_indices(self):
if self.query_handler:
self.query_handler.close()
if self.write_handler:
self.write_handler.flush()
self.write_handler.close()
keys = []
vals = []
# we read the valid values and write them to the intermediary file
read_handler = _ReadHandler(self.index_abspath, self.key_length)
for key in read_handler.header.keys():
pos_info = read_handler.header.get(key, None)
if pos_info:
p, r, l = pos_info
with mmap.mmap(read_handler.body, offset=p, length=l) as m:
keys.append(key)
vals.append(m[r:])
read_handler.close()
if len(keys) == 0:
return
# intermediary file
tmp_file = self.index_abspath + '-tmp'
self._start = 0
filtered_data_writer = _WriteHandler(tmp_file, 'ab')
# reset size
self._size = 0
self._add(keys, vals, filtered_data_writer)
filtered_data_writer.close()
# replace orig. file
# and .head file
head_path = self.index_abspath + '.head'
os.remove(self.index_abspath)
os.remove(head_path)
os.rename(tmp_file, self.index_abspath)
os.rename(tmp_file + '.head', head_path)
def __init__(self, delete_on_dump: bool = False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.delete_on_dump = delete_on_dump
def add(
self, keys: Iterable[str], values: Iterable[bytes], *args, **kwargs
) -> None:
"""Add the serialized documents to the index via document ids.
:param keys: a list of ``id``, i.e. ``doc.id`` in protobuf
:param values: serialized documents
:param args: extra arguments
:param kwargs: keyword arguments
"""
if not any(keys):
return
self._add(keys, values)
def sample(self) -> Optional[bytes]:
"""Return a random entry from the indexer for sanity check.
:return: A random entry from the indexer.
"""
k = random.sample(self.query_handler.header.keys(), k=1)[0]
return self[k]
def __iter__(self):
for k in self.query_handler.header.keys():
yield self[k]
def query(self, key: str, *args, **kwargs) -> Optional[bytes]:
"""Find the serialized document to the index via document id.
:param key: document id
:param args: extra arguments
:param kwargs: keyword arguments
:return: serialized documents
"""
return self._query(key)
def update(
self, keys: Iterable[str], values: Iterable[bytes], *args, **kwargs
) -> None:
"""Update the serialized documents on the index via document ids.
:param keys: a list of ``id``, i.e. ``doc.id`` in protobuf
:param values: serialized documents
:param args: extra arguments
:param kwargs: keyword arguments
"""
keys, values = self._filter_nonexistent_keys_values(
keys, values, self.query_handler.header.keys()
)
del self.query_handler
self.handler_mutex = False
if keys:
self._delete(keys)
self.add(keys, values)
def delete(self, keys: Iterable[str], *args, **kwargs) -> None:
"""Delete the serialized documents from the index via document ids.
:param keys: a list of ``id``, i.e. ``doc.id`` in protobuf
:param args: not used
:param kwargs: not used"""
super(BinaryPbIndexer, self).delete(keys)
class KeyValueIndexer(BinaryPbIndexer):
"""Alias for :class:`BinaryPbIndexer` """
class DataURIPbIndexer(BinaryPbIndexer):
"""Alias for BinaryPbIndexer"""
class UniquePbIndexer(CompoundExecutor):
"""A frequently used pattern for combining a :class:`BaseKVIndexer` and a :class:`DocCache` """
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] |
BOJ/dp_boj/drop.py | mrbartrns/swacademy_structure | 0 | 184361 | # BOJ 14501
import sys
si = sys.stdin.readline
def solve(n):
ans = 0
for i in range(n, 0, -1):
if i + t[i] > n + 1:
arr[i] = arr[i + 1]
else:
arr[i] = max(arr[i + 1], arr[i] + arr[i + t[i]])
ans = max(ans, arr[i])
return ans
n = int(si())
t = [0] * 17
arr = [0] * 17
for i in range(1, n + 1):
u, v = map(int, si().split())
t[i] = u
arr[i] = v
print(solve(n))
"""
n = 7
t = [0, 3, 5, 1, 1, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]
arr = [0, 10, 20, 10, 20, 15, 40, 200, 0, 0, 0, 0, 0, 0, 0, 0]
ans = 0
for y in range(n, 0, -1):
if y + t[y] > n + 1:
arr[y] = arr[y + 1]
else:
arr[y] = max(arr[y + 1], arr[y] + arr[y + t[y]])
ans = max(ans, arr[y])
print(ans)
# print(solve(n))
""" | [
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main/schema/components/media_stats.py | kristianmk/tator | 50 | 111511 | <gh_stars>10-100
media_stats = {
'type': 'object',
'properties': {
'count': {'type': 'integer', 'minimum': 0},
'download_size': {'type': 'integer', 'minimum': 0},
'total_size': {'type': 'integer', 'minimum': 0},
'duration': {'type': 'number', 'minimum': 0},
},
}
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python/vpc_flow_logs_enabled.py | jghaines/aws-config-rules | 1 | 92626 | #
# This file made available under CC0 1.0 Universal (https://creativecommons.org/publicdomain/zero/1.0/legalcode)
#
# Ensure VPC Flow Logs is enabled.
# Description: Checks that VPC Flow Logs is enabled at specific VPC
#
# Trigger Type: Change Triggered
# Scope of Changes: EC2:VPC
# Required Resource Identifier: VPC ID
# Example Value: vpc-xxxxxxxx
import boto3
import json
def evaluate_compliance(config_item, vpc_id):
if (config_item['resourceType'] != 'AWS::EC2::VPC'):
return 'NOT_APPLICABLE'
elif is_flow_logs_enabled(vpc_id):
return 'COMPLIANT'
else:
return 'NON_COMPLIANT'
def is_flow_logs_enabled(vpc_id):
ec2 = boto3.client('ec2')
response = ec2.describe_flow_logs(
Filter=[
{
'Name': 'resource-id',
'Values': [
vpc_id,
]
},
],
)
if len(response[u'FlowLogs']) != 0: return True
def lambda_handler(event, context):
invoking_event = json.loads(event['invokingEvent'])
compliance_value = 'NOT_APPLICABLE'
vpc_id = invoking_event['configurationItem']['resourceId']
compliance_value = evaluate_compliance(
invoking_event['configurationItem'], vpc_id)
config = boto3.client('config')
response = config.put_evaluations(
Evaluations=[
{
'ComplianceResourceType': invoking_event['configurationItem']['resourceType'],
'ComplianceResourceId': vpc_id,
'ComplianceType': compliance_value,
'OrderingTimestamp': invoking_event['configurationItem']['configurationItemCaptureTime']
},
],
ResultToken=event['resultToken'])
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peeringdb/migrations/0006_auto_20180213_0031.py | schiederme/peering-manager | 173 | 186435 | <filename>peeringdb/migrations/0006_auto_20180213_0031.py
# -*- coding: utf-8 -*-
# Generated by Django 1.11.10 on 2018-02-12 23:31
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [("peeringdb", "0005_auto_20180122_1845")]
operations = [
migrations.CreateModel(
name="Prefix",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("protocol", models.CharField(max_length=8)),
("prefix", models.CharField(max_length=64)),
("ixlan_id", models.PositiveIntegerField()),
],
options={
"verbose_name": "IX Prefix",
"verbose_name_plural": "IX Prefixes",
"ordering": ["prefix"],
},
),
migrations.AddField(
model_name="networkixlan",
name="ixlan_id",
field=models.PositiveIntegerField(default=0),
preserve_default=False,
),
]
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tests/openwisp2/sample_network_topology/migrations/0004_default_groups.py | Aryamanz29/openwisp-network-topology | 0 | 71234 | from django.conf import settings
from django.contrib.auth.management import create_permissions
from django.contrib.auth.models import Permission
from django.db import migrations
from swapper import dependency, split
def create_default_permissions(apps, schema_editor):
for app_config in apps.get_app_configs():
app_config.models_module = True
create_permissions(app_config, apps=apps, verbosity=0)
app_config.models_module = None
def assign_permissions_to_groups(apps, schema_editor):
create_default_permissions(apps, schema_editor)
operators_and_admin_can_change = ['link', 'node']
operators_read_only_admins_manage = ['topology']
manage_operations = ['add', 'change', 'delete']
Group = apps.get_model('openwisp_users', 'Group')
try:
admin = Group.objects.get(name='Administrator')
operator = Group.objects.get(name='Operator')
# consider failures custom cases
# that do not have to be dealt with
except Group.DoesNotExist:
return
for model_name in operators_and_admin_can_change:
for operation in manage_operations:
permission = Permission.objects.get(
codename='{}_{}'.format(operation, model_name)
)
admin.permissions.add(permission.pk)
operator.permissions.add(permission.pk)
for model_name in operators_read_only_admins_manage:
try:
permission = Permission.objects.get(codename="view_{}".format(model_name))
operator.permissions.add(permission.pk)
except Permission.DoesNotExist:
pass
for operation in manage_operations:
admin.permissions.add(
Permission.objects.get(
codename="{}_{}".format(operation, model_name)
).pk
)
class Migration(migrations.Migration):
dependencies = [
dependency(*split(settings.AUTH_USER_MODEL), version='0004_default_groups'),
('sample_network_topology', '0003_add_user_defined_properties_field'),
]
operations = [
migrations.RunPython(
assign_permissions_to_groups, reverse_code=migrations.RunPython.noop
)
]
| [
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4782,
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13,
13,
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1018,
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12650,
29889,
657,
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978,
2433,
26486,
1495,
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1678,
396,
2050,
4418,
1973,
2888,
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13,
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396,
393,
437,
451,
505,
304,
367,
316,
1997,
411,
13,
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5174,
6431,
29889,
25125,
3664,
1252,
391,
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13,
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363,
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978,
297,
12768,
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392,
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6406,
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3068,
29918,
3167,
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363,
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297,
10933,
29918,
3372,
800,
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2333,
29889,
12650,
29889,
657,
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13,
18884,
15234,
3871,
2433,
29912,
3227,
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4286,
4830,
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1904,
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1723,
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4113,
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6847,
29889,
1202,
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16074,
29889,
20571,
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13,
9651,
5455,
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20571,
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13,
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363,
1904,
29918,
978,
297,
12768,
29918,
949,
29918,
6194,
29918,
328,
29885,
1144,
29918,
1171,
482,
29901,
13,
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1018,
29901,
13,
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353,
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2333,
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12650,
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657,
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19284,
3871,
543,
1493,
648,
29913,
1642,
4830,
29898,
4299,
29918,
978,
876,
13,
9651,
5455,
29889,
17858,
6847,
29889,
1202,
29898,
16074,
29889,
20571,
29897,
13,
4706,
5174,
20894,
2333,
29889,
25125,
3664,
1252,
391,
29901,
13,
9651,
1209,
13,
4706,
363,
5858,
297,
10933,
29918,
3372,
800,
29901,
13,
9651,
4113,
29889,
17858,
6847,
29889,
1202,
29898,
13,
18884,
20894,
2333,
29889,
12650,
29889,
657,
29898,
13,
462,
1678,
15234,
3871,
10724,
3227,
29913,
1642,
4830,
29898,
16453,
29892,
1904,
29918,
978,
29897,
13,
18884,
13742,
20571,
13,
9651,
1723,
13,
13,
13,
1990,
341,
16783,
29898,
26983,
800,
29889,
29924,
16783,
1125,
13,
1678,
9962,
353,
518,
13,
4706,
10609,
10456,
5451,
29898,
11027,
29889,
20656,
29950,
29918,
11889,
29918,
20387,
29931,
511,
1873,
2433,
29900,
29900,
29900,
29946,
29918,
4381,
29918,
13155,
5477,
13,
4706,
6702,
11249,
29918,
11618,
29918,
3332,
3002,
742,
525,
29900,
29900,
29900,
29941,
29918,
1202,
29918,
1792,
29918,
12119,
29918,
11330,
29918,
2671,
5477,
13,
1678,
4514,
13,
1678,
6931,
353,
518,
13,
4706,
9725,
800,
29889,
6558,
11980,
29898,
13,
9651,
3566,
29918,
17858,
6847,
29918,
517,
29918,
13155,
29892,
11837,
29918,
401,
29922,
26983,
800,
29889,
6558,
11980,
29889,
1217,
459,
13,
4706,
1723,
13,
1678,
4514,
13,
2
] |
Scripts/ReverseSearch/social_media.py | balswyan/senior-capstone-fall-2018 | 1 | 31847 | <filename>Scripts/ReverseSearch/social_media.py
import urllib2
from cookielib import CookieJar
import os
import re
import time
import json
cookies = CookieJar()
opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cookies))
opener.addheaders = [('User-agent', 'Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 '
'(KHTML, like Gecko) Chrome/24.0.1312.27 Safari/537.17')]
with open("missing_people.json") as f:
people = json.load(f)
for person in people['results']:
facebook_profile = 'https://www.facebook.com/search/top/?q='+ person['firstname'] + '%20' + person['lastname']
facebook_post ='https://www.facebook.com/search/posts/?q='+ person['firstname'] + '%20' + person['lastname']
facebook_news = 'https://www.facebook.com/search/str/' + person['firstname'] + '%20' + person['lastname'] + '/links-keyword/stories-news-pivot'
instagram_tags = 'https://www.instagram.com/explore/tags/'+ person['firstname'] + person['lastname']
twitter_search = 'https://twitter.com/search?q='+ person['firstname'] + '%20' + person['lastname'] + '&src=typd'
twitter_hashtag = 'https://twitter.com/hashtag/' + person['firstname'] + person['lastname'] '?src=hash'
print(What ever you want)
| [
1,
529,
9507,
29958,
4081,
29879,
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1123,
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13,
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13,
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709,
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1053,
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347,
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279,
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763,
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408,
285,
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12,
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13,
13,
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297,
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13,
12,
12,
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510,
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4478,
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3332,
13401,
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2022,
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4102,
978,
2033,
718,
14210,
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29900,
29915,
718,
2022,
1839,
4230,
978,
2033,
13,
12,
12,
15445,
29918,
2490,
353,
29915,
991,
597,
1636,
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15445,
29889,
510,
29914,
4478,
29914,
14080,
13401,
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2433,
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2022,
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978,
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718,
14210,
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718,
2022,
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4230,
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2033,
13,
12,
12,
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353,
525,
991,
597,
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510,
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710,
22208,
718,
2022,
1839,
4102,
978,
2033,
718,
14210,
29906,
29900,
29915,
718,
2022,
1839,
4230,
978,
2033,
718,
8207,
4965,
29899,
26766,
29914,
303,
3842,
29899,
15753,
29899,
29886,
11002,
29915,
13,
12,
12,
2611,
14442,
29918,
11338,
353,
525,
991,
597,
1636,
29889,
2611,
14442,
29889,
510,
29914,
24516,
487,
29914,
11338,
29914,
18717,
2022,
1839,
4102,
978,
2033,
718,
2022,
1839,
4230,
978,
2033,
13,
12,
12,
24946,
29918,
4478,
353,
525,
991,
597,
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29889,
510,
29914,
4478,
29973,
29939,
2433,
29974,
2022,
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2033,
718,
14210,
29906,
29900,
29915,
718,
2022,
1839,
4230,
978,
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718,
525,
29987,
4351,
29922,
1017,
15926,
29915,
13,
12,
12,
24946,
29918,
5349,
400,
351,
353,
525,
991,
597,
24946,
29889,
510,
29914,
5349,
400,
351,
22208,
718,
2022,
1839,
4102,
978,
2033,
718,
2022,
1839,
4230,
978,
2033,
525,
29973,
4351,
29922,
8568,
29915,
13,
12,
12,
2158,
29898,
5618,
3926,
366,
864,
29897,
13,
13,
2
] |
docs/test.py | jelitox/proxylist | 0 | 119291 | from proxylists import get_proxies
proxies = get_proxies()
print(proxies)
| [
1,
515,
10166,
21513,
1053,
679,
29918,
771,
29916,
583,
13,
13,
13,
771,
29916,
583,
353,
679,
29918,
771,
29916,
583,
580,
13,
2158,
29898,
771,
29916,
583,
29897,
13,
2
] |
flow/networks/i24_subnetwork.py | kumarms1/flow | 0 | 196406 | <reponame>kumarms1/flow
from flow.networks.base import Network
EDGES_DISTRIBUTION = [
# Main Highway
"108162443",
"108162396",
"108162303",
"634155184",
"108162054",
"108161914#0",
"108161914#1",
"108161914#2",
"108161914#3",
"108161914#4",
"108161914#5",
"635462234",
"173720363#0",
"173720363#1",
"173720363#2",
"173720363#3",
"173720363#4",
"173720351",
"173720358#0",
"173720358#1",
"173720350",
"173720361",
"173720354",
"173720360",
]
class I24SubNetwork(Network):
def specify_routes(self, net_params):
return {
"108162443": ["108162443",
"108162396",
"108162303",
"634155184",
"108162054",
"108161914#0",
"108161914#1",
"108161914#2",
"108161914#3",
"108161914#4",
"108161914#5",
"635462234",
"173720363#0",
"173720363#1",
"173720363#2",
"173720363#3",
"173720363#4",
"173720351",
"173720358#0",
"173720358#1",
"173720350",
"173720361",
"173720354",
"173720360"]
} | [
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13,
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29955,
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613,
13,
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376,
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29955,
29941,
29955,
29906,
29900,
29941,
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] |
preprocess/python/h5py_utils.py | fredericsun/seq2seq-ChinesePoetryGenerater | 0 | 23125 | import numpy as np
import h5py
import os
import sys
from copy import deepcopy
#handle .(period) and slash specially since it is part of path
#replace with \period or \slash-forward when store, recover later
#not using '\forward-slash' is because \f is a special character
PERIOD='\period'
SLASH='\slash-forward'
'''
This function will save a python dictionary with format {'key':vector} to hdf5 format
target_dict is the target dictionary
f_name is the HDF5 file name including path to it
mode is file open mode, 'a' for append is the default setting
output file is a HDF5 file which the keys of HDF5 are keys of target dict
dataset is corresponding array of each key in target dict
'''
def dict2hdf5(target_dict, f_name, sub_group='/', mode='a'):
print ('Saving In HDF5 Format...')
index_count=0
bar_count=0
total=len(target_dict)
if total<50:
total=50
#open HDF5 file
f = h5py.File(f_name, mode)
for key in target_dict:
######### print progress #############
index_count +=1
if index_count % (total/50) ==0:
sys.stdout.write('\r'),
bar_count += 1
sys.stdout.write("[%-50s] %d%% %d/%d" % ('#'*bar_count, 2*bar_count, index_count, total))
sys.stdout.flush()
######### end print progress #########
#print os.path.join(sub_group,key)
#sys.stdout.flush()
#replace special handled chars, will not change string if / is not in it
dataset_key=key.replace('.',PERIOD).replace('/',SLASH)
f.create_dataset(os.path.join(sub_group,dataset_key), data=target_dict[key])
f.close()
'''
This function will convert saved hdf5 file from previous function back to {'key':vector} dictionary
f_name is the HDF5 file name including path to it
'''
def hdf52dict(f_name, sub_group='/' ):
rv=dict()
#open HDF5 file
f = h5py.File(f_name, 'r')
group=f[sub_group]
for key in group:
#replace back special handled chars
key=key.replace(PERIOD, '.').replace(SLASH,'/')
rv[key]=np.array(group[key])
f.close()
return rv
def main():
d=dict()
d['hello']=[1,0,0,0,0,0,0,0,0,0,0]
d['world']=[0,1,0,0,0,0,0,0,0,0,0]
dict2hdf5(target_dict=d, f_name='test')
rv=hdf52dict('test')
print (rv)
if __name__ == '__main__':
main()
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src/server/oasisapi/analysis_models/tests/test_analysis_model.py | macabuag/OasisPlatform_GLP | 36 | 127014 | import json
import string
from backports.tempfile import TemporaryDirectory
from django.test import override_settings
from django.urls import reverse
from django_webtest import WebTest, WebTestMixin
from hypothesis import given, settings
from hypothesis.extra.django import TestCase
from hypothesis.strategies import text
from rest_framework_simplejwt.tokens import AccessToken
from ...auth.tests.fakes import fake_user
from ..models import AnalysisModel
from .fakes import fake_analysis_model
# Override default deadline for all tests to 8s
settings.register_profile("ci", deadline=800.0)
settings.load_profile("ci")
class AnalysisModelApi(WebTest, TestCase):
@given(
supplier_id=text(alphabet=string.ascii_letters, min_size=1, max_size=10),
version_id=text(alphabet=string.whitespace, min_size=0, max_size=10),
)
def test_version_id_is_missing___response_is_400(self, supplier_id, version_id):
user = fake_user()
response = self.app.post(
reverse('analysis-model-list', kwargs={'version': 'v1'}),
expect_errors=True,
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
params=json.dumps({
'supplier_id': supplier_id,
'version_id': version_id,
}),
content_type='application/json',
)
self.assertEqual(400, response.status_code)
self.assertFalse(AnalysisModel.objects.exists())
@given(
supplier_id=text(alphabet=string.whitespace, min_size=0, max_size=10),
version_id=text(alphabet=string.ascii_letters, min_size=1, max_size=10),
)
def test_supplier_id_is_missing___response_is_400(self, supplier_id, version_id):
user = fake_user()
response = self.app.post(
reverse('analysis-model-list', kwargs={'version': 'v1'}),
expect_errors=True,
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
params=json.dumps({
'supplier_id': supplier_id,
'version_id': version_id,
}),
content_type='application/json',
)
self.assertEqual(400, response.status_code)
self.assertFalse(AnalysisModel.objects.exists())
@given(
supplier_id=text(alphabet=string.ascii_letters, min_size=1, max_size=10),
model_id=text(alphabet=string.ascii_letters, min_size=1, max_size=10),
version_id=text(alphabet=string.ascii_letters, min_size=1, max_size=10),
)
def test_data_is_valid___object_is_created(self, supplier_id, model_id, version_id):
user = fake_user()
response = self.app.post(
reverse('analysis-model-list', kwargs={'version': 'v1'}),
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
params=json.dumps({
'supplier_id': supplier_id,
'model_id': model_id,
'version_id': version_id,
}),
content_type='application/json',
)
model = AnalysisModel.objects.first()
self.assertEqual(201, response.status_code)
self.assertEqual(model.supplier_id, supplier_id)
self.assertEqual(model.version_id, version_id)
self.assertEqual(model.model_id, model_id)
class ModelSettingsJson(WebTestMixin, TestCase):
def test_user_is_not_authenticated___response_is_forbidden(self):
models = fake_analysis_model()
response = self.app.get(models.get_absolute_settings_url(), expect_errors=True)
self.assertIn(response.status_code, [401,403])
""" Add these check back in once models auto-update their settings fields
"""
def test_settings_json_is_not_present___get_response_is_404(self):
user = fake_user()
models = fake_analysis_model()
response = self.app.get(
models.get_absolute_settings_url(),
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
expect_errors=True,
)
self.assertEqual(404, response.status_code)
def test_settings_json_is_not_present___delete_response_is_404(self):
user = fake_user()
models = fake_analysis_model()
response = self.app.delete(
models.get_absolute_settings_url(),
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
expect_errors=True,
)
self.assertEqual(404, response.status_code)
def test_settings_json_is_not_valid___response_is_400(self):
with TemporaryDirectory() as d:
with override_settings(MEDIA_ROOT=d):
user = fake_user()
models = fake_analysis_model()
json_data = {
"model_settings":{
"event_set":{
"name": "Event Set",
"default": "P",
"options":[
{"id":"P", "desc": "Proabilistic"},
{"id":"H", "desc": "Historic"}
]
},
"event_occurrence_id":{
"name": "Occurrence Set",
"desc": "PiWind Occurrence selection",
"default": 1,
"options":[
{"id":"1", "desc": "Long Term"}
]
},
"boolean_parameters": [
{"name": "peril_wind", "desc":"Boolean option", "default": 1.1},
{"name": "peril_surge", "desc":"Boolean option", "default": True}
],
"float_parameter": [
{"name": "float_1", "desc":"Some float value", "default": False, "max":1.0, "min":0.0},
{"name": "float_2", "desc":"Some float value", "default": 0.3, "max":1.0, "min":0.0}
]
},
"lookup_settings":{
"supported_perils":[
{"i": "WSS", "desc": "Single Peril: Storm Surge"},
{"id": "WTC", "des": "Single Peril: Tropical Cyclone"},
{"id": "WW11", "desc": "Group Peril: Windstorm with storm surge"},
{"id": "WW2", "desc": "Group Peril: Windstorm w/o storm surge"}
]
}
}
response = self.app.post(
models.get_absolute_settings_url(),
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
params=json.dumps(json_data),
content_type='application/json',
expect_errors=True,
)
validation_error = {
'model_settings': ["Additional properties are not allowed ('float_parameter' was unexpected)"],
'model_settings-event_set': ["'desc' is a required property"],
'model_settings-event_occurrence_id-default': ["1 is not of type 'string'"],
'model_settings-boolean_parameters-0-default': ["1.1 is not of type 'boolean'"],
'lookup_settings-supported_perils-0': ["Additional properties are not allowed ('i' was unexpected)", "'id' is a required property"],
'lookup_settings-supported_perils-1': ["Additional properties are not allowed ('des' was unexpected)", "'desc' is a required property"],
'lookup_settings-supported_perils-2-id': ["'WW11' is too long"]
}
self.assertEqual(400, response.status_code)
self.assertDictEqual.__self__.maxDiff = None
self.assertDictEqual(json.loads(response.body), validation_error)
def test_settings_json_is_uploaded___can_be_retrieved(self):
with TemporaryDirectory() as d:
with override_settings(MEDIA_ROOT=d):
user = fake_user()
models = fake_analysis_model()
json_data = {
"model_settings":{
"event_set":{
"name": "Event Set",
"desc": "Either Probablistic or Historic",
"default": "P",
"options":[
{"id":"P", "desc": "Proabilistic"},
{"id":"H", "desc": "Historic"}
]
},
"event_occurrence_id":{
"name": "Occurrence Set",
"desc": "PiWind Occurrence selection",
"default": "1",
"options":[
{"id":"1", "desc": "Long Term"}
]
},
"boolean_parameters": [
{"name": "peril_wind", "desc":"Boolean option", "default": False},
{"name": "peril_surge", "desc":"Boolean option", "default": True}
],
"float_parameters": [
{"name": "float_1", "desc":"Some float value", "default": 0.5, "max":1.0, "min":0.0},
{"name": "float_2", "desc":"Some float value", "default": 0.3, "max":1.0, "min":0.0}
]
},
"lookup_settings":{
"supported_perils":[
{"id": "WSS", "desc": "Single Peril: Storm Surge"},
{"id": "WTC", "desc": "Single Peril: Tropical Cyclone"},
{"id": "WW1", "desc": "Group Peril: Windstorm with storm surge"},
{"id": "WW2", "desc": "Group Peril: Windstorm w/o storm surge"}
]
}
}
self.app.post(
models.get_absolute_settings_url(),
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
params=json.dumps(json_data),
content_type='application/json'
)
response = self.app.get(
models.get_absolute_settings_url(),
headers={
'Authorization': 'Bearer {}'.format(AccessToken.for_user(user))
},
)
self.assertDictEqual.__self__.maxDiff = None
self.assertDictEqual(json.loads(response.body), json_data)
self.assertEqual(response.content_type, 'application/json')
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2
] |
mmhoidet/core/hoi/samplers/__init__.py | noobying/mmhoidet | 2 | 177090 | from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
from .pseudo_sampler import PseudoSampler
__all__ = [
'BaseSampler', 'SamplingResult', 'PseudoSampler'
]
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starcheat/assets/core.py | chrmoritz/starcheat | 0 | 151009 | """
Module for reading and indexing Starbound assets
"""
import os
import json
import re
import sqlite3
import logging
import starbound
import starbound.btreedb4
from assets.blueprints import Blueprints
from assets.items import Items
from assets.species import Species
from assets.player import Player
from assets.monsters import Monsters
from assets.techs import Techs
from assets.images import Images
from assets.frames import Frames
from assets.common import asset_category
# Regular expression for comments
comment_re = re.compile(
'("(\\[\s\S]|[^"])*")|((^)?[^\S\n]*/(?:\*(.*?)\*/[^\S\n]*|/[^\n]*)($)?)',
re.DOTALL | re.MULTILINE
)
ignore_assets = re.compile(".*\.(db|ds_store|ini|psd|patch)", re.IGNORECASE)
def parse_json(content, key):
decoder = json.JSONDecoder(strict=False)
# Looking for comments
# Allows for // inside of the " " JSON data
content = comment_re.sub(lambda m: m.group(1) or '', content)
# Return json file
return decoder.decode(content)
def load_asset_file(filename):
with open(filename) as f:
content = ''.join(f.readlines())
return parse_json(content, filename)
class Assets():
def __init__(self, db_file, starbound_folder):
self.starbound_folder = starbound_folder
self.mods_folder = os.path.join(self.starbound_folder, "giraffe_storage", "mods")
self.db = sqlite3.connect(db_file)
self.vanilla_assets = os.path.join(self.starbound_folder, "assets", "packed.pak")
self.image_cache = {}
def init_db(self):
c = self.db.cursor()
c.execute("drop table if exists assets")
c.execute("""create table assets
(key text, path text, type text, category text, name text, desc text)""")
self.db.commit()
def total_indexed(self):
c = self.db.cursor()
try:
c.execute("select count(*) from assets")
except sqlite3.OperationalError:
# database may be corrupt
return 0
return c.fetchone()[0]
def create_index(self, asset_files=False):
logging.info("Creating new assets index...")
if not asset_files:
asset_files = self.find_assets()
blueprints = Blueprints(self)
items = Items(self)
species = Species(self)
monsters = Monsters(self)
techs = Techs(self)
frames = Frames(self)
new_index_query = "insert into assets values (?, ?, ?, ?, ?, ?)"
c = self.db.cursor()
for asset in asset_files:
yield (asset[0], asset[1])
tmp_data = None
if asset_category(asset[0]) != '':
if asset[0].endswith(".png"):
tmp_data = (asset[0], asset[1], "image", "", "", "")
elif blueprints.is_blueprint(asset[0]):
tmp_data = blueprints.index_data(asset)
elif species.is_species(asset[0]):
tmp_data = species.index_data(asset)
elif items.is_item(asset[0]):
tmp_data = items.index_data(asset)
elif monsters.is_monster(asset[0]):
tmp_data = monsters.index_data(asset)
elif techs.is_tech(asset[0]):
tmp_data = techs.index_data(asset)
elif frames.is_frames(asset[0]):
tmp_data = frames.index_data(asset)
else:
logging.warning("Skipping invalid asset (no file extension) %s in %s" % (asset[0], asset[1]))
if tmp_data is not None:
c.execute(new_index_query, tmp_data)
self.db.commit()
logging.info("Finished creating index")
def find_assets(self):
"""Scan all Starbound assets and return key/file list.
Includes mod files, .pak files.
"""
index = []
vanilla_path = os.path.join(self.starbound_folder, "assets")
logging.info("Scanning vanilla assets")
vanilla_assets = self.scan_asset_folder(vanilla_path)
[index.append(x) for x in vanilla_assets]
mods_path = self.mods_folder
if not os.path.isdir(mods_path):
return index
for mod in os.listdir(mods_path):
mod_folder = os.path.join(mods_path, mod)
if os.path.isdir(mod_folder):
logging.info("Scanning mod folder: " + mod)
mod_assets = self.scan_asset_folder(mod_folder)
[index.append(x) for x in mod_assets]
elif mod_folder.endswith(".modpak"):
logging.info("Scanning modpak: " + mod)
mod_assets = self.scan_modpak(mod_folder)
[index.append(x) for x in mod_assets]
return index
def scan_modpak(self, modpak):
# TODO: may need support for reading the mod folder from the pakinfo file
db = starbound.open_file(modpak)
index = [(x, modpak) for x in db.get_index()]
return index
def scan_asset_folder(self, folder):
pak_path = os.path.join(folder, "packed.pak")
if os.path.isfile(pak_path):
db = starbound.open_file(pak_path)
index = [(x, pak_path) for x in db.get_index()]
return index
else:
# old style, probably a mod
index = []
mod_assets = None
files = os.listdir(folder)
# TODO: will need more logic to handle .modpack with modinfo inside.
found_mod_info = False
for f in files:
if f.endswith(".modinfo"):
modinfo = os.path.join(folder, f)
try:
modinfo_data = load_asset_file(modinfo)
path = "./"
if "path" in modinfo_data.keys():
path = modinfo_data["path"]
mod_assets = os.path.join(folder, path)
found_mod_info = True
except ValueError:
# really old mods
folder = os.path.join(folder, "assets")
if os.path.isdir(folder):
mod_assets = folder
if mod_assets is None:
return index
elif found_mod_info and self.is_packed_file(mod_assets):
# TODO: make a .pak scanner function that works for vanilla and mods
pak_path = os.path.normpath(mod_assets)
db = starbound.open_file(pak_path)
for x in db.get_index():
# removes thumbs.db etc from user pak files
if re.match(ignore_assets, x) is None:
index.append((x, pak_path))
return index
elif not os.path.isdir(mod_assets):
return index
# now we can scan!
for root, dirs, files in os.walk(mod_assets):
for f in files:
if re.match(ignore_assets, f) is None:
asset_folder = os.path.normpath(mod_assets)
asset_file = os.path.normpath(os.path.join(root.replace(folder, ""), f))
index.append((asset_file, asset_folder))
return index
def is_packed_file(self, path):
"""
Returns true if the asset path is a file (will be assuming from the index that it is a packed type)
Returns false if the asset path is a folder (legacy/non-packed mods)
"""
return os.path.isfile(path)
def read(self, key, path, image=False):
if self.is_packed_file(path):
key = key.lower()
db = starbound.open_file(path)
# try the cache first
if image and key in self.image_cache:
return self.image_cache[key]
try:
data = db.get(key)
except KeyError:
if image and path != self.vanilla_assets:
img = self.read(key, self.vanilla_assets, image)
self.image_cache[key] = img
return img
else:
logging.exception("Unable to read db asset '%s' from '%s'" % (key, path))
return None
if image:
img = data
self.image_cache[key] = img
return img
else:
try:
asset = parse_json(data.decode("utf-8"), key)
return asset
except ValueError:
logging.exception("Unable to read db asset '%s' from '%s'" % (key, path))
return None
else:
asset_file = os.path.join(path, key[1:])
try:
if image:
img = open(asset_file, "rb").read()
self.image_cache[key] = img
return img
else:
asset = load_asset_file(asset_file)
return asset
except (FileNotFoundError, ValueError):
if image and path != self.vanilla_assets:
if self.is_packed_file(self.vanilla_assets):
img = self.read(key.replace("\\", "/"), self.vanilla_assets, image)
self.image_cache[key] = img
return img
else:
img = self.read(key, self.vanilla_assets, image)
self.image_cache[key] = img
return img
else:
logging.exception("Unable to read asset file '%s' from '%s'" % (key, path))
return None
def blueprints(self):
return Blueprints(self)
def items(self):
return Items(self)
def species(self):
return Species(self)
def player(self):
return Player(self)
def monsters(self):
return Monsters(self)
def techs(self):
return Techs(self)
def images(self):
return Images(self)
def frames(self):
return Frames(self)
def get_all(self, asset_type):
c = self.assets.db.cursor()
c.execute("select * from assets where type = ? order by name collate nocase", (asset_type,))
return c.fetchall()
def get_categories(self, asset_type):
c = self.assets.db.cursor()
c.execute("select distinct category from assets where type = ? order by category", (asset_type,))
return [x[0] for x in c.fetchall()]
def filter(self, asset_type, category, name):
if category == "<all>":
category = "%"
name = "%" + name + "%"
c = self.db.cursor()
q = """select * from assets where type = ? and category like ?
and (name like ? or desc like ?) order by desc, name collate nocase"""
c.execute(q, (asset_type, category, name, name))
result = c.fetchall()
return result
def get_total(self, asset_type):
c = self.assets.db.cursor()
c.execute("select count(*) from assets where type = ?", (asset_type))
return c.fetchone()[0]
def missing_icon(self):
return self.read("/interface/inventory/x.png", self.vanilla_assets, image=True)
def get_mods(self):
"""Return a list of all unique mod paths."""
c = self.db.cursor()
c.execute("select distinct path from assets order by category")
all_assets = [x[0].replace(self.starbound_folder, "") for x in c.fetchall()]
return [x for x in all_assets if not x.endswith("packed.pak")]
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)
assets = Assets("assets.db", "/opt/starbound")
assets.init_db()
logging.info("Started indexing...")
count = 0
for i in assets.create_index():
count += 1
print(count)
logging.info("Finished!")
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13,
462,
1678,
13128,
29918,
1272,
353,
734,
12168,
29889,
2248,
29918,
1272,
29898,
24129,
29897,
13,
18884,
25342,
16608,
29889,
275,
29918,
19935,
29898,
24129,
29961,
29900,
29962,
1125,
13,
462,
1678,
13128,
29918,
1272,
353,
16608,
29889,
2248,
29918,
1272,
29898,
24129,
29897,
13,
9651,
1683,
29901,
13,
18884,
12183,
29889,
27392,
703,
29903,
1984,
3262,
8340,
24342,
313,
1217,
934,
6081,
29897,
1273,
29879,
297,
1273,
29879,
29908,
1273,
313,
24129,
29961,
29900,
1402,
24342,
29961,
29896,
12622,
13,
13,
9651,
565,
13128,
29918,
1272,
338,
451,
6213,
29901,
13,
18884,
274,
29889,
7978,
29898,
1482,
29918,
2248,
29918,
1972,
29892,
13128,
29918,
1272,
29897,
13,
13,
4706,
1583,
29889,
2585,
29889,
15060,
580,
13,
4706,
12183,
29889,
3888,
703,
12881,
3276,
4969,
2380,
1159,
13,
13,
1678,
822,
1284,
29918,
16596,
29898,
1311,
1125,
13,
4706,
9995,
29083,
599,
7828,
9917,
21608,
322,
736,
1820,
29914,
1445,
1051,
29889,
13,
13,
4706,
512,
27722,
878,
2066,
29892,
869,
29886,
557,
2066,
29889,
13,
13,
4706,
9995,
13,
4706,
2380,
353,
5159,
13,
4706,
1109,
2911,
29918,
2084,
353,
2897,
29889,
2084,
29889,
7122,
29898,
1311,
29889,
8508,
9917,
29918,
12083,
29892,
376,
16596,
1159,
13,
4706,
12183,
29889,
3888,
703,
4421,
9450,
1109,
2911,
21608,
1159,
13,
4706,
1109,
2911,
29918,
16596,
353,
1583,
29889,
16192,
29918,
24129,
29918,
12083,
29898,
3703,
2911,
29918,
2084,
29897,
13,
4706,
518,
2248,
29889,
4397,
29898,
29916,
29897,
363,
921,
297,
1109,
2911,
29918,
16596,
29962,
13,
13,
4706,
878,
29879,
29918,
2084,
353,
1583,
29889,
1545,
29879,
29918,
12083,
13,
4706,
565,
451,
2897,
29889,
2084,
29889,
275,
3972,
29898,
1545,
29879,
29918,
2084,
1125,
13,
9651,
736,
2380,
13,
13,
4706,
363,
878,
297,
2897,
29889,
1761,
3972,
29898,
1545,
29879,
29918,
2084,
1125,
13,
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878,
29918,
12083,
353,
2897,
29889,
2084,
29889,
7122,
29898,
1545,
29879,
29918,
2084,
29892,
878,
29897,
13,
9651,
565,
2897,
29889,
2084,
29889,
275,
3972,
29898,
1545,
29918,
12083,
1125,
13,
18884,
12183,
29889,
3888,
703,
4421,
9450,
878,
4138,
29901,
376,
718,
878,
29897,
13,
18884,
878,
29918,
16596,
353,
1583,
29889,
16192,
29918,
24129,
29918,
12083,
29898,
1545,
29918,
12083,
29897,
13,
18884,
518,
2248,
29889,
4397,
29898,
29916,
29897,
363,
921,
297,
878,
29918,
16596,
29962,
13,
9651,
25342,
878,
29918,
12083,
29889,
1975,
2541,
17350,
1545,
29886,
557,
29908,
1125,
13,
18884,
12183,
29889,
3888,
703,
4421,
9450,
878,
29886,
557,
29901,
376,
718,
878,
29897,
13,
18884,
878,
29918,
16596,
353,
1583,
29889,
16192,
29918,
1545,
29886,
557,
29898,
1545,
29918,
12083,
29897,
13,
18884,
518,
2248,
29889,
4397,
29898,
29916,
29897,
363,
921,
297,
878,
29918,
16596,
29962,
13,
4706,
736,
2380,
13,
13,
1678,
822,
12812,
29918,
1545,
29886,
557,
29898,
1311,
29892,
878,
29886,
557,
1125,
13,
4706,
396,
14402,
29901,
1122,
817,
2304,
363,
5183,
278,
878,
4138,
515,
278,
27068,
3888,
934,
13,
4706,
4833,
353,
5810,
9917,
29889,
3150,
29918,
1445,
29898,
1545,
29886,
557,
29897,
13,
4706,
2380,
353,
17288,
29916,
29892,
878,
29886,
557,
29897,
363,
921,
297,
4833,
29889,
657,
29918,
2248,
580,
29962,
13,
4706,
736,
2380,
13,
13,
1678,
822,
12812,
29918,
24129,
29918,
12083,
29898,
1311,
29892,
4138,
1125,
13,
4706,
27068,
29918,
2084,
353,
2897,
29889,
2084,
29889,
7122,
29898,
12083,
29892,
376,
4058,
287,
29889,
29886,
557,
1159,
13,
13,
4706,
565,
2897,
29889,
2084,
29889,
275,
1445,
29898,
29886,
557,
29918,
2084,
1125,
13,
9651,
4833,
353,
5810,
9917,
29889,
3150,
29918,
1445,
29898,
29886,
557,
29918,
2084,
29897,
13,
9651,
2380,
353,
17288,
29916,
29892,
27068,
29918,
2084,
29897,
363,
921,
297,
4833,
29889,
657,
29918,
2248,
580,
29962,
13,
9651,
736,
2380,
13,
4706,
1683,
29901,
13,
9651,
396,
2030,
3114,
29892,
3117,
263,
878,
13,
9651,
2380,
353,
5159,
13,
9651,
878,
29918,
16596,
353,
6213,
13,
9651,
2066,
353,
2897,
29889,
1761,
3972,
29898,
12083,
29897,
13,
13,
9651,
396,
14402,
29901,
674,
817,
901,
5900,
304,
4386,
869,
1545,
4058,
411,
878,
3888,
2768,
29889,
13,
9651,
1476,
29918,
1545,
29918,
3888,
353,
7700,
13,
13,
9651,
363,
285,
297,
2066,
29901,
13,
18884,
565,
285,
29889,
1975,
2541,
17350,
1545,
3888,
29908,
1125,
13,
462,
1678,
878,
3888,
353,
2897,
29889,
2084,
29889,
7122,
29898,
12083,
29892,
285,
29897,
13,
462,
1678,
1018,
29901,
13,
462,
4706,
878,
3888,
29918,
1272,
353,
2254,
29918,
24129,
29918,
1445,
29898,
1545,
3888,
29897,
13,
462,
4706,
2224,
353,
376,
6904,
29908,
13,
462,
4706,
565,
376,
2084,
29908,
297,
878,
3888,
29918,
1272,
29889,
8149,
7295,
13,
462,
9651,
2224,
353,
878,
3888,
29918,
1272,
3366,
2084,
3108,
13,
462,
4706,
878,
29918,
16596,
353,
2897,
29889,
2084,
29889,
7122,
29898,
12083,
29892,
2224,
29897,
13,
462,
4706,
1476,
29918,
1545,
29918,
3888,
353,
5852,
13,
462,
1678,
5174,
7865,
2392,
29901,
13,
462,
4706,
396,
2289,
2030,
878,
29879,
13,
462,
4706,
4138,
353,
2897,
29889,
2084,
29889,
7122,
29898,
12083,
29892,
376,
16596,
1159,
13,
462,
4706,
565,
2897,
29889,
2084,
29889,
275,
3972,
29898,
12083,
1125,
13,
462,
9651,
878,
29918,
16596,
353,
4138,
13,
13,
9651,
565,
878,
29918,
16596,
338,
6213,
29901,
13,
18884,
736,
2380,
13,
9651,
25342,
1476,
29918,
1545,
29918,
3888,
322,
1583,
29889,
275,
29918,
4058,
287,
29918,
1445,
29898,
1545,
29918,
16596,
1125,
13,
18884,
396,
14402,
29901,
1207,
263,
869,
29886,
557,
885,
7310,
740,
393,
1736,
363,
1109,
2911,
322,
878,
29879,
13,
18884,
27068,
29918,
2084,
353,
2897,
29889,
2084,
29889,
12324,
2084,
29898,
1545,
29918,
16596,
29897,
13,
18884,
4833,
353,
5810,
9917,
29889,
3150,
29918,
1445,
29898,
29886,
557,
29918,
2084,
29897,
13,
18884,
363,
921,
297,
4833,
29889,
657,
29918,
2248,
7295,
13,
462,
1678,
396,
25388,
28968,
29879,
29889,
2585,
2992,
515,
1404,
27068,
2066,
13,
462,
1678,
565,
337,
29889,
4352,
29898,
17281,
29918,
16596,
29892,
921,
29897,
338,
6213,
29901,
13,
462,
4706,
2380,
29889,
4397,
3552,
29916,
29892,
27068,
29918,
2084,
876,
13,
18884,
736,
2380,
13,
9651,
25342,
451,
2897,
29889,
2084,
29889,
275,
3972,
29898,
1545,
29918,
16596,
1125,
13,
18884,
736,
2380,
13,
13,
9651,
396,
1286,
591,
508,
12812,
29991,
13,
9651,
363,
3876,
29892,
4516,
29879,
29892,
2066,
297,
2897,
29889,
20919,
29898,
1545,
29918,
16596,
1125,
13,
18884,
363,
285,
297,
2066,
29901,
13,
462,
1678,
565,
337,
29889,
4352,
29898,
17281,
29918,
16596,
29892,
285,
29897,
338,
6213,
29901,
13,
462,
4706,
24342,
29918,
12083,
353,
2897,
29889,
2084,
29889,
12324,
2084,
29898,
1545,
29918,
16596,
29897,
13,
462,
4706,
24342,
29918,
1445,
353,
2897,
29889,
2084,
29889,
12324,
2084,
29898,
359,
29889,
2084,
29889,
7122,
29898,
4632,
29889,
6506,
29898,
12083,
29892,
376,
4968,
285,
876,
13,
462,
4706,
2380,
29889,
4397,
3552,
24129,
29918,
1445,
29892,
24342,
29918,
12083,
876,
13,
9651,
736,
2380,
13,
13,
1678,
822,
338,
29918,
4058,
287,
29918,
1445,
29898,
1311,
29892,
2224,
1125,
13,
4706,
9995,
13,
9651,
16969,
1565,
565,
278,
24342,
2224,
338,
263,
934,
313,
14043,
367,
10241,
515,
278,
2380,
393,
372,
338,
263,
4870,
287,
1134,
29897,
13,
9651,
16969,
2089,
565,
278,
24342,
2224,
338,
263,
4138,
313,
1397,
4135,
29914,
5464,
29899,
4058,
287,
878,
29879,
29897,
13,
4706,
9995,
13,
4706,
736,
2897,
29889,
2084,
29889,
275,
1445,
29898,
2084,
29897,
13,
13,
1678,
822,
1303,
29898,
1311,
29892,
1820,
29892,
2224,
29892,
1967,
29922,
8824,
1125,
13,
4706,
565,
1583,
29889,
275,
29918,
4058,
287,
29918,
1445,
29898,
2084,
1125,
13,
9651,
1820,
353,
1820,
29889,
13609,
580,
13,
9651,
4833,
353,
5810,
9917,
29889,
3150,
29918,
1445,
29898,
2084,
29897,
13,
13,
9651,
396,
1018,
278,
7090,
937,
13,
9651,
565,
1967,
322,
1820,
297,
1583,
29889,
3027,
29918,
8173,
29901,
13,
18884,
736,
1583,
29889,
3027,
29918,
8173,
29961,
1989,
29962,
13,
13,
9651,
1018,
29901,
13,
18884,
848,
353,
4833,
29889,
657,
29898,
1989,
29897,
13,
9651,
5174,
7670,
2392,
29901,
13,
18884,
565,
1967,
322,
2224,
2804,
1583,
29889,
3703,
2911,
29918,
16596,
29901,
13,
462,
1678,
10153,
353,
1583,
29889,
949,
29898,
1989,
29892,
1583,
29889,
3703,
2911,
29918,
16596,
29892,
1967,
29897,
13,
462,
1678,
1583,
29889,
3027,
29918,
8173,
29961,
1989,
29962,
353,
10153,
13,
462,
1678,
736,
10153,
13,
18884,
1683,
29901,
13,
462,
1678,
12183,
29889,
11739,
703,
2525,
519,
304,
1303,
4833,
24342,
14210,
29879,
29915,
515,
14210,
29879,
11838,
1273,
313,
1989,
29892,
2224,
876,
13,
462,
1678,
736,
6213,
13,
9651,
565,
1967,
29901,
13,
18884,
10153,
353,
848,
13,
18884,
1583,
29889,
3027,
29918,
8173,
29961,
1989,
29962,
353,
10153,
13,
18884,
736,
10153,
13,
9651,
1683,
29901,
13,
18884,
1018,
29901,
13,
462,
1678,
24342,
353,
6088,
29918,
3126,
29898,
1272,
29889,
13808,
703,
9420,
29899,
29947,
4968,
1820,
29897,
13,
462,
1678,
736,
24342,
13,
18884,
5174,
7865,
2392,
29901,
13,
462,
1678,
12183,
29889,
11739,
703,
2525,
519,
304,
1303,
4833,
24342,
14210,
29879,
29915,
515,
14210,
29879,
11838,
1273,
313,
1989,
29892,
2224,
876,
13,
462,
1678,
736,
6213,
13,
4706,
1683,
29901,
13,
9651,
24342,
29918,
1445,
353,
2897,
29889,
2084,
29889,
7122,
29898,
2084,
29892,
1820,
29961,
29896,
29901,
2314,
13,
9651,
1018,
29901,
13,
18884,
565,
1967,
29901,
13,
462,
1678,
10153,
353,
1722,
29898,
24129,
29918,
1445,
29892,
376,
6050,
2564,
949,
580,
13,
462,
1678,
1583,
29889,
3027,
29918,
8173,
29961,
1989,
29962,
353,
10153,
13,
462,
1678,
736,
10153,
13,
18884,
1683,
29901,
13,
462,
1678,
24342,
353,
2254,
29918,
24129,
29918,
1445,
29898,
24129,
29918,
1445,
29897,
13,
462,
1678,
736,
24342,
13,
9651,
5174,
313,
2283,
17413,
2392,
29892,
7865,
2392,
1125,
13,
18884,
565,
1967,
322,
2224,
2804,
1583,
29889,
3703,
2911,
29918,
16596,
29901,
13,
462,
1678,
565,
1583,
29889,
275,
29918,
4058,
287,
29918,
1445,
29898,
1311,
29889,
3703,
2911,
29918,
16596,
1125,
13,
462,
4706,
10153,
353,
1583,
29889,
949,
29898,
1989,
29889,
6506,
703,
1966,
613,
5591,
4968,
1583,
29889,
3703,
2911,
29918,
16596,
29892,
1967,
29897,
13,
462,
4706,
1583,
29889,
3027,
29918,
8173,
29961,
1989,
29962,
353,
10153,
13,
462,
4706,
736,
10153,
13,
462,
1678,
1683,
29901,
13,
462,
4706,
10153,
353,
1583,
29889,
949,
29898,
1989,
29892,
1583,
29889,
3703,
2911,
29918,
16596,
29892,
1967,
29897,
13,
462,
4706,
1583,
29889,
3027,
29918,
8173,
29961,
1989,
29962,
353,
10153,
13,
462,
4706,
736,
10153,
13,
18884,
1683,
29901,
13,
462,
1678,
12183,
29889,
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1159,
13,
2
] |
test.py | lipengyuan1994/Tensorflow-Developer | 5 | 133166 | <gh_stars>1-10
import tensorflow as tf
print(tf.__version__)
| [
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] |
linux/functions_youtube_linux/check_version.py | Frxhb/youtubedl_script | 0 | 117836 | import os
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKCYAN = '\033[96m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def check_ver_func():
test = os.popen('youtube-dl --version').read()
print(" Your youtube-dl version is:\n" + bcolors.OKGREEN + " " + test + bcolors.ENDC) | [
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] |
public-engines/spark-mllib-engine/spark_mllib_engine/data_handler/training_preparator.py | lucasnil/incubator-marvin | 0 | 160656 | #!/usr/bin/env python
# coding=utf-8
"""TrainingPreparator engine action.
Use this module to add the project main code.
"""
from .._compatibility import six
from .._logging import get_logger
from ..spark_serializer import SparkSerializer
from marvin_python_toolbox.engine_base import EngineBaseDataHandler
__all__ = ['TrainingPreparator']
logger = get_logger('training_preparator')
class TrainingPreparator(SparkSerializer,EngineBaseDataHandler):
def __init__(self, **kwargs):
super(TrainingPreparator, self).__init__(**kwargs)
def execute(self, params, **kwargs):
# Training Preparator
from pyspark.sql.types import DoubleType
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from pyspark.mllib.regression import LabeledPoint
l_atributos = ["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"]
dataset = self.marvin_initial_dataset
# Chaniging atribute types to double
for coluna in l_atributos:
dataset = dataset.withColumn(coluna, dataset[coluna].cast(DoubleType()))
# Maping column "Species" to a numerical value in a new collumn named "label"
label_indexer = StringIndexer().setInputCol("Species").setOutputCol("label")
dataset = label_indexer.fit(dataset).transform(dataset)
# Concatenating all features into a single vector and naming the resulting column as "features"
assembler = VectorAssembler(inputCols=["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"], outputCol="features")
dataset = assembler.transform(dataset)
(train, test) = dataset.randomSplit([0.7, 0.3])
self.marvin_dataset = {'train': train, 'test': test}
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] |
Day 9/FLOW005.py | arpit1920/Python-Codechef-Problems | 0 | 95216 | '''
Consider a currency system in which there are notes of six denominations,
namely, Rs. 1, Rs. 2, Rs. 5, Rs. 10, Rs. 50, Rs. 100.
If the sum of Rs. N is input,
write a program to computer smallest number of notes that will combine to give Rs. N.
Input
The first line contains an integer T, total number of testcases.
Then follow T lines, each line contains an integer N.
Output
For each test case, display the smallest number of notes that will combine to give N, in a new line.
Constraints
1 ≤ T ≤ 1000
1 ≤ N ≤ 1000000
Example
Input
3
1200
500
242
Output
12
5
7
'''
# cook your dish here
t=int(input())
for i in range(t):
n=int(input())
c=0
c+=(n//100)
n=n%100
c+=(n//50)
n=n%50
c+=(n//10)
n=n%10
c+=(n//5)
n=n%5
c+=(n//2)
n=n%2
c+=(n//1)
n=n%1
print(c) | [
1,
14550,
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Exercicios/Exercicio062.py | RicardoMart922/estudo_Python | 0 | 111421 | <reponame>RicardoMart922/estudo_Python<filename>Exercicios/Exercicio062.py
# Faça um programa que leia um número qualquer e mostre seu fatorial.
numero = int(input('Digite um número: '))
fatorial = 1
i = 1
while i <= numero:
fatorial *= i
i += 1
print('{}! = '.format(numero), end='')
j = 1
k = 1
if numero == 0 or numero == 1:
print('{}'.format(fatorial))
else:
while numero >= j:
if k == 1:
print('{}'.format(numero), end='')
k = 2
numero -= 1
print(' x {}'.format(numero), end='')
numero -= 1
print(' = {}'.format(fatorial))
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] |
tests/core/test_core_renderer.py | timvink/pheasant | 24 | 12592 | from pheasant.renderers.jupyter.jupyter import Jupyter
jupyter = Jupyter()
jupyter.findall("{{3}}3{{5}}")
jupyter.page
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ap/apps/events/factories.py | shacker/ap | 1 | 48841 | import random
from typing import Any
import factory
import pytz
from django.utils.text import slugify
from ap.apps.events.models import EVENT_TYPE_CHOICES, Event, Organization, Route
from ap.apps.users.constants import COUNTRY_CHOICES
from ap.apps.users.models import User
from ap.apps.events.factory_data import RANDOM_EVENT_LIST, RANDOM_COORDS_LIST
class RouteFactory(factory.django.DjangoModelFactory):
"""Fabricate a route with realistic data. MUST have an event passed in when called."""
class Meta:
model = Route
title = factory.Faker('text', max_nb_chars=60)
url = factory.Faker('url')
class EventFactory(factory.django.DjangoModelFactory):
"""Fabricate an event with realistic data"""
class Meta:
model = Event
name = factory.LazyAttribute(lambda o: random.choice(RANDOM_EVENT_LIST))
event_type = factory.LazyAttribute(lambda o: random.choice(EVENT_TYPE_CHOICES)[0])
slug = factory.LazyAttribute(lambda o: slugify(o.name))
about = factory.Faker('paragraph', nb_sentences=9)
start = factory.Faker('date_time_this_century', tzinfo=pytz.UTC)
address = factory.Faker('street_address')
place_name = factory.Faker('company')
city = factory.Faker('city')
state_province = factory.Faker('state')
country = factory.LazyAttribute(lambda o: random.choice(COUNTRY_CHOICES)[0])
official_event_site_url = factory.Faker('url')
official_event_site_title = factory.Faker('text', max_nb_chars=30)
fee = factory.Faker('pydecimal', left_digits=4, right_digits=2, positive=True)
fee_paid = factory.Faker('pybool')
notes = factory.Faker('paragraph')
published = True
@factory.post_generation
def maybe_add_orgs(obj, build: bool, extracted: Any, **kwargs: dict) -> None:
# Maybe assign a random org to event.
dice = random.choice(range(1, 5))
for _ in range(dice):
org = Organization.objects.all().order_by('?').first()
obj.organizations.add(org)
@factory.post_generation
def maybe_add_humans(obj, build: bool, extracted: Any, **kwargs: dict) -> None:
# Maybe assign a random contact to event.
dice = random.choice(range(1, 5))
for _ in range(dice):
user = User.objects.all().order_by('?').first()
obj.humans.add(user)
@factory.post_generation
def add_routes(obj, build: bool, extracted: Any, **kwargs: dict) -> None:
numroutes = random.choice(range(0, 5))
RouteFactory.create_batch(numroutes, event=obj)
@factory.post_generation
def add_coords(obj, build: bool, extracted: Any, **kwargs: dict) -> None:
# Select from actual city coordinates guaranteed to be on land
coords = random.choice(RANDOM_COORDS_LIST)
obj.latitude = coords[0]
obj.longitude = coords[1]
class OrgFactory(factory.django.DjangoModelFactory):
"""Fabricate an organization with realistic data"""
class Meta:
model = Organization
name = factory.Faker('company')
slug = factory.LazyAttribute(lambda o: slugify(o.name))
address = factory.Faker('address')
phone = factory.Faker('phone_number')
email = factory.Faker('email')
human = factory.LazyAttribute(lambda o: User.objects.all().order_by('?').first())
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] |
runpandas/types/summary.py | pnposch/runpandas | 11 | 20854 | <filename>runpandas/types/summary.py
"""
Helper module for evaluation and display of the summary of training sessions.
"""
import numpy as np
import pandas as pd
from runpandas._utils import convert_pace_secmeters2minkms
def _build_summary_statistics(obj):
"""
Generate session statistics from a given DataFrame.
Parameters
----------
obj: The DataFrame to generate basic commute statistics from.
Returns:
--------
A Dictionary containing the following statistics:
- Total moving time
- Average speed
- Max speed
- Average moving speed
- Average cadence running
- Average cadence running moving
- Max cadence
- Average heart rate
- Average heart rate moving
- Max heart rate
- Average pace (per 1 km)
- Average pace moving (per 1 km)
- Max pace
- Average temperature
- Max temperature
- Min temperature
- Total distance
- Total ellapsed time
"""
start = obj.start
try:
moving_time = obj.moving_time
except AttributeError:
moving_time = np.nan
try:
mean_speed = obj.mean_speed()
max_speed = obj["speed"].max()
mean_pace = convert_pace_secmeters2minkms(obj.mean_pace().total_seconds())
max_pace = convert_pace_secmeters2minkms(
obj["speed"].to_pace().min().total_seconds()
)
except AttributeError:
mean_speed = np.nan
max_speed = np.nan
mean_pace = np.nan
try:
mean_moving_speed = obj.mean_speed(only_moving=True)
mean_moving_pace = convert_pace_secmeters2minkms(
obj.mean_pace(only_moving=True).total_seconds()
)
except (AttributeError, KeyError):
mean_moving_speed = np.nan
mean_moving_pace = np.nan
try:
mean_cadence = obj.mean_cadence()
max_cadence = obj["cad"].max()
except AttributeError:
mean_cadence = np.nan
max_cadence = np.nan
try:
mean_moving_cadence = obj.mean_cadence(only_moving=True)
except (AttributeError, KeyError):
mean_moving_cadence = np.nan
try:
mean_heart_rate = obj.mean_heart_rate()
max_heart_rate = obj["hr"].max()
except AttributeError:
mean_heart_rate = np.nan
max_heart_rate = np.nan
try:
mean_moving_heart_rate = obj.mean_heart_rate(only_moving=True)
except (AttributeError, KeyError):
mean_moving_heart_rate = np.nan
try:
mean_temperature = obj["temp"].mean()
min_temperature = obj["temp"].min()
max_temperature = obj["temp"].max()
except KeyError:
mean_temperature = np.nan
min_temperature = np.nan
max_temperature = np.nan
total_distance = obj.distance
ellapsed_time = obj.ellapsed_time
row = {k: v for k, v in locals().items() if not k.startswith("__") and k != "obj"}
return row
def _build_session_statistics(obj):
"""
Generate session statistics from a given DataFrame.
Parameters
----------
obj: The DataFrame to generate basic commute statistics from.
Returns:
--------
A ``pandas.Dataframe`` containing the following statistics:
- Total moving time
- Average speed
- Max speed
- Average moving speed
- Average cadence running
- Average cadence running moving
- Max cadence
- Average heart rate
- Average heart rate moving
- Max heart rate
- Average pace (per 1 km)
- Average pace moving (per 1 km)
- Max pace
- Average temperature
- Max temperature
- Min temperature
- Total distance
- Total ellapsed time
"""
stats = {key: [value] for key, value in _build_summary_statistics(obj).items()}
return pd.DataFrame(stats).set_index("start")
def _build_activity_statistics(obj):
"""
Generate basic statistics from a given pandas Series.
Parameters
----------
obj: The DataFrame to generate basic commute statistics from.
Returns:
--------
A Series containing the following statistics:
- Session times
- Total distance
- Total ellapsed time
- Total moving time
- Total and average elevation gain
- Average speed
- Average moving speed
- Average pace (per 1 km)
- Average pace moving (per 1 km)
- Average cadence running
- Average cadence running moving
- Average heart rate
- Average heart rate moving
- Average temperature
"""
# special conditions for methods that raise Exceptions
stats = _build_summary_statistics(obj)
rows = {
"Session": "Running: %s" % stats["start"].strftime("%d-%m-%Y %H:%M:%S"),
"Total distance (meters)": stats["total_distance"],
"Total ellapsed time": stats["ellapsed_time"],
"Total moving time": stats["moving_time"],
"Average speed (km/h)": stats["mean_speed"] * 3.6,
"Average moving speed (km/h)": stats["mean_moving_speed"] * 3.6,
"Average pace (per 1 km)": stats["mean_pace"],
"Average pace moving (per 1 km)": stats["mean_moving_pace"],
"Average cadence": stats["mean_cadence"],
"Average moving cadence": stats["mean_moving_cadence"],
"Average heart rate": stats["mean_heart_rate"],
"Average moving heart rate": stats["mean_moving_heart_rate"],
"Average temperature": stats["mean_temperature"],
}
series = pd.Series(
rows,
index=[
"Session",
"Total distance (meters)",
"Total ellapsed time",
"Total moving time",
"Average speed (km/h)",
"Average moving speed (km/h)",
"Average pace (per 1 km)",
"Average pace moving (per 1 km)",
"Average cadence",
"Average moving cadence",
"Average heart rate",
"Average moving heart rate",
"Average temperature",
],
)
return series
def activity_summary(activity):
"""
Returns the pandas Dataframe with the common basic statistics for the
given activity.
Parameters
----------
activity: runpandas.types.Activity. Runpandas Activity to be computed the statistics
Returns
-------
pandas.Dataframe: A pandas DataFrame containing the summary statistics, which
inclues estimates of the total distance covered, the total duration,
the time spent moving, and many others.
"""
summary_statistics = _build_activity_statistics(activity)
return summary_statistics.T
def session_summary(session):
"""
Returns the a pandas Dataframe with the common basic statistics for the
given activity.
Parameters
----------
session: runpandas.types.Activity. Runpandas Activity with pandas.MultiIndex
to be computed the statistics
Returns
-------
pandas.Dataframe: A pandas DataFrame containing the summary statistics
across all th activities, which includes estimates of the total distance covered,
the total duration, the time spent moving, and many others.
"""
frames = []
for index in session.index.unique(level="start"):
df = session.xs(index, level=0)
df.start = index
frames.append(_build_session_statistics(df))
session_summary = pd.concat(frames, axis=0, verify_integrity=True)
session_summary.sort_index(inplace=True)
return session_summary
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src/plex_posters/__dev/__init__.py | dtomlinson91/plex_posters | 0 | 40637 | from __future__ import annotations
from .__version__ import __version__ # noqa
from .lib import export
from typing import Type, TypeVar, List, Dict
import praw # type: ignore
import requests
__all__ = [] # type: List
__header__ = 'plex_posters'
# __section__ = 'module'
T_movie_poster_porn_scraper = TypeVar(
'T_movie_poster_porn_scraper', bound="movie_poster_porn_scraper"
)
@export
class movie_poster_porn_scraper(object):
"""Poster scraper
Attributes
----------
reddit_instance : praw.Reddit
A praw instance connected to Reddit
"""
def __init__(self, instance: praw.Reddit) -> None:
"""
Parameters
----------
instance : praw.Reddit
A praw instance connected to Reddit
"""
super().__init__()
self.reddit_instance = instance
@classmethod
def create_instance(
cls: Type[T_movie_poster_porn_scraper],
client_id: str,
client_secret: str,
user_agent: str,
) -> T_movie_poster_porn_scraper:
"""`classmethod` to connect to reddit using the api.
Parameters
----------
client_id : str
a valid client id
client_secret : str
the secret key for the client
user_agent : str
a user agent
"""
reddit_instance = praw.Reddit(
client_id=client_id,
client_secret=client_secret,
user_agent=user_agent,
)
return cls(reddit_instance)
def get_hot_posters(
self,
) -> T_movie_poster_porn_scraper:
"""
"""
self._poster_urls: Dict = {}
for post in self.reddit_instance.subreddit('MoviePosterPorn').hot(
limit=10
):
print(post.title)
print(post.url)
# print(dir(post))
# self._poster_urls.append(post.url)
self._poster_urls[post.title] = post.url
print(self._poster_urls)
return self
def get_posters(self):
"""download the posters
Returns
-------
self
"""
for title, url in self._poster_urls.items():
r = requests.get(url)
with open('posters/' + title + '.jpg', 'wb') as p:
p.write(r.content)
return self
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] |
pstraw/screws.py | pskelecton/straw | 2 | 175944 | #!/usr/local/bin/python3.6
# -*- coding:utf-8 -*-
# ========================================
# Description :
# 工具类
# 反射类、数据仓库类、路径加工、缓存
# Created : 2020.10.14
# Author : <NAME>
# ========================================
from promise import Promise
import os
import inspect
from dataclasses import dataclass
# 字典反射对象使用案例
# ConfStore = Store({})
# ConfStore.a = {'xxx': Store({'yyy': 123})}
# ConfStore.b = {'xxx': {'yyy': 123}}
# 配置对象，控制对象只读
class ConfStore():
def __init__(self, **kwargs):
self.__ConfStore__ = Store(kwargs)
def __repr__(self):
return str(self.__ConfStore__)
def __str__(self):
return str(self.__ConfStore__)
def __getattr__(self, name):
return self.__ConfStore__.get(name)
def create(self, name, value):
if self.__ConfStore__.get(name) == None:
self.__ConfStore__[name] = value
return True
else:
return False
def modify(self, name, value):
if self.__ConfStore__.get(name) != None:
self.__ConfStore__[name] = value
return True
else:
return False
def remove(self, name):
del self.__ConfStore__[name]
# 路径加工类
class PathPlant():
def __init__(self, *args, **kwargs):
super().__init__()
# 接收一个model名的参数
self.MODEL_FOLDER_NAME = kwargs.get('MODEL_FOLDER_NAME') if not hasattr(
self, "MODEL_FOLDER_NAME") else getattr(self, "MODEL_FOLDER_NAME")
# 转换绝对路径，纯静态方法，外部直接访问
# 规则转换: @/xxx/ 则只取相对路径 , 没有@/则转换绝对路径
@staticmethod
def transAbspath(path):
if type(path) == str:
if path[:2] == '@/':
# 相对路径转换
# return os.path.normpath(path[2:])
# 绝对路径转换
return os.path.realpath(path[2:])
else:
# 绝对路径转换
return os.path.abspath(path)
else:
return None
# 获取模型绝对路径
@classmethod
def getModelPath(cls, model_func):
# 获取模型绝对路径
model_path = os.path.abspath(inspect.getfile(model_func))
return model_path
# 初始化文件夹，静态+对象方法，可直接访问，可继承访问
@classmethod
def splitFolder(cls, path, modelStr, includeModel=True):
# 文件或目录检测,解决path最后带\\或/的情况
tempath = os.path.split(path)
if tempath[1] == '':
path = tempath[0]
# modelStr前的路径
head = path
# modelStr后的路径
tail = ""
#
skip = False
while not skip:
sp = os.path.split(head)
# 匹配到或者都没有匹配到则跳出
if sp[1] == modelStr or sp[0] == "":
skip = True
# 初始赋值
if tail == "":
tail = sp[1]
else:
if sp[1] == modelStr and includeModel == False:
# 不组装modelStr
pass
else:
# 每次循环都合并一下路径
tail = os.path.join(sp[1], tail)
head = sp[0]
return (head, tail)
@classmethod
def initFolder(cls, path):
# 判断路径是否存在，不存在则创建文件夹
if not os.path.exists(path):
# os.mkdir(path, mode=0o777)
os.makedirs(path, mode=0o777, exist_ok=False)
# 深度遍历文件，path一定要是绝对路径，静态+对象方法，可直接访问，可继承访问
@classmethod
def deepenFolder(cls, path):
_file_list = list()
_dep_file_dict = dict()
# 结构化文件夹
def traverseFile(dir_path):
if os.path.exists(dir_path) and os.path.isdir(dir_path):
file_list = os.listdir(dir_path)
dep_file_dict = dict()
for file_name in file_list:
file_path = os.path.join(dir_path, file_name)
if os.path.isdir(file_path):
dep_file_dict[file_name] = traverseFile(file_path)
else:
_file_list.append(file_path)
dep_file_dict[file_name] = file_path
return dep_file_dict
else:
raise Exception
# 生成文件结构化对象
_dep_file_dict = traverseFile(os.path.abspath(path))
return Store({'FILE_STRUCTURE': _dep_file_dict, 'PATH_LIST': _file_list})
# 字典反射成对象
class Store(dict):
# 缓冲数据
__cache_dict = None
def __init__(self, iterable=None):
iterable = iterable or {}
self.__cache_dict = dict(iterable)
super().__init__(iterable)
self.__reflect__(iterable)
# 构建时反射值
def __reflect__(self, _dict):
for key in _dict:
if type(_dict[key]) == dict:
self.__dict__[key] = Store(_dict[key])
else:
self.__dict__[key] = _dict[key]
# 更新时同步反射
def __updvalue__(self, key, value):
# 判断旧值不等于新值的情况
if str(self.__cache_dict.get(key)) != str(value):
# 更新缓冲数据
self.__cache_dict[key] = value
# 引用赋值
if type(value) == dict:
self.__dict__[key] = Store(value)
else:
self.__dict__[key] = value
# 赋值
super().__setitem__(key, value)
# 对象属性赋值拦截
def __setattr__(self, key, value):
if self.__cache_dict != None:
self.__updvalue__(key, value)
else:
self.__dict__[key] = value
# 字典属性赋值拦截
def __setitem__(self, key, value):
if self.__cache_dict != None:
self.__updvalue__(key, value)
# 字典取值拦截
def __getitem__(self, key):
try:
return self.__dict__[key]
except Exception as exc:
return None
# 解除属性引用
def __delattr__(self, key):
try:
del self.__dict__[key]
super().__delitem__(key)
except Exception as exc:
pass
# 解除字典属性引用
def __delitem__(self, key):
try:
del self.__dict__[key]
super().__delitem__(key)
except Exception as exc:
pass
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tests/test_fixtures.py | adammichaelwood/omk_core | 0 | 74204 | <gh_stars>0
import pytest
import omk_core as omk
@pytest.fixture
def tonal_tuples():
MS = [
(0, 0),
(1, 2),
(2, 4),
(3, 5),
(4, 7),
(5, 9),
(6,11)
]
return [(x[0],(x[1]+m)%12) for m in [0,1,2,-1,-2] for x in MS]
@pytest.fixture
def tonal_vectors(tonal_tuples):
return [omk.TonalVector(x) for x in tonal_tuples]
@pytest.fixture
def tonal_oct_tuples(tonal_tuples):
return [(x[0], x[1], y) for y in [0,1,2,-1,-2] for x in tonal_tuples]
@pytest.fixture
def tonal_oct_vectors(tonal_oct_tuples):
return [omk.TonalVector(x) for x in tonal_oct_tuples]
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azure-mgmt/tests/test_mgmt_network.py | SUSE/azure-sdk-for-python | 2 | 1024 | # coding: utf-8
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#--------------------------------------------------------------------------
import unittest
import azure.mgmt.network.models
from testutils.common_recordingtestcase import record
from tests.mgmt_testcase import HttpStatusCode, AzureMgmtTestCase
class MgmtNetworkTest(AzureMgmtTestCase):
def setUp(self):
super(MgmtNetworkTest, self).setUp()
self.network_client = self.create_mgmt_client(
azure.mgmt.network.NetworkManagementClient
)
if not self.is_playback():
self.create_resource_group()
@record
def test_network_interface_card(self):
vnet_name = self.get_resource_name('pyvnet')
subnet_name = self.get_resource_name('pysubnet')
nic_name = self.get_resource_name('pynic')
# Create VNet
async_vnet_creation = self.network_client.virtual_networks.create_or_update(
self.group_name,
vnet_name,
{
'location': self.region,
'address_space': {
'address_prefixes': ['10.0.0.0/16']
}
}
)
async_vnet_creation.wait()
# Create Subnet
async_subnet_creation = self.network_client.subnets.create_or_update(
self.group_name,
vnet_name,
subnet_name,
{'address_prefix': '10.0.0.0/24'}
)
subnet_info = async_subnet_creation.result()
# Create NIC
async_nic_creation = self.network_client.network_interfaces.create_or_update(
self.group_name,
nic_name,
{
'location': self.region,
'ip_configurations': [{
'name': 'MyIpConfig',
'subnet': {
'id': subnet_info.id
}
}]
}
)
nic_info = async_nic_creation.result()
nic_info = self.network_client.network_interfaces.get(
self.group_name,
nic_info.name
)
nics = list(self.network_client.network_interfaces.list(
self.group_name
))
self.assertEqual(len(nics), 1)
nics = list(self.network_client.network_interfaces.list_all())
self.assertGreater(len(nics), 0)
async_delete = self.network_client.network_interfaces.delete(
self.group_name,
nic_info.name
)
async_delete.wait()
@record
def test_load_balancers(self):
public_ip_name = self.get_resource_name('pyipname')
frontend_ip_name = self.get_resource_name('pyfipname')
addr_pool_name = self.get_resource_name('pyapname')
probe_name = self.get_resource_name('pyprobename')
lb_name = self.get_resource_name('pylbname')
front_end_id = ('/subscriptions/{}'
'/resourceGroups/{}'
'/providers/Microsoft.Network'
'/loadBalancers/{}'
'/frontendIPConfigurations/{}').format(
self.settings.SUBSCRIPTION_ID,
self.group_name,
lb_name,
frontend_ip_name
)
back_end_id = ('/subscriptions/{}'
'/resourceGroups/{}'
'/providers/Microsoft.Network'
'/loadBalancers/{}'
'/backendAddressPools/{}').format(
self.settings.SUBSCRIPTION_ID,
self.group_name,
lb_name,
addr_pool_name
)
probe_id = ('/subscriptions/{}'
'/resourceGroups/{}'
'/providers/Microsoft.Network'
'/loadBalancers/{}'
'/probes/{}').format(
self.settings.SUBSCRIPTION_ID,
self.group_name,
lb_name,
probe_name
)
# Create PublicIP
public_ip_parameters = {
'location': self.region,
'public_ip_allocation_method': 'static',
'idle_timeout_in_minutes': 4
}
async_publicip_creation = self.network_client.public_ip_addresses.create_or_update(
self.group_name,
public_ip_name,
public_ip_parameters
)
public_ip_info = async_publicip_creation.result()
# Building a FrontEndIpPool
frontend_ip_configurations = [{
'name': frontend_ip_name,
'private_ip_allocation_method': 'Dynamic',
'public_ip_address': {
'id': public_ip_info.id
}
}]
# Building a BackEnd adress pool
backend_address_pools = [{
'name': addr_pool_name
}]
# Building a HealthProbe
probes = [{
'name': probe_name,
'protocol': 'Http',
'port': 80,
'interval_in_seconds': 15,
'number_of_probes': 4,
'request_path': 'healthprobe.aspx'
}]
# Building a LoadBalancer rule
load_balancing_rules = [{
'name': 'azure-sample-lb-rule',
'protocol': 'tcp',
'frontend_port': 80,
'backend_port': 80,
'idle_timeout_in_minutes': 4,
'enable_floating_ip': False,
'load_distribution': 'Default',
'frontend_ip_configuration': {
'id': front_end_id
},
'backend_address_pool': {
'id': back_end_id
},
'probe': {
'id': probe_id
}
}]
# Building InboundNATRule1
inbound_nat_rules = [{
'name': 'azure-sample-netrule1',
'protocol': 'tcp',
'frontend_port': 21,
'backend_port': 22,
'enable_floating_ip': False,
'idle_timeout_in_minutes': 4,
'frontend_ip_configuration': {
'id': front_end_id
}
}]
# Building InboundNATRule2
inbound_nat_rules.append({
'name': 'azure-sample-netrule2',
'protocol': 'tcp',
'frontend_port': 23,
'backend_port': 22,
'enable_floating_ip': False,
'idle_timeout_in_minutes': 4,
'frontend_ip_configuration': {
'id': front_end_id
}
})
# Creating Load Balancer
lb_async_creation = self.network_client.load_balancers.create_or_update(
self.group_name,
lb_name,
{
'location': self.region,
'frontend_ip_configurations': frontend_ip_configurations,
'backend_address_pools': backend_address_pools,
'probes': probes,
'load_balancing_rules': load_balancing_rules,
'inbound_nat_rules' :inbound_nat_rules
}
)
lb_info = lb_async_creation.result()
# Get it
lb_info = self.network_client.load_balancers.get(
self.group_name,
lb_name
)
# List all
lbs = self.network_client.load_balancers.list_all()
lbs = list(lbs)
self.assertGreater(len(lbs), 0)
# List RG
lbs = self.network_client.load_balancers.list(self.group_name)
lbs = list(lbs)
self.assertGreater(len(lbs), 0)
# Delete
async_lb_delete = self.network_client.load_balancers.delete(
self.group_name,
lb_name
)
async_lb_delete.wait()
@record
def test_public_ip_addresses(self):
public_ip_name = self.get_resource_name('pyipname')
params_create = azure.mgmt.network.models.PublicIPAddress(
location=self.region,
public_ip_allocation_method=azure.mgmt.network.models.IPAllocationMethod.dynamic,
tags={
'key': 'value',
},
)
result_create = self.network_client.public_ip_addresses.create_or_update(
self.group_name,
public_ip_name,
params_create,
)
result_create.wait() # AzureOperationPoller
#self.assertEqual(result_create.status_code, HttpStatusCode.OK)
result_get = self.network_client.public_ip_addresses.get(
self.group_name,
public_ip_name,
)
#self.assertEqual(result_get.status_code, HttpStatusCode.OK)
self.assertEqual(result_get.location, self.region)
self.assertEqual(result_get.tags['key'], 'value')
result_list = self.network_client.public_ip_addresses.list(self.group_name)
#self.assertEqual(result_list.status_code, HttpStatusCode.OK)
result_list = list(result_list)
self.assertEqual(len(result_list), 1)
result_list_all = self.network_client.public_ip_addresses.list_all()
#self.assertEqual(result_list_all.status_code, HttpStatusCode.OK)
result_list_all = list(result_list_all)
self.assertGreater(len(result_list_all), 0)
result_delete = self.network_client.public_ip_addresses.delete(
self.group_name,
public_ip_name,
)
result_delete.wait() # AzureOperationPoller
#self.assertEqual(result_delete.status_code, HttpStatusCode.OK)
result_list = self.network_client.public_ip_addresses.list(self.group_name)
#self.assertEqual(result_list.status_code, HttpStatusCode.OK)
result_list = list(result_list)
self.assertEqual(len(result_list), 0)
@record
def test_virtual_networks(self):
network_name = self.get_resource_name('pyvnet')
subnet1_name = self.get_resource_name('pyvnetsubnetone')
subnet2_name = self.get_resource_name('pyvnetsubnettwo')
params_create = azure.mgmt.network.models.VirtualNetwork(
location=self.region,
address_space=azure.mgmt.network.models.AddressSpace(
address_prefixes=[
'10.0.0.0/16',
],
),
dhcp_options=azure.mgmt.network.models.DhcpOptions(
dns_servers=[
'10.1.1.1',
'10.1.2.4',
],
),
subnets=[
azure.mgmt.network.models.Subnet(
name=subnet1_name,
address_prefix='10.0.1.0/24',
),
azure.mgmt.network.models.Subnet(
name=subnet2_name,
address_prefix='10.0.2.0/24',
),
],
)
result_create = self.network_client.virtual_networks.create_or_update(
self.group_name,
network_name,
params_create,
)
vnet = result_create.result()
vnet = self.network_client.virtual_networks.get(
self.group_name,
vnet.name,
)
ip_availability = self.network_client.virtual_networks.check_ip_address_availability(
self.group_name,
vnet.name,
'10.0.1.35' # Should be available since new VNet sor Subnet 1
)
self.assertTrue(ip_availability.available)
result_list = list(self.network_client.virtual_networks.list(
self.group_name,
))
self.assertEqual(len(result_list), 1)
result_list_all = list(self.network_client.virtual_networks.list_all())
async_delete = self.network_client.virtual_networks.delete(
self.group_name,
network_name,
)
async_delete.wait()
@record
def test_dns_availability(self):
result_check = self.network_client.check_dns_name_availability(
self.region,
'pydomain',
)
#self.assertEqual(result_check.status_code, HttpStatusCode.OK)
self.assertTrue(result_check)
@record
def test_subnets(self):
network_name = self.get_resource_name('pysubnet')
subnet1_name = self.get_resource_name('pysubnetone')
subnet2_name = self.get_resource_name('pysubnettwo')
params_create = azure.mgmt.network.models.VirtualNetwork(
location=self.region,
address_space=azure.mgmt.network.models.AddressSpace(
address_prefixes=[
'10.0.0.0/16',
],
),
dhcp_options=azure.mgmt.network.models.DhcpOptions(
dns_servers=[
'10.1.1.1',
'10.1.2.4',
],
),
subnets=[
azure.mgmt.network.models.Subnet(
name=subnet1_name,
address_prefix='10.0.1.0/24',
),
],
)
result_create = self.network_client.virtual_networks.create_or_update(
self.group_name,
network_name,
params_create,
)
result_create.wait() # AzureOperationPoller
params_create = azure.mgmt.network.models.Subnet(
name=subnet2_name,
address_prefix='10.0.2.0/24',
)
result_create = self.network_client.subnets.create_or_update(
self.group_name,
network_name,
subnet2_name,
params_create,
)
result_create.wait() # AzureOperationPoller
result_get = self.network_client.virtual_networks.get(
self.group_name,
network_name,
)
self.assertEqual(len(result_get.subnets), 2)
result_get = self.network_client.subnets.get(
self.group_name,
network_name,
subnet2_name,
)
result_list = self.network_client.subnets.list(
self.group_name,
network_name,
)
subnets = list(result_list)
result_delete = self.network_client.subnets.delete(
self.group_name,
network_name,
subnet2_name,
)
result_delete.wait()
@record
def test_network_security_groups(self):
security_group_name = self.get_resource_name('pysecgroup')
security_rule_name = self.get_resource_name('pysecgrouprule')
params_create = azure.mgmt.network.models.NetworkSecurityGroup(
location=self.region,
security_rules=[
azure.mgmt.network.models.SecurityRule(
name=security_rule_name,
access=azure.mgmt.network.models.SecurityRuleAccess.allow,
description='Test security rule',
destination_address_prefix='*',
destination_port_range='123-3500',
direction=azure.mgmt.network.models.SecurityRuleDirection.inbound,
priority=500,
protocol=azure.mgmt.network.models.SecurityRuleProtocol.tcp,
source_address_prefix='*',
source_port_range='655',
),
],
)
result_create = self.network_client.network_security_groups.create_or_update(
self.group_name,
security_group_name,
params_create,
)
result_create.wait() # AzureOperationPoller
result_get = self.network_client.network_security_groups.get(
self.group_name,
security_group_name,
)
result_list = list(self.network_client.network_security_groups.list(
self.group_name,
))
self.assertEqual(len(result_list), 1)
result_list_all = list(self.network_client.network_security_groups.list_all())
# Security Rules
new_security_rule_name = self.get_resource_name('pynewrule')
async_security_rule = self.network_client.security_rules.create_or_update(
self.group_name,
security_group_name,
new_security_rule_name,
{
'access':azure.mgmt.network.models.SecurityRuleAccess.allow,
'description':'New Test security rule',
'destination_address_prefix':'*',
'destination_port_range':'123-3500',
'direction':azure.mgmt.network.models.SecurityRuleDirection.outbound,
'priority':400,
'protocol':azure.mgmt.network.models.SecurityRuleProtocol.tcp,
'source_address_prefix':'*',
'source_port_range':'655',
}
)
security_rule = async_security_rule.result()
security_rule = self.network_client.security_rules.get(
self.group_name,
security_group_name,
security_rule.name
)
self.assertEqual(security_rule.name, new_security_rule_name)
new_security_rules = list(self.network_client.security_rules.list(
self.group_name,
security_group_name
))
self.assertEqual(len(new_security_rules), 2)
result_delete = self.network_client.security_rules.delete(
self.group_name,
security_group_name,
new_security_rule_name
)
result_delete.wait()
# Delete NSG
result_delete = self.network_client.network_security_groups.delete(
self.group_name,
security_group_name,
)
result_delete.wait()
@record
def test_routes(self):
route_table_name = self.get_resource_name('pyroutetable')
route_name = self.get_resource_name('pyroute')
async_route_table = self.network_client.route_tables.create_or_update(
self.group_name,
route_table_name,
{'location': self.region}
)
route_table = async_route_table.result()
route_table = self.network_client.route_tables.get(
self.group_name,
route_table.name
)
self.assertEqual(route_table.name, route_table_name)
route_tables = list(self.network_client.route_tables.list(
self.group_name
))
self.assertEqual(len(route_tables), 1)
route_tables = list(self.network_client.route_tables.list_all())
self.assertGreater(len(route_tables), 0)
async_route = self.network_client.routes.create_or_update(
self.group_name,
route_table.name,
route_name,
{
'address_prefix': '10.1.0.0/16',
'next_hop_type': 'None'
}
)
route = async_route.result()
route = self.network_client.routes.get(
self.group_name,
route_table.name,
route.name
)
self.assertEqual(route.name, route_name)
routes = list(self.network_client.routes.list(
self.group_name,
route_table.name
))
self.assertEqual(len(routes), 1)
async_route_delete = self.network_client.routes.delete(
self.group_name,
route_table.name,
route.name
)
async_route_delete.wait()
async_route_table_delete = self.network_client.route_tables.delete(
self.group_name,
route_table_name
)
async_route_table_delete.wait()
@record
def test_usages(self):
usages = list(self.network_client.usages.list(self.region))
self.assertGreater(len(usages), 1)
self.assertTrue(all(hasattr(u, 'name') for u in usages))
@record
def test_express_route_service_providers(self):
ersp = list(self.network_client.express_route_service_providers.list())
self.assertGreater(len(ersp), 0)
self.assertTrue(all(hasattr(u, 'bandwidths_offered') for u in ersp))
@record
def test_express_route_circuit(self):
express_route_name = self.get_resource_name('pyexpressroute')
async_express_route = self.network_client.express_route_circuits.create_or_update(
self.group_name,
express_route_name,
{
"location": self.region,
"sku": {
"name": "Standard_MeteredData",
"tier": "Standard",
"family": "MeteredData"
},
"service_provider_properties": {
"service_provider_name": "Comcast",
"peering_location": "Chicago",
"bandwidth_in_mbps": 100
}
}
)
express_route = async_express_route.result()
express_route = self.network_client.express_route_circuits.get(
self.group_name,
express_route_name
)
routes = list(self.network_client.express_route_circuits.list(
self.group_name
))
self.assertEqual(len(routes), 1)
routes = list(self.network_client.express_route_circuits.list_all())
self.assertGreater(len(routes), 0)
stats = self.network_client.express_route_circuits.get_stats(
self.group_name,
express_route_name
)
self.assertIsNotNone(stats)
async_peering = self.network_client.express_route_circuit_peerings.create_or_update(
self.group_name,
express_route_name,
'AzurePublicPeering',
{
"peering_type": "AzurePublicPeering",
"peer_asn": 100,
"primary_peer_address_prefix": "192.168.1.0/30",
"secondary_peer_address_prefix": "192.168.2.0/30",
"vlan_id": 200,
}
)
peering = async_peering.result()
peering = self.network_client.express_route_circuit_peerings.get(
self.group_name,
express_route_name,
'AzurePublicPeering'
)
peerings = list(self.network_client.express_route_circuit_peerings.list(
self.group_name,
express_route_name
))
self.assertEqual(len(peerings), 1)
stats = self.network_client.express_route_circuits.get_peering_stats(
self.group_name,
express_route_name,
'AzurePublicPeering'
)
self.assertIsNotNone(stats)
auth_name = self.get_resource_name('pyauth')
async_auth = self.network_client.express_route_circuit_authorizations.create_or_update(
self.group_name,
express_route_name,
auth_name,
{}
)
auth = async_auth.result()
auth = self.network_client.express_route_circuit_authorizations.get(
self.group_name,
express_route_name,
auth_name
)
auths = list(self.network_client.express_route_circuit_authorizations.list(
self.group_name,
express_route_name
))
self.assertEqual(len(auths), 1)
async_auth = self.network_client.express_route_circuit_authorizations.delete(
self.group_name,
express_route_name,
auth_name
)
async_auth.wait()
async_peering = self.network_client.express_route_circuit_peerings.delete(
self.group_name,
express_route_name,
'AzurePublicPeering'
)
async_peering.wait()
async_express_route = self.network_client.express_route_circuits.delete(
self.group_name,
express_route_name
)
async_express_route.wait()
@record
def test_virtual_network_gateway_operations(self):
# https://docs.microsoft.com/en-us/azure/vpn-gateway/vpn-gateway-howto-site-to-site-resource-manager-portal
vnet_name = self.get_resource_name('pyvirtnet')
fe_name = self.get_resource_name('pysubnetfe')
be_name = self.get_resource_name('pysubnetbe')
gateway_name = self.get_resource_name('pysubnetga')
# Create VNet
async_vnet_creation = self.network_client.virtual_networks.create_or_update(
self.group_name,
vnet_name,
{
'location': self.region,
'address_space': {
'address_prefixes': [
'10.11.0.0/16',
'10.12.0.0/16'
]
}
}
)
async_vnet_creation.wait()
# Create Front End Subnet
async_subnet_creation = self.network_client.subnets.create_or_update(
self.group_name,
vnet_name,
fe_name,
{'address_prefix': '10.11.0.0/24'}
)
fe_subnet_info = async_subnet_creation.result()
# Create Back End Subnet
async_subnet_creation = self.network_client.subnets.create_or_update(
self.group_name,
vnet_name,
be_name,
{'address_prefix': '10.12.0.0/24'}
)
be_subnet_info = async_subnet_creation.result()
# Create Gateway Subnet
async_subnet_creation = self.network_client.subnets.create_or_update(
self.group_name,
vnet_name,
'GatewaySubnet',
{'address_prefix': '10.12.255.0/27'}
)
gateway_subnet_info = async_subnet_creation.result()
# Public IP Address
public_ip_name = self.get_resource_name('pyipname')
params_create = azure.mgmt.network.models.PublicIPAddress(
location=self.region,
public_ip_allocation_method=azure.mgmt.network.models.IPAllocationMethod.dynamic,
tags={
'key': 'value',
},
)
result_create = self.network_client.public_ip_addresses.create_or_update(
self.group_name,
public_ip_name,
params_create,
)
public_ip_address = result_create.result()
# Gateway itself
vng_name = self.get_resource_name('pyvng')
gw_params = {
'location': self.region,
'gateway_type': 'VPN',
'vpn_type': 'RouteBased',
'enable_bgp': False,
'sku': {
'tier': 'Standard',
'capacity': 2,
'name': 'Standard'},
'ip_configurations':[{
'name': 'default',
'private_ip_allocation_method': 'Dynamic',
'subnet': {
'id': gateway_subnet_info.id
},
'public_ip_address': {
'id': public_ip_address.id
}
}],
}
async_create = self.network_client.virtual_network_gateways.create_or_update(
self.group_name,
vng_name,
gw_params
)
vng = async_create.result()
self.assertEquals(vng.name, vng_name)
#------------------------------------------------------------------------------
if __name__ == '__main__':
unittest.main()
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] |
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